From be31e75688cfa90f3fbe4891df9614aabb47b899 Mon Sep 17 00:00:00 2001 From: aditya27 <120127383+Edgeshot27@users.noreply.github.com> Date: Wed, 19 Mar 2025 22:45:08 +0530 Subject: [PATCH 01/13] Add files via upload --- .../Deep_learning_Based_clustering.ipynb | 637 ++++++++++++++++++ 1 file changed, 637 insertions(+) create mode 100644 examples/clustering/Deep_learning_Based_clustering.ipynb diff --git a/examples/clustering/Deep_learning_Based_clustering.ipynb b/examples/clustering/Deep_learning_Based_clustering.ipynb new file mode 100644 index 0000000000..f61cee5cb4 --- /dev/null +++ b/examples/clustering/Deep_learning_Based_clustering.ipynb @@ -0,0 +1,637 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dne_kUDfuiYS", + "outputId": "5cb1dfa6-241e-43bc-9425-06595b24a733" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting aeon[deep_learning]\n", + " Downloading aeon-1.0.0-py3-none-any.whl.metadata (20 kB)\n", + "\u001b[33mWARNING: aeon 1.0.0 does not provide the extra 'deep-learning'\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: deprecated>=1.2.13 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (1.2.18)\n", + "Requirement already satisfied: numba<0.61.0,>=0.55 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (0.60.0)\n", + "Requirement already satisfied: numpy<2.1.0,>=1.21.0 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (2.0.2)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (24.2)\n", + "Requirement already satisfied: pandas<2.3.0,>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (2.2.2)\n", + "Collecting scikit-learn<1.6.0,>=1.0.0 (from aeon[deep_learning])\n", + " Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n", + "Requirement already satisfied: scipy<1.15.0,>=1.9.0 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (1.14.1)\n", + "Requirement already satisfied: typing-extensions>=4.6.0 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (4.12.2)\n", + "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.11/dist-packages (from deprecated>=1.2.13->aeon[deep_learning]) (1.17.2)\n", + "Requirement already satisfied: llvmlite<0.44,>=0.43.0dev0 in /usr/local/lib/python3.11/dist-packages (from numba<0.61.0,>=0.55->aeon[deep_learning]) (0.43.0)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas<2.3.0,>=2.0.0->aeon[deep_learning]) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas<2.3.0,>=2.0.0->aeon[deep_learning]) (2025.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas<2.3.0,>=2.0.0->aeon[deep_learning]) (2025.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn<1.6.0,>=1.0.0->aeon[deep_learning]) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn<1.6.0,>=1.0.0->aeon[deep_learning]) (3.6.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas<2.3.0,>=2.0.0->aeon[deep_learning]) (1.17.0)\n", + "Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m52.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading aeon-1.0.0-py3-none-any.whl (8.2 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: scikit-learn, aeon\n", + " Attempting uninstall: scikit-learn\n", + " Found existing installation: scikit-learn 1.6.1\n", + " Uninstalling scikit-learn-1.6.1:\n", + " Successfully uninstalled scikit-learn-1.6.1\n", + "Successfully installed aeon-1.0.0 scikit-learn-1.5.2\n" + ] + } + ], + "source": [ + "!pip install aeon[deep_learning]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2Ru1riLYWFE3" + }, + "source": [ + "# **Deep Learning Based Clustering**\n", + "The aeon.clustering.deeplearning module provides powerful deep learning models designed specifically for time series clustering. These models leverage advanced architectures like **Auto-Encoders, Residual Networks, Dilated Convolutions, and Attention Mechanisms** to effectively identify patterns, trends, and groupings in complex temporal data. Each model is tailored to handle different data characteristics, improving clustering accuracy and performance across various domains such as healthcare, finance, and signal processing." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "86gsiHDbuoz-" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "7xvwY48cur5i" + }, + "outputs": [], + "source": [ + "from aeon.datasets import load_arrow_head" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "EkRKdT47N7Oc" + }, + "outputs": [], + "source": [ + "X_train, y_train = load_arrow_head(split=\"train\")\n", + "X_test, y_test = load_arrow_head(split=\"test\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b-XHpTDjxeSd" + }, + "source": [ + "# **AEFCNClusterer (Auto-Encoder Fully Convolutional Network)**\n", + "The **AEFCNClusterer** is a deep learning model that leverages a **Fully Convolutional Network (FCN)** architecture with an **Auto-Encoder** structure for clustering. It combines feature extraction with convolutional layers and reconstruction capabilities via auto-encoders. \n", + "FCNs are effective for extracting spatial hierarchies in time series data without requiring fully connected layers, making them highly efficient.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "DGbXlOPLxdO-" + }, + "outputs": [], + "source": [ + "from aeon.clustering.deep_learning import AEFCNClusterer" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kxYYwMmKN_Uz", + "outputId": "0f2ccdef-1a79-41ad-94f2-a5708d41443f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 81ms/step\n" + ] + } + ], + "source": [ + "model = AEFCNClusterer(n_epochs=10,batch_size=3)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "q3oOIG3jOtbf", + "outputId": "258be6d8-6dc7-4c47-b8f3-f5b47412967b" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='viridis')\n", + "plt.title('Cluster Distribution with AEFCN')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wuFuNtkNP5tN" + }, + "source": [ + "# **AEResNetClusterer (Auto-Encoder Residual Network)**\n", + "The **AEResNetClusterer** applies an Auto-Encoder architecture integrated with a **Residual Network** (ResNet) backbone.ResNet models use skip connections, allowing gradients to flow directly through layers, reducing vanishing gradient issues.This approach enhances learning in deep networks and efficiently captures complex temporal patterns in time series data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "6atNZu4ADFxb" + }, + "outputs": [], + "source": [ + "from aeon.clustering.deep_learning import AEResNetClusterer" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ipdqBhoAP4-9", + "outputId": "9059875c-26ae-416e-e0ba-d31ca587b9e9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 468ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 147ms/step\n" + ] + } + ], + "source": [ + "model = AEResNetClusterer(n_epochs=10, random_state=42,batch_size=3)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "bHI_0_064GFu", + "outputId": "f1d5dd1d-3c4d-42cd-a75c-d3c00da85bca" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='plasma')\n", + "plt.title('Cluster Distribution with AEResNet')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H1CANB-oD-t_" + }, + "source": [ + "# **AEDCNNClusterer (Auto-Encoder Dilated Convolutional Network)**\n", + "The **AEDCNNClusterer** is built on an **Auto-Encoder** with a **Dilated Convolutional Network (DCNN)** backbone.Dilated convolutions use dilated filters to expand the receptive field exponentially, allowing the model to capture long-term dependencies in the data without losing resolution.This method is ideal for detecting patterns over extended time windows.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "gM5ja7I14GJK" + }, + "outputs": [], + "source": [ + "from aeon.clustering.deep_learning import AEDCNNClusterer" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qv-xuWhvE1_6", + "outputId": "3ce747a0-6495-485e-e57e-df93fa25bc0b" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.11/dist-packages/aeon/clustering/deep_learning/_ae_dcnn.py:209: UserWarning: Currently, the dilation rate has been set to `1` which is\n", + " different from the original paper of the `AEDCNNNetwork` due to CPU\n", + " Implementation issues with `tensorflow.keras.layers.Conv1DTranspose`\n", + " & `dilation_rate` > 1 on some Hardwares & OS combinations. You\n", + " can use the dilation rates as specified in the paper by passing\n", + " `dilation_rate=None` to the Network/Clusterer.\n", + " encoder, decoder = self._network.build_network(input_shape, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 272ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n" + ] + } + ], + "source": [ + "model = AEDCNNClusterer(n_epochs=10, random_state=42,dilation_rate=1)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "AQIaYKy6E5RK", + "outputId": "6de1c9b3-1d85-425c-9eca-e2edbbf36ae9" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='coolwarm')\n", + "plt.title('Cluster Distribution with AEDCNN')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ickHTlKQFDDZ" + }, + "source": [ + "# **AEDRNNClusterer (Auto-Encoder Dilated Recurrent Neural Network)**\n", + "The **AEDRNNClusterer** integrates an Auto-Encoder with a **Dilated Recurrent Neural Network (DRNN)** backbone.DRNNs combine the strengths of RNNs (sequence modeling) with dilated connections to capture patterns over long temporal sequences efficiently.they are Suitable for tasks where sequential relationships are vital (e.g., speech data, financial trends).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "O2Xj2LilFBjX" + }, + "outputs": [], + "source": [ + "from aeon.clustering.deep_learning import AEDRNNClusterer" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "k4z2dOrzFjx2", + "outputId": "392d57ef-54ed-4f86-e236-3245145cd0a2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 2s/step" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x785f3b7428e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 1s/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 231ms/step\n" + ] + } + ], + "source": [ + "model = AEDRNNClusterer(n_epochs=10, random_state=42)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "mE4l1C9AFm-U", + "outputId": "a04a18cf-e3bb-4cf0-88da-cbe8db8cb1fc" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='magma')\n", + "plt.title('Cluster Distribution with AEDRNN')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cRH_XEfeF6Cs" + }, + "source": [ + "# **AEAttentionBiGRUClusterer (Auto-Encoder Attention Bidirectional GRU Network)**\n", + "The **AEAttentionBiGRUClusterer** integrates an Auto-Encoder with an Attention-based **Bidirectional Gated Recurrent Unit (BiGRU)** network.\n", + "The Attention Mechanism allows the model to selectively focus on the most relevant parts of the time series during clustering.The Bidirectional GRU enhances this by processing the sequence from both forward and backward directions, improving sequence dependency recognition.It is Suitable for tasks requiring fine-grained sequence interpretation.Excels in datasets where certain segments of the sequence are more influential than others.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "iuy-H4F5F4dk" + }, + "outputs": [], + "source": [ + "from aeon.clustering.deep_learning import AEAttentionBiGRUClusterer" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9Zx6TvNFHBwg", + "outputId": "290fa9d2-0fcf-469d-f6c3-f69cbdebe9e3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 901ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 631ms/step\n" + ] + } + ], + "source": [ + "model = AEAttentionBiGRUClusterer(n_epochs=10, random_state=42)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "-AvTzfdlHGeF", + "outputId": "3461f910-6547-40c9-a402-275ee128a92b" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='inferno')\n", + "plt.title('Cluster Distribution with AEAttentionBiGRU')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FTnLddtrHMQE" + }, + "source": [ + "# **AEBiGRUClusterer (Auto-Encoder Bidirectional GRU Network)\n", + "**\n", + "The **AEBiGRUClusterer** is an Auto-Encoder with a **Bidirectional GRU (BiGRU)** architecture.GRUs are similar to LSTMs but with a simpler structure, making them faster and more efficient for time series data.The bidirectional structure enhances the model’s ability to detect patterns by combining forward and backward sequence insights.It Performs well on shorter sequences with frequent fluctuations.Suitable for tasks requiring fast training without compromising performance.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "duoJ4wMQHnFt" + }, + "outputs": [], + "source": [ + "from aeon.clustering.deep_learning import AEBiGRUClusterer" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XfGf-pgxHnRy", + "outputId": "84b30734-4b03-4b01-e099-4a761edcd0ab" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 918ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 127ms/step\n" + ] + } + ], + "source": [ + "model = AEBiGRUClusterer(n_epochs=10, random_state=42)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "RlH69d_rHnVX", + "outputId": "ebbb3fb9-13b4-4ccc-e5fe-214cbba0e0ef" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='cividis')\n", + "plt.title('Cluster Distribution with AEBiGRU')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sq0CNNhuI3_O" + }, + "source": [ + "**References:**\n", + "\n", + "[1]Zhao et. al, Convolutional neural networks for time series classification,\n", + "Journal of Systems Engineering and Electronics, 28(1):2017.\n", + "\n", + "[2]Wang et. al, Time series classification from scratch with deep neural networks: A strong baseline, International joint conference on neural networks (IJCNN), 2017.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-yv9NB8JHyUE" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 5ba8e4189862bac808d449f589a605de774e25c4 Mon Sep 17 00:00:00 2001 From: Edgeshot27 <120127383+Edgeshot27@users.noreply.github.com> Date: Thu, 20 Mar 2025 15:34:09 +0000 Subject: [PATCH 02/13] Automatic `pre-commit` fixes --- .../Deep_learning_Based_clustering.ipynb | 46 +++++++++---------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/examples/clustering/Deep_learning_Based_clustering.ipynb b/examples/clustering/Deep_learning_Based_clustering.ipynb index f61cee5cb4..750e5b7012 100644 --- a/examples/clustering/Deep_learning_Based_clustering.ipynb +++ b/examples/clustering/Deep_learning_Based_clustering.ipynb @@ -70,8 +70,8 @@ }, "outputs": [], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "import numpy as np" ] }, { @@ -140,7 +140,7 @@ } ], "source": [ - "model = AEFCNClusterer(n_epochs=10,batch_size=3)\n", + "model = AEFCNClusterer(n_epochs=10, batch_size=3)\n", "model.fit(X_train)\n", "y_pred = model.predict(X_test)" ] @@ -169,9 +169,9 @@ } ], "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='viridis')\n", - "plt.title('Cluster Distribution with AEFCN')\n", - "plt.colorbar(label='Cluster')\n", + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"viridis\")\n", + "plt.title(\"Cluster Distribution with AEFCN\")\n", + "plt.colorbar(label=\"Cluster\")\n", "plt.show()" ] }, @@ -217,7 +217,7 @@ } ], "source": [ - "model = AEResNetClusterer(n_epochs=10, random_state=42,batch_size=3)\n", + "model = AEResNetClusterer(n_epochs=10, random_state=42, batch_size=3)\n", "model.fit(X_train)\n", "y_pred = model.predict(X_test)" ] @@ -246,9 +246,9 @@ } ], "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='plasma')\n", - "plt.title('Cluster Distribution with AEResNet')\n", - "plt.colorbar(label='Cluster')\n", + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"plasma\")\n", + "plt.title(\"Cluster Distribution with AEResNet\")\n", + "plt.colorbar(label=\"Cluster\")\n", "plt.show()" ] }, @@ -307,7 +307,7 @@ } ], "source": [ - "model = AEDCNNClusterer(n_epochs=10, random_state=42,dilation_rate=1)\n", + "model = AEDCNNClusterer(n_epochs=10, random_state=42, dilation_rate=1)\n", "model.fit(X_train)\n", "y_pred = model.predict(X_test)" ] @@ -336,9 +336,9 @@ } ], "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='coolwarm')\n", - "plt.title('Cluster Distribution with AEDCNN')\n", - "plt.colorbar(label='Cluster')\n", + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"coolwarm\")\n", + "plt.title(\"Cluster Distribution with AEDCNN\")\n", + "plt.colorbar(label=\"Cluster\")\n", "plt.show()" ] }, @@ -429,9 +429,9 @@ } ], "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='magma')\n", - "plt.title('Cluster Distribution with AEDRNN')\n", - "plt.colorbar(label='Cluster')\n", + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"magma\")\n", + "plt.title(\"Cluster Distribution with AEDRNN\")\n", + "plt.colorbar(label=\"Cluster\")\n", "plt.show()" ] }, @@ -509,9 +509,9 @@ } ], "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='inferno')\n", - "plt.title('Cluster Distribution with AEAttentionBiGRU')\n", - "plt.colorbar(label='Cluster')\n", + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"inferno\")\n", + "plt.title(\"Cluster Distribution with AEAttentionBiGRU\")\n", + "plt.colorbar(label=\"Cluster\")\n", "plt.show()" ] }, @@ -588,9 +588,9 @@ } ], "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='cividis')\n", - "plt.title('Cluster Distribution with AEBiGRU')\n", - "plt.colorbar(label='Cluster')\n", + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"cividis\")\n", + "plt.title(\"Cluster Distribution with AEBiGRU\")\n", + "plt.colorbar(label=\"Cluster\")\n", "plt.show()" ] }, From 7ef773fde1c7e4391f27e3d76d7bfd8e90b90e63 Mon Sep 17 00:00:00 2001 From: aditya27 <120127383+Edgeshot27@users.noreply.github.com> Date: Thu, 20 Mar 2025 21:47:18 +0530 Subject: [PATCH 03/13] Update Deep_learning_Based_clustering.ipynb --- examples/clustering/Deep_learning_Based_clustering.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/clustering/Deep_learning_Based_clustering.ipynb b/examples/clustering/Deep_learning_Based_clustering.ipynb index 750e5b7012..f533f36727 100644 --- a/examples/clustering/Deep_learning_Based_clustering.ipynb +++ b/examples/clustering/Deep_learning_Based_clustering.ipynb @@ -70,8 +70,8 @@ }, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np" + "import matplotlib.pyplot as plt\n" + ] }, { From eb9a1cce4817c746590106edb371861f5382f1cd Mon Sep 17 00:00:00 2001 From: Edgeshot27 <120127383+Edgeshot27@users.noreply.github.com> Date: Thu, 20 Mar 2025 16:17:50 +0000 Subject: [PATCH 04/13] Automatic `pre-commit` fixes --- examples/clustering/Deep_learning_Based_clustering.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/clustering/Deep_learning_Based_clustering.ipynb b/examples/clustering/Deep_learning_Based_clustering.ipynb index f533f36727..d5fee99f32 100644 --- a/examples/clustering/Deep_learning_Based_clustering.ipynb +++ b/examples/clustering/Deep_learning_Based_clustering.ipynb @@ -70,8 +70,7 @@ }, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n" - + "import matplotlib.pyplot as plt" ] }, { From 5c0142871fdc347202504b958eb9eb164ffe05cf Mon Sep 17 00:00:00 2001 From: aditya27 <120127383+Edgeshot27@users.noreply.github.com> Date: Tue, 25 Mar 2025 20:49:49 +0530 Subject: [PATCH 05/13] Delete examples/clustering/Deep_learning_Based_clustering.ipynb --- .../Deep_learning_Based_clustering.ipynb | 636 ------------------ 1 file changed, 636 deletions(-) delete mode 100644 examples/clustering/Deep_learning_Based_clustering.ipynb diff --git a/examples/clustering/Deep_learning_Based_clustering.ipynb b/examples/clustering/Deep_learning_Based_clustering.ipynb deleted file mode 100644 index d5fee99f32..0000000000 --- a/examples/clustering/Deep_learning_Based_clustering.ipynb +++ /dev/null @@ -1,636 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "dne_kUDfuiYS", - "outputId": "5cb1dfa6-241e-43bc-9425-06595b24a733" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting aeon[deep_learning]\n", - " Downloading aeon-1.0.0-py3-none-any.whl.metadata (20 kB)\n", - "\u001b[33mWARNING: aeon 1.0.0 does not provide the extra 'deep-learning'\u001b[0m\u001b[33m\n", - "\u001b[0mRequirement already satisfied: deprecated>=1.2.13 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (1.2.18)\n", - "Requirement already satisfied: numba<0.61.0,>=0.55 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (0.60.0)\n", - "Requirement already satisfied: numpy<2.1.0,>=1.21.0 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (2.0.2)\n", - "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (24.2)\n", - 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"\u001b[?25hDownloading aeon-1.0.0-py3-none-any.whl (8.2 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m27.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: scikit-learn, aeon\n", - " Attempting uninstall: scikit-learn\n", - " Found existing installation: scikit-learn 1.6.1\n", - " Uninstalling scikit-learn-1.6.1:\n", - " Successfully uninstalled scikit-learn-1.6.1\n", - "Successfully installed aeon-1.0.0 scikit-learn-1.5.2\n" - ] - } - ], - "source": [ - "!pip install aeon[deep_learning]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2Ru1riLYWFE3" - }, - "source": [ - "# **Deep Learning Based Clustering**\n", - "The aeon.clustering.deeplearning module provides powerful deep learning models designed specifically for time series clustering. These models leverage advanced architectures like **Auto-Encoders, Residual Networks, Dilated Convolutions, and Attention Mechanisms** to effectively identify patterns, trends, and groupings in complex temporal data. Each model is tailored to handle different data characteristics, improving clustering accuracy and performance across various domains such as healthcare, finance, and signal processing." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "86gsiHDbuoz-" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "7xvwY48cur5i" - }, - "outputs": [], - "source": [ - "from aeon.datasets import load_arrow_head" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "EkRKdT47N7Oc" - }, - "outputs": [], - "source": [ - "X_train, y_train = load_arrow_head(split=\"train\")\n", - "X_test, y_test = load_arrow_head(split=\"test\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b-XHpTDjxeSd" - }, - "source": [ - "# **AEFCNClusterer (Auto-Encoder Fully Convolutional Network)**\n", - "The **AEFCNClusterer** is a deep learning model that leverages a **Fully Convolutional Network (FCN)** architecture with an **Auto-Encoder** structure for clustering. It combines feature extraction with convolutional layers and reconstruction capabilities via auto-encoders. \n", - "FCNs are effective for extracting spatial hierarchies in time series data without requiring fully connected layers, making them highly efficient.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "DGbXlOPLxdO-" - }, - "outputs": [], - "source": [ - "from aeon.clustering.deep_learning import AEFCNClusterer" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kxYYwMmKN_Uz", - "outputId": "0f2ccdef-1a79-41ad-94f2-a5708d41443f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 81ms/step\n" - ] - } - ], - "source": [ - "model = AEFCNClusterer(n_epochs=10, batch_size=3)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "q3oOIG3jOtbf", - "outputId": "258be6d8-6dc7-4c47-b8f3-f5b47412967b" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"viridis\")\n", - "plt.title(\"Cluster Distribution with AEFCN\")\n", - "plt.colorbar(label=\"Cluster\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wuFuNtkNP5tN" - }, - "source": [ - "# **AEResNetClusterer (Auto-Encoder Residual Network)**\n", - "The **AEResNetClusterer** applies an Auto-Encoder architecture integrated with a **Residual Network** (ResNet) backbone.ResNet models use skip connections, allowing gradients to flow directly through layers, reducing vanishing gradient issues.This approach enhances learning in deep networks and efficiently captures complex temporal patterns in time series data.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "6atNZu4ADFxb" - }, - "outputs": [], - "source": [ - "from aeon.clustering.deep_learning import AEResNetClusterer" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ipdqBhoAP4-9", - "outputId": "9059875c-26ae-416e-e0ba-d31ca587b9e9" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 468ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 147ms/step\n" - ] - } - ], - "source": [ - "model = AEResNetClusterer(n_epochs=10, random_state=42, batch_size=3)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "bHI_0_064GFu", - "outputId": "f1d5dd1d-3c4d-42cd-a75c-d3c00da85bca" - }, - "outputs": [ - { - "data": { - "image/png": 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JTIMGDUqdpmzM1/1hGzduhI+PD5YvX17o2Lfffovt27dj1apVRklSq1WrhvHjx2PWrFn47bff0K5dO+0gbR8fnzK97vXq1cPEiRMxceJEXLx4ES1btsT777+PL7/8ssjyBa0zM2fOxJAhQ4qs7/79+6Ve21SvP5E+OGamknrrrbfg7OyMV155BSkpKYWOX7p0CR9++GGx5xd8oD7cP14wzfdhd+/eLfSXbsuWLQEA2dnZAAAnJycAKPQF7OPjg9DQUHzyySe4efNmoRhu375dbHyGuHr1KoYOHQqFQoFJkyYVWy4tLQ15eXk6+5o3bw6ZTKZ9bgDg7OxcZHKhj5iYGJ2ps9euXcOOHTvQpUsXbUtDvXr1oFKpdLp0bt68ie3btxeqr6yxhYWFQaFQ4KOPPtL5ea5ZswYqlQrdunUz4FnpCg4Ohr29Pd577z14eXmhadOmAPKTnN9++w3/+9//ytQq4+zsrJ0ibywPHjzAt99+i+7du+O5554rtI0ePRrp6elGXatlzJgxcHJywoIFCwDkt9a4ublh3rx5RY5DK3hfZGZmFlo5uF69enB1ddX5/SzKG2+8AQ8PD8yePbvQseeffx4xMTH48ccfCx27d++e9j1R3PuayBLYMlNJ1atXD5s2bcILL7yAxo0b66wAfPToUWzdurXEezF16dIFtWrVwvDhwzFp0iTI5XKsXbsW3t7eOrcC2LBhA1asWIE+ffqgXr16SE9Px6effgo3Nzd07doVQH43S5MmTbBlyxY0aNAAXl5eaNasGZo1a4bly5fjiSeeQPPmzREVFYW6desiJSUFMTExuH79On7//XeDXofTp0/jyy+/hEajwb1793DixAl88803kCQJX3zxRYlTSQ8ePIjRo0ejf//+aNCgAfLy8vDFF19ALpejX79+2nJBQUHYv38/lixZgurVq6NOnTolLn9fkmbNmiE8PFxnajYAnQGbAwYMwOTJk9GnTx+MHTtWO223QYMGhdYQKWts3t7eiI6OxqxZsxAREYGePXsiLi4OK1aswOOPP16mFqyycnJyQlBQEH777TftGjNAfstMRkYGMjIyypTMBAUFYcuWLZgwYQIef/xxuLi4FLlkf3ns3LkT6enp2inij2rXrh28vb2xceNGvPDCC9r9Fy5cKLIlxNfXF88880yJ16xSpQqGDRuGFStW4Ny5c2jcuDFWrlyJwYMHo3Xr1hgwYID2fbd792506NABH3/8MS5cuIDOnTvj+eefR5MmTWBnZ4ft27cjJSVFZ2Xrori7u2PcuHFFDgSeNGkSdu7cie7du2Po0KEICgpCRkYG/vzzT2zbtg1XrlzRrilV3PuayOwsOZWKTO/ChQsiKipKBAQECIVCIVxdXUWHDh3EsmXLdKZDPzo1WwghTp06JYKDg4VCoRC1atUSS5YsKTSl9/Tp02LgwIGiVq1aQqlUCh8fH9G9e3ed6cVCCHH06FERFBQkFApFoemcly5dEpGRkcLPz0/Y29uLGjVqiO7du4tt27ZpyxRct6Qp4A8rmHJasNnZ2QkvLy8RHBwsoqOjxdWrVwud8+j038uXL4uXX35Z1KtXTzg4OAgvLy/RqVMnsX//fp3zzp8/L5566inh6OgoAGhfx4Kp0rdv3y50reKmZo8aNUp8+eWXon79+kKpVIpWrVrpTEcu8NNPP4lmzZoJhUIhGjZsKL788ssi6ywutkd/jgU+/vhj0ahRI2Fvby98fX3FiBEjxN27d3XKPDzV92HFTRkvyqRJkwQA8d577+nsDwwMFADEpUuXdPYXNTX7/v374sUXXxQeHh4CgPbaBWUfnVJf8Duxbt26YuPq0aOHcHBwEBkZGcWWGTp0qLC3txd37twRQpQ8Nbtjx47a8wqmZhfl0qVLQi6X67wHDx06JMLDw4W7u7twcHAQ9erVE0OHDtW+t+7cuSNGjRolGjVqJJydnYW7u7sIDg4WX3/9tU7dxf287t69K9zd3Yuc3p2eni6io6NFYGCgUCgUomrVqqJ9+/Zi8eLFIicnR1uupPc1kTlJQpRhxB4RERGRleKYGSIiIqrQmMwQERFRhcZkhoiIiCo0JjNEREQ26ueff0aPHj1QvXp1SJKE7777rtRzDh8+jNatW2vvqL5+/XqTx1kaJjNEREQ2KiMjAy1atChykciiJCQkoFu3bujUqRNiY2Pxxhtv4JVXXilyXSJz4mwmIiIigiRJ2L59O3r37l1smcmTJ2P37t06t8sZMGAA7t27h71795ohyqLZ5KJ5Go0GN27cgKurK5fkJiKiEgkhkJ6ejurVq+vcVd7YsrKykJOTY3A9QohC321KpbLUm/+WRUxMTKFbXYSHh+ONN94wuG5D2GQyc+PGDfj7+1s6DCIiqkCuXbtW5hvMlldWVhYC6rggJdnwG8a6uLjg/v37OvtmzJiBmTNnGlx3cnJyofum+fr6Ii0tDQ8ePDD6jXXLyiaTGVdXVwD5v5gl3TGZiIgoLS0N/v7+2u8OU8jJyUFKshp/XQyAq5v+rT/paRo0rX+l0PebMVplrJlNJjMFzW9ubm5MZoiIqEzMMSzB1U0GNwOSmQKm+n7z8/MrdPPilJQUuLm5WaxVBrDRZIaIiMgaSRpA0uifNEkaIwZThJCQEOzZs0dn3759+xASEmLaC5eCU7OJiIishZAM38rh/v37iI2NRWxsLID8qdexsbFITEwEAERHRyMyMlJb/vXXX8fly5fx1ltv4fz581ixYgW+/vprjB8/3mgvgT7YMkNERGQlJI1kYMtM+c49efIkOnXqpH08YcIEAMCQIUOwfv163Lx5U5vYAECdOnWwe/dujB8/Hh9++CFq1qyJzz77DOHh4XrHbAw2uc5MWloa3N3doVKpOGaGiIhKZI7vjIJrXL8eCDc3uQH1qFGzZrzNfb+xZYaIiMhK5I+ZMex8W8RkhoiIyFpo/t0MOd8GcQAwERERVWhsmSEiIrISksjfDDnfFjGZISKiSkctLkKtOQvAHnayEMikKpYOqUwkYeCYGSYzREREFZtGXENm3ptQixP/7VTbwV72PBzl0yBJlXtZf1vFZIaIiCoFjbiN+7n9IfDPI0fykKvZDCFS4GS32iy3JdCbRuRvhpxvgzgAmIiIKoUc9fp/E5mi7jytQZ44oNtiY4UKxswYstkiJjNERFQp5Gi+RtGJTAE5cjTfmCscMiN2MxERUaUgkFpKCTWEuG2WWPTGdWb0wmSGiIgqBQlVIVBSsiKHJPmZLR59SBoByYBxL4acW5Gxm4mIiCoFhWwASv5aU0Mh62eucPSjMcJmg5jMEBFRpaCQD4WEagCKulGjBDupG+RSa3OHRWbAZIaIiCoFmeQJF/ttsJOeBPDw9GsHKGSvwMluiXVPywZnM+mLY2aIiKjSkEm+cLZfC424DrX4C4A97KTHIUmulg6tbDgAWC9MZoiIqNKRSTUhk2paOgwyEyYzREREVkLSGHhvJrbMEBERkUUJAMKAgS82OmaGA4CJiIioQmPLDBERkZWQhIHdTDbaMsNkhoiIyFpwNpNe2M1EREREFRpbZoiIiKyEoQvfsZuJiIiILIvdTHphMkNERGQtmMzohWNmiIiIqEIzaTKTmpqKQYMGwc3NDR4eHhg+fDju379fbPkrV65AkqQit61bt2rLFXV88+bNpnwqREREJpc/ZkYyYLP0M7AMk3YzDRo0CDdv3sS+ffuQm5uLYcOG4dVXX8WmTZuKLO/v74+bN2/q7Fu9ejUWLVqEZ599Vmf/unXrEBERoX3s4eFh9PiJiIjMit1MejFZMnPu3Dns3bsXJ06cQJs2bQAAy5YtQ9euXbF48WJUr1690DlyuRx+fn46+7Zv347nn38eLi4uOvs9PDwKlS1OdnY2srOztY/T0tLK+3SIiMgILl++i9u3MlG9ugv8a7lbOhyqJEzWzRQTEwMPDw9tIgMAYWFhkMlkOHbsWJnqOHXqFGJjYzF8+PBCx0aNGoWqVauibdu2WLt2LUQJ97KYP38+3N3dtZu/v3/5nxAREentlyOJ6PTkBrRs+gme6fQFmjZcia5dNuLM6WRLh2ZdNEbYbJDJkpnk5GT4+Pjo7LOzs4OXlxeSk8v2y7tmzRo0btwY7du319k/e/ZsfP3119i3bx/69euHkSNHYtmyZcXWEx0dDZVKpd2uXbtW/idERER6OXggAT2e/apQ4hJz9Dq6PP0FTp64YaHIrJAwwmaDyt3NNGXKFLz33nslljl37pzeARV48OABNm3ahGnTphU69vC+Vq1aISMjA4sWLcLYsWOLrEupVEKpVBocExERlY9GIzBm5A/QaEShm0Gr1QJCaDBh3I/4+egwywRIlUK5k5mJEydi6NChJZapW7cu/Pz8cOvWLZ39eXl5SE1NLdNYl23btiEzMxORkZGllg0ODsacOXOQnZ3NpIWIyIoc+fkqriUWP05RoxGIPZOCv87eQtNmPsWWsxWSRoKkkQw63xaVO5nx9vaGt7d3qeVCQkJw7949nDp1CkFBQQCAgwcPQqPRIDg4uNTz16xZg549e5bpWrGxsfD09GQiQ0RkZa5eUZWp3JWEe0xmAMO7itjNZFyNGzdGREQEoqKisGrVKuTm5mL06NEYMGCAdiZTUlISOnfujM8//xxt27bVnhsfH4+ff/4Ze/bsKVTv999/j5SUFLRr1w4ODg7Yt28f5s2bhzfffNNUT4WIiPTk6elQxnKOJo6EKjOTrjOzceNGjB49Gp07d4ZMJkO/fv3w0UcfaY/n5uYiLi4OmZmZOuetXbsWNWvWRJcuXQrVaW9vj+XLl2P8+PEQQiAwMBBLlixBVFSUKZ8KERHpofMzdeHiosD9+znFlqlW3QXBITXMGJUVExJgSFeRsM1uJkmUNKe5kkpLS4O7uztUKhXc3NwsHQ4RUaX28YfH8faUg8UeX/VZN7w4qLkZIyofc3xnFFzj7r5AuDnL9a8nQw3PZ+Jt7vuN92YiIiKTGjX2ccyc3RFKZf6XtNwuv/XAydkeH3wUbtWJjNlxarZeeNdsIiIyKUmSMGFSCF6OaoXvd8Th1q1MVK/hip69GsDZWWHp8KgSYDJDRERm4eHhgMFDWlg6DOumMXDMDKdmExERkUUJybBBvDY6AJhjZoiIiKhCY8sMERGRlZA0+Zsh59siJjNERETWgmNm9MJuJiIiIqrQ2DJDRERkLXhvJr0wmSEiIrIW7GbSC7uZiIiIqEJjywwREZG14DozemEyQ0REZC00/26GnG+DmMwQERFZC7bM6IVjZoiIiKhCY8sMERGRlRBCgjBgRpKw0ZYZJjNERETWgt1MemE3ExEREVVobJkhIiKyFpzNpBcmM0RERNaC3Ux6YTcTERERVWhsmSEiIrIWvDeTXpjMEBERWQt2M+mF3UxERERUobFlhoiIyFqwm0kvTGaIiIishfh3M+R8G8RuJiIiIishNJLBmz6WL1+OgIAAODg4IDg4GMePHy+x/NKlS9GwYUM4OjrC398f48ePR1ZWll7XNgYmM0RERDZsy5YtmDBhAmbMmIHTp0+jRYsWCA8Px61bt4osv2nTJkyZMgUzZszAuXPnsGbNGmzZsgVvv/22mSP/D5MZIiIia1Ewm8mQrZyWLFmCqKgoDBs2DE2aNMGqVavg5OSEtWvXFln+6NGj6NChA1588UUEBASgS5cuGDhwYKmtOabEZIaIiMhaFAwANmQDkJaWprNlZ2cXebmcnBycOnUKYWFh2n0ymQxhYWGIiYkp8pz27dvj1KlT2uTl8uXL2LNnD7p27WrkF6PsmMwQERFVMv7+/nB3d9du8+fPL7LcnTt3oFar4evrq7Pf19cXycnJRZ7z4osvYvbs2XjiiSdgb2+PevXqITQ01KLdTJzNREREZC0EDFw0L/8/165dg5ubm3a3Uqk0LK6HHD58GPPmzcOKFSsQHByM+Ph4jBs3DnPmzMG0adOMdp3yYDJDRERkLYSB68z8mwi5ubnpJDPFqVq1KuRyOVJSUnT2p6SkwM/Pr8hzpk2bhsGDB+OVV14BADRv3hwZGRl49dVX8c4770AmM3+nj8muOHfuXLRv3x5OTk7w8PAo0zlCCEyfPh3VqlWDo6MjwsLCcPHiRZ0yqampGDRoENzc3ODh4YHhw4fj/v37JngGRERElZtCoUBQUBAOHDig3afRaHDgwAGEhIQUeU5mZmahhEUulwPI/x63BJMlMzk5Oejfvz9GjBhR5nMWLlyIjz76CKtWrcKxY8fg7OyM8PBwnbnrgwYNwl9//YV9+/Zh165d+Pnnn/Hqq6+a4ikQERGZlRCGb+U1YcIEfPrpp9iwYQPOnTuHESNGICMjA8OGDQMAREZGIjo6Wlu+R48eWLlyJTZv3oyEhATs27cP06ZNQ48ePbRJjbmZrJtp1qxZAID169eXqbwQAkuXLsXUqVPRq1cvAMDnn38OX19ffPfddxgwYADOnTuHvXv34sSJE2jTpg0AYNmyZejatSsWL16M6tWrF1l3dna2zkjutLQ0A54ZERGRiVjgRpMvvPACbt++jenTpyM5ORktW7bE3r17tYOCExMTdVpipk6dCkmSMHXqVCQlJcHb2xs9evTA3Llz9Y/bQFYzmykhIQHJyck608Pc3d0RHBysnR4WExMDDw8PbSIDAGFhYZDJZDh27Fixdc+fP19nVLe/v7/pnggREVEFM3r0aFy9ehXZ2dk4duwYgoODtccOHz6s0zBhZ2eHGTNmID4+Hg8ePEBiYiKWL19e5iElpmA1yUzBFLCSpoclJyfDx8dH57idnR28vLyKnUIGANHR0VCpVNrt2rVrRo6eiIjICIy0zoytKVcyM2XKFEiSVOJ2/vx5U8WqN6VSqR3ZXdYR3kREROYmhGTwZovKNWZm4sSJGDp0aIll6tatq1cgBVPAUlJSUK1aNe3+lJQUtGzZUlvm0XtF5OXlITU1tdgpZERERBWGoa0rNtoyU65kxtvbG97e3iYJpE6dOvDz88OBAwe0yUtaWhqOHTumnREVEhKCe/fu4dSpUwgKCgIAHDx4EBqNRqd/j4iIiGyHycbMJCYmIjY2FomJiVCr1YiNjUVsbKzOmjCNGjXC9u3bAQCSJOGNN97Au+++i507d+LPP/9EZGQkqlevjt69ewMAGjdujIiICERFReH48eP49ddfMXr0aAwYMKDYmUxEREQVhgVuNFkZmGxq9vTp07Fhwwbt41atWgEADh06hNDQUABAXFwcVCqVtsxbb72lXUXw3r17eOKJJ7B37144ODhoy2zcuBGjR49G586dIZPJ0K9fP3z00UemehpERERmY+i4F1sdMyMJSy3XZ0FpaWlwd3eHSqXiYGAiIiqROb4zCq5x+8P2cHPUv50h7UEevMcdtbnvN96biYiIyFpo/t0MOd8GMZkhIiKyFhZYAbgysJpF84iIiIj0wZYZIiIiKyE0EoQBa8UYcm5FxmSGiIjIWrCbSS/sZiIiIqIKjS0zREREVoLrzOiHyQwREZG1EAbem4nJDBEREVkUx8zohWNmiIiIqEJjywwREZGVECJ/M+R8W8RkhoiIyFpoDBwzY6PrzLCbiYiIiCo0tswQERFZCU7N1g+TGSIiImvB2Ux6YTcTERERVWhsmSEiIrISvNGkfpjMEBERWQsBA7uZjBZJhcJuJiIiIqrQ2DJDRERkJTibST9MZoiIiKwFF83TC5MZIiIiK8HbGeiHY2aIiIioQmPLDBERkZXgmBn9MJkhIiKyFhwzoxcmM0REBhBCQC1+h0ZcgSS5wk7qAElysHRYRDaFyQwRkZ7yNKfxIG8KNIh/aK8rHORjoJANhyTZ5l/JpD92M+mHyQwRkR7UmrPIyBsEIPeRI+nIUs+DwAM4yMdYIjSq0Ay80SRsM5nhbCYiIj08UC8EkAdAU+TxbPUyaESqWWMislVMZoiIykkjbkMtfgGgLqGUGrmaXeYKiSqJgm4mQzZbxG4mIqJyEuJOGUrJIcRtk8dClQxnM+mFLTNEROUkSVVR+tgENSTJxxzhENk8kyUzc+fORfv27eHk5AQPD49Sy+fm5mLy5Mlo3rw5nJ2dUb16dURGRuLGjRs65QICAiBJks62YMECEz0LIqLCZJI37KQnAchLKGUHe1l3c4VElUTB7QwM2WyRyZKZnJwc9O/fHyNGjChT+czMTJw+fRrTpk3D6dOn8e233yIuLg49e/YsVHb27Nm4efOmdhszhjMGiMi8HORvIb+nvuiPUaX8DcgkT7PGRBUfx8zox2RjZmbNmgUAWL9+fZnKu7u7Y9++fTr7Pv74Y7Rt2xaJiYmoVauWdr+rqyv8/PyMFisRUXnJZU3gbLcZD9RvQyPOafdL8IBSPg4KWaQFo6MKSxg4NdtGkxmrHjOjUqkgSVKhbqoFCxagSpUqaNWqFRYtWoS8vLwS68nOzkZaWprORkRkKDtZC7jY7YKL3fdwsvsYTnYb4GofA6V8CBfMIzIjq53NlJWVhcmTJ2PgwIFwc3PT7h87dixat24NLy8vHD16FNHR0bh58yaWLFlSbF3z58/XthQRERmTJEmQS00hR1NLh0KVgUaC4GymcitXy8yUKVMKDb59dDt//rzBQeXm5uL555+HEAIrV67UOTZhwgSEhobisccew+uvv473338fy5YtQ3Z2drH1RUdHQ6VSabdr164ZHCMREZGxccyMfsrVMjNx4kQMHTq0xDJ169Y1JB5tInP16lUcPHhQp1WmKMHBwcjLy8OVK1fQsGHDIssolUoolUqD4iIiIiLrVK5kxtvbG97e3qaKRZvIXLx4EYcOHUKVKlVKPSc2NhYymQw+PlzPgYiIKjgOANaLycbMJCYmIjU1FYmJiVCr1YiNjQUABAYGwsXFBQDQqFEjzJ8/H3369EFubi6ee+45nD59Grt27YJarUZycjIAwMvLCwqFAjExMTh27Bg6deoEV1dXxMTEYPz48XjppZfg6ckpkEREVLHxrtn6MVkyM336dGzYsEH7uFWrVgCAQ4cOITQ0FAAQFxcHlUoFAEhKSsLOnTsBAC1bttSpq+AcpVKJzZs3Y+bMmcjOzkadOnUwfvx4TJgwwVRPg4iIiKycyZKZ9evXl7rGjHhoqcKAgACdx0Vp3bo1fvvtN2OER0REZHWEJn8z5HxbZLVTs4mIjOX32GR8tvoMYk8nw8HRDt17NMBLQx5DlSqOlg6NSBfHzOiFyQwRVWqLFx7F7Bk/w85Ohry8/D9bjx9LwpLFMdi5ZwBatORq4kQVnVWvAExEZIg9uy5i9oyfAUCbyAD5N+NLS8tGn55f48GDXEuFR1QI15nRD5MZIqq0Plp6DHJ50R/uarXAnduZ+GbruSKPE1kCkxn9MJkhokopL0+Do79eh1pd/MQCuVzC4UNXzRgVUSkKxswYstkgJjNEVCmVNjsyvwyg0ZRejoisG5MZIqqU7O3laNHSFzJZ8X+pCiEQ3K6GGaMiKpkQgPj3ZpN6bTaamzOZIaJKa+SYx4tteZHJJDg722PgoGZmjoqoeBwzox8mM0RUaQ0Y2BSvvJq/+vjDA4HlcgkKhRybvu4HNzfehJaoouM6M0RUaUmShPeXdkGXiHpYvfIUYs+kQOkgR89eDfHayCDUrct7upGVEf9uhpxvg5jMEFGlJkkSIp4NRMSzgZYOhahUvNGkftjNRERERBUaW2aIyKpcuXIPF+L+gYuzAo8HV4e9vdzSIRGZDVtm9MOWGSKyCvHxqejZ9Ss81ngVnuu9FRHPbETDusuxasXJMq0ZQ1QZGDQt+99NH8uXL0dAQAAcHBwQHByM48ePl1j+3r17GDVqFKpVqwalUokGDRpgz549el3bGNgyQ0QWd/XqPXTu+DnSVNk6++/cycRbE/cjNfUB3p76pIWiIzIjC9w1e8uWLZgwYQJWrVqF4OBgLF26FOHh4YiLi4OPj0+h8jk5OXjmmWfg4+ODbdu2oUaNGrh69So8PDz0j9tAbJkhIotb8O4vSEvLLvbWAwvnH8WNpHQzR0VkG5YsWYKoqCgMGzYMTZo0wapVq+Dk5IS1a9cWWX7t2rVITU3Fd999hw4dOiAgIAAdO3ZEixYtzBz5f5jMEJFFZWbmYuvX56DOK74rSZKAzZvOmjEqIssw1qJ5aWlpOlt2dnaR18vJycGpU6cQFham3SeTyRAWFoaYmJgiz9m5cydCQkIwatQo+Pr6olmzZpg3bx7UarXxX5AyYjJDRBaV+s8D5OSU/CEok0m4zpYZsgHGSmb8/f3h7u6u3ebPn1/k9e7cuQO1Wg1fX1+d/b6+vkhOTi7ynMuXL2Pbtm1Qq9XYs2cPpk2bhvfffx/vvvuucV+McuCYGSKyKA9PB8hkUok3fNRoBLy9ncwYFVHFdu3aNbi5uWkfK5XGW+lao9HAx8cHq1evhlwuR1BQEJKSkrBo0SLMmDHDaNcpDyYzRGRRLi4KdO/ZALu/v1DsmBm1WuD5AU3NHBmR+QkBg24WWXCum5ubTjJTnKpVq0IulyMlJUVnf0pKCvz8/Io8p1q1arC3t4dc/t+yCY0bN0ZycjJycnKgUCj0fwJ6YjcTEVlc9DsdoFDIde6fVECSgJdfaYl69XjrAar8zH2jSYVCgaCgIBw4cEC7T6PR4MCBAwgJCSnynA4dOiA+Ph4ajUa778KFC6hWrZpFEhmAyQwRWYGmzXywa++LqPPIvZIUCjnGvNEWiz/oYqHIiCq/CRMm4NNPP8WGDRtw7tw5jBgxAhkZGRg2bBgAIDIyEtHR0dryI0aMQGpqKsaNG4cLFy5g9+7dmDdvHkaNGmWpp8BuJiKyDo+3rY5Tv0ch5tfrOH/+DpydFegSUQ+eng6WDo3IfDRS/mbI+eX0wgsv4Pbt25g+fTqSk5PRsmVL7N27VzsoODExETLZf20f/v7++PHHHzF+/Hg89thjqFGjBsaNG4fJkyfrH7eBJGGDS2umpaXB3d0dKpWqTH2KRERku8zxnVFwjdgBQ+FqQFdNek4OWm5eb3Pfb2yZISKjU6s12LsnHhvW/Y6EhHvw8XHGwEHN0K9/Yzg62ls6PCKqZJjMEJFRZWfnYdAL3+KnHy9DLpegVgtcvJCKIz8nYtmHx7F774uoymnWREXijSb1wwHARGRUc2b+jP37EgBAO9W6YA2ZC3H/YPiwnRaLjcjamXs2U2XBZIaIjCYjIweffXqm2AXw1GqBQweuIO78HTNHRlRRGJrIMJkhIjJI7JlkZGbkllhGkoCf/5dopoiIyBZwzAwRGU1Z50ba4CRKorIRUv5myPk2iMkMERnNYy184eBgh6ysvGLLCAGEtK9pxqiIKg6hyd8MOd8WsZuJiIzGzU2JwUMeg0xW9F+HcjsJwSE10Pwx3yKPE1Hlplar8fPPP+PevXtGrZfJDBEZ1ey5oWgbXB0AtEmNJOVvNWq4Yf3nvSwZHpFVq+yzmeRyObp06YK7d+8atV6TJTNz585F+/bt4eTkBA8PjzKdM3ToUEiSpLNFRETolElNTcWgQYPg5uYGDw8PDB8+HPfv3zfBMyAifTg7K7Br74tY+Wk3BD1eDb5+zmjSxBtz5z+NX48NQ42atrMqKVF5VfZkBgCaNWuGy5cvG7VOk42ZycnJQf/+/RESEoI1a9aU+byIiAisW7dO+1ipVOocHzRoEG7evIl9+/YhNzcXw4YNw6uvvopNmzYZLXYiMoxCIcegl5pj0EvNLR0KEVmZd999F2+++SbmzJmDoKAgODs76xzX5zYMJktmZs2aBQBYv359uc5TKpXw8/Mr8ti5c+ewd+9enDhxAm3atAEALFu2DF27dsXixYtRvXp1g2ImIiKyJFtYAbhr164AgJ49e0KS/otXCAFJkqBWq8tdp9XNZjp8+DB8fHzg6emJp59+Gu+++y6qVKkCAIiJiYGHh4c2kQGAsLAwyGQyHDt2DH369CmyzuzsbGRnZ2sfp6WlmfZJEBER6UEIwxKSirDqwaFDh4xep1UlMxEREejbty/q1KmDS5cu4e2338azzz6LmJgYyOVyJCcnw8fHR+ccOzs7eHl5ITk5udh658+fr20pIiIiIsvp2LGj0ess1wDgKVOmFBqg++h2/vx5vYMZMGAAevbsiebNm6N3797YtWsXTpw4gcOHD+tdJwBER0dDpVJpt2vXrhlUHxERkUkULJpnyFYBHDlyBC+99BLat2+PpKQkAMAXX3yBX375Ra/6ytUyM3HiRAwdOrTEMnXr1tUrkOLqqlq1KuLj49G5c2f4+fnh1q1bOmXy8vKQmppa7DgbIH8czqMDiYlIP3/+kYLPVp/BieNJEAIICPBAu/Y10SW8Lho38bZ0eEQVmi2Mmfnmm28wePBgDBo0CKdPn9YOA1GpVJg3bx727NlT7jrLlcx4e3vD29t8H1bXr1/HP//8g2rVqgEAQkJCcO/ePZw6dQpBQUEAgIMHD0Kj0SA4ONhscRHZqo8+OIapbx+CXA4UjNH76+xt7N51EdPePoQnO9bCmnU94VfNxbKBElVQtpDMvPvuu1i1ahUiIyOxefNm7f4OHTrg3Xff1atOk60zk5iYiNjYWCQmJkKtViM2NhaxsbE6a8I0atQI27dvBwDcv38fkyZNwm+//YYrV67gwIED6NWrFwIDAxEeHg4AaNy4MSIiIhAVFYXjx4/j119/xejRozFgwADOZCIysYMHEjD17fyBe8VNNjj6yzWEh32J9PTsogsQkc2Li4vDU089VWi/u7u73isDmyyZmT59Olq1aoUZM2bg/v37aNWqFVq1aoWTJ09qy8TFxUGlUgHIXxXwjz/+QM+ePdGgQQMMHz4cQUFBOHLkiE4X0caNG9GoUSN07twZXbt2xRNPPIHVq1eb6mkQ0b+WLT0Oubzkv/rUaoErCfew8Ys/zRQVUeVScG8mQzZr5+fnh/j4+EL7f/nlF72HqphsNtP69etLXWPm4TvnOjo64scffyy1Xi8vLy6QR2RmQgj8/L+rUKvLNu/zy8//xOsj25RekIh02EI3U1RUFMaNG4e1a9dCkiTcuHEDMTExePPNNzFt2jS96rSqqdlEZL3Kun6FEMDt2xmmDYaIKqwpU6ZAo9Ggc+fOyMzMxFNPPQWlUok333wTY8aM0atOJjNEVCpJkvB42+o4fiyp1NYZmUxCrVruZoqMqHKxhZYZSZLwzjvvYNKkSYiPj8f9+/fRpEkTuLjoP3GAd80mojIZNebxMnUzaTQCw15pafqAiCohW7jR5Msvv4z09HQoFAo0adIEbdu2hYuLCzIyMvDyyy/rVSeTGSJCbq4ahw9dwXffnsfvsck649kK9OjVAG9MKHkJBJlMQruQGniufxNThUpEFdyGDRvw4MGDQvsfPHiAzz//XK862c1EZOPWrYnFnJk/486dTO0+uVxCnTqeGDmmDQYNbg5HR3tIkoTZczsh9OkArFx+Akf+l4gHD/K0Y2kUSjkGRz6Gd+d3gkIht9CzIarYKnM3U1paGoQQEEIgPT0dDg4O2mNqtRp79uwpdMuismIyQ2TDli87gei3DhTar1YLxMenYsK4n/DFhj/w/Q8D4eaWv0TC053r4OnOdQAAaWnZOHP6JoQAWrbyg4eHQ6G6iKjsKnMy4+Hhob31UYMGDQodlyRJ7/soMpkhslEqVRZmTjtcarnfY5Px9uQD+Hhl10LH3NyU6BgaYPzgiKjSOXToEIQQePrpp/HNN9/Ay8tLe0yhUKB27dp6L4DLZIbIRu3YHofs7GKW8n2IRgNs/OJPzJ7bCV5ejmaIjMh2VeaWmYK7ZSckJKBWrVqQJOPFygHARDbq+LGkMpdVqwW+3vy3CaMhIgD5d73WGLBZcTJT4Ny5c/j111+1j5cvX46WLVvixRdfxN27d/Wqk8kMUSUkhMD/Dl/BB4t/w7Klx/D3X7cBAHfvZmH7N+cxZ+bP+Hz9H+Wq84fdF00RKhE9xBamZk+aNAlpaWkAgD///BMTJkxA165dkZCQgAkTJuhVJ7uZiCqZv/+6jZcGbkf8xVTI5RKEAN6JPgT/Wm5ISc5ATk7pXUtFuXgx1ciREpEtSkhIQJMm+cs3fPPNN+jRowfmzZuH06dPo2vXwmPzyoLJDFElknQ9Dc8+sxFpafl3rX54kbtriWkG1c3p1kSmV5nHzBRQKBTIzMxfCmL//v2IjIwEkH/vxYIWm/JiMkNUiaxacQppadllviFkWcnlErqE63c3WyIqOyHKfh+04s63dk888QQmTJiADh064Pjx49iyZQsA4MKFC6hZs6ZedXLMDFEl8tWms0ZPZCQpf2XfqNeDjFovEdmmjz/+GHZ2dti2bRtWrlyJGjVqAAB++OEHRERE6FUnW2aIKpE0VbZR65PLJcjlMmz4shfq1/cq/QQiMoyhg3grQDdTrVq1sGvXrkL7P/jgA73rZDJDVInUqu2Oixf+MbipWamUo3WQH54KDcDQYS1Qo6abcQIkohLZwpiZxMTEEo/XqlWr3HUymSGqRIZHtcKUSfsNqqNJk6o48HMknJ0VRoqKiOg/AQEBJS6Yp1aXf8YlkxmiSuTpsAB4eDrgbmqWXud3DgvA9u8HGDkqIiorW2iZOXPmjM7j3NxcnDlzBkuWLMHcuXP1qpPJDFElkZBwDxFhG3Hvrn6JTKtWfti6/XkjR0VE5WELyUyLFi0K7WvTpg2qV6+ORYsWoW/fvuWuk7OZiCqJcaN/wD93Hug1XmbU2Mfxv6NDYWfHjwQisoyGDRvixIkTep3LlhmiCiIzMxf303Pg6eUAe3s5HjzIxbo1sVj76RkkJNxDbq6m3HU2aOCFT9b0QFCbaiaImIjKS2gkCI0BLTMGnGsujy6MJ4TAzZs3MXPmTNSvX1+vOpnMEFm532OT8d78X7FnVzw0GgFnZ3sMeLEpYn5Nwt9/39arTjs7GWa/G4pRYx836p1ricgw+YvmGdLNZMRgTMTDw6PQ544QAv7+/ti8ebNedTKZIbJi/zt8Bf16bYVarYFGk/8plZGRizWfxhpUb6PGVTF6XFsjREhExmQLY2YOHTqk81gmk8Hb2xuBgYGws9MvLWEyQ2Sl8vI0eGXo98jL+y+RMZaQ9votGU5EZKiOHTsavU4mM0RW6qe9l5CSkmGSuqNea22SeonIMJW1ZWbnzp1lLtuzZ89y189khshK/f33bdjZyZCXV/6BvSXp1qM+GjWuatQ6icg4Kmsy07t37zKVkySJi+YRVSZOTvZG715yc1fg4xXPGrVOIqLSaDTG/aPsUVxUgshKdetRH8KIUxNcXRX46cBgVKnqZLQ6ici4ClpmDNms1cGDB9GkSZNCU7MBQKVSoWnTpjhy5IhedTOZIbJSeXkCwe2MM1DXwUGOc/Gj0KSpt1HqIyLTqMzJzNKlSxEVFQU3t8I3rnV3d8drr72GJUuW6FU3kxkiK3MjKR19emxBq2af4LeY6wbX5+3thJO/vwo3N6URoiMi0s/vv/+OiIiIYo936dIFp06d0qtujpkhsiKxZ5LxXJ+tuHM70yj1RU99AlPe7sCF8YgqiMo6ABgAUlJSYG9vX+xxOzs73L6t50Kg+gZFRMZz6uRNTHlrP47FJBmtzvoNvBD9zhNGq4+ITE8IA29nYMXJTI0aNXD27FkEBgYWefyPP/5AtWr63VqF3UxEFnbyxA088/TnRk1k5HIJazeUf60GIiJT6dq1K6ZNm4asrKxCxx48eIAZM2age/fuetVtsmRm7ty5aN++PZycnODh4VGmcyRJKnJbtGiRtkxAQECh4wsWLDDRsyAyvRef/wZ5uYbPWpLJ8/9bpaojtn//Alq09DO4TiIyr8o8AHjq1KlITU1FgwYNsHDhQuzYsQM7duzAe++9h4YNGyI1NRXvvPOOXnWbrJspJycH/fv3R0hICNasWVOmc27evKnz+IcffsDw4cPRr18/nf2zZ89GVFSU9rGrq6vhAROZWHZ2HnZ+dwHffnMOqnvZaNioCqpVd0FysmGr/MpkwGMt/NAloi6aNvVBtx71oVDIjRQ1EZlT/o0mDTvfWvn6+uLo0aMYMWIEoqOjtUtPSJKE8PBwLF++HL6+vnrVbbJkZtasWQCA9evXl/kcPz/dvyR37NiBTp06oW7dujr7XV1dC5UlsmbJN++j+7Nf4ULcP5DJJGg0Ar/FXDfK6r4ymQzB7Wpg6vSnjBApEVmSRkjQGNC6Ysi55lC7dm3s2bMHd+/eRXx8PIQQqF+/Pjw9PQ2q12rHzKSkpGD37t0YPnx4oWMLFixAlSpV0KpVKyxatAh5eXkl1pWdnY20tDSdjchchBAY0P8bXIpPBQDtqr7Guk1BXp4Gvfs2NEpdRETm4Onpiccffxxt27Y1OJEBrHg204YNG+Dq6oq+ffvq7B87dixat24NLy8vHD16FNHR0bh582aJC+3Mnz9f21JEZG7HYpJw+tTN0gvqQS6X0Da4Btp38DdJ/URkXpV5arYplatlZsqUKcUO0i3Yzp8/b5TA1q5di0GDBsHBwUFn/4QJExAaGorHHnsMr7/+Ot5//30sW7YM2dnZxdYVHR0NlUql3a5du2aUGInKYv/+y7CzM+4HjOzfd267kJr4ams/riNDVFkYOvjXRpOZcrXMTJw4EUOHDi2xzKPjW/Rx5MgRxMXFYcuWLaWWDQ4ORl5eHq5cuYKGDYtualcqlVAqufopWUZerubfm6wZ/iFjr1CjXYdUtGndA9161MfjbaszkSEim1euZMbb2xve3qa/t8uaNWsQFBSEFi1alFo2NjYWMpkMPj4+Jo+LSB9OznegMWARrAIeXg9w8vwmuDu9CaU81PDAiMjqsJtJPyYbM5OYmIjU1FQkJiZCrVYjNjYWABAYGAgXFxcAQKNGjTB//nz06dNHe15aWhq2bt2K999/v1CdMTExOHbsGDp16gRXV1fExMRg/PjxeOmll4wygIjI2I78fBXvzYsHIGBoy4yTkxqODg2gkL1klNiIyPowmdGPyZKZ6dOnY8OGDdrHrVq1AgAcOnQIoaGhAIC4uDioVCqd8zZv3gwhBAYOHFioTqVSic2bN2PmzJnIzs5GnTp1MH78eEyYMMFUT4NIL0IIDBrwLXbtvPjvHsM/YIJDlHCx+wqS5GxwXURElYkkhDUvsWMaaWlpcHd3h0qlKvJW5ESGuH0rA+2D1yLFwMXwHrX/8GC0Da5h1DqJqHTm+M4ouMaWxvPgJHco/YRiZKqz8MK5t8sd6/Lly7Fo0SIkJyejRYsWWLZsGdq2bVvqeZs3b8bAgQPRq1cvfPfdd3rHbSirXWeGqCLKyVHjyfbrjJ7IBNRxZyJDZAMscTuDLVu2YMKECZgxYwZOnz6NFi1aIDw8HLdu3SrxvCtXruDNN9/Ek08+qe/TNRomM0RG9Nknp3Ej6b7R6920pV/phYiI/vXoQrElLV+yZMkSREVFYdiwYWjSpAlWrVoFJycnrF27tthz1Go1Bg0ahFmzZhllFrOhmMwQGYkQAss/PmH0emfPDUWz5pytR2QLjNUy4+/vD3d3d+02f/78Iq+Xk5ODU6dOISwsTLtPJpMhLCwMMTExxcY5e/Zs+Pj4FLlKvyVY7QrARBXFtUQVPlhyDBs//wMPHpR8a43yqB3gjnfndUKvPo2MVicRWTdjzWa6du2azpiZ4tZau3PnDtRqdaEbPPr6+ha7CO4vv/yCNWvWaGcpWwMmM0QGuBD3D555+gukpWVDnWecsfSDhzyGl4e3ROs21bggHpGN0QjDbhb5763f4ObmZpLByunp6Rg8eDA+/fRTVK1a1ej164vJDJEBol7+Hqp7WdAYeM9IuTz/diAbvuyFHr1400giMo+qVatCLpcjJSVFZ39KSgr8/PwKlb906RKuXLmCHj16aPdp/v0AtLOzQ1xcHOrVq2faoIvAZIaoHITQQC3+gEbcwdcb1ThzOtngOmUyYMiwFnhtRBAaNzH9CttEZL3MvWieQqFAUFAQDhw4gN69ewPIT04OHDiA0aNHFyrfqFEj/Pnnnzr7pk6divT0dHz44Yfw97fMTW+ZzJBNEyILuZqdyNFshxB3IJNqQSF/AXZSZ0iSXKdsruYHPMibj9jTWRg/IhQXz3sZIwK8OqIpFi6OMEJdRFTRWWIF4AkTJmDIkCFo06YN2rZti6VLlyIjIwPDhg0DAERGRqJGjRqYP38+HBwc0KxZM53zPTw8AKDQfnNiMkM2SyPuICN3EDS4iPwVegU0IgF5eYdgJ4XCyW4lJCl/0FyOeiceqN/AhfMe6N+tD7Kz5CVVXUYCcjkwcvRTRqiLiEg/L7zwAm7fvo3p06cjOTkZLVu2xN69e7WDghMTEyGTWffkZyYzZLMy88ZDg8v/PioYvJvf95snfkaWegkc7aIhRA6y1DNx84YTpr/VHtlZcmg0hr6x86+3YWMHBAR4GFgXEVUWQgDCgDF4+q7pP3r06CK7lQDg8OHDJZ67fv16/S5qRExmyCapxUWoxa8llNAgR/MlHMRYxP6+D29Pbo9ff65ppKsLeHiqcfBIDwTWK/3O8ERkO3ijSf0wmSGblKf5DQVdS8V7gNOnYxDW8TzUauPdSkAul2H/oVcRWK+K0eokIrJlTGbIRpWtLbZX1zNQqwFj3PUaAOzsJOzZNwgNGjKRIaLCNEIycJ0ZtswQ2Qw76XGUltB8s7kB0tLURrtm+w418eVXfVHV28lodRJR5cJuJv0wmSGbJJc1hlxqA7U4A6BwwiIEsPyDxwy/kARUr+6KA4cHo0ZN46/GSUREvNEk2TAnuw8hoTryu5Dy/5opmAlw6pgPLp73NPwiAli4OIyJDBGVibFuNGlr2DJDNksmVYOr/ffI0WxFjvobqDU38PdZO3y0qBV++L4ODB0n4+Rsj8VLnkHP3rw9ARGVDbuZ9MNkhmyaJLlBKR8OpXw4Zs6cjA8WukMI/RssFUo5BgxsipD2NdGrTyO4uCiMGC0RVXbCwAHATGaIbJgQAus+dfn3g0CgvK0yMhkw+91OGDy0BTw9HUwSIxERFY3JDNksIdIgoIEEd2Rk5MLDMwN3U92hT/dSj54NMHZ8sPGDJCKbIoT+q/gWnG+LmMyQzcnV7EGWehU04iwAQEJN2Cn6on6ju0i45K5XnTI5x9ITkeGERoIwYLye0LCbiajSy1J/hFTVx9jyZWN8taEfUpKd4eKSg5AnYyE0Sr3rbdnK14hREhFReTCZIZuh1pxD8u2VeO7Z3rgY5/lvc6yEu6kOuL7JFVWqPtCrXrlcwuixbY0aKxHZJs5m0g+TGbIZOZqvMHnsU7h00aPQG14ICXduO0KShDbJKavZ74bC3l5u3GCJyCbxdgb6YTJDNuPa9Xjs3dW8hL9cJAiBEhIa3VlOnp4OmD2vE4YM5Z2viYgsickM2YwzJ73K0ASbn9B06XoFf8R6485tR0AAzi65qFXbAW+/0wd29nao6e+KJk28IUm2+VcQEZkGZzPph8kMVXpCCNy8cR/3VY8BSC39BEkg6ZorTpzbhLxcCXb2Au/Naot5czebPFYism0cM6MfJjNUaQkhsPazWHz4wTFcSbhXsBeljocRMlyKd0fGfTv89ms1fLr8MRw/6o95c00cMBER6YXJDFVKQghMfOMnfLb6DPTpCcp6YI9GNV7WPg4M1G/9GSKi8uAAYP0wmaFK6ddfruGz1WcAPNqHrN8b/fVRbQwPioioFBwzox8mM1ThqcVl5KjXIEezC0AmJPjh00+7QS6XoFaX952t2w0lk0lo2coXg4c8ZsyQiYiKxDEz+uEa7FSh5WlO4H5ud+RotgBIB6CGQBL+/P1uOROZ/LJ29hrtHqVSjmHDW+L7HwbC0dHeqHETEZHxsGWGKiwhcpCZNxJANoQQOHbUD19taIRLF91xOd4d5b379buLf4aDoxqTx3ZC57C6WLOhJ9zdeQdsIjIfjpnRj8laZq5cuYLhw4ejTp06cHR0RL169TBjxgzk5OSUeF5WVhZGjRqFKlWqwMXFBf369UNKSopOmcTERHTr1g1OTk7w8fHBpEmTkJeXZ6qnQlbqSuI2CPyD+/fl6NW5F/p37Ylvvw7E76d9odFIKN/4GAl5eXLUqn0fcrmE2XM7MZEhIrMTAhAaAzaOmTGu8+fPQ6PR4JNPPkFgYCDOnj2LqKgoZGRkYPHixcWeN378eOzevRtbt26Fu7s7Ro8ejb59++LXX38FAKjVanTr1g1+fn44evQobt68icjISNjb22PevHmmejpkZX7YE4+z57Yi4VIHbFzf5L9+YlGQn5f/r5OcbDlu33bC5m390aSpt/GCJSIik5KEMF8et2jRIqxcuRKXL18u8rhKpYK3tzc2bdqE5557DkB+UtS4cWPExMSgXbt2+OGHH9C9e3fcuHEDvr75dypetWoVJk+ejNu3b0OhUJQaR1paGtzd3aFSqeDm5ma8J0hmkZr6AI3qLUdA3RSc/7sKytudVJy9v2xFk4aD4eHyhsF1EVHlYY7vjIJrLHL/BI6So971PBAPMEn1ms19v5l1ALBKpYKXl1exx0+dOoXc3FyEhYVp9zVq1Ai1atVCTEwMACAmJgbNmzfXJjIAEB4ejrS0NPz1119F1pudnY20tDSdjSquTV/8iaysvH8TGcAYiUzNWmlo3twP7s5jDa6LiEhfBWNmDNlskdmSmfj4eCxbtgyvvfZasWWSk5OhUCjg4eGhs9/X1xfJycnaMg8nMgXHC44VZf78+XB3d9du/v7+BjwTsrSTJ28asTYBe/s8fLbxJzjavQ9J4gQ/IqKKptyf3FOmTIEkSSVu58+f1zknKSkJERER6N+/P6KioowWfFlFR0dDpVJpt2vXrpk9BjIeOzsZ7OzVBtSQ37MqSQKhz1zDj79+i6aPpUKSSu+iJCIyKfHfwnn6bOAA4LKZOHEihg4dWmKZunXrav9948YNdOrUCe3bt8fq1atLPM/Pzw85OTm4d++eTutMSkoK/Pz8tGWOHz+uc17BbKeCMo9SKpVQKpUlXpsqjs7P+GHHd7HIy5WX+1yZTIMDx7dALpPgVSUL7h75s+skeEOGWsYOlYioXDQC0BjQda5hMlM23t7e8PYu20yPpKQkdOrUCUFBQVi3bh1kspIbgoKCgmBvb48DBw6gX79+AIC4uDgkJiYiJCQEABASEoK5c+fi1q1b8PHxAQDs27cPbm5uaNKkSXmfDlUQ/9zJxM2b9+HifgSbNx9Gdlb1ctaQP0h4zqJfEVg/vdBRpfxlSBKXXSIiqohM9umdlJSE0NBQ1K5dG4sXL8bt27e1xwpaUJKSktC5c2d8/vnnaNu2Ldzd3TF8+HBMmDABXl5ecHNzw5gxYxASEoJ27doBALp06YImTZpg8ODBWLhwIZKTkzF16lSMGjWKrS+V0IW4fzBj2mHs2XXxofUTapS7Hjf3HEyfF4MXXroAIQBJAjQaGWQyDeykHlDIXjFq3ERE+hDCsJ4irjNjZPv27UN8fDzi4+NRs2ZNnWMFs8Fzc3MRFxeHzMxM7bEPPvgAMpkM/fr1Q3Z2NsLDw7FixQrtcblcjl27dmHEiBEICQmBs7MzhgwZgtmzZ5vqqZCFnPv7NjqHfoEHmbmPvEHLMhX733ExMoHhr5/F27OPwd5eQAggL0/CjevOsJM3Qv26r8NO6gRJn1trExEZmRAShAHdTLZ6byazrjNjLbjOjHUSIgu5mt3IE8cBCPR9tjqOxWTrcbNIABCI6JGA6fOOwr/Wf8nyL/+rhgkjOuFWsivOx4+Cr5+L0eInosrJnOvMzHH8FA6Sk971ZIlMTHsQZXPfbxwkQFYhT/MHMvOGQ+AfAHJcueyKo788r3d9zzx7FZ9+uV/7+N1pbbFvTwAux3tAkoCRo4OYyBARVRJMZsjiNOI2MvIiAdzHtasuOPBjbfz1R/GLK5ZOYP7SI/n/EsD61U2wZkVrqDUayOUSXh8ZhHfnP22U2ImIjIljZvTDZIYsLkfzFR48eIDJYzviu62BAABh0NtZQlXvLACukGtegV/VjngrOg2eno7o1bsh/KqxRYaIrJNGSAZOzbbNMTNMZsjictU/YNSwTjjwY62HBq/p+4YUcPfQwFXxOexkbSAplBgw0FiREhGRNWIyQxZ3+pQS+34IMEpdMhkw5o0GsJd3MEp9RETmxG4m/TCZIbPQiHvQiLMAJMilxyBJrtpjO7Y2hZ2dBnl5ht0XSS7XoGGTTIwY2dPAaImILIPJjH6YzJBJCZGBB+q5yNV8AyD3371KKGQD4SCfDElSIv1eIIQw7OaRjo65eGHwNcyePRmurlw8kYjIljCZIZMRIhsZeZFQi98BaB46ko0czedQi0twtluL2rXrASj6judlsfrzs3imy5PwchsLSXI3NGwiIovhAGD9GNauT1SCXM0OqMUZ6CYyBTRQiyPIE/sxeOhj0Gj0ewPa2Un4/cQwVHF/jYkMEVV4wgibLWIyQyaTo9mM0n7FMvPGw6/mLkyObqfXNfLyBBIS7up1LhERVQ7sZiKjuXs3C1u3/IVL8Xfh7qFEl55pqN+oqFaZh2UhSz0dYya3hq/fJCxacAI3btwHAMjlEiRJQl5e8XXI5RLc3R2M+CyIiCxHI4puyy7P+baIyQwZxYZ1v+PN8T8hJ0cNOzsZNBqBBXPD0L3PJXyw6jAcHNQlnq9BLF4c9hOGvvwOzv55C1kP8hBY3wtzZv2Mz9f/UWxCo1YL9Ovf2BRPiYjI7AQMvNGkAedWZOxmIoPt2nkBY0b+gOxsNYQAcnM12ptD7tlRB2+NeaoMtWiQo9kMmSwLLVr6ITikJqpUdcKYN4KhUMohlxd+g8rlEh5vWx1hz9Q18jMiIrIMIf5tndFzs9Wp2UxmyCBCCMydcwRSMX8MaDQybP+6Pq5cdi26gI4H0IhLOnvq1fPErh8Gam9BYGcn0yY2HTsFYNt3z0Mms82/RIiIKB+7mcggV6+q8NfZ2yWWkck0WPpeEP6544BrV13h7fsAzw28gF7PXSqi+6nwr2Sbx6vj7PkR2L8vAWdO34RSYYfwZ+uhSVNvIz4TIiLLM3RGko02zDCZIcPcT88ptYwQEr7Z3AByuQZqtQyXL7njt1+q4bPlzbFl1y54VckGAEioApkUWGQdcrkM4RH1EB5Rz6jxExFZEw4A1g+7mcgg/rXcYG9f8q9Rwc0j1er8ckIjAyDhYpwnxr3aSVtOKX8FkmRvsliJiKhyYjJDBnF3d0D/F5oUOUC3NGq1DIf318LleHfYS/2hkEWZIEIiooqDi+bph8kMGWzm7FBUq+aiV0IDCJw6OgOOdgsgSfx1JCLbZshMpoLNFvHbgwzmV80Fh38ZisFDA6B0KG9vrwQZakMqbjoUERFRKTgAmAzyz51M7Pr+Iv5JTUD70HXoPzgV3UL7lquOkPY1TRQdEVHFwtlM+mEyQ3rRaARmTf8fPv7oOPLyNJDJBNTqULi5Z5e5DrmdhJD2/mjchFOsiYiA/JlMBs1mMlYgFQyTGdLLjKmH8dHSY9rVJtXq/G6i9DQF8v82KL3bqFYtd6xZ18N0QRIRkU1gMkPllpJ8Hx9/dLzIZbPzp2EXNJQWndDY28swe24nRA59DK6uSlOGSkRUobCbST9MZqjcdnwXB1HiDUAKkhgBSfpvnRm5HHByUmDX3hfRqrWfyeMkIqpo2M2kH85monJLTX0Amaz0X52R42PRqEkqHBzzUNU7CyNGtUbMieFMZIiIiiGQf7NIvTdLPwELYcsMlVvtAA/k5ZWc/0uSwCsjzyJ65gkAgIP8bSjlXcwRHhER2Ri2zFC59ezVAM4uxd92QC7XoHP4NXj7ZAOQoJSNhkI23HwBEhFVUBojbLaIyQyVm7OzAos/yG9leXStO7lcgpOzDLPmekMpnwBX+1/gYDeBi+IREZUBb2egHyYzpJdBLzXHxs19EFjfS7tPkoDQTgE49HMUmjeZBQf5SMikahaMkoiIymL58uUICAiAg4MDgoODcfz48WLLfvrpp3jyySfh6ekJT09PhIWFlVjeHDhmhvTWo1dDdO/ZAOf+vgPVvSzUqu2OGjXdLB0WEVGFJWBYV5E+LTNbtmzBhAkTsGrVKgQHB2Pp0qUIDw9HXFwcfHx8CpU/fPgwBg4ciPbt28PBwQHvvfceunTpgr/++gs1atQwIHr9SaLkObaVUlpaGtzd3aFSqeDmxi9fIiIqnjm+Mwqu8QrWQgEnvevJQSY+w8u4du2aTqxKpRJKZdHregUHB+Pxxx/Hxx9/DADQaDTw9/fHmDFjMGXKlFKvqVar4enpiY8//hiRkZF6x24IdjMRERFVMv7+/nB3d9du8+fPL7JcTk4OTp06hbCwMO0+mUyGsLAwxMTElOlamZmZyM3NhZeXV+mFTcRkycyVK1cwfPhw1KlTB46OjqhXrx5mzJiBnJycYs9JTU3FmDFj0LBhQzg6OqJWrVoYO3YsVCqVTjlJkgptmzdvNtVTISIiMgtjDQC+du0aVCqVdouOji7yenfu3IFarYavr6/Ofl9fXyQnJ5cp5smTJ6N69eo6CZG5mWzMzPnz56HRaPDJJ58gMDAQZ8+eRVRUFDIyMrB48eIiz7lx4wZu3LiBxYsXo0mTJrh69Spef/113LhxA9u2bdMpu27dOkRERGgfe3h4mOqpEBERmYWxVgB2c3MzyzCKBQsWYPPmzTh8+DAcHBxMfr3imCyZiYiI0Ek26tati7i4OKxcubLYZKZZs2b45ptvtI/r1auHuXPn4qWXXkJeXh7s7P4L18PDA35+XEmWiIhIX1WrVoVcLkdKSorO/pSUlFK/YxcvXowFCxZg//79eOyxx0wZZqnMOmZGpVKVu0+tYMDVw4kMAIwaNQpVq1ZF27ZtsXbt2hLvFZSdnY20tDSdjYiIyNoII/yvPBQKBYKCgnDgwAHtPo1GgwMHDiAkJKTY8xYuXIg5c+Zg7969aNOmjd7P11jMNjU7Pj4ey5YtK7ZVpih37tzBnDlz8Oqrr+rsnz17Np5++mk4OTnhp59+wsiRI3H//n2MHTu2yHrmz5+PWbNmGRQ/ERGRqVniRpMTJkzAkCFD0KZNG7Rt2xZLly5FRkYGhg0bBgCIjIxEjRo1tIOI33vvPUyfPh2bNm1CQECAdmyNi4sLXFxcDIhef+Wemj1lyhS89957JZY5d+4cGjVqpH2clJSEjh07IjQ0FJ999lmZrpOWloZnnnkGXl5e2LlzJ+zti18+f/r06Vi3bh2uXbtW5PHs7GxkZ2fr1O3v78+p2UREVCpzTs0ejDUGT83+AsPLHevHH3+MRYsWITk5GS1btsRHH32E4OBgAEBoaCgCAgKwfv16AEBAQACuXr1aqI4ZM2Zg5syZesduiHInM7dv38Y///xTYpm6detCoVAAyB/UGxoainbt2mH9+vVluttyeno6wsPD4eTkhF27dpU6qGj37t3o3r07srKyip1H/zCuM0NERGVlC8lMRVfubiZvb294e3uXqWxSUhI6deqEoKAgrFu3rkyJTFpaGsLDw6FUKrFz584yjY6OjY2Fp6dnmRIZIiIia2WJbqbKwGRjZpKSkhAaGoratWtj8eLFuH37tvZYwQjppKQkdO7cGZ9//jnatm2LtLQ0dOnSBZmZmfjyyy91But6e3tDLpfj+++/R0pKCtq1awcHBwfs27cP8+bNw5tvvmmqp0JERGQWAgJC0n9hfhtc1B+ACZOZffv2IT4+HvHx8ahZs6bOsYIXOzc3F3FxccjMzAQAnD59GseOHQMABAYG6pyTkJCAgIAA2NvbY/ny5Rg/fjyEEAgMDMSSJUsQFRVlqqdSKWRn5+G7b+OwZ9dFZGbmomkzbwx5uSXq1PGwdGhEREQG4b2ZbKBP8cqVe+jx7Fe4ekUFmUyCRiMgl+f/973FYXh9pOWn1RERWStzjpkZgM+gkAwYMyMysRmv2Mz3WwHem6mSy8vToHf3Lbh+Lb+7TqPJz13VagEhgLcm7sdPP16yZIhERPQvjRE2W8RkppLbuycely/dhVpddAOcXC5h6fu/mTkqIiIi42EyU8n99OMl2NkV/2NWqwV+OXINDx7kmjEqIiIqmqGr/9rcyBEAZlwBmCwjJ0ddpuWtc3M1cHQ0Q0BERFQsTs3WD1tmKrlWratBU0wXEwBIElA7wB2urgozRkVERGQ8TGYquQEvNoWjoz0kqfgyr49sA6mkAkREZBbmvtFkZcFkppJzd3fAus97Qi6Xwc7uv4RFkvK3Z7sG4rURQRaMkIiICnA2k36YzNiAZ7vVx/9+HYLnnm8KJyd7yOUSmjT1xocfR+DLzX1LHCBMRETmIyTDN1vEAcA2ovljvli9pjtWr+lu6VCIiIiMiskMERGRlcjvKtJ/3IutdjMxmSEiIrISnJqtHw6WICIiogqNLTNERERWwtDp1bY6NZvJDBERkZVgN5N+2M1EREREFRpbZoiIiKyEBsLA2UzsZiIiIiILMnThO6H9P9vCbiYiIiKq0NgyQ0REZCXYzaQfJjNWJjdXjbupWXB2sYezs8LS4RARkVkZeudrJjNkQf/88wDvLzyKDet+R3p6DiQJCH82EJOjOyCoTTVLh0dERGbAqdn6YTJjBf65k4nOoV/g6pV7UKvzs2ohgH0/XsL+ny5jyzfP4ZkudS0cJRERkXXiAGArMHP6/3QSmQJqtYBGo8Erw3YiOzvPQtEREZG5FIyZMWSzRUxmLCw9PRtfbTpbKJEpoNEAd1OzsGvnRTNHRkRE5iaMsNkiJjMWlnhVhZxsdYll7O1lOHfutpkiIiIiqlg4ZsbCnMowY0mjEXBytDdDNEREZEkaSUAjcWp2ebFlxsICAtzRqHEVSCWs+KhWC/To1cB8QRERkUVwzIx+mMxYmCRJiH7nSYhifv/kcgk9ezdA/QZVzBsYERFRBcFkxgr06dcI7y0Og52dBJlMgp2dDHZ2+T+asGfq4pPPuls4QiIiMgcOANYPx8xYiRGj2qBvv0b4auNZXL58F25uSvTp15gL5hER2RDezkA/TGasiK+fC96Y2M7SYRAREVUoTGaIiIisBFtm9MNkhoiIyErw3kz6MdkA4CtXrmD48OGoU6cOHB0dUa9ePcyYMQM5OTklnhcaGgpJknS2119/XadMYmIiunXrBicnJ/j4+GDSpEnIy+Ny/0REVLEJI/zPFpmsZeb8+fPQaDT45JNPEBgYiLNnzyIqKgoZGRlYvHhxiedGRUVh9uzZ2sdOTk7af6vVanTr1g1+fn44evQobt68icjISNjb22PevHmmejpERERkpUyWzERERCAiIkL7uG7duoiLi8PKlStLTWacnJzg5+dX5LGffvoJf//9N/bv3w9fX1+0bNkSc+bMweTJkzFz5kwoFKWvqEtERGSNhIFjZmy1Zcas68yoVCp4eXmVWm7jxo2oWrUqmjVrhujoaGRmZmqPxcTEoHnz5vD19dXuCw8PR1paGv76668i68vOzkZaWprORkREZG0KbmdgyGaLzDYAOD4+HsuWLSu1VebFF19E7dq1Ub16dfzxxx+YPHky4uLi8O233wIAkpOTdRIZANrHycnJRdY5f/58zJo1ywjPgoiIiKxNuVtmpkyZUmiA7qPb+fPndc5JSkpCREQE+vfvj6ioqBLrf/XVVxEeHo7mzZtj0KBB+Pzzz7F9+3ZcunSpvKFqRUdHQ6VSabdr167pXRcREZGpaIyw2aJyt8xMnDgRQ4cOLbFM3bp1tf++ceMGOnXqhPbt22P16tXlDjA4OBhAfstOvXr14Ofnh+PHj+uUSUlJAYBix9kolUoolcpyX5uIiMicNBCQuM5MuZU7mfH29oa3t3eZyiYlJaFTp04ICgrCunXrIJOVf4hObGwsAKBatfxl/UNCQjB37lzcunULPj4+AIB9+/bBzc0NTZo0KXf9+tKIf5CniQGQC7msOeRSoNmuTURERP8x2ZiZpKQkhIaGonbt2li8eDFu376tPVbQgpKUlITOnTvj888/R9u2bXHp0iVs2rQJXbt2RZUqVfDHH39g/PjxeOqpp/DYY48BALp06YImTZpg8ODBWLhwIZKTkzF16lSMGjXKLK0vQmTjgXo2cjVbAfy7to0akEvBcLJbBJlU0+QxEBFR5WToWjG2OpvJZMnMvn37EB8fj/j4eNSsqfsFL0T+i52bm4u4uDjtbCWFQoH9+/dj6dKlyMjIgL+/P/r164epU6dqz5XL5di1axdGjBiBkJAQODs7Y8iQITrr0piKEAKZeSORJ/6HR3sm1eIk7uf2h4v995BJVU0eCxERVT7sZtKPJAoyCxuSlpYGd3d3qFQquLm5lfm8PE0MMvIGlVBCBqVsBBzsJhoeJBERWQV9vzP0uUZju0WQS45616MWD3Aub5JJY7VGZl1npqLL0XwLQF5CCQ1yNFvMFQ4REVUyBTeaNGSzRbzRZDloxC0A6hLLCNw1TzBERFTpsJtJP0xmykEm+UEt5CgpoZHA8TJERKQfDWBgMmOb2M1UDgrZcyi5ZUYGhewFc4VDREREYDJTLnKpDeykZwFIRR2FhOpQyIeYOywiIqokhARoDNhEUV9PNoDJTDlIkgQnu6VQyIYDeHhNGwl20lNwsd8KmeRpqfCIiKiC4wBg/XDMTDlJkj0c7d6GgxiDPHECQC7kUjPIpBqWDo2IiMgmMZnRkyS5wl562tJhEBFRJZLfssLZTOXFZIaIiMhKqA28nYGtJjMcM0NEREQVGltmiIiIrAS7mfTDZIaIiMhKMJnRD7uZiIiIqEJjywwREZGVUEsaCEn/mxJobPSGBmyZISIishJqCIM3fSxfvhwBAQFwcHBAcHAwjh8/XmL5rVu3olGjRnBwcEDz5s2xZ88eva5rLExmiIiIrITGwERGnzEzW7ZswYQJEzBjxgycPn0aLVq0QHh4OG7dulVk+aNHj2LgwIEYPnw4zpw5g969e6N37944e/asoU9fb5IQwuZGC6WlpcHd3R0qlQpubm6WDoeIiKyYOb4zCq7hrpgJSXLQux4hsqDKmVmuWIODg/H444/j448/BgBoNBr4+/tjzJgxmDJlSqHyL7zwAjIyMrBr1y7tvnbt2qFly5ZYtWqV3rEbwibHzBTkb2lpaRaOhIiIrF3Bd4U5/vbPk7IgGTAjSUjZAAp/vymVSiiVykLlc3JycOrUKURHR2v3yWQyhIWFISYmpshrxMTEYMKECTr7wsPD8d133+kdt6FsMplJT08HAPj7+1s4EiIiqijS09Ph7u5ukroVCgX8/PyQnLzA4LpcXFwKfb/NmDEDM2fOLFT2zp07UKvV8PX11dnv6+uL8+fPF1l/cnJykeWTk5MNC9wANpnMVK9eHdeuXYOrqyskybT3S09LS4O/vz+uXbtWIbu0GL/lVOTYAcZvaRU5fmuLXQiB9PR0VK9e3WTXcHBwQEJCAnJycgyuSwhR6LutqFaZysQmkxmZTIaaNWua9Zpubm5W8abUF+O3nIocO8D4La0ix29NsZuqReZhDg4OcHDQf7yMPqpWrQq5XI6UlBSd/SkpKfDz8yvyHD8/v3KVNwfOZiIiIrJRCoUCQUFBOHDggHafRqPBgQMHEBISUuQ5ISEhOuUBYN++fcWWNwebbJkhIiKifBMmTMCQIUPQpk0btG3bFkuXLkVGRgaGDRsGAIiMjESNGjUwf/58AMC4cePQsWNHvP/+++jWrRs2b96MkydPYvXq1RZ7DkxmTEypVGLGjBkVtr+S8VtORY4dYPyWVpHjr8ixV0QvvPACbt++jenTpyM5ORktW7bE3r17tYN8ExMTIZP915HTvn17bNq0CVOnTsXbb7+N+vXr47vvvkOzZs0s9RRsc50ZIiIiqjw4ZoaIiIgqNCYzREREVKExmSEiIqIKjckMERERVWhMZoiIiKhCYzJjgCtXrmD48OGoU6cOHB0dUa9ePcyYMaPU5ahDQ0MhSZLO9vrrr+uUSUxMRLdu3eDk5AQfHx9MmjQJeXl5Fo8/NTUVY8aMQcOGDeHo6IhatWph7NixUKlUOuUefX6SJGHz5s0Wjx8AsrKyMGrUKFSpUgUuLi7o169fodUszfH6A8DcuXPRvn17ODk5wcPDo0znFPXaSpKERYsWacsEBAQUOr5ggeH3fDE09qFDhxaKKyIiQqdMamoqBg0aBDc3N3h4eGD48OG4f/++UWPXJ/7c3FxMnjwZzZs3h7OzM6pXr47IyEjcuHFDp5w5Xnt94gfyl7mfPn06qlWrBkdHR4SFheHixYs6Zcz1+pf3OleuXCn2d3/r1q3acub47CHrw3VmDHD+/HloNBp88sknCAwMxNmzZxEVFYWMjAwsXry4xHOjoqIwe/Zs7WMnJyftv9VqNbp16wY/Pz8cPXoUN2/eRGRkJOzt7TFv3jyLxn/jxg3cuHEDixcvRpMmTXD16lW8/vrruHHjBrZt26ZTdt26dTpfVGX9wDVl/AAwfvx47N69G1u3boW7uztGjx6Nvn374tdffwVgvtcfyL9jbf/+/RESEoI1a9aU6ZybN2/qPP7hhx8wfPhw9OvXT2f/7NmzERUVpX3s6upqeMAP0Sd2AIiIiMC6deu0jx9dS2TQoEG4efMm9u3bh9zcXAwbNgyvvvoqNm3aZLTYgfLHn5mZidOnT2PatGlo0aIF7t69i3HjxqFnz544efKkTllTv/b6xA8ACxcuxEcffYQNGzagTp06mDZtGsLDw/H3339rl9E31+tf3uv4+/sX+t1fvXo1Fi1ahGeffVZnv6k/e8gKCTKqhQsXijp16pRYpmPHjmLcuHHFHt+zZ4+QyWQiOTlZu2/lypXCzc1NZGdnGyvUIpUl/kd9/fXXQqFQiNzcXO0+AGL79u1Gjq50pcV/7949YW9vL7Zu3ardd+7cOQFAxMTECCEs8/qvW7dOuLu763Vur169xNNPP62zr3bt2uKDDz4wPLAyKE/sQ4YMEb169Sr2+N9//y0AiBMnTmj3/fDDD0KSJJGUlGRgpEUz5LU/fvy4ACCuXr2q3WfO116Issev0WiEn5+fWLRokXbfvXv3hFKpFF999ZUQwnyvv7Gu07JlS/Hyyy/r7LPUZw9ZFruZjEylUsHLy6vUchs3bkTVqlXRrFkzREdHIzMzU3ssJiYGzZs317nFenh4ONLS0vDXX3+ZJO4CZY3/0XPc3NxgZ6fb0Ddq1ChUrVoVbdu2xdq1ayHMsD5jafGfOnUKubm5CAsL0+5r1KgRatWqhZiYGACWff3LKyUlBbt378bw4cMLHVuwYAGqVKmCVq1aYdGiRSbpJtPH4cOH4ePjg4YNG2LEiBH4559/tMdiYmLg4eGBNm3aaPeFhYVBJpPh2LFjlgi3RCqVCpIkFfrL3xpf+4SEBCQnJ+v87ru7uyM4OFjnd98cr78xrnPq1CnExsYW+btvic8esix2MxlRfHw8li1bVmoX04svvojatWujevXq+OOPPzB58mTExcXh22+/BQAkJyfrfJEC0D5OTk42TfAoe/wPu3PnDubMmYNXX31VZ//s2bPx9NNPw8nJCT/99BNGjhyJ+/fvY+zYscYOW6ss8ScnJ0OhUBT68vH19dW+tpZ6/fWxYcMGuLq6om/fvjr7x44di9atW8PLywtHjx5FdHQ0bt68iSVLllgo0nwRERHo27cv6tSpg0uXLuHtt9/Gs88+i5iYGMjlciQnJ8PHx0fnHDs7O3h5eVnda5+VlYXJkydj4MCBOnd2ttbXvuD1K+p3++HffXO8/sa4zpo1a9C4cWO0b99eZ78lPnvICli6acgaTZ48WQAocTt37pzOOdevXxf16tUTw4cPL/f1Dhw4IACI+Ph4IYQQUVFRokuXLjplMjIyBACxZ88eq4lfpVKJtm3bioiICJGTk1Ni2WnTpomaNWuWqV5Txr9x40ahUCgK7X/88cfFW2+9JYSwzOuvb1dHw4YNxejRo0stt2bNGmFnZyeysrKsJnYhhLh06ZIAIPbv3y+EEGLu3LmiQYMGhcp5e3uLFStWlFqfueLPyckRPXr0EK1atRIqlarEsmV97U0d/6+//ioAiBs3bujs79+/v3j++eeFEOZ7/Q29TmZmpnB3dxeLFy8utWx5Pnuo4mLLTBEmTpyIoUOHllimbt262n/fuHEDnTp1Qvv27fW6a2hwcDCA/JaFevXqwc/PD8ePH9cpUzDbxs/Pr9T6zBF/eno6IiIi4Orqiu3bt8Pe3r7E8sHBwZgzZw6ys7NLvXmcKeP38/NDTk4O7t27p9M6k5KSon1tzf366+vIkSOIi4vDli1bSi0bHByMvLw8XLlyBQ0bNiy2nLlif7iuqlWrIj4+Hp07d4afnx9u3bqlUyYvLw+pqalW89rn5ubi+eefx9WrV3Hw4EGdVpmilPW1B0wbf8Hrl5KSgmrVqmn3p6SkoGXLltoy5nj9Db3Otm3bkJmZicjIyFLLluezhyowS2dTFd3169dF/fr1xYABA0ReXp5edfzyyy8CgPj999+FEP8NQE1JSdGW+eSTT4Sbm1uZ/rorD33iV6lUol27dqJjx44iIyOjTOe8++67wtPT05BQi1Te+AsGAG/btk277/z580UOADbH619An9aBIUOGiKCgoDKV/fLLL4VMJhOpqal6RFcyQ1pmrl27JiRJEjt27BBC/Dcw9OTJk9oyP/74o9UMAM7JyRG9e/cWTZs2Fbdu3SrTOaZ87YUo/wDgh1szVCpVkQOATf36G3qdjh07in79+pXpWqb67CHrwmTGANevXxeBgYGic+fO4vr16+LmzZva7eEyDRs2FMeOHRNCCBEfHy9mz54tTp48KRISEsSOHTtE3bp1xVNPPaU9Jy8vTzRr1kx06dJFxMbGir179wpvb28RHR1t8fhVKpUIDg4WzZs3F/Hx8TrnFCQTO3fuFJ9++qn4888/xcWLF8WKFSuEk5OTmD59usXjF0KI119/XdSqVUscPHhQnDx5UoSEhIiQkBDtcXO9/kIIcfXqVXHmzBkxa9Ys4eLiIs6cOSPOnDkj0tPTtWUaNmwovv32W53zVCqVcHJyEitXrixU59GjR8UHH3wgYmNjxaVLl8SXX34pvL29RWRkpEVjT09PF2+++aaIiYkRCQkJYv/+/aJ169aifv36OkliRESEaNWqlTh27Jj45ZdfRP369cXAgQONGrs+8efk5IiePXuKmjVritjYWJ3ft4JZbuZ67fWJXwghFixYIDw8PMSOHTvEH3/8IXr16iXq1KkjHjx4oC1jrte/tOsU9d4VQoiLFy8KSZLEDz/8UKhOc332kPVhMmOAdevWFdsvXCAhIUEAEIcOHRJCCJGYmCieeuop4eXlJZRKpQgMDBSTJk0q1O9+5coV8eyzzwpHR0dRtWpVMXHiRJ2pz5aK/9ChQ8Wek5CQIITIn2LZsmVL4eLiIpydnUWLFi3EqlWrhFqttnj8Qgjx4MEDMXLkSOHp6SmcnJxEnz59dBIgIczz+guR37pSVPwPxwtArFu3Tue8Tz75RDg6Oop79+4VqvPUqVMiODhYuLu7CwcHB9G4cWMxb948o7cqlTf2zMxM0aVLF+Ht7S3s7e1F7dq1RVRUlM4UeCGE+Oeff8TAgQOFi4uLcHNzE8OGDdP5grZU/AW/SyWdY67XXp/4hchvnZk2bZrw9fUVSqVSdO7cWcTFxenUa67Xv7TrFPXeFUKI6Oho4e/vX+Tnibk+e8j6SEJwzhoRERFVXFxnhoiIiCo0JjNERERUoTGZISIiogqNyQwRERFVaExmiIiIqEJjMkNEREQVGpMZIiIiqtCYzBAREVGFxmSGiIiIKjQmM0RERFShMZkhIiKiCu3/3fzLCfSEEfEAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"plasma\")\n", - "plt.title(\"Cluster Distribution with AEResNet\")\n", - "plt.colorbar(label=\"Cluster\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "H1CANB-oD-t_" - }, - "source": [ - "# **AEDCNNClusterer (Auto-Encoder Dilated Convolutional Network)**\n", - "The **AEDCNNClusterer** is built on an **Auto-Encoder** with a **Dilated Convolutional Network (DCNN)** backbone.Dilated convolutions use dilated filters to expand the receptive field exponentially, allowing the model to capture long-term dependencies in the data without losing resolution.This method is ideal for detecting patterns over extended time windows.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "gM5ja7I14GJK" - }, - "outputs": [], - "source": [ - "from aeon.clustering.deep_learning import AEDCNNClusterer" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qv-xuWhvE1_6", - "outputId": "3ce747a0-6495-485e-e57e-df93fa25bc0b" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.11/dist-packages/aeon/clustering/deep_learning/_ae_dcnn.py:209: UserWarning: Currently, the dilation rate has been set to `1` which is\n", - " different from the original paper of the `AEDCNNNetwork` due to CPU\n", - " Implementation issues with `tensorflow.keras.layers.Conv1DTranspose`\n", - " & `dilation_rate` > 1 on some Hardwares & OS combinations. You\n", - " can use the dilation rates as specified in the paper by passing\n", - " `dilation_rate=None` to the Network/Clusterer.\n", - " encoder, decoder = self._network.build_network(input_shape, **kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 272ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n" - ] - } - ], - "source": [ - "model = AEDCNNClusterer(n_epochs=10, random_state=42, dilation_rate=1)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "AQIaYKy6E5RK", - "outputId": "6de1c9b3-1d85-425c-9eca-e2edbbf36ae9" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"coolwarm\")\n", - "plt.title(\"Cluster Distribution with AEDCNN\")\n", - "plt.colorbar(label=\"Cluster\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ickHTlKQFDDZ" - }, - "source": [ - "# **AEDRNNClusterer (Auto-Encoder Dilated Recurrent Neural Network)**\n", - "The **AEDRNNClusterer** integrates an Auto-Encoder with a **Dilated Recurrent Neural Network (DRNN)** backbone.DRNNs combine the strengths of RNNs (sequence modeling) with dilated connections to capture patterns over long temporal sequences efficiently.they are Suitable for tasks where sequential relationships are vital (e.g., speech data, financial trends).\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "O2Xj2LilFBjX" - }, - "outputs": [], - "source": [ - "from aeon.clustering.deep_learning import AEDRNNClusterer" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "k4z2dOrzFjx2", - "outputId": "392d57ef-54ed-4f86-e236-3245145cd0a2" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 2s/step" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x785f3b7428e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 1s/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 231ms/step\n" - ] - } - ], - "source": [ - "model = AEDRNNClusterer(n_epochs=10, random_state=42)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "mE4l1C9AFm-U", - "outputId": "a04a18cf-e3bb-4cf0-88da-cbe8db8cb1fc" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"magma\")\n", - "plt.title(\"Cluster Distribution with AEDRNN\")\n", - "plt.colorbar(label=\"Cluster\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cRH_XEfeF6Cs" - }, - "source": [ - "# **AEAttentionBiGRUClusterer (Auto-Encoder Attention Bidirectional GRU Network)**\n", - "The **AEAttentionBiGRUClusterer** integrates an Auto-Encoder with an Attention-based **Bidirectional Gated Recurrent Unit (BiGRU)** network.\n", - "The Attention Mechanism allows the model to selectively focus on the most relevant parts of the time series during clustering.The Bidirectional GRU enhances this by processing the sequence from both forward and backward directions, improving sequence dependency recognition.It is Suitable for tasks requiring fine-grained sequence interpretation.Excels in datasets where certain segments of the sequence are more influential than others.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "iuy-H4F5F4dk" - }, - "outputs": [], - "source": [ - "from aeon.clustering.deep_learning import AEAttentionBiGRUClusterer" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9Zx6TvNFHBwg", - "outputId": "290fa9d2-0fcf-469d-f6c3-f69cbdebe9e3" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 901ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 631ms/step\n" - ] - } - ], - "source": [ - "model = AEAttentionBiGRUClusterer(n_epochs=10, random_state=42)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "-AvTzfdlHGeF", - "outputId": "3461f910-6547-40c9-a402-275ee128a92b" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"inferno\")\n", - "plt.title(\"Cluster Distribution with AEAttentionBiGRU\")\n", - "plt.colorbar(label=\"Cluster\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FTnLddtrHMQE" - }, - "source": [ - "# **AEBiGRUClusterer (Auto-Encoder Bidirectional GRU Network)\n", - "**\n", - "The **AEBiGRUClusterer** is an Auto-Encoder with a **Bidirectional GRU (BiGRU)** architecture.GRUs are similar to LSTMs but with a simpler structure, making them faster and more efficient for time series data.The bidirectional structure enhances the model’s ability to detect patterns by combining forward and backward sequence insights.It Performs well on shorter sequences with frequent fluctuations.Suitable for tasks requiring fast training without compromising performance.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "duoJ4wMQHnFt" - }, - "outputs": [], - "source": [ - "from aeon.clustering.deep_learning import AEBiGRUClusterer" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XfGf-pgxHnRy", - "outputId": "84b30734-4b03-4b01-e099-4a761edcd0ab" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 918ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 127ms/step\n" - ] - } - ], - "source": [ - "model = AEBiGRUClusterer(n_epochs=10, random_state=42)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "RlH69d_rHnVX", - "outputId": "ebbb3fb9-13b4-4ccc-e5fe-214cbba0e0ef" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"cividis\")\n", - "plt.title(\"Cluster Distribution with AEBiGRU\")\n", - "plt.colorbar(label=\"Cluster\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sq0CNNhuI3_O" - }, - "source": [ - "**References:**\n", - "\n", - "[1]Zhao et. al, Convolutional neural networks for time series classification,\n", - "Journal of Systems Engineering and Electronics, 28(1):2017.\n", - "\n", - "[2]Wang et. al, Time series classification from scratch with deep neural networks: A strong baseline, International joint conference on neural networks (IJCNN), 2017.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-yv9NB8JHyUE" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 814d25f18a5af6569a32b3c92bc5a70bb6125338 Mon Sep 17 00:00:00 2001 From: aditya27 <120127383+Edgeshot27@users.noreply.github.com> Date: Tue, 25 Mar 2025 20:51:11 +0530 Subject: [PATCH 06/13] Add files via upload --- .../deep_learning_based_clustering.ipynb | 822 ++++++++++++++++++ 1 file changed, 822 insertions(+) create mode 100644 examples/clustering/deep_learning_based_clustering.ipynb diff --git a/examples/clustering/deep_learning_based_clustering.ipynb b/examples/clustering/deep_learning_based_clustering.ipynb new file mode 100644 index 0000000000..51e142f1f6 --- /dev/null +++ b/examples/clustering/deep_learning_based_clustering.ipynb @@ -0,0 +1,822 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dne_kUDfuiYS", + "outputId": "775fa5b6-e64e-4696-8eda-8d1f45bb0859" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting aeon[deep_learning]\n", + " Downloading aeon-1.0.0-py3-none-any.whl.metadata (20 kB)\n", + "\u001b[33mWARNING: aeon 1.0.0 does not provide the extra 'deep-learning'\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: deprecated>=1.2.13 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (1.2.18)\n", + "Requirement already satisfied: numba<0.61.0,>=0.55 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (0.60.0)\n", + "Requirement already satisfied: numpy<2.1.0,>=1.21.0 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) (2.0.2)\n", + "Requirement already 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python-dateutil>=2.8.2->pandas<2.3.0,>=2.0.0->aeon[deep_learning]) (1.17.0)\n", + "Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m47.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading aeon-1.0.0-py3-none-any.whl (8.2 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m63.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: scikit-learn, aeon\n", + " Attempting uninstall: scikit-learn\n", + " Found existing installation: scikit-learn 1.6.1\n", + " Uninstalling scikit-learn-1.6.1:\n", + " Successfully uninstalled scikit-learn-1.6.1\n", + "Successfully installed aeon-1.0.0 scikit-learn-1.5.2\n" + ] + } + ], + "source": [ + "!pip install aeon[deep_learning]" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Deep Learning Based Clustering**\n", + "The aeon.clustering.deeplearning module provides powerful deep learning models designed specifically for time series clustering. These models leverage advanced architectures like **Auto-Encoders, Residual Networks, Dilated Convolutions, and Attention Mechanisms** to effectively identify patterns, trends, and groupings in complex temporal data. Each model is tailored to handle different data characteristics, improving clustering accuracy and performance across various domains such as healthcare, finance, and signal processing." + ], + "metadata": { + "id": "2Ru1riLYWFE3" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ], + "metadata": { + "id": "86gsiHDbuoz-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from aeon.datasets import load_arrow_head" + ], + "metadata": { + "id": "7xvwY48cur5i" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X_train, y_train = load_arrow_head(split=\"train\")\n", + "X_test, y_test = load_arrow_head(split=\"test\")" + ], + "metadata": { + "id": "EkRKdT47N7Oc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(X_train[:5])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5upX35-AXSnH", + "outputId": "3bacd61e-0b2f-41bd-d07e-c7b9c3a886fb" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[[-1.9630089 -1.9578249 -1.9561449 ... -1.9053929 -1.9239049 -1.9091529]]\n", + "\n", + " [[-1.7745713 -1.7740359 -1.7765863 ... -1.7292269 -1.7756704 -1.7893245]]\n", + "\n", + " [[-1.8660211 -1.8419912 -1.8350253 ... -1.8625124 -1.8633682 -1.8464925]]\n", + "\n", + " [[-2.0737575 -2.0733013 -2.0446071 ... -2.0269634 -2.073405 -2.0752917]]\n", + "\n", + " [[-1.7462554 -1.7412629 -1.7227405 ... -1.7434421 -1.7627288 -1.7634281]]]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(y_train[:5])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3LzGs-PYeMJ1", + "outputId": "581523e0-782c-4b72-aa3a-0276bd89c39d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['0' '1' '2' '0' '1']\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **AEFCNClusterer (Auto-Encoder Fully Convolutional Network)**\n", + "The **AEFCNClusterer** is a deep learning model that leverages a **Fully Convolutional Network (FCN)** architecture with an **Auto-Encoder** structure for clustering. It combines feature extraction with convolutional layers and reconstruction capabilities via auto-encoders. \n", + "FCNs are effective for extracting spatial hierarchies in time series data without requiring fully connected layers, making them highly efficient.\n" + ], + "metadata": { + "id": "b-XHpTDjxeSd" + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEFCNClusterer" + ], + "metadata": { + "id": "DGbXlOPLxdO-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEFCNClusterer(n_epochs=10,batch_size=3)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kxYYwMmKN_Uz", + "outputId": "0f2ccdef-1a79-41ad-94f2-a5708d41443f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 81ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='viridis')\n", + "plt.title('Cluster Distribution with AEFCN')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "q3oOIG3jOtbf", + "outputId": "258be6d8-6dc7-4c47-b8f3-f5b47412967b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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xbdo0yOVyrF+/Hh4eHhqPAti0aRNWr16N/v37o2HDhsjIyMDnn38OZ2dn9OrVC0BBN0vz5s2xbds2NG7cGO7u7mjRogVatGiBVatWoXPnzmjZsiXCwsLQoEEDpKamIjo6Gjdv3sSff/6p0/tw9uxZfPXVV1CpVLh//z5OnTqFb7/9FpIk4csvv1RPES/O4cOHMX78eAwaNAiNGzdGfn4+vvzyS8jlco2VVv39/XHw4EEsX74ctWrVQv369Utd/r40LVq0QHBwsMbUbAAaX0yDBw/G9OnT0b9/f0ycOBFZWVlYs2YNGjdurJEIVSQ2Dw8PREREYO7cuQgJCUHfvn0RGxuL1atX46mnnirXgnDl5eDgAH9/f/z+++/qNWaAgpaZzMxMZGZmliuZ8ff3x7Zt2xAeHo6nnnoKTk5O6pYNbe3ZswcZGRnqKeKP69ChAzw8PLB582a8/PLL6v2XL18utjXFy8sLzz77bLmvb21tjZ07dyIoKAhPP/00XnrpJXTq1AnW1ta4cOECtmzZAjc3tyJrzTxKLpfj3XffxahRo8p9XSKzY7qJVGSOLl++LMLCwoSvr6+wsbER1apVE506dRIrV67UmA79+NRsIYQ4c+aMCAgIEDY2NqJu3bpi+fLlRab0nj17VgwZMkTUrVtX2NraCk9PT/H8889rTC8WQojjx48Lf39/YWNjU2Sa9tWrV0VoaKjw9vYW1tbWonbt2uL5558XO3bsUJcpvG5pU8AfVTg1u3CzsrIS7u7uIiAgQERERIgbN24UOefx6b/Xrl0Tr776qmjYsKGws7MT7u7uonv37uLgwYMa5126dEk8/fTTwt7eXgBQv4+FU6Xv3LlT5FolTc0eN26c+Oqrr0SjRo2Era2taNOmjcZ05EI//fSTaNGihbCxsRFNmjQRX331VbF1lhTb4/+OhT755BPRtGlTYW1tLby8vMTYsWPFvXv3NMp07dpVPPHEE0ViKmnKeHGmTZsmAIjFixdr7Pfz8xMAxNWrVzX2Fzc1+8GDB+KVV14Rrq6uAoD62oVlH59SX/gzsWHDhhLj6tOnj7CzsxOZmZkllhk5cqSwtrYWd+/eFUKUPjX70WnXj06jLsu9e/fE7NmzRcuWLYWDg4Ows7MTLVq0EBERESI5ObnMOvPy8kTDhg05NZsqLUkIjvoiIiKiyotjZoiIiKhSYzJDRERElRqTGSIiIqrUmMwQERFZqF9++QV9+vRBrVq1IEkSdu/eXeY5R48eRdu2bWFraws/Pz9s3LjR4HGWhckMERGRhcrMzESrVq2watWqcpWPj49H79690b17d8TExOCtt97Ca6+9hh9//NHAkZaOs5mIiIgIkiRh165d6NevX4llpk+fjn379mk8/mbw4MG4f/8+Dhw4YIQoi2eRi+apVCrcunUL1apVM9gS50REVDUIIZCRkYFatWppPCVe3x4+fIjc3Fyd6xFCFPlus7W1LfNhvuURHR1d5OGnwcHBeOutt3SuWxcWmczcunULPj4+pg6DiIgqkcTERNSpU8cgdT98+BD16zkh5bbuD4B1cnIq8uDeyMhIzJkzR+e6U1JSijwHzcvLC+np6cjOztb7g3LLyyKTmcKHESYmJpb6BGQiIqL09HT4+PgY7EG2QMFDWlNuKxF/ph6cq2nf+pOeoUJ9/xtFvt/00SpjziwymSlsfnN2dmYyQ0RE5WKMYQnO1WQ6JTPqegz0/ebt7V3kYcSpqalwdnY2WasMYKHJDBERkTlSChWUOkzLUQqV/oIpRmBgIPbv36+xLyoqCoGBgQa9blk4NZuIiMhMqCB03iriwYMHiImJQUxMDICCqdcxMTFISEgAAERERCA0NFRd/o033sC1a9fw9ttv49KlS1i9ejW++eYbTJ48WW/vgTbYMkNERGQmVFBBl7aVip59+vRpdO/eXf06PDwcADBixAhs3LgRycnJ6sQGAOrXr499+/Zh8uTJ+Oijj1CnTh3873//Q3BwsA5R684i15lJT0+Hi4sLFAoFx8wQEVGpjPGdUXiNW7F1dB4AXKvJTYv7fmPLDBERkZlQCgGlDm0MupxbmTGZISIiMhPajHt5/HxLxAHAREREVKmxZYaIiMhMqCCgZMtMhTGZISKiKufG34m4fOYarG2s0PqZFnD1cDF1SOXCbibtMJkhIqIqIzk+FUtHrsJfxy6q98mt5AgZ/Qze/HAkbOxsTBgdGQqTGSIiqhLupd7HW51n4f4dhcZ+Zb4S+z8/iH9upWHe7ulGeSyBtjibSTscAExERFXCzo/24/5tBVT5RReOEyqB378/o9FiY45UetgsEZMZIiKqEg6sPwSVsuSvc7mVDFGbjhovIDIadjMREVGVoLibUepxZb4KaSn3jROMlpQ6zmbS5dzKjC0zRERUJbh5lT5jSW4lQ43a7kaKRjtKoftmiZjMEBFRlfDc6B6QyUv+WlPmq9BzZPcSj5sDjpnRDpMZIiKqEgZM6g2POtUhsyr61SZJErq+1BHNAxubIDIyNCYzRERUJThXr4YVv72Pds+2Ah6ZfW1rb4MXw5/HjC8nmPW0bABQQYJSh00F874/Q+EAYCIiqjJq1HLH/H3vIOX6bcT9EQ9rGyu06NIMjs4Opg6tXFSiYNPlfEvEZIaIiKocb19PePt6mjoMMhImM0RERGaisLtIl/MtEZMZIiIiM8FkRjscAExERESVGltmiIiIzIRKSFAJ7VtXdDm3MmMyQ0REZCbYzaQddjMRERFRpcaWGSIiIjOhhAxKHdoZlHqMpTJhMkNERGQmhI5jZgTHzBAREZEpccyMdjhmhoiIiCo1gyYzaWlpGDp0KJydneHq6orRo0fjwYMHJZa/fv06JEkqdtu+fbu6XHHHt27dashbISIiMjilkOm8WSKDdjMNHToUycnJiIqKQl5eHkaNGoUxY8Zgy5YtxZb38fFBcnKyxr7PPvsMS5cuxXPPPaexf8OGDQgJCVG/dnV11Xv8RERExqSCBJUO7QwqWOaTJg2WzFy8eBEHDhzAqVOn0K5dOwDAypUr0atXLyxbtgy1atUqco5cLoe3t7fGvl27duGll16Ck5OTxn5XV9ciZUuSk5ODnJwc9ev09PSK3g4REenBraspuH9bgRq13eFZ18PU4VAVYbD2qOjoaLi6uqoTGQAICgqCTCbDiRMnylXHmTNnEBMTg9GjRxc5Nm7cONSoUQPt27fH+vXrIUTJ2ejChQvh4uKi3nx8fCp+Q0REpLU/f76A8QERGNFoAiZ1momhvm9iSvdIXD5z1dShmZXCAcC6bJbIYMlMSkoKPD01H79uZWUFd3d3pKSklKuOdevWoVmzZujYsaPG/nnz5uGbb75BVFQUBg4ciDfffBMrV64ssZ6IiAgoFAr1lpiYWPEbIiIirZyJ+hPTn51XJHE5/+slvNVlFi6dvGKiyMwPx8xop8J3PWPGjBIH6RZuly5d0jmw7OxsbNmypdhWmVmzZqFTp05o06YNpk+fjrfffhtLly4tsS5bW1s4OztrbEREZHgqlQrLx3wKlVJAqDRb0FVKFZS5+fj4zf+ZKDqqKio8ZmbKlCkYOXJkqWUaNGgAb29v3L59W2N/fn4+0tLSyjXWZceOHcjKykJoaGiZZQMCAvDee+8hJycHtra2ZZYnIiLj+PPoBdy+cafE4yqVwJWz1xD/1w3Ub1nPiJGZp4IBwDo8aNJCu5kqnMx4eHjAw6PsQVuBgYG4f/8+zpw5A39/fwDA4cOHoVKpEBAQUOb569atQ9++fct1rZiYGLi5uTGRISIyMynxt8suBCD52m0mMwBUOj7OgLOZ9KxZs2YICQlBWFgY1q5di7y8PIwfPx6DBw9Wz2RKSkpCjx498MUXX6B9+/bqc+Pi4vDLL79g//79Rer9/vvvkZqaig4dOsDOzg5RUVFYsGABpk6daqhbISIiLVVzdyq7UAXKERXHoOvMbN68GePHj0ePHj0gk8kwcOBAfPzxx+rjeXl5iI2NRVZWlsZ569evR506ddCzZ88idVpbW2PVqlWYPHkyhBDw8/PD8uXLERYWZshbISIiLbQLbg37anbIznhYYpkatd3RvGNjI0ZlvnQdxKssZWZvVSaJ0uY0V1Hp6elwcXGBQqHgYGAiIgPbsfx7fDr1ixKPv71xPJ4N7WrEiCrGGN8ZhdfYEtMCDtXkWteTlaHEK63PW9z3Gx80SUREBjVw8vPIz83HF/O2Iy8nD3K5DMp8FewcbTFmaahZJzLGphQSlDo8+VqXcyszJjNERGRQkiRh8Iz+eP6Nnvh15wncS1WgRh13dB4QAHtHO1OHR1UAkxkiIjIKJ1dHhLz6jKnDMGtKHWczKTmbiYiIiExJJWRQ6TAAWGV5w2ABGPBxBkRERETGwJYZIiIiM8FuJu0wmSEiIjITKug2I0mlv1AqFXYzERERUaXGlhkiIiIzoYIMKp2ezWSZbRRMZoiIiMyE7o8zsMxkxjLvmoiIiKoMtswQERGZCRUkqKDLAGA+zoCIiIhMiN1M2mEyQ0REZCZ0X2fGMpMZy7xrIiIiqjLYMkNERGQmVEKCSpdF83Q4tzJjMkNERGQmVDp2M1nqOjOWeddERERUZbBlhoiIyEyohAwqHWYk6XJuZcZkhoiIyEwoIUGpw1oxupxbmVlmCkdERERVBltmiIiIzAS7mbTDZIaIiMhMKKFbV5FSf6FUKpaZwhEREVGVwZYZIiIiM8FuJu0wmSEiIjITfNCkdizzromIiMyQgASVDpvQcrzNqlWr4OvrCzs7OwQEBODkyZOlll+xYgWaNGkCe3t7+Pj4YPLkyXj48KFW19YHJjNEREQWbNu2bQgPD0dkZCTOnj2LVq1aITg4GLdv3y62/JYtWzBjxgxERkbi4sWLWLduHbZt24Z33nnHyJH/h8kMERGRmSjsZtJlq6jly5cjLCwMo0aNQvPmzbF27Vo4ODhg/fr1xZY/fvw4OnXqhFdeeQW+vr7o2bMnhgwZUmZrjiExmSEiIjIThU/N1mUDgPT0dI0tJyen2Ovl5ubizJkzCAoKUu+TyWQICgpCdHR0sed07NgRZ86cUScv165dw/79+9GrVy89vxvlx2SGiIioivHx8YGLi4t6W7hwYbHl7t69C6VSCS8vL439Xl5eSElJKfacV155BfPmzUPnzp1hbW2Nhg0bolu3bibtZuJsJiIiIjOhhAxKHdoZCs9NTEyEs7Ozer+tra3OsRU6evQoFixYgNWrVyMgIABxcXGYNGkS3nvvPcyaNUtv16kIJjNERERm4tGuIm3PBwBnZ2eNZKYkNWrUgFwuR2pqqsb+1NRUeHt7F3vOrFmzMHz4cLz22msAgJYtWyIzMxNjxozBu+++C5nM+J0+Brvi/Pnz0bFjRzg4OMDV1bVc5wghMHv2bNSsWRP29vYICgrClStXNMqkpaVh6NChcHZ2hqurK0aPHo0HDx4Y4A6IiIiqNhsbG/j7++PQoUPqfSqVCocOHUJgYGCx52RlZRVJWORyOYCC73FTMFgyk5ubi0GDBmHs2LHlPmfJkiX4+OOPsXbtWpw4cQKOjo4IDg7WmLs+dOhQXLhwAVFRUdi7dy9++eUXjBkzxhC3QEREZFQqyHTeKio8PByff/45Nm3ahIsXL2Ls2LHIzMzEqFGjAAChoaGIiIhQl+/Tpw/WrFmDrVu3Ij4+HlFRUZg1axb69OmjTmqMzWDdTHPnzgUAbNy4sVzlhRBYsWIFZs6ciRdeeAEA8MUXX8DLywu7d+/G4MGDcfHiRRw4cACnTp1Cu3btAAArV65Er169sGzZMtSqVavYunNycjRGcqenp+twZ0RERIahFBKUOnQzaXPuyy+/jDt37mD27NlISUlB69atceDAAfWg4ISEBI2WmJkzZ0KSJMycORNJSUnw8PBAnz59MH/+fK3j1pXZzGaKj49HSkqKxvQwFxcXBAQEqKeHRUdHw9XVVZ3IAEBQUBBkMhlOnDhRYt0LFy7UGNXt4+NjuBshIiKqZMaPH48bN24gJycHJ06cQEBAgPrY0aNHNRomrKysEBkZibi4OGRnZyMhIQGrVq0q95ASQzCbZKZwClhp08NSUlLg6empcdzKygru7u4lTiEDgIiICCgUCvWWmJio5+iJiIh0p691ZixNhZKZGTNmQJKkUrdLly4ZKlat2draqkd2l3eENxERkbGJf5+are0mLPRBkxUaMzNlyhSMHDmy1DINGjTQKpDCKWCpqamoWbOmen9qaipat26tLvP4syLy8/ORlpZW4hQyIiKiykIJCUotHxZZeL4lqlAy4+HhAQ8PD4MEUr9+fXh7e+PQoUPq5CU9PR0nTpxQz4gKDAzE/fv3cebMGfj7+wMADh8+DJVKpdG/R0RERJbDYO1RCQkJiImJQUJCApRKJWJiYhATE6OxJkzTpk2xa9cuAIAkSXjrrbfw/vvvY8+ePfjrr78QGhqKWrVqoV+/fgCAZs2aISQkBGFhYTh58iR+++03jB8/HoMHDy5xJhMREVFloRK6jpsx9R2YhsGmZs+ePRubNm1Sv27Tpg0A4MiRI+jWrRsAIDY2FgqFQl3m7bffVq8ieP/+fXTu3BkHDhyAnZ2duszmzZsxfvx49OjRAzKZDAMHDsTHH39sqNsgIiIymsKxL7qcb4kkYarl+kwoPT0dLi4uUCgUHAxMRESlMsZ3RuE1RhwZDBsnG63ryX2Qi03dt1rc9xufzURERGQmVJCg0mEQry7nVmZMZoiIiMyEKVYArgoss3ONiIiIqgy2zBAREZkJDgDWDpMZIiIiM6GCbo8ksNQxM5aZwhEREVGVwZYZIiIiMyF0nM0kLLRlhskMERGRmdD1ydeW+tRsJjNERERmggOAtWOZd01ERERVBltmiIiIzAS7mbTDZIaIiMhM8HEG2mE3ExEREVVqbJkhIiIyE+xm0g6TGSIiIjPBZEY77GYiIiKiSo0tM0RERGaCLTPaYTJDRERkJpjMaIfdTERERFSpsWWGiIjITAjotlaM0F8olQqTGSIiIjPBbibtMJkhIiIyE0xmtMMxM0RERFSpsWWGiIjITLBlRjtMZoiIiMwEkxntMJkhItKBEALIOwcorwNSNcC2IyTJztRhEVkUJjNERFoSuX9AKN4BlFf/2yk5AU7jAIdXIUmW+VcyaU8ICUKH1hVdzq3MmMwQEWlB5F2ASBsOIP+xAw8gMhZDEg8LkhqiClBB0mmdGV3Orcw4m4mISAsiYxkKEhlV8ccfrIJQpRk1JiJLxWSGiKiChPIOkPsbSkpkCiiB7P3GComqiMIBwLpslojdTEREFaX6pxyF5BCqOxba6E/a4pgZ7bBlhoioomQ1gDLTFCUkuacxoiGyeAZLZubPn4+OHTvCwcEBrq6uZZbPy8vD9OnT0bJlSzg6OqJWrVoIDQ3FrVu3NMr5+vpCkiSNbdGiRQa6CyKioiR5DcCmMwB5KaWsALtexgqJqgh2M2nHYMlMbm4uBg0ahLFjx5arfFZWFs6ePYtZs2bh7Nmz2LlzJ2JjY9G3b98iZefNm4fk5GT1NmHCBH2HT0RUKqnaFBT01Bf/MSo5TYQkczNqTFT5FXYz6bJZIoONmZk7dy4AYOPGjeUq7+LigqioKI19n3zyCdq3b4+EhATUrVtXvb9atWrw9vbWW6xERBUlWTcHqm+GUMwE8i89csAFktMEwGG46YKjSkvo2LpiqcmMWY+ZUSgUkCSpSDfVokWLUL16dbRp0wZLly5Ffn5+8RX8KycnB+np6RobEZGuJOsnIVX/DlL13ZBcP4Lkth6S52+QHEO5YB6REZntbKaHDx9i+vTpGDJkCJydndX7J06ciLZt28Ld3R3Hjx9HREQEkpOTsXz58hLrWrhwobqliIhInyRJAqybF2xEOhIAhNDtfEtUoZaZGTNmFBl8+/h26dKlsisqQ15eHl566SUIIbBmzRqNY+Hh4ejWrRuefPJJvPHGG/jggw+wcuVK5OTklFhfREQEFAqFektMTNQ5RiIiIn0rXAFYl80SVahlZsqUKRg5cmSpZRo0aKBLPOpE5saNGzh8+LBGq0xxAgICkJ+fj+vXr6NJkybFlrG1tYWtra1OcREREZF5qlAy4+HhAQ8PD0PFok5krly5giNHjqB69eplnhMTEwOZTAZPT67nQERElRsXzdOOwcbMJCQkIC0tDQkJCVAqlYiJiQEA+Pn5wcnJCQDQtGlTLFy4EP3790deXh5efPFFnD17Fnv37oVSqURKSgoAwN3dHTY2NoiOjsaJEyfQvXt3VKtWDdHR0Zg8eTKGDRsGNzdOgSQiospNJSRIOiQklrrOjMGSmdmzZ2PTpk3q123atAEAHDlyBN26dQMAxMbGQqFQAACSkpKwZ88eAEDr1q016io8x9bWFlu3bsWcOXOQk5OD+vXrY/LkyQgPDzfUbRAREZGZM1gys3HjxjLXmBGPDNn29fXVeF2ctm3b4vfff9dHeERERGZHCB1nM1nodCaznZpNRKQvcX/EY8+aH3HlzDXY2FmjU7/2CHn1GThXr2bq0Ig0cMyMdpjMEFGVtmXBTmyY+TXkVjIo81UAgIsnrmDrot1YcnA2/NrUN3GERKQrs14BmIhIF8f3nMKGmV8DgDqRAQChEshMz0JEyPvIyS55jSoiY+OzmbTDZIaIqqwdH3wPmbz4jzmVUoX7d9JxZOtxI0dFVDI+NVs7TGaIqEpS5ivx17GLUClVJZaRyWWIOfyXEaMiKl3hAGBdNkvEZIaIqqSyZkf+WwgqVcnJDhFVDkxmiKhKsrK2gl+b+pBkJTe7q4RA88DiH4NCZAoFrSu6jJkx9R2YBpMZIqqyBrzVG0JV/Ke7JJNg72iHZ0O7GjkqopJxALB2mMwQUZUVNOxp9BnbEwA0BgLLrGSwtrHCnF1vw9HZwVThEZGecJ0ZIqqyJEnChE9eQ0Cvttj9yQFcOVuwaF7n/gHoN+E51GrobeoQiTSIfzddzrdETGaIqEqTJAkBvf0R0Nvf1KEQlYkrAGuH3UxERERUqbFlhojMSnJ8KhIv3YK9kx2adWgEK2t+TJEFYT+TVtgyQ0Rm4eaVZLz97DyENhyPd3svQHjX2Rhc53XsXvlD+daMIaoKdJ3JpGU306pVq+Dr6ws7OzsEBATg5MmTpZa/f/8+xo0bh5o1a8LW1haNGzfG/v37tbq2PvBPHiIyuZTrtzEx8B1kKrI09ivupGPVpPVI/ycDoXNeMlF0RMaj6yq+2py7bds2hIeHY+3atQgICMCKFSsQHByM2NhYeHp6Fimfm5uLZ599Fp6entixYwdq166NGzduwNXVVfvAdcSWGSIyuS/nbkdWelaJjx746v0duJv0j5GjIrIMy5cvR1hYGEaNGoXmzZtj7dq1cHBwwPr164stv379eqSlpWH37t3o1KkTfH190bVrV7Rq1crIkf+HyQwRmdTDrBwc/vpXjadaP06SJBz88hcjRkVkGvpaNC89PV1jy8kp/unwubm5OHPmDIKCgtT7ZDIZgoKCEB0dXew5e/bsQWBgIMaNGwcvLy+0aNECCxYsgFKp1P8bUk5MZojIpNL/yUB+bn6pZWQyCbcT2TJDFqBw3IsuGwAfHx+4uLiot4ULFxZ7ubt370KpVMLLy0tjv5eXF1JSUoo959q1a9ixYweUSiX279+PWbNm4YMPPsD777+v3/eiAjhmhohMqpqbI2QyCaoSHjsAACqVgJunixGjIqrcEhMT4ezsrH5ta2urt7pVKhU8PT3x2WefQS6Xw9/fH0lJSVi6dCkiIyP1dp2KYDJDRCZl72SPjv3a4/h3p0ocM6NSqvDM0M5GjozI+PQ1ANjZ2VkjmSlJjRo1IJfLkZqaqrE/NTUV3t7Fr5Bds2ZNWFtbQy6Xq/c1a9YMKSkpyM3NhY2NjfY3oCV2MxGRyYVGDoK1jRVk8qLTSiUJeP71Z1Hbr6YJIiMyMqGHrQJsbGzg7++PQ4cOqfepVCocOnQIgYGBxZ7TqVMnxMXFQaX674+Py5cvo2bNmiZJZAAmM0RkBuq3rIelh+cUeVaSlY0VXpzSF+NXjjZRZERVX3h4OD7//HNs2rQJFy9exNixY5GZmYlRo0YBAEJDQxEREaEuP3bsWKSlpWHSpEm4fPky9u3bhwULFmDcuHGmugV2MxGReWgW0AjrL36E879ewo2/b8LeyQ7te7VBNTcnU4dGZDSmeDbTyy+/jDt37mD27NlISUlB69atceDAAfWg4ISEBMhk/7V9+Pj44Mcff8TkyZPx5JNPonbt2pg0aRKmT5+uddy6koQFLq2Znp4OFxcXKBSKcvUpEhGR5TLGd0bhNep+Nhsyezut61FlP0TCmHkW9/3Glhki0julUokTe89i/7pDSL6WCjdPFwQN74rugzvC1l5/syqIiAAmM0SkZ7k5eZg7cClO7v8DMrkMKqUKiZeS8OfRC9ix/HssOxwJVw9OsyYqjim6maoCDgAmIr3aOPNrnDoQAwDqqdbi3zVkEi8lYeGwj00VGpH5M/JspqqCyQwR6U125kN8v/YndfLyOJVShbNR53Dj4k0jR0ZUWUh62CwPkxki0psrZ67hYWbxz4ApJEnAn0cuGCkiIrIEHDNDRHpTvsmRUjnLEVkgXbuKLPRXiy0zRKQ3fm3qw8bOutQyQgi06NzUSBERVTIcM6MVJjNEpDeOzg4IefUZjQW2HiW3kuGJjk3QsJWvcQMjIrOgVCrxyy+/4P79+3qtl8kMEenVa4uHoVlgIwCAJPt3MKIESJKEGnWq492tk00YHZGZE5LumxmTy+Xo2bMn7t27p9d6DZbMzJ8/Hx07doSDgwNcXV3Ldc7IkSMhSZLGFhISolEmLS0NQ4cOhbOzM1xdXTF69Gg8ePDAAHdARNqwd7TD0kORmLZhHJq2bwT3mq6o36Iuxiwdjk//WAqPOtVNHSKR2Sp8arYum7lr0aIFrl27ptc6DTYAODc3F4MGDUJgYCDWrVtX7vNCQkKwYcMG9WtbW83VQocOHYrk5GRERUUhLy8Po0aNwpgxY7Blyxa9xU5EurG2sUbPEd3Qc0Q3U4dCRGbm/fffx9SpU/Hee+/B398fjo6OGse1eQyDwZKZuXPnAgA2btxYofNsbW3h7e1d7LGLFy/iwIEDOHXqFNq1awcAWLlyJXr16oVly5ahVq1aOsVMRERkUhYwm6lXr14AgL59+0KS/usWE0JAkiQolcoK12l2U7OPHj0KT09PuLm54ZlnnsH777+P6tULmqWjo6Ph6uqqTmQAICgoCDKZDCdOnED//v2LrTMnJwc5Of+tfZGenm7YmyAiItKGruNezHzMDAAcOXJE73WaVTITEhKCAQMGoH79+rh69SreeecdPPfcc4iOjoZcLkdKSgo8PT01zrGysoK7uztSUlJKrHfhwoXqliIiIiIyna5du+q9zgoNAJ4xY0aRAbqPb5cuXdI6mMGDB6Nv375o2bIl+vXrh7179+LUqVM4evSo1nUCQEREBBQKhXpLTEzUqT4iIiJDkITuW2Vw7NgxDBs2DB07dkRSUhIA4Msvv8Svv/6qVX0VapmZMmUKRo4cWWqZBg0aaBVISXXVqFEDcXFx6NGjB7y9vXH79m2NMvn5+UhLSytxnA1QMA7n8YHERKSdq39ex/erf8TFE1cgVALeDTzRonMztH+uDXyf8DF1eESVmwWMmfn2228xfPhwDB06FGfPnlUPA1EoFFiwYAH2799f4TorlMx4eHjAw8OjwhfR1s2bN/HPP/+gZs2aAIDAwEDcv38fZ86cgb+/PwDg8OHDUKlUCAgIMFpcRJZq+7I9+OztLyGTS1ApCz41488nIHrPaXz+9pdo1f0JRHw1CdVrupk4UqJKygLGzLz//vtYu3YtQkNDsXXrVvX+Tp064f3339eqToOtM5OQkICYmBgkJCRAqVQiJiYGMTExGmvCNG3aFLt27QIAPHjwANOmTcPvv/+O69ev49ChQ3jhhRfg5+eH4OBgAECzZs0QEhKCsLAwnDx5Er/99hvGjx+PwYMHcyYTkYGdifoTn739JQCoE5nH/fXLRYQ/PQtZGdnGDI2IKpHY2Fg8/fTTRfa7uLhovTKwwZKZ2bNno02bNoiMjMSDBw/Qpk0btGnTBqdPn1aXiY2NhUKhAFCwKuC5c+fQt29fNG7cGKNHj4a/vz+OHTum0UW0efNmNG3aFD169ECvXr3QuXNnfPbZZ4a6DSL6144PvodMXvpHhkqpQvK12/hp41HjBEVU1VjAs5m8vb0RFxdXZP+vv/6q9VAVg81m2rhxY5lrzDz65Fx7e3v8+OOPZdbr7u7OBfKIjEwIgZgj56FSqsouC4EfNx5BvwnPGSEyoirGAsbMhIWFYdKkSVi/fj0kScKtW7cQHR2NqVOnYtasWVrVaVZTs4nIfInyrpMugHupCsMGQ0SV1owZM6BSqdCjRw9kZWXh6aefhq2tLaZOnYoJEyZoVSeTGSIqkyRJaNahMf6Ovlxm64wkk+Dta7yJAkRVigW0zEiShHfffRfTpk1DXFwcHjx4gObNm8PJyUnrOvnUbCIqlwFvPV++biaVQO8xzxohIqIqqIo/NRsAXn31VWRkZMDGxgbNmzdH+/bt4eTkhMzMTLz66qta1clkhoiQn5ePs4f+wi87ohH3R3yxXUqd+7fHS9NeKLUemUzCE52aoNvgjoYKlYgquU2bNiE7u+iMx+zsbHzxxRda1cluJiILt++zKGyYtRWKO/89s0wml6FWQy8MmNQbPUd2g629LSRJQtjiYWgb1BK7Pt6PmCPnkZOdq27Wtra1QvCoZzBm6XBY21ib6G6IKjddV/E15xWA09PTIYSAEAIZGRmws7NTH1Mqldi/f3+RRxaVF5MZIgu2c8U+rAnfWGS/SqnCzcvJ+Hjc/3Bg/WEsORQJR2cHAID/s63g/2wrAEBmehYun74KIYDG/g3g5OpozPCJqp4qPGbG1dVV/eijxo0bFzkuSZLWz1FkMkNkoTIVmfjfO5vLLHflj3h8OmUTwj8fW+SYo7MD2jzT0hDhEVEVc+TIEQgh8Mwzz+Dbb7+Fu7u7+piNjQ3q1aun9QK4TGaILNQvO35HXk5emeWESuDHjUfx2uJhcHavZoTIiKgqKnxadnx8POrWrQtJ0t9gZQ4AJrJQF3+/XO4maZVShcObtXuaLRGVnwQdn5pt6hsoh4sXL+K3335Tv161ahVat26NV155Bffu3dOqTiYzRFWQEAJ/HP4LWxfvxvYPvkf8+QQAQMa9B/h5ezQ2zvoaP6w7XKE6o78/XXYhItKNBUzNnjZtGtLTCyYc/PXXXwgPD0evXr0QHx+P8PBwrepkNxNRFRN/PgHzXlyGm5eTC56lJAQ+m/YFPOvVQFryfeTn5mtV783Lt/QcKRFZovj4eDRv3hwA8O2336JPnz5YsGABzp49i169emlVJ5MZoirkzs1/MKXrbGSmF6zh8Ogid7dv3NWpbisbflwQGVwVns1UyMbGBllZWQCAgwcPIjQ0FEDBsxcLW2wqip9ORFXI7o/3IzM9u1wr9VaEzEqG9s+10WudRFQMC0hmOnfujPDwcHTq1AknT57Etm3bAACXL19GnTp1tKqTY2aIqpCoL3/ReyIDCZBJEl4YF6LfeonIIn3yySewsrLCjh07sGbNGtSuXRsA8MMPPyAkRLvPGbbMEFUhmYpMvdYnk8sgk8swa1s46jTWbv0HIiq/qrwCcKG6deti7969RfZ/+OGHWtfJZIaoCvHy9cTN2FvFPlupImzsrNG4XUO07t4CvcKC4FGnup4iJKJSWUA3U0JCQqnH69atW+E6mcwQVSF93uiJNZM36lSHbwsffBy9APaOdmUXJiKqIF9f31IXzFMqlRWuk8kMURXi3/NJOLk5IiPtgVbnt332SSz+cZaeoyKicrOAlpk//vhD43VeXh7++OMPLF++HPPnz9eqTiYzRFVE8rVUhD89Gxn3tUtkGvk3wIJ97+g5KiKqCEsYM9OqVasi+9q1a4datWph6dKlGDBgQIXr5Gwmoiriwzc+heJuBqDFZKaBk3tj9anFkFvJ9R8YEVE5NGnSBKdOndLqXLbMEFUSQmQDqkxA5gJJskZOdg72fXYQe9f+hOT421qt7OvTtBamb5qAJk/5GSBiIqowXR9JUAkeZ/D4wnhCCCQnJ2POnDlo1KiRVnUymSEycyLvb4gHnwA5hwGoAMkBWcr+mPK8AldjErWqU24lx2uLhmLg5Of1+uRaItKRBYyZcXV1LfK5I4SAj48Ptm7dqlWdTGaIzJjIiYa49xoK+o4K+o+EKgvTnz+JqzH20PYZufWa18GL4X30FicR6YcljJk5cuSIxmuZTAYPDw/4+fnBykq7tITJDJGZEiIfQjEFgBKPDoTZ/KEXYmMcdKq7ReemugVHRKSlrl276r1OJjNE5irnZ0Cl+XDIh1kyfPOJp85V930zWOc6iMgAqmg30549e8pdtm/fvhWun8kMkbnKvwxAjoKWmQInoqoh56FukxA7vvAU6jX30S02IjIMHbuZzDWZ6devX7nKSZLERfOIqhTJAY92LymVBV1MunB0cUD452/oGBgRUcWoVHp+AO5juM4Mkbmy7aHx8tQhZ9y4bK91dQ7O9vjw2HtwqeGsa2REZChCD5uZOnz4MJo3b15kajYAKBQKPPHEEzh27JhWdbNlhshM3boux6Wfn4Hq4Vk4VMvHN6s8CtqftVhHwsbOGlsS1sLRWbeBw0RkYFV0zAwArFixAmFhYXB2LvoHlYuLC15//XUsX74cXbp0qXDdTGaIzMzdpH+wbPQanPnpz3/3VPwJso9y9XTGJycXMZEhIpP6888/sXjx4hKP9+zZE8uWLdOqbiYzRGbkytlreLf3AijuFG2G1cbwyEEYPnsQF8YjqiSq8jozqampsLa2LvG4lZUV7ty5o1XdTGaIzEDsqTismbwRF47H6q3OOk1qIjTyJb3VR0Ski9q1a+P8+fPw8yv+8Snnzp1DzZo1taqbA4CJTOzSySuY1OVdvSYyMrkM72x5S2/1ERHpqlevXpg1axYePnxY5Fh2djYiIyPx/PPPa1W3wZKZ+fPno2PHjnBwcICrq2u5zpEkqdht6dKl6jK+vr5Fji9atMhAd0FkePMGLYYyt+LrKjxOkhd0JbnUcMbCAzPRqE0DneskIiOrwrOZZs6cibS0NDRu3BhLlizBd999h++++w6LFy9GkyZNkJaWhnfffVerug3WzZSbm4tBgwYhMDAQ69atK9c5ycnJGq9/+OEHjB49GgMHDtTYP2/ePISFhalfV6tWTfeAiQwsNycPv+48gZ+/OY4H9zNRt2lt1KhTHXcSFdD2GUsAIMkk+LWuj/a92qB+y3ro+EI7WNuU3C9NROarKo+Z8fLywvHjxzF27FhERERAiIJgJUlCcHAwVq1aBS8v7dbSMlgyM3fuXADAxo0by32Ot7e3xuvvvvsO3bt3R4MGmn9hVqtWrUhZInP2T/I9TOsxF4mXkiDJJAiVwIXfLkGZr/tCUjKZDE90bIKR8wbrIVIiMjkzTkh0Va9ePezfvx/37t1DXFwchBBo1KgR3NzcdKrXbMfMpKamYt++fRg9enSRY4sWLUL16tXRpk0bLF26FPn5+aXWlZOTg/T0dI2NyFiEEIjstxhJcQUtj0JV8Emlj0SmoB4lurzYQS91EREZg5ubG5566im0b99e50QGMOPZTJs2bUK1atUwYMAAjf0TJ05E27Zt4e7ujuPHjyMiIgLJyclYvnx5iXUtXLhQ3VJEZGwXjsci9tRVg9Qtk8vQPLAxWnZpZpD6icjIqvCieYZUoZaZGTNmlDhIt3C7dOmSXgJbv349hg4dCjs7O4394eHh6NatG5588km88cYb+OCDD7By5Urk5OSUWFdERAQUCoV6S0xM1EuMROVx+scYyK30u86LJCuo74lOTTB399tcR4aoiigcM6PLZokq1DIzZcoUjBw5stQyj49v0caxY8cQGxuLbdu2lVk2ICAA+fn5uH79Opo0aVJsGVtbW9ja2uocF5E2lPnKfx+ypnvCYWWtwpOB+WjUYRA69muPZgGNmMgQkcWrUDLj4eEBDw8PQ8Witm7dOvj7+6NVq1Zllo2JiYFMJoOnp6fB4yLShr19GoRK94Sjmlsetpy9BNvqUyA5DdNDZERkdtjNpBWDjZlJSEhAWloaEhISoFQqERMTAwDw8/ODk5MTAKBp06ZYuHAh+vfvrz4vPT0d27dvxwcffFCkzujoaJw4cQLdu3dHtWrVEB0djcmTJ2PYsGF6GUBEpG8xR8/jy/d/RcEnjG4JjZ2DgI1jY8DhFb3ERkTmpypPzTYkgyUzs2fPxqZNm9Sv27RpAwA4cuQIunXrBgCIjY2FQqHQOG/r1q0QQmDIkCFF6rS1tcXWrVsxZ84c5OTkoH79+pg8eTLCw8MNdRtEWhFC4MOwRfhh/dl/9+jeMvNEhxqQ3FdAkjnqXBcRUVVisKnZGzduhBCiyFaYyAAFH/iPj8EZM2YMsrKy4OLiUqTOtm3b4vfff8f9+/eRnZ2Nv//+GxERERwPQ2ZFKP/Bh6NefiSR0Y/+U6ZDknGBSKIqzUQrAK9atQq+vr6ws7NDQEAATp48Wa7ztm7dCkmS0K9fP+0urCdmu84MUWUkRC6OrB+FH75QQZ+d1971PdG8Q2O91UdEZsoEycy2bdsQHh6OyMhInD17Fq1atUJwcDBu375d6nnXr1/H1KlT0aVLl4pfVM+YzBDp0a2/d2DRGzYo6FbS3yyjubve1ltdRFT1Pb5QbGnLlyxfvhxhYWEYNWoUmjdvjrVr18LBwQHr168v8RylUomhQ4di7ty5epnFrCsmM0R6IoTA9mX7IfQ8AO+1RUPR4Ml6+q2UiMySvtaZ8fHxgYuLi3pbuHBhsdfLzc3FmTNnEBQUpN4nk8kQFBSE6OjoEuOcN28ePD09i12l3xTMdgVgosridsIdbF38HX7ceAS52bnQV4uMl68HXl8aii4D+agCIouhp6nZiYmJcHZ2Vu8uaWzp3bt3oVQqizzg0cvLq8RFcH/99VesW7dOPUvZHDCZIdJBwqUkvNV5JrLSs/T2rKXgUd3x/Os90eSphlwQj8jS6CmZcXZ21khm9CUjIwPDhw/H559/jho1aui9fm0xmSHSwaLhH+PB/UwASugyTkYml0GSJMzcNhmd+wfoM0QiohLVqFEDcrkcqampGvtTU1Ph7e1dpPzVq1dx/fp19OnTR72vYIVzwMrKCrGxsWjYsKFhgy4GkxmiChBCBeT9BaH8Bwe/TsGVM1fhWE2JzAw5tE1kJJmE50b3QL8Jz8H3CR/9BkxElYqxF82zsbGBv78/Dh06pJ5erVKpcOjQIYwfP75I+aZNm+Kvv/7S2Ddz5kxkZGTgo48+go+PaT7DmMyQRRPiIZC9FyJ7N6D6B5D7QHIYBNg+A0mSa5Z9+CNExmJcPvsPlk6qi4TLdgAkZGbo8msk0G9cd7z50RhdboOIqgoTPM4gPDwcI0aMQLt27dC+fXusWLECmZmZGDVqFAAgNDQUtWvXxsKFC2FnZ4cWLVponO/q6goARfYbE5MZslhC+Q/EvVAg/woKWlUEoIyHyD0K2HQF3FZBkmwKymbvhVCE48ZlW0wd0Ah5OfqYCCggk0vo/9ZAPdRFRKSdl19+GXfu3MHs2bORkpKC1q1b48CBA+pBwQkJCZDJzHvyM5MZslhCMQXIv1b46t///juIN/cYRMaHkJynQ4hciPR5uJtshdUzayM3R6aHB0cWXG/W1uGoWd+rjLJEZClM9Wym8ePHF9utBABHjx4t9dyNGzdqd1E9YjJDFknkxwG5x0spoQKyt0A4jcfV09/j06nuiPlVX2u9CDi5Svjk92mo3bi9nuokoiqBT83WCpMZsky5J6DuWiqJyMaVU4cxofO3UCmd9HZpmZUcHx1fjtqNa+utTiIiS8ZkhixTOZfpfTt4G1RKQF8L4cmtZPjg6FzUbcpEhoiKwZYZrZj3iB4iQ7HxR1m/9Yd2uCMzveTnmVRUyy5NsTXpMzzRsane6iSiqkXSw2aJ2DJDFkmybgZh7Q/kxaBgwbuitq/Rw+qWElCjtjs+Pj4fHnXMZ7VMIqKqhC0zZLEk1w8BeU2U9PfMvTsVyfVLaOURwJsrXmUiQ0TlI/SwWSC2zJDFkuTeQPXvgOwdENk7AeUtQGTg1nUbfLHUG/fvVuTXo2gyZOdoi3Efj0aXAXw8ARGVj6mmZld2TGbIokmyaoDjKEiOo6C6E4wzhwVmDW8AZT6gTe+zta0VgoY9jSc6NcXTL3aAvZO93mMmoiqMA4C1wmSGCIAQAul3kzD31SZaJTKSDAhbNBwho59BNTf9TeMmIqKyMZkhiyVUGQCUgOQCiCxEfeOMnGzt5gN06heAQVP76j1GIrJAFtq6ogsmM2RxxMMfIB58BuRfKNghrwPY9cWFU45a1ymXcyw9EemOY2a0w2SGLIp48AmyUj/Bj19Xxw9bGiMt1RrO1fPRMXgn8vNtta63kX8DPUZJREQVwWSGLIbIuwRFwipMGeCHxCt2BS25QkL6fTm2r/aES/V8reqVyWUYOPl5vcZKRBaKA4C1wmSGLIbI3ooVb/vg5lU7CPHIuBghQQC4f9cKklQwGLgi42ZeWzwMVtb8VSIi3bGbSTv8BCaLcefGRRz/wVkzkdEgFSQyEv59dtPj5TT3Obk5YsyS4XhudA/DBExEROXCZIYsxqWzNhDiYRmlJEAIBAYrcOWcg3oVYAcnFZ4ZZIWuQycj64EdPH2qw7dFXUiSpT4JhYgMgt1MWmEyQ1WeEAL/3ErDg8zmAM6WfYIE3L5pg81nLkKZD8itCnbKvGMNHCkRWTp2M2mHyQxVWUII7P00CtuX7UHytdTCvShzPIyQcPOaHfJyJcRftEXjVg/BXxUiIvPFxTGoShJCYOX4/+HjNz9Hcnzq40fLPN/OQYmRgU3xMEtesENeW/9BEhE9jg+a1Ar/3KQq6dwvf+P7NT8VvND45S57jItMLlCnYQ46PJuOJwMzC85yCNV/kEREj+OYGa0wmaFKT+THQ2RuAB7uA0QWIPPG3lWNIZfLoFSqKlSXTCYglwv0e/UOnu6bDkAGWD0BOLxomOCJiB7BMTPaYTJDlZrIPQ2R9iqAXAD/Ji6qJFyLcYRSWZEnVheMpanmlo+5G6+jmX8WABvAfiCkatMgSXZ6j52IiPSDyQxVWkLkQtwfDyAHQgicP+GIHza74+ZVWyRes0O5Bvs+YvyCRNg5qvDZvNoI6N0aQ2bOgCSrZqjwiYiKYjeTVgw2APj69esYPXo06tevD3t7ezRs2BCRkZHIzc0t9byHDx9i3LhxqF69OpycnDBw4ECkpmoO4ExISEDv3r3h4OAAT09PTJs2Dfn52i1FT5WXyPwCUKUh6wEwqY8fpg7ww6Fv3RAb4wihAir29GsJynwJnrXzcPnPaggc8DoTGSIyOkkInTdLZLCWmUuXLkGlUuHTTz+Fn58fzp8/j7CwMGRmZmLZsmUlnjd58mTs27cP27dvh4uLC8aPH48BAwbgt99+AwAolUr07t0b3t7eOH78OJKTkxEaGgpra2ssWLDAULdDZkY8PAKRsRSfvFMbe7+oDqhX9X38v+WXmyPh/l1rzPtuBuq3qKu3WImIyLAkIYyXxi1duhRr1qzBtWvXij2uUCjg4eGBLVu24MUXCwZcXrp0Cc2aNUN0dDQ6dOiAH374Ac8//zxu3boFLy8vAMDatWsxffp03LlzBzY2NmXGkZ6eDhcXFygUCjg7O+vvBskohOo+xO0umP+GF37Z44aKdieVZHVULHyefBV2Hm/pXBcRVR3G+M4ovEbrYfMht9F+jJ4y9yFivnrX4r7fjLrOjEKhgLu7e4nHz5w5g7y8PAQFBan3NW3aFHXr1kV0dDQAIDo6Gi1btlQnMgAQHByM9PR0XLhwodh6c3JykJ6errFRJZa9E1cvyP5NZAB9JDJePjlo+KQvbGtM1LkuIiJtFc5m0mWzREZLZuLi4rBy5Uq8/vrrJZZJSUmBjY0NXF1dNfZ7eXkhJSVFXebRRKbweOGx4ixcuBAuLi7qzcfHR4c7IVMTeeew7RNPfdUGK2sVZq+7DrgugSRxHUkiosqmwp/cM2bMgCRJpW6XLl3SOCcpKQkhISEYNGgQwsLC9BZ8eUVEREChUKi3xMREo8dA+mSFaxd0mSr9758ukkC77hlYc/Ay/Fo8hCTZ6iU6IiKtcQVgrVR4APCUKVMwcuTIUss0aNBA/f+3bt1C9+7d0bFjR3z22Welnuft7Y3c3Fzcv39fo3UmNTUV3t7e6jInT57UOK9wtlNhmcfZ2trC1pZfVFWFUtYOt5OKH3dVFplM4LOjlyCTAS7V8+Hk8u/aNDIPQM4WOyIyLS6ap50KJzMeHh7w8PAoV9mkpCR0794d/v7+2LBhA2Sy0huC/P39YW1tjUOHDmHgwIEAgNjYWCQkJCAwMBAAEBgYiPnz5+P27dvw9CzoaoiKioKzszOaN29e0duhSkJxNx3/3LoHJ4ffsWLsF8jJdqhgDQWDhN+cnwQfv6LLA0gOIyFJXHaJiKgyMtind1JSErp164Z69eph2bJluHPnjvpYYQtKUlISevTogS+++ALt27eHi4sLRo8ejfDwcLi7u8PZ2RkTJkxAYGAgOnToAADo2bMnmjdvjuHDh2PJkiVISUnBzJkzMW7cOLa+VEEJl5KwLmIzovecRuHEu7qNZZi+MgFKJbD3ixqI+8se+XmlJ8qOzkq8PucWggffgxCAJAEqlQwymQqw6w04vmqM2yEiKh0XzdOKwZKZqKgoxMXFIS4uDnXq1NE4VvillJeXh9jYWGRlZamPffjhh5DJZBg4cCBycnIQHByM1atXq4/L5XLs3bsXY8eORWBgIBwdHTFixAjMmzfPULdCJnL9QiImdnwHOVm5eHQFgcQrdlg8oS4ACTKZKGEyU0F5SQL6vXYHr81MhpU1IASgzAfu3LKGZN0ENZtPAGy7QZJ0nxFFRKQrdjNpx6jrzJgLrjNjnoR4CDzcD5F7GoDAtD7pOB99F6piHxZZ3Noyj+4T6PTcfYyZcwvePv+tDv3HMQd8MLke/rlth68T1sLd2w1ERKUx5joz/i/pvs7MmW8sb50ZDhIgsyDy/oK4Fwao0gDIceu6Dc792riUM4prSflvX4ee6Zi9LkH9+vN5NfF7lDNuXrWDJAH9Jz3HRIaIqIpgMkMmJ5R3INJGAiITKYnWOBHljGsXKvLE6yI1YtLigun3QgB7NlTHrv/VhEqlgkwuod+E5zBm6XC9xE5EpG+W2lWkCyYzZHrZ25CTlYUV02rj8C43SChIQrQnwaWGEpCqQWk7CtXqtsDQmXdQzd0JXQZ2QPWabJEhIjMlhG4fgJY3cgQAkxkyA+Lhj1gw1gcnDjoDQtJxML5AdW9AXmMjJBt/2Ei2CBqmp0CJiMgsMZkhk7t0Jhe//+Sil7okGdB3bFfIbDvqpT4iImPibCbtMJkhoxCq+0DeBQASYP0kJJmT+tiR3d6QW2VCmV/a9OjC31DpsX3/vZbJBXybCvR76zX9BU5EZExcZ0YrTGbIoIQqEyJjEZC9E0Dev3ttIRwGQ6o2FZJkiwcZvhCi+Cee/6e0adiArb0KwUOyMWrJCjhU02XwMBERVTZMZshghMiFuDcKyDsH4NG1YnKArC8h8uMAt//Bq0EzAH9XsPb/EpmZ63LwVK9esPcYAkmmn+4qIiJTkFQFmy7nW6IKPzWbqNyy9wB5MdBMZAqpgNzfgJxDCHn1Gai0/AWUW8lw8a8BcPB6g4kMEVV+fGq2VpjMkMGI7G0o60dM3J8CL8+DGDarv1bXUOarcOtaqlbnEhFR1cBuJtKbjHsPcHjLr0i6kgwnV0d06XEb9RqV1eSSA5E+B8PGt4a790hsmb8Hd5PSABS0ugASlPnKEs+WyWVwcnXU2z0QEZkSZzNph8kM6cX+/x3CJxP+h/xcJeRWMqhUAl/O88TTfW0wbUUCbOxK/w2T8s+h9yt/olfYasSfS0BOdi7qNK6JDTO34sD6Q1DmF58UqZQqdHu5kyFuiYjI+LhonlbYzUQ6+233SXw4Zi3ycvIhhEB+nlL9cMhf97rgw6l1yqgBAFRA9jbIpBz4tamPJzo2gUsNZwya2gfWttaQyYv+qMrkMjTr0Ajtglvp+Y6IiEyjsGVGl80SMZkhnQghsClyGySp+DViVCoJh3e649Z1m3JUlg0or2nsqu1XE0sPRaJ6rYJHEMit5OrEpk2Plpi/7x3IZPwxJiKyZOxmIp2kXL+N+L8SSi0jkwl8tdwLin+skJpoAzePfAQNSkP3fveL6X6SFzm/aftG+PLaKpz+8U9cPn0V1rbWCOjdFvVb1NXjnRARmQEumqcVJjOkk+yMh2WWEULCoR3ukMkFVEoJN68JnIt2xM7PPLBk+1W4VP93gK+sOmDlV2wdcrkcAb3aIqBXW32GT0RkVjgAWDtsnyedeNWrASvroq0pjyocj6ZSFnRFCZUEQELCFTssmfhf64rk8CokydpQoRIRURXFZIZ04ujiiO5DOkNmVfEfJZVSwukjzrh51QawexFwHG2ACImIKpHC2Uy6bBaIyQzpbPTCoahe002rhAYAzv35FiSX+ZAk/jgSkWXjbCbt8NuDdFa9phtWnVyE50a1g42dFhXI65U4G4qIiKgsHABMOlHcTcdvu08h424c2gbsRsjATEwIaVShOlp0bmqg6IiIKhnOZtIKkxnSikqlwvp3tuDbD/ciP18JmUxApawFR+f8ctcht5KhReem8H3Cx4CREhFVHpzNpB0mM6SVdTM245sP9qj/CiicqZSZIUfBzrK7jbzqeWDGV5MMFyQREVkEJjNUYWkp9/Dtir3FN2cKCf+1kxaf0Mit5QhbPAzPje4Bh2r2BoyUiKiSUYmCTZfzLRCTGaqwY9+egKrUX5jCJEZAkgoWzQMAmVyCnaMdlh6KRGP/hgaPk4io0uGYGa0wmaEKS/8nAzKZDEqVstRyL42/jVOHnHEr3hb2TgI9hj+P/hP7wKueh5EiJSKqXCToOGZGb5FULkxmqMK863tCmV96IiNJAgPC7mL0OykFr6vNgOT4qjHCIyIiC8N1ZqjCOg8IgL1TyQvKyOQC7Xs8gJuHCoAEOL4JOIwyXoBERJUVVwDWCpMZqjB7RzuMX1nw6IHH17qTyWWwd7RC2MI2kJzeguTxM2TV3uKieERE5cAVgLXDZIa00nNEN0R+OxW1G9f6b6cEtO3REh///gHq+S+E5PQGJLm36YIkIqJyWbVqFXx9fWFnZ4eAgACcPHmyxLKff/45unTpAjc3N7i5uSEoKKjU8sbAMTOktc79A9CpX3tcv5CIzPuZ8PL1hEed6qYOi4io8jLBbKZt27YhPDwca9euRUBAAFasWIHg4GDExsbC09OzSPmjR49iyJAh6NixI+zs7LB48WL07NkTFy5cQO3atXUIXnuSEJbXwZaeng4XFxcoFAo4OzubOhwiIjJjxvjOKLxGl26RsLLS5iF3BfLzH+LY0blITEzUiNXW1ha2trbFnhMQEICnnnoKn3zyCYCCFd59fHwwYcIEzJgxo8xrKpVKuLm54ZNPPkFoaKjWseuC3UxERERVjI+PD1xcXNTbwoULiy2Xm5uLM2fOICgoSL1PJpMhKCgI0dHR5bpWVlYW8vLy4O7urpfYtWGwZOb69esYPXo06tevD3t7ezRs2BCRkZHIzc0t8Zy0tDRMmDABTZo0gb29PerWrYuJEydCoVBolJMkqci2detWQ90KERGRcaj0sAFITEyEQqFQbxEREcVe7u7du1AqlfDy8tLY7+XlhZSUlHKFPH36dNSqVUsjITI2g42ZuXTpElQqFT799FP4+fnh/PnzCAsLQ2ZmJpYtW1bsObdu3cKtW7ewbNkyNG/eHDdu3MAbb7yBW7duYceOHRplN2zYgJCQEPVrV1dXQ90KERGRUUhCQNJh9Efhuc7OzkYZRrFo0SJs3boVR48ehZ2d9t1jujJYMhMSEqKRbDRo0ACxsbFYs2ZNiclMixYt8O2336pfN2zYEPPnz8ewYcOQn58PK6v/wnV1dYW3N2fKEBERaatGjRqQy+VITU3V2J+amlrmd+yyZcuwaNEiHDx4EE8++aQhwyyTUcfMKBSKCvepFQ64ejSRAYBx48ahRo0aaN++PdavX4/SxjHn5OQgPT1dYyMiIjI7Qg9bBdjY2MDf3x+HDh1S71OpVDh06BACAwNLPG/JkiV47733cODAAbRr165iFzUAo03NjouLw8qVK0tslSnO3bt38d5772HMmDEa++fNm4dnnnkGDg4O+Omnn/Dmm2/iwYMHmDhxYrH1LFy4EHPnztUpfiIiIoPTdRVfLc4NDw/HiBEj0K5dO7Rv3x4rVqxAZmYmRo0qWLk9NDQUtWvXVg8iXrx4MWbPno0tW7bA19dXPbbGyckJTk5O2seugwpPzZ4xYwYWL15capmLFy+iadOm6tdJSUno2rUrunXrhv/973/luk56ejqeffZZuLu7Y8+ePbC2ti6x7OzZs7FhwwYkJiYWezwnJwc5OTkadfv4+HBqNhERlcmYU7O7dpyl89Tsn4+/V+FYP/nkEyxduhQpKSlo3bo1Pv74YwQEBAAAunXrBl9fX2zcuBEA4Ovrixs3bhSpIzIyEnPmzNE6dl1UOJm5c+cO/vnnn1LLNGjQADY2NgAKBvV269YNHTp0wMaNGyGTld2zlZGRgeDgYDg4OGDv3r1lDirat28fnn/+eTx8+LDEefSP4jozRERUXpaQzFR2Fe5m8vDwgIeHR7nKJiUloXv37vD398eGDRvKlcikp6cjODgYtra22LNnT7lGR8fExMDNza1ciQwREZHZMkE3U1VgsDEzSUlJ6NatG+rVq4dly5bhzp076mOFI6STkpLQo0cPfPHFF2jfvj3S09PRs2dPZGVl4auvvtIYrOvh4QG5XI7vv/8eqamp6NChA+zs7BAVFYUFCxZg6tSphroVIiIio5BUBZsu51sigyUzUVFRiIuLQ1xcHOrUqaNxrLBnKy8vD7GxscjKygIAnD17FidOnAAA+Pn5aZwTHx8PX19fWFtbY9WqVZg8eTKEEPDz88Py5csRFhZmqFupEnJz8nBsx+84vucUHmbmoEHLuugVFoSaDbzKPpmIiMiM8dlMFtCnmByfireD5iEl/jZkMgkqlYBMLoMQAm9+OAr9Jjxn6hCJiMyWMcfMdGv/rs5jZo6enG8x32+F+GymKk6Zr8SM4PdxO/EuAEClKshdVUoVhEpg1aT1OPnDH6YMkYiIChl5nZmqgslMFff73jO4FZcCVX7xHakyuQzbluw2blBERER6ZLRF88g0Tu4/C7mVHMp8ZbHHVUoVzv38N3Kyc2Brz9lgRESmpK9nM1kaJjNVXF5ePsrT7pifp4StveHjISKiUnBqtlbYzVTFNfZvCJWylB9uCfCu7wmHasxkiIiocmIyU8U9O/xp2NjbQJKKPy4B6D+hF6SSChARkfEIACodNstsmGEyU9U5ujji3a/fgkwuh9zqv39uSZIACejwfDu8MD7EhBESEVGhwjEzumyWiMmMBQjs0w6rTi1C9yGdYetgC5lcBt8WPpi89nVEfjsVciu5qUMkIiLg3+nVQofN1DdgGhwAbCEatvLF9E0TMH3TBFOHQkREpFdMZoiIiMwFZzNphckMERGRuVChYGaGLudbII6ZISIiokqNLTNERERmgisAa4fJDBERkbngmBmtsJuJiIiIKjW2zBAREZkLtsxohckMERGRuWAyoxV2MxEREVGlxpYZIiIic8F1ZrTCZMbM5OflIyPtAeyc7GDvaGfqcIiIyIg4NVs7TGbMRPo/GdiyYCd+WHcIWenZkGQSAnq1xbBZL6LJU36mDo+IiIyBY2a0wmTGDCjupmNix3eREn8bKmVBG6FQCZz84Q+cOhCD9/ZMx1MhbUwcJRERkXniAGAzsO6dLRqJTCGVUgWVSoWFwz5Gbk6eiaIjIiKjUQndNwvEZMbEsjKycfDLn4skMoWESiAj7QGO7z5p5MiIiMjoCruZdNksEJMZE0u9fht5OfmllpFby3H9QqKRIiIiIqpcOGbGxOzKMWNJqATsHGyNEA0REZmWrq0rbJkhE/Cu74l6zetAkkpeWEClVKFT//ZGjIqIiEyC3UxaYTJjYpIkYXjkSxAl/ADK5DJ0HhAAnya1jRwZERFR5cBkxgx0HRSIN1eMgtxKBkkmQW4lh9yq4J+mXXBrvL1pvIkjJCIio+BsJq1wzIyZ6D+xF7q+FIioL37BrbhkOLo4oOtLHblgHhGRJRGqgk2X8y0Qkxkz4u7thpfffsHUYRAREVUqTGaIiIjMBR9noBUmM0REROZCJaDT9GoLHTNjsAHA169fx+jRo1G/fn3Y29ujYcOGiIyMRG5ubqnndevWDZIkaWxvvPGGRpmEhAT07t0bDg4O8PT0xLRp05CfX/rCc0RERGaPU7O1YrCWmUuXLkGlUuHTTz+Fn58fzp8/j7CwMGRmZmLZsmWlnhsWFoZ58+apXzs4OKj/X6lUonfv3vD29sbx48eRnJyM0NBQWFtbY8GCBYa6HSIiIjJTBktmQkJCEBISon7doEEDxMbGYs2aNWUmMw4ODvD29i722E8//YS///4bBw8ehJeXF1q3bo333nsP06dPx5w5c2BjY6PX+yAiIjIaAR3HzOgtkkrFqOvMKBQKuLu7l1lu8+bNqFGjBlq0aIGIiAhkZWWpj0VHR6Nly5bw8vJS7wsODkZ6ejouXLhQbH05OTlIT0/X2IiIiMwOu5m0YrQBwHFxcVi5cmWZrTKvvPIK6tWrh1q1auHcuXOYPn06YmNjsXPnTgBASkqKRiIDQP06JSWl2DoXLlyIuXPn6uEuiIiIyNxUuGVmxowZRQboPr5dunRJ45ykpCSEhIRg0KBBCAsLK7X+MWPGIDg4GC1btsTQoUPxxRdfYNeuXbh69WpFQ1WLiIiAQqFQb4mJfAI1ERGZIZVK980CVbhlZsqUKRg5cmSpZRo0aKD+/1u3bqF79+7o2LEjPvvsswoHGBAQAKCgZadhw4bw9vbGyZMnNcqkpqYCQInjbGxtbWFry6dOExGRmeM6M1qpcDLj4eEBDw+PcpVNSkpC9+7d4e/vjw0bNkAmq/gQnZiYGABAzZo1AQCBgYGYP38+bt++DU9PTwBAVFQUnJ2d0bx58wrXry2hSgNyogHkAdYtIFnxsQNERESmYLAxM0lJSejWrRvq1auHZcuW4c6dO+pjhS0oSUlJ6NGjB7744gu0b98eV69exZYtW9CrVy9Ur14d586dw+TJk/H000/jySefBAD07NkTzZs3x/Dhw7FkyRKkpKRg5syZGDdunFFaX4TIgUh/H8j+FsB/a9sI6/aQXBZBsqpj8BiIiKiKYsuMVgyWzERFRSEuLg5xcXGoU0fzC178+2bn5eUhNjZWPVvJxsYGBw8exIoVK5CZmQkfHx8MHDgQM2fOVJ8rl8uxd+9ejB07FoGBgXB0dMSIESM01qUxFCEExL0JQO4vAB7rl8w7A5E2GKj+HSR5dYPHQkREVRBXANaKJITlpXHp6elwcXGBQqGAs7Nzuc8TOb9D3AstpYQMcHwdsmqTdQ+SiIjMgrbfGdpcI8h9FKxk2q+Xlq/KxcG0DQaN1RwZdZ2Zyk483A1AXkoJFZC93UjREBFRVSOESufNEvFBkxWhvA1AWXoZ1T2jhEJERFWQELp1FVleZwsAJjMVI/dGQctMKQmNrIaxoiEioqpG6DhmxkKTGXYzVYBkPwClt8zIAPtBxgqHiIiIwGSmYqz9AdsQAFIxB+WAvCYkx+HGjoqIiKoKrgCsFSYzFSBJEiTXDwCHUQAeXdNGAmy6QHLfCknmZqrwiIiosuODJrXCMTMVJEnWkJxnQDiNB3JPoWAF4CcgyWubOjQiIiKLxGRGS5LMCbDrbuowiIioChEqFYSkfVcRp2YTERGRaXE2k1Y4ZoaIiIgqNbbMEBERmQuVACS2zFQUkxkiIiJzIQSKPMi4wudbHnYzERERUaXGlhkiIiIzIVQCQoduJsGWGSIiIjIpodJ908KqVavg6+sLOzs7BAQE4OTJk6WW3759O5o2bQo7Ozu0bNkS+/fv1+q6+sJkhoiIyEwIldB5q6ht27YhPDwckZGROHv2LFq1aoXg4GDcvn272PLHjx/HkCFDMHr0aPzxxx/o168f+vXrh/Pnz+t6+1qThAW2SaWnp8PFxQUKhQLOzs6mDoeIiMyYMb4zCq/RTeoPK8la63ryRR6Oil0VijUgIABPPfUUPvnkEwCASqWCj48PJkyYgBkzZhQp//LLLyMzMxN79+5V7+vQoQNat26NtWvXah27LixyzExh/paenm7iSIiIyNwVflcY42//fJGjdVcRAOQjD0DR7zdbW1vY2toWKZ+bm4szZ84gIiJCvU8mkyEoKAjR0dHFXiM6Ohrh4eEa+4KDg7F7926t49aVRSYzGRkZAAAfHx8TR0JERJVFRkYGXFxcDFK3jY0NvL298WuK7mNPnJyciny/RUZGYs6cOUXK3r17F0qlEl5eXhr7vby8cOnSpWLrT0lJKbZ8SkqKboHrwCKTmVq1aiExMRHVqlWDJEkGvVZ6ejp8fHyQmJhYKbu0GL/pVObYAcZvapU5fnOLXQiBjIwM1KpVy2DXsLOzQ3x8PHJzc3WuSwhR5LutuFaZqsQikxmZTIY6deoY9ZrOzs5m8UupLcZvOpU5doDxm1pljt+cYjdUi8yj7OzsYGdnZ/DrPKpGjRqQy+VITU3V2J+amgpvb+9iz/H29q5QeWPgbCYiIiILZWNjA39/fxw6dEi9T6VS4dChQwgMDCz2nMDAQI3yABAVFVVieWOwyJYZIiIiKhAeHo4RI0agXbt2aN++PVasWIHMzEyMGjUKABAaGoratWtj4cKFAIBJkyaha9eu+OCDD9C7d29s3boVp0+fxmeffWaye2AyY2C2traIjIystP2VjN90KnPsAOM3tcocf2WOvTJ6+eWXcefOHcyePRspKSlo3bo1Dhw4oB7km5CQAJnsv46cjh07YsuWLZg5cybeeecdNGrUCLt370aLFi1MdQuWuc4MERERVR0cM0NERESVGpMZIiIiqtSYzBAREVGlxmSGiIiIKjUmM0RERFSpMZnRwfXr1zF69GjUr18f9vb2aNiwISIjI8tcjrpbt26QJElje+ONNzTKJCQkoHfv3nBwcICnpyemTZuG/Px8k8eflpaGCRMmoEmTJrC3t0fdunUxceJEKBQKjXKP358kSdi6davJ4weAhw8fYty4cahevTqcnJwwcODAIqtZGuP9B4D58+ejY8eOcHBwgKura7nOKe69lSQJS5cuVZfx9fUtcnzRokUmj33kyJFF4goJCdEok5aWhqFDh8LZ2Rmurq4YPXo0Hjx4oNfYtYk/Ly8P06dPR8uWLeHo6IhatWohNDQUt27d0ihnjPdem/iBgmXuZ8+ejZo1a8Le3h5BQUG4cuWKRhljvf8Vvc7169dL/Nnfvn27upwxPnvI/HCdGR1cunQJKpUKn376Kfz8/HD+/HmEhYUhMzMTy5YtK/XcsLAwzJs3T/3awcFB/f9KpRK9e/eGt7c3jh8/juTkZISGhsLa2hoLFiwwafy3bt3CrVu3sGzZMjRv3hw3btzAG2+8gVu3bmHHjh0aZTds2KDxRVXeD1xDxg8AkydPxr59+7B9+3a4uLhg/PjxGDBgAH777TcAxnv/gYIn1g4aNAiBgYFYt25duc5JTk7WeP3DDz9g9OjRGDhwoMb+efPmISwsTP26WrVqugf8CG1iB4CQkBBs2LBB/frxtUSGDh2K5ORkREVFIS8vD6NGjcKYMWOwZcsWvcUOVDz+rKwsnD17FrNmzUKrVq1w7949TJo0CX379sXp06c1yhr6vdcmfgBYsmQJPv74Y2zatAn169fHrFmzEBwcjL///lu9jL6x3v+KXsfHx6fIz/5nn32GpUuX4rnnntPYb+jPHjJDgvRqyZIlon79+qWW6dq1q5g0aVKJx/fv3y9kMplISUlR71uzZo1wdnYWOTk5+gq1WOWJ/3HffPONsLGxEXl5eep9AMSuXbv0HF3Zyor//v37wtraWmzfvl297+LFiwKAiI6OFkKY5v3fsGGDcHFx0ercF154QTzzzDMa++rVqyc+/PBD3QMrh4rEPmLECPHCCy+UePzvv/8WAMSpU6fU+3744QchSZJISkrSMdLi6fLenzx5UgAQN27cUO8z5nsvRPnjV6lUwtvbWyxdulS97/79+8LW1lZ8/fXXQgjjvf/6uk7r1q3Fq6++qrHPVJ89ZFrsZtIzhUIBd3f3Mstt3rwZNWrUQIsWLRAREYGsrCz1sejoaLRs2VLjEevBwcFIT0/HhQsXDBJ3ofLG//g5zs7OsLLSbOgbN24catSogfbt22P9+vUQRlifsaz4z5w5g7y8PAQFBan3NW3aFHXr1kV0dDQA077/FZWamop9+/Zh9OjRRY4tWrQI1atXR5s2bbB06VKDdJNp4+jRo/D09ESTJk0wduxY/PPPP+pj0dHRcHV1Rbt27dT7goKCIJPJcOLECVOEWyqFQgFJkor85W+O7318fDxSUlI0fvZdXFwQEBCg8bNvjPdfH9c5c+YMYmJiiv3ZN8VnD5kWu5n0KC4uDitXriyzi+mVV15BvXr1UKtWLZw7dw7Tp09HbGwsdu7cCQBISUnR+CIFoH6dkpJimOBR/vgfdffuXbz33nsYM2aMxv558+bhmWeegYODA3766Se8+eabePDgASZOnKjvsNXKE39KSgpsbGyKfPl4eXmp31tTvf/a2LRpE6pVq4YBAwZo7J84cSLatm0Ld3d3HD9+HBEREUhOTsby5ctNFGmBkJAQDBgwAPXr18fVq1fxzjvv4LnnnkN0dDTkcjlSUlLg6empcY6VlRXc3d3N7r1/+PAhpk+fjiFDhmg82dlc3/vC96+4n+1Hf/aN8f7r4zrr1q1Ds2bN0LFjR439pvjsITNg6qYhczR9+nQBoNTt4sWLGufcvHlTNGzYUIwePbrC1zt06JAAIOLi4oQQQoSFhYmePXtqlMnMzBQAxP79+80mfoVCIdq3by9CQkJEbm5uqWVnzZol6tSpU656DRn/5s2bhY2NTZH9Tz31lHj77beFEKZ5/7Xt6mjSpIkYP358meXWrVsnrKysxMOHD80mdiGEuHr1qgAgDh48KIQQYv78+aJx48ZFynl4eIjVq1eXWZ+x4s/NzRV9+vQRbdq0EQqFotSy5X3vDR3/b7/9JgCIW7duaewfNGiQeOmll4QQxnv/db1OVlaWcHFxEcuWLSuzbEU+e6jyYstMMaZMmYKRI0eWWqZBgwbq/7916xa6d++Ojh07avXU0ICAAAAFLQsNGzaEt7c3Tp48qVGmcLaNt7d3mfUZI/6MjAyEhISgWrVq2LVrF6ytrUstHxAQgPfeew85OTllPjzOkPF7e3sjNzcX9+/f12idSU1NVb+3xn7/tXXs2DHExsZi27ZtZZYNCAhAfn4+rl+/jiZNmpRYzlixP1pXjRo1EBcXhx49esDb2xu3b9/WKJOfn4+0tDSzee/z8vLw0ksv4caNGzh8+LBGq0xxyvveA4aNv/D9S01NRc2aNdX7U1NT0bp1a3UZY7z/ul5nx44dyMrKQmhoaJllK/LZQ5WYqbOpyu7mzZuiUaNGYvDgwSI/P1+rOn799VcBQPz5559CiP8GoKampqrLfPrpp8LZ2blcf91VhDbxKxQK0aFDB9G1a1eRmZlZrnPef/994ebmpkuoxapo/IUDgHfs2KHed+nSpWIHABvj/S+kTevAiBEjhL+/f7nKfvXVV0Imk4m0tDQtoiudLi0ziYmJQpIk8d133wkh/hsYevr0aXWZH3/80WwGAOfm5op+/fqJJ554Qty+fbtc5xjyvRei4gOAH23NUCgUxQ4ANvT7r+t1unbtKgYOHFiuaxnqs4fMC5MZHdy8eVP4+fmJHj16iJs3b4rk5GT19miZJk2aiBMnTgghhIiLixPz5s0Tp0+fFvHx8eK7774TDRo0EE8//bT6nPz8fNGiRQvRs2dPERMTIw4cOCA8PDxERESEyeNXKBQiICBAtGzZUsTFxWmcU5hM7NmzR3z++efir7/+EleuXBGrV68WDg4OYvbs2SaPXwgh3njjDVG3bl1x+PBhcfr0aREYGCgCAwPVx431/gshxI0bN8Qff/wh5s6dK5ycnMQff/wh/vjjD5GRkaEu06RJE7Fz506N8xQKhXBwcBBr1qwpUufx48fFhx9+KGJiYsTVq1fFV199JTw8PERoaKhJY8/IyBBTp04V0dHRIj4+Xhw8eFC0bdtWNGrUSCNJDAkJEW3atBEnTpwQv/76q2jUqJEYMmSIXmPXJv7c3FzRt29fUadOHRETE6Px81Y4y81Y77028QshxKJFi4Srq6v47rvvxLlz58QLL7wg6tevL7Kzs9VljPX+l3Wd4n53hRDiypUrQpIk8cMPPxSp01ifPWR+mMzoYMOGDSX2CxeKj48XAMSRI0eEEEIkJCSIp59+Wri7uwtbW1vh5+cnpk2bVqTf/fr16+K5554T9vb2okaNGmLKlCkaU59NFf+RI0dKPCc+Pl4IUTDFsnXr1sLJyUk4OjqKVq1aibVr1wqlUmny+IUQIjs7W7z55pvCzc1NODg4iP79+2skQEIY5/0XoqB1pbj4H40XgNiwYYPGeZ9++qmwt7cX9+/fL1LnmTNnREBAgHBxcRF2dnaiWbNmYsGCBXpvVapo7FlZWaJnz57Cw8NDWFtbi3r16omwsDCNKfBCCPHPP/+IIUOGCCcnJ+Hs7CxGjRql8QVtqvgLf5ZKO8dY77028QtR0Doza9Ys4eXlJWxtbUWPHj1EbGysRr3Gev/Luk5xv7tCCBERESF8fHyK/Twx1mcPmR9JCM5ZIyIiosqL68wQERFRpcZkhoiIiCo1JjNERERUqTGZISIiokqNyQwRERFVakxmiIiIqFJjMkNERESVGpMZIiIiqtSYzBAREVGlxmSGiIiIKjUmM0RERFSp/R86H8bJaHxluQAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Inferences from scatter plot:**\n", + "\n", + "1.The model has categorized the data into at least two clusters, as indicated \n", + " by the color differences. However, the separation is not very distinct, meaning the clusters might overlap in feature space.\n", + "\n", + "2.The data points appear to be aligned along a diagonal trend, which suggests\n", + " that the dataset might have a strong correlation between the two plotted dimensions.\n", + "\n", + "3.The majority of points belong to one dominant cluster (purple), while the\n", + " second cluster (yellow) contains fewer points. This might indicate an imbalance in the dataset or that the model is biased toward grouping most points into one cluster." + ], + "metadata": { + "id": "I9GhXLYBhq27" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEResNetClusterer:**" + ], + "metadata": { + "id": "rR3RUBPpihGC" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEResNetClusterer (Auto-Encoder Residual Network)**\n", + "The **AEResNetClusterer** applies an Auto-Encoder architecture integrated with a **Residual Network** (ResNet) backbone.ResNet models use skip connections, allowing gradients to flow directly through layers, reducing vanishing gradient issues.This approach enhances learning in deep networks and efficiently captures complex temporal patterns in time series data.\n" + ], + "metadata": { + "id": "wuFuNtkNP5tN" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "prOOOUzzioBS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEResNetClusterer" + ], + "metadata": { + "id": "6atNZu4ADFxb" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEResNetClusterer(n_epochs=10, random_state=42,batch_size=3)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ], + "metadata": { + "id": "ipdqBhoAP4-9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9059875c-26ae-416e-e0ba-d31ca587b9e9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 468ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 147ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='plasma')\n", + "plt.title('Cluster Distribution with AEResNet')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "bHI_0_064GFu", + "outputId": "f1d5dd1d-3c4d-42cd-a75c-d3c00da85bca" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Inferences of the scatter plot:**\n", + "\n", + "1.Cluster Formation: The model has classified the data into two clusters, but\n", + " similar to the AEFCN plot, the separation between them is not distinct. Most points belong to a single dominant cluster (dark purple), while a small number of points are assigned to the other cluster (yellow).\n", + "\n", + "2.Linear Data Distribution: The points in the plot follow a diagonal pattern,\n", + " indicating that the dataset might have a strong correlation between the features being visualized. This suggests that the data structure might be inherently linear.\n", + "\n", + "3.Imbalance in Clusters: The clustering results show a highly imbalanced\n", + " distribution, where one cluster contains most of the points. This could imply that either the model is struggling to identify meaningful cluster boundaries or that the dataset itself has an inherent class imbalance\n" + ], + "metadata": { + "id": "E0FZWgqhPe8v" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEDCNNClusterer:**" + ], + "metadata": { + "id": "6xWiuNT7izNY" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEDCNNClusterer (Auto-Encoder Dilated Convolutional Network)**\n", + "The **AEDCNNClusterer** is built on an **Auto-Encoder** with a **Dilated Convolutional Network (DCNN)** backbone.Dilated convolutions use dilated filters to expand the receptive field exponentially, allowing the model to capture long-term dependencies in the data without losing resolution.This method is ideal for detecting patterns over extended time windows.\n" + ], + "metadata": { + "id": "H1CANB-oD-t_" + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEDCNNClusterer" + ], + "metadata": { + "id": "gM5ja7I14GJK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEDCNNClusterer(n_epochs=10, random_state=42,dilation_rate=1)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qv-xuWhvE1_6", + "outputId": "d3c6eb67-8f10-4446-d46b-739f8c72219a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='coolwarm')\n", + "plt.title('Cluster Distribution with AEDCNN')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "AQIaYKy6E5RK", + "outputId": "6de1c9b3-1d85-425c-9eca-e2edbbf36ae9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#**Inferences from the scatter plot:**\n", + "1. Compared to AEFCN and AEResNet, AEDCNN appears to have a slightly better spread of cluster labels across the data points.\n", + "2. Most data points fall into one main cluster (red), with very few points classified into another cluster (blue).\n", + "3. The points follow a strong diagonal trend, indicating that the underlying feature space has a continuous, linear structure rather than distinct groups.\n" + ], + "metadata": { + "id": "_sqUBae7ePwg" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEDRNNClusterer:**" + ], + "metadata": { + "id": "ep5dTHIEi4xD" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEDRNNClusterer (Auto-Encoder Dilated Recurrent Neural Network)**\n", + "The **AEDRNNClusterer** integrates an Auto-Encoder with a **Dilated Recurrent Neural Network (DRNN)** backbone.DRNNs combine the strengths of RNNs (sequence modeling) with dilated connections to capture patterns over long temporal sequences efficiently.they are Suitable for tasks where sequential relationships are vital (e.g., speech data, financial trends).\n", + "\n" + ], + "metadata": { + "id": "ickHTlKQFDDZ" + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEDRNNClusterer" + ], + "metadata": { + "id": "O2Xj2LilFBjX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEDRNNClusterer(n_epochs=10, random_state=42)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "k4z2dOrzFjx2", + "outputId": "392d57ef-54ed-4f86-e236-3245145cd0a2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\r\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 2s/step" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x785f3b7428e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 1s/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 231ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='magma')\n", + "plt.title('Cluster Distribution with AEDRNN')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "mE4l1C9AFm-U", + "outputId": "a04a18cf-e3bb-4cf0-88da-cbe8db8cb1fc" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Inferences from the scatter plot:**\n", + "1.The scatter plot shows two clear clusters, one in black (cluster 0) and another in light yellow (cluster 1).Unlike completely mixed distributions, this separation indicates that AEDRNN has recognized some underlying structure in the data.\n", + "\n", + "2.Similar to other models, the data points are aligned along a diagonal trend, indicating that the latent space follows a strong linear pattern.This suggests that AEDRNN’s clustering is more of a gradual transition rather than a hard separation.\n", + "\n" + ], + "metadata": { + "id": "UES_F4Iedmbg" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEAttentionBiGRUClusterer:**" + ], + "metadata": { + "id": "n2poXz2KjAvo" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEAttentionBiGRUClusterer (Auto-Encoder Attention Bidirectional GRU Network)**\n", + "The **AEAttentionBiGRUClusterer** integrates an Auto-Encoder with an Attention-based **Bidirectional Gated Recurrent Unit (BiGRU)** network.\n", + "The Attention Mechanism allows the model to selectively focus on the most relevant parts of the time series during clustering.The Bidirectional GRU enhances this by processing the sequence from both forward and backward directions, improving sequence dependency recognition.It is Suitable for tasks requiring fine-grained sequence interpretation.Excels in datasets where certain segments of the sequence are more influential than others.\n", + "\n", + "\n" + ], + "metadata": { + "id": "cRH_XEfeF6Cs" + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEAttentionBiGRUClusterer" + ], + "metadata": { + "id": "iuy-H4F5F4dk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEAttentionBiGRUClusterer(n_epochs=10, random_state=42)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9Zx6TvNFHBwg", + "outputId": "290fa9d2-0fcf-469d-f6c3-f69cbdebe9e3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 901ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 631ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='inferno')\n", + "plt.title('Cluster Distribution with AEAttentionBiGRU')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "-AvTzfdlHGeF", + "outputId": "3461f910-6547-40c9-a402-275ee128a92b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Inferences from the plot:**\n", + "1.The plot shows two distinct clusters: one in black (cluster 0) and another in\n", + " light yellow (cluster 1).The dominance of black-colored points indicates that most of the data falls into a single cluster, with only a few points classified differently.\n", + "\n", + "2.The few yellow points suggest that AEAttentionBiGRU is detecting some\n", + " anomalies or outliers.\n", + " \n", + "3.These yellow points are often found at the edges or slightly away from the densest region, meaning that the model might be learning small variations and\n", + " assigning them to a different cluster.\n" + ], + "metadata": { + "id": "UyQ5LH82h5A-" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEBiGRUClusterer:**" + ], + "metadata": { + "id": "gLoHAkV-jGsX" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEBiGRUClusterer (Auto-Encoder Bidirectional GRU Network)**\n", + "The **AEBiGRUClusterer** is an Auto-Encoder with a **Bidirectional GRU (BiGRU)** architecture.GRUs are similar to LSTMs but with a simpler structure, making them faster and more efficient for time series data.The bidirectional structure enhances the model’s ability to detect patterns by combining forward and backward sequence insights.It Performs well on shorter sequences with frequent fluctuations.Suitable for tasks requiring fast training without compromising performance.\n", + "\n" + ], + "metadata": { + "id": "FTnLddtrHMQE" + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEBiGRUClusterer" + ], + "metadata": { + "id": "duoJ4wMQHnFt" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEBiGRUClusterer(n_epochs=10, random_state=42)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XfGf-pgxHnRy", + "outputId": "84b30734-4b03-4b01-e099-4a761edcd0ab" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 918ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 127ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='cividis')\n", + "plt.title('Cluster Distribution with AEBiGRU')\n", + "plt.colorbar(label='Cluster')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "RlH69d_rHnVX", + "outputId": "ebbb3fb9-13b4-4ccc-e5fe-214cbba0e0ef" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#**Inferences from the scatter plot:**\n", + "\n", + "1.The plot shows two distinct clusters (dark blue and yellow), indicating that AEBiGRU is effectively distinguishing between two groups.\n", + "\n", + "2.Unlike previous models where yellow points were more scattered, AEBiGRU has a larger, more structured region assigned to cluster 1.\n", + "\n", + "3.Since the clusters are positioned along a linear trend, this might imply that the underlying feature representation is influenced by a dominant latent factor." + ], + "metadata": { + "id": "NYnhRFqk2r_v" + } + }, + { + "cell_type": "markdown", + "source": [ + "**References:**\n", + "\n", + "[1]Zhao et. al, Convolutional neural networks for time series classification,\n", + "Journal of Systems Engineering and Electronics, 28(1):2017.\n", + "\n", + "[2]Wang et. al, Time series classification from scratch with deep neural networks: A strong baseline, International joint conference on neural networks (IJCNN), 2017.\n", + "\n" + ], + "metadata": { + "id": "sq0CNNhuI3_O" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "-yv9NB8JHyUE" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 7534e4a19d60636d27d88d4583ce83b9ba4aac75 Mon Sep 17 00:00:00 2001 From: Edgeshot27 <120127383+Edgeshot27@users.noreply.github.com> Date: Tue, 25 Mar 2025 15:21:41 +0000 Subject: [PATCH 07/13] Automatic `pre-commit` fixes --- .../deep_learning_based_clustering.ipynb | 1634 ++++++++--------- 1 file changed, 817 insertions(+), 817 deletions(-) diff --git a/examples/clustering/deep_learning_based_clustering.ipynb b/examples/clustering/deep_learning_based_clustering.ipynb index 51e142f1f6..e599e078b8 100644 --- a/examples/clustering/deep_learning_based_clustering.ipynb +++ b/examples/clustering/deep_learning_based_clustering.ipynb @@ -1,822 +1,822 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "dne_kUDfuiYS", + "outputId": "775fa5b6-e64e-4696-8eda-8d1f45bb0859" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting aeon[deep_learning]\n", + " Downloading aeon-1.0.0-py3-none-any.whl.metadata (20 kB)\n", + "\u001b[33mWARNING: aeon 1.0.0 does not provide the extra 'deep-learning'\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: deprecated>=1.2.13 in /usr/local/lib/python3.11/dist-packages (from aeon[deep_learning]) 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scikit-learn-1.5.2\n" - ] - } - ], - "source": [ - "!pip install aeon[deep_learning]" - ] - }, - { - "cell_type": "markdown", - "source": [ - "# **Deep Learning Based Clustering**\n", - "The aeon.clustering.deeplearning module provides powerful deep learning models designed specifically for time series clustering. These models leverage advanced architectures like **Auto-Encoders, Residual Networks, Dilated Convolutions, and Attention Mechanisms** to effectively identify patterns, trends, and groupings in complex temporal data. Each model is tailored to handle different data characteristics, improving clustering accuracy and performance across various domains such as healthcare, finance, and signal processing." - ], - "metadata": { - "id": "2Ru1riLYWFE3" - } - }, - { - "cell_type": "code", - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ], - "metadata": { - "id": "86gsiHDbuoz-" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from aeon.datasets import load_arrow_head" - ], - "metadata": { - "id": "7xvwY48cur5i" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "X_train, y_train = load_arrow_head(split=\"train\")\n", - "X_test, y_test = load_arrow_head(split=\"test\")" - ], - "metadata": { - "id": "EkRKdT47N7Oc" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "print(X_train[:5])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5upX35-AXSnH", - "outputId": "3bacd61e-0b2f-41bd-d07e-c7b9c3a886fb" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[[[-1.9630089 -1.9578249 -1.9561449 ... -1.9053929 -1.9239049 -1.9091529]]\n", - "\n", - " [[-1.7745713 -1.7740359 -1.7765863 ... -1.7292269 -1.7756704 -1.7893245]]\n", - "\n", - " [[-1.8660211 -1.8419912 -1.8350253 ... -1.8625124 -1.8633682 -1.8464925]]\n", - "\n", - " [[-2.0737575 -2.0733013 -2.0446071 ... -2.0269634 -2.073405 -2.0752917]]\n", - "\n", - " [[-1.7462554 -1.7412629 -1.7227405 ... -1.7434421 -1.7627288 -1.7634281]]]\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "print(y_train[:5])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3LzGs-PYeMJ1", - "outputId": "581523e0-782c-4b72-aa3a-0276bd89c39d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "['0' '1' '2' '0' '1']\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# **AEFCNClusterer (Auto-Encoder Fully Convolutional Network)**\n", - "The **AEFCNClusterer** is a deep learning model that leverages a **Fully Convolutional Network (FCN)** architecture with an **Auto-Encoder** structure for clustering. It combines feature extraction with convolutional layers and reconstruction capabilities via auto-encoders. \n", - "FCNs are effective for extracting spatial hierarchies in time series data without requiring fully connected layers, making them highly efficient.\n" - ], - "metadata": { - "id": "b-XHpTDjxeSd" - } - }, - { - "cell_type": "code", - "source": [ - "from aeon.clustering.deep_learning import AEFCNClusterer" - ], - "metadata": { - "id": "DGbXlOPLxdO-" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "model = AEFCNClusterer(n_epochs=10,batch_size=3)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kxYYwMmKN_Uz", - "outputId": "0f2ccdef-1a79-41ad-94f2-a5708d41443f" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 81ms/step\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='viridis')\n", - "plt.title('Cluster Distribution with AEFCN')\n", - "plt.colorbar(label='Cluster')\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "q3oOIG3jOtbf", - "outputId": "258be6d8-6dc7-4c47-b8f3-f5b47412967b" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "**Inferences from scatter plot:**\n", - "\n", - "1.The model has categorized the data into at least two clusters, as indicated \n", - " by the color differences. However, the separation is not very distinct, meaning the clusters might overlap in feature space.\n", - "\n", - "2.The data points appear to be aligned along a diagonal trend, which suggests\n", - " that the dataset might have a strong correlation between the two plotted dimensions.\n", - "\n", - "3.The majority of points belong to one dominant cluster (purple), while the\n", - " second cluster (yellow) contains fewer points. This might indicate an imbalance in the dataset or that the model is biased toward grouping most points into one cluster." - ], - "metadata": { - "id": "I9GhXLYBhq27" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **AEResNetClusterer:**" - ], - "metadata": { - "id": "rR3RUBPpihGC" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **AEResNetClusterer (Auto-Encoder Residual Network)**\n", - "The **AEResNetClusterer** applies an Auto-Encoder architecture integrated with a **Residual Network** (ResNet) backbone.ResNet models use skip connections, allowing gradients to flow directly through layers, reducing vanishing gradient issues.This approach enhances learning in deep networks and efficiently captures complex temporal patterns in time series data.\n" - ], - "metadata": { - "id": "wuFuNtkNP5tN" - } - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "prOOOUzzioBS" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from aeon.clustering.deep_learning import AEResNetClusterer" - ], - "metadata": { - "id": "6atNZu4ADFxb" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "model = AEResNetClusterer(n_epochs=10, random_state=42,batch_size=3)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)" - ], - "metadata": { - "id": "ipdqBhoAP4-9", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "9059875c-26ae-416e-e0ba-d31ca587b9e9" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 468ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 147ms/step\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='plasma')\n", - "plt.title('Cluster Distribution with AEResNet')\n", - "plt.colorbar(label='Cluster')\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "bHI_0_064GFu", - "outputId": "f1d5dd1d-3c4d-42cd-a75c-d3c00da85bca" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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JTIMGDUqdpmzM1/1hGzduhI+PD5YvX17o2Lfffovt27dj1apVRklSq1WrhvHjx2PWrFn47bff0K5dO+0gbR8fnzK97vXq1cPEiRMxceJEXLx4ES1btsT777+PL7/8ssjyBa0zM2fOxJAhQ4qs7/79+6Ve21SvP5E+OGamknrrrbfg7OyMV155BSkpKYWOX7p0CR9++GGx5xd8oD7cP14wzfdhd+/eLfSXbsuWLQEA2dnZAAAnJycAKPQF7OPjg9DQUHzyySe4efNmoRhu375dbHyGuHr1KoYOHQqFQoFJkyYVWy4tLQ15eXk6+5o3bw6ZTKZ9bgDg7OxcZHKhj5iYGJ2ps9euXcOOHTvQpUsXbUtDvXr1oFKpdLp0bt68ie3btxeqr6yxhYWFQaFQ4KOPPtL5ea5ZswYqlQrdunUz4FnpCg4Ohr29Pd577z14eXmhadOmAPKTnN9++w3/+9//ytQq4+zsrJ0ibywPHjzAt99+i+7du+O5554rtI0ePRrp6elGXatlzJgxcHJywoIFCwDkt9a4ublh3rx5RY5DK3hfZGZmFlo5uF69enB1ddX5/SzKG2+8AQ8PD8yePbvQseeffx4xMTH48ccfCx27d++e9j1R3PuayBLYMlNJ1atXD5s2bcILL7yAxo0b66wAfPToUWzdurXEezF16dIFtWrVwvDhwzFp0iTI5XKsXbsW3t7eOrcC2LBhA1asWIE+ffqgXr16SE9Px6effgo3Nzd07doVQH43S5MmTbBlyxY0aNAAXl5eaNasGZo1a4bly5fjiSeeQPPmzREVFYW6desiJSUFMTExuH79On7//XeDXofTp0/jyy+/hEajwb1793DixAl88803kCQJX3zxRYlTSQ8ePIjRo0ejf//+aNCgAfLy8vDFF19ALpejX79+2nJBQUHYv38/lixZgurVq6NOnTolLn9fkmbNmiE8PFxnajYAnQGbAwYMwOTJk9GnTx+MHTtWO223QYMGhdYQKWts3t7eiI6OxqxZsxAREYGePXsiLi4OK1aswOOPP16mFqyycnJyQlBQEH777TftGjNAfstMRkYGMjIyypTMBAUFYcuWLZgwYQIef/xxuLi4FLlkf3ns3LkT6enp2inij2rXrh28vb2xceNGvPDCC9r9Fy5cKLIlxNfXF88880yJ16xSpQqGDRuGFStW4Ny5c2jcuDFWrlyJwYMHo3Xr1hgwYID2fbd792506NABH3/8MS5cuIDOnTvj+eefR5MmTWBnZ4ft27cjJSVFZ2Xrori7u2PcuHFFDgSeNGkSdu7cie7du2Po0KEICgpCRkYG/vzzT2zbtg1XrlzRrilV3PuayOwsOZWKTO/ChQsiKipKBAQECIVCIVxdXUWHDh3EsmXLdKZDPzo1WwghTp06JYKDg4VCoRC1atUSS5YsKTSl9/Tp02LgwIGiVq1aQqlUCh8fH9G9e3ed6cVCCHH06FERFBQkFApFoemcly5dEpGRkcLPz0/Y29uLGjVqiO7du4tt27ZpyxRct6Qp4A8rmHJasNnZ2QkvLy8RHBwsoqOjxdWrVwud8+j038uXL4uXX35Z1KtXTzg4OAgvLy/RqVMnsX//fp3zzp8/L5566inh6OgoAGhfx4Kp0rdv3y50reKmZo8aNUp8+eWXon79+kKpVIpWrVrpTEcu8NNPP4lmzZoJhUIhGjZsKL788ssi6ywutkd/jgU+/vhj0ahRI2Fvby98fX3FiBEjxN27d3XKPDzV92HFTRkvyqRJkwQA8d577+nsDwwMFADEpUuXdPYXNTX7/v374sUXXxQeHh4CgPbaBWUfnVJf8Duxbt26YuPq0aOHcHBwEBkZGcWWGTp0qLC3txd37twRQpQ8Nbtjx47a8wqmZhfl0qVLQi6X67wHDx06JMLDw4W7u7twcHAQ9erVE0OHDtW+t+7cuSNGjRolGjVqJJydnYW7u7sIDg4WX3/9tU7dxf287t69K9zd3Yuc3p2eni6io6NFYGCgUCgUomrVqqJ9+/Zi8eLFIicnR1uupPc1kTlJQpRhxB4RERGRleKYGSIiIqrQmMwQERFRhcZkhoiIiCo0JjNEREQ26ueff0aPHj1QvXp1SJKE7777rtRzDh8+jNatW2vvqL5+/XqTx1kaJjNEREQ2KiMjAy1atChykciiJCQkoFu3bujUqRNiY2Pxxhtv4JVXXilyXSJz4mwmIiIigiRJ2L59O3r37l1smcmTJ2P37t06t8sZMGAA7t27h71795ohyqLZ5KJ5Go0GN27cgKurK5fkJiKiEgkhkJ6ejurVq+vcVd7YsrKykJOTY3A9QohC321KpbLUm/+WRUxMTKFbXYSHh+ONN94wuG5D2GQyc+PGDfj7+1s6DCIiqkCuXbtW5hvMlldWVhYC6rggJdnwG8a6uLjg/v37OvtmzJiBmTNnGlx3cnJyofum+fr6Ii0tDQ8ePDD6jXXLyiaTGVdXVwD5v5gl3TGZiIgoLS0N/v7+2u8OU8jJyUFKshp/XQyAq5v+rT/paRo0rX+l0PebMVplrJlNJjMFzW9ubm5MZoiIqEzMMSzB1U0GNwOSmQKm+n7z8/MrdPPilJQUuLm5WaxVBrDRZIaIiMgaSRpA0uifNEkaIwZThJCQEOzZs0dn3759+xASEmLaC5eCU7OJiIishZAM38rh/v37iI2NRWxsLID8qdexsbFITEwEAERHRyMyMlJb/vXXX8fly5fx1ltv4fz581ixYgW+/vprjB8/3mgvgT7YMkNERGQlJI1kYMtM+c49efIkOnXqpH08YcIEAMCQIUOwfv163Lx5U5vYAECdOnWwe/dujB8/Hh9++CFq1qyJzz77DOHh4XrHbAw2uc5MWloa3N3doVKpOGaGiIhKZI7vjIJrXL8eCDc3uQH1qFGzZrzNfb+xZYaIiMhK5I+ZMex8W8RkhoiIyFpo/t0MOd8GcQAwERERVWhsmSEiIrISksjfDDnfFjGZISKiSkctLkKtOQvAHnayEMikKpYOqUwkYeCYGSYzREREFZtGXENm3ptQixP/7VTbwV72PBzl0yBJlXtZf1vFZIaIiCoFjbiN+7n9IfDPI0fykKvZDCFS4GS32iy3JdCbRuRvhpxvgzgAmIiIKoUc9fp/E5mi7jytQZ44oNtiY4UKxswYstkiJjNERFQp5Gi+RtGJTAE5cjTfmCscMiN2MxERUaUgkFpKCTWEuG2WWPTGdWb0wmSGiIgqBQlVIVBSsiKHJPmZLR59SBoByYBxL4acW5Gxm4mIiCoFhWwASv5aU0Mh62eucPSjMcJmg5jMEBFRpaCQD4WEagCKulGjBDupG+RSa3OHRWbAZIaIiCoFmeQJF/ttsJOeBPDw9GsHKGSvwMluiXVPywZnM+mLY2aIiKjSkEm+cLZfC424DrX4C4A97KTHIUmulg6tbDgAWC9MZoiIqNKRSTUhk2paOgwyEyYzREREVkLSGHhvJrbMEBERkUUJAMKAgS82OmaGA4CJiIioQmPLDBERkZWQhIHdTDbaMsNkhoiIyFpwNpNe2M1EREREFRpbZoiIiKyEoQvfsZuJiIiILIvdTHphMkNERGQtmMzohWNmiIiIqEIzaTKTmpqKQYMGwc3NDR4eHhg+fDju379fbPkrV65AkqQit61bt2rLFXV88+bNpnwqREREJpc/ZkYyYLP0M7AMk3YzDRo0CDdv3sS+ffuQm5uLYcOG4dVXX8WmTZuKLO/v74+bN2/q7Fu9ejUWLVqEZ599Vmf/unXrEBERoX3s4eFh9PiJiIjMit1MejFZMnPu3Dns3bsXJ06cQJs2bQAAy5YtQ9euXbF48WJUr1690DlyuRx+fn46+7Zv347nn38eLi4uOvs9PDwKlS1OdnY2srOztY/T0tLK+3SIiMgILl++i9u3MlG9ugv8a7lbOhyqJEzWzRQTEwMPDw9tIgMAYWFhkMlkOHbsWJnqOHXqFGJjYzF8+PBCx0aNGoWqVauibdu2WLt2LUQJ97KYP38+3N3dtZu/v3/5nxAREentlyOJ6PTkBrRs+gme6fQFmjZcia5dNuLM6WRLh2ZdNEbYbJDJkpnk5GT4+Pjo7LOzs4OXlxeSk8v2y7tmzRo0btwY7du319k/e/ZsfP3119i3bx/69euHkSNHYtmyZcXWEx0dDZVKpd2uXbtW/idERER6OXggAT2e/apQ4hJz9Dq6PP0FTp64YaHIrJAwwmaDyt3NNGXKFLz33nslljl37pzeARV48OABNm3ahGnTphU69vC+Vq1aISMjA4sWLcLYsWOLrEupVEKpVBocExERlY9GIzBm5A/QaEShm0Gr1QJCaDBh3I/4+egwywRIlUK5k5mJEydi6NChJZapW7cu/Pz8cOvWLZ39eXl5SE1NLdNYl23btiEzMxORkZGllg0ODsacOXOQnZ3NpIWIyIoc+fkqriUWP05RoxGIPZOCv87eQtNmPsWWsxWSRoKkkQw63xaVO5nx9vaGt7d3qeVCQkJw7949nDp1CkFBQQCAgwcPQqPRIDg4uNTz16xZg549e5bpWrGxsfD09GQiQ0RkZa5eUZWp3JWEe0xmAMO7itjNZFyNGzdGREQEoqKisGrVKuTm5mL06NEYMGCAdiZTUlISOnfujM8//xxt27bVnhsfH4+ff/4Ze/bsKVTv999/j5SUFLRr1w4ODg7Yt28f5s2bhzfffNNUT4WIiPTk6elQxnKOJo6EKjOTrjOzceNGjB49Gp07d4ZMJkO/fv3w0UcfaY/n5uYiLi4OmZmZOuetXbsWNWvWRJcuXQrVaW9vj+XLl2P8+PEQQiAwMBBLlixBVFSUKZ8KERHpofMzdeHiosD9+znFlqlW3QXBITXMGJUVExJgSFeRsM1uJkmUNKe5kkpLS4O7uztUKhXc3NwsHQ4RUaX28YfH8faUg8UeX/VZN7w4qLkZIyofc3xnFFzj7r5AuDnL9a8nQw3PZ+Jt7vuN92YiIiKTGjX2ccyc3RFKZf6XtNwuv/XAydkeH3wUbtWJjNlxarZeeNdsIiIyKUmSMGFSCF6OaoXvd8Th1q1MVK/hip69GsDZWWHp8KgSYDJDRERm4eHhgMFDWlg6DOumMXDMDKdmExERkUUJybBBvDY6AJhjZoiIiKhCY8sMERGRlZA0+Zsh59siJjNERETWgmNm9MJuJiIiIqrQ2DJDRERkLXhvJr0wmSEiIrIW7GbSC7uZiIiIqEJjywwREZG14DozemEyQ0REZC00/26GnG+DmMwQERFZC7bM6IVjZoiIiKhCY8sMERGRlRBCgjBgRpKw0ZYZJjNERETWgt1MemE3ExEREVVobJkhIiKyFpzNpBcmM0RERNaC3Ux6YTcTERERVWhsmSEiIrIWvDeTXpjMEBERWQt2M+mF3UxERERUobFlhoiIyFqwm0kvTGaIiIishfh3M+R8G8RuJiIiIishNJLBmz6WL1+OgIAAODg4IDg4GMePHy+x/NKlS9GwYUM4OjrC398f48ePR1ZWll7XNgYmM0RERDZsy5YtmDBhAmbMmIHTp0+jRYsWCA8Px61bt4osv2nTJkyZMgUzZszAuXPnsGbNGmzZsgVvv/22mSP/D5MZIiIia1Ewm8mQrZyWLFmCqKgoDBs2DE2aNMGqVavg5OSEtWvXFln+6NGj6NChA1588UUEBASgS5cuGDhwYKmtOabEZIaIiMhaFAwANmQDkJaWprNlZ2cXebmcnBycOnUKYWFh2n0ymQxhYWGIiYkp8pz27dvj1KlT2uTl8uXL2LNnD7p27WrkF6PsmMwQERFVMv7+/nB3d9du8+fPL7LcnTt3oFar4evrq7Pf19cXycnJRZ7z4osvYvbs2XjiiSdgb2+PevXqITQ01KLdTJzNREREZC0EDFw0L/8/165dg5ubm3a3Uqk0LK6HHD58GPPmzcOKFSsQHByM+Ph4jBs3DnPmzMG0adOMdp3yYDJDRERkLYSB68z8mwi5ubnpJDPFqVq1KuRyOVJSUnT2p6SkwM/Pr8hzpk2bhsGDB+OVV14BADRv3hwZGRl49dVX8c4770AmM3+nj8muOHfuXLRv3x5OTk7w8PAo0zlCCEyfPh3VqlWDo6MjwsLCcPHiRZ0yqampGDRoENzc3ODh4YHhw4fj/v37JngGRERElZtCoUBQUBAOHDig3afRaHDgwAGEhIQUeU5mZmahhEUulwPI/x63BJMlMzk5Oejfvz9GjBhR5nMWLlyIjz76CKtWrcKxY8fg7OyM8PBwnbnrgwYNwl9//YV9+/Zh165d+Pnnn/Hqq6+a4ikQERGZlRCGb+U1YcIEfPrpp9iwYQPOnTuHESNGICMjA8OGDQMAREZGIjo6Wlu+R48eWLlyJTZv3oyEhATs27cP06ZNQ48ePbRJjbmZrJtp1qxZAID169eXqbwQAkuXLsXUqVPRq1cvAMDnn38OX19ffPfddxgwYADOnTuHvXv34sSJE2jTpg0AYNmyZejatSsWL16M6tWrF1l3dna2zkjutLQ0A54ZERGRiVjgRpMvvPACbt++jenTpyM5ORktW7bE3r17tYOCExMTdVpipk6dCkmSMHXqVCQlJcHb2xs9evTA3Llz9Y/bQFYzmykhIQHJyck608Pc3d0RHBysnR4WExMDDw8PbSIDAGFhYZDJZDh27Fixdc+fP19nVLe/v7/pnggREVEFM3r0aFy9ehXZ2dk4duwYgoODtccOHz6s0zBhZ2eHGTNmID4+Hg8ePEBiYiKWL19e5iElpmA1yUzBFLCSpoclJyfDx8dH57idnR28vLyKnUIGANHR0VCpVNrt2rVrRo6eiIjICIy0zoytKVcyM2XKFEiSVOJ2/vx5U8WqN6VSqR3ZXdYR3kREROYmhGTwZovKNWZm4sSJGDp0aIll6tatq1cgBVPAUlJSUK1aNe3+lJQUtGzZUlvm0XtF5OXlITU1tdgpZERERBWGoa0rNtoyU65kxtvbG97e3iYJpE6dOvDz88OBAwe0yUtaWhqOHTumnREVEhKCe/fu4dSpUwgKCgIAHDx4EBqNRqd/j4iIiGyHycbMJCYmIjY2FomJiVCr1YiNjUVsbKzOmjCNGjXC9u3bAQCSJOGNN97Au+++i507d+LPP/9EZGQkqlevjt69ewMAGjdujIiICERFReH48eP49ddfMXr0aAwYMKDYmUxEREQVhgVuNFkZmGxq9vTp07Fhwwbt41atWgEADh06hNDQUABAXFwcVCqVtsxbb72lXUXw3r17eOKJJ7B37144ODhoy2zcuBGjR49G586dIZPJ0K9fP3z00UemehpERERmY+i4F1sdMyMJSy3XZ0FpaWlwd3eHSqXiYGAiIiqROb4zCq5x+8P2cHPUv50h7UEevMcdtbnvN96biYiIyFpo/t0MOd8GMZkhIiKyFhZYAbgysJpF84iIiIj0wZYZIiIiKyE0EoQBa8UYcm5FxmSGiIjIWrCbSS/sZiIiIqIKjS0zREREVoLrzOiHyQwREZG1EAbem4nJDBEREVkUx8zohWNmiIiIqEJjywwREZGVECJ/M+R8W8RkhoiIyFpoDBwzY6PrzLCbiYiIiCo0tswQERFZCU7N1g+TGSIiImvB2Ux6YTcTERERVWhsmSEiIrISvNGkfpjMEBERWQsBA7uZjBZJhcJuJiIiIqrQ2DJDRERkJTibST9MZoiIiKwFF83TC5MZIiIiK8HbGeiHY2aIiIioQmPLDBERkZXgmBn9MJkhIiKyFhwzoxcmM0REBhBCQC1+h0ZcgSS5wk7qAElysHRYRDaFyQwRkZ7yNKfxIG8KNIh/aK8rHORjoJANhyTZ5l/JpD92M+mHyQwRkR7UmrPIyBsEIPeRI+nIUs+DwAM4yMdYIjSq0Ay80SRsM5nhbCYiIj08UC8EkAdAU+TxbPUyaESqWWMislVMZoiIykkjbkMtfgGgLqGUGrmaXeYKiSqJgm4mQzZbxG4mIqJyEuJOGUrJIcRtk8dClQxnM+mFLTNEROUkSVVR+tgENSTJxxzhENk8kyUzc+fORfv27eHk5AQPD49Sy+fm5mLy5Mlo3rw5nJ2dUb16dURGRuLGjRs65QICAiBJks62YMECEz0LIqLCZJI37KQnAchLKGUHe1l3c4VElUTB7QwM2WyRyZKZnJwc9O/fHyNGjChT+czMTJw+fRrTpk3D6dOn8e233yIuLg49e/YsVHb27Nm4efOmdhszhjMGiMi8HORvIb+nvuiPUaX8DcgkT7PGRBUfx8zox2RjZmbNmgUAWL9+fZnKu7u7Y9++fTr7Pv74Y7Rt2xaJiYmoVauWdr+rqyv8/PyMFisRUXnJZU3gbLcZD9RvQyPOafdL8IBSPg4KWaQFo6MKSxg4NdtGkxmrHjOjUqkgSVKhbqoFCxagSpUqaNWqFRYtWoS8vLwS68nOzkZaWprORkRkKDtZC7jY7YKL3fdwsvsYTnYb4GofA6V8CBfMIzIjq53NlJWVhcmTJ2PgwIFwc3PT7h87dixat24NLy8vHD16FNHR0bh58yaWLFlSbF3z58/XthQRERmTJEmQS00hR1NLh0KVgUaC4GymcitXy8yUKVMKDb59dDt//rzBQeXm5uL555+HEAIrV67UOTZhwgSEhobisccew+uvv473338fy5YtQ3Z2drH1RUdHQ6VSabdr164ZHCMREZGxccyMfsrVMjNx4kQMHTq0xDJ169Y1JB5tInP16lUcPHhQp1WmKMHBwcjLy8OVK1fQsGHDIssolUoolUqD4iIiIiLrVK5kxtvbG97e3qaKRZvIXLx4EYcOHUKVKlVKPSc2NhYymQw+PlzPgYiIKjgOANaLycbMJCYmIjU1FYmJiVCr1YiNjQUABAYGwsXFBQDQqFEjzJ8/H3369EFubi6ee+45nD59Grt27YJarUZycjIAwMvLCwqFAjExMTh27Bg6deoEV1dXxMTEYPz48XjppZfg6ckpkEREVLHxrtn6MVkyM336dGzYsEH7uFWrVgCAQ4cOITQ0FAAQFxcHlUoFAEhKSsLOnTsBAC1bttSpq+AcpVKJzZs3Y+bMmcjOzkadOnUwfvx4TJgwwVRPg4iIiKycyZKZ9evXl7rGjHhoqcKAgACdx0Vp3bo1fvvtN2OER0REZHWEJn8z5HxbZLVTs4mIjOX32GR8tvoMYk8nw8HRDt17NMBLQx5DlSqOlg6NSBfHzOiFyQwRVWqLFx7F7Bk/w85Ohry8/D9bjx9LwpLFMdi5ZwBatORq4kQVnVWvAExEZIg9uy5i9oyfAUCbyAD5N+NLS8tGn55f48GDXEuFR1QI15nRD5MZIqq0Plp6DHJ50R/uarXAnduZ+GbruSKPE1kCkxn9MJkhokopL0+Do79eh1pd/MQCuVzC4UNXzRgVUSkKxswYstkgJjNEVCmVNjsyvwyg0ZRejoisG5MZIqqU7O3laNHSFzJZ8X+pCiEQ3K6GGaMiKpkQgPj3ZpN6bTaamzOZIaJKa+SYx4tteZHJJDg722PgoGZmjoqoeBwzox8mM0RUaQ0Y2BSvvJq/+vjDA4HlcgkKhRybvu4HNzfehJaoouM6M0RUaUmShPeXdkGXiHpYvfIUYs+kQOkgR89eDfHayCDUrct7upGVEf9uhpxvg5jMEFGlJkkSIp4NRMSzgZYOhahUvNGkftjNRERERBUaW2aIyKpcuXIPF+L+gYuzAo8HV4e9vdzSIRGZDVtm9MOWGSKyCvHxqejZ9Ss81ngVnuu9FRHPbETDusuxasXJMq0ZQ1QZGDQt+99NH8uXL0dAQAAcHBwQHByM48ePl1j+3r17GDVqFKpVqwalUokGDRpgz549el3bGNgyQ0QWd/XqPXTu+DnSVNk6++/cycRbE/cjNfUB3p76pIWiIzIjC9w1e8uWLZgwYQJWrVqF4OBgLF26FOHh4YiLi4OPj0+h8jk5OXjmmWfg4+ODbdu2oUaNGrh69So8PDz0j9tAbJkhIotb8O4vSEvLLvbWAwvnH8WNpHQzR0VkG5YsWYKoqCgMGzYMTZo0wapVq+Dk5IS1a9cWWX7t2rVITU3Fd999hw4dOiAgIAAdO3ZEixYtzBz5f5jMEJFFZWbmYuvX56DOK74rSZKAzZvOmjEqIssw1qJ5aWlpOlt2dnaR18vJycGpU6cQFham3SeTyRAWFoaYmJgiz9m5cydCQkIwatQo+Pr6olmzZpg3bx7UarXxX5AyYjJDRBaV+s8D5OSU/CEok0m4zpYZsgHGSmb8/f3h7u6u3ebPn1/k9e7cuQO1Wg1fX1+d/b6+vkhOTi7ynMuXL2Pbtm1Qq9XYs2cPpk2bhvfffx/vvvuucV+McuCYGSKyKA9PB8hkUok3fNRoBLy9ncwYFVHFdu3aNbi5uWkfK5XGW+lao9HAx8cHq1evhlwuR1BQEJKSkrBo0SLMmDHDaNcpDyYzRGRRLi4KdO/ZALu/v1DsmBm1WuD5AU3NHBmR+QkBg24WWXCum5ubTjJTnKpVq0IulyMlJUVnf0pKCvz8/Io8p1q1arC3t4dc/t+yCY0bN0ZycjJycnKgUCj0fwJ6YjcTEVlc9DsdoFDIde6fVECSgJdfaYl69XjrAar8zH2jSYVCgaCgIBw4cEC7T6PR4MCBAwgJCSnynA4dOiA+Ph4ajUa778KFC6hWrZpFEhmAyQwRWYGmzXywa++LqPPIvZIUCjnGvNEWiz/oYqHIiCq/CRMm4NNPP8WGDRtw7tw5jBgxAhkZGRg2bBgAIDIyEtHR0dryI0aMQGpqKsaNG4cLFy5g9+7dmDdvHkaNGmWpp8BuJiKyDo+3rY5Tv0ch5tfrOH/+DpydFegSUQ+eng6WDo3IfDRS/mbI+eX0wgsv4Pbt25g+fTqSk5PRsmVL7N27VzsoODExETLZf20f/v7++PHHHzF+/Hg89thjqFGjBsaNG4fJkyfrH7eBJGGDS2umpaXB3d0dKpWqTH2KRERku8zxnVFwjdgBQ+FqQFdNek4OWm5eb3Pfb2yZISKjU6s12LsnHhvW/Y6EhHvw8XHGwEHN0K9/Yzg62ls6PCKqZJjMEJFRZWfnYdAL3+KnHy9DLpegVgtcvJCKIz8nYtmHx7F774uoymnWREXijSb1wwHARGRUc2b+jP37EgBAO9W6YA2ZC3H/YPiwnRaLjcjamXs2U2XBZIaIjCYjIweffXqm2AXw1GqBQweuIO78HTNHRlRRGJrIMJkhIjJI7JlkZGbkllhGkoCf/5dopoiIyBZwzAwRGU1Z50ba4CRKorIRUv5myPk2iMkMERnNYy184eBgh6ysvGLLCAGEtK9pxqiIKg6hyd8MOd8WsZuJiIzGzU2JwUMeg0xW9F+HcjsJwSE10Pwx3yKPE1Hlplar8fPPP+PevXtGrZfJDBEZ1ey5oWgbXB0AtEmNJOVvNWq4Yf3nvSwZHpFVq+yzmeRyObp06YK7d+8atV6TJTNz585F+/bt4eTkBA8PjzKdM3ToUEiSpLNFRETolElNTcWgQYPg5uYGDw8PDB8+HPfv3zfBMyAifTg7K7Br74tY+Wk3BD1eDb5+zmjSxBtz5z+NX48NQ42atrMqKVF5VfZkBgCaNWuGy5cvG7VOk42ZycnJQf/+/RESEoI1a9aU+byIiAisW7dO+1ipVOocHzRoEG7evIl9+/YhNzcXw4YNw6uvvopNmzYZLXYiMoxCIcegl5pj0EvNLR0KEVmZd999F2+++SbmzJmDoKAgODs76xzX5zYMJktmZs2aBQBYv359uc5TKpXw8/Mr8ti5c+ewd+9enDhxAm3atAEALFu2DF27dsXixYtRvXp1g2ImIiKyJFtYAbhr164AgJ49e0KS/otXCAFJkqBWq8tdp9XNZjp8+DB8fHzg6emJp59+Gu+++y6qVKkCAIiJiYGHh4c2kQGAsLAwyGQyHDt2DH369CmyzuzsbGRnZ2sfp6WlmfZJEBER6UEIwxKSirDqwaFDh4xep1UlMxEREejbty/q1KmDS5cu4e2338azzz6LmJgYyOVyJCcnw8fHR+ccOzs7eHl5ITk5udh658+fr20pIiIiIsvp2LGj0ess1wDgKVOmFBqg++h2/vx5vYMZMGAAevbsiebNm6N3797YtWsXTpw4gcOHD+tdJwBER0dDpVJpt2vXrhlUHxERkUkULJpnyFYBHDlyBC+99BLat2+PpKQkAMAXX3yBX375Ra/6ytUyM3HiRAwdOrTEMnXr1tUrkOLqqlq1KuLj49G5c2f4+fnh1q1bOmXy8vKQmppa7DgbIH8czqMDiYlIP3/+kYLPVp/BieNJEAIICPBAu/Y10SW8Lho38bZ0eEQVmi2Mmfnmm28wePBgDBo0CKdPn9YOA1GpVJg3bx727NlT7jrLlcx4e3vD29t8H1bXr1/HP//8g2rVqgEAQkJCcO/ePZw6dQpBQUEAgIMHD0Kj0SA4ONhscRHZqo8+OIapbx+CXA4UjNH76+xt7N51EdPePoQnO9bCmnU94VfNxbKBElVQtpDMvPvuu1i1ahUiIyOxefNm7f4OHTrg3Xff1atOk60zk5iYiNjYWCQmJkKtViM2NhaxsbE6a8I0atQI27dvBwDcv38fkyZNwm+//YYrV67gwIED6NWrFwIDAxEeHg4AaNy4MSIiIhAVFYXjx4/j119/xejRozFgwADOZCIysYMHEjD17fyBe8VNNjj6yzWEh32J9PTsogsQkc2Li4vDU089VWi/u7u73isDmyyZmT59Olq1aoUZM2bg/v37aNWqFVq1aoWTJ09qy8TFxUGlUgHIXxXwjz/+QM+ePdGgQQMMHz4cQUFBOHLkiE4X0caNG9GoUSN07twZXbt2xRNPPIHVq1eb6mkQ0b+WLT0Oubzkv/rUaoErCfew8Ys/zRQVUeVScG8mQzZr5+fnh/j4+EL7f/nlF72HqphsNtP69etLXWPm4TvnOjo64scffyy1Xi8vLy6QR2RmQgj8/L+rUKvLNu/zy8//xOsj25RekIh02EI3U1RUFMaNG4e1a9dCkiTcuHEDMTExePPNNzFt2jS96rSqqdlEZL3Kun6FEMDt2xmmDYaIKqwpU6ZAo9Ggc+fOyMzMxFNPPQWlUok333wTY8aM0atOJjNEVCpJkvB42+o4fiyp1NYZmUxCrVruZoqMqHKxhZYZSZLwzjvvYNKkSYiPj8f9+/fRpEkTuLjoP3GAd80mojIZNebxMnUzaTQCw15pafqAiCohW7jR5Msvv4z09HQoFAo0adIEbdu2hYuLCzIyMvDyyy/rVSeTGSJCbq4ahw9dwXffnsfvsck649kK9OjVAG9MKHkJBJlMQruQGniufxNThUpEFdyGDRvw4MGDQvsfPHiAzz//XK862c1EZOPWrYnFnJk/486dTO0+uVxCnTqeGDmmDQYNbg5HR3tIkoTZczsh9OkArFx+Akf+l4gHD/K0Y2kUSjkGRz6Gd+d3gkIht9CzIarYKnM3U1paGoQQEEIgPT0dDg4O2mNqtRp79uwpdMuismIyQ2TDli87gei3DhTar1YLxMenYsK4n/DFhj/w/Q8D4eaWv0TC053r4OnOdQAAaWnZOHP6JoQAWrbyg4eHQ6G6iKjsKnMy4+Hhob31UYMGDQodlyRJ7/soMpkhslEqVRZmTjtcarnfY5Px9uQD+Hhl10LH3NyU6BgaYPzgiKjSOXToEIQQePrpp/HNN9/Ay8tLe0yhUKB27dp6L4DLZIbIRu3YHofs7GKW8n2IRgNs/OJPzJ7bCV5ejmaIjMh2VeaWmYK7ZSckJKBWrVqQJOPFygHARDbq+LGkMpdVqwW+3vy3CaMhIgD5d73WGLBZcTJT4Ny5c/j111+1j5cvX46WLVvixRdfxN27d/Wqk8kMUSUkhMD/Dl/BB4t/w7Klx/D3X7cBAHfvZmH7N+cxZ+bP+Hz9H+Wq84fdF00RKhE9xBamZk+aNAlpaWkAgD///BMTJkxA165dkZCQgAkTJuhVJ7uZiCqZv/+6jZcGbkf8xVTI5RKEAN6JPgT/Wm5ISc5ATk7pXUtFuXgx1ciREpEtSkhIQJMm+cs3fPPNN+jRowfmzZuH06dPo2vXwmPzyoLJDFElknQ9Dc8+sxFpafl3rX54kbtriWkG1c3p1kSmV5nHzBRQKBTIzMxfCmL//v2IjIwEkH/vxYIWm/JiMkNUiaxacQppadllviFkWcnlErqE63c3WyIqOyHKfh+04s63dk888QQmTJiADh064Pjx49iyZQsA4MKFC6hZs6ZedXLMDFEl8tWms0ZPZCQpf2XfqNeDjFovEdmmjz/+GHZ2dti2bRtWrlyJGjVqAAB++OEHRERE6FUnW2aIKpE0VbZR65PLJcjlMmz4shfq1/cq/QQiMoyhg3grQDdTrVq1sGvXrkL7P/jgA73rZDJDVInUqu2Oixf+MbipWamUo3WQH54KDcDQYS1Qo6abcQIkohLZwpiZxMTEEo/XqlWr3HUymSGqRIZHtcKUSfsNqqNJk6o48HMknJ0VRoqKiOg/AQEBJS6Yp1aXf8YlkxmiSuTpsAB4eDrgbmqWXud3DgvA9u8HGDkqIiorW2iZOXPmjM7j3NxcnDlzBkuWLMHcuXP1qpPJDFElkZBwDxFhG3Hvrn6JTKtWfti6/XkjR0VE5WELyUyLFi0K7WvTpg2qV6+ORYsWoW/fvuWuk7OZiCqJcaN/wD93Hug1XmbU2Mfxv6NDYWfHjwQisoyGDRvixIkTep3LlhmiCiIzMxf303Pg6eUAe3s5HjzIxbo1sVj76RkkJNxDbq6m3HU2aOCFT9b0QFCbaiaImIjKS2gkCI0BLTMGnGsujy6MJ4TAzZs3MXPmTNSvX1+vOpnMEFm532OT8d78X7FnVzw0GgFnZ3sMeLEpYn5Nwt9/39arTjs7GWa/G4pRYx836p1ricgw+YvmGdLNZMRgTMTDw6PQ544QAv7+/ti8ebNedTKZIbJi/zt8Bf16bYVarYFGk/8plZGRizWfxhpUb6PGVTF6XFsjREhExmQLY2YOHTqk81gmk8Hb2xuBgYGws9MvLWEyQ2Sl8vI0eGXo98jL+y+RMZaQ9votGU5EZKiOHTsavU4mM0RW6qe9l5CSkmGSuqNea22SeonIMJW1ZWbnzp1lLtuzZ89y189khshK/f33bdjZyZCXV/6BvSXp1qM+GjWuatQ6icg4Kmsy07t37zKVkySJi+YRVSZOTvZG715yc1fg4xXPGrVOIqLSaDTG/aPsUVxUgshKdetRH8KIUxNcXRX46cBgVKnqZLQ6ici4ClpmDNms1cGDB9GkSZNCU7MBQKVSoWnTpjhy5IhedTOZIbJSeXkCwe2MM1DXwUGOc/Gj0KSpt1HqIyLTqMzJzNKlSxEVFQU3t8I3rnV3d8drr72GJUuW6FU3kxkiK3MjKR19emxBq2af4LeY6wbX5+3thJO/vwo3N6URoiMi0s/vv/+OiIiIYo936dIFp06d0qtujpkhsiKxZ5LxXJ+tuHM70yj1RU99AlPe7sCF8YgqiMo6ABgAUlJSYG9vX+xxOzs73L6t50Kg+gZFRMZz6uRNTHlrP47FJBmtzvoNvBD9zhNGq4+ITE8IA29nYMXJTI0aNXD27FkEBgYWefyPP/5AtWr63VqF3UxEFnbyxA088/TnRk1k5HIJazeUf60GIiJT6dq1K6ZNm4asrKxCxx48eIAZM2age/fuetVtsmRm7ty5aN++PZycnODh4VGmcyRJKnJbtGiRtkxAQECh4wsWLDDRsyAyvRef/wZ5uYbPWpLJ8/9bpaojtn//Alq09DO4TiIyr8o8AHjq1KlITU1FgwYNsHDhQuzYsQM7duzAe++9h4YNGyI1NRXvvPOOXnWbrJspJycH/fv3R0hICNasWVOmc27evKnz+IcffsDw4cPRr18/nf2zZ89GVFSU9rGrq6vhAROZWHZ2HnZ+dwHffnMOqnvZaNioCqpVd0FysmGr/MpkwGMt/NAloi6aNvVBtx71oVDIjRQ1EZlT/o0mDTvfWvn6+uLo0aMYMWIEoqOjtUtPSJKE8PBwLF++HL6+vnrVbbJkZtasWQCA9evXl/kcPz/dvyR37NiBTp06oW7dujr7XV1dC5UlsmbJN++j+7Nf4ULcP5DJJGg0Ar/FXDfK6r4ymQzB7Wpg6vSnjBApEVmSRkjQGNC6Ysi55lC7dm3s2bMHd+/eRXx8PIQQqF+/Pjw9PQ2q12rHzKSkpGD37t0YPnx4oWMLFixAlSpV0KpVKyxatAh5eXkl1pWdnY20tDSdjchchBAY0P8bXIpPBQDtqr7Guk1BXp4Gvfs2NEpdRETm4Onpiccffxxt27Y1OJEBrHg204YNG+Dq6oq+ffvq7B87dixat24NLy8vHD16FNHR0bh582aJC+3Mnz9f21JEZG7HYpJw+tTN0gvqQS6X0Da4Btp38DdJ/URkXpV5arYplatlZsqUKcUO0i3Yzp8/b5TA1q5di0GDBsHBwUFn/4QJExAaGorHHnsMr7/+Ot5//30sW7YM2dnZxdYVHR0NlUql3a5du2aUGInKYv/+y7CzM+4HjOzfd267kJr4ams/riNDVFkYOvjXRpOZcrXMTJw4EUOHDi2xzKPjW/Rx5MgRxMXFYcuWLaWWDQ4ORl5eHq5cuYKGDYtualcqlVAqufopWUZerubfm6wZ/iFjr1CjXYdUtGndA9161MfjbaszkSEim1euZMbb2xve3qa/t8uaNWsQFBSEFi1alFo2NjYWMpkMPj4+Jo+LSB9OznegMWARrAIeXg9w8vwmuDu9CaU81PDAiMjqsJtJPyYbM5OYmIjU1FQkJiZCrVYjNjYWABAYGAgXFxcAQKNGjTB//nz06dNHe15aWhq2bt2K999/v1CdMTExOHbsGDp16gRXV1fExMRg/PjxeOmll4wygIjI2I78fBXvzYsHIGBoy4yTkxqODg2gkL1klNiIyPowmdGPyZKZ6dOnY8OGDdrHrVq1AgAcOnQIoaGhAIC4uDioVCqd8zZv3gwhBAYOHFioTqVSic2bN2PmzJnIzs5GnTp1MH78eEyYMMFUT4NIL0IIDBrwLXbtvPjvHsM/YIJDlHCx+wqS5GxwXURElYkkhDUvsWMaaWlpcHd3h0qlKvJW5ESGuH0rA+2D1yLFwMXwHrX/8GC0Da5h1DqJqHTm+M4ouMaWxvPgJHco/YRiZKqz8MK5t8sd6/Lly7Fo0SIkJyejRYsWWLZsGdq2bVvqeZs3b8bAgQPRq1cvfPfdd3rHbSirXWeGqCLKyVHjyfbrjJ7IBNRxZyJDZAMscTuDLVu2YMKECZgxYwZOnz6NFi1aIDw8HLdu3SrxvCtXruDNN9/Ek08+qe/TNRomM0RG9Nknp3Ej6b7R6920pV/phYiI/vXoQrElLV+yZMkSREVFYdiwYWjSpAlWrVoFJycnrF27tthz1Go1Bg0ahFmzZhllFrOhmMwQGYkQAss/PmH0emfPDUWz5pytR2QLjNUy4+/vD3d3d+02f/78Iq+Xk5ODU6dOISwsTLtPJpMhLCwMMTExxcY5e/Zs+Pj4FLlKvyVY7QrARBXFtUQVPlhyDBs//wMPHpR8a43yqB3gjnfndUKvPo2MVicRWTdjzWa6du2azpiZ4tZau3PnDtRqdaEbPPr6+ha7CO4vv/yCNWvWaGcpWwMmM0QGuBD3D555+gukpWVDnWecsfSDhzyGl4e3ROs21bggHpGN0QjDbhb5763f4ObmZpLByunp6Rg8eDA+/fRTVK1a1ej164vJDJEBol7+Hqp7WdAYeM9IuTz/diAbvuyFHr1400giMo+qVatCLpcjJSVFZ39KSgr8/PwKlb906RKuXLmCHj16aPdp/v0AtLOzQ1xcHOrVq2faoIvAZIaoHITQQC3+gEbcwdcb1ThzOtngOmUyYMiwFnhtRBAaNzH9CttEZL3MvWieQqFAUFAQDhw4gN69ewPIT04OHDiA0aNHFyrfqFEj/Pnnnzr7pk6divT0dHz44Yfw97fMTW+ZzJBNEyILuZqdyNFshxB3IJNqQSF/AXZSZ0iSXKdsruYHPMibj9jTWRg/IhQXz3sZIwK8OqIpFi6OMEJdRFTRWWIF4AkTJmDIkCFo06YN2rZti6VLlyIjIwPDhg0DAERGRqJGjRqYP38+HBwc0KxZM53zPTw8AKDQfnNiMkM2SyPuICN3EDS4iPwVegU0IgF5eYdgJ4XCyW4lJCl/0FyOeiceqN/AhfMe6N+tD7Kz5CVVXUYCcjkwcvRTRqiLiEg/L7zwAm7fvo3p06cjOTkZLVu2xN69e7WDghMTEyGTWffkZyYzZLMy88ZDg8v/PioYvJvf95snfkaWegkc7aIhRA6y1DNx84YTpr/VHtlZcmg0hr6x86+3YWMHBAR4GFgXEVUWQgDCgDF4+q7pP3r06CK7lQDg8OHDJZ67fv16/S5qRExmyCapxUWoxa8llNAgR/MlHMRYxP6+D29Pbo9ff65ppKsLeHiqcfBIDwTWK/3O8ERkO3ijSf0wmSGblKf5DQVdS8V7gNOnYxDW8TzUauPdSkAul2H/oVcRWK+K0eokIrJlTGbIRpWtLbZX1zNQqwFj3PUaAOzsJOzZNwgNGjKRIaLCNEIycJ0ZtswQ2Qw76XGUltB8s7kB0tLURrtm+w418eVXfVHV28lodRJR5cJuJv0wmSGbJJc1hlxqA7U4A6BwwiIEsPyDxwy/kARUr+6KA4cHo0ZN46/GSUREvNEk2TAnuw8hoTryu5Dy/5opmAlw6pgPLp73NPwiAli4OIyJDBGVibFuNGlr2DJDNksmVYOr/ffI0WxFjvobqDU38PdZO3y0qBV++L4ODB0n4+Rsj8VLnkHP3rw9ARGVDbuZ9MNkhmyaJLlBKR8OpXw4Zs6cjA8WukMI/RssFUo5BgxsipD2NdGrTyO4uCiMGC0RVXbCwAHATGaIbJgQAus+dfn3g0CgvK0yMhkw+91OGDy0BTw9HUwSIxERFY3JDNksIdIgoIEEd2Rk5MLDMwN3U92hT/dSj54NMHZ8sPGDJCKbIoT+q/gWnG+LmMyQzcnV7EGWehU04iwAQEJN2Cn6on6ju0i45K5XnTI5x9ITkeGERoIwYLye0LCbiajSy1J/hFTVx9jyZWN8taEfUpKd4eKSg5AnYyE0Sr3rbdnK14hREhFReTCZIZuh1pxD8u2VeO7Z3rgY5/lvc6yEu6kOuL7JFVWqPtCrXrlcwuixbY0aKxHZJs5m0g+TGbIZOZqvMHnsU7h00aPQG14ICXduO0KShDbJKavZ74bC3l5u3GCJyCbxdgb6YTJDNuPa9Xjs3dW8hL9cJAiBEhIa3VlOnp4OmD2vE4YM5Z2viYgsickM2YwzJ73K0ASbn9B06XoFf8R6485tR0AAzi65qFXbAW+/0wd29nao6e+KJk28IUm2+VcQEZkGZzPph8kMVXpCCNy8cR/3VY8BSC39BEkg6ZorTpzbhLxcCXb2Au/Naot5czebPFYism0cM6MfJjNUaQkhsPazWHz4wTFcSbhXsBeljocRMlyKd0fGfTv89ms1fLr8MRw/6o95c00cMBER6YXJDFVKQghMfOMnfLb6DPTpCcp6YI9GNV7WPg4M1G/9GSKi8uAAYP0wmaFK6ddfruGz1WcAPNqHrN8b/fVRbQwPioioFBwzox8mM1ThqcVl5KjXIEezC0AmJPjh00+7QS6XoFaX952t2w0lk0lo2coXg4c8ZsyQiYiKxDEz+uEa7FSh5WlO4H5ud+RotgBIB6CGQBL+/P1uOROZ/LJ29hrtHqVSjmHDW+L7HwbC0dHeqHETEZHxsGWGKiwhcpCZNxJANoQQOHbUD19taIRLF91xOd4d5b379buLf4aDoxqTx3ZC57C6WLOhJ9zdeQdsIjIfjpnRj8laZq5cuYLhw4ejTp06cHR0RL169TBjxgzk5OSUeF5WVhZGjRqFKlWqwMXFBf369UNKSopOmcTERHTr1g1OTk7w8fHBpEmTkJeXZ6qnQlbqSuI2CPyD+/fl6NW5F/p37Ylvvw7E76d9odFIKN/4GAl5eXLUqn0fcrmE2XM7MZEhIrMTAhAaAzaOmTGu8+fPQ6PR4JNPPkFgYCDOnj2LqKgoZGRkYPHixcWeN378eOzevRtbt26Fu7s7Ro8ejb59++LXX38FAKjVanTr1g1+fn44evQobt68icjISNjb22PevHmmejpkZX7YE4+z57Yi4VIHbFzf5L9+YlGQn5f/r5OcbDlu33bC5m390aSpt/GCJSIik5KEMF8et2jRIqxcuRKXL18u8rhKpYK3tzc2bdqE5557DkB+UtS4cWPExMSgXbt2+OGHH9C9e3fcuHEDvr75dypetWoVJk+ejNu3b0OhUJQaR1paGtzd3aFSqeDm5ma8J0hmkZr6AI3qLUdA3RSc/7sKytudVJy9v2xFk4aD4eHyhsF1EVHlYY7vjIJrLHL/BI6So971PBAPMEn1ms19v5l1ALBKpYKXl1exx0+dOoXc3FyEhYVp9zVq1Ai1atVCTEwMACAmJgbNmzfXJjIAEB4ejrS0NPz1119F1pudnY20tDSdjSquTV/8iaysvH8TGcAYiUzNWmlo3twP7s5jDa6LiEhfBWNmDNlskdmSmfj4eCxbtgyvvfZasWWSk5OhUCjg4eGhs9/X1xfJycnaMg8nMgXHC44VZf78+XB3d9du/v7+BjwTsrSTJ28asTYBe/s8fLbxJzjavQ9J4gQ/IqKKptyf3FOmTIEkSSVu58+f1zknKSkJERER6N+/P6KioowWfFlFR0dDpVJpt2vXrpk9BjIeOzsZ7OzVBtSQ37MqSQKhz1zDj79+i6aPpUKSSu+iJCIyKfHfwnn6bOAA4LKZOHEihg4dWmKZunXrav9948YNdOrUCe3bt8fq1atLPM/Pzw85OTm4d++eTutMSkoK/Pz8tGWOHz+uc17BbKeCMo9SKpVQKpUlXpsqjs7P+GHHd7HIy5WX+1yZTIMDx7dALpPgVSUL7h75s+skeEOGWsYOlYioXDQC0BjQda5hMlM23t7e8PYu20yPpKQkdOrUCUFBQVi3bh1kspIbgoKCgmBvb48DBw6gX79+AIC4uDgkJiYiJCQEABASEoK5c+fi1q1b8PHxAQDs27cPbm5uaNKkSXmfDlUQ/9zJxM2b9+HifgSbNx9Gdlb1ctaQP0h4zqJfEVg/vdBRpfxlSBKXXSIiqohM9umdlJSE0NBQ1K5dG4sXL8bt27e1xwpaUJKSktC5c2d8/vnnaNu2Ldzd3TF8+HBMmDABXl5ecHNzw5gxYxASEoJ27doBALp06YImTZpg8ODBWLhwIZKTkzF16lSMGjWKrS+V0IW4fzBj2mHs2XXxofUTapS7Hjf3HEyfF4MXXroAIQBJAjQaGWQyDeykHlDIXjFq3ERE+hDCsJ4irjNjZPv27UN8fDzi4+NRs2ZNnWMFs8Fzc3MRFxeHzMxM7bEPPvgAMpkM/fr1Q3Z2NsLDw7FixQrtcblcjl27dmHEiBEICQmBs7MzhgwZgtmzZ5vqqZCFnPv7NjqHfoEHmbmPvEHLMhX733ExMoHhr5/F27OPwd5eQAggL0/CjevOsJM3Qv26r8NO6gRJn1trExEZmRAShAHdTLZ6byazrjNjLbjOjHUSIgu5mt3IE8cBCPR9tjqOxWTrcbNIABCI6JGA6fOOwr/Wf8nyL/+rhgkjOuFWsivOx4+Cr5+L0eInosrJnOvMzHH8FA6Sk971ZIlMTHsQZXPfbxwkQFYhT/MHMvOGQ+AfAHJcueyKo788r3d9zzx7FZ9+uV/7+N1pbbFvTwAux3tAkoCRo4OYyBARVRJMZsjiNOI2MvIiAdzHtasuOPBjbfz1R/GLK5ZOYP7SI/n/EsD61U2wZkVrqDUayOUSXh8ZhHfnP22U2ImIjIljZvTDZIYsLkfzFR48eIDJYzviu62BAABh0NtZQlXvLACukGtegV/VjngrOg2eno7o1bsh/KqxRYaIrJNGSAZOzbbNMTNMZsjictU/YNSwTjjwY62HBq/p+4YUcPfQwFXxOexkbSAplBgw0FiREhGRNWIyQxZ3+pQS+34IMEpdMhkw5o0GsJd3MEp9RETmxG4m/TCZIbPQiHvQiLMAJMilxyBJrtpjO7Y2hZ2dBnl5ht0XSS7XoGGTTIwY2dPAaImILIPJjH6YzJBJCZGBB+q5yNV8AyD3371KKGQD4SCfDElSIv1eIIQw7OaRjo65eGHwNcyePRmurlw8kYjIljCZIZMRIhsZeZFQi98BaB46ko0czedQi0twtluL2rXrASj6judlsfrzs3imy5PwchsLSXI3NGwiIovhAGD9GNauT1SCXM0OqMUZ6CYyBTRQiyPIE/sxeOhj0Gj0ewPa2Un4/cQwVHF/jYkMEVV4wgibLWIyQyaTo9mM0n7FMvPGw6/mLkyObqfXNfLyBBIS7up1LhERVQ7sZiKjuXs3C1u3/IVL8Xfh7qFEl55pqN+oqFaZh2UhSz0dYya3hq/fJCxacAI3btwHAMjlEiRJQl5e8XXI5RLc3R2M+CyIiCxHI4puyy7P+baIyQwZxYZ1v+PN8T8hJ0cNOzsZNBqBBXPD0L3PJXyw6jAcHNQlnq9BLF4c9hOGvvwOzv55C1kP8hBY3wtzZv2Mz9f/UWxCo1YL9Ovf2BRPiYjI7AQMvNGkAedWZOxmIoPt2nkBY0b+gOxsNYQAcnM12ptD7tlRB2+NeaoMtWiQo9kMmSwLLVr6ITikJqpUdcKYN4KhUMohlxd+g8rlEh5vWx1hz9Q18jMiIrIMIf5tndFzs9Wp2UxmyCBCCMydcwRSMX8MaDQybP+6Pq5cdi26gI4H0IhLOnvq1fPErh8Gam9BYGcn0yY2HTsFYNt3z0Mms82/RIiIKB+7mcggV6+q8NfZ2yWWkck0WPpeEP6544BrV13h7fsAzw28gF7PXSqi+6nwr2Sbx6vj7PkR2L8vAWdO34RSYYfwZ+uhSVNvIz4TIiLLM3RGko02zDCZIcPcT88ptYwQEr7Z3AByuQZqtQyXL7njt1+q4bPlzbFl1y54VckGAEioApkUWGQdcrkM4RH1EB5Rz6jxExFZEw4A1g+7mcgg/rXcYG9f8q9Rwc0j1er8ckIjAyDhYpwnxr3aSVtOKX8FkmRvsliJiKhyYjJDBnF3d0D/F5oUOUC3NGq1DIf318LleHfYS/2hkEWZIEIiooqDi+bph8kMGWzm7FBUq+aiV0IDCJw6OgOOdgsgSfx1JCLbZshMpoLNFvHbgwzmV80Fh38ZisFDA6B0KG9vrwQZakMqbjoUERFRKTgAmAzyz51M7Pr+Iv5JTUD70HXoPzgV3UL7lquOkPY1TRQdEVHFwtlM+mEyQ3rRaARmTf8fPv7oOPLyNJDJBNTqULi5Z5e5DrmdhJD2/mjchFOsiYiA/JlMBs1mMlYgFQyTGdLLjKmH8dHSY9rVJtXq/G6i9DQF8v82KL3bqFYtd6xZ18N0QRIRkU1gMkPllpJ8Hx9/dLzIZbPzp2EXNJQWndDY28swe24nRA59DK6uSlOGSkRUobCbST9MZqjcdnwXB1HiDUAKkhgBSfpvnRm5HHByUmDX3hfRqrWfyeMkIqpo2M2kH85monJLTX0Amaz0X52R42PRqEkqHBzzUNU7CyNGtUbMieFMZIiIiiGQf7NIvTdLPwELYcsMlVvtAA/k5ZWc/0uSwCsjzyJ65gkAgIP8bSjlXcwRHhER2Ri2zFC59ezVAM4uxd92QC7XoHP4NXj7ZAOQoJSNhkI23HwBEhFVUBojbLaIyQyVm7OzAos/yG9leXStO7lcgpOzDLPmekMpnwBX+1/gYDeBi+IREZUBb2egHyYzpJdBLzXHxs19EFjfS7tPkoDQTgE49HMUmjeZBQf5SMikahaMkoiIymL58uUICAiAg4MDgoODcfz48WLLfvrpp3jyySfh6ekJT09PhIWFlVjeHDhmhvTWo1dDdO/ZAOf+vgPVvSzUqu2OGjXdLB0WEVGFJWBYV5E+LTNbtmzBhAkTsGrVKgQHB2Pp0qUIDw9HXFwcfHx8CpU/fPgwBg4ciPbt28PBwQHvvfceunTpgr/++gs1atQwIHr9SaLkObaVUlpaGtzd3aFSqeDmxi9fIiIqnjm+Mwqu8QrWQgEnvevJQSY+w8u4du2aTqxKpRJKZdHregUHB+Pxxx/Hxx9/DADQaDTw9/fHmDFjMGXKlFKvqVar4enpiY8//hiRkZF6x24IdjMRERFVMv7+/nB3d9du8+fPL7JcTk4OTp06hbCwMO0+mUyGsLAwxMTElOlamZmZyM3NhZeXV+mFTcRkycyVK1cwfPhw1KlTB46OjqhXrx5mzJiBnJycYs9JTU3FmDFj0LBhQzg6OqJWrVoYO3YsVCqVTjlJkgptmzdvNtVTISIiMgtjDQC+du0aVCqVdouOji7yenfu3IFarYavr6/Ofl9fXyQnJ5cp5smTJ6N69eo6CZG5mWzMzPnz56HRaPDJJ58gMDAQZ8+eRVRUFDIyMrB48eIiz7lx4wZu3LiBxYsXo0mTJrh69Spef/113LhxA9u2bdMpu27dOkRERGgfe3h4mOqpEBERmYWxVgB2c3MzyzCKBQsWYPPmzTh8+DAcHBxMfr3imCyZiYiI0Ek26tati7i4OKxcubLYZKZZs2b45ptvtI/r1auHuXPn4qWXXkJeXh7s7P4L18PDA35+XEmWiIhIX1WrVoVcLkdKSorO/pSUlFK/YxcvXowFCxZg//79eOyxx0wZZqnMOmZGpVKVu0+tYMDVw4kMAIwaNQpVq1ZF27ZtsXbt2hLvFZSdnY20tDSdjYiIyNoII/yvPBQKBYKCgnDgwAHtPo1GgwMHDiAkJKTY8xYuXIg5c+Zg7969aNOmjd7P11jMNjU7Pj4ey5YtK7ZVpih37tzBnDlz8Oqrr+rsnz17Np5++mk4OTnhp59+wsiRI3H//n2MHTu2yHrmz5+PWbNmGRQ/ERGRqVniRpMTJkzAkCFD0KZNG7Rt2xZLly5FRkYGhg0bBgCIjIxEjRo1tIOI33vvPUyfPh2bNm1CQECAdmyNi4sLXFxcDIhef+Wemj1lyhS89957JZY5d+4cGjVqpH2clJSEjh07IjQ0FJ999lmZrpOWloZnnnkGXl5e2LlzJ+zti18+f/r06Vi3bh2uXbtW5PHs7GxkZ2fr1O3v78+p2UREVCpzTs0ejDUGT83+AsPLHevHH3+MRYsWITk5GS1btsRHH32E4OBgAEBoaCgCAgKwfv16AEBAQACuXr1aqI4ZM2Zg5syZesduiHInM7dv38Y///xTYpm6detCoVAAyB/UGxoainbt2mH9+vVluttyeno6wsPD4eTkhF27dpU6qGj37t3o3r07srKyip1H/zCuM0NERGVlC8lMRVfubiZvb294e3uXqWxSUhI6deqEoKAgrFu3rkyJTFpaGsLDw6FUKrFz584yjY6OjY2Fp6dnmRIZIiIia2WJbqbKwGRjZpKSkhAaGoratWtj8eLFuH37tvZYwQjppKQkdO7cGZ9//jnatm2LtLQ0dOnSBZmZmfjyyy91But6e3tDLpfj+++/R0pKCtq1awcHBwfs27cP8+bNw5tvvmmqp0JERGQWAgJC0n9hfhtc1B+ACZOZffv2IT4+HvHx8ahZs6bOsYIXOzc3F3FxccjMzAQAnD59GseOHQMABAYG6pyTkJCAgIAA2NvbY/ny5Rg/fjyEEAgMDMSSJUsQFRVlqqdSKWRn5+G7b+OwZ9dFZGbmomkzbwx5uSXq1PGwdGhEREQG4b2ZbKBP8cqVe+jx7Fe4ekUFmUyCRiMgl+f/973FYXh9pOWn1RERWStzjpkZgM+gkAwYMyMysRmv2Mz3WwHem6mSy8vToHf3Lbh+Lb+7TqPJz13VagEhgLcm7sdPP16yZIhERPQvjRE2W8RkppLbuycely/dhVpddAOcXC5h6fu/mTkqIiIi42EyU8n99OMl2NkV/2NWqwV+OXINDx7kmjEqIiIqmqGr/9rcyBEAZlwBmCwjJ0ddpuWtc3M1cHQ0Q0BERFQsTs3WD1tmKrlWratBU0wXEwBIElA7wB2urgozRkVERGQ8TGYquQEvNoWjoz0kqfgyr49sA6mkAkREZBbmvtFkZcFkppJzd3fAus97Qi6Xwc7uv4RFkvK3Z7sG4rURQRaMkIiICnA2k36YzNiAZ7vVx/9+HYLnnm8KJyd7yOUSmjT1xocfR+DLzX1LHCBMRETmIyTDN1vEAcA2ovljvli9pjtWr+lu6VCIiIiMiskMERGRlcjvKtJ/3IutdjMxmSEiIrISnJqtHw6WICIiogqNLTNERERWwtDp1bY6NZvJDBERkZVgN5N+2M1EREREFRpbZoiIiKyEBsLA2UzsZiIiIiILMnThO6H9P9vCbiYiIiKq0NgyQ0REZCXYzaQfJjNWJjdXjbupWXB2sYezs8LS4RARkVkZeudrJjNkQf/88wDvLzyKDet+R3p6DiQJCH82EJOjOyCoTTVLh0dERGbAqdn6YTJjBf65k4nOoV/g6pV7UKvzs2ohgH0/XsL+ny5jyzfP4ZkudS0cJRERkXXiAGArMHP6/3QSmQJqtYBGo8Erw3YiOzvPQtEREZG5FIyZMWSzRUxmLCw9PRtfbTpbKJEpoNEAd1OzsGvnRTNHRkRE5iaMsNkiJjMWlnhVhZxsdYll7O1lOHfutpkiIiIiqlg4ZsbCnMowY0mjEXBytDdDNEREZEkaSUAjcWp2ebFlxsICAtzRqHEVSCWs+KhWC/To1cB8QRERkUVwzIx+mMxYmCRJiH7nSYhifv/kcgk9ezdA/QZVzBsYERFRBcFkxgr06dcI7y0Og52dBJlMgp2dDHZ2+T+asGfq4pPPuls4QiIiMgcOANYPx8xYiRGj2qBvv0b4auNZXL58F25uSvTp15gL5hER2RDezkA/TGasiK+fC96Y2M7SYRAREVUoTGaIiIisBFtm9MNkhoiIyErw3kz6MdkA4CtXrmD48OGoU6cOHB0dUa9ePcyYMQM5OTklnhcaGgpJknS2119/XadMYmIiunXrBicnJ/j4+GDSpEnIy+Ny/0REVLEJI/zPFpmsZeb8+fPQaDT45JNPEBgYiLNnzyIqKgoZGRlYvHhxiedGRUVh9uzZ2sdOTk7af6vVanTr1g1+fn44evQobt68icjISNjb22PevHmmejpERERkpUyWzERERCAiIkL7uG7duoiLi8PKlStLTWacnJzg5+dX5LGffvoJf//9N/bv3w9fX1+0bNkSc+bMweTJkzFz5kwoFKWvqEtERGSNhIFjZmy1Zcas68yoVCp4eXmVWm7jxo2oWrUqmjVrhujoaGRmZmqPxcTEoHnz5vD19dXuCw8PR1paGv76668i68vOzkZaWprORkREZG0KbmdgyGaLzDYAOD4+HsuWLSu1VebFF19E7dq1Ub16dfzxxx+YPHky4uLi8O233wIAkpOTdRIZANrHycnJRdY5f/58zJo1ywjPgoiIiKxNuVtmpkyZUmiA7qPb+fPndc5JSkpCREQE+vfvj6ioqBLrf/XVVxEeHo7mzZtj0KBB+Pzzz7F9+3ZcunSpvKFqRUdHQ6VSabdr167pXRcREZGpaIyw2aJyt8xMnDgRQ4cOLbFM3bp1tf++ceMGOnXqhPbt22P16tXlDjA4OBhAfstOvXr14Ofnh+PHj+uUSUlJAYBix9kolUoolcpyX5uIiMicNBCQuM5MuZU7mfH29oa3t3eZyiYlJaFTp04ICgrCunXrIJOVf4hObGwsAKBatfxl/UNCQjB37lzcunULPj4+AIB9+/bBzc0NTZo0KXf9+tKIf5CniQGQC7msOeRSoNmuTURERP8x2ZiZpKQkhIaGonbt2li8eDFu376tPVbQgpKUlITOnTvj888/R9u2bXHp0iVs2rQJXbt2RZUqVfDHH39g/PjxeOqpp/DYY48BALp06YImTZpg8ODBWLhwIZKTkzF16lSMGjXKLK0vQmTjgXo2cjVbAfy7to0akEvBcLJbBJlU0+QxEBFR5WToWjG2OpvJZMnMvn37EB8fj/j4eNSsqfsFL0T+i52bm4u4uDjtbCWFQoH9+/dj6dKlyMjIgL+/P/r164epU6dqz5XL5di1axdGjBiBkJAQODs7Y8iQITrr0piKEAKZeSORJ/6HR3sm1eIk7uf2h4v995BJVU0eCxERVT7sZtKPJAoyCxuSlpYGd3d3qFQquLm5lfm8PE0MMvIGlVBCBqVsBBzsJhoeJBERWQV9vzP0uUZju0WQS45616MWD3Aub5JJY7VGZl1npqLL0XwLQF5CCQ1yNFvMFQ4REVUyBTeaNGSzRbzRZDloxC0A6hLLCNw1TzBERFTpsJtJP0xmykEm+UEt5CgpoZHA8TJERKQfDWBgMmOb2M1UDgrZcyi5ZUYGhewFc4VDREREYDJTLnKpDeykZwFIRR2FhOpQyIeYOywiIqokhARoDNhEUV9PNoDJTDlIkgQnu6VQyIYDeHhNGwl20lNwsd8KmeRpqfCIiKiC4wBg/XDMTDlJkj0c7d6GgxiDPHECQC7kUjPIpBqWDo2IiMgmMZnRkyS5wl562tJhEBFRJZLfssLZTOXFZIaIiMhKqA28nYGtJjMcM0NEREQVGltmiIiIrAS7mfTDZIaIiMhKMJnRD7uZiIiIqEJjywwREZGVUEsaCEn/mxJobPSGBmyZISIishJqCIM3fSxfvhwBAQFwcHBAcHAwjh8/XmL5rVu3olGjRnBwcEDz5s2xZ88eva5rLExmiIiIrITGwERGnzEzW7ZswYQJEzBjxgycPn0aLVq0QHh4OG7dulVk+aNHj2LgwIEYPnw4zpw5g969e6N37944e/asoU9fb5IQwuZGC6WlpcHd3R0qlQpubm6WDoeIiKyYOb4zCq7hrpgJSXLQux4hsqDKmVmuWIODg/H444/j448/BgBoNBr4+/tjzJgxmDJlSqHyL7zwAjIyMrBr1y7tvnbt2qFly5ZYtWqV3rEbwibHzBTkb2lpaRaOhIiIrF3Bd4U5/vbPk7IgGTAjSUjZAAp/vymVSiiVykLlc3JycOrUKURHR2v3yWQyhIWFISYmpshrxMTEYMKECTr7wsPD8d133+kdt6FsMplJT08HAPj7+1s4EiIiqijS09Ph7u5ukroVCgX8/PyQnLzA4LpcXFwKfb/NmDEDM2fOLFT2zp07UKvV8PX11dnv6+uL8+fPF1l/cnJykeWTk5MNC9wANpnMVK9eHdeuXYOrqyskybT3S09LS4O/vz+uXbtWIbu0GL/lVOTYAcZvaRU5fmuLXQiB9PR0VK9e3WTXcHBwQEJCAnJycgyuSwhR6LutqFaZysQmkxmZTIaaNWua9Zpubm5W8abUF+O3nIocO8D4La0ix29NsZuqReZhDg4OcHDQf7yMPqpWrQq5XI6UlBSd/SkpKfDz8yvyHD8/v3KVNwfOZiIiIrJRCoUCQUFBOHDggHafRqPBgQMHEBISUuQ5ISEhOuUBYN++fcWWNwebbJkhIiKifBMmTMCQIUPQpk0btG3bFkuXLkVGRgaGDRsGAIiMjESNGjUwf/58AMC4cePQsWNHvP/+++jWrRs2b96MkydPYvXq1RZ7DkxmTEypVGLGjBkVtr+S8VtORY4dYPyWVpHjr8ixV0QvvPACbt++jenTpyM5ORktW7bE3r17tYN8ExMTIZP915HTvn17bNq0CVOnTsXbb7+N+vXr47vvvkOzZs0s9RRsc50ZIiIiqjw4ZoaIiIgqNCYzREREVKExmSEiIqIKjckMERERVWhMZoiIiKhCYzJjgCtXrmD48OGoU6cOHB0dUa9ePcyYMaPU5ahDQ0MhSZLO9vrrr+uUSUxMRLdu3eDk5AQfHx9MmjQJeXl5Fo8/NTUVY8aMQcOGDeHo6IhatWph7NixUKlUOuUefX6SJGHz5s0Wjx8AsrKyMGrUKFSpUgUuLi7o169fodUszfH6A8DcuXPRvn17ODk5wcPDo0znFPXaSpKERYsWacsEBAQUOr5ggeH3fDE09qFDhxaKKyIiQqdMamoqBg0aBDc3N3h4eGD48OG4f/++UWPXJ/7c3FxMnjwZzZs3h7OzM6pXr47IyEjcuHFDp5w5Xnt94gfyl7mfPn06qlWrBkdHR4SFheHixYs6Zcz1+pf3OleuXCn2d3/r1q3acub47CHrw3VmDHD+/HloNBp88sknCAwMxNmzZxEVFYWMjAwsXry4xHOjoqIwe/Zs7WMnJyftv9VqNbp16wY/Pz8cPXoUN2/eRGRkJOzt7TFv3jyLxn/jxg3cuHEDixcvRpMmTXD16lW8/vrruHHjBrZt26ZTdt26dTpfVGX9wDVl/AAwfvx47N69G1u3boW7uztGjx6Nvn374tdffwVgvtcfyL9jbf/+/RESEoI1a9aU6ZybN2/qPP7hhx8wfPhw9OvXT2f/7NmzERUVpX3s6upqeMAP0Sd2AIiIiMC6deu0jx9dS2TQoEG4efMm9u3bh9zcXAwbNgyvvvoqNm3aZLTYgfLHn5mZidOnT2PatGlo0aIF7t69i3HjxqFnz544efKkTllTv/b6xA8ACxcuxEcffYQNGzagTp06mDZtGsLDw/H3339rl9E31+tf3uv4+/sX+t1fvXo1Fi1ahGeffVZnv6k/e8gKCTKqhQsXijp16pRYpmPHjmLcuHHFHt+zZ4+QyWQiOTlZu2/lypXCzc1NZGdnGyvUIpUl/kd9/fXXQqFQiNzcXO0+AGL79u1Gjq50pcV/7949YW9vL7Zu3ardd+7cOQFAxMTECCEs8/qvW7dOuLu763Vur169xNNPP62zr3bt2uKDDz4wPLAyKE/sQ4YMEb169Sr2+N9//y0AiBMnTmj3/fDDD0KSJJGUlGRgpEUz5LU/fvy4ACCuXr2q3WfO116Issev0WiEn5+fWLRokXbfvXv3hFKpFF999ZUQwnyvv7Gu07JlS/Hyyy/r7LPUZw9ZFruZjEylUsHLy6vUchs3bkTVqlXRrFkzREdHIzMzU3ssJiYGzZs317nFenh4ONLS0vDXX3+ZJO4CZY3/0XPc3NxgZ6fb0Ddq1ChUrVoVbdu2xdq1ayHMsD5jafGfOnUKubm5CAsL0+5r1KgRatWqhZiYGACWff3LKyUlBbt378bw4cMLHVuwYAGqVKmCVq1aYdGiRSbpJtPH4cOH4ePjg4YNG2LEiBH4559/tMdiYmLg4eGBNm3aaPeFhYVBJpPh2LFjlgi3RCqVCpIkFfrL3xpf+4SEBCQnJ+v87ru7uyM4OFjnd98cr78xrnPq1CnExsYW+btvic8esix2MxlRfHw8li1bVmoX04svvojatWujevXq+OOPPzB58mTExcXh22+/BQAkJyfrfJEC0D5OTk42TfAoe/wPu3PnDubMmYNXX31VZ//s2bPx9NNPw8nJCT/99BNGjhyJ+/fvY+zYscYOW6ss8ScnJ0OhUBT68vH19dW+tpZ6/fWxYcMGuLq6om/fvjr7x44di9atW8PLywtHjx5FdHQ0bt68iSVLllgo0nwRERHo27cv6tSpg0uXLuHtt9/Gs88+i5iYGMjlciQnJ8PHx0fnHDs7O3h5eVnda5+VlYXJkydj4MCBOnd2ttbXvuD1K+p3++HffXO8/sa4zpo1a9C4cWO0b99eZ78lPnvICli6acgaTZ48WQAocTt37pzOOdevXxf16tUTw4cPL/f1Dhw4IACI+Ph4IYQQUVFRokuXLjplMjIyBACxZ88eq4lfpVKJtm3bioiICJGTk1Ni2WnTpomaNWuWqV5Txr9x40ahUCgK7X/88cfFW2+9JYSwzOuvb1dHw4YNxejRo0stt2bNGmFnZyeysrKsJnYhhLh06ZIAIPbv3y+EEGLu3LmiQYMGhcp5e3uLFStWlFqfueLPyckRPXr0EK1atRIqlarEsmV97U0d/6+//ioAiBs3bujs79+/v3j++eeFEOZ7/Q29TmZmpnB3dxeLFy8utWx5Pnuo4mLLTBEmTpyIoUOHllimbt262n/fuHEDnTp1Qvv27fW6a2hwcDCA/JaFevXqwc/PD8ePH9cpUzDbxs/Pr9T6zBF/eno6IiIi4Orqiu3bt8Pe3r7E8sHBwZgzZw6ys7NLvXmcKeP38/NDTk4O7t27p9M6k5KSon1tzf366+vIkSOIi4vDli1bSi0bHByMvLw8XLlyBQ0bNiy2nLlif7iuqlWrIj4+Hp07d4afnx9u3bqlUyYvLw+pqalW89rn5ubi+eefx9WrV3Hw4EGdVpmilPW1B0wbf8Hrl5KSgmrVqmn3p6SkoGXLltoy5nj9Db3Otm3bkJmZicjIyFLLluezhyowS2dTFd3169dF/fr1xYABA0ReXp5edfzyyy8CgPj999+FEP8NQE1JSdGW+eSTT4Sbm1uZ/rorD33iV6lUol27dqJjx44iIyOjTOe8++67wtPT05BQi1Te+AsGAG/btk277/z580UOADbH619An9aBIUOGiKCgoDKV/fLLL4VMJhOpqal6RFcyQ1pmrl27JiRJEjt27BBC/Dcw9OTJk9oyP/74o9UMAM7JyRG9e/cWTZs2Fbdu3SrTOaZ87YUo/wDgh1szVCpVkQOATf36G3qdjh07in79+pXpWqb67CHrwmTGANevXxeBgYGic+fO4vr16+LmzZva7eEyDRs2FMeOHRNCCBEfHy9mz54tTp48KRISEsSOHTtE3bp1xVNPPaU9Jy8vTzRr1kx06dJFxMbGir179wpvb28RHR1t8fhVKpUIDg4WzZs3F/Hx8TrnFCQTO3fuFJ9++qn4888/xcWLF8WKFSuEk5OTmD59usXjF0KI119/XdSqVUscPHhQnDx5UoSEhIiQkBDtcXO9/kIIcfXqVXHmzBkxa9Ys4eLiIs6cOSPOnDkj0tPTtWUaNmwovv32W53zVCqVcHJyEitXrixU59GjR8UHH3wgYmNjxaVLl8SXX34pvL29RWRkpEVjT09PF2+++aaIiYkRCQkJYv/+/aJ169aifv36OkliRESEaNWqlTh27Jj45ZdfRP369cXAgQONGrs+8efk5IiePXuKmjVritjYWJ3ft4JZbuZ67fWJXwghFixYIDw8PMSOHTvEH3/8IXr16iXq1KkjHjx4oC1jrte/tOsU9d4VQoiLFy8KSZLEDz/8UKhOc332kPVhMmOAdevWFdsvXCAhIUEAEIcOHRJCCJGYmCieeuop4eXlJZRKpQgMDBSTJk0q1O9+5coV8eyzzwpHR0dRtWpVMXHiRJ2pz5aK/9ChQ8Wek5CQIITIn2LZsmVL4eLiIpydnUWLFi3EqlWrhFqttnj8Qgjx4MEDMXLkSOHp6SmcnJxEnz59dBIgIczz+guR37pSVPwPxwtArFu3Tue8Tz75RDg6Oop79+4VqvPUqVMiODhYuLu7CwcHB9G4cWMxb948o7cqlTf2zMxM0aVLF+Ht7S3s7e1F7dq1RVRUlM4UeCGE+Oeff8TAgQOFi4uLcHNzE8OGDdP5grZU/AW/SyWdY67XXp/4hchvnZk2bZrw9fUVSqVSdO7cWcTFxenUa67Xv7TrFPXeFUKI6Oho4e/vX+Tnibk+e8j6SEJwzhoRERFVXFxnhoiIiCo0JjNERERUoTGZISIiogqNyQwRERFVaExmiIiIqEJjMkNEREQVGpMZIiIiqtCYzBAREVGFxmSGiIiIKjQmM0RERFShMZkhIiKiCu3/3fzLCfSEEfEAAAAASUVORK5CYII=\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "**Inferences of the scatter plot:**\n", - "\n", - "1.Cluster Formation: The model has classified the data into two clusters, but\n", - " similar to the AEFCN plot, the separation between them is not distinct. Most points belong to a single dominant cluster (dark purple), while a small number of points are assigned to the other cluster (yellow).\n", - "\n", - "2.Linear Data Distribution: The points in the plot follow a diagonal pattern,\n", - " indicating that the dataset might have a strong correlation between the features being visualized. This suggests that the data structure might be inherently linear.\n", - "\n", - "3.Imbalance in Clusters: The clustering results show a highly imbalanced\n", - " distribution, where one cluster contains most of the points. This could imply that either the model is struggling to identify meaningful cluster boundaries or that the dataset itself has an inherent class imbalance\n" - ], - "metadata": { - "id": "E0FZWgqhPe8v" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **AEDCNNClusterer:**" - ], - "metadata": { - "id": "6xWiuNT7izNY" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **AEDCNNClusterer (Auto-Encoder Dilated Convolutional Network)**\n", - "The **AEDCNNClusterer** is built on an **Auto-Encoder** with a **Dilated Convolutional Network (DCNN)** backbone.Dilated convolutions use dilated filters to expand the receptive field exponentially, allowing the model to capture long-term dependencies in the data without losing resolution.This method is ideal for detecting patterns over extended time windows.\n" - ], - "metadata": { - "id": "H1CANB-oD-t_" - } - }, - { - "cell_type": "code", - "source": [ - "from aeon.clustering.deep_learning import AEDCNNClusterer" - ], - "metadata": { - "id": "gM5ja7I14GJK" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "model = AEDCNNClusterer(n_epochs=10, random_state=42,dilation_rate=1)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qv-xuWhvE1_6", - "outputId": "d3c6eb67-8f10-4446-d46b-739f8c72219a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='coolwarm')\n", - "plt.title('Cluster Distribution with AEDCNN')\n", - "plt.colorbar(label='Cluster')\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "AQIaYKy6E5RK", - "outputId": "6de1c9b3-1d85-425c-9eca-e2edbbf36ae9" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#**Inferences from the scatter plot:**\n", - "1. Compared to AEFCN and AEResNet, AEDCNN appears to have a slightly better spread of cluster labels across the data points.\n", - "2. Most data points fall into one main cluster (red), with very few points classified into another cluster (blue).\n", - "3. The points follow a strong diagonal trend, indicating that the underlying feature space has a continuous, linear structure rather than distinct groups.\n" - ], - "metadata": { - "id": "_sqUBae7ePwg" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **AEDRNNClusterer:**" - ], - "metadata": { - "id": "ep5dTHIEi4xD" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **AEDRNNClusterer (Auto-Encoder Dilated Recurrent Neural Network)**\n", - "The **AEDRNNClusterer** integrates an Auto-Encoder with a **Dilated Recurrent Neural Network (DRNN)** backbone.DRNNs combine the strengths of RNNs (sequence modeling) with dilated connections to capture patterns over long temporal sequences efficiently.they are Suitable for tasks where sequential relationships are vital (e.g., speech data, financial trends).\n", - "\n" - ], - "metadata": { - "id": "ickHTlKQFDDZ" - } - }, - { - "cell_type": "code", - "source": [ - "from aeon.clustering.deep_learning import AEDRNNClusterer" - ], - "metadata": { - "id": "O2Xj2LilFBjX" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "model = AEDRNNClusterer(n_epochs=10, random_state=42)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "k4z2dOrzFjx2", - "outputId": "392d57ef-54ed-4f86-e236-3245145cd0a2" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\r\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 2s/step" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x785f3b7428e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 1s/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 231ms/step\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='magma')\n", - "plt.title('Cluster Distribution with AEDRNN')\n", - "plt.colorbar(label='Cluster')\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "mE4l1C9AFm-U", - "outputId": "a04a18cf-e3bb-4cf0-88da-cbe8db8cb1fc" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# **Inferences from the scatter plot:**\n", - "1.The scatter plot shows two clear clusters, one in black (cluster 0) and another in light yellow (cluster 1).Unlike completely mixed distributions, this separation indicates that AEDRNN has recognized some underlying structure in the data.\n", - "\n", - "2.Similar to other models, the data points are aligned along a diagonal trend, indicating that the latent space follows a strong linear pattern.This suggests that AEDRNN’s clustering is more of a gradual transition rather than a hard separation.\n", - "\n" - ], - "metadata": { - "id": "UES_F4Iedmbg" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **AEAttentionBiGRUClusterer:**" - ], - "metadata": { - "id": "n2poXz2KjAvo" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **AEAttentionBiGRUClusterer (Auto-Encoder Attention Bidirectional GRU Network)**\n", - "The **AEAttentionBiGRUClusterer** integrates an Auto-Encoder with an Attention-based **Bidirectional Gated Recurrent Unit (BiGRU)** network.\n", - "The Attention Mechanism allows the model to selectively focus on the most relevant parts of the time series during clustering.The Bidirectional GRU enhances this by processing the sequence from both forward and backward directions, improving sequence dependency recognition.It is Suitable for tasks requiring fine-grained sequence interpretation.Excels in datasets where certain segments of the sequence are more influential than others.\n", - "\n", - "\n" - ], - "metadata": { - "id": "cRH_XEfeF6Cs" - } - }, - { - "cell_type": "code", - "source": [ - "from aeon.clustering.deep_learning import AEAttentionBiGRUClusterer" - ], - "metadata": { - "id": "iuy-H4F5F4dk" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "model = AEAttentionBiGRUClusterer(n_epochs=10, random_state=42)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9Zx6TvNFHBwg", - "outputId": "290fa9d2-0fcf-469d-f6c3-f69cbdebe9e3" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 901ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 631ms/step\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='inferno')\n", - "plt.title('Cluster Distribution with AEAttentionBiGRU')\n", - "plt.colorbar(label='Cluster')\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "-AvTzfdlHGeF", - "outputId": "3461f910-6547-40c9-a402-275ee128a92b" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# **Inferences from the plot:**\n", - "1.The plot shows two distinct clusters: one in black (cluster 0) and another in\n", - " light yellow (cluster 1).The dominance of black-colored points indicates that most of the data falls into a single cluster, with only a few points classified differently.\n", - "\n", - "2.The few yellow points suggest that AEAttentionBiGRU is detecting some\n", - " anomalies or outliers.\n", - " \n", - "3.These yellow points are often found at the edges or slightly away from the densest region, meaning that the model might be learning small variations and\n", - " assigning them to a different cluster.\n" - ], - "metadata": { - "id": "UyQ5LH82h5A-" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **AEBiGRUClusterer:**" - ], - "metadata": { - "id": "gLoHAkV-jGsX" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **AEBiGRUClusterer (Auto-Encoder Bidirectional GRU Network)**\n", - "The **AEBiGRUClusterer** is an Auto-Encoder with a **Bidirectional GRU (BiGRU)** architecture.GRUs are similar to LSTMs but with a simpler structure, making them faster and more efficient for time series data.The bidirectional structure enhances the model’s ability to detect patterns by combining forward and backward sequence insights.It Performs well on shorter sequences with frequent fluctuations.Suitable for tasks requiring fast training without compromising performance.\n", - "\n" - ], - "metadata": { - "id": "FTnLddtrHMQE" - } - }, - { - "cell_type": "code", - "source": [ - "from aeon.clustering.deep_learning import AEBiGRUClusterer" - ], - "metadata": { - "id": "duoJ4wMQHnFt" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "model = AEBiGRUClusterer(n_epochs=10, random_state=42)\n", - "model.fit(X_train)\n", - "y_pred = model.predict(X_test)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "XfGf-pgxHnRy", - "outputId": "84b30734-4b03-4b01-e099-4a761edcd0ab" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 918ms/step\n", - "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 127ms/step\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap='cividis')\n", - "plt.title('Cluster Distribution with AEBiGRU')\n", - "plt.colorbar(label='Cluster')\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 452 - }, - "id": "RlH69d_rHnVX", - "outputId": "ebbb3fb9-13b4-4ccc-e5fe-214cbba0e0ef" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#**Inferences from the scatter plot:**\n", - "\n", - "1.The plot shows two distinct clusters (dark blue and yellow), indicating that AEBiGRU is effectively distinguishing between two groups.\n", - "\n", - "2.Unlike previous models where yellow points were more scattered, AEBiGRU has a larger, more structured region assigned to cluster 1.\n", - "\n", - "3.Since the clusters are positioned along a linear trend, this might imply that the underlying feature representation is influenced by a dominant latent factor." - ], - "metadata": { - "id": "NYnhRFqk2r_v" - } - }, - { - "cell_type": "markdown", - "source": [ - "**References:**\n", - "\n", - "[1]Zhao et. al, Convolutional neural networks for time series classification,\n", - "Journal of Systems Engineering and Electronics, 28(1):2017.\n", - "\n", - "[2]Wang et. al, Time series classification from scratch with deep neural networks: A strong baseline, International joint conference on neural networks (IJCNN), 2017.\n", - "\n" - ], - "metadata": { - "id": "sq0CNNhuI3_O" - } - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "-yv9NB8JHyUE" - }, - "execution_count": null, - "outputs": [] + { + "cell_type": "markdown", + "source": [ + "# **Deep Learning Based Clustering**\n", + "The aeon.clustering.deeplearning module provides powerful deep learning models designed specifically for time series clustering. These models leverage advanced architectures like **Auto-Encoders, Residual Networks, Dilated Convolutions, and Attention Mechanisms** to effectively identify patterns, trends, and groupings in complex temporal data. Each model is tailored to handle different data characteristics, improving clustering accuracy and performance across various domains such as healthcare, finance, and signal processing." + ], + "metadata": { + "id": "2Ru1riLYWFE3" + } + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ], + "metadata": { + "id": "86gsiHDbuoz-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from aeon.datasets import load_arrow_head" + ], + "metadata": { + "id": "7xvwY48cur5i" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X_train, y_train = load_arrow_head(split=\"train\")\n", + "X_test, y_test = load_arrow_head(split=\"test\")" + ], + "metadata": { + "id": "EkRKdT47N7Oc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(X_train[:5])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5upX35-AXSnH", + "outputId": "3bacd61e-0b2f-41bd-d07e-c7b9c3a886fb" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[[-1.9630089 -1.9578249 -1.9561449 ... -1.9053929 -1.9239049 -1.9091529]]\n", + "\n", + " [[-1.7745713 -1.7740359 -1.7765863 ... -1.7292269 -1.7756704 -1.7893245]]\n", + "\n", + " [[-1.8660211 -1.8419912 -1.8350253 ... -1.8625124 -1.8633682 -1.8464925]]\n", + "\n", + " [[-2.0737575 -2.0733013 -2.0446071 ... -2.0269634 -2.073405 -2.0752917]]\n", + "\n", + " [[-1.7462554 -1.7412629 -1.7227405 ... -1.7434421 -1.7627288 -1.7634281]]]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(y_train[:5])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3LzGs-PYeMJ1", + "outputId": "581523e0-782c-4b72-aa3a-0276bd89c39d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['0' '1' '2' '0' '1']\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **AEFCNClusterer (Auto-Encoder Fully Convolutional Network)**\n", + "The **AEFCNClusterer** is a deep learning model that leverages a **Fully Convolutional Network (FCN)** architecture with an **Auto-Encoder** structure for clustering. It combines feature extraction with convolutional layers and reconstruction capabilities via auto-encoders. \n", + "FCNs are effective for extracting spatial hierarchies in time series data without requiring fully connected layers, making them highly efficient.\n" + ], + "metadata": { + "id": "b-XHpTDjxeSd" + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEFCNClusterer" + ], + "metadata": { + "id": "DGbXlOPLxdO-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEFCNClusterer(n_epochs=10, batch_size=3)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kxYYwMmKN_Uz", + "outputId": "0f2ccdef-1a79-41ad-94f2-a5708d41443f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 81ms/step\n" + ] } - ] -} \ No newline at end of file + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"viridis\")\n", + "plt.title(\"Cluster Distribution with AEFCN\")\n", + "plt.colorbar(label=\"Cluster\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "q3oOIG3jOtbf", + "outputId": "258be6d8-6dc7-4c47-b8f3-f5b47412967b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Inferences from scatter plot:**\n", + "\n", + "1.The model has categorized the data into at least two clusters, as indicated \n", + " by the color differences. However, the separation is not very distinct, meaning the clusters might overlap in feature space.\n", + "\n", + "2.The data points appear to be aligned along a diagonal trend, which suggests\n", + " that the dataset might have a strong correlation between the two plotted dimensions.\n", + "\n", + "3.The majority of points belong to one dominant cluster (purple), while the\n", + " second cluster (yellow) contains fewer points. This might indicate an imbalance in the dataset or that the model is biased toward grouping most points into one cluster." + ], + "metadata": { + "id": "I9GhXLYBhq27" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEResNetClusterer:**" + ], + "metadata": { + "id": "rR3RUBPpihGC" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEResNetClusterer (Auto-Encoder Residual Network)**\n", + "The **AEResNetClusterer** applies an Auto-Encoder architecture integrated with a **Residual Network** (ResNet) backbone.ResNet models use skip connections, allowing gradients to flow directly through layers, reducing vanishing gradient issues.This approach enhances learning in deep networks and efficiently captures complex temporal patterns in time series data.\n" + ], + "metadata": { + "id": "wuFuNtkNP5tN" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "prOOOUzzioBS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEResNetClusterer" + ], + "metadata": { + "id": "6atNZu4ADFxb" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEResNetClusterer(n_epochs=10, random_state=42, batch_size=3)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ], + "metadata": { + "id": "ipdqBhoAP4-9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9059875c-26ae-416e-e0ba-d31ca587b9e9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 468ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 147ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"plasma\")\n", + "plt.title(\"Cluster Distribution with AEResNet\")\n", + "plt.colorbar(label=\"Cluster\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "bHI_0_064GFu", + "outputId": "f1d5dd1d-3c4d-42cd-a75c-d3c00da85bca" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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JTIMGDUqdpmzM1/1hGzduhI+PD5YvX17o2Lfffovt27dj1apVRklSq1WrhvHjx2PWrFn47bff0K5dO+0gbR8fnzK97vXq1cPEiRMxceJEXLx4ES1btsT777+PL7/8ssjyBa0zM2fOxJAhQ4qs7/79+6Ve21SvP5E+OGamknrrrbfg7OyMV155BSkpKYWOX7p0CR9++GGx5xd8oD7cP14wzfdhd+/eLfSXbsuWLQEA2dnZAAAnJycAKPQF7OPjg9DQUHzyySe4efNmoRhu375dbHyGuHr1KoYOHQqFQoFJkyYVWy4tLQ15eXk6+5o3bw6ZTKZ9bgDg7OxcZHKhj5iYGJ2ps9euXcOOHTvQpUsXbUtDvXr1oFKpdLp0bt68ie3btxeqr6yxhYWFQaFQ4KOPPtL5ea5ZswYqlQrdunUz4FnpCg4Ohr29Pd577z14eXmhadOmAPKTnN9++w3/+9//ytQq4+zsrJ0ibywPHjzAt99+i+7du+O5554rtI0ePRrp6elGXatlzJgxcHJywoIFCwDkt9a4ublh3rx5RY5DK3hfZGZmFlo5uF69enB1ddX5/SzKG2+8AQ8PD8yePbvQseeffx4xMTH48ccfCx27d++e9j1R3PuayBLYMlNJ1atXD5s2bcILL7yAxo0b66wAfPToUWzdurXEezF16dIFtWrVwvDhwzFp0iTI5XKsXbsW3t7eOrcC2LBhA1asWIE+ffqgXr16SE9Px6effgo3Nzd07doVQH43S5MmTbBlyxY0aNAAXl5eaNasGZo1a4bly5fjiSeeQPPmzREVFYW6desiJSUFMTExuH79On7//XeDXofTp0/jyy+/hEajwb1793DixAl88803kCQJX3zxRYlTSQ8ePIjRo0ejf//+aNCgAfLy8vDFF19ALpejX79+2nJBQUHYv38/lixZgurVq6NOnTolLn9fkmbNmiE8PFxnajYAnQGbAwYMwOTJk9GnTx+MHTtWO223QYMGhdYQKWts3t7eiI6OxqxZsxAREYGePXsiLi4OK1aswOOPP16mFqyycnJyQlBQEH777TftGjNAfstMRkYGMjIyypTMBAUFYcuWLZgwYQIef/xxuLi4FLlkf3ns3LkT6enp2inij2rXrh28vb2xceNGvPDCC9r9Fy5cKLIlxNfXF88880yJ16xSpQqGDRuGFStW4Ny5c2jcuDFWrlyJwYMHo3Xr1hgwYID2fbd792506NABH3/8MS5cuIDOnTvj+eefR5MmTWBnZ4ft27cjJSVFZ2Xrori7u2PcuHFFDgSeNGkSdu7cie7du2Po0KEICgpCRkYG/vzzT2zbtg1XrlzRrilV3PuayOwsOZWKTO/ChQsiKipKBAQECIVCIVxdXUWHDh3EsmXLdKZDPzo1WwghTp06JYKDg4VCoRC1atUSS5YsKTSl9/Tp02LgwIGiVq1aQqlUCh8fH9G9e3ed6cVCCHH06FERFBQkFApFoemcly5dEpGRkcLPz0/Y29uLGjVqiO7du4tt27ZpyxRct6Qp4A8rmHJasNnZ2QkvLy8RHBwsoqOjxdWrVwud8+j038uXL4uXX35Z1KtXTzg4OAgvLy/RqVMnsX//fp3zzp8/L5566inh6OgoAGhfx4Kp0rdv3y50reKmZo8aNUp8+eWXon79+kKpVIpWrVrpTEcu8NNPP4lmzZoJhUIhGjZsKL788ssi6ywutkd/jgU+/vhj0ahRI2Fvby98fX3FiBEjxN27d3XKPDzV92HFTRkvyqRJkwQA8d577+nsDwwMFADEpUuXdPYXNTX7/v374sUXXxQeHh4CgPbaBWUfnVJf8Duxbt26YuPq0aOHcHBwEBkZGcWWGTp0qLC3txd37twRQpQ8Nbtjx47a8wqmZhfl0qVLQi6X67wHDx06JMLDw4W7u7twcHAQ9erVE0OHDtW+t+7cuSNGjRolGjVqJJydnYW7u7sIDg4WX3/9tU7dxf287t69K9zd3Yuc3p2eni6io6NFYGCgUCgUomrVqqJ9+/Zi8eLFIicnR1uupPc1kTlJQpRhxB4RERGRleKYGSIiIqrQmMwQERFRhcZkhoiIiCo0JjNEREQ26ueff0aPHj1QvXp1SJKE7777rtRzDh8+jNatW2vvqL5+/XqTx1kaJjNEREQ2KiMjAy1atChykciiJCQkoFu3bujUqRNiY2Pxxhtv4JVXXilyXSJz4mwmIiIigiRJ2L59O3r37l1smcmTJ2P37t06t8sZMGAA7t27h71795ohyqLZ5KJ5Go0GN27cgKurK5fkJiKiEgkhkJ6ejurVq+vcVd7YsrKykJOTY3A9QohC321KpbLUm/+WRUxMTKFbXYSHh+ONN94wuG5D2GQyc+PGDfj7+1s6DCIiqkCuXbtW5hvMlldWVhYC6rggJdnwG8a6uLjg/v37OvtmzJiBmTNnGlx3cnJyofum+fr6Ii0tDQ8ePDD6jXXLyiaTGVdXVwD5v5gl3TGZiIgoLS0N/v7+2u8OU8jJyUFKshp/XQyAq5v+rT/paRo0rX+l0PebMVplrJlNJjMFzW9ubm5MZoiIqEzMMSzB1U0GNwOSmQKm+n7z8/MrdPPilJQUuLm5WaxVBrDRZIaIiMgaSRpA0uifNEkaIwZThJCQEOzZs0dn3759+xASEmLaC5eCU7OJiIishZAM38rh/v37iI2NRWxsLID8qdexsbFITEwEAERHRyMyMlJb/vXXX8fly5fx1ltv4fz581ixYgW+/vprjB8/3mgvgT7YMkNERGQlJI1kYMtM+c49efIkOnXqpH08YcIEAMCQIUOwfv163Lx5U5vYAECdOnWwe/dujB8/Hh9++CFq1qyJzz77DOHh4XrHbAw2uc5MWloa3N3doVKpOGaGiIhKZI7vjIJrXL8eCDc3uQH1qFGzZrzNfb+xZYaIiMhK5I+ZMex8W8RkhoiIyFpo/t0MOd8GcQAwERERVWhsmSEiIrISksjfDDnfFjGZISKiSkctLkKtOQvAHnayEMikKpYOqUwkYeCYGSYzREREFZtGXENm3ptQixP/7VTbwV72PBzl0yBJlXtZf1vFZIaIiCoFjbiN+7n9IfDPI0fykKvZDCFS4GS32iy3JdCbRuRvhpxvgzgAmIiIKoUc9fp/E5mi7jytQZ44oNtiY4UKxswYstkiJjNERFQp5Gi+RtGJTAE5cjTfmCscMiN2MxERUaUgkFpKCTWEuG2WWPTGdWb0wmSGiIgqBQlVIVBSsiKHJPmZLR59SBoByYBxL4acW5Gxm4mIiCoFhWwASv5aU0Mh62eucPSjMcJmg5jMEBFRpaCQD4WEagCKulGjBDupG+RSa3OHRWbAZIaIiCoFmeQJF/ttsJOeBPDw9GsHKGSvwMluiXVPywZnM+mLY2aIiKjSkEm+cLZfC424DrX4C4A97KTHIUmulg6tbDgAWC9MZoiIqNKRSTUhk2paOgwyEyYzREREVkLSGHhvJrbMEBERkUUJAMKAgS82OmaGA4CJiIioQmPLDBERkZWQhIHdTDbaMsNkhoiIyFpwNpNe2M1EREREFRpbZoiIiKyEoQvfsZuJiIiILIvdTHphMkNERGQtmMzohWNmiIiIqEIzaTKTmpqKQYMGwc3NDR4eHhg+fDju379fbPkrV65AkqQit61bt2rLFXV88+bNpnwqREREJpc/ZkYyYLP0M7AMk3YzDRo0CDdv3sS+ffuQm5uLYcOG4dVXX8WmTZuKLO/v74+bN2/q7Fu9ejUWLVqEZ599Vmf/unXrEBERoX3s4eFh9PiJiIjMit1MejFZMnPu3Dns3bsXJ06cQJs2bQAAy5YtQ9euXbF48WJUr1690DlyuRx+fn46+7Zv347nn38eLi4uOvs9PDwKlS1OdnY2srOztY/T0tLK+3SIiMgILl++i9u3MlG9ugv8a7lbOhyqJEzWzRQTEwMPDw9tIgMAYWFhkMlkOHbsWJnqOHXqFGJjYzF8+PBCx0aNGoWqVauibdu2WLt2LUQJ97KYP38+3N3dtZu/v3/5nxAREentlyOJ6PTkBrRs+gme6fQFmjZcia5dNuLM6WRLh2ZdNEbYbJDJkpnk5GT4+Pjo7LOzs4OXlxeSk8v2y7tmzRo0btwY7du319k/e/ZsfP3119i3bx/69euHkSNHYtmyZcXWEx0dDZVKpd2uXbtW/idERER6OXggAT2e/apQ4hJz9Dq6PP0FTp64YaHIrJAwwmaDyt3NNGXKFLz33nslljl37pzeARV48OABNm3ahGnTphU69vC+Vq1aISMjA4sWLcLYsWOLrEupVEKpVBocExERlY9GIzBm5A/QaEShm0Gr1QJCaDBh3I/4+egwywRIlUK5k5mJEydi6NChJZapW7cu/Pz8cOvWLZ39eXl5SE1NLdNYl23btiEzMxORkZGllg0ODsacOXOQnZ3NpIWIyIoc+fkqriUWP05RoxGIPZOCv87eQtNmPsWWsxWSRoKkkQw63xaVO5nx9vaGt7d3qeVCQkJw7949nDp1CkFBQQCAgwcPQqPRIDg4uNTz16xZg549e5bpWrGxsfD09GQiQ0RkZa5eUZWp3JWEe0xmAMO7itjNZFyNGzdGREQEoqKisGrVKuTm5mL06NEYMGCAdiZTUlISOnfujM8//xxt27bVnhsfH4+ff/4Ze/bsKVTv999/j5SUFLRr1w4ODg7Yt28f5s2bhzfffNNUT4WIiPTk6elQxnKOJo6EKjOTrjOzceNGjB49Gp07d4ZMJkO/fv3w0UcfaY/n5uYiLi4OmZmZOuetXbsWNWvWRJcuXQrVaW9vj+XLl2P8+PEQQiAwMBBLlixBVFSUKZ8KERHpofMzdeHiosD9+znFlqlW3QXBITXMGJUVExJgSFeRsM1uJkmUNKe5kkpLS4O7uztUKhXc3NwsHQ4RUaX28YfH8faUg8UeX/VZN7w4qLkZIyofc3xnFFzj7r5AuDnL9a8nQw3PZ+Jt7vuN92YiIiKTGjX2ccyc3RFKZf6XtNwuv/XAydkeH3wUbtWJjNlxarZeeNdsIiIyKUmSMGFSCF6OaoXvd8Th1q1MVK/hip69GsDZWWHp8KgSYDJDRERm4eHhgMFDWlg6DOumMXDMDKdmExERkUUJybBBvDY6AJhjZoiIiKhCY8sMERGRlZA0+Zsh59siJjNERETWgmNm9MJuJiIiIqrQ2DJDRERkLXhvJr0wmSEiIrIW7GbSC7uZiIiIqEJjywwREZG14DozemEyQ0REZC00/26GnG+DmMwQERFZC7bM6IVjZoiIiKhCY8sMERGRlRBCgjBgRpKw0ZYZJjNERETWgt1MemE3ExEREVVobJkhIiKyFpzNpBcmM0RERNaC3Ux6YTcTERERVWhsmSEiIrIWvDeTXpjMEBERWQt2M+mF3UxERERUobFlhoiIyFqwm0kvTGaIiIishfh3M+R8G8RuJiIiIishNJLBmz6WL1+OgIAAODg4IDg4GMePHy+x/NKlS9GwYUM4OjrC398f48ePR1ZWll7XNgYmM0RERDZsy5YtmDBhAmbMmIHTp0+jRYsWCA8Px61bt4osv2nTJkyZMgUzZszAuXPnsGbNGmzZsgVvv/22mSP/D5MZIiIia1Ewm8mQrZyWLFmCqKgoDBs2DE2aNMGqVavg5OSEtWvXFln+6NGj6NChA1588UUEBASgS5cuGDhwYKmtOabEZIaIiMhaFAwANmQDkJaWprNlZ2cXebmcnBycOnUKYWFh2n0ymQxhYWGIiYkp8pz27dvj1KlT2uTl8uXL2LNnD7p27WrkF6PsmMwQERFVMv7+/nB3d9du8+fPL7LcnTt3oFar4evrq7Pf19cXycnJRZ7z4osvYvbs2XjiiSdgb2+PevXqITQ01KLdTJzNREREZC0EDFw0L/8/165dg5ubm3a3Uqk0LK6HHD58GPPmzcOKFSsQHByM+Ph4jBs3DnPmzMG0adOMdp3yYDJDRERkLYSB68z8mwi5ubnpJDPFqVq1KuRyOVJSUnT2p6SkwM/Pr8hzpk2bhsGDB+OVV14BADRv3hwZGRl49dVX8c4770AmM3+nj8muOHfuXLRv3x5OTk7w8PAo0zlCCEyfPh3VqlWDo6MjwsLCcPHiRZ0yqampGDRoENzc3ODh4YHhw4fj/v37JngGRERElZtCoUBQUBAOHDig3afRaHDgwAGEhIQUeU5mZmahhEUulwPI/x63BJMlMzk5Oejfvz9GjBhR5nMWLlyIjz76CKtWrcKxY8fg7OyM8PBwnbnrgwYNwl9//YV9+/Zh165d+Pnnn/Hqq6+a4ikQERGZlRCGb+U1YcIEfPrpp9iwYQPOnTuHESNGICMjA8OGDQMAREZGIjo6Wlu+R48eWLlyJTZv3oyEhATs27cP06ZNQ48ePbRJjbmZrJtp1qxZAID169eXqbwQAkuXLsXUqVPRq1cvAMDnn38OX19ffPfddxgwYADOnTuHvXv34sSJE2jTpg0AYNmyZejatSsWL16M6tWrF1l3dna2zkjutLQ0A54ZERGRiVjgRpMvvPACbt++jenTpyM5ORktW7bE3r17tYOCExMTdVpipk6dCkmSMHXqVCQlJcHb2xs9evTA3Llz9Y/bQFYzmykhIQHJyck608Pc3d0RHBysnR4WExMDDw8PbSIDAGFhYZDJZDh27Fixdc+fP19nVLe/v7/pnggREVEFM3r0aFy9ehXZ2dk4duwYgoODtccOHz6s0zBhZ2eHGTNmID4+Hg8ePEBiYiKWL19e5iElpmA1yUzBFLCSpoclJyfDx8dH57idnR28vLyKnUIGANHR0VCpVNrt2rVrRo6eiIjICIy0zoytKVcyM2XKFEiSVOJ2/vx5U8WqN6VSqR3ZXdYR3kREROYmhGTwZovKNWZm4sSJGDp0aIll6tatq1cgBVPAUlJSUK1aNe3+lJQUtGzZUlvm0XtF5OXlITU1tdgpZERERBWGoa0rNtoyU65kxtvbG97e3iYJpE6dOvDz88OBAwe0yUtaWhqOHTumnREVEhKCe/fu4dSpUwgKCgIAHDx4EBqNRqd/j4iIiGyHycbMJCYmIjY2FomJiVCr1YiNjUVsbKzOmjCNGjXC9u3bAQCSJOGNN97Au+++i507d+LPP/9EZGQkqlevjt69ewMAGjdujIiICERFReH48eP49ddfMXr0aAwYMKDYmUxEREQVhgVuNFkZmGxq9vTp07Fhwwbt41atWgEADh06hNDQUABAXFwcVCqVtsxbb72lXUXw3r17eOKJJ7B37144ODhoy2zcuBGjR49G586dIZPJ0K9fP3z00UemehpERERmY+i4F1sdMyMJSy3XZ0FpaWlwd3eHSqXiYGAiIiqROb4zCq5x+8P2cHPUv50h7UEevMcdtbnvN96biYiIyFpo/t0MOd8GMZkhIiKyFhZYAbgysJpF84iIiIj0wZYZIiIiKyE0EoQBa8UYcm5FxmSGiIjIWrCbSS/sZiIiIqIKjS0zREREVoLrzOiHyQwREZG1EAbem4nJDBEREVkUx8zohWNmiIiIqEJjywwREZGVECJ/M+R8W8RkhoiIyFpoDBwzY6PrzLCbiYiIiCo0tswQERFZCU7N1g+TGSIiImvB2Ux6YTcTERERVWhsmSEiIrISvNGkfpjMEBERWQsBA7uZjBZJhcJuJiIiIqrQ2DJDRERkJTibST9MZoiIiKwFF83TC5MZIiIiK8HbGeiHY2aIiIioQmPLDBERkZXgmBn9MJkhIiKyFhwzoxcmM0REBhBCQC1+h0ZcgSS5wk7qAElysHRYRDaFyQwRkZ7yNKfxIG8KNIh/aK8rHORjoJANhyTZ5l/JpD92M+mHyQwRkR7UmrPIyBsEIPeRI+nIUs+DwAM4yMdYIjSq0Ay80SRsM5nhbCYiIj08UC8EkAdAU+TxbPUyaESqWWMislVMZoiIykkjbkMtfgGgLqGUGrmaXeYKiSqJgm4mQzZbxG4mIqJyEuJOGUrJIcRtk8dClQxnM+mFLTNEROUkSVVR+tgENSTJxxzhENk8kyUzc+fORfv27eHk5AQPD49Sy+fm5mLy5Mlo3rw5nJ2dUb16dURGRuLGjRs65QICAiBJks62YMECEz0LIqLCZJI37KQnAchLKGUHe1l3c4VElUTB7QwM2WyRyZKZnJwc9O/fHyNGjChT+czMTJw+fRrTpk3D6dOn8e233yIuLg49e/YsVHb27Nm4efOmdhszhjMGiMi8HORvIb+nvuiPUaX8DcgkT7PGRBUfx8zox2RjZmbNmgUAWL9+fZnKu7u7Y9++fTr7Pv74Y7Rt2xaJiYmoVauWdr+rqyv8/PyMFisRUXnJZU3gbLcZD9RvQyPOafdL8IBSPg4KWaQFo6MKSxg4NdtGkxmrHjOjUqkgSVKhbqoFCxagSpUqaNWqFRYtWoS8vLwS68nOzkZaWprORkRkKDtZC7jY7YKL3fdwsvsYTnYb4GofA6V8CBfMIzIjq53NlJWVhcmTJ2PgwIFwc3PT7h87dixat24NLy8vHD16FNHR0bh58yaWLFlSbF3z58/XthQRERmTJEmQS00hR1NLh0KVgUaC4GymcitXy8yUKVMKDb59dDt//rzBQeXm5uL555+HEAIrV67UOTZhwgSEhobisccew+uvv473338fy5YtQ3Z2drH1RUdHQ6VSabdr164ZHCMREZGxccyMfsrVMjNx4kQMHTq0xDJ169Y1JB5tInP16lUcPHhQp1WmKMHBwcjLy8OVK1fQsGHDIssolUoolUqD4iIiIiLrVK5kxtvbG97e3qaKRZvIXLx4EYcOHUKVKlVKPSc2NhYymQw+PlzPgYiIKjgOANaLycbMJCYmIjU1FYmJiVCr1YiNjQUABAYGwsXFBQDQqFEjzJ8/H3369EFubi6ee+45nD59Grt27YJarUZycjIAwMvLCwqFAjExMTh27Bg6deoEV1dXxMTEYPz48XjppZfg6ckpkEREVLHxrtn6MVkyM336dGzYsEH7uFWrVgCAQ4cOITQ0FAAQFxcHlUoFAEhKSsLOnTsBAC1bttSpq+AcpVKJzZs3Y+bMmcjOzkadOnUwfvx4TJgwwVRPg4iIiKycyZKZ9evXl7rGjHhoqcKAgACdx0Vp3bo1fvvtN2OER0REZHWEJn8z5HxbZLVTs4mIjOX32GR8tvoMYk8nw8HRDt17NMBLQx5DlSqOlg6NSBfHzOiFyQwRVWqLFx7F7Bk/w85Ohry8/D9bjx9LwpLFMdi5ZwBatORq4kQVnVWvAExEZIg9uy5i9oyfAUCbyAD5N+NLS8tGn55f48GDXEuFR1QI15nRD5MZIqq0Plp6DHJ50R/uarXAnduZ+GbruSKPE1kCkxn9MJkhokopL0+Do79eh1pd/MQCuVzC4UNXzRgVUSkKxswYstkgJjNEVCmVNjsyvwyg0ZRejoisG5MZIqqU7O3laNHSFzJZ8X+pCiEQ3K6GGaMiKpkQgPj3ZpN6bTaamzOZIaJKa+SYx4tteZHJJDg722PgoGZmjoqoeBwzox8mM0RUaQ0Y2BSvvJq/+vjDA4HlcgkKhRybvu4HNzfehJaoouM6M0RUaUmShPeXdkGXiHpYvfIUYs+kQOkgR89eDfHayCDUrct7upGVEf9uhpxvg5jMEFGlJkkSIp4NRMSzgZYOhahUvNGkftjNRERERBUaW2aIyKpcuXIPF+L+gYuzAo8HV4e9vdzSIRGZDVtm9MOWGSKyCvHxqejZ9Ss81ngVnuu9FRHPbETDusuxasXJMq0ZQ1QZGDQt+99NH8uXL0dAQAAcHBwQHByM48ePl1j+3r17GDVqFKpVqwalUokGDRpgz549el3bGNgyQ0QWd/XqPXTu+DnSVNk6++/cycRbE/cjNfUB3p76pIWiIzIjC9w1e8uWLZgwYQJWrVqF4OBgLF26FOHh4YiLi4OPj0+h8jk5OXjmmWfg4+ODbdu2oUaNGrh69So8PDz0j9tAbJkhIotb8O4vSEvLLvbWAwvnH8WNpHQzR0VkG5YsWYKoqCgMGzYMTZo0wapVq+Dk5IS1a9cWWX7t2rVITU3Fd999hw4dOiAgIAAdO3ZEixYtzBz5f5jMEJFFZWbmYuvX56DOK74rSZKAzZvOmjEqIssw1qJ5aWlpOlt2dnaR18vJycGpU6cQFham3SeTyRAWFoaYmJgiz9m5cydCQkIwatQo+Pr6olmzZpg3bx7UarXxX5AyYjJDRBaV+s8D5OSU/CEok0m4zpYZsgHGSmb8/f3h7u6u3ebPn1/k9e7cuQO1Wg1fX1+d/b6+vkhOTi7ynMuXL2Pbtm1Qq9XYs2cPpk2bhvfffx/vvvuucV+McuCYGSKyKA9PB8hkUok3fNRoBLy9ncwYFVHFdu3aNbi5uWkfK5XGW+lao9HAx8cHq1evhlwuR1BQEJKSkrBo0SLMmDHDaNcpDyYzRGRRLi4KdO/ZALu/v1DsmBm1WuD5AU3NHBmR+QkBg24WWXCum5ubTjJTnKpVq0IulyMlJUVnf0pKCvz8/Io8p1q1arC3t4dc/t+yCY0bN0ZycjJycnKgUCj0fwJ6YjcTEVlc9DsdoFDIde6fVECSgJdfaYl69XjrAar8zH2jSYVCgaCgIBw4cEC7T6PR4MCBAwgJCSnynA4dOiA+Ph4ajUa778KFC6hWrZpFEhmAyQwRWYGmzXywa++LqPPIvZIUCjnGvNEWiz/oYqHIiCq/CRMm4NNPP8WGDRtw7tw5jBgxAhkZGRg2bBgAIDIyEtHR0dryI0aMQGpqKsaNG4cLFy5g9+7dmDdvHkaNGmWpp8BuJiKyDo+3rY5Tv0ch5tfrOH/+DpydFegSUQ+eng6WDo3IfDRS/mbI+eX0wgsv4Pbt25g+fTqSk5PRsmVL7N27VzsoODExETLZf20f/v7++PHHHzF+/Hg89thjqFGjBsaNG4fJkyfrH7eBJGGDS2umpaXB3d0dKpWqTH2KRERku8zxnVFwjdgBQ+FqQFdNek4OWm5eb3Pfb2yZISKjU6s12LsnHhvW/Y6EhHvw8XHGwEHN0K9/Yzg62ls6PCKqZJjMEJFRZWfnYdAL3+KnHy9DLpegVgtcvJCKIz8nYtmHx7F774uoymnWREXijSb1wwHARGRUc2b+jP37EgBAO9W6YA2ZC3H/YPiwnRaLjcjamXs2U2XBZIaIjCYjIweffXqm2AXw1GqBQweuIO78HTNHRlRRGJrIMJkhIjJI7JlkZGbkllhGkoCf/5dopoiIyBZwzAwRGU1Z50ba4CRKorIRUv5myPk2iMkMERnNYy184eBgh6ysvGLLCAGEtK9pxqiIKg6hyd8MOd8WsZuJiIzGzU2JwUMeg0xW9F+HcjsJwSE10Pwx3yKPE1Hlplar8fPPP+PevXtGrZfJDBEZ1ey5oWgbXB0AtEmNJOVvNWq4Yf3nvSwZHpFVq+yzmeRyObp06YK7d+8atV6TJTNz585F+/bt4eTkBA8PjzKdM3ToUEiSpLNFRETolElNTcWgQYPg5uYGDw8PDB8+HPfv3zfBMyAifTg7K7Br74tY+Wk3BD1eDb5+zmjSxBtz5z+NX48NQ42atrMqKVF5VfZkBgCaNWuGy5cvG7VOk42ZycnJQf/+/RESEoI1a9aU+byIiAisW7dO+1ipVOocHzRoEG7evIl9+/YhNzcXw4YNw6uvvopNmzYZLXYiMoxCIcegl5pj0EvNLR0KEVmZd999F2+++SbmzJmDoKAgODs76xzX5zYMJktmZs2aBQBYv359uc5TKpXw8/Mr8ti5c+ewd+9enDhxAm3atAEALFu2DF27dsXixYtRvXp1g2ImIiKyJFtYAbhr164AgJ49e0KS/otXCAFJkqBWq8tdp9XNZjp8+DB8fHzg6emJp59+Gu+++y6qVKkCAIiJiYGHh4c2kQGAsLAwyGQyHDt2DH369CmyzuzsbGRnZ2sfp6WlmfZJEBER6UEIwxKSirDqwaFDh4xep1UlMxEREejbty/q1KmDS5cu4e2338azzz6LmJgYyOVyJCcnw8fHR+ccOzs7eHl5ITk5udh658+fr20pIiIiIsvp2LGj0ess1wDgKVOmFBqg++h2/vx5vYMZMGAAevbsiebNm6N3797YtWsXTpw4gcOHD+tdJwBER0dDpVJpt2vXrhlUHxERkUkULJpnyFYBHDlyBC+99BLat2+PpKQkAMAXX3yBX375Ra/6ytUyM3HiRAwdOrTEMnXr1tUrkOLqqlq1KuLj49G5c2f4+fnh1q1bOmXy8vKQmppa7DgbIH8czqMDiYlIP3/+kYLPVp/BieNJEAIICPBAu/Y10SW8Lho38bZ0eEQVmi2Mmfnmm28wePBgDBo0CKdPn9YOA1GpVJg3bx727NlT7jrLlcx4e3vD29t8H1bXr1/HP//8g2rVqgEAQkJCcO/ePZw6dQpBQUEAgIMHD0Kj0SA4ONhscRHZqo8+OIapbx+CXA4UjNH76+xt7N51EdPePoQnO9bCmnU94VfNxbKBElVQtpDMvPvuu1i1ahUiIyOxefNm7f4OHTrg3Xff1atOk60zk5iYiNjYWCQmJkKtViM2NhaxsbE6a8I0atQI27dvBwDcv38fkyZNwm+//YYrV67gwIED6NWrFwIDAxEeHg4AaNy4MSIiIhAVFYXjx4/j119/xejRozFgwADOZCIysYMHEjD17fyBe8VNNjj6yzWEh32J9PTsogsQkc2Li4vDU089VWi/u7u73isDmyyZmT59Olq1aoUZM2bg/v37aNWqFVq1aoWTJ09qy8TFxUGlUgHIXxXwjz/+QM+ePdGgQQMMHz4cQUFBOHLkiE4X0caNG9GoUSN07twZXbt2xRNPPIHVq1eb6mkQ0b+WLT0Oubzkv/rUaoErCfew8Ys/zRQVUeVScG8mQzZr5+fnh/j4+EL7f/nlF72HqphsNtP69etLXWPm4TvnOjo64scffyy1Xi8vLy6QR2RmQgj8/L+rUKvLNu/zy8//xOsj25RekIh02EI3U1RUFMaNG4e1a9dCkiTcuHEDMTExePPNNzFt2jS96rSqqdlEZL3Kun6FEMDt2xmmDYaIKqwpU6ZAo9Ggc+fOyMzMxFNPPQWlUok333wTY8aM0atOJjNEVCpJkvB42+o4fiyp1NYZmUxCrVruZoqMqHKxhZYZSZLwzjvvYNKkSYiPj8f9+/fRpEkTuLjoP3GAd80mojIZNebxMnUzaTQCw15pafqAiCohW7jR5Msvv4z09HQoFAo0adIEbdu2hYuLCzIyMvDyyy/rVSeTGSJCbq4ahw9dwXffnsfvsck649kK9OjVAG9MKHkJBJlMQruQGniufxNThUpEFdyGDRvw4MGDQvsfPHiAzz//XK862c1EZOPWrYnFnJk/486dTO0+uVxCnTqeGDmmDQYNbg5HR3tIkoTZczsh9OkArFx+Akf+l4gHD/K0Y2kUSjkGRz6Gd+d3gkIht9CzIarYKnM3U1paGoQQEEIgPT0dDg4O2mNqtRp79uwpdMuismIyQ2TDli87gei3DhTar1YLxMenYsK4n/DFhj/w/Q8D4eaWv0TC053r4OnOdQAAaWnZOHP6JoQAWrbyg4eHQ6G6iKjsKnMy4+Hhob31UYMGDQodlyRJ7/soMpkhslEqVRZmTjtcarnfY5Px9uQD+Hhl10LH3NyU6BgaYPzgiKjSOXToEIQQePrpp/HNN9/Ay8tLe0yhUKB27dp6L4DLZIbIRu3YHofs7GKW8n2IRgNs/OJPzJ7bCV5ejmaIjMh2VeaWmYK7ZSckJKBWrVqQJOPFygHARDbq+LGkMpdVqwW+3vy3CaMhIgD5d73WGLBZcTJT4Ny5c/j111+1j5cvX46WLVvixRdfxN27d/Wqk8kMUSUkhMD/Dl/BB4t/w7Klx/D3X7cBAHfvZmH7N+cxZ+bP+Hz9H+Wq84fdF00RKhE9xBamZk+aNAlpaWkAgD///BMTJkxA165dkZCQgAkTJuhVJ7uZiCqZv/+6jZcGbkf8xVTI5RKEAN6JPgT/Wm5ISc5ATk7pXUtFuXgx1ciREpEtSkhIQJMm+cs3fPPNN+jRowfmzZuH06dPo2vXwmPzyoLJDFElknQ9Dc8+sxFpafl3rX54kbtriWkG1c3p1kSmV5nHzBRQKBTIzMxfCmL//v2IjIwEkH/vxYIWm/JiMkNUiaxacQppadllviFkWcnlErqE63c3WyIqOyHKfh+04s63dk888QQmTJiADh064Pjx49iyZQsA4MKFC6hZs6ZedXLMDFEl8tWms0ZPZCQpf2XfqNeDjFovEdmmjz/+GHZ2dti2bRtWrlyJGjVqAAB++OEHRERE6FUnW2aIKpE0VbZR65PLJcjlMmz4shfq1/cq/QQiMoyhg3grQDdTrVq1sGvXrkL7P/jgA73rZDJDVInUqu2Oixf+MbipWamUo3WQH54KDcDQYS1Qo6abcQIkohLZwpiZxMTEEo/XqlWr3HUymSGqRIZHtcKUSfsNqqNJk6o48HMknJ0VRoqKiOg/AQEBJS6Yp1aXf8YlkxmiSuTpsAB4eDrgbmqWXud3DgvA9u8HGDkqIiorW2iZOXPmjM7j3NxcnDlzBkuWLMHcuXP1qpPJDFElkZBwDxFhG3Hvrn6JTKtWfti6/XkjR0VE5WELyUyLFi0K7WvTpg2qV6+ORYsWoW/fvuWuk7OZiCqJcaN/wD93Hug1XmbU2Mfxv6NDYWfHjwQisoyGDRvixIkTep3LlhmiCiIzMxf303Pg6eUAe3s5HjzIxbo1sVj76RkkJNxDbq6m3HU2aOCFT9b0QFCbaiaImIjKS2gkCI0BLTMGnGsujy6MJ4TAzZs3MXPmTNSvX1+vOpnMEFm532OT8d78X7FnVzw0GgFnZ3sMeLEpYn5Nwt9/39arTjs7GWa/G4pRYx836p1ricgw+YvmGdLNZMRgTMTDw6PQ544QAv7+/ti8ebNedTKZIbJi/zt8Bf16bYVarYFGk/8plZGRizWfxhpUb6PGVTF6XFsjREhExmQLY2YOHTqk81gmk8Hb2xuBgYGws9MvLWEyQ2Sl8vI0eGXo98jL+y+RMZaQ9votGU5EZKiOHTsavU4mM0RW6qe9l5CSkmGSuqNea22SeonIMJW1ZWbnzp1lLtuzZ89y189khshK/f33bdjZyZCXV/6BvSXp1qM+GjWuatQ6icg4Kmsy07t37zKVkySJi+YRVSZOTvZG715yc1fg4xXPGrVOIqLSaDTG/aPsUVxUgshKdetRH8KIUxNcXRX46cBgVKnqZLQ6ici4ClpmDNms1cGDB9GkSZNCU7MBQKVSoWnTpjhy5IhedTOZIbJSeXkCwe2MM1DXwUGOc/Gj0KSpt1HqIyLTqMzJzNKlSxEVFQU3t8I3rnV3d8drr72GJUuW6FU3kxkiK3MjKR19emxBq2af4LeY6wbX5+3thJO/vwo3N6URoiMi0s/vv/+OiIiIYo936dIFp06d0qtujpkhsiKxZ5LxXJ+tuHM70yj1RU99AlPe7sCF8YgqiMo6ABgAUlJSYG9vX+xxOzs73L6t50Kg+gZFRMZz6uRNTHlrP47FJBmtzvoNvBD9zhNGq4+ITE8IA29nYMXJTI0aNXD27FkEBgYWefyPP/5AtWr63VqF3UxEFnbyxA088/TnRk1k5HIJazeUf60GIiJT6dq1K6ZNm4asrKxCxx48eIAZM2age/fuetVtsmRm7ty5aN++PZycnODh4VGmcyRJKnJbtGiRtkxAQECh4wsWLDDRsyAyvRef/wZ5uYbPWpLJ8/9bpaojtn//Alq09DO4TiIyr8o8AHjq1KlITU1FgwYNsHDhQuzYsQM7duzAe++9h4YNGyI1NRXvvPOOXnWbrJspJycH/fv3R0hICNasWVOmc27evKnz+IcffsDw4cPRr18/nf2zZ89GVFSU9rGrq6vhAROZWHZ2HnZ+dwHffnMOqnvZaNioCqpVd0FysmGr/MpkwGMt/NAloi6aNvVBtx71oVDIjRQ1EZlT/o0mDTvfWvn6+uLo0aMYMWIEoqOjtUtPSJKE8PBwLF++HL6+vnrVbbJkZtasWQCA9evXl/kcPz/dvyR37NiBTp06oW7dujr7XV1dC5UlsmbJN++j+7Nf4ULcP5DJJGg0Ar/FXDfK6r4ymQzB7Wpg6vSnjBApEVmSRkjQGNC6Ysi55lC7dm3s2bMHd+/eRXx8PIQQqF+/Pjw9PQ2q12rHzKSkpGD37t0YPnx4oWMLFixAlSpV0KpVKyxatAh5eXkl1pWdnY20tDSdjchchBAY0P8bXIpPBQDtqr7Guk1BXp4Gvfs2NEpdRETm4Onpiccffxxt27Y1OJEBrHg204YNG+Dq6oq+ffvq7B87dixat24NLy8vHD16FNHR0bh582aJC+3Mnz9f21JEZG7HYpJw+tTN0gvqQS6X0Da4Btp38DdJ/URkXpV5arYplatlZsqUKcUO0i3Yzp8/b5TA1q5di0GDBsHBwUFn/4QJExAaGorHHnsMr7/+Ot5//30sW7YM2dnZxdYVHR0NlUql3a5du2aUGInKYv/+y7CzM+4HjOzfd267kJr4ams/riNDVFkYOvjXRpOZcrXMTJw4EUOHDi2xzKPjW/Rx5MgRxMXFYcuWLaWWDQ4ORl5eHq5cuYKGDYtualcqlVAqufopWUZerubfm6wZ/iFjr1CjXYdUtGndA9161MfjbaszkSEim1euZMbb2xve3qa/t8uaNWsQFBSEFi1alFo2NjYWMpkMPj4+Jo+LSB9OznegMWARrAIeXg9w8vwmuDu9CaU81PDAiMjqsJtJPyYbM5OYmIjU1FQkJiZCrVYjNjYWABAYGAgXFxcAQKNGjTB//nz06dNHe15aWhq2bt2K999/v1CdMTExOHbsGDp16gRXV1fExMRg/PjxeOmll4wygIjI2I78fBXvzYsHIGBoy4yTkxqODg2gkL1klNiIyPowmdGPyZKZ6dOnY8OGDdrHrVq1AgAcOnQIoaGhAIC4uDioVCqd8zZv3gwhBAYOHFioTqVSic2bN2PmzJnIzs5GnTp1MH78eEyYMMFUT4NIL0IIDBrwLXbtvPjvHsM/YIJDlHCx+wqS5GxwXURElYkkhDUvsWMaaWlpcHd3h0qlKvJW5ESGuH0rA+2D1yLFwMXwHrX/8GC0Da5h1DqJqHTm+M4ouMaWxvPgJHco/YRiZKqz8MK5t8sd6/Lly7Fo0SIkJyejRYsWWLZsGdq2bVvqeZs3b8bAgQPRq1cvfPfdd3rHbSirXWeGqCLKyVHjyfbrjJ7IBNRxZyJDZAMscTuDLVu2YMKECZgxYwZOnz6NFi1aIDw8HLdu3SrxvCtXruDNN9/Ek08+qe/TNRomM0RG9Nknp3Ej6b7R6920pV/phYiI/vXoQrElLV+yZMkSREVFYdiwYWjSpAlWrVoFJycnrF27tthz1Go1Bg0ahFmzZhllFrOhmMwQGYkQAss/PmH0emfPDUWz5pytR2QLjNUy4+/vD3d3d+02f/78Iq+Xk5ODU6dOISwsTLtPJpMhLCwMMTExxcY5e/Zs+Pj4FLlKvyVY7QrARBXFtUQVPlhyDBs//wMPHpR8a43yqB3gjnfndUKvPo2MVicRWTdjzWa6du2azpiZ4tZau3PnDtRqdaEbPPr6+ha7CO4vv/yCNWvWaGcpWwMmM0QGuBD3D555+gukpWVDnWecsfSDhzyGl4e3ROs21bggHpGN0QjDbhb5763f4ObmZpLByunp6Rg8eDA+/fRTVK1a1ej164vJDJEBol7+Hqp7WdAYeM9IuTz/diAbvuyFHr1400giMo+qVatCLpcjJSVFZ39KSgr8/PwKlb906RKuXLmCHj16aPdp/v0AtLOzQ1xcHOrVq2faoIvAZIaoHITQQC3+gEbcwdcb1ThzOtngOmUyYMiwFnhtRBAaNzH9CttEZL3MvWieQqFAUFAQDhw4gN69ewPIT04OHDiA0aNHFyrfqFEj/Pnnnzr7pk6divT0dHz44Yfw97fMTW+ZzJBNEyILuZqdyNFshxB3IJNqQSF/AXZSZ0iSXKdsruYHPMibj9jTWRg/IhQXz3sZIwK8OqIpFi6OMEJdRFTRWWIF4AkTJmDIkCFo06YN2rZti6VLlyIjIwPDhg0DAERGRqJGjRqYP38+HBwc0KxZM53zPTw8AKDQfnNiMkM2SyPuICN3EDS4iPwVegU0IgF5eYdgJ4XCyW4lJCl/0FyOeiceqN/AhfMe6N+tD7Kz5CVVXUYCcjkwcvRTRqiLiEg/L7zwAm7fvo3p06cjOTkZLVu2xN69e7WDghMTEyGTWffkZyYzZLMy88ZDg8v/PioYvJvf95snfkaWegkc7aIhRA6y1DNx84YTpr/VHtlZcmg0hr6x86+3YWMHBAR4GFgXEVUWQgDCgDF4+q7pP3r06CK7lQDg8OHDJZ67fv16/S5qRExmyCapxUWoxa8llNAgR/MlHMRYxP6+D29Pbo9ff65ppKsLeHiqcfBIDwTWK/3O8ERkO3ijSf0wmSGblKf5DQVdS8V7gNOnYxDW8TzUauPdSkAul2H/oVcRWK+K0eokIrJlTGbIRpWtLbZX1zNQqwFj3PUaAOzsJOzZNwgNGjKRIaLCNEIycJ0ZtswQ2Qw76XGUltB8s7kB0tLURrtm+w418eVXfVHV28lodRJR5cJuJv0wmSGbJJc1hlxqA7U4A6BwwiIEsPyDxwy/kARUr+6KA4cHo0ZN46/GSUREvNEk2TAnuw8hoTryu5Dy/5opmAlw6pgPLp73NPwiAli4OIyJDBGVibFuNGlr2DJDNksmVYOr/ffI0WxFjvobqDU38PdZO3y0qBV++L4ODB0n4+Rsj8VLnkHP3rw9ARGVDbuZ9MNkhmyaJLlBKR8OpXw4Zs6cjA8WukMI/RssFUo5BgxsipD2NdGrTyO4uCiMGC0RVXbCwAHATGaIbJgQAus+dfn3g0CgvK0yMhkw+91OGDy0BTw9HUwSIxERFY3JDNksIdIgoIEEd2Rk5MLDMwN3U92hT/dSj54NMHZ8sPGDJCKbIoT+q/gWnG+LmMyQzcnV7EGWehU04iwAQEJN2Cn6on6ju0i45K5XnTI5x9ITkeGERoIwYLye0LCbiajSy1J/hFTVx9jyZWN8taEfUpKd4eKSg5AnYyE0Sr3rbdnK14hREhFReTCZIZuh1pxD8u2VeO7Z3rgY5/lvc6yEu6kOuL7JFVWqPtCrXrlcwuixbY0aKxHZJs5m0g+TGbIZOZqvMHnsU7h00aPQG14ICXduO0KShDbJKavZ74bC3l5u3GCJyCbxdgb6YTJDNuPa9Xjs3dW8hL9cJAiBEhIa3VlOnp4OmD2vE4YM5Z2viYgsickM2YwzJ73K0ASbn9B06XoFf8R6485tR0AAzi65qFXbAW+/0wd29nao6e+KJk28IUm2+VcQEZkGZzPph8kMVXpCCNy8cR/3VY8BSC39BEkg6ZorTpzbhLxcCXb2Au/Naot5czebPFYism0cM6MfJjNUaQkhsPazWHz4wTFcSbhXsBeljocRMlyKd0fGfTv89ms1fLr8MRw/6o95c00cMBER6YXJDFVKQghMfOMnfLb6DPTpCcp6YI9GNV7WPg4M1G/9GSKi8uAAYP0wmaFK6ddfruGz1WcAPNqHrN8b/fVRbQwPioioFBwzox8mM1ThqcVl5KjXIEezC0AmJPjh00+7QS6XoFaX952t2w0lk0lo2coXg4c8ZsyQiYiKxDEz+uEa7FSh5WlO4H5ud+RotgBIB6CGQBL+/P1uOROZ/LJ29hrtHqVSjmHDW+L7HwbC0dHeqHETEZHxsGWGKiwhcpCZNxJANoQQOHbUD19taIRLF91xOd4d5b379buLf4aDoxqTx3ZC57C6WLOhJ9zdeQdsIjIfjpnRj8laZq5cuYLhw4ejTp06cHR0RL169TBjxgzk5OSUeF5WVhZGjRqFKlWqwMXFBf369UNKSopOmcTERHTr1g1OTk7w8fHBpEmTkJeXZ6qnQlbqSuI2CPyD+/fl6NW5F/p37Ylvvw7E76d9odFIKN/4GAl5eXLUqn0fcrmE2XM7MZEhIrMTAhAaAzaOmTGu8+fPQ6PR4JNPPkFgYCDOnj2LqKgoZGRkYPHixcWeN378eOzevRtbt26Fu7s7Ro8ejb59++LXX38FAKjVanTr1g1+fn44evQobt68icjISNjb22PevHmmejpkZX7YE4+z57Yi4VIHbFzf5L9+YlGQn5f/r5OcbDlu33bC5m390aSpt/GCJSIik5KEMF8et2jRIqxcuRKXL18u8rhKpYK3tzc2bdqE5557DkB+UtS4cWPExMSgXbt2+OGHH9C9e3fcuHEDvr75dypetWoVJk+ejNu3b0OhUJQaR1paGtzd3aFSqeDm5ma8J0hmkZr6AI3qLUdA3RSc/7sKytudVJy9v2xFk4aD4eHyhsF1EVHlYY7vjIJrLHL/BI6So971PBAPMEn1ms19v5l1ALBKpYKXl1exx0+dOoXc3FyEhYVp9zVq1Ai1atVCTEwMACAmJgbNmzfXJjIAEB4ejrS0NPz1119F1pudnY20tDSdjSquTV/8iaysvH8TGcAYiUzNWmlo3twP7s5jDa6LiEhfBWNmDNlskdmSmfj4eCxbtgyvvfZasWWSk5OhUCjg4eGhs9/X1xfJycnaMg8nMgXHC44VZf78+XB3d9du/v7+BjwTsrSTJ28asTYBe/s8fLbxJzjavQ9J4gQ/IqKKptyf3FOmTIEkSSVu58+f1zknKSkJERER6N+/P6KioowWfFlFR0dDpVJpt2vXrpk9BjIeOzsZ7OzVBtSQ37MqSQKhz1zDj79+i6aPpUKSSu+iJCIyKfHfwnn6bOAA4LKZOHEihg4dWmKZunXrav9948YNdOrUCe3bt8fq1atLPM/Pzw85OTm4d++eTutMSkoK/Pz8tGWOHz+uc17BbKeCMo9SKpVQKpUlXpsqjs7P+GHHd7HIy5WX+1yZTIMDx7dALpPgVSUL7h75s+skeEOGWsYOlYioXDQC0BjQda5hMlM23t7e8PYu20yPpKQkdOrUCUFBQVi3bh1kspIbgoKCgmBvb48DBw6gX79+AIC4uDgkJiYiJCQEABASEoK5c+fi1q1b8PHxAQDs27cPbm5uaNKkSXmfDlUQ/9zJxM2b9+HifgSbNx9Gdlb1ctaQP0h4zqJfEVg/vdBRpfxlSBKXXSIiqohM9umdlJSE0NBQ1K5dG4sXL8bt27e1xwpaUJKSktC5c2d8/vnnaNu2Ldzd3TF8+HBMmDABXl5ecHNzw5gxYxASEoJ27doBALp06YImTZpg8ODBWLhwIZKTkzF16lSMGjWKrS+V0IW4fzBj2mHs2XXxofUTapS7Hjf3HEyfF4MXXroAIQBJAjQaGWQyDeykHlDIXjFq3ERE+hDCsJ4irjNjZPv27UN8fDzi4+NRs2ZNnWMFs8Fzc3MRFxeHzMxM7bEPPvgAMpkM/fr1Q3Z2NsLDw7FixQrtcblcjl27dmHEiBEICQmBs7MzhgwZgtmzZ5vqqZCFnPv7NjqHfoEHmbmPvEHLMhX733ExMoHhr5/F27OPwd5eQAggL0/CjevOsJM3Qv26r8NO6gRJn1trExEZmRAShAHdTLZ6byazrjNjLbjOjHUSIgu5mt3IE8cBCPR9tjqOxWTrcbNIABCI6JGA6fOOwr/Wf8nyL/+rhgkjOuFWsivOx4+Cr5+L0eInosrJnOvMzHH8FA6Sk971ZIlMTHsQZXPfbxwkQFYhT/MHMvOGQ+AfAHJcueyKo788r3d9zzx7FZ9+uV/7+N1pbbFvTwAux3tAkoCRo4OYyBARVRJMZsjiNOI2MvIiAdzHtasuOPBjbfz1R/GLK5ZOYP7SI/n/EsD61U2wZkVrqDUayOUSXh8ZhHfnP22U2ImIjIljZvTDZIYsLkfzFR48eIDJYzviu62BAABh0NtZQlXvLACukGtegV/VjngrOg2eno7o1bsh/KqxRYaIrJNGSAZOzbbNMTNMZsjictU/YNSwTjjwY62HBq/p+4YUcPfQwFXxOexkbSAplBgw0FiREhGRNWIyQxZ3+pQS+34IMEpdMhkw5o0GsJd3MEp9RETmxG4m/TCZIbPQiHvQiLMAJMilxyBJrtpjO7Y2hZ2dBnl5ht0XSS7XoGGTTIwY2dPAaImILIPJjH6YzJBJCZGBB+q5yNV8AyD3371KKGQD4SCfDElSIv1eIIQw7OaRjo65eGHwNcyePRmurlw8kYjIljCZIZMRIhsZeZFQi98BaB46ko0czedQi0twtluL2rXrASj6judlsfrzs3imy5PwchsLSXI3NGwiIovhAGD9GNauT1SCXM0OqMUZ6CYyBTRQiyPIE/sxeOhj0Gj0ewPa2Un4/cQwVHF/jYkMEVV4wgibLWIyQyaTo9mM0n7FMvPGw6/mLkyObqfXNfLyBBIS7up1LhERVQ7sZiKjuXs3C1u3/IVL8Xfh7qFEl55pqN+oqFaZh2UhSz0dYya3hq/fJCxacAI3btwHAMjlEiRJQl5e8XXI5RLc3R2M+CyIiCxHI4puyy7P+baIyQwZxYZ1v+PN8T8hJ0cNOzsZNBqBBXPD0L3PJXyw6jAcHNQlnq9BLF4c9hOGvvwOzv55C1kP8hBY3wtzZv2Mz9f/UWxCo1YL9Ovf2BRPiYjI7AQMvNGkAedWZOxmIoPt2nkBY0b+gOxsNYQAcnM12ptD7tlRB2+NeaoMtWiQo9kMmSwLLVr6ITikJqpUdcKYN4KhUMohlxd+g8rlEh5vWx1hz9Q18jMiIrIMIf5tndFzs9Wp2UxmyCBCCMydcwRSMX8MaDQybP+6Pq5cdi26gI4H0IhLOnvq1fPErh8Gam9BYGcn0yY2HTsFYNt3z0Mms82/RIiIKB+7mcggV6+q8NfZ2yWWkck0WPpeEP6544BrV13h7fsAzw28gF7PXSqi+6nwr2Sbx6vj7PkR2L8vAWdO34RSYYfwZ+uhSVNvIz4TIiLLM3RGko02zDCZIcPcT88ptYwQEr7Z3AByuQZqtQyXL7njt1+q4bPlzbFl1y54VckGAEioApkUWGQdcrkM4RH1EB5Rz6jxExFZEw4A1g+7mcgg/rXcYG9f8q9Rwc0j1er8ckIjAyDhYpwnxr3aSVtOKX8FkmRvsliJiKhyYjJDBnF3d0D/F5oUOUC3NGq1DIf318LleHfYS/2hkEWZIEIiooqDi+bph8kMGWzm7FBUq+aiV0IDCJw6OgOOdgsgSfx1JCLbZshMpoLNFvHbgwzmV80Fh38ZisFDA6B0KG9vrwQZakMqbjoUERFRKTgAmAzyz51M7Pr+Iv5JTUD70HXoPzgV3UL7lquOkPY1TRQdEVHFwtlM+mEyQ3rRaARmTf8fPv7oOPLyNJDJBNTqULi5Z5e5DrmdhJD2/mjchFOsiYiA/JlMBs1mMlYgFQyTGdLLjKmH8dHSY9rVJtXq/G6i9DQF8v82KL3bqFYtd6xZ18N0QRIRkU1gMkPllpJ8Hx9/dLzIZbPzp2EXNJQWndDY28swe24nRA59DK6uSlOGSkRUobCbST9MZqjcdnwXB1HiDUAKkhgBSfpvnRm5HHByUmDX3hfRqrWfyeMkIqpo2M2kH85monJLTX0Amaz0X52R42PRqEkqHBzzUNU7CyNGtUbMieFMZIiIiiGQf7NIvTdLPwELYcsMlVvtAA/k5ZWc/0uSwCsjzyJ65gkAgIP8bSjlXcwRHhER2Ri2zFC59ezVAM4uxd92QC7XoHP4NXj7ZAOQoJSNhkI23HwBEhFVUBojbLaIyQyVm7OzAos/yG9leXStO7lcgpOzDLPmekMpnwBX+1/gYDeBi+IREZUBb2egHyYzpJdBLzXHxs19EFjfS7tPkoDQTgE49HMUmjeZBQf5SMikahaMkoiIymL58uUICAiAg4MDgoODcfz48WLLfvrpp3jyySfh6ekJT09PhIWFlVjeHDhmhvTWo1dDdO/ZAOf+vgPVvSzUqu2OGjXdLB0WEVGFJWBYV5E+LTNbtmzBhAkTsGrVKgQHB2Pp0qUIDw9HXFwcfHx8CpU/fPgwBg4ciPbt28PBwQHvvfceunTpgr/++gs1atQwIHr9SaLkObaVUlpaGtzd3aFSqeDmxi9fIiIqnjm+Mwqu8QrWQgEnvevJQSY+w8u4du2aTqxKpRJKZdHregUHB+Pxxx/Hxx9/DADQaDTw9/fHmDFjMGXKlFKvqVar4enpiY8//hiRkZF6x24IdjMRERFVMv7+/nB3d9du8+fPL7JcTk4OTp06hbCwMO0+mUyGsLAwxMTElOlamZmZyM3NhZeXV+mFTcRkycyVK1cwfPhw1KlTB46OjqhXrx5mzJiBnJycYs9JTU3FmDFj0LBhQzg6OqJWrVoYO3YsVCqVTjlJkgptmzdvNtVTISIiMgtjDQC+du0aVCqVdouOji7yenfu3IFarYavr6/Ofl9fXyQnJ5cp5smTJ6N69eo6CZG5mWzMzPnz56HRaPDJJ58gMDAQZ8+eRVRUFDIyMrB48eIiz7lx4wZu3LiBxYsXo0mTJrh69Spef/113LhxA9u2bdMpu27dOkRERGgfe3h4mOqpEBERmYWxVgB2c3MzyzCKBQsWYPPmzTh8+DAcHBxMfr3imCyZiYiI0Ek26tati7i4OKxcubLYZKZZs2b45ptvtI/r1auHuXPn4qWXXkJeXh7s7P4L18PDA35+XEmWiIhIX1WrVoVcLkdKSorO/pSUlFK/YxcvXowFCxZg//79eOyxx0wZZqnMOmZGpVKVu0+tYMDVw4kMAIwaNQpVq1ZF27ZtsXbt2hLvFZSdnY20tDSdjYiIyNoII/yvPBQKBYKCgnDgwAHtPo1GgwMHDiAkJKTY8xYuXIg5c+Zg7969aNOmjd7P11jMNjU7Pj4ey5YtK7ZVpih37tzBnDlz8Oqrr+rsnz17Np5++mk4OTnhp59+wsiRI3H//n2MHTu2yHrmz5+PWbNmGRQ/ERGRqVniRpMTJkzAkCFD0KZNG7Rt2xZLly5FRkYGhg0bBgCIjIxEjRo1tIOI33vvPUyfPh2bNm1CQECAdmyNi4sLXFxcDIhef+Wemj1lyhS89957JZY5d+4cGjVqpH2clJSEjh07IjQ0FJ999lmZrpOWloZnnnkGXl5e2LlzJ+zti18+f/r06Vi3bh2uXbtW5PHs7GxkZ2fr1O3v78+p2UREVCpzTs0ejDUGT83+AsPLHevHH3+MRYsWITk5GS1btsRHH32E4OBgAEBoaCgCAgKwfv16AEBAQACuXr1aqI4ZM2Zg5syZesduiHInM7dv38Y///xTYpm6detCoVAAyB/UGxoainbt2mH9+vVluttyeno6wsPD4eTkhF27dpU6qGj37t3o3r07srKyip1H/zCuM0NERGVlC8lMRVfubiZvb294e3uXqWxSUhI6deqEoKAgrFu3rkyJTFpaGsLDw6FUKrFz584yjY6OjY2Fp6dnmRIZIiIia2WJbqbKwGRjZpKSkhAaGoratWtj8eLFuH37tvZYwQjppKQkdO7cGZ9//jnatm2LtLQ0dOnSBZmZmfjyyy91But6e3tDLpfj+++/R0pKCtq1awcHBwfs27cP8+bNw5tvvmmqp0JERGQWAgJC0n9hfhtc1B+ACZOZffv2IT4+HvHx8ahZs6bOsYIXOzc3F3FxccjMzAQAnD59GseOHQMABAYG6pyTkJCAgIAA2NvbY/ny5Rg/fjyEEAgMDMSSJUsQFRVlqqdSKWRn5+G7b+OwZ9dFZGbmomkzbwx5uSXq1PGwdGhEREQG4b2ZbKBP8cqVe+jx7Fe4ekUFmUyCRiMgl+f/973FYXh9pOWn1RERWStzjpkZgM+gkAwYMyMysRmv2Mz3WwHem6mSy8vToHf3Lbh+Lb+7TqPJz13VagEhgLcm7sdPP16yZIhERPQvjRE2W8RkppLbuycely/dhVpddAOcXC5h6fu/mTkqIiIi42EyU8n99OMl2NkV/2NWqwV+OXINDx7kmjEqIiIqmqGr/9rcyBEAZlwBmCwjJ0ddpuWtc3M1cHQ0Q0BERFQsTs3WD1tmKrlWratBU0wXEwBIElA7wB2urgozRkVERGQ8TGYquQEvNoWjoz0kqfgyr49sA6mkAkREZBbmvtFkZcFkppJzd3fAus97Qi6Xwc7uv4RFkvK3Z7sG4rURQRaMkIiICnA2k36YzNiAZ7vVx/9+HYLnnm8KJyd7yOUSmjT1xocfR+DLzX1LHCBMRETmIyTDN1vEAcA2ovljvli9pjtWr+lu6VCIiIiMiskMERGRlcjvKtJ/3IutdjMxmSEiIrISnJqtHw6WICIiogqNLTNERERWwtDp1bY6NZvJDBERkZVgN5N+2M1EREREFRpbZoiIiKyEBsLA2UzsZiIiIiILMnThO6H9P9vCbiYiIiKq0NgyQ0REZCXYzaQfJjNWJjdXjbupWXB2sYezs8LS4RARkVkZeudrJjNkQf/88wDvLzyKDet+R3p6DiQJCH82EJOjOyCoTTVLh0dERGbAqdn6YTJjBf65k4nOoV/g6pV7UKvzs2ohgH0/XsL+ny5jyzfP4ZkudS0cJRERkXXiAGArMHP6/3QSmQJqtYBGo8Erw3YiOzvPQtEREZG5FIyZMWSzRUxmLCw9PRtfbTpbKJEpoNEAd1OzsGvnRTNHRkRE5iaMsNkiJjMWlnhVhZxsdYll7O1lOHfutpkiIiIiqlg4ZsbCnMowY0mjEXBytDdDNEREZEkaSUAjcWp2ebFlxsICAtzRqHEVSCWs+KhWC/To1cB8QRERkUVwzIx+mMxYmCRJiH7nSYhifv/kcgk9ezdA/QZVzBsYERFRBcFkxgr06dcI7y0Og52dBJlMgp2dDHZ2+T+asGfq4pPPuls4QiIiMgcOANYPx8xYiRGj2qBvv0b4auNZXL58F25uSvTp15gL5hER2RDezkA/TGasiK+fC96Y2M7SYRAREVUoTGaIiIisBFtm9MNkhoiIyErw3kz6MdkA4CtXrmD48OGoU6cOHB0dUa9ePcyYMQM5OTklnhcaGgpJknS2119/XadMYmIiunXrBicnJ/j4+GDSpEnIy+Ny/0REVLEJI/zPFpmsZeb8+fPQaDT45JNPEBgYiLNnzyIqKgoZGRlYvHhxiedGRUVh9uzZ2sdOTk7af6vVanTr1g1+fn44evQobt68icjISNjb22PevHmmejpERERkpUyWzERERCAiIkL7uG7duoiLi8PKlStLTWacnJzg5+dX5LGffvoJf//9N/bv3w9fX1+0bNkSc+bMweTJkzFz5kwoFKWvqEtERGSNhIFjZmy1Zcas68yoVCp4eXmVWm7jxo2oWrUqmjVrhujoaGRmZmqPxcTEoHnz5vD19dXuCw8PR1paGv76668i68vOzkZaWprORkREZG0KbmdgyGaLzDYAOD4+HsuWLSu1VebFF19E7dq1Ub16dfzxxx+YPHky4uLi8O233wIAkpOTdRIZANrHycnJRdY5f/58zJo1ywjPgoiIiKxNuVtmpkyZUmiA7qPb+fPndc5JSkpCREQE+vfvj6ioqBLrf/XVVxEeHo7mzZtj0KBB+Pzzz7F9+3ZcunSpvKFqRUdHQ6VSabdr167pXRcREZGpaIyw2aJyt8xMnDgRQ4cOLbFM3bp1tf++ceMGOnXqhPbt22P16tXlDjA4OBhAfstOvXr14Ofnh+PHj+uUSUlJAYBix9kolUoolcpyX5uIiMicNBCQuM5MuZU7mfH29oa3t3eZyiYlJaFTp04ICgrCunXrIJOVf4hObGwsAKBatfxl/UNCQjB37lzcunULPj4+AIB9+/bBzc0NTZo0KXf9+tKIf5CniQGQC7msOeRSoNmuTURERP8x2ZiZpKQkhIaGonbt2li8eDFu376tPVbQgpKUlITOnTvj888/R9u2bXHp0iVs2rQJXbt2RZUqVfDHH39g/PjxeOqpp/DYY48BALp06YImTZpg8ODBWLhwIZKTkzF16lSMGjXKLK0vQmTjgXo2cjVbAfy7to0akEvBcLJbBJlU0+QxEBFR5WToWjG2OpvJZMnMvn37EB8fj/j4eNSsqfsFL0T+i52bm4u4uDjtbCWFQoH9+/dj6dKlyMjIgL+/P/r164epU6dqz5XL5di1axdGjBiBkJAQODs7Y8iQITrr0piKEAKZeSORJ/6HR3sm1eIk7uf2h4v995BJVU0eCxERVT7sZtKPJAoyCxuSlpYGd3d3qFQquLm5lfm8PE0MMvIGlVBCBqVsBBzsJhoeJBERWQV9vzP0uUZju0WQS45616MWD3Aub5JJY7VGZl1npqLL0XwLQF5CCQ1yNFvMFQ4REVUyBTeaNGSzRbzRZDloxC0A6hLLCNw1TzBERFTpsJtJP0xmykEm+UEt5CgpoZHA8TJERKQfDWBgMmOb2M1UDgrZcyi5ZUYGhewFc4VDREREYDJTLnKpDeykZwFIRR2FhOpQyIeYOywiIqokhARoDNhEUV9PNoDJTDlIkgQnu6VQyIYDeHhNGwl20lNwsd8KmeRpqfCIiKiC4wBg/XDMTDlJkj0c7d6GgxiDPHECQC7kUjPIpBqWDo2IiMgmMZnRkyS5wl562tJhEBFRJZLfssLZTOXFZIaIiMhKqA28nYGtJjMcM0NEREQVGltmiIiIrAS7mfTDZIaIiMhKMJnRD7uZiIiIqEJjywwREZGVUEsaCEn/mxJobPSGBmyZISIishJqCIM3fSxfvhwBAQFwcHBAcHAwjh8/XmL5rVu3olGjRnBwcEDz5s2xZ88eva5rLExmiIiIrITGwERGnzEzW7ZswYQJEzBjxgycPn0aLVq0QHh4OG7dulVk+aNHj2LgwIEYPnw4zpw5g969e6N37944e/asoU9fb5IQwuZGC6WlpcHd3R0qlQpubm6WDoeIiKyYOb4zCq7hrpgJSXLQux4hsqDKmVmuWIODg/H444/j448/BgBoNBr4+/tjzJgxmDJlSqHyL7zwAjIyMrBr1y7tvnbt2qFly5ZYtWqV3rEbwibHzBTkb2lpaRaOhIiIrF3Bd4U5/vbPk7IgGTAjSUjZAAp/vymVSiiVykLlc3JycOrUKURHR2v3yWQyhIWFISYmpshrxMTEYMKECTr7wsPD8d133+kdt6FsMplJT08HAPj7+1s4EiIiqijS09Ph7u5ukroVCgX8/PyQnLzA4LpcXFwKfb/NmDEDM2fOLFT2zp07UKvV8PX11dnv6+uL8+fPF1l/cnJykeWTk5MNC9wANpnMVK9eHdeuXYOrqyskybT3S09LS4O/vz+uXbtWIbu0GL/lVOTYAcZvaRU5fmuLXQiB9PR0VK9e3WTXcHBwQEJCAnJycgyuSwhR6LutqFaZysQmkxmZTIaaNWua9Zpubm5W8abUF+O3nIocO8D4La0ix29NsZuqReZhDg4OcHDQf7yMPqpWrQq5XI6UlBSd/SkpKfDz8yvyHD8/v3KVNwfOZiIiIrJRCoUCQUFBOHDggHafRqPBgQMHEBISUuQ5ISEhOuUBYN++fcWWNwebbJkhIiKifBMmTMCQIUPQpk0btG3bFkuXLkVGRgaGDRsGAIiMjESNGjUwf/58AMC4cePQsWNHvP/+++jWrRs2b96MkydPYvXq1RZ7DkxmTEypVGLGjBkVtr+S8VtORY4dYPyWVpHjr8ixV0QvvPACbt++jenTpyM5ORktW7bE3r17tYN8ExMTIZP915HTvn17bNq0CVOnTsXbb7+N+vXr47vvvkOzZs0s9RRsc50ZIiIiqjw4ZoaIiIgqNCYzREREVKExmSEiIqIKjckMERERVWhMZoiIiKhCYzJjgCtXrmD48OGoU6cOHB0dUa9ePcyYMaPU5ahDQ0MhSZLO9vrrr+uUSUxMRLdu3eDk5AQfHx9MmjQJeXl5Fo8/NTUVY8aMQcOGDeHo6IhatWph7NixUKlUOuUefX6SJGHz5s0Wjx8AsrKyMGrUKFSpUgUuLi7o169fodUszfH6A8DcuXPRvn17ODk5wcPDo0znFPXaSpKERYsWacsEBAQUOr5ggeH3fDE09qFDhxaKKyIiQqdMamoqBg0aBDc3N3h4eGD48OG4f/++UWPXJ/7c3FxMnjwZzZs3h7OzM6pXr47IyEjcuHFDp5w5Xnt94gfyl7mfPn06qlWrBkdHR4SFheHixYs6Zcz1+pf3OleuXCn2d3/r1q3acub47CHrw3VmDHD+/HloNBp88sknCAwMxNmzZxEVFYWMjAwsXry4xHOjoqIwe/Zs7WMnJyftv9VqNbp16wY/Pz8cPXoUN2/eRGRkJOzt7TFv3jyLxn/jxg3cuHEDixcvRpMmTXD16lW8/vrruHHjBrZt26ZTdt26dTpfVGX9wDVl/AAwfvx47N69G1u3boW7uztGjx6Nvn374tdffwVgvtcfyL9jbf/+/RESEoI1a9aU6ZybN2/qPP7hhx8wfPhw9OvXT2f/7NmzERUVpX3s6upqeMAP0Sd2AIiIiMC6deu0jx9dS2TQoEG4efMm9u3bh9zcXAwbNgyvvvoqNm3aZLTYgfLHn5mZidOnT2PatGlo0aIF7t69i3HjxqFnz544efKkTllTv/b6xA8ACxcuxEcffYQNGzagTp06mDZtGsLDw/H3339rl9E31+tf3uv4+/sX+t1fvXo1Fi1ahGeffVZnv6k/e8gKCTKqhQsXijp16pRYpmPHjmLcuHHFHt+zZ4+QyWQiOTlZu2/lypXCzc1NZGdnGyvUIpUl/kd9/fXXQqFQiNzcXO0+AGL79u1Gjq50pcV/7949YW9vL7Zu3ardd+7cOQFAxMTECCEs8/qvW7dOuLu763Vur169xNNPP62zr3bt2uKDDz4wPLAyKE/sQ4YMEb169Sr2+N9//y0AiBMnTmj3/fDDD0KSJJGUlGRgpEUz5LU/fvy4ACCuXr2q3WfO116Issev0WiEn5+fWLRokXbfvXv3hFKpFF999ZUQwnyvv7Gu07JlS/Hyyy/r7LPUZw9ZFruZjEylUsHLy6vUchs3bkTVqlXRrFkzREdHIzMzU3ssJiYGzZs317nFenh4ONLS0vDXX3+ZJO4CZY3/0XPc3NxgZ6fb0Ddq1ChUrVoVbdu2xdq1ayHMsD5jafGfOnUKubm5CAsL0+5r1KgRatWqhZiYGACWff3LKyUlBbt378bw4cMLHVuwYAGqVKmCVq1aYdGiRSbpJtPH4cOH4ePjg4YNG2LEiBH4559/tMdiYmLg4eGBNm3aaPeFhYVBJpPh2LFjlgi3RCqVCpIkFfrL3xpf+4SEBCQnJ+v87ru7uyM4OFjnd98cr78xrnPq1CnExsYW+btvic8esix2MxlRfHw8li1bVmoX04svvojatWujevXq+OOPPzB58mTExcXh22+/BQAkJyfrfJEC0D5OTk42TfAoe/wPu3PnDubMmYNXX31VZ//s2bPx9NNPw8nJCT/99BNGjhyJ+/fvY+zYscYOW6ss8ScnJ0OhUBT68vH19dW+tpZ6/fWxYcMGuLq6om/fvjr7x44di9atW8PLywtHjx5FdHQ0bt68iSVLllgo0nwRERHo27cv6tSpg0uXLuHtt9/Gs88+i5iYGMjlciQnJ8PHx0fnHDs7O3h5eVnda5+VlYXJkydj4MCBOnd2ttbXvuD1K+p3++HffXO8/sa4zpo1a9C4cWO0b99eZ78lPnvICli6acgaTZ48WQAocTt37pzOOdevXxf16tUTw4cPL/f1Dhw4IACI+Ph4IYQQUVFRokuXLjplMjIyBACxZ88eq4lfpVKJtm3bioiICJGTk1Ni2WnTpomaNWuWqV5Txr9x40ahUCgK7X/88cfFW2+9JYSwzOuvb1dHw4YNxejRo0stt2bNGmFnZyeysrKsJnYhhLh06ZIAIPbv3y+EEGLu3LmiQYMGhcp5e3uLFStWlFqfueLPyckRPXr0EK1atRIqlarEsmV97U0d/6+//ioAiBs3bujs79+/v3j++eeFEOZ7/Q29TmZmpnB3dxeLFy8utWx5Pnuo4mLLTBEmTpyIoUOHllimbt262n/fuHEDnTp1Qvv27fW6a2hwcDCA/JaFevXqwc/PD8ePH9cpUzDbxs/Pr9T6zBF/eno6IiIi4Orqiu3bt8Pe3r7E8sHBwZgzZw6ys7NLvXmcKeP38/NDTk4O7t27p9M6k5KSon1tzf366+vIkSOIi4vDli1bSi0bHByMvLw8XLlyBQ0bNiy2nLlif7iuqlWrIj4+Hp07d4afnx9u3bqlUyYvLw+pqalW89rn5ubi+eefx9WrV3Hw4EGdVpmilPW1B0wbf8Hrl5KSgmrVqmn3p6SkoGXLltoy5nj9Db3Otm3bkJmZicjIyFLLluezhyowS2dTFd3169dF/fr1xYABA0ReXp5edfzyyy8CgPj999+FEP8NQE1JSdGW+eSTT4Sbm1uZ/rorD33iV6lUol27dqJjx44iIyOjTOe8++67wtPT05BQi1Te+AsGAG/btk277/z580UOADbH619An9aBIUOGiKCgoDKV/fLLL4VMJhOpqal6RFcyQ1pmrl27JiRJEjt27BBC/Dcw9OTJk9oyP/74o9UMAM7JyRG9e/cWTZs2Fbdu3SrTOaZ87YUo/wDgh1szVCpVkQOATf36G3qdjh07in79+pXpWqb67CHrwmTGANevXxeBgYGic+fO4vr16+LmzZva7eEyDRs2FMeOHRNCCBEfHy9mz54tTp48KRISEsSOHTtE3bp1xVNPPaU9Jy8vTzRr1kx06dJFxMbGir179wpvb28RHR1t8fhVKpUIDg4WzZs3F/Hx8TrnFCQTO3fuFJ9++qn4888/xcWLF8WKFSuEk5OTmD59usXjF0KI119/XdSqVUscPHhQnDx5UoSEhIiQkBDtcXO9/kIIcfXqVXHmzBkxa9Ys4eLiIs6cOSPOnDkj0tPTtWUaNmwovv32W53zVCqVcHJyEitXrixU59GjR8UHH3wgYmNjxaVLl8SXX34pvL29RWRkpEVjT09PF2+++aaIiYkRCQkJYv/+/aJ169aifv36OkliRESEaNWqlTh27Jj45ZdfRP369cXAgQONGrs+8efk5IiePXuKmjVritjYWJ3ft4JZbuZ67fWJXwghFixYIDw8PMSOHTvEH3/8IXr16iXq1KkjHjx4oC1jrte/tOsU9d4VQoiLFy8KSZLEDz/8UKhOc332kPVhMmOAdevWFdsvXCAhIUEAEIcOHRJCCJGYmCieeuop4eXlJZRKpQgMDBSTJk0q1O9+5coV8eyzzwpHR0dRtWpVMXHiRJ2pz5aK/9ChQ8Wek5CQIITIn2LZsmVL4eLiIpydnUWLFi3EqlWrhFqttnj8Qgjx4MEDMXLkSOHp6SmcnJxEnz59dBIgIczz+guR37pSVPwPxwtArFu3Tue8Tz75RDg6Oop79+4VqvPUqVMiODhYuLu7CwcHB9G4cWMxb948o7cqlTf2zMxM0aVLF+Ht7S3s7e1F7dq1RVRUlM4UeCGE+Oeff8TAgQOFi4uLcHNzE8OGDdP5grZU/AW/SyWdY67XXp/4hchvnZk2bZrw9fUVSqVSdO7cWcTFxenUa67Xv7TrFPXeFUKI6Oho4e/vX+Tnibk+e8j6SEJwzhoRERFVXFxnhoiIiCo0JjNERERUoTGZISIiogqNyQwRERFVaExmiIiIqEJjMkNEREQVGpMZIiIiqtCYzBAREVGFxmSGiIiIKjQmM0RERFShMZkhIiKiCu3/3fzLCfSEEfEAAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Inferences of the scatter plot:**\n", + "\n", + "1.Cluster Formation: The model has classified the data into two clusters, but\n", + " similar to the AEFCN plot, the separation between them is not distinct. Most points belong to a single dominant cluster (dark purple), while a small number of points are assigned to the other cluster (yellow).\n", + "\n", + "2.Linear Data Distribution: The points in the plot follow a diagonal pattern,\n", + " indicating that the dataset might have a strong correlation between the features being visualized. This suggests that the data structure might be inherently linear.\n", + "\n", + "3.Imbalance in Clusters: The clustering results show a highly imbalanced\n", + " distribution, where one cluster contains most of the points. This could imply that either the model is struggling to identify meaningful cluster boundaries or that the dataset itself has an inherent class imbalance\n" + ], + "metadata": { + "id": "E0FZWgqhPe8v" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEDCNNClusterer:**" + ], + "metadata": { + "id": "6xWiuNT7izNY" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEDCNNClusterer (Auto-Encoder Dilated Convolutional Network)**\n", + "The **AEDCNNClusterer** is built on an **Auto-Encoder** with a **Dilated Convolutional Network (DCNN)** backbone.Dilated convolutions use dilated filters to expand the receptive field exponentially, allowing the model to capture long-term dependencies in the data without losing resolution.This method is ideal for detecting patterns over extended time windows.\n" + ], + "metadata": { + "id": "H1CANB-oD-t_" + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEDCNNClusterer" + ], + "metadata": { + "id": "gM5ja7I14GJK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEDCNNClusterer(n_epochs=10, random_state=42, dilation_rate=1)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qv-xuWhvE1_6", + "outputId": "d3c6eb67-8f10-4446-d46b-739f8c72219a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"coolwarm\")\n", + "plt.title(\"Cluster Distribution with AEDCNN\")\n", + "plt.colorbar(label=\"Cluster\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "AQIaYKy6E5RK", + "outputId": "6de1c9b3-1d85-425c-9eca-e2edbbf36ae9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#**Inferences from the scatter plot:**\n", + "1. Compared to AEFCN and AEResNet, AEDCNN appears to have a slightly better spread of cluster labels across the data points.\n", + "2. Most data points fall into one main cluster (red), with very few points classified into another cluster (blue).\n", + "3. The points follow a strong diagonal trend, indicating that the underlying feature space has a continuous, linear structure rather than distinct groups.\n" + ], + "metadata": { + "id": "_sqUBae7ePwg" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEDRNNClusterer:**" + ], + "metadata": { + "id": "ep5dTHIEi4xD" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEDRNNClusterer (Auto-Encoder Dilated Recurrent Neural Network)**\n", + "The **AEDRNNClusterer** integrates an Auto-Encoder with a **Dilated Recurrent Neural Network (DRNN)** backbone.DRNNs combine the strengths of RNNs (sequence modeling) with dilated connections to capture patterns over long temporal sequences efficiently.they are Suitable for tasks where sequential relationships are vital (e.g., speech data, financial trends).\n", + "\n" + ], + "metadata": { + "id": "ickHTlKQFDDZ" + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEDRNNClusterer" + ], + "metadata": { + "id": "O2Xj2LilFBjX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEDRNNClusterer(n_epochs=10, random_state=42)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "k4z2dOrzFjx2", + "outputId": "392d57ef-54ed-4f86-e236-3245145cd0a2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\r\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 2s/step" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:tensorflow:5 out of the last 17 calls to .one_step_on_data_distributed at 0x785f3b7428e0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 1s/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 231ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"magma\")\n", + "plt.title(\"Cluster Distribution with AEDRNN\")\n", + "plt.colorbar(label=\"Cluster\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "mE4l1C9AFm-U", + "outputId": "a04a18cf-e3bb-4cf0-88da-cbe8db8cb1fc" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Inferences from the scatter plot:**\n", + "1.The scatter plot shows two clear clusters, one in black (cluster 0) and another in light yellow (cluster 1).Unlike completely mixed distributions, this separation indicates that AEDRNN has recognized some underlying structure in the data.\n", + "\n", + "2.Similar to other models, the data points are aligned along a diagonal trend, indicating that the latent space follows a strong linear pattern.This suggests that AEDRNN’s clustering is more of a gradual transition rather than a hard separation.\n", + "\n" + ], + "metadata": { + "id": "UES_F4Iedmbg" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEAttentionBiGRUClusterer:**" + ], + "metadata": { + "id": "n2poXz2KjAvo" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEAttentionBiGRUClusterer (Auto-Encoder Attention Bidirectional GRU Network)**\n", + "The **AEAttentionBiGRUClusterer** integrates an Auto-Encoder with an Attention-based **Bidirectional Gated Recurrent Unit (BiGRU)** network.\n", + "The Attention Mechanism allows the model to selectively focus on the most relevant parts of the time series during clustering.The Bidirectional GRU enhances this by processing the sequence from both forward and backward directions, improving sequence dependency recognition.It is Suitable for tasks requiring fine-grained sequence interpretation.Excels in datasets where certain segments of the sequence are more influential than others.\n", + "\n", + "\n" + ], + "metadata": { + "id": "cRH_XEfeF6Cs" + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEAttentionBiGRUClusterer" + ], + "metadata": { + "id": "iuy-H4F5F4dk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEAttentionBiGRUClusterer(n_epochs=10, random_state=42)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9Zx6TvNFHBwg", + "outputId": "290fa9d2-0fcf-469d-f6c3-f69cbdebe9e3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 901ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 631ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"inferno\")\n", + "plt.title(\"Cluster Distribution with AEAttentionBiGRU\")\n", + "plt.colorbar(label=\"Cluster\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "-AvTzfdlHGeF", + "outputId": "3461f910-6547-40c9-a402-275ee128a92b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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Tp/HDDz/g0KFDeOedd7BkyRKcPn260DUYVCoVAGDixIkIDAzMt0zt2rU1fra3ty/JbRTqypUr8PT0LPQLZMmSJRg2bBj27t2Ln3/+GePHj8e8efNw+vTpYg9A1mfMQPF/N4ZUUOtYYX8p52rbti3mzJmDZ8+e4cSJE/j444/h6uqKRo0a4cSJE+pxGbomM9rGePToUdy7dw/btm3Dtm3b8hzfsmVLnmSmOL755hsAQGhoKEJDQ/Mc37VrF4YPH17iegHt3ku6/A51pWvraufOnQEAv/76a4HJTP369QHkvM+fH3vl4+MDHx8fADkJVH5jnerUqaNOoF577TXI5XJMmTIFHTt2RPPmzdXl7OzskJGRASFEnvelEEJjthVZFiYzevLaa69h3bp1iIqKgr+/f4nPz/0r7fHjxxoD9wpqgm7VqhVatWqFOXPmYOvWrRgyZAi2bduGd999t8Av35o1awLImVJZ1F+1+hYVFYUbN24Ua3Bg48aN0bhxY0ydOhWnTp1CmzZtsGbNGnzyyScA9PuX1/Xr1/Ps+/vvv+Hg4KD+S9bNzS3PDCMg/99NcWOrVq0aACAmJkb9ewFyBkHHxcXp9ffTrl07ZGZm4ttvv0ViYqI6aXn11VfVyUzdunULHWwKGO4v3i1btsDT01M9o+h5u3fvxp49e7BmzZoSJalCCGzduhUdO3bE+++/n+f47NmzsWXLFnUyU9C9Gfu9pM2/n06dOmkci4mJUR/Xl+zsbAD/ta7m57XXXsP8+fOxZcuWAgeSF9fHH3+ML774AlOnTsXBgwfV+6tVq4bs7GzcuHEjT7IYGxsLpVKp93un0oFjZvTkf//7HxwdHfHuu+8iOTk5z/EbN27gs88+K/D83Fkvz08Lzp3m+7x///03z19yTZs2BZCz8iYAODg4AECeL2BPT0906NABa9euxb179/LE8Pw0UX26ffs2hg0bBhsbG0yaNKnAcikpKeoPzVyNGzeGTCZT3xsAODo65ptcaCMqKgoXL15U/5yQkIC9e/eia9eu6r9ma9WqBYVCodGlc+/ePezZsydPfcWNLSAgADY2Nvj88881fp/r16+HQqFAjx49dLgrTX5+frC2tsaCBQvg7u6Ol156CUBOknP69Gn88ssvxWqVcXR0VE+R15f09HTs3r0br732Gl5//fU829ixY/HkyZM8052LcvLkSdy6dQvDhw/Pt94333wTx44dw927d9X3BuR9zxj7vVTcfz/NmzeHp6cn1qxZo/He+Omnn3D16lW9/vsB/puplLtGVH7atGmDLl26YN26ddi7d2++ZYrbCuXq6or33nsPhw4dQnR0tHp/7sy9FStW5DknNxkubHYflV1smdGTWrVqYevWrXjzzTfRoEEDBAcHo1GjRsjMzMSpU6ewY8eOQpfZ7tq1K6pWrYoRI0Zg0qRJkMvl2LBhAzw8PDQeBbB582asWrUKffv2Ra1atfDkyRN88cUXcHZ2Rvfu3QHkdLM0bNgQ27dvR926deHu7o5GjRqhUaNGWLlyJdq2bYvGjRsjJCQENWvWRHJyMqKionDnzh38/vvvOr0OFy9exDfffAOVSoXHjx/j3Llz2LVrFyRJwtdff62e1pqfo0ePYuzYsRgwYADq1q2L7OxsfP3115DL5ejfv7+6nK+vLw4fPoylS5eiUqVKqFGjRqHL3xemUaNGCAwM1JiaDeSsK5Jr4MCBmDx5Mvr27Yvx48cjLS0Nq1evRt26dTUSoZLE5uHhgfDwcMycORNBQUHo1asXYmJisGrVKrRo0UIv01tzOTg4wNfXF6dPn1avMQPktMykpqYiNTW1WMmMr68vtm/fjrCwMLRo0QJOTk4FdjkU1759+/DkyRP1FPEXtWrVCh4eHtiyZQvefPNN9f6///5b3Y30PC8vL3Tp0gVbtmyBXC4v8Eu9V69e+Pjjj7Ft2zaEhYWpB4GPHz8egYGBkMvlGDhwoNHfS76+vli9ejU++eQT1K5dG56ennlaXgCok9Phw4ejffv2GDRokHpqdvXq1fPtViuu51/btLQ0nD59Gps3b0bt2rXx9ttvF3ruN998g6CgIPTp0wfdunVDQEAA3NzckJSUhMOHD+PXX38tdrLxwQcfYNmyZZg/f766+7Fp06Z499138dlnn+H69evo0qULACAyMhIHDhzAu+++W2jCRWWYSeZQlWF///23CAkJEdWrVxc2NjaiXLlyok2bNmL58uUaUxvze5zBhQsXhJ+fn7CxsRFVq1YVS5cuzTOl9+LFi2LQoEGiatWqwtbWVnh6eorXXntNY3qxEEKcOnVK+Pr6ChsbmzxTS2/cuCGCg4OFt7e3sLa2FpUrVxavvfaa2Llzp7pM7nULmwL+vNwplrmblZWVcHd3F35+fiI8PFzcvn07zzkvTje9efOmeOedd0StWrWEnZ2dcHd3Fx07dhSHDx/WOO/atWvi1VdfFfb29gKA+nXMnSr94MGDPNcqaGr2mDFjxDfffCPq1KkjbG1tRbNmzTSmI+f6+eefRaNGjYSNjY2oV6+e+Oabb/Kts6DYXvw95lqxYoWoX7++sLa2Fl5eXmL06NEaU1eFyJma/dJLL+WJqaAp4/mZNGmSACAWLFigsb927doCgLhx44bG/vymZj99+lQMHjxYuLq6CgDqaxf0OIPcfxMbN24sMK6ePXsKOzs7kZqaWmCZYcOGCWtra/Hw4UMhROFTs9u3by8yMzNF+fLlRbt27Qp9TWrUqKGejp6dnS3GjRsnPDw8hCRJGr9XQ72X8nuNk5KSRI8ePUS5cuXU91NQWSGE2L59u2jWrJmwtbUV7u7uYsiQIeLOnTsaZQqazlzYcgW5m1wuF1WqVBEjR44UycnJeerN799fenq6WLZsmfD39xfOzs7CyspKeHt7i9dee01s2bJFY6mHopZ0GDZsmJDL5SI2Nla9T6lUis8++0w0adJE2NnZCTs7O9GkSRPx+eefG/xxD2S+JCGMMPqMiIiIyEA4ZoaIiIhKNSYzREREVKoxmSEiIqJSjckMERGRhcpdCLFSpUqQJAnff/99keccP34cr7zyCmxtbVG7dm1s2rTJ4HEWhckMERGRhUpNTUWTJk3yXbQyP3FxcejRowc6duyI6OhofPjhh3j33Xdx6NAhA0daOM5mIiIiIkiShD179qBPnz4Flpk8eTJ+/PFHjcf3DBw4EI8fP9ZYrdnYLHLRPJVKhbt376JcuXJ8KBkRERVKCIEnT56gUqVKGk+517dnz54hMzNT53pEPs+usrW1LfJhxMURFRWV5xEegYGB+PDDD3WuWxcWmczcvXtX/eAzIiKi4khISCj2A29L6tmzZ6hRwxtJSbo/MsTJySnPc7QiIiIwY8YMnetOSkrK8xw3Ly8vpKSkID09Xe8P+i0ui0xmch9Vn5CQUOgTnImIiFJSUuDj46P+7jCEzMxMJCUpcPP2p3B21j4hSElJR81qoXm+3/TRKmPOLDKZyW1+c3Z2ZjJDRETFYoxhCc7O9jolM//VY5jvN29v7zwPU05OToazs7PJWmUAC01miIiIzJEQ2RAiW6fzDcnf3x8HDhzQ2BcZGQl/f3+DXrconJpNRERkJoRQ6ryVxNOnTxEdHY3o6GgAOVOvo6OjER8fDwAIDw9HcHCwuvyoUaNw8+ZN/O9//8O1a9ewatUqfPfddzo9qV0f2DJDRERkJlQiGyodWldKeu758+fRsWNH9c9hYWEAgKFDh2LTpk24d++eOrEBgBo1auDHH39EaGgoPvvsM1SpUgVffvklAgMDtY5ZH5jMEBERWagOHTqgsOXm8lvdt0OHDrh06ZIBoyo5JjNERERmwtzHzJgrJjNERERmImfciy7JTMnGzJQVHABMREREpRpbZoiIiMyEUGVDqHRomdHh3NKMyQwREZU5AgoI/ANABgnekGBn6pCKR2TnbLqcb4GYzBARUZkh8BQqRAG4/9w+CUAtyNAcEuQmi40Mh8kMERGVCQLpUOFnAM/yHAFioUI6ZGgPCYZ/LIG2OJtJO0xmiIioTBCIQU4iU9C6KYnIabHxKuC4GVBlA6os3c63QJzNREREZYJALApOZABAgkCcscIhI2LLDBERlREZRRwXEEg3SiTayulm0n5cD7uZiIiISjU75B0v8zwJEhyMFYx2VNmASodByhbazcRkhoiIygQJtSHwJwruahKQUNOYIZUckxmtcMwMERGVCRLqA3AACpytVBVABeMFREbDlhkiIioTJNhChq5Q4QyAu88dkUNCXUhoatbTsnModVz4zjKfzcRkhoiIygwJDpCjIwSeAvgXOR0QnpBgbeLIikdSZUNSad9pIlloNxOTGSIiKnMkOAFwMnUYZCRMZoiIiMyFKhvQoWXGUgcAM5khIiIyF0xmtMLZTERERFSqsWWGiIjITEgiG5LQYQAwVwAmIiIik1KpAJUO06tVKv3FUoqwm4mIiIhKNbbMEBERmYmcdWa0X9iP68wQERGRaamUOs5m4grAREREZEqqbECHlhlOzSYiIiIqhQyazDx69AhDhgyBs7MzXF1dMWLECDx9+rTA8rdu3YIkSfluO3bsUJfL7/i2bdsMeStEREQGJ6mUOm+WyKDdTEOGDMG9e/cQGRmJrKwsDB8+HCNHjsTWrVvzLe/j44N79+5p7Fu3bh0WLVqEbt26aezfuHEjgoKC1D+7urrqPX4iIiKjEjqOmRFMZvTq6tWrOHjwIM6dO4fmzZsDAJYvX47u3btj8eLFqFSpUp5z5HI5vL29Nfbt2bMHb7zxBpycNB8Y5urqmqdsQTIyMpCRkaH+OSUlpaS3Q0REenDjxg3cv38flStXRtWqVU0dDpURButmioqKgqurqzqRAYCAgADIZDKcOXOmWHVcuHAB0dHRGDFiRJ5jY8aMQYUKFdCyZUts2LABQogC65k3bx5cXFzUm4+PT8lviIiItPbLL7/Cz681ateuj9atX0W1arXQoUNnXLhwwdShmRVJpdKxm4mL5ulVUlISPD09NfZZWVnB3d0dSUlJxapj/fr1aNCgAVq3bq2xf9asWfjuu+8QGRmJ/v374/3338fy5csLrCc8PBwKhUK9JSQklPyGiIhIK5GRhxEQEIjz5zUTl99+O4m2bTvg7NmzJorMDKmUum8WqMTdTFOmTMGCBQsKLXP16lWtA8qVnp6OrVu3Ytq0aXmOPb+vWbNmSE1NxaJFizB+/Ph867K1tYWtra3OMRERUcmoVCqEhLwHpVKZpwU9d9/o0eNw4ULxWuyJ8lPiZGbChAkYNmxYoWVq1qwJb29v3L9/X2N/dnY2Hj16VKyxLjt37kRaWhqCg4OLLOvn54fZs2cjIyODSQsRkRk5fvwX3L4dX+BxlUqFixcv4o8//kDjxo2NGJl5yukq0mUFYLbMFIuHhwc8PDyKLOfv74/Hjx/jwoUL8PX1BQAcPXoUKpUKfn5+RZ6/fv169OrVq1jXio6OhpubGxMZIiIzExcXV6xyN2/GMZkB/r+rSJdF85jM6FWDBg0QFBSEkJAQrFmzBllZWRg7diwGDhyonsmUmJiIzp0746uvvkLLli3V58bGxuLXX3/FgQMH8tT7ww8/IDk5Ga1atYKdnR0iIyMxd+5cTJw40VC3QkREWnJ3d9drOaL8GHSdmS1btmDs2LHo3LkzZDIZ+vfvj88//1x9PCsrCzExMUhLS9M4b8OGDahSpQq6du2ap05ra2usXLkSoaGhEEKgdu3aWLp0KUJCQgx5K0REpIXAwK4oV84JT54UvGBq5cqV0Lq1vxGjMl/sZtKOJAqb01xGpaSkwMXFBQqFAs7OzqYOh4ioTFu69FNMmPC/Ao9v3rwBwcFvGzGikjHGd0buNe7/4gtnJ+3bGVKeZsOz/QWL+37jgyaJiMigQkM/RGZmFmbOnIWMjEzI5XJkZ2fD0dERixcvMOtExtgkldBprRhJZXHtEwCYzBARkYFJkoQpU/6HUaNGYvfuPUhOvo8qVSqjX7++cHR0NHV4VAYwmSEiIqNwdXXFO+8MN3UY5k2lBHRZxNdCx8wwmSEiIjIXQsdkxkIfNGmwxxkQERERGQNbZoiIiMyEJFSQhA5Ts4VlPmiSyQwREZG54JgZrbCbiYiIiEo1tswQERGZC5VKx2czsZuJiIiITInJjFbYzURERESlGltmiIiIzISkUkHSoXFFl0chlGZMZoiIiMyFSqXjbCYmM0RERGRKTGa0wjEzREREVKqxZYaIiMhcsGVGK0xmiIiIzIVQAiqhw/mWmcywm4mIiIhKNbbMEBERmQlOzdYOkxkiIiJzwTEzWmE3ExEREZVqbJkhIiIyF2yZ0QqTGSIiInOhErolJLrMhCrF2M1EREREpRpbZoiIiMyFSujYzWSZLTNMZoiIiMyFSgWoJB3Ot8xkht1MRERE5kKl0n3TwsqVK1G9enXY2dnBz88PZ8+eLbT8smXLUK9ePdjb28PHxwehoaF49uyZVtfWByYzREREFmz79u0ICwtDREQELl68iCZNmiAwMBD379/Pt/zWrVsxZcoURERE4OrVq1i/fj22b9+Ojz76yMiR/4fJDBERkblQCd23Elq6dClCQkIwfPhwNGzYEGvWrIGDgwM2bNiQb/lTp06hTZs2GDx4MKpXr46uXbti0KBBRbbmGBKTGSIiInMhVLpvAFJSUjS2jIyMfC+XmZmJCxcuICAgQL1PJpMhICAAUVFR+Z7TunVrXLhwQZ283Lx5EwcOHED37t31/GIUH5MZIiKiMsbHxwcuLi7qbd68efmWe/jwIZRKJby8vDT2e3l5ISkpKd9zBg8ejFmzZqFt27awtrZGrVq10KFDB5N2M3E2ExERkbkQOk7NFjndTAkJCXB2dlbvtrW11TGw/xw/fhxz587FqlWr4Ofnh9jYWHzwwQeYPXs2pk2bprfrlASTGSIiInOhp3VmnJ2dNZKZglSoUAFyuRzJycka+5OTk+Ht7Z3vOdOmTcPbb7+Nd999FwDQuHFjpKamYuTIkfj4448hkxm/08dgV5wzZw5at24NBwcHuLq6FuscIQSmT5+OihUrwt7eHgEBAbh+/bpGmUePHmHIkCFwdnaGq6srRowYgadPnxrgDoiIiMo2Gxsb+Pr64siRI+p9KpUKR44cgb+/f77npKWl5UlY5HI5gJzvcVMwWDKTmZmJAQMGYPTo0cU+Z+HChfj888+xZs0anDlzBo6OjggMDNSYuz5kyBD8+eefiIyMxP79+/Hrr79i5MiRhrgFIiIi4zLBbKawsDB88cUX2Lx5M65evYrRo0cjNTUVw4cPBwAEBwcjPDxcXb5nz55YvXo1tm3bhri4OERGRmLatGno2bOnOqkxNoN1M82cORMAsGnTpmKVF0Jg2bJlmDp1Knr37g0A+Oqrr+Dl5YXvv/8eAwcOxNWrV3Hw4EGcO3cOzZs3BwAsX74c3bt3x+LFi1GpUqV8687IyNAYyZ2SkqLDnRERERnGcxOStD6/pN588008ePAA06dPR1JSEpo2bYqDBw+qBwXHx8drtMRMnToVkiRh6tSpSExMhIeHB3r27Ik5c+ZoH7iOzGY2U1xcHJKSkjSmh7m4uMDPz089PSwqKgqurq7qRAYAAgICIJPJcObMmQLrnjdvnsaobh8fH8PdCBERUSkzduxY3L59GxkZGThz5gz8/PzUx44fP67RMGFlZYWIiAjExsYiPT0d8fHxWLlyZbGHlBiC2SQzuVPACpselpSUBE9PT43jVlZWcHd3L3AKGQCEh4dDoVCot4SEBD1HT0REpAcm6GYqC0qUzEyZMgWSJBW6Xbt2zVCxas3W1lY9sru4I7yJiIiMTqWHzQKVaMzMhAkTMGzYsELL1KxZU6tAcqeAJScno2LFiur9ycnJaNq0qbrMi8+KyM7OxqNHjwqcQkZERFRq6JqQMJkpmoeHBzw8PAwSSI0aNeDt7Y0jR46ok5eUlBScOXNGPSPK398fjx8/xoULF+Dr6wsAOHr0KFQqlUb/HhEREVkOg42ZiY+PR3R0NOLj46FUKhEdHY3o6GiNNWHq16+PPXv2AAAkScKHH36ITz75BPv27cMff/yB4OBgVKpUCX369AEANGjQAEFBQQgJCcHZs2dx8uRJjB07FgMHDixwJhMREVGpIfSwWSCDTc2ePn06Nm/erP65WbNmAIBjx46hQ4cOAICYmBgoFAp1mf/973/qVQQfP36Mtm3b4uDBg7Czs1OX2bJlC8aOHYvOnTtDJpOhf//++Pzzzw11G0REREYjVBKEStLhfD0GU4pIwlTL9ZlQSkoKXFxcoFAoOBiYiIgKZYzvjNxrPFxkA2d77ZOZlHSBCpMyLe77jc9mIiIiMhccAKwVJjNERETmQkiADt1MljpmxmwWzSMiIiLSBltmiIiIzAQHAGuHyQwREZG5UOnYzWShyQy7mYiIiKhUY8sMERGRuRBSzqb1+foLpTRhMkNERGQmOGZGO0xmiIiIzIVKpuOYGctsmuGYGSIiIirV2DJDRERkLjibSStMZoiIiMyEEBKEDgOALe9piznYzURERESlGltmiIiIzAUHAGuFyQwREZGZECroODXbMpMZdjMRERFRqcaWGSIiInMhdJzNpMvqwaUYkxkiIiIzoftsJstMZtjNRERERKUaW2aIiIjMhUqWs2l9vv5CKU2YzBAREZkJ3R80aZndTExmiIiIzATHzGiHY2aIiIioVGPLDBERkbngmBmtMJkhIiIyExwzox0mM0REOhBC4OzZs7h+PRYuLi4ICOgMe3t7U4dFZFGYzBARaSkqKgojRryHq1evqve5uDhj2rSpCAv7EJJkmX8lk/Y4AFg7TGaIiLRw8eJFdOrUFZmZmRr7FYoUTJz4P6SlpWHatI9NFB2VWhwzoxXOZiIi0sKUKR8jKysLKlX+3x6zZ8/Bw4cPjRwVkWViMkNEVEJJSUmIjDwMpVJZYBmlUont278zYlRUFuQOANZls0TsZiIiKqH79+8XWUYul+PevSQjRENlCcfMaIctM0REJeTl5VXk4N7s7GxUqlTRSBERWTaDJTNz5sxB69at4eDgAFdX1yLLZ2VlYfLkyWjcuDEcHR1RqVIlBAcH4+7duxrlqlevDkmSNLb58+cb6C6IiPLy8vJCYGBXyOXyAstYW1vjzTffMGJUVCYI2X+DgLXZhGW2URjsrjMzMzFgwACMHj26WOXT0tJw8eJFTJs2DRcvXsTu3bsRExODXr165Sk7a9Ys3Lt3T72NGzdO3+ETERVq3rxPYGNjXWBCM3NmBMqXL2/kqKi045gZ7RhszMzMmTMBAJs2bSpWeRcXF0RGRmrsW7FiBVq2bIn4+HhUrVpVvb9cuXLw9vbWW6xERCXVtGlT/PLLUYSEjMLvv19W73d3d8eMGdMwduwYE0ZHpZUQuo17EUKPwZQiZj0AWKFQQJKkPN1U8+fPx+zZs1G1alUMHjwYoaGhsLIq+FYyMjKQkZGh/jklJcVQIRORBWnRogUuXTqP6OhoxMbegIuLC9q3fxW2tramDo3IophtMvPs2TNMnjwZgwYNgrOzs3r/+PHj8corr8Dd3R2nTp1CeHg47t27h6VLlxZY17x589QtRURE+iRJEpo1a4ZmzZqZOhQqC3TtKrLQbqYSjZmZMmVKnsG3L27Xrl3TOaisrCy88cYbEEJg9erVGsfCwsLQoUMHvPzyyxg1ahSWLFmC5cuXa7S8vCg8PBwKhUK9JSQk6BwjERGRvgkh03mzRCVqmZkwYQKGDRtWaJmaNWvqEo86kbl9+zaOHj2q0SqTHz8/P2RnZ+PWrVuoV69evmVsbW3Z7EtERFRGlSiZ8fDwgIeHh6FiUScy169fx7Fjx4o1EyA6OhoymQyenp4Gi4uIiMgoVJJuXUUW2s1ksDEz8fHxePToEeLj46FUKhEdHQ0AqF27NpycnAAA9evXx7x589C3b19kZWXh9ddfx8WLF7F//34olUokJeWsnunu7g4bGxtERUXhzJkz6NixI8qVK4eoqCiEhobirbfegpubm6FuhYiIyCi4ArB2DJbMTJ8+HZs3b1b/nDs47tixY+jQoQMAICYmBgqFAgCQmJiIffv2AciZ8vi83HNsbW2xbds2zJgxAxkZGahRowZCQ0MRFhZmqNsgIiIiMycJYXmz0lNSUuDi4gKFQlHkmBwiIrJsxvjOyL1G7JD6KGdT8MrSRXmSqUTtLdcs7vvNbKdmExHpy6VLl7Bq1VpcuHAB9vb26NOnN955ZxhX6CWzo+uMJAtsnwDAZIaIyri5c+fj44+nwcrKCtnZ2QCA06fPYP78BTh8+BDXhyEqAyxzQjoRWYR9+37Axx9PAwB1IgMAKpUKCkUKAgN7ID093VThEeXBZzNph8kMEZVZixcvLfBBkEqlEg8ePMC2bduNHBVRwXJnM+myWSImM0RUJmVnZ+PEid+gVCoLLCOXy3HkyFEjRkVUOCYz2mEyQ0RlUnEGQgohoFJZ5oBJorKEyQwRlUnW1tZo1qwpZLKCP+aEEGjdupURoyIqnBA6jplhywwRUdkSGvoBVCpVvsdkMhkcHR0RHPy2kaMiKhgfNKkdy7xrIrIIb701BKNHjwIAjYHAVlZy2NjY4Pvvd1nUwmJEZRWTGSIqsyRJwsqVn2P//r3o0qUzPDw84OPjgzFj3seVK9Ho3LmTqUMk0sCp2drhonlEVKZJkoQePbqjR4/upg6FqEh80KR22DJDREREpRpbZojIrMTFxeHatRg4OTmhVSs/WFtbmzokIqNhy4x22DJDRGbh+vXr6NIlEDVr1kX37j3x6qsdUblyNSxfvsJiH55HlkeodB03o911V65cierVq8POzg5+fn44e/ZsoeUfP36MMWPGoGLFirC1tUXdunVx4MAB7S6uB2yZISKTu3XrFlq1aguFQqGx/8GDBxg/PhT//PMIM2ZMN1F0RMZjipaZ7du3IywsDGvWrIGfnx+WLVuGwMBAxMTEwNPTM0/5zMxMdOnSBZ6enti5cycqV66M27dvw9XVVeu4dcWWGSIyuZkzZyMlJaXARw/Mnj0HiYmJRo6KyDIsXboUISEhGD58OBo2bIg1a9bAwcEBGzZsyLf8hg0b8OjRI3z//fdo06YNqlevjvbt26NJkyZGjvw/TGaIyKTS0tKwdes2jadav0iSJHz99RYjRkVkGvpaNC8lJUVjy8jIyPd6mZmZuHDhAgICAtT7ZDIZAgICEBUVle85+/btg7+/P8aMGQMvLy80atQIc+fOLfQ5aIbGZIaITOqff/5BZmZmoWVkMhkSEhKMFBGR6aiEpPMGAD4+PnBxcVFv8+bNy/d6Dx8+hFKphJeXl8Z+Ly8vJCUl5XvOzZs3sXPnTiiVShw4cADTpk3DkiVL8Mknn+j3xSgBjpkhIpNyc3ODTCYr8LEDAKBSqfLtuyei/CUkJGisbm1ra6u3unPfj+vWrYNcLoevry8SExOxaNEiRERE6O06JcFkhohMysnJCX379sb33+8rsJlaqVRiyJBBRo6MyAR0XcX3/891dnYu1qM6KlSoALlcjuTkZI39ycnJ8Pb2zvecihUrwtraWuMRIQ0aNEBSUhIyMzNhY2OjffxaYjcTEZlcRMQ02NjYaHw45pIkCe+9NxK1a9c2QWRExpU7m0mXrSRsbGzg6+uLI0eOqPepVCocOXIE/v7++Z7Tpk0bxMbGarSm/v3336hYsaJJEhmAyQwRmYHGjRvj2LFI1KpVU2O/jY0NJkwIxYoVn5koMqKyLywsDF988QU2b96Mq1evYvTo0UhNTcXw4cMBAMHBwQgPD1eXHz16NB49eoQPPvgAf//9N3788UfMnTsXY8aMMdUtsJuJiMyDn58frl37E7/9dhJ//fUXnJyc0L17N7i5uZk6NCKjMcU6M2+++SYePHiA6dOnIykpCU2bNsXBgwfVg4Lj4+Mhk/3X9uHj44NDhw4hNDQUL7/8MipXrowPPvgAkydP1jpuXUnCApfWTElJgYuLCxQKRbH6FImIyHIZ4zsj9xrnuraBk7X27QxPs7LR4ueTFvf9xm4mItI7pVKJvXv3oWfPPnjppZfRsWMANm7chPT0dFOHRkRlELuZiEivMjIy0K/fABw48BPkcjmUSiWuXYvB8eO/YMmST3Hs2GF4eHiYOkwis6QSMqiE9u0MupxbmlnmXRORwUydOh0HDx4CAPVU69xZD9euxWDIkGCTxUZk7oTQ5SGTuo23Kc2YzBCR3qSmpmL16rUFLoCnVCoRGXkYV69eNXJkRKWDsadmlxVMZohIby5cuIjU1NRCy0iShGPHjhsnICKyCBwzQ0R6U9zJkRY4iZKoWEwxNbssYDJDRHrTrFlT2NnZ4dmzZwWWEUKgbds2RoyKqPR4/mGR2p5vidjNRER64+zsjBEjhmsssPU8KysrtGnTGk2aNDFyZERkDpRKJX799Vc8fvxYr/UymSEivVqwYB78/VsBgDqpkSQJkiShSpXK2LZtiynDIzJrZX0AsFwuR9euXfHvv//qtV6DJTNz5sxB69at4eDgAFdX12KdM2zYMPWHXu4WFBSkUebRo0cYMmQInJ2d4erqihEjRuDp06cGuAMi0oajoyOOHo3Epk3r4efXEhUreqNRo5ewePECREdfQJUqVUwdIpHZKuvJDAA0atQIN2/e1GudBhszk5mZiQEDBsDf3x/r168v9nlBQUHYuHGj+mdbW1uN40OGDMG9e/cQGRmJrKwsDB8+HCNHjsTWrVv1FjsR6cbGxgZDhwZj6FCuKUNEmj755BNMnDgRs2fPhq+vLxwdHTWOa/MYBoMlMzNnzgQAbNq0qUTn2drawtvbO99jV69excGDB3Hu3Dk0b94cALB8+XJ0794dixcvRqVKlXSKmYiIyJQsYQBw9+7dAQC9evWCJP0XrxACkiSpF9ssCbObzXT8+HF4enrCzc0NnTp1wieffILy5csDAKKiouDq6qpOZAAgICAAMpkMZ86cQd++ffOtMyMjAxkZGeqfU1JSDHsTREREWhBCt+nVpWHVg2PHjum9TrNKZoKCgtCvXz/UqFEDN27cwEcffYRu3bohKioKcrkcSUlJ8PT01DjHysoK7u7uSEpKKrDeefPmqVuKiIiIyHTat2+v9zpLNAB4ypQpeQbovrhdu3ZN62AGDhyIXr16oXHjxujTpw/279+Pc+fO4fjx41rXCQDh4eFQKBTqLSEhQaf6iIiIDMESBgADwIkTJ/DWW2+hdevWSExMBAB8/fXX+O2337Sqr0QtMxMmTMCwYcMKLVOzZk2tAimorgoVKiA2NhadO3eGt7c37t+/r1EmOzsbjx49KnCcDZAzDufFgcREpJ3ff/8dq1atwenTZ6BSCdSqVRNt27ZGt25BeOmll0wdHlGpJnQcM1Makpldu3bh7bffxpAhQ3Dx4kX1MBCFQoG5c+fiwIEDJa6zRMmMh4cHPDw8SnwRbd25cwf//PMPKlasCADw9/fH48ePceHCBfj6+gIAjh49CpVKBT8/P6PFRWSpFi9eikmTJkMul6sH6V25cgV79+7DpElT0LFjB2zZ8pX6PUtEJWMJjzP45JNPsGbNGgQHB2Pbtm3q/W3atMEnn3yiVZ0GW2cmPj4e0dHRiI+Ph1KpRHR0NKKjozXWhKlfvz727NkDAHj69CkmTZqE06dP49atWzhy5Ah69+6N2rVrIzAwEADQoEEDBAUFISQkBGfPnsXJkycxduxYDBw4kDOZiAwsMvIwJk2aDAAFzjb49dcTaNeuI548eWLM0IioFImJicGrr76aZ7+Li4vWKwMbLJmZPn06mjVrhoiICDx9+hTNmjVDs2bNcP78eXWZmJgYKBQKADmrAl6+fBm9evVC3bp1MWLECPj6+uLEiRMaXURbtmxB/fr10blzZ3Tv3h1t27bFunXrDHUbRPT/liz5FHK5vNAySqUSN2/exKZNm40UFVHZYgljZry9vREbG5tn/2+//ab1UBVJWODja1NSUuDi4gKFQqHV4jxElkYIAVtbR2RlZRVZVpIkNG3aBBcvnjNCZESGZ4zvjNxrHGrVDY5W1lrXk5qdhcDTP5n199u8efPwzTffYMOGDejSpQsOHDiA27dvIzQ0FNOmTcO4ceNKXKdZTc0mIvNV3L97hBB5BuoTEeWaMmUKVCoVOnfujLS0NLz66quwtbXFxIkTtUpkACYzRFQMkiShVSs/REWdLnJ1TplMhmrVqhkpMqKyxRIGAEuShI8//hiTJk1CbGwsnj59ioYNG8LJyUnrOvnUbCIqltDQD4q1zLhKpcJ774UYISKisif3cQa6bObunXfewZMnT2BjY4OGDRuiZcuWcHJyQmpqKt555x2t6mQyQ0TIysrCkSNHsXPnLly6dCnfLqW+ffvgf/+bWGg9MpkMbdq0xsCBbxoqVCIq5TZv3oz09PQ8+9PT0/HVV19pVSeTGSILt27dF6hcuRoCAgIxYMBAvPJKS1hb26NevYZYvXqN+kNHkiQsWDAPP//8E3r06A4HBweNh8TZ2tpi5MgQHDp0ADY2Nqa6HaJSTUDSeTNXKSkpUCgUEELgyZMnSElJUW///vsvDhw4kOeRRcXFMTNEFmzZss8QGpq3tUWpVOLvv6/j/ffHYcOGTThy5Gf1zIguXQLQpUsAgJwPp/PnL0AIAV/fV+Dq6mrM8InKnLI8ZsbV1VX96KO6devmOS5JktbPUWQyQ2ShFAoFwsOnFlnu4sVLCAubhC+/XJvnmLOzMzp16miI8IiojDl27BiEEOjUqRN27doFd3d39TEbGxtUq1ZN6wVwmcwQWaidO3epn4lSGJVKhU2bNmPhwnkaHz5EpH+6DuI15wHAuU/LjouLQ9WqVTW6qXXFMTNEFioq6kyx145RKpXYsuVbA0dERJawAvDVq1dx8uRJ9c8rV65E06ZNMXjwYPz7779a1clkhqgMEkLg6NFjWLBgEZYs+RRXrlwBAPz777/YsWMnpk2bjvXrN5Sozn37fjBEqET0HBV0nJptxgOAc02aNAkpKSkAgD/++ANhYWHo3r074uLiEBYWplWd7GYiKmOuXLmC/v3fwN9/X4dcLocQAhMn/g/VqlXFvXtJyMzM1Krev//+W8+REpEliouLQ8OGDQEAu3btQs+ePTF37lxcvHgR3bt316pOJjNEZcidO3fw6qud1H/1PL/I3e3b8TrVzenWRIZXlmcz5bKxsUFaWhoA4PDhwwgODgYAuLu7qz+7SorJDFEZ8vnnK5CSklKslXpLwspKju7du+m1TiLKSwXduopKQzdT27ZtERYWhjZt2uDs2bPYvn07gJzW3ypVqmhVJ8fMEJUhX3/9jd4TmZx1IWQYM2a0XuslIsu0YsUKWFlZYefOnVi9ejUqV64MAPjpp58QFBSkVZ1smSEqQx4/Vui1PrlcDrlcju+++zbfRa6ISM90nZFUCrqZqlativ379+fZ/+mnn2pdJ5MZojKkRo3quHYtpthTrgtiZ2eH5s190alTR4SEjNC66ZeISqYsrzOTKz6+8PF7VatWLXGdTGaIypBRo0biww8n6FRHo0Yv4fTpk3B0dNRTVERE/6levXqhC+Zp01XOZIaoDOnatQvc3Nzw6NEjrc7v0qULfv75gJ6jIqLisoTZTJcuXdL4OSsrC5cuXcLSpUsxZ84crepkMkNURty8eRPt2nXE48ePtTrf1/cVHDiwT79BEVGJqP5/0+V8c9ekSZM8+5o3b45KlSph0aJF6NevX4nr5GwmojLivffex8OHD6FSlfzjLDT0Q5w/fwZWVvz7hohMo169ejh37pxW5/KTi6iUSEtLw5MnT+Du7g5ra2ukp6dj3bovsGbNOty8GafVyr7169fDV19tRIsWLQwQMRGVlCV0M724MJ4QAvfu3cOMGTNQp04drepkMkNk5i5duoRZs+Zg374foFKp4OTkhLfeGozffjuJK1f+1KpOKysrLFgwF6GhH+r1ybVEpBuV0G1Gkkq3iYxG4erqmudzRwgBHx8fbNu2Tas6mcwQmbGjR4+hW7fXoFQq1d1HT58+xZo163Sq96WXGiIsLFQfIRKRHglIEDqs4qvLucZy7NgxjZ9lMhk8PDxQu3Ztrbu6mcwQmans7GwMGRKM7OxsrcbBFKZt2zZ6rY+IqLjat2+v9zqZzBCZqQMHfkJSUpJB6n7//VEGqZeIdFNWF83bt6/4MyV79epV4vqZzBCZqStX/oSVlRWys7P1Wm+fPr3QsGFDvdZJRPqRM2ZGt/PNUZ8+fYpVTpIkLppHVJY4OjrovXvJxcUFX3yxVq91EhEVRd+fZS/iOjNEZqp37146P2Ppec7Ozvjtt+OoUKGC3uokIv3KHQCsy2aujh49ioYNG+aZmg0ACoUCL730Ek6cOKFV3UxmiMxUdnY2WrdurZe67OzskJAQh0aNGumlPiIyjNwxM7ps5mrZsmUICQmBs7NznmMuLi547733sHTpUq3qZjJDZGYSExMRGNgddeo0wMmTJ3Wuz9PTE9eu/ZnvBwgRkbH8/vvvCAoKKvB4165dceHCBa3qZjJDZEYuXryI5s39cPjwEb3UN2PGdCQl3UG1alX1Uh8RGZYQum/mKjk5GdbW1gUet7KywoMHD7Sqm8kMkRk4d+4c2rZtD19fPyQlJetlsFy9enURETGNK/wSlSICElQ6bOY8ZqZy5cq4cuVKgccvX76MihUralU3kxkiEzt79izatGmPkydP6a1OuVyOb7/9Rm/1ERHpqnv37pg2bRqePXuW51h6ejoiIiLw2muvaVW3wZKZOXPmoHXr1nBwcICrq2uxzpEkKd9t0aJF6jLVq1fPc3z+/PkGugsiw+vTpz+ysrJ0rkculwMAKlSogEOHDqBZs2Y610lExpX7oEldNnM1depUPHr0CHXr1sXChQuxd+9e7N27FwsWLEC9evXw6NEjfPzxx1rVbbB1ZjIzMzFgwAD4+/tj/fr1xTrn3r17Gj//9NNPGDFiBPr376+xf9asWQgJCVH/XK5cOd0DJjKwjIwM7N69B9u378Djx4/RoEF9VKlSGffu6bbKr0wmQ7NmTdG9ezc0btwIvXv3go2NjZ6iJiJjKqsrAAOAl5cXTp06hdGjRyM8PFy99IQkSQgMDMTKlSvh5eWlVd0GS2ZmzpwJANi0aVOxz/H29tb4ee/evejYsSNq1qypsb9cuXJ5yhKZs3v37qFTpy64di0GMpkMKpUKJ0+e0svqvjKZDK1b+2PWrBk610VEpiX+f9PlfHNWrVo1HDhwAP/++y9iY2MhhECdOnXg5uamU71mO2YmOTkZP/74I0aMGJHn2Pz581G+fHk0a9YMixYtKvILISMjAykpKRobkbEIIdC7dz/Ext4A8N9KmPp6TEF2djZef71/0QWJiMyEm5sbWrRogZYtW+qcyABm/DiDzZs3o1y5cujXr5/G/vHjx+OVV16Bu7s7Tp06hfDwcNy7d6/QhXbmzZunbikiMrZTp07h3LnzBqlbLpfD378V2rVra5D6ici4ynI3kyGVqGVmypQpBQ7Szd2uXbuml8A2bNiAIUOGwM7OTmN/WFgYOnTogJdffhmjRo3CkiVLsHz5cmRkZBRYV3h4OBQKhXpLSEjQS4xExXHoUKR6cK6+yGQ5b902bVpj797dnH5NVEao9LBZohK1zEyYMAHDhg0rtMyL41u0ceLECcTExGD79u1FlvXz80N2djZu3bqFevXq5VvG1tYWtra2OsdFpI2srCytngKbHxsbK7zaviF8XwlEnz694Ofnx0SGiCxeiZIZDw8PeHh4GCoWtfXr18PX1xdNmjQpsmx0dDRkMhk8PT0NHheRNh49eqSXetzdHZFwdxnsbFtAhpf0UicRmRddp1eb89RsQzLYmJn4+Hg8evQI8fHxUCqViI6OBgDUrl0bTk5OAID69etj3rx56Nu3r/q8lJQU7NixA0uWLMlTZ1RUFM6cOYOOHTuiXLlyiIqKQmhoKN566y29DCAi0rfjx49j/fqNeqnL0ckWtraekFBXL/URkfnhmBntGCyZmT59OjZv3qz+OXcBr2PHjqFDhw4AgJiYGCgUCo3ztm3bBiEEBg0alKdOW1tbbNu2DTNmzEBGRgZq1KiB0NBQhIWFGeo2iLQihED//gOwZ89evdXZps0rkCEAEgp+tgkRkSWShDDnx1IZRkpKClxcXKBQKPgkYdK7+/fvo0kTXyQl6bYY3ouiok6gVatWeq2TiIpmjO+M3GssrT0M9nLtF71MV2YiLHZTiWNduXIlFi1ahKSkJDRp0gTLly9Hy5Ytizxv27ZtGDRoEHr37o3vv/9e67h1ZbbrzBCVRpmZmf//sEj9JjI1atRgIkNkAXK7mXTZSmr79u0ICwtDREQELl68iCZNmiAwMBD3798v9Lxbt25h4sSJaNeunba3qzdMZoj0aPXqNbhz547e6/3++116r5OIyq4XF4otbPmSpUuXIiQkBMOHD0fDhg2xZs0aODg4YMOGDQWeo1QqMWTIEMycOVMvs5h1xWSGSE+EEPj008/0Xu+CBfPw8suN9V4vEZkffa0z4+PjAxcXF/U2b968fK+XmZmJCxcuICAgQL1PJpMhICAAUVFRBcY5a9YseHp65rtKvymY7QrARKVFfHw8FixYhI0bN0MI/TyiAMh5QvzixQvQv3+/ogsTUZmgr6nZCQkJGmNmClpr7eHDh1AqlXke8Ojl5VXgIri//fYb1q9fr56lbA6YzBDp4Nq1a2jTpj1SUlLw5sCW+P33eFz5Q7dupnfeGYb33gtBixYtuCAekYUR0G0V39wZPc7OzgYZrPzkyRO8/fbb+OKLL1ChQgW9168tJjNEOnjrrWF4/Pgxatb0wIZN72LShG24dvUusrNL9nEkl8shSRK+++5b9O3bxzDBEhG9oEKFCpDL5UhOTtbYn5ycDG9v7zzlb9y4gVu3bqFnz57qfbkPz7WyskJMTAxq1apl2KDzwTEzRCUgICDwECqRgM2b1+DChQtQqVQYNbojhBAYPaYTSrragUwmw7vvvoPo6PNMZIgsnICk7mrSakPJWnNtbGzg6+uLI0eOqPepVCocOXIE/v7+ecrXr18ff/zxB6Kjo9Vbr1690LFjR0RHR8PHx0fn10AbbJkhi5aeno5vv92Gr776Bvfv30fNmjXx7rvvoGfP1/I8HFIgHipcxPnzV/DO0C/w11931cfavVoPVlZy1KnjjQFv+mHb1tPFjuGdd4ZhzZpVersnIiq9VCJn0+X8kgoLC8PQoUPRvHlztGzZEsuWLUNqaiqGDx8OAAgODkblypUxb9482NnZoVGjRhrnu7q6AkCe/cbEZIYs1v3799GpUxf8+edfkMlkUKlU+Pvv6/jxxwPo3r0bdu/eoR40p8ItCJzEX38lolP7ecjIyNKoS/ncJ0ilSq6wspIVq6tJkiR89NEU/d4YEVEJvPnmm3jw4AGmT5+OpKQkNG3aFAcPHlQPCo6Pj4dMZt4dOUxmyGINGRKMmJi/AfzX55v7dOuDBw9h6tTpWLRoAQSUEDiHxMR/8eH4b/DsWRZUL/z5c/jnK3jllWqwspKjcmW3PMcL8vnnn6JGjRp6vCsiKs0E/hvEq+352hg7dizGjh2b77Hjx48Xeu6mTZu0vKr+mHeqRWQgf/31Fw4fPoLs7PynUqtUKqxevRZPnz7F778fQdfOs1GtSiiOHrmab6Kybu1xZGUpoVKpMHBQK8hkhfdbW1lZ4dChAxg7doxe7oeIygZTrABcFjCZIYt0/PgvRU57Tk1Nxbffbkdz3144evRqoWXv3HmEAf2WIzNTifLlnTB9Rp8Cy8rlcvzxxyV07dpFm9CJiOgF7GYii1TcGUehoRPUXU9FOXjwD9Sr/T+MfK8jArq+BMXjNKxedRRpaZnqMlZWVvjll6OoX7++VnETUdn2/Cq+2p5viZjMkEVq27ZNkQmNTCZDampqiepNTPwXEdN3I2L67jzH2rVri127voOHh0eJ6iQiy6GvFYAtDbuZyCI1adIEbdu2gZVVwfl87qBgXUiShCpVKiMhIQ6//nqMiQwRkQEwmSGL9e2338DHpwokSTLYYwOEEPjss09RpUoVg9RPRGWLvh40aWmYzJDFqlKlCi5dOo8lSxaiceNGcHV10Wv9jo6O2LDhC/Tr11ev9RJR2SWE7pslYjJDFs3FxQWhoR/i998v5nlqrDZsbW3x7rvvYOPGL5GUdAfDhw/TPUgishgqSDpvlogDgImQ0x0UG3tD6/NlMhkWLJiLESPegZubmx4jIyKiojCZIYulUCigVCrh5uaG1NTUYk/Bzk/fvr0xceIEPUZHRJbIFM9mKgvYzUQWZ8eOnfD19YOrawWUL++FWrXqYuHCxTrVKZPJiy5ERFQUXcfLWGgyw5YZsiizZn2CiIiZGrOX4uJuYfbsOTrV6+v7iq6hERGRlpjMkMW4fPkyIiJmAij+CsDFIZfLERb2od7qIyLLpesgXg4AJirj1q79wiD1LlgwD9bW1gapm4gsi67Tqzk1m6iMO3v2nF7rc3Nzw5dfrsWECaF6rZeIiEqGLTNkMbKysnQ639vbG2vWrIS1tTV8fHzQqNFLBls5mIgsEx80qR0mM1TmCSFw9+5dNG7cCL//flmrOiRJwr17CXqOjIhIE6dma4fJDJVZQgisXbsOixYtwc2bcTrVxTExRETmi8kMlUlCCIwZMx6rV6/RS1dQ9erV9BAVEVHhdF0qxkIbZpjMUNn0668nsHr1GgD6mYY9fvxYnesgIipKTjeTDlOzLTSbYTJDpZ5ACgSuQuA2gCwADliz9ktYWcmRna39IwoAQC6X4ZVXXsE77wzXS6xERIXh1GztcGo2lWoC96HCAQjEIieRAYA0REf/oXMiY2dni5CQd3HkyM+wt7fXOVYiIjIMtsxQqSWghAq/AlBCCIETJ/7G+i9/QczVe/g7JknreuVyGQIDA7F169dwcXHRX8BEREXg1GztGKxl5tatWxgxYgRq1KgBe3t71KpVCxEREcjMzCz0vGfPnmHMmDEoX748nJyc0L9/fyQnJ2uUiY+PR48ePeDg4ABPT09MmjQJ2dnZhroVMlMCMQAy8PTpM7Txn4VO7edh6zencP58HFQ6dBzL5XIsWDCXiQwRGZ0uD5nUtYuqNDNYy8y1a9egUqmwdu1a1K5dG1euXEFISAhSU1OxeHHBTygODQ3Fjz/+iB07dsDFxQVjx45Fv379cPLkSQCAUqlEjx494O3tjVOnTuHevXsIDg6GtbU15s6da6jbITMjcAcqcRHjxn6NtauPqt/Aur6Rra3l2PfD92jUqJHuQRIRkVFIQp9P3CvCokWLsHr1aty8eTPf4wqFAh4eHti6dStef/11ADlJUYMGDRAVFYVWrVrhp59+wmuvvYa7d+/Cy8sLALBmzRpMnjwZDx48gI2NTZFxpKSkwMXFBQqFAs7Ozvq7QTIKgQyosAeD3lyOHd+d1Wvd+374Aj1fG6bXOomodDPGd0buNUZ5vQdbma3W9WSoMrAmea3Ffb8ZdQCwQqGAu7t7gccvXLiArKwsBAQEqPfVr18fVatWRVRUFAAgKioKjRs3VicyABAYGIiUlBT8+eef+dabkZGBlJQUjY1KL4Gb+P33OL0nMt26t0eP7sF6rZOIqCSE+G8VYG02S+1mMloyExsbi+XLl+O9994rsExSUhJsbGzg6uqqsd/LywtJSUnqMs8nMrnHc4/lZ968eXBxcVFvPj4+OtwJmd4/WDDvR73W2L9/c+zatRUyGSf4ERGVNiX+5J4yZQokSSp0u3btmsY5iYmJCAoKwoABAxASEqK34IsrPDwcCoVCvSUk8Bk7pZuEy5f18zts2rQqTpyaiu07x8LOzk4vdRIRaUvoYbNEJR4APGHCBAwbNqzQMjVr1lT/9927d9GxY0e0bt0a69atK/Q8b29vZGZm4vHjxxqtM8nJyfD29laXOXtWs3shd7ZTbpkX2drawtZW+z5IMi8pCifY2sq1Olcmk/DHX3Mhk0nw8CgHV1fH/z9iB8BJbzESEWmDD5rUTomTGQ8PD3h4eBSrbGJiIjp27AhfX19s3LixyCZ8X19fWFtb48iRI+jfvz8AICYmBvHx8fD39wcA+Pv7Y86cObh//z48PT0BAJGRkXB2dkbDhg1LejtUSjx8+BB3797FiRO/Ye3aJQidEIh3hn1Z4no+X/EW6tWrmGe/hPqQuIYkEVGpZLCp2YmJiejQoQOqVauGxYsX48GDB+pjuS0oiYmJ6Ny5M7766iu0bNkSLi4uGDFiBMLCwuDu7g5nZ2eMGzcO/v7+aNWqFQCga9euaNiwId5++20sXLgQSUlJmDp1KsaMGcPWlzLo2rVrmDLlY+zb94P6GUsuLvaYMX1PiR5X4OLqgCVLB2HY8HYQQkCSJKhUAjKZBKAaJDQw4F0QERUPH2egHYMlM5GRkYiNjUVsbCyqVKmicSz3SykrKwsxMTFIS0tTH/v0008hk8nQv39/ZGRkIDAwEKtWrVIfl8vl2L9/P0aPHg1/f384Ojpi6NChmDVrlqFuhUzkzz//hL9/O6SlpWk8LFKhSIdCkV7k+ZIESJKEceO7YP7CN2BtbQUhBLKylLiT8AgymRtq1ugCoBIk6P5kbSIiXXEFYO0YdZ0Zc8F1ZsxTeno6vvtuB3799QSEEDh1KgqxsTegVBb/GUtWVjL07NUMtrbWsLaWYcbMvqhW/b9u0aNH/sI7w75EUlIKEhLiChxnRUSUy5jrzAwt/x5sdFhnJlOVgc3/WN46M3w2E5mF8+fPo3v3Xnjw4AGsrHJaUEqSxDyvfHknfDy1F3yqllfvmzxpO37Ydwl//50ESZLwwQfjmcgQEZURHPFIJpeUlIQuXYLw6NEjAEB2drbWiUx2tgpbt0ShUmU3ADldmiuWH8ZnyyIRG3sfcrkcH3wwHosXL9Bb/ERE+sKp2dphywyZ3Lp1X0KhSIG+ejzT0nIfZmoNZVYdlHeTMG1aLbi7u6F//36oWDHvbCYiInPAqdnaYTJDJrdr1269JTIA4OXlCit5ACR4wtZGjrfeaqa3uomIyPwwmSGT++efR3qtr3fv1yEDW1+IqPTh1GztMJkho3j06BEuXrwESZLQokVzjVH2Dg72eruOnZ0dFi9eqLf6iIiMiVOztcMBwGRQT58+xciRo1Gxog+6dAlCQEAgvLwq48MPw/Ds2TMAQPXq1fVyLUdHR5w+fRLlypXTS31ERFQ6MJkhg8nIyEDXrt2wYcNGZGZmqvc/e/YMy5evRO/e/aBUKuHn1xKSpP2idT4+Ppg9eyYSEuLQpMnL+gidiMgkVPhvELBWm6lvwESYzJDBbNmyFVFRp/OdZq1SqfDzz5HYt+8HjBgxXOtrWFlZ4fXX+2Hq1I/g5uamS7hERCbHqdnaYTJDBrNu3ZeFPlxULpewes0nqFo9E9Omf6TVNbKzs3Hjxk1tQyQiojKAA4BJb/79919s3fotrl+PhaurK27cuAmVquBGT6VSICH+PlTiLKZFtELFisvxyez5SExMBJDT6gLkJCwFkcvlcHV10e+NEBGZiNCxq4izmYh08OWX6zF27AfIzMyElZUVVCpVkav4ymQSvLxdIEkShPgHI99rgZB3b+Dy5ctIT09H3bp1MXXqdKxfv7HAhEapVGLgwDcNcUtEREYnhG5dRUxmiLT0/fd7ERIySv1zVlZWsc5TqQSGDmsLIOcJ1wKxkMmboFmz/xa5mzgxDN98szXfZzXJ5XK0aNEcgYFd9XAXRESmx6nZ2uGYGdKJEALTp88o8WwkuVyGxo2r4I03Wz63NxuAQqNc7dq1cfToz6hUKWcRPCsrK8jlcgBA586dcODAD4WOyyEiorKPLTOkk1u3buGPP66U+LwGDSvhhwNhsLOzeeFI3sSkZcuWiIuLxaFDP+P8+QuwtbVFjx7d0KhRIy2jJiIyTznTq7XvK+KzmYi08OTJk2KXlckkqFQCkiThyh938Fr3pTh8dDIqVMhd5M4WQP6DeeVyObp374bu3bvpHjQRkZnSdXq1heYy7GYi3VSrVg3W1tbFKqv6/z8Zch8qefWvuxj69rr/3wdIaACJ/ySJiKiE+M1BOnFxccHgwQPV06hLQqlU4dDBP/D330mQpFqQ0NAAERIRlR46rf4rLLebickM6WzevDmoWLEirKzkWp3/63EJMvhBgvaPNCAiKguEHv5niZjMkM4qVqyIc+ei0K9fX+1mFgknJjJERKQ1DgAmnTx8+BDff78Xf/zxB77/fp96PExJtG3bxgCRERGVPro+LNJSu5mYzJBWVCoVPvpoKpYuXYbs7GytkhgrKyu0bdsGL730kgEiJCIqfbhonnaYzJBWpkz5CIsXL9UqiclVrVo1bNnylR6jIiIiS8QxM1RiSUlJ+PTTz7ROZKytrfHpp4tx6dI5VKpUSc/RERGVXkIInTdLxJYZKrFdu3YX+jTsgshkMjg5OeLo0Uj4+voaIDIiotKN3UzaYcsMldg//zwq8awlSZIwbtwYXL58iYkMEVEB2DKjHbbMUInVqFEd2dnZJTpn8eIFCAsLNVBERERkydgyQyXWr19fODk5FVpGkiTI5XJIkoSpUz9CaOiHxgmOiKgUE/ivq0mbzTLbZdgyQ1pwdHTEihWfYdiwEZAkSaNZUy6Xw8rKCv369UXjxo3w9ttDUKVKFRNGS0RUeqiE0PGp2ZaZzrBlhrQydGgwdu/egbp166j3SZKEgIDOiI4+j61bv0Z4+GQmMkREpcDKlStRvXp12NnZwc/PD2fPni2w7BdffIF27drBzc0Nbm5uCAgIKLS8MTCZIa317dsHV69ewR9/XMKJE8cQH38TBw/+iPr165s6NCKiUskUz2bavn07wsLCEBERgYsXL6JJkyYIDAzE/fv38y1//PhxDBo0CMeOHUNUVBR8fHzQtWtXJCYm6nr7WpOEBQ59TklJgYuLCxQKBZydnU0dDhERmTFjfGfkXuNV+3dgJdloXU+2yMSv6RuQkJCgEautrS1sbW3zPcfPzw8tWrTAihUrAOSs8O7j44Nx48ZhypQpRV5TqVTCzc0NK1asQHBwsNax64ItM0RERGWMj48PXFxc1Nu8efPyLZeZmYkLFy4gICBAvU8mkyEgIABRUVHFulZaWhqysrLg7u6ul9i1YbBk5tatWxgxYgRq1KgBe3t71KpVCxEREcjMzCzwnEePHmHcuHGoV68e7O3tUbVqVYwfPx4KhUKjnCRJebZt27YZ6laIiIiMQgWh8wYACQkJUCgU6i08PDzf6z18+BBKpRJeXl4a+728vJCUlFSsmCdPnoxKlSppJETGZrDZTNeuXYNKpcLatWtRu3ZtXLlyBSEhIUhNTcXixYvzPefu3bu4e/cuFi9ejIYNG+L27dsYNWoU7t69i507d2qU3bhxI4KCgtQ/u7q6GupWiIiIjEJfs5mcnZ2NMoxi/vz52LZtG44fPw47OzuDX68gBktmgoKCNJKNmjVrIiYmBqtXry4wmWnUqBF27dql/rlWrVqYM2cO3nrrLWRnZ8PK6r9wXV1d4e3tbajwiYiIyrwKFSpALpcjOTlZY39ycnKR37GLFy/G/PnzcfjwYbz88suGDLNIRh0zo1AoStynljvg6vlEBgDGjBmDChUqoGXLltiwYUOhSzhnZGQgJSVFYyMiIjI3xp7NZGNjA19fXxw5ckS9T6VS4ciRI/D39y/wvIULF2L27Nk4ePAgmjdvrvX96ovRFs2LjY3F8uXLC2yVyc/Dhw8xe/ZsjBw5UmP/rFmz0KlTJzg4OODnn3/G+++/j6dPn2L8+PH51jNv3jzMnDlTp/iJiIgM7flxL9qeX1JhYWEYOnQomjdvjpYtW2LZsmVITU3F8OHDAQDBwcGoXLmyehDxggULMH36dGzduhXVq1dXj61xcnIqcnV4Qynx1OwpU6ZgwYIFhZa5evWqxlojiYmJaN++PTp06IAvv/yyWNdJSUlBly5d4O7ujn379sHa2rrAstOnT8fGjRuRkJCQ7/GMjAxkZGRo1O3j48Op2UREVCRjTs1uYfe2zlOzzz37usSxrlixAosWLUJSUhKaNm2Kzz//HH5+fgCADh06oHr16ti0aRMAoHr16rh9+3aeOiIiIjBjxgytY9dFiZOZBw8e4J9//im0TM2aNWFjk/PLuHv3Ljp06IBWrVph06ZNxXra8pMnTxAYGAgHBwfs37+/yEFFP/74I1577TU8e/aswHn0z+M6M0REVFyWkMyUdiXuZvLw8ICHh0exyiYmJqJjx47w9fXFxo0bi5XIpKSkIDAwELa2tti3b1+xRkdHR0fDzc2tWIkMERGRudJ2Fd/nz7dEBhszk5iYiA4dOqBatWpYvHgxHjx4oD6WO0I6MTERnTt3xldffYWWLVsiJSUFXbt2RVpaGr755huNwboeHh6Qy+X44YcfkJycjFatWsHOzg6RkZGYO3cuJk6caKhbISIiMgqh45gZJjN6FhkZidjYWMTGxuZ52GBuz1ZWVhZiYmKQlpYGALh48SLOnDkDAKhdu7bGOXFxcahevTqsra2xcuVKhIaGQgiB2rVrY+nSpQgJCTHUrZQJGRkZ2LlzF/bu/QGpqal4+eXGCAkZgZo1a5o6NCIiIp3w2UwW0KcYFxeHzp27Ii7uFmQyGVQqFeRyOYRQYdmypRg3bqypQyQiMlvGHDPTzH4Q5DqMmVGKTFxK/9Zivt9y8dlMZVx2dja6du2GhIQ7AHLWDwByHgymUgmMHx+Kn346aMoQiYjo/+nrcQaWhslMGbd//4+Ijb2B7OzsfI/L5XIsWLDIyFERERHpj9EWzSPTOHDgJ1hZWRWYzCiVSvzyy69IT0+Hvb29kaMjIqLn5bav6HK+JWIyU8YV9pTy52VlZTGZISIyMRUASacVgC0Tu5nKuObNfaFUKgs8LkkSatSojnLlyhkxKiIiIv1hMlPGvf32W7C3t4ckSQWWGT9+bKHHiYjIOFSSSufNEjGZKeNcXFywbdsWyOVyjSePS5IESZLQs2cPjB07xoQREhFRLpUe/meJmMxYgJ49X8P586cxaNBAODg4QC6Xo1Gjl7B27Srs2rVDI8khIiLTYTKjHX6LWYgmTZrgq6824quvNpo6FCIiIr1iMkNERGQmODVbO0xmiIiIzIRKUkHSYRCvpXYzccwMERERlWpsmSEiIjITQsdBvOxmIiIiIpMSUELo0GkiUPAiqWUZu5mIiIioVGPLDBERkZnI6WLiAOCSYjJDRERkJlQQ0C2Z0f4hlaUZu5mIiIioVGPLDBERkZnIGQCs/YN/LXUAMJMZM5MzrS4DgDUk/nqIiCwKx8xoh9+WZkIgAwJXIHADQNb/760MGRpDQnlThkZEREbCxxloh8mMGRB4BhV+BvAU0Bi8dRcq3IUMHSChkomiIyIiMm8cAGwGVIhG3kQG//+zgAonLbYflIjIkqig1HmzRGyZMTGBLABxyJvIPC8TAgmQUN04QRERkUmwm0k7bJkxuacoerCXBEBhhFiIiIhKH7bMmFxxfwX8VRERlXUqoQR0mJqdc77l4TekyTkBcEHhLS8CEnyMFA8REZkKu5m0w24mE5MgQYbGhZYAfCDB2VghERERlSpsmTEDEqpBQjoELiJnIHBuE6MAUBEytDZdcEREZDQ5LTPadxVZassMkxkzIUN9CFSDwE3kDAq2/v8khwvmERFZCiFUUOnyOAPBZIZMTII9JLxk6jCIiIhKFSYzREREZiKnm0iXB02yZYaIiIhMSOg4tVrX80srg81munXrFkaMGIEaNWrA3t4etWrVQkREBDIzMws9r0OHDpAkSWMbNWqURpn4+Hj06NEDDg4O8PT0xKRJk5CdnW2oWyEiIjIKlR7+Z4kM1jJz7do1qFQqrF27FrVr18aVK1cQEhKC1NRULF68uNBzQ0JCMGvWLPXPDg4O6v9WKpXo0aMHvL29cerUKdy7dw/BwcGwtrbG3LlzDXU7REREZKYMlswEBQUhKChI/XPNmjURExOD1atXF5nMODg4wNvbO99jP//8M/766y8cPnwYXl5eaNq0KWbPno3JkydjxowZsLGx0et9EBERGUvObCTOZiopoy6ap1Ao4O7uXmS5LVu2oEKFCmjUqBHCw8ORlpamPhYVFYXGjRvDy8tLvS8wMBApKSn4888/860vIyMDKSkpGhsREZG5EVDqvFkiow0Ajo2NxfLly4tslRk8eDCqVauGSpUq4fLly5g8eTJiYmKwe/duAEBSUpJGIgNA/XNSUlK+dc6bNw8zZ87Uw10QERGRuSlxy8yUKVPyDNB9cbt27ZrGOYmJiQgKCsKAAQMQEhJSaP0jR45EYGAgGjdujCFDhuCrr77Cnj17cOPGjZKGqhYeHg6FQqHeEhIStK6LiIjIUIQQEEKlwyZMfQsmUeKWmQkTJmDYsGGFlqlZs6b6v+/evYuOHTuidevWWLduXYkD9PPzA5DTslOrVi14e3vj7NmzGmWSk5MBoMBxNra2trC1tS3xtYmIiIxJ13ViuM5MMXl4eMDDw6NYZRMTE9GxY0f4+vpi48aNkMlKPkQnOjoaAFCxYkUAgL+/P+bMmYP79+/D09MTABAZGQlnZ2c0bNiwxPVrS+AZBJIAqCChPCS4GO3aRERE9B+DjZlJTExEhw4dUK1aNSxevBgPHjxQH8ttQUlMTETnzp3x1VdfoWXLlrhx4wa2bt2K7t27o3z58rh8+TJCQ0Px6quv4uWXXwYAdO3aFQ0bNsTbb7+NhQsXIikpCVOnTsWYMWOM0voioIQK5wHcQM6DIHP/3xMy+EOCk8FjICKisiln0Tvtu4osdTaTwZKZyMhIxMbGIjY2FlWqVNE4ltunl5WVhZiYGPVsJRsbGxw+fBjLli1DamoqfHx80L9/f0ydOlV9rlwux/79+zF69Gj4+/vD0dERQ4cO1ViXxlAEBFT4FcDdfI4+gAo/Q4bukGBn8FiIiKjs0TUZsdRkRhIWOFooJSUFLi4uUCgUcHZ2LvZ5AklQ4UghJSRIaAgZmuocIxERmQdtvzO0uYatVWVIkvarpgihQkZ2okFjNUdGXWemtBOIQ+GLGQkIaD/rioiILJuASufNEvFBkyUgkI6i+zIzjBEKERGVQexm0g6TmRKQ4AABCYUnNBwvQ0RE2uHUbO2wm6kEJNRE4YmMBAm1jRUOERERgS0zJeQBoCqA+HyOSQAcIKGecUMiIqIyg1OztcNkpgQkSJChDQQcIfA3oPFAr4qQwQ8SuNIwERFpSwA6dRVZ3ARlAExmSkyCDBJegUBjAPeR84/OjYvlERERmQiTGS1JsAZQ2dRhEBFRGZLTTVTYEiBFnc+WGSIiIjKhnNlIOiQzFtrNxNlMREREVKqxZYaIiMhs6NYywwHAREREZFo6jpmBhY6ZYTcTERERlWpsmSEiIjITHACsHbbMEBERmQ2VHraSW7lyJapXrw47Ozv4+fnh7NmzhZbfsWMH6tevDzs7OzRu3BgHDhzQ6rr6wmSGiIjIbIiccS/ablq0zGzfvh1hYWGIiIjAxYsX0aRJEwQGBuL+/fv5lj916hQGDRqEESNG4NKlS+jTpw/69OmDK1eu6Hjv2pOEBa6wk5KSAhcXFygUCjg7O5s6HCIiMmPG+M7IvQZgBUnnbqbsEsXq5+eHFi1aYMWKFQAAlUoFHx8fjBs3DlOmTMlT/s0330Rqair279+v3teqVSs0bdoUa9as0Tp2XVjkmJnc/C0lJcXEkRARkbnL/a4wzt/+Qi/jXl78frO1tYWtbd5nB2ZmZuLChQsIDw9X75PJZAgICEBUVFS+dUdFRSEsLExjX2BgIL7//nud49aWRSYzT548AQD4+PiYOBIiIiotnjx58v+tJ/pnY2MDb29vJCUl6VyXk5NTnu+3iIgIzJgxI0/Zhw8fQqlUwsvLS2O/l5cXrl27lm/9SUlJ+ZbXR+zasshkplKlSkhISEC5cuUgSbosTlS0lJQU+Pj4ICEhoVR2aTF+0ynNsQOM39RKc/zmFrsQAk+ePEGlSpUMdg07OzvExcUhMzNT57qEEHm+2/JrlSlLLDKZkclkqFKlilGv6ezsbBZvSm0xftMpzbEDjN/USnP85hS7oVpknmdnZwc7OzuDX+d5FSpUgFwuR3Jyssb+5ORkeHt753uOt7d3icobA2czERERWSgbGxv4+vriyJEj6n0qlQpHjhyBv79/vuf4+/trlAeAyMjIAssbg0W2zBAREVGOsLAwDB06FM2bN0fLli2xbNkypKamYvjw4QCA4OBgVK5cGfPmzQMAfPDBB2jfvj2WLFmCHj16YNu2bTh//jzWrVtnsntgMmNgtra2iIiIKLX9lYzfdEpz7ADjN7XSHH9pjr00evPNN/HgwQNMnz4dSUlJaNq0KQ4ePKge5BsfHw+Z7L+OnNatW2Pr1q2YOnUqPvroI9SpUwfff/89GjVqZKpbsMx1ZoiIiKjs4JgZIiIiKtWYzBAREVGpxmSGiIiISjUmM0RERFSqMZkhIiKiUo3JjA5u3bqFESNGoEaNGrC3t0etWrUQERFR5HLUHTp0gCRJGtuoUaM0ysTHx6NHjx5wcHCAp6cnJk2ahOzsbJPH/+jRI4wbNw716tWDvb09qlativHjx0OhUGiUe/H+JEnCtm3bTB4/ADx79gxjxoxB+fLl4eTkhP79++dZzdIYrz8AzJkzB61bt4aDgwNcXV2LdU5+r60kSVi0aJG6TPXq1fMcnz9/vsljHzZsWJ64goKCNMo8evQIQ4YMgbOzM1xdXTFixAg8ffpUr7FrE39WVhYmT56Mxo0bw9HREZUqVUJwcDDu3r2rUc4Yr7028QM5y9xPnz4dFStWhL29PQICAnD9+nWNMsZ6/Ut6nVu3bhX4b3/Hjh3qcsb47CHzw3VmdHDt2jWoVCqsXbsWtWvXxpUrVxASEoLU1FQsXry40HNDQkIwa9Ys9c8ODg7q/1YqlejRowe8vb1x6tQp3Lt3D8HBwbC2tsbcuXNNGv/du3dx9+5dLF68GA0bNsTt27cxatQo3L17Fzt37tQou3HjRo0vquJ+4BoyfgAIDQ3Fjz/+iB07dsDFxQVjx45Fv379cPLkSQDGe/2BnCfWDhgwAP7+/li/fn2xzrl3757Gzz/99BNGjBiB/v37a+yfNWsWQkJC1D+XK1dO94Cfo03sABAUFISNGzeqf35xLZEhQ4bg3r17iIyMRFZWFoYPH46RI0di69ateosdKHn8aWlpuHjxIqZNm4YmTZrg33//xQcffIBevXrh/PnzGmUN/dprEz8ALFy4EJ9//jk2b96MGjVqYNq0aQgMDMRff/2lXkbfWK9/Sa/j4+OT59/+unXrsGjRInTr1k1jv6E/e8gMCdKrhQsXiho1ahRapn379uKDDz4o8PiBAweETCYTSUlJ6n2rV68Wzs7OIiMjQ1+h5qs48b/ou+++EzY2NiIrK0u9D4DYs2ePnqMrWlHxP378WFhbW4sdO3ao9129elUAEFFRUUII07z+GzduFC4uLlqd27t3b9GpUyeNfdWqVROffvqp7oEVQ0liHzp0qOjdu3eBx//66y8BQJw7d06976effhKSJInExEQdI82fLq/92bNnBQBx+/Zt9T5jvvZCFD9+lUolvL29xaJFi9T7Hj9+LGxtbcW3334rhDDe66+v6zRt2lS88847GvtM9dlDpsVuJj1TKBRwd3cvstyWLVtQoUIFNGrUCOHh4UhLS1Mfi4qKQuPGjTUesR4YGIiUlBT8+eefBok7V3Hjf/EcZ2dnWFlpNvSNGTMGFSpUQMuWLbFhwwYII6zPWFT8Fy5cQFZWFgICAtT76tevj6pVqyIqKgqAaV//kkpOTsaPP/6IESNG5Dk2f/58lC9fHs2aNcOiRYsM0k2mjePHj8PT0xP16tXD6NGj8c8//6iPRUVFwdXVFc2bN1fvCwgIgEwmw5kzZ0wRbqEUCgUkScrzl785vvZxcXFISkrS+Lfv4uICPz8/jX/7xnj99XGdCxcuIDo6Ot9/+6b47CHTYjeTHsXGxmL58uVFdjENHjwY1apVQ6VKlXD58mVMnjwZMTEx2L17NwAgKSlJ44sUgPrnpKQkwwSP4sf/vIcPH2L27NkYOXKkxv5Zs2ahU6dOcHBwwM8//4z3338fT58+xfjx4/Udtlpx4k9KSoKNjU2eLx8vLy/1a2uq118bmzdvRrly5dCvXz+N/ePHj8crr7wCd3d3nDp1CuHh4bh37x6WLl1qokhzBAUFoV+/fqhRowZu3LiBjz76CN26dUNUVBTkcjmSkpLg6empcY6VlRXc3d3N7rV/9uwZJk+ejEGDBmk82dlcX/vc1y+/f9vP/9s3xuuvj+usX78eDRo0QOvWrTX2m+Kzh8yAqZuGzNHkyZMFgEK3q1evapxz584dUatWLTFixIgSX+/IkSMCgIiNjRVCCBESEiK6du2qUSY1NVUAEAcOHDCb+BUKhWjZsqUICgoSmZmZhZadNm2aqFKlSrHqNWT8W7ZsETY2Nnn2t2jRQvzvf/8TQpjm9de2q6NevXpi7NixRZZbv369sLKyEs+ePTOb2IUQ4saNGwKAOHz4sBBCiDlz5oi6devmKefh4SFWrVpVZH3Gij8zM1P07NlTNGvWTCgUikLLFve1N3T8J0+eFADE3bt3NfYPGDBAvPHGG0II473+ul4nLS1NuLi4iMWLFxdZtiSfPVR6sWUmHxMmTMCwYcMKLVOzZk31f9+9excdO3ZE69attXpqqJ+fH4CcloVatWrB29sbZ8+e1SiTO9vG29u7yPqMEf+TJ08QFBSEcuXKYc+ePbC2ti60vJ+fH2bPno2MjIwiHx5nyPi9vb2RmZmJx48fa7TOJCcnq19bY7/+2jpx4gRiYmKwffv2Isv6+fkhOzsbt27dQr169QosZ6zYn6+rQoUKiI2NRefOneHt7Y379+9rlMnOzsajR4/M5rXPysrCG2+8gdu3b+Po0aMarTL5Ke5rDxg2/tzXLzk5GRUrVlTvT05ORtOmTdVljPH663qdnTt3Ii0tDcHBwUWWLclnD5Vips6mSrs7d+6IOnXqiIEDB4rs7Gyt6vjtt98EAPH7778LIf4bgJqcnKwus3btWuHs7Fysv+5KQpv4FQqFaNWqlWjfvr1ITU0t1jmffPKJcHNz0yXUfJU0/twBwDt37lTvu3btWr4DgI3x+ufSpnVg6NChwtfXt1hlv/nmGyGTycSjR4+0iK5wurTMJCQkCEmSxN69e4UQ/w0MPX/+vLrMoUOHzGYAcGZmpujTp4946aWXxP3794t1jiFfeyFKPgD4+dYMhUKR7wBgQ7/+ul6nffv2on///sW6lqE+e8i8MJnRwZ07d0Tt2rVF586dxZ07d8S9e/fU2/Nl6tWrJ86cOSOEECI2NlbMmjVLnD9/XsTFxYm9e/eKmjVrildffVV9TnZ2tmjUqJHo2rWriI6OFgcPHhQeHh4iPDzc5PErFArh5+cnGjduLGJjYzXOyU0m9u3bJ7744gvxxx9/iOvXr4tVq1YJBwcHMX36dJPHL4QQo0aNElWrVhVHjx4V58+fF/7+/sLf31993FivvxBC3L59W1y6dEnMnDlTODk5iUuXLolLly6JJ0+eqMvUq1dP7N69W+M8hUIhHBwcxOrVq/PUeerUKfHpp5+K6OhocePGDfHNN98IDw8PERwcbNLYnzx5IiZOnCiioqJEXFycOHz4sHjllVdEnTp1NJLEoKAg0axZM3HmzBnx22+/iTp16ohBgwbpNXZt4s/MzBS9evUSVapUEdHR0Rr/3nJnuRnrtdcmfiGEmD9/vnB1dRV79+4Vly9fFr179xY1atQQ6enp6jLGev2Luk5+710hhLh+/bqQJEn89NNPeeo01mcPmR8mMzrYuHFjgf3CueLi4gQAcezYMSGEEPHx8eLVV18V7u7uwtbWVtSuXVtMmjQpT7/7rVu3RLdu3YS9vb2oUKGCmDBhgsbUZ1PFf+zYsQLPiYuLE0LkTLFs2rSpcHJyEo6OjqJJkyZizZo1QqlUmjx+IYRIT08X77//vnBzcxMODg6ib9++GgmQEMZ5/YXIaV3JL/7n4wUgNm7cqHHe2rVrhb29vXj8+HGeOi9cuCD8/PyEi4uLsLOzEw0aNBBz587Ve6tSSWNPS0sTXbt2FR4eHsLa2lpUq1ZNhISEaEyBF0KIf/75RwwaNEg4OTkJZ2dnMXz4cI0vaFPFn/tvqbBzjPXaaxO/EDmtM9OmTRNeXl7C1tZWdO7cWcTExGjUa6zXv6jr5PfeFUKI8PBw4ePjk+/nibE+e8j8SEJwzhoRERGVXlxnhoiIiEo1JjNERERUqjGZISIiolKNyQwRERGVakxmiIiIqFRjMkNERESlGpMZIiIiKtWYzBAREVGpxmSGiIiISjUmM0RERFSqMZkhIiKiUu3/ACARcSj2kuGOAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Inferences from the plot:**\n", + "1.The plot shows two distinct clusters: one in black (cluster 0) and another in\n", + " light yellow (cluster 1).The dominance of black-colored points indicates that most of the data falls into a single cluster, with only a few points classified differently.\n", + "\n", + "2.The few yellow points suggest that AEAttentionBiGRU is detecting some\n", + " anomalies or outliers.\n", + " \n", + "3.These yellow points are often found at the edges or slightly away from the densest region, meaning that the model might be learning small variations and\n", + " assigning them to a different cluster.\n" + ], + "metadata": { + "id": "UyQ5LH82h5A-" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEBiGRUClusterer:**" + ], + "metadata": { + "id": "gLoHAkV-jGsX" + } + }, + { + "cell_type": "markdown", + "source": [ + "# **AEBiGRUClusterer (Auto-Encoder Bidirectional GRU Network)**\n", + "The **AEBiGRUClusterer** is an Auto-Encoder with a **Bidirectional GRU (BiGRU)** architecture.GRUs are similar to LSTMs but with a simpler structure, making them faster and more efficient for time series data.The bidirectional structure enhances the model’s ability to detect patterns by combining forward and backward sequence insights.It Performs well on shorter sequences with frequent fluctuations.Suitable for tasks requiring fast training without compromising performance.\n", + "\n" + ], + "metadata": { + "id": "FTnLddtrHMQE" + } + }, + { + "cell_type": "code", + "source": [ + "from aeon.clustering.deep_learning import AEBiGRUClusterer" + ], + "metadata": { + "id": "duoJ4wMQHnFt" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = AEBiGRUClusterer(n_epochs=10, random_state=42)\n", + "model.fit(X_train)\n", + "y_pred = model.predict(X_test)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XfGf-pgxHnRy", + "outputId": "84b30734-4b03-4b01-e099-4a761edcd0ab" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 918ms/step\n", + "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 127ms/step\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.scatter(X_test[:, 0, 0], X_test[:, 0, 1], c=y_pred, cmap=\"cividis\")\n", + "plt.title(\"Cluster Distribution with AEBiGRU\")\n", + "plt.colorbar(label=\"Cluster\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 452 + }, + "id": "RlH69d_rHnVX", + "outputId": "ebbb3fb9-13b4-4ccc-e5fe-214cbba0e0ef" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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AAEdHRwAFEyNPT08EBQVhzZo1SExMLBDDvXv3ioxPHzdv3sSwYcNgZ2eHSZMmFVkuNTUVubm5WueaNWsGmUymeW1A3l++hSUXuoiKisK5c+c0j2/duoVdu3aha9eumpaGunXrQqlUanXpJCYmYufOnQXqK21swcHBsLOzw2effab181y7di2USiW6d++ux6vSFhAQAFtbW3z88cdwd3fHM888AyAvyTl58iT+7//+r1StMk5OTpop8oby+PFjfP/99+jRowdefvnlAse4ceOQlpZWYOkAfVy5cgWxsbGa9Y6KMnPmTNy/fx/h4eGFjk8rTatYvvfffx85OTlYunSp5ly1atXQokULbNq0qcDvTHR0NE6ePFnsTDUiU2M3kxWoW7cutmzZgldffRWNGzfWWgH4xIkT2L59e7F7MXXt2hU1a9bEyJEjMWnSJMjlcqxbtw4eHh5af81t3LgRK1euRN++fVG3bl2kpaXhyy+/hIuLC7p16wYgr5ulSZMm2LZtGxo0aAB3d3c0bdoUTZs2xYoVK9ChQwc0a9YM4eHhqFOnDpKTkxEVFYXbt2/jjz/+0Ot9OHfuHDZt2gS1Wo2HDx/izJkz+O677yBJEr755hvNFPHCHD58GOPGjcOAAQPQoEED5Obm4ptvvoFcLkf//v015fz9/XHw4EEsXboU1atXR+3atYtd/r44TZs2RUhIiNbUbACaFZQBYODAgZg8eTL69u2L8ePHIyMjA6tWrUKDBg20EqGyxObh4YHIyEjMmjULoaGh6NWrF2JjY7Fy5Uo899xzpVoQrrQcHR3h7++PkydPataYAfJaZtLT05Genl6qZMbf3x/btm1DREQEnnvuOTg7O6Nnz556xbZ7926kpaVppog/rW3btvDw8MDmzZvx6quvas5fuXIFmzZtKlDey8sLL774ouZxbm6uppxarcaNGzewevVqqNXqIhfkyzd48GBcuHAB8+fPx+nTpzFw4EDUrl0b6enpuHDhAr799ltUqlRJ06panCZNmqBbt2746quvMG3aNFSpUgUAsHTpUoSEhKBFixYYNmwYqlevjkuXLuGLL75AtWrVEBkZWWLdRCZjxplUZGJXrlwR4eHhws/PT9jZ2YlKlSqJ9u3bi+XLl2tNh356arYQQkRHR4uAgABhZ2cnatasKZYuXVpgSu+5c+fEoEGDRM2aNYVCoRCenp6iR48eWtOLhRDixIkTwt/fX9jZ2RWYpn316lURFhYmvL29ha2trahRo4bo0aOH2LFjh6ZM/n2LmwL+pPyp2fmHjY2NcHd3FwEBASIyMlLcvHmzwDVPT4e9du2aGDFihKhbt66wt7cX7u7uonPnzuLgwYNa112+fFk8//zzwsHBQQDQvI/5U6Xv3btX4F5FTc0eO3as2LRpk6hfv75QKBSiZcuWWtOR8/3yyy+iadOmws7OTjRs2FBs2rSp0DqLiu3pn2O+zz//XDRq1EjY2toKLy8vMWbMGPHgwQOtMp06dRLPPPNMgZiKmjJemPzpwR9//LHW+Xr16gkA4urVq1rnC5ua/ejRIzF48GDh5uYmAGjuXdQU4/zfifXr1xcZV8+ePYW9vb1IT08vssywYcOEra2tuH//vhCi+KnZnTp10lxX2NRsFxcX0aVLlwK/U4W93nxHjx4VL7/8sqhWrZqwtbUVLi4uonXr1mLGjBkiMTFRq2xRP6v8ep7+tyiEECdPnhQ9evQQlStXFjY2NqJGjRrijTfeELdv3y7yPSEyB0mIMrRHEhEREVkYjpkhIiKico3JDBEREZVrTGaIiIioXGMyQ0REZKV+/fVX9OzZE9WrV4ckSfjhhx9KvObo0aNo1aoVFAoF6tWrZxHbYTCZISIislLp6elo3rw5VqxYUary169fR/fu3dG5c2fExMTgnXfewRtvvIGff/7ZyJEWj7OZiIiICJIkYefOnejTp0+RZSZPnoy9e/dqbacxcOBAPHz4EPv37zdBlIWzykXz1Go17ty5g0qVKhltGXQiIqoYhBBIS0tD9erVtXaSN7TMzExkZ2frXY8QosB3m0KhKHHD39KIiooqsEFqSEgI3nnnHb3r1odVJjN37tyBr6+vucMgIqJy5NatW6XeVLasMjMzUbuWC5LuFtyeoqycnZ0L7O81Y8YMzJw5U++6k5KSCuyV5uXlhdTUVDx+/Njgm+mWllUmM/kbFt66davYXZKJiIhSU1Ph6+trtM1ugbyNXJPu5iD+bAu4VCp8l/fSSE1ToWbrmALfb4ZolbFkVpnM5De/ubi4MJkhIqJSMcWwBJdKcr2SGU09Rvp+8/b2LrBhcXJyMlxcXMzWKgNYaTJDRERkkYTIO/S53ogCAwOxb98+rXMHDhxAYGCgUe9bEk7NJiIishT5yYw+Rxk8evQIMTExiImJAZA39TomJgbx8fEAgMjISISFhWnKjx49GteuXcP777+Py5cvY+XKlfjf//6HiRMnGuwt0AVbZoiIiCyEqRtmzp49i86dO2seR0REAACGDh2KDRs2IDExUZPYAEDt2rWxd+9eTJw4EZ9++il8fHzw1VdfISQkRPegDcAq15lJTU2Fq6srlEolx8wQEVGxTPGdkX+PBxdb6j0AuHKT363u+40tM0RERBZCCBmE0H0EiBW2TwBgMkNERGQxhJD0TGbUBoym/OAAYCIiIirX2DJDRERkIdRCBrUeLTP6XFueMZkhIqIK5+Lf8Yi+cBV2tjZ4IfBZeFRxNXdIpaL/mBkmM0REROXa9VvJGDZpGX49c1FzzsZGjpEDgrFs2huwV9iZMToyFiYzRERUISTff4j2r0zGvX+UWudzc1X4ctsvuHM3BbvWfGiSbQl0xZYZ3VjnqyYiogrn0w0/4u4/SuSqCs7oUasFfjx0BseeaLGxRHmzmfQ7rBGTGSIiqhDW/u8AVIUkMvls5HJs/P6wCSMiU2E3ExERVQj3U1KLfT5XpULi3QcmikY37GbSjXW+aiIiqnC8qroV+7yNXAYf7yqmCUZHaiHpfVgjJjNERFQhvPHqi5DLiv5ay1WpMaz/CyaMqOwEZJrWGZ0OK/1at85XTUREFc6EYT3hU60KbOQFv9okScKr3TsgsFUjM0RGxsZkhoiIKoQqlV1wYvvH6NqxJZ6cfe1gb4d3R/bGN0smWvS0bICzmXTFAcBERFRhVPeqgr1rp+PG7WT8/tc12NnZomPrJnCp5Gju0EpF34SEyQwREVEF4efjBT8fL3OHQSbCZIaIiMhCcGq2bpjMEBERWQgh9OsqEsKAwZQj1pnCERERUYXBlhkiIiILwQHAumEyQ0REZCGEnqv4Wmsyw24mIiIiKtfYMkNERGQh2M2kGyYzREREFkJAv/2VrHVvJiYzREREFoItM7qxzhSOiIiIKgyjJjMpKSkYMmQIXFxc4ObmhpEjR+LRo0dFlr9x4wYkSSr02L59u6ZcYc9v3brVmC+FiIjI6LjRpG6M2s00ZMgQJCYm4sCBA8jJycHw4cMxatQobNmypdDyvr6+SExM1Dr3xRdfYNGiRXjppZe0zq9fvx6hoaGax25ubgaPn4iIyJTYzaQboyUzly5dwv79+3HmzBm0bt0aALB8+XJ069YNixcvRvXq1QtcI5fL4e3trXVu586deOWVV+Ds7Kx13s3NrUDZomRlZSErK0vzODU1tawvh4iIDODqzUTc/UeJGt5VULO6h7nDoQrCaN1MUVFRcHNz0yQyABAcHAyZTIZTp06Vqo7o6GjExMRg5MiRBZ4bO3YsqlatijZt2mDdunUQxWxIMX/+fLi6umoOX1/fsr8gIiLS2f+duoCAvu+h3guj0W7AZNTq+AaCBn+I6D/jzB2aRWE3k26MlswkJSXB09NT65yNjQ3c3d2RlJRUqjrWrl2Lxo0bo127dlrnZ8+ejf/97384cOAA+vfvj7feegvLly8vsp7IyEgolUrNcevWrbK/ICIi0smB32IQ/Po0nL2gnbj8duYiOrw6Baf/uGKmyCwPkxndlLmbacqUKfj444+LLXPp0iWdA8r3+PFjbNmyBdOmTSvw3JPnWrZsifT0dCxatAjjx48vtC6FQgGFQqF3TEREVDZqtRrhH3wOlVoUaEFXqdUQOcCY6asRvWupmSKkiqDMycy7776LYcOGFVumTp068Pb2xt27d7XO5+bmIiUlpVRjXXbs2IGMjAyEhYWVWDYgIABz5sxBVlYWkxYiIgty9OQF3Ey4V+TzarUa5y5cxZ+xN9CsoZ/pArNQaj33ZtLn2vKszMmMh4cHPDxKHrQVGBiIhw8fIjo6Gv7+/gCAw4cPQ61WIyAgoMTr165di169epXqXjExMahcuTITGSIiC3P9dnKpyl2LT2YyA85m0pXRZjM1btwYoaGhCA8Px+rVq5GTk4Nx48Zh4MCBmplMCQkJ6NKlC77++mu0adNGc21cXBx+/fVX7Nu3r0C9P/74I5KTk9G2bVvY29vjwIEDmDdvHt577z1jvRQiItKRu6tzyYUAuLuVrhxRYYy6zszmzZsxbtw4dOnSBTKZDP3798dnn32meT4nJwexsbHIyMjQum7dunXw8fFB165dC9Rpa2uLFStWYOLEiRBCoF69eli6dCnCw8ON+VKIiEgHIc+3QiUnB6SlPy6yTA0vd7Rr1ciEUVkutszoRhLFzWmuoFJTU+Hq6gqlUgkXFxdzh0NEVKEtXbsL785bV+TzGxdNQFi/F0wYUdmY4jsj/x7nD/dDJWdbnetJe5SDZ1/43uq+37jRJBERGdXEEb2QnZODWZ9uRVZ2DuRyGXJVajg5KLD4g+EWnciYGltmdMNkhoiIjEqSJEwZ/TJGDw7F9z+fRPL9h/DxroJ+IYFwcrQ3d3hUATCZISIik3BzccaIAcHmDsOiCQD6DP6wunEj/2IyQ0REZCEEJAjo0c2kx7XlmdG2MyAiIiIyBbbMEBERWQgOANYNkxkiIiJLoe9mkVaazLCbiYiIiMo1tswQERFZCCH06yqyvmVw8zCZISIishBqkXfoc701YjcTERERlWtsmSEiIrIQnM2kGyYzREREFoLJjG6YzBAREVkIJjO64ZgZIiIiKtfYMkNERGQh8qZm63e9NWIyQ0REZCG40aRu2M1ERERE5RpbZoiIiCwEBwDrhskMERGRhWAyoxt2MxEREVG5xpYZIiIiC8HZTLphMkNERGQh2M2kG3YzERERUbnGlhkiIiILwW4m3TCZISIishDsZtINu5mIiIgsRH4yo8+hixUrVsDPzw/29vYICAjA6dOniy2/bNkyNGzYEA4ODvD19cXEiRORmZmp070NgckMERGRFdu2bRsiIiIwY8YMnDt3Ds2bN0dISAju3r1baPktW7ZgypQpmDFjBi5duoS1a9di27Zt+OCDD0wc+X+YzBAREVkItQGOslq6dCnCw8MxfPhwNGnSBKtXr4ajoyPWrVtXaPkTJ06gffv2GDx4MPz8/NC1a1cMGjSoxNYcY2IyQ0REZCn07WL6t5spNTVV68jKyir0dtnZ2YiOjkZwcLDmnEwmQ3BwMKKiogq9pl27doiOjtYkL9euXcO+ffvQrVs3A78ZpcdkhoiIqILx9fWFq6ur5pg/f36h5e7fvw+VSgUvLy+t815eXkhKSir0msGDB2P27Nno0KEDbG1tUbduXQQFBZm1m4mzmYiIiCyEoWYz3bp1Cy4uLprzCoVC79jyHT16FPPmzcPKlSsREBCAuLg4TJgwAXPmzMG0adMMdp+yYDJDRERkIQT0XGfm3/+6uLhoJTNFqVq1KuRyOZKTk7XOJycnw9vbu9Brpk2bhtdffx1vvPEGAKBZs2ZIT0/HqFGj8OGHH0ImM32nj9HuOHfuXLRr1w6Ojo5wc3Mr1TVCCEyfPh3VqlWDg4MDgoOD8ffff2uVSUlJwZAhQ+Di4gI3NzeMHDkSjx49MsIrICIiqtjs7Ozg7++PQ4cOac6p1WocOnQIgYGBhV6TkZFRIGGRy+UA8r7HzcFoyUx2djYGDBiAMWPGlPqahQsX4rPPPsPq1atx6tQpODk5ISQkRGvu+pAhQ/DXX3/hwIED2LNnD3799VeMGjXKGC+BiIjIpMyxzkxERAS+/PJLbNy4EZcuXcKYMWOQnp6O4cOHAwDCwsIQGRmpKd+zZ0+sWrUKW7duxfXr13HgwAFMmzYNPXv21CQ1pma0bqZZs2YBADZs2FCq8kIILFu2DFOnTkXv3r0BAF9//TW8vLzwww8/YODAgbh06RL279+PM2fOoHXr1gCA5cuXo1u3bli8eDGqV69eaN1ZWVlaI7lTU1P1eGVERETGYY7tDF599VXcu3cP06dPR1JSElq0aIH9+/drBgXHx8drtcRMnToVkiRh6tSpSEhIgIeHB3r27Im5c+fqHrieLGY20/Xr15GUlKQ1PczV1RUBAQGa6WFRUVFwc3PTJDIAEBwcDJlMhlOnThVZ9/z587VGdfv6+hrvhRAREZUz48aNw82bN5GVlYVTp04hICBA89zRo0e1GiZsbGwwY8YMxMXF4fHjx4iPj8eKFStKPaTEGCwmmcmfAlbc9LCkpCR4enpqPW9jYwN3d/cip5ABQGRkJJRKpea4deuWgaMnIiLSn7m2MyjvypTMTJkyBZIkFXtcvnzZWLHqTKFQaEZ2l3aENxERkakJAxzWqExjZt59910MGzas2DJ16tTRKZD8KWDJycmoVq2a5nxycjJatGihKfP0XhG5ublISUkpcgoZERFRecFds3VTpmTGw8MDHh4eRgmkdu3a8Pb2xqFDhzTJS2pqKk6dOqWZERUYGIiHDx8iOjoa/v7+AIDDhw9DrVZr9e8RERGR9TDamJn4+HjExMQgPj4eKpUKMTExiImJ0VoTplGjRti5cycAQJIkvPPOO/joo4+we/du/PnnnwgLC0P16tXRp08fAEDjxo0RGhqK8PBwnD59GsePH8e4ceMwcODAImcyERERlRf5s5n0OayR0aZmT58+HRs3btQ8btmyJQDgyJEjCAoKAgDExsZCqVRqyrz//vuaVQQfPnyIDh06YP/+/bC3t9eU2bx5M8aNG4cuXbpAJpOhf//++Oyzz4z1MoiIiEyG3Uy6kYS5luszo9TUVLi6ukKpVHIwMBERFcsU3xn599izJRxOjnY615OekY0eg7+0uu837s1ERERkIcyxaF5FwGSGiIjIQrCbSTcWs2geERERkS7YMkNERGQh9F34zkp7mZjMEBERWQp2M+mG3UxERERUrrFlhoiIyEJwNpNumMwQERFZCCYzumEyQ0REZCHykhl9xswYMJhyhGNmiIiIqFxjywwREZGFYDeTbpjMEBERWQwJAvpMr+bUbCIiIqJyhy0zREREFoLdTLphMkNERGQhmMzoht1MREREVK6xZYaIiMhCcG8m3TCZISIishBqkXfoc701YjcTERERlWtsmSEiIrIQQs91ZvRbo6b8YjJDRERkITibSTdMZoiIiCyEgJ7JjMEiKV84ZoaIiIjKNbbMEBERWQhOzdYNkxkiIiILwTEzumEyQ0SkByEETv9xBX/fSIRrJUcEt28OB3uFucMisipMZoiIdBR17jJGRi7HpbjbmnOuzo6Y9variBjZG5JknU3+pDu2zOiGyQwRkQ7OXbiKF16biuycXK3zykcZeG/+emQ8zsK0t181U3RUXnGdGd1wNhMRkQ6mLPoaObkqqItYP37O59twPyXVxFERWScmM0REZZR07wEO/BYDlUpdZBmVSo1te4+ZMCqqCPK7mfQ5rBG7mYiIyujuP8oSy8jlMiTefWCCaKgi4ZgZ3bBlhoiojLyquqGksb25KjWqe7mbJiAiK2e0ZGbu3Llo164dHB0d4ebmVmL5nJwcTJ48Gc2aNYOTkxOqV6+OsLAw3LlzR6ucn58fJEnSOhYsWGCkV0FEVJBXVTeEdGwFubzoj1BbGzle7d7BhFFRRcBuJt0YLZnJzs7GgAEDMGbMmFKVz8jIwLlz5zBt2jScO3cO33//PWJjY9GrV68CZWfPno3ExETN8fbbbxs6fCKiYs2f9DrsbGwglxX+MTrrnUGoUtnFxFFReZe/ArA+hzUy2piZWbNmAQA2bNhQqvKurq44cOCA1rnPP/8cbdq0QXx8PGrWrKk5X6lSJXh7exssViKismrRpA7+b+s8hH+wAn9cuq457+7mjJnjB2FcWHczRkfllYB+m0VaacOMZQ8AViqVkCSpQDfVggULMGfOHNSsWRODBw/GxIkTYWNT9EvJyspCVlaW5nFqKqdLEpH+nnu2Pn7/8RPEXLyOuJt5KwB3atMUCoWtuUMjsioWm8xkZmZi8uTJGDRoEFxc/muqHT9+PFq1agV3d3ecOHECkZGRSExMxNKlS4usa/78+ZqWIiIiQ5IkCS2fqYOWz9QxdyhUAXA2k27KNGZmypQpBQbfPn1cvnxZ76BycnLwyiuvQAiBVatWaT0XERGBoKAgPPvssxg9ejSWLFmC5cuXa7W8PC0yMhJKpVJz3Lp1S+8YiYiIDE7fwb9WmsyUqWXm3XffxbBhw4otU6eOfn+d5CcyN2/exOHDh7VaZQoTEBCA3Nxc3LhxAw0bNiy0jEKhgELBjd+IiIgqojIlMx4eHvDw8DBWLJpE5u+//8aRI0dQpUqVEq+JiYmBTCaDp6en0eIiIiIyBX1nJHE2k4HFx8cjJSUF8fHxUKlUiImJAQDUq1cPzs7OAIBGjRph/vz56Nu3L3JycvDyyy/j3Llz2LNnD1QqFZKSkgAA7u7usLOzQ1RUFE6dOoXOnTujUqVKiIqKwsSJE/Haa6+hcuXKxnopREREJsHZTLoxWjIzffp0bNy4UfO4ZcuWAIAjR44gKCgIABAbGwulMm9Z8ISEBOzevRsA0KJFC6268q9RKBTYunUrZs6ciaysLNSuXRsTJ05ERESEsV4GERERWTijJTMbNmwocY0Z8cSwaz8/P63HhWnVqhVOnjxpiPCIiIgsDmcz6cZip2YTERnK739dw8rN+xB94SocFHbo82IARgwI5gq9ZHGYzOiGyQwRVWjzVm7Hh0s2wUYuQ65KDQA4+XssFqz+Dge/mcP1YYgqAO6aTUQV1u6Dp/Dhkk0AoElkAEAtBJRpGQgZPhOPM4teo4rI1Lg3k26YzBBRhbX4qx+K3AhSpVbj3j9KbN1zzMRRERWNu2brhskMEVVIubkqHDtzESq1usgycrkMh06cN2FURMUTBjisEZMZIqqQSpodmV9GrbbWj3+iioPJDBFVSLa2Nmj5TB3IpKLHEAgh0K5VIxNGRVQ8djPphskMEVVYE4f3grqIT3eZJMHJ0R5hfTubOCqiojGZ0Q2TGSKqsF7rE4QxQ14CkDc+Jp+NXAY7O1v8sOoDuFRyNFd4RGQgXGeGiCosSZKwYtab6N65NT7/eg+iL1yDvcIW/UIC8XZYd9StVc3cIRJp4UaTumEyQ0QVmiRJ6N65Nbp3bm3uUIhKxI0mdcNuJiIiIirX2DJDRBbl+q1kXL56G85O9mjboiFsbfkxRdZDQM+9mQwWSfnClhkisgh/X7+DF1+fjjpBo9Bt5Gw8P/AD1Gg3HMs37inVmjFEFYG5ZjOtWLECfn5+sLe3R0BAAE6fPl1s+YcPH2Ls2LGoVq0aFAoFGjRogH379ul2cwPgnzxEZHY3biej7cvvQ5mWrnX+Xkoqxs/+Ev88TMPMCYPMFB2R6Zhj1+xt27YhIiICq1evRkBAAJYtW4aQkBDExsbC09OzQPns7Gy8+OKL8PT0xI4dO1CjRg3cvHkTbm5uugeuJ7bMEJHZzfpsK1LT0qFSFb71wJzPtyEh6R8TR0VkHZYuXYrw8HAMHz4cTZo0werVq+Ho6Ih169YVWn7dunVISUnBDz/8gPbt28PPzw+dOnVC8+bNTRz5f5jMEJFZZTzOwpbdv2rtav00CRK++eGICaMiMg9DdTOlpqZqHVlZhe8On52djejoaAQHB2vOyWQyBAcHIyoqqtBrdu/ejcDAQIwdOxZeXl5o2rQp5s2bB5VKZfD3o7SYzBCRWf3zIBXZObnFlpHJJNxKvG+iiIjMR0DS+wAAX19fuLq6ao758+cXer/79+9DpVLBy8tL67yXlxeSkpIKvebatWvYsWMHVCoV9u3bh2nTpmHJkiX46KOPDPtmlAHHzBCRWVV2dYZMJhW74aNaCHhWcTNdUETl3K1bt+Di4qJ5rFAoDFa3Wq2Gp6cnvvjiC8jlcvj7+yMhIQGLFi3CjBkzDHafsmAyQ0Rm5ezkgL4vtsUPB08VOWZGpVJjSK9OJo6MyPQMNQDYxcVFK5kpStWqVSGXy5GcnKx1Pjk5Gd7e3oVeU61aNdja2kIul2vONW7cGElJScjOzoadnZ3uL0BH7GYiIrObMWEg7GxtIJcV/EiSJODNwaGo58etB8gKCAMcZWBnZwd/f38cOnRIc06tVuPQoUMIDAws9Jr27dsjLi4OavV/f3xcuXIF1apVM0siAzCZISIL0KyhH45s/gh1a2n/JWhna4N33+iDz2eMMlNkRBVfREQEvvzyS2zcuBGXLl3CmDFjkJ6ejuHDhwMAwsLCEBkZqSk/ZswYpKSkYMKECbhy5Qr27t2LefPmYezYseZ6CexmIiLLENCiIS4fWInfzl7Exb9vwdnJHt2CWqOyq7O5QyMyHT27mXRZAvjVV1/FvXv3MH36dCQlJaFFixbYv3+/ZlBwfHw8ZE+0mvr6+uLnn3/GxIkT8eyzz6JGjRqYMGECJk+erEfg+pGEFS6tmZqaCldXVyiVylL1KRIRkfUyxXdG/j0WzJkIe3vdB+tmZmZhyrRPrO77jS0zRGRwKpUKew6fxVfbfsG1W8nwrOKKsH6dMbBHRzjo8UFNRFQYJjNEZFBZWTno99Z87DsaDblcBpVKjctXb+PoqQtY8tUuHNn8ETyquJo7TCKLZI7tDCoCDgAmIoOaunQT9v96DgA0U63V/37CXr52G0MmLjVbbESWzlwbTZZ3TGaIyGDSMzKxavNPRS6Ap1KpceB4DC7F3TJxZETlA5MZ3TCZISKDib5wFemPC98DJp8kAUdO/mmiiIjIGnDMDBEZTOkmR0qlLEdkfXRY967A9daIyQwRGUzLJnVgr7BDZlZ2kWWEEOjQuokJoyIqPzgAWDfsZiIig3Gp5IiRrwRrLbD1JBu5DO39G6F549omjoyILIFKpcKvv/6Khw8fGrReJjNEZFAfvz8UgS0bAgBkMglA3jgZSZLgU60qtn46yZzhEVm0ij4AWC6Xo2vXrnjw4IFB6zVaMjN37ly0a9cOjo6OcHNzK9U1w4YNgyRJWkdoaKhWmZSUFAwZMgQuLi5wc3PDyJEj8ejRIyO8AiLShZOjPQ5vmoMNCycgoHkDVPOojKYNamFx5HDE7FkGn2pVzR0ikcWq6MkMADRt2hTXrl0zaJ1GGzOTnZ2NAQMGIDAwEGvXri31daGhoVi/fr3msUKhvVrokCFDkJiYiAMHDiAnJwfDhw/HqFGjsGXLFoPFTkT6sbOzxdD+L2Bo/xfMHQoRWZiPPvoI7733HubMmQN/f384OTlpPa/LNgxGS2ZmzZoFANiwYUOZrlMoFPD29i70uUuXLmH//v04c+YMWrduDQBYvnw5unXrhsWLF6N69ep6xUxERGRO1jCbqVu3bgCAXr16QZIkzXkhBCRJgkqlKnOdFjeb6ejRo/D09ETlypXxwgsv4KOPPkKVKlUAAFFRUXBzc9MkMgAQHJw32PDUqVPo27dvoXVmZWUhK+u/tS9SU1ON+yKIiIh0YA2zmY4cOWLwOi0qmQkNDUW/fv1Qu3ZtXL16FR988AFeeuklREVFQS6XIykpCZ6enlrX2NjYwN3dHUlJSUXWO3/+fE1LEREREZlPp06dDF5nmQYAT5kypcAA3aePy5cv6xzMwIED0atXLzRr1gx9+vTBnj17cObMGRw9elTnOgEgMjISSqVSc9y6xaXUiYjI8ggh6X2UB8eOHcNrr72Gdu3aISEhAQDwzTff4LffftOpvjK1zLz77rsYNmxYsWXq1KmjUyBF1VW1alXExcWhS5cu8Pb2xt27d7XK5ObmIiUlpchxNkDeOJynBxITkW7+uHQdKzf9hJMxsVCrBerW9EIH/yZ4KcgfzzSoae7wiMo1a+hm+u677/D6669jyJAhOHfunGYYiFKpxLx587Bv374y11mmZMbDwwMeHh5lvomubt++jX/++QfVqlUDAAQGBuLhw4eIjo6Gv78/AODw4cNQq9UICAgwWVxE1mrxlzsxacEGyGUSVP9uJnnhyk3sOngakz7egM5tm2HzJxGo5ulu5kiJyidrGAD80UcfYfXq1QgLC8PWrVs159u3b4+PPvpIpzqNts5MfHw8YmJiEB8fD5VKhZiYGMTExGitCdOoUSPs3LkTAPDo0SNMmjQJJ0+exI0bN3Do0CH07t0b9erVQ0hICACgcePGCA0NRXh4OE6fPo3jx49j3LhxGDhwIGcyERnZgd9iMGnBBgDQJDJP+/XMX+j4aiTSHmWYMDIiKk9iY2Px/PPPFzjv6uqq88rARktmpk+fjpYtW2LGjBl49OgRWrZsiZYtW+Ls2bOaMrGxsVAqlQDyVgU8f/48evXqhQYNGmDkyJHw9/fHsWPHtLqINm/ejEaNGqFLly7o1q0bOnTogC+++MJYL4OI/rXkqx8glxf/kaFSqXHtVjI2fHfYRFERVSzWsGiet7c34uLiCpz/7bffdB6qYrTZTBs2bChxjZknd851cHDAzz//XGK97u7uXCCPyMSEEDh88k+oVOrSlMb6HYfw9tAeRo+LqKKxhjEz4eHhmDBhAtatWwdJknDnzh1ERUXhvffew7Rp03Sq06KmZhOR5RLq0iQyeR+md/95aNxgiKjcmjJlCtRqNbp06YKMjAw8//zzUCgUeO+99/D222/rVCeTGSIqkSRJaNuyIaLOxUJVQlIjk0mo5eNZbBkiKpyA0Oq10OV6SydJEj788ENMmjQJcXFxePToEZo0aQJnZ2ed6+Su2URUKhNH9C4xkQEAtVrgzYEhJoiIqOIRBjgs3YgRI5CWlgY7Ozs0adIEbdq0gbOzM9LT0zFixAid6mQyQ0TIycnFoeN/YMdPx/H7X9cK/cuwb9e2eH9Uv2LrkclkaO/fGAN7dDRWqERUzm3cuBGPHz8ucP7x48f4+uuvdaqT3UxEVu6Lb3/G1KWbcC/lvz3L5DIZ6tb0xjsjemFY/xfgYK+AJEn4ePJQBLdvjk/X/4gjp87jcWa2ZsChws4Ww1/ugsWRw2FnZ2umV0NUzuk7I8mCm2ZSU1MhRF43WlpaGuzt7TXPqVQq7Nu3r8CWRaXFZIbIii1bvxsTP1pb4LxKrcaVG3fw1vTVWLf9AA598xFcKjkCAF7s0AIvdmgBAEhNy8DZP+MghIB/s7pwc9G9z5uIKvZsJjc3N83WRw0aNCjwvCRJOu+jyGSGyEop09IRuajkJt1zf11DxLx1+Gr+uALPuVRyxAvtnjVGeERUwRw5cgRCCLzwwgv47rvv4O7+30rhdnZ2qFWrls4L4DKZIbJSO346gaysnBLLqdUCG3YcwsLJQ+HuVskEkRFZsQq8n0H+btnXr19HzZo1IUmG2xSTA4CJrFTU75dL/bmnUquxeff/GTUeIrKO2UyXLl3C8ePHNY9XrFiBFi1aYPDgwXjw4IFOdTKZIaqAhBA4fOI8Pl7zHZZ89QMuxN4EADxQPsL2fccxbelmrP3fwTLVufvgaWOESkRPsIbtDCZNmoTU1LwJB3/++SciIiLQrVs3XL9+HRERETrVyW4mogrmQuxN9B+7AFeu34FcLoMQAu/NX49a1T2QeO8BsnNydar3yvU7Bo6UiKzR9evX0aRJEwDAd999h549e2LevHk4d+4cunXrplOdTGaIKpDbiffx/KAPkPrvrtVP7qV08849veq2s+XHBZGxVeTZTPns7OyQkZH3GXXw4EGEhYUByNt7Mb/Fpqz46URUgXy2cQ9SH2WUckPI0rORy9AtyN+gdRJRQRV4/K9Ghw4dEBERgfbt2+P06dPYtm0bAODKlSvw8fHRqU6OmSGqQL7ZecTgiYwk5a3/MPZ13Zp/iYie9Pnnn8PGxgY7duzAqlWrUKNGDQDATz/9hNDQUJ3qZMsMUQXyMC3DoPXJZTLI5TL8b/n7aFC7hkHrJqKC8lfI1ed6S1ezZk3s2bOnwPlPPvlE5zqZzBBVILV9PHH5WoLeH2j2Clu0blYPLwQ+i/BXu8KnWlUDRUhExbGGMTPx8fHFPl+zZs0y18lkhqgCGT04FO8Usj1BWTRtUBMnv1sIJ0cHA0VFRPQfPz+/YhfMU6lUZa6TyQxRBdK1YwtUdnVCysNHOl3/YscW+GWDbnujEJH+rKFl5vfff9d6nJOTg99//x1Lly7F3LlzdaqTyQxRBXEtPgkdB36Ah0rdEhn/pnWx76vpBo6KiMqm4s9nat68eYFzrVu3RvXq1bFo0SL069evzHVyNhNRBfHmhytxPyUVah0+yyaO6I2zu5bCxkZu+MCIiEqhYcOGOHPmjE7XsmWGqJzIeJyFtPTHcHd1hq2tDR5nZuGLb3/B6i0/4dqtZJ1W9m1Upwa+XjIRzz1b3wgRE1FZWUM309ML4wkhkJiYiJkzZ6J+fd0+i5jMEFm43/+6htnLt2L3odNQqwWcHe3xWp8g/Hb2Ei5cualTnTY2cnz8/lBMHNHLoDvXEpF+rGFqtpubW4HPHSEEfH19sXXrVp3qZDJDZMEOnziPl0bMgkqthvrf/qNHGZlYvWW/XvU+U88XESN7GyJEIjIga2iZOXLkiNZjmUwGDw8P1KtXDzY2uqUlTGaILFRurgpDIpYiV6XSJDKG0uG5Jgatj4iotDp16mTwOpnMEFmofUfPIuneA6PU/daQl4xSLxHpp6LOZdq9e3epy/bq1avM9TOZIbJQF67Ew0YuR64OC0gVp8+LAWhSv+wrbBKR8VXUMTN9+vQpVTlJkrhoHlFF4uRgD7XasJtGulZyxJfzxhm0TiKikhj6s+xpXGeGyEL1frENhAEbjV2cHfDbtgWo6u5isDqJyMCEAQ4LdfjwYTRp0qTA1GwAUCqVeOaZZ3Ds2DGd6mYyQ2ShcnPVaNeqsUHqslfY4dZv69C0YS2D1EdExlGBcxksW7YM4eHhcHEp+AeVq6sr3nzzTSxdulSnupnMEFmYhKR/EDJsJup3GY3j0Zf0rs+ziisuH1gBl0qOBoiOiEg3f/zxB0JDQ4t8vmvXroiOjtapbo6ZIbIg5y7EofvIObibojRIfTMnDMT0twdyYTyicqKiDgAGgOTkZNja2hb5vI2NDe7du6dT3UxmiCzAmfN/Y+JHX+F49GWD1dmwdnXMGD/IYPURkfFV5EXzatSogQsXLqBevXqFPn/+/HlUq1ZNp7rZzURkZqf/uIL2AyYbNJGRy2X49tP3DFYfEZG+unXrhmnTpiEzM7PAc48fP8aMGTPQo0cPneo2WjIzd+5ctGvXDo6OjnBzcyvVNZIkFXosWrRIU8bPz6/A8wsWLDDSqyAyvj6j5yMnV/+1ZOSyvK6kqu4u+Hn9TLR8pq7edRKRaeW3zOhzWKqpU6ciJSUFDRo0wMKFC7Fr1y7s2rULH3/8MRo2bIiUlBR8+OGHOtVttG6m7OxsDBgwAIGBgVi7dm2prklMTNR6/NNPP2HkyJHo37+/1vnZs2cjPDxc87hSpUr6B0xkZFlZOfj+5yhs2/cbHqamo3FdH/h4V0Hi3RS96pXJJLRsUgfdgvzRrKEfege3gZ1d0f3SRGTJhJ5LMlhuNuPl5YUTJ05gzJgxiIyM1IzvkSQJISEhWLFiBby8vHSq22jJzKxZswAAGzZsKPU13t7eWo937dqFzp07o06dOlrnK1WqVKAskSVLvJuCF16bhstXb0Mmk6BWCxw/exG5Kv0XkpLJZGjXqhFmTxxigEiJyJwq8pgZAKhVqxb27duHBw8eIC4uDkII1K9fH5UrV9arXosdM5OcnIy9e/di5MiRBZ5bsGABqlSpgpYtW2LRokXIzc0ttq6srCykpqZqHUSmIoRA7zfnIe7GHQDQbBppiEQGyNuQ8uWX2hukLiIiU6hcuTKee+45tGnTRu9EBrDg2UwbN25EpUqV0K9fP63z48ePR6tWreDu7o4TJ04gMjISiYmJxS60M3/+fE1LEZGpnYi+jDPn/zZK3XK5DIEtG6Ijd8EmqjgsvHXFEpWpZWbKlClFDtLNPy5fNsyMjHXr1mHIkCGwt7fXOh8REYGgoCA8++yzGD16NJYsWYLly5cjKyuryLoiIyOhVCo1x61btwwSI1Fp/HzsHOQywzaCyv5dN6Z9q8bYteZDriNDVEFU5BWAjalMLTPvvvsuhg0bVmyZp8e36OLYsWOIjY3Ftm3bSiwbEBCA3Nxc3LhxAw0bNiy0jEKhgEKh0DsuIl3k5KqgMuAma/YKW0wY1hN9XmyLgBYNmMgQkdUrUzLj4eEBDw8PY8WisXbtWvj7+6N58+Yllo2JiYFMJoOnp6fR4yLSRcrDNIPVJQGYNWEw3n+zX4lliaj8qcgrABuT0cbMxMfHIyUlBfHx8VCpVIiJiQEA1KtXD87OzgCARo0aYf78+ejbt6/mutTUVGzfvh1LliwpUGdUVBROnTqFzp07o1KlSoiKisLEiRPx2muvGWQAEZGhHT35J9ZuP2iw+p5tXBtvvfaSweojIstS0WczGYvRkpnp06dj48aNmsctW7YEABw5cgRBQUEAgNjYWCiV2nvQbN26FUIIDBpUcBl2hUKBrVu3YubMmcjKykLt2rUxceJEREREGOtlEOlECIH+YxZg54GTBquzXi1v/N+3c+Hs5GCwOomIKgKjTc3esGGDprnsySM/kQHyPvCfHoMzatQoZGRkwNXVtUCdrVq1wsmTJ/Hw4UM8fvwYFy9eRGRkJMfDkEW5e/8hqgcOM2giAwDfLJkI10pOBq2TiCyLuVYAXrFiBfz8/GBvb4+AgACcPn26VNdt3boVkiShT58+ut3YQCx2nRmi8ig7Owf+vSOQdO+hQeut7euFti0bGbROIrI85pjNtG3bNkRERGDGjBk4d+4cmjdvjpCQENy9e7fY627cuIH33nsPHTt21OGuhsVkhsiAVm3+CbeT/jF4vT+s/sDgdRJRxfX0QrHFLV+ydOlShIeHY/jw4WjSpAlWr14NR0dHrFu3rshrVCoVhgwZglmzZhlkFrO+mMwQGYgQAp+s223wej9+fyiebeRn8HqJyPIUNjyjrAcA+Pr6wtXVVXPMnz+/0PtlZ2cjOjoawcHBmnMymQzBwcGIiooqMs7Zs2fD09Oz0FX6zcFiVwAmKi/i79zDx2u+w/odh/A4M9tg9fr5eGJx5HD0D21nsDqJyLIZajbTrVu34OLiojlf1NjS+/fvQ6VSFdjg0cvLq8hFcH/77TesXbtWM0vZEjCZIdLD5au30f6VKUhNSzfYXksjXu6CNweH4rln63NBPCLSiYuLi1YyYyhpaWl4/fXX8eWXX6Jq1aoGr19XTGaI9PBaxFI8TH2k2TxSV3K5DJIk4X+fTULfkEADRUdEVLyqVatCLpcjOTlZ63xycjK8vb0LlL969Spu3LiBnj17as6p/13h3MbGBrGxsahbt65xgy4EkxmiMlCr1Thz/m/c/UeJPy7dQPSFq3rXKZMkvPHKi3g7rAeeaVDTAFESUXll6kXz7Ozs4O/vj0OHDmmmV6vVahw6dAjjxo0rUL5Ro0b4888/tc5NnToVaWlp+PTTT+Hr66tr6HphMkNW7XFmFr798Ri+/v4w7v6jRJ2a3njjlRfRs8tzkMvlWmW/238Ck+ZvwPXbyUXUppsRA4Kx+qO3DFonEZVP5lgBOCIiAkOHDkXr1q3Rpk0bLFu2DOnp6Rg+fDgAICwsDDVq1MD8+fNhb2+Ppk2bal3v5uYGAAXOmxKTGbJad+8/xAuvTcNff8dDJklQC4Er1+9g75Gz6Bbkj+9XRkKhsAUAfLv7VwyeWHCLDX1JAD54a4DB6yUiKq1XX30V9+7dw/Tp05GUlIQWLVpg//79mkHB8fHxkMkse/KzJKxwV6rU1FS4urpCqVQaZYAUlQ8vhk3H0ZN/FjpwVyaTEDGiNxZFDkd2dg6qBQ436IaR+ZbPGIVxYd0NXi8RGY4pvjPy7/Ha0NGws9N9Vfvs7Cxs2rja6r7fLDvVIjKSi3/H4+DxP4qcgaRWC6za/BMepT/Gik37DJ7I2Mjl+HnDTCYyRKTFHCsAVwTsZiKrdPTUBUhS8f3L6Y/zxtO8O2+9Qe8tl8vw50+foVFdH4PWS0RkrZjMkFXK612VUNLfMRPnfgVD9sTayOX4v61zmcgQUaHMMQC4ImA3E1mlDq2blJikyCQgPaPo/UzKquNzTXAnaj3atWpssDqJqGIx167Z5R1bZsgqNW9cGx38G+NkTGzR42YM8KEgAajhXQVROxbCp5rlrJZJRFSRsGWGrNa3n74H32oekCQJxto1QAD4dHo4ExkiKhUOANYNkxmyWj7VquL3PZ9gyQfD0axBLbhVcjJo/U6O9lj38dvox+0JiKi02M+kE3YzkVVzreSEiSN6Y+KI3mj04lt4mJauV30KO1u83icI7f0b4+WX2sHZycFAkRKRNeAAYN0wmSFC3uymuBuJOl8vkyR8PGUYRg4IRmVXZwNGRkREJWEyQ1ZLmZYOlUqNyq7OSM/IhEpd+EDg0ujbtS3ee6OP4YIjIquk77gXK22YYTJD1mf7vuNYsOY7nPt3x+vaPl54rU+QXnXK5Bx+RkQGoO+wFyvNZpjMkFWZvXwrZiz7Vmv20t37idi5bxPy/jnoNq3Jv2ldg8RHRERlx2SGrMb5yzcwY9m3APL/8slbBTgjU8KFa7onMnK5DBEjehsqTCKyYhwArBsmM2Q11ny7/6kzecmL0DGJyffx+0Nha8t/SkSkPyYzuuEnMFmN039cMWh9lV2dsWjKMIx85UWD1ktERGXDZIasRk6uSq/rvT0qY/Wc0bC1sYFv9apo2qAWJGMtHUxEVilvNpPuzStW2jDDZIYqPiEEoL6Hzm1q4o9LN0pzBfK7oOQyAZVagiRJSDy5wYhREhGxm0lXnE9KFZYQAiLje+Cfl4H7vRH5ynewkZfmX7oEQMDJQY3m9XIBALY2cqPGSkREumMyQxWSEAJIWwykLQJUeSv7elQWCHspEzJZyQmNR2WBtEP34eyYV9bPx9Oo8RIRAdyaSVdMZqhiyvkdePz9vw/++9f9WUQaOjbPAZDXhfT08wBgIxcYGJyJySuc8GuMHQBg/NAexo6YiIi7ZuuIY2ao3BO58UDGt0DmQUBkADJPQFIAkAPQHvTraA8c+PQhfjxuh/V7HBCfLIO7ixrnYm2Rmi6DXCZgawOcOC9HdKwCcpkMrZrWwYgBwWZ5bURkhaw1I9EDkxkq10T2H8CDCQByAPy7t5I6qdhrbGyAvp2y0bdTNgAgJxfYdlCBsNmusJEDj7MkRMcqYG9ni2Evd8HCyUPhYK8w7gshIiKdMZmhckuIHOBhJIBs6POnjK0NMPDFLDg7PERIQDb+iLNDrjwQz/pPg2slJ4PFS0RUEs5m0o3RxszcuHEDI0eORO3ateHg4IC6detixowZyM7OLva6zMxMjB07FlWqVIGzszP69++P5ORkrTLx8fHo3r07HB0d4enpiUmTJiE3N9dYL4UsVcb/APEAhmiTtZEDfTplw8EeaNtUjQ4dwpnIEJHJccyMbozWMnP58mWo1WqsWbMG9erVw4ULFxAeHo709HQsXry4yOsmTpyIvXv3Yvv27XB1dcW4cePQr18/HD9+HACgUqnQvXt3eHt748SJE0hMTERYWBhsbW0xb948Y70csjAi6zfg0efGqdx1ESQbbhxJRFReSEKYrlFq0aJFWLVqFa5du1bo80qlEh4eHtiyZQtefvllAHlJUePGjREVFYW2bdvip59+Qo8ePXDnzh14eXkBAFavXo3Jkyfj3r17sLOzKzGO1NRUuLq6QqlUwsXFxXAvkExCqJXAvd4AsgxfueMwSJXeNHy9RFRumeI7I/8ePV9+E7a2JX+PFSUnJxs/7lhjdd9vJp2arVQq4e7uXuTz0dHRyMnJQXDwfzNHGjVqhJo1ayIqKgoAEBUVhWbNmmkSGQAICQlBamoq/vrrr0LrzcrKQmpqqtZB5djjfTBKIiOvDziHG75eIqJS4jozujFZMhMXF4fly5fjzTeL/qs3KSkJdnZ2cHNz0zrv5eWFpKQkTZknE5n85/OfK8z8+fPh6uqqOXx9ffV4JWR2OReNU6/rNEgSl14iIipvyvzJPWXKFEiSVOxx+fJlrWsSEhIQGhqKAQMGIDzc9H/5RkZGQqlUao5bt26ZPAYyIMlIWwtInH5NROYlhND7sEZlHgD87rvvYtiwYcWWqVOnjub/79y5g86dO6Ndu3b44osvir3O29sb2dnZePjwoVbrTHJyMry9vTVlTp8+rXVd/myn/DJPUygUUCj4RVVhyJsD+NmwdcqqAPLqhq2TiKiM9J2RZJ2pjA7JjIeHBzw8PEpVNiEhAZ07d4a/vz/Wr18Pmaz4hiB/f3/Y2tri0KFD6N+/PwAgNjYW8fHxCAwMBAAEBgZi7ty5uHv3Ljw98/bLOXDgAFxcXNCkSZOyvhwqJ4T6IaC6D2T/DqQvM/wNHAdCkrjsEhFReWS0T++EhAQEBQWhVq1aWLx4Me7du6d5Lr8FJSEhAV26dMHXX3+NNm3awNXVFSNHjkRERATc3d3h4uKCt99+G4GBgWjbti0AoGvXrmjSpAlef/11LFy4EElJSZg6dSrGjh3L1pcKSOTeAB6tArKOwfB/c+Ttjg3Fi4DjIAPXTURUdlw0TzdGS2YOHDiAuLg4xMXFwcfHR+u5/D69nJwcxMbGIiMjQ/PcJ598AplMhv79+yMrKwshISFYuXKl5nm5XI49e/ZgzJgxCAwMhJOTE4YOHYrZs2cb66WQmYjca0BKOCAyYZTGU3kdoNIYwK4dJEkyfP1ERGXEZEY3Jl1nxlJwnRnLJEQmkHkIyI4BIICcPwHVbWj2XDIoOVB1FyR5FSPUTUQViSnXmQnpM0rvdWZ+/uELq/t+4yABsggi5xLw4N1/tyeQI68lxhhJDABIgMPLTGSIiCoIJjNkdkL1D/BgPCDyuxtVRriLHHnJkQQ4DAAqvW2EexAR6YfdTLphMkPm9/gHQKTD4ONiJGfA8VVAVgNQ3wFkLoCiMyR5VcPeh4jIQJjM6IbJDJlf5hEYNpGRA26fAHbPQuJCeEREFR6TGTI/YeC9sux7QFI8Z9g6iYhMgIvm6YbJDJmEUCuBnFgAEmDbBJLM6b8nDdp6Ygc4czwMEZVT+m4WaaXZDJMZMiqhzgDSPgMy9wHI+fesHYRjH8D5rbxuIHn1f6dg68seqPwFJLlTyUWJiKjCYDJDRiNENvBgApB7EdrTrLOBjO1A7g0It6WAzTNA9hno/CeF5AU49gIcX4Yks551FYio4uEAYN2UeddsolLL/BnIvYDC14sRQPbpvG0KHHvqcRM54NAZkvMIJjJEVO4JAxzWiMkMGU/GLuTtf1QM5UwgKwpwDNPxJipAlaDjtUREVBGwm4kMRqhTgcxfANUtQKr07ziYkv5OyALSFgE2TYFKE4H0TYA6f1NS+b//LW4RPVneejJERBUAu5l0w2SGDEJk7AbSFgPIxX/bEZRhJd/cvwDVM0DVnUBuHCCyAJuawKM1wOMfi6lLDdi/qG/4REQWgcmMbtjNRHoTmf8HpM1H3mwlgbyEpqxbEoh/u6WyIdk2hGT3LCSZG+A4GIAdCv9VleUNHrYL0Cd8IiKLkZ/M6HNYIyYzpBchBJD+JUocG1MqmUDuTa0zko0v4L4ckHn8e0YOza+t3XNA5aWQJP4aExFZM3YzkX7UiUDuVQNWKC9wRrJ9BqLqd0D2KSDnEiDZAYp2kGzqGvC+RETmxxWAdcNkhvSjzii5TGlJlQGb2oU/JckBRbu8g4ioguKYGd2wfZ70I/eGwXJip8GQJObXRERUNkxmSC+SzBmw74rCuofKxL7Hv4N9iYisFwcA64bJDOnPeQwgqwqdExrHIYDLBxzIS0RWjysA64bfHqQ3SV4VcF8HKIKg06+UvAYkyRCzoYiIyBpxgALpRagfApm/5i10l/UrCt+HqQR2zQ0dFhFRucQBwLphMkM6EUINPFoNZHyLvAXydPkXJAdsm0OyqWPg6IiIyicmM7phMkO6ebQSyNisXx1yb8B1pkHCISIi68VkhspMqP4BMrbqUYMN4DwWcOgJSeZksLiIiMo7LpqnGyYzVHZZR6DT2BjIAMkBqPw5JNtGho6KiKjcYzeTbpjMUNmplcibtVSWzSQlwOFlwGkgJHk1IwVGRFS+CeiZzBgskvKFyQyVnbw6yrwrtvPbkJwGGSUcIiKyblxnhsrOPiivu6hEMgAS4DQMcBxo3JiIiCoALpqnG7bMUJlJkgNEpXeB1I8ASND+5yMDIM9bQM+2LmD/EiS5p1niJCIqbzhmRjdsmSGdSA7dAdf5gNxX+wm754AqX0Nymw3JaSgTGSKicmDFihXw8/ODvb09AgICcPr06SLLfvnll+jYsSMqV66MypUrIzg4uNjypsBkhnQm2QcBVbYCVTYBlVcDVXdBqrwMko2fuUMjIiqXhADUehy6tMxs27YNERERmDFjBs6dO4fmzZsjJCQEd+/eLbT80aNHMWjQIBw5cgRRUVHw9fVF165dkZCQoOer150khPU1SqWmpsLV1RVKpRIuLi7mDoeIiCyYKb4z8u/RqvMoyG3sdK5HlZuNc0e+wK1bt7RiVSgUUCgUhV4TEBCA5557Dp9//jkAQK1Ww9fXF2+//TamTJlS8j1VKlSuXBmff/45wsLCdI5dH2yZISIiqmB8fX3h6uqqOebPn19ouezsbERHRyM4OFhzTiaTITg4GFFRUaW6V0ZGBnJycuDu7m6Q2HVhtGTmxo0bGDlyJGrXrg0HBwfUrVsXM2bMQHZ2dpHXpKSk4O2330bDhg3h4OCAmjVrYvz48VAqlVrlJEkqcGzdqs+KtEREROZnqNlMt27dglKp1ByRkZGF3u/+/ftQqVTw8vLSOu/l5YWkpKRSxTx58mRUr15dKyEyNaPNZrp8+TLUajXWrFmDevXq4cKFCwgPD0d6ejoWL15c6DV37tzBnTt3sHjxYjRp0gQ3b97E6NGjcefOHezYsUOr7Pr16xEaGqp57ObmZqyXQkREZBJCSBBC0ut6AHBxcTHJMIoFCxZg69atOHr0KOzt7Y1+v6IYLZkJDQ3VSjbq1KmD2NhYrFq1qshkpmnTpvjuu+80j+vWrYu5c+fitddeQ25uLmxs/gvXzc0N3t7exgqfiIiowqtatSrkcjmSk5O1zicnJ5f4Hbt48WIsWLAABw8exLPPPmvMMEtk0jEzSqWyzH1q+QOunkxkAGDs2LGoWrUq2rRpg3Xr1qG4ccxZWVlITU3VOoiIiCxN/joz+hxlYWdnB39/fxw6dEhzTq1W49ChQwgMDCzyuoULF2LOnDnYv38/WrdurevLNRiTLZoXFxeH5cuXF9kqU5j79+9jzpw5GDVqlNb52bNn44UXXoCjoyN++eUXvPXWW3j06BHGjx9faD3z58/HrFmz9IqfiIjI2Myxa3ZERASGDh2K1q1bo02bNli2bBnS09MxfPhwAEBYWBhq1KihGUT88ccfY/r06diyZQv8/Pw0Y2ucnZ3h7OysR/S6K/PU7ClTpuDjjz8utsylS5fQqNF/uyInJCSgU6dOCAoKwldffVWq+6SmpuLFF1+Eu7s7du/eDVtb2yLLTp8+HevXr8etW7cKfT4rKwtZWVladfv6+nJqNhERlciUU7ObPv+m3lOzL/y6psyxfv7551i0aBGSkpLQokULfPbZZwgICAAABAUFwc/PDxs2bAAA+Pn54ebNmwXqmDFjBmbOnKlz7PooczJz7949/PPPP8WWqVOnDuzs8n4Yd+7cQVBQENq2bYsNGzZAJiu5ZystLQ0hISFwdHTEnj17ShxUtHfvXvTo0QOZmZlFzqN/EteZISKi0rKGZKa8K3M3k4eHBzw8PEpVNiEhAZ07d4a/vz/Wr19fqkQmNTUVISEhUCgU2L17d6lGR8fExKBy5cqlSmSIiIgsFfdm0o3RxswkJCQgKCgItWrVwuLFi3Hv3j3Nc/kjpBMSEtClSxd8/fXXaNOmDVJTU9G1a1dkZGRg06ZNWoN1PTw8IJfL8eOPPyI5ORlt27aFvb09Dhw4gHnz5uG9994z1kshIiIyCXOMmakIjJbMHDhwAHFxcYiLi4OPj4/Wc/k9Wzk5OYiNjUVGRgYA4Ny5czh16hQAoF69elrXXL9+HX5+frC1tcWKFSswceJECCFQr149LF26FOHh4cZ6KRWCENlA5hEg61dAZAI2dQGH3pBsapg7NCIiIr1wbyYr6FMUqjtAytuA+g7yZuOr//2vACpNhOQ4wLwBEhFZMFOOmWnUQf8xM5d/s74xM9ybqYITIhd4MAFQ5y+IpH7ivwJIWwqRVbr9N4iIyLhMvc5MRcFkpqLLOg6obgNQFVFABqR/Y8qIiIiIDMpki+aRmWRHAZCj6GRGDeT8DiEyIUnm21eDiIjyBwDrsTeT4UIpV5jMVHQip5TlVNDj3w8RERmAWgCSHhmJ2kqzGXYzVXS2jfDfOJnCSICsOiA5mioiIiIig2IyU9HZvwRAgWKbXRwHQJLYLENEZG4cAKwbJjMVnCRzBtxmI+9HLX/ymbz/2LUHHF82Q2RERPQ0IfK6inQ9rDWZ4ZgZKyApOkK4rwcytgCZRwFkAza1AYcBgEN3SBJ/DYiILAFXANYNv8WshGRbH3CdkXcQERFVIExmiIiILIRaz6YZa53NxGSGiIjIQgghQQg91pnR49ryjAOAiYiIqFxjywwREZGFKG5VMFNcX14xmSEiIrIQHDOjG3YzERERUbnGlhkiIiILwZYZ3TCZISIishBMZnTDbiYiIiIq19gyQ0REZCHUkFDsxsClut76MJmxMELkAupUQOYASXIwdzhERGRCakC/biZDBVLOMJmxEEKtBNI3Ao93ASIDgARh1w5wHgHJtom5wyMiIhPI3zVbn+utEcfMWAChfgikvAFkbPs3kQEAAWRHASmjILKizBkeERGRRWMyYwnSVgGqRBRsIFTnHcqZECLbDIEREZEpqQxwWCMmM2Ym1OlA5k8o+ldQACIVyPrVlGEREZEZqIT+hzViMmNuqkQAOSUUsgFyr5kiGiIionKHA4DNrVQzltSAZG/0UIiIyLxyBSBxAHCZMZkxN3l1QF4bUN1A0fPx1ICikwmDIiIic1BBgqTHWjHCSteZYTeTmUmSBDiPRNGJjAxQBEGyqWXKsIiIiMoNtsxYAMm+C4T6HyDtM+QlNfmZtQqwawu4TjdjdEREZCrsZtINkxkLITm+AqHokjezSXUbkJwA+y5cMI+IyJoIPRMSJjNkbpK8CuD0mrnDICIiKleYzBAREVkMAf2aV6yzaYbJDBERkaVgLqMTo81munHjBkaOHInatWvDwcEBdevWxYwZM5CdXfyy/EFBQZAkSesYPXq0Vpn4+Hh0794djo6O8PT0xKRJk5Cbm2usl0JERGQiwgCH9TFay8zly5ehVquxZs0a1KtXDxcuXEB4eDjS09OxePHiYq8NDw/H7NmzNY8dHR01/69SqdC9e3d4e3vjxIkTSExMRFhYGGxtbTFv3jxjvRwiIiKyUEZLZkJDQxEaGqp5XKdOHcTGxmLVqlUlJjOOjo7w9vYu9LlffvkFFy9exMGDB+Hl5YUWLVpgzpw5mDx5MmbOnAk7OzuDvg4iIiKTEQIQT286XMbrrZBJF81TKpVwd3cvsdzmzZtRtWpVNG3aFJGRkcjIyNA8FxUVhWbNmsHLy0tzLiQkBKmpqfjrr78KrS8rKwupqalaBxERkcURQv/DCplsAHBcXByWL19eYqvM4MGDUatWLVSvXh3nz5/H5MmTERsbi++//x4AkJSUpJXIANA8TkpKKrTO+fPnY9asWQZ4FURERGRpytwyM2XKlAIDdJ8+Ll++rHVNQkICQkNDMWDAAISHhxdb/6hRoxASEoJmzZphyJAh+Prrr7Fz505cvXq1rKFqREZGQqlUao5bt27pXBcREZHxqA1wWJ8yt8y8++67GDZsWLFl6tSpo/n/O3fuoHPnzmjXrh2++OKLMgcYEBAAIK9lp27duvD29sbp06e1yiQnJwNAkeNsFAoFFApFme9NRERkUkKt55gZJjOl4uHhAQ8Pj1KVTUhIQOfOneHv74/169dDJiv7EJ2YmBgAQLVq1QAAgYGBmDt3Lu7evQtPT08AwIEDB+Di4oImTUy39L9QPwCyowGRA9g2hmTjZ7J7ExER0X+MNmYmISEBQUFBqFWrFhYvXox79+5pnstvQUlISECXLl3w9ddfo02bNrh69Sq2bNmCbt26oUqVKjh//jwmTpyI559/Hs8++ywAoGvXrmjSpAlef/11LFy4EElJSZg6dSrGjh1rktYXIbKA1E+AzD0AVP+dt20JuE6DJK9m9BiIiKiCYsuMToyWzBw4cABxcXGIi4uDj4+P1nPi39HWOTk5iI2N1cxWsrOzw8GDB7Fs2TKkp6fD19cX/fv3x9SpUzXXyuVy7NmzB2PGjEFgYCCcnJwwdOhQrXVpjEUIATz8AMiOQoGFiXLOAymjIKpshCQrecYWERFRQfqOe7HOZEYSwvrmcaWmpsLV1RVKpRIuLi6lvk5kRwMPxhVTQgY4hUFyflP/IImIyCLo+p2hyz3QOAyQ67FemiobuPS1UWO1RCZdZ6bce/wTAHkxBdTA492mioaIiCqa/G4mfQ4rxI0my0J9H0+Okym8zENTREJERBWRvgvfWV9nCwAmM2Uj80Bey0wxCY2siqmiISKiCodjZnTBbqaycOiB4ltmZIBDL1NFQ0RERGAyUza2zwKKFwBIhTwpB2RegOMAU0dFREQVBcfM6ITJTBlIkgS4zgIcBwJ4arS5XQDgvgaSzNUssRERUQWQv2u2zgfHzFApSJINUGk8hNNIICfm3xWAG3KxPCIiIjNhMqMjSeYEKNqbOwwiIqpQOABYF0xmiIiILAWnZuuEY2aIiIioXGPLDBERkaXgRpM6YTJDRERkKZjM6ITdTERERFSusWWGiIjIYoh/D32utz5smSEiIrIY+q7+q1s304oVK+Dn5wd7e3sEBATg9OnTxZbfvn07GjVqBHt7ezRr1gz79u3T6b6GwmSGiIjIUphhO4Nt27YhIiICM2bMwLlz59C8eXOEhITg7t27hZY/ceIEBg0ahJEjR+L3339Hnz590KdPH1y4cEHfV68zSQjrm5SempoKV1dXKJVKuLi4mDscIiKyYKb4zsi/B2p3A2S2ulekzgGu7ytTrAEBAXjuuefw+eef51WhVsPX1xdvv/02pkyZUqD8q6++ivT0dOzZs0dzrm3btmjRogVWr16te+x6sMoxM/n5W2pqqpkjISIiS5f/XWGSv/1VOfotfKfOBVDw+02hUEChUBQonp2djejoaERGRmrOyWQyBAcHIyoqqtBbREVFISIiQutcSEgIfvjhB93j1pNVJjNpaWkAAF9fXzNHQkRE5UVaWlpe64kR2NnZwdvbG0nxB/Suy9nZucD324wZMzBz5swCZe/fvw+VSgUvLy+t815eXrh8+XKh9SclJRVaPikpSb/A9WCVyUz16tVx69YtVKpUKW8nbCNKTU2Fr68vbt26VS67tBi/+ZTn2AHGb27lOX5Li10IgbS0NFSvXt1o97C3t8f169eRnZ2td11CiALfbYW1ylQkVpnMyGQy+Pj4mPSeLi4uFvGPUleM33zKc+wA4ze38hy/JcVurBaZJ9nb28Pe3t7o93lS1apVIZfLkZycrHU+OTkZ3t7ehV7j7e1dpvKmwNlMREREVsrOzg7+/v44dOiQ5pxarcahQ4cQGBhY6DWBgYFa5QHgwIEDRZY3BatsmSEiIqI8ERERGDp0KFq3bo02bdpg2bJlSE9Px/DhwwEAYWFhqFGjBubPnw8AmDBhAjp16oQlS5age/fu2Lp1K86ePYsvvvjCbK+ByYyRKRQKzJgxo9z2VzJ+8ynPsQOM39zKc/zlOfby6NVXX8W9e/cwffp0JCUloUWLFti/f79mkG98fDxksv86ctq1a4ctW7Zg6tSp+OCDD1C/fn388MMPaNq0qblegnWuM0NEREQVB8fMEBERUbnGZIaIiIjKNSYzREREVK4xmSEiIqJyjckMERERlWtMZvRw48YNjBw5ErVr14aDgwPq1q2LGTNmlLgcdVBQECRJ0jpGjx6tVSY+Ph7du3eHo6MjPD09MWnSJOTm5po9/pSUFLz99tto2LAhHBwcULNmTYwfPx5KpVKr3NOvT5IkbN261ezxA0BmZibGjh2LKlWqwNnZGf379y+wmqUp3n8AmDt3Ltq1awdHR0e4ubmV6prC3ltJkrBo0SJNGT8/vwLPL1iwwOyxDxs2rEBcoaGhWmVSUlIwZMgQuLi4wM3NDSNHjsSjR48MGrsu8efk5GDy5Mlo1qwZnJycUL16dYSFheHOnTta5Uzx3usSP5C3zP306dNRrVo1ODg4IDg4GH///bdWGVO9/2W9z40bN4r83d++fbumnCk+e8jycJ0ZPVy+fBlqtRpr1qxBvXr1cOHCBYSHhyM9PR2LFy8u9trw8HDMnj1b89jR0VHz/yqVCt27d4e3tzdOnDiBxMREhIWFwdbWFvPmzTNr/Hfu3MGdO3ewePFiNGnSBDdv3sTo0aNx584d7NixQ6vs+vXrtb6oSvuBa8z4AWDixInYu3cvtm/fDldXV4wbNw79+vXD8ePHAZju/QfydqwdMGAAAgMDsXbt2lJdk5iYqPX4p59+wsiRI9G/f3+t87Nnz0Z4eLjmcaVKlfQP+Am6xA4AoaGhWL9+vebx02uJDBkyBImJiThw4ABycnIwfPhwjBo1Clu2bDFY7EDZ48/IyMC5c+cwbdo0NG/eHA8ePMCECRPQq1cvnD17Vqussd97XeIHgIULF+Kzzz7Dxo0bUbt2bUybNg0hISG4ePGiZhl9U73/Zb2Pr69vgd/9L774AosWLcJLL72kdd7Ynz1kgQQZ1MKFC0Xt2rWLLdOpUycxYcKEIp/ft2+fkMlkIikpSXNu1apVwsXFRWRlZRkq1EKVJv6n/e9//xN2dnYiJydHcw6A2Llzp4GjK1lJ8T98+FDY2tqK7du3a85dunRJABBRUVFCCPO8/+vXrxeurq46Xdu7d2/xwgsvaJ2rVauW+OSTT/QPrBTKEvvQoUNF7969i3z+4sWLAoA4c+aM5txPP/0kJEkSCQkJekZaOH3e+9OnTwsA4ubNm5pzpnzvhSh9/Gq1Wnh7e4tFixZpzj18+FAoFArx7bffCiFM9/4b6j4tWrQQI0aM0Dpnrs8eMi92MxmYUqmEu7t7ieU2b96MqlWromnTpoiMjERGRobmuaioKDRr1kxri/WQkBCkpqbir7/+Mkrc+Uob/9PXuLi4wMZGu6Fv7NixqFq1Ktq0aYN169ZBmGB9xpLij46ORk5ODoKDgzXnGjVqhJo1ayIqKgqAed//skpOTsbevXsxcuTIAs8tWLAAVapUQcuWLbFo0SKjdJPp4ujRo/D09ETDhg0xZswY/PPPP5rnoqKi4ObmhtatW2vOBQcHQyaT4dSpU+YIt1hKpRKSJBX4y98S3/vr168jKSlJ63ff1dUVAQEBWr/7pnj/DXGf6OhoxMTEFPq7b47PHjIvdjMZUFxcHJYvX15iF9PgwYNRq1YtVK9eHefPn8fkyZMRGxuL77//HgCQlJSk9UUKQPM4KSnJOMGj9PE/6f79+5gzZw5GjRqldX727Nl44YUX4OjoiF9++QVvvfUWHj16hPHjxxs6bI3SxJ+UlAQ7O7sCXz5eXl6a99Zc778uNm7ciEqVKqFfv35a58ePH49WrVrB3d0dJ06cQGRkJBITE7F06VIzRZonNDQU/fr1Q+3atXH16lV88MEHeOmllxAVFQW5XI6kpCR4enpqXWNjYwN3d3eLe+8zMzMxefJkDBo0SGtnZ0t97/Pfv8J+t5/83TfF+2+I+6xduxaNGzdGu3bttM6b47OHLIC5m4Ys0eTJkwWAYo9Lly5pXXP79m1Rt25dMXLkyDLf79ChQwKAiIuLE0IIER4eLrp27apVJj09XQAQ+/bts5j4lUqlaNOmjQgNDRXZ2dnFlp02bZrw8fEpVb3GjH/z5s3Czs6uwPnnnntOvP/++0II87z/unZ1NGzYUIwbN67EcmvXrhU2NjYiMzPTYmIXQoirV68KAOLgwYNCCCHmzp0rGjRoUKCch4eHWLlyZYn1mSr+7Oxs0bNnT9GyZUuhVCqLLVva997Y8R8/flwAEHfu3NE6P2DAAPHKK68IIUz3/ut7n4yMDOHq6ioWL15cYtmyfPZQ+cWWmUK8++67GDZsWLFl6tSpo/n/O3fuoHPnzmjXrp1Ou4YGBAQAyGtZqFu3Lry9vXH69GmtMvmzbby9vUuszxTxp6WlITQ0FJUqVcLOnTtha2tbbPmAgADMmTMHWVlZJW4eZ8z4vb29kZ2djYcPH2q1ziQnJ2veW1O//7o6duwYYmNjsW3bthLLBgQEIDc3Fzdu3EDDhg2LLGeq2J+sq2rVqoiLi0OXLl3g7e2Nu3fvapXJzc1FSkqKxbz3OTk5eOWVV3Dz5k0cPnxYq1WmMKV97wHjxp///iUnJ6NatWqa88nJyWjRooWmjCnef33vs2PHDmRkZCAsLKzEsmX57KFyzNzZVHl3+/ZtUb9+fTFw4ECRm5urUx2//fabACD++OMPIcR/A1CTk5M1ZdasWSNcXFxK9dddWegSv1KpFG3bthWdOnUS6enppbrmo48+EpUrV9Yn1EKVNf78AcA7duzQnLt8+XKhA4BN8f7n06V1YOjQocLf379UZTdt2iRkMplISUnRIbri6dMyc+vWLSFJkti1a5cQ4r+BoWfPntWU+fnnny1mAHB2drbo06ePeOaZZ8Tdu3dLdY0x33shyj4A+MnWDKVSWegAYGO///rep1OnTqJ///6lupexPnvIsjCZ0cPt27dFvXr1RJcuXcTt27dFYmKi5niyTMOGDcWpU6eEEELExcWJ2bNni7Nnz4rr16+LXbt2iTp16ojnn39ec01ubq5o2rSp6Nq1q4iJiRH79+8XHh4eIjIy0uzxK5VKERAQIJo1aybi4uK0rslPJnbv3i2+/PJL8eeff4q///5brFy5Ujg6Oorp06ebPX4hhBg9erSoWbOmOHz4sDh79qwIDAwUgYGBmudN9f4LIcTNmzfF77//LmbNmiWcnZ3F77//Ln7//XeRlpamKdOwYUPx/fffa12nVCqFo6OjWLVqVYE6T5w4IT755BMRExMjrl69KjZt2iQ8PDxEWFiYWWNPS0sT7733noiKihLXr18XBw8eFK1atRL169fXShJDQ0NFy5YtxalTp8Rvv/0m6tevLwYNGmTQ2HWJPzs7W/Tq1Uv4+PiImJgYrd+3/FlupnrvdYlfCCEWLFgg3NzcxK5du8T58+dF7969Re3atcXjx481ZUz1/pd0n8L+7QohxN9//y0kSRI//fRTgTpN9dlDlofJjB7Wr19fZL9wvuvXrwsA4siRI0IIIeLj48Xzzz8v3N3dhUKhEPXq1ROTJk0q0O9+48YN8dJLLwkHBwdRtWpV8e6772pNfTZX/EeOHCnymuvXrwsh8qZYtmjRQjg7OwsnJyfRvHlzsXr1aqFSqcwevxBCPH78WLz11luicuXKwtHRUfTt21crARLCNO+/EHmtK4XF/2S8AMT69eu1rluzZo1wcHAQDx8+LFBndHS0CAgIEK6ursLe3l40btxYzJs3z+CtSmWNPSMjQ3Tt2lV4eHgIW1tbUatWLREeHq41BV4IIf755x8xaNAg4ezsLFxcXMTw4cO1vqDNFX/+71Jx15jqvdclfiHyWmemTZsmvLy8hEKhEF26dBGxsbFa9Zrq/S/pPoX92xVCiMjISOHr61vo54mpPnvI8khCcM4aERERlV9cZ4aIiIjKNSYzREREVK4xmSEiIqJyjckMERERlWtMZoiIiKhcYzJDRERE5RqTGSIiIirXmMwQERFRucZkhoiIiMo1JjNERERUrjGZISIionLt/wHj9iDZnnhvKwAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#**Inferences from the scatter plot:**\n", + "\n", + "1.The plot shows two distinct clusters (dark blue and yellow), indicating that AEBiGRU is effectively distinguishing between two groups.\n", + "\n", + "2.Unlike previous models where yellow points were more scattered, AEBiGRU has a larger, more structured region assigned to cluster 1.\n", + "\n", + "3.Since the clusters are positioned along a linear trend, this might imply that the underlying feature representation is influenced by a dominant latent factor." + ], + "metadata": { + "id": "NYnhRFqk2r_v" + } + }, + { + "cell_type": "markdown", + "source": [ + "**References:**\n", + "\n", + "[1]Zhao et. al, Convolutional neural networks for time series classification,\n", + "Journal of Systems Engineering and Electronics, 28(1):2017.\n", + "\n", + "[2]Wang et. al, Time series classification from scratch with deep neural networks: A strong baseline, International joint conference on neural networks (IJCNN), 2017.\n", + "\n" + ], + "metadata": { + "id": "sq0CNNhuI3_O" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "-yv9NB8JHyUE" + }, + "execution_count": null, + "outputs": [] + } + ] +} From 5841e8bb485f923c12178c988f4ef1d84b1dcfea Mon Sep 17 00:00:00 2001 From: aditya27 <120127383+Edgeshot27@users.noreply.github.com> Date: Tue, 25 Mar 2025 21:05:49 +0530 Subject: [PATCH 08/13] Update deep_learning_based_clustering.ipynb --- examples/clustering/deep_learning_based_clustering.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/clustering/deep_learning_based_clustering.ipynb b/examples/clustering/deep_learning_based_clustering.ipynb index e599e078b8..6aa1818e04 100644 --- a/examples/clustering/deep_learning_based_clustering.ipynb +++ b/examples/clustering/deep_learning_based_clustering.ipynb @@ -79,8 +79,8 @@ { "cell_type": "code", "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np" + "import matplotlib.pyplot as plt\n" + ], "metadata": { "id": "86gsiHDbuoz-" From a8251097530d8e7b124b98646d6c76f305f91964 Mon Sep 17 00:00:00 2001 From: Edgeshot27 <120127383+Edgeshot27@users.noreply.github.com> Date: Tue, 25 Mar 2025 15:36:16 +0000 Subject: [PATCH 09/13] Automatic `pre-commit` fixes --- examples/clustering/deep_learning_based_clustering.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/clustering/deep_learning_based_clustering.ipynb b/examples/clustering/deep_learning_based_clustering.ipynb index 6aa1818e04..dda480369a 100644 --- a/examples/clustering/deep_learning_based_clustering.ipynb +++ b/examples/clustering/deep_learning_based_clustering.ipynb @@ -79,8 +79,7 @@ { "cell_type": "code", "source": [ - "import matplotlib.pyplot as plt\n" - + "import matplotlib.pyplot as plt" ], "metadata": { "id": "86gsiHDbuoz-" From 879dc1d03dd32dc3e07282fe6791b0e202e6b97a Mon Sep 17 00:00:00 2001 From: aditya27 <120127383+Edgeshot27@users.noreply.github.com> Date: Wed, 26 Mar 2025 20:59:28 +0530 Subject: [PATCH 10/13] Update deep_learning_based_clustering.ipynb --- .../deep_learning_based_clustering.ipynb | 50 ++----------------- 1 file changed, 5 insertions(+), 45 deletions(-) diff --git a/examples/clustering/deep_learning_based_clustering.ipynb b/examples/clustering/deep_learning_based_clustering.ipynb index dda480369a..e9779c5ddf 100644 --- a/examples/clustering/deep_learning_based_clustering.ipynb +++ b/examples/clustering/deep_learning_based_clustering.ipynb @@ -260,15 +260,7 @@ "id": "I9GhXLYBhq27" } }, - { - "cell_type": "markdown", - "source": [ - "# **AEResNetClusterer:**" - ], - "metadata": { - "id": "rR3RUBPpihGC" - } - }, + { "cell_type": "markdown", "source": [ @@ -373,15 +365,7 @@ "id": "E0FZWgqhPe8v" } }, - { - "cell_type": "markdown", - "source": [ - "# **AEDCNNClusterer:**" - ], - "metadata": { - "id": "6xWiuNT7izNY" - } - }, + { "cell_type": "markdown", "source": [ @@ -471,15 +455,7 @@ "id": "_sqUBae7ePwg" } }, - { - "cell_type": "markdown", - "source": [ - "# **AEDRNNClusterer:**" - ], - "metadata": { - "id": "ep5dTHIEi4xD" - } - }, + { "cell_type": "markdown", "source": [ @@ -585,15 +561,7 @@ "id": "UES_F4Iedmbg" } }, - { - "cell_type": "markdown", - "source": [ - "# **AEAttentionBiGRUClusterer:**" - ], - "metadata": { - "id": "n2poXz2KjAvo" - } - }, + { "cell_type": "markdown", "source": [ @@ -691,15 +659,7 @@ "id": "UyQ5LH82h5A-" } }, - { - "cell_type": "markdown", - "source": [ - "# **AEBiGRUClusterer:**" - ], - "metadata": { - "id": "gLoHAkV-jGsX" - } - }, + { "cell_type": "markdown", "source": [ From 8c2633db5dfe7dfbcc9852a9f7ed53673dd33351 Mon Sep 17 00:00:00 2001 From: Edgeshot27 <120127383+Edgeshot27@users.noreply.github.com> Date: Wed, 26 Mar 2025 15:29:57 +0000 Subject: [PATCH 11/13] Automatic `pre-commit` fixes --- .../clustering/deep_learning_based_clustering.ipynb | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/clustering/deep_learning_based_clustering.ipynb b/examples/clustering/deep_learning_based_clustering.ipynb index e9779c5ddf..1e2561228c 100644 --- a/examples/clustering/deep_learning_based_clustering.ipynb +++ b/examples/clustering/deep_learning_based_clustering.ipynb @@ -260,7 +260,7 @@ "id": "I9GhXLYBhq27" } }, - + { "cell_type": "markdown", "source": [ @@ -365,7 +365,7 @@ "id": "E0FZWgqhPe8v" } }, - + { "cell_type": "markdown", "source": [ @@ -455,7 +455,7 @@ "id": "_sqUBae7ePwg" } }, - + { "cell_type": "markdown", "source": [ @@ -561,7 +561,7 @@ "id": "UES_F4Iedmbg" } }, - + { "cell_type": "markdown", "source": [ @@ -659,7 +659,7 @@ "id": "UyQ5LH82h5A-" } }, - + { "cell_type": "markdown", "source": [ From de8ffd7ec9474f083876f07790d34e165313f4b6 Mon Sep 17 00:00:00 2001 From: aditya27 <120127383+Edgeshot27@users.noreply.github.com> Date: Sun, 30 Mar 2025 12:13:14 +0530 Subject: [PATCH 12/13] Update deep_learning_based_clustering.ipynb --- examples/clustering/deep_learning_based_clustering.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/clustering/deep_learning_based_clustering.ipynb b/examples/clustering/deep_learning_based_clustering.ipynb index 1e2561228c..9c5209bbfe 100644 --- a/examples/clustering/deep_learning_based_clustering.ipynb +++ b/examples/clustering/deep_learning_based_clustering.ipynb @@ -69,7 +69,7 @@ { "cell_type": "markdown", "source": [ - "# **Deep Learning Based Clustering**\n", + " **Deep Learning Based Clustering**\n", "The aeon.clustering.deeplearning module provides powerful deep learning models designed specifically for time series clustering. These models leverage advanced architectures like **Auto-Encoders, Residual Networks, Dilated Convolutions, and Attention Mechanisms** to effectively identify patterns, trends, and groupings in complex temporal data. Each model is tailored to handle different data characteristics, improving clustering accuracy and performance across various domains such as healthcare, finance, and signal processing." ], "metadata": { From ca371abb6f49db07234937d95dcdb4a0a0f5539c Mon Sep 17 00:00:00 2001 From: aditya27 <120127383+Edgeshot27@users.noreply.github.com> Date: Sun, 30 Mar 2025 12:15:15 +0530 Subject: [PATCH 13/13] Update deep_learning_based_clustering.ipynb --- examples/clustering/deep_learning_based_clustering.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/clustering/deep_learning_based_clustering.ipynb b/examples/clustering/deep_learning_based_clustering.ipynb index 9c5209bbfe..1e2561228c 100644 --- a/examples/clustering/deep_learning_based_clustering.ipynb +++ b/examples/clustering/deep_learning_based_clustering.ipynb @@ -69,7 +69,7 @@ { "cell_type": "markdown", "source": [ - " **Deep Learning Based Clustering**\n", + "# **Deep Learning Based Clustering**\n", "The aeon.clustering.deeplearning module provides powerful deep learning models designed specifically for time series clustering. These models leverage advanced architectures like **Auto-Encoders, Residual Networks, Dilated Convolutions, and Attention Mechanisms** to effectively identify patterns, trends, and groupings in complex temporal data. Each model is tailored to handle different data characteristics, improving clustering accuracy and performance across various domains such as healthcare, finance, and signal processing." ], "metadata": {