4222 lines
514 KiB
Plaintext
4222 lines
514 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 76,
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"id": "e6e27297",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import random\n",
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"import time\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"from sklearn.metrics import silhouette_score\n",
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"from sklearn.decomposition import PCA\n",
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"from IPython.display import Image\n",
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"from sklearn.datasets import make_classification, make_blobs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e1e5a2b7",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# Analiza skupień metodą k-medoids "
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]
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},
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{
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"cell_type": "markdown",
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"id": "80d5deaf",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"### Co to jest klasteryzacja? "
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]
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},
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{
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"cell_type": "markdown",
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"id": "4040df16",
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"metadata": {
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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" Analiza skupień lub klasteryzacja to zadanie polegające na grupowaniu zbioru obiektów w taki sposób, aby obiekty w tej samej grupie lub klastrze były do siebie bardziej podobne niż obiekty w innych grupach lub klastrach. "
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]
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},
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{
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"cell_type": "markdown",
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"id": "493a0d16",
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"metadata": {},
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"source": [
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" Idea algorytmu klastrowania:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"id": "e84b8c18",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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jxo2en/3sZ/oz2l20UWjr9uzZ41Gi0IM2UAkjnZR6YNbx1MO/p6CgQF979dVX9TUlhPQ1NcWntXwzZszQ19SweWt+6iFZv8fvjpoT6VEdJfoz2vQzzjhD5402F8HXfWoET9/305/+VP8m+Nte+crP+E1Qos1TVlamyxITE+Mxyow81GiXLpuv+hqcEB+HEpP61dtvJH4PEUcJcc9dd93lwW+3cR9sYbyHHZTo1p+VANdlOPfcc/XnL774Qn9WPbf6M+ro9ICnHQYTCBiiEl9kfMkguIwvlXpCbRVpqufOo3ojW3OECFC9Sjrut7/9bY+avKzFCASQeorxrF692oM01VCJRz3hadGChuLQoUOtotCbqISQQf5//vOfPapn1JOQkOBRw8eebdu26fPqSU3/QUNgxsXFeQ4ePNgqXNavX99aXjQ0SEf1snlUj6Uut/GHumLFCv1Z9arqOKrXrlUcql5Nj1pY43nttdf0NTQyaOjUE5pH9ch51JxBD+qLtNWTr8/7kIkhKsELQtUbH1wzxJfqUdWMUGfUTz2ta3GG/NQTvC53238g7FTvqkf1BHtUL6cul+rd01HaikrV+6obrE2bNmkexgMBRCXuR/oQ8Qiqh1R/NsS8ISohTFXPrQcNIOKfdtpp+jO+M8ZDiSHw//KXv+i0/K0XRaXGxX9IIOwEDFGJtgq/CWjH8feOB0UEX+1o28Kj7UIbr3oXtchBe4bPqjfOg3ZWzX3Xbe1NN92k03/++ec9EIzIyxC0hljC7w/O//a3v9VZQNTg84ABA/RvDzpD8JuAcxs2bGgVh0Z8Q0hBxKIzAO05OidUD6VHzRnV90FwGqLS233Bikpf+bUVlc8995wuBzpEEH7xi1/ozxCV31Rfg9M555zj2bFjh8/fSDXFTaerhr91Poao/OEPf6g/n3766fr6008/7QFX/IbhdxjBzaKSw9/qr8fMgEUzWNSBeTSqN1IwLKx6F1uzUD10op56Wj9jSPqll16SwYMH6yFzdM1jqBir49B9jqEFzOfD8CzmaGL4Y8qUKXp4FV3tGEb2FtD9jqCeiEWJP1F/5Hp+JIZhEDD0oRomOe+88/QqvA8//FBmz54tStTornodSf1jDM1ipZ5qzPSQC+YGYdK5MdcPw+IIGJo3ArrylfjTw/Q4hyF0zDfFCu5rr71WDy188MEHRvTWV2/3tV5Ub3bv3t0uHwwrGQHDHqox1/NZMXcJQzvGHCYlvIxora/q6V0P62C6AYbuEdTffut14w2G0jFEBNvA1rCFEYyhDAwtIWCYCsyM8zinemf1dADVuOjhDuSLYTFj2gOGZXyFQOuFtLzVw1cevEYCJGAegU6dOunfBLTJGIpVo1Z+t6MoBdoutLXLli2TpUuXihrhEiUCRY1CCYZe0f6irUVbjoBhcrTnCMY5/UH9ozoVjLf61WizkSbmrKO9QruFYFzDe+M3C9OuEPA7hLYSv29o73Adw9rHBm/3HRvn2M/ttVf+5Ie00EYjYLoRAuY8GsGo0zfVF1OUMPfTn99II23jFW00AqYEIAwcOFBzxfcg1HPzdQFC/A9FpcnA+/btq//YMXcQ81GMP1AjG3yxjIBVYxCVEIZYbQ2hhoUZWCmIeGgw1JOVYJ4eDvVkq281hB7E5/79+43kjnrFHyDmM2I+nhoOEfX0pFfcqZ4vHQ+iEsFodFBWCFrjs76o/lG9bPpAA/bPf/5T/5GhkVM9m7Jr1y4dTQ3D6FeILSN4W1QDUayGzrVwRb28rf7zdp+RJl598VE9fW2j+v0ejRjmXKL+mEQPgddewDwbCPx9+/bpKG3nwBgC3+CBBhxi3DiPG9raHw0WRCQeIIz5t8fyb68c7Z1HmhCsSBMBDT0WjTGQAAmEh4DqRdO/CZj/qIZ8W8WKP+2oUWIIUjU6JZgreNxxx+l52//4xz/0OaMtxwN5oerQwMM65tGjg8D4zTDSUT1xWjxiLjjaO6PNVr2SRhQx2i/jGi6oXtbW63iDNhNpqB5KUT2kooac9e/NUZHUB2/3tY0TSHvlT35IG200ghpx069t22ijTt9UX/wWG8Fok9v7jTTi+fOK+iLgIQDBaKdVr6j+7IZ/KCrDZEWs/sOKZgQsDFHDr3LrrbfqnkUIUQgBLNxQcxH1UyBWmaE3E6IVjQYWfGAhDJ4Y2wtoiJAORAZWH+KAGEI6eApDQAMF8YkFNgjH9pSpYVd55JFH9DWUAb2BZ555pv6MidlqLknrdTWvRp9v7x/jKQ2NKXo7X3jhBR3VW89he2mgLu3xMf5g27vXOH9sPKNcEKUQtWg8ELw9MaOnGfHxBA6+xlMx4hv1x+R7iERj0rxxHnGODcaCGvCHgDd+BPBggYAeh7Y9ncfe3/azUS98R7DFFR4kYF9v9Wh7H9+TAAmEnkAg7SjaGgS054aoNB5ssfDTCGi/8dCL9tUQQ8Y1vKITA6NWSAcjMmpOvP4twAKSK6+8Ui655BLdkYHfBIhSX8H47VFzy/UoD/JECLS98be98jc/NZ1IlwO/p2hfMWJohGDq295vpNFGo332Nupm5Nn2tW0bjfNYkKuG2gUPHG4JFJUmWdL4shiv7SVrXDeGuREPPWNq7ok+MLyh5oTIWWedpVegYVWfWtQhao6kThKCBcLnqaeeEjzd4I/fSPPYPI2GCEMnGNbAqmQE48nWiG80Pm1FjXENr2rStl6xDqGCVckoE1YUY8Uznh7RQGH1GsSeURZvrygPnqbRGwixivwQMKzjLT6uGeeNP2Cc88UH1xHaxj9y5qtzRprGefDE0zZ6AtBAo1HEOTzhoiFrGx9iHiup1ZxK3VNrMMUTOaY5/OpXv9KNOl5RLwh/NadHZ9U2HSNvrKaEfREMO+A9hm4wXI+HCvAygj/1Qg8wxDdEO/aDw7QDb3kbafKVBEjAOgLe/maRm6929NjSoD3AvsEIaHOMdge7UGB41QjGQyo+t21PjOtoB36pVjhjahCmQmE4GCNQ6MHD57///e962BY9ovjNMIK3OqAtRAcB2k6MgBnTofAbZgRv9x3bFvnbXvmTH9KGSEUdkTemFhnTz9BGo4cwmPoaLNv+RqJtxYMBVrFDrBvBqJ/xapxv+4qRsULVq4zfCPz+43fANUH9gDLYlIB6EtWLdbD4om3AIh0splE9Zm1PB/3eWNGMieWBBDW/0aM25/ao3stAbvOoYeGA7/GWQXt8vMX15xwWEqkhfZ9RMTle9dx6VEPiUVsTeYyVhPhsBDWkoblgFaY/AZPtVQPoKS4uPio6Fm1hkj4W9QQasKgKZTr2uxNoOoxPAiRgLYFg21FvpTJWPmNXkUCC2utYL8zBivJAflfQRmHRYkfbGX/bK3/yU6JWt9FY2IN6KcGHyfEeNZ+1FUmg9fX1G4nfY9Wx0pp2IG/QRhsLYAO5z85x9V40rlHIrEjABPDEhCc3PHVh+MJYzBJwQhFyA3pJMXyEJ308+WJuKXoRsA9poAGLlzB0hHmxaqWgnsAfaBqMTwIkQAIgoFYfC/amxGJIjFLgfSQGzO/EvpCYqoWeVKwpwFx2jKxhgVOggb+RgRGLDSw6Y7uNAAQR5vFhuJ2C8puti3mwmHIAEYlhf8yHDbbxhijFxHnMXcL8VAYSIAESCJYAhsKxKAjDvaecckqwyTj+PqxQx7Ay5qOvW7dOt9cYpjamGQVaQf5GBkaMPZWB8WJsEiABEiABEiABEiABLwS4UMcLFJ4iARIgARIgARIgARIIjABFZWC8GJsESIAESIAESIAESMALAYpKL1B4igRIgARIgARIgARIIDACFJWB8WJsEiABEiABEiABEiABLwQoKr1A4SkSIAESIAESIAESIIHACFBUBsaLsUmABEiABEiABEiABLwQoKj0AoWnSIAESIAESIAESIAEAiNAURkYL8YmARIgARIgARIgARLwQoCi0gsUniIBEiABEiABEiABEgiMAEVlYLwYmwRIgARIgARIgARIwAsBikovUHiKBEiABEiABEiABEggMAIUlYHxYmwSIAESIAESIAESIAEvBCgqvUDhKRIgARIgARIgARIggcAIUFQGxouxSYAESIAESIAESIAEvBCgqPQChadIgARIgARIgARIgAQCI0BRGRgvxiYBEiABEiABEiABEvBCgKLSCxSeIgESIAESIAESIAESCIwARWVgvBibBEiABEiABEiABEjACwGKSi9QeIoESIAESIAESIAESCAwAhSVgfFibBIgARIgARIgARIgAS8EKCq9QOEpEiABEiABEiABEiCBwAhQVAbGi7FJgARIgARIgARIgAS8EKCo9AKFp0iABEiABEiABEiABAIjQFEZGC/GJgESIAESIAESIAES8EKAotILFJ4iARIgARIgARIgARIIjABFZWC8GJsESIAESIAESIAESMALAYpKL1B4igRIgARIgARIgARIIDACjhWVNXWN8tisLwSvDCRAAiRAAvYhwPbZPrZgSUgglAQcKyo/Wl4qW3cekNnLdoSSF/MiARIgARL4BgJsn78BEC+TgEsJOFJU1tY3yRwlKhuaWmTuip2CzwwkQAIkQALhJ8D2Ofw2YAlIIFwEHCkq0Tvp8RxBhtcPl24PFz/mSwIkQAIk0IYA2+c2MPiWBCKMgONEpfEU3NjUrE2FV/ZWRti3ltUlARKwJQG2z7Y0CwtFAiEj4DhR2fYpODoqSoNib2XIvi/MiARIgATaJcD2uV00vEACEUHAUaIST8H/+wxD3R5JSoiVFqUm8YrPH3y6nXMrI+Iry0qSAAnYkQDbZztahWUigdAScJSo3FxaJS0tHjl9ci+596qJmhRe8Rlh044q/cp/SIAESIAEQkuA7XNoeTM3ErAjgSiPCnYsWHtlalQrvuNij2jh6x+dK3+6cbqO2vZ8e/fyPAmQAAmQgHUE2rbDbJ+t48yUScCuBDB27KhgCMpjC93e+WPj8TMJkAAJkIA1BNprh9s7b00prEu1qqZCqmr2ycG6/VJdV9V6HFTv6xtrpb6pVhqa66W5pUmNqjWriVkeiVL/RUfHSEx0rMTHJEhCbJIkxiVJWmLGUUd6YqZkJGero7N1FWDKJGAxAceJSot5MHkSIAESIIEIJwBRWFZVLHurd8qegzhKZa86EuNStOiDAIQozEzOkZ5ZfaVTUpbExyYqwZioBWNUVLTERMVoMQlx2exRAtPTInVaeNapPZbr5EBtZaso3avygFCFaK1rrJHc9B7SRR/dJTetu+RlFGpRGuFmYfUdQICi0gFGYhFJgARIgASsIwDht1OJyOJ9G6S4YoN6v1VylJjLTcvT4m5Qt9H6NSk+JeBCoJdSSUx9H4SnEbpnFhlvj3qtbTh8RMQqQVtWVSKf71gk5ep994xeUpjdXwo791fvC7VgPepGfiABGxCgqLSBEVgEEiABEiCB0BJoaKqXjXtWytpdS2VbxXrJUr2OBUqwjes1UwqyfiQJaog6HAHCVYtHJSCNgKH1kspNWvC+t/pfUllTLkXZA2RQtzHSr8sw1UuaYETlKwmElQBFZVjxM3MSIAESIIFQEWhsbjgiJMuWytbytZKf1UcG5Y2R04dfooetQ1WOQPOBwIV4xIGAYfT1u1fIytJF8t+VL0ivnEG6HrgeFxMfaPKMTwKmEaCoNA0lEyIBEiABErAjAcxVXFo8Tw0lL5Dumb1kQNeRcuqwiySY4Ww71A8LfUbkT9QHhsvX7/5cCczF8u6qF2W4Oj+2cLpe9GOHsrIMkUWAojKy7M3akgAJkEDEENi8d7UWk6X7t2ixdcWUO1wntiCMR/acpA9DPD/zyX3SI7O3jCmcJn1yh0SMvVnR8BOgqAy/DVgCEiABEiABEwlsLV8nH61/Ta+4HqN67c4ZfWVEDAtjS6ITBp0j0/qfJqt3fipz1r8ucze8KTMGnKWGyAeaSJhJkYB3AhSV3rnwLAmQAAmQgMMI7KjcInM2vK636pne/wwZrOZLRmLAvMqRPSfrY42aP/ru6hf1FkjH9z9TzSPtHYlIWOcQEaCoDBFoZkMCJEACJGANgYO1++W91f+W3Qe3y9R+p8qwHhMkWu0VySBaWA/sNkov6nltxTPSNb2nnDTkAklPyiQeEjCdAEWl6Ujtk2DFod0yf/O7apXjGsFkbsy96ZUzWCb3OVmyU7vap6AsCQmQAAkEQQAbimMBzryNb6mtgE7Qw9zwXOOEEMr2GQJ7RP4kGdp9nCzc8oE89fGvZVq/0/ScS2zUzkACZhFwxl+fWbWNoHT2VpfJc/PvlwF5x8nUAedKUlyq1DYekm1718gTc++Wq6fdrTf2jSAkrCoJkICLCMDTzdtqO51YJSIvmXSrox6Uw9U+Q3BP6XuKoOfyvyv/ISt3Llar4H+oNnbv7qJvBqsSTgJ8RAknfYvyxhPwk/PukVGFM6Vf11GSHJ8mUVFR+nVwj/EyrvfJWnAiHgMJkAAJOI3AZ9vmyD8WP6LnDP5wws8cJSjt0D5jpArcRvWcojmCJwMJmEGAotIMijZLA0PeA7odJz2UT1pvAefRg4l4DCRAAiTgFALwmf3y0ifkC7Xp9+WT71CiaLJ+YHZK+VFOu7TP6GjAYh5wBE9wBV8GEugIAYrKjtCz6b1b1RB3Ue5gn6WDsMRcSwYSIAEScAKBysPl8uz8ByQlIV0unXSb2m+ysxOK/bUy2q19BkfwBFfwBWcGEgiWAEVlsORsfF9to1qUo+ZQ+gp6jqVavMNAAiRAAnYnULp/q/xt4UMyrmimfGfo9yUmOsbuRW63fHZsn8ETXMEXnMGbgQSCIUBRGQw1m9+TFJeiF+X4KiYW7TjVRZmvevEaCZCAuwhsq1gvL332uJymFpSMKpji+MrZuX0GX3B+6bPHBNwZSCBQAhSVgRJzQPxeaui7tHKTz5JiFTi2F2IgARIgAbsS2H1gh7y67Cm5YOx10rfLULsWM6By2b19BuezRl6hFvA8KvsO7QmoboxMAhSVLvwOYB/K9WWftissITjX7/pU71fpwuqzSiRAAi4gsOvAdnn6k9/I2aOulO6ZRS6o0ZEqOKF9hkvH74/7qTz58a9kR+Vm17BnRawnQFFpPeOQ54DtIi6bfLss2fKurCldLDUN1coHrke/bty9XJYXz9b7VHID9JCbhhmSAAn4QQD7OL645A/ynWEXuc5ntVPa5145g+S8MdeoVeF/kb1qT1AGEvCHQJQSGx5/ItoxzvWPzpU/3TjdjkU7qkyh9JzQNuPWfNVQ95HJ4cqjjhoap0edtpT4ngRIwAoCwbbP8P71zCf3yrT+pyt3i+OtKNpRaba2kyH2PNaar83b51WlS5Q/9Tfkyil3ch7+Ud8cfvBGgKLSGxUTz7X1nIBtfNp6tsEQND3bmAibSZEACdiGQDCiEn0c/1j8e+mR2UuOH3CG5XVh++wf4jnrX5edVdvUkPgNjtsX1L8aMpZZBDj8bRZJL+ngSZSebbyA4SkSIAES8EJg4Zb3pbmlSfVSnublqrmn2D77zxO9xk3NTcpv+Pv+38SYEUmAotJCs9vFc4KFVWTSJEACJGAKAczbW7z1Qzln9FUSHWX9TxPbZ//NBnvALou3/o/zK/3HFpExrf/LjUisRyptN88JEWwKVp0ESMDGBDDs/dbK52XmwLMkLbFTSErK9jkwzLDLjAFnKzu9oBd+BnY3Y0cKgdhIqWg46mlHzwnh4MA8SYAESMAXgS9KF+reyRH5k3xFM/Ua2+fAcY7sOUlWbJ+vfYWPyJ8YeAIuuaN2716pLd8rdfsqpK6yUuor96n3R16bamqkqVYddXXiaWqSluZmkZYWkehoiY6JkajYWIlNTJTYpGSJTUmRhMwsSeycJQlZnSUx68hrcpcukpST60haFJUWms3wnJAcn9ZuLvRs0y4aXiABEogAAo3NDTJn/RvyveOuD2lt2T4Hh/uUoRfKvz79kwzOGyNxMfHBJeKQu1oaG+XAls1yaHuJVJfgKNZHXGqqFn2JnTtrMZjcpatkDhwsidnZSiwmHRGMyckSpYQkRCTEJMQlRKZHCcxW4VlbK3UVFUdEqRKnh1T6EKm1e3ZL4+HDklZQ+OVRIKk9C6RT7z4SHRdna3oUlRaax/Cc0K/rqHZzoWebdtHwAgmQQAQQWF7yiV7t3bVTfkhry/Y5ONywE1bnw27jes0MLhGb3gXhd2DzJqlcvUofBzZtlNT8nkrQ9dTiruvESVrcxae131HUXtUgLAWHChCeIp31e+nb78jrMf82VFd/KWaLtbDdOecjObRju3RS8bOGDNVHpz59tWA95tawfqSotBA/9oPEfmvoqcR2QscGw7PNNdPvOfYSP5MACZCA6wm0tDTLoq0fqF7Kn4S8rmyfg0c+td9pqrfyjzK2cLoa1T0ilIJPLbx3Ypi6fOlnsnvRAtm3aqWg1xGireDU0yRL9T7Gqh7HcAQI16zBQ/Rh5I8ezsp1a5TgXS3rnnlKalSPZuehw6TrhEmSM2asHlY34obrlaLSQvKG5wRsK3SgZp8UqY3HjX0qtaBUrhSxTyU921hoBCZNAiRgWwIb9nwhGcnZ0iW9e8jLyPY5eOSwF+wG+w3s1v5IXPA5WHtnc329lC9TQnLhAqn44nPJHDBI0As55NrrJU7Nc7RrgMDNHT1WHygjhsj3frpEyubNlTVPPi7Zw0foeuSoODEJCWGpBjc/DwF2p3hOCAEKZkECJBAhBPzZ/PzFJX+U4fkT1Py8sWGjwvY5OPRryj6TL3YskgvHhb6XObgSi2CBzfb335WdH82WTv36SZdx46XLceMFcySdHhoPHZI9ny6WPUsWy4GNG6X78TOk50mnSFJuaBf8sKcyBN8kPBGfOeLSEOTELEiABEjAGQTgjnHH/i1y7uirw1pgts/B4e/XZbi8/cULAjsmxdu3dw+1K1+xXHYoMVm1YYMWWxMefDjkYis4yv7fBWHcY8YJ+jDE86LbbpaM/v0l/8STJWdkaHqUKSr9txljkgAJkAAJmERg095V0jtnsMTHhmeYzqRqRGwyWPndO3eIwI6h8NEeDOh9K7+QjS/+QzxqAQ567YbfeHPYhoWDKX+w96B3sv9FF0uf8y6QXfM/kU2KweaX/iX9LvyBdB42PNhk/bqPotIvTIxEAiRAAiRgJoHiig1S2Nn7ylcz82Fa1hEo7NxfiivW205U7t+wXgup+v37pe8F31PzDCdbB8HGKWNeZY+ZqvdSHbsXzpe1anFPQmam9FXiMrP/AEtKTlFpCVYmSgIkQAIk4ItASeVGmdTnJF9ReM3mBIqyB+jV+3YpZt2+fbLu2afk4LZt0vu750v3adMl6sttfOxSxnCVA8K6y7gJslMt6ln5+0ckvahIBl5+ldp4/cutjUwqGEWlSSCZDAnYlYBeiLDpHdlSvkbqGmv0/KdeatgRW6pw5wG7Ws3d5WpoqpNDdQckKyXH3RV1ee1gv0N1B6Ve2TMhNjFstcWG4liAs+Xll/RWQBjmtvsm4Qaswzt3ytZZ/5GKz1fo1dxYfZ49YpT0OvtcSelu7q4IENg9ZsyUvClTZdubr8vCm2+U3uedLz3VnEts1G5GoKg0gyLTIAGbEthbXSbPzb9fBuYdJ9MHnieJcSkCL07YdP+JuXfrLa1y0/JsWnoWy60Eyqt3Sa7aliYqypwfMrdysnu9YL/c9DypUPbsnlkUluLC082avzymReS439xvuhCzslLYzHzx7bdK4eQpMvriSyQ+NU3q1abnu9TCovk3/FgmPfIHvfm62WWA4O59znel6/iJeiuiXR/Pk8E/uk5t8F7Q4az4F91hhEyABOxJAD2U2CN1VOFM6au8OiXFp6of8Si9Gf/gHuNlXO+TteBEPAYSCCWBg3X7JS0xI5RZMi+LCMCOsGc4Qsm7/5XPfvULNWfwWzL2nt84SlCih3LBTT+V/qecKvlqa6OEtHTdPiemp0uRGrYfdNY5WnAinlUBPaHg1uOEb2uO4NnRQFHZUYK8nwRsSmD+5ndlQLfjvHpzQpHh5WmA6sFEPAYSCCUBDH2nJ2aFMkvmZRGBTklZUl1XZVHq3pNtUj6zV/z2ASmbO0cmPPBbJYq+pQWZ99j2PLt11itSMGmy5A4c6LWAOF+oriOelQEdDVjIA47gCa7gG2ygqAyWHO8jAZsT2KqGuOHFyVeAsNyq5loykEAoCTQ2N0hsNGdfhZK5VXnFKDvCnqEKNbt3yeI7bpX4Thky7t4HJCkntJt7m1XPii9WSLdv2DsyZ9Bg2afihSKAI3iCK/iCczCBf9XBUAvzPVx4EWYDOCT72ka1KXGcb08R2m2o2ryYgQRCSQAiBPscujHo9ln1/uNhzdgY3M0L4+Ki46WpuTEkpqzauEH3pPU5/3uSr4ZsnRzgASdB+ff2FXC9QcULVYiOjZXBV/1Idnz4gSy56w4ZecvPJaNf/4CyZ09lQLjCHxkLL5755F61UitOL7w4c/R1MnXAuepJsUUvvMB1BhIAgaQvF+X4ooFFO3b3huGr/LzmTAIQlKHs3QoVJaN9lqhY3S5HQvvc2KJ6nWPiLEe8b9VKWfHg/TJELShxuqAELHjAwaIcX6HhUHVYfJGDLzgvf/A+AfdAAkVlILTCHJcLL8JsAIdl30sNfZdWbvJZaqwCRy8KAwmEkgBEZVNLUyiztDyvtu1zP7UwLjk+LSIWxjUrO1rd64x9J7945GEZ+fM7JGf0GMttGYoMsoePlPK1vqcela1YobYXGhmK4nwtD3Ae/tObZOmv7pbDZf4vFqKo/BpK+57gwgv72saOJcM+lOvLPm1XWEJwrt/1qd6v0o7lZ5ncSyA1sZNaMVzpqgpGavt8oLbS0pX8B7dukUW33qRcLP5MMvq6xwMT9qEsXjBf9q5b5/XvAOdLlIvFXmd/1+v1UJyES8fR/3e33s9y/3rv5Ty2HBSVxxKx8WcuvLCxcWxYNGxsftnk22XJlndlTeliqWmoFo/Ho1837l4uy4tn630quQG6DY3n8iKlJ2aGfMWw1UgjtX3Gym/Y04qAfRyX3vsrGXz1tZb7rLai/L7SxHY+4+9/SNa+9qpsmzdX6g4e1O0zXncsXiQb3nlb71Np9gbovsrk7Vr28BEy4pbb5PPfPijV20u8RTnqHBfqHIXD3h+48MLe9vmm0oVjgRU2Nr9m+j1626CP172iNj7H4p0UwdD4FVPupEedbzIar1tCICetm+w9uFP9iLaoIWJ39G1EYvsM+8GOsKfZoUHNN1z+wL0y4OJLJW/qdLOT/1p6ofRsY2Semt9TJv/+z3rboOUv/F2weCdezbXsrIbGJzz4O9vsu5kzcrT0V3aAPVCueB8LjCgqDes64NVYeIG5Ou0FLrxoj0x4z4fTsw16Is8ccWl4ATB3EmhDIF659MMQeOXhcumc2qXNFee+jcT2GfaDHWFPMwNGVL545LfSbcq0kAjKcHm2ATP0RA69/qdm4rMkrbyp0+TwzlL54tGHZcxdv2x3X1B3PCJagtB+iXLhhf1s4k+J2k7gp2cbf4gxTiQQKMjqJ9sq/Jun5QQekdg+b6tYLwVqr1uzw7Y3XpOWpibp893zzU76a+nZwbPN1wpl0xN9zrtAWhobBfZpL1BUtkfGhue58MKGRvGjSJE6gd8PNIwSwQQKs/tL8b6NriEQie1z8b4NUpg9wFQbYt5eyVtvyoibbpaomBhT0/aWmF0823grm93OwR6wS/Fbb7Q7v5Ki0m5W81EeLrzwAcfGlyJ1Ar+NTcKi2YBA39yhskVtEN7QVG+D0nS8CJHWPsNusB/saFbAsPeaJx6Tvt+/SBIyQ+PG026ebcxiaVU6sEs/ZR/YCfY6NnBO5bFELPhs5gINLrywwEAWJxmJE/gtRsrkXUAAm+7nZ/aWTXtXyuC8sWGrkW6fTfKAE0ntM+wG+5npPGHnnI+UY49o6TFjZsi+D3b0bBOyygeZUY8ZJ0jp7A+1r/Dux884KhWKyqNwmP/BigUaXHhhvp2sTNGfCfx1alV2YlyylcVg2iRgOwIje06WJdtmh01UGu3zgLzjtAcc7bZUeZmCU4An5t6tt9yCUAwkREr7/FnxXBlXZJ74a66vl03/+qeMvuOuQHB3OK7h2SYxPb3dtMLl2abdAtngwqArrpZl9/1auk6cJDEJCa0l4vB3Kwrz33CBhvlMnZiiXxP4y1dLb3q2caJ5WeYOEOjfZbhU1VTIHrUtTahD2/Y5kjzgmMEZ9oLdYD+zQqnyNw0/0+lFRWYl6Vc6dvds41clwhAJdoK9YLe2gaKyLQ2T33OBhslAHZqcPxP41ynPN5P7nuLQGrLYJBAcgejoGJnY+0SZt/HN4BLowF1sn4OH9/HGt2RCr28L7GdGaGlulm1vviFYXRzq4ATPNqFm4m9+sBfsBvsZgaLSIGHBKxdoWADVgUlG2gR+B5qIRQ4jAQyB79y/TXYf2BHSUrB9Dg437FS6f6uMKpgSXAJe7ipf+qkk5eZKWkGBl6vWnnKKZxtrKQSXOuwFu8F+RqCoNEhY8MoFGhZAdWiSxgT+uJhogWeb15c9pl/F06Q92wQ6b8uhGFhsEvgagbiYeDl+wBny7uoXv3bNyhNsn4Oj+86qF5W9zhTYzaxQ+uH/pOdJ4RupMTzbNMbECjzbfPzQA7JCvdY2NWsPMrjO4J0A7Ab7GYELdQwSFrz6s0CDHnAsAG/TJCNlAr9N8bNYNiYwvMdEWVbysXy+Y4GMyJ8UkpKyfQ4c84rtC8Sj/hveY0LgN7dzB1Zf79+wXob/7NZ2YoTmtFM824SGhv+55I4ZK2v+8ph2MYlFT+yp9J9dwDH9WqChVhn24gKNgNnyBhIgAfcQiIqKktOG/VBmr5sl1XUHQlIxts+BYYZdPlr/mrLTRe266AssxSOxy5ctlewRIyU20VxXj8GUhfcETgArv7NHjhLYEYGiMnCGft/hzwKN9bvUAo0+J/udJiOSAAmQgBsJ5KZ3l/G9viWvLntKWjwtlleR7bP/iGEP2GV8rxMEdjIzVK5ZJVmDhpiZJNMKMYGswUNk3+qVOleKSgvhc4GGhXCZNAmQgOsIYCV4THSszNvwluV1Y/vsP+J5G96UWDXfEPYxO1SuWSOdhw0zO1mmF0ICnYcOk/1r1+ocOafSYvDGAg1sX4EFGkcmh6cIhl6umHKnoGELdzDT40+468L8SYAEnEsAw+Dnjr5KnvnkXumc2kWG9RhvaWUc0z6b5PEnGJirSpfIqp2fypXq9wr2MTM01dZKfdV+Se7azcxkmVaICcB+sCPsSVEZAvh2XqBheJQYqDxKTB94nvLqkqKEb8c8SoQAKbMgARJwKQG4/Tt/7HXywuJHJTk+VfrkWjs06oT22UyPP4F8bVZsn6/mUb4uF42/0VR3jEYZDpXukLSeBdo1o3GOr84jANeasCPsyeFv59nPtBK39SjRt+so1Wik6ifR5Pg0Gax6CMb1Plmem3+/IB4DCZAACYSKAObtnTfmR/KfpX+RLeVHhtVClbdd8mnbPofD48+2inXy9soX5MJxPzF9HqXBuG7fPknIyjI+8tXBBGBH2JOi0sFG7GjR6VGiowR5PwmQgFUE8rP6yFVT75IXl/xBtkagsAxn+4zN6F9d9rQa8v4/6dbJuj0a6/dXSmLnbKu+Qkw3hAQSs3OkvrKSojKEzG2XFT1K2M4kLBAJkEAbAphX+QM19Praiudk055Vba64/2242mdw/vdnj8k5am5r1075loJuqW+Q6Lg4S/Ng4qEhEB0XK80N9ZxTGRrc9syFHiXsaRd/S8UFVv6SYjwnEyjKHqDmWF4rLy99Qqb3O91U94B25hKO9nl5yScyV/lhB+8emb0sxwMRgn0O3RgO79wpW2f9Ryo+XyGNhw9LXEqK2o9zlMDXODZad1uIiU+Qlgb1kOC2irE+/hMwPEr4uqOu8bBavJPsKwqvhYEAFlhhhWxUdJxeYHXm6Otk6oBzpbG5RZ6Ye7fgOgMJuIUABM4lE2+VJdtmC9wENrc0u6Vq7dbDn/bZLI9s4Amu4AvOoRCUqDiESHN9fbsMnHrh0I7tsui2n0mS6oUdffElMvXWn8uoH14icc1NMv+GHwuuuy3gASE6Pp6i0m2GDaQ+fnmUKF8tvenxJxCslsdtO4GfC6wsx80MbEIgKyVHLp/8czmkPLv8dcGDUlWzzyYls6YYfrXPJnhkA0fwBFfwBedQheiEeGlpbAxVdiHJBz2UC276qfQ/5VTJHzdeEtLS9QLYxPR0KZo2XQaddY4svv1WQTw3hZbGJv2QwJ5KN1k1wLr441FiXZny+NP3lABTZnQrCYRzAr+V9WLaJPBNBOJjE+W8sddo39PPzr9Plqstbzwezzfd5sjr/rTPHfHIBm7gB47w5Q2u4BvKkJCJFcMVoczS8ry2znpFCiZNltyBA73mhfOF6jriuSnUVZTrlfwUlW6yaoB1oUeJAIHZJHq4JvDbpPosBgnI2KLj1QKemwT7KD6/6Heu3PbMyvYZox3gBn7gCJ7hCImdO+sVw+HI26o8K75YId2UL2xfIWfQYNmn4rkpYOU37MnNz91k1SDq4gSPEkFUy9W3hGMCv6uBsnKOJNBF7WV52aRbZWnxPPnbgodknPJLPbH3t7WbR0dWyEuhzW6fm1uaZOGWD2TJ1g9lWr/TZEzhNDU0G76+pdQe+VK9vUQ8LS2u2QC98dAhNeSd5sWaX53C9QYVzy0B9oMdYU+KSrdYtQP1sLNHiQ5Uy7W3GhP4sUl9e4ELrNojw/NuIgBBhF62/l1HyHur/y2PzblLpiqxBPeO0WEUS2YyNqN9bvG0yMrSxfLxxreka3pPvf9nelKmmcUMKq3YpCRJyMiUmt27JCXPHSui41JTpb66WjCHsr3QcKharwZv77rTzsN+sCPsSVHpNOuxvBFPwJjADy8b7YVtXGDVHhqedyEBCCTMCdxRuUW5FXxNFm5+T6b1P10G541xYW0Dq9KasqUyb8ObkpKQLmeNvELys3oHloDFsbMGq6HglStdIyqzh4+U8rVrJH/8hHbJla1YobYXGtnudadd2LdqpWQOGqSLHb5+b6dRY3lJwCYE/JnAzwVWNjEWixFSAhBMF0+8WU4acoEsUsO8T3/8Gz1vsLG5IaTlCHdmqC/mS6L+4AAe4GI3QQlOWYOHSuXa1eFGZlr+2IeyeMF82btundc0cb5k/idqv8rver3uxJOVa1ZL5yHDdNHZU+lEC7LMEU3AmMD/5Lx75IDaDqQod7DaDy1VsGddaeUmWa9W7F897W5BPAYSiEQCvXIGCY7Ne1frOZez182S4fkT1RzC6ZKZ7F63gPtrKlR958oXOxaqvSZ7y/EDzpQ+uUNs/RXIGT1G1j77lDTV1UlsYmhXn1sBBhubj7//Ib2t0OG9k/WiHcyhxJA4ejAhOCc98gfXbIAOu2GD98FXXaNxUlRa8a1imiTghYCZHnDMnsDvpbg8RQKOJwBBhaNKi6158uwn90leRqEM7DZKBnQdKUnxKY6vY23DYVm/e4Ws27VcyqqKZUT+JLliyh2S4RDxjDmImf0HSPmypdJNbbUTrmCmB5zU/J4y+fd/1tsGLX/h74LFO/Gqnp3V0PiEB3/nGkEJW8FusB/siBCl9qpy7CZf1z86V/5043RdEf5DAnYmAA83z82/XwbmHSc9svopL0Upumdxm9q8GHvNoWcRQpGBBNxCwI7tc1Nzo2zcs1LW7loqW9TfXn5WHxmUN1oJzFHqbzLJMejrGmuVkFwua8uWqXmkm6W3Gq0Y1G2M9OsyTGJjnOdLe8+SRVL89lsy7tf3hcUG8HCDDckLJ0+RXDU3MD71SM/irhXLpeTLnkUIRYavE1hy1x1SeOpp0mXckTmk7Kn8OiOeIQFTCRgecMb1PlkJyr6taWP19mC1SrVTcmctOK+YcieHrFvp8A0JmE8AggsiEkdDU71s2qsEphJm7695WQ+LF3buL4XK13iB+jtNsJHIrFciskRNbSmuWC/F+zYIhrl7ZQ9UQ/oT5NzRV6tNy53tPztnzHGy7rlnpbqkRNIKCsw3vI8UDQ848HTTdsNywwNOSm4XLTjd1sPoA4nfl2Cv2r17BfYzAkWlQYKvJGARAX884NQ0VAvinTniUotKwWRJgATaEoAQG5w3Vh8tyvd12YFiJdo2qD0cZ8us5U9LTlp3PXqQq167pPfQRyiGyzGcvedgqT72VpfK3updUl69U7pn9JKi7P5yytALJa9ToURHx7StjqPfR8fESNEZZ8rml/8lI2/5eUjr4o8HnPoDVXooe+j1Pw1p2eye2eaX/y1Fp58hsJ8RKCoNEnwlAYsIwAPO1IHn+kwdPZgfr3eX2y6fFeZFErARAQg0LGzBAbe02CS8rKpECTqIu516vuKegzvUEHmynquYnpgpaYkZrUenpCzt4jBBuTlEDyf2yIyOilEbsceotJqlxYOjRdDjWN9Up3pJ6+RAbaVU11W1Hgfr9uu5n3WNNUrA5kuu2tw9L6NIzZGcrF4LXLWpuzfT95j5Ldn62iw5uG2bpBcVeYtiyTl4wBl10cU+04YHnBVqbiTDVwRgp6qNG2TYT2746qR6R1F5FA5+IAHzCdADjvlMmSIJWEkgJjpWb79z7BY8VWq3BSz6gQCEINxfUy7b1bD0QfUegrGhGYKxXppaGrWI9CghiQ3aITJjo+P0MHV8TKKev9lWlEJAQqhicU2Gmg4TiSEmIUH6fu/7su6ZJ2XcvQ+EDEEkesAxA+5aZad+F/5AYLe2gaKyLQ2+JwELCNADjgVQmSQJhIEABF+kir5Q4O5+/Awp/d/7UvrRbOkxY2YostSrliPNA05HwZZ+9KGIcs2YN/3rPuO5+XlH6fJ+EvgGAoYHHF/R6AHHFx1eIwESiAQCUVFRMvia62TjP5+X+v2VIamy4QHHV2Zu84Djq67fdA122fTPf2g7wV7HBorKY4nwMwmYTIAecEwGyuRIgARcSyCtZ4EUnnaGfP7Iw+Jpbra8npHoASdYqLAH7FJw2ukCO3kLFJXeqPAcCZhIwPCAs2TLu7KmdLFgpTe2h8XrRrXX3PLi2fSAYyJvJkUCJOBsAkVnnCXRsbGy+T8vWV4RwwPO2tdelW3z5krdwYO6fcbrjsWLZMM7b7vKA05HgGK1d3RcnFqpf1a7yXBOZbtoeIEEzCPgBA842uOP2tZoa/kawbYm2D6lV85gQU8rXT6a911gSiRAAr4JYFh1+E23yOKf3ywpeXmSN3W67xs6eNUJHnDM9PgTDK6yj+dJ2SfztEcgb8PeRpoUlQYJvpKAxQQgzOy6D6Xh8WeA8vgzdcC5rb7E4fHnibl30+OPxd8NJk8CJHA0gXjlL3vkbXfI0nvuljj1Pmfk6KMjmPwJPZZ23Yeyrcef0RdfcpTHn/k3/Fj3pFrp8ad09oey6cV/yJi77xHYxVfg8LcvOrxGAhFAwPD4M6pwpvRT7urg6QdPoobHH3gCgotJxGMgARIggVARwLy9EbfcJp8//JBgP8lIDIbHn/6nnCr548ZLQlq6bp8Njz/wBAQXk4hnRdi3aqWs+ctjMvrOu9qdR9k2X4rKtjT4ngQikIA/Hn/Qg4l4DCRAAiQQSgKZAwbKxN8+Ist+8yslLD8PZda2yMsfjz+FkyZrjz9mF7hq00b5Qi3MmfDQI5Leq7dfyVNU+oWJkUjAvQTg8acod7DPCsLjD+ZaMpAACZBAqAmk5HWXMb+4R1b98fdSvmxpqLMPa37ooe02cpTPMsDjzz6Te3LBecUD96m5rTcH5OGIotKnqXiRBNxPgB5/3G9j1pAEnE6g89Bhao7l7bJaDcXu+PADp1fH7/KHw+MP+IIzeIN7IIGiMhBajEsCLiRgePzxVbXaxkN6NbivOLxGAiRAAlYSyOjXX8b9+j4p+e9bsvbpJ6WlqcnK7GyRdlxqqsDjj6/QcKha4lJSfEXx6xp4giv4gjN4BxooKgMlxvgk4DICfnn8UUPk2F6IgQRIgATCSSC5azcZf99D2uPOkjt/LrXle8NZHMvzDpXHH3AET3jMAV9wDiZQVAZDjfeQgIsI+OPxZ/2uT/V+lS6qNqtCAiTgUAKxSUky8tbbte/pRT+/RUo//J/esNyh1fFZbKs9/sARB/iBI3x5gyv4Bhu4T2Ww5HgfCbiEgOHx58l598iBmn160U5SXKpgyLu0cpOsL/uUHn9cYmtWgwTcRKDg5O9I1qAhesubso/nyuCrrxXsN+mmYHj8WXDTT+Xw3sl60U6C2isSQ+Lla9dI8YL5QXv8wTZEa558XFoaG2XsL34laQXeXS8GwjNKqVRPIDfYKe71j86VP9043U5FYllIwLEEWj3qqKHuI4t3lEcdtSqcHnUca9KwFpztc1jxR1TmnpYW2f7+u7Ll5Zek4NTTpOj0M7U7QTdBOOJR5xW9XycW78SruZadh48U9GQGKqQhIre9+bqUvP2W9D7vfOl54skSFW3OwDV7Kt30rWNdSKADBOzs8acD1eKtJEACLicAQYReyy7HjZd1zz4ln/zkOulz3gXKveM0iYqJcUXtzfD442luFrhbhA/v9KIimfjwo5LYubOpfCgqTcXJxEiABEiABEiABMJBAAIJcwL3b1iv3Qpuff1V6Xv+96TrxMnhKI6t8ty9cL5seulfkpCRKcNuuEky+w+wpHwUlZZgZaIkQAIkQAIkQALhIADBdNw9v9EeeDb965+y9bVZ0vOkU6Tb5CkSk5AQjiKFJc/m+nrZNf8T2f7eO7rHduBlV0r28BGWloWi0lK8TJwESIAESIAESCAcBCCgcJSvWC471JzLjf94XrofP0Py1RzC5C5dwlGkkORZs2ePru/OOR9JRv/+0vfCH0jON3jlMatgFJVmkWQ6JEACJEACJEACtiMAQYWjdu9evaBnsdo+p1OfvtJl/ATpMm68YINxpwcs3tmzZLHsWbxIDmzeJN1nzJQJDz4sSbm5Ia0aRWVIcTMzEiABEiABEiCBcBCAwOp/0cXSR82zLF/2mexeuEDW/+1ZyRwwULpMmHREYJrgmSZUdWs8fPiIkFy0QPavXyfZI0YqMTlDRtxym8TEx4eqGEflQ1F5FA5+IAESIAESIAEScDMBCK6uSkTiaKqrUwJzaavAxLB41uChkjVUHQMHS2xysm1QNNXUSOW6NVK5apVUrlklGObuPHS42rR8hgz/2a0Sm5gY9rJSVIbdBCwACZAACZAACZBAOAhAiHWbpDYVV0eL2nLnoBo6rlyzWu/huPLR30lqfk9J7dlT0noWSGpBodogvFDi1ebjVocGtbl5dUmxHFJHdUmJHNqxXR+d+vZTQnKYDLriaklXQ/jRNtsyiaLS6m8G0ycBEiABEiABErA9AQi0DLVyHAc2Fccm4Qe2bjki7LaXyG41X7G6eJvEqSHypC5dJTErSxKyOn/1mp2tXRzCzWFscoreUBz7ZEbHxkpLU5Ngn0hs1N5Uc1iaamv1UVdRIfWV+6SusvLI6759au7nHsHQdlphkfZyg/mf3WeeIJ169bb9pu4Ulbb/mrOAJEACJEACJEACoSYQHRen93M8dk/H2vK9eujZEIM1e3ZL5dq1WhRiiLqprlbobkHdAAAhcklEQVSa1bA6RKkhJLFBuxaYKs0Y1Tsam6iEpxKnCZlZagPyI+IUbhITO2frxTVJOaFdYGMWW4pKs0gyHRIgARIgARIgAdcTgOBzquiz2jjmOHu0upRMnwRIgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEKCptbR4WjgRIgARIgARIgAScQYCi0hl2YilJgARIgARIgARIwNYEYm1dOhaOBEiABEiABEjAVAIN1cXSqI6mw6XSVLNTve6Uxi9fWxoOSEvjQXUcEk9LgzoaRf0jEhWt/o9TR7xEx6WqI12i4zMkNiVP4pK7q1d1fPkan9ZL4tIKTC0zE3MGAYpKZ9iJpSQBEiABEiCBgAh4mhuktvwzqd+/SuorV7Ye0fGZSvQVSlxKDy0E49J7S1K3qepz/pdiMV1i4jspIRnzpZCMVbqy6UuB2SzNEJ4NEJ4HpfHwDi1KIUyRRyMEavVWdb1KErKGfXVkDpWknLEqyfiA6sDIziJAUekse7G0JEACJEACJOCVAIRfbfmnUlM2R2p2zZHavYslIXPIkUMJvLSi8yRRvcYkZnm939fJqOhYJTCPSAb0VIrqmURIknFeb2uuq5S6L4VsXflSObDxb0rcrpak3PGS3O14Sc47XonM41rT9JoITzqOAEWl40zGApMACZAACZDAEQItjYfl0Pa35ODWl5WYnC3odYRoyxpygyR1nap6HNPDggrCNSVvuj6MAjSr3s3a3R/LYSV69yz8sTQe3KLE5UxJ73WepPY8TfWSphhR+epQAhSVDjUci00CJEACJBCZBFqaarWQrFZC8vDODySpy2RJU8Ks27S/Hhm2tikWCNzUnqfqA0XEMHp18WtyYNPzsnv+Varz89u6HlpgxibZtBYsli8CFJW+6PAaCZAACZAACdiEABbYVK19XKo2PqeHkdMKz5auU56WmIRMm5QwsGJg3mZGv0v00Vy//yuBueBade5SyRh0ncSruZ8MziFAUekcW7GkJEACJEACEUjg0I73pGrd41KzZ6EWW4VnLnWd2IIwzuh/mT4M8Vz8+hhJ7jJRMgZeK6n5J0Wg5Z1XZYpK59mMJSYBEiABEogAAod3fijln90hHk+TZKpeu7wZL0l0BAwLo3cyd9xDkj36Hjm45UUpX3qHVCz7heSMvU8NkZ8QAZZ3bhUpKp1rO5acBEiABEjAhQTQI1mx9E61PU+Z5Iz5tV7I4sJqfmOVIKAz+l+uDyxE2r3gOrXtUZ5kj7lX92B+YwKMEHICFJUhR84MSYAESIAESODrBLDHI1ZF1+1bIdkj75ZOfX+ottyJ+XrECDyDFeJphefoRT1lcy6UxM4jpcvEPyuReWRrowhEYssqU1Ta0iwsVFsCFYd2y/xN78iW8jVS11gjSfEp0itnsEzuc7Jkp3ZtG5XvSYAESMBxBDzKYw0W4JQv/6XaCuhG6a6GuZ2ySXh91Qap/PxeOVz6nlrNvV+tPs+UlB4nS9aIOyQho7+ptoDAzuh/qXTq833Zt/K3sm3WcMkZ9Uu1oOdaiVIefxjCT4BWCL8NWAIfBPZWl8kzn9yrvTpMH3ienDn6Opk64FxpbG6RJ+beLbjOQAIkQAJOJVBXuUpK3pwoB7f9RwpOW6B6KO90jqDcv0ZKXh8tCXFZUjDp79LvpIVSMPFvEitxsu2VAWqz8zWWmAWCG5zAC9xK3pygNlpfZUleTDQwAhSVgfFi7BASQA/lk/PukVGFM6Vv11GqhzJVPY1GSXJ8mgzuMV7G9T5Znpt/vyAeAwmQAAk4jcD+NX+WHe/MVL1vV0jP78w1vWfPSh7oodz26hDpOuwuyep1kcQl5urewrikLpLT/xrJG/WAlLwxThDPqoCeUHDLGHCV5gieDOElQFEZXv7M3QeB+ZvflQHdjpMeWX29xsL5AXnHCeIxkAAJkIBTCLQ0HpLS/52t5gf+XQrP+EyJoiv0A7NTyo9yVn5+n3Tuc7mkd/O+Ghvns9V1xLMyoKMBi3nAETzBFXwZwkOAojI83JmrHwS27l0jRbmDfcaEsNyq5loykAAJkIATCDQo14TFqgcvVvXoFZy+UOLSCpxQ7K+V8fDO9yWj59lfO9/2RFreicrjz/ttT1n2HhzBE1zBF5wZQk+AojL0zJmjnwRqlU/bpLhUn7FxvbbhsM84vEgCJEACdiBQu3exlLw1Wfvl7jr5CT1X3A7lCqYMzfX7JDYxx+etuI54oQpR0XECrvB7Ds7gzRBaAhSVoeXN3AIgkBSXIrXfMIxRp4RnYlxyAKkyKgmQAAmEnsDhso+k9IMzpNuUZ9Rw95WhL4DJOcYkdJamunKfqTbVV+jV4D4jWXARfMG59IPTBdwZQkeAojJ0rJlTgAR6qaHv0spNPu/aVr5aeqvthRhIgARIwK4E6vZ9Ljtnnyc9vv2mpPb8jl2LGVC5UrqfKNVlvoe2q0pmqe2FwuNeEZzzjn9RL+BpOLAxoLoxcvAEKCqDZ8c7LSaAfSjXl33arrCE4Fynrk/ue4rFJWHyJEACJBAcgbqK5VL82ki19+S/JSl3XHCJ2PAu7ENZsflZObjrQ6+lw/l9m59R+1Xe6fV6KE7CpWP+SR/o/Sxrdi8IRZYRnwdFZcR/BewLABubXzb5dlmy5V1ZU7pYahqqlQ9cj37duHu5LC+eLVdPu5sboNvXhCwZCUQ0AezTuOO9k9Q8v6dd57Ma2/kUnLFEypb/XMo3PCGNtXtU+9yiXyu3PC+7V/5ais5ZHfZtklJ6fEu6nzBLdn54ttRXro7o72MoKk+POqGg7JI8wuHZJjctT66Zfo/eNujjda+oOZZYvKM86qih8Sum3ElB6ZLvFqtBAm4j0FxXKaXvnya54x5R7hZ/YHn1jni2uU+vtsbiGMx5xBC1FZ5tjMokZA6WonPX622DShZdphflGPkWnLks7ILSKGdq/smSO/4RNcfyNClU5YpJzDIu8dVkAhSVJgN1a3LwXIONxgeqfSHh2Sbxy0U029S2P/Bsgx5DCEArAnoszxxxqRVJM00SIAESMJ0ARlR2fnSepCt3giERlPBso7bRwb6Q2ROe06uysYimavss7dlG9xgqAWhFQI9lt+l/tyJpU9OEa8eGqnXKLudL/skfOG5fUFNhWJgYh78thOuWpOnZxi2WZD1IgARCQaBy5UPiaa6XbOWX2upwlGeb3j8UeLSBH+xQeraxuo5mpZ896h5llzqBfRisIUBRaQ1XV6VKzzauMicrQwIkYCEBzNurXPWIdJ/5stqHMsbCnI4kbRfPNpZX1IQMYA/YpXLV7zi/0gSe3pKgqPRGheeOIkDPNkfh4AcSIAES8EoAw967PrlccsY+ILHJ3bzGMfuk3TzbmF0/s9ODXWCfXZ9coRd+mp1+pKdHURnp3wA/6k/PNn5AYhQSIIGIJ3Bg49/U0HOs8kUdujngdvRsY/cvQkb/y0R1I2tf4XYvq9PKR1HpNIuFobz0bBMG6MySBEjAUQRammqkfOmd0mXSYyEtt1+ebdSiHcRj+IpA10mPS/lndwjsxmAeAYpK81i6NiV6tnGtaVkxEiABkwhUrX9akrpMkMTOI0xK0b9k/PJso1aBIx7DVwRgJ9gLdmMwjwBFpXksXZsSPdu41rSsGAmQgAkEPC1NakXxwyFZ7X1scf3zbPOs3q/y2Hsj/TNW58NusB+DOQQoKs3h6OpU6NnG1eZl5UiABDpI4FDJmxKXViSJWUM7mFLgtzvFs03gNbP+DtgLdoP9GMwhwM3PzeHo+lTo2cb1JmYFSYAEgiRQteFpyRx0XZB3d/w2p3i26XhNzU8BdoP90orONj/xCEyRojICjR5slenZJlhyvI8ESMCtBJrr90vtnoVq/8NXwlpFp3i2CSskL5mnFpwmu+dfqVxM7leLmTK9xOCpQAhw+DsQWoxLAiRAAiRAAm0IHNr+tqT0OFGiletaBucRiI5NVvY7SWBHho4ToKjsOEOmQAIkQAIkEKEEanbNkeRu0yO09u6odnK346Wm7CN3VCbMtaCoDLMBmD0JkAAJkIBzCdTsmqe26znBuRVgyZX9ZkrN7o9JwgQCFJUmQGQSJEACJEACkUegpfGQNNXskrj0PpFXeRfVGPZrqtktzQ3VLqpVeKpCURke7syVBEiABEjA4QTq96+RBLUtTZRy+cfgXAKwX0LWEGmoWuvcStik5PxLsIkhWAwSIAESIAFnEWg8XCqxyd2dVWiW1isB2BH2ZOgYAYrKjvHj3SRAAiRAAhFKoBlD36n5EVp7d1U7LrWnGgIvc1elwlAbisowQGeWJEACJEACzifQ0lQjUdEJzq8IayBRMQniUfZk6BgBbn7eMX68uwMEKg7tlvmb35Wt5WuktuGwJMWnSK+cwQJf49honYEESIAE7ExAi0q1z6EbQ33VBqn8/D45vPN9tTH4PrUPZ4ak5p+sfIjfKdho3W0hKiZJWppq3VatkNeHPZUhR84MQWBvdZk888m9IlGxMnXAuXLm6Ov0a2Nzizwx9259naRIgARIwM4EsHG2G3u3sACp5PXRkhCbIQUTnpN+Jy2UwsnPS6wkyLZXBgiuuy14mmslOjbJbdUKeX0oKkOOnBmih/LJeffIqMKZ0q/rKEmOT1OrJ6P06+Ae42Vc75Plufn3C+IxkAAJkIBdCWhR2VJv1+IFVS70UG57dYh0HXaXZPX+ocQlddGr2+MScyWn/48kb9QDUvLGOEE8NwVPc73q43Bnr3Mo7URRGUrazEsTwJD3gG7HSY+svl6J4PyAvOP00LjXCDxJAiRAAjYgEJPcTRoP7bBBScwrAoa8O/e5XNK7ed/QHeez1XXEc1NoPLRdreTPc1OVwlIXisqwYI/sTLfuXSNFuYN9QoCwxFxLBhIgARKwK4G4lB5qxfBOuxYvqHJhDmVGz7N93puWd6Kea+kzksMuwo6wJ0PHCFBUdowf7w6CQG2jWpQTl+rzTlzH4h0GEiABErArgYTMwVJfuUo8nha7FjHgcmFRTmxijs/7cB3x3BJgP9gR9mToGAGKyo7x491BEEiKS5Fa5d7MV6hTwjMxjvNbfDHiNRIggfASiFYPv7EYAj+4ObwFMTH3mITO0lRX7jPFpvoKiYnP9BnHSRdhP9gR9mToGAGKyo7x491BEOilhr5LKzf5vHNb+WrprbYXYiABEiABOxNI7jZNDQV/aOciBlS2lO4nSnXZ+z7vqSqZJSk9TvIZx0kXD++cLcldpzqpyLYtK0WlbU3j3oJhH8r1ZZ+2KywhONep65P7nuJeCKwZCZCAKwgkdzteanbNdUVdUImsEXdIxeZn5eAu70IZ5/dtfkbvV+mWStfsmiPJeTPcUp2w1oOiMqz4IzNzbGx+2eTbZcmWd2VN6WKpaahWc5I8+nXj7uWyvHi2XD3tbm6AHplfD9aaBBxFILXnqXK49H1pUVN23BCwsXnBGUukbPnPpXzDE9JYu0fPGcVr5ZbnZffKX0vROatdswE67Ab7wY4MHSdAjzodZxgxKZjpASc3LU+umX6P3jbo43WvqDmWWLyjPOqoofErptxJQRkx3ypWlAScTSAmIVOSukyUQ9vflvTe54etMsd6wMHcSAxlo+cxUA84WLBSdO56vW1QyaLL9KIcI72CM5cFnF7YoPiRMewG+8GODB0nQFHZcYYRkQI84GBDcuwfCQ84enW2WmyzTW0PBA846FmEUAwkoMfyzBGXBnIL45IACZCA7Qhk9L9SKlf/PmyiUnvAURuSY//IbOUBB6uzsdimavss7QFH9ywGuLIZQrTb9L/bjrXZBdq/9jHJGnKD2clGbHoc/o5Y0/tfcXrA8Z8VY5IACUQegdSC06WxepvUqW1pQh3a9YCjPOHk9L/GtR5wzOAMe8FusB+DOQQoKs3h6OpU6AHH1eZl5UiABDpIICo6VrKG3SIVy+7uYEqB3x6pHnACJ/X1OyqW/1LZ7WaB/RjMIUBRaQ5HV6dCDziuNi8rRwIkYAKBjAFXSO3exVK373MTUvM/iUj1gOM/Ie8xYafaPYskY8CV3iPwbFAEKCqDwhZZN9EDTmTZm7UlARIInEB0bLLkjLlX9iy4LvCbO3BHJHrA6QCu1lt3L7hWcsbeJ7Abg3kEKCrNY+nalOgBx7WmZcVIgARMJNCp3yVq+50mqdrwVxNT9Z2UXx5w1KIdxGM4QqBqw3MiyjVjp74XE4nJBCgqTQbqxuToAceNVmWdSIAEzCYQFRUl3aY8K+Wf3SZNNbvMTt5ren55wFGrwBGPQbRdyj+7XdnpGYG9GMwlQFFpLk9XpkYPOK40KytFAiRgAYGErCGSNfRnsnP2eaozrNmCHI5O0j8POM/q/SqPvjPyPsEesEvW0JsEdmIwnwBFpflMXZciPeC4zqSsEAmQgIUEsobdKlExCYLVxVaHSPOA0xGeFcvvVnZJVCu+b+1IMrzXBwGuo/cBh5e+IuAEDzhmevz5quZ8RwIkQAKBEcCwavcZL0vx62MkvlN/NXfvB4ElEGBsJ3jAOdbjT3RchqTmn6x9iAfq8SdAPDr6gc3/lIPqKFQegTjsHQxB/+6hqPSPE2MpAnb2gGOFxx8anQRIgASCJRCTmCU9vv2mbH9npsQkdtYCKti0/LnPzh5wvHr8qa+QqpLgPf74w8SIU7UB81zvkJ6nzFa2yDJO89UCAhz+tgAqkwwtAXr8CS1v5kYCJOAfAczb637CLNn54TlyuPQD/25yWax2Pf4k5iqPPz+y3OPP4Z2zZfcnV0j+Se9yHmUIvlsUlSGAzCysJUCPP9byZeokQALBE0juOkmKzv5cdrx3ohKW/ws+IYfeGU6PP7V7l8jOj86XwrNWSGL2KIcSdFaxKSqdZS+W1gsBevzxAoWnSIAEbEMgvlM/yVdDr2VzfyCHtv/XNuUKRUHC5fEHnEs/OF26z3xZEjuPCEVVmYciQFHJr4HjCdDjj+NNyAqQgOsJpOTNUHMs35Bdaii2av3Trq+vUcFwePwBX3AGb3BnCB0BisrQsWZOFhGgxx+LwDJZEiABUwkk5Y6XgtPmS+Xq3wvcBHpaGk1N346J+eXxRy3aiYnP7HDxwRNcwRecwZshtAQoKkPLm7lZQIAefyyAyiRJgAQsIRCf3lsKz1iiPbuUvDlRGqtLLMnHLon65fFHrQJP6XFSh4oMjuAJT0bgC84MoSdAURl65szRZAL0+GMyUCZHAiRgKYHouFTp8a3XtO/p4jfGquHwZ5TPcI+leYYrcf88/jyj96sMpozgBn7gCF/e4Aq+DOEhwH0qw8OduZpIwPD48+S8e+RAzT4pyh0sSapRqW08JKWVm2R92ady9bS79T6bJmbLpEiABEigQwQyB/9YkrpNU1veXCkHNr8gXSc/JaHYCLxDhQ7wZsPjz7ZXh0h9n8slo+fZEpuYI0115VJd9r5UbH5Wis5ZHVS9sV3R7vlXiae5Xi+ESswaGmDpGN1sAuypNJso0wsLAcPjT1xMtHy87hV5fdlj+lU8TXLFlDsF1xlIgARIwG4EIIQKTl8o6UXflZK3JknFinuVSGqwWzE7VB7D40+TNErJostk43sT9Wt9U5UUKA83uB5IAB9wAi9wAz8KykAIWheXPZXWsWXKISZgZ48/IUbB7EiABBxEICoqWtBrmVp4luxZ+GPZ8p9+kj3ql9Kpz0USFR3joJq0X1QzPP54Wpp1jy58qid2Hqn2//xC4lK6t58pr4ScAEVlyJEzQxIgARIgARL4OgEIJMwJrNmzULsVrPziAcke/StJ73Xe1yNH2JmDW1+WimW/kJikrpJ3/IuS3GVihBFwRnUpKp1hJ5aSBEiABEggQghAMBWcOld74ClfeqfsU+Iyc9B1kt77QomOTYoQCiItTbVycMuLsn/tYxIVFStdJvxJrRL/VsTU34kVpah0otVYZhIgARIgAdcTgIDCcWjHe1K17nHZ++ltktHvUskYdK3EpxW5tv4N1dukau3jUrXxr7pHMmfMfZKa37Eth1wLy2YVo6i0mUFYHBIgARIgARJoSwCCCkdDdbEWW8Wvj5WknOMkregcSSs8W2ISOr5xeNv8wvG+uX6/VBfPkuptr0pt+adKPF8mhWcuVeK5MBzFYZ5BEqCoDBIcbyMBEiABEiCBUBKAwMod95CeZ3lo+1tSreYZ7l18oyR1maQE5nnqUAIzvlMoi9ShvJobDigRCSH5stTuWSDYKL2T6ontfsIsNcyf2KG0eXN4CFBUhoc7cyUBEiABEiCBoAhAcKX3+q4+WhoPy6Htb8tBJcz2Lr5B4tJ6SXLe8drndVLXqUpkpgeVhxU3NTcclNrdH8vhso+kpmyO8ia0VZK7n6A2Lb9Eus98RW1anmJFtkwzhAQoKkMIm1mRAAmQAAmQgJkEIMTSe5+vD09Lk9SVfyaHd83R/q9rP7pA7QE55Mih9sNMyBqm9nMcLjGJWWYWwWtazXWVUlf5hdRXrjxy7F8j9ftXa3/cyXkzpOukxyUxZ6zaMokyxCtAh56kNR1qOBabBEiABEiABNoSgEBL6jJBHzLiDr2Jem3F0i+F3So1zPyKev+FRMdn6B5NbGEUm6wOvKojLiVf9Ramq+tp6rWTFnxIMyo6Tjwtjepo0kdL4wFpaaiWlsaD0nh4hzQd3nnkqNmpPpeqHsht6nqVErHDjwhZNf+zU//LJSl7jETFxLctMt+7jABFpcsMyuqQAAmQAAmQAAhAwGF7omP3dGysLlGLfrZ+JQQPbpEaNSwNcdii5jm2NEIwHlICsl6LSKVOkdiXIjNB+9aOjlPCU4nT2JQ8iftSmKInNDalh16ZHpdWQCNEIAGKygg0OqtMAiRAAiQQuQQg+Cj6Itf+Vtacvr+tpMu0SYAESIAESIAESCBCCFBURoihWU0SIAESIAESIAESsJIARaWVdJk2CZAACZAACZAACUQIAYrKCDE0q0kCJEACJEACJEACVhKgqLSSLtMmARIgARIgARIggQghQFEZIYZmNUmABEiABEiABEjASgIUlVbSZdokQAIkQAIkQAIkECEEKCojxNCsJgmQAAmQAAmQAAlYSYCi0kq6TJsESIAESIAESIAEIoQARWWEGJrVJAESIAESIAESIAErCVBUWkmXaZMACZAACZAACZBAhBCgqIwQQ7OaJEACJEACJEACJGAlAYpKK+kybRIgARIgARIgARKIEAIUlRFiaFaTBEiABEiABEiABKwkQFFpJV2mTQIRTGDl5gqprW+KYAKsOgmQAAnYk4BV7XOsPavLUpEACTiZQGNTizz91mqJjo6SE8bkq6OnJCWwuXGyTVl2EiABdxCwsn1mT6U7viOsBQnYikBcbLScM72PxChROWf5Tvm/pxfJWwu2sufSVlZiYUiABCKRgJXtM0VlJH6jWGcSCAGBSUPzJDYmWhqbmqWhsfkocVlT1xiCEjALEiABEiABbwSsap+jPCp4yxDn3llULO8uLsZbBhIgARIIikB0VJS0tGlm4lUvZq/uneS6s4cHlR5vOkKA7TO/CSRAAh0lYHb77FNUdrSwVt9//aNz5U83Trc6G6ZPAiQQBAHM27nzqYWtQ95xsTGi9KUcP6qHzFBHcmJcEKnyFqcQYPvsFEuxnJFIwKr2mTPnI/HbxDqTQAgILFhVJk3NLdJWTM4cnc8FOyFgzyxIgARIwBcBq9pnikpf1HmNBEggKAJ4Cn517mZ974njCoRiMiiMvIkESIAETCdgZftMUWm6uZggCZAAVhdedfoQ6dMjgz2T/DqQAAmQgI0IWNk+U1TayNAsCgm4icDQ3tluqg7rQgIkQAKuIWBV+8wthVzzFWFFSIAESIAESIAESCB8BCgqw8eeOZMACZAACZAACZCAawhQVLrGlKwICZAACZAACZAACYSPAEVl+NgzZxIgARIgARIgARJwDQGKSteYkhUhARIgARIgARIggfARoKgMH3vmTAIkQAIkQAIkQAKuIUBR6RpTsiIkQAIkQAIkQAIkED4CFJXhY8+cSYAESIAESIAESMA1BCgqXWNKVoQESIAESIAESIAEwkeAojJ87JkzCZAACZAACZAACbiGAEWla0zJipAACZAACZAACZBA+AhQVIaPPXMmARIgARIgARIgAdcQoKh0jSlZERIgARIgARIgARIIHwGKyvCxZ84kQAIkQAIkQAIk4BoCFJWuMSUrQgIkQAIkQAIkQALhI0BRGT72zJkESIAESIAESIAEXEOAotI1pmRFSIAESIAESIAESCB8BCgqw8eeOZMACZAACZAACZCAawhQVLrGlKwICZAACZAACZAACYSPAEVl+NgzZxIgARIgARIgARJwDQGKSteYkhUhARIgARIgARIggfARoKgMH3vmTAIkQAIkQAIkQAKuIUBR6RpTsiIkQAIkQAIkQAIkED4CFJXhY8+cSYAESIAESIAESMA1BCgqXWNKVoQESIAESIAESIAEwkeAojJ87JkzCZAACZAACZAACbiGAEWla0zJipAACZAACZAACZBA+AhQVIaPPXMmARIgARIgARIgAdcQoKh0jSlZERIgARIgARIgARIIHwGKyvCxZ84kQAIkQAIkQAIk4BoCFJWuMSUrQgIkQAIkQAIkQALhI0BRGT72zJkESIAESIAESIAEXEOAotI1pmRFSIAESIAESIAESCB8BCgqw8eeOZMACZAACZAACZCAawg4WlSePL7QNYZgRUiABEjATQTYPrvJmqwLCfhHIMqjgn9RGYsESIAESIAESIAESIAEvBNwdE+l9yrxLAmQAAmQAAmQAAmQQKgJUFSGmjjzIwESIAESIAESIAEXEqCodKFRWSUSIAESIAESIAESCDUBispQE2d+JEACJEACJEACJOBCAhSVLjQqq0QCJEACJEACJEACoSZAURlq4syPBEiABEiABEiABFxIgKLShUZllUiABEiABEiABEgg1AT+Hz8JfMoE6SI2AAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"execution_count": 77,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"Image(\"algorithm.png\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5813bd1a",
|
|
"metadata": {},
|
|
"source": [
|
|
" Sama analiza skupień nie jest jednym konkretnym algorytmem, lecz ogólnym zadaniem do rozwiązania. Można je zrealizować za pomocą różnych algorytmów (algorytm $k-średnich$, algorytm $k-medoid$), które różnią się znacznie w rozumieniu tego, czym jest klaster i jak skutecznie je znaleźć. Popularne pojęcia klastrów obejmują grupy o małych odległościach między elementami klastra. Klastrowanie można zatem sformułować jako wieloprzedmiotowy problem optymalizacyjny. Wybór odpowiedniego algorytmu grupowania i ustawień parametrów zależy od indywidualnego zbioru danych i przeznaczenia wyników. Analiza skupień jako taka nie jest zadaniem automatycznym, lecz iteracyjnym procesem odkrywania wiedzy lub interaktywnej optymalizacji wieloprzedmiotowej, który wymaga prób i błędów. Często konieczne jest modyfikowanie wstępnego przetwarzania danych i parametrów modelu, aż do uzyskania pożądanych właściwości."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3dc57d21",
|
|
"metadata": {
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
},
|
|
"source": [
|
|
" W naszym projekcie przedstawimy metodę $k-medoid$ i porównamy ją z metodą $k-średnich$."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2e28109b",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Algorytm k-średnich"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "01b24fd7",
|
|
"metadata": {},
|
|
"source": [
|
|
" W uczeniu maszynowym występują głównie dwa rodzaje algorytmów uczenia. Są to algorytm uczenia nadzorowanego i algorytm uczenia nienadzorowanego. \n",
|
|
"Klastrowanie metodą $k-średnich$ jest algorytmem uczenia nienadzorowanego, który grupuje nieoznakowane zbiory danych w różne klastry lub grupy. W algorytmie $k-średnich$ $k$ określa liczbę wstępnie zdefiniowanych klastrów lub grup, które należy utworzyć w danym zbiorze danych. Na przykład jeśli $k = 3$, powstaną trzy klastry, dla $k = 5$ będzie pięć klastrów itd."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7f4d7284",
|
|
"metadata": {},
|
|
"source": [
|
|
" Algorytm $k-średnich$ pomaga grupować dane w różne klastry w sposób konwencjonalny, umożliwiając samodzielne odkrywanie kategorii grup w nieoznakowanym zbiorze danych bez konieczności uczenia. Algorytm klasteryzacji $k-średnich$ jest algorytmem opartym na centroidach, w którym każdy klaster jest powiązany z centroidem. Głównym celem tego algorytmu jest zminimalizowanie sumy odległości wewnętrznych punktów danych w odpowiadających im klastrach. Algorytm przyjmuje jako dane wejściowe nieuporządkowany zbiór danych i dzieli go na $k$ grup, a następnie powtarza ten proces aż do uzyskania najlepszych klastrów. Wartość $k$ jest zawsze określona z góry."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "22642017",
|
|
"metadata": {},
|
|
"source": [
|
|
" Główne zadanie algorytmu $k-średnich$ jest następujące:\n",
|
|
"\n",
|
|
"  $1.$ Wybierz liczbę klastrów $k$. \n",
|
|
"  $2.$ Wybierz losowo $k$ punktów z zestawu danych jako centroidy. \n",
|
|
"  $3.$ Przypisz każdy punkt danych do najbliższego mu centroida. \n",
|
|
"  $4.$ Oblicz wariancję i umieść nowy centroid dla każdego klastra. \n",
|
|
"  $5.$ Powtórz $krok 3$, czyli ponownie przypisz każdy punkt danych do nowego najbliższego centroidu."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "15d683c2",
|
|
"metadata": {},
|
|
"source": [
|
|
" **Rozwiązanie**: Implementacja algorytmu $k-średnich$ przy użyciu Pythona. Do wykonania algorytmu $k-średnich$ potrzebne jest wstępne przetwarzanie danych. W naszym rozwiązaniu przeprowadziliśmy wstępne przetwarzanie danych w celu zaimplementowania algorytmu. Dodatkowo oceniliśmy jaka jest jakość naszego grupowania. Posłużyliśmy się tzw. sylwetką (ang. silhouette) $s(x_i)$ obliczaną dla każdego obiektu $x_i$. Najpierw dla $x_i$ znajduje się jego średnią odległość $a(x_i)$ od pozostałych obiektów grupy, do której został przydzielony, a następnie wybiera się minimalną wartość $b(x_i)$ spośród obliczonych odległości od $x_i$ do każdej spośród pozostałych grup osobno. Odległość $x_i$ od danej grupy oblicza się jako średnią odległość od $x_i$ do wszystkich elementów tej grupy. Obie wielkości zestawia się we wzorze: "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e276f546",
|
|
"metadata": {},
|
|
"source": [
|
|
"<h1><center>$s(x_i) = \\frac{b(x_i)-a(x_i)}{max(a(x_i),b(x_i))}$</center></h1>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9af1d9d2",
|
|
"metadata": {},
|
|
"source": [
|
|
"otrzymując wartość sylwetki dla danego obiektu $x_i$. Jej zbiór wartości to $[-1, 1]$. Zatem ma ona prostą interpretację: obiekty, dla których wskaźnik jest bliski $1$, zostały trafnie zgrupowane, pozostałe (o wartości ok. $0$ i $ujemnej$) prawdopodobnie trafiły do złych grup."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 78,
|
|
"id": "409d4187",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"class TrainModel_means:\n",
|
|
" def __init__(self, data, k_value, max_iteration):\n",
|
|
" self.data = data\n",
|
|
" scaler = MinMaxScaler()\n",
|
|
" self.data = scaler.fit_transform(self.data)\n",
|
|
" self.k_value = k_value\n",
|
|
" self.max_iteration = max_iteration\n",
|
|
" self.centroids = []\n",
|
|
" self.final_clusters, self.silhouette = self.data_cluster()\n",
|
|
"\n",
|
|
" def generate_column(self, col, data):\n",
|
|
" values = []\n",
|
|
" for i in range(len(data)):\n",
|
|
" values.append(data[i][col])\n",
|
|
" return values\n",
|
|
"\n",
|
|
" def calculateDistance(self, x, y):\n",
|
|
" return np.linalg.norm(x-y)\n",
|
|
"\n",
|
|
" def get_closest_centroid(self, points, centroids):\n",
|
|
" closest_centroids = []\n",
|
|
" for i in points:\n",
|
|
" distance = []\n",
|
|
" for c in centroids:\n",
|
|
" dis = self.calculateDistance(i, c)\n",
|
|
" distance.append(dis)\n",
|
|
" closest_centroids.append(np.argmin(distance))\n",
|
|
" return closest_centroids\n",
|
|
"\n",
|
|
" def calculate_new_centroids(self, clusters, X):\n",
|
|
" new_centroids = []\n",
|
|
" new_df = pd.concat([pd.DataFrame(X), pd.DataFrame(clusters, columns=['cluster'])], axis=1)\n",
|
|
" for c in set(new_df['cluster']):\n",
|
|
" current_cluster = new_df[new_df['cluster'] == c][new_df.columns[:-1]]\n",
|
|
" cluster_mean = current_cluster.mean(axis=0)\n",
|
|
" new_centroids.append(cluster_mean)\n",
|
|
" return new_centroids\n",
|
|
"\n",
|
|
" def get_clustered_data(self, points, centroids):\n",
|
|
" closest_centroids = self.get_closest_centroid(points, centroids)\n",
|
|
" clustered_data = {}\n",
|
|
" for i in range(self.k_value):\n",
|
|
" clustered_data[i] = []\n",
|
|
" for i in range(len(points)):\n",
|
|
" clustered_data[closest_centroids[i]].append(points[i])\n",
|
|
" return clustered_data\n",
|
|
"\n",
|
|
" def get_clusters_label(self, data_points, clusters):\n",
|
|
" labels = []\n",
|
|
" for i in range(len(data_points)):\n",
|
|
" labels.append(0)\n",
|
|
" for i in clusters.keys():\n",
|
|
" cluster = clusters[i]\n",
|
|
" for j in range(len(cluster)):\n",
|
|
" for k in range(len(data_points)):\n",
|
|
" if (cluster[j] == data_points[k]).all():\n",
|
|
" labels[k] = i\n",
|
|
" break\n",
|
|
" return labels\n",
|
|
"\n",
|
|
" def data_cluster(self):\n",
|
|
" centroid_points = random.sample(range(0, len(self.data)), self.k_value)\n",
|
|
" for i in centroid_points:\n",
|
|
" self.centroids.append(self.data[i])\n",
|
|
" for i in range(self.max_iteration):\n",
|
|
" closest_centroids = self.get_closest_centroid(self.data, self.centroids)\n",
|
|
" self.centroids = self.calculate_new_centroids(closest_centroids, np.array(self.data))\n",
|
|
" final_clusters = self.get_clustered_data(self.data, self.centroids)\n",
|
|
" cluster_labels = self.get_clusters_label(self.data, final_clusters)\n",
|
|
" silhouette_avg = silhouette_score(self.data, cluster_labels)\n",
|
|
" print(\"Sylwetka (ang.silhouette) dla metody k-medoid i dla k =\", self.k_value, round(silhouette_avg,2))\n",
|
|
" \n",
|
|
" return final_clusters, round(silhouette_avg,2)\n",
|
|
" \n",
|
|
" def return_values(self):\n",
|
|
" return self.centroids, self.final_clusters, self.silhouette"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 79,
|
|
"id": "b42a9194",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.44\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# iris\n",
|
|
"dataset = pd.read_csv('iris.csv')\n",
|
|
"dataset = dataset.iloc[: , 1:-1]\n",
|
|
"dataset = dataset.values\n",
|
|
"model = TrainModel_means(dataset, 3, 10)\n",
|
|
"centroids, final_clusters, silhouette = model.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 80,
|
|
"id": "15541704",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.34\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# glass\n",
|
|
"dataset2= pd.read_csv('glass.csv')\n",
|
|
"dataset2 = dataset2.iloc[:,:-1]\n",
|
|
"dataset2 = dataset2.values\n",
|
|
"model2 = TrainModel_means(dataset2, 4, 10)\n",
|
|
"centroids2, final_clusters2, silhouette2 = model2.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 81,
|
|
"id": "c29dca2b",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.3\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# wine\n",
|
|
"dataset3= pd.read_csv('wine.csv')\n",
|
|
"dataset3 = dataset3.values\n",
|
|
"model3 = TrainModel_means(dataset3, 3, 10)\n",
|
|
"centroids3, final_clusters3, silhouette3 = model3.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f7c684c9",
|
|
"metadata": {
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
},
|
|
"source": [
|
|
"### Algorytm k-medoid"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "af45d7c7",
|
|
"metadata": {
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
},
|
|
"source": [
|
|
"$1.$ Inicjalizacja: wybierz $k$ losowych punktów spośród $n$ punktów danych jako medoidy. \n",
|
|
"$2.$ Przyporządkuj każdy punkt danych do najbliższego medoidu, używając dowolnych popularnych metod metryki odległości. \n",
|
|
"$3.$ Podczas gdy koszt maleje: \n",
|
|
"  Dla każdej medoidy $m$, dla każdego punktu danych $o$, który nie jest medoidą: \n",
|
|
"    $i.$ Zamień punkty $m$ i $o$, przyporządkuj każdy punkt danych do najbliższej medoidy, ponownie oblicz koszt. \n",
|
|
"    $ii.$ Jeśli całkowity koszt jest większy niż w poprzednim kroku, cofnij zamianę."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d8f6dd1e",
|
|
"metadata": {
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
},
|
|
"source": [
|
|
" **Rozwiązanie**: Implementacja algorytmu k-medoid w Pythonie. Do wykonania algorytmu k-medoidy potrzebne jest wstępne przetworzenie danych. W naszym rozwiązaniu przeprowadziliśmy wstępne przetwarzanie danych w celu zaimplementowania algorytmu k-medoid. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 102,
|
|
"id": "73cffc81",
|
|
"metadata": {
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"class TrainModel_medoids:\n",
|
|
" def __init__(self, data, k_value):\n",
|
|
" self.data = data\n",
|
|
" scaler = MinMaxScaler()\n",
|
|
" self.data = scaler.fit_transform(self.data)\n",
|
|
" self.k_value = k_value\n",
|
|
" self.medoids, self.res_cluster, self.cluster_labels, self.silhouette = self.kmedoids(self.data)\n",
|
|
"\n",
|
|
" def get_random_medoids(self, data):\n",
|
|
" points = random.sample(range(0, len(data)), self.k_value)\n",
|
|
" medoids = []\n",
|
|
" for i in points:\n",
|
|
" medoids.append(data[i])\n",
|
|
" return medoids\n",
|
|
"\n",
|
|
" def get_closest_medoids(self, sample_point, medoids):\n",
|
|
" min_distance = float('inf')\n",
|
|
" closest_medoid = None\n",
|
|
" for i in range(len(medoids)):\n",
|
|
" distance = self.calculateDistance(sample_point, medoids[i])\n",
|
|
" if distance < min_distance:\n",
|
|
" min_distance = distance\n",
|
|
" closest_medoid = i\n",
|
|
" return closest_medoid\n",
|
|
"\n",
|
|
" def get_clusters(self, data_points, medoids):\n",
|
|
" clusters = [[] for _ in range(self.k_value)]\n",
|
|
" for i in range(len(data_points)):\n",
|
|
" x = self.get_closest_medoids(data_points[i], medoids)\n",
|
|
" clusters[x].append(data_points[i])\n",
|
|
" return clusters\n",
|
|
"\n",
|
|
" def calculate_cost(self, data_points, clusters, medoids):\n",
|
|
" cost = 0\n",
|
|
" for i in range(len(clusters)):\n",
|
|
" for j in range(len(clusters[i])):\n",
|
|
" cost += self.calculateDistance(medoids[i], clusters[i][j])\n",
|
|
" return cost\n",
|
|
"\n",
|
|
" def get_non_medoids(self, data_points, medoids):\n",
|
|
" non_medoids = []\n",
|
|
" for sample in data_points:\n",
|
|
" flag = False\n",
|
|
" for m in medoids:\n",
|
|
" if (sample == m).all():\n",
|
|
" flag = True\n",
|
|
" if flag == False:\n",
|
|
" non_medoids.append(sample)\n",
|
|
" return non_medoids\n",
|
|
"\n",
|
|
" def get_clusters_label(self, data_points, clusters):\n",
|
|
" labels = []\n",
|
|
" for i in range(len(data_points)):\n",
|
|
" labels.append(0)\n",
|
|
" for i in range(len(clusters)):\n",
|
|
" cluster = clusters[i]\n",
|
|
" for j in range(len(cluster)):\n",
|
|
" for k in range(len(data_points)):\n",
|
|
" if (cluster[j] == data_points[k]).all():\n",
|
|
" labels[k] = i\n",
|
|
" break\n",
|
|
" return labels\n",
|
|
" \n",
|
|
" def plot_results(self, clusters, medoids):\n",
|
|
" colors = ['b', 'g', 'r', 'c', 'm', 'k']\n",
|
|
" X = []\n",
|
|
" Y = []\n",
|
|
" plt.figure(figsize=(10,8))\n",
|
|
" for i in range(self.k_value):\n",
|
|
" X.append(np.squeeze(clusters[i])[:, 0])\n",
|
|
" Y.append(np.squeeze(clusters[i])[:, 1])\n",
|
|
"\n",
|
|
" for i in range(len(X)):\n",
|
|
" plt.scatter(X[i], Y[i], c=colors[i])\n",
|
|
"\n",
|
|
" mx = []\n",
|
|
" my = []\n",
|
|
"\n",
|
|
" for m in medoids:\n",
|
|
" mx.append(m[0])\n",
|
|
" my.append(m[1])\n",
|
|
"\n",
|
|
" plt.scatter(mx, my, c='yellow', marker='*')\n",
|
|
" plt.xlabel(\"X\")\n",
|
|
" plt.ylabel(\"Y\")\n",
|
|
" plt.title(f\"K-medoids. Number of clusters: {self.k_value}\")\n",
|
|
" plt.show()\n",
|
|
" time.sleep(4)\n",
|
|
"\n",
|
|
" def kmedoids(self, data):\n",
|
|
" medoids = self.get_random_medoids(data)\n",
|
|
" clusters = self.get_clusters(data, medoids)\n",
|
|
" initial_cost = self.calculate_cost(data, clusters, medoids)\n",
|
|
" while True:\n",
|
|
" best_medoids = medoids\n",
|
|
" lowest_cost = initial_cost\n",
|
|
" for i in range(len(medoids)):\n",
|
|
" non_medoids = self.get_non_medoids(data, medoids)\n",
|
|
" for j in range(len(non_medoids)):\n",
|
|
" new_medoids = medoids.copy()\n",
|
|
" for k in range(len(new_medoids)):\n",
|
|
" if (new_medoids[k] == medoids[i]).all():\n",
|
|
" new_medoids[k] = non_medoids[j]\n",
|
|
" new_clusters = self.get_clusters(data, new_medoids)\n",
|
|
" new_cost = self.calculate_cost(data, new_clusters, new_medoids)\n",
|
|
" if new_cost < lowest_cost:\n",
|
|
" lowest_cost = new_cost\n",
|
|
" best_medoids = new_medoids\n",
|
|
" if lowest_cost < initial_cost:\n",
|
|
" initial_cost = lowest_cost\n",
|
|
" medoids = best_medoids\n",
|
|
" else:\n",
|
|
" break\n",
|
|
" final_clusters = self.get_clusters(data, medoids)\n",
|
|
" cluster_labels = self.get_clusters_label(data, final_clusters)\n",
|
|
" silhouette_avg = silhouette_score(data, cluster_labels)\n",
|
|
" self.plot_results(final_clusters, medoids)\n",
|
|
"\n",
|
|
" print(\"Sylwetka (ang.silhouette) dla metody k-medoid i dla k =\", self.k_value, round(silhouette_avg,2))\n",
|
|
"\n",
|
|
" res_cluster = []\n",
|
|
" for i in range(0, self.k_value):\n",
|
|
" res_cluster.append([data[s] for s in range(0, len(data)) if cluster_labels[s] == i])\n",
|
|
" return medoids, res_cluster, cluster_labels, round(silhouette_avg,2)\n",
|
|
"\n",
|
|
" def calculateDistance(self, x, y):\n",
|
|
" return np.linalg.norm(x-y)\n",
|
|
"\n",
|
|
" def return_values(self):\n",
|
|
" return self.medoids, self.res_cluster, self.cluster_labels, self.silhouette"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c5290f06",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Uruchomienie algorytmu k-medoid dla zbioru danych iris"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 84,
|
|
"id": "4c1f8423",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.48\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
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"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
|
" }\n",
|
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"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: center;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Długość kielicha</th>\n",
|
|
" <th>Szerokość kielicha</th>\n",
|
|
" <th>Długość płatka</th>\n",
|
|
" <th>Szerokość płatka</th>\n",
|
|
" <th>Wartość medoidu 0</th>\n",
|
|
" <th>Wartość medoidu 1</th>\n",
|
|
" <th>Wartość medoidu 2</th>\n",
|
|
" <th>Medoid</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>0.72</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.66</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.51</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.49</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>0.44</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.51</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.44</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>21</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.49</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>22</th>\n",
|
|
" <td>0.44</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>23</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.51</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>24</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.66</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>26</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>27</th>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>28</th>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>29</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>30</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>31</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>32</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>33</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.49</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>34</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>35</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>36</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>37</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>38</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>39</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>40</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.51</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>41</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>42</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>43</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.51</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>44</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>45</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>46</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>47</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>48</th>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>49</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.34</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>51</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>52</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.59</td>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>53</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.68</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>54</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.68</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>55</th>\n",
|
|
" <td>0.36</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.66</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>56</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.66</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>57</th>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>58</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.66</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>59</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>60</th>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>61</th>\n",
|
|
" <td>0.47</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.64</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>62</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>63</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.68</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>64</th>\n",
|
|
" <td>0.44</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>65</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>66</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>67</th>\n",
|
|
" <td>0.11</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>68</th>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>69</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>70</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>71</th>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>72</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>73</th>\n",
|
|
" <td>0.03</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>74</th>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>76</th>\n",
|
|
" <td>0.14</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>77</th>\n",
|
|
" <td>0.14</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>78</th>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.02</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>79</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>0.03</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>80</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>81</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>82</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>83</th>\n",
|
|
" <td>0.39</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>84</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>85</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>86</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>87</th>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>88</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.17</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>89</th>\n",
|
|
" <td>0.14</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.15</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>90</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>91</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>92</th>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>93</th>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>94</th>\n",
|
|
" <td>0.11</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>95</th>\n",
|
|
" <td>0.14</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>96</th>\n",
|
|
" <td>0.31</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>97</th>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>0.87</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>98</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>99</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.03</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>100</th>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>101</th>\n",
|
|
" <td>0.03</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>102</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>103</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>104</th>\n",
|
|
" <td>0.06</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>105</th>\n",
|
|
" <td>0.03</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>106</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.62</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>107</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.15</td>\n",
|
|
" <td>0.12</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>108</th>\n",
|
|
" <td>0.14</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>109</th>\n",
|
|
" <td>0.22</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.10</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>110</th>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>111</th>\n",
|
|
" <td>0.28</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>112</th>\n",
|
|
" <td>0.19</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.07</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>113</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.68</td>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>114</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.85</td>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>115</th>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>116</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>117</th>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.88</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>118</th>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.95</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>119</th>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>0.38</td>\n",
|
|
" <td>0.90</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>120</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.21</td>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>121</th>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.86</td>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>122</th>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>123</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.29</td>\n",
|
|
" <td>0.73</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>124</th>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.76</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>125</th>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.96</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>126</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.73</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>127</th>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.76</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>128</th>\n",
|
|
" <td>0.94</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.97</td>\n",
|
|
" <td>0.88</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>129</th>\n",
|
|
" <td>0.94</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>130</th>\n",
|
|
" <td>0.72</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>131</th>\n",
|
|
" <td>0.94</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.97</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>132</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>133</th>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.85</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>134</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>135</th>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.81</td>\n",
|
|
" <td>0.63</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>136</th>\n",
|
|
" <td>0.86</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.86</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>137</th>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>138</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.33</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.88</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>139</th>\n",
|
|
" <td>0.94</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.86</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>140</th>\n",
|
|
" <td>0.56</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.96</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>141</th>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.76</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>142</th>\n",
|
|
" <td>0.72</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>143</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.78</td>\n",
|
|
" <td>0.96</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>144</th>\n",
|
|
" <td>0.72</td>\n",
|
|
" <td>0.46</td>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>145</th>\n",
|
|
" <td>0.69</td>\n",
|
|
" <td>0.50</td>\n",
|
|
" <td>0.83</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>146</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.54</td>\n",
|
|
" <td>0.80</td>\n",
|
|
" <td>1.00</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>147</th>\n",
|
|
" <td>0.67</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>148</th>\n",
|
|
" <td>0.61</td>\n",
|
|
" <td>0.42</td>\n",
|
|
" <td>0.71</td>\n",
|
|
" <td>0.79</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>149</th>\n",
|
|
" <td>0.53</td>\n",
|
|
" <td>0.58</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.92</td>\n",
|
|
" <td>(0.47, 0.38, 0.59, 0.58)</td>\n",
|
|
" <td>(0.19, 0.58, 0.08, 0.04)</td>\n",
|
|
" <td>(0.69, 0.42, 0.76, 0.83)</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Długość kielicha \\\n",
|
|
"0 0.17 \n",
|
|
"1 0.17 \n",
|
|
"2 0.75 \n",
|
|
"3 0.58 \n",
|
|
"4 0.72 \n",
|
|
"5 0.33 \n",
|
|
"6 0.61 \n",
|
|
"7 0.39 \n",
|
|
"8 0.56 \n",
|
|
"9 0.17 \n",
|
|
"10 0.64 \n",
|
|
"11 0.25 \n",
|
|
"12 0.19 \n",
|
|
"13 0.44 \n",
|
|
"14 0.47 \n",
|
|
"15 0.50 \n",
|
|
"16 0.36 \n",
|
|
"17 0.67 \n",
|
|
"18 0.36 \n",
|
|
"19 0.42 \n",
|
|
"20 0.53 \n",
|
|
"21 0.36 \n",
|
|
"22 0.44 \n",
|
|
"23 0.50 \n",
|
|
"24 0.56 \n",
|
|
"25 0.50 \n",
|
|
"26 0.58 \n",
|
|
"27 0.64 \n",
|
|
"28 0.69 \n",
|
|
"29 0.47 \n",
|
|
"30 0.39 \n",
|
|
"31 0.33 \n",
|
|
"32 0.33 \n",
|
|
"33 0.42 \n",
|
|
"34 0.47 \n",
|
|
"35 0.31 \n",
|
|
"36 0.47 \n",
|
|
"37 0.67 \n",
|
|
"38 0.56 \n",
|
|
"39 0.36 \n",
|
|
"40 0.33 \n",
|
|
"41 0.33 \n",
|
|
"42 0.50 \n",
|
|
"43 0.42 \n",
|
|
"44 0.19 \n",
|
|
"45 0.36 \n",
|
|
"46 0.39 \n",
|
|
"47 0.39 \n",
|
|
"48 0.53 \n",
|
|
"49 0.22 \n",
|
|
"50 0.39 \n",
|
|
"51 0.42 \n",
|
|
"52 0.17 \n",
|
|
"53 0.39 \n",
|
|
"54 0.47 \n",
|
|
"55 0.36 \n",
|
|
"56 0.56 \n",
|
|
"57 0.53 \n",
|
|
"58 0.50 \n",
|
|
"59 0.56 \n",
|
|
"60 0.50 \n",
|
|
"61 0.47 \n",
|
|
"62 0.42 \n",
|
|
"63 0.56 \n",
|
|
"64 0.44 \n",
|
|
"65 0.22 \n",
|
|
"66 0.17 \n",
|
|
"67 0.11 \n",
|
|
"68 0.08 \n",
|
|
"69 0.19 \n",
|
|
"70 0.31 \n",
|
|
"71 0.08 \n",
|
|
"72 0.19 \n",
|
|
"73 0.03 \n",
|
|
"74 0.17 \n",
|
|
"75 0.31 \n",
|
|
"76 0.14 \n",
|
|
"77 0.14 \n",
|
|
"78 0.00 \n",
|
|
"79 0.42 \n",
|
|
"80 0.39 \n",
|
|
"81 0.31 \n",
|
|
"82 0.22 \n",
|
|
"83 0.39 \n",
|
|
"84 0.22 \n",
|
|
"85 0.31 \n",
|
|
"86 0.22 \n",
|
|
"87 0.08 \n",
|
|
"88 0.22 \n",
|
|
"89 0.14 \n",
|
|
"90 0.19 \n",
|
|
"91 0.19 \n",
|
|
"92 0.25 \n",
|
|
"93 0.25 \n",
|
|
"94 0.11 \n",
|
|
"95 0.14 \n",
|
|
"96 0.31 \n",
|
|
"97 0.25 \n",
|
|
"98 0.33 \n",
|
|
"99 0.19 \n",
|
|
"100 0.33 \n",
|
|
"101 0.03 \n",
|
|
"102 0.22 \n",
|
|
"103 0.19 \n",
|
|
"104 0.06 \n",
|
|
"105 0.03 \n",
|
|
"106 0.19 \n",
|
|
"107 0.22 \n",
|
|
"108 0.14 \n",
|
|
"109 0.22 \n",
|
|
"110 0.08 \n",
|
|
"111 0.28 \n",
|
|
"112 0.19 \n",
|
|
"113 0.67 \n",
|
|
"114 0.56 \n",
|
|
"115 0.78 \n",
|
|
"116 0.56 \n",
|
|
"117 0.61 \n",
|
|
"118 0.92 \n",
|
|
"119 0.83 \n",
|
|
"120 0.67 \n",
|
|
"121 0.81 \n",
|
|
"122 0.61 \n",
|
|
"123 0.58 \n",
|
|
"124 0.69 \n",
|
|
"125 0.42 \n",
|
|
"126 0.58 \n",
|
|
"127 0.61 \n",
|
|
"128 0.94 \n",
|
|
"129 0.94 \n",
|
|
"130 0.72 \n",
|
|
"131 0.94 \n",
|
|
"132 0.67 \n",
|
|
"133 0.81 \n",
|
|
"134 0.58 \n",
|
|
"135 0.81 \n",
|
|
"136 0.86 \n",
|
|
"137 1.00 \n",
|
|
"138 0.58 \n",
|
|
"139 0.94 \n",
|
|
"140 0.56 \n",
|
|
"141 0.58 \n",
|
|
"142 0.72 \n",
|
|
"143 0.67 \n",
|
|
"144 0.72 \n",
|
|
"145 0.69 \n",
|
|
"146 0.67 \n",
|
|
"147 0.67 \n",
|
|
"148 0.61 \n",
|
|
"149 0.53 \n",
|
|
"\n",
|
|
" Szerokość kielicha \\\n",
|
|
"0 0.46 \n",
|
|
"1 0.46 \n",
|
|
"2 0.50 \n",
|
|
"3 0.50 \n",
|
|
"4 0.46 \n",
|
|
"5 0.12 \n",
|
|
"6 0.33 \n",
|
|
"7 0.33 \n",
|
|
"8 0.54 \n",
|
|
"9 0.17 \n",
|
|
"10 0.38 \n",
|
|
"11 0.29 \n",
|
|
"12 0.00 \n",
|
|
"13 0.42 \n",
|
|
"14 0.08 \n",
|
|
"15 0.38 \n",
|
|
"16 0.38 \n",
|
|
"17 0.46 \n",
|
|
"18 0.42 \n",
|
|
"19 0.29 \n",
|
|
"20 0.08 \n",
|
|
"21 0.21 \n",
|
|
"22 0.50 \n",
|
|
"23 0.33 \n",
|
|
"24 0.21 \n",
|
|
"25 0.33 \n",
|
|
"26 0.38 \n",
|
|
"27 0.42 \n",
|
|
"28 0.33 \n",
|
|
"29 0.38 \n",
|
|
"30 0.25 \n",
|
|
"31 0.17 \n",
|
|
"32 0.17 \n",
|
|
"33 0.29 \n",
|
|
"34 0.29 \n",
|
|
"35 0.42 \n",
|
|
"36 0.58 \n",
|
|
"37 0.46 \n",
|
|
"38 0.12 \n",
|
|
"39 0.42 \n",
|
|
"40 0.21 \n",
|
|
"41 0.25 \n",
|
|
"42 0.42 \n",
|
|
"43 0.25 \n",
|
|
"44 0.12 \n",
|
|
"45 0.29 \n",
|
|
"46 0.42 \n",
|
|
"47 0.38 \n",
|
|
"48 0.38 \n",
|
|
"49 0.21 \n",
|
|
"50 0.33 \n",
|
|
"51 0.29 \n",
|
|
"52 0.21 \n",
|
|
"53 0.21 \n",
|
|
"54 0.08 \n",
|
|
"55 0.33 \n",
|
|
"56 0.29 \n",
|
|
"57 0.33 \n",
|
|
"58 0.42 \n",
|
|
"59 0.33 \n",
|
|
"60 0.25 \n",
|
|
"61 0.42 \n",
|
|
"62 0.29 \n",
|
|
"63 0.21 \n",
|
|
"64 0.42 \n",
|
|
"65 0.62 \n",
|
|
"66 0.42 \n",
|
|
"67 0.50 \n",
|
|
"68 0.46 \n",
|
|
"69 0.67 \n",
|
|
"70 0.79 \n",
|
|
"71 0.58 \n",
|
|
"72 0.58 \n",
|
|
"73 0.38 \n",
|
|
"74 0.46 \n",
|
|
"75 0.71 \n",
|
|
"76 0.58 \n",
|
|
"77 0.42 \n",
|
|
"78 0.42 \n",
|
|
"79 0.83 \n",
|
|
"80 1.00 \n",
|
|
"81 0.79 \n",
|
|
"82 0.62 \n",
|
|
"83 0.75 \n",
|
|
"84 0.75 \n",
|
|
"85 0.58 \n",
|
|
"86 0.71 \n",
|
|
"87 0.67 \n",
|
|
"88 0.54 \n",
|
|
"89 0.58 \n",
|
|
"90 0.42 \n",
|
|
"91 0.58 \n",
|
|
"92 0.62 \n",
|
|
"93 0.58 \n",
|
|
"94 0.50 \n",
|
|
"95 0.46 \n",
|
|
"96 0.58 \n",
|
|
"97 0.87 \n",
|
|
"98 0.92 \n",
|
|
"99 0.50 \n",
|
|
"100 0.62 \n",
|
|
"101 0.42 \n",
|
|
"102 0.58 \n",
|
|
"103 0.62 \n",
|
|
"104 0.12 \n",
|
|
"105 0.50 \n",
|
|
"106 0.62 \n",
|
|
"107 0.75 \n",
|
|
"108 0.42 \n",
|
|
"109 0.75 \n",
|
|
"110 0.50 \n",
|
|
"111 0.71 \n",
|
|
"112 0.54 \n",
|
|
"113 0.42 \n",
|
|
"114 0.54 \n",
|
|
"115 0.42 \n",
|
|
"116 0.38 \n",
|
|
"117 0.42 \n",
|
|
"118 0.42 \n",
|
|
"119 0.38 \n",
|
|
"120 0.21 \n",
|
|
"121 0.67 \n",
|
|
"122 0.50 \n",
|
|
"123 0.29 \n",
|
|
"124 0.42 \n",
|
|
"125 0.33 \n",
|
|
"126 0.50 \n",
|
|
"127 0.42 \n",
|
|
"128 0.75 \n",
|
|
"129 0.25 \n",
|
|
"130 0.50 \n",
|
|
"131 0.33 \n",
|
|
"132 0.54 \n",
|
|
"133 0.50 \n",
|
|
"134 0.33 \n",
|
|
"135 0.42 \n",
|
|
"136 0.33 \n",
|
|
"137 0.75 \n",
|
|
"138 0.33 \n",
|
|
"139 0.42 \n",
|
|
"140 0.58 \n",
|
|
"141 0.46 \n",
|
|
"142 0.46 \n",
|
|
"143 0.46 \n",
|
|
"144 0.46 \n",
|
|
"145 0.50 \n",
|
|
"146 0.54 \n",
|
|
"147 0.42 \n",
|
|
"148 0.42 \n",
|
|
"149 0.58 \n",
|
|
"\n",
|
|
" Długość płatka \\\n",
|
|
"0 0.08 \n",
|
|
"1 0.08 \n",
|
|
"2 0.63 \n",
|
|
"3 0.59 \n",
|
|
"4 0.66 \n",
|
|
"5 0.51 \n",
|
|
"6 0.61 \n",
|
|
"7 0.59 \n",
|
|
"8 0.63 \n",
|
|
"9 0.39 \n",
|
|
"10 0.61 \n",
|
|
"11 0.49 \n",
|
|
"12 0.42 \n",
|
|
"13 0.54 \n",
|
|
"14 0.51 \n",
|
|
"15 0.63 \n",
|
|
"16 0.44 \n",
|
|
"17 0.58 \n",
|
|
"18 0.59 \n",
|
|
"19 0.53 \n",
|
|
"20 0.59 \n",
|
|
"21 0.49 \n",
|
|
"22 0.64 \n",
|
|
"23 0.51 \n",
|
|
"24 0.66 \n",
|
|
"25 0.63 \n",
|
|
"26 0.56 \n",
|
|
"27 0.58 \n",
|
|
"28 0.64 \n",
|
|
"29 0.59 \n",
|
|
"30 0.42 \n",
|
|
"31 0.47 \n",
|
|
"32 0.46 \n",
|
|
"33 0.49 \n",
|
|
"34 0.69 \n",
|
|
"35 0.59 \n",
|
|
"36 0.59 \n",
|
|
"37 0.63 \n",
|
|
"38 0.58 \n",
|
|
"39 0.53 \n",
|
|
"40 0.51 \n",
|
|
"41 0.58 \n",
|
|
"42 0.61 \n",
|
|
"43 0.51 \n",
|
|
"44 0.39 \n",
|
|
"45 0.54 \n",
|
|
"46 0.54 \n",
|
|
"47 0.54 \n",
|
|
"48 0.56 \n",
|
|
"49 0.34 \n",
|
|
"50 0.53 \n",
|
|
"51 0.69 \n",
|
|
"52 0.59 \n",
|
|
"53 0.68 \n",
|
|
"54 0.68 \n",
|
|
"55 0.66 \n",
|
|
"56 0.66 \n",
|
|
"57 0.64 \n",
|
|
"58 0.66 \n",
|
|
"59 0.69 \n",
|
|
"60 0.78 \n",
|
|
"61 0.64 \n",
|
|
"62 0.69 \n",
|
|
"63 0.68 \n",
|
|
"64 0.69 \n",
|
|
"65 0.07 \n",
|
|
"66 0.07 \n",
|
|
"67 0.05 \n",
|
|
"68 0.08 \n",
|
|
"69 0.07 \n",
|
|
"70 0.12 \n",
|
|
"71 0.07 \n",
|
|
"72 0.08 \n",
|
|
"73 0.07 \n",
|
|
"74 0.08 \n",
|
|
"75 0.08 \n",
|
|
"76 0.10 \n",
|
|
"77 0.07 \n",
|
|
"78 0.02 \n",
|
|
"79 0.03 \n",
|
|
"80 0.08 \n",
|
|
"81 0.05 \n",
|
|
"82 0.07 \n",
|
|
"83 0.12 \n",
|
|
"84 0.08 \n",
|
|
"85 0.12 \n",
|
|
"86 0.08 \n",
|
|
"87 0.00 \n",
|
|
"88 0.12 \n",
|
|
"89 0.15 \n",
|
|
"90 0.10 \n",
|
|
"91 0.10 \n",
|
|
"92 0.08 \n",
|
|
"93 0.07 \n",
|
|
"94 0.10 \n",
|
|
"95 0.10 \n",
|
|
"96 0.08 \n",
|
|
"97 0.08 \n",
|
|
"98 0.07 \n",
|
|
"99 0.03 \n",
|
|
"100 0.05 \n",
|
|
"101 0.05 \n",
|
|
"102 0.08 \n",
|
|
"103 0.05 \n",
|
|
"104 0.05 \n",
|
|
"105 0.05 \n",
|
|
"106 0.10 \n",
|
|
"107 0.15 \n",
|
|
"108 0.07 \n",
|
|
"109 0.10 \n",
|
|
"110 0.07 \n",
|
|
"111 0.08 \n",
|
|
"112 0.07 \n",
|
|
"113 0.68 \n",
|
|
"114 0.85 \n",
|
|
"115 0.83 \n",
|
|
"116 0.78 \n",
|
|
"117 0.81 \n",
|
|
"118 0.95 \n",
|
|
"119 0.90 \n",
|
|
"120 0.81 \n",
|
|
"121 0.86 \n",
|
|
"122 0.69 \n",
|
|
"123 0.73 \n",
|
|
"124 0.76 \n",
|
|
"125 0.69 \n",
|
|
"126 0.73 \n",
|
|
"127 0.76 \n",
|
|
"128 0.97 \n",
|
|
"129 1.00 \n",
|
|
"130 0.80 \n",
|
|
"131 0.97 \n",
|
|
"132 0.80 \n",
|
|
"133 0.85 \n",
|
|
"134 0.78 \n",
|
|
"135 0.81 \n",
|
|
"136 0.86 \n",
|
|
"137 0.92 \n",
|
|
"138 0.78 \n",
|
|
"139 0.86 \n",
|
|
"140 0.78 \n",
|
|
"141 0.76 \n",
|
|
"142 0.75 \n",
|
|
"143 0.78 \n",
|
|
"144 0.69 \n",
|
|
"145 0.83 \n",
|
|
"146 0.80 \n",
|
|
"147 0.71 \n",
|
|
"148 0.71 \n",
|
|
"149 0.75 \n",
|
|
"\n",
|
|
" Szerokość płatka \\\n",
|
|
"0 0.00 \n",
|
|
"1 0.00 \n",
|
|
"2 0.54 \n",
|
|
"3 0.58 \n",
|
|
"4 0.58 \n",
|
|
"5 0.50 \n",
|
|
"6 0.58 \n",
|
|
"7 0.50 \n",
|
|
"8 0.63 \n",
|
|
"9 0.38 \n",
|
|
"10 0.50 \n",
|
|
"11 0.54 \n",
|
|
"12 0.38 \n",
|
|
"13 0.58 \n",
|
|
"14 0.38 \n",
|
|
"15 0.54 \n",
|
|
"16 0.50 \n",
|
|
"17 0.54 \n",
|
|
"18 0.58 \n",
|
|
"19 0.38 \n",
|
|
"20 0.58 \n",
|
|
"21 0.42 \n",
|
|
"22 0.71 \n",
|
|
"23 0.50 \n",
|
|
"24 0.58 \n",
|
|
"25 0.46 \n",
|
|
"26 0.50 \n",
|
|
"27 0.54 \n",
|
|
"28 0.54 \n",
|
|
"29 0.58 \n",
|
|
"30 0.38 \n",
|
|
"31 0.42 \n",
|
|
"32 0.38 \n",
|
|
"33 0.46 \n",
|
|
"34 0.63 \n",
|
|
"35 0.58 \n",
|
|
"36 0.63 \n",
|
|
"37 0.58 \n",
|
|
"38 0.50 \n",
|
|
"39 0.50 \n",
|
|
"40 0.50 \n",
|
|
"41 0.46 \n",
|
|
"42 0.54 \n",
|
|
"43 0.46 \n",
|
|
"44 0.38 \n",
|
|
"45 0.50 \n",
|
|
"46 0.46 \n",
|
|
"47 0.50 \n",
|
|
"48 0.50 \n",
|
|
"49 0.42 \n",
|
|
"50 0.50 \n",
|
|
"51 0.75 \n",
|
|
"52 0.67 \n",
|
|
"53 0.79 \n",
|
|
"54 0.58 \n",
|
|
"55 0.79 \n",
|
|
"56 0.71 \n",
|
|
"57 0.71 \n",
|
|
"58 0.71 \n",
|
|
"59 0.58 \n",
|
|
"60 0.54 \n",
|
|
"61 0.71 \n",
|
|
"62 0.75 \n",
|
|
"63 0.75 \n",
|
|
"64 0.71 \n",
|
|
"65 0.04 \n",
|
|
"66 0.04 \n",
|
|
"67 0.04 \n",
|
|
"68 0.04 \n",
|
|
"69 0.04 \n",
|
|
"70 0.12 \n",
|
|
"71 0.08 \n",
|
|
"72 0.04 \n",
|
|
"73 0.04 \n",
|
|
"74 0.00 \n",
|
|
"75 0.04 \n",
|
|
"76 0.04 \n",
|
|
"77 0.00 \n",
|
|
"78 0.00 \n",
|
|
"79 0.04 \n",
|
|
"80 0.12 \n",
|
|
"81 0.12 \n",
|
|
"82 0.08 \n",
|
|
"83 0.08 \n",
|
|
"84 0.08 \n",
|
|
"85 0.04 \n",
|
|
"86 0.12 \n",
|
|
"87 0.04 \n",
|
|
"88 0.17 \n",
|
|
"89 0.04 \n",
|
|
"90 0.04 \n",
|
|
"91 0.12 \n",
|
|
"92 0.04 \n",
|
|
"93 0.04 \n",
|
|
"94 0.04 \n",
|
|
"95 0.04 \n",
|
|
"96 0.12 \n",
|
|
"97 0.00 \n",
|
|
"98 0.04 \n",
|
|
"99 0.04 \n",
|
|
"100 0.04 \n",
|
|
"101 0.04 \n",
|
|
"102 0.04 \n",
|
|
"103 0.08 \n",
|
|
"104 0.08 \n",
|
|
"105 0.04 \n",
|
|
"106 0.21 \n",
|
|
"107 0.12 \n",
|
|
"108 0.08 \n",
|
|
"109 0.04 \n",
|
|
"110 0.04 \n",
|
|
"111 0.04 \n",
|
|
"112 0.04 \n",
|
|
"113 0.67 \n",
|
|
"114 1.00 \n",
|
|
"115 0.83 \n",
|
|
"116 0.71 \n",
|
|
"117 0.88 \n",
|
|
"118 0.83 \n",
|
|
"119 0.71 \n",
|
|
"120 0.71 \n",
|
|
"121 1.00 \n",
|
|
"122 0.79 \n",
|
|
"123 0.75 \n",
|
|
"124 0.83 \n",
|
|
"125 0.96 \n",
|
|
"126 0.92 \n",
|
|
"127 0.71 \n",
|
|
"128 0.88 \n",
|
|
"129 0.92 \n",
|
|
"130 0.92 \n",
|
|
"131 0.79 \n",
|
|
"132 0.83 \n",
|
|
"133 0.71 \n",
|
|
"134 0.83 \n",
|
|
"135 0.63 \n",
|
|
"136 0.75 \n",
|
|
"137 0.79 \n",
|
|
"138 0.88 \n",
|
|
"139 0.92 \n",
|
|
"140 0.96 \n",
|
|
"141 0.71 \n",
|
|
"142 0.83 \n",
|
|
"143 0.96 \n",
|
|
"144 0.92 \n",
|
|
"145 0.92 \n",
|
|
"146 1.00 \n",
|
|
"147 0.92 \n",
|
|
"148 0.79 \n",
|
|
"149 0.92 \n",
|
|
"\n",
|
|
" Wartość medoidu 0 \\\n",
|
|
"0 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"1 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"2 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"3 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"4 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"5 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"6 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"7 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"8 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"9 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"10 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"11 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"12 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"13 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"14 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"15 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"16 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"17 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"18 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"19 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"20 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"21 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"22 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"23 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"24 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"25 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"26 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"27 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"28 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"29 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"30 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"31 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"32 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"33 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"34 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"35 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"36 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"37 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"38 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"39 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"40 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"41 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"42 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"43 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"44 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"45 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"46 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"47 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"48 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"49 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"50 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"51 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"52 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"53 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"54 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"55 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"56 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"57 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"58 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"59 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"60 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"61 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"62 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"63 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"64 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"65 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"66 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"67 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"68 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"69 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"70 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"71 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"72 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"73 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"74 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"75 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"76 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"77 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"78 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"79 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"80 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"81 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"82 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"83 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"84 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"85 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"86 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"87 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"88 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"89 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"90 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"91 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"92 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"93 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"94 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"95 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"96 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"97 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"98 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"99 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"100 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"101 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"102 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"103 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"104 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"105 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"106 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"107 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"108 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"109 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"110 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"111 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"112 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"113 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"114 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"115 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"116 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"117 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"118 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"119 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"120 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"121 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"122 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"123 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"124 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"125 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"126 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"127 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"128 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"129 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"130 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"131 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"132 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"133 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"134 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"135 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"136 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"137 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"138 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"139 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"140 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"141 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"142 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"143 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"144 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"145 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"146 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"147 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"148 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"149 (0.47, 0.38, 0.59, 0.58) \n",
|
|
"\n",
|
|
" Wartość medoidu 1 \\\n",
|
|
"0 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"1 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"2 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"3 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"4 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"5 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"6 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"7 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"8 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"9 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"10 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"11 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"12 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"13 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"14 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"15 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"16 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"17 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"18 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"19 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"20 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"21 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"22 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"23 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"24 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"25 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"26 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"27 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"28 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"29 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"30 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"31 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"32 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"33 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"34 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"35 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"36 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"37 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"38 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"39 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"40 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"41 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"42 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"43 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"44 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"45 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"46 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"47 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"48 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"49 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"50 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"51 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"52 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"53 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"54 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"55 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"56 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"57 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"58 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"59 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"60 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"61 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"62 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"63 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"64 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"65 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"66 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"67 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"68 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"69 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"70 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"71 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"72 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"73 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"74 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"75 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"76 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"77 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"78 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"79 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"80 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"81 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"82 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"83 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"84 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"85 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"86 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"87 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"88 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"89 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"90 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"91 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"92 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"93 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"94 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"95 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"96 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"97 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"98 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"99 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"100 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"101 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"102 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"103 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"104 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"105 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"106 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"107 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"108 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"109 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"110 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"111 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"112 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"113 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"114 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"115 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"116 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"117 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"118 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"119 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"120 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"121 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"122 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"123 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"124 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"125 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"126 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"127 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"128 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"129 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"130 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"131 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"132 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"133 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"134 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"135 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"136 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"137 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"138 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"139 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"140 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"141 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"142 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"143 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"144 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"145 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"146 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"147 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"148 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"149 (0.19, 0.58, 0.08, 0.04) \n",
|
|
"\n",
|
|
" Wartość medoidu 2 \\\n",
|
|
"0 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"1 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"2 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"3 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"4 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"5 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"6 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"7 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"8 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"9 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"10 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"11 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"12 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"13 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"14 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"15 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"16 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"17 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"18 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"19 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"20 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"21 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"22 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"23 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"24 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"25 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"26 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"27 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"28 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"29 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"30 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"31 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"32 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"33 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"34 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"35 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"36 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"37 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"38 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"39 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"40 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"41 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"42 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"43 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"44 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"45 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"46 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"47 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"48 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"49 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"50 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"51 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"52 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"53 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"54 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"55 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"56 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"57 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"58 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"59 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"60 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"61 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"62 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"63 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"64 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"65 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"66 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"67 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"68 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"69 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"70 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"71 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"72 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"73 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"74 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"75 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"76 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"77 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"78 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"79 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"80 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"81 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"82 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"83 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"84 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"85 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"86 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"87 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"88 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"89 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"90 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"91 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"92 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"93 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"94 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"95 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"96 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"97 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"98 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"99 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"100 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"101 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"102 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"103 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"104 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"105 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"106 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"107 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"108 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"109 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"110 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"111 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"112 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"113 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"114 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"115 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"116 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"117 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"118 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"119 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"120 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"121 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"122 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"123 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"124 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"125 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"126 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"127 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"128 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"129 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"130 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"131 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"132 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"133 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"134 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"135 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"136 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"137 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"138 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"139 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"140 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"141 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"142 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"143 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"144 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"145 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"146 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"147 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"148 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"149 (0.69, 0.42, 0.76, 0.83) \n",
|
|
"\n",
|
|
" Medoid \n",
|
|
"0 0 \n",
|
|
"1 0 \n",
|
|
"2 0 \n",
|
|
"3 0 \n",
|
|
"4 0 \n",
|
|
"5 0 \n",
|
|
"6 0 \n",
|
|
"7 0 \n",
|
|
"8 0 \n",
|
|
"9 0 \n",
|
|
"10 0 \n",
|
|
"11 0 \n",
|
|
"12 0 \n",
|
|
"13 0 \n",
|
|
"14 0 \n",
|
|
"15 0 \n",
|
|
"16 0 \n",
|
|
"17 0 \n",
|
|
"18 0 \n",
|
|
"19 0 \n",
|
|
"20 0 \n",
|
|
"21 0 \n",
|
|
"22 0 \n",
|
|
"23 0 \n",
|
|
"24 0 \n",
|
|
"25 0 \n",
|
|
"26 0 \n",
|
|
"27 0 \n",
|
|
"28 0 \n",
|
|
"29 0 \n",
|
|
"30 0 \n",
|
|
"31 0 \n",
|
|
"32 0 \n",
|
|
"33 0 \n",
|
|
"34 0 \n",
|
|
"35 0 \n",
|
|
"36 0 \n",
|
|
"37 0 \n",
|
|
"38 0 \n",
|
|
"39 0 \n",
|
|
"40 0 \n",
|
|
"41 0 \n",
|
|
"42 0 \n",
|
|
"43 0 \n",
|
|
"44 0 \n",
|
|
"45 0 \n",
|
|
"46 0 \n",
|
|
"47 0 \n",
|
|
"48 0 \n",
|
|
"49 0 \n",
|
|
"50 0 \n",
|
|
"51 0 \n",
|
|
"52 0 \n",
|
|
"53 0 \n",
|
|
"54 0 \n",
|
|
"55 0 \n",
|
|
"56 0 \n",
|
|
"57 0 \n",
|
|
"58 0 \n",
|
|
"59 0 \n",
|
|
"60 0 \n",
|
|
"61 0 \n",
|
|
"62 0 \n",
|
|
"63 0 \n",
|
|
"64 0 \n",
|
|
"65 1 \n",
|
|
"66 1 \n",
|
|
"67 1 \n",
|
|
"68 1 \n",
|
|
"69 1 \n",
|
|
"70 1 \n",
|
|
"71 1 \n",
|
|
"72 1 \n",
|
|
"73 1 \n",
|
|
"74 1 \n",
|
|
"75 1 \n",
|
|
"76 1 \n",
|
|
"77 1 \n",
|
|
"78 1 \n",
|
|
"79 1 \n",
|
|
"80 1 \n",
|
|
"81 1 \n",
|
|
"82 1 \n",
|
|
"83 1 \n",
|
|
"84 1 \n",
|
|
"85 1 \n",
|
|
"86 1 \n",
|
|
"87 1 \n",
|
|
"88 1 \n",
|
|
"89 1 \n",
|
|
"90 1 \n",
|
|
"91 1 \n",
|
|
"92 1 \n",
|
|
"93 1 \n",
|
|
"94 1 \n",
|
|
"95 1 \n",
|
|
"96 1 \n",
|
|
"97 1 \n",
|
|
"98 1 \n",
|
|
"99 1 \n",
|
|
"100 1 \n",
|
|
"101 1 \n",
|
|
"102 1 \n",
|
|
"103 1 \n",
|
|
"104 1 \n",
|
|
"105 1 \n",
|
|
"106 1 \n",
|
|
"107 1 \n",
|
|
"108 1 \n",
|
|
"109 1 \n",
|
|
"110 1 \n",
|
|
"111 1 \n",
|
|
"112 1 \n",
|
|
"113 2 \n",
|
|
"114 2 \n",
|
|
"115 2 \n",
|
|
"116 2 \n",
|
|
"117 2 \n",
|
|
"118 2 \n",
|
|
"119 2 \n",
|
|
"120 2 \n",
|
|
"121 2 \n",
|
|
"122 2 \n",
|
|
"123 2 \n",
|
|
"124 2 \n",
|
|
"125 2 \n",
|
|
"126 2 \n",
|
|
"127 2 \n",
|
|
"128 2 \n",
|
|
"129 2 \n",
|
|
"130 2 \n",
|
|
"131 2 \n",
|
|
"132 2 \n",
|
|
"133 2 \n",
|
|
"134 2 \n",
|
|
"135 2 \n",
|
|
"136 2 \n",
|
|
"137 2 \n",
|
|
"138 2 \n",
|
|
"139 2 \n",
|
|
"140 2 \n",
|
|
"141 2 \n",
|
|
"142 2 \n",
|
|
"143 2 \n",
|
|
"144 2 \n",
|
|
"145 2 \n",
|
|
"146 2 \n",
|
|
"147 2 \n",
|
|
"148 2 \n",
|
|
"149 2 "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# iris\n",
|
|
"model4 = TrainModel_medoids(dataset, 3)\n",
|
|
"medoids4, res_cluster4, cluster_labels4, silhouette4 = model4.return_values()\n",
|
|
"\n",
|
|
"res = res_cluster4[0] + res_cluster4[1] + res_cluster4[2]\n",
|
|
"\n",
|
|
"medoids_values = []\n",
|
|
"for i in range(0, len(res_cluster4[0])):\n",
|
|
" medoids_values.append(0)\n",
|
|
" \n",
|
|
"for i in range(0, len(res_cluster4[1])):\n",
|
|
" medoids_values.append(1)\n",
|
|
" \n",
|
|
"for i in range(0, len(res_cluster4[2])):\n",
|
|
" medoids_values.append(2)\n",
|
|
"\n",
|
|
"\n",
|
|
"data = {'Długość kielicha': [round(res[i][0],2) for i in range(0, len(res))],\n",
|
|
" 'Szerokość kielicha': [round(res[i][1],2) for i in range(0, len(res))],\n",
|
|
" 'Długość płatka': [round(res[i][2],2) for i in range(0, len(res))],\n",
|
|
" 'Szerokość płatka': [round(res[i][3],2) for i in range(0, len(res))],\n",
|
|
" 'Wartość medoidu 0': [(round(medoids4[0][0],2),round(medoids4[0][1],2), round(medoids4[0][2],2), round(medoids4[0][3],2)) for i in range(150)],\n",
|
|
" 'Wartość medoidu 1': [(round(medoids4[1][0],2), round(medoids4[1][1],2), round(medoids4[1][2],2), round(medoids4[1][3],2)) for i in range(150)],\n",
|
|
" 'Wartość medoidu 2': [(round(medoids4[2][0],2), round(medoids4[2][1],2), round(medoids4[2][2],2), round(medoids4[2][3],2)) for i in range(150)],\n",
|
|
" 'Medoid': medoids_values}\n",
|
|
"df = pd.DataFrame(data)\n",
|
|
"df.to_csv('iris_data.csv')\n",
|
|
"\n",
|
|
"pd.set_option('display.max_rows', None)\n",
|
|
"pd.set_option('display.max_columns', None)\n",
|
|
"pd.set_option('display.width', 10)\n",
|
|
"pd.set_option('display.colheader_justify', 'center')\n",
|
|
"pd.set_option('display.precision', 5)\n",
|
|
"display(df)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "655da39a",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Uruchomienie algorytmu k-medoid dla zbioru danych glass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 85,
|
|
"id": "bb83f704",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.35\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# glass\n",
|
|
"model5 = TrainModel_medoids(dataset2, 4)\n",
|
|
"medoids5, res_cluster5, cluster_labels5, silhouette5 = model5.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "17f279ec",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Uruchomienie algorytmu k-medoid dla zbioru danych wine"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 86,
|
|
"id": "6ffe3810",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.28\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# wine\n",
|
|
"model6 = TrainModel_medoids(dataset3, 3)\n",
|
|
"medoids6, res_cluster6, cluster_labels6, silhouette6 = model6.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3db40fc5",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Algorytm k-medoid + PCA"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 87,
|
|
"id": "662d4cf5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pca = PCA(n_components=2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ab279486",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Redukcja wymiaru z 4 do 2 przy pomocy PCA na zbiorze danych iris"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 88,
|
|
"id": "21f2332b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pca.fit(dataset)\n",
|
|
"dataset_pca = pca.transform(dataset)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 94,
|
|
"id": "71128af5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.5\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# iris\n",
|
|
"model7 = TrainModel_medoids(dataset_pca, 3)\n",
|
|
"medoids7, res_cluster7, cluster_labels7, silhouette7 = model7.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7a82f399",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Redukcja wymiaru z 9 do 2 przy pomocy PCA na zbiorze danych glass"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 95,
|
|
"id": "5c305f99",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pca.fit(dataset2)\n",
|
|
"dataset2_pca = pca.transform(dataset2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 96,
|
|
"id": "e4621daf",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.5\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# glass\n",
|
|
"model8 = TrainModel_medoids(dataset2_pca, 4)\n",
|
|
"medoids8, res_cluster8, cluster_labels8, silhouette8 = model8.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "14a5a5dd",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### Redukcja wymiaru z 13 do 2 przy pomocy PCA na zbiorze danych wine"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 98,
|
|
"id": "b63e355c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pca.fit(dataset3)\n",
|
|
"dataset3_pca = pca.transform(dataset3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 101,
|
|
"id": "bf13c5e4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.45\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# wine\n",
|
|
"model9 = TrainModel_medoids(dataset3_pca, 3)\n",
|
|
"medoids9, res_cluster9, cluster_labels9, silhouette9 = model9.return_values()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ba1dd5c6",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Porównanie metody k-średnich, k-medoid, k-medoid + PCA"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "80537977",
|
|
"metadata": {},
|
|
"source": [
|
|
" W celu porównania przedstawionych powyżej trzech metod został stworzony wykres. Na wykresie zostały porównane wartości sylwetek trzech metod na trzech zbiorach danych (iris, glass, wine): "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 103,
|
|
"id": "c5909200",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"result_data = {'Iris': [silhouette, silhouette4, silhouette7],\n",
|
|
" 'Glass': [silhouette2, silhouette5, silhouette8],\n",
|
|
" 'Wine': [silhouette3, silhouette6, silhouette9]}\n",
|
|
"df_shoulette = pd.DataFrame(result_data)\n",
|
|
"df_shoulette.index = ['k-średnich', 'k-medoid', 'k-medoid + PCA']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 104,
|
|
"id": "a8983527",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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\n",
|
|
"text/plain": [
|
|
"<Figure size 864x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"barWidth = 0.25\n",
|
|
"fig = plt.subplots(figsize =(12, 8))\n",
|
|
"\n",
|
|
"iris = [silhouette, silhouette2, silhouette3]\n",
|
|
"glass = [silhouette4, silhouette5, silhouette6]\n",
|
|
"wine = [silhouette7, silhouette8, silhouette9]\n",
|
|
"\n",
|
|
"br1 = np.arange(len(iris))\n",
|
|
"br2 = [x + barWidth for x in br1]\n",
|
|
"br3 = [x + barWidth for x in br2]\n",
|
|
"\n",
|
|
"plt.bar(br1, iris, color ='y', width = barWidth, edgecolor ='grey', label ='k-średnich')\n",
|
|
"plt.bar(br2, glass, color ='m', width = barWidth, edgecolor ='grey', label ='k-medoid')\n",
|
|
"plt.bar(br3, wine, color ='c', width = barWidth, edgecolor ='grey', label ='k-medoid + PCA')\n",
|
|
"\n",
|
|
"plt.xlabel('Zbiór danych', fontsize = 15)\n",
|
|
"plt.ylabel('Wartość sylwetki', fontsize = 15)\n",
|
|
"plt.xticks([r + barWidth for r in range(len(iris))], ['Iris', 'Glass', 'Wine'])\n",
|
|
"\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 105,
|
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"id": "74fc7277",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
|
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"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: center;\">\n",
|
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" <th></th>\n",
|
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" <th>Iris</th>\n",
|
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" <th>Glass</th>\n",
|
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" <th>Wine</th>\n",
|
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
|
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" <th>k-średnich</th>\n",
|
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" <td>0.44</td>\n",
|
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" <td>0.34</td>\n",
|
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" <td>0.30</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>k-medoid</th>\n",
|
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" <td>0.48</td>\n",
|
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" <td>0.35</td>\n",
|
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" <td>0.28</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>k-medoid + PCA</th>\n",
|
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" <td>0.50</td>\n",
|
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" <td>0.50</td>\n",
|
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" <td>0.45</td>\n",
|
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" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
|
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"</div>"
|
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],
|
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"text/plain": [
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" Iris \\\n",
|
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"k-średnich 0.44 \n",
|
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"k-medoid 0.48 \n",
|
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"k-medoid + PCA 0.50 \n",
|
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"\n",
|
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" Glass \\\n",
|
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"k-średnich 0.34 \n",
|
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"k-medoid 0.35 \n",
|
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"k-medoid + PCA 0.50 \n",
|
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"\n",
|
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" Wine \n",
|
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"k-średnich 0.30 \n",
|
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"k-medoid 0.28 \n",
|
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"k-medoid + PCA 0.45 "
|
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]
|
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},
|
|
"execution_count": 105,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
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],
|
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"source": [
|
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"df_shoulette"
|
|
]
|
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},
|
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{
|
|
"cell_type": "markdown",
|
|
"id": "cf18c7b0",
|
|
"metadata": {},
|
|
"source": [
|
|
" W wyniku tego porównania można dojść do wniosku, że najlepszą z metod jest metoda k-medoid wraz z redukcją wymiaru zbiorów danych przy pomocy PCA. "
|
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]
|
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},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "05d43597",
|
|
"metadata": {},
|
|
"source": [
|
|
"### K-medoids na innych zbiorach danych"
|
|
]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 106,
|
|
"id": "ce6fa598",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Load datasets\n",
|
|
"\n",
|
|
"dataset = np.array([[5, 6], [4, 7], [4, 8], [4, 6], [5, 7], [5, 8], [7, 6], [8, 8], [7, 7], [7, 8]])\n",
|
|
"dataset3 = np.array(\n",
|
|
" [[4.5, 6], [4, 7], [4, 8], [4, 6], [4.5, 7], [4.5, 8], [7, 6], [5.5, 7], [5.5, 8], [5.5, 6], [8, 8], [7, 7],\n",
|
|
" [7, 8]])\n",
|
|
"\n",
|
|
"X1, Y1 = make_blobs(n_features=2, centers=4)\n",
|
|
"X, y = make_classification(n_samples=100, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_classes=2,\n",
|
|
" n_clusters_per_class=2, class_sep=2, flip_y=0, weights=[0.5, 0.5], random_state=17)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 107,
|
|
"id": "9af6e339",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def print_sns_plot(data):\n",
|
|
" column_values = ['x', 'y']\n",
|
|
" df = pd.DataFrame(data=data, columns=column_values, index=None)\n",
|
|
" sns.set_theme(style='darkgrid')\n",
|
|
" plt.figure(figsize=(10,8))\n",
|
|
" plt.title(\"Dataset for clustering\")\n",
|
|
" sns.scatterplot(data=df, x='x', y='y')\n",
|
|
" plt.show()\n",
|
|
" time.sleep(4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 108,
|
|
"id": "c0d9a1c3",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
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\n",
|
|
"text/plain": [
|
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"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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9tZo3b65PPvlE3bt316pVq5SWlmYxymlO/uUcf00ZeU7dPX9NCUSGk385x19TRp5Td2/915Q+59zFfZ/vW3r16qXXXntNbdu2VWZmpiZMmKAuXbpo+/btys7OVnl5uZKSkjR79mw1a9bsvB7bi7cpT8Xl5PDEXsIPOwlP7CX8sJPw5PVe6nub0vMY8xIxFpnYS/hhJ+GJvYQfdhKerGOMf4EfAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhjyNsTVr1qhv375KT0/XkiVLTjtfWFioIUOGaMCAARo7dqyOHTvm5TgAAABhx7MYKy4u1sKFC5Wbm6u33npLS5cu1c6dO+vcZ+bMmZowYYJWr16ta6+9Vi+//LJX4wAAAIQlz2IsPz9fPXr0UFxcnGJiYpSRkaG8vLw69wkGgzpx4oQkqaKiQi1atPBqHAAAgLAU7dUDl5SUyO/3h47j4+O1ZcuWOveZMmWKRo8erVmzZumyyy7TsmXLzus52rS5/KLMei5+f6znz4Hzx17CDzsJT+wl/LCT8GS5F89iLBgMyufzhY6dc3WOKysrlZWVpVdeeUXJycn64x//qMmTJ2vx4sUNfo7S0nIFg+6izn0qvz9WgcBxzx4fF4a9hB92Ep7YS/hhJ+HJ671ERfnOeQHJs7cpExMTFQgEQseBQEDx8fGh4x07dqh58+ZKTk6WJN13333atGmTV+MAAACEJc9iLDU1VQUFBSorK1NFRYXWrVuntLS00Pn27durqKhIu3fvliStX79eXbp08WocAACAsOTZ25QJCQmaOHGiRo0aperqag0dOlTJycnKzMzUhAkT1KVLF82ePVuPPfaYnHNq06aNZs2a5dU4AAAAYcnnnPPuQ1ce4zNjkYm9hB92Ep7YS/hhJ+Hpkv3MGAAAAOpHjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhT2NszZo16tu3r9LT07VkyZLTzu/evVsjR47UgAED9NBDD+no0aNejgMAABB2PIux4uJiLVy4ULm5uXrrrbe0dOlS7dy5M3TeOadf/OIXyszM1OrVq/XDH/5Qixcv9mocAACAsORZjOXn56tHjx6Ki4tTTEyMMjIylJeXFzpfWFiomJgYpaWlSZIefvhhjRgxwqtxAAAAwlK0Vw9cUlIiv98fOo6Pj9eWLVtCx/v27dOVV16padOmadu2bbruuuv05JNPntdztGlz+UWb92z8/ljPnwPnj72EH3YSnthL+GEn4clyL57FWDAYlM/nCx075+oc19TUaNOmTXrjjTfUpUsXPf3005ozZ47mzJnT4OcoLS1XMOgu6tyn8vtjFQgc9+zxcWHYS/hhJ+GJvYQfdhKevN5LVJTvnBeQPHubMjExUYFAIHQcCAQUHx8fOvb7/Wrfvr26dOkiSerfv3+dK2cAAACRwLMYS01NVUFBgcrKylRRUaF169aFPh8mSd26dVNZWZm2b98uSXr//feVlJTk1TgAAABhybO3KRMSEjRx4kSNGjVK1dXVGjp0qJKTk5WZmakJEyaoS5cueu6555Sdna2KigolJiZq3rx5Xo0DAAAQlnzOOe8+dOUxPjMWmdhL+GEn4Ym9hB92Ep4u2c+MAQAAoH7EGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjZ1BQWKTpL6/TvqJ2mv7yOhUUFlmPhEZSUFikSc9/rNFz3tek5z9m90AE2FS0WU9tnK7KZu311Mbp2lS02XokRJizxtgzzzwj51xjzhIWCgqL9Oo729Wh7cdql7hf17XN16vvbOeXcgQ4ufvSY1WSpNJjVeweuMRtKtqs3O3LdV2brbrm/xzWdW22Knf7coIMjcrnzlJc999/v6Kjo7VgwQL5/f7GnqtBSkvLFQxe3GDcWTRA3a4vUHR0jaKb1KqmtolqaqL16Y4UdUxcfVGfCxfG749VIHD8oj/upOc/DoXYqdpc0Vy/G3fbRX++S4lXO8F3w17qt6fyViVf9aWio2oVHeVUE/SpJthEWw7+QNe22HjRn4+dhCev9xIV5VObNpef/fzZTixZskQpKSkaMmSIPvroI0+GC0d/XHOfSg77VVPbRJJUU9tEJYfj9X/X3Gc8Gbx2phA71+0Avv9e39JVh05crtrg338d1gajFDgRq9f/2tV2MESU6LOdiIqK0rhx4/TjH/9Y2dnZWr9+vdq1axc6/+CDDzbKgI3tm+p/1pK1wzXpp79XRVULNY2u1pK196u6+p+tR4PH2lzR/KxXxgBcmr6pbq+ln/+LHk15TxXV0WrapFbLPr9F39S0tx4NEaTeD/BHRUXJ5/Np586d2rFjR+g/l6rBd3TQHV0/VtU3zZW7dpiqvmmutK75GnxHB+vR4LHBd3RQs+i6PxLNoqPYPXAJG9Cht25rt0dVNU31p8J/UVVNU6W226MBHXpbj4YIctYrY845vfjii3r55Zc1ceJEjRgxojHnMpOSlKht+yZq6nPfaO/BGP11R7qG3NlcKUmJ1qPBYyd3vOLDXSo9VqU2VzTX4Ds6sHvgEvajxJv1/47+SjnrC7XvWLW2HLxFA3+QpB8l3mw9GiLIWT/Af99996miokLz58/X9ddf39hzNYgXH+A/FR+0DE/sJfywk/DEXsIPOwlPYfsB/qSkJP35z38O2xADAAC4FJz1bcrp06c35hwAAAARiX+BHwAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADHkaY2vWrFHfvn2Vnp6uJUuWnPV+H3zwgXr16uXlKAAAAGEp2qsHLi4u1sKFC7VixQo1a9ZMw4YN06233qqOHTvWud+hQ4c0d+5cr8YAAAAIa55dGcvPz1ePHj0UFxenmJgYZWRkKC8v77T7ZWdna/z48V6NAQAAENY8uzJWUlIiv98fOo6Pj9eWLVvq3Oe1117TDTfcoJtuuumCnqNNm8u/04wN4ffHev4cOH/sJfywk/DEXsIPOwlPlnvxLMaCwaB8Pl/o2DlX53jHjh1at26dXnnlFRUVFV3Qc5SWlisYdN951rPx+2MVCBz37PFxYdhL+GEn4Ym9hB92Ep683ktUlO+cF5A8e5syMTFRgUAgdBwIBBQfHx86zsvLUyAQ0JAhQzRmzBiVlJRo+PDhXo0DAAAQljyLsdTUVBUUFKisrEwVFRVat26d0tLSQucnTJigtWvXatWqVVq8eLHi4+OVm5vr1TgAAABhybMYS0hI0MSJEzVq1CgNHDhQ/fv3V3JysjIzM7V161avnhYAAOB7xeec8+5DVx7jM2ORib2EH3YSnthL+GEn4emS/cwYAAAA6keMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFPY2zNmjXq27ev0tPTtWTJktPOv/fee7r33ns1YMAAjRs3TkePHvVyHAAAgLDjWYwVFxdr4cKFys3N1VtvvaWlS5dq586dofPl5eX69a9/rcWLF2v16tXq1KmTFi1a5NU4AAAAYcmzGMvPz1ePHj0UFxenmJgYZWRkKC8vL3S+urpaOTk5SkhIkCR16tRJBw8e9GocAACAsBTt1QOXlJTI7/eHjuPj47Vly5bQcatWrXT33XdLkiorK7V48WKNHDnyvJ6jTZvLL86w5+D3x3r+HDh/7CX8sJPwxF7CDzsJT5Z78SzGgsGgfD5f6Ng5V+f4pOPHj+uRRx5R586dNWjQoPN6jtLScgWD7jvPejZ+f6wCgeOePT4uDHsJP+wkPLGX8MNOwpPXe4mK8p3zApJnb1MmJiYqEAiEjgOBgOLj4+vcp6SkRMOHD1enTp00c+ZMr0YBAAAIW57FWGpqqgoKClRWVqaKigqtW7dOaWlpofO1tbV6+OGH1adPH2VlZZ3xqhkAAMClzrO3KRMSEjRx4kSNGjVK1dXVGjp0qJKTk5WZmakJEyaoqKhIX3zxhWpra7V27VpJ0o033sgVMgAAEFF8zjnvPnTlMT4zFpnYS/hhJ+GJvYQfdhKeLtnPjAEAAKB+xBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGCIGAMAADBEjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8QYAACAIWIMAADAEDEGAABgiBgDAAAwRIwBAAAY8jTG1qxZo759+yo9PV1Lliw57fy2bds0ePBgZWRkKCsrSzU1NV6OAwAAEHaivXrg4uJiLVy4UCtWrFCzZs00bNgw3XrrrerYsWPoPpMmTdJTTz2lrl27atq0aVq2bJmGDx/u1UgNVlBYpBUf7lLZsSq1vqK5Bt/RQSlJidZjoRGc3H3psSq1YfdARNhUtFmrd+XpcNURtWoepwEdeutHiTdbj4UI4tmVsfz8fPXo0UNxcXGKiYlRRkaG8vLyQue/+uorVVZWqmvXrpKkwYMH1zlvpaCwSK++s12lx6rkJJUeq9Kr72xXQWGR9Wjw2Km7l9g9EAk2FW1W7vblOlx1RJJ0uOqIcrcv16aizbaDIaJ4FmMlJSXy+/2h4/j4eBUXF5/1vN/vr3PeyooPd+mbmmCd276pCWrFh7uMJkJjYfdA5Fm9K0/Vweo6t1UHq7V6l/3FAUQOz96mDAaD8vl8oWPnXJ3j+s43RJs2l3/3Qb+l7B9XRc50u98fe9GfDxfGi12w+++G1yg8sZdzO/KPK2Jnut2r146dhCfLvXgWY4mJifrv//7v0HEgEFB8fHyd84FAIHR86NChOucborS0XMGg++7DnqL1Fc1Db1N9+/ZA4PhFfS5cGL8/1pNdsPsL59VO8N2wl/rFNY8LvUX57du9eO3YSXjyei9RUb5zXkDy7G3K1NRUFRQUqKysTBUVFVq3bp3S0tJC56+++mo1b95cn3zyiSRp1apVdc5bGXxHBzWLrvuyNIuO0uA7OhhNhMbC7oHIM6BDbzWNalrntqZRTTWgQ2+jiRCJPLsylpCQoIkTJ2rUqFGqrq7W0KFDlZycrMzMTE2YMEFdunTR/PnzlZ2drfLyciUlJWnUqFFejdNgJ/9yjr+mjDyn7p6/pgQiw8m/muSvKWHJ55y7uO/zNSIv3qY8FZeTwxN7CT/sJDyxl/DDTsLTJfs2JQAAAOpHjAEAABgixgAAAAwRYwAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ9HWA3wXUVG+S+I5cP7YS/hhJ+GJvYQfdhKevNxLfY/tc845z54dAAAA58TblAAAAIaIMQAAAEPEGAAAgCFiDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAAAADBFjAAAAhogxAAAAQ8SYpDVr1qhv375KT0/XkiVLTju/bds2DR48WBkZGcrKylJNTY3BlJGnvr289957uvfeezVgwACNGzdOR48eNZgystS3k5M++OAD9erVqxEni2z17WX37t0aOXKkBgwYoIceeoiflUZQ304KCws1ZMgQDRgwQGPHjtWxY8cMpow85eXl6t+/vw4cOHDaOdPf9S7CFRUVuTvvvNMdPnzYnThxwt1zzz3uyy+/rHOffv36uU8//dQ559zUqVPdkiVLDCaNLPXt5fjx4+62225zRUVFzjnnnn76affb3/7WatyI0JCfFeecCwQCrnfv3u7OO+80mDLy1LeXYDDo0tPT3Ycffuicc+53v/udmzdvntW4EaEhPyv333+/++CDD5xzzs2ePdstWLDAYtSI8tlnn7n+/fu7pKQkt3///tPOW/6uj/grY/n5+erRo4fi4uIUExOjjIwM5eXlhc5/9dVXqqysVNeuXSVJgwcPrnMe3qhvL9XV1crJyVFCQoIkqVOnTjp48KDVuBGhvp2clJ2drfHjxxtMGJnq20thYaFiYmKUlpYmSXr44Yc1YsQIq3EjQkN+VoLBoE6cOCFJqqioUIsWLSxGjSjLli1TTk6O4uPjTztn/bs+4mOspKREfr8/dBwfH6/i4uKznvf7/XXOwxv17aVVq1a6++67JUmVlZVavHix7rrrrkafM5LUtxNJeu2113TDDTfopptuauzxIlZ9e9m3b5+uvPJKTZs2TYMGDVJOTo5iYmIsRo0YDflZmTJlirKzs3X77bcrPz9fw4YNa+wxI87MmTN1yy23nPGc9e/6iI+xYDAon88XOnbO1Tmu7zy80dDX/fjx4xozZow6d+6sQYMGNeaIEae+nezYsUPr1q3TuHHjLMaLWPXtpaamRps2bdL999+vlStX6pprrtGcOXMsRo0Y9e2ksrJSWVlZeuWVV/TRRx9p+PDhmjx5ssWo+Afr3/URH2OJiYkKBAKh40AgUOcS5rfPHzp06IyXOHFx1bcX6e//S2b48OHq1KmTZs6c2dgjRpz6dpKXl6dAIKAhQ4ZozJgxof3AW/Xtxe/3q3379urSpYskqX///tqyZUujzxlJ6tvJjh071Lx5cyUnJ0uS7rvvPm3atKnR58T/sv5dH/ExlpqaqoKCApWVlamiokLr1q0LfbZCkq6++mo1b95cn3zyiSRp1apVdc7DG/Xtpba2Vg8//LD69OmjrKwsrlY2gvp2MmHCBK1du1arVq3S4sWLFR8fr9zcXMOJI0N9e+nWrZvKysq0fft2SdL777+vpKQkq3EjQn07ad++vYqKirR7925J0vr160OxDBvWv+ujG+2ZwlRCQoImTpyoUaNGqbq6WkOHDlVycrIyMzM1YcIEdenSRfPnz1d2drbKy8uVlJSkUaNGWY99yatvL0VFRfriiy9UW1urtWvXSpJuvPFGrpB5qCE/K2h8DdnLc889p+zsbFVUVCgxMVHz5s2zHvuS1pCdzJ49W4899picc2rTpo1mzZplPXZECpff9T7nnGu0ZwMAAEAdEf82JQAAgCViDAAAwBAxBgAAYIgYAwAAMESMAQAAGCLGAES0wsJCde/eXVu3bg3dVlZWprvuuksffPCB3WAAIgb/tAWAiPfmm29q8eLFWrlypVq2bKnRo0crJSVFv/jFL6xHAxABiDEAkDRp0iR9/fXXateunfbv369Fixbx/+wAoFEQYwAg6euvv9bAgQNVU1OjNWvWqGXLltYjAYgQfGYMACTt2bNHJ06c0LFjx1RYWGg9DoAIwpUxABGvrKxMQ4cO1eOPP66qqiotXLhQK1eulN/vtx4NQAQgxgBEtNraWo0ePVodO3bUk08+KUmaOnWq9u/fr1dffVVNmjQxnhDApY63KQFEtHnz5qmiokKTJ08O3TZ9+nQdPXpUCxYsMJwMQKTgyhgAAIAhrowBAAAYIsYAAAAMEWMAAACGiDEAAABDxBgAAIAhYgwAAMAQMQYAAGDo/wO73BoeITCw5wAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 2 0.3\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# 2 clusters\n",
|
|
"print_sns_plot(dataset)\n",
|
|
"model9 = TrainModel_medoids(dataset, 2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 109,
|
|
"id": "a756e584",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.36\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print_sns_plot(dataset3)\n",
|
|
"model10 = TrainModel_medoids(dataset3, 3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 110,
|
|
"id": "ca919f2d",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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q6+uVlJT0leM1NTVat25ds7tsMTEx+vWvf62XXnpJ1113nebMmeP0NAEAAKziMcYYJ0+QlZWl22+//YKPLp977jnt37//gkXs+PHjSkxM1JtvvunkNAEAAKzi2DtpktTQ0KDdu3fr8ccfv+Axr776qiZNmhTcrqmp0caNG3XfffdJkowxCgkJuaLzVlXVqqnJ0e6pmJhIVVbWOHoOW5Hdndkld+d3c3bJ3fnJ7s7skvP527TxKDo64sLjjp1Z0v79+9W1a1f5fL6vHDfG6N1331Xfvn2D+3w+n1atWqV33nlHkrRmzRolJiY6OU0AAADrOFrSysrKFBsb22zfxIkTVVJSIun0H7vh9XoVFhYWHA8JCdGSJUuUn5+vpKQkvfvuu8rJyXFymgAAANZx/J20lsDjTmeR3Z3ZJXfnd3N2yd35ye7O7FIrf9wJAACAr4eSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFgo1KkvXr9+vdasWRPcPnDggFJSUjRr1qzgvuXLl2vjxo1q3769JGns2LEaN26cysvLlZOTo6qqKt1www1asGCB2rVr59RUAQAArONYSUtLS1NaWpok6YMPPlBmZqamTp3a7JjS0lItWrRIffv2bbZ/9uzZSk9P14gRI7RixQoVFBQoJyfHqakCAABY56o87szPz1d2draioqKa7S8tLdXKlSs1atQozZkzR6dOnZLf79fu3bs1bNgwSVJqaqq2bNlyNaYJAABgDcfupJ2xa9cu1dfXKykpqdn+EydOqEePHsrJydEPfvAD5ebmqqCgQOPGjVNERIRCQ09PLSYmRocPH76ic0ZHR3xr87+YmJjIq3IeG5Hdvdyc383ZJXfnJ7t7tWR+x0va2rVrdf/995+3v127dnr66aeD2xkZGZo+fbrS09Pl8XiaHXvu9qVUVdWqqcl8vQlfppiYSFVW1jh6DluR3Z3ZJXfnd3N2yd35ye7O7JLz+du08Vz0xpKjjzsbGhq0e/duDRky5Lyx8vJybdiwIbhtjFFoaKiioqJUU1OjQCAgSaqsrFSnTp2cnCYAAIB1HC1p+/fvV9euXeXz+c4bCw8P1xNPPKGysjIZY/Tcc88pMTFRXq9X/fv31+bNmyVJmzZtUkJCgpPTBAAAsI6jJa2srEyxsbHN9k2cOFElJSWKiorSnDlzNGXKFA0fPlzGmOBj0by8PK1bt07Jycnas2ePHnzwQSenCQAAYB2PMcbZl7daAO+kOYvs7swuuTu/m7NL7s5Pdndml1r5O2kAAAD4eihpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYKFQp754/fr1WrNmTXD7wIEDSklJ0axZs4L7Xn31VS1btkzGGF1//fWaN2+eOnTooMLCQi1cuFDR0dGSpEGDBik7O9upqQIAAFjHsZKWlpamtLQ0SdIHH3ygzMxMTZ06NTheW1ur/Px8bdy4UZ07d9aTTz6pZcuWaebMmSotLVVubq5Gjhzp1PQAAACsdlUed+bn5ys7O1tRUVHBfX6/X3l5eercubMkqVu3bjp06JAkqaSkRIWFhRo1apSmTZum48ePX41pAgAAWMNjjDFOnmDXrl1auHChNm7ceMFj6uvrlZ6ervHjx+uOO+5QZmamMjIy1K9fPy1atEjl5eVauHChk9MEAACwiuMlLSsrS7fffvsFH13W1NQoMzNT119/vR577LHzxo8fP67ExES9+eabl33OqqpaNTU5GksxMZGqrKxx9By2Irs7s0vuzu/m7JK785Pdndkl5/O3aeNRdHTEhccdO7OkhoYG7d69W0OGDPnK8YqKCqWnp6tbt26aO3eupNOl7dlnnw0eY4xRSEiIk9MEAACwjqMlbf/+/eratat8Pt95Y4FAQJMnT1ZSUpJmzJghj8cjSfL5fFq1apXeeecdSdKaNWuUmJjo5DQBAACs49hvd0pSWVmZYmNjm+2bOHGisrKy9Pnnn+u9995TIBDQK6+8Iknq1auX5s6dqyVLlig/P1/19fXq2rWr5s+f7+Q0AQAArOP4O2ktgXfSnEV2d2aX3J3fzdkld+cnuzuzS638nTQAAAB8PZQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKhTn3x+vXrtWbNmuD2gQMHlJKSolmzZgX37du3TzNmzNCJEyfUv39/zZ49W6GhoSovL1dOTo6qqqp0ww03aMGCBWrXrp1TU70yHqm6zq/PP6yULyxU7a8JlUxLT+oqcXN2t2Pt4UZc92hhHmOM45fcBx98oMzMTK1du1ZRUVHB/SNHjtSjjz6quLg4TZ8+Xb169VJ6eromTZqk0aNHa8SIEVqxYoXq6uqUk5Nz2eerqqpVU5MDsTzSvs+Oa+m6t3XKH1CYN0RZY+PU4/sdWv8PrpuznyUmJlKVlTUtPY2ri7WX5NK1P4vr8nPdS3Lhup/D6fxt2ngUHR1x4fFLfcG//Mu/aNeuXd9oEvn5+crOzm5W0A4ePKj6+nrFxcVJklJTU7Vlyxb5/X7t3r1bw4YNa7bfBtV1/uAPrCSd8ge0dN3bqq7zt/DMnOfm7G7H2sONuO5hg0s+7kxMTFRBQYFmz56tsWPHasyYMfrOd75z2SfYtWuX6uvrlZSU1Gx/RUWFYmJigtsxMTE6fPiwjh07poiICIWGhjbbfyUu1kq/ic8/rAz+wJ5xyh9QnT+gG78fdYFPtQ5uzn6umJjIlp7CVcXaf8lta38uN+Xnuv+Sm9b9q7Rk/kuWtNGjR2v06NH66KOPtHHjRt15553q27evxo8fr5tvvvmSJ1i7dq3uv//+8/Y3NTXJ4/EEt40x8ng8wX+e7dztS3HqcacvLFRh3pBmP7hh3hD5vCGt/nawm7OfzY23/ln709y49mdzW36u+9Pctu7nsv5xp3S6UH366af65JNPFAgEFB0drfz8fC1duvSin2toaNDu3bs1ZMiQ88ZiY2NVWVkZ3D5y5Ig6deqkqKgo1dTUKBA4/YNRWVmpTp06Xc40Hdf+mlBljY1TmDdEkoLvKLT3eVt4Zs5zc3a3Y+3hRlz3sMEl76QtXrxYL774or73ve8pPT1dTz75pLxer+rq6jR48GBlZWVd8LP79+9X165d5fP5zhvr0qWLwsLC9NZbb+mWW25RUVGREhIS5PV61b9/f23evFmjRo3Spk2blJCQ8M1SfluM1OP7HfS7X/9Edf6AfN6Q0z+wbniJ1M3Z3Y61hxtx3cMCl7yTdvToUT399NN6/vnnNXLkSHm9p/8rwufzaeHChRf9bFlZmWJjY5vtmzhxokpKSiRJCxYs0Lx58zR8+HDV1dXpF7/4hSQpLy9P69atU3Jysvbs2aMHH3zw62RzhpHaX+NV7xtj1P4al/3Aujm727H2cCOue7Swq/JHcFxtjv0RHGdx83N6srszu+Tu/G7OLrk7P9ndmV36O3knDQAAAFcXJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsFOrkl2/btk3Lly/XyZMnNWDAAM2cOTM4tm/fPuXm5ga3jx49qg4dOujll19WYWGhFi5cqOjoaEnSoEGDlJ2d7eRUAQAArOJYSSsrK1NeXp7Wr1+v6Oho3Xvvvdq+fbsGDhwoSerRo4eKiookSSdPnlRaWpry8/MlSaWlpcrNzdXIkSOdmh4AAIDVHCtpW7duVXJysmJjYyVJixcvVlhY2Fceu3LlSt16663q37+/JKmkpESffPKJVq5cqW7duumRRx5Rhw4dnJoqAACAdTzGGOPEF+fl5cnr9erAgQM6dOiQBg0apAcffFAej6fZcTU1NRo2bJj++7//O/h4MzMzUxkZGerXr58WLVqk8vJyLVy40IlpAgAAWMmxO2mBQEB79uzR6tWr5fP5NGXKFBUWFio1NbXZcS+99JKGDh0aLGiStGLFiuD/njBhghITE6/o3FVVtWpqcqR7BsXERKqyssbRc9iK7O7MLrk7v5uzS+7OT3Z3Zpecz9+mjUfR0REXHnfqxB07dlR8fLyioqIUHh6uoUOHau/evecd9+qrryo5OTm4XVNTo2effTa4bYxRSEiIU9MEAACwkmMlbfDgwSouLlZ1dbUCgYB27Nihnj17NjvGGKN3331Xffv2De7z+XxatWqV3nnnHUnSmjVrrvhOGgAAwN87xx539unTRxMmTFB6err8fr8GDBigMWPGaOLEicrKylLv3r119OhReb3eZr9QEBISoiVLlig/P1/19fXq2rWr5s+f79Q0AQAArOTYLw60JN5JcxbZ3Zldcnd+N2eX3J2f7O7MLrXid9IAAADw9VHSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACxESQMAALAQJQ0AAMBClDQAAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAuFOvnl27Zt0/Lly3Xy5EkNGDBAM2fObDa+fPlybdy4Ue3bt5ckjR07VuPGjVN5eblycnJUVVWlG264QQsWLFC7du2cnCoAAIBVHLuTVlZWpry8PBUUFOill17Se++9p+3btzc7prS0VIsWLVJRUZGKioo0btw4SdLs2bOVnp6uLVu2qFevXiooKHBqmgAAAFZyrKRt3bpVycnJio2Nldfr1eLFi9WnT59mx5SWlmrlypUaNWqU5syZo1OnTsnv92v37t0aNmyYJCk1NVVbtmxxapoAAABWcuxx56effiqv16vJkyfr0KFDGjRokB588MHg+IkTJ9SjRw/l5OToBz/4gXJzc1VQUKBx48YpIiJCoaGnpxYTE6PDhw9f0bmjoyO+zSgXFBMTeVXOYyOyu5eb87s5u+Tu/GR3r5bM71hJCwQC2rNnj1avXi2fz6cpU6aosLBQqampkqR27drp6aefDh6fkZGh6dOnKz09XR6Pp9l3nbt9KVVVtWpqMt88xEXExESqsrLG0XPYiuzuzC65O7+bs0vuzk92d2aXnM/fpo3nojeWHHvc2bFjR8XHxysqKkrh4eEaOnSo9u7dGxwvLy/Xhg0bgtvGGIWGhioqKko1NTUKBAKSpMrKSnXq1MmpaQIAAFjJsZI2ePBgFRcXq7q6WoFAQDt27FDPnj2D4+Hh4XriiSdUVlYmY4yee+45JSYmyuv1qn///tq8ebMkadOmTUpISHBqmgAAAFZyrKT16dNHEyZMUHp6upKTk/Xd735XY8aM0cSJE1VSUqKoqCjNmTNHU6ZM0fDhw2WM0f333y9JysvL07p165ScnKw9e/Y0e5cNAADADTzGGGdf3moBvJPmLLK7M7vk7vxuzi65Oz/Z3ZldasXvpAEAAODro6QBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhUKd/PJt27Zp+fLlOnnypAYMGKCZM2c2G3/11Ve1bNkyGWN0/fXXa968eerQoYMKCwu1cOFCRUdHS5IGDRqk7OxsJ6cKAABgFcdKWllZmfLy8rR+/XpFR0fr3nvv1fbt2zVw4EBJUm1trfLz87Vx40Z17txZTz75pJYtW6aZM2eqtLRUubm5GjlypFPTAwAAsJpjjzu3bt2q5ORkxcbGyuv1avHixerTp09w3O/3Ky8vT507d5YkdevWTYcOHZIklZSUqLCwUKNGjdK0adN0/Phxp6YJAABgJY8xxjjxxXl5efJ6vTpw4IAOHTqkQYMG6cEHH5TH4znv2Pr6eqWnp2v8+PG64447lJmZqYyMDPXr10+LFi1SeXm5Fi5c6MQ0AQAArOTY485AIKA9e/Zo9erV8vl8mjJligoLC5WamtrsuJqaGmVmZqp79+664447JEkrVqwIjk+YMEGJiYlXdO6qqlo1NTnSPYNiYiJVWVnj6DlsRXZ3Zpfcnd/N2SV35ye7O7NLzudv08aj6OiIC487deKOHTsqPj5eUVFRCg8P19ChQ7V3795mx1RUVCg9PV3dunXT3LlzJZ0ubc8++2zwGGOMQkJCnJomAACAlRwraYMHD1ZxcbGqq6sVCAS0Y8cO9ezZMzgeCAQ0efJkJSUlacaMGcHHoD6fT6tWrdI777wjSVqzZs0V30kDAAD4e+fY484+ffpowoQJSk9Pl9/v14ABAzRmzBhNnDhRWVlZ+vzzz/Xee+8pEAjolVdekST16tVLc+fO1ZIlS5Sfn6/6+np17dpV8+fPd2qaAAAAVnLsFwdaEu+kOYvs7swuuTu/m7NL7s5Pdndml1rxO2kAAAD4+ihpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFHS9q2bduUmpqqpKQkPfroo+eN79u3T6mpqRo2bJhmzJihxsZGSVJ5ebnGjRun4cOHa8qUKTpx4oST0wQAALCOYyWtrKxMeXl5Kigo0EsvvaT33ntP27dvb3ZMTk6OZs2apVdeeUXGGK1bt06SNHv2bKWnp2vLli3q1auXCgoKnJrmlfNI1Sf9KvmwUtX1jZKnpSd0Fbk5u9ux9nAjrnu0sFCnvnjr1q1KTk5WbGysJGnx4sUKCwsLjh88eFD19fWKi4uTJKWmpmrp0qVKS0vT7t27tWLFiuD+e+65Rzk5OU5N9fJ5pH2fHdfSdW/rlD+gMG+IssbGqcf3O0impSfnMDdndzvWHm7EdQ8LOHYn7dNPP1UgENDkyZOVkpKi559/Xh06dAiOV1RUKCYmJrgdExOjw4cP69ixY4qIiFBoaGiz/TaorvMHf2Al6ZQ/oKXr3lZ1nb+FZ+Y8N2d3O9YebsR1Dxs4dictEAhoz549Wr16tXw+n6ZMmaLCwkKlpqZKkpqamuTxfHnv2Bgjj8cT/OfZzt2+lOjoiG8e4Ct8/mFl8Af2jFP+gOr8Ad34/ShHzmkLN2c/V0xMZEtP4api7b/ktrU/l5vyc91/yU3r/lVaMr9jJa1jx46Kj49XVNTpi3no0KHau3dvsKTFxsaqsrIyePyRI0fUqVMnRUVFqaamRoFAQCEhIaqsrFSnTp2u6NxVVbVqavr270f7wkIV5g1p9oMb5g2Rzxuiysqab/18NnFz9rPFxES6Kq/E2p/hxrU/m9vyc92f5rZ1P5fT+du08Vz0xpJjjzsHDx6s4uJiVVdXKxAIaMeOHerZs2dwvEuXLgoLC9Nbb70lSSoqKlJCQoK8Xq/69++vzZs3S5I2bdqkhIQEp6Z5RdpfE6qssXEK84ZIUvAdhfY+bwvPzHluzu52rD3ciOseNvAYYxx7BXLDhg169tln5ff7NWDAAM2cOVOTJk1SVlaWevfurffff18zZ85UbW2tevbsqXnz5qlt27Y6ePCgcnNzVVVVpeuuu06LFi1q9j7bpTh1J03S6d/2qfOrzh+Qzxty+gfWLS+Rujn7/+fa/6pk7d279v+fK/Nz3btz3c/S0nfSHC1pLcXRkvb/ufnCJbs7s0vuzu/m7JK785Pdndmlli9p/I0DAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICFKGkAAAAWoqQBAABYiJIGAABgIUoaAACAhShpAAAAFgpt6Qk4oU0bT6s6j43I7l5uzu/m7JK785PdvZzMf6nv9hhjjGNnBwAAwNfC404AAAALUdIAAAAsREkDAACwECUNAADAQpQ0AAAAC1HSAAAALERJAwAAsBAlDQAAwEKUNAAAAAtR0gAAACzUKv/uzm/b7373Ox07dkyPP/54s/379u3TjBkzdOLECfXv31+zZ89WaGjr+ld6oezLly/Xxo0b1b59e0nS2LFjNW7cuJaYoiPGjx+vo0ePBtdzzpw56tOnT3C8ta/9pfK35vXftm2bli9frpMnT2rAgAGaOXNms/HWvPaXyt6a1339+vVas2ZNcPvAgQNKSUnRrFmzgvta69pfTvbWvPaSVFRUpKeeekqSlJCQoIceeqjZeIutvcFF7dq1y/zTP/2Teeihh84bGzFihPnLX/5ijDHm4YcfNs8999xVnp2zLpZ90qRJ5s9//nMLzMp5TU1N5rbbbjN+v/+Cx7Tmtb+c/K11/T/77DNz2223mUOHDpmGhgZz9913m9dff73ZMa117S8ne2td93P99a9/NYmJiaaqqqrZ/ta69me7UPbWvPZ1dXXm1ltvNVVVVcbv95s777zT7Ny5s9kxLbX2PO68iC+++EKLFy/W5MmTzxs7ePCg6uvrFRcXJ0lKTU3Vli1brvIMnXOx7JJUWlqqlStXatSoUZozZ45OnTp1lWfonL/97W+SpIyMDI0ePbrZf2FKrX/tL5Vfar3rv3XrViUnJys2NlZer1eLFy9udgexNa/9pbJLrXfdz5Wfn6/s7GxFRUUF97XmtT/bV2WXWvfaBwIBNTU16eTJk2psbFRjY6PCwsKC4y259pS0i5g1a5ays7ODt3fPVlFRoZiYmOB2TEyMDh8+fDWn56iLZT9x4oR69OihnJwcFRYWqrq6WgUFBS0wS2dUV1crPj5eK1as0LPPPqu1a9dq586dwfHWvvaXyt+a1//TTz9VIBDQ5MmTlZKSoueff14dOnQIjrfmtb9U9ta87mfbtWuX6uvrlZSU1Gx/a177My6UvbWvfUREhB544AElJSVp4MCB6tKli/r16xccb8m1p6RdwPr163XdddcpPj7+K8ebmprk8XiC28aYZtt/zy6VvV27dnr66ad14403KjQ0VBkZGdq+fftVnqVz+vbtq/nz5ysyMlJRUVG68847m+VrzWsvXTp/a17/QCCgN954Q4899ph+//vfa+/evSosLAyOt+a1v1T21rzuZ1u7dq3uv//+8/a35rU/40LZW/vav//++9q4caNee+017dixQ23atNEzzzwTHG/JtaekXcDmzZu1c+dOpaSkaOnSpdq2bZsee+yx4HhsbKwqKyuD20eOHFGnTp1aYqrfuktlLy8v14YNG4LbxphW8fLsGXv27NEbb7wR3D43X2tee+nS+Vvz+nfs2FHx8fGKiopSeHi4hg4dqr179wbHW/PaXyp7a173MxoaGrR7924NGTLkvLHWvPbSxbO39rUvLi5WfHy8oqOj1bZtW6WmpurNN98Mjrfk2lPSLuC//uu/9PLLL6uoqEhZWVkaMmSIpk+fHhzv0qWLwsLC9NZbb0k6/ZshCQkJLTXdb9WlsoeHh+uJJ55QWVmZjDF67rnnlJiY2IIz/nbV1NRo/vz5OnXqlGpra1VYWNgsX2tee+nS+Vvz+g8ePFjFxcWqrq5WIBDQjh071LNnz+B4a177S2Vvzet+xv79+9W1a1f5fL7zxlrz2ksXz97a17579+7atWuX6urqZIzRtm3b1Lt37+B4S649Je0KTZw4USUlJZKkBQsWaN68eRo+fLjq6ur0i1/8ooVn56wz2aOiojRnzhxNmTJFw4cPlzHmK2+R/70aPHiwBg4cqJ/97GcaM2aMxowZo759+7pm7S+VvzWvf58+fTRhwgSlp6crOTlZ3/3udzVmzBhXrP2lsrfmdT+jrKxMsbGxzfa5Ye2li2dv7Wt/2223acSIEUpNTdXo0aPV2NioX/3qV1asvccYY67KmQAAAHDZuJMGAABgIUoaAACAhShpAAAAFqKkAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAHABRQWFmro0KE6ceKE6urqlJSUpE2bNrX0tAC4BH+YLQBcxL/+678qMjJSDQ0NCgkJ0W9/+9uWnhIAl6CkAcBF1NbWKiUlReHh4XrxxRcVFhbW0lMC4BI87gSAi6iqqtKpU6dUXV2tioqKlp4OABfhThoAXIDf79ddd92lu+66S01NTVq/fr1eeOEFeb3elp4aABfgThoAXMCiRYvUsWNHpaWl6ec//7muvfZaLV68uKWnBcAluJMGAABgIe6kAQAAWIiSBgAAYCFKGgAAgIUoaQAAABaipAEAAFiIkgYAAGAhShoAAICF/h8YhJIAUruo/AAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.42\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print_sns_plot(dataset3)\n",
|
|
"model11 = TrainModel_medoids(dataset3, 4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e42aee29",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Przykłady z syntetycznymi zbiorami danych"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 111,
|
|
"id": "47339051",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Dataset with 4 centers\n",
|
|
"X1, Y1 = make_blobs(n_features=2, centers=4)\n",
|
|
"print_sns_plot(X1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 112,
|
|
"id": "437665cb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 2 0.53\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model12 = TrainModel_medoids(X1, 2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 113,
|
|
"id": "6d4c1c3e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 3 0.7\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model13 = TrainModel_medoids(X1, 3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 114,
|
|
"id": "0a90a853",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.69\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model14 = TrainModel_medoids(X1, 4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 115,
|
|
"id": "85bc48d6",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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BxoIKsDm68HVdKza8cYxFEYmIiFIMZ8CU1tO66ODn58XgC2BRRCIiolTCAExhnmrULkFgBX0iIqIUxQAsWmTyu7yrUbMoIhERUWpiDlg09Cwz9i54OK4gS6xGvf9IDRaVFGNreSWLIhIREaUYBmBRINf09tl7Z8Ks14jVqHcfPIV5s0Zj+EAjhlr0DL6IiIhSBAOwKAjW9Fa2GjWDLyIiopTAACwKPMuM3kGYT34Xq1ETUaSo3LPuLbZOZJu0MGem855ClAAYgEUBm94SkSIC5JuGdK9JtuAt2f4eSmoMwKKBTW+JSAEB8009M+xy+hu8xZtk+3so6bEMRbT0LDMW5BrcN0LeAIgowgLlmwYjF7wlajHoZPt7KPkxACMiSlCefFNvodYT7E/wFo+S7e+h5McAjIgoQXnyTT1BmE++aRBSwdtgix4GncavgHRAMkWnldafYJQoFpgDRkSUqPqRb9p7s9Bgix4LZxfj0d9/FHoOVRzlXXHzEyUalSAICfXRbGqyweVKqEPut7w8ExoaWmN9GCmB51o5PNfKkT3Xnl2D9k4YdBox+PLQatICJvRb251Ytf5gWM+JKq+/J1abn/i5Vk68n2u1WgWLxSj775wBIyJKVV41CWsa7AELSEsJVnRacayxSAmEOWBERNSnHCrmXRH1neIB2Mcff4zbbrsNpaWluOOOO3D27FmlD4GIiHoJO6FfBUAQcP+iySgrGYPcbF1YmwCIUp3iS5APP/ww1q9fj7Fjx+L111/HmjVrsGHDBqUPg4iIvIWT0C+RfH/PrRMxarAJRh2rzxOFQtEZsM7OTjzwwAMYO3YsAGDMmDGoq6tT8hCIiEhOiAWkpYqebnjjmHuDFIMvopAoOgOWkZGB0tJSAIDL5cK6deswe/bssF4j0I6CZJaXZ4r1IaQMnmvl8FwrJ5Ln+tzJBsnk+zZnN4oKciL2PomKn2vlJPK5jloAtmfPHjz99NM+vxs1ahQ2btyIzs5OPPLII+jq6sKyZcvCel2WoaBo4rlWDs+1ciJ9rvXadGg1aX7lJ/SatJT/35Sfa+XE+7mOWRmKOXPmYM6cOX6/t9vtuOeee5CdnY0NGzZAo2GyJhFRImHRU6L+i0kSfmFhIZ588kmo1ayCQUSUcPpRgZ+I3BQNwL788kvs27cPo0ePxrx58wAA+fn5+MMf/qDkYRARUX+x6ClRvygagH3rW9/CiRMnlHxLIiJKdJ4WQ7ZOZJu0MGey1AUlPrYiIiIiZYUTUMVRw2+iSGIARkREygkzoJKqOfbCts9i1/CbKEKYBU9ERIqRC6isbU7/B6sAW0c3Sq8twsLZxcjN1onPabF3KnnYRBHHGTAiIpIWhdyrFlunZBHXFnun74yWxEzZopJi7D54Cq12Jxt+U8JjAEZERP6ilHuVbdJKFnHtHVBJzZRtLa/EvFmjMTzfyLIXlPC4BElERH7CWioMg6eIq1aTBgC+RVy9yM2UjR6WxQR8SgqcASMiIj8hLxWGK8QirnIzZblmLYMvSgqcAYs3KsDa7kRNgx1WRxegivUBEVEq8gRA3qSWCvtFAKCSvsmFOlNGlKg4AxZPZHIuLDnyzTyJiKIhav0eQ80tY7sjSnIMwOKIXM5F0bBsZHAmjIiUFKUAKKy6Xmx3REmMS5BxRC7norm1PUZHREQprScAKsg1uIOgCARAgXLLiFIJAzAlhJjXJZdzkWPKVOAgiYiiT5HcMqIEwAAs2nryHVatP4gnXjmEZ189gtrGNtQ0+gdjckmng3MNMTp4IqLIYnI9kRtzwKLMO98hN1uHG2eMwC83HpZOPpXJuVCrmQBGREmCyfVEABiARZdXHzMAyDJo8F+7v/JLPl2zfAbsbU6x1QeTTokoqckl10eh9RFRvGIAFi0SW62XzZsAk0GDjpZLCagdzm7UnrfhhW2fRazVBxFRwolS6yOieMUcsP6SSbCX2mr9+53HMXtaoc/TtZo0aDPSxMdEotUHEVGiiVbrI6J4xRmw/ggwYpPbaj0oRy+219Bq0lBWUoz65jafx/S71QcRUYKJWusjojjFAKwfAhUUNGRqUFZSDJcA7D9Sg8YWB7SaNFiydJg3azRcggC1SgVdRhreOHBSfE1uxyaiVCTX+5H3Q0pWDMD6QW7EdrapDS9svTQrtqikGOWHqnH7jeMwNDcTpsx8cfdPQ4sDrXb3FHvEWn0QESWYqLU+IopTDMD6QW7EVnve5jMrtrW8EmuWzYDFlAG4fHf/mPUabscmImJ5CkoxDMD6waxPx+o7p6HidDNcAvD+p2ewqGQMNr1d4fO4Dmc37A4nLMYAU+kCABXrfRFRCmPvR0ohDMD6SgVUVPsm4N972yQMtOjEJUUP2TwGbrsmInJjDTBKMSxD0UdSCfjrXz+KL6qaUVZSHFKbDW67JqKUEKwfbq+Wbat+9yEqai7K9s0lSgacAesjuQT8DqcL+4/UYN6s0SgaloU8s1Y2j4Hbroko6YUw0x9oRznvhZSsOAPWR54EfG9aTRogAI0tDmwpP4Hubpf75iEzjS73Gtx2TUTJIpSZ/kCDUaJkxRmwPpLaMr2opBi7D54C4A6kLGZd0Nd4aMkUVH1jFeuCFQ0xc+cPESWNUGb6WQOMUhEDsHB5JYoOH2gUt0y7BOAPbx4XC64unz8BFrO77EQgnV0u7Dxw0mdqnogoIUkk0ocSXLEGGKUilSAICfXxbmqyweWK0SH35DJsersCV08eBrUaGDciB8Pz9O5js3aiyepAtkkLweVCplYTcCePtd2JVesP+t2Yeuc95OWZ0NDQGtU/jdx4rpXDc60cRc61XK5XYZbfjnHJ3d6e4C3Ba4Dxc62ceD/XarUKFotR9t85AxYGa5sTm96uQMn0QmwtrxRvJisWTML4wmxYTBloae3AZ5WNPkuKRUNMTMInoqQWKJF+eL4Bj909HY7OLuSaZDYmsQYYpRgGYGFosXXi6snDxOALcN9k1m0/imfvnQm1WoUzDTafJcWykmIMzMmEUet/qpn3QETJItTWbA8tmSI+nvW+KJVxF2QYsk1aqNWQnbWytndhS6/gbEt5JaztXZKv58l7CKVmGBFRPJPb1e3dms1k0OBMg431vojAGbCwmDPTMW5EjuysVaO1QzI4c3RIB2DsfUZEyUIqkf6eWyf6tGa7bmqBOEjNzdbhuqkFqK1vRf6ATHev3Gjc+1hhn+IUA7BwCMDwPD1WLJiEdduP+u/WUakkg7MsQ4Z7hCd10TPvgYiSgcSAUp2m9m3NpoIYfN00c6SYzrHzQFV02rCx3RvFMS5BhssFjC/MxrP3zsQTS6fj2Xtnihez1JLiopJiPP/aJ9LT7MHacxARJRIBMOs1yDZkoMXmLqLqfU9U9wxSr5ta4JdLG402bGz3RvGMM2B9ITdr1TMCXLNsBj4+UQ+XC9h98BQaWxz+bTU4MiOiZNPrvjbYosd9CyaJOyDzs3QYnm9EbX1r/3aAh7isyJ3mFM8YgEWaANjbndhSXunz694XPXufEVGy8b6v5WbrUDK9EE+98r9+dcHyB2Ri54Gqvu0AD2Pwyp3mFM+4BBkFofR49B6Z5WbrsPB7xSi9tgj2jm4uRRJRQvK+r8kuM9qdsJgy+rwDPJxlRe40p3jGGbBI8ZoSz8nSBW2r4QnSTAaNTzLqrnejlIxKRBRlPjNOKvmSPeZMTeg7wHstNza1Su82P3ehHVCpfJcjudOc4hgDsEiQmBJ/aMmUgBe9Z2RWW2+THCVyKZKIEo13KQoAgZf/QtkBLnFvfXDJFMnX7XYJ+PsX5y61h/P04Y3UTnOWs6AIYwAWAVJT4s9t/gTP3jsTBbkG94Mk2m6MK8iCNiNNfpSo18Da5sS5kw3Qa9N5wRNRfPOacbI5nBieb5Qu2RPifUzq3vrmgX9g+fwJeGnHcfF1l5Zejk17KlDX1ObTHi5i90tumqIoYAAWAX3eaSMAuVk6v9HcYIsexkwNKmovova8DXsPV6PV7uQFT0Txz2vGaUiOvl/Lf73vrbnZOkwbPxjb9lai9JoiqNVAccEAbN97AnVNbQB828NFahWBm6YoGpiEHwGhJN1DDTTZOlH5jRVN9k7xzPdOEh1s0WNRyRj8v5c+wq83fYydB07ippkjYTJoWL+GiBJLTzBWkGtwByphDh5731s9if11TW3Ytq8SW8or8ex/HcGE0fk+z/MMgCMlUJ9LbpqivmIAFgFBd9qogaNVzXj09x/hmf86gkdf+ghHq5rdZ99ryv6JpdPx4OIp2PDGMZ+R1tbySlw3tQAdzm40WjtYuJWIkotMUere91a5XrzqXt9kkS41EajPJQfF1FdcgoyEIDttmqydYr4C4L5hvLTjONYsmwGLMcNnyr6mwS55g4HKfcGfPHMRW8pPMAeBiJJDkPwq73urQaeRrB82tvBSj96+5JoFY85Mxz23ThQHx54uJ7sPnsLYwmwuQ1KfMACLlAA7bVrbO1F6TZE4qtt/pAaNLQ40WR3uAMyLXOFAtUqFpaWXY+veEwCYg0BEySFofpX3vVUFyRI/Bfn9yzULSgBGDTFj3qzRcAkCILi7nLTanSzqSn3GACza0oDWNid2vVflM3IqP1QNi1nn93CzPt2v2ffS0vEYbNHjT7sr0NjiEB/LlhpElOjC2sQkt9rgilCpiQCM2jQMzzcGrO9IFA4GYNGkAmrOt2H96/45XY/c8W1YzBmXatX0PL6i+iI2v/MVFs6+DANMOliydMjP0cPp7EbNOZvPy7OlBhElurDbBfWlrlc4NbzkHsuirhRhDMCiyNrmxFfVzZKju/aOLr8L13sq/tU9XwFw34h+++AsGLVpQavrExElGu/irVG5t4VTwyvYYyNV1JUIDMCiqsXWCZcgXQ265pwNQ3L0PlPsclPxza3tGGTWcfRFRMknyjNL4dTwYr0vUhLLUERRtkmL9z89g0UlxT4lKu7+/njsPVztV6dGbqtzjinT/UM/a+oQEcWlKN7bAuWY9eexRP3FACyKzJnpuP3GcSg/VI3Sa4pQVlKMn94+Fd0uF2ZPK0SOSev3eKl6YoM97YyIiCg0PbXFulwCykrGIDf70qYnuRyzkIpqE0UIlyCjqWdqfdUPp+JsYxvS1Gq8/JfPxX5lw/ONvnkIAjCuMAtrls1wl6gw62AxZ0CtZsVVIqKQSeRylZUU462e0hFyOWZRz0cj8sIALNp6EjfPN7dha3ll4NyCnl2QvS9+ywBj7I6fiCjBSOVybSmvxGN3T4dRmyYfUHGnIymIS5AKaLF1wtHZHTS3QC4BtK7R7v+iMq07iIhSnVwul6OzK3hAxVxbUghnwKLFq5aMQa+BWqUKWusm2C5I79cOeVs1EVE8Cqc2V5jkaoudrL2Ijo5u3ispLig+A3bkyBHMnz8ft9xyC5YvX46LFy8qfQjR1xMgrVp/EE+8cgjPb/4EwwcaUNZrN6RPw26EsAuyh9xMGZvCElFC6HWPXPW7D1FRczH8mfwQm3h7OpDsPVzNeyXFDcVnwH72s59hw4YNGD16NJ577jm88sorePDBB5U+jKjqHSDVNbVh056v8NAPpuKyggFwdHQh16z1mwqXSwAdnGtAU9OlKvhhte4gIoozEam3FUIT78funo5jJxvF3o2eVm68V1I8UDwA2717NzQaDZxOJ86fP48xY8YofQhRZ2t3+jXfrmtqQ2tbJwq8S0r0ngKXSABVq1X44utG6LXp4hR92K07iIjiSCQGkaE08Tbq0rHr3SreKykuKR6AaTQanDhxAj/60Y+Qnp4e9uyXxRLfOwJdLgFf1l6UbL49yGJEXp708btcAuoa7Wi+2I4ccyYmDcnGoS/OYe1rn4ivs3LxFMyYMBgWACsXT/H7t5HDBgQtWSG+j9X9PoNzDSxz0UteninWh5AyeK6VE0/nulOQzokNdI/s7dzJBskgrs3ZjaKCHACAxSUEvVdG454YT+c62SXyuVYJghCVVMQ9e/bg6aef9vndqFGjsHHjRvHnLVu24M0338SWLVtCft2mJhtcrjjKnuyVSKpWAQ+v+9DvxrL6zmkYnquXTvyUmEpfsWASNr/zFeqa2nxeRxzded43nK3STN4PKi/PhIaG1lgfRkrguVZO3J3rvtyLet1rAWDV7/zvtX7LmD3Pszmc0GrSYXc4kW3sSfpHkOPow0aBuDvXSSzez7VarQo4aRS1AExKR0cH3n//fcyePRsA0NbWhquuugqffvppyK8RVwGYxE3knlsnYtPbFWKugccTS6f7Lj96sbY7sWr9Qb8bSek1Rdi2r1L+dcK8Oci9D/ucXRLvF3Qy4blWTlye63AGkWqgtqENFaeb4RKA9z89g7tuHo/OLle/mmwPzzeIA+bcbB2um1oAtRqYOjYf2nQ1ahvsqD1vw97D1WIB12AD1rg810kq3s91sABM0SXI9PR0PPnkkxg0aBAuv/xy7NmzB1OmTFHyECJKKgdhwxvHMG/WaGwpPyE+LljOgVw+hLrXHlWf1+nDCJLJ+0QUN3rqbYn3ngAzX1+cbsG67Ud90jr++D9f4Gf//O2QiqbK5YutvnOaGHzdNHOkWCx754Eqn8r5i0qKsfvgKTbmpohStAxFWloa1q5di5///OcoLS3FO++8g1/84hdKHkJEyQU0wwcapctNyGyZlis/MW5EjmzZir6UomCfMyJKNNY2pxh8Ae573dbySlw9eRiaWztCKpoqd69uuuiAVpOG66YW+HUq2VJeieumFojv5/lvNuamSFE8Cf/b3/42duzYofTbRoXcbsShuXq/fo4Q3DNWm96uwNWTh0GtBsaNyMHwPL1s+YnheXo8e+9MtDm7odekwWzQwGp3otHagW6XEPZsFvucEVGiCbRCEOrgUe5efaHVgUUlxeh0Sncq8QySPf/NAStFEivh94NcQNNwwYHnNn8Ck0GD2dMKMXygEfnZmdj0dgVKpheKIy1Psv34wmzp/mMu9xR9UUEOGhpbffpElpWMCV6KQiJHjH3OiCiRyAVP40bkhHz/krpXezY6ObtduOvm8ZLv4XltrSYNapWKA1aKKEWT8CMhrpLwAb9EUrVahYfXfQiTQeOTU1BWUgyXC2J5Co9QkuDz8kyoqmn2SaDPzdZh7syR2OIVzPXevcMdj+GL96TOZMJzrZyEPNde91aVSoWXdhxDXVPbpYHriGzAFfrr2Nqd0Gb07II0ZMBs0IiDWpNB43c/9c4Bu+fWiRg12ASjjrsg40m8n+u4SsJPSr0SSWsa7OhwdqN0apFPToFLANRqSE5zn21qg3l4gG3P8J+Gb2xx4K2Dp3D/oslIU6swaECmz8gsIpWmiYhiQWIAee9tE5FtykBW5qUVgr68zv0LJ6MgzwC4fAtf55i0uOKyXJ/B9MghZt+VAg5eKYIU7wWZ7HKydCgrGYOBOXqUXluE3Gx3E+39R2owYkiWZBJ87XmbO3lepj+ayyVIJtC32p2oPW9zB1+9ElAD7XgkIopnUgPI9a8fQ8WpFpxtbAvybIgbnr4+Z0NtvQ0mg0Z8HZ/NSj0D6IJcA4zadJ+EfqMuHdmGDLTYOmFtv7RpiihSOAPWH71nq/TpqD1vw84DJ322S3t6kO068A/ce9skrH/9qN+/jy3MBgDJWauiYdmSOQxlJcUYlmeUzElguyIiSlRyA0iXIASeyVcBNkcXvq5rxYY3jkneh0MqvcMUDlIAA7C+krhAV985zS+A2lpeidJrirDrvSrc/N0iFAw2Yt6s0XAJgtggttXuFEdaUjed5tZ2DDLrxOnyRmsHdD29IeVyErjjkYgSldwAEkKA3d499+Ta+kuDYMD3PrxtX2VIA1GmcJASGID1kdQFWnG6WTKAGjXU7L5we2p4Dc83SgdGKun+aDmmTACCf+FCQD6YkmjszeCLiBKB1ADSM4slF0B57sml1xbJlpQIdSDKotWkBAZgfSR1gboESDeY7ZWjNa4wy79OmEt+1mpwrgFNTbbwDzLUStNERPHEawB5tqkNtedt4mqBXADlfU+Wug9PHJ2LmeMHhjQQjUoKRx/6SlJyYwDWR1IX6PufnsE9t03Ehtcv5R4snz8BZoPXjh0VfLY+e+qEDc01yNbpUquZ/UlECS7cAMQzgByehaEWPcYWZsvP5KsAQ6YGZSXFyNCoccfccfjTWxU+A9khA3TyOxkl8nkjmsLBnDKSwDpgfSVxQa1YMAm7P/waE0bnu3fMCMD7n53BqtunirNQ1nYnnt30MWZ/pwA55kycb24L2ug13mudJBOea+XwXCsn5uc6mgGIxGsvuWEMACA3OxNDLfrAgZPcsRVmwWoPsVm4F6lzbW13+tRxBEKrAUmBxfxzHQTrgEWLRI6Vrd2JiuoWVFS3+DzUO2/A1u7EjTNGYPM7J3xuFv/9wddM8iSipBTNpHap1978zgmsWTYDFlNG0PpdwY4tEikczCkjKawD1h9eNWTMmRoY9RlBm11rteli8AVculnMmTGCdbqIKClFsy6h3GvbHc6QgiYlaiZK1XFkWSBiABZBniR6z4XmkzfQ46LMxZ5t1PGCJKKkFM0AJOzX7inSWtNgh9XRFdljUwFn623ia3uKt4by3UCphzlgkdarN2TvvIG6Fgf+7ZVDfrkADyyaDE2aGnnZOr8k1ZDWubnDJiLiPacgmfBcKyfm51rhHDDZ1/Z6rGcT1OhhWcjUpuOr6mZ0OF14/9MzuP3GceEfW7DjCPLdQOGL+ec6iGA5YAzAFGbr7MbHX9X7NX2dXJyHbxrskhevJceIU2cuoNHagUxtOkx6DYzatEsXL3fYREy8X9DJhOdaOXFxrqMZgIT42p5keJNBg5tmjkT5oWqUTC8U+/aG1ei716BXrQIeXvchE+0VFBef6wCYhB9njNo0DMu7VA1frVJhWJ4Rqp4WG1KJoJVn67D2tU/8WhAVDTEBAqs2E1EC6E9dwmAz/N6vHeCxnnyv0qlFYnX88kPVKL2mSFwu3PzOVz471+WOp/eg955bJ8Jk0MAEDa6bWiC+ns3h5H2YJDEAU5oAFA0xuZcavUZrNfV2ydywRmuHGHx5frelvBLzZo1GXrYO5kwNd9gQUfLq4xKj1GPFfC+V+x6ZqUvzmwFbVFIcNGiSGvRueOMYFpUUA4DP6w3PN2JIjp6rEeSHSfix0Gv3pM+NwYtWkwZNulq2Ka1nlw532BBRsrI5ulBbb0PptUVYOLsYJoMGL2z7DNY2p99j5VYDPI/1JMOre9q+5Q/Qi8GS5/FbyysBlTrgt6PcoHdIntHv9dZtPyp5rEScAYsHKqCltQNLSy/Hy7s+98lFOF1nlWyJoVapxAAraONtJugTUSJSAV/XtYrNtb17QkrN8AddDeip3zg834BBFj1aWh1YOPsyZBt1yNSl4XxzG9768BROVDdDm54Li1F6ECvXqsis13A1gkLGACxWvIIig16DuiY7/vL+1yi9pghqNTBicBb0OjX+tPtLLCop9pnSXlo6HtkGLdRqFWoa7Mg2aTGuUKbxNhP0iShBWduc2PDGMb8ZqnmzRkvO8Aft4eh13x0xyIRzzW1Y+9qnPvm1t84aDUdnt7tXr0wAJjXoLSspRqu9E4MtetQ1tUm/f28cHKc0BmCxIBEUlZUUw9ntwrZ9lQDcF+3jP74SrXYndh88JSaJqlUqjBqSheaLDnHHjXdQ1TvBlQn6RJSo5Ga0hg80Su50lFsNUKtV+Ka5DRdsnVi3/ajPfddk0KCjpdsnvzZDo4bFrJM/MAEYnm8QN1NBAN7qaRb+/370HfziP/83eA9JDo5THgOwGJAKirb07MjxBGAdzm44nV3izWTbvkrxAtWkqfDc5k9CCqqYoE9EiUpuRmuoRSapvVeLOINOgxZbBw5+fh6adBW27f1H0PuuSxCQPyATFnNGwFIUzdYObCk/4ff7tDSV9GpELxwcEwOwGJALijzblgH3TSZLn4HhuQb89sFZONdkC7pjUiqoCjolT0QUp4Lmt0oRALNeg7ONbXj6T0fE5y2fPzGk+667NJAhaB0wuXurxZyJDJUQtNwGB8fEACwG5C5ctUol/ve9t02E2aABXMDQfCMyVD1XsRBeUNWnGxgRUTzoNaMVagFXqdml881tQe+7nhqLnoT9QOTurYNzDWhqsgX90zg4JlbCjwWJtf8lN4yByyWgvbMbEABDZhomFeXCnKnxr/Ybbu4AW2CELN4rKycTnmvlxP25jnAyek2DHU+8csjnd7nZOiz8XjFe+csXPvfNgkFGtNg60eboQo5ZC4sp8NKj5HF73VvzckM818wB67d4/1yzFVG86rlwG1s7cLL2IvYerkZjiwOAexRUek0RvjVyAApyDdIfMgZVURHvF3Qy4blWTlyf6ygEIlZHF1b9zr8t0JIbxmDEYDMEQXDfNw0aVFRH9r3DOte8j/dLXH+uETwAYyHWWOnJUxC6BWg1arTa3YX6tJo03HXLt6DTpqGrW4DV0SUdcEoUcyUiSjTBiqf2hWd50FOg2tMq6IrLcjE8Vy/eN632EN5b5e4hWdNgh9XR5ZMz1m+8j6c05oDFkLXNiec2fwKTQSOWmTDo0pGWpvap+7Vy8RQUDzPBandP0edk6eDqdrF2DBElvKgkowfKHfO6VwZ9by4TUhQxAIshz8Xf0dItboNe+L1ibH7nBDqc3cjN1uG6qQU429AKXUYafr/zGJzdLsydORJbvAI03hCIKFFFLRk9hObfwd6bpSIomrgEqTSv6WyDXuPXw9GQ6c7/uv3GsVhaOgHvf3YGr+75Ck//6TBKphf6BF9AZKbriYhiRWq58KElUwAgOst+Qd77/oWTYTZoYG134tyFdtkZMqL+4gyYknpNZw+26LF8/gS8tOO4+HOWUYfN7xzz63vW2OLA1vJKrL5zGkqvLQIA7D9Sg8YWB2vHEFHi6rVcmGPSorbejlXrD0Z2ll9mp6XfUqVXYn7ptUUsFUFRwwBMQb2ns+ua2rBtbyXWLJsBu8MJnTYdj//H3/36nnkqNXc4u1Fxuhnb9rqr4t8xdxzaO7rg7HLBoHPnK3AZkogSjtdyobU9xGW/cEpXSORyPbRkCvKydeLzC/IMgACfxPz9R2r8evGyjiJFCgMwBUklfNY1tcHucKIg14DKb6wBKzVrNWlw9dSnMRk06OjsFltr7DxQxVwwIkp4ISXlh5kc33vwazJocKbBJrZ0835+o7VDfFxji0PsxTtyqBmDB2Qy+KKIYQ6YgjwJn97E6WwVMEDm3yFAXI7c/3ENAOC6qQXMBSOipBPwPtkj3NIVvYO666YW4H+/qMP9iybjJwsm44GyyfifD6pgbXMiU5vu8/6NLQ7seq8KJn1G+KUiolnCghIeZ8AUJNsWqCfnYNPbFX7T3ffcNhGDcvSYdFku1m3/TCzWChXYR4yIkk4o7dPCLV3Re7fjAHMGrptWiBe2XnqPpaWXo93ZBZM+A2UlxT47zctKit1LnOGQzPmdiK5uF3QZ6TDpNTDq0sTyQiwplHoYgCmpJ+Hz1yuugrW9C46OLuRm6XxyDjzT3Wo1MLk4D/lZWuRZTGhoasWSG8Zi3faj6HB2Q61SMTmUiJJPCP0fQypd0StH7KElU8QlxyG5Rvxy42GfGbSXd32OJ//lShi1aRiWZ8S8WaPhEoSe5txGGHXhBUfes3S52TqUTC8U39NdlX8scrO1eGHrUZYUSlEMwGKgtt7uO7pbNNkn58BTE+yy4dmwpqvRYG2EGiqY9Bo8/uMrcaK6GRCAJTeMEWuGMTmUiJJGkBpeQWfJZHLEfr3iKtS3ONDa5pSeQWvtQL5Ji6IhJneCfj9aBHnP0l03tUBc2fC81+Z3vsK8WaNZYyyFMQBTmFTuQu15m+Roztkl4NlNH6NkeqHPsmRZSTHeOngKAMTZsqlj8t1NZBl8EVGyCzJLFqiAaoezG2o1JO+5FrNOfP1gRVyD8Zmlk0kZcfVqxcw0ktTCJHyFSeUu7D1cjXtunehTDPDu74/Hn976AldPHuY3ctpSXonZ0wrF5NDh+SYGX0SUvKSS2QP0UZTLEWu0dqD2vA3//V4VlpZe7nPPXT5/AizmyKVwSBV59abVpEGtUvn9jmkkqYMzYAqTyl1otTuRm63DY3dPx7GTjYAA2B1O1DW1yY6cLhuehSeWTu/z9DgRUUIIp+RET96XRpMmOcOl06Zj7+Fq3PLdUdh/uNqd/tHZjdzsTKghRPY+6jVLZ3M4MTzfKObweueAeY6TaSSphwGYwsyZ6VixYJLPhbiopBi/234UDy6egl3vVqHD2Y2Fs4t9Rk69byTpaWoMz9X7NZclIkomIfdj9ArUTAaN305GT3DTanfC5RIwqXggqs+1AgKw+a9fodXujHz+lddS5pAcPZ69dyYarR3QadNhzkyHMTM94GYDSm4MwJQmAAOMGSi9pkicRve0GupwduH+hZOx6e0KaDVqLJ8/EVZ7h1+y/aKSYry04xhW3T4VZr0m9GrQREQJJtSSE96BWkdLN946eArzZo3G6GFZyDVr3cENgPsXTkZtfSu2lFf6vZfN4RTfM5Tq+mHde3vnlQGAq/+5ZpS4GIDFgFGfgV3vVfnNahl1Ggyx6H3KTWg1aXigbDJWLp6CM/Wt6HS6xICtxd6Js41tIVeDJiJKNCGVnIB/oNbY4sCW8hN4Yun0SzliKmBorh4ZGjXKSsZg7+FqsbbiYIseF1o78dQfD4e01Cm7LEoUIibhR5NMFWSp5EzP9LjV7hSDL8A90vvtls9wus6KTW9/hW37KtHY4oBWkwaDThNWNWgiokQT6H7pLWgF/Z6gadX6g/jFxsPYeeAk5s4cidxsXU8S/kS/e6/c/dQz22YyaLDwe8UovbYItfU22BxdUTgDlKw4AxYtQRJH/bZQG9zB17kL7ZLT7cMHGn2SNVcsmIQOZxer4RNRcguhMCsQvDaYVC7ZlvJKPHb3dBi1aWixh15dv8XWCZNBg5tmjvQpETTIokfhkOxong1KIgzAoiRo4qh3PoAKqKh2B2ul1xZJTrcPzdVj9Z3TUHG6GS4XsPmdr7B8/kRWwyei5BdKXa4ggZpcLllXVzfM2TogjO4i2SYtZk8r9CsRtOGNYxhbmIMM9nykEARdgvzJT36CgwcPKnEsSSVQ4mhv1jYnNr1dgdJripCpddcA855uX7l4CtQqFV7acQwuFwAVcPUVw7Dlr19hxYJJQafmiYhSQoDaYMGWKENd6vQ8dvhAo+Q9vq7RxqbbFJKgM2AlJSVYv349nnzySSxcuBC33norsrOzFTi0OBPmjhe5xFGVSiXufvSwtTt9qt0PtuixcvEUqFTAwGwdRg4bgOP/qPeriL+opBi52VpuYyYiktNz77a1O/1KAPnU3QpxqRNwP3ZorkHyHn+ipgVtji5uhqKgVIIghPQRqaqqwhtvvIG//vWvuOKKK/DDH/4QEydOjPbx+WlqssHlUvhTHU4hQK/nfFHd4lfvq/xQtbt8hFdOQZOtE4/+/iO/C3nNshmwGDOQl2dCZXUzDlfUi60r9h+pQavdKT6GIiMvz4SGhtZYH0ZK4LlWTsqe61737sEWPZbPnwhBEPo/YJX4Xrj7++NhdzjR0enCzMsHwqiNYpZPuGUwklC8f67VahUsFqPsv4f06XC5XKiursbp06fR3d0Ni8WCJ554ArNmzcL9998fsYONVyEXAvQWoN5X76ROu8MJk0GD0qlF4tT1/iM1sDucsBgz4HIJqK23YeeBk+JNZOn3J7jLUnS5/GbUiIjI/95d19SGX2487JOL22cCMK4wC48vvRLnm+2wZGXitXcqUFHdIibkTxw5wPc9IhU09WVSgOJO0ABs7dq12LFjB4YPH44lS5bgt7/9LTQaDdra2vBP//RPKRGAhVoIsDe5el+9kzpzzDrMnTnSp2rz0tLxMOkzABVQ12jHhjeOocPZjdxsHUqmF2Lta5/0/8LjCIqIklhf790h8do85b3K0XDRgcYWBza8ccx3kB7BoKlPkwIUd4Im4Tc3N+MPf/gDNm/ejJtvvhkajft/XL1ej3//93+P+gHGg6D1ZWSEmtTp6naJwRfgvphe3vUFzje3o6quFY0X27Hge5fhp7dPxZ03fQudThdMBo342Be2fYam1k6/emMBedXEeeKVQ1j1uw9RUXORyaNElDT6eu8OhVQQtLW8EtdNLRB/9t50JRc09aVuYzibvCh+BZ0Be+qpp2T/7bvf/W5EDyZeBasvIyvEpE7viyk3W+e+gFVAhkaFsw12vzZE735Si5tmjvRqYdSNj0/UY0t5ZcijKo6giCjZ9fneHQK5IMgziO0d6EVyNi7U7gAU31gHLBTh7I6ReG6w+jWei6l3Yb+ykjFi3hdwaYRVek2R+P/3f1yD2dMKkZediYWzi7H/SE1IgVRUp+aJiOJBf+7dQMA0DbkgCAIkA72wgqYg6SHRDCxJOQzAQhVKIcA+8lxMtfU2n8J+LkGQHWF1OLuRqUvzyx1bVFKM3QdPBQ2kOIIiopQQ6r27d9CjT/fL8VqxYBLGj8juaaLtHwTdc+tEDMkz4LsTB8Go62PQFEquWH8DS4oLDMDiQc/FpM1I8wu4Ao2wxhQMwJMvH/KbIZs3a3TI+WkcQRFRypMIelYsmITN73zlc39dt/0oVt85DcNz9T5B0NmmNtSet2HT2xVotTulG3OHGDSFnB4SxUkBUkbMArAvv/wSCxcuxOeffx6rQ4gvApCbpfMJuPYfqUFZSbHfDFf5oWosnz8Rnc5u2b6R4oUtN5XNERQREaACmlo7UVvfitJri7D/SA0aWxxYt/0oSq8pwrZ9leJDO5zdqDjdjCy9xqeMxQtbP/O5F8umgYQQNHnSQ7zzgQHA5nAyPSTJxCQAa29vx1NPPQWnM/zdH0lLBTRcaMeSG8aKo65WuxODcvVYVFIMl0vA4FwD6i+04dopw2HK1EAl07tsqEUvBl8Bp7I5giKiVCZxj/SkcTS2OKDuVSdAq0mDywWfFI+I5dP2DJY1mjQMtuhxy9WjYLU74RIEqFUq2Nu7gBzwPp1EgpahiIZnnnkGd9xxRyzeOrZUgLXdKVkuwtrmxB//5wto0lWYN2s0Fs4uxrxZo2HUpWOASYft+/6BX2/6GFv+WglLlg5b953ASzuO4Z7bJvqUuVg+fwLMPSUqIrntmYgo2QQqJaHVpGFsYY7P/XVRSTHe/+yMT4pHREpdeJUFWrvlEywtnYCOzm7sPHAS2/ZWYueBk/im0Q6bo6v/fzTFDcVnwPbt2weHw4Ebb7yxT88PVNY/nrlcAj46XudTQHXl4imYMWEw1GoVzp1swNWTh+FPb1X4lKQAgMuLBuCJH1+Jc012ZGjS8Oa7J/GP2osAgIutDp9q+9v2VmLciBkYmm/EuZMNkiOzNmc3igpyFP37E0leninWh5AyeK6Vw3PtT+4eqVYDKxdPwYSiXPy/H30HX55qgssFlB+qxh1zx2PksAFQq90jaItLwMrFU/zu7d6PAdzfAXWNdjRb25FjzsTgXIP472frbWIg2NHSjc6ubr/akFvKKzF2RA5GDhug0NlJDIn8uY5aALZnzx48/fTTPr8bNWoUbDYbNm7c2OfXjUkvyAiwtjvFCxRwX1BrX/sEgwa48wT02nSo1fAJvjwlKbaUuy/qspJivNUzNQ5cmg4XqQBntwvnmmzIUAnQa9Mllyj1mrS47p8VS/HeWyyZ8Fwrh+damtw9cuqYfFhMGbh4sQ3DLJkwZw5Ei70TMy8fCLNeg6Ymm8/rFA81ifm0gyxGZKgF92M8Obj2TqhUKry04xjqmtr80kHONdp9jsHRIZ3f2+Zw8n9HL/H+uQ7WCzJqS5Bz5szBe++95/N/c+fORUtLC37wgx+gtLQUAFBaWgqbzRbk1RJAgOVFIHjlYnNmOsaNuDTdfd3UAp+SFJ4R0OxphQCAwRY9/t+PpmGAWQe1WoX9R2qw690qzJ05EjkmrfiaoVTiJyJKRXL3SIspw6fkgzlTg4Jcg2//SO97fnsXzHr3Y4bmG31ycFetP4gnXj6EX248jJLphcjN1vmlg/RexszUpkkua5r1LBOUTFSCIMRsOmnMmDE4ceJEWM+JyxkwuWT3wixY7e4diAa9Bs9v/gR1TW3i07SaNN+dMmrgi9Mt7t031xZh295Kv7d69EffgS5DjQutnVi3/ahf4mir3enXf8wzAuNOx+DifUSVTHiulcNzHUBf7pEBNjjl5brPtbXdiVXrD/rNrnnvrHxi6XQU5Br8Xu9HN49DtwviINxzjy8enoUhA/RRPBmJJd4/18FmwFgHLAKkEjk3vV2BJTeM9QmSls+fgG17K32moH0udhcwvjAba5bNQMPFdsmp8Vyze3brqT8elqyQv21fpe/uG+50JKJUF6iyfB/ukYFqdeX1PCbkVkU9x5Zl0GDNshmwO5ww6DR4/rVPfPJ7yw9VY8plU8P72yiuxTQAC3f2K15JXWhXTx4mBl+A+8J7acdx8QITR1pwT2N7XzwWUwZaWjv8aoB5AraaBrvshc1q9kREXkKpLB8mW7vzUnAEiLXDvJthh9SqyKDxq7Z//8LJKMg34PYbx0Wmaj7FLc6ARYDUheadUO/R4eyG3eF0Tzn3kLt4ioaYMDAnE5cVDIDT2Y0BxktT4wadRvLCVqtUrGZPROQl5MryoVIBF2yd2PVelV+BbO/Br1S3kRULJmGAKQPTxuWjo7MLTdZOyWP79YqrkJGuxrxZo8U6YBnp/inbEf/bSFEMwCJA6kLzJNQH6rUod/E8dvd0GHXpMOvT4XIJaFMBUF3K6u/o7MKikmKf/IC7vz8ewweZMNCsZfBFRNQjYoVSe1jbnH6rG1vLK7H6zmm+G5ykuo2IM17uFJKykmKfY/NUv69rbsepb6zYe7jaZ9d77/zeRmtHRP82UhYDsEiQudCC9VqUuzEcO9mIXe9W4Z7bJuKdg6dQUd3iMztm1Geg/FC1T37AzgMnser2qQy+iIi8yC0F9jVVQ+6+LQiC//23V36Z1X5p0J2brcPwgSaUlRTDJQCfnjiPGROG+CXee6ry+wRWPUuPtfW2iP5tpCwGYJHSO5HTFbzXYqAcgQ5nNza8fgwrF09Bw1+Oo7HFcWlqWa8JLT+AiCjFSa1Q9Od+2Z+AzrvP400zR4o9JD3FW3vXivTeXOX9Hp7VE5NB47cawu+CxMEALJqC7K6RujF4RjyA+wI8XXcRP7p5PKrPtWL/kRpxBMRG2kREIZBZofCUCAp352B/AjpP8CZV5/F03cWAm6u838MTyHW0dGP3wVPiasjE0bkYMkDH74IEwQAslrxuDOdbHDj1jVWcbgYuVbqvPteKXe9WoaykWCyy6hfcwX83JS9CIiL43i9VkNx5GPLOQa/7dqO1AzptesjFrT3BW219q1+w5erZHdl7Zm3i6FzMHD/QJ8DznoVrbHGIM2Qzxw/kfT+BMACLtZ4bgzpNDau9E612d2Vk7501V08eJlbCv+KyXP/XCKEQLIMyIqLI7Rw829gmHcQF0hO85Q/IxLufnMHVk4eJpSyO/6MeKxZM8qkdef/CyZdmtLzu3ZFeVqXYYAAWJ4zaNORl6bBy8RScrrsoNn4tmV7osyQptbsl1EKwrA9DRKkuErsi5e65D/1gKs61NMLe3glDpgbGTA2M2jTfe64AWMwZWDi7GC/tOO5TqHv8yOzQUkukllUZfCUcBmDxQgAK8g2wObrE/AS1WuW3JCmV6BlqIVjWhyGiVBeJXZG977m52TrccvUoHDvZ6FM8e8kNYzHEokfREJNPcGS1O8Xgy1N64nxzG5ouGmExZYRWlZ9dThJe1JpxUx8IgFGbjkFZOhQPM2FonsFnSVKukXbvRq6AfCFY70rNRESpRq4Bd6h5XID/PXfOjBGw2p1i8AW477eb3/kKVd9YxabbHr13Q+56rwpbyivx6O8/QkXNRXFZkpIbZ8DilQDoNGnylZC9+n9ZBujw2N3fgdXWifaObljbOjBysFlylGfQcfaLiFJYBJbvvHOwTAYNcsyZqGuSbhHnEgT38qZeI96zDXoNBlv0uHryML/dkFypSB0MwGKhd/NUfbpvsrw+HU3WTjy3+RO/AMpTB8yTdG8yaHDrrNFwdHb7TH3/ZMFELLlhDDa/c8KnxEWHswsAi/QRUQrr7/KdVxBn6+jG51VNUKtUsi3isg0Zfhulls+fgGarg5XsUxgDMKVJ7FhcPn8Ctu2tRF1TGwZb9Fg4uxjnm9tkL0y1WoXaehtKry3CqCFmnK5rxc4DJ31GUS9uP4YlN4zxqZZffqgaUy6bGoM/mogoSfQaQHd1dePjr87h5qtGoqykWDIHTK1W+SXtv7TjOJ748ZWsZJ/CGIBFU++Zrsx0yd0zL+04LlY7vnryMPfP1xZJXpgmfQa+9gq4PG0svJM5PfkDedmZ2PwOtykTEUWExAD6sbu/g5kThmBLeSWun16IlYuvgEqlglGvgUmXDqMuHTX10suTlTXNfpXsVyyYxPt0imAAFi09F+qmtytw9eRhUKuBcSNykCaTHC8mXarcP+8/UuNTB0ytBkYMycJX1c34r91fia/hEgC1SoXBFj1Kphf6XMj33jYRv/7JVWi2dvS7+jMRUaqTGkBX1rSI990/7a4AcCldxKh132Pldl7a27ux/+Man5WKAaYM3pdTBAOwKLG2ObHp7Qq/oOi+BZMw2KJHXVOb+FhP/0fvnxtbHPjo+DcStWIm+lzE+4/UYP6s0bhj7ni/PmLrXz+G1XdOQ0GeAUA/qz8TEaU4qZI/js7uoHlcUoVTVyyYhM3vfCVWsgfc9/6Zlw9U5o+hmGMZiihpsXVK7nD53fajWD5/os8W6OXzJ+D9z84AAN7/9AyWz58ArSYNVxQPFIMvz/PPN7f5bH9ubHHgv9//GoIgSN4EKk43w9rmlK3+3Ht7NBERSZMs+dOTfO/NL49LAMYVZmHNshl45J+/jTXLZmD8yGzcfuM48bmDLXqsvnMaWuydsDq6WIoiBXAGLEqyTVrZWlyCIPg1hh05aKrPz8/eOxPnLrT7PX/v4WosLR2Pl3d9IY6kbr3uMmjS1ZJT3C4X3LW/BPm6YNxtQ0QUnDkzHQ8tmYKqb6xieaCxBdnB2wLJ9Z8s7NlJ6XDiQmsnfrnxMFcoUghnwKLEnJmOcSNyZEdG5kyNuDRYc94OqFQoyDO4g6GeCy49TY2ykjHIzdaJz2+1OzFsoBGr75yGspJilF5ThDf2/wMNF9qw5IYxPjNri0qK8f5nZ5BtyJAcuXG3DRFReDq7XNh54CS27a3EzgMnYXd0iYHUE0un47cPzvILnORaFzVZO9Fi64RWky7ZuSQmKxQqwNruRE2DnTNxUcYZsGgRgOF5esnmqp6Ky3INtHuPlMpKivHWwVNotTuxYsEkZGrS8MR/HPKZ0dr4VoUYkKnVwPCBJmzaU4Hbbxwnvh+btxIR9V2wRt7mTA3y8oxoaGj1eZ535fvrphYgQ6PGsIEmPP/aJ3B2u7Dk+rHxsUIhscuTM3HRwwAsmlzA+ELp5qrWdqdYSLV0qnsHTG29DXk5mX4X+JbySjx293QYtWkw6zWyW5rbO7vFZM5V//xtrLp9qk+AxeatRER919dG3tkmreRO9SU3jIEmXY36C+2h1QOTKG0UyXt4sACTIotLkNHWU3G5INd3ebHF1gmTQSP2Adt/pAYAUN/chkUll/ksO3Y4u9HV1S0+X2450fPannph3u8X6FiIiCi4sFI5vJby1GoVls+f6Lcpa/M7J2C1O7H3cDUWlRQHTsjvmZ1atf4gnnjlEFb97sOI940MFGBS5HEGLEayTVrMnuYeDXkCMe+R0aKSYuw+eAqNLQ53D8dMDWoa7GKrot7LiZ5lSs9/mzP5Py0RUb9ItI0LKZVDYinvX8uukAxu8gZk4rpvF+Cj49+g9JoiZOrSkGPW+STkr1gwCbkD/FdHfGanIjA7JlevjLnC0cFv6RgxZ6ZjWL4RHc5ulE4t8hsZbS2vROk1Rdj1XhXKSorx5alm/Of/fOm3e6bF3ok0tRq19a1YXDIWmdo0mI0ZMGamA64Y/5FERIlKLh+qMHgqh9RSXvW5Vsng5nxTG3a9VyUOumdPK8T614/5PHfd9qO4f9FkyQDubFMbzAX+ucN9yd2SqlfGXOHoYQAWKwIwMCfTPeWski4RMXyQEQtnXwYAMGZmYOHsYuw/UuOX9Ak10GLr8Ev2Z+IkEVHfeAdRnuT52vpW5A/IhMWUIc462RxdsLZ3wdHRhdwsHSwuQXIpb+/hatxz60RseOOY30qHZ9A9b9ZoDB9olPw+cHR2SwZwtedtyM8OMjsWKoG5wkpiABZDFlMGls+fgPPN0gmYLa0dUKlUPs1dPResd9Kn1e7E5ne+utTOAsCmtyvcSfhMnCQiCpv3zkXvFJGdB6rEmbCqs60402DzuUevXDwFQ3L1fvf0VrsTowabxBqPX5+1imkmgDtoGj0sC7lZOpnvA4df30jP98GIwabI7aLsyRUWn8fgK2oYgMWSC5hUlIMLA50YmKPHSzsujYx+OGcsBubo8dyffdsLeUZJ4pq8CmjvcmHerNF45S9f+FyYNoeTARgRUR948qGum1rglyLywrbPsGbZDFR9Y8XOAyd9/m3ta5/g2ftmYvWd01Bxuhkuwd3h5PYbx8Go68nLUqmw9rVP/YKsXLNWchnQE2gBwLxZo5GXnYkWmwMqALOnFWAAc7cSEgOwWHMBA/QaDBg1QJz21es0aLK2o+rsRemlyYFGn1pitfU2v5vA1vJKrFk2Q/E/h4goGXgCodr6Vsn7cJPVAZdMC7izjW14Yatv38dxhVliXm6wXCvvZUCVSoWXdhwTN2QNzMnEX/9+GtPGD8ZfD1Xj6snD8PnXTXjkn7+N/3jzOOqa2pi7lSAYgMULATAbNWi2deJMvQ31F9qRoZFuLzTUovepJVZ6bZHkTcDucMJi5AiIiChsPYFQ/oBM7DxQ5Xcftph1OF0nnVhfe97ml0Tvk4/l1RuyyeqAxayDxZxxaeOU9zKgClh1+6VWdWnpamivHo1X93zpV1fsvgWTkGPKgFGnYfCVAFgHLF6ogM9PteDZ/zqCF7Z9hp0HTkKXke7XXsi7kr53oifbDBERRZjgztW9f+Fkv/uwxZyBoiFmlHnV79Jq0nDvbROx93C1z8v41dLq6Q356O8/wjP/dQSP/v4jVFTL1PTqVb+xqcWB0+esuHryML+l0d9tP+oOvljnMSFwBizWemq32DrcF4/3xfSnXu2Fpo7Jh8WUIV5YnhyF/Udq/JIzOf1MRBQBcjsDXUDREBMG5mTisoIB7l2QZi30+gzMnlYIl+C++e4/UoNWu9Mnb7eptRO19a0ovbYI+4/UoLHFEfKuxWyTFmqVClBJL38q3r6I+owBWCx51ZmRW0Zs7+zGrvfcu268gy8AMOvTxV6Tuw+ewrxZozEwR48LrQ5kpAeY3IxyOwsioqQitzNQAIzadBi1PV+lKqDy7KXEfE9h7GF5RtkewN5Ft0MJnsyZ6SgaYoYLYOJ9gmMAFkO9i/VJXUzfGpmDf5o6DB0OJ2rq7ZcCJrinsDe/8xXuXzQZtedb4XIJ+PM7FWKypuRoKpRmqwzQiIjCZm1zYu1rvjvXt5RX4tl7Z/rk7coV3Q4peBLcM2/2zm7ce9tEsWgrVz4SDwOwGPLO4ZJaRrxvwSQUDNSjsqYVa1/7BCaDBrOnFWL4QCPyB2Ri09sVqGtqQ/W5VmzbW+nz2nJT0UGbrYYSoBERkZ9gzbo9PYBLp16q2bj/SA3UaoQdPHV3uZBlyMCaZTPQ4exi4n0CYgAWQ959txpbHOIy4oghZgzK1sGs18Da6hSDL7l+kUDoU9HBbhBBAzQiIpIk10vRoHMPbnOydJg7c6RP4daykmJMHJ2LAYYQgyeZQfKQHD2DrwTDXZAxZM5053B5dtC02p3I0Kjxp//5wv0A4VLAJFUMcGt5Ja6bWiDOnsntlvTmuUF48w7WAgVoREQkz5yZjpWLp/jcixeVFOP51z5BRc1FuFwuMfgCLi1RpqkQcvAkN0j+5oIDVkeX9E5KlXv5s6bBLv8YUhxnwGJJAAYYMy61EBKAj45/g6snD8O5C+2ASnUpYOrpF+npSea5gAy6NDS2OFB+qBqr75wGQRAC9u8KVgBQbgTHxE4ioiAEYOQQMx5YdAVqzlvhckFMsH9h22d4+PapgXcuhpB/KzdIPnayEbverZLM6WVaSXxiABZjRn0Gdr1XJQZXt3x3FDa/c0K8UB5aMgUrF0/B6TorBlv0koX31iy70n/9X+7CCtJsNViARkRE8poutuP0OatkXm6mNl1+gBtioCQ3SIYgnTLCtJL4xSXIGDPr07H6zmkoKynG0u9fjrc/Ou1zoTy3+RMUDjZh5uUD8S/zJkSm8F6vwn4+z/MK0J5YOh3P3juTIyUiohDlmDOhVqn8Uj0GW/TI0Khx/6LJKCsZg9xsnc8AVy5QsrY5fV7HM0juvcy5/+Ma8XnnLrSLS41MK4lfnAGLpZ5qyHI1YYCenmMX2mDM1KDD2S15ITW2doQ3QxVsmluu5g0REQU0ONcgVsj35HsNtuixcHYxHn3pI/Fef8+tEzFqsEls0B1sg5TIa5Dc2NqBk7UXfb4ztJo0fH3WirWvfYr7F07G0DwD00riFGfAYkhqxONJrPcYbNGjqbUTq9YfxNdnrZIJ9CdrL6KiRqaNhYcnCbPRjtrGNjy76WM88cohrPrdh8GfS0REIVGrVSgaYsLUMXl47O7pePRH38GDi6fgpR3Hfe71G944BpdL8Ots4k02UOoZJI8aaMTwfCNa7U7x8Z7ZMM8MmloFyVZKUpu0SFmcAYsVFdBo7ZAc8ah7wmKtJg13zB0vFvaTqhW2fP4EtNg6sOntCqy6far0mr5EboH3TBvzAYiIIqhXhfyaBnvQ2a0+5d96zYadu9COr89a/VZQmls7Aub9RgwLeIeNAVgs9AREtfU2yanhgoFmLJxdDLVKBUG41O/LUyus9JoiDLTo0XChHS2tHdhSXolFJcWwOZySQZTcTFvpNUXYtq+S/cOIiKIopN3lQTZIyeqZDYNKhbWvfSr9HtFOK+FOyz7hEmQMeAKivYerJet3DcvT41sjBuDKb+VjsEXvMy3d2OLArveqcL6pDXsPVyM/R4/Sa4vQ6XQhU6uRrPcil1vgWXZkPgARUfRIJc77LQN6zyAZtWHPUnlv6Fo4uxiDLXrFlhpD3UBAvjgDFgOegKijpVuc0YIKmDg6F0MG6MTpawCwdXRhyQ1jsfmdr3yWDz86/g3mzhyJF7ZeGnEMsujRrE3Hc5s/8RmFDB9olN22zDITRERRFmx2K5wZJKmlPvhv6FqxYBLGFWYBriDHFs7SocxjQ95AQD4YgMVA7xZE2/ZVQqtJw8zxA/0++M3WDnxw9Az+7w+mwNnlgkqlws4D/8DUsYP8KipveOMY5s0a7fO7TW9X4MHFU3oadtuw93A1Wu1OrFgwCQNMGZh5+UAGX0RE0RZgGTDkWl0ygdrQXL3f89dtPxo8t1fi9e65dSJGDTHDqE3z/V4IECSygHffMACLgXCSLXOydJg5YQj+/c+XZrWWll4Oo14jOeJwCZdeIDdbh5LphXj09/Jbnz2s7UyeJCKKhVBnkOQCtaAV9mVIvZ5nID883+gzA+f9WE9Hltr6VuQPyITFnMEC3n3AACwWwki2dHX79w57edfnWLn4CskRh1p1qZ6EVP/IDW8cw5plM2DU9TyIyZNERDEV6gySd6Dm3ZbOkKnBYIsedU1tAZ/fm1zg5xIEvxk4z2Nzs3W4aeZI8btl54Ge9keFCuy0TDJMwo+VQNXovchdIHVNdskE/pGDTeLv1GpIPvfjE/Vi7S8mTxIRKSBAQ+yQkvRxKVDzBEG73qvCtr2VeOIPfxcT7z3PLyspRkOLA1DLv69c7TFPWyPvavmex0oN7F/Y9hmsdmdI32l0CWfA4pzcyKij04W9h6vdU8UDjRiaq0fDBQf++D9foPSaIqjVwISiXOw8UOX3XJcL4uiGyZNERFEWbKUhxFURT6BWW2/zC4Je2nEcDyy6AqfPWQEBeOvgKWjS1Fhyw1is235U8n2l0mE8NSJ7z6Bdeu9WfmdECAOwOGfOTMfKxVPEYqzizsZ8A8YWZosXqrXNKe5+3LbP3QT23U/O4L4Fk/A7r4vPc3F5LhgmTxIRRVdISfa9k/TRKzdXnw6r3YksgwYZQ82SQZCnCbhnebJwsEncKS/5vl6B39mmNtSet2H3wVNotTv9c7h6Hps/IFNyYM/vjPAxAItHvbb6Th8/SHJk5ClVIbcNuK6pDRes7eKM2IjBWXjjb5VobHGIF0yfqi8TEVHIwl5pUANfnG7xmblaPn8Ctu2tRF1TG8pKxsjmAHvnaJVeWxT8fT2B3/AsDLXofQb2ft8BAmAxMeE+UhiAxRuJqeqVi6egeKgpYBVjuZkse3s39n9cg+umFuB03UXMu3Y0Xt1TgdtvHCdeMIq0qSAiSiVeA2mDXhP6SoMKqG1oE4Mv4NIS4/2LJqP6XCs+/uqcT7NvTxCUka4GphX6LE9Kva9KpXLngnnf54PNwHl2x/M7I2IYgMUZqanqta99ErSei9xa/kfHv/HZsSJZoC/abSqIiFJJr4H0j27+ll/AVFZSDLVa5fdUa5sTFaebJWeuqs+1Yte7VVhUUoyDx7/BY3dPR1dX96UgCIBKrRKfK9U/eFFJMV7acUy+d7DE8UvlrPE7o/8YgMUZualqm8Mp/rtkra5eoxKDToPnX/sEV08e5pesGVKBPiIi6pPeA2m7owv7j9SIXU88SfIjh5gvpZL0aLF1wiVIz1xBAEwGDTqdLsyZMRLadLXYPcXzfZCbpfMp9L374CnMmzUaedmZON/cJjbrDpQ0H3JhWOoXBmBxRmopcbBFjwutnXjqj4cD1+ryHpWogNtvHMcdK0RECmu0dvjdd1vtTnGDFCC/BJlt0uL9T89IzlxJrWj0/i7ovRrSandCq1HjtfKv0NjiCPjeHtwdrwzF64Dt3LkT3/3ud1FaWorS0lKsXbtW6UOIa1L1YO65dZJfPoCnVpdcfRfPjNjUMfmSdV64Y4WIKDoytek+9939R2pQJlG3UapRtjkzHbffOA7lh6pRek0R7l84GfNmjcbug6dwRfFA6Rpc3nUbvVZDnlg6Hc/eOxPD8oxotTuDvreHXH0wfm9EluIzYJ9//jkeeeQR3HzzzUq/dWKQSHC0d3RLjkbONrX5NOP2mxXjjhUiIsWZ9BqfnK9WuxO6jDSsWT4D9nZn4MT1nu+AVbdPRYu9Ezk9wVCr3QmopItr+81M9crRMhs0WLNsBpqsDljMOljMGQGbdHN3vDJUgiAoejoXLlwIk8mE+vp6jBkzBo899hiysrJCfn5Tkw0uV2p9AjoFFR54/oBfPsC8WaOxpfyEz+8k1+g9u3G4YyWovDwTGhpaY30YKYHnWjk818rJyzOhobEVVd+0ouobK1yCALVKhaIhZhQNMfXt3ttzD7d3dOPfXjnk910QMDerr+3mEuB7I94/12q1ChaLUfbfFQ/A7rvvPtx1112YMmUKnn/+eXzzzTf493//dyUPIeG4XAI+Ol7nU4z13tsm4tU9FeKavscv752JCUV5MTpSIiIC3PftukY7mlvbkWPKxOBcg+Sux3Bfs/d3wcrFUzBjwmDZ1z5bb5McwP/2wVkYmm/0PVZrO3LMkTlWCi5qS5B79uzB008/7fO7UaNGYePGjeLPS5cuRUlJSVivm4ozYHl5JhQPNfksS6rT1OKavodWkwa9Ji2uRwTxLt5HVMmE51o5PNfK8T7XGSpgkFkHQEBTky0ir9/7u8Cs1/i/tlcNsi6XILlsea7JhgyV0PcZsjgQ75/rYDNgUQvA5syZgzlz5vj8rrW1FRs3bsSdd94JABAEAWlpaRLPJj+9666owDV6IqJUI1WDq1f3lIYL7WJrOrmq+Z6EepaciB1Fk/D1ej1efvllXHHFFZg0aRI2bdoU9gxYSuh1MVmkZvzkqhFDpnoxERElH4kZrLKSYpgMGnS0dGPv4WrJqvmewbqn5ISnf6RnN73N4WQAFmWKBmBpaWn4zW9+gyeeeAIOhwMjRozAr371KyUPIf4FaEUk1Zerd+uIRJ1KJiKi8EnNYG0pr0TpNUXYts/d+/etg6f8q+b3fCdkm7QYbNGjZHqhT32x4flGDMnR87sjihSvA/btb38bO3fuxJ49e7BhwwaYTCalDyGuybUi8qnzEsZz/WrEEBFRfFJBvrajDLmiqd7PbbU7YdSmoSDX4B6wewVV5sx0LJ8/UbJjirXN2adjotCwEn6c6U8FYk4lExElqFCS4Xulp5gz0yW7p2g1aVCrVOJ/S+YHe71WtyCdqG9zOHG2sY2rKlHCACzOyF1MoVQg5lQyEVFiCpoMLxegFWZJbsganm/A2MJs6RpePa+16e0KXD15GAZZ9CgrGYO9h6t92hVpNel4YdthJuhHieJLkBSYVCuilYunBGwb4f3cgFPJREQUlwKtfgABUkzsTr/WQ+MKsmDUpksuOXpea9PbFSiZXohd71XhhW2fYeeBk5g7cyRys3ViEGd3OAMeE/UPZ8DijcTuxpHDBoRWQ0Zwl/ZgE1UiosQSbPUjWHqKX2mKAFpsnbh68jC/wfqW8ko8dvd0GLVpMOs1sLZ3SR6TSqVyp7hwVaVfOAMWj3p2N3pGL+FUJM42KthElcmZREQRIbX64d00O5INsrNNWqjV0n0lu7q6xVkzc2Y6ViyY5HNMi0qK8dKOY1xViQDOgCWDXomZj939HXxe1QSXALz/6RncfuO40Aq0SiR4yj4ngasnExHFnWC1He2dWH3nNLy04xjqmtr6VXzbnJmOcSNygucbC8AAYwZKrykSZ7x2HzyFxhYHV1UigAFYopMpwrf3cA1a7U6sWDAJ4wqzEKjzvdzrBAqoWD2ZiCjCQqjtuGLBJAwwZcCo01wKvtRAk7UTTVYHLFk6WEwZge/5AjA8T48VCyZh3fajAbupGPUZ2PVeVZ82hlFgXIJMcHJF+K6bWnApAd8e+RpiwRJGiYiof6Tuy+u2H3UHX5mXgq+jVc149Pcf4Zn/OoJHX/oIR6uag3+7u4Dxhdl+yfu9B9zBlkap7zgDluCCFeELt4ZY79eRe25/ymUQEVEvEikgcvflRmuH+Dhnlwsv7TjuE6S9tOM41iybAYspI3BaiVRfyd7klkaZatJvDMASnFwg5Lk4PEFRsP6Q4QZUnlERm4ETEfWTTArI8IFGyfvyyTMXsaX8hPtxiyZLBmnNrR2ov9De/zzdcHKDKSxcgkxwUtPDZSXF2P9xjXjBNbQ4sGr9QTzxyiGs+t2HqKi56LdjMexpZq9RUaDpayIiCkwuBcTV7ZK8v+89XC0+TqVSSe6OzDJm9L81XU9g6P39cezUBdg6u7nrPQI4A5ZIZEYivaeH1WoVRg4xi//98LoPgyfL92WaOZTpayIiCihQCoj3fTk9PQ2/2fKJWK0eAHYe+AeWzZ+A3/csQ2o1aVg+fwK6urr7XRNSKjDc8MYxzJs1GsPzjRx09xMDsEQhMUW9YsEkDDBmwKh3B0veF5VR6/6ftqbBHvpFyICKiEhxAVNAvO7LVkcXNGlqLPxesTgD9f6nZ3DZsCysWTbDvQvSrIPFnAFrm3QR1ZDydHsG++cutKP02iLsP1IjBn0dzm64BIG73iOAS5AJQm43zLGqZtllRSCyxfuIiCjyQk0BMevTsXB2MXa9V4Vteyux690qLJxdDIM2DRZjBoqHmGExuktQ9Hn3otey4zP/dQS73q3CTT0tijyvA4G73iOBM2AJItBux0A1uJgsT0QU50JMAbHanZI7HiVnovq4e1FqsL+1vBKl1xRh13tVWFRSjN0HT/VtIM+Efh8MwBJEsN2OPsuKvT7k4wq5hZiIKK6FkAISbrmgvqSVyL1H4WAT5s0ajd0HT6HV7gx/IM/uKX4YgCUIqZksz0gE8FpWDPAhZ24XEVHiUqL+otx7DLXoMdSix9jC7D4N5Nk9xR9zwBJFr7IPq++chvJD1WhscfgsK4Zb0Z6IiBKDElXpA72HOVODglzDpSr8YWD3FH+cAUsk3tPJKmDV7VP9lhXDnqImIqLEoERV+ii9B7un+GMAlqhk1va9P+S52TpcN7UAajVg0GnEbvZERJSglCgXFIX34IYwfwzAkoznQ77p7QqUTC/E1vJKdDi7sfNAVconPBIRURgiuWuRPSX9MABLNj0f8gcXT8Gjv/+ICY9ERBS+aOxaZLFvH0zCT0QqwNruRE2DHVZHl38BVgGwtzuZ8EhERH3CDV3RxxmwRBPiqIQJj0RE5COMJUVu6Io+zoAlEhXQ1NoZ0qhEie3KRESUILxaDD3xyqGALewAtrFTAmfAEkXPxVNb3xraqIQJj0RE1CPcQqjctRh9DMAShOfiKb22KPSlRSY8EhER+tbGiIP46OISZILwXDz7j9RgUUkxlxaJiChkfVpS7BnE97X6PQXGGbB45ZUs2SmoxIunscWB3QdPofSaIqjVwNQx+bCYMnhhEBElokjW2gqAS4rxRyUIQkKd+qYmG1yuhDrk8EnsdHxoyRR0drnYST7K8vJMaGhojfVhpASea+XwXCsnrHMdjVpbQd7P2uZMmiXFeP9cq9UqWCxG2X9nABaHrO1OrFp/0C/P69crroLLJSTNxROP4v2CTiY818rhuVZOOOda7l4fVwWzFZqh64t4/1wHC8C4BBmH5JIlm1s7Lq3FA3FzERARUfjivtaW0jN0KYZJ+HEo4vVXglXOJyIixcV7rS1Ww48uBmBxKKJFVMMsvkdERMqI94LZgWboqP+4BBkLwdbUe9VfGWQxIkMtSE/5BnmtcIvvERGRQuK81hZb2kUXAzClhbqm7lVENS/PKJ1oGMJrxX2OARFRKovjgtksXRFdXIJUWCTX1EN5rbByDJgrRkREHl4zdE8snY5n750ZegK+CrB1dOGbFge+Pm/jd4oEzoApLJIzUqG8lloFlJUUY0t5pTiCKSsphlrd60oIZ7dLHG9LJiKiCOrLDJ0KqPqmFWcabD7fPdxB6YsBmMIiuaYeyms1WzvwVk/lfKgACMBbB09h5BAzjNpL//OHnCvGbclEROQhMSC3tjlR9Y0VOw+cZP5xAFyCVFgkd72E8lrZJi1a7U5s21eJbXsrsW1fJVrtTr+AL9TdLtyWTEREAGR32dvanXAJAndQBsEZMKVFctdLCK8VahJlqDNzTOonIkoxMmkncgPyNctmQK1ScQdlEAzAYiGSu16CvZZXkGZzOKHVpMPucMLa3uWTuxXpQI2IiJJAgLQTuQF5k9WBy4tykKkdi83vfMUdlDIYgCW6UBLiBcCs1+BsYxte2HZYOncrxJk5bksmIkodgfKD5QbkX5+1Ytd7VXhw8RV4fOl0tDm6kGvW8nuiFwZgiSyMhPiQkuxDmZmL88KBREQUOYHSTgryDH4D8kUlxdh98BQ6nN14/rVP8ey9MzEoS+d+Ir8nfDAAS2ByQdWvV1wFl0vwmRWLaO5WHBcOJCKiyAmYduI1ID93oR1fn7Xio+Pf4LqpBWLNL5vDyfxgGQzAEphUUGUyaPB1XSs2vHHMZ1ZsaJ6BuVtERBSWoGknPQNyqFT401tfomR6IbZ61f4anm/EkBw9B+oSWIYigUlVuZ89rVAMvoBLs2JqFeK66SsREcWhEKvhmzPTsXz+RDH4AtzfP+u2H2WZIhmcAUtgUiOT4QONkkuNza0dzN0iIqLwhZgfLASo/cVlSH8MwBKZREI8AtVeYe4WEREF0o9Wc9lGlikKBwOwRNc7qOpZanxh22cwGTSYPa0QwwcaAZVKbEXUZ+wBSUSUvNTAF6dbsG770T61mmOZovCoBEFIqNPS1GSDy5VQh9xveXkmNDS0hv4EFWBzdEkm4/tdSKEGVSnSAzLsc019xnOtHJ5r5STsuVYBtY1t+OXGw34zWGH1b/R8pyiQ6hLv51qtVsFiMcr/u4LHQkoRAJdLkEzG90mGlOnj5dk+7I09IImIkpe1zYmK083979/YsypTkGtwB21JNECPNAZgSSqU5trhBFWhNusmIqLE02LrhEuA38565nBFDwOwJCVVoqL3hRROUBXK6xERUWLKNmnx/qdnsKik2Kdc0YoFk1iuKEoYgCUpTzJkoLpf4QRVobweERElJnNmOm6/cRzKD1Wj9JoilJUUY/Wd0zB+RDaXEaOESfgJoM+JhsGSIcNNrFcwuTJW4j2pM5nwXCuH51o5CX2uE+weH+/nOlgSvuJlKOrr6/Hoo4+ivr4eOp0Ozz33HIYNG6b0YaSGYHW/wm2szTpiRETJq/c9HoC1naWHokXxJcif/vSn+Kd/+ie8+eabKC0txXPPPaf0IZA37lghIqLewtglT32jaADW3NyMr776CmVlZQCAW2+9Ff/6r/+q5CEQERFRECw9FH2KLkHW1tZiyJAheOaZZ3DkyBHk5eXhscceC+s1Aq2nJrO8PFNM39/lElDXaEeztR055kwMzjVArU7OoVCsz3Uq4blWDs+1cpLhXJ872SC5S77N2Y2igpwYHZW/RD7XUQvA9uzZg6efftrnd4WFhfjyyy/xk5/8BD/72c+wfft2PPLII3j11VdDfl0m4cdAilTBB+LgXKcQnmvl8FwrJ1nOtV6bLtnXUa9Ji5u/L97PdbAkfEV3QdbU1GDevHn4+OOPAQDt7e248sorcfTo0ZBfgwGY8qztTqxaf7B/7SkSRKzPdSrhuVYOz7VykuZcJ8DAO97PdVztgiwoKMCgQYPw7rvv4tprr8Xf/vY3jB8/XslDIG8h9oEMVLA12QIwIiJC+LvkKWyKl6F48cUX8fjjj+PXv/41jEYjnnnmGaUPgYCwRjeegq29Z8BYBZ+IKInJlR4KcfBOgSkegI0aNSqsnC+KDrkdLlLLip4q+L2DNY6GiIhSTAIsTSYKxQMwig9hLStyKpqIiBDe4J0CYwCWosJeVmQVfCKilMec4MhhM+4UFXZzbZV7N2RNgx1WRxerIRMRpSDP4N0bc4L7hjNgqSqcZUWu+RMREZgTHEkMwFJZiMuKXPMnIkpBMrsdew/e1WoVaurt3BEZJgZgFBTX/ImIUkyQlQ9zpgZmvSb01RGWrvDDHDAKimv+RESpJZRm3CE37O4J5latP4gnXjmEVb/7EBU1F1M+l5gBGAUVdsI+EREltEArH+E8BggjUEsxXIKk4FgHjIgopYRSqijUckZMY5HGGTAKTc+af0GuwX3BMPgiIkp8MiWGQln5CHV1hGks0lSCICTUV2lTkw0uV0Idcr/Fe8f3ZMJzrRyea+XwXCsnoc51sBJDnsT5QCsfIT4mGqWM4v1cq9UqWCxG2X/nEmSi484SIiLqg6AlhkIpVRTiY5jG4o8BWCJjgVQiIuojRXOz2M7OD3PAEhh3lhARUV8xNyu2GIAlsFC3ABMREfXGEkOxxSXIBBbqFmAiIiI/zM2KKc6AJTCOXoiIqF+StcSQTHmNeMIZsETG0QsREZGvBNmgxhmwRJesoxciIqI+SJQNapwBIyIiovjXU/fS1u6ENiMddS0NMOjS/epfJkrrIwZgREREFN96lhU3vV2BkumF2FpeKbu8mCgb1LgESURERHHNs6x49eRhYvAFSC8vJsoGNc6AERERUVwTlxVVCL68mCAb1BiAERERUVzzrtof0vJiArQ+4hIkERERxTXPsuL7n57BopLiuF9eDAVnwIiIiCj+9Ox6bLF1ItukxbjCLKy6fSpsDifWLJuBzm4XDBlpcbm8GAoGYERERBRfAhRT9Swr5uWZ0NDQmpDBF8AlSCIiIoozgYqpeloMna23xWWLoVBxBoyIiIjiilwx1bNNbXhha3y3GAoVZ8CIiIgornjvevTQatJQe94W9y2GQsUAjIiIiOKKVDHVe26diL2Hq30e56kBloi4BJkqeu0m6d07i4iIKG5IFFNVp6nRaved7YrHFkOhYgCWCgLsJmEQRkREcal3MVUVcP/CyX7fZSxDQXFLbjfJs/fOjKvO8ERERLJ6zYoNshiRoRYSMvgCmAOWEuR2kyTqujkREaWonlmxglwDhuYbEzb4AhiApQS53SSJum5ORESU6BiApQCp3SSJ2juLiIgoGTAHLBVI7CZJ1KRFIiKiZMAALFX03k3C4IuIiChmuARJREREpDAGYERERJQ6VBAbelsdXTFr6M0lyFTBSvhERJTq4qgwOQOwVBBHHzgiIqJYiafC5FyCTAFyH7hE7SBPRETkJ4SlxXgqTM4ZsBQQ6APHVkRERJTwQlzp8RQm9/5OjFVhcs6ApQBWwiciomQW6kpPPBUm5wxYCvB84JKlgzwREZG3kFd64qgwOQOwVBBHHzgiIqKIUgGGTA3KSorhEoD9R2rQ2OKQX+mJk8LkDMBSRZx84IiIiCJGIvdrUUkxyg9V4/Ybx8X1ZAMDMCIiIkpIUrlfW8srsWbZDFhMGXEbfAFMwiciIqIEJZf7ZXc44zr4AhiAERERUYJK5F3+DMCIiIgoIcVTWYlwMQeMiIiIElMC7/JnAEZERESJK0F3+XMJkoiIiEhhis6ANTU14a677hJ/bm1txYULF/Dpp58qeRhEREREMaVoAGaxWLBr1y4AgMvlwh133IGVK1cqeQhEREREMRezJcg33ngDmZmZuOWWW2J1CEREREQxoRIEQfF0te7ublx//fVYv349xowZo/TbExEREcVU1JYg9+zZg6efftrnd6NGjcLGjRvx/vvvY8SIEX0KvpqabHC5EmSLQ4Tk5ZnQ0NAa68NICTzXyuG5Vg7PtXJ4rpUT7+darVbBYjHK/nvUArA5c+Zgzpw5kv+2d+9e3HTTTdF6ayIiIqK4FpMcsM8++wzf/va3Y/HWRERERDEXkwCstrYWgwYNisVbExEREcVcTCrhHz16NBZvS0RERBQXWAmfiIiISGEMwIiIiIgUxgCMiIiISGExyQHrD7VaFetDiIlU/btjgedaOTzXyuG5Vg7PtXLi+VwHO7aYVMInIiIiSmVcgiQiIiJSGAMwIiIiIoUxACMiIiJSGAMwIiIiIoUxACMiIiJSGAMwIiIiIoUxACMiIiJSGAMwIiIiIoUxACMiIiJSGAMwIiIiIoUxAEswX375JS6//PJYH0ZS+/jjj3HbbbehtLQUd9xxB86ePRvrQ0o6//3f/42bbroJ119/Pf785z/H+nCS1rp16zB37lzMnTsXv/rVr2J9OCnh2WefxSOPPBLrw0hq+/fvx/z58zFnzhysWbMm1ofTZwzAEkh7ezueeuopOJ3OWB9KUnv44YexZs0a7Nq1C7fccktCX+Dx6Pz581i7di02b96MN998E1u3bsXJkydjfVhJ5+DBg/jggw+wc+dOvPnmm/jiiy9QXl4e68NKah999BF27twZ68NIarW1tXj88cexfv16/OUvf8GXX36Jd999N9aH1ScMwBLIM888gzvuuCPWh5HUOjs78cADD2Ds2LEAgDFjxqCuri7GR5VcDh48iCuvvBLZ2dnQ6/W44YYb8Pbbb8f6sJJOXl4eHnnkEWRkZECj0aCoqAjffPNNrA8rabW0tGDt2rVYvnx5rA8lqZWXl+Omm27CoEGDoNFosHbtWkyaNCnWh9UnDMASxL59++BwOHDjjTfG+lCSWkZGBkpLSwEALpcL69atw+zZs2N8VMmlvr4eeXl54s/5+fk4f/58DI8oOV122WWYPHkyAOD06dPYs2cPrr322tgeVBL7+c9/jpUrV8JsNsf6UJJadXU1uru7sXz5cpSWlmLz5s3IysqK9WH1SXqsD4B87dmzB08//bTP70aNGgWbzYaNGzfG5qCSlNy53rhxIzo7O/HII4+gq6sLy5Yti9ERJieXywWVSiX+LAiCz88UWf/4xz+wbNky/PSnP8WIESNifThJafv27Rg8eDBmzJiBHTt2xPpwklp3dzeOHDmCV199FXq9Hvfccw927tyJ+fPnx/rQwsYALM7MmTMHc+bM8fnd9u3b8fvf/x4/+MEPxN+Vlpbiz3/+M4xGo9KHmDSkzjUA2O123HPPPcjOzsaGDRug0WhicHTJa9CgQThy5Ij4c0NDA/Lz82N4RMnr448/xv3334/Vq1dj7ty5sT6cpLV79240NDSgtLQUFy9eRFtbG375y19i9erVsT60pJObm4sZM2YgJycHADB79mwcO3YsIQMwlSAIQqwPgsIzZswYnDhxItaHkbTuvfdeWCwWPPnkk1CruUofaefPn8fixYvx+uuvIzMzE2VlZXjqqacwceLEWB9aUqmrq8O8efOwdu1azJgxI9aHkzJ27NiB//3f/8UzzzwT60NJSkePHsWqVauwbds2GAwG3Hffffje976HBQsWxPrQwsYZMCIvX375Jfbt24fRo0dj3rx5ANw5Sn/4wx9ifGTJY+DAgVi5ciX++Z//GU6nE7fddhuDryh45ZVX0NHR4RMIlJWVYfHixTE8KqL+mTRpEpYuXYolS5bA6XTiqquuwq233hrrw+oTzoARERERKYzrK0REREQKYwBGREREpDAGYEREREQKYwBGREREpDAGYEREREQKYwBGREREpDAGYEREREQKYwBGRClp586dmD17Nux2O9ra2jBnzhy8+eabsT4sIkoRLMRKRCnr//7f/wuTyYTOzk6kpaXhqaeeivUhEVGKYABGRCnLZrOhtLQUOp0OO3bsgFarjfUhEVGK4BIkEaWspqYmdHR0wGq1or6+PtaHQ0QphDNgRJSSnE4nysrKUFZWBpfLhe3bt+O1116DRqOJ9aERUQrgDBgRpaTnn38eubm5WLBgARYtWoQBAwZg7dq1sT4sIkoRnAEjIiIiUhhnwIiIiIgUxgCMiIiISGEMwIiIiIgUxgCMiIiISGEMwIiIiIgUxgCMiIiISGEMwIiIiIgU9v8B7mS3K0V2ZxQAAAAASUVORK5CYII=\n",
|
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"text/plain": [
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"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 2 clusters\n",
|
|
"X2, y2 = make_classification(n_samples=500, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_classes=2,\n",
|
|
" n_clusters_per_class=1, class_sep=4, flip_y=0, weights=[0.5, 0.5], random_state=17)\n",
|
|
"print_sns_plot(X2)\n",
|
|
"\n",
|
|
"# 4 clusters\n",
|
|
"X3, y3 = make_classification(n_samples=500, n_features=2, n_informative=2, n_redundant=0, n_repeated=0, n_classes=2,\n",
|
|
" n_clusters_per_class=2, class_sep=3, flip_y=0, weights=[0.5, 0.5], random_state=17)\n",
|
|
"print_sns_plot(X3)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 116,
|
|
"id": "7fb14dda",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 2 0.72\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model15 = TrainModel_medoids(X2, 2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 117,
|
|
"id": "11960f33",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 720x576 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Sylwetka (ang.silhouette) dla metody k-medoid i dla k = 4 0.77\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model16 = TrainModel_medoids(X3, 4)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.13"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|