From 1646895bc931f5e47553da51c1b533461cafb688 Mon Sep 17 00:00:00 2001 From: baraline Date: Wed, 4 Dec 2024 22:47:52 +0100 Subject: [PATCH 01/36] WIP remake module structure --- aeon/similarity_search/__init__.py | 6 +- aeon/similarity_search/_commons.py | 504 -------------- aeon/similarity_search/base.py | 209 ++---- .../distance_profiles/__init__.py | 18 - .../euclidean_distance_profile.py | 102 --- .../squared_distance_profile.py | 319 --------- .../distance_profiles/tests/__init__.py | 1 - .../tests/test_euclidean_distance.py | 208 ------ .../tests/test_squared_distance.py | 200 ------ .../matrix_profiles/__init__.py | 14 - .../matrix_profiles/stomp.py | 633 ------------------ .../matrix_profiles/tests/test_stomp.py | 205 ------ aeon/similarity_search/query_search.py | 428 ------------ aeon/similarity_search/series_search.py | 436 ------------ .../series_search/__init__.py | 7 + aeon/similarity_search/series_search/base.py | 22 + .../subsequence_search/__init__.py | 5 + .../subsequence_search/_brute_force.py | 284 ++++++++ .../subsequence_search/_commons.py | 138 ++++ .../subsequence_search/_stomp.py | 596 +++++++++++++++++ .../subsequence_search/base.py | 358 ++++++++++ .../tests/__init__.py | 0 .../subsequence_search/tests/test__commons.py | 64 ++ .../subsequence_search/tests/test_stomp.py | 238 +++++++ aeon/similarity_search/tests/test__commons.py | 49 -- .../tests/test_query_search.py | 176 ----- .../tests/test_series_search.py | 74 -- aeon/utils/numba/general.py | 3 + 28 files changed, 1785 insertions(+), 3512 deletions(-) delete mode 100644 aeon/similarity_search/_commons.py delete mode 100644 aeon/similarity_search/distance_profiles/__init__.py delete mode 100644 aeon/similarity_search/distance_profiles/euclidean_distance_profile.py delete mode 100644 aeon/similarity_search/distance_profiles/squared_distance_profile.py delete mode 100644 aeon/similarity_search/distance_profiles/tests/__init__.py delete mode 100644 aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py delete mode 100644 aeon/similarity_search/distance_profiles/tests/test_squared_distance.py delete mode 100644 aeon/similarity_search/matrix_profiles/__init__.py delete mode 100644 aeon/similarity_search/matrix_profiles/stomp.py delete mode 100644 aeon/similarity_search/matrix_profiles/tests/test_stomp.py delete mode 100644 aeon/similarity_search/query_search.py delete mode 100644 aeon/similarity_search/series_search.py create mode 100644 aeon/similarity_search/series_search/__init__.py create mode 100644 aeon/similarity_search/series_search/base.py create mode 100644 aeon/similarity_search/subsequence_search/__init__.py create mode 100644 aeon/similarity_search/subsequence_search/_brute_force.py create mode 100644 aeon/similarity_search/subsequence_search/_commons.py create mode 100644 aeon/similarity_search/subsequence_search/_stomp.py create mode 100644 aeon/similarity_search/subsequence_search/base.py rename aeon/similarity_search/{matrix_profiles => subsequence_search}/tests/__init__.py (100%) create mode 100644 aeon/similarity_search/subsequence_search/tests/test__commons.py create mode 100644 aeon/similarity_search/subsequence_search/tests/test_stomp.py delete mode 100644 aeon/similarity_search/tests/test__commons.py delete mode 100644 aeon/similarity_search/tests/test_query_search.py delete mode 100644 aeon/similarity_search/tests/test_series_search.py diff --git a/aeon/similarity_search/__init__.py b/aeon/similarity_search/__init__.py index f576c41f03..53f80b2cdf 100644 --- a/aeon/similarity_search/__init__.py +++ b/aeon/similarity_search/__init__.py @@ -1,7 +1,3 @@ """Similarity search module.""" -__all__ = ["BaseSimilaritySearch", "QuerySearch", "SeriesSearch"] - -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.query_search import QuerySearch -from aeon.similarity_search.series_search import SeriesSearch +__all__ = [] diff --git a/aeon/similarity_search/_commons.py b/aeon/similarity_search/_commons.py deleted file mode 100644 index 1d20a6a5b0..0000000000 --- a/aeon/similarity_search/_commons.py +++ /dev/null @@ -1,504 +0,0 @@ -"""Helper and common function for similarity search estimators and functions.""" - -__maintainer__ = ["baraline"] - -import warnings - -import numpy as np -from numba import njit, prange -from numba.typed import List -from scipy.signal import convolve - -from aeon.utils.numba.general import ( - get_all_subsequences, - normalise_subsequences, - sliding_mean_std_one_series, - z_normalise_series_2d, -) - - -@njit(cache=True, fastmath=True) -def _compute_dist_profile(X_subs, q): - """ - Compute the distance profile between subsequences and a query. - - Parameters - ---------- - X_subs : array, shape=(n_samples, n_channels, query_length) - Input subsequences extracted from a time series. - q : array, shape=(n_channels, query_length) - Query used for the distance computation - - Returns - ------- - dist_profile : np.ndarray, 1D array of shape (n_samples) - The distance between the query all subsequences. - - """ - n_candidates, n_channels, q_length = X_subs.shape - dist_profile = np.zeros(n_candidates) - for i in range(n_candidates): - for j in range(n_channels): - for k in range(q_length): - dist_profile[i] += (X_subs[i, j, k] - q[j, k]) ** 2 - return dist_profile - - -@njit(cache=True, fastmath=True) -def naive_squared_distance_profile( - X, - q, - mask, - normalise=False, - X_means=None, - X_stds=None, -): - """ - Compute a squared euclidean distance profile. - - Parameters - ---------- - X : array, shape=(n_samples, n_channels, n_timepoints) - Input time series dataset to search in. - q : array, shape=(n_channels, query_length) - Query used during the search. - mask : array, shape=(n_samples, n_timepoints - query_length + 1) - Boolean mask indicating candidates for which the distance - profiles computed for each query should be set to infinity. - normalise : bool - Wheter to use a z-normalised distance. - X_means : array, shape=(n_samples, n_channels, n_timepoints - query_length + 1) - Mean of each candidate (subsequence) of length query_length in X. The - default is None, meaning that these values will be computed if normalise - is True. If provided, the computations will be skipped. - X_stds : array, shape=(n_samples, n_channels, n_timepoints - query_length + 1) - Standard deviation of each candidate (subsequence) of length query_length - in X. The default is None, meaning that these values will be computed if - normalise is True. If provided, the computations will be skipped. - - Returns - ------- - out : np.ndarray, 1D array of shape (n_samples, n_timepoints_t - query_length + 1) - The distance between the query and all candidates in X. - - """ - query_length = q.shape[1] - dist_profiles = List() - # Init distance profile array with unequal length support - for i in range(len(X)): - dist_profiles.append(np.zeros(X[i].shape[1] - query_length + 1)) - if normalise: - q = z_normalise_series_2d(q) - else: - q = q.astype(np.float64) - for i in range(len(X)): - # Numba don't support strides with integers ? - - X_subs = get_all_subsequences(X[i].astype(np.float64), query_length, 1) - if normalise: - if X_means is None and X_stds is None: - _X_means, _X_stds = sliding_mean_std_one_series(X[i], query_length, 1) - else: - _X_means, _X_stds = X_means[i], X_stds[i] - X_subs = normalise_subsequences(X_subs, _X_means, _X_stds) - dist_profile = _compute_dist_profile(X_subs, q) - dist_profile[~mask[i]] = np.inf - dist_profiles[i] = dist_profile - return dist_profiles - - -@njit(cache=True, fastmath=True) -def naive_squared_matrix_profile(X, T, query_length, mask, normalise=False): - """ - Compute a squared euclidean matrix profile. - - Parameters - ---------- - X : array, shape=(n_samples, n_channels, n_timepoints_x) - Input time series dataset to search in. - T : array, shape=(n_channels, n_timepoints_t) - Time series from which queries are extracted. - query_length : int - Length of the queries to extract from T. - mask : array, shape=(n_samples, n_timepoints_x - query_length + 1) - Boolean mask indicating candidates for which the distance - profiles computed for each query should be set to infinity. - normalise : bool - Wheter to use a z-normalised distance. - - Returns - ------- - out : np.ndarray, 1D array of shape (n_timepoints_t - query_length + 1) - The minimum distance between each query in T and all candidates in X. - """ - X_subs = List() - for i in range(len(X)): - i_subs = get_all_subsequences(X[i].astype(np.float64), query_length, 1) - if normalise: - X_means, X_stds = sliding_mean_std_one_series(X[i], query_length, 1) - i_subs = normalise_subsequences(i_subs, X_means, X_stds) - X_subs.append(i_subs) - - n_candidates = T.shape[1] - query_length + 1 - mp = np.full(n_candidates, np.inf) - - for i in range(n_candidates): - q = T[:, i : i + query_length] - if normalise: - q = z_normalise_series_2d(q) - for id_sample in range(len(X)): - dist_profile = _compute_dist_profile(X_subs[id_sample], q) - dist_profile[~mask[id_sample]] = np.inf - mp[i] = min(mp[i], dist_profile.min()) - return mp - - -def fft_sliding_dot_product(X, q): - """ - Use FFT convolution to calculate the sliding window dot product. - - This function applies the Fast Fourier Transform (FFT) to efficiently compute - the sliding dot product between the input time series `X` and the query `q`. - The dot product is computed for each channel individually. The sliding window - approach ensures that the dot product is calculated for every possible subsequence - of `X` that matches the length of `q` - - Parameters - ---------- - X : array, shape=(n_channels, n_timepoints) - Input time series - q : array, shape=(n_channels, query_length) - Input query - - Returns - ------- - out : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - Sliding dot product between q and X. - """ - n_channels, n_timepoints = X.shape - query_length = q.shape[1] - out = np.zeros((n_channels, n_timepoints - query_length + 1)) - for i in range(n_channels): - out[i, :] = convolve(np.flipud(q[i, :]), X[i, :], mode="valid").real - return out - - -def get_ith_products(X, T, L, ith): - """ - Compute dot products between X and the i-th subsequence of size L in T. - - Parameters - ---------- - X : array, shape = (n_channels, n_timepoints_X) - Input data. - T : array, shape = (n_channels, n_timepoints_T) - Data containing the query. - L : int - Overall query length. - ith : int - Query starting index in T. - - Returns - ------- - np.ndarray, 2D array of shape (n_channels, n_timepoints_X - L + 1) - Sliding dot product between the i-th subsequence of size L in T and X. - - """ - return fft_sliding_dot_product(X, T[:, ith : ith + L]) - - -@njit(cache=True) -def numba_roll_1D_no_warparound(array, shift, warparound_value): - """ - Roll the rows of an array. - - Wheter to allow values at the end of the array to appear at the start after - being rolled out of the array length. - - Parameters - ---------- - array : np.ndarray of shape (n_columns) - Array to roll. - shift : int - The amount of indexes the values will be rolled on each row of the array. - Must be inferior or equal to n_columns. - warparound_value : any type - A value of the type of array to insert instead of the value that got rolled - over the array length - - Returns - ------- - rolled_array : np.ndarray of shape (n_rows, n_columns) - The rolled array. Can also be a TypedList in the case where n_columns changes - between rows. - - """ - length = array.shape[0] - _a1 = array[: length - shift] - array[shift:] = _a1 - array[:shift] = warparound_value - return array - - -@njit(cache=True) -def numba_roll_2D_no_warparound(array, shift, warparound_value): - """ - Roll the rows of an array. - - Wheter to allow values at the end of the array to appear at the start after - being rolled out of the array length. - - Parameters - ---------- - array : np.ndarray of shape (n_rows, n_columns) - Array to roll. Can also be a TypedList in the case where n_columns changes - between rows. - shift : int - The amount of indexes the values will be rolled on each row of the array. - Must be inferior or equal to n_columns. - warparound_value : any type - A value of the type of array to insert instead of the value that got rolled - over the array length - - Returns - ------- - rolled_array : np.ndarray of shape (n_rows, n_columns) - The rolled array. Can also be a TypedList in the case where n_columns changes - between rows. - - """ - for i in prange(len(array)): - length = len(array[i]) - _a1 = array[i][: length - shift] - array[i][shift:] = _a1 - array[i][:shift] = warparound_value - return array - - -@njit(cache=True) -def extract_top_k_and_threshold_from_distance_profiles_one_series( - distance_profiles, - id_x, - k=1, - threshold=np.inf, - exclusion_size=None, - inverse_distance=False, -): - """ - Extract the top-k smallest values from distance profiles and apply threshold. - - This function processes a distance profile and extracts the top-k smallest - distance values, optionally applying a threshold to exclude distances above - a given value. It also optionally handles exclusion zones to avoid selecting - neighboring timestamps. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) - Precomputed distance profile. Can be a TypedList if n_candidates vary between - cases. - id_x : int - Identifier of the series or subsequence from which the distance profile - is computed. - k : int - Number of matches to returns - threshold : float - All matches below this threshold will be returned - exclusion_size : int or None, optional, default=None - Size of the exclusion zone around the current subsequence. This prevents - selecting neighboring subsequences within the specified range, useful for - avoiding trivial matches in time series data. If set to `None`, no - exclusion zone is applied. - inverse_distance : bool, optional - Wheter to return the worst matches instead of the bests. The default is False. - - Returns - ------- - top_k_dist : np.ndarray - Array of the top-k smallest distance values, potentially excluding values above - the threshold or those within the exclusion zone. - top_k : np.ndarray - Array of shape (k, 2) where each row contains the `id_x` identifier and the - index of the corresponding subsequence (or timestamp) with the top-k smallest - distances. - """ - if inverse_distance: - # To avoid div by 0 case - distance_profiles += 1e-8 - distance_profiles[distance_profiles != np.inf] = ( - 1 / distance_profiles[distance_profiles != np.inf] - ) - - if threshold != np.inf: - distance_profiles[distance_profiles > threshold] = np.inf - - _argsort = np.argsort(distance_profiles) - - if distance_profiles[distance_profiles <= threshold].shape[0] < k: - _k = distance_profiles[distance_profiles <= threshold].shape[0] - elif _argsort.shape[0] < k: - _k = _argsort.shape[0] - else: - _k = k - - if exclusion_size is None: - indexes = np.zeros((_k, 2), dtype=np.int_) - for i in range(_k): - indexes[i, 0] = id_x - indexes[i, 1] = _argsort[i] - return distance_profiles[_argsort[:_k]], indexes - else: - # Apply exclusion zone to avoid neighboring matches - top_k = np.zeros((_k, 2), dtype=np.int_) - exclusion_size - top_k_dist = np.zeros((_k), dtype=np.float64) - - top_k[0, 0] = id_x - top_k[0, 1] = _argsort[0] - - top_k_dist[0] = distance_profiles[_argsort[0]] - - n_inserted = 1 - i_current = 1 - - while n_inserted < _k and i_current < _argsort.shape[0]: - candidate_timestamp = _argsort[i_current] - - insert = True - LB = candidate_timestamp >= (top_k[:, 1] - exclusion_size) - UB = candidate_timestamp <= (top_k[:, 1] + exclusion_size) - if np.any(UB & LB): - insert = False - - if insert: - top_k[n_inserted, 0] = id_x - top_k[n_inserted, 1] = _argsort[i_current] - top_k_dist[n_inserted] = distance_profiles[_argsort[i_current]] - n_inserted += 1 - i_current += 1 - return top_k_dist[:n_inserted], top_k[:n_inserted] - - -def extract_top_k_and_threshold_from_distance_profiles( - distance_profiles, - k=1, - threshold=np.inf, - exclusion_size=None, - inverse_distance=False, -): - """ - Extract the best matches from a distance profile given k and threshold parameters. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) - Precomputed distance profile. Can be a TypedList if n_candidates vary between - cases. - k : int - Number of matches to returns - threshold : float - All matches below this threshold will be returned - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - inverse_distance : bool, optional - Wheter to return the worst matches instead of the bests. The default is False. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - # This whole function could be optimized and maybe made in numba to avoid stepping - # out of numba mode during distance computations - - n_cases_ = len(distance_profiles) - - id_timestamps = np.concatenate( - [np.arange(distance_profiles[i].shape[0]) for i in range(n_cases_)] - ) - id_samples = np.concatenate( - [[i] * distance_profiles[i].shape[0] for i in range(n_cases_)] - ) - - distance_profiles = np.concatenate(distance_profiles) - - if inverse_distance: - # To avoid div by 0 case - distance_profiles += 1e-8 - distance_profiles[distance_profiles != np.inf] = ( - 1 / distance_profiles[distance_profiles != np.inf] - ) - - if threshold != np.inf: - distance_profiles[distance_profiles > threshold] = np.inf - - _argsort_1d = np.argsort(distance_profiles) - _argsort = np.asarray( - [ - [id_samples[_argsort_1d[i]], id_timestamps[_argsort_1d[i]]] - for i in range(len(_argsort_1d)) - ], - dtype=int, - ) - - if distance_profiles[distance_profiles <= threshold].shape[0] < k: - _k = distance_profiles[distance_profiles <= threshold].shape[0] - warnings.warn( - f"Only {_k} matches are bellow the threshold of {threshold}, while" - f" k={k}. The number of returned match will be {_k}.", - stacklevel=2, - ) - elif _argsort.shape[0] < k: - _k = _argsort.shape[0] - warnings.warn( - f"The number of possible match is {_argsort.shape[0]}, but got" - f" k={k}. The number of returned match will be {_k}.", - stacklevel=2, - ) - else: - _k = k - - if exclusion_size is None: - return distance_profiles[_argsort_1d[:_k]], _argsort[:_k] - else: - # Apply exclusion zone to avoid neighboring matches - top_k = np.zeros((_k, 2), dtype=int) - top_k_dist = np.zeros((_k), dtype=float) - - top_k[0] = _argsort[0, :] - top_k_dist[0] = distance_profiles[_argsort_1d[0]] - - n_inserted = 1 - i_current = 1 - - while n_inserted < _k and i_current < _argsort.shape[0]: - candidate_sample, candidate_timestamp = _argsort[i_current] - - insert = True - is_from_same_sample = top_k[:, 0] == candidate_sample - if np.any(is_from_same_sample): - LB = candidate_timestamp >= ( - top_k[is_from_same_sample, 1] - exclusion_size - ) - UB = candidate_timestamp <= ( - top_k[is_from_same_sample, 1] + exclusion_size - ) - if np.any(UB & LB): - insert = False - - if insert: - top_k[n_inserted] = _argsort[i_current] - top_k_dist[n_inserted] = distance_profiles[_argsort_1d[i_current]] - n_inserted += 1 - i_current += 1 - return top_k_dist[:n_inserted], top_k[:n_inserted] diff --git a/aeon/similarity_search/base.py b/aeon/similarity_search/base.py index 5b0ce8c555..8a9e9547d7 100644 --- a/aeon/similarity_search/base.py +++ b/aeon/similarity_search/base.py @@ -3,15 +3,13 @@ __maintainer__ = ["baraline"] from abc import abstractmethod -from collections.abc import Iterable -from typing import Optional, final +from typing import Optional, Union, final import numpy as np from numba import get_num_threads, set_num_threads from numba.typed import List from aeon.base import BaseCollectionEstimator -from aeon.utils.numba.general import sliding_mean_std_one_series class BaseSimilaritySearch(BaseCollectionEstimator): @@ -20,36 +18,10 @@ class BaseSimilaritySearch(BaseCollectionEstimator): Parameters ---------- - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function of the child classes. - n_jobs : int, default=1 - Number of parallel jobs to use. - - Attributes - ---------- - X_ : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. + normalize : bool, optional + Whether the inputs should be z-normalized. The default is False. + n_jobs : int, optional + Number of parallel jobs to use. The default is 1. """ _tags = { @@ -63,30 +35,27 @@ class BaseSimilaritySearch(BaseCollectionEstimator): @abstractmethod def __init__( self, - distance: str = "euclidean", - distance_args: Optional[dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, + normalize: Optional[bool] = False, + n_jobs: Optional[int] = 1, ): - self.distance = distance - self.distance_args = distance_args - self.inverse_distance = inverse_distance - self.normalise = normalise self.n_jobs = n_jobs - self.speed_up = speed_up + self.normalize = normalize super().__init__() @final - def fit(self, X: np.ndarray, y=None): + def fit( + self, + X: Union[np.ndarray, List], + y=None, + ): """ Fit method: data preprocessing and storage. Parameters ---------- X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input array to be used as database for the similarity search + Input array to be used as database for the similarity search. If it is an + unequal length collection, it should be a list of 2d numpy arrays. y : optional Not used. @@ -113,120 +82,80 @@ def fit(self, X: np.ndarray, y=None): self.is_fitted = True return self - def _store_mean_std_from_inputs(self, query_length: int) -> None: + @abstractmethod + def find_motifs( + self, + k: int, + threshold: float, + X: Optional[np.ndarray] = None, + allow_overlap: Optional[bool] = True, + ): """ - Store the mean and std of each subsequence of size query_length in X_. + Find the top-k motifs in the training data. + + Given ``k`` and ``threshold`` parameters, this methods returns the top-k motif + sets. We define a motif set as a set of candidates which all are at a distance + of at most ``threshold`` from each other. The top-k motifs sets are the + motif sets with the most candidates. Parameters ---------- - query_length : int - Length of the query. + X : np.ndarray, optional + The query in which we want to indentify motifs. If provided, the motifs + extracted should appear in X and in the database given in fit. If not + provided, the motifs will be extracted only from the database given in fit. + k : int, optional + Number of motifs to return + threshold : int, optional + A threshold on the similarity measure to determine which candidates will be + part of a motif set. + allow_overlap: bool, optional + Wheter a candidate can be part of multiple motif sets (True), or if motif + sets should be mutually exclusive (False). Returns ------- - None + list of ndarray, shape=(k,) + A list of at most ``k`` numpy arrays containing the indexes of the + candidates in each motif. """ - means = [] - stds = [] - - for i in range(len(self.X_)): - _mean, _std = sliding_mean_std_one_series(self.X_[i], query_length, 1) - - stds.append(_std) - means.append(_mean) - - self.X_means_ = List(means) - self.X_stds_ = List(stds) + ... - def _init_X_index_mask( + @abstractmethod + def find_neighbors( self, - X_index: Optional[Iterable[int]], - query_length: int, - exclusion_factor: Optional[float] = 2.0, - ) -> np.ndarray: + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + ): """ - Initiliaze the mask indicating the candidates to be evaluated in the search. + Find the top-k neighbors of X in the database. + + Given ``k`` and ``threshold`` parameters, this methods returns the top-k + neighbors of X, such as each of the ``k`` neighbors as a distance inferior or + equal to ``threshold``. By default, ``threshold`` is set to infinity. It is + possible for this method to return less than ``k`` neighbors, either if there + is less than ``k`` admissible candidate in the database, or if in the top-k + candidates, some do not meet the ``threshold`` condition. Parameters ---------- - X_index : Iterable - Any Iterable (tuple, list, array) of length two used to specify the index of - the query X if it was extracted from the input data X given during the fit - method. Given the tuple (id_sample, id_timestamp), the similarity search - will define an exclusion zone around the X_index in order to avoid matching - X with itself. If None, it is considered that the query is not extracted - from X_ (the training data). - query_length : int - Length of the queries. - exclusion_factor : float, optional - The exclusion factor is used to prevent candidates close or equal to the - query sample point to be returned as best matches. It is used to define a - region between :math:`id_timestamp - query_length//exclusion_factor` and - :math:`id_timestamp + query_length//exclusion_factor` which cannot be used - in the search. The default is 2.0. - - Raises - ------ - ValueError - If the length of the q_index iterable is not two, will raise a ValueError. - TypeError - If q_index is not an iterable, will raise a TypeError. + X: np.ndarray + The query for which we want to identify nearest neighbors in the database. + k : int, optional + Number of neighbors to return. + threshold : int, optional + A threshold on the distance to determine which candidates will be returned. Returns ------- - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean array which indicates the candidates that should be evaluated in the - similarity search. + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the indexes of the + candidates in each motif. """ - if self.metadata_["unequal_length"]: - mask = List( - [ - np.ones(self.X_[i].shape[1] - query_length + 1, dtype=bool) - for i in range(self.n_cases_) - ] - ) - else: - mask = np.ones( - (self.n_cases_, self.min_timepoints_ - query_length + 1), - dtype=bool, - ) - if X_index is not None: - if isinstance(X_index, Iterable): - if len(X_index) != 2: - raise ValueError( - "The X_index should contain an interable of size 2 such as " - "(id_sample, id_timestamp), but got an iterable of " - "size {}".format(len(X_index)) - ) - else: - raise TypeError( - "If not None, the X_index parameter should be an iterable, here " - "X_index is of type {}".format(type(X_index)) - ) - - if exclusion_factor <= 0: - raise ValueError( - "The value of exclusion_factor should be superior to 0, but got " - "{}".format(len(exclusion_factor)) - ) - - i_instance, i_timestamp = X_index - profile_length = self.X_[i_instance].shape[1] - query_length + 1 - exclusion_LB = max(0, int(i_timestamp - query_length // exclusion_factor)) - exclusion_UB = min( - profile_length, - int(i_timestamp + query_length // exclusion_factor), - ) - mask[i_instance][exclusion_LB:exclusion_UB] = False - - return mask + ... @abstractmethod def _fit(self, X, y=None): ... - - @abstractmethod - def get_speedup_function_names(self): - """Return a dictionnary containing the name of the speedup functions.""" - ... diff --git a/aeon/similarity_search/distance_profiles/__init__.py b/aeon/similarity_search/distance_profiles/__init__.py deleted file mode 100644 index 4be73f9d8e..0000000000 --- a/aeon/similarity_search/distance_profiles/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Distance profiles.""" - -__all__ = [ - "euclidean_distance_profile", - "normalised_euclidean_distance_profile", - "squared_distance_profile", - "normalised_squared_distance_profile", -] - - -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) diff --git a/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py b/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py deleted file mode 100644 index 1dd781e467..0000000000 --- a/aeon/similarity_search/distance_profiles/euclidean_distance_profile.py +++ /dev/null @@ -1,102 +0,0 @@ -"""Optimized distance profile for euclidean distance.""" - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba.typed import List - -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) - - -def euclidean_distance_profile( - X: Union[np.ndarray, List], q: np.ndarray, mask: np.ndarray -) -> np.ndarray: - """ - Compute a distance profile using the squared Euclidean distance. - - It computes the distance profiles between the input time series and the query using - the squared Euclidean distance. The distance between the query and a candidate is - comptued using a dot product and a rolling sum to avoid recomputing parts of the - operation. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - of 2D arrays of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = squared_distance_profile(X, q, mask) - # Need loop as we can return a list of np array in the unequal length case - for i in range(len(distance_profiles)): - distance_profiles[i] = distance_profiles[i] ** 0.5 - return distance_profiles - - -def normalised_euclidean_distance_profile( - X: Union[np.ndarray, List], - q: np.ndarray, - mask: np.ndarray, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - q_means: np.ndarray, - q_stds: np.ndarray, -) -> np.ndarray: - """ - Compute a distance profile in a brute force way. - - It computes the distance profiles between the input time series and the query using - the specified distance. The search is made in a brute force way without any - optimizations and can thus be slow. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - of 2D arrays of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length. Should be a numba - TypedList if X is unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length. Should be a numba - TypedList if X is unequal length. - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - # Need loop as we can return a list of np array in the unequal length case - for i in range(len(distance_profiles)): - distance_profiles[i] = distance_profiles[i] ** 0.5 - return distance_profiles diff --git a/aeon/similarity_search/distance_profiles/squared_distance_profile.py b/aeon/similarity_search/distance_profiles/squared_distance_profile.py deleted file mode 100644 index a42beeac2f..0000000000 --- a/aeon/similarity_search/distance_profiles/squared_distance_profile.py +++ /dev/null @@ -1,319 +0,0 @@ -"""Optimized distance profile for euclidean distance.""" - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba import njit, prange -from numba.typed import List - -from aeon.similarity_search._commons import fft_sliding_dot_product -from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD - - -def squared_distance_profile( - X: Union[np.ndarray, List], q: np.ndarray, mask: np.ndarray -) -> np.ndarray: - """ - Compute a distance profile using the squared Euclidean distance. - - It computes the distance profiles between the input time series and the query using - the squared Euclidean distance. The distance between the query and a candidate is - comptued using a dot product and a rolling sum to avoid recomputing parts of the - operation. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profile : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - QX = [fft_sliding_dot_product(X[i], q) for i in range(len(X))] - if isinstance(X, np.ndarray): - QX = np.asarray(QX) - elif isinstance(X, List): - QX = List(QX) - distance_profiles = _squared_distance_profile(QX, X, q, mask) - if isinstance(X, np.ndarray): - distance_profiles = np.asarray(distance_profiles) - return distance_profiles - - -def normalised_squared_distance_profile( - X: Union[np.ndarray, List], - q: np.ndarray, - mask: np.ndarray, - X_means: np.ndarray, - X_stds: np.ndarray, - q_means: np.ndarray, - q_stds: np.ndarray, -) -> np.ndarray: - """ - Compute a distance profile in a brute force way. - - It computes the distance profiles between the input time series and the query using - the specified distance. The search is made in a brute force way without any - optimizations and can thus be slow. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - query_length = q.shape[1] - QX = [fft_sliding_dot_product(X[i], q) for i in range(len(X))] - if isinstance(X, np.ndarray): - QX = np.asarray(QX) - elif isinstance(X, List): - QX = List(QX) - - distance_profiles = _normalised_squared_distance_profile( - QX, mask, X_means, X_stds, q_means, q_stds, query_length - ) - if isinstance(X, np.ndarray): - distance_profiles = np.asarray(distance_profiles) - return distance_profiles - - -@njit(cache=True, fastmath=True, parallel=True) -def _squared_distance_profile(QX, X, q, mask): - """ - Compute squared distance profiles between query subsequence and time series. - - Parameters - ---------- - QX : List of np.ndarray - List of precomputed dot products between queries and time series, with each - element corresponding to a different time series. - Shape of each array is (n_channels, n_timepoints - query_length + 1). - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between q and the input time series X. - - """ - distance_profiles = List() - query_length = q.shape[1] - - # Init distance profile array with unequal length support - for i_instance in range(len(X)): - profile_length = X[i_instance].shape[1] - query_length + 1 - distance_profiles.append(np.full((profile_length), np.inf)) - - for _i_instance in prange(len(QX)): - # prange cast iterator to unit64 with parallel=True - i_instance = np.int_(_i_instance) - - distance_profiles[i_instance][mask[i_instance]] = ( - _squared_dist_profile_one_series(QX[i_instance], X[i_instance], q)[ - mask[i_instance] - ] - ) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _squared_dist_profile_one_series(QT, T, Q): - """ - Compute squared distance profile between query subsequence and a single time series. - - This function calculates the squared distance profile for a single time series by - leveraging the dot product of the query and time series as well as precomputed sums - of squares to efficiently compute the squared distances. - - Parameters - ---------- - QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - The dot product between the query and the time series. - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - Q : np.ndarray - 2D array of shape (n_channels, query_length) representing query subsequence. - - Returns - ------- - distance_profile : np.ndarray - 2D array of shape (n_channels, n_timepoints - query_length + 1) - The squared distance profile between the query and the input time series. - """ - n_channels, profile_length = QT.shape - query_length = Q.shape[1] - _QT = -2 * QT - distance_profile = np.zeros(profile_length) - for k in prange(n_channels): - _sum = 0 - _qsum = 0 - for j in prange(query_length): - _sum += T[k, j] ** 2 - _qsum += Q[k, j] ** 2 - - distance_profile += _qsum + _QT[k] - distance_profile[0] += _sum - for i in prange(1, profile_length): - _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 - distance_profile[i] += _sum - return distance_profile - - -@njit(cache=True, fastmath=True, parallel=True) -def _normalised_squared_distance_profile( - QX, mask, X_means, X_stds, q_means, q_stds, query_length -): - """ - Compute the normalised squared distance profiles between query subsequence and input time series. - - Parameters - ---------- - QX : List of np.ndarray - List of precomputed dot products between queries and time series, with each element - corresponding to a different time series. - Shape of each array is (n_channels, n_timepoints - query_length + 1). - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length - q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - query_length : int - The length of the query subsequence used for the distance profile computation. - - Returns - ------- - List of np.ndarray - List of 2D arrays, each of shape (n_channels, n_timepoints - query_length + 1). - Each array contains the normalised squared distance profile between the query subsequence and the corresponding time series. - Entries in the array are set to infinity where the mask is False. - """ - distance_profiles = List() - Q_is_constant = q_stds <= AEON_NUMBA_STD_THRESHOLD - # Init distance profile array with unequal length support - for i_instance in range(len(QX)): - profile_length = QX[i_instance].shape[1] - distance_profiles.append(np.full((profile_length), np.inf)) - - for _i_instance in prange(len(QX)): - # prange cast iterator to unit64 with parallel=True - i_instance = np.int_(_i_instance) - - distance_profiles[i_instance][mask[i_instance]] = ( - _normalised_squared_dist_profile_one_series( - QX[i_instance], - X_means[i_instance], - X_stds[i_instance], - q_means, - q_stds, - query_length, - Q_is_constant, - )[mask[i_instance]] - ) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _normalised_squared_dist_profile_one_series( - QT, T_means, T_stds, Q_means, Q_stds, query_length, Q_is_constant -): - """ - Compute the z-normalised squared Euclidean distance profile for one time series. - - Parameters - ---------- - QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - The dot product between the query and the time series. - T_means : np.ndarray, 1D array of length n_channels - The mean values of the time series for each channel. - - T_stds : np.ndarray, 2D array of shape (n_channels, profile_length) - The standard deviations of the time series for each channel and position. - Q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - Q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - query_length : int - The length of the query subsequence used for the distance profile computation. - Q_is_constant : np.ndarray - 1D array of shape (n_channels,) where each element is a Boolean indicating - whether the query standard deviation for that channel is less than or equal - to a specified threshold. - - Returns - ------- - np.ndarray - 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the - z-normalised squared distance profile between the query subsequence and the time - series. Entries are computed based on the z-normalised values, with special - handling for constant values. - """ - n_channels, profile_length = QT.shape - distance_profile = np.zeros(profile_length) - - for i in prange(profile_length): - Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD - for k in prange(n_channels): - # Two Constant case - if Q_is_constant[k] and Sub_is_constant[k]: - _val = 0 - # One Constant case - elif Q_is_constant[k] or Sub_is_constant[k]: - _val = query_length - else: - denom = query_length * Q_stds[k] * T_stds[k, i] - - p = (QT[k, i] - query_length * (Q_means[k] * T_means[k, i])) / denom - p = min(p, 1.0) - - _val = abs(2 * query_length * (1.0 - p)) - distance_profile[i] += _val - - return distance_profile diff --git a/aeon/similarity_search/distance_profiles/tests/__init__.py b/aeon/similarity_search/distance_profiles/tests/__init__.py deleted file mode 100644 index 566dda7367..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for distance profiles.""" diff --git a/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py b/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py deleted file mode 100644 index 2eafff78bb..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/test_euclidean_distance.py +++ /dev/null @@ -1,208 +0,0 @@ -"""Tests for naive Euclidean distance profile.""" - -__maintainer__ = [] - - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_array_almost_equal, assert_array_equal - -from aeon.similarity_search._commons import naive_squared_distance_profile -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["float64", "int64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance(dtype): - """Test Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_constant_case(dtype): - """Test Euclidean distance profile calculation.""" - X = np.ones((2, 1, 10), dtype=dtype) - q = np.zeros((1, 3), dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_euclidean(): - """Test if input is altered during Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = euclidean_distance_profile(X, q, mask) - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 for T in naive_squared_distance_profile(X, q, mask, normalise=True) - ] - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - X_means = List() - X_stds = List() - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds.append(_std) - X_means.append(_mean) - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 - for T in naive_squared_distance_profile( - X, q, mask, normalise=True, X_means=X_means, X_stds=X_stds - ) - ] - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - expected = [T**0.5 for T in naive_squared_distance_profile(X, q, mask)] - dist_profile = euclidean_distance_profile(X, q, mask) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_constant_case(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.ones((2, 2, 10), dtype=dtype) - q = np.zeros((2, 3), dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = [ - T**0.5 for T in naive_squared_distance_profile(X, q, mask, normalise=True) - ] - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_normalised_euclidean(): - """Test if input is altered during normalised Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = normalised_euclidean_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) diff --git a/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py b/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py deleted file mode 100644 index cdb7b35cbc..0000000000 --- a/aeon/similarity_search/distance_profiles/tests/test_squared_distance.py +++ /dev/null @@ -1,200 +0,0 @@ -"""Tests for naive Euclidean distance profile.""" - -__maintainer__ = [] - - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_array_almost_equal, assert_array_equal - -from aeon.similarity_search._commons import naive_squared_distance_profile -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["float64", "int64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance(dtype): - """Test Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_constant_case(dtype): - """Test Euclidean distance profile calculation.""" - X = np.ones((2, 1, 10), dtype=dtype) - q = np.zeros((1, 3), dtype=dtype) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_euclidean(): - """Test if input is altered during Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = squared_distance_profile(X, q, mask) - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - - assert_array_almost_equal(dist_profile, expected) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - X_means = List() - X_stds = List() - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds.append(_std) - X_means.append(_mean) - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_euclidean_distance_unequal_length(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - mask = List( - [np.ones(X[i].shape[1] - q.shape[1] + 1, dtype=bool) for i in range(len(X))] - ) - - expected = naive_squared_distance_profile(X, q, mask) - dist_profile = squared_distance_profile(X, q, mask) - for i in range(len(X)): - assert_array_almost_equal(dist_profile[i], expected[i]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_normalised_euclidean_constant_case(dtype): - """Test normalised Euclidean distance profile calculation.""" - X = np.ones((2, 2, 10), dtype=dtype) - q = np.zeros((2, 3), dtype=dtype) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - - dist_profile = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - expected = naive_squared_distance_profile(X, q, mask, normalise=True) - - assert_array_almost_equal(dist_profile, expected) - - -def test_non_alteration_of_inputs_normalised_euclidean(): - """Test if input is altered during normalised Euclidean distance profile.""" - X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) - X_copy = np.copy(X) - q = np.asarray([[3, 4, 5]]) - q_copy = np.copy(q) - - search_space_size = X.shape[-1] - q.shape[-1] + 1 - - X_means = np.zeros((X.shape[0], X.shape[1], search_space_size)) - X_stds = np.zeros((X.shape[0], X.shape[1], search_space_size)) - - for i in range(X.shape[0]): - _mean, _std = sliding_mean_std_one_series(X[i], q.shape[-1], 1) - X_stds[i] = _std - X_means[i] = _mean - - q_means = q.mean(axis=-1) - q_stds = q.std(axis=-1) - - mask = np.ones((X.shape[0], X.shape[2] - q.shape[1] + 1), dtype=bool) - _ = normalised_squared_distance_profile( - X, q, mask, X_means, X_stds, q_means, q_stds - ) - - assert_array_equal(q, q_copy) - assert_array_equal(X, X_copy) diff --git a/aeon/similarity_search/matrix_profiles/__init__.py b/aeon/similarity_search/matrix_profiles/__init__.py deleted file mode 100644 index d04f1cbfd3..0000000000 --- a/aeon/similarity_search/matrix_profiles/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Distance profiles.""" - -__all__ = [ - "stomp_normalised_euclidean_matrix_profile", - "stomp_euclidean_matrix_profile", - "stomp_normalised_squared_matrix_profile", - "stomp_squared_matrix_profile", -] -from aeon.similarity_search.matrix_profiles.stomp import ( - stomp_euclidean_matrix_profile, - stomp_normalised_euclidean_matrix_profile, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) diff --git a/aeon/similarity_search/matrix_profiles/stomp.py b/aeon/similarity_search/matrix_profiles/stomp.py deleted file mode 100644 index 509e68ad49..0000000000 --- a/aeon/similarity_search/matrix_profiles/stomp.py +++ /dev/null @@ -1,633 +0,0 @@ -"""Implementation of stomp for euclidean and squared euclidean distance profile.""" - -from typing import Optional - -__maintainer__ = ["baraline"] - - -from typing import Union - -import numpy as np -from numba import njit -from numba.typed import List - -from aeon.similarity_search._commons import ( - extract_top_k_and_threshold_from_distance_profiles_one_series, - get_ith_products, - numba_roll_1D_no_warparound, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - _normalised_squared_dist_profile_one_series, - _squared_dist_profile_one_series, -) -from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD - - -def stomp_euclidean_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a euclidean euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - MP, IP = stomp_squared_matrix_profile( - X, - T, - L, - mask, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - for i in range(len(MP)): - MP[i] = MP[i] ** 0.5 - return MP, IP - - -def stomp_squared_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a squared euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - XdotT = [get_ith_products(X[i], T, L, 0) for i in range(len(X))] - if isinstance(X, np.ndarray): - XdotT = np.asarray(XdotT) - elif isinstance(X, List): - XdotT = List(XdotT) - - MP, IP = _stomp( - X, - T, - XdotT, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, - ) - return MP, IP - - -def stomp_normalised_euclidean_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - T_means: np.ndarray, - T_stds: np.ndarray, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - MP, IP = stomp_normalised_squared_matrix_profile( - X, - T, - L, - X_means, - X_stds, - T_means, - T_stds, - mask, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - for i in range(len(MP)): - MP[i] = MP[i] ** 0.5 - return MP, IP - - -def stomp_normalised_squared_matrix_profile( - X: Union[np.ndarray, List], - T: np.ndarray, - L: int, - X_means: Union[np.ndarray, List], - X_stds: Union[np.ndarray, List], - T_means: np.ndarray, - T_stds: np.ndarray, - mask: np.ndarray, - k: int = 1, - threshold: float = np.inf, - inverse_distance: bool = False, - exclusion_size: Optional[int] = None, -): - """ - Compute a squared euclidean matrix profile using STOMP [1]_. - - This improves on the naive matrix profile by updating the dot products for each - sucessive query in T instead of recomputing them. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - References - ---------- - .. [1] Matrix Profile II: Exploiting a Novel Algorithm and GPUs to break the one - Hundred Million Barrier for Time Series Motifs and Joins. Yan Zhu, Zachary - Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael Yeh, Gareth Funning, Abdullah - Mueen, Philip Berisk and Eamonn Keogh. IEEE ICDM 2016 - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - XdotT = [get_ith_products(X[i], T, L, 0) for i in range(len(X))] - if isinstance(X, np.ndarray): - XdotT = np.asarray(XdotT) - elif isinstance(X, List): - XdotT = List(XdotT) - - MP, IP = _stomp_normalised( - X, - T, - XdotT, - X_means, - X_stds, - T_means, - T_stds, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, - ) - return MP, IP - - -def _stomp_normalised( - X, - T, - XdotT, - X_means, - X_stds, - T_means, - T_stds, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, -): - """ - Compute the Matrix Profile using the STOMP algorithm with normalised distances. - - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - Length of the subsequences used for the distance computation. - XdotT : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Precomputed dot products between each time series in X and the query series T. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for the - first query of size L in T. This mask will be updated during the algorithm. - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - tuple of np.ndarray - - MP : array of shape (n_queries,) - Matrix profile distances for each query subsequence. - - IP : array of shape (n_queries,) - Indexes of the top matches for each query subsequence. - """ - n_queries = T.shape[1] - L + 1 - MP = np.empty(n_queries, dtype=object) - IP = np.empty(n_queries, dtype=object) - for i_x in range(len(X)): - for i in range(n_queries): - dist_profiles = _normalised_squared_dist_profile_one_series( - XdotT[i_x], - X_means[i_x], - X_stds[i_x], - T_means[:, i], - T_stds[:, i], - L, - T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD, - ) - dist_profiles[~mask[i_x]] = np.inf - if i + 1 < n_queries: - XdotT[i_x] = _update_dot_products_one_series( - X[i_x], T, XdotT[i_x], L, i + 1 - ) - - mask[i_x] = numba_roll_1D_no_warparound(mask[i_x], 1, True) - ( - top_dists, - top_indexes, - ) = extract_top_k_and_threshold_from_distance_profiles_one_series( - dist_profiles, - i_x, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - if i_x > 0: - top_dists, top_indexes = _sort_out_tops( - top_dists, MP[i], top_indexes, IP[i], k - ) - MP[i] = top_dists - IP[i] = top_indexes - else: - MP[i] = top_dists - IP[i] = top_indexes - - return MP, IP - - -def _stomp( - X, - T, - XdotT, - L, - mask, - k, - threshold, - exclusion_size, - inverse_distance, -): - n_queries = T.shape[1] - L + 1 - MP = np.empty(n_queries, dtype=object) - IP = np.empty(n_queries, dtype=object) - for i_x in range(len(X)): - for i in range(n_queries): - Q = T[:, i : i + L] - dist_profiles = _squared_dist_profile_one_series(XdotT[i_x], X[i_x], Q) - dist_profiles[~mask[i_x]] = np.inf - if i + 1 < n_queries: - XdotT[i_x] = _update_dot_products_one_series( - X[i_x], T, XdotT[i_x], L, i + 1 - ) - - mask[i_x] = numba_roll_1D_no_warparound(mask[i_x], 1, True) - ( - top_dists, - top_indexes, - ) = extract_top_k_and_threshold_from_distance_profiles_one_series( - dist_profiles, - i_x, - k=k, - threshold=threshold, - exclusion_size=exclusion_size, - inverse_distance=inverse_distance, - ) - if i_x > 0: - top_dists, top_indexes = _sort_out_tops( - top_dists, MP[i], top_indexes, IP[i], k - ) - MP[i] = top_dists - IP[i] = top_indexes - else: - MP[i] = top_dists - IP[i] = top_indexes - - return MP, IP - - -def _sort_out_tops(top_dists, prev_top_dists, top_indexes, prev_to_indexes, k): - """ - Sort and combine top distance results from previous and current computations. - - Parameters - ---------- - top_dists : np.ndarray - Array of distances from the current computation. Shape should be (n,). - prev_top_dists : np.ndarray - Array of distances from previous computations. Shape should be (n,). - top_indexes : np.ndarray - Array of indexes corresponding to the top distances from current computation. - Shape should be (n,). - prev_to_indexes : np.ndarray - Array of indexes corresponding to the top distances from previous computations. - Shape should be (n,). - k : int, default=1 - The number of best matches to return during predict for each subsequence. - - Returns - ------- - tuple - A tuple containing two elements: - - A 1D numpy array of sorted distances, of length min(k, - total number of distances). - - A 1D numpy array of indexes corresponding to the sorted distances, - of length min(k, total number of distances). - """ - all_dists = np.concatenate((prev_top_dists, top_dists)) - all_indexes = np.concatenate((prev_to_indexes, top_indexes)) - if k == np.inf: - return all_dists, all_indexes - else: - idx = np.argsort(all_dists)[:k] - return all_dists[idx], all_indexes[idx] - - -@njit(cache=True, fastmath=True) -def _update_dot_products_one_series( - X, - T, - XT_products, - L, - i_query, -): - """ - Update dot products of the i-th query of size L in T from the dot products of i-1. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input time series on which the sliding dot product is computed. - T: np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - i_query : int - Query starting index in T. - - Returns - ------- - XT_products : np.ndarray of shape (n_cases, n_channels, n_timepoints - L + 1) - Sliding dot product between the i-th subsequence of size L in T and X. - - """ - n_channels = T.shape[0] - Q = T[:, i_query : i_query + L] - n_candidates = X.shape[1] - L + 1 - - for i_ft in range(n_channels): - # first element of all 0 to n-1 candidates * first element of previous query - _a1 = X[i_ft, : n_candidates - 1] * T[i_ft, i_query - 1] - # last element of all 1 to n candidates * last element of current query - _a2 = X[i_ft, L : L - 1 + n_candidates] * T[i_ft, i_query + L - 1] - - XT_products[i_ft, 1:] = XT_products[i_ft, :-1] - _a1 + _a2 - - # Compute first dot product - XT_products[i_ft, 0] = np.sum(Q[i_ft] * X[i_ft, :L]) - return XT_products diff --git a/aeon/similarity_search/matrix_profiles/tests/test_stomp.py b/aeon/similarity_search/matrix_profiles/tests/test_stomp.py deleted file mode 100644 index ffcf7d0b6a..0000000000 --- a/aeon/similarity_search/matrix_profiles/tests/test_stomp.py +++ /dev/null @@ -1,205 +0,0 @@ -"""Tests for stomp algorithm.""" - -__maintainer__ = ["baraline"] - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_almost_equal, assert_array_almost_equal, assert_equal - -from aeon.distances import get_distance_function -from aeon.similarity_search._commons import get_ith_products -from aeon.similarity_search.matrix_profiles.stomp import ( - _update_dot_products_one_series, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -DATATYPES = ["int64", "float64"] -K_VALUES = [1] - - -def test__update_dot_products_one_series(): - """Test the _update_dot_product function.""" - X = np.random.rand(1, 50) - T = np.random.rand(1, 25) - L = 10 - current_product = get_ith_products(X, T, L, 0) - for i_query in range(1, T.shape[1] - L + 1): - new_product = get_ith_products( - X, - T, - L, - i_query, - ) - current_product = _update_dot_products_one_series( - X, - T, - current_product, - L, - i_query, - ) - assert_array_almost_equal(new_product, current_product) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_squared_matrix_profile(dtype, k): - """Test stomp series search.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile(X, S, L, mask, k=k) - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = np.array( - [ - [distance(q, X[j, :, _i : _i + L]) for _i in range(X.shape[-1] - L + 1)] - for j in range(X.shape[0]) - ] - ) - id_bests = np.vstack( - np.unravel_index( - np.argsort(expected.ravel(), kind="stable"), expected.shape - ) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - assert_equal(ip[i][j], id_bests[j]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_normalised_squared_matrix_profile(dtype, k): - """Test stomp series search.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - X_means = [] - X_stds = [] - - for i in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i], L, 1) - - X_stds.append(_std) - X_means.append(_mean) - X_means = np.asarray(X_means) - X_stds = np.asarray(X_stds) - - S_means, S_stds = sliding_mean_std_one_series(S, L, 1) - - mp, ip = stomp_normalised_squared_matrix_profile( - X, S, L, X_means, X_stds, S_means, S_stds, mask, k=k - ) - - for i in range(S.shape[-1] - L + 1): - q = (S[:, i : i + L] - S_means[:, i]) / S_stds[:, i] - - expected = np.array( - [ - [ - distance( - q, - (X[j, :, _i : _i + L] - X_means[j, :, _i]) / X_stds[j, :, _i], - ) - for _i in range(X.shape[-1] - L + 1) - ] - for j in range(X.shape[0]) - ] - ) - id_bests = np.vstack( - np.unravel_index(np.argsort(expected.ravel()), expected.shape) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_stomp_squared_matrix_profile_unequal_length(dtype): - """Test stomp with unequal length.""" - X = List( - [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6]], dtype=dtype), - ] - ) - L = 3 - mask = List( - [ - np.ones(X[0].shape[1] - L + 1, dtype=bool), - np.ones(X[1].shape[1] - L + 1, dtype=bool), - ] - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile(X, S, L, mask) - - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = [ - [ - distance(q, X[j][:, _i : _i + q.shape[-1]]) - for _i in range(X[j].shape[-1] - q.shape[-1] + 1) - ] - for j in range(len(X)) - ] - assert_almost_equal(mp[i][0], np.concatenate(expected).min()) - - -@pytest.mark.parametrize("dtype", DATATYPES) -@pytest.mark.parametrize("k", K_VALUES) -def test_stomp_squared_matrix_profile_inverse(dtype, k): - """Test stomp series search for inverse distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5, 3, 2, 4, 5]], dtype=dtype) - L = 3 - mask = np.ones((X.shape[0], X.shape[2] - L + 1), dtype=bool) - distance = get_distance_function("squared") - mp, ip = stomp_squared_matrix_profile( - X, - S, - L, - mask, - k=k, - inverse_distance=True, - ) - - for i in range(S.shape[-1] - L + 1): - q = S[:, i : i + L] - - expected = np.array( - [ - [ - distance(q, X[j, :, _i : _i + q.shape[-1]]) - for _i in range(X.shape[-1] - q.shape[-1] + 1) - ] - for j in range(X.shape[0]) - ] - ) - expected += 1e-8 - expected = 1 / expected - id_bests = np.vstack( - np.unravel_index(np.argsort(expected.ravel()), expected.shape) - ).T - - for j in range(k): - assert_almost_equal(mp[i][j], expected[id_bests[j, 0], id_bests[j, 1]]) - assert_equal(ip[i][j], id_bests[j]) diff --git a/aeon/similarity_search/query_search.py b/aeon/similarity_search/query_search.py deleted file mode 100644 index 393439148d..0000000000 --- a/aeon/similarity_search/query_search.py +++ /dev/null @@ -1,428 +0,0 @@ -"""Base class for query search.""" - -__maintainer__ = ["baraline"] - -from typing import Optional, final - -import numpy as np -from numba import get_num_threads, set_num_threads - -from aeon.similarity_search._commons import ( - extract_top_k_and_threshold_from_distance_profiles, -) -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.distance_profiles.euclidean_distance_profile import ( - euclidean_distance_profile, - normalised_euclidean_distance_profile, -) -from aeon.similarity_search.distance_profiles.squared_distance_profile import ( - normalised_squared_distance_profile, - squared_distance_profile, -) - - -class QuerySearch(BaseSimilaritySearch): - """ - Query search estimator. - - The query search estimator will return a set of matches of a query in a search space - , which is defined by a time series dataset given during fit. Depending on the `k` - and/or `threshold` parameters, which condition what is considered a valid match - during the search, the number of matches will vary. If `k` is used, at most `k` - matches (the `k` best) will be returned, if `threshold` is used and `k` is set to - `np.inf`, all the candidates which distance to the query is inferior or equal to - `threshold` will be returned. If both are used, the `k` best matches to the query - with distance inferior to `threshold` will be returned. - - - Parameters - ---------- - k : int, default=1 - The number of best matches to return during predict for a given query. - threshold : float, default=np.inf - The number of best matches to return during predict for a given query. - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - n_jobs : int, default=1 - Number of parallel jobs to use. - store_distance_profiles : bool, default=False. - Whether to store the computed distance profiles in the attribute - "distance_profiles_" after calling the predict method. It will store the raw - distance profile, meaning without potential inversion or thresholding applied. - - Attributes - ---------- - X_ : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. This is the - database we search in when given a query. - distance_profile_function : function - The function used to compute the distance profile. This is determined - during the fit method based on the distance and normalise - parameters. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - def __init__( - self, - k: int = 1, - threshold: float = np.inf, - distance: str = "euclidean", - distance_args: Optional[dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - store_distance_profiles: bool = False, - ): - self.k = k - self.threshold = threshold - self.store_distance_profiles = store_distance_profiles - self._previous_query_length = -1 - self.axis = 1 - - super().__init__( - distance=distance, - distance_args=distance_args, - inverse_distance=inverse_distance, - normalise=normalise, - speed_up=speed_up, - n_jobs=n_jobs, - ) - - def _fit(self, X: np.ndarray, y=None): - """ - Check input format and store it to be used as search space during predict. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input array to used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - - """ - self.X_ = X - self.distance_profile_function_ = self._get_distance_profile_function() - return self - - @final - def predict( - self, - X: np.ndarray, - axis=1, - X_index=None, - exclusion_factor=2.0, - apply_exclusion_to_result=False, - ) -> np.ndarray: - """ - Predict method : Check the shape of X and call _predict to perform the search. - - If the distance profile function is normalised, it stores the mean and stds - from X and X_, with X_ the training data. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, query_length) - Input query used for similarity search. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - X_index : Iterable - An Interable (tuple, list, array) of length two used to specify the index of - the query X if it was extracted from the input data X given during the fit - method. Given the tuple (id_sample, id_timestamp), the similarity search - will define an exclusion zone around the X_index in order to avoid matching - X with itself. If None, it is considered that the query is not extracted - from X_. - exclusion_factor : float, default=2. - The factor to apply to the query length to define the exclusion zone. The - exclusion zone is define from - :math:`id_timestamp - query_length//exclusion_factor` to - :math:`id_timestamp + query_length//exclusion_factor`. This also applies to - the matching conditions defined by child classes. For example, with - TopKSimilaritySearch, the k best matches are also subject to the exclusion - zone, but with :math:`id_timestamp` the index of one of the k matches. - apply_exclusion_to_result : bool, default=False - Wheter to apply the exclusion factor to the output of the similarity search. - This means that two matches of the query from the same sample must be at - least spaced by +/- :math:`query_length//exclusion_factor`. - This can avoid pathological matching where, for example if we extract the - best two matches, there is a high chance that if the best match is located - at :math:`id_timestamp`, the second best match will be located at - :math:`id_timestamp` +/- 1, as they both share all their values except one. - - Raises - ------ - TypeError - If the input X array is not 2D raise an error. - ValueError - If the length of the query is greater - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - self._check_is_fitted() - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) - - query_dim, query_length = self._check_query_format(X, axis) - - mask = self._init_X_index_mask( - X_index, - query_length, - exclusion_factor=exclusion_factor, - ) - - if self.normalise: - self.query_means_ = np.mean(X, axis=-1) - self.query_stds_ = np.std(X, axis=-1) - if self._previous_query_length != query_length: - self._store_mean_std_from_inputs(query_length) - - if apply_exclusion_to_result: - exclusion_size = query_length // exclusion_factor - else: - exclusion_size = None - - self._previous_query_length = query_length - - X_preds = self._predict( - self._call_distance_profile(X, mask), - exclusion_size=exclusion_size, - ) - set_num_threads(prev_threads) - return X_preds - - def _predict( - self, distance_profiles: np.ndarray, exclusion_size: Optional[int] = None - ) -> np.ndarray: - """ - Private predict method for QuerySearch. - - It takes the distance profiles and apply the `k` and `threshold` conditions to - return the set of best matches. - - Parameters - ---------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) # noqa: E501 - Precomputed distance profile. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(n_matches)``, contains the distance between - the query and its best matches in X_. The second array, of shape - ``(n_matches, 2)``, contains the indexes of these matches as - ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - - """ - if self.store_distance_profiles: - self.distance_profiles_ = distance_profiles - # Define id sample and timestamp to not "loose" them due to concatenation - return extract_top_k_and_threshold_from_distance_profiles( - distance_profiles, - k=self.k, - threshold=self.threshold, - exclusion_size=exclusion_size, - inverse_distance=self.inverse_distance, - ) - - def _check_query_format(self, X, axis): - if axis not in [0, 1]: - raise ValueError("The axis argument is expected to be either 1 or 0") - if self.axis != axis: - X = X.T - if not isinstance(X, np.ndarray) or X.ndim != 2: - raise TypeError( - "Error, only supports 2D numpy for now. If the query X is univariate " - "do X = X[np.newaxis, :]." - ) - - query_dim, query_length = X.shape - if query_length >= self.min_timepoints_: - raise ValueError( - "The length of the query should be inferior or equal to the length of " - "data (X_) provided during fit, but got {} for X and {} for X_".format( - query_length, self.min_timepoints_ - ) - ) - - if query_dim != self.n_channels_: - raise ValueError( - "The number of feature should be the same for the query X and the data " - "(X_) provided during fit, but got {} for X and {} for X_".format( - query_dim, self.n_channels_ - ) - ) - return query_dim, query_length - - def _get_distance_profile_function(self): - """ - Given distance and speed_up parameters, return the distance profile function. - - Raises - ------ - ValueError - If the distance parameter given at initialization is not a string nor a - numba function or a callable, or if the speedup parameter is unknow or - unsupported, raisea ValueError. - - Returns - ------- - function - The distance profile function matching the distance argument. - - """ - if isinstance(self.distance, str): - distance_dict = _QUERY_SEARCH_SPEED_UP_DICT.get(self.distance) - if distance_dict is None: - raise NotImplementedError( - f"No distance profile have been implemented for {self.distance}." - ) - else: - speed_up_profile = distance_dict.get(self.normalise).get(self.speed_up) - - if speed_up_profile is None: - raise ValueError( - f"Unknown or unsupported speed up {self.speed_up} for " - f"{self.distance} distance function with" - ) - self.speed_up_ = self.speed_up - return speed_up_profile - else: - raise ValueError( - f"Expected distance argument to be str but got {type(self.distance)}" - ) - - def _call_distance_profile(self, X: np.ndarray, mask: np.ndarray) -> np.ndarray: - """ - Obtain the distance profile function and call it with the query and the mask. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, query_length) - Input query used for similarity search. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean array which indicates the candidates that should be evaluated in - the similarity search. - - Returns - ------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_timepoints - query_length + 1) # noqa: E501 - The distance profiles between the input time series and the query. - - """ - if self.normalise: - distance_profiles = self.distance_profile_function_( - self.X_, - X, - mask, - self.X_means_, - self.X_stds_, - self.query_means_, - self.query_stds_, - ) - else: - distance_profiles = self.distance_profile_function_(self.X_, X, mask) - - return distance_profiles - - @classmethod - def get_speedup_function_names(self) -> dict: - """ - Get available speedup for query search in aeon. - - The returned structure is a dictionnary that contains the names of all - avaialble speedups for normalised and non-normalised distance functions. - - Returns - ------- - dict - The available speedups name that can be used as parameters in - similarity search classes. - - """ - speedups = {} - for dist_name in _QUERY_SEARCH_SPEED_UP_DICT.keys(): - for normalise in _QUERY_SEARCH_SPEED_UP_DICT[dist_name].keys(): - speedups_names = list( - _QUERY_SEARCH_SPEED_UP_DICT[dist_name][normalise].keys() - ) - if normalise: - speedups.update({f"normalised {dist_name}": speedups_names}) - else: - speedups.update({f"{dist_name}": speedups_names}) - return speedups - - -_QUERY_SEARCH_SPEED_UP_DICT = { - "euclidean": { - True: { - "fastest": normalised_euclidean_distance_profile, - "Mueen": normalised_euclidean_distance_profile, - }, - False: { - "fastest": euclidean_distance_profile, - "Mueen": euclidean_distance_profile, - }, - }, - "squared": { - True: { - "fastest": normalised_squared_distance_profile, - "Mueen": normalised_squared_distance_profile, - }, - False: { - "fastest": squared_distance_profile, - "Mueen": squared_distance_profile, - }, - }, -} diff --git a/aeon/similarity_search/series_search.py b/aeon/similarity_search/series_search.py deleted file mode 100644 index 3c36cf9c4a..0000000000 --- a/aeon/similarity_search/series_search.py +++ /dev/null @@ -1,436 +0,0 @@ -"""Base class for series search.""" - -__maintainer__ = ["baraline"] - -from typing import Union, final - -import numpy as np -from numba import get_num_threads, set_num_threads - -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.matrix_profiles.stomp import ( - stomp_euclidean_matrix_profile, - stomp_normalised_euclidean_matrix_profile, - stomp_normalised_squared_matrix_profile, - stomp_squared_matrix_profile, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - - -class SeriesSearch(BaseSimilaritySearch): - """ - Series search estimator. - - The series search estimator will return a set of matches for each subsequence of - size L in a time series given during predict. The matching of each subsequence will - be made against all subsequence of size L inside the time series given during fit, - which will represent the search space. - - Depending on the `k` and/or `threshold` parameters, which condition what is - considered a valid match during the search, the number of matches will vary. If `k` - is used, at most `k` matches (the `k` best) will be returned, if `threshold` is used - and `k` is set to `np.inf`, all the candidates which distance to the query is - inferior or equal to `threshold` will be returned. If both are used, the `k` best - matches to the query with distance inferior to `threshold` will be returned. - - - Parameters - ---------- - k : int, default=1 - The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf - The number of best matches to return during predict for each subsequence. - distance : str, default="euclidean" - Name of the distance function to use. A list of valid strings can be found in - the documentation for :func:`aeon.distances.get_distance_function`. - If a callable is passed it must either be a python function or numba function - with nopython=True, that takes two 1d numpy arrays as input and returns a float. - distance_args : dict, default=None - Optional keyword arguments for the distance function. - normalise : bool, default=False - Whether the distance function should be z-normalised. - speed_up : str, default='fastest' - Which speed up technique to use with for the selected distance - function. By default, the fastest algorithm is used. A list of available - algorithm for each distance can be obtained by calling the - `get_speedup_function_names` function. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - n_jobs : int, default=1 - Number of parallel jobs to use. - - Attributes - ---------- - X_ : array, shape (n_cases, n_channels, n_timepoints) - The input time series stored during the fit method. This is the - database we search in when given a query. - distance_profile_function : function - The function used to compute the distance profile. This is determined - during the fit method based on the distance and normalise - parameters. - - Notes - ----- - For now, the multivariate case is only treated as independent. - Distances are computed for each channel independently and then - summed together. - """ - - def __init__( - self, - k: int = 1, - threshold: float = np.inf, - distance: str = "euclidean", - distance_args: Union[None, dict] = None, - inverse_distance: bool = False, - normalise: bool = False, - speed_up: str = "fastest", - n_jobs: int = 1, - ): - self.k = k - self.threshold = threshold - self._previous_query_length = -1 - self.axis = 1 - - super().__init__( - distance=distance, - distance_args=distance_args, - inverse_distance=inverse_distance, - normalise=normalise, - speed_up=speed_up, - n_jobs=n_jobs, - ) - - def _fit(self, X, y=None): - """ - Check input format and store it to be used as search space during predict. - - Parameters - ---------- - X : array, shape (n_cases, n_channels, n_timepoints) - Input array to used as database for the similarity search - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. - - Returns - ------- - self - - """ - self.X_ = X - self.matrix_profile_function_ = self._get_series_method_function() - return self - - @final - def predict( - self, - X: np.ndarray, - length: int, - axis: int = 1, - X_index=None, - exclusion_factor=2.0, - apply_exclusion_to_result=False, - ): - """ - Predict method : Check the shape of X and call _predict to perform the search. - - If the distance profile function is normalised, it stores the mean and stds - from X and X_, with X_ the training data. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, series_length) - Input time series used for the search. - length : int - The length parameter that will be used to extract queries from X. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - X_index : int - An integer indicating if X was extracted is part of the dataset that was - given during the fit method. If so, this integer should be the sample id. - The search will define an exclusion zone for the queries extarcted from X - in order to avoid matching with themself. If None, it is considered that - the query is not extracted from X_. - exclusion_factor : float, default=2. - The factor to apply to the query length to define the exclusion zone. The - exclusion zone is define from - ``id_timestamp - query_length//exclusion_factor`` to - ``id_timestamp + query_length//exclusion_factor``. This also applies to - the matching conditions defined by child classes. For example, with - TopKSimilaritySearch, the k best matches are also subject to the exclusion - zone, but with :math:`id_timestamp` the index of one of the k matches. - apply_exclusion_to_result : bool, default=False - Wheter to apply the exclusion factor to the output of the similarity search. - This means that two matches of the query from the same sample must be at - least spaced by +/- ``query_length//exclusion_factor``. - This can avoid pathological matching where, for example if we extract the - best two matches, there is a high chance that if the best match is located - at ``id_timestamp``, the second best match will be located at - ``id_timestamp`` +/- 1, as they both share all their values except one. - - Raises - ------ - TypeError - If the input X array is not 2D raise an error. - ValueError - If the length of the query is greater - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - self._check_is_fitted() - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) - series_dim, series_length = self._check_series_format(X, length, axis) - - mask = self._init_X_index_mask( - None if X_index is None else [X_index, 0], - length, - exclusion_factor=exclusion_factor, - ) - - if self.normalise: - _mean, _std = sliding_mean_std_one_series(X, length, 1) - self.T_means_ = _mean - self.T_stds_ = _std - if self._previous_query_length != length: - self._store_mean_std_from_inputs(length) - - if apply_exclusion_to_result: - exclusion_size = length // exclusion_factor - else: - exclusion_size = None - - self._previous_query_length = length - - X_preds = self._predict( - X, - length, - mask, - exclusion_size, - X_index, - exclusion_factor, - apply_exclusion_to_result, - ) - set_num_threads(prev_threads) - return X_preds - - def _predict( - self, - X, - length, - mask, - exclusion_size, - X_index, - exclusion_factor, - apply_exclusion_to_result, - ): - """ - Private predict method for SeriesSearch. - - This method calculates the matrix profile for a given time series dataset by - comparing all possible subsequences of a specified length against a reference - time series. It handles exclusion zones to prevent nearby matches from being - selected and supports normalization. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, series_length) - Input time series used for the search. - length : int - The length parameter that will be used to extract queries from X. - axis : int - The time point axis of the input series if it is 2D. If ``axis==0``, it is - assumed each column is a time series and each row is a time point. i.e. the - shape of the data is ``(n_timepoints,n_channels)``. ``axis==1`` indicates - the time series are in rows, i.e. the shape of the data is - ``(n_channels,n_timepoints)``. - mask : np.ndarray, 2D array of shape (n_cases, n_timepoints - length + 1) - Boolean mask of the shape of the distance profiles indicating for which part - of it the distance should be computed. In this context, it is the mask for - the first query of size L in T. This mask will be updated during the - algorithm. - exclusion_size : int, optional - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - - Returns - ------- - Tuple(ndarray, ndarray) - The first array, of shape ``(series_length - length + 1, n_matches)``, - contains the distance between all the queries of size length and their best - matches in X_. The second array, of shape - ``(series_length - L + 1, n_matches, 2)``, contains the indexes of these - matches as ``(id_sample, id_timepoint)``. The corresponding match can be - retrieved as ``X_[id_sample, :, id_timepoint : id_timepoint + length]``. - - """ - if self.normalise: - return self.matrix_profile_function_( - self.X_, - X, - length, - self.X_means_, - self.X_stds_, - self.T_means_, - self.T_stds_, - mask, - k=self.k, - threshold=self.threshold, - inverse_distance=self.inverse_distance, - exclusion_size=exclusion_size, - ) - else: - return self.matrix_profile_function_( - self.X_, - X, - length, - mask, - k=self.k, - threshold=self.threshold, - inverse_distance=self.inverse_distance, - exclusion_size=exclusion_size, - ) - - def _check_series_format(self, X, length, axis): - if axis not in [0, 1]: - raise ValueError("The axis argument is expected to be either 1 or 0") - if self.axis != axis: - X = X.T - if not isinstance(X, np.ndarray) or X.ndim != 2: - raise TypeError( - "Error, only supports 2D numpy for now. If the series X is univariate " - "do X = X[np.newaxis, :]." - ) - - series_dim, series_length = X.shape - if series_length < length: - raise ValueError( - "The length of the series should be superior or equal to the length " - "parameter given during predict, but got {} < {}".format( - series_length, length - ) - ) - - if series_dim != self.n_channels_: - raise ValueError( - "The number of feature should be the same for the series X and the data" - " (X_) provided during fit, but got {} for X and {} for X_".format( - series_dim, self.n_channels_ - ) - ) - return series_dim, series_length - - def _get_series_method_function(self): - """ - Given distance and speed_up parameters, return the series method function. - - Raises - ------ - ValueError - If the distance parameter given at initialization is not a string nor a - numba function or a callable, or if the speedup parameter is unknow or - unsupported, raisea ValueError. - - Returns - ------- - function - The series method function matching the distance argument. - - """ - if isinstance(self.distance, str): - distance_dict = _SERIES_SEARCH_SPEED_UP_DICT.get(self.distance) - if distance_dict is None: - raise NotImplementedError( - f"No distance profile have been implemented for {self.distance}." - ) - else: - speed_up_series_method = distance_dict.get(self.normalise).get( - self.speed_up - ) - - if speed_up_series_method is None: - raise ValueError( - f"Unknown or unsupported speed up {self.speed_up} for " - f"{self.distance} distance function with" - ) - self.speed_up_ = self.speed_up - return speed_up_series_method - else: - raise ValueError( - f"Expected distance argument to be str but got {type(self.distance)}" - ) - - @classmethod - def get_speedup_function_names(self): - """ - Get available speedup for series search in aeon. - - The returned structure is a dictionnary that contains the names of all - avaialble speedups for normalised and non-normalised distance functions. - - Returns - ------- - dict - The available speedups name that can be used as parameters in - similarity search classes. - - """ - speedups = {} - for dist_name in _SERIES_SEARCH_SPEED_UP_DICT.keys(): - for normalise in _SERIES_SEARCH_SPEED_UP_DICT[dist_name].keys(): - speedups_names = list( - _SERIES_SEARCH_SPEED_UP_DICT[dist_name][normalise].keys() - ) - if normalise: - speedups.update({f"normalised {dist_name}": speedups_names}) - else: - speedups.update({f"{dist_name}": speedups_names}) - return speedups - - -_SERIES_SEARCH_SPEED_UP_DICT = { - "euclidean": { - True: { - "fastest": stomp_normalised_euclidean_matrix_profile, - "STOMP": stomp_normalised_euclidean_matrix_profile, - }, - False: { - "fastest": stomp_euclidean_matrix_profile, - "STOMP": stomp_euclidean_matrix_profile, - }, - }, - "squared": { - True: { - "fastest": stomp_normalised_squared_matrix_profile, - "STOMP": stomp_normalised_squared_matrix_profile, - }, - False: { - "fastest": stomp_squared_matrix_profile, - "STOMP": stomp_squared_matrix_profile, - }, - }, -} diff --git a/aeon/similarity_search/series_search/__init__.py b/aeon/similarity_search/series_search/__init__.py new file mode 100644 index 0000000000..f576c41f03 --- /dev/null +++ b/aeon/similarity_search/series_search/__init__.py @@ -0,0 +1,7 @@ +"""Similarity search module.""" + +__all__ = ["BaseSimilaritySearch", "QuerySearch", "SeriesSearch"] + +from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search.query_search import QuerySearch +from aeon.similarity_search.series_search import SeriesSearch diff --git a/aeon/similarity_search/series_search/base.py b/aeon/similarity_search/series_search/base.py new file mode 100644 index 0000000000..db83519c04 --- /dev/null +++ b/aeon/similarity_search/series_search/base.py @@ -0,0 +1,22 @@ +"""Base class for whole series search.""" + +__maintainer__ = ["baraline"] + +from aeon.similarity_search.base import BaseSimilaritySearch + + +class BaseSeriesSearch(BaseSimilaritySearch): + """.""" + + ... + + +class BaseIndexSearch(BaseSimilaritySearch): + """.""" + + ... + + def batch_fit(sourcefiles, batch_size): + """.""" + # fit + # and then update diff --git a/aeon/similarity_search/subsequence_search/__init__.py b/aeon/similarity_search/subsequence_search/__init__.py new file mode 100644 index 0000000000..dfca8ee18c --- /dev/null +++ b/aeon/similarity_search/subsequence_search/__init__.py @@ -0,0 +1,5 @@ +"""Similarity search module.""" + +__all__ = ["StompMatrixProfile"] + +from aeon.similarity_search.subsequence_search._stomp import StompMatrixProfile diff --git a/aeon/similarity_search/subsequence_search/_brute_force.py b/aeon/similarity_search/subsequence_search/_brute_force.py new file mode 100644 index 0000000000..7f95083f3b --- /dev/null +++ b/aeon/similarity_search/subsequence_search/_brute_force.py @@ -0,0 +1,284 @@ +"""Implementation of matrix profile with brute force.""" + +from typing import Optional + +__maintainer__ = ["baraline"] + + +import numpy as np +from numba import njit, prange +from numba.typed import List + +from aeon.similarity_search.subsequence_search._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile_list, +) +from aeon.similarity_search.subsequence_search.base import BaseMatrixProfile +from aeon.utils.numba.general import ( + get_all_subsequences, + z_normalise_series_3d, + z_normalize_series_2d, +) + +# TODO : check function params and make docstrings +# TODO : make tests + + +class BruteForceMatrixProfile(BaseMatrixProfile): + """.""" + + def compute_matrix_profile( + self, + k, + threshold, + exclusion_size, + inverse_distance, + allow_overlap, + X: Optional[np.ndarray] = None, + X_index: Optional[int] = None, + ): + """ + . + + Parameters + ---------- + k : TYPE + DESCRIPTION. + threshold : TYPE + DESCRIPTION. + exclusion_size : TYPE + DESCRIPTION. + inverse_distance : TYPE + DESCRIPTION. + X : Optional[np.ndarray], optional + DESCRIPTION. The default is None. + X_index : Optional[int], optional + DESCRIPTION. The default is None. + : TYPE + DESCRIPTION. + + Returns + ------- + MP : TYPE + DESCRIPTION. + IP : TYPE + DESCRIPTION. + + """ + # pairwise if none + if X is None: + MP = [] + IP = [] + for i in range(len(self.X_)): + _MP, _IP = self.compute_matrix_profile( + k, + threshold, + exclusion_size, + inverse_distance, + X=self.X_[i], + X_index=i, + ) + MP.append(_MP) + IP.append(_IP) + else: + MP, IP = _naive_squared_matrix_profile( + self.X_, + X, + self.length, + X_index, + k, + allow_overlap, + threshold, + exclusion_size, + inverse_distance, + normalize=self.normalize, + ) + + return MP, IP + + def compute_distance_profile(self, X: np.ndarray): + """ + Compute the distance profile of X to all samples in X_. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The query to use to compute the distance profiles. + + Returns + ------- + distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) + The distance profile of X to all samples in X_. The ``n_candidates`` value + is equal to ``n_timepoins - length + 1``. If X_ is an unequal length + collection, returns a numba typed list instead of an ndarray. + + """ + distance_profiles = _naive_squared_distance_profile( + self.X_, X, normalize=self.normalize + ) + + if not self.metadata_["unequal_length"]: + distance_profiles = np.asarray(distance_profiles) + return distance_profiles + + +@njit(cache=True, fastmath=True) +def _compute_dist_profile(X_subs, q): + """ + Compute the distance profile between subsequences and a query. + + Parameters + ---------- + X_subs : array, shape=(n_samples, n_channels, query_length) + Input subsequences extracted from a time series. + q : array, shape=(n_channels, query_length) + Query used for the distance computation + + Returns + ------- + dist_profile : np.ndarray, 1D array of shape (n_samples) + The distance between the query all subsequences. + + """ + n_candidates, n_channels, q_length = X_subs.shape + dist_profile = np.zeros(n_candidates) + for i in range(n_candidates): + for j in range(n_channels): + for k in range(q_length): + dist_profile[i] += (X_subs[i, j, k] - q[j, k]) ** 2 + return dist_profile + + +@njit(cache=True, fastmath=True, parallel=True) +def _naive_squared_distance_profile( + X, + Q, + normalize=False, +): + """ + Compute a squared euclidean distance profile. + + Parameters + ---------- + X : array, shape=(n_samples, n_channels, n_timepoints) + Input time series dataset to search in. + Q : array, shape=(n_channels, query_length) + Query used during the search. + normalize : bool + Wheter to use a z-normalized distance. + + Returns + ------- + out : np.ndarray, 1D array of shape (n_samples, n_timepoints_t - query_length + 1) + The distance between the query and all candidates in X. + + """ + query_length = Q.shape[1] + dist_profiles = List() + # Init distance profile array with unequal length support + for i in range(len(X)): + dist_profiles.append(np.zeros(X[i].shape[1] - query_length + 1)) + if normalize: + Q = z_normalize_series_2d(Q) + else: + Q = Q.astype(np.float64) + + for i in prange(len(X)): + X_subs = get_all_subsequences(X[i], query_length, 1) + if normalize: + X_subs = z_normalise_series_3d(X_subs) + + dist_profile = _compute_dist_profile(X_subs, Q) + dist_profiles[i] = dist_profile + return dist_profiles + + +@njit(cache=True, fastmath=True, parallel=True) +def _naive_squared_matrix_profile( + X, + T, + L, + k, + T_index, + threshold, + inverse_distance, + allow_overlap, + exclusion_size, + normalize=False, +): + """ + Compute a squared euclidean matrix profile. + + Parameters + ---------- + X : array, shape=(n_samples, n_channels, n_timepoints_x) + Input time series dataset to search in. + T : array, shape=(n_channels, n_timepoints_t) + Time series from which queries are extracted. + query_length : int + Length of the queries to extract from T. + T_index : int, + If ``X`` is a subsequence of the database given in fit, specify its starting + index as (i_case, i_timestamp). If specified, this subsequence and the + neighboring ones (according to ``exclusion_factor``) won't be considered as + admissible candidates. + normalize : bool + Wheter to use a z-normalized distance. + + Returns + ------- + out : np.ndarray, 1D array of shape (n_timepoints_t - query_length + 1) + The minimum distance between each query in T and all candidates in X. + """ + n_queries = T.shape[1] - L + 1 + MP = List() + IP = List() + + # Init List to allow parallel, we'll re-use it for all dist profiles + dist_profiles = List() + for i_x in range(len(X)): + dist_profiles.append(np.zeros(X[i_x].shape[1] - L + 1)) + + X_subs = List() + for i in range(len(X)): + i_subs = get_all_subsequences(X[i], L, 1) + if normalize: + i_subs = z_normalise_series_3d(X_subs) + X_subs.append(i_subs) + + for i_q in range(n_queries): + Q = T[:, i : i + L] + if normalize: + Q = z_normalize_series_2d(Q) + for i_x in prange(len(X)): + dist_profiles[i_x][0 : X[i_x].shape[1] - L + 1] = _compute_dist_profile( + X_subs[i_x], Q + ) + + if T_index is not None: + _max_timestamp = X[T_index].shape[1] - L + ub = min(i_q + exclusion_size, _max_timestamp) + lb = max(0, i_q - exclusion_size) + dist_profiles[T_index][lb:ub] = np.inf + + if inverse_distance: + dist_profiles = _inverse_distance_profile_list(dist_profiles) + + # Deal with self-matches + if T_index is not None: + _max_timestamp = X[T_index].shape[1] - L + ub = min(i_q + exclusion_size, _max_timestamp) + lb = max(0, i_q - exclusion_size) + dist_profiles[T_index][lb:ub] = np.inf + + top_dists, top_indexes = _extract_top_k_from_dist_profile( + dist_profiles, + k, + threshold, + allow_overlap, + exclusion_size, + ) + + MP.append(top_dists) + IP.append(top_indexes) + return MP, IP diff --git a/aeon/similarity_search/subsequence_search/_commons.py b/aeon/similarity_search/subsequence_search/_commons.py new file mode 100644 index 0000000000..e2e0aa54df --- /dev/null +++ b/aeon/similarity_search/subsequence_search/_commons.py @@ -0,0 +1,138 @@ +"""Helper and common function for similarity search estimators and functions.""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numba import njit, prange +from scipy.signal import convolve + + +def fft_sliding_dot_product(X, q): + """ + Use FFT convolution to calculate the sliding window dot product. + + This function applies the Fast Fourier Transform (FFT) to efficiently compute + the sliding dot product between the input time series `X` and the query `q`. + The dot product is computed for each channel individually. The sliding window + approach ensures that the dot product is calculated for every possible subsequence + of `X` that matches the length of `q` + + Parameters + ---------- + X : array, shape=(n_channels, n_timepoints) + Input time series + q : array, shape=(n_channels, query_length) + Input query + + Returns + ------- + out : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + Sliding dot product between q and X. + """ + n_channels, n_timepoints = X.shape + query_length = q.shape[1] + out = np.zeros((n_channels, n_timepoints - query_length + 1)) + for i in range(n_channels): + out[i, :] = convolve(np.flipud(q[i, :]), X[i, :], mode="valid").real + return out + + +def get_ith_products(X, T, L, ith): + """ + Compute dot products between X and the i-th subsequence of size L in T. + + Parameters + ---------- + X : array, shape = (n_channels, n_timepoints_X) + Input data. + T : array, shape = (n_channels, n_timepoints_T) + Data containing the query. + L : int + Overall query length. + ith : int + Query starting index in T. + + Returns + ------- + np.ndarray, 2D array of shape (n_channels, n_timepoints_X - L + 1) + Sliding dot product between the i-th subsequence of size L in T and X. + + """ + return fft_sliding_dot_product(X, T[:, ith : ith + L]) + + +@njit(cache=True, fastmath=True, parallel=True) +def _inverse_distance_profile_list(dist_profiles): + for i in prange(len(dist_profiles)): + dist_profiles[i] = 1 / (dist_profiles[i] + 1e-8) + return dist_profiles + + +@njit(cache=True) +def _extract_top_k_from_dist_profile( + dist_profiles, + k, + threshold, + allow_overlap, + exclusion_size, +): + top_k_indexes = np.zeros((2 * k, 2), dtype=np.int64) - 1 + top_k_distances = np.full(2 * k, np.inf) + for i_profile in range(len(dist_profiles)): + # Extract top-k without neighboring matches + if not allow_overlap: + _sorted_indexes = np.argsort(dist_profiles[i_profile]) + _top_k_indexes = np.zeros(k, dtype=np.int64) - 1 + _current_k = 1 + _top_k_indexes[0] = _sorted_indexes[0] + _current_j = 1 + # Until we extract k value or explore all the array + while _current_k < k and _current_j < len(_sorted_indexes): + _insert = True + # Check for validity with each previously inserted + for i_k in range(_current_k): + ub = min(_top_k_indexes[i_k] + exclusion_size, len(dist_profiles)) + lb = max(_top_k_indexes[i_k] - exclusion_size, 0) + if ( + _sorted_indexes[_current_j] < lb + or _sorted_indexes[_current_j] > ub + ): + _insert = False + break + + if _insert: + _top_k_indexes[_current_k] = _sorted_indexes[_current_j] + _current_k += 1 + _current_j += 1 + + _top_k_indexes = _top_k_indexes[:_current_k] + _top_k_distances = dist_profiles[i_profile][_top_k_indexes] + # Extract top-k with neighboring matches + else: + _top_k_indexes = np.argpartition(dist_profiles[i_profile], k)[:k] + _top_k_distances = dist_profiles[i_profile][_top_k_indexes] + + # Select overall top k by using the buffer array of size 2*k + # Inset top from current sample + top_k_distances[k : k + len(_top_k_distances)] = _top_k_distances + top_k_indexes[k : k + len(_top_k_distances), 1] = _top_k_indexes + top_k_indexes[k : k + len(_top_k_distances), 0] = i_profile + + # Sort overall + idx = np.argsort(top_k_distances) + # Keep top k overall + top_k_distances[:k] = top_k_distances[idx[:k]] + top_k_indexes[:k] = top_k_indexes[idx[:k]] + + top_k_distances[k:] = np.inf + + # get the actual number of extracted values and apply threshold + true_k = 0 + for i in range(k): + # if top_k is inf, it means that no value was extracted + if top_k_distances[i] != np.inf and top_k_distances[i] <= threshold: + true_k += 1 + else: + break + + return top_k_indexes[:true_k], top_k_distances[:true_k] diff --git a/aeon/similarity_search/subsequence_search/_stomp.py b/aeon/similarity_search/subsequence_search/_stomp.py new file mode 100644 index 0000000000..8986a41926 --- /dev/null +++ b/aeon/similarity_search/subsequence_search/_stomp.py @@ -0,0 +1,596 @@ +"""Implementation of STOMP with squared euclidean distance.""" + +from typing import Optional + +__maintainer__ = ["baraline"] + + +import numpy as np +from numba import njit, prange +from numba.typed import List + +from aeon.similarity_search.subsequence_search._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile_list, + fft_sliding_dot_product, + get_ith_products, +) +from aeon.similarity_search.subsequence_search.base import BaseMatrixProfile +from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD + +# TODO : check and order parameters of functions in base and here +# TODO : check function params and make docstrings to be consistent with brute force +# TODO : validate tests + + +class StompMatrixProfile(BaseMatrixProfile): + """.""" + + def compute_matrix_profile( + self, + k, + threshold, + exclusion_size, + inverse_distance, + allow_overlap, + X: Optional[np.ndarray] = None, + X_index: Optional[int] = None, + ): + """ + . + + Parameters + ---------- + k : TYPE + DESCRIPTION. + threshold : TYPE + DESCRIPTION. + exclusion_size : TYPE + DESCRIPTION. + inverse_distance : TYPE + DESCRIPTION. + X : Optional[np.ndarray], optional + DESCRIPTION. The default is None. + X_index : Optional[int], optional + If ``X`` is a series of the database given in fit, specify its index in + ``X_``. If specified, each query of this series won't be able to match with + its neighboring subsequences. + : TYPE + DESCRIPTION. + + Returns + ------- + MP : TYPE + DESCRIPTION. + IP : TYPE + DESCRIPTION. + + """ + # pairwise if none + if X is None: + MP = [] + IP = [] + for i in range(len(self.X_)): + _MP, _IP = self.compute_matrix_profile( + k, + threshold, + exclusion_size, + inverse_distance, + X=self.X_[i], + X_index=i, + ) + MP.append(_MP) + IP.append(_IP) + else: + XdotT = [ + get_ith_products(self.X[i], X, self.length, 0) + for i in range(len(self.X_)) + ] + if isinstance(X, np.ndarray): + XdotT = np.asarray(XdotT) + elif isinstance(X, List): + XdotT = List(XdotT) + if X_index is None: + X_means, X_stds = 0 + else: + X_means, X_stds = self.X_means_[i], self.X_stds_[i] + if self.normalize: + MP, IP = _stomp_normalized( + self.X_, + X, + XdotT, + self.X_means_, + self.X_stds_, + X_means, + X_stds, + self.length, + X_index, + k, + threshold, + allow_overlap, + exclusion_size, + inverse_distance, + ) + + else: + MP, IP = _stomp( + self.X_, + X, + XdotT, + self.length, + X_index, + k, + allow_overlap, + threshold, + exclusion_size, + inverse_distance, + ) + + return MP, IP + + def compute_distance_profile(self, X: np.ndarray): + """ + Compute the distance profile of X to all samples in X_. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The query to use to compute the distance profiles. + + Returns + ------- + distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) + The distance profile of X to all samples in X_. The ``n_candidates`` value + is equal to ``n_timepoins - length + 1``. If X_ is an unequal length + collection, returns a numba typed list instead of an ndarray. + + """ + QX = [fft_sliding_dot_product(self.X_[i], X) for i in range(len(self.X_))] + if self.metadata_["unequal_length"]: + QX = List(QX) + else: + QX = np.asarray(QX) + + if self.normalize: + distance_profiles = _normalized_squared_distance_profile( + QX, + self.X_means_, + self.X_stds_, + X.mean(axis=1), + X.std(axis=1), + self.length, + ) + else: + distance_profiles = _squared_distance_profile( + QX, + self.X_, + X, + ) + + if not self.metadata_["unequal_length"]: + distance_profiles = np.asarray(distance_profiles) + return distance_profiles + + +@njit(cache=True, parallel=True, fastmath=True) +def _stomp_normalized( + X, + T, + XdotT, + X_means, + X_stds, + T_means, + T_stds, + L, + T_index, + k, + threshold, + allow_overlap, + exclusion_size, + inverse_distance, +): + """ + Compute the Matrix Profile using the STOMP algorithm with normalized distances. + + X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + The input samples. If X is an unquel length collection, expect a TypedList + of 2D arrays of shape (n_channels, n_timepoints) + T : np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + L : int + Length of the subsequences used for the distance computation. + XdotT : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) + Precomputed dot products between each time series in X and the query series T. + X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) + Means of each subsequences of X of size L. Should be a numba TypedList if X is + unequal length. + X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) + Stds of each subsequences of X of size L. Should be a numba TypedList if X is + unequal length. + T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) + Means of each subsequences of T of size L. + T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) + Stds of each subsequences of T of size L. + T_index : int, + If ``T`` is a series of the database given in fit, specify its index + in ``X_``. If specified, each query of this series won't be able to + match with its neighboring subsequences. + k : int, default=1 + The number of best matches to return during predict for each subsequence. + threshold : float, default=np.inf + The number of best matches to return during predict for each subsequence. + inverse_distance : bool, default=False + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + exclusion_size : int, optional + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestomp - exclusion_size` and + :math:`id_timestomp + exclusion_size` which cannot be returned + as best match if :math:`id_timestomp` was already selected. By default, + the value None means that this is not used. + + Returns + ------- + tuple of np.ndarray + - MP : array of shape (series_length - L + 1,) + Matrix profile distances for each query subsequence. + - IP : array of shape (series_length - L + 1,) + Indexes of the top matches for each query subsequence. + """ + n_queries = T.shape[1] - L + 1 + MP = List() + IP = List() + + # Init List to allow parallel, we'll re-use it for all dist profiles + dist_profiles = List() + for i_x in range(len(X)): + dist_profiles.append(np.zeros(X[i_x].shape[1] - L + 1)) + + for i_q in range(n_queries): + for i_x in prange(len(X)): + dist_profiles[i_x][0 : X[i_x].shape[1] - L + 1] = ( + _normalized_squared_dist_profile_one_series( + XdotT[i_x], + X_means[i_x], + X_stds[i_x], + T_means[:, i_q], + T_stds[:, i_q], + L, + T_stds[:, i_q] <= AEON_NUMBA_STD_THRESHOLD, + ) + ) + if i_q + 1 < n_queries: + XdotT[i_x] = _update_dot_products_one_series( + X[i_x], T, XdotT[i_x], L, i_q + 1 + ) + + if inverse_distance: + dist_profiles = _inverse_distance_profile_list(dist_profiles) + + # Deal with self-matches + if T_index is not None: + _max_timestamp = X[T_index].shape[1] - L + ub = min(i_q + exclusion_size, _max_timestamp) + lb = max(0, i_q - exclusion_size) + dist_profiles[T_index][lb:ub] = np.inf + + top_indexes, top_dists = _extract_top_k_from_dist_profile( + dist_profiles, + k, + threshold, + allow_overlap, + exclusion_size, + ) + + MP.append(top_dists) + IP.append(top_indexes) + + return MP, IP + + +@njit(cache=True, parallel=True, fastmath=True) +def _stomp( + X, + T, + XdotT, + L, + T_index, + k, + allow_overlap, + threshold, + exclusion_size, + inverse_distance, +): + n_queries = T.shape[1] - L + 1 + MP = List() + IP = List() + + # Init List to allow parallel, we'll re-use it for all dist profiles + dist_profiles = List() + for i_x in range(len(X)): + dist_profiles.append(np.zeros(X[i_x].shape[1] - L + 1)) + # For each query of size L in T + for i_q in range(n_queries): + Q = T[:, i_q : i_q + L] + # For each series in X compute distance profile to the query + for i_x in prange(len(X)): + dist_profiles[i_x][0 : X[i_x].shape[1] - L + 1] = ( + _squared_dist_profile_one_series(XdotT[i_x], X[i_x], Q) + ) + if i_q + 1 < n_queries: + XdotT[i_x] = _update_dot_products_one_series( + X[i_x], T, XdotT[i_x], L, i_q + 1 + ) + + if inverse_distance: + dist_profiles = _inverse_distance_profile_list(dist_profiles) + + # Deal with self-matches + if T_index is not None: + _max_timestamp = X[T_index].shape[1] - L + ub = min(i_q + exclusion_size, _max_timestamp) + lb = max(0, i_q - exclusion_size) + dist_profiles[T_index][lb:ub] = np.inf + + top_indexes, top_dists = _extract_top_k_from_dist_profile( + dist_profiles, + k, + threshold, + allow_overlap, + exclusion_size, + ) + + MP.append(top_dists) + IP.append(top_indexes) + + return MP, IP + + +@njit(cache=True, fastmath=True) +def _update_dot_products_one_series( + X, + T, + XT_products, + L, + i_query, +): + """ + Update dot products of the i-th query of size L in T from the dot products of i-1. + + Parameters + ---------- + X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + Input time series on which the sliding dot product is computed. + T: np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + L : int + The length of the subsequences considered during the search. This parameter + cannot be larger than n_timepoints and series_length. + i_query : int + Query starting index in T. + + Returns + ------- + XT_products : np.ndarray of shape (n_cases, n_channels, n_timepoints - L + 1) + Sliding dot product between the i-th subsequence of size L in T and X. + + """ + n_channels = T.shape[0] + Q = T[:, i_query : i_query + L] + n_candidates = X.shape[1] - L + 1 + + for i_ft in range(n_channels): + # first element of all 0 to n-1 candidates * first element of previous query + _a1 = X[i_ft, : n_candidates - 1] * T[i_ft, i_query - 1] + # last element of all 1 to n candidates * last element of current query + _a2 = X[i_ft, L : L - 1 + n_candidates] * T[i_ft, i_query + L - 1] + + XT_products[i_ft, 1:] = XT_products[i_ft, :-1] - _a1 + _a2 + + # Compute first dot product + XT_products[i_ft, 0] = np.sum(Q[i_ft] * X[i_ft, :L]) + return XT_products + + +@njit(cache=True, fastmath=True, parallel=True) +def _squared_distance_profile(QX, X, Q): + """ + Compute squared distance profiles between query subsequence and time series. + + Parameters + ---------- + QX : List of np.ndarray + List of precomputed dot products between queries and time series, with each + element corresponding to a different time series. + Shape of each array is (n_channels, n_timepoints - query_length + 1). + X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + The input samples. If X is an unquel length collection, expect a numba TypedList + 2D array of shape (n_channels, n_timepoints) + Q : np.ndarray, 2D array of shape (n_channels, query_length) + The query used for similarity search. + mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) + Boolean mask of the shape of the distance profile indicating for which part + of it the distance should be computed. + + Returns + ------- + distance_profiles : np.ndarray + 3D array of shape (n_cases, n_timepoints - query_length + 1) + The distance profile between Q and the input time series X. + + """ + distance_profiles = List() + query_length = Q.shape[1] + + # Init distance profile array with unequal length support + for i_instance in range(len(X)): + profile_length = X[i_instance].shape[1] - query_length + 1 + distance_profiles.append(np.full((profile_length), np.inf)) + + for _i_instance in prange(len(QX)): + # prange cast iterator to unit64 with parallel=True + i_instance = np.int_(_i_instance) + + distance_profiles[i_instance] = _squared_dist_profile_one_series( + QX[i_instance], X[i_instance], Q + ) + return distance_profiles + + +@njit(cache=True, fastmath=True) +def _squared_dist_profile_one_series(QT, T, Q): + """ + Compute squared distance profile between query subsequence and a single time series. + + This function calculates the squared distance profile for a single time series by + leveraging the dot product of the query and time series as well as precomputed sums + of squares to efficiently compute the squared distances. + + Parameters + ---------- + QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + The dot product between the query and the time series. + T : np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + Q : np.ndarray + 2D array of shape (n_channels, query_length) representing query subsequence. + + Returns + ------- + distance_profile : np.ndarray + 2D array of shape (n_channels, n_timepoints - query_length + 1) + The squared distance profile between the query and the input time series. + """ + n_channels, profile_length = QT.shape + query_length = Q.shape[1] + _QT = -2 * QT + distance_profile = np.zeros(profile_length) + for k in prange(n_channels): + _sum = 0 + _qsum = 0 + for j in prange(query_length): + _sum += T[k, j] ** 2 + _qsum += Q[k, j] ** 2 + + distance_profile += _qsum + _QT[k] + distance_profile[0] += _sum + for i in prange(1, profile_length): + _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 + distance_profile[i] += _sum + return distance_profile + + +@njit(cache=True, fastmath=True, parallel=True) +def _normalized_squared_distance_profile( + QX, X_means, X_stds, Q_means, Q_stds, query_length +): + """ + Compute the normalized squared distance profiles between query subsequence and input time series. + + Parameters + ---------- + QX : List of np.ndarray + List of precomputed dot products between queries and time series, with each element + corresponding to a different time series. + Shape of each array is (n_channels, n_timepoints - query_length + 1). + X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 + Means of each subsequences of X of size query_length + X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 + Stds of each subsequences of X of size query_length + Q_means : np.ndarray, 1D array of shape (n_channels) + Means of the query q + Q_stds : np.ndarray, 1D array of shape (n_channels) + Stds of the query q + query_length : int + The length of the query subsequence used for the distance profile computation. + + Returns + ------- + List of np.ndarray + List of 2D arrays, each of shape (n_channels, n_timepoints - query_length + 1). + Each array contains the normalized squared distance profile between the query subsequence and the corresponding time series. + Entries in the array are set to infinity where the mask is False. + """ + distance_profiles = List() + Q_is_constant = Q_stds <= AEON_NUMBA_STD_THRESHOLD + # Init distance profile array with unequal length support + for i_instance in range(len(QX)): + profile_length = QX[i_instance].shape[1] + distance_profiles.append(np.zeros(profile_length)) + + for _i_instance in prange(len(QX)): + # iterator is uint64 with prange and parallel so cast to int to avoid warnings + i_instance = np.int64(_i_instance) + distance_profiles[i_instance] = _normalized_squared_dist_profile_one_series( + QX[i_instance], + X_means[i_instance], + X_stds[i_instance], + Q_means, + Q_stds, + query_length, + Q_is_constant, + ) + return distance_profiles + + +@njit(cache=True, fastmath=True) +def _normalized_squared_dist_profile_one_series( + QT, T_means, T_stds, Q_means, Q_stds, query_length, Q_is_constant +): + """ + Compute the z-normalized squared Euclidean distance profile for one time series. + + Parameters + ---------- + QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + The dot product between the query and the time series. + T_means : np.ndarray, 1D array of length n_channels + The mean values of the time series for each channel. + T_stds : np.ndarray, 2D array of shape (n_channels, profile_length) + The standard deviations of the time series for each channel and position. + Q_means : np.ndarray, 1D array of shape (n_channels) + Means of the query q + Q_stds : np.ndarray, 1D array of shape (n_channels) + Stds of the query q + query_length : int + The length of the query subsequence used for the distance profile computation. + Q_is_constant : np.ndarray + 1D array of shape (n_channels,) where each element is a Boolean indicating + whether the query standard deviation for that channel is less than or equal + to a specified threshold. + + Returns + ------- + np.ndarray + 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the + z-normalized squared distance profile between the query subsequence and the time + series. Entries are computed based on the z-normalized values, with special + handling for constant values. + """ + n_channels, profile_length = QT.shape + distance_profile = np.zeros(profile_length) + + for i in prange(profile_length): + Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD + for k in prange(n_channels): + # Two Constant case + if Q_is_constant[k] and Sub_is_constant[k]: + _val = 0 + # One Constant case + elif Q_is_constant[k] or Sub_is_constant[k]: + _val = query_length + else: + denom = query_length * Q_stds[k] * T_stds[k, i] + + p = (QT[k, i] - query_length * (Q_means[k] * T_means[k, i])) / denom + p = min(p, 1.0) + + _val = abs(2 * query_length * (1.0 - p)) + distance_profile[i] += _val + + return distance_profile diff --git a/aeon/similarity_search/subsequence_search/base.py b/aeon/similarity_search/subsequence_search/base.py new file mode 100644 index 0000000000..1d49004472 --- /dev/null +++ b/aeon/similarity_search/subsequence_search/base.py @@ -0,0 +1,358 @@ +"""Base class for subsequence search.""" + +__maintainer__ = ["baraline"] + +import warnings +from abc import abstractmethod +from typing import Optional, final + +import numpy as np +from numba import get_num_threads, set_num_threads +from numba.typed import List + +from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search.subsequence_search._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile_list, +) +from aeon.utils.numba.general import sliding_mean_std_one_series + + +# We can define a BaseVariableLengthSubsequenceSearch later for VALMOD and the likes. +class BaseSubsequenceSearch(BaseSimilaritySearch): + """ + Base class for similarity search on time series subsequences. + + Parameters + ---------- + length : int + The length of the subsequence to be considered. + normalize : bool, optional + Whether the inputs should be z-normalized. The default is False. + n_jobs : int, optional + Number of parallel jobs to use. The default is 1. + """ + + @abstractmethod + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + n_jobs: Optional[int] = 1, + ): + self.length = length + super().__init__(n_jobs=n_jobs, normalize=normalize) + + @final + def find_motifs( + self, + k: int, + threshold: float, + X: Optional[np.ndarray] = None, + allow_overlap: Optional[bool] = False, + exclusion_factor: Optional[float] = 2.0, + ): + """ + Find the top-k motifs in the training data. + + Given ``k`` and ``threshold`` parameters, this methods returns the top-k motif + sets. We define a motif set as a set of candidates which all are at a distance + of at most ``threshold`` from each other. The top-k motifs sets are the + motif sets with the most candidates. + + Parameters + ---------- + k : int, optional + Number of motifs to return + threshold : int, optional + A threshold on the similarity measure to determine which candidates will be + part of a motif set. + X : np.ndarray, 2D array of shape (n_channels, n_timestamps), optional + A series in which we want to indentify motifs. If provided, the motifs + extracted should appear in X and in the database given in fit. If not + provided, the motifs will be extracted only from the database given in fit. + allow_overlap: bool, optional + Wheter a candidate can be part of multiple motif sets (True), or if motif + sets should be mutually exclusive (False). + exclusion_factor : float, default=2. + A factor of the query length used to define the exclusion zone when + ``allow_overlap`` is set to False. For a given timestamp, the exclusion zone + starts from :math:`id_timestamp - query_length//exclusion_factor` and end at + :math:`id_timestamp + query_length//exclusion_factor`. + + Returns + ------- + list of ndarray, shape=(k,) + A list of at most ``k`` numpy arrays containing the indexes of the + candidates in each motif. + + """ + self._check_is_fitted() + + @final + def find_neighbors( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + inverse_distance: Optional[bool] = False, + X_index: Optional[np.ndarray] = None, + allow_overlap: Optional[bool] = False, + exclusion_factor: Optional[float] = 2.0, + ): + """ + Find the top-k neighbors of X in the database. + + Given ``k`` and ``threshold`` parameters, this methods returns the top-k + neighbors of X, such as each of the ``k`` neighbors as a distance inferior or + equal to ``threshold``. By default, ``threshold`` is set to infinity. It is + possible for this method to return less than ``k`` neighbors, either if there + is less than ``k`` admissible candidate in the database, or if in the top-k + candidates, some do not meet the ``threshold`` condition. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The subsequence for which we want to identify nearest neighbors in the + database. + k : int, optional + Number of neighbors to return. + threshold : int, optional + A threshold on the distance to determine which candidates will be returned. + inverse_distance : bool, optional + Wheter to inverse the computed distance, meaning that the method will return + the k most dissimilar neighbors instead of the k most similar. + X_index : np.ndarray, shape=(2,), optional + If ``X`` is a subsequence of the database given in fit, specify its starting + index as (i_case, i_timestamp). If specified, this subsequence and the + neighboring ones (according to ``exclusion_factor``) won't be considered as + admissible candidates. + allow_overlap: bool, optional + Wheter the top-k candidates can be neighboring subsequences. + exclusion_factor : float, default=2. + A factor of the query length used to define the exclusion zone when + ``allow_overlap`` is set to False. For a given timestamp, the exclusion zone + starts from :math:`id_timestamp - query_length//exclusion_factor` and end at + :math:`id_timestamp + query_length//exclusion_factor`. + + Returns + ------- + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the indexes of the + neighbors. + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the distances of the + neighbors to X. + + """ + self._check_is_fitted() + if self.length != X.shape[1] or self.n_channels_ != X.shape[0]: + raise ValueError( + f"Expected a subsequence of shape {(self.n_channels_, self.length)} but" + f" got {X.shape}" + ) + self._check_X_index(X_index) + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) + neighbors, distances = self._find_neighbors( + X, + k=k, + threshold=threshold, + inverse_distance=inverse_distance, + X_index=X_index, + allow_overlap=allow_overlap, + exclusion_factor=exclusion_factor, + ) + set_num_threads(prev_threads) + if len(neighbors) < k: + warnings.warn( + f"The number of admissible neighbors found is {len(neighbors)}, instead" + f" of {k}", + stacklevel=2, + ) + return neighbors, distances + + def _check_X_index(self, X_index: np.ndarray): + """ + Check wheter the X_index parameter is correctly formated and is admissible. + + Parameters + ---------- + X_index : np.ndarray, 1D array of shape (2) + Array of integer containing the sample and timestamp identifiers of the + starting point of a subsequence in X_. + + Returns + ------- + X_index : np.ndarray, 1D array of shape (2) + Array of integer containing the sample and timestamp identifiers of the + starting point of a subsequence in X_. + + """ + if X_index is not None: + if ( + isinstance(X_index, list) + and len(X_index) == 2 + and isinstance(X_index[0], int) + and isinstance(X_index[1], int) + ): + X_index = np.asarray(X_index, dtype=int) + elif len(X_index) != 2: + raise ValueError( + "Expected a numpy array or list of integers with 2 elements " + f"for X_index but got {X_index}" + ) + elif ( + not (isinstance(X_index[0], int) and isinstance(X_index[1], int)) + or X_index.dtype != int + ): + raise TypeError( + "Expected a numpy array or list of integers for X_index but got " + f"{X_index}" + ) + + if X_index[0] >= self.n_cases_: + raise ValueError( + "The sample ID (first element) of X_index cannot exced the number " + "of series in the collection given during fit. Expected a value " + f"between [0, {self.n_cases_ - 1}] but got {X_index[0]}" + ) + _max_timestamp = self.X_[X_index[0]].shape[1] - self.length + 1 + if X_index[1] >= _max_timestamp: + raise ValueError( + "The timestamp ID (second element) of X_index cannot exced the " + "number of timestamps minus the length parameter plus one. Expected" + f" a value between [0, {_max_timestamp - 1}] but got {X_index[1]}" + ) + return X_index + + def _compute_mean_std_from_collection(self, X: np.ndarray): + """ + Compute the mean and std of each subsequence of size ``length`` in X. + + Parameters + ---------- + X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + Collection of series from which we extract mean and stds. If it is an + unequal length collection, it should be a list of 2d numpy arrays. + + Returns + ------- + Tuple(np.ndarray, np.ndarray) + Both array are of shape (n_cases, n_timepoints-length+1, n_channels), + the first contains the means and the second the stds for each subsequence + of size ``length`` in X. + + """ + means = [] + stds = [] + + for i_x in range(len(X)): + _mean, _std = sliding_mean_std_one_series(X[i_x], self.length, 1) + stds.append(_std) + means.append(_mean) + + if self.metadata_["unequal_length"]: + return List(means), List(stds) + else: + return np.asarray(means), np.asarray(stds) + + @abstractmethod + def _fit(self, X, y=None): ... + + +class BaseMatrixProfile(BaseSubsequenceSearch): + """Base class for Matrix Profile methods using a length parameter.""" + + def _fit(self, X, y=None): + if self.length >= self.min_timepoints_: + raise ValueError( + "The length of the query should be inferior or equal to the length of " + "data (X_) provided during fit, but got {} for X and {} for X_".format( + self.length, self.min_timepoints_ + ) + ) + + if self.normalize: + self.X_means_, self.X_stds_ = self._compute_mean_std_from_collection(X) + self.X_ = X + return self + + def _find_motifs(): + raise NotImplementedError() + + def _find_neighbors( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + inverse_distance: Optional[bool] = False, + X_index=None, + allow_overlap: Optional[bool] = False, + exclusion_factor: Optional[float] = 2.0, + ): + """ + Find the top-k neighbors of X in the database. + + Given ``k`` and ``threshold`` parameters, this methods returns the top-k + neighbors of X, such as each of the ``k`` neighbors as a distance inferior or + equal to ``threshold``. By default, ``threshold`` is set to infinity. It is + possible for this method to return less than ``k`` neighbors, either if there + is less than ``k`` admissible candidate in the database, or if in the top-k + candidates, some do not meet the ``threshold`` condition. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The subsequence for which we want to identify nearest neighbors in the + database. + k : int, optional + Number of neighbors to return. + threshold : int, optional + A threshold on the distance to determine which candidates will be returned. + inverse_distance : bool, optional + Wheter to inverse the computed distance, meaning that the method will return + the k most dissimilar neighbors instead of the k most similar. + X_index : np.ndarray, shape=(2,), optional + If ``X`` is a subsequence of the database given in fit, specify its starting + index as (i_case, i_timestamp). If specified, this subsequence and the + neighboring ones (according to ``exclusion_factor``) won't be considered as + admissible candidates. + allow_overlap: bool, optional + Wheter the top-k candidates can be neighboring subsequences. + exclusion_factor : float, default=2. + A factor of the query length used to define the exclusion zone when + ``allow_overlap`` is set to False. For a given timestamp, the exclusion zone + starts from :math:`id_timestamp - query_length//exclusion_factor` and end at + :math:`id_timestamp + query_length//exclusion_factor`. + """ + exclusion_size = self.length // exclusion_factor + dist_profiles = self.compute_distance_profile(X) + + if inverse_distance: + dist_profiles = _inverse_distance_profile_list(dist_profiles) + + # Deal with self-matches + if X_index is not None: + _max_timestamp = self.X_[X_index[0]].shape[1] - self.length + ub = min(X_index[1] + exclusion_size, _max_timestamp) + lb = max(0, X_index[1] - exclusion_size) + dist_profiles[X_index[0]][lb:ub] = np.inf + + return _extract_top_k_from_dist_profile( + dist_profiles, + k, + threshold, + allow_overlap, + exclusion_size, + ) + + @abstractmethod + def compute_matrix_profile(X: Optional[np.ndarray] = None): + """Compute matrix profiles between X_ and X or between all series in X_.""" + ... + + @abstractmethod + def compute_distance_profile(X: np.ndarray): + """Compute distrance profiles between X_ and X (a series of size length).""" + ... diff --git a/aeon/similarity_search/matrix_profiles/tests/__init__.py b/aeon/similarity_search/subsequence_search/tests/__init__.py similarity index 100% rename from aeon/similarity_search/matrix_profiles/tests/__init__.py rename to aeon/similarity_search/subsequence_search/tests/__init__.py diff --git a/aeon/similarity_search/subsequence_search/tests/test__commons.py b/aeon/similarity_search/subsequence_search/tests/test__commons.py new file mode 100644 index 0000000000..23b07d78f7 --- /dev/null +++ b/aeon/similarity_search/subsequence_search/tests/test__commons.py @@ -0,0 +1,64 @@ +"""Test _commons.py functions.""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numba.typed import List +from numpy.testing import assert_array_almost_equal + +from aeon.similarity_search.subsequence_search._commons import ( + _inverse_distance_profile_list, + fft_sliding_dot_product, + get_ith_products, +) +from aeon.testing.data_generation import ( + make_example_2d_numpy_list, + make_example_2d_numpy_series, +) + + +def test_fft_sliding_dot_product(): + """Test the fft_sliding_dot_product function.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=4) + + values = fft_sliding_dot_product(X, Q) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0], X[0, i : i + 5]) for i in range(X.shape[1] - 5 + 1)], + ) + + +def test_get_ith_products(): + """Test i-th dot product of a subsequence of size L.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + L = 5 + + values = get_ith_products(X, Q, L, 0) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0, 0:L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + values = get_ith_products(X, Q, L, 4) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0, 4 : 4 + L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + +def test__inverse_distance_profile_list(): + """Test method to inverse a TypedList of distance profiles.""" + X = make_example_2d_numpy_list(n_cases=2, return_y=False) + T = _inverse_distance_profile_list(List(X)) + assert_array_almost_equal(1 / (X[0] + 1e-8), T[0]) + assert_array_almost_equal(1 / (X[1] + 1e-8), T[1]) + + +def test__extract_top_k_from_dist_profile(): + """Test method to esxtract the top k candidates from a list of distance profiles.""" + ... diff --git a/aeon/similarity_search/subsequence_search/tests/test_stomp.py b/aeon/similarity_search/subsequence_search/tests/test_stomp.py new file mode 100644 index 0000000000..169eee135e --- /dev/null +++ b/aeon/similarity_search/subsequence_search/tests/test_stomp.py @@ -0,0 +1,238 @@ +"""Tests for stomp algorithm.""" + +__maintainer__ = ["baraline"] + +import numpy as np +import pytest +from numba.typed import List +from numpy.testing import assert_almost_equal, assert_array_almost_equal + +from aeon.similarity_search.subsequence_search._commons import get_ith_products +from aeon.similarity_search.subsequence_search._stomp import ( + _normalized_squared_dist_profile_one_series, + _normalized_squared_distance_profile, + _squared_dist_profile_one_series, + _squared_distance_profile, + _stomp, + _stomp_normalized, + _update_dot_products_one_series, +) +from aeon.testing.data_generation import ( + make_example_2d_numpy_series, + make_example_3d_numpy, + make_example_3d_numpy_list, +) +from aeon.utils.numba.general import ( + sliding_mean_std_one_series, + z_normalise_series_2d_with_mean_std, +) + +K_VALUES = [1, 3] + + +def _get_mean_sdts_inputs(X, Q, L): + X_means = [] + X_stds = [] + + for i_x in range(len(X)): + _mean, _std = sliding_mean_std_one_series(X[i_x], L, 1) + X_stds.append(_std) + X_means.append(_mean) + + Q_means = Q.mean(axis=1) + Q_stds = Q.std(axis=1) + + return X_means, X_stds, Q_means, Q_stds + + +def test__update_dot_products_one_series(): + """Test the _update_dot_product function.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=20) + T = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + L = 7 + current_product = get_ith_products(X, T, L, 0) + for i_query in range(1, T.shape[1] - L + 1): + new_product = get_ith_products( + X, + T, + L, + i_query, + ) + current_product = _update_dot_products_one_series( + X, + T, + current_product, + L, + i_query, + ) + assert_array_almost_equal(new_product, current_product) + + +def test__squared_dist_profile_one_series(): + """Test Euclidean distance.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = get_ith_products(X, Q, L, 0) + dist_profile = _squared_dist_profile_one_series(QX, X, Q) + for i_t in range(X.shape[1] - L + 1): + assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) + + +def test__normalized_squared_dist_profile_one_series(): + """Test Euclidean distance.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = get_ith_products(X, Q, L, 0) + X_mean, X_std = sliding_mean_std_one_series(X, L, 1) + Q_mean = Q.mean(axis=1) + Q_std = Q.std(axis=1) + + dist_profile = _normalized_squared_dist_profile_one_series( + QX, X_mean, X_std, Q_mean, Q_std, L, Q.std(axis=1) <= 0 + ) + Q = z_normalise_series_2d_with_mean_std(Q, Q_mean, Q_std) + for i_t in range(X.shape[1] - L + 1): + S = z_normalise_series_2d_with_mean_std( + X[:, i_t : i_t + L], X_mean[:, i_t], X_std[:, i_t] + ) + assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) + + +def test__squared_distance_profile(): + """Test Euclidean distance profile calculation.""" + L = 3 + X = make_example_3d_numpy(n_cases=3, n_channels=1, n_timepoints=10, return_y=False) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = np.asarray([get_ith_products(X[i_x], Q, L, 0) for i_x in range(len(X))]) + dist_profiles = _squared_distance_profile(QX, X, Q) + for i_x in range(len(X)): + for i_t in range(X[i_x].shape[1] - L + 1): + assert_almost_equal( + dist_profiles[i_x][i_t], np.sum((X[i_x, :, i_t : i_t + L] - Q) ** 2) + ) + + # test unequal length and multivariate + X = List( + make_example_3d_numpy_list( + n_cases=3, + n_channels=2, + min_n_timepoints=10, + max_n_timepoints=20, + return_y=False, + ) + ) + + Q = make_example_2d_numpy_series(n_channels=2, n_timepoints=L) + QX = List([get_ith_products(X[i_x], Q, L, 0) for i_x in range(len(X))]) + dist_profiles = _squared_distance_profile(QX, X, Q) + for i_x in range(len(X)): + for i_t in range(X[i_x].shape[1] - L + 1): + assert_almost_equal( + dist_profiles[i_x][i_t], np.sum((X[i_x][:, i_t : i_t + L] - Q) ** 2) + ) + + +def test__normalized_squared_distance_profile(): + """Test Euclidean distance profile calculation.""" + L = 3 + X = make_example_3d_numpy(n_cases=3, n_channels=1, n_timepoints=10, return_y=False) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = np.asarray([get_ith_products(X[i_x], Q, L, 0) for i_x in range(len(X))]) + + X_means, X_stds, Q_means, Q_stds = _get_mean_sdts_inputs(X, Q, L) + + X_means = np.asarray(X_means) + X_stds = np.asarray(X_stds) + + dist_profiles = _normalized_squared_distance_profile( + QX, X_means, X_stds, Q_means, Q_stds, L + ) + + Q_norm = z_normalise_series_2d_with_mean_std(Q, Q_means, Q_stds) + for i_x in range(len(X)): + for i_t in range(X[i_x].shape[1] - L + 1): + X_sub_norm = z_normalise_series_2d_with_mean_std( + X[i_x, :, i_t : i_t + L], X_means[i_x][:, i_t], X_stds[i_x][:, i_t] + ) + assert_almost_equal( + dist_profiles[i_x][i_t], np.sum((X_sub_norm - Q_norm) ** 2) + ) + + # test unequal length and multivariate + X = List( + make_example_3d_numpy_list( + n_cases=5, + n_channels=2, + min_n_timepoints=10, + max_n_timepoints=20, + return_y=False, + ) + ) + Q = make_example_2d_numpy_series(n_channels=2, n_timepoints=L) + + QX = List([get_ith_products(X[i_x], Q, L, 0) for i_x in range(len(X))]) + + X_means, X_stds, Q_means, Q_stds = _get_mean_sdts_inputs(X, Q, L) + # Convert to numba typed list + X_means = List(X_means) + X_stds = List(X_stds) + + dist_profiles = _normalized_squared_distance_profile( + QX, X_means, X_stds, Q_means, Q_stds, L + ) + + Q_norm = z_normalise_series_2d_with_mean_std(Q, Q_means, Q_stds) + for i_x in range(len(X)): + for i_t in range(X[i_x].shape[1] - L + 1): + X_sub_norm = z_normalise_series_2d_with_mean_std( + X[i_x][:, i_t : i_t + L], X_means[i_x][:, i_t], X_stds[i_x][:, i_t] + ) + assert_almost_equal( + dist_profiles[i_x][i_t], np.sum((X_sub_norm - Q_norm) ** 2) + ) + + +# K_VALUES = [1, 3] +@pytest.mark.parametrize("k", K_VALUES) +def test__stomp(k): + """Test STOMP method.""" + L = 3 + X = np.array([[[1, 2, 3, 2, 1, 2, 3, 4, 5, 2, 1, 2, 2]]]) + T = np.array([[1, 1, 3, 2, 2]]) + XdotT = np.asarray([get_ith_products(X[i_x], T, L, 0) for i_x in range(len(X))]) + + T_index = None + allow_overlap = False + threshold = np.inf + exclusion_size = L + inverse_distance = False + + MP, IP = _stomp( + X, + T, + XdotT, + L, + T_index, + k, + allow_overlap, + threshold, + exclusion_size, + inverse_distance, + ) + Expected_MP = [[1, 6], [1, 2], [1, 2, 5]] + Expected_IP = [[[0, 0], [0, 1]], [[0, 1], [0, 0]], [[0, 2], [0, 1], [0, 0]]] + for i in range(len(Expected_MP)): + assert_array_almost_equal(Expected_IP[i], IP[i]) + assert_array_almost_equal(Expected_MP[i], MP[i]) + + +@pytest.mark.parametrize("k", K_VALUES) +def test__stomp_normalized(k): + """Test STOMP normalized method.""" + _stomp_normalized + ... + + +# TODO : add tests for StompMatrixProfile diff --git a/aeon/similarity_search/tests/test__commons.py b/aeon/similarity_search/tests/test__commons.py deleted file mode 100644 index a97519ad31..0000000000 --- a/aeon/similarity_search/tests/test__commons.py +++ /dev/null @@ -1,49 +0,0 @@ -"""Test _commons.py functions.""" - -__maintainer__ = ["baraline"] - -import numpy as np -from numpy.testing import assert_array_almost_equal - -from aeon.similarity_search._commons import ( - fft_sliding_dot_product, - naive_squared_distance_profile, - naive_squared_matrix_profile, -) - - -def test_fft_sliding_dot_product(): - """Test the fft_sliding_dot_product function.""" - X = np.random.rand(1, 10) - q = np.random.rand(1, 5) - - values = fft_sliding_dot_product(X, q) - - assert_array_almost_equal( - values[0], - [np.dot(q[0], X[0, i : i + 5]) for i in range(X.shape[1] - 5 + 1)], - ) - - -def test_naive_squared_distance_profile(): - """Test naive squared distance profile computation is correct.""" - X = np.zeros((1, 1, 6)) - X[0, 0] = np.arange(6) - Q = np.array([[1, 2, 3]]) - query_length = Q.shape[1] - mask = np.ones((X.shape[0], X.shape[2] - query_length + 1), dtype=bool) - dist_profile = naive_squared_distance_profile(X, Q, mask) - assert_array_almost_equal(dist_profile[0], np.array([3.0, 0.0, 3.0, 12.0])) - - -def test_naive_squared_matrix_profile(): - """Test naive squared matrix profile computation is correct.""" - X = np.zeros((1, 1, 6)) - X[0, 0] = np.arange(6) - Q = np.zeros((1, 6)) - - Q[0] = np.arange(6, 12) - query_length = 3 - mask = np.ones((X.shape[0], X.shape[2] - query_length + 1), dtype=bool) - matrix_profile = naive_squared_matrix_profile(X, Q, query_length, mask) - assert_array_almost_equal(matrix_profile, np.array([27.0, 48.0, 75.0, 108.0])) diff --git a/aeon/similarity_search/tests/test_query_search.py b/aeon/similarity_search/tests/test_query_search.py deleted file mode 100644 index f97f6a50bf..0000000000 --- a/aeon/similarity_search/tests/test_query_search.py +++ /dev/null @@ -1,176 +0,0 @@ -"""Tests for QuerySearch.""" - -__maintainer__ = ["baraline"] - -import numpy as np -import pytest -from numpy.testing import assert_almost_equal, assert_array_equal - -from aeon.similarity_search.query_search import QuerySearch - -DATATYPES = ["int64", "float64"] - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_mean_std_equal_length(dtype): - """Test the mean and std computation of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(normalise=True) - search.fit(X) - _ = search.predict(q, X_index=(1, 2)) - for i in range(len(X)): - for j in range(X[i].shape[1] - q.shape[1] + 1): - subsequence = X[i, :, j : j + q.shape[1]] - assert_almost_equal(search.X_means_[i][:, j], subsequence.mean(axis=-1)) - assert_almost_equal(search.X_stds_[i][:, j], subsequence.std(axis=-1)) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_mean_std_unequal_length(dtype): - """Test the mean and std computation of QuerySearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(normalise=True) - search.fit(X) - _ = search.predict(q, X_index=(1, 2)) - for i in range(len(X)): - for j in range(X[i].shape[1] - q.shape[1] + 1): - subsequence = X[i][:, j : j + q.shape[1]] - assert_almost_equal(search.X_means_[i][:, j], subsequence.mean(axis=-1)) - assert_almost_equal(search.X_stds_[i][:, j], subsequence.std(axis=-1)) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_threshold_and_k(dtype): - """Test the k and threshold combination of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=3, threshold=1) - search.fit(X) - dist, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_inverse_distance(dtype): - """Test the inverse distance parameter of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1, inverse_distance=True) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 5)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_euclidean(dtype): - """Test the functionality of QuerySearch with Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch(k=3) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 1)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 4)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(1, 2)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - _, idx = search.predict(q, X_index=(1, 2)) - assert_array_equal(idx, [(1, 0)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_euclidean_unequal_length(dtype): - """Test the functionality of QuerySearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch(k=3) - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 1)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(0, 2), (1, 2), (1, 4)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) - - _, idx = search.predict(q, apply_exclusion_to_result=True) - assert_array_equal(idx, [(1, 2)]) - - search = QuerySearch(k=1, normalise=True) - search.fit(X) - _, idx = search.predict(q, X_index=(1, 2)) - assert_array_equal(idx, [(1, 0)]) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_QuerySearch_speedup(dtype): - """Test the speedup functionality of QuerySearch.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - q = np.asarray([[3, 4, 5]], dtype=dtype) - - search = QuerySearch(k=1, speed_up="fastest") - search.fit(X) - _, idx = search.predict(q) - assert_array_equal(idx, [(0, 2)]) - - search = QuerySearch( - k=1, - distance="euclidean", - speed_up="fastest", - normalise=True, - ) - search.fit(X) - q = np.asarray([[8, 8, 10]], dtype=dtype) - _, idx = search.predict(q) - assert_array_equal(idx, [(1, 2)]) diff --git a/aeon/similarity_search/tests/test_series_search.py b/aeon/similarity_search/tests/test_series_search.py deleted file mode 100644 index a10109359c..0000000000 --- a/aeon/similarity_search/tests/test_series_search.py +++ /dev/null @@ -1,74 +0,0 @@ -"""Tests for SeriesSearch similarity search algorithm.""" - -__maintainer__ = ["baraline"] - - -import numpy as np -import pytest - -from aeon.similarity_search.series_search import SeriesSearch - -DATATYPES = ["int64", "float64"] -K_VALUES = [1, 3] -normalise = [True, False] - -# See #2236 -# @pytest.mark.parametrize("k", K_VALUES) -# @pytest.mark.parametrize("normalise", normalise) -# def test_SeriesSearch_k(k, normalise): -# """Test the k and threshold combination of SeriesSearch.""" -# X = np.asarray([[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]]) -# S = np.asarray([[3, 4, 5, 4, 3, 4]]) -# L = 3 -# -# search = SeriesSearch(k=k, normalise=normalise) -# search.fit(X) -# mp, ip = search.predict(S, L) -# -# assert mp[0].shape[0] == ip[0].shape[0] == k -# assert len(mp) == len(ip) == S.shape[1] - L + 1 -# assert ip[0].shape[1] == 2 - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_SeriesSearch_error_predict(dtype): - """Test the functionality of SeriesSearch with Euclidean distance.""" - X = np.asarray( - [[[1, 2, 3, 4, 5, 6, 7, 8]], [[1, 2, 4, 4, 5, 6, 5, 4]]], dtype=dtype - ) - S = np.asarray([[3, 4, 5, 4, 3, 4, 5]], dtype=dtype) - L = 100 - - search = SeriesSearch() - search.fit(X) - with pytest.raises(ValueError): - mp, ip = search.predict(S, L) - L = 3 - S = np.asarray( - [ - [3, 4, 5, 4, 3, 4], - [6, 5, 3, 2, 4, 5], - ], - dtype=dtype, - ) - with pytest.raises(ValueError): - mp, ip = search.predict(S, L) - - S = [6, 5, 3, 2, 4, 5] - with pytest.raises(TypeError): - mp, ip = search.predict(S, L) - - -@pytest.mark.parametrize("dtype", DATATYPES) -def test_SeriesSearch_process_unequal_length(dtype): - """Test the functionality of SeriesSearch on unequal length data.""" - X = [ - np.array([[1, 2, 3, 4, 5, 6, 7, 8]], dtype=dtype), - np.array([[1, 2, 4, 4, 5, 6, 5]], dtype=dtype), - ] - S = np.asarray([[3, 4, 5, 4, 3, 4]], dtype=dtype) - L = 3 - - search = SeriesSearch() - search.fit(X) - mp, ip = search.predict(S, L) diff --git a/aeon/utils/numba/general.py b/aeon/utils/numba/general.py index 10e96abde6..b398a8414b 100644 --- a/aeon/utils/numba/general.py +++ b/aeon/utils/numba/general.py @@ -8,7 +8,9 @@ "first_order_differences_3d", "z_normalise_series_with_mean", "z_normalise_series", + "z_normalise_series_with_mean_std", "z_normalise_series_2d", + "z_normalise_series_2d_with_mean_std", "z_normalise_series_3d", "set_numba_random_seed", "choice_log", @@ -20,6 +22,7 @@ "slope_derivative_2d", "slope_derivative_3d", "generate_combinations", + "get_all_subsequences", ] From 52b0692a26353f302570323d9b6d4498e09071db Mon Sep 17 00:00:00 2001 From: Antoine Guillaume Date: Thu, 5 Dec 2024 17:20:47 +0100 Subject: [PATCH 02/36] Update _brute_force.py --- aeon/similarity_search/subsequence_search/_brute_force.py | 7 ------- 1 file changed, 7 deletions(-) diff --git a/aeon/similarity_search/subsequence_search/_brute_force.py b/aeon/similarity_search/subsequence_search/_brute_force.py index 7f95083f3b..691b81367a 100644 --- a/aeon/similarity_search/subsequence_search/_brute_force.py +++ b/aeon/similarity_search/subsequence_search/_brute_force.py @@ -264,13 +264,6 @@ def _naive_squared_matrix_profile( if inverse_distance: dist_profiles = _inverse_distance_profile_list(dist_profiles) - # Deal with self-matches - if T_index is not None: - _max_timestamp = X[T_index].shape[1] - L - ub = min(i_q + exclusion_size, _max_timestamp) - lb = max(0, i_q - exclusion_size) - dist_profiles[T_index][lb:ub] = np.inf - top_dists, top_indexes = _extract_top_k_from_dist_profile( dist_profiles, k, From 7973a306c98c7c0b38fff56abd01e415732ca31d Mon Sep 17 00:00:00 2001 From: Antoine Guillaume Date: Thu, 5 Dec 2024 17:47:19 +0100 Subject: [PATCH 03/36] Update test__commons.py --- .../subsequence_search/tests/test__commons.py | 57 ++++++++++++++++++- 1 file changed, 55 insertions(+), 2 deletions(-) diff --git a/aeon/similarity_search/subsequence_search/tests/test__commons.py b/aeon/similarity_search/subsequence_search/tests/test__commons.py index 23b07d78f7..b0e2764b0b 100644 --- a/aeon/similarity_search/subsequence_search/tests/test__commons.py +++ b/aeon/similarity_search/subsequence_search/tests/test__commons.py @@ -4,7 +4,7 @@ import numpy as np from numba.typed import List -from numpy.testing import assert_array_almost_equal +from numpy.testing import assert_array_almost_equal, assert_array_equal from aeon.similarity_search.subsequence_search._commons import ( _inverse_distance_profile_list, @@ -61,4 +61,57 @@ def test__inverse_distance_profile_list(): def test__extract_top_k_from_dist_profile(): """Test method to esxtract the top k candidates from a list of distance profiles.""" - ... + X = List([ + [5,4,3,3,1,3,2,5,1,4,1,0,1,2,2,7,8,1,5], + [5,1,0,1,0,0,5,4,3,5,6,1,4,2], + ]) + + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + X, + 1, + np.inf, + False, + 3 + ) + assert_array_equal(top_k_indexes, [[0,11]]) + assert_array_equal(top_k_indexes, [0]) + + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + X, + 5, + np.inf, + False, + 3 + ) + assert_array_equal(top_k_indexes, [[0,11],[1,2],[0,4],[0,17],[1,11]]) + assert_array_equal(top_k_indexes, [0,0,1,1,1]) + + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + X, + 5, + np.inf, + True, + 3 + ) + assert_array_equal(top_k_indexes, [[0,11],[1,2],[1,4],[1,5],[0,4]]) + assert_array_equal(top_k_indexes, [0,0,0,0,1]) + + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + X, + 5, + 0.5, + True, + 3 + ) + assert_array_equal(top_k_indexes, [[0,11],[1,2],[1,4],[1,5]]) + assert_array_equal(top_k_indexes, [0,0,0,0]) + + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + X, + 5, + 0.5, + False, + 3 + ) + assert_array_equal(top_k_indexes, [[0,11],[1,2]]) + assert_array_equal(top_k_indexes, [0,0]) From ad02b8466fb51e2fde63f1bcfec43166d5026116 Mon Sep 17 00:00:00 2001 From: baraline Date: Fri, 27 Dec 2024 09:05:05 +0100 Subject: [PATCH 04/36] WIP mock and test --- .../series_search/__init__.py | 8 +- .../subsequence_search/__init__.py | 8 +- .../subsequence_search/_brute_force.py | 140 +++++++----- .../subsequence_search/_commons.py | 48 +++- .../subsequence_search/_stomp.py | 211 ++++++++++-------- .../subsequence_search/base.py | 172 +++++++++++--- .../tests/test_brute_force.py | 155 +++++++++++++ .../{test__commons.py => test_commons.py} | 84 +++---- .../subsequence_search/tests/test_stomp.py | 166 +++++++++++--- aeon/testing/mock_estimators/__init__.py | 3 - .../_mock_similarity_search.py | 21 -- .../_mock_similarity_searchers.py | 72 ++++++ aeon/utils/base/_register.py | 14 +- docs/api_reference/utils.rst | 1 - 14 files changed, 807 insertions(+), 296 deletions(-) create mode 100644 aeon/similarity_search/subsequence_search/tests/test_brute_force.py rename aeon/similarity_search/subsequence_search/tests/{test__commons.py => test_commons.py} (59%) delete mode 100644 aeon/testing/mock_estimators/_mock_similarity_search.py create mode 100644 aeon/testing/mock_estimators/_mock_similarity_searchers.py diff --git a/aeon/similarity_search/series_search/__init__.py b/aeon/similarity_search/series_search/__init__.py index f576c41f03..2f69dab51a 100644 --- a/aeon/similarity_search/series_search/__init__.py +++ b/aeon/similarity_search/series_search/__init__.py @@ -1,7 +1,5 @@ -"""Similarity search module.""" +"""Series search module.""" -__all__ = ["BaseSimilaritySearch", "QuerySearch", "SeriesSearch"] +__all__ = ["BaseSeriesSearch", "BaseIndexSearch"] -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.query_search import QuerySearch -from aeon.similarity_search.series_search import SeriesSearch +from aeon.similarity_search.series_search.base import BaseIndexSearch, BaseSeriesSearch diff --git a/aeon/similarity_search/subsequence_search/__init__.py b/aeon/similarity_search/subsequence_search/__init__.py index dfca8ee18c..c5de805eb6 100644 --- a/aeon/similarity_search/subsequence_search/__init__.py +++ b/aeon/similarity_search/subsequence_search/__init__.py @@ -1,5 +1,9 @@ -"""Similarity search module.""" +"""Subsequence search module.""" -__all__ = ["StompMatrixProfile"] +__all__ = ["BaseSubsequenceSearch", "BaseMatrixProfile", "StompMatrixProfile"] from aeon.similarity_search.subsequence_search._stomp import StompMatrixProfile +from aeon.similarity_search.subsequence_search.base import ( + BaseMatrixProfile, + BaseSubsequenceSearch, +) diff --git a/aeon/similarity_search/subsequence_search/_brute_force.py b/aeon/similarity_search/subsequence_search/_brute_force.py index 691b81367a..6c26925a32 100644 --- a/aeon/similarity_search/subsequence_search/_brute_force.py +++ b/aeon/similarity_search/subsequence_search/_brute_force.py @@ -16,8 +16,8 @@ from aeon.similarity_search.subsequence_search.base import BaseMatrixProfile from aeon.utils.numba.general import ( get_all_subsequences, + z_normalise_series_2d, z_normalise_series_3d, - z_normalize_series_2d, ) # TODO : check function params and make docstrings @@ -25,7 +25,7 @@ class BruteForceMatrixProfile(BaseMatrixProfile): - """.""" + """Estimator to compute matrix profile and distance profile using brute force.""" def compute_matrix_profile( self, @@ -33,37 +33,51 @@ def compute_matrix_profile( threshold, exclusion_size, inverse_distance, - allow_overlap, + allow_neighboring_matches, X: Optional[np.ndarray] = None, X_index: Optional[int] = None, ): """ - . + Compute matrix profiles. + + The matrix profiles are computed on the collection given in fit. If ``X`` is + not given, computes the matrix profile of each series in the collection. If it + is given, only computes it for ``X``. Parameters ---------- - k : TYPE - DESCRIPTION. - threshold : TYPE - DESCRIPTION. - exclusion_size : TYPE - DESCRIPTION. - inverse_distance : TYPE - DESCRIPTION. + k : int + The number of best matches to return during predict for each subsequence. + threshold : float + The number of best matches to return during predict for each subsequence. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the worst matches to the query will be returned instead of the best ones. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestomp - exclusion_size` and + :math:`id_timestomp + exclusion_size` which cannot be returned + as best match if :math:`id_timestomp` was already selected. By default, + the value None means that this is not used. X : Optional[np.ndarray], optional - DESCRIPTION. The default is None. + The time series on which the matrix profile will be compute. + The default is None, meaning that the series in the collection given in fit + will be used instead. X_index : Optional[int], optional - DESCRIPTION. The default is None. - : TYPE - DESCRIPTION. + If ``X`` is a series of the database given in fit, specify its index in + ``X_``. If specified, each query of this series won't be able to match with + its neighboring subsequences. Returns ------- - MP : TYPE - DESCRIPTION. - IP : TYPE - DESCRIPTION. - + MP : array of shape (series_length - L + 1,) + Matrix profile distances for each query subsequence. If X is none, this + will be a list of MP for each series in X_. + IP : array of shape (series_length - L + 1,) + Indexes of the top matches for each query subsequence. If X is none, this + will be a list of MP for each series in X_. """ # pairwise if none if X is None: @@ -87,11 +101,11 @@ def compute_matrix_profile( self.length, X_index, k, - allow_overlap, threshold, + allow_neighboring_matches, exclusion_size, inverse_distance, - normalize=self.normalize, + normalise=self.normalise, ) return MP, IP @@ -114,7 +128,7 @@ def compute_distance_profile(self, X: np.ndarray): """ distance_profiles = _naive_squared_distance_profile( - self.X_, X, normalize=self.normalize + self.X_, X, normalise=self.normalise ) if not self.metadata_["unequal_length"]: @@ -153,7 +167,7 @@ def _compute_dist_profile(X_subs, q): def _naive_squared_distance_profile( X, Q, - normalize=False, + normalise=False, ): """ Compute a squared euclidean distance profile. @@ -164,8 +178,8 @@ def _naive_squared_distance_profile( Input time series dataset to search in. Q : array, shape=(n_channels, query_length) Query used during the search. - normalize : bool - Wheter to use a z-normalized distance. + normalise : bool + Wheter to use a z-normalised distance. Returns ------- @@ -178,14 +192,16 @@ def _naive_squared_distance_profile( # Init distance profile array with unequal length support for i in range(len(X)): dist_profiles.append(np.zeros(X[i].shape[1] - query_length + 1)) - if normalize: - Q = z_normalize_series_2d(Q) + if normalise: + Q = z_normalise_series_2d(Q) else: Q = Q.astype(np.float64) - for i in prange(len(X)): + for _i in prange(len(X)): + # cast uint64 due to parallel prange + i = np.int64(_i) X_subs = get_all_subsequences(X[i], query_length, 1) - if normalize: + if normalise: X_subs = z_normalise_series_3d(X_subs) dist_profile = _compute_dist_profile(X_subs, Q) @@ -198,32 +214,50 @@ def _naive_squared_matrix_profile( X, T, L, - k, T_index, + k, threshold, - inverse_distance, - allow_overlap, + allow_neighboring_matches, exclusion_size, - normalize=False, + inverse_distance, + normalise=False, ): """ Compute a squared euclidean matrix profile. Parameters ---------- - X : array, shape=(n_samples, n_channels, n_timepoints_x) - Input time series dataset to search in. - T : array, shape=(n_channels, n_timepoints_t) - Time series from which queries are extracted. - query_length : int - Length of the queries to extract from T. + X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + The input samples. If X is an unquel length collection, expect a TypedList + of 2D arrays of shape (n_channels, n_timepoints) + T : np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + L : int + Length of the subsequences used for the distance computation. T_index : int, - If ``X`` is a subsequence of the database given in fit, specify its starting - index as (i_case, i_timestamp). If specified, this subsequence and the - neighboring ones (according to ``exclusion_factor``) won't be considered as - admissible candidates. - normalize : bool - Wheter to use a z-normalized distance. + If ``T`` is a series of ``X``, specify its index + in ``X``. If specified, each query of this series won't be able to + match with its neighboring subsequences. + k : int + The number of best matches to return during predict for each subsequence. + threshold : float + The number of best matches to return during predict for each subsequence. + allow_neighboring_matches : bool + Wheter the top-k candidates can be neighboring subsequences. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestomp - exclusion_size` and + :math:`id_timestomp + exclusion_size` which cannot be returned + as best match if :math:`id_timestomp` was already selected. By default, + the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + normalise : bool + Wheter to use a z-normalised distance. Returns ------- @@ -242,14 +276,14 @@ def _naive_squared_matrix_profile( X_subs = List() for i in range(len(X)): i_subs = get_all_subsequences(X[i], L, 1) - if normalize: - i_subs = z_normalise_series_3d(X_subs) + if normalise: + i_subs = z_normalise_series_3d(i_subs) X_subs.append(i_subs) for i_q in range(n_queries): Q = T[:, i : i + L] - if normalize: - Q = z_normalize_series_2d(Q) + if normalise: + Q = z_normalise_series_2d(Q) for i_x in prange(len(X)): dist_profiles[i_x][0 : X[i_x].shape[1] - L + 1] = _compute_dist_profile( X_subs[i_x], Q @@ -264,11 +298,11 @@ def _naive_squared_matrix_profile( if inverse_distance: dist_profiles = _inverse_distance_profile_list(dist_profiles) - top_dists, top_indexes = _extract_top_k_from_dist_profile( + top_indexes, top_dists = _extract_top_k_from_dist_profile( dist_profiles, k, threshold, - allow_overlap, + allow_neighboring_matches, exclusion_size, ) diff --git a/aeon/similarity_search/subsequence_search/_commons.py b/aeon/similarity_search/subsequence_search/_commons.py index e2e0aa54df..03e0ee1ac3 100644 --- a/aeon/similarity_search/subsequence_search/_commons.py +++ b/aeon/similarity_search/subsequence_search/_commons.py @@ -73,29 +73,61 @@ def _extract_top_k_from_dist_profile( dist_profiles, k, threshold, - allow_overlap, + allow_neighboring_matches, exclusion_size, ): + """ + Given an array (or list) of distance profiles, extract the top k lower distances. + + Parameters + ---------- + dist_profiles : np.ndarray, shape = (n_samples, n_timepoints - length + 1) + A collection of distance profiles computed from ``n_samples`` time series of + size ``n_timepoints``, giving distance profiles of length + ``n_timepoints - length + 1``, with ``length`` the size of the query used to + compute the distance profiles. + k : int + Number of best matches to return + threshold : float + A threshold on the distances of the best matches. To be returned, a candidate + must have a distance bellow this threshold. This can reduce the number of + returned matches to be bellow ``k`` + allow_neighboring_matches : bool + Wheter to allow returning matches that are in the same neighborhood. + exclusion_size : int + The size of the exlusion size to apply when ``allow_neighboring_matches`` is + False. It is applied on both side of existing matches (+/- their indexes). + + Returns + ------- + top_k_indexes : np.ndarray, shape = (k, 2) + The indexes of the best matches in ``distance_profiles``. + top_k_distances : np.ndarray, shape = (k) + The distances of the best matches. + + """ top_k_indexes = np.zeros((2 * k, 2), dtype=np.int64) - 1 top_k_distances = np.full(2 * k, np.inf) for i_profile in range(len(dist_profiles)): # Extract top-k without neighboring matches - if not allow_overlap: + if not allow_neighboring_matches: _sorted_indexes = np.argsort(dist_profiles[i_profile]) _top_k_indexes = np.zeros(k, dtype=np.int64) - 1 - _current_k = 1 - _top_k_indexes[0] = _sorted_indexes[0] - _current_j = 1 + _current_k = 0 + _current_j = 0 # Until we extract k value or explore all the array while _current_k < k and _current_j < len(_sorted_indexes): _insert = True # Check for validity with each previously inserted for i_k in range(_current_k): - ub = min(_top_k_indexes[i_k] + exclusion_size, len(dist_profiles)) + ub = min( + _top_k_indexes[i_k] + exclusion_size, + len(dist_profiles[i_profile]), + ) lb = max(_top_k_indexes[i_k] - exclusion_size, 0) if ( - _sorted_indexes[_current_j] < lb - or _sorted_indexes[_current_j] > ub + _sorted_indexes[_current_j] >= lb + and _sorted_indexes[_current_j] <= ub ): _insert = False break diff --git a/aeon/similarity_search/subsequence_search/_stomp.py b/aeon/similarity_search/subsequence_search/_stomp.py index 8986a41926..0dd75971f8 100644 --- a/aeon/similarity_search/subsequence_search/_stomp.py +++ b/aeon/similarity_search/subsequence_search/_stomp.py @@ -1,10 +1,10 @@ """Implementation of STOMP with squared euclidean distance.""" -from typing import Optional - __maintainer__ = ["baraline"] +from typing import Optional + import numpy as np from numba import njit, prange from numba.typed import List @@ -16,15 +16,14 @@ get_ith_products, ) from aeon.similarity_search.subsequence_search.base import BaseMatrixProfile -from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD - -# TODO : check and order parameters of functions in base and here -# TODO : check function params and make docstrings to be consistent with brute force -# TODO : validate tests +from aeon.utils.numba.general import ( + AEON_NUMBA_STD_THRESHOLD, + sliding_mean_std_one_series, +) class StompMatrixProfile(BaseMatrixProfile): - """.""" + """Estimator to compute matrix profile and distance profile using STOMP.""" def compute_matrix_profile( self, @@ -32,41 +31,53 @@ def compute_matrix_profile( threshold, exclusion_size, inverse_distance, - allow_overlap, + allow_neighboring_matches, X: Optional[np.ndarray] = None, X_index: Optional[int] = None, ): """ - . + Compute matrix profiles. + + The matrix profiles are computed on the collection given in fit. If ``X`` is + not given, computes the matrix profile of each series in the collection. If it + is given, only computes it for ``X``. Parameters ---------- - k : TYPE - DESCRIPTION. - threshold : TYPE - DESCRIPTION. - exclusion_size : TYPE - DESCRIPTION. - inverse_distance : TYPE - DESCRIPTION. + k : int + The number of best matches to return during predict for each subsequence. + threshold : float + The number of best matches to return during predict for each subsequence. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the worst matches to the query will be returned instead of the best ones. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestomp - exclusion_size` and + :math:`id_timestomp + exclusion_size` which cannot be returned + as best match if :math:`id_timestomp` was already selected. By default, + the value None means that this is not used. X : Optional[np.ndarray], optional - DESCRIPTION. The default is None. + The time series on which the matrix profile will be compute. + The default is None, meaning that the series in the collection given in fit + will be used instead. X_index : Optional[int], optional If ``X`` is a series of the database given in fit, specify its index in ``X_``. If specified, each query of this series won't be able to match with its neighboring subsequences. - : TYPE - DESCRIPTION. Returns ------- - MP : TYPE - DESCRIPTION. - IP : TYPE - DESCRIPTION. - + MP : array of shape (series_length - L + 1,) + Matrix profile distances for each query subsequence. If X is none, this + will be a list of MP for each series in X_. + IP : array of shape (series_length - L + 1,) + Indexes of the top matches for each query subsequence. If X is none, this + will be a list of MP for each series in X_. """ - # pairwise if none + # If we compute matrix profiles for each series in X_ if X is None: MP = [] IP = [] @@ -81,6 +92,7 @@ def compute_matrix_profile( ) MP.append(_MP) IP.append(_IP) + # else we compute matrix profiles using X on the series in X_ else: XdotT = [ get_ith_products(self.X[i], X, self.length, 0) @@ -90,12 +102,13 @@ def compute_matrix_profile( XdotT = np.asarray(XdotT) elif isinstance(X, List): XdotT = List(XdotT) + if X_index is None: - X_means, X_stds = 0 + X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) else: X_means, X_stds = self.X_means_[i], self.X_stds_[i] - if self.normalize: - MP, IP = _stomp_normalized( + if self.normalise: + MP, IP = _stomp_normalised( self.X_, X, XdotT, @@ -107,11 +120,10 @@ def compute_matrix_profile( X_index, k, threshold, - allow_overlap, + allow_neighboring_matches, exclusion_size, inverse_distance, ) - else: MP, IP = _stomp( self.X_, @@ -120,12 +132,11 @@ def compute_matrix_profile( self.length, X_index, k, - allow_overlap, threshold, + allow_neighboring_matches, exclusion_size, inverse_distance, ) - return MP, IP def compute_distance_profile(self, X: np.ndarray): @@ -151,8 +162,8 @@ def compute_distance_profile(self, X: np.ndarray): else: QX = np.asarray(QX) - if self.normalize: - distance_profiles = _normalized_squared_distance_profile( + if self.normalise: + distance_profiles = _normalised_squared_distance_profile( QX, self.X_means_, self.X_stds_, @@ -172,8 +183,8 @@ def compute_distance_profile(self, X: np.ndarray): return distance_profiles -@njit(cache=True, parallel=True, fastmath=True) -def _stomp_normalized( +@njit(cache=True, fastmath=True) +def _stomp_normalised( X, T, XdotT, @@ -185,12 +196,12 @@ def _stomp_normalized( T_index, k, threshold, - allow_overlap, + allow_neighboring_matches, exclusion_size, inverse_distance, ): """ - Compute the Matrix Profile using the STOMP algorithm with normalized distances. + Compute the Matrix Profile using the STOMP algorithm with normalised distances. X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) The input samples. If X is an unquel length collection, expect a TypedList @@ -198,8 +209,6 @@ def _stomp_normalized( T : np.ndarray, 2D array of shape (n_channels, series_length) The series used for similarity search. Note that series_length can be equal, superior or inferior to n_timepoints, it doesn't matter. - L : int - Length of the subsequences used for the distance computation. XdotT : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) Precomputed dot products between each time series in X and the query series T. X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) @@ -212,18 +221,19 @@ def _stomp_normalized( Means of each subsequences of T of size L. T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) Stds of each subsequences of T of size L. + L : int + Length of the subsequences used for the distance computation. T_index : int, If ``T`` is a series of the database given in fit, specify its index in ``X_``. If specified, each query of this series won't be able to match with its neighboring subsequences. - k : int, default=1 + k : int, The number of best matches to return during predict for each subsequence. - threshold : float, default=np.inf + threshold : float The number of best matches to return during predict for each subsequence. - inverse_distance : bool, default=False - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - exclusion_size : int, optional + allow_neighboring_matches : bool + Wheter the top-k candidates can be neighboring subsequences. + exclusion_size : int The size of the exclusion zone used to prevent returning as top k candidates the ones that are close to each other (for example i and i+1). It is used to define a region between @@ -231,6 +241,9 @@ def _stomp_normalized( :math:`id_timestomp + exclusion_size` which cannot be returned as best match if :math:`id_timestomp` was already selected. By default, the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. Returns ------- @@ -244,25 +257,12 @@ def _stomp_normalized( MP = List() IP = List() - # Init List to allow parallel, we'll re-use it for all dist profiles - dist_profiles = List() - for i_x in range(len(X)): - dist_profiles.append(np.zeros(X[i_x].shape[1] - L + 1)) - for i_q in range(n_queries): - for i_x in prange(len(X)): - dist_profiles[i_x][0 : X[i_x].shape[1] - L + 1] = ( - _normalized_squared_dist_profile_one_series( - XdotT[i_x], - X_means[i_x], - X_stds[i_x], - T_means[:, i_q], - T_stds[:, i_q], - L, - T_stds[:, i_q] <= AEON_NUMBA_STD_THRESHOLD, - ) - ) - if i_q + 1 < n_queries: + dist_profiles = _normalised_squared_distance_profile( + XdotT, X_means, X_stds, T_means[:, i_q], T_stds[:, i_q], L + ) + if i_q + 1 < n_queries: + for i_x in range(len(X)): XdotT[i_x] = _update_dot_products_one_series( X[i_x], T, XdotT[i_x], L, i_q + 1 ) @@ -281,7 +281,7 @@ def _stomp_normalized( dist_profiles, k, threshold, - allow_overlap, + allow_neighboring_matches, exclusion_size, ) @@ -291,7 +291,7 @@ def _stomp_normalized( return MP, IP -@njit(cache=True, parallel=True, fastmath=True) +@njit(cache=True, fastmath=True) def _stomp( X, T, @@ -299,28 +299,65 @@ def _stomp( L, T_index, k, - allow_overlap, threshold, + allow_neighboring_matches, exclusion_size, inverse_distance, ): + """ + Compute the Matrix Profile using the STOMP algorithm with non-normalised distances. + + X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + The input samples. If X is an unquel length collection, expect a TypedList + of 2D arrays of shape (n_channels, n_timepoints) + T : np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + XdotT : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) + Precomputed dot products between each time series in X and the query series T. + L : int + Length of the subsequences used for the distance computation. + T_index : int, + If ``T`` is a series of the database given in fit, specify its index + in ``X_``. If specified, each query of this series won't be able to + match with its neighboring subsequences. + k : int, + The number of best matches to return during predict for each subsequence. + threshold : float + The number of best matches to return during predict for each subsequence. + allow_neighboring_matches : bool + Wheter the top-k candidates can be neighboring subsequences. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestomp - exclusion_size` and + :math:`id_timestomp + exclusion_size` which cannot be returned + as best match if :math:`id_timestomp` was already selected. By default, + the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + + Returns + ------- + tuple of np.ndarray + - MP : array of shape (series_length - L + 1,) + Matrix profile distances for each query subsequence. + - IP : array of shape (series_length - L + 1,) + Indexes of the top matches for each query subsequence. + """ n_queries = T.shape[1] - L + 1 MP = List() IP = List() - # Init List to allow parallel, we'll re-use it for all dist profiles - dist_profiles = List() - for i_x in range(len(X)): - dist_profiles.append(np.zeros(X[i_x].shape[1] - L + 1)) # For each query of size L in T for i_q in range(n_queries): Q = T[:, i_q : i_q + L] + dist_profiles = _squared_distance_profile(XdotT, X, Q) # For each series in X compute distance profile to the query - for i_x in prange(len(X)): - dist_profiles[i_x][0 : X[i_x].shape[1] - L + 1] = ( - _squared_dist_profile_one_series(XdotT[i_x], X[i_x], Q) - ) - if i_q + 1 < n_queries: + if i_q + 1 < n_queries: + for i_x in range(len(X)): XdotT[i_x] = _update_dot_products_one_series( X[i_x], T, XdotT[i_x], L, i_q + 1 ) @@ -339,7 +376,7 @@ def _stomp( dist_profiles, k, threshold, - allow_overlap, + allow_neighboring_matches, exclusion_size, ) @@ -486,11 +523,11 @@ def _squared_dist_profile_one_series(QT, T, Q): @njit(cache=True, fastmath=True, parallel=True) -def _normalized_squared_distance_profile( +def _normalised_squared_distance_profile( QX, X_means, X_stds, Q_means, Q_stds, query_length ): """ - Compute the normalized squared distance profiles between query subsequence and input time series. + Compute the normalised squared distance profiles between query subsequence and input time series. Parameters ---------- @@ -513,7 +550,7 @@ def _normalized_squared_distance_profile( ------- List of np.ndarray List of 2D arrays, each of shape (n_channels, n_timepoints - query_length + 1). - Each array contains the normalized squared distance profile between the query subsequence and the corresponding time series. + Each array contains the normalised squared distance profile between the query subsequence and the corresponding time series. Entries in the array are set to infinity where the mask is False. """ distance_profiles = List() @@ -526,7 +563,7 @@ def _normalized_squared_distance_profile( for _i_instance in prange(len(QX)): # iterator is uint64 with prange and parallel so cast to int to avoid warnings i_instance = np.int64(_i_instance) - distance_profiles[i_instance] = _normalized_squared_dist_profile_one_series( + distance_profiles[i_instance] = _normalised_squared_dist_profile_one_series( QX[i_instance], X_means[i_instance], X_stds[i_instance], @@ -539,11 +576,11 @@ def _normalized_squared_distance_profile( @njit(cache=True, fastmath=True) -def _normalized_squared_dist_profile_one_series( +def _normalised_squared_dist_profile_one_series( QT, T_means, T_stds, Q_means, Q_stds, query_length, Q_is_constant ): """ - Compute the z-normalized squared Euclidean distance profile for one time series. + Compute the z-normalised squared Euclidean distance profile for one time series. Parameters ---------- @@ -568,8 +605,8 @@ def _normalized_squared_dist_profile_one_series( ------- np.ndarray 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the - z-normalized squared distance profile between the query subsequence and the time - series. Entries are computed based on the z-normalized values, with special + z-normalised squared distance profile between the query subsequence and the time + series. Entries are computed based on the z-normalised values, with special handling for constant values. """ n_channels, profile_length = QT.shape diff --git a/aeon/similarity_search/subsequence_search/base.py b/aeon/similarity_search/subsequence_search/base.py index 1d49004472..7df358e5ec 100644 --- a/aeon/similarity_search/subsequence_search/base.py +++ b/aeon/similarity_search/subsequence_search/base.py @@ -17,8 +17,9 @@ ) from aeon.utils.numba.general import sliding_mean_std_one_series - # We can define a BaseVariableLengthSubsequenceSearch later for VALMOD and the likes. + + class BaseSubsequenceSearch(BaseSimilaritySearch): """ Base class for similarity search on time series subsequences. @@ -27,8 +28,8 @@ class BaseSubsequenceSearch(BaseSimilaritySearch): ---------- length : int The length of the subsequence to be considered. - normalize : bool, optional - Whether the inputs should be z-normalized. The default is False. + normalise : bool, optional + Whether the inputs should be z-normalised. The default is False. n_jobs : int, optional Number of parallel jobs to use. The default is 1. """ @@ -37,19 +38,21 @@ class BaseSubsequenceSearch(BaseSimilaritySearch): def __init__( self, length: int, - normalize: Optional[bool] = False, + normalise: Optional[bool] = False, n_jobs: Optional[int] = 1, ): self.length = length - super().__init__(n_jobs=n_jobs, normalize=normalize) + super().__init__(n_jobs=n_jobs, normalise=normalise) @final def find_motifs( self, - k: int, - threshold: float, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, X: Optional[np.ndarray] = None, - allow_overlap: Optional[bool] = False, + X_index: Optional[int] = None, + inverse_distance: Optional[bool] = False, + allow_neighboring_matches: Optional[bool] = False, exclusion_factor: Optional[float] = 2.0, ): """ @@ -71,13 +74,21 @@ def find_motifs( A series in which we want to indentify motifs. If provided, the motifs extracted should appear in X and in the database given in fit. If not provided, the motifs will be extracted only from the database given in fit. - allow_overlap: bool, optional + X_index : Optional[int], optional + If ``X`` is a series of the database given in fit, specify its index in + ``X_``. If specified, each query of this series won't be able to match with + its neighboring subsequences. + inverse_distance : bool, optional + Wheter to inverse the computed distance, meaning that the method will return + the anomalies instead of motifs. + allow_neighboring_matches: bool, optional Wheter a candidate can be part of multiple motif sets (True), or if motif sets should be mutually exclusive (False). exclusion_factor : float, default=2. A factor of the query length used to define the exclusion zone when - ``allow_overlap`` is set to False. For a given timestamp, the exclusion zone - starts from :math:`id_timestamp - query_length//exclusion_factor` and end at + ``allow_neighboring_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - query_length//exclusion_factor` and end at :math:`id_timestamp + query_length//exclusion_factor`. Returns @@ -88,6 +99,19 @@ def find_motifs( """ self._check_is_fitted() + prev_threads = get_num_threads() + X_index = self._check_X_index_int(X_index) + motifs = self._find_motifs( + k=k, + threshold=threshold, + exclusion_factor=exclusion_factor, + inverse_distance=inverse_distance, + allow_neighboring_matches=allow_neighboring_matches, + X=X, + X_index=X_index, + ) + set_num_threads(prev_threads) + return motifs @final def find_neighbors( @@ -97,7 +121,7 @@ def find_neighbors( threshold: Optional[float] = np.inf, inverse_distance: Optional[bool] = False, X_index: Optional[np.ndarray] = None, - allow_overlap: Optional[bool] = False, + allow_neighboring_matches: Optional[bool] = False, exclusion_factor: Optional[float] = 2.0, ): """ @@ -127,12 +151,13 @@ def find_neighbors( index as (i_case, i_timestamp). If specified, this subsequence and the neighboring ones (according to ``exclusion_factor``) won't be considered as admissible candidates. - allow_overlap: bool, optional + allow_neighboring_matches: bool, optional Wheter the top-k candidates can be neighboring subsequences. exclusion_factor : float, default=2. A factor of the query length used to define the exclusion zone when - ``allow_overlap`` is set to False. For a given timestamp, the exclusion zone - starts from :math:`id_timestamp - query_length//exclusion_factor` and end at + ``allow_neighboring_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - query_length//exclusion_factor` and end at :math:`id_timestamp + query_length//exclusion_factor`. Returns @@ -151,7 +176,7 @@ def find_neighbors( f"Expected a subsequence of shape {(self.n_channels_, self.length)} but" f" got {X.shape}" ) - self._check_X_index(X_index) + X_index = self._check_X_index_array(X_index) prev_threads = get_num_threads() set_num_threads(self._n_jobs) neighbors, distances = self._find_neighbors( @@ -160,7 +185,7 @@ def find_neighbors( threshold=threshold, inverse_distance=inverse_distance, X_index=X_index, - allow_overlap=allow_overlap, + allow_neighboring_matches=allow_neighboring_matches, exclusion_factor=exclusion_factor, ) set_num_threads(prev_threads) @@ -172,10 +197,41 @@ def find_neighbors( ) return neighbors, distances - def _check_X_index(self, X_index: np.ndarray): + def _check_X_index_int(self, X_index: int): """ Check wheter the X_index parameter is correctly formated and is admissible. + This check is made for motif search functions. + + Parameters + ---------- + X_index : int + Index of a series in X_. + + Returns + ------- + X_index : int + Index of a series in X_ + + """ + if X_index is not None: + if not isinstance(X_index, int): + raise TypeError("Expected an integer for X_index but got {X_index}") + + if X_index >= self.n_cases_ or X_index < 0: + raise ValueError( + "The value of X_index cannot exced the number " + "of series in the collection given during fit. Expected a value " + f"between [0, {self.n_cases_ - 1}] but got {X_index}" + ) + return X_index + + def _check_X_index_array(self, X_index: np.ndarray): + """ + Check wheter the X_index parameter is correctly formated and is admissible. + + This check is made for neighbour search functions. + Parameters ---------- X_index : np.ndarray, 1D array of shape (2) @@ -198,12 +254,12 @@ def _check_X_index(self, X_index: np.ndarray): ): X_index = np.asarray(X_index, dtype=int) elif len(X_index) != 2: - raise ValueError( + raise TypeError( "Expected a numpy array or list of integers with 2 elements " f"for X_index but got {X_index}" ) elif ( - not (isinstance(X_index[0], int) and isinstance(X_index[1], int)) + not (isinstance(X_index[0], int) or not isinstance(X_index[1], int)) or X_index.dtype != int ): raise TypeError( @@ -211,7 +267,7 @@ def _check_X_index(self, X_index: np.ndarray): f"{X_index}" ) - if X_index[0] >= self.n_cases_: + if X_index[0] >= self.n_cases_ or X_index[0] < 0: raise ValueError( "The sample ID (first element) of X_index cannot exced the number " "of series in the collection given during fit. Expected a value " @@ -260,6 +316,30 @@ def _compute_mean_std_from_collection(self, X: np.ndarray): @abstractmethod def _fit(self, X, y=None): ... + @abstractmethod + def _find_motifs( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + inverse_distance: Optional[bool] = False, + X_index=None, + allow_neighboring_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 2.0, + ): ... + + @abstractmethod + def _find_neighbors( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + inverse_distance: Optional[bool] = False, + X_index=None, + allow_neighboring_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 2.0, + ): ... + class BaseMatrixProfile(BaseSubsequenceSearch): """Base class for Matrix Profile methods using a length parameter.""" @@ -273,13 +353,33 @@ def _fit(self, X, y=None): ) ) - if self.normalize: + if self.normalise: self.X_means_, self.X_stds_ = self._compute_mean_std_from_collection(X) self.X_ = X return self - def _find_motifs(): - raise NotImplementedError() + def _find_motifs( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + inverse_distance: Optional[bool] = False, + X_index=None, + allow_neighboring_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 2.0, + ): + exclusion_size = self.length // exclusion_factor + + MP, IP = self.compute_matrix_profile( + k, + threshold, + exclusion_size, + inverse_distance, + allow_neighboring_matches, + X=X, + X_index=X_index, + ) + # TODO : implement logic here def _find_neighbors( self, @@ -288,7 +388,7 @@ def _find_neighbors( threshold: Optional[float] = np.inf, inverse_distance: Optional[bool] = False, X_index=None, - allow_overlap: Optional[bool] = False, + allow_neighboring_matches: Optional[bool] = False, exclusion_factor: Optional[float] = 2.0, ): """ @@ -318,12 +418,13 @@ def _find_neighbors( index as (i_case, i_timestamp). If specified, this subsequence and the neighboring ones (according to ``exclusion_factor``) won't be considered as admissible candidates. - allow_overlap: bool, optional + allow_neighboring_matches: bool, optional Wheter the top-k candidates can be neighboring subsequences. exclusion_factor : float, default=2. A factor of the query length used to define the exclusion zone when - ``allow_overlap`` is set to False. For a given timestamp, the exclusion zone - starts from :math:`id_timestamp - query_length//exclusion_factor` and end at + ``allow_neighboring_matches`` is set to False. For a given timestamp, the + exclusion zone starts from + :math:`id_timestamp - query_length//exclusion_factor` and end at :math:`id_timestamp + query_length//exclusion_factor`. """ exclusion_size = self.length // exclusion_factor @@ -343,16 +444,25 @@ def _find_neighbors( dist_profiles, k, threshold, - allow_overlap, + allow_neighboring_matches, exclusion_size, ) @abstractmethod - def compute_matrix_profile(X: Optional[np.ndarray] = None): + def compute_matrix_profile( + self, + k, + threshold, + exclusion_size, + inverse_distance, + allow_neighboring_matches, + X: Optional[np.ndarray] = None, + X_index: Optional[int] = None, + ): """Compute matrix profiles between X_ and X or between all series in X_.""" ... @abstractmethod - def compute_distance_profile(X: np.ndarray): + def compute_distance_profile(self, X: np.ndarray): """Compute distrance profiles between X_ and X (a series of size length).""" ... diff --git a/aeon/similarity_search/subsequence_search/tests/test_brute_force.py b/aeon/similarity_search/subsequence_search/tests/test_brute_force.py new file mode 100644 index 0000000000..3c92138b3d --- /dev/null +++ b/aeon/similarity_search/subsequence_search/tests/test_brute_force.py @@ -0,0 +1,155 @@ +""" +Tests for stomp algorithm. + +We do not test equality for returned indexes due to the unstable nature of argsort +and the fact that the "kind=stable" parameter is not yet supported in numba. We instead +test that the returned index match the expected distance value. +""" + +__maintainer__ = ["baraline"] + +import numpy as np +import pytest +from numba.typed import List +from numpy.testing import assert_almost_equal, assert_array_almost_equal + +from aeon.similarity_search.subsequence_search._brute_force import ( + _compute_dist_profile, + _naive_squared_distance_profile, + _naive_squared_matrix_profile, +) +from aeon.similarity_search.subsequence_search._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile_list, +) +from aeon.testing.data_generation import ( + make_example_2d_numpy_series, + make_example_3d_numpy, + make_example_3d_numpy_list, +) +from aeon.utils.numba.general import sliding_mean_std_one_series + +K_VALUES = [1, 3, 5] +NN_MATCHES = [True, False] +INVERSE = [True, False] +NORMALISE = [True, False] + + +def _get_mean_sdts_inputs(X, Q, L): + X_means = [] + X_stds = [] + + for i_x in range(len(X)): + _mean, _std = sliding_mean_std_one_series(X[i_x], L, 1) + X_stds.append(_std) + X_means.append(_mean) + + Q_means = Q.mean(axis=1) + Q_stds = Q.std(axis=1) + + return X_means, X_stds, Q_means, Q_stds + + +def test__compute_dist_profile(): + """Test Euclidean distance.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + dist_profile = _compute_dist_profile(X, Q) + for i_t in range(X.shape[1] - L + 1): + assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) + + +def test__naive_squared_distance_profile(normalise): + """Test Euclidean distance profile calculation.""" + L = 3 + X = make_example_3d_numpy(n_cases=3, n_channels=1, n_timepoints=10, return_y=False) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + dist_profiles = _naive_squared_distance_profile(X, Q) + for i_x in range(len(X)): + for i_t in range(X[i_x].shape[1] - L + 1): + assert_almost_equal( + dist_profiles[i_x][i_t], np.sum((X[i_x, :, i_t : i_t + L] - Q) ** 2) + ) + + # test unequal length and multivariate + X = List( + make_example_3d_numpy_list( + n_cases=3, + n_channels=2, + min_n_timepoints=10, + max_n_timepoints=20, + return_y=False, + ) + ) + + Q = make_example_2d_numpy_series(n_channels=2, n_timepoints=L) + dist_profiles = _naive_squared_distance_profile(X, Q) + for i_x in range(len(X)): + for i_t in range(X[i_x].shape[1] - L + 1): + assert_almost_equal( + dist_profiles[i_x][i_t], np.sum((X[i_x][:, i_t : i_t + L] - Q) ** 2) + ) + + +@pytest.mark.parametrize( + [ + ("k", K_VALUES), + ("allow_neighboring_matches", NN_MATCHES), + ("inverse_distance", INVERSE), + ("normalise", NORMALISE), + ] +) +def test__naive_squared_matrix_profile( + k, allow_neighboring_matches, inverse_distance, normalise +): + """Test STOMP method.""" + L = 3 + + X = make_example_3d_numpy_list( + n_cases=3, + n_channels=2, + min_n_timepoints=6, + max_n_timepoints=8, + return_y=False, + ) + T = make_example_2d_numpy_series(n_channels=2, n_timepoints=5) + + T_index = None + threshold = np.inf + exclusion_size = L + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query + MP, IP = _naive_squared_matrix_profile( + X, + T, + L, + T_index, + k, + threshold, + allow_neighboring_matches, + exclusion_size, + inverse_distance, + normalise=normalise, + ) + # For each query of size L in T + for i in range(T.shape[1] - L + 1): + dist_profiles = _naive_squared_distance_profile( + X, T[:, i : i + L], normalise=normalise + ) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + if inverse_distance: + dist_profiles = _inverse_distance_profile_list(dist_profiles) + + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profiles, k, threshold, allow_neighboring_matches, exclusion_size + ) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profiles[index[0]][index[1]]) diff --git a/aeon/similarity_search/subsequence_search/tests/test__commons.py b/aeon/similarity_search/subsequence_search/tests/test_commons.py similarity index 59% rename from aeon/similarity_search/subsequence_search/tests/test__commons.py rename to aeon/similarity_search/subsequence_search/tests/test_commons.py index b0e2764b0b..70e443a78c 100644 --- a/aeon/similarity_search/subsequence_search/tests/test__commons.py +++ b/aeon/similarity_search/subsequence_search/tests/test_commons.py @@ -1,12 +1,13 @@ """Test _commons.py functions.""" __maintainer__ = ["baraline"] - import numpy as np +import pytest from numba.typed import List -from numpy.testing import assert_array_almost_equal, assert_array_equal +from numpy.testing import assert_, assert_array_almost_equal from aeon.similarity_search.subsequence_search._commons import ( + _extract_top_k_from_dist_profile, _inverse_distance_profile_list, fft_sliding_dot_product, get_ith_products, @@ -59,59 +60,38 @@ def test__inverse_distance_profile_list(): assert_array_almost_equal(1 / (X[1] + 1e-8), T[1]) -def test__extract_top_k_from_dist_profile(): - """Test method to esxtract the top k candidates from a list of distance profiles.""" - X = List([ - [5,4,3,3,1,3,2,5,1,4,1,0,1,2,2,7,8,1,5], - [5,1,0,1,0,0,5,4,3,5,6,1,4,2], - ]) - - top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( - X, - 1, - np.inf, - False, - 3 - ) - assert_array_equal(top_k_indexes, [[0,11]]) - assert_array_equal(top_k_indexes, [0]) - - top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( - X, - 5, - np.inf, - False, - 3 - ) - assert_array_equal(top_k_indexes, [[0,11],[1,2],[0,4],[0,17],[1,11]]) - assert_array_equal(top_k_indexes, [0,0,1,1,1]) +K_VALUES = [1, 3, 5] +THRESHOLDS = [np.inf, 0.7] +NN_MATCHES = [False, True] - top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( - X, - 5, - np.inf, - True, - 3 - ) - assert_array_equal(top_k_indexes, [[0,11],[1,2],[1,4],[1,5],[0,4]]) - assert_array_equal(top_k_indexes, [0,0,0,0,1]) - top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( - X, - 5, - 0.5, - True, - 3 +@pytest.mark.parametrize( + [("k", K_VALUES), ("threshold", THRESHOLDS), ("allow_nn_matches", NN_MATCHES)] +) +def test__extract_top_k_from_dist_profile(k, threshold, allow_nn_matches): + """Test method to esxtract the top k candidates from a list of distance profiles.""" + X = make_example_2d_numpy_list( + n_cases=2, min_n_timepoints=5, max_n_timepoints=7, return_y=False ) - assert_array_equal(top_k_indexes, [[0,11],[1,2],[1,4],[1,5]]) - assert_array_equal(top_k_indexes, [0,0,0,0]) + X_sort = [X[i][np.argsort(X[i])] for i in range(len(X))] top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( - X, - 5, - 0.5, - False, - 3 + X, k, threshold, allow_nn_matches, 3 ) - assert_array_equal(top_k_indexes, [[0,11],[1,2]]) - assert_array_equal(top_k_indexes, [0,0]) + for i, index in enumerate(top_k_indexes): + assert_(X[index[0]][index[1]] == top_k_distances[i]) + assert_(np.all(top_k_distances <= threshold)) + if allow_nn_matches: + for i in range(len(X)): + assert_(np.all(top_k_distances <= X_sort[i][k - 1])) + if not allow_nn_matches: + for i_x in range(len(X)): + # test same index X respect exclusion + same_X = [ + top_k_indexes[i][1] + for i in range(len(top_k_indexes)) + if top_k_indexes[i][0] == i_x + ] + same_X = np.sort(same_X) + if len(same_X) > 1: + assert_(np.all(np.diff(same_X) >= 3)) diff --git a/aeon/similarity_search/subsequence_search/tests/test_stomp.py b/aeon/similarity_search/subsequence_search/tests/test_stomp.py index 169eee135e..7a655035a5 100644 --- a/aeon/similarity_search/subsequence_search/tests/test_stomp.py +++ b/aeon/similarity_search/subsequence_search/tests/test_stomp.py @@ -1,4 +1,10 @@ -"""Tests for stomp algorithm.""" +""" +Tests for stomp algorithm. + +We do not test equality for returned indexes due to the unstable nature of argsort +and the fact that the "kind=stable" parameter is not yet supported in numba. We instead +test that the returned index match the expected distance value. +""" __maintainer__ = ["baraline"] @@ -7,14 +13,18 @@ from numba.typed import List from numpy.testing import assert_almost_equal, assert_array_almost_equal -from aeon.similarity_search.subsequence_search._commons import get_ith_products +from aeon.similarity_search.subsequence_search._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile_list, + get_ith_products, +) from aeon.similarity_search.subsequence_search._stomp import ( - _normalized_squared_dist_profile_one_series, - _normalized_squared_distance_profile, + _normalised_squared_dist_profile_one_series, + _normalised_squared_distance_profile, _squared_dist_profile_one_series, _squared_distance_profile, _stomp, - _stomp_normalized, + _stomp_normalised, _update_dot_products_one_series, ) from aeon.testing.data_generation import ( @@ -27,7 +37,9 @@ z_normalise_series_2d_with_mean_std, ) -K_VALUES = [1, 3] +K_VALUES = [1, 3, 5] +NN_MATCHES = [True, False] +INVERSE = [True, False] def _get_mean_sdts_inputs(X, Q, L): @@ -79,7 +91,7 @@ def test__squared_dist_profile_one_series(): assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) -def test__normalized_squared_dist_profile_one_series(): +def test__normalised_squared_dist_profile_one_series(): """Test Euclidean distance.""" L = 3 X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) @@ -89,7 +101,7 @@ def test__normalized_squared_dist_profile_one_series(): Q_mean = Q.mean(axis=1) Q_std = Q.std(axis=1) - dist_profile = _normalized_squared_dist_profile_one_series( + dist_profile = _normalised_squared_dist_profile_one_series( QX, X_mean, X_std, Q_mean, Q_std, L, Q.std(axis=1) <= 0 ) Q = z_normalise_series_2d_with_mean_std(Q, Q_mean, Q_std) @@ -134,7 +146,7 @@ def test__squared_distance_profile(): ) -def test__normalized_squared_distance_profile(): +def test__normalised_squared_distance_profile(): """Test Euclidean distance profile calculation.""" L = 3 X = make_example_3d_numpy(n_cases=3, n_channels=1, n_timepoints=10, return_y=False) @@ -146,7 +158,7 @@ def test__normalized_squared_distance_profile(): X_means = np.asarray(X_means) X_stds = np.asarray(X_stds) - dist_profiles = _normalized_squared_distance_profile( + dist_profiles = _normalised_squared_distance_profile( QX, X_means, X_stds, Q_means, Q_stds, L ) @@ -179,7 +191,7 @@ def test__normalized_squared_distance_profile(): X_means = List(X_means) X_stds = List(X_stds) - dist_profiles = _normalized_squared_distance_profile( + dist_profiles = _normalised_squared_distance_profile( QX, X_means, X_stds, Q_means, Q_stds, L ) @@ -194,21 +206,32 @@ def test__normalized_squared_distance_profile(): ) -# K_VALUES = [1, 3] -@pytest.mark.parametrize("k", K_VALUES) -def test__stomp(k): +@pytest.mark.parametrize( + [ + ("k", K_VALUES), + ("allow_neighboring_matches", NN_MATCHES), + ("inverse_distance", INVERSE), + ] +) +def test__stomp(k, allow_neighboring_matches, inverse_distance): """Test STOMP method.""" L = 3 - X = np.array([[[1, 2, 3, 2, 1, 2, 3, 4, 5, 2, 1, 2, 2]]]) - T = np.array([[1, 1, 3, 2, 2]]) - XdotT = np.asarray([get_ith_products(X[i_x], T, L, 0) for i_x in range(len(X))]) + + X = make_example_3d_numpy_list( + n_cases=3, + n_channels=2, + min_n_timepoints=6, + max_n_timepoints=8, + return_y=False, + ) + T = make_example_2d_numpy_series(n_channels=2, n_timepoints=5) + XdotT = List([get_ith_products(X[i_x], T, L, 0) for i_x in range(len(X))]) T_index = None - allow_overlap = False threshold = np.inf exclusion_size = L - inverse_distance = False - + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query MP, IP = _stomp( X, T, @@ -216,23 +239,102 @@ def test__stomp(k): L, T_index, k, - allow_overlap, threshold, + allow_neighboring_matches, exclusion_size, inverse_distance, ) - Expected_MP = [[1, 6], [1, 2], [1, 2, 5]] - Expected_IP = [[[0, 0], [0, 1]], [[0, 1], [0, 0]], [[0, 2], [0, 1], [0, 0]]] - for i in range(len(Expected_MP)): - assert_array_almost_equal(Expected_IP[i], IP[i]) - assert_array_almost_equal(Expected_MP[i], MP[i]) + # For each query of size L in T + for i in range(T.shape[1] - L + 1): + dist_profiles = _squared_distance_profile( + List([get_ith_products(X[i_x], T, L, i) for i_x in range(len(X))]), + X, + T[:, i : i + L], + ) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + if inverse_distance: + dist_profiles = _inverse_distance_profile_list(dist_profiles) + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profiles, k, threshold, allow_neighboring_matches, exclusion_size + ) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profiles[index[0]][index[1]]) + + +@pytest.mark.parametrize( + [ + ("k", K_VALUES), + ("allow_neighboring_matches", NN_MATCHES), + ("inverse_distance", INVERSE), + ] +) +def test__stomp_normalised(k, allow_neighboring_matches, inverse_distance): + """Test STOMP normalised method.""" + L = 3 + X = make_example_3d_numpy_list( + n_cases=3, + n_channels=2, + min_n_timepoints=6, + max_n_timepoints=8, + return_y=False, + ) + T = make_example_2d_numpy_series(n_channels=2, n_timepoints=5) -@pytest.mark.parametrize("k", K_VALUES) -def test__stomp_normalized(k): - """Test STOMP normalized method.""" - _stomp_normalized - ... + XdotT = List([get_ith_products(X[i_x], T, L, 0) for i_x in range(len(X))]) + T_index = None + threshold = np.inf + exclusion_size = L + X_means, X_stds, _, _ = _get_mean_sdts_inputs(X, T, L) + T_means, T_stds = sliding_mean_std_one_series(T, L, 1) + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query + MP, IP = _stomp_normalised( + X, + T, + XdotT, + X_means, + X_stds, + T_means, + T_stds, + L, + T_index, + k, + threshold, + allow_neighboring_matches, + exclusion_size, + inverse_distance, + ) + # For each query of size L in T + for i in range(T.shape[1] - L + 1): + dist_profiles = _normalised_squared_distance_profile( + List([get_ith_products(X[i_x], T, L, i) for i_x in range(len(X))]), + X_means, + X_stds, + T_means[:, i], + T_stds[:, i], + L, + ) + + if inverse_distance: + dist_profiles = _inverse_distance_profile_list(dist_profiles) -# TODO : add tests for StompMatrixProfile + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profiles, k, threshold, allow_neighboring_matches, exclusion_size + ) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profiles[index[0]][index[1]]) diff --git a/aeon/testing/mock_estimators/__init__.py b/aeon/testing/mock_estimators/__init__.py index 219fc3e987..e517e07ca0 100644 --- a/aeon/testing/mock_estimators/__init__.py +++ b/aeon/testing/mock_estimators/__init__.py @@ -29,8 +29,6 @@ "MockUnivariateSeriesTransformer", "MockMultivariateSeriesTransformer", "MockSeriesTransformerNoFit", - # similarity search - "MockSimilaritySearch", ] from aeon.testing.mock_estimators._mock_anomaly_detectors import ( @@ -64,4 +62,3 @@ MockSeriesTransformerNoFit, MockUnivariateSeriesTransformer, ) -from aeon.testing.mock_estimators._mock_similarity_search import MockSimilaritySearch diff --git a/aeon/testing/mock_estimators/_mock_similarity_search.py b/aeon/testing/mock_estimators/_mock_similarity_search.py deleted file mode 100644 index 55c9c435c7..0000000000 --- a/aeon/testing/mock_estimators/_mock_similarity_search.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Mock similarity searchers useful for testing and debugging.""" - -__maintainer__ = ["baraline"] -__all__ = [ - "MockSimilaritySearch", -] - -from aeon.similarity_search.base import BaseSimilaritySearch - - -class MockSimilaritySearch(BaseSimilaritySearch): - """Mock similarity search for testing base class predict.""" - - def _fit(self, X, y=None): - """_fit dummy.""" - self.X_ = X - return self - - def predict(self, X): - """Predict dummy.""" - return [(0, 0)] diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py new file mode 100644 index 0000000000..f7c0acfc98 --- /dev/null +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -0,0 +1,72 @@ +"""Mock series transformers useful for testing and debugging.""" + +__maintainer__ = [] +__all__ = [ + "MockSubsequenceSearch", + "MockMatrixProfile", +] + +import numpy as np + +from aeon.similarity_search.subsequence_search.base import ( + BaseMatrixProfile, + BaseSubsequenceSearch, +) + + +class MockMatrixProfile(BaseMatrixProfile): + def __init__(self, length=3): + super().__init__(length=length) + + def compute_matrix_profile( + self, + k, + threshold, + exclusion_size, + inverse_distance, + allow_neighboring_matches, + X=None, + X_index=None, + ): + """Compute matrix profiles between X_ and X or between all series in X_.""" + return np.zeros((X.shape[1] - self.length + 1, k)), np.zeros( + (X.shape[1] - self.length + 1, k, 2) + ) + + def compute_distance_profile(self, X): + """Compute distrance profiles between X_ and X (a series of size length).""" + return np.zeros(X.shape[1] - self.length + 1) + + +class MockSubsequenceSearch(BaseSubsequenceSearch): + """MockSeriesTransformer to set tags.""" + + def __init__(self, length=3): + super().__init__(length=length) + + def _fit(self, X, y=None): + return self + + def _find_motifs( + self, + X, + k=1, + threshold=np.inf, + inverse_distance=False, + X_index=None, + allow_neighboring_matches=False, + exclusion_factor=2.0, + ): + return [[0, 0]], self.X_[0][0:1] # TODO: update after logic is implemented + + def _find_neighbors( + self, + X, + k=1, + threshold=np.inf, + inverse_distance=False, + X_index=None, + allow_neighboring_matches=False, + exclusion_factor=2.0, + ): + return [[0, 0]], self.X_[0][0:1] diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 1d81c2512c..5bf4045ad5 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -25,6 +25,10 @@ from aeon.regression.base import BaseRegressor from aeon.segmentation.base import BaseSegmenter from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search.subsequence_search.base import ( + BaseMatrixProfile, + BaseSubsequenceSearch, +) from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer from aeon.transformations.series import BaseSeriesTransformer @@ -45,6 +49,8 @@ "regressor": BaseRegressor, "segmenter": BaseSegmenter, "similarity_searcher": BaseSimilaritySearch, + "subsequence_searcher": BaseSubsequenceSearch, + "matrixprofile_searcher": BaseMatrixProfile, "series-transformer": BaseSeriesTransformer, "forecaster": BaseForecaster, } @@ -53,5 +59,11 @@ VALID_ESTIMATOR_BASES = { k: BASE_CLASS_REGISTER[k] for k in BASE_CLASS_REGISTER.keys() - - {"estimator", "collection-estimator", "series-estimator", "transformer"} + - { + "estimator", + "collection-estimator", + "series-estimator", + "transformer", + "similarity_searcher", + } } diff --git a/docs/api_reference/utils.rst b/docs/api_reference/utils.rst index 40dea9f67c..6f43398a44 100644 --- a/docs/api_reference/utils.rst +++ b/docs/api_reference/utils.rst @@ -87,7 +87,6 @@ Mock Estimators MockUnivariateSeriesTransformer MockMultivariateSeriesTransformer MockSeriesTransformerNoFit - MockSimilaritySearch Utilities ^^^^^^^^^ From bb2aa33820b914b8a5dd390e4f087c61925c7a0c Mon Sep 17 00:00:00 2001 From: baraline Date: Wed, 1 Jan 2025 09:36:10 +0100 Subject: [PATCH 05/36] Add test for base subsequence --- aeon/similarity_search/__init__.py | 4 +- aeon/similarity_search/base.py | 59 ++++- .../similarity_search/series_search/_r_lsh.py | 239 ++++++++++++++++++ .../subsequence_search/_stomp.py | 145 +++++------ .../subsequence_search/base.py | 89 ++++--- .../subsequence_search/tests/test_base.py | 78 ++++++ aeon/similarity_search/tests/test_base.py | 1 + .../_mock_similarity_searchers.py | 15 +- 8 files changed, 487 insertions(+), 143 deletions(-) create mode 100644 aeon/similarity_search/series_search/_r_lsh.py create mode 100644 aeon/similarity_search/subsequence_search/tests/test_base.py create mode 100644 aeon/similarity_search/tests/test_base.py diff --git a/aeon/similarity_search/__init__.py b/aeon/similarity_search/__init__.py index 53f80b2cdf..cdf5cebd84 100644 --- a/aeon/similarity_search/__init__.py +++ b/aeon/similarity_search/__init__.py @@ -1,3 +1,5 @@ """Similarity search module.""" -__all__ = [] +__all__ = ["BaseSimilaritySearch"] + +from aeon.similarity_search.base import BaseSimilaritySearch diff --git a/aeon/similarity_search/base.py b/aeon/similarity_search/base.py index 8a9e9547d7..d486491fa4 100644 --- a/aeon/similarity_search/base.py +++ b/aeon/similarity_search/base.py @@ -18,8 +18,8 @@ class BaseSimilaritySearch(BaseCollectionEstimator): Parameters ---------- - normalize : bool, optional - Whether the inputs should be z-normalized. The default is False. + normalise : bool, optional + Whether the inputs should be z-normalised. The default is False. n_jobs : int, optional Number of parallel jobs to use. The default is 1. """ @@ -35,11 +35,11 @@ class BaseSimilaritySearch(BaseCollectionEstimator): @abstractmethod def __init__( self, - normalize: Optional[bool] = False, + normalise: Optional[bool] = False, n_jobs: Optional[int] = 1, ): self.n_jobs = n_jobs - self.normalize = normalize + self.normalise = normalise super().__init__() @final @@ -68,7 +68,9 @@ def fit( ------- self """ + self.reset() prev_threads = get_num_threads() + self._check_fit_format(X) X = self._preprocess_collection(X) # Store minimum number of n_timepoints for unequal length collections self.min_timepoints_ = min([X[i].shape[-1] for i in range(len(X))]) @@ -85,9 +87,9 @@ def fit( @abstractmethod def find_motifs( self, + X: np.ndarray, k: int, threshold: float, - X: Optional[np.ndarray] = None, allow_overlap: Optional[bool] = True, ): """ @@ -100,10 +102,8 @@ def find_motifs( Parameters ---------- - X : np.ndarray, optional - The query in which we want to indentify motifs. If provided, the motifs - extracted should appear in X and in the database given in fit. If not - provided, the motifs will be extracted only from the database given in fit. + X : np.ndarray, + A series in which we want to indentify motifs. k : int, optional Number of motifs to return threshold : int, optional @@ -115,9 +115,12 @@ def find_motifs( Returns ------- - list of ndarray, shape=(k,) - A list of at most ``k`` numpy arrays containing the indexes of the - candidates in each motif. + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the indexes of the + motifs. + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the distances of the + motifs to . """ ... @@ -157,5 +160,37 @@ def find_neighbors( """ ... + def _check_fit_format(self, X): + if isinstance(X, np.ndarray): # "numpy3D" or numpy2D + if X.ndim != 3: + raise TypeError( + f"A np.ndarray given in fit must be 3D but found {X.ndim}D" + ) + elif isinstance(X, list): # np-list or df-list + if isinstance(X[0], np.ndarray): # if one a numpy they must all be 2D numpy + for a in X: + if not (isinstance(a, np.ndarray) and a.ndim == 2): + raise TypeError( + "A np-list given in fit must contain 2D np.ndarray but" + f" found {a.ndim}D" + ) + + def _check_find_neighbors_motif_format(self, X): + if isinstance(X, np.ndarray): + if X.ndim != 2: + raise TypeError( + "A np.ndarray given in find_neighbors must be 2D" + f"(n_channels, n_timepoints) but found {X.ndim}D." + ) + else: + raise TypeError( + "Expected a 2D np.ndarray in find_neighbors but found" f" {type(X)}." + ) + if self.n_channels_ != X.shape[0]: + raise ValueError( + f"Expected X to have {self.n_channels_} channels but" + f" got {X.shape[0]} channels." + ) + @abstractmethod def _fit(self, X, y=None): ... diff --git a/aeon/similarity_search/series_search/_r_lsh.py b/aeon/similarity_search/series_search/_r_lsh.py new file mode 100644 index 0000000000..9921aeeba1 --- /dev/null +++ b/aeon/similarity_search/series_search/_r_lsh.py @@ -0,0 +1,239 @@ +"""Random projection LSH index.""" + +import numpy as np +from numba import njit, prange + +TPB = 16 + + +@njit(cache=True) +def _hamming_dist(X, Y): + d = 0 + for i in prange(X.shape[0]): + d += X[i] ^ Y[i] + return d + + +@njit(cache=True, parallel=True) +def _hamming_dist_matrix(bool_hashes_value_list, bool_hashes): + n_hashes = bool_hashes.shape[0] + res = np.zeros((n_hashes, bool_hashes_value_list.shape[0]), dtype=np.int64) + for i in prange(n_hashes): + for j in prange(bool_hashes_value_list.shape[0]): + res[i, j] = _hamming_dist(bool_hashes_value_list[j], bool_hashes[i]) + return res + + +@njit(cache=True, fastmath=True, parallel=True) +def _series_to_bool(X, hash_funcs, start_points, length): + n_hash_funcs = hash_funcs.shape[0] + res = np.empty(n_hash_funcs, dtype=np.bool_) + for j in prange(n_hash_funcs): + res[j] = ( + np.dot(X[start_points[j] : start_points[j] + length], hash_funcs[j]) >= 0 + ) + return res + + +@njit(cache=True, fastmath=True, parallel=True) +def _collection_to_bool(X, hash_funcs, start_points, length): + n_hash_funcs = hash_funcs.shape[0] + n_samples = X.shape[0] + res = np.empty((n_samples, n_hash_funcs), dtype=np.bool_) + for i in prange(n_samples): + for j in prange(n_hash_funcs): + res[i, j] = ( + np.dot(X[i, start_points[j] : start_points[j] + length], hash_funcs[j]) + >= 0 + ) + return res + + +class LSH: + """ + . + + Parameters + ---------- + n_vectors : TYPE + DESCRIPTION. + custom_table : TYPE, optional + DESCRIPTION. The default is None. + + Returns + ------- + None. + + """ + + def __init__(self, n_hash_funcs=128, window_length=1.0, seed=None): + self.n_hash_funcs = n_hash_funcs + self.window_length = window_length + self.seed = seed + + def fit(self, X): + """ + . + + Parameters + ---------- + X : TYPE + DESCRIPTION. + + Returns + ------- + TYPE + DESCRIPTION. + + """ + self.rng_ = np.random.default_rng(self.seed) + self.X_ = np.array( + [X[i].flatten() for i in range(len(X))] + ) # n_samples, n_channels * n_timepoints + + self.window_length_ = max(1, int(self.X_.shape[1] * self.window_length)) + # Can replace with choice [-1, 1] + self.hash_funcs_ = self.rng_.uniform( + low=-1, high=1.0, size=(self.n_hash_funcs, self.window_length_) + ) + self.start_points_ = self.rng_.choice( + self.X_.shape[1] - self.window_length_ + 1, + size=self.n_hash_funcs, + replace=True, + ) + + bool_hashes = _collection_to_bool( + self.X_, self.hash_funcs_, self.start_points_, self.window_length_ + ) + # could yield this + str_hashes = [hash(bool_hashes[i].tobytes()) for i in range(len(bool_hashes))] + self.dict_X_index = {} + self.dict_bool_hashes = {} + for i in range(len(str_hashes)): + if str_hashes[i] in self.dict_X_index: + self.dict_X_index[str_hashes[i]].append(i) + else: + self.dict_X_index[str_hashes[i]] = [i] + self.dict_bool_hashes[str_hashes[i]] = bool_hashes[i] + + self.bool_hashes_value_list = np.asarray(list(self.dict_bool_hashes.values())) + self.bool_hashes_key_list = np.asarray(list(self.dict_bool_hashes.keys())) + + return self + + def update(self, X): + """ + . + + Parameters + ---------- + X : TYPE + DESCRIPTION. + + Returns + ------- + TYPE + DESCRIPTION. + + """ + X_ = np.array( + [X[i].flatten() for i in range(len(X))] + ) # n_samples, n_channels * n_timepoints + bool_hashes = _collection_to_bool( + X_, self.hash_funcs_, self.start_points_, self.window_length_ + ) + + str_hashes = [hash(bool_hashes[i].tobytes()) for i in range(len(bool_hashes))] + base_index = self.X_.shape[0] + for i in range(len(str_hashes)): + if str_hashes[i] in self.dict_X_index: + self.dict_X_index[str_hashes[i]].append(i + base_index) + else: + self.dict_X_index[str_hashes[i]] = [i + base_index] + self.dict_bool_hashes[str_hashes[i]] = bool_hashes[i] + self.X_ = np.concatenate((self.X_, X_)) + + self.bool_hashes_value_list = np.asarray(list(self.dict_bool_hashes.values())) + self.bool_hashes_key_list = np.asarray(list(self.dict_bool_hashes.keys())) + return self + + def get_bucket_collection_indexes(self, X): + """ + . + + Parameters + ---------- + X : TYPE + DESCRIPTION. + + Returns + ------- + TYPE + DESCRIPTION. + + """ + bool_hash = _series_to_bool( + X.flatten(), self.hash_funcs_, self.start_points_, self.window_length_ + ) + str_hash = hash(bool_hash.tobytes()) + if str_hash in self.dict_X_index: + return self.dict_X_index[str_hash] + else: + return [] + + def predict(self, X, k=1): + """ + . + + Parameters + ---------- + X : TYPE + DESCRIPTION. + k : TYPE, optional + DESCRIPTION. The default is 1. + + Returns + ------- + top_k : TYPE + DESCRIPTION. + + """ + X_ = np.array([X[i].flatten() for i in range(len(X))]) + bool_hashes = _collection_to_bool( + X_, self.hash_funcs_, self.start_points_, self.window_length_ + ) + top_k = np.zeros((len(X), k), dtype=int) + dists = _hamming_dist_matrix(self.bool_hashes_value_list, bool_hashes) + self.h_dists = dists + # Deal with equality by merging bucket contents ? + for i_x in range(len(X)): + ids = np.argsort(dists[i_x]) + _i = 0 + c = k + while c > 0: + candidates = self.dict_X_index[self.bool_hashes_key_list[ids[_i]]] + # Can do exact search by computing distances here + if len(candidates) > c: + candidates = candidates[:c] + top_k[i_x, k - c : k - c + len(candidates)] = candidates + c -= len(candidates) + _i += 1 + return top_k + + def find_motif(Index, X=None): + """ + . + + Parameters + ---------- + Index : TYPE + DESCRIPTION. + X : TYPE, optional + DESCRIPTION. The default is None. + + Returns + ------- + None. + + """ + pass diff --git a/aeon/similarity_search/subsequence_search/_stomp.py b/aeon/similarity_search/subsequence_search/_stomp.py index 0dd75971f8..21fdacdfcd 100644 --- a/aeon/similarity_search/subsequence_search/_stomp.py +++ b/aeon/similarity_search/subsequence_search/_stomp.py @@ -2,9 +2,6 @@ __maintainer__ = ["baraline"] - -from typing import Optional - import numpy as np from numba import njit, prange from numba.typed import List @@ -27,13 +24,13 @@ class StompMatrixProfile(BaseMatrixProfile): def compute_matrix_profile( self, - k, - threshold, - exclusion_size, - inverse_distance, - allow_neighboring_matches, - X: Optional[np.ndarray] = None, - X_index: Optional[int] = None, + X: np.ndarray, + k: int, + threshold: float, + exclusion_size: int, + inverse_distance: bool, + allow_neighboring_matches: bool, + X_index=None, ): """ Compute matrix profiles. @@ -44,6 +41,8 @@ def compute_matrix_profile( Parameters ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A 2D array time series on which the matrix profile will be computed. k : int The number of best matches to return during predict for each subsequence. threshold : float @@ -55,14 +54,10 @@ def compute_matrix_profile( The size of the exclusion zone used to prevent returning as top k candidates the ones that are close to each other (for example i and i+1). It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, the value None means that this is not used. - X : Optional[np.ndarray], optional - The time series on which the matrix profile will be compute. - The default is None, meaning that the series in the collection given in fit - will be used instead. X_index : Optional[int], optional If ``X`` is a series of the database given in fit, specify its index in ``X_``. If specified, each query of this series won't be able to match with @@ -70,73 +65,55 @@ def compute_matrix_profile( Returns ------- - MP : array of shape (series_length - L + 1,) + MP : array of shape (n_timepoints - L + 1,) Matrix profile distances for each query subsequence. If X is none, this will be a list of MP for each series in X_. - IP : array of shape (series_length - L + 1,) + IP : array of shape (n_timepoints - L + 1,) Indexes of the top matches for each query subsequence. If X is none, this will be a list of MP for each series in X_. """ - # If we compute matrix profiles for each series in X_ - if X is None: - MP = [] - IP = [] - for i in range(len(self.X_)): - _MP, _IP = self.compute_matrix_profile( - k, - threshold, - exclusion_size, - inverse_distance, - X=self.X_[i], - X_index=i, - ) - MP.append(_MP) - IP.append(_IP) - # else we compute matrix profiles using X on the series in X_ + XdotT = [ + get_ith_products(self.X[i], X, self.length, 0) for i in range(len(self.X_)) + ] + if isinstance(X, np.ndarray): + XdotT = np.asarray(XdotT) + elif isinstance(X, List): + XdotT = List(XdotT) + + if X_index is None: + X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) else: - XdotT = [ - get_ith_products(self.X[i], X, self.length, 0) - for i in range(len(self.X_)) - ] - if isinstance(X, np.ndarray): - XdotT = np.asarray(XdotT) - elif isinstance(X, List): - XdotT = List(XdotT) - - if X_index is None: - X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) - else: - X_means, X_stds = self.X_means_[i], self.X_stds_[i] - if self.normalise: - MP, IP = _stomp_normalised( - self.X_, - X, - XdotT, - self.X_means_, - self.X_stds_, - X_means, - X_stds, - self.length, - X_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, - ) - else: - MP, IP = _stomp( - self.X_, - X, - XdotT, - self.length, - X_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, - ) + X_means, X_stds = self.X_means_[X_index], self.X_stds_[X_index] + if self.normalise: + MP, IP = _stomp_normalised( + self.X_, + X, + XdotT, + self.X_means_, + self.X_stds_, + X_means, + X_stds, + self.length, + X_index, + k, + threshold, + allow_neighboring_matches, + exclusion_size, + inverse_distance, + ) + else: + MP, IP = _stomp( + self.X_, + X, + XdotT, + self.length, + X_index, + k, + threshold, + allow_neighboring_matches, + exclusion_size, + inverse_distance, + ) return MP, IP def compute_distance_profile(self, X: np.ndarray): @@ -237,9 +214,9 @@ def _stomp_normalised( The size of the exclusion zone used to prevent returning as top k candidates the ones that are close to each other (for example i and i+1). It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, the value None means that this is not used. inverse_distance : bool If True, the matching will be made on the inverse of the distance, and thus, the @@ -331,9 +308,9 @@ def _stomp( The size of the exclusion zone used to prevent returning as top k candidates the ones that are close to each other (for example i and i+1). It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, the value None means that this is not used. inverse_distance : bool If True, the matching will be made on the inverse of the distance, and thus, the diff --git a/aeon/similarity_search/subsequence_search/base.py b/aeon/similarity_search/subsequence_search/base.py index 7df358e5ec..dcc3f17dc8 100644 --- a/aeon/similarity_search/subsequence_search/base.py +++ b/aeon/similarity_search/subsequence_search/base.py @@ -47,9 +47,9 @@ def __init__( @final def find_motifs( self, + X: np.ndarray, k: Optional[int] = 1, threshold: Optional[float] = np.inf, - X: Optional[np.ndarray] = None, X_index: Optional[int] = None, inverse_distance: Optional[bool] = False, allow_neighboring_matches: Optional[bool] = False, @@ -65,15 +65,13 @@ def find_motifs( Parameters ---------- + X : np.ndarray, 2D array of shape (n_channels, n_timestamps) + A series in which we want to indentify motifs. k : int, optional Number of motifs to return threshold : int, optional A threshold on the similarity measure to determine which candidates will be part of a motif set. - X : np.ndarray, 2D array of shape (n_channels, n_timestamps), optional - A series in which we want to indentify motifs. If provided, the motifs - extracted should appear in X and in the database given in fit. If not - provided, the motifs will be extracted only from the database given in fit. X_index : Optional[int], optional If ``X`` is a series of the database given in fit, specify its index in ``X_``. If specified, each query of this series won't be able to match with @@ -93,25 +91,30 @@ def find_motifs( Returns ------- - list of ndarray, shape=(k,) - A list of at most ``k`` numpy arrays containing the indexes of the - candidates in each motif. + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the indexes of the + motifs in X. + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the distances of the + motifs macthes to the motif in X. """ self._check_is_fitted() + if X is not None: + self._check_find_neighbors_motif_format(X) prev_threads = get_num_threads() X_index = self._check_X_index_int(X_index) - motifs = self._find_motifs( + motifs_indexes, distances = self._find_motifs( + X, k=k, threshold=threshold, exclusion_factor=exclusion_factor, inverse_distance=inverse_distance, allow_neighboring_matches=allow_neighboring_matches, - X=X, X_index=X_index, ) set_num_threads(prev_threads) - return motifs + return motifs_indexes, distances @final def find_neighbors( @@ -171,11 +174,14 @@ def find_neighbors( """ self._check_is_fitted() - if self.length != X.shape[1] or self.n_channels_ != X.shape[0]: + + self._check_find_neighbors_motif_format(X) + if self.length != X.shape[1]: raise ValueError( - f"Expected a subsequence of shape {(self.n_channels_, self.length)} but" - f" got {X.shape}" + f"Expected X to be of shape {(self.n_channels_, self.length)} but" + f" got {X.shape} in find_neighbors." ) + X_index = self._check_X_index_array(X_index) prev_threads = get_num_threads() set_num_threads(self._n_jobs) @@ -313,8 +319,19 @@ def _compute_mean_std_from_collection(self, X: np.ndarray): else: return np.asarray(means), np.asarray(stds) - @abstractmethod - def _fit(self, X, y=None): ... + def _fit(self, X, y=None): + if self.length >= self.min_timepoints_ or self.length < 1: + raise ValueError( + "The length of the query should be inferior or equal to the length of " + "data (X_) provided during fit, but got {} for X and {} for X_".format( + self.length, self.min_timepoints_ + ) + ) + + if self.normalise: + self.X_means_, self.X_stds_ = self._compute_mean_std_from_collection(X) + self.X_ = X + return self @abstractmethod def _find_motifs( @@ -322,8 +339,8 @@ def _find_motifs( X: np.ndarray, k: Optional[int] = 1, threshold: Optional[float] = np.inf, + X_index: Optional[int] = None, inverse_distance: Optional[bool] = False, - X_index=None, allow_neighboring_matches: Optional[bool] = False, exclusion_factor: Optional[float] = 2.0, ): ... @@ -344,27 +361,13 @@ def _find_neighbors( class BaseMatrixProfile(BaseSubsequenceSearch): """Base class for Matrix Profile methods using a length parameter.""" - def _fit(self, X, y=None): - if self.length >= self.min_timepoints_: - raise ValueError( - "The length of the query should be inferior or equal to the length of " - "data (X_) provided during fit, but got {} for X and {} for X_".format( - self.length, self.min_timepoints_ - ) - ) - - if self.normalise: - self.X_means_, self.X_stds_ = self._compute_mean_std_from_collection(X) - self.X_ = X - return self - def _find_motifs( self, X: np.ndarray, k: Optional[int] = 1, threshold: Optional[float] = np.inf, + X_index: Optional[int] = None, inverse_distance: Optional[bool] = False, - X_index=None, allow_neighboring_matches: Optional[bool] = False, exclusion_factor: Optional[float] = 2.0, ): @@ -379,7 +382,15 @@ def _find_motifs( X=X, X_index=X_index, ) - # TODO : implement logic here + # TODO check motif extraction logic, sure its not this one + MP_avg = np.array([np.mean(MP[i]) for i in range(len(MP))]) + return _extract_top_k_from_dist_profile( + MP_avg, + k, + threshold, + allow_neighboring_matches, + exclusion_size, + ) def _find_neighbors( self, @@ -451,12 +462,12 @@ def _find_neighbors( @abstractmethod def compute_matrix_profile( self, - k, - threshold, - exclusion_size, - inverse_distance, - allow_neighboring_matches, - X: Optional[np.ndarray] = None, + X: np.ndarray, + k: int, + threshold: float, + exclusion_size: int, + inverse_distance: bool, + allow_neighboring_matches: bool, X_index: Optional[int] = None, ): """Compute matrix profiles between X_ and X or between all series in X_.""" diff --git a/aeon/similarity_search/subsequence_search/tests/test_base.py b/aeon/similarity_search/subsequence_search/tests/test_base.py new file mode 100644 index 0000000000..a037879992 --- /dev/null +++ b/aeon/similarity_search/subsequence_search/tests/test_base.py @@ -0,0 +1,78 @@ +"""Test for subsequence search base class.""" + +import pytest + +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockMatrixProfile, + MockSubsequenceSearch, +) +from aeon.testing.testing_data import ( + make_example_1d_numpy, + make_example_2d_numpy_series, + make_example_3d_numpy, + make_example_3d_numpy_list, +) + +BASES = [MockMatrixProfile, MockSubsequenceSearch] + + +@pytest.mark.parametrize("base", BASES) +def test_input_shape_fit_neighbord_motifs(base): + """Test input shapes.""" + estimator = base() + # dummy data to pass to fit when testing predict/predict_proba + X_3D_uni = make_example_3d_numpy(n_channels=1, return_y=False) + X_3D_multi = make_example_3d_numpy(n_channels=2, return_y=False) + X_3D_uni_list = make_example_3d_numpy_list(n_channels=1, return_y=False) + X_3D_multi_list = make_example_3d_numpy_list(n_channels=2, return_y=False) + X_2D_uni = make_example_2d_numpy_series(n_channels=1) + X_2D_multi = make_example_2d_numpy_series(n_channels=2) + X_1D = make_example_1d_numpy() + + valid_inputs_fit = [X_3D_uni, X_3D_multi, X_3D_uni_list, X_3D_multi_list] + # Valid inputs + for _input in valid_inputs_fit: + estimator.fit(_input) + + invalid_inputs_fit = [X_2D_uni, X_2D_multi, X_1D] + for _input in invalid_inputs_fit: + with pytest.raises(TypeError): + estimator.fit(_input) + + valid_inputs_neighboord_motifs_uni = [X_2D_uni] + invalid_inputs_neighboord_motifs_uni = [ + X_1D, + X_3D_uni, + X_3D_uni_list, + ] + invalid_inputs_neighboord_motifs_multi = [ + X_3D_multi, + X_3D_multi_list, + ] + L = 5 + estimator_multi = base(length=L).fit(X_3D_multi) + estimator_uni = base(length=L).fit(X_3D_uni) + + for _input in valid_inputs_neighboord_motifs_uni: + estimator_uni.find_neighbors(_input[:, :L]) + estimator_uni.find_motifs(X=_input) + with pytest.raises(ValueError): + # Wrong number of channels + estimator_multi.find_neighbors(_input) + estimator_multi.find_motifs(X=_input) + # X length not of size L + estimator_uni.find_neighbors(X=_input[:, : L + 2]) + + for _input in invalid_inputs_neighboord_motifs_uni: + with pytest.raises(TypeError): + estimator_uni.find_neighbors(_input) + estimator_uni.find_motifs(X=_input) + estimator_multi.find_neighbors(_input) + estimator_multi.find_motifs(X=_input) + + for _input in invalid_inputs_neighboord_motifs_multi: + with pytest.raises(TypeError): + estimator_uni.find_neighbors(_input) + estimator_uni.find_motifs(X=_input) + estimator_multi.find_neighbors(_input) + estimator_multi.find_motifs(X=_input) diff --git a/aeon/similarity_search/tests/test_base.py b/aeon/similarity_search/tests/test_base.py new file mode 100644 index 0000000000..e066e14680 --- /dev/null +++ b/aeon/similarity_search/tests/test_base.py @@ -0,0 +1 @@ +"""Tests for base similarity search.""" diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py index f7c0acfc98..89c6121c38 100644 --- a/aeon/testing/mock_estimators/_mock_similarity_searchers.py +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -15,6 +15,8 @@ class MockMatrixProfile(BaseMatrixProfile): + """Mock estimator for BaseMatrixProfile.""" + def __init__(self, length=3): super().__init__(length=length) @@ -35,25 +37,24 @@ def compute_matrix_profile( def compute_distance_profile(self, X): """Compute distrance profiles between X_ and X (a series of size length).""" - return np.zeros(X.shape[1] - self.length + 1) + return [ + np.zeros(self.X_[i].shape[1] - self.length + 1) for i in range(len(self.X_)) + ] class MockSubsequenceSearch(BaseSubsequenceSearch): - """MockSeriesTransformer to set tags.""" + """Mock estimator for BaseSubsequenceSearch.""" def __init__(self, length=3): super().__init__(length=length) - def _fit(self, X, y=None): - return self - def _find_motifs( self, - X, k=1, threshold=np.inf, - inverse_distance=False, + X=None, X_index=None, + inverse_distance=False, allow_neighboring_matches=False, exclusion_factor=2.0, ): From f23c7203f9cc6ba295c187d1d227c8af1329daa4 Mon Sep 17 00:00:00 2001 From: baraline Date: Thu, 2 Jan 2025 11:23:49 +0100 Subject: [PATCH 06/36] Fix subsequence_search tests --- aeon/similarity_search/__init__.py | 2 +- aeon/similarity_search/{base.py => _base.py} | 0 aeon/similarity_search/series_search/base.py | 2 +- .../subsequence_search/__init__.py | 10 ++++- .../subsequence_search/base.py | 6 +-- .../subsequence_search/tests/__init__.py | 2 +- .../subsequence_search/tests/test_base.py | 24 +++++++---- .../tests/test_brute_force.py | 42 ++++++++++--------- .../subsequence_search/tests/test_commons.py | 15 ++++--- .../subsequence_search/tests/test_stomp.py | 10 ++--- .../_mock_similarity_searchers.py | 4 +- aeon/testing/utils/estimator_checks.py | 2 +- aeon/utils/base/_register.py | 14 +++---- aeon/utils/discovery.py | 1 + 14 files changed, 73 insertions(+), 61 deletions(-) rename aeon/similarity_search/{base.py => _base.py} (100%) diff --git a/aeon/similarity_search/__init__.py b/aeon/similarity_search/__init__.py index cdf5cebd84..26b79c7da2 100644 --- a/aeon/similarity_search/__init__.py +++ b/aeon/similarity_search/__init__.py @@ -2,4 +2,4 @@ __all__ = ["BaseSimilaritySearch"] -from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search._base import BaseSimilaritySearch diff --git a/aeon/similarity_search/base.py b/aeon/similarity_search/_base.py similarity index 100% rename from aeon/similarity_search/base.py rename to aeon/similarity_search/_base.py diff --git a/aeon/similarity_search/series_search/base.py b/aeon/similarity_search/series_search/base.py index db83519c04..bcbc92c042 100644 --- a/aeon/similarity_search/series_search/base.py +++ b/aeon/similarity_search/series_search/base.py @@ -2,7 +2,7 @@ __maintainer__ = ["baraline"] -from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search._base import BaseSimilaritySearch class BaseSeriesSearch(BaseSimilaritySearch): diff --git a/aeon/similarity_search/subsequence_search/__init__.py b/aeon/similarity_search/subsequence_search/__init__.py index c5de805eb6..5d64f901bd 100644 --- a/aeon/similarity_search/subsequence_search/__init__.py +++ b/aeon/similarity_search/subsequence_search/__init__.py @@ -1,7 +1,15 @@ """Subsequence search module.""" -__all__ = ["BaseSubsequenceSearch", "BaseMatrixProfile", "StompMatrixProfile"] +__all__ = [ + "BaseSubsequenceSearch", + "BaseMatrixProfile", + "StompMatrixProfile", + "BruteForceMatrixProfile", +] +from aeon.similarity_search.subsequence_search._brute_force import ( + BruteForceMatrixProfile, +) from aeon.similarity_search.subsequence_search._stomp import StompMatrixProfile from aeon.similarity_search.subsequence_search.base import ( BaseMatrixProfile, diff --git a/aeon/similarity_search/subsequence_search/base.py b/aeon/similarity_search/subsequence_search/base.py index dcc3f17dc8..238b749b5f 100644 --- a/aeon/similarity_search/subsequence_search/base.py +++ b/aeon/similarity_search/subsequence_search/base.py @@ -10,7 +10,7 @@ from numba import get_num_threads, set_num_threads from numba.typed import List -from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search._base import BaseSimilaritySearch from aeon.similarity_search.subsequence_search._commons import ( _extract_top_k_from_dist_profile, _inverse_distance_profile_list, @@ -34,7 +34,6 @@ class BaseSubsequenceSearch(BaseSimilaritySearch): Number of parallel jobs to use. The default is 1. """ - @abstractmethod def __init__( self, length: int, @@ -383,7 +382,8 @@ def _find_motifs( X_index=X_index, ) # TODO check motif extraction logic, sure its not this one - MP_avg = np.array([np.mean(MP[i]) for i in range(len(MP))]) + MP_avg = np.array([[np.mean(MP[i]) for i in range(len(MP))]]) + # TODO: appening IP of identified motifs to return to get motifs matches in X_ return _extract_top_k_from_dist_profile( MP_avg, k, diff --git a/aeon/similarity_search/subsequence_search/tests/__init__.py b/aeon/similarity_search/subsequence_search/tests/__init__.py index 3feb8d4ca5..0287f2ee04 100644 --- a/aeon/similarity_search/subsequence_search/tests/__init__.py +++ b/aeon/similarity_search/subsequence_search/tests/__init__.py @@ -1 +1 @@ -"""Tests for series methods.""" +"""Tests for subsequence search methods.""" diff --git a/aeon/similarity_search/subsequence_search/tests/test_base.py b/aeon/similarity_search/subsequence_search/tests/test_base.py index a037879992..e1a314f38a 100644 --- a/aeon/similarity_search/subsequence_search/tests/test_base.py +++ b/aeon/similarity_search/subsequence_search/tests/test_base.py @@ -55,24 +55,32 @@ def test_input_shape_fit_neighbord_motifs(base): for _input in valid_inputs_neighboord_motifs_uni: estimator_uni.find_neighbors(_input[:, :L]) - estimator_uni.find_motifs(X=_input) + estimator_uni.find_motifs(_input) with pytest.raises(ValueError): # Wrong number of channels estimator_multi.find_neighbors(_input) - estimator_multi.find_motifs(X=_input) - # X length not of size L - estimator_uni.find_neighbors(X=_input[:, : L + 2]) + with pytest.raises(ValueError): + estimator_multi.find_motifs(_input) + # X length not of size L + with pytest.raises(ValueError): + estimator_uni.find_neighbors(_input[:, : L + 2]) for _input in invalid_inputs_neighboord_motifs_uni: with pytest.raises(TypeError): estimator_uni.find_neighbors(_input) - estimator_uni.find_motifs(X=_input) + with pytest.raises(TypeError): + estimator_uni.find_motifs(_input) + with pytest.raises(TypeError): estimator_multi.find_neighbors(_input) - estimator_multi.find_motifs(X=_input) + with pytest.raises(TypeError): + estimator_multi.find_motifs(_input) for _input in invalid_inputs_neighboord_motifs_multi: with pytest.raises(TypeError): estimator_uni.find_neighbors(_input) - estimator_uni.find_motifs(X=_input) + with pytest.raises(TypeError): + estimator_uni.find_motifs(_input) + with pytest.raises(TypeError): estimator_multi.find_neighbors(_input) - estimator_multi.find_motifs(X=_input) + with pytest.raises(TypeError): + estimator_multi.find_motifs(_input) diff --git a/aeon/similarity_search/subsequence_search/tests/test_brute_force.py b/aeon/similarity_search/subsequence_search/tests/test_brute_force.py index 3c92138b3d..cc87b34a9f 100644 --- a/aeon/similarity_search/subsequence_search/tests/test_brute_force.py +++ b/aeon/similarity_search/subsequence_search/tests/test_brute_force.py @@ -27,7 +27,7 @@ make_example_3d_numpy, make_example_3d_numpy_list, ) -from aeon.utils.numba.general import sliding_mean_std_one_series +from aeon.utils.numba.general import sliding_mean_std_one_series, z_normalise_series_2d K_VALUES = [1, 3, 5] NN_MATCHES = [True, False] @@ -60,17 +60,22 @@ def test__compute_dist_profile(): assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) +@pytest.mark.parametrize("normalise", NORMALISE) def test__naive_squared_distance_profile(normalise): """Test Euclidean distance profile calculation.""" L = 3 X = make_example_3d_numpy(n_cases=3, n_channels=1, n_timepoints=10, return_y=False) Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) - dist_profiles = _naive_squared_distance_profile(X, Q) + dist_profiles = _naive_squared_distance_profile(X, Q, normalise=normalise) + + if normalise: + Q = z_normalise_series_2d(Q) for i_x in range(len(X)): for i_t in range(X[i_x].shape[1] - L + 1): - assert_almost_equal( - dist_profiles[i_x][i_t], np.sum((X[i_x, :, i_t : i_t + L] - Q) ** 2) - ) + _x = X[i_x, :, i_t : i_t + L] + if normalise: + _x = z_normalise_series_2d(_x) + assert_almost_equal(dist_profiles[i_x][i_t], np.sum((_x - Q) ** 2)) # test unequal length and multivariate X = List( @@ -84,22 +89,21 @@ def test__naive_squared_distance_profile(normalise): ) Q = make_example_2d_numpy_series(n_channels=2, n_timepoints=L) - dist_profiles = _naive_squared_distance_profile(X, Q) + dist_profiles = _naive_squared_distance_profile(X, Q, normalise=normalise) + if normalise: + Q = z_normalise_series_2d(Q) for i_x in range(len(X)): for i_t in range(X[i_x].shape[1] - L + 1): - assert_almost_equal( - dist_profiles[i_x][i_t], np.sum((X[i_x][:, i_t : i_t + L] - Q) ** 2) - ) - - -@pytest.mark.parametrize( - [ - ("k", K_VALUES), - ("allow_neighboring_matches", NN_MATCHES), - ("inverse_distance", INVERSE), - ("normalise", NORMALISE), - ] -) + _x = X[i_x][:, i_t : i_t + L] + if normalise: + _x = z_normalise_series_2d(_x) + assert_almost_equal(dist_profiles[i_x][i_t], np.sum((_x - Q) ** 2)) + + +@pytest.mark.parametrize("k", K_VALUES) +@pytest.mark.parametrize("allow_neighboring_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) +@pytest.mark.parametrize("normalise", NORMALISE) def test__naive_squared_matrix_profile( k, allow_neighboring_matches, inverse_distance, normalise ): diff --git a/aeon/similarity_search/subsequence_search/tests/test_commons.py b/aeon/similarity_search/subsequence_search/tests/test_commons.py index 70e443a78c..50c5cfad31 100644 --- a/aeon/similarity_search/subsequence_search/tests/test_commons.py +++ b/aeon/similarity_search/subsequence_search/tests/test_commons.py @@ -17,6 +17,10 @@ make_example_2d_numpy_series, ) +K_VALUES = [1, 3, 5] +THRESHOLDS = [np.inf, 0.7] +NN_MATCHES = [False, True] + def test_fft_sliding_dot_product(): """Test the fft_sliding_dot_product function.""" @@ -60,14 +64,9 @@ def test__inverse_distance_profile_list(): assert_array_almost_equal(1 / (X[1] + 1e-8), T[1]) -K_VALUES = [1, 3, 5] -THRESHOLDS = [np.inf, 0.7] -NN_MATCHES = [False, True] - - -@pytest.mark.parametrize( - [("k", K_VALUES), ("threshold", THRESHOLDS), ("allow_nn_matches", NN_MATCHES)] -) +@pytest.mark.parametrize("k", K_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLDS) +@pytest.mark.parametrize("allow_nn_matches", NN_MATCHES) def test__extract_top_k_from_dist_profile(k, threshold, allow_nn_matches): """Test method to esxtract the top k candidates from a list of distance profiles.""" X = make_example_2d_numpy_list( diff --git a/aeon/similarity_search/subsequence_search/tests/test_stomp.py b/aeon/similarity_search/subsequence_search/tests/test_stomp.py index 7a655035a5..12d7738eaf 100644 --- a/aeon/similarity_search/subsequence_search/tests/test_stomp.py +++ b/aeon/similarity_search/subsequence_search/tests/test_stomp.py @@ -206,13 +206,9 @@ def test__normalised_squared_distance_profile(): ) -@pytest.mark.parametrize( - [ - ("k", K_VALUES), - ("allow_neighboring_matches", NN_MATCHES), - ("inverse_distance", INVERSE), - ] -) +@pytest.mark.parametrize("k", K_VALUES) +@pytest.mark.parametrize("allow_neighboring_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) def test__stomp(k, allow_neighboring_matches, inverse_distance): """Test STOMP method.""" L = 3 diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py index 89c6121c38..824a627d7a 100644 --- a/aeon/testing/mock_estimators/_mock_similarity_searchers.py +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -32,7 +32,7 @@ def compute_matrix_profile( ): """Compute matrix profiles between X_ and X or between all series in X_.""" return np.zeros((X.shape[1] - self.length + 1, k)), np.zeros( - (X.shape[1] - self.length + 1, k, 2) + (X.shape[1] - self.length + 1, k, 2), dtype=np.int64 ) def compute_distance_profile(self, X): @@ -50,9 +50,9 @@ def __init__(self, length=3): def _find_motifs( self, + X, k=1, threshold=np.inf, - X=None, X_index=None, inverse_distance=False, allow_neighboring_matches=False, diff --git a/aeon/testing/utils/estimator_checks.py b/aeon/testing/utils/estimator_checks.py index b2e0973dbf..d556ff0249 100644 --- a/aeon/testing/utils/estimator_checks.py +++ b/aeon/testing/utils/estimator_checks.py @@ -7,7 +7,7 @@ import numpy as np -from aeon.similarity_search.base import BaseSimilaritySearch +from aeon.similarity_search import BaseSimilaritySearch from aeon.testing.testing_data import FULL_TEST_DATA_DICT from aeon.utils.validation import get_n_cases diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 5bf4045ad5..9c576c350a 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -24,15 +24,13 @@ from aeon.forecasting.base import BaseForecaster from aeon.regression.base import BaseRegressor from aeon.segmentation.base import BaseSegmenter -from aeon.similarity_search.base import BaseSimilaritySearch -from aeon.similarity_search.subsequence_search.base import ( - BaseMatrixProfile, - BaseSubsequenceSearch, -) +from aeon.similarity_search.subsequence_search.base import BaseSubsequenceSearch from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer from aeon.transformations.series import BaseSeriesTransformer +# from aeon.similarity_search.series_search.base import BaseSeriesSearch + # all base classes BASE_CLASS_REGISTER = { # abstract - no estimator directly inherits from these @@ -48,11 +46,10 @@ "early_classifier": BaseEarlyClassifier, "regressor": BaseRegressor, "segmenter": BaseSegmenter, - "similarity_searcher": BaseSimilaritySearch, - "subsequence_searcher": BaseSubsequenceSearch, - "matrixprofile_searcher": BaseMatrixProfile, "series-transformer": BaseSeriesTransformer, "forecaster": BaseForecaster, + "subsequence_searcher": BaseSubsequenceSearch, + # "series_searcher": BaseSeriesSearch, } # base classes which are valid for estimator to directly inherit from @@ -64,6 +61,5 @@ "collection-estimator", "series-estimator", "transformer", - "similarity_searcher", } } diff --git a/aeon/utils/discovery.py b/aeon/utils/discovery.py index 8fd4a05efe..d6e5ce61fc 100644 --- a/aeon/utils/discovery.py +++ b/aeon/utils/discovery.py @@ -92,6 +92,7 @@ def all_estimators( # ignore test modules and base classes "base", "tests", + "similarity_search" # ignore these submodules "benchmarking", "datasets", From c372969a92257da14ea40a8f238d9f6160b331e4 Mon Sep 17 00:00:00 2001 From: baraline Date: Thu, 2 Jan 2025 13:56:20 +0100 Subject: [PATCH 07/36] debug brute force mp --- .../subsequence_search/_brute_force.py | 2 +- .../subsequence_search/_commons.py | 2 +- .../tests/test_brute_force.py | 41 ++++++++++++------- 3 files changed, 29 insertions(+), 16 deletions(-) diff --git a/aeon/similarity_search/subsequence_search/_brute_force.py b/aeon/similarity_search/subsequence_search/_brute_force.py index 6c26925a32..a7227499b6 100644 --- a/aeon/similarity_search/subsequence_search/_brute_force.py +++ b/aeon/similarity_search/subsequence_search/_brute_force.py @@ -281,7 +281,7 @@ def _naive_squared_matrix_profile( X_subs.append(i_subs) for i_q in range(n_queries): - Q = T[:, i : i + L] + Q = T[:, i_q : i_q + L] if normalise: Q = z_normalise_series_2d(Q) for i_x in prange(len(X)): diff --git a/aeon/similarity_search/subsequence_search/_commons.py b/aeon/similarity_search/subsequence_search/_commons.py index 03e0ee1ac3..c13a7381bc 100644 --- a/aeon/similarity_search/subsequence_search/_commons.py +++ b/aeon/similarity_search/subsequence_search/_commons.py @@ -141,7 +141,7 @@ def _extract_top_k_from_dist_profile( _top_k_distances = dist_profiles[i_profile][_top_k_indexes] # Extract top-k with neighboring matches else: - _top_k_indexes = np.argpartition(dist_profiles[i_profile], k)[:k] + _top_k_indexes = np.argsort(dist_profiles[i_profile])[:k] _top_k_distances = dist_profiles[i_profile][_top_k_indexes] # Select overall top k by using the buffer array of size 2*k diff --git a/aeon/similarity_search/subsequence_search/tests/test_brute_force.py b/aeon/similarity_search/subsequence_search/tests/test_brute_force.py index cc87b34a9f..9ef0eb44e8 100644 --- a/aeon/similarity_search/subsequence_search/tests/test_brute_force.py +++ b/aeon/similarity_search/subsequence_search/tests/test_brute_force.py @@ -11,7 +11,11 @@ import numpy as np import pytest from numba.typed import List -from numpy.testing import assert_almost_equal, assert_array_almost_equal +from numpy.testing import ( + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, +) from aeon.similarity_search.subsequence_search._brute_force import ( _compute_dist_profile, @@ -27,7 +31,11 @@ make_example_3d_numpy, make_example_3d_numpy_list, ) -from aeon.utils.numba.general import sliding_mean_std_one_series, z_normalise_series_2d +from aeon.utils.numba.general import ( + get_all_subsequences, + sliding_mean_std_one_series, + z_normalise_series_2d, +) K_VALUES = [1, 3, 5] NN_MATCHES = [True, False] @@ -51,18 +59,18 @@ def _get_mean_sdts_inputs(X, Q, L): def test__compute_dist_profile(): - """Test Euclidean distance.""" + """Test Euclidean distance with brute force.""" L = 3 X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) - dist_profile = _compute_dist_profile(X, Q) + dist_profile = _compute_dist_profile(get_all_subsequences(X, L, 1), Q) for i_t in range(X.shape[1] - L + 1): assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) @pytest.mark.parametrize("normalise", NORMALISE) def test__naive_squared_distance_profile(normalise): - """Test Euclidean distance profile calculation.""" + """Test Euclidean distance profile calculation with brute force.""" L = 3 X = make_example_3d_numpy(n_cases=3, n_channels=1, n_timepoints=10, return_y=False) Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) @@ -107,18 +115,20 @@ def test__naive_squared_distance_profile(normalise): def test__naive_squared_matrix_profile( k, allow_neighboring_matches, inverse_distance, normalise ): - """Test STOMP method.""" + """Test brute force matrix profile method.""" L = 3 - - X = make_example_3d_numpy_list( - n_cases=3, - n_channels=2, - min_n_timepoints=6, - max_n_timepoints=8, - return_y=False, + X = List( + make_example_3d_numpy_list( + n_cases=3, + n_channels=2, + min_n_timepoints=6, + max_n_timepoints=8, + return_y=False, + ) ) + X_copy = X.copy() T = make_example_2d_numpy_series(n_channels=2, n_timepoints=5) - + T_copy = T.copy() T_index = None threshold = np.inf exclusion_size = L @@ -136,6 +146,9 @@ def test__naive_squared_matrix_profile( inverse_distance, normalise=normalise, ) + assert_array_equal(T, T_copy) + for i in range(len(X)): + assert_array_equal(X[i], X_copy[i]) # For each query of size L in T for i in range(T.shape[1] - L + 1): dist_profiles = _naive_squared_distance_profile( From d7da68bf4f7806b78b2532baba5c77f21d80f447 Mon Sep 17 00:00:00 2001 From: baraline Date: Thu, 2 Jan 2025 14:50:20 +0100 Subject: [PATCH 08/36] more debug of subsequence tests --- aeon/similarity_search/_base.py | 89 +++++++- .../similarity_search/series_search/_r_lsh.py | 2 +- aeon/similarity_search/series_search/base.py | 209 +++++++++++++++++- .../subsequence_search/_brute_force.py | 8 + .../subsequence_search/_stomp.py | 9 + .../subsequence_search/base.py | 1 + .../subsequence_search/tests/test_commons.py | 5 +- .../subsequence_search/tests/test_stomp.py | 10 +- .../_mock_similarity_searchers.py | 18 +- aeon/utils/numba/general.py | 89 ++++++-- 10 files changed, 396 insertions(+), 44 deletions(-) diff --git a/aeon/similarity_search/_base.py b/aeon/similarity_search/_base.py index d486491fa4..476dc664c2 100644 --- a/aeon/similarity_search/_base.py +++ b/aeon/similarity_search/_base.py @@ -90,7 +90,6 @@ def find_motifs( X: np.ndarray, k: int, threshold: float, - allow_overlap: Optional[bool] = True, ): """ Find the top-k motifs in the training data. @@ -109,9 +108,6 @@ def find_motifs( threshold : int, optional A threshold on the similarity measure to determine which candidates will be part of a motif set. - allow_overlap: bool, optional - Wheter a candidate can be part of multiple motif sets (True), or if motif - sets should be mutually exclusive (False). Returns ------- @@ -194,3 +190,88 @@ def _check_find_neighbors_motif_format(self, X): @abstractmethod def _fit(self, X, y=None): ... + + def _check_X_index_int(self, X_index: int): + """ + Check wheter the X_index parameter is correctly formated and is admissible. + + This check is made for motif search functions. + + Parameters + ---------- + X_index : int + Index of a series in X_. + + Returns + ------- + X_index : int + Index of a series in X_ + + """ + if X_index is not None: + if not isinstance(X_index, int): + raise TypeError("Expected an integer for X_index but got {X_index}") + + if X_index >= self.n_cases_ or X_index < 0: + raise ValueError( + "The value of X_index cannot exced the number " + "of series in the collection given during fit. Expected a value " + f"between [0, {self.n_cases_ - 1}] but got {X_index}" + ) + return X_index + + def _check_X_index_array(self, X_index: np.ndarray): + """ + Check wheter the X_index parameter is correctly formated and is admissible. + + This check is made for neighbour search functions. + + Parameters + ---------- + X_index : np.ndarray, 1D array of shape (2) + Array of integer containing the sample and timestamp identifiers of the + starting point of a subsequence in X_. + + Returns + ------- + X_index : np.ndarray, 1D array of shape (2) + Array of integer containing the sample and timestamp identifiers of the + starting point of a subsequence in X_. + + """ + if X_index is not None: + if ( + isinstance(X_index, list) + and len(X_index) == 2 + and isinstance(X_index[0], int) + and isinstance(X_index[1], int) + ): + X_index = np.asarray(X_index, dtype=int) + elif len(X_index) != 2: + raise TypeError( + "Expected a numpy array or list of integers with 2 elements " + f"for X_index but got {X_index}" + ) + elif ( + not (isinstance(X_index[0], int) or not isinstance(X_index[1], int)) + or X_index.dtype != int + ): + raise TypeError( + "Expected a numpy array or list of integers for X_index but got " + f"{X_index}" + ) + + if X_index[0] >= self.n_cases_ or X_index[0] < 0: + raise ValueError( + "The sample ID (first element) of X_index cannot exced the number " + "of series in the collection given during fit. Expected a value " + f"between [0, {self.n_cases_ - 1}] but got {X_index[0]}" + ) + _max_timestamp = self.X_[X_index[0]].shape[1] - self.length + 1 + if X_index[1] >= _max_timestamp: + raise ValueError( + "The timestamp ID (second element) of X_index cannot exced the " + "number of timestamps minus the length parameter plus one. Expected" + f" a value between [0, {_max_timestamp - 1}] but got {X_index[1]}" + ) + return X_index diff --git a/aeon/similarity_search/series_search/_r_lsh.py b/aeon/similarity_search/series_search/_r_lsh.py index 9921aeeba1..a98dbfc562 100644 --- a/aeon/similarity_search/series_search/_r_lsh.py +++ b/aeon/similarity_search/series_search/_r_lsh.py @@ -3,7 +3,7 @@ import numpy as np from numba import njit, prange -TPB = 16 +# TPB = 16 @njit(cache=True) diff --git a/aeon/similarity_search/series_search/base.py b/aeon/similarity_search/series_search/base.py index bcbc92c042..9f0e4f4ca6 100644 --- a/aeon/similarity_search/series_search/base.py +++ b/aeon/similarity_search/series_search/base.py @@ -2,13 +2,218 @@ __maintainer__ = ["baraline"] +import warnings +from abc import abstractmethod +from typing import Optional, final + +import numpy as np +from numba import get_num_threads, set_num_threads + from aeon.similarity_search._base import BaseSimilaritySearch +from aeon.utils.numba.general import compute_mean_stds_collection_parallel class BaseSeriesSearch(BaseSimilaritySearch): - """.""" + """ + Base class for similarity search on whole time series. - ... + Parameters + ---------- + normalise : bool, optional + Whether the inputs should be z-normalised. The default is False. + n_jobs : int, optional + Number of parallel jobs to use. The default is 1. + """ + + @final + def find_motifs( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + X_index: Optional[int] = None, + inverse_distance: Optional[bool] = False, + ): + """ + Find the top-k motifs in the training data. + + Given ``k`` and ``threshold`` parameters, this methods returns the top-k motif + sets. We define a motif set as a set of candidates which all are at a distance + of at most ``threshold`` from each other. The top-k motifs sets are the + motif sets with the most candidates. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, n_timestamps) + A series in which we want to indentify motifs. + k : int, optional + Number of motifs to return + threshold : int, optional + A threshold on the similarity measure to determine which candidates will be + part of a motif set. + X_index : Optional[int], optional + If ``X`` is a series of the database given in fit, specify its index in + ``X_``. If specified, this series won't be able to match with itself. + inverse_distance : bool, optional + Wheter to inverse the computed distance, meaning that the method will return + the anomalies instead of motifs. + + Returns + ------- + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the indexes of the + motifs in X. + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the distances of the + motifs macthes to the motif in X. + + """ + self._check_is_fitted() + if X is not None: + self._check_find_neighbors_motif_format(X) + prev_threads = get_num_threads() + X_index = self._check_X_index_int(X_index) + motifs_indexes, distances = self._find_motifs( + X, + k=k, + threshold=threshold, + inverse_distance=inverse_distance, + X_index=X_index, + ) + set_num_threads(prev_threads) + return motifs_indexes, distances + + @final + def find_neighbors( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + X_index: Optional[int] = None, + inverse_distance: Optional[bool] = False, + ): + """ + Find the top-k neighbors of X in the database. + + Given ``k`` and ``threshold`` parameters, this methods returns the top-k + neighbors of X, such as each of the ``k`` neighbors as a distance inferior or + equal to ``threshold``. By default, ``threshold`` is set to infinity. It is + possible for this method to return less than ``k`` neighbors, either if there + is less than ``k`` admissible candidate in the database, or if in the top-k + candidates, some do not meet the ``threshold`` condition. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The subsequence for which we want to identify nearest neighbors in the + database. + k : int, optional + Number of neighbors to return. + threshold : int, optional + A threshold on the distance to determine which candidates will be returned. + X_index : Optional[int], optional + If ``X`` is a series of the database given in fit, specify its index in + ``X_``. If specified, this series won't be able to match with itself. + inverse_distance : bool, optional + Wheter to inverse the computed distance, meaning that the method will return + the k most dissimilar neighbors instead of the k most similar. + + + Returns + ------- + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the indexes of the + neighbors. + ndarray, shape=(k,) + A numpy array of at most ``k`` elements containing the distances of the + neighbors to X. + + """ + self._check_is_fitted() + + self._check_find_neighbors_motif_format(X) + if self.length != X.shape[1]: + raise ValueError( + f"Expected X to be of shape {(self.n_channels_, self.length)} but" + f" got {X.shape} in find_neighbors." + ) + + X_index = self._check_X_index_int(X_index) + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) + neighbors, distances = self._find_neighbors( + X, + k=k, + threshold=threshold, + inverse_distance=inverse_distance, + X_index=X_index, + ) + set_num_threads(prev_threads) + if len(neighbors) < k: + warnings.warn( + f"The number of admissible neighbors found is {len(neighbors)}, instead" + f" of {k}", + stacklevel=2, + ) + return neighbors, distances + + def _compute_mean_std_from_collection(self, X: np.ndarray): + """ + Compute the mean and std of each channel for all series in X. + + Parameters + ---------- + X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + Collection of series from which we extract mean and stds. If it is an + unequal length collection, it should be a list of 2d numpy arrays. + + Returns + ------- + Tuple(np.ndarray, np.ndarray) + Both array are of shape (n_cases, n_channels), the first contains the means + and the second the stds for each series in X. + + """ + means, stds = compute_mean_stds_collection_parallel(X) + return means, stds + + def _fit(self, X, y=None): + if self.length >= self.min_timepoints_ or self.length < 1: + raise ValueError( + "The length of the query should be inferior or equal to the length of " + "data (X_) provided during fit, but got {} for X and {} for X_".format( + self.length, self.min_timepoints_ + ) + ) + + if self.normalise: + self.X_means_, self.X_stds_ = self._compute_mean_std_from_collection(X) + self.X_ = X + return self + + @abstractmethod + def _find_motifs( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + X_index: Optional[int] = None, + inverse_distance: Optional[bool] = False, + allow_neighboring_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 2.0, + ): ... + + @abstractmethod + def _find_neighbors( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + inverse_distance: Optional[bool] = False, + X_index=None, + allow_neighboring_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 2.0, + ): ... class BaseIndexSearch(BaseSimilaritySearch): diff --git a/aeon/similarity_search/subsequence_search/_brute_force.py b/aeon/similarity_search/subsequence_search/_brute_force.py index a7227499b6..2225fbd0ad 100644 --- a/aeon/similarity_search/subsequence_search/_brute_force.py +++ b/aeon/similarity_search/subsequence_search/_brute_force.py @@ -27,6 +27,14 @@ class BruteForceMatrixProfile(BaseMatrixProfile): """Estimator to compute matrix profile and distance profile using brute force.""" + def __init__( + self, + length: int, + normalise: Optional[bool] = False, + n_jobs: Optional[int] = 1, + ): + super().__init__(length=length, n_jobs=n_jobs, normalise=normalise) + def compute_matrix_profile( self, k, diff --git a/aeon/similarity_search/subsequence_search/_stomp.py b/aeon/similarity_search/subsequence_search/_stomp.py index 21fdacdfcd..a1d6ac8730 100644 --- a/aeon/similarity_search/subsequence_search/_stomp.py +++ b/aeon/similarity_search/subsequence_search/_stomp.py @@ -1,6 +1,7 @@ """Implementation of STOMP with squared euclidean distance.""" __maintainer__ = ["baraline"] +from typing import Optional import numpy as np from numba import njit, prange @@ -22,6 +23,14 @@ class StompMatrixProfile(BaseMatrixProfile): """Estimator to compute matrix profile and distance profile using STOMP.""" + def __init__( + self, + length: int, + normalise: Optional[bool] = False, + n_jobs: Optional[int] = 1, + ): + super().__init__(length=length, n_jobs=n_jobs, normalise=normalise) + def compute_matrix_profile( self, X: np.ndarray, diff --git a/aeon/similarity_search/subsequence_search/base.py b/aeon/similarity_search/subsequence_search/base.py index 238b749b5f..4c26a6a06a 100644 --- a/aeon/similarity_search/subsequence_search/base.py +++ b/aeon/similarity_search/subsequence_search/base.py @@ -34,6 +34,7 @@ class BaseSubsequenceSearch(BaseSimilaritySearch): Number of parallel jobs to use. The default is 1. """ + @abstractmethod def __init__( self, length: int, diff --git a/aeon/similarity_search/subsequence_search/tests/test_commons.py b/aeon/similarity_search/subsequence_search/tests/test_commons.py index 50c5cfad31..e5b4272285 100644 --- a/aeon/similarity_search/subsequence_search/tests/test_commons.py +++ b/aeon/similarity_search/subsequence_search/tests/test_commons.py @@ -24,14 +24,15 @@ def test_fft_sliding_dot_product(): """Test the fft_sliding_dot_product function.""" + L = 4 X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) - Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=4) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) values = fft_sliding_dot_product(X, Q) # Compare values[0] only as input is univariate assert_array_almost_equal( values[0], - [np.dot(Q[0], X[0, i : i + 5]) for i in range(X.shape[1] - 5 + 1)], + [np.dot(Q[0], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], ) diff --git a/aeon/similarity_search/subsequence_search/tests/test_stomp.py b/aeon/similarity_search/subsequence_search/tests/test_stomp.py index 12d7738eaf..757c8a3133 100644 --- a/aeon/similarity_search/subsequence_search/tests/test_stomp.py +++ b/aeon/similarity_search/subsequence_search/tests/test_stomp.py @@ -265,13 +265,9 @@ def test__stomp(k, allow_neighboring_matches, inverse_distance): assert_almost_equal(MP[i][j], dist_profiles[index[0]][index[1]]) -@pytest.mark.parametrize( - [ - ("k", K_VALUES), - ("allow_neighboring_matches", NN_MATCHES), - ("inverse_distance", INVERSE), - ] -) +@pytest.mark.parametrize("k", K_VALUES) +@pytest.mark.parametrize("allow_neighboring_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) def test__stomp_normalised(k, allow_neighboring_matches, inverse_distance): """Test STOMP normalised method.""" L = 3 diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py index 824a627d7a..5362137ba9 100644 --- a/aeon/testing/mock_estimators/_mock_similarity_searchers.py +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -17,8 +17,13 @@ class MockMatrixProfile(BaseMatrixProfile): """Mock estimator for BaseMatrixProfile.""" - def __init__(self, length=3): - super().__init__(length=length) + def __init__( + self, + length=3, + normalise=False, + n_jobs=1, + ): + super().__init__(length=length, n_jobs=n_jobs, normalise=normalise) def compute_matrix_profile( self, @@ -45,8 +50,13 @@ def compute_distance_profile(self, X): class MockSubsequenceSearch(BaseSubsequenceSearch): """Mock estimator for BaseSubsequenceSearch.""" - def __init__(self, length=3): - super().__init__(length=length) + def __init__( + self, + length=3, + normalise=False, + n_jobs=1, + ): + super().__init__(length=length, n_jobs=n_jobs, normalise=normalise) def _find_motifs( self, diff --git a/aeon/utils/numba/general.py b/aeon/utils/numba/general.py index b398a8414b..958c584459 100644 --- a/aeon/utils/numba/general.py +++ b/aeon/utils/numba/general.py @@ -276,7 +276,7 @@ def z_normalise_series_2d_with_mean_std( Parameters ---------- - X : array, shape = (n_channels, n_timestamps) + X : array, shape = (n_channels, n_timepoints) Input array to normalise. mean : array, shape = (n_channels) Mean of each channel of X. @@ -285,7 +285,7 @@ def z_normalise_series_2d_with_mean_std( Returns ------- - arr : array, shape = (n_channels, n_timestamps) + arr : array, shape = (n_channels, n_timepoints) The normalised array """ arr = np.zeros(X.shape) @@ -379,10 +379,10 @@ def get_subsequence( Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) Input time series. i_start : int - A starting index between [0, n_timestamps - (length-1)*dilation] + A starting index between [0, n_timepoints - (length-1)*dilation] length : int Length parameter of the subsequence. dilation : int @@ -411,10 +411,10 @@ def get_subsequence_with_mean_std( Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) Input time series. i_start : int - A starting index between [0, n_timestamps - (length-1)*dilation] + A starting index between [0, n_timepoints - (length-1)*dilation] length : int Length parameter of the subsequence. dilation : int @@ -454,15 +454,56 @@ def get_subsequence_with_mean_std( return values, means, stds +@njit(cache=True, fastmath=True, parallel=True) +def compute_mean_stds_collection_parallel(X): + """ + Return the mean and standard deviation for each channel of all series in X. + + Parameters + ---------- + X : array, shape (n_cases, n_channels, n_timepoints) + A time series collection + + Returns + ------- + means : array, shape (n_cases, n_channels) + The mean of each channel of each time series in X. + stds : array, shape (n_cases, n_channels) + The std of each channel of each time series in X. + + """ + n_channels = X[0].shape[0] + n_cases = len(X) + means = np.zeros((n_cases, n_channels)) + stds = np.zeros((n_cases, n_channels)) + for i_x in prange(n_cases): + n_timepoints = X[i_x].shape[1] + _s = np.zeros(n_channels) + _s2 = np.zeros(n_channels) + for i_t in range(n_timepoints): + for i_c in range(n_channels): + _s += X[i_x][i_c, i_t] + _s2 += X[i_x][i_c, i_t] ** 2 + + for i_c in range(n_channels): + means[i_x, i_c] = _s / n_timepoints + _std = _s2 / n_timepoints - means[i_x, i_c] ** 2 + if _s > AEON_NUMBA_STD_THRESHOLD: + stds[i_x, i_c] = _std**0.5 + + return means, stds + + @njit(fastmath=True, cache=True) def sliding_mean_std_one_series( X: np.ndarray, length: int, dilation: int ) -> tuple[np.ndarray, np.ndarray]: - """Return the mean and standard deviation for all subsequence (l,d) in X. + """ + Return the mean and standard deviation for all subsequence (l,d) in X. Parameters ---------- - X : array, shape (n_channels, n_timestamps) + X : array, shape (n_channels, n_timepoints) An input time series length : int Length of the subsequence @@ -471,14 +512,14 @@ def sliding_mean_std_one_series( Returns ------- - mean : array, shape (n_channels, n_timestamps - (length-1) * dilation) + mean : array, shape (n_channels, n_timepoints - (length-1) * dilation) The mean of each subsequence with parameter length and dilation in X. - std : array, shape (n_channels, n_timestamps - (length-1) * dilation) + std : array, shape (n_channels, n_timepoints - (length-1) * dilation) The standard deviation of each subsequence with parameter length and dilation in X. """ - n_channels, n_timestamps = X.shape - n_subs = n_timestamps - (length - 1) * dilation + n_channels, n_timepoints = X.shape + n_subs = n_timepoints - (length - 1) * dilation if n_subs <= 0: raise ValueError( "Invalid input parameter for sliding mean and std computations" @@ -496,7 +537,7 @@ def sliding_mean_std_one_series( _sum2 = np.zeros(n_channels) # Initialize first subsequence if it is valid - if np.all(_idx_sub < n_timestamps): + if np.all(_idx_sub < n_timepoints): for i_length in prange(length): _idx_sub[i_length] = (i_length * dilation) + i_mod_dil for i_channel in prange(n_channels): @@ -513,7 +554,7 @@ def sliding_mean_std_one_series( _idx_sub += dilation # As long as subsequences further subsequences are valid - while np.all(_idx_sub < n_timestamps): + while np.all(_idx_sub < n_timepoints): # Update sums and mean stds arrays for i_channel in prange(n_channels): _v_new = X[i_channel, _idx_sub[-1]] @@ -537,17 +578,17 @@ def normalise_subsequences(X_subs: np.ndarray, X_means: np.ndarray, X_stds: np.n Parameters ---------- - X_subs : array, shape (n_timestamps-(length-1)*dilation, n_channels, length) - The subsequences of an input time series of size n_timestamps given the + X_subs : array, shape (n_timepoints-(length-1)*dilation, n_channels, length) + The subsequences of an input time series of size n_timepoints given the length and dilation parameter. - X_means : array, shape (n_channels, n_timestamps-(length-1)*dilation) + X_means : array, shape (n_channels, n_timepoints-(length-1)*dilation) Mean of the subsequences to normalise. - X_stds : array, shape (n_channels, n_timestamps-(length-1)*dilation) + X_stds : array, shape (n_channels, n_timepoints-(length-1)*dilation) Stds of the subsequences to normalise. Returns ------- - array, shape = (n_timestamps-(length-1)*dilation, n_channels, length) + array, shape = (n_timepoints-(length-1)*dilation, n_channels, length) Z-normalised subsequences. """ n_subsequences, n_channels, length = X_subs.shape @@ -758,8 +799,8 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra Parameters ---------- - X : array, shape = (n_channels, n_timestamps) - An input time series as (n_channels, n_timestamps). + X : array, shape = (n_channels, n_timepoints) + An input time series as (n_channels, n_timepoints). length : int Length of the subsequences to generate. dilation : int @@ -767,11 +808,11 @@ def get_all_subsequences(X: np.ndarray, length: int, dilation: int) -> np.ndarra Returns ------- - array, shape = (n_timestamps-(length-1)*dilation, n_channels, length) + array, shape = (n_timepoints-(length-1)*dilation, n_channels, length) The view of the subsequences of the input time series. """ - n_features, n_timestamps = X.shape + n_features, n_timepoints = X.shape s0, s1 = X.strides - out_shape = (n_timestamps - (length - 1) * dilation, n_features, np.int64(length)) + out_shape = (n_timepoints - (length - 1) * dilation, n_features, np.int64(length)) strides = (s1, s0, s1 * dilation) return np.lib.stride_tricks.as_strided(X, shape=out_shape, strides=strides) From da2758c4fbfb1c9405b4aff1fa12fa14b94522ba Mon Sep 17 00:00:00 2001 From: baraline Date: Thu, 2 Jan 2025 16:12:53 +0100 Subject: [PATCH 09/36] more debug of subsequence tests --- .../subsequence_search/base.py | 23 +++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/aeon/similarity_search/subsequence_search/base.py b/aeon/similarity_search/subsequence_search/base.py index 4c26a6a06a..9df76d233b 100644 --- a/aeon/similarity_search/subsequence_search/base.py +++ b/aeon/similarity_search/subsequence_search/base.py @@ -357,6 +357,29 @@ def _find_neighbors( exclusion_factor: Optional[float] = 2.0, ): ... + @classmethod + def _get_test_params(cls, parameter_set: str = "default") -> dict: + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + For classifiers, a "default" set of parameters should be provided for + general testing, and a "results_comparison" set for comparing against + previously recorded results if the general set does not produce suitable + probabilities to compare against. + + Returns + ------- + params : dict or list of dict, default={} + Parameters to create testing instances of the class. + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + return {"length": 3} + class BaseMatrixProfile(BaseSubsequenceSearch): """Base class for Matrix Profile methods using a length parameter.""" From 2191ac27c259ab09d9e2d21d267af1a1628b3783 Mon Sep 17 00:00:00 2001 From: baraline Date: Fri, 10 Jan 2025 12:03:53 +0100 Subject: [PATCH 10/36] Add functional LSH neighbors --- aeon/similarity_search/_base.py | 1 - .../series_search/__init__.py | 2 +- .../series_search/{base.py => _base.py} | 85 ++++-- .../similarity_search/series_search/_r_lsh.py | 239 ---------------- .../series_search/_rp_lsh.py | 263 ++++++++++++++++++ .../subsequence_search/__init__.py | 8 +- .../subsequence_search/{base.py => _base.py} | 4 + .../subsequence_search/_brute_force.py | 2 +- .../subsequence_search/_stomp.py | 2 +- .../_mock_similarity_searchers.py | 2 +- aeon/utils/base/_register.py | 7 +- 11 files changed, 339 insertions(+), 276 deletions(-) rename aeon/similarity_search/series_search/{base.py => _base.py} (79%) delete mode 100644 aeon/similarity_search/series_search/_r_lsh.py create mode 100644 aeon/similarity_search/series_search/_rp_lsh.py rename aeon/similarity_search/subsequence_search/{base.py => _base.py} (99%) diff --git a/aeon/similarity_search/_base.py b/aeon/similarity_search/_base.py index 476dc664c2..68e35c6cbb 100644 --- a/aeon/similarity_search/_base.py +++ b/aeon/similarity_search/_base.py @@ -26,7 +26,6 @@ class BaseSimilaritySearch(BaseCollectionEstimator): _tags = { "capability:multivariate": True, - "capability:unequal_length": True, "capability:multithreading": True, "fit_is_empty": False, "X_inner_type": ["np-list", "numpy3D"], diff --git a/aeon/similarity_search/series_search/__init__.py b/aeon/similarity_search/series_search/__init__.py index 2f69dab51a..9a618540db 100644 --- a/aeon/similarity_search/series_search/__init__.py +++ b/aeon/similarity_search/series_search/__init__.py @@ -2,4 +2,4 @@ __all__ = ["BaseSeriesSearch", "BaseIndexSearch"] -from aeon.similarity_search.series_search.base import BaseIndexSearch, BaseSeriesSearch +from aeon.similarity_search.series_search._base import BaseIndexSearch, BaseSeriesSearch diff --git a/aeon/similarity_search/series_search/base.py b/aeon/similarity_search/series_search/_base.py similarity index 79% rename from aeon/similarity_search/series_search/base.py rename to aeon/similarity_search/series_search/_base.py index 9f0e4f4ca6..f9c6ed8097 100644 --- a/aeon/similarity_search/series_search/base.py +++ b/aeon/similarity_search/series_search/_base.py @@ -132,11 +132,6 @@ def find_neighbors( self._check_is_fitted() self._check_find_neighbors_motif_format(X) - if self.length != X.shape[1]: - raise ValueError( - f"Expected X to be of shape {(self.n_channels_, self.length)} but" - f" got {X.shape} in find_neighbors." - ) X_index = self._check_X_index_int(X_index) prev_threads = get_num_threads() @@ -178,14 +173,6 @@ def _compute_mean_std_from_collection(self, X: np.ndarray): return means, stds def _fit(self, X, y=None): - if self.length >= self.min_timepoints_ or self.length < 1: - raise ValueError( - "The length of the query should be inferior or equal to the length of " - "data (X_) provided during fit, but got {} for X and {} for X_".format( - self.length, self.min_timepoints_ - ) - ) - if self.normalise: self.X_means_, self.X_stds_ = self._compute_mean_std_from_collection(X) self.X_ = X @@ -199,8 +186,6 @@ def _find_motifs( threshold: Optional[float] = np.inf, X_index: Optional[int] = None, inverse_distance: Optional[bool] = False, - allow_neighboring_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2.0, ): ... @abstractmethod @@ -211,17 +196,69 @@ def _find_neighbors( threshold: Optional[float] = np.inf, inverse_distance: Optional[bool] = False, X_index=None, - allow_neighboring_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2.0, ): ... -class BaseIndexSearch(BaseSimilaritySearch): - """.""" +# TODO : Add an update method to add series to the index +class BaseIndexSearch(BaseSeriesSearch): + """ + Base class for similarity search on whole time series using indexes. + + Parameters + ---------- + normalise : bool, optional + Whether the inputs should be z-normalised. The default is False. + n_jobs : int, optional + Number of parallel jobs to use. The default is 1. + """ + + def _fit(self, X, y=None): + super()._fit(X) + self._build_index() + return self + + @abstractmethod + def _build_index(self): ... + + @abstractmethod + def _query_index( + self, + X, + k=1, + inverse_distance=False, + threshold=np.inf, + ): ... + + @abstractmethod + def _get_bucket_sizes(self): ... + + @abstractmethod + def _get_bucket_content(self, key): ... - ... + def _find_motifs( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + X_index: Optional[int] = None, + inverse_distance: Optional[bool] = False, + ): + bucket_sizes = self._get_bucket_sizes() + idx_motifs = np.argsort(list(bucket_sizes.values()))[::-1][:, k] + # TODO : review distance return on motif for whole series and buckets + return [self._get_bucket_content(idx_motif) for idx_motif in idx_motifs], [ + 0 for _ in idx_motifs + ] - def batch_fit(sourcefiles, batch_size): - """.""" - # fit - # and then update + def _find_neighbors( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + inverse_distance: Optional[bool] = False, + X_index=None, + ): + top_k, top_k_dist = self._query_index( + X, k=k, inverse_distance=inverse_distance, threshold=threshold + ) + return top_k, top_k_dist diff --git a/aeon/similarity_search/series_search/_r_lsh.py b/aeon/similarity_search/series_search/_r_lsh.py deleted file mode 100644 index a98dbfc562..0000000000 --- a/aeon/similarity_search/series_search/_r_lsh.py +++ /dev/null @@ -1,239 +0,0 @@ -"""Random projection LSH index.""" - -import numpy as np -from numba import njit, prange - -# TPB = 16 - - -@njit(cache=True) -def _hamming_dist(X, Y): - d = 0 - for i in prange(X.shape[0]): - d += X[i] ^ Y[i] - return d - - -@njit(cache=True, parallel=True) -def _hamming_dist_matrix(bool_hashes_value_list, bool_hashes): - n_hashes = bool_hashes.shape[0] - res = np.zeros((n_hashes, bool_hashes_value_list.shape[0]), dtype=np.int64) - for i in prange(n_hashes): - for j in prange(bool_hashes_value_list.shape[0]): - res[i, j] = _hamming_dist(bool_hashes_value_list[j], bool_hashes[i]) - return res - - -@njit(cache=True, fastmath=True, parallel=True) -def _series_to_bool(X, hash_funcs, start_points, length): - n_hash_funcs = hash_funcs.shape[0] - res = np.empty(n_hash_funcs, dtype=np.bool_) - for j in prange(n_hash_funcs): - res[j] = ( - np.dot(X[start_points[j] : start_points[j] + length], hash_funcs[j]) >= 0 - ) - return res - - -@njit(cache=True, fastmath=True, parallel=True) -def _collection_to_bool(X, hash_funcs, start_points, length): - n_hash_funcs = hash_funcs.shape[0] - n_samples = X.shape[0] - res = np.empty((n_samples, n_hash_funcs), dtype=np.bool_) - for i in prange(n_samples): - for j in prange(n_hash_funcs): - res[i, j] = ( - np.dot(X[i, start_points[j] : start_points[j] + length], hash_funcs[j]) - >= 0 - ) - return res - - -class LSH: - """ - . - - Parameters - ---------- - n_vectors : TYPE - DESCRIPTION. - custom_table : TYPE, optional - DESCRIPTION. The default is None. - - Returns - ------- - None. - - """ - - def __init__(self, n_hash_funcs=128, window_length=1.0, seed=None): - self.n_hash_funcs = n_hash_funcs - self.window_length = window_length - self.seed = seed - - def fit(self, X): - """ - . - - Parameters - ---------- - X : TYPE - DESCRIPTION. - - Returns - ------- - TYPE - DESCRIPTION. - - """ - self.rng_ = np.random.default_rng(self.seed) - self.X_ = np.array( - [X[i].flatten() for i in range(len(X))] - ) # n_samples, n_channels * n_timepoints - - self.window_length_ = max(1, int(self.X_.shape[1] * self.window_length)) - # Can replace with choice [-1, 1] - self.hash_funcs_ = self.rng_.uniform( - low=-1, high=1.0, size=(self.n_hash_funcs, self.window_length_) - ) - self.start_points_ = self.rng_.choice( - self.X_.shape[1] - self.window_length_ + 1, - size=self.n_hash_funcs, - replace=True, - ) - - bool_hashes = _collection_to_bool( - self.X_, self.hash_funcs_, self.start_points_, self.window_length_ - ) - # could yield this - str_hashes = [hash(bool_hashes[i].tobytes()) for i in range(len(bool_hashes))] - self.dict_X_index = {} - self.dict_bool_hashes = {} - for i in range(len(str_hashes)): - if str_hashes[i] in self.dict_X_index: - self.dict_X_index[str_hashes[i]].append(i) - else: - self.dict_X_index[str_hashes[i]] = [i] - self.dict_bool_hashes[str_hashes[i]] = bool_hashes[i] - - self.bool_hashes_value_list = np.asarray(list(self.dict_bool_hashes.values())) - self.bool_hashes_key_list = np.asarray(list(self.dict_bool_hashes.keys())) - - return self - - def update(self, X): - """ - . - - Parameters - ---------- - X : TYPE - DESCRIPTION. - - Returns - ------- - TYPE - DESCRIPTION. - - """ - X_ = np.array( - [X[i].flatten() for i in range(len(X))] - ) # n_samples, n_channels * n_timepoints - bool_hashes = _collection_to_bool( - X_, self.hash_funcs_, self.start_points_, self.window_length_ - ) - - str_hashes = [hash(bool_hashes[i].tobytes()) for i in range(len(bool_hashes))] - base_index = self.X_.shape[0] - for i in range(len(str_hashes)): - if str_hashes[i] in self.dict_X_index: - self.dict_X_index[str_hashes[i]].append(i + base_index) - else: - self.dict_X_index[str_hashes[i]] = [i + base_index] - self.dict_bool_hashes[str_hashes[i]] = bool_hashes[i] - self.X_ = np.concatenate((self.X_, X_)) - - self.bool_hashes_value_list = np.asarray(list(self.dict_bool_hashes.values())) - self.bool_hashes_key_list = np.asarray(list(self.dict_bool_hashes.keys())) - return self - - def get_bucket_collection_indexes(self, X): - """ - . - - Parameters - ---------- - X : TYPE - DESCRIPTION. - - Returns - ------- - TYPE - DESCRIPTION. - - """ - bool_hash = _series_to_bool( - X.flatten(), self.hash_funcs_, self.start_points_, self.window_length_ - ) - str_hash = hash(bool_hash.tobytes()) - if str_hash in self.dict_X_index: - return self.dict_X_index[str_hash] - else: - return [] - - def predict(self, X, k=1): - """ - . - - Parameters - ---------- - X : TYPE - DESCRIPTION. - k : TYPE, optional - DESCRIPTION. The default is 1. - - Returns - ------- - top_k : TYPE - DESCRIPTION. - - """ - X_ = np.array([X[i].flatten() for i in range(len(X))]) - bool_hashes = _collection_to_bool( - X_, self.hash_funcs_, self.start_points_, self.window_length_ - ) - top_k = np.zeros((len(X), k), dtype=int) - dists = _hamming_dist_matrix(self.bool_hashes_value_list, bool_hashes) - self.h_dists = dists - # Deal with equality by merging bucket contents ? - for i_x in range(len(X)): - ids = np.argsort(dists[i_x]) - _i = 0 - c = k - while c > 0: - candidates = self.dict_X_index[self.bool_hashes_key_list[ids[_i]]] - # Can do exact search by computing distances here - if len(candidates) > c: - candidates = candidates[:c] - top_k[i_x, k - c : k - c + len(candidates)] = candidates - c -= len(candidates) - _i += 1 - return top_k - - def find_motif(Index, X=None): - """ - . - - Parameters - ---------- - Index : TYPE - DESCRIPTION. - X : TYPE, optional - DESCRIPTION. The default is None. - - Returns - ------- - None. - - """ - pass diff --git a/aeon/similarity_search/series_search/_rp_lsh.py b/aeon/similarity_search/series_search/_rp_lsh.py new file mode 100644 index 0000000000..8d1701793e --- /dev/null +++ b/aeon/similarity_search/series_search/_rp_lsh.py @@ -0,0 +1,263 @@ +"""Random projection LSH index.""" + +import numpy as np +from numba import njit, prange + +from aeon.similarity_search.series_search._base import BaseIndexSearch + +# TPB = 16 + + +@njit(cache=True) +def _hamming_dist(X, Y): + d = 0 + for i in prange(X.shape[0]): + d += X[i] ^ Y[i] + return d + + +@njit(cache=True, parallel=True) +def _hamming_dist_matrix(bool_hashes_value_list, bool_hashes): + n_hash_funcs = bool_hashes.shape[0] + res = np.zeros((n_hash_funcs, bool_hashes_value_list.shape[0]), dtype=np.int64) + for i in prange(n_hash_funcs): + for j in range(bool_hashes_value_list.shape[0]): + res[i, j] = _hamming_dist(bool_hashes_value_list[j], bool_hashes[i]) + return res + + +@njit(cache=True, fastmath=True, parallel=True) +def _series_to_bool(X, hash_funcs, start_points, length): + n_hash_funcs = hash_funcs.shape[0] + res = np.empty(n_hash_funcs, dtype=np.bool_) + for j in prange(n_hash_funcs): + res[j] = _nb_flat_dot( + X[start_points[j] : start_points[j] + length], hash_funcs[j] + ) + return res + + +@njit(cache=True, fastmath=True) +def _nb_flat_dot(X, Y): + n_channels, n_timepoints = X.shape + out = 0 + for i in prange(n_channels): + for j in prange(n_timepoints): + out += X[i, j] * Y[i, j] + return out >= 0 + + +@njit(cache=True, parallel=True) +def _collection_to_bool(X, hash_funcs, start_points, length): + n_hash_funcs = hash_funcs.shape[0] + n_samples = X.shape[0] + res = np.empty((n_samples, n_hash_funcs), dtype=np.bool_) + for j in prange(n_hash_funcs): + for i in range(n_samples): + res[i, j] = _nb_flat_dot( + X[i, :, start_points[j] : start_points[j] + length], hash_funcs[j] + ) + + return res + + +class RP_LSH_Cosine(BaseIndexSearch): + """ + Random Projection Locality Sensitive Hashing index for cosine similarity. + + In this method based on SimHash, we define a hash function as a boolean operation + such as, given a random vector ``V`` of shape ``(n_channels, L)`` and a time series + ``X`` of shape ``(n_channels, n_timeponts)`` (with ``L<=n_timepoints``), we compute + ``X.V > 0`` to obtain the boolean result. + In the case where ``L>> from aeon.datasets import load_classification + >>> from aeon.similarity_search.series_search import RP_LSH_Cosine + >>> index = RP_LSH_Cosine() + >>> X, y = load_classification("ArrowHead") + >>> index.fit(X[:200]) + >>> r = index.find_neighbors(X[201]) + """ + + _tags = { + "capability:unequal_length": False, + } + + def __init__( + self, + n_hash_funcs=128, + hash_func_coverage=0.25, + use_discrete_vectors=True, + random_state=None, + normalise=False, + n_jobs=1, + ): + self.n_hash_funcs = n_hash_funcs + self.hash_func_coverage = hash_func_coverage + self.use_discrete_vectors = use_discrete_vectors + self.random_state = random_state + super().__init__(normalise=normalise, n_jobs=n_jobs) + + def _build_index(self): + """ + Build the index based on the data stored in X_. + + Returns + ------- + self + + """ + rng = np.random.default_rng(self.random_state) + n_timepoints = self.X_.shape[2] + self.window_length_ = max(1, int(n_timepoints * self.hash_func_coverage)) + + if self.use_discrete_vectors: + self.hash_funcs_ = rng.choice( + [-1, 1], size=(self.n_hash_funcs, self.n_channels_, self.window_length_) + ) + else: + self.hash_funcs_ = rng.uniform( + low=-1, + high=1.0, + size=(self.n_hash_funcs, self.n_channels_, self.window_length_), + ) + self.start_points_ = rng.choice( + n_timepoints - self.window_length_ + 1, + size=self.n_hash_funcs, + replace=True, + ) + + bool_hashes = _collection_to_bool( + self.X_, self.hash_funcs_, self.start_points_, self.window_length_ + ) + + str_hashes = [hash(bool_hashes[i].tobytes()) for i in range(len(bool_hashes))] + self.dict_X_index_ = {} + self.dict_bool_hashes_ = {} + for i in range(len(str_hashes)): + if str_hashes[i] in self.dict_X_index_: + self.dict_X_index_[str_hashes[i]].append(i) + else: + self.dict_X_index_[str_hashes[i]] = [i] + self.dict_bool_hashes_[str_hashes[i]] = bool_hashes[i] + + self.bool_hashes_value_list_ = np.asarray(list(self.dict_bool_hashes_.values())) + self.bool_hashes_key_list_ = np.asarray(list(self.dict_bool_hashes_.keys())) + + return self + + def _get_bucket_content(self, key): + return self.dict_X_index_[key] + + def _get_bucket_sizes(self): + return {key: len(self.dict_X_index_[key]) for key in self.dict_X_index_} + + def _get_series_bucket(self, X): + """ + Get the matching bucket of a single series X if it exists in the index. + + Parameters + ---------- + X : TYPE + DESCRIPTION. + + Returns + ------- + TYPE + DESCRIPTION. + + """ + bool_hash = _series_to_bool( + X, self.hash_funcs_, self.start_points_, self.window_length_ + ) + str_hash = hash(bool_hash.tobytes()) + if str_hash in self.dict_X_index_: + return str_hash + else: + return None + + def _query_index( + self, + X, + k=1, + threshold=np.inf, + inverse_distance=False, + ): + """ + Find approximate nearest neighbors of a collection in the index. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_tiempoints) + Series for which we want to find neighbors. + k : int, optional + Number of neighbors to return for each series. The default is 1. + threshold : int, optional + A threshold on the distance to determine which candidates will be returned. + inverse_distance : bool, optional + Wheter to inverse the computed distance, meaning that the method will return + the k most dissimilar neighbors instead of the k most similar. + + Returns + ------- + top_k : np.ndarray, shape = (n_cases, k) + Indexes of k series in X_ (the index) that are close to each series in X. + top_k_dist : np.ndarray, shape = (n_cases, k) + Distance of k series in X_ (the index) to each series in X. The distance + is the hamming distance between the result of each hash function. + """ + bool_hashes = _series_to_bool( + X, self.hash_funcs_, self.start_points_, self.window_length_ + ) + top_k = np.zeros(k, dtype=int) + top_k_dist = np.zeros(k, dtype=float) + dists = _hamming_dist_matrix( + self.bool_hashes_value_list_, bool_hashes[np.newaxis, :] + )[0] + if inverse_distance: + dists = 1 / (dists + 1e-8) + # Get top k buckets + ids = np.argpartition(dists, kth=k)[:k] + # and reoder them + ids = ids[np.argsort(dists[ids])] + + _i_bucket = 0 + current_k = 0 + while current_k < k: + if dists[ids[_i_bucket]] <= threshold: + candidates = self.dict_X_index_[ + self.bool_hashes_key_list_[ids[_i_bucket]] + ] + # Can do exact search by computing distances here + if len(candidates) > k - current_k: + candidates = candidates[: k - current_k] + top_k[current_k : current_k + len(candidates)] = candidates + top_k_dist[current_k : current_k + len(candidates)] = dists[ + ids[_i_bucket] + ] + current_k += len(candidates) + else: + break + _i_bucket += 1 + return top_k[:current_k], top_k_dist[:current_k] diff --git a/aeon/similarity_search/subsequence_search/__init__.py b/aeon/similarity_search/subsequence_search/__init__.py index 5d64f901bd..eb062c46b8 100644 --- a/aeon/similarity_search/subsequence_search/__init__.py +++ b/aeon/similarity_search/subsequence_search/__init__.py @@ -7,11 +7,11 @@ "BruteForceMatrixProfile", ] +from aeon.similarity_search.subsequence_search._base import ( + BaseMatrixProfile, + BaseSubsequenceSearch, +) from aeon.similarity_search.subsequence_search._brute_force import ( BruteForceMatrixProfile, ) from aeon.similarity_search.subsequence_search._stomp import StompMatrixProfile -from aeon.similarity_search.subsequence_search.base import ( - BaseMatrixProfile, - BaseSubsequenceSearch, -) diff --git a/aeon/similarity_search/subsequence_search/base.py b/aeon/similarity_search/subsequence_search/_base.py similarity index 99% rename from aeon/similarity_search/subsequence_search/base.py rename to aeon/similarity_search/subsequence_search/_base.py index 9df76d233b..0c55881ad0 100644 --- a/aeon/similarity_search/subsequence_search/base.py +++ b/aeon/similarity_search/subsequence_search/_base.py @@ -44,6 +44,10 @@ def __init__( self.length = length super().__init__(n_jobs=n_jobs, normalise=normalise) + _tags = { + "capability:unequal_length": True, + } + @final def find_motifs( self, diff --git a/aeon/similarity_search/subsequence_search/_brute_force.py b/aeon/similarity_search/subsequence_search/_brute_force.py index 2225fbd0ad..269cdc369b 100644 --- a/aeon/similarity_search/subsequence_search/_brute_force.py +++ b/aeon/similarity_search/subsequence_search/_brute_force.py @@ -9,11 +9,11 @@ from numba import njit, prange from numba.typed import List +from aeon.similarity_search.subsequence_search._base import BaseMatrixProfile from aeon.similarity_search.subsequence_search._commons import ( _extract_top_k_from_dist_profile, _inverse_distance_profile_list, ) -from aeon.similarity_search.subsequence_search.base import BaseMatrixProfile from aeon.utils.numba.general import ( get_all_subsequences, z_normalise_series_2d, diff --git a/aeon/similarity_search/subsequence_search/_stomp.py b/aeon/similarity_search/subsequence_search/_stomp.py index a1d6ac8730..66d5270872 100644 --- a/aeon/similarity_search/subsequence_search/_stomp.py +++ b/aeon/similarity_search/subsequence_search/_stomp.py @@ -7,13 +7,13 @@ from numba import njit, prange from numba.typed import List +from aeon.similarity_search.subsequence_search._base import BaseMatrixProfile from aeon.similarity_search.subsequence_search._commons import ( _extract_top_k_from_dist_profile, _inverse_distance_profile_list, fft_sliding_dot_product, get_ith_products, ) -from aeon.similarity_search.subsequence_search.base import BaseMatrixProfile from aeon.utils.numba.general import ( AEON_NUMBA_STD_THRESHOLD, sliding_mean_std_one_series, diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py index 5362137ba9..1d3161514a 100644 --- a/aeon/testing/mock_estimators/_mock_similarity_searchers.py +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -8,7 +8,7 @@ import numpy as np -from aeon.similarity_search.subsequence_search.base import ( +from aeon.similarity_search.subsequence_search._base import ( BaseMatrixProfile, BaseSubsequenceSearch, ) diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 9c576c350a..024ad447ee 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -24,13 +24,12 @@ from aeon.forecasting.base import BaseForecaster from aeon.regression.base import BaseRegressor from aeon.segmentation.base import BaseSegmenter -from aeon.similarity_search.subsequence_search.base import BaseSubsequenceSearch +from aeon.similarity_search.series_search._base import BaseSeriesSearch +from aeon.similarity_search.subsequence_search._base import BaseSubsequenceSearch from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer from aeon.transformations.series import BaseSeriesTransformer -# from aeon.similarity_search.series_search.base import BaseSeriesSearch - # all base classes BASE_CLASS_REGISTER = { # abstract - no estimator directly inherits from these @@ -49,7 +48,7 @@ "series-transformer": BaseSeriesTransformer, "forecaster": BaseForecaster, "subsequence_searcher": BaseSubsequenceSearch, - # "series_searcher": BaseSeriesSearch, + "series_searcher": BaseSeriesSearch, } # base classes which are valid for estimator to directly inherit from From cd33d0a02ea98e9364c7e766f1a5c3520795e388 Mon Sep 17 00:00:00 2001 From: baraline Date: Mon, 13 Jan 2025 10:43:09 +0100 Subject: [PATCH 11/36] add notebook for sim search tasks --- .../subsequence_search/_base.py | 2 + .../similarity_search_tasks.ipynb | 136 ++++++++++++++++++ 2 files changed, 138 insertions(+) create mode 100644 examples/similarity_search/similarity_search_tasks.ipynb diff --git a/aeon/similarity_search/subsequence_search/_base.py b/aeon/similarity_search/subsequence_search/_base.py index 0c55881ad0..a8d78b029d 100644 --- a/aeon/similarity_search/subsequence_search/_base.py +++ b/aeon/similarity_search/subsequence_search/_base.py @@ -19,6 +19,8 @@ # We can define a BaseVariableLengthSubsequenceSearch later for VALMOD and the likes. +# BaseSubSeries 'replace sub by series' + class BaseSubsequenceSearch(BaseSimilaritySearch): """ diff --git a/examples/similarity_search/similarity_search_tasks.ipynb b/examples/similarity_search/similarity_search_tasks.ipynb new file mode 100644 index 0000000000..a42339c611 --- /dev/null +++ b/examples/similarity_search/similarity_search_tasks.ipynb @@ -0,0 +1,136 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2347de94-27a7-486e-a900-e80db5c7f427", + "metadata": {}, + "source": [ + "# Similarity search tasks\n", + "\n", + "To discuss : the term subsequences appear more often than subseries in similarity search papers, so maybe stick to subsequences ?\n", + "\n", + "## Notations\n", + "- A single time point $x \\in \\mathbb{R}^{d}$ representing a vector of size $d$, with $d$ the number of channels\n", + "- A single time series $X \\in \\mathbb{R}^{d,m}$ of $d$ channels and $m$ timepoints\n", + "- A collection ${\\cal X} \\in \\mathbb{R}^{n,d,m}$ of $n$ time series \n", + "- $l$ a length parameter for subseries extracted using a sliding window on a time series $X$ over its timepoints\n", + "- $W_{i,j} \\in \\mathbb{R}^{d,l}$ a subseries extracted from a collection ${\\cal X}$, with $i$ the sample id and $j$ the starting timepoint, such as $W_{i,j} = X_{i,[j:j+l[}$. Denoted $W_{j}$ if used outside of the context of a collection. ${\\cal W}$ denotes the set of all admissible subseries.\n", + " \n", + "## Series tasks\n", + "Given a single series $X$, we want to be able to do the following tasks :\n", + "\n", + "#### Subseries Neighbor search:\n", + "$K$-nn based and/or range ($r$) based search (radius only for now, extent necessary for [k-Motiflefts](https://www.vldb.org/pvldb/vol16/p725-schafer.pdf) ?). Given a series $X$ and a subseries $W_i$, find the other subseries in $X$ that are the most similar to $W_i$. In terms of parameterization, we want to be able to toggle on/off :\n", + "- ignore neighboring matches. Given $W_j$ a neighboring subseries of $W_i$, the subseries $W_{j-l//\\epsilon}, ..., W_{j+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations\n", + "\n", + "#### Subseries Motif search :\n", + "Extract $k$-motifs or range $r$-motifs.\n", + "\n", + "The $k^{th}$ motif is the $k^{th}$ most similar pair of subseries in $X$. Given $\\forall a,b,i,j$ the pair ${W_i, W_j}$ is the motif if $dist(W_i, W_j) ≤ dist(W_a, W_b), i \\neq j$ and $a \\neq b$\n", + "\n", + "For the $r$-motif,: $S$ is a maximal set of subseries with range $r$ if $\\forall\\ W_i,W_j \\in S,\\ dist(W_i, W_j) \\leq 2r$ and $\\forall\\ W_a \\in {\\cal W}-S,\\ dist(W_a, W_i) > 2r$\n", + "\n", + "\n", + "#### Compute self distance profile\n", + "Given a subseries $W_i$, compute the self distance profile to $X$. Returns a vector of size $m-l+1$ containing the distance to all subseries. \n", + "\n", + "In terms of parameterization, we want to be able to toggle on/off :\n", + "- ignore neighboring matches. Given $W_j$ a neighboring subseries of $W_i$, the subseries $W_{j-l//\\epsilon}, ..., W_{j+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations\n", + "\n", + "\n", + "#### Compute self matrix profile\n", + "Given a series $X$ and a length parameter $l$, compute its self matrix profile. Returns a vector of size $m-l+1$ containing the distances to the best matches of each subseries $W_i$, and another vector of size $m-l+1$ containg the timestamp of the best matches in $X$ for each subseries. Implement it as A/B matrix profile with B=A.\n", + "\n", + "In terms of parameterization, we want to be able to toggle on/off :\n", + "- $k$ : number of best matches to return for each subseries in $X$\n", + "- $r$ : maximal distance of the best matches to be in the returned set for each subseries in $X$\n", + "- ignore neighboring matches. Given $W_j$ a neighboring subseries of $W_i$, the subseries $W_{j-l//\\epsilon}, ..., W_{j+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "7313599d-66e2-4d03-959e-bd0abe05baed", + "metadata": {}, + "source": [ + "\n", + "## Collection tasks\n", + "Given a time series collection $\\cal X$, we want to be able to do the following tasks :\n", + "(we consider all subseries $W_{i,j}$ part its of $\\cal X$ due to notation but doesn't have to be when given as inputs for example in neighbor search).\n", + "\n", + "#### Subseries Neighbor search :\n", + "$K$-nn based and/or range ($r$) based search (radius only for now, extent necessary for [k-Motiflefts](https://www.vldb.org/pvldb/vol16/p725-schafer.pdf) ?). Given a subseries $W_{i,j}$, find the other subseries in $\\cal X$ that are the most similar to $W_{i,j}$. In terms of parameterization, we want to be able to toggle on/off :\n", + "- ignore neighboring matches. Given ${W_a,b}$ a neighboring subseries of $W_{i,j}$, the subseries $W_{a, b-l//\\epsilon}, ..., W_{a,b+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations\n", + "\n", + "#### Series Neighbor search :\n", + "$K$-nn based and/or range ($r$) based search. Given a series $X_i$, find the other series in $\\cal X$ that are the most similar to $X_i$. In terms of parameterization, we want to be able to toggle on/off :\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations\n", + "\n", + "#### Subseries Motif search :\n", + "Extract $k$-motifs or range $r$-motifs.\n", + "\n", + "The $k^{th}$ motif is the $k^{th}$ most similar pair of subseries in $X$. Given $\\forall a,b,a^\\prime,b^\\prime,i,j,i^\\prime,j^\\prime$ the pair $(W_{i,j}, W_{i^\\prime,j^\\prime})$ is the motif if $dist(W_{i,j}, W_{i^\\prime,j^\\prime}) ≤ dist(W_{a,b}, W_{a^\\prime,b^\\prime}), i \\neq i^\\prime, j \\neq j^\\prime, a \\neq a^\\prime, b \\neq b^\\prime$.\n", + "\n", + "For the $r$-motif,: $S$ is a maximal set of subseries with range $r$ if $\\forall\\ (W_{i,j},W_{i^\\prime,j^\\prime}) \\in S,\\ dist(W_{i,j}, W_{i^\\prime,j^\\prime}) \\leq 2r$ and $\\forall\\ W_{a,b} \\in {\\cal W}-S,\\ dist(W_{i,j}, W_{a,b}) > 2r$\n", + "\n", + "\n", + "#### Compute distance profiles :\n", + "Given a subseries $W_{i,j}$, compute the distance profiles to all series in $\\cal X$. Returns a vector of size $n, m-l+1$ containing the distance to all subseries. \n", + "\n", + "In terms of parameterization, we want to be able to toggle on/off :\n", + "- ignore neighboring matches. Given $W_{i,b}$ a neighboring subseries of $W_{i,j}$ the subseries $W_{i,b-l//\\epsilon}, ..., W_{i,b+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations.\n", + "\n", + "\n", + "#### Compute matrix profiles :\n", + "Given a series $X_i \\in {\\cal X}$ and a length parameter $l$, compute its matrix profile over the collection. Returns a vector of size $m-l+1$ containing the distances to the best matches of each subseries $W_{i,j}$, and another vector of size $m-l+1$ containg the timestamp of the best matches in ${\\cal X}$ for each subseries.\n", + "\n", + "In terms of parameterization, we want to be able to toggle on/off :\n", + "- $k$ : number of best matches to return for each subseries in $X$\n", + "- $r$ : maximal distance of the best matches to be in the returned set for each subseries in $X$\n", + "- ignore neighboring matches. Given $W_{a,b}$ a neighbor of subseries $W_{i,j}$ the subseries $W_{a,b-l//\\epsilon}, ..., W_{a,b+l//\\epsilon}$ cannot be in the returned set.\n", + "- inverse distance. Return the worst matches instead of the best ones.\n", + "- normalize. Wheter subseries should be normalized prior to distance computations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be1430f0-dce0-4de4-b702-11ee5e33f462", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (Spyder)", + "language": "python3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b841b79796e4f7214fd0bb2dc4c381c42cfe554b Mon Sep 17 00:00:00 2001 From: baraline Date: Thu, 16 Jan 2025 09:55:29 +0100 Subject: [PATCH 12/36] Updated series similarity search --- aeon/similarity_search/_base.py | 252 ++----- aeon/similarity_search/collection/__init__.py | 1 + aeon/similarity_search/collection/_base.py | 96 +++ .../collection/motifs/__init__.py | 1 + .../collection/neighbors/__init__.py | 1 + .../neighbors/_rp_cosine_lsh.py} | 0 aeon/similarity_search/series/__init__.py | 7 + aeon/similarity_search/series/_base.py | 115 ++++ aeon/similarity_search/series/_commons.py | 210 ++++++ .../series/motifs/__init__.py | 7 + .../similarity_search/series/motifs/_stomp.py | 457 +++++++++++++ .../series/motifs/tests/__init__.py | 1 + .../series/motifs/tests/test_stomp.py | 149 +++++ .../series/neighbors/__init__.py | 9 + .../series/neighbors/_dummy.py | 159 +++++ .../series/neighbors/_mass.py | 247 +++++++ .../series/neighbors/tests/__init__.py | 1 + .../series/neighbors/tests/test_dummy.py | 40 ++ .../series/neighbors/tests/test_mass.py | 44 ++ .../series/tests/__init__.py | 1 + .../tests/test_base.py | 31 +- .../series/tests/test_commons.py | 174 +++++ .../series_search/__init__.py | 5 - aeon/similarity_search/series_search/_base.py | 264 -------- .../subsequence_search/__init__.py | 17 - .../subsequence_search/_base.py | 509 -------------- .../subsequence_search/_brute_force.py | 319 --------- .../subsequence_search/_commons.py | 170 ----- .../subsequence_search/_stomp.py | 619 ------------------ .../subsequence_search/tests/__init__.py | 1 - .../tests/test_brute_force.py | 172 ----- .../subsequence_search/tests/test_commons.py | 97 --- .../subsequence_search/tests/test_stomp.py | 332 ---------- .../_mock_similarity_searchers.py | 81 +-- aeon/transformations/collection/base.py | 4 +- .../similarity_search_tasks.ipynb | 2 +- 36 files changed, 1793 insertions(+), 2802 deletions(-) create mode 100644 aeon/similarity_search/collection/__init__.py create mode 100644 aeon/similarity_search/collection/_base.py create mode 100644 aeon/similarity_search/collection/motifs/__init__.py create mode 100644 aeon/similarity_search/collection/neighbors/__init__.py rename aeon/similarity_search/{series_search/_rp_lsh.py => collection/neighbors/_rp_cosine_lsh.py} (100%) create mode 100644 aeon/similarity_search/series/__init__.py create mode 100644 aeon/similarity_search/series/_base.py create mode 100644 aeon/similarity_search/series/_commons.py create mode 100644 aeon/similarity_search/series/motifs/__init__.py create mode 100644 aeon/similarity_search/series/motifs/_stomp.py create mode 100644 aeon/similarity_search/series/motifs/tests/__init__.py create mode 100644 aeon/similarity_search/series/motifs/tests/test_stomp.py create mode 100644 aeon/similarity_search/series/neighbors/__init__.py create mode 100644 aeon/similarity_search/series/neighbors/_dummy.py create mode 100644 aeon/similarity_search/series/neighbors/_mass.py create mode 100644 aeon/similarity_search/series/neighbors/tests/__init__.py create mode 100644 aeon/similarity_search/series/neighbors/tests/test_dummy.py create mode 100644 aeon/similarity_search/series/neighbors/tests/test_mass.py create mode 100644 aeon/similarity_search/series/tests/__init__.py rename aeon/similarity_search/{subsequence_search => series}/tests/test_base.py (76%) create mode 100644 aeon/similarity_search/series/tests/test_commons.py delete mode 100644 aeon/similarity_search/series_search/__init__.py delete mode 100644 aeon/similarity_search/series_search/_base.py delete mode 100644 aeon/similarity_search/subsequence_search/__init__.py delete mode 100644 aeon/similarity_search/subsequence_search/_base.py delete mode 100644 aeon/similarity_search/subsequence_search/_brute_force.py delete mode 100644 aeon/similarity_search/subsequence_search/_commons.py delete mode 100644 aeon/similarity_search/subsequence_search/_stomp.py delete mode 100644 aeon/similarity_search/subsequence_search/tests/__init__.py delete mode 100644 aeon/similarity_search/subsequence_search/tests/test_brute_force.py delete mode 100644 aeon/similarity_search/subsequence_search/tests/test_commons.py delete mode 100644 aeon/similarity_search/subsequence_search/tests/test_stomp.py diff --git a/aeon/similarity_search/_base.py b/aeon/similarity_search/_base.py index 68e35c6cbb..fc58838eb4 100644 --- a/aeon/similarity_search/_base.py +++ b/aeon/similarity_search/_base.py @@ -1,176 +1,87 @@ """Base class for similarity search.""" __maintainer__ = ["baraline"] +__all__ = [ + "BaseSimilaritySearch", +] + from abc import abstractmethod -from typing import Optional, Union, final +from typing import Union import numpy as np -from numba import get_num_threads, set_num_threads from numba.typed import List -from aeon.base import BaseCollectionEstimator - +from aeon.base import BaseAeonEstimator -class BaseSimilaritySearch(BaseCollectionEstimator): - """ - Base class for similarity search applications. - Parameters - ---------- - normalise : bool, optional - Whether the inputs should be z-normalised. The default is False. - n_jobs : int, optional - Number of parallel jobs to use. The default is 1. - """ +class BaseSimilaritySearch(BaseAeonEstimator): + """Base class for similarity search applications.""" _tags = { + "requires_y": False, "capability:multivariate": True, - "capability:multithreading": True, "fit_is_empty": False, - "X_inner_type": ["np-list", "numpy3D"], } @abstractmethod - def __init__( - self, - normalise: Optional[bool] = False, - n_jobs: Optional[int] = 1, - ): - self.n_jobs = n_jobs - self.normalise = normalise + def __init__(self): super().__init__() - @final + @abstractmethod def fit( self, X: Union[np.ndarray, List], y=None, ): """ - Fit method: data preprocessing and storage. + Fit estimator to X. + + State change: + Changes state to "fitted". + + Writes to self: + _is_fitted : flag is set to True. Parameters ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input array to be used as database for the similarity search. If it is an - unequal length collection, it should be a list of 2d numpy arrays. - y : optional - Not used. - - Raises - ------ - TypeError - If the input X array is not 3D raise an error. + X : Series or Collection, any supported type + Data to fit transform to, of python type as follows: + Series: 2D np.ndarray shape (n_channels, n_timepoints) + Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints) + or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i) + y: ignored, exists for API consistency reasons. Returns ------- - self + self : a fitted instance of the estimator """ - self.reset() - prev_threads = get_num_threads() - self._check_fit_format(X) - X = self._preprocess_collection(X) - # Store minimum number of n_timepoints for unequal length collections - self.min_timepoints_ = min([X[i].shape[-1] for i in range(len(X))]) - self.n_channels_ = X[0].shape[0] - self.n_cases_ = len(X) - if self.metadata_["unequal_length"]: - X = List(X) - set_num_threads(self._n_jobs) - self._fit(X, y) - set_num_threads(prev_threads) - self.is_fitted = True - return self + ... @abstractmethod - def find_motifs( + def predict( self, - X: np.ndarray, - k: int, - threshold: float, + X: Union[np.ndarray, None] = None, ): """ - Find the top-k motifs in the training data. - - Given ``k`` and ``threshold`` parameters, this methods returns the top-k motif - sets. We define a motif set as a set of candidates which all are at a distance - of at most ``threshold`` from each other. The top-k motifs sets are the - motif sets with the most candidates. + Predict method. Parameters ---------- - X : np.ndarray, - A series in which we want to indentify motifs. - k : int, optional - Number of motifs to return - threshold : int, optional - A threshold on the similarity measure to determine which candidates will be - part of a motif set. - - Returns - ------- - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the indexes of the - motifs. - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the distances of the - motifs to . - + X : 2D np.array of shape ``(n_cases, n_timepoints)`` + Optional data to use for predict. """ ... - @abstractmethod - def find_neighbors( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - ): + def _check_predict_series_format(self, X): """ - Find the top-k neighbors of X in the database. - - Given ``k`` and ``threshold`` parameters, this methods returns the top-k - neighbors of X, such as each of the ``k`` neighbors as a distance inferior or - equal to ``threshold``. By default, ``threshold`` is set to infinity. It is - possible for this method to return less than ``k`` neighbors, either if there - is less than ``k`` admissible candidate in the database, or if in the top-k - candidates, some do not meet the ``threshold`` condition. + Check wheter a series X in predict is correctly formated. Parameters ---------- - X: np.ndarray - The query for which we want to identify nearest neighbors in the database. - k : int, optional - Number of neighbors to return. - threshold : int, optional - A threshold on the distance to determine which candidates will be returned. - - Returns - ------- - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the indexes of the - candidates in each motif. - + X : np.ndarray, shape = (n_channels, n_timepoints) + A series to be used in predict. """ - ... - - def _check_fit_format(self, X): - if isinstance(X, np.ndarray): # "numpy3D" or numpy2D - if X.ndim != 3: - raise TypeError( - f"A np.ndarray given in fit must be 3D but found {X.ndim}D" - ) - elif isinstance(X, list): # np-list or df-list - if isinstance(X[0], np.ndarray): # if one a numpy they must all be 2D numpy - for a in X: - if not (isinstance(a, np.ndarray) and a.ndim == 2): - raise TypeError( - "A np-list given in fit must contain 2D np.ndarray but" - f" found {a.ndim}D" - ) - - def _check_find_neighbors_motif_format(self, X): if isinstance(X, np.ndarray): if X.ndim != 2: raise TypeError( @@ -186,91 +97,8 @@ def _check_find_neighbors_motif_format(self, X): f"Expected X to have {self.n_channels_} channels but" f" got {X.shape[0]} channels." ) - - @abstractmethod - def _fit(self, X, y=None): ... - - def _check_X_index_int(self, X_index: int): - """ - Check wheter the X_index parameter is correctly formated and is admissible. - - This check is made for motif search functions. - - Parameters - ---------- - X_index : int - Index of a series in X_. - - Returns - ------- - X_index : int - Index of a series in X_ - - """ - if X_index is not None: - if not isinstance(X_index, int): - raise TypeError("Expected an integer for X_index but got {X_index}") - - if X_index >= self.n_cases_ or X_index < 0: - raise ValueError( - "The value of X_index cannot exced the number " - "of series in the collection given during fit. Expected a value " - f"between [0, {self.n_cases_ - 1}] but got {X_index}" - ) - return X_index - - def _check_X_index_array(self, X_index: np.ndarray): - """ - Check wheter the X_index parameter is correctly formated and is admissible. - - This check is made for neighbour search functions. - - Parameters - ---------- - X_index : np.ndarray, 1D array of shape (2) - Array of integer containing the sample and timestamp identifiers of the - starting point of a subsequence in X_. - - Returns - ------- - X_index : np.ndarray, 1D array of shape (2) - Array of integer containing the sample and timestamp identifiers of the - starting point of a subsequence in X_. - - """ - if X_index is not None: - if ( - isinstance(X_index, list) - and len(X_index) == 2 - and isinstance(X_index[0], int) - and isinstance(X_index[1], int) - ): - X_index = np.asarray(X_index, dtype=int) - elif len(X_index) != 2: - raise TypeError( - "Expected a numpy array or list of integers with 2 elements " - f"for X_index but got {X_index}" - ) - elif ( - not (isinstance(X_index[0], int) or not isinstance(X_index[1], int)) - or X_index.dtype != int - ): - raise TypeError( - "Expected a numpy array or list of integers for X_index but got " - f"{X_index}" - ) - - if X_index[0] >= self.n_cases_ or X_index[0] < 0: - raise ValueError( - "The sample ID (first element) of X_index cannot exced the number " - "of series in the collection given during fit. Expected a value " - f"between [0, {self.n_cases_ - 1}] but got {X_index[0]}" - ) - _max_timestamp = self.X_[X_index[0]].shape[1] - self.length + 1 - if X_index[1] >= _max_timestamp: - raise ValueError( - "The timestamp ID (second element) of X_index cannot exced the " - "number of timestamps minus the length parameter plus one. Expected" - f" a value between [0, {_max_timestamp - 1}] but got {X_index[1]}" - ) - return X_index + if hasattr(self, "length") and X.shape[1] != self.length: + raise ValueError( + f"Expected X to have {self.length} timepoints but" + f" got {X.shape[1]} timepoints." + ) diff --git a/aeon/similarity_search/collection/__init__.py b/aeon/similarity_search/collection/__init__.py new file mode 100644 index 0000000000..0aef46ef49 --- /dev/null +++ b/aeon/similarity_search/collection/__init__.py @@ -0,0 +1 @@ +"""Similarity search for time series collection.""" diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py new file mode 100644 index 0000000000..402b7342a2 --- /dev/null +++ b/aeon/similarity_search/collection/_base.py @@ -0,0 +1,96 @@ +"""Base similiarity search for collections.""" + +__maintainer__ = ["baraline"] +__all__ = [ + "BaseCollectionSimilaritySearch", +] + +from abc import abstractmethod +from typing import Union, final + +import numpy as np +from numba import get_num_threads, set_num_threads + +from aeon.base import BaseCollectionEstimator +from aeon.similarity_search._base import BaseSimilaritySearch + + +class BaseCollectionSimilaritySearch(BaseCollectionEstimator, BaseSimilaritySearch): + """Similarity search base class for collections.""" + + # tag values specific to CollectionTransformers + _tags = { + "input_data_type": "Collection", + } + + @abstractmethod + def __init__(self, n_jobs=1): + self.n_jobs = n_jobs + super().__init__() + + @final + def fit( + self, + X: np.ndarray, + y=None, + ): + """ + Fit method: data preprocessing and storage. + + Parameters + ---------- + X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + Input array to be used as database for the similarity search. If it is an + unequal length collection, it should be a list of 2d numpy arrays. + y : optional + Not used. + + Raises + ------ + TypeError + If the input X array is not 3D raise an error. + + Returns + ------- + self + """ + self.reset() + X = self._preprocess_collection(X) + # Store minimum number of n_timepoints for unequal length collections + self.n_channels_ = X[0].shape[1] + self.n_cases_ = len(X) + self.X_ = X + + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) + self._fit(X, y=y) + set_num_threads(prev_threads) + + self.is_fitted = True + return self + + @abstractmethod + def _fit( + self, + X: np.ndarray, + y=None, + ): ... + + def _pre_predict( + self, + X: Union[np.ndarray, None] = None, + ): + """ + Predict method. + + Parameters + ---------- + X : Union[np.ndarray, None], optional + Optional data to use for predict.. The default is None. + + """ + self._check_is_fitted() + if X is not None: + # Could we call somehow _preprocess_series from a BaseCollectionEstimator ? + self._check_predict_format(X) + return X diff --git a/aeon/similarity_search/collection/motifs/__init__.py b/aeon/similarity_search/collection/motifs/__init__.py new file mode 100644 index 0000000000..fc014bcced --- /dev/null +++ b/aeon/similarity_search/collection/motifs/__init__.py @@ -0,0 +1 @@ +"""Motif search for time series collection.""" diff --git a/aeon/similarity_search/collection/neighbors/__init__.py b/aeon/similarity_search/collection/neighbors/__init__.py new file mode 100644 index 0000000000..e9a5d49d49 --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/__init__.py @@ -0,0 +1 @@ +"""Neighbors search for time series collection.""" diff --git a/aeon/similarity_search/series_search/_rp_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py similarity index 100% rename from aeon/similarity_search/series_search/_rp_lsh.py rename to aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py diff --git a/aeon/similarity_search/series/__init__.py b/aeon/similarity_search/series/__init__.py new file mode 100644 index 0000000000..23df7d1b53 --- /dev/null +++ b/aeon/similarity_search/series/__init__.py @@ -0,0 +1,7 @@ +"""Similarity search for series.""" + +__all__ = [ + "BaseSeriesSimilaritySearch", +] + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py new file mode 100644 index 0000000000..b9cca5d5cb --- /dev/null +++ b/aeon/similarity_search/series/_base.py @@ -0,0 +1,115 @@ +"""Base similiarity search for series.""" + +from abc import abstractmethod +from typing import Union, final + +import numpy as np +from numba import get_num_threads, set_num_threads + +from aeon.base import BaseSeriesEstimator +from aeon.similarity_search._base import BaseSimilaritySearch +from aeon.utils.validation import check_n_jobs + + +class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): + """Base class for similarity search applications on single series.""" + + _tags = { + "input_data_type": "Series", + } + + @abstractmethod + def __init__(self, axis=1, n_jobs=1): + self.n_jobs = n_jobs + super().__init__(axis=axis) + + @final + def fit( + self, + X: np.ndarray, + y=None, + ): + """ + Fit method: data preprocessing and storage. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, n_timepoints) + Input series to be used for the similarity search operations. + y : optional + Not used. + + Raises + ------ + TypeError + If the input X array is not 2D raise an error. + + Returns + ------- + self + """ + self.reset() + self._n_jobs = check_n_jobs(self.n_jobs) + X = self._preprocess_series(X, self.axis, True) + # Store minimum number of n_timepoints for unequal length collections + self.n_channels_ = X.shape[0] + self.n_timepoints_ = X.shape[1] + self.X_ = X + + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) + self._fit(X, y=y) + set_num_threads(prev_threads) + + self.is_fitted = True + return self + + @abstractmethod + def _fit( + self, + X: np.ndarray, + y=None, + ): ... + + def _pre_predict( + self, + X: Union[np.ndarray, None] = None, + ): + """ + Predict method. + + Parameters + ---------- + X : Union[np.ndarray, None], optional + Optional data to use for predict.. The default is None. + + """ + self._check_is_fitted() + if X is not None: + X = self._preprocess_series(X, self.axis, False) + self._check_predict_format(X) + return X + + def _check_X_index(self, X_index: int): + """ + Check wheter a X_index parameter is correctly formated and is admissible. + + Parameters + ---------- + X_index : int + Index of a timestamp in X_. + + """ + if X_index is not None: + if not isinstance(X_index, int): + raise TypeError("Expected an integer for X_index but got {X_index}") + + max_timepoints = self.n_timepoints_ + if hasattr(self, "length"): + max_timepoints -= self.length + if X_index >= max_timepoints or X_index < 0: + raise ValueError( + "The value of X_index cannot exced the number " + "of timepoint in series given during fit. Expected a value " + f"between [0, {max_timepoints - 1}] but got {X_index}" + ) diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py new file mode 100644 index 0000000000..d14281573f --- /dev/null +++ b/aeon/similarity_search/series/_commons.py @@ -0,0 +1,210 @@ +"""Helper and common function for similarity search series estimators.""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numba import njit +from scipy.signal import convolve + + +def fft_sliding_dot_product(X, q): + """ + Use FFT convolution to calculate the sliding window dot product. + + This function applies the Fast Fourier Transform (FFT) to efficiently compute + the sliding dot product between the input time series `X` and the query `q`. + The dot product is computed for each channel individually. The sliding window + approach ensures that the dot product is calculated for every possible subsequence + of `X` that matches the length of `q` + + Parameters + ---------- + X : array, shape=(n_channels, n_timepoints) + Input time series + q : array, shape=(n_channels, query_length) + Input query + + Returns + ------- + out : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + Sliding dot product between q and X. + """ + n_channels, n_timepoints = X.shape + query_length = q.shape[1] + out = np.zeros((n_channels, n_timepoints - query_length + 1)) + for i in range(n_channels): + out[i, :] = convolve(np.flipud(q[i, :]), X[i, :], mode="valid").real + return out + + +def get_ith_products(X, T, L, ith): + """ + Compute dot products between X and the i-th subsequence of size L in T. + + Parameters + ---------- + X : array, shape = (n_channels, n_timepoints_X) + Input data. + T : array, shape = (n_channels, n_timepoints_T) + Data containing the query. + L : int + Overall query length. + ith : int + Query starting index in T. + + Returns + ------- + np.ndarray, 2D array of shape (n_channels, n_timepoints_X - L + 1) + Sliding dot product between the i-th subsequence of size L in T and X. + + """ + return fft_sliding_dot_product(X, T[:, ith : ith + L]) + + +def _inverse_distance_profile(dist_profile): + return 1 / (dist_profile + 1e-8) + + +@njit(cache=True) +def _extract_top_k_from_dist_profile( + dist_profile, + k, + threshold, + allow_trivial_matches, + exclusion_size, +): + """ + Given a distance profiles, extract the top k lower distances. + + Parameters + ---------- + dist_profile : np.ndarray, shape = (n_timepoints - length + 1) + A distance profile of length ``n_timepoints - length + 1``, with + ``length`` the size of the query used to compute the distance profiles. + k : int + Number of best matches to return + threshold : float + A threshold on the distances of the best matches. To be returned, a candidate + must have a distance bellow this threshold. This can reduce the number of + returned matches to be bellow ``k`` + allow_trivial_matches : bool + Wheter to allow returning matches that are in the same neighborhood. + exclusion_size : int + The size of the exlusion size to apply when ``allow_trivial_matches`` is + False. It is applied on both side of existing matches (+/- their indexes). + + Returns + ------- + top_k_indexes : np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + top_k_distances : np.ndarray, shape = (k) + The distances of the best matches. + + """ + if k == np.inf: + k = dist_profile.shape[0] + top_k_indexes = np.zeros((k), dtype=np.int64) - 1 + top_k_distances = np.full(k, np.inf, dtype=np.float64) + ub = np.full(k, np.inf) + lb = np.full(k, -1.0) + # Could be optimized by using argpartition + sorted_indexes = np.argsort(dist_profile) + _current_k = 0 + if not allow_trivial_matches: + _current_j = 0 + # Until we extract k value or explore all the array or until dist is > threshold + while _current_k < k and _current_j < len(sorted_indexes): + # if we didn't insert anything or there is a conflict in lb/ub + if _current_k > 0 and np.any( + (sorted_indexes[_current_j] >= lb[:_current_k]) + & (sorted_indexes[_current_j] <= ub[:_current_k]) + ): + pass + else: + _idx = sorted_indexes[_current_j] + if dist_profile[_idx] <= threshold: + top_k_indexes[_current_k] = _idx + top_k_distances[_current_k] = dist_profile[_idx] + ub[_current_k] = min( + top_k_indexes[_current_k] + exclusion_size, + len(dist_profile), + ) + lb[_current_k] = max(top_k_indexes[_current_k] - exclusion_size, 0) + _current_k += 1 + else: + break + _current_j += 1 + else: + _current_k += min(k, len(dist_profile)) + dist_profile = dist_profile[sorted_indexes[:_current_k]] + dist_profile = dist_profile[dist_profile <= threshold] + _current_k = len(dist_profile) + + top_k_indexes[:_current_k] = sorted_indexes[:_current_k] + top_k_distances[:_current_k] = dist_profile[:_current_k] + + return top_k_indexes[:_current_k], top_k_distances[:_current_k] + + +# Could add aggregation function as parameter instead of just max +def _extract_top_k_motifs(MP, IP, k): + criterion = np.zeros(len(MP)) + for i in range(len(MP)): + criterion[i] = max(MP[i]) + idx = np.argsort(criterion) + return [MP[i] for i in idx[:k]], [IP[i] for i in idx[:k]] + + +def _extract_top_r_motifs(MP, IP, k): + criterion = np.zeros(len(MP)) + for i in range(len(MP)): + criterion[i] = len(MP[i]) + idx = np.argsort(criterion)[::-1] + return [MP[i] for i in idx[:k]], [IP[i] for i in idx[:k]] + + +@njit(cache=True, fastmath=True) +def _update_dot_products( + X, + T, + XT_products, + L, + i_query, +): + """ + Update dot products of the i-th query of size L in T from the dot products of i-1. + + Parameters + ---------- + X: np.ndarray, 2D array of shape (n_channels, n_timepoints) + Input time series on which the sliding dot product is computed. + T: np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + L : int + The length of the subsequences considered during the search. This parameter + cannot be larger than n_timepoints and series_length. + i_query : int + Query starting index in T. + + Returns + ------- + XT_products : np.ndarray of shape (n_channels, n_timepoints - L + 1) + Sliding dot product between the i-th subsequence of size L in T and X. + + """ + n_channels = T.shape[0] + Q = T[:, i_query : i_query + L] + n_candidates = X.shape[1] - L + 1 + + for i_ft in range(n_channels): + # first element of all 0 to n-1 candidates * first element of previous query + _a1 = X[i_ft, : n_candidates - 1] * T[i_ft, i_query - 1] + # last element of all 1 to n candidates * last element of current query + _a2 = X[i_ft, L : L - 1 + n_candidates] * T[i_ft, i_query + L - 1] + + XT_products[i_ft, 1:] = XT_products[i_ft, :-1] - _a1 + _a2 + + # Compute first dot product + XT_products[i_ft, 0] = np.sum(Q[i_ft] * X[i_ft, :L]) + return XT_products diff --git a/aeon/similarity_search/series/motifs/__init__.py b/aeon/similarity_search/series/motifs/__init__.py new file mode 100644 index 0000000000..d4853a68fe --- /dev/null +++ b/aeon/similarity_search/series/motifs/__init__.py @@ -0,0 +1,7 @@ +"""Subsequence Neighbor search for series.""" + +__all__ = [ + "StompMotif", +] + +from aeon.similarity_search.series.motifs._stomp import StompMotif diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py new file mode 100644 index 0000000000..9287d80241 --- /dev/null +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -0,0 +1,457 @@ +"""Implementation of STOMP with squared euclidean distance.""" + +__maintainer__ = ["baraline"] +__all__ = ["StompMotif"] + +from typing import Optional + +import numpy as np +from numba import njit +from numba.typed import List + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _extract_top_k_motifs, + _extract_top_r_motifs, + _inverse_distance_profile, + _update_dot_products, + get_ith_products, +) +from aeon.similarity_search.series.neighbors._mass import ( + _normalized_squared_distance_profile, + _squared_distance_profile, +) +from aeon.utils.numba.general import sliding_mean_std_one_series + + +class StompMotif(BaseSeriesSimilaritySearch): + """ + Estimator to extract top k motifs using STOMP, descibed in [1]_. + + This estimators allows to perform multiple type of motif search operations by using + different parameterization. We base oursleves on Figure 3 of [2]_ to establish the + following list, we do not yet support "Learning" and "Valmod" motifs : + + - for "Pair Motifs" : This is the default configuration + + - for "k-Motiflets" : { + "motif_size": k, + } + + - for "k-motifs" (naming is confusing here, it is a range based motif): { + "motif_size":np.inf, + "dist_threshold":r, + "motif_extraction_method":"r_motifs" + } + + Parameters + ---------- + length : int + The length of the motifs to extract. This is the length of the subsequence + that will be used in the computations. + normalize : bool + Wheter the computations between subsequences should use a z-normalied distance. + + Notes + ----- + This estimator only provide exact computation method, faster approximate methods + also exists in the litterature. We use a squared euclidean distance instead of the + euclidean distance, if you want euclidean distance results, you should square root + the obtained results. + + References + ---------- + .. [1] Yan Zhu, Zachary Zimmerman, Nader Shakibay Senobari, Chin-Chia Michael + Yeh, Gareth Funning, Abdullah Mueen, Philip Brisk, and Eamonn Keogh. 2016. + Matrix profile II: Exploiting a novel algorithm and GPUs to break the one hundred + million barrier for time series motifs and joins. In 2016 IEEE 16th international + conference on data mining (ICDM). IEEE, 739–748. + .. [2] Patrick Schäfer and Ulf Leser. 2022. Motiflets: Simple and Accurate Detection + of Motifs in Time Series. Proc. VLDB Endow. 16, 4 (December 2022), 725–737. + https://doi.org/10.14778/3574245.3574257 + """ + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + ): + self.length = length + self.normalize = normalize + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + if self.normalize: + self.X_means_, X_stds_ = sliding_mean_std_one_series(X, self.length, 1) + return self + + def predict( + self, + X: np.ndarray = None, + k: Optional[int] = 1, + motif_size: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 2, + inverse_distance: Optional[bool] = False, + motif_extraction_method: Optional[str] = "k_motifs", + ): + """ + Exctract the motifs of X_ relative to a series X using STOMP matrix prfoile. + + To compute self-motifs, X is set to None. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, n_timepoint) + Series to use to compute the matrix profile against X_. If None, will + compute the self matrix profile of X_. Motifs will then be extracted from + the matrix profile. + k : int + The number of motifs to return. The default is 1, meaning we return only + the motif set with the minimal sum of distances to its query. + motif_size : int + The number of subsequences in a motif. Default is 1, meaning we extract + motif pairs (the query and its best match) + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches: bool, optional + Wheter a neighbors of a match to a query can be also considered as matches + (True), or if an exclusion zone is applied around each match to avoid + trivial matches with their direct neighbors (False). + exclusion_factor : float, default=1. + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - length//exclusion_factor` and end at + :math:`id_timestamp + length//exclusion_factor`. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + motif_extraction_method : str + A string indicating the methodology to use to extract the top motifs. + Available methods are "r_motifs" and "k_motifs". "r_motifs" means we rank + motif set by their cardinality, with higher is better. "k_motifs" means + we rank motif set by their maximum distance to their query + + Returns + ------- + np.ndarray, shape = (k, motif_size) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k, motif_size) + The distances of the best matches. + + """ + X = self._pre_predict(X) + if motif_extraction_method not in ["k_motifs", "r_motifs"]: + raise ValueError( + "Expected motif_extraction_method to be either 'k_motifs' or 'r_motifs'" + f"but got {motif_extraction_method}" + ) + exclusion_size = self.length // exclusion_factor + MP, IP = self.compute_matrix_profile( + X, + motif_size=motif_size, + dist_threshold=dist_threshold, + allow_trivial_matches=allow_trivial_matches, + exclusion_size=exclusion_size, + inverse_distance=inverse_distance, + ) + if motif_extraction_method == "k_motifs": + return _extract_top_k_motifs(MP, IP, k) + elif motif_extraction_method == "r_motifs": + return _extract_top_r_motifs(MP, IP, k) + + def compute_matrix_profile( + self, + X: np.ndarray, + motif_size: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_size: Optional[float] = 2, + inverse_distance: Optional[bool] = False, + ): + """ + Compute matrix profile. + + The matrix profile is computed on the series given in fit (X_). If X is + not given, computes the self matrix profile of X_. Otherwise, compute the matrix + profile of X_ relative to X. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A 2D array time series on against which the matrix profile of X_ will be + computed. + motif_size : int + The number of subsequences in a motif. Default is 1, meaning we extract + motif pairs (the query and its best match). + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the worst matches to the query will be returned instead of the best ones. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, + the value None means that this is not used. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + if X is None: + is_self_mp = True + X = self.X_ + if self.normalize: + X_means, X_stds = self.X_means_, self.X_stds_ + else: + is_self_mp = False + if self.normalize: + X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) + + X_dotX = get_ith_products(X, self.X_, self.length, 0) + + if self.normalize: + MP, IP = _stomp_normalized( + self.X_, + X, + X_dotX, + self.X_means_, + self.X_stds_, + X_means, + X_stds, + self.length, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, + ) + else: + MP, IP = _stomp( + self.X_, + X, + X_dotX, + self.length, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, + ) + return MP, IP + + +@njit(cache=True, fastmath=True) +def _stomp_normalized( + X_A, + X_B, + AdotB, + X_A_means, + X_A_stds, + X_B_means, + X_B_stds, + L, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, +): + """ + Compute the Matrix Profile using the STOMP algorithm with normalized distances. + + X_A : np.ndarray, 2D array of shape (n_channels, n_timepoints) + The series from which the queries will be extracted. + X_B : np.ndarray, 2D array of shape (n_channels, series_length) + The time series on which the distance profile of each query will be computed. + AdotB : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Precomputed dot products between the first query of size L of X_A and X_B. + X_A_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) + Means of each subsequences of X_A of size L. + X_A_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) + Stds of each subsequences of X of size L. + X_B_means : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Means of each subsequences of X_B of size L. + X_B_stds : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Stds of each subsequences of X_B of size L. + L : int + Length of the subsequences used for the distance computation. + motif_size : int + The number of subsequences to extract from each distance profile. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches : bool + Wheter the top-k candidates can be neighboring subsequences. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, + the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + is_self_mp : bool + Wheter X_A == X_B. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + n_queries = X_A.shape[1] - L + 1 + _max_timestamp = X_B.shape[1] - L + MP = List() + IP = List() + + for i_q in range(n_queries): + # size T.shape[1] - L + 1 + dist_profile = _normalized_squared_distance_profile( + AdotB, X_B_means, X_B_stds, X_A_means[:, i_q], X_A_stds[:, i_q], L + ) + + if i_q + 1 < n_queries: + AdotB = _update_dot_products(X_B, X_A, AdotB, L, i_q + 1) + + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + if is_self_mp: + ub = min(i_q + exclusion_size, _max_timestamp) + lb = max(0, i_q - exclusion_size) + dist_profile[lb:ub] = np.inf + + top_indexes, top_dists = _extract_top_k_from_dist_profile( + dist_profile, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + + MP.append(top_dists) + IP.append(top_indexes) + + return MP, IP + + +@njit(cache=True, fastmath=True) +def _stomp( + X_A, + X_B, + AdotB, + L, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + is_self_mp, +): + """ + Compute the Matrix Profile using the STOMP algorithm with non-normalized distances. + + X_A : np.ndarray, 2D array of shape (n_channels, n_timepoints) + The series from which the queries will be extracted. + X_B : np.ndarray, 2D array of shape (n_channels, series_length) + The time series on which the distance profile of each query will be computed. + AdotB : np.ndarray, 2D array of shape (n_channels, series_length - L + 1) + Precomputed dot products between the first query of size L of X_A and X_B. + L : int + Length of the subsequences used for the distance computation. + motif_size : int + The number of subsequences to extract from each distance profile. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X to a query + subsequence from X_ for the candidate to be considered as a neighbor. + allow_trivial_matches : bool + Wheter the top-k candidates can be neighboring subsequences. + exclusion_size : int + The size of the exclusion zone used to prevent returning as top k candidates + the ones that are close to each other (for example i and i+1). + It is used to define a region between + :math:`id_timestamp - exclusion_size` and + :math:`id_timestamp + exclusion_size` which cannot be returned + as best match if :math:`id_timestamp` was already selected. By default, + the value None means that this is not used. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, the + worst matches to the query will be returned instead of the best ones. + is_self_mp : bool + Wheter X_A == X_B. + + Returns + ------- + MP : TypedList of np.ndarray (n_timepoints - L + 1) + Matrix profile distances for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + IP : TypedList of np.ndarray (n_timepoints - L + 1) + Indexes of the top matches for each query subsequence. n_timepoints is the + number of timepoint of X_. Each element of the list contains array of + variable size. + """ + n_queries = X_A.shape[1] - L + 1 + _max_timestamp = X_B.shape[1] - L + MP = List() + IP = List() + + # For each query of size L in X_A + for i_q in range(n_queries): + Q = X_A[:, i_q : i_q + L] + dist_profile = _squared_distance_profile(AdotB, X_B, Q) + if i_q + 1 < n_queries: + AdotB = _update_dot_products(X_B, X_A, AdotB, L, i_q + 1) + + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + if is_self_mp: + ub = min(i_q + exclusion_size, _max_timestamp) + lb = max(0, i_q - exclusion_size) + dist_profile[lb:ub] = np.inf + + top_indexes, top_dists = _extract_top_k_from_dist_profile( + dist_profile, + motif_size, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + + MP.append(top_dists) + IP.append(top_indexes) + + return MP, IP diff --git a/aeon/similarity_search/series/motifs/tests/__init__.py b/aeon/similarity_search/series/motifs/tests/__init__.py new file mode 100644 index 0000000000..d0d8f2c42c --- /dev/null +++ b/aeon/similarity_search/series/motifs/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for series motif search methods.""" diff --git a/aeon/similarity_search/series/motifs/tests/test_stomp.py b/aeon/similarity_search/series/motifs/tests/test_stomp.py new file mode 100644 index 0000000000..67ff930de1 --- /dev/null +++ b/aeon/similarity_search/series/motifs/tests/test_stomp.py @@ -0,0 +1,149 @@ +""" +Tests for stomp algorithm. + +We do not test equality for returned indexes due to the unstable nature of argsort +and the fact that the "kind=stable" parameter is not yet supported in numba. We instead +test that the returned index match the expected distance value. +""" + +__maintainer__ = ["baraline"] + +import numpy as np +import pytest +from numpy.testing import assert_almost_equal, assert_array_almost_equal + +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile, + get_ith_products, +) +from aeon.similarity_search.series.motifs._stomp import _stomp, _stomp_normalized +from aeon.similarity_search.series.neighbors._dummy import ( + _naive_squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import ( + get_all_subsequences, + sliding_mean_std_one_series, + z_normalise_series_3d, +) + +MOTIFS_SIZE_VALUES = [1, 3] +THRESHOLD = [np.inf, 0.75] +THRESHOLD_NORM = [np.inf, 4.5] +NN_MATCHES = [True, False] +INVERSE = [True, False] + + +@pytest.mark.parametrize("motif_size", MOTIFS_SIZE_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLD) +@pytest.mark.parametrize("allow_trivial_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) +def test__stomp(motif_size, threshold, allow_trivial_matches, inverse_distance): + """Test STOMP method.""" + L = 3 + + X_A = make_example_2d_numpy_series( + n_channels=2, + n_timepoints=10, + ) + X_B = make_example_2d_numpy_series(n_channels=2, n_timepoints=10) + AdotB = get_ith_products(X_B, X_A, L, 0) + + exclusion_size = L + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query + MP, IP = _stomp( + X_A, + X_B, + AdotB, + L, + motif_size, + threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + False, + ) + # For each query of size L in T + X_B_subs = get_all_subsequences(X_B, L, 1) + X_A_subs = get_all_subsequences(X_A, L, 1) + for i in range(X_A.shape[1] - L + 1): + dist_profile = _naive_squared_distance_profile(X_B_subs, X_A_subs[i]) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profile, motif_size, threshold, allow_trivial_matches, exclusion_size + ) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profile[index]) + + +@pytest.mark.parametrize("motif_size", MOTIFS_SIZE_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLD_NORM) +@pytest.mark.parametrize("allow_trivial_matches", NN_MATCHES) +@pytest.mark.parametrize("inverse_distance", INVERSE) +def test__stomp_normalised( + motif_size, threshold, allow_trivial_matches, inverse_distance +): + """Test STOMP normalised method.""" + L = 3 + + X_A = make_example_2d_numpy_series( + n_channels=2, + n_timepoints=10, + ) + X_B = make_example_2d_numpy_series(n_channels=2, n_timepoints=10) + X_A_means, X_A_stds = sliding_mean_std_one_series(X_A, L, 1) + X_B_means, X_B_stds = sliding_mean_std_one_series(X_B, L, 1) + AdotB = get_ith_products(X_B, X_A, L, 0) + + exclusion_size = L + # MP : distances to best matches for each query + # IP : Indexes of best matches for each query + MP, IP = _stomp_normalized( + X_A, + X_B, + AdotB, + X_A_means, + X_A_stds, + X_B_means, + X_B_stds, + L, + motif_size, + threshold, + allow_trivial_matches, + exclusion_size, + inverse_distance, + False, + ) + # For each query of size L in T + X_B_subs = z_normalise_series_3d(get_all_subsequences(X_B, L, 1)) + X_A_subs = z_normalise_series_3d(get_all_subsequences(X_A, L, 1)) + for i in range(X_A.shape[1] - L + 1): + dist_profile = _naive_squared_distance_profile(X_B_subs, X_A_subs[i]) + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + dist_profile, motif_size, threshold, allow_trivial_matches, exclusion_size + ) + + # Check that the top matches extracted have the same value that the + # top matches in the distance profile + assert_array_almost_equal(MP[i], top_k_distances) + + # Check that the index in IP correspond to a distance profile point + # with value equal to the corresponding MP point. + for j, index in enumerate(top_k_indexes): + assert_almost_equal(MP[i][j], dist_profile[index]) diff --git a/aeon/similarity_search/series/neighbors/__init__.py b/aeon/similarity_search/series/neighbors/__init__.py new file mode 100644 index 0000000000..047bfbe9c4 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/__init__.py @@ -0,0 +1,9 @@ +"""Subsequence Neighbor search for series.""" + +__all__ = [ + "DummySNN", + "MassSNN", +] + +from aeon.similarity_search.series.neighbors._dummy import DummySNN +from aeon.similarity_search.series.neighbors._mass import MassSNN diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py new file mode 100644 index 0000000000..bbea714eda --- /dev/null +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -0,0 +1,159 @@ +"""Implementation of NN with brute force.""" + +from typing import Optional + +__maintainer__ = ["baraline"] +__all__ = ["DummySNN"] + +import numpy as np +from numba import njit, prange + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile, +) +from aeon.utils.numba.general import ( + get_all_subsequences, + z_normalise_series_2d, + z_normalise_series_3d, +) + + +class DummySNN(BaseSeriesSimilaritySearch): + """Estimator to compute the on profile and distance profile using brute force.""" + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + n_jobs: Optional[int] = 1, + ): + self.length = length + self.normalize = normalize + super().__init__(n_jobs=n_jobs) + + def _fit( + self, + X: np.ndarray, + y=None, + ): + self.X_subs = get_all_subsequences(self.X_, self.length, 1) + if self.normalize: + self.X_subs = z_normalise_series_3d(self.X_subs) + return self + + def predict( + self, + X: np.ndarray, + k: Optional[int] = 1, + threshold: Optional[float] = np.inf, + exclusion_factor: Optional[float] = 2, + inverse_distance: Optional[bool] = False, + allow_neighboring_matches: Optional[bool] = False, + X_index: Optional[int] = None, + ): + """ + Compute nearest neighbors to X in subsequences of X_. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, length) + Subsequence we want to find neighbors for. + k : int + The number of neighbors to return. + threshold : float + The maximum distance of neighbors to X. + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + exclusion_factor : float, default=1. + A factor of the query length used to define the exclusion zone when + ``allow_neighboring_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - length//exclusion_factor` and end at + :math:`id_timestamp + length//exclusion_factor`. + X_index : Optional[int], optional + If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. + If specified, neighboring subsequences of X won't be able to match as + neighbors. + + Returns + ------- + np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k) + The distances of the best matches. + + """ + X = self._pre_predict(X) + X_index = self._check_X_index(X_index) + dist_profile = self.compute_distance_profile(X) + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + if X_index is not None: + exclusion_size = self.length // exclusion_factor + _max_timestamp = self.n_timepoints_ - self.length + ub = min(X_index + exclusion_size, _max_timestamp) + lb = max(0, X_index - exclusion_size) + dist_profile[lb:ub] = np.inf + + return _extract_top_k_from_dist_profile( + dist_profile, + k, + threshold, + allow_neighboring_matches, + exclusion_size, + ) + + def compute_distance_profile(self, X: np.ndarray): + """ + Compute the distance profile of X to all samples in X_. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The query to use to compute the distance profiles. + + Returns + ------- + distance_profile : np.ndarray, 1D array of shape (n_candidates) + The distance profile of X to X_. The ``n_candidates`` value + is equal to ``n_timepoins - length + 1``, with ``n_timepoints`` the + length of X_. + + """ + if self.normalize: + X = z_normalise_series_2d(X) + return _naive_squared_distance_profile(self.X_subs, X) + + +@njit(cache=True, fastmath=True, parallel=True) +def _naive_squared_distance_profile( + X_subs, + Q, +): + """ + Compute a squared euclidean distance profile. + + Parameters + ---------- + X_subs : array, shape=(n_subsequences, n_channels, length) + Subsequences of size length of the input time series to search in. + Q : array, shape=(n_channels, query_length) + Query used during the search. + + Returns + ------- + out : np.ndarray, 1D array of shape (n_samples, n_timepoints_t - query_length + 1) + The distance between the query and all candidates in X. + + """ + n_subs, n_channels, length = X_subs.shape + dist_profile = np.zeros(n_subs) + for i in prange(n_subs): + for j in range(n_channels): + for k in range(length): + dist_profile[i] += (X_subs[i, j, k] - Q[j, k]) ** 2 + return dist_profile diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py new file mode 100644 index 0000000000..bb56815f4e --- /dev/null +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -0,0 +1,247 @@ +"""Implementation of NN with MASS.""" + +from typing import Optional + +__maintainer__ = ["baraline"] +__all__ = ["MassSNN"] + +import numpy as np +from numba import njit, prange + +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _inverse_distance_profile, + fft_sliding_dot_product, +) +from aeon.utils.numba.general import ( + AEON_NUMBA_STD_THRESHOLD, + sliding_mean_std_one_series, +) + + +class MassSNN(BaseSeriesSimilaritySearch): + """Estimator to compute the on profile and distance profile using MASS.""" + + def __init__( + self, + length: int, + normalize: Optional[bool] = False, + ): + self.length = length + self.normalize = normalize + super().__init__() + + def _fit( + self, + X: np.ndarray, + y=None, + ): + if self.normalize: + self.X_means_, X_stds_ = sliding_mean_std_one_series(X, self.length, 1) + return self + + def predict( + self, + X: np.ndarray, + k: Optional[int] = 1, + dist_threshold: Optional[float] = np.inf, + allow_trivial_matches: Optional[bool] = False, + exclusion_factor: Optional[float] = 2, + inverse_distance: Optional[bool] = False, + X_index: Optional[int] = None, + ): + """ + Compute nearest neighbors to X in subsequences of X_. + + Parameters + ---------- + X : np.ndarray, shape=(n_channels, length) + Subsequence we want to find neighbors for. + k : int + The number of neighbors to return. + dist_threshold : float + The maximum allowed distance of a candidate subsequence of X_ to X + for the candidate to be considered as a neighbor. + allow_trivial_matches: bool, optional + Wheter a neighbors of a match to a query can be also considered as matches + (True), or if an exclusion zone is applied around each match to avoid + trivial matches with their direct neighbors (False). + inverse_distance : bool + If True, the matching will be made on the inverse of the distance, and thus, + the farther neighbors will be returned instead of the closest ones. + exclusion_factor : float, default=1. + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - length//exclusion_factor` and end at + :math:`id_timestamp + length//exclusion_factor`. + X_index : Optional[int], optional + If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. + If specified, neighboring subsequences of X won't be able to match as + neighbors. + + Returns + ------- + np.ndarray, shape = (k) + The indexes of the best matches in ``distance_profile``. + np.ndarray, shape = (k) + The distances of the best matches. + + """ + X = self._pre_predict(X) + X_index = self._check_X_index(X_index) + dist_profile = self.compute_distance_profile(X) + if inverse_distance: + dist_profile = _inverse_distance_profile(dist_profile) + + if X_index is not None: + exclusion_size = self.length // exclusion_factor + _max_timestamp = self.n_timepoints_ - self.length + ub = min(X_index + exclusion_size, _max_timestamp) + lb = max(0, X_index - exclusion_size) + dist_profile[lb:ub] = np.inf + + return _extract_top_k_from_dist_profile( + dist_profile, + k, + dist_threshold, + allow_trivial_matches, + exclusion_size, + ) + + def compute_distance_profile(self, X: np.ndarray): + """ + Compute the distance profile of X to all samples in X_. + + Parameters + ---------- + X : np.ndarray, 2D array of shape (n_channels, length) + The query to use to compute the distance profiles. + + Returns + ------- + distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) + The distance profile of X to all samples in X_. The ``n_candidates`` value + is equal to ``n_timepoins - length + 1``. If X_ is an unequal length + collection, returns a numba typed list instead of an ndarray. + + """ + QT = fft_sliding_dot_product(self.X_, X) + + if self.normalize: + distance_profile = _normalized_squared_distance_profile( + QT, + self.X_means_, + self.X_stds_, + X.mean(axis=1), + X.std(axis=1), + self.length, + ) + else: + distance_profile = _squared_distance_profile( + QT, + self.X_, # T + X, # Q + ) + + return distance_profile + + +@njit(cache=True, fastmath=True) +def _squared_distance_profile(QT, T, Q): + """ + Compute squared distance profile between query subsequence and a single time series. + + This function calculates the squared distance profile for a single time series by + leveraging the dot product of the query and time series as well as precomputed sums + of squares to efficiently compute the squared distances. + + Parameters + ---------- + QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + The dot product between the query and the time series. + T : np.ndarray, 2D array of shape (n_channels, series_length) + The series used for similarity search. Note that series_length can be equal, + superior or inferior to n_timepoints, it doesn't matter. + Q : np.ndarray + 2D array of shape (n_channels, query_length) representing query subsequence. + + Returns + ------- + distance_profile : np.ndarray + 2D array of shape (n_channels, n_timepoints - query_length + 1) + The squared distance profile between the query and the input time series. + """ + n_channels, profile_length = QT.shape + query_length = Q.shape[1] + _QT = -2 * QT + distance_profile = np.zeros(profile_length) + for k in prange(n_channels): + _sum = 0 + _qsum = 0 + for j in prange(query_length): + _sum += T[k, j] ** 2 + _qsum += Q[k, j] ** 2 + + distance_profile += _qsum + _QT[k] + distance_profile[0] += _sum + for i in prange(1, profile_length): + _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 + distance_profile[i] += _sum + return distance_profile + + +@njit(cache=True, fastmath=True) +def _normalized_squared_distance_profile( + QT, T_means, T_stds, Q_means, Q_stds, query_length +): + """ + Compute the z-normalized squared Euclidean distance profile for one time series. + + Parameters + ---------- + QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) + The dot product between the query and the time series. + T_means : np.ndarray, 1D array of length n_channels + The mean values of the time series for each channel. + T_stds : np.ndarray, 2D array of shape (n_channels, profile_length) + The standard deviations of the time series for each channel and position. + Q_means : np.ndarray, 1D array of shape (n_channels) + Means of the query q + Q_stds : np.ndarray, 1D array of shape (n_channels) + Stds of the query q + query_length : int + The length of the query subsequence used for the distance profile computation. + + + Returns + ------- + np.ndarray + 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the + z-normalized squared distance profile between the query subsequence and the time + series. Entries are computed based on the z-normalized values, with special + handling for constant values. + """ + n_channels, profile_length = QT.shape + distance_profile = np.zeros(profile_length) + Q_is_constant = Q_stds <= AEON_NUMBA_STD_THRESHOLD + for i in prange(profile_length): + Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD + for k in prange(n_channels): + # Two Constant case + if Q_is_constant[k] and Sub_is_constant[k]: + _val = 0 + # One Constant case + elif Q_is_constant[k] or Sub_is_constant[k]: + _val = query_length + else: + denom = query_length * Q_stds[k] * T_stds[k, i] + + p = (QT[k, i] - query_length * (Q_means[k] * T_means[k, i])) / denom + p = min(p, 1.0) + + _val = abs(2 * query_length * (1.0 - p)) + distance_profile[i] += _val + + return distance_profile diff --git a/aeon/similarity_search/series/neighbors/tests/__init__.py b/aeon/similarity_search/series/neighbors/tests/__init__.py new file mode 100644 index 0000000000..00ef2e73ec --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for series neighbors search methods.""" diff --git a/aeon/similarity_search/series/neighbors/tests/test_dummy.py b/aeon/similarity_search/series/neighbors/tests/test_dummy.py new file mode 100644 index 0000000000..df8ff72655 --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/test_dummy.py @@ -0,0 +1,40 @@ +""" +Tests for stomp algorithm. + +We do not test equality for returned indexes due to the unstable nature of argsort +and the fact that the "kind=stable" parameter is not yet supported in numba. We instead +test that the returned index match the expected distance value. +""" + +__maintainer__ = ["baraline"] + +import numpy as np +import pytest +from numpy.testing import assert_almost_equal + +from aeon.similarity_search.series.neighbors._brute_force import ( + _naive_squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import get_all_subsequences, z_normalise_series_2d + +NORMALIZE = [True, False] + + +@pytest.mark.parametrize("normalize", NORMALIZE) +def test__naive_squared_distance_profile(normalize): + """Test Euclidean distance with brute force.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + dist_profile = _naive_squared_distance_profile( + get_all_subsequences(X, L, 1), Q, normalize=normalize + ) + + if normalize: + Q = z_normalise_series_2d(Q) + for i_t in range(X.shape[1] - L + 1): + S = X[:, i_t : i_t + L] + if normalize: + S = z_normalise_series_2d(X[:, i_t : i_t + L]) + assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) diff --git a/aeon/similarity_search/series/neighbors/tests/test_mass.py b/aeon/similarity_search/series/neighbors/tests/test_mass.py new file mode 100644 index 0000000000..b6bf1953ea --- /dev/null +++ b/aeon/similarity_search/series/neighbors/tests/test_mass.py @@ -0,0 +1,44 @@ +"""Tests for MASS algorithm.""" + +__maintainer__ = ["baraline"] + +import numpy as np +from numpy.testing import assert_almost_equal + +from aeon.similarity_search.series._commons import fft_sliding_dot_product +from aeon.similarity_search.series.neighbors._mass import ( + _normalized_squared_distance_profile, + _squared_distance_profile, +) +from aeon.testing.data_generation import make_example_2d_numpy_series +from aeon.utils.numba.general import sliding_mean_std_one_series, z_normalise_series_2d + + +def test__squared_distance_profile(): + """Test squared distance profile.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = fft_sliding_dot_product(X, Q) + dist_profile = _squared_distance_profile(QX, X, Q) + for i_t in range(X.shape[1] - L + 1): + assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) + + +def test__normalized_squared_distance_profile(): + """Test Euclidean distance.""" + L = 3 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + QX = fft_sliding_dot_product(X, Q) + X_mean, X_std = sliding_mean_std_one_series(X, L, 1) + Q_mean = Q.mean(axis=1) + Q_std = Q.std(axis=1) + + dist_profile = _normalized_squared_distance_profile( + QX, X_mean, X_std, Q_mean, Q_std, L + ) + Q = z_normalise_series_2d(Q) + for i_t in range(X.shape[1] - L + 1): + S = z_normalise_series_2d(X[:, i_t : i_t + L]) + assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) diff --git a/aeon/similarity_search/series/tests/__init__.py b/aeon/similarity_search/series/tests/__init__.py new file mode 100644 index 0000000000..4762fe16ce --- /dev/null +++ b/aeon/similarity_search/series/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for base class and commons functions.""" diff --git a/aeon/similarity_search/subsequence_search/tests/test_base.py b/aeon/similarity_search/series/tests/test_base.py similarity index 76% rename from aeon/similarity_search/subsequence_search/tests/test_base.py rename to aeon/similarity_search/series/tests/test_base.py index e1a314f38a..d3dc953c6a 100644 --- a/aeon/similarity_search/subsequence_search/tests/test_base.py +++ b/aeon/similarity_search/series/tests/test_base.py @@ -1,10 +1,11 @@ """Test for subsequence search base class.""" +__maintainer__ = ["baraline"] + import pytest from aeon.testing.mock_estimators._mock_similarity_searchers import ( - MockMatrixProfile, - MockSubsequenceSearch, + MockSeriesSimilaritySearch, ) from aeon.testing.testing_data import ( make_example_1d_numpy, @@ -13,11 +14,11 @@ make_example_3d_numpy_list, ) -BASES = [MockMatrixProfile, MockSubsequenceSearch] +BASES = [MockSeriesSimilaritySearch] @pytest.mark.parametrize("base", BASES) -def test_input_shape_fit_neighbord_motifs(base): +def test_input_shape_fit_predict(base): """Test input shapes.""" estimator = base() # dummy data to pass to fit when testing predict/predict_proba @@ -29,31 +30,31 @@ def test_input_shape_fit_neighbord_motifs(base): X_2D_multi = make_example_2d_numpy_series(n_channels=2) X_1D = make_example_1d_numpy() - valid_inputs_fit = [X_3D_uni, X_3D_multi, X_3D_uni_list, X_3D_multi_list] + valid_inputs_fit = [X_2D_uni, X_2D_multi] # Valid inputs for _input in valid_inputs_fit: estimator.fit(_input) - invalid_inputs_fit = [X_2D_uni, X_2D_multi, X_1D] + invalid_inputs_fit = [X_1D, X_3D_multi_list, X_3D_uni_list, X_3D_multi, X_3D_uni] for _input in invalid_inputs_fit: with pytest.raises(TypeError): estimator.fit(_input) - valid_inputs_neighboord_motifs_uni = [X_2D_uni] - invalid_inputs_neighboord_motifs_uni = [ + valid_inputs_predict = [X_2D_uni, X_2D_multi] + invalid_inputs_predict_uni = [ X_1D, X_3D_uni, X_3D_uni_list, ] - invalid_inputs_neighboord_motifs_multi = [ + invalid_inputs_predict_multi = [ X_3D_multi, X_3D_multi_list, ] - L = 5 - estimator_multi = base(length=L).fit(X_3D_multi) - estimator_uni = base(length=L).fit(X_3D_uni) + L = 3 + estimator_multi = base(length=L).fit(X_2D_multi) + estimator_uni = base(length=L).fit(X_2D_uni) - for _input in valid_inputs_neighboord_motifs_uni: + for _input in valid_inputs_predict: estimator_uni.find_neighbors(_input[:, :L]) estimator_uni.find_motifs(_input) with pytest.raises(ValueError): @@ -65,7 +66,7 @@ def test_input_shape_fit_neighbord_motifs(base): with pytest.raises(ValueError): estimator_uni.find_neighbors(_input[:, : L + 2]) - for _input in invalid_inputs_neighboord_motifs_uni: + for _input in invalid_inputs_predict_uni: with pytest.raises(TypeError): estimator_uni.find_neighbors(_input) with pytest.raises(TypeError): @@ -75,7 +76,7 @@ def test_input_shape_fit_neighbord_motifs(base): with pytest.raises(TypeError): estimator_multi.find_motifs(_input) - for _input in invalid_inputs_neighboord_motifs_multi: + for _input in invalid_inputs_predict_multi: with pytest.raises(TypeError): estimator_uni.find_neighbors(_input) with pytest.raises(TypeError): diff --git a/aeon/similarity_search/series/tests/test_commons.py b/aeon/similarity_search/series/tests/test_commons.py new file mode 100644 index 0000000000..774eee8dee --- /dev/null +++ b/aeon/similarity_search/series/tests/test_commons.py @@ -0,0 +1,174 @@ +"""Test _commons.py functions.""" + +__maintainer__ = ["baraline"] +import numpy as np +import pytest +from numba.typed import List +from numpy.testing import assert_, assert_array_almost_equal + +from aeon.similarity_search.series._commons import ( + _extract_top_k_from_dist_profile, + _extract_top_k_motifs, + _extract_top_r_motifs, + _inverse_distance_profile, + _update_dot_products, + fft_sliding_dot_product, + get_ith_products, +) +from aeon.testing.data_generation import ( + make_example_1d_numpy, + make_example_2d_numpy_series, +) + +K_VALUES = [1, 3, np.inf] +THRESHOLDS = [np.inf, 0.7] +NN_MATCHES = [False, True] +EXCLUSION_SIZE = [3, 5] + + +def test_fft_sliding_dot_product(): + """Test the fft_sliding_dot_product function.""" + L = 4 + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) + + values = fft_sliding_dot_product(X, Q) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + +def test__update_dot_products(): + """Test the _update_dot_product function.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=20) + T = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + L = 7 + current_product = get_ith_products(X, T, L, 0) + for i_query in range(1, T.shape[1] - L + 1): + new_product = get_ith_products( + X, + T, + L, + i_query, + ) + current_product = _update_dot_products( + X, + T, + current_product, + L, + i_query, + ) + assert_array_almost_equal(new_product, current_product) + + +def test_get_ith_products(): + """Test i-th dot product of a subsequence of size L.""" + X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) + L = 5 + + values = get_ith_products(X, Q, L, 0) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0, 0:L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + values = get_ith_products(X, Q, L, 4) + # Compare values[0] only as input is univariate + assert_array_almost_equal( + values[0], + [np.dot(Q[0, 4 : 4 + L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], + ) + + +def test__inverse_distance_profile(): + """Test method to inverse a TypedList of distance profiles.""" + X = make_example_1d_numpy() + X_inv = _inverse_distance_profile(X) + assert_array_almost_equal(1 / (X + 1e-8), X_inv) + + +def test__extract_top_k_motifs(): + """Test motif extraction based on max distance.""" + MP = List( + [ + [1.0, 2.0], + [1.0, 4.0], + [0.5, 0.9], + [0.6, 0.7], + ] + ) + IP = List( + [ + [1, 2], + [1, 4], + [0, 3], + [0, 7], + ] + ) + MP_k, IP_k = _extract_top_k_motifs(MP, IP, 2) + assert_(len(MP_k) == 2) + assert_(MP_k[0] == [0.6, 0.7]) + assert_(IP_k[0] == [0, 7]) + assert_(MP_k[1] == [0.5, 0.9]) + assert_(IP_k[1] == [0, 3]) + + +def test__extract_top_r_motifs(): + """Test motif extraction based on motif set cardinality.""" + MP = List( + [ + [1.0, 1.5, 2.0, 1.5], + [1.0, 4.0], + [0.5, 0.9, 1.0], + [0.6, 0.7], + ] + ) + IP = List( + [ + [1, 2, 3, 4], + [1, 4], + [0, 3, 6], + [0, 7], + ] + ) + MP_k, IP_k = _extract_top_r_motifs(MP, IP, 2) + assert_(len(MP_k) == 2) + assert_(MP_k[0] == [1.0, 1.5, 2.0, 1.5]) + assert_(IP_k[0] == [1, 2, 3, 4]) + assert_(MP_k[1] == [0.5, 0.9, 1.0]) + assert_(IP_k[1] == [0, 3, 6]) + + +@pytest.mark.parametrize("k", K_VALUES) +@pytest.mark.parametrize("threshold", THRESHOLDS) +@pytest.mark.parametrize("allow_nn_matches", NN_MATCHES) +@pytest.mark.parametrize("exclusion_size", EXCLUSION_SIZE) +def test__extract_top_k_from_dist_profile( + k, threshold, allow_nn_matches, exclusion_size +): + """Test method to esxtract the top k candidates from a list of distance profiles.""" + X = make_example_1d_numpy(n_timepoints=30) + X_sort = np.argsort(X) + exclusion_size = 3 + top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( + X, k, threshold, allow_nn_matches, exclusion_size + ) + + if len(top_k_indexes) == 0 or len(top_k_distances) == 0: + raise AssertionError("_extract_top_k_from_dist_profile returned empty list") + for i, index in enumerate(top_k_indexes): + assert_(X[index] == top_k_distances[i]) + + assert_(np.all(top_k_distances <= threshold)) + + if allow_nn_matches: + assert_(np.all(top_k_distances <= X_sort[len(top_k_indexes) - 1])) + + if not allow_nn_matches: + same_X = np.sort(top_k_indexes) + if len(same_X) > 1: + assert_(np.all(np.diff(same_X) >= exclusion_size)) diff --git a/aeon/similarity_search/series_search/__init__.py b/aeon/similarity_search/series_search/__init__.py deleted file mode 100644 index 9a618540db..0000000000 --- a/aeon/similarity_search/series_search/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Series search module.""" - -__all__ = ["BaseSeriesSearch", "BaseIndexSearch"] - -from aeon.similarity_search.series_search._base import BaseIndexSearch, BaseSeriesSearch diff --git a/aeon/similarity_search/series_search/_base.py b/aeon/similarity_search/series_search/_base.py deleted file mode 100644 index f9c6ed8097..0000000000 --- a/aeon/similarity_search/series_search/_base.py +++ /dev/null @@ -1,264 +0,0 @@ -"""Base class for whole series search.""" - -__maintainer__ = ["baraline"] - -import warnings -from abc import abstractmethod -from typing import Optional, final - -import numpy as np -from numba import get_num_threads, set_num_threads - -from aeon.similarity_search._base import BaseSimilaritySearch -from aeon.utils.numba.general import compute_mean_stds_collection_parallel - - -class BaseSeriesSearch(BaseSimilaritySearch): - """ - Base class for similarity search on whole time series. - - Parameters - ---------- - normalise : bool, optional - Whether the inputs should be z-normalised. The default is False. - n_jobs : int, optional - Number of parallel jobs to use. The default is 1. - """ - - @final - def find_motifs( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - X_index: Optional[int] = None, - inverse_distance: Optional[bool] = False, - ): - """ - Find the top-k motifs in the training data. - - Given ``k`` and ``threshold`` parameters, this methods returns the top-k motif - sets. We define a motif set as a set of candidates which all are at a distance - of at most ``threshold`` from each other. The top-k motifs sets are the - motif sets with the most candidates. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, n_timestamps) - A series in which we want to indentify motifs. - k : int, optional - Number of motifs to return - threshold : int, optional - A threshold on the similarity measure to determine which candidates will be - part of a motif set. - X_index : Optional[int], optional - If ``X`` is a series of the database given in fit, specify its index in - ``X_``. If specified, this series won't be able to match with itself. - inverse_distance : bool, optional - Wheter to inverse the computed distance, meaning that the method will return - the anomalies instead of motifs. - - Returns - ------- - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the indexes of the - motifs in X. - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the distances of the - motifs macthes to the motif in X. - - """ - self._check_is_fitted() - if X is not None: - self._check_find_neighbors_motif_format(X) - prev_threads = get_num_threads() - X_index = self._check_X_index_int(X_index) - motifs_indexes, distances = self._find_motifs( - X, - k=k, - threshold=threshold, - inverse_distance=inverse_distance, - X_index=X_index, - ) - set_num_threads(prev_threads) - return motifs_indexes, distances - - @final - def find_neighbors( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - X_index: Optional[int] = None, - inverse_distance: Optional[bool] = False, - ): - """ - Find the top-k neighbors of X in the database. - - Given ``k`` and ``threshold`` parameters, this methods returns the top-k - neighbors of X, such as each of the ``k`` neighbors as a distance inferior or - equal to ``threshold``. By default, ``threshold`` is set to infinity. It is - possible for this method to return less than ``k`` neighbors, either if there - is less than ``k`` admissible candidate in the database, or if in the top-k - candidates, some do not meet the ``threshold`` condition. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, length) - The subsequence for which we want to identify nearest neighbors in the - database. - k : int, optional - Number of neighbors to return. - threshold : int, optional - A threshold on the distance to determine which candidates will be returned. - X_index : Optional[int], optional - If ``X`` is a series of the database given in fit, specify its index in - ``X_``. If specified, this series won't be able to match with itself. - inverse_distance : bool, optional - Wheter to inverse the computed distance, meaning that the method will return - the k most dissimilar neighbors instead of the k most similar. - - - Returns - ------- - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the indexes of the - neighbors. - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the distances of the - neighbors to X. - - """ - self._check_is_fitted() - - self._check_find_neighbors_motif_format(X) - - X_index = self._check_X_index_int(X_index) - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) - neighbors, distances = self._find_neighbors( - X, - k=k, - threshold=threshold, - inverse_distance=inverse_distance, - X_index=X_index, - ) - set_num_threads(prev_threads) - if len(neighbors) < k: - warnings.warn( - f"The number of admissible neighbors found is {len(neighbors)}, instead" - f" of {k}", - stacklevel=2, - ) - return neighbors, distances - - def _compute_mean_std_from_collection(self, X: np.ndarray): - """ - Compute the mean and std of each channel for all series in X. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Collection of series from which we extract mean and stds. If it is an - unequal length collection, it should be a list of 2d numpy arrays. - - Returns - ------- - Tuple(np.ndarray, np.ndarray) - Both array are of shape (n_cases, n_channels), the first contains the means - and the second the stds for each series in X. - - """ - means, stds = compute_mean_stds_collection_parallel(X) - return means, stds - - def _fit(self, X, y=None): - if self.normalise: - self.X_means_, self.X_stds_ = self._compute_mean_std_from_collection(X) - self.X_ = X - return self - - @abstractmethod - def _find_motifs( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - X_index: Optional[int] = None, - inverse_distance: Optional[bool] = False, - ): ... - - @abstractmethod - def _find_neighbors( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - inverse_distance: Optional[bool] = False, - X_index=None, - ): ... - - -# TODO : Add an update method to add series to the index -class BaseIndexSearch(BaseSeriesSearch): - """ - Base class for similarity search on whole time series using indexes. - - Parameters - ---------- - normalise : bool, optional - Whether the inputs should be z-normalised. The default is False. - n_jobs : int, optional - Number of parallel jobs to use. The default is 1. - """ - - def _fit(self, X, y=None): - super()._fit(X) - self._build_index() - return self - - @abstractmethod - def _build_index(self): ... - - @abstractmethod - def _query_index( - self, - X, - k=1, - inverse_distance=False, - threshold=np.inf, - ): ... - - @abstractmethod - def _get_bucket_sizes(self): ... - - @abstractmethod - def _get_bucket_content(self, key): ... - - def _find_motifs( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - X_index: Optional[int] = None, - inverse_distance: Optional[bool] = False, - ): - bucket_sizes = self._get_bucket_sizes() - idx_motifs = np.argsort(list(bucket_sizes.values()))[::-1][:, k] - # TODO : review distance return on motif for whole series and buckets - return [self._get_bucket_content(idx_motif) for idx_motif in idx_motifs], [ - 0 for _ in idx_motifs - ] - - def _find_neighbors( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - inverse_distance: Optional[bool] = False, - X_index=None, - ): - top_k, top_k_dist = self._query_index( - X, k=k, inverse_distance=inverse_distance, threshold=threshold - ) - return top_k, top_k_dist diff --git a/aeon/similarity_search/subsequence_search/__init__.py b/aeon/similarity_search/subsequence_search/__init__.py deleted file mode 100644 index eb062c46b8..0000000000 --- a/aeon/similarity_search/subsequence_search/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Subsequence search module.""" - -__all__ = [ - "BaseSubsequenceSearch", - "BaseMatrixProfile", - "StompMatrixProfile", - "BruteForceMatrixProfile", -] - -from aeon.similarity_search.subsequence_search._base import ( - BaseMatrixProfile, - BaseSubsequenceSearch, -) -from aeon.similarity_search.subsequence_search._brute_force import ( - BruteForceMatrixProfile, -) -from aeon.similarity_search.subsequence_search._stomp import StompMatrixProfile diff --git a/aeon/similarity_search/subsequence_search/_base.py b/aeon/similarity_search/subsequence_search/_base.py deleted file mode 100644 index a8d78b029d..0000000000 --- a/aeon/similarity_search/subsequence_search/_base.py +++ /dev/null @@ -1,509 +0,0 @@ -"""Base class for subsequence search.""" - -__maintainer__ = ["baraline"] - -import warnings -from abc import abstractmethod -from typing import Optional, final - -import numpy as np -from numba import get_num_threads, set_num_threads -from numba.typed import List - -from aeon.similarity_search._base import BaseSimilaritySearch -from aeon.similarity_search.subsequence_search._commons import ( - _extract_top_k_from_dist_profile, - _inverse_distance_profile_list, -) -from aeon.utils.numba.general import sliding_mean_std_one_series - -# We can define a BaseVariableLengthSubsequenceSearch later for VALMOD and the likes. - -# BaseSubSeries 'replace sub by series' - - -class BaseSubsequenceSearch(BaseSimilaritySearch): - """ - Base class for similarity search on time series subsequences. - - Parameters - ---------- - length : int - The length of the subsequence to be considered. - normalise : bool, optional - Whether the inputs should be z-normalised. The default is False. - n_jobs : int, optional - Number of parallel jobs to use. The default is 1. - """ - - @abstractmethod - def __init__( - self, - length: int, - normalise: Optional[bool] = False, - n_jobs: Optional[int] = 1, - ): - self.length = length - super().__init__(n_jobs=n_jobs, normalise=normalise) - - _tags = { - "capability:unequal_length": True, - } - - @final - def find_motifs( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - X_index: Optional[int] = None, - inverse_distance: Optional[bool] = False, - allow_neighboring_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2.0, - ): - """ - Find the top-k motifs in the training data. - - Given ``k`` and ``threshold`` parameters, this methods returns the top-k motif - sets. We define a motif set as a set of candidates which all are at a distance - of at most ``threshold`` from each other. The top-k motifs sets are the - motif sets with the most candidates. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, n_timestamps) - A series in which we want to indentify motifs. - k : int, optional - Number of motifs to return - threshold : int, optional - A threshold on the similarity measure to determine which candidates will be - part of a motif set. - X_index : Optional[int], optional - If ``X`` is a series of the database given in fit, specify its index in - ``X_``. If specified, each query of this series won't be able to match with - its neighboring subsequences. - inverse_distance : bool, optional - Wheter to inverse the computed distance, meaning that the method will return - the anomalies instead of motifs. - allow_neighboring_matches: bool, optional - Wheter a candidate can be part of multiple motif sets (True), or if motif - sets should be mutually exclusive (False). - exclusion_factor : float, default=2. - A factor of the query length used to define the exclusion zone when - ``allow_neighboring_matches`` is set to False. For a given timestamp, - the exclusion zone starts from - :math:`id_timestamp - query_length//exclusion_factor` and end at - :math:`id_timestamp + query_length//exclusion_factor`. - - Returns - ------- - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the indexes of the - motifs in X. - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the distances of the - motifs macthes to the motif in X. - - """ - self._check_is_fitted() - if X is not None: - self._check_find_neighbors_motif_format(X) - prev_threads = get_num_threads() - X_index = self._check_X_index_int(X_index) - motifs_indexes, distances = self._find_motifs( - X, - k=k, - threshold=threshold, - exclusion_factor=exclusion_factor, - inverse_distance=inverse_distance, - allow_neighboring_matches=allow_neighboring_matches, - X_index=X_index, - ) - set_num_threads(prev_threads) - return motifs_indexes, distances - - @final - def find_neighbors( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - inverse_distance: Optional[bool] = False, - X_index: Optional[np.ndarray] = None, - allow_neighboring_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2.0, - ): - """ - Find the top-k neighbors of X in the database. - - Given ``k`` and ``threshold`` parameters, this methods returns the top-k - neighbors of X, such as each of the ``k`` neighbors as a distance inferior or - equal to ``threshold``. By default, ``threshold`` is set to infinity. It is - possible for this method to return less than ``k`` neighbors, either if there - is less than ``k`` admissible candidate in the database, or if in the top-k - candidates, some do not meet the ``threshold`` condition. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, length) - The subsequence for which we want to identify nearest neighbors in the - database. - k : int, optional - Number of neighbors to return. - threshold : int, optional - A threshold on the distance to determine which candidates will be returned. - inverse_distance : bool, optional - Wheter to inverse the computed distance, meaning that the method will return - the k most dissimilar neighbors instead of the k most similar. - X_index : np.ndarray, shape=(2,), optional - If ``X`` is a subsequence of the database given in fit, specify its starting - index as (i_case, i_timestamp). If specified, this subsequence and the - neighboring ones (according to ``exclusion_factor``) won't be considered as - admissible candidates. - allow_neighboring_matches: bool, optional - Wheter the top-k candidates can be neighboring subsequences. - exclusion_factor : float, default=2. - A factor of the query length used to define the exclusion zone when - ``allow_neighboring_matches`` is set to False. For a given timestamp, - the exclusion zone starts from - :math:`id_timestamp - query_length//exclusion_factor` and end at - :math:`id_timestamp + query_length//exclusion_factor`. - - Returns - ------- - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the indexes of the - neighbors. - ndarray, shape=(k,) - A numpy array of at most ``k`` elements containing the distances of the - neighbors to X. - - """ - self._check_is_fitted() - - self._check_find_neighbors_motif_format(X) - if self.length != X.shape[1]: - raise ValueError( - f"Expected X to be of shape {(self.n_channels_, self.length)} but" - f" got {X.shape} in find_neighbors." - ) - - X_index = self._check_X_index_array(X_index) - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) - neighbors, distances = self._find_neighbors( - X, - k=k, - threshold=threshold, - inverse_distance=inverse_distance, - X_index=X_index, - allow_neighboring_matches=allow_neighboring_matches, - exclusion_factor=exclusion_factor, - ) - set_num_threads(prev_threads) - if len(neighbors) < k: - warnings.warn( - f"The number of admissible neighbors found is {len(neighbors)}, instead" - f" of {k}", - stacklevel=2, - ) - return neighbors, distances - - def _check_X_index_int(self, X_index: int): - """ - Check wheter the X_index parameter is correctly formated and is admissible. - - This check is made for motif search functions. - - Parameters - ---------- - X_index : int - Index of a series in X_. - - Returns - ------- - X_index : int - Index of a series in X_ - - """ - if X_index is not None: - if not isinstance(X_index, int): - raise TypeError("Expected an integer for X_index but got {X_index}") - - if X_index >= self.n_cases_ or X_index < 0: - raise ValueError( - "The value of X_index cannot exced the number " - "of series in the collection given during fit. Expected a value " - f"between [0, {self.n_cases_ - 1}] but got {X_index}" - ) - return X_index - - def _check_X_index_array(self, X_index: np.ndarray): - """ - Check wheter the X_index parameter is correctly formated and is admissible. - - This check is made for neighbour search functions. - - Parameters - ---------- - X_index : np.ndarray, 1D array of shape (2) - Array of integer containing the sample and timestamp identifiers of the - starting point of a subsequence in X_. - - Returns - ------- - X_index : np.ndarray, 1D array of shape (2) - Array of integer containing the sample and timestamp identifiers of the - starting point of a subsequence in X_. - - """ - if X_index is not None: - if ( - isinstance(X_index, list) - and len(X_index) == 2 - and isinstance(X_index[0], int) - and isinstance(X_index[1], int) - ): - X_index = np.asarray(X_index, dtype=int) - elif len(X_index) != 2: - raise TypeError( - "Expected a numpy array or list of integers with 2 elements " - f"for X_index but got {X_index}" - ) - elif ( - not (isinstance(X_index[0], int) or not isinstance(X_index[1], int)) - or X_index.dtype != int - ): - raise TypeError( - "Expected a numpy array or list of integers for X_index but got " - f"{X_index}" - ) - - if X_index[0] >= self.n_cases_ or X_index[0] < 0: - raise ValueError( - "The sample ID (first element) of X_index cannot exced the number " - "of series in the collection given during fit. Expected a value " - f"between [0, {self.n_cases_ - 1}] but got {X_index[0]}" - ) - _max_timestamp = self.X_[X_index[0]].shape[1] - self.length + 1 - if X_index[1] >= _max_timestamp: - raise ValueError( - "The timestamp ID (second element) of X_index cannot exced the " - "number of timestamps minus the length parameter plus one. Expected" - f" a value between [0, {_max_timestamp - 1}] but got {X_index[1]}" - ) - return X_index - - def _compute_mean_std_from_collection(self, X: np.ndarray): - """ - Compute the mean and std of each subsequence of size ``length`` in X. - - Parameters - ---------- - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Collection of series from which we extract mean and stds. If it is an - unequal length collection, it should be a list of 2d numpy arrays. - - Returns - ------- - Tuple(np.ndarray, np.ndarray) - Both array are of shape (n_cases, n_timepoints-length+1, n_channels), - the first contains the means and the second the stds for each subsequence - of size ``length`` in X. - - """ - means = [] - stds = [] - - for i_x in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i_x], self.length, 1) - stds.append(_std) - means.append(_mean) - - if self.metadata_["unequal_length"]: - return List(means), List(stds) - else: - return np.asarray(means), np.asarray(stds) - - def _fit(self, X, y=None): - if self.length >= self.min_timepoints_ or self.length < 1: - raise ValueError( - "The length of the query should be inferior or equal to the length of " - "data (X_) provided during fit, but got {} for X and {} for X_".format( - self.length, self.min_timepoints_ - ) - ) - - if self.normalise: - self.X_means_, self.X_stds_ = self._compute_mean_std_from_collection(X) - self.X_ = X - return self - - @abstractmethod - def _find_motifs( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - X_index: Optional[int] = None, - inverse_distance: Optional[bool] = False, - allow_neighboring_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2.0, - ): ... - - @abstractmethod - def _find_neighbors( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - inverse_distance: Optional[bool] = False, - X_index=None, - allow_neighboring_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2.0, - ): ... - - @classmethod - def _get_test_params(cls, parameter_set: str = "default") -> dict: - """Return testing parameter settings for the estimator. - - Parameters - ---------- - parameter_set : str, default="default" - Name of the set of test parameters to return, for use in tests. If no - special parameters are defined for a value, will return `"default"` set. - For classifiers, a "default" set of parameters should be provided for - general testing, and a "results_comparison" set for comparing against - previously recorded results if the general set does not produce suitable - probabilities to compare against. - - Returns - ------- - params : dict or list of dict, default={} - Parameters to create testing instances of the class. - Each dict are parameters to construct an "interesting" test instance, i.e., - `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. - """ - return {"length": 3} - - -class BaseMatrixProfile(BaseSubsequenceSearch): - """Base class for Matrix Profile methods using a length parameter.""" - - def _find_motifs( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - X_index: Optional[int] = None, - inverse_distance: Optional[bool] = False, - allow_neighboring_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2.0, - ): - exclusion_size = self.length // exclusion_factor - - MP, IP = self.compute_matrix_profile( - k, - threshold, - exclusion_size, - inverse_distance, - allow_neighboring_matches, - X=X, - X_index=X_index, - ) - # TODO check motif extraction logic, sure its not this one - MP_avg = np.array([[np.mean(MP[i]) for i in range(len(MP))]]) - # TODO: appening IP of identified motifs to return to get motifs matches in X_ - return _extract_top_k_from_dist_profile( - MP_avg, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - ) - - def _find_neighbors( - self, - X: np.ndarray, - k: Optional[int] = 1, - threshold: Optional[float] = np.inf, - inverse_distance: Optional[bool] = False, - X_index=None, - allow_neighboring_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2.0, - ): - """ - Find the top-k neighbors of X in the database. - - Given ``k`` and ``threshold`` parameters, this methods returns the top-k - neighbors of X, such as each of the ``k`` neighbors as a distance inferior or - equal to ``threshold``. By default, ``threshold`` is set to infinity. It is - possible for this method to return less than ``k`` neighbors, either if there - is less than ``k`` admissible candidate in the database, or if in the top-k - candidates, some do not meet the ``threshold`` condition. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, length) - The subsequence for which we want to identify nearest neighbors in the - database. - k : int, optional - Number of neighbors to return. - threshold : int, optional - A threshold on the distance to determine which candidates will be returned. - inverse_distance : bool, optional - Wheter to inverse the computed distance, meaning that the method will return - the k most dissimilar neighbors instead of the k most similar. - X_index : np.ndarray, shape=(2,), optional - If ``X`` is a subsequence of the database given in fit, specify its starting - index as (i_case, i_timestamp). If specified, this subsequence and the - neighboring ones (according to ``exclusion_factor``) won't be considered as - admissible candidates. - allow_neighboring_matches: bool, optional - Wheter the top-k candidates can be neighboring subsequences. - exclusion_factor : float, default=2. - A factor of the query length used to define the exclusion zone when - ``allow_neighboring_matches`` is set to False. For a given timestamp, the - exclusion zone starts from - :math:`id_timestamp - query_length//exclusion_factor` and end at - :math:`id_timestamp + query_length//exclusion_factor`. - """ - exclusion_size = self.length // exclusion_factor - dist_profiles = self.compute_distance_profile(X) - - if inverse_distance: - dist_profiles = _inverse_distance_profile_list(dist_profiles) - - # Deal with self-matches - if X_index is not None: - _max_timestamp = self.X_[X_index[0]].shape[1] - self.length - ub = min(X_index[1] + exclusion_size, _max_timestamp) - lb = max(0, X_index[1] - exclusion_size) - dist_profiles[X_index[0]][lb:ub] = np.inf - - return _extract_top_k_from_dist_profile( - dist_profiles, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - ) - - @abstractmethod - def compute_matrix_profile( - self, - X: np.ndarray, - k: int, - threshold: float, - exclusion_size: int, - inverse_distance: bool, - allow_neighboring_matches: bool, - X_index: Optional[int] = None, - ): - """Compute matrix profiles between X_ and X or between all series in X_.""" - ... - - @abstractmethod - def compute_distance_profile(self, X: np.ndarray): - """Compute distrance profiles between X_ and X (a series of size length).""" - ... diff --git a/aeon/similarity_search/subsequence_search/_brute_force.py b/aeon/similarity_search/subsequence_search/_brute_force.py deleted file mode 100644 index 269cdc369b..0000000000 --- a/aeon/similarity_search/subsequence_search/_brute_force.py +++ /dev/null @@ -1,319 +0,0 @@ -"""Implementation of matrix profile with brute force.""" - -from typing import Optional - -__maintainer__ = ["baraline"] - - -import numpy as np -from numba import njit, prange -from numba.typed import List - -from aeon.similarity_search.subsequence_search._base import BaseMatrixProfile -from aeon.similarity_search.subsequence_search._commons import ( - _extract_top_k_from_dist_profile, - _inverse_distance_profile_list, -) -from aeon.utils.numba.general import ( - get_all_subsequences, - z_normalise_series_2d, - z_normalise_series_3d, -) - -# TODO : check function params and make docstrings -# TODO : make tests - - -class BruteForceMatrixProfile(BaseMatrixProfile): - """Estimator to compute matrix profile and distance profile using brute force.""" - - def __init__( - self, - length: int, - normalise: Optional[bool] = False, - n_jobs: Optional[int] = 1, - ): - super().__init__(length=length, n_jobs=n_jobs, normalise=normalise) - - def compute_matrix_profile( - self, - k, - threshold, - exclusion_size, - inverse_distance, - allow_neighboring_matches, - X: Optional[np.ndarray] = None, - X_index: Optional[int] = None, - ): - """ - Compute matrix profiles. - - The matrix profiles are computed on the collection given in fit. If ``X`` is - not given, computes the matrix profile of each series in the collection. If it - is given, only computes it for ``X``. - - Parameters - ---------- - k : int - The number of best matches to return during predict for each subsequence. - threshold : float - The number of best matches to return during predict for each subsequence. - inverse_distance : bool - If True, the matching will be made on the inverse of the distance, and thus, - the worst matches to the query will be returned instead of the best ones. - exclusion_size : int - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - X : Optional[np.ndarray], optional - The time series on which the matrix profile will be compute. - The default is None, meaning that the series in the collection given in fit - will be used instead. - X_index : Optional[int], optional - If ``X`` is a series of the database given in fit, specify its index in - ``X_``. If specified, each query of this series won't be able to match with - its neighboring subsequences. - - Returns - ------- - MP : array of shape (series_length - L + 1,) - Matrix profile distances for each query subsequence. If X is none, this - will be a list of MP for each series in X_. - IP : array of shape (series_length - L + 1,) - Indexes of the top matches for each query subsequence. If X is none, this - will be a list of MP for each series in X_. - """ - # pairwise if none - if X is None: - MP = [] - IP = [] - for i in range(len(self.X_)): - _MP, _IP = self.compute_matrix_profile( - k, - threshold, - exclusion_size, - inverse_distance, - X=self.X_[i], - X_index=i, - ) - MP.append(_MP) - IP.append(_IP) - else: - MP, IP = _naive_squared_matrix_profile( - self.X_, - X, - self.length, - X_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, - normalise=self.normalise, - ) - - return MP, IP - - def compute_distance_profile(self, X: np.ndarray): - """ - Compute the distance profile of X to all samples in X_. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, length) - The query to use to compute the distance profiles. - - Returns - ------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) - The distance profile of X to all samples in X_. The ``n_candidates`` value - is equal to ``n_timepoins - length + 1``. If X_ is an unequal length - collection, returns a numba typed list instead of an ndarray. - - """ - distance_profiles = _naive_squared_distance_profile( - self.X_, X, normalise=self.normalise - ) - - if not self.metadata_["unequal_length"]: - distance_profiles = np.asarray(distance_profiles) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _compute_dist_profile(X_subs, q): - """ - Compute the distance profile between subsequences and a query. - - Parameters - ---------- - X_subs : array, shape=(n_samples, n_channels, query_length) - Input subsequences extracted from a time series. - q : array, shape=(n_channels, query_length) - Query used for the distance computation - - Returns - ------- - dist_profile : np.ndarray, 1D array of shape (n_samples) - The distance between the query all subsequences. - - """ - n_candidates, n_channels, q_length = X_subs.shape - dist_profile = np.zeros(n_candidates) - for i in range(n_candidates): - for j in range(n_channels): - for k in range(q_length): - dist_profile[i] += (X_subs[i, j, k] - q[j, k]) ** 2 - return dist_profile - - -@njit(cache=True, fastmath=True, parallel=True) -def _naive_squared_distance_profile( - X, - Q, - normalise=False, -): - """ - Compute a squared euclidean distance profile. - - Parameters - ---------- - X : array, shape=(n_samples, n_channels, n_timepoints) - Input time series dataset to search in. - Q : array, shape=(n_channels, query_length) - Query used during the search. - normalise : bool - Wheter to use a z-normalised distance. - - Returns - ------- - out : np.ndarray, 1D array of shape (n_samples, n_timepoints_t - query_length + 1) - The distance between the query and all candidates in X. - - """ - query_length = Q.shape[1] - dist_profiles = List() - # Init distance profile array with unequal length support - for i in range(len(X)): - dist_profiles.append(np.zeros(X[i].shape[1] - query_length + 1)) - if normalise: - Q = z_normalise_series_2d(Q) - else: - Q = Q.astype(np.float64) - - for _i in prange(len(X)): - # cast uint64 due to parallel prange - i = np.int64(_i) - X_subs = get_all_subsequences(X[i], query_length, 1) - if normalise: - X_subs = z_normalise_series_3d(X_subs) - - dist_profile = _compute_dist_profile(X_subs, Q) - dist_profiles[i] = dist_profile - return dist_profiles - - -@njit(cache=True, fastmath=True, parallel=True) -def _naive_squared_matrix_profile( - X, - T, - L, - T_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, - normalise=False, -): - """ - Compute a squared euclidean matrix profile. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - Length of the subsequences used for the distance computation. - T_index : int, - If ``T`` is a series of ``X``, specify its index - in ``X``. If specified, each query of this series won't be able to - match with its neighboring subsequences. - k : int - The number of best matches to return during predict for each subsequence. - threshold : float - The number of best matches to return during predict for each subsequence. - allow_neighboring_matches : bool - Wheter the top-k candidates can be neighboring subsequences. - exclusion_size : int - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestomp - exclusion_size` and - :math:`id_timestomp + exclusion_size` which cannot be returned - as best match if :math:`id_timestomp` was already selected. By default, - the value None means that this is not used. - inverse_distance : bool - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - normalise : bool - Wheter to use a z-normalised distance. - - Returns - ------- - out : np.ndarray, 1D array of shape (n_timepoints_t - query_length + 1) - The minimum distance between each query in T and all candidates in X. - """ - n_queries = T.shape[1] - L + 1 - MP = List() - IP = List() - - # Init List to allow parallel, we'll re-use it for all dist profiles - dist_profiles = List() - for i_x in range(len(X)): - dist_profiles.append(np.zeros(X[i_x].shape[1] - L + 1)) - - X_subs = List() - for i in range(len(X)): - i_subs = get_all_subsequences(X[i], L, 1) - if normalise: - i_subs = z_normalise_series_3d(i_subs) - X_subs.append(i_subs) - - for i_q in range(n_queries): - Q = T[:, i_q : i_q + L] - if normalise: - Q = z_normalise_series_2d(Q) - for i_x in prange(len(X)): - dist_profiles[i_x][0 : X[i_x].shape[1] - L + 1] = _compute_dist_profile( - X_subs[i_x], Q - ) - - if T_index is not None: - _max_timestamp = X[T_index].shape[1] - L - ub = min(i_q + exclusion_size, _max_timestamp) - lb = max(0, i_q - exclusion_size) - dist_profiles[T_index][lb:ub] = np.inf - - if inverse_distance: - dist_profiles = _inverse_distance_profile_list(dist_profiles) - - top_indexes, top_dists = _extract_top_k_from_dist_profile( - dist_profiles, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - ) - - MP.append(top_dists) - IP.append(top_indexes) - return MP, IP diff --git a/aeon/similarity_search/subsequence_search/_commons.py b/aeon/similarity_search/subsequence_search/_commons.py deleted file mode 100644 index c13a7381bc..0000000000 --- a/aeon/similarity_search/subsequence_search/_commons.py +++ /dev/null @@ -1,170 +0,0 @@ -"""Helper and common function for similarity search estimators and functions.""" - -__maintainer__ = ["baraline"] - -import numpy as np -from numba import njit, prange -from scipy.signal import convolve - - -def fft_sliding_dot_product(X, q): - """ - Use FFT convolution to calculate the sliding window dot product. - - This function applies the Fast Fourier Transform (FFT) to efficiently compute - the sliding dot product between the input time series `X` and the query `q`. - The dot product is computed for each channel individually. The sliding window - approach ensures that the dot product is calculated for every possible subsequence - of `X` that matches the length of `q` - - Parameters - ---------- - X : array, shape=(n_channels, n_timepoints) - Input time series - q : array, shape=(n_channels, query_length) - Input query - - Returns - ------- - out : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - Sliding dot product between q and X. - """ - n_channels, n_timepoints = X.shape - query_length = q.shape[1] - out = np.zeros((n_channels, n_timepoints - query_length + 1)) - for i in range(n_channels): - out[i, :] = convolve(np.flipud(q[i, :]), X[i, :], mode="valid").real - return out - - -def get_ith_products(X, T, L, ith): - """ - Compute dot products between X and the i-th subsequence of size L in T. - - Parameters - ---------- - X : array, shape = (n_channels, n_timepoints_X) - Input data. - T : array, shape = (n_channels, n_timepoints_T) - Data containing the query. - L : int - Overall query length. - ith : int - Query starting index in T. - - Returns - ------- - np.ndarray, 2D array of shape (n_channels, n_timepoints_X - L + 1) - Sliding dot product between the i-th subsequence of size L in T and X. - - """ - return fft_sliding_dot_product(X, T[:, ith : ith + L]) - - -@njit(cache=True, fastmath=True, parallel=True) -def _inverse_distance_profile_list(dist_profiles): - for i in prange(len(dist_profiles)): - dist_profiles[i] = 1 / (dist_profiles[i] + 1e-8) - return dist_profiles - - -@njit(cache=True) -def _extract_top_k_from_dist_profile( - dist_profiles, - k, - threshold, - allow_neighboring_matches, - exclusion_size, -): - """ - Given an array (or list) of distance profiles, extract the top k lower distances. - - Parameters - ---------- - dist_profiles : np.ndarray, shape = (n_samples, n_timepoints - length + 1) - A collection of distance profiles computed from ``n_samples`` time series of - size ``n_timepoints``, giving distance profiles of length - ``n_timepoints - length + 1``, with ``length`` the size of the query used to - compute the distance profiles. - k : int - Number of best matches to return - threshold : float - A threshold on the distances of the best matches. To be returned, a candidate - must have a distance bellow this threshold. This can reduce the number of - returned matches to be bellow ``k`` - allow_neighboring_matches : bool - Wheter to allow returning matches that are in the same neighborhood. - exclusion_size : int - The size of the exlusion size to apply when ``allow_neighboring_matches`` is - False. It is applied on both side of existing matches (+/- their indexes). - - Returns - ------- - top_k_indexes : np.ndarray, shape = (k, 2) - The indexes of the best matches in ``distance_profiles``. - top_k_distances : np.ndarray, shape = (k) - The distances of the best matches. - - """ - top_k_indexes = np.zeros((2 * k, 2), dtype=np.int64) - 1 - top_k_distances = np.full(2 * k, np.inf) - for i_profile in range(len(dist_profiles)): - # Extract top-k without neighboring matches - if not allow_neighboring_matches: - _sorted_indexes = np.argsort(dist_profiles[i_profile]) - _top_k_indexes = np.zeros(k, dtype=np.int64) - 1 - _current_k = 0 - _current_j = 0 - # Until we extract k value or explore all the array - while _current_k < k and _current_j < len(_sorted_indexes): - _insert = True - # Check for validity with each previously inserted - for i_k in range(_current_k): - ub = min( - _top_k_indexes[i_k] + exclusion_size, - len(dist_profiles[i_profile]), - ) - lb = max(_top_k_indexes[i_k] - exclusion_size, 0) - if ( - _sorted_indexes[_current_j] >= lb - and _sorted_indexes[_current_j] <= ub - ): - _insert = False - break - - if _insert: - _top_k_indexes[_current_k] = _sorted_indexes[_current_j] - _current_k += 1 - _current_j += 1 - - _top_k_indexes = _top_k_indexes[:_current_k] - _top_k_distances = dist_profiles[i_profile][_top_k_indexes] - # Extract top-k with neighboring matches - else: - _top_k_indexes = np.argsort(dist_profiles[i_profile])[:k] - _top_k_distances = dist_profiles[i_profile][_top_k_indexes] - - # Select overall top k by using the buffer array of size 2*k - # Inset top from current sample - top_k_distances[k : k + len(_top_k_distances)] = _top_k_distances - top_k_indexes[k : k + len(_top_k_distances), 1] = _top_k_indexes - top_k_indexes[k : k + len(_top_k_distances), 0] = i_profile - - # Sort overall - idx = np.argsort(top_k_distances) - # Keep top k overall - top_k_distances[:k] = top_k_distances[idx[:k]] - top_k_indexes[:k] = top_k_indexes[idx[:k]] - - top_k_distances[k:] = np.inf - - # get the actual number of extracted values and apply threshold - true_k = 0 - for i in range(k): - # if top_k is inf, it means that no value was extracted - if top_k_distances[i] != np.inf and top_k_distances[i] <= threshold: - true_k += 1 - else: - break - - return top_k_indexes[:true_k], top_k_distances[:true_k] diff --git a/aeon/similarity_search/subsequence_search/_stomp.py b/aeon/similarity_search/subsequence_search/_stomp.py deleted file mode 100644 index 66d5270872..0000000000 --- a/aeon/similarity_search/subsequence_search/_stomp.py +++ /dev/null @@ -1,619 +0,0 @@ -"""Implementation of STOMP with squared euclidean distance.""" - -__maintainer__ = ["baraline"] -from typing import Optional - -import numpy as np -from numba import njit, prange -from numba.typed import List - -from aeon.similarity_search.subsequence_search._base import BaseMatrixProfile -from aeon.similarity_search.subsequence_search._commons import ( - _extract_top_k_from_dist_profile, - _inverse_distance_profile_list, - fft_sliding_dot_product, - get_ith_products, -) -from aeon.utils.numba.general import ( - AEON_NUMBA_STD_THRESHOLD, - sliding_mean_std_one_series, -) - - -class StompMatrixProfile(BaseMatrixProfile): - """Estimator to compute matrix profile and distance profile using STOMP.""" - - def __init__( - self, - length: int, - normalise: Optional[bool] = False, - n_jobs: Optional[int] = 1, - ): - super().__init__(length=length, n_jobs=n_jobs, normalise=normalise) - - def compute_matrix_profile( - self, - X: np.ndarray, - k: int, - threshold: float, - exclusion_size: int, - inverse_distance: bool, - allow_neighboring_matches: bool, - X_index=None, - ): - """ - Compute matrix profiles. - - The matrix profiles are computed on the collection given in fit. If ``X`` is - not given, computes the matrix profile of each series in the collection. If it - is given, only computes it for ``X``. - - Parameters - ---------- - X : np.ndarray, shape = (n_channels, n_timepoints) - A 2D array time series on which the matrix profile will be computed. - k : int - The number of best matches to return during predict for each subsequence. - threshold : float - The number of best matches to return during predict for each subsequence. - inverse_distance : bool - If True, the matching will be made on the inverse of the distance, and thus, - the worst matches to the query will be returned instead of the best ones. - exclusion_size : int - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - X_index : Optional[int], optional - If ``X`` is a series of the database given in fit, specify its index in - ``X_``. If specified, each query of this series won't be able to match with - its neighboring subsequences. - - Returns - ------- - MP : array of shape (n_timepoints - L + 1,) - Matrix profile distances for each query subsequence. If X is none, this - will be a list of MP for each series in X_. - IP : array of shape (n_timepoints - L + 1,) - Indexes of the top matches for each query subsequence. If X is none, this - will be a list of MP for each series in X_. - """ - XdotT = [ - get_ith_products(self.X[i], X, self.length, 0) for i in range(len(self.X_)) - ] - if isinstance(X, np.ndarray): - XdotT = np.asarray(XdotT) - elif isinstance(X, List): - XdotT = List(XdotT) - - if X_index is None: - X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) - else: - X_means, X_stds = self.X_means_[X_index], self.X_stds_[X_index] - if self.normalise: - MP, IP = _stomp_normalised( - self.X_, - X, - XdotT, - self.X_means_, - self.X_stds_, - X_means, - X_stds, - self.length, - X_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, - ) - else: - MP, IP = _stomp( - self.X_, - X, - XdotT, - self.length, - X_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, - ) - return MP, IP - - def compute_distance_profile(self, X: np.ndarray): - """ - Compute the distance profile of X to all samples in X_. - - Parameters - ---------- - X : np.ndarray, 2D array of shape (n_channels, length) - The query to use to compute the distance profiles. - - Returns - ------- - distance_profiles : np.ndarray, 2D array of shape (n_cases, n_candidates) - The distance profile of X to all samples in X_. The ``n_candidates`` value - is equal to ``n_timepoins - length + 1``. If X_ is an unequal length - collection, returns a numba typed list instead of an ndarray. - - """ - QX = [fft_sliding_dot_product(self.X_[i], X) for i in range(len(self.X_))] - if self.metadata_["unequal_length"]: - QX = List(QX) - else: - QX = np.asarray(QX) - - if self.normalise: - distance_profiles = _normalised_squared_distance_profile( - QX, - self.X_means_, - self.X_stds_, - X.mean(axis=1), - X.std(axis=1), - self.length, - ) - else: - distance_profiles = _squared_distance_profile( - QX, - self.X_, - X, - ) - - if not self.metadata_["unequal_length"]: - distance_profiles = np.asarray(distance_profiles) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _stomp_normalised( - X, - T, - XdotT, - X_means, - X_stds, - T_means, - T_stds, - L, - T_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, -): - """ - Compute the Matrix Profile using the STOMP algorithm with normalised distances. - - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - XdotT : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Precomputed dot products between each time series in X and the query series T. - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Means of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Stds of each subsequences of X of size L. Should be a numba TypedList if X is - unequal length. - T_means : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Means of each subsequences of T of size L. - T_stds : np.ndarray, 2D array of shape (n_channels, n_timepoints - L + 1) - Stds of each subsequences of T of size L. - L : int - Length of the subsequences used for the distance computation. - T_index : int, - If ``T`` is a series of the database given in fit, specify its index - in ``X_``. If specified, each query of this series won't be able to - match with its neighboring subsequences. - k : int, - The number of best matches to return during predict for each subsequence. - threshold : float - The number of best matches to return during predict for each subsequence. - allow_neighboring_matches : bool - Wheter the top-k candidates can be neighboring subsequences. - exclusion_size : int - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - inverse_distance : bool - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - - Returns - ------- - tuple of np.ndarray - - MP : array of shape (series_length - L + 1,) - Matrix profile distances for each query subsequence. - - IP : array of shape (series_length - L + 1,) - Indexes of the top matches for each query subsequence. - """ - n_queries = T.shape[1] - L + 1 - MP = List() - IP = List() - - for i_q in range(n_queries): - dist_profiles = _normalised_squared_distance_profile( - XdotT, X_means, X_stds, T_means[:, i_q], T_stds[:, i_q], L - ) - if i_q + 1 < n_queries: - for i_x in range(len(X)): - XdotT[i_x] = _update_dot_products_one_series( - X[i_x], T, XdotT[i_x], L, i_q + 1 - ) - - if inverse_distance: - dist_profiles = _inverse_distance_profile_list(dist_profiles) - - # Deal with self-matches - if T_index is not None: - _max_timestamp = X[T_index].shape[1] - L - ub = min(i_q + exclusion_size, _max_timestamp) - lb = max(0, i_q - exclusion_size) - dist_profiles[T_index][lb:ub] = np.inf - - top_indexes, top_dists = _extract_top_k_from_dist_profile( - dist_profiles, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - ) - - MP.append(top_dists) - IP.append(top_indexes) - - return MP, IP - - -@njit(cache=True, fastmath=True) -def _stomp( - X, - T, - XdotT, - L, - T_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, -): - """ - Compute the Matrix Profile using the STOMP algorithm with non-normalised distances. - - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a TypedList - of 2D arrays of shape (n_channels, n_timepoints) - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - XdotT : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - L + 1) - Precomputed dot products between each time series in X and the query series T. - L : int - Length of the subsequences used for the distance computation. - T_index : int, - If ``T`` is a series of the database given in fit, specify its index - in ``X_``. If specified, each query of this series won't be able to - match with its neighboring subsequences. - k : int, - The number of best matches to return during predict for each subsequence. - threshold : float - The number of best matches to return during predict for each subsequence. - allow_neighboring_matches : bool - Wheter the top-k candidates can be neighboring subsequences. - exclusion_size : int - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. - inverse_distance : bool - If True, the matching will be made on the inverse of the distance, and thus, the - worst matches to the query will be returned instead of the best ones. - - Returns - ------- - tuple of np.ndarray - - MP : array of shape (series_length - L + 1,) - Matrix profile distances for each query subsequence. - - IP : array of shape (series_length - L + 1,) - Indexes of the top matches for each query subsequence. - """ - n_queries = T.shape[1] - L + 1 - MP = List() - IP = List() - - # For each query of size L in T - for i_q in range(n_queries): - Q = T[:, i_q : i_q + L] - dist_profiles = _squared_distance_profile(XdotT, X, Q) - # For each series in X compute distance profile to the query - if i_q + 1 < n_queries: - for i_x in range(len(X)): - XdotT[i_x] = _update_dot_products_one_series( - X[i_x], T, XdotT[i_x], L, i_q + 1 - ) - - if inverse_distance: - dist_profiles = _inverse_distance_profile_list(dist_profiles) - - # Deal with self-matches - if T_index is not None: - _max_timestamp = X[T_index].shape[1] - L - ub = min(i_q + exclusion_size, _max_timestamp) - lb = max(0, i_q - exclusion_size) - dist_profiles[T_index][lb:ub] = np.inf - - top_indexes, top_dists = _extract_top_k_from_dist_profile( - dist_profiles, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - ) - - MP.append(top_dists) - IP.append(top_indexes) - - return MP, IP - - -@njit(cache=True, fastmath=True) -def _update_dot_products_one_series( - X, - T, - XT_products, - L, - i_query, -): - """ - Update dot products of the i-th query of size L in T from the dot products of i-1. - - Parameters - ---------- - X: np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - Input time series on which the sliding dot product is computed. - T: np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - L : int - The length of the subsequences considered during the search. This parameter - cannot be larger than n_timepoints and series_length. - i_query : int - Query starting index in T. - - Returns - ------- - XT_products : np.ndarray of shape (n_cases, n_channels, n_timepoints - L + 1) - Sliding dot product between the i-th subsequence of size L in T and X. - - """ - n_channels = T.shape[0] - Q = T[:, i_query : i_query + L] - n_candidates = X.shape[1] - L + 1 - - for i_ft in range(n_channels): - # first element of all 0 to n-1 candidates * first element of previous query - _a1 = X[i_ft, : n_candidates - 1] * T[i_ft, i_query - 1] - # last element of all 1 to n candidates * last element of current query - _a2 = X[i_ft, L : L - 1 + n_candidates] * T[i_ft, i_query + L - 1] - - XT_products[i_ft, 1:] = XT_products[i_ft, :-1] - _a1 + _a2 - - # Compute first dot product - XT_products[i_ft, 0] = np.sum(Q[i_ft] * X[i_ft, :L]) - return XT_products - - -@njit(cache=True, fastmath=True, parallel=True) -def _squared_distance_profile(QX, X, Q): - """ - Compute squared distance profiles between query subsequence and time series. - - Parameters - ---------- - QX : List of np.ndarray - List of precomputed dot products between queries and time series, with each - element corresponding to a different time series. - Shape of each array is (n_channels, n_timepoints - query_length + 1). - X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) - The input samples. If X is an unquel length collection, expect a numba TypedList - 2D array of shape (n_channels, n_timepoints) - Q : np.ndarray, 2D array of shape (n_channels, query_length) - The query used for similarity search. - mask : np.ndarray, 3D array of shape (n_cases, n_timepoints - query_length + 1) - Boolean mask of the shape of the distance profile indicating for which part - of it the distance should be computed. - - Returns - ------- - distance_profiles : np.ndarray - 3D array of shape (n_cases, n_timepoints - query_length + 1) - The distance profile between Q and the input time series X. - - """ - distance_profiles = List() - query_length = Q.shape[1] - - # Init distance profile array with unequal length support - for i_instance in range(len(X)): - profile_length = X[i_instance].shape[1] - query_length + 1 - distance_profiles.append(np.full((profile_length), np.inf)) - - for _i_instance in prange(len(QX)): - # prange cast iterator to unit64 with parallel=True - i_instance = np.int_(_i_instance) - - distance_profiles[i_instance] = _squared_dist_profile_one_series( - QX[i_instance], X[i_instance], Q - ) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _squared_dist_profile_one_series(QT, T, Q): - """ - Compute squared distance profile between query subsequence and a single time series. - - This function calculates the squared distance profile for a single time series by - leveraging the dot product of the query and time series as well as precomputed sums - of squares to efficiently compute the squared distances. - - Parameters - ---------- - QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - The dot product between the query and the time series. - T : np.ndarray, 2D array of shape (n_channels, series_length) - The series used for similarity search. Note that series_length can be equal, - superior or inferior to n_timepoints, it doesn't matter. - Q : np.ndarray - 2D array of shape (n_channels, query_length) representing query subsequence. - - Returns - ------- - distance_profile : np.ndarray - 2D array of shape (n_channels, n_timepoints - query_length + 1) - The squared distance profile between the query and the input time series. - """ - n_channels, profile_length = QT.shape - query_length = Q.shape[1] - _QT = -2 * QT - distance_profile = np.zeros(profile_length) - for k in prange(n_channels): - _sum = 0 - _qsum = 0 - for j in prange(query_length): - _sum += T[k, j] ** 2 - _qsum += Q[k, j] ** 2 - - distance_profile += _qsum + _QT[k] - distance_profile[0] += _sum - for i in prange(1, profile_length): - _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 - distance_profile[i] += _sum - return distance_profile - - -@njit(cache=True, fastmath=True, parallel=True) -def _normalised_squared_distance_profile( - QX, X_means, X_stds, Q_means, Q_stds, query_length -): - """ - Compute the normalised squared distance profiles between query subsequence and input time series. - - Parameters - ---------- - QX : List of np.ndarray - List of precomputed dot products between queries and time series, with each element - corresponding to a different time series. - Shape of each array is (n_channels, n_timepoints - query_length + 1). - X_means : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Means of each subsequences of X of size query_length - X_stds : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints - query_length + 1) # noqa: E501 - Stds of each subsequences of X of size query_length - Q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - Q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - query_length : int - The length of the query subsequence used for the distance profile computation. - - Returns - ------- - List of np.ndarray - List of 2D arrays, each of shape (n_channels, n_timepoints - query_length + 1). - Each array contains the normalised squared distance profile between the query subsequence and the corresponding time series. - Entries in the array are set to infinity where the mask is False. - """ - distance_profiles = List() - Q_is_constant = Q_stds <= AEON_NUMBA_STD_THRESHOLD - # Init distance profile array with unequal length support - for i_instance in range(len(QX)): - profile_length = QX[i_instance].shape[1] - distance_profiles.append(np.zeros(profile_length)) - - for _i_instance in prange(len(QX)): - # iterator is uint64 with prange and parallel so cast to int to avoid warnings - i_instance = np.int64(_i_instance) - distance_profiles[i_instance] = _normalised_squared_dist_profile_one_series( - QX[i_instance], - X_means[i_instance], - X_stds[i_instance], - Q_means, - Q_stds, - query_length, - Q_is_constant, - ) - return distance_profiles - - -@njit(cache=True, fastmath=True) -def _normalised_squared_dist_profile_one_series( - QT, T_means, T_stds, Q_means, Q_stds, query_length, Q_is_constant -): - """ - Compute the z-normalised squared Euclidean distance profile for one time series. - - Parameters - ---------- - QT : np.ndarray, 2D array of shape (n_channels, n_timepoints - query_length + 1) - The dot product between the query and the time series. - T_means : np.ndarray, 1D array of length n_channels - The mean values of the time series for each channel. - T_stds : np.ndarray, 2D array of shape (n_channels, profile_length) - The standard deviations of the time series for each channel and position. - Q_means : np.ndarray, 1D array of shape (n_channels) - Means of the query q - Q_stds : np.ndarray, 1D array of shape (n_channels) - Stds of the query q - query_length : int - The length of the query subsequence used for the distance profile computation. - Q_is_constant : np.ndarray - 1D array of shape (n_channels,) where each element is a Boolean indicating - whether the query standard deviation for that channel is less than or equal - to a specified threshold. - - Returns - ------- - np.ndarray - 2D array of shape (n_channels, n_timepoints - query_length + 1) containing the - z-normalised squared distance profile between the query subsequence and the time - series. Entries are computed based on the z-normalised values, with special - handling for constant values. - """ - n_channels, profile_length = QT.shape - distance_profile = np.zeros(profile_length) - - for i in prange(profile_length): - Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD - for k in prange(n_channels): - # Two Constant case - if Q_is_constant[k] and Sub_is_constant[k]: - _val = 0 - # One Constant case - elif Q_is_constant[k] or Sub_is_constant[k]: - _val = query_length - else: - denom = query_length * Q_stds[k] * T_stds[k, i] - - p = (QT[k, i] - query_length * (Q_means[k] * T_means[k, i])) / denom - p = min(p, 1.0) - - _val = abs(2 * query_length * (1.0 - p)) - distance_profile[i] += _val - - return distance_profile diff --git a/aeon/similarity_search/subsequence_search/tests/__init__.py b/aeon/similarity_search/subsequence_search/tests/__init__.py deleted file mode 100644 index 0287f2ee04..0000000000 --- a/aeon/similarity_search/subsequence_search/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for subsequence search methods.""" diff --git a/aeon/similarity_search/subsequence_search/tests/test_brute_force.py b/aeon/similarity_search/subsequence_search/tests/test_brute_force.py deleted file mode 100644 index 9ef0eb44e8..0000000000 --- a/aeon/similarity_search/subsequence_search/tests/test_brute_force.py +++ /dev/null @@ -1,172 +0,0 @@ -""" -Tests for stomp algorithm. - -We do not test equality for returned indexes due to the unstable nature of argsort -and the fact that the "kind=stable" parameter is not yet supported in numba. We instead -test that the returned index match the expected distance value. -""" - -__maintainer__ = ["baraline"] - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import ( - assert_almost_equal, - assert_array_almost_equal, - assert_array_equal, -) - -from aeon.similarity_search.subsequence_search._brute_force import ( - _compute_dist_profile, - _naive_squared_distance_profile, - _naive_squared_matrix_profile, -) -from aeon.similarity_search.subsequence_search._commons import ( - _extract_top_k_from_dist_profile, - _inverse_distance_profile_list, -) -from aeon.testing.data_generation import ( - make_example_2d_numpy_series, - make_example_3d_numpy, - make_example_3d_numpy_list, -) -from aeon.utils.numba.general import ( - get_all_subsequences, - sliding_mean_std_one_series, - z_normalise_series_2d, -) - -K_VALUES = [1, 3, 5] -NN_MATCHES = [True, False] -INVERSE = [True, False] -NORMALISE = [True, False] - - -def _get_mean_sdts_inputs(X, Q, L): - X_means = [] - X_stds = [] - - for i_x in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i_x], L, 1) - X_stds.append(_std) - X_means.append(_mean) - - Q_means = Q.mean(axis=1) - Q_stds = Q.std(axis=1) - - return X_means, X_stds, Q_means, Q_stds - - -def test__compute_dist_profile(): - """Test Euclidean distance with brute force.""" - L = 3 - X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) - Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) - dist_profile = _compute_dist_profile(get_all_subsequences(X, L, 1), Q) - for i_t in range(X.shape[1] - L + 1): - assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) - - -@pytest.mark.parametrize("normalise", NORMALISE) -def test__naive_squared_distance_profile(normalise): - """Test Euclidean distance profile calculation with brute force.""" - L = 3 - X = make_example_3d_numpy(n_cases=3, n_channels=1, n_timepoints=10, return_y=False) - Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) - dist_profiles = _naive_squared_distance_profile(X, Q, normalise=normalise) - - if normalise: - Q = z_normalise_series_2d(Q) - for i_x in range(len(X)): - for i_t in range(X[i_x].shape[1] - L + 1): - _x = X[i_x, :, i_t : i_t + L] - if normalise: - _x = z_normalise_series_2d(_x) - assert_almost_equal(dist_profiles[i_x][i_t], np.sum((_x - Q) ** 2)) - - # test unequal length and multivariate - X = List( - make_example_3d_numpy_list( - n_cases=3, - n_channels=2, - min_n_timepoints=10, - max_n_timepoints=20, - return_y=False, - ) - ) - - Q = make_example_2d_numpy_series(n_channels=2, n_timepoints=L) - dist_profiles = _naive_squared_distance_profile(X, Q, normalise=normalise) - if normalise: - Q = z_normalise_series_2d(Q) - for i_x in range(len(X)): - for i_t in range(X[i_x].shape[1] - L + 1): - _x = X[i_x][:, i_t : i_t + L] - if normalise: - _x = z_normalise_series_2d(_x) - assert_almost_equal(dist_profiles[i_x][i_t], np.sum((_x - Q) ** 2)) - - -@pytest.mark.parametrize("k", K_VALUES) -@pytest.mark.parametrize("allow_neighboring_matches", NN_MATCHES) -@pytest.mark.parametrize("inverse_distance", INVERSE) -@pytest.mark.parametrize("normalise", NORMALISE) -def test__naive_squared_matrix_profile( - k, allow_neighboring_matches, inverse_distance, normalise -): - """Test brute force matrix profile method.""" - L = 3 - X = List( - make_example_3d_numpy_list( - n_cases=3, - n_channels=2, - min_n_timepoints=6, - max_n_timepoints=8, - return_y=False, - ) - ) - X_copy = X.copy() - T = make_example_2d_numpy_series(n_channels=2, n_timepoints=5) - T_copy = T.copy() - T_index = None - threshold = np.inf - exclusion_size = L - # MP : distances to best matches for each query - # IP : Indexes of best matches for each query - MP, IP = _naive_squared_matrix_profile( - X, - T, - L, - T_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, - normalise=normalise, - ) - assert_array_equal(T, T_copy) - for i in range(len(X)): - assert_array_equal(X[i], X_copy[i]) - # For each query of size L in T - for i in range(T.shape[1] - L + 1): - dist_profiles = _naive_squared_distance_profile( - X, T[:, i : i + L], normalise=normalise - ) - # Check that the top matches extracted have the same value that the - # top matches in the distance profile - if inverse_distance: - dist_profiles = _inverse_distance_profile_list(dist_profiles) - - top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( - dist_profiles, k, threshold, allow_neighboring_matches, exclusion_size - ) - # Check that the top matches extracted have the same value that the - # top matches in the distance profile - assert_array_almost_equal(MP[i], top_k_distances) - - # Check that the index in IP correspond to a distance profile point - # with value equal to the corresponding MP point. - for j, index in enumerate(top_k_indexes): - assert_almost_equal(MP[i][j], dist_profiles[index[0]][index[1]]) diff --git a/aeon/similarity_search/subsequence_search/tests/test_commons.py b/aeon/similarity_search/subsequence_search/tests/test_commons.py deleted file mode 100644 index e5b4272285..0000000000 --- a/aeon/similarity_search/subsequence_search/tests/test_commons.py +++ /dev/null @@ -1,97 +0,0 @@ -"""Test _commons.py functions.""" - -__maintainer__ = ["baraline"] -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_, assert_array_almost_equal - -from aeon.similarity_search.subsequence_search._commons import ( - _extract_top_k_from_dist_profile, - _inverse_distance_profile_list, - fft_sliding_dot_product, - get_ith_products, -) -from aeon.testing.data_generation import ( - make_example_2d_numpy_list, - make_example_2d_numpy_series, -) - -K_VALUES = [1, 3, 5] -THRESHOLDS = [np.inf, 0.7] -NN_MATCHES = [False, True] - - -def test_fft_sliding_dot_product(): - """Test the fft_sliding_dot_product function.""" - L = 4 - X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) - Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) - - values = fft_sliding_dot_product(X, Q) - # Compare values[0] only as input is univariate - assert_array_almost_equal( - values[0], - [np.dot(Q[0], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], - ) - - -def test_get_ith_products(): - """Test i-th dot product of a subsequence of size L.""" - X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) - Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) - L = 5 - - values = get_ith_products(X, Q, L, 0) - # Compare values[0] only as input is univariate - assert_array_almost_equal( - values[0], - [np.dot(Q[0, 0:L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], - ) - - values = get_ith_products(X, Q, L, 4) - # Compare values[0] only as input is univariate - assert_array_almost_equal( - values[0], - [np.dot(Q[0, 4 : 4 + L], X[0, i : i + L]) for i in range(X.shape[1] - L + 1)], - ) - - -def test__inverse_distance_profile_list(): - """Test method to inverse a TypedList of distance profiles.""" - X = make_example_2d_numpy_list(n_cases=2, return_y=False) - T = _inverse_distance_profile_list(List(X)) - assert_array_almost_equal(1 / (X[0] + 1e-8), T[0]) - assert_array_almost_equal(1 / (X[1] + 1e-8), T[1]) - - -@pytest.mark.parametrize("k", K_VALUES) -@pytest.mark.parametrize("threshold", THRESHOLDS) -@pytest.mark.parametrize("allow_nn_matches", NN_MATCHES) -def test__extract_top_k_from_dist_profile(k, threshold, allow_nn_matches): - """Test method to esxtract the top k candidates from a list of distance profiles.""" - X = make_example_2d_numpy_list( - n_cases=2, min_n_timepoints=5, max_n_timepoints=7, return_y=False - ) - X_sort = [X[i][np.argsort(X[i])] for i in range(len(X))] - - top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( - X, k, threshold, allow_nn_matches, 3 - ) - for i, index in enumerate(top_k_indexes): - assert_(X[index[0]][index[1]] == top_k_distances[i]) - assert_(np.all(top_k_distances <= threshold)) - if allow_nn_matches: - for i in range(len(X)): - assert_(np.all(top_k_distances <= X_sort[i][k - 1])) - if not allow_nn_matches: - for i_x in range(len(X)): - # test same index X respect exclusion - same_X = [ - top_k_indexes[i][1] - for i in range(len(top_k_indexes)) - if top_k_indexes[i][0] == i_x - ] - same_X = np.sort(same_X) - if len(same_X) > 1: - assert_(np.all(np.diff(same_X) >= 3)) diff --git a/aeon/similarity_search/subsequence_search/tests/test_stomp.py b/aeon/similarity_search/subsequence_search/tests/test_stomp.py deleted file mode 100644 index 757c8a3133..0000000000 --- a/aeon/similarity_search/subsequence_search/tests/test_stomp.py +++ /dev/null @@ -1,332 +0,0 @@ -""" -Tests for stomp algorithm. - -We do not test equality for returned indexes due to the unstable nature of argsort -and the fact that the "kind=stable" parameter is not yet supported in numba. We instead -test that the returned index match the expected distance value. -""" - -__maintainer__ = ["baraline"] - -import numpy as np -import pytest -from numba.typed import List -from numpy.testing import assert_almost_equal, assert_array_almost_equal - -from aeon.similarity_search.subsequence_search._commons import ( - _extract_top_k_from_dist_profile, - _inverse_distance_profile_list, - get_ith_products, -) -from aeon.similarity_search.subsequence_search._stomp import ( - _normalised_squared_dist_profile_one_series, - _normalised_squared_distance_profile, - _squared_dist_profile_one_series, - _squared_distance_profile, - _stomp, - _stomp_normalised, - _update_dot_products_one_series, -) -from aeon.testing.data_generation import ( - make_example_2d_numpy_series, - make_example_3d_numpy, - make_example_3d_numpy_list, -) -from aeon.utils.numba.general import ( - sliding_mean_std_one_series, - z_normalise_series_2d_with_mean_std, -) - -K_VALUES = [1, 3, 5] -NN_MATCHES = [True, False] -INVERSE = [True, False] - - -def _get_mean_sdts_inputs(X, Q, L): - X_means = [] - X_stds = [] - - for i_x in range(len(X)): - _mean, _std = sliding_mean_std_one_series(X[i_x], L, 1) - X_stds.append(_std) - X_means.append(_mean) - - Q_means = Q.mean(axis=1) - Q_stds = Q.std(axis=1) - - return X_means, X_stds, Q_means, Q_stds - - -def test__update_dot_products_one_series(): - """Test the _update_dot_product function.""" - X = make_example_2d_numpy_series(n_channels=1, n_timepoints=20) - T = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) - L = 7 - current_product = get_ith_products(X, T, L, 0) - for i_query in range(1, T.shape[1] - L + 1): - new_product = get_ith_products( - X, - T, - L, - i_query, - ) - current_product = _update_dot_products_one_series( - X, - T, - current_product, - L, - i_query, - ) - assert_array_almost_equal(new_product, current_product) - - -def test__squared_dist_profile_one_series(): - """Test Euclidean distance.""" - L = 3 - X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) - Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) - QX = get_ith_products(X, Q, L, 0) - dist_profile = _squared_dist_profile_one_series(QX, X, Q) - for i_t in range(X.shape[1] - L + 1): - assert_almost_equal(dist_profile[i_t], np.sum((X[:, i_t : i_t + L] - Q) ** 2)) - - -def test__normalised_squared_dist_profile_one_series(): - """Test Euclidean distance.""" - L = 3 - X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) - Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) - QX = get_ith_products(X, Q, L, 0) - X_mean, X_std = sliding_mean_std_one_series(X, L, 1) - Q_mean = Q.mean(axis=1) - Q_std = Q.std(axis=1) - - dist_profile = _normalised_squared_dist_profile_one_series( - QX, X_mean, X_std, Q_mean, Q_std, L, Q.std(axis=1) <= 0 - ) - Q = z_normalise_series_2d_with_mean_std(Q, Q_mean, Q_std) - for i_t in range(X.shape[1] - L + 1): - S = z_normalise_series_2d_with_mean_std( - X[:, i_t : i_t + L], X_mean[:, i_t], X_std[:, i_t] - ) - assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) - - -def test__squared_distance_profile(): - """Test Euclidean distance profile calculation.""" - L = 3 - X = make_example_3d_numpy(n_cases=3, n_channels=1, n_timepoints=10, return_y=False) - Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) - QX = np.asarray([get_ith_products(X[i_x], Q, L, 0) for i_x in range(len(X))]) - dist_profiles = _squared_distance_profile(QX, X, Q) - for i_x in range(len(X)): - for i_t in range(X[i_x].shape[1] - L + 1): - assert_almost_equal( - dist_profiles[i_x][i_t], np.sum((X[i_x, :, i_t : i_t + L] - Q) ** 2) - ) - - # test unequal length and multivariate - X = List( - make_example_3d_numpy_list( - n_cases=3, - n_channels=2, - min_n_timepoints=10, - max_n_timepoints=20, - return_y=False, - ) - ) - - Q = make_example_2d_numpy_series(n_channels=2, n_timepoints=L) - QX = List([get_ith_products(X[i_x], Q, L, 0) for i_x in range(len(X))]) - dist_profiles = _squared_distance_profile(QX, X, Q) - for i_x in range(len(X)): - for i_t in range(X[i_x].shape[1] - L + 1): - assert_almost_equal( - dist_profiles[i_x][i_t], np.sum((X[i_x][:, i_t : i_t + L] - Q) ** 2) - ) - - -def test__normalised_squared_distance_profile(): - """Test Euclidean distance profile calculation.""" - L = 3 - X = make_example_3d_numpy(n_cases=3, n_channels=1, n_timepoints=10, return_y=False) - Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) - QX = np.asarray([get_ith_products(X[i_x], Q, L, 0) for i_x in range(len(X))]) - - X_means, X_stds, Q_means, Q_stds = _get_mean_sdts_inputs(X, Q, L) - - X_means = np.asarray(X_means) - X_stds = np.asarray(X_stds) - - dist_profiles = _normalised_squared_distance_profile( - QX, X_means, X_stds, Q_means, Q_stds, L - ) - - Q_norm = z_normalise_series_2d_with_mean_std(Q, Q_means, Q_stds) - for i_x in range(len(X)): - for i_t in range(X[i_x].shape[1] - L + 1): - X_sub_norm = z_normalise_series_2d_with_mean_std( - X[i_x, :, i_t : i_t + L], X_means[i_x][:, i_t], X_stds[i_x][:, i_t] - ) - assert_almost_equal( - dist_profiles[i_x][i_t], np.sum((X_sub_norm - Q_norm) ** 2) - ) - - # test unequal length and multivariate - X = List( - make_example_3d_numpy_list( - n_cases=5, - n_channels=2, - min_n_timepoints=10, - max_n_timepoints=20, - return_y=False, - ) - ) - Q = make_example_2d_numpy_series(n_channels=2, n_timepoints=L) - - QX = List([get_ith_products(X[i_x], Q, L, 0) for i_x in range(len(X))]) - - X_means, X_stds, Q_means, Q_stds = _get_mean_sdts_inputs(X, Q, L) - # Convert to numba typed list - X_means = List(X_means) - X_stds = List(X_stds) - - dist_profiles = _normalised_squared_distance_profile( - QX, X_means, X_stds, Q_means, Q_stds, L - ) - - Q_norm = z_normalise_series_2d_with_mean_std(Q, Q_means, Q_stds) - for i_x in range(len(X)): - for i_t in range(X[i_x].shape[1] - L + 1): - X_sub_norm = z_normalise_series_2d_with_mean_std( - X[i_x][:, i_t : i_t + L], X_means[i_x][:, i_t], X_stds[i_x][:, i_t] - ) - assert_almost_equal( - dist_profiles[i_x][i_t], np.sum((X_sub_norm - Q_norm) ** 2) - ) - - -@pytest.mark.parametrize("k", K_VALUES) -@pytest.mark.parametrize("allow_neighboring_matches", NN_MATCHES) -@pytest.mark.parametrize("inverse_distance", INVERSE) -def test__stomp(k, allow_neighboring_matches, inverse_distance): - """Test STOMP method.""" - L = 3 - - X = make_example_3d_numpy_list( - n_cases=3, - n_channels=2, - min_n_timepoints=6, - max_n_timepoints=8, - return_y=False, - ) - T = make_example_2d_numpy_series(n_channels=2, n_timepoints=5) - XdotT = List([get_ith_products(X[i_x], T, L, 0) for i_x in range(len(X))]) - - T_index = None - threshold = np.inf - exclusion_size = L - # MP : distances to best matches for each query - # IP : Indexes of best matches for each query - MP, IP = _stomp( - X, - T, - XdotT, - L, - T_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, - ) - # For each query of size L in T - for i in range(T.shape[1] - L + 1): - dist_profiles = _squared_distance_profile( - List([get_ith_products(X[i_x], T, L, i) for i_x in range(len(X))]), - X, - T[:, i : i + L], - ) - # Check that the top matches extracted have the same value that the - # top matches in the distance profile - if inverse_distance: - dist_profiles = _inverse_distance_profile_list(dist_profiles) - - top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( - dist_profiles, k, threshold, allow_neighboring_matches, exclusion_size - ) - # Check that the top matches extracted have the same value that the - # top matches in the distance profile - assert_array_almost_equal(MP[i], top_k_distances) - - # Check that the index in IP correspond to a distance profile point - # with value equal to the corresponding MP point. - for j, index in enumerate(top_k_indexes): - assert_almost_equal(MP[i][j], dist_profiles[index[0]][index[1]]) - - -@pytest.mark.parametrize("k", K_VALUES) -@pytest.mark.parametrize("allow_neighboring_matches", NN_MATCHES) -@pytest.mark.parametrize("inverse_distance", INVERSE) -def test__stomp_normalised(k, allow_neighboring_matches, inverse_distance): - """Test STOMP normalised method.""" - L = 3 - X = make_example_3d_numpy_list( - n_cases=3, - n_channels=2, - min_n_timepoints=6, - max_n_timepoints=8, - return_y=False, - ) - T = make_example_2d_numpy_series(n_channels=2, n_timepoints=5) - - XdotT = List([get_ith_products(X[i_x], T, L, 0) for i_x in range(len(X))]) - - T_index = None - threshold = np.inf - exclusion_size = L - X_means, X_stds, _, _ = _get_mean_sdts_inputs(X, T, L) - T_means, T_stds = sliding_mean_std_one_series(T, L, 1) - # MP : distances to best matches for each query - # IP : Indexes of best matches for each query - MP, IP = _stomp_normalised( - X, - T, - XdotT, - X_means, - X_stds, - T_means, - T_stds, - L, - T_index, - k, - threshold, - allow_neighboring_matches, - exclusion_size, - inverse_distance, - ) - # For each query of size L in T - for i in range(T.shape[1] - L + 1): - dist_profiles = _normalised_squared_distance_profile( - List([get_ith_products(X[i_x], T, L, i) for i_x in range(len(X))]), - X_means, - X_stds, - T_means[:, i], - T_stds[:, i], - L, - ) - - if inverse_distance: - dist_profiles = _inverse_distance_profile_list(dist_profiles) - - top_k_indexes, top_k_distances = _extract_top_k_from_dist_profile( - dist_profiles, k, threshold, allow_neighboring_matches, exclusion_size - ) - # Check that the top matches extracted have the same value that the - # top matches in the distance profile - assert_array_almost_equal(MP[i], top_k_distances) - - # Check that the index in IP correspond to a distance profile point - # with value equal to the corresponding MP point. - for j, index in enumerate(top_k_indexes): - assert_almost_equal(MP[i][j], dist_profiles[index[0]][index[1]]) diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py index 1d3161514a..89061a6dc2 100644 --- a/aeon/testing/mock_estimators/_mock_similarity_searchers.py +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -1,83 +1,30 @@ """Mock series transformers useful for testing and debugging.""" -__maintainer__ = [] +__maintainer__ = ["baraline"] __all__ = [ - "MockSubsequenceSearch", - "MockMatrixProfile", + "MockSeriesSimilaritySearch", ] -import numpy as np +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch -from aeon.similarity_search.subsequence_search._base import ( - BaseMatrixProfile, - BaseSubsequenceSearch, -) - -class MockMatrixProfile(BaseMatrixProfile): +class MockSeriesSimilaritySearch(BaseSeriesSimilaritySearch): """Mock estimator for BaseMatrixProfile.""" def __init__( self, length=3, - normalise=False, + normalize=False, n_jobs=1, ): - super().__init__(length=length, n_jobs=n_jobs, normalise=normalise) - - def compute_matrix_profile( - self, - k, - threshold, - exclusion_size, - inverse_distance, - allow_neighboring_matches, - X=None, - X_index=None, - ): - """Compute matrix profiles between X_ and X or between all series in X_.""" - return np.zeros((X.shape[1] - self.length + 1, k)), np.zeros( - (X.shape[1] - self.length + 1, k, 2), dtype=np.int64 - ) + self.length = length + self.normalize = normalize + super().__init__(n_jobs=n_jobs) - def compute_distance_profile(self, X): - """Compute distrance profiles between X_ and X (a series of size length).""" - return [ - np.zeros(self.X_[i].shape[1] - self.length + 1) for i in range(len(self.X_)) - ] + def _fit(self, X, y=None): + return self - -class MockSubsequenceSearch(BaseSubsequenceSearch): - """Mock estimator for BaseSubsequenceSearch.""" - - def __init__( - self, - length=3, - normalise=False, - n_jobs=1, - ): - super().__init__(length=length, n_jobs=n_jobs, normalise=normalise) - - def _find_motifs( - self, - X, - k=1, - threshold=np.inf, - X_index=None, - inverse_distance=False, - allow_neighboring_matches=False, - exclusion_factor=2.0, - ): - return [[0, 0]], self.X_[0][0:1] # TODO: update after logic is implemented - - def _find_neighbors( - self, - X, - k=1, - threshold=np.inf, - inverse_distance=False, - X_index=None, - allow_neighboring_matches=False, - exclusion_factor=2.0, - ): - return [[0, 0]], self.X_[0][0:1] + def predict(self, X): + """Compute matrix profiles between X_ and X or between all series in X_.""" + X = self._pre_predict(X) + return [0], [0.1] diff --git a/aeon/transformations/collection/base.py b/aeon/transformations/collection/base.py index 013001d80e..54442dd6db 100644 --- a/aeon/transformations/collection/base.py +++ b/aeon/transformations/collection/base.py @@ -31,10 +31,10 @@ class name: BaseCollectionTransformer import pandas as pd from aeon.base import BaseCollectionEstimator -from aeon.transformations.base import BaseTransformer +from aeon.similarity_search._base import BaseSimilaritySearch -class BaseCollectionTransformer(BaseCollectionEstimator, BaseTransformer): +class BaseCollectionTransformer(BaseCollectionEstimator, BaseSimilaritySearch): """Transformer base class for collections.""" # tag values specific to CollectionTransformers diff --git a/examples/similarity_search/similarity_search_tasks.ipynb b/examples/similarity_search/similarity_search_tasks.ipynb index a42339c611..86fe5b5274 100644 --- a/examples/similarity_search/similarity_search_tasks.ipynb +++ b/examples/similarity_search/similarity_search_tasks.ipynb @@ -26,7 +26,7 @@ "- normalize. Wheter subseries should be normalized prior to distance computations\n", "\n", "#### Subseries Motif search :\n", - "Extract $k$-motifs or range $r$-motifs.\n", + "Extract $k$-motifs or range motifs or $r$-motifs.\n", "\n", "The $k^{th}$ motif is the $k^{th}$ most similar pair of subseries in $X$. Given $\\forall a,b,i,j$ the pair ${W_i, W_j}$ is the motif if $dist(W_i, W_j) ≤ dist(W_a, W_b), i \\neq j$ and $a \\neq b$\n", "\n", From 57e5e7b5e324426ea69a168709f7d0bac291054e Mon Sep 17 00:00:00 2001 From: baraline Date: Thu, 16 Jan 2025 15:56:05 +0100 Subject: [PATCH 13/36] Fix mistake addition in transformers and fix base classes --- aeon/similarity_search/_base.py | 5 +- aeon/similarity_search/collection/_base.py | 12 ++- .../collection/neighbors/__init__.py | 6 ++ .../collection/neighbors/_rp_cosine_lsh.py | 96 +++++++++---------- .../collection/neighbors/tests/__init__.py | 1 + .../collection/tests/__init__.py | 1 + .../collection/tests/test_base.py | 62 ++++++++++++ aeon/similarity_search/series/_base.py | 5 +- .../series/tests/test_base.py | 81 ++++++---------- .../_mock_similarity_searchers.py | 31 +++--- aeon/transformations/collection/base.py | 4 +- 11 files changed, 180 insertions(+), 124 deletions(-) create mode 100644 aeon/similarity_search/collection/neighbors/tests/__init__.py create mode 100644 aeon/similarity_search/collection/tests/__init__.py create mode 100644 aeon/similarity_search/collection/tests/test_base.py diff --git a/aeon/similarity_search/_base.py b/aeon/similarity_search/_base.py index fc58838eb4..5204163315 100644 --- a/aeon/similarity_search/_base.py +++ b/aeon/similarity_search/_base.py @@ -20,7 +20,6 @@ class BaseSimilaritySearch(BaseAeonEstimator): _tags = { "requires_y": False, - "capability:multivariate": True, "fit_is_empty": False, } @@ -85,12 +84,12 @@ def _check_predict_series_format(self, X): if isinstance(X, np.ndarray): if X.ndim != 2: raise TypeError( - "A np.ndarray given in find_neighbors must be 2D" + "A np.ndarray given in predict must be 2D" f"(n_channels, n_timepoints) but found {X.ndim}D." ) else: raise TypeError( - "Expected a 2D np.ndarray in find_neighbors but found" f" {type(X)}." + "Expected a 2D np.ndarray in predict but found" f" {type(X)}." ) if self.n_channels_ != X.shape[0]: raise ValueError( diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index 402b7342a2..3fde03d420 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -15,12 +15,16 @@ from aeon.similarity_search._base import BaseSimilaritySearch -class BaseCollectionSimilaritySearch(BaseCollectionEstimator, BaseSimilaritySearch): +class BaseCollectionSimilaritySearch(BaseSimilaritySearch, BaseCollectionEstimator): """Similarity search base class for collections.""" # tag values specific to CollectionTransformers _tags = { "input_data_type": "Collection", + "capability:multivariate": True, + "capability:unequal_length": True, + "capability:multithreading": True, + "X_inner_type": ["np-list", "numpy3D"], } @abstractmethod @@ -57,10 +61,8 @@ def fit( self.reset() X = self._preprocess_collection(X) # Store minimum number of n_timepoints for unequal length collections - self.n_channels_ = X[0].shape[1] + self.n_channels_ = X[0].shape[0] self.n_cases_ = len(X) - self.X_ = X - prev_threads = get_num_threads() set_num_threads(self._n_jobs) self._fit(X, y=y) @@ -92,5 +94,5 @@ def _pre_predict( self._check_is_fitted() if X is not None: # Could we call somehow _preprocess_series from a BaseCollectionEstimator ? - self._check_predict_format(X) + self._check_predict_series_format(X) return X diff --git a/aeon/similarity_search/collection/neighbors/__init__.py b/aeon/similarity_search/collection/neighbors/__init__.py index e9a5d49d49..f5cf0d925b 100644 --- a/aeon/similarity_search/collection/neighbors/__init__.py +++ b/aeon/similarity_search/collection/neighbors/__init__.py @@ -1 +1,7 @@ """Neighbors search for time series collection.""" + +__all__ = ["RandomProjectionIndexANN"] + +from aeon.similarity_search.collection.neighbors._rp_cosine_lsh import ( + RandomProjectionIndexANN, +) diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index 8d1701793e..6774803984 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -3,9 +3,8 @@ import numpy as np from numba import njit, prange -from aeon.similarity_search.series_search._base import BaseIndexSearch - -# TPB = 16 +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.utils.numba.general import z_normalise_series_2d, z_normalise_series_3d @njit(cache=True) @@ -17,12 +16,11 @@ def _hamming_dist(X, Y): @njit(cache=True, parallel=True) -def _hamming_dist_matrix(bool_hashes_value_list, bool_hashes): - n_hash_funcs = bool_hashes.shape[0] - res = np.zeros((n_hash_funcs, bool_hashes_value_list.shape[0]), dtype=np.int64) - for i in prange(n_hash_funcs): - for j in range(bool_hashes_value_list.shape[0]): - res[i, j] = _hamming_dist(bool_hashes_value_list[j], bool_hashes[i]) +def _hamming_dist_series_to_collection(X_bool, collection_bool): + n_buckets = collection_bool.shape[0] + res = np.zeros(n_buckets, dtype=np.int64) + for i in prange(n_buckets): + res[i] = _hamming_dist(collection_bool[i], X_bool) return res @@ -32,7 +30,7 @@ def _series_to_bool(X, hash_funcs, start_points, length): res = np.empty(n_hash_funcs, dtype=np.bool_) for j in prange(n_hash_funcs): res[j] = _nb_flat_dot( - X[start_points[j] : start_points[j] + length], hash_funcs[j] + X[:, start_points[j] : start_points[j] + length], hash_funcs[j] ) return res @@ -57,13 +55,12 @@ def _collection_to_bool(X, hash_funcs, start_points, length): res[i, j] = _nb_flat_dot( X[i, :, start_points[j] : start_points[j] + length], hash_funcs[j] ) - return res -class RP_LSH_Cosine(BaseIndexSearch): +class RandomProjectionIndexANN(BaseCollectionSimilaritySearch): """ - Random Projection Locality Sensitive Hashing index for cosine similarity. + Random Projection Locality Sensitive Hashing index with cosine similarity. In this method based on SimHash, we define a hash function as a boolean operation such as, given a random vector ``V`` of shape ``(n_channels, L)`` and a time series @@ -74,8 +71,8 @@ class RP_LSH_Cosine(BaseIndexSearch): as ``X[:, s:s+L].V`` Note that this method will not provide exact results, but will perform approximate - searchs. This also ignore any temporal correlation and treat series as - high dimensional points. + searchs. This also ignore any temporal correlation and consider series as + high dimensional points due to the cosine similarity distance. Parameters ---------- @@ -93,15 +90,16 @@ class RP_LSH_Cosine(BaseIndexSearch): Example ------- >>> from aeon.datasets import load_classification - >>> from aeon.similarity_search.series_search import RP_LSH_Cosine - >>> index = RP_LSH_Cosine() + >>> from aeon.similarity_search.collection.neighbors import RandomProjectionIndexANN + >>> index = RandomProjectionIndexANN() >>> X, y = load_classification("ArrowHead") >>> index.fit(X[:200]) - >>> r = index.find_neighbors(X[201]) + >>> r = index.predict(X[201]) """ _tags = { "capability:unequal_length": False, + "capability:multithreading": True, } def __init__( @@ -110,18 +108,26 @@ def __init__( hash_func_coverage=0.25, use_discrete_vectors=True, random_state=None, - normalise=False, + normalize=True, n_jobs=1, ): self.n_hash_funcs = n_hash_funcs self.hash_func_coverage = hash_func_coverage self.use_discrete_vectors = use_discrete_vectors self.random_state = random_state - super().__init__(normalise=normalise, n_jobs=n_jobs) + self.normalize = normalize + super().__init__(n_jobs=n_jobs) - def _build_index(self): + def _fit(self, X, y=None): """ - Build the index based on the data stored in X_. + Build the index based on the X. + + Parameters + ---------- + X : np.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints) + Input array to be used to build the index. + y : optional + Not used. Returns ------- @@ -129,8 +135,10 @@ def _build_index(self): """ rng = np.random.default_rng(self.random_state) - n_timepoints = self.X_.shape[2] - self.window_length_ = max(1, int(n_timepoints * self.hash_func_coverage)) + if self.normalize: + X = z_normalise_series_3d(X) + self.n_timepoints_ = X.shape[2] + self.window_length_ = max(1, int(self.n_timepoints_ * self.hash_func_coverage)) if self.use_discrete_vectors: self.hash_funcs_ = rng.choice( @@ -143,13 +151,12 @@ def _build_index(self): size=(self.n_hash_funcs, self.n_channels_, self.window_length_), ) self.start_points_ = rng.choice( - n_timepoints - self.window_length_ + 1, + self.n_timepoints_ - self.window_length_ + 1, size=self.n_hash_funcs, replace=True, ) - bool_hashes = _collection_to_bool( - self.X_, self.hash_funcs_, self.start_points_, self.window_length_ + X, self.hash_funcs_, self.start_points_, self.window_length_ ) str_hashes = [hash(bool_hashes[i].tobytes()) for i in range(len(bool_hashes))] @@ -174,20 +181,6 @@ def _get_bucket_sizes(self): return {key: len(self.dict_X_index_[key]) for key in self.dict_X_index_} def _get_series_bucket(self, X): - """ - Get the matching bucket of a single series X if it exists in the index. - - Parameters - ---------- - X : TYPE - DESCRIPTION. - - Returns - ------- - TYPE - DESCRIPTION. - - """ bool_hash = _series_to_bool( X, self.hash_funcs_, self.start_points_, self.window_length_ ) @@ -197,7 +190,7 @@ def _get_series_bucket(self, X): else: return None - def _query_index( + def predict( self, X, k=1, @@ -222,19 +215,26 @@ def _query_index( Returns ------- top_k : np.ndarray, shape = (n_cases, k) - Indexes of k series in X_ (the index) that are close to each series in X. + Indexes of k series in the index that are similar to X. top_k_dist : np.ndarray, shape = (n_cases, k) - Distance of k series in X_ (the index) to each series in X. The distance + Distance of k series in the index to X. The distance is the hamming distance between the result of each hash function. """ - bool_hashes = _series_to_bool( + X = self._pre_predict(X) + if X.shape[1] != self.n_timepoints_: + raise ValueError( + f"Expected a series with {self.n_timepoints_} but got {X.shape[1]}." + "Unequal length is not supported by this estimator." + ) + if self.normalize: + X = z_normalise_series_2d(X) + + X_bool = _series_to_bool( X, self.hash_funcs_, self.start_points_, self.window_length_ ) top_k = np.zeros(k, dtype=int) top_k_dist = np.zeros(k, dtype=float) - dists = _hamming_dist_matrix( - self.bool_hashes_value_list_, bool_hashes[np.newaxis, :] - )[0] + dists = _hamming_dist_series_to_collection(X_bool, self.bool_hashes_value_list_) if inverse_distance: dists = 1 / (dists + 1e-8) # Get top k buckets diff --git a/aeon/similarity_search/collection/neighbors/tests/__init__.py b/aeon/similarity_search/collection/neighbors/tests/__init__.py new file mode 100644 index 0000000000..89bc3412fb --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for similarity search for time series collection neighbors module.""" diff --git a/aeon/similarity_search/collection/tests/__init__.py b/aeon/similarity_search/collection/tests/__init__.py new file mode 100644 index 0000000000..d136a8571e --- /dev/null +++ b/aeon/similarity_search/collection/tests/__init__.py @@ -0,0 +1 @@ +"""Tests for similarity search for time series collection base class and commons.""" diff --git a/aeon/similarity_search/collection/tests/test_base.py b/aeon/similarity_search/collection/tests/test_base.py new file mode 100644 index 0000000000..9180a071cf --- /dev/null +++ b/aeon/similarity_search/collection/tests/test_base.py @@ -0,0 +1,62 @@ +"""Test for collection similarity search base class.""" + +__maintainer__ = ["baraline"] + +import pytest + +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockCollectionSimilaritySearch, +) +from aeon.testing.testing_data import ( + make_example_1d_numpy, + make_example_2d_numpy_series, + make_example_3d_numpy, + make_example_3d_numpy_list, +) + + +def test_input_shape_fit_predict_collection(): + """Test input shapes.""" + estimator = MockCollectionSimilaritySearch() + # dummy data to pass to fit when testing predict/predict_proba + X_3D_uni = make_example_3d_numpy(n_channels=1, return_y=False) + X_3D_multi = make_example_3d_numpy(n_channels=2, return_y=False) + X_3D_uni_list = make_example_3d_numpy_list(n_channels=1, return_y=False) + X_3D_multi_list = make_example_3d_numpy_list(n_channels=2, return_y=False) + X_2D_uni = make_example_2d_numpy_series(n_channels=1) + X_2D_multi = make_example_2d_numpy_series(n_channels=2) + X_1D = make_example_1d_numpy() + + # 2D are converted to 3D + valid_inputs_fit = [ + X_3D_uni, + X_3D_multi, + X_3D_multi_list, + X_3D_uni_list, + X_2D_uni, + X_2D_multi, + ] + # Valid inputs + for _input in valid_inputs_fit: + estimator.fit(_input) + + with pytest.raises(ValueError): + estimator.fit(X_1D) + + estimator_multi = MockCollectionSimilaritySearch().fit(X_3D_multi) + estimator_uni = MockCollectionSimilaritySearch().fit(X_3D_uni) + + estimator_uni.predict(X_2D_uni) + estimator_multi.predict(X_2D_multi) + + with pytest.raises(ValueError): + estimator_uni.predict(X_2D_multi) + with pytest.raises(ValueError): + estimator_multi.predict(X_2D_uni) + + for _input in [X_3D_uni, X_3D_uni_list]: + with pytest.raises(TypeError): + estimator_uni.predict(_input) + for _input in [X_3D_multi, X_3D_multi_list]: + with pytest.raises(TypeError): + estimator_multi.predict(_input) diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index b9cca5d5cb..bd07f25ba6 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -11,11 +11,12 @@ from aeon.utils.validation import check_n_jobs -class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): +class BaseSeriesSimilaritySearch(BaseSimilaritySearch, BaseSeriesEstimator): """Base class for similarity search applications on single series.""" _tags = { "input_data_type": "Series", + "capability:multivariate": True, } @abstractmethod @@ -87,7 +88,7 @@ def _pre_predict( self._check_is_fitted() if X is not None: X = self._preprocess_series(X, self.axis, False) - self._check_predict_format(X) + self._check_predict_series_format(X) return X def _check_X_index(self, X_index: int): diff --git a/aeon/similarity_search/series/tests/test_base.py b/aeon/similarity_search/series/tests/test_base.py index d3dc953c6a..1b4d17b991 100644 --- a/aeon/similarity_search/series/tests/test_base.py +++ b/aeon/similarity_search/series/tests/test_base.py @@ -1,4 +1,4 @@ -"""Test for subsequence search base class.""" +"""Test for series similarity search base class.""" __maintainer__ = ["baraline"] @@ -14,13 +14,10 @@ make_example_3d_numpy_list, ) -BASES = [MockSeriesSimilaritySearch] - -@pytest.mark.parametrize("base", BASES) -def test_input_shape_fit_predict(base): +def test_input_shape_fit_predict_series(): """Test input shapes.""" - estimator = base() + estimator = MockSeriesSimilaritySearch() # dummy data to pass to fit when testing predict/predict_proba X_3D_uni = make_example_3d_numpy(n_channels=1, return_y=False) X_3D_multi = make_example_3d_numpy(n_channels=2, return_y=False) @@ -30,58 +27,38 @@ def test_input_shape_fit_predict(base): X_2D_multi = make_example_2d_numpy_series(n_channels=2) X_1D = make_example_1d_numpy() - valid_inputs_fit = [X_2D_uni, X_2D_multi] - # Valid inputs + valid_inputs_fit = [X_1D, X_2D_uni, X_2D_multi] + # 1D is converted to 2D univariate for _input in valid_inputs_fit: estimator.fit(_input) - invalid_inputs_fit = [X_1D, X_3D_multi_list, X_3D_uni_list, X_3D_multi, X_3D_uni] - for _input in invalid_inputs_fit: - with pytest.raises(TypeError): - estimator.fit(_input) - - valid_inputs_predict = [X_2D_uni, X_2D_multi] - invalid_inputs_predict_uni = [ - X_1D, - X_3D_uni, - X_3D_uni_list, - ] - invalid_inputs_predict_multi = [ + invalid_inputs_fit = [ X_3D_multi, + X_3D_uni, X_3D_multi_list, + X_3D_uni_list, ] - L = 3 - estimator_multi = base(length=L).fit(X_2D_multi) - estimator_uni = base(length=L).fit(X_2D_uni) - - for _input in valid_inputs_predict: - estimator_uni.find_neighbors(_input[:, :L]) - estimator_uni.find_motifs(_input) - with pytest.raises(ValueError): - # Wrong number of channels - estimator_multi.find_neighbors(_input) - with pytest.raises(ValueError): - estimator_multi.find_motifs(_input) - # X length not of size L + for _input in invalid_inputs_fit: with pytest.raises(ValueError): - estimator_uni.find_neighbors(_input[:, : L + 2]) + estimator.fit(_input) + + estimator_multi = MockSeriesSimilaritySearch().fit(X_2D_multi) + estimator_uni = MockSeriesSimilaritySearch().fit(X_2D_uni) - for _input in invalid_inputs_predict_uni: - with pytest.raises(TypeError): - estimator_uni.find_neighbors(_input) - with pytest.raises(TypeError): - estimator_uni.find_motifs(_input) - with pytest.raises(TypeError): - estimator_multi.find_neighbors(_input) - with pytest.raises(TypeError): - estimator_multi.find_motifs(_input) + estimator_uni.predict(X_2D_uni) + # 1D is converted to 2D univariate + estimator_uni.predict(X_1D) + estimator_multi.predict(X_2D_multi) - for _input in invalid_inputs_predict_multi: - with pytest.raises(TypeError): - estimator_uni.find_neighbors(_input) - with pytest.raises(TypeError): - estimator_uni.find_motifs(_input) - with pytest.raises(TypeError): - estimator_multi.find_neighbors(_input) - with pytest.raises(TypeError): - estimator_multi.find_motifs(_input) + with pytest.raises(ValueError): + estimator_uni.predict(X_2D_multi) + with pytest.raises(ValueError): + estimator_multi.predict(X_2D_uni) + + for _input in [X_3D_uni, X_3D_uni_list]: + with pytest.raises(ValueError): + estimator_uni.predict(_input) + + for _input in [X_3D_multi, X_3D_multi_list]: + with pytest.raises(ValueError): + estimator_multi.predict(_input) diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py index 89061a6dc2..a2919c939d 100644 --- a/aeon/testing/mock_estimators/_mock_similarity_searchers.py +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -1,25 +1,32 @@ """Mock series transformers useful for testing and debugging.""" __maintainer__ = ["baraline"] -__all__ = [ - "MockSeriesSimilaritySearch", -] +__all__ = ["MockSeriesSimilaritySearch", "MockCollectionSimilaritySearch"] +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch class MockSeriesSimilaritySearch(BaseSeriesSimilaritySearch): """Mock estimator for BaseMatrixProfile.""" - def __init__( - self, - length=3, - normalize=False, - n_jobs=1, - ): - self.length = length - self.normalize = normalize - super().__init__(n_jobs=n_jobs) + def __init__(self): + super().__init__() + + def _fit(self, X, y=None): + return self + + def predict(self, X): + """Compute matrix profiles between X_ and X or between all series in X_.""" + X = self._pre_predict(X) + return [0], [0.1] + + +class MockCollectionSimilaritySearch(BaseCollectionSimilaritySearch): + """Mock estimator for BaseMatrixProfile.""" + + def __init__(self): + super().__init__() def _fit(self, X, y=None): return self diff --git a/aeon/transformations/collection/base.py b/aeon/transformations/collection/base.py index 54442dd6db..013001d80e 100644 --- a/aeon/transformations/collection/base.py +++ b/aeon/transformations/collection/base.py @@ -31,10 +31,10 @@ class name: BaseCollectionTransformer import pandas as pd from aeon.base import BaseCollectionEstimator -from aeon.similarity_search._base import BaseSimilaritySearch +from aeon.transformations.base import BaseTransformer -class BaseCollectionTransformer(BaseCollectionEstimator, BaseSimilaritySearch): +class BaseCollectionTransformer(BaseCollectionEstimator, BaseTransformer): """Transformer base class for collections.""" # tag values specific to CollectionTransformers From 2078086a8c8a142f2a8d5cf166d8eb2b7898f830 Mon Sep 17 00:00:00 2001 From: baraline Date: Thu, 16 Jan 2025 16:18:38 +0100 Subject: [PATCH 14/36] Fix registry and api reference --- aeon/similarity_search/collection/__init__.py | 4 ++ aeon/testing/testing_config.py | 4 -- aeon/utils/base/_register.py | 11 ++-- docs/api_reference/similarity_search.rst | 63 +++++++++++-------- 4 files changed, 49 insertions(+), 33 deletions(-) diff --git a/aeon/similarity_search/collection/__init__.py b/aeon/similarity_search/collection/__init__.py index 0aef46ef49..ab3a546193 100644 --- a/aeon/similarity_search/collection/__init__.py +++ b/aeon/similarity_search/collection/__init__.py @@ -1 +1,5 @@ """Similarity search for time series collection.""" + +__all__ = ["BaseCollectionSimilaritySearch"] + +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch diff --git a/aeon/testing/testing_config.py b/aeon/testing/testing_config.py index 4c46058318..61ff90cdd1 100644 --- a/aeon/testing/testing_config.py +++ b/aeon/testing/testing_config.py @@ -57,10 +57,6 @@ "ClaSPSegmenter": ["check_non_state_changing_method"], "HMMSegmenter": ["check_non_state_changing_method"], "RSTSF": ["check_non_state_changing_method"], - # Keeps length during predict to avoid recomputing means and std of data in fit - # if the next predict calls uses the same query length parameter. - "QuerySearch": ["check_non_state_changing_method"], - "SeriesSearch": ["check_non_state_changing_method"], # Unknown issue not producing the same results "RDSTRegressor": ["check_regressor_against_expected_results"], "RISTRegressor": ["check_regressor_against_expected_results"], diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 024ad447ee..5e81e29b33 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -24,8 +24,9 @@ from aeon.forecasting.base import BaseForecaster from aeon.regression.base import BaseRegressor from aeon.segmentation.base import BaseSegmenter -from aeon.similarity_search.series_search._base import BaseSeriesSearch -from aeon.similarity_search.subsequence_search._base import BaseSubsequenceSearch +from aeon.similarity_search._base import BaseSimilaritySearch +from aeon.similarity_search.collection import BaseCollectionSimilaritySearch +from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer from aeon.transformations.series import BaseSeriesTransformer @@ -37,6 +38,7 @@ "estimator": BaseAeonEstimator, "series-estimator": BaseSeriesEstimator, "transformer": BaseTransformer, + "similarity-search": BaseSimilaritySearch, # estimator types "anomaly-detector": BaseAnomalyDetector, "collection-transformer": BaseCollectionTransformer, @@ -47,8 +49,8 @@ "segmenter": BaseSegmenter, "series-transformer": BaseSeriesTransformer, "forecaster": BaseForecaster, - "subsequence_searcher": BaseSubsequenceSearch, - "series_searcher": BaseSeriesSearch, + "series-similarity-search": BaseSeriesSimilaritySearch, + "collection-similarity-search": BaseCollectionSimilaritySearch, } # base classes which are valid for estimator to directly inherit from @@ -60,5 +62,6 @@ "collection-estimator", "series-estimator", "transformer", + "similarity-search", } } diff --git a/docs/api_reference/similarity_search.rst b/docs/api_reference/similarity_search.rst index eb13cafd23..7212179953 100644 --- a/docs/api_reference/similarity_search.rst +++ b/docs/api_reference/similarity_search.rst @@ -4,51 +4,47 @@ Similarity search ================= The :mod:`aeon.similarity_search` module contains algorithms and tools for similarity -search tasks. +search tasks. First, we distinguish between `series` estimator and `collection` +estimators, similarly to the `aeon.transformer` module. Secondly, we distinguish between +estimators used `neighbors` (with sufix SNN for subsequence nearest neighbors, or ANN +for approximate nearest neighbors) search and estimators used for `motifs` search. -Similarity search estimators ----------------------------- +Series Similarity search estimators +----------------------------------- -.. currentmodule:: aeon.similarity_search +.. currentmodule:: aeon.similarity_search.series.neighbors .. autosummary:: :toctree: auto_generated/ :template: class.rst - QuerySearch - SeriesSearch + DummySNN + MassSNN -Distance profile functions --------------------------- - -.. currentmodule:: aeon.similarity_search.distance_profiles +.. currentmodule:: aeon.similarity_search.series.motifs .. autosummary:: :toctree: auto_generated/ - :template: function.rst + :template: class.rst + + StompMotif - euclidean_distance_profile - normalised_euclidean_distance_profile - squared_distance_profile - normalised_squared_distance_profile -Matrix profile functions --------------------------- +Collection Similarity search estimators +----------------------------------- -.. currentmodule:: aeon.similarity_search.matrix_profiles +.. currentmodule:: aeon.similarity_search.collection.neighbors .. autosummary:: :toctree: auto_generated/ - :template: function.rst + :template: class.rst + + RandomProjectionIndexANN - stomp_normalised_euclidean_matrix_profile - stomp_euclidean_matrix_profile - stomp_normalised_squared_matrix_profile - stomp_squared_matrix_profile -Base ----- +Base Estimators +--------------- .. currentmodule:: aeon.similarity_search.base @@ -57,3 +53,20 @@ Base :template: class.rst BaseSimilaritySearch + + +.. currentmodule:: aeon.similarity_search.series.base + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + BaseSeriesSimilaritySearch + +.. currentmodule:: aeon.similarity_search.collection.base + +.. autosummary:: + :toctree: auto_generated/ + :template: class.rst + + BaseCollectionSimilaritySearch From 9effbd9a9ed1afc69428d76501a7e143717ff90d Mon Sep 17 00:00:00 2001 From: baraline Date: Fri, 17 Jan 2025 22:40:49 +0100 Subject: [PATCH 15/36] Update documentation and fix some leftover bugs --- aeon/similarity_search/_base.py | 6 +- aeon/similarity_search/collection/_base.py | 7 +- .../collection/neighbors/_rp_cosine_lsh.py | 8 +- aeon/similarity_search/series/__init__.py | 6 +- aeon/similarity_search/series/_base.py | 7 +- aeon/similarity_search/series/_commons.py | 5 +- .../similarity_search/series/motifs/_stomp.py | 38 +- .../series/neighbors/_dummy.py | 14 +- .../series/neighbors/_mass.py | 28 +- docs/getting_started.md | 5 +- examples/similarity_search/code_speed.ipynb | 2 +- .../similarity_search/distance_profiles.ipynb | 2 +- .../similarity_search/similarity_search.ipynb | 425 +++++------------- 13 files changed, 201 insertions(+), 352 deletions(-) diff --git a/aeon/similarity_search/_base.py b/aeon/similarity_search/_base.py index 5204163315..a87487fde1 100644 --- a/aeon/similarity_search/_base.py +++ b/aeon/similarity_search/_base.py @@ -72,7 +72,7 @@ def predict( """ ... - def _check_predict_series_format(self, X): + def _check_predict_series_format(self, X, length=None): """ Check wheter a series X in predict is correctly formated. @@ -96,8 +96,8 @@ def _check_predict_series_format(self, X): f"Expected X to have {self.n_channels_} channels but" f" got {X.shape[0]} channels." ) - if hasattr(self, "length") and X.shape[1] != self.length: + if length is not None and X.shape[1] != length: raise ValueError( - f"Expected X to have {self.length} timepoints but" + f"Expected X to have {length} timepoints but" f" got {X.shape[1]} timepoints." ) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index 3fde03d420..618a531081 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -15,7 +15,7 @@ from aeon.similarity_search._base import BaseSimilaritySearch -class BaseCollectionSimilaritySearch(BaseSimilaritySearch, BaseCollectionEstimator): +class BaseCollectionSimilaritySearch(BaseCollectionEstimator, BaseSimilaritySearch): """Similarity search base class for collections.""" # tag values specific to CollectionTransformers @@ -81,6 +81,7 @@ def _fit( def _pre_predict( self, X: Union[np.ndarray, None] = None, + length: int = None, ): """ Predict method. @@ -89,10 +90,12 @@ def _pre_predict( ---------- X : Union[np.ndarray, None], optional Optional data to use for predict.. The default is None. + length: int, optional + If not None, the number of timepoint of X should be equal to length. """ self._check_is_fitted() if X is not None: # Could we call somehow _preprocess_series from a BaseCollectionEstimator ? - self._check_predict_series_format(X) + self._check_predict_series_format(X, length=length) return X diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index 6774803984..a6f3097f78 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -220,12 +220,8 @@ def predict( Distance of k series in the index to X. The distance is the hamming distance between the result of each hash function. """ - X = self._pre_predict(X) - if X.shape[1] != self.n_timepoints_: - raise ValueError( - f"Expected a series with {self.n_timepoints_} but got {X.shape[1]}." - "Unequal length is not supported by this estimator." - ) + X = self._pre_predict(X, length=self.n_timepoints_) + if self.normalize: X = z_normalise_series_2d(X) diff --git a/aeon/similarity_search/series/__init__.py b/aeon/similarity_search/series/__init__.py index 23df7d1b53..d1b5494c13 100644 --- a/aeon/similarity_search/series/__init__.py +++ b/aeon/similarity_search/series/__init__.py @@ -1,7 +1,7 @@ """Similarity search for series.""" -__all__ = [ - "BaseSeriesSimilaritySearch", -] +__all__ = ["BaseSeriesSimilaritySearch", "MassSNN", "StompMotif"] from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series.motifs._stomp import StompMotif +from aeon.similarity_search.series.neighbors._mass import MassSNN diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index bd07f25ba6..2e1b6d40e0 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -11,7 +11,7 @@ from aeon.utils.validation import check_n_jobs -class BaseSeriesSimilaritySearch(BaseSimilaritySearch, BaseSeriesEstimator): +class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): """Base class for similarity search applications on single series.""" _tags = { @@ -75,6 +75,7 @@ def _fit( def _pre_predict( self, X: Union[np.ndarray, None] = None, + length: int = None, ): """ Predict method. @@ -83,12 +84,14 @@ def _pre_predict( ---------- X : Union[np.ndarray, None], optional Optional data to use for predict.. The default is None. + length: int, optional + If not None, the number of timepoint of X should be equal to length. """ self._check_is_fitted() if X is not None: X = self._preprocess_series(X, self.axis, False) - self._check_predict_series_format(X) + self._check_predict_series_format(X, length=length) return X def _check_X_index(self, X_index: int): diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py index d14281573f..4e62e5aacb 100644 --- a/aeon/similarity_search/series/_commons.py +++ b/aeon/similarity_search/series/_commons.py @@ -61,6 +61,7 @@ def get_ith_products(X, T, L, ith): return fft_sliding_dot_product(X, T[:, ith : ith + L]) +@njit(cache=True, fastmath=True) def _inverse_distance_profile(dist_profile): return 1 / (dist_profile + 1e-8) @@ -101,9 +102,7 @@ def _extract_top_k_from_dist_profile( The distances of the best matches. """ - if k == np.inf: - k = dist_profile.shape[0] - top_k_indexes = np.zeros((k), dtype=np.int64) - 1 + top_k_indexes = np.zeros(k, dtype=np.int64) - 1 top_k_distances = np.full(k, np.inf, dtype=np.float64) ub = np.full(k, np.inf) lb = np.full(k, -1.0) diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index 9287d80241..fbd459b890 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -154,13 +154,13 @@ def predict( "Expected motif_extraction_method to be either 'k_motifs' or 'r_motifs'" f"but got {motif_extraction_method}" ) - exclusion_size = self.length // exclusion_factor + MP, IP = self.compute_matrix_profile( X, motif_size=motif_size, dist_threshold=dist_threshold, allow_trivial_matches=allow_trivial_matches, - exclusion_size=exclusion_size, + exclusion_factor=exclusion_factor, inverse_distance=inverse_distance, ) if motif_extraction_method == "k_motifs": @@ -170,11 +170,11 @@ def predict( def compute_matrix_profile( self, - X: np.ndarray, + X: np.ndarray = None, motif_size: Optional[int] = 1, dist_threshold: Optional[float] = np.inf, allow_trivial_matches: Optional[bool] = False, - exclusion_size: Optional[float] = 2, + exclusion_factor: Optional[float] = 2, inverse_distance: Optional[bool] = False, ): """ @@ -198,14 +198,12 @@ def compute_matrix_profile( inverse_distance : bool If True, the matching will be made on the inverse of the distance, and thus, the worst matches to the query will be returned instead of the best ones. - exclusion_size : int - The size of the exclusion zone used to prevent returning as top k candidates - the ones that are close to each other (for example i and i+1). - It is used to define a region between - :math:`id_timestamp - exclusion_size` and - :math:`id_timestamp + exclusion_size` which cannot be returned - as best match if :math:`id_timestamp` was already selected. By default, - the value None means that this is not used. + exclusion_factor : float, default=1. + A factor of the query length used to define the exclusion zone when + ``allow_trivial_matches`` is set to False. For a given timestamp, + the exclusion zone starts from + :math:`id_timestamp - length//exclusion_factor` and end at + :math:`id_timestamp + length//exclusion_factor`. Returns ------- @@ -229,7 +227,7 @@ def compute_matrix_profile( X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) X_dotX = get_ith_products(X, self.X_, self.length, 0) - + exclusion_size = self.length // exclusion_factor if self.normalize: MP, IP = _stomp_normalized( self.X_, @@ -353,14 +351,17 @@ def _stomp_normalized( lb = max(0, i_q - exclusion_size) dist_profile[lb:ub] = np.inf - top_indexes, top_dists = _extract_top_k_from_dist_profile( + _top_indexes, top_dists = _extract_top_k_from_dist_profile( dist_profile, motif_size, dist_threshold, allow_trivial_matches, exclusion_size, ) - + top_indexes = np.zeros((len(_top_indexes), 2), dtype=np.int64) + for i_idx in range(len(_top_indexes)): + top_indexes[i_idx, 0] = i_q + top_indexes[i_idx, 1] = _top_indexes[i_idx] MP.append(top_dists) IP.append(top_indexes) @@ -443,14 +444,17 @@ def _stomp( lb = max(0, i_q - exclusion_size) dist_profile[lb:ub] = np.inf - top_indexes, top_dists = _extract_top_k_from_dist_profile( + _top_indexes, top_dists = _extract_top_k_from_dist_profile( dist_profile, motif_size, dist_threshold, allow_trivial_matches, exclusion_size, ) - + top_indexes = np.zeros((len(_top_indexes), 2), dtype=np.int64) + for i_idx in range(len(_top_indexes)): + top_indexes[i_idx, 0] = i_q + top_indexes[i_idx, 1] = _top_indexes[i_idx] MP.append(top_dists) IP.append(top_indexes) diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index bbea714eda..7b4d4d89da 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -47,7 +47,7 @@ def predict( self, X: np.ndarray, k: Optional[int] = 1, - threshold: Optional[float] = np.inf, + dist_threshold: Optional[float] = np.inf, exclusion_factor: Optional[float] = 2, inverse_distance: Optional[bool] = False, allow_neighboring_matches: Optional[bool] = False, @@ -62,7 +62,7 @@ def predict( Subsequence we want to find neighbors for. k : int The number of neighbors to return. - threshold : float + dist_threshold : float The maximum distance of neighbors to X. inverse_distance : bool If True, the matching will be made on the inverse of the distance, and thus, @@ -73,7 +73,7 @@ def predict( the exclusion zone starts from :math:`id_timestamp - length//exclusion_factor` and end at :math:`id_timestamp + length//exclusion_factor`. - X_index : Optional[int], optional + X_index : int, optional If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. If specified, neighboring subsequences of X won't be able to match as neighbors. @@ -86,12 +86,13 @@ def predict( The distances of the best matches. """ - X = self._pre_predict(X) + X = self._pre_predict(X, length=self.length) X_index = self._check_X_index(X_index) dist_profile = self.compute_distance_profile(X) if inverse_distance: dist_profile = _inverse_distance_profile(dist_profile) + exclusion_size = self.length // exclusion_factor if X_index is not None: exclusion_size = self.length // exclusion_factor _max_timestamp = self.n_timepoints_ - self.length @@ -99,10 +100,13 @@ def predict( lb = max(0, X_index - exclusion_size) dist_profile[lb:ub] = np.inf + if k == np.inf: + k = len(dist_profile) + return _extract_top_k_from_dist_profile( dist_profile, k, - threshold, + dist_threshold, allow_neighboring_matches, exclusion_size, ) diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py index bb56815f4e..9565407fc8 100644 --- a/aeon/similarity_search/series/neighbors/_mass.py +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -21,7 +21,22 @@ class MassSNN(BaseSeriesSimilaritySearch): - """Estimator to compute the on profile and distance profile using MASS.""" + """ + Estimator to compute the subsequences nearest neighbors using MASS _[1]. + + Parameters + ---------- + length : int + The length of the subsequences to use for the search. + normalize : bool + Wheter the subsequences should be z-normalized. + + References + ---------- + .. [1] Abdullah Mueen, Yan Zhu, Michael Yeh, Kaveh Kamgar, Krishnamurthy + Viswanathan, Chetan Kumar Gupta and Eamonn Keogh (2015), The Fastest Similarity + Search Algorithm for Time Series Subsequences under Euclidean Distance. + """ def __init__( self, @@ -38,7 +53,7 @@ def _fit( y=None, ): if self.normalize: - self.X_means_, X_stds_ = sliding_mean_std_one_series(X, self.length, 1) + self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) return self def predict( @@ -76,7 +91,7 @@ def predict( the exclusion zone starts from :math:`id_timestamp - length//exclusion_factor` and end at :math:`id_timestamp + length//exclusion_factor`. - X_index : Optional[int], optional + X_index : int, optional If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. If specified, neighboring subsequences of X won't be able to match as neighbors. @@ -89,19 +104,22 @@ def predict( The distances of the best matches. """ - X = self._pre_predict(X) + X = self._pre_predict(X, length=self.length) X_index = self._check_X_index(X_index) dist_profile = self.compute_distance_profile(X) if inverse_distance: dist_profile = _inverse_distance_profile(dist_profile) + exclusion_size = self.length // exclusion_factor if X_index is not None: - exclusion_size = self.length // exclusion_factor _max_timestamp = self.n_timepoints_ - self.length ub = min(X_index + exclusion_size, _max_timestamp) lb = max(0, X_index - exclusion_size) dist_profile[lb:ub] = np.inf + if k == np.inf: + k = len(dist_profile) + return _extract_top_k_from_dist_profile( dist_profile, k, diff --git a/docs/getting_started.md b/docs/getting_started.md index 36f18583cb..ce519359f2 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -21,8 +21,9 @@ classical techniques for the following learning tasks: - [**Clustering**](api_reference/clustering), where a collection of time series without any labels are used to train a model to label cases ([more details](examples/clustering/clustering.ipynb)). -- [**Similarity search**](api_reference/similarity_search), where the goal is to evaluate - the similarity between a query time series and a collection of other longer time series +- [**Similarity search**](api_reference/similarity_search), where the goal is to find + time series motifs or nearest neighbors in an efficient way for either single series + or collections. ([more details](examples/similarity_search/similarity_search.ipynb)). - [**Anomaly detection**](api_reference/anomaly_detection), where the goal is to find values or areas of a single time series that are not representative of the whole series. diff --git a/examples/similarity_search/code_speed.ipynb b/examples/similarity_search/code_speed.ipynb index f31155333d..9b4c08acf3 100644 --- a/examples/similarity_search/code_speed.ipynb +++ b/examples/similarity_search/code_speed.ipynb @@ -554,7 +554,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/examples/similarity_search/distance_profiles.ipynb b/examples/similarity_search/distance_profiles.ipynb index ec56fcc6bf..d5ea595ff5 100644 --- a/examples/similarity_search/distance_profiles.ipynb +++ b/examples/similarity_search/distance_profiles.ipynb @@ -146,7 +146,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index cdbaa86948..803398d551 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -7,12 +7,27 @@ "source": [ "# Time Series Similarity Search with aeon\n", "\n", - "The goal of Time Series Similarity Search is to asses the similarities between a time\n", - " series, denoted as a query `q` of length `l`, and a collection of time series,\n", - " denoted as `X`, with lengths greater than or equal to `l`. In this\n", - " context, the notion of similiarity between `q` and the other series in `X` is quantified by similarity functions. Those functions are most of the time defined as distance function, such as the Euclidean distance. Knowing the similarity between `q` and other admissible candidates, we can then perform many other tasks for \"free\", such as anomaly or motif detection.\n", + "\"time\n", "\n", - "\"time" + "The objectives of the similarity search module in aeon is to provide estimators with a `fit`/`predict` interface to solve the following use cases :\n", + "\n", + "- Nearest neighbors search on time series subesequences or whole series\n", + "- Motifs search on time series subsequences\n", + "\n", + "Similarly to the `transformer` module, the `similarity_search` module split estimators between `series` estimators and `collection` estimators, such as :\n", + "\n", + "- `series` estimators take as input a single time series of shape `(n_channels, n_timepoints)` during fit and predict.\n", + "- `collection` estimators take as input a time series collection of shape `(n_cases, n_channels, n_timepoints)` during fit, and a single series of shape `(n_channels, n_timepoints)` during predict.\n", + "\n", + "Note that the above is a general guideline, and that some estimators can also take `None` as input during predict, or series of length different to `n_timepoints`. We'll explore the different estimators in the next sections.\n", + "\n", + "### Other similarity search notebooks\n", + "\n", + "This notebook gives an overview of similarity search module and the available estimators. The following notebooks are also avaiable to go more in depth with specific subject of similarity search in aeon:\n", + "\n", + "- [The theory and math behind the similarity search estimators in aeon](distance_profiles.ipynb)\n", + "- [Analysis of the performance of the estimators provided by similarity search module](code_speed.ipynb)\n", + "\n" ] }, { @@ -22,25 +37,34 @@ "metadata": {}, "outputs": [], "source": [ - "def plot_best_matches(top_k_search, best_matches):\n", + "# Define some plotting functions we'll use later !\n", + "def plot_best_matches(\n", + " X_fit, X_predict, idx_predict, idx_matches, length, normalize=False\n", + "):\n", " \"\"\"Plot the top best matches of a query in a dataset.\"\"\"\n", - " fig, ax = plt.subplots(figsize=(20, 5), ncols=3)\n", - " for i_k, (id_sample, id_timestamp) in enumerate(best_matches):\n", + " fig, ax = plt.subplots(figsize=(20, 5), ncols=len(idx_matches))\n", + " if len(idx_matches) == 1:\n", + " ax = [ax]\n", + " for i_k, id_timestamp in enumerate(idx_matches):\n", " # plot the sample of the best match\n", - " ax[i_k].plot(top_k_search.X_[id_sample, 0], linewidth=2)\n", + " ax[i_k].plot(X_fit[0], linewidth=2)\n", " # plot the location of the best match on it\n", + " match = X_fit[0, id_timestamp : id_timestamp + length]\n", " ax[i_k].plot(\n", - " range(id_timestamp, id_timestamp + q.shape[1]),\n", - " top_k_search.X_[id_sample, 0, id_timestamp : id_timestamp + q.shape[1]],\n", + " range(id_timestamp, id_timestamp + length),\n", + " match,\n", " linewidth=7,\n", " alpha=0.5,\n", " color=\"green\",\n", " label=\"best match location\",\n", " )\n", " # plot the query on the location of the best match\n", + " Q = X_predict[0, idx_predict : idx_predict + length]\n", + " if normalize:\n", + " Q = Q * np.std(match) + np.mean(match)\n", " ax[i_k].plot(\n", - " range(id_timestamp, id_timestamp + q.shape[1]),\n", - " q[0],\n", + " range(id_timestamp, id_timestamp + length),\n", + " Q,\n", " linewidth=5,\n", " alpha=0.5,\n", " color=\"red\",\n", @@ -66,73 +90,32 @@ " plt.show()" ] }, - { - "cell_type": "markdown", - "id": "7e06b213-6038-4901-b98e-2433625115c4", - "metadata": {}, - "source": [ - "## Similarity search Notebooks\n", - "\n", - "This notebook gives an overview of similarity search module and the available estimators. The following notebooks are avaiable to go more in depth with specific subject of similarity search in aeon:\n", - "\n", - "- [Deep dive in the distance profiles](distance_profiles.ipynb)\n", - "- [Analysis of the speedups provided by similarity search module](code_speed.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "ca967c08-9a05-411a-a09a-ad8a13c0adb9", - "metadata": {}, - "source": [ - "## Expected inputs and format\n", - "For both `QuerySearch` and `SeriesSearch`, the `fit` method expects a time series dataset of shape `(n_cases, n_channels, n_timepoints)`. This can be 3D numpy array or a list of 2D numpy arrays if `n_timepoints` varies between cases (i.e. unequal length dataset).\n", - "\n", - "The `predict` method expects a 2D numpy array of shape `(n_channels, query_length)` for `QuerySearch`. In `SeriesSearch`, the predict methods also expects a 2D numpy array, but of shape `(n_channels, n_timepoints)` (`n_timepoints` doesn't have to be the same as in fit) and a `query_length` parameter." - ] - }, { "cell_type": "markdown", "id": "d1fd75ae-84c2-40be-95f6-bd7de409317d", "metadata": {}, "source": [ - "## Available estimators\n", + "### A word on base clases\n", "\n", - "All estimators of the similarity search module in aeon inherit from the `BaseSimilaritySearch` class, which requires the following arguments:\n", - "- `distance` : a string indicating which distance function to use as similarity function. By default this is `\"euclidean\"`, which means that the Euclidean distance is used.\n", - "- `normalise` : a boolean indicating whether this similarity function should be z-normalised. This means that the scale of the two series being compared will be ignored, and that, loosely speaking, we will only focus on their shape during the comparison. By default, this parameter is set `False`.\n", + "All estimators of the similarity search module in aeon inherit from the `BaseSimilaritySearch` class, which define the some abstract methods that estimator must implement, such as `fit` and `predict` and some private function used to validate the format of the time series you will provide. Then, the two submodules `series` and `collection` also define a base class (`BaseSeriesSimilaritySearch` and `BaseCollectionSeriesSearch`) that their respective estimator will inherit from. If you ever want to extend the module or create your own estimators, these are the classes you'll want to use to define the base structure of your estimator.\n", "\n", - "Another parameter, which has no effect on the output of the estimators, is a boolean named `store_distance_profile`, set to `False` by default. If set to `True`, the estimators will expose an attribute named `_distance_profile` after the `predict` function is called. This attribute will contain the computed distance profile for query given as input to the `predict` function.\n", + "### Load a dataset\n", + "In the following, we'll use an easy dataset (`GunPoint`) to help build intuition. Don't hesitate to swap it with other datasets to explore ! We load it using the `load_classification` function.\n", "\n", - "To illustrate how to work with similarity search estimators in aeon, we will now present some example use cases." - ] - }, - { - "cell_type": "markdown", - "id": "01fa67c2-0126-4152-98a9-fa0df84c4629", - "metadata": {}, - "source": [ - "### Query search" - ] - }, - { - "cell_type": "markdown", - "id": "8e99b251-d156-4989-b5a0-3a2c79cb75d4", - "metadata": {}, - "source": [ - "We will use the GunPoint dataset for this example, which can be loaded using the `load_classification` function." + "The GunPoint dataset is composed of two classes which are discriminated by the \"bumps\" located before and after the central peak. These bumps correspond to an actor drawing a fake gun from a holster before pointing it (hence the name \"GunPoint\" !). In the second class, the actor simply points his fingers without making the motion of taking the gun out of the holster." ] }, { "cell_type": "code", "execution_count": 2, - "id": "f8a6bb7e-b219-41f1-b508-b849c45672eb", + "id": "20d3b591-f275-4548-a7d2-75b16380b055", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -162,205 +145,97 @@ }, { "cell_type": "markdown", - "id": "5392f7f4-1825-4b15-9248-27eeecb1af3c", + "id": "01fa67c2-0126-4152-98a9-fa0df84c4629", "metadata": {}, "source": [ - "The GunPoint dataset is composed of two classes which are discriminated by the \"bumps\" located before and after the central peak. These bumps correspond to an actor drawing a fake gun from a holster before pointing it (hence the name \"GunPoint\" !). In the second class, the actor simply points his fingers without making the motion of taking the gun out of the holster.\n", + "## 1. Series estimators\n", "\n", - "Suppose that we define our input query for the similarity search task as one of these bumps:" + "First, we'll explore estimators of the `series` module, where you must provide single series of shape `(n_channels, n_timepoints)` during fit." ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "a494a0be-4459-414d-9fc2-1400feefd171", + "cell_type": "markdown", + "id": "78f17f93-28b3-49c0-be5f-1d430a273b0c", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "# We will use the fourth sample an testing data\n", - "X_test = X[3]\n", - "mask = np.ones(X.shape[0], dtype=bool)\n", - "mask[3] = False\n", - "# Use this mask to exluce the sample from which we will extract the query\n", - "X_train = X[mask]\n", + "### 1.1 Subsequence nearest neighbors with MASS\n", "\n", - "q = X_test[:, 20:55]\n", - "plt.plot(q[0])\n", - "plt.show()" + "To perform nearest neighbors search on subsequences on a series, we can use the `MassSNN` estimator.\n", + "\n", + "It takes as parameter during initialisation :\n", + "- `length` : an integer giving the length of the subsequences to extract from the series. It is also the expected length of the series given in `predict`\n", + "- `normalize`: a boolean indicating wheter the subsequences should be independently z-normalized (`(X-mean(X))/std(X)`) before the distance computations. This results in a scale-independent matching.\n", + " \n", + "To parameterize the search, additional parameters are available when calling the `predict` method:\n", + "\n", + "- `k` (int) : the number of nearest neighbors to return.\n", + "- `dist_threshold` (float) : the maximum allowed distance for a candidate subsequence to be considered as a neighbor.\n", + "- `allow_trivial_matches` (bool) : wheter a neighbors of a match to a query can be also considered as matches (True), or if an exclusion zone is applied around each match to avoid trivial matches with their direct neighbors (False).\n", + "- `inverse_distance` (bool) : if True, the matching will be made on the inverse of the distance, and thus, the farther neighbors will be returned instead of the closest ones.\n", + "- `exclusion_factor` (float): A factor of the `length` used to define the exclusion zone when `allow_trivial_matches` is set to False. For a given timestamp, the exclusion zone starts from `id_timestamp - length//exclusion_factor` and end at `id_timestamp + length//exclusion_factor`.\n", + "- `X_index` (int): If series given during predict is a subsequence of series given during fit, specify its starting timestamp. If specified, neighboring subsequences of X won't be able to match as neighbors." ] }, { "cell_type": "markdown", - "id": "fcf10a34-930a-4fce-86f8-4dfa207cad11", + "id": "33105406-fc83-4143-9345-af589a06a00a", "metadata": {}, "source": [ - "Then, we can use the `QuerySearch` class to search for the top `k` matches of this query in a collection of series. The training data for `QuerySearch` can be seen as the database in which want to search for the query on." + "First, we'll select a series from the dataset to use during fit. This is the series we want our neighbors to come from." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "80eaab8f-204f-439f-84c8-ad3462f1575e", + "execution_count": 3, + "id": "a494a0be-4459-414d-9fc2-1400feefd171", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n" + "(1, 150)\n" ] } ], "source": [ - "from aeon.similarity_search import QuerySearch\n", + "from aeon.similarity_search.series import MassSNN\n", "\n", - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, distance=\"euclidean\")\n", - "# Call fit to store X_train as the database to search in\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" - ] - }, - { - "cell_type": "markdown", - "id": "3dc402cf-80b7-4d0c-b07c-2f8e7822ac97", - "metadata": {}, - "source": [ - "The similarity search estimators return a list of size `k`, which contains a tuple containing the location of the best matches as `(id_sample, id_timestamp)`. We can then plot the results as:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "23efe48e-8257-4ecc-93a2-d72f19024ab5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_best_matches(top_k_search, best_matches)" + "length = 35\n", + "# We'll take a sample of the class with a \"bump\".\n", + "series_fit = X[2]\n", + "print(series_fit.shape)\n", + "snn = MassSNN(length=length, normalize=False).fit(series_fit)" ] }, { "cell_type": "markdown", - "id": "877b1b32-d978-4c54-a4e7-b475496f710a", + "id": "320ef728-ca92-4fd5-9686-2b9739fcab83", "metadata": {}, "source": [ - "You may also want to search not for the top-k matches, but for all matches below a threshold on the distance from the query to a candidate. To do so, you can use the `threshold` parameter of `QuerySearch` :" + "Then we'll take a subsequence of size `length` in another series of the same class to use in `predict` :" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "23ad7adb-2b01-4425-a2e8-c393f3721a0f", + "execution_count": 4, + "id": "98560db4-4289-4072-8662-2cde2ad5c44a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n", - "match 3 : [176 25] with distance 0.21889484294879047 to q\n", - "match 4 : [23 20] with distance 0.22668346183441293 to q\n", - "match 5 : [167 23] with distance 0.24774491003815066 to q\n" + "match 0 : 27 with distance 0.3020071566139322\n", + "match 1 : 28 with distance 0.48913603040398357\n", + "match 2 : 26 with distance 0.889697094966067\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\antoine\\Documents\\aeon\\aeon\\similarity_search\\query_search.py:270: UserWarning: Only 6 matches are bellow the threshold of 0.25, while k=inf. The number of returned match will be 6.\n", - " return extract_top_k_and_threshold_from_distance_profiles(\n" - ] - } - ], - "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=np.inf, threshold=0.25, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" - ] - }, - { - "cell_type": "markdown", - "id": "0efd83a5-b36f-4809-be96-94de734d931c", - "metadata": {}, - "source": [ - "You may also combine the `k` and `threshold` parameter :" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "65db1593-3873-4a47-9e2a-d8dfcf42dd1a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "match 0 : [195 26] with distance 0.1973741999473598 to q\n", - "match 1 : [92 23] with distance 0.20753669049486048 to q\n", - "match 2 : [154 22] with distance 0.21538593730366784 to q\n" - ] - } - ], - "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, threshold=0.25, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "for i in range(len(best_matches)):\n", - " print(f\"match {i} : {best_matches[i]} with distance {distances_to_matches[i]} to q\")" - ] - }, - { - "cell_type": "markdown", - "id": "ff62a385-d58e-4fb1-95dd-eb0474711531", - "metadata": {}, - "source": [ - "It is also possible to return the **worst** matches (not that the title of the plots are not accurate here) to the query, by using the `inverse_distance` parameter :" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6d6078ab-9104-462e-9856-1d0fc9594b24", - "metadata": {}, - "outputs": [ { "data": { - "image/png": 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22mvYtm0bzjzzTDz99NP48Y9/jCuuuALPP/88Lr/8cnzpS1/CXXfdBQC4/vrr8de//hVXXXUV7r33Xr2kMxqN4pprrsGTTz6J0047DV6vFzabDYCahX///fdhsVgwf/583HHHHWhtbc04np6eHnzjG9/AunXrUFVVhQ9/+MN45plncPbZZ+PWW2/F66+/jlmzZmFoaAgAcMIJJ+CNN96A0WjEK6+8gn//93/H008/je9+97tpZamPPPKI/hl33HEHbrjhBtxwww146KGH8OUvfxnPPPMMAKC3txdvvvkmduzYgRUrVnDyTzQBvogPv9nwGwyGBsd8T5YVjIRi2NrjQcdAAL6IhOioOOG2mlDnsqDKbsbJrRVwWU3YN7wP+4b3wW6yY3HDYpzSeAoanA25/LEKEuME4wRRsRoIDuDJLU+mVQ4FIhLWHxzGth4vQrH4Mb+XxShCEARYjCLaZ1RjUbMbMTmGVR2rsK5nHS6dfynm1cybih+j4DFOFH+cYJKICpokS3hs82N6gmjPYT9e39WPUCwOSVagKMqY1xgNAloq7WhwW7H7sA9DgSj2D0gAAtjUNYLTZ1Zj6YwqmAwiIvEI1veux/re9ZhRMQMXt12Maa7CKz3MldbWVpx99tkAgE9/+tP42c9+ho985CPYsmULPvShDwEA4vE4mprUfdeLFy/Gddddh8svvxyXX375MX3GRz/6UZhMJpx00kmIx+P4yEc+AgA46aST0NHRAQB47bXX8OMf/xjBYBBDQ0NYtGgRLr300rT32blzJ5qamnDaaacBANxut/69D37wg6ioqAAALFy4EAcOHBj3ov7ee+/hggsuQF1dHQDguuuuw+uvvw6DwYDzzjtPPy6yuroaAODxeHDDDTdg9+7dEAQBsVjsqD/zO++8gz/96U8A1CD1b//2b/r3Lr/8coiiiIULF+LQoUNHfS8iSheMBfHbjb/VE0Sbukbw7r4hROIy4rKMDGECZqOI6VV2VDnM2NHngzccgzes/lve1D2Cs9tqceK0ChhEAcFYEKu7VmN112rMr5mPD835EGrttbn8EQsK4wTjBFEx6g/045ENjyAQCwAAPMEY1h4YwrZeL+KyGigqbCa0Vif60imA22aCw2KAJCuQ4jJicQWeUAwHBoMIRiUAQDgWx6s7DmFz9wgumF+PaZU2+KI+PL75cSyfuxynNZ+Wl583nxgnij9OMElEBe0f+/+Bg56DAIC9h/342+ZeyKkzfgEwG0TMrHFg4TQ3GlxWWE2iXhF0xuxqDPij8ISi2NcfwLZeL97ZN4i1B4bRVufE/EYXplfbIYoCDngOYOW6lTi16VR8eM6HYTFa8vEj59XoSipBEKAoChYtWoR33nlnzPOff/55vP7663juuefw/e9/H5s3bz7qZ1gs6u9VFEWYTCb9M0VRhCRJCIfD+MIXvoC1a9eitbUVd99993Ef26h9BgAYDAZIknRcrz+S//iP/8CFF16IP//5z+jo6MAFF1wwqfdLHWumpCcRHdnzu57XV4U3do3gtR2H074vCIDZaEBbnRMLm9yocZr1FWAAOHNODfp9EXhCMezo82Ffvx+v7TiMd/YOYm69Eyc0ujGt0gpBELBzcCd2D+3GWa1n4cKZF8IgGnL+8+Yb48TRMU4QFZbDgcP4zYbfIBALQFEUrN43hDUdQ/q/p7Y6J06dWYVGt/WYdhUoioJgNA5BADqHQnhjdz8O+yL4w9pOLGh044IT6mAxGvD87ucRjAVx/szzp/pHLCiME0dX6HGicDbZE43iCXvwbte7AIDukRCe36ImiE6dUYUvXNCGOz4wF1/5wFx84YI2LD+pCTNrHLCZDWkXJkEQUOeyoK3ehQ8vasTHl7ag0W1FLC5je58Xz2zoxq/f3Ie1HUOQZHX7wbredXhg3QPo9nbn5efOp4MHD+oX79///vc455xzMH/+fPT39+uPx2IxbN26FbIso7OzExdeeCF+9KMfwePxwO/3w+VywefzTXgM2gW8trYWfr8fTz31lP691PeeP38+ent78d577wEAfD7fhC7ep59+Ov75z39iYGAA8Xgcjz/+OM4//3ycccYZeP3117F//34A0MtDPR4PmpubAaSXgB7p5z7rrLPwxBNPAAAee+yxtH3VRDRx3d5ubO3fCgDYdcinJ4jOn1eHL1zQhi9/YC6+8sF5+Pz5c/ChhQ1orrLBakqPE6IgoMFtxbwGFy5d3IRLTmpCjcOMcCyOzd0e/HFdJx56qwNbuj2QFQWyIuPNg2/iwfcfxGBw7Pa2Usc4wThBVExGJ4je3DOAd/er1+4FTW585swZ+NjJ09BUYRuT3BAFERWWClRYKmA32fX+dIIgwGExwm42Yn6jCzecNROnz6qGQRSwvc+Lp9d16ZVGr3W8hnc6xyZGShnjRPHHCVYSUcF6/cDriCtxKIqC13f1Q5YVnNxSiXPaajNm+W1GG06oPQFVtirUO+pRYanAjoEd6PR2Yt/wPgBAa7Udnzx9OkaCUezo82Fnnw/DwSje3DOAzd0efPCEBkyvsWMoNISHNzyMT574SbRVt+X6R8+b+fPn47777sNNN92EhQsX4vOf/zzMZjOeeuopfPnLX4bH44EkSfjqV7+KefPm4dOf/jQ8Hg8URcGXv/xlVFZW4tJLL8VVV12FZ599dkyjuWNRWVmJW2+9FSeeeCIaGxv18k8AuPHGG3H77bfrjeaefPJJ3HHHHQiFQrDZbHjllVeO+2duamrCPffcgwsvvFBvNHfZZZcBAFauXIkrr7wSsiyjvr4ef//73/Fv//ZvuOGGG/C9730Pl1xyif4+F154Ie655x4sWbIE3/zmN9M+4+c//zk++9nP4ic/+YneaI6IJu+1jtcAAFJcxuu71Gqic9pqccr0zKfLuMwuzK+djwpLBZpcTbAardjevx2d3k4c9ByEIAiY2+DC3AYX+n0R7OzzYWefF75wDK9sP4SNnSO4aGEDGtxW9Ph68Kv1v8INJ99QVkcfM04wThAVi6HQ0JgE0boDwxAFActPakJbvVN/rsPkQPu0dsyrmQeXxQVFUeA0O9MqRmVFRo+vB/uH92MgOIDdQ7sRjAVhMog4a04tFjS58ez73Ymqoi5ctbQFTqsRL+19CS6LCyfWn5iPX0POMU4Uf5wQlAKtW21vb8fatWvzPQzKk6HQEO5dcy9kRcbew348t6kHdrMRnz17JkwGNYt/Yv2JaHA0wCgaUeeow8zKmeOeVBaRInh1/6tY17MOcSXZlE5RFBwYDOL13f0YCkQBAEunV+HstloYRAFmgxk3LrkxJ32Ktm/fjicPPTnln6PJdBoBFabt27djwYIFaY/xGsnfQbk76DmIh95/CACw4eAwVu3qR63TguuWTdcXEtqntaPCUgGTwYRGZyOmV0wf96Qyf9SPl/e+jM2HNkNBcmqkKAp2HfLjzT0D8IVjEEUBZ82uwakzqiAIApxmJ24+5eacHHvMOEHjYZzIjL+D8qUoCh7d9Cj2Du+Foih4Y/cA1h8cmyCym+y4YOYFOKXxFJgMpuP+nD1De/DSnpf0bc+BiIRnNnSj3xdBrdOCT7S3wGI0wCAYcOupt6LR2ZjVn3M0xgnK5HhjBCuJqCC9efBNyIoMRVHwzj61JPS0mVV6gui6k67D3Jq5x/x+FqMFy+cux3kzzsOOgR3YcngLOkY6IAgCZtY6ML3ajvc6hrB6/xDWHxzGSCiGjy1uQjQexWObHsMtS2/JyQ0AL7RERMfmnx3/BADE4jLWdAwDAM6YXQNBECAKIm479bbjmow7zU5cueBKfGDWB7C9fzs2H96MHl8PBEHA/EYX5tQ58OaeAWzoHEkkjCRcML8O/qgfj256FLcsvQU2k21KftZUjBNEREfX5+/D3uG9AIB1B4YzJoha3C247qTrJnXtbqtuw+zTZmNVxyq8fuB1OCxGXHlKC/6wthMD/gie29iLK05pBsQ4XtzzIm44+YYpP02ZcYImiz2JqODE5Ti2HlZ7TOzt92PAH4HTYsRJzWp3+TlVc44rQZTKaXaifVo7blxyI76y7Cs4bdppECBAFAUsm12DT5zaAotRxL5+P1btPAxFURCIBfDMjmfYLJKIqEAEogF98r+524NgVEK9y4o5dQ4AwJLGJRNera20VuLM1jNx26m34QunfUHfHmA0iLhgfj0uPXkaDKKAjV0jWHtATU4Nhgbxwp4XsvCTERFRNuwa3AVArSja2DkCALh4UWNaguj6xddnJbkvCiI+MOsD+GjbRwEANrMBl5/SDLvZiK7hIDZ0qrGiY6RDb4FBVMiYJKKC0+PrQSQeAQDsOaweU3nK9CoYE1VEF866MCufU2WrwiXzLsHt7bejwdEAAJhWacOKk5thEAVs6vJgY5cHAHDAcwCbDx+90z4REU29/SP79a/3HvYDANpnqtu/DIIB5804LyufU++ox1ULr8Jnl3wWldZKAMCcOicuXtQICMBbewawJ/H5mw5twoGRA1n5XCIimhwtSTTgj8IXkWA3GzGvQU0QucwuXL/4+qyfZLysZRk+MOsDAIAKmwkfWqjeX7zXMYyIpLa7eHX/q1x4poLHJBEVHC3DrigKDg4FAQAza+wAgOkV09Hibsnq5zU4G3DrqbdiUd0iAEBzlQ0fXqiuQL+9dwD+iNrh/uW9LyMsHd/RiURElH1anIhKMnq9YQgCML1ajRML6xbqCZ1smVE5A7e3344ZFTMAAPMaXDhnTi0AYNXOw4jF1dMxn9/9POJyfNz3ISKiqeeL+NDtU08p3j+gJvJn1Tr0bV5XLLgi6wkizbnTz8XJDScDUO9fplXaEI7F8f7BEQDqYviOgR1T8tlE2cIkERUcbfI/GIgiGJXgMBtR7TADwJSdNGYUjbhiwRWYVTkLADC/0YXZtU5EJRlv7FYb0fmjfrx+4PUp+XwiIjo2iqJg75C61ax7JARZVtDgssJqUk+gmao4YTVace1J16LeUQ8AWDqjCvUuC/wRCe/uV4+0PRw4jLU9bJJLdLy0I7AXLlyIRYsW4f/+7//GPEc7+aitrQ2LFy/G+vXr8zBSKga7h3brX+8bUHclzKpVtyM7TA59vj8VBEHARbMvgkk0QRAEnJ1YUFh/cBihqLqIsL6Xf3epsDFJRAUlGo+i09sJAHoV0fRqu575n101e8o+2yga8YlFn4DNqO5NPn9+HQyigJ19PnQNq2NZ17MOkixN2RiIiOjIhsPD8ETUrcCpcUIzq2rqJv9WoxVXL7oaBsEAURBw4Qlqwmj9wWEMJ07IXNO9hlsJiI6T0WjE//zP/2Dbtm1YvXo17rvvPmzbti3tOS+88AJ2796N3bt3Y+XKlfj85z+fp9FSodO2mgWjEvq8YRhEQY8Tc2vmTnnjaJfFhdObTweg7lCYXm1HVJKxt1+tajroOQhZkad0DESTMekkETP/lE0HRg7oF83OxOS/NXFRtxgsU34Uvd1kxwdnfxCAupe4fUY1AOgN7yLxCDpGOqZ0DESlhnGCsim16efoOFFnr4Pb4p7Sz6+11+Ks1rMAAE0VNixsckOWFWzuVhNXg6FBDAQHpnQMRKWmqakJS5cuBQC4XC4sWLAA3d3dac959tln8ZnPfAaCIOCMM87AyMgIent78zFcKmCSLOlxYv9AAFCAlio7zEb1tnd+zfycjOPs6WfDIKgVrjNr1CqmXo/atiISjzBOUEGbdJKImX/KJu2iHpcVdA2HACRXiGdVzYIoTH3x29KmpWhyNgEAFk5TbzYODAUhyWryaufAzikfQ6mQJFZdEeMEZZe21SwQkTDgj8BoENFUaQUwtdWmqc6dca6ejFo0TT15c1+/X68g2jnIOHGsGCdotI6ODrz//vtYtmxZ2uPd3d1obW3V/9zS0jImkUTU6+tFNK5WdnYMqgsJsxNbzQyCIWdxwm6yo8ml3k80Vqgxqs8T0r/f5e3KyThKAeNE7k36jpuZf8omLUnU5w0jFpdR7TDDaTUCyN3kXxRELGlcAkCtJqp1WhCVZHQnkla7BneV7FaC73//+5g3bx7OOeccXHvttfjv//5vXHDBBVi7Vu2xMTAwgJkzZwIA4vE4/vVf/xWnnXYaFi9ejAceeAAAsGrVKpx77rlYsWIFFi5ciLvuugs//elP9c/41re+lbGShEoX4wRli6IoejVnZ2IbcHOlDUZRnc7kKk6YDWacWH8iAKCpQu2HNBKKYTgYA5Dc6lCKGCdoKvn9fnz84x/HT3/6U7jdE6sKXLlyJdrb29He3o7+/v4sj5AKXZ+/T//6kFet3GmuUltJzKicMWUNqzPRDtupd1kgigIGg1H9lLNSThIxThQ/Yzbf7Hgz/01NTWnPW7lyJVauXAkAvKiXIUmWcDhwGAD0hExrVbLPRK4m/wAwr2YeXtjzgvq5tQ4M+CPY1x/AjBoHPBEPXtr7Ei6YeQGsRmv2P/zuu7P/nsfwGevWrcMTTzyBDRs2QJIkLF26FKeeeuq4b/Hggw+ioqIC7733HiKRCM4++2x8+MMfBgCsX78eW7ZswaxZs9DR0YErr7wSX/3qVyHLMp544gmsWbNmqn4yKnCMEzQZI+ERhCQ1PiTjhDr5FwURMypn5Gws82vm4+3OtyGKAmbVOLC9z4t9/X5UO6px0HMQ73a9i1OnnQqjmNWplopxgkpQLBbDxz/+cVx33XW48sorx3y/ubkZnZ2d+p+7urrQ3Nw85nm33XYbbrvtNgBAe3v71A2YCpKWJArH4vCGYjCIAqrt6gE4za6xf1+mkpYkMhpE1DktOOQN45Anguk19qlPEjFO0CRkbe9ONjL/t912G9auXYu1a9eirq4uW0OjIjEcGoYCtUJHawBa51Kz/XaTHTW2mpyNpcpWpZ9gM7vOCUA9HUGrIFrdtRq/WvcrDIWGcjamqfbGG2/giiuugN1uh9vtxooVK474/Jdffhm//e1vsWTJEixbtgyDg4PYvVs9TeL000/HrFlq89iZM2eipqYG77//Pl5++WWccsopqKnJ3f8vqXAwTtBkDYYG9a+HRsWJekf91CTux9Fa0aofdDC7Tt3KoJ2iAwAv7HkBD7//MALRQMbXFyPGCZoqiqLg5ptvxoIFC/C1r30t43NWrFiB3/72t1AUBatXr0ZFRcWYhQQiLUk04I8AAGqdahUPADQ6G3M6Fi1JBKhVpwDQm9hy1h/oR0SK5HQ8ucA4URqysryVrcw/lbfUyf9wUJ38V9pNAIAaW82Un0Qw2vya+TgcOIwGtwV2sxG+cAzdIyHUuSywGA0YDA3ilX2v4OpFV+d0XLlmNBohJ/oxhcNh/XFFUfDzn/8cF198cdrzV61aBYfDkfbYLbfcgkceeQR9fX246aabpn7QVHAYJygbBoPJODGS2NpVmVghrrXX5nQsoiBibs1cbDq0CdNr7BBFAb2eEIaDUTjMRpiNIrp93Xjz4Ju4uO3io79hEWOcoMl666238Lvf/Q4nnXQSlixZAgD4wQ9+gIMHDwIAbr/9dixfvhx/+9vf0NbWBrvdjocffjiPI6ZCJCsyDgUOAQD6fWoCRltIAHKfJKqwVMBpdsIf9at9iTrVlhoAoEDBPW/eg8+1fy7n48oHxoniMulKImb+KVtSJ//DIXXyX5WY/NfYc58pnl+rnn4gCILe8O6pdV345aq92JI4xWZ7/3aEYqFx36OYnHfeeXjmmWcQCoXg8/nw3HPPAVAz9+vWrQMAPPXUU/rzL774Yvzyl79ELJbowbFrFwKBzCvmV1xxBV588UW89957Y4IAlT7GCcoWbTEhKskIRCUYRAGuRN+6XFabarRTcixGA1oqbVAU4Ddvd+CX/9yrnqoD4P2+90umjx3jBE2Vc845B4qiYNOmTdiwYQM2bNiA5cuX4/bbb8ftt98OQJ2P3Xfffdi7dy82b97MrWQ0xmBwEJKsNjnWk0RONUlkNphRZavK6XgEQdCriZoq1MrTXk9YjwkKFDz0/kN6u41SwDhRGiZdScTMP2WLNvkPReOIxOIwGUTYzerRkfmY/De7mvXs/+KWChwcDiIciyMqydh92I8TmyugQEGPrwdzqufkfHzZtnTpUlxzzTU4+eSTUV9fj9NOOw0A8PWvfx1XX301Vq5ciUsuuUR//i233IKOjg4sXboUiqKgrq4OzzzzTMb3NpvNuPDCC1FZWQmDwZCLH4cKCOMEZYu2mDCiVZvaTBATVab5WEyYUz0HBsGAuBLHKdOrMBSIIiLJiMVl7O33Y1atA2EpjMHQYM4rnaYC4wQRFbLUptX9/vRKogZHQ05OSR6txd2CHQM74LYaYTcbEIzG8cbuAbitRixuqUQUUbzT+Q4uO+GynI9tKjBOlAZBKdDlrfb2dr0DOpWHh99/GAc8B9AzEsIf1nai3mXBp5apTUivXnQ1FtYtzPmY3jz4Jl7Z94r+Z284hofe3A+ryYDPnTcbgiDgg7M+iHNnnDvpz9q+fTsWLFgw6ffJlrvvvhtOpxNf//rXJ/1esixj6dKl+OMf/4i5c+dmYXTlJdPfDV4j+TsoRz9d/VOMhEews8+HF7b0Yk6dE5eePA0AcOvSW9Hszv0Wxed3PY/3et7T/9w9HMIf13WizmnBdWeoMezKBVdiccPiSX8W4wSNh3EiM/4Oysvf9/4db3W+hbis4Ber9iCuKPjC+W0wG0WcNu00XDLvkqO/SZZ1jHTgkQ2PAAD+urEHe/r9+veWn9SEeQ0uuC1ufO3MzJXWx4txgjI53hiR+3Qq0Ti0SqLRfSaA/FQSAcAZLWfgpPqT9D+7LOoqgHpiglrO2u3rHu/lBGDbtm1oa2vDBz/4QV7QiWjCJFmCJ6xu9dUqiapS40QeKokA4AOzPoA5Vclq0nq3BYIADASikOJq/4VuL+PEkTBOEFE2aJVEQ4Eo4rKCSpsZZqN6u5uvvj/Nrmb9kIPz5tXhrDk1aK1WT2/WtsR5I16EpfC470GME7k2BeeyEh2/iBSBP6pm1oczTP6rbdV5GZdRNOLKBVdimmsaXtr7EgRBQIPbiv0DAfR5w6iwm9Dl7YKiKDlvrD3V7s7S0ZkLFy7Evn37svJeRFS+hkJDyRMwg1rfOvVwA4fJkdOTzVLZTDZ8evGn8fzu57G2Zy1MBhE1DgsG/BEc9kUwrdJWsosJjBNEVCgURdGTRKP7EQH5SxKZDCacO+NcvLz3ZbhtJpw+qwZumxedQ0EMJk7pBNTTzlorWvMyxqnEOFGcWElEBSH1KPnkCrE6+a+wVMBkMOVlXIDadO6UplMgIHF8plu9EdFOJ/BH/fBFfXkbHxFROUiNE/oJmI78HW6QShAELGtepv+5YVSc6PX1Ii7H8zI2IqJy4I/6EYipDY9Hn2wmQEC9oz5vYzur9SysmJ88Cr7GoY5rKNE3CQD6g/05HxfReJgkooKgbTUDkivElXk82Ww0q9GqNx1tqFAn/4e8ybLQbG0lKNAWYZRH/DtBpNKaViuKMmYxIV9bklPV2GtgMagTf20xQYsTcSWuH8s8Wbwm0Gj8O0EEDAQH9K8HA1qSSL2XqLXX5nXBGQCWNi3FWa1nAQCqHCYIgoCRcEzflpzNE854TaBUE/n7wCQRFYS0yX8osUJcQJN/AHpD1AaXOvk/7AtDltV/dNnYSmC1WjE4OMgLO+kURcHg4CCs1vxsoyEqJPoJmLE4IpIMs1GEzZQ4AbMAFhNEQcQ0l9pEu6FCTRYd8mR3MYFxgkZjnCBSDYeH9a89IXXBucKWTBIVgjp7HQDAKIqotJkARe2fBKjbzbKBcYJSTTRGsCcRFQRt8u+PSJDiCuxmA6wFNPkH1MZzG/o2wGY2oMJmgicUw2AgijqXJSuT/5aWFnR1daG/n+WmlGS1WtHS0pLvYRDlnbaYMKL3IzLrveAKaTFh/8h+1DgsMBoEjIRiCMfisJoM6PZ14zScNqn3Z5ygTBgniICR8AgAQFYU+MLq4TJuq3qrW2Wrytew0qRueatxmDEcjGIwEEW925q1SiLGCRptIjGCSSIqCNrkf/RWM6CwJv+aRrcVnlAMfd4w6lwW9Ph6Jt282mQyYdasWdkYKhFRydEWE/R+RPbk1oFCWkwAAIMooN5pRY8nhD5vGDNrHFlZTGCcICLKbDikVhL5IxJkRYHDbITRoG6aqbRW5nFkSXWOOv3rGqcFe/r9evNqX9SHsBSe9CEMjBOUDdxuRgVh9OS/qgAn/w2OBhgEtbqpYVS/iUg8klbmSkRE2ZN+AmaykghQG5JWWQtjlTh1MUGLE4cTcWIgOIBYPJaXcRERlTqtksib2GrmtiXvJQolRpgNZj1hVeNUY1hq8+ps9iUimgwmiSjvIlIEYUmdRPtCWnmoemEXBbFgsv8G0YAGZwOA5GkJAykX9kP+7DQlJSKidN6IV//ap90AJOKE2+LOe0NSjcvsgsPkAJCME/1+dfFDgcLTa4iIpkgySZS4l7AlN8wUyr0EkOxLVJM4nVOrJAKy15eIaLKYJKK801aHASAQUS/szsQeYqfZCVEonL+mDQ41SVTrVCf/g/6o3hguWyfXEBFROl/Up3/tHxUn3BZ3XsaUiSAI+mJCbWKVeMDHxQQioqkUi8f0OKE3rbYmFw8KKUmk9SWqtJshCgI84RhiiRPOuJBAhaJw7r6pbKVN/qPq5N9hUSf/LrMrL2MaT6OzEQBgMxvgtBgRi8t6MOrz9+VzaEREJcsXScaJQDQOAHBa1O2/LkthxQltMaHaqTbWHglF9RsAxgkiouzzRDz6195w+nYzp9lZMNWmQLIvkUEU1N56CjCcqCbidjMqFEwSUd6lTf61FWItSVRok//ECjGQrCbqT6wSc4WYiGhqaIsJiqLocaLQFxOMoohquwmKoladAqw4JSKaClrTamBsT6JCqiICxp5wBgBDiSTRUGgoL2MiGo1JIsq7TNsIHObCnPxrK8QAUDuqL9FweBgRKZLxdURENHHaYkI0LiMWl2E0iDAnTq0p6MWEUXHikP+QvkWZiIiyQ+tHBCSTRNp2s0JpWq2ptlXrX1fY1CSRtivBE/YgLsfzMi6iVEwSUd5pk/9YXEZUkmEQBVhNhTn5t5lsev8Lrd+E1pQUYJkoEdFU0BYTApHEVjOzAYIgACi8xYRae63eS0+rONWSRCEplLYwQkREk6edMByXFfiiEgQh2beu0CqJrEYrbEYbgGRzbS1JpEBJ2zpHlC9MElHeJSf/ySqiQp38A8mtBHXa5D+lKSn7TRARZZ+2mDB6qxlQeIsJRtGIWnstgGSc6GecICKaMlolkS8cAxTAaTHBIKr3ElW2wqokApJjqkhsidP6KAHpW+eI8oVJIsq75ORfXSFOnfw7zc68jOlItC1nVXYzDKIAbziGiKSOnf0miIiyb8xiQpHEidTtZvpJmOxfR0SUVVpixRtSY4TbmowRhVZJBCS3wGlJIk9i3ED61jmifGGSiPJOm/zr/YgSJ9YAhbdCDCT7TYiioDecG9CaknLyT0SUVYqi6IsJ/kyVRAVcceowG2AzGRCRZPgSY+diAhFRdmmJFU+iIkdLvgCF15MISCauXFYTIAD+SAxxWV1I0LbOEeUTk0SUV6mT/9EnmwGFOflPa149asvZoQCbkhIRZVNICiGuqNWaek+ixGKCUTTCarTmbWzj0RYTBEEYEye43YyIKHvCUhghKQRg7MlmAgS9l2gh0babGUQBLot6CqYvkeDidjMqBEwSUV5F4hHEZPWiGIimrxAbBAPsJnvexjaeGnsNjKI6Rm3yPxhQJ//ReBTeiDdvYyMiKjXaQgIA+EfFCZfZpfewKySZFhMGU444lhU5L+MiIio1mU42cydONnNb3DCIhkwvy6vU6iZta5zWvJrbzagQMElEeeWP+pNfpzSuBtQ+E4U4+RcFETW2GgBApX3sXmKWiRIRZU9qnAiMihOFuCUZUOOXxaAmh5JxQr0BkBUZnjBPryEiyobUpMro7WaF2LQaSO+TpDevTtxL8D6CCgGTRJRXqSvEo7ebFerkHxh7KoE2+QdYJkpElE2pR8aPiRMFuCUZULeZVduqAYwTJ3gTQESUFemVRInG1Ymj5QuxaTWgjkuAuhCux4hEgisYCyIiRcZ9LVEuMElEeZU++ddON1PLQgt18g9An/y7U46ulBO9iIZCQ3kbFxFRqdEWExRFGdO4ulgXExgniIiyQ1ucleIyglEJoijoMaIQm1YDgEE06L2S3BliBLecUb4xSUR5ldZropgm/4mgYzKIsJuNkOXkzQtXiImIskdbTIhIMuKyArNRhNmoTl+KYTHBZTMCAuALS8nTa1hxSkSUFVpCxZuoxHFbjBAT7SoKtZIIGLuQ4GW1KRUQJokor7TJf1SSEYvLMBoEWIpg8p+6x1lfJQ7yVAIiomwbfQKm1o8IKI7FBKMowmk2qpVQYS4mEBFlk5Yk8uhbzUz69wo5SaSNLWOSiPcSlGdMElFeZZr8a82qC3nyr60QA2Mv7txGQESUPdpiwugtyUARLiYwThARZY2iKHrSXT/ZrEiSRNpCgt1sgEEUEIrFEZXUky+53YzyjUkiyitt8j96qxlQ2JP/CkvF2IZzieAUkkIIS+G8jY2IqJRoiwkZ40SRLSZocWI4NAwl0ceOiIgmJiSFEI1HAaRsN7Oq11uDYCjoGKEtJAiCoI9ZjxGsNqU8Y5KI8kqvJIoW1+TfIBpQYa0AwKakRERTRVGUZCVRNP1kM6CwFxPcFjdEQZ1mjY4TkXgEISmUt7EREZWC1Iob7fqqXW8rrBX6NbgQpVY5ZVpIIMqnwv2XQyUvbfKvbzcrjm0EAI58vDEv7kREkxaMBSEravn96J5EZoMZZoM5b2M7GlEQx/Sc4GICEVH2pM63k9vN1BhRyFvNgPST17Qx+xLVUJ6Ih9WmlFdMElHeROIRSLI66dd6TWgrxAbBAKvRmrexHQvt4p4xScQyUSKiSfNH/frXgVHbzZxmp97DrlBpiwmZjjjmYgIR0eSkVhJ5EwcDaFu3UpMwhchpdsIgqIvj2pi1nyEaj7LalPKKSSLKm9TJfzCxjcBuLp7Jv7aX2GEZ23COK8RERJOXHifUxQR7ouLUaXbmZUzHY8xiQpiLCURE2aIliSJSHOFYHEaDoMeIQq8kEgRBb13hSiSJfCkLCZ6wJy/jIgKYJKI8Slsh1ib/luKZ/GsrxBkbznGFmIho0jLFCUcRxgm72QCjQUQkpt7IAFxMICKarOTJZskqIm2RudCTRIB6EA6Q3G6mVRIBPOGM8otJIsqbQDSgfx0c1WuiGCb/qWWsYxrOcYWYiGjSArGUOJGh4rTQpZ5eU6FtJ+BiAhFRVmiJFL0fUeI6CySvv4VMS2Rp4/alVJt6Iqwkovxhkojypui3EdjGJom0IOUJexCX43kZFxFRqdDiRCwuIyrJMIgCLEZ16lIUcYKLCUTH5KabbkJ9fT1OPPHEjN9ftWoVKioqsGTJEixZsgTf/e53czxCKjSKouhJIm0rr3adBYqkkiix3cxuTrauiMXV1hWsJKJ8ykqSiBd2mght8h+XFYRicQgCYEskiRxmRz6HdkysRivsJjuAsZN/BQov7kQJjBE0UVqc0BcSTAZ9K4HDVPhxItNighYnvBGvfngDUbm78cYb8eKLLx7xOeeeey42bNiADRs24K677srRyKhQBWIB/Ro6+mQzk2gqihihJbIEQYArcShD6oIzUb5kJUnECztNhDb5DyUm/zaTEWJi8l8MK8TAkU84Y5KISMUYQROlxQntZDN7YhINFEecMBvM+jgrEjcvjBNEY5133nmorq7O9zCoiKRu2U3tSQSoyZdCPwAHSPYkAgCXLf2EM8YHyqesJIl4YaeJSK4Qa30mDPr3imHyDyRXACrsGY435lYCIgCMETRxoyuJHCUWJ3gTQHTs3nnnHZx88sn46Ec/iq1bt+Z7OJRnqfNs76jtZsWw1QxIH6fbqi4kaH2JGB8on3LWk4gXdhpNa0gaGNWPCCieyb+2lUBbufCGY1AUBQAv7kTHgzGCMtEOOBjdtBooojgxquLUm7qYwObVRMdk6dKlOHDgADZu3Ig77rgDl19++bjPXblyJdrb29He3o7+/v7cDZJySrt+KoqiJ9/dietsMTStBgC3xQ0BasWTy5peSRSSQojGo3kbG5W3nCSJjvXCzot6edFXiLWTzYpsGwGQXAEwG0XYzQbEZQWBiJr04uSf6Nhw8k+ZxOU4grEgAOjXVbsluZhQDL3rgLGn13jDEuTEYgIrTomOjdvthtOpzg2XL1+OWCyGgYGBjM+97bbbsHbtWqxduxZ1dXW5HCblkHb9DMdkxOIyzEZRP9ig2lYc1csG0QCXxQUg5YQzVptSAchJkuhYL+y8qJcPRVGSvSaKuZIo5eQaN0+uIZoQTv4pk2AsCAVqMkWrJHIkKolsRhuMonHc1xYSbUXbaBDhsBghKwr87DlBdFz6+vr0Su01a9ZAlmXU1NTkeVSUT9pirLbVzG016X2IUufnhU5fSEj0rdMqiQA2r6b8yckMq6+vDw0NDRAEgRd2AqCWUMqKesTj6Mm/STTBbDDnbWzHY/TJNX2eMDyhGJqrbJz8Ex0jxgjKRFtIAFJONyuiEzA1qTcrFTYTAhEJnlAMbpuJFadECddeey1WrVqFgYEBtLS04Dvf+Q5iMfXm//bbb8dTTz2FX/7ylzAajbDZbHjiiSeKojExTR1tMVZbnNW29ALFs90MSDav1rabaT2JAC4kUP5kJUnECzsdr9TJv15JlNhGUCxVRIB6YRcgQIEy5oSzYCyIiBSBxWjJ5xCJ8o4xgiYiPU6kn25WTHEitTFphc2EnpEQPKEYWsGKUyLN448/fsTvf+lLX8KXvvSlHI2GCl0sHoM34gWQ7POmbdcCirOSyGkxQhAAf1RCXFZgEAV4IqwkovzISpKIF3Y6XmkrxJH0SqJimvwbRAPcFjc8EQ8qrJlPrmlwNuRreEQFgTGCJiJTJZF2ulkxxYkKawVEQYSsyPpNjBYnwlIYYSkMq9GazyESERWV1AobfbuZLXkfYTKYMr2sIFVY1UoigyjAaTbCF5HgD0uosJtYSUR5k7PTzYhSaSfWAGO3ERTT5B9I3Us8NknEVWIioonRTsBUFGXM6WbFFCdEQYTb4gaAMRWnAA85ICI6Xqnza09IjQ/a9bWYqoiA9GpTly15WjLAnkSUP0wSUV5k3EZQhJN/ILnvucLOyT8RUbZocSIalyHFFRgNIsyJk2uKLk5Yx48TXCkmIjo+qfNrfbtZIsFSLCebabSeRADgtmrNqxNJIm43ozxhkojyQpv8S3EZUUmGKAqwmop78u+0GCGKAoJRCbG42pSbk38ioonR4sTorWZAEcYJbTFBWyVmxSkR0YRp101FUdJONwOKq2k1kNxuBgDOxM8QSLTi8Ef9+kE/RLnEJBHlxejJv91k0BvVFtvkXysTFQVBD1DaDQAn/0REE6PHiYi2JTnZRrFY44TDbIBBFBCKxRGR1J+LFadERMdHu24Go3HEZQU2k0GvNC227WZmg1nvS+dKHM7gC6tJIlmR03ZfEOUKk0SUF9oFb/RWM6C4jjYG0lcsRvebYCUREdHEjI0TyUoih6nI4kTipkUQhJRqIvXnYpwgIjo+2iKsZ9RWM6D4KokA6H3rnIkkkT9RSQRAP8WNKJeYJKK8GLONwFK82wjSjzdWL+5a0BoODUNRlHwMi4ioqJVunEhfTGDFKRHRsVMURa8kGr3VDCi+SiIgJUmU6EnkDzNJRPnFJBHlhXZqTSlsI3CZXTCK6vhHT/5jckz/WYmI6NhIsoSwFAYABCPpFacChJKrOOViAhHRsfFH/YjJ6vUz2bRajQ9G0Vh09xHA2EoiHyuJKM+YJKKckxUZgaiaOMm0jaDYLu6CIOgnE+g9iVJWAHh8JRHR8dFihPq1tpigxgm7yQ5RKK7pi8PkgElU44MWJ3yJFXBJlriYQER0jFK36HoS23a17WZV1iq9x2kx0ZJEdrMBoiggHItDShyCwyQR5UNxzbKoJARjQShQV03DMXXyb0tM/q1Gq16VU0y0rQQu69iTa9hvgojo+ARjQf3rUCw9SVRsVURAYjEhcYKNSzvimHGCiOi4pV4vte1mFUV6splGSxIJggCnOb0vEZNElA9MElHOpXbpTz3dDCi+KiKNliTSyl21FWKAk38iouOVliRKxAmbSb2+2k32vIxpspJxYmzFKeMEEdGxSUsSjWpcrVX2FxstSQQk+xJpJ5wxSUT5wCQR5UWruxVV1ipEJbUk1KqtEBfZiTUabfJvMxlgNAiISLJ+vDEn/0RExyc1SZSsOFWnLEWfJNK3JXMxgYjoeHkiahsHWVH0RIo7kVhJPSSgmKQmt0afcMYkEeVD8e3roaLX6GzEzUtvBgC8sPp1DEtDuGXJyWitEYuuz4RGC0qCIMBlMWE4GIU3JKHOZeDkn4joOKUmiYKx0qoksppEGA0iopKMcCwOq4lxgojoWGnXS39YgqwocJiNMBrU+wdtW2+xSa0k0rYkpyaJZEUu2nskKk7820Z5NRKMQYAJs2oaMaNyBlorWvM9pAlJXbnQSl61LWec/BMRHR8tSSTLCiKxOCAAFlNpVBIJgqCvemur4IwTRETHRjsQRqvG1ObdQPFWElmMFlgMFgAplUSJ+JB64A9RrjBJRHmjKAqGAlEAQLXdnOfRTE5akkhrSpoy+efxxkREx05LEmlNq21GA8TEiTXFniQCxm45Y5KIiOjoFEXRr5cevR9RcmNMsfYkApLVRK5RiwgAt5xR7jFJRHkTiMYRjcuwmQz66WbFyml2wiCoP8PoE85icgwhKZS3sRERFRvtSHitH5HVlIwRpZAkGn3CmSfs4WICEdFRhKQQYrJ63fSG1CSKdrKZQTAU7QE4QDJJ5LSoP48/kuxbxyQR5RqTRJQ3Q/5EFZGjuKuIAHX7AE84IyLKDq2SSD8BM2UhoVgPOHCYHDCKanxIbktWb3JiciytDxMREY2VdrLZqO1mldZKCImK02KkJ4lG9SQCmCSi3GOSiPJmKFg6SSIg08k1PN6YiGgitIRJKVUSpS0m8IQzIqLjpvUjApKVmBWJJFGxNq3WaEkiu9kAQRAQjMYhyTIAJoko95gkorwZCkQAlHCSKMTJPxHRROiVRFpPInPxJ4mAZJxIbjfjYgIR0bFKvU7qPYmsyUqiYqYliURBgDMR8wIRNQYySUS5xiQR5c1gCW03A5LByWExQBQFhGJxxOLqCgAn/0REx0ZRlGTj6sR2M1sJVBIBKYsJo07BBBgniIiOxhNRK4nisgJ/VIIgJLdnFXPTaiCZJAJStpwldiUwSUS5xiQR5c1wiW0308pcBUGAy8LjjYmIJiISj0BW1AR7eFQlkUk0wWQwjfvaQqfdxDjMBhi4mEBEdFy066QvHAMUtcmzQVT7EJVKJRGQbF7tSzSvZpKIco1JIsqbwUBpJYkyHm8c4vHGRETHI7WBc3BUJVExVxEByTiRupjAOEFEdGy062Ryq5lR/16p9CQC1F0JQHK7mS/q4wmYlFNMElHeDJdwkkjvNxFOTv55cSciOrpANKB/PbqSqFSSRADgsqUfcsAkERHRkWmNq7Xrpta0Gij+SiKr0aqfgOlMLCIEEiecSbKEsBTO29io/DBJRHkzVGJJIpfZBYOg3siMPt44Go8iJIXyNjYiomKRqZLIXmKVRECy4tTHxQQioqOKSBF9Lq1VYGrzbQECXGZX3saWDYIgwGl2AgAciSSRP5I83MAX9eVlXFSemCSivCm1JJEgCHqpa6YTzlKP7SQiosxSk0RaJZE1UUnkMDvyMqZscZqdycWEUSecxeQYFxOIiMahNa0Gxp5s5ra4YRANGV9XTLRE1+hKIgDwR/15GROVJyaJKG9KLUkEJJuSJrebJS/uqcGNiIgy05JEiqIgFCutnkSpiwkubTEhzMUEIqKjSd2Sqy3CatvNir0fkcZlUZNEWiVRIFFNCwC+CCuJKHeYJKK80ZNE9hJKEmmVRBmON+bkn4jo6LQkUSyuIC4rMBpEmAzqdKXYk0RAcjHBbdNOwUyJE1xMICLKKHUerS3CatfRYu9HpNEqiZKNqyV9GzK3m1EuMUlEeRGLy/CGJYhCetO5YqdN/p0WIwQB8EclxGX14s7JPxHR0WlJotCok82AEkkSjd6WnFpxysUEIqKMtEqiWFxGMCpBFAW94kabfxc7rZLInFgcicVlROMyAFYSUW4Zj/4UouwbDqpVRFV2M0RRyPNoskeb/BtEAU6zEb6IBF84hkq7mZN/IqJjoCeJYiWaJEpbTBAQiEiQZBlGUeRiAhHROE5qOAlVtips7e2FSZZQ7ZBQaa2AL+IruUoiQRDgMBswEpIRiMRhMRpYSUQ5xSQR5UUp9iMC0lcy3DZTIkkkqUkiTv6JiI4qmSRSK2xs5hJLEiUWE0RRgNNihC8cg1+LE1xMICLKqNHZiEZnI3zew3DIIk6vr8XXzlyGuByHgtI4GVKrJALUvkQjoRgCEQnVDjMriSinuN2M8kJLElWVWpIopXHe6KaknPwTER1dIBYAAISiaol9qVYSASknnCW2nHExgYjoyDqH1YWEliobAMAgGmAUS6PuQaskApLNq/2JE85YSUS5xCQR5YWWJKoptSRRpsl/KHlxl2Qp4+uIiEhVLpVEQEpfohAXE4iIjkXnkJYkKv54MFpqJZFTO+FMSxJFfHoTa6KpxiQR5cVwiVYSmQwmOEwOAMkTzlKPN2apKBHR+OJyHGEpDKBMKolGnXDGxQQioiPrGg4BAFqriz8ejGYxWGAS1fsHh54kUvvzxZU4QlIob2Oj8sIkEeXFYIlWEgHJVWKXlccbExEdj9QJ8OhKIpvRBlEo/mlL6mKCK8MJZ1xMoHJ00003ob6+HieeeGLG7yuKgi9/+ctoa2vD4sWLsX79+hyPkArF6O1mpUQQBDjNTgCAw6LGPn+U8YFyLyuzLV7Y6XjplUT2EkwSWUYdbxxKXty14zuJyg3jBB0LbasZMLaSqBSqiDTaYsLo7WYA4wSVpxtvvBEvvvjiuN9/4YUXsHv3buzevRsrV67E5z//+RyOjgpJ51CikqgEt5sByS1no7ebAexLRLmTlSQRL+x0vPRKImcJJolGVRL5IxLkxB5i9pugcsU4QcciLUk0qpKopJJE2mKCLb1xNcCKUypP5513Hqqrq8f9/rPPPovPfOYzEAQBZ5xxBkZGRtDb25vDEVIh8IZj8IRisJpE1JbgPQSQbF7tyJQkYiUR5UhWkkS8sNPxGg6WfiWR0SDCbjZCVhT9As/JP5Urxgk6FulJotKvJHKmLibIXEwgGk93dzdaW1v1P7e0tKC7uzuPI6J86EpUEbVU2SEIQp5HMzW0SiKHORkftIbVrCSiXMnJeYHjXdibmppy8fFUgAb9apKouoR7EgHqCWfBqARvSILLauLkn2gcjBMEAPNq5uHLy76MYCyIx1e9BHs8iOVzF8NojKHGVpPv4WWNvpgginCYjQhEJfgjEtw2ExcTiCZp5cqVWLlyJQCgv78/z6OhbNL6EbWWYD8ijVZJZDaKMBtFRCUZEUmG1WRgJRHlTE6SRMeKF/XyoVUSlWSSKO3kGhP6vGF4wzE0w8bJP9EkMU6UNqNoRLWtGi5zJSLhRlgF4MNt50EUS2vFOG0xwaYmibzhmJok4mIC0RjNzc3o7OzU/9zV1YXm5uaMz73ttttw2223AQDa29tzMj7Kjc6hRJKoBE8202iVRIBaTRSVoghEJFhNBvij/jyOjMpJTo4JOdYL+2233Ya1a9di7dq1qKury8XQKA8URcFQoISTRCmT/9EnnHnCHr1klIiSGCcoVeqW5FJLEAHpiwnaCWe+MLclE41nxYoV+O1vfwtFUbB69WpUVFSw0rQMdQ2XdtNqIFlJBKT2JYoDALwRb17GROUnJ0kiXtgplS8iIRZXYDcbYE30miglDpMDRlG9qI8+4Swmx9J6bhCRinGCUmkLCVUluJAAjN6WnH7CGRcTqBxde+21OPPMM7Fz5060tLTgwQcfxP3334/7778fALB8+XLMnj0bbW1tuPXWW/GLX/wizyOmfOhKbDdrKeXtZimVRE6Lep/ki6jxgT2JKFeyst3s2muvxapVqzAwMICWlhZ85zvfQSym/mW+/fbbsXz5cvztb39DW1sb7HY7Hn744Wx8LBWp4RKuIgIAQRBQYanAYGgQblti8h9OHm/siXjgMDvyNTyivGCcoONRytWmQHIxQZKlMSecaYsJjBNUTh5//PEjfl8QBNx33305Gg0VKq2SqKVMKomcFvU+QjsAxxfxQVZkiEJO6jyojGUlScQLOx2PwRKf/ANApbVSTRLp282Sx1eOhEcwzTUtX0MjygvGCToeepKoBE/ABNS/75XWSgwEB1K2myUXE0bCI0wSERGN0j2iJomaS7iSyGK0wGq0IiyF9RMwtfsIBQr8UT/cFnc+h0hlgGlIyrlSryQC1CQRkOw14Q3H9O0DI+GRPI2KiKg46HHCWfpxQltM0LabAYwTRESjecMx+MISrCYRVXZTvoczpbQkkDPRk8gfSS42sy8R5QKTRJRz5VJJBKjHV1pNBsRlBcGo2nSOk38ioiPT4kRNGcQJbVuyLyJxMYGIaBzdia1mzZU2CELpHWiQSksSaQfg+MNMElFuZWW7GdHxGC7xbQRAcvIPqKvE4Vgc3nAMDotx/Ml/PA4YSq+RNxHR8dLiRFUZxAmTQYTNZEAoFkcwGs8cJxgfiKjM9ehbzUq3H5HmuCqJnnkGEEWgsjL5X3MzYwZNCpNElHNDZbSNAFC3nB32ReANSWiqOMIK8TPPALt3p1/kTzkFaGiY8vESERUSvZKojOJEKBaHJzTOYsJvfgMMDKTHh2XLgIoKEBGVA70fUWXp9iPSaEkiu9kAURAQisUhxWUYDWJ6kkhRgC1bAElKf4NvfAOwlf7viaYOk0SUc6XekBQYVUlkS29KOhIegaIoY0tlPR4gHAb6+tT/AGDuXCaJiKjsDAfLp5IIANw2Iw77ks1JxySJRkaAYFD9r6dHfWzp0pyMk4ioEHTrJ5uVfvJDSxIJggCHxQhfOAZ/REKl3ZyeJAoExiaILBbAas3haKkUsScR5VypH20MAE6zEwZBLfPUm5ImJv/ReBQhKTT2RSMjYx+rrJyiERIRFa5Bf+nHifRtyclDDoDkYgIAdauZzzf2DVhFRERlpCtRSTStsvQTIKmnl7lGbTlLSxJluneoqABKvGcTTT0miSjnhoKlP/nXjjcGMO7xxmkkiTcBREQJw2UQJxwmB4yiOvl36cccq3EiJscQjAXVJ3o86paCVE4nYCrt032IiFLpPYkqy6cnEQA4RzWv9oQ9ySdygZmmCJNElHPlUEkEHOfxxl7v2JsAlwswckcoEZUXRVHKIk6kLibolUSh5LYBPU7wJoCIKHm6WRlsN6uwJBeJtebVvkQlkS/qg6zI6jcZH2iKMElEOVcOk39g7PHG3vARjjfmRZ6ICIBaUh+LK7CbDbCaSvt0lrFxIsNigseDMRgfiKiMRKQ4DvsiMIgCGlyWfA9nylmMFlgM6s85upJIVmQEogH1ibx/oCnCJBHlVFSS4QtLMIiCvnJaqrTJv8UowmwUEYvLiEhq5v+YkkTcakZEZahcFhIApGxL1rabZVhM4E0AEZW53pEwAKDRbYXRUB63r9qWs9E9iYCUvkRcRKApUh7/yqhgjOgn1pggiqXdVE2b/AuCAJdF20qQbEqahjcBREQAyjNJZDUZ9MWEcGzUYgLjAxGVuW69H1HpbzXTaEkivZIoU5KIi8w0RZgkopwaLMPJP6AebwwkTzhjkoiIKLNyTBIBmU84U78YGftC3gQQURnRk0Rl0I9Ik6wk0g7AGZUkUhTeP9CUYZKIcmo4oFUScfKvpDaq5kWeiAhASpKo7OJE+glnrCQiIlLpTavLsJLIbjFAEAQEYxLisnrv4I14gWAQiMXSX2QyAfbSP/2Nph6TRJRTWiVRjbP0J/9Os3PM8cbadrNoPJo83hjgTQARUUK5VhK5Rp1wNhIegSJJ6umXY15YOfYxIqISVc6VRKIgwGE2AAoQSGw580a84987CKXdzoNyg0kiyqnhYPlUEgmCoB9hqZ1ck1oq6okkms3F44DPN/YNuJ2AiMrQkBYnyiBJ5DA59MWE0SecxeQYgoN96paCtBc51NViIqIyoVUSTSvDSiJgbF8ib8TLptU0pZgkopwa8Ccqicpg8g+kHG9sTW9cDaRsJfB6x94EOJ28CSCisjRYRnFCEISUOJHeuw4AfIc6x76INwFEVGa6RtTq+9YyrCQCkiecaYvNnoiHuxBoSjFJRDnV71OPsKxzW/M8ktyosGqVRInJf8rJBOw3QUQ01mFfBABQ77bkeSS5MbriNHUxITDQM/YFjA9EVEakuIyeEfX+oWwriRLNq7VKIl/EB2V4eOyLuAuBsoRJIsqpw97E5N9VHpN/bYXYZjLAaBAQicURkeIAAE84USbKJBERke6wV70ZqHeVx2KCFidcoxpXA0BooDfDCypzMCoiosLQ5w0jLitocFtgNRnyPZycsRqtMIlqckjfbpaoJIorcUQGD499EeMDZQmTRJRT+gpxmSWJBEEYc4QljzcmIhqrv0zjhL6YIMn6YkJk4FCGF1TmbnBERHnWOaT2I2qpKq9TuwRB0KuJnNp2s0hyESEy0Df2RYwPlCVMElFOHU5sN6svl+1mlmSyZ/RWAm43IyJKF4vLGAxEIQpAjbM8kkTatmRBEFL616mLCdEhrhQTUXnrHC6/fkQaLUnkGlVJBEVBdKh/7AsYHyhLmCSinInLir5CXFcmk//0443Tm5Lqp5sxSUREBCBZRVTjtMAglscxvulxQqs4VRcT5KGhDC+oHPsYEVGJ6kqcbNZaXV6VRADGVBJpPYmMUQnR4KiTkY1G9fRLoixgkohyZjAQgawA1Q4zzMby+KvnsrggCurP6h41+Q9LYYSlMJNEREQJ2pbkhjJpWg2kJ4n0E85CMQiygrhnGMro0y+5HZmIykjXkFpJ1FLGlUQOixEQgEBUgiwrsPrDiMQj6U+uqACE8lhcoalXHnfqVBDKrWk1AIiCmHJyTXLyrxkJDgE+39gX8iaAiMpQuTWtBgCn2QmDoDZj1bclhyWYgxHIcQmSnDwVE3Y7YDbnY5hERHmhVxKVWU8iIJkkMogC7CYjFEVNFFkDEUSkUUkiLjBTFjFJRDmjbzUroyQRkOw3ofeaCCcn/N6BbkCW01/AmwAiKlPldrgBoC4maDcCyTgRgzWg/i7CUjj5ZC4gEFGZ0XoSlVvjaiCZJAJS+hJFJFjGqyQiyhImiShn9KbVZbRCDCS3EiQbkiYriQL9PWNfwIs8EZWpckwSAck4od0E+MISLAE1ZqbdCDA+EFEZiUoy+rxhiALQVFle9w9AepJI70sUHqeSiPGBsohJIsqZQ9p2szLqNQEkJ/8OiwGiKCAUiyMWV6uHggO9Y1/AizwRlSltu1ldmZyAqdEXE1JOwcxYScTtBERURnpGQlAUoKnCBpOh/G5bMyWJfBF1ESESj6T3rOP9A2VR+f1ro7zRKokaymyFWOtJJAgCXNoFXmtePXgowwt4kSei8qQ3ri63OJHYluwwG2BILCYYvWofDm43I6JypfUjKsem1QBgN9n1nnVOa3olkazI6T3rGB8oi5gkopzRG1eX6QoxkLrlTL2oR4f6x76AF3kiKlP6tuQyjROpiwnKSAAAk0REVL7KuR8RoMYErZpIiw3+CLcj09Rjkohyptx7TQApJ5wlKomk4cGxL+BFnojKVDmeggmMjhPqYgJGWElEROWtK5Ekaq0uz0oiILnlTKskCgajMIeiAJDel8jtHvNaoolikohypl9PEpXXCrHb4oYAAQDgGnXCmeD1ppeKArwJIKKyFJcVDPjVOFHrLN8kkRYnDB715ohJIiIqV51D2naz8qwkAlKSRIlKInhDEBKtiPRKIqcTMBrzMDoqVUwSUU4oipKyjaC8Jv8G0aBf4CsSK8SeoFpJZA1E0m8AAN4EEFFZGvRHICtAtcMMs7G8picuswuioP7MFTYjLFIc8cRKsSRLiMVjgMEAOBz5HCYRUU5p281ay7QnEQC4LC4AySSRwRvSG1brlUS8d6AsK69ZGOXNcDCGWFyB22qE1WTI93ByrtpWDQCotKtJopFgFMaoBGNUQjAWTD7RYFBXA4iIyky5bkkG1MUErZqo0m6GOxxFOCbr3w9JIfUmQBDyNEIiotzTGle3VrOSyGgQYTMZ4ApHEYurSaJoXF1MYJKIso1JIsqJcm1GqtGSRFV2MwBgJBSD2acGvlAslHwibwKIqEwxTiQXE9yRGMKxuP69YCzImwAiKivhWBz9vghMBgENZRoXgGSSCFCridyRGKJxdRFB327G+EBZlpUk0Ysvvoj58+ejra0N99xzz5jvP/LII6irq8OSJUuwZMkS/PrXv87Gx1IRKddmpJoaew0AwGoywGoyIBaXIY+oFUQhaVSSiKgEMU7Q0ZR9nLCpcaLSZlaTRFJc31IQioUYH6jkMU5QKq2KaFqlDQaxfBdQ05JEViPc4Riikpok0ltWMD5Qlk26w1U8HscXv/hF/P3vf0dLSwtOO+00rFixAgsXLkx73jXXXIN77713sh9HRaqctxEAyck/oK4S93nikAb9AJC+3YwXeSpBjBN0LMo+TiQWE8xGEQ2yDEUBIpIMq8mQ3G5GVKIYJ2g0rR9RSxn3IwKACkvy2u+2muCORBERk0kiRVEgMD5Qlk26kmjNmjVoa2vD7NmzYTab8clPfhLPPvtsNsZGJeSQl9sINFU2dctZPBH8xmw3IyoxjBN0LPQ4UaZJotQ40SgnbgASW8643YxKHeMEjab3Iyrjk80AwGl2wiiqdR0VNnU7ciTRs05WZMTkGOMDZd2kk0Td3d1obW3V/9zS0oLu7u4xz3v66aexePFiXHXVVejs7Jzsx1KR6RlJlIxWlGeSqMpWBQFqqazWvFoZDgAAYnJMPbkG4EWeShLjBB0LPU5UlueqcWrFaYOeJFL/NxQLQXG7M76OqBQwTtBoXUOsJAIAQRD0gw3cVqO+HVnD7cg0FXLSuPrSSy9FR0cHNm3ahA996EO44YYbMj5v5cqVaG9vR3t7O/r7+3MxNMqRzsRqQEuZrgYYRWPayTUAIHiSFUR6XyJe5KlMMU5QuceJCmsFDIJ6+mdtXAKQrCSKK3EEHKa8jY2oEDBOlBeebJak3UPUGgSY47JeSQQAIUiAnb8jyq5JJ4mam5vTMvldXV1obm5Oe05NTQ0sFrV8/JZbbsG6desyvtdtt92GtWvXYu3atairq5vs0KiAdGn7iqvLdzVA6zdRlagkMniTvYj0LWdMElEJYpygo1EUpezjhCiIqLZVQ5AVVCdWiUMpJ5wNGmP5GhrRlGOcoNHYkyipyloFAKhT1JiQerCB3yrwZGTKukkniU477TTs3r0b+/fvRzQaxRNPPIEVK1akPae3t1f/+i9/+QsWLFgw2Y+lIqIoCrrLfIUYSD3e2AwoCgyBSPLkGq2SiNsJqAQxTtDRDAaiCMdktd+CtXwrZqpt1TCHorCZ1OmZVkkUs5gwJPnyOTSiKcU4QaN1JrablXtPIkBtWwEArmgcRlGAogCxuHoP4bMwQUTZN+nTzYxGI+69915cfPHFiMfjuOmmm7Bo0SLcddddaG9vx4oVK/Czn/0Mf/nLX2A0GlFdXY1HHnkkC0OnYtHviyAiyaiym+C0TPqvXNHS+k2YjSJqAQiyop9cE4wFAasVMJvzO0iiKcA4QUfTyd4TANSK055gBFajuu0sIsmQFQURuxmDocE8j45o6jBOUCp/RMJwMKbOmZ3leZhBKm27mSUQgWAyQIpIiEhxmI0iRkzxI7+YaAKycse+fPlyLF++PO2x7373u/rXP/zhD/HDH/4wGx9FRaiTe4oBJLebAUCzOv9HOBZXjzeOhQCXK08jI5p6jBN0JDzFRlVjq4ElGIEoCrAYRUQktfdExG5BIMgkEZU2xgnSdKVsNRNFVspo283MwQhEo4hARD3YwGUFhk1SnkdHpSgnjaupvHVxTzGA9OONmxLxTju5JhgLQnE68zEsIqK869K3JDNOWIJRAIDVpK4mhGNxRO0WVhIRUdnoGuLCQSptu5klFIXFpFWaqhVEw6Y44jKriSi7mCSiKdfFfkQA1FJRUVD/yTVAO944eXJN2M6tZkRUntigVFVjr4E5GAGQTBKFYnFE7GYMhYb0PnZERKWMMSGd1WiF1WiFORiB1aj1rEvcS9hM8Ea8+RwelSAmiWjKaZVErWV+oddOrgGAeq1hdcrJNV4zJ/9EVJ541LHKZXbBEVLjgjWleXXUboEkS/BEPPkcHhFRTjAmjFVlrYIlEBlTSRS1WzAcHs7n0KgEMUlEU46VREm19loAQN2oSiIAGOLxxkRUppLbkss7TgiCgOq4WlVqS6skUhu3DgQH8jY2IqJc4WEGY1XZqtTtZqMqiSIOC0bCI3kcGZUiJoloyvFCn6QliaoTyaGIJEOW1QqiQSaJiKgMybLCnkQpqmJqcijZk0hGJLEdmUkiIioHh7xhAEBTBWOCptLkgimsJokEALG4eg8RtZkxHGIlEWUXk0Q0JcKxOFbtPIyIFEf3CCuJNDU29YQzaziavAFIlIseFkN5GxcRUa55wzG8sbsfh30RRCUZ1Q4zHJasHLpa1Cqi6skGFqMIQVBvBHxm9ffCJBERlYN+n9qbrd5lyfNICkeNbIWgqBWn5kQ1kc9ggGwQud2Mso6zMZoSj685iO88tw3nzatDLK6g1mmGzWzI97DyTqsksgSjsJpEhGNxhKJx2M1GHBKDeR4dEVHu/PzV3fjVG/tx3rw6AKwiAgBEo3AqJgDqjYDNZIA/JuNQHGgEk0REVPoURcGAXz3lsdbJJJGmOmaEL/G11WRARJIxYlTvrVhJRNnGSiKaEgcTW8xe39UPAGhmFREANUkkxmWYIjG930Q4FociAIeFICRZyvMIiYhyY3ScYJIIgM8HuykZL60mA/xmI4ZD6nZkJomIqNR5QxKicRlOi5ELzCkqY8nfhdaXaDCRJGJPIso2JoloSvjC6cmOcj/ZTGMz2VAZTe83EYrJiNrMUEQBQ6GhfA6PiChnxsYJLibA64XNmIyXNpMBPrMJwwF1Vd0f9SMshfM1OiKiKdfvV7ea1XGrWRpXNPm1dg8xKKq38oFYABEpko9hUYlikoimhDex6qlluqfzCEtdo+IAkH5yTdTGk2uIqLx4w2qc0I5651HHAHw+GEQDrEYrgESSyGLCUDB5dzAYHMzX6IiIppzWj6jWac7zSAqLMRCCxaDeL2j3V/1C8laefYkom9iTiKaEtkL83csWYWefH9efOSPPIyoc9fHE5N+c3G4WtvHkGiIqL1qc+Ok1S7Bm/zAuWzItzyMqAD6144TdZEdYCsNqNsCvGDEcSJ5+ORAcQLO7OV8jJCKaUqwkGofXC5vJhkg8AktiofkwBGiRczg0jEZnY/7GRyWFSSKaEtoK8YImN645bXqeR1NYquNmDAEwigIMooC4rMDLk2uIqMxoFaenzazGR05syvNoCkQiSaRtObOaRPgFE0ZCUciKAlEQGCeIqKQNJCqJ6ti0Op3Pp1eZWhOVRL0QcLKiQBAEVhJRVnG7GU0JLUnktpryPJLCUy2pCSHt5BoAGEiUi3LyT0TlQFEUeBOVRC7GiSSvFwD05tVGUYTksCIuK3rlFeMEEZUyrZKIJ5uN4vPpCwhGgwiDKGDEYEAoFgfAE84ou5gkoinh0yf/LFYbrSKa/GenNZ47LAgA1Mm/oih5GRcRUa6EYnHEZQVWkwizkVMRXcp2M42hUv1aa17NJBERlTKtJxG3m43i88FmSh5sYDWK8FuM8CSqcllJRNnEmRllnaIo+jYCrhCP5QjHIUBNCmmVRH2JSqJoPApf1Je3sRER5YI3pC4ksNp0lAxJIlPidFCtefVQaAiyIud+bEREOTDASqKxolEgHNa3mwGA2WxEwJRMEvGEZMomJoko6wLROGRFTYBwhXgs0R/QVwJsZvX30xNPfp8n1xBRqdO2JLPaNIWi6Ekis8EMg6AuIpir1RMxtUqiuBLHSHgkL0MkIppqrCTKwOMBkOxXBwBxpxUQBH1hfiQ8wgUEyhrewVPW+Tj5H58kASMj+iqxtt2sO+WaPhhikoiISpsWJ9w2VhLpRkaAuLpiIAgC7CY7JLMRrorEdrNg8oQzLiYQUaka4OlmY/X3AwCMolFfQIgntiJrlUSyIsMb8eZnfFRymCSirNO3EXDyP9bgICDLaUkiv8WI/rgMSVYzRew3QUSljtvNMjh8OO2PNpMNgQo7qh1mAMBwYrsZwDhBRKVJlhUM+NVrXY3TnOfRFJBEkkg99EatJpJqnQAATyKeAmxeTdnDJBFlXfJkM1YSjZG4CdCSRKIgwF/hgKIAnsQqMVeIiajUcbtZBombAI3dZEew0gGX1QiDKCAQkRCR1EojVpwSUSkaDkYRlxVU2EywGA35Hk7hSFlE0LacyfVuAMlKIoDNqyl7mCSirEtuN+MK8RiJm4DUPcWRGheA5CoxV4iJqNR5w6w4HWNUJZHdZEegygFREFBpU1fURxKLCYwTRFSKtCqiWlYRpUtZRNCbV9dXQBAAfySGuKyejMzm1ZQtTBJR1nG72RGMqiQCgHidmiQaCiQbz0myNPa1REQlQmu0ye1mKTIliSrVptVVDvX3pDWvZsUpEZUiNq3OIB5X21UkaNvNwtVOOC0m9cyDxAI9t5tRtjBJRFnn43az8SVuAkwGE0yiOulXEuWiWiWRAoUXeSIqab5EJRG3myXIMjCQXh1kM9oQSDQmrban9yXyRX2ISJHcjpGIaIr1+8MAgDqX9SjPLCNDQ/qhBoBaSRS1mhCzmlCRWJDXtpxxuxllC5NElHVeffLPFeI0sRgwnLx4a9VEhsZEkijApqREVB68PN0s3fCwevplCoPDCWtlLQCgKtG8eij1hDP2JSKiEjPg43azMUYfamC06VWmWpJI24o8FBqCoii5HR+VJCaJKOv0bQQ2rhCnGRgAUi7cNpMNYYcl5XjjqH5h5+SfiEpZcrsZ4wSAMU2rAQB1dah11AEAqrRKopTFBG45I6JS0+/ndrMxRsUHq9GKUJV6slmlPb2SKCyFEZJCuR0flSQmiSjr9IakrCRKl+HkmkClA3azAWajiIgkIxhVy0lZSUREpczHOJFu1EoxAKC+HjW2GgDJnkQjoeRiAuMEEZUarSdRrZNJIt2o+CAIAgwNTQCAylGVRACbV1N2MElEWcejjceRoSlpsNIBQRCSq8RBNiUlotKX3G7GOAEgcyVRfT1q7ep2M4vRALvZCCmu6Ak2VpwSUanRG1czSZSUIT5YGlsAAJWJ+4eRULLKlEkiygYmiSjrktvNuEKcJtOe4ip1T3GyKSmPNyai0qfFCfauS8hUSVRXhxp7jf7HqsS2Am0xgXGCiErN/oEAAGB6jf0ozywTo04209iaZwBAWuNqWWtZwYVmygImiSjruI1gHKNWAmwmG0KV6p7i0ccbh6QQfBFfbsdHRJQjjBMpMpxsBiCtkghIaV4dSFacskEpEZWKQERC90gIJoOA6dVMEgFQTzaT5fTHnE5UVqnbzcxGEXazEXFZgT+ixlVWElE2MElEWadvI+B2syRZBjyetIdEQYS5YRqA5N7rQ96w/v0ub1fuxkdElEPcbpbC60073hgAYLMBDgfcFjeMovo70rZfHPaq2zFicgy+KBcTiKg0aFVEM2ocMBl4iwoAGBkZ+1hNDapt1foftSrT1BPOiCaL/wIp67yhxAoxt5sleTxjVwIcDkyrnQUAaKqwAQD6vGHEZXVluNPbmdMhEhHlQlSSEY7JMIgCbCZDvoeTf5luAqqqAKiLCdrNQFOFFQDQ40meXMMtZ0RUKvYc9gMA2uqceR5JARkeHvtYVVVakqhCb16tVpkySUTZwCQRZZ1PryRikkiX6SagshItbrXxnM1sQJXdjLis6E37Oj1MEhFR6fGlVJsKgpDn0RSAcW4CNNqWs1qnBSaDCE8ohkBEggCB25KJqGTs7U8kieqZJNKNc/9Qaa2EKKi38Vrzak+i119ICiEUC419HdFxYJ03ZVU4FkdEkmEUBVhNzEHqxrkJaK1o1f/YVGHFcDCKXk8IjRVW9Pp7IcmSvtWAiKgUeMOsNk0zzk2AZpprGnwRH2rttVhSNw3buhW0152Djy85gfGBiEqGVkk0p96R55EUkHHig0E0oNJaiaHQECr1Qw1i+lOGQkNoNjXnaJBUingXT1nlS5n8c4U4xTgX+XpHPcwGdQWgqVLdctbrUfsSSbKEPn9frkZIRJQTyZPNmOAAcNRKonOmn4Obl96My064DB+ZdwFMSiv2HTIwQUREJUWvJKpz5XkkBeQI8UHbclapnXCW2G4GcMsZTR6TRJRVPjatzmyci7woiPqWM63fRO9IskSUW86IqNTwZLNRjlJJlGrpdPXmYP2BDDGFqMi9+OKLmD9/Ptra2nDPPfeM+X4kEsE111yDtrY2LFu2DB0dHbkfJE0JKS7rjatn17GSSHeE+KAliSq0xtWhmH7i5WBoMBejoxKWlSQRL+qk0bYRuDj5T5cpSZS4yLe61S1nNQ4zzEYRvoikJ9vYvJpKBeMEabSTzVhJlHCUSqJUp0yvBABs7PIgKskZn0NUjOLxOL74xS/ihRdewLZt2/D4449j27Ztac958MEHUVVVhT179uBf/uVf8I1vfCNPo6Vs6xwOIRZXMK3CCoeFsQEAEA4DoVG9hQwGwKVWWmlJIovRALvZgLiswB9R78NYSUSTNekkES/qlMrHY40zO8LpNVpfIkEQ9FPOtC1nnZ5OfVWAqFgxTlAqHm6QQpIA36jm04IAVFRkfHql3Yy2eieikoytPZ4cDJAoN9asWYO2tjbMnj0bZrMZn/zkJ/Hss8+mPefZZ5/FDTfcAAC46qqr8Oqrr3KOVCKS/YjYtFqX6d6hogIQ1dv31BPOKm1q64qRRF8iJolosiadJOJFnVJ5Q9xGMEYsBvj96Y+l3ARo282A1C1napLIF/XBE+GNABU3xglKpccJNq7OfBPgcgHG8RdaTk1sOVvHLWdUQrq7u9HamjzMo6WlBd3d3eM+x2g0oqKiAoOD3FZTCrR+RHPqmCTSHaXKtMZWo3+dbF6t9iVikogma9JJomxe1FeuXIn29na0t7ejv79/skOjPOA2ggwy3QS43WrJKACr0Yp6Rz0AoMGtJokO+8L6U9mXiIod4wSlYpxIcRz9iDSLW9UFhq093uyPh6gEME4UH1YSZXCU+FBprYQA9ZCgaodaSTQUUJNEwVgQoVhozMuJjlVBNa6+7bbbsHbtWqxduxZ1dXX5Hg5NALcRZHCEfkQarZqo3mUBAPT7I3oVBfsSESUxThQ/Nq5OcRz9iDQnTlOTRFu6WWVKpaO5uRmdncn5TldXF5qbm8d9jiRJ8Hg8qKmpwWiME8VnZ5+67XYuk0RJR4kPBtGASmslgLFJIgAYDrPalCZu0kmibF7Uqfit2a9ekOrdljyPpIAcoR+RRmte7bAY4bAYEZVkBCLArMpZepURUbFinCCNoihYs18tg2ecwIQqieY3umAQBezt9yMUjU/JsIhy7bTTTsPu3buxf/9+RKNRPPHEE1ixYkXac1asWIHf/OY3AICnnnoKH/jAByAIQj6GS1kUjsWxvdcLQQBObM7cj60sHUN80PoS1TqtEBU3gsF6LGtehuVzl8Nldk39GKlkTbrWO/Wi3tzcjCeeeAK///3v056jXdTPPPNMXtRL2LYeL17ZfghWk4grTmk5+gvKxTFUErVWtKLKWoUWdwuW1s/E+/sNOK/pQqw4mb9HKn6ME6T5x47D2NbrRZ3LgosWNOR7OPk3gUoiq8mAufVO7OjzYXufF0unH/n5RMXAaDTi3nvvxcUXX4x4PI6bbroJixYtwl133YX29nasWLECN998M66//nq0tbWhuroaTzzxRL6HTVmwrdcLSVYwr8EJJ082SzqGReaPtH0ERtEIp9mN5996GRG/jLNbL2KlLk3apP8l8qJOmvte2wMAuPb06ahzcYVYdwwX+Vp7Lb5yxlcAAAe6dmLz/j3Y1uPHipNzMD6iKcY4QYBaRfSzf6hx4nPnzYbVZMjziArAMSwiZLJoWgV29PmwtdvDJBGVjOXLl2P58uVpj333u9/Vv7ZarfjjH/+Y62HRFNvYOQIAOLmlMq/jKCiKckzxoc6R3E45u86J7b1e7D3sxymMCzRJWUnX8qJOew778LctvTAbRHzuvDn5Hk5hOc6bgIXTtKak7DdBpYNxgt7YPYCNnSOodpjxqWXT8z2cwnAMiwiZLJrmxtPr2byaiIqfniRqrczrOApKMKiejpzKZALs9nFfMrdeTRLtZpKIsqCgGldT8Xp09UEoCvCJ9hY0Jo5xJ4y/EnCEm4ATm90A1Mk/jwAnolLx6OoDAICbz5kFu5lbChAOA6FRp88YDIDr6H0ktL4dW7iYQERFbmOXeh1bwiRR0nj3DkfYhq81/dZOiiOaDCaJaNIURcHftx0CAFx1KnvopAmHgUgk/TGj8Yg3Ac2VNlTaTRgKRNHrCU/xAImIpl4oGsfru9WjqD++lHECQOYqoooKQDz61GzhNHUxYWefD1FJzvLAiIhyYyQYxf6BACxGEfMb2WhZN4GtyG2JJNHuQ74pGBCVGyaJaNK29njRPRJCvcvC/cSjZbrIV1QccSVAEAQsmpasJiIiKnZv7O5HOCbj5NZKVptqJtC0WuO0GDGr1oFYXMHuw7whIKLipFURndhcAZOBt6W6CWxFntuQqCTqZyURTR7/NdKkvZyoIvrQwgaIIk8jSjPBm4ATE32JNndzKwERFT8tTnx4IU80000iSQRAX0zYwjhBREWKTavHMYH4MKPGAaMooGs4hGBUmqKBUblgkogm7eWtfQDUJBGNMsGmpFq/ic1dGV5PRFREpLiMV7czSTRGpvhwDCebaU5KxIlNXUwSEVFxSjatrsjvQArNBOKDySBiZq0DigLs6w9MybCofDBJRJPSORTEjj4fnBYjzpxTk+/hFJ4JrhRrzfs2dI6weTURFbV1B4YxHIxhVq1D75lAmHQlkRYn3j84kp3xEBHlkKIo2JhYDD2lladxpZlgfNCaV+9iXyKaJCaJaFK0LQQXzK+DxWjI82gK0AQazwFAS5UNtU4LhoMxHBgMZn9cREQ5krrVTDhCP7ayM8kk0UktFTCIAnYe8nFrAREVne6REAb8UVTZTWittuV7OIVDlgFPhgrRY7h/OKFR3Ya8o49JIpocJoloUlbtPAwAuGgBtxBkNMHtZoIgJFeJOzPcSBARFYnXEnHig4wTSYoy6e1mdrMR8xtciMsKNnPLGREVmQ36VrNKLiCk8nrVRFEqux2wWI760gVN6glx23jwDU0Sk0Q0YYGIhHf3DUEQgPPm1eV7OIVHliecJAKAU6ZXAuBWAiIqXgcGA9jXH4DbasTSxDWNAPj9gDSq+sdiAWzHt5qux4nEzRYRUbFg0+pxTKLKdGHiQIPtvV62q6BJYZKIJuztvYOIxmUsaa1EtcOc7+EUHp8PiMfTH7Na1f+OgTb538DJPxEVqVU7+wEA586rg5HHGyeNdxNwnKvpp0xXbxw2cDGBiIrMxk61AnIJFxDSTbBVBQA0V9rgthoxGIjisC+S3XFRWeGMjSZM20Jw4fz6PI+kQE2iiggAFrdUQhDUktFwLH70FxARFRjGiXFMsh+RRtuWvP7gMFeNiahoSHEZm7vVJBEriUaZxP2DIAhY0KRWE23r5ZYzmjgmiWhCFEXBPxMrxBfM51azjCZ5E+C0qP0mJFnBlm72myCi4hKOxfHO3kEAwPnckpxukv2INLNrHXBbjTjsi6DXE570sIiIcmH3YT9CsTimV9u5G2G0Sd4/6Eki9iWiSWCSiCZk92E/ukdCqHWaceK0inwPpzBNolxUwy1nRFSs3tk3iIgkY3FLBepcR2+4WVayVEkkigKWaFvOGCeIqEhsTGlaTaNMchFB60vESiKaDCaJaEJe2a4eaXz+vHqIIk8kyGiS282AZL+JtxOr8URExeLVRJy4gFvNxspSkgiA3hD87b0DkxgQEVHu6CebtXCheYxJxoeFTcnm1UQTxSQRHTdFUfDM+90AgIsX8UjjcWXhJuCCxBaNt/YMIBiVjvJsIqLCEJHi+OumXgCMExllodJUo/V7enX7YfYlIqKisKZjCEByMZQSYjH19MtUggBUHHsybW6DE0ZRwP6BAO8daMKYJKLjtrnbg12H/KhxmHHhCVwhzkiWgf7+sY8fZ5Ko3m3FktZKRCQZb+zmKjERFYfXdhzGSDCGExpdWMQtyemCQfX0y9EmmCQ6qbkCDW4Lej1hbGUPCiIqcF3DQezrD8BlNbKSaLSenrGPud2AwXDMb2ExGtBW74SiADv7MsQaomPAJBEdt6fXdQEAViyZBhOPNM7s4EEgFEp/zGSa0E3Ahxaqq/B/33YoCwMjIpp6T61Tq02vOrUlzyMpQLt2AaMrfqqr1RgxAaIo4IML1DjxMuMEERW413epi55nz6mFkfcR6XbsGPtYU9Nxv4225YwH39BE8V8mHZeIFMezG9UsNyf/R5DpIt/WdlwrARotSfSPHYcRl7mVgIgK24A/glU7D8MgCrhsSXO+h1N4MsWHefMm9ZZanHiFSSIiKnCv71Ir7c+dV5vnkRQYRckcH0444bjfakmiV936gyOTGxOVLSaJ6Lj8Yzu3EByVogDbt499fAIXeQCYW+/E9Go7hgJRrD+YoY8FEVEBeeb9bkiyggvn1/FUs9FiMWDv3rGPL1gwqbc9c3YN7GYDtvV60TUcnNR7ERFNFSku461Ek/3z5tbleTQF5vDhsf3qBGFCiwhLE72eeN9AE8UkER0zRVHwy3+qk9ur21vzPJoC1tcHeEaVd4rihFeKBUHQV4lf3to32dEREU2ZiBTHQ2/uBwBcdSrjxBh796qJolR2O9A6ud+V1WTA+YmDDrg1mYgK1cauEfjCEmbXOtBabc/3cApLpiqiGTPUGHGcTmh0wWYy4MBgEAP+SBYGR+WGSSI6Zq9sP4xNXR7UOi249vTp+R5O4RrvIm+zTfgtP3JiIwDg+U29kLnljIgK1BNrOtHjCeOERhc+vJCnmo2Rqcp0/nx1IWGStDihnSpHRFRo/pnoR3TePFYRjZGlrWYAYDSIWJxoCv4+t5zRBDBJRMdElhX8v7/vAgB84YI5sJmPv7dO2ch0kZ/kVoJTp1dhWoUVPZ4w1h5g6SgRFZ5QNI57X9sDAPjqRfMgikKeR1RgZFltWj3aBG8CRrtoQQOsJhHrDgyjc4hbzoiosCiKolfEn8d+ROk8HqA3Q4J/EvFh6QxuOaOJY5KIjslzm3qwvdeLRrcVn1rGKqJxjYwAhzKU+s+fP6m3FUUBly6ZBgD4y8buSb0XEdFUeOTtDvT7Ijix2Y2LF7GKaIzOzsynXs6enZW3d1iM+NBCtZrouU0ZjlEmIsqjjV0e7OjzodphxtltTBKlybSA0Ng4oVORNXpfIi4u0wQwSURH1ecJ4z//shUA8JWL5sJqYhXRuDJd5JuagIrJN/m+7GT1lKC/be5DLC5P+v2IiLJle68X/5uoNv3Xi0+AILCKaIydO8c+NmeOmijKkhUnJxYTNjBJRESF5fF3DwJQT0e2GHkvkSbT/cMkF5hPSZxwtqnLA4n3DXScmCSiI5JlBV/7wwaMBGO4YH4dPnkaG5EeUaabgEkebaxZ0ORCW70TQ4Eo3twzkJX3JCKarHAsji8//j6icRnXnj5db6BMo0zBTcBo582rhdtqxI4+H3Yd8mX1vYmIJsoXjuEvG9XkNe8lRolGgf37xz4+yfuHWqcFM2rsCMXi2NHHeEDHh0kiOqKH3tqPt/cOotZpxk+uOpmrw0cSiQAdHWMfz9JNgCAIuCyxSvz/Xt6FYFTKyvsSEU3Gj17cgd2H/Zhd58B/fGxy/ddK1uAgMDAquS8IwNy5Wf0Yi9GA5Sc1AQB++LftXD0mooLwl409CMXiWDarGrPrnPkeTmHZtw+QRs3pnU5g2rRJv/Wpib5Eb+/l4jIdHyaJaFydQ0H8z8vqyuePPr4YdS5LnkdU4PbtA+Lx9MdcLnW7WZZcf+YMTK+2Y3O3B//y5AaedEZEebWhcwSPvN0Bgyjg/645BXazMd9DKkyZqoiam9UbgSz7wgVtqLSb8NrOfnzv+QynqRER5VBEiuPXb6iVMjwdOYNM8WHuXHUhYZIunF8PQD2hmuh4MElEGSmKgrue3YJQLI5LFjfhgwvYhPSoxttqlsXqq0q7GQ/d2A6X1YiXth7ST5wjIso1KS7jm3/aDEUBbj5nFk5qmXzvtZKV6SYgS1uRR5teY8fK69thNoh45O0O/D7RB4SIKB9+/cZ+7B8IYE6dQ690pARFAXbvHvt4luLD+fPrYDIIWNsxhKFANCvvSeWBSSLK6K+bevHazn64rEb856UL8z2cwheP5+wmoK3ehfs/fSpEAbj3tT34567+rH8GEdHRPPjmfmzv9aKlyoavXpTdbVMlJRAADhwY+/gUJYkA4PRZ1fjhlScBAO5+biu29nim7LOIiMbTPRLCvf/YAwD47mUnwmzkrWeazk7AN6pfkMGgHmqQBW6rCWfMroGsAK/tYDURHTv+S6Ux+n0R3PXsFgDANz+6APUua55HVAT27QOCwfTHjMasHW082tlttfiXi9QbjH95cgP6POEp+Rwiokz2HPbjfxKVjN+7/ERuMzuSbdsAeVRvoIoKoGFqK3Q/fmoLrj29FVFJxpd+/z78EfaxI6LcCcfi+LenNqq7Ek5q4rH3mWzZMvaxWbMAszlrH3FRYjfIK9sPZe09qfQxSVQmej0h/PadDhzyHjmZoCgKvv3MZgwHYzinrRbXns4TCI7J5s1jH5s/P6tHG4/2hQvbcO7cWgwFovjkynewt98/ZZ9FRKVvb78fv1t9AJ5g7IjPk+Iy/r8/bkRUknF1ewsuSPQ8oHFkuglYtCirW5HH85+XLsIJjS7sHwjgU79ajcNHmQMQEWVDVJLxxcfW4609g6hxmPFtHmowliwDW7eOffzEE7P6MRctVJNE/9zVj3AsfpRnE6mYJCpxsqzgd+904EP/73Xc9exWnP+T1/DfL+3EcIZ9qYqi4ME39+OlrYfgtBjxo6sW8zSzYxGLATt2jH38pJOm9GMNooCfXrMEi6a50TEYxBX3vYWn1nUhKvE0GyI6dlFJxs9e3Y2P/vQN/MczW3DeT17Dytf3IpCh8kSWFfzwhR3Y2DmCpgorvv0xbkc+Io8n81azKY4PGqvJgPs/fSpaq23Y1OXBZfe9hZe29vHQAyKaMoqi4Ot/3IhXdxxGpd2ER29ZhqYKW76HVXj271e3I6cyGoETTsjqxzRX2rCwyY1gNI43dvOUMzo2TBKVuO/+dRv+49mt8EckzGtwIhyTce9re3DmPa/im3/ahJ196j7YQETCt5/Zop+E8p+XLkRzJS/ox2TXLiA6KulmtQJtbVP+0TVOC/7wuTPxoYUN8IYlfP2PG3H+T17DY+8e4NHHRHRUiqLgjsfX4//9fReicRlz653whGL4wd924Iwfvorv/XUbOofUrbTDgShuf3QdHnxzPwyigB99fDHc1qmrliwJmVaJa2uBxsacDWFmrQPPfOFstM+oQq8njM/9bh0u+t9/4q+beqAoTBYR0bELx+IYCR65AfIjb3fgLxt74DAb8OjNy7CgyZ2j0RWZTFWmc+eq9xBZtmLJNADAr9/Yl/X3ptI0qSYCQ0NDuOaaa9DR0YGZM2fiD3/4A6qqqsY8z2Aw4KTEqtn06dPxl7/8ZTIfS8foLxt78MjbHTAZBPz0mlNwyeImrDswjJ//YzdW7ezH42s68fiaTpzQ6MK+/gCicRlmo4ifXLUYly1pzvfwi8fGjWMfW7BAXQ3IAYfFiAc+fSr+9H43HvjnXuw+7Me3/rwFv3m7A+fOrUOt04LTZ1XjlNZKiCIrwyi3GCcK2wOv78NLWw/BZTXigetPxZmza7BqVz9+8doevNcxjF+/uR8PvbUfJzS6sfOQD3FZgdtqxH3XLcW5c+vyPfzCpiiZ48OJJ+Zkq1mqGqcFj926DL9/9yB+/cZ+7OsP4Eu/fx8PTt+P02ZWo85pwdlttVjQ5GIFMeUc40Th6xwKYuXr+/Dshm54wxLOn1eHm8+ZhXPn1qZdM97eM4DvJxacf/KJk3FiM0+9zCgcBrZvH/t4lreaaT61bDrue20P3t0/hHUHhnDqjOop+RwqHYIyiWWkf/u3f0N1dTXuvPNO3HPPPRgeHsaPfvSjMc9zOp3w+4+vX0p7ezvWrl070aGVvT2HfVhx71sIRuP4r8sW4fozZ6Z9f2+/H795uwNPretCMBqHIACnTq/Cty5ZgFOmjw3MNI6ODuCRR8Y+fv31WTuZ4HjIsoK/benFPS/sQNdwKO17jW4r5jY4Ueu04MzZNfjISepKdsdAAEOBKELROE6ZXoXGCjYqLwbFco1knChcq/cN4lO/Wg1ZAX79mXa9b4Fmc5cHD7+1H89t6kEsrsAoCjhzTg3uXrEIc+qceRp1Edm4Efjzn8c+fscdQE1N7seTEIvL+OPaLvy/v+/EgD+9ImB6tR0zax2od1lw/rw6XLSgAaFYHAcGAxgJxhCR4lg2qwZVjuw1VaWpUyzXSMaJwtY5FMSVv3wb/b4IAMAoCpASW1bPnVuL286bjcPeCJ7b1INVO9UTd286exbu4unI43v5ZeDtt9MfM5uBf/3XKetn+pOXduC+1/bigyfU48EbT5uSz6DicqTr46SSRPPnz8eqVavQ1NSE3t5eXHDBBdi5c+eY5/GinluyrOATD7yDdQeGcdmSafjpNUvGXRn0hGLY0DmCRdPcqHVacjzSIifLwMqVQF9f+uNuN/DVrwJi/nZzhmNx/H3bIfSMhNA1HMKr2w+hZ9QJaAZRQHxUXwpRAM6bV4er21vxwQX1iMUVbOn24OBgEH3eMOpdFsxtcOHEZjcsRkMufyQapViukYwThSkci+PD//s6Dg4F8fkL5uAbHxm/B8JhXxg7+3xY3FKJChu3lx2TaBT4+c/HHm3c0gLcckt+xjSKLxzDy1sP4ZAvjAMDQbyy/RAGR/UrzBQnzAYRH1rYgE+0t+DcuXXwhyVs7vbg4FAQA/4IplXaML/BhQVNLhgN7GqQT8VyjWScKFzDgSg+fv/b2NcfwOmzqnH3pYvQVGHF79ccxAP/3AtvOL13nc1kwGfOnIGvXzwfJv77z2xwEPjFL4D4qCbSS5cCK1ZM2ccO+CM450f/QDgm4693nMMqLzri9XFS+2EOHTqEpqYmAEBjYyMOHcp8tF44HEZ7ezuMRiPuvPNOXH755ZP5WDqKP73fjXUHhlHrtOC/Lj/xiKXjFTYTzp/HLQMT8u67YxNEAPCBD+Q1QQSozUovPXma/ufvrFiEXYd96POE0Tkcwt829WL1/kFYTSJm1jhQ51IThKv3DWLVzn6s2tkPp8WIYFRCpv6mDrMB582rwy3nzsapM1h5RuNjnChMK1/fh4NDQcxrcOJrH5p3xOfWu6yod7HC8JgpCvDqq2MTRADwwQ/mfjzjcFlN+PipLfqfpbiMHX0+HPaFsa8/gOc29WJj5wgcZgNm1TlQ7bAgKsWxZv8Qnt/ci+c398JlNcIXHtvgHAAq7SZcOL8en79gDuY1uHL1Y1ERYpwoTOFYHLf8di329QdwQqMLv76hXe9D98UL23Dt6dPxf6/swtoDw5hZ48CJzRW45rRWVLPScHySBDz//NgEkckEnH/+lH50rdOC65bNwINv7se3n9mCpz9/FgxsQ0HjOGqS6KKLLkJfhhvh73//+2l/FgRh3GTEgQMH0NzcjH379uEDH/gATjrpJMzJsBVn5cqVWLlyJQCgv7//mH4ASucJxfDDv6l7XL91yQlsKjpV1qwBXnpp7OPTpgEnn5z78RyFKAo4odGNExrV5oHXnzEDwagEq9GQ1qdoKBDFsxu68Ye1Xdje64VRFHBisxttdU7Uu6045A1ja48Huw758cKWPry4tQ83nz0LX794PqwmVhaVK8aJ4tI5FMR9r+0BAHz3shO52ptNWoLo3XfHfu+EE4BZs3I/pmNkNIiJleUKfOAE4JZzZyMQkWA3G9L+3fZ6QvjT+m78cW0nOgaDMBtFnNRcgVm1DtQ6LegaDmJztwcHBoP48/vdeH5TL75y0Vx87rzZrCwqY4wTxSUuK/jKE+9j3YFhTKuw4pHPnj7mnqLaYcZ3LpuaHjolKRYDnnwS2JehefTZZwMVU1/Z89WL5uL5Tb3Y0DmCR97uwM3nFG5MovzKyXazVDfeeCM+9rGP4aqrrjri81geOjHfeW4rHn6rA6fPrMaTnzuDDSizTZaB114D3ngj8/dvugmYPj23Y5oiPSMhVDvMGZM/XcNBPLr6IFa+vheyApzQ6MJ91y1ln5IcKpZrJONE4fnc79bipa2HcPmSafjpJ0/J93BKh7ZC/P77Y79nMABf/CJQXTrNQhVFQddwCA1uK8zGscmfvf1+/PqNfXh8TScA4PRZ1fj5taegwc2qtFwplmsk40Th+e5z2/DQW/vhthrx1OfPYjXgZPl8wB/+AHR2jv2e2632qpuiXkSjvbLtEG757VrYTAY8/+VzMJtz97J1pOvjpJZ0VqxYgd/85jcAgN/85je47LLLxjxneHgYkYja6GxgYABvvfUWFi5kI7OpsH8ggN+9cwCCANy9YhETREcjy8DBg8C2bcCBA+qfjyQUAh5/fPwE0dKlJZMgAoBplbZxq4Naquy486Mn4M9fOBuzah3Y0efDip+/ib9vy1wiTuWLcaKwvLtvEC9tPQSbyYBvLl+Q7+EULkkC9u9X40NXl1ohdCQeD/Dww5kTRABw3nkllSAC1IqP1mp7xgQRAMypc+KHVy7Gb286HfUuC9bsH8Ly/3sD6w4M53ikVOgYJwrLcxt78NBb+2EyCFj5mXYmiEbz+YBNm4C1a9U4cbT7h85OtYdppgSRIAAf+1jOEkQAcNHCBqw4eRpCsTg+89Aa9I3qWUpTQ4rLOOQNY0efF1u6PdjQOYLXdhzGn9/vwotb+vDWngG8s3cw7b939w3ivY4hrDswjPcPDmNT1wi2dHuwrceL7b1ebOoawXsdQ+gcCmZ9vJPqSXTnnXfi6quvxoMPPogZM2bgD3/4AwBg7dq1uP/++/HrX/8a27dvx+c+9zmIoghZlnHnnXfyoj5F7nlhOyRZwdXtLVg4zZ3v4RQmSQL27gW2bgV27VKPoNTY7cD8+cCiReqWAENKgmT/fvWUGq838/suWgRccsnUjr0AndxaiefuOAff/NNmPLexB198bD0evLGdR2OTjnGicMiygu8ntiN/7vzZrOgYLRpV48LWrWqciKY0cXa5gAUL1OOJW1uTR9grippIeu659HiS6owz1CRRmTpvXh2e//K5+OqT7+OtPYO48eE1eOK2M7BoGpumkopxonDs7ffjzqc3AQD+42MLccbs/J3EWFA8HmDLFjU+9PSkf89uV7cTa/cPWl9SSQLeegv45z8zJ5IEAbjiCmDekfsCToUfXnkSDg4FsaFzBJ956F08dssZen9SOrK4rGBfvx+7DvnhCcXgj8TgD0vwRST1f8MS/BHtzzH4E48HovGjv/kEffHCOfjXi8c/gGQiJrXdbCqxPPT4rN43iE+uXA2byYBV/3oBJ/+ZdHUBTz8NDB/DKqbVCrS1qTcGnZ3qa8dz8snAZZflvVl1PimKgu/+dRsefqsDNpMBj96yjA2tpxivkfwdHK8/re/C1/6wEQ1uC177+gWwmye1TlRadu0Cnn0WCASO/lynU40PFgvQ0QGM02QXAHDuuephBqzsRVxWcMfj6/G3zX2ocZjxx9vP5DaHKcZrJH8Hx+OwL4xrHliN/QMBXHryNPzsk+OfjlxW3nkHeOWVsc2mM7Fagdmz1XuC/fvHjykGA3DllWpiKU+GA1Fc/cA72H3Yj0a3Fb/49FIsnZ7duftQIIrV+wbR4LZiTp0DDosRRnH8vmOFZvchH1bt7MfBoSC6R0LoGg6icyiEUOz4Ez6ioPbxqrKbYTKIMIgCqhxmVNpMCMXi8IVjaUXLSuL/xBUFsqJAlhXIihpLZUWBogBmowizUcTlpzTj+jNmHPeYpux0MyoMh31hfPWJDQC4Ojyujg7g979PXxk+knBYXTE4mgsvVFeIi+RiN1UEQcB/XLIQvrCEp9Z14ZbfvIc/f+FszKx15HtoRARgz2Ef7np2KwDg//vwfCaIUm3dqi4gHG3LgMbvBzZsOPJzDAZg+XLg1FMnPbxSYRAF/O81S+CPrMPru/px48Pv4c9fOAs1Tq5eE+XbcCCK63+9BvsHAljY5MYPrzypaG7kp4yiAKtWqZVAxyocVqtLj8TpBK6+Ou8tKqocZjx26zJ88bH1eK9jGNc88A5uPXc2vnhhGxyWic8RZFntV/e3Lb247x974Iukn4BpNopY0OjCktZKXHZKM05prSyIv2uD/gjWHhjGlm4PukdC2N7rw/bezDtImittWNDkRq3TDKfFCKfVCJfVBFfia6fFCJdV/c9pMcFpNcJuSj8sqNBxlljkIlIcn390Pfq8YZw2swpfuKAt30MqPJ2dwGOPqacKZIvVqpaIzp+fvfcscqIo4J4rT8KAP4JVO/tx0yPv4U9fOAuVdh6FSpRPnlAMt/52HfwRCZcsbsInUo49L3vbtwNPPXX0nkPHw+0GPvEJdVsapbEYDbj/00txzQOrsbnbg9t+tw6P3bKMp2MS5Um/L4I/ruvEQ292YMAfQVu9E7+7+XQ4J5EkKBlvvnl8CaJj0dKiJojchdEWpN5lxe9vPQM/+Nt2PPxWB36xai+eWteFOz96Ai5f0nzEpEZUknFgMIA9h/3YfdiPPYn/9vb7EZGSiy5Lp1ciHJNxcCiIcCyOqCRjY5cHG7s8+M07B7BomhvXLZuBy5ZMm1RyaiJGglE8t7EHz27owdoM/fJcViMuXtSIBU1utFTZ0FxpQ2uVHRX20j89nFeAIhaMSrjj98njKX9x3anjNpAsW7KsbiEYL0FkMqlbBsJhde/wsZg5U00Q5eCoymJjNIi491NLcdUv38aOPh8+89Aa/Pam05koIsqTQX8EN/9mLfYPBLCgyY2fXLW4IFbsCkIkAvzlL+MniCwWNUYEg8deZbRwIXDppYDNlr1xlhi72Yhf39COy+97C+sODOP2R9fh/k+fykQRUZYpioKRYAzdIyF0DAbQMxLCYCCK4UAUQ4EY9g34sa8/uR1qcUsFfvWZdlb3AcDQEPCPf4z//epqtWJ0ZOTYFqEFQd15cN556T1PC4DJIOI/L12ES0+ehu88tw0bO0fwtT9sxCNvd+DC+fVoq3fCZBAQjMaxt9+vJ4UODgYhyZnjZ4PbgvmNbtxyziycNy+9T6k3HMOWLg9W7erHH9d2YmuPF//+58344d+246sfmocbz5oJwxRW3CiKgvc6hvH4moN4fnMvoomElsUoYun0KiydUYkZ1Q40V9lw6oyqso1NTBIViXAsjp19PoyEYvCGYvCGY3hiTSc2d3tQaTdh5Wfa2XAsk23bgIGBsY9XVgIXXwzMnQsYjeo2tN271W0He/Zk3pZWWwucfbbag6iM+w8djdNixEM3noZrVr6DTV0eXPPAavzultNR7+I2SKKp5I9I2HXIB28oBk8iVvz6zf04MBhEc6UNK68/ldvMUq1dq55aOVpDgxofZs5Ur/XhMLBjhxof9u/PvKAwbZraf+iEE8p++/GxaHBb8fBnT8O1K1dj1c5+3PjwGvz6htNYvUB0FIqiwBuS0O8Po98XRb8/ggFfRP/fAb/2dRQD/si4N/Eam8mAZbOrcdPZs3Du3FouImjefDPzAsIJJwAf+hBQk2jofbT7B0FQDz0491ygqWnqxz0JS6dX4c+fPwt/er8bP3pxBzZ1ebCpyzPu8wUBmF5tR1u9E3PrnZhT70Rb4j+3dfxKG7fVhLPaanFWWy2+9qF5eHFLHx5dfQBrDwzjv/66DX/Z0I0VS5rRPqMKi1sqsvZ3cigQxZ/Wd+HxNQexN5EcFQT1cIWPL23GRQsacl7JVMhK7jfxjac24ZkN3TAZRBgNAoyiAKOofm0xiqh3WVHtNCMSkyEril46ZjGKMBhEmEQBBlHQG0oZRQEKgMFAFN5QDBajCKvJoDeMspkNsBhFhKJxRCQZFqOYeEx9XJIVSHEZ0bgMKZ5oNAX1Ih+Xgaikvi4qyYhIMiJSPOVr9c8D/ig2dI7omc5U06vteOSzp7H5YyaKkvm4+sZG4IYb0ld6zWa1edyiReoNwMGDQF+f+rUoqhf22bM5+T9G0ypt+OPnzsJ1v16NnYd8uOaB1Xj0lmVoruTqOuXf9Q++i/c6hmASRRgMaowwGdRrv81kQIPbigq7CZFEY8LWajumVdhgMggwGkQ1ruj/q8YJSVYw6I/CH5FgNanXfzkxwbSbjfoqXFSSYTWpccRmMsBkFCHFFcTiMmIZ4oQkK3pMiCZiQiSmxpRILBEz4jJ6PWFs6vIgnuGG4MRmNx664TTUs19dUiwGvP322MdnzwauvTb9OGKrFViyRP0vGlV73PX3q01MDQZ1W1nqiWd0TP7/9u4+OKry3gP49+ye3WxeNuElCYmbhLAEkpCQgBAoWtsLClcvTLCACmKdqr3Qt2k7jtqZO3M791pLOt5hWufeOo5SlToo0+t1lAGLrehtGSBeJGin+IawgYSEAHkhr/t6fvePs7vkDSG7yZ6T5fsZdjY5eTk/nt093+xznvM8ZXmZ+MPWZdi84wPUn+7A5h0fYOdDNRx5Sqbww1cbcLHbB9U68n1B5PhvCb/m9SM2gOGTzkI/jg/7UrTvYei2Yd836IsDgRAuRTuB/PCHrnNkI4BMh4oZmQ4UZ6ejcGoapmfYMS1dv+VnOVCenwmblSc+h7h8Gfj445Hba2r0ueYGH+uHv39oatJHIWmaniMzZwJTJ89CLhaLgg2LCnBnZR4OfNqGT1q70XhJ71CxWS2YlZ0e7QianZMR9ygbh82Kuxe6cPdCF/78SRv+9c2/Ry9FA4DFM6fiZ3eVoaZ4Wsz7aO/14T/f+xKvfnA2+trJdabg3sWFuK+mEIXT0uL6PySrpOsk8oU7XXyjdKgAiPYcTkZzZ2RgRqYDmQ4bnA4VuZkOPLhsJrI5LHR0X3wx+qozd9751ZcCqKr+RsHtnrjabgB5WQ78YesyfPt3/4dPWrtx73NH8LvvLEZZnjmuw6Ybly+gwRvQ4MXoOXHyQm+CKxofVouCefmZyHamwOlQkemwoWBqKr5zSzHPjg3X0DBy1RlFAdasGdpBNJzdri9XbMCSxclozgwnXv/eLbh/Rz0+burCxufr8cKDi/lHOxnu46YuNHeOMtLQBJwpKrKdKcjJSEG2067fZ6Qgxzno3pmC6en2G/ZSmbgcPjxyJbPUVOCOO776ZICqArNm6bdJLiNFxdoFLqxd4ErYPlfOm4Gvuadh399a0XC2E+9+egEfnunEPc8dwR3luXjsH0tROsN5zZFF3kAIfz93GQ1nO9FwpgsHT15Enz8ERQH+oTQHm5YUYUVZLjtHryHp/mr8j3uqUbeuCgFNPyMbDGnh0TyCgUAIbd1edPb7owfNpo5+tF726mdwNUEoJAhoGkKaREcBAfqSdZmpNviD+puLyPNqwK/BGwwhzWaFw2aFP6hhIBDCQHhiLjV89iFyttkSPvNgUQCLosCuWpCiWpCiWgd9bEGKzQq71YIUmwXpdhXVhVMwLZ1n166bCPDXv47cXlSk9+pTQkzPSMFrW76Gh18+imNnOnHXMwextvom3LO4EAuLpsR16cuAP4Qv2vSVBz5p7cYXbT0Y8IcgQHSkn4h+Mi7VZoHTYYPVokS/polAURRYFf3NtUXRzxZaBp0dHC7dbkVmqg0WRUFICx8zwseKUCh8r2lQFP13RF7zaniEor4PIKQBgZCGwmmp2PKN2TG3AcVm1z8vDY/cCT9+IQ2B8H2fL4S2Hi+6BwJItVkR0gRnOvrR1u1FSJPwz1zJFf3x16BAwfQMOzIcaniEjxYeiSro9+mjfdLtKuyqBb5gCAMBDQN+fbs9MppJtYSXhkU0J6wWJZwF+uikSC4MzQwrMlNVLCicAudXDPGmsFAIOHRo5PbKSn2eCUqooulp+O/vLcMDOz7AZ+d7sGL7/2JjTRFqF9yEqoIspKixv8nt8Qbw+flITvTg1IVe+IKj50S63YoMhwqLoueEJvrojmg2hI/fkY9He6OiAEhPUZGZqmfblVy4kg+RzyO/16oosFrD94P2E9T0EYZVrim4t4aToCfas5tvRr8/FD7ua4OO//oxPxgShEQQeRZEng+DnxWRp0j0ftBXR/szY/jviHyP3WqJdgrlOFPY8TOR+vqAY8dGbl+6VJ+njiaU02HDxiVF2LikCD3eAF446MGOg6fx7qcX8O6nF8KjvVOQm+nAtDQ7vMEQ+nxB9HiD6PMH0esNotsbHDGqenlpDn52VxlPVI9B0nUS2awW2KxAKkY/gJbmORNcERnC4wHOnRu5ncvVJ1xWqg2vPLIEdW9/ht1Hz+LNj1rw5kctUC0KZmWnw52TjkyHDXbVEr1FLtcEgJCmX34TCGk42zGALy/0ovXyALr6x3G1OoMsnjmVnUQG0HPi6meQ5oF/RCS1v/0N6B5lWdvbbkt8LQQAyM9KxR+2LsOTez/Bno9b8Er9GbxSfwZ21QJ3OCcyUvROVrvVOigrLJBwh44mAl9QQ+OlPpy62IvWy170eK9zQQoTq60OspPIAFUFU4wugYxQXz9y3jm7Xe8kooRyOmx4dOVcfPtrM/Ff753E/zScQ68viMb2fjS291/15xQFKMtzYmHRVNxcNAWLi6dhVnZ6AitPDknXSUQEQJ9wbrj8fGA235AbIc2u4hd3V2LLN9x4pf4Mjpxqx4mWyzgZXiEhFqpFgTsnHeX5mZiXn4my/ExkOtTwKB79jF2kP3AgEEKPNwARfYQG9H/62eTwWV0tPE9YSCQ6N8BgIkCfP4jugWB0/9bwnDSReQoiZ4YB/SywFj57rIlE55oJaaKPDlEtmME5YogSS9NGz4fyciA3N/H1UNT0jBQ8s3Ehfri8BK8cOYMPPO34oq0Xn53vwWfne2L6nXbVgjm5GSjPz0R5fibK8pxIs1vDI4GG5kSfL4hen358H5ITop+sCIl+TA+Fj+OjEQF6fEH0eANQoIyaE1aLBVaL/lSM/K5IXlzJIoFq1edKc2dzzkmihPD5gKNHR25fvJgrVhoox5mCf19biX+rrUCvL4i2bh8udHvR0e9Hmt2KdLuKDIeKjJTwzaHGNQKVdOwkouRz7hxw+vTI7bfdxlFEBiucloZ/+adyAEC/P4jTF/vgudSHfn/wyuS84Ql5/SENCsKX3Vj0jp+bpqSiJDdDn3wx3Q7LBC6RSURJ6LPPgPb2kdu//vXE10KjmjvDiV/cXQlAv1zs9MU+NLb3RS/PHDyReyQnrBb98i/VoqBwWipKcpxwTU3F1DQbV2siouvz4Yf6apaDWa3AsmXG1ENDKIoCp8MGp8OGklx2nk80dhJRctE04M9/Hrk9O1s/U0ymkWZXUenKQqUry+hSiOhG4PcD778/crvbDbgSNzknXT+nw4bqwimoLpxidClElMx6ekZf8XLBAsDJqUroxsNpvSm5/PGP+vLEw916K0cRERHdqESA11/Xl64fjqOIiIhuXIEAsGvX6Cte3nqrMTURGYwjicg47e36AdhqBSwW/V5R9FswqN8GBvShn6GQfgsE9O2hkH7f369/j88HNDUBHR0j95OVBVRVJf7/R0REY6dpej5YrVduFot+A/RjfyBw5dg/OB8CAf3nA4Gh+fDll/rnwxUUJMVyxUREN4RgEOjsHJoPkfcPInoW+P3Xzoe+Pv39hdcLfPrpyCXvAa54STc0dhKRcZ5/Xj+ATyRFAWpr9QAhIiLz8/mA3/524vdjswFr1nCUKRHRZHHpEvDccxO/n4wMYNWqid8PkUnxcjMyzmi99uPtjju4ohkR0WSSiGwAgLVrgby8xOyLiIjil4h8sFqBe+/lXER0Q2MnERkjMiR0Ii1YANxyy8Tug4iIxlci3gR885v6pQRERDR5THQ+KAqwejVQVDSx+yEyOV5uRsYQ0W8T4aabgCVLgOpqXkZARDTZaNrE/e6ZM/WTB6WlE7cPIiKaGBPVSWSx6Fce3HYbO4iIwE4iMoqm6cvSh0L6x5GJ5SKdR6qqD/dMTQUcjiuf22xXPo58PS0NSEkB7Hb90oHp043+3xERUTwi+RC5adrQfFDVK/kQyQObTb9FJrpOS9NvdrueES6XvpABERFNTlbryHwY3HEUyYLUVP24P9r7B1XVsyE1Vc+H1FR9EYP0dOP+X0Qmw04iMoaqAj/6kdFVEBGR2UydynwgIqKRioqYD0QJwDmJiIiIiIiIiIiInURERERERERERMROIiIiIiIiIiIiAjuJiIiIiIiIiIgI7CQiIiIiIiIiIiKwk4iIiIiIiIiIiMBOIiIiIiIiIiIiAjuJiIiIiIiIiIgI7CQiIiIiIiIiIiIAioiI0UWMJjs7G8XFxTH97MWLF5GTkzO+BY0j1hc/s9fI+uLD+q6tsbERly5dMrQGozEnjMP64mP2+gDz18j6ro05wZwwEuuLD+uLj9nrA4yv8asywrSdRPFYvHgxPvzwQ6PLuCrWFz+z18j64sP6aKKZ/TFkffFhffEze42sjyaa2R9D1hcf1hcf1hc/M9fIy82IiIiIiIiIiIidRERERERERERElKSdRFu2bDG6hK/E+uJn9hpZX3xYH000sz+GrC8+rC9+Zq+R9dFEM/tjyPriw/riw/riZ+Yak3JOIiIiIiIiIiIiGpukHElERERERERERERjk3SdRPv370dpaSlKSkrwq1/9yuhy0NTUhOXLl2PevHmoqKjAM888AwDo6OjAypUrMWfOHKxcuRKdnZ2G1hkKhbBw4UKsWbMGAODxeLB06VKUlJTgvvvug9/vN6y2rq4ubNiwAWVlZSgvL8eRI0dM1X6//vWvUVFRgcrKSmzatAler9fQ9nv44YeRm5uLysrK6LartZeI4Mc//jFKSkpQVVWFhoYGQ+p7/PHHUVZWhqqqKnzrW99CV1dX9Gt1dXUoKSlBaWkp3nnnHUPqi9i+fTsURYkuF2lE+1H8mBOxYU7EjjkRf33MCUok5sTYmTkjAObEWDEnxr++iEmRE5JEgsGguN1uOXXqlPh8PqmqqpITJ04YWlNLS4scO3ZMRES6u7tlzpw5cuLECXn88celrq5ORETq6urkiSeeMLJM2b59u2zatElWr14tIiL33HOPvPbaayIisnXrVnn22WcNq+3BBx+UF154QUREfD6fdHZ2mqb9mpubpbi4WPr7+0VEb7eXXnrJ0Pb7y1/+IseOHZOKiorotqu11759++TOO+8UTdPkyJEjsmTJEkPqe+eddyQQCIiIyBNPPBGt78SJE1JVVSVer1dOnz4tbrdbgsFgwusTETl79qysWrVKioqK5OLFiyJiTPtRfJgTsWNOxIY5MT71MScoUZgTsTFzRogwJ8aKOTH+9YlMnpxIqk6iw4cPy6pVq6Kfb9u2TbZt22ZgRSPV1tbKn/70J5k7d660tLSIiH7gnzt3rmE1NTU1yYoVK+TAgQOyevVq0TRNpk+fHn2RDW/XROrq6pLi4mLRNG3IdrO0X3NzsxQUFEh7e7sEAgFZvXq17N+/3/D283g8Qw5KV2uvLVu2yKuvvjrq9yWyvsHeeOMNuf/++0Vk5Gt41apVcvjwYUPqW79+vXz00Ucyc+bM6EHdqPaj2DEnYsOciB1zYnzqG4w5QROJOTF2Zs4IEeZErJgT41/fZMmJpLrc7Ny5cygsLIx+XlBQgHPnzhlY0VCNjY04fvw4li5dira2NuTn5wMA8vLy0NbWZlhdP/3pT/H000/DYtGfDu3t7ZgyZQpUVQVgbDt6PB7k5OTgoYcewsKFC/Hd734XfX19pmk/l8uFxx57DEVFRcjPz0dWVhYWLVpkmvaLuFp7mfE18+KLL+Kuu+4CYJ763nrrLbhcLlRXVw/Zbpb66PqZ/TFjTowdc2J8MCfiw5xIHmZ/zMyYE2bOCIA5MV6YE/GZTDmRVJ1EZtbb24v169fjN7/5DTIzM4d8TVEUKIpiSF179+5Fbm4uFi1aZMj+ryUYDKKhoQHf//73cfz4caSnp4+4NtzI9uvs7MRbb70Fj8eDlpYW9PX1Yf/+/YbUcr2MbK9r+eUvfwlVVbF582ajS4nq7+/Htm3b8OSTTxpdCiU55kRsmBPjjzkxNswJShQz5oTZMwJgTkwE5sTYTLacSKpOIpfLhaampujnzc3NcLlcBlakCwQCWL9+PTZv3ox169YBAGbMmIHW1lYAQGtrK3Jzcw2p7dChQ9izZw+Ki4uxceNGvPfee/jJT36Crq4uBINBAMa2Y0FBAQoKCrB06VIAwIYNG9DQ0GCa9nv33Xcxa9Ys5OTkwGazYd26dTh06JBp2i/iau1lptfMyy+/jL1792LXrl3R0DFDfadOnYLH40F1dTWKi4vR3NyMm2++GefPnzdFfTQ2Zn3MmBOxY06MD+ZE7JgTycWsj5lZc8LsGQEwJ8YLcyJ2ky0nkqqTqKamBidPnoTH44Hf78fu3btRW1traE0igkceeQTl5eV49NFHo9tra2uxc+dOAMDOnTuxdu1aQ+qrq6tDc3MzGhsbsXv3bqxYsQK7du3C8uXL8frrrxteX15eHgoLC/H5558DAA4cOIB58+aZpv2KiopQX1+P/v5+iEi0PrO0X8TV2qu2tha///3vISKor69HVlZWdBhpIu3fvx9PP/009uzZg7S0tCF17969Gz6fDx6PBydPnsSSJUsSWtv8+fNx4cIFNDY2orGxEQUFBWhoaEBeXp5p2o+uH3Ni7JgT8WFOjA/mBCUKc2JszJ4RAHNivDAnYjfpcsKYqZAmzr59+2TOnDnidrvlqaeeMrocOXjwoACQ+fPnS3V1tVRXV8u+ffvk0qVLsmLFCikpKZHbb79d2tvbjS5V3n///eiKBKdOnZKamhqZPXu2bNiwQbxer2F1HT9+XBYtWiTz58+XtWvXSkdHh6na7+c//7mUlpZKRUWFPPDAA+L1eg1tv40bN0peXp6oqioul0t27Nhx1fbSNE1+8IMfiNvtlsrKSjl69Kgh9c2ePVsKCgqir5GtW7dGv/+pp54St9stc+fOlbffftuQ+gYbPNGcEe1H8WNOxI45ERvmRPz1MScokZgTsTFrRogwJ8aKOTH+9Q1m9pxQRESM7aYiIiIiIiIiIiKjJdXlZkREREREREREFBt2EhERERERERERETuJiIiIiIiIiIiInURERERERERERAR2EhEREREREREREdhJREREREREREREYCcRERERERERERGBnURERERERERERATg/wHYm9QfAzcDgAAAAABJRU5ErkJggg==", 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uWbAAWLKk3iskIiKiZvHaa8Drr7sfAKBpwKxZbtxx1FFAe3tdl0dEjY1JE6JGtmmTmzDxy2SANWvczx13XOBTBdPGqo1DeGr9APozBdgOIITAlGQEh0xLYsHUJFJRFboqI6opaI9pUGRWrRAREdE+2LABlmNhzY41GC4OY6gwBFmSMXVgAEingSVLULRs/HXTMJ5aP4DtwwVYjoAQAl0J3YtF2mKaG4uoMjriOmMRIiIiCnIcYMOG4HOmCbzxhvuxYMEekyam7eC5zcP4y7oBbB3KwxYCjiPQHtdwyNQkDpmWREdc92KR9pgGVeFhVKJWw6QJUSMbHST4zZnjPXxxaxorfv8qVr6yE4bt7Pk1o8gS0BnX0ZVwP7qTOroTEXQldHTGNeRMG7tHDAznTe/+iKqgryOKmZ1xHDI1iUN7U5C52UFERNT0jLWv4vkdzyFdTAMABARe3PkilkhLUGhbhm/8/Fn8/sUdKFr7HotIEtAR09CddOOP7lI80pWIoCuuIW86GMgWMZirxCK6KqOvPYaZnTEsmJrE4X1tjEWIiIiaSX8/UCiM/zldB/r6xjy9YXcW33vkNfzPmu3Im/Z+fbmOuIauhI4ppf2QrqSOKQkdnQkdRcvBQNbAYNaAIwBFBjRFRl97FDM6Y5g3JYkjprcx8ULUYJg0IWpk69fjpZ0voT3ajp5EDxTZ179z7lxs3J3Dtx96Bff/desB/fKOAHZnDezOGge8xClJHe9YOBVzuhMA3M2ProSO3rYoZnbFsLgnBYkzWIiIiBpa3sjhuSfvQ76UMCkrWBZ+9cKf8f/lZqI/uXS/f10hgMGc6SVFDkRnXMOJC6di/tQEJEiQJPe5njZ3M+OwXiZViIiIGsr69Xv+3OzZgTblOzNFrPj9q7jrqU2wHHFAX24oZ2IoZ+KNndkDen1bVMXbF07Bop4UJLgxR2fCjUWmt8dw+PQ2VtYShQyTJkSNyjQxvO4l7MjuwI7sDqwbXIfpqemY0TYDmqzhP7cD193zeOAExbRUBCcvnoq3zuvGwp4kVFmGJAFbBvN4rX8EG3ZnUTBtGLaDnGFjMGtg14iBgayx3ycxynaNGLhn1ZY9fv6QaUlcsGwO3v+mmUhG+C2JiIio0eTMHH756E2YkxnwnhNCYGemiA0DORQg4fW2NYiJqeiNz8ZJi6fi+HndWNybgqa4sci24Txe7x/Bul055A0Lpi2QMywM5EwMZIvYPWIgZxxYLDKYM/d6gGRudxwfXTYX5x47E+0x7YC+BhEREdXQPnTdEELg/r9uxdfufwFDvsMX7TENyxdPxVvnd+OwvjboigxZBrYPF/B6/wje2JVF3rBhWA7ypo2BrLsnsnukiOwBxiLpgoUH12zHg2u2j/v5GR0x/N3xc/CRN89CZ0I/oK9BRJNLEkIcWJo1pNLpNNrb2zE8PIy2trZ6L4eoet54Ay98+/PYmdsZeNqwBF4dSeL6xR+BInUBcCs7Ll1+CM5762xENWW8X21COcPC7lICpfyRiCjoTkbQEdMgSRKEEMgaNrYM5rFxIIdnNgzgj2t379MmR0JX8M7De/CupX14x6KpB7xOIhoffz7WFv+8qVWMGCO4/a+3Q3t6FRb++TUAgGE5WLtzxGvfub4jiXvftABvnTcN1591CQ6bdsgBfa2CabsVsCNF7C61wYhpits2NKFDLsUiOcPG1qE8Ng3m8MyGQTzx+m6MFK0Jf/2oJuPUQ3tw9tI+LD90KuI6D3MQTSb+bKwt/nlT0xICuOEGIJcb//Mf/ziGpvTi83c/h9+9sMN7OqEruOjE+bjoxHlIRQ/skETBl0TZNVLEYM5ARC3FIqU5bEII5E03Ftk8mMeqjUP439d2Il2YOBbRVRknL5qKdx3Zh1MOnXbA6ySi8e3Pz0a+EyBqUCOvvTAmYVIwbby4NY3V3Q4y2v9AFX348JGn4pvvO/mgf9jGdRXxLhWzuuIT3nv0rI7SowUoWjbWbB5GprRZYdsCu0aK2J4u4I+v78Zf1runUrOGjV+t3opfrd4KVZZwyLQkDutrw3FzO7F88TRM74gd1PqJiIhocjnCwV3P34X+bD8O3zEEAChaNl7amkbBN7dEnj8VF75tHqKagntf+S8kIx/FrPZZ+/31opqCGR0xzNiHmOAoLxZxB76u2TLsJXEcR2D3iIHt6QL+vG43nnh9NwCgYDr4zZpt+M2abZAlYMHUYCyyLzEQERERVdnOnXtOmGgadrVPwd/d8iRe3p7xnn7XkX34+nuXYEoyclBfOqopmN4R26f9iSNndniPLdvBC1vTGMi5rc+FENg1YmDHcAHPbBzEylfcvR3DcvDQizvw0Is7IEnAvCkJHN7XhjfN7sTyQ6dh3pTEQa2fiPYdkyZEDWrd6pWB67xh48VtaZi2g41tcaSiGk4/XEFP99MQ0tsB1OeEQkRVcNzcrnE/95l3ukPq73hyPX7z3Dbv5IXlCLy8PYOXt2dwb6m118JpSaSiKmxHIK6rOOXQaXjv0dPR0xat2e+FiIiIKv606U/YlN4ECIGO7UMomDZe2pb2Br1rioz5UxOIHD8fw6UK0qSeRFuktieeNUXGm2Z37uGzC/F6fwa3/2kDfv3Xrd7sFEcAr/WP4LX+kVJrrxcwf2oCHTENtgAiqoyTF0/F+46esU9JHCIiIpoke2nNle7uwUd+/BRe7x8B4Hbd+Oe/OQJnLx07GL6WVEUOHOgYbf2uLO54cgN+tXordo0UAbgFNW/szOKNnVk88Nw2XPvAi5jTHUd3QoctAF2RcOLCqXjf0dO9GbJENHnYnouoARUKI/jzZe+HsNw39gXTxgtb3YQJAPz3O47A8hMXIBFRsXTaUpxz+Dn1XO4+MSwHT7y+C799fjv+unkIr/ePTDikTZKAZfO78TdHz8CZS3vRxtJVoj3iz8fa4p83NTvLsfDdP30XWTOL+HAOR//ySTy/dRhGKWESVWUcNr0NWkTD//2/t8NRZEyJT8EFR12AVCRV59WPz7Id/OmN3fif57fjr5uG8OqODEx74rdKb5nbhfcdMx3vWtqHjjj7kBPtCX821hb/vKlp/fKXwAsvjHm6YNr4wkA37mtfCADoa4/i5/9wfENVZ9iOwF/WDeC3z2/Dqk1DeHl7xout9uaY2R34m6Nn4F1H9h10NQ1RM2N7LqIm98Kqh7yECQBsGsh5CRMkozj1xAWIlYaqL5u1rB5L3G+6KmP5odOw/NBpANz2Hi9uTeOxV3fi0Zf78dyWYYxO8QoB/HHtbvxx7W58+VfP491L+3D56Ysws5PtM4iIiKrpuR3PIWtmAQAd24eweTBXSZhoCg7va4Ouyhia2gZHkTEtMQ3nH3U+knqynsveK1WRceLCqThx4VQA7oGOV7Zn8PhrO/GHl/uxauMgxjvP8Zf1A/jL+gFcc/8LOH1JL648fXFDbdAQERE1DCGADRsghIAkSYFPPb1hEE+n5gFwB6v/4hPHN1xrTUWWsGxBN5Yt6AbgHuh4dceIF4s8u2Fw3MOlqzYOYdXGIVz7wIs45dBpuPL0xVjcG85DKkSNgkkTogbjCAdrVz2CaaXrouUORQUAVZbQd+xsFEoJkzntczA9Nb1OKz04EVXBMbM7cczsTnzmnYtgOwISAFmWsHbnSGn+yRZs2O32MjUsB/es2oIH1mzDRW+fhxMXToUkuS05ZnTEMC0VgSxLe/+iRERENCEhBP606U/edWzzALaVWknIkuQlTABgqKcdvclefPTIjyKhN1YiQVdlLJ3ZjqUz23Hp8kMCscjG3Tn8avUW3Ld6C9budJNHpi3wm+e24XfPb8f5y+bitMN7SrGIhL72GHrbooxFiIiIDsbu3cDICF7Z/QqyRhapSAptkTbE1CRWbc1g23FToClSQyZMxqMqMg6f3obDp7fh4pMWwCklTGRZwpahPO4v7YuU57fYjsDDL+7AIy/twEfeMhvvPrIPsiRBlSX0tkfR1x6DwliEaJ8waULUYF7c+SK0TVu96+3DBe9xT1sUuemV+SEnzDqhpmurJv8P9gVTk7j8tEX47DsXYvWmIfxq9Vbct3oLhnImDMvBzSvX4uaVawOvj6gyDutrwz+9cyGWL542+pcnIiKifbR2cC125tyBpRAChVe3exUY01IRL2ECAOq8BbjgqAsQ0xp/7oc/FpndHcenTl2Iy045BC9sTeNXq7fg3lVbsGvEgOUI/OSJdfjJE+sCr9dVGYt6krhs+SE4Y0nvmBOyRERENIHSPJPhwjDyVh4ZI4Otma3YPlzAywkVw9rjOGXhoSiILciZMxDXGj9x4uc/fDGjI4ZPnrwAnzx5AV7ZnsF9q7fgnmc3Y0e6CEcAP//zRvz8zxsDr9dL8+YuPmkB3nf0dMYiRHshT3wLEYWFEAJPrn8CbTuHAbinCPoz7slOSXKTJkO9HQCArlgXFnUvqtdSa0KSJBwzuxPXvHcJHr9qOf7xpPnQlfG/rRUtB6s3DeHCW5/CpT9/Fv3pwrj3ERER0d75q0y04RzS/Rnvurc96j12ZAnvP+3TTZEw2RNJknDEjHZ86V2H47HPLcenT12IqDZ+LGJYDp7fksbFP3sW/3D709g8mKvxaomIiBrc+vUwbRN5K+89JYTAtuE8NrZHYElbMbV7Le5ccydueeaWOi60thb3pvD5Mw/FY59bjqvOXIxkZPwz8obt4OXtGXzmrtX46I//gnW7sjVeKVHjYKUJUQPZlN6E9PqXoZR6hvdnCrBLRzunJCOQEjqyHe5JimUzl7XUqYG2qIarzzoM571lDu7/6xaMFG0ICBQMG5sG81i7c8Rr5fWb57bh/17bhf/6x2Xs80lERLQfdozswNrBSjXn0Itb0V2aq9aV0BHVFO9zMw99CxKJjlovsW4SERWXn7YIf/uWWbh31Rak8xYEBIqmg82DObyxM4s3SpsTv3+pH39auxt3/sPxOHpWR30XTkRE1Cg2bEC6mA48NZgzUbQcbGxPYHZXHFNT7iD0GakZ9VhhXUU1BZecfAg+eOws3PPsZgzmTAgIGJaDzYN5rN+VxWv9IwCA/3t9F85c8ThuvfDNOGHBlDqvnCh8mDQhaiDP7XgOyUH3zbYQItCaq689iuGeDkCSEFNjOKr3qDqtsr5md8dx2SkLxzwvhMA9z27BP//mRQzmTAznTXzijqdx/6VvR3tcq8NKiYiIGs+a/jXeYyEEdr/Wj+7SdZ+vykSWZMw56qQary4c+tpjuOTkQ8Y8L4TA/zy/Hdfc/wL6M0VkDRsX3/EM7v/U2zAtFR3nVyIiIiJPPg+k02OSJtuG83AkCVvbYnjX7E7v+ZltM2u9wtCYmorgH09aMO7nHnlpB776qxewZSiPouXg0jufxf2Xvb0pZsAQTSa25yJqIBuGNiCSddtxDefd0xQA0B7TENdVZLrdqonjph8HXdHrts4wkiQJ5xw7E3+44mQsndEOANiwO4dP/2KVV61DREREe7d+aL33eEe6CDHsVnEmIypS0cohhL5kHyKz59V6eaEmSRLOXtqH319xEt4y151Btz1dwKV3PgujFNMRERHRHqTdZEnGqLQFzRs2MgULu+IRpNpimNNd2fhv5aTJ3px6WA8evvwdOHnxVABupc4/3vEM8oZd55URhQuTJkQNomAVsDO3E5GsW12SKVje58rlp4VEBIqk4C0z3lKXNTaCzoSOH370WHQl3KTSY6/uxL899EqdV0VERBR+lmNh+8h273rrUB6pogkAmFKKRcpmtM0A2ttrur5G0RbV8O/nvcmrzHlq/SCue+DFOq+KiIgo5IaHIYQIVJpkCm4cko5oOKyvzWtRLksyepO9dVlmI4jrKr73kWMwt5RkenFbGl+45zkIwQOlRGVMmhA1iC3pLQDgVZqMFCtJk/KQr2IyikO6DkEqwjkdezOjI4Z//39vgiK7AdXNK9fij2t31XlVRERE4bZjZAcspxJ/bE8X0FZKmqR8A0c7oh2Ia3Ggra3ma2wUU1MR/PDvjoWuum/H7nhyAx5+cUedV0VERBRi6TTyVj4Qi5T3RdIRLdAmtDfZC01hG+69aY9puOX845DQ3Xl0v1q9Ffeu2lLnVRGFB5MmRA1iS6aUNMkVIYTwggNNkRApveEuxiOY3T67bmtsJMsWdOMLZx7qXX/+7ueQM6y9vIKIiKi1bU5vDlzvGMwhYViQJQlxvTIAvi3SBigKkEjUeokN5ahZHbj2vUu86y/euwZDOaOOKyIiIgqxceaZlPdFRqIaetoqSRO25to3i3pS+Na5lXm4X//1i+hPF/byCqLWwaQJUYPYnN4MCIFotoiC6XhzOJIR1StBLSYibjsM2id///Z5Xk/xTQN5fOu3bNNFRES0J+UDHACQLVpAOg8JQCKieLEIUEqatLUBvudofB9+8ywsL/UU35kp4lq26SIiIhrf8HAgaWI7ArnSHA65IwFNqWxxzkhxX2RfvevIPrz3qOkA3Nm5X7rvebbpIgKTJkQNQQiBzenN0IoWZNsZtzWXEdUgFAXTU9PrtcyGI8sSrj/3SEQ191vhbX9cjz+/sbvOqyIiIgonf6XJ9uFKa66krzUX4Eua0IQkScI3PnAkUlH3z/CeZ7fgDy+zTRcREdEYoypNsr59kejUZOBWVprsn2veuwRTku7c14df3IH7/7q1zisiqj8mTYgawFBhCDkzh0hunHkmUbdPZzEewbTENOiKXpc1Nqp5UxK48vTF3vXX7n+hjqshIiIKp5yZw0B+wLveni54Q+DLG/4AEFWjbizCIfD7rLc9iq+863Dv+qu/egGOwxOeREREfvbQILJG1rv274ukplUOa8TUGLpiXTVdW6PrSui47n1HeNfX/vpFGJZTxxUR1R+TJkQNoHyyMzLi9pYcKZje5xIRt4d4MRnlaYoDdOHb5uGIGW6Q9fL2DF7bkanzioiIiMJlSzo4GHT7cCVpkoxUBq22RUqbFqw02S8fPG4m3jrP3eDZPJjHqk1D9V0QERFRmAiBzM7NEKgcKsgUKkmTtp5K3DGjbUagbSjtm7OW9uGdh00DAOzOGvjj2l11XhFRfTFpQtQAvKRJrgjH17czpilQ5coQeCZNDowiS3j/MZU/u98+v72OqyEiIgoff2suRwhsT7vtuXRFhq5W3lKk9JT7gEmT/SJJEs45thKL/O4FxiJERESefB6Z7GDgqXKlSTGioYND4CfFuYxFiDxMmhA1gPLg1Wi2iKxheWcrkr52GBwCf3DOPKLXe/w/TJoQEREF+JMmA1kDpu0gVTQDsQjgqzRhe679dtphPVBk92Ts/zy/jUNYiYiIykYNgS9aNkzbbR8ldcQDlSUcAn/gTlo0DTHN7Wby0As7YLNdKLUwJk2IQs5yLGzLbAMARLLFQAmqf/Cqk0phanxqzdfXLGZ0xHDUTHeD58VtaWzcnavzioiIiMJBCOEd4ADc1lwA3KSJLxaRICGplwaxstJkv3UmdCyb3w0A2DSQxwtb0xO8goiIqEWMGgI/4tsXUTsTgVt5mPTAxXQFJy9295V2Zw38Zd3ABK8gal5MmhCF3I6RHbCF244rkisGh8D7Nio6e+awb+dBOsNXbfLbF7bVcSVEREThsTu/GwWr4F2XkyZto5ImST0JRXZPJzJpcmD8sQjbYhAREbmyu7ahaBe9a/++iN5dSZp0x7oR1+I1XVuzOZOxCBGAKidNHn/8cbznPe/B9OnTIUkS7rvvvr3ev3LlSkiSNObj5ZdfruYyiULN3w4jMlLwggNZkhDXFe9zU3rn13xtzeasI/q8x2zRRdQcGIsQHbwxQ+DTBSiOg4RpIeFLmnituVQViHPD4kCcsaQH5TMwjEWImgNjEaKDt3v7G4Frf9IkPjXlPWaVycE75dBp0BV3u/i3z2+HwxZd1KKqmjTJZrM46qijcNNNN+3X61555RVs27bN+1i4cGGVVkgUfrvzu90HQkDJFGBYbt/ORETxKkuEBPROX1SvJTaNeVMSOLTXDbhWbRzCtuF8nVdERAeLsQjRwduV2+U9thwHu7NFpIom4rrizeAAgFTENwSe1a8HZFoqiuPmdAIAXu8fwev9mTqviIgOFmMRooOX3VU5SCCEQLZY6sahykBH5aAGh8AfvFRUw9sXTgHgHpRZvXmovgsiqhN14lsO3FlnnYWzzjprv183bdo0dHR0TP6CiBpQuW+nXjBhGpXTFOXhXABgRnX0dTA4mAxnLOnFy9vdDYqHXtiBC06YW98FEdFBYSxCdPBG9xAXwm3NFfXFIgCQ0ktJEw6BPyhnLOnFU+sHAbgnPC87JTXBK4gozBiLEB08c3Cn99hyBBzhVj9ENQWFeMT7XF+yb8xraf+duaQXf3i5HwDwu+e3402zO+u8IqLaC+VMk2OOOQZ9fX049dRT8eijj+713mKxiHQ6HfggaibDhWEA7hD4YqnKBAB0tfLf10hEKxsVdFDOWlrp3/noK/11XAkR1dP+xCIA4xFqboGkSakdRqpouqc7faJq1H3AeSYHxd9L/NFXdu7lTiJqZoxFiCqsocpAcsO3LxJRZRQTlaRJR7SjlstqWqcd3uNVE3NfhFpVqJImfX19uOWWW3D33XfjnnvuweLFi3Hqqafi8ccf3+NrvvGNb6C9vd37mDVrVg1XTFR95Y2KSK4YCA78SRO5o5ND4CfJ4p4UOuIaAOD5LcMQgv07iVrJgcQiAOMRam6jK00At9LEH4uossoh8JNkZmccs7piAIAXt6Zhs5c4UUthLEI0ihCwh4e8S/++iKYqKJYqTWRJRkJPjH41HYDOhI7FPe7B3Nf7R5A37DqviKj2qtqea38tXrwYixcv9q6XLVuGTZs24dvf/jbe8Y53jPuaq6++Gpdffrl3nU6nGRxQ07AcC1kzC8AdAm/YvqSJ4tuo6Oiq+dqalSRJOGJ6O/7v9V3YNWKgP1NET1u03ssioho5kFgEYDxCzUsIEUiaZHyVJv5YJKJUTnmyPdfBO2J6OzYN5JE3bazbNYJDprGimKhVMBYhGiWbhWlU5o0WffsiIhGBKMUjKT0FWQrV2fCGdsSMNry4LQ1HAC9tT7NFF7Wc0H83Of744/Haa6/t8fORSARtbW2BD6JmkSlWhn+OrjSJqJU+4lrnlJquq9kdMaOy2bNm83AdV0JEYTBRLAIwHqHmlbfyMB3Tu95TpUlE9SVN+O//oAVikS2MRYhaHWMRamX20CAM2/Cu/fsidlvMe9wW4b/5ybTUF4s8z1iEWlDokyarVq1CXx8HOVFr8p/sjGSLMKxKSWRgo6Jrak3X1eyOmFEJtp7fyuCAqNUxFqFW5o9FgGClif8AR6DShBt1B+2IwEYF5xIQtTrGItTKsru3Ba79SRO0M2lSLUuYNKEWV9X2XCMjI3j99de963Xr1mH16tXo6urC7NmzcfXVV2PLli24/fbbAQArVqzA3LlzsWTJEhiGgZ/97Ge4++67cffdd1dzmUSh5d+oiPoGwSuy5A3lAoBYV0/N19bMjpjOjQqiZsFYhOjgjE6ajBTcqpM2w4KmVGKRQKUJ23MdtCOm+w5wcKOCqKExFiE6OLld+5Y0aY8y/phMh/W2QZYAR3BfhFpTVZMmTz/9NJYvX+5dl/trXnDBBbjtttuwbds2bNy40fu8YRi48sorsWXLFsRiMSxZsgS/+c1vcPbZZ1dzmUShNVysvEnWs5WZJv4e4gAQn8JTR5NpTnccqaiKTMHiRgVRg2MsQnRwxiRNihZU20G740CSfEmTcqWJpgFRzgI7WN3JCKa3R7F1uIAXtqbhOAKy78AMETUOxiJEBye/e0fgurwvosgSnLa49zwrTSZXTFewcFoKr+zI4NUdGRRMG1FNmfiFRE2iqkmTk08+GUKIPX7+tttuC1xfddVVuOqqq6q5JKKGUt6okBwBJVNA+b+TvzWXkIBkV289lte0JEnCkultePKNAWxPF7AzU8TUVGTiFxJR6DAWITo4/qSJ5TjIGTY6R80zAXyVJm1tgMTN/cmwZEY7tg4XMFK0sGEgh3lTEvVeEhEdAMYiRAenuLvfeyyE8NqW64qMYqLyPp1Jk8m3ZEYbXtmRgeUIvLojgyNndtR7SUQ1E/qZJkStrLxRoRUMmGZlnknEt1FhxHS0xTtrvrZm52/R9QLnmhARUYvyJ02yBTcWaTPGJk2iaqm6hK25Jk2wXShjESIiak3m0G7vseUIOL7DpMU4kybVxNbl1MqYNCEKsfJGhTsEvtK3079RYSRiSOg8eTjZls7kRgUREdFwofIzMFN055mkxqk00RXdfcAh8JNm6UzONSEiIrKGBrzHo/dFCqw0qSr/vsgaxiLUYpg0IQoxL2mSN1C0fcGBb6aJ3NYOWeJ/5cm2hCcqiIiIApUmI0ULgJs0ifhiEVVWocqlrr9MmkyawOlOVr0SEVErEgJ2esi7NHz7IpqqwIi5hzZkSUZST9Z6dU3vsL42r+sqO3BQq+FOK1FI2Y6NEWMEAKDn9lxponV213xtrWDelATiujvkjBsVRETUioQQgaRJpuAmTZKGFYhFvCHwAJBK1Wx9zW5aW9Sbqfb8lvReZyIQERE1pXwehpH3Lv37IkhEIEqHOFJ6iodJqyAZUb2Zai9vy8D0Ja2Imh2/oxCFlH+TYm/tubT2rpquq1UosjsMHgA2D+YxmDXqvCIiIqLaKlgFmI7pXY94SZNgey5vCDzApMkkWzrDrTYZzpvYPJif4G4iIqLmYg8NwrAr78X9+yJOKuo9Zmuu6inHIobt4NUdmTqvhqh2mDQhCqlA0iRvBIKDiKpUHndOqem6WsmSwDB4tugiIqLW4o9FgGB7rj1WmrA916Q6YjrnmhARUevKDmwPXBf9lSZMmtSEv13oC2xdTi2ESROikPJvVLjtuWwAbgWEIkve52JdPTVfW6son6gA2L+TiIhaz+ikSbk9V8qwAvPVWGlSPUfM4AEOIiJqXdndwaSJ/zCp1BH3HjNpUj1HcF+EWhSTJkQhNbo9V3kQvH+TAgDiTJpUzeLeysbPa/0jdVwJERFR7Y1XaSIJgU7bgSRVDnB4lSayDCQStVxi0wvGImyJQUREraUw0B+4Lg+ClyUJdrJyaINJk+rhvgi1KiZNiELKv1EhjxRQnv3pb4cBAInu3louq6XMn1rZ+Fm7k8EBERG1luFi5TSh7QjkDAtx00JUkQL3eZUmyaSbOKFJM7Mz7sV+a3dm67waIiKi2ioM7vQeCyG8ShNdlWHG2Z6rFroSOroSOgDui1Br4bsaopAqJ01ky4aTqww+8ydNLE1BW9vUmq+tVcR1FTM6YgCA1/tHIMqZKyIiohbgP8Cxp3kmgK/ShK25Jp0iS5g/xT3EsX5XFqbtTPAKIiKi5mEM7fYe246AU3pPHlFlFBOsNKmVBaUDpTvSRaQLZp1XQ1QbTJoQhVR5oyKSLQb6dvrbc5nxKJJ6suZrayULprl/vpmChZ0jxTqvhoiIqHYCSZPSPJPkqHkmgK/ShEmTqijHIpYjsHEgV+fVEBER1Y41NOA9Lo7aFynGdO+6PdoOqp5DplX2nd5g5Su1CCZNiEKq3BIjkje8eSaAe6KiTG5rhyzxv3E1HTK1Ehy8zv6dRETUQvxJk0zRPVWYHFVpokgKVFl1L9p4yrMaGIsQEVGrsoYHvceGb19EV2UYcffQhgSJh0mrbAFjEWpB3G0lCiHbsZE13Oy9nhtVaeLfqGjvrPnaWs2Cab65JgwOiIioRQghxq00aTPMwAEOr8oEYKVJlSyYxo0KIiJqQbYNe6QSi/j3RZSICjPiHtpIRVI8TFpljEWoFfG7ClEIZYwMBEq9Oke35/JtVGgdXTVfW6vxn+7kAFYiImoVRbsIw67MVCvPNEkWrUAsElUrQ1iZNKmOYCzCjQoiImoN9vBQIBbx74s4ySggSQA4z6QWGItQK2LShCiE/Cc7I3ljj0mTSMeUmq6rFfFEBRERtaLhwnDgOuPNNAm25/KGwANsz1Ul86cmyvtCrHolIqKWkd29PXDtn2mCtsqhDSZNqm9GRwxRzY3/GItQq2DShCiE/EkTPVf0enfKkgRV9p3u7JpW87W1mu6Ejo64BoAnKoiIqHVkjEzgulxpkjIsaArbc9VSVFMwszMGwK16FULUeUVERETVlx0IJk38M03QFq88ZNKk6mRZwvwp7oHSDQO5wMFeombFpAlRCI0Ylc15f3su/8lOAIgxaVJ1kiR5Q8+2DRe8TSMiIqJmlikGkybZ0s+/LsuGXC57AKAreuUmJk2qphyLjBQt7EgX67waIiKi6isM7Axcm1b5MCngpCqHNpg0qY1yFw7bEdiwm63LqfkxaUIUQv6NCiVbhFM6UagpUuC+WCeTJrUQ6N/JUlQiImoB/gMcQgjkDBuq7SAlgicLvfZcug5EIqDq8McibBdKREStoDgUTJqUK000RYYZr7TnSupJUPUxFqFWw6QJUQh5GxVCQM7kved1XzsMIQHJ7t5aL60lLZiW8B6zRRcREbUCf9Ikb9pwhHDnmSjBtw9epUkq5Q1kpcnnn7HGWISIiFqBOTTgPXaEgO2UD5PKKCYqBzVSOitda4H7ItRqmDQhCqHyRoVWtGAZtve8v4e4GdWRjLXXfG2t6BAOgyciohbjn2mSLbqxyOh5JsCopAlVDWMRIiJqNdbwoPfY9M3Q0BUZxVilPSgrTWqDsQi1GiZNiEKonDSJ5IowfcPO/DNN7EQ82EecqmbBVJ7uJCKi1uKvNMkZ7jyTZNGEpu4hadLGfuLVxFiEiIhaje1LmviHwGuqDCNeqTRh0qQ25nYnIJeKitfu5EwTan5MmhCFUPl0p54rBoMD3+lOqb2j1stqWTM7417CiicqiIioFfiTJpVKExO6b76aJmuQyi25WGlSVV0JHV0JN0HFWISIiJqeEHDSw96laQvvsaZIKMbdn4mqrCKqRse8nCZfVFMwqysOwD3A4ThiglcQNTYmTYhCxnZs5MwcACCSM8YEB2VKG1tz1YoiS5g/xe3fuWF3LlD9Q0RE1IwCSROv0iTYnitQ8cqkSdWVB7D2Z4pIF8w6r4aIiKiKDANWIeddBt6Dx3Q4qgLArTKROFOtZsqxSM6wsS1dqPNqiKqLSROikMmalTLHSK4Y7N3pa4mhtnfVdF2trjyA1XIENuzOTXA3ERFR4ypaRRi24V1ni6WkiWEGYpGIWmmNwfZc1RcYwMpqEyIiamLO8BBMu3JAwPDti9ipSmUJW3PV1gLfXBPGItTsmDQhCplMsTJ4dW/tufTO7pquq9X5e4m/wV7iRETUxPxVJgCQNdz2XG1Fk5UmdRSMRdhLnIiImld+oB8Cla4b/koTkYp5j5k0qa0FUysHOLgvQs2OSROikPFvVLjtudzgQJIAVa6UnUY6p9R8ba1sbnfce7xxgJUmRETUvEYnTXJepQnbc9XTnO7KRsUGxiJERNTE8oP9getAe662SqVJSmf8UUuMRaiVMGlCFDLBpEnRCw40WQ706ox2Tq352lqZPzhYv5unO4mIqHlljEzgOmvYgBBotywovgMcTJrUlv8AxwbGIkRE1MQKA8GkieGb9cpKk/qZ60+asG05NTkmTYhCxp800bJFbxC8pgb/u8a6emq6rlY3J7BRweCAiIia15hKE8NCzLIRGzVo1UuaJBKAotRqeS1rVhdjESIiag3FoV2B6/KsV02RYSY506RepqUiiJT2pniAg5odkyZEIVM+3SnbDqRs0Xte97XDcBQZyTa256ql7oSOZEQFwI0KIiJqbv6kiWk7MCwHyaIJTdlD0oRVJjUR1RT0tbsbRdyoICKiZmYO7PYeCyEqHTgUCcVYpdI1FWEMUkuyLHkHSjcN5GE7YoJXEDUuJk2IQqa8UaHnjUDfTv9GRTGmI8ngoKYkqRIcbBnKB3uqEhERNRF/0iRbmmeSMqzAAQ7AlzRpa6vZ2lpdORYZzJkYzpt1Xg0REVF1mMOD3mPLqYyE1xQZRjzifY6VJrVXbl1u2A62DefrvBqi6mHShChkyhsVkWwRRiBpUvnvaiRjiGvxMa+l6ipvVNiOwJZBBgdERNScMsXKTJOsYQMAUkUzEIsAQEQpbVqw0qRm5nRVeolvZOUrERE1KceXNPEfWNQVGcUEkyb1NMfXLpSxCDUzJk2IQqa8UaHnil7fTgDQfTNNpFQqMBSeaoPD4ImIqBX4K01yXqWJGZivpkgKFLk0x4SVJjUzZ0plo4KxCBERNSXHgZ0Z9i4N376IqikwohoAQIKEhJYY83KqrjlT/PsiTJpQ82LShChEhBCVSpPcnitNlI6umq+NgLm+YfAbBxgcEBFRcxqvPVeyGGzP5bXmAlhpUkNzfQc4ONeEiIia0sgITMvwLk27MjfDSkSA0gHSuBavHOCgmvHvizAWoWbGpAlRiBSsAmzhtsGI5IxAcODfqNDaO2u+NgJm+1pirN/FpAkRETUf27GRNStvgL32XEZwEHwgacJKk5qZ3eXfqGAsQkRETSiTgWFXkib+w6ROKuo9Zmuu+vC3CmUsQs2MSROiEPGf7IyMas+lqZWNCq2ju6brItfcKf5KE56oICKi5uNPmAD+ShMz0CqUlSb1MaebSRMiImpuxuAu7zApgMC+iNMW8x4zaVIf0zuiUGV3f4qtQqmZMWlCFCIZozJ4Vd9Le65Ix5SarotcPamot2HE3p1ERNSM/Ac4gNGVJntImrDSpGZSUQ3dCffPnhsVRETUjPID/YFr/yB4+CpNUhEe2qgHVZExs9NNXm0cyEEIMcEriBoTkyZEIRKoNMkWveBAlSXIvsHv0a5pNV8bAbIsYU6pLcbGgRwch8EBERE1l0wxE7jOFS0ojoOEaXunCgFf0kRVgWgUVDvlapP+TBE5w6rzaoiIiCZXYVTSxH+YFO2ViktWmtTPnNKMtZxhY+dIsc6rIaoOJk2IQsRLmggRqDTxn+wEgBiTJnVTDg4My8H2dKHOqyEiIppc41WaJIsWNEWG5DvAEVEj7oO2Nm8gK9WGfxj8xgFWvhIRUXMpDu0KXJdnvSqyBCfJmSZhMJftQqkFMGlCFCLl051a0YKwbJSrHP09xI2ohmSsvR7LIwR7ibMtBhERNRt/0sQRAnnTcltzqcG3DV6lCeeZ1NxsblQQEVETMwZHJ00qh0mLiYj3PJMm9TO7m8PgqfkxaUIUIuWNCj1XhGlVWj/5K02MeIS9O+vIf6JiI4MDIiJqMv6kSd5wD3CkiiZ0JVhN4iVNOM+k5uYGNip4gIOIiJqLNTzoPbYdAbvUFltTJBRjlZlqKZ37IvUSrDRhLELNiUkTohApb1RExgyBr2xUFOMRnqioozm+jQoOgyciomaTMSozTbJFd15G0rDGtAplpUn9BKteGYsQEVFzcYaHvMf+IfA6K01Cg/si1AqYNCEKkUrSxBgTHJSJVBKqrNZ8beSawxMVRETUxPyVJlnDBuBWmviTJhIkaLLmXrDSpOb8GxWseiUioqZSLMIqVH62+fdFRFSDoyreNZMm9TOrK+aNtNvIfRFqUkyaEIVI+XRnJFeEafkqTXx9xJX2zpqviypmdMSgym50wN6dRETUbAJJE6/SxAzMV9MUrTIUnpUmNdcZ15CKugdoOF+NiIiaSjqNolX0Lg3fvoiVqgyB1xUdETUCqo+IqmB6ewwAK02oeTFpQhQSpm2iYBUAuDNNgu25Kv9VVSZN6kpVZMzodIODDbuzEEJM8AoiIqLGIIRAplhpz5Uz3KRJ26hKE681F8BKkzqQJMmrfN06lA9sKBERETW0TAaGbXiXpl15v20nKkkTVpnU3+wuNxYZzpsYyhkT3E3UeJg0IQqJrFk5KRjJFgPBgb89l9bRXdN10Vjl4CBr2BjIMjggIqLmULSLsIXtXWeL7mN3pkllvlpE8Z3sZKVJXczpclt0OQLYMpSv82qIiIgmhzM8BNMxvetAe662mPeYSZP687cu3zjAahNqPkyaEIVEzqz8kInkgzNN/BsVeieTJvU2s7MSHHCjgoiImkXWCLZ6ypk2IASSRrDSRFO0yk1MmtTFzM7KxtGWQcYiRETUHIqDuwLX/n0Rx9eeK6ElQPXFWISaHZMmRCERSJpki7B8lSaK7Dvd2Tm1puuisfzBwWYGB0RE1CT8sQgAFAwbMcuG6ohg0qQ8BD6RABQFVHvBWISnO4mIqDkYY5ImvnbYvkqThM6kSb35D5NyX4SaEZMmRCFR3qiQbQda0YTpuCcqNEXyhq06ioxosqNeS6QSblQQEVEzGp00yZs2UkUTkgT4zm9UZppwnkndsOqViIiakTG0O3BtOb65XW2VSpO4FgfVV6DShLEINSEmTYhCotwSQ88VAcCrNFHlyn/TYjyCOE9U1B3LUImIqBmNSZoYtjvPRJa9AxyArz0XW3PVzQxWvRIRURMyRyVNypUmsiTBSlV+9jFpUn8zeJiUmhyTJkQhUd6oiOQM2I6AI0pJE988k2JcZ3AQAixDJSKiZuRPmgghkLfcShN/LAL42nOx0qRuZnRwo4KIiJqPPTwUuLbsSgcOIx7xnue+SP1NS0W9+bvcF6FmxKQJUUhUkibFQAmqv4d4MR5h784QmJqMQC/9vbAMlYiImoU/aWLYDhxHIDVqCDzASpMwSERUdCXcNmmseiUioqbgOLAzae9SCAHLKVWaqAqMqOZ9joPg60+RJUwvHeLYMpiHEGKCVxA1FiZNiEKivFGh54JD4FVfE3EjHuGJihCQZQnTO9x+qpsZHBARUZPImlnvcd6wAcCtNJFZaRJG5WqT7ekCTNuZ4G4iIqKQGxmBZRveZTlhAgBmXAd8rUK5LxIO5VgkU7SQzlt1Xg3R5GLShCgkvEqTbDHwxjdQaZKIIKpGx7yWaq/comukaGE4b9Z5NURERAfPX2mSN92kSdKwWGkSUuUZa44Atg8X6rwaIiKig5ROw7Qr7639h0mNRHAfhEmTcPDPe908xHah1FyYNCEKCS9pkjeClSa+PuJSqg2yxP+2YTCTA1iJiKjJBJIm/koTfywCCYqkuBesNKkrfyyyiXNNiIio0WUyMJ1K0sR/mNRMRAK3MmkSDpz3Ss2Mu69EIVFuiaHnijD9M03kyn9TpaOz5uui8QUHsDI4ICKixjd+pYkJ1ReLaIoGqdweg5UmdcVYhIiImkomA2MP7bmsZKXSRJO1StUr1RVjEWpmTJoQhYAjHORN9wdMJLfnShOtvavma6PxzezyBwc83UlERI3PnzQpmDYUx0HctKH5Y5HyPBNNA6JsGVpP/tOdHAZPREQNL5MJtOfyV5o4qUrMkdA5BD4s/FWvjEWo2TBpQhQCBasAAQEIAT2/55kmWhsrTcIisFExxOCAiIgam+3YKFiVuRg5w0bScAd6qkqw0gSAW2UiBQfEU20FD3AwFiEiogY3qj2X/zCp46s0YWuu8JjZ5W/PxcOk1FyYNCEKgfLJTtW0oVhOoAy1XGliRjTEYmyDERYsQyUiombirzIB3EqTpOFuXIxbacLWXHUXjEW4UUFERI3NGh6EI3xzTHxty9HGpEkY9aQiUGQ3TuS+CDUbJk2IQqC8UaHnigCCZajlPuLFRITBQYj0tEWhMjggIqImMTppkjccJIulShPfTBNd0d0HTJrUXSqqoT3mJrFY9UpERI3OHBoIXPsrTURb5aAA90XCQ1Vk9LW7CS3GItRsqpo0efzxx/Ge97wH06dPhyRJuO+++yZ8zWOPPYZjjz0W0WgU8+fPxw9/+MNqLpEoFLKGOwQ+knOHnpWDA1mSvKx9MaYzOAgRRZYwvXTCcwtPdxKFFmMRon0zJmliWl6lSWC+msJKkzAp9xLfNlyA5Tt0Q0ThwViEaN+Yw8Gkif8wqZxi0iSsyrHIcN5EpmBOcDdR46hq0iSbzeKoo47CTTfdtE/3r1u3DmeffTZOPPFErFq1Cl/84hfx6U9/GnfffXc1l0lUd+WNikip0sQqlaH6NymMOCtNwqbcFiNdsDCcZ3BAFEaMRYj2zdikiYOkYUGRJcgS23OFVTkWsR2BHZlinVdDRONhLEK0D0wTVjYTeKrcttxQZWgJ3Xue+yLhMqOD816pOanV/MXPOussnHXWWft8/w9/+EPMnj0bK1asAAAcdthhePrpp/Htb38b55xzTpVWSVR//vZcQgiYpUoTzdcOoxjXkdATdVkfja98ogIAtgzmvRYZRBQejEWI9s3YpImNVNGEJgeHvbPSJFxmdvoGsA7kAnNOiCgcGIsQ7YNMBqYdPIhYrqAsRCPQfQc4Ehr3RcLEvy+yeSCPQ3vb6rgaoskTqpkmf/rTn3D66acHnjvjjDPw9NNPwzR5ipuaV6XSxIA9zhB4gJUmYeTfqOCJCqLmwFiEWlXWzHqPbUegaNpIGhZUJfh2gZUm4RLYqOCMNaKmwFiEWlImA9MJ/vsuHyY1fFUmACtNwiYYi7B1OTWPqlaa7K/t27ejp6cn8FxPTw8sy8KuXbvQ19c35jXFYhHFYqUUPZ1OV32dRJPNX2li+oadab6NCs40CZ8ZDA6Ims6BxCIA4xFqfP5Kk4JpAwCShglNYaVJmPljER7gIGoOjEWoJY2qNHEcAUe4eyNmPBK4lfsi4cJYhJpVqCpNAECSgm/MROmb5Ojny77xjW+gvb3d+5g1a1bV10g02bxKk7zhzTMBAFVmpUmY8XQnUXPa31gEYDxCjc+fNMl7SRMLqsxKkzDj6U6i5sRYhFrOqEoT07cvYiWYNAmzWf5WodwXoSYSqqRJb28vtm/fHniuv78fqqqiu7t73NdcffXVGB4e9j42bdpUi6USTapyS4yxlSaVoNhOxisbFRQKo2eaEFHjO5BYBGA8Qo0vkDQxbGi2jahlB1qFAqVKk0gE0PXRvwTVAVuFEjUfxiLUkjIZGLbhXVq+fRErGQ3cyqRJuPS2R1E+78tYhJpJqNpzLVu2DL/+9a8Dzz300EM47rjjoGnjbxZHIhFEIpFxP0fUKHJmDhACet5A2vZVmpTacwkJ0FIdez1ZRLXX2xaFIkuwHYHNQzzdSdQMDiQWARiPUOMb3Z4rYVgAgq1CFUmBLMmsMgmR9piGVERFpmjxdCdRk2AsQi1pVHsu07cv4vgqTSRIiGkxUHhoiozetii2DhcYi1BTqWqlycjICFavXo3Vq1cDANatW4fVq1dj48aNANyTEOeff753/8UXX4wNGzbg8ssvx0svvYSf/OQn+PGPf4wrr7yymsskqrucmYNWMCE7AqYzttLEiOmIR5L1Wh7tgVoKDgBg61ChzqshovEwFiHaN/6kSc60kSolTfyVJpxnEk7lXuLbhgpwfHEkEYUDYxGifTCqPZfl+3lmt1WSJDEt5h7goFApxyIDWQN5w67zaogmR1W/0zz99NM45phjcMwxxwAALr/8chxzzDH46le/CgDYtm2bFygAwLx58/Dggw9i5cqVOProo3HdddfhxhtvxDnnnFPNZRLVleVYMGwDkbxbimr5K01KfcSNGOeZhNX0DjdpwuCAKJwYixBNTAiBrJH1rguGjWTR3bjQfDNNvDahbW01XR/t3fQOd6PCsB3syhYnuJuIao2xCNE+2EulieRrz8V9kXAqxyIAsHWY1SbUHKranuvkk0/2BpaN57bbbhvz3EknnYRnn322iqsiCpfyyU49577JHW+mSTGuMzgIqb72GIBBAMC24TzmT2VFEFGYMBYhmphhG7BFJfGfN20kx6k00ZXSHBNWmoRKX3tlM2nbUAHTUtG93E1EtcZYhGhiIp0OVpr49kVEe2VDnvsi4dTn+zvaNlTAAu6LUBNgTRtRnZWTJpHcOJUmpT7iRpyVJmEVOFHBFl1ERNSA/K25AHcQfNIoVZr4ZpqwPVc4BWMRnu4kIqIGUyzCKgRjkXKlSV5VEIlWZvlwXyScZnRUDmwwFqFmwaQJUZ2V22F4lSa+3p2qXKo0ibHSJKwYHBARUaMbkzQxbaRK7bkCM01kJk3CaIYvabKFsQgRETWaUfNMgMpMkxFdQ0xXvOe5LxJO0xmLUBNi0oSozrxKk1EzTVRZgiSVBsHHI0joifoskPbKX4bK3p1ERNSIxqs0SRkWJACKxEHwYRdozzXMqlciImowo+aZAJVKk5GIiphWSZokNO6LhFGgPRf3RahJMGlCVGejZ5qUT1SovnYYnGkSXmyJQUREjS5rZgPX7kwTE6oiewc4AFaahBVjESIiamjjVZqUZprkolqgVSj3RcJpBtuWUxNi0oSozvwzTRwhYJeSJpqvHQZnmoQXgwMiImp0/koTIQTyhoVk0QrEIoCv0iTJ4Z5h0tseRTm3xaQJERE1nHEqTcqHSc14NPA890XCqS2mIlFqo8ZYhJoFkyZEdeZVmuSL3mkKAFBlX6UJZ5qEFoMDIiJqdP6kiWkLqKYNzXECsQhQqjSJxwFVrfUSaS80RUZPyt1U2sIDHERE1GhGVZoIIbz2XFZCD9zKfZFwkiTJq3zdMpSHEGKCVxCFH5MmRHWWNbOQHAE9b3iBAVCpNHFkCWZUY3AQUpIkoa8UHGwdZnBARESNx580yZs2kuMMgQdKlSZszRVKfR1u0mTXSBFFy67zaoiIiPZDJgPDNrzLcvcNADATrDRpFOV9kaLlYDBnTnA3UfgxaUJUZzkzB61gQBKVElSgMtPEiOmAJCGmxvb0S1CdlU9UFEwGB0RE1Hj8SZOCaSNpWAAQ6CEOlCpNmDQJJf9ck+0cBk9ERI1kVHsu07cv4iQjgVuZNAmvGR2VBBe7cFAzYNKEqM5yZg6RnHuqIlBpIrunO4vxCGJqDIqs1GV9NDEGB0RE1Mj8SZOcYSNllCpN5EqliQQJqqwyaRJS/hlrWxiLEBFRIxnVnsvy7YvYqeDh0YSeqNmyaP9Mb2csQs2FSROiOsuZOei5IgAEZ5qUK004BD70+tr9w+AZHBARUWPZc6VJJWmiyiokSWLSJKT62isHOLZxrgkRETUKIcZWmpT2RQQAyVdposqqW/VKodTnO8Cxjfsi1ASYNCGqIyGEW2mSdytNLKdyoqLcR7wY5xD4sPO3xGDShIiIGs2eZ5pU3ipoSmmTgkmTUGIsQkREDalQACwrWGlS2hfJ6ioi0UqSJK7F3QMcFErT/R042CqUmgCTJkR1ZNgGHOEgkh2n0qTUEsOIRxDTOM8kzBgcEBFRoxJCIG9WNtn9lSb+9lzeyU4mTULJ355r6zCTJkRE1CDSaQCA5VjeU+V9kRFdQ1SrtCnnnNdwY6tQajZMmhDVUd5yf5CU23OZgUqTyiB4BgfhNp3tuYiIqEEVrAIEKoc2CqZvpgkrTRqGvz3XVrbnIiKiRpHJQAgRTJqUBsFndTWYNOFh0lDrDbQK5b4INT4mTYjqqNwOw2vP5as08Q+CZ3uucOtt5yB4IiJqTOUDHN616SBVHDsIXpVV9wGTJqHUldARUd23doxFiIioYYwaAg9UBsFndA1RrbJtyX2RcIuoCqaUZtDwAAc1AyZNiOqo3A7DGwTvVJImilyZacITFeEW1RgcEBFRY/LPMwGAgmEhUW7PpYxqzyVJQDJZ0/XRvpEkyWuLsXUoDyHEBK8gIiIKgUwmUGUC+NpzRVTE2J6rocwotS7fkSnAtJ0J7iYKNyZNiOrIqzQpJ01KP1RUWfIGnBmsNGkIDA6IiKgR+eeZAICULUIRAhIAxTdsVVM0N2Ei8+1DWJWHwWcNG+m8NcHdREREIZBOw7SDlSbltuWZUTNNuC8SfuVYRAhgO+e9UoPjux6iOspbeci2A63ovrEtV5qUe4jbqgJLU3iiogH0tVeCgx1pBgdERNQYRleaqFm3ZaiqyN4BDqBUacLWXKEWmGvCYfBERNQIxmnPZZf2RXJRDZpvvho7cIRfn2/e6zYmTajBMWlCVEc5M+e15hJCeMGB6mvNBUniiYoGUD5RAbBFFxERNY7RM020rPszzD/PxL1WmTQJuWAswqQJERE1gExmbKVJqT2XlYgGnue+SPhN7+C8V2oeTJoQ1VHezCOSHTvPpNxD3Ii7czJ4oiL8GBwQEVEj8rfnshwH0Xy50iSYNNEUDWhrq+naaP/MYNKEiIgaTTodmGkihIBVas9lleaGlrEDR/j5Y5EtjEWowTFpQlRHeSuPSGlzwvLNwVBL/cKLMR0Ag4NGEDjdyZYYRETUIPztuQqmg5ThnvZUR80uYXuu8OvzH+BgSwwiIgo7xwGy2UB7Lke4La9tSYKI64HbeZg0/Po6/O25uC9CjY1JE6I68rfnMsepNCkm3JMVLEMNP3/SZMsggwMiImoM/vZcBdNGqlhKmihsz9VoGIsQEVFDGRkBhAi05ypXmYxEVER1NXA790XCz9+Bg7EINTomTYjqKG/mEcmVK00qSRNNrrTnUmXVbYlBocb2XERE1Ij8lSZ5w0bScFtkjJ5pwvZc4Te9ne25iIiogWQyABBoz1XeFxnRNUQ1JXA7O3CE35REBLribjVz1is1OiZNiOooZ+YQyZVnmvjacymV9lw8TdEYpiYjiKju39tmnqggIqIG4Z9pUrRsJL1Kk8rbBEVSIEsyK01CLqYrmJJ0W5kwFiEiotBLpwEg0J6rPOs1o2uIjU6asD1X6MmyhBmd7t/T5sEchBATvIIovJg0IaqjvJX32nP5K03KpzuLiQhPUzQISZK8oWdbhvIMDoiIqCEEKk1Mx6s00XyVJl7FK5MmoVeORXZkCjAsZ4K7iYiI6qhUaRJoz1Wa9ZqJqIFKk6gadQ9wUOiVY5GsYWM4b05wN1F48TsOUZ04wkHBKlTacwVmmrj/NY14hJUmDaR8oiJn2BjKMTggIqLw8880MQomEmapPZdvpokqq4CqAtHomNdTuJRjESGA7RwGT0REYTZOe67yrFe3PVdly5L7Io1jhm/GGitfqZExaUJUJ3kzDwgBPV+uNPG15yrPNInpLEFtIP7gYAt7iRMRUcjZjg3DNrxraaSyya7KlbcJmlyaZyIF55xQ+AQ2KoZye7mTiIiozsqVJs7YSpMRPVhpwg4cjaN8gAPgvgg1NiZNiOokb+WhGhaUUuuEQHsuRYIZ0eAoMoODBsITFURE1Ej8rbkAQM74kibKqPZcbM3VEAIHOBiLEBFRmKXTsB0bjqgcIPUGwUeCM014mLRxMBahZsGkCVGduEPgK6c7Tf8geFlGMREBwDLURjKzy5804elOIiIKN39rLgBQRoreY3+liSqrTJo0iJmdlbiRBziIiCjUMplAlQlQaVue1rVApQn3RRrHzE4eJqXmwKQJUZ3kzcoQeACwS8GBJAGKLKEY0wHwREUjmdFRCeRYhkpERGE3utJEy+6h0kRmpUmjYEsMIiJqGJlMYJ4JAFilw6QjETUw04QdOBpHMBbhYVJqXEyaENVJzswhkvdVmpTKUDW5MgQe4ImKRhIIDniigoiIQi5vBn9W6Vn3MIcsSZClUe252tpqujY6MIxFiIioIRgGUCjAtEdVmtgCRUWGqSiIqqw0aUS9bVEopTm9PMBBjYxJE6I6yVt5REqbE0II70RF+WRnMV6qNOGJiobRk4owOCAiooYxptKkVAHrrzIBWGnSSNqiGlJRFQBjESIiCrFxhsADbnuuEV2DrsqQ5Uo8wg4cjUNVZPS2RQHwAAc1NiZNiOrE357LEYAozYFX5XLShJUmjSYQHHCjgoiIQs4/00QI4c1aU+Vg0oQzTRpLeQDrtuG81/6ViIgoVEpJkzHtuWwHmVFD4AHuizSaciwymDORLVoT3E0UTkyaENWJvz2X5R8CrwTbc/FERWMpDz0bypkYYXBAREQh5m/PZdgOkkX3tKemBN8iaAorTRpJeRi8aQv0ZwoT3E1ERFQH5UoTX3sutwOHwIiuBobAA+zA0WhmcsYaNQEmTYjqxN+ey7IrpwArlSZuey6eqGgs7CVORESNwt+eq1C0kTTcjYvRlSZsz9VYZjIWISKisEunAQTbc1ml6shMRBuTNOG+SGPhvgg1AyZNiOokZ+ag50tJE1/rhNGVJlE1WvvF0QGb2eE/UZHby51ERET15W/PZRUM6HZ5vlrwLYKaSAGaVtO10YGb0cHTnUREFHLjVJqU90VGdBUxLRiLsANHY/HHIpsZi1CDYtKEqE7yxSz0cnsu29eeS5YgJAlGVENUjUKW+N+0kfBEBRERNYrAIPjhys+sMTNN2jtrtSSaBP5YZDNjESIiCqNxZpqU90XGqzRhe67Gwn0RagbcjSWqEzM9BKlUYGIGKk0ktzWXJLEEtQHN6Kj8nfFEBRERhZl/pomU8SVNlErSRJM1SG1tNV0XHRxWmhARUeiVK03Gac81ogeTJoqkQFf02q6PDgpjEWoGTJoQ1YEQAiI97F0HK03kyhB4nqZoODN5upOIiBqEv9JE9g0MV+XKWwRN0QAmTRoKYxEiIgq98kwTf3suu9Key580iWkxSFKwCpbCbbq/Pdcg25ZTY2LShKgOLMeCkq384PAPgtcUCcUYh8A3qr6OygwalqESEVFYCSECM03kkaL32F9posoqh8A3mK6EjmipF/wWblQQEVHYCAGMjAAY255LoJw0qWxXcl+k8UQ1BVNT7mFg7otQo2LShKgO3CHwhncdGATvrzThsLOGE1EVTCsHByxDJSKikDJsA47wVbqO+CtNgu25mDRpLJIkeW0xtgzlIYSY4BVEREQ1VCgAlgUhxJj2XDlNhSPLiPkrTdiBoyGVY5H+TBFFy67zaoj2H5MmRHWQt/LQc5UTnZbj27QozzQBT1Q0qvLQs52ZIgomgwMiIgqfwBB4AIrvMIemjGrPlUzWbF00OWZ0ujFkwXQwkDUmuJuIiKiGxhkC7147GNFVAAi05+K+SGPyD4PfNlTYy51E4cSkCVEdjKk0sf2VJhKMUnsunqhoTP6hZ9uGGRwQEVH4+FtzAYCe9bXnktmeq9FxACsREYVWqTWXv8oEAExbIDtO0oQdOBrTTMYi1OCYNCGqg7yZRyQ3tj2XIkuQJIntuRrczM7KSRj27yQiojAaXWnir4BVRrfnYqVJw/EPg2csQkREobLHShOBEV0DgMBMEx4mbUyMRajRMWlCVAc5Mxdsz2W77bnKJzsNDoJvaP4y1M0cwEpERCGUN31vXoWAVqqAVUsHOMrYnqsxzQzEItyoICKiECklTUw7WGli2W57LlmSoCscBN/ouC9CjY5JE6I6yFt56AV3c0II4VWaqKXAoMj2XA1tRkfUe7yV7bmIiCiE/JUmqmlDKs3gUpXg2wMlkQJUtaZro4M33dcSY+swkyZERBQie2jPZdlupUlUUwIHONiBozEFYxHui1DjYdKEqA7yxaw306ScMAHc051CkmBG3ZJUnqhoTH3t/oFn3KggIqLw8c80UbJF2OUDHL7WXACgtLXXdF00OfraKwc4OHyViIhCZW/tuSJqoDUXwH2RRhXYF+EBDmpAPDZGVAfF9CAipVyJP2miKaUh8KVTFTxRMbls24ZpmhPfeJCmRCXMSLmD6/KFPAoFblZQbei6DlnmeQgimligPVem8lhVgkkTrb2rVktqCbWKRdp1YEabAgigWCwwFqGaYSxCRBMqV5r42nM5joAj3EHwMd8QeIAdOCab4zgwDGPiGw+SDoFDunTkTRumYTAWoZrRNA2Kokx84wSYNCGqA3N4wHtcnmcCAKosoxjXvWsGB5NDCIHt27djaGioZl/z2lOmwRFuImzdunU1+7rU2mRZxrx586Dr+sQ3E1FLGzFGvMcinUc5VaKO2uzU2jtruKrmVY9Y5LpTemA7AorMWIRqh7EIEU2oVGli2JWNe9Nx90VGdA1TRidNeJh00hiGgXXr1sFxnIlvngRfPmkKTFtAksBYhGqqo6MDvb29gVZ/+4tJE6I6KAzt8h4bVuWHla7K3hB4XdGhK3yzMRnKmxTTpk1DPB4/qG+a+2xnFoZtQ5IkzJ2WrM3XpJbmOA62bt2Kbdu2Yfbs2fw3R0R7lS6mvcfC1zJBV4NJk0jHlJqtqZnVIxaRd2dRKM2qmdOTgsyfC1RljEWIaJ+UKk2KdtF7qrwvMqKrmBsJblWm9FTt1tbEhBDYtm0bFEXBrFmzalIVqA3mkC26bdhmTU2OmZ1HNNmEEMjlcujv7wcA9PX1HfCvxaQJUR0YQ7srj0cnTeIRAEBbpI1vNCaBbdveJkV3d3fNvm40ZsMsuOXGmh5hcEA1MXXqVGzduhWWZUHTtHovh4hCzJ80cTIFlM90+pMmuqJzpskkqFssErVRFG4somg6IurBtykgmghjESLaq2IRKLWGKlrBpEleVWDLMpLRylalJmuIqtExvwztP8uykMvlMH36dMTjtZkTE406yJUqihRNR1TnNjRVXyzmVqf19/dj2rRpB9yqi7t4RDVWtIpeOSoAGL72XLpSqTRpi7TVfG3NqNw3vFZBQZnm6wlv2rUpfSUqt8KwbbvOKyGiMHOEg4xRiUWQrlSaRHxJ/ogSAVI83Xmw6hWL6L6/S9MSe7mTaPIwFiGivfLthQQqTWwH2dKGetJXacLDpJOn/H25lu0TNV8sYtiMRah2ynH3wcwSZNKEqMYyRgZ6vtK7sziq0qTIpElV1DrQ8gcHJoMDqhG+oSCifZE1snBEJf6QRyqDOf2VJhGVSZPJVN9YhAc4qDYYixDRXpVac9mODcuxvKcNy8GI7lanjU6a0OSq5fdpxiJUL5Px75xJE6IaSxfTiPiSJoH2XEqwPRc1ruCJigMLDk4++WR85jOfmaQVNYf169dDkiSsXr16v14nSRLuu+++qqxpf8ydOxcrVqyo9zKIqMX5W3MBgDxSOekZSJooESCZrNm6aHJp6sFvVDAWGYuxCBHRQShVmvirTIBy0sRNlqSiTJo0C30SOnAwFhmLsUhtsJkcUY2li+lApUl5Q12VJciyxPZcVfKzNT9DzsnV7OsVTRu7su7fczKioj02tqdze6QdFxx9Qc3WNNrKlSuxfPlyDA4OoqOjo+W+fjXddttt+MxnPoOhoaHA80899RQSiUR9FkVEVDI6aaLlirAASJIbj5Sx0mRy1ToWMSwbO0fcWCShK+iIj23HwViEsQgRUU2Vh8Bbo5ImdiVpwkqT6rlv/X3Qd+s1mzll2Q52ZNy/67imoDPBWCRsX7+aGj0WYdKEqMbSxTT0nPtDQwgBs1RpUj7ZWYwzaVINw8VhZKzMxDdOEst2kC66GxWGrcAGB2GSOxyViKje/EkT2bIhCm6vX12RA6XsWjwFcJDzpKl1LGI7AumiG3MWbBmOVLse5hRejEWIqK72VmkS0RDVFKi+rg3cF5lcGSMDuSBDtWqzHSxEJRbJWzKEzFiEGicWYXsuohpLF4a9ShPTFihPu4ioMoQEmFEmTZqB4jupa4sDn2liWRYuu+wydHR0oLu7G1/+8pchfL+eYRi46qqrMGPGDCQSCbz1rW/FypUrvc9v2LAB73nPe9DZ2YlEIoElS5bgwQcfxPr167F8+XIAQGdnJyRJwsc+9rFx13Dbbbeho6MDDzzwABYvXox4PI5zzz0X2WwWP/3pTzF37lx0dnbiU5/6VGDo589+9jMcd9xxSKVS6O3txf/7f/8P/f39ALDXr+84Dq6//noccsghiEQimD17Nv7lX/4lsKY33ngDy5cvRzwex1FHHYU//elP+/XnumbNGpxyyimIxWLo7u7GJz7xCYyUTj2V/eQnP8GSJUsQiUTQ19eHyy67zPvcd77zHSxduhSJRAKzZs3CJZdc4r1+5cqVuPDCCzE8PAxJkiBJEq655hoAY8tQN27ciPe9731IJpNoa2vDhz70IezYscP7/DXXXIOjjz4ad9xxB+bOnYv29nZ85CMfQSZTu003Imo+gaRJpgDbcX+u+FtzAYDe3lXTddHk8oUicBzGIoxFrgHAWISI6mycShMhhDsIXlMDVSYA90UanSRJkEsHcmzGIoxFGiwWYdKEqMayw7sgl35YGKOGwJtRHaL0DpfBQWPzBwcHs1Hx05/+FKqq4s9//jNuvPFGfPe738WPfvQj7/MXXnghnnjiCfziF7/Ac889hw9+8IM488wz8dprrwEALr30UhSLRTz++ONYs2YNrr/+eiSTScyaNQt33303AOCVV17Btm3b8L3vfW+P68jlcrjxxhvxi1/8Ar/97W+xcuVKfOADH8CDDz6IBx98EHfccQduueUW/Pd//7f3GsMwcN111+Gvf/0r7rvvPqxbt84LAPb29a+++mpcf/31+MpXvoIXX3wRP//5z9HT0xNYz5e+9CVceeWVWL16NRYtWoS//du/hWVZ2Be5XA5nnnkmOjs78dRTT+GXv/wlfv/73wd++P/gBz/ApZdeik984hNYs2YN7r//fhxyyCHe52VZxo033ojnn38eP/3pT/GHP/wBV111FQDghBNOwIoVK9DW1oZt27Zh27ZtuPLKK8esQwiBv/mbv8HAwAAee+wxPPzww1i7di0+/OEPB+5bu3Yt7rvvPjzwwAN44IEH8Nhjj+Gb3/zmPv1eiYjG40+aWEOVdlGjkyaRju6arYkmnyRJ3iGOgznAwViEsQhjESKaNONUmpi2gBDASEQLzDMBuC/SDMqHOBwhAomO/cFYhLFIPWIRtuciqrH80E7vsX9AuK4o3jwTVVYRU2M1XxtNLlkCHOFuVAghAi1P9tWsWbPw3e9+F5IkYfHixVizZg2++93v4h/+4R+wdu1a/Od//ic2b96M6dOnAwCuvPJK/Pa3v8Wtt96Kf/3Xf8XGjRtxzjnnYOnSpQCA+fPne792V5d7gnjatGkT9s40TRM/+MEPsGDBAgDAueeeizvuuAM7duxAMpnE4YcfjuXLl+PRRx/1frh9/OMf914/f/583HjjjXjLW96CkZERJJPJcb9+JpPB9773Pdx000244AK3r+mCBQvw9re/PbCeK6+8Eu9617sAAF//+texZMkSvP766zj00EMn/DO98847kc/ncfvtt3t9NG+66Sa85z3vwfXXX4+enh788z//M6644gr80z/9k/e6N7/5zd5j/yC6efPm4brrrsMnP/lJ3HzzzdB1He3t7ZAkCb29vXtcx+9//3s899xzWLduHWbNmgUAuOOOO7BkyRI89dRT3tdzHAe33XYbUqW5Ah/96EfxyCOPjDllQkS0rzJG5VSWPZz3HutKMGkS65xWszVRdSiSBBvuZpQjhHegY38wFmEswliEiCbNOJUm5X2REU3FTFaaNB1FlmCVDpI6AlD2PxRhLALGIvWIRVhpQlRjxtDuymOrUrKnqzKKviHwB7LBTuHib9F1oMUmxx9/fODfwrJly/Daa6/Btm08++yzEEJg0aJFSCaT3sdjjz2GtWvXAgA+/elP45//+Z/xtre9DV/72tfw3HPPHdA64vG4FxgAQE9PD+bOnYtkMhl4rlxmCgCrVq3C+973PsyZMwepVAonn3wyALf0ck9eeuklFItFnHrqqXtdz5FHHuk97uvrA4DA196bl156CUcddVRg8Njb3vY2OI6DV155Bf39/di6dete1/Doo4/itNNOw4wZM5BKpXD++edj9+7dyGaz+7SG8jpmzZrlBQYAcPjhh6OjowMvvfSS99zcuXO9wKD8+93X3ysR0Xj8lSaOL2kS8VWaaLIGpa29puuiySf7Y5EDDEYYi4yPsQhjESI6AONUmpQ7cIxEgu25FElBXIvXdn006QKtyxmLMBYZZx1hjUWYNCGqIcux4KSHveviqPZcRjwCgKcpmoU8CcHB3jiOA0VR8Mwzz2D16tXex0svveSVdF500UV444038NGPfhRr1qzBcccdh+9///v7/bW0UYOAJUka9znHcf9NZ7NZnH766Ugmk/jZz36Gp556Cvfeey8Atzx1T2Kxfauw8n9tyWuD5uzp9oC9Vf1IkjThGjZs2ICzzz4bRxxxBO6++24888wz+Pd//3cA7smTfbWndYx+fm9/zkRE+8sdyFlJmoiMr9JEVbzHETUC+N6YUGNSfD9PDqZF154wFql8XYCxCBHRXpkmUCgAGFVpYjkoKjJMRUHS156Lh0mbg7/K1WEswlhkH9cRhlikJkmTm2++GfPmzUM0GsWxxx6L//3f/93jvStXrvQGxPg/Xn755Voslaiq0sW0NwQeGN2eS/baczFp0hyUSQgOnnzyyTHXCxcuhKIoOOaYY2DbNvr7+3HIIYcEPvzlj7NmzcLFF1+Me+65B1dccQX+4z/+AwCg6+6/N/+Qssny8ssvY9euXfjmN7+JE088EYceeuiYUwDjff2FCxciFovhkUcemfQ1lR1++OFYvXp14PTDE088AVmWsWjRIqRSKcydO3ePa3j66adhWRb+7d/+DccffzwWLVqErVu3Bu7RdX3CP9fDDz8cGzduxKZNm7znXnzxRQwPD+Owww47iN8hjYexCJErb+VhOZVex9JIwXvsb88VUZg0aQay793egR7gYCwy+RiLtCbGItTySq25HOHAdCqbqobtIKu7G6L+ShPuizSHyag0YSwy+RiLTKzqSZO77roLn/nMZ/ClL30Jq1atwoknnoizzjprr2VIQGUATvlj4cKF1V4qUdWli2noubFlqECpPVecSZNmMhnBwaZNm3D55ZfjlVdewX/+53/i+9//vtdPctGiRTjvvPNw/vnn45577sG6devw1FNP4frrr8eDDz4IwO0x+bvf/Q7r1q3Ds88+iz/84Q/eD545c+ZAkiQ88MAD2LlzJ0ZKQexkmD17NnRdx/e//3288cYbuP/++3HdddcF7hnv60ejUXz+85/HVVddhdtvvx1r167Fk08+iR//+MeTtrbzzjsP0WgUF1xwAZ5//nk8+uij+NSnPoWPfvSj3mC1a665Bv/2b/+GG2+8Ea+99hqeffZZ7yTKggULYFmW93u744478MMf/jDwNebOnYuRkRE88sgj2LVrF3K53Jh1vPOd78SRRx6J8847D88++yz+8pe/4Pzzz8dJJ52E4447btJ+v8RYhMjPX2UCAHLGlzTxteeKqBHA12qAGlOgVegBHsZjLMJYhA4eYxEiVFpz+apMAHdfZER3kyWpKJMmzWYyKk0YizAWqYeqJ02+853v4O///u9x0UUX4bDDDsOKFSswa9Ys/OAHP9jr66ZNm4be3l7vQ1GUvd5P1AjGVJqUkiaKLEGRJVaaVFF7pB1dsa6afnTHu9AW6fQ+Rn++PTJxr/jzzz8f+Xweb3nLW3DppZfiU5/6FD7xiU94n7/11ltx/vnn44orrsDixYvx3ve+F3/+85+9fpC2bePSSy/FYYcdhjPPPBOLFy/GzTffDACYMWMGvv71r+MLX/gCenp6cNlll03an/fUqVNx22234Ze//CUOP/xwfPOb38S3v/3twD17+vpf+cpXcMUVV+CrX/0qDjvsMHz4wx+e1F6V8Xgcv/vd7zAwMIA3v/nNOPfcc3Hqqafipptu8u654IILsGLFCtx8881YsmQJ3v3ud+O1114DABx99NH4zne+g+uvvx5HHHEE7rzzTnzjG98IfI0TTjgBF198MT784Q9j6tSp+Na3vjVmHZIk4b777kNnZyfe8Y534J3vfCfmz5+Pu+66a9J+r+RiLEJUMSZpkq1sXGi+yZysNJl89YhFpsS7vTgkFelgLMJYJICxSO0wFiFCZQi8PTppYntJk2Sk0oKH+yKTL6Wn0BkduzdR1VgkUYlFkhpjEcYiQWGORSQhqtBQrsQwDMTjcfzyl7/E+9//fu/5f/qnf8Lq1avx2GOPjXnNypUrsXz5csydOxeFQgGHH344vvzlL2P58uXjfo1isYhisfINN51OY9asWRgeHkZbG7/BUrj838b/w+6bbkB7/zCEEPjL+gEIAcQ0BUfN6sCz73oT0lPb8JEjPoJDpxxa7+U2hUKhgHXr1nml8LVkWA5e3u5uTrVFNcydkpjgFUQHZ2//3tPpNNrb21vu52MtYhGA8Qg1jqe3Po0HXn3Au1b+5X5oRRO6IuNNczq95w+dcih6/3kFUOOfnc2onrGI7Th4YasbiyQjKuZPZfUQVRdjkbEYixCV/PnPwP/8D3aM7MBLuyoDnldvHMTjPV34v8UzcMnJh3jPn3XIWXjrzLfWY6VNqV7xiBACz29JQ0AgpilY2MNDOVR9e/r3vj+xSFUrTXbt2gXbtr2ynrKenh5s37593Nf09fXhlltuwd1334177rkHixcvxqmnnorHH3983Pu/8Y1voL293fsoZxGJwshfaWI5AuWUZbkdBitNmoumSCif2TVtDsskqodaxCIA4xFqHP5KE9uwoBXdnuL+1lwAoEcTQCRS07XR5FNk2ZuxZjAWIaoLxiJEJeNUmgghULQdZHU1MM8E4L5Is5AkCWqpmtm0q3Zun2jSqRPfcvD80+4B95vi6OfKFi9ejMWLF3vXy5Ytw6ZNm/Dtb38b73jHO8bcf/XVV+Pyyy/3rsunKYjCKF0YxtS8GyD455lEmDRpSm5wIMO0HQYHRHVWzVgEYDxCjcOfNLGG897jMUmT9i5gD/9HqLFoqgzbtGHaYq/f+4iouhiLUMsbZ6ZJ+TDpCJMmTU0v7YtYjgPHEZBlxiIUflWtNJkyZQoURRlzeqK/v3/MKYu9Of74472eaaNFIhG0tbUFPojCamRkAEopWTJ6CLwZ0eAoMmRJRkJjG6dmoSnut9lycEBEtVWLWARgPEKNI5A0GaoMYxydNIl2Tq3Zmqi6yrGIEAIWYxGimmMsQlQyTqVJeV9kJKIFhsADTJo0k3IsArALBzWOqiZNdF3Hsccei4cffjjw/MMPP4wTTjhhn3+dVatWoa+vb7KXR1RzhaFd3uOi7weFrsiBKhOeAGweOoMDorpiLEIUFEiaZCqVJhHfzytVVqG2ddRyWVRFmlKJKxmLENUeYxGiknLSxFdpUm4dOaKpgSHwsiQjofMwabPQVMYi1Hiq3p7r8ssvx0c/+lEcd9xxWLZsGW655RZs3LgRF198MQC3hHTLli24/fbbAQArVqzA3LlzsWTJEhiGgZ/97Ge4++67cffdd1d7qURVZTs2zPSQdz260sSIugECT1M0l9HBQURT6rgaotbEWITIJYQIJE2cdMF77K80iSgRIMmB4c0ieICDlSZE9cBYhAhANgtg/EqTnK4i6as0SekpyFJVz3lTDfkrTQzGItQgqp40+fCHP4zdu3fj2muvxbZt23DEEUfgwQcfxJw5cwAA27Ztw8aNG737DcPAlVdeiS1btiAWi2HJkiX4zW9+g7PPPrvaSyWqqoyRgVYwvOvRSZMRzjNpSgwOiOqPsQiRq2gXYdiVWMQZ2UPSRI0ACZ7ubBZsiUFUf4xFqOUJAWSzcIQTiEUMy4EjScirSmCmCfdFmgtjEWpENRkEf8kll+CSSy4Z93O33XZb4Pqqq67CVVddVYNVEdVWupiGXjC9a1aatAYGB0ThwFiEKNiaCwDESOWk55hKEyZNmkYgFrEYixDVC2MRammFAuAEEyaAuy+S0xRAkgIzTbgv0lx0tgqlBsRaN6IaSRfT0PK+ExWlHxSyJEGRJJhRVpo0IwYHREQUFqOTJrIvaeLfWGelSXPxtwo1GIsQEVE9lFtz+eaZAO7PpZzmJktYadK8NLYKpQbEpAlRjQwXhr1KEyGEV2miqzIkSWKlSZNicEBERGExXBgOXMs5d+NCU2TIUmVjnZUmzYWxCBER1d0480wAoGi5SRNVkRHxVb1yX6S5KLLkxZoGq16pQTBpQlQjQ4Uhb6aJ5Qg4wn3TWg4MDM40aUr+4IAtMYiIqJ4GC4PeY8t2oOTcuMS/SeFeM2nSTGRJgiq7f8eseiUiorooJU0KVmWemnuY1EZWV5GKqJB8Bzi4L9JcJEnyDnGYtgMheIiDwo9JE6IaGSwMepUmRbPyhrW8UWGWkiYd0Y6ar42qxx8cGAwOiIiojoYKQ97jdMFC3LQAjE2aRNUokyZNptyiy7Id7+AOERFRzZSSJnkz7z1l2gKOALKaivaYFrid+yLNRyu1LneEgM1YhBoAkyZENTKYH4RemmlSsGzv+aimAACMqAZN1pDQuEnRbBotODAMY+KbiIio4QzmK5Um6axRSZqUYpGySCQBRCI1XRtVl146wCHgJk7CjrEIEVGTGRkBEKw0KZb2RXL62KRJZ6yzdmujmgi0C7W4L0Lhp058CxEdLCFEoD3XuJUmUR3dsc5ASSpVwb/+K2DbE983ibrzJuKG+zVFUgcUGVAU4ItfnPC12WwWn/zkJ3HPPfcglUrhyiuvxK9//WscffTRWLFiBSRJwr333ou/+Zu/8V7T0dGBFStW4GMf+xgAYMuWLbj88svx0EMPQZZlvP3tb8f3vvc9zJ07FwDwsY99DENDQ3jrW9+K73//+9B1HR//+Mfxy1/+EmvWrAms59hjj8W73vUuXHvttZPyZ0NERLXjb89VSOdRjjiivkqTiBKBkkwBjEeqpw6xSGfBRKRY+poJHVAZixARUQ2N056rvC+S0xR0+JImESWCmBqr7fpaTR1ikY6iBa3gHtiR4hqgKYxFKNRYaUJUAxkjA8cyoRXdHxBFX6VJRFNgqzJsVWYJai3Yds0/FMeB5NiQHBuOaVU+tw8+97nP4dFHH8W9996Lhx56CCtXrsQzzzyzz7/dXC6H5cuXI5lM4vHHH8f//d//IZlM4swzzwycnHjkkUfw0ksv4eGHH8YDDzyAj3/843jxxRfx1FNPefc899xzWLVqlRd0EBFR4yhaReTMnHdtDFUe+ytN2JqrBuodi1iMRYiIqMayWQghAkmTcgeOrKaizZc06Yh28DBptdUhFlFLcQhjEWoUrDQhqoHB/CC0ouldF3wDwaOqDDOqA5KEzihLUJuRrwp1v9pzjYyM4Mc//jFuv/12nHbaaQCAn/70p5g5c+Y+/xq/+MUvIMsyfvSjH3mB56233oqOjg6sXLkSp59+OgAgkUjgRz/6EXRd9157xhln4NZbb8Wb3/xm73UnnXQS5s+fv89fn4iIwsE/zwQArHSlp7h/pklMiwHJZK2WRTWiyJXNJ9thLEJERDWWzaJoFyFQ+RlUqTQJtudia67mJPtiEYexCDUAVpoQ1YB/CDwAFE03m67IElRFhhF1AwQGB83pQIODtWvXwjAMLFu2zHuuq6sLixcv3udf45lnnsHrr7+OVCqFZDKJZDKJrq4uFAoFrF271rtv6dKlgcAAAP7hH/4B//mf/4lCoQDTNHHnnXfi4x//+D5/bSIiCg9/ay4AsNKVk57+pAkrTZqT7Duxuz8HOBiLEBHRpMhmA0PggUqlSU5X0RarnOnmYdLmpARikX1/HWMRqhdWmhDVwFBhCFppCLwQAsVSpYl/ngnA4KBZHWhwIPZhU0OSpDH3mWYlQec4Do499ljceeedY147depU73FinA2y97znPYhEIrj33nsRiURQLBZxzjnn7PtvgIiIQsM/BF4IATHiJk0iqhxogcGkSXNSDvAAB2MRIiKaFNlsoDUXAG9fxI5HEFErrUJ5mLQ5yQdY9cpYhOqFSROiGhjMVypNioHWXG5gUK404UyT5nSgwcEhhxwCTdPw5JNPYvbs2QCAwcFBvPrqqzjppJMAuD/gt23b5r3mtddeQy5X6VP/pje9CXfddRemTZuGtra2/Vq3qqq44IILcOuttyISieAjH/kI4vH4fv0aREQUDv5Kk6LleIc5/PNMALiDV5k0aTq+UISxCBER1ZZlAYVCIGniCAHDcmAoMhLJSOB27os0J1mSIEmAEO7f/75iLEL1wqQJUQ0MFgahFdzNCX/SJKK5lSZGrFRpwhMV1acoE98zyWQAkmLBEYAtSe4a9mEdyWQSf//3f4/Pfe5z6O7uRk9PD770pS9BlittVE455RTcdNNNOP744+E4Dj7/+c9D0yr9YM877zzccMMNeN/73odrr70WM2fOxMaNG3HPPffgc5/73IR9QC+66CIcdthhAIAnnnjiwP4AiIio7vwzTYbzJhKmBcCdrebHSpMaqEMsIgGQVRu2I2BLYCxCRES1U9q8zluV9lxGaV8kqweHwAPswFETdYhFAEBWVVi2G4sIWYbEWIRCjEkTohoYzA9iarnSpDTPBIBXgmrGdCS0BHRFH/f1NIm++MW6fNmhHRkUTBuSJGHK9LZAK5S9ueGGGzAyMoL3vve9SKVSuOKKKzA8POx9/t/+7d9w4YUX4h3veAemT5+O733ve3jmmWe8z8fjcTz++OP4/Oc/jw984APIZDKYMWMGTj311H06YbFw4UKccMIJ2L17N9761rfu/2+ciIhCwd+eazhvIl5KmvjbYciS7MYiTJpUV51ikXT/CLKG+/c+ZXp7oBJ2bxiLEBHRQclmASBQaVIo7YvkNBXt0WDShJUmNVCnWGRkVxbp0t5YV18bNGXfRm0zFqF6YNKEqMosx0LGyGBGqQ1Gwd+eq1xpEtVYZdLkdEVGwbQhhIDlCGjKvm1UJJNJ3HHHHbjjjju8537zm994j6dPn47f/e53gdcMDQ0Frnt7e/HTn/50j1/jtttu2+PnhBDYsWMH/vEf/3Gf1ktEROEjhAi050rnTXQb7mZFueoVcKtMJEli0qRJaaoMuOEoDNtBVN63U6aMRYiI6KCMjAAIJk3KHTiymop2X6VJSk9BU4JJFGoe/n0Q03b2OWnCWITqgUkToiort8Mo9w4ft9IkqmMaT1M0tQMNDuqpv78fd9xxB7Zs2YILL7yw3sshIqIDNGKMwHIs7zrYnquycR5Vo+4DJk2a0uhYJKrVpzXH/mAsQkTUBLJZ2I4Nwza8p/yVJm2xytYkq0yam+ZrC2taDtAAzVYYi7QuJk2IqqzcDmO8QfAR1Vdpwr6dTc2fJGmU4KCnpwdTpkzBLbfcgs5O/vskImpU/nkmwKj2XL5Kk5gacx8wadKUdH8sYu/7ANZ6YixCRNQEstlAlQlQ2RfJ6Qp6fZUm7MDR3Pz7IgZjEQo5Jk2IqqzcDqM8CL58okJXZK+XtBnTGRw0Of+JioMNDlauXHmQq9k3QjRGEENERHvnb80FAPlMAbrtQJYkqL65FlE1CsRidRsOStUVOMBhO3u5c2KMRYiIaJ9ls4Eh8ABQNMtJExUp30wTHiZtboxFqJGEvz8MUYMbKgwBQkAvmLAdd54FUDnZKSTAjLDSpNlNZnBARES0P/xD4B0hYGaLANzZapI0KmnCKpOmNabqlYiIqBbGqTQpWKW25YkoFN8BDh4mbW76qFahRGHGpAlRlQ3mB6GYNmTbQdEaO8/E0jUIWWLvzibH4ICIiOrFX2kyUrQQL7UMjajBipKYFmPSpIn5Z5oYjEWIiKhWRiVNLNuBXTpMqqRigVu5L9Lc1AZsFUqti0kToiobLAx680wKZuUNalSrzDORJRnt0fa6rK9V1LukksEB1UK9/50TUTj5Z5oM58afZwKw0qTa6v09WpElyKXKIsYiVC31/ndORCGUzSJvVtpz+ee8qu3RwK3swFF99fw+LUuSV/nKAxxUTZPx75xJE6IqEkJgMD/ozTMZr9LEiOloj7RDlvjfsRo0ze2Pmsvl6roOBgdUC4bhfq9ROI+AiHz87bnSBRMJw02aRH2VJqqsQpVVJk2qICyxiOSLRUzb4eY2VQVjESIaY1SliX9fRG+vVJookoJUJFXTpbWS8vfl8vfpeinHIpbtwGEsQlVSjrvLcfiB4CB4oioqWAUU7SJSpUqToq/SpHy6k0Pgq0tRFHR0dKC/vx8AEI/HA/3ba7oWx4Rh2TAtIJtTochMlNHkcRwHO3fuRDweh6ryxzsRuWzHRrqY9q6H8+NXmsTU0qYFkyaTLlSxiDAhLAs2gEw2D11lLEKTh7EIEY0hBMTISCBpUu7AIQBEfEmTjmgHD5NWkaqqiMfj2LlzJzRNg1yn/QjZNiEsd48sM5JDRGOSnSaPEAK5XA79/f3o6Og4qEMcjGSIqqjcQ1zPu5n8gu9ERfl0pxHV2Lezynp7ewHA26yol8GcgWyx9G8gE+FGBU06WZYxe/bsum3GEVH4DBeHIVA5xZfOm5hhlitNKj+HomqpPQaTJlURllhkOG8iU3D//p20jig3KmiSMRYhooBCAaZZgC0qeyHl9lxZXUV7XPee575IdUmShL6+Pqxbtw4bNmyo2zoyBRPDeTcWsYZ1xHXGIjT5Ojo6vPj7QDFpQlRFObNUDlZKmhi+3p3lYZxmVGdwUGXl4GDatGkwTbNu61i9agtuevQ1AMBnT1uEdx85vW5roeak63rdTgwRUThljWzgeqRoIWG6Gxe6rz0XkybVFZZY5KEXtuP6R18GAHziHfPx4TfPrttaqDkxFiGigGwWph38uVfeF8lpKlKRSusc7otUn67rWLhwYV1bdD3x+i5c8+DzAIC/e+scXPj2eXVbCzUnTdMmpU0okyZEVVQedlYeBG877klPVZa801dGVENC4wZFLSiKUtf+ynOndWBLxt2oen57Hue+JTrBK4iIiA6Ovx0G4LbEiBsWJAmQfQfBNaW0acGkSVXVOxaZ39vpxSLPbcvjgihjESIiqqJsFpZjBZ6ynErSZIqvVWhCZwxSC7IsI1rHn/8L+7q8WGTV1iw+yViEQopHQIiqqLxRUR4Eb5WTJkrlv54Z0yunO6mpLepJeo9f3ZGp40qIiKhV5K184LpoOYibVuAABwBocilpkkyCmtch05JesoyxCBERVd04SZPyYdJCRA3sjXBfpDXM7IwhVmoPyliEwoxJE6IqKm9U6AUTQggvOFB8RzuNqIaYFhv39dRcupMRTElGADA4ICKi2hhdaVK0bMRNC8qo9jmqXCpAZ6VJU4tqCuZ2u3/Hr/VnvNiUiIioKrJZmE6wPZdluz97jKgeeD6mcl+kFciy5B0o3TCQQ96wJ3gFUX0waUJUReWNCj1vBN6Uqr6kiRllpUkrWdzrBge7RgzsGinWeTVERNTsyq1CAcARAqZpI2FYgVgEKCVNFAWIRGq9RKqxRT0pAG6rto0DuTqvhoiImtrIyDjtudy9ETsWTJpwX6R1lGMRIdxDHERhxKQJURXlzTwgBKIjBS8wAIJJEyOm80RFCykHBwDw6nYGB0REVF3+SpOi6aCtYEICxk+aJJOAJIGa26LeSizyCmMRIiKqpqGhQNLEEQKOcPdGzHjwoAY7cLSOxYxFqAEwaUJURQWrgEjOgGw7waSJUhoCH9NhawpPVLSQxb6kySts0UVERFXmn2lStGx0FtwqR0UJJkc0RQO6umq6NqoPfyzCdqFERFRVAwOBpIm/A0ehLbgPwn2R1rGIsQg1ACZNiKoob+URy7ibFbZdCQ7KfcTzqRgkSAwOWoj/dCeDAyIiqjZ/pUnBdNCZNwAA6ngzTZg0aQnlVqEAD3AQEVGVDQzAtCszTSzbnzSJB25lB47WEag02TFSx5UQ7RmTJkRVVLAKiKXdpInlON7z5ZYY+bYYImoEElthtIyF03wbFSxDJSKiKvPPNClati9pEow9FElh0qRFzOlOQFfct4FsFUpERFVTKADZbKDSpLwvYsoynGSwPRcPk7aOaakI2mMaAMYiFF5MmhBVUd6sVJqMN9Mkn4rxNEWLSUU1zOhw/85f3TECIcQEryAiIjpwYypNSu25/EkTVVbdAxxMmrQETZExf2oCALBuVxZFy67zioiIqCkNDgLAuO25BmM6IprqPS9LMnQlOBiempckSV670O3pAoZz5gSvIKo9Jk2IqihYaTJ2pkm+LcbTFC2oXIo6UrSwdbgwwd1EREQHbsxMk1KliX+miSa7J/2YNGkd5VjEcgTW7crWeTVERNSUBgYAYFSliT9pUtmSjKpRduBoMYt87UJf7We1CYUPkyZEVeIIB0W7iFg6B2DPM01iGitNWk1g6BlLUYmIqIoClSaGjY7C2Jkmqlw66dnZWdO1Uf34YxG2CyUioqrYvRsAYDpjZ5oMRnVEVcV7nh04Ws9ixiIUckyaEFVJwSoAQvjac40/04SVJq2HA1iJiKgWbMeGYRvetZrJQy2d8BzdngupFKCzLUar8G9UvMpYhIiIqmGcShO7tC8yGIuMqTSh1rKIsQiFHJMmRFWSN/PQ8wYUyw0KRs80MSMaLF3liYoWxNOdRERUC/4qEwCIDFdadSmjkyZszdVSyu25AMYiRERUJQMDcIQDR1QOkJb3RYZioypN2IGj5fj3RV5mLEIhxKQJUZX455kAlYFngNtHPN/mBgU8UdF6FkxNotyu9Q32EScioirxzzMBgEgm5z1W/TNNFI1JkxYzoyOGaOmEL2MRIiKqioGBQJUJ4JtpEh0704RaS2dCx5RkBAA4X41CiUkToirJW3mvNRdQ6d0JAIokIZ9ykyY8UdF6opqC3jY3KNw0kJvgbiIiogMzutIknq5cj5lpwqRJS5FlCbO74gCAzQN5OL7DPURERAfNMICREZi2GXjasgUsWUI6onGmCWFOtxuL7MwUkTfsOq+GKIhJE6IqGV1pUp5posoSJImVJq1uVmmjYiBrIFMwJ7ibiIho/+XNYKVJ+TCHBMDXnYtJkxZVTpoYtoMdmcIEdxMREe2HceaZAO5Mk6GoDkgSK03Ii0UAYNMgD5RSuDBpQlQleXNUpcmowatepQlPVLSkQHAwkN/LnURERAdmdKVJYsS9VkoHOMqYNGlNs3yxyMbd3KggIqJJtIekieUIDMZ0KLLk7Y0A7MDRqhiLUJgxaUJUJf5KEyGEN9NEUdz/dqw0aW3+pMlGtugiIqIqCMw0EQLJUtJEVYJvATRZAzo7a7k0CgHGIkREVDV7S5pEI4ioSuAAB/dFWhNjEQozJk2IqiRv5rxKE/8Q+DGVJjxR0ZKClSYMDoiIaPL5K03UXBGy5faK9p/sBAA5mQKi3KxoNYxFiIioakpJE9MJtqK2S5UmETW4HckOHK2JSRMKMyZNiKrEzAxDNd3NCWtU0sTSVZgRFQBPVLSqWQwOiIioyvwzTdTBrPdYGZU0UaZMrdmaKDy4UUFERFUzTqVJuQPHYExHVAtuR3JfpDXxAAeFGZMmRFVi795ZeexLmiiy5FaZlEpReaKiNc3p5kYFERFVl7/SRPElTUZXmqjd02q2JgoPHuAgIqKqGSdpUj5MWm7P5ccOHK1pWiriVR0xFqGwYdKEqFp2D3gPA5UmiuzNMwGAiBqp6bIoHLoTOuK6GyjyRAUREVWDf6aJNlT5WTNmpsm03pqticIjqinoaXPj0I0D+QnuJiIi2kemCaTTAMYmTWxJQjqqsdKEAACyLHmHODYO5CCEmOAVRLXDpAlRtQz6kia24z1Wy5UmcAMDWeJ/w1YkSZJXirp5MB+oRiIiIpoM/kqTQNJkVKWJPqWnZmuicCnHIrtGisgZ1gR3ExER7YPBQe+haVdmmti2wHBUh5CksZUm7MDRssqxSNFysDNTrPNqiCq4W0tUJdLQsPd49EyTcqUJT1O0tvKJCsN2sCNdmOBuIiKi/eOfaRJJVx77Z5qosgqpu7um66LwmBXoJc5qEyIimgS+pEmw0sTBYEwHgECliSzJ0BW9duujUOGMNQorJk2IqkQqlaMCY2eaFJJusoSnKVobgwMiIqqmQKXJSOWxOippgo6OWi6LQoSxCBERTTrfXsjo9lzDEQ0AApUmUTUKSQpWwVLr4Iw1CismTYiqwBEOlExl4OromSaFhNs/mpUmrY0bFUREVE3lpIlkO1Dzhve8f6aJokWAeHzMa6k1MBYhIqJJt5ekSTrqVpREfJUm3BdpbYxFKKyYNCGqgqJZgJ6v9GIcPdPEiDNpQsHggMPgiYhoMjnCQdF2Y5FI3gjEIv72XCKVAni6s2UxFiEiokmXyXgP/UkT2xYY0VUAYytNqHUxaUJhxaQJURXkRwahWJXNCX+liRPT4ZROeMY0tudqZSxDJSKiavG35opki4FWoYFB8G2pWi6LQoYbFURENOlKlSaOcGAL23vachxkSu25/DNN2La8tc3qqvz98wAHhQmTJkRVYAzuClz7NyqsVOUUBU9UtLaZnZXggBsVREQ0mfxD4PVcMdgq1Jc0kds6arksCpmpqQgiqvuWkLEIERFNilLSxF9l4l4LpPXxZ5pQ64rrKqYk3W4sjEUoTJg0IaoCYyCYNPFvVNi+pAlPVLS2qKagt83998ATFURENJkClSajkiaKUkmaSG1tNV0XhYskSV61yaaBHBzfvxMiIqL9JsQekya2LcavNGEHjpY3u1RtsiNdRMG0J7ibqDaYNCGqAnNod+C63EdckSUYCVaaUEV5o2LXiIFs0ZrgbiIion2TtyqVJpFsEbZ/polvhona0VXTdVH4lGORouVg50hxgruJiIj2olgEDAPA2KTJiCLDKrUqZ6UJ+fnbhW4e5IFSCgcmTYiqwBoeDFyX23OpsoRiTPee54kK8s812cTggIiIJsmeKk1UWYLkS5ooHZ01XxuFC2esERHRpPENgTdtM/CpYdUdAi9LEjRf1Ss7cBBnrFEYMWlCVAW2L2kihPBtVMgoJiLe53iiggLBwW4GB0RENDn8M00i2WDSxE/r6K7puih8GIsQEdGkKbXmAsZWmgxqbtIkosqBAxzcF6FZjEUohJg0IaoCe6iSNLGFb/CqIqEYryRNeKKCZndzGDwREU0+f6WJnit6Va+KEgz/mTQhf9JkA2MRIiI6GHtNmrgtuaKaEnieHTiIsQiFEZMmRFUg/IGC7Ru8KkusNKGAOd0J7/Handk6roSIiJqJN9NECKgjlQSKv9JESEC0c0qtl0YhM3dKZaPijZ0jdVwJERE1vD0kTYQQGCzNMYmowa1I7ovQ3CmVfZE3uC9CIVGTpMnNN9+MefPmIRqN4thjj8X//u//7vX+xx57DMceeyyi0Sjmz5+PH/7wh7VYJtHkyVQChfLJTqA008RfacITFS3vkGlJ7/FrOzJ7uZOIDgZjEWo15UoTvWDCtipD4P1JEyOmI6rHx7yWWsuc7oTXW/61HUyaEFULYxFqCf6ZJk5lponlCGR0DQAQGV1pwg4cLW9aKoJU1G3fxn0RCouqJ03uuusufOYzn8GXvvQlrFq1CieeeCLOOussbNy4cdz7161bh7PPPhsnnngiVq1ahS9+8Yv49Kc/jbvvvrvaSyWaHKYJkauUE1q+pInQVdi66l3zRAW1RTX0tbv/Dl7dkYHwtXMjosnBWIRaUXmmiX+eCeBWvZYV4xFuVBA0Rca80gnPN3aNwLSdCV5BRPuLsQi1jD1UmtiOQCbiJk2irDShUSRJwqKeFABg63ABmYI5wSuIqq/qSZPvfOc7+Pu//3tcdNFFOOyww7BixQrMmjULP/jBD8a9/4c//CFmz56NFStW4LDDDsNFF12Ej3/84/j2t79d7aUSTY50OniiwvfG0/C15oooEcgSO+QRsLAUHKQLFvozxTqvhqj5MBahVuRVmuSKsG3/fLVK7FGMRxBRI2NeS62nHIuYtsCG3WyLQTTZGItQy9hD0sSyBTKlA6RjKk3YgYMALOrxdeHoZ+Ur1V9Vd2wNw8AzzzyD008/PfD86aefjj/+8Y/jvuZPf/rTmPvPOOMMPP300zBNZhqpAaTTY05UlJnJygkKnqagskWBFl0MDogmE2MRalXlmSaRXBGWM357LtGW4gEOAgAsmpbyHr/KWIRoUjEWoZayp6SJ44xbaSJBQkThAQ4CFvpiEbboojCo6rukXbt2wbZt9PT0BJ7v6enB9u3bx33N9u3bx73fsizs2rVrzP3FYhHpdDrwQVRXo5Im/pYYli9pwtMUVFYuQwXcFl1ENHlqEYsAjEcofMqVJqPbc/mTJkilRr+MWpT/dCdjEaLJxViEWoZlAb5W5aZdSfAVJRmF8iB4X6VJVI1CknyxCbWs4L4ID3BQ/dXkaNnob4BCiL1+Uxzv/vGeB4BvfOMbaG9v9z5mzZo1CSsmOnBiH5MmrDShsoWBMlRuVBBVQzVjEYDxCIWPN9Mkt+eZJlJbe83XReG0sMd/upMbFUTVwFiEml4m+F7Wvy+SiahA6d+uv9KE+yJUxgMcFDZVTZpMmTIFiqKMOT3R398/5tREWW9v77j3q6qK7u7uMfdfffXVGB4e9j42bdo0eb8BogNgDAZP/vhnmjgpX6UJB69SyUKeqCCqmlrEIgDjEQoXRzgo2u6MrEh2zzNNpHYmTcg1tzsOTXE3s7hRQTS5GItQyxhV3eRPmqQ1zXvsrzRhBw4qm5qKoD3m/jvhAQ4Kg6omTXRdx7HHHouHH3448PzDDz+ME044YdzXLFu2bMz9Dz30EI477jhovm+yZZFIBG1tbYEPonoyh3YHrv0zTZxUJSDgiQoqS0ZUTG93/z28uiPjnSIjooNXi1gEYDxC4VJuzQUAkbyxx5kmakdXTddF4aUqMuZPcU94rtuVhWE5E7yCiPYVYxFqGb6kiSMc2ML2rof1SqIkwkoTGockSVhYmve6PV3AcJ7zm6i+qt6e6/LLL8ePfvQj/OQnP8FLL72Ez372s9i4cSMuvvhiAO5piPPPP9+7/+KLL8aGDRtw+eWX46WXXsJPfvIT/PjHP8aVV15Z7aUSTQpraCB47UuaiDbONKHxlatNMgULO9LFOq+GqLkwFqFW4yVNhBgz08Tfnktp66jxyijMyu1CLUdg/e5snVdD1FwYi1BL2MMQeAAY1ivJvqi/0oQdOMjH34XjdbYupzpTq/0FPvzhD2P37t249tprsW3bNhxxxBF48MEHMWfOHADAtm3bsHHjRu/+efPm4cEHH8RnP/tZ/Pu//zumT5+OG2+8Eeecc061l0o0KezhocB1MGnCShMa36KeJB57dScAt9qkt53/PogmC2MRajXleSaqaUOx7EDVq1pqwWRGVERjHARPFe4A1m0A3FjEP5CViA4OYxFqCb6ZJqOTJoOqr9JEY6UJjS8412QEx85hVTTVT9WTJgBwySWX4JJLLhn3c7fddtuY50466SQ8++yzVV4VURXYNuzMcPCp8kaFIsOK6d7zPFFBfsG5Jpn/v737Dm/jutLH/86gEiAI9l5Eqvdq2ZLtOG5yb9k4cYnsNCfOxi3ZOMlukl/aOk7ZlE2csnHKblxiJ/m6xlVusi1ZVrc6qUJRFHsFQHTM3N8fQw4AsZMgQZDv53n0iMAA4L0qwOE9956DD83LS+JoiKYfxiI0k/SdNLF4tZOLcSdNepuwBm0WOLlQQTHOXKggosRiLELT3hAnTbqM0eVHq5E9TWhg885YFyFKpgkvz0U0o/T0IKLE113sawQftJkBKVoSgzsqKFZscMCmZ0RENB7+iHbSxOzrTZr0xiJGWYIUkzThQgXFmhsXi3ChgoiIRmmopIlBW36UJAkmA9dFaGBzYzZwcF2Eko1JE6JEcrvjggMhhL67M5BmiXsoFyooVl/DMwCoYe1OIiIaB/2kSW/SpO/Ua2w/k6DdwoUKilORbYO5d1GLuzuJiGjUYpIm4TM2k7b3njSxGmV9AwfAChwULy/dgkyb1v+GsQglG5MmRIl0RtIkphoGgvb4pAkXKiiW3WJESaYWMB5r6YEQYphnEBERDayvp4nFG4zbwGE0REP/kM3ChQqKYzTIqMqzAwBOdvgQjChJHhEREaUMVQV6oicD4jaTShK6ehMllpgm8ADXRSieJEmYl6+dfG31BOHyhYd5BtHEYdKEKJE8nrgdFRFV1b8O28xxD+VCBZ2pr5a4JxhBkyuQ5NEQEVGqij1posQk4Y2xJ01sPGlC/fWVC1VUgdp2b5JHQ0REKcPr1RInvWKTJsE0EwK9GzgsxvhlSFbgoDPFluhiFQ5KJiZNiBLpjJMmESW6UBFyxAcDXKigM8X1NWll/U4iIhqbvp4mFm8wLhY5szwXFyroTPNYS5yIiMYipjQXEJ806bGagd5wxMqTJjQM9nulqYJJE6JEOjNpElOfK8zyXDSM2XnRhYoTbQwOiIhobOJOmqg8aUIjFx+L8KQJERGN0BlJk7AarcDRY4lW3eh30oQVOOgMXBehqYJJE6JEOiNpErtQEYk5aWI2mGGQ43dYEPXVEQeA4wwOiIhojPSeJr5g3AaO2J4mbARPA6mKXahoZyxCREQjNMRJE7fFqH9tNcUvQzIWoTPFroucYKlQSiImTYgSye2O21ERUaI1PdX06EkT7qaggXB3JxERJUIgEoAcUWAKRuJikb6TJorRAIPVBlnijwIUryLHhr4DSYxFiIhoxIZImnhMJv1rizG6eVSCxKQJ9VOYYYXNrP074UkTSib+pESUKEIAHs+A5bkEAOGIBgMMDGggWXYzsmxaQMmFCiIiGit/xA+LLwQg/tRrX0+ToN2CNLMtKWOjqc1qMqA0S/u3caKtB0KIYZ5BRESEIZMmrkFOmliMFkiSBKJYsiyhMlc7bVLf5UcwoiR5RDRTMWlClCheL0QkMmB5Lq/ZCLM5Giiw8SoNpq8sRrM7AG8wMsyjiYiI+gtEArD4ggDi+6v1nTRhPxMaSl9ZDG9IQYs7mOTREBFRSjizp4kSrcDhMkbXQmJPmrACBw2mb11EUQVOdfiSPBqaqZg0IUoUtxuKiM+A9y1UuC0mWE3R4IALFTSYqtxo/c5a1u8kIqJRUoWqJU28AyRNenuaBO0WLlTQoKpy2YCViIhGaYiTJl3mgU+acF2EBhO7LnKcVTgoSZg0IUoUtztuNwUQ7WnisZhgMUb/u3GhggYT24CVzeCJiGi0ghEtWaKfNBmgpwlPmtBQYhuwHucGDiIiGo4QcUkTIYS+oVRIQLchuoE07qQJK3DQIOKbwXNdhJKDSROiRHG743ZTANHdnR4zT5rQyMQFB9xRQUREo+SP+AFAP2kyaE8TLlTQIOJjES5UEBHRMHw+QIlW3YhdFwlbzfDFxCIWnjShEZidF3vqlesilBxMmhAlygBJk76FCo/FBHPsSRMuVNAgZsftqGBwQEREoxOIBABg6J4maWYuVNCguFBBRESjcmY/EzVagSNosyAYjp56tbKnCY1AZS43cFDyMWlClCgez6AnTQI2M2RJ0u/nQgUNpjzbru8EZnBARESj5Q/HnzSJDHbShAsVNIh8hwV2s7aoxZIYREQ0LI8n7mbsukjQbkEwEj2FwpMmNBJ2ixGFGdq/D24mpWRh0oQoUdzuuB0VQLSOeMgeHwxwoYIGYzbKKMvS/n3UtnshhBjmGURERFFnnjRRemMRgyxBktjThIYnSZLeY+10lx+BsDLMM4iIaEYbogl80GZBoPekiSRJMBtYgYNGpq9caLcvjE5vKMmjoZmISROiRDmjPJcQQi/PFbJb4h7KhQoaSt9ChS+koNkdSPJoiIgolfgjfkiKClNA++Gy76RJX2kuVZYQtpq4UEFD6luoEAKo6/AleTRERDSlDZU0iTlpYjHK+gYOgOsiNDT2WKNkY9KEKBGE6Jc0UQXQd0ZAOfOkCRcqaAhVuWwGT0REYxOIBGDxhyAJbQNHNGmihf1BmwWQJC5U0JCqcmP7mnChgoiIhnBmTxMlvqdJIKKdNLEa45cgWYGDhhIfi3BdhCYfkyZEiRAIAOFwXNIkomqBgddkhNFqjHs4FypoKFUxDViPc6GCiIhGwR/26/1MlJgSj0ZDtJ8JwIUKGlrc7k7WEicioqEMcdIkYDNHT5qYDHGP47oIDSU2FjnOHmuUBEyaECVCb5AQu6MiomgLFT1mI6zG+OCACxU0lPhjqFyoICKikQtEAjH9TAZoAm/TkiZcqKChxC1UcAMHERENZYikicds1EtwWM48acIKHDSE2Xk8aULJxaQJUSL0BgmxwUFfPxOPxQSLKf6/GhcqaCizedKEiIjGyB/xw9ybNOkrzQVEe5roJ024UEFDYEkMIiIakd5S5bFi10VcZpP+tZUnTWgUijPT9EQbS4VSMjBpQpQIAyRNIrFJk5iTJmaDGQY5PlggipWbboajt6QbFyqIiGg0ApGAXp4rPmkS09MEXKigoaWZDSjJ1BJrJ9p6IGJKvREREemCQSAUirsrrGoVOMIWI/wxHx/9TpqwAgcNwSBLqOzt93qq04eIoiZ5RDTTMGlClAgDJk20N3SPxQRrzEkTLlLQcCRJ0vuaNLr88IeUJI+IiIhShT/sj5bnUqM/XBpieppYDBbIEn8MoKH1lehyByJo7wkN82giIpqRzjhlAkTXRWKbwAP9T5pYjJaJHRulvL5YJKwInOr0JXk0NNPwpyWiROjraaJGe5r01RH3mONPmnA3BY3EvHwtaSIEUNPiSfJoiIgoVcSdNFEGKM9ls3ADB43I3HyH/nV1M2MRIiIawDBJk2A4ugEw9qSJ1WjlBg4aFmMRSia+QxElgtsNIcSA5bncPGlCY7CoOEP/+mBj/0CUiIhoIP5I9KTJYD1N2M+ERiI+FnElcSRERDRlDZU0sQ9+0oTrIjQSXBehZGLShCgR3G4oIr6E0mA9TbhQQSOxqCgaHBxq4kIFERENTwiBYMgPi08rpXRmTxMhSQhbzVyooBGJj0W4UEFERAMYIGkSVrQKHEOdNGEFDhoJxiKUTEyaECWC2x13ygSI7Wli5EkTGrWF3FFBRESjFIgEYAqEIPU27T6zp0kozQwhS1yooBGZk58Os0GLYRmLEBHRgM5Imggh9A2lPGlC41WalYYMqxEAT73S5GPShGi8gkEgGNR3U/RRFIGA0YCwwcCeJjRqGVYTyrNtAIAjTR4oMbuFiYiIBhLbzwTo39MkaNcarnKhgkbCbJQxt0DrsXairQf+kDLMM4iIaMbxxPeZiN1MOuRJE1bgoBGQJEkv0dXiDqK9JzjMM4gSh0kTovHq7gaAAU6aCHjMJgCAhSdNaAz6jqL6wwpq271JHg0REU11gUgA1p6AfvvMniZBm5Y04UIFjVRfLKIK4EgzT5sQEdEZurribsYlTc44aWLhSRMag0VFTv3rQzz5SpOISROi8aqrAzBI0sRihNkoQ5Yk/X4uVNBILS5m/U4iIho5f8SPzJZo6YLYpIlBlhDgSRMaJcYiREQ0KK8XaG+Pu2uokyaxZctZgYNGirEIJQuTJkTjVVsLoH/SRFFVNGbY4kpzAVyooJFbFNfXhPU7iYhoaIFIAFlN0R2ffaUdDbIESZLgydM+V7hQQSO1qDi6u5N9TYiIKE7vWkissKqVLfc6bVDMRgR7T5pIEvQ+WQDXRWjkFrHfKyUJkyZE46GqAyZNhBCIqAJ1memwGuP/m3GhgkZqcTGPoRIR0cgFO9tgc/n02xFFW6gwytqJ167CTABcqKCRW1jk0L9mLEJERHFOnOh3V9+6SHdRFgAg0HvSxGI0QGIFDhqDOfnpesLtEDeT0iRi0oRoPJqagIBWO7xvRwWg1X0OSTIaHGlxdTsBLlTQyBVkWJBtNwPQFiqEYDN4IiIaQsyOz74NHICWNOnJsiOcpn2mcKGCRsphNaEixwZA62miqIxFiIio1xBJk65iLWnSd9LEcsZmUq6L0EiZDDLmFaYDAE60e+ELRYZ5BlFiMGlCNB4xixOxJ00iqop6pw2qLPc/acKFChohSZL0Bqwd3hBaPcEkj4iIiKYyufak/nXs2rbBIKOrd8cnwIUKGp2+WCQQVlHb3pPk0RAR0ZTQ1QV0d/e7O6JGICSguzATQggEI9pJE+sZm0lZgYNGoy8WEQI40uxJ8mhopmDShGg8YnZWxCZNFFXgZJaWCedJExqPuKZnLItBRESDEQLGU6f1mxFV1b82ypJeJgPgQgWNzmLWEiciojMNcMoEAMJKGJ4cByJmI0KKir5iCTxpQuPB0uWUDEyaEI1VJAKcOhW9GXvSRBGoc/YmTdjThMaBzeCJiGhEOjoAV/RzIqJEj5rIBhndBdEfNrlQQaOxiBs4iIjoTAM0gQe0dZFoP5PoBg6L8YyTJqzAQaPAZvCUDEyaEI1Vfb2WOOkVjERLJ/mMBrSkawsSVlP0v5lJNsEgxwcLREOJO2nSxOCAiIgGceIEQkpIvxmJqc/VnZMOxWzUbzNpQqMRt7uTsQgREQkx6EmTkBLSS4IGe5vAA4DFxJMmNHYLi7guQpOPSROisTqjNJc37NVv12bYAUkCAKRbTPr9NpNt8sZH00JlbrqeePugnidNiIhoYMrxY/CEojWe/aHoQkV3cXw/E27goNHId1iQYzcDAPaddkFlM3giopmtpQXw+frdLYRAV8QDV360L2cfhyW6eUOWZCZNaFTSLUbMytHW0w43ufVeOUQTiUkTorGKOY7qCsQvZu9Ps+hfF2VGg4F8e/7Ej4umFYMsYWWZttjV0O1HfWf/4JSIiGY4VYWneh9UES2D4QmE9a/F7AL9a8YiNFqSJGF1hRaLuPxhHG7mDk8iohltkNJcvrAPHTk2qL2luBq7/fq1osxoOa5cWy5kicuRNDqrK7IBAKGIij2nupM7GJoR+C5FNBbBINDYqN90BaNJEyEEdpq00yU2sxGZadGTJuXO8skbI00b62fn6F9vPd6exJEQEdGU1NICt6tVvymEgCeglRBVjQYYK3L1a4xFaCziYpFjHUkcCRERJd0gSRNX0IWumNOtDb1JE0mSUOSMbiZlLEJjER+LcF2EJh6TJkRjceoUoEZ3c8aeNOkwGdFq1I6eFmdaIfWW6QKAisyKyRsjTRvr50QXu7ZwoYKIiM508mRcLBKMqAgpWpzSU+iEZIqW4+JCBY3FuTGxCDdwEBHNYKoK1NUNeKk70K03gfeHFHT2lufKd1hgMkSXHyucXBeh0Vs/J3YzKddFaOIxaUI0FidP6l+qQoU7GC1TcMyepvczKY45gmqUjSh2FE/aEGn6WFbqhN2sLXhtPd4BIVhLnIiIokRtbdyp175TJgAQjDllIkFi0oTGZE5+OnLTtfKz22s7EVbUYZ5BRETTUnOzVnljAJ2KF57sdABAkytamit2XQTgBg4amyJnGqpy7QCAvfXd8AYjwzyDaHyYNCEai5ikiTvohkB0EfuAJdrPpCQmOChxlMAoR5ufEY2UySBjbaVWv7O9J4hjrT1JHhEREU0Zqoqeo4cQUaM/OMb2M5Gqoj1M8u35bLxKYyJJkl4WwxtSsO+0a5hnEBHRtBSzFhIrEAmgNccC0XuipCGmn0nsukimNRNOq3NCh0jT17reWCSiCuw42Znk0dB0x6QJ0WgFg0BTk34zthyGEAK7e0tzmQwy8tKjCRTupqDxWD87tiwGj6ISEVGvlha4XS1xd/WdNAkbDLBWREsZsEwojQdriRMR0WCluVwBF7oLM/XbsU3gizPZz4QSg+siNJmYNCEarfr6+H4mMeUwXBYzGiXtv1WR0wpZjvYzYXBA4xFfv5MLFURE1OvkybhYJKyo8IcVAIC7wAmjOXrKlbEIjUd8XxMuVBARzThD9DNxBaNJk4iiosWjlfDKsplhYyxCCbJuNtdFaPIwaUI0WjHHUYUQcSdNjqUP3M9EgoQyZ9mkDZGmn4WFGci0mQAA2050QlHZ14SIiHr7mQQG6WdSnhP3WC5U0HiUZdtQmqXFt7tOdSHQm5wjIqIZoqUFCAQGvNSp9Oj9TJrdAai9P6+e2c+ETeBpPLLtZiwsygAAHGx0o9sXSvKIaDpj0oRotGKSJt6wF4qI/sB40DpwP5OC9ALWEKdxkWUJ66q0xS+XP4xDje4kj4iIiJJOVeE/Xo2gEm3IGtvPBJV5+pdZ1ixkWDImc3Q0DfWV6ApFVOyq60ryaIiIaFIN0s8krITRmGXU+5kMVprLZrIh15bb7/lEo9EXiwgBbDvBk680cZg0IRqNYBBobNRvdge64y7v6e1nIksSCp3R4IC7KSgR1seUxdjCo6hERDREP5OQQUbarOhJE54yoUSILdG1hX1NiIhmlkGSJrGluQCgoTt6GiV2M2m5sxySJIFoPM6NKV2+5RiTJjRxmDQhGo0z+5nElsOwmnGy99BJnsMCkyH634sLFZQIsQ1Y36puTeJIiIhoSjh5Mi4WUVUBb0hLmnTlZSAtzaxfYyxCidB36hUA3qpuS+JIiIhoUg3VzySmCbwqBJpc2kkTm9kIZ5pJfxxjEUqEs2Zlw9jbP/itmlYIwdLlNDGYNCEajTP7mcQ0Xj3uiPYzKTmjbieDA0qEqlw7KnPtAIDttZ3o8rJ+JxHRjHZGE/ieYAR9PzcGSuP7mVRk8tQrjV9+hhXLSp0AgENNbtR3+pI8IiIimhRD9DPpUr16P5P2niBCEW2jaUmmNe5kCddFKBEcVhPOrsoGANR3+nGk2ZPkEdF0xaQJ0WgcP65/GVSCCCnRRevDMf1MYpudZadlw2FxTM74aFqTJAkbFhUAAFQBvHa4ZZhnEBHRtKUoCB2vgS8cXbSO7WeiVkbLKNlMNuSkxSdRiMaqLxYBgE2HGIsQEc0IMWshsRRVwWmnHNPPJJpYiV0XMckmFKUXTewYacbYsKhQ//rVg4xFaGIwaUI0Uh4P0NSk34wthwEAe4zRY6exzc64m4ISacPimOCACxVERDPXqVNwu+PLI7lj+pnYZkWTJqwhTol0WUws8srB5iSOhIiIJs3RowPe7Ql50FmYod9uiGsCH02alGaUwiAbJm58NKNcGrOBg7EITRQmTYhG6owgIbYchifdimO9/UyybGbYzEb9WllG2aQMj2aGlWWZyHNop5rermmDr7d2PRERzTBHj8Zt4BBCoCeofSY052YgIz16ApYbOCiR5uSn6+VCd5zsRCfLhRIRTW9+v9bfdQCugAudJVqpJCEEGnuTJiaDjLyYWKTMyXURSpzizDSWC6UJx6QJ0UidmTSJWag4luWAqmpFxNnPhCaSLEv6ropgRMXbNWzCSkQ0Ix09GreBwxdSoPTGIp5ZuawhThNGkiRsWMxyoUREM8bx41oj+AG0G0LoydIS6e5ABN7eDRxFTitkmbEITZzLWIWDJhiTJkQjoSjAiRP6zbAShjfs1W/vT48mSmKPoNpMNuTaouUxiBIhtpY463cSEc1AXV1QW1vgCUYbX3oC0ZOHkdnRzwmjbGQNcUo41hInIppBBinNJYRAbZ4B6N2o0ThIaS4JEitwUMLFr4uwRBclHpMmRCNx6hQQDOo33UG3/rViNGCvYeB+JmUZZawhTgm3fnYuHBatBNxrh1sQVgbe9UNERNPU0aPwBD0QEPpd7t4m8K12K7KKM/X7WUOcJkJsudB3jrJcKBHRtCXEoEkTb9iL5uKB+5nEVuAoSC+AxWgBUSLNyU9HVUy50I6e4DDPIBodJk2IRqKmJu5mbDmMjqJMnO59c7abjXCmRRMoPIJKE8FslPHhBfkAtCPQ2050JHlEREQ0qc4ozSWE0E+a1OVmIJf9TGiCnVku9K1qlgslIpqWGhoA38D9IrrDPegqzNRv9500kSUJhc7oZlLGIjQRJEnCpSwXShOISROikRiin8nxbAdCEW2nf3FmGmuI06S4PKZ+5992nk7iSIiIaFKFw0BtbVwsEoyo+qlDz6w81hCnSREfiwzcIJiIiFLcIKdMAKAp2wTFrFVA8IcUdHpDAIA8hwUmQ3S5kbEITRSui9BEYtKEaDhdXUB7u35TFSo8oWgN8X226A6K2NJcRtmIIgdriNPEuGRRPrLtZgDAywea0OoJJHlEREQ0KWprIcLhuJMmfadM/EYDjBXRXmoSJJRmlE76EGlmOHdOrl5+ZXNNG+o6vMM8g4iIUs4QSZPjOdElxcZBSnMBTJrQxFlRlon5BQ4AwK66LhxsdA3zDKKRY9KEaDj798fd7A50QxXabs6e7HQcC0XriccGByWOEhhl4+SMkWYci9GAj5+lNdMLKwJ/28EdnkREM8L+/fCEPIio0R4Snt5+JrVZ6SjKtun359vzYTVa+70EUSIYZAm3nqMthAkBPP7+qSSPiIiIEqq9HWhsHPCSL+xDXUG0HGija+Am8JnWTGRYMkA0ESRJwifWVei3H93GWIQSZ0KTJl1dXdi4cSOcTiecTic2btyI7u7uIZ/zyU9+EpIkxf0655xzJnKYRIMLhYBt2+Luau5p1r9uK87C6U6tvqfZKCPXwRriNHluWVuOvmpwj79/Cooqhn4C0QzEWISmlc5O4MCBuFhECAGXvzdpkpPBGuI0qT62pgzm3hIsf9tZj0BYSfKIiKYexiKUst59d9BL9QYv/BnR5EhdR7TvSWwFDsYiNNFuWFkCu9kAAHhmTwPcvZuJiMZrQpMmt9xyC/bu3YuXX34ZL7/8Mvbu3YuNGzcO+7zLL78cTU1N+q8XX3xxIodJNLhdu+KanoWVMNp90VJde+w2+Ht/OKzItkNmPxOaRGXZNlw4X2sI3+gK4I0jrUkeEdHUw1iEppUtW6AoEbR6o+/37kAEwYgKVZIQrMpnDXGaVLnpFlyxVKsn3uUL48X9TUkeEdHUw1iEUlJ3N7Bv34CXhBDYnxVG3w6+VncA7T1BAEBBhhU2c7TiBmMRmmjpFiNuWFUCAPCHFTy9uyHJI6LpYsJqBx0+fBgvv/wytm3bhrPPPhsA8PDDD2PdunWorq7G/PnzB32uxWJBYWHhoNeJJkUkAmzdGndXq7dVL83lznXg3UB0N93i4uiRU7PBjIrMChBNtI3nVOjJkke21eHSRQVJHhHR1MFYhKYVtxvYuxftvva40lxtHm2R4lCeE7Nn5ej3y5KM2dmzJ32YNPNsPKcCz+7Vyrc8sq0OH1nFPjpEfRiLUMrasgVQ1QEvdQS6cHx2ln77YKNb/zp2XQQA5mTPmZjxEcX4xDkVemmuR7bV4bZ1FZBiNjUTjcWEnTR577334HQ69cAAAM455xw4nU5sPWMh+kxvvfUW8vPzMW/ePNxxxx1obR1893QwGITb7Y77RZQQH3wAeDxxd8WWwziyoAS1nVrdznSLEeU50Rrii/MWw2wwT844aUb70Lw8lGVrx6LfrmnDoUa+BxL1maxYBGA8QpNg61ZAUdDUE93JH1FVdHiDEAB2VxVidr5dvzY/Zz5sJtsAL0SUWKsrsrCgUGvCuudUN3ac7EzyiIimDsYilJI8HmDPnkEvVxca9dJcEUXFkWbt35rRIGF+7+cBAFRlVSHTmjmhQyUCgAWFGVg7KxsAcKy1B29WswoHjd+EJU2am5uRn5/f7/78/Hw0NzcP8AzNFVdcgcceewxvvPEGfvrTn2LHjh246KKLEAwGB3z8gw8+qNcGdTqdKCsrS9gcaAbzeoG33467yxP0wBPSkijeTDveNpgghNZDYlFxRlxprpVFKydvrDSjGWQJt6+bpd/+znMH9X+XRDPdZMUiAOMRmmCtrcCuXfCH/egOdOt3d/SEIARQnetE/uw8GOVoaM9YhCaLJEn41Lmz9NvffvYg+6wR9WIsQilHCOC117TKGwMIKSFsnWXQbx9v8yIY0U6kzM1zwGKMXltZyFiEJs8nY2KR7//zMIIR9lmj8Rl10uQ73/lOv4ZkZ/7auXMnAAx4FEoIMeQRqY9//OO46qqrsGTJElxzzTV46aWXUFNTgxdeeGHAx//7v/87XC6X/qu+vn60UyKKFw4Df/0r4HLpdwkhUNNRo98+ubQMB5uip1AWFUWPoOak5aAsgwEqTZ6N6yowq/ek0/aTnXjug8Ykj4hoYk21WARgPEITqKcHeOwxiFAoLhYBgNbe0lzvleVhUUw5DIfZwXIYNKk+urpMj4cPNbnx1+2nkjwioonFWISmrXff1apuDGJnhhferOjJ1oON0XWT2FjEarRiQe6CiRkj0QCuWFKIs2ZpZeNq273407snkzsgSnmj7mly11134aabbhryMbNmzcK+ffvQ0tLS71pbWxsKCkZec7+oqAgVFRU4evTogNctFgssFsuIX49oSEIATz8NnD4dd3dtd61+ysTvSMO+rAx01WpHUEuz0pBpi5biWlm0krUTaVJZjAZ8+5rF+NT/7gAA/ODFw7hkYQHslglrW0WUVFMtFgEYj9AECYWAxx8HXC7Uu+vRFejSL/lCEXiDERzPdkAtzkS+w6pfW164HLI0YQfKifoxyBK+e91i3Pi79wAA//VqNa5aWoQsO8vV0vTEWISmpQMHgNdfH/Ryg7sB7y2Olt9y+8M41eUDADjTTCjNStOvLc1fCpPBNHFjJTqDJEn47rVLcPWv3oEqgF+9cRQ3rCxBodM6/JOJBjDqFbXc3Fzk5uYO+7h169bB5XJh+/btWLt2LQDg/fffh8vlwvr160f8/To6OlBfX4+ioqLRDpVodFQVeOEF4NAh/S5FVVDbXYvT7mgSpXZxGbbUdui3Fxc79a9lScbyguWTM16iGBcuyMfFC/Lx+pFWtLiD+OUbR/HvVyxM9rCIJgRjEZoRQiHgiSegNpzGye6TOOWK7twXQuBUp7ZIsbU8Ly4WAVgOg5LjrFnZuH5FMZ7Z24huXxg/ebUaP7hhabKHRTQhGIvQtFNdrW0gHYAqVNS76rHb5oInrwSAFotsOdYO9FZjXFSUEbd5lGVCKRkWFWfg1rMr8Mi2OvhCCh548TB+dTP/LdLYTNgWtIULF+Lyyy/HHXfcgW3btmHbtm244447cPXVV2P+/Pn64xYsWICne9+Ye3p68JWvfAXvvfceTp48ibfeegvXXHMNcnNzccMNN0zUUIm0hMkzzwC7dgHQAoB2Xzu2N2yPS5h0Fmbi/0LA6S6tAbzNbMCc/HT9+pzsOXBYHCBKhm9dvQhmg/a2/vDbJ6ZU8zMhBP66/RSufehdnP/jN7DmP1/DuT98Ay/saxr+yURjxFiEUlYwCDz2GDoP7sSOhh39Eia17V50+8LYU5SNtmyH3oQbACqcFcix5SRj1ET49ysXwm7W6tk//v4p/HPf1CkZKoTAM3sacP2vt+BDP34TZz3wGs75wev4+06WMKKJw1iEUsLBg8CTTwJK/x4Q3YFu7GrchaPeU6g5O1r6891j7ahu0apxGA1SXGmuAnsBitKZ4KPk+LcN85Bl0045Pf9BI56YYiVDXz7QhH/57VY9Fln7wGv4v60nkz0sGsCE1m557LHHcM8992DDhg0AgGuvvRYPPfRQ3GOqq6vh6u0dYTAYsH//fvzlL39Bd3c3ioqKcOGFF+LJJ5+Ew8GFaBpEOAzU1AANDYDJBFRVAeXlwEhLZEUiwFNP6SdMekI9ON55PK4EBgB4M9LwP6UFqG6OBgZXLyuGyRDNPa4qWpWYORGNwaxcO+65eA7+69UaqAK45/E9ePqL58Yl9pKhyxvC1/7fPrx6qH9pgrv/uhsRdQWuW1GShJHRTMBYhCad3w8cOQK0tAA2GzBvHlBYOPLn+3zw/Pl/cPLAu+jwd/S73NDtR6sniJOZ6Xh9bjGuWVoEqynadJWxCCVTQYYVX718Ab793EEAwFf+/gFm5dixpMQ5zDMnlicQxjefOYBn9/ZP4tz/j30IKSpuPbsiCSOjmYCxCE0YIbTS4idOaPFHcTGwYAFgHkVpxD17gOee014rhj/sx4muE2jztUFIEg5eshR+p9ZHc8+pLuyq09ZLJAm4fHERHNZoKa5VRatYspySJtNmxreuXoQv/03rzfOtZw+gKi8dayuzkzouXyiC7//zEP66vf9mjW8/dxCBsILPXzA7CSOjwUhCnPHOmOLcbjecTidcLhcyMjKGfwKlJiG0wGDvXu0YaSgUfz07G1i5ElixAhgqsPR6gSeeAOrrIYRAvbseJ7pO9HuYR5bxX7NL8IFf23khScA1y4pRlRddjJ6VOQu3L7+dwQEllaoK/Otju/HywWYAQGWuHY/fcTaKnGnDPHNi7K3vxp2P7EKzO6Dfl203wyBLaOttYCxLwE8/thw3rCxNyhhnCn4+Ti7+ec8wigIcPgzs2wccO6adYI1VXKzFJUuXAtYh6ip3dKDu1z/AyRO7IRAfoociKk52eNHpDaHdZsEjK2bjwhUlWFAY/fdVlF6EO1bfwX4mlFRCCNz/j334xy7ttHax04onPrcO5Tm2pIzncJMbn3tkJ+o7/fp92XYzTAYJLe6gft/3r1uMjetmJWGEMwc/GycX/7ynsY4OLdlx4ADQ3R1/zWwGliwBVq/W4o/B1ieEAN56C9i8ud+l5p5m1HTUQBVaPFO9fj6a5hXBF4rgnaPtONzk1h970YJ8LCvN1G/n2nJx55o7YZTZX5OS67vPH8Sft5wEAOTYzfjr587BvILkJJ5PtPXgc4/swrHWHv2+TJsJVqMhbq3kq5fPx79+eM5AL0EJMprPRiZNKLWEQsCOHcDOnUBX1/CPl2Vtp8Xq1UBlpXa7T1MT8Pe/A52dAIBjncfiSnEBgCoEGjwh/KS8EKfSo4vOlywsiNsxZzfZ8ZlVn0F2WnIz10QA4A1G8C+/3YojvaeiAGBJSQYuXViIz5xfifRJahD/VnUrvvDobvjDWrIxy2bCjz+6HJcuKoCqCnzr2QN47H3tqKwkAX/65Fm4cH7+pIxtJuLn4+Tin/cM0dMDbNumLVx4vcM/3mTSEicDLWQcO4YTf/gJTjVXxz1FCIEWdxD1XT4oqkCP2YhHVszGsqUlWF2RpT/OarTi0ys/jXw730cp+YIRBTf/fht2n+rW71tQ6MAlCwtwx/lVcNompznw+yc68Nm/7IQnEAEAOCxGPPCRpbh2eTGEEPjhy0fwP5ujG6YeumUlrl5WPCljm4n42Ti5+Oc9zQihbRjdvl3bQDoShYXAmjVaEiV204bfD7z4IrB/f7+nnHafxrHOY/rtuqXlOLGqEgcb3Xj3WDsC4WgJr7Mrs7FudrS3j0k24ZMrPomSDFYRoOSLKCo++ecdePdYu37fnPx0XLwwH3ecX4XcdMukjOOD+m586n93oNOrbfZOMxnw3WsX48Y1pZAkCQ+9cRT/9WqN/vgffmQpblpbPiljm4mYNGFgMD21tmp1Njv6l6oYEYdDK5GRnq4FGadP60dQmzxNqO6ILlIIIdDlC+N4tx+Pzi9DXaZ2osRuNuLCBflx5Y7MBjNuX347AwOaUuo7fbj+11vQ4Y0/hVWZa8evbl454WUyntnTgK/8/QNEVO3/2NpZ2fjlzStR6IwG60II/H/PHsQj2+oAAIUZVrz65Q8hwzo5CykzDT8fJxf/vGeAEyeAf/wD8PnG9vycHGDuXMBoBI4eRduJAzjYdjDuId2+EOo6fHryucdsxNOr5mDpyjIsKIr+uzJIBmxcvhGzMmeNdTZECdfqCeC6h7agyRWIu78kMw2/vHkFVldM7GajVw424+6/7kEoou2UXlbqxK9vWYWy7OiJFyEEfvJKNX7z1nEA2gaPTV++YNIWUmYafjZOLv55TyPhMPDss9rJkrEwGoE5c4CiIq186PHjWv+0M3T6O7GvZZ9+u35xGTbPLsTmo+16lQAAsBhlnDc3D0uKo83fZUnGTUtuwryceWMbI9EE6PaFcP2vt+BkR3y8nuew4BcfX4Fz5+QO8szEeOdoGz7/yC74QlosP7/Agd98YhVm58WXUP/NW8fw45e1NUm72YBXv3wBSjKTUy1kumPShIHB9ONyAX/4A+DxDP/YUXIH3djTtEcvgyGEwLHWHjQGIvj7klk47bQDErC8JBPr5+TAYozWDZclGbcsvQVzsnl8jqaeJpcfj79/Cm9Wt+JAQ/QItdkg4xtXLcRt6yoSXk5OVQV+8VoNfvlGdHfSlUsL8bOPrYirud9HCIHb/7wDb9e0AQBuXluOBz+yNKFjIg0/HycX/7ynuYYG4H//V1vESICeUA/2NO2BIrQfqPqavbfGLFC4LSbs3rAMy5eXIc0cfT+VIOHGxTdiUd6ihIyFKJHaPEE8uq0Ob9W0Yd/pbr1kvkGW8JUN83HnBVUJj0WEEPift0/gxy8fQe/eDXx4fh5+c+sq2Mz9T9sKIXDXX/fghX1NAICrlxXhoVvYG2gi8LNxcvHPe5oQQtukcfDg8I8dB3/Yj11NuxBRtZN5tcvK8Yg9Hbvru+MeN7/AgQ/Ny4P9jOoF182/DiuLVk7oGInGwuUL49H36/DmkVbsPtWlxwaSBNx14Rzcd8k8GOTExyKPbqvDd58/FN1IWpmNh29bA2fawJtE//2pfXq/kwvm5eF/P3UWy/9PACZNGBhML8Eg8Kc/aTsiEv3SkSB2Ne1CSInuxj/V6cPxnhD+tmQWmjJsKMlMwwXz8pCfEV+D3Cgb8ZGFH+EiBaWE2nYv7n1iD/addun3femSebj3krkJ+x4uXxj3PbkHb1a36fd94pxyfPfaJUMGIQ3dfmz42WZ4e3dfPH7H2Vg/e2J3fMxE/HycXPzznsa6u7WNHD09wz50JMJKGLuadiEQie7Gb+j2o74zuiMunGVH+8fOQVZJZtxzDZIB18y/BisKVyRkLEQT6XSXD19+8gNsP9mp3/fZ8yrxjasWJmxRwBuM4Kv/2IcX9jfp992wsgQ//ugymAyD9/pp7wni0p9tRpdPS4T+fuNqbFhcmJAxURQ/GycX/7yniddeA959d0K/haIq2N20G96wVmr0xOoqPJ+VgTePtOqPyU234IJ5eXGn9QBt88YVc6/A2pK1EzpGokRodQfwb3//AO8cjZbsuumsMjz4kaUJi0UCYQXffOaA3tsNADYsKsAvb1454EbSPu5AGBt+9rbe4+SnNy7Hv6xm39dEG81nI7tE0tSmqtquiglImAghcKjtUFzCpL0niANBFX9ZMRvNTjsuX1KIj64u7ZcwKXGU4LOrPsuECaWMylw7/nHnenzmvEr9vp+/VoOH3x5hPdxhNLsCuOE3W/SEiSwB/3HlAnz/uqETJoBWpuPrVyzQb3/9/+2HP6QM8QwioiQJBoHHH09YwkQIgSPtR+ISJl2+UFzCxDG/CNKdF/VLmBTYC/DplZ9mwoRSRmmWDY/fcTbuuWiO3s7nD+/W4uebaoZ+4gh1ekP4l99ujUuY3HvxXPz0xuVDJkwAbTHw29cs1m9/85kDcPkTc5KMiGjMdu+e8ISJEALVHdXwhr1QZQmHz1+IraW52ByzEe5Dc/Nwy9nl/RImubZcfHLFJ5kwoZSRn2HF/31qLb52+QJ9neKJHfX47vOHkIgzBe5AGDc/vC0uYXLH+ZX4za2rhkyYAECG1YQHblii3/7ePw/FlcWjycekCU1t27cDR48O/zhJim/yPgK13bVwBaO77r3BCDaHBR5ZUYUumwUfmpuLBYUZcdlmi8GCK+deic+s+gwK07n7jFKL2SjjW1cvwjevWqjf98CLh/WeImPV3hPErX/YhhPt2s6kLJsJj3zmbHzuQ7NHvFvj1rMrsLZSq21+qtOHx7efGteYiIgmxKuvaj3WhiPL8U3eB1HvrkeHP9qrzR9ScKw1mpAJLy1Dx03nIJJm1u8zySZcWnUpPrf6c+ynRinHaJDx5Q3z8eAN0VKcv3zjGH7b21NkrFz+MDb+8X0cadZK+TosRvzhtjX40qXzII+w5MZ1K4px0YJ8AECrJ4g/b6kd15iIiMalq0tr1j4ShqEXY4fS6GlEq7cVYYsRH2xYjqMl2XhhXxPU3gXkVeVZWFWRBTkmrjFIBlw460LcueZOVGRWjPl7EyWDLEv4wodn4xcfX4G+EOF/t57ET16pHvqJw/CFIvj0n3dgz6luAFrD91/evBLfuGoRjMNs3uhz8cICXLeiGIAW2/xu8/jiIxqf/kVdiaYKRRl+V4XBAFx+ObBsGWCxaPU+GxuBXbuA/fshQiH4wj50+DvgCXqgCAUWgwVtvja9VicAhATwmDUNr1TmQJFlLCrKwIqyzLhvtbJwJS6uuhjp5nQQpbLPnl8FX0jBz3p3dv5/zx5Als2Eq5cVj/q1un0hbPzjdhxv0xImZdlpePyz5/TbhTQcWZbw/euW4LJfvA0A+P3bx3Hr2eXD7sYgIpo0Hg+wZ8/Qj8nIADZsABYs0JquCgFx/Dg8770F19734Qm4EFEjsBgs6PR3IqhEd48pqkBNiweKKhA0yKhdMQsV1yyHFLMpZGn+UlxSdQmcVudEzZJoUty0thz+sILvPn8IAPCjl48g227Cx88qH/VreYMRfOrP23GwUevflu+w4K+fO6dfk9XhSJKE7167GJtr2qCoAn/echKfOa8SDuvAtceJiCbU1q1AJDL0Y84+G1i7FsjJ0W739AB79wI7d2rlRGP4w350+jvhCroQVsKwGq3oDnTDH/GjqzATNevnoyfdihd21sMf1k79l2fbcN4ZjbLn58zHZXMuQ3ZadoImSpQc1ywvRiCs4P5/7AMA/Oat48i2m/HZ86tG/VqBsILP/WUXdtZ1AdA2kj762bOxuHj0Mfs3r1qEVw42IxBW8dj7dfjCh2cjN90y6teh8WPShKau6uqhy1/k5wOf+YyWLOkjSXDlpKN2bTlOVQTQs2sbbNXHkdnlGvRlWmbl4bcWG/Z6tSP4BRlWXLQgP26HPJua0XRz90Vz4Asp+N3m4xAC+PKTHyDHbsG62Tkjfg1/SMGn/ncHDjdpixRFTuuYEiZ95hc6cNniArxysAUt7iD+vus0Np7DnUtENEXs2aOVDR3MvHnATTcBsgxP0IPajlqc7D6J2q5adJV1wZyTi4LjCgpqW5He2Rn31L7G796Iit3FOTi8sBTXnVsVlzC5fM7lOKf0nImaHdGk+9S5lfCFFH1n5388fQC56RZcvLBgxK8Riqj43CM7sbt3V2eO3YzH7zh71AmTPmXZNtywsgT/2HUaLn8Yj2yrw79+eM6YXouIaMxCIWDfvsGvG43Axo1AxRk/K6WnA+edB5x7LnyH96Nt2+vwH9gDl7sN/oi/38t4nTYcP2spOkuyAUnCO9WtaOntp+BMM+HKpUVxp/UunHUhLph1QUKmSDQV3LimDIGwgm89exAA8J8vHEZ+hhXXLh/5htKIouLuv+7Bu8e0PikOqxGPfGZsCRMAyHNYcPPacvx5y0kEwir++G4tvnb5guGfSAnHpAlNXbt2DX7N4QA+8Ym4hIkr4MILR1/A0Y6jEOitRViVAVSthKUngKymLlh7ApBUAQlAIN2KrqIsbOkKYO9RrV6n1WTAVcuK4o7OXVJ1CRMmNO1IkoSvXT4fnd4g/rbzNEKKis/9ZSee/Pw6LCoevlGkFhjs1o+e5qZb8Nhnzx5zwqTPXRfOxSsHtR5Gv3vrOG46q2zYOuRERBNOVYeOS4qLgY9+FD0RH148+iIOtx2OxiK9QjYL6peWo35pOWzdXmQ2d8PsD0FSBU62e7HJbMPJzHQEbRbcvKYcZmP0ve+88vOYMKFp6V8/PBsdPSH8aUstFFXgi4/vxmOfPQerK7KGfa6qCnz1Hx9gyzGtxJ0zTSsPOiffMe4xPbX7NFQB/OGdWnxy/SzYzPyxmYgm0YEDWh+1wVx/ff+ESS9/2I+Xjr2EA20HoFapkMsrkN2YAXuXF7KiQhICEZMBroJMuPMyIHqTIjUtHuyt7wYAGGQJVy0tijv1f1bxWUyY0LS0cd0stPeE8N+va60B/u1ve5FtM+O8ubnDPFPb+PSd5w9i0yFtDcNmNuB/P7UWS0rGdyr88x+ajce2nUJIUfGXrSfx+Q9VIdNmHv6JlFBciaKpqbMTOD5E7b5LLtFKYPTq8HXgD7v/gJqOmn6LFAAQTLeiujQXWysLUbNiFk6srkLt7AK80+nXs8EAcNniAmTEHMFfnLcY55adm5g5EU0xkiThgRuW4sL5eQAATzCCmx/ehvdPdAz5PCEEvv3cQbx2WKvrn24x4i+fXouqMe7qjLW01KmPp6Hbj6d3N4z7NYmIxu34ccA1+KlVXH01XKoff9z9RxxqOzRgLAJozSEbu/3oTk9D44ISHF1WgafzsvBLkxUHCrLQYzHhkoUFyLZHfyiamz0XF1VelOgZEU0JkiThm1ctxNXLigAAgbCK2/74Pt6sHr530I9fqcYzexsBABajjD998qwRbfwYTlVeul6ytNMbwuPvs88aEU2ynTsHvzZvHrBkyYCXvCEv/rTnT9jXsg+q0E7HqkYD2stzUbe8ArWrKrFvSTm2leejJTsdQpYQUVTsP+3Ca4db9Ne5YF4e8jOs+u1yZzkun3N5YuZGNAXdd8lc3Ly2DAAQVgQ+83878NL+pmGf95u3juPRbVqcYJQl/H7jmhFt/BhOodOKG9eUAgC8IQV/3nJy3K9Jo8ctMzQ17d49+LW0NGDxYv1mMBLEEweegCfkiXtYMKKgsduPU51+nGz3ossXAgDIkoQ8hwVdvhBCkWiZjbNmZaMyN7rom2vLxXULrhtxI2uiVGQyyPj1ratwy8PvY299d28j1e348UeX4fqV8Q2Gm10BbDrcgn9+0Ij3azt7ny/hfzauTsgiRZ+7LpqLN6u101+/fusYrl9ZErfjmoho0g21eFFcjHBBHp7c+2d0BbriLoUiKhpdftR3+nCy3YcOr7ZrVJKAvHQL3IEIAr11wwFgWWkm5hdGd8lnWjPxkYUfgSzxPZCmL1mW8NOPLUenN4StxzvgDSn47P/txPeuW4xb1pbHxeKtngBeP9yKF/c34Z2j2sYnWQJ+dfPKhCxS9PnihXPw3AdaQub3b5/ALWeX87QJEU2OpiatT+tg1qwZ8G5FVfC3g39Dm68t7v6woqLJFeiNRbxo6+k9wSIBuXYLfCEFvlC0d8qCQgeWxuySTzen48ZFN8Igs9ckTV+SpPVYbfOE8NrhFgQjKv718d349ysW4I7zq+JikU5vCK8fbsFLB5rxxpHoJo+f3LhsRKdTRurOC2bjyR31iKgCf95Si0+dO4unTSYZIz+aehRl6EarK1ZoNTyh7Xh/+sjTcYFBizuAN3trcYoBNnqqQuh1OvssKsrAuqpoLweTbMLHFn8MZgPfkGj6s5mNeOQza3HX43uwuaYNIUXFfU/uxff+eQiz8+yQJQlHW3vQ6Q31e+5PProc585JXGAAAKsrsrB+dg62Hu9AXYcPf9pSizsvmJ3Q70FENGJuN1BTM+hlsWoVnq95Ho2e6AJHe08Qbx5pRaMrADFAMCIE0OqJL7sxt8CBD82Lvp8aJANuXHQj0kxpCZgE0dRmMRrwp0+ehS89uRcvHWiGogp84+kD+Mkr1Zidlw6zQcbRVg/ae/rHIt+9bgk2LC5M6HjmFzpw+eJCvHywGa2eIH795jHcfxnriRPRJBhqo4bTCcwZuM/SK8dfQZ2rTr/d5QvhzSOtON3lhzrQwojQ4pVYlbl2XLSgQF8gliDhXxb+CxyW8ZU9JEoFRoOM39y6Cl9/ah+e2t0AIYAfvHgEv3rjGGbnpcNuMeBoS0+/GB4Avnr5fNywsjSh4+nrs/b3XafhDkTws001+N51A58yo4nBbWs09Rw5Ani9g19fvVr/cnPdZhxpP6LfdvnDeHpPA5pd8QkTSZJQkpmGxcUZeskLgyxhcbETt62bhQ2LC+ManF017yrk2/MTNyeiKc5hNeGPt6/BLWeX6/d1ekPYcbIL79d29kuYVOTY8NMbl/c7jZIo/3HlQvRt5vjl60fR7AoM/QQioomyZw8G3IUBABYLtmf7sa8l2qzVG4zgmT0NaOj2xydMJO2o/eJiJ3LTLYCkxScLCjNw69kVuGppEYwxjd83zN6AkoyJeY8lmoqsJgN+fcsqfO5DVfp93b4wdtV14b0THf0SJiWZafjP65dg4zkD1/Ufr69dsQDm3r5qD79di9r2IX4+ISJKhGAQ2L9/8OurVgFy/2W8PU17sL1hu347EFbwzJ4GnOr09UuYFGRYsaTYiTyHBZKknX6dm+/AzWvLcd2K+BP+F1ZeiMqsyvHPiyhFmI0yfnrjcnzpknn6fZ5ABHvru7HlWEe/hElhhhXfuHIhvjBBmzy/vGEebGbtlNej2+pwsHGIcsGUcDxpQlPPUI1WZ80CcrVdmM09zdh8crN+Kayo+OcHjXqZC2eaCbNy7CjNSkNZti2uiVkgrMAoS3EN3/usLVmLFYUrEjIVolRiNMh44PolWFGWiWf2NOBEmxfNvaey8hwWzCtIx6ryLFyxpAgLixwTWrpuSYkTt55djke3nYIvpOCBFw/jVzevnLDvR0Q0IFUdsmSoe14FXq1/S7+tqAIv7GtCT1Arc+GwmlCZa0NJpg3l2TakmaOxSDCiQJYkmAaIRZYXLMfakrUJmwZRqpBlCf9x5UIsKXHi7zvrcby1B429Gydy082Ym+/AivJMXLGkEEtLnBMai1Tm2vHZ8yvxm7eOI6So+O7zB/HnT57F0r1ENHEOHABC/U/UAdCSJatW9bvbHXTjxaMv6rdVIfDSgWa4/GEAgN1iRGWuHaWZ2rqI3RJdBuwrVz5QKeQFuQtwfvn545kNUUqSJAn3XjIXC4sceOz9UzjW2oOGbj8AIMtmwtwCB5aXOnH5kiKsLMuM24CdaEXONNx90Vz86OUjUAXw7WcP4u93rmMsMkmYNKGppasLOHFi8Osxp0zeqH1Db7QqhMBrh1r0+pyZaSbctLY8LlESa6D7jbIRF1RcgPPKzxvHBIhSmyRJ+NiaMnxsjdYEzRMIQ1UBp8006WP5yob5eGFfE7p8YTz/QSNuWVuOdbNzhn8iEVGiDNMA/u1cHxQR7UnyVnUrGl3aD1XpFiNuOqssbnEilsXYPxYxSAasL1uPCysv5A9DNKNdu7wY1y7XmrF7gxGEFTUpdbzvumgOnt7TgCZXAG9Vt+G1w624dFHBpI+DiGaIoXq7zpsHOPqXyXqn7h2E1bB+e+uxDtR1aCfj0kwGfHxNGTLSBv5ZbqBkiSzJOLvkbFxcdTFjEZrRNiwu1Mt/+kIRBMIqsmymSf9/8enzZuHvO+txot2LnXVdeHpPAz6yKrGlwGhgLM9FU8tQQYLNBixcCADoCfXgaMdR/VJ1iwfVLVojeJNBxjXLiwdNmJwpzZiGCyouwJfO+RLOrzifgQFRDIfVlJSECQBk2sz46uXR+uHfeHo//CFliGcQESXYEHFJuDAfe6Vm/XZtew/2N2gJFoMs4eplxYMmTM5kMVhwbtm5uO+c+3Bx1cVs/E4Uw24xJq3xqc1sxDeuWqjf/vazB/Td20RECdXcDDQ0DH49ZgNpn4gawf7WaDmvhi4/dtZ1AtA2w125tGjQhMmZTLIJ55Seg3vOvgeXzbkMRpl7rIn62MxGZNvNSVkvtBgN+Pa1i/XbD7xwuF8/IpoYfBekqUNVgb17B7++fLneAP5A6wH9lAkA7D8d3QW6YVEBctItcU9dlLcIc7PnItOaiQ5/B7r8XVCEgnJnOeZkz2HDd6Ip6mNryvDEjnp8UN+NE+1ePPjSYTY/I6LJ0dMDVFcPevl4ZSYiaqt+e/9pt/71hfPzUei0xj1+Xs48zM+Zj+y0bHQFutDp70RYCaPMWYY52XNgNcY/noimhquWFuGJOfV491g7Gl0BfOe5g/j5x1cke1hENN0MtYHU6QRm9++ZcLTjKAKRaO/HAw3RdZHz5+SiLNsW9/iqrCoszF2IXFsuXEEX2n3tCCthlGSUYG72XKSZ0sY/DyJKuAvm5eGKJYV46UAzOrwhfP3/7cPDt63hpu8JxqQJTR01NYDHM/j1mPqdsQ1XPYFwTH1BM+bkp8c97f7198Nutuu32ciMKHUYZAk/+9hyXPXLdxAIq/jLe3W4aEE+Pjw/P9lDI6Lpbu9ebUPHQMxm7Mz2A719oQNhBSc7tRt2sxGLijPiHv6lc74Ep9Wp364EYxGiVCFJEn700WW4/OdvwxOM4Ok9Dbh4YT6uXlac7KER0XQRDgP79g1+fZAG8LHrImFFxbG2HgBa2a1lpc64x9699m7k2FjqmChVff/6Jdhe24kObwivHW7FkzvqcdPa8mQPa1rj2X+aOobaWVFeDuTlAQDafe1o9DTql6qbo4mWBYXxzalvWXpLXMKEiFLP7Lx0/MeV0dIYX/3HPnR6B2mQSESUCEIMGZf45s/Gce9p/XZNiweqqp2AnV/ogBwTi9yw4Ia4hAkRpZ6SzDR87/poaYxvPH0Aza7AEM8gIhqFQ4eAwCDvKZIErFzZ7+5AJICajhr99ok2L8KKttljbr4DRkN0ue/yOZczYUKU4nLTLfjhvyzTb3/vn4dwst2bxBFNf0ya0NTQ2QkcPTr49Zj6nftb9sddOhKTNJlfGG2MVmAvwLyceYkbIxElzcZzKvCheVritNUTxMf+5z0GCEQ0cY4d02KTQRwqt8aVCT1yxgaOPpnWTCwrWAYiSn3XryjBVUuLAAAufxg3/s9W1LQMcUqeiGgkhAC2bx/8+ty5QEZGv7sPtR2CIqL9Ho80R8uExsYiNpMNZxWflZixElFSXbqoADedVQYA8IUU3PT7bdh3uju5g5rGWJ6Lkk8I4Nlntd8HYrUCixb1PlTEHUFt8wT1BkhFTmtck0guUhBNH5Ik4ScfXYarfvkO2ntCONbag+t+vQW/vXUV1s/JTfbwBtTpDeGt6la8e6wdXd4QfCEFgYgKkyzBZJBhMsowG7SvM20mzM13YH6hA840E4IRFWFF+3X+3LxkT4VoZgkGgRdeGPx6QQF2oUm/6fKH0dhbJjTbbkaeI9pXbWn+UtYaJpomJEnCf16/BHtOdaHRFUB9px83/HoL/vumlbhkUUGyhzcgly+Mt2pa8e7RdrT3BOEPKwiEVRhkCabeGMRskGEyyHCmmTC3IB3zCx3IspkRUlSEeuORdVU5cbvWiSiBtm8fugF8TJnyWLHrIr5QBHUdPgBAusWI0qxob5Il+UtgkA2JGSsRJd03r16E7bWdONHuRbM7gBt/9x7+68bluGb51Cwb6gmE8c7Rdrxd04ZWTxC+UGTQWMRhNWJuQTrmFTiQm25BSFERjqgIKSrOmpUNq2ly38uYNKHke/99oK5u8OvLlgEmEwDgtPs0ugJd+qX43RTR3RcSJCzJZ7NooumkIMOK//eF9fjM/+3EsdYeuPxhbPzTdnz1svn43IeqkrowGVFU7Gtw4YP6blQ3e3C4yY19Da5Bc8GjceIHV0KWuehKNGlefRXo7h70cteiKjR539Nvx5cJzYh7L+IGDqLpJctuxj++sB6fe2QnDjS44Q0puOORnbjnorm45+K5MCTx81pRBQ40uPDB6W4c6YtFTrugqOMPRj749gY405g0IUq4jg7gtdcGv+5wAPP6V89wBVw42X1Sv320pQeqiJYJjY1FluYvTdhwiSj50i1GPPn5dbjz0V3YVdeFYETF3X/dg32nu/HVyxfAlMRNDqoqcKjJjb313TjS7MaRJg8+ON2NsDL+WOTdr12I0ixbAkY5ckyaUPIoCrBtG7Bp09CPW7NG/3JH4w79ayEEanoXKiRJwtyCaAP4iswK1g8nmoYqcux4+l/X494n9uKNI61QVIEHXzqCnXVd+K8bl8OZZprQ7y+EQE8wghZ3EMdaPahp6cEH9d14v7YTPcHIhHzPsKrCwt1hRBMvFAJefnnoHmsWC7ZleYHeyl1CiLgNHLFlQgvTC5Fn50kxoummODMNf//8enzlHx/ghX1NEAL479ePYvepLvzi4yuQk24Z/kXGQQgBb0hBqzuAY609qGnxYN9pF7ad6IA7MEGxSG+fBCJKoIYG4OGHh37M6tUDNoDf2bgz7vZgm0mzrFkozSgd3ziJaMrJc1jw+B1n45tPH8Dfd2l9Fh9+pxZ7TnXjoVtWodBpnfAx+EIRtLqDWizS6sGBBhfeO96BLl94Qr5fIhIvo8WkCY2f1ws0N2uLDX2/gkHt90hES44oivY4j0f7PRgEfL7hX3vNGiA/HwDQ5e/CgdYD+qVTnT54ehcpZ+XYYDNH/zlzZyfR9OWwmvDwbWvw8001eOjNYwCATYdacNnP38a9l8zFjatLx1xCotsXwv4GFxq6tFI7kgQ0u4I41OTCkWYPmlwBhCIjWziYm5+Oixbm4+IFBZiTnw6b2QCLUYYqtMWH2KOmLe4gapo9qGnxIBhRtSOqveW7JPCUCdGodHcD7e2DxyWqqv3e06PFJT6fdt3vH/alfeedjV3dW/Xbze4AOr0hANpCamzilrEI0fSVZjbgoZtXYnFxBv7rlWqoAnjnaDsu+8XbuOvCObj57HJYjGPb8ODyh3GwwYX6Lh+EAGRJQqsngENNbhxu8qDJ5UcgPLJYpCrXjgsX5OPihfmYX+CA3WKMi0W0XwKhiIpWTwDVzR4cbe2BP6T0lhKVYDHIsBh5yoRoQEIAJ08OHHOEw9G1kEAgGncEAtqayHBH0p1OYN26fncHIgFsb4j2QOn0BtHk0prI56Zb4sqELitYxjKhRNOUxWjAjz+6DEtKnPjPFw4hrAjsrOvCZb94G//64dm4ff2sMZez6glGcKDBhboOL4TQ1kU6vCEcanTjcJMbjd0B+MPK8C8EoDzbhosW5OOiBflYVJwBu9kIq6l/LBJWVLR5gqhp8aC6xQNvMAKzwQCTUYLZICPDOvkpDCZNaPzq64Ennkj862ZmApdeqt/cWr8Vqoj+gLDjZLRM16Ki6G4Kg2TAorxFiR8PEU0ZBlnCVy6bj9WzsvClJ/ei2xdGszuAf39qP37/9gnctq4ClywsQFn20Mc3vcEI3j3WjjePtGLr8Q6c6hxBMncAOXYz1s/JxdmV2VhcnIF5vQsTA45dAgyyIS6AKXKmYUVZ5pi+NxGdYf9+4PXXE/+6FRV4r1hFpD66k3tnTCyyuIhlQolmEkmS8K8fnoOVZVm4+6970N4TRHtPCN95/hAefqcWn1w/C5cuKsCsXPuQr+MPKdh6vB1v9MYite3eMY0n02bC+tk5OKcqR49FHNaBT+AOFIsUOq1YVpo5pu9NNGNJEvDoo1piJNGuuw6w9D+5trNxJ4JKUL892LoIACwtYGkuoulMkiTcvn4WlpU6cdfje9DQ7YfLH8aDLx3Bn7bU4rZ1s3DZ4gLMzksfMoEaCCt4v7YTbx5pxTtH23Ci3TumUuMOqxHrqnKwbnYOFhc79Z6tAxkoFinIsGJJydSpGsSkCY2f2Tz8Y4Yhev83xv0njgkSPEFPXGmuJpcfp7u0xc0smxmz86OluebnzofVOPFH0Ygo+S6cn49/3n0evvPcQbx2uBUAUNvuxXefP4TvPn8Is/PsyLSZYTJIEALwhiLoCUTQE1TgDUZGvDsCANJMBpRn25DrMCPHbkFlrh3zChyYX5iOqtx09h0hmioGWGAYiQFjkT5mMwJXX453Dv6PfldHTxDH23oAAHaLEfOLoqW5KrMqkWHJ6PcyRDT9rJudgxfvOQ/fff4QXtjfBABo6PbjgRcP44EXD6My145se3ws4g0q6AlG4A1G4AuNPBaxGGUtFkm3INdhwawcG+YWODC/wIG5+YxFiCbbia4TsIU6YYtISDOmwSgbx32yQwgBae1aoKqq37WwEsZrJ6I9UNyBsN5bzWIyxC02ljhKkGvLHddYiCg1rCzPwj/vPg/f/+chPL23AUIALe4gfvJKNX7ySnVv7GCGuffkqLd3PaSn99doYhGzHouYkZNuQUW2DfMLHZib78D8QkdS+7slGpMmNC5HO46i7vQWVHQchSRpZWRkSda/NsgGGGUjTLIJsiRDQCCkhOAJeuAJeRCMBKEIBYqqQEBAggRJktCyeBZa216E3WWHoiqod9fHfd/Y3RSrK7IgxwQm60r7H2EloumrNMuGP9x+FnbVdeLHL1fj/dpO/drxNi+Ake3YNBtlLC1xYmmJE3ML0mGQJAgAGVYTFhY5UJFjn1YBANF0VNNRg0MnXsSc1gMwySaYDKa43/tikbAShjvoRk+oB4FIYMBYxCAZYDKYYDaYceq8pdgdkzABgJ11MbFIeRaMMTXHGYsQzSz5GVb8+tZVuPO0Cz95tRpv17Tp12rbvSM+PWI2yFhUnIFlpU7MLXDAJGuxSLrFiIVFDszKsY+5BCkRJd72hu3I7DwEq1c7+WGQDEgzpcFqtMJqtGpJlN41kr5YxCAboAoVYSUMT8gDT9CDoBJERI1AURX40i3YZRGQ3t4Lo2yE3WyH3aSdWKtz1cV9/911XXoD+BWlmfqCKACsK2MsQjSTZNnN+NnHV+DzF8zGT1+txquHWvRrpzp9I66qYZQlLCrOwNISJxYUOmAyyBAAbGYDFhZloCp35sQiTJrQuDR4GrCn4yCMnoaEvJ6AgCsnHUeWl0DxtaHN19bvMR09QZzo3dmZbjFiQczOzgpnBcqcZQkZCxGlltUV2Xjy8+tQ3ezBpkPN2HSoBQcb3Yio0XOlBllCusWIdIsRdosB6RYjFhVn4KIF+VhXlYs0MxuuE6WyTn8nGgJtyPS1j/k1BASEENqChhrGyRI7DpXE/2Dg9g++s7MwvRBzsueM+fsTUepaWurEXz69FsfberDpUAs2HWrBvtPdcc1LZQkxsYgR6VYjFhQ6cOH8fJw7J3fQ8p5ENPV0+bvgiOlfpAgFPaEe9IR6xvR6ilHGoQsWIWKUATWMsBqGP+JHO/rHNb5QBAcaXQAAo0GOK/WbnZbNkuVEM9T8Qgd+f9sa1HV49Vhkz6luhJRouwNZ0k7Kx8Yjc/PTceGCfJw3NxcZg5T3nGkYkU2SsKKirsOHY609ONXphcVoQJbdjGybGVl2E7LtZjisJhhlCUZZgkGWUqJhlypURMbYWOhMIasJDQtKUL+kDOogjRMjior3jnfot1edsbPzvPLzEjIWIkpd8wu1Y6F3XTQXAKCoWoNTSdLKWqTCeyvRRFBUgVOdWixyst0Lk0HSYhG7GVk2M7LsZjjTUi8WieUL+xIWl/gdaahfUoamuUVazfJeEVXFeyc6Bt3ZeV75eSn350ZEiTU7Lx2zL0jHnRfMBqC9/4YVFUIAVhNjEZq5FFXgdJcWi9S2e2GQJWTbzci0RddGnGkmmAzylI9FhBDoCnShxDz+ZTUhSWiblYfaFbPgdw7dkxHQ/hzfP9GJSG9CdklxRtzmr/Vl6yFLM2MnOBENrCLHjs+eX4XPnq+V+uuLRVQhkGYyTNn31qmESZMJFFFUbDnegWf3NuDVgy3oCUaGf1IMQ2+QkGs3ozTbhopsGy5ckI9LFxXANElHoVRVYMfJThxr64GqCqhCaxDUV/euzluLTpcble4ALEYZVqMMs9EASQIkDFIXHNp/Vm9IgQtAa4YNR4tzcKIwC5JJhnyqG2ajDJvZqH3wCy3p1N4TxN76br0HgZU7O4loBAyyxBMkNGNpP1R34Nm9jXjpQBPcgbHFItk2M8qy01CWbcMF8/Jw+ZJCWAbZ4JBoQgjsPtWF6uYeKEJAVQWCEQU9gQg8wQjMBhkZaSZkWI2o8zYgGFIQUVXIkjRkLKKqAt6QVsNXUQUiAPwmIzqy03F8Vj7airNgsxhh6/IDkhaLdHpD2FvfDW9vTMednUQ0Etp7KWMRmplUVWBnXRee3duAF/c3ocsXHtXz+2KRzDQTyrJtKM+2Yf3sHFyzvDiugfBEEkJg32kXDja6oQjtRKo74MXmE41AuxclHd7eJI8Ms1GG1aT9PlQsohhl9DjScCwnA3sKs+EyGRFq90Np9cEgA7IkwWSQYTMbkGbWFjjDigqXL4w99d3wBLQ/R1mWsLoiS3/ddHM6VhSumJQ/FyJKHYxFRo9Jk2F4gxEcafYAEBACEACCYRWeQBieYAQStDr4BllCly+Mdk8QDd1+VDd7cLTVg0BYHeY7DE5RBRRVoNEVQKMrgO21nfj7rtPId1hw09pybFhUgEVFGf0a/rn8YRxpcqPbH4YvpDU89gUj8IYUCCFQnJmGsiwbKvPsKHZa4z7AhRBocQdR3eLB1mPteO6DRjS5AoOO0S83IIxOZAsDwqqMkCIjCBlhgwEhgwxhMsBoMsBkNiBiMqLHbES3wYDmiIqwJEV3bkYAnO4e1Z/PeXNyubOTiIimvUBYwcFGN2JjkVCkNxbpTYKYjTKMsowuXwjtPUE0dvtR3dKDoy2eUTX2O1NfLNLsDqDZHcCOk114ancDcuxmfOysMly2uBBLS5z9+v14AmEcafag0xuCr7fpcd/viipQlGnVYpFcO0qz0vrFIm09QdQ09+C9E+14dm8jTnf5RzRer+EgbIEWnO5REJZlhAwyImYDZLMRktUIYTAgIkkICYFWRcBjNMJrNsJvMkCJjUu8CnB0+BJf66ty4pKy55ady52dREQ07YQVFftOu6BFIYAQWiziDmibKVUhYOmNRVz+MNo8QTS5/Khu8aCm2QNvAmKRVk8QrZ4gdtV14ek9DfjPFw7jxtWluGJpIZaVZvbbWOoLRXC4yY2OnhB8IUXbKBHUNoBGVBWFzjSUZaWhMteO8mxbv7WEjp4gqps92H6yE8/ubezXmyiCdvQYO2FQJOSaLQgbZAQNBoQNWvwRMshQJQmKLEE1yFDsFih2K0JWI0KyAWEBdPlDUAMCiOnZOlpnVWTDEVNKZ13pOhhlLvUREY0X30mHcaLNi3/57dZxv47DasT5c3MxN9+Bqjw7FFWg0xtCly+ETm8Y3b4QPAHtw1tRBSKqQETRjk61uANxuzFaPUH88vWj+OXrR5FpM2FJsRNGg/YBX9fhG3GjQQDIsZuxpMQJoyyh0RVAQ5dvlLtQVaiyjKcWV4zmKYA8tgUFSQLm5juwpiIL+RlW/f6ctBzu7CQiommp2RVISCxiNxtw3txczCtwYHZeOgQEOr1hdPXGI12+ENz++Fikr7xdmyeIDm9If60Obwi/fes4fvvWcTisRiwrdcJkkCFBazR4ot0LIQYfS6xMmwlLS5ywGGU0uQJo6Paje5S7UPsIBOFKM+PpRaOIS8Zgdl461szKQpEzTb/PYXZgeeHyCf2+REREyeD2hxMSi6SZDDh3Ti7mF6Zjdl46ZElCpzeEbl8Inb4QurxhuANhhJX4WCSsCLR5gmjvCeqv5fKH8Yd3a/GHd2uRbjFqsYRJi0Uauv041toDdYSxSIbViKWlTtjMRjT3xiKdMXHPQFRJ61uytTx/5H8AKgBfBNqu0fGZlWPHmllZKMmMxiJpxjSsKV4z7tcmIiImTSaMJGkfYktLnLhqWRE+PD9vXGUsPIEw9tZ347Ftp7DpcAuU3k//bl8Y7x4be7PTDm8Im2v6N1vvY5QlXDAvDxcuyIfNrJXdshoNSLcaYTMb8eZJFz5o6YE/rMAdCMPlD8MbVKD2ls8IKSoCYQX+sAohBCRJgkEGstLMKHBake+wIM1k0I+uqkILioIRFf6QAl9YgQyt/IXFKKMix4ZMmzlujBIkXDP/Gu7sJCKiaWk8hyjLs21YWuLElUuLcPHC/HGVsfAGI9h32oXHt5/CS/ubEOmNRTyBCLYc6xjm2YPr9oXxzhCnOmQJOHdOLjYsKoDNbIQsA2aDAQ6r1rQwrKjwBCLo8oXwxKHtaHT74Q1G9FJeYUWFP6zFI2pvLCJLQGaaGYVOK/IzLLCZDDAZZRhiYpGQosIXUuDv3R1rMmilNsqz05Btt/Qb5zXzr+HOTiIimpbGU9GhNCsNi4szcMWSIly6qAB2y9g/K32hCA42uvHX7afwz31NCEW0yh49wQjeOzH2WMQ9TCwjScA5lTm4bHEBHFYTZBmo6QrhcFcJzAYZqtA2mfjDCjyBCFz+MHoCsbGI0NZFIgpUNRqLZFhNKHRaUZBhhd1sgMmgVTFRhYAqYtZF9FhEK9lVmmVDnqN/LHLl3CthMfa/n4iIRo8/2Q0j12HGp8+tjOnRoZXAcFhN+od9KKJCUVVkppmR6zAj32HF7Lz0hNbQd1hNOH9uHs6fm4cmlx+bDrVgy7F2vHe8I+5kiNkoY1FRBpaVOlHotMJu1hYU7GYD7BYjFCFwusuP050+HG72YP/pbv0Ui1GWUOi0oiovHQsKHVhQ6MCH5+cj224ebFg47bejR6QPO37Ru9000eWzLAYLrp53NWZlzkro6xIREU0VGVZTXCwCRGORdIsBkCSEIioiiopMmwm56RbkO6yoyrOPa2HiTHaLEetm52Dd7By0Xr0Qmw61YOuxDmw93h53ItZkkLCwKANLS5wozkxDusUIW28cYrcYIYRAQ7cf9Z1+VDe7se+0Sz/FYpAlFGZoY59XoMUiF8zPQ77DOtiw4tQF0+ENDxxrTFQsYpJNuGLuFZiXMy+hr0tERDRVWE2yHosAWjxiMspwWI1wWIyQemORsKLCmWZCTroF+Q4LqvLscaWjxstmNuKsWdk4a1Y2vnnVImw61IwtvbFIe0/0ZIhRljC/0IFlpU6UZtlgNxtgsxj1mESSJDR2+1Hf6UNNiwcfnHahzaOdYpEloCDDispcO+YXOjC/QItFYk+XAsBz1bvhl+2jGv9ExSJG2YhLqi7B0oKlCX1dIqKZTBJipMUTUoPb7YbT6YTL5UJGRkayhzPhVFXAE4wAAlCFQLrVOKom8UIINLkCMMoSctMt/fqjDOepw09hX8u+0Q573MwGM1YXrca6snXIsEz/v2ciovGaaZ+PyTaT/rxVVaAnFIFQAUUIpFuMcT3HhtPXTw0A8hyWfv1RRvM633/7+1DF2PvJjYZRNmJV0SqsL1uPTGvmpHxPIqJUNpM+G6eCmfTnLYS2LiJUbV3EZjGMutJHqyeAiCKQ77DAOII1lf/b+3+o7a4d65ATwiAZsLxwOc4tOxc5tpykjoWIKBWM5rORJ01SnCxLcKaNfeeGJEkozkwb/oGDGO/ChNVoRZY1C2aDGUbZCEUoiKgRBCIBeENe+CN+SJBgMphgNphRlF6EyqxKrCxciTTT2MdNREREiSHLEjLGsYtUkrSTruMVUkJIM6bBF/ZBYOR7giwGC7LSsmAxWPRYRFEV+CP+AWORAnuBHovYzaPbYUpERESJJ0nji0UAjPhUa5/L51yONl8bugPd6PJ3oSvQhS5/F1xB16jXScwGM7LTsvVYRBVqdF0k7IU/7IeAgNlghkk2Id+ej8qsSqwoXMFNpEREE4RJExqXK+deiYsrL4YiFKhChaIqcV8HlSB8YZ+2gCEEZEmGQTYg3ZyOovQiZKdlD3k0daKOrxIREdH0YjFacP+590MIgUAkoMcf3rC3XywiSzLSzekoTC9Eri2XsQgRERGNSkF6AQrSC/rdrwoVroALvrBP34gRUkJ6LKIKVY9F7GY7CuwFyLPnDdmjlbEIEdHkY9KExsVmssFmsk3Y6zMoICIiotGQJAlppjSkmdKQg/GXqmAsQkRERCMlSzKy0rKQlZaVsNdkLEJENPlGXnCaiIiIiIiIiIiIiIhoGmPShIiIiIiIiIiIiIiICEyaEBERERERERERERERAWDShIiIiIiIiIiIiIiICACTJkRERERERERERERERACYNCEiIiIiIiIiIiIiIgLApAkREREREREREREREREAJk2IiIiIiIiIiIiIiIgAMGlCREREREREREREREQEgEkTIiIiIiIiIiIiIiIiAEyaEBERERERERERERERAQCMyR5AogkhAAButzvJIyEiIpo6+j4X+z4naWIxHiEiIorHWGRyMRYhIiKKN5pYZNolTTweDwCgrKwsySMhIiKaejweD5xOZ7KHMe0xHiEiIhoYY5HJwViEiIhoYCOJRSQxzbZ5qKqKxsZGOBwOSJKUkNd0u90oKytDfX09MjIyEvKaU8l0nt90nhvA+aWy6Tw3gPObioQQ8Hg8KC4uhiyzOudES3Q8kor/5kaD80td03luwPSe33SeG8D5TUWMRSYXY5HR4fxS13SeGzC95zed5wZwflPRaGKRaXfSRJZllJaWTshrZ2RkpMw/grGYzvObznMDOL9UNp3nBnB+Uw13dU6eiYpHUu3f3GhxfqlrOs8NmN7zm85zAzi/qYaxyORhLDI2nF/qms5zA6b3/Kbz3ADOb6oZaSzC7R1ERERERERERERERERg0oSIiIiIiIiIiIiIiAgAkyYjYrFY8O1vfxsWiyXZQ5kQ03l+03luAOeXyqbz3ADOjyjRpvu/Oc4vdU3nuQHTe37TeW4A50eUaNP93xznl7qm89yA6T2/6Tw3gPNLddOuETwREREREREREREREdFY8KQJERERERERERERERERmDQhIiIiIiIiIiIiIiICwKQJERERERERERERERERACZNiIiIiIiIiIiIiIiIADBpMiK/+c1vUFlZCavVitWrV+Odd95J9pBG7cEHH8RZZ50Fh8OB/Px8XH/99aiuro57jBAC3/nOd1BcXIy0tDR8+MMfxsGDB5M04rF78MEHIUkS7rvvPv2+VJ9bQ0MDPvGJTyAnJwc2mw0rVqzArl279OupPL9IJIJvfvObqKysRFpaGqqqqvC9730Pqqrqj0ml+b399tu45pprUFxcDEmS8Mwzz8RdH8lcgsEg7r77buTm5sJut+Paa6/F6dOnJ3EWAxtqbuFwGF/72tewdOlS2O12FBcX47bbbkNjY2Pca0zVuQHD/93F+vznPw9JkvCLX/wi7v6pPD9KbYxFUgtjkdSaH2MRxiJTYW4AYxGa2hiLpBbGIqk1P8YijEWmwtwAxiKxmDQZxpNPPon77rsP3/jGN7Bnzx6cf/75uOKKK3Dq1KlkD21UNm/ejC9+8YvYtm0bNm3ahEgkgg0bNsDr9eqP+fGPf4yf/exneOihh7Bjxw4UFhbi0ksvhcfjSeLIR2fHjh34/e9/j2XLlsXdn8pz6+rqwrnnnguTyYSXXnoJhw4dwk9/+lNkZmbqj0nl+f3oRz/C7373Ozz00EM4fPgwfvzjH+MnP/kJfvWrX+mPSaX5eb1eLF++HA899NCA10cyl/vuuw9PP/00nnjiCbz77rvo6enB1VdfDUVRJmsaAxpqbj6fD7t378a3vvUt7N69G0899RRqampw7bXXxj1uqs4NGP7vrs8zzzyD999/H8XFxf2uTeX5UepiLDI13+8Hw1gk9ebHWISxyFSYG8BYhKYuxiJT8/1+MIxFUm9+jEUYi0yFuQGMReIIGtLatWvFnXfeGXffggULxNe//vUkjSgxWltbBQCxefNmIYQQqqqKwsJC8cMf/lB/TCAQEE6nU/zud79L1jBHxePxiLlz54pNmzaJCy64QNx7771CiNSf29e+9jVx3nnnDXo91ed31VVXiU9/+tNx933kIx8Rn/jEJ4QQqT0/AOLpp5/Wb49kLt3d3cJkMoknnnhCf0xDQ4OQZVm8/PLLkzb24Zw5t4Fs375dABB1dXVCiNSZmxCDz+/06dOipKREHDhwQFRUVIif//zn+rVUmh+lFsYiU//9vg9jkdScH2MRxiJTbW5CMBahqYWxyNR/v+/DWCQ158dYhLHIVJubEIxFeNJkCKFQCLt27cKGDRvi7t+wYQO2bt2apFElhsvlAgBkZ2cDAGpra9Hc3Bw3V4vFggsuuCBl5vrFL34RV111FS655JK4+1N9bs899xzWrFmDG2+8Efn5+Vi5ciUefvhh/Xqqz++8887D66+/jpqaGgDABx98gHfffRdXXnklgNSfX6yRzGXXrl0Ih8NxjykuLsaSJUtSbr4ulwuSJOm7f1J9bqqqYuPGjbj//vuxePHiftdTfX40NTEWSa33e8YiqTk/xiKMRVJlboxFKBkYi6TW+z1jkdScH2MRxiKpMreZFIsYkz2Aqay9vR2KoqCgoCDu/oKCAjQ3NydpVOMnhMCXv/xlnHfeeViyZAkA6PMZaK51dXWTPsbReuKJJ7B7927s2LGj37VUn9uJEyfw29/+Fl/+8pfxH//xH9i+fTvuueceWCwW3HbbbSk/v6997WtwuVxYsGABDAYDFEXBAw88gJtvvhlA6v/9xRrJXJqbm2E2m5GVldXvMan0vhMIBPD1r38dt9xyCzIyMgCk/tx+9KMfwWg04p577hnweqrPj6YmxiKp837PWCR158dYhLFIqsyNsQglA2OR1Hm/ZyySuvNjLMJYJFXmNpNiESZNRkCSpLjbQoh+96WSu+66C/v27cO7777b71oqzrW+vh733nsvXn31VVit1kEfl4pzA7Qs7po1a/CDH/wAALBy5UocPHgQv/3tb3Hbbbfpj0vV+T355JN49NFH8fjjj2Px4sXYu3cv7rvvPhQXF+P222/XH5eq8xvIWOaSSvMNh8O46aaboKoqfvOb3wz7+FSY265du/Df//3f2L1796jHmgrzo6lvOr0HAoxF+qTC3ADGIn1SdX4DYSwSLxXmxliEkm06vQcCjEX6pMLcAMYifVJ1fgNhLBIvFeY202IRlucaQm5uLgwGQ79MWGtra7+MaKq4++678dxzz+HNN99EaWmpfn9hYSEApORcd+3ahdbWVqxevRpGoxFGoxGbN2/GL3/5SxiNRn38qTg3ACgqKsKiRYvi7lu4cKHedC+V/+4A4P7778fXv/513HTTTVi6dCk2btyIL33pS3jwwQcBpP78Yo1kLoWFhQiFQujq6hr0MVNZOBzGxz72MdTW1mLTpk36bgogtef2zjvvoLW1FeXl5fr7TF1dHf7t3/4Ns2bNApDa86Opi7FIasyVsUjq/t0BjEUAxiKpMDfGIpQsjEVSY66MRVL37w5gLAIwFkmFuc20WIRJkyGYzWasXr0amzZtirt/06ZNWL9+fZJGNTZCCNx111146qmn8MYbb6CysjLuemVlJQoLC+PmGgqFsHnz5ik/14svvhj79+/H3r179V9r1qzBrbfeir1796Kqqipl5wYA5557Lqqrq+Puq6mpQUVFBYDU/rsDAJ/PB1mOfysyGAxQVRVA6s8v1kjmsnr1aphMprjHNDU14cCBA1N+vn2BwdGjR/Haa68hJycn7noqz23jxo3Yt29f3PtMcXEx7r//frzyyisAUnt+NHUxFkmN93vGIqn7dwcwFmEskhpzYyxCycJYJDXe7xmLpO7fHcBYhLFIasxtxsUiE95qPsU98cQTwmQyiT/+8Y/i0KFD4r777hN2u12cPHky2UMblS984QvC6XSKt956SzQ1Nem/fD6f/pgf/vCHwul0iqeeekrs379f3HzzzaKoqEi43e4kjnxsLrjgAnHvvffqt1N5btu3bxdGo1E88MAD4ujRo+Kxxx4TNptNPProo/pjUnl+t99+uygpKRH//Oc/RW1trXjqqadEbm6u+OpXv6o/JpXm5/F4xJ49e8SePXsEAPGzn/1M7NmzR9TV1QkhRjaXO++8U5SWlorXXntN7N69W1x00UVi+fLlIhKJJGtaQoih5xYOh8W1114rSktLxd69e+PeZ4LBoP4aU3VuQgz/d3emiooK8fOf/zzuvqk8P0pdjEWm5vv9cBiLpM78GIswFpkKcxOCsQhNXYxFpub7/XAYi6TO/BiLMBaZCnMTgrFILCZNRuDXv/61qKioEGazWaxatUps3rw52UMaNQAD/vrzn/+sP0ZVVfHtb39bFBYWCovFIj70oQ+J/fv3J2/Q43BmcJDqc3v++efFkiVLhMViEQsWLBC///3v466n8vzcbre49957RXl5ubBaraKqqkp84xvfiPtASaX5vfnmmwP+X7v99tuFECObi9/vF3fddZfIzs4WaWlp4uqrrxanTp1KwmziDTW32traQd9n3nzzTf01purchBj+7+5MAwUHU3l+lNoYi6QexiKpMz/GIoxFpsLchGAsQlMbY5HUw1gkdebHWISxyFSYmxCMRWJJQggx0lMpRERERERERERERERE0xV7mhAREREREREREREREYFJEyIiIiIiIiIiIiIiIgBMmhAREREREREREREREQFg0oSIiIiIiIiIiIiIiAgAkyZEREREREREREREREQAmDQhIiIiIiIiIiIiIiICwKQJERERERERERERERERACZNiIiIiIiIiIiIiIiIADBpQkREREREREREREREBIBJEyIiIiIiIiIiIiIiIgBMmhAREREREREREREREQFg0oSIiIiIiIiIiIiIiAgA8P8D44YC6P1yUcsAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, @@ -368,141 +243,87 @@ } ], "source": [ - "# Here, the distance function (distance and normalise arguments)\n", - "top_k_search = QuerySearch(k=3, inverse_distance=True, distance=\"euclidean\")\n", - "top_k_search.fit(X_train)\n", - "distances_to_matches, best_matches = top_k_search.predict(q)\n", - "plot_best_matches(top_k_search, best_matches)" - ] - }, - { - "cell_type": "markdown", - "id": "b5240535-5123-4ac5-a5e0-e0502ef80b3e", - "metadata": {}, - "source": [ - "## Using the speed_up option for similarity search" + "series_predict = X[3]\n", + "starting_timestep_predict = 25\n", + "indexes, distances = snn.predict(\n", + " series_predict[:, starting_timestep_predict : starting_timestep_predict + length],\n", + " k=3,\n", + " allow_trivial_matches=True,\n", + ")\n", + "for i in range(len(indexes)):\n", + " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", + "plot_best_matches(\n", + " series_fit, series_predict, starting_timestep_predict, indexes, length\n", + ")" ] }, { "cell_type": "markdown", - "id": "b5e13c31-2aa3-4987-8d44-8a296c81a318", + "id": "fcf10a34-930a-4fce-86f8-4dfa207cad11", "metadata": {}, "source": [ - "In the similarity search module, we implement different kind of optimization to decrease the time necessary to extract the best matches to a query. You can find more information about these optimization in the other notebooks of the similarity search module. An utility function is available to list the optimizations currently implemented in aeon :" + "The `predict` method returns two lists, containing the starting timesteps of the matches in `series_fit` and the squared euclidean distance of these matches to the subsequence we gave in `predict`. Now, you can then play with the different parameters of `predict` to customize your search results to your needs!\n", + "\n", + "It is also possible to get the distance profile which is used to extract the best matches :" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "d22e2d74-f44d-4c81-ba1b-72d618bd5862", + "execution_count": 5, + "id": "7d2bd3f7-7eb9-4406-be1c-b6fcd9c76730", "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "{'normalised euclidean': ['fastest', 'Mueen'],\n", - " 'euclidean': ['fastest', 'Mueen'],\n", - " 'normalised squared': ['fastest', 'Mueen'],\n", - " 'squared': ['fastest', 'Mueen']}" + "
" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "QuerySearch.get_speedup_function_names()" - ] - }, - { - "cell_type": "markdown", - "id": "bf12616c-6ace-478b-806f-5419c2c19f2b", - "metadata": {}, - "source": [ - "By default, the `fastest` option is used, which use the best optimisation available. You can change this behavior by using the values of t with the corresponding distance function and normalization options in the estimators, for example with a `QuerySearch` using the `normalised euclidean` distance:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6313f26a-5788-42dc-881a-40746458414c", - "metadata": {}, - "outputs": [], - "source": [ - "top_k_search = QuerySearch(distance=\"euclidean\", normalise=True, speed_up=\"Mueen\")" - ] - }, - { - "cell_type": "markdown", - "id": "6ab51d84-7220-4333-b50e-2db695eaf45d", - "metadata": {}, - "source": [ - "For more information on these optimizations you can refer to the [distance profile notebook](distance_profiles.ipynb) for the theory, and to the [analysis of the speedups provided by similarity search module](code_speed.ipynb) for a comparison of their performance." + "distance_profile = snn.compute_distance_profile(\n", + " series_predict[:, starting_timestep_predict : starting_timestep_predict + length],\n", + ")\n", + "plt.figure(figsize=(5, 3))\n", + "plt.plot(distance_profile)\n", + "plt.show()" ] }, { "cell_type": "markdown", - "id": "4149c40f", + "id": "b5240535-5123-4ac5-a5e0-e0502ef80b3e", "metadata": {}, "source": [ - "# Series search\n", - "For series search, we are not interest in exploring the relationship of the input dataset `X` (given in `fit`) and a single query, but to all queries of size `query_length` that exists in another time series `T`. For example, with using again our simple GunPoint dataset:" + "### 1.2 Motif search with STOMP" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "d510c4cc", + "execution_count": 6, + "id": "ff23faf5-2941-441a-8c4c-0cf66eaca121", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "([array([0.02910451])], [array([[7, 0]], dtype=int64)])" ] }, + "execution_count": 6, "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index of the 20-th query best matches : [[195 26]]\n" - ] + "output_type": "execute_result" } ], "source": [ - "from aeon.similarity_search import SeriesSearch\n", - "\n", - "query_length = 35\n", - "estimator = SeriesSearch(distance=\"euclidean\").fit(X_train) # X_test is a 3D array\n", - "mp, ip = estimator.predict(X_test, query_length) # X_test is a 2D array\n", - "plot_matrix_profile(X_test, mp, 0)\n", - "print(f\"Index of the 20-th query best matches : {ip[20]}\")" - ] - }, - { - "cell_type": "markdown", - "id": "0dca5122", - "metadata": {}, - "source": [ - "Notice that we find the same best match for the 20-ith query, which was the query that we used for `QuerySearch` !\n", - "\n", - "`SeriesSearch` returns two lists, `mp` and `ip`, which respectively contain the distances to the best matches of all queries of size `query_length` in `X_test` (the `i-th` query being `X_test[:, i : i + query_length]`) and the indexes of these best matches in `X_train` in the `(ix_case, ix_timepoint)` format, such as `X_train[ix_case, :, ix_timepoint : ix_timepoint + query_length]` will be the matching subsquence.\n", + "from aeon.similarity_search.series import StompMotif\n", "\n", - "Most of the options (`k`, `threshold`, `inverse_distance`, etc.) from `QuerySearch` are also available for `SeriesSearch`." + "motif = StompMotif(length=length).fit(series_fit)\n", + "motif.predict(series_predict)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ff23faf5-2941-441a-8c4c-0cf66eaca121", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -521,7 +342,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.0" } }, "nbformat": 4, From f51d66a6efb78c7bc1b261af6fdc1f1b5e13128e Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 19 Jan 2025 13:34:51 +0100 Subject: [PATCH 16/36] Update documentation and add default test params --- aeon/similarity_search/series/_commons.py | 26 ++- .../similarity_search/series/motifs/_stomp.py | 49 +++++- .../series/neighbors/_dummy.py | 26 +++ .../series/neighbors/_mass.py | 26 +++ .../similarity_search/similarity_search.ipynb | 157 +++++++++++++++++- 5 files changed, 261 insertions(+), 23 deletions(-) diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py index 4e62e5aacb..8b309bb6b2 100644 --- a/aeon/similarity_search/series/_commons.py +++ b/aeon/similarity_search/series/_commons.py @@ -146,20 +146,32 @@ def _extract_top_k_from_dist_profile( # Could add aggregation function as parameter instead of just max -def _extract_top_k_motifs(MP, IP, k): +def _extract_top_k_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): criterion = np.zeros(len(MP)) for i in range(len(MP)): - criterion[i] = max(MP[i]) - idx = np.argsort(criterion) - return [MP[i] for i in idx[:k]], [IP[i] for i in idx[:k]] + if len(MP[i]) > 0: + criterion[i] = max(MP[i]) + else: + criterion[i] = np.inf + idx, _ = _extract_top_k_from_dist_profile( + criterion, k, np.inf, allow_trivial_matches, exclusion_size + ) + return [MP[i] for i in idx], [IP[i] for i in idx] -def _extract_top_r_motifs(MP, IP, k): + +def _extract_top_r_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): criterion = np.zeros(len(MP)) for i in range(len(MP)): criterion[i] = len(MP[i]) - idx = np.argsort(criterion)[::-1] - return [MP[i] for i in idx[:k]], [IP[i] for i in idx[:k]] + idx, _ = _extract_top_k_from_dist_profile( + _inverse_distance_profile(criterion), + k, + np.inf, + allow_trivial_matches, + exclusion_size, + ) + return [MP[i] for i in idx], [IP[i] for i in idx] @njit(cache=True, fastmath=True) diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index fbd459b890..c912cdfacd 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -87,7 +87,7 @@ def _fit( y=None, ): if self.normalize: - self.X_means_, X_stds_ = sliding_mean_std_one_series(X, self.length, 1) + self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) return self def predict( @@ -164,9 +164,13 @@ def predict( inverse_distance=inverse_distance, ) if motif_extraction_method == "k_motifs": - return _extract_top_k_motifs(MP, IP, k) + return _extract_top_k_motifs( + MP, IP, k, allow_trivial_matches, self.length // exclusion_factor + ) elif motif_extraction_method == "r_motifs": - return _extract_top_r_motifs(MP, IP, k) + return _extract_top_r_motifs( + MP, IP, k, allow_trivial_matches, self.length // exclusion_factor + ) def compute_matrix_profile( self, @@ -225,9 +229,12 @@ def compute_matrix_profile( is_self_mp = False if self.normalize: X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) - X_dotX = get_ith_products(X, self.X_, self.length, 0) exclusion_size = self.length // exclusion_factor + + if motif_size == np.inf: + motif_size = X.shape[1] - self.length + 1 + if self.normalize: MP, IP = _stomp_normalized( self.X_, @@ -260,6 +267,32 @@ def compute_matrix_profile( ) return MP, IP + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 3} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + @njit(cache=True, fastmath=True) def _stomp_normalized( @@ -330,7 +363,7 @@ def _stomp_normalized( variable size. """ n_queries = X_A.shape[1] - L + 1 - _max_timestamp = X_B.shape[1] - L + _max_timestamp = X_B.shape[1] - L + 1 MP = List() IP = List() @@ -347,7 +380,7 @@ def _stomp_normalized( dist_profile = _inverse_distance_profile(dist_profile) if is_self_mp: - ub = min(i_q + exclusion_size, _max_timestamp) + ub = min(i_q + exclusion_size, _max_timestamp + 1) lb = max(0, i_q - exclusion_size) dist_profile[lb:ub] = np.inf @@ -425,7 +458,7 @@ def _stomp( variable size. """ n_queries = X_A.shape[1] - L + 1 - _max_timestamp = X_B.shape[1] - L + _max_timestamp = X_B.shape[1] - L + 1 MP = List() IP = List() @@ -440,7 +473,7 @@ def _stomp( dist_profile = _inverse_distance_profile(dist_profile) if is_self_mp: - ub = min(i_q + exclusion_size, _max_timestamp) + ub = min(i_q + exclusion_size, _max_timestamp + 1) lb = max(0, i_q - exclusion_size) dist_profile[lb:ub] = np.inf diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index 7b4d4d89da..a3120f98dd 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -132,6 +132,32 @@ def compute_distance_profile(self, X: np.ndarray): X = z_normalise_series_2d(X) return _naive_squared_distance_profile(self.X_subs, X) + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 3} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + @njit(cache=True, fastmath=True, parallel=True) def _naive_squared_distance_profile( diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py index 9565407fc8..befc8b33fd 100644 --- a/aeon/similarity_search/series/neighbors/_mass.py +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -165,6 +165,32 @@ def compute_distance_profile(self, X: np.ndarray): return distance_profile + @classmethod + def _get_test_params(cls, parameter_set: str = "default"): + """Return testing parameter settings for the estimator. + + Parameters + ---------- + parameter_set : str, default="default" + Name of the set of test parameters to return, for use in tests. If no + special parameters are defined for a value, will return `"default"` set. + There are currently no reserved values for transformers. + + Returns + ------- + params : dict or list of dict, default = {} + Parameters to create testing instances of the class + Each dict are parameters to construct an "interesting" test instance, i.e., + `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. + """ + if parameter_set == "default": + params = {"length": 3} + else: + raise NotImplementedError( + f"The parameter set {parameter_set} is not yet implemented" + ) + return params + @njit(cache=True, fastmath=True) def _squared_distance_profile(QT, T, Q): diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index 803398d551..53e5a9bcc5 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -275,9 +275,9 @@ "outputs": [ { "data": { - 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21sJXxY25Xc1XpXCuobps2ynYuPMG3QQGI1ErsdTb8eUhx04QvIzqPo8NikOwrxKnz1dj82G91OVQO8RgJGol20+cR0WNFRFBagzuyn0X3SXIV4Unhzh6jUu3noIQ7DVSyzAYiVpJ44Lh4/vooOC+i2711ODO8PdR4HCJCTtPlUldDrUzDEaiVlBlrseWo+cAAPf342VUd+sQ4IPJKbEAgA+2n5a4GmpvWhyM27dvx/jx46HT6SCTybBx48ZmzwshsGDBAuh0Ovj5+WHEiBE4fPhws3PMZjNmzZqF8PBwBAQEYMKECSgq4n5q5Lk2H9ajzmpHfHgA+sRopC7HKzw9NB4KuQw7TpbhSIlJ6nKoHWlxMFZXV6Nv375YunTpFZ9fvHgxlixZgqVLlyI7OxtarRb33HMPKisrneekpaUhIyMD69atw86dO1FVVYVx48bBZuNqFeSZNjZMHbi/n+MPSnK/2FB/jEl0rCy0Ygd7jdQC4hYAEBkZGc6v7Xa70Gq14q233nI+VldXJzQajXj//feFEEJUVFQIlUol1q1b5zynuLhYyOVy8c0339zQ9zUajQKAMBqNt1I+UaswmOpEl3lfik5zvxCnz1dJXY5XOVhYITrN/UJ0nfelKC6vkboccgN35IFL7zHm5+dDr9cjNTXV+Zharcbw4cOxa9cuAEBOTg6sVmuzc3Q6HRISEpznEHmSLw+WwGYX6BujQXx4gNTleJXEGA3u7BKGervAP3fmS10OtRMuDUa93jFnKCqq+YoeUVFRzuf0ej18fHzQoUOHq55zKbPZDJPJ1Owgai8+O9B4GbWjxJV4p982LBO3dk8BjLVWiauh9sAto1IvvYcihLjufZVrnZOeng6NRuM8YmNjXVYrkTudvVCN/QUVkMuAcX25k4YURnSPQPeoQFRbbFxcnG6IS4NRq9UCwGU9P4PB4OxFarVaWCwWlJeXX/WcS82bNw9Go9F5FBbyl5vah00Ng26G3BaOyCBfiavxTjKZDE81TPhfuesMl4mj63JpMMbHx0Or1SIzM9P5mMViQVZWFgYPHgwASE5OhkqlanZOaWkp8vLynOdcSq1WIzg4uNlB1NYJIZyT+if05dxFKT2Q1BEd/FUoKq9F5pFzUpdDbZyypS+oqqrCqVOnnF/n5+cjNzcXoaGhiIuLQ1paGhYtWoRu3bqhW7duWLRoEfz9/TF16lQAgEajwdNPP42XX34ZYWFhCA0NxSuvvILExESMHj3adS0jktjhEhN+Pl8NH6Uc9yZopS7Hq/mqFHh0YByWbfsZH/2Qj1/xvwddQ4uDce/evRg5cqTz69mzZwMApk+fjpUrV2LOnDmora3FjBkzUF5ejkGDBmHz5s0ICgpyvubdd9+FUqnE5MmTUVtbi1GjRmHlypVQKLjbAHmOxg2JR/eKRLCvSuJqaNqdnfDB9tPYnX8Rh0uM6K3jQgt0ZTIh2t8KuyaTCRqNBkajkZdVqU2y2QUGv/UdzpnM+N9pybi3N3sobcGstfvx+YESPJQcgz8/3FfqcsgF3JEHXCuVyA1251/AOZMZwb5KjOgRIXU51OCpIZ0BOAZFna80S1sMtVkMRiI3+Gy/YzTqmMRoqJW8RdBW9I/rgL6xIbDY7FifXSB1OdRGMRiJXKzOasNXeY4NiTmpv+2ZfmcnAMCa3QWot9klrobaIgYjkYttO34elXX10Ab7YlB8qNTl0CXGJEYjNMAHJcY6fHfMIHU51AYxGIlcrHE06oR+Osi5IXGb46tSOPdqXP3TWYmrobaIwUjkQqY6q7MXwg2J267HBsVBJgN2nCzD6fNVUpdDbQyDkciFvjmkh6Xejm6Rgbg9mlOJ2qrYUH/c3SMSALD6Jw7CoeYYjEQulLHfcRl1YlJHbkjcxj3eMAjnk5xC1FjqJa6G2hIGI5GL6I11+Cn/AgCujdoeDO8WgU5h/qisq8fnDVuDEQEMRiKX2XSgGEIAAzp3QGyov9Tl0HXI5TI8OjAOALBmD3fsoV8wGIlcJKNhUv/EJM5dbC8eSo6BSiHDgcIKHC4xSl0OtREMRiIXOHGuEkdLTVApZBibyA2J24vwQDVSG9axXbuHg3DIgcFI5AIbGwbdDO8eiRB/H4mroZaY2nA5deP+Eg7CIQAMRqJbZrcLfJbruIz6AC+jtjt3dglD5zB/VJk5CIccGIxEtyj7zEUUV9QiUK3EqF6RUpdDLSSXyzCFg3CoCQYj0S3a2LAE3JhELXxV3EmjPeIgHGqKwUh0C+qsNnxx0LGTBkejtl9NB+Gsz2av0dsxGIluwbbjBlTW1SNa44s74sOkLoduwaMDHJdTM/YXo85qk7gakhKDkegWbNjHnTQ8xeCuYYjp4IfKunp83bCfJnknBiPRTaqosWDrccdOGhyN2v7J5TI80rAd1VoOwvFqLg/G+vp6vP7664iPj4efnx+6dOmCN998E3b7LztlCyGwYMEC6HQ6+Pn5YcSIETh8+LCrSyFyqy8PlcJqE+gVHYyeWu6k4QkeSomBXAbsyb/I7ai8mMuD8e2338b777+PpUuX4ujRo1i8eDHeeecd/P3vf3ees3jxYixZsgRLly5FdnY2tFot7rnnHlRWVrq6HCK3aZzU/0ASFwz3FNEaP4xo2I5q/V72Gr2Vy4Pxxx9/xP3334+xY8eic+fOeOihh5Camoq9e/cCcPQW33vvPcyfPx+TJk1CQkICVq1ahZqaGqxZs8bV5RC5ReHFGmSfKYdMBkzoy8uonuSRAY7LqZ/mFMFqs1/nbPJELg/GoUOH4rvvvsOJEycAAAcOHMDOnTsxZswYAEB+fj70ej1SU1Odr1Gr1Rg+fDh27drl6nKI3KJx38XBXcOg1fhKXA250t09IxEeqEZZlQXfHTVIXQ5JQOnqN5w7dy6MRiN69uwJhUIBm82GhQsX4tFHHwUA6PV6AEBUVFSz10VFReHs2bNXfE+z2Qyz2ez82mQyubpsohsmhHAG46SkGImrIVdTKeR4MLkj/jfrND7ZW4hfJWilLolamct7jOvXr8fq1auxZs0a7Nu3D6tWrcKf//xnrFq1qtl5l+5uLoS46o7n6enp0Gg0ziM2NtbVZRPdsP2FFcgvq4afSsEPTQ/1cLLjM2bbifMwVNZJXA21NpcH46uvvorXXnsNU6ZMQWJiIqZNm4aXXnoJ6enpAACt1vFB0thzbGQwGC7rRTaaN28ejEaj8ygs5E1xkk5Gw9zFe3tHIUDt8osu1AbcFhmIpLgQ2OzCOciKvIfLg7GmpgZyefO3VSgUzuka8fHx0Gq1yMzMdD5vsViQlZWFwYMHX/E91Wo1goODmx1EUrDU2/H5wYadNPrzMqona+w1frK3CEIIiauh1uTyYBw/fjwWLlyIL7/8EmfOnEFGRgaWLFmCBx54AIDjEmpaWhoWLVqEjIwM5OXl4cknn4S/vz+mTp3q6nKIXGrbcQMqaqyICFJjSFcuAefJxvWNhq9KjpOGKhwo4sLi3sTl14H+/ve/4/e//z1mzJgBg8EAnU6HZ599Fn/4wx+c58yZMwe1tbWYMWMGysvLMWjQIGzevBlBQUGuLofIpRoH3Uzsp4NSwYWjPFmwrwq/6q3FxtwSfLK3EP1iQ6QuiVqJTLTDawQmkwkajQZGo5GXVanVGGusGLBwCyw2O7564S7cruPvnqf74VQZHvu/3QjyVSJ7/mhuK9YGuSMP+Ccv0Q364lAJLDY7emqDGIpe4s4uYegY4lhY/NvD+uu/gDwCg5HoBjXupPEgB914DblchgeTHf+9P93H0anegsFIdAPOlFUj52w55DLg/n5cG9WbPNjfseTfzpPncc7EOY3egMFIdAM27CsCANzVLQKRwVwCzpt0CgvAgM4dYBe/DL4iz8ZgJLoOu11gQ+MScP25YLg3mtRw+fzTHM5p9AYMRqLryD5zEUXltQhUK5F6O5eA80Zj+0RDrXTMacwr5lrNno7BSHQdjYNuxiRq4efD4freKNhXhdTejj+KPm24rE6ei8FIdA21Fhu+PFQK4JfLaeSdGi+jf5ZbDEs992n0ZAxGomvYfESPKnM9Oob4YWDnUKnLIQnddVs4IoLUKK+xYttx7tPoyRiMRNfw3xzHZbMH+3eEXH7lbdHIOygVcjyQ5Og18nKqZ2MwEl2F3liHH06VAeBlVHJoXNzh+2MGlFdbJK6G3IXBSHQVGfuLYRfAgM4d0Dk8QOpyqA3ooQ1Cb10wrDbh3H6MPA+DkegKhBDOy2VcAo6aerDJnEbyTAxGois4WGTEKUMV1Eo5xvSJlrocakMm9NNBKZfhQJERpwyVUpdDbsBgJLqCxt7ivb21CPZVSVwNtSXhgWqM6BEBAPhvDpeI80QMRqJLmOtt2HTAcf/ooWReRqXLNV5O3bi/GDY7l4jzNAxGoktsPWZARY0VUcFqDLktXOpyqA26u1ckNH4q6E112PVzmdTlkIsxGIku0Th3cVL/GCg4d5GuQK1UYHxfx73n/3IQjsdhMBI1cb7SjK3HzwPgaFS6toeSYwEA3+TpYay1SlwNuRKDkaiJz3Id94yS4kJwW2Sg1OVQG9Y3RoPuUYEw19vxBec0ehQGI1EDIQQ+2cu5i3RjZDIZHm7oNf5nLy+nehK3BGNxcTEef/xxhIWFwd/fH/369UNOTo7zeSEEFixYAJ1OBz8/P4wYMQKHDx92RylEN+xwiQnHz1XCRynH+D46qcuhdmBiUkfHnMbCCpw4xzmNnsLlwVheXo4hQ4ZApVLh66+/xpEjR/CXv/wFISEhznMWL16MJUuWYOnSpcjOzoZWq8U999yDykr+YpF0GgdRpN4eBY0/5y7S9UUEqXF3z0gAwCd7CyWuhlzF5cH49ttvIzY2Fh999BEGDhyIzp07Y9SoUejatSsAR2/xvffew/z58zFp0iQkJCRg1apVqKmpwZo1a1xdDtENMdfbsDHXMVmbcxepJSanOC6nZuwvhtXGfRo9gcuDcdOmTUhJScHDDz+MyMhIJCUlYcWKFc7n8/PzodfrkZqa6nxMrVZj+PDh2LVr1xXf02w2w2QyNTuIXKnp3MW7ukVIXQ61IyN6RCA8UI2yKgu2HuM+jZ7A5cF4+vRpLF++HN26dcO3336L5557Di+88AL+9a9/AQD0ej0AICoqqtnroqKinM9dKj09HRqNxnnExsa6umzyco2XUR9I4txFahmlQo4H+zv2afwPL6d6BJcHo91uR//+/bFo0SIkJSXh2WefxTPPPIPly5c3O08ma/7hI4S47LFG8+bNg9FodB6FhfzlI9cxVNY55y7yMirdjMkDHH+sf3/MAL2xTuJq6Fa5PBijo6Nx++23N3usV69eKCgoAABotVoAuKx3aDAYLutFNlKr1QgODm52ELlK43qXnLtIN6trRCAGxYfCLoD12fzDvb1zeTAOGTIEx48fb/bYiRMn0KlTJwBAfHw8tFotMjMznc9bLBZkZWVh8ODBri6H6JqEEM7LqI1z0ohuxtRBcQCA9dkFXFi8nXN5ML700kv46aefsGjRIpw6dQpr1qzBBx98gJkzZwJwXEJNS0vDokWLkJGRgby8PDz55JPw9/fH1KlTXV0O0TUdLDLixDnHvovj+nLfRbp59/bWIsRfhRJjHbafOC91OXQLXB6MAwYMQEZGBtauXYuEhAT8z//8D9577z089thjznPmzJmDtLQ0zJgxAykpKSguLsbmzZsRFBTk6nKIrumTHMdlr18lcN9FujW+KoVzxaR/7y6QuBq6FTIhRLvr85tMJmg0GhiNRt5vpJtWZ7Vh4MItMNXVY/XTgzC0G7eYoltzylCF0UuyIJcBu14bBa3GV+qSPJ478oBrpZLXyjxyDqa6enQM8cPgrmFSl0Me4LbIQAzkIJx2j8FIXusT576LHSHn3EVykakDHYNw1uw5y5Vw2ikGI3mlkopa7DjpGCDB0ajkSvclahERpMY5kxlfHSqVuhy6CQxG8kob9hVBCGBQfCjiwvylLoc8iFqpwLQ7HNPT/rkzH+1wGIfXYzCS12k2dzGFvUVyvamD4uCjlONAkRH7CiqkLodaiMFIXif7TDnOXKhBgI8CYxK1UpdDHig8UI2J/Rx7ev7zh3yJq6GWYjCS12lc6HlcHx38fZQSV0Oe6tdD4wEA3+TpUVxRK3E11BIMRvIq1eZ654CIh1O4YDi5T09tMIbcFgabXWDVrjNSl0MtwGAkr/LloVLUWGzoEh6A5E4dpC6HPNyvhzh6jWt2F6CixiJxNXSjGIzkVT5puIz6YHLMVbc5I3KVkT0i0Ss6GFXmeny4k/ca2wsGI3mN0+erkH2mHHIZ912k1iGXy/DiqNsAACt/OMNeYzvBYCSv0bjSzYgekYgK5hqW1DpSb9eipzYIleZ6/JO9xnaBwUheod5mx6cNwTiZg26oFcnlMqSN7gYA+Ii9xnaBwUheYfvJ8zBUmhEa4IO7e0ZJXQ55GfYa2xcGI3mF/2Q7eosPJHWEj5K/9tS6HPcaHb3G/9uZj3OmOokromvhJwR5vAtVZmw5eg4AMJlLwJFE7u2tRVJcCGosNqR/dVTqcugaGIzk8TL2F6PeLtA3NgQ9tEFSl0NeSi6X4c0JCZDJgI25Jcg+c1HqkugqGIzk0YQQzg1jH+YUDZJYYowGUwY4rlq88dlh2OzceaMtYjCSR8strMBJQxV8VXJMaFjUmUhKr6T2QLCvEkdKTVi7p0DqcugKGIzk0RoXDB+TEI1gX5XE1RABYYFqzL6nOwBg8TfHUGrkAuNtjduDMT09HTKZDGlpac7HhBBYsGABdDod/Pz8MGLECBw+fNjdpZCXqbHU4/MDjgXDJw/goBtqOx6/oxP6xmhgqqvH7PUHYOcl1TbFrcGYnZ2NDz74AH369Gn2+OLFi7FkyRIsXboU2dnZ0Gq1uOeee1BZWenOcsjLfHmwFFXmenQO88eg+FCpyyFyUirkeG9KEvxUCvx4+gJW7DgtdUnUhNuCsaqqCo899hhWrFiBDh1+2cVACIH33nsP8+fPx6RJk5CQkIBVq1ahpqYGa9ascVc55IUaL6M+nBLLBcOpzYkPD8CCCbcDAP68+Tjyio0SV0SN3BaMM2fOxNixYzF69Ohmj+fn50Ov1yM1NdX5mFqtxvDhw7Fr164rvpfZbIbJZGp2EF3Lz1wwnNqBySmx+FVvLaw2gRfW7udycW2EW4Jx3bp12LdvH9LT0y97Tq/XAwCiopovyxUVFeV87lLp6enQaDTOIzaW94vo2hp7i1wwnNoymUyG9EmJiNb44nRZNX6zai/qrDapy/J6Lg/GwsJCvPjii1i9ejV8fa/+gXTppS0hxFUvd82bNw9Go9F5FBYWurRm8izWZguG848oats6BPhg5VMDEeSrxN6z5Xhh7X7Ob5SYy4MxJycHBoMBycnJUCqVUCqVyMrKwt/+9jcolUpnT/HS3qHBYLisF9lIrVYjODi42UF0Nd8dPYeyKgvCA9UY1StS6nKIrquHNggrnkiBj0KOzUfO4fef5XGkqoRcHoyjRo3CoUOHkJub6zxSUlLw2GOPITc3F126dIFWq0VmZqbzNRaLBVlZWRg8eLCryyEvtK5xpZuUGKgUnKpL7cMdXcLw3pR+kMmANbsLMHPNPtRaeFlVCkpXv2FQUBASEhKaPRYQEICwsDDn42lpaVi0aBG6deuGbt26YdGiRfD398fUqVNdXQ55mZKKWmSdOA+Al1Gp/RmTGI13J/fDnP8exNd5epRU/IgVT6QgkvfJW5XLg/FGzJkzB7W1tZgxYwbKy8sxaNAgbN68GUFBXOCZbs0ne4sgBHBHl1DEhwdIXQ5Ri01M6ghdiB+e/XgvDhQZMWHpD1j4QAJG9eI+oq1FJoRodxeyTSYTNBoNjEYj7zeSk80uMGzxVhRX1OKvU/rh/n4dpS6J6KadKavG06uy8fP5agDAuD7ReGN8b0QEqSWurG1xRx7wBgx5jJ2nylBcUQuNnwr39tZKXQ7RLekcHoDPZw3Fb4d1gVwGfHGwFCP/vA0LvzyC4gqur+pOklxKbQsuVlsw9O3vAQAyOKaPyGWAj1IBH4UMwX4qxHTwQ0wHf3SNDMSdXULRNSKQK6i0Yesadip4IKkjfFUKiashunX+Pkr8bkwvTOirw2sbDiKv2IQVO/Lxzx/O4Fe9tfhVghbDe0RwgXwX89pgtAuBmiuO+KoHAJQY63BM33zt1oggNYZ0DcPdvaIwvHsENH78ZWwrDJV1yDxyDgAwZSAH3ZBnSeiowaaZQ7HthAErtufjx9MX8OWhUnx5qBRKuQzJnTpgUHwo+nfqgKS4DvxsukVee4/RZhcoqaiFEICA40dQbxew1NthtdlxsdqCwvJaFF2swaFiI/aeLYel3u58vVIuw8D4UNzdMxIje0aiS3gAe5MSWrbtFBZ/cxz940KwYcYQqcshcqu8YiM+P1CCLUfPOe9BNhUZpEZ8eAC6RAQgWuMHbbAvIoPV0PipEOSrRIBaCT+VAr4qBdRKebv+7HLHPUavDcaWqrPasK+gHFknzuO7owacMlQ1e75TmD+Gd4/A8O4RuLNrGPx9vLYz3ursdoERf96Ggos1eOehPniY0zTIi5wpq8YPP5ch52w59p0tx5kLNS1+D7VSDh+lHGqlIyjVKsf/91PJ4eejgJ9KAT8fJQJ8FPD3USJQrUCgrxKBahUC1AoEqh1hG+CjdJzvo4Bvw3uqFI5DLrt8xTNXYDA2aAujUs+UVeO7YwZsPWbA7vwLsNp++TGqFDL0jQnBwPhQDIwPRXKnDgjiPQC32XHyPKZ9uAdBvkrs+d1o+Pnw/iJ5L2OtFWfKqnG6rApnympwzlSHUmMdDJVmVJmtqKyrR1VdPerbyMo6//r1QAzrHnHTr3dHHrBbc5M6hwfg6aHxeHpoPKrM9fjhVBm2nziPrBPnUVRei71ny7H3bDmWbfsZchnQKzoYAzqHYnDXMNzZNYxB6UJrdjsG3UxK6shQJK+n8VOhb2wI+saGXPM8q82OOqsNdVY7zPU2WOrtMDceVhvq6huft6HGYkOtxYYaSz2qzA3/W1ePSnM9qhuOKnM9aiwN51ptzW49tTfsMbqYEAJnL9Rgz5mL2JPvOAouNr+0oZDL0C82BCO6R+DuXpG4PTq4XV/jl5Khsg6D079HvV3gm7S70FPbtn4fiLyV3S5gsdlhsdlRbxMQQsDeMKZDBsfnnUwGBPkqoVbe/B+07DG2AzKZDJ3DA9A5PMC5JNk5Ux325F/E7vwL2HmyDGcu1CDnbDlyzpbjL5knoA32xYgeERjRIwJDbgtnb7IF/ptThHq7QP+4EIYiURsil8vgK1e0y6lTDMZWEBXsi/F9dRjfVwcAKLxYgx0ny/D9MQN+OFUGvakO67ILsS67EEq5DP07dXAO5Lk9OhhyOXuTV2KzC6xtmLs4ZWCcxNUQkafgpVSJ1Vlt+On0BWw77rg/mV/WfOh1eKAaI3pEYGSPSAzrzt5kU1uPGfDUymxo/FT4ad4o3l8k8kK8lOqBfFUKjOgRiRE9HPsGnr1Q3TCIpww//lyGsioz/ptThP/mFEGtlCO1txaT+nfEXbeFQ+nlWyp9/NNZAMDDyTEMRSJyGfYY2zBLvR17z1zE1uMGfHfUgNNNepMxHfzwwqhumJTU0SsDsvBiDYa9sxVCAFtfGcGdNIi8FOcxNvCWYGxKCIFDxUZs2FeMTQdKcLHaAgCIDw/Ay6ndMTYx2qtGtr79zTEs3/Yz7uoWjo+fHiR1OUQkEe6u4cVkMhn6xIRgwYTe+GHu3Zg/phdCA3yQX1aN59fsx1Mrs1F4seUrXrRH5nob1mcXAgAev6OTxNUQkadhMLZDfj4KPDOsC7bPGYm00d3go5Bj2/HzSH13O/714xmpy3O7rw/pcbHagmiNL0b1jJS6HCLyMAzGdixQrUTa6O74Ou0uDIwPRa3Vhj98dhgrf8iXujS3ahx08+jAOK+8v0pE7sVPFQ/QNSIQ6565Ay+O6gYA+OMXR/DtYb3EVbnHwaIK5Jwth0oh4/ZSROQWDEYPIZfLkDa6Gx4dGAchgBfW7se+gnKpy3K5lT+cAQCM66NDZJCvtMUQkUdiMHoQmUyG/7m/N+7uGQlzvR2/WbUXBlOd1GW5jKGyDp8fLAEAPDm4s7TFEJHHcnkwpqenY8CAAQgKCkJkZCQmTpyI48ePNztHCIEFCxZAp9PBz88PI0aMwOHDh11dildSKuT4+6NJ6BUdjIvVFry+MQ/tcEbOFf37pwJYbY51Ua+3cwAR0c1yeTBmZWVh5syZ+Omnn5CZmYn6+nqkpqaiuvqXyemLFy/GkiVLsHTpUmRnZ0Or1eKee+5BZWWlq8vxSgFqJZZM7gulXIbNR87hi4OlUpd0y8z1Nvy7YXupp4bES1wNEXkyt0/wP3/+PCIjI5GVlYVhw4ZBCAGdToe0tDTMnTsXAGA2mxEVFYW3334bzz777HXf0xsn+N+MdzNP4K/fnURogA8yXxqGsEC11CXdtA37ijD7PwegDfbFjrkjoeJoVCJCO53gbzQaAQChoaEAgPz8fOj1eqSmpjrPUavVGD58OHbt2nXF9zCbzTCZTM0Our6ZI29DT20QLlZb8Mam9nupWgiBfzZMQZl2ZyeGIhG5lVs/YYQQmD17NoYOHYqEhAQAgF7vmEYQFRXV7NyoqCjnc5dKT0+HRqNxHrGxHKZ/I3yUcrzzUF8o5DJ8cbAUmUfOSV3STfnx5wvIKzbBVyXHo9xeiojczK3B+Pzzz+PgwYNYu3btZc9duq6nEOKqa33OmzcPRqPReRQWFrqlXk+UGKPBb+5y3JP7w2d5qDLXS1xRy72//TQAYHJKLEIDfCSuhog8nduCcdasWdi0aRO2bt2KmJgY5+NarRYALusdGgyGy3qRjdRqNYKDg5sddOPSRnVHbKgfSo11+Mvm49d/QRtypMSE7SfOQy4DfjO0i9TlEJEXcHkwCiHw/PPPY8OGDfj+++8RH998BGF8fDy0Wi0yMzOdj1ksFmRlZWHw4MGuLofgWFt14cREAMDKXWeQW1ghbUEtsGKHo7c4JjEacWH+EldDRN7A5cE4c+ZMrF69GmvWrEFQUBD0ej30ej1qa2sBOC6hpqWlYdGiRcjIyEBeXh6efPJJ+Pv7Y+rUqa4uhxoM6x6BB5I6Qghg3oZDsNrsUpd0XUXlNdh0wDGh/9lhXSWuhoi8hcuDcfny5TAajRgxYgSio6Odx/r1653nzJkzB2lpaZgxYwZSUlJQXFyMzZs3IygoyNXlUBOvj+2FEH8Vjpaa8FE7WGj8nzvPwGYXGHJbGBJjNFKXQ0ReghsVe5n/ZBdizqcH4adSYMvLw9ExxE/qkq6orMqMu97eilqrDat+PRDDu0dIXRIRtUHtch4jtS0Pp8RgYGfHFlVvfNZ25zZ+sP00aq029I3RYFi3cKnLISIvwmD0MjKZDAsfSIBKIcOWo+fa5PZUZVVm54bLaaO7X3UaDxGROzAYvVC3qCD8dphj6sOCTYfb3NzG/836GXVWO/rGhmBED15CJaLWxWD0UrPu7oa4UH+UGuvwt+9OSl2O0/lKMz7+6SwAIG10N/YWiajVMRi9lK9KgT/e3xsA8M+d+Thxrm3sbNLYW+wXG4IRHHBDRBJgMHqxkT0ikXp7FOrtok3s21hSUcveIhFJjsHo5f4w/nb4quTYk38RG3OLJa1l8TfHYK63Y2DnUE7PICLJMBi9XEwHf8y6uxsAYOGXx2CstUpSR25hBTbmlkAmA34/7nb2FolIMgxGwjN3dUGXiACUVZnxzrfHWv37CyHwpy+OAAAmJcVwlRsikhSDkeCjlDsXGV/9UwH2nrnYqt//q0N67D1bDj+VAq/e26NVvzcR0aUYjAQAuLNrGCanOLYHm/vpQZjrba3yfWstNqR/fRQA8OzwLtBqfFvl+xIRXQ2DkZx+N6YXwgN98PP5aizb+nOrfM/F3x5DUXktdBpf56IDRERSYjCSU4i/D94Y75jbuGzbKbfPbdyTfxErd50BAKQ/2Af+Pkq3fj8iohvBYKRmxvWJxqiekbDaBF5Yux91VvdcUq212DDnvwcgBPBISiynZxBRm8FgpGZkMhnSH0xEeKAPjukrsfDLo275Pou/PYYzF2oQrfHF/HG93PI9iIhuBoORLhMZ5Iu/TO4HAPj4p7P4Jq/Upe//7WE9PvrhDAAgfVIign1VLn1/IqJbwWCkKxrePQLPDncMhpnz34MovFjjkvfNKzYibV0uAODJwZ0xokekS96XiMhVGIx0Va+k9kC/2BCY6uox/aM9KKsy39L7GSrr8My/9qLWasNd3cLx+lheQiWitofBSFelUsix7LH+0Gl8cfp8NZ74cM9NLxlnqrPimX/loNRYhy4RAVg6tT+UCv76EVHbw08muiZdiB9W/2YQwgJ8cKTUhKdXZqPG0rKNjYvKa/DQ8l04UFgBjZ8K/5w+ABo/3lckorZJ0mBctmwZ4uPj4evri+TkZOzYsUPKcugqukQE4l9PD0SQrxJ7z5Zj4j9+QF6x8YZee6jIiAeW7cKJc1WIDFLj378ZhM7hAW6umIjo5kkWjOvXr0daWhrmz5+P/fv346677sJ9992HgoICqUqia+it02DVrwciPFCNE+eq8MCyH/CPradgtdmveL6hsg5/+uIIHnp/F85XmtFTG4SNM4cgoSMXCCeitk0mJNqddtCgQejfvz+WL1/ufKxXr16YOHEi0tPTr/lak8kEjUYDo9GI4OBgd5dKTVyoMmN+Rh6+OawHAASplRjWIwIje0RCpZDhfKUZP5+vwoZ9xTDXO0JzRI8I/P3RJARxWgYRuZg78kCSNbgsFgtycnLw2muvNXs8NTUVu3btuux8s9kMs/mXEZEmk8ntNdKVhQWqsfzx/sjYX4z0r4/hfKUZXx4sxZcHL5/r2D8uBC+O7o5h3cK5vyIRtRuSBGNZWRlsNhuioqKaPR4VFQW9Xn/Z+enp6fjjH//YWuXRdchkMkzqH4OJ/TriQFEFvjtqwK6fy+CjlCMyyBcRQWqM7BGJIbeFMRCJqN2RdNXmSz80hRBX/CCdN28eZs+e7fzaZDIhNjbW7fXRtcnlMiTFdUBSXAcA3EeRiDyDJMEYHh4OhUJxWe/QYDBc1osEALVaDbVa3VrlERGRF5NkVKqPjw+Sk5ORmZnZ7PHMzEwMHjxYipKIiIgASHgpdfbs2Zg2bRpSUlJw55134oMPPkBBQQGee+45qUoiIiKSLhgfeeQRXLhwAW+++SZKS0uRkJCAr776Cp06dZKqJCIiIunmMd4KzmMkIiLAPXnAtVKJiIiaYDASERE1Iek8xpvVePWXK+AQEXm3xhxw5V3BdhmMlZWVAMBJ/kREBMCRCxqNazYpaJeDb+x2O0pKShAUFHRLS441rqBTWFjoNYN42GbvaDPgne1mm72jzcAv7S4oKIBMJoNOp4Nc7pq7g+2yxyiXyxETE+Oy9wsODvaqXyiAbfYm3thuttl7aDQal7ebg2+IiIiaYDASERE14dXBqFar8cYbb3jVAuVss/fwxnazzd7Dne1ul4NviIiI3MWre4xERESXYjASERE1wWAkIiJqgsFIRETUhNcG47JlyxAfHw9fX18kJydjx44dUpfkMunp6RgwYACCgoIQGRmJiRMn4vjx483OEUJgwYIF0Ol08PPzw4gRI3D48GGJKna99PR0yGQypKWlOR/z1DYXFxfj8ccfR1hYGPz9/dGvXz/k5OQ4n/e0dtfX1+P1119HfHw8/Pz80KVLF7z55puw2+3Oczyhzdu3b8f48eOh0+kgk8mwcePGZs/fSBvNZjNmzZqF8PBwBAQEYMKECSgqKmrFVrTMtdpstVoxd+5cJCYmIiAgADqdDk888QRKSkqavYdL2iy80Lp164RKpRIrVqwQR44cES+++KIICAgQZ8+elbo0l7j33nvFRx99JPLy8kRubq4YO3asiIuLE1VVVc5z3nrrLREUFCQ+/fRTcejQIfHII4+I6OhoYTKZJKzcNfbs2SM6d+4s+vTpI1588UXn457Y5osXL4pOnTqJJ598UuzevVvk5+eLLVu2iFOnTjnP8bR2/+lPfxJhYWHiiy++EPn5+eKTTz4RgYGB4r333nOe4wlt/uqrr8T8+fPFp59+KgCIjIyMZs/fSBufe+450bFjR5GZmSn27dsnRo4cKfr27Svq6+tbuTU35lptrqioEKNHjxbr168Xx44dEz/++KMYNGiQSE5ObvYermizVwbjwIEDxXPPPdfssZ49e4rXXntNoorcy2AwCAAiKytLCCGE3W4XWq1WvPXWW85z6urqhEajEe+//75UZbpEZWWl6Natm8jMzBTDhw93BqOntnnu3Lli6NChV33eE9s9duxY8etf/7rZY5MmTRKPP/64EMIz23xpSNxIGysqKoRKpRLr1q1znlNcXCzkcrn45ptvWq32m3WlPwYutWfPHgHA2alxVZu97lKqxWJBTk4OUlNTmz2empqKXbt2SVSVexmNRgBAaGgoACA/Px96vb7Zz0CtVmP48OHt/mcwc+ZMjB07FqNHj272uKe2edOmTUhJScHDDz+MyMhIJCUlYcWKFc7nPbHdQ4cOxXfffYcTJ04AAA4cOICdO3dizJgxADyzzZe6kTbm5OTAarU2O0en0yEhIcFjfg5GoxEymQwhISEAXNfmdrmI+K0oKyuDzWZDVFRUs8ejoqKg1+slqsp9hBCYPXs2hg4dioSEBABwtvNKP4OzZ8+2eo2usm7dOuzbtw/Z2dmXPeepbT59+jSWL1+O2bNn43e/+x327NmDF154AWq1Gk888YRHtnvu3LkwGo3o2bMnFAoFbDYbFi5ciEcffRSA5/63bupG2qjX6+Hj44MOHTpcdo4nfNbV1dXhtddew9SpU52LiLuqzV4XjI0u3a5KCHFLW1i1Vc8//zwOHjyInTt3XvacJ/0MCgsL8eKLL2Lz5s3w9fW96nme1GbAsQVbSkoKFi1aBABISkrC4cOHsXz5cjzxxBPO8zyp3evXr8fq1auxZs0a9O7dG7m5uUhLS4NOp8P06dOd53lSm6/mZtroCT8Hq9WKKVOmwG63Y9myZdc9v6Vt9rpLqeHh4VAoFJf99WAwGC7766u9mzVrFjZt2oStW7c226ZLq9UCgEf9DHJycmAwGJCcnAylUgmlUomsrCz87W9/g1KpdLbLk9oMANHR0bj99tubPdarVy8UFBQA8Mz/1q+++ipee+01TJkyBYmJiZg2bRpeeuklpKenA/DMNl/qRtqo1WphsVhQXl5+1XPaI6vVismTJyM/Px+ZmZnNtpxyVZu9Lhh9fHyQnJyMzMzMZo9nZmZi8ODBElXlWkIIPP/889iwYQO+//57xMfHN3s+Pj4eWq222c/AYrEgKyur3f4MRo0ahUOHDiE3N9d5pKSk4LHHHkNubi66dOnicW0GgCFDhlw2FefEiRPo1KkTAM/8b11TU3PZhrQKhcI5XcMT23ypG2ljcnIyVCpVs3NKS0uRl5fXbn8OjaF48uRJbNmyBWFhYc2ed1mbWzBIyGM0Ttf48MMPxZEjR0RaWpoICAgQZ86ckbo0l/h//+//CY1GI7Zt2yZKS0udR01NjfOct956S2g0GrFhwwZx6NAh8eijj7a74ezX03RUqhCe2eY9e/YIpVIpFi5cKE6ePCn+/e9/C39/f7F69WrnOZ7W7unTp4uOHTs6p2ts2LBBhIeHizlz5jjP8YQ2V1ZWiv3794v9+/cLAGLJkiVi//79zhGYN9LG5557TsTExIgtW7aIffv2ibvvvrtNT9e4VputVquYMGGCiImJEbm5uc0+28xms/M9XNFmrwxGIYT4xz/+ITp16iR8fHxE//79nVMZPAGAKx4fffSR8xy73S7eeOMNodVqhVqtFsOGDROHDh2Srmg3uDQYPbXNn3/+uUhISBBqtVr07NlTfPDBB82e97R2m0wm8eKLL4q4uDjh6+srunTpIubPn9/sw9ET2rx169Yr/juePn26EOLG2lhbWyuef/55ERoaKvz8/MS4ceNEQUGBBK25Mddqc35+/lU/27Zu3ep8D1e0mdtOERERNeF19xiJiIiuhcFIRETUBIORiIioCQYjERFREwxGIiKiJhiMRERETTAYiYiImmAwEhERNcFgJCIiaoLBSERE1ASDkYiIqAkGIxERURP/Hwx1ewg8qwNBAAAAAElFTkSuQmCC", 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", 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" + "
" ] }, "metadata": {}, @@ -288,7 +288,7 @@ "distance_profile = snn.compute_distance_profile(\n", " series_predict[:, starting_timestep_predict : starting_timestep_predict + length],\n", ")\n", - "plt.figure(figsize=(5, 3))\n", + "plt.figure(figsize=(7, 2))\n", "plt.plot(distance_profile)\n", "plt.show()" ] @@ -301,19 +301,104 @@ "### 1.2 Motif search with STOMP" ] }, + { + "attachments": { + "f492cb89-5bf3-4641-8be2-a77805f20b88.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "6aecb58e-9de9-4264-959e-4180ab3fa27a", + "metadata": {}, + "source": [ + "When doing motif search, it's important to define the type of motif you want to extract from a series. We'll use the figure and definitions given by [1] :\n", + "\n", + "![image.png](attachment:f492cb89-5bf3-4641-8be2-a77805f20b88.png)\n", + "\n", + "For now, the `StompMotif` estimators supports only \"Pair motifs\", \"k-Motiflets\", and \"k-motifs\". Note that the naming \"k-motifs\" is a bit confusing, it extract motifs based on a range parameter and not by number of closests neihbors. To choose the type of motifs you want to extract, you will have to use the parameters of the `predict` method :\n", + "\n", + "- for **\"Pair Motifs\"** : This is the default configuration\n", + "\n", + "- for **\"k-Motiflets\"** : ```{\"motif_size\": k}```\n", + "\n", + "- for **\"k-motifs\"** : ```{\"motif_size\": np.inf, \"dist_threshold\": r, \"motif_extraction_method\": \"r_motifs\"}```\n", + "\n", + "These configuration will extract the best motif only, if you want to extract more than one motifs, you can use the `k` parameter to extract the `top-k` motifs. \n", + "\n", + "**The term `k` of `top-k` motifs, while also used in `k-Motiflets`, is not the same. We use `motif_size` as the `k` in `k-Motiflets`. This is to avoid \"extraction the `top-k` `k-motiflets`\", which can lead to confusions. Rather, we extract the `top-k` `motif_size-motiflets`**.\n", + "\n", + "The `top-k` using `motif_extraction_method=\"r_motifs\"` will be the motif with the highest cardinality (i.e. the more matches in range `r`), while for `motif_extraction_method=\"k_motifs\"`,which is the default value, the best motifs will be those who minimize the maximum pairwise distance." + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "id": "ff23faf5-2941-441a-8c4c-0cf66eaca121", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "([array([0.02910451])], [array([[7, 0]], dtype=int64)])" + "([array([2.16605047]),\n", + " array([3.23155459]),\n", + " array([8.15076681]),\n", + " array([8.15076681]),\n", + " array([26.42906254])],\n", + " [array([[13, 30]], dtype=int64),\n", + " array([[31, 13]], dtype=int64),\n", + " array([[108, 77]], dtype=int64),\n", + " array([[ 77, 108]], dtype=int64),\n", + " array([[59, 76]], dtype=int64)])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from aeon.similarity_search.series import StompMotif\n", + "\n", + "motif = StompMotif(length=length, normalize=True).fit(series_fit)\n", + "motif.predict(\n", + " k=5,\n", + " motif_size=1,\n", + " motif_extraction_method=\"k_motifs\",\n", + " allow_trivial_matches=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d16036a3-f5b9-41d2-ae23-a1bcf0737c93", + "metadata": {}, + "source": [ + "\n", + "Note that we also support giving another series in `predict`, which will use this series to search for the motifs matching subsequences in the series given during `fit`. For those familiar with the matrix profile notations, this is the case of using `MP(A,B)`, while not using a series in `predict` is doing a self matrix profile `MP(A,A)`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "59117ea7-2cbf-49d6-829a-792805b4aaf7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([array([0.01197907]),\n", + " array([0.0622802]),\n", + " array([0.14565364]),\n", + " array([0.70546699]),\n", + " array([1.19303001])],\n", + " [array([[83, 78]], dtype=int64),\n", + " array([[50, 49]], dtype=int64),\n", + " array([[32, 30]], dtype=int64),\n", + " array([[9, 4]], dtype=int64),\n", + " array([[101, 95]], dtype=int64)])" ] }, - "execution_count": 6, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -321,9 +406,65 @@ "source": [ "from aeon.similarity_search.series import StompMotif\n", "\n", - "motif = StompMotif(length=length).fit(series_fit)\n", - "motif.predict(series_predict)" + "motif.predict(\n", + " series_predict,\n", + " k=5,\n", + " motif_size=1,\n", + " motif_extraction_method=\"k_motifs\",\n", + " allow_trivial_matches=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9190fdf4-db3d-4d51-b2c8-41b88a9f6f74", + "metadata": {}, + "source": [ + "You can also return the matrix profile with the same parameterization as `predict` (minus `motif_extraction_method` parameter) using :" ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4c36738a-e6a0-4452-aee2-ccbad99d6d8b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "MP, IP = motif.compute_matrix_profile()\n", + "\n", + "plt.figure(figsize=(7, 2))\n", + "plt.plot([MP[i][0] for i in range(len(MP))])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1610adf3-5cb1-466e-9cad-fb248148fd5a", + "metadata": {}, + "source": [ + "## References\n", + "[1] Patrick Schäfer and Ulf Leser. 2022. Motiflets: Simple and Accurate Detection\n", + " of Motifs in Time Series. Proc. VLDB Endow. 16, 4 (December 2022), 725–737." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "989ba9f2-6dd8-4db7-9dfc-783aac5e6fcb", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 763bdcf2794670ceee7ce375868d66c859ad830e Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 19 Jan 2025 20:41:47 +0100 Subject: [PATCH 17/36] Fix identifiers and test data shape for all_estimators tests --- aeon/similarity_search/collection/__init__.py | 5 +- aeon/similarity_search/collection/_base.py | 14 +---- .../collection/neighbors/_rp_cosine_lsh.py | 6 ++- aeon/similarity_search/series/_base.py | 9 +--- .../similarity_search/series/motifs/_stomp.py | 1 + .../series/neighbors/_dummy.py | 17 ++++-- .../series/tests/test_commons.py | 4 +- aeon/testing/testing_data.py | 52 +++++-------------- aeon/utils/base/_identifier.py | 2 + aeon/utils/tags/_tags.py | 2 +- .../similarity_search/similarity_search.ipynb | 12 ++--- 11 files changed, 46 insertions(+), 78 deletions(-) diff --git a/aeon/similarity_search/collection/__init__.py b/aeon/similarity_search/collection/__init__.py index ab3a546193..3a08ed22d6 100644 --- a/aeon/similarity_search/collection/__init__.py +++ b/aeon/similarity_search/collection/__init__.py @@ -1,5 +1,8 @@ """Similarity search for time series collection.""" -__all__ = ["BaseCollectionSimilaritySearch"] +__all__ = ["BaseCollectionSimilaritySearch", "RandomProjectionIndexANN"] from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.similarity_search.collection.neighbors._rp_cosine_lsh import ( + RandomProjectionIndexANN, +) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index 618a531081..cbbf8de1e9 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -9,7 +9,6 @@ from typing import Union, final import numpy as np -from numba import get_num_threads, set_num_threads from aeon.base import BaseCollectionEstimator from aeon.similarity_search._base import BaseSimilaritySearch @@ -22,16 +21,9 @@ class BaseCollectionSimilaritySearch(BaseCollectionEstimator, BaseSimilaritySear _tags = { "input_data_type": "Collection", "capability:multivariate": True, - "capability:unequal_length": True, - "capability:multithreading": True, - "X_inner_type": ["np-list", "numpy3D"], + "X_inner_type": ["numpy3D"], } - @abstractmethod - def __init__(self, n_jobs=1): - self.n_jobs = n_jobs - super().__init__() - @final def fit( self, @@ -63,11 +55,7 @@ def fit( # Store minimum number of n_timepoints for unequal length collections self.n_channels_ = X[0].shape[0] self.n_cases_ = len(X) - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) self._fit(X, y=y) - set_num_threads(prev_threads) - self.is_fitted = True return self diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index a6f3097f78..1fad3f08f7 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -1,7 +1,7 @@ """Random projection LSH index.""" import numpy as np -from numba import njit, prange +from numba import get_num_threads, njit, prange, set_num_threads from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch from aeon.utils.numba.general import z_normalise_series_2d, z_normalise_series_3d @@ -134,6 +134,8 @@ def _fit(self, X, y=None): self """ + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) rng = np.random.default_rng(self.random_state) if self.normalize: X = z_normalise_series_3d(X) @@ -171,7 +173,7 @@ def _fit(self, X, y=None): self.bool_hashes_value_list_ = np.asarray(list(self.dict_bool_hashes_.values())) self.bool_hashes_key_list_ = np.asarray(list(self.dict_bool_hashes_.keys())) - + set_num_threads(prev_threads) return self def _get_bucket_content(self, key): diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index 2e1b6d40e0..6ee1f27270 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -4,7 +4,6 @@ from typing import Union, final import numpy as np -from numba import get_num_threads, set_num_threads from aeon.base import BaseSeriesEstimator from aeon.similarity_search._base import BaseSimilaritySearch @@ -20,8 +19,7 @@ class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): } @abstractmethod - def __init__(self, axis=1, n_jobs=1): - self.n_jobs = n_jobs + def __init__(self, axis=1): super().__init__(axis=axis) @final @@ -56,12 +54,7 @@ def fit( self.n_channels_ = X.shape[0] self.n_timepoints_ = X.shape[1] self.X_ = X - - prev_threads = get_num_threads() - set_num_threads(self._n_jobs) self._fit(X, y=y) - set_num_threads(prev_threads) - self.is_fitted = True return self diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index c912cdfacd..43bc76f049 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -233,6 +233,7 @@ def compute_matrix_profile( exclusion_size = self.length // exclusion_factor if motif_size == np.inf: + # convert infs here as numba seem to not be able to do == np.inf ? motif_size = X.shape[1] - self.length + 1 if self.normalize: diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index a3120f98dd..12bb7a1035 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -6,7 +6,7 @@ __all__ = ["DummySNN"] import numpy as np -from numba import njit, prange +from numba import get_num_threads, njit, prange, set_num_threads from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch from aeon.similarity_search.series._commons import ( @@ -23,6 +23,8 @@ class DummySNN(BaseSeriesSimilaritySearch): """Estimator to compute the on profile and distance profile using brute force.""" + _tags = {"capability:multithreading": True} + def __init__( self, length: int, @@ -31,16 +33,21 @@ def __init__( ): self.length = length self.normalize = normalize - super().__init__(n_jobs=n_jobs) + self.n_jobs = n_jobs + super().__init__() def _fit( self, X: np.ndarray, y=None, ): + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) + self.X_subs = get_all_subsequences(self.X_, self.length, 1) if self.normalize: self.X_subs = z_normalise_series_3d(self.X_subs) + set_num_threads(prev_threads) return self def predict( @@ -128,9 +135,13 @@ def compute_distance_profile(self, X: np.ndarray): length of X_. """ + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) if self.normalize: X = z_normalise_series_2d(X) - return _naive_squared_distance_profile(self.X_subs, X) + distance_profile = _naive_squared_distance_profile(self.X_subs, X) + set_num_threads(prev_threads) + return distance_profile @classmethod def _get_test_params(cls, parameter_set: str = "default"): diff --git a/aeon/similarity_search/series/tests/test_commons.py b/aeon/similarity_search/series/tests/test_commons.py index 774eee8dee..36e8b6babc 100644 --- a/aeon/similarity_search/series/tests/test_commons.py +++ b/aeon/similarity_search/series/tests/test_commons.py @@ -20,8 +20,8 @@ make_example_2d_numpy_series, ) -K_VALUES = [1, 3, np.inf] -THRESHOLDS = [np.inf, 0.7] +K_VALUES = [1, 3, 5] +THRESHOLDS = [np.inf, 1.5] NN_MATCHES = [False, True] EXCLUSION_SIZE = [3, 5] diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py index eb134cddda..e6730c9958 100644 --- a/aeon/testing/testing_data.py +++ b/aeon/testing/testing_data.py @@ -10,7 +10,8 @@ from aeon.forecasting import BaseForecaster from aeon.regression import BaseRegressor from aeon.segmentation import BaseSegmenter -from aeon.similarity_search import BaseSimilaritySearch +from aeon.similarity_search.collection import BaseCollectionSimilaritySearch +from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.testing.data_generation import ( make_example_1d_numpy, make_example_2d_dataframe_collection, @@ -219,7 +220,7 @@ }, } -EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH = { +EQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH = { "numpy3D": { "train": ( make_example_3d_numpy( @@ -401,7 +402,7 @@ }, } -EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH = { +EQUAL_LENGTH_MULTIVARIATE_COLLETION_SIMILARITY_SEARCH = { "numpy3D": { "train": ( make_example_3d_numpy( @@ -553,7 +554,7 @@ }, } -UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH = { +UNEQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH = { "np-list": { "train": ( make_example_3d_numpy_list( @@ -685,30 +686,6 @@ }, } -UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH = { - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=2, - min_n_timepoints=10, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=2, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - X_classification_missing_train, y_classification_missing_train = make_example_3d_numpy( n_cases=10, n_channels=1, @@ -828,7 +805,7 @@ FULL_TEST_DATA_DICT.update( { f"EqualLengthUnivariate-SimilaritySearch-{k}": v - for k, v in EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH.items() + for k, v in EQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH.items() } ) FULL_TEST_DATA_DICT.update( @@ -846,7 +823,7 @@ FULL_TEST_DATA_DICT.update( { f"EqualLengthMultivariate-SimilaritySearch-{k}": v - for k, v in EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH.items() + for k, v in EQUAL_LENGTH_MULTIVARIATE_COLLETION_SIMILARITY_SEARCH.items() } ) FULL_TEST_DATA_DICT.update( @@ -863,8 +840,8 @@ ) FULL_TEST_DATA_DICT.update( { - f"UnequalLengthUnivariate-SimilaritySearch-{k}": v - for k, v in UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH.items() + f"UnequalLengthUnivariate-CollectionSimilaritySearch-{k}": v + for k, v in UNEQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH.items() } ) FULL_TEST_DATA_DICT.update( @@ -879,12 +856,6 @@ for k, v in UNEQUAL_LENGTH_MULTIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"UnequalLengthMultivariate-SimilaritySearch-{k}": v - for k, v in UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"MissingValues-Classification-{k}": v @@ -1017,14 +988,15 @@ def _get_task_for_estimator(estimator): # collection data with continuous target labels elif isinstance(estimator, BaseRegressor): data_label = "Regression" - elif isinstance(estimator, BaseSimilaritySearch): - data_label = "SimilaritySearch" + elif isinstance(estimator, BaseCollectionSimilaritySearch): + data_label = "CollectionSimilaritySearch" # series data with no secondary input elif ( isinstance(estimator, BaseAnomalyDetector) or isinstance(estimator, BaseSegmenter) or isinstance(estimator, BaseSeriesTransformer) or isinstance(estimator, BaseForecaster) + or isinstance(estimator, BaseSeriesSimilaritySearch) ): data_label = "None" else: diff --git a/aeon/utils/base/_identifier.py b/aeon/utils/base/_identifier.py index cf2722cfcb..03e8d8beaf 100644 --- a/aeon/utils/base/_identifier.py +++ b/aeon/utils/base/_identifier.py @@ -55,6 +55,8 @@ def get_identifier(estimator): identifiers.remove("collection-estimator") if len(identifiers) > 1 and "transformer" in identifiers: identifiers.remove("transformer") + if len(identifiers) > 1 and "similarity-search" in identifiers: + identifiers.remove("similarity-search") if len(identifiers) > 1: TypeError( diff --git a/aeon/utils/tags/_tags.py b/aeon/utils/tags/_tags.py index e1bacdd5ad..d85ba87caa 100644 --- a/aeon/utils/tags/_tags.py +++ b/aeon/utils/tags/_tags.py @@ -138,7 +138,7 @@ class : identifier for the base class of objects this tag applies to "point belongs to.", }, "requires_y": { - "class": ["transformer", "anomaly-detector", "segmenter"], + "class": ["transformer", "anomaly-detector", "segmenter", "similarity-search"], "type": "bool", "description": "Does this estimator require y to be passed in its methods?", }, diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index 53e5a9bcc5..91024292ef 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -332,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "id": "ff23faf5-2941-441a-8c4c-0cf66eaca121", "metadata": {}, "outputs": [ @@ -351,7 +351,7 @@ " array([[59, 76]], dtype=int64)])" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -363,8 +363,6 @@ "motif.predict(\n", " k=5,\n", " motif_size=1,\n", - " motif_extraction_method=\"k_motifs\",\n", - " allow_trivial_matches=False,\n", ")" ] }, @@ -379,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "id": "59117ea7-2cbf-49d6-829a-792805b4aaf7", "metadata": {}, "outputs": [ @@ -398,7 +396,7 @@ " array([[101, 95]], dtype=int64)])" ] }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -410,8 +408,6 @@ " series_predict,\n", " k=5,\n", " motif_size=1,\n", - " motif_extraction_method=\"k_motifs\",\n", - " allow_trivial_matches=False,\n", ")" ] }, From 85c71741af20e7587971b4c7b8a4a06939474a24 Mon Sep 17 00:00:00 2001 From: baraline Date: Mon, 20 Jan 2025 08:50:40 +0100 Subject: [PATCH 18/36] Fix missing params --- .../collection/neighbors/_rp_cosine_lsh.py | 3 ++- .../collection/tests/test_base.py | 16 ++++------------ .../series/tests/test_commons.py | 4 ++-- 3 files changed, 8 insertions(+), 15 deletions(-) diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index 1fad3f08f7..61142d6f83 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -116,7 +116,8 @@ def __init__( self.use_discrete_vectors = use_discrete_vectors self.random_state = random_state self.normalize = normalize - super().__init__(n_jobs=n_jobs) + self.n_jobs = n_jobs + super().__init__() def _fit(self, X, y=None): """ diff --git a/aeon/similarity_search/collection/tests/test_base.py b/aeon/similarity_search/collection/tests/test_base.py index 9180a071cf..c1efaa30f0 100644 --- a/aeon/similarity_search/collection/tests/test_base.py +++ b/aeon/similarity_search/collection/tests/test_base.py @@ -11,7 +11,6 @@ make_example_1d_numpy, make_example_2d_numpy_series, make_example_3d_numpy, - make_example_3d_numpy_list, ) @@ -21,8 +20,6 @@ def test_input_shape_fit_predict_collection(): # dummy data to pass to fit when testing predict/predict_proba X_3D_uni = make_example_3d_numpy(n_channels=1, return_y=False) X_3D_multi = make_example_3d_numpy(n_channels=2, return_y=False) - X_3D_uni_list = make_example_3d_numpy_list(n_channels=1, return_y=False) - X_3D_multi_list = make_example_3d_numpy_list(n_channels=2, return_y=False) X_2D_uni = make_example_2d_numpy_series(n_channels=1) X_2D_multi = make_example_2d_numpy_series(n_channels=2) X_1D = make_example_1d_numpy() @@ -31,8 +28,6 @@ def test_input_shape_fit_predict_collection(): valid_inputs_fit = [ X_3D_uni, X_3D_multi, - X_3D_multi_list, - X_3D_uni_list, X_2D_uni, X_2D_multi, ] @@ -53,10 +48,7 @@ def test_input_shape_fit_predict_collection(): estimator_uni.predict(X_2D_multi) with pytest.raises(ValueError): estimator_multi.predict(X_2D_uni) - - for _input in [X_3D_uni, X_3D_uni_list]: - with pytest.raises(TypeError): - estimator_uni.predict(_input) - for _input in [X_3D_multi, X_3D_multi_list]: - with pytest.raises(TypeError): - estimator_multi.predict(_input) + with pytest.raises(TypeError): + estimator_uni.predict(X_3D_uni) + with pytest.raises(TypeError): + estimator_multi.predict(X_3D_multi) diff --git a/aeon/similarity_search/series/tests/test_commons.py b/aeon/similarity_search/series/tests/test_commons.py index 36e8b6babc..6f2c816193 100644 --- a/aeon/similarity_search/series/tests/test_commons.py +++ b/aeon/similarity_search/series/tests/test_commons.py @@ -109,7 +109,7 @@ def test__extract_top_k_motifs(): [0, 7], ] ) - MP_k, IP_k = _extract_top_k_motifs(MP, IP, 2) + MP_k, IP_k = _extract_top_k_motifs(MP, IP, 2, True, 0) assert_(len(MP_k) == 2) assert_(MP_k[0] == [0.6, 0.7]) assert_(IP_k[0] == [0, 7]) @@ -135,7 +135,7 @@ def test__extract_top_r_motifs(): [0, 7], ] ) - MP_k, IP_k = _extract_top_r_motifs(MP, IP, 2) + MP_k, IP_k = _extract_top_r_motifs(MP, IP, 2, True, 0) assert_(len(MP_k) == 2) assert_(MP_k[0] == [1.0, 1.5, 2.0, 1.5]) assert_(IP_k[0] == [1, 2, 3, 4]) From fd7caadd1a5e03f5394fb70aceae89692d7f7e94 Mon Sep 17 00:00:00 2001 From: baraline Date: Sat, 1 Feb 2025 14:06:55 +0100 Subject: [PATCH 19/36] Fix n_jobs params and tags, add some docs --- .../collection/neighbors/_rp_cosine_lsh.py | 68 +++--- aeon/similarity_search/series/_base.py | 2 - .../similarity_search/series/motifs/_stomp.py | 12 +- .../series/neighbors/_dummy.py | 6 +- aeon/utils/tags/_tags.py | 2 +- examples/similarity_search/code_speed.ipynb | 2 +- .../similarity_search/distance_profiles.ipynb | 2 +- .../similarity_search/similarity_search.ipynb | 201 +++++++++++++++--- .../similarity_search_tasks.ipynb | 2 +- 9 files changed, 234 insertions(+), 63 deletions(-) diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index 61142d6f83..1e5bb914bb 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -233,30 +233,46 @@ def predict( ) top_k = np.zeros(k, dtype=int) top_k_dist = np.zeros(k, dtype=float) - dists = _hamming_dist_series_to_collection(X_bool, self.bool_hashes_value_list_) - if inverse_distance: - dists = 1 / (dists + 1e-8) - # Get top k buckets - ids = np.argpartition(dists, kth=k)[:k] - # and reoder them - ids = ids[np.argsort(dists[ids])] - - _i_bucket = 0 - current_k = 0 - while current_k < k: - if dists[ids[_i_bucket]] <= threshold: - candidates = self.dict_X_index_[ - self.bool_hashes_key_list_[ids[_i_bucket]] - ] - # Can do exact search by computing distances here - if len(candidates) > k - current_k: - candidates = candidates[: k - current_k] - top_k[current_k : current_k + len(candidates)] = candidates - top_k_dist[current_k : current_k + len(candidates)] = dists[ - ids[_i_bucket] - ] - current_k += len(candidates) - else: - break - _i_bucket += 1 + X_hash = hash(X_bool.tobytes()) + remove_X_hash = False + if not inverse_distance and X_hash in self.dict_X_index_: + current_k = min(len(self.dict_X_index_[X_hash]), k) + top_k[:current_k] = self.dict_X_index_[X_hash][:current_k] + remove_X_hash = True + else: + current_k = 0 + + if current_k < k: + dists = _hamming_dist_series_to_collection( + X_bool, self.bool_hashes_value_list_ + ) + + if inverse_distance: + dists = 1 / (dists + 1e-8) + if remove_X_hash: + dists[np.where(self.bool_hashes_value_list_ == X_hash)[0]] = np.iinfo( + dists.dtype + ).max + # Get top k buckets + ids = np.argpartition(dists, kth=k)[:k] + # and reoder them + ids = ids[np.argsort(dists[ids])] + + _i_bucket = 0 + while current_k < k: + if dists[ids[_i_bucket]] <= threshold: + candidates = self.dict_X_index_[ + self.bool_hashes_key_list_[ids[_i_bucket]] + ] + # Can do exact search by computing distances here + if len(candidates) > k - current_k: + candidates = candidates[: k - current_k] + top_k[current_k : current_k + len(candidates)] = candidates + top_k_dist[current_k : current_k + len(candidates)] = dists[ + ids[_i_bucket] + ] + current_k += len(candidates) + else: + break + _i_bucket += 1 return top_k[:current_k], top_k_dist[:current_k] diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index 6ee1f27270..bad22168f3 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -7,7 +7,6 @@ from aeon.base import BaseSeriesEstimator from aeon.similarity_search._base import BaseSimilaritySearch -from aeon.utils.validation import check_n_jobs class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): @@ -48,7 +47,6 @@ def fit( self """ self.reset() - self._n_jobs = check_n_jobs(self.n_jobs) X = self._preprocess_series(X, self.axis, True) # Store minimum number of n_timepoints for unequal length collections self.n_channels_ = X.shape[0] diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index 43bc76f049..ad5f0b189b 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -31,15 +31,19 @@ class StompMotif(BaseSeriesSimilaritySearch): This estimators allows to perform multiple type of motif search operations by using different parameterization. We base oursleves on Figure 3 of [2]_ to establish the - following list, we do not yet support "Learning" and "Valmod" motifs : + following list, but modify the confusing naming for some of them. We do not yet + support "Learning" and "Valmod" motifs : - - for "Pair Motifs" : This is the default configuration + - for "Pair Motifs" : This is the default configuration: { + "motif_size": 1, + } - - for "k-Motiflets" : { + - for "k-motifs" : the extension of pair motifs: { "motif_size": k, } - - for "k-motifs" (naming is confusing here, it is a range based motif): { + - for "r-motifs" (originaly named k-motifs, which was confusing as it is a range + based motif): { "motif_size":np.inf, "dist_threshold":r, "motif_extraction_method":"r_motifs" diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index 12bb7a1035..2f4e773bb2 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -18,6 +18,7 @@ z_normalise_series_2d, z_normalise_series_3d, ) +from aeon.utils.validation import check_n_jobs class DummySNN(BaseSeriesSimilaritySearch): @@ -42,7 +43,8 @@ def _fit( y=None, ): prev_threads = get_num_threads() - set_num_threads(self._n_jobs) + + set_num_threads(check_n_jobs(self.n_jobs)) self.X_subs = get_all_subsequences(self.X_, self.length, 1) if self.normalize: @@ -136,7 +138,7 @@ def compute_distance_profile(self, X: np.ndarray): """ prev_threads = get_num_threads() - set_num_threads(self._n_jobs) + set_num_threads(check_n_jobs(self.n_jobs)) if self.normalize: X = z_normalise_series_2d(X) distance_profile = _naive_squared_distance_profile(self.X_subs, X) diff --git a/aeon/utils/tags/_tags.py b/aeon/utils/tags/_tags.py index d85ba87caa..45801ef09a 100644 --- a/aeon/utils/tags/_tags.py +++ b/aeon/utils/tags/_tags.py @@ -155,7 +155,7 @@ class : identifier for the base class of objects this tag applies to "values?", }, "input_data_type": { - "class": "transformer", + "class": ["transformer", "similarity-search"], "type": ("str", ["Series", "Collection"]), "description": "The input abstract data type of the transformer, input X. " "Series indicates a single series input, Collection indicates a collection of " diff --git a/examples/similarity_search/code_speed.ipynb b/examples/similarity_search/code_speed.ipynb index 9b4c08acf3..f31155333d 100644 --- a/examples/similarity_search/code_speed.ipynb +++ b/examples/similarity_search/code_speed.ipynb @@ -554,7 +554,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/examples/similarity_search/distance_profiles.ipynb b/examples/similarity_search/distance_profiles.ipynb index d5ea595ff5..ec56fcc6bf 100644 --- a/examples/similarity_search/distance_profiles.ipynb +++ b/examples/similarity_search/distance_profiles.ipynb @@ -146,7 +146,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index 91024292ef..b720286d65 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -113,9 +113,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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uWbAAWLKk3iskIiKiZvHaa8Drr7sfAKBpwKxZbtxx1FFAe3tdl0dEjY1JE6JGtmmTmzDxy2SANWvczx13XOBTBdPGqo1DeGr9APozBdgOIITAlGQEh0xLYsHUJFJRFboqI6opaI9pUGRWrRAREdE+2LABlmNhzY41GC4OY6gwBFmSMXVgAEingSVLULRs/HXTMJ5aP4DtwwVYjoAQAl0J3YtF2mKaG4uoMjriOmMRIiIiCnIcYMOG4HOmCbzxhvuxYMEekyam7eC5zcP4y7oBbB3KwxYCjiPQHtdwyNQkDpmWREdc92KR9pgGVeFhVKJWw6QJUSMbHST4zZnjPXxxaxorfv8qVr6yE4bt7Pk1o8gS0BnX0ZVwP7qTOroTEXQldHTGNeRMG7tHDAznTe/+iKqgryOKmZ1xHDI1iUN7U5C52UFERNT0jLWv4vkdzyFdTAMABARe3PkilkhLUGhbhm/8/Fn8/sUdKFr7HotIEtAR09CddOOP7lI80pWIoCuuIW86GMgWMZirxCK6KqOvPYaZnTEsmJrE4X1tjEWIiIiaSX8/UCiM/zldB/r6xjy9YXcW33vkNfzPmu3Im/Z+fbmOuIauhI4ppf2QrqSOKQkdnQkdRcvBQNbAYNaAIwBFBjRFRl97FDM6Y5g3JYkjprcx8ULUYJg0IWpk69fjpZ0voT3ajp5EDxTZ179z7lxs3J3Dtx96Bff/desB/fKOAHZnDezOGge8xClJHe9YOBVzuhMA3M2ProSO3rYoZnbFsLgnBYkzWIiIiBpa3sjhuSfvQ76UMCkrWBZ+9cKf8f/lZqI/uXS/f10hgMGc6SVFDkRnXMOJC6di/tQEJEiQJPe5njZ3M+OwXiZViIiIGsr69Xv+3OzZgTblOzNFrPj9q7jrqU2wHHFAX24oZ2IoZ+KNndkDen1bVMXbF07Bop4UJLgxR2fCjUWmt8dw+PQ2VtYShQyTJkSNyjQxvO4l7MjuwI7sDqwbXIfpqemY0TYDmqzhP7cD193zeOAExbRUBCcvnoq3zuvGwp4kVFmGJAFbBvN4rX8EG3ZnUTBtGLaDnGFjMGtg14iBgayx3ycxynaNGLhn1ZY9fv6QaUlcsGwO3v+mmUhG+C2JiIio0eTMHH756E2YkxnwnhNCYGemiA0DORQg4fW2NYiJqeiNz8ZJi6fi+HndWNybgqa4sci24Txe7x/Bul055A0Lpi2QMywM5EwMZIvYPWIgZxxYLDKYM/d6gGRudxwfXTYX5x47E+0x7YC+BhEREdXQPnTdEELg/r9uxdfufwFDvsMX7TENyxdPxVvnd+OwvjboigxZBrYPF/B6/wje2JVF3rBhWA7ypo2BrLsnsnukiOwBxiLpgoUH12zHg2u2j/v5GR0x/N3xc/CRN89CZ0I/oK9BRJNLEkIcWJo1pNLpNNrb2zE8PIy2trZ6L4eoet54Ay98+/PYmdsZeNqwBF4dSeL6xR+BInUBcCs7Ll1+CM5762xENWW8X21COcPC7lICpfyRiCjoTkbQEdMgSRKEEMgaNrYM5rFxIIdnNgzgj2t379MmR0JX8M7De/CupX14x6KpB7xOIhoffz7WFv+8qVWMGCO4/a+3Q3t6FRb++TUAgGE5WLtzxGvfub4jiXvftABvnTcN1591CQ6bdsgBfa2CabsVsCNF7C61wYhpits2NKFDLsUiOcPG1qE8Ng3m8MyGQTzx+m6MFK0Jf/2oJuPUQ3tw9tI+LD90KuI6D3MQTSb+bKwt/nlT0xICuOEGIJcb//Mf/ziGpvTi83c/h9+9sMN7OqEruOjE+bjoxHlIRQ/skETBl0TZNVLEYM5ARC3FIqU5bEII5E03Ftk8mMeqjUP439d2Il2YOBbRVRknL5qKdx3Zh1MOnXbA6ySi8e3Pz0a+EyBqUCOvvTAmYVIwbby4NY3V3Q4y2v9AFX348JGn4pvvO/mgf9jGdRXxLhWzuuIT3nv0rI7SowUoWjbWbB5GprRZYdsCu0aK2J4u4I+v78Zf1runUrOGjV+t3opfrd4KVZZwyLQkDutrw3FzO7F88TRM74gd1PqJiIhocjnCwV3P34X+bD8O3zEEAChaNl7amkbBN7dEnj8VF75tHqKagntf+S8kIx/FrPZZ+/31opqCGR0xzNiHmOAoLxZxB76u2TLsJXEcR2D3iIHt6QL+vG43nnh9NwCgYDr4zZpt+M2abZAlYMHUYCyyLzEQERERVdnOnXtOmGgadrVPwd/d8iRe3p7xnn7XkX34+nuXYEoyclBfOqopmN4R26f9iSNndniPLdvBC1vTGMi5rc+FENg1YmDHcAHPbBzEylfcvR3DcvDQizvw0Is7IEnAvCkJHN7XhjfN7sTyQ6dh3pTEQa2fiPYdkyZEDWrd6pWB67xh48VtaZi2g41tcaSiGk4/XEFP99MQ0tsB1OeEQkRVcNzcrnE/95l3ukPq73hyPX7z3Dbv5IXlCLy8PYOXt2dwb6m118JpSaSiKmxHIK6rOOXQaXjv0dPR0xat2e+FiIiIKv606U/YlN4ECIGO7UMomDZe2pb2Br1rioz5UxOIHD8fw6UK0qSeRFuktieeNUXGm2Z37uGzC/F6fwa3/2kDfv3Xrd7sFEcAr/WP4LX+kVJrrxcwf2oCHTENtgAiqoyTF0/F+46esU9JHCIiIpoke2nNle7uwUd+/BRe7x8B4Hbd+Oe/OQJnLx07GL6WVEUOHOgYbf2uLO54cgN+tXordo0UAbgFNW/szOKNnVk88Nw2XPvAi5jTHUd3QoctAF2RcOLCqXjf0dO9GbJENHnYnouoARUKI/jzZe+HsNw39gXTxgtb3YQJAPz3O47A8hMXIBFRsXTaUpxz+Dn1XO4+MSwHT7y+C799fjv+unkIr/ePTDikTZKAZfO78TdHz8CZS3vRxtJVoj3iz8fa4p83NTvLsfDdP30XWTOL+HAOR//ySTy/dRhGKWESVWUcNr0NWkTD//2/t8NRZEyJT8EFR12AVCRV59WPz7Id/OmN3fif57fjr5uG8OqODEx74rdKb5nbhfcdMx3vWtqHjjj7kBPtCX821hb/vKlp/fKXwAsvjHm6YNr4wkA37mtfCADoa4/i5/9wfENVZ9iOwF/WDeC3z2/Dqk1DeHl7xout9uaY2R34m6Nn4F1H9h10NQ1RM2N7LqIm98Kqh7yECQBsGsh5CRMkozj1xAWIlYaqL5u1rB5L3G+6KmP5odOw/NBpANz2Hi9uTeOxV3fi0Zf78dyWYYxO8QoB/HHtbvxx7W58+VfP491L+3D56Ysws5PtM4iIiKrpuR3PIWtmAQAd24eweTBXSZhoCg7va4Ouyhia2gZHkTEtMQ3nH3U+knqynsveK1WRceLCqThx4VQA7oGOV7Zn8PhrO/GHl/uxauMgxjvP8Zf1A/jL+gFcc/8LOH1JL648fXFDbdAQERE1DCGADRsghIAkSYFPPb1hEE+n5gFwB6v/4hPHN1xrTUWWsGxBN5Yt6AbgHuh4dceIF4s8u2Fw3MOlqzYOYdXGIVz7wIs45dBpuPL0xVjcG85DKkSNgkkTogbjCAdrVz2CaaXrouUORQUAVZbQd+xsFEoJkzntczA9Nb1OKz04EVXBMbM7cczsTnzmnYtgOwISAFmWsHbnSGn+yRZs2O32MjUsB/es2oIH1mzDRW+fhxMXToUkuS05ZnTEMC0VgSxLe/+iRERENCEhBP606U/edWzzALaVWknIkuQlTABgqKcdvclefPTIjyKhN1YiQVdlLJ3ZjqUz23Hp8kMCscjG3Tn8avUW3Ld6C9budJNHpi3wm+e24XfPb8f5y+bitMN7SrGIhL72GHrbooxFiIiIDsbu3cDICF7Z/QqyRhapSAptkTbE1CRWbc1g23FToClSQyZMxqMqMg6f3obDp7fh4pMWwCklTGRZwpahPO4v7YuU57fYjsDDL+7AIy/twEfeMhvvPrIPsiRBlSX0tkfR1x6DwliEaJ8waULUYF7c+SK0TVu96+3DBe9xT1sUuemV+SEnzDqhpmurJv8P9gVTk7j8tEX47DsXYvWmIfxq9Vbct3oLhnImDMvBzSvX4uaVawOvj6gyDutrwz+9cyGWL542+pcnIiKifbR2cC125tyBpRAChVe3exUY01IRL2ECAOq8BbjgqAsQ0xp/7oc/FpndHcenTl2Iy045BC9sTeNXq7fg3lVbsGvEgOUI/OSJdfjJE+sCr9dVGYt6krhs+SE4Y0nvmBOyRERENIHSPJPhwjDyVh4ZI4Otma3YPlzAywkVw9rjOGXhoSiILciZMxDXGj9x4uc/fDGjI4ZPnrwAnzx5AV7ZnsF9q7fgnmc3Y0e6CEcAP//zRvz8zxsDr9dL8+YuPmkB3nf0dMYiRHshT3wLEYWFEAJPrn8CbTuHAbinCPoz7slOSXKTJkO9HQCArlgXFnUvqtdSa0KSJBwzuxPXvHcJHr9qOf7xpPnQlfG/rRUtB6s3DeHCW5/CpT9/Fv3pwrj3ERER0d75q0y04RzS/Rnvurc96j12ZAnvP+3TTZEw2RNJknDEjHZ86V2H47HPLcenT12IqDZ+LGJYDp7fksbFP3sW/3D709g8mKvxaomIiBrc+vUwbRN5K+89JYTAtuE8NrZHYElbMbV7Le5ccydueeaWOi60thb3pvD5Mw/FY59bjqvOXIxkZPwz8obt4OXtGXzmrtX46I//gnW7sjVeKVHjYKUJUQPZlN6E9PqXoZR6hvdnCrBLRzunJCOQEjqyHe5JimUzl7XUqYG2qIarzzoM571lDu7/6xaMFG0ICBQMG5sG81i7c8Rr5fWb57bh/17bhf/6x2Xs80lERLQfdozswNrBSjXn0Itb0V2aq9aV0BHVFO9zMw99CxKJjlovsW4SERWXn7YIf/uWWbh31Rak8xYEBIqmg82DObyxM4s3SpsTv3+pH39auxt3/sPxOHpWR30XTkRE1Cg2bEC6mA48NZgzUbQcbGxPYHZXHFNT7iD0GakZ9VhhXUU1BZecfAg+eOws3PPsZgzmTAgIGJaDzYN5rN+VxWv9IwCA/3t9F85c8ThuvfDNOGHBlDqvnCh8mDQhaiDP7XgOyUH3zbYQItCaq689iuGeDkCSEFNjOKr3qDqtsr5md8dx2SkLxzwvhMA9z27BP//mRQzmTAznTXzijqdx/6VvR3tcq8NKiYiIGs+a/jXeYyEEdr/Wj+7SdZ+vykSWZMw56qQary4c+tpjuOTkQ8Y8L4TA/zy/Hdfc/wL6M0VkDRsX3/EM7v/U2zAtFR3nVyIiIiJPPg+k02OSJtuG83AkCVvbYnjX7E7v+ZltM2u9wtCYmorgH09aMO7nHnlpB776qxewZSiPouXg0jufxf2Xvb0pZsAQTSa25yJqIBuGNiCSddtxDefd0xQA0B7TENdVZLrdqonjph8HXdHrts4wkiQJ5xw7E3+44mQsndEOANiwO4dP/2KVV61DREREe7d+aL33eEe6CDHsVnEmIypS0cohhL5kHyKz59V6eaEmSRLOXtqH319xEt4y151Btz1dwKV3PgujFNMRERHRHqTdZEnGqLQFzRs2MgULu+IRpNpimNNd2fhv5aTJ3px6WA8evvwdOHnxVABupc4/3vEM8oZd55URhQuTJkQNomAVsDO3E5GsW12SKVje58rlp4VEBIqk4C0z3lKXNTaCzoSOH370WHQl3KTSY6/uxL899EqdV0VERBR+lmNh+8h273rrUB6pogkAmFKKRcpmtM0A2ttrur5G0RbV8O/nvcmrzHlq/SCue+DFOq+KiIgo5IaHIYQIVJpkCm4cko5oOKyvzWtRLksyepO9dVlmI4jrKr73kWMwt5RkenFbGl+45zkIwQOlRGVMmhA1iC3pLQDgVZqMFCtJk/KQr2IyikO6DkEqwjkdezOjI4Z//39vgiK7AdXNK9fij2t31XlVRERE4bZjZAcspxJ/bE8X0FZKmqR8A0c7oh2Ia3Ggra3ma2wUU1MR/PDvjoWuum/H7nhyAx5+cUedV0VERBRi6TTyVj4Qi5T3RdIRLdAmtDfZC01hG+69aY9puOX845DQ3Xl0v1q9Ffeu2lLnVRGFB5MmRA1iS6aUNMkVIYTwggNNkRApveEuxiOY3T67bmtsJMsWdOMLZx7qXX/+7ueQM6y9vIKIiKi1bU5vDlzvGMwhYViQJQlxvTIAvi3SBigKkEjUeokN5ahZHbj2vUu86y/euwZDOaOOKyIiIgqxceaZlPdFRqIaetoqSRO25to3i3pS+Na5lXm4X//1i+hPF/byCqLWwaQJUYPYnN4MCIFotoiC6XhzOJIR1StBLSYibjsM2id///Z5Xk/xTQN5fOu3bNNFRES0J+UDHACQLVpAOg8JQCKieLEIUEqatLUBvudofB9+8ywsL/UU35kp4lq26SIiIhrf8HAgaWI7ArnSHA65IwFNqWxxzkhxX2RfvevIPrz3qOkA3Nm5X7rvebbpIgKTJkQNQQiBzenN0IoWZNsZtzWXEdUgFAXTU9PrtcyGI8sSrj/3SEQ191vhbX9cjz+/sbvOqyIiIgonf6XJ9uFKa66krzUX4Eua0IQkScI3PnAkUlH3z/CeZ7fgDy+zTRcREdEYoypNsr59kejUZOBWVprsn2veuwRTku7c14df3IH7/7q1zisiqj8mTYgawFBhCDkzh0hunHkmUbdPZzEewbTENOiKXpc1Nqp5UxK48vTF3vXX7n+hjqshIiIKp5yZw0B+wLveni54Q+DLG/4AEFWjbizCIfD7rLc9iq+863Dv+qu/egGOwxOeREREfvbQILJG1rv274ukplUOa8TUGLpiXTVdW6PrSui47n1HeNfX/vpFGJZTxxUR1R+TJkQNoHyyMzLi9pYcKZje5xIRt4d4MRnlaYoDdOHb5uGIGW6Q9fL2DF7bkanzioiIiMJlSzo4GHT7cCVpkoxUBq22RUqbFqw02S8fPG4m3jrP3eDZPJjHqk1D9V0QERFRmAiBzM7NEKgcKsgUKkmTtp5K3DGjbUagbSjtm7OW9uGdh00DAOzOGvjj2l11XhFRfTFpQtQAvKRJrgjH17czpilQ5coQeCZNDowiS3j/MZU/u98+v72OqyEiIgoff2suRwhsT7vtuXRFhq5W3lKk9JT7gEmT/SJJEs45thKL/O4FxiJERESefB6Z7GDgqXKlSTGioYND4CfFuYxFiDxMmhA1gPLg1Wi2iKxheWcrkr52GBwCf3DOPKLXe/w/TJoQEREF+JMmA1kDpu0gVTQDsQjgqzRhe679dtphPVBk92Ts/zy/jUNYiYiIykYNgS9aNkzbbR8ldcQDlSUcAn/gTlo0DTHN7Wby0As7YLNdKLUwJk2IQs5yLGzLbAMARLLFQAmqf/Cqk0phanxqzdfXLGZ0xHDUTHeD58VtaWzcnavzioiIiMJBCOEd4ADc1lwA3KSJLxaRICGplwaxstJkv3UmdCyb3w0A2DSQxwtb0xO8goiIqEWMGgI/4tsXUTsTgVt5mPTAxXQFJy9295V2Zw38Zd3ABK8gal5MmhCF3I6RHbCF244rkisGh8D7Nio6e+awb+dBOsNXbfLbF7bVcSVEREThsTu/GwWr4F2XkyZto5ImST0JRXZPJzJpcmD8sQjbYhAREbmyu7ahaBe9a/++iN5dSZp0x7oR1+I1XVuzOZOxCBGAKidNHn/8cbznPe/B9OnTIUkS7rvvvr3ev3LlSkiSNObj5ZdfruYyiULN3w4jMlLwggNZkhDXFe9zU3rn13xtzeasI/q8x2zRRdQcGIsQHbwxQ+DTBSiOg4RpIeFLmnituVQViHPD4kCcsaQH5TMwjEWImgNjEaKDt3v7G4Frf9IkPjXlPWaVycE75dBp0BV3u/i3z2+HwxZd1KKqmjTJZrM46qijcNNNN+3X61555RVs27bN+1i4cGGVVkgUfrvzu90HQkDJFGBYbt/ORETxKkuEBPROX1SvJTaNeVMSOLTXDbhWbRzCtuF8nVdERAeLsQjRwduV2+U9thwHu7NFpIom4rrizeAAgFTENwSe1a8HZFoqiuPmdAIAXu8fwev9mTqviIgOFmMRooOX3VU5SCCEQLZY6sahykBH5aAGh8AfvFRUw9sXTgHgHpRZvXmovgsiqhN14lsO3FlnnYWzzjprv183bdo0dHR0TP6CiBpQuW+nXjBhGpXTFOXhXABgRnX0dTA4mAxnLOnFy9vdDYqHXtiBC06YW98FEdFBYSxCdPBG9xAXwm3NFfXFIgCQ0ktJEw6BPyhnLOnFU+sHAbgnPC87JTXBK4gozBiLEB08c3Cn99hyBBzhVj9ENQWFeMT7XF+yb8xraf+duaQXf3i5HwDwu+e3402zO+u8IqLaC+VMk2OOOQZ9fX049dRT8eijj+713mKxiHQ6HfggaibDhWEA7hD4YqnKBAB0tfLf10hEKxsVdFDOWlrp3/noK/11XAkR1dP+xCIA4xFqboGkSakdRqpouqc7faJq1H3AeSYHxd9L/NFXdu7lTiJqZoxFiCqsocpAcsO3LxJRZRQTlaRJR7SjlstqWqcd3uNVE3NfhFpVqJImfX19uOWWW3D33XfjnnvuweLFi3Hqqafi8ccf3+NrvvGNb6C9vd37mDVrVg1XTFR95Y2KSK4YCA78SRO5o5ND4CfJ4p4UOuIaAOD5LcMQgv07iVrJgcQiAOMRam6jK00At9LEH4uossoh8JNkZmccs7piAIAXt6Zhs5c4UUthLEI0ihCwh4e8S/++iKYqKJYqTWRJRkJPjH41HYDOhI7FPe7B3Nf7R5A37DqviKj2qtqea38tXrwYixcv9q6XLVuGTZs24dvf/jbe8Y53jPuaq6++Gpdffrl3nU6nGRxQ07AcC1kzC8AdAm/YvqSJ4tuo6Oiq+dqalSRJOGJ6O/7v9V3YNWKgP1NET1u03ssioho5kFgEYDxCzUsIEUiaZHyVJv5YJKJUTnmyPdfBO2J6OzYN5JE3bazbNYJDprGimKhVMBYhGiWbhWlU5o0WffsiIhGBKMUjKT0FWQrV2fCGdsSMNry4LQ1HAC9tT7NFF7Wc0H83Of744/Haa6/t8fORSARtbW2BD6JmkSlWhn+OrjSJqJU+4lrnlJquq9kdMaOy2bNm83AdV0JEYTBRLAIwHqHmlbfyMB3Tu95TpUlE9SVN+O//oAVikS2MRYhaHWMRamX20CAM2/Cu/fsidlvMe9wW4b/5ybTUF4s8z1iEWlDokyarVq1CXx8HOVFr8p/sjGSLMKxKSWRgo6Jrak3X1eyOmFEJtp7fyuCAqNUxFqFW5o9FgGClif8AR6DShBt1B+2IwEYF5xIQtTrGItTKsru3Ba79SRO0M2lSLUuYNKEWV9X2XCMjI3j99de963Xr1mH16tXo6urC7NmzcfXVV2PLli24/fbbAQArVqzA3LlzsWTJEhiGgZ/97Ge4++67cffdd1dzmUSh5d+oiPoGwSuy5A3lAoBYV0/N19bMjpjOjQqiZsFYhOjgjE6ajBTcqpM2w4KmVGKRQKUJ23MdtCOm+w5wcKOCqKExFiE6OLld+5Y0aY8y/phMh/W2QZYAR3BfhFpTVZMmTz/9NJYvX+5dl/trXnDBBbjtttuwbds2bNy40fu8YRi48sorsWXLFsRiMSxZsgS/+c1vcPbZZ1dzmUShNVysvEnWs5WZJv4e4gAQn8JTR5NpTnccqaiKTMHiRgVRg2MsQnRwxiRNihZU20G740CSfEmTcqWJpgFRzgI7WN3JCKa3R7F1uIAXtqbhOAKy78AMETUOxiJEBye/e0fgurwvosgSnLa49zwrTSZXTFewcFoKr+zI4NUdGRRMG1FNmfiFRE2iqkmTk08+GUKIPX7+tttuC1xfddVVuOqqq6q5JKKGUt6okBwBJVNA+b+TvzWXkIBkV289lte0JEnCkultePKNAWxPF7AzU8TUVGTiFxJR6DAWITo4/qSJ5TjIGTY6R80zAXyVJm1tgMTN/cmwZEY7tg4XMFK0sGEgh3lTEvVeEhEdAMYiRAenuLvfeyyE8NqW64qMYqLyPp1Jk8m3ZEYbXtmRgeUIvLojgyNndtR7SUQ1E/qZJkStrLxRoRUMmGZlnknEt1FhxHS0xTtrvrZm52/R9QLnmhARUYvyJ02yBTcWaTPGJk2iaqm6hK25Jk2wXShjESIiak3m0G7vseUIOL7DpMU4kybVxNbl1MqYNCEKsfJGhTsEvtK3079RYSRiSOg8eTjZls7kRgUREdFwofIzMFN055mkxqk00RXdfcAh8JNm6UzONSEiIrKGBrzHo/dFCqw0qSr/vsgaxiLUYpg0IQoxL2mSN1C0fcGBb6aJ3NYOWeJ/5cm2hCcqiIiIApUmI0ULgJs0ifhiEVVWocqlrr9MmkyawOlOVr0SEVErEgJ2esi7NHz7IpqqwIi5hzZkSUZST9Z6dU3vsL42r+sqO3BQq+FOK1FI2Y6NEWMEAKDn9lxponV213xtrWDelATiujvkjBsVRETUioQQgaRJpuAmTZKGFYhFvCHwAJBK1Wx9zW5aW9Sbqfb8lvReZyIQERE1pXwehpH3Lv37IkhEIEqHOFJ6iodJqyAZUb2Zai9vy8D0Ja2Imh2/oxCFlH+TYm/tubT2rpquq1UosjsMHgA2D+YxmDXqvCIiIqLaKlgFmI7pXY94SZNgey5vCDzApMkkWzrDrTYZzpvYPJif4G4iIqLmYg8NwrAr78X9+yJOKuo9Zmuu6inHIobt4NUdmTqvhqh2mDQhCqlA0iRvBIKDiKpUHndOqem6WsmSwDB4tugiIqLW4o9FgGB7rj1WmrA916Q6YjrnmhARUevKDmwPXBf9lSZMmtSEv13oC2xdTi2ESROikPJvVLjtuWwAbgWEIkve52JdPTVfW6son6gA2L+TiIhaz+ikSbk9V8qwAvPVWGlSPUfM4AEOIiJqXdndwaSJ/zCp1BH3HjNpUj1HcF+EWhSTJkQhNbo9V3kQvH+TAgDiTJpUzeLeysbPa/0jdVwJERFR7Y1XaSIJgU7bgSRVDnB4lSayDCQStVxi0wvGImyJQUREraUw0B+4Lg+ClyUJdrJyaINJk+rhvgi1KiZNiELKv1EhjxRQnv3pb4cBAInu3louq6XMn1rZ+Fm7k8EBERG1luFi5TSh7QjkDAtx00JUkQL3eZUmyaSbOKFJM7Mz7sV+a3dm67waIiKi2ioM7vQeCyG8ShNdlWHG2Z6rFroSOroSOgDui1Br4bsaopAqJ01ky4aTqww+8ydNLE1BW9vUmq+tVcR1FTM6YgCA1/tHIMqZKyIiohbgP8Cxp3kmgK/ShK25Jp0iS5g/xT3EsX5XFqbtTPAKIiKi5mEM7fYe246AU3pPHlFlFBOsNKmVBaUDpTvSRaQLZp1XQ1QbTJoQhVR5oyKSLQb6dvrbc5nxKJJ6suZrayULprl/vpmChZ0jxTqvhoiIqHYCSZPSPJPkqHkmgK/ShEmTqijHIpYjsHEgV+fVEBER1Y41NOA9Lo7aFynGdO+6PdoOqp5DplX2nd5g5Su1CCZNiEKq3BIjkje8eSaAe6KiTG5rhyzxv3E1HTK1Ehy8zv6dRETUQvxJk0zRPVWYHFVpokgKVFl1L9p4yrMaGIsQEVGrsoYHvceGb19EV2UYcffQhgSJh0mrbAFjEWpB3G0lCiHbsZE13Oy9nhtVaeLfqGjvrPnaWs2Cab65JgwOiIioRQghxq00aTPMwAEOr8oEYKVJlSyYxo0KIiJqQbYNe6QSi/j3RZSICjPiHtpIRVI8TFpljEWoFfG7ClEIZYwMBEq9Oke35/JtVGgdXTVfW6vxn+7kAFYiImoVRbsIw67MVCvPNEkWrUAsElUrQ1iZNKmOYCzCjQoiImoN9vBQIBbx74s4ySggSQA4z6QWGItQK2LShCiE/Cc7I3ljj0mTSMeUmq6rFfFEBRERtaLhwnDgOuPNNAm25/KGwANsz1Ul86cmyvtCrHolIqKWkd29PXDtn2mCtsqhDSZNqm9GRwxRzY3/GItQq2DShCiE/EkTPVf0enfKkgRV9p3u7JpW87W1mu6Ejo64BoAnKoiIqHVkjEzgulxpkjIsaArbc9VSVFMwszMGwK16FULUeUVERETVlx0IJk38M03QFq88ZNKk6mRZwvwp7oHSDQO5wMFeombFpAlRCI0Ylc15f3su/8lOAIgxaVJ1kiR5Q8+2DRe8TSMiIqJmlikGkybZ0s+/LsuGXC57AKAreuUmJk2qphyLjBQt7EgX67waIiKi6isM7Axcm1b5MCngpCqHNpg0qY1yFw7bEdiwm63LqfkxaUIUQv6NCiVbhFM6UagpUuC+WCeTJrUQ6N/JUlQiImoB/gMcQgjkDBuq7SAlgicLvfZcug5EIqDq8McibBdKREStoDgUTJqUK000RYYZr7TnSupJUPUxFqFWw6QJUQh5GxVCQM7kved1XzsMIQHJ7t5aL60lLZiW8B6zRRcREbUCf9Ikb9pwhHDnmSjBtw9epUkq5Q1kpcnnn7HGWISIiFqBOTTgPXaEgO2UD5PKKCYqBzVSOitda4H7ItRqmDQhCqHyRoVWtGAZtve8v4e4GdWRjLXXfG2t6BAOgyciohbjn2mSLbqxyOh5JsCopAlVDWMRIiJqNdbwoPfY9M3Q0BUZxVilPSgrTWqDsQi1GiZNiEKonDSJ5IowfcPO/DNN7EQ82EecqmbBVJ7uJCKi1uKvNMkZ7jyTZNGEpu4hadLGfuLVxFiEiIhaje1LmviHwGuqDCNeqTRh0qQ25nYnIJeKitfu5EwTan5MmhCFUPl0p54rBoMD3+lOqb2j1stqWTM7417CiicqiIioFfiTJpVKExO6b76aJmuQyi25WGlSVV0JHV0JN0HFWISIiJqeEHDSw96laQvvsaZIKMbdn4mqrCKqRse8nCZfVFMwqysOwD3A4ThiglcQNTYmTYhCxnZs5MwcACCSM8YEB2VKG1tz1YoiS5g/xe3fuWF3LlD9Q0RE1IwCSROv0iTYnitQ8cqkSdWVB7D2Z4pIF8w6r4aIiKiKDANWIeddBt6Dx3Q4qgLArTKROFOtZsqxSM6wsS1dqPNqiKqLSROikMmalTLHSK4Y7N3pa4mhtnfVdF2trjyA1XIENuzOTXA3ERFR4ypaRRi24V1ni6WkiWEGYpGIWmmNwfZc1RcYwMpqEyIiamLO8BBMu3JAwPDti9ipSmUJW3PV1gLfXBPGItTsmDQhCplMsTJ4dW/tufTO7pquq9X5e4m/wV7iRETUxPxVJgCQNdz2XG1Fk5UmdRSMRdhLnIiImld+oB8Cla4b/koTkYp5j5k0qa0FUysHOLgvQs2OSROikPFvVLjtudzgQJIAVa6UnUY6p9R8ba1sbnfce7xxgJUmRETUvEYnTXJepQnbc9XTnO7KRsUGxiJERNTE8oP9getAe662SqVJSmf8UUuMRaiVMGlCFDLBpEnRCw40WQ706ox2Tq352lqZPzhYv5unO4mIqHlljEzgOmvYgBBotywovgMcTJrUlv8AxwbGIkRE1MQKA8GkieGb9cpKk/qZ60+asG05NTkmTYhCxp800bJFbxC8pgb/u8a6emq6rlY3J7BRweCAiIia15hKE8NCzLIRGzVo1UuaJBKAotRqeS1rVhdjESIiag3FoV2B6/KsV02RYSY506RepqUiiJT2pniAg5odkyZEIVM+3SnbDqRs0Xte97XDcBQZyTa256ql7oSOZEQFwI0KIiJqbv6kiWk7MCwHyaIJTdlD0oRVJjUR1RT0tbsbRdyoICKiZmYO7PYeCyEqHTgUCcVYpdI1FWEMUkuyLHkHSjcN5GE7YoJXEDUuJk2IQqa8UaHnjUDfTv9GRTGmI8ngoKYkqRIcbBnKB3uqEhERNRF/0iRbmmeSMqzAAQ7AlzRpa6vZ2lpdORYZzJkYzpt1Xg0REVF1mMOD3mPLqYyE1xQZRjzifY6VJrVXbl1u2A62DefrvBqi6mHShChkyhsVkWwRRiBpUvnvaiRjiGvxMa+l6ipvVNiOwJZBBgdERNScMsXKTJOsYQMAUkUzEIsAQEQpbVqw0qRm5nRVeolvZOUrERE1KceXNPEfWNQVGcUEkyb1NMfXLpSxCDUzJk2IQqa8UaHnil7fTgDQfTNNpFQqMBSeaoPD4ImIqBX4K01yXqWJGZivpkgKFLk0x4SVJjUzZ0plo4KxCBERNSXHgZ0Z9i4N376IqikwohoAQIKEhJYY83KqrjlT/PsiTJpQ82LShChEhBCVSpPcnitNlI6umq+NgLm+YfAbBxgcEBFRcxqvPVeyGGzP5bXmAlhpUkNzfQc4ONeEiIia0sgITMvwLk27MjfDSkSA0gHSuBavHOCgmvHvizAWoWbGpAlRiBSsAmzhtsGI5IxAcODfqNDaO2u+NgJm+1pirN/FpAkRETUf27GRNStvgL32XEZwEHwgacJKk5qZ3eXfqGAsQkRETSiTgWFXkib+w6ROKuo9Zmuu+vC3CmUsQs2MSROiEPGf7IyMas+lqZWNCq2ju6brItfcKf5KE56oICKi5uNPmAD+ShMz0CqUlSb1MaebSRMiImpuxuAu7zApgMC+iNMW8x4zaVIf0zuiUGV3f4qtQqmZMWlCFCIZozJ4Vd9Le65Ix5SarotcPamot2HE3p1ERNSM/Ac4gNGVJntImrDSpGZSUQ3dCffPnhsVRETUjPID/YFr/yB4+CpNUhEe2qgHVZExs9NNXm0cyEEIMcEriBoTkyZEIRKoNMkWveBAlSXIvsHv0a5pNV8bAbIsYU6pLcbGgRwch8EBERE1l0wxE7jOFS0ojoOEaXunCgFf0kRVgWgUVDvlapP+TBE5w6rzaoiIiCZXYVTSxH+YFO2ViktWmtTPnNKMtZxhY+dIsc6rIaoOJk2IQsRLmggRqDTxn+wEgBiTJnVTDg4My8H2dKHOqyEiIppc41WaJIsWNEWG5DvAEVEj7oO2Nm8gK9WGfxj8xgFWvhIRUXMpDu0KXJdnvSqyBCfJmSZhMJftQqkFMGlCFCLl051a0YKwbJSrHP09xI2ohmSsvR7LIwR7ibMtBhERNRt/0sQRAnnTcltzqcG3DV6lCeeZ1NxsblQQEVETMwZHJ00qh0mLiYj3PJMm9TO7m8PgqfkxaUIUIuWNCj1XhGlVWj/5K02MeIS9O+vIf6JiI4MDIiJqMv6kSd5wD3CkiiZ0JVhN4iVNOM+k5uYGNip4gIOIiJqLNTzoPbYdAbvUFltTJBRjlZlqKZ37IvUSrDRhLELNiUkTohApb1RExgyBr2xUFOMRnqioozm+jQoOgyciomaTMSozTbJFd15G0rDGtAplpUn9BKteGYsQEVFzcYaHvMf+IfA6K01Cg/si1AqYNCEKkUrSxBgTHJSJVBKqrNZ8beSawxMVRETUxPyVJlnDBuBWmviTJhIkaLLmXrDSpOb8GxWseiUioqZSLMIqVH62+fdFRFSDoyreNZMm9TOrK+aNtNvIfRFqUkyaEIVI+XRnJFeEafkqTXx9xJX2zpqviypmdMSgym50wN6dRETUbAJJE6/SxAzMV9MUrTIUnpUmNdcZ15CKugdoOF+NiIiaSjqNolX0Lg3fvoiVqgyB1xUdETUCqo+IqmB6ewwAK02oeTFpQhQSpm2iYBUAuDNNgu25Kv9VVSZN6kpVZMzodIODDbuzEEJM8AoiIqLGIIRAplhpz5Uz3KRJ26hKE681F8BKkzqQJMmrfN06lA9sKBERETW0TAaGbXiXpl15v20nKkkTVpnU3+wuNxYZzpsYyhkT3E3UeJg0IQqJrFk5KRjJFgPBgb89l9bRXdN10Vjl4CBr2BjIMjggIqLmULSLsIXtXWeL7mN3pkllvlpE8Z3sZKVJXczpclt0OQLYMpSv82qIiIgmhzM8BNMxvetAe662mPeYSZP687cu3zjAahNqPkyaEIVEzqz8kInkgzNN/BsVeieTJvU2s7MSHHCjgoiImkXWCLZ6ypk2IASSRrDSRFO0yk1MmtTFzM7KxtGWQcYiRETUHIqDuwLX/n0Rx9eeK6ElQPXFWISaHZMmRCERSJpki7B8lSaK7Dvd2Tm1puuisfzBwWYGB0RE1CT8sQgAFAwbMcuG6ohg0qQ8BD6RABQFVHvBWISnO4mIqDkYY5ImvnbYvkqThM6kSb35D5NyX4SaEZMmRCFR3qiQbQda0YTpuCcqNEXyhq06ioxosqNeS6QSblQQEVEzGp00yZs2UkUTkgT4zm9UZppwnkndsOqViIiakTG0O3BtOb65XW2VSpO4FgfVV6DShLEINSEmTYhCotwSQ88VAcCrNFHlyn/TYjyCOE9U1B3LUImIqBmNSZoYtjvPRJa9AxyArz0XW3PVzQxWvRIRURMyRyVNypUmsiTBSlV+9jFpUn8zeJiUmhyTJkQhUd6oiOQM2I6AI0pJE988k2JcZ3AQAixDJSKiZuRPmgghkLfcShN/LAL42nOx0qRuZnRwo4KIiJqPPTwUuLbsSgcOIx7xnue+SP1NS0W9+bvcF6FmxKQJUUhUkibFQAmqv4d4MR5h784QmJqMQC/9vbAMlYiImoU/aWLYDhxHIDVqCDzASpMwSERUdCXcNmmseiUioqbgOLAzae9SCAHLKVWaqAqMqOZ9joPg60+RJUwvHeLYMpiHEGKCVxA1FiZNiEKivFGh54JD4FVfE3EjHuGJihCQZQnTO9x+qpsZHBARUZPImlnvcd6wAcCtNJFZaRJG5WqT7ekCTNuZ4G4iIqKQGxmBZRveZTlhAgBmXAd8rUK5LxIO5VgkU7SQzlt1Xg3R5GLShCgkvEqTbDHwxjdQaZKIIKpGx7yWaq/comukaGE4b9Z5NURERAfPX2mSN92kSdKwWGkSUuUZa44Atg8X6rwaIiKig5ROw7Qr7639h0mNRHAfhEmTcPDPe908xHah1FyYNCEKCS9pkjeClSa+PuJSqg2yxP+2YTCTA1iJiKjJBJIm/koTfywCCYqkuBesNKkrfyyyiXNNiIio0WUyMJ1K0sR/mNRMRAK3MmkSDpz3Ss2Mu69EIVFuiaHnijD9M03kyn9TpaOz5uui8QUHsDI4ICKixjd+pYkJ1ReLaIoGqdweg5UmdcVYhIiImkomA2MP7bmsZKXSRJO1StUr1RVjEWpmTJoQhYAjHORN9wdMJLfnShOtvavma6PxzezyBwc83UlERI3PnzQpmDYUx0HctKH5Y5HyPBNNA6JsGVpP/tOdHAZPREQNL5MJtOfyV5o4qUrMkdA5BD4s/FWvjEWo2TBpQhQCBasAAQEIAT2/55kmWhsrTcIisFExxOCAiIgam+3YKFiVuRg5w0bScAd6qkqw0gSAW2UiBQfEU20FD3AwFiEiogY3qj2X/zCp46s0YWuu8JjZ5W/PxcOk1FyYNCEKgfLJTtW0oVhOoAy1XGliRjTEYmyDERYsQyUiombirzIB3EqTpOFuXIxbacLWXHUXjEW4UUFERI3NGh6EI3xzTHxty9HGpEkY9aQiUGQ3TuS+CDUbJk2IQqC8UaHnigCCZajlPuLFRITBQYj0tEWhMjggIqImMTppkjccJIulShPfTBNd0d0HTJrUXSqqoT3mJrFY9UpERI3OHBoIXPsrTURb5aAA90XCQ1Vk9LW7CS3GItRsqpo0efzxx/Ge97wH06dPhyRJuO+++yZ8zWOPPYZjjz0W0WgU8+fPxw9/+MNqLpEoFLKGOwQ+knOHnpWDA1mSvKx9MaYzOAgRRZYwvXTCcwtPdxKFFmMRon0zJmliWl6lSWC+msJKkzAp9xLfNlyA5Tt0Q0ThwViEaN+Yw8Gkif8wqZxi0iSsyrHIcN5EpmBOcDdR46hq0iSbzeKoo47CTTfdtE/3r1u3DmeffTZOPPFErFq1Cl/84hfx6U9/GnfffXc1l0lUd+WNikip0sQqlaH6NymMOCtNwqbcFiNdsDCcZ3BAFEaMRYj2zdikiYOkYUGRJcgS23OFVTkWsR2BHZlinVdDRONhLEK0D0wTVjYTeKrcttxQZWgJ3Xue+yLhMqOD816pOanV/MXPOussnHXWWft8/w9/+EPMnj0bK1asAAAcdthhePrpp/Htb38b55xzTpVWSVR//vZcQgiYpUoTzdcOoxjXkdATdVkfja98ogIAtgzmvRYZRBQejEWI9s3YpImNVNGEJgeHvbPSJFxmdvoGsA7kAnNOiCgcGIsQ7YNMBqYdPIhYrqAsRCPQfQc4Ehr3RcLEvy+yeSCPQ3vb6rgaoskTqpkmf/rTn3D66acHnjvjjDPw9NNPwzR5ipuaV6XSxIA9zhB4gJUmYeTfqOCJCqLmwFiEWlXWzHqPbUegaNpIGhZUJfh2gZUm4RLYqOCMNaKmwFiEWlImA9MJ/vsuHyY1fFUmACtNwiYYi7B1OTWPqlaa7K/t27ejp6cn8FxPTw8sy8KuXbvQ19c35jXFYhHFYqUUPZ1OV32dRJPNX2li+oadab6NCs40CZ8ZDA6Ims6BxCIA4xFqfP5Kk4JpAwCShglNYaVJmPljER7gIGoOjEWoJY2qNHEcAUe4eyNmPBK4lfsi4cJYhJpVqCpNAECSgm/MROmb5Ojny77xjW+gvb3d+5g1a1bV10g02bxKk7zhzTMBAFVmpUmY8XQnUXPa31gEYDxCjc+fNMl7SRMLqsxKkzDj6U6i5sRYhFrOqEoT07cvYiWYNAmzWf5WodwXoSYSqqRJb28vtm/fHniuv78fqqqiu7t73NdcffXVGB4e9j42bdpUi6USTapyS4yxlSaVoNhOxisbFRQKo2eaEFHjO5BYBGA8Qo0vkDQxbGi2jahlB1qFAqVKk0gE0PXRvwTVAVuFEjUfxiLUkjIZGLbhXVq+fRErGQ3cyqRJuPS2R1E+78tYhJpJqNpzLVu2DL/+9a8Dzz300EM47rjjoGnjbxZHIhFEIpFxP0fUKHJmDhACet5A2vZVmpTacwkJ0FIdez1ZRLXX2xaFIkuwHYHNQzzdSdQMDiQWARiPUOMb3Z4rYVgAgq1CFUmBLMmsMgmR9piGVERFpmjxdCdRk2AsQi1pVHsu07cv4vgqTSRIiGkxUHhoiozetii2DhcYi1BTqWqlycjICFavXo3Vq1cDANatW4fVq1dj48aNANyTEOeff753/8UXX4wNGzbg8ssvx0svvYSf/OQn+PGPf4wrr7yymsskqrucmYNWMCE7AqYzttLEiOmIR5L1Wh7tgVoKDgBg61ChzqshovEwFiHaN/6kSc60kSolTfyVJpxnEk7lXuLbhgpwfHEkEYUDYxGifTCqPZfl+3lmt1WSJDEt5h7goFApxyIDWQN5w67zaogmR1W/0zz99NM45phjcMwxxwAALr/8chxzzDH46le/CgDYtm2bFygAwLx58/Dggw9i5cqVOProo3HdddfhxhtvxDnnnFPNZRLVleVYMGwDkbxbimr5K01KfcSNGOeZhNX0DjdpwuCAKJwYixBNTAiBrJH1rguGjWTR3bjQfDNNvDahbW01XR/t3fQOd6PCsB3syhYnuJuIao2xCNE+2EulieRrz8V9kXAqxyIAsHWY1SbUHKranuvkk0/2BpaN57bbbhvz3EknnYRnn322iqsiCpfyyU49577JHW+mSTGuMzgIqb72GIBBAMC24TzmT2VFEFGYMBYhmphhG7BFJfGfN20kx6k00ZXSHBNWmoRKX3tlM2nbUAHTUtG93E1EtcZYhGhiIp0OVpr49kVEe2VDnvsi4dTn+zvaNlTAAu6LUBNgTRtRnZWTJpHcOJUmpT7iRpyVJmEVOFHBFl1ERNSA/K25AHcQfNIoVZr4ZpqwPVc4BWMRnu4kIqIGUyzCKgRjkXKlSV5VEIlWZvlwXyScZnRUDmwwFqFmwaQJUZ2V22F4lSa+3p2qXKo0ibHSJKwYHBARUaMbkzQxbaRK7bkCM01kJk3CaIYvabKFsQgRETWaUfNMgMpMkxFdQ0xXvOe5LxJO0xmLUBNi0oSozrxKk1EzTVRZgiSVBsHHI0joifoskPbKX4bK3p1ERNSIxqs0SRkWJACKxEHwYRdozzXMqlciImowo+aZAJVKk5GIiphWSZokNO6LhFGgPRf3RahJMGlCVGejZ5qUT1SovnYYnGkSXmyJQUREjS5rZgPX7kwTE6oiewc4AFaahBVjESIiamjjVZqUZprkolqgVSj3RcJpBtuWUxNi0oSozvwzTRwhYJeSJpqvHQZnmoQXgwMiImp0/koTIQTyhoVk0QrEIoCv0iTJ4Z5h0tseRTm3xaQJERE1nHEqTcqHSc14NPA890XCqS2mIlFqo8ZYhJoFkyZEdeZVmuSL3mkKAFBlX6UJZ5qEFoMDIiJqdP6kiWkLqKYNzXECsQhQqjSJxwFVrfUSaS80RUZPyt1U2sIDHERE1GhGVZoIIbz2XFZCD9zKfZFwkiTJq3zdMpSHEGKCVxCFH5MmRHWWNbOQHAE9b3iBAVCpNHFkCWZUY3AQUpIkoa8UHGwdZnBARESNx580yZs2kuMMgQdKlSZszRVKfR1u0mTXSBFFy67zaoiIiPZDJgPDNrzLcvcNADATrDRpFOV9kaLlYDBnTnA3UfgxaUJUZzkzB61gQBKVElSgMtPEiOmAJCGmxvb0S1CdlU9UFEwGB0RE1Hj8SZOCaSNpWAAQ6CEOlCpNmDQJJf9ck+0cBk9ERI1kVHsu07cv4iQjgVuZNAmvGR2VBBe7cFAzYNKEqM5yZg6RnHuqIlBpIrunO4vxCGJqDIqs1GV9NDEGB0RE1Mj8SZOcYSNllCpN5EqliQQJqqwyaRJS/hlrWxiLEBFRIxnVnsvy7YvYqeDh0YSeqNmyaP9Mb2csQs2FSROiOsuZOei5IgAEZ5qUK004BD70+tr9w+AZHBARUWPZc6VJJWmiyiokSWLSJKT62isHOLZxrgkRETUKIcZWmpT2RQQAyVdposqqW/VKodTnO8Cxjfsi1ASYNCGqIyGEW2mSdytNLKdyoqLcR7wY5xD4sPO3xGDShIiIGs2eZ5pU3ipoSmmTgkmTUGIsQkREDalQACwrWGlS2hfJ6ioi0UqSJK7F3QMcFErT/R042CqUmgCTJkR1ZNgGHOEgkh2n0qTUEsOIRxDTOM8kzBgcEBFRoxJCIG9WNtn9lSb+9lzeyU4mTULJ355r6zCTJkRE1CDSaQCA5VjeU+V9kRFdQ1SrtCnnnNdwY6tQajZMmhDVUd5yf5CU23OZgUqTyiB4BgfhNp3tuYiIqEEVrAIEKoc2CqZvpgkrTRqGvz3XVrbnIiKiRpHJQAgRTJqUBsFndTWYNOFh0lDrDbQK5b4INT4mTYjqqNwOw2vP5as08Q+CZ3uucOtt5yB4IiJqTOUDHN616SBVHDsIXpVV9wGTJqHUldARUd23doxFiIioYYwaAg9UBsFndA1RrbJtyX2RcIuoCqaUZtDwAAc1AyZNiOqo3A7DGwTvVJImilyZacITFeEW1RgcEBFRY/LPMwGAgmEhUW7PpYxqzyVJQDJZ0/XRvpEkyWuLsXUoDyHEBK8gIiIKgUwmUGUC+NpzRVTE2J6rocwotS7fkSnAtJ0J7iYKNyZNiOrIqzQpJ01KP1RUWfIGnBmsNGkIDA6IiKgR+eeZAICULUIRAhIAxTdsVVM0N2Ei8+1DWJWHwWcNG+m8NcHdREREIZBOw7SDlSbltuWZUTNNuC8SfuVYRAhgO+e9UoPjux6iOspbeci2A63ovrEtV5qUe4jbqgJLU3iiogH0tVeCgx1pBgdERNQYRleaqFm3ZaiqyN4BDqBUacLWXKEWmGvCYfBERNQIxmnPZZf2RXJRDZpvvho7cIRfn2/e6zYmTajBMWlCVEc5M+e15hJCeMGB6mvNBUniiYoGUD5RAbBFFxERNY7RM020rPszzD/PxL1WmTQJuWAswqQJERE1gExmbKVJqT2XlYgGnue+SPhN7+C8V2oeTJoQ1VHezCOSHTvPpNxD3Ii7czJ4oiL8GBwQEVEj8rfnshwH0Xy50iSYNNEUDWhrq+naaP/MYNKEiIgaTTodmGkihIBVas9lleaGlrEDR/j5Y5EtjEWowTFpQlRHeSuPSGlzwvLNwVBL/cKLMR0Ag4NGEDjdyZYYRETUIPztuQqmg5ThnvZUR80uYXuu8OvzH+BgSwwiIgo7xwGy2UB7Lke4La9tSYKI64HbeZg0/Po6/O25uC9CjY1JE6I68rfnMsepNCkm3JMVLEMNP3/SZMsggwMiImoM/vZcBdNGqlhKmihsz9VoGIsQEVFDGRkBhAi05ypXmYxEVER1NXA790XCz9+Bg7EINTomTYjqKG/mEcmVK00qSRNNrrTnUmXVbYlBocb2XERE1Ij8lSZ5w0bScFtkjJ5pwvZc4Te9ne25iIiogWQyABBoz1XeFxnRNUQ1JXA7O3CE35REBLribjVz1is1OiZNiOooZ+YQyZVnmvjacymV9lw8TdEYpiYjiKju39tmnqggIqIG4Z9pUrRsJL1Kk8rbBEVSIEsyK01CLqYrmJJ0W5kwFiEiotBLpwEg0J6rPOs1o2uIjU6asD1X6MmyhBmd7t/T5sEchBATvIIovJg0IaqjvJX32nP5K03KpzuLiQhPUzQISZK8oWdbhvIMDoiIqCEEKk1Mx6s00XyVJl7FK5MmoVeORXZkCjAsZ4K7iYiI6qhUaRJoz1Wa9ZqJqIFKk6gadQ9wUOiVY5GsYWM4b05wN1F48TsOUZ04wkHBKlTacwVmmrj/NY14hJUmDaR8oiJn2BjKMTggIqLw8880MQomEmapPZdvpokqq4CqAtHomNdTuJRjESGA7RwGT0REYTZOe67yrFe3PVdly5L7Io1jhm/GGitfqZExaUJUJ3kzDwgBPV+uNPG15yrPNInpLEFtIP7gYAt7iRMRUcjZjg3DNrxraaSyya7KlbcJmlyaZyIF55xQ+AQ2KoZye7mTiIiozsqVJs7YSpMRPVhpwg4cjaN8gAPgvgg1NiZNiOokb+WhGhaUUuuEQHsuRYIZ0eAoMoODBsITFURE1Ej8rbkAQM74kibKqPZcbM3VEAIHOBiLEBFRmKXTsB0bjqgcIPUGwUeCM014mLRxMBahZsGkCVGduEPgK6c7Tf8geFlGMREBwDLURjKzy5804elOIiIKN39rLgBQRoreY3+liSqrTJo0iJmdlbiRBziIiCjUMplAlQlQaVue1rVApQn3RRrHzE4eJqXmwKQJUZ3kzcoQeACwS8GBJAGKLKEY0wHwREUjmdFRCeRYhkpERGE3utJEy+6h0kRmpUmjYEsMIiJqGJlMYJ4JAFilw6QjETUw04QdOBpHMBbhYVJqXEyaENVJzswhkvdVmpTKUDW5MgQe4ImKRhIIDniigoiIQi5vBn9W6Vn3MIcsSZClUe252tpqujY6MIxFiIioIRgGUCjAtEdVmtgCRUWGqSiIqqw0aUS9bVEopTm9PMBBjYxJE6I6yVt5REqbE0II70RF+WRnMV6qNOGJiobRk4owOCAiooYxptKkVAHrrzIBWGnSSNqiGlJRFQBjESIiCrFxhsADbnuuEV2DrsqQ5Uo8wg4cjUNVZPS2RQHwAAc1NiZNiOrE357LEYAozYFX5XLShJUmjSYQHHCjgoiIQs4/00QI4c1aU+Vg0oQzTRpLeQDrtuG81/6ViIgoVEpJkzHtuWwHmVFD4AHuizSaciwymDORLVoT3E0UTkyaENWJvz2X5R8CrwTbc/FERWMpDz0bypkYYXBAREQh5m/PZdgOkkX3tKemBN8iaAorTRpJeRi8aQv0ZwoT3E1ERFQH5UoTX3sutwOHwIiuBobAA+zA0WhmcsYaNQEmTYjqxN+ey7IrpwArlSZuey6eqGgs7CVORESNwt+eq1C0kTTcjYvRlSZsz9VYZjIWISKisEunAQTbc1ml6shMRBuTNOG+SGPhvgg1AyZNiOokZ+ag50tJE1/rhNGVJlE1WvvF0QGb2eE/UZHby51ERET15W/PZRUM6HZ5vlrwLYKaSAGaVtO10YGb0cHTnUREFHLjVJqU90VGdBUxLRiLsANHY/HHIpsZi1CDYtKEqE7yxSz0cnsu29eeS5YgJAlGVENUjUKW+N+0kfBEBRERNYrAIPjhys+sMTNN2jtrtSSaBP5YZDNjESIiCqNxZpqU90XGqzRhe67Gwn0RagbcjSWqEzM9BKlUYGIGKk0ktzWXJLEEtQHN6Kj8nfFEBRERhZl/pomU8SVNlErSRJM1SG1tNV0XHRxWmhARUeiVK03Gac81ogeTJoqkQFf02q6PDgpjEWoGTJoQ1YEQAiI97F0HK03kyhB4nqZoODN5upOIiBqEv9JE9g0MV+XKWwRN0QAmTRoKYxEiIgq98kwTf3suu9Key580iWkxSFKwCpbCbbq/Pdcg25ZTY2LShKgOLMeCkq384PAPgtcUCcUYh8A3qr6OygwalqESEVFYCSECM03kkaL32F9posoqh8A3mK6EjmipF/wWblQQEVHYCAGMjAAY255LoJw0qWxXcl+k8UQ1BVNT7mFg7otQo2LShKgO3CHwhncdGATvrzThsLOGE1EVTCsHByxDJSKikDJsA47wVbqO+CtNgu25mDRpLJIkeW0xtgzlIYSY4BVEREQ1VCgAlgUhxJj2XDlNhSPLiPkrTdiBoyGVY5H+TBFFy67zaoj2H5MmRHWQt/LQc5UTnZbj27QozzQBT1Q0qvLQs52ZIgomgwMiIgqfwBB4AIrvMIemjGrPlUzWbF00OWZ0ujFkwXQwkDUmuJuIiKiGxhkC7147GNFVAAi05+K+SGPyD4PfNlTYy51E4cSkCVEdjKk0sf2VJhKMUnsunqhoTP6hZ9uGGRwQEVH4+FtzAYCe9bXnktmeq9FxACsREYVWqTWXv8oEAExbIDtO0oQdOBrTTMYi1OCYNCGqg7yZRyQ3tj2XIkuQJIntuRrczM7KSRj27yQiojAaXWnir4BVRrfnYqVJw/EPg2csQkREobLHShOBEV0DgMBMEx4mbUyMRajRMWlCVAc5Mxdsz2W77bnKJzsNDoJvaP4y1M0cwEpERCGUN31vXoWAVqqAVUsHOMrYnqsxzQzEItyoICKiECklTUw7WGli2W57LlmSoCscBN/ouC9CjY5JE6I6yFt56AV3c0II4VWaqKXAoMj2XA1tRkfUe7yV7bmIiCiE/JUmqmlDKs3gUpXg2wMlkQJUtaZro4M33dcSY+swkyZERBQie2jPZdlupUlUUwIHONiBozEFYxHui1DjYdKEqA7yxaw306ScMAHc051CkmBG3ZJUnqhoTH3t/oFn3KggIqLw8c80UbJF2OUDHL7WXACgtLXXdF00OfraKwc4OHyViIhCZW/tuSJqoDUXwH2RRhXYF+EBDmpAPDZGVAfF9CAipVyJP2miKaUh8KVTFTxRMbls24ZpmhPfeJCmRCXMSLmD6/KFPAoFblZQbei6DlnmeQgimligPVem8lhVgkkTrb2rVktqCbWKRdp1YEabAgigWCwwFqGaYSxCRBMqV5r42nM5joAj3EHwMd8QeIAdOCab4zgwDGPiGw+SDoFDunTkTRumYTAWoZrRNA2Kokx84wSYNCGqA3N4wHtcnmcCAKosoxjXvWsGB5NDCIHt27djaGioZl/z2lOmwRFuImzdunU1+7rU2mRZxrx586Dr+sQ3E1FLGzFGvMcinUc5VaKO2uzU2jtruKrmVY9Y5LpTemA7AorMWIRqh7EIEU2oVGli2JWNe9Nx90VGdA1TRidNeJh00hiGgXXr1sFxnIlvngRfPmkKTFtAksBYhGqqo6MDvb29gVZ/+4tJE6I6KAzt8h4bVuWHla7K3hB4XdGhK3yzMRnKmxTTpk1DPB4/qG+a+2xnFoZtQ5IkzJ2WrM3XpJbmOA62bt2Kbdu2Yfbs2fw3R0R7lS6mvcfC1zJBV4NJk0jHlJqtqZnVIxaRd2dRKM2qmdOTgsyfC1RljEWIaJ+UKk2KdtF7qrwvMqKrmBsJblWm9FTt1tbEhBDYtm0bFEXBrFmzalIVqA3mkC26bdhmTU2OmZ1HNNmEEMjlcujv7wcA9PX1HfCvxaQJUR0YQ7srj0cnTeIRAEBbpI1vNCaBbdveJkV3d3fNvm40ZsMsuOXGmh5hcEA1MXXqVGzduhWWZUHTtHovh4hCzJ80cTIFlM90+pMmuqJzpskkqFssErVRFG4somg6IurBtykgmghjESLaq2IRKLWGKlrBpEleVWDLMpLRylalJmuIqtExvwztP8uykMvlMH36dMTjtZkTE406yJUqihRNR1TnNjRVXyzmVqf19/dj2rRpB9yqi7t4RDVWtIpeOSoAGL72XLpSqTRpi7TVfG3NqNw3vFZBQZnm6wlv2rUpfSUqt8KwbbvOKyGiMHOEg4xRiUWQrlSaRHxJ/ogSAVI83Xmw6hWL6L6/S9MSe7mTaPIwFiGivfLthQQqTWwH2dKGetJXacLDpJOn/H25lu0TNV8sYtiMRah2ynH3wcwSZNKEqMYyRgZ6vtK7sziq0qTIpElV1DrQ8gcHJoMDqhG+oSCifZE1snBEJf6QRyqDOf2VJhGVSZPJVN9YhAc4qDYYixDRXpVac9mODcuxvKcNy8GI7lanjU6a0OSq5fdpxiJUL5Px75xJE6IaSxfTiPiSJoH2XEqwPRc1ruCJigMLDk4++WR85jOfmaQVNYf169dDkiSsXr16v14nSRLuu+++qqxpf8ydOxcrVqyo9zKIqMX5W3MBgDxSOekZSJooESCZrNm6aHJp6sFvVDAWGYuxCBHRQShVmvirTIBy0sRNlqSiTJo0C30SOnAwFhmLsUhtsJkcUY2li+lApUl5Q12VJciyxPZcVfKzNT9DzsnV7OsVTRu7su7fczKioj02tqdze6QdFxx9Qc3WNNrKlSuxfPlyDA4OoqOjo+W+fjXddttt+MxnPoOhoaHA80899RQSiUR9FkVEVDI6aaLlirAASJIbj5Sx0mRy1ToWMSwbO0fcWCShK+iIj23HwViEsQgRUU2Vh8Bbo5ImdiVpwkqT6rlv/X3Qd+s1mzll2Q52ZNy/67imoDPBWCRsX7+aGj0WYdKEqMbSxTT0nPtDQwgBs1RpUj7ZWYwzaVINw8VhZKzMxDdOEst2kC66GxWGrcAGB2GSOxyViKje/EkT2bIhCm6vX12RA6XsWjwFcJDzpKl1LGI7AumiG3MWbBmOVLse5hRejEWIqK72VmkS0RDVFKi+rg3cF5lcGSMDuSBDtWqzHSxEJRbJWzKEzFiEGicWYXsuohpLF4a9ShPTFihPu4ioMoQEmFEmTZqB4jupa4sDn2liWRYuu+wydHR0oLu7G1/+8pchfL+eYRi46qqrMGPGDCQSCbz1rW/FypUrvc9v2LAB73nPe9DZ2YlEIoElS5bgwQcfxPr167F8+XIAQGdnJyRJwsc+9rFx13Dbbbeho6MDDzzwABYvXox4PI5zzz0X2WwWP/3pTzF37lx0dnbiU5/6VGDo589+9jMcd9xxSKVS6O3txf/7f/8P/f39ALDXr+84Dq6//noccsghiEQimD17Nv7lX/4lsKY33ngDy5cvRzwex1FHHYU//elP+/XnumbNGpxyyimIxWLo7u7GJz7xCYyUTj2V/eQnP8GSJUsQiUTQ19eHyy67zPvcd77zHSxduhSJRAKzZs3CJZdc4r1+5cqVuPDCCzE8PAxJkiBJEq655hoAY8tQN27ciPe9731IJpNoa2vDhz70IezYscP7/DXXXIOjjz4ad9xxB+bOnYv29nZ85CMfQSZTu003Imo+gaRJpgDbcX+u+FtzAYDe3lXTddHk8oUicBzGIoxFrgHAWISI6mycShMhhDsIXlMDVSYA90UanSRJkEsHcmzGIoxFGiwWYdKEqMayw7sgl35YGKOGwJtRHaL0DpfBQWPzBwcHs1Hx05/+FKqq4s9//jNuvPFGfPe738WPfvQj7/MXXnghnnjiCfziF7/Ac889hw9+8IM488wz8dprrwEALr30UhSLRTz++ONYs2YNrr/+eiSTScyaNQt33303AOCVV17Btm3b8L3vfW+P68jlcrjxxhvxi1/8Ar/97W+xcuVKfOADH8CDDz6IBx98EHfccQduueUW/Pd//7f3GsMwcN111+Gvf/0r7rvvPqxbt84LAPb29a+++mpcf/31+MpXvoIXX3wRP//5z9HT0xNYz5e+9CVceeWVWL16NRYtWoS//du/hWVZ2Be5XA5nnnkmOjs78dRTT+GXv/wlfv/73wd++P/gBz/ApZdeik984hNYs2YN7r//fhxyyCHe52VZxo033ojnn38eP/3pT/GHP/wBV111FQDghBNOwIoVK9DW1oZt27Zh27ZtuPLKK8esQwiBv/mbv8HAwAAee+wxPPzww1i7di0+/OEPB+5bu3Yt7rvvPjzwwAN44IEH8Nhjj+Gb3/zmPv1eiYjG40+aWEOVdlGjkyaRju6arYkmnyRJ3iGOgznAwViEsQhjESKaNONUmpi2gBDASEQLzDMBuC/SDMqHOBwhAomO/cFYhLFIPWIRtuciqrH80E7vsX9AuK4o3jwTVVYRU2M1XxtNLlkCHOFuVAghAi1P9tWsWbPw3e9+F5IkYfHixVizZg2++93v4h/+4R+wdu1a/Od//ic2b96M6dOnAwCuvPJK/Pa3v8Wtt96Kf/3Xf8XGjRtxzjnnYOnSpQCA+fPne792V5d7gnjatGkT9s40TRM/+MEPsGDBAgDAueeeizvuuAM7duxAMpnE4YcfjuXLl+PRRx/1frh9/OMf914/f/583HjjjXjLW96CkZERJJPJcb9+JpPB9773Pdx000244AK3r+mCBQvw9re/PbCeK6+8Eu9617sAAF//+texZMkSvP766zj00EMn/DO98847kc/ncfvtt3t9NG+66Sa85z3vwfXXX4+enh788z//M6644gr80z/9k/e6N7/5zd5j/yC6efPm4brrrsMnP/lJ3HzzzdB1He3t7ZAkCb29vXtcx+9//3s899xzWLduHWbNmgUAuOOOO7BkyRI89dRT3tdzHAe33XYbUqW5Ah/96EfxyCOPjDllQkS0rzJG5VSWPZz3HutKMGkS65xWszVRdSiSBBvuZpQjhHegY38wFmEswliEiCbNOJUm5X2REU3FTFaaNB1FlmCVDpI6AlD2PxRhLALGIvWIRVhpQlRjxtDuymOrUrKnqzKKviHwB7LBTuHib9F1oMUmxx9/fODfwrJly/Daa6/Btm08++yzEEJg0aJFSCaT3sdjjz2GtWvXAgA+/elP45//+Z/xtre9DV/72tfw3HPPHdA64vG4FxgAQE9PD+bOnYtkMhl4rlxmCgCrVq3C+973PsyZMwepVAonn3wyALf0ck9eeuklFItFnHrqqXtdz5FHHuk97uvrA4DA196bl156CUcddVRg8Njb3vY2OI6DV155Bf39/di6dete1/Doo4/itNNOw4wZM5BKpXD++edj9+7dyGaz+7SG8jpmzZrlBQYAcPjhh6OjowMvvfSS99zcuXO9wKD8+93X3ysR0Xj8lSaOL2kS8VWaaLIGpa29puuiySf7Y5EDDEYYi4yPsQhjESI6AONUmpQ7cIxEgu25FElBXIvXdn006QKtyxmLMBYZZx1hjUWYNCGqIcux4KSHveviqPZcRjwCgKcpmoU8CcHB3jiOA0VR8Mwzz2D16tXex0svveSVdF500UV444038NGPfhRr1qzBcccdh+9///v7/bW0UYOAJUka9znHcf9NZ7NZnH766Ugmk/jZz36Gp556Cvfeey8Atzx1T2Kxfauw8n9tyWuD5uzp9oC9Vf1IkjThGjZs2ICzzz4bRxxxBO6++24888wz+Pd//3cA7smTfbWndYx+fm9/zkRE+8sdyFlJmoiMr9JEVbzHETUC+N6YUGNSfD9PDqZF154wFql8XYCxCBHRXpkmUCgAGFVpYjkoKjJMRUHS156Lh0mbg7/K1WEswlhkH9cRhlikJkmTm2++GfPmzUM0GsWxxx6L//3f/93jvStXrvQGxPg/Xn755Voslaiq0sW0NwQeGN2eS/baczFp0hyUSQgOnnzyyTHXCxcuhKIoOOaYY2DbNvr7+3HIIYcEPvzlj7NmzcLFF1+Me+65B1dccQX+4z/+AwCg6+6/N/+Qssny8ssvY9euXfjmN7+JE088EYceeuiYUwDjff2FCxciFovhkUcemfQ1lR1++OFYvXp14PTDE088AVmWsWjRIqRSKcydO3ePa3j66adhWRb+7d/+DccffzwWLVqErVu3Bu7RdX3CP9fDDz8cGzduxKZNm7znXnzxRQwPD+Owww47iN8hjYexCJErb+VhOZVex9JIwXvsb88VUZg0aQay793egR7gYCwy+RiLtCbGItTySq25HOHAdCqbqobtIKu7G6L+ShPuizSHyag0YSwy+RiLTKzqSZO77roLn/nMZ/ClL30Jq1atwoknnoizzjprr2VIQGUATvlj4cKF1V4qUdWli2noubFlqECpPVecSZNmMhnBwaZNm3D55ZfjlVdewX/+53/i+9//vtdPctGiRTjvvPNw/vnn45577sG6devw1FNP4frrr8eDDz4IwO0x+bvf/Q7r1q3Ds88+iz/84Q/eD545c+ZAkiQ88MAD2LlzJ0ZKQexkmD17NnRdx/e//3288cYbuP/++3HdddcF7hnv60ejUXz+85/HVVddhdtvvx1r167Fk08+iR//+MeTtrbzzjsP0WgUF1xwAZ5//nk8+uij+NSnPoWPfvSj3mC1a665Bv/2b/+GG2+8Ea+99hqeffZZ7yTKggULYFmW93u744478MMf/jDwNebOnYuRkRE88sgj2LVrF3K53Jh1vPOd78SRRx6J8847D88++yz+8pe/4Pzzz8dJJ52E4447btJ+v8RYhMjPX2UCAHLGlzTxteeKqBHA12qAGlOgVegBHsZjLMJYhA4eYxEiVFpz+apMAHdfZER3kyWpKJMmzWYyKk0YizAWqYeqJ02+853v4O///u9x0UUX4bDDDsOKFSswa9Ys/OAHP9jr66ZNm4be3l7vQ1GUvd5P1AjGVJqUkiaKLEGRJVaaVFF7pB1dsa6afnTHu9AW6fQ+Rn++PTJxr/jzzz8f+Xweb3nLW3DppZfiU5/6FD7xiU94n7/11ltx/vnn44orrsDixYvx3ve+F3/+85+9fpC2bePSSy/FYYcdhjPPPBOLFy/GzTffDACYMWMGvv71r+MLX/gCenp6cNlll03an/fUqVNx22234Ze//CUOP/xwfPOb38S3v/3twD17+vpf+cpXcMUVV+CrX/0qDjvsMHz4wx+e1F6V8Xgcv/vd7zAwMIA3v/nNOPfcc3Hqqafipptu8u654IILsGLFCtx8881YsmQJ3v3ud+O1114DABx99NH4zne+g+uvvx5HHHEE7rzzTnzjG98IfI0TTjgBF198MT784Q9j6tSp+Na3vjVmHZIk4b777kNnZyfe8Y534J3vfCfmz5+Pu+66a9J+r+RiLEJUMSZpkq1sXGi+yZysNJl89YhFpsS7vTgkFelgLMJYJICxSO0wFiFCZQi8PTppYntJk2Sk0oKH+yKTL6Wn0BkduzdR1VgkUYlFkhpjEcYiQWGORSQhqtBQrsQwDMTjcfzyl7/E+9//fu/5f/qnf8Lq1avx2GOPjXnNypUrsXz5csydOxeFQgGHH344vvzlL2P58uXjfo1isYhisfINN51OY9asWRgeHkZbG7/BUrj838b/w+6bbkB7/zCEEPjL+gEIAcQ0BUfN6sCz73oT0lPb8JEjPoJDpxxa7+U2hUKhgHXr1nml8LVkWA5e3u5uTrVFNcydkpjgFUQHZ2//3tPpNNrb21vu52MtYhGA8Qg1jqe3Po0HXn3Au1b+5X5oRRO6IuNNczq95w+dcih6/3kFUOOfnc2onrGI7Th4YasbiyQjKuZPZfUQVRdjkbEYixCV/PnPwP/8D3aM7MBLuyoDnldvHMTjPV34v8UzcMnJh3jPn3XIWXjrzLfWY6VNqV7xiBACz29JQ0AgpilY2MNDOVR9e/r3vj+xSFUrTXbt2gXbtr2ynrKenh5s37593Nf09fXhlltuwd1334177rkHixcvxqmnnorHH3983Pu/8Y1voL293fsoZxGJwshfaWI5AuWUZbkdBitNmoumSCif2TVtDsskqodaxCIA4xFqHP5KE9uwoBXdnuL+1lwAoEcTQCRS07XR5FNk2ZuxZjAWIaoLxiJEJeNUmgghULQdZHU1MM8E4L5Is5AkCWqpmtm0q3Zun2jSqRPfcvD80+4B95vi6OfKFi9ejMWLF3vXy5Ytw6ZNm/Dtb38b73jHO8bcf/XVV+Pyyy/3rsunKYjCKF0YxtS8GyD455lEmDRpSm5wIMO0HQYHRHVWzVgEYDxCjcOfNLGG897jMUmT9i5gD/9HqLFoqgzbtGHaYq/f+4iouhiLUMsbZ6ZJ+TDpCJMmTU0v7YtYjgPHEZBlxiIUflWtNJkyZQoURRlzeqK/v3/MKYu9Of74472eaaNFIhG0tbUFPojCamRkAEopWTJ6CLwZ0eAoMmRJRkJjG6dmoSnut9lycEBEtVWLWARgPEKNI5A0GaoMYxydNIl2Tq3Zmqi6yrGIEAIWYxGimmMsQlQyTqVJeV9kJKIFhsADTJo0k3IsArALBzWOqiZNdF3Hsccei4cffjjw/MMPP4wTTjhhn3+dVatWoa+vb7KXR1RzhaFd3uOi7weFrsiBKhOeAGweOoMDorpiLEIUFEiaZCqVJhHfzytVVqG2ddRyWVRFmlKJKxmLENUeYxGiknLSxFdpUm4dOaKpgSHwsiQjofMwabPQVMYi1Hiq3p7r8ssvx0c/+lEcd9xxWLZsGW655RZs3LgRF198MQC3hHTLli24/fbbAQArVqzA3LlzsWTJEhiGgZ/97Ge4++67cffdd1d7qURVZTs2zPSQdz260sSIugECT1M0l9HBQURT6rgaotbEWITIJYQIJE2cdMF77K80iSgRIMmB4c0ieICDlSZE9cBYhAhANgtg/EqTnK4i6as0SekpyFJVz3lTDfkrTQzGItQgqp40+fCHP4zdu3fj2muvxbZt23DEEUfgwQcfxJw5cwAA27Ztw8aNG737DcPAlVdeiS1btiAWi2HJkiX4zW9+g7PPPrvaSyWqqoyRgVYwvOvRSZMRzjNpSgwOiOqPsQiRq2gXYdiVWMQZ2UPSRI0ACZ7ubBZsiUFUf4xFqOUJAWSzcIQTiEUMy4EjScirSmCmCfdFmgtjEWpENRkEf8kll+CSSy4Z93O33XZb4Pqqq67CVVddVYNVEdVWupiGXjC9a1aatAYGB0ThwFiEKNiaCwDESOWk55hKEyZNmkYgFrEYixDVC2MRammFAuAEEyaAuy+S0xRAkgIzTbgv0lx0tgqlBsRaN6IaSRfT0PK+ExWlHxSyJEGRJJhRVpo0IwYHREQUFqOTJrIvaeLfWGelSXPxtwo1GIsQEVE9lFtz+eaZAO7PpZzmJktYadK8NLYKpQbEpAlRjQwXhr1KEyGEV2miqzIkSWKlSZNicEBERGExXBgOXMs5d+NCU2TIUmVjnZUmzYWxCBER1d0480wAoGi5SRNVkRHxVb1yX6S5KLLkxZoGq16pQTBpQlQjQ4Uhb6aJ5Qg4wn3TWg4MDM40aUr+4IAtMYiIqJ4GC4PeY8t2oOTcuMS/SeFeM2nSTGRJgiq7f8eseiUiorooJU0KVmWemnuY1EZWV5GKqJB8Bzi4L9JcJEnyDnGYtgMheIiDwo9JE6IaGSwMepUmRbPyhrW8UWGWkiYd0Y6ar42qxx8cGAwOiIiojoYKQ97jdMFC3LQAjE2aRNUokyZNptyiy7Id7+AOERFRzZSSJnkz7z1l2gKOALKaivaYFrid+yLNRyu1LneEgM1YhBoAkyZENTKYH4RemmlSsGzv+aimAACMqAZN1pDQuEnRbBotODAMY+KbiIio4QzmK5Um6axRSZqUYpGySCQBRCI1XRtVl146wCHgJk7CjrEIEVGTGRkBEKw0KZb2RXL62KRJZ6yzdmujmgi0C7W4L0Lhp058CxEdLCFEoD3XuJUmUR3dsc5ASSpVwb/+K2DbE983ibrzJuKG+zVFUgcUGVAU4ItfnPC12WwWn/zkJ3HPPfcglUrhyiuvxK9//WscffTRWLFiBSRJwr333ou/+Zu/8V7T0dGBFStW4GMf+xgAYMuWLbj88svx0EMPQZZlvP3tb8f3vvc9zJ07FwDwsY99DENDQ3jrW9+K73//+9B1HR//+Mfxy1/+EmvWrAms59hjj8W73vUuXHvttZPyZ0NERLXjb89VSOdRjjiivkqTiBKBkkwBjEeqpw6xSGfBRKRY+poJHVAZixARUQ2N056rvC+S0xR0+JImESWCmBqr7fpaTR1ikY6iBa3gHtiR4hqgKYxFKNRYaUJUAxkjA8cyoRXdHxBFX6VJRFNgqzJsVWYJai3Yds0/FMeB5NiQHBuOaVU+tw8+97nP4dFHH8W9996Lhx56CCtXrsQzzzyzz7/dXC6H5cuXI5lM4vHHH8f//d//IZlM4swzzwycnHjkkUfw0ksv4eGHH8YDDzyAj3/843jxxRfx1FNPefc899xzWLVqlRd0EBFR4yhaReTMnHdtDFUe+ytN2JqrBuodi1iMRYiIqMayWQghAkmTcgeOrKaizZc06Yh28DBptdUhFlFLcQhjEWoUrDQhqoHB/CC0ouldF3wDwaOqDDOqA5KEzihLUJuRrwp1v9pzjYyM4Mc//jFuv/12nHbaaQCAn/70p5g5c+Y+/xq/+MUvIMsyfvSjH3mB56233oqOjg6sXLkSp59+OgAgkUjgRz/6EXRd9157xhln4NZbb8Wb3/xm73UnnXQS5s+fv89fn4iIwsE/zwQArHSlp7h/pklMiwHJZK2WRTWiyJXNJ9thLEJERDWWzaJoFyFQ+RlUqTQJtudia67mJPtiEYexCDUAVpoQ1YB/CDwAFE03m67IElRFhhF1AwQGB83pQIODtWvXwjAMLFu2zHuuq6sLixcv3udf45lnnsHrr7+OVCqFZDKJZDKJrq4uFAoFrF271rtv6dKlgcAAAP7hH/4B//mf/4lCoQDTNHHnnXfi4x//+D5/bSIiCg9/ay4AsNKVk57+pAkrTZqT7Duxuz8HOBiLEBHRpMhmA0PggUqlSU5X0RarnOnmYdLmpARikX1/HWMRqhdWmhDVwFBhCFppCLwQAsVSpYl/ngnA4KBZHWhwIPZhU0OSpDH3mWYlQec4Do499ljceeedY147depU73FinA2y97znPYhEIrj33nsRiURQLBZxzjnn7PtvgIiIQsM/BF4IATHiJk0iqhxogcGkSXNSDvAAB2MRIiKaFNlsoDUXAG9fxI5HEFErrUJ5mLQ5yQdY9cpYhOqFSROiGhjMVypNioHWXG5gUK404UyT5nSgwcEhhxwCTdPw5JNPYvbs2QCAwcFBvPrqqzjppJMAuD/gt23b5r3mtddeQy5X6VP/pje9CXfddRemTZuGtra2/Vq3qqq44IILcOuttyISieAjH/kI4vH4fv0aREQUDv5Kk6LleIc5/PNMALiDV5k0aTq+UISxCBER1ZZlAYVCIGniCAHDcmAoMhLJSOB27os0J1mSIEmAEO7f/75iLEL1wqQJUQ0MFgahFdzNCX/SJKK5lSZGrFRpwhMV1acoE98zyWQAkmLBEYAtSe4a9mEdyWQSf//3f4/Pfe5z6O7uRk9PD770pS9BlittVE455RTcdNNNOP744+E4Dj7/+c9D0yr9YM877zzccMMNeN/73odrr70WM2fOxMaNG3HPPffgc5/73IR9QC+66CIcdthhAIAnnnjiwP4AiIio7vwzTYbzJhKmBcCdrebHSpMaqEMsIgGQVRu2I2BLYCxCRES1U9q8zluV9lxGaV8kqweHwAPswFETdYhFAEBWVVi2G4sIWYbEWIRCjEkTohoYzA9iarnSpDTPBIBXgmrGdCS0BHRFH/f1NIm++MW6fNmhHRkUTBuSJGHK9LZAK5S9ueGGGzAyMoL3vve9SKVSuOKKKzA8POx9/t/+7d9w4YUX4h3veAemT5+O733ve3jmmWe8z8fjcTz++OP4/Oc/jw984APIZDKYMWMGTj311H06YbFw4UKccMIJ2L17N9761rfu/2+ciIhCwd+eazhvIl5KmvjbYciS7MYiTJpUV51ikXT/CLKG+/c+ZXp7oBJ2bxiLEBHRQclmASBQaVIo7YvkNBXt0WDShJUmNVCnWGRkVxbp0t5YV18bNGXfRm0zFqF6YNKEqMosx0LGyGBGqQ1Gwd+eq1xpEtVYZdLkdEVGwbQhhIDlCGjKvm1UJJNJ3HHHHbjjjju8537zm994j6dPn47f/e53gdcMDQ0Frnt7e/HTn/50j1/jtttu2+PnhBDYsWMH/vEf/3Gf1ktEROEjhAi050rnTXQb7mZFueoVcKtMJEli0qRJaaoMuOEoDNtBVN63U6aMRYiI6KCMjAAIJk3KHTiymop2X6VJSk9BU4JJFGoe/n0Q03b2OWnCWITqgUkToiort8Mo9w4ft9IkqmMaT1M0tQMNDuqpv78fd9xxB7Zs2YILL7yw3sshIqIDNGKMwHIs7zrYnquycR5Vo+4DJk2a0uhYJKrVpzXH/mAsQkTUBLJZ2I4Nwza8p/yVJm2xytYkq0yam+ZrC2taDtAAzVYYi7QuJk2IqqzcDmO8QfAR1Vdpwr6dTc2fJGmU4KCnpwdTpkzBLbfcgs5O/vskImpU/nkmwKj2XL5Kk5gacx8wadKUdH8sYu/7ANZ6YixCRNQEstlAlQlQ2RfJ6Qp6fZUm7MDR3Pz7IgZjEQo5Jk2IqqzcDqM8CL58okJXZK+XtBnTGRw0Of+JioMNDlauXHmQq9k3QjRGEENERHvnb80FAPlMAbrtQJYkqL65FlE1CsRidRsOStUVOMBhO3u5c2KMRYiIaJ9ls4Eh8ABQNMtJExUp30wTHiZtboxFqJGEvz8MUYMbKgwBQkAvmLAdd54FUDnZKSTAjLDSpNlNZnBARES0P/xD4B0hYGaLANzZapI0KmnCKpOmNabqlYiIqBbGqTQpWKW25YkoFN8BDh4mbW76qFahRGHGpAlRlQ3mB6GYNmTbQdEaO8/E0jUIWWLvzibH4ICIiOrFX2kyUrQQL7UMjajBipKYFmPSpIn5Z5oYjEWIiKhWRiVNLNuBXTpMqqRigVu5L9Lc1AZsFUqti0kToiobLAx680wKZuUNalSrzDORJRnt0fa6rK9V1LukksEB1UK9/50TUTj5Z5oM58afZwKw0qTa6v09WpElyKXKIsYiVC31/ndORCGUzSJvVtpz+ee8qu3RwK3swFF99fw+LUuSV/nKAxxUTZPx75xJE6IqEkJgMD/ozTMZr9LEiOloj7RDlvjfsRo0ze2Pmsvl6roOBgdUC4bhfq9ROI+AiHz87bnSBRMJw02aRH2VJqqsQpVVJk2qICyxiOSLRUzb4eY2VQVjESIaY1SliX9fRG+vVJookoJUJFXTpbWS8vfl8vfpeinHIpbtwGEsQlVSjrvLcfiB4CB4oioqWAUU7SJSpUqToq/SpHy6k0Pgq0tRFHR0dKC/vx8AEI/HA/3ba7oWx4Rh2TAtIJtTochMlNHkcRwHO3fuRDweh6ryxzsRuWzHRrqY9q6H8+NXmsTU0qYFkyaTLlSxiDAhLAs2gEw2D11lLEKTh7EIEY0hBMTISCBpUu7AIQBEfEmTjmgHD5NWkaqqiMfj2LlzJzRNg1yn/QjZNiEsd48sM5JDRGOSnSaPEAK5XA79/f3o6Og4qEMcjGSIqqjcQ1zPu5n8gu9ERfl0pxHV2Lezynp7ewHA26yol8GcgWyx9G8gE+FGBU06WZYxe/bsum3GEVH4DBeHIVA5xZfOm5hhlitNKj+HomqpPQaTJlURllhkOG8iU3D//p20jig3KmiSMRYhooBCAaZZgC0qeyHl9lxZXUV7XPee575IdUmShL6+Pqxbtw4bNmyo2zoyBRPDeTcWsYZ1xHXGIjT5Ojo6vPj7QDFpQlRFObNUDlZKmhi+3p3lYZxmVGdwUGXl4GDatGkwTbNu61i9agtuevQ1AMBnT1uEdx85vW5roeak63rdTgwRUThljWzgeqRoIWG6Gxe6rz0XkybVFZZY5KEXtuP6R18GAHziHfPx4TfPrttaqDkxFiGigGwWph38uVfeF8lpKlKRSusc7otUn67rWLhwYV1bdD3x+i5c8+DzAIC/e+scXPj2eXVbCzUnTdMmpU0okyZEVVQedlYeBG877klPVZa801dGVENC4wZFLSiKUtf+ynOndWBLxt2oen57Hue+JTrBK4iIiA6Ovx0G4LbEiBsWJAmQfQfBNaW0acGkSVXVOxaZ39vpxSLPbcvjgihjESIiqqJsFpZjBZ6ynErSZIqvVWhCZwxSC7IsI1rHn/8L+7q8WGTV1iw+yViEQopHQIiqqLxRUR4Eb5WTJkrlv54Z0yunO6mpLepJeo9f3ZGp40qIiKhV5K184LpoOYibVuAABwBocilpkkyCmtch05JesoyxCBERVd04SZPyYdJCRA3sjXBfpDXM7IwhVmoPyliEwoxJE6IqKm9U6AUTQggvOFB8RzuNqIaYFhv39dRcupMRTElGADA4ICKi2hhdaVK0bMRNC8qo9jmqXCpAZ6VJU4tqCuZ2u3/Hr/VnvNiUiIioKrJZmE6wPZdluz97jKgeeD6mcl+kFciy5B0o3TCQQ96wJ3gFUX0waUJUReWNCj1vBN6Uqr6kiRllpUkrWdzrBge7RgzsGinWeTVERNTsyq1CAcARAqZpI2FYgVgEKCVNFAWIRGq9RKqxRT0pAG6rto0DuTqvhoiImtrIyDjtudy9ETsWTJpwX6R1lGMRIdxDHERhxKQJURXlzTwgBKIjBS8wAIJJEyOm80RFCykHBwDw6nYGB0REVF3+SpOi6aCtYEICxk+aJJOAJIGa26LeSizyCmMRIiKqpqGhQNLEEQKOcPdGzHjwoAY7cLSOxYxFqAEwaUJURQWrgEjOgGw7waSJUhoCH9NhawpPVLSQxb6kySts0UVERFXmn2lStGx0FtwqR0UJJkc0RQO6umq6NqoPfyzCdqFERFRVAwOBpIm/A0ehLbgPwn2R1rGIsQg1ACZNiKoob+URy7ibFbZdCQ7KfcTzqRgkSAwOWoj/dCeDAyIiqjZ/pUnBdNCZNwAA6ngzTZg0aQnlVqEAD3AQEVGVDQzAtCszTSzbnzSJB25lB47WEag02TFSx5UQ7RmTJkRVVLAKiKXdpInlON7z5ZYY+bYYImoEElthtIyF03wbFSxDJSKiKvPPNClati9pEow9FElh0qRFzOlOQFfct4FsFUpERFVTKADZbKDSpLwvYsoynGSwPRcPk7aOaakI2mMaAMYiFF5MmhBVUd6sVJqMN9Mkn4rxNEWLSUU1zOhw/85f3TECIcQEryAiIjpwYypNSu25/EkTVVbdAxxMmrQETZExf2oCALBuVxZFy67zioiIqCkNDgLAuO25BmM6IprqPS9LMnQlOBiempckSV670O3pAoZz5gSvIKo9Jk2IqihYaTJ2pkm+LcbTFC2oXIo6UrSwdbgwwd1EREQHbsxMk1KliX+miSa7J/2YNGkd5VjEcgTW7crWeTVERNSUBgYAYFSliT9pUtmSjKpRduBoMYt87UJf7We1CYUPkyZEVeIIB0W7iFg6B2DPM01iGitNWk1g6BlLUYmIqIoClSaGjY7C2Jkmqlw66dnZWdO1Uf34YxG2CyUioqrYvRsAYDpjZ5oMRnVEVcV7nh04Ws9ixiIUckyaEFVJwSoAQvjac40/04SVJq2HA1iJiKgWbMeGYRvetZrJQy2d8BzdngupFKCzLUar8G9UvMpYhIiIqmGcShO7tC8yGIuMqTSh1rKIsQiFHJMmRFWSN/PQ8wYUyw0KRs80MSMaLF3liYoWxNOdRERUC/4qEwCIDFdadSmjkyZszdVSyu25AMYiRERUJQMDcIQDR1QOkJb3RYZioypN2IGj5fj3RV5mLEIhxKQJUZX455kAlYFngNtHPN/mBgU8UdF6FkxNotyu9Q32EScioirxzzMBgEgm5z1W/TNNFI1JkxYzoyOGaOmEL2MRIiKqioGBQJUJ4JtpEh0704RaS2dCx5RkBAA4X41CiUkToirJW3mvNRdQ6d0JAIokIZ9ykyY8UdF6opqC3jY3KNw0kJvgbiIiogMzutIknq5cj5lpwqRJS5FlCbO74gCAzQN5OL7DPURERAfNMICREZi2GXjasgUsWUI6onGmCWFOtxuL7MwUkTfsOq+GKIhJE6IqGV1pUp5posoSJImVJq1uVmmjYiBrIFMwJ7ibiIho/+XNYKVJ+TCHBMDXnYtJkxZVTpoYtoMdmcIEdxMREe2HceaZAO5Mk6GoDkgSK03Ii0UAYNMgD5RSuDBpQlQleXNUpcmowatepQlPVLSkQHAwkN/LnURERAdmdKVJYsS9VkoHOMqYNGlNs3yxyMbd3KggIqJJtIekieUIDMZ0KLLk7Y0A7MDRqhiLUJgxaUJUJf5KEyGEN9NEUdz/dqw0aW3+pMlGtugiIqIqCMw0EQLJUtJEVYJvATRZAzo7a7k0CgHGIkREVDV7S5pEI4ioSuAAB/dFWhNjEQozJk2IqiRv5rxKE/8Q+DGVJjxR0ZKClSYMDoiIaPL5K03UXBGy5faK9p/sBAA5mQKi3KxoNYxFiIioakpJE9MJtqK2S5UmETW4HckOHK2JSRMKMyZNiKrEzAxDNd3NCWtU0sTSVZgRFQBPVLSqWQwOiIioyvwzTdTBrPdYGZU0UaZMrdmaKDy4UUFERFUzTqVJuQPHYExHVAtuR3JfpDXxAAeFGZMmRFVi795ZeexLmiiy5FaZlEpReaKiNc3p5kYFERFVl7/SRPElTUZXmqjd02q2JgoPHuAgIqKqGSdpUj5MWm7P5ccOHK1pWiriVR0xFqGwYdKEqFp2D3gPA5UmiuzNMwGAiBqp6bIoHLoTOuK6GyjyRAUREVWDf6aJNlT5WTNmpsm03pqticIjqinoaXPj0I0D+QnuJiIi2kemCaTTAMYmTWxJQjqqsdKEAACyLHmHODYO5CCEmOAVRLXDpAlRtQz6kia24z1Wy5UmcAMDWeJ/w1YkSZJXirp5MB+oRiIiIpoM/kqTQNJkVKWJPqWnZmuicCnHIrtGisgZ1gR3ExER7YPBQe+haVdmmti2wHBUh5CksZUm7MDRssqxSNFysDNTrPNqiCq4W0tUJdLQsPd49EyTcqUJT1O0tvKJCsN2sCNdmOBuIiKi/eOfaRJJVx77Z5qosgqpu7um66LwmBXoJc5qEyIimgS+pEmw0sTBYEwHgECliSzJ0BW9duujUOGMNQorJk2IqkQqlaMCY2eaFJJusoSnKVobgwMiIqqmQKXJSOWxOippgo6OWi6LQoSxCBERTTrfXsjo9lzDEQ0AApUmUTUKSQpWwVLr4Iw1CismTYiqwBEOlExl4OromSaFhNs/mpUmrY0bFUREVE3lpIlkO1Dzhve8f6aJokWAeHzMa6k1MBYhIqJJt5ekSTrqVpREfJUm3BdpbYxFKKyYNCGqgqJZgJ6v9GIcPdPEiDNpQsHggMPgiYhoMjnCQdF2Y5FI3gjEIv72XCKVAni6s2UxFiEiokmXyXgP/UkT2xYY0VUAYytNqHUxaUJhxaQJURXkRwahWJXNCX+liRPT4ZROeMY0tudqZSxDJSKiavG35opki4FWoYFB8G2pWi6LQoYbFURENOlKlSaOcGAL23vachxkSu25/DNN2La8tc3qqvz98wAHhQmTJkRVYAzuClz7NyqsVOUUBU9UtLaZnZXggBsVREQ0mfxD4PVcMdgq1Jc0kds6arksCpmpqQgiqvuWkLEIERFNilLSxF9l4l4LpPXxZ5pQ64rrKqYk3W4sjEUoTJg0IaoCYyCYNPFvVNi+pAlPVLS2qKagt83998ATFURENJkClSajkiaKUkmaSG1tNV0XhYskSV61yaaBHBzfvxMiIqL9JsQekya2LcavNGEHjpY3u1RtsiNdRMG0J7ibqDaYNCGqAnNod+C63EdckSUYCVaaUEV5o2LXiIFs0ZrgbiIion2TtyqVJpFsEbZ/polvhona0VXTdVH4lGORouVg50hxgruJiIj2olgEDAPA2KTJiCLDKrUqZ6UJ+fnbhW4e5IFSCgcmTYiqwBoeDFyX23OpsoRiTPee54kK8s812cTggIiIJsmeKk1UWYLkS5ooHZ01XxuFC2esERHRpPENgTdtM/CpYdUdAi9LEjRf1Ss7cBBnrFEYMWlCVAW2L2kihPBtVMgoJiLe53iiggLBwW4GB0RENDn8M00i2WDSxE/r6K7puih8GIsQEdGkKbXmAsZWmgxqbtIkosqBAxzcF6FZjEUohJg0IaoCe6iSNLGFb/CqIqEYryRNeKKCZndzGDwREU0+f6WJnit6Va+KEgz/mTQhf9JkA2MRIiI6GHtNmrgtuaKaEnieHTiIsQiFEZMmRFUg/IGC7Ru8KkusNKGAOd0J7/Handk6roSIiJqJN9NECKgjlQSKv9JESEC0c0qtl0YhM3dKZaPijZ0jdVwJERE1vD0kTYQQGCzNMYmowa1I7ovQ3CmVfZE3uC9CIVGTpMnNN9+MefPmIRqN4thjj8X//u//7vX+xx57DMceeyyi0Sjmz5+PH/7wh7VYJtHkyVQChfLJTqA008RfacITFS3vkGlJ7/FrOzJ7uZOIDgZjEWo15UoTvWDCtipD4P1JEyOmI6rHx7yWWsuc7oTXW/61HUyaEFULYxFqCf6ZJk5lponlCGR0DQAQGV1pwg4cLW9aKoJU1G3fxn0RCouqJ03uuusufOYzn8GXvvQlrFq1CieeeCLOOussbNy4cdz7161bh7PPPhsnnngiVq1ahS9+8Yv49Kc/jbvvvrvaSyWaHKYJkauUE1q+pInQVdi66l3zRAW1RTX0tbv/Dl7dkYHwtXMjosnBWIRaUXmmiX+eCeBWvZYV4xFuVBA0Rca80gnPN3aNwLSdCV5BRPuLsQi1jD1UmtiOQCbiJk2irDShUSRJwqKeFABg63ABmYI5wSuIqq/qSZPvfOc7+Pu//3tcdNFFOOyww7BixQrMmjULP/jBD8a9/4c//CFmz56NFStW4LDDDsNFF12Ej3/84/j2t79d7aUSTY50OniiwvfG0/C15oooEcgSO+QRsLAUHKQLFvozxTqvhqj5MBahVuRVmuSKsG3/fLVK7FGMRxBRI2NeS62nHIuYtsCG3WyLQTTZGItQy9hD0sSyBTKlA6RjKk3YgYMALOrxdeHoZ+Ur1V9Vd2wNw8AzzzyD008/PfD86aefjj/+8Y/jvuZPf/rTmPvPOOMMPP300zBNZhqpAaTTY05UlJnJygkKnqagskWBFl0MDogmE2MRalXlmSaRXBGWM357LtGW4gEOAgAsmpbyHr/KWIRoUjEWoZayp6SJ44xbaSJBQkThAQ4CFvpiEbboojCo6rukXbt2wbZt9PT0BJ7v6enB9u3bx33N9u3bx73fsizs2rVrzP3FYhHpdDrwQVRXo5Im/pYYli9pwtMUVFYuQwXcFl1ENHlqEYsAjEcofMqVJqPbc/mTJkilRr+MWpT/dCdjEaLJxViEWoZlAb5W5aZdSfAVJRmF8iB4X6VJVI1CknyxCbWs4L4ID3BQ/dXkaNnob4BCiL1+Uxzv/vGeB4BvfOMbaG9v9z5mzZo1CSsmOnBiH5MmrDShsoWBMlRuVBBVQzVjEYDxCIWPN9Mkt+eZJlJbe83XReG0sMd/upMbFUTVwFiEml4m+F7Wvy+SiahA6d+uv9KE+yJUxgMcFDZVTZpMmTIFiqKMOT3R398/5tREWW9v77j3q6qK7u7uMfdfffXVGB4e9j42bdo0eb8BogNgDAZP/vhnmjgpX6UJB69SyUKeqCCqmlrEIgDjEQoXRzgo2u6MrEh2zzNNpHYmTcg1tzsOTXE3s7hRQTS5GItQyxhV3eRPmqQ1zXvsrzRhBw4qm5qKoD3m/jvhAQ4Kg6omTXRdx7HHHouHH3448PzDDz+ME044YdzXLFu2bMz9Dz30EI477jhovm+yZZFIBG1tbYEPonoyh3YHrv0zTZxUJSDgiQoqS0ZUTG93/z28uiPjnSIjooNXi1gEYDxC4VJuzQUAkbyxx5kmakdXTddF4aUqMuZPcU94rtuVhWE5E7yCiPYVYxFqGb6kiSMc2ML2rof1SqIkwkoTGockSVhYmve6PV3AcJ7zm6i+qt6e6/LLL8ePfvQj/OQnP8FLL72Ez372s9i4cSMuvvhiAO5piPPPP9+7/+KLL8aGDRtw+eWX46WXXsJPfvIT/PjHP8aVV15Z7aUSTQpraCB47UuaiDbONKHxlatNMgULO9LFOq+GqLkwFqFW4yVNhBgz08Tfnktp66jxyijMyu1CLUdg/e5snVdD1FwYi1BL2MMQeAAY1ivJvqi/0oQdOMjH34XjdbYupzpTq/0FPvzhD2P37t249tprsW3bNhxxxBF48MEHMWfOHADAtm3bsHHjRu/+efPm4cEHH8RnP/tZ/Pu//zumT5+OG2+8Eeecc061l0o0KezhocB1MGnCShMa36KeJB57dScAt9qkt53/PogmC2MRajXleSaqaUOx7EDVq1pqwWRGVERjHARPFe4A1m0A3FjEP5CViA4OYxFqCb6ZJqOTJoOqr9JEY6UJjS8412QEx85hVTTVT9WTJgBwySWX4JJLLhn3c7fddtuY50466SQ8++yzVV4VURXYNuzMcPCp8kaFIsOK6d7zPFFBfsG5Jpn/v737Dm/jutLH/86gEiAI9l5Eqvdq2ZLtOG5yb9k4cYnsNCfOxi3ZOMlukl/aOk7ZlE2csnHKblxiJ/m6xlVusi1ZVrc6qUJRFHsFQHTM3N8fQw4AsZMgQZDv53n0iMAA4L0qwOE9956DD83LS+JoiKYfxiI0k/SdNLF4tZOLcSdNepuwBm0WOLlQQTHOXKggosRiLELT3hAnTbqM0eVHq5E9TWhg885YFyFKpgkvz0U0o/T0IKLE113sawQftJkBKVoSgzsqKFZscMCmZ0RENB7+iHbSxOzrTZr0xiJGWYIUkzThQgXFmhsXi3ChgoiIRmmopIlBW36UJAkmA9dFaGBzYzZwcF2Eko1JE6JEcrvjggMhhL67M5BmiXsoFyooVl/DMwCoYe1OIiIaB/2kSW/SpO/Ua2w/k6DdwoUKilORbYO5d1GLuzuJiGjUYpIm4TM2k7b3njSxGmV9AwfAChwULy/dgkyb1v+GsQglG5MmRIl0RtIkphoGgvb4pAkXKiiW3WJESaYWMB5r6YEQYphnEBERDayvp4nFG4zbwGE0REP/kM3ChQqKYzTIqMqzAwBOdvgQjChJHhEREaUMVQV6oicD4jaTShK6ehMllpgm8ADXRSieJEmYl6+dfG31BOHyhYd5BtHEYdKEKJE8nrgdFRFV1b8O28xxD+VCBZ2pr5a4JxhBkyuQ5NEQEVGqij1posQk4Y2xJ01sPGlC/fWVC1VUgdp2b5JHQ0REKcPr1RInvWKTJsE0EwK9GzgsxvhlSFbgoDPFluhiFQ5KJiZNiBLpjJMmESW6UBFyxAcDXKigM8X1NWll/U4iIhqbvp4mFm8wLhY5szwXFyroTPNYS5yIiMYipjQXEJ806bGagd5wxMqTJjQM9nulqYJJE6JEOjNpElOfK8zyXDSM2XnRhYoTbQwOiIhobOJOmqg8aUIjFx+L8KQJERGN0BlJk7AarcDRY4lW3eh30oQVOOgMXBehqYJJE6JEOiNpErtQEYk5aWI2mGGQ43dYEPXVEQeA4wwOiIhojPSeJr5g3AaO2J4mbARPA6mKXahoZyxCREQjNMRJE7fFqH9tNcUvQzIWoTPFroucYKlQSiImTYgSye2O21ERUaI1PdX06EkT7qaggXB3JxERJUIgEoAcUWAKRuJikb6TJorRAIPVBlnijwIUryLHhr4DSYxFiIhoxIZImnhMJv1rizG6eVSCxKQJ9VOYYYXNrP074UkTSib+pESUKEIAHs+A5bkEAOGIBgMMDGggWXYzsmxaQMmFCiIiGit/xA+LLwQg/tRrX0+ToN2CNLMtKWOjqc1qMqA0S/u3caKtB0KIYZ5BRESEIZMmrkFOmliMFkiSBKJYsiyhMlc7bVLf5UcwoiR5RDRTMWlClCheL0QkMmB5Lq/ZCLM5Giiw8SoNpq8sRrM7AG8wMsyjiYiI+gtEArD4ggDi+6v1nTRhPxMaSl9ZDG9IQYs7mOTREBFRSjizp4kSrcDhMkbXQmJPmrACBw2mb11EUQVOdfiSPBqaqZg0IUoUtxuKiM+A9y1UuC0mWE3R4IALFTSYqtxo/c5a1u8kIqJRUoWqJU28AyRNenuaBO0WLlTQoKpy2YCViIhGaYiTJl3mgU+acF2EBhO7LnKcVTgoSZg0IUoUtztuNwUQ7WnisZhgMUb/u3GhggYT24CVzeCJiGi0ghEtWaKfNBmgpwlPmtBQYhuwHucGDiIiGo4QcUkTIYS+oVRIQLchuoE07qQJK3DQIOKbwXNdhJKDSROiRHG743ZTANHdnR4zT5rQyMQFB9xRQUREo+SP+AFAP2kyaE8TLlTQIOJjES5UEBHRMHw+QIlW3YhdFwlbzfDFxCIWnjShEZidF3vqlesilBxMmhAlygBJk76FCo/FBHPsSRMuVNAgZsftqGBwQEREoxOIBABg6J4maWYuVNCguFBBRESjcmY/EzVagSNosyAYjp56tbKnCY1AZS43cFDyMWlClCgez6AnTQI2M2RJ0u/nQgUNpjzbru8EZnBARESj5Q/HnzSJDHbShAsVNIh8hwV2s7aoxZIYREQ0LI8n7mbsukjQbkEwEj2FwpMmNBJ2ixGFGdq/D24mpWRh0oQoUdzuuB0VQLSOeMgeHwxwoYIGYzbKKMvS/n3UtnshhBjmGURERFFnnjRRemMRgyxBktjThIYnSZLeY+10lx+BsDLMM4iIaEYbogl80GZBoPekiSRJMBtYgYNGpq9caLcvjE5vKMmjoZmISROiRDmjPJcQQi/PFbJb4h7KhQoaSt9ChS+koNkdSPJoiIgolfgjfkiKClNA++Gy76RJX2kuVZYQtpq4UEFD6luoEAKo6/AleTRERDSlDZU0iTlpYjHK+gYOgOsiNDT2WKNkY9KEKBGE6Jc0UQXQd0ZAOfOkCRcqaAhVuWwGT0REYxOIBGDxhyAJbQNHNGmihf1BmwWQJC5U0JCqcmP7mnChgoiIhnBmTxMlvqdJIKKdNLEa45cgWYGDhhIfi3BdhCYfkyZEiRAIAOFwXNIkomqBgddkhNFqjHs4FypoKFUxDViPc6GCiIhGwR/26/1MlJgSj0ZDtJ8JwIUKGlrc7k7WEicioqEMcdIkYDNHT5qYDHGP47oIDSU2FjnOHmuUBEyaECVCb5AQu6MiomgLFT1mI6zG+OCACxU0lPhjqFyoICKikQtEAjH9TAZoAm/TkiZcqKChxC1UcAMHERENZYikicds1EtwWM48acIKHDSE2Xk8aULJxaQJUSL0BgmxwUFfPxOPxQSLKf6/GhcqaCizedKEiIjGyB/xw9ybNOkrzQVEe5roJ024UEFDYEkMIiIakd5S5bFi10VcZpP+tZUnTWgUijPT9EQbS4VSMjBpQpQIAyRNIrFJk5iTJmaDGQY5PlggipWbboajt6QbFyqIiGg0ApGAXp4rPmkS09MEXKigoaWZDSjJ1BJrJ9p6IGJKvREREemCQSAUirsrrGoVOMIWI/wxHx/9TpqwAgcNwSBLqOzt93qq04eIoiZ5RDTTMGlClAgDJk20N3SPxQRrzEkTLlLQcCRJ0vuaNLr88IeUJI+IiIhShT/sj5bnUqM/XBpieppYDBbIEn8MoKH1lehyByJo7wkN82giIpqRzjhlAkTXRWKbwAP9T5pYjJaJHRulvL5YJKwInOr0JXk0NNPwpyWiROjraaJGe5r01RH3mONPmnA3BY3EvHwtaSIEUNPiSfJoiIgoVcSdNFEGKM9ls3ADB43I3HyH/nV1M2MRIiIawDBJk2A4ugEw9qSJ1WjlBg4aFmMRSia+QxElgtsNIcSA5bncPGlCY7CoOEP/+mBj/0CUiIhoIP5I9KTJYD1N2M+ERiI+FnElcSRERDRlDZU0sQ9+0oTrIjQSXBehZGLShCgR3G4oIr6E0mA9TbhQQSOxqCgaHBxq4kIFERENTwiBYMgPi08rpXRmTxMhSQhbzVyooBGJj0W4UEFERAMYIGkSVrQKHEOdNGEFDhoJxiKUTEyaECWC2x13ygSI7Wli5EkTGrWF3FFBRESjFIgEYAqEIPU27T6zp0kozQwhS1yooBGZk58Os0GLYRmLEBHRgM5Imggh9A2lPGlC41WalYYMqxEAT73S5GPShGi8gkEgGNR3U/RRFIGA0YCwwcCeJjRqGVYTyrNtAIAjTR4oMbuFiYiIBhLbzwTo39MkaNcarnKhgkbCbJQxt0DrsXairQf+kDLMM4iIaMbxxPeZiN1MOuRJE1bgoBGQJEkv0dXiDqK9JzjMM4gSh0kTovHq7gaAAU6aCHjMJgCAhSdNaAz6jqL6wwpq271JHg0REU11gUgA1p6AfvvMniZBm5Y04UIFjVRfLKIK4EgzT5sQEdEZurribsYlTc44aWLhSRMag0VFTv3rQzz5SpOISROi8aqrAzBI0sRihNkoQ5Yk/X4uVNBILS5m/U4iIho5f8SPzJZo6YLYpIlBlhDgSRMaJcYiREQ0KK8XaG+Pu2uokyaxZctZgYNGirEIJQuTJkTjVVsLoH/SRFFVNGbY4kpzAVyooJFbFNfXhPU7iYhoaIFIAFlN0R2ffaUdDbIESZLgydM+V7hQQSO1qDi6u5N9TYiIKE7vWkissKqVLfc6bVDMRgR7T5pIEvQ+WQDXRWjkFrHfKyUJkyZE46GqAyZNhBCIqAJ1memwGuP/m3GhgkZqcTGPoRIR0cgFO9tgc/n02xFFW6gwytqJ167CTABcqKCRW1jk0L9mLEJERHFOnOh3V9+6SHdRFgAg0HvSxGI0QGIFDhqDOfnpesLtEDeT0iRi0oRoPJqagIBWO7xvRwWg1X0OSTIaHGlxdTsBLlTQyBVkWJBtNwPQFiqEYDN4IiIaQsyOz74NHICWNOnJsiOcpn2mcKGCRsphNaEixwZA62miqIxFiIio1xBJk65iLWnSd9LEcsZmUq6L0EiZDDLmFaYDAE60e+ELRYZ5BlFiMGlCNB4xixOxJ00iqop6pw2qLPc/acKFChohSZL0Bqwd3hBaPcEkj4iIiKYyufak/nXs2rbBIKOrd8cnwIUKGp2+WCQQVlHb3pPk0RAR0ZTQ1QV0d/e7O6JGICSguzATQggEI9pJE+sZm0lZgYNGoy8WEQI40uxJ8mhopmDShGg8YnZWxCZNFFXgZJaWCedJExqPuKZnLItBRESDEQLGU6f1mxFV1b82ypJeJgPgQgWNzmLWEiciojMNcMoEAMJKGJ4cByJmI0KKir5iCTxpQuPB0uWUDEyaEI1VJAKcOhW9GXvSRBGoc/YmTdjThMaBzeCJiGhEOjoAV/RzIqJEj5rIBhndBdEfNrlQQaOxiBs4iIjoTAM0gQe0dZFoP5PoBg6L8YyTJqzAQaPAZvCUDEyaEI1Vfb2WOOkVjERLJ/mMBrSkawsSVlP0v5lJNsEgxwcLREOJO2nSxOCAiIgGceIEQkpIvxmJqc/VnZMOxWzUbzNpQqMRt7uTsQgREQkx6EmTkBLSS4IGe5vAA4DFxJMmNHYLi7guQpOPSROisTqjNJc37NVv12bYAUkCAKRbTPr9NpNt8sZH00JlbrqeePugnidNiIhoYMrxY/CEojWe/aHoQkV3cXw/E27goNHId1iQYzcDAPaddkFlM3giopmtpQXw+frdLYRAV8QDV360L2cfhyW6eUOWZCZNaFTSLUbMytHW0w43ufVeOUQTiUkTorGKOY7qCsQvZu9Ps+hfF2VGg4F8e/7Ej4umFYMsYWWZttjV0O1HfWf/4JSIiGY4VYWneh9UES2D4QmE9a/F7AL9a8YiNFqSJGF1hRaLuPxhHG7mDk8iohltkNJcvrAPHTk2qL2luBq7/fq1osxoOa5cWy5kicuRNDqrK7IBAKGIij2nupM7GJoR+C5FNBbBINDYqN90BaNJEyEEdpq00yU2sxGZadGTJuXO8skbI00b62fn6F9vPd6exJEQEdGU1NICt6tVvymEgCeglRBVjQYYK3L1a4xFaCziYpFjHUkcCRERJd0gSRNX0IWumNOtDb1JE0mSUOSMbiZlLEJjER+LcF2EJh6TJkRjceoUoEZ3c8aeNOkwGdFq1I6eFmdaIfWW6QKAisyKyRsjTRvr50QXu7ZwoYKIiM508mRcLBKMqAgpWpzSU+iEZIqW4+JCBY3FuTGxCDdwEBHNYKoK1NUNeKk70K03gfeHFHT2lufKd1hgMkSXHyucXBeh0Vs/J3YzKddFaOIxaUI0FidP6l+qQoU7GC1TcMyepvczKY45gmqUjSh2FE/aEGn6WFbqhN2sLXhtPd4BIVhLnIiIokRtbdyp175TJgAQjDllIkFi0oTGZE5+OnLTtfKz22s7EVbUYZ5BRETTUnOzVnljAJ2KF57sdABAkytamit2XQTgBg4amyJnGqpy7QCAvfXd8AYjwzyDaHyYNCEai5ikiTvohkB0EfuAJdrPpCQmOChxlMAoR5ufEY2UySBjbaVWv7O9J4hjrT1JHhEREU0Zqoqeo4cQUaM/OMb2M5Gqoj1M8u35bLxKYyJJkl4WwxtSsO+0a5hnEBHRtBSzFhIrEAmgNccC0XuipCGmn0nsukimNRNOq3NCh0jT17reWCSiCuw42Znk0dB0x6QJ0WgFg0BTk34zthyGEAK7e0tzmQwy8tKjCRTupqDxWD87tiwGj6ISEVGvlha4XS1xd/WdNAkbDLBWREsZsEwojQdriRMR0WCluVwBF7oLM/XbsU3gizPZz4QSg+siNJmYNCEarfr6+H4mMeUwXBYzGiXtv1WR0wpZjvYzYXBA4xFfv5MLFURE1OvkybhYJKyo8IcVAIC7wAmjOXrKlbEIjUd8XxMuVBARzThD9DNxBaNJk4iiosWjlfDKsplhYyxCCbJuNtdFaPIwaUI0WjHHUYUQcSdNjqUP3M9EgoQyZ9mkDZGmn4WFGci0mQAA2050QlHZ14SIiHr7mQQG6WdSnhP3WC5U0HiUZdtQmqXFt7tOdSHQm5wjIqIZoqUFCAQGvNSp9Oj9TJrdAai9P6+e2c+ETeBpPLLtZiwsygAAHGx0o9sXSvKIaDpj0oRotGKSJt6wF4qI/sB40DpwP5OC9ALWEKdxkWUJ66q0xS+XP4xDje4kj4iIiJJOVeE/Xo2gEm3IGtvPBJV5+pdZ1ixkWDImc3Q0DfWV6ApFVOyq60ryaIiIaFIN0s8krITRmGXU+5kMVprLZrIh15bb7/lEo9EXiwgBbDvBk680cZg0IRqNYBBobNRvdge64y7v6e1nIksSCp3R4IC7KSgR1seUxdjCo6hERDREP5OQQUbarOhJE54yoUSILdG1hX1NiIhmlkGSJrGluQCgoTt6GiV2M2m5sxySJIFoPM6NKV2+5RiTJjRxmDQhGo0z+5nElsOwmnGy99BJnsMCkyH634sLFZQIsQ1Y36puTeJIiIhoSjh5Mi4WUVUBb0hLmnTlZSAtzaxfYyxCidB36hUA3qpuS+JIiIhoUg3VzySmCbwqBJpc2kkTm9kIZ5pJfxxjEUqEs2Zlw9jbP/itmlYIwdLlNDGYNCEajTP7mcQ0Xj3uiPYzKTmjbieDA0qEqlw7KnPtAIDttZ3o8rJ+JxHRjHZGE/ieYAR9PzcGSuP7mVRk8tQrjV9+hhXLSp0AgENNbtR3+pI8IiIimhRD9DPpUr16P5P2niBCEW2jaUmmNe5kCddFKBEcVhPOrsoGANR3+nGk2ZPkEdF0xaQJ0WgcP65/GVSCCCnRRevDMf1MYpudZadlw2FxTM74aFqTJAkbFhUAAFQBvHa4ZZhnEBHRtKUoCB2vgS8cXbSO7WeiVkbLKNlMNuSkxSdRiMaqLxYBgE2HGIsQEc0IMWshsRRVwWmnHNPPJJpYiV0XMckmFKUXTewYacbYsKhQ//rVg4xFaGIwaUI0Uh4P0NSk34wthwEAe4zRY6exzc64m4ISacPimOCACxVERDPXqVNwu+PLI7lj+pnYZkWTJqwhTol0WUws8srB5iSOhIiIJs3RowPe7Ql50FmYod9uiGsCH02alGaUwiAbJm58NKNcGrOBg7EITRQmTYhG6owgIbYchifdimO9/UyybGbYzEb9WllG2aQMj2aGlWWZyHNop5rermmDr7d2PRERzTBHj8Zt4BBCoCeofSY052YgIz16ApYbOCiR5uSn6+VCd5zsRCfLhRIRTW9+v9bfdQCugAudJVqpJCEEGnuTJiaDjLyYWKTMyXURSpzizDSWC6UJx6QJ0UidmTSJWag4luWAqmpFxNnPhCaSLEv6ropgRMXbNWzCSkQ0Ix09GreBwxdSoPTGIp5ZuawhThNGkiRsWMxyoUREM8bx41oj+AG0G0LoydIS6e5ABN7eDRxFTitkmbEITZzLWIWDJhiTJkQjoSjAiRP6zbAShjfs1W/vT48mSmKPoNpMNuTaouUxiBIhtpY463cSEc1AXV1QW1vgCUYbX3oC0ZOHkdnRzwmjbGQNcUo41hInIppBBinNJYRAbZ4B6N2o0ThIaS4JEitwUMLFr4uwRBclHpMmRCNx6hQQDOo33UG3/rViNGCvYeB+JmUZZawhTgm3fnYuHBatBNxrh1sQVgbe9UNERNPU0aPwBD0QEPpd7t4m8K12K7KKM/X7WUOcJkJsudB3jrJcKBHRtCXEoEkTb9iL5uKB+5nEVuAoSC+AxWgBUSLNyU9HVUy50I6e4DDPIBodJk2IRqKmJu5mbDmMjqJMnO59c7abjXCmRRMoPIJKE8FslPHhBfkAtCPQ2050JHlEREQ0qc4ozSWE0E+a1OVmIJf9TGiCnVku9K1qlgslIpqWGhoA38D9IrrDPegqzNRv9500kSUJhc7oZlLGIjQRJEnCpSwXShOISROikRiin8nxbAdCEW2nf3FmGmuI06S4PKZ+5992nk7iSIiIaFKFw0BtbVwsEoyo+qlDz6w81hCnSREfiwzcIJiIiFLcIKdMAKAp2wTFrFVA8IcUdHpDAIA8hwUmQ3S5kbEITRSui9BEYtKEaDhdXUB7u35TFSo8oWgN8X226A6K2NJcRtmIIgdriNPEuGRRPrLtZgDAywea0OoJJHlEREQ0KWprIcLhuJMmfadM/EYDjBXRXmoSJJRmlE76EGlmOHdOrl5+ZXNNG+o6vMM8g4iIUs4QSZPjOdElxcZBSnMBTJrQxFlRlon5BQ4AwK66LhxsdA3zDKKRY9KEaDj798fd7A50QxXabs6e7HQcC0XriccGByWOEhhl4+SMkWYci9GAj5+lNdMLKwJ/28EdnkREM8L+/fCEPIio0R4Snt5+JrVZ6SjKtun359vzYTVa+70EUSIYZAm3nqMthAkBPP7+qSSPiIiIEqq9HWhsHPCSL+xDXUG0HGija+Am8JnWTGRYMkA0ESRJwifWVei3H93GWIQSZ0KTJl1dXdi4cSOcTiecTic2btyI7u7uIZ/zyU9+EpIkxf0655xzJnKYRIMLhYBt2+Luau5p1r9uK87C6U6tvqfZKCPXwRriNHluWVuOvmpwj79/Cooqhn4C0QzEWISmlc5O4MCBuFhECAGXvzdpkpPBGuI0qT62pgzm3hIsf9tZj0BYSfKIiKYexiKUst59d9BL9QYv/BnR5EhdR7TvSWwFDsYiNNFuWFkCu9kAAHhmTwPcvZuJiMZrQpMmt9xyC/bu3YuXX34ZL7/8Mvbu3YuNGzcO+7zLL78cTU1N+q8XX3xxIodJNLhdu+KanoWVMNp90VJde+w2+Ht/OKzItkNmPxOaRGXZNlw4X2sI3+gK4I0jrUkeEdHUw1iEppUtW6AoEbR6o+/37kAEwYgKVZIQrMpnDXGaVLnpFlyxVKsn3uUL48X9TUkeEdHUw1iEUlJ3N7Bv34CXhBDYnxVG3w6+VncA7T1BAEBBhhU2c7TiBmMRmmjpFiNuWFUCAPCHFTy9uyHJI6LpYsJqBx0+fBgvv/wytm3bhrPPPhsA8PDDD2PdunWorq7G/PnzB32uxWJBYWHhoNeJJkUkAmzdGndXq7dVL83lznXg3UB0N93i4uiRU7PBjIrMChBNtI3nVOjJkke21eHSRQVJHhHR1MFYhKYVtxvYuxftvva40lxtHm2R4lCeE7Nn5ej3y5KM2dmzJ32YNPNsPKcCz+7Vyrc8sq0OH1nFPjpEfRiLUMrasgVQ1QEvdQS6cHx2ln77YKNb/zp2XQQA5mTPmZjxEcX4xDkVemmuR7bV4bZ1FZBiNjUTjcWEnTR577334HQ69cAAAM455xw4nU5sPWMh+kxvvfUW8vPzMW/ePNxxxx1obR1893QwGITb7Y77RZQQH3wAeDxxd8WWwziyoAS1nVrdznSLEeU50Rrii/MWw2wwT844aUb70Lw8lGVrx6LfrmnDoUa+BxL1maxYBGA8QpNg61ZAUdDUE93JH1FVdHiDEAB2VxVidr5dvzY/Zz5sJtsAL0SUWKsrsrCgUGvCuudUN3ac7EzyiIimDsYilJI8HmDPnkEvVxca9dJcEUXFkWbt35rRIGF+7+cBAFRlVSHTmjmhQyUCgAWFGVg7KxsAcKy1B29WswoHjd+EJU2am5uRn5/f7/78/Hw0NzcP8AzNFVdcgcceewxvvPEGfvrTn2LHjh246KKLEAwGB3z8gw8+qNcGdTqdKCsrS9gcaAbzeoG33467yxP0wBPSkijeTDveNpgghNZDYlFxRlxprpVFKydvrDSjGWQJt6+bpd/+znMH9X+XRDPdZMUiAOMRmmCtrcCuXfCH/egOdOt3d/SEIARQnetE/uw8GOVoaM9YhCaLJEn41Lmz9NvffvYg+6wR9WIsQilHCOC117TKGwMIKSFsnWXQbx9v8yIY0U6kzM1zwGKMXltZyFiEJs8nY2KR7//zMIIR9lmj8Rl10uQ73/lOv4ZkZ/7auXMnAAx4FEoIMeQRqY9//OO46qqrsGTJElxzzTV46aWXUFNTgxdeeGHAx//7v/87XC6X/qu+vn60UyKKFw4Df/0r4HLpdwkhUNNRo98+ubQMB5uip1AWFUWPoOak5aAsgwEqTZ6N6yowq/ek0/aTnXjug8Ykj4hoYk21WARgPEITqKcHeOwxiFAoLhYBgNbe0lzvleVhUUw5DIfZwXIYNKk+urpMj4cPNbnx1+2nkjwioonFWISmrXff1apuDGJnhhferOjJ1oON0XWT2FjEarRiQe6CiRkj0QCuWFKIs2ZpZeNq273407snkzsgSnmj7mly11134aabbhryMbNmzcK+ffvQ0tLS71pbWxsKCkZec7+oqAgVFRU4evTogNctFgssFsuIX49oSEIATz8NnD4dd3dtd61+ysTvSMO+rAx01WpHUEuz0pBpi5biWlm0krUTaVJZjAZ8+5rF+NT/7gAA/ODFw7hkYQHslglrW0WUVFMtFgEYj9AECYWAxx8HXC7Uu+vRFejSL/lCEXiDERzPdkAtzkS+w6pfW164HLI0YQfKifoxyBK+e91i3Pi79wAA//VqNa5aWoQsO8vV0vTEWISmpQMHgNdfH/Ryg7sB7y2Olt9y+8M41eUDADjTTCjNStOvLc1fCpPBNHFjJTqDJEn47rVLcPWv3oEqgF+9cRQ3rCxBodM6/JOJBjDqFbXc3Fzk5uYO+7h169bB5XJh+/btWLt2LQDg/fffh8vlwvr160f8/To6OlBfX4+ioqLRDpVodFQVeOEF4NAh/S5FVVDbXYvT7mgSpXZxGbbUdui3Fxc79a9lScbyguWTM16iGBcuyMfFC/Lx+pFWtLiD+OUbR/HvVyxM9rCIJgRjEZoRQiHgiSegNpzGye6TOOWK7twXQuBUp7ZIsbU8Ly4WAVgOg5LjrFnZuH5FMZ7Z24huXxg/ebUaP7hhabKHRTQhGIvQtFNdrW0gHYAqVNS76rHb5oInrwSAFotsOdYO9FZjXFSUEbd5lGVCKRkWFWfg1rMr8Mi2OvhCCh548TB+dTP/LdLYTNgWtIULF+Lyyy/HHXfcgW3btmHbtm244447cPXVV2P+/Pn64xYsWICne9+Ye3p68JWvfAXvvfceTp48ibfeegvXXHMNcnNzccMNN0zUUIm0hMkzzwC7dgHQAoB2Xzu2N2yPS5h0Fmbi/0LA6S6tAbzNbMCc/HT9+pzsOXBYHCBKhm9dvQhmg/a2/vDbJ6ZU8zMhBP66/RSufehdnP/jN7DmP1/DuT98Ay/saxr+yURjxFiEUlYwCDz2GDoP7sSOhh39Eia17V50+8LYU5SNtmyH3oQbACqcFcix5SRj1ET49ysXwm7W6tk//v4p/HPf1CkZKoTAM3sacP2vt+BDP34TZz3wGs75wev4+06WMKKJw1iEUsLBg8CTTwJK/x4Q3YFu7GrchaPeU6g5O1r6891j7ahu0apxGA1SXGmuAnsBitKZ4KPk+LcN85Bl0045Pf9BI56YYiVDXz7QhH/57VY9Fln7wGv4v60nkz0sGsCE1m557LHHcM8992DDhg0AgGuvvRYPPfRQ3GOqq6vh6u0dYTAYsH//fvzlL39Bd3c3ioqKcOGFF+LJJ5+Ew8GFaBpEOAzU1AANDYDJBFRVAeXlwEhLZEUiwFNP6SdMekI9ON55PK4EBgB4M9LwP6UFqG6OBgZXLyuGyRDNPa4qWpWYORGNwaxcO+65eA7+69UaqAK45/E9ePqL58Yl9pKhyxvC1/7fPrx6qH9pgrv/uhsRdQWuW1GShJHRTMBYhCad3w8cOQK0tAA2GzBvHlBYOPLn+3zw/Pl/cPLAu+jwd/S73NDtR6sniJOZ6Xh9bjGuWVoEqynadJWxCCVTQYYVX718Ab793EEAwFf+/gFm5dixpMQ5zDMnlicQxjefOYBn9/ZP4tz/j30IKSpuPbsiCSOjmYCxCE0YIbTS4idOaPFHcTGwYAFgHkVpxD17gOee014rhj/sx4muE2jztUFIEg5eshR+p9ZHc8+pLuyq09ZLJAm4fHERHNZoKa5VRatYspySJtNmxreuXoQv/03rzfOtZw+gKi8dayuzkzouXyiC7//zEP66vf9mjW8/dxCBsILPXzA7CSOjwUhCnPHOmOLcbjecTidcLhcyMjKGfwKlJiG0wGDvXu0YaSgUfz07G1i5ElixAhgqsPR6gSeeAOrrIYRAvbseJ7pO9HuYR5bxX7NL8IFf23khScA1y4pRlRddjJ6VOQu3L7+dwQEllaoK/Otju/HywWYAQGWuHY/fcTaKnGnDPHNi7K3vxp2P7EKzO6Dfl203wyBLaOttYCxLwE8/thw3rCxNyhhnCn4+Ti7+ec8wigIcPgzs2wccO6adYI1VXKzFJUuXAtYh6ip3dKDu1z/AyRO7IRAfoociKk52eNHpDaHdZsEjK2bjwhUlWFAY/fdVlF6EO1bfwX4mlFRCCNz/j334xy7ttHax04onPrcO5Tm2pIzncJMbn3tkJ+o7/fp92XYzTAYJLe6gft/3r1uMjetmJWGEMwc/GycX/7ynsY4OLdlx4ADQ3R1/zWwGliwBVq/W4o/B1ieEAN56C9i8ud+l5p5m1HTUQBVaPFO9fj6a5hXBF4rgnaPtONzk1h970YJ8LCvN1G/n2nJx55o7YZTZX5OS67vPH8Sft5wEAOTYzfjr587BvILkJJ5PtPXgc4/swrHWHv2+TJsJVqMhbq3kq5fPx79+eM5AL0EJMprPRiZNKLWEQsCOHcDOnUBX1/CPl2Vtp8Xq1UBlpXa7T1MT8Pe/A52dAIBjncfiSnEBgCoEGjwh/KS8EKfSo4vOlywsiNsxZzfZ8ZlVn0F2WnIz10QA4A1G8C+/3YojvaeiAGBJSQYuXViIz5xfifRJahD/VnUrvvDobvjDWrIxy2bCjz+6HJcuKoCqCnzr2QN47H3tqKwkAX/65Fm4cH7+pIxtJuLn4+Tin/cM0dMDbNumLVx4vcM/3mTSEicDLWQcO4YTf/gJTjVXxz1FCIEWdxD1XT4oqkCP2YhHVszGsqUlWF2RpT/OarTi0ys/jXw730cp+YIRBTf/fht2n+rW71tQ6MAlCwtwx/lVcNompznw+yc68Nm/7IQnEAEAOCxGPPCRpbh2eTGEEPjhy0fwP5ujG6YeumUlrl5WPCljm4n42Ti5+Oc9zQihbRjdvl3bQDoShYXAmjVaEiV204bfD7z4IrB/f7+nnHafxrHOY/rtuqXlOLGqEgcb3Xj3WDsC4WgJr7Mrs7FudrS3j0k24ZMrPomSDFYRoOSLKCo++ecdePdYu37fnPx0XLwwH3ecX4XcdMukjOOD+m586n93oNOrbfZOMxnw3WsX48Y1pZAkCQ+9cRT/9WqN/vgffmQpblpbPiljm4mYNGFgMD21tmp1Njv6l6oYEYdDK5GRnq4FGadP60dQmzxNqO6ILlIIIdDlC+N4tx+Pzi9DXaZ2osRuNuLCBflx5Y7MBjNuX347AwOaUuo7fbj+11vQ4Y0/hVWZa8evbl454WUyntnTgK/8/QNEVO3/2NpZ2fjlzStR6IwG60II/H/PHsQj2+oAAIUZVrz65Q8hwzo5CykzDT8fJxf/vGeAEyeAf/wD8PnG9vycHGDuXMBoBI4eRduJAzjYdjDuId2+EOo6fHryucdsxNOr5mDpyjIsKIr+uzJIBmxcvhGzMmeNdTZECdfqCeC6h7agyRWIu78kMw2/vHkFVldM7GajVw424+6/7kEoou2UXlbqxK9vWYWy7OiJFyEEfvJKNX7z1nEA2gaPTV++YNIWUmYafjZOLv55TyPhMPDss9rJkrEwGoE5c4CiIq186PHjWv+0M3T6O7GvZZ9+u35xGTbPLsTmo+16lQAAsBhlnDc3D0uKo83fZUnGTUtuwryceWMbI9EE6PaFcP2vt+BkR3y8nuew4BcfX4Fz5+QO8szEeOdoGz7/yC74QlosP7/Agd98YhVm58WXUP/NW8fw45e1NUm72YBXv3wBSjKTUy1kumPShIHB9ONyAX/4A+DxDP/YUXIH3djTtEcvgyGEwLHWHjQGIvj7klk47bQDErC8JBPr5+TAYozWDZclGbcsvQVzsnl8jqaeJpcfj79/Cm9Wt+JAQ/QItdkg4xtXLcRt6yoSXk5OVQV+8VoNfvlGdHfSlUsL8bOPrYirud9HCIHb/7wDb9e0AQBuXluOBz+yNKFjIg0/HycX/7ynuYYG4H//V1vESICeUA/2NO2BIrQfqPqavbfGLFC4LSbs3rAMy5eXIc0cfT+VIOHGxTdiUd6ihIyFKJHaPEE8uq0Ob9W0Yd/pbr1kvkGW8JUN83HnBVUJj0WEEPift0/gxy8fQe/eDXx4fh5+c+sq2Mz9T9sKIXDXX/fghX1NAICrlxXhoVvYG2gi8LNxcvHPe5oQQtukcfDg8I8dB3/Yj11NuxBRtZN5tcvK8Yg9Hbvru+MeN7/AgQ/Ny4P9jOoF182/DiuLVk7oGInGwuUL49H36/DmkVbsPtWlxwaSBNx14Rzcd8k8GOTExyKPbqvDd58/FN1IWpmNh29bA2fawJtE//2pfXq/kwvm5eF/P3UWy/9PACZNGBhML8Eg8Kc/aTsiEv3SkSB2Ne1CSInuxj/V6cPxnhD+tmQWmjJsKMlMwwXz8pCfEV+D3Cgb8ZGFH+EiBaWE2nYv7n1iD/addun3femSebj3krkJ+x4uXxj3PbkHb1a36fd94pxyfPfaJUMGIQ3dfmz42WZ4e3dfPH7H2Vg/e2J3fMxE/HycXPzznsa6u7WNHD09wz50JMJKGLuadiEQie7Gb+j2o74zuiMunGVH+8fOQVZJZtxzDZIB18y/BisKVyRkLEQT6XSXD19+8gNsP9mp3/fZ8yrxjasWJmxRwBuM4Kv/2IcX9jfp992wsgQ//ugymAyD9/pp7wni0p9tRpdPS4T+fuNqbFhcmJAxURQ/GycX/7yniddeA959d0K/haIq2N20G96wVmr0xOoqPJ+VgTePtOqPyU234IJ5eXGn9QBt88YVc6/A2pK1EzpGokRodQfwb3//AO8cjZbsuumsMjz4kaUJi0UCYQXffOaA3tsNADYsKsAvb1454EbSPu5AGBt+9rbe4+SnNy7Hv6xm39dEG81nI7tE0tSmqtquiglImAghcKjtUFzCpL0niANBFX9ZMRvNTjsuX1KIj64u7ZcwKXGU4LOrPsuECaWMylw7/nHnenzmvEr9vp+/VoOH3x5hPdxhNLsCuOE3W/SEiSwB/3HlAnz/uqETJoBWpuPrVyzQb3/9/+2HP6QM8QwioiQJBoHHH09YwkQIgSPtR+ISJl2+UFzCxDG/CNKdF/VLmBTYC/DplZ9mwoRSRmmWDY/fcTbuuWiO3s7nD+/W4uebaoZ+4gh1ekP4l99ujUuY3HvxXPz0xuVDJkwAbTHw29cs1m9/85kDcPkTc5KMiGjMdu+e8ISJEALVHdXwhr1QZQmHz1+IraW52ByzEe5Dc/Nwy9nl/RImubZcfHLFJ5kwoZSRn2HF/31qLb52+QJ9neKJHfX47vOHkIgzBe5AGDc/vC0uYXLH+ZX4za2rhkyYAECG1YQHblii3/7ePw/FlcWjycekCU1t27cDR48O/zhJim/yPgK13bVwBaO77r3BCDaHBR5ZUYUumwUfmpuLBYUZcdlmi8GCK+deic+s+gwK07n7jFKL2SjjW1cvwjevWqjf98CLh/WeImPV3hPErX/YhhPt2s6kLJsJj3zmbHzuQ7NHvFvj1rMrsLZSq21+qtOHx7efGteYiIgmxKuvaj3WhiPL8U3eB1HvrkeHP9qrzR9ScKw1mpAJLy1Dx03nIJJm1u8zySZcWnUpPrf6c+ynRinHaJDx5Q3z8eAN0VKcv3zjGH7b21NkrFz+MDb+8X0cadZK+TosRvzhtjX40qXzII+w5MZ1K4px0YJ8AECrJ4g/b6kd15iIiMalq0tr1j4ShqEXY4fS6GlEq7cVYYsRH2xYjqMl2XhhXxPU3gXkVeVZWFWRBTkmrjFIBlw460LcueZOVGRWjPl7EyWDLEv4wodn4xcfX4G+EOF/t57ET16pHvqJw/CFIvj0n3dgz6luAFrD91/evBLfuGoRjMNs3uhz8cICXLeiGIAW2/xu8/jiIxqf/kVdiaYKRRl+V4XBAFx+ObBsGWCxaPU+GxuBXbuA/fshQiH4wj50+DvgCXqgCAUWgwVtvja9VicAhATwmDUNr1TmQJFlLCrKwIqyzLhvtbJwJS6uuhjp5nQQpbLPnl8FX0jBz3p3dv5/zx5Als2Eq5cVj/q1un0hbPzjdhxv0xImZdlpePyz5/TbhTQcWZbw/euW4LJfvA0A+P3bx3Hr2eXD7sYgIpo0Hg+wZ8/Qj8nIADZsABYs0JquCgFx/Dg8770F19734Qm4EFEjsBgs6PR3IqhEd48pqkBNiweKKhA0yKhdMQsV1yyHFLMpZGn+UlxSdQmcVudEzZJoUty0thz+sILvPn8IAPCjl48g227Cx88qH/VreYMRfOrP23GwUevflu+w4K+fO6dfk9XhSJKE7167GJtr2qCoAn/echKfOa8SDuvAtceJiCbU1q1AJDL0Y84+G1i7FsjJ0W739AB79wI7d2rlRGP4w350+jvhCroQVsKwGq3oDnTDH/GjqzATNevnoyfdihd21sMf1k79l2fbcN4ZjbLn58zHZXMuQ3ZadoImSpQc1ywvRiCs4P5/7AMA/Oat48i2m/HZ86tG/VqBsILP/WUXdtZ1AdA2kj762bOxuHj0Mfs3r1qEVw42IxBW8dj7dfjCh2cjN90y6teh8WPShKau6uqhy1/k5wOf+YyWLOkjSXDlpKN2bTlOVQTQs2sbbNXHkdnlGvRlWmbl4bcWG/Z6tSP4BRlWXLQgP26HPJua0XRz90Vz4Asp+N3m4xAC+PKTHyDHbsG62Tkjfg1/SMGn/ncHDjdpixRFTuuYEiZ95hc6cNniArxysAUt7iD+vus0Np7DnUtENEXs2aOVDR3MvHnATTcBsgxP0IPajlqc7D6J2q5adJV1wZyTi4LjCgpqW5He2Rn31L7G796Iit3FOTi8sBTXnVsVlzC5fM7lOKf0nImaHdGk+9S5lfCFFH1n5388fQC56RZcvLBgxK8Riqj43CM7sbt3V2eO3YzH7zh71AmTPmXZNtywsgT/2HUaLn8Yj2yrw79+eM6YXouIaMxCIWDfvsGvG43Axo1AxRk/K6WnA+edB5x7LnyH96Nt2+vwH9gDl7sN/oi/38t4nTYcP2spOkuyAUnCO9WtaOntp+BMM+HKpUVxp/UunHUhLph1QUKmSDQV3LimDIGwgm89exAA8J8vHEZ+hhXXLh/5htKIouLuv+7Bu8e0PikOqxGPfGZsCRMAyHNYcPPacvx5y0kEwir++G4tvnb5guGfSAnHpAlNXbt2DX7N4QA+8Ym4hIkr4MILR1/A0Y6jEOitRViVAVSthKUngKymLlh7ApBUAQlAIN2KrqIsbOkKYO9RrV6n1WTAVcuK4o7OXVJ1CRMmNO1IkoSvXT4fnd4g/rbzNEKKis/9ZSee/Pw6LCoevlGkFhjs1o+e5qZb8Nhnzx5zwqTPXRfOxSsHtR5Gv3vrOG46q2zYOuRERBNOVYeOS4qLgY9+FD0RH148+iIOtx2OxiK9QjYL6peWo35pOWzdXmQ2d8PsD0FSBU62e7HJbMPJzHQEbRbcvKYcZmP0ve+88vOYMKFp6V8/PBsdPSH8aUstFFXgi4/vxmOfPQerK7KGfa6qCnz1Hx9gyzGtxJ0zTSsPOiffMe4xPbX7NFQB/OGdWnxy/SzYzPyxmYgm0YEDWh+1wVx/ff+ESS9/2I+Xjr2EA20HoFapkMsrkN2YAXuXF7KiQhICEZMBroJMuPMyIHqTIjUtHuyt7wYAGGQJVy0tijv1f1bxWUyY0LS0cd0stPeE8N+va60B/u1ve5FtM+O8ubnDPFPb+PSd5w9i0yFtDcNmNuB/P7UWS0rGdyr88x+ajce2nUJIUfGXrSfx+Q9VIdNmHv6JlFBciaKpqbMTOD5E7b5LLtFKYPTq8HXgD7v/gJqOmn6LFAAQTLeiujQXWysLUbNiFk6srkLt7AK80+nXs8EAcNniAmTEHMFfnLcY55adm5g5EU0xkiThgRuW4sL5eQAATzCCmx/ehvdPdAz5PCEEvv3cQbx2WKvrn24x4i+fXouqMe7qjLW01KmPp6Hbj6d3N4z7NYmIxu34ccA1+KlVXH01XKoff9z9RxxqOzRgLAJozSEbu/3oTk9D44ISHF1WgafzsvBLkxUHCrLQYzHhkoUFyLZHfyiamz0XF1VelOgZEU0JkiThm1ctxNXLigAAgbCK2/74Pt6sHr530I9fqcYzexsBABajjD998qwRbfwYTlVeul6ytNMbwuPvs88aEU2ynTsHvzZvHrBkyYCXvCEv/rTnT9jXsg+q0E7HqkYD2stzUbe8ArWrKrFvSTm2leejJTsdQpYQUVTsP+3Ca4db9Ne5YF4e8jOs+u1yZzkun3N5YuZGNAXdd8lc3Ly2DAAQVgQ+83878NL+pmGf95u3juPRbVqcYJQl/H7jmhFt/BhOodOKG9eUAgC8IQV/3nJy3K9Jo8ctMzQ17d49+LW0NGDxYv1mMBLEEweegCfkiXtYMKKgsduPU51+nGz3ossXAgDIkoQ8hwVdvhBCkWiZjbNmZaMyN7rom2vLxXULrhtxI2uiVGQyyPj1ratwy8PvY299d28j1e348UeX4fqV8Q2Gm10BbDrcgn9+0Ij3azt7ny/hfzauTsgiRZ+7LpqLN6u101+/fusYrl9ZErfjmoho0g21eFFcjHBBHp7c+2d0BbriLoUiKhpdftR3+nCy3YcOr7ZrVJKAvHQL3IEIAr11wwFgWWkm5hdGd8lnWjPxkYUfgSzxPZCmL1mW8NOPLUenN4StxzvgDSn47P/txPeuW4xb1pbHxeKtngBeP9yKF/c34Z2j2sYnWQJ+dfPKhCxS9PnihXPw3AdaQub3b5/ALWeX87QJEU2OpiatT+tg1qwZ8G5FVfC3g39Dm68t7v6woqLJFeiNRbxo6+k9wSIBuXYLfCEFvlC0d8qCQgeWxuySTzen48ZFN8Igs9ckTV+SpPVYbfOE8NrhFgQjKv718d349ysW4I7zq+JikU5vCK8fbsFLB5rxxpHoJo+f3LhsRKdTRurOC2bjyR31iKgCf95Si0+dO4unTSYZIz+aehRl6EarK1ZoNTyh7Xh/+sjTcYFBizuAN3trcYoBNnqqQuh1OvssKsrAuqpoLweTbMLHFn8MZgPfkGj6s5mNeOQza3HX43uwuaYNIUXFfU/uxff+eQiz8+yQJQlHW3vQ6Q31e+5PProc585JXGAAAKsrsrB+dg62Hu9AXYcPf9pSizsvmJ3Q70FENGJuN1BTM+hlsWoVnq95Ho2e6AJHe08Qbx5pRaMrADFAMCIE0OqJL7sxt8CBD82Lvp8aJANuXHQj0kxpCZgE0dRmMRrwp0+ehS89uRcvHWiGogp84+kD+Mkr1Zidlw6zQcbRVg/ae/rHIt+9bgk2LC5M6HjmFzpw+eJCvHywGa2eIH795jHcfxnriRPRJBhqo4bTCcwZuM/SK8dfQZ2rTr/d5QvhzSOtON3lhzrQwojQ4pVYlbl2XLSgQF8gliDhXxb+CxyW8ZU9JEoFRoOM39y6Cl9/ah+e2t0AIYAfvHgEv3rjGGbnpcNuMeBoS0+/GB4Avnr5fNywsjSh4+nrs/b3XafhDkTws001+N51A58yo4nBbWs09Rw5Ani9g19fvVr/cnPdZhxpP6LfdvnDeHpPA5pd8QkTSZJQkpmGxcUZeskLgyxhcbETt62bhQ2LC+ManF017yrk2/MTNyeiKc5hNeGPt6/BLWeX6/d1ekPYcbIL79d29kuYVOTY8NMbl/c7jZIo/3HlQvRt5vjl60fR7AoM/QQioomyZw8G3IUBABYLtmf7sa8l2qzVG4zgmT0NaOj2xydMJO2o/eJiJ3LTLYCkxScLCjNw69kVuGppEYwxjd83zN6AkoyJeY8lmoqsJgN+fcsqfO5DVfp93b4wdtV14b0THf0SJiWZafjP65dg4zkD1/Ufr69dsQDm3r5qD79di9r2IX4+ISJKhGAQ2L9/8OurVgFy/2W8PU17sL1hu347EFbwzJ4GnOr09UuYFGRYsaTYiTyHBZKknX6dm+/AzWvLcd2K+BP+F1ZeiMqsyvHPiyhFmI0yfnrjcnzpknn6fZ5ABHvru7HlWEe/hElhhhXfuHIhvjBBmzy/vGEebGbtlNej2+pwsHGIcsGUcDxpQlPPUI1WZ80CcrVdmM09zdh8crN+Kayo+OcHjXqZC2eaCbNy7CjNSkNZti2uiVkgrMAoS3EN3/usLVmLFYUrEjIVolRiNMh44PolWFGWiWf2NOBEmxfNvaey8hwWzCtIx6ryLFyxpAgLixwTWrpuSYkTt55djke3nYIvpOCBFw/jVzevnLDvR0Q0IFUdsmSoe14FXq1/S7+tqAIv7GtCT1Arc+GwmlCZa0NJpg3l2TakmaOxSDCiQJYkmAaIRZYXLMfakrUJmwZRqpBlCf9x5UIsKXHi7zvrcby1B429Gydy082Ym+/AivJMXLGkEEtLnBMai1Tm2vHZ8yvxm7eOI6So+O7zB/HnT57F0r1ENHEOHABC/U/UAdCSJatW9bvbHXTjxaMv6rdVIfDSgWa4/GEAgN1iRGWuHaWZ2rqI3RJdBuwrVz5QKeQFuQtwfvn545kNUUqSJAn3XjIXC4sceOz9UzjW2oOGbj8AIMtmwtwCB5aXOnH5kiKsLMuM24CdaEXONNx90Vz86OUjUAXw7WcP4u93rmMsMkmYNKGppasLOHFi8Osxp0zeqH1Db7QqhMBrh1r0+pyZaSbctLY8LlESa6D7jbIRF1RcgPPKzxvHBIhSmyRJ+NiaMnxsjdYEzRMIQ1UBp8006WP5yob5eGFfE7p8YTz/QSNuWVuOdbNzhn8iEVGiDNMA/u1cHxQR7UnyVnUrGl3aD1XpFiNuOqssbnEilsXYPxYxSAasL1uPCysv5A9DNKNdu7wY1y7XmrF7gxGEFTUpdbzvumgOnt7TgCZXAG9Vt+G1w624dFHBpI+DiGaIoXq7zpsHOPqXyXqn7h2E1bB+e+uxDtR1aCfj0kwGfHxNGTLSBv5ZbqBkiSzJOLvkbFxcdTFjEZrRNiwu1Mt/+kIRBMIqsmymSf9/8enzZuHvO+txot2LnXVdeHpPAz6yKrGlwGhgLM9FU8tQQYLNBixcCADoCfXgaMdR/VJ1iwfVLVojeJNBxjXLiwdNmJwpzZiGCyouwJfO+RLOrzifgQFRDIfVlJSECQBk2sz46uXR+uHfeHo//CFliGcQESXYEHFJuDAfe6Vm/XZtew/2N2gJFoMs4eplxYMmTM5kMVhwbtm5uO+c+3Bx1cVs/E4Uw24xJq3xqc1sxDeuWqjf/vazB/Td20RECdXcDDQ0DH49ZgNpn4gawf7WaDmvhi4/dtZ1AtA2w125tGjQhMmZTLIJ55Seg3vOvgeXzbkMRpl7rIn62MxGZNvNSVkvtBgN+Pa1i/XbD7xwuF8/IpoYfBekqUNVgb17B7++fLneAP5A6wH9lAkA7D8d3QW6YVEBctItcU9dlLcIc7PnItOaiQ5/B7r8XVCEgnJnOeZkz2HDd6Ip6mNryvDEjnp8UN+NE+1ePPjSYTY/I6LJ0dMDVFcPevl4ZSYiaqt+e/9pt/71hfPzUei0xj1+Xs48zM+Zj+y0bHQFutDp70RYCaPMWYY52XNgNcY/noimhquWFuGJOfV491g7Gl0BfOe5g/j5x1cke1hENN0MtYHU6QRm9++ZcLTjKAKRaO/HAw3RdZHz5+SiLNsW9/iqrCoszF2IXFsuXEEX2n3tCCthlGSUYG72XKSZ0sY/DyJKuAvm5eGKJYV46UAzOrwhfP3/7cPDt63hpu8JxqQJTR01NYDHM/j1mPqdsQ1XPYFwTH1BM+bkp8c97f7198Nutuu32ciMKHUYZAk/+9hyXPXLdxAIq/jLe3W4aEE+Pjw/P9lDI6Lpbu9ebUPHQMxm7Mz2A719oQNhBSc7tRt2sxGLijPiHv6lc74Ep9Wp364EYxGiVCFJEn700WW4/OdvwxOM4Ok9Dbh4YT6uXlac7KER0XQRDgP79g1+fZAG8LHrImFFxbG2HgBa2a1lpc64x9699m7k2FjqmChVff/6Jdhe24kObwivHW7FkzvqcdPa8mQPa1rj2X+aOobaWVFeDuTlAQDafe1o9DTql6qbo4mWBYXxzalvWXpLXMKEiFLP7Lx0/MeV0dIYX/3HPnR6B2mQSESUCEIMGZf45s/Gce9p/XZNiweqqp2AnV/ogBwTi9yw4Ia4hAkRpZ6SzDR87/poaYxvPH0Aza7AEM8gIhqFQ4eAwCDvKZIErFzZ7+5AJICajhr99ok2L8KKttljbr4DRkN0ue/yOZczYUKU4nLTLfjhvyzTb3/vn4dwst2bxBFNf0ya0NTQ2QkcPTr49Zj6nftb9sddOhKTNJlfGG2MVmAvwLyceYkbIxElzcZzKvCheVritNUTxMf+5z0GCEQ0cY4d02KTQRwqt8aVCT1yxgaOPpnWTCwrWAYiSn3XryjBVUuLAAAufxg3/s9W1LQMcUqeiGgkhAC2bx/8+ty5QEZGv7sPtR2CIqL9Ho80R8uExsYiNpMNZxWflZixElFSXbqoADedVQYA8IUU3PT7bdh3uju5g5rGWJ6Lkk8I4Nlntd8HYrUCixb1PlTEHUFt8wT1BkhFTmtck0guUhBNH5Ik4ScfXYarfvkO2ntCONbag+t+vQW/vXUV1s/JTfbwBtTpDeGt6la8e6wdXd4QfCEFgYgKkyzBZJBhMsowG7SvM20mzM13YH6hA840E4IRFWFF+3X+3LxkT4VoZgkGgRdeGPx6QQF2oUm/6fKH0dhbJjTbbkaeI9pXbWn+UtYaJpomJEnCf16/BHtOdaHRFUB9px83/HoL/vumlbhkUUGyhzcgly+Mt2pa8e7RdrT3BOEPKwiEVRhkCabeGMRskGEyyHCmmTC3IB3zCx3IspkRUlSEeuORdVU5cbvWiSiBtm8fugF8TJnyWLHrIr5QBHUdPgBAusWI0qxob5Il+UtgkA2JGSsRJd03r16E7bWdONHuRbM7gBt/9x7+68bluGb51Cwb6gmE8c7Rdrxd04ZWTxC+UGTQWMRhNWJuQTrmFTiQm25BSFERjqgIKSrOmpUNq2ly38uYNKHke/99oK5u8OvLlgEmEwDgtPs0ugJd+qX43RTR3RcSJCzJZ7NooumkIMOK//eF9fjM/+3EsdYeuPxhbPzTdnz1svn43IeqkrowGVFU7Gtw4YP6blQ3e3C4yY19Da5Bc8GjceIHV0KWuehKNGlefRXo7h70cteiKjR539Nvx5cJzYh7L+IGDqLpJctuxj++sB6fe2QnDjS44Q0puOORnbjnorm45+K5MCTx81pRBQ40uPDB6W4c6YtFTrugqOMPRj749gY405g0IUq4jg7gtdcGv+5wAPP6V89wBVw42X1Sv320pQeqiJYJjY1FluYvTdhwiSj50i1GPPn5dbjz0V3YVdeFYETF3X/dg32nu/HVyxfAlMRNDqoqcKjJjb313TjS7MaRJg8+ON2NsDL+WOTdr12I0ixbAkY5ckyaUPIoCrBtG7Bp09CPW7NG/3JH4w79ayEEanoXKiRJwtyCaAP4iswK1g8nmoYqcux4+l/X494n9uKNI61QVIEHXzqCnXVd+K8bl8OZZprQ7y+EQE8wghZ3EMdaPahp6cEH9d14v7YTPcHIhHzPsKrCwt1hRBMvFAJefnnoHmsWC7ZleYHeyl1CiLgNHLFlQgvTC5Fn50kxoummODMNf//8enzlHx/ghX1NEAL479ePYvepLvzi4yuQk24Z/kXGQQgBb0hBqzuAY609qGnxYN9pF7ad6IA7MEGxSG+fBCJKoIYG4OGHh37M6tUDNoDf2bgz7vZgm0mzrFkozSgd3ziJaMrJc1jw+B1n45tPH8Dfd2l9Fh9+pxZ7TnXjoVtWodBpnfAx+EIRtLqDWizS6sGBBhfeO96BLl94Qr5fIhIvo8WkCY2f1ws0N2uLDX2/gkHt90hES44oivY4j0f7PRgEfL7hX3vNGiA/HwDQ5e/CgdYD+qVTnT54ehcpZ+XYYDNH/zlzZyfR9OWwmvDwbWvw8001eOjNYwCATYdacNnP38a9l8zFjatLx1xCotsXwv4GFxq6tFI7kgQ0u4I41OTCkWYPmlwBhCIjWziYm5+Oixbm4+IFBZiTnw6b2QCLUYYqtMWH2KOmLe4gapo9qGnxIBhRtSOqveW7JPCUCdGodHcD7e2DxyWqqv3e06PFJT6fdt3vH/alfeedjV3dW/Xbze4AOr0hANpCamzilrEI0fSVZjbgoZtXYnFxBv7rlWqoAnjnaDsu+8XbuOvCObj57HJYjGPb8ODyh3GwwYX6Lh+EAGRJQqsngENNbhxu8qDJ5UcgPLJYpCrXjgsX5OPihfmYX+CA3WKMi0W0XwKhiIpWTwDVzR4cbe2BP6T0lhKVYDHIsBh5yoRoQEIAJ08OHHOEw9G1kEAgGncEAtqayHBH0p1OYN26fncHIgFsb4j2QOn0BtHk0prI56Zb4sqELitYxjKhRNOUxWjAjz+6DEtKnPjPFw4hrAjsrOvCZb94G//64dm4ff2sMZez6glGcKDBhboOL4TQ1kU6vCEcanTjcJMbjd0B+MPK8C8EoDzbhosW5OOiBflYVJwBu9kIq6l/LBJWVLR5gqhp8aC6xQNvMAKzwQCTUYLZICPDOvkpDCZNaPzq64Ennkj862ZmApdeqt/cWr8Vqoj+gLDjZLRM16Ki6G4Kg2TAorxFiR8PEU0ZBlnCVy6bj9WzsvClJ/ei2xdGszuAf39qP37/9gnctq4ClywsQFn20Mc3vcEI3j3WjjePtGLr8Q6c6hxBMncAOXYz1s/JxdmV2VhcnIF5vQsTA45dAgyyIS6AKXKmYUVZ5pi+NxGdYf9+4PXXE/+6FRV4r1hFpD66k3tnTCyyuIhlQolmEkmS8K8fnoOVZVm4+6970N4TRHtPCN95/hAefqcWn1w/C5cuKsCsXPuQr+MPKdh6vB1v9MYite3eMY0n02bC+tk5OKcqR49FHNaBT+AOFIsUOq1YVpo5pu9NNGNJEvDoo1piJNGuuw6w9D+5trNxJ4JKUL892LoIACwtYGkuoulMkiTcvn4WlpU6cdfje9DQ7YfLH8aDLx3Bn7bU4rZ1s3DZ4gLMzksfMoEaCCt4v7YTbx5pxTtH23Ci3TumUuMOqxHrqnKwbnYOFhc79Z6tAxkoFinIsGJJydSpGsSkCY2f2Tz8Y4Yhev83xv0njgkSPEFPXGmuJpcfp7u0xc0smxmz86OluebnzofVOPFH0Ygo+S6cn49/3n0evvPcQbx2uBUAUNvuxXefP4TvPn8Is/PsyLSZYTJIEALwhiLoCUTQE1TgDUZGvDsCANJMBpRn25DrMCPHbkFlrh3zChyYX5iOqtx09h0hmioGWGAYiQFjkT5mMwJXX453Dv6PfldHTxDH23oAAHaLEfOLoqW5KrMqkWHJ6PcyRDT9rJudgxfvOQ/fff4QXtjfBABo6PbjgRcP44EXD6My145se3ws4g0q6AlG4A1G4AuNPBaxGGUtFkm3INdhwawcG+YWODC/wIG5+YxFiCbbia4TsIU6YYtISDOmwSgbx32yQwgBae1aoKqq37WwEsZrJ6I9UNyBsN5bzWIyxC02ljhKkGvLHddYiCg1rCzPwj/vPg/f/+chPL23AUIALe4gfvJKNX7ySnVv7GCGuffkqLd3PaSn99doYhGzHouYkZNuQUW2DfMLHZib78D8QkdS+7slGpMmNC5HO46i7vQWVHQchSRpZWRkSda/NsgGGGUjTLIJsiRDQCCkhOAJeuAJeRCMBKEIBYqqQEBAggRJktCyeBZa216E3WWHoiqod9fHfd/Y3RSrK7IgxwQm60r7H2EloumrNMuGP9x+FnbVdeLHL1fj/dpO/drxNi+Ake3YNBtlLC1xYmmJE3ML0mGQJAgAGVYTFhY5UJFjn1YBANF0VNNRg0MnXsSc1gMwySaYDKa43/tikbAShjvoRk+oB4FIYMBYxCAZYDKYYDaYceq8pdgdkzABgJ11MbFIeRaMMTXHGYsQzSz5GVb8+tZVuPO0Cz95tRpv17Tp12rbvSM+PWI2yFhUnIFlpU7MLXDAJGuxSLrFiIVFDszKsY+5BCkRJd72hu3I7DwEq1c7+WGQDEgzpcFqtMJqtGpJlN41kr5YxCAboAoVYSUMT8gDT9CDoBJERI1AURX40i3YZRGQ3t4Lo2yE3WyH3aSdWKtz1cV9/911XXoD+BWlmfqCKACsK2MsQjSTZNnN+NnHV+DzF8zGT1+txquHWvRrpzp9I66qYZQlLCrOwNISJxYUOmAyyBAAbGYDFhZloCp35sQiTJrQuDR4GrCn4yCMnoaEvJ6AgCsnHUeWl0DxtaHN19bvMR09QZzo3dmZbjFiQczOzgpnBcqcZQkZCxGlltUV2Xjy8+tQ3ezBpkPN2HSoBQcb3Yio0XOlBllCusWIdIsRdosB6RYjFhVn4KIF+VhXlYs0MxuuE6WyTn8nGgJtyPS1j/k1BASEENqChhrGyRI7DpXE/2Dg9g++s7MwvRBzsueM+fsTUepaWurEXz69FsfberDpUAs2HWrBvtPdcc1LZQkxsYgR6VYjFhQ6cOH8fJw7J3fQ8p5ENPV0+bvgiOlfpAgFPaEe9IR6xvR6ilHGoQsWIWKUATWMsBqGP+JHO/rHNb5QBAcaXQAAo0GOK/WbnZbNkuVEM9T8Qgd+f9sa1HV49Vhkz6luhJRouwNZ0k7Kx8Yjc/PTceGCfJw3NxcZg5T3nGkYkU2SsKKirsOHY609ONXphcVoQJbdjGybGVl2E7LtZjisJhhlCUZZgkGWUqJhlypURMbYWOhMIasJDQtKUL+kDOogjRMjior3jnfot1edsbPzvPLzEjIWIkpd8wu1Y6F3XTQXAKCoWoNTSdLKWqTCeyvRRFBUgVOdWixyst0Lk0HSYhG7GVk2M7LsZjjTUi8WieUL+xIWl/gdaahfUoamuUVazfJeEVXFeyc6Bt3ZeV75eSn350ZEiTU7Lx2zL0jHnRfMBqC9/4YVFUIAVhNjEZq5FFXgdJcWi9S2e2GQJWTbzci0RddGnGkmmAzylI9FhBDoCnShxDz+ZTUhSWiblYfaFbPgdw7dkxHQ/hzfP9GJSG9CdklxRtzmr/Vl6yFLM2MnOBENrCLHjs+eX4XPnq+V+uuLRVQhkGYyTNn31qmESZMJFFFUbDnegWf3NuDVgy3oCUaGf1IMQ2+QkGs3ozTbhopsGy5ckI9LFxXANElHoVRVYMfJThxr64GqCqhCaxDUV/euzluLTpcble4ALEYZVqMMs9EASQIkDFIXHNp/Vm9IgQtAa4YNR4tzcKIwC5JJhnyqG2ajDJvZqH3wCy3p1N4TxN76br0HgZU7O4loBAyyxBMkNGNpP1R34Nm9jXjpQBPcgbHFItk2M8qy01CWbcMF8/Jw+ZJCWAbZ4JBoQgjsPtWF6uYeKEJAVQWCEQU9gQg8wQjMBhkZaSZkWI2o8zYgGFIQUVXIkjRkLKKqAt6QVsNXUQUiAPwmIzqy03F8Vj7airNgsxhh6/IDkhaLdHpD2FvfDW9vTMednUQ0Etp7KWMRmplUVWBnXRee3duAF/c3ocsXHtXz+2KRzDQTyrJtKM+2Yf3sHFyzvDiugfBEEkJg32kXDja6oQjtRKo74MXmE41AuxclHd7eJI8Ms1GG1aT9PlQsohhl9DjScCwnA3sKs+EyGRFq90Np9cEgA7IkwWSQYTMbkGbWFjjDigqXL4w99d3wBLQ/R1mWsLoiS3/ddHM6VhSumJQ/FyJKHYxFRo9Jk2F4gxEcafYAEBACEACCYRWeQBieYAQStDr4BllCly+Mdk8QDd1+VDd7cLTVg0BYHeY7DE5RBRRVoNEVQKMrgO21nfj7rtPId1hw09pybFhUgEVFGf0a/rn8YRxpcqPbH4YvpDU89gUj8IYUCCFQnJmGsiwbKvPsKHZa4z7AhRBocQdR3eLB1mPteO6DRjS5AoOO0S83IIxOZAsDwqqMkCIjCBlhgwEhgwxhMsBoMsBkNiBiMqLHbES3wYDmiIqwJEV3bkYAnO4e1Z/PeXNyubOTiIimvUBYwcFGN2JjkVCkNxbpTYKYjTKMsowuXwjtPUE0dvtR3dKDoy2eUTX2O1NfLNLsDqDZHcCOk114ancDcuxmfOysMly2uBBLS5z9+v14AmEcafag0xuCr7fpcd/viipQlGnVYpFcO0qz0vrFIm09QdQ09+C9E+14dm8jTnf5RzRer+EgbIEWnO5REJZlhAwyImYDZLMRktUIYTAgIkkICYFWRcBjNMJrNsJvMkCJjUu8CnB0+BJf66ty4pKy55ady52dREQ07YQVFftOu6BFIYAQWiziDmibKVUhYOmNRVz+MNo8QTS5/Khu8aCm2QNvAmKRVk8QrZ4gdtV14ek9DfjPFw7jxtWluGJpIZaVZvbbWOoLRXC4yY2OnhB8IUXbKBHUNoBGVBWFzjSUZaWhMteO8mxbv7WEjp4gqps92H6yE8/ubezXmyiCdvQYO2FQJOSaLQgbZAQNBoQNWvwRMshQJQmKLEE1yFDsFih2K0JWI0KyAWEBdPlDUAMCiOnZOlpnVWTDEVNKZ13pOhhlLvUREY0X30mHcaLNi3/57dZxv47DasT5c3MxN9+Bqjw7FFWg0xtCly+ETm8Y3b4QPAHtw1tRBSKqQETRjk61uANxuzFaPUH88vWj+OXrR5FpM2FJsRNGg/YBX9fhG3GjQQDIsZuxpMQJoyyh0RVAQ5dvlLtQVaiyjKcWV4zmKYA8tgUFSQLm5juwpiIL+RlW/f6ctBzu7CQiommp2RVISCxiNxtw3txczCtwYHZeOgQEOr1hdPXGI12+ENz++Fikr7xdmyeIDm9If60Obwi/fes4fvvWcTisRiwrdcJkkCFBazR4ot0LIQYfS6xMmwlLS5ywGGU0uQJo6Paje5S7UPsIBOFKM+PpRaOIS8Zgdl461szKQpEzTb/PYXZgeeHyCf2+REREyeD2hxMSi6SZDDh3Ti7mF6Zjdl46ZElCpzeEbl8Inb4QurxhuANhhJX4WCSsCLR5gmjvCeqv5fKH8Yd3a/GHd2uRbjFqsYRJi0Uauv041toDdYSxSIbViKWlTtjMRjT3xiKdMXHPQFRJ61uytTx/5H8AKgBfBNqu0fGZlWPHmllZKMmMxiJpxjSsKV4z7tcmIiImTSaMJGkfYktLnLhqWRE+PD9vXGUsPIEw9tZ347Ftp7DpcAuU3k//bl8Y7x4be7PTDm8Im2v6N1vvY5QlXDAvDxcuyIfNrJXdshoNSLcaYTMb8eZJFz5o6YE/rMAdCMPlD8MbVKD2ls8IKSoCYQX+sAohBCRJgkEGstLMKHBake+wIM1k0I+uqkILioIRFf6QAl9YgQyt/IXFKKMix4ZMmzlujBIkXDP/Gu7sJCKiaWk8hyjLs21YWuLElUuLcPHC/HGVsfAGI9h32oXHt5/CS/ubEOmNRTyBCLYc6xjm2YPr9oXxzhCnOmQJOHdOLjYsKoDNbIQsA2aDAQ6r1rQwrKjwBCLo8oXwxKHtaHT74Q1G9FJeYUWFP6zFI2pvLCJLQGaaGYVOK/IzLLCZDDAZZRhiYpGQosIXUuDv3R1rMmilNsqz05Btt/Qb5zXzr+HOTiIimpbGU9GhNCsNi4szcMWSIly6qAB2y9g/K32hCA42uvHX7afwz31NCEW0yh49wQjeOzH2WMQ9TCwjScA5lTm4bHEBHFYTZBmo6QrhcFcJzAYZqtA2mfjDCjyBCFz+MHoCsbGI0NZFIgpUNRqLZFhNKHRaUZBhhd1sgMmgVTFRhYAqYtZF9FhEK9lVmmVDnqN/LHLl3CthMfa/n4iIRo8/2Q0j12HGp8+tjOnRoZXAcFhN+od9KKJCUVVkppmR6zAj32HF7Lz0hNbQd1hNOH9uHs6fm4cmlx+bDrVgy7F2vHe8I+5kiNkoY1FRBpaVOlHotMJu1hYU7GYD7BYjFCFwusuP050+HG72YP/pbv0Ui1GWUOi0oiovHQsKHVhQ6MCH5+cj224ebFg47bejR6QPO37Ru9000eWzLAYLrp53NWZlzkro6xIREU0VGVZTXCwCRGORdIsBkCSEIioiiopMmwm56RbkO6yoyrOPa2HiTHaLEetm52Dd7By0Xr0Qmw61YOuxDmw93h53ItZkkLCwKANLS5wozkxDusUIW28cYrcYIYRAQ7cf9Z1+VDe7se+0Sz/FYpAlFGZoY59XoMUiF8zPQ77DOtiw4tQF0+ENDxxrTFQsYpJNuGLuFZiXMy+hr0tERDRVWE2yHosAWjxiMspwWI1wWIyQemORsKLCmWZCTroF+Q4LqvLscaWjxstmNuKsWdk4a1Y2vnnVImw61IwtvbFIe0/0ZIhRljC/0IFlpU6UZtlgNxtgsxj1mESSJDR2+1Hf6UNNiwcfnHahzaOdYpEloCDDispcO+YXOjC/QItFYk+XAsBz1bvhl+2jGv9ExSJG2YhLqi7B0oKlCX1dIqKZTBJipMUTUoPb7YbT6YTL5UJGRkayhzPhVFXAE4wAAlCFQLrVOKom8UIINLkCMMoSctMt/fqjDOepw09hX8u+0Q573MwGM1YXrca6snXIsEz/v2ciovGaaZ+PyTaT/rxVVaAnFIFQAUUIpFuMcT3HhtPXTw0A8hyWfv1RRvM633/7+1DF2PvJjYZRNmJV0SqsL1uPTGvmpHxPIqJUNpM+G6eCmfTnLYS2LiJUbV3EZjGMutJHqyeAiCKQ77DAOII1lf/b+3+o7a4d65ATwiAZsLxwOc4tOxc5tpykjoWIKBWM5rORJ01SnCxLcKaNfeeGJEkozkwb/oGDGO/ChNVoRZY1C2aDGUbZCEUoiKgRBCIBeENe+CN+SJBgMphgNphRlF6EyqxKrCxciTTT2MdNREREiSHLEjLGsYtUkrSTruMVUkJIM6bBF/ZBYOR7giwGC7LSsmAxWPRYRFEV+CP+AWORAnuBHovYzaPbYUpERESJJ0nji0UAjPhUa5/L51yONl8bugPd6PJ3oSvQhS5/F1xB16jXScwGM7LTsvVYRBVqdF0k7IU/7IeAgNlghkk2Id+ej8qsSqwoXMFNpEREE4RJExqXK+deiYsrL4YiFKhChaIqcV8HlSB8YZ+2gCEEZEmGQTYg3ZyOovQiZKdlD3k0daKOrxIREdH0YjFacP+590MIgUAkoMcf3rC3XywiSzLSzekoTC9Eri2XsQgRERGNSkF6AQrSC/rdrwoVroALvrBP34gRUkJ6LKIKVY9F7GY7CuwFyLPnDdmjlbEIEdHkY9KExsVmssFmsk3Y6zMoICIiotGQJAlppjSkmdKQg/GXqmAsQkRERCMlSzKy0rKQlZaVsNdkLEJENPlGXnCaiIiIiIiIiIiIiIhoGmPShIiIiIiIiIiIiIiICEyaEBERERERERERERERAWDShIiIiIiIiIiIiIiICACTJkRERERERERERERERACYNCEiIiIiIiIiIiIiIgLApAkREREREREREREREREAJk2IiIiIiIiIiIiIiIgAMGlCREREREREREREREQEgEkTIiIiIiIiIiIiIiIiAEyaEBERERERERERERERAQCMyR5AogkhAAButzvJIyEiIpo6+j4X+z4naWIxHiEiIorHWGRyMRYhIiKKN5pYZNolTTweDwCgrKwsySMhIiKaejweD5xOZ7KHMe0xHiEiIhoYY5HJwViEiIhoYCOJRSQxzbZ5qKqKxsZGOBwOSJKUkNd0u90oKytDfX09MjIyEvKaU8l0nt90nhvA+aWy6Tw3gPObioQQ8Hg8KC4uhiyzOudES3Q8kor/5kaD80td03luwPSe33SeG8D5TUWMRSYXY5HR4fxS13SeGzC95zed5wZwflPRaGKRaXfSRJZllJaWTshrZ2RkpMw/grGYzvObznMDOL9UNp3nBnB+Uw13dU6eiYpHUu3f3GhxfqlrOs8NmN7zm85zAzi/qYaxyORhLDI2nF/qms5zA6b3/Kbz3ADOb6oZaSzC7R1ERERERERERERERERg0oSIiIiIiIiIiIiIiAgAkyYjYrFY8O1vfxsWiyXZQ5kQ03l+03luAOeXyqbz3ADOjyjRpvu/Oc4vdU3nuQHTe37TeW4A50eUaNP93xznl7qm89yA6T2/6Tw3gPNLddOuETwREREREREREREREdFY8KQJERERERERERERERERmDQhIiIiIiIiIiIiIiICwKQJERERERERERERERERACZNiIiIiIiIiIiIiIiIADBpMiK/+c1vUFlZCavVitWrV+Odd95J9pBG7cEHH8RZZ50Fh8OB/Px8XH/99aiuro57jBAC3/nOd1BcXIy0tDR8+MMfxsGDB5M04rF78MEHIUkS7rvvPv2+VJ9bQ0MDPvGJTyAnJwc2mw0rVqzArl279OupPL9IJIJvfvObqKysRFpaGqqqqvC9730Pqqrqj0ml+b399tu45pprUFxcDEmS8Mwzz8RdH8lcgsEg7r77buTm5sJut+Paa6/F6dOnJ3EWAxtqbuFwGF/72tewdOlS2O12FBcX47bbbkNjY2Pca0zVuQHD/93F+vznPw9JkvCLX/wi7v6pPD9KbYxFUgtjkdSaH2MRxiJTYW4AYxGa2hiLpBbGIqk1P8YijEWmwtwAxiKxmDQZxpNPPon77rsP3/jGN7Bnzx6cf/75uOKKK3Dq1KlkD21UNm/ejC9+8YvYtm0bNm3ahEgkgg0bNsDr9eqP+fGPf4yf/exneOihh7Bjxw4UFhbi0ksvhcfjSeLIR2fHjh34/e9/j2XLlsXdn8pz6+rqwrnnnguTyYSXXnoJhw4dwk9/+lNkZmbqj0nl+f3oRz/C7373Ozz00EM4fPgwfvzjH+MnP/kJfvWrX+mPSaX5eb1eLF++HA899NCA10cyl/vuuw9PP/00nnjiCbz77rvo6enB1VdfDUVRJmsaAxpqbj6fD7t378a3vvUt7N69G0899RRqampw7bXXxj1uqs4NGP7vrs8zzzyD999/H8XFxf2uTeX5UepiLDI13+8Hw1gk9ebHWISxyFSYG8BYhKYuxiJT8/1+MIxFUm9+jEUYi0yFuQGMReIIGtLatWvFnXfeGXffggULxNe//vUkjSgxWltbBQCxefNmIYQQqqqKwsJC8cMf/lB/TCAQEE6nU/zud79L1jBHxePxiLlz54pNmzaJCy64QNx7771CiNSf29e+9jVx3nnnDXo91ed31VVXiU9/+tNx933kIx8Rn/jEJ4QQqT0/AOLpp5/Wb49kLt3d3cJkMoknnnhCf0xDQ4OQZVm8/PLLkzb24Zw5t4Fs375dABB1dXVCiNSZmxCDz+/06dOipKREHDhwQFRUVIif//zn+rVUmh+lFsYiU//9vg9jkdScH2MRxiJTbW5CMBahqYWxyNR/v+/DWCQ158dYhLHIVJubEIxFeNJkCKFQCLt27cKGDRvi7t+wYQO2bt2apFElhsvlAgBkZ2cDAGpra9Hc3Bw3V4vFggsuuCBl5vrFL34RV111FS655JK4+1N9bs899xzWrFmDG2+8Efn5+Vi5ciUefvhh/Xqqz++8887D66+/jpqaGgDABx98gHfffRdXXnklgNSfX6yRzGXXrl0Ih8NxjykuLsaSJUtSbr4ulwuSJOm7f1J9bqqqYuPGjbj//vuxePHiftdTfX40NTEWSa33e8YiqTk/xiKMRVJlboxFKBkYi6TW+z1jkdScH2MRxiKpMreZFIsYkz2Aqay9vR2KoqCgoCDu/oKCAjQ3NydpVOMnhMCXv/xlnHfeeViyZAkA6PMZaK51dXWTPsbReuKJJ7B7927s2LGj37VUn9uJEyfw29/+Fl/+8pfxH//xH9i+fTvuueceWCwW3HbbbSk/v6997WtwuVxYsGABDAYDFEXBAw88gJtvvhlA6v/9xRrJXJqbm2E2m5GVldXvMan0vhMIBPD1r38dt9xyCzIyMgCk/tx+9KMfwWg04p577hnweqrPj6YmxiKp837PWCR158dYhLFIqsyNsQglA2OR1Hm/ZyySuvNjLMJYJFXmNpNiESZNRkCSpLjbQoh+96WSu+66C/v27cO7777b71oqzrW+vh733nsvXn31VVit1kEfl4pzA7Qs7po1a/CDH/wAALBy5UocPHgQv/3tb3Hbbbfpj0vV+T355JN49NFH8fjjj2Px4sXYu3cv7rvvPhQXF+P222/XH5eq8xvIWOaSSvMNh8O46aaboKoqfvOb3wz7+FSY265du/Df//3f2L1796jHmgrzo6lvOr0HAoxF+qTC3ADGIn1SdX4DYSwSLxXmxliEkm06vQcCjEX6pMLcAMYifVJ1fgNhLBIvFeY202IRlucaQm5uLgwGQ79MWGtra7+MaKq4++678dxzz+HNN99EaWmpfn9hYSEApORcd+3ahdbWVqxevRpGoxFGoxGbN2/GL3/5SxiNRn38qTg3ACgqKsKiRYvi7lu4cKHedC+V/+4A4P7778fXv/513HTTTVi6dCk2btyIL33pS3jwwQcBpP78Yo1kLoWFhQiFQujq6hr0MVNZOBzGxz72MdTW1mLTpk36bgogtef2zjvvoLW1FeXl5fr7TF1dHf7t3/4Ns2bNApDa86Opi7FIasyVsUjq/t0BjEUAxiKpMDfGIpQsjEVSY66MRVL37w5gLAIwFkmFuc20WIRJkyGYzWasXr0amzZtirt/06ZNWL9+fZJGNTZCCNx111146qmn8MYbb6CysjLuemVlJQoLC+PmGgqFsHnz5ik/14svvhj79+/H3r179V9r1qzBrbfeir1796Kqqipl5wYA5557Lqqrq+Puq6mpQUVFBYDU/rsDAJ/PB1mOfysyGAxQVRVA6s8v1kjmsnr1aphMprjHNDU14cCBA1N+vn2BwdGjR/Haa68hJycn7noqz23jxo3Yt29f3PtMcXEx7r//frzyyisAUnt+NHUxFkmN93vGIqn7dwcwFmEskhpzYyxCycJYJDXe7xmLpO7fHcBYhLFIasxtxsUiE95qPsU98cQTwmQyiT/+8Y/i0KFD4r777hN2u12cPHky2UMblS984QvC6XSKt956SzQ1Nem/fD6f/pgf/vCHwul0iqeeekrs379f3HzzzaKoqEi43e4kjnxsLrjgAnHvvffqt1N5btu3bxdGo1E88MAD4ujRo+Kxxx4TNptNPProo/pjUnl+t99+uygpKRH//Oc/RW1trXjqqadEbm6u+OpXv6o/JpXm5/F4xJ49e8SePXsEAPGzn/1M7NmzR9TV1QkhRjaXO++8U5SWlorXXntN7N69W1x00UVi+fLlIhKJJGtaQoih5xYOh8W1114rSktLxd69e+PeZ4LBoP4aU3VuQgz/d3emiooK8fOf/zzuvqk8P0pdjEWm5vv9cBiLpM78GIswFpkKcxOCsQhNXYxFpub7/XAYi6TO/BiLMBaZCnMTgrFILCZNRuDXv/61qKioEGazWaxatUps3rw52UMaNQAD/vrzn/+sP0ZVVfHtb39bFBYWCovFIj70oQ+J/fv3J2/Q43BmcJDqc3v++efFkiVLhMViEQsWLBC///3v466n8vzcbre49957RXl5ubBaraKqqkp84xvfiPtASaX5vfnmmwP+X7v99tuFECObi9/vF3fddZfIzs4WaWlp4uqrrxanTp1KwmziDTW32traQd9n3nzzTf01purchBj+7+5MAwUHU3l+lNoYi6QexiKpMz/GIoxFpsLchGAsQlMbY5HUw1gkdebHWISxyFSYmxCMRWJJQggx0lMpRERERERERERERERE0xV7mhAREREREREREREREYFJEyIiIiIiIiIiIiIiIgBMmhAREREREREREREREQFg0oSIiIiIiIiIiIiIiAgAkyZEREREREREREREREQAmDQhIiIiIiIiIiIiIiICwKQJERERERERERERERERACZNiIiIiIiIiIiIiIiIADBpQkREREREREREREREBIBJEyIiIiIiIiIiIiIiIgBMmhAREREREREREREREQFg0oSIiIiIiIiIiIiIiAgA8P8D44YC6P1yUcsAAAAASUVORK5CYII=", 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", 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" + "
" ] }, "metadata": {}, @@ -275,9 +275,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -298,7 +298,7 @@ "id": "b5240535-5123-4ac5-a5e0-e0502ef80b3e", "metadata": {}, "source": [ - "### 1.2 Motif search with STOMP" + "### 1.2 Motif search with StompMotif estimator" ] }, { @@ -311,23 +311,23 @@ "id": "6aecb58e-9de9-4264-959e-4180ab3fa27a", "metadata": {}, "source": [ - "When doing motif search, it's important to define the type of motif you want to extract from a series. We'll use the figure and definitions given by [1] :\n", + "When doing motif search, it's important to define the type of motif you want to extract from a series. We'll use the figure and definitions given by [1] and make some adjustement to clear out some confusion due to the naming of each method:\n", "\n", "![image.png](attachment:f492cb89-5bf3-4641-8be2-a77805f20b88.png)\n", "\n", - "For now, the `StompMotif` estimators supports only \"Pair motifs\", \"k-Motiflets\", and \"k-motifs\". Note that the naming \"k-motifs\" is a bit confusing, it extract motifs based on a range parameter and not by number of closests neihbors. To choose the type of motifs you want to extract, you will have to use the parameters of the `predict` method :\n", + "For now, the `StompMotif` estimators supports only the following configuration, which you will have to specify using the parameters of the `predict` method :\n", "\n", - "- for **\"Pair Motifs\"** : This is the default configuration\n", + "- for **\"Pair Motifs\"** : This is the default configuration with ```{\"motif_size\": 1}```, meaning we extract the closest match to each candidate, so we end up with the pair ```(candidate, closest match)```\n", "\n", - "- for **\"k-Motiflets\"** : ```{\"motif_size\": k}```\n", + "- for **\"k-motif\"**, which we define as the extension of **Pair motifs** to : ```{\"motif_size\": k}```. For ```k=2```, we would extract ```(candidate, closest match 1, closest match 2)```\n", "\n", - "- for **\"k-motifs\"** : ```{\"motif_size\": np.inf, \"dist_threshold\": r, \"motif_extraction_method\": \"r_motifs\"}```\n", + "- for **\"r-motifs\"**, which we renamed from **k-motif** in the figure, because it is a range-based method : ```{\"motif_size\": np.inf, \"dist_threshold\": r, \"motif_extraction_method\": \"r_motifs\"}```\n", "\n", "These configuration will extract the best motif only, if you want to extract more than one motifs, you can use the `k` parameter to extract the `top-k` motifs. \n", "\n", - "**The term `k` of `top-k` motifs, while also used in `k-Motiflets`, is not the same. We use `motif_size` as the `k` in `k-Motiflets`. This is to avoid \"extraction the `top-k` `k-motiflets`\", which can lead to confusions. Rather, we extract the `top-k` `motif_size-motiflets`**.\n", + "**The term `k` of `top-k` motifs, while also used in `k-motifs`, is not the same. We use `motif_size` as the `k` in `k-motifs`. This is to avoid \"extraction the `top-k` `k-motif`\", which would be confusing and ill defined. Rather, we extract the `top-k` `motif_size-motifs`**.\n", "\n", - "The `top-k` using `motif_extraction_method=\"r_motifs\"` will be the motif with the highest cardinality (i.e. the more matches in range `r`), while for `motif_extraction_method=\"k_motifs\"`,which is the default value, the best motifs will be those who minimize the maximum pairwise distance." + "The `top-k` using `motif_extraction_method=\"r_motifs\"` will be the motifs with the highest cardinality (i.e. the more matches in range `r`), while for `motif_extraction_method=\"k_motifs\"`,which is the default value, the best motifs will be those who minimize the maximum pairwise distance." ] }, { @@ -427,9 +427,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -446,21 +446,172 @@ }, { "cell_type": "markdown", - "id": "1610adf3-5cb1-466e-9cad-fb248148fd5a", + "id": "5f9f5a86-19b9-4259-8cdc-2664b22532ae", "metadata": {}, "source": [ - "## References\n", - "[1] Patrick Schäfer and Ulf Leser. 2022. Motiflets: Simple and Accurate Detection\n", - " of Motifs in Time Series. Proc. VLDB Endow. 16, 4 (December 2022), 725–737." + "## 1.3 Motif search with KMotiflets estimator" + ] + }, + { + "cell_type": "markdown", + "id": "7d2522e0-e6f4-412e-b0cb-2945016d188a", + "metadata": {}, + "source": [ + "# 2. Collection estimators\n", + "\n", + "Now, we'll explore estimators of the `collection` module, where you must provide single series of shape `(n_cases, n_channels, n_timepoints)` during fit." + ] + }, + { + "cell_type": "markdown", + "id": "5aea3e4f-e613-4646-b012-e64c5ec9586f", + "metadata": {}, + "source": [ + "## 2.1 Approximate nearest neighbors with RandomProjectionIndexANN\n", + "\n", + "This method uses a random projection locality sensitive hashing index based on cosine similarity. W we define a hash function as a boolean operatio such as, given a random vector ``V`` of shape ``(n_channels, L)`` and a time ser ``X`` of shape ``(n_channels, n_timeponts)`` (with ``L<=n_timepoints``), we com \n", + " ``X.V > 0`` to obtainhash of ``X``e \r\n", + " In the case where ``L 0``` instead.\n", + "\n", + "The ```RandomProjectionIndexANN``` estimators use the parameter ```n_hash_funcs``` to create that much random hash function as defined above. Each series `X` of the collection given in fit is then represented as an array of ```n_hash_funcs``` boolean, which is then hashed to a dictionnary as ```{hash(bool_array): case_id_array}```.\n", + "\n", + "To compute the nearest neighbors of a series ``X`` given in predict, we first transform this series to a boolean array using our previously defined hash functions, and then do ```hash(bool_array)``` to look at the bucket in which ``X`` falls, and consider the ```case_id_array``` as the indexes of its neighbors. If this bucket doesn't exists, we compute a distance matrix between the boolean array of ``X`` and every boolean array making the keys of the dictionnary to get similar buckets.\n", + "\n", + "This method will not provide exact results, but will perform approximate searchs. This also ignore any temporal correlation and consider series as high dimensional points due to the cosine similarity distance.y distance.\r\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "989ba9f2-6dd8-4db7-9dfc-783aac5e6fcb", + "execution_count": 9, + "id": "cc719800-0119-42f9-9018-32288c2db69b", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 0 : 130 with distance 1.0\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from aeon.similarity_search.collection import RandomProjectionIndexANN\n", + "\n", + "X_fit = X[:199]\n", + "X_predict = X[199]\n", + "index = RandomProjectionIndexANN().fit(X_fit)\n", + "indexes, distances = index.predict(X_predict, k=2)\n", + "\n", + "for i in range(len(indexes)):\n", + " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", + " # A bit of hacking of the function defined for series estimator to show best mathces\n", + " plot_best_matches(X_fit[indexes[i]], X_predict, 0, [0], X_predict.shape[1])" + ] + }, + { + "cell_type": "markdown", + "id": "c4c7a34a-3620-475c-96b8-a9bb605d09c3", + "metadata": {}, + "source": [ + "You can then play with the different parameter of the estimator to affect the speed vs accuracy of the index, for example increasing ```n_hash_funcs``` from the default 128 to 512, and considering larger vectors (``V`` of shape ``(n_channels, L)``) for the hash functions by tuning ```hash_func_coverage``` (a float between 0 and 1, with 0.25 as default) such as ```L = n_timepoints * hash_func_coverage```:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1b22b743-5710-4691-b740-8edaa3bbac2e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 0 : 130 with distance 3.0\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "index = RandomProjectionIndexANN(n_hash_funcs=512, hash_func_coverage=0.75).fit(X_fit)\n", + "indexes, distances = index.predict(X_predict, k=2)\n", + "\n", + "for i in range(len(indexes)):\n", + " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", + " # A bit of hacking of the function defined for series estimator to show best mathces\n", + " plot_best_matches(X_fit[indexes[i]], X_predict, 0, [0], X_predict.shape[1])" + ] + }, + { + "cell_type": "markdown", + "id": "7828c48c-abdb-4807-bc94-d9b8414b5282", + "metadata": {}, + "source": [ + "This type of method is mostly interesting where speed of the search is paramount, or when the dataset size grows large (> 10k samples)." + ] + }, + { + "cell_type": "markdown", + "id": "1610adf3-5cb1-466e-9cad-fb248148fd5a", + "metadata": {}, + "source": [ + "## References\n", + "[1] Patrick Schäfer and Ulf Leser. 2022. Motiflets: Simple and Accurate Detection\n", + " of Motifs in Time Series. Proc. VLDB Endow. 16, 4 (December 2022), 725–737." + ] } ], "metadata": { @@ -479,7 +630,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/examples/similarity_search/similarity_search_tasks.ipynb b/examples/similarity_search/similarity_search_tasks.ipynb index 86fe5b5274..54d17857d5 100644 --- a/examples/similarity_search/similarity_search_tasks.ipynb +++ b/examples/similarity_search/similarity_search_tasks.ipynb @@ -128,7 +128,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.10.13" } }, "nbformat": 4, From 6e3157b1bbdd1f7bb746491a155ea20b224f9ef2 Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 2 Feb 2025 12:46:51 +0100 Subject: [PATCH 20/36] Fix numba test bug and update testing data for sim search --- aeon/similarity_search/collection/_base.py | 40 ++--- .../collection/neighbors/_rp_cosine_lsh.py | 87 ++++++----- .../neighbors/tests/test_rp_cosine_lsh.py | 1 + aeon/similarity_search/series/_commons.py | 2 +- .../series/neighbors/tests/test_dummy.py | 2 +- .../series/tests/test_commons.py | 51 +++---- aeon/testing/testing_data.py | 142 +----------------- .../similarity_search/similarity_search.ipynb | 26 ++-- 8 files changed, 124 insertions(+), 227 deletions(-) create mode 100644 aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index cbbf8de1e9..8277a36e96 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -6,7 +6,7 @@ ] from abc import abstractmethod -from typing import Union, final +from typing import final import numpy as np @@ -60,30 +60,30 @@ def fit( return self @abstractmethod - def _fit( - self, - X: np.ndarray, - y=None, - ): ... + def _fit(self, X: np.ndarray, y=None): ... - def _pre_predict( - self, - X: Union[np.ndarray, None] = None, - length: int = None, - ): + @final + def predict(self, X, **kwargs): """ - Predict method. + Predict function. Parameters ---------- - X : Union[np.ndarray, None], optional - Optional data to use for predict.. The default is None. - length: int, optional - If not None, the number of timepoint of X should be equal to length. + X : np.ndarray, shape = (n_cases, n_channels, n_tiempoints) + Collections of series to predict on. + + Returns + ------- + indexes : np.ndarray, shape = (n_cases, k) + Indexes of series in the that are similar to X. + distances : np.ndarray, shape = (n_cases, k) + Distance of the matches to each series """ self._check_is_fitted() - if X is not None: - # Could we call somehow _preprocess_series from a BaseCollectionEstimator ? - self._check_predict_series_format(X, length=length) - return X + X = self._preprocess_collection(X) + indexes, distances = self._predict(X, **kwargs) + return indexes, distances + + @abstractmethod + def _predict(self, X: np.ndarray): ... diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index 1e5bb914bb..11f3dc5dd4 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -4,7 +4,7 @@ from numba import get_num_threads, njit, prange, set_num_threads from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch -from aeon.utils.numba.general import z_normalise_series_2d, z_normalise_series_3d +from aeon.utils.numba.general import z_normalise_series_3d @njit(cache=True) @@ -158,42 +158,22 @@ def _fit(self, X, y=None): size=self.n_hash_funcs, replace=True, ) - bool_hashes = _collection_to_bool( - X, self.hash_funcs_, self.start_points_, self.window_length_ - ) - - str_hashes = [hash(bool_hashes[i].tobytes()) for i in range(len(bool_hashes))] + X_bools, X_hashes = self._collection_to_hashes(X) self.dict_X_index_ = {} self.dict_bool_hashes_ = {} - for i in range(len(str_hashes)): - if str_hashes[i] in self.dict_X_index_: - self.dict_X_index_[str_hashes[i]].append(i) + for i in range(len(X_hashes)): + if X_hashes[i] in self.dict_X_index_: + self.dict_X_index_[X_hashes[i]].append(i) else: - self.dict_X_index_[str_hashes[i]] = [i] - self.dict_bool_hashes_[str_hashes[i]] = bool_hashes[i] + self.dict_X_index_[X_hashes[i]] = [i] + self.dict_bool_hashes_[X_hashes[i]] = X_bools[i] self.bool_hashes_value_list_ = np.asarray(list(self.dict_bool_hashes_.values())) self.bool_hashes_key_list_ = np.asarray(list(self.dict_bool_hashes_.keys())) set_num_threads(prev_threads) return self - def _get_bucket_content(self, key): - return self.dict_X_index_[key] - - def _get_bucket_sizes(self): - return {key: len(self.dict_X_index_[key]) for key in self.dict_X_index_} - - def _get_series_bucket(self, X): - bool_hash = _series_to_bool( - X, self.hash_funcs_, self.start_points_, self.window_length_ - ) - str_hash = hash(bool_hash.tobytes()) - if str_hash in self.dict_X_index_: - return str_hash - else: - return None - - def predict( + def _predict( self, X, k=1, @@ -205,8 +185,8 @@ def predict( Parameters ---------- - X : np.ndarray, shape = (n_channels, n_tiempoints) - Series for which we want to find neighbors. + X : np.ndarray, shape = (n_cases, n_channels, n_tiempoints) + Collections of series for which we want to find neighbors. k : int, optional Number of neighbors to return for each series. The default is 1. threshold : int, optional @@ -223,17 +203,47 @@ def predict( Distance of k series in the index to X. The distance is the hamming distance between the result of each hash function. """ - X = self._pre_predict(X, length=self.n_timepoints_) + if X[0].shape[1] != self.n_timepoints_: + raise ValueError( + "Expected series of the same length as the series given in fit, but got" + f"{X[0].shape[1]} instead of {self.n_timepoints_}" + ) + prev_threads = get_num_threads() + set_num_threads(self._n_jobs) if self.normalize: - X = z_normalise_series_2d(X) + X = z_normalise_series_3d(X) - X_bool = _series_to_bool( + X_bools = _collection_to_bool( X, self.hash_funcs_, self.start_points_, self.window_length_ ) + X_bools, X_hashes = self._collection_to_hashes(X) + top_k = [] + top_k_dist = [] + for i in range(len(X_bools)): + idx, dists = self._extract_neighors_one_series( + X_bools[i], + X_hashes[i], + k=k, + threshold=threshold, + inverse_distance=inverse_distance, + ) + top_k.append(idx) + top_k_dist.append(dists) + + set_num_threads(prev_threads) + return top_k, top_k_dist + + def _extract_neighors_one_series( + self, + X_bool, + X_hash, + k=1, + threshold=np.inf, + inverse_distance=False, + ): top_k = np.zeros(k, dtype=int) top_k_dist = np.zeros(k, dtype=float) - X_hash = hash(X_bool.tobytes()) remove_X_hash = False if not inverse_distance and X_hash in self.dict_X_index_: current_k = min(len(self.dict_X_index_[X_hash]), k) @@ -275,4 +285,13 @@ def predict( else: break _i_bucket += 1 + return top_k[:current_k], top_k_dist[:current_k] + + def _collection_to_hashes(self, X): + bool_hashes = _collection_to_bool( + X, self.hash_funcs_, self.start_points_, self.window_length_ + ) + return bool_hashes, [ + hash(bool_hashes[i].tobytes()) for i in range(len(bool_hashes)) + ] diff --git a/aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py new file mode 100644 index 0000000000..82c1d102f3 --- /dev/null +++ b/aeon/similarity_search/collection/neighbors/tests/test_rp_cosine_lsh.py @@ -0,0 +1 @@ +"""Tests for RandomProjectionIndexANN.""" diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py index 8b309bb6b2..8aa63826a8 100644 --- a/aeon/similarity_search/series/_commons.py +++ b/aeon/similarity_search/series/_commons.py @@ -148,12 +148,12 @@ def _extract_top_k_from_dist_profile( # Could add aggregation function as parameter instead of just max def _extract_top_k_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): criterion = np.zeros(len(MP)) + for i in range(len(MP)): if len(MP[i]) > 0: criterion[i] = max(MP[i]) else: criterion[i] = np.inf - idx, _ = _extract_top_k_from_dist_profile( criterion, k, np.inf, allow_trivial_matches, exclusion_size ) diff --git a/aeon/similarity_search/series/neighbors/tests/test_dummy.py b/aeon/similarity_search/series/neighbors/tests/test_dummy.py index df8ff72655..1e500e16b1 100644 --- a/aeon/similarity_search/series/neighbors/tests/test_dummy.py +++ b/aeon/similarity_search/series/neighbors/tests/test_dummy.py @@ -12,7 +12,7 @@ import pytest from numpy.testing import assert_almost_equal -from aeon.similarity_search.series.neighbors._brute_force import ( +from aeon.similarity_search.series.neighbors._dummy import ( _naive_squared_distance_profile, ) from aeon.testing.data_generation import make_example_2d_numpy_series diff --git a/aeon/similarity_search/series/tests/test_commons.py b/aeon/similarity_search/series/tests/test_commons.py index 6f2c816193..e8d6e76915 100644 --- a/aeon/similarity_search/series/tests/test_commons.py +++ b/aeon/similarity_search/series/tests/test_commons.py @@ -4,7 +4,7 @@ import numpy as np import pytest from numba.typed import List -from numpy.testing import assert_, assert_array_almost_equal +from numpy.testing import assert_, assert_array_almost_equal, assert_array_equal from aeon.similarity_search.series._commons import ( _extract_top_k_from_dist_profile, @@ -93,7 +93,7 @@ def test__inverse_distance_profile(): def test__extract_top_k_motifs(): """Test motif extraction based on max distance.""" - MP = List( + MP = np.array( [ [1.0, 2.0], [1.0, 4.0], @@ -101,7 +101,8 @@ def test__extract_top_k_motifs(): [0.6, 0.7], ] ) - IP = List( + + IP = np.array( [ [1, 2], [1, 4], @@ -111,36 +112,32 @@ def test__extract_top_k_motifs(): ) MP_k, IP_k = _extract_top_k_motifs(MP, IP, 2, True, 0) assert_(len(MP_k) == 2) - assert_(MP_k[0] == [0.6, 0.7]) - assert_(IP_k[0] == [0, 7]) - assert_(MP_k[1] == [0.5, 0.9]) - assert_(IP_k[1] == [0, 3]) + assert_array_equal(MP_k[0], [0.6, 0.7]) + assert_array_equal(IP_k[0], [0, 7]) + assert_array_equal(MP_k[1], [0.5, 0.9]) + assert_array_equal(IP_k[1], [0, 3]) def test__extract_top_r_motifs(): """Test motif extraction based on motif set cardinality.""" - MP = List( - [ - [1.0, 1.5, 2.0, 1.5], - [1.0, 4.0], - [0.5, 0.9, 1.0], - [0.6, 0.7], - ] - ) - IP = List( - [ - [1, 2, 3, 4], - [1, 4], - [0, 3, 6], - [0, 7], - ] - ) + MP = List() + MP.append(List([1.0, 1.5, 2.0, 1.5])) + MP.append(List([1.0, 4.0])) + MP.append(List([0.5, 0.9, 1.0])) + MP.append(List([0.6, 0.7])) + + IP = List() + IP.append(List([1, 2, 3, 4])) + IP.append(List([1, 4])) + IP.append(List([0, 3, 6])) + IP.append(List([0, 7])) + MP_k, IP_k = _extract_top_r_motifs(MP, IP, 2, True, 0) assert_(len(MP_k) == 2) - assert_(MP_k[0] == [1.0, 1.5, 2.0, 1.5]) - assert_(IP_k[0] == [1, 2, 3, 4]) - assert_(MP_k[1] == [0.5, 0.9, 1.0]) - assert_(IP_k[1] == [0, 3, 6]) + assert_array_equal(MP_k[0], [1.0, 1.5, 2.0, 1.5]) + assert_array_equal(IP_k[0], [1, 2, 3, 4]) + assert_array_equal(MP_k[1], [0.5, 0.9, 1.0]) + assert_array_equal(IP_k[1], [0, 3, 6]) @pytest.mark.parametrize("k", K_VALUES) diff --git a/aeon/testing/testing_data.py b/aeon/testing/testing_data.py index e6730c9958..3337f83b0c 100644 --- a/aeon/testing/testing_data.py +++ b/aeon/testing/testing_data.py @@ -220,50 +220,6 @@ }, } -EQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH = { - "numpy3D": { - "train": ( - make_example_3d_numpy( - n_cases=10, - n_channels=1, - n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=1, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=1, - min_n_timepoints=20, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=1, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION = { "numpy3D": { "train": make_example_3d_numpy( @@ -402,50 +358,6 @@ }, } -EQUAL_LENGTH_MULTIVARIATE_COLLETION_SIMILARITY_SEARCH = { - "numpy3D": { - "train": ( - make_example_3d_numpy( - n_cases=10, - n_channels=2, - n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=2, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=2, - min_n_timepoints=20, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=2, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION = { "np-list": { "train": make_example_3d_numpy_list( @@ -554,30 +466,6 @@ }, } -UNEQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH = { - "np-list": { - "train": ( - make_example_3d_numpy_list( - n_cases=10, - n_channels=1, - min_n_timepoints=10, - max_n_timepoints=20, - random_state=data_rng.randint(np.iinfo(np.int32).max), - return_y=False, - ), - None, - ), - "test": ( - make_example_2d_numpy_series( - n_timepoints=10, - n_channels=1, - random_state=data_rng.randint(np.iinfo(np.int32).max), - ), - None, - ), - }, -} - UNEQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION = { "np-list": { "train": make_example_3d_numpy_list( @@ -802,12 +690,6 @@ for k, v in EQUAL_LENGTH_UNIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"EqualLengthUnivariate-SimilaritySearch-{k}": v - for k, v in EQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"EqualLengthMultivariate-Classification-{k}": v @@ -820,12 +702,6 @@ for k, v in EQUAL_LENGTH_MULTIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"EqualLengthMultivariate-SimilaritySearch-{k}": v - for k, v in EQUAL_LENGTH_MULTIVARIATE_COLLETION_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"UnequalLengthUnivariate-Classification-{k}": v @@ -838,12 +714,6 @@ for k, v in UNEQUAL_LENGTH_UNIVARIATE_REGRESSION.items() } ) -FULL_TEST_DATA_DICT.update( - { - f"UnequalLengthUnivariate-CollectionSimilaritySearch-{k}": v - for k, v in UNEQUAL_LENGTH_UNIVARIATE_COLLETION_SIMILARITY_SEARCH.items() - } -) FULL_TEST_DATA_DICT.update( { f"UnequalLengthMultivariate-Classification-{k}": v @@ -887,9 +757,12 @@ def _get_datatypes_for_estimator(estimator): FULL_TEST_DATA_DICT. Each tuple is formatted (data_key, label_key). """ datatypes = [] - univariate, multivariate, unequal_length, missing_values = ( - _get_capabilities_for_estimator(estimator) - ) + ( + univariate, + multivariate, + unequal_length, + missing_values, + ) = _get_capabilities_for_estimator(estimator) task = _get_task_for_estimator(estimator) inner_types = estimator.get_tag("X_inner_type") @@ -983,13 +856,12 @@ def _get_task_for_estimator(estimator): or isinstance(estimator, BaseEarlyClassifier) or isinstance(estimator, BaseClusterer) or isinstance(estimator, BaseCollectionTransformer) + or isinstance(estimator, BaseCollectionSimilaritySearch) ): data_label = "Classification" # collection data with continuous target labels elif isinstance(estimator, BaseRegressor): data_label = "Regression" - elif isinstance(estimator, BaseCollectionSimilaritySearch): - data_label = "CollectionSimilaritySearch" # series data with no secondary input elif ( isinstance(estimator, BaseAnomalyDetector) diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index b720286d65..d521e880ad 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -325,7 +325,7 @@ "\n", "These configuration will extract the best motif only, if you want to extract more than one motifs, you can use the `k` parameter to extract the `top-k` motifs. \n", "\n", - "**The term `k` of `top-k` motifs, while also used in `k-motifs`, is not the same. We use `motif_size` as the `k` in `k-motifs`. This is to avoid \"extraction the `top-k` `k-motif`\", which would be confusing and ill defined. Rather, we extract the `top-k` `motif_size-motifs`**.\n", + "**The term `k` of `top-k` motifs, while also used in `k-motifs`, is not the same. To avoid confusion of both terms, we use `motif_size` instead of `k` to specify the size of the motifs to extract. This avoids the phrasing \"extracting the `top-k` `k-motif`\", which would be confusing and ill defined. Rather, we extract the `top-k` `motif_size-motifs`**.\n", "\n", "The `top-k` using `motif_extraction_method=\"r_motifs\"` will be the motifs with the highest cardinality (i.e. the more matches in range `r`), while for `motif_extraction_method=\"k_motifs\"`,which is the default value, the best motifs will be those who minimize the maximum pairwise distance." ] @@ -459,7 +459,7 @@ "source": [ "# 2. Collection estimators\n", "\n", - "Now, we'll explore estimators of the `collection` module, where you must provide single series of shape `(n_cases, n_channels, n_timepoints)` during fit." + "Now, we'll explore estimators of the `collection` module, where you must provide single series of shape `(n_cases, n_channels, n_timepoints)` during fit and predict." ] }, { @@ -483,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "id": "cc719800-0119-42f9-9018-32288c2db69b", "metadata": {}, "outputs": [ @@ -491,7 +491,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 0 : 130 with distance 1.0\n" + "match 0 : 130 with distance 0.0\n" ] }, { @@ -508,12 +508,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 1 : 174 with distance 3.0\n" + "match 1 : 130 with distance 0.0\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -526,9 +526,14 @@ "from aeon.similarity_search.collection import RandomProjectionIndexANN\n", "\n", "X_fit = X[:199]\n", + "# you can use a single series, but it will be converted into a collection internally !\n", "X_predict = X[199]\n", "index = RandomProjectionIndexANN().fit(X_fit)\n", "indexes, distances = index.predict(X_predict, k=2)\n", + "# as X_predict is converted to a collection, we select the first returns\n", + "# to obtain its results\n", + "indexes = indexes[0]\n", + "distances = distances[0]\n", "\n", "for i in range(len(indexes)):\n", " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", @@ -546,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "1b22b743-5710-4691-b740-8edaa3bbac2e", "metadata": {}, "outputs": [ @@ -554,7 +559,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 0 : 130 with distance 3.0\n" + "match 0 : 130 with distance 6.0\n" ] }, { @@ -571,7 +576,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 1 : 184 with distance 15.0\n" + "match 1 : 184 with distance 23.0\n" ] }, { @@ -589,6 +594,9 @@ "index = RandomProjectionIndexANN(n_hash_funcs=512, hash_func_coverage=0.75).fit(X_fit)\n", "indexes, distances = index.predict(X_predict, k=2)\n", "\n", + "indexes = indexes[0]\n", + "distances = distances[0]\n", + "\n", "for i in range(len(indexes)):\n", " print(f\"match {i} : {indexes[i]} with distance {distances[i]}\")\n", " # A bit of hacking of the function defined for series estimator to show best mathces\n", From e3ccb3f9bb43a1a2e27d01f7651fe4a03b21fc09 Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 2 Feb 2025 13:13:42 +0100 Subject: [PATCH 21/36] Fix imports, testing data tests, and impose predict/_predict interface to all sim search estimators --- aeon/similarity_search/_base.py | 30 ----- aeon/similarity_search/collection/_base.py | 5 +- .../collection/neighbors/_rp_cosine_lsh.py | 8 -- aeon/similarity_search/series/_base.py | 65 +++++++--- .../similarity_search/series/motifs/_stomp.py | 6 +- .../series/neighbors/_dummy.py | 12 +- .../series/neighbors/_mass.py | 12 +- .../series/neighbors/tests/test_dummy.py | 17 +-- .../_mock_similarity_searchers.py | 8 +- aeon/testing/tests/test_testing_data.py | 113 ------------------ .../similarity_search/similarity_search.ipynb | 34 +++--- 11 files changed, 97 insertions(+), 213 deletions(-) diff --git a/aeon/similarity_search/_base.py b/aeon/similarity_search/_base.py index a87487fde1..07140c7495 100644 --- a/aeon/similarity_search/_base.py +++ b/aeon/similarity_search/_base.py @@ -71,33 +71,3 @@ def predict( Optional data to use for predict. """ ... - - def _check_predict_series_format(self, X, length=None): - """ - Check wheter a series X in predict is correctly formated. - - Parameters - ---------- - X : np.ndarray, shape = (n_channels, n_timepoints) - A series to be used in predict. - """ - if isinstance(X, np.ndarray): - if X.ndim != 2: - raise TypeError( - "A np.ndarray given in predict must be 2D" - f"(n_channels, n_timepoints) but found {X.ndim}D." - ) - else: - raise TypeError( - "Expected a 2D np.ndarray in predict but found" f" {type(X)}." - ) - if self.n_channels_ != X.shape[0]: - raise ValueError( - f"Expected X to have {self.n_channels_} channels but" - f" got {X.shape[0]} channels." - ) - if length is not None and X.shape[1] != length: - raise ValueError( - f"Expected X to have {length} timepoints but" - f" got {X.shape[1]} timepoints." - ) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index 8277a36e96..f45a324546 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -71,6 +71,9 @@ def predict(self, X, **kwargs): ---------- X : np.ndarray, shape = (n_cases, n_channels, n_tiempoints) Collections of series to predict on. + kwargs : dict, optional + Additional keyword argument as dict or individual keywords args + to pass to use. Returns ------- @@ -86,4 +89,4 @@ def predict(self, X, **kwargs): return indexes, distances @abstractmethod - def _predict(self, X: np.ndarray): ... + def _predict(self, X, **kwargs): ... diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index 11f3dc5dd4..c6c0b72eb7 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -87,14 +87,6 @@ class RandomProjectionIndexANN(BaseCollectionSimilaritySearch): random_state: int, optional A random seed to seed the index building. The default is None. - Example - ------- - >>> from aeon.datasets import load_classification - >>> from aeon.similarity_search.collection.neighbors import RandomProjectionIndexANN - >>> index = RandomProjectionIndexANN() - >>> X, y = load_classification("ArrowHead") - >>> index.fit(X[:200]) - >>> r = index.predict(X[201]) """ _tags = { diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index bad22168f3..93778200d6 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -1,7 +1,7 @@ """Base similiarity search for series.""" from abc import abstractmethod -from typing import Union, final +from typing import final import numpy as np @@ -18,7 +18,8 @@ class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): } @abstractmethod - def __init__(self, axis=1): + def __init__(self, length, axis=1): + self.length = length super().__init__(axis=axis) @final @@ -63,27 +64,38 @@ def _fit( y=None, ): ... - def _pre_predict( - self, - X: Union[np.ndarray, None] = None, - length: int = None, - ): + @final + def predict(self, X=None, **kwargs): """ - Predict method. + Predict function. Parameters ---------- - X : Union[np.ndarray, None], optional - Optional data to use for predict.. The default is None. - length: int, optional - If not None, the number of timepoint of X should be equal to length. + X : np.ndarray, shape = (n_channels, n_tiempoints) + Series to predict on. + kwargs : dict, optional + Additional keyword argument as dict or individual keywords args + to pass to use. + + Returns + ------- + indexes : np.ndarray, shape = (k) + Indexes of series in the that are similar to X. + distances : np.ndarray, shape = (k) + Distance of the matches to each series """ self._check_is_fitted() if X is not None: X = self._preprocess_series(X, self.axis, False) - self._check_predict_series_format(X, length=length) - return X + self._check_predict_series_format(X) + else: + X = self.X_ + indexes, distances = self._predict(X, **kwargs) + return indexes, distances + + @abstractmethod + def _predict(self, X, **kwargs): ... def _check_X_index(self, X_index: int): """ @@ -108,3 +120,28 @@ def _check_X_index(self, X_index: int): "of timepoint in series given during fit. Expected a value " f"between [0, {max_timepoints - 1}] but got {X_index}" ) + + def _check_predict_series_format(self, X): + """ + Check wheter a series X in predict is correctly formated. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A series to be used in predict. + """ + if isinstance(X, np.ndarray): + if X.ndim != 2: + raise TypeError( + "A np.ndarray given in predict must be 2D" + f"(n_channels, n_timepoints) but found {X.ndim}D." + ) + else: + raise TypeError( + "Expected a 2D np.ndarray in predict but found" f" {type(X)}." + ) + if self.n_channels_ != X.shape[0]: + raise ValueError( + f"Expected X to have {self.n_channels_} channels but" + f" got {X.shape[0]} channels." + ) diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index ad5f0b189b..4865005070 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -81,9 +81,8 @@ def __init__( length: int, normalize: Optional[bool] = False, ): - self.length = length self.normalize = normalize - super().__init__() + super().__init__(length) def _fit( self, @@ -94,7 +93,7 @@ def _fit( self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) return self - def predict( + def _predict( self, X: np.ndarray = None, k: Optional[int] = 1, @@ -152,7 +151,6 @@ def predict( The distances of the best matches. """ - X = self._pre_predict(X) if motif_extraction_method not in ["k_motifs", "r_motifs"]: raise ValueError( "Expected motif_extraction_method to be either 'k_motifs' or 'r_motifs'" diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index 2f4e773bb2..0be77c4064 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -32,10 +32,9 @@ def __init__( normalize: Optional[bool] = False, n_jobs: Optional[int] = 1, ): - self.length = length self.normalize = normalize self.n_jobs = n_jobs - super().__init__() + super().__init__(length) def _fit( self, @@ -52,7 +51,7 @@ def _fit( set_num_threads(prev_threads) return self - def predict( + def _predict( self, X: np.ndarray, k: Optional[int] = 1, @@ -95,7 +94,12 @@ def predict( The distances of the best matches. """ - X = self._pre_predict(X, length=self.length) + if X.shape[1] != self.length: + raise ValueError( + f"Expected X to have {self.length} timepoints but" + f" got {X.shape[1]} timepoints." + ) + X_index = self._check_X_index(X_index) dist_profile = self.compute_distance_profile(X) if inverse_distance: diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py index befc8b33fd..36dd9bcd5a 100644 --- a/aeon/similarity_search/series/neighbors/_mass.py +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -43,9 +43,8 @@ def __init__( length: int, normalize: Optional[bool] = False, ): - self.length = length self.normalize = normalize - super().__init__() + super().__init__(length) def _fit( self, @@ -56,7 +55,7 @@ def _fit( self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) return self - def predict( + def _predict( self, X: np.ndarray, k: Optional[int] = 1, @@ -104,7 +103,12 @@ def predict( The distances of the best matches. """ - X = self._pre_predict(X, length=self.length) + if X.shape[1] != self.length: + raise ValueError( + f"Expected X to have {self.length} timepoints but" + f" got {X.shape[1]} timepoints." + ) + X_index = self._check_X_index(X_index) dist_profile = self.compute_distance_profile(X) if inverse_distance: diff --git a/aeon/similarity_search/series/neighbors/tests/test_dummy.py b/aeon/similarity_search/series/neighbors/tests/test_dummy.py index 1e500e16b1..e064b39fbf 100644 --- a/aeon/similarity_search/series/neighbors/tests/test_dummy.py +++ b/aeon/similarity_search/series/neighbors/tests/test_dummy.py @@ -9,32 +9,23 @@ __maintainer__ = ["baraline"] import numpy as np -import pytest from numpy.testing import assert_almost_equal from aeon.similarity_search.series.neighbors._dummy import ( _naive_squared_distance_profile, ) from aeon.testing.data_generation import make_example_2d_numpy_series -from aeon.utils.numba.general import get_all_subsequences, z_normalise_series_2d +from aeon.utils.numba.general import get_all_subsequences -NORMALIZE = [True, False] - -@pytest.mark.parametrize("normalize", NORMALIZE) -def test__naive_squared_distance_profile(normalize): +def test__naive_squared_distance_profile(): """Test Euclidean distance with brute force.""" L = 3 X = make_example_2d_numpy_series(n_channels=1, n_timepoints=10) Q = make_example_2d_numpy_series(n_channels=1, n_timepoints=L) - dist_profile = _naive_squared_distance_profile( - get_all_subsequences(X, L, 1), Q, normalize=normalize - ) - if normalize: - Q = z_normalise_series_2d(Q) + dist_profile = _naive_squared_distance_profile(get_all_subsequences(X, L, 1), Q) + for i_t in range(X.shape[1] - L + 1): S = X[:, i_t : i_t + L] - if normalize: - S = z_normalise_series_2d(X[:, i_t : i_t + L]) assert_almost_equal(dist_profile[i_t], np.sum((S - Q) ** 2)) diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py index a2919c939d..35251bf558 100644 --- a/aeon/testing/mock_estimators/_mock_similarity_searchers.py +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -16,9 +16,8 @@ def __init__(self): def _fit(self, X, y=None): return self - def predict(self, X): + def _predict(self, X): """Compute matrix profiles between X_ and X or between all series in X_.""" - X = self._pre_predict(X) return [0], [0.1] @@ -31,7 +30,6 @@ def __init__(self): def _fit(self, X, y=None): return self - def predict(self, X): + def _predict(self, X): """Compute matrix profiles between X_ and X or between all series in X_.""" - X = self._pre_predict(X) - return [0], [0.1] + return [0 for _ in range(len(X))], [0.1 for _ in range(len(X))] diff --git a/aeon/testing/tests/test_testing_data.py b/aeon/testing/tests/test_testing_data.py index f9afe264dd..891bd5851a 100644 --- a/aeon/testing/tests/test_testing_data.py +++ b/aeon/testing/tests/test_testing_data.py @@ -6,19 +6,15 @@ from aeon.testing.testing_data import ( EQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION, EQUAL_LENGTH_MULTIVARIATE_REGRESSION, - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH, EQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, EQUAL_LENGTH_UNIVARIATE_REGRESSION, - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH, FULL_TEST_DATA_DICT, MISSING_VALUES_CLASSIFICATION, MISSING_VALUES_REGRESSION, UNEQUAL_LENGTH_MULTIVARIATE_CLASSIFICATION, UNEQUAL_LENGTH_MULTIVARIATE_REGRESSION, - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH, UNEQUAL_LENGTH_UNIVARIATE_CLASSIFICATION, UNEQUAL_LENGTH_UNIVARIATE_REGRESSION, - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH, ) from aeon.utils.data_types import COLLECTIONS_DATA_TYPES from aeon.utils.validation import ( @@ -108,31 +104,6 @@ def test_equal_length_univariate_collection(): EQUAL_LENGTH_UNIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0], include_2d=True - ) - assert is_univariate(EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0]) - assert is_equal_length( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert is_univariate( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) - assert is_equal_length( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - EQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_unequal_length_univariate_collection(): """Test the contents of the unequal length univariate data dictionary.""" @@ -182,34 +153,6 @@ def test_unequal_length_univariate_collection(): UNEQUAL_LENGTH_UNIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0], - include_2d=True, - ) - assert is_univariate( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_equal_length( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert is_univariate( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) - assert is_equal_length( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_UNIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_equal_length_multivariate_collection(): """Test the contents of the equal length multivariate data dictionary.""" @@ -259,34 +202,6 @@ def test_equal_length_multivariate_collection(): EQUAL_LENGTH_MULTIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0], - include_2d=True, - ) - assert not is_univariate( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert is_equal_length( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not is_univariate( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) - assert is_equal_length( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - EQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_unequal_length_multivariate_collection(): """Test the contents of the unequal length multivariate data dictionary.""" @@ -348,34 +263,6 @@ def test_unequal_length_multivariate_collection(): UNEQUAL_LENGTH_MULTIVARIATE_REGRESSION[key]["test"][1].dtype, np.floating ) - for key in UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH: - assert is_collection( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0], - include_2d=True, - ) - assert not is_univariate( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_equal_length( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["train"][0] - ) - assert not is_collection( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not is_univariate( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0], - is_collection=False, - ) - assert is_equal_length( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - assert not has_missing( - UNEQUAL_LENGTH_MULTIVARIATE_SIMILARITY_SEARCH[key]["test"][0] - ) - def test_missing_values_collection(): """Test the contents of the missing value data dictionary.""" diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index d521e880ad..f14cdb15a9 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -339,16 +339,16 @@ { "data": { "text/plain": [ - "([array([2.16605047]),\n", - " array([3.23155459]),\n", - " array([8.15076681]),\n", - " array([8.15076681]),\n", - " array([26.42906254])],\n", - " [array([[13, 30]], dtype=int64),\n", - " array([[31, 13]], dtype=int64),\n", - " array([[108, 77]], dtype=int64),\n", - " array([[ 77, 108]], dtype=int64),\n", - " array([[59, 76]], dtype=int64)])" + "([array([0.]),\n", + " array([0.]),\n", + " array([0.]),\n", + " array([3.88578059e-14]),\n", + " array([7.77156117e-14])],\n", + " [array([[30, 30]], dtype=int64),\n", + " array([[48, 48]], dtype=int64),\n", + " array([[0, 0]], dtype=int64),\n", + " array([[69, 69]], dtype=int64),\n", + " array([[87, 87]], dtype=int64)])" ] }, "execution_count": 6, @@ -483,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "cc719800-0119-42f9-9018-32288c2db69b", "metadata": {}, "outputs": [ @@ -491,7 +491,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 0 : 130 with distance 0.0\n" + "match 0 : 130 with distance 1.0\n" ] }, { @@ -508,12 +508,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 1 : 130 with distance 0.0\n" + "match 1 : 56 with distance 3.0\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -551,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "1b22b743-5710-4691-b740-8edaa3bbac2e", "metadata": {}, "outputs": [ @@ -559,7 +559,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 0 : 130 with distance 6.0\n" + "match 0 : 130 with distance 9.0\n" ] }, { @@ -576,7 +576,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 1 : 184 with distance 23.0\n" + "match 1 : 184 with distance 19.0\n" ] }, { From ee7aa5846509a92f492aa407e7cb7edabd29d4a9 Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 2 Feb 2025 13:42:08 +0100 Subject: [PATCH 22/36] Fix args --- aeon/similarity_search/series/_base.py | 3 +-- aeon/similarity_search/series/motifs/_stomp.py | 3 ++- aeon/similarity_search/series/neighbors/_dummy.py | 5 +++-- aeon/similarity_search/series/neighbors/_mass.py | 6 +++--- 4 files changed, 9 insertions(+), 8 deletions(-) diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index 93778200d6..435715db69 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -18,8 +18,7 @@ class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): } @abstractmethod - def __init__(self, length, axis=1): - self.length = length + def __init__(self, axis=1): super().__init__(axis=axis) @final diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index 4865005070..b3b5fcd41f 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -82,7 +82,8 @@ def __init__( normalize: Optional[bool] = False, ): self.normalize = normalize - super().__init__(length) + self.length = length + super().__init__() def _fit( self, diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index 0be77c4064..5ca1bb5c85 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -34,7 +34,8 @@ def __init__( ): self.normalize = normalize self.n_jobs = n_jobs - super().__init__(length) + self.length = length + super().__init__() def _fit( self, @@ -168,7 +169,7 @@ def _get_test_params(cls, parameter_set: str = "default"): `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. """ if parameter_set == "default": - params = {"length": 3} + params = {"length": 20} else: raise NotImplementedError( f"The parameter set {parameter_set} is not yet implemented" diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py index 36dd9bcd5a..7d052d9d89 100644 --- a/aeon/similarity_search/series/neighbors/_mass.py +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -44,7 +44,8 @@ def __init__( normalize: Optional[bool] = False, ): self.normalize = normalize - super().__init__(length) + self.length = length + super().__init__() def _fit( self, @@ -108,7 +109,6 @@ def _predict( f"Expected X to have {self.length} timepoints but" f" got {X.shape[1]} timepoints." ) - X_index = self._check_X_index(X_index) dist_profile = self.compute_distance_profile(X) if inverse_distance: @@ -188,7 +188,7 @@ def _get_test_params(cls, parameter_set: str = "default"): `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance. """ if parameter_set == "default": - params = {"length": 3} + params = {"length": 20} else: raise NotImplementedError( f"The parameter set {parameter_set} is not yet implemented" From bf0c5e89e39d50026b18d4c945271ddf25e7d64e Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 2 Feb 2025 14:02:45 +0100 Subject: [PATCH 23/36] Fix extract test --- aeon/similarity_search/collection/_base.py | 28 +++++++++++++++++++ .../collection/tests/test_base.py | 10 ++++--- .../series/tests/test_commons.py | 2 +- 3 files changed, 35 insertions(+), 5 deletions(-) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index f45a324546..ecb3e31ccc 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -84,9 +84,37 @@ def predict(self, X, **kwargs): """ self._check_is_fitted() + if X[0].ndim == 1: + X = X[np.newaxis, :, :] X = self._preprocess_collection(X) + self._check_predict_series_format(X) indexes, distances = self._predict(X, **kwargs) return indexes, distances + def _check_predict_series_format(self, X): + """ + Check wheter a series X in predict is correctly formated. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A series to be used in predict. + """ + if isinstance(X, np.ndarray): + if X[0].ndim != 2: + raise TypeError( + "A np.ndarray given in predict must be 3D" + f"(n_channels, n_timepoints) but found {X.ndim}D." + ) + else: + raise TypeError( + "Expected a 3D np.ndarray in predict but found" f" {type(X)}." + ) + if self.n_channels_ != X[0].shape[0]: + raise ValueError( + f"Expected X to have {self.n_channels_} channels but" + f" got {X[0].shape[0]} channels." + ) + @abstractmethod def _predict(self, X, **kwargs): ... diff --git a/aeon/similarity_search/collection/tests/test_base.py b/aeon/similarity_search/collection/tests/test_base.py index c1efaa30f0..c9d7af012b 100644 --- a/aeon/similarity_search/collection/tests/test_base.py +++ b/aeon/similarity_search/collection/tests/test_base.py @@ -42,13 +42,15 @@ def test_input_shape_fit_predict_collection(): estimator_uni = MockCollectionSimilaritySearch().fit(X_3D_uni) estimator_uni.predict(X_2D_uni) + estimator_uni.predict(X_3D_uni) estimator_multi.predict(X_2D_multi) + estimator_multi.predict(X_3D_multi) with pytest.raises(ValueError): estimator_uni.predict(X_2D_multi) with pytest.raises(ValueError): estimator_multi.predict(X_2D_uni) - with pytest.raises(TypeError): - estimator_uni.predict(X_3D_uni) - with pytest.raises(TypeError): - estimator_multi.predict(X_3D_multi) + with pytest.raises(ValueError): + estimator_multi.predict(X_3D_uni) + with pytest.raises(ValueError): + estimator_uni.predict(X_3D_multi) diff --git a/aeon/similarity_search/series/tests/test_commons.py b/aeon/similarity_search/series/tests/test_commons.py index e8d6e76915..6ad6fcf589 100644 --- a/aeon/similarity_search/series/tests/test_commons.py +++ b/aeon/similarity_search/series/tests/test_commons.py @@ -163,7 +163,7 @@ def test__extract_top_k_from_dist_profile( assert_(np.all(top_k_distances <= threshold)) if allow_nn_matches: - assert_(np.all(top_k_distances <= X_sort[len(top_k_indexes) - 1])) + assert_(np.all(top_k_distances <= X[X_sort[len(top_k_indexes) - 1]])) if not allow_nn_matches: same_X = np.sort(top_k_indexes) From 0c2d7635c791f3402c2804fcee732df14d4e66d1 Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 2 Feb 2025 15:53:57 +0100 Subject: [PATCH 24/36] update docs api and notebooks --- aeon/similarity_search/series/__init__.py | 3 +- docs/api_reference/similarity_search.rst | 6 +- examples/similarity_search/code_speed.ipynb | 184 +++++------------- .../similarity_search_tasks.ipynb | 2 +- 4 files changed, 55 insertions(+), 140 deletions(-) diff --git a/aeon/similarity_search/series/__init__.py b/aeon/similarity_search/series/__init__.py index d1b5494c13..6eb2c521be 100644 --- a/aeon/similarity_search/series/__init__.py +++ b/aeon/similarity_search/series/__init__.py @@ -1,7 +1,8 @@ """Similarity search for series.""" -__all__ = ["BaseSeriesSimilaritySearch", "MassSNN", "StompMotif"] +__all__ = ["BaseSeriesSimilaritySearch", "MassSNN", "StompMotif", "DummySNN"] from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch from aeon.similarity_search.series.motifs._stomp import StompMotif +from aeon.similarity_search.series.neighbors._dummy import DummySNN from aeon.similarity_search.series.neighbors._mass import MassSNN diff --git a/docs/api_reference/similarity_search.rst b/docs/api_reference/similarity_search.rst index 7212179953..ec9d866b3f 100644 --- a/docs/api_reference/similarity_search.rst +++ b/docs/api_reference/similarity_search.rst @@ -46,7 +46,7 @@ Collection Similarity search estimators Base Estimators --------------- -.. currentmodule:: aeon.similarity_search.base +.. currentmodule:: aeon.similarity_search._base .. autosummary:: :toctree: auto_generated/ @@ -55,7 +55,7 @@ Base Estimators BaseSimilaritySearch -.. currentmodule:: aeon.similarity_search.series.base +.. currentmodule:: aeon.similarity_search.series._base .. autosummary:: :toctree: auto_generated/ @@ -63,7 +63,7 @@ Base Estimators BaseSeriesSimilaritySearch -.. currentmodule:: aeon.similarity_search.collection.base +.. currentmodule:: aeon.similarity_search.collection._base .. autosummary:: :toctree: auto_generated/ diff --git a/examples/similarity_search/code_speed.ipynb b/examples/similarity_search/code_speed.ipynb index f31155333d..65d2907604 100644 --- a/examples/similarity_search/code_speed.ipynb +++ b/examples/similarity_search/code_speed.ipynb @@ -27,15 +27,7 @@ "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "\n", - "from aeon.similarity_search._commons import (\n", - " naive_squared_distance_profile,\n", - " naive_squared_matrix_profile,\n", - ")\n", - "from aeon.similarity_search.distance_profiles.squared_distance_profile import (\n", - " normalised_squared_distance_profile,\n", - " squared_distance_profile,\n", - ")\n", - "from aeon.similarity_search.matrix_profiles import stomp_squared_matrix_profile\n", + "from aeon.similarity_search.series import DummySNN, MassSNN\n", "from aeon.utils.numba.general import sliding_mean_std_one_series\n", "\n", "ggplot_styles = {\n", @@ -158,9 +150,9 @@ "for size in sizes:\n", " for query_length in query_lengths:\n", " X = rng.random((1, size))\n", - " _times = %timeit -r 7 -n 10 -q -o get_means_stds(X, query_length)\n", + " _times = %timeit -r 3 -n 3 -q -o get_means_stds(X, query_length)\n", " times.loc[(size, query_length), \"full computation\"] = _times.average\n", - " _times = %timeit -r 7 -n 10 -q -o sliding_mean_std_one_series(X, query_length, 1)\n", + " _times = %timeit -r 3 -n 3 -q -o sliding_mean_std_one_series(X, query_length, 1)\n", " times.loc[(size, query_length), \"sliding_computation\"] = _times.average" ] }, @@ -172,7 +164,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -219,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "56c34efd-2927-4a6f-9f3c-bc723e5229c2", "metadata": {}, "outputs": [], @@ -233,30 +225,33 @@ "for size in sizes:\n", " for _query_length in query_lengths:\n", " query_length = int(_query_length * size)\n", - " X = rng.random((1, 1, size))\n", + " X = rng.random((1, size))\n", " q = rng.random((1, query_length))\n", " mask = np.ones((1, size - query_length + 1), dtype=bool)\n", " # Used for numba compilation before timings\n", - " naive_squared_distance_profile(X, q, mask)\n", - " _times = %timeit -r 3 -n 7 -q -o naive_squared_distance_profile(X, q, mask)\n", + " mass = MassSNN(length=query_length).fit(X)\n", + " mass.compute_distance_profile(q)\n", + " dummy = DummySNN(length=query_length).fit(X)\n", + " dummy.compute_distance_profile(q)\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o dummy.compute_distance_profile(q)\n", " times.loc[(size, _query_length), \"Naive Euclidean distance\"] = _times.average\n", - " # Used for numba compilation before timings\n", - " squared_distance_profile(X, q, mask)\n", - " _times = %timeit -r 3 -n 7 -q -o squared_distance_profile(X, q, mask)\n", - " times.loc[(size, _query_length), \"Euclidean distance as dot product\"] = (\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o mass.compute_distance_profile(q)\n", + " times.loc[(size, _query_length), \"Euclidean distance with MASS\"] = (\n", " _times.average\n", " )" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "a082b60c-b6ec-41a7-8566-2c6deca59860", "metadata": {}, "outputs": [ { "data": { - "image/png": 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4wo/NrOogSZmZmeHHXq93So8PAADwUeQn/chPyE8k8pPxHv/ChQv667/+a23YsEGXLl0a1bkAAIA1kc/0I58hn5Gsl88AAABMNuQn/chPyE8k8pPrCYVC+trXvhY+Xr16tVasWDEuYwMYPYpmAGCEnnrqKRmGIak/EXrooYeu2zY/P/+qX5TGU1NTk5577jlJ/b8k/tM//dOQ7R0Oh/7xH/8xfPyLX/xi2DEee+wx5efnD9vukUceCT/++c9/rmAwOGi73t5ePfvss+Hj8diK8+jRoyotLZUk3XLLLcPGKykpSd/61rfCx2Y+p+9+97tKTk6+7usDk+xDhw4N2mZg8vipT31Kt99++3XPt2LFCn3uc58bdl5j4d5779Vdd9113dfvueeecJLS3t4+6u0w29vbw48TExNN9RnYbmD/qTg+AADAR5GfXEF+Qn5CfjJ240+fPl2PPvqofvvb36q+vl6dnZ3q7u5WfX29nn76aa1cuTLc9uDBg7rvvvvCFx8BAADMIp+5gnyGfMZq+QwAAMBkQ35yBfkJ+Qn5yeD+/u//Xm+99Zak/iKn73//++MyLoCxQdEMAIxQWVlZ+PGf/MmfDNt+qCQqknbt2hWuLH/ggQeUkJAwbJ/Vq1crKSlJkrR3795h23/qU58yNZeVK1fq5ptvliQ1NDTo9ddfH7Td//zP/6ipqUmStHjxYq1bt87U+Ufj97//ffjxpz/96Wuq4wfz4QoM0vCfU0JCgu67774h29xyyy3hx6dPnx60TUVFRfjxZz/72WHnaKbNWPjjP/7jIV+32Wxavnx5+Ph678+s7u7u8OO4uDhTfT5c5ULSqG/imujxAQAAPor85AryE/IT8pOxGX/FihU6f/68/u3f/k3333+/Fi5cqMTERMXHx2vhwoX63Oc+pzfffPOqC4hvvfWW/vmf//kG3wkAALAq8pkryGfIZ6yWzwAAAEw25CdXkJ+Qn5CfXOuVV17R3/3d34WP/+Zv/kbr16+P+LgAxk7MRE8AAKYSwzB09OjR8PHatWuH7ZOfny+32z3u25QfOHAg/PjYsWN67LHHRtTf5/Opo6MjnDR9VGxsrG666SbT5/vSl76kRx99VFJ/lfw999xzTZuB1fN/+qd/OqL53qiBn1NZWZnOnDkzbJ8PV5aQpHPnzg3ZtqCgQLGxsUO2SU9PDz9ubW295vWGhoZw8ij1J7PDWblypWw221VzjQQzPwPDvb+RGJjsm91utKenJ/zY7GoEk3V8AACAgchPriA/6Ud+Qn4yFuMPtTLeh2w2m7797W+rrq5Ozz//vCTpX/7lX/SNb3xDMTH8yRkAAAyPfOYK8pl+5DPWymcAAAAmE/KTK8hP+pGfkJ8M9NZbb+nTn/50+HO/88479e1vfzuiYwIYe1zBBIAR8Pv9V/1iNm/ePFP95s2bN+5J0oULF8KP9+7da2qlgI/y+XzXTZJcLteIboT5zGc+o7/+679WR0eHXnnlFTU1NWn69Onh18+fPx9ecSAuLm7ctpMc+Dlt3759xP19Pt+QrzudzmHPMTCJ6uvru+b1gQnStGnT5Ha7hz1nSkqKnE6nWlpahm07GiN9f729vaMab+DNW2ZXCRjYzszNX5N5fAAAgIHIT64gP+lHfkJ+Mp7jS9K3v/3tcNGMz+fTwYMHWVkNAACYQj5zBflMP/IZa+UzAAAAkwn5yRXkJ/3IT8hPPvT+++/r7rvvVkdHh6T+wqXf/va3LCAGTEH2iZ4AAEwl7e3tVx1PmzbNVL/rJRqR5Pf7R32OwX5h/9BIK7RTU1P1yU9+UlL/L8rPPPPMVa///Oc/VygUkiR97GMfU0ZGxghne2NG+zkFg8EhXzezvedwBv7cmf2Zk8bngsVYvL+RGLhKwaVLl0z1aWxsDD82k2BO5vEBAAAGIj+5gvykH/kJ+cl4ji9JOTk5WrBgQfj45MmToz4nAACwBvKZK8hn+pHPWCufAQAAmEzIT64gP+lHfkJ+IkmnTp3S5s2b5fF4JElLlizRa6+9xiICwBRF0QwAjMBHf+Hp7Ow01e/DSuOx9GFCcT0DE7N/+Zd/kWEYI/438MaXsfDII4+EHw/cetMwDP3sZz8LH3/xi18c03GHMvBz+s1vfnNDn1OkDfy5M/szJ0Xm526iFRQUhB+b2TpVks6ePRt+XFhYOKXHBwAAGIj8ZHTIT24M+ckVE50fTPT4H8rKygo/bm5uHpNzAgCA6Ec+MzrkMzeGfOaKyZJPAAAATAbkJ6NDfnJjyE+umIz5SUNDgzZt2hTetSgnJ0c7d+5kAQFgCqNoBgBGwOl0Ki4uLnw88JevoZw7d27YNsNtyfhRw1XEz5w5M/x4YGX1RFqzZo2WLVsmqX/12QMHDkiSysrKVF9fL0maP3++7rzzznGb02T8nD5q4CoLnZ2dw24BKvWvRhDprTgnwqJFi8KP33nnHVN9jhw5Mmj/qTg+AADAQOQno0N+cmPIT66Y6Pxgosf/0MALdBOxsiIAAJiayGdGh3zmxpDPXDFZ8gkAAIDJgPxkdMhPbgz5yRWTLT+5dOmSNm3apFOnTkmS5syZo127dl21iBiAqYeiGQAYAZvNpuXLl4ePDx48OGyfmpqa8BZ9Q0lNTQ0/NtP+vffeG/L11atXhx/v27dv2PONl8FWFxi4ysDnP/952e3j95+nyfo5DTRnzpyrEqU333xz2D6HDx82terBeG+nOVolJSXhx1VVVbp48eKQ7S9cuKCamprw8caNG6f0+AAAAAORn4we+cnIkZ9cMdH5wUSPL/VfyKuqqgofz5o1a9TnBAAA1kA+M3rkMyNHPnPFZMgnAAAAJgvyk9EjPxk58pMrJlN+4vF4tHnz5vC1jxkzZmjXrl1jvkMTgPFH0QwAjNDAX9Kee+65Yds/88wzps478Berd999d8i2hw8fDlcyX8/WrVsVExMjSdq/f7+OHj1qah6R9tnPflaJiYmSpF/96lc6f/68fvOb30iS7Ha7vvCFL4zrfO69997w49/85je6dOnSuI5vVlFRUfjxL37xi2Hbm/nZlKSEhITw497e3pFPbJzl5eVp8eLF4eOnn356yPYDX7/pppuUnZ09pccHAAD4KPKT0SE/uTHkJ/0mOj+Y6PEl6fnnn1dPT4+k/otwGzZsGPU5AQCAdZDPjA75zI0hn+k3GfIJAACAyYT8ZHTIT24M+Um/yZKftLa2auvWreHiNZfLpZ07d6qgoGBMzg9gYlE0AwAj9Kd/+qfhxwcPHhzyl9Ha2lr94Ac/MHXegRXuQ/3i19fXp69+9avDnm/27Nn67Gc/K0kyDEOf+9zn1NraamouoVBITU1NptqOVFpamh588EFJ/VtGfuITn1B3d7ckacuWLZo7d25Exr2eVatWqbi4WJLU1dWlP/mTP1EgEDDVNxAImNoacywMTB6ff/75IVe1OHLkyLDJw4fS09PDjxsaGm58guPo0UcfDT/+/ve/f93EtrGxUd///vfDx9u2bYuK8QEAAAYiPxkd8pMbQ35yxUTnB2M9fmdnp0KhkKmxa2pq9I1vfCN8vGXLFs2YMcNUXwAAAIl8ZrTIZ24M+cwVE53PAAAATCbkJ6NDfnJjyE+umOj8pLOzU3/wB3+gt99+W5KUkpKi1157TcuWLRuT8wOYeBTNAMAI5efn6+GHHw4ff/GLXxz0F9LDhw9r8+bN6ujoUFxc3LDn/eQnPxnehvLAgQP6xje+oWAweFWb8+fP695779X+/fsVHx8/7Dn/8R//UVlZWZKkY8eOadWqVdqxY8d1258/f14/+MEPVFBQoF/96lfDnv9GDdySc+DWkl/84hcjNuZQnnjiCSUnJ0uSdu7cqQ0bNgy55WV1dbX+/u//XgsWLBi3LTzvvvturV+/XlJ/Envfffdp165d17QrLy/XPffco2AwaOrnbunSpeHHL730kqktPCfal770JeXk5Ejq3xLz7rvv1pkzZ65qc+bMGd1zzz3yer2S+r+3A//A8VHl5eWy2Wzhf+Xl5eM6PgAAwI0iPxk98pORIz+5Itryk0OHDmnJkiV68skndfny5UHbBINBPffcc1q7dq08Ho8kKS4uTv/0T/903XkCAAAMhnxm9MhnRo585oqJzmcAAAAmE/KT0SM/GTnykysmMj/p6enR/fffr71790qSEhMT9eqrr2rVqlVj8M4ATBYxEz0BAJiK/uVf/kUHDhxQVVWVenp69PDDD+vb3/621q5dq/j4eJ04cUKHDh2SYRh64IEH5PF4VFFRMeQ558+fr6985Sv693//d0nSP/3TP+mXv/ylNmzYoISEBNXV1Wnfvn0KBAK68847lZmZOeyWi7NmzdJ///d/65577lFzc7Oqqqq0detWzZ49W6tWrdL06dPV29ur5uZmHT9+fNgtPsfK7bffriVLlujEiRPh52bMmKE//MM/HJfxP2rp0qX65S9/qU9+8pPq7OzUm2++qTVr1ignJ0e33nqr3G63uru7dfnyZR07dmxCKvBtNpt++tOfhm9Kam5u1ubNm7V8+XLdfPPNkqSjR4+Gt3L9+te/rhdffDGcPHyYgH/UAw88oP/9v/+3DMPQ7373Oy1btkzr1q1TSkpKuM2nPvUprVixIqLvbyRiY2P161//WuvXr1d7e7veeecd5eXladOmTZo9e7bOnz+v0tLS8Paiqamp+vWvfx3ennayjv/FL35Rhw8fvuq5D5M8Sbpw4UI41gP913/916SKDwAAGH/kJ6NDfjJy5CdXRGN+UllZqUcffVSPPfaYcnNztWTJErndbtntdjU2NurAgQNqbm4Ot3c4HHrmmWe0fPnyMXlPAADAWshnRod8ZuTIZ66Y6HxG0qDXPc6ePRt+/D//8z+DtvkwPgAAAGOJ/GR0yE9GjvzkionMT771rW9p586d4eNFixbphRde0AsvvDBs3/T0dP3d3/3dqOcAYBwYAIAb0tDQYKxYscKQdN1/f/iHf2i0trYaRUVF4efKysque86uri7jnnv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" ] @@ -281,12 +276,12 @@ "id": "f10127f4-6515-4ea5-a1d9-e14671eb70be", "metadata": {}, "source": [ - "The same reasoning holds for the normalised (squared) euclidean distance, we can use the `ConvolveDotProduct` value for the `speed_up` argument in `TopKSimilaritySearch` to use this optimization for both normalised and non normalised distances. In the normalised case, the formula used to computed the normalised (squared) euclidean distance is taken from the paper [Matrix Profile I: All Pairs Similarity Joins for Time Series](https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf), see MASS algortihm." + "The same reasoning holds for the normalised (squared) euclidean distance, we can use the `normalize` parameter of the two estimators to set this option. In the normalised case, the formula used to computed the normalised (squared) euclidean distance is taken from the paper [Matrix Profile I: All Pairs Similarity Joins for Time Series](https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf), see MASS algortihm." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "id": "bb8dabbd-d11f-4aa9-a8f6-d489548ca852", "metadata": {}, "outputs": [], @@ -300,47 +295,35 @@ "for size in sizes:\n", " for _query_length in query_lengths:\n", " query_length = int(_query_length * size)\n", - " X = rng.random((1, 1, size))\n", + " X = rng.random((1, size))\n", " q = rng.random((1, query_length))\n", - " n_cases, n_channels = X.shape[0], X.shape[1]\n", - " search_space_size = size - query_length + 1\n", - " X_means = np.zeros((n_cases, n_channels, search_space_size))\n", - " X_stds = np.zeros((n_cases, n_channels, search_space_size))\n", - " mask = np.ones((n_channels, search_space_size), dtype=bool)\n", - " for i in range(X.shape[0]):\n", - " _mean, _std = sliding_mean_std_one_series(X[i], query_length, 1)\n", - " X_stds[i] = _std\n", - " X_means[i] = _mean\n", - " q_means, q_stds = sliding_mean_std_one_series(q, query_length, 1)\n", - " q_means = q_means[:, 0]\n", - " q_stds = q_stds[:, 0]\n", + " mask = np.ones((1, size - query_length + 1), dtype=bool)\n", " # Used for numba compilation before timings\n", - " naive_squared_distance_profile(\n", - " X, q, mask, normalise=True, X_means=X_means, X_stds=X_stds\n", - " )\n", - " _times = %timeit -r 3 -n 7 -q -o naive_squared_distance_profile(X, q, mask, normalise=True, X_means=X_means, X_stds=X_stds)\n", + " mass = MassSNN(length=query_length, normalize=True).fit(X)\n", + " mass.compute_distance_profile(q)\n", + " dummy = DummySNN(length=query_length, normalize=True).fit(X)\n", + " dummy.compute_distance_profile(q)\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o dummy.compute_distance_profile(q)\n", " times.loc[(size, _query_length), \"Naive Normalised Euclidean distance\"] = (\n", " _times.average\n", " )\n", - " # Used for numba compilation before timings\n", - " normalised_squared_distance_profile(\n", - " X, q, mask, X_means, X_stds, q_means, q_stds\n", - " )\n", - " _times = %timeit -r 3 -n 7 -q -o normalised_squared_distance_profile(X, q, mask, X_means, X_stds, q_means, q_stds)\n", - " times.loc[(size, _query_length), \"Normalised Euclidean as dot product\"] = (\n", + "\n", + " _times = %timeit -r 3 -n 3 -q -o mass.compute_distance_profile(q)\n", + " times.loc[(size, _query_length), \"Normalised Euclidean distance with MASS\"] = (\n", " _times.average\n", " )" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "id": "f701000b-9c17-45f7-a58c-64c22b88f641", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -359,31 +342,27 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "df47932f-d736-4e23-b3ee-d79a94c6b46e", - "metadata": {}, - "source": [ - "# Series Search" - ] - }, { "cell_type": "markdown", "id": "a716ea8f-9b1d-428c-8b41-0ab17af814d1", "metadata": {}, "source": [ - "## Dot products" + "## Updating the dot products used in MASS when computing matrix profiles\n", + "\n", + "This is part of the STOMP algorithm, which update the dot products of the sliding query instead of recomputing it everytime. When you compute $MASS(X,q_i)$, and $q_i$ is taken from a series $Y$ such as $q_i = Y[i:i+L]$, you can compute the dot product of $q_0$, and then only update it for subsequent $q_1, ...$" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "id": "9ab17a04-76ff-422f-ab91-165959af6924", "metadata": {}, "outputs": [], "source": [ - "from aeon.similarity_search._commons import get_ith_products\n", - "from aeon.similarity_search.matrix_profiles.stomp import _update_dot_products_one_series\n", + "from aeon.similarity_search.series._commons import (\n", + " _update_dot_products,\n", + " get_ith_products,\n", + ")\n", "\n", "\n", "def compute_all_products(X, T, L):\n", @@ -409,7 +388,7 @@ " \"\"\"\n", " prods = get_ith_products(X, T, L, 0)\n", " for i in range(T.shape[1] - L + 1):\n", - " prods = _update_dot_products_one_series(X, T, prods, L, i)\n", + " prods = _update_dot_products(X, T, prods, L, i)\n", " return prods\n", "\n", "\n", @@ -428,23 +407,24 @@ " mask = np.ones((1, search_space_size), dtype=bool)\n", " # Used for numba compilation before timings\n", " compute_all_products(X, T, query_length)\n", - " _times = %timeit -r 3 -n 7 -q -o compute_all_products(X, T, query_length)\n", - " times.loc[(size, _query_length), \"compute_all_products\"] = _times.average\n", - " # Used for numba compilation before timings\n", " update_products(X, T, query_length)\n", - " _times = %timeit -r 3 -n 7 -q -o update_products(X, T, query_length)\n", + "\n", + " _times = %timeit -r 2 -n 2 -q -o compute_all_products(X, T, query_length)\n", + " times.loc[(size, _query_length), \"compute_all_products\"] = _times.average\n", + "\n", + " _times = %timeit -r 2 -n 2 -q -o update_products(X, T, query_length)\n", " times.loc[(size, _query_length), \"update_products\"] = _times.average" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "id": "84953d86-45c6-41d7-be9d-6b136d1505b2", "metadata": {}, "outputs": [ { "data": { - "image/png": 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zZpKkn376qdL177rrLo/aMmjQIA0YMECSlJiYqO+//77c9b7++mudPXtWknTJJZdo6NChHu2/Jv773/+6bt99991lRp4oT/HoJlLlr1NwcLDGjx9f4TqXXXaZ6/axY8fKXWfdunWu25MnT660jZ6sUxt+85vfVLjcZDKpf//+rvvunp+n8vLyXLcDAwM92qZ4BBlJNb5AwtvHBwAAAABvIxe5gFyEXIRcpHaOf8UVVyghIUEffPCBJkyYoC5duigkJERBQUHq0qWLpk6dqi1btpS6gGjbtm16++23q/lMAAAAAKDukaFcQIZChtLUMhQAQMPj7+0GAADgbYZhaPfu3a77Q4YMqXSbmJgYRURE1Pu0mZs2bXLd3rNnjx5//PEqbW+z2XTu3DlXeHGxgIAA9e3b1+P9PfTQQ5oxY4Yk5wgU48aNK7NOyZEpHnzwwSq1t7pKvk5r167V8ePHK92meNQWSTp58mSF6/bs2VMBAQEVrhMZGem6nZmZWWZ5YmKiK8SRnKFSZQYNGiSTyVSqrXXBkz5Q2fOripKhm6fT8+bn57tuezrSR0M9PgAAAAB4E7nIBeQiTuQi5CK1cfyKRrstZjKZ9Kc//UmHDx/WwoULJUl/+9vf9OKLL8rfn68wAQAAADQsZCgXkKE4kaE0rQwFANDwkCIDAJq8jIyMUidIHTt29Gi7jh071ntYcerUKdftn376yaNROC5ms9nchhVWq7VKXzLfe++9+v3vf69z587pm2++0dmzZ9WqVSvX8oSEBNdoHoGBgfU2/WrJ1+nbb7+t8vY2m63C5RaLpdJ9lAwzioqKyiwvGVSEhoYqIiKi0n02b95cFotF6enpla5bE1V9foWFhTU6XskLIzwdgaPkep5cWNGQjw8AAAAA3kQucgG5iBO5CLlIfR5fkv70pz+5CmFsNps2b96sYcOG1Xi/AAAAAFCbyFAuIENxIkNpWhkKAKDhMXu7AQAAeFt2dnap+6GhoR5t5+6Evy5lZGTUeB/lnTgXq+roBy1atNCkSZMkOU9Y//3vf5daPnfuXDkcDknSrbfeqpYtW1axtdVT09fJbrdXuNyT6XArU7LfedrnpPo5Ma+N51cVJUcASU5O9mibpKQk121Pgp6GfHwAAAAA8CZykQvIRZzIRchF6vP4ktStWzd17tzZdf/XX3+t8T4BAAAAoLaRoVxAhuJEhtK0MhQAQMNDIQwAoMm7+OQvJyfHo+3OnTtX620pPrF3p2RA8re//U2GYVT5X8kvlWvDb3/7W9ftklPVGoahOXPmuO5Pnz69Vo9bkZKv09KlS6v1OtW1kv3O0z4n1U2/87aePXu6bnsy1bAknThxwnW7V69ePn18AAAAAPAmcpGaIRepHnKRC7ydS3j7+MXatWvnup2SklIr+wQAAACA2kSGUjNkKNVDhnJBQ8kwAAANB4UwAIAmz2KxKDAw0HW/5ElQRU6ePFnpOpVNYXqxykabaNOmjet2yVELvOmqq65Sv379JDlHa9y0aZMkae3atTpy5IgkqVOnThozZky9takhvk4XKzmCSU5OTqVT5krOkT7qeupab+jdu7fr9s6dOz3a5ueffy53e188PgAAAAB4E7lIzZCLVA+5yAXeziW8ffxiJS/Q8cZoyQAAAABQGTKUmiFDqR4ylAsaSoYBAGg4KIQBADR5JpNJ/fv3d93fvHlzpdscOnRIqampla7XokUL121P1t+7d2+Fy6+88krX7Q0bNlS6v/pS3sgdJUfwmDZtmszm+vuzo6G+TiVFR0eXCiy2bNlS6Tbbt2/3aESR+p5+tqauvfZa1+2DBw/q9OnTFa5/6tQpHTp0yHV/1KhRPn18AAAAAPAmcpGaIxepOnKRC7ydS3j7+JLzQp6DBw+67rdv377G+wQAAACA2kaGUnNkKFVHhnJBQ8gwAAANC4UwAACo9MnSp59+Wun6//73vz3ab8mpYnft2lXhutu3b9fRo0crXOf666+Xv7+/JGnjxo3avXu3R+2oa5MnT1ZISIgk6bPPPlNCQoKWLl0qSTKbzXrggQfqtT0333yz6/bSpUuVnJxcr8f31IgRI1y3FyxYUOn6nvRNSQoODnbdLiwsrHrD6lmPHj10ySWXuO7PmzevwvVLLu/bt6+6du3q08cHAAAAAG8jF6kZcpHqIRdx8nYu4e3jS9LChQuVn58vyXkRzvDhw2u8TwAAAACoC2QoNUOGUj1kKE4NIcMAADQsFMIAACDpwQcfdN3evHlzhSeF8fHxeueddzzab8nRIyo6ASsqKtJTTz1V6f6ioqI0efJkSZJhGJo6daoyMzM9aovD4dDZs2c9WreqwsPDNXHiREnOKVbvvPNO5eXlSZLGjh2rDh061Mlx3Rk8eLBGjhwpScrNzdWUKVNUUFDg0bYFBQUeTSVbG0qGOAsXLqxwxJiff/650pP4YpGRka7biYmJ1W9gPZoxY4br9ltvveU2YEpKStJbb73luv/YY481iuMDAAAAgDeRi9QMuUj1kItc4O1coraPn5OTI4fD4dGxDx06pBdffNF1f+zYsWrdurVH2wIAAABAfSNDqRkylOohQ7nA2xkKAKBhoRAGAABJMTExuv/++133p0+fXu6J4fbt23Xdddfp3LlzCgwMrHS/kyZNck3bumnTJr344ouy2+2l1klISNDNN9+sjRs3KigoqNJ9vvrqq2rXrp0kac+ePRo8eLBWrlzpdv2EhAS988476tmzpz777LNK919dJaewLTkV6/Tp0+vsmBV5//33FRYWJkn64YcfNHz48AqniI2Li9Of//xnde7cud6mvL3xxhs1bNgwSc4wafz48Vq1alWZ9WJjYzVu3DjZ7XaP+t2ll17quv3ll196NOWttz300EPq1q2bJOdUzzfeeKOOHz9eap3jx49r3LhxSktLk+T8uS0ZNF4sNjZWJpPJ9S82NrZejw8AAAAAvoJcpObIRaqOXOSCxpaLbN26VX369NGsWbN05syZctex2+369NNPNWTIEKWmpkqSAgMD9frrr7ttJwAAAAB4GxlKzZGhVB0ZygXezlAAAA2Lv7cbAABAQ/G3v/1NmzZt0sGDB5Wfn6/7779ff/rTnzRkyBAFBQVp//792rp1qwzD0O23367U1FStW7euwn126tRJjzzyiD788ENJ0uuvv65FixZp+PDhCg4O1uHDh7VhwwYVFBRozJgxatu2baVTlLZv315fffWVxo0bp5SUFB08eFDXX3+9oqKiNHjwYLVq1UqFhYVKSUnRvn37Kp0St7ZcffXV6tOnj/bv3+96rHXr1rrlllvq5fgXu/TSS7Vo0SJNmjRJOTk52rJli6666ip169ZNAwcOVEREhPLy8nTmzBnt2bPHK6NbmEwmzZ492/WFf0pKiq677jr1799fAwYMkCTt3r3bNfXxc889py+++MJ1El8chF3s9ttv1x/+8AcZhqEVK1aoX79+Gjp0qJo3b+5a56677tIVV1xRp8+vKgICArRkyRINGzZM2dnZ2rlzp3r06KHRo0crKipKCQkJWrNmjWs63hYtWmjJkiWu6Zwb6vGnT5+u7du3l3qsOGyRpFOnTrne65L+9a9/Naj3BwAAAEDjRy5SM+QiVUcuckFjzEUOHDigGTNm6PHHH1f37t3Vp08fRUREyGw2KykpSZs2bVJKSoprfT8/P/373/9W//79a+U5AQAAAEBdIUOpGTKUqiNDucDbGYqkcq/xOHHihOv2119/Xe46xe8PAKAWGQAAwCUxMdG44oorDElu/91yyy1GZmamMWLECNdja9eudbvP3NxcY9y4cRXu8+abbzZsNptx3333uR6bM2dOhW09duyYMXr06Ar3W/JfmzZtjO+++67Mfo4ePepap1OnTjV6/f7+97+XOuZzzz1Xo/2506lTJ9cxjh49WuG6u3btMi6//HKPX6fOnTsbO3fuLLOftWvXutYZMWKER+0sud/K2tilS5cK2/XQQw8ZBQUFRvv27V2P2Ww2t/t86aWXKtzfxf2rKq+pYRhV6qtVsXHjxkpfi65duxqbNm2qdF8l37PKfk7r4viGYZT6PVGVf560FQAAAABqG7lIpxq9fuQipZGLVF1jyUUuPnZl/3r27Gls2LDB05cJAAAAALyODKVTjV4/MpTSyFCqzpsZSlUyD0/fWwBA9TAjDAAAJbRv316bN2/Wv//9by1YsEB79uxRRkaG2rRpo/79++u+++7THXfcIZPJ5PE+g4OD9Z///EeLFi3SvHnz9PPPPysjI0OtW7dW//79df/99+vOO++s0j4l54ggq1at0qZNm/TFF19o/fr1OnnypGw2m/z9/RUZGakePXroiiuu0NixYzVy5MhaHeGgPLfffruefvpp131vTV1bUv/+/bV9+3atXLlSy5cv14YNG3Tq1Cmlp6crKChIrVq1Us+ePXXllVfq+uuv15AhQ6r8XtRGG/ft26ePPvpIX3zxheLi4pSTk6N27dpp8ODB+u1vf6vRo0dLkmw2myTniB0tWrRwu8//+7//07BhwzRnzhzt2LFDycnJysnJqZfnUxNDhgzRnj179O9//1uff/654uLilJqaqsjISMXExGjixImaOnWqa2rixnZ8AAAAAPAmcpGaIRepfhvJRZy8nUvU1vGvueYabd++XZs2bdLGjRt18OBBpaamKjU1Vfn5+bJYLOrQoYOuvPJK3XLLLbrhhhvqvd8BAAAAQE2QodQMGUr120iG4uTtDAUA0DCYDMMwvN0IAAB80ciRI13T165du1YjR470boMagHnz5un++++XJA0bNkw//vijdxvUyBw6dEgxMTGSpF69eunXX3/1cosAAAAAAE0VuUhZ5CJ1i1wEAAAAAOCLyFDKIkOpW2QoAICmwuztBgAAgMbjk08+cd3+7W9/68WWNE6fffaZ6/agQYO82BIAAAAAAHAxcpG6RS4CAAAAAEDjQIZSt8hQAABNBYUwAACgVuzcudM1SkdERIQmTpzo5RY1LkePHtV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" ] @@ -463,76 +443,10 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "d11a14ed-65f0-41d5-ad00-1e3877733d2e", - "metadata": {}, - "source": [ - "## Stomp vs naive" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "63d8fe31-86b8-408b-b5c6-6c18987fdc08", - "metadata": {}, - "outputs": [], - "source": [ - "# Sizes are limited to not time-out the CI, you can test with more sizes locally !\n", - "sizes = [500, 1000, 2500, 5000]\n", - "query_lengths = [0.05, 0.1]\n", - "times = pd.DataFrame(\n", - " index=pd.MultiIndex(levels=[[], []], codes=[[], []], names=[\"size\", \"query_length\"])\n", - ")\n", - "\n", - "for size in sizes:\n", - " for _query_length in query_lengths:\n", - " query_length = int(_query_length * size)\n", - " X = rng.random((1, 1, size))\n", - " T = rng.random((1, size))\n", - " search_space_size = size - query_length + 1\n", - " mask = np.ones((1, search_space_size), dtype=bool)\n", - " # Used for numba compilation before timings\n", - " naive_squared_matrix_profile(X, T, query_length, mask)\n", - " _times = %timeit -r 1 -n 3 -q -o naive_squared_matrix_profile(X, T, query_length, mask)\n", - " times.loc[(size, _query_length), \"Naive\"] = _times.average\n", - " # Used for numba compilation before timings\n", - " stomp_squared_matrix_profile(X, T, query_length, mask)\n", - " _times = %timeit -r 1 -n 3 -q -o stomp_squared_matrix_profile(X, T, query_length, mask)\n", - " times.loc[(size, _query_length), \"Stomp\"] = _times.average" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cc4801ae-bb48-46d1-8e71-21c045c69773", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(ncols=len(query_lengths), figsize=(20, 5), dpi=200)\n", - "for j, (i, grp) in enumerate(times.groupby(\"query_length\")):\n", - " grp.droplevel(1).plot(label=i, ax=ax[j])\n", - " ax[j].set_title(f\"query length {i}\")\n", - " ax[j].set_yscale(\"log\")\n", - "ax[0].set_ylabel(\"time in seconds\")\n", - "plt.show()" - ] - }, { "cell_type": "code", "execution_count": null, - "id": "391737ea-a185-4ac9-906d-90724a279017", + "id": "61dac86c-a1f3-4899-bcd5-33c8468e4c07", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/similarity_search/similarity_search_tasks.ipynb b/examples/similarity_search/similarity_search_tasks.ipynb index 54d17857d5..3c204f4c72 100644 --- a/examples/similarity_search/similarity_search_tasks.ipynb +++ b/examples/similarity_search/similarity_search_tasks.ipynb @@ -7,7 +7,7 @@ "source": [ "# Similarity search tasks\n", "\n", - "To discuss : the term subsequences appear more often than subseries in similarity search papers, so maybe stick to subsequences ?\n", + "\n", "\n", "## Notations\n", "- A single time point $x \\in \\mathbb{R}^{d}$ representing a vector of size $d$, with $d$ the number of channels\n", From db104992718fd0b3c514dc5836558b814991670a Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 2 Feb 2025 16:07:17 +0100 Subject: [PATCH 25/36] remove notes --- .../similarity_search_tasks.ipynb | 136 ------------------ 1 file changed, 136 deletions(-) delete mode 100644 examples/similarity_search/similarity_search_tasks.ipynb diff --git a/examples/similarity_search/similarity_search_tasks.ipynb b/examples/similarity_search/similarity_search_tasks.ipynb deleted file mode 100644 index 3c204f4c72..0000000000 --- a/examples/similarity_search/similarity_search_tasks.ipynb +++ /dev/null @@ -1,136 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "2347de94-27a7-486e-a900-e80db5c7f427", - "metadata": {}, - "source": [ - "# Similarity search tasks\n", - "\n", - "\n", - "\n", - "## Notations\n", - "- A single time point $x \\in \\mathbb{R}^{d}$ representing a vector of size $d$, with $d$ the number of channels\n", - "- A single time series $X \\in \\mathbb{R}^{d,m}$ of $d$ channels and $m$ timepoints\n", - "- A collection ${\\cal X} \\in \\mathbb{R}^{n,d,m}$ of $n$ time series \n", - "- $l$ a length parameter for subseries extracted using a sliding window on a time series $X$ over its timepoints\n", - "- $W_{i,j} \\in \\mathbb{R}^{d,l}$ a subseries extracted from a collection ${\\cal X}$, with $i$ the sample id and $j$ the starting timepoint, such as $W_{i,j} = X_{i,[j:j+l[}$. Denoted $W_{j}$ if used outside of the context of a collection. ${\\cal W}$ denotes the set of all admissible subseries.\n", - " \n", - "## Series tasks\n", - "Given a single series $X$, we want to be able to do the following tasks :\n", - "\n", - "#### Subseries Neighbor search:\n", - "$K$-nn based and/or range ($r$) based search (radius only for now, extent necessary for [k-Motiflefts](https://www.vldb.org/pvldb/vol16/p725-schafer.pdf) ?). Given a series $X$ and a subseries $W_i$, find the other subseries in $X$ that are the most similar to $W_i$. In terms of parameterization, we want to be able to toggle on/off :\n", - "- ignore neighboring matches. Given $W_j$ a neighboring subseries of $W_i$, the subseries $W_{j-l//\\epsilon}, ..., W_{j+l//\\epsilon}$ cannot be in the returned set.\n", - "- inverse distance. Return the worst matches instead of the best ones.\n", - "- normalize. Wheter subseries should be normalized prior to distance computations\n", - "\n", - "#### Subseries Motif search :\n", - "Extract $k$-motifs or range motifs or $r$-motifs.\n", - "\n", - "The $k^{th}$ motif is the $k^{th}$ most similar pair of subseries in $X$. Given $\\forall a,b,i,j$ the pair ${W_i, W_j}$ is the motif if $dist(W_i, W_j) ≤ dist(W_a, W_b), i \\neq j$ and $a \\neq b$\n", - "\n", - "For the $r$-motif,: $S$ is a maximal set of subseries with range $r$ if $\\forall\\ W_i,W_j \\in S,\\ dist(W_i, W_j) \\leq 2r$ and $\\forall\\ W_a \\in {\\cal W}-S,\\ dist(W_a, W_i) > 2r$\n", - "\n", - "\n", - "#### Compute self distance profile\n", - "Given a subseries $W_i$, compute the self distance profile to $X$. Returns a vector of size $m-l+1$ containing the distance to all subseries. \n", - "\n", - "In terms of parameterization, we want to be able to toggle on/off :\n", - "- ignore neighboring matches. Given $W_j$ a neighboring subseries of $W_i$, the subseries $W_{j-l//\\epsilon}, ..., W_{j+l//\\epsilon}$ cannot be in the returned set.\n", - "- inverse distance. Return the worst matches instead of the best ones.\n", - "- normalize. Wheter subseries should be normalized prior to distance computations\n", - "\n", - "\n", - "#### Compute self matrix profile\n", - "Given a series $X$ and a length parameter $l$, compute its self matrix profile. Returns a vector of size $m-l+1$ containing the distances to the best matches of each subseries $W_i$, and another vector of size $m-l+1$ containg the timestamp of the best matches in $X$ for each subseries. Implement it as A/B matrix profile with B=A.\n", - "\n", - "In terms of parameterization, we want to be able to toggle on/off :\n", - "- $k$ : number of best matches to return for each subseries in $X$\n", - "- $r$ : maximal distance of the best matches to be in the returned set for each subseries in $X$\n", - "- ignore neighboring matches. Given $W_j$ a neighboring subseries of $W_i$, the subseries $W_{j-l//\\epsilon}, ..., W_{j+l//\\epsilon}$ cannot be in the returned set.\n", - "- inverse distance. Return the worst matches instead of the best ones.\n", - "- normalize. Wheter subseries should be normalized prior to distance computations\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "7313599d-66e2-4d03-959e-bd0abe05baed", - "metadata": {}, - "source": [ - "\n", - "## Collection tasks\n", - "Given a time series collection $\\cal X$, we want to be able to do the following tasks :\n", - "(we consider all subseries $W_{i,j}$ part its of $\\cal X$ due to notation but doesn't have to be when given as inputs for example in neighbor search).\n", - "\n", - "#### Subseries Neighbor search :\n", - "$K$-nn based and/or range ($r$) based search (radius only for now, extent necessary for [k-Motiflefts](https://www.vldb.org/pvldb/vol16/p725-schafer.pdf) ?). Given a subseries $W_{i,j}$, find the other subseries in $\\cal X$ that are the most similar to $W_{i,j}$. In terms of parameterization, we want to be able to toggle on/off :\n", - "- ignore neighboring matches. Given ${W_a,b}$ a neighboring subseries of $W_{i,j}$, the subseries $W_{a, b-l//\\epsilon}, ..., W_{a,b+l//\\epsilon}$ cannot be in the returned set.\n", - "- inverse distance. Return the worst matches instead of the best ones.\n", - "- normalize. Wheter subseries should be normalized prior to distance computations\n", - "\n", - "#### Series Neighbor search :\n", - "$K$-nn based and/or range ($r$) based search. Given a series $X_i$, find the other series in $\\cal X$ that are the most similar to $X_i$. In terms of parameterization, we want to be able to toggle on/off :\n", - "- inverse distance. Return the worst matches instead of the best ones.\n", - "- normalize. Wheter subseries should be normalized prior to distance computations\n", - "\n", - "#### Subseries Motif search :\n", - "Extract $k$-motifs or range $r$-motifs.\n", - "\n", - "The $k^{th}$ motif is the $k^{th}$ most similar pair of subseries in $X$. Given $\\forall a,b,a^\\prime,b^\\prime,i,j,i^\\prime,j^\\prime$ the pair $(W_{i,j}, W_{i^\\prime,j^\\prime})$ is the motif if $dist(W_{i,j}, W_{i^\\prime,j^\\prime}) ≤ dist(W_{a,b}, W_{a^\\prime,b^\\prime}), i \\neq i^\\prime, j \\neq j^\\prime, a \\neq a^\\prime, b \\neq b^\\prime$.\n", - "\n", - "For the $r$-motif,: $S$ is a maximal set of subseries with range $r$ if $\\forall\\ (W_{i,j},W_{i^\\prime,j^\\prime}) \\in S,\\ dist(W_{i,j}, W_{i^\\prime,j^\\prime}) \\leq 2r$ and $\\forall\\ W_{a,b} \\in {\\cal W}-S,\\ dist(W_{i,j}, W_{a,b}) > 2r$\n", - "\n", - "\n", - "#### Compute distance profiles :\n", - "Given a subseries $W_{i,j}$, compute the distance profiles to all series in $\\cal X$. Returns a vector of size $n, m-l+1$ containing the distance to all subseries. \n", - "\n", - "In terms of parameterization, we want to be able to toggle on/off :\n", - "- ignore neighboring matches. Given $W_{i,b}$ a neighboring subseries of $W_{i,j}$ the subseries $W_{i,b-l//\\epsilon}, ..., W_{i,b+l//\\epsilon}$ cannot be in the returned set.\n", - "- inverse distance. Return the worst matches instead of the best ones.\n", - "- normalize. Wheter subseries should be normalized prior to distance computations.\n", - "\n", - "\n", - "#### Compute matrix profiles :\n", - "Given a series $X_i \\in {\\cal X}$ and a length parameter $l$, compute its matrix profile over the collection. Returns a vector of size $m-l+1$ containing the distances to the best matches of each subseries $W_{i,j}$, and another vector of size $m-l+1$ containg the timestamp of the best matches in ${\\cal X}$ for each subseries.\n", - "\n", - "In terms of parameterization, we want to be able to toggle on/off :\n", - "- $k$ : number of best matches to return for each subseries in $X$\n", - "- $r$ : maximal distance of the best matches to be in the returned set for each subseries in $X$\n", - "- ignore neighboring matches. Given $W_{a,b}$ a neighbor of subseries $W_{i,j}$ the subseries $W_{a,b-l//\\epsilon}, ..., W_{a,b+l//\\epsilon}$ cannot be in the returned set.\n", - "- inverse distance. Return the worst matches instead of the best ones.\n", - "- normalize. Wheter subseries should be normalized prior to distance computations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be1430f0-dce0-4de4-b702-11ee5e33f462", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (Spyder)", - "language": "python3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From f32877963ffd86a4f02ba5791c0fcd3d0488610d Mon Sep 17 00:00:00 2001 From: baraline Date: Sat, 15 Mar 2025 15:06:31 +0100 Subject: [PATCH 26/36] Patrick comments --- aeon/similarity_search/_base.py | 14 ++- aeon/similarity_search/collection/_base.py | 8 +- .../collection/motifs/__init__.py | 2 +- .../collection/neighbors/_rp_cosine_lsh.py | 99 ++++++++++++++----- aeon/similarity_search/series/_base.py | 13 +-- aeon/similarity_search/series/_commons.py | 14 ++- .../series/motifs/__init__.py | 2 +- .../series/neighbors/_mass.py | 18 ++-- 8 files changed, 111 insertions(+), 59 deletions(-) diff --git a/aeon/similarity_search/_base.py b/aeon/similarity_search/_base.py index 07140c7495..a2345ee558 100644 --- a/aeon/similarity_search/_base.py +++ b/aeon/similarity_search/_base.py @@ -40,7 +40,7 @@ def fit( Changes state to "fitted". Writes to self: - _is_fitted : flag is set to True. + _is_fitted : flag is set to True. Parameters ---------- @@ -65,9 +65,17 @@ def predict( """ Predict method. + Can either work with new series or with None (for case when predict can be made + using the data given in fit against itself) depending on the estimator. + Parameters ---------- - X : 2D np.array of shape ``(n_cases, n_timepoints)`` - Optional data to use for predict. + X : Series or Collection, any supported type + Data to fit transform to, of python type as follows: + Series: 2D np.ndarray shape (n_channels, n_timepoints) + Collection: 3D np.ndarray shape (n_cases, n_channels, n_timepoints) + or list of 2D np.ndarray, case i has shape (n_channels, n_timepoints_i + None : If None type is accepted, it means that the predict function will + work only with the data given in fit. (e.g. self matrix profile instead) """ ... diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index ecb3e31ccc..609fcdfc82 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -69,11 +69,11 @@ def predict(self, X, **kwargs): Parameters ---------- - X : np.ndarray, shape = (n_cases, n_channels, n_tiempoints) + X : np.ndarray, shape = (n_cases, n_channels, n_timepoints) Collections of series to predict on. kwargs : dict, optional - Additional keyword argument as dict or individual keywords args - to pass to use. + Additional keyword arguments to be passed to the _predict function of the + estimator. Returns ------- @@ -93,7 +93,7 @@ def predict(self, X, **kwargs): def _check_predict_series_format(self, X): """ - Check wheter a series X in predict is correctly formated. + Check whether a series X in predict is correctly formated. Parameters ---------- diff --git a/aeon/similarity_search/collection/motifs/__init__.py b/aeon/similarity_search/collection/motifs/__init__.py index fc014bcced..b7169f1ade 100644 --- a/aeon/similarity_search/collection/motifs/__init__.py +++ b/aeon/similarity_search/collection/motifs/__init__.py @@ -1 +1 @@ -"""Motif search for time series collection.""" +"""Motif discovery for time series collection.""" diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index c6c0b72eb7..dcc5310aff 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -4,34 +4,58 @@ from numba import get_num_threads, njit, prange, set_num_threads from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch -from aeon.utils.numba.general import z_normalise_series_3d +from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD, z_normalise_series_3d @njit(cache=True) -def _hamming_dist(X, Y): - d = 0 - for i in prange(X.shape[0]): +def _bool_hamming_dist(X, Y): + """ + Compute a hamming distance on boolean arrays. + + Parameters + ---------- + X : np.ndarray of shape (n_timepoints) + A boolean array + + Y : np.ndarray of shape (n_timepoints) + A boolean array + + Returns + ------- + d : int + The hamming distance between X and Y. + + """ + d = np.uint64(0) + for i in range(X.shape[0]): d += X[i] ^ Y[i] return d @njit(cache=True, parallel=True) -def _hamming_dist_series_to_collection(X_bool, collection_bool): - n_buckets = collection_bool.shape[0] - res = np.zeros(n_buckets, dtype=np.int64) - for i in prange(n_buckets): - res[i] = _hamming_dist(collection_bool[i], X_bool) - return res +def _bool_hamming_dist_matrix(X_bool, collection_bool): + """ + Compute the distances between X_bool and each boolean array of collection_bool. + Each array of collection_bool represent the hash value of a bucket in the index. -@njit(cache=True, fastmath=True, parallel=True) -def _series_to_bool(X, hash_funcs, start_points, length): - n_hash_funcs = hash_funcs.shape[0] - res = np.empty(n_hash_funcs, dtype=np.bool_) - for j in prange(n_hash_funcs): - res[j] = _nb_flat_dot( - X[:, start_points[j] : start_points[j] + length], hash_funcs[j] - ) + Parameters + ---------- + X_bool : np.ndarray of shape (n_timepoints) + A 1D boolean array + collection_bool : np.ndarray of shape (n_cases, n_timepoints) + A 2D boolean array + + Returns + ------- + res : np.ndarray of shape (n_cases) + The distance of X_bool to all buckets in the index + + """ + n_buckets = collection_bool.shape[0] + res = np.zeros(n_buckets, dtype=np.uint64) + for i in prange(n_buckets): + res[i] = _bool_hamming_dist(collection_bool[i], X_bool) return res @@ -47,6 +71,27 @@ def _nb_flat_dot(X, Y): @njit(cache=True, parallel=True) def _collection_to_bool(X, hash_funcs, start_points, length): + """ + Transform a collection of time series X to their boolean hash representation. + + Parameters + ---------- + X : np.ndarray of shape (n_cases, n_channels, n_timepoints) + Time series collection to transform. + hash_funcs : np.ndarray of shape (n_hash, n_channels, length) + The random projection vectors used to compute the boolean hash + start_points : np.ndarray of shape (n_hash) + The starting index where the random vector should be applied when computing + the distance to the input series. + length : int + Length of the random vectors. + + Returns + ------- + res : np.ndarray of shape (n_cases, n_hash) + The boolean representation of all series in X. + + """ n_hash_funcs = hash_funcs.shape[0] n_samples = X.shape[0] res = np.empty((n_samples, n_hash_funcs), dtype=np.bool_) @@ -71,7 +116,7 @@ class RandomProjectionIndexANN(BaseCollectionSimilaritySearch): as ``X[:, s:s+L].V`` Note that this method will not provide exact results, but will perform approximate - searchs. This also ignore any temporal correlation and consider series as + searches. This also ignore any temporal correlation and consider series as high dimensional points due to the cosine similarity distance. Parameters @@ -80,13 +125,17 @@ class RandomProjectionIndexANN(BaseCollectionSimilaritySearch): Number of random hashing function to use to index series. The default is 128. hash_func_coverage : float, optional A value in the interval ]0,1] which defines the size L fo the random vectors - relative to the size of the input time series. The default is 1.0. + relative to the size of the input time series. The default is 0.25. use_discrete_vectors: bool, optional, Wheter to use dicrete vectors with values -1 or 1 as random vector. If false, the values of the random vectors are drawn uniformly between [-1,1]. random_state: int, optional A random seed to seed the index building. The default is None. - + normalize: bool, optional + Wheter to z-normalize the input the series during fit and predict before + indexing them. + n_jobs: int, optional + Number of parallel threads to use when computing boolean hashes. """ _tags = { @@ -237,6 +286,7 @@ def _extract_neighors_one_series( top_k = np.zeros(k, dtype=int) top_k_dist = np.zeros(k, dtype=float) remove_X_hash = False + # Case where X_hash is an existing bucket of the index if not inverse_distance and X_hash in self.dict_X_index_: current_k = min(len(self.dict_X_index_[X_hash]), k) top_k[:current_k] = self.dict_X_index_[X_hash][:current_k] @@ -244,13 +294,12 @@ def _extract_neighors_one_series( else: current_k = 0 + # Case where we want to find more neighboors in buckets with similar hash if current_k < k: - dists = _hamming_dist_series_to_collection( - X_bool, self.bool_hashes_value_list_ - ) + dists = _bool_hamming_dist_matrix(X_bool, self.bool_hashes_value_list_) if inverse_distance: - dists = 1 / (dists + 1e-8) + dists = 1 / (dists + AEON_NUMBA_STD_THRESHOLD) if remove_X_hash: dists[np.where(self.bool_hashes_value_list_ == X_hash)[0]] = np.iinfo( dists.dtype diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index 435715db69..04323d4b52 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -122,23 +122,14 @@ def _check_X_index(self, X_index: int): def _check_predict_series_format(self, X): """ - Check wheter a series X in predict is correctly formated. + Check wheter a series X is correctly formated regarding series given in fit. Parameters ---------- X : np.ndarray, shape = (n_channels, n_timepoints) A series to be used in predict. + """ - if isinstance(X, np.ndarray): - if X.ndim != 2: - raise TypeError( - "A np.ndarray given in predict must be 2D" - f"(n_channels, n_timepoints) but found {X.ndim}D." - ) - else: - raise TypeError( - "Expected a 2D np.ndarray in predict but found" f" {type(X)}." - ) if self.n_channels_ != X.shape[0]: raise ValueError( f"Expected X to have {self.n_channels_} channels but" diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py index 8aa63826a8..e342ef5a83 100644 --- a/aeon/similarity_search/series/_commons.py +++ b/aeon/similarity_search/series/_commons.py @@ -6,6 +6,8 @@ from numba import njit from scipy.signal import convolve +from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD + def fft_sliding_dot_product(X, q): """ @@ -63,7 +65,7 @@ def get_ith_products(X, T, L, ith): @njit(cache=True, fastmath=True) def _inverse_distance_profile(dist_profile): - return 1 / (dist_profile + 1e-8) + return 1 / (dist_profile + AEON_NUMBA_STD_THRESHOLD) @njit(cache=True) @@ -75,7 +77,7 @@ def _extract_top_k_from_dist_profile( exclusion_size, ): """ - Given a distance profiles, extract the top k lower distances. + Given a distance profile, extract the top k lowest distances. Parameters ---------- @@ -86,10 +88,12 @@ def _extract_top_k_from_dist_profile( Number of best matches to return threshold : float A threshold on the distances of the best matches. To be returned, a candidate - must have a distance bellow this threshold. This can reduce the number of - returned matches to be bellow ``k`` + must have a distance below this threshold. This can reduce the number of + returned matches to be below ``k`` allow_trivial_matches : bool - Wheter to allow returning matches that are in the same neighborhood. + Whether to allow returning matches that are in the same neighborhood by + ignoring the exclusion zone defined by the ``exclusion_size`` parameter. + If False, the exclusion zone is applied. exclusion_size : int The size of the exlusion size to apply when ``allow_trivial_matches`` is False. It is applied on both side of existing matches (+/- their indexes). diff --git a/aeon/similarity_search/series/motifs/__init__.py b/aeon/similarity_search/series/motifs/__init__.py index d4853a68fe..56e3bc276f 100644 --- a/aeon/similarity_search/series/motifs/__init__.py +++ b/aeon/similarity_search/series/motifs/__init__.py @@ -1,4 +1,4 @@ -"""Subsequence Neighbor search for series.""" +"""Motif discovery for single series.""" __all__ = [ "StompMotif", diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py index 7d052d9d89..1eb03970ba 100644 --- a/aeon/similarity_search/series/neighbors/_mass.py +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -6,7 +6,7 @@ __all__ = ["MassSNN"] import numpy as np -from numba import njit, prange +from numba import njit from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch from aeon.similarity_search.series._commons import ( @@ -29,7 +29,7 @@ class MassSNN(BaseSeriesSimilaritySearch): length : int The length of the subsequences to use for the search. normalize : bool - Wheter the subsequences should be z-normalized. + Whether the subsequences should be z-normalized. References ---------- @@ -79,7 +79,7 @@ def _predict( The maximum allowed distance of a candidate subsequence of X_ to X for the candidate to be considered as a neighbor. allow_trivial_matches: bool, optional - Wheter a neighbors of a match to a query can be also considered as matches + Whether a neighbors of a match to a query can be also considered as matches (True), or if an exclusion zone is applied around each match to avoid trivial matches with their direct neighbors (False). inverse_distance : bool @@ -199,7 +199,7 @@ def _get_test_params(cls, parameter_set: str = "default"): @njit(cache=True, fastmath=True) def _squared_distance_profile(QT, T, Q): """ - Compute squared distance profile between query subsequence and a single time series. + Compute squared Euclidean distance profile between query and a time series. This function calculates the squared distance profile for a single time series by leveraging the dot product of the query and time series as well as precomputed sums @@ -225,16 +225,16 @@ def _squared_distance_profile(QT, T, Q): query_length = Q.shape[1] _QT = -2 * QT distance_profile = np.zeros(profile_length) - for k in prange(n_channels): + for k in range(n_channels): _sum = 0 _qsum = 0 - for j in prange(query_length): + for j in range(query_length): _sum += T[k, j] ** 2 _qsum += Q[k, j] ** 2 distance_profile += _qsum + _QT[k] distance_profile[0] += _sum - for i in prange(1, profile_length): + for i in range(1, profile_length): _sum += T[k, i + (query_length - 1)] ** 2 - T[k, i - 1] ** 2 distance_profile[i] += _sum return distance_profile @@ -274,9 +274,9 @@ def _normalized_squared_distance_profile( n_channels, profile_length = QT.shape distance_profile = np.zeros(profile_length) Q_is_constant = Q_stds <= AEON_NUMBA_STD_THRESHOLD - for i in prange(profile_length): + for i in range(profile_length): Sub_is_constant = T_stds[:, i] <= AEON_NUMBA_STD_THRESHOLD - for k in prange(n_channels): + for k in range(n_channels): # Two Constant case if Q_is_constant[k] and Sub_is_constant[k]: _val = 0 From 0a9434f0084683ef612c9afec20c5713d1f86983 Mon Sep 17 00:00:00 2001 From: baraline Date: Thu, 27 Mar 2025 09:59:13 +0100 Subject: [PATCH 27/36] Adress comments and clean index code --- .../collection/neighbors/_rp_cosine_lsh.py | 80 +++++++------------ .../similarity_search/series/motifs/_stomp.py | 2 +- aeon/testing/mock_estimators/__init__.py | 7 ++ aeon/utils/discovery.py | 1 - aeon/utils/numba/general.py | 1 + aeon/utils/tags/_tags.py | 2 +- docs/api_reference/utils.rst | 7 ++ docs/getting_started.md | 65 +++++++-------- examples/similarity_search/code_speed.ipynb | 2 +- .../similarity_search/similarity_search.ipynb | 48 +++++------ 10 files changed, 102 insertions(+), 113 deletions(-) diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index c6c0b72eb7..abf4facec1 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -4,7 +4,7 @@ from numba import get_num_threads, njit, prange, set_num_threads from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch -from aeon.utils.numba.general import z_normalise_series_3d +from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD, z_normalise_series_3d @njit(cache=True) @@ -17,7 +17,7 @@ def _hamming_dist(X, Y): @njit(cache=True, parallel=True) def _hamming_dist_series_to_collection(X_bool, collection_bool): - n_buckets = collection_bool.shape[0] + n_buckets = len(collection_bool) res = np.zeros(n_buckets, dtype=np.int64) for i in prange(n_buckets): res[i] = _hamming_dist(collection_bool[i], X_bool) @@ -150,18 +150,15 @@ def _fit(self, X, y=None): size=self.n_hash_funcs, replace=True, ) - X_bools, X_hashes = self._collection_to_hashes(X) - self.dict_X_index_ = {} - self.dict_bool_hashes_ = {} - for i in range(len(X_hashes)): - if X_hashes[i] in self.dict_X_index_: - self.dict_X_index_[X_hashes[i]].append(i) + X_bools = self._collection_to_hashes(X) + self.index_ = {} + self._raw_index_bool_arrays = np.unique(X_bools, axis=0) + for i in range(len(X_bools)): + key = X_bools[i].tostring() + if key in self.index_: + self.index_[key].append(i) else: - self.dict_X_index_[X_hashes[i]] = [i] - self.dict_bool_hashes_[X_hashes[i]] = X_bools[i] - - self.bool_hashes_value_list_ = np.asarray(list(self.dict_bool_hashes_.values())) - self.bool_hashes_key_list_ = np.asarray(list(self.dict_bool_hashes_.keys())) + self.index_[key] = [i] set_num_threads(prev_threads) return self @@ -169,7 +166,6 @@ def _predict( self, X, k=1, - threshold=np.inf, inverse_distance=False, ): """ @@ -181,8 +177,6 @@ def _predict( Collections of series for which we want to find neighbors. k : int, optional Number of neighbors to return for each series. The default is 1. - threshold : int, optional - A threshold on the distance to determine which candidates will be returned. inverse_distance : bool, optional Wheter to inverse the computed distance, meaning that the method will return the k most dissimilar neighbors instead of the k most similar. @@ -206,18 +200,14 @@ def _predict( if self.normalize: X = z_normalise_series_3d(X) - X_bools = _collection_to_bool( - X, self.hash_funcs_, self.start_points_, self.window_length_ - ) - X_bools, X_hashes = self._collection_to_hashes(X) + X_bools = self._collection_to_hashes(X) top_k = [] top_k_dist = [] + for i in range(len(X_bools)): idx, dists = self._extract_neighors_one_series( X_bools[i], - X_hashes[i], k=k, - threshold=threshold, inverse_distance=inverse_distance, ) top_k.append(idx) @@ -229,61 +219,53 @@ def _predict( def _extract_neighors_one_series( self, X_bool, - X_hash, k=1, - threshold=np.inf, inverse_distance=False, ): + key = X_bool.tostring() top_k = np.zeros(k, dtype=int) top_k_dist = np.zeros(k, dtype=float) remove_X_hash = False - if not inverse_distance and X_hash in self.dict_X_index_: - current_k = min(len(self.dict_X_index_[X_hash]), k) - top_k[:current_k] = self.dict_X_index_[X_hash][:current_k] + if not inverse_distance and key in self.index_: + current_k = min(len(self.index_[key]), k) + top_k[:current_k] = self.index_[key][:current_k] remove_X_hash = True else: current_k = 0 if current_k < k: dists = _hamming_dist_series_to_collection( - X_bool, self.bool_hashes_value_list_ + X_bool, self._raw_index_bool_arrays ) if inverse_distance: - dists = 1 / (dists + 1e-8) + dists = 1 / (dists + AEON_NUMBA_STD_THRESHOLD) # to avoid div 0 if remove_X_hash: - dists[np.where(self.bool_hashes_value_list_ == X_hash)[0]] = np.iinfo( + dists[np.where(self._raw_index_bool_arrays == key)[0]] = np.iinfo( dists.dtype ).max - # Get top k buckets + # Get top k index of keys ids = np.argpartition(dists, kth=k)[:k] # and reoder them ids = ids[np.argsort(dists[ids])] _i_bucket = 0 while current_k < k: - if dists[ids[_i_bucket]] <= threshold: - candidates = self.dict_X_index_[ - self.bool_hashes_key_list_[ids[_i_bucket]] - ] - # Can do exact search by computing distances here - if len(candidates) > k - current_k: - candidates = candidates[: k - current_k] - top_k[current_k : current_k + len(candidates)] = candidates - top_k_dist[current_k : current_k + len(candidates)] = dists[ - ids[_i_bucket] - ] - current_k += len(candidates) - else: - break + key_index = self._raw_index_bool_arrays[ids[_i_bucket]].tostring() + candidates = self.index_[key_index] + # Can do exact search by computing distances here + if len(candidates) > k - current_k: + candidates = candidates[: k - current_k] + top_k[current_k : current_k + len(candidates)] = candidates + top_k_dist[current_k : current_k + len(candidates)] = dists[ + ids[_i_bucket] + ] + current_k += len(candidates) _i_bucket += 1 return top_k[:current_k], top_k_dist[:current_k] def _collection_to_hashes(self, X): - bool_hashes = _collection_to_bool( + return _collection_to_bool( X, self.hash_funcs_, self.start_points_, self.window_length_ ) - return bool_hashes, [ - hash(bool_hashes[i].tobytes()) for i in range(len(bool_hashes)) - ] diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index b3b5fcd41f..7b29360ed6 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -43,7 +43,7 @@ class StompMotif(BaseSeriesSimilaritySearch): } - for "r-motifs" (originaly named k-motifs, which was confusing as it is a range - based motif): { + based motif): { "motif_size":np.inf, "dist_threshold":r, "motif_extraction_method":"r_motifs" diff --git a/aeon/testing/mock_estimators/__init__.py b/aeon/testing/mock_estimators/__init__.py index e517e07ca0..e9e83aa263 100644 --- a/aeon/testing/mock_estimators/__init__.py +++ b/aeon/testing/mock_estimators/__init__.py @@ -29,6 +29,9 @@ "MockUnivariateSeriesTransformer", "MockMultivariateSeriesTransformer", "MockSeriesTransformerNoFit", + # similarity search + "MockSeriesSimilaritySearch", + "MockCollectionSimilaritySearch", ] from aeon.testing.mock_estimators._mock_anomaly_detectors import ( @@ -62,3 +65,7 @@ MockSeriesTransformerNoFit, MockUnivariateSeriesTransformer, ) +from aeon.testing.mock_estimators._mock_similarity_searchers import ( + MockCollectionSimilaritySearch, + MockSeriesSimilaritySearch, +) diff --git a/aeon/utils/discovery.py b/aeon/utils/discovery.py index d6e5ce61fc..8fd4a05efe 100644 --- a/aeon/utils/discovery.py +++ b/aeon/utils/discovery.py @@ -92,7 +92,6 @@ def all_estimators( # ignore test modules and base classes "base", "tests", - "similarity_search" # ignore these submodules "benchmarking", "datasets", diff --git a/aeon/utils/numba/general.py b/aeon/utils/numba/general.py index 958c584459..58ab9d15e9 100644 --- a/aeon/utils/numba/general.py +++ b/aeon/utils/numba/general.py @@ -23,6 +23,7 @@ "slope_derivative_3d", "generate_combinations", "get_all_subsequences", + "compute_mean_stds_collection_parallel", ] diff --git a/aeon/utils/tags/_tags.py b/aeon/utils/tags/_tags.py index 45801ef09a..2c132902e4 100644 --- a/aeon/utils/tags/_tags.py +++ b/aeon/utils/tags/_tags.py @@ -157,7 +157,7 @@ class : identifier for the base class of objects this tag applies to "input_data_type": { "class": ["transformer", "similarity-search"], "type": ("str", ["Series", "Collection"]), - "description": "The input abstract data type of the transformer, input X. " + "description": "The input abstract data type of the estimator, input X. " "Series indicates a single series input, Collection indicates a collection of " "time series.", }, diff --git a/docs/api_reference/utils.rst b/docs/api_reference/utils.rst index 6f43398a44..8c4891dde0 100644 --- a/docs/api_reference/utils.rst +++ b/docs/api_reference/utils.rst @@ -87,6 +87,8 @@ Mock Estimators MockUnivariateSeriesTransformer MockMultivariateSeriesTransformer MockSeriesTransformerNoFit + MockSeriesSimilaritySearch + MockCollectionSimilaritySearch Utilities ^^^^^^^^^ @@ -192,7 +194,9 @@ Numba first_order_differences_3d z_normalise_series_with_mean z_normalise_series + z_normalise_series_with_mean_std z_normalise_series_2d + z_normalise_series_2d_with_mean_std z_normalise_series_3d set_numba_random_seed choice_log @@ -204,6 +208,9 @@ Numba slope_derivative_2d slope_derivative_3d generate_combinations + get_all_subsequences + compute_mean_stds_collection_parallel + .. currentmodule:: aeon.utils.numba.stats diff --git a/docs/getting_started.md b/docs/getting_started.md index ce519359f2..ba270b5a42 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -310,45 +310,38 @@ new data. ### Similarity Search -The similarity search module in `aeon` offers a set of functions and estimators to solve -tasks related to time series similarity search. The estimators can be used standalone -or as parts of pipelines, while the functions give you the tools to build your own -estimators that would rely on similarity search at some point. - -The estimators are inheriting from the [BaseSimiliaritySearch](similarity_search.base.BaseSimiliaritySearch) -class accepts as inputs 3D time series (n_cases, n_channels, n_timepoints) for the -fit method. Univariate and single series can still be used, but will need to be reshaped -to this format. - -This collection, asked for the fit method, is stored as a database. It will be used in -the predict method, which expects a single 2D time series as input -(n_channels, query_length). This 2D time series will be used as a query to search for in -the 3D database. - -The result of the predict method will then depends on wheter you use the [QuerySearch](similarity_search.query_search.QuerySearch) -and the [SeriesSearch](similarity_search.series_search.SeriesSearch) estimator. In [QuerySearch](similarity_search.query_search.QuerySearch), the 2D series is a subsequence -for which we want to indentify the best (or worst !) matches in the 3D database. -For [SeriesSearch](similarity_search.series_search.SeriesSearch), we require a `length` parmater, and we will search for the best -matches of all subsequences of size `length` in the 2D series inside the 3D database. -By default, these estimators will use the Euclidean (or squared Euclidean) distance, -but more distance will be added in the future. - +The similarity search module in `aeon` offers a set of estimators to solve tasks +related to time series similarity search. The estimators can be used standalone for +data analysis purposes or as parts of pipelines, to perform other tasks such as +classification or clustering. + +Similarly to the transformation module, similarity search estimators are either defined +for single series or for collection of series. The estimators are inheriting from the +[BaseSimiliaritySearch](similarity_search._base.BaseSimiliaritySearch) class, which +both [BaseSeriesSimiliaritySearch](similarity_search.series._base.BaseSeriesSimiliaritySearch) +and [BaseCollectionSimiliaritySearch](similarity_search.collection._base.BaseCollectionSimiliaritySearch) +inherit from. + +All estimators use a `fit` `predict` interface, where `predict` outputs both the +indexes of the neighbors or motifs and a distance or similarity measure linked to them. +For example, using `StompMotif` to compute the matrix profile between two series : ```{code-block} python >>> import numpy as np ->>> from aeon.similarity_search import QuerySearch ->>> X = [[[1, 2, 3, 4, 5, 6, 7]], # 3D array example (univariate) -... [[4, 4, 4, 5, 6, 7, 3]]] # Two samples, one channel, seven series length ->>> X = np.array(X) # X is of shape (2, 1, 7) : (n_cases, n_channels, n_timepoints) ->>> top_k = QuerySearch(k=2) ->>> top_k.fit(X) # fit the estimator on train data -... ->>> q = np.array([[4, 5, 6]]) # q is of shape (1,3) : ->>> top_k.predict(q) # Identify the two (k=2) most similar subsequences of length 3 in X -[(0, 3), (1, 2)] +>>> from aeon.similarity_search.series import StompMotif +>>> X1 = np.array([1, 1, 2, 4, 6, 6, 7]) # single series (univariate) +>>> X2 = np.array([0, 1, 2, 2, 4, 5, 7, 9, 4, 6]) # single series (univariate) +>>> top_k = StompMotif(4).fit(X1) # 4 is length of the motif to search +>>> distances, indexes = top_k.predict(X2, k=1) ``` -The output of predict gives a list of size `k`, where each element is a set indicating -the location of the best matches in X as `(id_sample, id_timestamp)`. This is equivalent -to the subsequence `X[id_sample, :, id_timestamps:id_timestamp + q.shape[0]]`. +Some things to note on this example : + +- We defined `1D` series of shape `(n_timepoints)`, but internally, series estimator +will use a `2D` representation as `(n_channels, n_timepoints)`. +- The output of predict gives a two lists of size `k` (the number of motifs to extract) +which can be read as follows : `distances[i] = d(X1[:, indexes[i][0]],X2[:, indexes[i][1]])` + +For more examples and use cases you can check the example section of the module, +starting with the general [similarity search notebook](examples/similarity_search/similarity_search.ipynb) ## Transformers diff --git a/examples/similarity_search/code_speed.ipynb b/examples/similarity_search/code_speed.ipynb index 65d2907604..3e1c369cf8 100644 --- a/examples/similarity_search/code_speed.ipynb +++ b/examples/similarity_search/code_speed.ipynb @@ -468,7 +468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index f14cdb15a9..1ada9538eb 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -113,9 +113,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" + "
" ] }, "metadata": {}, @@ -275,9 +275,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -346,7 +346,7 @@ " array([7.77156117e-14])],\n", " [array([[30, 30]], dtype=int64),\n", " array([[48, 48]], dtype=int64),\n", - " array([[0, 0]], dtype=int64),\n", + " array([[10, 10]], dtype=int64),\n", " array([[69, 69]], dtype=int64),\n", " array([[87, 87]], dtype=int64)])" ] @@ -427,9 +427,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -474,9 +474,9 @@ " In the case where ``L 0``` instead.\n", "\n", - "The ```RandomProjectionIndexANN``` estimators use the parameter ```n_hash_funcs``` to create that much random hash function as defined above. Each series `X` of the collection given in fit is then represented as an array of ```n_hash_funcs``` boolean, which is then hashed to a dictionnary as ```{hash(bool_array): case_id_array}```.\n", + "The ```RandomProjectionIndexANN``` estimators use the parameter ```n_hash_funcs``` to create that much random hash function as defined above. Each series `X` of the collection given in fit is then represented as an array of ```n_hash_funcs``` boolean, which is then hashed to a dictionnary as ``h(bool_arry): case_id_array}```.\n", "\n", - "To compute the nearest neighbors of a series ``X`` given in predict, we first transform this series to a boolean array using our previously defined hash functions, and then do ```hash(bool_array)``` to look at the bucket in which ``X`` falls, and consider the ```case_id_array``` as the indexes of its neighbors. If this bucket doesn't exists, we compute a distance matrix between the boolean array of ``X`` and every boolean array making the keys of the dictionnary to get similar buckets.\n", + "To compute the nearest neighbors of a series ``X`` given in predict, we first transform this series to a boolean array using our previously defined hash functions, and theusedthe resulting o `h(bool_aryy)``` to look at the bucket in which ``X`` falls, and consider the ```case_id_array``` as the indexes of its neighbors. If this bucket doesn't exists, we compute a distance matrix between the boolean array of ``X`` and every boolean array making the keys of the dictionnary to get similar buckets.\n", "\n", "This method will not provide exact results, but will perform approximate searchs. This also ignore any temporal correlation and consider series as high dimensional points due to the cosine similarity distance.y distance.\r\n" ] @@ -491,14 +491,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 0 : 130 with distance 1.0\n" + "match 0 : 130 with distance 2.0\n" ] }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -508,14 +508,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 1 : 56 with distance 3.0\n" + "match 1 : 56 with distance 4.0\n" ] }, { "data": { - "image/png": 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", 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vgLX3e9EbdO39fg20ffLr+tse7PlMwyTLm9Xn65P+dcv15ZLtzQZwfrem/w5Lf7axk0HSnqBsepA2PWAqIiLn73dHf4e3xYvH4xntqTjyfHncu+TeURt/3bp1XHfddbS1tZGfn3/RjT+cHnvsMT72sY/R3t7eZ/+mTZvIysoanUkNgoImIiIiIuNAMBakobuB+u56GgINNHQ30B3txjTMPhei+ssYsGzrlIt/J+/rveB4uouQvft6Myx6L8ilX4jtbZ+LLE8WhRmFpzxyfDlE4pHTZh2kP6dfuLwQ9M61M9I52lMZlxJ2AuwzHzcW2NhOoGU8s7HpjHQmf+a7RmcO0USU5mDzGYNuAqF4aMCvk4HRJ7vsfPQGUNKDKafb9rv9fQJvCriIiPTVFe3CDJu447ocLKOnuLh4tKdwVvSvREREROQCYdkWoViIrmgXTYEmGgI9QZLuBrqi/V9l7Ih0jOAsh0dvxkN1Z/VoT2VciCcsgrEEoWiCYDT5HIrFCUYTROIWHpeJz23idSefkw+X0/a6TVxm6mLkSatt9WmffOH05GMBXKaB12Vimmd/gdO2baIJi2AkQTCWIBiJE4ol8LhMsnxusrwusnxufG7zgrhwmrybH8wLYK6DZds24ZhFdySO2zTI9rvxuC6sJcdkcIYqYAIks1ISkUEHkT2mxwmi9C7Nl+PNId+fT3FWMYUZhX2WGRQRkbEpHo/z4Q9/mMcffxyXy8UHP/hBvvzlLzuf76LRKJ///Of52c9+Rnt7OwsWLODf//3fWb16NQDHjh3jwx/+MK+88grRaJSpU6fyta99jfnz53PdddcBUFBQAMC9997LY489dsocerMmHn/8cT7xiU9QXV3N7bffzk9+8hN+85vf8MUvfpGOjg7e/e53s3btWlyuZKbx448/ztq1a9m/fz9ZWVlcf/31rF27lokTJ3L06NF+x7csi6997Wv84Ac/oLq6mpKSEj7wgQ/wuc99zpnTkSNH+PjHP86GDRuYNWsW3//+91mxYsVZf1137tzJRz/6UdavX09mZiZ33XUXjzzyCNnZ2c4xP/7xj/n617/OoUOHKCws5K677uI73/kOAI888giPPvooR44cobCwkDvuuIP/+I//IDs7m3Xr1nHfffcBqWWCv/jFL/LQQw+dsjzX8ePH+chHPsJzzz2HaZrceuutfPvb36akpASAhx56iN/97nd84hOf4Atf+AJtbW3cdttt/OAHPyAnJ+es3+9g6ROCiIiIyBgQS8Q40XmC5mDzKXUmerdDsdCQXoiS00sufWQTjVtE4skgQiRmEYlbROMW0YRFrOcRTyQv3McTFrGE7exPWMnvlG3T85xq944B4HGlBSY8rj4BiuQ+s2fpI4toPNEzp555xC0iidR2ut7r7n0qnBgQTViEogliiZFZ4muw3C4Dr8uF12Xg7QnOeHu+RoZhpAV54gRjCRLWmf89uEyjJ4jiJsvnItvnxu9xndV8TMPANMA0DVyGgWkm2y7T6OkzerZPv6/3dYYBwWiCQCROIJIgEI33bMcJRBN0R5IBK7DJ8CTn2TvfLK+bzLQgUIbXlfwZTSR/ThNW+rNFome/YSR/vpIP47TbbtPAwsa2wbJPfbbs1M9qckmu5M+W0fPzRFo7Ydl0hmN0heN0hmN0hlLbXeH4KT9zPrdJts9Nls9Njt9Nts9Nts9Dtt9FhseNyzRwmT3fg96vv2FgmjjbCbvnfSeS7z3es937tej9GsUtK+04m0TasXHLxrJsfB6zZw7JOWX73OcV3LF75hZL+x0RTfs9YdvJnxlX789Lz/tKvu/Ue07+Dkl+L7Bxvl/pv1N6t3u/d87vHDvZf6bXYJP8XpN6jZ3+c3HSa0zDcP4t9X6txmIQLGbFaA210hpqPW2/aZgU+AsoziqmOLOY4qxiijKLKMoswuvyjvBsRUSkPz/5yU943/vex4YNG9i8eTP/8A//wJQpU3j/+98PwH333cfRo0f55S9/SXl5OU8++SS33norO3fuZNasWfzjP/4j0WiUl156iaysLPbs2UN2djaVlZX89re/5a677mL//v3k5uaSkZHR7zyCwSDf+ta3+OUvf0lXVxd33nknd955J/n5+Tz11FMcOXKEu+66i6uvvpp3vvOdQDKg8+Uvf5k5c+bQ2NjIxz/+cd773vfy1FNPDTj+gw8+yA9+8AO+8Y1vcPXVV1NXV8e+ffv6zOdzn/sc//mf/8msWbP43Oc+x9/93d9x6NAh3O4zX+4PBoPceuutXHnllWzatInGxkbuv/9+PvzhDztBo+9973s88MADfPWrX+W2226jo6ODV1991TmHaZp861vfYurUqVRVVfGhD32If/7nf+a73/0uK1euZO3atfzLv/wL+/fvB+gTjOll2zZve9vbyMrK4sUXXyQej/OhD32Id77znaxbt8457vDhw/zud7/jj3/8I21tbbzjHe/gq1/9Kv/3//7fM77Xc6WgiYiIiMgo6A2SHG0/SlV7FTWdNWdd4+BMApE4zd0R2oIxXIaRdlG+N0sgeUHebRr93oFv2zYJ28ayIGHZWPapFyaTFyUtiMTxBCO4g1FIWMRcJlGXSdxlEnW5iJsGUdPENnAuwvW+PpboucgZT0AsQSKRgJgF8QQ+0yDf76bA7ybPl3pke0xcgNFzJS/m9xDN8BLzebBdJnHLci6u92ZRROMJomlBjWi8b5AjmhZ8iMSt5IXEMcBlWfjjCTwJC1/cwmtZeBIW3oRFZiJBfs+2O2GBAQnDJGEaWAYkDAOr55HcZyS/Fy6TmMskZpqnbNPPz8Owsm1n3HjCJp6IExzC0ycsm85Q8iL+hSAYjROMxmkiMtpTGVaRuEUkHqUlMLaXzPOmBXe8LhOrJxjS+3sxYdFnX8KyifX8brPHyO+RkZAeBOt9tsH5vZoMOCd6nm2nbdk4gVFfWhC59/8pr9vsk+F2csabryewei7ZZJZt0RJqoSXUwj76XojK9+dTkVPB1PypTM2fSlFm0QWRsSYiMh5VVlbyjW98A8MwmDNnDjt37uQb3/gG73//+zl8+DC/+MUvOHHiBOXl5QB88pOf5C9/+QuPPvoo//Zv/8bx48e56667WLhwIQDTp093zl1YWAjAxIkTz1hTJBaL8b3vfY8ZM2YA8Pa3v53//u//pqGhgezsbCdz5YUXXnCCJn//93/vvH769Ol861vf4vLLL6e7u5vs7OzTjt/V1cU3v/lNvvOd73Dvvcl6LzNmzODqq6/uM59PfvKTvOlNbwLg4Ycf5pJLLuHQoUPMnTv3jF/Tn/3sZ4RCIX7605869UW+853vcMcdd/Dv//7vlJSU8K//+q984hOf4KMf/ajzussuu8zZTi/kPm3aNL785S/zwQ9+kO9+97t4vV7y8vIwDIPS0tJ+5/Hss8+yY8cOqqqqqKysBOC///u/ueSSS9i0aZMznmVZPPbYY05myXve8x6ee+45BU1ERERELnRxK+4ESY62H+VE54nzLiQetyzaAjGauiI0d6ceyTvW+2HbuGwbb8LCZ9lkA15sSCRrj2BZ2AkwLAvTtjFtMG0bj2WRFY2THY2RFY2TE41T2LPtPcushd4L8wnTwLRtXJaN20rOxxzExcW4AS63C58nebEs3hv8sGw6TYOwy0XQ6ybgcRPwugl4PQQ9Lrp7ngNeDwGPC8s8izuje75WGfEEGbE4/ngCl5X8uhikvj6mbWP0bgNuw8A2wTYMMAxsw8A2e557+gDckRieUIyMWJysWHKMzFiczFiCzNjZf21Nwzgl3nG6L6lpgDst28Bt9s1AMLxuDK+buAExyyZm0/Po2bZsYrZN1AbbsjFIvmcDMNPbNpjYGGlfF8PG6QcblwWQ3B81DbrdbrrdLjpdJh1uFx0uFwGPi2DP9zHkdiXPZUCG20WmxyTTbZLpMfG7XWS5TfxuM7n0WDS5/FgwFncCaJG4lZ56k/wanfztPumLmAw8JZ8Tpplsm+n7jJ59536XvWFAhsdNts8FGASjyeyT8XLB3WUa5Pg95PqTGSXxhE13JO48ziZbaDRF4xat8SitYzy4M9rOJwgWthKEY+d300BvQCXb5+oTuEnPMBvMcn3t4Xbaw+3sbtoNJOtq9QZQFEQRERlZV155ZZ/fuStWrODrX/86iUSCrVu3Yts2s2fP7vOaSCTChAkTAPinf/onPvjBD/L0009z4403ctddd7Fo0aJBzyMzM9MJmACUlJQwderUPhkUJSUlNDY2Ou1t27bx0EMP8cYbb9Da2oplJT/bHz9+nPnz5592nL179xKJRLjhhhsGnE/6eygrKwOgsbHxrIIme/fuZfHixX0Ksl911VVYlsX+/fsxDIPa2toB5/DCCy/wb//2b+zZs4fOzk7i8TjhcJhAIHDWhd737t1LZWWlEzABmD9/Pvn5+ezdu9cJmkydOrXPUlxlZWV9vs7DQUETERERkWGSsBLsb9nPG/VvcKTtyFkHSRKWTTjWkyURiRMNRbC7ItjdYQhEMAIRot1RgsEorkQCt2VTZFmUWTYuy8Jt2bgtC49lO1kJ3kTC2R6tyzwey8JjDRwEMAycJY7ilnXaC/+2DeHY6S+yeYEi4hQFz3yXfsjtIuB1E/S4Cfo8WB4XOZZFtmWRnUg+shIJvCQv/LrNU5fOST73Xb4puYTRIGpzYJNw2SQMm7jbJOHzELfcJCyv8zVIH/uUh9F/xtC56VnfxyAtyGCc9Dz8bDuOnYiTiIdJBJJ37btdA2dIDcSybWI9y6vFz/ZCfT/LEznLIPUsY5QwDGKmQdwwiZlGMnunZ1+0J9ji8rrweN14vW58Xhcenxuf143X6wKXjW0ksF0mtt9FwnQRTtiE4jaBhEUwbhFMWATiFqGEhZvkz53b6Pm57NlOf7YNiFoQAaIkt6PYROy0/TbYLgPbNLGTa4xh9TzbLhPbNMHsCXYlbFwJCzNhYVrJZ1fCxpVIYFo2LsCb6cWX7cOf7SMjx09Gto9Mn3vAjLbeWifOIxwnHEv0ZLr1ZHPYpLat5FJVlmVj9vy7dJsGbpeB20zW2kluG7hMs98+t2nidhnOsmqhWHL5tO5wnEA0+dzds4RadzROPHH6nxkz7fdA77lOWQrNbeI9aYk0w0guaWZZtrPMWCLtPfZmsPT+PumJvzpLo5lp20af7eTScL1z69OXdi7zpNf0Lr1mGsaAY9KzFFsg7XsWGEQQzOjJfvT21E0ywMn0iyQsrHMMovVms3SFB84oc7sM8jO8FGYlHxN6nvMzvX3qNJ0sEAuwu2n3KUGUaQXTmFs0l2zvqUuOiIjI8LMsC5fLxZYtW5w6Ir16gxn3338/t9xyC3/60594+umn+cpXvsLXv/51PvKRjwxqLI/H06dtGMZp9/UGRgKBADfffDM333wzjz/+OMXFxRw/fpxbbrmFaLT/mwwGWiKsv/n0ftayzvC3Vi/btvv9fGYYxhnncOzYMW6//XbWrFnDl7/8ZQoLC3nllVd43/veRyx29tnd/c3j5P0DfZ2Hi4ImIiIiIkOsvrueN+rfYEfDDoKxUxcaMuMJvOEYnlAUTyhKoC1Ie1MX3S0BQu0B3MEombEE+bE4ZbE47kFcRHL31G/I9LrI8LjAY5KwDOKW6VyU661/0Luuf+/qSL0X2/q7gGYaqQuEznZazYf0bIf0wEHvxTpInbu3NoFzjrTtXrZtO/VEwvFET6DEIhxLEIknl3VJ53KyJgw8PRdEndoNrlStCVdvXYQ+dSp6xzV7HiPHMHou5LrAN6Ijj23OxV0MzrIEyYBMw0jWjRmKkw1KTzGKuAXxGEO69tgYZxuQ8LiJe1zOs20aGFZPFlJ/zzbJzCzoydRKz9pKbcc9LuI+DzG/h5jXTczvIe72EEvbF/e6kwGgniwh2zQGvQxd7++iuGWfUoNE2QYpvV+n3kCKmRYg6V1m64zLQlq2U7cpkrasV+Tkek7xZJ2n9GPDsQShM2SsxBO2k5WZzjAM8jM8TiClKMfHpIIMMr2nv2SSHkR56uBTzC2ay7KyZUwvmK6fCRGRIfb666+f0p41axYul4ulS5eSSCRobGxk1apV/Z6jsrKSNWvWsGbNGqdeyEc+8hG83mQNq0RiaJZJTrdv3z6am5v56le/6mRSbN68uc8xpxt/1qxZZGRk8Nxzz3H//fcP+bwgmc3xk5/8pE9WyKuvvoppmsyePZucnBymTp3Kc8895xSrT7d582bi8Thf//rXMXsyrn/961+f8t7O9HWdP38+x48fp7q62vka7dmzh46ODubNmzcUb/WcKWgiIiIiMgRCsRA7G3eyrW4bdd11zv6MzhBFx5sprGnF3xXCE44SDUbpDKWKJRuWTQFQMIjxDCDD6yLT6yLT63aee+9gHg8Mw8DvceH3uMij791Ftm07NUl6gyPmOHnfI83AwOPy4DE9znPCThC34iSs5HPcig9ZzZ3h4jJceFweZ74y+gwb3NE47mgcxlCNlt7l8nqDKLbZN6jSm3lj9S6p5xr6fmw7mbVz8sPqu5+ebJPkxG2nlpNxhjZw6r6eoFNv0Cg9KIVhpBLMeoJX9AayTtoHPcEwbzIQFu8JTiW8p9lnuIm7DOIGJM4QsEoFkE2yzjGCHLcsgpGEE7hJZgolnO2ucJyOUOyUulW2bdMWjNIWjHK4KbW/MMvLpIJMJhVk9BtEsWyLPU172NO0h8KMQi4tu5SlpUvJ8p7d0iQiIjKw6upqHnjgAT7wgQ+wdetWvv3tb/P1r38dgNmzZ3P33Xdzzz338PWvf52lS5fS3NzM888/z8KFC7n99tv52Mc+xm233cbs2bNpa2vj+eefdy7IT5kyBcMw+OMf/8jtt99ORkbGaQuWn4vJkyfj9Xr59re/zZo1a9i1axdf/vKX+xzT3/if/vSn+ed//me8Xi9XXXUVTU1N7N69m/e9731DMre7776bL37xi9x777089NBDNDU18ZGPfIT3vOc9lJSUAPDQQw+xZs0aJk6cyG233UZXVxevvvoqH/nIR5gxYwbxeJxvf/vb3HHHHbz66qt8//vf7zPG1KlT6e7u5rnnnmPx4sVkZmaSmZnZ55gbb7yRRYsWcffdd7N27VqnEPy1117L8uXLh+S9nisFTURERETOkW3bHGk7wrb6bext2pu8qGzbZLd2U3S8meJjzWS1B0hYNq2BKNXBKJ2h2IBLA7nNZKCgN2PCfYbMiYtVcomX5F3MIzIeBm7Tjct04TbdmIaJy3DhMl19nk3DxGUmn5NL2hjO63vn3du2sfsEJfp7AM64LsPVZx69+1ymyxnjZHZa5Q7TMJ3giNflxWN6cJv9L5/U5zy27QRP4la8T82NgV7f37zSX5OwEn0CNScHbRJ2ssaH23TjdXmTc097D16XF5eZyiCxbItoIko0ESUSj6S2E8nthHV2ASCb5LJglm0NuG3ZZ7c8gIHhzN/r8uJz+1LbruRV4t65pj96593fOKZh9nn0fs0TdsL5eo4Ul+HC5/bhd/udh8+Valu2RSQRIRKPOM+977F32z6l2szgGRinfF16/22e7usVTUSJxpLzGO6gm8tI/qwO5mfnfJz8u+hstqE3M/DkbYOEnSCaCAz662QbJAMpaQGXhNs1cDCnd5u07dMcCyTrDbldJNxm8tllpto5bhL5Xiy3i0CGlwbDoDUYozWQrMPSGkgGTE5eXqy1p2/HiXYAJqQFUSpOE0RpDbXy7JFneaHqBeYVz2NZ2TKm5k8dNzcziMiFLcebg9fvPWWpo9GU58s74zH33HMPoVCIyy+/HJfLxUc+8hH+4R/+wel/9NFHnaLlNTU1TJgwgRUrVnD77bcDySyOf/zHf+TEiRPk5uZy66238o1vfAOAiooKHn74YT7zmc9w3333cc899/DYY48NyXsrLi7mscce47Of/Szf+ta3uPTSS/nP//xP3vKWtzjH9Df+F77wBdxuN//yL/9CbW0tZWVlrFmzZkjmBcn6LH/961/56Ec/ymWXXUZmZiZ33XUXjzzyiHPMvffeSzgc5hvf+Aaf/OQnKSoq4u1vfzsAS5Ys4ZFHHuHf//3fefDBB7nmmmv4yle+wj333OO8fuXKlaxZs4Z3vvOdtLS08MUvfpGHHnqozzwMw+B3v/sdH/nIR7jmmmswTZNbb72Vb3/720P2Xs+VYY+XCoM9Ojs7ycvLo6Ojg9zc3NGejoiIiIxDtm2zr3kfz1c9T1OwCcOyyWvsoOh4M0XHm/F3h7HtZLHjpq4ILYFTL8T0cpkGuX4PuRlucv0eMr2uIbu44nV5MQ3TufA8FBci053uoqRpmKdchDv5oltyqS/TCTykX8zsbZuGSSQRIRQLEYqHCMVCxKyzXx83fY4el+eUAMNAz73Hpj/S35fI6STrnfQEUk6z7TbdeEzPOf8c2bbtBJMMjNMG5wZ6rWVbWLbVJ5Bi2Vaff6cnP0Pf4Fbvn469v0vS270BobMNwp3pfZ7p62nZ1ml///T+DjmfOSSsRJ9gVW8wp3fM3t9hvb8DT273/k7rL7B68txODsClv084UxBj4O3hlLASfb5G6dsxK+b8nJ4u4NvbBvrNbHO2TwouD8X/ZXGPi2B+FoH8LAIFyeeuvAyaMWgJxqhrD3OiLUhDV4SBLplUFmaysCKPGcXZ/dZEKcosYlnZMpaVL8Pr8p733EVEzkU4HKaqqopp06bh9/tHezoiw6q/n/fBxA2UaSIiIiIyCEfajvDckeeo6arBG4wwc+dxSo404okkL+hH4xY13RGauiKnLVTuMg1y/G7y/B5yM4YmSGJgkOXNItubTZan59mb1efiTO9FufQLT+kZA6e7CHjyBcHTPQDIyIDsbMjJAY8HolGIxfo+926fMnkDXC5wu5OP3u3eZ8MgWfDEJGYnCCRCdMdDdMeDBOJB4rEIGVGLjIhFRiSB1zb7ZB+c7wXcUefzQWYmeL3Jba839UhvezzJZXMsCxKJU597t2Oxvt+Tk79HAxSlHDK91aVNM7U9EuMOs/R/M8N1frfhdi40D/a1LsOFCxcexs7dpafT+z5Hk8t0kWFmkOE5u0Ks56v3+3OhcZkuMs1MMj2ZZz54CKX/X5YeUIlZsT6ZS+kBnJO5YwlymzrJberssz/mcxPIzyKYl0kk00egPJPquM2RSIJD4ThHw4k+IZvq1iDVrUEyvS7ml+exsDyPvMy+/8aag8389fBfea36Ne6YcwezJ8weji+LiIiIDCEFTURERETOQm1XLc8eeZYjbUfAtik50sisDQdxR+NYtk1rMEpjV4T24KkXZ1ym4RSWzfEN/iJ+eq2J9GWJMtwZZHuzyfBknPFCrXPR1OvDl37h3etNBid6AhP9Plyu5MX73uBITk5q232WHyltO3mBPh5PBUV6L5yfzdcByO959Hv+aBS6uyEQ6Pt8un2nC+KczDCS7zsjI/Xs8fT92qQFdpxH73wsK/VIb/du+3yQlZU898nPmZln/7UdKrad/P6cPN/TtU8Ofpzt8+m+34kEBIPJ70369ym9HY2mXn/y+dLbp3tPZ9rX3zHpQaeTg1C9j3g89RCRYWcaprOs3dlIX64vFAsRjAUJxAIEY0FCsVCfzBVPJE5+Qwf5DR3OvgVp54rb0GgY1FtQFUlQa5q0+720+71UtQV440gzFcU5LKzIY1pRVp/sk65oFz/f+XOWlC7h1pm34nfrTm8REZGxSkETERERkQE0B5t5vup59jTtAcAbijJ7/QGKjjdjWTa1nWFq20OnrVOS63dTnOOnMMvb77IdvQwMcn255PvzyfPnOReE3C4vZlZW3wvqWVmpi/ceTypLI327t+3x9A2QmCNTA+T0b9JIzWO4zu/zJR8TJpz5+JMDLLFY8uuaHiDx+886qDMuGEbyZ2akuVypYNyFKj0o2JvRk76dnv1zuoygM+1L304PFvX3sO3+Az0DBYFOt783cJr+++Xkh2FAJNL3EQ4rmCSjzjRMp6ZOrq/vUhyWbZ0SSOmKdBGKh057LrcB5diUm7DUb9AZjtFQ30VbIOqEXrp8Htr9Xmqy/RRUFlA6rQizOJdgbgYJr5s36t/gSNsR7ph9B7MmzBrmdy8iIiLnYliDJi+99BJf+9rX2LJlC3V1dTz55JO87W1vG/A1L774Ig888AC7d++mvLycf/7nfx7SQjciIiIiZ6Mz0sm6o+t4o/6NZKFe22bi0SZmvX4AdzhGY1eEE20hoom+RXy9LpPiHB/FOT78ntMvtxL3uolleMnKL6FgQgVFxVMoKp6CJycvFRRJD46MZqBjPPN6obAw+RA5X8MdFLxQJRJ9Aylw+uysk7O3ejN9bDv1SG8nEsmgTDAIodDpn4PBZHB0oECUXNRMwyTLm0WWN4tiip394XiYjnAH7eF22sPtpw2iGIZBXoaHvAwP0bhFU1eExq4wRGLkRGLQEYCaFqwNh6goyKQsz08s00cwL5NgbgYvbdnN8RnLuOrSt+IvKk0GJkVERGRMGNagSSAQYPHixdx3333cddddZzy+qqqK22+/nfe///08/vjjvPrqq3zoQx+iuLj4rF4vIiIicr5s2+bV6ldZd3QdcSt5h7QnFGX26wcpOtpIayBKdVvolHolhVleJub4yMs4tdBzID+L5slFBGZUMm3OlcyaOJfJeZNVEFZExr/epf0yR7buxVkZqA7QcD8bxukzdk6u8XTyMnTpS9ydTcZR+vG9y9ClB6J6H5DKYjrd0oPp25aVyiTqfU7fPt2+3u0LJPPI7/bjz/ZTkl0CJIMovQGU9nA74Xi4z/Fet0lFQQbl+X46QjEaOiO0BZO1mmwbjrcGaQ1EmVGcTX4oSn59e/KFmw+z7Te/Z/bEuUyYtRiWLIEFCxR8FRERGWWGbZ9uAd9hGMgwzphp8ulPf5r//d//Ze/evc6+NWvWsH37dtavX39W43R2dpKXl0dHRwe5ublnfoGIiIhIj3A8zBN7n+BAywFnX3FPdkmoNcjx1iCBaN8LPgWZXioLM8j09r0XpaM4l+bJRbRMLmbS9MUsLV3K7AmzcZm6k1RERC5S8Xjf4Eosdvpgzrm0E4nUcnj9PQIBaGpKZiOdh65IF7VdtTQGGknYidMeE41b1LaHqO9MBVgMAyYXZFKa5z/lBouy7DJmFM7AnZEFixfDsmVQUnJe8xQR6RUOh6mqqmLatGn4/aqpJONbfz/vg4kbjKmaJuvXr+fmm2/us++WW27hRz/6EbFYDM9p1leORCJEetO8Sb55ERERkcFqDjbzy12/pDnYDIAnHGPmhoNk7a3lUGuQznDfouE5fjeTCzPJ8ac+n7SV5tM0bSLNlRPILapgSekSFpcsJsd3AddpEBERGSq9WTRZWaM3B9tO1rFqbEw+mppS29HoWZ0ix5fDHN8cZhTOoKG7gbruOrqj3X2O8bpNphZlUZjt5UhjN+G4hW3DsZ6sk+kTs8lIW8azrruO1lAr84rnkb9xI2zcCJWVsHw5zJ8/OvWmRERELlJjKmhSX19PyUl3UpSUlBCPx2lubqasrOyU13zlK1/h4YcfHqkpioiIyDh0oOUAv93zWyKJ5I0Y+XVtzFm3h5rqNqq6I32OzfS6mFyY2WcZrqjfw4EVs+mcXsGCiQu4rXQpk3InnXIXqYiIiIwyw4CcnORjxozUftuGjo5kEKWzMxlY6erq++juTmW2AG7TTUVuBeU55XRFU9knlp2ql5Pr97BwUj7VrUEn66QrEmfniXYqCzMpzU1lnUQSEbbXb2dm4Uwqciugujr5+Mtfkkt3LVsGRUUj8mUSERG5mI2poAlwysWF3tXD+rvo8OCDD/LAAw847c7OTiorK4dvgiIiIjJu2LbNy8df5oWqF7BJLvExZfsxyjYf4UB9J8FoaskNn9uksjCTCVnePp9LmqYUc3jFXJbMvIpVk1eR5R3Fu2dFRETk3BgG5OcnH/2xrOQSX93d0N6efLS2YrS1kdvWRm57ATNjM6nrquNo+1Fn6S6XaSSzTrK8HG7qJhK3sGw41pKqdeLvyTqxsTnYepBALMDMwpmYhplcTmz9+uRj9my4+WYFT0RERIbRmAqalJaWUl9f32dfY2MjbrebCRMmnPY1Pp8Pn883EtMTERGRcSQSj/C7fb9jb3OylponFGXey3txHW5kV0MXcSt544bLNKgszGRijg8zLVgS83k4eOVsylfcxJqpq8n354/G2xAREZGRYpqpLJXTrISBZeHu6qKyrY38+uOs3/EnOmqOkNEZJLMzRG6Gh0UnZ52E4+w40cHUokwm5qTWXa/tqiUYC3JJ8SV4XGlLcx04AIcOwRVXwLXXgmoTiIiIDLkxFTRZsWIFf/jDH/rse/rpp1m+fPlp65mIiIiInIvWUCu/3PVLGgONAOTVtzP/xT201nVwrDXoHOf3uJhTkkOGt2/x9ubJRfDmN/P2S25nYtbEEZ27iIiIjFGmCXl5kJdHztSp3HTFKrbUbeHpw08TjUfwByJkdCQDKP4TbRzdX092e4DsaJwjTQGCkQRTJmQ6Ga3t4Xa21G1hwcQFZHuzU+NYVjLrZMcOuOGG5NJdpjk671lERGQcGtagSXd3N4cOHXLaVVVVvPHGGxQWFjJ58mQefPBBampq+OlPfwrAmjVr+M53vsMDDzzA+9//ftavX8+PfvQjfvGLXwznNEVEROQicqj1EL/Z8xvC8TDYNpN3HmfK1iqqGrtpTqtfkp/pYebEbNxpFyFiPjfBG67lylvey6Q8LQcqIiIi/TMMg+Xly5lZOJPf7/s9VUYV4Ww/bRXAvAoyr5vPi/sbCe46wdK6VuzWLkKxBLMmZuN2JT9/hONhttVtY17xPIoyT1qSKxCA//1f2LQJbrsNJk8e+TcpIjIORaNRvF7vaE9DRpFh22lVzIbYunXruO66607Zf++99/LYY4/x3ve+l6NHj7Ju3Tqn78UXX+TjH/84u3fvpry8nE9/+tOsWbPmrMfs7OwkLy+Pjo4OcnNzh+JtiIiIyDixsWYjfz74Z2xsPOEYc1/eS/axJg7UdxFIq19SkZ/BpIKMPrVLQtMqmf3eTzB98mIVeBcREZFBsW2bzbWbeebIM0QT0T59O2s6eGF/I9nBCIvr27iipYNlBX4yvH3vc52WP43JeZP7/xyycCHcdBPoWoiInCQcDlNVVcW0adPwP/IIJBJnftFwc7ngs58942GBQIAPfvCDPPHEE+Tk5PDJT36SP/zhDyxZsoS1a9diGAZPPvkkb3vb25zX5Ofns3btWt773vcCUFNTwwMPPMDTTz+NaZpcffXVfPOb32Tq1KkAvPe976W9vZ0rrriCb3/723i9Xv7+7/+e//mf/2Hnzp195rNs2TLe9KY38aUvfWmovhIyxPr8vKctYzmYuMGwZpqsXr2agWIyjz322Cn7rr32WrZu3TqMsxIREZGL0esnXucvh/4CQG5jB5es20OkpZudafVLTMNgRnEWE7JT9dJswyC0+mpW/91nyFSRdxERETkHhmFwWcVlyayT/b/naPtRp29hRR6FmV7+uKOWl/1eXp1czLyOAPeYCeZ0pZYNrWqvojvazdyiubhM16mD7NwJ+/bBqlWwciW4x9SK7CIyViQSYyNocpY+9alP8cILL/Dkk09SWlrKZz/7WbZs2cKSJUvO6vXBYJDrrruOVatW8dJLL+F2u/nXf/1Xbr31Vnbs2OFklDz33HPk5ubyzDPPYNs2+fn5PPzww2zatInLLrsMgB07drBt2zb+53/+Z7jerowR+h9URERExr30gEn5/lpmvn6AxvYwx1oC9N7e4XObzCnNITPtrs5wlg/fu97NrVffjWlorXARERE5PwUZBdy7+F5eP/E6zxx5Bsu2AKgoyODvLp/M/26vpbk7wu6CHD5twK1zJvHmri4qDtRj2DZNwSZC9SEWlSzC6zrN0jGxGDz/POzdC+98J+Tnj+wbFBEZQt3d3fzoRz/ipz/9KTfddBMAP/nJT5g0adJZn+OXv/wlpmnywx/+0MnUe/TRR8nPz2fdunXcfPPNAGRlZfHDH/6wz7Jct9xyC48++qgTNHn00Ue59tprmT59+lC9RRmj9Ne/iIiIjGsbTmxwAiZl+2uZ9dp+TrQEOZoWMMnL8LCwIq9PwKStspjyT36J61a9RwETERERGTKGYbCicgXvXvRuMtwZzv7cDA/vWF7JzIk9Rd9t+Et9N9/JyeX1N11KW2k+AN3RbnY07CBhDXCneF0d/L//B4cPD+M7EREZXocPHyYajbJixQpnX2FhIXPmzDnrc2zZsoVDhw6Rk5NDdnY22dnZFBYWEg6HOZz2O3LhwoWn1DF5//vfzy9+8QvC4TCxWIyf/exn/P3f//35vzEZ85RpIiIiIuPWxpqN/PnQnwEoOdzA7PUHqG4LUdseco4py/MzuTDTuevINgwarlzANX/3GcpzK0Zl3iIiIjL+TS+YzvuXvZ9f7PwFTcEmALxukzctLGNDVSuvH2kBYF99F23BGHdcewlTG9uZuekwdHezt3kvlxRf0n+Nk1AIHn8cbrgBrroKVJNNRC4wZ1OK2zCMU46LxWLOtmVZLFu2jJ/97GenvLa4uNjZzso6dSnmO+64A5/Px5NPPonP5yMSiXDXXXcN5i3IBUq3TYqIiMi4tLFmI08dfAqAouPNzHllLydag30CJlMnZDFlQpZzsSGS6aPxb2/nzfd9RQETERERGXaFGYXcf+n9zJ4w29lnGAZXTp/AmxaW4XYlL9s0dIb55eZqqkry2fg3l1O1dBoN0VaOdRwbeADbhmefhf/5H4hEhvOtiIgMuZkzZ+LxeHj99dedfW1tbRw4cMBpFxcXU1dX57QPHjxIMJiqB3XppZdy8OBBJk6cyMyZM/s88vLyBhzf7XZz77338uijj/Loo4/yrne9i8zMzCF8hzJWKWgiIiIi486mmk1OwKSwppX563ZT0xKk5qSASWme32m3VhRi/8M/8PZbHyDbmz3icxYREZGLk8/t410L3sXVk6/us39WSQ7vXF5Jjt8DQHckzh+21xEFji2ewsa/uYINBUEaA41nHmTPHvjhD6GlZRjegYhcUFyusfM4g+zsbN73vvfxqU99iueee45du3bx3ve+F9NMXdK+/vrr+c53vsPWrVvZvHkza9aswePxOP133303RUVFvPWtb+Xll1+mqqqKF198kY9+9KOcOHHijHO4//77ef755/nzn/+spbkuIlqeS0RERMaVzbWb+dPBPwGQV9/OJS/soqY5MGDA5Oil01l414dYVrF8xOcrIiIiYhomN06/kZKsEn6///fErTgAxTk+3nVZJb/aXE1nKEZjV5hn9jRw64JSIlk+9l47n8amAPec8JPXER54kKYm+K//gjvvhEHUAxCRceaznx3tGQzK1772Nbq7u3nLW95CTk4On/jEJ+jo6HD6v/71r3PfffdxzTXXUF5ezje/+U22bNni9GdmZvLSSy/x6U9/mjvvvJOuri4qKiq44YYbyM3NPeP4s2bNYuXKlbS0tHDFFVcMy3uUscewz2ZxuAtIZ2cneXl5dHR0nNUPvoiIiIwfm2s388cDfwQgp6mTxU9vp76xixNt/QdMDl8+iyve/lEWTFww4vMVEREROVlNZw2/3PVLuqJdzr7m7gi/2lRNLGEBsHJGEZdPK3T6C1zZ/ENdGRl7Dp7dINdeC6tXq86JyEUiHA5TVVXFtGnT8Pv9Z37BGLd69WqWLFnC2rVrh30s27aZO3cuH/jAB3jggQeGfTw5f/39vA8mbqDluURERGRc2FK7xQmYZLV2s+jZHacETKZMyOwTMKlaOk0BExERERlTKnIr+Idl/0BZdpmzryjbx62XlDrt1440c7ip22m3Jbr5xcww8ZtvBPMsLvW8+CL8/OeqcyIiMoDGxkYeeeQRampquO+++0Z7OjKCFDQRERGRC97uxt384cAfAMjoCLL4mR001HeeEjApy8tw2scXTmb5Oz+ugImIiIiMOTm+HO5edDe5vtSdsDMmZrNyxoRkw4a/7KqnuTsV9DjeWc2fCluw3/MeyMo68yAHD8Jjj0EgMMSzFxEZH0pKSvjqV7/Kf/3Xf1FQUDDa05ERpKCJiIiIXNBagi38fv/vAfB3h1ny1+001rYPGDCpmVvB0rs/ycLSRSM+XxEREZGzke3N5u8W/B0eM1XQ+LKphcwuyQEglrD43+21hKIJp39b/TY2eBrgAx+AioozD1JXBz/+MbS3D/X0RUSGzbp160Zsaa6mpib+z//5P8M+lowtCpqIiIjIBStuxfmfPf9DNBHFG4qy+K/baTk5YFLYN2BSP7OUhe/9ZwVMREREZMwryynjbXPf5rQNw+Cm+SVMzEkuN9oZivGnnbUkrFS52r8e+iuH401w331w6aVnHqSlJRk4aWoa6umLiIhckBQ0ERERkQvWM4efob67HmybeS/uIdLQwfHWoNM/uTCTsvxUwKRpSjHz3/cZFpUuHo3pioiIiAzaJRMv4Zop1zhtj8vkjsVlZHrdAJxoC/HigVTAw8bmf/b8Dy3RDnjLW+COO8DlGniQzk549FGoqRmW9yAiInIhUdBERERELkj7mvexoWYDAJP21pB1orVPQdRJBRmUpwVMWismMOcDn2VR2ZKRnqqIiIjIeblu6nXMLZrrtHP8Hu5YXIbLNADYcaKdHSfanf5wPMwvdv2CcDwMy5Yls05yc08+bV/BIPzkJ3DkyHC8BREZA2zbPvNBIhc4y7LO+xyGPc7+tXR2dpKXl0dHRwe5Z/pAICIiIhekjnAH39/8fULxEFltAS79w2YO1HTQEYoBkJfhYW5pDoaRvJDQXlrAtH/8HIsrl4/mtEVERETOWTQR5Udbf0RDoMHZt6e2k6f31ANgGgZ/s7SCysJMp3/hxIXcNf+uZKOzE/77v8+8DJfLBW9/O8ybN+TvQURGRyKR4ODBg2RmZlJcXOz8nSQynti2TTQapampiUQiwaxZszDNVM7IYOIGCpqIiIjIBcWyLR574zGOdxzHSFgs+9NWOo40Ud2zLJfHZbJoUh4eV/LDUWdxLlM+/HkWT7l8NKctIiIict7aw+3815b/IhhLLUf60oEmth5vA8DvcfF3l08mLyNVPP7OeXeyqKSnllswCD/72ZmX4TKM5LJeZ1MTRUQuCN3d3Zw4cULZJjLuZWZmUlZWhtfr7bNfQRMFTURERMatF6pe4MVjLwIwbcsRCjcdZndtp9M/ryzXuVAQzvJhfvBD3LzgraMyVxEREZGhdrT9KD/d/lMsO7n8iGXb/P6NWo61BAAoyfXzjuWVztJdPpePNcvXUJBRkDxBJAK/+tXZLcN1001w1VXD8j5EZOQlEglisdhoT0Nk2LhcLtxu92mzqRQ0UdBERERkXKpqq+Kn23+KjU1eQwcL/rSVnSfaicaTFw0q8jOcJSlsA2r/5ibe+eZP4zbdozltERERkSG1pXYLfzjwB6cdjiX45aZq2oNRAC6bWshVM4uc/srcSu5beh+m0bNMSTwOTzwBe/acebBVq+D665PZJyIiIheowcQNVAheRERELgiBaIAn9j6BjY0rGmfuy3upaup2AiY5PjeTClKF3+sXTOPWG9coYCIiIiLjzrLyZVxekVp61O9xcduCUsyewMamY62caEst4VXdWc1Lx15KncDtTtYtOZvlt15+GV57bcjmLiIiMtYpaCIiIiJjnm3b/G7f7+iKdgEwc9NhOmvbaQ0k76Z0mQYzS7KdFNxAfhYL3vVPTMicMGpzFhERERlOt8y4hWn505x2Sa6fFTN6PvvY8Ndd9YRjCaf/xaMvcrzjeOoEppmsW3I2y2898wxs3z5UUxcRERnTFDQRERGRMW/9ifUcbD0IwITjzeTuquZoz7rdADOKs/G5XQBYpoHrrr9lYcXSUZmriIiIyEhwmS7unHcnmZ5MZ9+yKQVO5m1XJM5z+xqdos82Nk/sfYJwPJw6iWEk65bcdNOZB/z97+HgwSF9DyIiImORgiYiIiIyptV01vDskWcB8ISizHxlHwcbu+mtylaa66cwy+sc33blEq5f+X9GY6oiIiIiIyrHl8Nb57zVaZuGwS2XlOLzJG8mOdjQxZ66Tqe/PdzOUwefOvVEV10Fb3nLwHVLLAt+/Ws4cWLI5i8iIjIWKWgiIiIiY1YkHuE3e36DZVtg28xZf4DamnZnqYlMr4vJham7K7tKCrjqXZ/C6/L2d0oRERGRcWVO0RyWly932jl+DzfOnei01+1vcgrEA+xo2MGOhh2nnujSS+Fv/3bgwEksBj//OTQ3D8ncRURExiIFTURERGTMeuHoC7SF2wAoPVQPe2po6ooAyTspZ03MwTSTf9gn3C4q7vkwpbnlozZfERERkdFwy4xbKMosctqzSnK4pDwXgFjC4s+76klYttP/pwN/oi3UduqJ5s9P1jkZSDAIjz8OXV1DMncREZGxRkETERERGZPqu+vZcGIDAP6uEJWv7KeqOVXHZFpRFhlel9OO33ITS+dfP+LzFBERERltHpeHu+bdhctIfTa6dvZE8jOT2bcNnWE2HGlx+iKJCE/sfSKZzXuySy+F68/wmaq9PRk4CYcHPk5EROQCpKCJiIiIjDm2bfPHA3/ExgbbZu7Lezle3+ncIVmU7aM4x+ccH5g+mWve+k8YAy0nISIiIjKOleWUccP0G5y2121y6yWlmD2fjzYea+VEW9Dpr+6s5qVjL53+ZKtWweWXDzxgQwP84hcQj5/33EVERMYSBU1ERERkzNlat5UTnckio6WH6kkcaaIjFAPA6zKZVpTlHBvL8LL4/s+R4c087blERERELhYrJq1gesF0p12a5+fK6ROSDRv+uqveqQ0H8OLRF6nuqD71RIYBt96aXK5rIMeOwW9/mywSLyIiMk4oaCIiIiJjSiAa4NkjzwLgisaZvPkIx1pSd0VOLcrEZaYySgr+9l4mlc8Z8XmKiIiIjDWGYfA3c/+GTE/qZpLlUwuoyM8AoCsS5/l9jdh2MnvXxuaJvU8QS8ROPZlpwp13wtSpAw+6dy/8+c9g2wMfJyIicoFQ0ERERETGlGePPEsoHgJg8s7jNNW2E00k717Mz/BQ0LM2N0B08QKWXfd/RmWeIiIiImNRji+Ht8x5i9M2DYNbF5Ti8yTrnRxo6GJvXaqIe1u4jQ01G05/Mrcb3vUuKC0deNBNm+C118577iIiImOBgiYiIiIyZhzvOM62+m1Asvh74baj1HUkC4waBkwtynLqlkSy/Sy651OqYyIiIiJykrlFc1levtxp5/g93DB3otN+YX8jgUiqFsnLx14mEA2c/mR+P9x9N+TnDzzos8/C0aPnMWsREZGxQUETERERGRMSVoI/Hvij0562+QjHGlJ3QVbkZ+DvuUMSIOtNf0PphCkjOkcRERGRC8UtM26hKLPIac8uyWF+WS4AsYTF+sMtTl8kEeGFoy/0f7KcHHjPeyBzgBpytg2/+Q10dfV/jIiIyAVAQRMREREZEzbUbKAx0AhAXn07xs5qunvugPR7XJT3rMUNECkv4bIb7x2VeYqIiIhcCDwuD3fNuwuXkbrp5OpZRXjdyUtBu+o6aOqKOH1barc4n8VOa8KEZMaJ19v/Md3dycCJCsOLiMgFTEETERERGXUd4Q7WHV2XbNg2U9Yf4Hhrqvj7tAlZmD3LcNkGTH3XGnwe/yjMVEREROTCUZZTxnXTrnPamV43l08tTDZsePlgU5+i8M8cfmbgE1ZUwDvekSwS359jx+C558536iIiIqNGQRMREREZdX859BeiiSgApYcbaDvUSNxK/gE/IctLXqbHOdZYtITZC1ePxjRFRERELjgrJq2gMKPQaS+pzCfXn/xsdbw1yNGW1I0qB1sPcrj18MAnnDkTbrxx4GNefRX27TvnOYuIiIwmBU1ERERkVB1sOcje5r0AuGIJJryy31kqwmUaTJmQ5RxrezwsuvvjKv4uIiIicpZcpoubpt/ktN0uk6tnpWqdvHywCavnZhWApw8/jWWfYXmtFStg3ryBj/nd76C19VymLCIiMqoUNBEREZFRE0vEeOrgU067cvtR6k60Oe1JBRnOutsA+Te+mcKJKv4uIiIiMhhzi+YyJS/1GWrWxGzK8pJLnbYGouys7XD6GgINvFH/xsAnNAx461uhsLD/Y8Jh+PWvIRY7n6mLiIiMOAVNREREZNS8cvwV2sLJIIm/O4x7/SGC0QQAmV4XpbmpuiVmfgEL3/YPozJPERERkQuZYRjcMvOWPu1Vs4qd9uuHW4jEE077+arnicQjDMjvT9Y3cbv7P6a+Hv7853Oet4iIyGhQ0ERERERGRUuwhVeOv+K0y9YfoLY54LSnFWX1WYZr6t++H7cvY0TnKCIiIjJelOeUs7hkcaqdn8HskhwAQrEEm6pS2b7d0W5erX71zCctLYU3vWngY7ZuhW3bzmnOIiIio0FBExERERlxtm3zp4N/ImEn72jMa+ggsvUYlp1cT3tijo8cf6r4e9b0eUxeeduozFVERERkvLh+2vW4zVRmyFUzi3CZyZtUtlW30RFKLaX1WvVrdIQ7TjnHKZYuTT4G8qc/JbNORERELgAKmoiIiMiI29m4kyNtR5IN2yb/hd20BqIAuE2DyYWZzrEuw8Xcd380uXa2iIiIiJyzPH8eKytXptoZHpZU5gOQsGxePdTs9MWtOM9XPX92J7799mTWSX/i8WR9k3D4XKYtIiIyohQ0ERERkREVjAX5y6G/OO2iA3W0H2hw2lMmZOF2pT6iFF91EznT547oHEVERETGq6sqryLbm+20L59WSIbHBcCBhi7qOkJO3/aG7dR21Z75pB5Psr6J39//Ma2t8LvfQU9msYiIyFiloImIiIiMqGcOP0MwFgTAFUvgeW43kbgFQI7fTVG21zk2IyOX2e/44KjMU0RERGQ88rl9XDf1urS2iyunT3DaLx1oxk4LbPz10F/7tPtVWAhve9vAx+zbB9u3D3bKIiIiI0pBExERERkxR9uPsq0+VQg0b9Nh2us7geTqW9NPKv4+7Y57MHPzRnyeIiIiIuPZ0rKllGSVOO2FFXkUZCZvXKnrCHGwsdvpO9ZxjP0t+8/uxHPnwlVXDXzMX/8K3d0DHyMiIjKKFDQRERGRERG34vzxwB+dtisSI/7yAXrvWyzPyyDDmypMWlQ2g4k3vXWEZykiIiIy/pmGyc0zbk61TYNrZhc57VcONRO3LKf99OGnSViJszv5DTfAlCn994dC8Oc/D3rOIiIiI0VBExERERkRrx5/leZgqrho5JWDRAMRAPweFxX5GU6fx/Qw8x0fTK6PLSIiIiJDbkbhDGYVznLaUydkUVmYCUBnKMb26nanrzXUyqbaTWd3YtOEt78dsrP7P2b37uRSXSIiImOQgiYiIiIy7JqDzbx07CWnHQpE8Lx+yGlPK8rCNFPLck1eeBX+JctGdI4iIiIiF5ubZtyEQfIzmGEYXDOrmJ4mG6paCUVT2SUvHn2RUCx0utOcKicH3vzmgY/5058gHD6XaYuIiAwrBU1ERERkWNm2zR8P/JGEnfqju+GFffgiMQCKsn3kZaQySgr8BUx6y3uSRU5EREREZNhMzJrIsvLUjSrFOT4uKcsFIBq3eP1Ii9MXiof63ARzRnPnwvz5/fd3dcHTTw96ziIiIsNNQRMREREZVtsbtnO0/ajTPtbQRfn2YwC4TYMpEzKdPtMwmbngGozp00d6miIiIiIXpeumXofP5XPaK2YU4XYlLxftqOmgNRB1+jbWbKQ11Hr2J7/9dsjI6L9/61aoqhr0nEVERIaTgiYiIiIybIKxIE8fTt1BGEtY1L58gNyeLJPJEzLxuFIfR6bkTSHrhluVZSIiIiIyQrK8WVw9+Wqnne1zs3xKAZDMGH7lYJPTl7ATPHP4mbM/eXY23HLLwMf84Q8Qiw1qziIiIsNJQRMREREZNk8ffppgLOi0NxxuYeGhOgBy/G6Ks1N3NWZ5sqicsTS5lIOIiIiIjJgrJ11Jni/PaS+bUkCWzw3AkeYA1a2pz3N7m/dyrP3Y2Z988WKYMaP//tZWeOGFQc9ZRERkuChoIiIiIsOiqq2KN+rfcNrN3RG6tx2jKBjBMGB6UTZGWkbJ7AmzMVddoywTERERkRHmcXm4cfqNaW2Tq2YUOe2XDjRh2bbT/uvhv2KntQdkGHDHHeDx9H/M+vVQWzvoeYuIiAwHBU1ERERkyMWtOH888Eenbds2z+6p5/LjjQBU5GeQ4XU5/eU55eSVTIaFC0d8riIiIiICCyYuoCKnwmnPK8uhOCeZFdzUHWFvXafTV9tVy87GnWd/8vx8uOGG/vttG37/e0gkBjttERGRIaegiYiIiAy5l4+9TEuoxWnvqOnAd6yF8q4Qfo+L8vxUQVCvy8u0/GmwciW4XKc7nYiIiIgMM8MwuGXmLX3a18wqdtqvHW4hlrCc9nNHniOWGEQtkssvh0mT+u9vaIBXXx3UnEVERIaDgiYiIiIypFqCLbxy/BWnHYjEefVQMyuqk0VEpxVlYaYtwTWzcCae3Hy49NKRnqqIiIiIpJmcN5n5xfOddmVhJtOLsoHkZ7otx9qcvo5IB6+feP3sT26a8Ja3DHyTzIsvQlNT//0iIiIjQEETERERGVIvHnuRhJ1aWmHd/iYmtHYztb2b4mwfeRmp9awLMwopziyGK64YeJ1rERERERkRN06/EZeRCmxcPavIueFl87E2uiNxp+/l4y/THe0++5NPnAirVvXfn0jAH/6QXK5LRERklChoIiIiIkOmJdjCzobU+tbVrUEONnZx5Ykm3KbB5AmZTp9pmMwqnIXh9yeXaxARERGRUVeYUcjlFanPZoVZXhZNygMgnrB47VCz0xdNRHmh6oXBDbBqVTJ40p/jx2HTpsGdU0REZAgpaCIiIiJD5uXjL2OTujPw9SMtTAiEmdPcyeQJmXhcqY8eU/OnkuHJgOXLwe8fjemKiIiIyGlcM+UaMtypGnRXTJuA1538HLenvpPGrrDTt7VuK42BxrM/ucuVXKYrbbnWUzz3HHR1DXreIiIiQ0FBExERERkSbaE2djTscNon2oLUtIdYUd2E3+OiONvn9GV5spiUOwncblixYjSmKyIiIiL9yPBksHrq6lTb6+KKaROSDRtePtCM3bOElo3N04efHtwAkybBlVf23x+JwF//OshZi4iIDI0RCZp897vfZdq0afj9fpYtW8bLL7/c77Hr1q3DMIxTHvv27RuJqYqIiMg5evn4y1i25bQ3HGklLxxlflMHFfkZGGl3E86eMBvTMGHpUsjOHo3pioiIiMgAlpcvZ0LGBKe9uDLPqU1X3Rakqjng9B1qPcSh1kODG+C666CgoP/+Xbvg8OHBnVNERGQIDHvQ5Fe/+hUf+9jH+NznPse2bdtYtWoVt912G8ePHx/wdfv376eurs55zJo1a7inKiIiIueoPdzOG/VvOO3a9hDVbUEuP9FMpsugKNvr9BX4C8jz54FpwsqVozBbERERETkTl+niphk3OW23aXLVzCKn/fLBZhJWalnWpw8/3ecGmjPyeuHNbx74mKeegnh84GNERESG2LAHTR555BHe9773cf/99zNv3jzWrl1LZWUl3/ve9wZ83cSJEyktLXUeLpdruKcqIiIi5+iV46/0zTKpaiUzGmdxfSvlBX2zTKbkT0luLFgw8N2FIiIiIjKq5kyYw9T8qU571sRsyvKStU7aglF21XQ4fY2BRrbVbRvcADNmJD8T9qelBV59dXDnFBEROU/DGjSJRqNs2bKFm2++uc/+m2++mddee23A1y5dupSysjJuuOEGXnjhheGcpoiIiJyHzkhnnz+Q6zpCHGsJsLymmSzToCitlkm+P598f36ycfXVIzxTERERERkMwzC4ZcYtGBhO+5rZqWyT14+0EI4lnPbzVc8TTUQHN8gtt4DP13//yy9Da+vgzikiInIehjVo0tzcTCKRoKSkpM/+kpIS6uvrT/uasrIy/uu//ovf/va3PPHEE8yZM4cbbriBl1566bTHRyIROjs7+zxERERk5Lx6/FUSduqP5Y1VrXjjCS6ta6U8PwMzPcskryfLZM4cmDhxpKcqIiIiIoNUllPGopJFqXZeBnNKcgAIxRJsOpoKaARiATbWbBzcADk5cP31/ffH4/CnP4Ft93+MiIjIEBqRQvDpS3IA2LZ9yr5ec+bM4f3vfz+XXnopK1as4Lvf/S5vetOb+M///M/THv+Vr3yFvLw851FZWTnk8xcREZHT64p0saVui9Nu7AxT1RxgSX0rubZNcU7qrsFcX24qy2TVqhGeqYiIiIicqxum34DH9Djtq2YW4TKT13XeqG6nKxxz+l49/iqReGRwA1x2GZSV9d9/+DDs2TO4c4qIiJyjYQ2aFBUV4XK5TskqaWxsPCX7ZCBXXnklBw8ePG3fgw8+SEdHh/Oorq4+rzmLiIjI2Xut+jXiVqo454aqVgzbZnlNC+UFfbNMpuZPTd40MXUqTJo0CrMVERERkXOR68tlZeXKVDvDw5LKfAASlt0n2yQUD7GhZsPgBjDNZFH4fm6wBeAvf4HIIIMxIiIi52BYgyZer5dly5bxzDPP9Nn/zDPPsHLlyn5edapt27ZR1s8dBz6fj9zc3D4PERERGX6BaIDNtZuddlNXhMNN3cxt6qAonmBiWpZJjjeHAn9P0XfVMhERERG54Fw1+SqyvdlOe/mUQjyu5GWlXbWddIZS2SavVb9GOB4e3AAVFbBsWf/9XV2gmrciIjIChn15rgceeIAf/vCH/PjHP2bv3r18/OMf5/jx46xZswZIZorcc889zvFr167ld7/7HQcPHmT37t08+OCD/Pa3v+XDH/7wcE9VREREBuG16teIWak/jjdWtYBtc/mJZsry/afPMikpgRkzRmO6IiIiInIevC4v10651mlneF0s7ck2sSybjWnZJuF4mNdPvD74QW64AbKy+u/fsAHq6gZ/XhERkUEY9qDJO9/5TtauXcuXvvQllixZwksvvcRTTz3FlCnJQrB1dXUcP37cOT4ajfLJT36SRYsWsWrVKl555RX+9Kc/ceeddw73VEVEROQsBWNBNtVuctot3REONnVT2RlkcihCSY7f6cv2ZlOYUZhsrFgx8LILIiIiIjJmLS1bSp4vz2lfOqUArzt5aWlPbScdwdQNNeur1xOKhQY3QEYG3Hxz//22raLwIiIy7AzbHl//03R2dpKXl0dHR4eW6hIRERkmzx15jpePv+y0/7yzjv0NXdy5+xg32XHK8jOcvgUTF1CUWQTZ2fCxj4HbPQozFhEREZGhsKV2C3848Aenvf5wCxuqWgCYX5bLzZeUOn3XTLmG66ddP7gBbBt+8hM4erT/Y978Zli+fHDnFRGRi9pg4gbDnmkiIiIi40soFmJjzUan3RqIsr+xi4JghHnt3UzMTWWZZHmymJAxIdm4/HIFTEREREQucEtKl6Rq1QFLJ+fj68k22VvfRVsw6vS9fuJ1grHg4AYwDHjTm8Dl6v+YZ5+FQGBw5xURETlLCpqIiIjIoGyo2UAkEXHam6pawYbLapopz/PjMlPLb03Jn5KsZeLx6G5AERERkXHAZbq4dmqqtonf4+LSyckgim3bbDySqm0STUR5rfq1wQ9SXAwrV/bfHw7D008P/rwiIiJnQUETEREROWsnF/VsD0bZ19CFPxZnaXMHpWlZJpmeTIozi5ONJUsgM3OEZysiIiIiw2FRyaJUzTpgyeR8/J5kZsi+hk5aA6lsk401GwlEzyEr5JprID+///7t2+HYscGfV0RE5AwUNBEREZGztrFmI+F42GlvOtqKbdssrWtlcra3b5ZJXk+WiWHAlVeOxnRFREREZBiYhsnqqaudts/tYtmU3mwT2HCkxemLJqK8Wv3q4AfxeOD22wc+5s9/Bssa/LlFREQGoKCJiIiInJVYItYny6QjFGNvXRcuy+KK+lZK8lJZJhnuDCZmTUw25syBCRNGeroiIiIiMowWTFxAUWaR0148KZ+MnmyT/Y1dtHSnLedas4nuaPfgB5k9G+bN67+/vh62bBn8eUVERAagoImIiIiclR0NO/oU8tx8tBXLtpnf2MEMvxu3mfpY4dQyAVixYqSnKiIiIiLD7ORsE6/bZPnUngLxNryelm0Ss2K8cvyVcxvo1lvB6+2///nnITjIYvMiIiIDUNBEREREzsi27T5ZJsFonD11nWDbrKhroSwty8Tv9lOSVZJslJfD5MkjPV0RERERGQHzi+ensouBRZPyyfS6ATjY2E1TVyrbZHPtZroiXYMfJC8PVq3qvz8UghdeGPx5RURE+qGgiYiIiJzRkbYjNAWbnPaumg4Sls2U9gCXmDZuV1qWSV5alsnKlcmaJiIiIiIy7pycbeJxpWWb0DfbJG7Fefn4y+c20IoVUFjYf//mzcmlukRERIaAgiYiIiJyRhtqNjjbCctm+4kOAK6oaaYkN5Vl4nP5KMnuyTLJyxt4DWoRERERueDNK5qXyjIGFlXkkdWTbXK4qZuGzrDTt6V2Cx3hjsEP4nYnl+nqj20ni8Lb9uDPLSIichIFTURERGRALcEWDrQccNoHG7sIROJMCIRZFo7g7yn4CVCRW4Fp9Hy8uOIKcLlOPp2IiIiIjCOGYXDdtOuctttlctm0VFZIerZJwk6ce7bJrFnJR3+OHYNdu87t3CIiImkUNBEREZEBpWeZ2LbNtuPtAFxe09ynlolpmJRllyUbPh9ceulITlNERERERsmcCXNSnwOBBeW5ZPuS2SZVzQHqO1LZJtvqttEebh/8IIaRzDYZ6Kacp5+GaHTw5xYREUmjoImIiIj0KxwP80b9G067riNMQ2eYrGiMy9u7yfG7nb6SrBI8Lk+yceml4PcjIiIiIuPf6bJNLk/LNll/UrbJK8dfObeBJkyAK6/sv7+rC14+x0wWERGRHgqaiIiISL+21m0lmkjdrbetuh2AS2tbmZTjSxV8ByblTkpuGEZyaS4RERERuWjMKpxFRU6F076kPI8cf/KGmmMtAWrbQ07fG/VvEIgGzm2ga66BnJz++197DVpbz+3cIiIiKGgiIiIi/bBsi401G512ZzjGocZu3AmLKxrbmJDtdfoK/AVkebOSjfnzIT9/hGcrIiIiIqPp5GwTl2lwRVq2yeajbc523IqzqXbTuQ3k88FNN/Xfn0jAX/5ybucWERFBQRMRERHpx77mfX3Wm95e3Y5t21zS2M7UDDfm6bJMAFauHMFZioiIiMhYMaNgBpW5lU57XlmqtsmR5m5aA6kM5o01G4klYuc20MKFMHly//0HDsDBg+d2bhERuegpaCIiIiKnteFEqgB8LGGxq7YTbJvL61qYmJOqV5LhzqAwo+cuwsmToaLi5FOJiIiIyEXAMAxWT13ttF2mwZLKfKe99Xgq2yQYC/apnTfIgeC225LP/fnLXyAeP7fzi4jIRU1BExERETlFbVctxzqOOe29dZ1EYgkqO4PMwcbrTn2EmJQ7KVXbZMWKkZ6qiIiIiIwh0wumU5pd6rQXTspzPjvureskGE0FMtafWI9lW+c2UFkZLFvWf39LC7z++rmdW0RELmoKmoiIiMgp0rNMbNtm2/F2AJbWtlCWl8oycZvu1B/FBQUwZ85ITlNERERExhjDMFhZmVqu1ed2saA8D4CEZbO9usPpaw21sr95/7kPdv31kJHRf/9LL0FX17mfX0RELkoKmoiIiEgf3dFudjXuctrHWoO0BaNkRWNc3h0kq2ddaoCy7DJcpivZuPxyMPXRQkRERORid0nxJeT6cp320sn5Tj287SfaiSVS2SWvVb927gNlZsJ11/XfH43Cs8+e+/lFROSipCsbIiIi0semmk0k7ITT7s0yWVLXRnmuv8+xFbk99UvcbliyZIRmKCIiIiJjmct0ceWkK512jt/D7JJsAMKxBHvqOp2+6s5qqjuqz32w5cuhpKT//u3bobb23M8vIiIXHQVNRERExBG34myu3ey0WwMRjrUEMGybK5o7KMj0OH3FmcX43T1BlIULB14aQUREREQuKpeWXYrP5Uu1Jxc429uOtWHZttM+r2wT00wWhR/I009D2ngiIiIDUdBEREREHLsadxGIBZx2b5bJrJZOZvnMVMF3kgXgHZddNlJTFBEREZELgN/tZ1l5qlD7xFw/lYWZALSHYhxpSn3m3Ne8j9ZQ67kPNnUqLFjQf//Ro3DgwLmfX0RELioKmoiIiAiQLPj++onXnXY4lmBvfbJw5mX1bRTnpu4UzPHmpNaprqiA8vIRnauIiIiIjH1XVFyBaaQuPS1LyzbZcqzN2baxWV+9/vwGu/HG5JKx/XnmGUgk+u8XERHpoaCJiIiIAHCs4xj13fVOe1dNB/GERWEwwmXxGO60Iu+Tcielsk6UZSIiIiIip5Hnz2PBxFQGyJQJmUzISt6IU9cRorY95PS9Uf8GwVjw3AfLz4crrui/v7kZtmw59/OLiMhFQ0ETERERAeiTZWJZNtur2wFYWt9CSV6qALzX5aU4qzjZyMiASy4ZyWmKiIiIyAVkxaQVzrZhGFw6Jd9pb03LNolZMTbVbDq/wVatgszM/vvXrYNw+PzGEBGRcU9BExEREaEt1Mb+5v1O+1BTN12ROJ5Egms7g2R4XE5fRU5FapmFpUvB4zn5dCIiIiIiAJTllDG9YLrTnlOaQ5Y3uYzWoeZu2oNRp29jzUbiVvzcB/P7YfXq/vuDQXjllXM/v4iIXBQUNBERERE21mzExnbavQXg5zd2MCUrFRQxDZOynLLUC5cvH6kpioiIiMgFamXlSmfbbZosmZyfbNiwtedzJ0AgFmB7/fbzG2zZMigq6r//9dehvb3/fhERuegpaCIiInKRiyVibKvf5rQbOsPUdYTAtlnV0kGuP1VQc2LWRLwub7IxcyYUFo70dEVERETkAjOjYAYTsyY67YUVeXhcyUtSe+o6CEVTBdrXn1iPbdunnOOsuVxw003998fj8Nxz535+EREZ9xQ0ERERucjtbNxJOJ5a2/mNnlom5V0hFpp2quA7yQLwjssvH6kpioiIiMgFzDCMPtkmfo+LS8pzAYgnbHacaHf6moPNHGg5cH4Dzp4NU6f2379zJ9TUnN8YIiIybiloIiIichGzbZuNNRuddiia4EBDFwCXN7RRlO1z+vL9+WR7s3sa+clMExERERGRs7Bg4gJyvDlOe+nkAnrvzXmjup14wnL6Xqt+7fwGMwy4+eaBj3n6aTifjBYRERm3FDQRERG5iFV3VlPfXe+0d9V2kLBsMqNxrgmHcZmpLJOKnIrUC5cvB1MfI0RERETk7LhNN1dMusJp52V4mDUxGUQJxRLsrety+o51HONE54nzG7C8HBYt6r//2DHYt+/8xhARkXFJVztEREQuYptqNjnblm2z40QHAIsa2ijP9jp9PpePosyegpouFyxdOqLzFBEREZEL3/Ly5an6eMCyKQXO9tbjbX1qmayvXn/+A95wA7jd/fc/8wwkEv33i4jIRUlBExERkYtUd7SbPU17nHZVc4CucAzDtrmhoxu/x+X0leeUp2qbXHIJZGWN9HRFRERE5ALnd/u5tOxSp12S62dSQQYAbcEoR5oDTt+epj20hdrOb8C8PFixov/+1lbYvPn8xhARkXFHQRMREZGL1JbaLSTs1J1123sKwE9v7WKmN7Usl4FBWU5Z6oWXXTZSUxQRERGRcebKSVdiGqnLUX2yTY6lgiQ2Nq+feP38B7z66oFv+Fm3DsLh8x9HRETGDQVNRERELkIJK8GWui1Ouy0Q5XhrEICrmzvIz/A4fROzJqaWUSgthUmTRnSuIiIiIjJ+5PvzmV8832lPnZBFYVbys2ZNe4i6jpDTt7VuK6FY6JRzDIrPB6tX998fCsFLL53fGCIiMq4oaCIiInIR2t+yn85Ip9PefqIdgLxQlMvj0dRSXEBFbloB+Msug7Q+EREREZHBWlm50tk2DINLJ6dnm7Q72zErxubaIVg+a9kyKCrqv3/DBmhv779fREQuKgqaiIiIXIQ21mx0tqNxiz11yQDK8oZWSrJ9Tl+ON4ccb06y4fPBwoUjOk8RERERGX/Kc8qZmj/Vac8tzSHTmyzYfqipi45gzOnbULOBuBU/vwFNE26+uf/+RAJeeOH8xhARkXFDQRMREZGLTGOgkaPtR532vvpOonELl2VxfSCI25X6eNCnAPySJeD1juxkRURERGRcSs82cbtMllTmAWDbsK06VdukO9rNzoad5z/grFkwbVr//Tt2QEPD+Y8jIiIXPAVNRERELjKbajY527Zts+NEBwBzmjuZ6nc7fW7TzcSsiakXqgC8iIiIiAyRWYWzKM4sdtoLK/Kdm3d21XYSjiWcvteqX8O27fMb0DAGzjaxbXj++fMbQ0RExgUFTURERC4i4XiY7Q3bnXZte5jm7ggA13R2k+VLBU3Ksstwma5kY/r0gdeBFhEREREZBMMwWFG5wmlneF1cUpYLQDxhOTf2ADQFmzjUeuj8By0rG3i52f374fjx8x9HREQuaAqaiIiIXES2128nmog67Td6C8CHoyyPxfocW55TnmosXz4S0xMRERGRi8iikkVke7Od9tLJ+fSuDPtGdTtxy3L6Xqt+bWgGvf76ZI2T/jz7bDLrRERELloKmoiIiFwkbNtmU21qaa7uSJzDjd0AXNbSQWF2ql7JhIwJZHgyko3MTJgzZ0TnKiIiIiLjn9t0c3nF5U47P9PLzOJkECUYjbO/rsvpq2qvorar9vwHLSgY+Iag48fh4MHzH0dERC5YCpqIiIhcJKraq2gONjvtnSc6sGwbw7a5MRjC7L2tD6jIrUi9cPFicLlGcqoiIiIicpFYXr4cj+lx2pdOKXC2txxv61PLZMiyTa65Brze/vuffRbSslxEROTioqCJiIjIRWJjzUZnO2HZ7KxJrhM9tT3ANE8qYJLhzqDAn/pjlaVLR2yOIiIiInJxyfRksrQs9XmzLC+D8vxkxnNrIMrRlqDTt6dpD+3h9vMfNDsbVqzov7+xEXbuPP9xRETkgqSgiYiIyEWgI9zB/ub9TvtQYzfBaByAm4NBfO5UJkl5TjlGb9ZJRQVMnDiicxURERGRi8uVk67EIHUTz7LJadkmx9qcbcu2eP3E60Mz6MqVyWVo+/PCCxCPD81YIiJyQVHQRERE5CKwuXYzNqmlDXb0FID3x+JcGQ47+03DpDS7NPVCZZmIiIiIyDArzChkXvE8pz29OIuCzOTyWSfagjR0pj6vbq3bSigWOv9BfT5Ytar//vZ22Lz5/McREZELjoImIiIi41zcirOlbovTbuqKUNOe/ENzRVeAfG8qy6QkqwSPq2dNaY8HFiwY0bmKiIiIyMVpZeVKZ9swDC5NyzbZmpZtEk1E+3y2PS+XXQZ5ef33v/QSRCJDM5aIiFwwFDQREREZ53Y37iYYS60Fvb0nywTgllAotRQXJxWAnz8f/P6RmKKIiIiIXOQm5U5ict5kpz2vLIcMT/LmngON3XSGYk7fhhMbSFiJ8x/U7Ybrruu/PxiE14ao+LyIiFwwFDQREREZ5zbVbnK2w7EE++q7AJgUijArkfpjM8+XR7Y3O/VCLc0lIiIiIiMoPdvE7TJZXJkPgG3bbKtud/q6ol3sbByiQu2LFg1cw2/9eggEhmYsERG5IChoIiIiMo41B5s50XnCae+p6ySesAC4PRLGZfaTZVJYCFOmjNg8RURERETmTJjDhIwJTnvxpHzn8+qumg7CsdQNP69Vv4Zt26ecY9BME264of/+aDS5TJeIiFw0FDQREREZx3Y07OjT3l3TCYA7YXF1V+qOOa/LS1FmUerAJUsgbdkuEREREZHhZhhGn2yTDK+LS8pzAYglLHbVdDh9jYFGDrcdHpqBZ8+Gysr++zdvhra2/vtFRGRcUdBERERknLJtm50NqWULmroitASShSxXRiLkpsVEyrLLMI2ejwWGkQyaiIiIiIiMsEUli8jyZDntpZMLoOdz67bqdhJWKrvkteohqjdiGHDjjf33JxLwwgtDM5aIiIx5CpqIiIiMUyc6T9AWTt0Rt6++09m+IRDsc2xZTlmqMXMm5OYO+/xERERERE7mcXm4vOJyp12Q6WVGUbLuXiASZ39PfT6AI21HqO+uH5qBp0xJZpz0Z+dOqB+isUREZExT0ERERGScSl+ay7Zt5w/M/EiM+cGQ05fvz8fv9qdeqALwIiIiIjKKLqu4DLfpdtrLphQ421uPt/WpZbK+ev3QDXzDDf0vUWvbsG7d0I0lIiJjloImIiIi41DCSrC7abfTPtEWojsSB+DGUBCPK/URoCSrJPXCzEyYM2fE5ikiIiIicrJMTyZLS1M38pTnZ1CWl7zJp7k7wvHWVNb07qbdBGPBU85xTkpKYOHC/vv37YOGhqEZS0RExiwFTURERMahw22H+/zxuK8ny8Swba7pTBWANw2T4qzi1AsXLQKXa8TmKSIiIiJyOldOuhKDVNbHpZNT2Sbbq9ud7bgVZ1vdtqEb+LrrBv48/NJLQzeWiIiMSQqaiIiIjEPpS3PFExYHG5NBk5mdQSpILWcwIWNCn6UPuPTSEZujiIiIiEh/JmROYG7RXKc9ozibbF/yc+uRlgAdoZjTt7l2c58lu85LQQEsW9Z//5490NQ0NGOJiMiYpKCJiIjIOBOJR9jXvM9pVzUHiMYtAG4KhXCZqTv2SrLTluaqqICJE0dsniIiIiIiA7li0hXOtmkaLKzISzZs2FXT4fS1hds41Hpo6Aa+6qr+s01sG15+eejGEhGRMWdEgibf/e53mTZtGn6/n2XLlvHyGf5zefHFF1m2bBl+v5/p06fz/e9/fySmKSIiMi7sbd5L3Io77d6lufyxOMu7U0t2uU03hRmFqReqALyIiIiIjCFT8qYwMSt1U8+CijzMnhuAdtV0ELcsp29T7aahGzgvD5Ys6b9/505oaRm68UREZEwZ9qDJr371Kz72sY/xuc99jm3btrFq1Spuu+02jh8/ftrjq6qquP3221m1ahXbtm3js5/9LP/0T//Eb3/72+GeqoiIyLiQvjRXOJbgaEuyhsmlbd0U+FJ3zBVnFmMaPR8F3G5YsGBE5ykiIiIiMhDDMLis/DKnneVzM7M4G4BQLMGhhm6n72DLQdpCbUM3+NVXg9nPZTPbhldeGbqxRERkTBn2oMkjjzzC+973Pu6//37mzZvH2rVrqays5Hvf+95pj//+97/P5MmTWbt2LfPmzeP+++/n7//+7/nP//zP4Z6qiIjIBa8r0kVVW5XTPtjQTcJKru98UyCAYfSzNNf8+eD3j9g8RURERETOxqKSRXhdXqe9eFK+s739RLuzbWOzuXbz0A1cUAALF/bfv307tLf33y8iIhesYQ2aRKNRtmzZws0339xn/80338xrr7122tesX7/+lONvueUWNm/eTCwWO+X4SCRCZ2dnn4eIiMjFalfjLuy0Qu/76pP/L5Z0hZhtJZz9PpePPF9e6oUqAC8iIiIiY5DP7WNxyWKnXZ7vpyjbB0BdR5jGzrDTt61+W59las/bqlWQdtNRH5albBMRkXFqWIMmzc3NJBIJSkpK+uwvKSmhvr7+tK+pr68/7fHxeJzm5uZTjv/KV75CXl6e86isrBy6NyAiInKBSV+aqzMUo6Y9BMBVHV1kelNLc5Vkl6SyTgoKYMqUEZ2niIiIiMjZuqwitUSXYRgsmpS6+WdHWkH4YCzI7sbdQzdwURFcckn//du2gW7eFREZd0akELxxUlTetu1T9p3p+NPtB3jwwQfp6OhwHtXV1UMwYxERkQtPU6CJuu46p72/IVkA3rBtrgqG+i7NlZV2g8LSpf3fQSciIiIiMsomZk1kav5Upz23NBevO3lJa199F+FYKqN6SAvCA1xzTf99iQS8+urQjiciIqNuWIMmRUVFuFyuU7JKGhsbT8km6VVaWnra491uNxMmTDjleJ/PR25ubp+HiIjIxWhn405n27Zt9tUl73qb0t5NhSf1X362N5ssb1ayYRiwZMlITlNEREREZNDSC8J73SbzSpPXf+IJi711qWyPE50nqO2qHbqBJ06EefP679+yBbq7++8XEZELzrAGTbxeL8uWLeOZZ57ps/+ZZ55h5cqVp33NihUrTjn+6aefZvny5Xg8nmGbq4iIyIXMtu0+S3M1d0dpCUQBuDoQwu9JW5orPctk2jTQDQciIiIiMsbNLZpLtjfbafdZoutEh7NKCcCmmhHMNonHoZ+6vSIicmEa9uW5HnjgAX74wx/y4x//mL179/Lxj3+c48ePs2bNGiC5vNY999zjHL9mzRqOHTvGAw88wN69e/nxj3/Mj370Iz75yU8O91RFREQuWNWd1bSH2512bwF4l2VxRSjU59iJWRNTjQULRmJ6IiIiIiLnxWW6WFa2zGlPyPYxqSATgLZglOrW1GfenY07CcVCp5zjnJWVwezZ/fdv3gzB4NCNJyIio2rYgybvfOc7Wbt2LV/60pdYsmQJL730Ek899RRTegrO1tXVcfz4cef4adOm8dRTT7Fu3TqWLFnCl7/8Zb71rW9x1113DfdURURELljpWSaWbbO/PlnPZEZ7gNK0AvD5/nx8bl+yYZoDLzUgIiIiIjKGLCtfhmmkLmUtTss22X6i3dmOW3HeqH9jaAcfKNskGoX164d2PBERGTXukRjkQx/6EB/60IdO2/fYY4+dsu/aa69l69atwzwrERGR8SFhJdjduNtp17SF6I7EAbgmFMbjSv1h2WdprpkzISNjxOYpIiIiInI+cn25zC2ay56mPQBML84my+cmEIlzpLmbrnCMHH9yafdNtZu4ctKVGIYxNINPmgQzZsDhw6fv37gRVq7U52sRkXFg2DNNREREZHgdaj1EKJ5afqB3aS53wuKytKW5TMOkOKs49UItzSUiIiIiF5j0gvAu02BhRTLbxLZhZ02H09caauVI25GhHXygbJNIBDZsGNrxRERkVChoIiIicoFLX5ornrA42NgNwLyOborTluaakDEBt9mTZOp2w5w5IzpPEREREZHzNTV/KkWZRU57QUUeZk82ya6aThJWqiD8xpqNQzv4lCkwdWr//Rs2JIMnIiJyQVPQRERE5AIWiUfY37LfaVc1B4jGLQBWh8OYZmo5gpLstKW5Zs8Gn2/E5ikiIiIiMhQMw+iTbZLtczOjOBuAYDTOoZ4biAAOtBygPdw+tBMYKNskFIJNm4Z2PBERGXEKmoiIiFzA9jbvJW7FU+2eAvC+eIKl4dRdbm7TTWFGYeqFWppLRERERC5Qi0sX4zE9TntRPwXhbWy21G4Z2sGnTYPKyv77X3stWRheREQuWAqaiIiIXMDSl+YKxxIcbQkAsKgzQIEn9d/8xKyJmEZP2+uFWbNGdJ4iIiIiIkPF7/azqGSR055UkEFhlheA2vYQTV2pm4e21m3tc5PReTOMgbNNgkHYMsSBGhERGVEKmoiIiFygOiOdVLVVOe0DDV1YPWs4r45EMIy0pbmy0pbmmjsXPKk780RERERELjSXVaSW6DIMg8WT8p32jrRsk0AswN6mvUM7+MyZUF7ef/+rr0IsNrRjiojIiFHQRERE5AK1s2EnNqlCl/t7lubKiMVZFAo7+/1uP7m+3NQLtTSXiIiIiFzgSrNLmZw32WnPLcvB40pe5tpX30UknnD6NtUOcZ2RM2WbdHfDtm1DO6aIiIwYBU1EREQuQLZts60+9YdYRyhGTXsIgMsCIbJPWprLyTrJyIAZM0Z0riIiIiIiwyG9ILzP7WJeWQ4AsYTF3roup+94x3EaA41DO/icOVBS0n//K69AfAiXBRMRkRGjoImIiMgFqLqzmuZgs9PeU9vpbF8bDve/NNf8+eByjcgcRURERESG07zieWR5spz2opOW6LLtVFb21rqtQzv4mbJNOjth+/ahHVNEREaEgiYiIiIXoG11qSwTy7bZU9sBQE40xoJIqvBltjebLG/qD0ktzSUiIiIi44XbdHNp2aVOuyjbR3l+BgCtgSh1Hakla3c07BjagvAA8+ZBUVH//S+/DIlE//0iIjImKWgiIiJygYnEI+xu2u20j7cG6Yok/wBcHYvic6X+ey/LLku9MDsbpkwZsXmKiIiIiAy35eXLMUhlWS8oz3O2d6dlYwdjQfY37x/awU1z4GyT9nbYuXNoxxQRkWGnoImIiMgFZnfTbqKJaKpdk/pj8Jq0AvCmYTIxa2LqhZdckvzDTkRERERknMjz5zGzcKbTnlWSjded/Mx7oKGLaNxy+oZ8iS5IZnIXFvbf//LLYFn994uIyJijKyciIiIXmPQ/9kLRBIebuwEoTVjMSAuaFGUW4XF5Ui/U0lwiIiIiMg6lL9HlcZnMLkkVhD/QkCoIf6TtCO3h9qEd3DRh1ar++1taYPfu/vtFRGTMUdBERETkAtIUaOJE5wmnvbe+E8tKFri8JRHFTCsA32dprrw8mDRpxOYpIiIiIjJSZk+Y3acgfN8lujqcbRubN+rfGPoJLFoE+fn997/8MqQVpRcRkbFNQRMREZELSHqWiW3bfZbmWhkIOtt+t598f37qhQsWQFpARURERERkvHCZLhaXLnbaJbk+irJ9ANR1hGnpjjh92+q2YdlDvFyWywVXX91/f2Mj7N07tGOKiMiwUdBERETkApGwEmxv2O60GzojtASSfwDOdxuUBFJLc5Vml2KkB0m0NJeIiIiIjGNLS5c624ZhcEl5rtNOLwjfEemgqq1q6CewZAnk5vbf/9JLyjYREblAKGgiIiJygdjfsp9gLJVNsittqYGbYpE+x5Zml6YaEyZAaSkiIiIiIuNVcVYxlbmVTntuaS4uM3kT0d66ThJWKmAxLAXh3W646qr+++vr4cCBoR9XRESGnIImIiIiF4htdduc7fSilh7TYFlHwOkrzCjE7/anXqiluURERETkIpBeED7D62JGcTYAoViCI03dTt++5n19bkYauglcCllZ/fcr20RE5IKgoImIiMgFoDPSyaHWQ077YEM30XhyLeYrM9zkdoecvj4F4EFLc4mIiIjIReGSiZfgdXlT7bQlunalLdGVsBPsaNgx9BPweGDlyv77a2rg8OGhH1dERIaUgiYiIiIXgDfq38AmdVfa7rSlua6PpmqZeEwPEzInpF5YUgLFxSMyRxERERGR0eR1eVkwMXXD0OTCTHL8HgCOtQboCsecvq11W7GHI+vjsssgM7P/fmWbiIiMeQqaiIiIjHG2bfdZmqstEKWmPZlZUpjpYW5ju9NXkl2CaaT9964sExERERG5iKQv0dWnILwNe9KyTRoDjdR01Qz9BLxeuPLK/vuPH4djx4Z+XBERGTIKmoiIiIxxR9uP0hZuc9q70/7Yu95rkBFIFYHX0lwiIiIicjGryKlgYtZEpz2/PBd6yvvtru3sk12SfmPSkLr8cvD7++9/8cXhGVdERIaEgiYiIiJj3Lb61B9zlmWzpy4ZNDENg+taUwGUHG8OWd60wpMVFVBQMGLzFBEREREZbYZhsLR0qdPO9XuYUpj8jNwZjlHdmqoFuLNxJ9FEdOgn4ffDFVf0319VBdXVQz+uiIgMCQVNRERExrBwPMyepj1Ou6olQDAaB2BhlofyulQGSlnOSVkmixaNyBxFRERERMaSxaWLcRkup72gT0H4VG3AaCLK7sbdwzOJK69MLtXVH2WbiIiMWQqaiIiIjGE7G3YSt+JOe3dNKrPktlAIo2d5AdMw+yxDgMcDixeP2DxFRERERMaKTE8mc4vmOu3pxdlkeJJBlMNN3YSiCacvPat7SGVkJJfp6s+hQ3DixPCMLSIi50VBExERkTFsa91WZzsQiVPVEgAgz22ytL7V6ZuYNRG36U69cNGigddRFhEREREZx5aWpZbocpkG88qS2SYJy2ZffepGpOMdx2kKNA3PJFasSN7M1B9lm4iIjEkKmoiIiIxR9d311HXXOe09danClbeQwBeJOX2l2aV9XzzQXW0iIiIiIuPc9ILp5PnynPYlaUt0nVIQfriyTbKyYPny/vsPHoSamuEZW0REzpmCJiIiImNUepaJbdvsqU3dEXd9W2o7w53R5w9CJk+GkpIRmaOIiIiIyFhkGiZLSpc47QnZPkrzkpnYzd0RGrsiTt/2+u0krMTJpxgaV10Fbnf//S+9NDzjiojIOVPQREREZAyKW3F2Nux02rXtYdqCUQCWuKC0I+D0leWUYRhG6sXKMhERERERYWnZUgxSn5MXlKduNNpVkyoIH4gFONByYHgmkZ09cLbJ/v1QV9d/v4iIjDgFTURERMagvU17CcVDTntXbeqPujcFUgETA6Pv0lzZ2TBv3ojMUURERERkLMv35zO9YLrTnl2Sg8eVvBS2v6GLWMJy+tKzvIfcmbJNVNtERGRMUdBERERkDEr/oy0ST3CwsRuAXNtiaUtqaa7CjEK8Lm/qhcuWgcs1YvMUERERERnL0gvCe90ms0uyAYjGLQ42dDt9h1oP0RnpPOX1QyInJ/k5vT/79kF9/fCMLSIig6agiYiIyBjT0N1AVXuV0z5Q30285y64N0ejuNOKVpbllKVeaJoDp/6LiIiIiFxk5hbNJcOd4bQvSVuia3daNreNzebazcM3kauuGvjmJtU2EREZMxQ0ERERGWPWn1jvbNu2zfYT7b0NrmtP3f3mdXmZkDEh9cJ585J3sYmIiIiICABu083i0sVOuyzPT2FWMlO7pj1EayDq9G2s2Ug4Hh6eieTmwqWX9t+/Zw80NAzP2CIiMigKmoiIiIwh/7+9+w6P4zrM/f+d7Qtgd9EbUdh7FYsK1QtlWcWWHdlykVtc8lw7seIk18nNTeLc37V9nTxJbkkcR7bjbktKLLlIltVFSaYkNrF3EgRA9Lq7wPbd+f2x5AArAKwoJPB+nmeenXPO7OxZaQBi951zTjgezlkAvrk3SvdAHICrkwnKkimrrbKgMncB+PXrJ62fIiIiIiJXijWVQ1N0GYaRsyD8jsY+az+WirGtZdvEdeT66zXaRETkCqDQRERE5DKyrXUbaTNtlXc2DX2Iuy8azTm2qmDY1Fzl5VBfP+H9ExERERG50lQUVDDLN8sqL5vlx+3IfiV2sC1EKJq02t449QaJdGLEOcZFIABr1ozdfuAAdHVNzGuLiMh5U2giIiJymUimkznzKPcMxDnZMwhAbSbDknDEaivNK8XrHJqbmQ0bYPioExERERERsWyYtcHadzvsrK4tAiBjmmwfNtokkoywo3XHxHXk+uuzaxGOxjRh8+aJe20RETkvCk1EREQuE7s7dhNJDgUjbzf3W/v3J2M5/2jX+GuGCm43rFw58R0UEREREblCrahYQZGnyCqvri3Eac/+hb2/NchAfGga3C3NW0hlUiPOMS4KC2H16rHb9++H7u6JeW0RETkvCk1EREQuA6Zp8uapN61yJJHiYFt20fc8A67tC1ttPpePgHtoHmZWrwaXa7K6KiIiIiJyxbEZNq6vu94qe112VtZk/6ZOZ0x2DhttEk6Eebvt7YnrzA03nH20idY2ERGZUgpNRERELgPHeo/RHRm6o2zvqSDpjAnAvWYKb2ponZMaf40WgBcRERERuUCrKlfhd/ut8lV1Rdht2b+r97QEiSSGRpe83vQ66Ux6xDnGRVERrFo1dvvevdDTMzGvLSIi56TQRERE5DLwxqk3rP1UOsPuU/0AGJjcHhwaZeK2uynLLxt64ty5UFo6Wd0UEREREbliOWwONtZutMr5bgcrZmVHm6TSGd5u6rfagvEgezr2TFxnNNpEROSypdBERERkirUPtHOi74RVPtwRJpLI3tW20WWjLBy12mb5Z2Ezhv3zvWFoQUsRERERETm7q6quIt+Zb5XX1hdhOz3aZPepfmLJodElrzW9RsbMTExHiothxYqx2/fuhd7eiXltERE5K4UmIiIiU2z4WiamabKzsd8q3zM4aO3bDTtVBVVDTwwEYOHCyeiiiIiIiMi04LQ7ua72Oqvs8zhZWpWdsiuRyrC7ud9q6432sr9z/8R15sYbYfi0u8NlMvDaaxP32iIiMiaFJiIiIlMoHA+zt2OvVW7qjdAzGAdgnsfBwq6g1VZZUInT7hx68rp1Yw/pFxERERGRUa2rXofX4bXK6+uLrTUD327uJ5EaGl3yauOrmKY5MR0pKTn7aJNdu6CjY2JeW0RExqRvWkRERKbQttZtpM2hKQB2DptH+b2pOLbM0Ae0Gn/N0BPtdrjqqsnoooiIiIjItOJ2uLmm5hqrHMhzsrjSB0AsmWbP6fUFAboiXRzqPjRxnTnbaBPThGeeyT6KiMikUWgiIiIyRZLpJNtatlnlnoE4jT3Z6bgCbgfrO/usttK8UrzOobvhWL4c8ofmYhYRERERkfN3dc3VuO1uq7x+djGczi52NvWRTE/SaJPS0uzf9mM5eRIOHJiY1xYRkVEpNBEREZkiuzt2E00NLfL+9rBRJvc4TTyRhFWu9dfmPnn9+onunoiIiIjItOVxeLi65mqrXJzvYkF5drRJJJFmX8vQNLltA20c6z02cZ25+eazT7v77LOQSIzdLiIi40qhiYiIyBQwTZM3mt+wypFEioPtIQBcDhs394asNp/Lh9/tH3pydTXMmjVpfRURERERmY6uqbkGp21ozcANs4ut/R2NfaQyQ6NNNjdunti1Ta65Zuz2UAhef31iXltEREZQaCIiIjIFjvYepSfaY5X3nAqSPr1+yfV+F2WdQ3e21QZqrYUpAdiwYex5j0VERERE5LzkOfNYP2toBHeZz83csgIABuIpDraGrbZToVOc7D85cZ256SYoKBi7fcsW6Osbu11ERMaNQhMREZEpMHyUSSqdsRabNAyDuwYGrTa33U1pXunQE71eWLZssropIiIiIjKtXVtzLQ6bwyoPH22y7WSvdWMTZNc2mTBuN9xxx9jtqVR2mi4REZlwCk1EREQmWVu4jYb+Bqt8uD1MJJEGYGmRl7lN3VZbjb8GmzHsn+urrgLn0BQCIiIiIiJy8XxuH1dVXWWVKwMe6kvyAQjFkhxuHxpt0tDfQFOwaeI6s3Il1NSM3X7oEBw/PnGvLyIigEITERGRSffmqTetfdM02dk0NMz+3nQCeyoboNgNO1W+qqEnGgasWzdp/RQRERERmQk21m7MuVFpw5zc0SaZYWuZbD65eeI6Yhjw7neffSreZ56BdHri+iAiIgpNREREJlMoHmJf5z6r3NQboWcwAUC138OKU0OjTKp8VTlTBbBgARQVTVpfRURERERmgoAnwOrK1VZ5VqGXWYVeAPoiCY52DFhtx/uOc7Tn6MR1proa1qwZu727G7ZunbjXFxERhSYiIiKT6c1Tb5I2h+4M29nUb+3fmWcjPxixyrN8s3KfvGHDRHdPRERERGRGur7uegyGRnhcPafE2n/zRE/O2ibPHn+WdGYCR3vcdht4PGO3v/IKDAyM3S4iIpdEoYmIiMgkiSajbG/dbpW7wnEae7KLvvu9Tq7t7LfaSvNK8Tq9Q08uLoZ58yarqyIiIiIiM0qxt5gVFSuscm2xl+rA0GiTPaf6rbbuSHfO3/XjLj8fbr557PZ4HF58ceJeX0RkhlNoIiIiMkm2tmwlkU5Y5e0ne639jaVeypp7rHKtvzb3yevXn31uYxERERERuSQ31N1gjTYxDIMbF5ZZbW829BJNDI0ueeXkK0ST0YnrzPr1UFY2dvvbb0NLy8S9vojIDKbQREREZBIk0gneannLKgcjSY50hgHwOu3cHh7EOL3ApM/lw+/2Dz3Z6YTVqyezuyIiIiIiM05ZfhkrK1Za5cqAhyVV2b/L48k0bzUM3eQUTUV55eQrE9cZux3uuuvsx/zmNzBskXoRERkfCk1EREQmwdttbxNJDq1Xsr2x1/p8s3aWn9pj7VZbXaAOY/iokpUrwTtsqi4REREREZkQt829DafNaZU3zivFYc9+fbb7VJDewbjVtq11G12DXRPXmblzYenSsdtbWmD37ol7fRGRGWpCQ5O+vj4eeughAoEAgUCAhx56iP7+/rM+5xOf+ASGYeRs11xzzUR2U0REZEKlM2m2NG+xyoPxFAfaQgA47TZuy6RwxZIA5DnzKM0rzT3B+vWT1lcRERERkZnM7/Zzfd31VrnA42B9fREApmny6pFuqy1jZnju+HMT26FNm8DhGLv9+echFpvYPoiIzDATGpp8+MMfZteuXfz2t7/lt7/9Lbt27eKhhx465/Pe9a530dbWZm2/+c1vJrKbIiIiE2pv516C8aBVfrupn3QmO8xkZU2AuUfbrLZaf23uKJO6OqisnLS+ioiIiIjMdNfVXkfAHbDKV9UX4XNng4uTPYM0dA9abUd7j3Ks99jEdaawEK6/fuz2wUF45ZWJe30RkRlowkKTgwcP8tvf/pbvfOc7XHvttVx77bV8+9vf5qmnnuLw4cNnfa7b7aaystLaiouLJ6qbIiIiE8o0TX7X9DurHE+l2dPSD4DdZnBjvoNAV3bUidvupqKgIvcEGzZMVldFRERERARw2p3cPvf2YWUbGxcMjQZ/7WiXdRMUwLPHniVjZiauQxs3QiAwdvtbb0F7+9jtIiJyQSYsNHnjjTcIBAJcffXVVt0111xDIBBgy5YtZ3kmvPLKK5SXl7Nw4UI+85nP0NnZOeax8XicUCiUs4mIiFwuDvccpisyNM/x7uYgiVT2A9WSKj8LG4b+jasN1GIzhv3TXFAAS5ZMWl9FRERERCRreflyavw1VnlRhY/KgAeA3sEEe1uGRpJ3RbrY0bpj4jrjdMKdd47dbprw9NNaFF5EZJxMWGjS3t5OeXn5iPry8nLaz5J+33XXXfzkJz/hpZde4h/+4R/Ytm0bt956K/F4fNTjv/71r1trpgQCAWpra8ftPYiIiFwK0zR5rfE1q5xKZ9jV3AeAYcC1lQWUN3QA4LA5qCqoyj3B2rVgt09af0VEREREJMswDN41/1055ZsWllnlN0/0EEumrfLLJ18mmoxOXIeWLMkuDD+W5mbYtWviXl9EZAa54NDkK1/5yoiF2t+5bd++HSB3TvbTTNMctf6MD37wg9x9990sX76ce++9l2eeeYYjR47w9NNPj3r8X/zFXxAMBq2tubn5Qt+SiIjIhDjZf5KWcItV3t8aIpLIfrBaUO5jcUsv9tOjTmr8NdhtwwISmy0bmoiIiIiIyJSo8dewsmKlVa4KeFlc6QMglkzz5okeqy2SjPBq46sT1xnDgHe/++w3VT3/PEQiE9cHEZEZwnGhT/jCF77Agw8+eNZjZs+ezZ49e+jo6BjR1tXVRUVFxSjPGl1VVRX19fUcPXp01Ha3243b7T7v84mIiEyW15tet/YzGZMdjX1WeV1dIdUv7AHAbtiZ5ZuV++QlS8Dvn5R+ioiIiIjI6G6fezsHuw6SzCQB2Di/lGNdA6TSJntOBVlZU0hxvguAt1reYl31OkrySiamM6WlcO218Prro7dHIvDii3DvvRPz+iIiM8QFjzQpLS1l8eLFZ908Hg/XXnstwWCQrVu3Ws996623CAaDXHfddef9ej09PTQ3N1NVVXXug0VERC4TreFWjvcdt8pHOsKEYtkPWvUl+SwOR/CGs8P3q3xVOO3O3BOsXz9pfRURERERkdH53X421m20yj6Pk7V1xQBkTJPXjg6tX5gxMzx3/LmJ7dCNN559UfidO+HUqYntg4jINDdha5osWbKEd73rXXzmM5/hzTff5M033+Qzn/kM99xzD4sWLbKOW7x4MU8++SQAAwMD/Omf/ilvvPEGJ0+e5JVXXuHee++ltLSU+++/f6K6KiIiMu5+1/Q7a980TbadHBplsn52EbMOZqftMjCo9b9jPa7ycqivn5R+ioiIiIjI2W2s3YjfPTQKfN3sIgrc2clbGroHaewZtNoO9xzmeO/xEecYNy4X3HXX2O1nFoXPZCauDyIi09yEhSYAP/nJT1ixYgWbNm1i06ZNrFy5kh/96Ec5xxw+fJhgMAiA3W5n7969vOc972HhwoV8/OMfZ+HChbzxxhv4fL6J7KqIiMi46Yn0cKDrgFVu6B6kZzAOQFXAw6J0ipKWXgAqCipwO94xzeSGDdk5i0VEREREZMo57U5un3v7sLKNjfNLrfKrR7rIZEyr/OzxZ0ln0kyYRYtg4cKx29va4PR6wyIicuEueE2TC1FcXMyPf/zjsx5jmkP/qHi9Xp599tmJ7JKIiMiE+13z7zDJ/vv2zlEm62YXM/ftE1a5LlCX+2S3G1auRERERERELh8rylewtWUrp0LZqa8WV/rY1dxPRyhGz2CCva1BVtUUAtA52MkrJ1/htrm3TUxnDCM72uTECUilRj/mpZdg6VIoKJiYPoiITGMTOtJERERkpgnFQ+xu322VW/tjtAWza5eU5LtYM2yUSVleGXnOvNwTrF6dHXIvIiIiIiKXDcMweNf8d+WUb1pYZpXfON5DNDE0uuT1ptdp6GuYuA4VFcENN4zdHovB889P3OuLiExjCk1ERETG0Zun3iRtDn1Y2nay19pfV1/E3LeHPjiNGGVit8N11014H0VERERE5MLV+GtYUb7CKlcXellUkZ1OPpZM8/zBDmtGFROTJw89STQZnbgObdwIxcVjt+/eDSdPTtzri4hMUwpNRERExkk0GWV769DcwV3hOCdPLwrp8zi5OpOisCO7jleRpwif+x3rda1fD4HApPVXREREREQuzO1zb8dpc1rlGxeW4XXaATjRNcDelqDVFoqH+PWRX+dMTT+uHA5497vPfszTT0N6AtdXERGZhhSaiIiIjAPTNHnu+HMk0gmrbvgok7V1hSx4+6RVHjHKxOU6+/B6ERERERGZcgFPgI11G61yvtvB7UsrrPKrR7voHYxb5QNdB3i7/e2J69D8+dm1S8bS1QVvvjlxry8iMg0pNBERERkH21u353wY6o8kONoZBsDrtHNzKoGvJ1v2uXwUegpzT3DNNZCfP1ndFRERERGRi3R93fVU5A8FJfPKClhZkx0xnkqb/GZvO6lMxmp/5ugzdEe6J65Dd9559nURN2+GUGjiXl9EZJpRaCIiInKJGvsbeebYM1Y5nTF54WAHZ0bhX1UTYMHuRqu9LlCHYRhDJ/B4tJaJiIiIiMgVwmFz8P6l78dhc1h1Nywoozg/G1x0D8TZcqzHaktmkjxx8AnSmQmaJisQgJtuGrs9kYDf/nZiXltEZBpSaCIiInIJQvEQj+9/nIw5dCfZa0e7ONWXXfAx3+XgjlSC/GAEgDxnHqV5pbkn2bgxG5yIiIiIiMgVoTy/nE3zNlllp93GXcursNuyN0ftbOqj8fT6hgCt4VZeanhp4jp0zTVQVjZ2+4EDcOLExL2+iMg0otBERETkIqUyKR7b9xiDyaEPQwdaQ+xq7gfAZjO4Z1kFi/Y2We0jRpnk58PVV09Wl0VEREREZJysr17PwpKFVrnM52bj/KEbpJ7d30EkkbLKW5q30NDXMDGdsdvh7rvPfswzz2hReBGR86DQRERE5CKYpslTR56iJdxi1XWEYrx4qMMq37KonLXdQbwDMSC7lsnwuY+B7OLvZ5t/WERERERELkuGYfCeRe+hwFVg1a2pLaS+JLtWYSSR4vkDHZin5+01MXni4BNEkpGJ6dDs2bBy5djtXV2wdevEvLaIyDSi0EREROQibGvdxq72XVZ5MJ7i17tbSWeyH4hWzAqwqqKA+tNrmThtTpaVL8sdZRIIwLp1k9ltEREREREZR/mufN67+L1W2TAMNi2twOu0A9DQPcieU0GrPZwI86vDv7KClHG3aRO43WO3v/IKDAxMzGuLiEwTCk1EREQuUGN/I789NrSQYjpj8pu9bQzEs0PvqwJebl5UTvXhVtyROAYGS8uW4nG8Y92Sm24ChwMREREREblyzS+ezzU111jlfLeDTcuGRpi/erSLnoG4VT7UfYidbTsnpjMFBXDzzWO3x+Pw/PMT89oiItOEQhMREZELEIwFRyz8/uqRLlr6Ty/87nZwz8oqXKk09Xuya5nMLZpLkbco90QlJbBq1aT1W0REREREJs7tc2/PmYp3TmkBq2oKgexNVs/sayeVHvoM8cyxZzjUfWhiOrNhw9kXhd+9G5qbJ+a1RUSmAYUmIiIi5ymZTvLY/tyF3/e3Btl9qh8Au83gnpVV5Lsd1BxswRlPUpFfQY2/ZuTJbr45u1ijiIiIiIhc8Rw2B7+39Pdw2IZGkt+woJSS/OxUWd0DcV4/1m21pTIpHtv3GK83vT7+U3XZ7XDXXWc/5je/gUzm7MeIiMxQCk1ERETOw5mF31vDrVZdezDGS4c6rfIti8qpCnhxxJPU7muiwFXAwpKFueuYAFRUwPLlk9V1ERERERGZBGX5Zdw5706r7LDbuGtFJXZb9vPAruZ+jnaErXYTkxdOvMAvDv2CVCY1vp2ZOxeWLh27va0Ndk7QFGEiIlc4hSYiIiLnEIqH+Mnen7C7Y7dVNxhP8dSeoYXfV9YUsnxWAIC6fc140zaWly/HbhtlNMmtt8I7gxQREREREbniratex6KSRVa5tMDNDfNLrfLT+9rY2diXM7pkd8dufrj7hwwmBhlXd94JTufY7S++CJHI+L6miMg0oNBERERkDKZpsrdjL9/c9k2O9R6z6tMZk6eHLfxeXejlpoXZOYM94Sg1B1tYVr5s5MLvALNmwcKFk9J/ERERERGZXIZhcN+i+yhwFVh1q2oLWVzpyxbM7MLwLxzssG7AAmgKNvHtnd+mY6Bj/DoTCMANN4zdHo3Cyy+P3+uJiEwTCk1ERERGEUlG+I8D/8HPD/6cWCpm1afSGV440EHr6YXfC9wO7l5Rhd1m4EikWPHCXhb651DoKRz9xLfdplEmIiIiIiLTWL4rn/sX32+VDcPgzmWVXD2nxKrb3xriiZ2niCbSVl1/rJ/vvv1dDncfHr/OXHcdFBWN3b59e3aqLhERsSg0EREReYfD3Yf55rZvcqDrQE59ezDGT95q4mB7CDiz8Hs1+W4HRjrD0lf2MzflY5Zv1ugnnjMnO7ewiIiIiIhMa/OK53FT/U1W2TAMrp1XwruWD61x0tIf5dFtTfQMxK3jEukEj+57lN81/W58Foh3OOBd7xq73TThmWeyjyIiAig0ERERscRTcX556Jf8bN/PGEgMWPXpjMmW4908tr2ZvkgCyAYmm5ZWUhnwgGmyYOsx6rtToy/8fsatt07G2xARERERkcvAzbNv5oa63OmxFlf6eWBtLXkuBwDBaJLHtjfT0D20nomJyfMnnueJg0+MzzonCxfCggVjtzc1wd69l/46IiLThGGOS2x9+QiFQgQCAYLBIH6/f6q7IyIilznTNOmL9XGi7wSvNb5GMB7Mae8eiPPs/na6wkN3f1X4Pdy5rILifDcANfubWbKjibXVa0dfxwRg1Sq4//7R20REREREZNra3b6bXx3+FWlzaCqucCzJr3a3Wp8zDANuWFDGmtrCnJuwPA4Pt8y+hfWz1mMzLuHe554e+OY3IZ0evb2gAP7wD8HtvvjXEBG5jF1IbqDQREREZpzBxCAN/Q2c6DvBib4T9Mf6RxyTMU12Nvbxxokea4FGm2GwYU4x62cXW0PqS5q6WfXKQVaWrxh7HZPqavjkJ8HpnKB3JCIiIiIil7PmYDOP7nuUweTQyJFkOsOz+9o51jU0yn35rAC3LCq3Pm+cUZ5fzl3z72JO0ZyL78QLL8Drr4/dft11sGnTxZ9fROQyptBEoYmIiAyTTCdpDDZaIUn7QPtZj++PJHhu2GLvAMX5Lu5cVkmFf2gkSUFPmGueO8CKosUEPIHRTxYIwKc/DT7fuLwXERERERG5MvXH+vnZ3p/RMdhh1ZmmyRvHe9h6steqqwp4uXVxOWW+kaM+lpYtZdO8TWPfsHU2iQT88z9DKDR6u80Gn/gE1NVd+LlFRC5zCk0UmoiIzHjJdJJjvcc40HWAwz2HSaQT53xONJHmUHuILcd7SKYz2UoD1tYVce3cEhz2oeHw7sE4d7/axhLXLJz2MUaQuFzw+78PFRXj8ZZEREREROQKl0gn+PmBn3O453BO/aH2EM8f6LBGuRsGrJxVyLXzSvA47TnHOm1Orq+7nutqrxv7s8hY9u2D//zPsdvz8+Ezn4HCwgs7r4jIZU6hiUITEZEZKZlOcrT3KAe6DnCk58g5g5JkOkNLf5Tm3ghNPRG6BuI57QGvk01LK5lV5M2pL8DFh7cnqB5gbIYBH/7w2RdcFBERERGRGSdjZnjxxIv8rvl3OfVtwSi/3ddOMJq06jxOO9fNK2H5rAA2I3fKrkJPIZvmbWJJ6ZKcdVDOyjThBz+AkyfHPqaiInvzl8t1vm9JROSyp9BEoYmIyIxxJijZ37mfo71HzxqUZDIm7aFYNiTpjdAWipHJjP7P4IpZAW5YUIbLkbvY4qKiBdy/J4HneOPZO3bXXXD11Rf8fkREREREZGbY1b6LXx/+dc4C8al0hp1N/Ww92UvqzOh3oKzAzc2Ly5lV6B1xnlp/LXfMu4O6wHlOq9XRAf/2b5DJjH3M4sXwwQ9mbwYTEZkGFJooNBERmfb6on28eepN3m5/+6xBSTKd4WT3IEc7BzjZM0giNcYHAwPKC9zUFucxv7yAqkDuhxG33c27q25k5cFejB07zt65DRvg3e++0LckIiIiIiIzTFOwicf2PZazQDxAOJbk9WPdHG4P59QvrvRx/fwyCjyOEedaVLKI2+feTll+2blf+JVXstvZ3HAD3Hbbuc8lInIFUGii0EREZNpqDbeypXkL+zv3YzL6P2FngpIjnQOc7B4cWp/kHQJeJ3XFedQV51FTlIfXZR9xjCORYlWvi1sj5eQ3d2SHs5/NggXwoQ9lF1EUERERERE5h2AsyC8P/5ITfSdGtLX0RXn5cCfdw6YSdtptbJhTzJrawpx1FwEMDNZUreHm2Tfjd5/lezHThMcfh4MHz965970PVq68oPcjInI5Umii0EREZFoxTZNjvcfY0ryFhv6GUY9JpjM0dA9ytGOAhp7BnKHsZ7iddupPhyS1xXkEvKMvmmhLpSlp7qHqZDfrB4uoya88vzmCKyrgU58Ct/uC3p+IiIiIiMxspmlyqPsQzx5/lv5Yf05bxjTZ1xJky/EeYsmhqbx8HifXzi1hcZVvxHonDpuDa2qu4fq66/E4PKO/aCIB//7v0N4+dsccDvjEJ6Cm5iLfmYjI5UGhiUITEZFpIZ1Js7dzL1uat9A52DnqMe3BGDub+jjRPUAqPfKfNI/TzvyyAhZUFFBTlIfd9o7wwzRxxZJ4gxHyghEKO4LUtg5S4Sqixl+D23GeAUhBAXzmMxAIXOjbFBERERERAbJrNv6u+Xe83vQ6qUwqpy2WTLPleA97W/pzBsCXFrjZOL+U2SV5I2728jq83Fh/I+uq1+G0j3LTWDAI3/42DAyM3Sl91hGRaUChiUITEZEr1mBikKZgE43BRg50HSAUD404xjRNGroH2dHYR0t/dES7x2lnfnkBC8qHghIjncEbjpIXjJAXOv14evOmDAo9hRR5iyjyFOF1jlxc8awcDvjkJ2HWrIt92yIiIiIiIpb+WD/PHX+OA10HRrR1heP87lg3J3ty10GpKfJy/fwyKgMjR5bkOfNYX72eDbM2kO/Kz208dQq+/31IpUY8z1JZmR1V73JdzNsREZlyCk0UmoiIXBFM06Q/1k9jsJGmYBNNwSa6I91jHp/KZDjcFmZHUx+9g7mLv58JShaW+6gp8uJKpgh0hvB3Bgl0BvF3h7GdnrLLZtiyIYmniCJvEfnO/PObfms0hgEPPABLl17c80VERERERMZwou8Evz3221FH3jf3Rnj9WDcdoVhO/YJyH9fNL6Eob2TA4bA5WF25mmtrrqUkr2SoYc8eeOKJs3dm6dLsZ5+L/ewkIjKFFJooNBERuSxFk1HaB9ppH2inJdxCY38j4UT4nM+LJdPsbQmyq6mfwUTu3U9FeS7W1hWyJs9BSU/YCkryg5Gc45w2J6V5pZTll1HoKcRmjMNC7UVFcOedsHjxpZ9LRERERERkFOlMmu2t23n55MvEUrkBiWmaHO0cYMuxbvqjSaveZhgsnxXg6jnF5LsdI85pYLCodBHX1V5HXaAuW/nCC/D662fvzE03wS23XPJ7EhGZbApNFJqIiEwp0zQJJ8K0D7TTFm7LPg60jVjQ8GzSGZOOUIxjnQPsbQmSfMfC7tWFXm4o8XJDew/ljd24YskR53DanJTll1GWlw1KLno0yXB5ebBsGSxfDnV1ustKREREREQmxWBikM2Nm9neup2Mmfv5KJ3JLhb/VkMPkcTQYvF2m8GSKj9X1RVRnD/61Fq1/lquq72ORSULsT3+H3Do0Nk7cu+9sHbtJb8fEZHJpNBEoYmIyKQxTZPeaC9tA205IclgcvDcTx4mkcrQGozS2h+lpS9KeyhGOvOOf6IMmF9awI3FHjY0dlJxvAPjHf+MTUhQ4nZnR5OsWAFz5oDdfunnFBERERERuQi90V5eaniJfZ37RrQlUhl2NvWxo7FvxI1nc0sLWFtfRHWhZ9TPSQF3gLXFy9nw3D48PcGzd+Kuu+Dqqy/pfYiITCaFJgpNRGSGy5gZYqkY0WSUWCpG2kyT78zH7/bjtDsv+rypTIquwa6cgKRjsINEOnHuJw9jmiaDiTRt/VFa+rNBSddAnLH+RbLbDJZW+bmh0M2qI62Un+zEeMexhZ5Cav21FHuLxycocThgwYJsULJgATgv/r+biIiIiIjIeGsJtfDCiRdo6G8Y0TYYT7GzqY+9LUESqdzwpNLvYW19EfPKC7CN8tnJOxDnzpebqXMUU+QpGvvz1aZNcN114/JeREQmmkIThSYiMk0l00n6Yn30RHroifbQH+snmowSTUWtx1gqNmKe2+G8Di9+t3/ULZVJMZgcZDAxyGBykIHEgLU/mBgkkoxgcmH/bJimSX80SVc4Tmc4Tlc4Tlc4ljNkfDR+j5NZhV6qi7yscpgsPthCWWPXiLCkLK+M2kAtfvcl/M73eKCkBEpLs1tZGcyena0XERERERG5TJmmyfG+4zx//Hk6BjtGtMdTafa1BHm7qZ+BeO76kAGvk6vqilha7cdpz13zMdARZNWzu8izuakqqKLKV4XLPsr0XrfeCjfeOK7vSURkIig0UWgiIle4/lg/nYOdVjjSG+2lJ9JDKB664NBiMqUzJj2DZ4KRbEjSPRAfcWfTCAaU5rupLvRSXehhVqEXn8dJQU+Y2bsbKW3qzjncZtioKqiixl+D1+k9/w7m50NV1VA4cmbLz9faJCIiIiIicsXKmBn2duzlpYaXCMZHTq2Vzpgc6Qizo7GP7oF4TpvHaWdplZ/lswI5655UHmtn8evZ9U0MDErzSinNK8Xn9uF1eIdGoNx4Y3ZxeH2mEpHLmEIThSYicgVKpBPsbt/NttZtdA525rQZGRPPQAxvKEJeKEpeMII3GMEzGMc0IOl2kvS6SHicJD0ukh6ntZ/wDLWN5x+xiVSG7oEzo0didIbj9AwmyLxzHZJReJx2yn1uyn0eqgs9VBd68Tiz64Q4ownKGzqpPN6Bryec8zynzcks/yxm+Wad3zRjZWXZxdpra7NbcbH+kBcRERERkWkrlUmxvXU7W5q3EIqHRrSbpklTb4QdjX009UZGtNcUeVkxq5B55fk4bDZqDpxi/tZjI45z2Bz4XD5r1oL8G2/Hc/d9+rwlIpcthSYKTUTkChKMBdnaspUdbTuIpWLYUmlKTvXi6w6RF4riDUbwhqPY3hFGmKZJMm2STGdIZUzSmQyptEkqc3o7XZ/KmJimSTrPTbDcT7iykGh1IanyQvLzXeS57CPmsTVNk1gyQySRIpJIE0mkiZ7ePzPVVl80wfkMevF5nJT53JT73NZjgduRMy+uLZWmtLmHiuMdFLf05izubjfs+N1+yvPLKc8vx24bYxF2w8gGJGe2mhrwXsAoFBERERERkWkinUmzv2s/W5q30D7QPuoxneEYOxv7OdoZJv2Oz5tep52l1X5WzAqwtKmLhW8ePedr9q9YQOLO2wl4Cslz5uF1eslz5lmb15Etj/mZTkRkAik0UWgiIleAU6FTvHnqTQ50HSBjZrClM1QdbqV+TyOuWBLIhhepjEksmSaWzBBLpokm01Y5cwm/wpM2G61+L6cCBfSV+giW+4kaNqKJ7Gtc6LkNA4ryXMMCEg9lBW68rjH+IDZNCtv7qTjeQVljF45kdo0Tp81JwBOg0FNIwB2gwFVw9oXdDQOWL88OCS8ru6A+i4iIiIiITGemaXKi7wRbmrdwvO/4qMdEE2kOtIXY1xKkL5IY0V5XnMc9iTi3HWzCfo6RJG0Lqjhy7UJM29jHue1uClwFBDwBAu7AiEe/239+MwuIiFwAhSYKTUTkMpUxMxzsOsgbp97gVOgUkJ16q+J4O7N3ncQzmF3/oyMUIxhNEk2mR9zxM1FMoDPfQ5svjzafl1afl+58D+YofxTbbQalBdmRI2dCktIC94jFAzFNnPEUnoEYnnA0+3h6K+gbxB2J43F4CLhPhySeQO7cuGdjs8HKlXDDDdlF3EVERERERGRM7QPtvNH8Bns795IxR647aZomp/qi7G0JcqxrYMTUy8u6gnzgRCs+l50Ct4MCjwOv0z7i81vH3AoOXb/4rMHJueQ788l35WM37NgM26ibYRjYDBsOmwOnzYnD5hh1c9qduOwufC4fPrcPn8unUEZkBlJootBERC4zpmmys20nrza+OrQon2lSdrKLObtOkheMMBhP0RaM0TMY53x+M3ucdjwOGy6HDbvNwGG34bAZ2W3Yvt1uYGCQTGeIpzIkzmzpM/tpEukMybSJYYDTZsNpz57DcDkIlRQwUOYnUuYnUhHAXpxPsceJJ57EGU/iiiVxxpI4Ywlr3xVNWOGIPZW2+pznzKPAVUCBqwCfy0eBq+DC/1i122HVqmxYUlR0Yc8VERERERGZ4YKxIG+1vMWO1h3E0/FRj4kkUhxoDbG3JUgwmrTqF3UFec+hZmynP7TaDIN89+kQxe0gz+XAaTfom13GwZuWknFcnlNxeRyenBDlzKPL7sJpd1ohzGj7bocbh80x1W9BRC6QQhOFJiJyGRlIDPDkwSeHhkKbJsUtvczZ2UBBT5i+SJL2YIxQLDniuS6HDY/Tjtdpw+Ow43HZ8TjtuB22EeuQXKqMaWLAOUd5ZOw2bOmRdyUNZ2BY4cjw7ZLmrrXb4aqr4PrrIRC4+POIiIiIiIgIiXSCfZ372NG6g5Zwy6jHmKZJc2+UIx1h2kPZm/zmdYe4/0AT9nN8pdhdmM8za+ZhFniyn2tdZz7f2inwOPB5nPg8DgpcDmyXMCplKuQ78wl4slOJnZlSbHjZ5/ZhM2znPpGITBqFJgpNROQycaLvBE8cfIKBxAAAgY4gc3ecoKC9n65wjPZgjFgqN4Cw2wzKfW4qAx7cl+ldOcPZDXt25IjbZ4Ujec688fsDsbAwOw3XunWg3+siIiIiIiLjri3cxvbW7ezt3EsiPXJdkzMSqQyd4RjGkXauevUA0UiSxFluqgt6nDy+bDY9+Z4xjzEMyHcNhSg+jwO/x4nbmb1ZMLtxejourDrj9L7DbmRnX7AZOGxnZmIwxv1GwwthYOCyu865eZ1eCj2F1pqefrf/0m42FJExKTRRaCIiUyxjZth8cjOvNr6KiYmRzjB/23EqD5ziVF+EznB8xFolHqedSr+HMp8b+yh32cTz3ET8XiKBPKKBPCJ+L1F/HqbNwBlNkJ80KEgaFKQM8pKQl4C8RAZvPIOrp59EPEI8HSeeihNPx0edw3YsZ+aIHf6H3ZnptTwOz/mtQXIhPB5Ytiw7DVdtbfavaBEREREREZlQ8VScvZ172dG6g7aBtrMeW9jez4oX9pKOJRmIpxiIp4in0qTSJqmMSSqdIZUxGbTZeGJpHU2FBZP0LrJsxlCY4nHayXfbyT89jVi+yzG0f7p+xBqdU8DAwOf2WSHKmUClyFtEsbcYv9uvESwiF0mhiUITEZlCoXiInx/4OY3BRgCc0QTLXtmPs6mHo50DxJLpnOP9XidVAQ+FXmdO+DAYyKN5eS0DJT6iPi92j5cSbwkleSUUe4ut/UJPIV6H9+x3o6RS0NYGTU3Q1ITZ1ERqIJQToiTTSQwjezfM8IDEaXdOzh9ldjssWJAdVbJwITg0R6yIiIiIiMhUME2T1nArO9p2cLDrINFUdNTjAh1BVrywB8c7PucOlzFNkibsWD+f47NKiCbTDMRShGJJwrEU4ViKgXiSSGLsc0wGp92G027DYR8ateI8PYrFeWbdULsNr8uOz50dEVPgceBzO/E4beN/M+Eo7IadYm/xqJtGqYicnUIThSYiMkWO9hzlyUNPEklGAPB1h1n20j7624M09gxaC7wbBpQWuKkKeMhz5YYD0QIPJ1fPJr50IetqNlDrr6XYW0yBq2D8/ggzTejpsUIUmpqgt3d8zn0hDANqarJBybJlkJc3+X0QERERERGRMZmmSU+0h1OhU7SEWmgJt9Ax0EHazIYcvu4wK17Yg2uUdTrfqWH1bBpX1Y86m0AqnSEcT50OUpIkUhkyZjZ0Ma1H06rLmJDJmKQz2ZEt6Ux2ZEsqY5JOm6SGlWPJNInU+c+2cKEcdgOf23k6RMmOYnE5bKeDGAPX6UDG6cgte532cV3PxW1343V68Tq85DnzrP3hdQWuAvJd+eM/tbbIZU6hiUITEZlk6Uyalxpe4nfNv7PqKo53MO/1g5xsD9M7ODQnbL7LzoIKHx5n7h0g8TwXjSvrca67mqvrr2Nx6eLJ/eNlcBBaW6GlZWiLRC79vHl52XVJzmxFRbllp/PSX0NEREREREQmTSqTon2g3QpRuk8dpeapzeQFz/0Zsn1+JYevXYg5ydNhJVIZBk9PIzaYSJ3eTzMYz+5HEumcacVSmQwT/a2pYRgUuB34T6/j4vOefvQ48Hud+NwOHBP438nAsAKUM1u+M7d8ZpuQqblFJpFCE4UmIjKJ+mP9/OeB/+RU6BQARsZk7vbjFO08ydHOMPFhd7NU+j3UleTlLEiXdDs4tWI2gRvu4Oo51zPLP2vS38OoTBP6+3ODlLY2SCSya47k5UF+/sjHM/t+PwQC4HZP9TsRERERERGRCZYcCJH82Y9JnzhOMpMkmU5aj6lMytpPpBN0lOWx6+ZFpFyX77TMZ0a1JNMZ0hmT5Jk1WqzRMNlpxYb2UyTT4z+aJd/loDDPSWGek4DXZe0Xel24HJMXPNkN+4iAJeAOEPAEtJC9XBEUmig0EZFJ0h3p5ge7fkA4EQbAEU+y5JUDJA620tQXse5KsdsM5pUVUJzvsp6bdtjoXDmfyjvuZ+3cjfjdV8jvLNPUwuwiIiIiIiIyUjoNv/oV7N59zkOThX7633Uz/WU+gvEgwVjQegzFQyQzSTJmZtTtcmSaJvFUhnAsRSSRIpnOBi1ntkQqtxxPZRg4HcC8c+3T85XnclDodRLIc5LvcuBx2vA47ae3YfsO24SOWDnDwKDAVZANUTwBAu4APrePfGc++a5861HTgslUUGii0EREJkEsFeORHY/QG82uBZLfO8DiF/bSeqKLvsjQXK4FbgcLygtwD5uOK+rzknzg/Wy67qN4HJ5J77uIiIiIiIjIhDBN2LwZXnnl3McaBlx9Ndx6K7hc5z6ebDhhYpIxM6QzadJm2hrNMtqWSCcYTA4SjocJJ8I5j/F0/NLe6zhJpDKEYknCsSShaOr0fvYxFE0SSVxcqDKc4/QaKnkuO/kuO3lux+n904+ny3kux6SMYMlz5uWEKO8MVoY/amowGQ8KTRSaiMgEM02Tx/c/zsHugwCUnexi1gt7OdESJDFsOG5VwENtce50XMFZpdR86mGumrtR/+iLiIiIiIjI9LRrV3bUSeY8RoYUFcF998GcORPereES6YQVokST0ZxpxIbvJzPZcjwVJ5wIE4wFCSfCkzbqJZ5KE4wk6Y8m6Y8k6Y8kCJ7eH0ykxv31nHabFaBkA5Yz+47T+3YKTi94b5uE7zVsho18Zz5uhxu33W09uuyunDqX3UWeMw+/22+NctGIFjlDoYlCExGZYG80v8Gzx58FoKyhk3kv7mPvqX7SmeyvVIfNYF55AUV5uXfK9F+1jGs+8ZdU+qsnvc8iIiIiIiIik6qhAR57DGKx8zt+3Tq4444rYm3MjJlhIDFAKB6yphQ7M71YLBUjkU7kbPF0fEJCljOjVKKJNLFkmlgqk31Mpoklh++niSYzRJIpGKdvg4cvZO/zOK3H7IL22X3nJEwLNhabYcPn8llThQ1/LM8vp9BTOGV9k8mn0EShiYhMoKZgE9/f9X0yZoaC3gFWPbWDg0191nBZn9vBggpfznDWtMOGee+93HjP53HZz2/IsYiIiIiIiMgVr6sLfvIT6O8/v+MDAbjnHliwYEK7NRXSmbQVokRTUYKxIP2xfoLx04+ny4PJwQnrQyZjEk2mGUykiCTSROLD91MMJtJETpcvdq2V4Zx2G16XnbzTU4N5T08H5nVly3lOh7Xvcdqx2yZvRo5CTyFzCucwt2guc4rmUOAqmLTXlsmn0EShiYhMkMHEIN/a/i3CiTDOWJKrntpBW0M3neHsPKgep53ls/w4bEOBSbIgj8pPP8zyVXdoOi4RERERERGZeQYG4NFH4dSp83/O6tVw553g9U5Yty5XyXTSClJ6o705W1+0j7R56WHG+UhlMkQTaQbjQ0HKYDz7GI6nCJ9ee2U8wpUzPE67tfZKTsjitONy2HDZbdlHhw2n3Yb79KPTblzydy7l+eVWiFJfWK81aKcZhSYKTURkAmTMDD/e82NO9J3AyJiseGEP6cPtHO8aAMBmwPJZAfJcDus56fpaVn7+/6OsfPYU9VpERERERETkMpDJwJYt2QXiU+e5DkdeHqxfn90KNAoAst9NhOIhK0TpifQQioeIpqJEk1EiyQjRVJREOjFpfUqkMtlF7GNDQcqZx0giTTQ5PqNWzsYwzqzF4sDnceBzv2PKME+23nGe04XZDBvVvmqWlC5hTdUa8px5E9p/mXgKTRSaiMgEeLnhZTY3bgZg7vbjlO5sYF9LkNPLmDCvrIAy39C8q6l1a7nps1/F6dKdCSIiIiIiIiIAdHfDL38Jzc3n/xy7HVasgGuugcrKievbNJLOpK0gJZrKhimDiUEGEgMMJAYYTA7tDyQGJjxkSWdMYsl0NkRJpIkkU0P7iTTRxFDAEkmkSabHf/0XAK/Tjt/rZFahl9riPGYVenOmVx+Ny+5ibdVarq29Fr9b3zdfqS6b0OSrX/0qTz/9NLt27cLlctF/HnMXmqbJ3/7t3/LII4/Q19fH1Vdfzb/8y7+wbNmy83pNhSYiMhGO9hzlJ3t/AmQXfl/08j72tYSsOyXKfW7mlg3d9dK9aiF3ff6fyHflT0l/RURERERERC5bmQxs3QovvgjJ5IU9d86cbHiycGF2eMFMk0plpzsLhSAczm6Dg9n/Fg5HNmAa69HpBI8nO+WZxwNut/XfMJFODAUqidxAZfg2mBwklTnPkUKXIJnODAUqyRTRRIZEOkMiNfSYTGWIp7OPZ+oG4qkLClxsNoNKv4faojzqivOoDHjGXFfFbthZWbGSjXUbKc0rHa+3KpPksglN/uZv/obCwkJOnTrFd7/73fMKTb7xjW/w1a9+le9///ssXLiQ//k//yevvvoqhw8fxufznfP5Ck1EZLwFY0G+tf1bRFNR8nsHuOrpnRxvDdI7mL0LI89lZ3l1ANvpf1T7q4q56r/+I3VFs6ew1yIiIiIiIiKXud5e+PWvoaHhwp9bXJwNT1avBpdr3Ls2JZJJCAazWyg09BgODz1GIuP3eoaRDU/ObGfCFJ8PAoGhrbAQ8vPBMDBNk1gqRjAeJBgLWo9nFrQPxoKEE+Hx6+MFMk2TeCqTM0VYOJYiHB+2Hxs7qHPabVQXeqkr9jK/zEcgzzniGAODJWVLuL7ueqp91RP5dmQcXTahyRnf//73efjhh88ZmpimSXV1NQ8//DBf/vKXAYjH41RUVPCNb3yDz33uc+d8LYUmIjKeUpkU33v7e7SEW3DEk6z99Q76W/po7Mn+kWK3GayYFcDjtAMQy3dT9Edf5upFt05lt0VERERERESuDKYJO3bA889DPH7hz3c6oaYG6uqyW01NdgTFZDNNSKchkchuyWR2VEgyOfp+IjEUjJzZxjMQGW92e26QUlQEpaVQXp4NsOx269BUJkUoHiIcD2enBUsOMpgYHPUxmoxiMrmrR0QTaU71RWjqjdDcF6U/MvrUZIYBC8p9rJtdRLlv9KnX5xbN5Ya6G5hdOPuSF6KXiXUhuYHjrK2TrKGhgfb2djZt2mTVud1ubrrpJrZs2XJeoYmIyHh67vhztIRbMDImS189SKo7TFPP0B8x88oKrMAkY7cR/7372bDwlqnqroiIiIiIiMiVxTBg3TpYsACeegqOHr2w5yeT2ZEqZ0arGEZ23ZMzIUpdXXbkBGSDjUwmG26kUtlt+H4ikQ1uzgQfw/fPpy0zMetwXBbS6ezIoN7ekW02G5SUZAOUsjIc5eUUl5VRXFyTE6aMJmNmrPVWhocp76yLJCMk0gni6fglr7/iddlZUOFjQUX2ugjFkjT3RmjujdLcG2EwkZ1+zDThSEeYIx1h6kvyWT+7iFmF3pxw5ETfCU70naA+UM89C++hLL/skvoml4fLKjRpb28HoKKiIqe+oqKCxsbGUZ8Tj8eJD0uhQ6HQxHVQRGaUfZ372NqyFYDZbzfga+pmb8eAdf9DVcBDcf7QEOD2W9bzvhs+oTsLRERERERERC5UIAAf/jAcPgxbtkBT08WdxzShrS27vfVWts7tzn7pn05n22V8ZTLQ1ZXdhrPbwe/PhlbDt4ICa9/m81HgzqfAVTD6uUd7OTNDMp0kno4TT8WtICWajBKKh0ZMHTaYHDzr+fweJ8uqAyyrDmCaJn2RBMc6B9jV3E8kkV3LtrFnkMaeQaoCXtbPLmJOaX7O9z+NwUb+bce/ceucW7mm5hpsxtkXl5fL2wWHJl/5ylf427/927Mes23bNtatW3fRnXrnF46maY75JeTXv/71c/ZHRORCNQeb+eWhXwJQdrKLuj2NHOocIHF6MTGfx0FdcZ51fNvSOm597x/jcYw+XFNEREREREREzsEwYPHi7NbSAm++Cfv3X/oIjouZ9ksuXToNfX3Z7Wzs9qH1VM72WFAAfj82nw+3x4Pb4YbzmIktmU4SiocIxUP0RHs42X+Shr6GUcMUwzAoznezYY6bq+qK2N8aYkdjH6HT66C0BaP8aneUknw362YXsbDCZy0cn8qkeO74cxzqPsR7F7+XYm/xBf8nk8vDBa9p0t3dTXd391mPmT17Nh7P0BeH57umyYkTJ5g3bx47d+5kzZo1Vv173vMeCgsL+cEPfjDiOaONNKmtrdWaJiJy0ToGOvjeru8RS8XI7xvkqqd30tYV5lRfFMguCrZiVgCXI3vXQH9lIfVf+O+sqbn4sFhERERERERERhEKwdat2XVPotGp7o1cLpzO7GiVMyNZhj+e2S8oGHN6MNM06Rzs5ETfCRr6GzjZf3LMab8yGZMjHWG2neyjZzA3gPN7nNyyuIw5pbkjZZw2J5vmbWJd9TrNSHKZmNA1TUpLSyktLb3ozp3NnDlzqKys5Pnnn7dCk0QiwebNm/nGN74x6nPcbjfuqVjcSUSmpd5oLz/a8yNiqRj2RIplL+8jGIxagQnA/PICKzCJ57lxfOBDCkxEREREREREJoLfD7ffDjfeCHv2ZEefnOOG7mnN4Rh9yivDGFqfZazHRAJisWz4FItlt1Rqqt/RxUkmx15j5QzDgPz8UYMVw+ejwu+nomwN19ZcQ9rM0Bpu5UTfCfZ17qMrMjTVmM1msLjKz6JKHw3dg2w72UdbMPs9USiW5Je7WllWHeDGhaW4HdmQJplJ8vTRpznUfYj7Ft1HwBOY0P8cMr4mdE2TpqYment7aWpqIp1Os2vXLgDmz59PQUE2fVu8eDFf//rXuf/++zEMg4cffpivfe1rLFiwgAULFvC1r32NvLw8PvzhD09kV0VECMfD/Gj3jxhIDIBpsuiNIyQ7QhztDFvH1BbnEfA6gezC75333MKDq943VV0WERERERERmRlcruyC8WvXwrFj8PbbcPIkRCJT3bPx43Rm13Y5s/n9Q49nwhGPJxsGjJdUKjdEGRyEYHDkFg6f+1yXG9OEgYHsdjYuF3afj1q/n1q/nxsLFtJolrE1fJgGR5hYgQfTbsMwDOaWFTCnNJ/W/hhvnuihuS97/e1vDdLUG2HT0gpqh03nfrzvON/c9k3uWnAXqypWadTJFWJCQ5O//uu/zplS68zokZdffpmbb74ZgMOHDxMMBq1j/ut//a9Eo1H+y3/5L/T19XH11Vfz3HPP4fP5JrKrIjLDRZNRfrTnR/TFsvNsVh1pI+9gKwfaw9YacaUFbqoDQ1MPNmxcxv23fBan3TkVXRYRERERERGZeQwDFizIbqYJPT3ZRePPbGcbeTCR7PZssON2Z8OP4ZvDMXLf4ciOghgejni94xuInA+HYyiQOZtUKjtVWjAI/f1DC793dmbrrmSJRPY66ukBwABmA/WmSX8sTmP4OK3OKJFAHhG/l0ggj0J/HvVLy9nVG2XzkS6S6QzhWJKf7zzF6tpCNs4vxWk/PUtJOs4vDv2Cg10HuXfRvRe06L1MjQte0+RydyFzk4mIACTSCX60+0c0h5oByO8dYMmTWznU3E8qk/0VWZTnYmFFgXVHQMviWVz1+/+dJWVLpqzfIiIiIiIiIvIO4TA0N0NjYzZE6ezMTk9ls2WDDYdj7MczoYfLdfb90drGWDtjRojHh0KUM0FKV9eVH6YME46HaQo25UzbBZB0O2kP5PFsNM12m4OOAi+mYVCY52LT0gqqC705x/vdfj668qOU55dPZveFC8sNFJqIyIyWyqT42d6fcbzvOAD2ZJplT27l5MF2EukMAD6PgyWVfmy2bGASLPPj/9wfccv826es3yIiIiIiIiJyHkwzu9lsU92TmSedzk6NFQ7nbu+si0bhCvmKOpKM0BRson2gPafeNE3aQzGOhuKc9OfTVJjd6pZUc838EhzDrr8CVwGfXP1JSvJKJrv7M5pCE4UmI6QyKd469RbX1FyD3TaDk2+RYTJmhp8f+Dn7u/ZnK0yTOS/tp2/LMWLJNAD5LjtLqv3WP25Jt5PMZz/Dpqse0DyUIiIiIiIiIiKXyjSzo1WGL1L/zsfBwWzAEgplH6d4LZv+WD+Hug8RS8Vy6qOJNMe7BhiIpwCIOO30lRdSs74ec2EVsYLstO8Bd4BPrvkkhZ7Cye76jKXQRKFJDtM0efLQk+zp2MOcwjl8YNkH8Dq9536iyDRmmiZPHXmKHW07rLqSg6cwf76dSCIbmHicdpZV+605KAFiH3w/d77rCwpMRERERERERESmSiqVG6IMfzyzHw5nR7tMkHQmzfG+47SGW3PqTdOkNRjjVF/EGkBjMwyWVvkxKgP01JbQU1OCva6eT171+/jcWst7Mig0UWiS45WTr/DKyVescmleKR9Z8RGKvEVT1ymRKfbiiRd5rek1q+zuDpP33VeIDiYAcNltLJvlx+0YGpmVuvZqbvnM1zRaS0RERERERETkcmea2REpowUrwx+j0Ut6md5oL4e7DxNPx3PqI4kUxzoHrJtz7bZscJLvdgCQdDtIz5vLTXd8Bu/i5eDVTe4TSaGJQhPLno49PHHwiRH1ec48PrT8Q9QGaqegVyJTa/PJzbx88uWhingK97dfxugMAeCwGSyrDuB1DYUj9vo5XPOX/4rT5Zns7oqIiIiIiIiIyERJJkeOUgkGoacHurvPa0H7VCbF0Z6jdAx25NRnMiaH2kOEYtnpukb7zqnAVcCq6qtwzp4Lq1fDihVg1w27402hiUITABr7G/nh7h+SNkcfhuawOXjv4veyvHz5JPdMZOq8MzAxTZPoT9+k+HB2KKXdZrCkyk/B6dQfwF1QxJq//RaekopJ76+IiIiIiIiIiEyhRAJ6e7MBypkg5cxjIpFzaHekm8Pdh0lmklZdOmNysC1krXPisttYWu3H4xwKRvxuP6sqVmVnN5k1C97/figunpz3N0MoNFFoQm+0l+/s/A6R5NCiSDsb+6gMeKguzB3qdeucW7mh7gat0SDT3junqjNNk+YXDrLwtYMAGAYsrvQT8DqtY7wOL8u/+FXyV1w12d0VEREREREREZHLVSYD7e3Q0JDdmpogkSCRTnC4+zA90R7r0FQ6w4G2kDVVl9thY1l1AJdjaB3dIk8RKypWYDNs4HLB3XfDypXZL6zkkik0meGhSSQZ4bs7v5vzg3moPcRv97VjtxlsWlrJosrcBYZWV67m3oX3aq0GmbZGC0ze3t7E+qd34sxkAFhY4aM432Ud47K7WPGez+K77/cmu7siIiIiIiIiInIlSaehtRUaGsicOMG+Hb+hN9xpNSfTGQ60hogms8GJx2lnWbUfp30oOCnxlrCsfFk2OIHsVF133w0eTRd/qS4kN7CdtVWuSE8deSonMDFNk0NtYSA7HOyZfW1sbehleF62q30XP97zY6LJS1v4SORyNFpg8tqBdha8uM8KTOaVFeQEJg6bg+Vr7sR39/2T3V0REREREREREbnS2O1QWws33ojtE59g6T/8kJ4P3EvTijoGC/Nx2m0sqfLjPj26JJZMc7AtRCqdsU7RE+3hYNfBoe9t9+6Fb30rO4pFJo1GmkxDfdE+frL3J3RHuq26dMbkpUMd7G8NWXVLq/zctqQCu21oiFdpXikfXvFhir2aM0+mh9ECk80HO1jw3B7m9WbDxHllBZT53NYxdsPOyvoNBB7+MhQWTnKPRURERERERERkOkikE/x4z49pCjbhGYhR0tyD90QnbTsbSZ8ecVLgdrCkyp/zHW1VQRULSxYOLadgGHDTTXDjjWDTOIiLoem5ZnhoAhBNRnl8/+M09DdYdaZpsu1kH1uOD4UpNUV53LOyKmfhoTxnHg8uf5C6QN2k9llkvI0WmLxysJP6F/eytCsIjAxMDAxWVKyg+JP/BRYvnuwui4iIiIiIiIjINBJLxfjh7h/SGm616oL9UXa8eJBZHf3M6w1Tbcuus2sbFpwsKllEla8q92S1tdlF4nWT7wXT9FyC1+nloys/yprKNVadYRhsmFPMu1dUWcnlqb4Ij21rpj+SsI6LJCP8cPcP2duxd9L7LTJeRgtMXj7USeXmA2cNTJaXL6f45rsUmIiIiIiIiIiIyCXzODx8dOVHKc8vt+oChV5WbFrGS8vq+db6RWwu8nOkI0xm2PiGY73HRi6l0NwM//qvsG/fZHV/RlJoMo3ZbXbuW3Qft825Lad+YYWP31tbg/f06JK+SILHtjXT2j/0Q5jKpPj5wZ/zetPrk9pnkfEwamByuJPC1w+zpq0XGDswKZm/Au64Y7K7LCIiIiIiIiIi01SeM4+PrfpYzpIIZT4396+eheFy8OyCWfz7nCqOhYdubE+baQ52H2TERFHxOPznf8Ibb0xW92cchSbTnGEY3FB/Aw8sfQCHzWHVVwW8PLihzlr4OppM8/OdpzjcHs55/gsnXmB76/ZJ7bPIxUqkE/zq8K9GDUy8bxzjuuYuAOaPFZiU18MDD4DD8c5Ti4iIiIiIiIiIXLQCVwEfX/VxAu6AVVcZ8PCe1dXYbQZHSgP8f/NrOO7Ls9pD8RBNwTEWgX/2WQUnE0ShyQyxrHwZn1j9CfKd+VZdwOvkA+tqqS3K/iCmMybP7Gtja0NPToL5zNFnxv7hFLlMdAx08MiOR9jZttOqOxOYGNsauKWhHcgGJqWjBSbFs+Chh6CoaNL7LiIiIiIiIiIi01/AE+Djqz9OgavAqqspymPjvFIAwi4nf1tawqHV9ZinF4E/2X+ScDw86vl49lnYsmXC+z3TKDSZQWr8NXz6qk9Tmldq1Xmcdt67ZhbLqocWv9lyvIfXjg4tFp820zy+//GxfzhFppBpmmxt2cq3d36b7sjQdZvJmLx0qJPE202862gLAPPLxwhMApXwkY9ARcWk919ERERERERERGaOYm8xH1v1MfKcQyNK1tQVUnP6xvZQMs33HV52vnsNUZ8XE5OD3QdJZ9Kjn/C55xScjDOFJjNMkbeIT1/1aeYUzrHq7DaD25dUsHH+UJiys6mPg20hqzyQGODx/Y+P/cMpMgWiySiP7X+M3xz9DalMyqoPx5L8fOcpBvae4t5DzRicDkwKRglMfOXw4INQWzsF70BERERERERERGaa8vxy7l98v1U2DINNyypwObJf1x9qD7EjY7D9vnW0za8kkozQ0N8w9gmfew5+97uJ7vaModBkBvI4PHx05UdZU7nGqjMMg/Wzi7llcblV98LBDjpDMavcHGrmmWPPTGpfRcbS2N/It7Z/i0Pdh3Lqj3cO8OO3mqCpm/cdaMJumiMCE5thywYm+aXw/vfDvHmT3X0REREREREREZnBFpQsYH31eqvs9zi5ZdHQd7MvHuokmDY5fP1ijly7kFOhU/RF+8Y+4fPPKzgZJwpNZii7zc59i+7j9rm359Svqilk+azsYkTpjMmv97QRTQyNLtneuj1nzQiRyZYxM2w+uZnv7/o+wXjQqk+lM7x8qJNf72mluCfMA/saybfBsmr/6IFJXgncdx8sXToVb0NERERERERERGa4O+bdQYm3xCovrvSxoMIHQDyZ5rkD7ZimSeuiag5ft4hD3YdIppNjn/D55+H11ye629OeY6o7IFPHMAyur7ueIk8R/3ngPzHJLv5+86IyusNx2kMxwrEkv9nXxv2rZ2GzZRcfevrI05Tnl1Pjr5nK7ssMkUwn6RjsoDXcSlu4jaZgEz3RnpxjegcT/GZvGz3hGNec6ubGkx2U5jmZW5qPwz6UDZ8JTIq9xbBpE6xZ886XExERERERERERmRQuu4v3LXkf3337u2TMDIZhcNviclr7ogwmUjT1RthzKsiq2kLaFlYBENhzjKWlS8Y+6QsvgGnCDTdM0ruYfgzTNM2p7sR4CoVCBAIBgsEgfr//3E8QALY0b+G5489Z5YFYip9ubSKSyK4Tsba+iBsWlFntfrefz679LAWugknvq0wvGTNDIp0gnooTT8eJJqM5IUlXpIuMmRn1uaZpcqAtxMuHu8gbjHHP4VPUhwaZXZJPuc+NYRjWsQ6bg2VlyyjyFsGNN8Ktt07WWxQRERERERERERnTKydf4ZWTr1jlkz2D/OLtFgAcdoMPb6inON8FQOXRNu4/Yqciv3y0Uw257TYFJ8NcSG6g0ESA7JfPPz/4c/Z17rPqWvqi/HznKTKnL5G7llexqNJntdcH6vnYqo9ht9knvb8ysRLpBJFkhMHEINFUlFQmRTqTJm2mz+sxY2ZGbUtlUjkBSSwVI5FOXFQf46k0Lx3s5HBHmEVdQd51tIViAxZUFJDnyh1EF3AHWFK2BI/DA+vXw7vfDcMCFRERERERERERkamSzqT597f/nZZwi1X38qFOdp/qB6DC7+ED62qxn54JqP5ELx9qKMBjd539xLfemr15WBSaKDS5OIl0gu/u/C4dgx1W3e7mfl4+3AmAw27jg+tqKfMNrQ9x9ayruWvBXZPeVxlimiapTIpUJkUyk8w+ppPnVY6lYtlwJDlohSSRZIRk5ixzI06haCJNZzhGRyjO/tYgkYEYdxxrY2VHH+U+N/Ul+dY/HmfUB+qZXTg7O+pkxQp43/sUmIiIiIiIiIiIyGWlJ9LDt7Z/y/peLpnO8NO3muiLZG84vnpOCdfOG1r/ZG2HnXsOpjnnt1wKTgCFJgpNLkFftI9HdjxCNBUFsl/IP3+ggwNtIQACXicf2lCHxzk0uuT+xfezqnLVlPT3cmaaJj3RHpqDzQTjwTGnmBp+/JnRGOcbepwpT0eRRIrOcJzOUJzOUIyOcJxwbCjMqQpFuO9wM6XxJHNL8ykZttg7ZOeEXFK6JDsdF8DixfDAA2DXyCgREREREREREbn8bGvZxtNHn7bKHaEYj21rJmOaGIbBB9bVUBXwWu3vjc1m9VuN2TVMzkbByQXlBloIXnIUeYt4/9L385M9P8Ek+8N46+JyegYTdIRiBKNJntnXzntWV2M7fbf+r4/8mrL8Mqp91VPc+6kXjAVp6G+goa+Bhv4GQvHQVHdpwpmmScaEdMYkY5qkMyZp0yRjlXPbEukMiVR2i6cyOeVEKk08lSEUTRKOjx4GeZIp1rT1ckNjJ36XnfmzAjkhHkCxt5jFpYtx2V3ZUSU33ZT9h8FmG/WcIiIiIiIiIiIiU21d9ToO9xzmWO8xIDst19VzinnjRA+mafLsvnY+fHU9Lkf2O65fehopufkqal/ZOXZwYhjg843eJqPSSBMZ1etNr/PCiResciiW5GdvNRFNpgFYP7uYjfNLrfaAO8Dn1n2OPGfepPd1KkWSESsgaehroCfaM9VdOm/pjEkynSGaSBNNpokk0qf3U9n9ZNpqS6WHQo+hYAQyp8ORieSLJ6kJDjInHGVxPE5dIkm+20G+247Xac9Z7N3AYG7RXGr8Ndn6wkJ4//uhtnZC+ygiIiIiIiIiIjIewvEw39z2TWsmoEzG5PEdzbQHYwAsrw5w+9IK63ivw8vnHNdQ+Owrowcn73kPrFkzGV2/rGl6LoUml8w0Tf7jwH9woOuAVdfcG+GJt1s4c8ncvaKKBRVDKeW8onl8ZOVHsBnneTe/acKpU9DaCtEopNOQSo18PLNvGJCfn01Gh29+f7Z+kqZdMk2Twz2HeaP5DRqDjef1nFQmc85RcqYJmUwGWzSBIxTFORDHORDDNRDFNRjHPRDHPRjDEU+RBlJA0jBIGQbJ0+UUBkkDkhgkDIOo3SBq2IjZbURsNqKGQdRmI2IziNlsJBw2UoaNlM0gZbORsRmn97NlczzX/jBN7KaJzTSxZ0xsJtl908Qws2V7JoPdNJkVTbAsHmdhNE5FOk2B247nHQHJO3kdXpaWLcXnPn1NrlyZXfDd4xm/9yAiIiIiIiIiIjLBDnQd4PH9j1vl/kiCH7/VRCqdnf7/1sXlrKwptNrL8sr4tH097qeeyQ1O7rsPrrpqsrp9WVNootBkXMRTcb6z8zt0Rbqsup1Nfbx6JFt22m18aEMtxflDa0ncVH8Tt8y55ewn7umBPXuyW1/f+HR2eKDi9YLbDS5X9nGs/XfWuVznnL6pJ9LDb47+huN9x8c+yDRJRpN0dobp6grT3T1ANBTDk0rjTmXwpNO4U+mhcipb9qbSFCSSOCZ45MaFyBgGaZsN7AaGkd2wkf3vZBjZ9dRtp9tsBg7TxAbYTXCYJnZM7CbYTRO7AYZhYDfAbjOw22ynH09vRvbRYcue92wByXAGBpUFlcwrnofD5sj+/7znnuyi7yIiIiIiIiIiIlegJw8+ye6O3Vb5QGuI5w60A2AzDN531SxqioZm/VlQvIAPmcuw/eKX2eBEgUkOhSYKTcZNT6SHb+/8NrFUdviXaZo8u7+dQ+1hAIryXHxoQ501jx7Ah1d8mIUlC3NPFI3Cvn2we3d2dMnl6kx4ciZIsdvBMEibGY72H+dI71HSZgbTZmBCNjjIZLBFk0SCUQZCUWKhGJFhC5ZPNcPI/iLNbsP2bdl9p93AYbPhtNtw2A2cdtuwumyQcb4BxmRx2V0UuAoIuANUFlTidpwO7urq4H3vy07LJSIiIiIiIiIicoWKpWL867Z/JRgPWnWvHuliZ1P2JnSP086HNtQR8Dqt9o21G7kjUgmJBKxdO+l9vpwpNFFoMq6O9BzhZ3t/hpmNCUimMzy2rZnugTgAC8oLePeKKuuLdY/Dw+fWfo4ilx+OHs0GJUeOZKfYugL1Rns52nPUmkfwjIxp0hGK0TeYIBxPnXX6rXyXA7v93MFDduDGUKCRE3TY3hF6GJyuGz0MOVN/uQUeF8pld+Fz+ShwFeBz+/C5fLjsrtz3ZbNlF3u/4QYt9i4iIiIiIiIiItNCU7CJ7+/6PhkzOy1XxjT55a5WGnsGASgtcPOBdbU5N7S/b8n7WFmxckr6ezlTaKLQZNxtPrmZl0++bJX7Iwl+urWJRCr7A3vjgjKuqi8CwMiYrGqIcG9XMfZYfEr6Ox7iqTjHeo/lTE92RjCSpKFnkFhy9CDI67QT8Drxe534vQ4c+iLfYjNsOGwO7IYdh82R3bfZR9R5nV58Lt/QKJKxFBfD/fdrsXcREREREREREZl2trdu56kjT1nlWDLNY9ua6YskAJhXVsA9K4duaHfYHHxy9SeZ5Z81Jf29XCk0UWgy7kzT5NF9j3K457BVd7xrgF/vbgWyoxnef9Us5nidLN18gKK2PioLKllUsuiKG+lgmiYt4RYa+hpIm7mhSCKVobFnkJ7BRE6922EbCkk8zpx090phM2w4bU5cdhdOuzNn327YsRk2DMPIPmKMWT5X27hcD6WlUF8P8+bB4sUaXSIiIiIiIiIiItPW00eeZlvrNqvcO5jg0W1DN7RfPaeEa+eVWO0+l4/Prv0sPrdv0vt6uVJootBkQsRSMR7Z8Qi90V6r7nfHutl2Mluujyf5H+EggdhQoLCwZCHVvupJ7+vFGkgMcKj7EAOJgZx68/RUXM19UdLDFmovcDuYU5pPvtsxof06EzzYDBt2m31o3xjaf2fb+bafCUjsNvuEvoeLZhhQVZVdr6S+PvuYnz/VvRIREREREREREZkU6UyaH+35ESf7T1p1J3sG+eWuFmvJgHevqGJhxVBIMss3i0+s/gROuxNRaKLQZAK1D7Tz3Z3fJZnJLnSeMU1+8XYLBYdauetIC8VOG0uq/dhOjyYwMFhTtQa/+yz/L6qrs6MF3G5wOLKLr4/2mEpBODz2dolrprSF2zjae9SaI/CMgViKE90DRBJD53fYDOqK8yjzuc86csJld1HkKaLIW0SeM++cfTAwsBX4MAoLsRUWYQ8UYS8qxlZYBH4/BAJQUJANEtLps2+JBMTj597S6ex/21Qqd3943aX8mrDbsyNBzjwO3x+rzuWCyspsSFJTk702REREREREREREZqhIMsK3d3ybvlifVbezsY9Xj2aXFnDYDT6wtpZyv8dqX1mxkvsX33/FzQQ0ERSaKDSZULvbd/PkoSeB7Pol1W8dJfzcfhLpbNhQ6fcwu3RoJIDb7mZd9brcVDMQgJUrs1tZ2aV3yjQhGoWBgdxA4ExwMDxAeMd+OhbhUMse2nsasaeGgpFUOkNTb4TOcO66LOU+N7XFeTjtI6eEctgcFOYVEyispKioinxfCYbXCx7P+W1ebzYgutyYZnbLZIb2Ryu/MwwxjOwmIiIiIiIiIiIil6RzsJPv7PwOiXR2ph/TNHnuQAcH20IA+NwOHtxQlzMrzh1z72Bj3cYp6e/lRKGJQpMJ9/SRp9l1You1fslALMX+tqA1IGF+eQGlBUOjA4o8RayoXYdt2TJYtQpmz74svkzvi/bx+P7HaRtoy1aYJvZkmqbWfrYc6CCWTGMzTQygJM/FDfNKqPR7wDQxMtl60zBYVLOKq+ZspKK0HsPpvCzem4iIiIiIiIiIiEwvh7sP8+i+RzHJfhGbSmf4z52naA/GAKgOeHnf2lk4Tq8BbGDwoRUfYmHJwinr8+VAoYlCkwmXaj3Ftn/4E+I9HVZdRyhGQ/cgADbDYPksP3kuB7ECDw1r5rDo+vdy66I7p6rLIxztOcoTB58gmopadZmMye+Od7OjcWiYm9Nu49q5JayuLcRmyw1DyvPLuWfhPdQF6iat3yIiIiIiIiIiIjJzvdb4Gi82vGiVB+Mpfra1iYF4CoBl1X5uX1JhTcvltrv59FWfpix/HGb8uUJdSG4wcn4hkXPZtw/H937AGu8cnLahKbfKfW5rdEnGNDnSMUBnRYAd96ylY14Fr7a9weHuw1PVa0vGzPByw8v8dO9PcwKTwXiKn+88lROYzCsr4GPX1nNVfVFOYOKyu9g0bxOfW/s5BSYiIiIiIiIiIiIyaa6vu57l5cutcr7bwb2rqrGf/v5yf2uIXc39Vns8Hedn+35GNBl956lkFApN5PyZJrz4Ivznf0IyicfhYWnZUqvZMAzmlOaT57IDsLmiiL+vLCcxbA69Jw89SV+0b8SpJ0skGeGne3/K5sbN1hA2gJb+KD99q4mW/uwvDpthcPOicu5ZWYXP48w5x5LSJXx+/ee5rvY67Db7pPZfREREREREREREZjbDMHjPovdQVVBl1VX4PdyxtMIqD8bTOc/pjfby3PHnJq2PVzJNzyXnJ5WCX/4S9u4d0dTY30hDf4NVHsyY/F1+AbtLAgDcsKCUtfXFVnu1r5pPrfkUDtvkLnjeGm7l8f2P0x/rt+pM02RXcz+vHe0mc/pHId/t4O4VVVQXenOeX+Qp4t0L3s2CkgWT2W0RERERERERERGREYKxIN/e+W0GEgNW3RvHewh4nSytzv1ufE7hHB5Y9gB5zrzJ7uZl4UJyg8n91lquTLEYPPYYNDSM2lwXqCOcCNMd6SZW4OHgLcuozxjs3t0KwOvHeqgMeJl1OoRoDbfy7LFnuXvh3ZP2FvZ27OWXh39JKpOy6hKpDC8c7OBIR9iqqynK490rKslzDf1oGBhsrNvITfU34bTnjjoRERERERERERERmQoBT4APLvsg39/1fdJmdmTJtfNKRhy3YdYG7px3p2bNOU+ankvOLhSCf//3MQMTyA4HW1y6mERdDTvuWctAiY+5ZQVsmJ0dXWKaJr/Z20YkMRRYbGvdxt6OkaNWxptpmrzW+Bo/P/jznMCkdzDBo9uacgKTdfXFvG/NrJzAxOvw8pGVH+H2ubcrMBEREREREREREZHLSm2glnsW3jNqm82wce/Ce3n3gncrMLkACk1kbB0d8J3vQGfnOQ91bLyBVX/695A3NLzrmnkl1BRly4PxFM/sa7emwAL49ZFf0zXYNf79Pi1jZnjqyFO82PBiTv3RjjA/29pE72ACAJfDxj0rq7l+QWnOYu/Vvmo+t+5zzC+eP2F9FBEREREREREREbkUa6rWcG3NtTl1ec48Pr7q46ytXjtFvbpyKTSR0TU0ZEeYhEJnP87hgPe9D+68kwp/FfcuutdqshkGdy2vJP/0yI3m3ghvneix2hPpBI/vf5xEOjHu3Y+n4vx070/Z0bbDqjNNk60NPTy9t41kOgNASb6bD22oY355Qc7z11at5VNrPkWhp3Dc+yYiIiIiIiIiIiIynu6Yd4d183dFfgWfXftZ6gvrp7hXVyaFJjLS3r3w4x9DPH7247xeeOghWLnSqlpZsZK1VUPpZb7bwV0rKjGM7AiOt072crJ70GrvinTx1JGnMIeNQLlUoXiI7+36Hsd6j1l16YzJCwc72XJ8KLRZXOnjwQ21FOW5rDqHzcF7F7+XexfdO+kL1YuIiIiIiIiIiIhcDJth4/eW/h7X1FzD71/1+7oZ/BIoNJEhpgm/+x38/OeQTp/92MJC+NSnoH5kWnnXgruoKqiyyjVFeWw8swCRCb/d3044lrTa93TsyRkRcik6Bjr4zs7v0D7QbtXFU2l+uauF/a1Bq+76+aXcuawSp33oR6DIU8Snr/o0qytXj0tfRERERERERERERCaLx+HhXfPfhcvuOvfBMiaFJjOdaUJPD2zdmh1d8vzz535OVRX8/u9DWdmozQ6bgw8s+wAeh8eqW1tfxNzSfABiyTRP720jnRkaXfLM0WdoDbde0ls53nucf3/73wnFh6YUC8eS/Mf2UzT1RgCw2wzuWl7FutnF1ugXgIUlC/ns2s9SWVB5SX0QERERERERERERkSuXYY7nvEiXgVAoRCAQIBgM4vf7p7o7l6doNLtmyfHj2a2///yfO38+PPAAuN3nPPRQ9yEe3feoVY4l0/z0rSZCp0eZrK4t5OZF5VZ7oaeQz639HF6n9/z7Q3atkl3tu/j1kV+TMTNWfVc4zi93tTAQTwHgcdq5d1U1swpzz3/L7Fu4sf7GnBBFRERERERERERERKaHC8kNFJrMBOk0nDqVDUhOnICWluwIkwu1ejXcey/Y7ef9lOeOP8eW5i1WuSMU4/HtzdYok7tXVLGgwme1zymcwy1zbqHWX3vOEGMwMcjezr283fY2HYMdOW0newb5zd42EqlsiBLwOnnP6lkU5+euX3L/4vtZVr7svN+PiIiIiIiIiIiIiFxZLiQ30ErX01lPDzz3HJw8ee5F3c/lppvg5pvhAkdj3DbnNk6FTtEUbAKgwu/hpoVlvHSoE4DnD3ZQWuCm6HSY0dDfQMPbDfhcPpaWLWVp2VJqA7XYjOxMchkzw7HeY7zd9jZHeo6QNkeuvbKvJciLhzqtxeUr/R7uW11Nnmvocs9z5vHg8gepC9Rd8H8KEREREREREREREZmeNNJkOhschL//+0s7h80G99wDV1110acIxUP82/Z/YzA5CGSn03p2fzuH2sMAlBa4+eD62pxF2YcrcBWwtGwpTpuT3R27GUgMjHqcaZq8cbyHrSd7rbp5ZQW8a3nugu/F3mI+suIjlOSVXPR7EhEREREREREREZErg0aaSFZ+fnbR9ra2i3u+0wkf+AAsWHBJ3fC7/bx/6fv50e4fYWJiGAa3Lq6gMxyndzBB90Cc3+xtY93sYqoDnhHTcg0kBtjasnXM84djSQ60hjjQFiIYTVr1a+qKuGFBKbZh56v11/Lg8gfJd+Vf0nsSERERERERERERkelHocl0N2/ehYcmbjcsWZKdjquwcFy6MbdoLjfPvpmXT74MgMth456VVfxsazPJdIaG7kEaugfJdztYUF7A/PICqgu9OYHHcKl0huNdg+xvDdLUF4Hh46UMuHlBGavrinKes7RsKfcvvh+n3Tku70lEREREREREREREpheFJtPdvHnw+utnP8YwYNas7LHz5mX3L2Cx9/N1Y/2NNIeaOdZ7DIDifDd3Lqvkt/vbSaWzC7YPxlPsau5nV3M/eS4H88sLWFBewKwiLwbQGY6zvzXE4fYQ8dOLvA+9D6grymPd7GLqivNymq6rvY475t5xzsXlRURERERERERERGTm0pom010qBd/4BiSTufWFhUMhyZw54PVOSnciyQjf2v4tQvGQVRdLpjnRNcixzjCNvRHSmZGXZJ7Ljsdpp3cwMaIt4HWytMrPkmo/fk/uKBIDg7sW3MWGWRvG/82IiIiIiIiIiIiIyGVPa5rIEIcDZs+GpqZsOHImKCkqyo4wmWR5zjw+sOwD/GDXD0hmskGOx2lnabWfpdV+4qk0DV2DHOkcoLFn0ApQIok0kUR66G3ZbSwoL2BplZ+aIu+oI0hcdhfvX/J+FpUumpw3JyIiIiIiIiIiIiJXNI00mQkGBrIjSSZgyq2L1djfyBMHnyAYD455TCKVXevkaEeYhtMBSlXAy7JqPwsqCnA7Rn8/NsPGsrJl3DHvDvxuXQMiIiIiIiIiIiIiM9mF5AYTGpp89atf5emnn2bXrl24XC76+/vP+ZxPfOIT/OAHP8ipu/rqq3nzzTfP6zUVmlw5UpkUB7oOsL9zP8d6j5E202Mem0xnSGdMPM6xg5/y/HLWVK5hZcVK8l35E9FlEREREREREREREbnCXDbTcyUSCR544AGuvfZavvvd75738971rnfxve99zyq7XK6J6J5MMYfNwcqKlaysWEk8FedIzxH2d2UDlFQmlXOs025jtLzE4/CwonwFa6rWUFVQpYXeRUREREREREREROSiTWho8rd/+7cAfP/737+g57ndbiorKyegR3K5cjvcrKhYwYqKFcRTcY72HuVA1wGO9hy11j45w8BgbtFc1lStYXHpYhw2Lc0jIiIiIiIiIiIiIpfusvy2+ZVXXqG8vJzCwkJuuukmvvrVr1JeXj7V3ZJJ4na4WV6+nOXly0mkExzrPUb7QDvRZJSKggoWFC8g4AlMdTdFREREREREREREZJq57EKTu+66iwceeID6+noaGhr4q7/6K2699VZ27NiB2+0ecXw8Hicej1vlUCg0md2VCeayu1hatpSlZUunuisiIiIiIiIiIiIiMs3ZLvQJX/nKVzAM46zb9u3bL7pDH/zgB7n77rtZvnw59957L8888wxHjhzh6aefHvX4r3/96wQCAWurra296NcWEREREREREREREZGZ64JHmnzhC1/gwQcfPOsxs2fPvtj+jFBVVUV9fT1Hjx4dtf0v/uIv+NKXvmSVQ6GQghMREREREREREREREblgFxyalJaWUlpaOhF9GVVPTw/Nzc1UVVWN2u52u0edtktERERERERERERERORCXPD0XBeiqamJXbt20dTURDqdZteuXezatYuBgQHrmMWLF/Pkk08CMDAwwJ/+6Z/yxhtvcPLkSV555RXuvfdeSktLuf/++yeyqyIiIiIiIiIiIiIiMsNN6ELwf/3Xf80PfvADq7xmzRoAXn75ZW6++WYADh8+TDAYBMBut7N3715++MMf0t/fT1VVFbfccguPPfYYPp9vIrsqIiIiIiIiIiIiIiIznGGapjnVnRhPoVCIQCBAMBjE7/dPdXdERERERERERERERGQKXUhuMKHTc4mIiIiIiIiIiIiIiFwpFJqIiIiIiIiIiIiIiIig0ERERERERERERERERARQaCIiIiIiIiIiIiIiIgKAY6o7MN7OrGsfCoWmuCciIiIiIiIiIiIiIjLVzuQFZ/KDs5l2oUk4HAagtrZ2insiIiIiIiIiIiIiIiKXi3A4TCAQOOsxhnk+0coVJJPJ0Nrais/nwzCMqe7OZSUUClFbW0tzczN+v3+quyMy4XTNy0yja15mGl3zMtPompeZSNe9zDS65mWm0TUvk8U0TcLhMNXV1dhsZ1+1ZNqNNLHZbNTU1Ex1Ny5rfr9fv4RkRtE1LzONrnmZaXTNy0yja15mIl33MtPompeZRte8TIZzjTA5QwvBi4iIiIiIiIiIiIiIoNBEREREREREREREREQEUGgyo7jdbv7mb/4Gt9s91V0RmRS65mWm0TUvM42ueZlpdM3LTKTrXmYaXfMy0+ial8vRtFsIXkRERERERERERERE5GJopImIiIiIiIiIiIiIiAgKTURERERERERERERERACFJiIiIiIiIiIiIiIiIoBCExEREREREREREREREUChyYzxzW9+kzlz5uDxeFi7di2vvfbaVHdJZFx8/etfZ/369fh8PsrLy3nve9/L4cOHc44xTZOvfOUrVFdX4/V6ufnmm9m/f/8U9VhkfH3961/HMAwefvhhq07XvEw3LS0tfPSjH6WkpIS8vDxWr17Njh07rHZd8zLdpFIp/vt//+/MmTMHr9fL3Llz+R//43+QyWSsY3Tdy5Xs1Vdf5d5776W6uhrDMPjFL36R034+13c8HucP//APKS0tJT8/n/vuu49Tp05N4rsQOX9nu+aTySRf/vKXWbFiBfn5+VRXV/Oxj32M1tbWnHPompcrybl+zw/3uc99DsMw+N//+3/n1Oual6mk0GQGeOyxx3j44Yf5y7/8S95++21uuOEG7rrrLpqamqa6ayKXbPPmzXz+85/nzTff5PnnnyeVSrFp0yYGBwetY/7u7/6Of/zHf+Sf//mf2bZtG5WVldxxxx2Ew+Ep7LnIpdu2bRuPPPIIK1euzKnXNS/TSV9fHxs3bsTpdPLMM89w4MAB/uEf/oHCwkLrGF3zMt184xvf4Fvf+hb//M//zMGDB/m7v/s7/v7v/57/9//+n3WMrnu5kg0ODrJq1Sr++Z//edT287m+H374YZ588kkeffRRXn/9dQYGBrjnnntIp9OT9TZEztvZrvlIJMLOnTv5q7/6K3bu3MkTTzzBkSNHuO+++3KO0zUvV5Jz/Z4/4xe/+AVvvfUW1dXVI9p0zcuUMmXa27Bhg/kHf/AHOXWLFy82//zP/3yKeiQycTo7O03A3Lx5s2mappnJZMzKykrzf/2v/2UdE4vFzEAgYH7rW9+aqm6KXLJwOGwuWLDAfP75582bbrrJ/OIXv2iapq55mX6+/OUvm9dff/2Y7brmZTq6++67zU996lM5de973/vMj370o6Zp6rqX6QUwn3zySat8Ptd3f3+/6XQ6zUcffdQ6pqWlxbTZbOZvf/vbSeu7yMV45zU/mq1bt5qA2djYaJqmrnm5so11zZ86dcqcNWuWuW/fPrO+vt78p3/6J6tN17xMNY00meYSiQQ7duxg06ZNOfWbNm1iy5YtU9QrkYkTDAYBKC4uBqChoYH29vacnwG3281NN92knwG5on3+85/n7rvv5vbbb8+p1zUv082vfvUr1q1bxwMPPEB5eTlr1qzh29/+ttWua16mo+uvv54XX3yRI0eOALB7925ef/113v3udwO67mV6O5/re8eOHSSTyZxjqqurWb58uX4GZFoIBoMYhmGNrNU1L9NNJpPhoYce4s/+7M9YtmzZiHZd8zLVHFPdAZlY3d3dpNNpKioqcuorKipob2+fol6JTAzTNPnSl77E9ddfz/LlywGs63y0n4HGxsZJ76PIeHj00UfZuXMn27ZtG9Gma16mmxMnTvCv//qvfOlLX+K//bf/xtatW/mjP/oj3G43H/vYx3TNy7T05S9/mWAwyOLFi7Hb7aTTab761a/yoQ99CNDvepnezuf6bm9vx+VyUVRUNOIYfc6VK10sFuPP//zP+fCHP4zf7wd0zcv0841vfAOHw8Ef/dEfjdqua16mmkKTGcIwjJyyaZoj6kSudF/4whfYs2cPr7/++og2/QzIdNHc3MwXv/hFnnvuOTwez5jH6ZqX6SKTybBu3Tq+9rWvAbBmzRr279/Pv/7rv/Kxj33MOk7XvEwnjz32GD/+8Y/56U9/yrJly9i1axcPP/ww1dXVfPzjH7eO03Uv09nFXN/6GZArXTKZ5MEHHySTyfDNb37znMfrmpcr0Y4dO/g//+f/sHPnzgu+fnXNy2TR9FzTXGlpKXa7fUQK29nZOeLOHZEr2R/+4R/yq1/9ipdffpmamhqrvrKyEkA/AzJt7Nixg87OTtauXYvD4cDhcLB582b+7//9vzgcDuu61jUv00VVVRVLly7NqVuyZAlNTU2Afs/L9PRnf/Zn/Pmf/zkPPvggK1as4KGHHuKP//iP+frXvw7oupfp7Xyu78rKShKJBH19fWMeI3KlSSaTfOADH6ChoYHnn3/eGmUCuuZlennttdfo7Oykrq7O+kzb2NjIn/zJnzB79mxA17xMPYUm05zL5WLt2rU8//zzOfXPP/8811133RT1SmT8mKbJF77wBZ544gleeukl5syZk9M+Z84cKisrc34GEokEmzdv1s+AXJFuu+029u7dy65du6xt3bp1fOQjH2HXrl3MnTtX17xMKxs3buTw4cM5dUeOHKG+vh7Q73mZniKRCDZb7kc1u91OJpMBdN3L9HY+1/fatWtxOp05x7S1tbFv3z79DMgV6UxgcvToUV544QVKSkpy2nXNy3Ty0EMPsWfPnpzPtNXV1fzZn/0Zzz77LKBrXqaepueaAb70pS/x0EMPsW7dOq699loeeeQRmpqa+IM/+IOp7prIJfv85z/PT3/6U375y1/i8/msO9ICgQBerxfDMHj44Yf52te+xoIFC1iwYAFf+9rXyMvL48Mf/vAU917kwvl8PmvNnjPy8/MpKSmx6nXNy3Tyx3/8x1x33XV87Wtf4wMf+ABbt27lkUce4ZFHHgHQ73mZlu69916++tWvUldXx7Jly3j77bf5x3/8Rz71qU8Buu7lyjcwMMCxY8esckNDA7t27aK4uJi6urpzXt+BQIDf//3f50/+5E8oKSmhuLiYP/3TP2XFihXcfvvtU/W2RMZ0tmu+urqa3/u932Pnzp089dRTpNNp63NtcXExLpdL17xccc71e/6dwaDT6aSyspJFixYB+j0vlwFTZoR/+Zd/Mevr602Xy2VeddVV5ubNm6e6SyLjAhh1+973vmcdk8lkzL/5m78xKysrTbfbbd54443m3r17p67TIuPspptuMr/4xS9aZV3zMt38+te/NpcvX2663W5z8eLF5iOPPJLTrmtepptQKGR+8YtfNOvq6kyPx2POnTvX/Mu//EszHo9bx+i6lyvZyy+/POrf8B//+MdN0zy/6zsajZpf+MIXzOLiYtPr9Zr33HOP2dTUNAXvRuTcznbNNzQ0jPm59uWXX7bOoWteriTn+j3/TvX19eY//dM/5dTpmpepZJimaU5SPiMiIiIiIiIiIiIiInLZ0pomIiIiIiIiIiIiIiIiKDQREREREREREREREREBFJqIiIiIiIiIiIiIiIgACk1EREREREREREREREQAhSYiIiIiIiIiIiIiIiKAQhMRERERERERERERERFAoYmIiIiIiIiIiIiIiAig0ERERERERERERERERARQaCIiIiIiIiIiIiIiIgIoNBEREREREREREREREQEUmoiIiIiIiIiIiIiIiAAKTURERERERERERERERAD4/wFLAhfMj2MDSwAAAABJRU5ErkJggg==", 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" + "
" ] }, "metadata": {}, @@ -559,14 +559,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 0 : 130 with distance 9.0\n" + "match 0 : 130 with distance 6.0\n" ] }, { "data": { - "image/png": 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", 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", 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" + "
" ] }, "metadata": {}, @@ -576,14 +576,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 1 : 184 with distance 19.0\n" + "match 1 : 56 with distance 19.0\n" ] }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -638,7 +638,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.12.0" } }, "nbformat": 4, From 75ae9786f34f1304571fd5d2c7b3b93c76abf2de Mon Sep 17 00:00:00 2001 From: baraline Date: Mon, 31 Mar 2025 14:39:34 +0200 Subject: [PATCH 28/36] Fix Patrick comments --- aeon/similarity_search/collection/_base.py | 9 +++- aeon/similarity_search/series/_base.py | 7 ++- .../similarity_search/series/motifs/_stomp.py | 54 ++++++++++--------- .../series/neighbors/_dummy.py | 11 ++-- .../series/neighbors/_mass.py | 8 +-- .../similarity_search/distance_profiles.ipynb | 6 +-- .../similarity_search/similarity_search.ipynb | 12 ++--- 7 files changed, 61 insertions(+), 46 deletions(-) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index 609fcdfc82..20fd325497 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -15,7 +15,12 @@ class BaseCollectionSimilaritySearch(BaseCollectionEstimator, BaseSimilaritySearch): - """Similarity search base class for collections.""" + """ + Similarity search base class for collections. + + Such estimators include nearest neighbors on whole series or subsequences with + indexing or concenssus motifs search over a collection. + """ # tag values specific to CollectionTransformers _tags = { @@ -69,7 +74,7 @@ def predict(self, X, **kwargs): Parameters ---------- - X : np.ndarray, shape = (n_cases, n_channels, n_timepoints) + X : np.ndarray, 3D array of shape = (n_cases, n_channels, n_timepoints) Collections of series to predict on. kwargs : dict, optional Additional keyword arguments to be passed to the _predict function of the diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index 04323d4b52..0b3a8daa8b 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -10,7 +10,12 @@ class BaseSeriesSimilaritySearch(BaseSeriesEstimator, BaseSimilaritySearch): - """Base class for similarity search applications on single series.""" + """ + Base class for similarity search applications on single series. + + Such estimators include nearest neighbors on subsequences extracted from a series + or motif discovery on single series. + """ _tags = { "input_data_type": "Series", diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index 7b29360ed6..a577fb4e82 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -59,8 +59,8 @@ class StompMotif(BaseSeriesSimilaritySearch): Notes ----- - This estimator only provide exact computation method, faster approximate methods - also exists in the litterature. We use a squared euclidean distance instead of the + This estimator only provides an exact computation method, faster approximate methods + also exist in the litterature. We use a squared euclidean distance instead of the euclidean distance, if you want euclidean distance results, you should square root the obtained results. @@ -101,7 +101,7 @@ def _predict( motif_size: Optional[int] = 1, dist_threshold: Optional[float] = np.inf, allow_trivial_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2, + exclusion_factor: Optional[float] = 0.5, inverse_distance: Optional[bool] = False, motif_extraction_method: Optional[str] = "k_motifs", ): @@ -120,29 +120,35 @@ def _predict( The number of motifs to return. The default is 1, meaning we return only the motif set with the minimal sum of distances to its query. motif_size : int - The number of subsequences in a motif. Default is 1, meaning we extract - motif pairs (the query and its best match) + The number of subsequences in a motif excluding the motif candidate. This + means that the number of subsequences in the returned motifs will be + ``motif_size + 1``. For example, with the default is 1, this means that we + extract motif pairs (the motif candidate from X and its best match in X_) dist_threshold : float The maximum allowed distance of a candidate subsequence of X to a query subsequence from X_ for the candidate to be considered as a neighbor. allow_trivial_matches: bool, optional - Wheter a neighbors of a match to a query can be also considered as matches + Whether a neighbor of a match to a query can also be considered as matches (True), or if an exclusion zone is applied around each match to avoid trivial matches with their direct neighbors (False). - exclusion_factor : float, default=1. + exclusion_factor : float, default=0.5. A factor of the query length used to define the exclusion zone when ``allow_trivial_matches`` is set to False. For a given timestamp, the exclusion zone starts from - :math:`id_timestamp - length//exclusion_factor` and end at - :math:`id_timestamp + length//exclusion_factor`. + :math:`id_timestamp - floor(length*exclusion_factor)` and end at + :math:`id_timestamp + floor(length*exclusion_factor)`. inverse_distance : bool If True, the matching will be made on the inverse of the distance, and thus, the farther neighbors will be returned instead of the closest ones. motif_extraction_method : str - A string indicating the methodology to use to extract the top motifs. - Available methods are "r_motifs" and "k_motifs". "r_motifs" means we rank - motif set by their cardinality, with higher is better. "k_motifs" means - we rank motif set by their maximum distance to their query + A string indicating the methodology to use to extract the top k motifs from + the matrix profile. Available methods are "r_motifs" and "k_motifs": + - "r_motifs" means we rank motif set by their cardinality (number of matches + with a distance at most dist_threshold to the candidate motif), with higher + is better. + - "k_motifs" means rank motifs by their maximum distance to their matches. + For example, if a 3-motif has distances to its matches equal to + ``[0.1,0.2,0.5]`` will have a score of ``max([0.1,0.2,0.5])=0.5``. Returns ------- @@ -168,11 +174,11 @@ def _predict( ) if motif_extraction_method == "k_motifs": return _extract_top_k_motifs( - MP, IP, k, allow_trivial_matches, self.length // exclusion_factor + MP, IP, k, allow_trivial_matches, int(self.length * exclusion_factor) ) elif motif_extraction_method == "r_motifs": return _extract_top_r_motifs( - MP, IP, k, allow_trivial_matches, self.length // exclusion_factor + MP, IP, k, allow_trivial_matches, int(self.length * exclusion_factor) ) def compute_matrix_profile( @@ -181,7 +187,7 @@ def compute_matrix_profile( motif_size: Optional[int] = 1, dist_threshold: Optional[float] = np.inf, allow_trivial_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2, + exclusion_factor: Optional[float] = 0.5, inverse_distance: Optional[bool] = False, ): """ @@ -194,7 +200,7 @@ def compute_matrix_profile( Parameters ---------- X : np.ndarray, shape = (n_channels, n_timepoints) - A 2D array time series on against which the matrix profile of X_ will be + A 2D array time series against which the matrix profile of X_ will be computed. motif_size : int The number of subsequences in a motif. Default is 1, meaning we extract @@ -205,12 +211,12 @@ def compute_matrix_profile( inverse_distance : bool If True, the matching will be made on the inverse of the distance, and thus, the worst matches to the query will be returned instead of the best ones. - exclusion_factor : float, default=1. + exclusion_factor : float, default=0.5 A factor of the query length used to define the exclusion zone when ``allow_trivial_matches`` is set to False. For a given timestamp, the exclusion zone starts from - :math:`id_timestamp - length//exclusion_factor` and end at - :math:`id_timestamp + length//exclusion_factor`. + :math:`id_timestamp - floor(length * exclusion_factor)` and end at + :math:`id_timestamp + floor(length * exclusion_factor)`. Returns ------- @@ -233,9 +239,9 @@ def compute_matrix_profile( if self.normalize: X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) X_dotX = get_ith_products(X, self.X_, self.length, 0) - exclusion_size = self.length // exclusion_factor + exclusion_size = int(self.length * exclusion_factor) - if motif_size == np.inf: + if np.isinf(motif_size): # convert infs here as numba seem to not be able to do == np.inf ? motif_size = X.shape[1] - self.length + 1 @@ -340,7 +346,7 @@ def _stomp_normalized( The maximum allowed distance of a candidate subsequence of X to a query subsequence from X_ for the candidate to be considered as a neighbor. allow_trivial_matches : bool - Wheter the top-k candidates can be neighboring subsequences. + Whether the top-k candidates can be neighboring subsequences. exclusion_size : int The size of the exclusion zone used to prevent returning as top k candidates the ones that are close to each other (for example i and i+1). @@ -353,7 +359,7 @@ def _stomp_normalized( If True, the matching will be made on the inverse of the distance, and thus, the worst matches to the query will be returned instead of the best ones. is_self_mp : bool - Wheter X_A == X_B. + Whether X_A == X_B. Returns ------- diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index 5ca1bb5c85..9c13d9abec 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -57,7 +57,7 @@ def _predict( X: np.ndarray, k: Optional[int] = 1, dist_threshold: Optional[float] = np.inf, - exclusion_factor: Optional[float] = 2, + exclusion_factor: Optional[float] = 0.5, inverse_distance: Optional[bool] = False, allow_neighboring_matches: Optional[bool] = False, X_index: Optional[int] = None, @@ -76,12 +76,12 @@ def _predict( inverse_distance : bool If True, the matching will be made on the inverse of the distance, and thus, the farther neighbors will be returned instead of the closest ones. - exclusion_factor : float, default=1. + exclusion_factor : float, default=0.5 A factor of the query length used to define the exclusion zone when ``allow_neighboring_matches`` is set to False. For a given timestamp, the exclusion zone starts from - :math:`id_timestamp - length//exclusion_factor` and end at - :math:`id_timestamp + length//exclusion_factor`. + :math:`id_timestamp - floor(length * exclusion_factor)` and end at + :math:`id_timestamp + floor(length * exclusion_factor)`. X_index : int, optional If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. If specified, neighboring subsequences of X won't be able to match as @@ -106,9 +106,8 @@ def _predict( if inverse_distance: dist_profile = _inverse_distance_profile(dist_profile) - exclusion_size = self.length // exclusion_factor if X_index is not None: - exclusion_size = self.length // exclusion_factor + exclusion_size = int(self.length * exclusion_factor) _max_timestamp = self.n_timepoints_ - self.length ub = min(X_index + exclusion_size, _max_timestamp) lb = max(0, X_index - exclusion_size) diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py index 1eb03970ba..71d6e6aae0 100644 --- a/aeon/similarity_search/series/neighbors/_mass.py +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -62,7 +62,7 @@ def _predict( k: Optional[int] = 1, dist_threshold: Optional[float] = np.inf, allow_trivial_matches: Optional[bool] = False, - exclusion_factor: Optional[float] = 2, + exclusion_factor: Optional[float] = 0.5, inverse_distance: Optional[bool] = False, X_index: Optional[int] = None, ): @@ -89,8 +89,8 @@ def _predict( A factor of the query length used to define the exclusion zone when ``allow_trivial_matches`` is set to False. For a given timestamp, the exclusion zone starts from - :math:`id_timestamp - length//exclusion_factor` and end at - :math:`id_timestamp + length//exclusion_factor`. + :math:`id_timestamp - floor(length * exclusion_factor)` and end at + :math:`id_timestamp + floor(length * exclusion_factor)`. X_index : int, optional If ``X`` is a subsequence of X_, specify its starting timestamp in ``X_``. If specified, neighboring subsequences of X won't be able to match as @@ -114,7 +114,7 @@ def _predict( if inverse_distance: dist_profile = _inverse_distance_profile(dist_profile) - exclusion_size = self.length // exclusion_factor + exclusion_size = int(self.length * exclusion_factor) if X_index is not None: _max_timestamp = self.n_timepoints_ - self.length ub = min(X_index + exclusion_size, _max_timestamp) diff --git a/examples/similarity_search/distance_profiles.ipynb b/examples/similarity_search/distance_profiles.ipynb index ec56fcc6bf..d2bf3fd87f 100644 --- a/examples/similarity_search/distance_profiles.ipynb +++ b/examples/similarity_search/distance_profiles.ipynb @@ -37,11 +37,11 @@ "We can then find the \"best match\" between $Q$ and $X$ by looking at the distance profile minimum value and extract the subsequence $W_{\\text{argmin} P(X,Q)}$ as the best match.\n", "\n", "### Trivial matches\n", - "One should be careful of what is called \"trivial matches\" in this situation. If $Q$ is extracted from $X$, it is extremely likely that it will match with itself, as $dist(Q,Q)=0$. To avoid this, it is common to set the parts of the distance profile that are neighbors to $Q$ to $\\infty$. This is the role of the `q_index` parameter in the similarity search `predict` methods. The `exclusion_factor` parameter is used to define the neighbors of $Q$ that will also get $\\infty$ value.\n", + "One should be careful of what is called \"trivial matches\" in this situation. If $Q$ is extracted from $X$, it is extremely likely that it will match with itself, as $dist(Q,Q)=0$. To avoid this, it is common to set the parts of the distance profile that are neighbors to $Q$ to $\\infty$. This is the role of the `q_index` parameter in the similarity search `predict` methods. The `exclusion_factor` parameter is used to define the neighbors of $Q$ that will also get $\\infty$ value.\n", "\n", - "For example, if $Q$ was extracted at index $i$ in $X$ (i.e. $Q = \\{x_i, \\ldots, x_{i+(l-1)}\\}$), then all points in the interval `[i - l//exclusion_factor, i + l//exclusion_factor]` will the set to $\\infty$ in the distance profile to avoid a trivial match.\n", + "For example, if $Q$ was extracted at index $i$ in $X$ (i.e. $Q = \\{x_i, \\ldots, x_{i+(l-1)}\\}$), then all points in the interval `[i - floor(l*exclusion_factor), i + floor(l*exclusion_factor)]` will the set to $\\infty$ in the distance profile to avoid a trivial match.\n", "\n", - "The same reasoning can also be applied for the best matches of $Q$. It is highly likely that the two best matches will be neighbours, as if $W_i$ and $W_{i+/-1}$ share $l-1$ values. The `apply_exclusion_to_result` boolean parameter in `predict` allows you to apply the exclusion zone defined by `[i - l//exclusion_factor, i + l//exclusion_factor]` to the output of the algorithm.\n" + "The same reasoning can also be applied for the best matches of $Q$. It is highly likely that the two best matches will be neighbours, as if $W_i$ and $W_{i+/-1}$ share $l-1$ values. The `apply_exclusion_to_result` boolean parameter in `predict` allows you to apply the exclusion zone defined by `[i - floor(l*exclusion_factor), i + floor(l*exclusion_factor)]` to the output of the algorithm.\n" ] }, { diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index 1ada9538eb..a6e4af20c1 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -172,7 +172,7 @@ "- `dist_threshold` (float) : the maximum allowed distance for a candidate subsequence to be considered as a neighbor.\n", "- `allow_trivial_matches` (bool) : wheter a neighbors of a match to a query can be also considered as matches (True), or if an exclusion zone is applied around each match to avoid trivial matches with their direct neighbors (False).\n", "- `inverse_distance` (bool) : if True, the matching will be made on the inverse of the distance, and thus, the farther neighbors will be returned instead of the closest ones.\n", - "- `exclusion_factor` (float): A factor of the `length` used to define the exclusion zone when `allow_trivial_matches` is set to False. For a given timestamp, the exclusion zone starts from `id_timestamp - length//exclusion_factor` and end at `id_timestamp + length//exclusion_factor`.\n", + "- `exclusion_factor` (float): A factor of the `length` used to define the exclusion zone when `allow_trivial_matches` is set to False. For a given timestamp, the exclusion zone starts from `id_timestamp - floor(length*exclusion_factor)` and end at `id_timestamp + floor(length*exclusion_factor)`.\n", "- `X_index` (int): If series given during predict is a subsequence of series given during fit, specify its starting timestamp. If specified, neighboring subsequences of X won't be able to match as neighbors." ] }, @@ -491,7 +491,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 0 : 130 with distance 2.0\n" + "match 0 : 130 with distance 1.0\n" ] }, { @@ -508,12 +508,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 1 : 56 with distance 4.0\n" + "match 1 : 184 with distance 2.0\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -576,12 +576,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 1 : 56 with distance 19.0\n" + "match 1 : 184 with distance 19.0\n" ] }, { "data": { - "image/png": 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", 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" ] From fa43a107d7fcc4df85b4e9c6210e70b4c2420674 Mon Sep 17 00:00:00 2001 From: baraline Date: Mon, 31 Mar 2025 15:33:08 +0200 Subject: [PATCH 29/36] Fix variable suppression mistake --- aeon/similarity_search/series/neighbors/_dummy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index 9c13d9abec..9f3ea0ea9f 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -106,8 +106,8 @@ def _predict( if inverse_distance: dist_profile = _inverse_distance_profile(dist_profile) + exclusion_size = int(self.length * exclusion_factor) if X_index is not None: - exclusion_size = int(self.length * exclusion_factor) _max_timestamp = self.n_timepoints_ - self.length ub = min(X_index + exclusion_size, _max_timestamp) lb = max(0, X_index - exclusion_size) From f4a64145ec356e7ebcb7de621f0c7d9cb0862951 Mon Sep 17 00:00:00 2001 From: baraline Date: Sat, 19 Apr 2025 12:02:58 +0200 Subject: [PATCH 30/36] Divide base class into task specific --- aeon/similarity_search/collection/__init__.py | 13 +- aeon/similarity_search/collection/_base.py | 12 + .../collection/neighbors/_rp_cosine_lsh.py | 4 +- .../collection/tests/test_base.py | 18 +- aeon/similarity_search/series/__init__.py | 15 +- aeon/similarity_search/series/_base.py | 67 +++-- aeon/similarity_search/series/_commons.py | 7 +- .../similarity_search/series/motifs/_stomp.py | 38 +-- .../series/neighbors/_dummy.py | 4 +- .../series/neighbors/_mass.py | 4 +- .../series/tests/test_base.py | 71 ++--- .../series/tests/test_commons.py | 4 +- aeon/testing/mock_estimators/__init__.py | 12 +- .../_mock_similarity_searchers.py | 50 +++- aeon/utils/base/_register.py | 20 +- docs/api_reference/similarity_search.rst | 4 + docs/api_reference/utils.rst | 6 +- examples/similarity_search/code_speed.ipynb | 8 +- .../similarity_search/similarity_search.ipynb | 257 ++++++++++++++---- 19 files changed, 433 insertions(+), 181 deletions(-) diff --git a/aeon/similarity_search/collection/__init__.py b/aeon/similarity_search/collection/__init__.py index 3a08ed22d6..cb69faf9a1 100644 --- a/aeon/similarity_search/collection/__init__.py +++ b/aeon/similarity_search/collection/__init__.py @@ -1,8 +1,17 @@ """Similarity search for time series collection.""" -__all__ = ["BaseCollectionSimilaritySearch", "RandomProjectionIndexANN"] +__all__ = [ + "BaseCollectionSimilaritySearch", + "BaseCollectionNeighbors", + "BaseCollectionMotifs", + "RandomProjectionIndexANN", +] -from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.similarity_search.collection._base import ( + BaseCollectionMotifs, + BaseCollectionNeighbors, + BaseCollectionSimilaritySearch, +) from aeon.similarity_search.collection.neighbors._rp_cosine_lsh import ( RandomProjectionIndexANN, ) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index 9bd6f7cb31..15f960863f 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -110,3 +110,15 @@ def _check_predict_series_format(self, X): @abstractmethod def _predict(self, X, **kwargs): ... + + +class BaseCollectionMotifs(BaseCollectionSimilaritySearch): + """Base class for motif search on collections.""" + + ... + + +class BaseCollectionNeighbors(BaseCollectionSimilaritySearch): + """Base class for neighbors search on collections.""" + + ... diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index 167ec538c6..8574bfdbc5 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -3,7 +3,7 @@ import numpy as np from numba import get_num_threads, njit, prange, set_num_threads -from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.similarity_search.collection._base import BaseCollectionNeighbors from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD, z_normalise_series_3d @@ -103,7 +103,7 @@ def _collection_to_bool(X, hash_funcs, start_points, length): return res -class RandomProjectionIndexANN(BaseCollectionSimilaritySearch): +class RandomProjectionIndexANN(BaseCollectionNeighbors): """ Random Projection Locality Sensitive Hashing index with cosine similarity. diff --git a/aeon/similarity_search/collection/tests/test_base.py b/aeon/similarity_search/collection/tests/test_base.py index 7f538cdd59..23460ebda0 100644 --- a/aeon/similarity_search/collection/tests/test_base.py +++ b/aeon/similarity_search/collection/tests/test_base.py @@ -3,14 +3,26 @@ __maintainer__ = ["baraline"] from aeon.testing.mock_estimators._mock_similarity_searchers import ( - MockCollectionSimilaritySearch, + MockCollectionMotifsSearch, + MockCollectionNeighborsSearch, ) from aeon.testing.testing_data import FULL_TEST_DATA_DICT, _get_datatypes_for_estimator -def test_input_shape_fit_predict_collection(): +def test_input_shape_fit_predict_collection_motifs(): """Test input shapes.""" - estimator = MockCollectionSimilaritySearch() + estimator = MockCollectionMotifsSearch() + datatypes = _get_datatypes_for_estimator(estimator) + # dummy data to pass to fit when testing predict/predict_proba + for datatype in datatypes: + X_train, y_train = FULL_TEST_DATA_DICT[datatype]["train"] + X_test, y_test = FULL_TEST_DATA_DICT[datatype]["test"] + estimator.fit(X_train, y_train).predict(X_test) + + +def test_input_shape_fit_predict_collection_neighbors(): + """Test input shapes.""" + estimator = MockCollectionNeighborsSearch() datatypes = _get_datatypes_for_estimator(estimator) # dummy data to pass to fit when testing predict/predict_proba for datatype in datatypes: diff --git a/aeon/similarity_search/series/__init__.py b/aeon/similarity_search/series/__init__.py index 6eb2c521be..d940f59e0c 100644 --- a/aeon/similarity_search/series/__init__.py +++ b/aeon/similarity_search/series/__init__.py @@ -1,8 +1,19 @@ """Similarity search for series.""" -__all__ = ["BaseSeriesSimilaritySearch", "MassSNN", "StompMotif", "DummySNN"] +__all__ = [ + "BaseSeriesSimilaritySearch", + "BaseSeriesMotifs", + "BaseSeriesNeighbors", + "MassSNN", + "StompMotif", + "DummySNN", +] -from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._base import ( + BaseSeriesMotifs, + BaseSeriesNeighbors, + BaseSeriesSimilaritySearch, +) from aeon.similarity_search.series.motifs._stomp import StompMotif from aeon.similarity_search.series.neighbors._dummy import DummySNN from aeon.similarity_search.series.neighbors._mass import MassSNN diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index be1e57b504..cbb33c2d34 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -53,7 +53,6 @@ def fit( """ self.reset() X = self._preprocess_series(X, self.axis, True) - self.n_channels_ = self.metadata_["n_channels"] timepoint_idx = 1 if self.axis == 1 else 0 self.n_timepoints_ = X.shape[timepoint_idx] @@ -99,6 +98,32 @@ def predict(self, X, **kwargs): @abstractmethod def _predict(self, X, **kwargs): ... + def _check_predict_series_format(self, X): + """ + Check wheter a series X is correctly formated regarding series given in fit. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_timepoints) + A series to be used in predict. + + """ + channel_idx = 0 if self.axis == 1 else 1 + if self.n_channels_ != X.shape[channel_idx]: + raise ValueError( + f"Expected X to have {self.n_channels_} channels but" + f" got {X.shape[channel_idx]} channels." + ) + + +class BaseSeriesNeighbors(BaseSeriesSimilaritySearch): + """ + Base class for neighbor search estimators. + + The goal of this base class is to define a fit_predict method to, for example, + compute self matrix profiles. + """ + def _check_X_index(self, X_index: int): """ Check wheter a X_index parameter is correctly formated and is admissible. @@ -123,25 +148,33 @@ def _check_X_index(self, X_index: int): f"between [0, {max_timepoints - 1}] but got {X_index}" ) - def _check_predict_series_format(self, X): + +class BaseSeriesMotifs(BaseSeriesSimilaritySearch): + """ + Base class for motif search estimators. + + The goal of this base class is to define a fit_predict method to, for example, + compute self matrix profiles. + """ + + def fit_predict(self, X, **kwargs): """ - Check wheter a series X is correctly formated regarding series given in fit. + Fit and predict on a single series X in order to compute self-motifs. Parameters ---------- - X : np.ndarray, shape = (n_channels, n_timepoints) - A series to be used in predict. + X : np.ndarray, shape = (n_channels, n_tiempoints) + Series to fit and predict on. + kwargs : dict, optional + Additional keyword argument as dict or individual keywords args + to pass to the estimator during predict. + Returns + ------- + indexes : np.ndarray + Indexes of series in the that are similar to X. + distances : np.ndarray + Distance of the matches to each series """ - channel_idx = 0 if self.axis == 1 else 1 - if self.n_channels_ != X.shape[channel_idx]: - raise ValueError( - f"Expected X to have {self.n_channels_} channels but" - f" got {X.shape[channel_idx]} channels." - ) - - -# class BaseSeriesNeighbors(BaseSeriesSimilaritySearch): ... - - -# class BaseSeroesMotifs(BaseSeriesSimilaritySearch): ... + self.fit(X) + return self.predict(X, is_self_computation=True, **kwargs) diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py index e342ef5a83..22bccd15e6 100644 --- a/aeon/similarity_search/series/_commons.py +++ b/aeon/similarity_search/series/_commons.py @@ -161,7 +161,10 @@ def _extract_top_k_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): idx, _ = _extract_top_k_from_dist_profile( criterion, k, np.inf, allow_trivial_matches, exclusion_size ) - return [MP[i] for i in idx], [IP[i] for i in idx] + return ( + [IP[i] for i in idx], + [MP[i] for i in idx], + ) def _extract_top_r_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): @@ -175,7 +178,7 @@ def _extract_top_r_motifs(MP, IP, k, allow_trivial_matches, exclusion_size): allow_trivial_matches, exclusion_size, ) - return [MP[i] for i in idx], [IP[i] for i in idx] + return [IP[i] for i in idx], [MP[i] for i in idx] @njit(cache=True, fastmath=True) diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index a577fb4e82..a047b268f5 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -9,7 +9,7 @@ from numba import njit from numba.typed import List -from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._base import BaseSeriesMotifs from aeon.similarity_search.series._commons import ( _extract_top_k_from_dist_profile, _extract_top_k_motifs, @@ -25,7 +25,7 @@ from aeon.utils.numba.general import sliding_mean_std_one_series -class StompMotif(BaseSeriesSimilaritySearch): +class StompMotif(BaseSeriesMotifs): """ Estimator to extract top k motifs using STOMP, descibed in [1]_. @@ -96,7 +96,7 @@ def _fit( def _predict( self, - X: np.ndarray = None, + X: np.ndarray, k: Optional[int] = 1, motif_size: Optional[int] = 1, dist_threshold: Optional[float] = np.inf, @@ -104,6 +104,7 @@ def _predict( exclusion_factor: Optional[float] = 0.5, inverse_distance: Optional[bool] = False, motif_extraction_method: Optional[str] = "k_motifs", + is_self_computation: Optional[bool] = False, ): """ Exctract the motifs of X_ relative to a series X using STOMP matrix prfoile. @@ -113,9 +114,8 @@ def _predict( Parameters ---------- X : np.ndarray, shape=(n_channels, n_timepoint) - Series to use to compute the matrix profile against X_. If None, will - compute the self matrix profile of X_. Motifs will then be extracted from - the matrix profile. + Series to use to compute the matrix profile against X_. Motifs will then be + extracted from the matrix profile. k : int The number of motifs to return. The default is 1, meaning we return only the motif set with the minimal sum of distances to its query. @@ -149,6 +149,8 @@ def _predict( - "k_motifs" means rank motifs by their maximum distance to their matches. For example, if a 3-motif has distances to its matches equal to ``[0.1,0.2,0.5]`` will have a score of ``max([0.1,0.2,0.5])=0.5``. + is_self_computation : bool + Wheter X is equal to the series X_ given during fit. Returns ------- @@ -171,6 +173,7 @@ def _predict( allow_trivial_matches=allow_trivial_matches, exclusion_factor=exclusion_factor, inverse_distance=inverse_distance, + is_self_computation=is_self_computation, ) if motif_extraction_method == "k_motifs": return _extract_top_k_motifs( @@ -183,12 +186,13 @@ def _predict( def compute_matrix_profile( self, - X: np.ndarray = None, + X: np.ndarray, motif_size: Optional[int] = 1, dist_threshold: Optional[float] = np.inf, allow_trivial_matches: Optional[bool] = False, exclusion_factor: Optional[float] = 0.5, inverse_distance: Optional[bool] = False, + is_self_computation: Optional[bool] = False, ): """ Compute matrix profile. @@ -217,6 +221,8 @@ def compute_matrix_profile( the exclusion zone starts from :math:`id_timestamp - floor(length * exclusion_factor)` and end at :math:`id_timestamp + floor(length * exclusion_factor)`. + is_self_computation : bool + Wheter X is equal to the series X_ given during fit. Returns ------- @@ -229,15 +235,11 @@ def compute_matrix_profile( number of timepoint of X_. Each element of the list contains array of variable size. """ - if X is None: - is_self_mp = True - X = self.X_ - if self.normalize: - X_means, X_stds = self.X_means_, self.X_stds_ - else: - is_self_mp = False - if self.normalize: - X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) + if is_self_computation and self.normalize: + X_means, X_stds = self.X_means_, self.X_stds_ + elif not is_self_computation and self.normalize: + X_means, X_stds = sliding_mean_std_one_series(X, self.length, 1) + X_dotX = get_ith_products(X, self.X_, self.length, 0) exclusion_size = int(self.length * exclusion_factor) @@ -260,7 +262,7 @@ def compute_matrix_profile( allow_trivial_matches, exclusion_size, inverse_distance, - is_self_mp, + is_self_computation, ) else: MP, IP = _stomp( @@ -273,7 +275,7 @@ def compute_matrix_profile( allow_trivial_matches, exclusion_size, inverse_distance, - is_self_mp, + is_self_computation, ) return MP, IP diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index 9f3ea0ea9f..edf49a9ee8 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -8,7 +8,7 @@ import numpy as np from numba import get_num_threads, njit, prange, set_num_threads -from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._base import BaseSeriesNeighbors from aeon.similarity_search.series._commons import ( _extract_top_k_from_dist_profile, _inverse_distance_profile, @@ -21,7 +21,7 @@ from aeon.utils.validation import check_n_jobs -class DummySNN(BaseSeriesSimilaritySearch): +class DummySNN(BaseSeriesNeighbors): """Estimator to compute the on profile and distance profile using brute force.""" _tags = {"capability:multithreading": True} diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py index 71d6e6aae0..f549531c84 100644 --- a/aeon/similarity_search/series/neighbors/_mass.py +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -8,7 +8,7 @@ import numpy as np from numba import njit -from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.series._base import BaseSeriesNeighbors from aeon.similarity_search.series._commons import ( _extract_top_k_from_dist_profile, _inverse_distance_profile, @@ -20,7 +20,7 @@ ) -class MassSNN(BaseSeriesSimilaritySearch): +class MassSNN(BaseSeriesNeighbors): """ Estimator to compute the subsequences nearest neighbors using MASS _[1]. diff --git a/aeon/similarity_search/series/tests/test_base.py b/aeon/similarity_search/series/tests/test_base.py index 1b4d17b991..754580bc2d 100644 --- a/aeon/similarity_search/series/tests/test_base.py +++ b/aeon/similarity_search/series/tests/test_base.py @@ -2,63 +2,30 @@ __maintainer__ = ["baraline"] -import pytest - from aeon.testing.mock_estimators._mock_similarity_searchers import ( - MockSeriesSimilaritySearch, -) -from aeon.testing.testing_data import ( - make_example_1d_numpy, - make_example_2d_numpy_series, - make_example_3d_numpy, - make_example_3d_numpy_list, + MockSeriesMotifSearch, + MockSeriesNeighborsSearch, ) +from aeon.testing.testing_data import FULL_TEST_DATA_DICT, _get_datatypes_for_estimator -def test_input_shape_fit_predict_series(): +def test_input_shape_fit_predict_collection_motifs(): """Test input shapes.""" - estimator = MockSeriesSimilaritySearch() + estimator = MockSeriesMotifSearch() + datatypes = _get_datatypes_for_estimator(estimator) # dummy data to pass to fit when testing predict/predict_proba - X_3D_uni = make_example_3d_numpy(n_channels=1, return_y=False) - X_3D_multi = make_example_3d_numpy(n_channels=2, return_y=False) - X_3D_uni_list = make_example_3d_numpy_list(n_channels=1, return_y=False) - X_3D_multi_list = make_example_3d_numpy_list(n_channels=2, return_y=False) - X_2D_uni = make_example_2d_numpy_series(n_channels=1) - X_2D_multi = make_example_2d_numpy_series(n_channels=2) - X_1D = make_example_1d_numpy() - - valid_inputs_fit = [X_1D, X_2D_uni, X_2D_multi] - # 1D is converted to 2D univariate - for _input in valid_inputs_fit: - estimator.fit(_input) - - invalid_inputs_fit = [ - X_3D_multi, - X_3D_uni, - X_3D_multi_list, - X_3D_uni_list, - ] - for _input in invalid_inputs_fit: - with pytest.raises(ValueError): - estimator.fit(_input) - - estimator_multi = MockSeriesSimilaritySearch().fit(X_2D_multi) - estimator_uni = MockSeriesSimilaritySearch().fit(X_2D_uni) + for datatype in datatypes: + X_train, y_train = FULL_TEST_DATA_DICT[datatype]["train"] + X_test, y_test = FULL_TEST_DATA_DICT[datatype]["test"] + estimator.fit(X_train, y_train).predict(X_test) - estimator_uni.predict(X_2D_uni) - # 1D is converted to 2D univariate - estimator_uni.predict(X_1D) - estimator_multi.predict(X_2D_multi) - with pytest.raises(ValueError): - estimator_uni.predict(X_2D_multi) - with pytest.raises(ValueError): - estimator_multi.predict(X_2D_uni) - - for _input in [X_3D_uni, X_3D_uni_list]: - with pytest.raises(ValueError): - estimator_uni.predict(_input) - - for _input in [X_3D_multi, X_3D_multi_list]: - with pytest.raises(ValueError): - estimator_multi.predict(_input) +def test_input_shape_fit_predict_collection_neighbors(): + """Test input shapes.""" + estimator = MockSeriesNeighborsSearch() + datatypes = _get_datatypes_for_estimator(estimator) + # dummy data to pass to fit when testing predict/predict_proba + for datatype in datatypes: + X_train, y_train = FULL_TEST_DATA_DICT[datatype]["train"] + X_test, y_test = FULL_TEST_DATA_DICT[datatype]["test"] + estimator.fit(X_train, y_train).predict(X_test) diff --git a/aeon/similarity_search/series/tests/test_commons.py b/aeon/similarity_search/series/tests/test_commons.py index 6ad6fcf589..abed374318 100644 --- a/aeon/similarity_search/series/tests/test_commons.py +++ b/aeon/similarity_search/series/tests/test_commons.py @@ -110,7 +110,7 @@ def test__extract_top_k_motifs(): [0, 7], ] ) - MP_k, IP_k = _extract_top_k_motifs(MP, IP, 2, True, 0) + IP_k, MP_k = _extract_top_k_motifs(MP, IP, 2, True, 0) assert_(len(MP_k) == 2) assert_array_equal(MP_k[0], [0.6, 0.7]) assert_array_equal(IP_k[0], [0, 7]) @@ -132,7 +132,7 @@ def test__extract_top_r_motifs(): IP.append(List([0, 3, 6])) IP.append(List([0, 7])) - MP_k, IP_k = _extract_top_r_motifs(MP, IP, 2, True, 0) + IP_k, MP_k = _extract_top_r_motifs(MP, IP, 2, True, 0) assert_(len(MP_k) == 2) assert_array_equal(MP_k[0], [1.0, 1.5, 2.0, 1.5]) assert_array_equal(IP_k[0], [1, 2, 3, 4]) diff --git a/aeon/testing/mock_estimators/__init__.py b/aeon/testing/mock_estimators/__init__.py index e9e83aa263..254c2b17e5 100644 --- a/aeon/testing/mock_estimators/__init__.py +++ b/aeon/testing/mock_estimators/__init__.py @@ -30,8 +30,10 @@ "MockMultivariateSeriesTransformer", "MockSeriesTransformerNoFit", # similarity search - "MockSeriesSimilaritySearch", - "MockCollectionSimilaritySearch", + "MockSeriesMotifSearch", + "MockSeriesNeighborsSearch", + "MockCollectionMotifsSearch", + "MockCollectionNeighborsSearch", ] from aeon.testing.mock_estimators._mock_anomaly_detectors import ( @@ -66,6 +68,8 @@ MockUnivariateSeriesTransformer, ) from aeon.testing.mock_estimators._mock_similarity_searchers import ( - MockCollectionSimilaritySearch, - MockSeriesSimilaritySearch, + MockCollectionMotifsSearch, + MockCollectionNeighborsSearch, + MockSeriesMotifSearch, + MockSeriesNeighborsSearch, ) diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py index 35251bf558..02ae23ddcb 100644 --- a/aeon/testing/mock_estimators/_mock_similarity_searchers.py +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -1,13 +1,21 @@ """Mock series transformers useful for testing and debugging.""" __maintainer__ = ["baraline"] -__all__ = ["MockSeriesSimilaritySearch", "MockCollectionSimilaritySearch"] +__all__ = [ + "MockSeriesMotifSearch", + "MockSeriesNeighborsSearch", + "MockCollectionMotifsSearch", + "MockCollectionNeighborsSearch", +] -from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch -from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch +from aeon.similarity_search.collection._base import ( + BaseCollectionMotifs, + BaseCollectionNeighbors, +) +from aeon.similarity_search.series._base import BaseSeriesMotifs, BaseSeriesNeighbors -class MockSeriesSimilaritySearch(BaseSeriesSimilaritySearch): +class MockSeriesMotifSearch(BaseSeriesMotifs): """Mock estimator for BaseMatrixProfile.""" def __init__(self): @@ -17,11 +25,11 @@ def _fit(self, X, y=None): return self def _predict(self, X): - """Compute matrix profiles between X_ and X or between all series in X_.""" + """top-1 motif start timestamp index in X, and distances to the match in X_.""" return [0], [0.1] -class MockCollectionSimilaritySearch(BaseCollectionSimilaritySearch): +class MockSeriesNeighborsSearch(BaseSeriesNeighbors): """Mock estimator for BaseMatrixProfile.""" def __init__(self): @@ -31,5 +39,33 @@ def _fit(self, X, y=None): return self def _predict(self, X): - """Compute matrix profiles between X_ and X or between all series in X_.""" + """top-1 neighbor start timestamp index in X_, and distances to the query.""" + return [0], [0.1] + + +class MockCollectionMotifsSearch(BaseCollectionMotifs): + """Mock estimator for BaseMatrixProfile.""" + + def __init__(self): + super().__init__() + + def _fit(self, X, y=None): + return self + + def _predict(self, X): + """top-1 motif start timestamp index in X, and distances to the match in X_.""" + return [0, 0], [0.1] + + +class MockCollectionNeighborsSearch(BaseCollectionNeighbors): + """Mock estimator for BaseMatrixProfile.""" + + def __init__(self): + super().__init__() + + def _fit(self, X, y=None): + return self + + def _predict(self, X): + """top-1 neighbor sample index in X_, and distances to the each query.""" return [0 for _ in range(len(X))], [0.1 for _ in range(len(X))] diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 5e81e29b33..a6b174d0a0 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -25,8 +25,16 @@ from aeon.regression.base import BaseRegressor from aeon.segmentation.base import BaseSegmenter from aeon.similarity_search._base import BaseSimilaritySearch -from aeon.similarity_search.collection import BaseCollectionSimilaritySearch -from aeon.similarity_search.series import BaseSeriesSimilaritySearch +from aeon.similarity_search.collection import ( + BaseCollecionNeighbors, + BaseCollectionSimilaritySearch, + BaseColletionMotifs, +) +from aeon.similarity_search.series import ( + BaseSeriesMotifs, + BaseSeriesNeighbors, + BaseSeriesSimilaritySearch, +) from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer from aeon.transformations.series import BaseSeriesTransformer @@ -39,6 +47,8 @@ "series-estimator": BaseSeriesEstimator, "transformer": BaseTransformer, "similarity-search": BaseSimilaritySearch, + "series-similarity-search": BaseSeriesSimilaritySearch, + "collection-similarity-search": BaseCollectionSimilaritySearch, # estimator types "anomaly-detector": BaseAnomalyDetector, "collection-transformer": BaseCollectionTransformer, @@ -49,8 +59,10 @@ "segmenter": BaseSegmenter, "series-transformer": BaseSeriesTransformer, "forecaster": BaseForecaster, - "series-similarity-search": BaseSeriesSimilaritySearch, - "collection-similarity-search": BaseCollectionSimilaritySearch, + "series-motifs-search": BaseSeriesMotifs, + "series-neighbors-search": BaseSeriesNeighbors, + "collection-motifs-search": BaseColletionMotifs, + "collection-neighbors-search": BaseCollecionNeighbors, } # base classes which are valid for estimator to directly inherit from diff --git a/docs/api_reference/similarity_search.rst b/docs/api_reference/similarity_search.rst index ec9d866b3f..4936afd36b 100644 --- a/docs/api_reference/similarity_search.rst +++ b/docs/api_reference/similarity_search.rst @@ -62,6 +62,8 @@ Base Estimators :template: class.rst BaseSeriesSimilaritySearch + BaseSeriesNeighbors + BaseSeriesMotifs .. currentmodule:: aeon.similarity_search.collection._base @@ -70,3 +72,5 @@ Base Estimators :template: class.rst BaseCollectionSimilaritySearch + BaseCollectionNeighbors + BaseCollectionMotifs diff --git a/docs/api_reference/utils.rst b/docs/api_reference/utils.rst index 8c4891dde0..0d943f28c1 100644 --- a/docs/api_reference/utils.rst +++ b/docs/api_reference/utils.rst @@ -87,8 +87,10 @@ Mock Estimators MockUnivariateSeriesTransformer MockMultivariateSeriesTransformer MockSeriesTransformerNoFit - MockSeriesSimilaritySearch - MockCollectionSimilaritySearch + MockSeriesMotifSearch + MockSeriesNeighborsSearch + MockCollectionMotifsSearch + MockCollectionNeighborsSearch Utilities ^^^^^^^^^ diff --git a/examples/similarity_search/code_speed.ipynb b/examples/similarity_search/code_speed.ipynb index 28cfdd64ed..0433b44962 100644 --- a/examples/similarity_search/code_speed.ipynb +++ b/examples/similarity_search/code_speed.ipynb @@ -164,7 +164,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -251,7 +251,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -323,7 +323,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -424,7 +424,7 @@ "outputs": [ { "data": { - "image/png": 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" ] diff --git a/examples/similarity_search/similarity_search.ipynb b/examples/similarity_search/similarity_search.ipynb index 652fc2db6e..6bb339f13f 100644 --- a/examples/similarity_search/similarity_search.ipynb +++ b/examples/similarity_search/similarity_search.ipynb @@ -100,9 +100,7 @@ "All estimators of the similarity search module in aeon inherit from the `BaseSimilaritySearch` class, which define the some abstract methods that estimator must implement, such as `fit` and `predict` and some private function used to validate the format of the time series you will provide. Then, the two submodules `series` and `collection` also define a base class (`BaseSeriesSimilaritySearch` and `BaseCollectionSeriesSearch`) that their respective estimator will inherit from. If you ever want to extend the module or create your own estimators, these are the classes you'll want to use to define the base structure of your estimator.\n", "\n", "### Load a dataset\n", - "In the following, we'll use an easy dataset (`GunPoint`) to help build intuition. Don't hesitate to swap it with other datasets to explore ! We load it using the `load_classification` function.\n", - "\n", - "The GunPoint dataset is composed of two classes which are discriminated by the \"bumps\" located before and after the central peak. These bumps correspond to an actor drawing a fake gun from a holster before pointing it (hence the name \"GunPoint\" !). In the second class, the actor simply points his fingers without making the motion of taking the gun out of the holster." + "In the following, we'll use an easy dataset (`ArrowHead`) to help build intuition. Don't hesitate to swap it with other datasets to explore ! We load it using the `load_classification` function." ] }, { @@ -113,9 +111,9 @@ "outputs": [ { "data": { - "image/png": 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22r05OVKYdMH/jPfIAAAAAPhSR4tdqrdmr1SzT6opsrPQW2vs35teJeffYZ8zBIDuHpcaWjtN6EI9Lpe1mB96XH0/m3I2afFRzjavnrTMnnz25q+lf3xJ+sJ6KTbFue0DgJ8haAIAo1WxXXr4I1JHozRphfSxPzoWMDHJgBWN7dpb0aQDNS1q6+y2DojN/eaAOSEmUjnJscpOirHW6QnRiooIsw6U0Y/JKjn3h1LuCdLfPmMf7BecKC346HiPDAAAAIAvFL0i/eNGqXb/0b9Lm2I3Ng+QjImm9i79+Y0DeuCVIlU1tQ/rOYkxkcpOjrHOFadmJWpRQaoWFqZqenaiIsKPPl9saOtUcU2LimtaVVLXqtyUWJ00OV1ZSTH2A87+nrTrObt35wvfkz74f06/TADwG5TnAoDRaK2T7jtVaiyVJiyVrnpKiknyenPmT/K7h+r1z62lWr+3Wvsqm60D5JEyB8FmiQoPU1x0pJJiI5UQE6GE6Ejr4DnR+tl9O8ZzO0KJMVGKjQpXS0e3mtu7rH03t3er28xYcvdeD1OY9Zi0hGilxZslSpmJMSpMjx/04NvvrPmh9MqdUlS83cgwZ954jwjwOdLMgwPlueAkvjMAglZ7k/TS96W3f2v/bHpNTjpNSp8qpU+x1/lLxr0yQFFVs3aVNw64LyIsTKnxUda5Vnp8tMLDwvTH9fv1u9eKVNfSedQ2zPmZeYw5C7PWYeacUuro7hlyv/HREZqUkaCu7h7rce2dPWru6FJj2+DnnVOzEnTKlHTNyklSQcNGnfPmtdb9b5/+oLomn6m0hCjrvNCMOyYyYtTvCzCeOG8KfG2U5wIAP/Duo3bAxMxU+q/HvQ6YvHeoXk9tPqx/bS3T4brWAb8zgYjCtDhNyUywghvmYNjEJsy6sb1LFQ1tKm9oV2VTu5V9Ypi1WTokNXd0D3s20mjERIZbs5Zm5SZpZk6SNdYjf58SF2UtybFRykmOUUaie9bSWDrrO9Lhd6R9a6VHPil9bi2p5QAAAEAwOPiG9MR1Ut1B++eln5bOvd1vepX09Li0dleFfv/afr2yu2pEzzXng184c5o+uChfUeHh9oS2QSoMmIl4ZvKbqVhQ0dCuisY2vV/aoC3FddYEPTNBbntpw6D7yEiIVkF6vPJTYq2gzs7yRmsin1lssbot8jx9OvIF5a37hla9+L9qVlzv8yemx2tBQYpOmJBirwtSrUl6ABBo+MsFAN4yU3g2/sG+feoXpPj0ET3dlNp65t1S/Wn9fm05VD9g5s9Zs7N13twczctP1sT0BEVHhh93eyZI0tTWpa6eHut2V49Lnd09VqaImTlkDpzN7z0ZJHYWiVn3ZZWYxYwrLirCnZ1iL2bGk8v8zy6Zq9aObtW2dKi2pVO1zR0qb2hTe1ePtpU0WMtwzchO1GnTM7VieqZOmZqupNgxaM4eHiF95EHpNyvtusZ/v0G64mEp/PjvMQAAAAA/1dUh/fUTUku1lDLRLh817awxHUJjW6fW7qzUi++X650DtUqOsyeL5STFKiU+Ss9vK9OB6hbrsSbesWBCiqIi+s5DTPZHXWunapo6rAlynnOmGz8wXRefkD+szH4TSDHnVWaZlpVo3XfpognW2pwn7q5oVGl9mzWpzWSGmHVsVITyUmKPmvhW19KhDftr9WZRtQ7Vtqqz26X1XTfqwpJ3VdBdpluTntb/9nzSepyZv3ewpsVann231Hq+Ge8JBSlaPi1Dy6ZmaumkNMVFk40CwP8RNAGA0TR/L98qRcZKJ1w+7KfVt3Zq9X/26q9vHbSCDkZ0RLhWzc/VxSfkaeXMLOugdaTMAak5EB8P5uDb1L81M5F2ljVqb2WTOrr6UsJNsKWtq9t67WYxzQurmzu0u6LJWh56fb81fpOhsrAgxaq1aw6uTeaKT1K8EzKkj/1BevB8aeez0hv3SctvdH4/AAAAAMbG/lfsgElClvSF10dVNnkkzAS0Z98rtSbErd9bZQUWPEwVge12/KBXcmykPn7yRH3q1ElWieOhmPMpE4QxfSud6llpzrlm5yZby3CkxkfrnLk51jLArl9Kf7lcV3Q/qytu/L56UqdYk+q2lzbq3cN1ViUFk9ViXv+mg3XWcu/Le63+m3PykrWwIFWLClO1eGKq1W8FAPwNQRMA8NbGh+z13EuluLTjPtykST+5+bB+/Ox2VTWZwlnShNQ4feKUibripEKrL0igMgffkzMTrGXVvNxhPcdkqKzfV63X9lRZy/7qFitN3Cx/fbu493HmfZmQGqv81Dgr3Xt2XpJ1kG9mTQ0nA2dIpgfN+f8jPXuz9J+fSov/a1ifIwAAAAA/tONZez3rAp8HTMy53abiOj36drGe3lJilUT2mJqZoHPn5WjljCyrZ4gpkWUy803J5Fm5ybpscb7io49/Oc6c64xLOePhmHGuNO0D0t5/Sy/eqvArHrbGumKGWTJ7H3aotsXq1WmW1/ZWWWWlTTDFLH9644D1mFXzcvTTjywctwmAADAYgiYA4I32Rum9v9m3l1x93IfvLm/Ud5/cqjeLanqb6X3z/Nk6Z05OYDRP9wHT3PDCBXnWYpTWt1p1dk2pMrM2s5NMSro5uTBL/xJmhpmlZBoYRoaHWZksPS5TQEyanBGvxRPTrFlLZgbTkSnmAyy9xm4QWfG+9PqvpLO/5+uXDQAAAMBpPT19QZPZlzi66ZK6ViswYspTmf4gpldIaV2byhraeh9jzkE+urRA58/Ps7Llg57JfDnvx9Lq06TtT0v7X5Mmn3bUwwrS4nX5iWYptAJN5j3cbM75iuustVme31aurYdf0f9duUhLJ42s5DUA+ApBEwDwxtYnpM5mKWO6NGn5MR9qGrx/9dEtVo+R2Khw/fcHZui606eOLksiCOWlxFmLOdEwzEG1KV9mTlLMYlK7TTNCk4myo7TRCqjsqWg6ajvmvpe2V1i3TTzKzOZaMjFVS9yBFNNAsTe93fQxMY3hH/kv6Y1fS6feICX0zYwCgKF8+tOfVl1dnZ588snxHgoAADj8jtRUJkUnSVNXOrJJM4nrgVf2WaW3TDniI5leIGYCmKkacMqUdMdKaAWMnLnS0k9LGx6Unv+2dN3Lx+wTad4fU47MLJcszO99j//7/220qg587P439NXzZur6M6YpPEQnFgLB5tMBfM5E0AQAvOFpAL/kKnuWzRD2VTbpW397zwqYnD07W7d9cN4x69Zi4EG1qd9rlvkTUgb8zjNLyTRRNPkl4WFh1sdgTTAra7BS5TcdqFVJfVtvya8/v3nQem5GQrQuWJBrNUNcOjFN4bMvkvIXSyWbpFfvllb9eJxeMYBQc9ttt+mvf/2riouLFR0draVLl+rHP/6xTjnllPEeGgAAgWXHM31loyJHV9LKnDt8/x/b9Ja7SoBhgiInTU5XdnKMspNilJUUqxk5iUqODfGSUmd+W3rvcal0s/TuX6VFnxjR0xcUpOjp/15hVWV4anOJfvrcTj2/tUxXLZusi07I86rXJ4Dg8sQTT2j16tV65513VFNTo02bNmnRokU+3y9BEwAYqbKt9kym8Chp4SeO2bjvy3/drNbObi2flqEHrjqRGTMO6T9L6Uj9a+iW1bdp48FabTpYq40H6/Te4XqrAf3Dbxy0FtNT5uKFeTop7zqdU/IFdb35gJ6MuVThyflWTV4TYDE9VTISoxUVQWYQAGfNnDlTv/rVrzR16lS1trbq7rvv1nnnnac9e/YoKytrvIcHAEBgMLV6PUETMyFqFDq7e3T9w+9Yk7NMGWCTEfGZFVOOmsQFt8Qs6fSvSi99X1rzQ7vfZ3TCiDaRFBule65YpNOmZerWf2y1yjJ/9bEt+uEz7+vDSyboU6dOolk8EMKam5u1YsUKfexjH9N11103ZvvlChAAeJtlMvtC+yBxCHe9uMu6SJ8aH6W7PraIgMk4yE2JtVLmv3PRXP3thuV677bz9MdrT9ZHlhQoMSbSKvl1/3/26bOvpeitnlmK7GlXy0s/1c2PbtHVD76li3/5qk69Y41OuO0Ffe2xLdqwv8bKcgEwdh5//HEtWLBAcXFxysjI0DnnnGMdOBtvv/22zj33XGVmZiolJUUrV67Uxo0bjwqy3n///br44osVHx+vOXPmaP369VZg4swzz1RCQoKWL1+uvXv39j7HZICY2UvmeYWFhdbzzEF6ff3A3kr99fT06I477tCUKVOssS5cuNAa+7F84hOfsF6PCZrMmzdPd911lxoaGvTuu++O+n0DACBkVO6UqvdIEdHSjPNGtaknNh6yAiaZidF65Ztn6e4rFhEwOR5T4jh1ktRYKr3+S682YY7XPnZSodZ94yx9fdUsa3JbfWunfv/afp3/i1esHigAQvOc6VOf+pRuvfVW6zWNJYImADASna3Su4/0leYawut7q3T/Ovs/Jv/z4ROsi/cYfzGRETpjZpbu/NhCbfjuObrvv5ZYAZSLFuTrtcLrrcd8IvJlfWhKl+bkJSsrKUYR4WFWttDj7xzSR1ev1zl3/Ue/WbdXze1d4/1ygNExAcCO5vFZhhl8LC0t1ZVXXqlrr71W27dv19q1a/XhD3+4N3jZ2Nioq6++Wq+++qreeOMNzZgxQxdeeKF1f3+33367rrrqKm3evFmzZ8+2ghWf//zndcstt2jDhg3W9m688cYBzzEnCI8++qiefvppPffcc1Ya+Be+8IUhx2oO/v/4xz9aqePbtm3TV77yFX3yk5/Uf/7zn2G91o6ODv3mN7+xTmTMyQMAABimHU/b6ykrpdhkrzdjKgX835o91u3rV06z+i1iGEw5tLNvtW9v/KNdM9lL2Umx+uJZ063gye+vOUlLJ6VZn8u3nnjPygICxhznTPKnc6axRHkuABiJHc9KbfVSykRp6gcGfUhtc4dufmSL9d+3K08u1Pnzc8d8mDg+Ux/XZKGYxbZE+uNTity3VnfnvCBddq91b0+Pyyrx9cjbxXrm3VLtrWzWT/65Q49uOKTVn1yq6dmkiiNAdbZIP7GbcI65b5cMq3SDOQHo6uqyDvonTZpk3WdmUHl84AMD/w6boENqaqp10G1mSXlcc8011qwn45vf/KaWLVum733ve1q1apV135e//GXrMf21tbVZB/QTJkywfv7lL3+piy66SHfeeadycwf+XW9vb9dPfvITvfTSS9a2DZM9Yk5MzMwrM5trKM8884w+/vGPq6WlRXl5eXrxxRetWWAAAGAE52jGnL7/9nvj0Q3FVia66VnyyVPt4w4M0+yLpehEqeGwVLJRKjhxVJszE9fOmpWtBRNSrElrps/Mg68W6fMrpzk2ZGBYOGeSP5wzjQcyTQBgJA6ut9dzPyiFD/4n1DQNLGto09TMBH3v4rljOz6MzlnftdemiWGrnQJuyqqdODldP7t8od76ztn6yYcWKCc5RnsqmnTZva/pua2l4ztmIIiZjIuzzz7bOui//PLL9cADD6i2trb39+Xl5VZdWzNbymRoJCcnq6mpSQcPHhywnRNOOKH3dk5OzlEnEuY+c8BvSmN5TJw4sffg3zAH9iadfOfOnUeN08ywMkEPk/aemJjYu5gTiP4p7IM566yzrNlcr7/+us4//3zrRKWiomLE7xUAACGp/pBUsskUl5FmXej1Zto6u3Xvy3aWicl0oAH5CEXFSjPPt2+//6RjmzX9Jb9z4Rzr9t0v7dLB6hbHtg0Ei1A4ZxoPZJoAwEiUb7PXeYOXTqlqatcz75ZYt0392/ho/swGlMKTpMxZUtVOac9L0oKPHtWk8BOnTNS5c3N041826s2iGl3/8EZ9fuVUff28WYqkWTwCSVS8PXtpvPY9DBEREVbmhQkovPDCC9bMpe985zt68803rTq4Js28urpav/jFL6xZVTExMdaBuil1NWB3UVED6vUOdZ85wPeGOekwnn322QEnDYYZ07GY+sDTp0+3llNPPdU6mfnd735npcEDAIBhZpkUniIlZnu9mb++dVCl9W3KS4nVFScVOje+UGKawG99XHr/Kenc280BliOb/ejSAj2x8bDW76vWd558z+pR6Tl2A3yOcyb5wznTeODqDgAMl6m35Qma5Mwb9CH/fK9UPS5pYUGKFhamju344IzZ7hlqO54Z8iGm18mfP3uKPnfGVOtn00z+mofeVksHfU4QQMxBr0n3Ho9lBCe65uD8tNNO0w9+8AOrRm50dLT+/ve/W7977bXX9KUvfcmqyWsaqZuD7aqqKkfeHjPzqqSk7wTJ1P8NDw/XrFmzjnrs3LlzrX2b53gCIJ7FNEUcCXMSYlLXAQDAMHiO2UdRmsvKMllrz3K+8QNkmXht+jn2Rd66g1LpZsc2a44Ff/LhBYqODNcru6v01OZxuoCN0MQ5k1+eM40FpkADwHDVF0vtDVJ4lJQxY9CHPL3F/o/FJQvHqeYlRm/WRdKrd0u7X5K62u3GhoMwWSXfvnCOFhWm6muPbbEO4D/52zf1+2tOVkpc32wMAN4zs6PWrFmj8847T9nZ2dbPlZWVmjPHLtNgsjL+9Kc/6cQTT7TSxL/+9a8rLs6Zpq2xsbHWrKyf//zn1rbNiYYpnXVkbV4jKSlJX/va16xGhibosWLFCtXX11snKCb93WznSM3Nzfrxj3+sD37wg1YvE3Picu+99+rw4cNWWj0AADiOlhpp/2v27dkXeb2Zh984oMrGdhWkxenypf534S5gRMdLM86zy3OZbJP8xY5tekpmgr589gz97Pmd+uEz72vlzCylJUQ7tn0gkAXzOZNRU1MzIDjjKf1l9jHYfpxCpgkADJcnyyRrlhR59AGaaRr49v5aazLAxScQNAlYE5ZKiTlSR6O0/5XjPtw0kjdZJyZQsvFgnT7+mzesMm0ARs8cPK9bt86aFTVz5kx997vftZoKXnDBBdbvTRkrU693yZIl+tSnPmUdpJsTBSeYGU+mmaLZtzkBMTV+77vvviEff/vtt1uNEu+44w7rBMX0JzGp5yYlfqg0+h07dugjH/mI9douueQSK23+lVdesWaAAQCA49j3suTqlrLmSOl2BvhIdXb3aPV/7CyTL31ghpXNgFGW6DK2PWlXanDQdadP1aycJNU0d/T2nwEQ3OdMxj/+8Q8tXrzYajBvfPzjH7d+Xr16tXwpzOVy+K/YODNRLdPUxkSqzJcGAByz7mfSv38knXCF9OHfHPXr36zbq5/8c4dOnpKuRz+/bFyGCIf840vSxj9IJ35GuviuYT1le2mDPvW7t6yAydSsBD38mVOUn+rM7A1gtEzDvqKiIutg1MwGwrHddtttevLJJ60G7YHyOXIMjJHiOwMg4D39Zemdh6RTvyCdf4dXm1i/t1pXPvCGMhKi9ca3z1YUPQpHp71J+tl0qatV+vwrUl5fY2knvLyjwiqLnBQbqTduOVsJMRTQgbM4bxq+24L8nIn/GgDAcB2nn8k/3KW5PkhprsA3210Teee/hj1Dak5esh67fpkmpMZpX2WzPnb/etW3dvp2nAAAAECoKlpnr6es9HoTa3dVWOuVs7IImDghJlGacY5925TocpgpyzU5I16NbV36+6bDjm8fADz4LwIAOBA02VfZpK2HGxQRHqYL5vuupiLGyJQzpKgEqbFEKtk0/KdlJujR65epMD1Oh2pb9btX9vl0mAAAAEBIqiuWavZJYRHSpOVeb2btjkprfeYsZ0rVwJTousxem94mDhe3CQ8P06eWTbZu/3H9fgVZ8RwAfoSgCQAMR2erVO2um5oz/6hfP72l1FqvmJ6pjMTBG4cjgETFStPPtm/v/OeInmoyTb5zod1w7XevFlk1dwEEXqq5v6WZAwCAQbJMTLPxWO9KDJqelDvLGxUeJp0xI9PZ8YUy0ww+IsY+f67Y7vjmLz+xQPHREdpV3mSVVwMwPm4L8nMmgiYAMByVOyRXjxSfYTcJ78fMbvnHFjs1+BJKcwWP2XaTMe0YWdDEWDUvV/Pyk9Xc0a373Y0lAQAAADgcNJk6itJcO+3SXEsmpik1PtqpkcEEsTwT0HxQois5NkofWVJg3X7o9f2Obx8ADIImADDS0lxhYQN+tb20UXsrmxUdGa5V8wYGVBDgM6RMun/FNql2ZAfjYWFh+tp5s6zbf1i/XxUNbT4aJAAAABBiTEmmov/0ldX10svu0lxnzaY0l+PmXtpXossHrl4+yVq/tL1cxTUtPtkHgNBG0AQARhQ0mT9kA/gPzMpWUmzUWI8MvhKf3lcf2YtskzNnZWnJxFS1dfbo3pfdpd2AcdbT0zPeQ8Ao8PkBACC77FNjqV0CqvAUrzbR3tWt1/ZU9R63w2Ezz7cnoJmKDXUHHd/89OwkqzR2j0t6+I0Djm8foF9O4HLqnClSPrRu3Tr97Gc/0zvvvKPS0lL9/e9/12WXuRtCDWLt2rU666yzjrrfPDc3l8bKAMZR+dZBm8Cb/5A+7Q6aUJorCM26UNr/irTjWWnZF0aebbJqlj7xwJv6y1sHdd0ZU1WQFu+zoQLHEh0drfDwcJWUlCgrK8v62XxHERjMf2s6OjpUWVlpfY7m8wMAIGR5skwKT5ai4rzaxFtFNWrt7FZ2Uozm5nnXEwXHEJcqTVgiHXpbKnpFWvxfju/i6uWT9eqeKv317WLddM5MxUVHOL4PhJ6oqCjrPMkcd5vzJs6ZQvecyadBk+bmZi1cuFDXXnutPvzhDw/7eTt37lRyct9/tLKzSZUEMI7MDIOywYMmO8oarQaCphHdB0jrDj6zL5Sev0U6+LrUUmNnn4zA8mmZWj4tQ6/vrdav/r1H//ORE3w2VOBYzEHjlClTrIkoJnCCwBQfH6+JEydanycAACFrn6c018rRl+aalc1FUV+ZfLodNNnvm6CJOf8uSIvTodpWPbX5sD5+8kTH94HQExERoYKCAh06dEj799MzJ5TPmXwaNLngggusZaRMkCQ1NdUnYwKAEWsql1prpLBwKWv2gF9tL22w1vPzU5jZEozSJkvZ8+y+JnvWSCdcPuJNfPW8WXr916/rsXcO6fMrp2lKZoJPhgocj5lpYw4eu7q61N3dPd7DgRcncJGRkVzYAQCENlN2xVyEd6gJPKW5fGjK6dKrd9mZJmYiosPHMBHhYbp62WT9+J/b9fCbBwiawDGJiYmaMWOGOjs7x3soGMdzJp8GTby1aNEitbe3a/78+brtttt02mmnDflY8zizeDQ02BcwAcDx0lwZ049K/zaZJsbsvKTxGBnGwvSz7aDJ3n97FTRZOilNZ83K0ss7K/XXtw7qlgvn+GSYwHCYg0eTcm4WAACAgDw3a62VohOl/MVebWJ/VbP2VTUrMjxMp83IdHyIcCs8VQqPkhoOSbVFUvpUx3fx0aUF+t/ndmjr4QbtLm/UjBzOy+HcxXezIHT5VW5/Xl6eVq9erb/97W/WUlhYqDPPPFMbN24c8jl33HGHUlJSehfzHADwTRP4gaW5+gdNZuVycBbUQRPDBE28bAZ3+Yn2f5ue31ZGQzkAAABgtP1MJi2XIqJGlWVy4uQ0JccykcRnouOlgpPs20XrfLKLtITo3myhJzcf9sk+AIQmvwqazJo1S5///Oe1dOlSLV++XA8++KC1vvvuu4d8zi233KL6+vrepbi4eEzHDCC0gyY7y+zsttm5NA8M6hlSkXFSU5lU8b5Xm1g5M0vRkeHaX92i3RVNjg8RAAAACAmei++j6Weys6+fCcagRJdhSnT5yGWLJ1jrJzeVqKeHCWoAgjBoMpiTTz5Ze/bsGfL3MTExVtP4/gsA+CZoMn/A3bXNHSpvsMsDkmkSxKJipckr+rJNvJAQE6nTp9up/89vLXNydAAAAEBo6O6UDrxu355yhlebaO3o1hv7qq3bZ80maDImzeAN04fGRxn358zJUWJMpA7Xteqdg7U+2QeA0OP3QZPNmzdbZbsAYFx0dUiVOwfNNPGU5ipIi7MO0hDEpn3AXptm8F5aNS/XWj//PkETAAAAYMQOb5Q6mqS49KMmtA3Xm0XVau/q0YTUOM3ITnR8iDiCKc8VESM1lUtVu3yyi9ioCJ0/3z7X+vsmSnQBCICgSVNTkxX0MItRVFRk3T548GBvaa2rrrqq9/H33HOPnnrqKSuzZOvWrbrpppv073//W1/84hd9OUwAGFr1bqmnU4pJllIG9kyiNFcI9jUxM9s6W73axNlzshUeJqtJoZkFBQAAAGAE9ntKc50uhXt3OWtbSUNvP5OwsDAnR4ehsvYnnuLTvibGh9wlup59t1QdXT0+2w+A0OHToMmGDRu0ePFiazFuvvlm6/att95q/VxaWtobQDE6Ojr01a9+VQsWLNDKlSu1ZcsWvfTSSzr7bPfFKgAYz34mRxxUezJNZlOaK/hlzpSSJ0jd7dKB17zaREZijE6cnG7dfmEb2SYAAADAiBzeZK8L3RfhvdB3DsfEtzEz+Yy+El0+curUDGUnxai+tVNrd1b4bD8AQodPgyZnnnmmXC7XUctDDz1k/d6s165d2/v4b3zjG1aWSWtrq6qrq/Xyyy/rrLPO8uUQAeDYyrcO2QTec8BNP5MQYAJmnhJde1/2ejPnzc2x1i9sK3dqZAAAAEBoKN1ir/MWeb2JHaWeagGcw415M/j9r0o9vskCiQgP06WL8q3bT26mRBeAEOhpAgB+k2nST0+PS7vK7aDJnDwOuENCb9DEu2bw/fuavLW/RrXNHU6NDAAAAAhuzVVSwyH7dt4JXm2ivatb+6qardtMfBtD+UukqHippVqqeN9nu7nMXaLrpe0Vamjr9Nl+AIQGgiYAcCwV2+119twBdxfXtqilo1vRkeGanJEwPmPD2Jp6pkk5sQ/0G0q82kRherzm5CWru8ell7aTbQIAAAAMS6ndK1cZ06UY7wIeeyuarePw5NhI5aXEOjs+DC0yWpq4zOcluubmJWtGdqLV0+S59yiHDGB0CJoAwFDa6qUGd2pv1qxBS3OZg7LICP6UhoT4dGnCklGX6Fo1z12i632CJgAAAMDISnMt9HoTO8o8pbmSaQI/XiW6inwXNDGfqSfbhBJdAEaLK30AMJTKXfY6MVeKSxvwq530MwnxEl1rRl2ia92uSrV0dDk1MgAAACB4OdDPxHMON5vyyuPYDN70Nen22W48fU3W76tWRWObz/YDIPgRNAGAoVR6SnPNPsYsJQ64Q8q0s/syTbxsYmi+M4XpcWrv6rECJwAAAACOo2TzqDNNtjPxbfyYzy06SWqvl8re9dluCtLiNX9Cslwu6bU9VT7bD4DgR9AEAIZSudNeZw0WNHHPUspNHutRYTwVnGgf7LfWSGXu2W5epI2vmmtnm7ywjRJdAABp3bp1uuSSS5Sfn2/9d+LJJ5885uPXrl1rPe7IpayMGu4AglBrrVR3YNRBk51MfBs/EZHSJHdfkwPrfbqrFdOzrPUruwmaAPAeQRMAOF4T+COCJm2d3dpf1Wzd5oA7xERESVNX2rf3eF+i6+w5dl+Tdbur5DLToAAAIa25uVkLFy7UvffeO6Ln7dy5U6Wlpb1Ldna2z8YIAONemittshSX6tUmaps7VN7Qbt2emcM53LgoPMVeF7/p092cPiPTWptME861AHgr0utnAkCoZJpkzxlw9+7yJvW4pLT4KGUlxYzP2DB+ppwh7XhGOuj9DKklk1IVExmuqqZ27a1s0vRsTtwAIJRdcMEF1jJSJkiSmurdBUQACK0m8HalgIK0OCXFRjk1MozExFP7giYmmBEW5pPdLJ2UZp1rmSDZnoomzSBIBsALZJoAwGDaGqSGQ/btrFlD9DNJtkphIFRnSL3tdV+TmMgI62DeWL+vxsnRAQBCyKJFi5SXl6dzzz1Xr7322jEf297eroaGhgELAIROE/i+cziMk/wlUliE1Fgq1Rf7bDexURE6eUq6dZsSXQC8RdAEAAZTtcteJ+ZKcfbFbY+dNBAMbTnzpagEu4lh5Q6vN7Nsaoa1fmNvtYODAwCEAhMoWb16tf72t79ZS2Fhoc4880xt3LhxyOfccccdSklJ6V3McwAgVJrA9/Wk5Bxu3ETHS3kn2LeL3/LprlZMt0t0vUozeABeImgCAMfsZzIwy6T/AfecPA64Q7aJYcFS+3bxG15vZtk0d9BkXzW1dgEAIzJr1ix9/vOf19KlS7V8+XI9+OCD1vruu+8e8jm33HKL6uvre5fiYt/N8gUARysA1OwddaaJ5xyOiW/jrLBfiS4fWuHua2LOtTq7vasOACC0ETQBgMF4MgiO6Gcy8ICb1O6Q1Xuw7/0MqRMKUhUXFaHq5g7tKm9ybmwAgJB08skna8+ePUP+PiYmRsnJyQMWAPB7Ze/Z65RCKcGedDRSPT0u7Spn4ptfKDzZXh/0fvLZcMzJTVZGQrRaOrq16WCdT/cFIDgRNAGAYwVNjsg0MY27zWJamczMSRyfsWH8TTxl1Af70ZHhOnGyu6/JXtLGAQCjs3nzZqtsFwAEldLRl+Yqrm2xLp6b4+/JGQnOjQ3e94cs3yq1+27iWHh4mJZ7SnTtrvTZfgAEL4ImADCYyp32OmvOoP1MJqXHKz46cjxGBn9QcJKkMKm2SGqq8Hozp7r7mqzfR18TAAhlTU1NVtDDLEZRUZF1++DBg72lta666qrex99zzz166qmnrMySrVu36qabbtK///1vffGLXxy31wAA/toE3lMpYEZ2oiIjuAw2rlIm2FlDrh7p8Aaf7up0d9DkFfqaAPAC/7UAgCO1N0r1xYNmmlALF5bYFCl77qjr8Xr6mrxZVGOVDQAAhKYNGzZo8eLF1mLcfPPN1u1bb73V+rm0tLQ3gGJ0dHToq1/9qhYsWKCVK1dqy5Yteumll3T22WeP22sAAL9tAl/KOZxfZpv4uBn8ae6+JluK69TQ1unTfQEIPkyTBoAjVe6y14k5Unz6gF/trbRTiGdkc8Ad8kw93optdomuOZd4tYkFE1KUEB2hupZObS9r0Lz8FMeHCQDwf2eeeaZcrqGD5w899NCAn7/xjW9YCwAEtY5mqWrXqIMmO8sbevtcwE+CJlsf93kz+AmpcZqamaB9Vc1av7daq+bl+nR/AIILmSYAcKTK7YNmmRh7K+ygybRsauGGvImjbwYfFRGuk6bYgTlzIA8AAADArWyrJJeUlCcl5Xi9GaoF+Gl/yOK3pZ4en+5qhTvb5NXdlOgCMDIETQBgyCbwA/uZGGaWijE1kybwIc+TVm6aU3a2eb2ZZe6+Jm/Q1wQAAABwtAl8W2e39rvP4WYTNPEP2fOkqASpvb7v3NtHTvM0g6evCYARImgCAEeq2DFopompg1rZ2G7dnppFpknIS5ssJWRL3R1SySZH+pp009cEAAAAcKwJ/O7yJplD7PSEaGUlxTg3NngvIlIqWGrf9nGJLnOuFREepqKqZh2qbfHpvgAEF4ImAHCkyp32Ontgpsm+SnuGUnZSjJJio8ZjZPAnYWH9Usu9P9g3fUySYiPV2NalbSX1zo0PAAAACIqgifeZJqZvoDErJ0lh5vgd/qHw1DEJmiTHRmlhgd03khJdAEaCoAkA9NfeJNUftG9nzR7wq33uJvBkmeCoEl2jONg3M59Ooa8JAAAA0KezVarYPvom8O5+JrPzKM0VbOdRw3X6jCxr/QolugCMAEETAOivyp1lYsouxdsXso/MNJmaRT8TDDJDyuV9aa1T3X1N1tPXBAAAAJDK35dc3VJClpSc7/VmdpW7m8DnEDTxKwUnmtR9qWaf1FTp012dMdPua/LanirKIQMYNoImADCMfibGXnemyTSCJvAws94iYqSWaql676j7mrxdVKOu7h4HBwgAAAAEoNJNfcfboyirZXqaGDMImviXuNS+ctg+zjZZWJCqpJhI1bV06r3DlEMGMDwETQCgv8odg/YzGZhpQnkuuEVGSxOWjPpgf05uspJjI9Xc0a0d7hICAAAAQMhyoJ9JQ1unyhrarNvTs5n45r8lut7w6W4iI8K1fLo9Se2VXb7NagEQPAiaAMBgQZMj+pmYNN6iajtoMi2TA244e7AfHh6mRRPTrNsbD9Y6NTIAAAAgwIMmi0adZZKbHKuUuCinRganTHSXOj7wus93dcZMd18TmsEDGCaCJgAwaHmugUGTw7Wt6ujqUXRkuCakxY3P2ODfQZODo0srXzIx1VpvPEDQBAAAACGsq93uaTLKTJM9FXYG94wcJr35pUmn2euSzVK7b7Ptz3A3gzcT1BrbOn26LwDBgaAJAHi0N0n1Bwctz7W3yp6lNCUjQRHh3tfURRAqPNleV+2UWuu83syS3kwT77cBAAAABLyK7VJPpxSXJqVO9Hozuzz9TLLpZ+KXUgul1EmSq9vnfU0K0+M1OSNeXT0urd9b7dN9AQgOBE0AwMNc9DYSsqT49AG/op8JhpSQKaVNsW8f3uD1ZhZNTLV6XB6saVFVU7tz4wMAAAACtZ/JaJrAV3iawJNp4rcmr7DX+1/z+a4o0QVgJAiaAIBH5c5BS3MZeyvtA+5pWRxw4xjZJoe8D5okx0ZphrtBJSW6AAAAELJKN4+6NJexu9wu+TSToIn/l+g64PugyenuEl2v7KYZPIDjI2gCAP3TwIcImuxzB03INMGgCk6y18VvjWozlOgCAABAyHOgCbzpW1Fa32bdnk55Lv812R00OfyO1GFXd/CVU6emKzI8TPurW3SwusWn+wIQ+AiaAMCRmSbZgwVNPOW5mKWEYwRNTHmunh6vN9MXNCHTBAAAACGou1Mq2zrqTBNPaa6c5BilxEU5NTo4zfQ0SSmUerpGPQHteJJio3rPt9aRbQLgOAiaAIBH5eCZJmaWUkWj3WOCTBMMKmeeFBkntdVL1bu93sySSanW+t1Dders9j74AgAAAATsRLbudikmua9voBf20AQ+MJieNWNYouuMmZnWmhJdAI6HoAkAGCYVuO6gfTtrzqBZJllJMVbfCeAoEVHShCX27UNve72ZqZmJSo6NVFtnj3aU2jWYAQAAgJBsAh/u/SWrXe5+JtPdPQMRACW69o9dX5PX91QzSQ3AMRE0AYD+pbniM6WEjAG/2lfl7meSSZYJjqHgRHs9irTy8PAwLaZEFwAAAEKVU03g3eW5ZuaQaeL3PJkmptRxZ6tPdzV/QopS46PU2N6lLcX0kQQwNIImADCgn8nALBNjb4WdaTKNWUo4loKT7fWhDaPaDH1NAAAAELIcaAJv7HEHTWbkcA7n99KnSkl5UnfHqLL2hyMiPEwrptslutbtrvLpvgAENoImADCgn8mso35FpglG1Ay+4n2prWHUfU0ImgAAACCk9HRLZe+NOtOkqb1Lh+vsjIUZTHwLjL4mk1eMWYmuM9wluuhrAuBYCJoAQP9MkyOawPfvaTItiwNuHENSjpQ6UZJLKtno9WYWFaZa5w3FNa2qbGx3dIgAAACA36raLXW2SFEJUsa0UWeZmJ6UqfHRDg4QPjOGzeBXzLAzTUx5rvqWTp/vD0BgImgCAEbF9kGDJt09Lu2rImiCEWabFHufVp4UG6WZ2XbtZbJNAAAAEHqluU6QwiNG3QR+JqW5Aocn08T0h+xs8+mu8lPjND07UT0u6bW9lOgCMDiCJgDQ0SzVHRy0p0lJXas6unoUHRmuCWlx4zM+BGBfk9HV4qVEFwAAAEKOQ03ge/uZuCciIQBkTJcSsqXudunwOz7fHSW6ABwPQRMAqNpll1SKz5AS7FRdj72V9gH35Ix4q2kcMKxMExM0cbm83sxidzP4TQfqnBoZAAAAEBJN4D2ZJjSBD7S+JmNXouv0me5m8Luq5BrFeRuA4EXQBAB6+5kMzDIx9tLPBCORu0CKiJFaa6SafV5vZok7aLLlUJ06u3scHCAAAADgh3p6pNJ3Hck02V1OpklA6m0G/6rPd3XKlHRFR4TrcF1rbzluAOiPoAkAePqZZA/WBN4+4J6alTDWo0IgioyW8heNukTX1MwEpcRFqb2rR++XNDg3PgAAAMAf1eyVOhqlyFgpc6bXm2lu77IuhBszspn4FlAm9etr0tXh013FR0fqpCn2RLVXdlGiC8DRCJoAQG+myWBBE3vWydRMDrgx0mbwb3m9ifDwMC2emNqbbQIAAAAEtcMb+0pzRUSOup9JZmKM0hKinRodxkLWLCk+U+pqlUrc3wcfOr23rwnN4AEcjaAJAFRuHzJocrCmxVpPzowf61EhGPqajMKCCSnW+r1D9U6MCgAAAPBfnubfE5aOajO73UGTmfQzCcy+JpOWj1mJrtNn2H1N1u+rVkcXJZEBDETQBEBo62iRag8MGjTp6u5RWUObdbsgjaAJRhg0Kd8mdXhfH3e+O2iylfJcAAAACJmgyZJRbWa3pwk8pbkCu6/JGDSDn5ObrMzEaLV0dOudA7U+3x+AwELQBEBoq9olySXFZ0iJdnquR2l9m7p7XIqODFdWYsy4DREBJmWClJQvubql0i2jDpqYE7+2zm4HBwgAAAD4ka52qexdRzNNZuTQBD6ggyYH35S6O326K1MSua9EF31NAAxE0ARAaDtGP5Nid2mugrQ464AKGDZPM/hRBE3yU2KVnhCtrh6XdpTZM+YAAACAoFO+VerukOLSpbTJo9rULjJNAlvWHCkuTepslko2j1mJLvqaADgSQRMAoe0Y/UwO1bZaa0pzYcRMA0tjFAf6YWFhfSW6DtPXBAAAAEHeBN5kmZi+Fl5q7ejW4Tr7HG46QZPAFB4uTTrNvn3A931NVriDJltL6lXd1O7z/QEIHARNAIS2ih1DZ5rU2pkmhWlxYz0qBLq8haPONDHm5ydba4ImAAAACFoONYEvqmqWyyWlxUcpg/LKgcsTNNnv+74m2UmxmpOXbH1vXt1DtgmAPgRNAIS2SnfQJJtME/igPFfVzlE1g1/gzjR5j6AJAAAAgpVDQZO9lXY/k2lZZJkER1+TN6TuLp/v7gxKdAEYBEETAKGro0Wq3X/cniaF6WSaYISScqXEHMnVI5Vv83oznvJcpjZzexfN4AEAABBk2uqlql327QlLHAmaTM1KcGJkGC8586TYFKmjUSp71+e78zSDX7erUi6TcgIABE0AhLTq3ZJcdsPBBPtAafDyXGSaYHz6mhSkxSk1Pkqd3S7tKrNPAgEAAICgUbLJXqdOkhLsGf/e2ltpZ3iTaRLgwiOkicvt2/t939fkxMlpio4MV0Vju/ZX29cAAICgCYDQ1b+fyRENB82s/vKG9t4L18B49DWxmsHnU6ILAAAAQcqh0lzGPspzBY/Jnmbwvu9rEhsVoYUF9jnX20U1Pt8fgMBA0ARA6DpGP5PD7n4m8dERSk+IHuuRIaiCJt5nmvQv0bW1hKAJAAAAgszhjY4ETXp6XNrnyTTJJmgSNM3gD6yXenxfpvjEyenW+u39BE0A2AiaAAhdnqBJ1rGawMdZs/0Br5vBV2yXOttG3Qx+K5kmAAAACDYOZZqUNrSptbNbURFhKqRSQODLPUGKSZba66XyrT7f3ckETQAcgaAJgNB1jKAJ/UwwaskTpPgMydU9ymbwydZ6R2mjOrp6HBwgAAAAMI4aSqTGUiksQso7YVSb2lthl+aanJGgyAgudQW8iEhp4qlj1tdkyaQ0q2K36WlS0ej9hDcAwYP/kgAITZ2tUk3RsDJNAK+Yo25PM/hRlOiamB6v5NhIdXT3aHdFo3PjAwAAAPwhyyR7rhSdMKpN7XX3M5maNbrtwA9LdO33fV+TlLgozcpJsm5v2F/r8/0BCPGgybp163TJJZcoPz/fKm/z5JNPHvc5a9eu1ZIlSxQTE6Pp06froYce8uUQAYSqqt2SXFJcmpSYfdSvi2vcmSbpZJpgfPuaWM3gKdEFAACAoC3NtWTUm/IETWgCH0Qmr7DXB183TWt8vruTp9glut6iGTwAXwdNmpubtXDhQt17773DenxRUZEuuuginXXWWdq8ebNuuukmffazn9Xzzz/vy2ECCOnSXHPsjIAjFPdmmhA0gQN9TUq3jGoznqDJewRNAAAAECwc6mdi7K1wN4EnaBJcE9CiEqTWWqni/TFrBr/hAEETAFKkLzd+wQUXWMtwrV69WlOmTNGdd95p/Txnzhy9+uqruvvuu7Vq1SofjhRA6AZNZg3668PuniaU54IjmSbl70td7VJkjFeb6cs0aXBydAAAAMD4MJkDhzc5FjTZV+XONMkmaBI0IqLsviZ719h9TXLn+3R3J01Os9bvlzSosa1TSbFRPt0fAP/mVz1N1q9fr3POOWfAfSZYYu4fSnt7uxoaGgYsAHBcFe6gSfaco37V0tGlqqYO6zbluTAqqZOk2FSpp1Oq2O71Zha4gybbSxvU1U0zeAAAAAS46t1SR6MUFT9oj8mRMBe4yxvardv0NAkyk919TQ74vhl8XkqcNWmyxyVtOljn8/0B8G9+FTQpKytTTk7OgPvMzyYQ0tpql8o50h133KGUlJTepbCwcIxGCyBYM00Ou0tzJcVGWg3hgNE1gx99X5NJ6fFKjIlUe5dpBm/PogMAAAACvjRX/mIpYnRFUPZV2qW5spNilEx2QHCZ5O5rcuB1yeXy+e5Odpfoens/JbqAUOdXQRNv3HLLLaqvr+9diouLx3tIAPxdZ5tUW9TX0+QIxe7SXIX0M4Gf9DUJDw/TvPxk6zZ9TQAAABDwfNAEniyTIGSCapFxUkt138RHHzrJ3QyeoAkAvwqa5Obmqry8fMB95ufk5GTFxQ3eVyAmJsb6ff8FAI6bCu7qscsmJWYf9eviGjvTpDCdfiZwgCfTpMT7TJP+Jbq2EjQBAABAoHOyCbw7aEIT+CAUGS0VnmzfNn1NfMzT18SU5+rooiwyEMr8KmiybNkyrVmzZsB9L774onU/APikn4kpn3SEQ71N4Mk0gQPy3Jkm5duk7k6vN+NpBm8aEwIAAAABnflfttW5oEmFXZ6LoEmQmnz6mAVNzHcoLT7KKotMhj8Q2nwaNGlqatLmzZutxSgqKrJuHzx4sLe01lVXXdX7+Ouvv1779u3TN77xDe3YsUP33XefHn30UX3lK1/x5TABhJpj9DMZkGmSRqYJHJA2RYpJlrrbpcqdXm9mTp6dSbmjrFE9pjshAAAAEIjKt0o9nVJClpRS6FymSTZBk+BuBv+az/uahIWF6UR3X5MNlOgCQppPgyYbNmzQ4sWLrcW4+eabrdu33nqr9XNpaWlvAMWYMmWKnn32WSu7ZOHChbrzzjv129/+VqtWrfLlMAGEbNDk6H4mxqE6Mk3goPBwR5rBmxrN0ZHhamrv6u27AwAAAAR0aa5BMv9Hoqu7Rweq7WPjafQ0CU7mexIZKzVXSlW7fb47msEDMCJ9+TaceeaZch0jCvzQQw8N+pxNmzbx6QAY/0yTdIImcEjuCdL+V6Sy97zeRFREuGbmJGrr4QZtL23QpAxOCgEAABCADm1wrDTXodpWdXT3KDYqXPkpVAoISpExUsFJ9vnUgVelrJlj0gx+w4FaK8M/PHx0gT0AgcmvepoAwJjUz63Z19fT5AgNbZ2qb7X7ThRQngtOyV1gr0cRNDHmukt00dcEAAAAgZ9pssSx0lxTMxO5uB3MJp02Zn1N5uUnKy4qQnUtndrj/n4BCD0ETQCEluo9kqtHik2REnOO+vUhd5ZJekK0EmJ8moyHUJI7316bhpejqMPr6WvyfmmjUyMDAPiBdevW6ZJLLlF+fr5VT/3JJ5887nPWrl2rJUuWKCYmRtOnTx80ix8A/E5LjVSz176d72DQhNJcwW3yCnu93/d9TUyG/wkFKdbtLcV1Pt0XAP9F0ARA6PYzGaR+rqdXBE3g4ajMWVJ4lNReL9X19fLyNtPElOcCAASP5uZmq6fjvffeO6zHFxUV6aKLLtJZZ52lzZs366abbtJnP/tZPf/88z4fKwCMSom7HHv6VCneLoM0Gnsrmq31tCyawAe1ghOliGipqayvcoQPzc5Nsta7K8g0AUIV06gBhJaK7cfsZ2Jq4ho0gYejIqOlrNlS+XtS+VYpbZJXm5ntDpocrmtVfUunUuKjHB4oAGA8XHDBBdYyXKtXr9aUKVN05513Wj/PmTNHr776qu6++26tWrXKhyMFgFE6vNGxfib9M02mZRM0CWpRcdKEE6WDr9slujKm+XR3M91Bk51lZPgDoYpMEwChmWkySD8To7jGzjQpSCfTBP7X1yQlLqq31877ZJsAQMhav369zjnnnAH3mWCJuX8o7e3tamhoGLAAwPj1M3E4aEJ5ruA3eez6mszKcWealBM0AUIVQRMAIVqe69iZJoVkmsBnfU1G1wze09eEEl0AELrKysqUkzOwN5v52QRCWlvtY5kj3XHHHUpJSeldCgsLx2i0AOBmelE4GDSpae5QbUtnbyN4hEgz+AO+72sywx00KalvU0Ob/R0DEFoImgAIHV3tffVPTU+TQRxy9zTxzOYH/CnTpH9fEzJNAAAjccstt6i+vr53KS4uHu8hAQg19Yek5gopPLLv2NiBLJMJqXGKi45wYIDwa4Un230iGw5Ltft9uiuT4Z+bHGvdJtsECE0ETQCEjqrdkqtHik2RknKP+rXL5eotz1WYTqYJHJbjzjSpOyC11Xu9mbn57qBJCUETAAhVubm5Ki8vH3Cf+Tk5OVlxcYNP/IiJibF+338BgDHlyTLJmWf3qBilHe5+EzNzyDIJCdEJ0oQlY1aiq6+vCc3ggVBE0ARACJbmmi2FhR3167qWTjV3dPfOVgIcFZ8uJRfYt8u3jTrTZE9Fkzq6epwaHQAggCxbtkxr1qwZcN+LL75o3Q8AodLPZGeZPYloVi5B4JAx+XR7vfffPt/VLHcwbheZJkBIImgCIDSDJoM4XGfXAM9MjFFsFOnd8GVfk61eb8KUjkuKiVRHd09vSQIAQGBramrS5s2brcUoKiqybh88eLC3tNZVV13V+/jrr79e+/bt0ze+8Q3t2LFD9913nx599FF95StfGbfXAADHdXijw0ET+2L2bHdGAELAzFX2es8aqdu3vUZmuvuaEDQBQhNBEwCh4zhBkxJ30GRCql27FPBZia5y7/uahIWF0QweAILMhg0btHjxYmsxbr75Zuv2rbfeav1cWlraG0AxpkyZomeffdbKLlm4cKHuvPNO/fa3v9WqVe6LSQDgb3q6pZJNjgVNTGllT3muWQRNQof57sRnSu310oHXfborz/eKoAkQmiLHewAAMGYq3EGT7GMHTfIpzQV/bwafn6y39tdYfU0+7C7rCwAIXGeeeaZ1AXAoDz300KDP2bTJfQESAPxd5U6ps1mKTpQyZ456c6X1bWps61JEeJimZiU4MkQEgPAIO9tk85+lXc9LU1f6bFfTs+3yXFVNHapualdGYozP9gXA/5BpAiA0dLVLNfuGVZ6LfibwedCkYrvU3eX1Zubk2bOetrvrOAMAAAAB0c8kf7F94XuUdrpn/0/NTFBMJKWVQ8rM8+31rn+ZlCOf7SY+OlIT0+PtXZVTFhkINQRNAISG6j2Sq1uKSZGS8gZ9SEldm7Um0wQ+kzZFikqQutrs76SX5ualWGuTaXKsmckAAACAfzWBX+JoPxNKc4WgaWdJEdH2pMiq3T7dFX1NgNBF0ARAiPUzmWWaQhwz04SgCXwmPFzKmWffLve+GfyMnESrFEFtS6fKG9qdGx8AAADgC/tftdcFJzmyOZrAh7CYJGnyir5sEx+alZs4ILMJQOggaAIgNBynn8nARvAETTAWfU3e9XoTsVERmuau3fx+ab1TIwMAAACcV3tAqt4thUVIU85wZJN9TeCTHdkeAszMC+y16WsyFpkm7u8bgNBB0ARAiGWaDB40ae/qVkWjPWM/PzV2LEeGUJM7316XeZ9pYszJs08Qt5dyAA8AAAA/tneNvS48RYq1y8yORmd3j/ZW2D0myDQJUaYZvHHwDamlxudBE5NpQllkILQQNAEQGo4TNCmvtwMmsVHhSk+IHsuRIdTknmCvy94b1WbmuoMmpq8JAAAA4Lf2uIMm0z/gyOb2VzWro7tHCdERVAkIVWmTpOy5dt/SPS/5bDdTsxKsssiNbV2URQZCDEETAMGvq0Oq3nvMoMmhupbefiZhQ/Q8ARyRPUdSmNRcITWWjzrT5P1SgiYAAADw43Oxff+xb08/x9HSXDNzkxQezrlbyJp5vr3e9ZzPdhETGaEpmXZZZPqaAKGFoAmA4Fe9x56BEpMsJecP+pCSujZrzUwl+Fx0gpQx3b5d7n22ydx8O2iyv7pZTe1dTo0OAAAAcM6ht6SORik+U8pd6GgT+Fnu0kkIUbPcfU12vyR1d/psNzNz7Gbw9DUBQgtBEwAhVJprljREFomnCXx+CkETBEZfk8zEGOUmx8qU1t1OtgkAAAD8kad00vSzpfBwh5vAEzQJaROW2sG49nrp4Pox6WsCIHQQNAGgUO9nMiBoQqYJxkLuAkf6msyfYGebbD1c78SoAAAAAN8ETaad7dgmd7kvXhM0CXHhEdKM8+zbu5732W48GU27CZoAIYWgCYDgN4ygyeHeoEnsWI0KoSzHHTQp9z7TxJiXn2Kttx4m0wQAAAB+xvTv80wSmuZME/jm9i4drLH7Uc7OtScQIYTNcvc12f2Cz3ZheucYu8qb1NPj8tl+APgXgiYAgl+FO2iSffygCT1NMKbluap2S532d88b8yfYQZNtJWSaAAAAwM/s/be9zlskJWY5mmWSlRSj9IRoR7aJADb5dElhUtUuqanCJ7uYlB6v6MhwtXZ261Ct9+duAAILQRMAwa2rQ6rZa9/OmjPoQ1wuV295rglpBE0wBpLypPgMydUtVWwfdXmu3RVNauvsdnCAAAAAgFP9TM5xbJOeJvCzKc0FIz69b0La/ld9sovIiHBNy7KbwdPXBAgdBE0ABDcTMOnpkmKSpeT8QR9S29Kpts4e63ZuCuW5MAbCwqSc+aMu0WUawWckRKu7x9XbEBMAAAAYdz3dfZkmDgZNepvAu/tMAJq0wqdBE2NWTuKATCcAwY+gCYDg5pnFnzXLvlA9CE+WiUnxjomMGMvRIZQ50Aw+LCxM89wlumgGDwAAAL9RsllqrbEnrxWc6HimCU3g0Wuy74Mmc/PtDP9Xdlf6bB8A/AtBEwDBrXJnX9DkuE3gKc2F8QiajK4Z/Hz3ATx9TQAAAOB3pbmmrpQiohzZpCmr7CmPRBN49Jq03N3XZKfU5JugxkUn5Cs8THpjX432VTb5ZB8A/AtBEwDBrXL7MfuZGL39TFIpzYVxCJqY8lwu16ibwW893ODUyAAAAAC/62dS2dSumuYOq4DA9Gy7XBJg9TXJmWffPuCbbJMJqXE6c1a2dfv/vXXQJ/sA4F8ImgAIkUyT2UM+5HCtO9MkhUwTjKHMmVJEtNTeINUd8Hoz8/NTeksVdHTZvXkAAACAcdNSIx3eYN+edrbjpbkmZyQoLpqyyhjbEl2fOHmitX78nUNq7+r22X4A+AeCJgCCV1eHVL3Hvp09dNCkpN6daZJG0ARjyJQp8ATzRtHXpDA9Tkmxkero7tHuChoTAgAAYJztWSO5eqTseVJqofP9TGgCjyGDJq/5bBdnzspSbnKsals69dzWMp/tB4B/IGgCIHjV7JN6uqToJCl5wpAPO1zXZq3paYJA7GtimsF7sk22UaILAAAA423Xc/Z65ipHN0sTeAxp4vK+8tw+6msSGRGuK06yg4CU6AKCH0ETACHQz2SWubI8jJ4mBE0wXkET7zNNjPkTaAYPAAAAP9DdJe150b4983xHN93XBJ6gCY6QkGFnNhkHfJdtYoImnobwe2kIDwQ1giYAQrqfialFWtnYbt0m0wRjLme+vS4fbdDE3Qy+hEwTAAAAjKPiN6W2eikuXSo40bHNdve4tMsdNCHTBOPV18RcMzjL0xD+TbJNgGBG0ARA8KrYftx+JmX1dmmu2KhwpcVHjdXIAFuuO2hSd1BqrfN6M/Pc5bneL2mwTigBAACAcS3NNeM8Kdy5Zu0Ha1rU1tljnbdNykhwbLsIwqCJDzNNjE+cYjeE/9vGQ2rrpCE8EKwImgAI6UyTw7WtvTNGTG8IYEzFpUkp7uaY5du83syUzATFR0eotbNbRVWkiQMAAGCc7HreR/1M7IzqGdlJijD1kYAjTTrNXle8LzVX+Ww3K2dmKS/Fbgj//DYawgPBiqAJgOCtpVu9p6+nyRAO088EflOiy/tm8ObEcW6e3ddkK83gAQAAMB5q9klVO6XwSGnaBxzd9A53E/iZOZTmwrH6msz1ebYJDeGB0EDQBEBwqt0v9XRKUQlScsGQDyups8tzETTB+DeDf9eZviaHaQYPAACAcbDrBXs9cZkUl+ropne6gyY0gcd49zUxLlmYb603HayTy0V5ZCAYETQBEJzMDCcjc7oUPvSfuhJ3pglN4DHufU3KvM80MebluzNNSgiaAAAAYBz7mcw83/FN76QJPEZSomu/b/uaFKbFy1SJa+/qUWVTu0/3BWB8EDQBEJyqdtnrzJnHfFhJPUET+EmmScV2u6zcKDNNth1uUA/N4AEAADCW2hv7Zvc7HDQxzbb3VzVbt8k0wfD6mmyTmip8tpvoyHDlJsdat4tr7GsKAIILQRMAwalq97CCJn2N4O0DHmDMpU6WohOl7nap2v299cL07ETr4L2xvUvFtS2ODhEAAAA4pr0v2+WR06fZ2f4O2lPRJDMnKC0+SllJMY5uG0EmMUvKW2Tf3vGsT3dVkB5vrQ9x7gUEJYImAEI208TUHqURPMadKR/naQZf9p7Xm4mKCO+debethGbwAAAAGEO7nvdZaS5PE3hTmissLMzx7SPIzLvMXr//pE93U5BmX0M45J6ICSC4EDQBEHxMI7bK4wdNapo7rBqk5rg7N4VME/hDXxPvgybGnFy7r8mOUoImAAAAGCM9PdJuT9BkleOb31lmH9vOdh/rAsc054P2uugVqbnap31NDDJNgOBE0ARA8DG1S9vrpbBwKX3qkA8rqWuz1lmJMYqJjBjDAQJD9DUZZdBkdp6dabLdPRsPAAAA8LmSTVJzpRSTLE1a7tNME+C4MqbZ51eubmmn70p0FbrLc9HTBAhOBE0ABG9prtRJUtTQGSSe0lw0gce4y3EHTcq3jmozntl3O9yz8QAAAACfK37TXk9eIUVEOb75nQRNMFJzPSW6nvJ5eS76SQLBiaAJgOANmmTNOubD9lY2WeuJ7hkiwLjJnmNnRpkZeo1lXm/G09PEzHZqbOt0cIAAAADAEEo32+v8xY5vura5QxWN7dbtmTkETTDCoMm+tVJLjU8zTUrqWtXd4/LJPgCMH4ImAIK4CfyMYz6MGUvwG9Hxff13St/1ejNpCdHKTY4d8P0GAAAAfKrEHTTJW+Sz0lxmVn9iTKTj20eQypwuZc+Terqknf/yyS7MeVdkeJg6u10qb7BLfwMIHgRNAARx0GToJvDGrvLGAbPzgXGVe4K9Lt0yqs3Moa8JAAAAxkp7U9/5V77zQRPO2eC1eb4t0RURHtZb6vtQLX1NgGBD0ARA8Knaba8zhy7P1dnd01uei0wT+IU8d9CkbHRBk9l57r4mpfQ1AQAAgI+VvSfJJSXlS4nZjm+eJvDw2txL7fXef0utdT7ZRWG6u69JDX1NgGBD0ARA8M10qi8+bnmufZXNVhptUkykJtAIHn6VaeJ9ea7+s/A8J5gAAACAz3iypPMW+mTzO8vsiUCzcu2JQcCwmR6nWbOlnk5p13M+2UVBqt3XhEwTIPgQNAEQXKr32Ov4TCk+fciH7XAffM/MTVJYWNhYjQ44fqZJ3QGptdbrzcxxZ5qYniY9NCQEAADAmDSBd740l8vl0q5yuzoA5bkwqobwPirR1ZtpUkumCRBsCJoACNLSXMfuZ+Jpkj0zh4Nv+Im4NCl1Yr8yB96Zkpmg6IhwNbV3MeMJAAAAAdsE3hzLmmPaqIgw6xgX8LpE1541Upvz5YsL0uxME8pzAcGHoAmA4OJpQphFE3iEZomuqIhwzchJtG5vd2dUAQAAAI7raJaqdvos08Qz0W1aVqJ1jAuMWPYcKWOG1N0u7X7BZ5kmTFYDgg//1QEQXDwH7cfJNKGhIPySZ4Ze2Wj7mniawdPXBAAAAD5StlVy9UiJuVJSruOb3+me6MY5G7xmSnFPP8e+XfyWzzJNSutb1dnd4/j2AYwfgiYAQq48V/+yRWSawC/7mngaanppTp6nGTyZJgAAAAi8fiYGE93giAlL7HXJRsc3nZUYo+jIcJlWkmX1bY5vH8D4IWgCIHj0dPc1gs+ccdw075zkGKXGR4/V6IDhl+cyZeY6WkadabK9lKAJAAAAfN3PZKFPNr/tcL21ZqIbRmXC0r6+kd2djm46PDxMBWnuZvD0NQGCCkETAMGj7oDU3SFFxkop7obax+hnMst9YRnwG6asQUKWXeag4n2vNzPbnWlyoKZFze1dDg4QAAAA0MDsaB80gT9c16p9Vc2KCA/T0knpjm8fISR9qhSbInW1jeoc67jN4GsJmgDBZEyCJvfee68mT56s2NhYnXLKKXrrraHrCD700EMKCwsbsJjnAcBxVbqbwJtGb+Hhx800meVulg34Vc1dz0y9UZToykyMUVZSjFyuviAhAAAA4JjOVqlyh8/Kc63bVWmtFxWmKiUuyvHtI8TOsfIX27cPO1+iq9CdaUIzeCC4+Dxo8sgjj+jmm2/W97//fW3cuFELFy7UqlWrVFFRMeRzkpOTVVpa2rscOHDA18MEEAxMSaPjlObq3+eBTBP4dYmuUfY18ZQx8NSCBgAAAJxtAt8tJWRLSXk+C5qcMSPL8W0jBOX7rq9Jb6YJ5bmAoOLzoMldd92l6667Ttdcc43mzp2r1atXKz4+Xg8++OCQzzHZJbm5ub1LTk6Or4cJIJiCJlmzhnyIy+XqzTShNi78uhl82buj2sycPDsouIO+JgAAAPBlE3gzk99BXd09enVPlXX7jJmZjm4bId4M3heZJulkmgDByKdBk46ODr3zzjs655xz+nYYHm79vH79+iGf19TUpEmTJqmwsFCXXnqptm3bNuRj29vb1dDQMGABEKKqdh8306SysV21LZ0KD5OmZ1OeC36caVL+/qgaFXqCgttLyTQBAACAr5rAO1+aa8uhOjW2dVlluU4oSHV8+wjhTJOK7VKHsxkhhfQ0AYKST4MmVVVV6u7uPipTxPxcVlY26HNmzZplZaE89dRTevjhh9XT06Ply5fr0KFDgz7+jjvuUEpKSu9iAi0AQpBp3lC1076dOXPIh+1093eYnJmg2KiIsRodMHxpU6SYZKm7vS97yguz3eXntpc1WBlWAAAAgOOZJp5+fA76zy47y2TF9EyrETwwasn5UmKOXVJulBn9Rypw9zQpb2hXe1e3o9sGEOSN4Edi2bJluuqqq7Ro0SKtXLlSTzzxhLKysnT//fcP+vhbbrlF9fX1vUtxcfGYjxmAH2ipllprTYE/KWP6kA+jNBf8Xni4lLtg1H1NTCZVZHiYNUuvpL7NufEBAAAgtJkm8GbGvo+bwFOaC842g/dNia70hGjFR9sTMg9TogsIGj4NmmRmZioiIkLl5eUD7jc/m14lwxEVFaXFixdrz549g/4+JibGahzffwEQgjwz8lMLpSh7psdgPE2xZ+YQNEEgNIP3fhZUdGR4bwk6+poAAADAMaaMrJmxH58pJU9wdNN1LR1691CddfuMmTSBhw/6mjjcDN70ZfZkmxQTNAGChk+DJtHR0Vq6dKnWrFnTe58pt2V+Nhklw2HKe7333nvKy8vz4UgBBLyaffb6GFkmBpkmCAieMgejTB3v62tC0AQAAAAOKd3ksybwpgF8j0uakZ2ovJShJ8MBI+ajTJP+fU0O0dcECBo+L891880364EHHtAf/vAHbd++XTfccIOam5t1zTXXWL83pbhMiS2PH/7wh3rhhRe0b98+bdy4UZ/85Cd14MABffazn/X1UAEEQ9AkfeqQD+nucWl3hR00meXu9wD4pTx3pknZe2a2gdebmZPn7mtCM3gA8Hv33nuvJk+erNjYWJ1yyil66623hnzsQw89ZM1s7b+Y5wHAmPCUkPVBP5NX3P1MyDKBzzJNavZKrXY2k1N6M01qyDQBgkWkr3dwxRVXqLKyUrfeeqvV/N30Knnuued6m8MfPHhQ4aZ+u1ttba2uu+4667FpaWlWpsrrr7+uuXPn+nqoAAJZTVFfE+0hHKxpUVtnj2KjwjUx3Z4JAvilzJlSRIzU3iDVFkkZ07zazNx8O2jyPpkmAODXHnnkEWuy2erVq62AyT333KNVq1Zp586dys7OHvQ5piyx+b2HCZwAwJiodJdGznb2Oo3L5dK63Z5+JgRN4LD4dCltslS7XyrZJE07y7FNF7qvL5BpAgQPnwdNjBtvvNFaBrN27doBP999993WAgBOZ5rsLGvo7WcSEc6FBfixiCgpZ659MG9KdHkZNPFkmuyvblZTe5cSY8bkP/sAgBG66667rIljnmx8Ezx59tln9eCDD+pb3/rWoM8xQZLh9okEAMe4XFKVO2CbNcvRTe+paFJpfZtiIsN1ypR0R7cN9JbosoImGx0NmhS4y3PR0wQIHj4vzwUAY3Lg7sk0SR8604Qm8AgonnIHo2gGn5kYo5zkGOufiCdoCADwLx0dHXrnnXd0zjnn9N5nMvHNz+vXrx/yeU1NTZo0aZIKCwt16aWXatu2bcfcT3t7uxoaGgYsADBizVVSa60J3R63n+RI/WeXnWVy8pR0xUZFOLptYECJLof7mnjKcx2qIdMECBYETQAEPnPQ3l5v3zbptkPY4e7rQBN4BITcEwbWjPbSXHe2yfslXBwDAH9UVVWl7u7u3vLFHuZnU7J4MLNmzbKyUJ566ik9/PDD6unp0fLly3Xo0KEh93PHHXcoJSWldzHBFgAYsSp3aa7UiVKUs43a1+22+5mspDQXAqwZvKc8V3Vzh1o6uhzdNoDxQdAEQPCU5krKP+aB+7uH7GZvCyakjNXIgNFnmpjyXCZVxEt9fU1oBg8AwWLZsmW66qqrrH6RK1eu1BNPPKGsrCzdf//9Qz7nlltuUX19fe9SXFw8pmMGECQ8pblMDz4HtXV268191dbt02cQNIEPz7HCwqXGEqlx8IkJ3kiJi1JSrF0K+auPbtFOd5ULAIGLoAmAwNdbmmvofiYVjW0qqW+TaWUyn6AJAoFprGkO6JsrR3VA7+lrQjN4APBPmZmZioiIUHl5+YD7zc/D7VkSFRWlxYsXa8+ePUM+JiYmxmoe338BgBGr2u2TfiYbD9SqvatH2UkxmpmT6Oi2gV4xiVLmLJ9km1x58kRr/a+tZVp1zzpd/6d3tPWwuyIGgIBD0ARAEDWBH7o017vF9sHKjOwkJdAMG4EgOr7vgN5km4yyPNeO0gZ1dfc4NToAgEOio6O1dOlSrVmzpvc+U27L/GwySobDlPd67733lJeX58ORAoCkSt9kmry+184yWT4tQ2FhYY5uGxi0r4lpBu+gb184R//80um6cEGuzFf4uW1luviXr+q5raWO7gfA2CBoAiDw1R4/02SLuzTXwkKyTBBA8kbf12RSRoLioyOsmXv7q5udGxsAwDE333yzHnjgAf3hD3/Q9u3bdcMNN6i5uVnXXHON9XtTisuU1/L44Q9/qBdeeEH79u3Txo0b9clPflIHDhzQZz/72XF8FQBCqqeJw0GT9e7SXMumZTi6XeAo+Ysd6R05VGnk+/5rqV646QydNDnNuu+tolrH9wPA95huDSB4Mk3Spgz5kM3FdtDkhILUsRoV4Ewz+HcfGdUBfUR4mGbnJmnjwTptK2nQ9OwkR4cIABi9K664QpWVlbr11lut5u+mV8lzzz3X2xz+4MGDCg/vm+9WW1ur6667znpsWlqalany+uuva+7cueP4KgAEvY5mqb7Y8fJcze1d2uI+X1s2NdOx7QKD8nx3q/f6bBczcpJ04YI8vb2/VuWNbT7bDwDfIWgCIOh7mrhcLr17yC7PtaiQoAkCtBn8KGc8maCJ6Wty6aIJzowNAOCoG2+80VoGs3bt2gE/33333dYCAOPSzyQ+Q4pPd2yzGw7UqqvHpQmpcSpMj3Nsu8CgPNcN6g5I3V1ShG8ujeYkx1rrigaCJkAgojwXgMDW3ig1V9i30wfPNDlQ3aL61k5FR4ZrVi6z7BFAchfY67qDUqv3ad1z8+yydO+X0AweAAAAowyaePruOWT93r7SXPQzgc8l5UuRsVJPV1/mlA/kJMdY6/KGdp/tA4DvEDQBENhq9/fNdopNOWY/k3n5yYqK4M8eAkhcqpQ6yb5dOopm8PnJvUETk3kFAAAAjFiVpwn8DN/0M5lKPxOMAVPu0lPau8Z3Jbqyk+xMk/KGNs7BgADE1UMAIdPPZCH9TBDIzeBHUaJrVk6SwsOk6uYOVTYy0wkAAABeqNzpeD+ThrZOveee5EYTeIx5iS5PqW8fyHZnmrR39aihtctn+wHgGwRNAARH0GSIfiaGp5/JwsLBM1GAgOhrMopMk7joCE3NSrRubyulRBcAAAD8ozzX20U16nFJkzPilZ9KPxOMEU9pb8/1BB+IiYxQWnyUdZtm8EDgIWgCIEiawA+eadLZ3aOth91BEzJNEIhyPUGTLaPazNy8vhJdAAAAwIiYhtnVexwvz9W/nwkw9pkmvgua9G8Gb0p0AQgsBE0ABHWmyc6yRisdNjk2UpMzEsZ2bICT5bmqd0sdLaPua7KdTBMAAACMVN0BqadTioyTUgod72dyKv1MEIRBk+zeoAklkoFAQ9AEQHA0gh8iaOIpzXVCQarCTVMHINAk5UqJOZKrRyrfNvpME4ImAAAA8LafSeZ0u5G2A+paOnqPTWkCjzHluX5grif0dPtsN9lJdl8TMk2AwEPQBEDg6mqX6g8dsxH8Fk8TePqZIJDleprBe1+ia447aFJU1ayWDhoRAgAAYASqdjrez+TNohq5XNL07MTeGfnAmEgpkCKipe6OvmsKPpDjbgZfQdAECDgETQAErtoDklxSdJKUkDnoQ7YccgdN6GeCYCjRNYq+JllJMdZMJ3NiuqOs0bmxAQAAIHSawGfNcr6fCVkmGGvhEVLaZJ+X6OrraUJ5LiDQEDQBEAT9TCZLYUeX3jKz6XeV2xeHFxYSNEEQZJqUvjuqzXj6mtAMHgAAAN6V56IJPILEGPQ1yU5yB00ayTQBAg1BEwCBq7bomP1Mth5uUI9Lyk2O7Z3hAQSkvIX2uuJ9qbtz1CW66GsCAACAYTOpyp5ME4fKc1U3tWune4IbTeARrEGTvvJcZJoAgYagCYDA5Tm4OU4/kxMK6GeCAGdSx2NS7Jq7nll+o2gGv41MEwAAAAxXU7nUXi+FhUsZ0xzrZ2LMzk1SekK0I9sEvAuauCdj+oBn8mZFY5t6zIxOAAGDoAmAwFVz7EyTzZ5+JpTmQqAz5edyF4y6r8ki97+FrYfr1djmfcYKAAAAQkjVrr6JPJH2zPnReu9wvbVeOinNke0B3gdN9vpsF6avpNHZ7VJtS4fP9gPAeQRNAARBT5PBM03edQdNPBeKgaBoBl/mfV+TwvR4TclMUHePq7eGNAAAADC8fibONYHfWWaX5prtzoQGxjXTpKfHJ7uIighXZqKdSUUzeCCwEDQBEJi6u6S6g0NmmrR3dau4prU35RsIePmL7fXhd0a1mTNmZFrrdbsrnRgVAAAAQiXTxMEm8L1BE87VMF5SCqXwSKm7XWos8dluaAYPBCaCJgACU8MhqadTioiRkvKP+rWn0Vp0ZDg1chEcCk7sK8/V5f0spdNnZFnrV3ZXOTUyAAAAhELQJMuZTJOGtk4drrMnuM3MJmiCcRIRKaVOGsNm8ARNgEBC0ARAgDeBnyyFH/2nrNx9QGIOUMJMPwgg0KVNkeIz7Wbwo+hrcuq0DEWGh+lAdYsOVDc7OkQAAAAEoUpPpslMRza3y51lkpcSq5T4KEe2CYyuRJcvgybuTBPKcwEBhaAJgABvAj94P5Myd9Ak132AAgQ8E/wrPNm+fehtrzeTGBPZ23BzHdkmAAAAOJb2xr7SRQ6V59rhDprMojQXxlvGNHtd7btm8Nm9QRMyTYBAQtAEQGDyHNSY2feDKKv3ZJoQNEEQlugqfmtUmzljprtE1y76mgAAAGAYpbkSsqU4e+LNaO0qJ2iCUMo0cZfnaiTTBAgkBE0ABKbDG+x13sJjBk1MyjcQNApGn2linOHua/L63mp1dvc4MTIAAAAEc2kuh/qZ9M80oQk8/Cdo4q5k4QM57kbw9DQBAgtBEwCBxzTBLtls3/aUKxqiPBeZJggqE5ZIYRFSw2Gp/rDXm5mXn6z0hGg1tXdpc3Gdo0MEAABAEGaaONTPxOVyaaenPFdOsiPbBBzJNHG5fLILepoAgYmgCYDAU/qu1N0uxWf0HeQcwVMvNJdMEwST6AQpZ559+5D3JbrCw8O0YnqmdXsdJboAAAAwRkETc+G4vrVTEeFhmpad4Mg2Aa+lFNqT0rpapcYyn5bnqmxqV3ePbwIzAJxH0ARA4Cl+014XnmI3xx4EjeARtHqbwbtL1Hnp9BnuoAnN4AEAAHC8oEmWM0GTHWUN1npKZoJiIiMc2SbgtchoKbXQp31NMhJjFB4mK2BS3Uy2CRAoCJoACDyeGfYFJw2Z8l1ebx+MUJ4LQcfzvR9lM/jT3X1N3j1Up9rmDidGBgAAgGDS3dl3IdmhTJPe0lz0M4G/SJ9mr2v2+mTzJqsqK8ndDJ4SXUDAIGgCILCYOqOei8Um02QQNc0d6nA3tyZogqANmpRutvv7eMmUrpuVk2T9k3ptL9kmAAAAOIIJmPR0SdGJUvIER4Mms3MImsAP+5r4SLa7GbynjDgA/0fQBEBgqS+WGkul8Egpf/ExS3NlJkYrOpI/cwjCg3rTz6e7w+7v40CJrld2ETQBAADAUP1MZgxZFnmkdpaTaYLQC5p4+prQDB4IHFxNBBBYPFkmuSdI0fGDPsQze4MsEwQlc8Ja4OlrMroSXWfMtEt0rdtdaZW1AwAAAHpV7nS0NFdXd492VzRZt2fnJjuyTSAgMk3c1ybINAECB0ETAIGltzSX+6LxIMrc/UxoAo+gVehMX5OTp6Rb2Vil9W0qqmp2ZmwAAAAIDlW7HQ2a7K9uUUdXj+KjI1SQFufINgHngiZFdjlwH8hxl+eqaCRoAgQKgiYAAkvxm8cPmngyTVIImiDI+5oc2jCqzcRGReiECSnW7c3FdU6MDAAAAMGiytlME08/k5k5SQoPd6bcFzBqaZOksHCpo0lqqvDJLijPBQQegiYAAkdHs1T23jGbwBvl9XbQhEwTBK38JfaBfcMhqaFkVJtaWJhqrQmaAAAAoJeZce/JNMma5cgmd5Y1WOtZNIGHP4mMkVIKfFqiy1M6nPJcQOAgaAIgcJRsklzdUlJ+30HNIErdByK5ZJogWMUkSjnzHCnRtcgdNNlC0AQAAAAeZmKOmXkfHtlXvmiUdrgzTWgCj1Dra5JNpgkQcAiaAAiq0lwGmSYICb3N4N92JGjyfmmD2jq7nRgZAAAAgqU0V9oUKSLKkU3uLLeDJrMJmiDEgiaeTJPq5nZ1dvf4ZB8AnEXQBEAANoEfujRX/54mZJogqHmCh6PMNDFNODMSotXZ7dL2UrtkAgAAAEKcw6W5Wjq6dLCmxbpNpglCLWiSHh+tyPAwu+pdE9kmQCAgaAIgMJiji96gydCZJmamfH1r54DZHEBQN4Mv3Sx1tnq9mbCwMPqaAAAAYKBKTxP4GY5sbnd5k3VKl5kYo4xEu1QR4DfSp9nrmr0+2Xx4eJiykyjRBQQSgiYAAkP1Xqm1RoqIkXJPGPJhZe7SXHFREUqOjRzDAQLjMBsqMVfq7pAObRjVpuhrAgAAgAGqdtnrTKeawFOaC4GQaVJkT9j0gWyawQMBhaAJgMDqZzJhiRQZPeTDSj39TFJirRn0QNAy3+/JK+zbB14b1abINAEAAMDgQZOZjmyOJvDwa2mTzQmW1N4gtVT7ZBc57mbwFQRNgIBA0ARAYDh0/NJc/Wdt0AQeIWHyafZ6/6uj2szCghR7M9UtqmvpcGJkAAAACFStdVJTuaPlufZUNlnrmTmJjmwPcFRUrJQ8YUyawVOeCwgMBE0ABIbit+11wbGDJjSBR0iZfLq9Nv1+Or2fsZQaH60pmQnWbbJNAAAAQpynCXxSvhSb7Mgmy90VASakxjuyPcBx6VPGKGhCpgkQCAiaAPB/XR1SlbsRYf6iYz7U09OEJvAICRnTpcQcqbtdOrzBkWyTLcX1Dg0OAAAAAclz7uVQlolR3mifp2W7SxQBfidjmk+DJr2N4BvJNAECAUETAP6verfU0yXFpPSlzB63PBcH4wixvib7X3OkGfzm4lonRgYAAIBAVekOmmQ50wS+vatbdS2d1u2cJCa3wc+bwVfv9cnm81PjrPWB6mafbB+AswiaAPB/FdvtdfYc+yLxMVCeCyFnkqevySuONIPfcqheLpfLiZEBAAAgkMtzOdQEvsLdwyE6MlzJcZGObBPwWdDER5km8/PtzP4D1S2qbaaPJODvCJoA8H/l2/qCJsfhKc+Vm2LP4gBCpq/JobelLu9TvefmJysqIkw1zR0qrml1bnwAAAAI0PJcDgVN3KW5cpJjFHacSXBAsAZNUuKjNNXTR/IQfSQBf0fQBEDgZJrkzDvmw7p7XKpw1wfNpacJQoWpNZ2QLXW1SYff8XozMZERmptnN/rkIB4AACBEmUk4tfsdLc/lyTShNBf8Wtpke91WJ7XU+GQXnpLIW4o53wL8HUETAP6vYniZJtVN7VbgJDxMykyMHpuxAX7R18RToutVZ/qaHOQgHgAAICSZfg6uHikmWUrMcWSTnr6TNIGHX4tOkJLy7Ns1RT7ZxaKJnj6SnG8B/o6gCQD/1t4o1R20b2fPHVY/k6ykGEVG8OcNIaS3GbxTfU04iAcAAAhJFe/b66zZx+0nOVzl7moA2WSawN+lT7PXNb5pBr+woC/ThD6SgH/jqiIA/1bprqebmCvFpw+vnwmluRCqfU2KR9fXxJNpsvVwvTq7e5waHQAAAAJF2Xv2One+Y5vsLc/FeRr8XfoUn/Y1mZOXrOjIcNW2dFoN4QH4L4ImAIKmCbwn7ZuDcYQc06QzIUvqapUOb/R6M5MzEpQcG6n2rh7tLGt0dIgAAAAIoPOv4/ST9KYRfHYS5bkQ2s3gTcBkXr67jyQlugC/RtAEQGA0gT9OaS6j1J1pkpdC0AQhxpROmDT6vibh4WG9Jbo2Hax1anQAAAAIFOVb7XXOAuc2yeQ2BAofB00G9JEkaAL4NYImAAKjpm7O8YMmnp4mOQRNEMp9TQ6Mrhn8SZPtMniv7al2YlQAAAAIFM3VUmPpsM+/hqvC3dMkh0bw8HcETQCMZdDk3nvv1eTJkxUbG6tTTjlFb7311jEf/9hjj2n27NnW4xcsWKB//vOfYzFMAP4cNBlBeS56miCk+5ocfFPq6vB6MytmZFrr1/dWqbuH5oQAAAAhl2WSNlmKSXJkk22d3apr6bRu0wgeARM0aamWWut8GjR5v6RB7V3dPtkHgAAImjzyyCO6+eab9f3vf18bN27UwoULtWrVKlVUVAz6+Ndff11XXnmlPvOZz2jTpk267LLLrGXrVvd/vAGEjqZKqbnS1B6SsmYf9+E0gkdIy5olxWfafU1KvO9rcsKEFCXFRqqhrUvvHa53dIgAAAAIhH4mzjWBr3RnmcREhis5LtKx7QI+EZMoJeb4NNtkYnq80hOi1dHdo+2l9JEEQjZoctddd+m6667TNddco7lz52r16tWKj4/Xgw8+OOjjf/GLX+j888/X17/+dc2ZM0e33367lixZol/96le+HioAf1O5vW+mU3TCcR9e3uBO+6Y8F0K1r8nk0fc1iYwI1/JpGdbtV3eboCUAAABCgg+CJp4m8KafSZg5XgVCvESX+XewsCDFur2ZPpJAaAZNOjo69M477+icc87p22F4uPXz+vXrB32Oub//4w2TmTLU49vb29XQ0DBgARAkyj2luY5fT7exrVNN7V3WbTJNoFAv0TWKoImxYkaWtX5ld5UTowIAAEAgKH/PXufMc26T7olt2Un0M0GgBU2KfLaLhe4SXVsOkdkPhGTQpKqqSt3d3crJcae2uZmfy8rKBn2OuX8kj7/jjjuUkpLSuxQWFjr4CgAEWj8TU1YoIYa0b4SoSe5Mk+JR9jWZbvc12XiwVi0ddjASAOBb9IEEMK66u6SKHfbtXOcyTTznaSbTBAgI6VPsNc3ggZA2Jo3gfemWW25RfX1971JcXDzeQwLglAp3ea6c42ealNXbM5jIMkFIM71/4jOkzhapZJPXm5mcEa8JqXHq7HbpzaIaR4cIADgafSABjLvqPVJ3uxSdKKVOdmyzFe6eJtnJZJogQKRPG7OgSVFVs+pavJ/sBiBAgyaZmZmKiIhQeXn5gPvNz7m5uYM+x9w/ksfHxMQoOTl5wAIgCLhcfUGTYZTnKnPPYMqlnwlCWXh4X7bJgVdHVWf39Bl2tsmrlOgCAJ+jDySAcVe+te/cyxxTOrVZ93ladhLnaQi08lx7fbaL1PhoTcm0+7aSbQKEYNAkOjpaS5cu1Zo1a3rv6+npsX5etmzZoM8x9/d/vPHiiy8O+XgAQaq+WOpolMKjpIzpx304ad+A2+QVDvU1sYMmr+0haAIAgd4H0qAXJIBhBU0c7GdiVLh7muSQaYJAK8/VXCm1+e6/lb3N4AmaAKFZnsukmT/wwAP6wx/+oO3bt+uGG25Qc3OzNYvKuOqqq6wSWx5f/vKX9dxzz+nOO+/Ujh07dNttt2nDhg268cYbfT1UAP7YBD5zphQRddyHl9W7M00ImiDUeYImB9+Uuju93szyaZkKC5N2lDWqotH+9wUACMw+kAa9IAEcU/k2x/uZGJ7jSDJNEDBiU6R4ewKZaot8XqJrC0ETIDSDJldccYV+/vOf69Zbb9WiRYu0efNmKyjiOcg/ePCgSktLex+/fPly/eUvf9FvfvMbq5bv448/rieffFLz5zv7H24AwdME3qA8F+CWNUeKS5c6m6WSzV5vJj0hWvPy7ZKXZJsAQOCjFySAYyrzZJo4e+2lnEwTBHKJLtPrx0cWTUzrzTRxmfLkAPxK5FjsxGSJDJUpsnbt2qPuu/zyy60FQAgbQRN4g0wTwM3UoJ58mrT9aWn/K1LhSV5vasX0LG093KBXdlfpQ4sLHB0mAGDs+kB6ekGaBQCO0lIjNZYMu5/kcLV1dqu+1c58zuY8DYHElAg/9JZU7btm8HPykhQVEabalk4drmtVQVq8z/YFwA8zTQBgdJkmwwyakGkC9JnkTF8TTzN4k2nC7CcA8A36QALwm34mqZOkWDvT2AmVjXaWSUxkuJJjx2TOLuCMTHdf1erdPttFTGSEJmXYzeD3VTb7bD8AvEPQBID/MX0YqnYNuzxXZ3ePqpo8ad8ETYDevibFo+trsnRSmnWSa8oq7Klocm58AIAB6AMJwD/6mSxwdrPuiW3mHC3MNMsDAinTxMfluYypmZ6gCedagL8haALA/9Tsk7o7pOhEKWXisGYwmUnwJrU1IyF6TIYI+DWToRWXJnU0SaVbvN5MbFSETp6Sbt02JboAAL5BH0gA/tHPZJ6jm6WfCQJWxoy+oIkPM+6nZiVa631VZJoA/ob8SAD+O9Mpa7bdn2GYpbmyk2IVHs4MJsD6dzPpNGnHM3Zfk4ITvd7UiumZVsDElOi6dsUUR4cJAOhDH0gA416ey+Em8BWNfedpQEBJN+c9YVJbvdRcJSVm+WQ3U7MozwX4KzJNAPhvE/hhlOYyyj1N4OlnAhxdomv/a6PazGnT7b4mb+2vUXcPfU0AAACCSneXVLnDp5km2WSaINBExUkphT4v0TWtN2hCeS7A3xA0AeC/TeCHedBe6gma0M8EODpocnC9fTLspTl5yUqKiVRjW5e2lzY4Nz4AAACMv5q9UlebFJUgpTmbVVzRr6cJEHDGoBn81Ey7PFdJfZtaOrw/ZwPgPIImAPw3aDLcTBMOxoGjZc+TYlNH3dckIjxMJ05Os26/WVTj4AABAADgP6W55g6rNPJIVDS6M02SyDRBABqDZvBpCdFKi4+ybhfR1wTwKwRNAPiXjhappqivmfUweHqa5KZwMA4c1dfEMH1NRuHkKRnW+q2iaidGBgAAAH9xeKNP+pkYTG5DcARN9vp0N55m8HvpawL4FYImAPxL1U5JLik+U0rMHtZTytzluTgYB44wdaW93v3iqDZzytR0a/1WUY166GsCAAAQPDzHiZ7jRp8ETZjchgAOmlT5rjyXMTWTviaAPyJoAsC/lI+sNFf/g/G8lDhfjQoITDPP7+tr0lrr9WYWTEhRXFSEals6tYeDeQAAgOBQu9+etBYWIU09y9FNt3V2q6HN7tGQlcTkNgRw0KRmn9TT7fNMk31kmgB+haAJAD/tZzK80lwul6uvPBeZJsBAaZPsf0uubmn3S15vJioiXEsnufua7KNEFwAAQFBlmUxcJsWlOrrpiga7n0lsVLiSYyMd3TYwJlIKpIgYqadTqjvos91MzXJnmlQxOQ3wJwRNAPhn0MQ0IhyG+tZOtXX2WLezSfsGhs422fnPUW3m5Cl2iS6awQMAAASJXc/b6xnnOr7p8kZ7Ylt2UqzCwsIc3z7gc+ERUsY0nzeDn+YOmhRVNluTQgH4B4ImAPxLxXavmsCnxUcpNirClyMDAtOsC+z1njVSd6fXmzmlX9CEg3kAAIAA19Ei7X/Fvj1zleOb92Sa0M8EAW0MgiYT0xMUER6m5o5ulbv/3QAYfwRNAPiPlhqpsdS+nTV7WE+hCTxwHBOWSvGZUnu9dOB1rzezsDBV0ZHhqmxs1/7qFkeHCAAAgDFmAiZdbVLKxGGfe42Ep+9kNudpCGRj0AzenGMVptn9WWkGD/gPgiYA/C/LxBy4xyaPsAk8B+PAkGnlntmDu57zejMmk2tRoV3rmr4mAAAAAW73C32luXxQPquvPBeZJghgGTN8nmliTHM3g99bRTN4wF8QNAHgh03g5wz7KWX1dvpqLkET4Pglunb+SxpFaa3+JboAAAAQoMzx4K4XfFaay6jsLc/FeRqCINOkeq9Pd9PbDJ5ME8BvEDQB4If9TEYQNGlotdYcjAPHMPUsKSJaqi2SKnd6vZlTpmRY67cImgAAAAQuczxYf1CKjJUmn+6TXXgyTehpgqAImjQckjp8lwUy1Z1psq+STBPAXxA0AeB/mSY584b9FE9Pk1yCJsDQYhKlKWfYt3f9y+vNLJmU+v/buw/wOKrrbeDvFmnVi9Vlyb1XXLCxMcVgY3qHACahhZKEhBYgJED+X4BACiWU0EvovZpibOMGuFfc5G7LVres3rbM95x7d2XJuGjlGa0kv788k5ld7c6MrsXu3Dn3ngOn3YbdZbXILWVdEyIiIqIOadN0vZaASXiUJYcIFLROjWU/jTqw6CQgMlFvl2617DC9kv0zTUo404SovWDQhIjazxTx1qTnCkz7ZnouokPrd7pe57S+rklUuBNDs+LVNmebEBEREXVQm2bodd/TLDtEkb/2JGeaUIfXBsXgAzNNdu2tRZ3ba9lxiKjlGDQhovahMh+oKwdsDiC5X4vfFigEz5kmRC0MmuxaDFS3vpD7mMa6JiwGT0RERNThSJ9r5wK93c+aoIlkA6io86htplGmzlMM3rq6Jskx4YiNcKqxpDv2cEY/UXvAoAkRtQ+F6/aN4nC2bDRSvceL0uoGtZ3BmSZEh5aQDaQNBQwfsMlf+LMVjmNdEyIiIqKOa8t3gM8DJPcHEntYcohv1xWo9chuCYiNCLPkGERtJqm3Xu/ZbNkhbDZbk7omTNFF1B4waEJE7UMrUnMV+VNzuZx2xEfyYpzosPqfodc5X7V6F6N6JMJuA7bvqWFdEyIiIqIOm5prsmWHmL5WB01OH5Ju2TGI2jw91x7r0nOJ3o11TVgMnqg9YNCEiNpZ0GRQi9+SHygCHx+hRmYQUQuDJptnAe7aVu0iLiIMo3voFF2z1heaeXZEREREZCXJ/SMzTSwMmuytbsDCrXpG8pTBDJpQJ5AcSM+1Wf83ZJFeKTposoUzTYjaBQZNiKh9BU3SWh40KWgsLsjUXEQtkjkCiM8G3NU6cNJKkwamqvWsDUUmnhwRERERWap0q64l6XAB2cdZcgi5PvT6DAxIj0X3JH0TmKhD69JrXz2gGuvqOu5Lz8WZJkTtAYMmRBR6Pi9QnBP0TJPCwEwTBk2IWkZmZA08V2+v+6zVu5k0ME2tF27dg8o6t1lnR0RERERW2j5fr7OOBcKs6UN9s0an5uIsE+o0wiL1wDNRsqlNZpoYFs5oIaKWYdCEiEKvdBvgqQOckUEVIwzMNGEReKIgDDpPrzd+A3h0XaDWjIKSi3q318C8jSXmnh8RERERWWP793rdY4Ilu6+u92D+pmK1zXom1DnrmlhXDL5HUrQa41ZZ50FJVYNlxyGilmHQhIjaT2qulP6A3dHitzE9F1EryMjC2AygvgLYOueIZ5vMZF0TIiIiovZPRq5vm29p0GTuxmLUe3zonhSl0nMRdRptUAw+IsyBrgmRansr65oQhRyDJkQUeltn63X6kKDe1pieizNNiFrObjc1RdfsnCJ4vD6zzo6IiIiIrLBnC1BVoOuZyCAaC0xfuy81l02GzBN1FqkD9TpvhaWH6e2va7KFdU2IQo5BEyIKLSmmtuo9vT300qDemu8PmnCmCVGQBvmDJhumAZ7WTf0e2S0BCVFhKKtxY9mOveaeHxERERFZU88ke4wl9UwaPD58t75IbbOeCXU63cbp9a6lgNe6mo79/TO0cgoqLDsGEbUMgyZEFFqr3gXc1UByf6DniS1+m89noKiSM02IWn3RH52ig5bb57VqF06HHaf0T1XbszboDjIRERERHZ31TH7cUoLKeg9SY10YkZ1gyTGIQiZlABCRALhrgPxVlh0mkNZufUGlZccgopZh0ISIQptXd/GLenvM9VBVz1qotKZBFaGWt8iFOREFQWoHDTxHb6/7vNW7mTTIX9dkHeuaEBEREbXrfldgpolFQZPpa/X14GmD02C3MzUXdcIUx4HZJjt+tOwwA9Lj1DqnoBKG/HdLRCHDoAkRhY4UoZZCauGxwPDLgnprgT81V3KMC2EOfpQRBW3QeftSdHk9rdrFCX2TEeawYWtJNbawWCERERFR+7RnM1BVqOuZdB1t+u69PgMz1u2rZ0LUKXX3B012LrTsEL1To+Gw21Be60ZBhb7nQUShwTuNRBQ6S17SawmYuPQ01JYq9F9ApLOeCVHrdJ8ARHYBavYAO35o1S5iI8JwXK8ktT1rPWebEBERER2N9UyWbC9FSVUD4iKcjdeGRJ1Ot/F6vXOB5Au35BAupwO9U6LV9oZ8pugiCiUGTYgoNMpygZyv9qXmClJg1AWLwBO1ksMJDDhLb6/7rNW7mTTQn6LLX/iTiIiIiNprPZMTLNn9az9sV+uzhmUwCwB1XhnDAWckUFsKlGy0PEXXehaDJwopfpsRUWgsfQUwfLr4e0r/oN8eSM+VHs96JkStNuh8vV7/BeDztmoXpw7UxeCXbi/F3uoGM8+OiIiIiI6U1EXYZl09kx17qjHdn5rrmuN7mr5/onbDGQ5k+dPb7bSwrkmGzsLBmSZEocWgCRG1PXcdsPx/envMDa3axdyNxWrdLy24tF5E1IQELSMSgOoiYH3rCsJnJUZhYEYcfAbwzVrdYSYiIiKidqJkk77Wc0YAXUeZvvtXf9iu4jIn9Uth34w6v8Zi8AssO8RA/0yTDZxpQhRSDJoQUdtb96muoxCXBfQ7I+i35xRUYvWucjjtNpw1NMOSUyQ6akZLHfcbvT374VbPNrlwRFe1fnPhDhjSayYiIiKi9lXPJOtY0+uZlNe48f7SXLX96xM4y4SOAm1QDD4w02RLcTXqPa3rnxHRkWPQhIhCVwB+9DW6rkKQPlyW25gWKCmG6bmIjogETWS2SUkOsObjVu3i4lFZcDntWJtXgRW5ZaafIhERERG1v3om7yzZiZoGLwakx2JCn2TT90/U7mSNAWwOoHwnUL7LkkOkx0UgPjIMXp+BzUVVlhyDiA6PQRMialsFa4BdSwC7Exj5q6Df7vb68MmK3Wr74lHZFpwg0VEmIh4Y/3u9PedhwOsJeheJ0eE4Z3im2n5zwQ6zz5CIiIiIWkNmAAeCJj3NDZpIvyxQAP66CT1hs9lM3T9Ru+SKATKGWZqiS/5b6p8e25hlg4hCg0ETImpby17T6wFnAzG6gHQw5uYUo6SqAckx4Ti5f4r550d0NBp7IxDZBSjdAvz0fqt28cvjuqv1tNX5KGVBeCIiIqLQ2zxT1zMJizK9nsmXq/NRUFGHlFgXzj1GD54hOqrqmlhYDH6gP2iygUETopBh0ISI2k5DDbD6Pb096upW7eIDf2quC0Z0RZiDH2FEpnDFAhNu1dtz/wF43UHvYnh2AoZlxaPB62vMbU1EREREISK16mb8VW8fex3gNC+tsdSwe+n7rWr7qnHd4XI6TNs3UbvXBsXgB2ToYvDr81kMnihUeMeRiNrO2o+B+gogsQfQ86Sg376nqh6z1hepbabmIjLZsb8GolOAvduBlW+1ahdX+mebvLVoh8rBS0REREQhsvp9oGitTsU64XZTd71oWynW7K5ARJgdV4zV139ER13QpHg9UFNqySGkTpDgTBOi0GHQhIjaPjWXzDKxB//x89nKPHh8hhrNHsjxSUQmCY8GJtymt+f9G/DUB72Lc4ZlqqKFuaW1mLex2PxzJCIiIqLDc9cBsx/S2xIwiepi6u7fXrRTrS8cmYUu0eGm7puo3YtJAZL66u3cRZYcol9aLKRMUHFlPUqqgu+XEdGRY9CEiNq+APwxU1u1iw+W7VLri0dlmXxyRKSMvhaISQfKc4Glrwb99shwBy7x//f5xkIWhCciIiIKicUv6Ou5uK66dp2J6j1efLchMPuf/TI6SnUPpOiypq5JtMuJ7l2i1DaLwROFBoMmRNTGBeDPalUB+LV55SqfZ7jDjnOHs9AgkSXCIoGT7tLbc/4OVO8JehdT/Sm6ZucUIbe0xuwzJCIiIqJDqd0LzH9Ub0/8s76+M9GPW/agqt6D1FgXjslKMHXfRB1Gt/F6vdPCuibprGtCFEoMmhBRGxeAv6ZVu/hgqZ5lMnlQGhKiOAWcyDKSPi9tCFBXDsx+MOi390yOxgl9k2EYnG1CRERE1Oa+fxyoKwNSBgLDLzd999+uLVDr0wanwW63mb5/og6huz9osns5UFtmySECKclZ14QoNBg0IaJ2XwC+wePDZyt3q+2LR3MKOJGl7A7gjH/smyFW8FPQu7jm+B5q/ebCHdjDHLxEREREbaN8F7DwOb096f/0dZ2JvD4DM9YVqu0pg9NN3TdRh5LYHUjuBxheYOtsSw4xMEMHTZieiyg0GDQhorZLzTXyqlYVgP9uQyH21riRFufCiX1TzD8/ImquxwRg0PmA4QO+/hPUtJEgTOyfimFZ8ahp8OKFeVstO00iIiIiamLBM4C3XqcO6jfF9N0v37kXJVUNiI1wYmzPJNP3T9Sh9D1NrzfNtDQ918bCSni8PkuOQUQHx6AJEVlr19J9BeBHXHlEqbkuGJEFB6eAE7WN0x4AnBHAju+BdZ8G9VabzYZbJ/VV268v2IESzjYhIiIislZdBbD8Db19wh1yQWb6Iaav0am5Th2QinAnbyfRUa7PJL3ePAPwmR/U6NYlCpFhDtR7fNi+h7Uiidoav+WIyPqcumLopa0qAF9UWYc5G4vV9sWjmJqLqM0kdAOOv0Vvf3ufrk0U5GyT4dkJqHV78fzcLdacIxERERFpK94EGip1yqDep5i+e8Mw8C1TcxE1r2sSFg1UFQKFwac0PhypGbSvrgmLwRO1NQZNiMg6JZuADV/q7cDN1yB9umK3yp07olsC+qTGmHt+RHRox98KxGUB5bnAXH+dk1bMNpGC8BIAJSIiIiIL+LzAIn8tk+N+06qUyIcjxah3ltbA5bTjpP5MmUwEpwvo5a/ZuulbS+uabMhnXROitsagCRFZ54f/yJgkoP+ZQOqAVo1m+nCZTs11yahsC06QiA4pPAqY8qDe/uEJYP6jQb395H4pOCY7AXVuH56fy9omRERERJbI+Qoo2wFEJgLDLrPkENPX6tRcJ/RNQVS405JjEHU4fSfr9aYZltY1Wb273JL9E9HBMWhCRNaoyAdWv7dvtHorrN5Vjo2FVWo009nDM8w9PyJqmcEXABP/ordn/Q2Y96+gZpvcNrmf2n5TZptUcLYJERERkekWPqvXo67Rg14sMH2tTs112uA0S/ZP1CH18QdNpI5rTanpux/bq4taL962B3Vur+n7J6KDY9CEiKyx8L+AtwHoNg7oNrZVuwjMMjl9SDriIsJMPkEiarGT7gJOuVdvf/cgMPefLX7riX2TMbJbgipg+CxrmxARERGZK28lsOMHwO4ExlxvySFyS2uwPr8CdhswaSCDJkSNErKBlIGA4QO2fGf67vunxSI9LkLN3F+0zfygDBEdHIMmRGS+2jJg6at6e8JtrdqFjKL4bOVutc0C8ETtwIl3Aqf+VW/PfgiY9YDOnx3kbJOcAubjJSIiIjJ9lonMDo7LtDQ115ieXdAlOtySYxB1+BRdm2eavmvpS53UT9cQmpNTZPr+iejgGDQhIvMtfRloqARSBwF9T2vVLmasK0RFnQeZ8REY3zvZ9FMkolY44XZg0v/T2/P/DbwyBSjacNi3TeiTjFMHpMLtNXDnh6vg8fqsP1ciIiKizq6yAFjz0b4C8BYJBE2mDE637BhEnaKuic/8fs7J/XXQZO7GYtP3TUQHx6AJEZnLXQcsfE5vH3+LDI04otRcF43KgkPmgRNR+zDhVuC8/wKuOJ279/kTgLn/ArzuQ46Q+vuFQxEX4VS1il6Yz6LwREREREfsh/8APjeQfRzQdZQlh1iXV4El2/eq1FwMmhAdgPz3Fx4L1JQA+StM3/34PsnqnsjW4mqVKo+IOnjQpLS0FFOnTkVcXBwSEhJw3XXXoaqq6pDvOfnkk9WNlabLTTfdZNUpEpEV5j4CVBcB8dnAkItatYttJdWYv0mPorhoJFNzEbU7I6YCv10I9J2iaxfNfhB4YSJQsvmgb0mLi8B9Zw9S20/M2ITNRUzTRUQk2G8iolb58SldR1Ic/wfLDvP8PF2T7syhGchMiLTsOEQdljMc6H2y3t5kfoqu+MgwjOqWqLbncLYJUccPmsiF/9q1azFjxgxMmzYN8+bNww033HDY911//fXIz89vXP75z5YXmyWiENvwFfD943r7tAcAR/DF2w3DwP2frYHP0NNQeyRHm3+eRHTk4rsCV7wHXPgiEJkIFP4EvHgKsPHbg75F6hPJf9cNXh/u/HA1vPIfOhHRUY79JiIKmszs//ZevX3yn4EBZ1lyGBnVPm11vtq+6aTelhyDqFPoE0jRdfC+0JE4KZCii3VNiDp20GT9+vX45ptv8NJLL2Hs2LGYMGECnnrqKbz77rvIy8s75HujoqKQnp7euMiIKyLqAEq3AZ/6RziOvUkXImwFuSifv6kE4U47/u+cweaeIxGZS9LvDbsU+O0iPS29vhx4+1Jg/qMSAT3Ay214+MKhiHU5sWJnGV75fltITpuIqL1gv4mIgrbkJeCbu/X2iXcCJ/u3LfDy99vUIJcT+iZjSNd4y45D1GnqmuxeBlTvMX33gWLwP27Zg3qP1/T9E1EbBU0WLFigppaPHj268blJkybBbrdj0aJFh3zvW2+9heTkZAwZMgT33HMPamoOna+vvr4eFRUVzRYiCkEdkw+uAurKgawxwOQHWrWbyjo3Hpi2Tm3/9uTenGVC1FHEpgFXfQGMukbmiwGz/gZ8cLX+TNhPRnwk7j17oNr+97c5WJVbFoITJiJqH9hvIqKgLH8d+PIOvX38rcDEv1h2qNLqBry7ZKfavvFEzjIhOqS4TCBtiO4LbZll+u4HZ8YhJdaFmgYvlm7fa/r+iaiNgiYFBQVITU1t9pzT6USXLl3Uzw7miiuuwJtvvonZs2erC/833ngDV1555SGP9fDDDyM+Pr5xyc7ONu33IKIW+vouIH8VEJUEXPKazunZCo9+uxFFlfXokRTF6d9EHY38d3/OE8DZjwP2MGDdp8CjA4BPfwfkLm428+TS0dmY2D8F9R4ffvXKYmwo4I07Ijo6sd9ERC1WsgmYdpveHnczMOn/9Kxfi/zvx+2oc/swpGscju+TZNlxiDrdbBMLUnTJjP0T++rZJnOYoouo/QVN/vSnP/2s4OD+y4YNG1p9MpK7d8qUKRg6dKjK7fv666/jk08+wZYtuvDYgUgnoby8vHHJzc1t9fGJKEhyE/SHJ4Hl/5OvceCil3Sdg1ZYs7scry/YrrYfOH8IIsIcJp8sEbWJ0dfqWScpAwB3DbDyTeDlycCz44H5jwF5K2EzDDx9xUiM6JaA8lo3rnxpMbaVVIf6zImITMN+ExGZbvqfAZ8H6DsFOO1BSwMmNQ2exr6ZDGaTzywiOoy+p+n15lmAz/wUWlIbUsxlMXiiNuEM5sV33HEHrr766kO+plevXiqnblFR88inx+NBaWmp+llLSV5fsXnzZvTufeBR5y6XSy1E1MYkT+fnNwM5X+nHE/8M9D6lVbuSPLl/+VQXfz97WAZO8I+gIKIOqvs44LcLgdxFwLL/AWs/AYrWAbP+n16ikhDd62S8Neok3FAXi++LgCtfWoT3bxqHrgmRoT57IqIjxn4TEZlq00w9el1m857+sKUBE/H+klzsrXGjW5conDEkw9JjEXUakqrcFQ/UlgK7lwPZx5q6e6ktZLcBGwurkFdWi0z2m4jaT9AkJSVFLYczbtw4lJWVYdmyZRg1apR67rvvvoPP52u8oG+JlStXqnVGBr+kidqVbfOAj28AKvMBRzgw+W+6+HsrVNS5cf+na1RdgxiXE/edPcj00yWiEJDOfLfj9CKd+7UfA5tm6M+Pmj3Amo8QteYjvAlgR2QWvqsajGeeW4rbfnkRUjJ6AnZLMogSEbUJ9puIyDReNzD9Hr099kYgydo0xm6vDy/O36a2rz+xFxxyl5aIDs/hBHpP1GmKN88wPWiSEBWOY7ITsHxnGebkFOOKsd1M3T8RHUHQpKUGDhyI008/Hddffz2ee+45uN1u3HzzzbjsssuQmZmpXrN7926ceuqpair5mDFj1FTyt99+G2eeeSaSkpKwevVq3HbbbTjxxBMxbNgwK06TiILVUA3MeQT48Sld4Cy5H3DRy0BG6/4bXbBlD/74wSrsLqtVIyb+79zBSIuLMP20iSjEIhN02i5ZpOO/awmwZTaw5Tsgbzm6G7twjXMXUDcdePEheB2RcKT0BZL7AtnHAQPOBOKz0Jmsz6/AR8t2qVGccnOiweNT6/7psbjp5N6IiwgL9SkSURtgv4mIDmvJy0DJRiAqGTjpLssP99VP+ap/lhwTjktGda7rL6I2qWsiQROZGSbZOEx2cv9UFTSZu7GIQROijhg0EW+99Za64JcLfLvdjosuughPPvlk48+lQ5CTk4Oamhr1ODw8HDNnzsQTTzyB6upqVZhQ3nPvvfdadYpEFEztkvVfAN/cA1Ts0s+N/BVw+iNAeHTQu6tze/Hotzl46fttatfdk6Lw2KXDMap7F/PPnYjaF0cY0H28Xk75C1C7V80+qVw3A3vXzUaGNx9h3lqgYLVe1nwEfH0nkDkCGHC2Hr0V3w2ITrY8NYXZDMPA/E0leHH+VrU+kFkbivDhsl24/5xBOGtoBnOIEx0F2G8iooOqKQXmPKy3T7kXiIi3/Frlublb1fbV43uwziRRsPpM0uu8FUBVERCTauruT+qXgsdmbMQPm/eoQVfhTs7OJ7KKzZBvxU6koqIC8fHxqrhhXFxcqE+HqOPbswX46k5gyyz9WG5WnvEPPfI7SJsKK/Heklx8vGI3Sqsb1HOXj+mGe88aiGiXZTFcIuogahu8uP+TlVi6cgV62/Jwemopzolcg/D8pbDJ7LamJDVgXCYQkw6ERwHOSMDpApwRgOEDfG5dLNXr0dte/2NZbHYgJk2/PzYDiPXXDfA2+Bc3EJ0CZB4DJHQ/4uCMXGpNX1uAJ2ZuwoaCSvWczK47fUg6hmUlIMxhVx0eed2rP2zHtpLqxk7RA+cNQbekqCM6Ph0deA1MweLfDFEH8OUfgSUvAmlDgRvnAnZrgxhzcopw9atLEBXuwI9/OkWlAyKiID1/IpC/Cjj/OeCYy03dtc9nYMzfZ6Gkqh5vXDeG9WCJLLz+5V1Koo5G4pxlO4Hdy/SNP6kXELjhZ6aqYmD+v/V0cLnhKDcoj78FmHC7vkHZQuW1bnyzJh/vL92FZTv2Nj6fER+hbgZOGpRm/rkTUYcUGe7Av34xCu/3SsV9n63BzAIf/ojJ6BVRhcvi12CisRjZ9Zvhqt8DmwQ39m7Xi6UnlQhkHANkHQsMOhdIGxJUEGXN7nL8bdo6LN5Wqh7LTYhLR2fjugk9kd3l55+l8rNn52xRy9yNxTjtibl46vKRmMzPSiIioqPL2k+ApS/rbakPZ3HARDzvn2UiA9sYMCFqpT6TddBEUnSZHDSx222YNDAV7y7JxYx1hQyaEFmIM02I2jsZ8SxTO7fPB3Yt1bUAqoubvyaxp051022cXqQ4YGtHRtdXAgue0XVLGqr2TTE9458tLjpY7/GqwmSfrtitUs3ItFEhRQRPHZCKy8Zk48S+KXA6OJWUiA5sXV4F7v30J6zeVQ6Pr/mlSnacA78bHYNze9sQVVcMeOr04vavJaAsacDsTr2o7TBdnFHWEgiuLAAq8vQiU+flM1OCwzJbRW5KSHC6cJ1+bVNdegODL9BL+pCDnn9RRR3+NT0HHy7fpWLdLqcdN5zYC7+e0AvxUYevV7K1uAp//uQnLNxaCqfdhsd/cQzOGa7rGxAdCK+BKVj8myFqpzz1wLf3Aotf0I+HXgJc9JLlh12VW4bznvlBXXfMu2siMhMiLT8mUae0cxHwymlARAJw5xbdBzHRrPWFuO5/S5EZH4Ef/nQK0/kSWXT9y6AJUXtUXQKsfEvl+ceOBYBbp2ppJDcB04fqNDMFa3RR9qaiU/UMlEAgRV57qJFJ8jFQ8JOuHbDiTaDGn2tfRldP+j9dQ6CFZFbJPR//pIobB/RNjcGFI7Nw0aiuSI1loXciajkJwm4uqsKG/Eqsy6/A56vyUFxZr34WF+HElcd1t65wuty0KFqnA9ebZwGbZ+qgTEC/M4AzHgESezQLlkgu8LcW7UC9P2B83jGZuOv0Aega5M0Hj9eHP36wCp+uzFPpvP5x0TBcMjrbvN+POhVeA1Ow+DdD1A6VbgM+vEZfewiZ5T/xL6bfdD2Q37y5DF+vKcCFI7visUuPsfx4RJ2Wzwv8sxdQVwZcO13fmzGR1Igd8bcZqHV7Me33EzCkq7W1jog6EwZNePFPHZncmPvkxuazSSQ9TI8JOgDSdTSQMQwI8998qysHchcDO34Edi7QabskbU1T4bFA9hj9/kTJz2/3z0SxASUbdbBE1gFdegGn3AcMOl/mf7botN1eH/75zQa8OH+bepwW58J5x3RVNwsHZcRx9AMRmdZJkFlsL8zbiq3+2h/pcRF46IIhOHWgxSmsZCbexuk6XcbGb3TgWmqonHAHCobcgOd+2I23F+9snF03qnsi/nLWQIzsltjqQ3p9hppx887iXPX4gfMG45fj9gVpiAJ4DUzB4t8MUQv5fLpemgj0oWQtgytkZn59BVAv60r/40q9uGuA8Gg92lwKuEcm6JppUldNZsEGyOzXnQv1svJtoL5c9/8ueAHod1qb/IpST+2UR+eosXTf3nYi+qXFtslxiTqtD6/V91lOuAM49X7Td3/D60vx7bpC3DqpL26d1M/0/RN1Vgya8OKfOiJPA/DdA8CPT+rHKQOAkVcBPU8AUge3OHih0tPkLdcBFJmlkrtIX8gfjsMF9JsCDLkIGHBW8wv5w5CR1Te/vQKLt+uc/ZKC5s4p/VVxYyIiK0gRROkoPPL1emzfU6OeO/+YTNx/zmB0iW6DHNzFOcCXd+jUiQB2GGm42309FvoGYXT3RNwyqS8m9Ek2JWAsl2pSF0WKxIsHzh+CXx7X/Yj3S50Lr4EpWPyboU5B+j4ymlsGktX61/L4Z9v7vUYGmUkgxPDqta/ptn8deGyFqCQgNkMHWfavz5Y1BrjkVSA+C21FMgW8s3inSqX88tXHttlxiTqtVe/qwbDpw4CbdH/BTO8vzcVdH67G4Mw4fPmHE0zfP1FnxULwRB1N6Vbgw+t0sEMc+2vgtAf3zSYJRliETssli3x3ykV/4VodRJHRS7Wl/k6AoRcZ8TTwHKD/mUBE8B3mJdtL8du3lqt0OTEuJ/59yTCcPiQj+PMmIgqyCOLpQ9JxUr8UPD5zI16av1WlsZq/qQQPnj8EZwy1+HMopT/Wn/YWpr3zNH5V8QK62wrxRvgj2DrhUfQ79UxTZ9fJvu4/e5AqIv/M7C34f5+vxcD0WIzu0cW0YxARUScg1/YyWKqyEKgq0OvKfH1jXoIEUitR1jJTMlD3S1L4BrZtjp8/1/j4QM81fd/+Pz/A+2S2u8zO8NbrAWPN1vJ8w37resBd2yTYUfbz7aZpM0MlLBpwxQKuGCA8xr8dq/tyDdXNz1myCUj71+zRi2LTddIkK4D04QacHdQAtiNVVFmHj5bvUts3ntSyGpZEdBi9T9XrgtV6Nllsuqm7lwCndDfW5lUgr6yWNYiILMCZJkShJhfQ/z1Od2hk6vZ5T+sgRgfw7uKduO+zNXB7DfRPi8WzV45Er5SYUJ8WER2FVuaW4a4PV2FjYZV6LPm4/+/cwZbUOpH0W0/P3oz/zt6sitSnR7jxfuY76Jb3jb7xcdajwLHXmX5cuWT7/TsrMG11vkqBOO33JyAl1mX6cahj4jUwBYt/Mx2QdN3l5lvpFmDPFj3wqmyHfk76EhIk8dTi6GPbl/5K1tKnOtS2Kx5wunRAR6Ut9q9lZn/g8f4/azoYQg0+8wHOcB0kOVTtyP3JjBYZxCb/ZhLYkn13HaXPL0Qzd+Xa4suf8jGyWwI++s14plUmMssLE/XA2HOfBkb+0vTdX/zsj1i6Yy/T9xIFgTNNiDqSGffrTk6X3sBVn7fpNOzWkuLED365Hq/9qKeSnzUsA/+6eBiiwvmRQkShcUx2ggoi/GfWRjw7Zws+Xr4bi7aW4tFLh+O4XkmmHWdzUSX+8M5KVZRenDYoTc1sSY05B/jqTmDpy8CXtwO1e3UOY5NnnEgx+A0FldhcVIXfv7Mcb143Fk6mQiQi6pjkBnpglkWgPkZVIVCRp/sHFRIIydu3lhvt+9cuPBBXnK6bISObZZHHEiRwhOtFbvLLbHSZ8aCWptv+x8YBnvvZ48PsQ9bN9uMPNEha4J+t/efXbO3Ss+gPFwSR+o0tTWUcanKe0cl6wZBQnw0em7FRBUzCHDb8+cyBDJgQmanf6Tposv5zS4ImkwelqaCJpCxm0ITIfJxpQhRK278HXjtLb1/ztZ6O3c6V1TSo+iXfby5Rj++Y3A83n9KHF9hE1G4s3V6K299fhZ2lNSpmcc34nrjl1L6Ij2r9rBO5XHpj4Q489OV61Ht8SIwKw4PnD8WZQ9P3ff7JJdXsh4B5/9KPx92sUy2a/PkoAZPznv4e1Q1e3HhiL9xz5kBT908dE6+BKVj8mwmM+t8LuKv9T/gLfMssAqmVIbM2JD2UFPT2BgIA/oBCYxHwKp2CSc3wkPf7ZyvA0PuuLgFqSoGaEv/r6vxBkrqWBUD2J/tO6KYHXCX1BhJ76NoYgQBJTDoQHmV2S1En9OGyXfjjB6vU9r8vGY6LR7X/wXtEHUrxRuCZY3V6wj9uAqLMTa27pbgKpz46VwU9l983GbEWzLA/2tR7vCiqqFdpCwsr6lFe61ZdPGHI97pf0zvp+99UD7PbEB8ZhrjIMLWOdjnh9flUhha3V68ddhvCHXaEO+1wOfU68FhqAzvtNjR4fWpxe/R75N/ZFeZQr5eft/k9OE+D/5pJ6on60/2rBvCn/w88F6gJ1jgAwwvUVej0lI1LCTDxHn0908Y404SoI5CO2Be36O1RV4c0YOL1GdhWUo0NBRXqJmNGfAR6JsegZ3K0+pDPL6/FnJxizN5QhB82l6gbdZJb/7FLj1E1BYiI2hOp9fHVLSfgwWnr8O6SXLzywzaVq/t3E3vjV+N6ICLMEVSwRD4fH5i2DrNzitVzJ/ZLwb8vHobUuIjmL5YL11PuBSK7ANPvARY8rW9kjb/Z1N+vT2oM/nXJcFVP6vl5W9UsG8truBARWajO7cWmwirsKK1GQXkd8srq1PXnnuoGCUOoj1cbbDp7kvxPxTZsjT+zB7ZhIMlRjQGR5egdtgddbSVI8pYg3FsNp6caDk8N7A1VsKlgRvG+Wn/tgk3XwJAZInGZ+vsjToIhmfutM9q03gWZN1O/zuNTa0nt6W2yNH3s8fmaPefxGoh2OZAQGa4Gf8S6nKqu25FauHUP7vl4tdq+eWIfBkyIrJDSD0gbChT+pGebyH0fE/VOiUGvlGhsLa7G3I3FOHtYpqn770ykT7drb6263xVY5PHe6gYVGCmrbUBZjRuVdR7LzsEOH8LgabJ4EW7T2/IzH+zwqi07vIasbU2esyEK9Yiy1SEGdYi11SLaKYEU6GCLfx0RZkOk04bIMNm2w6Gun+Q6yVDbLl89XL5qRPhq1BIua281XLL4qhHmrVPXRdJehgp++NTrorwVcPnMTf+Z2+MCZI9o+6BJMBg0IQoVGYm8Z7MeDTbp/7XJISUP//Y91dhaXIUtxdVqZIKMWM4pqFQjpw8kLsKJiv2+OCSY8t+pIzEw4ygdlUhE7V6My4lHLhqmArsPf7UBOYWV+PtXG/DaD9vxm4l9MCA9FqmxLqTGRiAy3KFu2MmoooIKfaNOPhd/2l2O1bvK1YW0kJE/95wxAFeN63HoGxbjfqvXEjiZcZ8qGo++k039/c4cmoHrT+iJF+dvw23vr0RSjAtjerIwPBF1DLt+fA/pM3+PBoShznCixudElBGGXghHV4RhCMLQYDhRj3DUI0wvhl474IMTXr3YvIhDNZJt5Ui2VSAF5XDZ9Gd2MOQ48Add9MBNGzx2F7yOSHidEYAjAjZnGOx2B+wOJxxOJ8LCI2GPiAXCo3VdCwl4NI609F9XSwqpKEnFlAREJeni4LI/tfjTT8laHSPM9JmJFBy5SVTr9qKqzoOqeg+q672orHerdVW9G1X1XlTXexp/LiOA1aBaw4DPkBHEhrpmCCwVtW61P7nGkJ+ZQS4/5Du/W5cotWR3iULP5Cgc26MLshJbNsNoY2ElbnxjmTonSbN8++R+ppwbER3AkAt10GTNx6YHTcTkgWl4vngrZq4rZNBEyExOmcVQXYyyknxs3rYVu3fnorw4DxHuMkTbapGBevSRoAPq/eEIY981gGSwtBlqJofTDjUjRP3c0D8XXpsTHpsTXluY2rYZBhyGG0544DTcsBseOHyydsNp6OfkesUJCwZpyCnJ7TrrYj3NeA2bumbSrRZY0LgdeD4Q7FEBH8OGSkRhjxGHEsSrtSynO9KQjfaN6bmIQqFwLfD8iTq376VvAIPOtfyQ8zcV47b3VqGkqv6AP48Mc6B/eiy6J0WhsKJOjayW6YhC+m8jshMwsX8qJg5IxaCMOFNGOBERtQUZqfnx8l0qb3d+ed3Pfi4z52oavAd9v4zaGdU9URWWl8/JFpHLq89/D6x4Q+eS//UsPdrMRDJa9devL1UzASVI9Pb1YzEsK8HUY1DHwWtg6kh/M0s+fw7HLr/bsv1XOpNQ5EjFTm8ytrkTUeqNRJXhQjUiUGNEYC9iUKo67bHYi1i5zRH0MeRGSu+UaAxIj8OAjFj0T4tVA4vkJrak1qCfz7KQAVzSbpJiRNZOu12lQpGgRG2DF9UNHjWzQs8k0jOI5OtUghP1bq8a5CX7CHPaESFLmEMt+ze3zNKod/tUsELeU9PgUbOWSiobVF9oT3U9Kmp14EMHR/atfW10d0R+f4ct0A4yi2rfWlK7OBy6faTLJdcoMgJaAjCHIv248b2TMb53khqBnh4fodKJyqws+b2//ikfX6zKx5IdpapdZabquzccF9QMXCIK0t7twH+G69SKd+QAMamm7n7J9lJc8twCNdh12X2Tj57vn7pyIH81kL8SRt5KGPmr4avMh7OhEh2GzbGv1pj6vvPtS2nl37bJtp9hDwNcMTDCYuALi4bPEa4CEhKKkc90CWjId5g3sMhEERXQsDcGNtz2CNQ7IlFvj0a9Ixp19ijU2aP9izwfAYfDqWpmOh0OtW5wRKPGEY8aZyxqbdHwwqb2LYMF5Dtb1mq2pAwg8OnHUu9Y+qeSmizG5Tjg3+UpA1PVAMb2fP3LoAlRW5MPwZdPA3YvBQacDVz2lrWH8xn475zNeHTGRvVBKh9c0sHrJVM5k6NVmpcBGXFqpJJctDclnYfc0hqkxUWgS7QegUdE1FHJzZM3FuzAt+sKVFBYctXWufeN+JH8sHKDQT7z5PNRAhDDsuLRLy1WzTJpVd7X188Fdi7QueevnwVEJpr+O1396mIs3FqKhKgwvHfDuJYHdqhT4TUwdaS/ma15xXh79gr0Sw5D70QneiY4kBjug01qfATqfTSuG5o/liLmcuNArZ1ARBwQnapvREWn6PRWUjz8ADfuVX5wj6/x5rvcsJfvAbkpXVmn03JU1OkZAvu2/es6Dypl9oA8X+tW+zoQuZ7OSoxU19ZyE1xu4MtNBbmh4PZJeiadeknOR24sqBvjcpNcRrT6b6LLIgEDp0OvG2+sN/l54Ka7kGCDDgDomRAyGCAhKhxdosLUWt5b5/9d5ca7/O56doS+6WHst9aL7kd4m2wHbozomzP+1zXeKNn3XnWjxjBUG5k5y6KtSLPGhPtv9kTsu+mz7waQXst1g/ybSBdKAhMSDJLUxiqXfYTOZy//FjKjNcKpAzxyPRF4fbCkLWUGiwxwyy3VaWZy99ZgfX6Fmhkr/zb7k+OlxLjULNqmPz6+TxKe+MUIpMS6jrS5iOhwXjwF2L0MOPPfwJjrTd21/Hd/7EMzUVrdgDeuG4MT+qagI5BgtqTilJSc0ieT70WdEkrf6Fffjw0VSCxfjy4V6xFVuQ3h1QWIrC9CnLsIsb6DB0fchgN7EKcGR9S5usAVn4bElEykpmXAGRmvZ4e6YvRMUbmOCNQzUwLbTdbq6SbbPreuSeZ162sTeyD4EbYvCCLbcq1yoOcbf97CgLV8wUoQxcFkUWZg0IQdRmrPFj4HfHO3Hnn8u0U6Z7FFymvcuP39lZi1oUg9vuzYbDVSmqOJiIj0DZ3Keg9KqxpUwEFucpheUK+qGHhxIlCeC/SaCEz90PQLXrlRduVLi7AytwzJMS58cNM4NdqZji68BqZg8W/myL4/JPi+vqACG/IrVV3AjYVV2F5SfdjZAHRg0Sq44FSBh33BG/0zCU5IbnaX06FmmaiAlz/tlcxgkdc2JcEk6e/I+6Rorrw3KdqFlNhw9T2ZFK3rg8S4pECvDobENAmGSKCjzQvsHiEJ+i3eVoofNu9RI8/zynRNoKZkIMg5wzJVSq7MhMiQnSvRUWfBM8D0PwPdxgHXfmP67u/7dA3eWLgDZw3NwDNTR6I9kc8iCepKavgtRVXYXFyFHXtqGtMfi2jUopctH71teehtz0Mf224Msu1Ad7u+j3Uwu4xk/OTriTW+nlhr9EBVVBYyM7PRK7srhmUnYHhWgkpnSNQUgya8+Kf2qiwXeGYs4K4GznoMOPY6yw4lM0SmvrRIjUCSEUYPnjcElx7b3jMGEhF1QjJ1/JUpgLsGOP4WYPLfLAmSX/biQjXaNDM+Am/8eqxKzUFHD14DU7D4N2NdMEXS3MoMABkuK7NFVJoL/2wRSVGhZo74Z5H4AkW//bM2ZDswe6NpofD9nwssIjD7QZYolwM19V7srdFFbWUt75dUvBJIkOKw4U6d0koCA7pArKSF0uvAc3q2i/9n/hkudv9zDv/rGl8TmAXTZD+B2TGBWRaylt89UOxcZp/ItgQ25NyY+td8MpNK6rXJKG5JgSKp44goBMp3A48P0tu3rQPiu5q6e7n+P+M/89Vn7o/3nBKSlEdNyYzHr9cU4MNluWo2/IH0tOXjnPDlmOJchoHeHH8CqZ8rdmYg19UXRZG94YlJhz0+E+FdshGZ1A3R8Unqey82Yl/Qm8jM61/+RRG1FYlPfnmHDpjICINR11h6uPs/W6MCJpIe4LkrR2FI13hLj0dERAeRMQw4/7/AB1cDP/xHfwf0P8PUQ8iIWZmS/4vnF2BLcTUufvZHvHL1sRjRzdx0YEREdHASSJA0j7KMQ1KoT6ddkmBKOI6SnPshJjNzJFDCYAlRiEmQpNt4YOePwNpPgPE3m7r7gRlxGNktAct3luGDpbvwu4l90NZk5t8Pm0vw1U8F+HpNfmO9SJm0NzQjGuMTyjA6bBv6ejYivXQJXHs36TcGJmdKms3kfkByX71OHQhkDEdKVBd0jIRj1BkxaELUVtZ+DGyarvMXnvMfGZJl2aHm5BRhdk6xmt7+xnVjmaaFiCjUBl8A7FwILHoO+OQm4MZ5QGJ3Uw8hKUfeu3EcrnttCVbtKsflLy7Ef6eOxCkD0kw9DhERERERBWHIhf6gycemB03E1LHdVdDkncU7cdNJvX9Wr9YKtQ1eTF9boJa5G4sbAyVRqMNFCbn4ReouDDPWI6JwJVBa1fzNUkuk54lA/zP1YvLsGyIzMGhC1BZqSoGv79bbJ9wBpPS37FBurw8PfrlebV89vgcDJkRE7cXkB4BdS3QhyA+uAq6dDjhdpgdO3r7+OPz2reWq83L968vw8AVDmZ6RiIiIiChUBp0HfH2X7geUbgO69DR191Kr6G/T1mHX3lrM21SMif1TYaWFW/fgrg9Xq+wmkotyiG0bLohejSmun9C1diNsdV5gZ5M3hEWpmSPIHAlkjQb6nApEMBsKtW8MmhC1hW/vA6qLgZQBwITbLD3UWwt3YHNRFbpEh+PmU/paeiwiIgqCMxy45DXguROAvBXAt/cCZ/7L9MNIPt+XrhqNP330Ez5avgt3fbQa8zeX4LZJfdGLdU6IiIiIiNpWTKqeWbF1jp5tIoNpTSQ1qy4amYVXftiGtxbutCxoUtPgwT+/ycEbP27BePta3B61DKc6ViLWXaxTbUkMRcRn65TE3ccB2WOB5P6Ag7egqWPhXyyR1XK+AVa+KdkcgXOeNH1UcVNlNQ14fKbODXnHaf0QHxlm2bGIiKgVEroBF74AvH0psPgF3YkYerHph5Fiu/++ZBgy4iPw9OzN+GJVHr5cnac6U384te9Rm99cijTLCDwp0Fzv8aHBv0hR4kDhYilk3LRIs9OuizZHhTtUeybFuNok5QERERERdSKDL9RBk+WvA+NuNv3e0BVjs1XQ5LsNhcgvr0VGfKSp+1+yrQSvv/cuxlTNxmLXIiTZKgEf9BIWDfSeCPQ7Heh1MpDAWe7U8TFoQmSltZ8CH/1abx/7a6DbWEsP98TMTSivdWNAeix+MZpfUkRE7VK/KcCE24HvHwM+/Q0Q1QXofYolBYn/OKU/zhiajsdnbMTM9UX4YNkufLpyNy4elaXyHXdPiu6UgRH5Lswvr1MdxryyOjUDc11+BdbnV6CyznNE+5eASVqsC8mxuqPr9Rlq8fgM9E+PxTNXjDTpNyEiIiKiTmPIRcDsh4C924GFzwITbjV1931SYzG2Zxcs2laKdxfn4rbJ/Y58p+46eLbMwdrv3kG3wjl4yla2705yVJJOOzbgLKD7BCAs4siPR9SO2AzpWXYiFRUViI+PR3l5OeLi4kJ9OnQ0W/Y/YNqtgOEDBp2vRxZbOMtkc1ElpjwxX924efvXYzG+T7JlxyIioiPk8wIfXgOs+0zn+P3VZ0D2GEsPuWLnXjw2YyPmbypRj2WyxLnDM/Gbk/uom/0diVy+7q1xo6C8DrvLalVQZFNRpVpvKapCtb8Q5YGEO+zI7hKJyHCH2g532tXMHLkilhkngSBIIBAS2JZgS1FlHXyHuHIenhWPz26egFDgNTAFi38zREREbWzl23rQVHgM8PvlQGyaqbv/fFUe/vDOCqTFufDD3afA6bC3bkfFOcCch+HLmQ67J5BzC6i1x8Ax+FyED78Y6HkSU25Rp77+5V83kRW+fwKY+Ve9PfIq4OzHAbvDssPJzZy/fLJGrScPSmPAhIiovZPvhAtfBOqrgC2zgLcuBq7+EkgfatkhR3RLxBvXjcXS7aUqZdecnGJ8ujJPLSf3T8E5wzIxaVCa+akdfT5g1xJg/edA+S6goUr93r76ShjuWsDnUQMMbD4PDJ8XhlfWHh1Y8nngtTngQRg8cKIBDlQZESjwxqPQiEeRkYAiIxE7jDRsM9KRa6SiAfr8k6LDkR4foVJqyYyaQRlxGJQZh94pMSpQ8jOeeqC+Eqgr12t57G3Q5+eV83HD625AZU0tyqtqUF1bBwe8sMOr1g7DB0ec5I8OTdCEiIiIiNq5YZcBS17SBeFn/Q04/xlTdz9lcJqqb1tYUY+Z6wtx+pCM4HZQWwbMeQTG4hdgM+Q6F8gzumCe7Vj0Ov5ijJl4nqWDgYnaE840ITLL3h06P+XGb4Ccr/RzUvT91L9KjhRLD/3Ytzl48rvNKt/617ec0CnTrRARdUoN1cAbFwK5C4HoFODa6UBS7zY59Jrd5fjvnM34ek2BmmUhwhw2HN8nGVMGp2NEtwT0SYlp3Qg12WHeChhrPoZ3zcdwVu42/fwPeFjY4YnNhCMsAnbDq2d7yuILbEsgpsnzgZ+pII37yE+g62jg+lkIBV4DU7D4N0NERBQCuUuAlyfp7eu/A7qOMnX3j3y9Ac/N3YKEqDC8c/1xGJjRgu94rxtY8Sbw3QNAzR711LfeUXjKcwFieozGY5cdY3qNFKL2fv3LoAkdXeSLQEaNylqNHJW1+wCP/TdP5LEaeVoB1JUBdRV6210HeGSpB9w1QP4qYO+25sea/Dfg+Fss/5XmbSzGVa8uVven/nPZMTjvmK6WH5OIiEwkI7r+dzZQ8BMQnw1c+w0Qn9Vmh99aXIXPVubh6zX52FhY1exnMiND6mTJLI34qDA4pUC63a7WXsNQxdTr3F61ttWVo1fFYvSvXITBNYuR6Ctt3I/MDpnhG4VVvt6oQiSqjEhUIwK1Rriap+GBAz6Zs2GzIz46EsmxkUiKi0ZKXCQSI52IDTMQ6/QhxulFvL0WyShDvHcPnDUlevZK6Va9yCyWIyXpElyxgDMCcIQB9jC9btx27nvO7my+dOkJnPwnhAKvgSlY/JshIiIKkY9vBFa/C2SNAa771tSBtlX1Hlz50iKszC1Ts07eu+E49E07QCpeud+1dS6w7hNgw5dA7V719A57Nv5SdyUWYBj+eFp/3HhiL9glry9RJ8CgSagv/j0NOrVDYKlvsh1Y5Ma9NL0a5ShJtGv1TRP5kJKb85IWYv9/mrBInfc8XJYYwBEO2Ow6xYesGxfbvu2WaPGfQLB/Kv4PVfXhbzvIGgf/mQQlVJCiUgcq5LH6vRzNf2e17X9OjRb1BzukjSW4Ie9VqUAq9XNWkXPIOhbodRLQ73Sgq/WFYCWX+5lPzkdpdQOmju2Ghy6wLq0LERFZqKoYePV0YM9mIKmvDpxEt32qRakJ8s2afMzbWKIKp0un61BiUYMpjiU41/4jxtvXwmnzNf6sxnDhO98IfGOMx67k49E7MwW9UqKR3SUK2YmRyEqMQnJM+M8uR1rdKZMdVBUBZTv0QIjG6wXbftcO/nWzawkn4IrR11cWptPs9NfA1KHwb4aIiChEKvKAp0YD7mrgwpeAYZeYuvvyWjemvrQQa3ZXICXWpQInvVJi9A8rC4DvHwdWvavvP/rVu5LxeN3ZeKn+FCTGRuPpy0dgbK8kU8+LKNQYNAnlxf/zJ+pZB9QxqBsl+48g9Y8cDTx2hgOuOCAiXi+yLQEsGYEqP3O49MjS7scDEW33N+fx+nD5iwuxZPteDM6Mw0e/GY+IsI55o4eIiACU5QKvnA5U7ALShwFXT9PfOyHi8xnYWVqDtXkVyCmoQE2DVxVG93k96FP2A0aUTcfAih/hNPYNSCiL7omClAkoST8R1elj0T09UdUQkULr1MmvganD4d8MERFRCM37t06HFZMO/HoGkNDN1N3vrW5Q94w2FFQiPS4Cb1zeE31yXoJt6ct6ULKMOYpOxc70SXinaiRe2JGuZn6P6dkFT18xAqmxEaaeD1F7wKBJKC/+X5oM7Fqst/e/0R7YlhvrUjip6ewQufEemQBEJgIRCfo18nzgn0dmpMiHmqSCaqhR0WjDU4+6Bg8qaxtQVVeP+gYP5J55uB1wOWwIdxjqJoWk1pBUGvZDTfdr8aDOlr4wcN7+/1O/x/7rQ/3M0EEJaTdJkaHaLOIAecmb5if36tGjMgNHBUHCdTvL+2WR0aPh0ft+LgERe8e8ibNrbw2emb0F7yzeiViXE1/8fgJ6JLOOCRFRh1eyGXhlCiBpp7qNA678WM8wbQ+q9wDL/wcsfQUoz933fHI/YOilwJAL26weC7XDa2DqcPg3Q0REFEKSGeW54/VM87iuwK8+A5L7mnqIPVX1uO75mZi8911c7ZiOaFu9ej4vbjjmZVyLxzano6ja2/h6ScV155T+ratpSNQBMGgSyot/SQshN+XlJr2J6R1ktOfWkios31mGFTv3YsXOMmwrqVY5xFtKioS7nDp4IqkvJPuFw2aDzWaDw/9YPhjjI8NU3sOk6HC17p8ei3G9kpAa13GizDILI6ewEnlldfAZhj9GY8BnQOVer3Pvy8EuRW9lhkakLOEOxLic+veP0b+/y9n6f0dJa5JbWoO8slrUur1o8PjUMWUtpN0Di/zbyLHVEuFU5+P2Guq1DV4vKus8WLB1D2ZvKGqWc/7ZqSNxxtAMM5qNiIjag/zVwGtn6/SefSYBF78Smhkn8uUpnbjcxcC2ucDaTwGv7mghsgtwzBXAsEv1rBgT8zBTB70Gpg6HfzNEREQhVr4beON8oGQjEJUM/PITIGOYeaUDlr4M35x/wl6naw2u9PXCY55LMM8nx9DX75K+6+JRWfjF6GwOxqVOryKI619nm53VUSK3IUbdjAdqGiuAyE37ev9N+jqPD7UNOoorN+vlZrnMBvH6DHVTvKrerdaSf1DqVRRU1CG/vE7ddJe0GAciQY60OBcSIsPVjfnqBg+q6z2oqdfbEigQ8v6D7aMl+qTGYHzvJIzu0QX902LRIznqsAEF+Z13l9WirKZBBQvU4pa1DlgEgghur08FCyRQk+gP2EiQRn63Q5F2K6mqV20lbfTT7nIs37kXq3eVH9Hv2pQEL6Tgrb1JgEMCUNHhTkS7HIh2OXVBXJ8Br5ooY6CizoNdpTXYU21NDRUJcI3qnojLx3RjwISIqLORjtLU94HXzwc2zwSeHAGcfA8w6mo9U9Iq9VXA7mU6SCKzZnctaSwIue/cjgHG3KBnlUiqSiIiIiIiap34rsA1XwNvXAAU+AdOTf0A6Da29fuUGr/rPwdm/Q3Yux1qzkhyfzRMvB/e6HE4aVcF4nPL1L3K84ZnYuKAVKbSJToAzjQx2TlPfa9u3FshIsyOYVkJGNEtASO7JWJgehxS41yHrGMh/7wyqyIQSJHghFcyXBmGuskvawmqyLa8tsHrQ3mNW93sl+LixZX1WJG7V+Uz3/8vRYIH3ZOi0DMpWn3AyiBTmcViwEBRRT1y99agsMI/IrWV4iKcqlBsdpdIJEaFo6LOjbIaveytaUBRZb069wORtFVScFaCHRI/VzNsbDa4wuwq2BPhX3t9PhVsqpXAVoNXHUN+f8n/KLnbj1RiVBi6JkaqIIukSpNjyqwSOSmv19D54dW/k1f9G1XWe1BV51HBtTCnHeH+FGuyDMmMU19oJ/VLQULUvuK5RETUCe1YAHxxC1CSox9LgfjJfwP6nX7k6SXlS710674ASe4SoGitTn/ZlKTGzBwBZB0LDDwXyBrNWSXtVKivganj4d8MERFRO1FXDrz9C2DnAiAsChh+OTD0EiB7bMuu+yvy9WCrTd8CW+cA9RX6+Zg0YOKfgWOuBBwcN09UwfRcobv4n/rSQqzLq1Apr0TgtoIENuRmfYT/Zr38XG6WSxopuekvj2MjnOpGv6RmiosIQ3p8hCrWlCHr+Ahkd4kKWfRXZoos3FqKH7eUqKDQ5sIqdXO/JaLDHUiKkeCODhjowIFeAkEEp8OmZtiU+oMVErSQ2TYtnXWRFhehln5pMSqgNLJ7IvqkxKiASWupGSO1HhVEkX+rQJBJAk8S4Kiq96JGAhz1MptH/xtKujP5PosMc6pAj/ybyb8lERFRq3g9wPLXgNkP6zonQup9SUqsjOF6kXponlrA7V9UDbTAtqzrmm83VAKFa4GaPT8/Xny2DpBkj9FL2lDAySB9RxDqa2DqePg3Q0RE1I5I/eL3f6mDH02vzVXdwD6AMxIIi9Dr6iKgaD1QvEEvZTub7ysqSc8OH3cz4Ipp81+FqL1i0IQX/5aTPxuZRbKpqBK5pbXwyp+R1A7xD16VeiDZaoZIlJppEQgiBaOmwYNde2tVTRBZJOWVpOtKiArzr8NVUCk5JpxFqoiIqPOPPvv+cWDRC4C72px9OlxA5jH7giRZY4A4pnzsqHgNTMHi3wwREVE74/MB2+YAP30IrPtcD3ZqERvQdSTQZzLQd7KeKW5inWWizoJBE178ExERUWckOYqlUGT+KiBvpc59LLNIpL6IpNKS6fyBEWiyVs8HtqP8r4nUo9XSZRaJK9S/EZmE18AULP7NEBERtWNyjb9xOpDzta4zKLPGPfX6+Yh4IHUgkDJg3zqqS6jPmKjdYyF4IiIios5ICsGnDdbLMVeE+myIiIiIiMgKMtBp8Pl6IaI2x5xGREREREREREREREREDJoQERERERERERERERFpDJoQERERERERERERERExaEJERERERERERERERKQxaEJERERERERERERERMSgCRERERERERERERERkcagCREREREREREREREREYMmREREREREREREREREGoMmREREREREREREREREDJoQERERERERERERERFpDJoQERERERERERERERExaEJERERERERERERERKQxaEJERERERERERERERMSgCRERERERERERERERkeZEJ2MYhlpXVFSE+lSIiIiIiNpE4No3cC1MdDjsNxERERHR0aQiiD5TpwuaVFZWqnV2dnaoT4WIiIiIqM2vhePj40N9GtQBsN9EREREREejyhb0mWxGJxuO5vP5kJeXh9jYWNhstpBErKTjkZubi7i4uDY/fmfFdrUG29UabFdrsF2twXa1BtvVGmzXg5NLern4z8zMhN3ODLx0eOw3dU5sV2uwXa3BdrUG29UabFdrsF2twXY98j5Tp5tpIr9wVlZWqE9D/UHyj9J8bFdrsF2twXa1BtvVGmxXa7BdrcF2PTDOMKFgsN/UubFdrcF2tQbb1RpsV2uwXa3BdrUG27X1fSYOQyMiIiIiIiIiIiIiImLQhIiIiIiIiIiIiIiISGPQxGQulwt//etf1ZrMw3a1BtvVGmxXa7BdrcF2tQbb1RpsV6LOg/89W4Ptag22qzXYrtZgu1qD7WoNtqs12K5HrtMVgiciIiIiIiIiIiIiImoNzjQhIiIiIiIiIiIiIiJi0ISIiIiIiIiIiIiIiEhj0ISIiIiIiIiIiIiIiIhBEyIiIiIiIiIiIiIiIo1BEyIiIiIiIiIiIiIiIgZNzPfMM8+gR48eiIiIwNixY7F48eJQn1KH8fDDD+PYY49FbGwsUlNTcf755yMnJ6fZa+rq6vC73/0OSUlJiImJwUUXXYTCwsKQnXNH9Mgjj8Bms+HWW29tfI7t2jq7d+/GlVdeqdotMjISQ4cOxdKlSxt/bhgG7r//fmRkZKifT5o0CZs2bQrpObd3Xq8X9913H3r27KnarHfv3njggQdUWwawXQ9v3rx5OOecc5CZman+e//000+b/bwlbVhaWoqpU6ciLi4OCQkJuO6661BVVYWj2aHa1e124+6771afA9HR0eo1v/rVr5CXl9dsH2zX4P9em7rpppvUa5544olmz7NdiToW9pmODPtNbYP9JvOw32Q+9pvMwX6TNdhvsgb7TW2LQRMTvffee7j99tvx17/+FcuXL8fw4cMxZcoUFBUVhfrUOoS5c+eqC9CFCxdixowZ6oP0tNNOQ3V1deNrbrvtNnzxxRf44IMP1OvlQ/XCCy8M6Xl3JEuWLMHzzz+PYcOGNXue7Rq8vXv34vjjj0dYWBi+/vprrFu3Do8++igSExMbX/PPf/4TTz75JJ577jksWrRIXRDIZ4J0tujA/vGPf+DZZ5/F008/jfXr16vH0o5PPfVU42vYrocnn5vyHSQ3pQ6kJW0oF1Jr165Vn8fTpk1TF2g33HADjmaHateamhr13S+dV1l//PHH6gbWueee2+x1bNfg/14DPvnkE3WNIJ2E/bFdiToO9pmOHPtN1mO/yTzsN1mD/SZzsN9kDfabrMF+UxszyDRjxowxfve73zU+9nq9RmZmpvHwww+H9Lw6qqKiIhkiYcydO1c9LisrM8LCwowPPvig8TXr169Xr1mwYEEIz7RjqKysNPr27WvMmDHDOOmkk4xbbrlFPc92bZ27777bmDBhwkF/7vP5jPT0dONf//pX43PS1i6Xy3jnnXfa6Cw7nrPOOsu49tprmz134YUXGlOnTlXbbNfgyX/Ln3zySePjlrThunXr1PuWLFnS+Jqvv/7asNlsxu7du9v4N+gY7XogixcvVq/bsWOHesx2bX277tq1y+jatauxZs0ao3v37sbjjz/e+DO2K1HHwj6T+dhvMhf7TeZiv8ka7DeZj/0ma7DfZA32m6zHmSYmaWhowLJly9RUvQC73a4eL1iwIKTn1lGVl5erdZcuXdRa2ldGUTVt4wEDBqBbt25s4xaQ0WhnnXVWs/YTbNfW+fzzzzF69GhccsklKi3CiBEj8OKLLzb+fNu2bSgoKGjWrvHx8SoFBdv14MaPH49Zs2Zh48aN6vGqVavw/fff44wzzlCP2a5HriVtKGuZqit/4wHyevlekxFW1PLvMZkSLW0p2K6t4/P58Mtf/hJ33nknBg8e/LOfs12JOg72mazBfpO52G8yF/tN1mC/yXrsN7Ud9pvMwX6TuZwm7++oVVJSonJKpqWlNXteHm/YsCFk59WR/0OX3LEyjXfIkCHqOfmyCg8Pb/wQbdrG8jM6uHfffVdNe5Rp5vtju7bO1q1b1XRoSS/x5z//WbXtH/7wB9WWV111VWPbHegzge16cH/6059QUVGhOqAOh0N9rj700ENqCqlgux65lrShrKVT25TT6VQ3Y9jOLSNT9iVX7+WXX67yxQq2a+tIuglpJ/mMPRC2K1HHwT6T+dhvMhf7TeZjv8ka7DdZj/2mtsF+k3nYbzIXgybUbkf3rFmzRo2UoCOTm5uLW265ReUrlGKbZF4HVaLzf//739VjGTElf7OS61Qu/ql13n//fbz11lt4++231ciIlStXqhsBkouT7UodhYxCvfTSS1XhSLlJQK0no3r/85//qBtYMvqMiIiaY7/JPOw3WYP9Jmuw30SdAftN5mG/yXxMz2WS5ORkFd0vLCxs9rw8Tk9PD9l5dUQ333yzKkY0e/ZsZGVlNT4v7ShT+svKypq9nm18+A9OKaw5cuRIFUGWRYoWSjEz2ZZREmzX4GVkZGDQoEHNnhs4cCB27typtgNtx8+E4Mg0Uhk1ddlll2Ho0KFqaqkU3Hz44YfVz9muR64lbSjr/QvyejwelJaWsp1beOG/Y8cOddMlMFpKsF2DN3/+fNVmkvok8B0mbXvHHXegR48e6jVsV6KOg30mc7HfZC72m6zBfpM12G+yHvtN1mK/yVzsN5mPQROTyNTSUaNGqZySTUdUyONx48aF9Nw6Coksy4X/J598gu+++w49e/Zs9nNp37CwsGZtnJOToy622MYHd+qpp+Knn35SI08Ci4z0kWm7gW22a/AkBYK0U1OST7Z79+5qW/5+5UunabvK9GnJE8l2PbiamhqVT7Mpubkin6eC7XrkWtKGspYbAnLzIEA+l+XfQXL40qEv/Ddt2oSZM2ciKSmp2c/ZrsGTGwCrV69u9h0mIyjlRsH06dPVa9iuRB0H+0zmYL/JGuw3WYP9Jmuw32Q99pusw36T+dhvskAbFJs/arz77ruGy+UyXnvtNWPdunXGDTfcYCQkJBgFBQWhPrUO4Te/+Y0RHx9vzJkzx8jPz29campqGl9z0003Gd26dTO+++47Y+nSpca4cePUQsE56aSTjFtuuaXxMds1eIsXLzacTqfx0EMPGZs2bTLeeustIyoqynjzzTcbX/PII4+oz4DPPvvMWL16tXHeeecZPXv2NGpra0N67u3ZVVddZXTt2tWYNm2asW3bNuPjjz82kpOTjbvuuqvxNWzXw6usrDRWrFihFvmqf+yxx9T2jh07WtyGp59+ujFixAhj0aJFxvfff2/07dvXuPzyy42j2aHataGhwTj33HONrKwsY+XKlc2+x+rr6xv3wXYN/u91f927dzcef/zxZs+xXYk6DvaZjhz7TW2H/aYjx36TNdhvMgf7TdZgv8ka7De1LQZNTPbUU0+pi6jw8HBjzJgxxsKFC0N9Sh2G/Ad/oOXVV19tfI18Mf32t781EhMT1YXWBRdcoD5Y6cgu/tmurfPFF18YQ4YMUR3/AQMGGC+88EKzn/t8PuO+++4z0tLS1GtOPfVUIycnJ2Tn2xFUVFSov035HI2IiDB69epl/OUvf2l28cR2PbzZs2cf8PNUOlctbcM9e/aoi6eYmBgjLi7OuOaaa9RF2tHsUO0qndWDfY/J+wLYrsH/vbbk4p/tStSxsM90ZNhvajvsN5mD/Sbzsd9kDvabrMF+kzXYb2pbNvk/K2awEBERERERERERERERdSSsaUJERERERERERERERMSgCRERERERERERERERkcagCREREREREREREREREYMmREREREREREREREREGoMmREREREREREREREREDJoQERERERERERERERFpDJoQERERERERERERERExaEJERERERERERERERKQxaEJERERERERERERERMSgCRERERERERERERERkcagCRERERERERERERERgYD/D38MorDgx/bfAAAAAElFTkSuQmCC", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -129,7 +127,7 @@ "from aeon.datasets import load_classification\n", "\n", "# Load GunPoint dataset\n", - "X, y = load_classification(\"GunPoint\")\n", + "X, y = load_classification(\"ArrowHead\")\n", "\n", "classes = np.unique(y)\n", "\n", @@ -190,22 +188,38 @@ "id": "a494a0be-4459-414d-9fc2-1400feefd171", "metadata": {}, "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "(1, 150)\n" + "(1, 251)\n" ] } ], "source": [ "from aeon.similarity_search.series import MassSNN\n", "\n", - "length = 35\n", - "# We'll take a sample of the class with a \"bump\".\n", "series_fit = X[2]\n", - "print(series_fit.shape)\n", - "snn = MassSNN(length=length, normalize=False).fit(series_fit)" + "series_predict = X[3]\n", + "\n", + "length = 50\n", + "snn = MassSNN(length=length, normalize=False).fit(series_fit)\n", + "\n", + "plt.plot(series_fit[0], label=\"series fit\")\n", + "plt.plot(series_predict[0], label=\"series predict\")\n", + "plt.legend()\n", + "plt.show()\n", + "print(series_fit.shape)" ] }, { @@ -226,14 +240,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "match 0 : 27 with distance 0.3020071566139322\n", - "match 1 : 28 with distance 0.48913603040398357\n", - "match 2 : 26 with distance 0.889697094966067\n" + "match 0 : 177 with distance 2.550008590853018\n", + "match 1 : 176 with distance 2.6262080735121316\n", + "match 2 : 31 with distance 2.7331649479116393\n" ] }, { "data": { - "image/png": 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" ] @@ -243,8 +257,8 @@ } ], "source": [ - "series_predict = X[3]\n", - "starting_timestep_predict = 25\n", + "starting_timestep_predict = 30\n", + "\n", "indexes, distances = snn.predict(\n", " series_predict[:, starting_timestep_predict : starting_timestep_predict + length],\n", " k=3,\n", @@ -275,7 +289,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -337,25 +351,54 @@ "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "BaseSeriesSimilaritySearch.predict() missing 1 required positional argument: 'X'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[6], line 4\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01maeon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msimilarity_search\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mseries\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m StompMotif\n\u001b[0;32m 3\u001b[0m motif \u001b[38;5;241m=\u001b[39m StompMotif(length\u001b[38;5;241m=\u001b[39mlength, normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\u001b[38;5;241m.\u001b[39mfit(series_fit)\n\u001b[1;32m----> 4\u001b[0m \u001b[43mmotif\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mmotif_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[43m)\u001b[49m\n", - "\u001b[1;31mTypeError\u001b[0m: BaseSeriesSimilaritySearch.predict() missing 1 required positional argument: 'X'" - ] + "data": { + "text/plain": [ + "([array([[ 40, 192]]), array([[192, 40]]), array([[158, 8]])],\n", + " [array([0.21749257]), array([0.21749257]), array([0.23961497])])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "from aeon.similarity_search.series import StompMotif\n", "\n", - "motif = StompMotif(length=length, normalize=True).fit(series_fit)\n", - "motif.predict(\n", - " k=5,\n", - " motif_size=1,\n", - ")" + "motif = StompMotif(length=length, normalize=True)\n", + "motif.fit_predict(series_fit, k=3, motif_size=1)" + ] + }, + { + "cell_type": "markdown", + "id": "ace51787-71c2-4f0e-bf37-b46b51ace354", + "metadata": {}, + "source": [ + "The above use of `fit_predict` is equivalent to the following calls, with `is_self_computation=True` indicating that the series in fit is the same that the series in predict, so it shouldn't match the same subsequences as motifs : " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c5dde2db-178b-444c-99ab-f659137638b8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "([array([[ 40, 192]]), array([[192, 40]]), array([[158, 8]])],\n", + " [array([0.21749257]), array([0.21749257]), array([0.23961497])])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "motif = StompMotif(length=length, normalize=True)\n", + "motif.fit(series_fit)\n", + "motif.predict(series_fit, k=3, motif_size=1, is_self_computation=True)" ] }, { @@ -363,22 +406,33 @@ "id": "d16036a3-f5b9-41d2-ae23-a1bcf0737c93", "metadata": {}, "source": [ - "\n", - "Note that we also support giving another series in `predict`, which will use this series to search for the motifs matching subsequences in the series given during `fit`. For those familiar with the matrix profile notations, this is the case of using `MP(A,B)`, while not using a series in `predict` is doing a self matrix profile `MP(A,A)`." + "While the above example only use `series_fit` to search motifs in the same series, we also support giving another series in `predict`, which will use this series to search for the motifs matching subsequences in the series given during `fit`. For those familiar with the matrix profile notations, this is the case of using `MP(A,B)`, while not using a series in `predict` is doing a self matrix profile `MP(A,A)`." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "59117ea7-2cbf-49d6-829a-792805b4aaf7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "([array([[149, 4]]), array([[ 62, 201]]), array([[3, 5]])],\n", + " [array([0.15686187]), array([0.27831027]), array([0.29831867])])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from aeon.similarity_search.series import StompMotif\n", "\n", "motif.predict(\n", " series_predict,\n", - " k=5,\n", + " k=3,\n", " motif_size=1,\n", ")" ] @@ -393,26 +447,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "4c36738a-e6a0-4452-aee2-ccbad99d6d8b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "MP, IP = motif.compute_matrix_profile()\n", + "MP, IP = motif.compute_matrix_profile(series_predict)\n", "\n", "plt.figure(figsize=(7, 2))\n", "plt.plot([MP[i][0] for i in range(len(MP))])\n", "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "5f9f5a86-19b9-4259-8cdc-2664b22532ae", - "metadata": {}, - "source": [ - "## 1.3 Motif search with KMotiflets estimator" - ] - }, { "cell_type": "markdown", "id": "7d2522e0-e6f4-412e-b0cb-2945016d188a", @@ -444,18 +501,71 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "cc719800-0119-42f9-9018-32288c2db69b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 0 : 32 with distance 1.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from aeon.similarity_search.collection import RandomProjectionIndexANN\n", "\n", - "X_fit = X[:199]\n", - "# you can use a single series, but it will be converted into a collection internally !\n", - "X_predict = X[199]\n", + "X_fit = X[:-2]\n", + "# we use a single series for this example but it will be converted into a collection\n", + "# as this is a collection estimators.\n", + "X_predict = X[-1]\n", "index = RandomProjectionIndexANN().fit(X_fit)\n", - "indexes, distances = index.predict(X_predict, k=2)\n", + "indexes, distances = index.predict(X_predict, k=3)\n", "# as X_predict is converted to a collection, we select the first returns\n", "# to obtain its results\n", "indexes = indexes[0]\n", @@ -477,10 +587,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "1b22b743-5710-4691-b740-8edaa3bbac2e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "match 0 : 17 with distance 12.0\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "index = RandomProjectionIndexANN(n_hash_funcs=512, hash_func_coverage=0.75).fit(X_fit)\n", "indexes, distances = index.predict(X_predict, k=2)\n", From 5c08b438a0082ee61699cd86a1be34f64b9ebad5 Mon Sep 17 00:00:00 2001 From: baraline Date: Sat, 19 Apr 2025 13:37:15 +0200 Subject: [PATCH 31/36] Fix typo in imports --- aeon/similarity_search/collection/_base.py | 2 ++ aeon/similarity_search/series/_base.py | 3 +++ aeon/utils/base/_register.py | 4 ++-- 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index 15f960863f..2334297a49 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -3,6 +3,8 @@ __maintainer__ = ["baraline"] __all__ = [ "BaseCollectionSimilaritySearch", + "BaseCollectionMotifs", + "BaseCollectionNeighbors", ] from abc import abstractmethod diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index cbb33c2d34..a00056b908 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -1,5 +1,8 @@ """Base similiarity search for series.""" +__maintainer__ = ["baraline"] +__all__ = ["BaseSeriesSimilaritySearch", "BaseSeriesNeighbors", "BaseSeriesMotifs"] + from abc import abstractmethod from typing import final diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index a6b174d0a0..4fbb0b588f 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -26,7 +26,7 @@ from aeon.segmentation.base import BaseSegmenter from aeon.similarity_search._base import BaseSimilaritySearch from aeon.similarity_search.collection import ( - BaseCollecionNeighbors, + BaseCollectionNeighbors, BaseCollectionSimilaritySearch, BaseColletionMotifs, ) @@ -62,7 +62,7 @@ "series-motifs-search": BaseSeriesMotifs, "series-neighbors-search": BaseSeriesNeighbors, "collection-motifs-search": BaseColletionMotifs, - "collection-neighbors-search": BaseCollecionNeighbors, + "collection-neighbors-search": BaseCollectionNeighbors, } # base classes which are valid for estimator to directly inherit from From 40c7e1a38442bfc38566f2a8e3393e5ac5b50793 Mon Sep 17 00:00:00 2001 From: baraline <10759117+baraline@users.noreply.github.com> Date: Sat, 19 Apr 2025 22:26:50 +0000 Subject: [PATCH 32/36] Empty commit for CI From 58dd7b427ccb3835eb937b69823b51ade3c2a2fd Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 20 Apr 2025 00:36:02 +0200 Subject: [PATCH 33/36] Fix typo again --- aeon/utils/base/_register.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index 4fbb0b588f..d8686cda75 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -26,9 +26,9 @@ from aeon.segmentation.base import BaseSegmenter from aeon.similarity_search._base import BaseSimilaritySearch from aeon.similarity_search.collection import ( + BaseCollectionMotifs, BaseCollectionNeighbors, BaseCollectionSimilaritySearch, - BaseColletionMotifs, ) from aeon.similarity_search.series import ( BaseSeriesMotifs, @@ -61,7 +61,7 @@ "forecaster": BaseForecaster, "series-motifs-search": BaseSeriesMotifs, "series-neighbors-search": BaseSeriesNeighbors, - "collection-motifs-search": BaseColletionMotifs, + "collection-motifs-search": BaseCollectionMotifs, "collection-neighbors-search": BaseCollectionNeighbors, } From 6911bd9c7e50b67c708deee228829b0632910117 Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 20 Apr 2025 00:51:00 +0200 Subject: [PATCH 34/36] Add check_inheritance exception for similarity search --- .../testing/estimator_checking/_yield_estimator_checks.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/aeon/testing/estimator_checking/_yield_estimator_checks.py b/aeon/testing/estimator_checking/_yield_estimator_checks.py index d583104e6b..6cf4ee7948 100644 --- a/aeon/testing/estimator_checking/_yield_estimator_checks.py +++ b/aeon/testing/estimator_checking/_yield_estimator_checks.py @@ -22,6 +22,7 @@ from aeon.regression import BaseRegressor from aeon.regression.deep_learning.base import BaseDeepRegressor from aeon.segmentation import BaseSegmenter +from aeon.similarity_search import BaseSimilaritySearch from aeon.testing.estimator_checking._yield_anomaly_detection_checks import ( _yield_anomaly_detection_checks, ) @@ -231,9 +232,10 @@ def check_inheritance(estimator_class): # Only transformers can inherit from multiple base types currently if n_base_types > 1: - assert issubclass( - estimator_class, BaseTransformer - ), "Only transformers can inherit from multiple base types." + assert issubclass(estimator_class, BaseTransformer) or issubclass( + estimator_class, BaseSimilaritySearch + ), "Only transformers or similarity search estimators can inherit from multiple" + "base types." def check_has_common_interface(estimator_class): From 7fd98995fb6b90bc4d2b951329a3bb1ba89a9034 Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 20 Apr 2025 01:12:04 +0200 Subject: [PATCH 35/36] Revert back to non per type base classes --- aeon/similarity_search/collection/__init__.py | 8 +-- aeon/similarity_search/collection/_base.py | 14 ---- .../collection/neighbors/_rp_cosine_lsh.py | 4 +- .../collection/tests/test_base.py | 18 +---- aeon/similarity_search/series/__init__.py | 4 -- aeon/similarity_search/series/_base.py | 66 +------------------ .../similarity_search/series/motifs/_stomp.py | 26 +++++++- .../series/neighbors/_dummy.py | 28 +++++++- .../series/neighbors/_mass.py | 28 +++++++- .../series/tests/test_base.py | 16 +---- aeon/similarity_search/tests/test_base.py | 1 - aeon/testing/mock_estimators/__init__.py | 12 ++-- .../_mock_similarity_searchers.py | 45 ++----------- aeon/utils/base/_register.py | 20 ++---- docs/api_reference/similarity_search.rst | 5 +- docs/api_reference/utils.rst | 6 +- 16 files changed, 102 insertions(+), 199 deletions(-) delete mode 100644 aeon/similarity_search/tests/test_base.py diff --git a/aeon/similarity_search/collection/__init__.py b/aeon/similarity_search/collection/__init__.py index cb69faf9a1..dea25853be 100644 --- a/aeon/similarity_search/collection/__init__.py +++ b/aeon/similarity_search/collection/__init__.py @@ -2,16 +2,10 @@ __all__ = [ "BaseCollectionSimilaritySearch", - "BaseCollectionNeighbors", - "BaseCollectionMotifs", "RandomProjectionIndexANN", ] -from aeon.similarity_search.collection._base import ( - BaseCollectionMotifs, - BaseCollectionNeighbors, - BaseCollectionSimilaritySearch, -) +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch from aeon.similarity_search.collection.neighbors._rp_cosine_lsh import ( RandomProjectionIndexANN, ) diff --git a/aeon/similarity_search/collection/_base.py b/aeon/similarity_search/collection/_base.py index 2334297a49..9bd6f7cb31 100644 --- a/aeon/similarity_search/collection/_base.py +++ b/aeon/similarity_search/collection/_base.py @@ -3,8 +3,6 @@ __maintainer__ = ["baraline"] __all__ = [ "BaseCollectionSimilaritySearch", - "BaseCollectionMotifs", - "BaseCollectionNeighbors", ] from abc import abstractmethod @@ -112,15 +110,3 @@ def _check_predict_series_format(self, X): @abstractmethod def _predict(self, X, **kwargs): ... - - -class BaseCollectionMotifs(BaseCollectionSimilaritySearch): - """Base class for motif search on collections.""" - - ... - - -class BaseCollectionNeighbors(BaseCollectionSimilaritySearch): - """Base class for neighbors search on collections.""" - - ... diff --git a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py index 8574bfdbc5..167ec538c6 100644 --- a/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py +++ b/aeon/similarity_search/collection/neighbors/_rp_cosine_lsh.py @@ -3,7 +3,7 @@ import numpy as np from numba import get_num_threads, njit, prange, set_num_threads -from aeon.similarity_search.collection._base import BaseCollectionNeighbors +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD, z_normalise_series_3d @@ -103,7 +103,7 @@ def _collection_to_bool(X, hash_funcs, start_points, length): return res -class RandomProjectionIndexANN(BaseCollectionNeighbors): +class RandomProjectionIndexANN(BaseCollectionSimilaritySearch): """ Random Projection Locality Sensitive Hashing index with cosine similarity. diff --git a/aeon/similarity_search/collection/tests/test_base.py b/aeon/similarity_search/collection/tests/test_base.py index 23460ebda0..937c59f643 100644 --- a/aeon/similarity_search/collection/tests/test_base.py +++ b/aeon/similarity_search/collection/tests/test_base.py @@ -3,26 +3,14 @@ __maintainer__ = ["baraline"] from aeon.testing.mock_estimators._mock_similarity_searchers import ( - MockCollectionMotifsSearch, - MockCollectionNeighborsSearch, + BaseCollectionSimilaritySearch, ) from aeon.testing.testing_data import FULL_TEST_DATA_DICT, _get_datatypes_for_estimator -def test_input_shape_fit_predict_collection_motifs(): +def test_input_shape_fit_predict_collection(): """Test input shapes.""" - estimator = MockCollectionMotifsSearch() - datatypes = _get_datatypes_for_estimator(estimator) - # dummy data to pass to fit when testing predict/predict_proba - for datatype in datatypes: - X_train, y_train = FULL_TEST_DATA_DICT[datatype]["train"] - X_test, y_test = FULL_TEST_DATA_DICT[datatype]["test"] - estimator.fit(X_train, y_train).predict(X_test) - - -def test_input_shape_fit_predict_collection_neighbors(): - """Test input shapes.""" - estimator = MockCollectionNeighborsSearch() + estimator = BaseCollectionSimilaritySearch() datatypes = _get_datatypes_for_estimator(estimator) # dummy data to pass to fit when testing predict/predict_proba for datatype in datatypes: diff --git a/aeon/similarity_search/series/__init__.py b/aeon/similarity_search/series/__init__.py index d940f59e0c..1ecc20614a 100644 --- a/aeon/similarity_search/series/__init__.py +++ b/aeon/similarity_search/series/__init__.py @@ -2,16 +2,12 @@ __all__ = [ "BaseSeriesSimilaritySearch", - "BaseSeriesMotifs", - "BaseSeriesNeighbors", "MassSNN", "StompMotif", "DummySNN", ] from aeon.similarity_search.series._base import ( - BaseSeriesMotifs, - BaseSeriesNeighbors, BaseSeriesSimilaritySearch, ) from aeon.similarity_search.series.motifs._stomp import StompMotif diff --git a/aeon/similarity_search/series/_base.py b/aeon/similarity_search/series/_base.py index a00056b908..6139835e77 100644 --- a/aeon/similarity_search/series/_base.py +++ b/aeon/similarity_search/series/_base.py @@ -1,7 +1,7 @@ """Base similiarity search for series.""" __maintainer__ = ["baraline"] -__all__ = ["BaseSeriesSimilaritySearch", "BaseSeriesNeighbors", "BaseSeriesMotifs"] +__all__ = ["BaseSeriesSimilaritySearch"] from abc import abstractmethod from typing import final @@ -117,67 +117,3 @@ def _check_predict_series_format(self, X): f"Expected X to have {self.n_channels_} channels but" f" got {X.shape[channel_idx]} channels." ) - - -class BaseSeriesNeighbors(BaseSeriesSimilaritySearch): - """ - Base class for neighbor search estimators. - - The goal of this base class is to define a fit_predict method to, for example, - compute self matrix profiles. - """ - - def _check_X_index(self, X_index: int): - """ - Check wheter a X_index parameter is correctly formated and is admissible. - - Parameters - ---------- - X_index : int - Index of a timestamp in X_. - - """ - if X_index is not None: - if not isinstance(X_index, int): - raise TypeError("Expected an integer for X_index but got {X_index}") - - max_timepoints = self.n_timepoints_ - if hasattr(self, "length"): - max_timepoints -= self.length - if X_index >= max_timepoints or X_index < 0: - raise ValueError( - "The value of X_index cannot exced the number " - "of timepoint in series given during fit. Expected a value " - f"between [0, {max_timepoints - 1}] but got {X_index}" - ) - - -class BaseSeriesMotifs(BaseSeriesSimilaritySearch): - """ - Base class for motif search estimators. - - The goal of this base class is to define a fit_predict method to, for example, - compute self matrix profiles. - """ - - def fit_predict(self, X, **kwargs): - """ - Fit and predict on a single series X in order to compute self-motifs. - - Parameters - ---------- - X : np.ndarray, shape = (n_channels, n_tiempoints) - Series to fit and predict on. - kwargs : dict, optional - Additional keyword argument as dict or individual keywords args - to pass to the estimator during predict. - - Returns - ------- - indexes : np.ndarray - Indexes of series in the that are similar to X. - distances : np.ndarray - Distance of the matches to each series - """ - self.fit(X) - return self.predict(X, is_self_computation=True, **kwargs) diff --git a/aeon/similarity_search/series/motifs/_stomp.py b/aeon/similarity_search/series/motifs/_stomp.py index a047b268f5..0f43bbf487 100644 --- a/aeon/similarity_search/series/motifs/_stomp.py +++ b/aeon/similarity_search/series/motifs/_stomp.py @@ -9,7 +9,7 @@ from numba import njit from numba.typed import List -from aeon.similarity_search.series._base import BaseSeriesMotifs +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch from aeon.similarity_search.series._commons import ( _extract_top_k_from_dist_profile, _extract_top_k_motifs, @@ -25,7 +25,7 @@ from aeon.utils.numba.general import sliding_mean_std_one_series -class StompMotif(BaseSeriesMotifs): +class StompMotif(BaseSeriesSimilaritySearch): """ Estimator to extract top k motifs using STOMP, descibed in [1]_. @@ -94,6 +94,28 @@ def _fit( self.X_means_, self.X_stds_ = sliding_mean_std_one_series(X, self.length, 1) return self + def fit_predict(self, X, **kwargs): + """ + Fit and predict on a single series X in order to compute self-motifs. + + Parameters + ---------- + X : np.ndarray, shape = (n_channels, n_tiempoints) + Series to fit and predict on. + kwargs : dict, optional + Additional keyword argument as dict or individual keywords args + to pass to the estimator during predict. + + Returns + ------- + indexes : np.ndarray + Indexes of series in the that are similar to X. + distances : np.ndarray + Distance of the matches to each series + """ + self.fit(X) + return self.predict(X, is_self_computation=True, **kwargs) + def _predict( self, X: np.ndarray, diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index edf49a9ee8..4b51716ce1 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -8,7 +8,7 @@ import numpy as np from numba import get_num_threads, njit, prange, set_num_threads -from aeon.similarity_search.series._base import BaseSeriesNeighbors +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch from aeon.similarity_search.series._commons import ( _extract_top_k_from_dist_profile, _inverse_distance_profile, @@ -21,7 +21,7 @@ from aeon.utils.validation import check_n_jobs -class DummySNN(BaseSeriesNeighbors): +class DummySNN(BaseSeriesSimilaritySearch): """Estimator to compute the on profile and distance profile using brute force.""" _tags = {"capability:multithreading": True} @@ -175,6 +175,30 @@ def _get_test_params(cls, parameter_set: str = "default"): ) return params + def _check_X_index(self, X_index: int): + """ + Check wheter a X_index parameter is correctly formated and is admissible. + + Parameters + ---------- + X_index : int + Index of a timestamp in X_. + + """ + if X_index is not None: + if not isinstance(X_index, int): + raise TypeError("Expected an integer for X_index but got {X_index}") + + max_timepoints = self.n_timepoints_ + if hasattr(self, "length"): + max_timepoints -= self.length + if X_index >= max_timepoints or X_index < 0: + raise ValueError( + "The value of X_index cannot exced the number " + "of timepoint in series given during fit. Expected a value " + f"between [0, {max_timepoints - 1}] but got {X_index}" + ) + @njit(cache=True, fastmath=True, parallel=True) def _naive_squared_distance_profile( diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py index f549531c84..40c09e545b 100644 --- a/aeon/similarity_search/series/neighbors/_mass.py +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -8,7 +8,7 @@ import numpy as np from numba import njit -from aeon.similarity_search.series._base import BaseSeriesNeighbors +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch from aeon.similarity_search.series._commons import ( _extract_top_k_from_dist_profile, _inverse_distance_profile, @@ -20,7 +20,7 @@ ) -class MassSNN(BaseSeriesNeighbors): +class MassSNN(BaseSeriesSimilaritySearch): """ Estimator to compute the subsequences nearest neighbors using MASS _[1]. @@ -169,6 +169,30 @@ def compute_distance_profile(self, X: np.ndarray): return distance_profile + def _check_X_index(self, X_index: int): + """ + Check wheter a X_index parameter is correctly formated and is admissible. + + Parameters + ---------- + X_index : int + Index of a timestamp in X_. + + """ + if X_index is not None: + if not isinstance(X_index, int): + raise TypeError("Expected an integer for X_index but got {X_index}") + + max_timepoints = self.n_timepoints_ + if hasattr(self, "length"): + max_timepoints -= self.length + if X_index >= max_timepoints or X_index < 0: + raise ValueError( + "The value of X_index cannot exced the number " + "of timepoint in series given during fit. Expected a value " + f"between [0, {max_timepoints - 1}] but got {X_index}" + ) + @classmethod def _get_test_params(cls, parameter_set: str = "default"): """Return testing parameter settings for the estimator. diff --git a/aeon/similarity_search/series/tests/test_base.py b/aeon/similarity_search/series/tests/test_base.py index 754580bc2d..33b78082c3 100644 --- a/aeon/similarity_search/series/tests/test_base.py +++ b/aeon/similarity_search/series/tests/test_base.py @@ -3,26 +3,14 @@ __maintainer__ = ["baraline"] from aeon.testing.mock_estimators._mock_similarity_searchers import ( - MockSeriesMotifSearch, - MockSeriesNeighborsSearch, + MockSeriesSimilaritySearch, ) from aeon.testing.testing_data import FULL_TEST_DATA_DICT, _get_datatypes_for_estimator def test_input_shape_fit_predict_collection_motifs(): """Test input shapes.""" - estimator = MockSeriesMotifSearch() - datatypes = _get_datatypes_for_estimator(estimator) - # dummy data to pass to fit when testing predict/predict_proba - for datatype in datatypes: - X_train, y_train = FULL_TEST_DATA_DICT[datatype]["train"] - X_test, y_test = FULL_TEST_DATA_DICT[datatype]["test"] - estimator.fit(X_train, y_train).predict(X_test) - - -def test_input_shape_fit_predict_collection_neighbors(): - """Test input shapes.""" - estimator = MockSeriesNeighborsSearch() + estimator = MockSeriesSimilaritySearch() datatypes = _get_datatypes_for_estimator(estimator) # dummy data to pass to fit when testing predict/predict_proba for datatype in datatypes: diff --git a/aeon/similarity_search/tests/test_base.py b/aeon/similarity_search/tests/test_base.py deleted file mode 100644 index e066e14680..0000000000 --- a/aeon/similarity_search/tests/test_base.py +++ /dev/null @@ -1 +0,0 @@ -"""Tests for base similarity search.""" diff --git a/aeon/testing/mock_estimators/__init__.py b/aeon/testing/mock_estimators/__init__.py index 254c2b17e5..e9e83aa263 100644 --- a/aeon/testing/mock_estimators/__init__.py +++ b/aeon/testing/mock_estimators/__init__.py @@ -30,10 +30,8 @@ "MockMultivariateSeriesTransformer", "MockSeriesTransformerNoFit", # similarity search - "MockSeriesMotifSearch", - "MockSeriesNeighborsSearch", - "MockCollectionMotifsSearch", - "MockCollectionNeighborsSearch", + "MockSeriesSimilaritySearch", + "MockCollectionSimilaritySearch", ] from aeon.testing.mock_estimators._mock_anomaly_detectors import ( @@ -68,8 +66,6 @@ MockUnivariateSeriesTransformer, ) from aeon.testing.mock_estimators._mock_similarity_searchers import ( - MockCollectionMotifsSearch, - MockCollectionNeighborsSearch, - MockSeriesMotifSearch, - MockSeriesNeighborsSearch, + MockCollectionSimilaritySearch, + MockSeriesSimilaritySearch, ) diff --git a/aeon/testing/mock_estimators/_mock_similarity_searchers.py b/aeon/testing/mock_estimators/_mock_similarity_searchers.py index 02ae23ddcb..ddf001daf3 100644 --- a/aeon/testing/mock_estimators/_mock_similarity_searchers.py +++ b/aeon/testing/mock_estimators/_mock_similarity_searchers.py @@ -2,20 +2,15 @@ __maintainer__ = ["baraline"] __all__ = [ - "MockSeriesMotifSearch", - "MockSeriesNeighborsSearch", - "MockCollectionMotifsSearch", - "MockCollectionNeighborsSearch", + "MockSeriesSimilaritySearch", + "MockCollectionSimilaritySearch", ] -from aeon.similarity_search.collection._base import ( - BaseCollectionMotifs, - BaseCollectionNeighbors, -) -from aeon.similarity_search.series._base import BaseSeriesMotifs, BaseSeriesNeighbors +from aeon.similarity_search.collection._base import BaseCollectionSimilaritySearch +from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch -class MockSeriesMotifSearch(BaseSeriesMotifs): +class MockSeriesSimilaritySearch(BaseSeriesSimilaritySearch): """Mock estimator for BaseMatrixProfile.""" def __init__(self): @@ -29,21 +24,7 @@ def _predict(self, X): return [0], [0.1] -class MockSeriesNeighborsSearch(BaseSeriesNeighbors): - """Mock estimator for BaseMatrixProfile.""" - - def __init__(self): - super().__init__() - - def _fit(self, X, y=None): - return self - - def _predict(self, X): - """top-1 neighbor start timestamp index in X_, and distances to the query.""" - return [0], [0.1] - - -class MockCollectionMotifsSearch(BaseCollectionMotifs): +class MockCollectionSimilaritySearch(BaseCollectionSimilaritySearch): """Mock estimator for BaseMatrixProfile.""" def __init__(self): @@ -55,17 +36,3 @@ def _fit(self, X, y=None): def _predict(self, X): """top-1 motif start timestamp index in X, and distances to the match in X_.""" return [0, 0], [0.1] - - -class MockCollectionNeighborsSearch(BaseCollectionNeighbors): - """Mock estimator for BaseMatrixProfile.""" - - def __init__(self): - super().__init__() - - def _fit(self, X, y=None): - return self - - def _predict(self, X): - """top-1 neighbor sample index in X_, and distances to the each query.""" - return [0 for _ in range(len(X))], [0.1 for _ in range(len(X))] diff --git a/aeon/utils/base/_register.py b/aeon/utils/base/_register.py index d8686cda75..5e81e29b33 100644 --- a/aeon/utils/base/_register.py +++ b/aeon/utils/base/_register.py @@ -25,16 +25,8 @@ from aeon.regression.base import BaseRegressor from aeon.segmentation.base import BaseSegmenter from aeon.similarity_search._base import BaseSimilaritySearch -from aeon.similarity_search.collection import ( - BaseCollectionMotifs, - BaseCollectionNeighbors, - BaseCollectionSimilaritySearch, -) -from aeon.similarity_search.series import ( - BaseSeriesMotifs, - BaseSeriesNeighbors, - BaseSeriesSimilaritySearch, -) +from aeon.similarity_search.collection import BaseCollectionSimilaritySearch +from aeon.similarity_search.series import BaseSeriesSimilaritySearch from aeon.transformations.base import BaseTransformer from aeon.transformations.collection import BaseCollectionTransformer from aeon.transformations.series import BaseSeriesTransformer @@ -47,8 +39,6 @@ "series-estimator": BaseSeriesEstimator, "transformer": BaseTransformer, "similarity-search": BaseSimilaritySearch, - "series-similarity-search": BaseSeriesSimilaritySearch, - "collection-similarity-search": BaseCollectionSimilaritySearch, # estimator types "anomaly-detector": BaseAnomalyDetector, "collection-transformer": BaseCollectionTransformer, @@ -59,10 +49,8 @@ "segmenter": BaseSegmenter, "series-transformer": BaseSeriesTransformer, "forecaster": BaseForecaster, - "series-motifs-search": BaseSeriesMotifs, - "series-neighbors-search": BaseSeriesNeighbors, - "collection-motifs-search": BaseCollectionMotifs, - "collection-neighbors-search": BaseCollectionNeighbors, + "series-similarity-search": BaseSeriesSimilaritySearch, + "collection-similarity-search": BaseCollectionSimilaritySearch, } # base classes which are valid for estimator to directly inherit from diff --git a/docs/api_reference/similarity_search.rst b/docs/api_reference/similarity_search.rst index 4936afd36b..c62b0636f3 100644 --- a/docs/api_reference/similarity_search.rst +++ b/docs/api_reference/similarity_search.rst @@ -62,8 +62,7 @@ Base Estimators :template: class.rst BaseSeriesSimilaritySearch - BaseSeriesNeighbors - BaseSeriesMotifs + .. currentmodule:: aeon.similarity_search.collection._base @@ -72,5 +71,3 @@ Base Estimators :template: class.rst BaseCollectionSimilaritySearch - BaseCollectionNeighbors - BaseCollectionMotifs diff --git a/docs/api_reference/utils.rst b/docs/api_reference/utils.rst index 0d943f28c1..8c4891dde0 100644 --- a/docs/api_reference/utils.rst +++ b/docs/api_reference/utils.rst @@ -87,10 +87,8 @@ Mock Estimators MockUnivariateSeriesTransformer MockMultivariateSeriesTransformer MockSeriesTransformerNoFit - MockSeriesMotifSearch - MockSeriesNeighborsSearch - MockCollectionMotifsSearch - MockCollectionNeighborsSearch + MockSeriesSimilaritySearch + MockCollectionSimilaritySearch Utilities ^^^^^^^^^ From 470dcf907f2c37440376e1b6abde4658f4c940ab Mon Sep 17 00:00:00 2001 From: baraline Date: Sun, 20 Apr 2025 01:21:06 +0200 Subject: [PATCH 36/36] Factor check index and typo in test --- .../collection/tests/test_base.py | 4 +-- aeon/similarity_search/series/_commons.py | 27 +++++++++++++++++++ .../series/neighbors/_dummy.py | 27 ++----------------- .../series/neighbors/_mass.py | 27 ++----------------- 4 files changed, 33 insertions(+), 52 deletions(-) diff --git a/aeon/similarity_search/collection/tests/test_base.py b/aeon/similarity_search/collection/tests/test_base.py index 937c59f643..7f538cdd59 100644 --- a/aeon/similarity_search/collection/tests/test_base.py +++ b/aeon/similarity_search/collection/tests/test_base.py @@ -3,14 +3,14 @@ __maintainer__ = ["baraline"] from aeon.testing.mock_estimators._mock_similarity_searchers import ( - BaseCollectionSimilaritySearch, + MockCollectionSimilaritySearch, ) from aeon.testing.testing_data import FULL_TEST_DATA_DICT, _get_datatypes_for_estimator def test_input_shape_fit_predict_collection(): """Test input shapes.""" - estimator = BaseCollectionSimilaritySearch() + estimator = MockCollectionSimilaritySearch() datatypes = _get_datatypes_for_estimator(estimator) # dummy data to pass to fit when testing predict/predict_proba for datatype in datatypes: diff --git a/aeon/similarity_search/series/_commons.py b/aeon/similarity_search/series/_commons.py index 22bccd15e6..646c38e5ff 100644 --- a/aeon/similarity_search/series/_commons.py +++ b/aeon/similarity_search/series/_commons.py @@ -9,6 +9,33 @@ from aeon.utils.numba.general import AEON_NUMBA_STD_THRESHOLD +def _check_X_index(X_index: int, n_timepoints: int, length: int): + """ + Check wheter a X_index parameter is correctly formated and is admissible. + + Parameters + ---------- + X_index : int + Index of a timestamp in X_. + n_timepoints: int + Number of timepoints in the serie X_ + length: int + Length parameter of the estimator + + """ + if X_index is not None: + if not isinstance(X_index, int): + raise TypeError("Expected an integer for X_index but got {X_index}") + + max_timepoints = n_timepoints - length + if X_index >= max_timepoints or X_index < 0: + raise ValueError( + "The value of X_index cannot exced the number " + "of timepoint in series given during fit. Expected a value " + f"between [0, {max_timepoints - 1}] but got {X_index}" + ) + + def fft_sliding_dot_product(X, q): """ Use FFT convolution to calculate the sliding window dot product. diff --git a/aeon/similarity_search/series/neighbors/_dummy.py b/aeon/similarity_search/series/neighbors/_dummy.py index 4b51716ce1..399297b5c9 100644 --- a/aeon/similarity_search/series/neighbors/_dummy.py +++ b/aeon/similarity_search/series/neighbors/_dummy.py @@ -10,6 +10,7 @@ from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch from aeon.similarity_search.series._commons import ( + _check_X_index, _extract_top_k_from_dist_profile, _inverse_distance_profile, ) @@ -101,7 +102,7 @@ def _predict( f" got {X.shape[1]} timepoints." ) - X_index = self._check_X_index(X_index) + X_index = _check_X_index(X_index, self.n_timepoints_, self.length) dist_profile = self.compute_distance_profile(X) if inverse_distance: dist_profile = _inverse_distance_profile(dist_profile) @@ -175,30 +176,6 @@ def _get_test_params(cls, parameter_set: str = "default"): ) return params - def _check_X_index(self, X_index: int): - """ - Check wheter a X_index parameter is correctly formated and is admissible. - - Parameters - ---------- - X_index : int - Index of a timestamp in X_. - - """ - if X_index is not None: - if not isinstance(X_index, int): - raise TypeError("Expected an integer for X_index but got {X_index}") - - max_timepoints = self.n_timepoints_ - if hasattr(self, "length"): - max_timepoints -= self.length - if X_index >= max_timepoints or X_index < 0: - raise ValueError( - "The value of X_index cannot exced the number " - "of timepoint in series given during fit. Expected a value " - f"between [0, {max_timepoints - 1}] but got {X_index}" - ) - @njit(cache=True, fastmath=True, parallel=True) def _naive_squared_distance_profile( diff --git a/aeon/similarity_search/series/neighbors/_mass.py b/aeon/similarity_search/series/neighbors/_mass.py index 40c09e545b..695dce8844 100644 --- a/aeon/similarity_search/series/neighbors/_mass.py +++ b/aeon/similarity_search/series/neighbors/_mass.py @@ -10,6 +10,7 @@ from aeon.similarity_search.series._base import BaseSeriesSimilaritySearch from aeon.similarity_search.series._commons import ( + _check_X_index, _extract_top_k_from_dist_profile, _inverse_distance_profile, fft_sliding_dot_product, @@ -109,7 +110,7 @@ def _predict( f"Expected X to have {self.length} timepoints but" f" got {X.shape[1]} timepoints." ) - X_index = self._check_X_index(X_index) + X_index = _check_X_index(X_index, self.n_timepoints_, self.length) dist_profile = self.compute_distance_profile(X) if inverse_distance: dist_profile = _inverse_distance_profile(dist_profile) @@ -169,30 +170,6 @@ def compute_distance_profile(self, X: np.ndarray): return distance_profile - def _check_X_index(self, X_index: int): - """ - Check wheter a X_index parameter is correctly formated and is admissible. - - Parameters - ---------- - X_index : int - Index of a timestamp in X_. - - """ - if X_index is not None: - if not isinstance(X_index, int): - raise TypeError("Expected an integer for X_index but got {X_index}") - - max_timepoints = self.n_timepoints_ - if hasattr(self, "length"): - max_timepoints -= self.length - if X_index >= max_timepoints or X_index < 0: - raise ValueError( - "The value of X_index cannot exced the number " - "of timepoint in series given during fit. Expected a value " - f"between [0, {max_timepoints - 1}] but got {X_index}" - ) - @classmethod def _get_test_params(cls, parameter_set: str = "default"): """Return testing parameter settings for the estimator.