Is your feature request related to a problem? Please describe.
I'm running the tutorial of PAA and SAX in the doc
import numpy
import matplotlib.pyplot as plt
from tslearn.generators import random_walks
from tslearn.preprocessing import TimeSeriesScalerMeanVariance
from tslearn.piecewise import PiecewiseAggregateApproximation
from tslearn.piecewise import SymbolicAggregateApproximation, \
OneD_SymbolicAggregateApproximation
numpy.random.seed(0)
# Generate a random walk time series
n_ts, sz, d = 1, 100, 1
dataset = random_walks(n_ts=n_ts, sz=sz, d=d)
scaler = TimeSeriesScalerMeanVariance(mu=0., std=1.) # Rescale time series
dataset = scaler.fit_transform(dataset)
n_paa_segments = 10
paa = PiecewiseAggregateApproximation(n_segments=n_paa_segments)
paa_data = paa.fit_transform(dataset)
paa_dataset_inv = paa.inverse_transform(paa_data)After reading the docs, it seems that there is no function to find the index when the paa_data changes occured.
Describe the solution you'd like
paa.get_index(paa_data) should return the index where the paa_data changes occured. That will be nice.
Is your feature request related to a problem? Please describe.
I'm running the tutorial of PAA and SAX in the doc
After reading the docs, it seems that there is no function to find the index when the paa_data changes occured.
Describe the solution you'd like
paa.get_index(paa_data)should return the index where thepaa_datachanges occured. That will be nice.