{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "reserved-pursuit", "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2021-12-02 14:34:26.619157: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", "2021-12-02 14:34:26.619177: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" ] } ], "source": [ "from library.analysis import runExerciseForSpheredNoise" ] }, { "cell_type": "code", "execution_count": 2, "id": "sweet-equivalent", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "///////////////////////////////////////////\n", "// Running SpheredNoise on folding_abalone_17_vs_7_8_9_10\n", "///////////////////////////////////////////\n", "\n", "-> Shuffling data\n", "### Start exercise for synthetic point generator\n", "\n", "====== Step 1/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 1/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2021-12-02 14:34:27.821660: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n", "2021-12-02 14:34:27.821679: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", "2021-12-02 14:34:27.821712: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (sbi-klabautermann): /proc/driver/nvidia/version does not exist\n", "2021-12-02 14:34:27.821943: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "trained 46 points min:0.02728552729928453 max:0.41828518979280144\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 452, 4\n", "LR fn, tp: 9, 3\n", "LR f1 score: 0.316\n", "LR cohens kappa score: 0.303\n", "LR average precision score: 0.361\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 455, 1\n", "GB fn, tp: 11, 1\n", "GB f1 score: 0.143\n", "GB cohens kappa score: 0.137\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 1/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 450, 6\n", "LR fn, tp: 6, 6\n", "LR f1 score: 0.500\n", "LR cohens kappa score: 0.487\n", "LR average precision score: 0.612\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 452, 4\n", "GB fn, tp: 7, 5\n", "GB f1 score: 0.476\n", "GB cohens kappa score: 0.464\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 1/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.6966004952625284\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 454, 2\n", "LR fn, tp: 11, 1\n", "LR f1 score: 0.133\n", "LR cohens kappa score: 0.124\n", "LR average precision score: 0.263\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 454, 2\n", "GB fn, tp: 11, 1\n", "GB f1 score: 0.133\n", "GB cohens kappa score: 0.124\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 1/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02923183196448697 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 455, 1\n", "LR fn, tp: 9, 3\n", "LR f1 score: 0.375\n", "LR cohens kappa score: 0.367\n", "LR average precision score: 0.548\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 455, 1\n", "GB fn, tp: 10, 2\n", "GB f1 score: 0.267\n", "GB cohens kappa score: 0.259\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 1/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 48 points min:0.030983866769659363 max:0.6446939196238787\n", "-> create 1776 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 453, 3\n", "LR fn, tp: 8, 2\n", "LR f1 score: 0.267\n", "LR cohens kappa score: 0.256\n", "LR average precision score: 0.317\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 453, 3\n", "GB fn, tp: 9, 1\n", "GB f1 score: 0.143\n", "GB cohens kappa score: 0.132\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 10, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 2/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 2/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.031772629730634515 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 452, 4\n", "LR fn, tp: 7, 5\n", "LR f1 score: 0.476\n", "LR cohens kappa score: 0.464\n", "LR average precision score: 0.513\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 456, 0\n", "GB fn, tp: 12, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: 0.000\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 453, 3\n", "LR fn, tp: 7, 5\n", "LR f1 score: 0.500\n", "LR cohens kappa score: 0.490\n", "LR average precision score: 0.514\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 453, 3\n", "GB fn, tp: 9, 3\n", "GB f1 score: 0.333\n", "GB cohens kappa score: 0.322\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02923183196448697 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 454, 2\n", "LR fn, tp: 11, 1\n", "LR f1 score: 0.133\n", "LR cohens kappa score: 0.124\n", "LR average precision score: 0.180\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 455, 1\n", "GB fn, tp: 10, 2\n", "GB f1 score: 0.267\n", "GB cohens kappa score: 0.259\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.6966004952625284\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 454, 2\n", "LR fn, tp: 8, 4\n", "LR f1 score: 0.444\n", "LR cohens kappa score: 0.435\n", "LR average precision score: 0.419\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 455, 1\n", "GB fn, tp: 9, 3\n", "GB f1 score: 0.375\n", "GB cohens kappa score: 0.367\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 48 points min:0.02728552729928453 max:0.37681593649950645\n", "-> create 1776 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 452, 4\n", "LR fn, tp: 7, 3\n", "LR f1 score: 0.353\n", "LR cohens kappa score: 0.341\n", "LR average precision score: 0.370\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 453, 3\n", "GB fn, tp: 8, 2\n", "GB f1 score: 0.267\n", "GB cohens kappa score: 0.256\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 10, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 3/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 3/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.35035874471746814\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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dPLj5MUUBgwGGpKL06wepaZjvmkXtpk3+j1N4hSRXIfzA3iXQYIH9+5qfEB9v35bF9PoqKNiLWlhI9V/faH6uCArS5yqED9mnYP3wA5yp1WYLONQLyOs3jOz0n5J5/GtGWU7aW6ykpqEAsXPvpDpw4YsOkOQqRAc5Fk9xHXyxLF8GP56wP87rP4LswVeT+U0OGcXfkD3+dkosEXzc7wLG3jUW812zUAsLUdBqBkQnxTnNFhDBQ5KrEB3kOBfTujdfu/0fMVIrCeiQWAGyL76KkrgeZKdOIMNYyZRJI9mYX2Ivbh3MU4+EM0muQnSAJTcHtbwc4uMxzJipLQaoq4MvPnd7fmbBFrJTJ2j7XhUe4tI/L2H0sy/Yjwfz1CPhTJKrEB1gefF5rYV6Xj+M46/RugEct19xkVG4h4zCPU1P/PML3wcpAkJmCwjhDSdPUn/FSBg8RJtS5Y6iEPn1foxPPwcJCdr0qyvH+TVM4T/SchWiA4z3ztP6XPcVNHUHtLTPW1y89hq59Q8L0nIVooPU8nLn1mpLK7C6d/dPQEIXpOUqRAdYV2dpfa4NDS2fpChwQX+M987zX2Ai4KTlKsQ5cqoxmpoGVmvLJ3fqhDJrjlPZQBEepOUqxDmw116tq8OS/zVERLScXE0mIvP2uD8mQp4kVyFa4bj6yro3H3XVm03LVxsaWugOUAAVUs7zZ6hCZyS5CtEKx9VX6sEDTnUBnDT2qypjf4K6bSuA9LGGOUmuQrQmNQ32FaAePQqdOkG1+zIqyqw5mOb/Tntg+1OENUmuQjSy5Obwr+xtfJx6DTeOG8zoAYlsL6oke8I8Bh7/np3npQEKM3ZtaFplpSgowy9tSqxCNJLZAkLQOFC1ZBHZcRdyrOIMH679B+aVK8iOHUBJXA82XzyOkoSelCQkk506Aa1ftVFqmv09gnWnUuF9klyFoHG+akMDmXs306uqlEn5n6JmvaU9rjzBtd9+Qa/Tx+l9+oRWdCXCCFFREBsLBXvt7yFbtAgb6RYQYc02G4DUNDh4gIwT35Lx6X7toMHoVGhl5s73m17Yf4B96autPKCUCxSOJLmKsGXrCkAxQHk59EiGY8WAou0YEGGEBrPb1xrvndesRoDUDBCOJLmKsGXfjbX+bGNSRZu3arFoxVcURduGpbH+qn0QKz5Bkqhok/S5ivCVmgZms7bCSlHg7Fktuaqq9qfZTHbqBEriGwexTCZtRZYUYBEekOQqwpa6batWJtBshvp6twsEMg//i15VP5L5w06UGTNR0i+RxQHCI9ItIMKOfRCr5JjDk27KBI67mrHPvsBYAO7wT3AiIFrbZLK9pOUqwkrtpk1YHrofdc8eqK1t+cSUFCId9rYSoc0X0+gkuYqw8tlfN7B4wjzyUtJaPEe5dASRH33mx6hEoBlmzETp18+r0+h0m1w3b97M7NmzufTSS7nooosCHY4IQq4rpswrV7AxcUjTAJU7PZJlnmoYMo6/BtPrq7w6C0S3fa61tbWMGjWKMWPG8MwzzwQ6HBGEbDuzWsrLAbQVVynpTVtbO1IUjCuelSlWwmt0m1xvvPFGAPLy8gIciQgFluXLwGptvrW1TVIPSazCq3TbLSBERxnvnQfn9dMenCpzOpbXbxiLJ/2evH7DtPmrsbFScCWMfXWojAffL+CrQ2Vtn+whSa4itB0rhiNHICZWWyjQKDt1AiUJyXw8aqq2Y0BlpRRcCWMb80sorqhlY36J195Tt90C3pCYGBvoEM5ZUlJcoEM4J3qKt3bTJk4/9SfUqmoMcbGoVdXaIgGAs3XE/OoX1Lz8KlitZBZsYdO1s7jt5ol0u6Yn1X99g9i5dxKto5/HRk/X2FPBFvPtYy/g3e1F3DLqPK/FrqhqS/tW6ENeXh6zZs3i4MGD5/zasrJqrFZd/3hOkpLiKC2tCnQYHtNbvPXXXgU/nnB5VtFKr8bGogy8iM4ZI6hZ9TeU6TcHRYFrvV1jTwRbzB2Jt7VELN0CQrfOufh05Wk3T6oQFw/RnVHLy+k0fDiRX+4IisQqgptuk2tFRQXffvstRUVFAHz77bd8++231NfXBzgy4S9trZpxTb7KLTPcv9GZGu2/ykqq//pGi68Xwpt02+f6+eef8+CDD9ofT5kyBYDc3Fz69u0boKiEP7VVfNq6Ogt1104su3ZiSUwkMncb9Zs+buoa6NxZmwlQVwcjRqJUVRI7906qHV/fmLxlGlZo80XtgLboNrlOmzaNadOmBToM4Weu/wha+4dgmDETy66d2oOyMiy5ORgXPmQvgK0MHAiAWliIUlWJ6fVVRCfFUd3YvyY7B4QP17sgfyRar3QLnDlzhn//+9/eeCsR5lrrCrDk5lA/7Qbqp92g7SLw0YdA05zVf726BgDjo0tRBg7EMGNmq2vGfbHkUeiT4/fAX3udeaXlWlRUxKxZs/j222+98XYijLXUmrTk5mB56H6toDWNS1uP/BeA1ZdO4ViXnnyfdD5k/4OxKx9rtv2KCG+ud0H+uGPRbbeACE/uugLse105DmYeK4aLB8O3+yEmBqvBgJkIPk69prH+qghHnvSt+muvM4+Sa2pqqq/jEKIZ2z8UtbxcW10VGQldumpLWevNUFtL5O4CZh0q42/bi1CBG0edF+iwRQDpaZDSo+QaERHBz3/+cwY2DhC4Ki4u5sUXX/RqYEI43vpjNGp//nhC28fKaoFqbdx/9IBERg9IDFCUQg8ct0hXQBeDlB4l14EDB9K7d2+mTp3q9viBAwckuQqvsuTmaLf+Vqv2hO1P0Pa8ioiA2OBb3ix8w9ZiVQDT66sCHQ7g4WyBSy65hMLCwhaPd+7cmREjRngtKBHe7H2sKO5PGJIqGwUKJ77YSaCjdF9boCOktoBv+Spe812zUPfmay1URxERKLfN6tDS1VC7xrbb4R2Zd5Ct9mByeq+Ad5GE2jVu67Ut8dpsgaKiIs47TwYTxLmzJYjtyYPJjuhNZvJgMiy7tYMmExgM0KUrxvsfDPgghZ7YW/iKgY0HTlGSHMfG/JKAJ1eh6dAigoaGBj755BNmz57Ndddd562YRJixvPg86p7drI7qz8HE81kdPQBl5h1aJasZM1FSh4LFIvVWXVhXZ2mzKFQrkwd1o0+XaCan9wp0WKJRu1quR44cYe3atWzYsIGGhgauuuoqXnrpJW/HJkKcJTfHvs+VNmCl9bEqxgjt1r/x9t9x7qJo4rjg4vLxV3N5oAMSTjxOrmazmc2bN7NmzRr27NnDmDFjqKioYMOGDS1O0RLClT2hnjzZrETgjF0byE6dwOQR/Zye99ek72Aj16XJV4fK2Jhfoos+ZxuPkuvy5cvZsGEDiYmJTJkyhWeeeYakpCSGDBmCwaDbqoVCZ1yXsDoxGMg4ms+occMwTZ3j/+BEUHPcpkUvydWjzPj222/z85//nI0bNzJ37lySkpJ8HZcIQdbVWc1nAADQuPrqynGo69diXrnC77HpmdSdbdvk9F6663P2KLkuWLCATz75hCuvvJLly5fz3Xff+TouEWLMK1eg7tntvBjAJsIIvfvAzh1QU4O6fq3/A9Qxf1VxCmajByTyxLRU3bRawcNugbvuuou77rqL7du3s3btWqZPn86FF16IqqqcOXPG1zGGpV1lO9hybBMTek/k0sSRgQ6nXRyXJKpZbzVPrIoCg4egREVpgzN781HXr0WZfnNA4tUrqTsbnNq1iODUqVOsX7+edevWUVxczGWXXcb111/PTTfd5IsY2y2YFxEs/+ZRTtSWkBzdi4VDlwQ4MvfamnxtvmsW6nffQXUVuPuadU9C6dfPr8sVw2mCe6AEW8y62qCwW7du/O///i+fffYZr732GvHx8Tz22GPtCk64N6H3RJKjezGh98RAh+IRx35B88oV1F8xEjUuHurPNiVWkwnGXa3VBBh3te6WKwrhTR1eoTV69GhGjx7NqVOnvBGPaHRp4kjddwfUbtqE+aVXm1V3Vw8egJoa2P4v7dbfxmLVtlv5ckfgghZBKRB7YHWURy3X/fv3c/vtt1NV1bzpXFlZycyZMyktLfV6cKFkV9kO/rB7AYt3L2BXWfPksqtsBw9uW+j2mF5V//V11MJCbYXV0aNQXaW1Vg2N5QEjO2mtVZNJq2KVlCQt1TDUntkOrq8JxkE9j5Lrm2++yWWXXUZcXPP+hfj4eEaOHMmbb77p9eBCyQeF71JSW8zx2hK2HNvkdGxX2Q7e/M9LHDn932bH3NlVtoPl3zzq90Ts+oWPnXsXSr/GCf+lP2q7rP7fNq3OqqpCVBTKwIsg5Tzo0hWlb9+gaXUI72lPYmz2mtQ0qKvV/gwSHiXX/Px8JkyY0OLx8ePHs2vXLq8FFYpsN8cqKv3jLnQ6tqHwXc5azmK2NrTYx+qYULcc28QJN0na11y/8NETJ2ob/N07r6lf1WKByMZiK7Gx9uPSvxq+2ioH6K5l2+w1BXshKlr7M0h4lFyPHz9Oly5dWjyekJDAiRMnvBVTSHBtXU7pdwvRxmiijZ05XPW907kqoKCQ3LlHi/2sjgk1UINdrf4jse0UEBeHcelylGHD7fVWZZfV8Nba//+2yl7qd985tWxdX6PHeq1t8WhAKy4ujqKiIvr06eP2eFFRkdsug3DmmAxtCbNLZDcUlGZJcWq/W9hybBPTB01r8f0m9J7oNO81EINdxvHXYN2bj2XJIqx786kdOwrzS69q/a0Wi3ZSYndZ8+7CtfBMsA3M+IJT0Z4GC0R1ajVxBuN3yqPkOnz4cNatW8fo0aPdHn/vvfcYNmyYVwMLVrbb9s4RMdRbz9q7ALYc28SZhhqSo3vZE+Oush18UPguClrLdnTvMZSWVrldQKCX2QPq+rVQXY2a9RaVX21DPVWhzWM1GMBkkt0B3HCaSVFeDkWFWMrLgy5ZtEdLBVWsq7MaE2uD9kTvPiF3PTzqFrjzzjv59NNPefTRR51u/0+cOMEjjzzCZ599xp133umzIIPJlmObOFpdSP6pXaAq9i4A11v59468w18OPENJbTEltcec+k8D1afqyrUvzJKb03T7bzCioqD064dyywytG2DZUyH3D8QbWrul/epQGQ++X8BXh8rsz4VSLQHHgiqODDNmwnn9oEcy9B8Qkr+UPWq5Dhs2jMcee4xHH32UNWvWENu4MVx1dTUmk4lHHnmE4cOH+zTQYDGh90Te/M9LmAyRNKhmqs2V/PnbFRw8vY+f9NQGBRfvXsCx2mL7a0yKyamrwLELoC2+XCbruk2xdXWWNn/VYISoTiTc/zuqR1zh1c8MFa7zMm3dKYwY6XTOxtO9mlVzcho4vLXlrqJgMDm9l73l6igYb/PP1Tktfz1x4gTr16+ntLQUVVW54IILuO6660hOTvZljO0WqOWvttv9sjpt7m+9Wt/ma2IMMcwb8f+40DTU/h4tJU3HY7ZWrjeWydoSghoXrxVRGTESpapSm/5SsFerEbBtKwDGe+fR89ZpQbXMEfy3NNN81yxtN9LG5b31V4zUfjHFxKBcNMh+bOdDK5vdNjsm5lC+xnpZGBDQPbQqKip44IEH2LZtG1arlfT0dFasWEHfvn3bFVAosyU+BUBRqLe6qV3qRo21ho8OfciVSbVsObaJKnMlZxpqnAbEbFxnDnjaym1L0wBDYz/Y/22DtHQtoVZWogCR73/U4c8JC6lpcPCAfV6mMv1me1EaQ1q6PamMHpDYrJJTqLfq7L/Ey8uhstJ+ZxRqPEquK1euJD8/n9/85jd06tSJ1atXs2TJEl5//XVfx6d7tlZqnaWWKGM0tQ21VJjL0SZYnZvT9ZVsKHyXmoYaYiJiW5xu5Y2ZA25bDdXVTYkVQFVRCwshPj7opsEEnMu8TNu2NfYBnodW6qo8nr84bqpIUlJIf688Sq7btm1j6dKlXH311QCMHTuWyZMnYzabMZlMPg1Q7zY0rrwCba6qpn1dEcVVP5Ac3cueVFtKmt6YOeDanwpoBVVOlUG3RJSUFHt3QKBv24KRYcZMbVlweTmW3Bz79dNjxXxvsf3Crv3V3VhO19rLTTp+hxw3VTTeOy+kv1ceJdcTJ04wZMgQ++MLL7wQk8lEaWkpvXv39llwwUB1+nvH+netWJna7xa/TLlyVyPUeO88XfSBBSPXOwFbInH9BdbSAE8osP281X99A6u5QbvrOXgAoqLt18Dxexfq3zGPBrQGDRrEv/71L7p162Z/btiwYWzcuJGUlBSfBtgRHRnQ8nRACeCt717hjLWmw/H2iu5FnKkL/eMu5HDV97ovlB1sdTvBdzFrtWsPatvY9O5jn1rU0UQSTNfY9gum66/u5vTpWq0Pv7oaYmN13UoN6IAWwH333efUBVBfX8/ChQuJioqyP/fGG2+0K0A9crfCyt2xCb0neiWxdjV1o1vnRH6o/IGjNUeINHRy+9lCfyy5OdrgjNkM9WYo0lqr4bbk19Zij06Ko7q0SmvJVlaidO0aVtfBxqPkOnXq1GbPTZ482evB6Elro/COxz4ofNcrnxcd0ZmLug7icMUhLkoYwpmGmqAplB2uzCtXaCvWOsdoW9j0blweXl3drK81nNjq/JKahgIhO2DVlnZt8xIs/DHP1XVBQHt0MkRxVa9r+OeJXFQrpMT2s89ZtXVB6LGrIJhuWW28FbN55QrUtxvv1KKi7InV1m/tOMe1I4LxGvPLOdR/f9jvW/i0l662eRGaP369qN2JVWn8H4CqWtl6fAtmi5k6a61TSUJbF8TW41t0sSQ2nLguQ3Vcqqo61iYdNQala1f7nM1grODUUY7XylbnN5x+fnd0nVxfeeUVrrjiCtLT07nnnnsoKytr+0V+dKTmkMfnmpRITEQCYMBAr+g+/LRPJtHGaDpHxIKqYFEtRBminUoS2moS/KTnhKDaUysUuNav/fCL/RQXlvDhF/u1/lWbokKnhBouJRYdE6rjtbLX+Q3xn78tHd5Dy1fWr1/Pyy+/zFNPPUXfvn1ZtmwZv/3tb3n77bcDGpfjTIGkyGRK65vXsTVgIM4UjwEDddZae02Brce3MCRhhL0/9dLEkdx0/m3290ztMYSCH/c5JVC9VMMKR67T1SblfUB2jzQmfbtX27rGIcGG+qoqdxwTqmz/3Zxuk+vf/vY35syZwzXXaF/YZcuWMWHCBL777jsGDhzo93jeO/IOW49vIcoQjRUrGwrXUm2ppLMxBrPVjFW1EGdKoEd0sr1/tNpcibXBam+JKhg4eHofc/7nV04J05ZAg7J/LQxY9+ZjefF5Mo78lwzrp9AjGWXGTNQ170CXriFZ0akt9hkS8fFOc3tFE112C9TX13PgwAFGjRplfy4lJYU+ffqQn58fkJi2Ht9CnaWO6oZqkqN7aQsGVAUVK+N7/5QB8QOZMeAOFg5dwk3n38aE3hNRgZiIWCb0ntj42AqqIv2mftJa6b6Wjjk+b3nxedT8r7X+1cOHtBkBAJWnMaSlE5m3h8jPPg/LpGJdnaXVmwjTaVae0GXLtby8HKvVSmKi8/LAbt26ndMW3omJsV6LaeIFk8g+vJG4yHj7jgEfHfqQgV0HcbD8ANMHTWN07zH287ce2Eyd9Qx9Yvtw3aDxACTER/PRoQ+5YcCNLY4ytjb6qEd6jrf0vdWoR4swvvd3khxK9yUlxbk9VrtpE+WP/AEMBtQVT0KJcw3SvH7DyE6dQGbBFsa88qLTe/qSHq9xRcYIznx3kM4ZI+jiJj49xtwaX8Sry+TqLe2diuW6OmtX2Q4KftxH905J1DTUsP7A+ywcuoT5g4ay/JtHOVFbwvoD7zuVCzx1ppxoQwxXJl1rv9W/0DSU+YO0c9zd/gdbt4De47XcNAN1dRaWm35uj9MWs+sxe0GRhgaIiEA9ebLZ+2WnTqAkPpns1AlkfPu+X352PV1jx10FRuTtRO0UxZm8nZhd4tNTzJ4Iq6lYXbt2xWAwNJsdcOrUKacluL7iuhOA7bEKzUbs3W0WaNvSJdYUJ4NRAdTaqL3rMcuLz8OZWkArD+g0G6BRZsEWelWeIPPwV2HXz2rJzWHD6s0U/7eYD7/YH5bTzc6VLluukZGRDBo0iLy8PEaO1JLT0aNHKS4uJj093Sef6dhadV2d5Vriz5G70Xxv1lgV3mPJzaH0vdVYbprhvp/QoICiNC0OsFMgNoYx8+5g7PhrgDn+CDfgnIqn//MLMlPSyU6dwKQdX8LdtwbFAoFA0u0KrXXr1rFs2TKnqVgAq1Z5/n/ouXQL2G7vvVHRv73C6XYqEMx3zUI5WoSacl6zxGDJzcHy2MNw+nTzFyoKxhXPBmTgJlDX2LxyBWrWWxAZCfX1TYN5igKdolCGDGkxuQbb9yLghVv87Wc/+xllZWU88sgjVFVVMWbMGP74xz/67POktRn6DDNmYnzv71hu+rn9OXvr7McT7hOrwYAy846wGhG35OZoidVqhbo6SEmBH37QEmtkJKCGde0ET+m25eoNgdpDq73C6Td+oCQlxXF8zfv2Ce/NtrZxFBWFcenyoN3fqb3Md81Czc+HBrO2WKJTJy3JWlXoHK3t2FpZ2WLtgGD7XoRdy1UIX7E89QScOI5l9y5oqW2R0AXjw4+GZcvMMGMmloMHoCECVCvExELnGPLOH8bHGVO5oZuZkdlvyWBWGyS5irBSu2kTnDiuPXCXWI1GInd9Y+8uAMIywdI5BipPQ5euYLGg9OvHJ9fdybGKWrLVrlwug1lt0uVULCF85fRTf2r5oMGAcvtsoHnRlmDX2mo12/H6aTdQP+0GravkVJnWVRITY59yNTm9F326RIfkFjW+IC1XEZIc97Sy7s23b2ut/lja4muUYcO1XVpxv8dYsHLccdW2l5Xrnl/W1Vla3zPAef20/8Bpe5bREHKbKvqSJFcRkqyrs1C/O6gllbo6aGhAfXe128UBAJhMzps1hlAhEscdV20/o20gz1Jerv2cqWmwrwDiE3S931UwkW4BEZIMM2ZqibS6umkmgMWiJRlH465GuXQExidXhGxCMcyYiTLwIoyPLm3+M1ZXa7MDtm2F2DiUlJSQvQ7+Ji1XEXLMK1dordS6OpcDZoyXpGP5urGyWo9kIp99wf8B+pm7Vrh9O5offkDds0dbnXaeLGf1Jmm5iqDSVhnB+tEjtOWrronVds6Bg9B/AEREQKz3qqYFA/ug1U/GYHnofq0rICYGrBZ7615ard4jLVcRVBxH8e1FVxprr3Lkv03LNFtg6NYVpbHVFk6tNPug1plaLZkCatZbKDPvYLuhG9kXjSOz9BvGBjjOUCLJVQQVd6P4tkUBbeo/gC4PLaR6xBVh10LTqn6dcf7lo6qo27aSPe4eSs7Cx8k9Jbl6kSRXEVQcSwRaXnxemzLUVmJN7onx/gcxjr+G6KQ4qoNoaabXHC9xLr6iqmA0ApD536/IThjIpGPfAVJbw1skuYqgYsnNwbJoYVOf6uE2duBN6ELkZ5/7PjA/cZ2f2tZ5pKahfvqJ1mq1iYmBfudD4RGUsT9hTFo6o8Ksm8QfZEBLBA17v2ELg1XN9EjG+PCjvg3KzzxdOWZ58XnUXbu0wT3Hln18ApFf7kCJioKoaCjYGzZbgfubJFehW05LMm0tMdd5qu5ERaHMvpPIzf8IuYThyQ4AltwcKDwCuNRO6JGMccljHr+P6BgpOagj4VSqrS32Vmp1DaBqU6cMBq1wc2viE4j851ctHg6Ha1w/7Ybm3SU9konc/A8vRtaycLjGjq9tifS5Cl3SWqkG7K0vd/VWHRmN2iBN9+4+j01vHPtXKdirDV65Ol0hxa39TLoFhC4ZZsyEsx72rY67GuNTz6AMGx5WGwdacnPYNv9hHvrXSbbXRKJmvYW6r8B58MrGbNZmVwi/kZar8DpPR7RdX2P7x2+8dx7WvfltdwEAjLvavoQ1XFpl9q1pysvJTr2Jkk4JZA+4nIwD/9IG+0yRYG68dhGN/8SDqHssVEjLVXhde2qhWldnwZEjcPgQlgX3udmB1YGiaP2v465GqapssUZpqLJdX2jc7vv0CTILtjSd0K2bllR7JGvzgKOjoXN0WLXq9UBarsLr2lULNTUNdu1s+7z4BOjevanwiMtS2LCQmgYHD0DqUDK2/oOMww7XzWQir8sFrL78l2AwcvuQrvYtWcLqGumAJFfhdZ7UQnUdhFGPHm37jaOims0ECLcaAQDqpo+1Uopf/rNptVWjvH7DeOGK2dRaFQwGhWy1i2zJEiCSXIXPtNb3ar+13bO7zWIrgDZ39ZYZTk+FUkFrTzQrpehmBkV2xo0onTphsljpGR8lW7IEkCRX4TMt3bZbcnNQ9++HWjej2u6YTERu3+2jKIODeeWK1vuhAQwGMo/t4ZMB/8Pk9F6yJUuASXIVPuPa92rJzcGyfBn8eKLtF8cnaLuPAqSc58Mo9cnW6q/IGIE5byfq13taPjkuXpsdkNCFjMI9jMkci3FAqv+CFW5JchU+43rbbnnxeY8SqzL7Tgxp6U5Ts8KJvThNvZmatrpNjEZISgrvAT6dkuQqvMK1f9XxMeBRi1W5dIRT/2y4Jgjr6iyoO0uz2gCuFAU6x6B07Wq/VuE4wKdXklyFV7i2mrSdAY5g8XTAatzVmMJgPytPGGbMbLxurSRXgwGuHIdSVWlPpuE2wKd3klyFV9j6V0lNc184pBXKpSPCPrE61V/dttXpF1Jev2Fkp04gs2ALGYV7tBZrTAxKVSUmmWalW5JchVdY9+ajHjwAX+/RtrD2VI9kuY3FYWpa/tfNplhlp06gJD6Z7PSfknGsADp3hsTuct10Tpa/ig6x1VxV335Dm9juSWI1RUJsLMannwvJmquubDvWmleuaHHnWsOMmRAf73buambBFnpVniAz/zMwm+G0Nosi1K9bsJOWq+gQy4vPe94F0H+AfVQ7nJZj2lulBw9AVDSWF59vdg2M46/RznPz+ozCPVp3QESE9p8nfdgi4CS5inarWLrU88RqMGC8d15YDro49kfblvqqRYVYysudtgdX//Of5v2rNp06YVz2FCAzAoKFJFfRLvX/79fUf9HGxn/xCShTp0PB3rBqqdo4TkdzHHiqn3YDnDwJRYXUT7sBZexP7Mtas8c29q+mTnBOrn36hv0UtWAjyVW0qdlI9n8POxULcSslhciPPvNPgDrV0qR+473zsPx+PjRY4PAh1KJCe191ZsEWreX63T/BZNKeT+oRdgspQoEkV9GMa+Fqy2NL4HSFZyUBASIiUHok+y7AINFS6UXj+GuwOPab2gexFDJ+2Ku1WJN7Evnv/KDbj0o0keQqmtEKV/8XrFYsC+7z7EW2gRajEVLOkz5Bmm7fLU89gWXhAkjsDhcPJq+kluxJv2/er2qKgK7d4FQZxMQEKGrhLbpMrps3b+add96hoKCA6upqDh48GOiQwooaF+/5iHSPZIiNRRn7k7DtW3XUbNnvkkXaFDWAE8fhxHGyJ/3efb8qYFz4kAxYhQhdJtfa2lpGjRrFmDFjeOaZZwIdTliwdwWUHIPa2tZPNhjAZCLmrjmY75a+QHDe14rKyqYtbhRFa807zP+196sWbNGOg9aH3bVbWM6mCFW6TK433ngjAHl5eQGOJPSdUxlAcNoQsIv0B9rZ57LGx6P066f1t+7N11ZcuSyssM9btTEYISYa48KH/Bu08CldJlfhW463rq2WAQzTSf/t4Th4ZasKpv7t7bZXrCUkQGJ3+xxgETpCOrkmJsYGOoRzlpQU59P3r1i6lPqXXgFVxXLoe6iocD7BYEBJTsYYF0f8/b+j+q+vox4twvje30m6dZrf4/UFX8T8RdoY3j3Tl1vSzqN+85es2foDmX3TmvWpAhgHDiTh/t8RPXGiR+8t19j3fBGvX5PrAw88wAcffNDi8alTp/Lkk0967fPKyqqxBtF+7f6YdlO/6m9Nc1QdE6ttf/vz+mF6/yMAqgHL6VrU1VlYbvp5s9iCcZqQr2LO2ribYxVnWLVrF1gsFHbtwwtXzoF/4pxgx12N8dkXqAaqPYhDrrHvdSTe1pKyX5ProkWLWLBgQYvHo6Ki/BhNeFKm34y66k0twSYkaJvddemKct319tF+RzLA0jpbF8skY3eye6SR+Y1WlOWFK+egoDrPCIiLQ6mqDGC0wp/8mlzj4uKIiwuu24VQY5r/Oyxp6dKP2kH22QHf/wdOnyYDyODTphP+SdOMAADFoP1CS00LSLzC/3TZ51pRUUFJSQlFRUUAfPvttwAMGDCAyMjIQIYWEqQ12nH22QGV7luizWYEJCVpc4cL9vopQhFouqzn+vnnnzNlyhT+8Ic/ADBlyhSmTJnCjz/+GODIRDiz1WW15OY01V+Ni9dqALQmuSfGhQ/Zp2iJ8KDLluu0adOYNq35yLQQ/uS66aJjIRZS0xrLLSoQYWz5TaKiMN7/oNwthCFdJlch9MC1qtWOzDvYeOAUkwd147InbQOzqtvdAwAtsS5dLkk1TOmyW0AIPTDMmInSr59908WNBSco6RTPxp2FcPZs6y+OiJDEGuYkuQrRBnXbVigqJHPPJ/QqK26aAdCSTp0wLn9aEmuYk24BIVpgefF5OHIEFMBiIePwLjKO7HFfMcxgABQwRWBc9pR9CaxMeQtfklyFcGCvDlZdDaU/Nt9xwTGxmkwoM2Zq+2LFxcPOHSjTb7Yn0pZ2IhDhQZKrEA483s3WYMD45Ap70jTfNQs1KtppHmtLOxGI8CDJVQhHZWUtH0tIgJoaUBQUl1t9d4lUpl+FN0muQjiqadw1wGCATlFQe8bhoELkTvcrrCSRClcyW0CEHceVVo7MK1c0zVm1WsFc3zhQ1aiujR0ahHAgyVWEHaeVVjQlW/WdVc4nNjRoydVo1Ha0vfW2AEQrgpUkVxHyXFuqalw8nCpDjYvXZgc8dD/qrp3uV1rFxJI3+AoWT3+YnZPn+DlyEcwkuYqQ59pSZecO7bb/i8+1rcNbW21VU0324PGUdOnJxvwS/wQsQoIkVxHyHJexnrjqas/2tbJVuuqWyJSbr6JPchcmp/fyfbAiZMhsARE21G1baSg80npyNZkwPvwYgH1q1egBiYwekOifIEXIkOQqQpLj0lN7YWuj0X1ije4MVgt06WovDwjI1CrRIZJcRUhy7Ge1J9ijR5ufGBWF8fEnJJEKr5M+VxGSbP2shhkzse7NR91XAD+eaH5i7z6SWIVPSMtVhBT7xoF1dbB/H5a9e7XFAO5ERGC8d55/AxRhQ5KrCAn2pFpeDqWlYNvC2l1iTUlB6ZEspQCFT0lyFSHBPmhlMDTVB3BlMtHtpRepHnGFf4MTYUn6XEVIsM9lPXvWfTFrgJTziJ440b+BibAlLVcRMtSjR+F0RfMDJhOknCf9q8KvJLmKoGbvaz16tPlsgIgISOzefDcBIfxAugVEUDKvXEH9ZelYFtynFV1xM83KuPxplL594eRJOHxI22VACD+RlqsIGpbcHCxPPQFlJ91XsLKJiEC5bZZ9JoBlXwHUS+tV+JckVxE0rKuz4MTxVs9RZt+Jaf7v7I8dNwuUvayEP0lyFUHBkpuD+p//uD9oMoHZDD2SnRKrjWzBIgJBkqvQLadtrstPaQnUHaMRJS1dWqZCVyS5Cl1xrGbV4jbXigIxMdDvfCg8gjL9ZrctViECSZKr0A1Lbg6WJYtAUbT+1bKTzU+KikK5ZYYkU6F7klyFblhXZ4FiANWqFV45fbrpYEQEXHElSlUlhrT0wAUphIckuYqAsXUBkJqGum2rNh/1TI1227+voOnEhC4oF16IWlSIWlmJdXWWDFAJ3ZPkKvzK7Q4BBw9AXV3Lc1cTE7Xz4uPtNVqF0DtJrsKv3O4QEBcP2/+lFVxxLbrSIxll7E+gYK+UCBRBRZKr8CtbQnVqfe7fp7VauyehpKRodQJOlWl7XlmtULAX0+urAhe0EO0gyVX4hSU3B8vyZVB5GuWWGRjHX4P5rlna7X51lXZSbCym11c59cXaWqxCBBtdJteXXnqJTz/9lMLCQuLj47n22muZP38+MTExgQ5NtJN1dZa9uIr69hvUr3oTBg/RarC6JFFZUSVCgS6T6549e5g7dy5DhgyhrKyMxYsXU1NTwxNPPBHo0EQ7qXHxLk+osH8fpj37AhOQED6my+T66quv2v/ev39/7rvvPpYsWRLAiMS5Mq9cgbp+LYwYqfWputt51TXhChFCdJlcXZWXlxMXFxfoMISHLLk5qFlvaYNRX3zu/iRFwbjkMb/GJYQ/Kaqq7zLtVVVVTJ06lWnTpnHPPfcEOhzRhtpNmyifvwC1pqb5tCqTSfuvro6YX95Nl0WLAhOkEH7g1+T6wAMP8MEHH7R4fOrUqTz55JP2x/X19dx9990oisJrr71GRMS5NbTLyqqxWnX9u8NJUlIcpaVVgQ7DY47x2rsBjBHu97HqkYwycVLA56sG8zUOFsEWc0fiTUpq+Y7ar90CixYtYsGCBS0ej4qKsv+9oaGB+fPnU1NTw1tvvXXOiVX4h313gJaKWPcfgPHeeTL6L8KOXzNWXFycR32nVquVhQsXUlRURFZWlkzB0iFLbg6l763G8uPJlhNrj2Qi3//Iv4EJoRO6bA4uXryYvLw8XnvtNcxmM6WlpQB069YNo9EY4OjCm72AdVEhFlWFmNimnQAATCaUGTNl8r8Ie7pMruvWrQNgypQpTs/n5ubSt2/fAEQUnuyJtKYGYmIw3jtPWwxw5L9Ng1VVlRAbi5KWLktUhXCgy+R68ODBQIcgoFkitTx0P8qtt2nlAOvOggJ0igJVlVaqEC4MgQ5A6IslNwfzXbOw5OZoy1IdJ5OYzVCwF+PS5SiXXkrMr36BMmQIxkeXyoCVEC502XIV/mefShUTCxaL1moFiI3Vkmp8AsTG2qdRGcdfQ5ekOMxBNOVGCH+S5BomHItUA/a/21qc6vq1Wt+q2YySOrTF84QQnpHkGiYci1QD9r/bkqYy/WbU9Wub7aQqSVWI9pHkGiZci1S7Fqw2zf8dyI6qQniNJNcw4VojVVqkQviWzBYQQggfkOQqhBA+IMlVCCF8QJKrEEL4gCRXIYTwAUmuQgjhA5JchRDCByS5CiGED0hyFUIIHwjpFVoGgxLoEM5ZsMUcbPFC8MUcbPFC8MXsi3h1v7W2EEIEI+kWEEIIH5DkKoQQPiDJVQghfECSqxBC+IAkVyGE8AFJrkII4QOSXIUQwgckuQohhA9IchVCCB+Q5CqEED4QVsn13nvv5aKLLiIvLy/QobTqpZde4sYbb+SSSy7hyiuv5PHHH6empibQYTl55ZVXuOKKK0hPT+eee+6hrKws0CG5FQzXsi3B8r3dt28fs2fPJj09ncsuu4z77rsv0CG1qrKykoceeojLL7+cYcOGceutt/Lvf//ba+8fNsl1w4YN1NbWBjoMj+zZs4e5c+fy/vvv8/TTT/Pll1/y+OOPBzosu/Xr1/Pyyy+zZMkS1qxZQ1VVFb/97W8DHZZber+WbQmW7+2hQ4eYPXs2l112GevWrWPNmjVMmjQp0GG16oknnmDfvn385S9/4cMPP2To0KH84he/oKqqyjsfoIaB48ePq+PGjVOLi4vVgQMHqtu3bw90SOfkk08+US+77LJAh2E3ZcoU9bnnnrM/LioqUgcOHKgePHgwgFF5Rm/XsjXB9L399a9/rT700EOBDuOcXH/99WpWVpb9cVVVlTpw4EB17969Xnn/sGi5Llq0iF/84hf07t070KG0S3l5OXFxcYEOA4D6+noOHDjAqFGj7M+lpKTQp08f8vPzAxiZZ/R0LdsSLN9bi8XCtm3b6N27NzNnzuTyyy/nzjvv5Lvvvgt0aK265JJLyMnJoby8HIvFwvr16+nZsycXXnihV94/5JPrmjVraGho4NZbbw10KO1SVVXFG2+8wfTp0wMdCqAlJ6vVSmJiotPz3bp149SpUwGKyjN6u5atCabv7alTp6itreWvf/0rkyZN4tVXXyU5OZk5c+ZQXV0d6PBatHjxYuLi4hg1ahRDhw7ltdde45VXXiE6Otor7x+0xbIfeOABPvjggxaPT506lXnz5vHCCy+wZs0aP0bWMk9ifvLJJ+2P6+vr+c1vfkNKSgp33323P0IMWcF0LY8dO6ar721brFYrANddd539l8Fjjz3GlVdeyRdffEFmZmYgw2vR22+/TXFxMW+99RYJCQls2LCBe+65hw8++ICEhIQOv3/QJtdFixaxYMGCFo9HRUWRl5fHyZMnufbaa52O3XHHHUydOpVly5b5OkwnnsRs09DQwPz586mpqeGtt94iIkIf/1d17doVg8FAWVkZAwYMsD9/6tQpunXrFsDIWqbXa9mS/fv36+p725auXbtiNBq54IIL7M+ZTCZSUlIoKSkJYGQtq6ur489//jOrVq1i+PDhAAwePJitW7fy8ccfM2PGjA5/hr6/Za2Ii4trs+9s1KhRbNy40em5G264gccff5wrrrjCl+G55UnMoLUEFi5cSFFREVlZWcTExPghOs9ERkYyaNAg8vLyGDlyJABHjx6luLiY9PT0AEfXnJ6vZUv09r1tS2RkJBdffDGFhYX25xoaGiguLtZtf3FDQwNmsxmj0ej0vKIoqF7anCVok6snYmNjGThwYLPn+/btS3JycgAi8szixYvJy8vjtddew2w2U1paCmj9mq5fhkC47bbbWLZsGRdffDF9+/Zl2bJlZGRkuL3Wgab3a+lOMH5v77jjDhYtWkRGRgZDhw4lKysLg8HAuHHjAh2aW7GxsQwfPpxly5axaNEiEhISWL9+PcXFxYwZM8YrnxHSyTVYrVu3DoApU6Y4PZ+bm0vfvn0DEJGzn/3sZ5SVlfHII49QVVXFmDFj+OMf/xjosNzS+7UMFTfccANlZWU8/fTTVFZWkpaWxptvvqnrO4Vnn32W5cuX88tf/pLa2loGDBjAiy++6NS90RGyQaEQQvhAyE/FEkKIQJDkKoQQPiDJVQghfECSqxBC+IAkVyGE8AFJrkII4QOSXIUQwgckuYqQ8MADD3DRRRdx0UUXMXjwYK666ioefvhhysvL7efs2bOHX//614wZM4ahQ4cyYcIEfve737Fv375m7/f6669z8cUX88QTT7j9vOrqav7whz+QkZHBJZdcwty5cykqKvLZzyeCjyRXETJGjBjBl19+yeeff86iRYvYvHkzCxcuBLTdE2677TYiIiJYsWIFn3zyCStXrqRPnz4sXbq02XutXbuWX/ziF2zYsIH6+vpmx++//36++uornnvuOVavXo2qqsyZM4e6ujqf/5wiSHil5LYQAbZw4UJ19uzZTs/95S9/UQcNGqQeP35cTU1NVRcvXuz2tRUVFU6Pv/rqK3X06NGq2WxWr7/+enXjxo1Oxw8fPqwOHDhQ3bZtm9N7DBkyRF2/fr13fiAR9KTlKkJWVFQUVquVdevWUV9fzz333OP2PNfane+++y433HADERERTJkyhbVr1zod3717NyaTidGjRzu9R1paGrt27fL+DyKCkiRXEZK+//573nnnHdLT0yktLSU2NpaePXu2+bpTp06Rk5PD1KlTAbjxxhvZvXs3hw8ftp9TWlpKly5dmlXV6t69u73qlhCSXEXI2LFjB8OGDSMtLY3MzExSUlJ4+umnz6k+5/r16xkwYACDBg0CoEePHowZM4b33nvPo9critKu2EXokZKDImSkpaWxfPlyjEYjPXr0IDIyEoALLriA6upqjh8/3mrrVVVV1q1bR2FhIYMHD7Y/b7Va+eabb5g/fz6RkZEkJSVRUVGBxWJxar2WlZVx/vnn++znE8FFWq4iZERFRdGvXz/69u1rT6wAEydOJDIykr/85S9uX3f69GkAtm/fztGjR/n73//Ohg0bnP5raGggJycHgOHDh2M2m9m+fbv9PSorK8nPz+fSSy/14U8ogom0XEXIS05O5uGHH+bhhx+mqqqKm2++mZSUFE6fPk1ubi55eXm88847rFmzhssuu4xhw4Y1e4+rr76ad999l0mTJnHBBRcwfvx4HnnkEZYuXUpcXBzPPPMMycnJXH/99QH4CYUeSctVhIWbbrqJrKwszp49y29/+1uuu+465s2bxw8//MAf/vAHysrKyM3NZeLEiW5ff/3117Njxw6OHDkCwFNPPcXIkSP59a9/za233orVauWNN95w2mRShDfZiUAIIXxAWq5CCOEDklyFEMIHJLkKIYQPSHIVQggfkOQqhBA+IMlVCCF8QJKrEEL4gCRXIYTwAUmuQgjhA/8fMCynkK6Ea4YAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 455, 1\n", "LR fn, tp: 7, 5\n", "LR f1 score: 0.556\n", "LR cohens kappa score: 0.548\n", "LR average precision score: 0.550\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 453, 3\n", "GB fn, tp: 9, 3\n", "GB f1 score: 0.333\n", "GB cohens kappa score: 0.322\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.6966004952625284\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 451, 5\n", "LR fn, tp: 10, 2\n", "LR f1 score: 0.211\n", "LR cohens kappa score: 0.195\n", "LR average precision score: 0.280\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 453, 3\n", "GB fn, tp: 10, 2\n", "GB f1 score: 0.235\n", "GB cohens kappa score: 0.224\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 453, 3\n", "LR fn, tp: 9, 3\n", "LR f1 score: 0.333\n", "LR cohens kappa score: 0.322\n", "LR average precision score: 0.432\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 454, 2\n", "GB fn, tp: 10, 2\n", "GB f1 score: 0.250\n", "GB cohens kappa score: 0.240\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.030983866769659363 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 452, 4\n", "LR fn, tp: 10, 2\n", "LR f1 score: 0.222\n", "LR cohens kappa score: 0.209\n", "LR average precision score: 0.219\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 454, 2\n", "GB fn, tp: 12, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: -0.007\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 48 points min:0.02923183196448697 max:0.6446939196238787\n", "-> create 1776 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 450, 6\n", "LR fn, tp: 5, 5\n", "LR f1 score: 0.476\n", "LR cohens kappa score: 0.464\n", "LR average precision score: 0.534\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 453, 3\n", "GB fn, tp: 7, 3\n", "GB f1 score: 0.375\n", "GB cohens kappa score: 0.365\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 10, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 4/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 4/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.37681593649950645\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 448, 8\n", "LR fn, tp: 5, 7\n", "LR f1 score: 0.519\n", "LR cohens kappa score: 0.504\n", "LR average precision score: 0.576\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 455, 1\n", "GB fn, tp: 9, 3\n", "GB f1 score: 0.375\n", "GB cohens kappa score: 0.367\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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AGbkK0UquNgmoBfmYb7kJdfajaDu+pDC2C/Nv+w2FPYdQ2HMI82+fwxel1Xqt1WFrqwgOnx+qYO77xXx+qMJr31OSqxCt1HRZln3nlcPJAY5bWvMyb6EspQsfZ+ijVNuSLVkdEDw2FJVxtKqeDUVlXvueUhYQopVsS6e0hETMwwbrx103kV28xd6MBaORj2/8GRNvHAjI6oBgNGFwZzYUlTFhcGevfc+gPongYslJBP4VyrHDheNvukvLPGJosx1XLikKxMUR/dl2L0bbXCjf/1CNPSRPIhAi2NjKAeqSxZivHa4nTXcSk/SjWrp3hzg5TDASSVlACA/ZywG7i/RSQAtHDxkX/E4++kc4Sa5CtIJWWXn+WBZXFTWDEXr1ksQqpCwghKesq3KhtOT8+lXHdayglwl69bI3uxaRTZKrEB4yTMuBlFT3F8QnoCQny6hVAFIWEOKCLMuWor23Sh+Z1rs4UBD0hiz9+snaVWEnyVUIN+xJtaXlVrHtoXNnjA/PlBGrcCJlASEcOG5t1datbjmxxsToy6ykFCBckOQqhAOnra09e7m/0GRCuWuabGMVbklZQEQ0tSCff+Vt4+OMseRMGEq/jEw4sF/vuequsUpSEsanZR2raJmMXEVEs67KJS+pH8eqG3jvi1L9sMAz9Wircl32DAAg9RIAl8dnC2EjyVVEtoxMsou30LmyjFvWrYDD34NVbZ5Yk5KgXTt9SyueHVgoIpuUBURkK95N1ne7yPr2Ak1VGhowLn7B6SBBOVRQtESSq4hIakE+6orlFCb0IG/8bLL35JNVsktfy+piW6ty9z3NWgVKzTVwmnYoC0ZSFhARybaVNa/7MMqSO5M3ZBxEmyAhsXm3q+7dMc16PDCBCpdCoSwjyVVEFNs6VjIyoUdPskv/Teeq42Tv2gRmC4XJvZk//nEKew45/6LKysAFLFwKhdMcpCwgIoptxGNLmFkHPydr9z/szzsez5JVfwzO1Ekv1iAUCqc5SHIVkSUjE/YWw4/lLp+2H89y5EuMc54K+v+BRfCS5CoiS/FuOHvW7dOjZv6C60aPBe7zX0wiLEnNVUQE29HX2q6dzVcDxMaCohB96y0yUg0hvjgO25skuYqI8K+8bcwfNp3C7oOdn+iYDnHxKEOvIm3lysAEJ9rEF8dhe5MkVxGWbKsCLMuWYp5yO3nxfewTVXaKopcIEhPts86OXbFEcJswuDNdO8R69Thsb5KaqwhL6orl+lbWnTsAyFY36xNVxVvOX6RpcLoKpW9feznAcf2klAiC28g+qYzs08LJEAEWtMl18+bNvPvuuxQXF1NbW8uBAwcCHZIINbaDBIGskl36DizQa6y2EwXSOzmtlbSd8BrM6ydFaAjaskB9fT0jRozgwQcfDHQoIgQZH54J6Z2aPV7YcwjzR/+awgGjID4e4xNzm21pNa18R0at4qIFbXKdOHEiv/zlL7nyyisDHYoIQdbdRXDyRLPH9U0CHcnrdz0oSlBvnxShXQMP2rKAEK1ha8RCbS3Ex8Oxo+eXXEVF6f+pKtl7C/j45nu4/bJuKEf7y8f/IBfKNXBF01y0AAoihYWFTJ8+XWquokXHb7oZ9eA3zg8aDHrdNTYWY/fuGFNSiJ9xP7HjxgUmSNFq9Zs2Ufs/b4bkn1tYj1wrKmqxWoP6345m0tISKC+vCXQYbeKL2D1tLac2Wl0/YYqGs2exjrwW46zHqQVq3cQYyvceQjt+t7FffS1cfW2Lf26BlJaW4Pa5oK25CgEtt5ZTC/IxT7kd8y036eUAQ5O/zgYjWMwQHa1ve3V4XajW8cJV/aZNYfdn4pXkeubMGf79739741sJ4aSl1nLWVbnw3SF94urkCTCZzj9pMunHtZiiISrK6fWh0As00tT+z8qw+zPxSnItLS1l+vTp3vhWdlVVVXz99deUlpYC8PXXX/P1119jNpu9+nNEcLL3XQW3S6OaJdyYWH3iymAEoxHi4qBdNMaFi5xeHwq9QCOJWpCPeuqU0065cBC0NdetW7cyd+5c+9eTJk0CoKCggG7dugUoKuEv7maJLcuWoq1bjTL1TrSSw4C+djUvYwzZe7eSlVqFkpystxYs3u2yVhsKvUAjiXVVLkrVaZTuPcLqz8Wj5JqRkeHrOJqZMmUKU6ZM8fvPFcGh6U4p28SWVrwHzGa0davhzBkA/mfEXZyKT+V4wiWMaHdAT6qZgzHK0SwhwTAtB+Oav6Le8bNAh+JVHiXXqKgofvazn9GvXz+Xzx89epQVK1Z4NTAR2ZqOLtUVy6G0RJ+cUhS4erj+xP/+k9r2SQDUJqTAzt0huy4yUhlHjyXt7ikhu9LBHY+Sa79+/ejSpQuTJ092+fz+/fsluQqfsG8OKC3R16yqKsTFw5fbMS5cBLdPZPzWPWzuMoRxV3bFMEB6A4jW88Vpsh4l1yuvvJKSkhK3z7dv356rr77aKwEJ4UhdsVxfEQDnWwSeO0lAXbEcJTmZnJISpvfcjunhd4BeMmIVreaLnWAeJdennnqqxed79OhBbm74LKEQwUEtyNe3sdq42EwYCV2sPj9UwYaiMiYM7hzULfZCmS/+HnlttUBpaSk9evTw1rcTEajpRzN1xXLX510ZjZB6CcaHZ4b1zL/tfnw46j84psSwoahMkquP+OLv0UWtc21sbGTjxo3ce++93Hrrrd6KSUSoZov7K35sPlodlEH0jj1Eb/5H2CZVG9v9uK04P6g77l+szw9V8Ms3twftWVht1aaR6+HDh1m9ejXr16+nsbGRm266iVdeecXbsYlIk5FJ4WkDed3HkP1fK8mqrnZ+XlHON7mOALaPqqOyr+O60f5fDukvG4rKKKtuCLuRucfJ1WKxsHnzZv72t7+xa9cuRo0aRVVVFevXr3e7REuI1tC2fcqqK6dTlpROTU05We1iwHwWJecXaNs+1VcMRJBQLHm0ZdZ9wuDObNp3knEDO/o4Ov/yKLkuWbKE9evXk5qayqRJk3jxxRdJS0tj0KBBGJo2yxDCQ7b/EcnI1JNnyWG4Ui8DaLGxKIMGna+/Zg4O24krXywDCpS2zLqP7JPKhBG9InOd69tvv81//ud/8sgjj0gyDUE7Kraz5dgmxnQZx1WpwwMdjp19mdWOL+2PTdvxob6VtXwPppXv2B8PxVGcp0K5IXRTkbB6w1MeZcrZs2ezceNGrr/+epYsWcLBgwd9HZfwoi3HNnGivowtxzYFOhQ7y7Kl59evOsgq2cWzH/+BrB+/cfGq8BROjWTkDLLzPBq5PvDAAzzwwAN88cUXrF69mqlTp9K3b180TePMuf3dIniN6TLOPnKFwI9k1YJ8tNw/u78gKgrluhv8Fk+ghfOoPJK1arXAiBEjGDFiBKdOnWLdunWcOXOGadOmMWzYMMaPH88dd9zhqzjFRbgqdbhTEnUcyfo7uaoF+ajz5jgde23Xu4/+a3U12rZPsbjpaiVCTzjVlT3VpgJqSkoK//Ef/8Enn3zCG2+8QWJiIr/73e+8HZs4Z0fFdpbsWciaw++yZM9CdlRsv6jvN6bLONJjO9tHsv6krlgODQ3Nn7jxZqLf/wjjwzNRevYECLvmyaGuNSc4NL02EhuUX/QOrZEjRzJy5EhOnTrljXgijicf0W0jze9rDmHVVGotNRc14mw6kvUHewOWw983fzImBqVGX9Nq+4jsONIJJ6G8lbU1E2/Nrs3IhAP79V8jhEcj13379vHzn/+cmprmSyWqq6vJycmhvLzc68FFAk8mm2wjzfioeAA0gv/QRdvIxbJsqT6CcexsBfoWVtCPY2lyDAuEz8RI0xHchqIyjlbVs6GoLMCRtV5rJt6aXVu8Wz8pwuEss3DnUXJ96623GDZsGAkJzU86TExMZPjw4bz11lteDy4SePIR/arU4cy5YgE/6/ML+iT2Y3LPu5pdYysdXGzJwFtsIxdt3Wq0khKoq9MTalKSfhSLBnRMR8kc3OwYlnDS9OPwhMGdQ3Yra2v+wWt6bTitiPCUomkuWg01ccstt7Bs2TIGDRrk8vl9+/bx6KOPkp8fXCc3RsrR2jsqtvPWN6+gYKBDdDLxpoSArASI//IzKl95Xd8UsOljqD4NaWlw9CjEJ+iNrhMT9U5XZgv06kX0+x/5NcaWBPJocG/w99Ha9lIP2JvotFWoHgve0tHaHtVcjx8/TocOHdw+n5SUxIkTJ1odmPCMq7qs42Nbjm0CTaFRs1DecIIqc6XTSgB/Lb2yneDJgf3nJ62OHtWbr5jPolx2GVplpb0UYHx4ps9iCRbhsszKVa3YuirXviU5HDZAeJtHZYGEhAT7KayulJaWuiwZiOZcfXxv+ljTrx3rsrbnVh36M4eqD/JByXuM6TKO7vE9SY1JI0ox0Wi1UGOpdvl6b3OsKcbPeECf6b96uF5bbWyE5GS94cqIUfrHxIdnovTrH5alAMd70ZqZ9VDgqlZsmJYDPXpCj8j6uO8pj0auQ4cOZe3atYwcOdLl82vWrGHIkCFeDSxcuVpjur7kPU7UH6fWUsOtA0Y7fX1V6nCnTQC219c21qKi0qDW22f/bSPUGks1Zxrr7D+j6SYCb3KqKf7yQf1Bx4mrykpISW22GiAcOd4LrbISSktQKyvD4v1OGNzZPnK1Cec/S2/waOR6//338/e//52FCxc6ffw/ceIEzzzzDJ988gn333+/z4IMJ64msDT7r5rLr20TWlelDqd3Ql/M1rO0U9phxAga9lGu7brJPe9y+hmOr/c2w7QcSExEq6ykav4CtKKv9D6sth4Ulw+MmIkMd5M2nx+qYO77xU79SkNtZDuyTyrPTclotnws1N6HP3k0oQWwbt06Fi5ciMViIT5eXxJUW1uLyWRi/vz5Qbk7K1QmtBxrorcOGM2f/v0qnx7fQnpMF040HOOGTmO4o9c9LieuaizVVJ2tBEXjvst+6fdJLMuypfpWVqNRLwPYKAoYDCiDr3RqwBLMvDmp4jjZ8/RtT3BMiaFrh1iem6L3ZbU8MB2tpASlZ0+v3R9/TQo5dTNbtxoUA0q/fhf1PiJ2Qgtg6tSpXHvttaxbt47y8nI0TePSSy/l1ltvJT093SuBRhrHpDrnigX2x7+r+RY0hcN1emOT/KOb2HrsExq1RqKUKIwGmNTzTkAvKTRqFkxKtF+2szad/dbeW6WXAKxWiI3Vj2WJbgeaFbp2i4gRqyPH+6MkJ9tPEth43Z1OH6lDuXuUrfzBgf36P6KaNSTfh695lFyrqqp48skn2bZtG1arlcGDB7N06VK6devm6/jCTtNZfseJpi3HNjF1wBR6J/TlYPXX9teoNKKeG4CbNTPRarT9+rrGOtJi0u3Lr3yt2c6bxCR9ZUBSEtGXX46l/0CIsJ4AjgnV8f60dJJAqNQrHUeptj9X2/tyfCwU3ou/eVQWWLBgAZs3b+bee++lXbt2rFq1ih49erBy5Up/xNhmwVgWWLJnISfqy+w1UcdEe6j6ICqqR9+nc2xXJve8y+dLrCzLlqKtW40yVR8pa397FzokY3xibrNtqp3unhKSH+1s2vrR1PEjvuOI1N8JxxcfrW3vjYZ6iIn1ahnDUcSWBbZt28aiRYu4+eabAbjuuuuYMGECFosFk8nknSgjhC2R9k7oy6pDf+ZMYy3/Ovm/1FiqPU6sANWW0z6M8jxt3Wqoq9N/BTCboa7WnjhCZQTmUxmZULwH7YcfAEKmxuwJw7QcvXZsNEJcnHz8bwWPVgucOHHCaXdW3759MZlM0k/AAzsqtvPbnbOZv3O204z+dzXfUmU5hVkz89WpLymrP9qq72tWz/qlCbYy9U5o1w7ax+nrV6OjIS4+ImeHbbP+//fBVucZ8uLdoKpQ8WPYdX0yjh6LkpwMqoqSnCz/kLaCRyNXVVWbjVANBgOq6vlIK1xdaPfTlmObOFl/3P57gA9K3js38lSgjU1YBiUPZlTH6722ftVVbc04eiymWY9jKd6tf+ytqYaMK8LmSJLW+vCf+zhW3cCG2h8ZVlKCumK5Pqqrq4OUVIiPD6uRnePfCQXC6r35g8erBR599FGnBGs2m5kzZw4xMTH2x958803vRhcCWmo8vaNiO7WWahJMSbSPam8vCZysP46KigED1jYkVwWFM411Xm0d6DQDHBPrlDybzmyH6iz3xbqtOJ+8hL5k79uqr+M9UgoWCxiMKEOGhFU5AM7/nVAIr1KHv3iUXCdPntzssQkTJng9mFDU0u4n22x+emxnp6VW5fUnOG2pwoqLbvwXEGeIw6xZaB8Vx5I9C702meVqBtimaV010kasoI/isvZuI6t8HbSLgSgjqOf+/DRr2PQpdVyfq1x3g4xYL4JHyfW5557zdRwhq6XRo7vE22Ctb1NiBUhq14G6xjoOnN5LtKGd19a2ysRUy6yrcuHUuR1WDfWQ1AEuuQROV+l16BDvU2orAdi27QJQvFtGrBfhok8iELodFdtZX/IeGjC5510uE55th5VqbXutWgPiouJRGg32UoPwHselZdt7DeXDf+7jtuJ8sg5/f34HmsEIdbUofftimPNUWJRJ7GWhxES9GQsyYr1YbTpDK9J40ohar70e52T9cadNAU03CaiaFYtmaVMcCgqTe96FhkaN5TQaWkBObw1njpsANhSVcazqDHlxvaHifF8AevWyd4IKlxMTbH0RjA/PJPr9j/TzzEL8PQWaJFcPtOYolo6xnRjTZZx9Mqt9VJx9dGlrutLWY1qiFP2Dxo8N5WhoKChBdwJBqHNsvpKtnKRzZRnZxQVO10jyEZ4I6rLAa6+9Rm5uLjU1NVxzzTU8++yzpKb6/1C3C7Xsc1USWLJnIXWNdbSPirMn5d2ndrbp5ysoRClRjO5yK1uObSJKiSLKYKRjbCde2b8Mk8E/fQUihVZZifrCcwyr+JFhjY0QE6PXWE9XQXqnQIfndWpBPuqCeaAYInKJna8E7ch13bp1vPrqqyxYsIC//e1v1NTU8NhjjwUklgu17HNVErCNUqstpzlUfZD1JavbfKzgLwfM4tVRudzR6x57Y+z7LvslB07vRUPDYjVL7bUJd63wLtQiz95d/8Rxpy5fyqQpKFddjfGJuT6NOxCsq3KlAYsPBO3I9S9/+Qv33XcfY8fq/4ouXryYMWPGcPDgQfr16xfg6JyN6TKOWksNGhq9E/qyZM9CaizVRBvaUWOpxoqVY/U/YMCAASMmg4mz1gaPvneyKcUpqTuuTviu5hs+Pb6FGzqNcTuB5o/jXYKRu2OgXT1u659wvG8ftD3F+m4rRaGw73Dy+l1PdvEWsv6+kehPtgbq7XhN0yPLm24SkFGr9wRlcjWbzezfv5+5c8+PErp3707Xrl0pKiryW3L1NDk5JjxbY5a4qHjSYztjUAxUmk8B2JdfWTUDydEp9seb6hLbzV5TndTzTrdx3NHrHu7odY/buJueSBBJ3LX0a/q4WpCv96O1WlG/Kjp/oaaR1+96yhLTycsYQ9Yny/wYvW/YP/6rqv5rx3SorpZNAj4SlMm1srISq9XarL6akpLCqVOuE5IrqanxFxXHp/s3U372OP9bvplbB4z26DVTB0zho0MfcnufiQC8vvs1vUaqRNGoNaJqKo1aI3WNtcRFxWGxNpJgiqfybCVWrBgxMqLrCA5U7mdCn4mM7DKKudvmtCoOW9zx0Ql0b9+N2/tMbLF7jzf56+dc0N1T9P8u8Hj5mlWoMTF628SoKL0xzTnZxVvIyxhDdvEWDB3Tgue9taClGI8tfR5qa/UvDAY4VYGiqrTPupoOQfDeQuH+tkZQJldvudiWgzek3cIWyyauT7vF43ZofU1XMGvAFYA+ij3VUIGGRlpMOr8f+kfm75zNifrjxBnjiY1qj4bG4JShFJ3aiYLCvVfcy7r973Oivox1+9+nr+mKVsdhu95xpOuPdm6h1jZOLchHPfkjRJlAOeuUWAGySnaRdawYuvdAmflk0L+3lu6/WpCPVnb+cEGsVqiqQouJpe6dv9DQ5/KAlgRC7e+OjVdOIvCn5ORkDAYDFRUV9OnTx/74qVOnSElJ8Vscbdm77/gRfkyXcRyuOUSj1ki15TSPfHEf/ZMGEW9KdGqW/enxLSgY0M6VDZquTrDFYJssu1BM3uw5EA6anp5gY12VC2XHoL7e+QWKApf2xvjwzLCpQbrt1mU+C6Z4WSXgA0G5WiA6OpoBAwZQWFhof+zIkSMcPXqUwYMHBzCyC2vayGVG/0fom9gfs3qWBrWBA6f32lce2NbG3tBpjJ5YNYWPDn3ocnWCP9oLhiunE2odaCdPNE+sAO3ahd06VsO0HL11JAqYTHq9tXcflJxfoPTrJ6sEfCAoR64A99xzD4sXL+byyy+nW7duLF68mKysrKBbKdCUq1HnVanDWXP4XfvMPjSfLOudcBlbjm2y12ov9H2F51xNbqkF+XDkiOsXJCahFuSHVXIFoKt+LFM4jciDWdAm15/+9KdUVFTwzDPPUFNTw6hRo3j22WcDHdYFNf1I7phEbTP7th4DaIrHM/nyUb/tbE1p1IJ8zFNu14/+Pu3mJAeTCazWkPmY7LS0qskEnmOHK2pr9cYzPXqGxPsKB0GbXAEeeughHnrooUCH4ZYtcfZO6Mt3Nd+6XLLlqt/rlmOb9BqrYrWPRD8oeY+T9cf5y753eGbwH/z+XiKBumI5fHfI/QUmEySnhFTTa6eSx91Tmh2WaO9wFRXU/6uHJbnjbeQ4+jxSd9ht+z9XH+cdH7Ndr9ieVBQuJJI3B1yUI6WuHzcaoWcvoqIMNJ6qCpnjTNSCfL1FYGLi+XW7K5ZDaQlqZSXKdTdA8R7okIxy6/hmfXqFb0lybSPH0ecNncbYR65NE5+rj/OuHpt07iTXqQNcrM108bPdnX4gXLMsW6qfGtBUTAzGRUswjh5L/JefUfnK6yGTgKyrcvVNAD2bfNS3WuHYUbRtn0JCIkq3bphmPR64QCOUJNc2cjX6BH1t6w91pfqolgsvm7KxJVxP1vvJ5NZ5rpZZOZ79pG37FH78Eard1Fi7dLW/LnbcOGqvvtZfoV80VxN1xodnos6bA2fP6iP17j1C5h+LcCPJtY3cTTD1TujLN9X7icLks5GlTG6d56pXgHVVLtrBA7Brpz6Kc6ddO4wPz/RTpN7X9PSI+k2b9H9UotvpO84slpApcYQjSa5e9l3Nt8Qa26NhlZGlHzie/WV5YLo+SsvIhB1ftvzCjukY5zwVFonHtirgVNlRQIGGBgp7DtG37qZZuS7QAUYoSa5e5q5c4MnKAtF6ttGb5YHpaPv2os5+9MIvMhhRuncPicTqbneZjWXZUnvjGQwG0DS96UzGGMqS0smrOS3JNUCCcodWKHPX+9Vxq6vstPI+raHB9W6rpqKioFevkKlDuttdBucS69tvni99tIuGhESIiiL763/Qufok2Uf+7eeIhY2MXP3ENqJ1HLnaOK4wuDXNs+5b4jy1IB/2Frd8UVIH/UDBEOtZ6q51or1VoiNTtN4roEdPRlyXyYjirSHzj0g4kuR6jq/XjrY0CeW4tMrT1oZCZ+9R2pL27SE1NSQSa9MygKsjz+2lgCYUTUMzRaMkJ8vSqyAgZYFzAtkYxdbARSbAPKMW5GO+5SbMwwajPv7/zvcodSH6q30olw+E6mr3naGCSNMygO1YGsuypfZf7aUAqxUSkyAmBuXe+0le9kdpwhJEZOR6TiDXjsrSqtZRVyyHkycufGFSEuD+o3UwahqrLdlyYD8oBti5w/kFtbXQqxemWY8Tm5YQUut0w50k13MkwYUGtSAfvv/+gtcpV11tT1CuPloHq6axGqbl6P+YGI16wxmtSfN3q+rnCIWnpCwgQoq6YjloLWwMAOiYjmnlOyGTUFtiHD0WJTkZzpzRD05sqmN6SG+ECGcycm0FaZjiG+7Wctoe1xoaYN9evalNSzuuQO8VMOcpH0fsW47344tqIxu6jCb7R5Wsml3NLz5T5/8AhUdk5NoKchqAb7hby2nfxrq3WP843FJijY3V17A69AoIVbb7oa5YzoZdP1AWn0Zeht5kvVnXtMbGkJioi0SSXFtBZvV9wzAtB6VnT3uN1LJsKeZrh6MlJLa4EsBGufd+jL9/HmXwlWHxEdl2P6j4keyiT+hcfYLs4i2QmkphjyuZf9tvKOw7zL5hIBQm6iKRomlNK+Th42JPfw2EUD0FE9zHfqEtnE2v0/YW612doPkEThPKvfd7bU1nMNx7p45e77zl/P7btWP+T2ZRlpBGl3bwbNFfne5pMMTfVqEae8id/houpEarc9W5qqXrUK0XTKrEx6NMvROKd4fNeVdqQb7eLrChwWXjmcIBo6hOSCE+MZ6JYwZimimfoIKZlAV8SGq0uqYf+93KyITaGrCYL/ANDRgXLoLi3W733Yca+06zhgbXFxgM5F2VTV1qOklpyYzsk+rfAEWrycjVh6Spta6ldaZOH4PXrXafXOzfzIjxhRedereGcs3RXgr54Qc400LjGauV7OJ8Nt56PxMGd/ZfgKLNJLn6kGxMcOaq9movBRTvOV9ndadJD9ZQ2hzQlP1k1mNH9YMR6+ouuMwsa/c/GXXXOIx9MvwUpbgYUhYQfuNqyZXW0AA/ll84scbGEr35HyGbTJuyrsqFw9/rI/UzZ9wnVoMB5d77IT4eTNFhUQKJFJJchd+4WnJ1wVaBMTH6+tXOXfwQof84lTJc7bwCPbHm/ALTrMcxLlwkTVlCjJQFhN+pH32IOmc2NDa2fGHHdJRxt4XdkdBqQT7qb59suQzQuw/Gh2eGRQkkUklyFT7RtL5qnw1v6SOwTRidb+XIsmypPmlnNLo/NSEqCuWe6dKPNQxIchU+0awv6ROPuf/468CbmwKCjbZutT5x5W4Nr8EAKalo2z5FzRwcdv+4RBqpuQrfOLdmVTtyBPV3CzxKrHRMD/nEqhbkY55yO+Ypt+vtER0oU+/U68cOCnsO0bez9hyij+hPnoDS8Fi7G+kkuQqf0P6+UZ8JP3kCTldd+AXnSgGhzroqF0pLoFRvvGJ5YLo9yZpmPQ7t2jldn5cxhrLEdL0xi9EIHdOhhwcbLkTQk7KA8Cp7XfHMGc9flN6J6E+2+i4oP3DcDEFlpf1x7eAB5zO+DEYKew4hL2MM2cVbyC7eYv89PXsR/f5HAYhe+IIkV+FV2rrVHnWyQlEgMVGvQcbF+T4wH1NXLNdHrJWV9gSpFuTbV0WoC5/WTw04c4a8686PVp/9+A9klezS+9AuWhLgdyG8SZKr8JqqRYs8G7GaTBifXwqE/vbVC7ItN6s+rR99rWnOo9WoKEi9BOMTc2UCK8xIy8EgE6qt19SCfNTfzGp5mdWNN6PUVAftEdcXc+/t21lrayE+HuPDM1FfeA5OHHe+MMoEjRb99yYT0f8uusiozwvVvzsQurFLy0HhU+b/9wj88wI10xtvJvq/XvZPQH7kuJ4X0CfwfvwRdcliCmO7kHdbjj5CBftoNevkfn2da3JKACMXvibJVXisaSJRlyzWP+5eqJNVVBRKTbUfIvQv+8YIxaCPWo/+ACj6AYonT5B32/TzKwHA/vusr6tRkpPDuxwiJLkKz1lX5aLt24s6+9ELX2w0ggYYDdC9R9glEntibWw8v3Y1Olpfz3uuzupUW+XcyHVvAcZfzwzKsojwrqBMrps3b+bdd9+luLiY2tpaDhw4EOiQIp5akI/27bfut206atcO5e577D0BwjGRWFflgmLQR+VT70Tb9DHUONcMs0p26SsBbF8fKULJ+UVY3g/RXFAm1/r6ekaMGMGoUaN48cUXAx1OxFML8lEf/38XPnrFZILklLDsC9CUYVqOc5PvlpafKQZIS4uI+yLOC8rkOnHiRAAKCwsDHEnkcVoMX7xbP4H103+0nFgVBWX6fXRZvDAkZ3zbYnuvoWy4rTN9d/yTgzf8Up+ochilAhj/+JIk0wgWlMlVBI66ZLE+4130FcS2B1cTUb37oCQn2xNwuH70d0ctyGf9thOUtU/hu9TLUTQrL19/H/yvXgqw9WGNpHsimgvr5JqaGh/oENqkpbVzvlC/aRO1/7MSOiTpiRX0SRnHxKqA0qkzxoQENDS0I6VEmaJI2/CB0/fyd+ze5i7++k2bqH7hD2iAAmSfjScvYwz9Thxi8+U3omhWfSVAyS6wWjEd2BeQexHK9z+UY3fFr8n1ySef5IMPPnD7/OTJk3n++ee99vNkE4FnLK+8rp9jdarC9QUGI/TqhclhW6e2Khf1jp85xRqqC8FtWorf8srraN99D40qoJEF9jJAv/LvnVYFYDJh6T/Q7/cilO9/qMYeNJsI5s2bx+zZs90+HxMT48dohI1tckbLuAI+/z+IiQXzWX396sBBKDExTkupIrErvmFaDmplJXx3qNlzTqsCeveB6moo3u3nCEWw8WtyTUhIICEhvIb+4SASk6UnXJ1Wqz75OFgsbl9jfHhm+PdLEB4JypprVVUVZWVllJaWAvD1118D0KdPH6KjowMZmghzjgnVdpqCumK53ifg5Am9m5c7HdPlHyphF5TJdevWrcydO9f+9aRJkwAoKCigW7duAYpKRALH42kM03L4V9428pL6kb1rE1nacfdL0sKk2bfwnqBMrlOmTGHKlCmBDkNEIMO0HNQVy9EqK7HuLiKvYyZlUXHnVwK4oXTvLiNW4SQok6sQAXXsKJgtaMeOkn1ZFnk9s86vBHClyYSfECDJVQgn6orl57t8Nahk7fmUrD2fur7YYIBel2J8eCYAlgemR9yGCuGeHFAoBPrZX0f7XOZyqZUTRYGkJL05Tc4viH7/I4yjxzY7SlwISa5CcO7sLzd9aR2Pv1am34fS9zJISHRay2qYloPSU05tFedJWUBEPLUgX9+F5kZexhjKktL5eMJ/ct0D1zU/fQBZKyyak+QqIp66Yrl+ooIrikL2gU/5eNwDTLxxICCJVHhGkquIaJZlS1uusyoKWdYKrnvgOv8FJcKC1FxFxFIL8tHeecv9BQYDtG9vXw0gRGvIyFWEtab1UacDFhfMc7vjqvCyLPIuv4kJQ7pxzeib/RavCB+SXEVYa7pEytYrgCOlzRuwGAxgtUL79uQNvY2yDp3I0zpwTQDiFqFPygIirDkukdISEvWetYe/b55YTSb99FYAVWXSnTfRNb0DEwZ39n/QIizIyFWENXurwBXLW564Sk4h7qeTqXvnLyhT72Rkn1RG9kn1U5QiHElyFWFNLchHnTfH7QYBFAUSEjHOeYoOd0/B8qBMXgnvkOQqwk7TnqyYXTS3jopCuWc6plmP+z9AERGk5irCjtMkVkamfqJgUzExkliFT8nIVYSfjEzYW4y2by/s+NLlJcrUO/0clIg0klxFWLGdTNvSOVfKvffLqFX4nCRXEVasLSXWc0exSF8A4Q+SXEXYsCxbira32PWTN95M9H+97N+AREST5CrCgmXZUrS333T9ZEwMSk21fwMSEU+SqwhpakG+fuz1iePNn4yKgpRUiI+XJtbC7yS5ipBkW8uqffstnK5qfkFMDES3g5MnAKTOKvxO1rmKkKQuWYy240vXiRUF46IlYD6rf+muEbYQPiTJVYQUtSAf86ir7SPSZuLjMf7xvzCOHoty1zSIj9d/FcLPpCwgQsYF+wQAxoWL7CUA06zHQdazigCR5CpChrpiufvEGh+PMvVOqa2KoCHJVQQlx+Yr6kcfwqf/aH5qQGIS1NbA9TfKGlYRdCS5iqDk1HzFVX+ApCSMT/9ORqoiaMmElghKthMEtKZlgHbtMP7xJaI//VwSqwhqklxFUDKOHqsn1ibbWZW775GkKkKClAVEwDU9oVVd+LTrtakJCVC828/RCdE2klxFwNh3WVVWQnW1/YRWl4m1YzpK9+6yjVWEDEmuImBsk1aYzVBbgxYbA+Xl+kmsFgvExupbWBvqUcbdJj1YRUiRmqsICLUgXx+xJibq21Q1DY4cgbNnoV07or/ahzJwENTVgqpKOUCEHBm5Cr9TC/JR58yGxkZ9dNrYqHew6tcfSg7bj2AxTMtBray0/16IUCLJVfiddVWunlAB6uv1X+PjiX53tdN1xtFjZWWACFlBWRZ45ZVXmDhxIldeeSXXX389v//976mrqwt0WMJLtITE5g9ePRzLA9NRC/L9H5AQPhCUI9ddu3YxY8YMBg0aREVFBfPnz6euro7nnnsu0KGJNrIsW+r+4MCkDig11fYdWTJaFeEgKJPr66+/bv997969efTRR1mwYEEAIxIXQy3IR3vnrea9AWwa6jFMy3Fa6ypEqAvK5NpUZWUlCQkJgQ5DtIFl2VLXidVk0iexzp6177qSEasIJ4qmuRtOBIeamhomT57MlClT+NWvfhXocISHqhYt4kzuu2gN9WBpPP+EyYThkkuInTwRy86viJ9xP7HjxgUuUCF8xK/J9cknn+SDDz5w+/zkyZN5/vnn7V+bzWYefPBBFEXhjTfeICqqdQPtioparNag/rejmbS0BMrLawIdRqupBfkY16zCHBPnuj0g6OtXC3f5PzgPheq9twnl+EM19rQ095+o/VoWmDdvHrNnz3b7fExMjP33jY2NzJo1i7q6Ov785z+3OrEK/1JXLEctOawv+G+qYzqcqbOvXxUiEvg1YyUkJHhUO7VarcyZM4fS0lJyc3OJi4vzQ3SirdSCfDhS6jqxmkwY5zwl9VQRcYJyODh//nwKCwt54403sFgslJeXA5CSkoLRaAxwdMK+rEpV4ZI0iI8/vykAIDYWZeAgTCvfCVyQQgRYUCbXtWvXAjBp0iSnxwsKCujWrVsAIhKObQG191adX6968oSeXNu1O3e+lQJGoyypEhEvKJPrgQMHAh2CaMLWwUpdsdx5lNoxHePDM/Xfv7YCtVHF+PBMKQOIiBeUyVUED9uIlYxMFNA7WcW2B83qdIw1QNrdU0JyxlcIX5DkKpqdBGD7vXH0WPuIVQFMK99xulZGp0K4J8lVOJ+0Ck57/JtuS5WdVEJ4RpKraJZAJZkKcfEkuYpmCVSSqRAXLyj7uQohRKiT5CqEED4gyVUIIXxAkqsQQviAJFchhPABSa5CCOEDklyFEMIHJLkKIYQPSHIVQggfCOsdWgaDEugQ2iRU44bQjh0k/kAK5dhdCfrTX4UQIhRJWUAIIXxAkqsQQviAJFchhPABSa5CCOEDklyFEMIHJLkKIYQPSHIVQggfkOQqhBA+IMlVCCF8QJKrEEL4QFj3Fgh2r7zyCn//+98pKSkhMTGRW265hVmzZhEXF+fy+h9++IHRo0c7PZaQkMCXX37pj3ABeO2118jNzaWmpoZrrrmGZ599ltTUVJfXfv/99zz99NMUFRVxySWX8MgjjzBlyhS/xeooFO+1zZNPPskHH3zg9NjcuXP5xS9+4fY1wXTvb775Zo4ePdrs8TVr1pCZmen0WDDd94slyTWAdu3axYwZMxg0aBAVFRXMnz+furo6nnvuuRZft2bNGjp37gyAweC/Dx/r1q3j1Vdf5YUXXqBbt24sXryYxx57jLfffrvZtRaLhYceeoiBAweydu1aioqKePrpp+nWrRvDhw/3W8w2oXavmxo3bhzz5s2zfx0fH+/22mC792vXrkVVVfvX7777Lu+//z4ZGRluXxMs9/2iaCJobNy4URs2bJjb548cOaL169dPO3LkiB+jOm/SpEnaSy+9ZP+6tLRU69evn3bgwIFm127ZskXLzMzU6urq7I/95je/0X7961/7JdYLCfZ77WjOnDnanDlzPL4+2O/9hAkTtBdeeMHlc8F03y9WiP6TEJ4qKytJSEi44HU///nPufbaa3nwwQc5ePCgHyIDs9nM/v37GTFihP2x7t2707VrV4qKippdv3v3bjIzM2nfvr39sZEjR7q8NhCC+V67snXrVkaMGMHtt9/Oa6+9RmNjo9trg/neHzx4kP379zNp0qQWrwuW+34xpCwQJGpqanjzzTeZOnWq22vat2/Pb3/7W4YMGYLZbObtt9/m5z//ORs3buSSSy7xaXyVlZVYrdZm9dWUlBROnTrV7PpTp065vLaiosKncXoi2O91U9dffz3jx48nPT2dvXv38sILL1BXV8djjz3m8vpgvvfr169n4MCBXHbZZS6fD6b7frEkufqAqwkIR5MnT+b555+3f202m/n1r39N9+7defDBB92+LiUlhZycHPvXgwcPZvz48axfv54ZM2Z4J3gv0YK0TXAo3uvx48fbf9+/f38MBgMLFy5k1qxZKErzBtPBeu+tVit5eXncf//9bq8Jpvt+sSS5+sC8efOYPXu22+djYmLsv29sbGTWrFnU1dXx5z//magoz/9IjEYj/fv3dzkT623JyckYDAYqKiro06eP/fFTp06RkpLS7PrU1FRKSkqcHnM1ovKnULnXFzJo0CDOnDlDZWVlyNx7gM8//5yKigqys7M9fk0w3ffWkpqrDyQkJJCWlub2P1utz2q1MmfOHEpLS3njjTfcLgtyR9M0Dh06RJcuXXzxNpxER0czYMAACgsL7Y8dOXKEo0ePMnjw4GbXZ2Zmsnv3burr6+2PffHFFy6v9YdQutcX8s033xAbG0tycrLL54Pt3tusX7+ea665plUf74PpvrdaQKfTItxTTz2lXXPNNdq+ffu0kydP2v9rbGzUNE3TioqKtJ/85Cfa8ePHNU3TZ7g/+ugj7bvvvtP279+vPfXUU9rQoUO1srIyv8S7Zs0abciQIVp+fr729ddfazk5OVpOTo7LWM+ePauNGTNGmzVrlnbw4EFtzZo12qBBg7TCwkK/xNpUqN1rR4sXL9a++uor7ciRI9onn3yiXXvttdrixYvtzwf7vdc0Taurq9OuvPJKLS8vz+nxYL7vF0vKAgG0du1agGYzpwUFBXTr1o36+nq+//57LBYLAIqi8Kc//YmjR48SExPDoEGDePvtt+nUqZNf4v3pT39KRUUFzzzzDDU1NYwaNYpnn30WoFms0dHRvPbaayxYsIApU6aQlpbG7373u4Css4TQu9eOvv32Wx566CFqa2vp0qULd911l1O9ONjvPcDmzZsxGAzNNggE832/WHJAoRBC+IDUXIUQwgckuQohhA9IchVCCB+Q5CqEED4gyVUIIXxAkqsQQviAJFchhPABSa4iLDz55JP079+f/v37M3DgQG666SaefvppKisr7dfs2rWLRx55hFGjRnHFFVcwZswYHn/8cfbu3dvs+61cuZLLL7/cbTPt2tpafvvb35KVlcWVV17JjBkzKC0t9dn7E6FHkqsIG1dffTWfffYZW7duZd68eWzevJk5c+YA+ikK99xzD1FRUSxdupSNGzeybNkyunbtyqJFi5p9r9WrV/PQQw+xfv16zGZzs+efeOIJPv/8c1566SVWrVqFpmncd999NDQ0+Px9ihAR4O23QnjFnDlztHvvvdfpsT/96U/agAEDtOPHj2sZGRna/PnzXb62qqrK6evPP/9cGzlypGaxWLTx48drGzZscHr+u+++0/r166dt27bN6XsMGjRIW7dunXfekAh5MnIVYSsmJgar1cratWsxm8386le/cnldUlKS09fvvfcet99+O1FRUUyaNInVq1c7Pb9z505MJhMjR450+h6ZmZns2LHD+29EhCRJriIsffvtt7z77rsMHjyY8vJy4uPjPWr+cerUKfLz85k8eTIAEydOZOfOnXz33Xf2a8rLy+nQoQNGo9HptZdccgnl5eXefSMiZElyFWFj+/btDBkyhMzMTLKzs+nevTt//OMfW9WZf926dfTp04cBAwYA0LFjR0aNGsWaNWs8er2rkwFEZJKWgyJsZGZmsmTJEoxGIx07diQ6OhqASy+9lNraWo4fP97i6FXTNNauXUtJSQkDBw60P261WtmzZw+zZs0iOjqatLQ0qqqqUFXVafRaUVFBr169fPb+RGiRkasIGzExMfTs2ZNu3brZEyvAuHHjiI6O5k9/+pPL150+fRrQu/UfOXKEv/71r6xfv97pv8bGRvLz8wEYOnQoFouFL774wv49qqurKSoq4qqrrvLhOxShREauIuylp6fz9NNP8/TTT1NTU8Odd95J9+7dOX36NAUFBRQWFvLuu+/yt7/9jWHDhjFkyJBm3+Pmm2/mvffe47bbbuPSSy9l9OjRPPPMMyxatIiEhARefPFF0tPTnQ4TFJFNRq4iItxxxx3k5uZy9uxZHnvsMW699VZmzpzJDz/8wG9/+1sqKiooKChg3LhxLl8/fvx4tm/fzuHDhwF44YUXGD58OI888gh33303VquVN9980+nwSRHZ5CQCIYTwARm5CiGED0hyFUIIH5DkKoQQPiDJVQghfECSqxBC+IAkVyGE8AFJrkII4QOSXIUQwgckuQohhA/8f8jtS5aY+yYlAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 452, 4\n", "LR fn, tp: 8, 4\n", "LR f1 score: 0.400\n", "LR cohens kappa score: 0.387\n", "LR average precision score: 0.485\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 453, 3\n", "GB fn, tp: 9, 3\n", "GB f1 score: 0.333\n", "GB cohens kappa score: 0.322\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02923183196448697 max:0.6966004952625284\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 453, 3\n", "LR fn, tp: 10, 2\n", "LR f1 score: 0.235\n", "LR cohens kappa score: 0.224\n", "LR average precision score: 0.294\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 454, 2\n", "GB fn, tp: 12, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: -0.007\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 453, 3\n", "LR fn, tp: 10, 2\n", "LR f1 score: 0.235\n", "LR cohens kappa score: 0.224\n", "LR average precision score: 0.366\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 453, 3\n", "GB fn, tp: 10, 2\n", "GB f1 score: 0.235\n", "GB cohens kappa score: 0.224\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 48 points min:0.02728552729928453 max:0.6446939196238787\n", "-> create 1776 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 455, 1\n", "LR fn, tp: 8, 2\n", "LR f1 score: 0.308\n", "LR cohens kappa score: 0.301\n", "LR average precision score: 0.256\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 453, 3\n", "GB fn, tp: 8, 2\n", "GB f1 score: 0.267\n", "GB cohens kappa score: 0.256\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 10, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 5/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 5/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 454, 2\n", "LR fn, tp: 8, 4\n", "LR f1 score: 0.444\n", "LR cohens kappa score: 0.435\n", "LR average precision score: 0.386\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 451, 5\n", "GB fn, tp: 10, 2\n", "GB f1 score: 0.211\n", "GB cohens kappa score: 0.195\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02728552729928453 max:0.6966004952625284\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 450, 6\n", "LR fn, tp: 10, 2\n", "LR f1 score: 0.200\n", "LR cohens kappa score: 0.183\n", "LR average precision score: 0.179\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 454, 2\n", "GB fn, tp: 12, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: -0.007\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.02923183196448697 max:0.6446939196238787\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 451, 5\n", "LR fn, tp: 8, 4\n", "LR f1 score: 0.381\n", "LR cohens kappa score: 0.367\n", "LR average precision score: 0.431\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 453, 3\n", "GB fn, tp: 9, 3\n", "GB f1 score: 0.333\n", "GB cohens kappa score: 0.322\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 46 points min:0.030983866769659363 max:0.19717631703630126\n", "-> create 1778 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 456, 0\n", "LR fn, tp: 8, 4\n", "LR f1 score: 0.500\n", "LR cohens kappa score: 0.494\n", "LR average precision score: 0.666\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 455, 1\n", "GB fn, tp: 11, 1\n", "GB f1 score: 0.143\n", "GB cohens kappa score: 0.137\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 48 points min:0.02728552729928453 max:0.6446939196238787\n", "-> create 1776 synthetic samples\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 454, 2\n", "LR fn, tp: 7, 3\n", "LR f1 score: 0.400\n", "LR cohens kappa score: 0.391\n", "LR average precision score: 0.430\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 452, 4\n", "GB fn, tp: 7, 3\n", "GB f1 score: 0.353\n", "GB cohens kappa score: 0.341\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 10, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "### Exercise is done.\n", "\n", "-----[ LR ]-----\n", "maximum:\n", "LR tn, fp: 456, 8\n", "LR fn, tp: 11, 7\n", "LR f1 score: 0.556\n", "LR cohens kappa score: 0.548\n", "LR average precision score: 0.666\n", "\n", "\n", "average:\n", "LR tn, fp: 452.64, 3.36\n", "LR fn, tp: 8.24, 3.36\n", "LR f1 score: 0.357\n", "LR cohens kappa score: 0.346\n", "LR average precision score: 0.407\n", "\n", "\n", "minimum:\n", "LR tn, fp: 448, 0\n", "LR fn, tp: 5, 1\n", "LR f1 score: 0.133\n", "LR cohens kappa score: 0.124\n", "LR average precision score: 0.179\n", "\n", "\n", "-----[ GB ]-----\n", "maximum:\n", "GB tn, fp: 456, 5\n", "GB fn, tp: 12, 5\n", "GB f1 score: 0.476\n", "GB cohens kappa score: 0.464\n", "\n", "\n", "average:\n", "GB tn, fp: 453.64, 2.36\n", "GB fn, tp: 9.64, 1.96\n", "GB f1 score: 0.234\n", "GB cohens kappa score: 0.224\n", "\n", "\n", "minimum:\n", "GB tn, fp: 451, 0\n", "GB fn, tp: 7, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: -0.007\n", "\n", "\n", "-----[ KNN ]-----\n", "maximum:\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 12, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "average:\n", "KNN tn, fp: 456.0, 0.0\n", "KNN fn, tp: 11.6, 0.0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "minimum:\n", "KNN tn, fp: 456, 0\n", "KNN fn, tp: 10, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n" ] } ], "source": [ "runExerciseForSpheredNoise(\"folding_abalone_17_vs_7_8_9_10\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "graphic-fancy", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "///////////////////////////////////////////\n", "// Running SpheredNoise on folding_abalone9-18\n", "///////////////////////////////////////////\n", "\n", "-> Shuffling data\n", "### Start exercise for synthetic point generator\n", "\n", "====== Step 1/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 1/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.3975704340113837\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 137, 1\n", "LR fn, tp: 3, 6\n", "LR f1 score: 0.750\n", "LR cohens kappa score: 0.736\n", "LR average precision score: 0.803\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 135, 3\n", "GB fn, tp: 5, 4\n", "GB f1 score: 0.500\n", "GB cohens kappa score: 0.472\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 1/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03311344137959691 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 135, 3\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.500\n", "LR cohens kappa score: 0.472\n", "LR average precision score: 0.464\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 136, 2\n", "GB fn, tp: 7, 2\n", "GB f1 score: 0.308\n", "GB cohens kappa score: 0.281\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 1/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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bNMB1MyxMygCiHkmuQlyCZ5MAgMV6frlV8hgID0e5O03fAIUhSVlAiAvU3nF18qvtqPkFENbKddy1RSH1+7+R1ecWUou+JWznN3qHKwxKkqsQF/DsuNr3LTVnz3quK/dMh317GZrcj6H7tknvVXFRhk2uX3zxBR9++CH79u3Dbrdz8OBBvUMSAU7NyUZ9+UWwnXLVV8PDwZ1cuyTKagDRJIatuVZWVjJ06FAeeOABvUMRQUDNyUZdMM/VyaqmBk4VQXiE61iW8HCsD89p8GwsIRpj2JHrpEmTAMjNzdU5EhHoPIm1qspzLbdrCplDpqGEhTG9Z2tuGH0LjvtnyFHXwmuGHbkK0RIcK1eg/m4uVJ2tcz3r2rEURrWnsHUcWVoHQI66Fk1j2JGrL8TFRV36QUB8fLSfI/E9M8YMxov7+MfrPX1XAQgNxdq3D6mHdmDvlIg1LobfjOjuivueqa4/JmG019obZoy5MQGdXG02O06ndtHHxMdHU1RU3kIR+YYZYwZjxe3Zzlqjnr9osaD0S8EJDDn1I0P3fEDC5k8oKio3TNzeMtJr7S2zxtyYgE6uQjREzclGXbQA7Pa6N8LCPG/5nZkZ8vZfXBZJriJouDcHaIcOnU+siuJaEXBFEtaH53gmqmTCSlwuwybX0tJSCgsLKSgoAOD7778HoGfPnoSFhekZmjARd0K1pKWf3xxwutR1MyQEJaU/lrR0SabC5wybXLdt28ZTTz3l+Xjy5MkA5OTkkJiYqFNUwkzUnGzUp58ERw1qSQnWh+e4EmzytZ4zrWRjgPAXwybXqVOnMnWqeWZmhfE4MzOgutp1FMuRwzj35hG65gO9wxJBQta5ioCj5mRTPXUC2tGj5HYf6GkPqG1cp3doIohIchUBR31jFRw5DEW/kNV3DIVtO5HV71Y52rqJvjps46mP9/HVYZveoZiSJFcRmBQFNI3U7/9GQk0Fk2emSn21iTbnFXKstJLNeYV6h2JKklxFwLE+PAciIyEigiEn9rN0cAzDesbpHZbpTEzpTJe2EUxM6ax3KKZk2AktIS6l9jKr2kup3P9u6J7w3rCecfJL6TJIchWm5V632lCXKuvosZJUha6kLCBMSc3JRvv5Zygvg+R+0mtVGI6MXIVp1D7bStu4DuwVgIa2/Uuc+/ZKr1VhKDJyFabhLgNoG9e5VgMogMUKSK9VYTwychWmoOZko5WUQJs2KLdPQNv+JUS0hqgoT8MVGbEKI5GRqzCkC2uozswMKCtDiY0ldO4TKLGx4HSixMZKUhWGJMlVGJIzMwPthx9QFy1Azcmu97ZfygDC6HxSFjhz5gzfffcdgwYN8sXTiSDnLgHkduxNVp9bSM3azoiVz9dbyyojVuErja2Zvhw+GbkWFBQwY8YMXzyVCGLuhivq00+6+gKk/IrC2M58lixJVPhX7TXTviJlAWEYzswMKMgHRw1oGhOTO9IlqTOTbuqjd2giwPmjzORVWSA5OdlnX1Cct9u2i63HtzAmYRzXxQ3WOxxd1Fm7evSoa4lV+zis857mhtG3cIPeAYqg4I8yk1fJNSQkhF//+tf06tWrwfvHjh3jjTfe8GlgwWDr8S2crCxk6/EtQZtcXRNXB2HPN+ePuI6KknqqMD2vkmuvXr1ISEhgypQpDd4/cOCAJNdmGJMwzjNyDVaWtHTXSaytWkFVFShSqRKBwavk2r9/f/Lz8xu937p1a66//nqfBRUsrosbHHQjVjUn29XMGlBGjIJ9e11NrPftheR+sG+vLK8SAUHRNE3TOwh/sdnsOJ0X//bi46MpKipvoYh8w4wxgyvu4xOnoOX9r+tCSAjU1MAVSYR9/KmusTXGTK917eVEne6Zapq43cz0WrvFx0c3es9n78HcR2ALUduFO60saelwRZLrT5sYnaMLHGpONuqiBWg/HPTpciLRfJe1iaCmpoYvvviCjz76iH/+85/s37/fV3EJE6s9gnJPWKmLFlAZE1FnVrb248TlcWZmuOrVmlNeT4NoVnL96aefWLduHZs2baKmpoabb76ZN99809exiUsw6lKu2guyPRNWigX72+/A9Td6Hie7rC7fV4dtbM4rJDV1JoOz3pOTFwzE6+TqcDj44osvWLt2LXv27GH48OGUlpayadOmRpdoCf8y6lIu94i19n/ozswMombfh13n2AKN+xDBrLYduGHNB3qHI2rxKrkuX76cTZs2ERcXx+TJk3n11VeJj4+nb9++WCyydEYv7qVcPaKvZPm3iw0zgr1wROr+OCI+GrvJJiyMbmJKZzbnFcohggbkVXJ9//33+fd//3ceeeQRSaYG4l7KtfzbxYYcwQr/k0MEjcurTPn444/z+eefM3LkSJYvX84PP/zg77hEE4xJGEfHiM5BvRlBCKPxauR6//33c//997Nz507WrVvHtGnTuPLKK9E0jTNnzvg7RnGBr47/g40HPvaUAYywGcEfLduEMLMmvccfOnQor776Kl9++SXjx4+na9eupKWlMWvWLNavX++vGMUFNh/+b08ZwCj80bJNCDNrVgG1Xbt2/Nu//Rt//etfWb16NW3atOH555/3dWyiERN7TmqxMoC3R1bLyQBC1OWz7a/FxcW0a9fOF0/lM7L99fI57p+Blp+PkpRE6GUu9ZHXuuWYMW6zxtwYr0au+/fv5ze/+Q3l5fW/8bKyMtLT0ykqKmp+hEFk/U8f8sjOWaz/6UO9Q/GKjEj9z9t3B8JcvJrQevfddxk0aBDR0fWzdJs2bRg8eDDvvvsuL730ks8DDDRfnthKlVrFlye20iP6Ks861SPlhzwTVLV3Xh0p/5EvT2xlVKcx3NltepO/3sV2cXmzw0t2Uflf7Xq1vNaBw6uRa15eHmPGjGn0/ujRo9m9e7fPggpkozqNIUQJJdwSwZ+PvMcPZd/zl2Of8lP5Yc8EVe2dV7WTMbgS4lPb57Hbtsurr1f7uZpyT7QceXcQmLxKridOnKBt27aN3o+JieHkyZO+isnUdtt2sfzbxQ0mv922XRwpP0T78HicOLE7zpdZHJqDMQnj2G3bRbmjjMiQKMYkjGNUpzGEW8MZ1cn1y23r8S0ctx/zOiFebA2srI81BuvosYSu+UBGrQHGq7JAdHQ0BQUFdOnSpcH7BQUFDZYMgs1u2y7e/fFNVKfK6oO/Z1N4ByYn3e15y+0eKUaGRNExojOtQyL5tngPoHBtu/5sPb6FXypPctpRQogSCsCd3abXKQeMSRjH/xR9wcj4W72K6WJrYI2wPjaYydrgwObVyHXgwIFs2LCh0fvr169nwIABPgvKTGqPVLce34KCBYfmoEZTOVl5os4I0z1SnJx0F/OuXUS+/QgqKlbFwv8Wf80PZd9T6ihGQ8OhVbP1+JZ6I+Hr4gazbMRySYoBQNYGBzavkut9993HX/7yFxYvXlzn7f/Jkyd57rnn+Otf/8p9993ntyCNrHbdskf0lWg4SWl3HZ0iOnvecrsTJMC8axd5Jq1KqosBqNaqG3zuckcZn+R/JHXRACW11sDmVVlgwIABPP/88yxevJi1a9cSFRUFgN1uJzQ0lOeee46BAwf6NVCjqn3I4NbjWwiztOJMTQVLBr4CuJZe/eVYFqDxs72At7XXaR0SxRnHpZvvFVYeA8CChWEdRvjz2xA6kJUYgc3rfq7Tpk3jxhtvZOPGjRQVFaFpGt27d+e2226jY8eO/ozR0C6sW154mqtrlt+1keGMswKAakdxk76GEyd7i/c0aymWEEIfXiXX0tJS5s+fz/bt23E6naSkpLBixQoSExP9HZ+pNDRBNKrTGLYd/2ujb/29pRGw50gKEZC8qrmuXLmSvLw8fvvb3/Lkk09is9lYtGiRv2MzpQsnoHpEX0W36J5Nfh4FC50jupAQ0YXY0Hae5xZCmINXI9ft27ezdOlSbrnlFgBGjBjBxIkTcTgchIaG+i24P/3pT2RkZFBeXs4NN9zACy+8QFycsRsDX3j0ivvjpuoU0RkFqKipoNp5FmeNU5phC2EiXo1cT548Sd++fT0fX3nllYSGhvq1n8DGjRv54x//yKJFi1i7di3l5eU89thjfvt6vjImYRytQyKxO8pY/9OHlDvKsDSh+ZgFC7d1mcCSga8wOeluOkZ0ZlSnMbLY34SkZ0Bw82rkqqpqvRGqxWJBVVW/BAXwX//1X8yaNYuxY12zqcuWLWPMmDH88MMPuhyI6O1Jq9fFDWZT/kecrDxBzvG/0jqkNacdp73+Op0iEjwTV7LI39ykZ0BdF9s08dVhG1s+/Z5xfToEzLE1Xq8WePTRR+sk2OrqaubNm0d4eLjn2jvvvOOToKqrqzlw4ABPPfWU51rXrl3p0qULeXl5uiTXppy06p56igqJIj6iI6cdpV59DQVFJq5M6sLEoeZko5WUQJs2so71nIv9stmcV0hhWRWb8wqDK7lOmTKl3rWJEyf6PBi3kpISnE5nvfpqu3btKC72fhlTXFyUV4+7WE9Gt2m9p/Lp4f9mQs9Jl3z8zGtn8unh/6ZXbG/+eSKXcEs4Vc4qwi3hxLeOZ1CnIfxQcoBesb358ujfKHfYGdBhAIdKfsR29hSfndzAzORZlx2zEZkxbm9iPvmn19EOH0Hdv4+YmAjs6zPR7OWE9OhB/D1TWyDK+oz2Wlf+nwewv/0OUbPvI+KC2H4zojsf7Szg7qFXGC7u5vJZs2xfOnnyJCNHjuTzzz+nZ8/zM+133HEHY8eO5cEHH/TqefzVLPvCEkFjJYPl3y7mcJnrMMeebXox79pFns//85H3sDvKGZ1wm6cM8MjOWVSpVYRbw3l96Ls+jdkIzBi3tzFXT50ARw6DxYIyYCCWtHRd+wYE8mttJJfdLLulxcbGYrFYsNlsda4b5bSDC1v1Nda6b0zCODpEdKo3GbX1+BZKqotxaA5PK0GgXgcsYR7Wh+dAj57QrbsnoUqnq+Dmdc21JYWFhdG7d29yc3MZPNg1Ejx69CjHjh0jJSVFl5hqj05rb3kF6n3s1tiE1JiEcRRVncTuKK+TSC/sgCWMS83JRn1jFVRUQGQk1ofnEPbxp3qHJQzEkMkVYPr06SxbtoxrrrmGxMREli1bxpAhQ3SZzIK6o1N38xW3ps7qN+Xx3q5SEC3LmZkB/zoC56pq6hurZJQq6jBscr3jjjuw2Ww899xzlJeXM3z4cF544QXd4mlsdOpvTVmlIPzDM0oFuCKJ3MJKsvqlknpKZUj+Hn2DM6Fg6WNr2OQK8OCDD3o9eeVvvl5z6u2IVK+kLs5zZmbAT/8CpxOOHCbr9t9RGBpFVvIYV3INDXXVXIVXgmX9ryEntIKBt+dXXRc3uF4ZQrScyi1bXOtV3Vq3JvXQDjorZ0kt+hZ69MT60oqAThK+Fix9bA09cg1Uu227sDvKaB0SKSNSA3OsXEHxB++6doWEhoDFAp06M+KjNbi669Zf/y0uLVj62MrIVQdbj2+hoqYCBcVzlIswHu2jzHMTVhrU1MAVSSgjRkm/AOEVSa46cJ+lpaHJES4GpeZkuxKqm6ahxMbCvr1y7pWXgr1xjZQFdOCeHKs9qSX0V3sWW335RVdytVhcf+Lae2qE7seIiwuWiavGSHLVkXS9Mg41Jxt1/hPgcKAePQqnS103wsII2/lNnccGY6JojtpbgIORJFcR9NScbNQF88DhcF0otqFMn4G2cR2RM36DQ9/wTCtYJq4aI8lVBDU1Jxt10QKoqjp/Ma49oXOfgLlP0NaEzUSEMUhyFUGnzo4rux3OVJ6/qShYn3yq4U8UogkkuYqgo76xytUeECA0FCwKWENdZYE+fYP6razwHVmKdc6Fp7aKwKPmZLv6rhbkn7/orrO6/87/qcXjEoFJkus53m5HFeblGbHWXr8K5z+2WFCm3dXygYmAJGWBc6RBSmDy1Fftdvjl5PkbViuoKsS0hVatICoK68NzpCQgfEaS6zmy5jQwOTMzXGWAC0erERFYFy+VZNpE/++TbWw+UMzE3u24YcoteodjaFIWEAHJXV/V9u+vn1hjYiSxNoOak83mPT9TGBLJpwe8Pyg0WMnIVQQcx8oVaO83cMy7xYKSPtO1hlV4pfaWYGdmBqkVYWT1GsmEAYl6h2Z4klxFQKidBLSN6+o/oE0M1kXPy2i1idQ3VkFBPmpJCdaH5zA0M4PhozpjHS0lgUuR5CoCgvryi3DyBOrPP8P1g+Hv287fjI5GueoqSayXKdi3szaV1FyF6ak52edXApw8ATv/ASGhrm5WN92C0uvqoG0e0pCmtAK0PjwHJaW/HGPTDDJyFaZTuwQAuLpZnTuFFRSodkBrWQ3QmKa0ApTRavNJchWm48zMQMv7X9TdX0ObmPO7qwBahUGXRFmzehEXtgIMltNYW5okV2Eqak6268BA9/KqstOuBFt5BmLbYZ33tCSIS7hwNOrMzED74QdXdzCkX62vSM1VGFJjdUH37HVtylVXEfbPPMK++JskhmawpKWD5gRFkeNrfEhGrsKQLqwLut+6YreD81x9VVEgLEwmq7zw1WEbm/MKubpjFLt+ch0VPr11CYOz3oPkftChI4C8lj4kyVUYkiUtHXX5MrS9eVTferMrkdpOQWQUuVekkJU8htQftzP8gXtktOqFzXmFHCut5Ejhac6qTrBY+LSgmEH5+XDwAIRHoCQlyWvpQ1IWEIbjGaUW21yTVb+cdC2xqlGhqpKsfrdSGNORz26dKcnASxNTOpOgVZGcn4dVraGtvZjbyw+hJCWhTLsLJSlJRq0+JiNXYTjOzAy0776r3xPAoqDcM53UgkN8ltSdSTf10SdAExrWM47rfvcyC6+ZSmR1JR3LTzHkfz4mdIf0L/YXSa7CMNwjVi26DVTVOnrFYgGrFSUtndC5TzACGKFblOai5mS7dq+VlkB4OKnfbyMreSyph3ZI71o/k+QqdOdJqiUlUPQLVFRAaBioNdJo5TI5MzNcJRUAVWXolYqrN8Dza/QNLAhIchW6c68MoLoaystdI9VWYVgX/6fUVC+TFt0GgNykAWQNGMfElAQGZ74HyHpWf5MJLaE7S1o6tGkD5WWea7J1tXncfWxP3nyLa43w166aalbyGArjEvn0QLFniZvwL0muokW5/+Ov/tUtVE+dgJqTjXX0WJTYWNdxK+d6rkpibR51+TI4cpiaQ4ddCfR61+kaqd9vI8F+igm928nKgBYiZQHRouocuxIS4tkkUHu/uyTWy1B22vW3prk2B2xcB+ERDPnlIMMHx7j6sMrxLC1CkqtoWcn9YN+3EBkFcXGeEZR0X7p8ak42hLWCs2cJ+9VYHPv2gmKBEFeZZVe3gWz+eB8TUzozrGec3uEGPCkLCL/xlADOvf0HYN9eiG6DcuWVhH38qSTUy1S7B4P6xirXyFVRoLQMS1o6Sq9envq1e5fW5rxCvcMOCpJchV84Vq5A/d1cOHIECs5PoFjS0qXm5yNqTjbqgnnsPKXy9N+Pkxt9BVisYLXiLLYBELrmA88vsIkpnenSNoKJKZ31DDtoSFlA+IW2cR04XZ2WuCJJ3v77kGdd8M8/Q1WVayVAdDxZ3Ycx5PA/oXUkztLTaBc0wx7WM07KAS1IkqvwKU9fgOsHw9e7UKbdJZsAfMSTVA/9CKdPe66nfpdD1nW3k3r8GwiPgKgoQjrEo975ax2jFZJchU+pb6yCn/4FoaFYl70so1Qf8ry2Tmed60Mspxmx8NeoOe1xZp7CkpZO/D1TKSoq1ylSAZJchQ9ceKYVTic4arw6o0l4R83JhuPH6iVWWrXyHB4oJRdjkQkt0Sy1Z6nrNLZ+eA706AndusmklY+oOdmeI1hQlPM3oqLk3YGBychVNIknmR49CsU21JISrA/PqbMBQP5j9x33igDOnoVWrVBmzELb/qXrRAZFcZUKkD4BRmTI5PrFF1/w4Ycfsm/fPux2OwcPHtQ7pKDU0KmgniYr9vP1PEmovlWn9eLft52/UVUFQNjHn+K4fwZa3v+C7ZSUXwzKkGWByspKhg4dygMPPKB3KEHNdSroQdRFCzybANzrVJW701BS+nvqfcJ33Kex1kms52gffgCca3ZzRVKdZW7CWAw5cp00aRIAubm5OkcS3Cxp6a5an2LxjI5klOp/lrR0VymgITU1nmY38v+DsRly5CqMwTp6LNbFS1F69ZLRkQ9deGx4nS2s7m2sqnp+8spigTYxrr8jIqRdoEkomqZpegfRmNzcXGbMmCE1VxFQiqbdQc2RfxHSowfxG9fX+Rg0qnf98/ySq4gIlJAQYle+AoD97XeImn0fEePG6fcNCK+0aFlg/vz5fPLJJ43enzJlCi+99JLPvp7NZsfpvPjvjvj4aNMttjZjzGDOuP0Rs3pnGlpmBuqdv6aoqBzH1X3Rvt5N9alTrgcoiqtHQHw8SmIilrR07Nff6Lp3/Y3YAfslYpLXumXEx0c3eq9Fk+uCBQt4/PHHG70fHh7egtEIoY/a9VI1J9vVh6H2SbcWC8qAgdLb1uRaNLlGR0cTHd14phci0Hlqqjab61ibC3dcAVithK75oOWDEz5lyNUCpaWlFBYWUlBQAMD3338PQM+ePQkLC9MzNCEuS52TGBqQ220gWbdMZ/Jhm3SwMjlDJtdt27bx1FNPeT6ePHkyADk5OSQmJuoUlRBN09AmDC26TcOJVVGgVSs+m/AghUo4m/MKJbmanCGT69SpU5k6dareYQhxWer0XHDXTs+dxuoREoIyfQbs24slLZ1J3fqwOa9QGloHAEMmVyHMzNPTNrkfCuc2BdTuc1t751VISJ1+t8NARqwBQpKrED7m6b9QUoISG4tzb55rRYCqgtUKN90C/287aBrK3Wl6hyv8RJKrED7mPiZcKylB27sXdn99/mZ4uKs0ENMWJSlJTmkIYJJchfARzzIr8DS0UR9/tO6DQkJQpt3lqbGKwCXJVQgf8SyzcmqoixZgXbzUVQZQVdcDFMVzzLUIfJJchfARS1o6akmJ6zgWdyPr9vHkRiSQlTyG1PYqIySxBg1JrkL4WlgrqD4LFRWgqmQNmUxhxyQ+bxvBCL1jEy1GkqsQXqq9KWBXt4Ge9aiDf/rm/NE3v5w8/wnVv0C37kzs3Y4sLULWrgYZSa5CeMm9xEp9YxWbBt1LYdtObM6D6/5rFRw5AlzQgc3pRImN5YYpt3CDLhELPUmzbCG85D7iBiA17y90LjxCqvKL6+2/O7FaLK51rOHh0KGjrAgIYjJyFaKJlBGjGPJRJkMO/AM2AxERgOL6X/pMWbsqAEmuQnhFzclGnf8EOBzw88+Q0AWOHHbdrKpCue466b8q6pDkKsRFVG7ZguPNt1yTVQ6H62JpCdYnn0J9Zj5UVkKfvtJ/VdQjNVchLsL+9ppzfQKKPdeUe6ZjHT0WpU9faB+PIidoiAZIchXiIqJm3++axGrd2nUhNBRLvxTg/ASXTFqJhkhyFaIBjpUrqL5xMGe/+caVPKuqXDdU1XO0tXX0WELXfCB1VtEgqbkK0QBt4zqoqOBMxofQ6+rzZ11FR8tIVXhFRq4i6Kg52Tjun4Gak93gvepbb4YzZyAkhNbp013J1N18RVVlpCq8IslVBJ3ax6/U5li5gn/8/n0WDppBbtcUaNWKtgsWuCav0mdCVJSrXaAQXpCygAg67mbWJPfDcf8MSO4H+/aifbePrNG/pbBNR7L63crQdgM8nxM69wmQzQGiCSS5iqBjHT0W6+ixOO6f4VpmdfAAhEdAmxhSD+3gsxvuYNIdqYTKWVbiMkhyFUFLi24DxTbo0sV13tW0uxgx9wlpCyh8QmquInh9vcu1CuDoUVAU2LdX74hEAJHkKoJK7ZUCyrS7XF2sQkJB02SJlfApKQuIoKK+sQoK8lFLSgj7+FPUfimeBtiyxEr4kiRXEdBqnx7QUPJ0T24J4WtSFhAB7cI1rdaH56Ck9PccfS2Ev8jIVQQsNSfb1SrQXu5aywqeUWrt/gBC+IMkVxFw3KUAraTEtdQK6qwEqD2aleQq/EWSqwg46vJlrlNY28TAFa4zr2qvBHDv0JLVAcKfJLkK06s3aVV22nWj+ixhH39a7/EyiSVagkxoCdO7cNJKuTvN1WTl7jSdIxPBTEauwvyS+7n6A5ybtJImK8IIZOQqTOPCPqzu0wK0LZ+5Gq/I9lVhIDJyFaZRe3eVdfRYz2kB1NSg9E2WCSphKDJyFeZSUwP/OoJj5QpXb4DISJS70+QsK2E4klyFKXiOZFEU0DS0jPew9EshbMcuV41VCIOR5CoMTc3JpnrqBNSnn4SiIojv4OpkFdaq3jEtQhiJ1FyFoTkzM6Ag39V3NTQU67ynPdelxiqMTJKr0N2FmwBqf2xJS0ctKQFcTVfcdVWprwqjk+QqdHfhXv/aH8tElTArqbkK3VnS0sFqRdv3LY6VK7CkpaMkJcnbfmFqhkyub775JpMmTaJ///6MHDmSJUuWUFFRoXdYwk+so8fC6VI4exbto0y9wxHCJwxZFtizZw+zZ8+mb9++2Gw2Fi5cSEVFBS+++KLeoQkfcqxcgbZxnWu9apsYqKqCNjHSElAEBEMm17feesvz7x49evDoo4+yaNEiHSMS/qBtXAd2O1rGeyjpM2HfXk8pQFYDCLMzZHK9UElJCdHR0XqHIXzEPWIlqRt8vx/CWsG+vYSu+cDzGBmxCrNTNE3T9A7iYsrLy5kyZQpTp07loYce0jsc4QPHe/dBs9tRoqKIXfkK9rffIWr2fUSMG6d3aEL4TIsm1/nz5/PJJ580en/KlCm89NJLno+rq6t54IEHUBSF1atXExLStIG2zWbH6bz4txcfH01RUXmTnldvZoq59prVmJgISt58Cy26DXy9C2XaXYbfumqm17o2M8Zt1pgb06JlgQULFvD44483ej88PNzz75qaGubOnUtFRQXvvfdekxOr0Jeak426+FnXqQChoTgBe6gVLT8fJSmJ0B279A5RCL9q0YwVHR3tVe3U6XQyb948CgoKyMjIIDIysgWiE77kzMw4f9yKw4ElLZ2ocyNXmagSwcCQw8GFCxeSm5vL6tWrcTgcFBUVAdCuXTusVqvO0QlvWNLSUX/8EcrLYNTNWEePJSI+Gvv1N+odmhAtwpDJdcOGDQBMnjy5zvWcnBwSExN1iEg0lRwCKIKdIZPrwYMH9Q5BCCEuiyG3vwohhNlJchVCCD+Q5CqEEH4gyVUIIfxAkqsQQviBJFchhPADSa5CCOEHklyFEMIPDLmJwFcsFsWnjzMSM8YM5ozbjDGDOeM2Y8yNMXw/VyGEMCMpCwghhB9IchVCCD+Q5CqEEH4gyVUIIfxAkqsQQviBJFchhPADSa5CCOEHklyFEMIPJLkKIYQfSHIVQgg/kOTagIcffpirr76a3NxcvUO5qDfffJNJkybRv39/Ro4cyZIlS6ioqNA7rAb96U9/4sYbbyQlJYWHHnoIm82md0iNMtPr2hiz/Ay7fffdd9x7772kpKQwaNAgHn30Ub1DumwB3bilOTZt2kRlZaXeYXhlz549zJ49m759+2Kz2Vi4cCEVFRW8+OKLeodWx8aNG/njH//Iyy+/TGJiIsuWLeOxxx7j/fff1zu0BpnldW2MmX6GAQ4fPsy9997LzJkzeeaZZ7BYLBw+fFjvsC6fJjxOnDih3XTTTdqxY8e0Xr16aTt37tQ7pCb5/PPPtUGDBukdRj2TJ0/WXnvtNc/HBQUFWq9evbSDBw/qGJX3jPq6NsSMP8OPPPKI9vTTT+sdhs9JWaCWBQsW8OCDD5KQkKB3KM1SUlJCdHS03mHUUV1dzYEDBxg6dKjnWteuXenSpQt5eXk6RuY9I76ujTHbz7Cqqmzfvp2EhATS09O54YYbuO+++/jhhx/0Du2ySXI9Z+3atdTU1HDPPffoHUqzlJeX88477zBt2jS9Q6mjpKQEp9NJXFxcnevt2rWjuLhYp6i8Z9TXtSFm/BkuLi6msrKSt99+m9tvv5233nqLjh07MmvWLOx2u97hXZaAr7nOnz+fTz75pNH7U6ZMYc6cObz++uusXbu2BSNrnDcxv/TSS56Pq6ur+e1vf0vXrl154IEHWiLEoGCm1/X48eOG+hn2ltPpBOC2227z/FJ4/vnnGTlyJH//+99JTU3VM7zLEvDJdcGCBTz++OON3g8PDyc3N5dTp05x66231rk3c+ZMpkyZwrJly/wdZh3exOxWU1PD3Llzqaio4L333iMkxFj/l8bGxmKxWLDZbPTs2dNzvbi4mHbt2ukY2cUZ/XW90P79+w31M+yt2NhYrFYr3bt391wLDQ2la9euFBYW6hjZ5TP2T4wPREdHX7JeNnToUDZv3lzn2oQJE1iyZAk33nijP8NrkDcxg+u3/rx58ygoKCAjI4PIyMgWiK5pwsLC6N27N7m5uQwePBiAo0ePcuzYMVJSUnSOrmFmeF0vZLSfYW+FhYVxzTXXkJ+f77lWU1PDsWPHTFM3bkzAJ1dvREVF0atXr3rXExMT6dixow4ReWfhwoXk5uayevVqHA4HRUVFgKueabVadY7uvOnTp7Ns2TKuueYaz1KsIUOGNPiaG4FZXtfazPozDK7R9YIFCxgyZAjXXnstGRkZWCwWbrrpJr1DuyySXE1sw4YNAEyePLnO9ZycHBITE3WIqGF33HEHNpuN5557jvLycoYPH84LL7ygd1iNMsvrGigmTJiAzWbjlVdeoaysjH79+vHuu++a4h3DxcgBhUII4QeyFEsIIfxAkqsQQviBJFchhPADSa5CCOEHklyFEMIPJLkKIYQfSHIVQgg/kOQqAsL8+fO5+uqrufrqq+nTpw8333wzzz77LCUlJZ7H7Nmzh0ceeYThw4dz7bXXMmbMGJ544gm+++67es+3Zs0arrnmmkYbZNvtdp555hmGDBlC//79mT17NgUFBX77/oT5SHIVAeP6669nx44dbNu2jQULFvDFF18wb948wHUawvTp0wkJCWHFihV8/vnnrFy5ki5durB06dJ6z7Vu3ToefPBBNm3aRHV1db37Tz75JF999RWvvfYamZmZaJrGrFmzqKqq8vv3KUxC52bdQvjEvHnztHvvvbfOtT/84Q9a7969tRMnTmjJycnawoULG/zc0tLSOh9/9dVX2rBhwzSHw6GNHz9e27x5c537R44c0Xr16qVt3769znP07dtX27hxo2++IWF6MnIVASs8PByn08mGDRuorq7moYceavBxMTExdT7+6KOPmDBhAiEhIUyePJl169bVuf/NN98QGhrKsGHD6jxHv3792L17t++/EWFKklxFQDp06BAffvghKSkpFBUVERUVRadOnS75ecXFxWRnZzNlyhQAJk2axDfffMORI0c8jykqKqJt27b1OmS1b9/e00FLCEmuImDs2rWLAQMG0K9fP1JTU+natSuvvPIKWhN6E23cuJGePXvSu3dvADp06MDw4cNZv369V5+vKEqzYheBR1oOioDRr18/li9fjtVqpUOHDoSFhQHQvXt37HY7J06cuOjoVdM0NmzYQH5+Pn369PFcdzqdfPvtt8ydO5ewsDDi4+MpLS1FVdU6o1ebzUa3bt389v0Jc5GRqwgY4eHhJCUlkZiY6EmsAOPGjSMsLIw//OEPDX7e6dOnAdi5cydHjx7lz3/+M5s2barzp6amhuzsbAAGDhyIw+Fg586dnucoKysjLy+P6667zo/foTATGbmKgNexY0eeffZZnn32WcrLy7nrrrvo2rUrp0+fJicnh9zcXD788EPWrl3LoEGDGDBgQL3nuOWWW/joo4+4/fbb6d69O6NHj+a5555j6dKlREdH8+qrr9KxY0fGjx+vw3cojEhGriIo3HnnnWRkZHD27Fkee+wxbrvtNubMmcPPP//MM888g81mIycnh3HjxjX4+ePHj2fXrl389NNPALz88ssMHjyYRx55hHvuuQen08k777xT5/BIEdzkJAIhhPADGbkKIYQfSHIVQgg/kOQqhBB+IMlVCCH8QJKrEEL4gSRXIYTwA0muQgjhB5JchRDCDyS5CiGEH/x/gLkDII77GT4AAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 136, 2\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.533\n", "LR cohens kappa score: 0.509\n", "LR average precision score: 0.663\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 137, 1\n", "GB fn, tp: 6, 3\n", "GB f1 score: 0.462\n", "GB cohens kappa score: 0.440\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 1/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 137, 1\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.571\n", "LR cohens kappa score: 0.552\n", "LR average precision score: 0.574\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 137, 1\n", "GB fn, tp: 5, 4\n", "GB f1 score: 0.571\n", "GB cohens kappa score: 0.552\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 1/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 36 points min:0.03211308144666282 max:0.6863390197271316\n", "-> create 516 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 134, 3\n", "LR fn, tp: 3, 3\n", "LR f1 score: 0.500\n", "LR cohens kappa score: 0.478\n", "LR average precision score: 0.466\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 136, 1\n", "GB fn, tp: 6, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: -0.012\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 137, 0\n", "KNN fn, tp: 6, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 2/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 2/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03311344137959691 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 136, 2\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.533\n", "LR cohens kappa score: 0.509\n", "LR average precision score: 0.576\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 135, 3\n", "GB fn, tp: 7, 2\n", "GB f1 score: 0.286\n", "GB cohens kappa score: 0.253\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03311344137959691 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 136, 2\n", "LR fn, tp: 4, 5\n", "LR f1 score: 0.625\n", "LR cohens kappa score: 0.604\n", "LR average precision score: 0.716\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 136, 2\n", "GB fn, tp: 6, 3\n", "GB f1 score: 0.429\n", "GB cohens kappa score: 0.402\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.3752825602129679\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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1XbtLB0wQ3z1gEqtoGhnQEqIZXANYJCWj7vpCq7mqKNA+nKCOHXCMGgMFB6RPtQ0zbHL99NNPef/99ykoKKCqqorDhw/rHZIQbo61a+D4v+CrL93H8hKGkH39JKZfLGSUFLJu8wzbLVBTU8PIkSOZO3eu3qEIUYcjN6fOxoAABAWRPWQKxdHd2J40Ub/ghGEYtuU6bdo0APLy8nSORIjL3DuwKgqEhmrlAiMiMM97nOm9h7Itv5h7x/bRO0xhAIZNrkIYiauPVT1xQutfBRh/KyH/53fu88NWzGdEegZdB40K6BF14ZmATq4xMRF6h9BsV5viEQj87f7OvPk71H8dr3swb4/7Pko2Z6GeKMK8+U9wz0y/u7+mCOR7A+/dX0AnV5nnakz+dH/uFmt5hTbNqlMMlJVqRVgio9z34bgrHTUrE8dd9wL4zf01lT99d83hzXmuAZ1chWgud1K1WLTBq4sXIbYL5gXPA1fuZWWeINWsRF2SXIWgXuEVuDxoFdsFrFat1Xq+2p1AJZH6xp6jpWzLL2ZqSjdG9YvRO5wWMWxyLS8vp7i4mKKiIgC+++47APr160dISIieoYkA48jNwbFwAVitOCwWlOhoUEygOjHPe9xd0UqZNbvR97sSsyTdltmWX8zJ8hqttq0kV9/YuXMnzz33nPv19OnTAcjNzaVHjx46RSUCkTMrE6w297xVU3pGnWRpnjAJrrIooHZFLEmuLTM1pZu75ervDJtcZ86cycyZM6/9g0K0gCM3B9ViIe/6cWQnpJL27U5GHsgneN0Gj69ROxmLlhnVL8bvW6wuhl2hJYSvubsDjh0lu9cIiiM6kz3gZq32ahOYJ0wieN0GabWKOiS5ijbLsXYNXLgAQNrBz+h23kLaD7sb7VsVoikM2y0gRKswmcGkkGoqZ/TYLpiXrNM7IhEgJLmKNss873EZ5Rc+I90CIiDV3r+qsWPSVyp8SZKrCEgNbRjY0DEhfEWSqwgYtVumtTcMdLVUTekZKAkJMmVKtArpcxUBoXadVcfaNdrBokJwOnGsXeNeDCBdAKK1SHIVAcGZlelesgpoidVu1zco0aZJt4AICKb0DJTERMxLl2Oe9zjEdIagIIjrqr0WopVJy1UEhPqP/M6sTFSHA6VHD+kKELqQlqsISDJ4JfQmLVfhN1yl/UhKhoIDV538L4NXQm/SchWGVnt6lWueqrplk8xXFYYnyVUYmjMrE/XIEW2aVVIySkKCVlil3hxWIYxGkqswlPpLVE3pGdr0KkWBggMEr9tA8Pyntd0CKiqk9SoMS5KrMJTaS1QduTnagoD24RDbpc7glAxY6aNmx44rajaIhsmAljAM164AREVBUrLWFVBVDagQEVFngEoGrFqfIzcHy5IXUFFkSxsPSMtVGIYzKxMqKgC03QDsdlDQaq4K3TmzMlEVBVSnPDF4QFquwjiSkuHwITCZtKWsQUEod6e7p10JfZnSMzBv/hOOu+6VVqsHJLkKw1B3faFtu9I+HCUxUYpYG8Ceo6Xu3VhHTZhE7D0zKSmp1Dssv+CVboHz58/zj3/8wxuXEgGqoeLVjYqIkCLWBrEtv5iT5TVsyy/WOxS/45XkWlRUxP333++NS4kAUT+ZumYBONauoWTWfzSYZM3zHkdJuUEKrRjI1JRudO8YxtSUbnqH4nekW0D4RO0pVeYJkzClZ2jHLBbsx/6F2sBos8wAMJ5R/WIY1S9G7zD8kkfJNSkpyddxiADjSqaugShX4nTk5rgHRYQIZB4l16CgIO69914SExMbPH/y5EnWrl3r1cD8zVel+/js1A4mxk/mxpgReoeju8ZaoWYZFBFthEfJNTExkfj4eGbMmNHg+UOHDrX55PrZqR2cqSnms1M7JLkKITwb0LrhhhsoLCxs9Hz79u0ZNmyY14LyRxPjJxMX1o2J8ZOv+bNfle5j5cGlfFW6rxUiax1Nmg0gRBugqKqq6h2Er5SWVuF0Gu/2Vh5cypmaYuLCurHg+sVXnI+NjfS7x2bbw/ejFhaiJCQQvG7DVX/WH++vKQL5/gL53qDp9xcbG9noOa8tfy0qKvLWpQKWq8XaN7K/u5UbMK3YpGS4UKP9KXQlTxHG0KLkarfb+eSTT3jggQe4/fbbvRWTX7tasnT1yx6r/IEF1y/mxpgRdfpq/VrBAQgN0/4Uuqo9DU7op1nJ9fjx47zyyiuMGzeOJUuW0LVrV9544w1vx+aXrpYsG+qXbUpfrZFJCUDjkO/CGDzuc7XZbHz66ads3LiR/fv3M3r0aHbv3s3WrVsbnaKlNz36XL0xJcso/VpN2bOqKYxyf74SyPcXyPcG3u1z9Wgq1sqVK9m6dSsxMTFMnz6d1157jdjYWAYPHozJJFULPVU78QKGnxfrerzk8CEIDZMankI0gUfJ9Y9//CM///nPeeyxxySZXkND811dSbXKVkG1vdrdZXCiqpB3v9e6U4yYYF2rrGq3XIVxuZ40pJqYMXiUKZ966ik++eQTxo0bx8qVKzly5Iiv49JNS0fv+0b2p8Z+nrM1Z9zXcCVcFYgL60bfyP5U2ipwqHYUTIYdzDJPmOTes0qqVBmfDGQZi0ct14cffpiHH36YvXv3smnTJmbNmkX//v1RVZXz58/7OsZW1ZyVVq6WafugcPLLvkJFxWE7506aVbYK2geFk9JpKMcqfyC/7GvO26uJCY0lMjjK7wezhDHUr+cg9NWkqlgjR45k5MiRlJWVsWXLFs6fP096ejrDhw9nypQp3HXXXb6Ks9VMjJ9cp1/UkwGqz07t4ERVITXOy79oHDiwOqy8c/h32FQb3cK6c6zyB87UFBMeFOGeIWDE7gDhn6SqmLG0eIXWnj172LhxIzt37uTgwYPeissrvDFbYOXBpZyoKgRF5cF/+0WDyfCr0n28ffh32FRro9dpbwpnTuKjHg1iyYisfwvk+wvkewODrdAaNWoUr7/+Ol988UVLL2VIE+Mng6J60Dd69SR+3lnN1sIP6iTW2v279ft6A2bllhAG01or2DzqFvj2229ZsWIFb7zxBpGRdTN1RUUF8+bN44UXXqBTp04+CVJPrkToanF+VbqPrYUfoAIpnYaSV/J3LNYyFA9+T52qOcnvD71G7/B+hJhDqLRVcL7W7AFXX+/tAydIlS0hfKR+IXdf8ajl+u677zJ8+PArEitAVFQUI0aM4N133/V6cEZxY8yIestVT3O25jR/O5mNxVoGgIrT4+sdrz7K8cqjlF4ooX1QOBPjJ1+xUitQVm4JYTSttYLNo5Zrfn4+c+bMafT8hAkTeOKJJ7wVk+HUHtTqG9mff1UeJSIoAoutrNnXtKpW2pvDiQyOcrdMpYUqhO+11sCfRy3X06dP07Fjx0bPd+jQgTNnzngrJsOp/Yh+rPIHUGlRYgWIDulEj/BejbZMA6agi2iS+v2BUuHKf3mUXCMjI69aUrCoqKjBLoNAUfsRfWL8ZGw0PivAEyZMpMbe5O5quNZniraj/kIAWRjgvzxKrkOHDuXDDz9s9PzmzZsZMmSI14JyefPNNxkzZgwpKSn88pe/pLS01Ouf4QlXnyvAx4UfNOMKCsFKsPuVE6fWAvbgM6WroO1w5Oag/vgjVFZAUrL22mKBqChZGOCHPEquDz30EH/9619ZunRpncf/M2fOsGTJEv72t7/x0EMPeTWwLVu28Ic//IHFixezceNGKisrefLJJ736GZ5wTYnafPx93v3+DYprTjbjKiqDo1OIDu6ECTPRIZ2kRSrqcOTm4Fi8EM6egYsXUXd9obVWKypQoqNlcYCPubpfanZ4rxvOowGtIUOG8Otf/5qlS5eyceNGIiIiAKiqqiI4OJglS5YwdOhQrwUF8F//9V88+OCDTJqk/Z9qxYoVTJw4kSNHjrRqicOso+9Rbivjh4rDBClNWtDmZsLEeXs1q0b83svRiUDhzMoERdFeXCqOJMtZfc9V7Ea1WKCigqp31sOwMV65tsfZYtasWYwZM4YtW7ZQUlKCqqr06dOH22+/nbi4OK8E42K1Wjl06BDPPfec+1jPnj3p3r07+fn5rZJcXTMEKmzl7mNmk5kgZxB21UF0cDSpXW5i79m/U36VwS0zQXQJi5OWahvWWLWq2scbqkAmy1l9z11WMyoKJSGBiEceospL1/YouZaXl/Pss8+ya9cunE4nKSkprFq1ih49engpjLosFgtOp5OYmJg6xzt16kRZmeej9DExEc2O4YtDn1Jy8TQKWmsiul00c1N+zl+O/pk7+01jZ1Eun57czvCuI7i11wT+cvTPhAdHcPCnA4SZw7jguIDNaSMuvCs/uy6DUfGjm/T5V1tWFwja0v2VbM5CPVGEefOfiL1nJjU7dlD1zjrUoiLUkp9QKiuI++9cuGemjhF7LpC+u5pfzKXqnfVEPPIQYZO1BlCYl67tUXJdvXo1+fn5/OpXv6Jdu3ZkZWWxePFi1q1b56UwfKMltQVujr2Nz2w7SO08hmOVPzAxfjL9g69n/sDrAXj59ApUVPadzmNuv/90H6df3fqtFRcq2HLoI/oHX+/xZ8v6bf9W//4cd6WjZmXiuOteSkoqsb3xltZaKreA3Y792DFOb/zIL1qpAffdDRsDw8ZQBVSVVLb+TgS7du1i+fLl3HrrrQCMHTuWqVOnYrPZCA4Ovsa7my46OhqTyURpaSn9+vVzHy8rK/PpEtv6FbCuNlKf0ulG8su+IqXTjQ2+78aYEVfsPCDaJtfjvWvQhKRkFEC1WqHiHNjtONau8YvkKjznUXI9c+YMgwcPdr/u378/wcHBlJSUEB8f7/WgQkJCGDhwIHl5eYwYoSW4EydOcPLkSVJSUrz+eS6eruf/qnQf5+3V/GLgfG6MGcHKg0sbfN+1ErQIbI7cHBxr10B1NYSHA5AX3oPsqu6kRVWQqnyvc4TClzxKrg6H44oWqslkwuFw+CQogPvuu48VK1Zw3XXX0aNHD1asWEFqaqpPB7Pq13Ktr/bjfrm13L1Fy7XeJ9omZ1YmHP8XOJ3aDIDefcgeNIHikEiyHYmkXswGkxmCgzDPe1zvcAPanqOlbMsvZmpKN0b1i7n2G7zA49kCTzzxRJ0Ea7VaWbBgAaGhoe5j69ev91pg//Ef/0FpaSlLliyhsrKS0aNH85vf/MZr12/ItVqarpZt+6BwrVCLqvDZqR0y2V80yJSegeNAvpZczWbM8x5neu+h/Pnzb7njxyMod6d7dVdd0bht+cWcLK9hW36xsZLrjBkzrjg2depUrwdT36OPPsqjjz7q88/xVP0WqrRWRX2O3BxOv/k7HOcqta6A6E5QVgo9e2GeMIlRwKh+Y4GxeofapkxN6eZuubaWFu9EYGRNmS3gyXYurSXgRmTrCeT7s868E44dvXygSxxKz54B0zoN5O8ODLYTQaCQKlTCa1zbz5tMEBHh3jlXKly1LZJcL/FWFSrZnqVtqZ0wHbk52syAkBAYfyvKkKF1BqqkwpX3GfkXVvMWywcgT6ZNeboTrGzP0jY4cnNwPPs02GzawFXPXlD6EygKSmUFwes2uP/x117iKrUCvKe1tmxpDmm5NoEnXQdSh7XtcGZlgs2mvXD92SsBc58+7gRa/x+/q4tAeEdrbdnSHNJybQJP5rPKwoG2wZGbg/p9rUUAl/pZzfMep+s9M92DItJa9S0jF7eR2QIGJCOyxuUuUXfihFZ7FdwLBKioQElIIH7bx357f9fiz9+dJ1q9toAQQuNYuwaKCqFWm0TJmIMpOUVaqKIOSa5CNKB2rVW4lFQBqi5V+3Ql1y5xBM9/GsCwj6f+rHZ9hryEG9ieOoNp4we12iqrlpDkKkQ9jtwcHAsXwMWLOPL/qSVSh0PbKSC2C4SGgtmsTbuKaH7NYHFtzqxM7UnBbid7eDLFFRdadQlrS0hyFaIeZ1YmWK1aUrXb656MiNBqBURFofT/N+kG8BHXkwNJyWCxQHU1aWcPsL1Pv1ZdwtoSklyFoO4/ZvX777UE6hIUpCXZm2/BfOe0BrdsEd7j3qxR0fYBCfnoL4BWjcGfKjJIchUCrbW6tzqE7J9iSIvuS2rF/lonndA5FqWywtBTfwKFtlmjCVSnXz8ZyCIC0aY5cnOwzrwT9cQJsvuPoTgqjuykiVr/aliY1r86brxhJ6oHgvpLWE3pGSiJiZiXLvfrX2TSchVtkm31KtQPsrTHfbsDUEk7mEN20kTSCj6D8HBCdkt9iJZqbOfb2uqvYguUpwNpuYo2Sd2yCS5cuDRgpU2rSi3cz2+2/5bUon+izJqtb4ABon6xmoYKrRh5CWtLSHIVbYLr8d86807tH/awekuUg4OhSxz07Yd51f9xz10VLVM/cTZUGSxQay5It4BoExxr12hFrE1mnFmZ2u6roWFwoQZQoGcv96i08J76j/htqdaCJFcRUBrq43Pk5mgbBQIoaNOtdn0B8fEoY29272MlfC9Q+lM9IclVBBTXY6dj7ZrL81a3bLo8b9Xp1BLrpSIr8vjvXfV3WXW9TlPOMiL7PW1RQK1fZoE8Z1iSqwgYjtwcVItFq1J1/F+ox45C/j8hrL22EMDhgHbtAAJyAMUIau+yOuL412zdd47ijl35S3kZwwsL4fAhCA1z97katdC1N0hyFQHDXbEqKOhyS9Vuh9hY93YrgdxSMoKpKd348+ffMmXXX3B8nUNax75kD5nMnSnxKD8kNNpyDUSSXIVfueq8yaoqLZkGB9c6qKBER7t/VpKqb43qF8ON//8r7mIrqef2k3rmO0KWfA0zbr3i5wP5+5DkKvxK/QnndQt8lGk/VFOjra5SVYjvHrAtI8Pr0AEcjjY7Z1iSq/AvScnwTQHqiRPaKqstm7SlqocPgc1OXsIQbZXVD7sZPfeegG4Z+ZInK6vq/7zjlZeg3AKjbkKJjm7z3S+yiED4l4ID2qN/yVnUP66HCxe18oDhERBkJjtpIsUdurL9tjlt+h92SzV1G3DH2jVw5jR5XQexKGoEXz6/us3/7y/JVfgN2+pVqN8UQHDI5Z0A7DZt8Op8NfRKIO38v4iPiWDa+EH6BuvnmrwktfQnAO2XW2wvtuUX+zA6/yDdAsJvuOsB1Odwgqpinvc4YydM8quan0bV5Mn+Fy8CkHb4Cz65cbjfFLT2JWm5CsO6osiHqx6A2ayVAuwSp/3Xu7ffl6czOtd3UbNjB7bVq7COGYFt9Sr3eeXudIiIYORNSbw0M8kvtmHxNWm5CsNyZmWiHjmC4/lncHTvoR2MjNRaqZJMW5WrD7bqnfWoBQVQXa09Scx/WvvlV3BAvpN6pOUqDKOhosmoTrDZtHmTgJI4QP4RtyLXd0JSMkRFYSss1J4cgoKgfbh7VkFTBr/aCmm5CsNoqGgyXN7W2jzvcUmqrcz1nSiXXqvFxWAyQ/swcDrd07UCeaVVc0lyFbpy70sPKGNvRkFrsdaeZymlAFufu0UaGaWVZUxKhl1faHUbQoK1/u8v90FScpuqdNUUklyFrtz70gMUHCB43QYAbA/fH9BFPYzKnVQtFqiogAs15HUbrG3cOPAmxnaJxXHXvdrPhIZp845Fg6TPVejGXcWqUwz0qjunMlC3/jA6VzcApaVQVgoJvclOHKdt3GjuhmtLHPl+rk1arqJV1H7M556ZwKVWq6uu6qUWq4s8arae2t+NGhmlJVXQFmoUHidtuJPsn86S9u1OrD8dQcnKDMhtWbxNWq7C5xy5OTgWL0Q9cqTOiLK0foxBm/J2GMfzz8DnO7UVb4oC4eEos2Yz9j8fZlnxTlJLjqCoqnxfHpKWq/A5Z1am9o9Vddb5hymtU/25u2ZsNq1Gg0t4BCH/s8f90jUjIPoXc6kaNkaHSP2PJFfhc7Wn6kgyNQb3LI1TJyE4mLzk8WR3SSbtYA6phfuhc+c6P+/6RRgWG0lVSaVOUfsXSa7C6+qXq5MWqnG4ZwOcOAFnz7iPb0+dQbFVIbtdO1LNFdArAeuYESizZss+Y80kfa7C69zLVhcvvFwXQOjKvfpt5QrUr7+qk1i5cIE7Ko/QPa4j09Nv0+YVf7nv8hJX0SySXMU1XVFA5Rrn3ctWFUWWRBqE6xceZ89cLtdYS+qG1SzrUOwuuKLMmu0e0BLNI8lVXNO11o7XP2+eMAnz0uUoiQNkZNkg3L/wajObtYFGAFV1r5QDCJ7/NCG790mXQAtIchXX5JoyRVIytofvx7Z61RUt1fpTqswTJslcSJ00+qQRHqEVXAlrryVVxQTh4RDXVUu0wqtkQEtcobEBKdeS1Np7z8uAlfHUL4ADl7dhwWSCixe0rgGnA/PS5e73yFOGd0nLVVzBPam83oCUq4WqzJotk/8NzJSeASYT6oF8rLfdon2HVVWXTppg3HiIiEDJmOP+xShPGd6nqGoDvds6+/TTT3n//fcpKCigqqqKw4cPN+s6paVVOJ2Gu71rio2NpETHuYSuFVUoJpTExCuWpraU3vfna0a4P+vIoZe3xGnXTlt15XBA7z4tqjJmhHvzpabeX2xsZKPnDNlyrampYeTIkcydO1fvUNqkywNSidI6Nbja/at1+lqjOlz+IatV22esfXvM8x7XL9g2xpB9rtOmTQMgLy9P50jaLulH9Q/uOcULF2hbjjudOE6cuDwLIDgETAp07yHFxluZIZOrEELjGlwkKRkKDlyxhNiUnqF14Vit2qM/QMlZbSaAyQTtQvjHUy+TrXZhau9ujNLpPtqigE6uMTEReofQbFfrywkEcn+eKdmchXqiCPXIYZSwMMyb/0TspZKNNTt2UL7qZW1b6w5RKKFhqOUWuLQpi7lnTzo88zR/K46huLSaHd+eZerI3i2OSb47z7Rqcn322Wf5+OOPGz0/Y8YMXn75Za99ngxoGZPcn+ccd6WjXmq5qgUHcNx1r/vatjfe0va0AvJiEvkk/WnSlLOMyH7P3cKtAiYfLWVbfjGTB3VpcVzy3V35841p1eS6cOFCnnrqqUbPh4aGtmI0QhhfQ33fdboK8v8JdjvZSRMpLq8hu2MXbqo3u2NUvxj3slbRelo1uUZGRhIZGdiPFEJ4U50dHLi0GKCoEJwqWCwo992PumUTaR0v8knHMKamdNM5YuFiyD7X8vJyiouLKSoqAuC7774DoF+/foSEhOgZmhCtypmViXogH8dXX0KHjlBZCU6H+7y66wu4cIHU/9nK2I8e1i9QcQVDznPduXMn06dP54UXXgBg+vTpTJ8+nbNnz+ocmRC+1WCFMZtNO3mu/HJiDQ6WOasGZ8iW68yZM5k5c6beYQjRampvaZ0XEkf2F8WkHVzHyINfQFgY1NTUfUPPXu6+WKkLYEyGTK5CtDXuLa2jorStrCNiyS45TeqRI9pqq9rJVVHcrVZZ7GFcklyFMADXPmMkJZP23T/Jjksh7fAXYL0INqu2z1V8EtnXTyKts4OxklANT5KrEAbgLg24eCGpdjup9s+02qsdo+H8eQhysn3qzylWQvmkYxhjdY5XXJshB7SECCTX2ibHRduC3KQtZbXbtapWFedAdaLMms208YPoLtOt/IYkVyF87Frb5Dhyc7DOvFPbkTU2FiVjzuXCKxcuQGgYFBxgVL8YXpqZJAsC/IQkVyF8rKFtcGpzrF0Dx466d2QNnv80yv0PQkQE3HyLFCb3U9LnKkQrs/7nY/DFf0NoKHSLh+rqK34meP7TIJsD+jVJrkL4mGvJqsNi0Qau/udzbQ+rmho4/i/o3Qf69gOQhQEBRJKrED7kyM2Bkz9qW6yc/FF7PW48fL5T+wGTSYpYByhJrkL4QO0VV4SEaEtYQ0JwZmUSsm4D1pl3agVYeiVIYg1QMqAlhA+4V1xVV2uP/4oCYe3dA1PmeY+jpNwg3QABTFquQniRIzeHks1ZkJSMAlrL9cxp7eT5ancrVZatBj5JrkJ4kWPtGhwniuDsT4R89BdtV9aVK6DiHMqs2XqHJ1qRdAsI4U3V1Vr/6qXpVc4D+XC+GuXudG16lWgzJLkKcRUNLV2tf8yRm4P1tluwjhyq9a8GB0N4OADqlk1QXa39KdoUSa5CXEVDS1cda9eg5v9T6wLIzeF/39rIouH3kxd3HVgvEnLjje6BKmXWbAgPly6BNkhRVdX/tkf1kOz+akz+dH8N7mF1/F/gdEKXOJSePXmh+0SKIzrTrbKEZZ1KiF+x1G/ur6n86btrDr/d/VUIf1N7VN/28P2XNgd0aicjIjClZ5CWvYvtCX2YNiuVYCmqIi6R5CqEp5KS4ZsCaB+ubb1yydjVv5b6quIK0ucqhIfUHdu1EoAA1VVQcrbRMoJCSHIVwlMV57Q/Kyu0otaqKqUARaMkuQrRgIamYCl3p1+usZqYiHnpclllJRolfa5CNMA1K8DxTQGgDWxJjVXRFNJyFYK6LVV3mUCnE6w26VcVzSItVyGou1hAtVi0JaxBQdBLtlgRzSMtV9Gm1dkcMCrqciI1maBXAiEf/UX6VUWzSMtVtGnOrMzLK64iItyJ1LUqq/YKLUmyoimk5SraJNvqVVjHjECNjNJ2CjCZ3efMEyYRvG4D5gmTrrktthCNkeQq2iR14/tQVQV7/o55+UqUIUMa3BXgWttiC9EY6RYQbVPHaG2HgI7RV90VQHYMEM0lLVcR0BpaDABgfuY5lBuHYX7mOZ0iE4FOWq4iIDlyc7SFAKdOaq8XLwSQPaxEq5GWqwgI9VuozqxMrTyg1QZWKygmGZQSrUpariIgONaugaJCHBYL5gmTtGlUFgsAytiboeCADEqJViXJVQSGqiqw27U/kcd+oT/pFhB+z7Z6FZw9A4qivW5gAEuI1ibJVfgl1yIA2+pVl3dWVRSIiJBJ/8IQJLkKv1R7y2pl1mxo1w5iu6CMvVkm/QtDkOQqDKexuam11d6yOnj+0yhJ14PDAQUH3EtXhdCTJFdhOJ6s5w+e/zQhu/dpBayRZarCeGS2gDAcU3qGuxJVQ/YcLWVbfjFTU7ox6tJW1jI7QBiNtFyFobhK/KmRUTgWL9RmAtSzLb+Yk+U1bMsv1iFCITwjyVUYijMrE/XIYfh8J1RVoW7ZdEUf7NSUbnTvGMbUlG46RytE4yS5CkOoU19VVSE4BEwmlFmzr+iDHdUvhpdmJrm7BIQwIulzFbpydwN8UwAXL8Le/4X47gCY5z2OecKkOrsBCOEvDJlc33jjDf76179SWFhIVFQUt912G/Pnzyc8PFzv0IQXOXJzcCxcoBVWiYjUNgRsHw4VFSgJCVLBSvg1Q3YL7N+/n0ceeYSPPvqIV199ld27d7Ns2TK9wxIt5Oo7rdmxA7hUucpq0/av6tyZkN37MC94XqZUiYBgyJbrW2+95f573759eeKJJ1i8eLGOEQlvcPWdVr2zHoaNqVO5yrXFirRSRaAwZHKtz2KxEBkZqXcYoglqT6li7/9CSDsIC4OoKCIeeYgqJJGKwGb45FpZWcn69euZNWtWk98bExPhg4haR2ysf/8yKdmchfOH7+HcOe3AhQtQc56QPr0JmzyZMH3D8zl///6uJpDvDbx3f62aXJ999lk+/vjjRs/PmDGDl19+2f3aarXyq1/9ip49ezJ37twmf15paRVOp9qsWPUUGxtJSUml3mG0iOOudNTFCyEoGBx2iIyCzp1x3HUvgN/f39UEwvfXmEC+N2j6/V0tEbdqcl24cCFPPfVUo+dDQ0Pdf7fb7cyfP5/q6mree+89goIM38hus1xl/1xFVODyXlWuKVTy+C/amlbNWJGRkR71nTqdThYsWEBRURGZmZkyBcuAas89rV3+j0vJFaRPVbRthpyKtWjRIvLy8njllVew2WyUlJRQUlKCw+HQOzRxSe1VU7XL/wkhNIZ81v7www8BmD59ep3jubm59OjRQ4eIRH21K1eZJ0yq02IVQhg0uR4+fFjvEMQ1yCO/EFdnyG4BIYTwd5JchRDCByS5CiGED0hyFUIIH5DkKoQQPiDJVQghfECSqxBC+IAkVyGE8AFJrkII4QOGXKHlLSaToncIzebPsXtC7s9/BfK9gffuT1FV1f8KngohhMFJt4AQQviAJFchhPABSa5CCOEDklyFEMIHJLkKIYQPSHIVQggfkOQqhBA+IMlVCCF8QJKrEEL4gCRXIYTwAUmuBvbGG28wbdo0brjhBsaNG8eyZcuorq7WO6wWefPNNxkzZgwpKSn88pe/pLS0VO+QWiwQv6ermTdvHgMGDCAvL0/vULzmm2++4YEHHiAlJYXhw4fzxBNPtPiaAV24xd/t37+fRx55hMGDB1NaWsqiRYuorq7mpZde0ju0ZtmyZQt/+MMfeOWVV+jRowcrVqzgySef5I9//KPeobVIoH1PV7N161Zqamr0DsOrjh49ygMPPMCcOXN44YUXMJlMHD16tOUXVoXf+OSTT9Thw4frHUazTZ8+XX399dfdr4uKitTExET18OHDOkblff7+PTXm9OnT6vjx49WTJ0+qiYmJ6t69e/UOySsee+wx9fnnn/f6daVbwI9YLBYiIyP1DqNZrFYrhw4dYuTIke5jPXv2pHv37uTn5+sYmff58/d0NQsXLuTRRx8lPj5e71C8xuFwsGvXLuLj48nIyOCmm27ioYce4siRIy2+tiRXP1FZWcn69euZNWuW3qE0i8Viwel0EhMTU+d4p06dKCsr0ykq7/P376kxGzduxG63c8899+gdileVlZVRU1PDO++8wx133MFbb71FXFwcDz74IFVVVS26tvS56uDZZ5/l448/bvT8jBkzePnll92vrVYrv/rVr+jZsydz585tjRBFMwTq93Tq1Cl+97vfsXHjRr1D8Tqn0wnA7bff7v7F8etf/5px48bx+eefk5aW1uxrS3LVwcKFC3nqqacaPR8aGur+u91uZ/78+VRXV/Pee+8RFOSfX1l0dDQmk4nS0lL69evnPl5WVkanTp10jMw7AuV7asi3337LTz/9xG233Vbn+Jw5c5gxYwYrVqzQKbKWi46Oxmw206dPH/ex4OBgevbsSXFxcYuuHTj/D/AjkZGRHvXJOZ1OFixYQFFREZmZmYSHh7dCdL4REhLCwIEDycvLY8SIEQCcOHGCkydPkpKSonN0LRNI31NDRo4cybZt2+ocu/POO1m2bBljxozRKSrvCAkJ4brrrqOwsNB9zG63c/LkyRb3LUtyNbBFixaRl5fH22+/jc1mo6SkBND6Kc1ms87RNd19993HihUruO6669xTsVJTU0lMTNQ7tBYJtO+pvoiIiAa/ox49ehAXF6dDRN41Z84cFi5cSGpqKtdffz2ZmZmYTCbGjx/fouvKHloGNmDAgAaP5+bm0qNHj1aOxjvefPNNMjMzqaysZPTo0fzmN7+hc+fOeofVIoH4PV3LgAED2LBhA6mpqXqH4hXvvfce7777LhUVFSQnJ7No0SL69+/fomtKchVCCB+QqVhCCOEDklyFEMIHJLkKIYQPSHIVQggfkOQqhBA+IMlVCCF8QJKrEEL4gCRXERCeffZZBgwYwIABAxg0aBC33HILL774IhaLxf0z+/fv57HHHmP06NFcf/31TJw4kaeffppvvvnmiuutW7eO6667rtGC11VVVbzwwgukpqZyww038Mgjj1BUVOSz+xP+R5KrCBjDhg1j9+7d7Ny5k4ULF/Lpp5+yYMECQNsF4b777iMoKIhVq1bxySefsHr1arp3787y5cuvuNamTZt49NFH2bp1K1ar9YrzzzzzDHv27OH1118nKysLVVV58MEHuXDhgs/vU/gJr5ffFkIHCxYsUB944IE6x37/+9+rAwcOVE+fPq0mJSWpixYtavC95eXldV7v2bNHHTVqlGqz2dQpU6ao27Ztq3P+2LFjamJiorpr16461xg8eLC6ZcsW79yQ8HvSchUBKzQ0FKfTyYcffojVauWXv/xlgz/XoUOHOq8/+OAD7rzzToKCgpg+fTqbNm2qc/7rr78mODiYUaNG1blGcnIyX331lfdvRPglSa4iIP3www+8//77pKSkUFJSQkREBF27dr3m+8rKysjJyWHGjBkATJs2ja+//ppjx465f6akpISOHTteUfGqc+fO7opYQkhyFQFj3759DBkyhOTkZNLS0ujZsyevvvoqahNqE23ZsoV+/foxcOBAALp06cLo0aPZvHmzR+9XFKVZsYvAI/VcRcBITk5m5cqVmM1munTpQkhICAB9+vShqqqK06dPX7X1qqoqH374IYWFhQwaNMh93Ol0cvDgQebPn09ISAixsbGUl5fjcDjqtF5LS0vp3bu3z+5P+BdpuYqAERoaSkJCAj169HAnVoDJkycTEhLC73//+wbfd+7cOQD27t3LiRMn+NOf/sTWrVvr/Ge328nJyQFg6NCh2Gw29u7d675GRUUF+fn53HjjjT68Q+FPpOUqAl5cXBwvvvgiL774IpWVlcyePZuePXty7tw5cnNzycvL4/3332fjxo0MHz6cIUOGXHGNW2+9lQ8++IA77riDPn36MGHCBJYsWcLy5cuJjIzktddeIy4ujilTpuhwh8KIpOUq2oS77rqLzMxMLl68yJNPPsntt9/O448/zo8//sgLL7xAaWkpubm5TJ48ucH3T5kyhX379nH8+HEAXnnlFUaMGMFjjz3GPffcg9PpZP369XU2lxRtm+xEIIQQPiAtVyGE8AFJrkII4QOSXIUQwgckuQohhA9IchVCCB+Q5CqEED4gyVUIIXxAkqsQQviAJFchhPCB/wchw0spX+W1PAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 136, 2\n", "LR fn, tp: 6, 3\n", "LR f1 score: 0.429\n", "LR cohens kappa score: 0.402\n", "LR average precision score: 0.594\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 136, 2\n", "GB fn, tp: 6, 3\n", "GB f1 score: 0.429\n", "GB cohens kappa score: 0.402\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 137, 1\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.571\n", "LR cohens kappa score: 0.552\n", "LR average precision score: 0.671\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 137, 1\n", "GB fn, tp: 5, 4\n", "GB f1 score: 0.571\n", "GB cohens kappa score: 0.552\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 2/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 36 points min:0.03211308144666282 max:0.6176281243596343\n", "-> create 516 synthetic samples\n" ] }, { "data": { "image/png": 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ZDJE93YtcCwFyQUuIZjW3ZKBpdiqEdgEgu99wlt/y32T3G+4aglVZIS1W0YiEqxDNaDoqoGrHDtd/l54BqB9uFXcThITIPlfiHNItIERz4uJdi67ExeN4aQ3FaW+CxQLh3SAoiJSjn5Mx8BpSir7GIhMFRDMMG66ffPIJb7/9Nrm5uZSXl3P48GG9SxKdSe4BCAlF2/GRe3lAamowL34Mc/J4krIyGS2jAsR5GDZcq6qqGD16NElJSbz44ot6lyM6CTUrE3X1KrCddl28arDltZI6192vKqMCxIUYNlynTp0KQHZ2ts6ViM5CzcpEXbIIHA7AddEqPXEmODXuiSxn1H/Nca/XKmuxiguRC1pCcDZYly12BytAxhXjKQzrRWGP3mTEuNYJaG5vLCGaI+EqOj335ICff64/aDaT8k0WvStO07dbKLePvgQ4d28sIVqiaFrTEdHGkp2dzd133y0XtITPnLrhRmqPfO+aHGA2ofSKRquuAnsJpt696f3lXr1LFH7IsH2u3mCzleN0XvizIyoqjKKisg6oyLv8sW6j1axmZaLm5dfPutIU6NMX7HYoK8cZ2oWiojLD1e0Jqdn3oqLCWjwX0OEqREvcGwkWFDToZ1XAEuz+yi8LsIj2MGy4lpSUUFhYSH5+PgDffvstAAMGDMBisehZmggAzvQ0tO8Ouxa6VhSIvhglJqbRKAAZDSDaw7DhumvXLpYuXeq+PW3aNACysrKIiYnRqSrhj+q2YlFmziJ44SLAdWFKXbEMQkLAbMb86FIJU+FVhg3XGTNmMGPGDL3LEH5OzcpEe2sjANrbm+BsuNYFqYxZFb4iQ7FEQHOmp9WvYNX3ChwvrXGfMyePJ3jDJgnWFnxx1MbSD3L54qhN71L8koSrCEhqViY1/3kjWs6/yIi/2b2ClbZ1i96l+Y3tOYUcL6lie06h3qX4JQlXEZCc6Wlw6iTU1pJycCd9QhVSvv9MlgZshSkJvenbPZQpCb31LsUvGbbPVYj2MM1ORd33JQCJBQcYuyANFkzUuSr/MmZAJGMGROpdht+SlqsISObk8Shz7gWrFUXGqgodSMtVBKzghYvcowOE6GjSchVCCB+QlqvwO3VTV4mLR9v9KYB751UZtyqMQsJV+J26NVU5fAgqK8HpRF23FiUiwr3WqoSr0Jt0Cwi/U7emqjJzlmvTQJO50XFZbEW0VnNbqbeXhKvwG3V/AADBGzZhik9wLQ/Yvz/mBxbIjCvRZr7YYULCVRhW09ZE0z8AZ3oalJaiRERIoIp28cW3Hq+Ea2VlJf/85z+98aOEcGsapk3/AKQbQHiLL771eOWCVn5+Pnfffbd7zVUh2sO9kHVYONmRA8lIuJVpR22MabKdtWxvLYxMugWE4ajr1qLt/wo+/RsZg6+j0GHiT1v+5tWLDUL4mkct17i4OF/XITqxupZqo/GpTicoCimHPyUj7iZu+e4fOL+vkJaq8BsehWtQUBC/+MUvGDRoULPnjx8/zrp167xamOg86vpW1XVrUdetdW290isarFaS7ruDJMB5rEL6VoVf8ShcBw0aRJ8+fZg+fXqz5w8dOiThKtrMNDvVFaoF+a7NAk1mlOHDCd6wyX0fabEKf+NRn+uVV15JXl5ei+e7dOnC1Vdf7bWiRGBrOsTKnDweJSICVNV1h+AgaaW2kS8Gw4u28ajl+thjj533/CWXXEJamvcG3wrYZ9vLzhM7uKnPRK6KHKV3OV7l2nn1O9cGgWdpdjv0jAKr1T0hQLSOmpXpek8Vk2v42h2yB52evDZaoG4LbOEdO0/s4FRVITtP7NC7lHZr2poyzU4FzXXBypmeVj8ZIDYWywd/lmBtI2d6mmubcM0pLX8DaFe41tbW8vHHHzNnzhwmTJjgrZoEcFOfiUSH9uamPv6/en7DC1Ynb7jRtcjKzFkogwZjmp0qkwG8xDQ7FWXQYMxPrZQPKANo0ySCY8eOsWXLFrZt20ZtbS033HAD69ev93ZtndpVkaMCpjvANDvVFbB2O+q/j4GmQe4BuWDlZTKpwlg8DleHw8Enn3zC5s2b2b9/P0lJSZSUlLBt27YWh2gJAfV/9GpWJry6DrVWlVaqCHgehevq1avZtm0bkZGRTJs2jRdffJGoqCiGDRuGySSTvIRnzMnjibpjBkVFZXqXIoTPeRSub731Fr/61a948MEHJUyFEMIDHiXlI488wscff8y4ceNYvXo13333na/rEkIIv+ZRy3XevHnMmzePPXv2sGXLFmbOnMnAgQPRNI3Kykpf1yiEEH6nVaMFRo8ezejRoykuLmbr1q1UVlYye/ZsRo4cyaRJk7jtttt8VWfAaMvkgECeUCBEoGpTB2qPHj345S9/yV//+ldef/11wsPD+e1vf+vt2gJSWyYHBNKEAiE6i3ZfnRozZgwvv/wyn376qTfqMYR9tr2s/vop9tn2ev1nt2VygBEmFMicdd+S9zfweBSu33zzDXfddRdlZecOoSktLSU1NZWioiKvF6cXX7YUr4ocxeIrVrT49b65YL/QYzqCLzZwE/Xk/Q08HoXrG2+8wciRIwkLCzvnXHh4OKNGjeKNN97wenF60bOl+M7RN/mu9FveOvKqz1rPbSFTVH1L3t/A49EFrZycHObOndvi+eTkZB566CFv1aS7jpx6Wnex6rKwgfxQ9j1nHCUAVKgV/FiRz84TOwxxEUumVvqWvL+Bx6NwPXnyJN27d2/xfLdu3Th16pS3aupUdp7YwY8V+RwpPUSouQtOnO5zlWoFXYK66lidEKKtPOoWCAsLO++Sgvn5+c12GYjm7bPtZdHeX/Orz++mS1BXNJwomKhSzx0znFO8zzBdA8L75EJW4PIoXEeMGMH777/f4vn33nuP4cOHe62oQLfzxA7sjmIcWg3/Kt5HlVqFExUN7Zz7ami888Ob7tu+HMkgOp5cyApcHnUL3Hvvvdx1112Eh4fzq1/9iujoaABOnTrF+vXr+etf/yo7EZx1vgH/DftXj1fkU6FWQDOB2tSZmhKWf/WI+56VtRXsPLGDCUOSvf8CRIeqW45RLmQFHo/Cdfjw4fz2t7/lqaeeYvPmzVitVgDKy8sJDg7mySefZMSIET4t1F80HMbVMFz32fbyxpH1/KzWcKT0ECYPvjRYFAs1moPQoC6cqjoJQHRob93HvArvkQtZgcvj6a8zZ87k2muvZevWrRQVFaFpGpdeeikTJkxwt2SFaxhXXcu1oZ0ndqBgwolrEz717L9bMqHvZH4o+55TVYV0CepKt+DuaGhM6zfLEKMHhBDn51G4lpSUsGTJEnbv3o3T6SQhIYE1a9YQExPj6/r8TkvDuG7qM5FteVuorXJQQ80Ff86nJ3cyuNswdytVAlUEsi+O2tieU8hdYy9lWFRgjJDx6ILWSy+9RE5ODr/5zW949NFHsdlsrFixwte1+b19tr08/tUjLP/qET4u+BMnqn70KFgBqtQq/lX8pQSrCBjnGxmxPaeQ4yVVvLsncDY69ajlunv3blauXMmNN94IwNixY5kyZQoOh4Pg4GCfFffqq6+SlpZGWVkZ11xzDU8//TSRkZE+e772aO5C1s4TO/ip6iQaWqPxq63x6qG1RIdGM63f7c2GrKyYZSxqVmajC1R1/y39qo1HRjR9P6Yk9GZ7TiG3j75Ep+q8z6OW66lTpxg2bJj79sCBAwkODvbpegJbt27lD3/4AytWrGDz5s2UlZXx8MMP++z5WuLp0Kfm1iO4LGxgu4IVQKWWk1WFvHFkfbM1yIpZxtIwQGSYVWPnm+I7ZkAkz86I4/rLL9ahMt/wKFxVVT2nhWoymVDV81+UaY//+7//45577mH8+PEMHTqUVatWsWfPng7fBWHniR0UlOedE25NQ7fpegT7bHv59ORODwZaNS/C0oPuwT2ICO6BWQmiVlWbDVgjrJglGoiLh+oqiIuX9QKaMCePJ3jDpk7Tivd4tMBDDz3UKGBrampYvHgxISEh7mMbN270SlE1NTUcOnSIpUuXuo/FxsbSt29fcnJyOnS32Zv6TOSNI+tRMDUaXtXSkKs6daMDFAAUzIqZWq32nPv1CY3h6RFrAHjv2Nv89XgGwUowUSHRLL7C1a9dN4yraQ1fnPhcugQMQs3KRF23Fo7/CBYL5B7AvHBRpwkScS6PwnX69OnnHJsyZYrXi6ljt9txOp3n9K/26NGD4uJinz1vcxqGacPWYdMhV03D9rKwgRRUHKNbcHeq1WqqnFXuxwYpwYQFuaYLV9ZWsvyrR5jW73Zu638nl4X9Bx/mvUu5o5R9tr2NRh80rWH70T+dN+BFx3Gmp0F+Hjg1CA6W1qrwLFyfffZZX9fhE5GRVo/v+73ja7Yf/RNTBkxlTJ+kRucmRCWfMxuq6bGZQ2bw56N/YvKAqURFhVFw6N+EBIVgtVixYuVE2XH32NZazUHf8D6c+fkMBeUFlDpM/KPoEyYMSWZCVDKfFn3CifLj7mMt1TDFMbXRc/oLf6q1oaioMKp27KD8jxuwzp9H6MT6D7qq/7qP0ufXoAHdHl3U6JyejPxet/ReGrnm1mjVHlodJSIiApPJhM1mY8CAAe7jxcXF9OjRw+OfY7OV43ReuNczKiqMrYc+4FRVIVsPfcDA4CtaXfPA4CtYOMT1uL8cyqK40k6oqSvDwhP49OROTJjR0FBQCLd0Y1zUzWzLexcFhSAliHFRN1NU5FqM/Lqom9np2NHoWHPG9Ely13q++xlJVFSY39TaUF3djvWvoeXlYV//GuVXX9todIB5yzYAyoFyA7xGo7/XTd9LMH7NTZ3vg6Dd27z4gsViYciQIWRnZ7uPFRQUcPz4cRISEnzynN68MLTzxA4qayuwBofxQ9n3KJgIMpu5ue8tDAgfxC8umwu4VhXobokgMiSq0eONsPOAaF7Ti1TqurVoOf9y9beKVgn0C36GbLkC3HnnnaxatYqhQ4cSExPDqlWrSExM9NnFLG8ukN20P/bDvHdRgAPFX1FxdtEVcC3AUuP8GafmlH5TA1OzMil6Lx3H4GGQewAtLJzPX9tMxsFaUiwXk1h7FI79GzUrUy5gtUKgr6tg2HC99dZbsdlsPPnkk5SVlZGUlMTTTz+td1keaRrUdRe7ugR1bdQ6brgDgQylMi5nehpaQT7a/n+BqoKqkjFpEYXVGhkDryHx0P8PTmezg+NF52XYcAW4//77uf/++/Uuo90atmQbhq60VP2DaXYq5vfeoebfx8B2GsLDSTmYRcYV40kp+CeEhEB4t4D9eivaxtDhamStmXbakXtyCe9zHsjBefAgXDUSpawU0+xUkoDR6WmY7k7FnLxO7xINq9EFv07WqpdwbaMLTSIQgcHx0hq0t85OjvlyL8Gf1c+Q62xh0RafZ+wmI/ZmUjJ2M7aTvV+GHC3gDxqOLmjP1iuybYtxqVmZaJsabBnvdLpmYsm+Vx77KG48hd2i+SiucwUrSLi2WcPhUu1ZPEUWXjGWhsGpPv8saA3GSVdWyoIsrTT1+svp2683U6+/XO9SOpx0C3hBS7sP+PqxwvvU55+FUydRD+Q0DtbgYOjSFc1uRxl7HQrIBSwPjBkQyZgBxlwm1NckXL2g4QWr1q6vKhe7jEPNyoRTrr3KcDgg+mKoKEeZOYs+q57ixJTpaHl5kHuA4A2b9C1WGJ50C3iZfM33T2pWJuqKZfUHFAW6dsX81EqCFy4CAn9GkfAuabl6mXzN9w9NdwxQVyyD6p9dJxUFLroISksbTQwI9BlFwrskXL1Mvub7B2d6GtrBXNT/XghRvUAxgVrrDlbl9tmQe0BaqaLNJFxFp1LXYtXCwqG62nXw1El3/ypOJ4R3c3cFCNFWEq6iU1HXrYVjx0Brsq9Z0U/1owOsnq8D3BnVbYM9JaF3px0J4AkJVxHw6lqrxMXDD0fPvYOiuLZm0TToG4P5gQUdX6TBubexqahg27W/pLBnLNtzkHA9DwlXEfCc6WnsqelKRnEUKf2Gk5i3v/6k1Yoyc5a7f1UuWJ3LPZKishKcTlL2f8xHI6cy5eahepdmaBKuIqCpWZloBQVkJM6lMCyKjLib6sM1+mLMjy6VQL0AZ3qa64KfxQLdupNICUmjumGWVut5SbiKgOV4aQ1a2puu1taBT8iIu4mU3J0QEoJlz1d6l+cX1KxMNLsdoqIwP7BAPohaQcJVBCzt7U2uq/9AYt5+d4tVmXOvnmUZXsMxwM70NCgtRenXT4K1lWSGlggY56xWpSiN7xAUhDLn3k4/zOpCq3o1XJhGZqW1nYSrCBjqurVo+/ejLllEzYzJcM3Y+oA1mVESruz0wQqcs6pX07BtGKjm5PEEb9gkrdY2kHAVfqlpIDheWuMaZuVUXYuu5OehlJViXvO/cNkA6N9fWl9nNW2NNg1bCVTvUDSt4bpqgcVmK8fpvPDL87e90uv4Y93eqtkx72607w67gjS8W+NJAN26QWRPr16ACeT32j2GFXS/aOVv73NUVFiL56TlKvyO46U1aAdzoazMNYX1p1NQ9xkaEoIy8D+wfPBnaXk18cVRG0s/yOWLozagvvUPoEREuBeqEd4h4SoMrbmLL9rbm+rXBagT7LpYpQyLk6//LdieU8i/T5fzv385xIZlr/DY56fZU9PV1Vdtt0N4OMTFyxY2XiLhKgytYX+gu6VVq557x9hLCF64SPoKz2NKQm80h4pSXcknkUMp7NKDjy5Pdp0sLXW1XnMPyBY2XiLjXIWhaWHhUGxDi452TcFUFOr7AICQEOjTV9YD8MCYAZHUfv9X0iPjqQq6iC5dQpg67QbMo7o1Wtu24X+LtpNwFcb25V7XRICDuWA6+0UrNBSqqiAoCPPK1dJS9ZCalUli3n4yul5GRbeuhFeccS28MqDxIuDyfnqHhKswLDUrE8zm+gNOp2ujwN59UCIiZKEVD7l3sj11EkJDSel5gI+6JjF5SA+9SwtoEq7CUBwvrUHbusW9UhUVFY3voKq6DxfyN+rqVWSH9iHjllRScneSePoISbdPxJx8o96lBTS5oCUMoe5ilfZuOpSXo6W96epvDQpyjWMN7+YaZpU6V4K1tUrPkBF3E4Xh0WQMnyhDrjqItFyF7tzrhSom18aA1dXg1GDP52ANQ+nXT7ay9kDDRcG13Z9yKsiEOmYsWC4iJXcnGdfeypSEPigFslZAR5BwFbpzrRequLZeiYx2TQ44u5eVEhsrQeChumFrHMyF6mpqzWY4UQjV1SRWfs3oQ2aC/3sTTJfugI4g4Sp00XDKpTL2OhRwt7joGQVWq/SttlLdEoFaQQHU1IDFgjJzlus9BfamzCXjg1zZ+6qDSJ+r0IUzPQ3y81z/5B5wfe3PPeAazB4bK9NXW6HhNFbi4qH0DFjDCIqNwRSfgOWDP2P54M9kaL04XlLF9pxCfQvuJKTlKvQRF+/6+hrezf21v67lJd0AraOuXgU/nUI9cgTKz3apVFdTW1UJ69a6P6SmJPR279oqfE/CVfhUw1XtG7VEcw+4LlbFxrqPm5PHS2u1FdwXsGynye433LWNzeF/kPjDPyEsHCobD2MbMyBSugM6kISr8Cl13VrIz0O12xsFp7RS26+uxQrUD7Uach2jrxmGKT4B83vvoN72C52r7LwkXIXPqFmZcOK4a1hVE9JKbR81K9MdrJhMpHz/GRkDryXl210QoWBeuIioO2b41dqogUbCVfiMMz3NNV01KEgWVvEydfWq+htRvUi67w4Sz46+MM2W99oIJFyFT9RvydxLhlR5QcO+a6C+1QqYH10q3wQMSMJVtEpLF6ia/vHXLQ+oDBosf/Tt5HhpDdqmN0DT+Nzck4xLk0jpN9y1VXhwsLy/BiXhKlql6WZ2Dfe3b7TIsmICzSkXrNpJzcpES3vTvT9YRuxICrtfTMbwiSSeyIWIHqhZmRKwBiSTCESrmGanQng4mt2O+vyzaDn/Ql23ttGOoqbZqSiDBmF+aqX80beT+vyzrnGrZ6Xs30Efi8a01Iko8QngdMoiLAYlLVdxQU27Atxz2MtK3fdp2ucnodo+7jGsRT81Op74w5ckfmTHMu/PqDKczdAM2XL95JNPmDNnDldddRWDBw/Wu5xOT1231t1Chfp975U77kRJuFJGArRS000Xm9uEUV23Fm3//kat1qbMyeNlzzADM2TLtaqqitGjR5OUlMSLL76odzmiCbky3T4N+6cbfhOou61mZUJBPjgbbMQYFIRy592Qe0Baqn7CkOE6depUALKzs3WuRACYH1ggXz+9qOnstLrbddtaa3Y7OBwNHmCGS/oRvHCRThWLtjBkuApjkZaqdzXXP+08kOMaFaAorq4As9m1yW3PnrKmrZ+ScBWigzVcy5ZL+rl2uK2ubty/GhSEEneFbMLoxzo0XJcsWcKHH37Y4vnp06fz3HPPee35IiOtHt83KirMa8/bkfyxbn+sGbxXd9F76agF+VBbCz8cbfY+lhuuI2rDhnY/lz++1/5Yc3M6NFyXLVvGI4880uL5kJAQrz6fzVaOs5lFQ5qKigrzywUu/LFuf6wZ2l53czPa1Ntmw0+nmwSrAiZXl0B2v+FkdLuWaXuOtWuJQH98r/2t5vN9EHRouIaFhREWFhifSkJ4oulIAADngRzXamENBZmhRyRYrWQMT6Gw+8VszymU9Vf9mCH7XEtKSigsLCQ/Px+Ab7/9FoABAwZgsVj0LE2IVmlu3Vpt6xZXHyu4WqkJE0gpO0JSyljMyeOZdtQmOwYEAEOG665du1i6dKn79rRp0wDIysoiJiZGp6qE8FzD7oDgDZvq97mKi4cuXaG8HDi7yHW3Xnx85XDGJscBsmNAoDBkuM6YMYMZM2boXYYQbVbXHaCuW+v6b7sdSkvh8CEICYWQEKipIeXwp3w0cZ60UgOQIcNVCH/XcJtr7dgxQHONYdU0V8t1dBJ8uZfR18Qxdt5YvcsVPmDItQWE8FdqVia773uYxz4/zZ4rroNim2saq9MJ6tl/V1aglJW6WrC5B/QuWfiIhKsQXuRMTyOjVzyFlm5kFJlcY1mbUGbOarREowhM0i0ghBeZZqeS8tY2MmKvJiV3J0CDba8/Jem/7my0lbgIXBKuQnhJ3bTWxPw8Eg/8zXUwvBsZCf9JYVgUH02cx9hk6V/tLKRbQIg2+uKojaUf5PLFUZsrWFcsg2PHGncFVFWScvJf9OlhZer1l+tWq+h40nIVog3UrEy27T1zdiYVXPXqs66xq2Yz9Ip2XciqrQVVZbR6WkYEdELSchWiDZzpaaTk/IXehT+QovwEttOuE4qC5ZO/YV79Alw2APpfKhetOilpuQrRCu69reLiSXw3ncRDn8N2oG5admRPQNbAFRKuQnhMzcpEfexR1y4Bdjv06Vu/spXDgXLV1dJKFW7SLSCEh5zpaeCodS9qbX5gAYSGuk5ePkw2CxSNSLgK4SHT7FTo3x8uG4D5gQWYk8ejXD4MekaheHktYuH/JFyFaAeZaSVaIn2uQtDCjgENjlV1C3WNY62sBJPJvfi1XLgSLZFwFYLmdwxQ162F/DxUu53yXj3h559d/a1h4dJSFRck3QJCUP/1nrh4amZMpmbcGNdIgFoVAOv8ea5VrQDUWmmtiguSlqsQuPa10g4fgiNHoPRM/QmTgvmBBYROnIiSOhdt6xaUmbP0K1T4DQlX0anV9atq+79yD7HCZAYFCA5GuX22u5UavHARLFykX7HCr0i4ik6trl/VHawA/fu7h1oJ0VbS5yo6t/Lycxa0ViIiJFhFu0nLVXRuitLwBlxkkZEAwiskXEWnU7eoNeDaMBBcIdu1K+anVkqrVXiFhKvoVBwvrUFLexOcGgSZ4ZJ+YLUCSD+r8CoJVxHwGs600t5Nr794dUk/CVThMxKuIuA1nH1FeDeoroZe0Vg++LPepYkAJuEqApa7b9Vmg4pyNJMJZeItkHtALloJn5NwFQHJvWFgZRU4z05bLbZB7gGCN2zStzjRKUi4ioCkrl7lGsNqMkH0xa5RAVartFhFh5FwFQGh4fAq8wML6tcHsFiw/HWXjpWJzkpmaImA4ExPc01jPfZvV3fA6CSwWlFun613aaKTkpar8GvuFuvp066JACYTKCaUslKCP9urd3miE5NwFX7N3WKtrYWgILikH0pEhPStCt1JuAq/0nQ7FtPsVFS73XXxymqVSQHCMCRchV9xpqehHTyI+t8LcabOJXjhIglTYUhyQUv4DTUrE62gAKqrwOlES3sTNStT77KEaJa0XIXhuS9anTgONTW4tgnQ4KKLGm0oKISRSLgKw3NftHJqYLG41gc4u0SgXLgSRiXdAsIQ1KxMHPPubvQ1v+4YcfGupQH798e8cjVKbCyoquwYIAxNWq7CEBquXFUXmM70NPbUdCWjuj/T/ucexgyIbHR/abUKI5OWq9CdmpWJZreDyYRmt7tbr6bZqWRcnkxh94vZnlPovr85eTzBGzZJq1UYmoSr0J0zPQ1KS13rAeTn1a8RkDyeabNuoG90d6Yk9Na5SiFaR7oFhK7crdbwcNfU1WJbo/NjBkQ26g4Qwl9IuApd1M200ux2KPoJNA1l5ixZyFoEDEOG6/r16/nLX/5CXl4e4eHh3HzzzSxcuJCuXbvqXZrwEnXdWtfwqh6RrrVWFZMsZC0CiiH7XPfv38/8+fP54IMPeOGFF/jss8945pln9C5LtEPdsKqqHTsan7BaMT+1EmXQIGmxioCiaFrdxu3GtWPHDlasWMHeva1bQs5mK8fpvPDLi4oKo6iorK3l6caf6nbMuxstL4+gHhGoYeGusatnuwD84aq/P73XdaRm34uKCmvxnCG7BZqy2+2EhbX8IoTxuKesFp5w7bZ6+TCUfv3Qys6g5eWhgHQBiIBm+HAtKytj48aNzJw5s9WPjYy0enzf830CGZlR6y56Lx21IB8cDteBb7+hT0EeVTt2UP7HjVjn30uoQWtviVHf6/ORmvXTod0CS5Ys4cMPP2zx/PTp03nuuefct2tqarjvvvtQFIXXX3+doKDWfRZIt0DHcby0Bm3rFpSZswheuOjclut1N2D531cMVXNr+GPdUrPvne+DoEPDtaysjOrq6hbPh4SEuL/+19bW8tBDD/HTTz/x5ptvtmmkgISr77mHVOV+7VqxqmtXLOfZXsUINbeFP9YtNfueYfpcw8LCPOo7dTqdLF68mPz8fNLS0mQIloHVrQlA9wioKHeNVRVCGLPPdfny5WRnZ/P666/jcDgoKioCoEePHpjNZp2rEw2ZZqc22nZFCOFiyHB9//33AZg2bVqj41lZWcTExOhQkWiJOXm8hKoQzTBkuB4+fFjvEoQQol0MOUNLCCH8nYSrEEL4gISrEEL4gISrEEL4gISrEEL4gISrEEL4gISrEEL4gISrEEL4gCEnEXiLyaT45L5G4o91+2PN4J91S8368YudCIQQwt9It4AQQviAhKsQQviAhKsQQviAhKsQQviAhKsQQviAhKsQQviAhKsQQviAhKsQQviAhKsQQviAhKsQQviAhGsLHnjgAQYPHkx2drbepZzX+vXrmTp1KldeeSXjxo3jmWeeoaKiQu+ymvXqq69y7bXXkpCQwK9//WtsNpveJbXIn97XlvjL73CdgwcPMmfOHBISEhg5ciQPPfSQ3iW1S0Av3NJW27Zto6qqSu8yPLJ//37mz5/PsGHDsNlsLF++nIqKCp599lm9S2tk69at/OEPf+D5558nJiaGVatW8fDDD/PWW2/pXVqz/OV9bYk//Q4DHD16lDlz5jB37lwef/xxTCYTR48e1bus9tFEIydPntSuv/567fjx49qgQYO0PXv26F1Sq3z88cfayJEj9S7jHNOmTdNefvll9+38/Hxt0KBB2uHDh3WsynNGfV+b44+/ww8++KD22GOP6V2GV0m3QBPLli3j/vvvp0+fPnqX0iZ2u52wsDC9y2ikpqaGQ4cOMXr0aPex2NhY+vbtS05Ojo6Vec6I72tL/O13WFVVdu/eTZ8+fUhNTeWaa67h3nvv5bvvvtO7tHaRcG1g8+bN1NbWcscdd+hdSpuUlZWxceNGZs6cqXcpjdjtdpxOJ5GRkY2O9+jRg+LiYp2q8pxR39fm+OPvcHFxMVVVVfzxj3/klltu4bXXXiM6Opp77rmH8vJyvctrs07R57pkyRI+/PDDFs9Pnz6dBQsW8Morr7B58+YOrKxlntT83HPPuW/X1NTwm9/8htjYWO67776OKLFT8Kf39cSJE4b6HfaU0+kEYMKECe4Phd/+9reMGzeOv//976SkpOhZXpt1inBdtmwZjzzySIvnQ0JCyM7O5vTp09x8882Nzs2dO5fp06ezatUqX5fZiCc116mtrWXhwoVUVFTw5ptvEhRkrP+tERERmEwmbDYbAwYMcB8vLi6mR48eOlZ2fkZ/X5v65ptvDPU77KmIiAjMZjOXXnqp+1hwcDCxsbEUFhbqWFn7GPu3xUvCwsIu2F82evRotm/f3ujY5MmTeeaZZ7j22mt9WV6zPKkZXJ/6ixcvJj8/n7S0NLp27doB1bWOxWJhyJAhZGdnM2rUKAAKCgo4fvw4CQkJOlfXPH94X5sy2u+wpywWC0OHDiUvL899rLa2luPHj/tNv3FzOkW4esJqtTJo0KBzjsfExBAdHa1DRZ5Zvnw52dnZvP766zgcDoqKigBXf6bZbNa5unp33nknq1atYujQoe6hWImJic2+50bgL+9rQ/76Owyu1vWyZctITEzkiiuuIC0tDZPJxPXXX693aW0m4ern3n//fQCmTZvW6HhWVhYxMTE6VNS8W2+9FZvNxpNPPklZWRlJSUk8/fTTepfVIn95XwPF5MmTsdlsvPDCC5SWlhIfH88bb7zhF98YWiIbFAohhA/IUCwhhPABCVchhPABCVchhPABCVchhPABCVchhPABCVchhPABCVchhPABCVcREJYsWcLgwYMZPHgwl19+OTfccANPPPEEdrvdfZ/9+/fz4IMPkpSUxBVXXMFNN93EokWLOHjw4Dk/b8OGDQwdOrTFxbHLy8t5/PHHSUxM5Morr2T+/Pnk5+f77PUJ/yPhKgLG1VdfzWeffcauXbtYtmwZn3zyCYsXLwZcOyHceeedBAUFsWbNGj7++GNeeukl+vbty8qVK8/5WVu2bOH+++9n27Zt1NTUnHP+0Ucf5YsvvuDll18mPT0dTdO45557qK6u9vnrFH5C58W6hfCKxYsXa3PmzGl07Pe//702ZMgQ7eTJk1pcXJy2fPnyZh9bUlLS6PYXX3yhjRkzRnM4HNqkSZO07du3Nzr/ww8/aIMGDdJ2797d6GcMGzZM27p1q3dekPB70nIVASskJASn08n7779PTU0Nv/71r5u9X7du3Rrdfvfdd5k8eTJBQUFMmzaNLVu2NDr/1VdfERwczJgxYxr9jPj4ePbt2+f9FyL8koSrCEjff/89b7/9NgkJCRQVFWG1Wrn44osv+Lji4mIyMzOZPn06AFOnTuWrr77ihx9+cN+nqKiI7t27n7M6Vs+ePd2rZwkh4SoCxt69exk+fDjx8fGkpKQQGxvLCy+8gNaKtYm2bt3KgAEDGDJkCAC9evUiKSmJ9957z6PHK4rSptpF4JElB0XAiI+PZ/Xq1ZjNZnr16oXFYgHg0ksvpby8nJMnT5639appGu+//z55eXlcfvnl7uNOp5Ovv/6ahQsXYrFYiIqKoqSkBFVVG7VebTYb/fv399nrE/5FWq4iYISEhNCvXz9iYmLcwQowceJELBYLv//975t93JkzZwDYs2cPBQUFvPPOO2zbtq3RP7W1tWRmZgIwYsQIHA4He/bscf+M0tJScnJyuOqqq3z4CoU/kZarCHjR0dE88cQTPPHEE5SVlTFr1ixiY2M5c+YMWVlZZGdn8/bbb7N582ZGjhzJ8OHDz/kZN954I++++y633HILl156KcnJyTz55JOsXLmSsLAwXnzxRaKjo5k0aZIOr1AYkbRcRadw2223kZaWxs8//8zDDz/MhAkTWLBgAT/++COPP/44NpuNrKwsJk6c2OzjJ02axN69ezl27BgAzz//PKNGjeLBBx/kjjvuwOl0snHjxkYbR4rOTXYiEEIIH5CWqxBC+ICEqxBC+ICEqxBC+ICEqxBC+ICEqxBC+ICEqxBC+ICEqxBC+ICEqxBC+ICEqxBC+MD/AzZ8iHbKzSOzAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 134, 3\n", "LR fn, tp: 2, 4\n", "LR f1 score: 0.615\n", "LR cohens kappa score: 0.597\n", "LR average precision score: 0.499\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 135, 2\n", "GB fn, tp: 4, 2\n", "GB f1 score: 0.400\n", "GB cohens kappa score: 0.379\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 137, 0\n", "KNN fn, tp: 6, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 3/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 3/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 133, 5\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.444\n", "LR cohens kappa score: 0.408\n", "LR average precision score: 0.447\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 134, 4\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.143\n", "GB cohens kappa score: 0.104\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 137, 1\n", "LR fn, tp: 4, 5\n", "LR f1 score: 0.667\n", "LR cohens kappa score: 0.649\n", "LR average precision score: 0.684\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 137, 1\n", "GB fn, tp: 6, 3\n", "GB f1 score: 0.462\n", "GB cohens kappa score: 0.440\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03311344137959691 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 136, 2\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.533\n", "LR cohens kappa score: 0.509\n", "LR average precision score: 0.577\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 138, 0\n", "GB fn, tp: 7, 2\n", "GB f1 score: 0.364\n", "GB cohens kappa score: 0.349\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.6610300673948196\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 136, 2\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.533\n", "LR cohens kappa score: 0.509\n", "LR average precision score: 0.640\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 137, 1\n", "GB fn, tp: 4, 5\n", "GB f1 score: 0.667\n", "GB cohens kappa score: 0.649\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 3/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 36 points min:0.0358887168898527 max:0.4738359420727812\n", "-> create 516 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 135, 2\n", "LR fn, tp: 4, 2\n", "LR f1 score: 0.400\n", "LR cohens kappa score: 0.379\n", "LR average precision score: 0.572\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 133, 4\n", "GB fn, tp: 5, 1\n", "GB f1 score: 0.182\n", "GB cohens kappa score: 0.149\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 137, 0\n", "KNN fn, tp: 6, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 4/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 4/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03311344137959691 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 133, 5\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.444\n", "LR cohens kappa score: 0.408\n", "LR average precision score: 0.540\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 135, 3\n", "GB fn, tp: 6, 3\n", "GB f1 score: 0.400\n", "GB cohens kappa score: 0.369\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03311344137959691 max:0.3975704340113837\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 137, 1\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.571\n", "LR cohens kappa score: 0.552\n", "LR average precision score: 0.593\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 132, 6\n", "GB fn, tp: 5, 4\n", "GB f1 score: 0.421\n", "GB cohens kappa score: 0.381\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 136, 2\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.533\n", "LR cohens kappa score: 0.509\n", "LR average precision score: 0.616\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 134, 4\n", "GB fn, tp: 6, 3\n", "GB f1 score: 0.375\n", "GB cohens kappa score: 0.340\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 137, 1\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.571\n", "LR cohens kappa score: 0.552\n", "LR average precision score: 0.735\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 137, 1\n", "GB fn, tp: 8, 1\n", "GB f1 score: 0.182\n", "GB cohens kappa score: 0.163\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 36 points min:0.03211308144666282 max:0.6610300673948196\n", "-> create 516 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 135, 2\n", "LR fn, tp: 4, 2\n", "LR f1 score: 0.400\n", "LR cohens kappa score: 0.379\n", "LR average precision score: 0.485\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 136, 1\n", "GB fn, tp: 5, 1\n", "GB f1 score: 0.250\n", "GB cohens kappa score: 0.234\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 137, 0\n", "KNN fn, tp: 6, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "====== Step 5/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 5/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.4217019089356842\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 133, 5\n", "LR fn, tp: 2, 7\n", "LR f1 score: 0.667\n", "LR cohens kappa score: 0.642\n", "LR average precision score: 0.703\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 135, 3\n", "GB fn, tp: 7, 2\n", "GB f1 score: 0.286\n", "GB cohens kappa score: 0.253\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.6610300673948196\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 137, 1\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.571\n", "LR cohens kappa score: 0.552\n", "LR average precision score: 0.734\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 136, 2\n", "GB fn, tp: 7, 2\n", "GB f1 score: 0.308\n", "GB cohens kappa score: 0.281\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.037393181196576475 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 134, 4\n", "LR fn, tp: 6, 3\n", "LR f1 score: 0.375\n", "LR cohens kappa score: 0.340\n", "LR average precision score: 0.454\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 136, 2\n", "GB fn, tp: 6, 3\n", "GB f1 score: 0.429\n", "GB cohens kappa score: 0.402\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 33 points min:0.03211308144666282 max:0.6176281243596343\n", "-> create 518 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 137, 1\n", "LR fn, tp: 5, 4\n", "LR f1 score: 0.571\n", "LR cohens kappa score: 0.552\n", "LR average precision score: 0.652\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 138, 0\n", "GB fn, tp: 6, 3\n", "GB f1 score: 0.500\n", "GB cohens kappa score: 0.484\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 5/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 36 points min:0.03311344137959691 max:0.6176281243596343\n", "-> create 516 synthetic samples\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 137, 0\n", "LR fn, tp: 3, 3\n", "LR f1 score: 0.667\n", "LR cohens kappa score: 0.657\n", "LR average precision score: 0.790\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 136, 1\n", "GB fn, tp: 3, 3\n", "GB f1 score: 0.600\n", "GB cohens kappa score: 0.586\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 137, 0\n", "KNN fn, tp: 6, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "### Exercise is done.\n", "\n", "-----[ LR ]-----\n", "maximum:\n", "LR tn, fp: 137, 5\n", "LR fn, tp: 6, 7\n", "LR f1 score: 0.750\n", "LR cohens kappa score: 0.736\n", "LR average precision score: 0.803\n", "\n", "\n", "average:\n", "LR tn, fp: 135.64, 2.16\n", "LR fn, tp: 4.44, 3.96\n", "LR f1 score: 0.543\n", "LR cohens kappa score: 0.520\n", "LR average precision score: 0.610\n", "\n", "\n", "minimum:\n", "LR tn, fp: 133, 0\n", "LR fn, tp: 2, 2\n", "LR f1 score: 0.375\n", "LR cohens kappa score: 0.340\n", "LR average precision score: 0.447\n", "\n", "\n", "-----[ GB ]-----\n", "maximum:\n", "GB tn, fp: 138, 6\n", "GB fn, tp: 8, 5\n", "GB f1 score: 0.667\n", "GB cohens kappa score: 0.649\n", "\n", "\n", "average:\n", "GB tn, fp: 135.76, 2.04\n", "GB fn, tp: 5.84, 2.56\n", "GB f1 score: 0.381\n", "GB cohens kappa score: 0.356\n", "\n", "\n", "minimum:\n", "GB tn, fp: 132, 0\n", "GB fn, tp: 3, 0\n", "GB f1 score: 0.000\n", "GB cohens kappa score: -0.012\n", "\n", "\n", "-----[ KNN ]-----\n", "maximum:\n", "KNN tn, fp: 138, 0\n", "KNN fn, tp: 9, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "average:\n", "KNN tn, fp: 137.8, 0.0\n", "KNN fn, tp: 8.4, 0.0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "minimum:\n", "KNN tn, fp: 137, 0\n", "KNN fn, tp: 6, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n" ] } ], "source": [ "runExerciseForSpheredNoise(\"folding_abalone9-18\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "fiscal-villa", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "///////////////////////////////////////////\n", "// Running SpheredNoise on folding_car_good\n", "///////////////////////////////////////////\n", "\n", "-> Shuffling data\n", "### Start exercise for synthetic point generator\n", "\n", "====== Step 1/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 1/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 313, 19\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.049\n", "LR average precision score: 0.038\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 5, 9\n", "GB f1 score: 0.783\n", "GB cohens kappa score: 0.775\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 1\n", "KNN fn, tp: 12, 2\n", "KNN f1 score: 0.235\n", "KNN cohens kappa score: 0.224\n", "\n", "\n", "------ Step 1/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 314, 18\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.048\n", "LR average precision score: 0.033\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 3, 11\n", "GB f1 score: 0.880\n", "GB cohens kappa score: 0.876\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 2\n", "KNN fn, tp: 12, 2\n", "KNN f1 score: 0.222\n", "KNN cohens kappa score: 0.208\n", "\n", "\n", "------ Step 1/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 313, 19\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.049\n", "LR average precision score: 0.043\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 1\n", "GB fn, tp: 5, 9\n", "GB f1 score: 0.750\n", "GB cohens kappa score: 0.741\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 2\n", "KNN fn, tp: 10, 4\n", "KNN f1 score: 0.400\n", "KNN cohens kappa score: 0.385\n", "\n", "\n", "------ Step 1/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 311, 21\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.051\n", "LR average precision score: 0.038\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 1\n", "GB fn, tp: 8, 6\n", "GB f1 score: 0.571\n", "GB cohens kappa score: 0.560\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 2\n", "KNN fn, tp: 13, 1\n", "KNN f1 score: 0.118\n", "KNN cohens kappa score: 0.105\n", "\n", "\n", "------ Step 1/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 56 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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YiAEoTkCJhZhVvPUIsN9zZcW7vgBbu96OsnMXUFRSxrwBIYtXywt5tdrEpS1ARB5N31LG+2P1aVmrSEnzY/XUYNV6bak1oJYf6/dc9Xi1Y2uOoVPrFrr2yCKv1lrisudKRB6eYWSQT6vQ69Wz71VITQIdNVhZ1vULVVXAuXMBIZO2l2WbF6U2jsjOwQjmM3zkZnbEh1VVGFNqvFdLtQy0oZ4rERF4MgGMzKmVEwOWySSWHpr/hwOArsUNrEt+d39TgafW7sKuuc8p9orlGHx8P1762+uGWwKB2Ck/VhUSVyIieA+UQDh6xFdYmhHWMno8K63EXq2WSIntCbXhvuuRObqGyqxLfqXDe1Zh+9vWXXi26+3429ZdTPHogWoSaEPiGgfYIW2Gd9ksT34sSz0APV6tOHa15bAAAu/L0it1Zef4euWCAPTpq/j+0lQsVmH7uE8Oytp0wMd99PdaeTxyIhgS1zjADkM43iE+T36s3HJUqRiw5sdKY1cTNvF1Zi5Ec/AA0KKl718FpMN7VmEbf0svdO7WEeNv6aV6nBx2+M5EOzShFQfYIW2Gd9msWfmxepDGrra+Xyr0WtedOYXrUi9Y/JyZk0l2+M5EO3FZLFsPlOfKhhnXibcINutMNmuhaGl+bLjCpvdaeYp3BnZbcKRnAABT3E9vPoiTZ6rRqfo0lgxuwxzv7m8qUFRShtzMjsw5tWYQDfceFcsmohLv+gIIh76G57/nKnqucn6nKzsH6NMXnkULDKllwFs/lhUWf1NcjpE17tzMjuhUfRpjDhXrireopAwnqxtQVFKmqx1EMCSuhG1x5k8FLl4EvF7NiTCp6LFMoLF6l7z1Y1nw90qFY0cVBZA3p3Ro9/ZYMrgNspLqdcWbm9kRndu21LVggRU7TK5GChJXwra4snPgmHo/kJysOREmFT2tCTSWrVPEcZg1M+5dXwA4nIAgMBfCZu09S0XZDsIWTxNlJK6ErflH7gNY9Pg6/CP3AdXjpAKolSPLuvxV7jxWYZMKmf+5hu3bA88586fCkZ4O1wtLFAWQp8i3XKyssReVlOHkmWp8uPEvhgtxPOXHkrgSitihp8Pj/3mKd6LxjpFoHNJf0XMNlCBs1963GwDjyideYRM/V/fHdYHnpDUJ5PJj5dLI9NoZemLn9WpZiKf8WBJXQhE7DOF4/D/v+gLgzGng4kVFz9W/dYqja9dAPQCW7Vp4hU38nL9wizhePfmxZv/o8Xq1RDAkroQidhjCDe3eHi9P6qMrJciZPxXocBVw2WW6vVopcrsSsHq1UuSKZUtjYNmokfVHT7rEl/U898oVvtSvPn3joodpFrYV1x07dmD69OkYOHAgMjIyrA4nLrF6CMe7jbcrOwdJ//spkvZ+pVmXQNxGuRKAcr4lT6lCI3ubrD960iW+rOfxLlW2g41kJ2wrrg0NDcjKysLs2bOtDiVusfpmCWcbb1ZhFrfRe6AEwoESeOY/EThPtiYBw75X0vPUeo16J57kcnuVjhMv8WXdjoZ3qbIdbCQ7YVtxHT9+PB566CH069fP6lAswWphA/hvFqNiD6fkIKswi9soFG4E3G6gqSlwnmxNgkfmwJGaqvq60vPUeo1GZQNIkUvF0sqpBQBn30w4MnrA2TdT9f212hHv2FZc4x079AJ4bxajYmctOSgHqzCL2+jImwwkJgIJCarnsbRPzpsVqqrgWfVqUCqW3AKBcCbN1OJkyakNnMdYjpFQQbA5e/bsEdLT060OI+Kc37ZNODvpLuH8tm1Wh6Ibq2M/v22b8OH0x4XZv9wu/OXrMlNeX6t9ZyflCT90u1b4odu1wtlJd4U8Fh93qt+AwHPnt20Tym65VTh9y62a188XR55wftu2oP9XipP1czm/bZtwMqOncLJn76BYtZC2Jd6J6apYUV24ZdBwYNBw1AG6tgSxCvF18tQ0wOtuQk1NgyWxu1evwZaut6Os8jz+tOs79E5rpXisuOe475oBbBv6DRoOT00DqlavQU1Ng+xxnrvzgbM/Xvr/+3xPXnqcPGtG87W6Ox/C+gJ47r4P5eW1cK9eA+Hb7wAAVavXoE5lI0P36jUQTpxA1eo1ABD4/8A50u/Qpbgrly4Dli5T3oVh0HA4n1/s82YvxcWCtC3hQoVbCFsRTV6tWbHq2dBPHCvrhn7N3uUxxWwAfx5t0uaPAjVY5bxaOW+WZcLMf6zemgcs2Q68y2atzi6xGySuMUY0ebVmxerKzsGIlS/ilZkjNPNjxbFKK/6rxQ2HAxC8TNkAQHCBFvEKrXAISiPT4dNqiTfvslne1LlYxbbiWl1djcOHD6O0tBQAcPjwYRw+fBiNjY0WR2Zv7DBja9SETDi8+cVx3LNmL9784rjqceJYBx/fjzEHd+LjPjnYd80AxXN89QAyAvUAAs9pTC75J5OkK7RCjhP1LJV6jdIJMz2LG7Q2ZuTNXggndS4WsW2x7M2bN+Ppp58Oeb64uBhdunRheo2o9lyjjCDP1QbbLt+zZi8aGj1omeTCe7OHMJ3jnjkNz3a+DWUpaejULhmvzFTezFpaQNuzbClwrgaOe/JlsxvEx1917yTF75SneGdg+a3rkTmBXqO0MLZ75jQIJf8EADgy+wFA0GOlItrumdMgHDsKCELQj4MRuFeuCOwawZPhISUa7r2o9FwnTZqEo0ePhvzHKqzxSjR5rlJ4Ylc6Z1TvDmiZ5MKo3h2YX8uZPxVjj36GjrXlGHNQPQZpAW2cPQNcuKBay8AveOV5d6km/ou9WqVeo3R4r2dxA8vGjDyfBW9+bKxiW3El+Igmz1UKT+xy57hXrkD+/Mn4U/0u3H/jNcyv5crOwbDZ92LxyU8wbKxyrxUInUzClR2AFi0082q96wvQ9O13zMKmZLFIRRjQHu77z2PZmNGoz4IFO3QIzIDENcaw2nMNxxLgiV3unHDWxntWvQqhqkrzWKnoObp2hWvJMs3hsDN/KhKuu85wYWOp6AXIt5GlhiwLVi86sRskroShhHOjGJXKE87aeNYC2kE1CdYXYE99Ep75rAxffPCp6jne9QVInjUjaDNFI4SNFbk2yn1mcp8Fy15fPJ+f1R0CsyBxjTEi2QuItDDIva/cUk25ZbMss+l6ckylNVi3pt+EspQ0fHSkUvMccSqWXI9TKlIsw2a5il6sbTSylgHP8D5W82NJXGOMSPYCWHs8Zr4v6wQNS6/Ue6AEOHsGjhE360ojc2XnILd/F3Rqqse4Hu00z1FLxZKrH8tSbAVorl2gKsIyXq1ZtQxYIc+ViAoi2QuwajgnX1xafYKGpVcqFG4E6uogFLypazXSFx98iqIjlRjXox1unDhS8xxxsWxpjzOcYissloZcfiyLsLH40eS5BkPiSgDg6z0YLeSsMXgPlEA4egTeAyXME2hyPTYpjrzJgNMJJCXputGLjlSiLKEVir76gUmk/KlYX3zwKZ7ZV4N9Y+9XXIzgzJ8KpKX5shFUYLU0pCLMKmws4q30fdD6XMlzJWIaPbPN0mGrdEdTXlhvdHE2gM9zPcZUHk+6PFPalsS58+D65Uo4evcJ3OgsSzpze7RDx9pyjD32f0wi5U/F8olycpBPK1c/1pGaCpSfVW0jy48HIJ8fy7yrAaMfLdfmWOyZakHiSuhCad25EevlWW90cTaAz3NV39DPjzRFS9oWub2jWNK6slp78NLB9zGk8QyTSPlTsXJ7tEOnpjpVn9Z/jpavrPSjZ1SxFVbxVorfDK/W7pC4EgD0zTbLrTtXm6QxGnE2AMuGfn6kKVrStsgJKUtal2fVq8Dx48DJH1TfX5qKldXag5f+U4Ss1h7V81h8ZdZiK3rSzaSx8046aQk42QIEAflhq9yOpjyY2YPxFO+EsOsz4MoOcPbNlPVq5YSUeTcErwdwuzUzFoRjx1A194nmNLJDX8Pz33NVbQcWX5m12Arv8N7MTIBYTcWybeEWI6DCLey4Z06TLRCihrjISLtn5qsWdmYRCN7VXSyxu2dOg/DVfsDrBa7rDkdqKoRjxwDBq1nARCsuabEVtfZ5Fi0AnE44/p+vF+r577m+mJKTkfT5PuXYGWM1CzM/GyWi4d6LysItRGThGZqJh5hanitLz8fMFT7O/Km+/bGcrubHjF4tS+x61vUn9eoVECnH1PuB5GRV24E1ViML3xiFmUN+u+fHkrgSAPiETTzE1PJczbrJ9KRiuZYuh6N//4AIsnq1TBMyjBv6ubJzkFb4PlzZOWicMhnCW+uAbtdo2w5XdgDSrrSs2IodJ53sGJMYElcCAH+eq38GWctzNctX03ODSfNjeYu0SGFdJRbC1weD/1WKe30BUH7WV9bwEnIpYmYWW7HjAgG7T4SRuBIA+G8C/01evWSJ6nFi8dZTNV8LPTeYND+WeUXTHSPROKS/Yn6s0my+5g9W7z7B/6q0USrecpkNZhZbsWNRFrtPhJG4EgD4bwL/TX6+4B3V48TizZsOJIeeGywkP5Z1RdOZ08DFi4r5sUBwr1h8rvg46YKLpHc2Iumfh5D0jnppRDnxZq38ZVSxFbv7m3aExJUAwN8L8N/kl0+donqcWLzDWe0jhbUX7CneCRw8ANcLSwL5scwrmjpcBVx2mWJ+LCDfk5QeJ1sVi0G0pL6ynu1UjErg17MSLih2xpV/sQiJK8GNOHf0sgHKG/oBoTuV8q72kcLaC5brRbKIsis7B1/+/n0sml+Af+Q+oDiBJteTlP5gyS24YBE36YQZazFwI4ut6MmuMIqG7dujurdM4kpwoycVS9pDY92GmaXoB+veUdJeJKs1seXjfTj5zff48ON9imIoXWwg1z65BRfMaWQiz1WPJWBUsRU92RVBr8+48k+Ouj+utXU2gBYkriYz84t7A/9ZhVl+mZ5ULKkosfa+lHxL8U3P0guW60WyWhNjv9iEjtWnMeaLTcw9PZb2ydUyUIpd7LmyrhozsthKONv38JI8a6atswG0IHGNA8xKh9GTiiUVJdbel5JvqSS2emBJ/AeArKyeeOmz1cjK6snsTbO0j3d47yneicbbb0Vj1gDNnj9rG6VoXXdWuL3a4p2o++PaiIq50ZC4xgGRWCWjVXJQLErSySXW8wCDb3qG89wrV0DY8A7QKjlQk4DFq/1H7gNY9Pg6/CP3AcVjeIf33vXaW3kH2si4uEGMXC+V9zvE69WKSzNGKwlWBxCLiC2AtTdusDASH/5JJDMImgFXqS0Qcs6l3ow/PiXEM+POvpkhs87O/Km+G7BPX7hnTmPu6fjPYxreX7wInDndfKOXngi0Q+m9ikrK8N3JCvzm+Bk0OY9jxH/NDDkmce48QObHRSpuzvyp8Fzqtfrj9Xz/PXCuRnPZrGfRgoBXy/odEP/wiAv08HyH/OewXGtp7K7334Xn7vt0v6ddoJ6rgVjtrRoNy6QTT8lB3hqschM0/p4tDh7Q1YPVNby/7DKgw1W60shyMztCcDfBIXixtTJR8Ti5ayzXq5YO71m28mbdAkeK0SMd/mT/6K4pReJKKKLlCYp7WHpKDvLWYFUTNr2CwOrVJs6d56tJ0KVLc0wMPubQ7u3xqPM4utWUYWw7t+JxevJjAz6zjl0jxD1gowtom4ln1ato/HJ/VOfHkriajB2yBXjR8gTDmSiTW9Ekh3RmXEnY9AqCntjFYsYqbO6VK4DdfwOuSEPCuAm+15Hxp/Xkx4Y748/aZiOXJ8czJK4GYgd/1Ui0Un7CGT7qmSn397Z4Z57lMLvoh1C4EVuvH46yiz7/FZBfoSW9xru/qcD8V7dj1z0zA1ZBSIHyMHeN0GqzkcuTeXE9MgdJAwdy5cfaBRJXDXK3jJHtecr1SFl7p9HSm+Ut+sHi1fKsjTdylVDAqwU0954SixmrsDnyJmPsfz5Hp8t8/ivA5k8XlZSh7CKw9frhij88ruwcoE9feBYtUL3GSrtGaPXujVyeDISTLkeeK86fP4+///3vRrxUTLL2xg1R2avlHfazeLWsqVji3hbvKiE1tIbOUt+SVaAS587DiPfW4pU5ozC0e3sAbFvi5GZ2RKfLgLH/+dywHFm/sLGuigP482Pl4K0zG+2pWIaIa2lpKaZNm2bES0UlLL3QaOmtiuEdOjN5tZyJ5UavEtIaOofjK4vFjLX3NqjoDYz5YhO23niXYTmy/vh5V8XpQa6dvHVm/bvkRitkC2hQNOFj2Z5ntPZGIwGLV8uzxYqepHg9vTQ/RibPA6FpZCyCJRRuxPred+Co0AoFe0oVj5O7xlrCxrsqTg9y7eTJPhDv2BCtMC0i6NNHvZgvIU844mvFWm4pcsnkLGiVxJNLLFfa5E+a7M+aFB/US1OxHkJqHlwSb3+cSsnzLJ+PI29y0AIIlkR6R95koN7l28RQ9chQPKteBUpPwFNVJRuTs28mvAcPwNk3U/V1wll0wro4wyzscN/4YRLXhIQE3HfffUhPT5f9+8mTJ7Fq1SpDA4sF/BaAlsjKrejiFTYj4b1RWIVNTGCGGsErn8Q3uqd4p28vKYBJpPzCpgaveGt9PmJfWdwWLRLnzsO0bypQVFIWmAgLhyBboKpKVXyNwMzVgCzY4b7xwySu6enp6NSpEyZOnCj79yNHjsS8uCotaZUKp5ynKn2OpUdrdQ8A4L9RWIRNehPILfGUOwfl5T5LQQPx0lK13oxcG1muu9bnI3eT++NoeGi24lLhN784ju3/PInbT32FwW0ygO46htKPzAmJSRyn2Qn5dug12uG+CcTCclC/fv1w4sQJxb9ffvnlGDRokGFBRRNmTVTZYZWMmUh9PZbSgXq82pD8WAW/U+pTsi5u0Pp85HxLuTxXKdu/PoMGr4AdV/TUNaG0+5sK/OL0FXhs5Fzsu6a5cHlQkfIwaquyYFb1NT3Y6b5hEtdnnnkGzz77rOLfr776ahQURG/KhFHwCqx/csyICTI77HUkbHgHqKvz/auAv7fqXV/AXEBbTyqWND9WaYImxHPlSHGSeyx7k/fpC1xoQOKAfoqvO6p3B7R0OnD7j4d19b6KSspwqqYBp2oaAosWpJgtPGYvzGDBDt9/P4ZlC5SWKs9sxgKsAmikUPJgh94D2qYG/6uAXmHTM+yU5scqiQpPnVlP8U54Fi2AcOyYan6seAlpYHsWhwPu/f9UfO0p+zah4N25mHbFBV0imJvZEZ3atETnNi0N8WrF2EmwtLDF9/8SYYlrU1MTtm3bhunTp+POO+80KibL0DvE1/JXrRBaO/QeXE8+DcfAQXA9+bTqcXqFjffGUVsrz1Nn1ru+AHA4AMGrmh8bUoPV4QQEQXWFllC40dfrf2udrvzYo6drUVHfiBuuSQ0sWjAK1utuB2Gzw/ffD1c91+PHj2Pjxo3YsmULmpqacOutt2L16tVGxxa1yE1+RUpgrZ6tBYB91wxA0ZiOyL2mI4aqHBeSCSCZXZeiZ7Ii5EZnqMEqzqP1x6cUh3hySCk/VjpBJ64gVldeK/vajrzJEN7yebJC4UZ4RaUU1T7X7V+fQUNjE7b//TtMvfBvQ78DrNfdDpNJdvj++2EWV7fbjR07dmDDhg346quvMGzYMFRXV2PLli2KKVrh8oc//AEFBQWora3FjTfeiJdeegnt2xv7q8yLHn9V6VgzBNcOM7ZFJWU4We3z/lh7UZ5VrwLffQvP/i/hnfaAYn6suHSev41yebUhKVYamQj+v7GkYrmyc7jyY/2xl+fdBc/d+bKvnzh3HtyA7vzYUb07YPvfv8Pt3+yG919HDP3sWQXLTsJmB5hsgWXLluGmm27C6tWrcfPNN+Mvf/kL1qxZA4fDAafTnEVehYWFeO2117Bo0SJs2LABtbW1ePzxx015Lz9mDOMjvdw1nKGzUb5abmZHdG7L4f0JAiAIqpNJzX7nUUWvVq4eAOsmhqzFpcXDT18WQ/PurFqxNx46rHice+UKCO+tBy5vpZnsL2bqhX+j4P9+jaknd1tWbCWavFkpZsTOpIxvvfUW7rvvPhQVFWHWrFlIS0szLAAl/vSnP+GBBx5ATk4OevbsiaVLl2LPnj04duyY6e+tRDTUBzCq9mc4DD6+Hy9+vByDj+9nPsf1yBzfAoEWLTTzY/3epZJXqzW5xIraDReU4sQoyv7YHaLYpQiFG4ELF4CzZ+BZ9Srz5+JfneWPLVx4i61Y/cPOixl+MZO4PvHEE9i2bRtuuukmLFu2zHSBa2xsxJEjR5CVlRV4rmvXrujcuTNKStTzD3mRCqf//3O3jAk8ZkWt52v2JBdvuo2REwGsBaXFuLJz8PeHFmLh+GeC8jRl40xPD/JmpevstSaXWGPXkx/LRJ++gODF5VOnqC6ZhaN54WskJmjkUuB4i61Y/cPOixnXmclznTlzJmbOnIk9e/Zg48aNyMvLw/XXXw9BEHD+/HnDgvFTVVUFr9cb4q+2a9cOlZWVzK/Tvn0y1/unpaWoPpajaMLHQUIsfqz3taygoU1L1CW6kNymJVpyxuhv2+kEJzwOB1wJLl3t/ejrsyhLao2Pvj6LCbMVzrt3ki/WP65FcpuWAICqhYsgVFeh1QP3o+2CBcC9k3z/+dv20GycW74CntpauGprkPyPzxXL/p1JcKLJ4UBCggutH5qNuj+uQ/KsGSHXpPz99RC+L4Xr/XeRdu+kkMdylB/9Gm6XC+cL3kHqgAFoOWoUGrZv97Vl1kxfTEtfQMOIrOb3HTXK1+bt21H38weaj5PQ8Mx8xVi1OLX5faC+Htj8PtKWvhC4zlBohyI858D3+SjFHrH7hTN2NXRlC2RlZSErKwuVlZUoLCzE+fPnkZ+fjxtuuAGjR4/G3XffbWhw4VJRUQevV3/B3XLJTK70sb/XKe7NlpfXBtX2lRNWudeyC+7VayCcOIGq1WtQx7iLK9Dsb6Y+NLv5vAcfhWN9AZA/VVd7x5Tuw9Yr+2LM2QMoL1ceKYhjBQChzJc0X//2n+CeLbP6aNBwODcOh2fmNDRptFG4FLuQP9V3zKDhqANCZvc9d+dDWF8Az933oby8NuSxHJ678yEsWgA4nYEY5K67e9ceCAcPomrXnubnND4fT00DvO4m1NQ0KGYiKDLpbqBwIzDpbku+n0qxp6Wl2PZ+8aMm/lypWO3atcPPfvYz/OxnP8Pu3buxYcMGvPjii4aJa2pqKpxOJyoqKtC9e/fA85WVlWjXrp0h7yFFrqQg0PwBMw3jvQCcAiBTz8ju5Ql502iClnReuul5Z42H3TMKWRwpPyzbTMudJ0Vcmcv/mDXzgqXNgQIuoi2j5WKSK3zDVMuAcbtyMVoVzCKBnYqtGAmXuIoZOnQohg4dqmu4rkVSUhJ69OiBvXv3YvDgwQCA77//HidPnkRmJvsMqlGwFG2Z+cW9Pgfb65B1smd+ca+tBZZXEP03ffKsGagL4/3dK1dgzxf/wtY+dyD3nAs3Mp4XVL4wzFJ60spcABRveqkgsAqxKzvHZyOo9MjkCt9oxd6cRubQJVI8FcyMxg75sWbANKF16NAh/PSnP0VtbegX4ty5c5g6dSrKy8sNDWzKlCl444038Mknn+DIkSNYsGABhgwZYlpOrRTphJbaMQHxdQBwQa7jyv4aFsFaXFppDb2erbXlEAo3YmvGLShrmYqPjqj/UIdkAyxbCuHLf8CzbKmu2KVtduYH7x2lNskhN2nGMykjdx5rIWwxeuouiGEtoG0mdiq2YiRM4vrGG2/ghhtuQEpKqL/QunVrDB48GG+88Yahgd1111148MEH8fzzz+Oee+5Bq1at8Otf/9rQ9+DBFCEUfP9ZWmyFcwsQue2ieXDkTcbYo39Fx4YqjOuhbv2EiN65muB/ZZCrByBtc6ByVGpq4LHSTS/9G+9sM+t5WuIt7TmzZjNo7RoRCeyQimUGTOJaUlKC2267TfHv2dnZ+PLLLw0Lys+DDz6Izz//HCUlJVi9ejWuuOIKw9/DSJTSrJhSr7weS1NReLcAYSmjx0Li3HkYsektLHt+Cm6cOFL1WKmwOe7JB5KTff8qIFcPQK7NYhFjven9Xq1waRWY2nHSHyLWXpuWCEvFlzk/tngnGu8YicYh/XVtiWMkdkjFMgMmcT19+jTatm2r+Pc2bdrgzJkzRsUUE4T4saL/F/d+1964AWsuzgTgws8faLTMHmDtwSj12NSKkbCy+5sKPL35IHZ/U8F8DuuKJl9+bIZqfmzguEsipqdgCUserX/SqWruE4FKWTyLGxTbp7LRomrsZ04DFy9qjlrMwk7FVoyEaUIrJSUFpaWl6Ny5s+zfS0tLZS2DWEEu9UrvBJVqBa0W4cVnBNFakyCwounCBc16AEDzRJX3QAmE9QWAIMAxZVpAYKUTR6wFS/TULhCcomWyDAVlAO39saRxK9VhkI3phx+A6irLfNdYrUnAJK4DBgzApk2bMHSofI2j999/H/379zc0MKuRpmJpYUWP00hB5E2HkUvF4o01N7Oj7r2jHHmTfT3X1m10+ZbC0SOA2w0AijPlRhcskUvFYhFlVuTSqlg+V0eXLnA++XRMCpyVMNkCM2bMwJ///Ge88MILQcP/M2fO4Pnnn8f//u//YsaM8IeFsQhr75ZnWayRXlW4EzJatoBWrO6VKzBw+ii8eOLPuuqRJs6dh6Q9+5G04y+6fEtH3mQgMRFISAi7xyZXGFutJoF/y2jpBJoaLFu0yE1K6vVqWTHS0ohVmHqu/fv3x4svvogXXngBGzZsQHKyb1lpXV0dEhMT8fzzz2PAAOX14LGC0Xmq4b6eHfID/SLRMi1FdWWQVqzh5Fvu+s1abK1MxNh2boz4r5lM5zj7ZsKz67PA/4eDnvxYuXO1jpUubvA/Jx0JSPNjWUYL4SweYbU04hXmRQR5eXkYPnw4CgsLUV5eDkEQcO211+LOO+9Ehw4dzIzRVrDuAqsH3uG9kV4Vry3gH4pWT/spILf0lBHWrbDl2FqZiLJWV2Br5Y8YoXJcSC/t+HHA64Fn1auqwib9bKTDb7XC2FqwiJuckMl9XuIdbwPt1Vi1Fc7iESMtjViEyRaorq7Gz3/+c4wcORL/8z//g8OHD2PWrFmYPn16XAmrXpRyYqXD/0iXapM7j9cW8Pc4zxcob0YIhFbKksaglK3AsrhhbDs3Otb/iLHt3KoxhNRgTUoENOoRy302rPmxADQ/H5ZULOniBmlbVM9j2C2X93vkSE2F65E51GtVgElcV65ciZKSEvziF7/Ak08+iYqKCixatMjs2KICuRVWehcaRLpUm9x5vKtk/Lmil0+dEnYMcmgtbnCvXIEh7/4OL+19E8N+crXqa4XUYF2yDI7+A1R9TLnPRis/FpAv6i3Nc2UVNTlvluXzYl21xfM94ikrGW8w2QK7du3CkiVLMHKkL7l7xIgRyM3NhdvtRmJioqkB2g1xWpZRNV7DXdfPMwlllFfrH4q21ciqcD0yJ+g9WWPQsguEwo3AxYvAmdOqw3sgeIjvPVAC4Z23fb26Pn1VU7ikf5MOv+Xa41u04AxatCDNrNBjxcgtEtCykVjtJjt497EIU8/1zJkz6N27d+Dx9ddfj8TERMPrCRCRwci13LzLX70HSiAcPQLvAfXi51qLGwKi69Au6BCUilW4EWhqAtxuzeR5qTUh1+MMuaZ9+gLuRuDyVoFjpJkVekYsvIsbWI6T+z5o1jJgyF6Id5jE1ePxhPRQnU4nPB6PKUFFG9LerFxKlRk1CViHZtIbxci13KzLX6U3OmstAy1hS5w7D65f/RaOAQODbnSt6vqOvMlAQgKQmKg5iSaNlUm0Dh7wiXdlReA4aZEbPT9y4mNZRTmclU+xuiQ1kjBnCzz22GNBAtvY2Ij58+ejRYvm5UXr1oW3vjyaYMkOkFvZJX0ciTKE0uEnb2aAHKwlB6VDT9bsAGmKllzs4l6w/zm51C7xEN97oCSwX5fWkl9prCzDaNbZdPHQHVAe7kuPE6qqAj+qeiwNVpjqx8ZgDVYjYRLXiRMnhjyXm5treDCEPqQ+phLSG8UKj018o3uKdwIHDwSt81eCRdjkhJTJq62rg/DWOrgBRYH1FO+EsOsz4MoOcPbNbK5l0LoNc3vVCOnRM9SPBcCUYyrOjzV6Vp98Wm2YxPXll182O46ohaUXanWRbDPXbrMufw05h6NqvhJyQio36RRyzlu+kZbawgVpjqlw9EigloGeCTTWJH4lwZIex9IrDifRX6tnGqv1AIyEyXMlmjGiuLVRO8AamYolB4tXy1MVizX/EgCEDe/4epgb3lGMXW7SSys/NnHuPDimz/CVKlSxJqQ5ps27s+qbQGNBLT9WmkaWtPkjJG3+SHd+LCtmVaqK1dqtcpC4mkQkdhgItx6AnkInco/FPTM9OxGw5l8CANqmBv3LGjtLfqyw4R2gVbLq8lepkDn7ZgLXXgdcd53mTDlLrHICLPecWJQiIVBGZpSIiaf8WBLXMDGqF8oD7w3AW6BZ+jgSM8quJ5+GY+AguJ58WtcyYa3i39L8WDXEYuZdXwCUnwXOqtcv1pNjKhVguefE11pPKhZLnVm52KkoS/iEvUFhvGG1fxpJlGqE+glnUkOrPqnce7pnToNw7KghXm3Ac9WZH+vM928E6FT1MeV8Zbk6DNJrqiTKrN6sGN71/+F4tVqwTsLGAtRzjUP0bF+iNhRV6gGbNWz1ebVCQNjUENYX+LxaydLTcPNjmy2NdN3r+lnqMIhFWc1zZRl5sHqzsrFzerVamGU32BES1ziEZ4WPHgtA69iAaFzdTdcKH1ZhA+ATYdG/st6mzCoxqVcr7Um6V67w9UZVlswGx9rsK7PUYdAz2adFOMN7KsoSPiSucQjPCh/epZpysPqWQGhPkrXn45gyzZcJMGWaYkxyk15Sr1Z2ZZk/P1bHhn7iMoVtFyxQP/jKDkDalWH3Gnk9V6XeM6EPEleTsHKiyyh4hqLS8+TQNbyX6UmyWA57HKlYOPJR7HEoV/mXm/SSpnVJRVl8rJ5tyPVsXY7ycqYfHi14h/fh9J7jKdVKCxLXOIR3lp+ltiqgnR+rZ3iv1ZNUYmtFgq+AdkWC4nlSIZUbRkt/KFjzYwGZWgaMW5cbJWy8nquuVDkJVJOgGRJXm2JmD4A3P1ZP70stPxZgr8Sk1ZNUYuyJfeh47gzGntjHfB7rMNrZNxOOjB6q+bFSr5blHMB4YeMt2sM78WTW4oNohMTVppjZAwi3MLaeHpvcYyVYJsI8q16FcCm9SI1h0ydg8em/YNj0CQD4K/7zxBk4RuRbih+rlWcMZ0dfrfxY1tj9cej9cbfD9ux2gsTVppjZA+C9cfzFVrSqSEmFjHfRghQ9EzR7zrnw7PW52HPOpau39vepc7HwhunYc87FHaf/GPHwXvxYrTyj0ZNJZv3QGXVOLEPialPMzAc0+8aRerNKXq1ekdczQVN0pBJlCcn46EilrtiLSspQ5rwcRSVlisewWBrS4b34sVodhnA8Vxb7xagfOqPOiWVIXGMMFsEy+8aRerNKXq3eIaueCZrcHu3QqakO43q00xX72IM7fF7twR2ax6q1RYo4P1atDkM4nisVW7EXJK4xhllDMz09aak3q+TV6h2y6k2KF8T/f6m4tNZ5WTf+BC99thpZN/5E8/XFaPmdrJOBcr4yi7gZ7Xfy1jLwL08mISZxjTl4KzGxwNqDEc+Mq3m1eoesejxXqS3Acp6neCf2HD6FhXfMxb5rBuhqsxw8qVhysbJPoBknbDwLSPTkL8cDVLglxmApYsxbcEV8k6u9R8iqJsbC2Fq9r0Ahkvp6X0+0eKfi6+X2aIePjlT6bIHBbAVMvOsLsLXjSJRd1gYfHanEjTra/Le3tmBr11sx9q0tGCH6sRD/cKgV7w5poyhWls+LtaAMK3JFe1jOAdiKysQD1HONMVh6WmbnMEp7PawTNCyeq+uROUB9HVB+VvX1bpw4Eq88fRdunDhSV3HpsWcPoOPFGozr0U5Xm7d2G4yy1h2wtdtgxWP8E3vVS5aovpZ4XT/rcF9X3QUGKBUrfKjnGmOw9rT0oufGkes9s5bI0zrOu74AcDgBwat6nHRDP5a9pFzZORiRnYMRGm2RY2z7Jmyt/BFj2zcpHiOuipU4W75gjfTzYy3NKN4vywh4vkfhfPdiUZip5xpjmDVjrMen5fUpWRP9HenpmpsbSidkWL3aLz74FPNf3oQvPviUOW73yhXA5/8HOF1wdrtW8TjWqlg8nx9vkRZAffueSKVixWKOLIkrwYSeG8fMVUGu7JxAD9dfZ1YugyDEmuDIj2VFKNyIrRm3oKxlqup5/qW84qpYcvmxQZN8j8yBI7Mf05YyvDVYWZcnaxFObnYs5siSuMYYZmUCaBXGFi/pNHtVEEuvVFrRiyc/lhVH3mSMPfpXdGyo0nWetC1+xJ+FKzsH6NMXnkULVAvm8BZpAYwVNvJqmyFxjTHCGVZqCZvcjeM/T7ykUyxsem4c7gkzhh4by03vXrkCNyybhxf/sgpZrT3MQpE4dx5GbHoLy56fghsnjlQ9Vq0tfkKyLRhyZHmtGDPzY808JxogcY0xzMwEkLsJ/OcpLenUs1ZevDOAmljw9EpZbmChcCNw4QJw9oyu5HlP8U7smv045r/0ri6vVgnpZ8GSI8srUEYLGy2bbYbElWBG7ibwC53Skk49qVjiHppaUrxUeFnqzDrzpwKtWwfyY+Vw5E0GWrQAruygK3neu74AW6/sG8iP1cPftu7Cs11vx9+27go8J1dDVlx2Ual9Zm6zzkqkvVo7Q+JKAGDrwUgnkwB5z1V6DutaeXEPTW21D8+wGYCvun95eeA8uU0Lk/bsR9KOvwTtvgBAdcgtlx/Lysd9clDWpgM+7hN+0r90os/Muq1GE4v1C2wprjt27MD06dMxcOBAZGRkWB1OXMA76STnufIS0kO7sgOQlhYSE++wGQ5HUH6s3A+KuBccuOFXvar6w+M9UIIhB/6Kl3atQVZrj642j7+lFzp364jxt/TSdZ5sHKL2eFa9CqHkn5q5r+FsYmgksei72lJcGxoakJWVhdmzZ1sdStzAW4pOy3MFoOtG9/devOuVNzHkHjanZwTlx8r9oIRYEydOAIDqD4/Yq/W3kaUntvubCnz410MYvWsjBh/fr3ptWOAZ4oeTH2sksei72lJcx48fj4ceegj9+vWzOpS4wQ7DSHHvRU8REJatTORSmuS21pZaE45uvu2/1drsyJvs6xXDIdsWJYpKynDq3AVsbZNuiLAFTfRFID/WSOxiTxiJLcWViDzhzjar2QKuR+b4bmBAVQDFvRc9a+VZFy2w1JkV94JZb/jEufPgWvEbOAYODIgZS08sN7MjOrVugbE1xwzPMWWNPZz8WKuxvU8r2Jg9e/YI6enpVocRF5zftk04O+ku4fy2bYafd37bNuFkRk/hZM/ewtlJdwmCIAhnJ+UJp/oNCDyWf908pnikMSjFVLV4sXAyo6dQtXix7ONw4jg7Y4bwQ5erhbMzZjCfw/L+etC6pnLouc5GnmsEPO2NJBEt3PLUU0/hgw8+UPz7xIkT8corrxj2fhUVdfB6Be0DVUhLS0F5ea1BEdmYQcOBQcNRB6BOT3svnddS5Tq5V6/xFa72eOC5+z6Ul9fCc3c+hPUFgcey5xw7isr/ehyumgb1oiU1DfC6m1BT04C68tqQxwFmz0Hi7DlwAzi9YTO8e/8B5/OL4c7OUY/9xAlUrV6DukHDFWNo3PEJ4PWicccnqCqvVD3H/51qfPtPQF0d6n//Gs7XN2ruTRbUZpnkf61rGk77jD6XBa17j6e9RpOWlqL4N4cgCOGpjw5qa2tx4cIFxb+3aNECKSnNwe7duxfTpk3D0aNHud6PxNV8/Dd56kOzFW8wnlVAnuKdgfqkjvT0QEqUHO6Z0yCcOAFHt25IXPt2yGPFc44dAwSvahEYT/FOeJYtBc7VwHFPvqIANv7Xo8D//RW46Ra4xo1Xba//O+VeuQLCW5fslORkJH2+T/2iiN9v0jjfRNTV3ZC0+SPm86SEs0LL7GWr0XDvqYlrRD3XlJQUpKWlKf4nFlYiOvBnAtQsV1/3Ls2P1Ur893uuSEtTTfwH+GoZsC5ucGXnAOfrgYsXA96snNeX9JvfIWn/QST95nfM+bGJc+fBMX0GkJysuUOBWch9NnrOjbVJKCOx5YRWdXU1Dh8+jNLSUgDA4cOHcfjwYTQ2NlocGaGEQ+Pv4SX+KxfGlus9sdz0vIsb5NoChP5Y+HvewrGjqrELuz7zrQjrm6kaQ0j8jNkALPBMZtp+MskG2FJcP/30U0yYMAHPPvssAGDChAmYMGECzp49a3FkhBTHiJuBFi1w2W3qBUv4E/+dgCAoCqDcMlmlG59XEKR5tFr5sXpix/HvgG+/0VXoWm4Tw3DgzY+NtaR/o7GluE6aNAlHjx4N+a9Lly5Wh0ZIEHZ9Bly4gIuf6CtYIt7EUPEYhsLYcvmwSje+9HnWxQ0stQtCfiz69AUELxx5k9VjT0wEnC7V95didOI/z/A+FpP+jcaW4koYSySGcFrThjzCplbLQFxcWpoPq3Tj8wqCXK9UKt4hq8QOHgBatPT9q9I+19LlcPTvr2t4b4fEf/JbtSFxjQPMHML5vb82T6qnEfEIm5xvGU5beKv8S3ulrBNmTLUaGMssSs/B2TNwjLjZUnEj31UdEtc4wMwhnFbJQelxeoRNzrdkKS7NWvg7JE9UQSzEFoae3VhZenZytQyYSjPW1UF4a52qVWE25LuqQ+IaB0TrEM6ZPxVIS/NVx7qEXFt4UrHkhEFJLMQWhp7i3yw9O7laBkzZC5fQzLYwEfJd1SFxJSyBtX4sAN/W0pe8WbnJpZBeMUPuppwwGJkfC7D5ynK1DAD758cC0fujHSlIXAlLMGpySQ6WHFM5AVYSC7GFoSc/lgW1fcnMyI+1mnjyaUlcibDQ2olACeaqTRJv1qj8WCC4Z8l60+vJMWX1lZX2JVPNj+VMxbJa3OLJpyVxJcKCdycCltxRIHQnWZYeG0t+rFI75G568d+MEDa1bchZCCcVy2pxiyefNqKFWyINFW4xH5bCLXI0Dh8M1NcDrVoxFyxxz5wGoeSfAABHZj/1Yi4rV0Ao3AhH3mTFYivimX/gUo+wT1/g4IGQLALxcX4P1W8TqMYrU1Am6fruwGvKP0YshWd4MbvYipFEw71nm8ItROwhl4qld5acFT09NubaBZfwe7BC4cYQr5ZnK29/vHq3xJE7Tw6e4b175QpfpbE+fW0vrLEAiSthOGZNyOgRNlZvVpofq+XVRmJDPxY/mmd4r/cHhwgPElfCcIyckJH20Fi9WubaBdKepYZXqyt2SSoWqz/N0ivl8S55RgsEP+S5ahANvo9Z6PHn9Fwn/4w7oN+3ZPVqeYtJa3m1emKXxsDqT/N6rtHkp7IQDfceea4EF7wzy1o9L6XhvdamhYD5vS+toXNgZ9XUVM3XkqZisS4V5p1RZ132Gy95plZD4kooYuZNznqeNBULBw/A9cISzf2meItJ83i1coIlzYcVe7V6c4JZ4V32S5gDiSuhCO/yRl5RZvFqpYWxWWHtsYWUDmSIU6lOgdibFT/W9FwZ68zyEE95plZD4krYBi0xlyuMrQRPpSyAbcJMGqczfyrQunXQXl/StDHxY61ULF5Y6zVQPYDIQOJKGI6ZXq20MLYSPJWyAPbaBdKC3Y7UVODcuUCbpb6y+LFmeUZOS8OsXmkk0s9iERJXwnAi7dXKIfVqWWfReTxXQF/iv5rnKle7gNXSMKtXavS2MvECiSthOGZ6tawCLBYkPaLNkx8L6Ev8V/Nc5YSMNQvArN6lHbaViUZIXAnbwCJQzNuniARJT0+aJRNALk4Wr5Zl+auckLH+6JjVu9SzMo5oJsHqAAjCD8vw3e9fap3nr9Xqf45VFMTnAcFiq/YaQV6tRpqYGnKxssTvzJ8KzyUrgXqX9oB6roRt4E2CZ6llwJo4L5cJwLz1CqNXq2f5KyX9Ry8kroTh8AoCr+eqtWmhnn2vWGsXSNGTH6uVihUSO4NfHM0FtGMVElfCcLhXaIm2mVaCtScpPk7PvlfSVCyeCTQleJa/6mlvtBbQjlXIcyUMR+pbssLiW8p6rqte9W1iWFUVtEmh9DiWmBx5kwOFW/S0hdWb1QurX6zHV5bC+3kR6lDPlTAc3lQsO5TEEw/v9eTHGpnAz5KxICWcVCxatWUOJK6EbWDJMZWDdyNAOULyYxm9WhZLgxWW2gUh70+eq+0gcSVsgx0KlkjzY3m9WlZY8mhZYifP1X6Q50pEPXKeqxTe/FiAz6tlhcWrZfFTyXO1H9RzJWwDb8ESFnjyYwE2P1JPnVkpRnq1PMN7uVoGhDGQuBJRD6/nqpUfy4rcOVb4mLyxU1EWcyBxJWyDmd4fT35sOK+tJ/nfqDbzxk5FWcyBPFfCNvB6f7y+JWt+rBZy57C2xWq/MxyvllCHeq6EbYj0tjIs8A7vWdtiZI6pWUXKCT5IXAnbYLaQhZxnUn4sK0aLWqSLlJMoq0PiStiGSN7krLPkPPmxrITjt7LWmWXBDjtHxCIkroRtiORNzjpLLidYcsJm1ESYnvjN2BJHD7STrDokroRtiORNbvaKJrOHzFbnxwJUk0ALElci6uFZ1x/O1iUsqVes9QDsMKy2Sxyxhi3FdfXq1Rg/fjz69euHm266CYsXL0Z9fb3VYRE2xch1/bzwbOVtF1uAhvfm4BAEQbA6CCmzZ8/GuHHj0Lt3b1RUVGDhwoXo378/Xn75ZV2vU1FRB683vOalpaWgvLw2rNeIB6y8Tu6VKwLr+vUsP3XPnAbhxAk4unVD4tq3TT/PjxHXSk85xGglGu69tLQUxb/ZUlylbN++HYsWLcK+fft0nUfiGjmi8TrxClS4wia+VtLXiibRNDvWaPhOqYmrLW0BKVVVVUhJUW4EEd9EumCJmYn/rJNlvIWxjYS8WnVs33Otra3FxIkTMWnSJDz88MNWh0PYkPK8u9D07XdIuO46pBW+z3xO45f7AQBJAwcynwcADdu3o+6Pa5E8a6bmflhsr7UOybNmoOWoUSGPjY7dSFhijWciKq5PPfUUPvjgA8W/T5w4Ea+88krgcWNjI2bPng2Hw4HXX38dCQn6SiGQLRA5rLxOPMNTf88V8K3U0tMLtdpzDSf2aCIa7j3beK61tbW4cOGC4t9btGgRGP43NTXhsccew9mzZ/Hmm2+iVatWut+PxDVyxNN1MtJztSoGK2GNPRq+U7bxXFNSUpCWlqb4n19YvV4v5s+fj9LSUrz++utcwkoQWrhXrkDj8MFwr1zBfI5dRC2a6wHEi1drywmthQsXYu/evVi+fDncbjfKy8tRXl4Oj8djdWhEDMGTH6tn00IpfmFr2L5db6ghRHM9gHjJq7XlhFZGRobs88XFxejSpQvz65AtEDmi8Trx5Md6infCs2gB4HDAkZ7BlR+bdH134LV1vGGHhV163ixEw3fKNp5rpCFxjRzxdJ3CzY9NfWg26gYNtySGaCIavlO28VwJwk5E2n/058cakbZkh+E9oQ6JKxG38ApUuJNJVnquROQgcSXiFl6BCncyqe6P4futVO7P/pDnqkE0+D52gK6TNkZ6ruHGEA1ebTR8p8hzJQgD4a1l4Bc1K5eKklcbOUhcCUIn3PtlHTsKz6IFhniuvJBXGzlIXAlCJ9zbyggC4HAa4rnyQl5t5NBXCYUgCLiyc/Rv433peO/6AiTPmoE6MwIjbAX1XAlCJ3ZYnx9p4rHN4ULiShA6CTc/Vq8tYAdho4kw/ZC4EoROws2PTZ41Q9d5dhA2mgjTD+W5ahANuXZ2gK4TO3qvVTTlphpJNHynKM+VIGyAkctfCftD4koQEYLXc7WDLUDoh8SVICIEr+dKfmd0Qp6rBtHg+9gBuk7s0LViIxquE3muBBHF2CEVi9APiStB2BzyXKMTEleCsDnkuUYnVFuAIGwOTy0Dwnqo50oQMQp5tdZC4koQMQp5tdZC4koQMQp5tdZCnitBxCjk1VoL9VwJgiBMgMSVIAjCBEhcCYIgTIDElSAIwgRIXAmCIEyAxJUgCMIESFwJgiBMgMSVIAjCBEhcCYIgTCCmV2g5nQ5bvU6sQ9eJHbpWbETzdYrpbV4IgiCsgmwBgiAIEyBxJQiCMAESV4IgCBMgcSUIgjABEleCIAgTIHElCIIwARJXgiAIEyBxJQiCMAESV4IgCBMgcSUIgjABEldGVq9ejfHjx6Nfv3646aabsHjxYtTX11sdli34wx/+gOHDhyMzMxMPP/wwKioqrA7JVtB3h49HHnkEGRkZ2Lt3r9WhcEHiyshXX32FWbNmYfPmzfjVr36Fzz//HIsXL7Y6LMspLCzEa6+9hkWLFmHDhg2ora3F448/bnVYtoK+O/rZsmULGhoarA4jPASCi23btgk33HCD1WFYzoQJE4Tf/va3gcelpaVCenq6cPToUQujsjf03VHn9OnTwi233CKcPHlSSE9PF/bs2WN1SFxQz5WTqqoqpKSkWB2GpTQ2NuLIkSPIysoKPNe1a1d07twZJSUlFkZmb+i7o86CBQvw4IMPolOnTlaHEhYkrhzU1tZi3bp1yMvLszoUS6mqqoLX60X79u2Dnm/Xrh0qKystisre0HdHnQ0bNqCpqQn33nuv1aGETUwXy2bhqaeewgcffKD494kTJ+KVV14JPG5sbMQvfvELdO3aFbNnz45EiESMQN8ddU6dOoXf/e532LBhg9WhGELci+uCBQvwxBNPKP69RYsWgf9vamrC3LlzUV9fjzfffBMJCfF9+VJTU+F0OlFRUYHu3bsHnq+srES7du0sjMx+0HdHm0OHDuHHH3/E7bffHvT8/fffj4kTJ2Lp0qUWRcZH3H/CKSkpTP6X1+vF/PnzUVpaioKCArRq1SoC0dmbpKQk9OjRA3v37sXgwYMBAN9//z1OnjyJzMxMi6OzD/TdYSMrKwtFRUVBz40bNw6LFy/G8OHDLYqKn7gXV1YWLlyIvXv34vXXX4fb7UZ5eTkAn7/ocrksjs46pkyZgqVLl6Jnz57o0qULli5diiFDhiA9Pd3q0GwDfXfYSE5Olv3edOnSBR06dLAgovCgPbQYycjIkH2+uLgYXbp0iXA09uIPf/gDCgoKUFtbi2HDhuGll17CFVdcYXVYtoG+O/xkZGTg7bffxpAhQ6wORTckrgRBECZAqVgEQRAmQOJKEARhAiSuBEEQJkDiShAEYQIkrgRBECZA4koQBGECJK4EQRAmQOJKxARPPfUUMjIykJGRgV69euHWW2/Fc889h6qqqsAxX331FR599FEMGzYMP/nJT3Dbbbdh3rx5+Prrr0Neb+3atejZsydefvll2ferq6vDs88+iyFDhqBfv36YNWsWSktLTWsfEX2QuBIxw6BBg/D555/j008/xYIFC7Bjxw7Mnz8fgG/HhClTpiAhIQErVqzAtm3bsHLlSnTu3BlLliwJea2NGzfiwQcfxJYtW9DY2Bjy9yeffBK7d+/Gb3/7W6xfvx6CIOCBBx7AhQsXTG8nESVYWqqbIAxi/vz5wvTp04Oe+/3vfy/06NFDOH36tNCnTx9h4cKFsudWV1cHPd69e7cwdOhQwe12C6NHjxaKioqC/v7tt98K6enpwq5du4Jeo3fv3kJhYaExDSKiHuq5EjFLixYt4PV6sWnTJjQ2NuLhhx+WPa5NmzZBj9977z2MGzcOCQkJmDBhAjZu3Bj09/379yMxMRFDhw4Neo2+ffviyy+/NL4hRFRC4krEJP/5z3/wzjvvIDMzE+Xl5UhOTsZVV12leV5lZSV27tyJiRMnAgDGjx+P/fv349tvvw0cU15ejrZt24ZUtLriiisCFa8IgsSViBn27duH/v37o2/fvhg7diy6du2KX/3qVxB01CYqLCxE9+7d0aNHDwDAlVdeiWHDhuH9999nOt/hcHDFTsQeVM+ViBn69u2LZcuWweVy4corr0RSUhIA4Nprr0VdXR1Onz6t2nsVBAGbNm3CiRMn0KtXr8DzXq8X//rXvzB37lwkJSUhLS0N1dXV8Hg8Qb3XiooKXHPNNaa1j4guqOdKxAwtWrRAt27d0KVLl4CwAsCoUaOQlJSE3//+97Ln1dTUAAD27NmD77//Hu+++y62bNkS9F9TUxN27twJABgwYADcbjf27NkTeI1z586hpKQEAwcONLGFRDRBPVci5unQoQOee+45PPfcc6itrcXkyZPRtWtX1NTUoLi4GHv37sU777yDDRs24IYbbkD//v1DXmPkyJF47733MGbMGFx77bXIzs7G888/jyVLliAlJQW//vWv0aFDB4wePdqCFhJ2hHquRFxw9913o6CgABcvXsTjjz+OO++8E3PmzMEPP/yAZ599FhUVFSguLsaoUaNkzx89ejT27duH48ePAwCWL1+OwYMH49FHH8W9994Lr9eLdevWBW1oScQ3tBMBQRCECVDPlSAIwgRIXAmCIEyAxJUgCMIESFwJgiBMgMSVIAjCBEhcCYIgTIDElSAIwgRIXAmCIEyAxJUgCMIE/j/jU86YT3dUhAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 311, 20\n", "LR fn, tp: 13, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.048\n", "LR average precision score: 0.048\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 328, 3\n", "GB fn, tp: 2, 11\n", "GB f1 score: 0.815\n", "GB cohens kappa score: 0.807\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 1\n", "KNN fn, tp: 13, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.005\n", "\n", "\n", "====== Step 2/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 2/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 311, 21\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.051\n", "LR average precision score: 0.041\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 1\n", "GB fn, tp: 4, 10\n", "GB f1 score: 0.800\n", "GB cohens kappa score: 0.793\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 329, 3\n", "KNN fn, tp: 14, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.014\n", "\n", "\n", "------ Step 2/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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nSkQlen2Jgzo3w+IJ3RXhWyIdX0VqsLKwq+9S7/XGEjGrXKM1d0KSvAAcZothOnqVFkuRchWJPKxLMAzKs3MHCic+EDi+lsIshL2ISeUaF5eAsrLrUaVgJUlCdXUViouvICEh0WxxTEev0tJi8Spm5xmdCHj1a6uOHoNn/jyuHHawCkW63cY6MelzbdKkBdzuQpSWFgtt73Q64fV61Tc0GafThaSkFKSkWDu7KBKwfIIi/k1m91dOJACrK6x8G9b5nNk58OQ+A1RX+zq/Qp+v1mzsOoEXSWJSubpccWjevLX6hjXYIceZUMJTbGphRyylwVMkVcuXBSIInD161j1Y9x7A8WO+T9mxPPFxPhfC9Wsx167FrhN4eohJtwAR/USiDYq8fuq7nx3HlO6P4N3Pjteu3/U5UFHh+5SR/OhUICUFjoezheUUcWnYwVdr1wk8PcRkVSytkOUqRrTeJ14lLbnlOiWuP8rhRBK8WPvzu0Pup3afWEPnygljgNOngfg4uBYtZVvSFq1upRc7/J5CVcUiy5UgZGix/uT1U0dc/BpJ1Tcx4uLXgfWsSZ+q5ctwvmu3kGm03AktrweoqoI3P48pZyxZhXaAlCtByGBW8BeYGZ86vAv+cvg9TB3eJeTxpQ3rIJWWBpoWMpU5oyyia+Ys4LbOQMdOAd8xdRqwNqRcLYwdfGjRRjh9tawiMI6JD8GRkhJIo2VaqaymhRmZSNj4CRI2fgJXRqawlUq/IfMg5WphqOVx5AmnIt3SaRAuJDfD1k6DAsviZz+LNseO+NJpeTJw3Any9jOictJvyDwsq1y3b9+ORx55BH369EGXLqGHWtEK+dCsgYj1x1Kko0/tQeuyIow6tYe7H0uRshSntDYfKC2FtDafKw/5Ya2FZZVreXk5Bg4ciBkzZpgtimmEO3SI0IeI9cdSpLyasn54AfV7vivCnI2Hsee7otqN/W1n0hr55DlxvE6WVzj9sPQbqj+WVa7jxo3DE088gV69epktiu2w01DQDg+xiPXHUqS8eFV/bQFvfh72ViZj7v5rCkWa9/cjOPq9G3l/P1J7rNy5cPTpC1fuXJ8ckgQ4nIrvmCWn3vtrp9+QVYnJDK1ox04N5+yQsSOS6snaRh4H6/exevPzIJ0tgFTz/eQfqMCFhk2Rt7cgUIlLun4NSEgDrl8Pefw6abo6mhbysNNvyLJIFmfv3r1Senq62WLEBO4FC6RzXe6Q3AsWROycNz79VLo84QHpxqefRuyckeJclzuk79u2l851uSOwLPh6H1q8TRr8wlbp4cXbAtv87S+fStOeeUf6218+DdpvYsj7xNrmXJ9+0vdt2knn+vQTOgYRPqLacqUMrVpECmZUrn4XqKhA2ep3UTVDe7UjXfep71Cg71CUAii1+T2uw4QHgQ3rgAkP1t6XvkPRYuRIFBaWoLSwBA8f/hu2NErH6FMnUFg4BADQw1WO7ic+hrNvTmC/ykVLgIIzuHr5ClzXypnfZdXKtyCdOQP3yrdQ2ncoAEBKagjExUFKagg3Y72VscNzRxlahJgPTTZpYnXs4KuVZ3DxYPlq1b4rXgYXM0ZXFo2gJXLADvfX6pByjRFEHiz5pIkW/DGYxQsX1ldMYew64SJSLBvdewAV5cpqWsEhW14JOPe9QvmpRQZoiRywQ01Zq2NZ5VpcXIyjR4+ioKAAAHD06FEcPXoUlZWVJktmT0QeLL3VmPwxmGXvvBdWmUNh1/hNb34eqk+eCv1SOPw1kJjk+6xB/t24Zs4CGiYBCQmB43BjX0lJmoZlletnn32G8ePH44UXXgAAjB8/HuPHj8fly5dNlix64VXOV7UQGzTwfSY2MFjCWvS+CP710WfIXfwh/vXRZ5EQsw7O7BzE3XZbyJeC2ovDlZEJ1ysL4UjvEtiG+z1dueKrU3DliqahPnUaqD9UclAAOzjWwwGrRJ7QRFjNfq7bboNr3Sahc9W3Ij1rf5GSe7mLP8SFuBS0qS7Fq3Me0HzecKDn98QK65Lj2bkDX2zZha3dMzHunm6BsK7Kgb2BigogMRGOO7vbqiShHZ47mtAidCPkTqixcho9V/vQq1lJviyjEyF7SYWCZamJuArGdm2KNtWlGNO1qfC5rDC5Iy/MzcKVkYmt3TNx/noFPv5nbfKB4+HskIW5eahdsxXuidUhy1UAO7xBw4GoNcnbTn6fKkf8CLh8CbilJRK2/4N5DM+8XKCyEujYSVFMOpyyhuNYq+b9Htub34ERV45i2sKn6nUuwBjLFQB2zX7JF9Z17QSGLf+V0HF51672/UWiMLcdnjuyXAkhRP2YQn7Y69eUn4xzoU1bwKnvJ6gnZ17vpM/2NnehPK4BtjfvZpqlpjesS9UC5V27yvdn1wnFSELKlQiJyPDb/wCXb9sW2EY+HOUhUlovmPoMR3kFT9QY2astkjxVGHFyr6VDv5gvR53RAmrfHxXmVoeUKxESkeLRfgVc+qfVgW3ULC3ecFRa876vtN6a99n71SO0iFfwRG1mfOqQjsj/r2r85MYxTUVR1F4UVoB37SKWMgs7XHOkIOVKhETEQvEr4JTpjwWWsRSPfBlXSSYmKT/DSG0IU7qmbqu8F4GaopfXYDULtRcH7/vVO6GlNvEWS0R1bQEiMvirMSW1SA3UB2BVY6rjYqiuBk6dRNXyZbUWUrNmQFmp75N1rpmzmNWaRCe4RCpcBcu6N60jtrQfgdFbdmGYfN/SUt81lJayD5LWyBcGZWI6sej1BqNWTYu33jHxocDEW6xDlitRb0TTX+UuBtfMWb7JLElSWDksS0tuJXEtLRUrMpQlplbBn9VlAACQkuLrdZWSwjyn3nRiS6D24uCg150QjZByJeqNfyh4I6/WT6rWvsSVkQlHzlTfpImKlSNXnFwlqaIMQkU4qFXw57VrUXsR6MUSMaRqLw6Drj2aIOVK1BvHxIeA5GQ0zJkcWCYS1sWyclQt0CWLIB34Ep4li5QrVJRBqNAhtbAitXYtCvlk8rOaFgJA8cKFISd9rFCURo+vViQxJJYUMCURCGCHYGYroHaf5IHn6N6DGRTPCl6X+1M983ID6ZwJe78K7KcnqaC+iQisdGH5spdGPYfz1yvQJi0Rr06rVcxVwwZAKi0FkpORsHt/2OUyC5HEEC3JB3Z47iiJgIg4an5M7qwywwKVW0ms+EvubL5QCq563Kvegifj7umGth1aY9w93RTbNMyZDCQnK9whan5lERnMtgpFEkNiKfmALFcB7PAGNQu5Yms1aULgPqlZKLx0TpaiVLPkqqb9BNKJ44AkwfXKwsA2civSMeyHdc7n2bkDnvnzAIcTjvR0riXFslBF5OLdL9f6fHgezFamm8rOIY+I0FKUJhIpqWqEcwRhh+eOLFfCMHj+QTULhTerrJZlxApSd2bn+IaiN8r5MacMS5kX9yqKniylL7bswvOtMur4YVFW5puQKytj+p1FLD4rWIV67okVfMxGQMqVqBe8B1pveqTa0JanJNG4CeD1+JSUf7lsiO6fdAuOTGDJGazAgyd3RMO6WNtt7Z6J86m3YEvTO5T7Jyf73CHJyQpFG0rO4OPrdSeYjRVeCkZAbgEB7DA8sQJa7hO3GpNKTVnPJx8D//NP4O57kPDfvw+5n24ZZDVQXQuXaBqiy9ftTetYp0rVnu+KsPnDf+D+wzsxMKEssL/iGmsC9NWuxecOOQFIXoU7RI6e+2IV7PDckVuAsBxaagQoJrRKrgNNm/k+5dvoqJzPlUHWqFFr3Vj5uq3dM3GhUUts7V6r9AZ1bobfZbTCwIQybgou61r2fFeEORsPY893RYpzoaIcKC2FZ8kiW1ipsQSlvxKWgpfe6seZnVNnfahoAfm2ohMtrty5zP0C6zkppcFyjPuuCJsPXcDYnq0V2yWNHFmntbV8gs/Zo2edY+ftLcD5a+W4Vl4V6DLgysiEx+UCqqqA69eYKalq95MwDrJcCVPgWZp6/Ibe/DxI33wDzy9nKya65FYnMwtLwNrV4jsOPsegzs2weEL3gDL0X4u8+6sfuS+Zl3wAAI7gv3WUBiQLNzKQciVMQUs1JjUl6YsWuAl4vYqJLvkQXaR0Iut8LHjKiVfnVlEZbMUbqDzwVR1XhHzCjVXLIGfgrbijVRqmDLxVsZ886kI0JdWz4g1IB7+CZ14uKVgDIbcAYSnkis2v9OSuAO/Xh4Djx4DuPQL7uDIy4c2ZWqcaU/DwPViJclundO8ROAcz7pZTESr4fGqVpeTEz34WqJFhdE27llHnTwAYCcBnBcst4MA5guRjFbRhyuD1ApVVgReIHTPCrA5ZrgYz7V+TAv8IdVQtzMNf+2q9Hv5asR+zToFKWBSv3qq063OgogLSrs/rVZybhWvmLCT06RPSFaGlXYtqURtGQRvXzFnAbZ2Bjh0DL65ojDM1G1KuMYJd/GxqPk5NHUxlioepQPxRAdXVmu6LaGQCKz7Wm5+HlOmPaS5OLaIAmS8CTjpxwsZPkLDxE7gyMoXvKXUZ0AYp1xghWqwT3RM03Xv4wpbk7oTcuT6lk9SQO9GlJcRLLaif1Q7Hj/z7YU1ocZM11KpX6Zy0Y1r61GVAE+RzNZhVQ9aYLQIAdghTtMDy0wLKMCRvfl4dd4Jc6YUKtQo14cXzscqPG+w7Tpn+GIKrzsq/ny3fVOPCTWBr00HwOwZ44V/By0W+Y5EQNZavlroMaIMytASwQ6aIUXAnfRiYdZ9EioVoKQgTHHMqup+iUEyTJtwsLrX7tKtmQkue2aX3ulnHkhe6wS0tgcLCOlle4czs0ltC0Q7PXagMLVKuAgR/yfLJqWDLNNQ6O1I5tL8vx51Te1SO1R4GtYeap0Dk14xbWgorGdGkBbX7JPoiCJZ/jyxpwR9Z8ORbu3G+QkKbRAfenDE0cCx/NTC0aOGrn+twwJHehZmOK1qdjIfeal1W+z2xoPRXQje8gid2QNXPzGkNo+eaWSFRPD9m+bZtdZbJfZvcKv8qPvPNhy7gXHE5Nh+6ULswORmIc/k+Zcf3VwNzzZxV8/8u6h1xBSIn1Or4xhKkXMNEtIZb2bl/vepDzWkNozswX03x1GxzfSk7i0zrtQTLNtpxGW0uncFox+XANv7kgxxZ8oGWF4FiklBeFpEziajWjyyWoAktHagN96PBHVBfpLX5vljRtfmB4PhIw5sECqxn5N2zFI94YL4EnD/nO8bXh7i+armjSnSikSVH8LL+W95FvzNn4Pi2A5A13Lfs9FfouzUPzkY5QGd2i3PRCS15WUTepF40T5xqhSxXg7F7EoHu+FhZZSmrwh1+qzTZ4wbmN0wC4uN9x2DVna2xNBs9V6tsw1n3lmXdMusUyMLSNFUnk1nK4a7jG42Qcg0Tq4asCfyLJvTGx7py58LRp68vllQDZrsTnNk5gOQFHA7+NTscyk/I/Zg+3yXLb+tXPEkjRwaWiVwvt95C0EuApdhYdQp4WW5y9HR/5clqlwSWcEPKlQiJ3skIvcH+0pr3fSmpa97XLXN9CFaSTORdA2oIdieESseVT2iJBOZzi9WovQQAjD61B63LijDq1B7Fvv7vVFOShAa/snybaElg0Qr5XMOAlvAru4VqqfkteWgpeKIgMQm4edP3aRK6fLUC16bI0Kqp5yoSmM/yY/ISIIIZPHoYBmpMkpDLqvCpXrnic4dcucI9n6j8sQApV8IQeBMeqjRrBpSV+j41oDcGUw9M5csJ61JQU20rvncvVNUsklfD0nI+0etl7SuSGLJ/9FRsPnYVY7s2xRD/wsqbgU/e+VkvHr0v6PJt21C18i3bVusit4AgWiem5NtGoy9WDZHQIRasbYT8kmGuXqUZTliXghpfZ9VX/1vv09XnekVcEZ9cjceFxEb45Gp8YJm8MDdvqK9nQovnLir90ypbuxNIuepATcGy1seasmU9ZFomQeTwSgNaCZEXh/+FkzL9scAyET90uCeERJIkRh3egdbXLmHU4dpzyv3IrJcnT061lyNPUadMn2br5ANyCxARgzeUlD9ckttd13eZ1sjXjTVEWJfZvaJEhr7+bZJapKK0Jq1TxFcb7t5YIq4Ilq822J3ALGhz4rgvtRa1vly5pexh1Grg+WRZvcbsRFgs1xs3buDf//53OA5lWUQtzWCrNdotVC3wLBSFFcSKIRUI62JZxWaHdQkh4qtlEM74WNHjq7kTnNk5vmaJN8oV7gq5pRxLGVxhKdxy7NgxZGVl4ejRo+GQKWxEqiqWHoXKcy3YWRnL75Pewh96qjFxq1QNuMsXedCgARL2HRTez2jk90nkevXKydpPbxEVQGwiTO16tFxLype74bb4hFaowi3kFtCJ3owru2ZqaYUVLSA0dBZISQ1exh1aq4R16Y5oCCMiw3u90QJq/ci0IhTZwLgeNXcC71rkE1pWVa6hELJcu3fvrnogj8cT1Zbr2E2jVLfzW52iJQlFjmUn1CxXvbCsIbkFJrndwOlTQHw8XIuWCtcktYLlqhcRC9Ss6wtGbQTBu5aYsFzj4uLw4x//GOnp6cz1586dw4oVK/RJZ1HkSnDz+K2GnEOv4rUDemMbRYtzB1tg/vqkCktZsDBLMFZRSqEQsUD1fgdhJzGxZgSRyFwd7gktq3x/Qso1PT0dbdq0QVZWFnP9sWPHok65hotgK9aOFmkkUUya1ChX0cB0oRYnSxcDly7C8/33mmbnrUY4M+cMp1lzX7nCZs2Zq3nXojeJwAruHkBQufbq1Qtnzpzhrm/YsCH69u0bNqGsSLiUIs9lYLe0WKNgpYOK+Bx5yrbOQ1bsDnxqaWETLZjx4tAbNnZt6W8gnTzFlZX3orBKui21eRGA5SMLVoZ6hvTRplwj2ZZD7qdD9x5MJclSnvJlfis5uIWN0dadme1Lwtkby2iqR2bAe+ECcEtLJGz/R5319Yl8CBcRafNSUFAQrkPZHhr+hxe12qXc+EtGaT15lhErUylU00I9pQGthpYqWGbjTA2dUmz19jH1slyrq6uxfft2rF27Fv/+979x5MiRcMpWb8yyXGMpQkBOOCwyPbGZvOG9HgtU3hlV0Q11YG9fllhiIhwPZ7MtZUFLyg6N94Ixw1erJ1og0nKG3XI9ffo0li5dirvvvhsvv/wyWrVqhZUrV+oW0MpM+9ckjN00Kqpm8q0Mr3ZpKAuF1+dLLfOHZY366qRKgciDALLOCjxL2eqWVH0woyZr0siRmjO35Cm4Zo8ghJMIqqqqsH37dqxZswYHDx7E4MGDUVxcjE2bNnFDtOrLH//4R+Tl5aGkpARDhgzBr3/9azTTWIouUkRzWFUk4dUuNWJmXFrzPnDzpu/TH5mQkRnof4XuPWplyJ0bOJa8P5YcPcH+VgkbUsMqk0RqOLNzmGF5ZiCkXJcsWYJNmzahWbNmGD9+PF5//XW0aNECd955J5xOYwprbdiwAX/4wx+wdOlStGvXDosWLcLTTz+N9957z5DzhQuWUlVTvHZ3B4QTPYpUpCAM85iNmwCXLvo+5ai0QWFlKvFkkM/O+2Uqf2JGoFi2VcKG1LBMzKwKokXEI4GQcn3vvffws5/9DE899ZRhyjSYv/zlL3j00UeRmem7WYsWLcK9996LEydOGGYpa0GvQiTFGn544UVq1pbruTnM9az9WEpSa5cFVicCu1iEdiKcL+j6IKRcn3nmGaxfvx7r1q3DmDFjkJWVZaiCq6ysxLFjxzBnzpzAsvbt26Nt27Y4dOiQIefmTUitGrJGMQEhFDIlAfDC59F2sDcJuT8RFtQeMk3DeFn1Kq616fUGWmvzssS8+XlImf4Y/DWw7GIRRjtGjCCElOu0adMwbdo07N27F+vWrcPEiRNx++23Q5Ik3LhxIyyCyHG73fB6vXX8q02bNsXVq1eFj9OsWYiq8CFgzQCKLts8fivGbhgFOCX4NStvRpHlQjAq1TZShJo9NYryubko/dNqpEx/DEkq5y/ftg2lf1qFlOnTACDwf39H1sL1+ZDOFsC1/gO0mDQhsN+lxmmodl9FXONGaDCgL26cOI6GA/qicc35yufmwj37GcDpVOxb3igJpfEupDRK8p2jZrlahzC5nPJusbFGpH5P5U/MEP4NiaKpKtbAgQMxcOBAXL16FRs2bMCNGzeQnZ2Nfv364f7778eDDz4YFqHChd5QrOAwGV7oDDecxgnITdbCwhLhCS67hejIMS3EqO9QoO9QlAKBItQAJ6xr5VuQzpyBe+VbgcLcVy9fQULNMN3zYDak/Dx4Hvyx4lqkx5+CIz8PUnYObuTnQWqQiBv7vkSVf5u+Q+F8eQG8QftWLlpS5xzy+8QLI5PLaeeC0fUhor8nzm9IjbCXHGzatCl++tOf4qc//Sn27NmDNWvW4Fe/+lXYlGuTJk3gdDpRVFSEzp07B5ZfvXoVTZs2Dcs5ghEdooeanAqsc/C351LjSpAPKYn6oVZyz7NkUd3C3JxhevBykbKIAHwZYNXVQFlZYBv5hBarlkKwnIQ9qXc910GDBmHQoEGahutqJCQkoGvXrti3bx/69+8PADh79izOnTuHnj17hu089YHXJ8sPL7U1eJ0ffwC6t4O1Z43thFpYl2fFG8DVotBNBQXhTmj5c3QkSVNrbatMyhD6EZr6P3LkCKZMmYKSkrrm8vXr15GTk4PCwsKwCjZ58mS88847+Pvf/45jx45h3rx5GDBggCUiBeoLSzHbKQDdDmmegHoSgZZUUPk1a+q8KusKy2pQyEqA0Nu00IxAf4KPkHJ955130K9fP6Sm1vUvpKWloX///njnnXfCKtgDDzyAxx9/HC+//DIefvhhJCcn4/XXXw/rOayE0T2Rwkm0PMSse867n4prvnLFN9S/cqX2WBxFLV/uP598gkqvktSTyUZEFiHleujQIdx7773c9RkZGThw4EDYhPLz+OOPY/fu3Th06BBWrlyJ5s3Z9SCtjpGFXMxQdNH8EAs1Uay86Vvo/wT/5aj20uQpSaSlQXK7fZaySuEa0XMRkUXI53rx4kU0btyYu75Ro0a4dOlSuGSyJLyi1yIZWWrLQ6HqR+veAzh+TJGqaTS2KtSsEd5EkvyaeemvLOTRAM6attLyCS3W9+fKyKyjdPX0IyPMRchyTU1NDVlSsKCggOkyiAXCZZEKDUdZqKRqaj2fkdjBncCz/uT3S4ufVB4N4Fm6GNKBL1H80vzaDTjfn9wyFR0p2MUXHisIKdfevXvjww8/5K5fv3497rrrrrAJZQem/WtS4J8cvcpWaDjKgLde7UHTNCkTJuzsTlB7MfDup6JmbE0XBO9Vd2A9757Ilbyob9iM75TgI+QWeOyxxzBlyhSkpaXhZz/7GVq2bAkAuHTpElauXIm//e1vyMuzrjUSDrQqTa1KV2Q4yoK3XjWdTxZ/GSns7E7QG3cqL/JSBZ8lm/yTKahS2U+1mpYN+nzFOkLK9a677sKvfvUrvPLKK1izZg1SauICS0tLER8fj5dffhm9e/c2VFCroMXHGrxfqO3CrXhUlUFysi9EKDlZ8znDiYjitIIiUX3JCXSa9SvaxvIW5JxrUysUIyoDYR7CSQQTJ07E0KFDsWHDBhQWFkKSJHTq1An33XdfwJKNVsZuGsVcrseaDXfUAO/hDIcyYJ6vHlYka1+7lNxTg9sgsaZws3+bOsgKwvBg3SPRjrgiWGFkEI0I+VyLi4vxs5/9DMOHD8fvfvc7HD16FNOnT8cjjzwS9YpVC3I/rNUrXjH9eAI+u/r49fTGZurt+2T2BA+3q4EA8msWDbvS2+fLDhONdkTIcl2+fDkOHTqEn//852jQoAHy8/Mxf/58rFq1ymj5LAdPaZrVeUDvUJBrrTDK5imoh69Wb5cBPRX+AfPdCUKFm1NS6qTgsq5N5PqltflARQWktfnw1IR9iRQQpzoGxiDUoHD48OF44YUXMHz4cADAt99+i7Fjx+LQoUOIj483XEi9GNGgULSldqjaAsHrjUBN+VSO+BFw+ZKibbFn545AiwxHejqzyR6rNbP/XE2emGFIBSc9TQt512gF5L8n1rWJtL9mbSO/Xkf79sz7YycXgB0aOda7QeGlS5dw5513Bv6+/fbbER8fH/Z6AtFCJF0CuuNjr19TfqLGQnxlIRzp6fzq/Ywhut9VcG2pMW2ndad6poRuzWwFmHG1IqMDxjau3Llw9OkLV+5coRAvP2a7T6IVIeXq8XjqWKhOpxMej8cQoewCK9wq0r5WvfGxjoezgZQU36cMvb48IGTThXr5avWmejJfBDqLokQUkUgOlW20pMKSz9UYhNwCXbt2Rb9+/RQKdt++fejZsycSExMDy1avXm2MlDoxwi0gR6jlC2Nbke1FCecwj3esyqH9fRZScjJwS0tdbgGRoW44kF8DUDeEScSdILJNfVAb7uqN2hCRm7WfVV0FdncLCE1oZWVl1Vk2duxY/RLZHLVarnKMtmTDGX7DmwCS1xyVtm3lFphOapHKreKuN/RLi/yA0grzdxqQX4/I5I3ZEzx6J/lE6kwww7qoToEhCCnXxYsXGy2HbQlVwEVtHzPRMpMuzzKq3PW5UIFpf91TAIFye3VmvAVk0No2W9FpYOniun5JnYqL145FTc5IIu363BctsOvzOq2//YTzxWGFa7YykemTHUUEK1GzQrCMQCSeVDTm1JufB5w+BZz8Dp4Vb7D9mKIB9DWB+Gol94AgX6NgFpqIj1Va8z5QWur71CCn1QhnWULy1Yam3m1eCB9q1qgVrFU5WjJ8gv2YIjizc+A5/B+gqtp3DJaVKhDj6czOCYSHaR3KiroihLLEGjcBLl30ffKulyFnJAl7zLMKZrtPrA4p1zChZXLLCmjxs6n5MVmN91wZmcCipbVDdEaUAEsZBCs6ViC+qDLgpqSqBNEzg/ifmxNSkfASBiI5dNbtf9eZbEG+2tCQctWIHRRnuFHzY/oVlrzxHqDeMZVJ0KSMlom3YFj7imSJiU76BB9fVJmryUhEB+RzNQBerVcroSWWU82P6fd/yhvvBZ+LpUCYsa9BxaPDXctAxOfI8ueq1U/l3s/uPYCKcu4MvhX8lnat3WB1SLnGKHofaj0PIneyhzGhpbeQi2ifKRbB+7IUsNr98ixZBOnAl/AsWaRcodIpwgoFxPVOclFx7tCQco1R9D7UoappXeekvzqzc4CqKuBGufJBZKSnBh+fpUhDySA/fvB2IqnCvGw0ZpaYXDZGOjFvv/Jt2wJy8BQbWYX2h5SrAfjTYK3sn9VtrYR46Hm5cK6MTCCtEeD1KJMPBCxQYTllVrDWegtyBSjveRV8DcFyyJex0ol57pBrS39jaGnHSKHXnRArkHIlNMG0EGseskbPsQPXAQgXURHxYzKXy47vV6KeFW8othOJjVX0vFK7FzI5nD16wtGlK5w9egbW85S55H8RlJXZ2kI14gUdTQjVFrArRtcWiEVC1QgIdZ9ES+vJlwFgnouVQ8+qKSC53cD165pz7fXcC5as3Im8h8bBc/IUcGsHOJo0ES4NGC2RBaK1G+zw3NW75CBB+NEzFOQqBcaElsjx1fzFAYtq5izFdvXpmKpqbbGuhWPZNXrulyG7DPD2VZPVLhahFSbxIgHFuRKaCBXvKU8ikOPNz4N05Bt4fjkb3pyptbn5jAwtkdhY0ToFIvGrwYQKG6tzfFkSRJ0oAUFE4mcDqNR5Fco0swCxknxAlitRbxRJBAyc2TnAzZuA16uYKFKzUjXNpBcV+RRPURHXglOd8Ye2iSSFfBoKc8sntDT1tFKplRArFqFdIOVqceww1FNLInBlZMKRM9U3my6bKGIpT3kolKZZ/4rywCdPQarN+IeijhIOjmrgRD5UThiDygljFNcgLyqupctCWF9GhOGQcrU4VsjgUcP/UCeNHMndJn72s0jYvV9Rro/10MtDoTxLF/sC85cGlbxkZD05Jk32Ke9JkzXJrqYk/esBKBSXyPfiqwx2OlAZzE+ais+VpyQp2N9ekHK1ONE81GMpKEUoVLHbt/DyJaXVxch6kitvLZNuwZlVwQpMNKyLpcCc2TlAfBzgVD5mSSNHBs6hRWGqWaBkoVoLUq4WR6+1YgdYLw65knRMmuxTTImJqsNmuTtB0/BYllkVKoUWgOJFUOccrIaBGZlwLVoKx129NUdXqLlDtPhqKdjfHEi5Ril2sGLUXhzxs5+F6zfL4eh2p6rlzsusksOyLuWZVSELvQSFddWBM9nEukZ5+qum8DCZO4RnKYuGdYlgh9+QlSHlGqXYwVfLQiT9lakkZe4ELUpBnlkVygUTLEdwDQItnWavy6MFeIrU6wXOn1N1h4SSMZQMItj1N2QVSLlGKXp8tVawVIQeaMYQXO5O0DI8lis3Lcop2FLmVtI6caJONTBFzuCVK75ruXJFIScaNgTiE7juEC3tdvS2n4lmf38kIOUapeixYqwwqyz0QOuI99SbOsq7JyI1CJzZOYDkBRwOhZKUZ2ih8qZvof8TNb7aVxbCkZ6unoWm8v0GVyTTXceX0AxlaMUQqhk8Ag0DjUYke0ekN5ZIBhcAOIb90NfVddgPNSlgeUfcUNcC1G1RU7o+P3AO79eHAl1ledcSWL50MXDpIjzff684bqD2ACdLzb/cv48dsriiAVKuUQjv4VRtKMdIR7UiWpSnciMPcPI7VC1fVhtvK/Njeg9/Xbe9i86mfzxZvfl58H77f5DmzwPAVtJcBegPTSt2+0LHLl+C5+xZzS8jaioYGcgtEIVwfY4qwzyeH080vrJ827bwXIABuGbOAhy+3Ch5RIHchaAloF9vzKkzO8c3WRXkKlDAaQ2jSJRgFOfmfX/yaxB1J1jB/253SLlGIeHsMgAItDhR6UQQCURSQ1kpuMHbiPqp1fzToV5wTZa/Bkd6F/73w4kKUMQAM4pzs+Cl4KpdC0UK1B9yC0QheqsO6XYn1PhqvSUlpr2tRa6ZNQRXcyfobTsd6p4ljRyJUkb1MLV95d+P6LX4UnBPAV5vQHmK+JVVv3NCFbJciQBaCp4oqKkI5UitLRwsMqy0xNBTpYwfDz1FVETdJ3pHELyGj0hIAJwu7jHCmcFlie/UIpByJeoNq83LF1t24YX2I/DFll3c/awQ+qUW1sVLDtAT1uWPe3XPfkaX8lF199T4lAOfqLHoFy6B4667NBWK0fvdkDuhFlKuRABNBU9ULJSt3TNxIbUFtjS9Q1P1/kijxwLlJQfI4cacSl5IoSazQsmqNoJgvChYIWpaY1c1ZbxR4kEAUq5EAC0z44oiIjWlAYtfmh9YP+6ebmhTWYLRJ/6Hr0gYBaYjPazUo2yc2Tm+oP8bNzRNaPmTAxK6dTNE+bBeFCKWJOuey4+lxRqlxINaSLkSAbQUp1ZYKDXxl96r7sD6QZ2bYWH/RhiYUMbPMmIoAyF3gsl+PVdGJtC2XZ1SgnJCFVFpsWG9IZYjS7E5s3OAtDRIbremaAH5sbRYo2Z/N1aClCsRgGehqPnp/PGXyY9NVezHetiDC54E88EdI3C8aQd8cMcIrpxW8NXq7Qqghqa2LyJyZmTC0aQJcP26pv3lcmiqOWuB78YqkHIlAugtWeePv2w8b15gmUjBE+aD6O/0bvGO73p9l5UTxuDij4ZzLTstbV9EUe2Wq1LQhofaizLWIeVKBKivtSUPMdJb8OTHx3agy9Uz+PEx/rBSS3k/K+HNz/PFoZ46zbUitWSJiaKamcdaLzDZyKqhS4W5a7Gkct2+fTseeeQR9OnTB126dDFbHEIFte6vctTasQwePQwLzm7H4NHDAss8O3dg1+yX8PyqXdjzXVFIGbRO3EQSZ3YOcGsHuDp10lTtyqqwXpRUmLsWSyrX8vJyDBw4EDNmzDBbFEIAVvdXEQtG9EH05udhS+rtOH+1FB//84imKvzBxzEzBtOVkYmEjZ+g1T92alI+pgy/BVqFizadDCbcfmWrYknlOm7cODzxxBPo1auX2aIQAviVpLz7q0hYF+shY0ULOLNzMPr452hdUohRh3dw+1Wp1QWwawymSAubcKN3eC/iqzXCr2xFqLYAEVHkD5bkdtfJhd/SaRAu3AS2Nh0Ev2PAlZGJwQAG1gTDe84cDJlV5eeLLbuwpf0IjN6yC8NqAujtMNwOxjHxoTo1X41G970SSCdm1S2w63cTEsnC7N27V0pPTzdbDEIHNz79VLo8YaJ049NPGcsfkG58+ql0vk8/6fs27aTzffoF1n/8yNPSY0++KX38yDMqx35AcWzW+R5avE0a/MJW6eHF27jb8OSMBsy4tgv3/Ej6vkMn6cI9wyN2TqsSUcv1+eefx0cffcRdn5WVhVdffTVs5ysqKoXXW/+QnhYtUlFYWBIGiaITf4plkydmBKo9Va18C9KZM3CvfEtZAarvUKDvUJQC8CY1BOLi4E1qGLi//e8bgr41Vg3rnsvTOUv7DkVpzTarPvpfbO8yBSM++l9MqzmfN7EhUH4DnqKruLhmY8BqlsvEldNAIvV7MuPa8PhTcOTnAZzvjwerXoMdnrsWLVK56yKqXOfNm4dnnnmGuz4xMTGC0hDhQhEtUPMQi5Ss01Ll3//wSW43UHgZnppK/v4HcXurHiiHE9tb9cC0mn1yBt6Kj9f9A6OO7IT3eBlTpmgurWfGtekud6mztKOViahyTU1NRWoqX9MT9sT/EKdMfwz+qEihXliMbXgtTvzLkZbma7hXWRXo2AoAIy5+je3N78CIK0cB3A3Al4Lbv38jeL8t4/bWCnftWysRlX5MG2HJaIHi4mIcPXoUBQUFAICjR4/i6NGjqKysNFkyggUrWkAEXuUo1qyxf7lr5iwgrRHg9SgC3KcO74K/HH4PU4cr46LVwr30hjlFY+iQmURj8oElowU+++wzzJkzJ/D3+PHjAQA7d+5Eu3btTJKKCDe8bqUsRShf7lnxhlAjxT3fFWHzoQsY27M1+p/+imlpSmveB27e9H2qdHOVE83uBDOIxhGEJZXrhAkTMGHCBLPFICwKy1fLcid8/M8jOH+9Ah+73eizlePTa9wEuHTR96lFBoYysPKDHq1YuVW4Jd0CRGwgOhQMTjxgKjBGx9RRh3eg9bVLvsQDwNd19fw5Zd3S5+bA0acvXM/VjpT0ZhlRRajIY+XkA0tarkRsIDoUDPZvSieO14kWYHVMHTx6WCDxABjm28fhVLVygpMP1GSymsUUS1h50o6UK2F5gv2bLCUpmvUTvA3L75t/2zBc8DZASbOWGAY2zPNpCC0joh9SrjGEXX2CIkqStU3V8mWBtFFnj57sa2ela5ZXAA0aAOUV3HsWa2FdhHbI5xpDREv4kEiRFiCoMHdNny/P0sXKgzGKc0/5QTK63riMyT9I1nTPRH21hRMfiPqKUAQp15jCys7/+sKaTFLUG63p84XLl5TKj1Fab0jWcLw65wEMyRrOvGc8JepZssinwJcs4srpzc9D9clTUV8RiiC3QExhZee/EcTPfjYQu1oFQMp7F2jQQOGrZflJWe2o5fhaazMm1a5fC3zyhvrO7By41n8Az4M/DiyLte8lViDLlVDFDlXi1cK64mc/C9dvlsPR7U5VC1FuBbMyuJzZOb4U3BvlSkv54WwgJQWOh7O5Q31W91c73F9CO6RcCVXs4BMU8cMyt1GJTZXWvA+Ulvo+ZedCm7aA06HY1tmjJxxdusLZo6e2dtQUHxuVkHIlVNHjE7SCNab3paCwgv2ZW0EZXCxLOTj21S69sAhjIOVKqCI6Oy/HCtaYyEuBpSTl18vL4OL5U/3nY2ZwMbrk8mQg7A9NaBG6EMpQkqWbmmHB6S17qDah5VnxBnD6NDzfHA4cIxhWcgKr7q2onCwoPtbakOVKhIRnoapZha6Zs4CGDYH4BE3Dciu4E4Ssbq8HqKhQbKPmhmB1ya0PdvCFxzKkXImQhJr1DuVTdGVkwvXKQjjS04ViRAPrLeBOUMM1c5Yvg8upfHzkL5xQ7obgureq90TnC44wF3ILECGpT91S7pDa4u081GoEuDIygUVLVbfhuRvKn5ihcAvI7wmAOkN9nguG4mOtDVmuREj0znrrHd5bYXJH5JpZ23yxZRdeaD8CX2zZxdzHb5VfX8rvesAaKZCFak9IuRKGwBveqylPPZEJotsYzZZOg3AhuRm2dhoUcrvgfsTye8JSpHrvCWEu5BYgIoqeoaw3Pw97K5OxZf81jO9YhEGdm9XZxgruhtGn9mBLo3SMOn8CQN1+Yn53Q6MnZgQaObJm/EU6HFA9WetDlithCHqH97ymhVvS78aFhFR8/M8j7B1ZpQMjzODRw7Dg7HYMHs2uAsua0PK/OObuv4Y93xUx92ONAshVYH3IciUMgWehqsVmepYs8lWuOntWYcmN3vIStnjSMer7EwCrhHVysq+6VXKy8LnCjR6r3Jmdg/wDFbjQsCny9hYwrfJwnYuILGS5EobA8wmqxmbKKkvJUbUKGZay2gRTKDkjhSsjE4627QCnAw7eNmGc5DP7emMJUq6EIfCUqNpwVl5ZSo6eSZ2t3TNxoVFLbO3Ot/CsEIifM/BW3NEqDVMG3spcH85JPitcb6zgkCQpePIyaigqKoXXW//La9EiFYWFJWGQKLqR36dwDsl5x6qa9hNIZ87A0aEDpLNngcuXgFtaImH7PwAAe74rwsf/PIJRh3dg8OhhutwURhCO31PlhDHA6VNAQgJcC5cwZZffn/hVfwZgr5RZOzx3LVqkcteR5UoYQjjjY3lhXQor2H3Vt9D/CWBQ52b49RdvY8B/PudaanYO/YLXC1RWqabbqoV1EcZAypWwFFrSXxWKoklT30L/Zw1a2rRokcHs4bVr5izgts5Ax44hs8RIkZoHRQsQlkekZbUrd65qu5bAcpV0U5SW+sK6SkvBoz5pweFAb7SAvCNufE0LHMIYSLkSloKlSEXCuliIJBYwt0lJAa4WKZoWssoQ2tEilHfEBSlXQyG3AGEptPhA5UNzTe4EtRRcxnqR41vCD6uCoiMuYSikXAnLIxTWxRjK85SootOAlhhSrxQo/s3CDuUS42c/i4Td+8klEAFIuRKWhxcbq7ByU1J8GVqyobzupoVLFkE68KUvW8x/rJmzgIR4oLISnhVv2MJKJcyFlCtheYRKALKG8rwg+hPH4Zk/D56dO9hKkhHWVdvx1Vl7nCBrWlQGIjagCS0iKhAtzO3MzoFn/jzA4fQpSLe77oRWk6a+hISgsK7gyTaRiTehSTUbBfYT4pDlSsQUddrPsHy1uXN9HV9z5waWsaIFgq3pquXLUDm0P6qWy4phy47P6/5qdswsYQykXImoRWRCi+WrZaGITOAM9aW1+UBpqe/Tj+z4iu6vMqh8YHRCbgEiahFqrc2Iq2UO5bv3AI4fA7r3UPht/ecBAKQ1AioqfJ+c43vz85Ay/THI0xNE2nsT9oMsVyJm4SowVobW4a+BxCTg8Nc+RVlZCdwoV4RdsdwJcnjdX5my2SCsiwgNKVciZgmOHAjAcBUo2mZnZAI1NVjVsENNWcIYSLkSMYszOweQpEDkgB+WrzZ4Aks0iyv/tmE43uRWfHDbME0TWlbogkvUD/K5EjGLvNmfWkiVvOCJs0dPcXdCeQXQoAFQXgHPireAgjO4tnQZXOuGBjZhFYHRW7uAfLXWgSxXIqYRrWUgL3jiWbrYl8G1dLHyYA6H8hPAlB8ko+uNy5j8g9reXsHOhHDWvqWwLutAypUggmApKEXBk2JfqcLApx9Gk8QhWcPx6pwHMCRreGCon/acMq+fpSSZMbMCclJYl3UgtwBBBMEapsfPfjZQoq8KCLgI5IjUnWXBCv1SWMocN0Q43QlE+CHLlSCCUBuma6ksJbdK/RNe15YGWaNlZT5fbVlZYJHcUuYN9fW2qCEiAylXgggTrGgBhWKsUaJSaVDTPYY7Qa7AtQz1yedqHUi5EkS4YFigCsVYo0QdKcqOoaywqz3fFWHOxsPY812Rpgkv8rlaB/K5EkS4YFigwT5Qb34eGj0xI5D+ygudyttbgPPXynG9vAqDOjcTFoF8rtaBLFeCCBNq9VxZ6a+eFW9AOngQnrnPKf2kpaVAtQeSrJqWkX5U8tWGH1KuBBEmmBNMIjUCvB7g5k3FNj8+tgNdrp7Bj4/t0ORH1askyVcbfki5EoSJuGbOAhITAadLsXzw6GFYcHY7Bo8epsmPqqbMecqXfLXhx5I+15UrV+Kvf/0rzpw5g7S0NIwYMQKzZ89GssyXRRB2QC32lZeCG7xNqHRcRUiYbFKN5c+VW6jyY5KvNvxY0nI9ePAgpk+fjo0bN+K1117D7t27sWDBArPFIgjNCPX/YmyjVk2LWZgbUEyqUQaXuVjScn3rrbcC/7/tttvwi1/8AvPnzzdRIoKILFu7Z+LC9Qpsbd8Bw1gbMApzAzr7fFGxF0OwpHINxu12IzU1VX1DgogSxt3TDZsPXcDYnq2Z6125c+t2UGAoSRFF6qtre6JuZwWiXjgkSZLMFiIUJSUlyMrKwoQJE/Dkk0+aLQ5BWJbCiQ9g940kbL0zA1Meuw/3dGvF3Kb65CnE3XYbWmxYDwAo37YN7tnPQHI4kNCtW2A5UT8iqlyff/55fPTRR9z1WVlZePXVVwN/V1ZWYsaMGXA4HHj77bcRF6fN0C4qKoXXW//La9EiFYWFJeobxjh0n8Qw6j55du7AU/8uw4WGTdGmgQNvPl7XocBzAVjRNWCH31OLFvwRdUSVa0lJCSoqKrjrExMTA8P/6upq/OIXv8Dly5fx7rvv6ooUIOUaWeg+iWHkfXriN1twISENbSqv481fjjbkHJHCDr+nUMo1oj7X1NRUId+p1+tFbm4uCgoKkJeXRyFYBCHIlNsb4pNjlzGma1PhfUSsVitatlbHkhNaL774Ivbt24e3334bVVVVKCwsBAA0bdoULpdLZW+CiF2GZA3HEI37eFa8AZw+Dc83hwGwJ7R48bEEH0sq1w8//BAAMH78eMXynTt3ol27diZIRBBRjtcDVEpc5ckqzE2ExpLK9fjx42aLQBAxg2vmrEC6rJYsMXIVhMaSypUgiMihu9Msoz0NUYsl018JgiDsDilXgiB0wapfS9RCbgGCIHSh250QI75aslwJgogosVKYm5QrQRARJVbKHpJbgCCIiBIrhbnJciUIgjAAUq4EQRAGQMqVIAjCAEi5EgRBGAApV4IgCAMg5UoQBGEApFwJgiAMgJQrQRCEAZByJQiCMICoztByOh2WPFY0Q/dJDLpPYtj5PkW0+ytBEESsQG4BgiAIAyDlShAEYQCkXAmCIAyAlCtBEIQBkHIlCIIwAFKuBEEQBkDKlSAIwgBIuRIEQRgAKVeCIAgDIOVKEARhAKRcNbBy5UqMGzcOvXr1wt13340FCxagrKzMbLEswR//+EcMHToUPXv2xJNPPomioiKzRbIU9NvRzsyZM9GlSxfs27fPbFF0QcpVAwcPHsT06dOxceNGvPbaa9i9ezcWLFhgtlims2HDBvzhD3/A/PnzsWbNGpSUlODpp582WyxLQb8dbWzatAnl5eVmi1E/JEI3n376qdSvXz+zxTCd8ePHS7/97W8DfxcUFEjp6enS8ePHTZTK2tBvh8/Fixele+65Rzp37pyUnp4u7d2712yRdEGWaz1wu91ITU01WwxTqaysxLFjxzBw4MDAsvbt26Nt27Y4dOiQiZJZG/rt8Jk3bx4ef/xxtGnTxmxR6gUpV52UlJRg9erVmDhxotmimIrb7YbX60WzZs0Uy5s2bYqrV6+aJJW1od8OnzVr1qC6uhqTJk0yW5R6E9XFskV5/vnn8dFHH3HXZ2Vl4dVXXw38XVlZiZ///Odo3749ZsyYEQkRiSiBfjt8zp8/j9///vdYs2aN2aKEBVKu8A1DnnnmGe76xMTEwP+rq6sxe/ZslJWV4d1330VcXGzfwiZNmsDpdKKoqAidO3cOLL969SqaNm1qomTWg347oTly5AiuXLmCESNGKJZPnToVWVlZWLRokUmS6YO+XQCpqalC/i+v14vc3FwUFBQgLy8PycnJEZDO2iQkJKBr167Yt28f+vfvDwA4e/Yszp07h549e5osnXWg3446AwcOxObNmxXLxowZgwULFmDo0KEmSaUfUq4aePHFF7Fv3z68/fbbqKqqQmFhIQCff9HlcpksnXlMnjwZixYtwh133IF27dph0aJFGDBgANLT080WzTLQb0edlJQU5m+mXbt2aNmypQkS1Q/qoaWBLl26MJfv3LkT7dq1i7A01uKPf/wj8vLyUFJSgsGDB+PXv/41mjdvbrZYloF+O/ro0qUL/vznP2PAgAFmi6IZUq4EQRAGQKFYBEEQBkDKlSAIwgBIuRIEQRgAKVeCIAgDIOVKEARhAKRcCYIgDICUK0EQhAGQciWigueffx5dunRBly5d0K1bN/zoRz/CSy+9BLfbHdjm4MGDeOqppzB48GD813/9F+699148++yz+Oabb+ocb9WqVbjjjjuwePFi5vlKS0vxwgsvYMCAAejVqxemT5+OgoICw66PsB+kXImooW/fvti9ezc+++wzzJs3D9u3b0dubi4AX7eEyZMnIy4uDsuWLcOnn36K5cuXo23btli4cGGdY61btw6PP/44Nm3ahMrKyjrrn3vuOezZswe//e1vkZ+fD0mS8Oijj6KiosLw6yRsgqmlugkiTOTm5kqPPPKIYtmbb74pde3aVbp48aLUvXt36cUXX2TuW1xcrPh7z5490qBBg6Sqqirp/vvvlzZv3qxYf/LkSSk9PV3atWuX4hh33nmntGHDhvBcEGF7yHIlopbExER4vV58+OGHqKysxJNPPsncrlGjRoq/165dizFjxiAuLg7jx4/HunXrFOu/+uorxMfHY9CgQYpj9OjRAwcOHAj/hRC2hJQrEZV8++23eP/999GzZ08UFhYiJSUFrVq1Ut3v6tWr2LFjB7KysgAA48aNw1dffYWTJ08GtiksLETjxo3rVLNq3rx5oNoVQZByJaKG/fv346677kKPHj0wevRotG/fHq+99hokDbWJNmzYgM6dO6Nr164AgFtuuQWDBw/G+vXrhfZ3OBy6ZCeiD6rnSkQNPXr0wJIlS+ByuXDLLbcgISEBANCpUyeUlpbi4sWLIa1XSZLw4Ycf4syZM+jWrVtgudfrxX/+8x/Mnj0bCQkJaNGiBYqLi+HxeBTWa1FRETp27GjY9RH2gixXImpITExEhw4d0K5du4BiBYCRI0ciISEBb775JnO/a9euAQD27t2Ls2fP4oMPPsCmTZsU/6qrq7Fjxw4AQO/evVFVVYW9e/cGjnH9+nUcOnQIffr0MfAKCTtBlisR9bRs2RIvvfQSXnrpJZSUlOChhx5C+/btce3aNezcuRP79u3D+++/jzVr1qBfv36466676hxj+PDhWLt2LUaNGoVOnTohIyMDL7/8MhYuXIjU1FS8/vrraNmyJe6//34TrpCwImS5EjHBgw8+iLy8PNy8eRNPP/007rvvPsyaNQvff/89XnjhBRQVFWHnzp0YOXIkc//7778f+/fvx+nTpwEAS5cuRf/+/fHUU09h0qRJ8Hq9WL16taKZJRHbUCcCgiAIAyDLlSAIwgBIuRIEQRgAKVeCIAgDIOVKEARhAKRcCYIgDICUK0EQhAGQciUIgjAAUq4EQRAGQMqVIAjCAP4/+dswvYdULuQAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 317, 15\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.044\n", "LR average precision score: 0.035\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 3, 11\n", "GB f1 score: 0.880\n", "GB cohens kappa score: 0.876\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 1\n", "KNN fn, tp: 11, 3\n", "KNN f1 score: 0.333\n", "KNN cohens kappa score: 0.321\n", "\n", "\n", "------ Step 2/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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nfFstmJrZWaoryxxcdseOce1wne+N0iNAfIL8b6tVFq3+mQE/s3uGFpz+fpMp4Ph7cm/DtiM1mDYoFZdNvIp5bd3tRuU/y2QDUJxrqKCdawQjvCMUPPiQkpKBGot85bHb9QVw+rR85cFSLfs6LdWuh7iqVNXXnRURhswu53tj5GihA6XY4ZeetTOb5dhVBlEukHqgomcmCqQeXOO90ZSC0m698UZTClfmGjVBDA20c41ghA8i3DKCuNi7R96R7d3DZ1fdLpLV1YFf646zxbhbq3EW/6Bh/OXyodDI0TA01HPHuaK87GwSAUegfus3+4Tscg2V+PBUDXJTUtnm54UBfH5T8rGGBhJXoiPOW1beULWRo4F/fS5feWi0eV5ZSUkFKk/JVw6cO2WUlyH23Q+Z7VxugeRk186OR4jM8+fB2l4Vi8dOtBNuXk5/bNtfgWnDfB/eEspB4hrBCPvOBE6/AQCHvgUcDvnKg6iYD74YqK6SryrgficgsrNLmDwZtpHjghqXhzED0jBmQBr3eERoIJ9rBKN2e2pYazyvrDhbhfO2DC/6Uhbzoi+5zIQ/X5gQzSQjwguJK9ER0dqfzttzXt9gfLznldkuwfPKiuDno0wlggcS1whGVAxEd7ympQ/CMGIkTEsf5LLjqaXqQVqafOKexnnrK5g+qrdTdKolEF5IXCMYYZFU+zZU0OcqfHuvclV+p1jVPv64JmsehMpOlEgVcxLXCEZUJNXOtBLduQrHxwZb+GT1Kq71ca5LU/4bQusj2pm2JP1CLL/2XpSkX8hlJ4rq3xuNo1lx/eSTT3DrrbdixIgRyMrKCvd0ogq13Qlobva8siJQMAUIQpSd41lruNbHuS5d824WWp+C88agIjEN288bw2W3PXsmKtL6YHt2gM6JXvw7bzGWz34Y/85bzGWn+vdG42hWXJubm5GTk4MFCxaEeypRh+pf9uYmz6vSiCZJOLOqUlKF1qfL8OFCdxK5PxehV6MFU38u4rKbfsVg9MnshelXDOayE80I040bSiU0G+c6ffp0AEBJSUmYZ0KwItrWWjQZQPggjKdflvtw7ZldhslTEbv4j8x2zh2d7aWNgECc69jc8chRMc512rBeQskHlNnliWZ3rkT4UN0HNvhiwGjkTwYIQiRFDsJE3QnOHZ15/u1cdk7U3tmNGZCGJ2YNoQSEINHszjUUpKVxZhi5kZHBGdAeQdRmj0TTd6Xomj0S3RnWwblWzQ8uhe2ljTDPvx0JHOt3vPgrORmg+CuudRcd72TRLtjPnIGxaDcyVv2Z2a7idBMcbW0wnm7im2e3BNhiTQCi+3vFi97XKqLF1WKxweHgb+US7cWyW0v2QuoSj6aSvWgNsA7ua2Wva4ajtQ11dc2w8axft+7A6ZNAt+586z5yHDByHGwA13j22nqgtRX22jqu8RzxXYGYGDjiu3LZta7f4HILiKS/RiN6+Rn09wuA3AJEB0QPJkTdCaYlD8jJB0se4LJrffZptIwbjdZnn+ayE0XUnRCsW0AUSiIILySuRAdEfXxqRxlIW7cAjY3yVY3xBH2uzvVMmDyZy66zzgc8iP6yU9vnHqm/BDTrFqitrUVFRQXKy8sBAIcPHwYADBgwAHFxceGcGuED0dNi4SgD0RKHZ854XhXG2dmhNnskWkv2MlfVcpU4/PYg0NbGvz6CHR6E7QRRvaGkSmh25/rZZ59hxowZeOihhwAAM2bMwIwZM1BZWRnmmRGa4fAh+SDs8CE+O9H6sf0z5fH6Z3KZOTO7Gl/5u1DyAZLFuvmK7rRF7UR3kqJ3PFpPPtDsznXWrFmYNWtWuKdBqIArfpS3RoCzvYslcAcDj06oXROB+jr5yoNbVAPXeM7MLrNZrATk5KnAwQOaFREnojtJ0TsercfVanbnSugP0Z2LcDpqaprn1Q8h6TDLkbTgMV57ZldMjx4Bfdnu47ncAgcPcNk54emb5Y6ondZ3kmqj2Z0roT94di4eOzsOPOx4OhG4+xH3fSM/Zgsc6mNfvQqoPAX7sWNc1bvcuwc4DuyHtHULulx9FVoD2HmIMofvs7O1V3tHqPWdpNrQzpUIHRzVptzFgCfEyUN8GG/T7YU7IW1+A2hqknfHPGmzNRbX1XDDHPnW/oY5Acezr1srt93G2Z356U8/CzxPqxVITpZ/eXCURqRdo/YgcSVCh6gYMNp1EJ+4LvITzqsvu3Vr5cgAh0N+IC3d8+pnPJcAp6XDOHQYDFmDYBw6LPB4P/0IHD3q4YYIJOXOqAlA3gWyCqb7bp52jtqBxJUIGaJiwGrn2JQP1NfDkJIii0h6ulxbIN2/SLpKEsbGyn5ExloG9nVrAbsdiI+HackD7EWkneOZjPJnbPdhJi9hL/bCQ6TWQ9U7JK5EyGBNPvAWA1Y7bxFmdic4SwX26y+PsXeP7Dfdu8e/XWOjvNvt1l22Y60f6z0eI94HSayi6b0uWg+ujxZIXAnVCVXdT9Yogw6n3yNHy7f7gZIPvKtuMSYfeI/n3PHWrfGfpuv9+VjXyduOdrLagKIFCNVR+1S5w3iHvpV3pIe+9W+3cJFnRENDveeVdbz24twSQ3SC3/dhxD1SgQgftHMldItwg0JrjefVB05/sGNTvnyLbTK1P2EKOITHrXn7Dthg5iuhx3N7T64A7UHiSugWnuQDD/Hp2lV+0Hn1g8ct9sD2Xm4DA/d0cz/8cv4SiL/6qoB2RT9a8MC7B1H0o4Xr9t79tTx2JMrKQeJKCOH8oWzesSPcU2HC46Q/rT3KIEAoFgDP2N3vSuXHnFc/lCT1x/JfL0ZJUn/XL4EzAeJcASC/uByHT9bj9eJyLt/0ntzbsHzi3diTe5twvDERWkhcoxzhcm+MhzQs47HMIZTpnTzuBGnHdsBmk68cGVoFmdmoSO6Jgszss+/FMjmbDWizQ+LsaPthTSwq4rvhw5pYSj7QCCSuGkPt2pbMsZs+4GwN2Ol4LLunzl7DEsLlXBf0z3QJKlctA3f/7LgJcnzsuAkBzXIPfYZe9aeQe+gzLrfATV+/h6zKH3HT1+9x7SqnlryHXpbjmFryHpdgRmrnVS1A0QIaQ7SykHBNVME2085KViz58h5UV8vjVZ+tZMVyui16Au4qfnK6mXk350FCV6C1DkjoCkNDPaTUNBgCRAsAQHZfM7J3PANMuMK1k2z95j+B7cr+g+w9HwM9z4FxyQPMnzm7+ntkf/Mp0D8Tpol/IbHUALRz1RjCt2mCIslzq+th1n673PzeB3zjtZzxvELh3ZPT/zhytGtdudwJbllgXP835WXyLre8jK/Ni1tsLc+6iLpICOWgnWukINhmmquIiTvtt8uO6sC1VD3IGSt3D8gZy2UmvDNv3zUaGuoR+/JrrodZ38M91jXYalEJGUkBGyl2iK3lHIPQDrRz1Riip7fCMZ+CO1cktIcx8Yq5245ODYI9sBHdVdNOkiBxDYJ5X97o+hMqhMWA44TYA2cevNnMZ9d+uxzTowefnSDChZ91dmATkqLeKtgRgSFx1Rhqd14Ntlo9b6UnvYmk6lEYHDGqoRiP4lyVg3yuEUK4qs6z+BG1gGjNU2FfryDFh0+g4Iq7kHv4AMZz2JWY+6Pg17OQe2wvl53anV6jCdq5BsHLl212/Yk2RDO0wnbbu24t17iu+FjOYH4Xgp1iC3oPR0VSDxT0Hs40P+d6FFw4DhXdeqLgwnF88xR0J5E7ITAkrhGC2l92521oPWeGltq3vU47WKq5xnXFxxoMYgeFRV/K4lr0pf/5ef2/5f5cJCcf/FzEND/nL4tpQ3qit70J04b05BqP9f/D247cCYEhcY0Q1N4ROpMB2iorucyEO712knzAxenTXDs0pw8bksQ03w7rGJ/gefVl5/VLI7uhHI/941lkN5T7n6CXKOYk2/HYD9uQk2znGo+5xY6XHU/XiWjd4ZK4RgiiB1rCBy8tbIWjOyCa7NDc5HllxBmihjGXiUVhMMb/dljHtLT24jCB2353Nt9AO2XvX1LMv1y9uimEqnC5L6J5h0viyoASIVeagbV1iTc5YwGjEXGXcx2fiCc7pKR6XhlxihDKy7iiDVxugcTEoKIpAtl1EFNWH6ioSHqF3rGKpGiURzQXhqFogQhBtCYBzGa5fTRvnGt7MoD9p6MIXDr6LM6aBLw+TMPkqbLd5Kl88xTEvZYBa/qpe2YVcxSGl5gy11Dw+n9jHU/tDLBozhyjnWuEoHacqxPeqljCyQ6CdmrF1YY7PpnVt+k9z2j2iSqNQZJ48x71g8Vig8PB//EyMpIw7f2zOyT3UCt310CgECye1+oNZ9xoyu8XwDaSPfxHON5U0C5YQjnfjIwkVAWICW6ZdZ18V9A/E3Hvfsg8Xuu8WyCVlcGQmelRQyEQouOJwrqeLGulBTIyfLfuIbdACIlkMfVGNIlA7dvLYEVZ1N0i7KYRPPCTkpKBGgukIb/isgs6CoMT4XXRIeQWYCDUB1qRcEAmmkSg9G1oqOIxXUkEQ4YK3bYLH+SIFtL5cpccV/vlLr8v67D+nZSAZEH0/zGaDrho5+oDVleAP4HUw+5VdGfnFC3bSxsBDreAcDFwxnl6p6uyHhB5v7/zffDLLzD07cs9L9UPcux2z6uvl3mtj+GGOfJB4ezr/dv5WJ9AacHedtF0wEU71yhHOA6xPYg9dvglQna8GUHChUkO7IdUegSOA/v9v857HZyhTpZqv+vja16BdnbO51uffdrzdYxVyjq8f1KS55UR49BhMGQNgnHoMP/jBZkhx2vXvGOH7g/aaOfaCd6HWSI70M52tM730dKOVrR9ijN+9PSnn8G0wPdJfIedHUdGkMfOiNE36B3qJW3dAjQ2ytfFfip4eRcwcYY6pabB0K+fcHsZXzt0VxzttweBtjbX52QNleqwPmnpsp82QEdb7/URvZMQDelixfbSy7r3zYZk59rU1IR///vfoXgr3cFTvCUSfK0u2g9eJJv/wyzRtMkOBzuMvkHp44/kbq0ffyQ/MHK0nGU1crT/8bxE3xXqtPRBvyFWvjKqAn1OV3ptcjePx5Uuzu2d2cX6/+H9/konH5jnz9O9bzYkO9fy8nLccsstOHz4cCjeTnfoWSxZdy4ddqDtmVYGM99tKDPeBzus7WEs1R5X5qaCXjtXVt+gh1i57YwD2Tufd19XHoSTFnzMI1SvC5VdwuTJXCF+WoTcAp2wbcb2DjF2vGFWvg7EtAarW8BbhJ0/3N1+vwD+Eme9RUD0NpRZJBMTgbo6V3ot6+fzJZIBEU0fbkf1OrwK384TZ2ES1yFDhig9j6hBS/5WgP2H1FukRONcRX287HYGjyuzCImKpGj6cJiIptP6cMMkrjExMbjpppswcODATp8/fvw41q1bF9KJaQnvnafz36wHVFoTVBGEg/i9Dl6Y30cwdx6nmz2uzKFmgl1wRWslEJEPk7gOHDgQvXv3xsyZMzt9/siRIxEtrrx4i3EkiGuoYBU7YdEac5nsmx1zmTwea5uWxETAaAJqrbAX7mT/RSJaK8GLcKX3sqL1+WkRpmiBSy65BGVlvlshd+3aFSNHjgzZpPQGSxRAREUKcNChwAhr3KOgaBka6mWh3LtHjpG0VLviVQPNE10TgLg4rphf45w8IDkZklUWZZ7MJffX8sQbB5PlpnrDRUFE41y1VIiGaef64IMP+n2+f//+yM+PnGK4voq2dPZvb6JNPBXD6/SededknJMH+4plgMEoC9Xp0/ITzqsPnO/pHINrp1Z56ux4AHP0hX3FMqDNDvuKZTDMvh6G9vkHwrEpH9J3pbK929xZEG64KOiTFt3x1q15CtJPP3PPU0u1C0KWoVVeHqAtRZQRzc0L3fHekYlW2ufaOfXoCWRkyDGcN94MmM3y1Q+dpXeyjGdfvUoWnOYmGOfkMceNOjblAwajHLdrMAAHDzDHtxrn5AEtLUBTM/9OUrQTBGPmmDeiGYDcpSzb0VLtgqDEta2tDR999BFuvfVWXHvttaGak6bwdZjlTjACqld3AWvhFu8vO3MPLUERcGzKB+rrYUhJkWsLsKZ3rnkC0td7YV/zBNd4qK+TrzExrkM3JpEcMhSQHMCEK2AYmMUlBqaJk4A+fQGjgAQJdoJg/aXojajYJS/5kyp1eJVEKM716NGj2LJlC95//320tbXhyiuvxPr160M9N80y78sb/caxdia2WtjBhvJQQrRwCzNeIsB8wOXtTmC9Da6q9LiyxoOyFj7pQLtP2dBQz1V/1YlwRwHROFfRYuVRnETAvHNtbW3F9u3bkZeXh9zcXPzwww+ora3F66+/jtWrV+Pyy0MfivLCCy9g3LhxGDZsGO666y5YLJaQj6FHRJ32oewQ69yRmOff7tfOvmIZpO++c43Jmg7Z4XWMP9zFh09g+RV3ofjwCcZP145XKJbiOyDBFuNOhNNkVe6YEM0wievq1asxYcIErF+/Hpdffjn++c9/YsOGDTAYDDAalSmstXXrVjz//PNYsWIFNm/ejIaGBtx7772KjOXNthnbPf7dme/U3+18Z4939np74U48vzEOG07P49rZBlvJivcHujP/o/OHNGHyZL/zhMEASA6P5INAP9z2wp34qmAXlo/9HfacOxwA+w93Qe8RqEjqgYLeI+TxWHPbE82eV0akzW/ItQw2v8FnJ9hiPFyn4Vq63dYLTMr497//HTfddBO2bduG+fPnIyMjQ+l54fXXX8fcuXMxadIkXHTRRVi1ahWKi4vx3XffKT42N5Lbn07w5bcVDW8R3kWIxmQKnhQb5+QBGT3kAyYOHJvyUZB0AU7U2PDB54cAsP9w5574Gr0aKpF74msuOyQkeF5ZiY/3vCpMNLeq1htM4nrffffho48+woQJE7B69WrFBa6lpQVHjhxBTk6O67F+/fqhT58+2L/ff11OUVgOlvzvLnXQikz0VlTwpNg0cRIMKSlAfT137Ghu6Rfo1VCFqQf5dmhjb5iMlRWfYewNvnfUnSLa8jstXbYLUOrPm2hpVa2luFO1YTrQmjdvHubNm4fi4mJs2bIFs2fPxgUXXABJktDU1BTySVmtVjgcDqSlpXk8npqaipqaGub3SUsTz/f2dg34e82096cCkgEwsNkB7bvX+xLxcv5ImOffjgQ/jc68qXp7E6Rj5TC9/SYybpzFblf6Ldq6dkVM6SG/jdW8aX5wKWwvbfQ5T3/v1fz7BX5tO+XGWZjcLQHjee3abZu7JcD20sswd0vw67bwmOeDS1G35mkYIMG8dzeXXf2apyEx2rnW6sZZ8h9eBD8fIAfm1695ChKAbkv+xG1re+llmOfP47I79cJfIf18FIaGeq7vKuD/exVqRD+fP7iiBXJycpCTk4Oamhps3boVTU1NmDNnDkaNGoUpU6bgN7/5TUgmFSpEu78CYOo86fEag+djzK1hnt8IG8BV/MT+mzmQNuXD/pubuDpkitph5Dhg5LhO5xmwS6cf20Bj2uuaYV2/AXV1zVy+vtb1GyCVlcG6fgP7ifPIcXAkidm1ta0GystQs2o14vzYea+VaPRGy6r28Sqr/Y7nTev6DZB++hkA+D5jEGO21dYDra1oq63j+s6Jdn8VXVOh7wwU6P6ampqK3/3ud/jd736HoqIibN68GY8++mjIxDUlJQVGoxEWiwUDBgxwPV5TU4PU1NSQjMEKi0gGG2bFW85Q7TJ14UI428a7qwAD9sKdkI4dA2wN/G4TwZhc+5ongFMnYf/lF1X+X4xz8mD/5Reg1sr/GVWuGta8Ywda128Q7u3G+50Rrdbmj6DruY4ZMwZjxozhul0PRFxcHAYNGoSSkhKMHi1XkD927BiOHz+OYcP8B4OL0pmosSQQOB9n7UTA8hghI/KFtxfulFu6GIxcB3eOTfmyCABcdq76BUDAjq3uggEAOHVSvtZaA47hvhsTKWrjfA8kJspNDHkPNQVFUjSuVrTNi6hIKrHxYDrQOnToEH7729+ioaHjNr2+vh55eXmoqqoK6cRuvvlmvPLKK/j0009x5MgRLFu2DNnZ2T7LHmoF90OxSEl/DcehhOjtnX3dWqCpCWhtYer46vpcQ4bKB1OpaVx29nVr5R0dEFB46tc85YoOsa9b64qpDZSa674bO/vLwxC4D1knhWEABDwQ6+z/W/QATjSES7TNi5ZCxph2rq+88gpGjRqFpE46SyYnJ2P06NF45ZVX8OSTT4ZsYv/zP/8Di8WCRx55BA0NDRg7diwee+yxkL2/N+47SNZDqVChlgAH20ZbrWIYrqImBgPTmB1apRiNQO8+Ae3cP5f0yy9ycZdu3QOP55b15dq1Go0Bhcfe4HZrbTDIO92e5yA2UOcDNzeH/MujGYiL5Wti6LaD5FkX52vVdilFQoYWk7ju378ft912m8/nJ06ciHvuuSdUc3Jxxx134I477gj5+wbCX1WsYG7jQyWiQe3qRCoiCfgwg5mnY1O+fOt65gzTmE4xcMUL989k22G5f679/5EfC1CaUH6Npb2MoeVsta3Y2ICf0WBoD9hzCisQ0JUAeCUc2GyAww4kpwde0yDik6kVTPAwievJkyfRvXt3n89369YNp06dCtWcNIUSXQdcAm0Hnv97nHoiKXjwItpfKpjDBfuKZbJ/kME36BQDyWqVawMwCBbg9blS0+TSgalpgQ3dux14Fef2O57k9heezgfu/288vk+312qpFF+0wORzTUpK8ltSsLy8vFOXgZ4IVCIwUKqr0I7WCHWzbUQD5QVFWTTg3TRxEkx/fpy5WpTLz7ZwUbtwGbnX1LT0QRhGjIRpqf/axQBkITUa5Wt5mfz3ct/F5J24dNRgaO98YATqagP7st12uaaFi4D+mQAQ0M69khXP/wVlgYUGJnEdPnw43nnnHZ/Pv/3227j00ktDNqlws23GdqHTf3ehZRJbR+DDhc4QPlwQtBMV5WAOF0Rsz4ryQDZRdlsPrvG8BdUhAcd/YRBJ57VdJOPigJbWwOnPbhlyrvmVl3F1czBNnOTa4Qeap7cQR3OWVTAwuQVuv/12/Pa3v0VycjLuvPNO9Owp54qfOnUK69evxz/+8Y+I6kTg5OXLNisSPuUh3BP47akds294PqP3a0V8xKaFi2B/cAnQKouk375gSWag6qxI2levkl0RAXyiouvv7TtldSd5rwu5FMRgEtdLL70Ujz76KP785z9j8+bNMLf7e2w2G2JjY/HII49g+PDhik5UC0RCWJUIeks+EIVVRLxP3u3dU+SY1QBuk25L/gSre5wro9+1w/r3zwSO/uxyDzDbCUIHXGIwJxHMnj0b48aNw9atW1FVVQVJknDeeefh2muvde1kI51QdyEgNAZjVEQH0WI8+e8QXsRo12FHXfwV4HDIVw47Pd2BRAJMPtfa2lrceeeduOqqq/B///d/OHz4MObPn49bb7014oU1UhIBiMCw1ljt4INkrBrWoaPpmTOeV1/jeZemjOvieWW1Y8T789EBlxhM4vrss89i//79uPvuu7FkyRJYLBasWLFC6bnpBr32wWJFuB0zo53365QeL1i8RYu1v1Sde4ZW4U6grlZ+ojFAHKp3tIZADdnOukL4okNGmNUKJCfTjpcTJnHdtWsXHn/8cdx5552YO3cu1q9fj6KiIrS2tio9P83Q2Q6WZ0cbSgFW+/SWuROq4I7H+3Wsdh1Eed1aSPu+gX3ZUiFhBsCegOAOYxFyR1W1K/nAYy0Dxda6uQ/shTvlpohGU+BY1/6ZsvugfyZXWrB73V/vho8EO0zieurUKVx88cWuf19wwQWIjY0NeT0BPcAaD6vkLjaUvbCCeV0HO28RZi3O7f06RrtORd/hAFpa+dem/SQdgE8Rca6LYfzlrhAunp2d5KzN4dypGo1AfDxbbK37PFtagLjYwL8EvH2zjGnB7r8seOJjKWTLEyZxtdvtiI2N9TQ0GmG32xWZlN5Q2y/L+oUX3Ul6i5ZofCxznyjvnR9rOxqv9E7TwkXA+QOAc89VRAyc6+f+eXh2dsYe7e2R0tJlF0LXrjDcMId/R8gqkm6+Wa7/Q7dfbjzxv+Sb9YQ5WuCee+7xENiWlhYsXboU8W5+n40bN4Z2djrCXzxsqMWXNcTGO7RI8XJsjCFCHfA6pWeeZycdW1nm6aoSZbW6WtAwnaQ752kyQWq341nT7o/+2RWK5diUz97PzO1zcp34p6cDTY1AejpfWJZgrzUK2fKESVxnzpzZ4bFp06aFfDJao7Mi1iy3+76SD9TG+8vO+gMmUi8UALB3j+wf3LtHHo9VCLx+mJmFgKP4iTsuF0BqmusOgGlM5zwTE2FISWG3a8c7FItZiNwy5LiSJEKUfMBKtMRDs8Ikrk888YTS8yAUQPTLLlqoxTD7elmUZ1/PN75g1S2u4iedYTYj9uXXmF/uLjrBiogaIhktHSu0StCdCAiZYCIJIoXYxX/kEmMnomIuXPNAZbEKF8KlKQXtCE+YDrSilc4iA3yJaCTFugoXeAkRrAdN3vNkttNQtXoWRA+K1LbTenyy2pC4Eh3wFh+lf2i8RTJSRaFDhhYjoqUbpaRkoMYiX3lgDaHzQu3/N61D4koERFi0BNMvWX+4O8yLNT62s+QDBZMknLg33ePBcWA/pNIjcBzYz2WH3f+S41x3/4vLjDmEzhtBURb95aF1SFwFYEkkiCgERYu1zUgHcWMNBfKaF6soBLvDsq9bi9Z5twBDhnKJgmjTPemtTYDNJl95CPbAjxfBEC69uWlYIXENAZFW3KWDSDL+0HQQScaCJh1EmFHMpR3bZdHZwddQ0vt2mdXH7NxhAXIHCRw8oI4odOnieWXEMCcPMJvlKweiPvdI3YGKQtECRAdClnzAeCpfcu6lKBg9FLmVBzAeYBbzEnM/FIy6BbmH/4nxHON1iMfljAIwjL/clRrKgvP0vb6hDlJNLX/R6bR0uWhLWjq7DcSjNyiEKzSQuIaQSNm9iiYfeIsbq92bI2bixBmgod8AjO9kfF8UDLkaFYnpKBhytSyurD/cI0fLTQVHjg78Wjecv3QMAFd8rCttNjVFuK0PZT7pDxJXogOq70AMBgASdxpr7sn/oKCHvOMFbmEfj6OpoDuiO3inXbffL/Asls0I7Qj1CflciZAhelB00+FPkFVThpsOf8Jll3NRbzz2+d+Qc1FvLjtRRA9enHYJkycrNLPOUb00JcW5ekDiSoQM0QONsbnjsfLYJxibO55vQNHT6TAnSfASshKQGh1P7fhktSC3ABEy1D4I0VsVpuYdO9DaXhWLKx2VsWtrqLCveQI4dRL2X37hG48x9K4DgrUltN6VlnauhG4RvU0XTm4QxLnDql/zlKqZSKztZzpQVel5ZR5QLK5W7aQFtSBxJUKGlm/TQuEPDPp2ucEW0G3S2XjonykkksKiJYpgIR1hBN1CakHiSoQMNXLERcXOY25uvaUCjue2y7WvXgXp672wr14lNHdTUlLAnbb7PJ1/R9GXAUWy07XwbmzISnqG55UR0Z1ypCYtkLgSIUP0y170owUPvHsQRT9aAr7WQ+w4bu895ubdW4qVGovn1d883cTOKTpdrr4q8Bjut7rOv8cnBDTr9Beb4E7SMHmqnNk1eSqXndrpr1pPmyVxJcLOB58fwvGyCnzw+SEuu5L0C7H82ntRkn4h34BuvaUC4bEbc2ZIMWRKuYud8/b8zKefBbTzuJV3ilVaWsCdXWe/2ISjIoJo86LlnaTakLgSIUPULTD14E70qjuFqQcD3967C0bBkEmo6NYT24cw9M1y3+UmtO8EEwLvCN2FxnDtFHlHd+2UwHadHLYwNaNxO3F3ipVp4SJVd3aiIqn1naTakLgSIUPtONfcn4vQq9GCqT8XBX6xe5gQx+2yR5EXjh2d+w7U+Quh2xKGPH+3Yjfh6rxKIhkaKM6VCBlKx7naC3fCvmIZYDDCsSkfY+fkIUfBgjL2wp3AF/+Ui7wUfQnjqjXsdieOy35dnP18CRlJsFU1+LUVbQ6pt5jfaIDElVAd4d5O69YCTU1AXBxX51V74U6gvg4wmlw7Qia7dWvPdpbtnsLV0hyxcUBMDJe/0164E9LWLXKcqMChEO00tQW5BQjVCeoW1mgEeveBaeIk5lAs+7q1QEsLEBcL08JF7CFczkyjmBiYljzAbGeckwdkZAA9evJ8Mnk9DEZAkmCck0e5+jqHxJVQHW/frCq9ttxFmTWEy2yW7WLkGzxWO9PESTCkpAD19XBsynd9vuYdO/xP0UuU1c7VJ0ILiSuhOt4HJqxi4G3HeoBmWrjIlTBgL9wJWCzy4ZbFf7yqaeEiIC4OaGnlFjj3uTmTD2qXr/A/nvO2vrwsqGIrPAeLtMtVDhJXIuwoLQbeO0mcbpafcF792CG5G+CwAzYbV9yoR1NBa438WHV14MlWV8vCX13NlfHkXcQkHFEGhCd0oEWEHZ7DGI/gfKuVuVqU+2m6/cMP5E4EYy5TbJ7SpnygtVW+JnQFWuvYMqVazpy9coR+BVvIm6IMQg/tXAl9IVgJyWM3x9OJwK3SE9eu2RllIElAejoQE4OYHj0C2+WMleeWM1aVjCeKaVUOEtcoJ9gT6UCHNKEaz4Xbbo7nNl143OZm15XrFnrcBFkkx01wzTOZJYng0LdyfOyhb+n2XueQWyBCEI0dFS047CzgXLfmaZi2sPeFEi387Px8GDIUBoA5ztVpJx07BtRY5HF5Gv653abz3EKXNMWhYMof5Y62HEkEJcmZckfb0i/Ala8mWHCaUA4SV40RVIC9SLX6IH8o+coii+Mqv2e1yodTvHZ1ta7yezy+05IbF6KgJha5qa0ukWRh0/kTUJGQinpzdy6R3DThZlRIXdDQsy+XXXF5PQquXoTc8u/4RJlQDE26BT755BPceuutGDFiBLKyssI9HVVR+/ZOtKAy162uG6I1P53+RwBc6+OyS0kVKr+3PWkgKlJ6YXvSQC475zgG3nJ/8Qmyn5ehzKA72zmK2BDqoElxbW5uRk5ODhYsWBDuqaiOcEUilZvuCXc0Dbbm58JFXOvjslv6oND68FTscmfOf3cgq+on3PRfPp/0TXvfRVblj7hp77tcdtOvGIw+mb0w/YrBXHYU56ocBkmSmCqhhYOSkhLccsstKC0tFbK3WGxwOPg/XkZGEqoC+MYiBVE3hBPetQp2PLURnW/Lr68CTp0Eep6DuH/IdVxZ1qpl1nVyFEP/TMS9+6Hy81R5PFb08jOYkZHk8znyuUY5ahf8iJoCI+6hWDz0zwSO/szUgsYd++pVQOUp2I8dU2V9td55VQtEtLimpZmFbf39RiI8ieS1OvnCXyH9fBRoqEfGjbOY7SpMRjgAGE1Gj/UJtFbHS9pb0JQUca3r8Vo5Cwy1NVx2tddOQlP+G+h67SR057HLHomm70rRNXsklx0Pev9eqSqu999/P9577z2fz8+cORNPPvlkyMYjt4Dy6GWthKMw2hyAJMHeZuf6nA67w3V12rGt1dn4C6517Z4KVJ4Cuqdy2bV8vBNobkbjxzvRuoDdH91ashdSl3g0lexFqwL//3r5XmnGLbBs2TLcd999Pp+Pj49XcTZEMDjFqvn3C4CRHHGuoiIXpJ1ktbpqC/DYc8XEuuOW2cVFa6vnlZXBFwPVVfJVBShtNjCqimtSUhKSkvS91SdkVE8iELRzxbkmJ3NFGbiLeezLrzGP50Kw8yoSzXJMbiKnS6voS9mdUPQll5nwLw8iIJoMxaqtrcXhw4dRXl4OADh8+DAOHz6MlpaWMM8s8gg2FEetJAJRnKFtvHG1TlG2r1srtD7CoXFpabIop6VxmZUMGovlU/+EkkFjuexEawtQum1gNCmun332GWbMmIGHHnoIADBjxgzMmDEDlZWVYZ5Z5CH6Q+JMBuhy9VVCdrxiJypWTvHAwQNiyQfgS1oIFtH1KRiRi4ru52D7iFwuO9FfrtRGOzCaFNdZs2ahtLS0w5++ffuGe2oRh/APSXsyQOs3/xGyE04iEAz74f2cokkLTkS7CIhmzOUKJjvQDlQ5IjoUiwiMaNyp80DDPP922ATs1N7xKN2ZNmS4twDnILvsP8je8zHQ8xwuO9H/D4pzDYwmd66E9hFNf42W+qGit/fCCCYteHRM4IDcAoEhcSUIBRC9vYfZLB9omTmjBc6c8bwyIr21CbDZ5CsRUkhcCV0QrgIjwuMK3t4LRxk02jyvrHTp4nllRNSnHE2QuBKqIipWogcvwXZasK9bK3bgI7gDFb1Nd8XF8sbHdon3vLLS2Oiqj0t0DokroSqsO54OosjYO8vbTnQ8px0aG4V8i6I+V+HbdMH4WGFEC9NEESSuhBCsPbQ6iCTjjqfDTpUxhKuDmDKO51OEA4iHr51xIJ+rzx214G266gdoor7hKILElWDCWwyc4md7aaN/O2/RYk0L9d6psnZ99fZ1CqahOn2fMJv9ugVEfY8+3Rxp6e070HSu91P7AE3t4ux6hMSVYMJbDJyhOOb5t3O9D/MPpfdOVTT5gHW8/plybn57HVXmJAIfB1eBxvUVyqR6R4kgM98iPaQuGCiJgGDCO9jcGVwfqKOpYfzlkLZucd2usgble48nGuzOnATgo/BJQHsf1a/UTloQLcASNcXLwwCJK6EoHreri/maGbqjuAjEJ8gxopyNAUXdDqHOcCKR1B7kFiCYUDsHXfWcd8HTdtHbaspwinxIXAkm1O5KKyw+Xgc0rHGuavseyWcZ+Wi6+2uwUJsX5VFrrVg7EXi/Tu3upv7s6HvFjl7WSjNtXghCFNZOBM7nRNwJ7sLI0/kgFHbOOeul5TgRGHILEKqidK6+vXAn7CuWQfquFI5N+VzB9R5+Xo7aAKGIdeXxMYerzgLBB4kroSi+kg8CiUgHAWEMdndsygcMRkCSYJyTxxxc3/rs05C+PQgYjfJOUoXgene/Mo+PmQpc6wNyCxCK4h1yxBqv6m3HGse5J/c2bDtSg2mDUnHZxKuYd5PFX/4XBRPvRm7p5xjv5loINJ5L/PtnwrRwEXNIVGe+WVZ3AHVe1QckroSyDBkKlB5xpa2yio+HSHLYFUg9UNEzCQVSgmzHKMoFQ65BRUIKCoZcg/Ec431VsAsFQ29Abt13LlFm4auCXSjodw1yC3Zx2YketBHqQ+JKKItg2uqHNbGoiO+GD2ticRmHXa6hEh+eqkFuSioAdpHMGtgHRyslZPXryjXPNwdNwokzQEOvTIznsNs+ZBIq6k9jez8+O2qvoh/I50ooimi86lTBhnujC17Fo4X/h9EFr3LZfR+fji7JZnwfz1cwRfbNmmDg9M1Ov2Iw+mT2wvQrBnPZUfKBfqCdK6EoommZY3PHI0fAryjqj5w2rBe27a/AtGG9uOzycvoL2Y0ZkIYxA/hrr1Kaq36gJIJO0EsAsxagtWL3g9JasaOXtfKXREBuAYJoRy8taAh9QOJKRBzCfbNEm+6xFvIO0XhB9wUjMVcFEldCs6gukoIdW4W7AAg2+RPdKVPygbqQuBKaRTdiUF0ti2R1NZ+dYC1Y0Z2ysB0hBIkroUnshTshWa1AcrIqZQ6LfrRg+fgFKDl/BFfKa9GPFiy//E6UZF4KtJzhmqdIU0F74U5IW7fInQ84Y4dFY44JMUhcCU3i2JQP1NfDkJISspJ//vjg80Oo6JqKgkuncInytv0VqOiRiYJhv4bhhjnMdgCExM67dgIPFCOrLiSuhCYRFQJRV8LUgzvRq6EKuTWHuUQ511CJ3rZqTLu0L2I529iIfEbjnDwYBg6E6c+PU4FujUNxrp2glxg7LaC1tVKiyLU/WufdAqmsDIbMTMS+/Jrf12ptrbSMXtaKimUTUYPaXVeDqVBFRVgiG3ILEEQQBHOrTXGukQ2JKxFxqC0+aouWsCirLObRDokrEXGoHWQvKlqiHWdhaY+rtXDG1Qqim3hjjUHiSkQcwiFHokH2gpldjgP7IZUegePAfr7xmpo8r4yIxNUCoOQDQUhciYhD2A8qGmRvMHheGZHe2gTYbPKVh/ZC4K4rK6Kfj5IPhCBxJQgnojs0ZzQjb1RjcjfPKyOGyVMBs1m+ciC6o6fkAzEoFIsIO2rHpvpEdIdmNgM1Fv5OsUsfFAvjEpyn2mFq0Q7tXImQoXYVq1DXUQ20Q/NlJ3owJeq+oJ2kPqCdawSjdpC6fd1aoLwMdquVbzzB0nvenWWDnWegHZqv5oBq7+xoJ6kPaOcawageQiN4ai7qsxSuoyoI7RgJHkhcIxjVxcBsluuTcvoehe0EUfs2nohOyC0Qwah+u7pwkdABjep2dFtNqIAmq2KtX78eH3/8McrKypCcnIxrrrkGixcvRiJnxXaqiqU8tFbs0Fqxo5e10l1VrH379mH+/Pm4+OKLYbFYsHz5cjQ2NuKJJ54I99QIgiCY0KS4btiwwfX3888/H/fccw9WrFgRxhkRBEHwoYsDLavViqQk39tvQt9EehUrIjrRpM/VnYaGBsycOROzZs3CXXfdFe7pEH5o3rEDtpdehnn+PCRMnsxsVzX7f9D208+IOf98ZGx9m9nu5JVXwf7zUZjOOw/n/LOQ2e7UlVeh7eejiDnvPPTksBP9fKJ2hL5R1S1w//3347333vP5/MyZM/Hkk0+6/t3S0oK7774b/fr1w4IFC7jHowMtMXiSD9zXqmXVaqC8DDWV1YgbOY55vNasiyEd/BatWYO51t1eWw+0tsJeW8dl19bmACQJbW12Ljvhz7d+A6SyMthe2ggbh100o5efQc0caC1btgz33Xefz+fj4+Ndf29ra8PixYvR2NiIV199FTExmnQPRyS+MpEUQ+WqS4bxl0PauoW/9J4gzlYw5vm3gzO9gtAxqipWUlISk+/U4XBg6dKlKC8vR35+PncIFhEcon2hRONOhftQCRZMES58EmRcbUJGEmw62I0RoUGTPtdly5bhiy++wIsvvoj09HTX46mpqTCZTMzvQ24B5QnnWmmmmhYj9L1iRy9r5c8toElxzcrK6vTxwsJC9O3bl/l9SFyVh9aKHVordvSyVprxubJSWloa7ikQBEEEhS7iXAmCIPQGiSuhWyiJgNAyJK5E2BEVO7VbYastyvRLQN+QuBIhQ+02L2q3wg5WlJt37OCyU73YORFSSFyJkKG2GAh3IlC5OLdzXWwvbeSyo84H+obElQgZomIg2hlAFOFOBIJ2znUxz7+dbzzqfKBrNBnnGioozlV59JhEEC7oe8WOXtZKd3GuBMECtWshtAy5BQiCIBSAxJUgGKHQKIIHElci6lA9rlYwFIvQNySuhG7RS1ytc7y6NU/z2VEGmq4hcSXCjuoiKYhwXG1jI9DWBsnGd/otulOm5ANtQOJKhB21xUDtuFokJgIxMTCY+ZpsisYNU/KBNqA4107QS4ydFgjFWuml6HWw80z5/QLqocWIXn4GdVcsO1SQuCoPrRU7tFbs6GWt/IkruQUIgiAUgMSVIAhCAUhcCYIgFIDElSAIQgFIXAmCIBSAxJUgCEIBSFwJgiAUgMSVIAhCAUhcCYIgFCCiOxEYjYaw2EYbtFbs0Fqxo/e1iuj0V4IgiHBBbgGCIAgFIHElCIJQABJXgiAIBSBxJQiCUAASV4IgCAUgcSUIglAAEleCIAgFIHElCIJQABJXgiAIBSBxJQiCUAASVz+sX78e06dPxyWXXIIJEyZg5cqVaGxsDPe0NMMLL7yAcePGYdiwYbjrrrtgsVjCPSXNQd8hMRYuXIisrCyUlJSEeyrCkLj6Yd++fZg/fz7effddPPPMM9i9ezdWrlwZ7mlpgq1bt+L555/HihUrsHnzZjQ0NODee+8N97Q0B32H+Hn//ffR3Nwc7mkEj0Qw89FHH0mjRo0K9zQ0wYwZM6TnnnvO9e/y8nJp4MCBUmlpaRhnpX3oO+SfkydPSldccYV0/PhxaeDAgVJxcXG4pyQM7Vw5sFqtSEpKCvc0wk5LSwuOHDmCnJwc12P9+vVDnz59sH///jDOTPvQd8g/y5Ytwx133IHevXuHeypBQ+LKSENDAzZu3IjZs2eHeyphx2q1wuFwIC0tzePx1NRU1NTUhGlW2oe+Q/7ZvHkz2tracOONN4Z7KiEhootl++L+++/He++95/P5mTNn4sknn3T9u6WlBXfffTf69euHBQsWqDFFIsKg75B/Tpw4gb/+9a/YvHlzuKcSMqJSXJctW4b77rvP5/Px8fGuv7e1tWHx4sVobGzEq6++ipiYqFwyD1JSUmA0GmGxWDBgwADX4zU1NUhNTQ3jzLQJfYcCc+jQIVRXV+Oaa67xePy2227DzJkzsWrVqjDNTJyo/F9OSkpi8ns5HA4sXboU5eXlyM/PR2Jiogqz0z5xcXEYNGgQSkpKMHr0aADAsWPHcPz4cQwbNizMs9MW9B1iIycnB9u2bfN47LrrrsPKlSsxbty4MM0qOKJSXFlZvnw5SkpK8OKLL6K1tRVVVVUAZN+iyWQK8+zCy80334xVq1bhoosuQt++fbFq1SpkZ2dj4MCB4Z6apqDvEBtms7nT707fvn3Rs2fPMMwoeKiHlh+ysrI6fbywsBB9+/ZVeTba44UXXkB+fj4aGhowduxYPPbYY0hPTw/3tDQFfYfEycrKwmuvvYbs7OxwT0UIEleCIAgFoFAsgiAIBSBxJQiCUAASV4IgCAUgcSUIglAAEleCIAgFIHElCIJQABJXgiAIBSBxJSKC+++/H1lZWcjKysLgwYNx5ZVX4uGHH4bVanW9Zt++ffjDH/6AsWPH4le/+hWuvvpq/PGPf8S3337b4f1efvllXHTRRXjiiSc6Hc9ms+Ghhx5CdnY2LrnkEsyfPx/l5eWKfT5Cf5C4EhHDyJEjsXv3bnz22WdYtmwZPvnkEyxduhSA3Dnh5ptvRkxMDJ5++ml89NFHePbZZ9GnTx88/vjjHd5ry5YtuOOOO/D++++jpaWlw/NLlixBUVERnnvuOWzatAmSJGHu3Lk4ffq04p+T0AlhLdVNECFi6dKl0q233urx2N/+9jdp0KBB0smTJ6UhQ4ZIy5cv79S2trbW499FRUXSmDFjpNbWVmnKlCnStm3bPJ7/6aefpIEDB0q7du3yeI+LL75Y2rp1a2g+EKF7aOdKRCzx8fFwOBx455130NLSgrvuuqvT13Xr1s3j32+99Rauu+46xMTEYMaMGdiyZYvH89988w1iY2MxZswYj/cYOnQovv7669B/EEKXkLgSEckPP/yAN954A8OGDUNVVRXMZjPOOeecgHY1NTXYuXMnZs6cCQCYPn06vvnmG/z000+u11RVVaF79+4dqlqlp6e7ql4RBIkrETHs2bMHl156KYYOHYrc3Fz069cPzzzzDCSO2kRbt27FgAEDMGjQIABAjx49MHbsWLz99ttM9gaDQWjuRORB9VyJiGHo0KFYvXo1TCYTevTogbi4OADAeeedB5vNhpMnT/rdvUqShHfeeQdlZWUYPHiw63GHw4H//ve/WLx4MeLi4pCRkYHa2lrY7XaP3avFYsG5556r2Ocj9AXtXImIIT4+HpmZmejbt69LWAFg8uTJiIuLw9/+9rdO7erq6gAAxcXFOHbsGN588028//77Hn/a2tqwc+dOAMDw4cPR2tqK4uJi13vU19dj//79GDFihIKfkNATtHMlIp6ePXvi4YcfxsMPP4yGhgZcf/316NevH+rq6lBYWIiSkhK88cYb2Lx5M0aNGoVLL720w3tcddVVeOuttzB16lScd955mDhxIh555BE8/vjjSEpKwl/+8hf07NkTU6ZMCcMnJLQI7VyJqOA3v/kN8vPzcebMGdx777249tprsWjRIvzyyy946KGHYLFYUFhYiMmTJ3dqP2XKFOzZswdHjx4FAKxZswajR4/GH/7wB9x4441wOBzYuHGjR3NLIrqhTgQEQRAKQDtXgiAIBSBxJQiCUAASV4IgCAUgcSUIglAAEleCIAgFIHElCIJQABJXgiAIBSBxJQiCUAASV4IgCAX4f6U9YDRy+gNPAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 317, 15\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.044\n", "LR average precision score: 0.043\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 8, 6\n", "GB f1 score: 0.600\n", "GB cohens kappa score: 0.590\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 332, 0\n", "KNN fn, tp: 12, 2\n", "KNN f1 score: 0.250\n", "KNN cohens kappa score: 0.242\n", "\n", "\n", "------ Step 2/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 314, 18\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.048\n", "LR average precision score: 0.040\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 3, 11\n", "GB f1 score: 0.880\n", "GB cohens kappa score: 0.876\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 1\n", "KNN fn, tp: 14, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.005\n", "\n", "\n", "------ Step 2/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 56 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 308, 23\n", "LR fn, tp: 13, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.051\n", "LR average precision score: 0.039\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 330, 1\n", "GB fn, tp: 4, 9\n", "GB f1 score: 0.783\n", "GB cohens kappa score: 0.775\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 328, 3\n", "KNN fn, tp: 11, 2\n", "KNN f1 score: 0.222\n", "KNN cohens kappa score: 0.206\n", "\n", "\n", "====== Step 3/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 3/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.4142135623730951\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 312, 20\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.050\n", "LR average precision score: 0.040\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 3, 11\n", "GB f1 score: 0.880\n", "GB cohens kappa score: 0.876\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 329, 3\n", "KNN fn, tp: 12, 2\n", "KNN f1 score: 0.211\n", "KNN cohens kappa score: 0.193\n", "\n", "\n", "------ Step 3/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 315, 17\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.046\n", "LR average precision score: 0.045\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 330, 2\n", "GB fn, tp: 3, 11\n", "GB f1 score: 0.815\n", "GB cohens kappa score: 0.807\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 2\n", "KNN fn, tp: 10, 4\n", "KNN f1 score: 0.400\n", "KNN cohens kappa score: 0.385\n", "\n", "\n", "------ Step 3/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 317, 15\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.044\n", "LR average precision score: 0.039\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 1\n", "GB fn, tp: 6, 8\n", "GB f1 score: 0.696\n", "GB cohens kappa score: 0.686\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 329, 3\n", "KNN fn, tp: 14, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.014\n", "\n", "\n", "------ Step 3/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 318, 14\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.042\n", "LR average precision score: 0.037\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 1\n", "GB fn, tp: 8, 6\n", "GB f1 score: 0.571\n", "GB cohens kappa score: 0.560\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 2\n", "KNN fn, tp: 14, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.010\n", "\n", "\n", "------ Step 3/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 56 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 304, 27\n", "LR fn, tp: 13, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.054\n", "LR average precision score: 0.037\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 328, 3\n", "GB fn, tp: 5, 8\n", "GB f1 score: 0.667\n", "GB cohens kappa score: 0.655\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 329, 2\n", "KNN fn, tp: 11, 2\n", "KNN f1 score: 0.235\n", "KNN cohens kappa score: 0.221\n", "\n", "\n", "====== Step 4/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 4/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 309, 23\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.053\n", "LR average precision score: 0.040\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 2, 12\n", "GB f1 score: 0.923\n", "GB cohens kappa score: 0.920\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 2\n", "KNN fn, tp: 13, 1\n", "KNN f1 score: 0.118\n", "KNN cohens kappa score: 0.105\n", "\n", "\n", "------ Step 4/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 318, 14\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.042\n", "LR average precision score: 0.037\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 7, 7\n", "GB f1 score: 0.667\n", "GB cohens kappa score: 0.657\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 332, 0\n", "KNN fn, tp: 13, 1\n", "KNN f1 score: 0.133\n", "KNN cohens kappa score: 0.129\n", "\n", "\n", "------ Step 4/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 317, 15\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.044\n", "LR average precision score: 0.037\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 8, 6\n", "GB f1 score: 0.600\n", "GB cohens kappa score: 0.590\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 2\n", "KNN fn, tp: 11, 3\n", "KNN f1 score: 0.316\n", "KNN cohens kappa score: 0.301\n", "\n", "\n", "------ Step 4/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 308, 24\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.054\n", "LR average precision score: 0.038\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 1\n", "GB fn, tp: 2, 12\n", "GB f1 score: 0.889\n", "GB cohens kappa score: 0.884\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 328, 4\n", "KNN fn, tp: 13, 1\n", "KNN f1 score: 0.105\n", "KNN cohens kappa score: 0.086\n", "\n", "\n", "------ Step 4/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 56 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 314, 17\n", "LR fn, tp: 13, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.045\n", "LR average precision score: 0.046\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 330, 1\n", "GB fn, tp: 4, 9\n", "GB f1 score: 0.783\n", "GB cohens kappa score: 0.775\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 0\n", "KNN fn, tp: 11, 2\n", "KNN f1 score: 0.267\n", "KNN cohens kappa score: 0.259\n", "\n", "\n", "====== Step 5/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 5/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 309, 23\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.053\n", "LR average precision score: 0.035\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 0, 14\n", "GB f1 score: 1.000\n", "GB cohens kappa score: 1.000\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 2\n", "KNN fn, tp: 14, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.010\n", "\n", "\n", "------ Step 5/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 317, 15\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.044\n", "LR average precision score: 0.041\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 1\n", "GB fn, tp: 4, 10\n", "GB f1 score: 0.800\n", "GB cohens kappa score: 0.793\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 2\n", "KNN fn, tp: 13, 1\n", "KNN f1 score: 0.118\n", "KNN cohens kappa score: 0.105\n", "\n", "\n", "------ Step 5/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 316, 16\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.045\n", "LR average precision score: 0.043\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 1\n", "GB fn, tp: 4, 10\n", "GB f1 score: 0.800\n", "GB cohens kappa score: 0.793\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 1\n", "KNN fn, tp: 10, 4\n", "KNN f1 score: 0.421\n", "KNN cohens kappa score: 0.408\n", "\n", "\n", "------ Step 5/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 55 points min:1.0 max:1.0\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 311, 21\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.051\n", "LR average precision score: 0.045\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 0\n", "GB fn, tp: 5, 9\n", "GB f1 score: 0.783\n", "GB cohens kappa score: 0.775\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 328, 4\n", "KNN fn, tp: 14, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.018\n", "\n", "\n", "------ Step 5/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 56 points min:1.0 max:1.4142135623730951\n", "-> create 1272 synthetic samples\n" ] }, { "data": { "image/png": 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A4AzEbfqAOQYfRt0r0aQFtetzLfwVpPaHhNz9NoJw+P6lp8vXVo5KtwARvtjzC7yLQG634vQ+OPTLMXkq4LB3KsrSCUZngFCHgzl/czecY7Lg/M3dmvZTW3iTXdBTCf2i+FkxSFyJsMIxeSrgaS9aozC9Z8WPcokkoyoWVzeEu+6FbeQoOO661/+acOLE9k8Aj8f7UwaRljiimV3k4xWDxJUwBZ84uFau0FzujkskGfGjPCLJOjaPSLIEiCdxgnXsstHX4JEZ/x/KRl8ju59QSxyZtjNkmRoDiSvRJXzi0LpVW11bnzhIG9drLncnKpI8sI4tXHKPo4cW69jFA3JQndwXxQNyZPcTEkSZtjNkmRoDiSsBQHwK6xOH5jfWajuhT3hyxhpS7k7PzC7RMUiffQqcOeP9KQdDgPNSXeh3+iTyUl2yu4kIIlmooSUqQ7GIzogG2PtCkGLHjIK8FDBot6JsTY2KK9BWCVdSQzhciWFNxsycA3t5NWJG9tP1XOFyLyMFslwJLzxtoFm0i4Nr9zeadjPDiuqqda7kFmBtUzZxTntVrDnyB2fc983l1TjW0IrN5dXC4yHMh8SV8MLTBppFuzjEjhmlaTcz/HwiouQuLYG07ztvZteZM/IbMkSyuLUnqpP6YEurQlIH476rtaMJxYPJqL5Z0dSPi8SV8BJiyzXUuEtLIB06BNSe1JT+6ikqBFpbvf/Zv09+Q5ZINh1Gv4ZqzGg6LD8mRnUrtXY0Wh5MRlnr7tISOOfNhHPeTE3Hjiarm8SV8CJoufqsqMRFtxk0sI505UuNxlOKRahZ2PMLAHv71yQlRXG7YGsyd/smPPnhCuRu36Qwps7VrUJZ6FvufnJV5qqqBKq0CWU0LapZVlw/+ugj3HzzzbjsssuQmZlp9nDCBlFLpatFRBKmGdsF10dXvtRISPD+JyD7So0O1mF9Pfd+gHo4mNw9F201w8rsEhVJVes4KxtwOIC03po+M9EU9mVZcW1tbcW4ceOwePFis4diGiJCKTrtCvWHvisPAQzOAAZrexB4018dqumvzHFNvNprvU68WnY/kXhcuXvOGgPX35UjsysYe34BkNbbe2+0uIT27gGSkmEbODAqhFIEy4rr7Nmzcccdd2DUqFFmD8U0RIQyXKZdXfG92VJSVCtiiWZ2scYV99KriNu9F3EvvSq7H+u+6+rv5PCJl12R583suiJP+VgBOCZPhW3gQCApWZNLKFw+Z2ZiWXElxD7AYTPtysoGmhohHT1qiGUumtmlZ41V1hjcpSWomX+d8jWzhJTDJ148IMdbdzYgs4vregQWM8Pmc2YiEZ1EkJaW2OVjKJUUM5wF87z/wgQt96qm4js4PR6grhaODW8jnfM6G3Jz0HKwAt1zc9BL4XytdyxG8xtrkbjoNiRo+RvqeM9ZY6jZUIS27/+DGIVrri7ZCqm5GbaSrUh/+nEAfNc9v9dpbKpuwbx+0rm/Bcf11FR8h7bu3RFTsc/czzsDq41HCxEtrlTPNXRovVeuzBHA198AvVLgvv5G7n1dZV9C6haPlrIv4VLaJ2cCkDMBzQCaTfobfn7Eic0Z0zHriBNXto/BlTkC+G4fXJmXyF6zp7bO/9O3Dc91nz34v5B6DsXZgwc1/S3c1+dDKirU9HcIBeHw/aN6roRhiBZuEV0QsUJmF284WGAPLT9798CWoDy9L5v8C6/vdPIv/K/xXPeWrKmo7tkXW7K0TdVpim8MJK4EABMLt2hMWrBCZhdvONhQZx3O2mNwsfOcuNrzCxBzwQWKIsnq2cVz3TNTXeh/5hRmKhR8IUKHZcW1oaEB+/fvR1VVFQBg//792L9/P5xOp8kji0y6WlZPa/qrcLqtjvBYoO7SEkhHjgDNTf4HAW/40kF3PLq1OXHQfa5hpGPyVKRv3KAokmqZXXKoZXZpwbVyBZwTxsK1ckWXjxUIpb9agG3btmHOnDl4+OGHAQBz5szBnDlzcOIEO9+a6CJhXLiF9wsbvB2PBeopKgTqaoG2Nv+DgDd8Ka9uH/o1HkdenULaLAO1zC459Lynai1jRMWX0l8twLx581BRUdHp38CBA80eWmQS4vRX0SwkFsLhWb4Hic0m/1DJyvamzLa1dahJwCNkud9s84rkN9s0XY+ePbt47ilzG5W+WmriK0c0xcdaVlwJMcI5/VW0LQqv1d3pGn0PEkmSf6js3QO4O/fs4vGB6tkNgefvyrImRcslqvXVUhNfOaJp8YzENcIIl/RXJoJtUXisblaBaZ5UWnt+AdD3PKBbN91EUi2JQDT9lWVNsho1Bp+LVZlL7WGrKr4EiWukYYVpl/CiBY9rIisbaG6CdOTIueMLirJj8lTEbfoAcZs+kH2oOCZPRdw/tiGu7GtdWsZ4igrR9v1/NFuSu/JuwSOT78GuvFvkD85oC85q1Bh8LlZlLrWHrRU+Z1aHxDXCsIIFamRDP+zd411cqqs9d3xeURZZsNMZKSkZnpPKNWVZwlUs9UF13wwUS33kD85oC642fdcz3ZfoCIkroTvCVg2vSMbEAKlp/uNznU/n0C9h61zFkpTj4jMncbaxGRefOSm7DcvHqzZ9J5E0DhJXQnd4vrDCC1N79wCJSbANGuQ/Ps/59J7Gii6+lU2c195XSz7fn3XsisqT6OY6i4OVbHF1l5Z4u8z26Qt79kj/6zR9Nw8SV0IRNQtNNP1VdGHKCpldrMQCFqxrLE66CNUpHbOvgtl56SQ8csVC7Lx0kv+1vIRT6Nd0AjMSTsmeS6jotQWI1MQCEldCETULjZX+yvVlYYikGdN7EdyvvQKcOA44ncxx+K5/Z99LmCLZv1FeJAF2Y8Nx336KJ/+1BuO+/ZS5jz2/AEhN87pMTPIrU1+tjpC4ErLIhel0gJH+yvVlYYhkqKf3omLgHYgDiI1ljsN3/cUx/ZkiuXznWlmRBNjpr2rX7Zg8FbZBg4DEJF0ePKJdMNQy3ljHjVTXBYlrlCD8ZWGE6XSAkf7K82XRc5W6K6mYIv24bFdNAmJjgF4yDQvbHzh5dfs6TeV5Crew0l9D/uB57RVI5d94rXTW+zIiqWY9y4XEWd11IQKJa5TA0wVUxKJgpb/yfFn0/ELxpGKyrk9KSvaGdQWltgbv5/yva+DMHX1OvH1ZW7Un2fez/YGT+822TlN5nsIteqa/GlWARU4k1aznSLVSWZC4Rgk8XUBFLIpQd39l4gued7tlLXPmwyUwHEopyP74T8DZs37xVsvs8t1r2/wbhIRENP2V+QARrAGAwRneZoeDM5hvy32eeNwXkWilsojoTgTEORyTp6pOKX3poWbx+XvbsPlAHWYNS8WVc6/h39EXNN/aCk9RIfM67fkF3qlufT3cpSXebXLGAp9sAxwOxfbX7qNHgYZ6/zZq91LtfRECHw5yx2ZtU3bd7Siui0VeqgtXMfZhpQUDUI3HlbtGI649XCHLlQCgf6iSSGgNs3I/z7F96Z4JCcoLPikpQGOj33q1NTUCvdNhGzWamdrqEx7Hg7/TLf1VFLUaAQDbavxwcC6qhwzHh4NzmfvIuosEC7MQ5yBxJXRHNLQmM6M3zsZ2w9CM3tqO7Uv7dDhkHw6syAcRV0lX4er+yoIjs4v1gMzsm4izbR5k9mU365S7B1SYpeuQuBK6I7pocSi+N7olJ+JQvLy4suJjeRaAWJEPZhQnCS7cwrLERTO7WFQcb0a3GDsqjjcz35e7B9G08GQUJK6E7uwaMgaPzngQu4aMkd2GJSCzRvbDgF4JmDWyn/zBGfGxrAWgYETEQourhHfBSaqvh71XL/84WNaxbGZXL+XMLhZ5thPof7wSeTZtHTzCYeHJ6pldJK6ELMK+0/JqHGtoxebyatltWAKSs/lNPP7ibcjZ/Kb8wQ1OfzWyG4L7tVeAqkpI7eMB2KLPKi9ohb5aRhGpLWNIXAlZRD+806vK0O+H/ZheVSa/EUMkucKGONNfjRRJ18oVcOaOhvO/rlGsKcscg0eC+0iV/zWW6LPKC6r11ZK73lD3KhO575HaMobE1WC+qt2F5759HF/VaisxZwkErcTcd1/Hkx88h9x3X5ffiCGSPL5T3i+U6IOB5/jSxvXA2bPA8Z8Ua8oGj8F21SRA8gBnzspmPgFs94javZG73lD3KhO67xHaMobE1WA+/nErjrdW4+MftVWN0huhLxRn+5TgqliiIsnjO+UVC0OLQAdU+veFRjHPF/xw2rsHsNm9q/4KjL8wDc/My8L4C9P8r6ndGy3Xy22dB03VjUprjtTIBBJXg5nSfxr6JvTDlP4mZjBBzKLg+aKwqmKJiqQozLYonItqPIVbOol3YKX/9tAo5vUEPZzs+QXAkCGIGToUjrvu1X6hCsjdT9G6uaypulGWotWn96KQuBrMZWljseTSZbgszdxgbKNWy0Vba/PCY3GzFoB4F9V4Crd0Em/f9FUhswvofM99Pbv6/rM0ZMkaonVzRUO/RB7iVp/ei0LiGiUY9QE2urYAz5eVtQCkFjwPtE/p3W6gew9NU121zC4fRosGV0dYRmYXz4NWJPSLq0RlFEHiSlganq6nrP5SasHzAM5lO3ncmip46T2NFV2F5xoHI7OLR/SHdnfjrCMWF3d3c18HV4nKKILE1UTCOpJAAJFFNZ6up6z+UjzB83qW9usKIqvw7tISfyEaJUSn9wdbHOjmduFQi4N7n0j1nYpCVbG6wMLPF/h/X3PlOs37B0YSmO2TDQVqlZ2++N9abC6vxqyR/fwr5Xm2E/jgeB3yUlJlj5uXcArFdS2Ykeryvza2+C1cXlkJ2+EMQKbCVux9vwVMLMYCtE+ljx4FmhplF5jcpSWQDh0CmhohZV0KIMBf3P67nNAXJ12E6phEbGlLYlbFkoN1T9WgilgdIctVgIWfL+ggrKJYJZJAb+Ty5dX8caxFKJ4Mo9zt77cH2L/vfy3UVlRX+keh9qTX9yuzwOSdbp/qML235xcAab0Bh0Nx1V+tsaEcaj27QkHr1q2WTm9VgyxXDSgJqprYDk0ejin9p3WwUC9LGxuWFqtsDdB2WBaq3x+XkSFr3UyvKkNxXSymp7oAZAEQrzMrakWpXZscPJakbyr/U4wduP1u7zZZ2cCecq9wyoikPb8A7iNHgMZTHWrKeooKISmIMuAVydzKStgyMgAs5L4ePev7qt1Tufeb31ijWsPWypDl2kXWXLmOyyVghUQCvVDzEYquULMyu7i6Idx1L2wjR3WIHTU8/TUowN7fMsbjUewfhapKuP/zQ8fMLl9SgUwnWU9RIRxLfo+4nbs7RCeEuleZUfdU7v3ERQvD2odL4ioIr6j6iKTpv+oXVnCFWs8FJq5CKgyx2HnpJG877L6XyAoJMxfeH3ngkbcks7K9aZ4BVqpayxil6wh1rzLRe6qWtCD3eUqYNi2s419JXBWY9f6MDv7VQDEN9rvKCa1PhK2QSKAXal9YUZHkyeziRUt2WYfSfq09UZ3UB8Ux/eWFxNezy+M5JyK+/PjknvLn9FmpNptfgH2JBXGbPpBtT2OG9Saa2SWatBCJkLgajC/U6qvaXX5B1mMxzMroKZJGwhKuWcNS0b+tGXmpLnlR86W/trR0bBmT1hu2iy+Wt7SysoGYGNh69+YWSyMSEYzK7JJbtDSj44MVIHENQM+40zVXrsPQ5OF+X2uk+FtDjWiAPc8XliVc9uyRsGUOQ8zMOfKi5kt/7Xmu6DWXhbl3D5CYhFiFRb1QwCVmLCtVxXKVSyIwo+ODFdBFXFtaWvDvf/9bj0OZSnAFq81ztuDOYfdjaPJwv+D6pvksn2vw61P6T8MpVwMONu7Hwcb9ob2YCEG4zB3HFJYlyjw1CfzprxddxN0yBtC/DoOhmV0sK1XFcjW0ElkYoou4VlVV4Ve/+pUehzIVVtwpT8lAuek+y8eqdSEsmvEHz9ee1Nz1lMfPxxJlrrYoXeyGoFcdBtHMLq5QM8Y1qolnpIqkKBTnGgAr7nRK/2n4+MetHQSXNzMr0n2rvPjiOwFv2BTvl88fPA8odj1l4Q8Ha89oYpKVDVQc6CAgPJldVligcZeWQDpyBGhuUhb5oGtUy5Lzw7hGysDSBpflmpWVpfjvuuuuM3qcIYFlgV6WNhYHG/fjjwde1CSWJKzn4C3tFzyFtecXAMk9VavUMy04jlbU0mefAmfOeH+2Y2TsKAve/lHB98ZTVAjU1Xpja5VEnlVTNjkZUn29rDshnKtbWalpIZflGhMTgxtvvBFDhw5lvn/s2DG89tprug7MKgSLJOv/vNP8qHUH+LKQ3G5IR4/CXVrCldnlz0KqrFSsUs/KJrLNvwHSxvVCMbNq1hlrG9fKFf7zaYmS6BAzy9jPN42X6uuBxsZzFidHZheATpZr4D2Vs155sumsCrdlHgK4LNehQ4eif//+mDt3LvPflClTjB6npQm0eMPBYg35090X3+nxALUn5a1XAT8fwPb1cXVDYGR2iXYi5Wmyx2qJ44+ZdbuZfw+fWADoeB9UMrv8MKb3avfUjJKKemGlyAMucR01ahQq2//ALLp3746cnBzdBmUWclEAge/zECiwQ5OH485h91vKahWNK+T9kjCn94MzgD59ZTORAMj6+YxaJGEdW4tIBt6HslHXeEv79Rshe39YLXH8MbOtrey/h++BMzijw8tqmV2B2wWLjdo91dQyhgPRz5tI4RYrLapxuQV+//vfK74/ePBgFBZGVgCwD1a4VTBK1qovBCt4oayr5Qq7BGMhhwfeKRdres/zYdezWIgwOWOB7Z9w+3h911Wcegmqe/RG8dCJGCd3f7Kyge/2wlVZCXu7a8Q2/wZI7xTJZ3b5Hjhf7oIUn6D5nuq5CCU85Rb8vIV74RbdogWqqqowePBgvQ5nOmrip2X6b7maAoKr3bziF+pKVrpSVQnY7f4KV0wYYpHX9iOKG93I+/4L2BcpWOZtbZBOnvQLhlpNWd+9RFY2sHePuQ8eQZEU/bwlLlqI+lWrLTHFF6FL4trW1oaPPvoI77zzDv79739j3759eo3L0mj1q1qupoDgl8QMa0mUz9/bhs0H6jBrWCqulAupEoUlFq0tQM9Y4LzzFDOR3PX1cMQ4AA3pr2bfSz+iIWiCn7eEadPQnDNB27kshJC4/vDDD1i/fj3ef/99tLW14Wc/+xlWrVql99hMIxSLUnq4AkRrj1ohTtNoNh+oQ3VMIj44UIcrNexXNnEOiutikZfqkq/czxCLLVlTUd14BlsGZcju5xPK9PQk1NQ0aRiVvoh+boTdNlHweWPBnaHlcrmwZcsWFBQUIC8vD4cPH0ZDQwP+9re/4bnnnsOkSZN0H9zrr7+OCRMmYOTIkbjzzjtRW1ur+znU8ImgXN0BpXRYNQKPKbJYIFzwQjDDKFxwl5ZgaPUhnHXEKDbYY91zX1WsLa095U/AEIuZqS70P3MKMzW0RTELrZ8b330CILRYZKUV/FDCJa7PPfccJk6ciFWrVmHSpEn45z//idWrV8Nms8FuN6b2y8aNG/GnP/0Jy5Ytw7p169DU1IT777/fkHPxIJcGyyo9KCeyweFagccUEkpRkQwTS8JdWoLPFt+PJU++jc/f28a9n6eoEAeT+6Fbm1OxwR4z/bXpMPo1VGNG02HZ/VhiwdOOhhejQpd8rWikw4fUM7sC6GrVKiut4IcSLmX8y1/+ghtvvBGbN2/GokWLkJ6ebvS48Le//Q233norpk6diuHDh+Ppp5/Gzp07cfDgQcPP7RPIzXO2+F/z1R3gLcDCY836jnnNyf5iGTFdWJgKtSUhapkX98lGdbee+OBAHfex7fkFyKv4FP1OHceMY1/K7se6D7nbN7X349okux+zmpaO95RHzJy/uRvO0SPgnDie+576M+VOnVLP7ArAd23IyrZM9pPeGPFA4xLXBx54AB9++CEmTpyI5557znCBczqdOHDgAMaNG+d/bdCgQRgwYADKy8sNPbePhZ8v8BfLBryLUksuXaa6D28iwVe1u/w1C0b97TOhfu/hVIVI1DLPO/AJ+rXUYeYw+e6vwcd2TJ6K3P/9N57c8gfk/qtY0zj17IbA84V1/uZuOMdkwfmbu/2vcf1dt3/iTSJoPMV9T+35BUBqmrepIcBtufquDXv3WL7uaqhjcZXgWtBauHAhFi5ciJ07d2L9+vWYP38+LrroIkiShJaWFt0G46O+vh4ejwdpaWkdXk9NTUVdnbwFE0xaWqIu40lPT1LdpoOgSkDilzvwi7Ov+l/63dilHbb/tOYj1Jz9CdtrPsKkOxaj+Y21SFx0GxI4zuVnwTzvvxDRunUrmt9Yg8RFC5mVnZTuU016Kpxf70Zszmiu+wkANRXfYXzjD7jqp21If22D7HYNuTloOViB7rk56NV+7Jprp8D50ceIu3aK7PlqNhRBOlIFx4a3ke67j08/7v0XgNp1y23DPH47vjEd2/6JN3Nt+yfnxsnxd/1x8CBIP3jDxQKvW5EF81CzoQjOeu93KLZiH/ffAgBaRT+ngrRu3Qqo3PdglO654rkMuDZN0QLjxo3DuHHjUFdXh40bN6KlpQX5+fm4/PLLMX36dFx//fW6DEovamub4fFIXT6OppVdCYDHjfpVq4GAsp0bD3ScZk5KvxYfu7ZiYvq1aB42FsiZgGYAzSauIqvhWrUaUmUl6let7hQio7QC7i4tgfujjwGPB84d/1LcLnAV2319PqSiQrivv1Hxb+Aq+xJSt3i0lH0Jl2+7Z19C3LPeX2XPxzg+ayVd6bqV7o0rcwSkvd/BlXlJhzEE3quy0deg+LzRyNv/Ca5Yt4l7NiGdPLe42+G6VXBfnw+cONn+u/J97UTOhNB+Tt9YA+fh7xXvezC8n5lOCF6b0sNJaDUqNTUV//3f/41//OMf+POf/4zk5GQ88cQTIodikpKSArvd3ik6oK6uDqmp8tNDPQn0uWqpGbD67EL86S8J+D83Ozu8Huyr9bkZLBcDq4TgApqnqBDo1g2w2xWn26zpPY/7Qk/3iGjhbdGassUDclCd3BfFw6/WNiXl6dnFQK1nl5WIHTNa8+fNSotnXV7qHz9+PF5++WV8+umn6htzEhcXh2HDhqGsrMz/2pEjR3Ds2DGMHDlSt/MYweL4Nfg/tzkB+UVq0xF23ncliNzhgK3gFsVCKlbwIQsX3t5TDqniADx7zq0J8FxPXtuP6Nd4HHnf/0vTdXP17LIIop831+6vwyKqRQ4ucd23bx9++ctfoqmps7nc2NiIgoIC1NTU6Dqwm266CW+++SY+/vhjHDhwAEuXLkVubq5s2cNww8wKWiGPj+UUZStYHTsbHXj4olnY2RjwdOS4blbBF67raW0BYmOB8/qFZDGzK4RyschdWgJ3XV1Y1pT1wSWub775Ji6//HIkJXX2LyQnJ2Ps2LF48803dR3Yddddh9tvvx2PPfYYfvGLX6BHjx548cUXdT0HD8EhVb7fhyYPV93P138r+HXTCXF8rBWEwBfj6Zw3U1EcAjO7/PBct690YMYQTePckjUV1T37YkuWtgdK2ER8AEKfN09RIaSGU5ojaKwEl7iWl5cr1mydPHkyvvrqK90G5eP222/Hjh07UF5ejlWrVqF37966n0MJXygWy8JUKsay5sp1WPj5AvzxwIudfK16WqumTO+70DvKTCHg7YaQl3AK/ZpOYEbCqXMv8lx35Q9eH6ivhCAn4ZTZJfyQFPi82fMLEHPBBWFrtQKc0QI//fQTevXqJft+z549cfz4cb3GFBZoWYgKtFb1LDUY6hJwVsrsUsuP33npJBQPOFcjwJ5fAPeRI96eXArXPe7bT5FbWekNmsdC74u8lut3ezVbrlw9uzgR7Yaghu9ei6ZLi9QkcEyeivQF80ytwdBVuCzXpKQkVFVVyb5fVVXFdBmEE3K1A3wEdxtQskBD5UsNpSXRpfMx4G0LLTeVV5uiBtcIcEyeCtugQUBikuJ1+xsbBnSb5bpuQcuVdWzRGQlPoW8RfPda2rje8kkEVoJLXMeMGYN3331X9v0NGzZg9OjRug3KDFi1AwLTX+VQsj5Z6a+iRV5YiE639VyVZ7Yu4YC3LXTwVN6fH3/kiOJiR96xL701AgLSX7mum9HYkOc+65rZ9dorkMq/8XfMZcFqRyM6BjX86a85Y4VD8aJRlLnE9bbbbsP//M//4PHHH+8w/T9+/Dgee+wx/OMf/8Btt92mcATr48vzFyls3VXBDHVPKz19oMzWJTy0+zGlpGT5a8/K9mYvtbX5LUm/4NbVKi525P6ruFP6q5EiyerZxdOPS8+eXWp9w3yfM9fKFZo+b777ZmtqNH3GE05w+VxHjx6NJ554Ao8//jjWrVuHxERvWmlzczNiY2Px2GOPYcyYMYYO1GguSxvL9KOqCWawC2Bo8nBM6T+N2Ypb7lhW6lipmXb/beyYUdC0JCPTvqTTNh6P9/d2S9JXcNr3uywTr/bm30+8WsuomJ0BeOqfsrZR6+wqt43tqkle3+lV7DKe7tISwO7wWtiOGNluusH4mx1WHADk7rkCYd1hwgS401/nz5+PCRMmYOPGjaipqYEkSTj//PPx85//HH379jVyjKYSWGAlUHzl/KpKVbPkFrMs0TsKgkWU20XStfsbxeP6priOu+491xq64gCQMxa2pkbmtQcuQvksSd4vatxLr6puwwvPw4+5Dc8iF2sbFZ+4p6gQaDnt/c/pZm6R7GrLmGgVSVG4xLWhoQEPPfQQPvvsM3g8HowcORIrVqzAwIEDjR6f6QT6YnkjBKb0n8ZdmhCwzodWTUSY4sthufqn8u2/OyZP9QuIranRW3FJBtugQbDn/97U++Nf5Mq6VHYb5gOSZ5GLtY1KNIf/oVNXC6T15hZJq3zOogUun+vKlStRXl6Oe+65Bw8++CBqa2uxbJly+b1IQcQXGyzCei1gGYm7tES1pixzYYLDcvWlvwYKAY8fTu+FEGHfNmORKxhmZpcv/1+hkyzTx6tiufojH3qlwDZwIAmmReGyXD/77DM89dRTuOYabxzeVVddhVmzZsHlciE2NtbQAZoNyxcrGqtqZYH1FBV6a8pmZMh/WVkWFY/Pde8eICm5gxDwWFF6u0t4pvedKnOVlgBut2r2Fatnl62pEVJqmnchSAZ79kh49u6BPftczQye67aKK4mQh8tyPX78OEaMGOH//0UXXYTY2Fjd6wmEI6z4WD0TBUQQsdC4VnRZFhWv5WpyZpe7tATSoUNA7ckOMazBMDO7Wlu9bypM74d2d+OsI7Zjzy6O62ZZ5zzXbYU6DKGOcgk3uMTV7XZ3slDtdjvcbvnmb9GCXG8tH2YUZxGZTnN9WRli4RPlxEUKoXgWyOzyWuanVKf3wddozy8AEhK87ylYrgdbHOjmdnXs2cVz3RZoFikaDhat8au8cEcL/PrXv+4gsE6nE0uWLEF8fLz/tbVrNcY6himB1mhgNIFlUFkQ0bMlt296n5CeJFtkWM8prGvlCkjvFAHJPeFYwl7oYl0fK/KASdA1OiZPhXvZUtWFqbymwyi29cUM6VwcONd16/jgEf27qoWMyR2XXBPKcInr3LlzO702a9Ys3QdjZeSm+iyfrK9wC8/+SugpgoGIxtXqGecomgcvbVwPnDkDnDkjO36R6/Pda+nMmU6RAbb5N/jHKkfu9k3IPX0a6NEDwEMAQu9Xdr/2ClBVCXd9PX+0B+BdcNv+iezCm9z9pOgDZbjE9ZlnnjF6HBGFXj5XodAoqH9h9RRJ3xha71jsbZXBCU+APRNfXCgA6ehRdgB9VjZQ/g2kUw1wrVyB2Pt+6526njh+7txB5/QH2NfVdnIdsBILguERYBZ6PnjUkPs8qS28kYUqRpc7ERAd0dPHqrbIJOfzUvOfWiL91RemlDFE26JI4NS89iTb37d3j7d1tMvlTw+15xcAffoC8fFMAfTnz0+8Wrf0V97iNMHbCBdgGZzhzWgbnMF+X8a/q/Y5s8LiWTiiqUFhNHPnsPuFfKsLP18gHOeqNu2ygkXhC7BHL22Fe3zWEip/kE9/BcM6zxkLfPpPID4e6Ndft8wuI6a4opldZdfdjuK6c+USWTBnLWrxuDLuIpreGwNZrpyoRQWYgd6hSl0JsHd9/oWm3fyinDFEk3Xu7x11yQjFJnu2QYPgeOo5XafVWmGVLwyGZTV+ODgX1UOG48PBubL7MWctKkkL0VpAxSxIXDnhydSScwmY1StLC8JhNe1f6Ngrx2vbz2dlVf6g+IAIFoRIy+zaNWQMHp3xIHYNOVf46OIzJ3G2sRkXnzkpOx5WNp2tqRFQ8J3S9D60kLhyorUVtpWzsViIWjW+LzQa5LOQWOLEW9ovWBB4BEJvC41HrJkCzJH+urm8GscaWrG5vNr/WkXlSXRzncXBSra4+rPpgkoukmVqLUhcQ4TVrVdWa+hgWALCk0TAEie12qNy5+OB10LjXXBSq7kAsK9RzZIEgMy+iTjb5kFm30T/a8w+XgHIiWg4WKbRlNVF4qojrE6xLNRaynQVkQ8wzwo1S0B8onx2927Z/UQtKt7pvZEtn32xowA0W8s8PteK483oFmNHxfFm/2vjvv0UT/5rDcZ9+ylzn1CIqNo95e2mG0w0ZXWRuJqA0YtjQh9gjiksK5THJ8othX+X3U3UkuQVZZ7rda1cAee4MXBe+zPF4zNFxSMBPx5TFBHmNXL4XPNsJ9D/eCXybCf8r5nRqyx4G7V7ymrBw0M0uS5IXA2G1QKmKy1leBD5APNMYVmhPD7fafeCm7oyZACdv9AswWLlwfNcrz+z68RxxeN3ik64ahIgeYAzZxR7Won6lccWv4UnSv8vxha/5X9NqVeZEdY5axvVe5qVDcTEAKlpmru6Wt11oRckribAuzhmlM9RyXeqVuYueBuf77TX0qWaxsi8No4iJiz3BdcXNqDoitI0vdMY9u4BbOpfE1G/su7WeXABFo57yhqD6j3duwdITPKGvEWBUIpA4mphjPJPWaHMnVLhbcUiJhlDvFNtt9svzCyh7vRaYGaXUlWsoDHY8wuAIUOACy6E4657ZXczoqOuHg8ennvKs5gZTDRN70UhcbUwRn2AQ/HFULW6FcoXKo7LJ5KtrR1qrnZaaAue3s+/wTuNjY1VnKYHj8ExeSriNn2gmLDg247Xr1wz/zrF2Yjwg4fhN+d2mWhMt42m6b0olP5qYYxKS9TzuHKFW1RTPxXKFyriK9yS0L1DYkFwGnDwazzFV7jH0AU8RYWQjlRBUkiJZaY1q5SRBNgFWLiuR6UqFiEGiSvRJToUbmkXV57YUJ6mf0x8Df0cdsWWMUaLpGi7bXt+ARwb3ob7+htlj80cO4fluvPSSSgeoFyTgAVPOxpCO+QWILoEK4lALoOoAxxhSkx4QsZCgMgqvK/FuLuulrm9z5Xy2UtrsOSZd/H5e9v87/FM74tbe6I6qQ+2tPbUdC3kPzUGEldCd7i+rIIiyRUyZjDu0hJIR48CTY2K3R6kwx17dvliQ93/+YEpyj4xLq6L9Tc79MHj41TL7JKD/KfGQOJKAJBfgFJbmGLVc+X6slZVAna7P/uJFyMC7F0rV2gKefMUFQK1J71dYRW6PeBUx55d9vwCIK23t804Q5R915ZXtw/9Gqoxo+mwputRy+wKBTyZXZT+SkQUvCIZbFGpTX/P1XNViB3VEd7EAh581yZtXC97jazMLmRle61u3+8MWMW5HZOnwjZwIOzJyUxR9l1b7jfb8OSHK5C7fZOm6wnl9F7u88ST2UXpr0REwSuSwQH2ql9YwXqujrvuhW3kKMXYUV54QolYAsxTU5aV2YW9e7wWqe93ORITgf4DYM8e6X/Jnl+AmAsuUBRA3ophweiZ2aWG3OdJ7fMSTf5dEtcIQ+7LxCuSwQtMalN80fRXUT+fYmk/hZYxTAHmqSkb2E47sN324AxgsHKRb1buvWPyVKRv3KB43aItY+TGIVIuUSROGQhtiyGrQ+IaYYj21RK1lkTTX0VRKu2Hyh/khcSf2eU5Jxg8i2qBmV0B7bbVEgt4BFgLwjVlOTK7eJIwOqFjS/BIhcQ1whCddvHkwVsBlvvCd822+TfIX7s/s6ulY8sYtcgDnwAn99RcoIQns4sbQZHkEkGBbLlomt6LQkkEEYYVms2JBthzoRAfa88eCYfcw4EzsysYW1MjpLTesGVkmHtfeUUyOIuLI7NLJFvOCp8zq0OWK6E7omXuuFb9fdP7AF8o1wq0TGZXqFvG8NQWEJ3eMwVYRZR5Oy0Q2iFxJfQnKxtoaoR09KisiDAr9/MUEPGJZKAvlEd4BJMW9F6A8RQVou37/2jO7JI2rveWPlSwXFn3VO3hwJVNRwhB4kroz9493gD72pPy8Y6sMncMq7QTLJHkmDJbJrOrvh72Xr2UrcSgh4WnqNB7zZJHcT/Wg0Dt4UC+U+MgcSV0h2elnGmlsqzSYBiZXaKFvkVx/uZuOEePgHPieE3Te5+VaE9NVbYSWTVl0/t4kxIUEAnXiqbQqFBD4krIItqEjmulnGGl6hk8z7ONaGYXtn/iHXvjKVnL3F1aAveypZAOHjy3TVY20NyEtspK5fsZZLk6Jk+FLSUFaGzU7McmzIPElZCFpwmdz1pq3aqx2SLDSuUJB2NldomKpEiRaADA8Eu8P202WT8vcyq/dw/Q1gbPSXl3iX+7IDdHqK3zaKoBYBQkroQsvJlIwYVbeGBZqTxfaJYFyiOSzGOnpHgt0LNnNYmILT7e29XA4WD6ef0r8Ol94Hj8qQ61XDE4A47zz9cskpZow8MBifI5LCmuH330EW6++WZcdtllyMzMNHs4UQtvJlJwPVceWFaq8LSWIxKAeexjx7w/XS5Fy7yTa0Sl86ncCrzvfp73z9KQiaQwPBEYDMg1cQ5LimtrayvGjRuHxYsXmz0UQgWfECRM63qbcOFp7f59gMfj/SkHSyx80/uEBG01AlQ6n1ppBV7YkhRMb7XStZuNJTO0Zs+eDQAoKyszeSREKAkMz9KU2dVQ3/EnC5ZYtLZ6LdB+/RUtc3d9vf9330+lzC4rZS+p9jKTgyezi4GVrt1sLGm5EuGNqLXE4ztlTjvHX+kNzxp/pfzBBae5LNeIGdN2YQtU8LqpMEvXIXEldMdI3ylr2smVIMAQC9tVk7zFrK+apG2cOsIrmsL3lKb3phFSt8BDDz2E9957T/b9uXPn4tlnn9XtfGlpiV0+Rnp6kg4jiQ5896omPRXOr3cjNme0pvtXc+Y02nr3RsyZFvn9Fszz/gvcj+N8Dbk5aDlYge65OejlG2fFd2jr3h0xFfu6/Hdu3boVzW+sQeKihVz+Z/+92lAE6UgVHBveRnrQdQUeu6H6GKTmpg7j5xrXHYvR/MZaJC66DQlarpFxn81A6e+i9Z6HmpCK69KlS/HAAw/Ivh8fH6/r+Wprm+HxSML7p6cnoaamSccRRS6B98q541+AJMG541+y94/lO3XG9wBOnoTzkixN953nfK6yLyF1i0dL2ZdwtW/jvj4fUlEh3Nff2OW/s2vVakiVlahftRrN7S3GO43zN3cDn2yDLSUF9ocfg2PyVK4xuFathnSiBgA6jJ8H96lWeFxtOHWqFc1h9llW+/7x3HOjURL/kLoFkpKSkJ6eLvsvKYmsxIiAo0YAc5rL0W6bNY3myezijR3lmaazkha4ptHbPwEASPX1/uvm8t+qhH4pES6hUSI+Zau7Lizpc21oaMD+/ftRVVUFANi/fz/2798Pp9Np8siiC9H0V64aAYyFFh6RZIkFT1sUXiHlESMjM7uYqIR+KRFqATKyHU0wlogHVsCS4rpt2zbMmTMHDz/8MABgzpw5mDNnDk6cOGHyyKIL0fRXrhoBjIUWnvRXlliIiiRzG57VdcGasv7MrpgYTQtMXRFIXXuVcSAikpFaU9aS4jpv3jxUVFR0+jdw4ECzhxa2iE67RNJfRUWSB5ZYsL7QXMdnCSnP6jrDMuc+X0wMbL17a24ZE2oLTdidIBD6Fak1ZS0proT+iE67jEp/1SIYag8G0Vx8aesWoLnZ+9OHYOFtrutpn97Hmt0yhgNha1kg9MvqvlNRSFyjBKM+wHqmv8qh9mBgFd7mstQbT3X8CfCJA6OmLA+iD6KuIDq9F7aWBSxXq/tORSFxjRLC+QOs9mBgLTBxWerp6R1/cpyrK4TiQRRMyKMFKLPLD4krYXlUHwys0C8eC8pXFcv3k+dcCK/MrlBPuSN1ii8CiSsRMtQEgRU/yiUirNAvHgvKFxrl+8mLjtaZaKFvNYvUd98AhHTGEs4zJL0hcSV0R04Q1QRBeHrPsFx5LChbfDzQO937UwN6WmdqMbNyscY8XV274g6gotddh8SV0B25L7aqKPk6A6Sk8O8DMC1XHgtKz3AwHjFitsRRKVYjF2vM29UVWdkhi1clOkLiSuiOlJQM1NV6fwagKniiPlAdGxvyTNO7mrQQGBOsWtFLMPXVd23Yu0dIJMl32nVIXAn94agRwGTi1d4Qp4lXa9qNJ/2VBWsb4ZqyHAtovocOep176KiKWBdSX7mOLwP5TrsOiSshi6jfTdSSjHvpVcTt3ou4l17VtB8L4fRXjpqyTMucZ5Gr/aHj+vwL/0u803tRC5JE0jxIXAlZRP1uPOmvhsNhSQoX3mZZ5hoyu2KvHM99GSSO4QuJKyGLFfxuRjbYY2V2yfmLA2Fa5hzn8wk3GhSEm4gYSFwJWaxgNRlZQITpXxX0F/M8iMxIfyXMg8SVsDT2/AIgORlSfb0265XHB8rK7OLxuTJEmedBZEb6azAUvxo6SFwJS+OYPBW2lBSgsVHWemWFT/FM71nxsTw+V9EFOz0xKrOL0A8SVyJkqFlNcu+LFG7hmt4LZnbpuWBnVBty0XtJ6AeJKxEyVEsHMt53rVwB97KlQFa2psItXNalYGaXnrhfewVS+Tdwv/YK+3058eXI7GLdayv40aMFElciJLhLSyAdPQo0NcouMrGm8lz9qhgiyWNd6jm915LZ1SH9VQU5kVRzX5CFaj4kroRmRKaynqJCoPYk4HbLLzIxpvI8AigqkqLT+65mdgWmv2JwBuDxeH+ykIl6UBNPslDNh8SV0AxvubvgKk5q/bhYIskjgKFOWmBeP0c7cZ9Qxo4Zde41Nd+wTNQDiaf1IXElNCNS7o6nH5cVMrt4pvfM6+dpJ94ulK7d35x7TcV3StP78IXEldCM0fnwemDUKrwsKiIZ2D46MIlAzXdKFmr4QuJK6I4VBIEnnpMpwBzTe9ax1UQysH10YBKBFR5EhDGQuBIRCU8SAVOAeab3rEUmlXRbORG1woOIMAYSVyIy4UkiYAkiz8IUa5FJJd02FCLK4woRzewitEPiSlgaQ2vKsgRR0HLVc3ovKpI8rhBhnzKhGRJXwtKI5sLbs0fCljkM9uyR8tuw6rkKirKelqmoSHIJPEdhGkIfSFwJS8PjOxXtacUSRJ6WMXpaqcxpOke5RNZDgKvfGE8xcEIXSFyJkCFUuIXDd8oSUj0FMPj4vN1feab3zGk6R7lEHsucuR9FJ4QMElciZIgUbuGZprMEQ9cFJA5LkjV2HuuZdX08AijqLqHohNARY/YAiOjBnl8AT1GhrGiw3o+977eASsaWY/JUY8WCp/B2VjZQcaBzeFbwa0Gwro/rejiOTZgLWa5EyFCzmqwSrhQM11RaIDxLdDy8xybMhcSViCoMq8QvGJ4lHA1BvlPLQ+JKRBVqoiQaPyocnsXTkpsB+U6tD4krEZaITqfVRIm5es/TSZanZxcLC0zvqWmhMZC4EmGJSE1ZLhFhBdnzCKCBLbmNhpoWGgOJKxGeqFiToqFRqKoE7Hbvz3Z4BFC0GwJvzKyRWEHgIxESVyI8UbEmWdN0URHh8W/yZHbxIlou0ShXCSEGiSthaYRbRDOm6Twi4rjrXthGjoLjrnu7PHZRkRSNMqDpvbUgcSUsjWiLaD2n6cIIZnbxjIElwDS9txaUoUVYGrWsLjl4MrsMRzCzy11a4r9mpYSL4PcMz1QjNEGWK2FprOAP5PFlMrfhiWFlCLBwixrCUpC4EoQKPGLH3IbXcg1hZhcROkhcCUINHgtUtDuBYGYX+VetD4krETUIT+95LFDRTKswTn8l14QylhTXVatWYfbs2Rg1ahQmTpyI5cuX4/Tp02YPiwhzhKf3gparaE2CUGNkPG40Y0lx/frrr7Fo0SJs2rQJL7zwAnbs2IHly5ebPSwi3OEQSeZ0W9By5Zm6h7qxIQuqzGUMlgzFWr16tf/3Cy64AL/+9a+xbNkyE0dERASCViJPOBhrG57QKNY2PKFYLAJFUst+ouFuFPqljCUt12Dq6+uRlJRk9jCIMELPzCeuzK4Qd39lEsb+20jEkpZrIE1NTVi7di3mz5+ved+0tMQunz89nUSdFyvdq5oNRZCOVMGx4W2kL5jnfXHBPO8/BVrvWIzmN9YicdFtSDDwepTuVUNuDloOVqB7bg56aRhDTcV3aOveHTEV+yz1t+gK4XwdIRXXhx56CO+9957s+3PnzsWzzz7r/7/T6cQ999yDQYMGYfHixZrPV1vbDI9HEhor4P3D1tQ0Ce8fTVjtXrmvz4dUVAj39TdqG1fOBCBnApoBNBt0PWr3ylX2JaRu8Wgp+xIuDWNwZY6AtPc7uDIvsdTfQhSrfaZYKIl/SMV16dKleOCBB2Tfj4+P9//e1taG++67D6dPn8Zbb72FmBjLG9mEhQhrf6Bo80ELRB4Q5wipYiUlJXH5Tj0eD5YsWYKqqioUFhaiR48eIRgdQVgEAxfeiNBhyQWtRx55BGVlZXj++efhcrlQU1ODmpoauN1us4dGENyIhkYZWXfWbKIp8cCS4vruu++ipqYGc+bMwYQJE/z/qqurzR4aEYVQkL1+RNM9saQjs6KiwuwhEIQf0fhRUd+p8PlCjEg8bjS5LixpuRKEpRCMH+2K7zSUmU+htMzDwXWhFySuRNQg7O8LsUiKClDI3ReiD50ogcSViBpCnUMfaist5CJJoV+KkLgSUUO4iKQwEe6+CDcsuaBFEEYQLokFooVbulRTVmDhLVzup1mQ5UoQFoOm95EBiStBWAzh6TZN7y0FuQUIIkKguqzWgixXgjAIyuyKbkhcCcIgQu07JVG2FiSuBGEQ5DuNbsjnShAqiIZGifoyyXcaGZC4EoQKoS6kQiIZGZBbgCBUoOk2IQJZrgShAlmShAhkuRIEQRgAiStBEIQBkLgSBEEYAIkrQRCEAZC4EgRBGACJK0EQhAGQuBIEQRgAiStBEIQBkLgSBEEYQERnaNntNkscI1qge8UP3Ss+wvk+2SRJksweBEEQRKRBbgGCIAgDIHElCIIwABJXgiAIAyBxJQiCMAASV4IgCAMgcSUIgjAAEleCIAgDIHElCIIwABJXgiAIAyBxJQiCMAASVw5WrVqF2bNnY9SoUZg4cSKWL1+O06dPmz0sy/D6669jwoQJGDlyJO68807U1taaPSRLQZ8fce666y5kZmairKzM7KFohsSVg6+//hqLFi3Cpk2b8MILL2DHjh1Yvny52cOyBBs3bsSf/vQnLFu2DOvWrUNTUxPuv/9+s4dlKejzI8b777+P1tZWs4chjkRo5sMPP5Quv/xys4dhCebMmSO9/PLL/v9XVVVJQ4cOlSoqKkwclbWhz486P/30k3T11VdLx44dk4YOHSrt3LnT7CFphixXAerr65GUlGT2MEzH6XTiwIEDGDdunP+1QYMGYcCAASgvLzdxZNaGPj/qLF26FLfffjv69+9v9lCEIXHVSFNTE9auXYv58+ebPRTTqa+vh8fjQVpaWofXU1NTUVdXZ9KorA19ftRZt24d2trasGDBArOH0iUiuli2Gg899BDee+892ffnzp2LZ5991v9/p9OJe+65B4MGDcLixYtDMUQigqDPjzo//vgjXn31Vaxbt87soXSZqBbXpUuX4oEHHpB9Pz4+3v97W1sb7rvvPpw+fRpvvfUWYmKi+tYBAFJSUmC321FbW4sLL7zQ/3pdXR1SU1NNHJn1oM8PH/v27cPJkydx7bXXdnj9lltuwdy5c/H000+bNDLtRPVfOCkpicv35fF4sGTJElRVVaGwsBA9evQIweisT1xcHIYNG4aysjKMHTsWAHDkyBEcO3YMI0eONHl01oE+P/yMGzcOmzdv7vDazJkzsXz5ckyYMMGkUYkR1eLKyyOPPIKysjL8+c9/hsvlQk1NDQCvb9HhcJg8OnO56aab8PTTT2P48OEYOHAgnn76aeTm5mLo0KFmD80y0OeHn8TEROZnZ+DAgejbt68JIxKHemhxkJmZyXy9tLQUAwcODPForMfrr7+OwsJCNDU14YorrsCTTz6J3r17mz0sy0Cfn66RmZmJv/71r8jNzTV7KJogcSUIgjAACsUiCIIwABJXgiAIAyBxJQiCMAASV4IgCAMgcSUIgjAAEleCIAgDIHElCIIwABJXIiJ46KGHkJmZiczMTFxyySX42c9+hkcffRT19fX+bb7++mvcfffduOKKK3DppZdiypQp+O1vf4vvvvuu0/HWrFmD4cOH45lnnmGer7m5GQ8//DByc3MxatQoLFq0CFVVVYZdHxF+kLgSEUNOTg527NiBbdu2YenSpfjoo4+wZMkSAN6OCTfddBNiYmKwYsUKfPjhh1i5ciUGDBiAp556qtOx1q9fj9tvvx3vv/8+nE5np/cffPBBfPHFF3j55ZdRVFQESZJw66234syZM4ZfJxEmmFqqmyB0YsmSJdLNN9/c4bU//vGP0rBhw6SffvpJysrKkh555BHmvg0NDR3+/8UXX0jjx4+XXC6XNH36dGnz5s0d3v/++++loUOHSp999lmHY4wYMULauHGjPhdEhD1kuRIRS3x8PDweD9599104nU7ceeedzO169uzZ4f/vvPMOZs6ciZiYGMyZMwfr16/v8P7u3bsRGxuL8ePHdzhGdnY2vvrqK/0vhAhLSFyJiOTw4cP4+9//jpEjR6KmpgaJiYk477zzVPerq6tDSUkJ5s6dCwCYPXs2du/eje+//96/TU1NDXr16tWpolXv3r39Fa8IgsSViBh27dqF0aNHIzs7G3l5eRg0aBBeeOEFSBpqE23cuBEXXnghhg0bBgDo06cPrrjiCmzYsIFrf5vNJjR2IvKgeq5ExJCdnY3nnnsODocDffr0QVxcHADg/PPPR3NzM3766SdF61WSJLz77ruorKzEJZdc4n/d4/Hg22+/xX333Ye4uDikp6ejoaEBbre7g/VaW1uLIUOGGHZ9RHhBlisRMcTHxyMjIwMDBw70CysATJs2DXFxcfjjH//I3O/UqVMAgJ07d+LIkSN4++238f7773f419bWhpKSEgDAmDFj4HK5sHPnTv8xGhsbUV5ejssuu8zAKyTCCbJciYinb9++ePTRR/Hoo4+iqakJN9xwAwYNGoRTp06htLQUZWVl+Pvf/45169bh8ssvx+jRozsd45prrsE777yDGTNm4Pzzz8fkyZPx2GOP4amnnkJSUhJefPFF9O3bF9OnTzfhCgkrQpYrERVcf/31KCwsxNmzZ3H//ffj5z//Oe69914cPXoUDz/8MGpra1FaWopp06Yx958+fTp27dqFH374AQDw/PPPY+zYsbj77ruxYMECeDwerF27tkNTSyK6oU4EBEEQBkCWK0EQhAGQuBIEQRgAiStBEIQBkLgSBEEYAIkrQRCEAZC4EgRBGACJK0EQhAGQuBIEQRgAiStBEIQB/D/6FjVEGRARxQAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 311, 20\n", "LR fn, tp: 13, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.048\n", "LR average precision score: 0.034\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 330, 1\n", "GB fn, tp: 0, 13\n", "GB f1 score: 0.963\n", "GB cohens kappa score: 0.961\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 0\n", "KNN fn, tp: 13, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "### Exercise is done.\n", "\n", "-----[ LR ]-----\n", "maximum:\n", "LR tn, fp: 318, 27\n", "LR fn, tp: 14, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.042\n", "LR average precision score: 0.048\n", "\n", "\n", "average:\n", "LR tn, fp: 313.0, 18.8\n", "LR fn, tp: 13.8, 0.0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.048\n", "LR average precision score: 0.040\n", "\n", "\n", "minimum:\n", "LR tn, fp: 304, 14\n", "LR fn, tp: 13, 0\n", "LR f1 score: 0.000\n", "LR cohens kappa score: -0.054\n", "LR average precision score: 0.033\n", "\n", "\n", "-----[ GB ]-----\n", "maximum:\n", "GB tn, fp: 332, 3\n", "GB fn, tp: 8, 14\n", "GB f1 score: 1.000\n", "GB cohens kappa score: 1.000\n", "\n", "\n", "average:\n", "GB tn, fp: 331.04, 0.76\n", "GB fn, tp: 4.24, 9.56\n", "GB f1 score: 0.783\n", "GB cohens kappa score: 0.776\n", "\n", "\n", "minimum:\n", "GB tn, fp: 328, 0\n", "GB fn, tp: 0, 6\n", "GB f1 score: 0.571\n", "GB cohens kappa score: 0.560\n", "\n", "\n", "-----[ KNN ]-----\n", "maximum:\n", "KNN tn, fp: 332, 4\n", "KNN fn, tp: 14, 4\n", "KNN f1 score: 0.421\n", "KNN cohens kappa score: 0.408\n", "\n", "\n", "average:\n", "KNN tn, fp: 330.0, 1.8\n", "KNN fn, tp: 12.32, 1.48\n", "KNN f1 score: 0.164\n", "KNN cohens kappa score: 0.152\n", "\n", "\n", "minimum:\n", "KNN tn, fp: 328, 0\n", "KNN fn, tp: 10, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: -0.018\n", "\n" ] } ], "source": [ "runExerciseForSpheredNoise(\"folding_car_good\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "indie-refrigerator", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "///////////////////////////////////////////\n", "// Running SpheredNoise on folding_car-vgood\n", "///////////////////////////////////////////\n", "\n", "-> Shuffling data\n", "### Start exercise for synthetic point generator\n", "\n", "====== Step 1/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 1/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 313, 20\n", "LR fn, tp: 12, 1\n", "LR f1 score: 0.059\n", "LR cohens kappa score: 0.013\n", "LR average precision score: 0.168\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 0, 13\n", "GB f1 score: 1.000\n", "GB cohens kappa score: 1.000\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 3\n", "KNN fn, tp: 9, 4\n", "KNN f1 score: 0.400\n", "KNN cohens kappa score: 0.384\n", "\n", "\n", "------ Step 1/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 314, 19\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.222\n", "LR cohens kappa score: 0.183\n", "LR average precision score: 0.183\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 1, 12\n", "GB f1 score: 0.960\n", "GB cohens kappa score: 0.959\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 332, 1\n", "KNN fn, tp: 7, 6\n", "KNN f1 score: 0.600\n", "KNN cohens kappa score: 0.589\n", "\n", "\n", "------ Step 1/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 314, 19\n", "LR fn, tp: 7, 6\n", "LR f1 score: 0.316\n", "LR cohens kappa score: 0.280\n", "LR average precision score: 0.266\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 1, 12\n", "GB f1 score: 0.960\n", "GB cohens kappa score: 0.959\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 332, 1\n", "KNN fn, tp: 5, 8\n", "KNN f1 score: 0.727\n", "KNN cohens kappa score: 0.719\n", "\n", "\n", "------ Step 1/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 319, 14\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.258\n", "LR cohens kappa score: 0.224\n", "LR average precision score: 0.235\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 1, 12\n", "GB f1 score: 0.960\n", "GB cohens kappa score: 0.959\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 333, 0\n", "KNN fn, tp: 10, 3\n", "KNN f1 score: 0.375\n", "KNN cohens kappa score: 0.366\n", "\n", "\n", "------ Step 1/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.4142135623730951\n", "-> create 1280 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 320, 11\n", "LR fn, tp: 8, 5\n", "LR f1 score: 0.345\n", "LR cohens kappa score: 0.316\n", "LR average precision score: 0.215\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 329, 2\n", "GB fn, tp: 0, 13\n", "GB f1 score: 0.929\n", "GB cohens kappa score: 0.926\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 328, 3\n", "KNN fn, tp: 8, 5\n", "KNN f1 score: 0.476\n", "KNN cohens kappa score: 0.461\n", "\n", "\n", "====== Step 2/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 2/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 316, 17\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.235\n", "LR cohens kappa score: 0.198\n", "LR average precision score: 0.326\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 1\n", "GB fn, tp: 0, 13\n", "GB f1 score: 0.963\n", "GB cohens kappa score: 0.961\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 2\n", "KNN fn, tp: 9, 4\n", "KNN f1 score: 0.421\n", "KNN cohens kappa score: 0.407\n", "\n", "\n", "------ Step 2/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.4142135623730951\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 308, 25\n", "LR fn, tp: 8, 5\n", "LR f1 score: 0.233\n", "LR cohens kappa score: 0.190\n", "LR average precision score: 0.158\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 2\n", "GB fn, tp: 0, 13\n", "GB f1 score: 0.929\n", "GB cohens kappa score: 0.926\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 327, 6\n", "KNN fn, tp: 9, 4\n", "KNN f1 score: 0.348\n", "KNN cohens kappa score: 0.326\n", "\n", "\n", "------ Step 2/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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nMPT0IQCtwv2nT/ZpLWG3G+hapIifOp8VCIkjzjSGto0gwvK2hZXrYhJSasCSvJC4EkmDbqnrl18isHC+Zm2qaELN9g2QBfn6vRsVS1gukXVdeZUicLymKq7JU4DcXLim3a4RTl6llbr8VQTKZ01eSFwJRyDSgEWvbyqkIOByKfuy2/EasLB9A2S/7JCqXVjU7iiG9eqoZAtIlWuVJiy8pip6vQDUIs8rf+W9RxbyySYvaSuukiRF3ygJkaQgAJfdw4gZkQYsen1T/+/+p7Fg7H9hxw23c7f70yf7cDQrD+8Xj9Tkvqr7BigWZ3WVpg0hW6WVOfcBeB5fDOzdowiiyFLXeuWv1IAldUlLcc3IyMKZM6dTSmAlSUJzsx+nTp1AVla23cMxBdFigPUHTuJo+wvwvtQJgU0bMXbLWnSRzinbyYsK3rD3r8p+rX0DPoq6FIuruFjpucprJxiPdUmWaeqSlnmuBQWF8Hpr0dh4Kuq2brcbwWDQ+kGZgNvtQU5OLnJz29k9lJgxmu/JFgMElyzDkKoqDKnaBdffCxAom4Yh0kkMWf80kJmFYIVfyX0NWxxw9hxN8j4vwb/VktUuKqi8phoXu5/iFhg4wsQrRziVtBRXjycD55+vn7qjprAwT1mtk7AOXlUSK27q6b7cmNpdNg1DK8oxfHA7eHp1RKBsWmhaf/g7SPLaVAcPtBywWZMWxQogC0/w9fZjp/d6++XOvBONUc5BpAZp6RYgnAcvWMUKFs8twPZzVXJas7IU61KO5mPk1Urknj0fr2CA10hFD/X0PtKiguquWOx+RGpB4ko4AjYIFdi0EZLXC+TnK8LDy2nl9XNll6zOnPsAsrbugKvhtCLWf/pkHw5XHcWfPtkHQLVYoN+nVHLxGqkIrXJgcFFBIrUgcSUSjkilVbCiHKitCTVEkffjLSrI6efKVmTxmlDLQa7r97bmxqKwEyBJylLXvP30LE3e2Ij0Ji19roS98PyMsg9Vaf9XNi3UEKWl05SeZecuKQVO7kPwbxsRyA8ox9d0pGo5nwsIWaIAhgMY0iKEspUarCiHVFujuBN4+8kWtSyiohan7D9uunsWBbRMghdwdBJkuRIJh7vUNdNf1TNqNDyPL4aruFgTzcdF3ZXtgZAYHzl9Du+3K9ZM20W6+aPmuGKlKuNSL3XdrwQ41xT624LeooKuK68CsrNDfznoFREQxnF69RqJK5FweIsKBivKccO+Tehy6pjiGmCDTjt6DMCCQdOxvW2RbgMWpQPWt9/CN2lclBVatalYALPU9d49QHZO6G8LeosK8rZVo1dEQBjH6cFAcgsQCUcdhPphy3NsShWAsNQrdcnq8MGhXN5hvTpiWK8rAVwJoMWirTkOnG0CTtZFTI1i/aOKVYrWPFXZxeCfcZtyE7sKCsKmotEaXMvbNr6yCoH6JkdOY5MNp/e9JcuVMBWRLk68IBTvOGyl1Y2lF6Jr5/YY16eDEnBiz6dYpFmZinUZi1uAt6igSLaASNSf18+VSN3OXySuhKmI+MFEFhUMVpRjyD8344kPlmHgkrmaGy/4t090xU6J+nfpqixZDTDTffDdAp5Ro5H19nvIevu9MKs0araAgEDw+rmmG7zr5HTfqVEcK64fffQRpk+fjiuuuAK9e/e2eziEICJ+ML3VA7b/4CosGP6f+PSrOm43f3m/D/qN1hU7XgMWZbp/6JDSmpDNhY2EqFUqkv8aaz/XVEOk126q4FhxbWpqwtChQzFr1iy7h0LEAK/ySXT1gPc7leCIz6URXdeVVyk3nrzf+Kv7KmIX3LMb0sEDCO7ZrTsmZbqflam0JtRb2TUaof6xBzX9Y+VzpKJAxIKo9a5XvZZqPzqOFdfx48fj7rvvRv/+/e0eChEDrFUqOuVjswV4KVXDenXEE1V/xhXTxygrAfCaV8tNrj2z54Qet0z3PYuXwlXcW9NfgJcSphYI9nGof6ykWelVPkcqCkQsmF29luw4VlwJ5yNilfIWB9RzCwzNOoPFg9thWK+OusInlb8GNDYqYsprXq2Hkek9+5iXf5uO8D57st61kLgSXESmeDxLZVivjrix9EKs330Un35Vp7s4YLQGLHrnQ9Z5oQdtcxHYtDGsebXIiq1AaC2swH/NhfTFFxH9fyQY4kEoo1ZpqmYLpHSea8eOuaYcp7Awz5TjJBO16yogfVsNz7o3UDh1EnebprtnofGV1UpivHydNry3H0dPn8OGfTUY7d2P0w31kDoUoN3ds5BTmIfR/9iKYR+tQu5FM5BTGOoS9f6XJ3E0Mxfvf+nFhMI87vmb7p6F08uWo/nbarjONcGz7g3ktsuB97FHALcbnnVvAJAQcLvhkiQUtJyvacMGNL6yCrkzZyhdqY68vQ4IBgHf98p2ANDULgeNmR7ktssJPTd1UuhfjNcmEk7/PrHXS++zkD/7nDjfj971dPp1ikZKi2tdXSOCwfhWG0jXfq6Bm8ogVZQjcNPNuu8/UN+EoL8Z9fVNyAGU7cb07YT1u49iTN9O8C55CtLJU3B1747GgSPQWNsA35KlwKFDOLnrc3iWLINn1GjcUNwB7x04iRv6dEBtbQMCN5Uh+OLz8NXU4tiat0PW0MARCOStBDIyIQUCCNx0M7wrVkICgJbHAOBqSfz3rliJ+vqmUNZAdRVO1pxAllzXP+kmoHItXJOnKOMCAP+KlZCqquBdsRKNOj0ARK6NHk77PvHq89lrwH2/A0cAA0egEVCuneExcI7vtOukR6QfgJQWV8JaNFNDlcURqpoKVVkF9CqXggHALymR+6H5AQz6v98D/welAQtqjmui+0B4s+rgnt3AF3uB/FDFlryduukLj8y5DwBzHwh7PlqllXyOVAnIRGsIDqTW+00kjvW5njp1Cvv370d1dTUAYP/+/di/fz98Pp/NI0sPhIoBBKLtPD+cZ/YcoGcv4PxCJdDFNkRpXU5Fipi2I23ZDJw7B5w4oQlCqQsEojVVSWfMTI0y6julIoIE8/HHH2PChAl45JFHAAATJkzAhAkTUFNTY/PI0gORQI5eAxbejaK+8TyjRuOzX72GBT+arTRhYUtPQ0n+hZokf/+zy+EbMVhJw2odrBvIzNAGodTdraI0VVGTqje6HmYKqdFrl6pBQ8eK66RJk3Dw4MGwf0VFRXYPLS0Quel0VwFgUq8A/jLZchMWeYqvLj0N7tkNHPoGOHqktfmKTk4relwMdG39XrBjj6VkVeRGT9XodiyYWWmVqrmvjhVXwllwc1oNrgIAtDZhGT/lR8qS1erjS5VrQ9H8779v7Uall9PKNGBh0bt5jaYTpbp1a7TSitBC4krElNMaePF5ZdsfTvwxnn74J/jhxB9rtlXfeLxKq8CmjWh+4bnQGlnM8WXBck2eAmRnA+cXth5Xp+ELry9rtEordpyxkErCYub0PtV/dGKFxJWIKXgFIKYSR27Q68Xn8X7bnjhy6qxSpcW6E9wlpUBGBtDUpFstpezHacASrdIqHlJpGmt0ep9ODViMQuJK6N4Un35Vh4ff3otPv6pTnlM3UomHG/ZtwoVnTypVWqw7gWeRivZl5W3L25csLbLerYTyXAndPEZ1D4CBH4Qv1geEBHj97qO49cqLcVlh27Bj8PIoPbPnYMiLz2PIrgp4hp8P9OLnV8qrAKjr+pXnWx7ruQVEcjNFclqtxOoF9kSOz7tOvM/MrP2cvqigmZDlSuj6XNU9APRyWuVFBd/cVs09dizWpnq6zVsFAAi3NsPSrnQwsxbeLKy2nEWOn2hfdDrNFkhcUxyjDViAUKXVU5P6YVivjty+qeo2gT8depHwmGIJQkleL5CfH3F6z64ywEOk4CHRWD21Zo9v9SoAIj9W6eROIHFNcYxWWrHwckzdZdMw5Mx3eOL/fo8hVbuEz8+zNlu7WR3UVlqdPg1XQYHuDctWdsmIVIrZbUUl2nIWDUJZeV3sni0kEhLXFMcsSyFyjmkNGl9ZHfH88gqqkZe61pa76mUaSLs/V5ZwYSu71Mcz40cl0Ri1pkWsUtH3S4UU5kDimuKYlRSvn2MaEkS57SAQXuoqtIJqvxJACsI1eUpr4Etg7J5Ro0MrDxQUaMfrQOEUwaifVMQqFbXe4/nOkOi2QuKaZogGMHhLVvOn98XwPL5Y6ZMacdsIK6jKDVikLZsjjp/XhCWVKq0SnWNq1Betdz4nXlO7IHFNM3hffnZRQd52ejfTtvwemH8oG5/sO6Y8J9JpSSQIxYXThCWVcjXNDApZWWmlN04nXlO7IHFNM3hffr01rUSmlO+3K8aR0+c0qVjqbQObNsI3aRx8k8ZprGA2COWZPSfkOwUSVtOeSsEVo9Ys68MGEHLRnGsK/Y2RVLqm8ULiSuguda1Gz51wQ/2X6JKfraRi8dwJrJDyglCiS107MepvtZ9R2EVjVhBq7x7A5YJUuZZ8p3FA4ppCmLWoIG87PQEbevoQFvc4h6v7XsDdThHSDh2VvgFmB6HsnoomuhhAVEjZhRq5vmnOjMFdxl8+nIgNEtcUIp70I9Y1ELYdZ6qoZ0GpG7DIQoqzZ4Damsg3epJOKRNdDMC7Tuz0XrQ3A2/GQMuHmwOJawoRz03Ougb0llNRR/Mjl7ZqhVQohzVJlwmxelkU9vii0Xy2WxgFoRILiWsKEU/6kbrUNRbkqH/Thg3K8blC2pKyFSmH1ahI2i0OVv8oiPhc2RQ1UR+2vG2snwXls0aHxDUOZvzvVOVfsmDUYvTMngNXaX+4rrxK2VYdrJIrtHhCGs/YRLDbncATIqO5orz+CmxZMPc6mZiiJrKv3bOFZIDElRCqtpEbX0uVa5UbXR31lyu07Ijm225FCfqjReAFEtmZgPxZBCvKNUGoaLnFZl4nu2cLyQCJa5ohEoTS25a90dWLCqortFhErWUrk94txaDVKJJS5S6bBrRpA/j9YR3JYn3PsewTbVu7ZwvJAIlrHKz64RrlX7IQOQgl0M2fmfLLIin7XHmIWrPJmooVS46p+jrx9mNbO3pGjQbONAI+X1hHMvW+RjNF9KxZu69pKkDiSnDTdoDYAmSyz1V06plKlVYiPx7sdQLChRTgt3bkdSSLZ/lwNbZb/SkMiWuSEov/TL2tfgMWY9385Zta9rnGc7PafaOb2e6PO73Pz0fgZJ12+XABIWU7kvHOJ7J8eCyzBbs/i1SAxDVJMeo/szKCLLqN3vjtnopa3ejEVVAA6VS9sp2IkPKOLxKAVI6nuqYiQS/efoQxSFwtxMpUrVi+/OptzY7ms9Nd0Sl6LDd6ojBan8/1ner8eGT07NlqzQoIKe/4er5TNmWLJZbra/dnkQqQuKYB0VJy4onms24Bo2NKFnjXgOc71RdqKeKxRARe7weSTdkCdLpexYjtqW5JComrA4lnUUERRPZ14rTdaoxG3Hm+U94PWmDhfPj3H4hYDMDmsPIasHDpVwL4fUCb8OXNI2H1dy2dybB7AMmIeprPpmFFek0U9ZdZz7KTb0BDQtevBDh4QEl6551P9Pgat8DAEbGPxUGIvGd52W81rslTIFWu1fhO5eCh7IpRMjKCrRkZvGMBHDHjZHKEsXcP0NwMnKzTfI6e2XMivifLv2tpDFmuFmLU52p0aigMk/RuRhAqVrdAKpE59wFkbd2BzLkPKM+x10rOyCh49hmNNcs2Ele2VQWhUNgJaJurrNyg5/flLdSoRtRfzJKsLhy7IcvVRMwKXOlZNGbBWiK884las/K+OYV5aKxtsGzMiUDEiuPBWqkA/xoDQOMrqxCob2q1ZqurlHPzzilvJ1VXAXUnFLFmx8l+hkranderad7C208+v/oxET9kuRqAV5nlhOYtRlvY8XBiNN9qRAJ7Rst29XyuPGsz2vLhIuOUxwQgYiqW6PiJ2CFxjZFYp/pqETYzNSue2nyhLlhxCGkqRZdFckxFUrGCFeVAczOkhoaY16ZiV24QSaeTx6ReJZdyWhMLiWsc6IlkInoOxBPNN9NS4dXM220JmdlfVSTHlCda3AotILQ2VUvDccUtUK09p+jy4WrY88ljwt49hldyJeKDxNUEomUIWFFIEM+03UxLhVczb7clJDpNNxLciYWw5cMlSfM6zy0Q2LQxlNLlcgF79yjjRL+SqGPjLVdu92eRzrgkifnEDXD27Fl88cUXGDRokBljMo26ukYEg/G9vcLCPNQygZpIQhkpNUtvG6PwAilmInJ8eZuCu2eh0SGpWCLj9s+4DVJVFVzdu4csPB3Y7Xj78c7nmzQO+PorwO2B6/LLAQDSlwfhAuB+bBF3XLJfFoEA4PHA8/jiVt9pfj5cBQW678k/4zZIuz8HALhK+3Pfk9XfFzPh3XdOpLAwT/c1UyzX6upq3HbbbWYcKu2Ixz9p9fRbtGY+c9XrEfu5OhHRqjSh0lO9Kii3G8jM0KRUeS68QHnZ/+xy+EYMhv/Z5QBU3clahFUuKHB1D63OGs0tEC0Vy253TbpBbgGbiecLb+aULxHT5EQRy49CrD0WRF0vntlzgB4XA12LlP1cBQUIqhq3sJVdci6sa/IUpUJLOd/sORE/CzboxSNZP89kRSjPtV+/flaPI6nQS8HiTffVmQI84ql+MZoPy5se8nI8rc63tQqRayqSmwqErFJUVyHg9epeC8/sOYrVKgsigFADcperVUy9Xrjbt4er5fhsZZdSXLBwvrJfLNc/Wp5usn6eyYqQuGZkZODmm29GcXEx9/XDhw/jxRdfNHVgdmFG+Wos2PGFj6fcNVXgXYPAe38Cdu1EIC8/9s+k5jjgcitCqvadytcWp0/D1aEAATldqqQUwb17wrpihRqwaMtdealdrO802meYTD7XVEDILVBcXIwuXbpg4sSJ3H/XXHON1eN0HOoMAJG0q0QVHBhddTSV0nGMNmDB3z4BgsHQ3xbkVW89s+cA0M8vVtf/q/sIoFNn5XzIz0fzt9VKW0DuOPuVAFIQrslTdFO7jLqSyOeaWITEtX///qhqqfbg0aZNGwwcONC0QTkdUXG0eulto4UEqSSkPESDVWGMvDoUhBp5te4mvOCV7CtVB6GQ0wbw+YAjRxTr2FVQAFfWeUpbQK7AcxY7BLSfmdFKK/K5JhYhcf3lL3+JRx55RPf1iy66COXlqfFrqC4AmPG/U3Hju9c7orSVRzyFBKkMr7+qSKVV1m9egOdXz8LVcDqsjDTSj1Vwz25IX+xFYOmSVp/r2TOhvFbf95pMg8xLL1VEOJY+sOwy50YqrVL9R9VpmJYtUF1dbdahUhazK7dSfXpvFF5/VaGUKk7v1LDKp9lzQilPLdsr5zt3Dqg5ruznmjwFyM4Gzi9Uju8ZNRqFleuUz0akD6wMTemTj7jEtbm5GR9++CGmT5+O6667zqwxOR5RcTQqpmY2YEl1eNeKtzYVi343f23vVLZ5tTy9V3eaUoS0U2dlv8y5D8B1WT8gGFT2ZcuE9cZpNC2OBNhZGBLXQ4cOYdmyZRg5ciQee+wxXHDBBVixYoXZY7MNtZ90/YQPolZdxes2mPG3qZixdSpm/C10HLpJxOG6RgTXpgqjXwkgSWHBJL2mKIoVXFIK12X94Jn3S3hGjeaWrMrHOL1suSKcmXMfgOfxxUqZa6Sxqn8M9H58ySXkLIT7ufr9fnz00UdYs2YNdu3aheHDh+PUqVN49913dVO04uWll15CeXk5Ghoa8MMf/hBPPvkkOnbsaMm54oVN4Yqp7NUNABLgdoUepllalB4iqUO8a8WmWQU2bQz1Nc3Pj1ybv2UzcO5c6K+q8bX6HLwxsbmwSsmq16sk9SsC21CvEU5eTmu0z18vn5XyWJ2FkOW6dOlSjBw5EitWrMBVV12F//mf/8HKlSvhcrngdltT5FVZWYnf/e53WLhwIdasWYOGhgbcd999lpwrUciCG5ZFEAQAV8tfmu7LGM18YC24YEU5UFsTykVtQdT1wluuRSTNC/n5wJHDiv9WHme7B/9LY83yFhWMBlmoyYGQMv7+97/HzTffjPXr12PmzJkoLCyMvlOc/OEPf8Add9yB0aNH49JLL8WSJUuwbds2fPnll5afOxrRfKiR3AQ8l8KqkWsAFwCPM5puOwWjIsIKrrtsWihyr0ry5069OcGqaC4BILxFoOKXzcwMKwbIGTNGm1JVXKxkD8iQWyg1EBLX+++/Hx9++CFGjhyJpUuXWi5wPp8PBw4cwNChQ5XnunXrhq5du2L37t0R9rQeo42yicTBWqWeUaPheXwxXMXFmmwB5OeH2vSprdea40BtjX62AMdS1rgTWmBzX2Mh2o8Kia/5WNHgXcjnOmPGDMyYMQPbtm3D2rVrMXnyZFxyySWQJAlnz541bTAyXq8XwWAwzL/aoUMHnDx5Uvg4HTvmmjKeSG3FRPZbP+ED5bkb370+6vGNns9uzB537boKSN9Ww7PuDRROncTdpmnDBjS+sgq5M2conbl4+zW1y0Fjpge57XKQU5iHpnY58NbWAG63sl3tugoEPR4gGETB3bOQU5gHTJ0U2veVVaF9x4wJO+fxDDeaXS5kZHiUa9DULgenG+ohvfQCctvlAAitn9U0cwYKI4wTADB1UuifDk13z0LjK6uRO/PO0BhTlETeByLftViJaYHCoUOHYujQoTh58iQqKytx9uxZlJWVYdCgQRg7dixuuukmUwZlFrH2c9WzSPX6SvKasqgtVd5+vH1kwZVfS4Y+lixW9N8M3FQGqaIcgZtu1j22b8lSoLoKJ2tOIKulnyxvP/+KlZCqquBdsRKNA0eE9jtzBsjKUrYL3FQGqaXyqr6+CY21Da09Vl1uZV/2WNJdPwdefB7NzQEcW/M2PKNGh7b5+hsAgHfFSgBQsgW8K1aGrFKB98dl4Ahg4Ag0Akm/KKQeie7nKvJd42F6P9cOHTrgP//zP/GXv/wFL7/8MvLz8/HEE08YORSXgoICuN1u1NXVaZ4/efIkOnToYNp5jCCau2p16Sshjn62gCt8Y8YtwMt95bkK2NxXtr+qvI8EyZQpfSqtU2YmRq+LFUHkuEP9w4YNw3PPPYfNmzdH31iQrKws9OnTB9u3b1ee+/bbb3H48GGUlpZG2NO5sGJL/tjoCGULMI1VePspHakKCpSbJxR8klpfl/8y0XvWdxrYtDHUX8Dr1YyDJ7i8/qrZ14yK2oBFRCDI78rHSddFSFz37duHW2+9FQ0N4eby6dOnMW3aNNTW1po6sFtuuQWvvvoq/vrXv+LAgQOYP38+hgwZYllOLaC1Snnip2eNsh2vyFqNHdGqJCNdv5Ru/v1KlH1DwafWvGJlO070Xk2wgr+oYLTm2/L//Ts/j7sBi961STec3uBdyOf66quvYtCgQcjLC/cv5OfnY/DgwXj11Vfx9NNPmzawn/zkJ6irq8Njjz2GhoYGDB8+HE8++aRpxxeFF4AyE7JeQ0Rr9Ky3nV6Tb3lbNdKWzZqpO9wuoEtXbeS/ZZE/GdnfKh/fXTYNgRarVb6BZWsWCFnSesUAwYpy5M68E43ye1E1bomlL6v8HikP2tkN3oXEdffu3bj99tt1Xx81ahTuvfdes8akcNddd+Guu+4y/bhmE23BQr2AV6IbczsZo5VW3NUDlCCUahUAeZE/priAPR+qq1r/D3B7DfCaqqj3i3SjN76yCoH6JnhGjdY2blFVhDlJIJyM0ysZhdwCx44dQ/v27XVfb9euHY4fP677eipC03/r4Vda1WoqrXiwvlOlYqrlmEpXKmYpal4QCoWdlIbXgP5UlLc4ILttsKIczV9/o234EqXBDKGP0ysZhcQ1Ly8vYkvB6upqrsuAML/JS6oSrVkJIFdaBTUWqUg3f140X7E2Dx1CYOF8peNV1tvvIevt9yLuJxow4VV3ZfTsqemcxWvcQhjHSVkUQuI6YMAAvPXWW7qvr1u3Dpe3rM2eCsSSRkViGR2jS8+wtFZa9Y7Yl1W3YooJcqFDx5BYNzdr2gIaWVobX38FHPomXOTPNYX+IryfK+Cs6HYq4KTrKeRzvfPOO3HrrbciPz8fP/vZz9C5c2iadPz4caxYsQJ/+ctfkn4lAhGRjLaSq3q7aNvwXk9VH6xIsErUz8huF8uigmywCmfPhPqwqhYRjBYc0wtCwe0BsjK1Pw46S7aocbrfMNlw0vUUEtfLL78cTzzxBB5//HGsWbMGubmhstLGxkZkZmbisccew4ABAywdqN3EI3axLF6Yilj6hddZVFC91DXQEvU/2wS4W10KcLkAj0eTeiXnsMpuAjY4Jh08EBaE8syew22N6KQbPRE4YXVZJwUDhctfJ0+ejBEjRqCyshK1tbWQJAkXX3wxrrvuOsWSTRVELVT1tpEg14Ex2JuVe/OOvDokrJf2hX/GbfqtAV0uICsT6NI1LFtAczOqlsiWj6Ne6jq4ZzekyrWaIJTeDS1yo4umoDkN3meRrO/FKoTE9dSpU3jooYewZcsWBINBlJaWYvny5SgqKrJ6fAkjFuvRDEuTd4xUtWBFbjqRm1V+HHjx+dZtx41HsOE0JK8X0vHjmnxUNsdU0+C6Ja1LyU9VhDS81FW9r2fUaE3aVCSMNvtOBnifqdH34gSL1wpckiRF7WyycOFCfPTRR5g+fTrOO+88VFRU4KKLLsKqVasSMUbDxNq4hQfbQCIWv2i0/NdUIlKjDZGbxzdpXCh6f1F3ZL39nrKfOjkfaLmpvd5QOWv37qH/V1cBHc+Hq6iIew5ekr9/xm2Qdn8OAHCV9kfmqtd1iwGM4p9xG6SqKri6d0fmqtcBJL4hiVWYKYjJfJ3ibtyyZcsWLF68GD/72c9wxx13YMWKFfj000/h9/tNG2Sqw5bUplOeLJtSFVO6TM1xoLZWE/11XXlVeGZB27aaNaZ8k8bBN2lcq2B+/RVw6JBuYxXt+Wo05xPB6aWYejih0QnvOjVt2OCYlCqjCLkFjh8/jssuu0x5fMkllyAzMxO1tbXo0qWLZYNLdlLNOjULblReFRRSb6eepsv7uQDFwgGgCV4p03u20srtBjIztCsIFBSEuSHU/tW435ODgit6WO0nNWrhNr6yKun9t0LiGggEkJmZqXnO7XYjEAhYMignE2v5aqqmV8UDzzfHEyLW3wmEl6wCCAtC8er/RYIvvPPF856SATPHbTTIxdsmd+aM1r63SYpwtsC9996rEVifz4d58+YhOztbeW716tXmji7JEFkBVn4t3WBvPCM5rTx4QSi9xi1q9ESFDXKlOkata1EhFRFv3jY5Y8agsaX5ebIi5HOdOHEiioqK0LlzZ+XfjTfeiO7du2ueIwg91DdeLCuvsrX5bPWNXv1/YOF8ZeVVkdJa+fi8doKxvj87sLrsU/SzMGNByVRByHJ96qmnrB5HUpLo9K1kQM/HprZORP187HbusmlhSf4AdHJTtdZssKJc6eeq5yrguRNEsdstYKbvVMQqFX2/Vvp0nZ7CFdMaWoQW1g2gdgXM+N+padleUO9mYqefQkLUrwQ4eECpzQcgJKSyCKvPDYDbhpAdJxvkEsXu4JWZ4m50em9m7mvThg3wt/hcY/HVOom4l3kh0gezGrAIw9Tm6wkp6xYAEJbC1ZoJ0LKEC9NURd5G+vJLpUtWMmF0ai2aQsYe30y3AA91toAeTk91I3ElhDHLr8g7DpubCuh0smKEVLedIG9RQfUSLnv3AC4XpMq12n6uTEvDVEfUH80iKmwi3xmewOfOnCHUJc3JvloS1zjgrbWlVyggumqskxG5oURuJt5xeMEk7s3DSfLnibB6UUG9MUCSFBeDfD62paEoTuojGgu8z0LkvegFBI1Ys7z9csaMcbRwikDiSggjUmll9GbVrZhSETa11xmX3r7qG7hVSItjPpbI8RONmeJu9L2ICGkslWzJ+oMlQ+KaYGJpxO10RG9Cdju9qShvKWo1YVN7AP5nl8M3YjD8zy6POC53WWipFznTQD6nlSWcicSoIFrtOxX97Hmfg90/WPFC4moRqeAGiIbe9D7azap38xq5mTSL/KnOxxNS1jdrJnb7/+KZMUQLXokSePF5SLs/12RqiH72omNLJkhcCVOJ54Zg9+UmrjPRfN1F/gR8szySfSqqxkzLz+h1Ua/cAMQm3Hb/YMULiWuCSSWLNpbKp2hTQ96+7Ha8aD5vkT9R3yxPMJJ1KmpmMMnodp7Zc0J+c7SuAMGbWaQLJK4EFzNzWkWmhjzfKbudHIRCYSfNlJ8nwqxvlofV/sZEYnTcIu4EubE48vOjpkaxrheR5cNTabaghsSV4CJkqQhO28IsUmaqCPAtHHWZpebGY6b8PBEWGZeZ/kYnYtQKZ32nwYryUHPygoKYr6nI8uHJOluIBokrwcVK/yRPSF2TpwDnnQe0aauxmNQNWAD+lN+oIDpRSA37NgWt8DCrlBOEYuEFCPUQzX9lj5+Ms4VokLgSXIzkjgJi4iBPFTFwsLKtu6QUyMgAmppiq7QSPGeyYHWOKftjxcN15VVAdnboL8QzLfQ+h1QVz2iQuBLG0avP54iD+sZzl5TC1bsPUF2lbKsvpNpKK1HLyKjg2i3UVlZM8a4xLwjF9nTgjSuWYGC0H2pyCxAEg7RlM3DuXOhvCyI5rPL/ASjbipSs6hKDyEfD7hvdyLQaEAsIusumATltgOZmTWoUa5UazTIwM6iWCpC4EqYjd/PXK3GU/++ZPUcREl7wSljoBCwtUZx4o4uMSS8gqBZqz6jRwNkzwPffhxVdxCqmZl4nJ/q+zYDElTCMZ/YcuEr7K8teA/rd/NU3kGi5o9FUr7jekwNvdJH8XAwcHJryDxwc8Vi81Cgj7QSNWtjpBIkrYXjZFR4iDVgi7WvWMiF23+hW+nx5lWqorgqtcNuy6q0esr/bXVKqez69FDX1zCKdGrAYhcSVEApC8bbTq9DKevs9ZL39XtypUaICGcuNniis9PnyKtV48IoBeKltRhqpxPL+7P6hswsSV0K4kYpIpZXZVrDRhsx2T++N5gmLVK/x+s7yXDTcYgBeRobgZ8jzm6v30xNRu3/o7ILEldDFSOWTaFqUaAUYt0IryjidgNFrxUMkWMWremNxl+ksiSM4LvU5eO9P73NQb5tOLgISV8LU/ERRi0bUYjKzDNdpiF4rkfp8rgBzrFne2mIivW/1zqHGzB+UVIDENcWxclHBeKL5Zi4TkiwENm1E7eSfRGzWzXu/IkEongCz1qyoW0CvIiuayCd8AUuHQ+Ka4lhpKain7U0bNuiejyciRlcdFcWJ089gRTmav/4mZitcJJDIa5DCWpqibgF522giz3t/qTrLMAKJa4pjtNpGFHnfxldW655PdLpv5o1n9/RTr2Iqo2fPuINceteYzQRgLU3FIq2tUdK4YnEJqbd1YoaG0yBxTXFEBCueqL+8b+7MO5XziVRaWX0j2n2j6wWhCivXWdKsmzvlLykFOnWGtGWzco5QGlfrqrdGswX0fhxFApDpAokrwcUMazZSpZWolWp0em/39JPnn2R9rtxrLNAnQTcIxUz5edVyrWlcxbo/PKK5tmatg5aqkLgScVmWrFuAt288Qmf3zWpU3Hn+SdbnyrvGIs1w3GXTALcb0hd7NW4HNhNApFpOZFFB9fjlz0LvM7V7xuAkSFyJuG4I1i0A0JLVevuxPtdYVnIIm26fqA2JcIvbgecW8IziL1du9D2JfBZ2zxicBIkrEVdtvrxvzpgxCRtbIjGz0iqEFHE/tlG1vF0oWHVQKT1F1nmA2624Hdxl4S0b9Zpjq8em13zHSN4zoYXENYWIJ/2I3ddsi9GJqVEimJkYz7oFghXlkL48qGnAIm3ZDJw9C2nNHzW+WfXSNu6yaUCXLkCPi3XTolr301qzvPfHWsVGf1AILSSuKYSZQah4rBQzo+DJQCxVaWq3ABu5b905CPibtb7TwkJtfirjX+WuhdWvBPD7gTZtNeMVCZBFI5U/T7NwpLh+9NFHmD59Oq644gr07t3b7uEkDWb4TmPdVxZSuYgASP1KKxG4grtnN/xffKFZBYCN3HtmzwF69gJ69NBeK5WYBl58Hjh7FvD7Il5POTiGEycifhbs43iyBYhWHCmuTU1NGDp0KGbNmmX3UNIGo6lRItkCsRw/GRG14qTKtZAaGyM2YAEQWsJ69hyl0Ulg4XyguVk7vXe7gfx2rdP5i7qHLN6W9bAU3G4gM0M/W6ClkMBdNk0ZAwmnOThSXMePH4+7774b/fv3t3soSYXZUzWR6b3V2QJ2Y7RenjdNd02eAldubljua7SeuXC5gYwMJVglB6GQm9u67T92hFwM/9ihHNszew5clw+AZ8ky3WCl0WIAcgtEx5HiShjDbItDZHpvdbaA3fCuQTy5r5mX9dU0YGGj+dyc1sJCoE1bZV0y5cdr9hxlW5HOWfK40a8kYl9WvfeteS9k3UZHcjDbtm2TiouL7R5GWnD2ww+lmkmTpbMffsg89xPNc+kG7xrUTJosHek/QKqZ9BPuY0mSJO+iRdLh3pdK3kWLIu73XZ++0uHelyrHP/vhh9Kxq38kHb36R8pzNZMmS991v1j6rvvFmnOEj3Oy0DiPXf3jsG1F3jcRGxmJFPKHHnoI77zzju7rEydOxNNPP23a+erqGhEMStE3jEBhYR5qaxtMGpFz8a9YCamqCt4VK9E4cEToyYEjgIEj0AigMco1SNnrxLkGgZvKIFWUI3DTzaitbQh7DAC+P28Emppw5s8b4Z81J2Sl1pyAp317zX548XlIAOrrm9BY2wDfkqXA118Bbo/yWQRuKgNqTrSc+2budfYtWQpUV+FkzQlktXx+euNs9nqBf38N74qVqK9vCvO56r3vRJIs36fCwjzd1xIqrvPnz8f999+v+3p2dnYCR5Me8AIWPGQfG03zrCFYUQ6cPg1XhwIEZH/qnt3AoW+A885TSkoBRA1CiaJei0yN68qrgL17whqwsC0hRb43hD4J9bnm5eWhsLBQ919env6vACFGtACJHqkUhLIakTxRz+w5SuQ+sGljSCjz89FcXa34WKXKtaEI//ffa1Kx1EEoJVvg0Ddhy5WrYc/HG6v8f+zd0/pZcxrF6L0nIjYcGdA6deoU9u/fj+rqagDA/v37sX//fvh8PptH5nxEOlKlI2YtdR3YtBGS1wvk50fsrwqgJTe1RrEKXQUFQFaWklLlmjwFyM2Fa9rtumthKdkCWVlhDVjC3pPqfDLqsXHHuXcPkJ0T+quCvjfxk1C3gCgff/wxHn74YeXxhAkTAACbNm1CUVGRTaNKDtjpvWfUaLJGEd7Ryeh+yvS+e/eoJbGhklVmSZWXXkRzcyD0uKQUwb17NCWsmj6wcx9QPk/0K1EEUGly7fVql2JhzsfC+y7ouYPoexM/jhTXSZMmYdKkSXYPIymhm4KPiE9ZL6FevZ+7bFooh7Vl8T5ZcFnhlrcLO8fRowBcreJYXYWA16vs55o8BVLlWs3qAQBCrgFVmaxUVQXk54dVVrE+0mg/KvR9sQ5HugUIwmziacAieb2aHFN28T7Zn8qulqqepis5rWylFQCcOaNM7zPnPoCsrTuQOfcBAK25sOoKLXnK7pk9R/Oe1OOUoem9fZC4EkQLeg1Y2G7+XMFi/J1sJ6vWDlWS0oBFCUKd8oa1BdScPxAAfD64Jk9RLE1ei0h2nIS9kLgSRAtsMAkQ6+bPCqm8n6u4WClZDVVadQJc0HSzQs3xiG0B3WXTAI8HyMqCVLlWNyCnN06K+tsHiStBtKC3qCDbzZ+bkaESUnm/sPr8muMAWoWU1zcgsGkjfJPGwTdpnOKG8Dy+GMjICGtNyGYLqBu+yFBvVvtwZECLIMxGJCmeDSbJ+4WCSS4lKMTLyABak/XVjzVWo8sNV4YL7scWtQaqmKBX4MXnWyq03Mr51Mdzl03TzRYwkg3BjpOCW+ZBliuRcog26w5bfUFnUUF2ys9apewyLDJsjikKC+G58ALtYDm5qXB7gMzMiG4IqaoKAOJuwMKOkzAPElci5RBt1i1SacVdBQDhlU+sALOWspxlEDxVHx708vlaU7tmzwF69AC6FumeTy9bgIeIcFJ1njWQuBIph2iz7mgd+RWYJVXYbXk+18DSJZA++wcCS5do9nG3b68IqbwfuhZpiwGinC+WBSVJOO2DxJVIOcwUlNYUqmBEsQ7LMT1dr/3bso+7Q2uOLK8vq975WKxeUJKIHxJXIm0RdQvwlqxmm+OwOaaun5aF+gb8tEyzT+aAyyNa1UrKVgQ3hN5YCWdB4kokFVYuH871yzK5r3qrB+Ci7kDH81un/CWlQKfOkLZsbo3uV1XBv/Pz6FZ1lAYsvMckts6DxJVIKuIREXZfdmrPKyJgc1+Vabvve01/Ac/sOcCZRkUUWWtWFkP1WmP+Z5fDN2Iw/M8u14yRDY7xYMdObgHnQeJKJBXxJMVH25dXROCaPAU477zQGlZKEKq3Jgil5MIGAoooshVTshgCUMbGOx8vOAZE/1GhwJXzIHElUg69nFZ1ahRPgOVF/jBwsPKau6Q0VB3V1KQ5nuvKq8KDUB5PmCiyNL6yShmbyKKCMlRplXyQuBJJhdGkeKHgVUsRAaqrmBxWbckqt5u/3w+0aascS6m0OnRIc47MAZcrnf/1ihbMcnsQ9kLiSiQVRn2LegEh9CtRrL3Ai89D2v050NjI5LD21jZgYdoLSls2A+fOASe0ualwu4EsbaWVf+cupfO/aLEDQJVWyQj1FiCSCpHmzrxaeb3F+qQtm7UJ/ACQm6v4R9XHVKg5rjRRaV1U0KNZVNB15VWQDn8HtGuvOU7uzBnwrlgZ1uA6GiLNvglnQeJKJDUiqwfI26kbsACRu/mrCW/Aok3y98yeE94UZu+eUICr7oRGhHPGjGldulx1fAC6qxrIrxn5USHsg9wChGMRCdCI+hnZFCej3fz1kvxZRPrA8sYfz9Se3ALOgsSVcCx6wqkWXZHgFRCe4hRXN3/OqgNhS2tz+sDyYMcvB8gCC+dr8l9FoHQsZ0HiSjgWo8Edo2lL7rJpQMfzQ53/+5Vwz8dL8o8nCMUTRF7+K5F8kLgSSYdazPSsxmhdo3SF70wjkHWesow1ez7ZAnZNnqIUEIiMMxZiyX8lnAsFtAjHIhrc4QWvogW5eEEvkY5U2qWutcExI0EoHu6SUgT37tHkvxLJB1muhC2IBKtELD9ePwCjyfR6HbDYVQb0gmNmBZKoGCA1IHElbMGoP5JFrz6fTfQXcQvwFhXU9bEy9f+8LAMeTRs2mPKjQjgfElfCFswSEF3/JNPNn1ehxQqwXjtB3squmm5aolkG0PYW0IOi/qkBiSthC6yAGG06or+oIJPor7fUtUqARXyuvLHq5bTy3lPuzBnUgCVNIHElHIFRP6NeTmu0bv5cIe1XAkgSXJOnaJa+lnZ/rlkCO1hRDunLgwgsnK/p58rmtPLGljNmTFSrlHyuqQGJK+EIeG6CuIJeQosKaoNXcgMWacvmqGOFJCn9BQDxFWdFIJ9rakCpWIQj4KUtGa2V15ve651Dfo07rtlzFKtVsVJVvtZIqV1GMZrCRTgLslwJR6BXMRXNmtUvdY28qCBvX8/sOXCV9g8t2dKCZ9To0HSf6ZwlEnSi6X16Q+JKWI7RBix6qVHRGp2I5r7yMgZ4COXbmugWIFIDElfCcuJp9Gyka5Ro7itrleqNU8RK5Y2LUqrSGxJXwnJEBFFPiHhdo9gcU1YQ2UUFFaIsWa03ThHLm4SUYCFxJRxNNNHiWaS8RQV5lVbs8bm5sCDfKWEMElfCcoyKk6jFyAaceNkCvEor0bEa9Z1SMUB6Q+JKWI6IOPmfXQ7fiMGaBtGioswrbWWzBcwcqyhk8aY3JK6E5ZjZgEXk+CJZBrEQV5ctyhZIW0hcCUcQuQFLTczCZnRtKjPdAhTkSm9IXAlTMb8BS3gQSuScVHpK2A2JK2EqZjdgMRqEEknZ4iGyRAxBiEDiSpiK0VzReJLwRazNeCxSsmYJI1DjFsJU9JqOGG3CYua45HGoH4vuS35TIlbIciUSQjTrT3TqLdKARQ+a3hOJhMSVECaepHiRSiszo/lmBrkIwgiOdAusWLECf/7zn1FVVYX8/Hxce+21mDt3Ltq2bWv30NIau6f2AL9vqmgvWJreE4nEkZbrrl27MHPmTLz99tt45plnsHXrVixatMjuYaU9IpafUevW7Ck7WamE3bgkSZLsHkQ0NmzYgIULF2LHjh0x7VdX14hgML63V1iYh9rahriOkQ7I18k/4zZIVVVwde+OzFWvAwgJrmxt6lmOetuwz/OOn0zQ90mMZLlOhYV5uq850nJl8Xq9yMvTfxOEc+A2rxawSkU7UpFFSiQLjrdcGxoaMHHiREyaNAn33HOP3cMhDNC0YQMaX1mN3Jl3ImfMGN3taif/BM1ff4OMnj1RWLkupn3Fx7EKuTNnxH0sgohGQsX1oYcewjvvvKP7+sSJE/H0008rj30+H2bNmgWXy4WXX34ZGRmxxd/ILZA4zLhOIu6DeI7lBJcCfZ/ESJbrFMktkFBxbWhowLlz53Rfz87OVqb/zc3NuPfee1FTU4PXXnvNUKYAiWviiHSdzBRNUYz6fq2Gvk9iJMt1iiSuCU3FysvLE/KdBoNBzJs3D9XV1SgvL6cUrCTHjhQu0ZQtgrAKRwa0FixYgO3bt2PZsmXw+/2ora1FbW0tAoGA3UMjDBBPEMpoahe1+yPsxpEBrd69e3Of37RpE4qKioSPQ26BxGHVdXKCn9RM6PskRrJcJ8e4BUQ5ePCg3UMgbIDnE+VN7wkiGXCkW4CwH6sX1xNtwELTeyJZIXEluJhdjsqKqZnLqRCEEyFxJbiYHYQSqbQiK5VIJRzpcyXsJ560JV7qFes7pbQoItUhy5UQRtQPS1YpQZDlSsSAaDEAWaUEQZYrEQHWUqWAE0GIQ5YroQtrqZJFShDikOVK6EKWKkEYhyzXFCeeTlBkqRKEcchyTXFoOWmCsAcS1xSHpvYEYQ/kFkhxaGpPEPZAlitBEIQFkLgSBEFYAIkrQRCEBZC4EgRBWACJK0EQhAWQuBIEQVgAiStBEIQFkLgSBEFYAIkrQRCEBaR0hZbb7XLUcVIduk5i0HUSI9mvk0uSJMnuQRAEQaQa5BYgCIKwABJXgiAICyBxJQiCsAASV4IgCAsgcSUIgrAAEleCIAgLIHElCIKwABJXgiAICyBxJQiCsAASV4IgCAsgcRVkxYoVGD9+PPr374+RI0di0aJFOHPmjN3DcgQvvfQSRowYgdLSUtxzzz2oq6uze0iOgr47xpg9ezZ69+6N7du32z0UQ5C4CrJr1y7MnDkTb7/9Np555hls3boVixYtsntYtlNZWYnf/e53WLhwIdasWYOGhgbcd999dg/LUdB3J3beffddNDU12T2M+JAIQ3z44YfSoEGD7B6G7UyYMEF67rnnlMfV1dVScXGxdPDgQRtH5WzouxOZY8eOSVdffbV0+PBhqbi4WNq2bZvdQzIEWa4G8Xq9yMvLs3sYtuLz+XDgwAEMHTpUea5bt27o2rUrdu/ebePInA19dyIzf/583HXXXejSpYvdQ4kLElcDNDQ0YPXq1Zg8ebLdQ7EVr9eLYDCIjh07ap7v0KEDTp48adOonA19dyKzZs0aNDc3Y+rUqXYPJW5Sulm2CA899BDeeecd3dcnTpyIp59+Wnns8/nwi1/8At26dcOsWbMSMUQiRaDvTmSOHDmCF154AWvWrLF7KKaQ9uI6f/583H///bqvZ2dnK/9vbm7G3LlzcebMGbz22mvIyEjvy1dQUAC32426ujr06tVLef7kyZPo0KGDjSNzHvTdic6+fftw4sQJXHvttZrnb7/9dkycOBFLliyxaWTGSPtPOC8vT8j/FQwGMW/ePFRXV6O8vBxt27ZNwOicTVZWFvr06YPt27dj8ODBAIBvv/0Whw8fRmlpqc2jcw703RFj6NChWL9+vea5cePGYdGiRRgxYoRNozJO2ourKAsWLMD27dvx8ssvw+/3o7a2FkDIv+jxeGwenX3ccsstWLJkCS699FIUFRVhyZIlGDJkCIqLi+0emmOg744Yubm53O9NUVEROnfubMOI4oPW0BKkd+/e3Oc3bdqEoqKiBI/GWbz00ksoLy9HQ0MDhg8fjieffBLnn3++3cNyDPTdMU7v3r3x+uuvY8iQIXYPJWZIXAmCICyAUrEIgiAsgMSVIAjCAkhcCYIgLIDElSAIwgJIXAmCICyAxJUgCMICSFwJgiAsgMSVSAkeeugh9O7dG71790bfvn3xox/9CI8++ii8Xq+yza5du/Dzn/8cw4cPxw9+8ANcc801eOCBB/DFF1+EHW/VqlW49NJL8dRTT3HP19jYiEceeQRDhgxB//79MXPmTFRXV1v2/ojkg8SVSBkGDhyIrVu34uOPP8b8+fPx0UcfYd68eQBCKybccsstyMjIwPLly/Hhhx/i2WefRdeuXbF48eKwY61duxZ33XUX3n33Xfh8vrDXH3zwQXz66ad47rnnUFFRAUmScMcdd+DcuXOWv08iSbC1VTdBmMS8efOk6dOna5777W9/K/Xp00c6duyY1K9fP2nBggXcfU+dOqV5/Omnn0rDhg2T/H6/NHbsWGn9+vWa17/++mupuLhY2rJli+YYl112mVRZWWnOGyKSHrJciZQlOzsbwWAQb731Fnw+H+655x7udu3atdM8fvPNNzFu3DhkZGRgwoQJWLt2reb1nTt3IjMzE8OGDdMco6SkBJ999pn5b4RISkhciZTk3//+N/74xz+itLQUtbW1yM3NxQUXXBB1v5MnT2Ljxo2YOHEiAGD8+PHYuXMnvv76a2Wb2tpatG/fPqyj1fnnn690vCIIElciZdixYwcuv/xylJSU4IYbbkC3bt3wzDPPQIqhN1FlZSV69eqFPn36AAA6deqE4cOHY926dUL7u1wuQ2MnUg/q50qkDCUlJVi6dCk8Hg86deqErKwsAMDFF1+MxsZGHDt2LKL1KkkS3nrrLVRVVaFv377K88FgEP/85z8xd+5cZGVlobCwEKdOnUIgENBYr3V1dejRo4dl749ILshyJVKG7OxsdO/eHUVFRYqwAsCYMWOQlZWF3/72t9z96uvrAQDbtm3Dt99+izfeeAPvvvuu5l9zczM2btwIABgwYAD8fj+2bdumHOP06dPYvXs3rrjiCgvfIZFMkOVKpDydO3fGo48+ikcffRQNDQ2YMmUKunXrhvr6emzatAnbt2/HH//4R6xZswaDBg3C5ZdfHnaMH//4x3jzzTdx/fXX4+KLL8aoUaPw2GOPYfHixcjLy8Ovf/1rdO7cGWPHjrXhHRJOhCxXIi246aabUF5eju+//x733XcfrrvuOsyZMwffffcdHnnkEdTV1WHTpk0YM2YMd/+xY8dix44dOHToEABg2bJlGDx4MH7+859j6tSpCAaDWL16tWZBSyK9oZUICIIgLIAsV4IgCAsgcSUIgrAAEleCIAgLIHElCIKwABJXgiAICyBxJQiCsAASV4IgCAsgcSUIgrAAEleCIAgL+P+d5EXih4zZqAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 320, 13\n", "LR fn, tp: 10, 3\n", "LR f1 score: 0.207\n", "LR cohens kappa score: 0.173\n", "LR average precision score: 0.212\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 1, 12\n", "GB f1 score: 0.960\n", "GB cohens kappa score: 0.959\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 329, 4\n", "KNN fn, tp: 7, 6\n", "KNN f1 score: 0.522\n", "KNN cohens kappa score: 0.506\n", "\n", "\n", "------ Step 2/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 322, 11\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.286\n", "LR cohens kappa score: 0.256\n", "LR average precision score: 0.308\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 2, 11\n", "GB f1 score: 0.917\n", "GB cohens kappa score: 0.914\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 332, 1\n", "KNN fn, tp: 9, 4\n", "KNN f1 score: 0.444\n", "KNN cohens kappa score: 0.433\n", "\n", "\n", "------ Step 2/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1280 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 317, 14\n", "LR fn, tp: 10, 3\n", "LR f1 score: 0.200\n", "LR cohens kappa score: 0.164\n", "LR average precision score: 0.180\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 0\n", "GB fn, tp: 0, 13\n", "GB f1 score: 1.000\n", "GB cohens kappa score: 1.000\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 1\n", "KNN fn, tp: 9, 4\n", "KNN f1 score: 0.444\n", "KNN cohens kappa score: 0.433\n", "\n", "\n", "====== Step 3/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 3/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 319, 14\n", "LR fn, tp: 11, 2\n", "LR f1 score: 0.138\n", "LR cohens kappa score: 0.101\n", "LR average precision score: 0.182\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 1\n", "GB fn, tp: 3, 10\n", "GB f1 score: 0.833\n", "GB cohens kappa score: 0.827\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 2\n", "KNN fn, tp: 8, 5\n", "KNN f1 score: 0.500\n", "KNN cohens kappa score: 0.486\n", "\n", "\n", "------ Step 3/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 312, 21\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.211\n", "LR cohens kappa score: 0.169\n", "LR average precision score: 0.176\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 2\n", "GB fn, tp: 0, 13\n", "GB f1 score: 0.929\n", "GB cohens kappa score: 0.926\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 3\n", "KNN fn, tp: 9, 4\n", "KNN f1 score: 0.400\n", "KNN cohens kappa score: 0.384\n", "\n", "\n", "------ Step 3/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 309, 24\n", "LR fn, tp: 6, 7\n", "LR f1 score: 0.318\n", "LR cohens kappa score: 0.280\n", "LR average precision score: 0.236\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 2\n", "GB fn, tp: 0, 13\n", "GB f1 score: 0.929\n", "GB cohens kappa score: 0.926\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 3\n", "KNN fn, tp: 7, 6\n", "KNN f1 score: 0.545\n", "KNN cohens kappa score: 0.531\n", "\n", "\n", "------ Step 3/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 320, 13\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.267\n", "LR cohens kappa score: 0.234\n", "LR average precision score: 0.233\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 1\n", "GB fn, tp: 0, 13\n", "GB f1 score: 0.963\n", "GB cohens kappa score: 0.961\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 2\n", "KNN fn, tp: 7, 6\n", "KNN f1 score: 0.571\n", "KNN cohens kappa score: 0.559\n", "\n", "\n", "------ Step 3/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1280 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 322, 9\n", "LR fn, tp: 10, 3\n", "LR f1 score: 0.240\n", "LR cohens kappa score: 0.211\n", "LR average precision score: 0.266\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 0\n", "GB fn, tp: 2, 11\n", "GB f1 score: 0.917\n", "GB cohens kappa score: 0.914\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 0\n", "KNN fn, tp: 10, 3\n", "KNN f1 score: 0.375\n", "KNN cohens kappa score: 0.366\n", "\n", "\n", "====== Step 4/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 4/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 319, 14\n", "LR fn, tp: 10, 3\n", "LR f1 score: 0.200\n", "LR cohens kappa score: 0.164\n", "LR average precision score: 0.212\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 0, 13\n", "GB f1 score: 1.000\n", "GB cohens kappa score: 1.000\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 333, 0\n", "KNN fn, tp: 13, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n", "\n", "------ Step 4/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 313, 20\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.216\n", "LR cohens kappa score: 0.176\n", "LR average precision score: 0.213\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 1\n", "GB fn, tp: 1, 12\n", "GB f1 score: 0.923\n", "GB cohens kappa score: 0.920\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 329, 4\n", "KNN fn, tp: 7, 6\n", "KNN f1 score: 0.522\n", "KNN cohens kappa score: 0.506\n", "\n", "\n", "------ Step 4/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 315, 18\n", "LR fn, tp: 10, 3\n", "LR f1 score: 0.176\n", "LR cohens kappa score: 0.136\n", "LR average precision score: 0.175\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 1\n", "GB fn, tp: 0, 13\n", "GB f1 score: 0.963\n", "GB cohens kappa score: 0.961\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 2\n", "KNN fn, tp: 8, 5\n", "KNN f1 score: 0.500\n", "KNN cohens kappa score: 0.486\n", "\n", "\n", "------ Step 4/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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roWiKkI9XlqtA5koywhwSVyLp4WN26d3cxyazWZ+VCSdtebxCo+6LZKIMfCV3AdnZ8BVNix3TvvdLIR8vjwXK0zc0eeUsFOdqQCrF2yVDsrGVVvuJVZ+elShqParPAyIChaHDgI8/ivtMXe7puFYffHn5cUH3VuJj9Y7BiysQag8hMGt2XJ3qc3n6hvf34mmX03G1PKTSc5eSca7vvfce7rzzTlxyySXIz893uzkpQaokZxa1QJON5VSq1hpGLIQrKwCfH1AUS33BY4EGCsYj0DPRNRGaXwZlz25dC5sFrwUqOjlGyMGz4tra2oqxY8di5syZbjclZUjGpxa10pwYHlrxK8ZFGax4AUrtPxB6brElH2S0DF/RNMOIBX9xCXx5eQg8uQiBgvGxMkIrXrAcjsX6LbJLZyS4JnjEXDS0TObkGGEdz4rrpEmTcN9992HEiBFuNyVlSMYKcTLcJukg+OPHTK1ZVl/4hw2PCYmVKAPs3xdnXbKuJ2KB7rFugarEHMDpF8iKF3SvhVdIZW9bI4OOJNyeFVfCOslYoLKGh7KD4OMWH8yaDd/wEfD9a3GiNasSIyBeWFgWqKUog8ysmHWp92KAzwco4dgxLF9ny6pyy6KllxTGyThXmXSomFnF42zdulXJy8tzuxkpw+HCImX/iJHK4cIpttd1cuNG5XBhkXJy40bT+tXHRv49Je48VlmnP5+iOi/+mANXXa18M+A85cBV15i0c4py4KqrTftG235tWw8XFinfXHCRsi//wthnJzduVA5cdY1y8KqrY23U74P4srTH8dTHi+i9oPdbWD1G/7wpls9LRTwfLbBt2zbccccd2L17t+VzO2K0gJNZjqIzzVnnDwZ+s9qwfu2stMgMeLJRBryz+tooA3ZEwelMWaGazQj9/BGgrR0YOBCBWbOZ9WjvJ972hMrmAsE2YOBAZFW/I3QtVu4F0d9CFG07U+m5S8loAUIMUT+byBBSNM5V5iy53nBY7QONXhsAS32j55fVZsoKV1YAbW1AOMRVrlnbtVEG6N0H8PvijtH6eHnL1yIzL4Io6eoqIMvVgFR6gyZLMpaIup+4rZ49ewAlHDeZI4tI+bsBRUHgyUURf2x9HdCzF3z9+ulacjxxrnoWcdTnG7Valbo6oFs3+HJyDC0y4ThX1fVZscyZfWWTBSp6Xio9dylpuR49ehS7du1CfX09AGDXrl3YtWsXgsGgyy1LT7SWiJ2TIaxZcp46edvkLy4Bcs8Czjo7/guTKAOe0CjW5FigYDyyqt9BVvU7sUlFdOsG7PvG1Lo0W0mmd32+vPyE/hOxAL1ggabr8lrPWq7V1dWYN29ewuc1NTXo27cvVxlkufIjugoIkNNPLF+mFisrk1gWqHqFVsya7T9A148ZrU9tgQKnRKSpCTh+3NiHrLIuASCwrhJt+UNibQgUsHdJkLEiK9pOEd+7LAtUlFR67lLSci0sLMTu3bsT/vAKK2ENEQtKD96kKNqge3U4E+s4KyuTWBaoOs41RktLnAXKWosPIHFl16zZDB+yJs5VZV2GKyvQtutTKBWvxvmaefKp8v4WZuFnvKSrD9RpPCuuhBiyhvPJDNVEVhSxhrpa0WLF8erGzJqstIrGzCI7O85VECyciGDhxHixVQmp3rVoXwzatvqLS4BwGMj6gWHMrPa3ELUGeRdA6J0rc2vyjgqJa5rhtN8rVMOXSk8LS8xZwqkVLR6rTtgCXfECsPcL4KsvTaMMtMtrWT7euAUQBeORs3wZfEOGJIi+9rcStUBFFkDIxM7FB6kIiWua4fQNHq7kS6XHW5aZNRu1yKKJr3nDmVhDeaZw+gNAVlZM6PWELaGfDx8CGhp0V52FajajZVW56UorLawXg7X+S7TgtWXw5EXgwckwwFSAxDXNcDoRh7+4BBmDBpn6QbXwLIFlXUvMIjNIfG3FIox7KcyaDd/FFyOw6LmYJa0VNpYo6/mLtfW17/0yIcqAJUZaC5THNcHqP97vZfrbRUhXdwKJawfA/ps3PiKDpz6e4TyvTzI0vwzKzk8Qml9mmDfArH4teq4DZmIVRmiUdjIuY9CgSG9ZsECjZYQ/qtU9nqecuOvRiLksf7sXcxm4iWdDsWRAoVgR7FwS2zbjDvi+rofSr7/pklSr7eRfkLAbOHEC6NIFvrx8ZJa/hrblS6FUrYWvaBoy5zwsrb5g4USuBQmxtqnKy83tioNrqi0tW40tSPiuFejU2dKS1GR+dzcXJKTSc5eSoViEPESXQfLAcguI1pdMQL2v5K5YTGuoZjOUileBlpbYHlYseOuLG6Yzogzali9FcNyYhM0ItRZ2Q9EUyxZotAyzXLSs9tsd8aHX1nSzQEUhy9WAVHqDJous5a+A+PYiMpKtxCzQnZ8A338PX8ld3JarHmZtDc0vi1nOWVu265bh+7oe4ZMnLVugVr8XuUaj8wDxBQkipNJzR5YrYYpMq0OWz5VVFrfFe9EQBH6xPE5YdaMITCb/tBao1gfqK5oWcUmc2oyQVVbUwhexQHn6jrdveDGb/CPMIcvVgFR6g7oJj+UqYoGyPrOSdlCL+tyoP1N7Dq/VKOID1faT7M0BzfoG4LdAyXLlgyxXwlFkWaCssngtXjMLVC+GlddqFLVAzZbz6rWd53uztluxQLULINIxsYrdkLgSlpAVbsMT58qqj/dBNxNqK8H5amJZr1TJV2SLlll5PHG8rGOtuH5E3UTpuiBABHILGJBKwxOnYIX7BNZVIjS12JYMSqJDZ97ytcNfdTatZEOezEKxAMTlgTVyocgY3jthecpwdaTSc0duAUIaLAtUu/yVB14L1M7wHq3wRFZaJWbT0lpjvCFPZi6McGVFJGa23tgVAphbvHoWIy0IcA+yXA1IpTeoW0Qs1zcQmnqbpckruxHZB4rXAhWF13JlYWa56rVR9LeQPdlmhVR67shyJWwjUDAeuVXrDCevkoHHgjKbvNKDZYFGJ7uMykrGr6i1QNU7GBhhFhqld72ivwVZoMlD4kpIx+mYWZ4og2Tq05YVrqyAsmdPLJeBFWRMFPFGTCRTH5E8JK6EdGRm5hKxQHnrs2IBauuDEgZ8Pt3QMtk+UNHQKJkvGcIaJK6EI8hYKSS7PisWoLZNke218+N8p+psWbLFSaZrQqQ+wjokroQj2PmwilqgyVjYZiLMe72iFq/dliUtHEgeihYwINlZyxl/uzXu/+VXrBEuy8u4PbsrMzrBzrhaVj9p6+Od9fdCRIZduH0/WYGiBSQy42+3xv4Q3sBpHy8PvPVr6+Od9edNLE64B4krkZaYDZvtnHAyqt9sOa/orL/T4W+EOSSuNlJ+xZq4P4R17LJAecXPbgtUVpSB0+FvhDkkrhYhsXQWuyxQXvHjFRrR5byi9fHU77ZrpKND4poEav8r+WDtQdZwmDe7Fq8FagZv5ipZ9dmVKJsQh8SV8AR2D4dlW6Bm8LaLx+dq54IIwj5IXAlPwJNMm4Xs7Fraer1g/YkuiCDchcQ1CWjCSh48ybRZyJ7Z1tYrc2VU66ZNtvlAeXZ/sBuKMoiHxJWwFVELNNnhvqgFaparNRnRb1lVLmSd8yCaGUwmFGUQD4krYSuyJ5y08M7681qgZj7PZEQ/u3SGqXUuOjElMzOYzCiDjmzNkrjqMONvt+Lm9TdSFECSyJ5w0sI765+MBRqaXwZlzx5LGalEs3klk6nLi1EGor70dIByC+igFlW1P5UlttHv9c5Jd9RrwUXXvDtxnsg+VG0z7oCyZzegKAg8uYj7PCb3Tkfw872GeQucziGQzFblPGjL4sndQLkFCEO8avHabTk4balYqc8sJ6qu3zIvH4EnF53eBUAwUbbWLcCCnZh7t2l9MofyPNa53b70dIDEVYfyK9Zgwy3vpp0Favekg+isv2i7rNSnPlZ0OMxKlM1L5wkTLPtA/cUlQFsbcLI1liuWhd2+bdH6tHSkBQokrhYwcxWwhNhrK7jsthxELRXRkCdWfejWDUpTk+FkFa9vk5WNSpsoWyYs8UPvPoDfp3uMXtt5XiC0RNY+pIjryZMn8T//8z8yivIU0QktK8Lo9bhXpy0Ho/rUD7askKdAwXj4cnKA48ctB92z2soSfR5/ZKhmM4KFExEsnGhJuJiz/rNmwzd8BAKzZp8+hsM1wfMCkWnxdqTJKh6kiGt9fT3uuOMOGUV5gmQtTT1rVfu5G1Yt7wMg60Hh9YFqj0smykA06J7nOF4faLiyAqivA+qtJZ3hjQxI2MNLs82MXlk8IwtRi5fiXOMht4AFtFapFStVtiUrKn6iPlA76lM/2Kzht8xlrVqsXI82zhWKAvj8hn3jLy4B+g8A+usLl+hvweOa4LWweVd28Vi8FOcaTwbPQUOHDrW7HZ7FSBSNwrLsRjvzzUtUyHh8oOrjQiteAOrrEGpqMh0Oqx9qVn3qY9ThODzt4oGnb6LHhFa8EBGIocOAjz9iipG6vGh7tSFd2voCBeMTymndtAmh+WUxYWb1Dass1nHa8gOzZie2aecnCP1sDsIldyFzzsPMfmCJME99vPeR6H2aDnCJa0ZGBm677Tbk5eUxv9+3bx9WrFghtWFOw5qsEo23Y8W9qj/Xq88KvDe3KCxx4EH7MLHK4RUjHuuLV4y05cWs5aYmKHV1wO5PgVCIKUbq8qJlYOiwmNXGU1+gYDxaVpUDPh+ghHV/t4SXmk4faD9niV/oZ3OAcBhK1VpAR1xFfwve39XO+9TuGOBk4XIL5OXloXfv3pg8eTLzz7XXXmt3O1MOu/2oToTQxE04aSZV1N+3btoU+4xn1j9ZP1/b8qW6y1GNHjjWtfv++UfwDRgAX9E04PvvT4uRClaUgVK11jBmllVfdukMRsxsvP+W18dr9jsGCsbDV3IXkJ0duTawJ9lEfwsWojGzonjdx8slriNGjEBdXZ3u92eccQZGjRolrVGpjIirQESIk/EZ8voozW7e6Pctq1bHPuOZ9U/Wz2ckbEYz6Swfr/LXv0S+GzY8QYyM+s9XNM0wZlZbX6hmc8RyPWXx6vlvtej5eJWmJqBbN8PfMXPOw8jasj1mhbMm2dSum2j7ZU4S2ulz9bqPl0tcf/7zn+Oxxx7T/b5///6oqPDm24MXvckp7W4DsizSZH2zVt7a2uEaryVhNOGk/j679O7YZzyz/jzWbKhmc2QGvKkp4RgjYWPOpJ86BkDs2qNlATjtT9WIUawd88ug7Nkdf+3DhhvGzAKJFm/73i8TXgyRiak84/jes84GcnPjXjJoOAwcPmT4+zH7RjPJdvr69sS5OWSt2rLTuuR9SbuFtGiB+vp6WUWlPDP+dit/JIECIARbg7jVx7YtX4rguDFoW77U9Dz1zWv0wHWeMCH2Gc/NzWPN6llZmeWvIXPOw8bCdtbZQO5ZhjPbsfpmzTZdtQWfH1CUyDHPLYbywd8ReuoJ09GA1uLNGDQo4cWgdWFo2xqurACOH4/9m9fiZZUVKBiPrOp3kFX9Tnw/a/zAohaoqMUrE6frM2xLMie3t7dj48aNuPPOO3H99dfLapPnUa/ISsYCLb9iDX7zShZWPnXA8pvWigWqPlapWgucOJHgV5RVn2jIDsAIeWJYWWYWL8uqM7KeARgKtb+4BMjNjQg2ABw/durv43HRBmY+3kDBeORWrTN9MWjb6i+OrDjDvm9i1jOvxcszavAXx+dOAMB8AXvR58rC6fqMEBLXr776CkuWLMGVV16JBQsW4JxzzsHKlStlt811bl5/Y+zfdoVYib5pRX1LvqJpQJcuCX5FWfXxBKnzTgD5cnIQmDXbkv+WZdUxLTHOoHsAEaFuOIxwZQV8/1oMZGcDP7r6tFtBZ1GB2W+r1zdqH2hsxVnWD05bzxwWr951s6xZ7THaFzCvj9dLwuYFuEKxAKCtrQ3vvfce1qxZgx07duDyyy/H0aNHsX79et0QrWR58cUXUVFRgebmZlxxxRV4+umn0atXL1vqEkHP/8ojxLFzM4HfIMty3aywFx4y5zycEJYjGvLEAytkp235UihVa+ErmqYb8hStT20ZssJ6WCFC0fZqIwjUsaxaoj5e1nVH3AKnh83hjz+Cf+IkBArGx3yW8PmZMbN6afX0+kZdXrSv1dcdKDiVtu/UpF20HN6QJ57jfEXTYr9PrA8aDkdeWgZ4PTTKabjyuT733HNYv349evXqhVtuuQWTJk1Cbm4uhgwZgt///vc4//zzpTesqqoKCxcuxJIlS9C3b18sXrwYfr8fv/3tb7nLSCafK2Aei2o0ucV9vAKsXHDAML8lC9EbmXWeXo5N9bFAYh7T6Pc5981Ey6hx3PUFx40BTpwAunRB1pbthucpTU3A8eO6/RMsnBjxzfYfgKzqd5hlxXKynqozurIpKqbRAHyl9h8AAN/wEbq5TaN9hW7d4MvJiXd/nGorvmsFOnWOOyZQMD4hblr3t1Dlj9X2deyFMb8M8Pngy8vnvm+SuWdCZXOBYBswcKBxP5vkauUhN7crDq6pdlSoRfsm6Xyuv/3tb3Hbbbdhw4YNKC0tRW5uLnflovzud7/D9OnTMX78eFx44YVYvHgxtm7dij179thet0xMowzCEHILyIxz1Ru+ioRi8dTHck1o3Q68E05a9GI5oSjAD04PrQHEDff1fLzaB44VZaBtq69oWsRPun9f3Cy8Xt+EVrwQ72vOPeu0j5fRj6d9rvlxbeWdcFLXp9eHdmbm4sHpWX876uMS14ceeggbN27ElVdeieeee852gQsGg/j0008xduzY2Gf9+vVDnz59UFtba2vdPMgIyYpFE1y5xlE/lRUfqPpYIx+lOhSLpz5myJOmfN4Jp8Cs2RFRhGr4z4gyCDy5CL6LhsSswdCKF4CTrUAwGBNP1ky6NmbWSPTDH0XuTf+w4RE/aWZmzJ0QqtmMhqIpzAUQAOKv6/AhoKHB8AXIE0+s7kfD+jTo9XNCZi6Gj1ftLzZqlxnpEGXA5XOdMWMGZsyYga1bt2Lt2rUoKirC+eefD0VRcPLkSWmNidLU1IRwOJzgX+3ZsyeOHDnCXU6vXtnS2jTjb7diwy3vxk1yadlwy7tx/9ceq/3eDVq7d0ZLZgDZ3Tujs8GQBgBwa2HkD4BDL/4K7T4f/N+dBO6djuzSGeis+r4zRxlGHMzwI+TzIZARQG5uVxx68VdQPv8CoZ2foHv3zug8YQJa75uJllWrkV16d6ztrd07o6nhMBSfD4F1b6D7fTNxfMlSKAC63zcTnXO7onXTJrSsq4z8/1TY2KEMP9oDfmQMGICzddrXet9MNM15CIrfj8C6N5B7a2GkrFXl6F46A503vA0AaCiaAuXreih7dsPXubOmHQq6d++MllXlaN/7JfDJztgx2aV3oyUzgMzrx6Ptw39E/r+qHCG/Hz5FQc6p9uv1Y7Qt2aUzAEbfAEDDukooX9dH2l+1DohdQ+Kx6utm9bP6vmEdk1DfrYW6x5lxDud9Iw0b6uOe0AKAsWPHYuzYsThy5Aiqqqpw8uRJFBcXY/To0bjhhhswdepUqY1LlmR9rloxNcszYPR9+RVrYt/btdcWj9+obeVLUOrq0LTyJV0/KQvlnp/AV1mBcFMTgp/vRdPKl3DsWKuQz5XJPT8BVryAUHsIB9dUI9QeBsJh4Pvg6bae+tMCoOVUX7atfAkKfEA4jNDU2yJt6hqZ1W4ZNQ4tDc2RY/bsxpH/8yACx1ojIWmnrkcpLknY/0s9IeVfsDBiiU29DQ0NzQgufg6or8ORw98i69Q1h6YWQzl1nvLxR7F2hPbvB3w+NK18KRLnuu4NtOVfFDvmyKmygoe/RVb1O2gBEDrWCt+p/oq2Xw/1b5lZ/lpC36jbFm0/AGY/xjFqHELHWmO/caBgfOJ9o1NGQn1mdTFww+cqivQ9tHr27Il/+7d/wx//+Ee8/PLL6NatG5566inhBmrJycmB3+9HY2Nj3OdHjhxBz549pdVjhtbyNErEYoYdeVu1/izRWEQemMPhU0P5Y0v0FyTwDgtjIUenklwHZs0GBg0GBg40j+XMy4tfr8+KVWWEZ+kNYaOrqKKz/0rXbgjNLzNdeKFdtaVefMCKc9VidVIlzm2j49vUug94faDafuS9b2QtgRV1J6Tc8lcjLrvsMjz//PP4y1/+IqM9AICsrCxccMEF2LZtW+yzr7/+Gvv27cPw4cOl1WMFq9ECoudYQeQBSPbmZ53v0xxjlsuAZy2+ZR+oSVwtK+heu6w1em50FRUAKHv2AP/9Z6ClBUrV2gQfb6xdrElCleizrl10hwEWPD5X1nE8vwWrn2W0zQhRI8DpiTAjuMR1586d+PGPf4zm5kSz/vjx4ygpKUFDQ4PUht1+++145ZVX8Kc//QmffvopysrKcOmll9oWU+sk2qWxom9bWQ+A6A0ZFYduj7DW4pvPkmu/NxP+iAWamDdAHR2g1wdaS1VrWaqJWaCzZkfqy8gA/H74iqYlWNiAta1gtLP+CQsggt8DJ08abkbIKos34kN7nOhvYQUeC1sWTk+EGcHlc33llVcwevRodO2a6F/o1q0bxowZg1deeQXPPvustIZNmTIFjY2NWLBgAZqbm3H55Zfj6aeflla+HmrLcsMt78b5UfXytBrBc46sAH2eYaVeeBFPADoPEdE6vVadlWTbSn3qvmElqg6teAHY+wXg95v2n1aMtAKWEO6kWZCgbn80qUxU0BNWQWmC/KOor525uKFP30i0Awc8/ag9RttW0d9C9D6NxsJa+a2SXbTiFlyWa21trWHO1oKCAnzwwQfSGhXlnnvuwZYtW1BbW4uVK1fizDPPlF6HTIz2xtJLlA3Ie9vyWKB6ITQ8Fq/eEFMd5xoZDucnDIfR0qK7GaERWqsnKkZqXyn8ASAry9T1kNDPqjhX1vd6LyuW9apts9722+pr1/p4o75mbd5cLbxhatr6WH2TzG8hshcb773uJQtUFC5xPXjwIHr06KH7fffu3XHokHH6s46C2aaELGQNwXhuyGRuWr0hpjrOVXstUbFAdrbuZoQsjAREK0YYODAS5K7TVr2ytG4BZuwoR27YaPnRGFYAzCD/hqIpTLFXZ8oS+R207WH1o7peGX5J3jK0x/GKeTI+Xq/AJa5du3Y1TClYX1/PdBmkIjypAp3YOlvkAeC5IVmz5Lzo+XjVKQe5VloxFiRoMRKQBDHSWKDatuougDCZcBK1QNU5BtQ+3va9XyaMGPzFJXF7d1mJ+MDQYaYWqBUhNkPdj05YoF6a+ReBS1xHjhyJt956S/f7devW4eKLL5bWqFTEitiWX7HGNAG3ncMi4QksHQtbbZHpzUYDMBR+K8NHo5Ano7bixAlLYsS7zJQdZbAbSsWrsYk9f3Ekn6vVCSdW/0Tbjo8/Mh0NuD28djrKwEtwievdd9+NP/zhD3jyySfjhv+HDh3CggUL8Mc//hF3362/BDIdYflTZVq8PDelrCiDZMrWWmQ8s9HM/bg01iXvUkorIU/o0sWSGLF8vHo+6zgLdNbsSFxt1g80mxEmLmjhjfgws0D1RgOs8pKNDokluhHYi80Kbr8YkoVLXC+++GI89dRTqKqqwlVXXYXRo0dj9OjRuOqqq1BdXY0FCxZg5MiRdrfVs/AuEJC9kEC2BSoaD8myyNTfy/bxmlqoSLRAeV0TZhNOPCFPMYt3yJC4xQ3te79MSJrCG+Rv5dqNytEriwd1fVbKkHmfphJcKQejHDp0CFVVVWhoaICiKDjvvPNw/fXX4+yzzzY/2QWsLH+1urV2MiKpZ71aXRZrdUWPEdp0cdqyjdLJqfuJdRxPWaxrCdVsjksLaOUa1eUBieFUeqkKWedp88Cy+tzstwjVbEZg3RsIHm4wTKEomrbPyu8liuj9ZvU80S3t3SDp5a9Hjx7Fvffei2uuuQb/8R//gV27dqG0tBR33nmnZ4XVbszEz+4JL8C+QO9o2ephOctSidZltLU2IG6BskKeeK+PZYGauSZYbQUSfbyiIU+5VevirGfWcaxwMFk7QCRLR7VAReFaRLB8+XLU1tbipz/9KX7wgx+gsrIS8+fPR3l5ud3t8xR2WKuykRl8rS7LaOjdsmp1JEGHTjlmgeyAvnWjPje6+gvt7cwAfT209fMey+pLK2Wxzufp5+hnemXwYBRML2qBWrl2gtMtcM011+Cxxx7DNddcAwD4/PPPcfPNN6O2thaZmZm2N1KUZLNiaYcnVnMJsI63W2T1htd2DOei3xtlxeItm2cYG93eBCdagC7ZsVwBItfGU1+ybhetiyGwrhKhqcWGLxYAsW1ekrk+I8xcQG7TodwChw4dwpAhQ2L/P//885GZmSk9n0Cqw8qa5ZTFGoVndlh0qKn3vVGcqx68IUhqopEBvpK7YsLDEzOrWxZnyJP6+q1sTa6GFecaRevC0G51LRueiA4iebjcAqFQKMFC9fv9CIVCtjTKq/BMRPFiV05XFtrhnOhQk0WoZjMaVBYZb9k8rgItTHeCRVE1Kgsw75u4nVE1Gz0aleUvLkFg3RsITb3N8Bytpcq7Fh+g4b7X4E6W/cADD8QJbDAYxNy5c9GpU6fYZ6tX6++l1JFx2nrVohUSmQ9TuLIikoW/MnGnUittEiUac8myQGUJjbos7c6ovGUFCsYj99ZC0+FuMr8V74stIWxM0m9BxMPlc503bx5XYc8880zSDZKJbJ+rVYysUyctVx6S8ctGLTIrPl67/XwyQ5Fk7mrKcz/x9o32ONHzvEa6+Fy5LFeviWY64AVBVZPMrLTWIuMpS5ZrgifKwOxYM5weNotaoLyQpeoMSe9EQKQHRnGsdixbFI3nNFtJFkVkYk+vDU7HafLGqNLElLchcXUIntSDbsIjRsmUZQZvXSJRBlHUx0ZjZtU7JtgpTqyUg8nixMIBQhwSVxfwutBGsfNhFRVJs5VkAJ8Fygp5Er1eHqvbKBSLdSythEp9SFwJXXgeVlGLjCWSokmUtWLEE9cbiZmN3zFBVJz0xFC7rNUowY0askDTA0uJW1INp6IFrM78ey1SIBnaZtwB39f1UPr1T3pWXtZKMt7VX6Kz7bzt1tarvZ+8PmvvFukSLUDiaoBIVqxUF0ursEKxRDELebJb/HhFWLRe7f0kM1wsnYQ6XcSV3AJEUsSyPelkg7KC2XCYZ/jN+r/ecF9bn179dkUZyBz+U6SA9yDL1QBey7WjWatarFpkblmgopi5GXjr47HInMqZ6mXIcu3g8Gxk2FERtUC1yLZAzcrntUBF6zOrH7A3UkB0REGIQeJKCMNKlg3IGw7zCo2sWX7R+lj184ZnydqZlQdyHTgLiSshTPRhbVllLWEPrxiKWqC8IuK2Bep0yBWFeDkLiSshTPRhzS61tvPv+180Yl71x3j/i0Yp7TBbkMA73HfaApW5Ko4HWmTgLCSuhDCsZNk8bKg9gH1HW7Gh9kDc57IsUK2IiPp4WefqLkiQZPGSdZk+kLgSwkQt0P/eedDwOK2I3Dz8XPTp0Rk3Dz837jhZFqj2uGR8vKLJUXiOYx0jal3KnKyiiS85kLgSwkQt0De31hsepxWRMV99iKfeXYIxX30Yd5w0C1Sz9QvvcF8kmxervmTK4kFmlAELHmudMIfElRB+eKIW6L+O7W94HK/1J8sC5UHUAk0mBwLPMTJ9vLIWc1CUgRgkroTww3PZ4F54pnAorrroHMPjRFPj8QgbS0ACs2bDN3wEArNm6x7HaoPwxBRHfbzIjDKQFTNLfmAxSFwJ2x8e3sUAZu1iRRnI9IGy+kHYApW4Ky1P/9kZMytqYXd0SFwJbB84Ek/c+Ai2DxxpS/k8wsa0QDUPNSvKQNQClTmZxEMyybJFIxbsvB5yFZhD4krohkaxELFYRC1Q3igDs/oAvqGuTGtM6yoIV1YguHMXQvPLDJfbynwx8FyPzCQ7ZM3GQ4lbDEilBBJARLQ21B7AzcPPxWWDe9lynjpJib84sgNAzn0z0TJqHHd986o/xr6jrejTozOeKRwKgJ14hCchCm/SFJ7EJsHCiUB9HdB/ALKq3xEuR/e8BY9BgQ++vDxklr/GrI+nfN42aPtGtI95cXqXXC9AiVs6CFYsUDXRiSkeQVZbLFG/4rElSy3VJ2qB8g6HZYUqhWo2I1g4EcHCiYYLG3gstkDBeOQsXwZfXp7lVVs8PleevrE7lwFNfMVDlqsBqfQGBcQtV1FrLGp5ZQwaBP/a9QItPo1MK5V1nPYaWdfMTC2440MgHAYGDUZW9TvM83gsXiDxfhK1QEWP4blmL5BKzx1Zrh0EKxaoGuGQnVN+xW6PPBz7THaibHV5vFYqy8db9lUnbO02MHYMb/QAsrIAf8DStej1QeumTVIsUL22yooysJOO5JclcU0jZAubGazcArLzkarL4w15YkUZ7D/+Hf6ze57u0lpWWYGC8Qgseg6+iy+Om5hKmExiTF4pe/bETV4BQMuqctNZf1ZkQGjFC1Camiz1p15btTg9lO9IUQYkrmmEbGETgccCZaH3PW9+AaOybh5+Lnp364Sbju2xJCKsIbO/uATo1g1KU9NpP+xHtVB2f4rwR7Wnj1HCgM8X91tkl86w7AMNV1YAX30J7P3CMGZWZmYuijKQA4mrB3HaApWZApDHAm1bvhTBcWPQtnwp83uj8hJWbTFWR7FyGTz9/17G5Tf9s2GeArYFujvOAg0UjIcvJwc4fjx2rvJmJdDSEvn71DGBJxfBl5cf91I4vuQXcRYo73Bf65qQGecqOvkn80WertYsiasHcdoCFY0yYMFjgSpVa4ETJ6C8WWnoT2Uh4n/kTawSKBgfCy+LtgmKAvj8ccN0pakJ6Nbt9Lndusf9zbJ4w5UVaP/yK6DeeDkva8kvl2vC4Vy0duYySBdIXD2I0zcbb2iUlujDpN7mhccC9RVNA7p0Abp1t/wS4cmUxXzJhMPAvm9MJ5NC88ug7NkT8/FGLNDT4VPhygqgoQE4fOh0fXN/Dt8loxCY+3PdNvmLS5Bx3nlAf+uhZTwLIKyMBtTIXNnlBbeUlyBx9SBO32zJRhmot3nheTFkznkYWVu2R0RJEzMrYy2+lsCs2cAZZwBZP4g9+HpiBJ8PUMLwF5fg/S8a8dixc7H9prs01myiP5WnTf6eOQjMmm35xcAz6x8bDVSttdY3Dlu8HQkSVyLp4ZzVbV6iyHiJ8IjD9oEj8cT0Zdg26rrYg69UrY34SStejc+UlZePwJOLECgYH3OXvPPpkbiIhQR/Kkf+2HBlBdp2fRrnv+UVIx6LMDoa8BVNi/UBT5SBqM9VpsWbrpC4phGiIpnscE5KKJZg2j4ecdhQewD7fZ3w7tDxMQvUVzQN8PuBrKzYcdpZ/6i7ZOIFPWNCErVm//7z5ZYmivzFJRHXhMri1fp49bAyGsic83CsD1BfF+fjZeF0lEFHgsQ1jRAVNtHJCdY2LzKHhtrr4fUratsQFckbP94cOzZzzsMI/GI5fEOGnrZm17wesWbXvA7g9I4JY7uFYkKyofYA9h06it+v/a/Tw3SOiIXTy1/z4/pG1kw5U8z7D5Di4+UlXWf9RfGkuL733nu48847cckllyA/P9/t5qQMyS4GsBoew9rmReaDqb2emF9xzeuWogyiPuXLb/pnYwu0R07c36zFADcPPxe9jx7EjTtrDC1QVnxuy6ryhGWmPC82kdCoQMF4ZFW/g6zqd5L28fIiev+lq8XrSXFtbW3F2LFjMXPmTLebklI4/aDwbvOiRdSCikUZ9MgxXLXFE7EQfTH8/r93xtoSeGQeMGgw0KWL7uTVZYN7YdGY7hibdSJOOCNRBrt1w6zClYkpB1nHscriWUjBI9K8IxSnZ/3T1eL1pLhOmjQJ9913H0aMGOF2U1IeOx+U6LD50rodSbeJ58UQizJ4ZJ6hSFh5MahdBQAiIVYNh/Unr3RiWOHzA4pi3CZNzKxe32jL4gk/4+ln3heR0xZoukYZeFJcCXnYeeOyQrFE28RjQUV9vNsHjjQUCZ6JIpargFvYNKu2IlEGebEoA7028aQcZJUVqolPfcjqP5mLAUShOFcNiofZunWrkpeX53YzCB1ObtyoHC6copzcuJHjuCLD4w4XFin7R4xUDhdO0S3/3vJtyg1L/qzcW77NsC1NCxcq3/Ttr3wz+J9in5nVb3Q96vNPbtyo7Mu/UNl34ZBYW/XKV5f3X58cUO4t36b81ycHLPfV4cIi5ZsB5ynfDDgvrn942i7CgauuVr4ZcJ5y4KprLJ0n2gbe3yfVcDSf66OPPoq3335b9/vJkyfj2Wefjf1/27ZtuOOOO7B7926h+jpaPle3UPcTa9iszTXKyjvLOk8L6zzWZ8FxY4CWFsDvR+AXy2MWlWiGfLOM/jz5XOdVf4wDx7/DOaFWPP3/Xta9Tr1ctLEYWtUSWKO+ip7Hc5wW3vy0surTXnMqPXeeyedaVlaGLVu26P4pKytzsjmEDfAMT1m5DHh8rpcN7oWF3Q9g1OI5sc9YZfmKpgHZ2fCV3BVzEfgGDACGDkvaJ8hataWHuv03Dz8X/Xt1SfTxGtSl7hv1zL/e8FskyoAFK7SMB5mhgOmAo+LatWtX5Obm6v7p2lX/LUA4j8gEhZXJJLNcBqyHVfsZqyxtQH1UuPHxR7FzrVwbK8ogbtWWjhip2xqLme3fzbB/ePyPen2s7RueKAPRNlhplxnp6nPNcLsBLI4ePYoDBw6gvj4SP7lr1y4AwODBg5GVleVm0zoU2gkZHgIF4xOO1ZZz2eBe3Pt1RYeZep+xytIbnqrPFbk2ICLmG2oP4KacnvB9bj5ZFFff1/VAWyjpjQBZfaytL3ocgNMr0ASvOdl2dVQ8uYdWdXU15s2bl/B5TU0N+vbty10O+VyTgyVSrM/M+knUFyeK6H5SdtcXWPcGQlNvc0yAojGz8Pli4WR2/g6y+jSVnjsjn6snxVUWJK7yYU3uBNZVIjS1WPoDK/qwCk+sLF8KpWotfEXTYi6FZOrTfi66QaHoNbXNuAPKnj2AEkbgyUUA+CbCRKGtteOhOFfCElq/WriyAu17v5SyukbWhAwPrN0XWGn7eNDzGZq130qaRZG+0MbM8pZBiwHkQOJKWEIrJP7iksjW2jYEoYs+rFbyIiREGajS9kWRKTYyyhKdmOLtT1oMIAdyCxiQSsMTN0lmuKuG18erRmbMrB5mca685OZ2xf6bJ8fKEvWBWhl+i7TVaR+5llR67sgtQDiKTMvHrCzemFktrN0X9CxCliuEtXU2D+qynAh5EvktePPMEsaQuBLSsWs4zEJ0/y9Wm/SEiDW8VmfLEo2ZFcVKGXa6VghjPBnnSqQ2PLGuyZSlxkqcqxa9ZC9mQqSOH/UXR/b/Qn0dQk1NjgyjnRi28/YFoQ9ZroQj2DmTLBploG2TleGwm5M3VqxKmpxyDxJXwhHsfFhFowxEfLzMcgTX4ltxJ1jNVxuFwqPcg6IFDEilWUs3kdVPTi8aECkrmbq0/WRl1l9WgH4qkErPHUULECmBF4awZmWx2uhE0D1ZoKkHiSvhGWRGGdgF7y4APKSKj5cQg8SV8AyyfKCi8Ag5q43JWJUU8pS+kLgSnsZMuGRatm64JWi4n76QuBKeQE8kRXygovW5IXQ03E9fSFwJT+D0FiGs+kjoCJmQuBKeQFQktw8ciSdufATbB450pD6C4IWWvxKeQHSLEHXiFm0SFqN4VNqShLAbslwJT8BKXs2DXuIW00TVHgvxItIPElfCE7BSB/LASh0ImA/7ZYZAkVATLEhcCamICo1o6sC25UsRHDcGbcuXxn1uNjkl0+dqZ6wqCXfqQuJKSEVUaPQsUDNk7nvlxb2jaJFB6kLiSkjF6Vl4vX2vzGD5eO1cRBCq2YyGoimeEm7CXkhcCamIhkaJkjnnYWRt2R63FTaPBcry8YoKGU99orvkyrSwCWchcSWkIjoxJYqoBSrq42UJG099du6SS3gTEleCiezQKDNErTFRC3TMVx/iqXeXYMxXH8Y+4xEt1jE89QUKxiO3ap2U2NpUyB5G0CICQge94HwzWHta8SC6D9XNw8+NbZGdbH08+0axjnF6QQLPHmVub49NkOVK6JDMrqpuY+fElFd9oMztv8l14CokrgQT0dAoUVj7UNk5MSW675XMKAOe6+MVbtb23xRl4C4krgQTp60xVpSB6MSUnRYor5jzRhCI+Hh58KqF3ZEgcSWYOD2sFLVAWRa2qIiIijnPi4EV58pzfamykoxIhMQ1zXF61ZHMKAPe4HyR0CgWPBYoK8qA58XAinPlsS7tXklG1qx90NbaBqTSFr96OLEls7qf5lV/jH1HW9GnR2c8Uzg0qXJ5ZryDhROB+jqg/wBkVb/DfR6L979ojEUeRC1hbf+x6mOdx7qWwLo3EJp6m2GbtPWxrkXmb+rFLbtT6bmjrbU7ME5PbDgdnC8Ky8K20zXBinPl2WqGN67Wi3kROjokrmmO01uXiPpAhYPzGbP+PKLMElIe1wRvVIO2De9/0Yj7Vm83jTIQnfV3Y3NFwhgSV0IqySwPlbXNC09ZLCHltUATfKArXoBS+4/IwgSdNmyoPYD6xhOWowx4txu30wIlv6wY5HM1IJV8P26i7ieWD0/UB8rjD5Tp4+XxubKOYflhWWVv2nkYEy46K3YeT30snF595bRfNpWeO/K5Eo4hM8pA1AJlobW+WNYYj8+VdQyPq0A0yoCF6FCeogychcSVkArvMFYLU7Q4RIQ1lOdZRcVqE49Qi8a5skKxRMPPRJHpl6WYWXNIXAlu7JyRlpnLgMciZLVJNFMWb33alIOik39esEApysAc8rkakEq+HyfQ873Z1U888aO858mKmWX5eHnbqe0n0RhWinP1DuRzJaTg9Iy0aOJtHguUdyWZdkgsGmXAvGaOKAMWZIGmBiSuBDdO+wNZQiYrZlZ0YkrUxysT0SQ3LCjO1T5IXAmpyLSgRC1QVlk8FihPDKtolAELmQsg7Nz/SyYdKcqAxJWQit0z0lwWKEeyE14LlOf6RCfjWO0UnfwTtUCdnvXvSFEGJK6EVGT68EQtUBasFIA8FqjWupQZZcBCNPxMFKct3o7k46VoAQNSadbSTbzWTzzRAqxZctHoBN7MXLL6yQv7Y9kZZeC1+8kIihYg0hZuC5QjIQrLAhXFzlVUXhhadyQLVBRPiuvKlSsxadIkjBgxAldeeSUWLlyIEydOuN0sQiIsEREZaopGGchcdcSamGpbvhTBcWPQtnyp7nmiUQZeWI5KUQbmeFJcd+zYgdLSUlRXV2PZsmXYsmULFi5c6HazCImwRERE3GT6QGVm5lKq1gInTkT+1kFmbgFWzCzhLp4U15deegkTJ07EoEGDMHr0aDzwwAOoqalxu1mERFgiIiJuMlPyydy00Fc0DejSJfK3Dk7nFiCcxZPiqqWpqQldu+o7jonUgyUisvaL4okyYCE6TGeJZOach5G1ZTsy5zyse55UH6/gVuEdKe7UaTwfLdDc3IzJkyejsLAQ999/v9vNIRymoWgK2vd+iYxBg5BbtU64nNZNm9CyqhzZpTPQecIE5jH3rd6O+sYT6N+rC1bePUZ13mpkl96te55ofbKujbc+u9tAxOOouD766KN4++23db+fPHkynn322dj/g8EgZs6cCZ/Ph5dffhkZGRmW6qNQLGews5+0YUd2Jt4WDcXiTcDCk7hFFNHQKC+EdWlJpefOKBTLmlolSVlZGR566CHd7zt16hT7d3t7O+bMmYMTJ07g1VdftSysRHoQKBiv60+1Igb+4pKYiOhx2eBeCaLKIz6sNvHUp7023vpY8NRHOIsn3QLhcBg/+9nPsGfPHlRUVKBHjx5C5ZDl6gxO9pPdlhbPYgPRNvH0k9MpACnlYHKk3CKCxx9/HNu2bcOSJUvQ1taGhoYGNDQ0IBQKud00wmXsnk3XTmDZGWXAwungfFoMYB+etFzz8/OZn9fU1KBv377c5ZDl6gzp1E8sK1TEWubxuRJsUqmfPONz5WX37t1uN4HooLD8oCJ+XlEfqBcnmAgxPOkWIAg7cHKo7nQKQIpX9R4krkSHwcls/U77XL2QzIWIh8SV6DA4OXmTjJBHXQrJrkoj3MWTPleCsAOWP9Uukok7FfHxOnltBB8krgRhA1Ghi1qutCCg40HiShA2IbqajKzQ9IB8rgRhQqosCCC8BVmuBGECWaCECGS5EoQJZIESIpDlShAmkAVKiECWK0EQhA2QuBIEQdgAiStBEIQNkLgSBEHYAIkrQRCEDZC4EgRB2ACJK0EQhA2QuBIEQdgAiStBEIQNpPUKLb/f54kyOgLUT3xQP/GRDv3kyd1fCYIgUh1yCxAEQdgAiStBEIQNkLgSBEHYAIkrQRCEDZC4EgRB2ACJK0EQhA2QuBIEQdgAiStBEIQNkLgSBEHYAIkrQRCEDZC4crBy5UpMmjQJI0aMwJVXXomFCxfixIkTbjfLE7z44osYN24chg8fjvvvvx+NjY1uN8lT0L0jxqxZs5Cfn49t27a53RRhSFw52LFjB0pLS1FdXY1ly5Zhy5YtWLhwodvNcp2qqir85je/wfz587FmzRo0NzfjwQcfdLtZnoLuHeusX78era2tbjcjeRTCMhs3blRGjx7tdjNc55ZbblGef/752P/r6+uVvLw8Zffu3S62ytvQvWPMwYMHlauuukrZt2+fkpeXp2zdutXtJglDlqsATU1N6Nq1q9vNcJVgMIhPP/0UY8eOjX3Wr18/9OnTB7W1tS62zNvQvWNMWVkZ7rnnHvTu3dvtpiQNiatFmpubsXr1ahQVFbndFFdpampCOBxGr1694j7v2bMnjhw54lKrvA3dO8asWbMG7e3tuPXWW91uihTSOlm2GY8++ijefvtt3e8nT56MZ599Nvb/YDCIn/70p+jXrx9mzpzpRBOJNIHuHWP279+PX/3qV1izZo3bTZFGhxbXsrIyPPTQQ7rfd+rUKfbv9vZ2zJkzBydOnMCrr76KjIwO3XXIycmB3+9HY2MjBg8eHPv8yJEj6Nmzp4st8x5075izc+dOfPvtt7juuuviPr/rrrswefJkLF682KWWidOhf+WuXbty+b/C4TDmzp2L+vp6VFRUoEuXLg60zttkZWXhggsuwLZt2zBmzBgAwNdff419+/Zh+PDhLrfOO9C9w8fYsWOxYcOGuM8mTpyIhQsXYty4cS61Kjk6tLjy8vjjj2Pbtm14+eWX0dbWhoaGBgAR/2IgEHC5de5x++23Y/HixbjwwgvRt29fLF68GJdeeiny8vLcbppnoHuHj+zsbOZ907dvX5x99tkutCh5aA8tDvLz85mf19TUoG/fvg63xlu8+OKLqKioQHNzMy6//HI8/fTTOPPMM91ulmege0ec/Px8vPbaa7j00kvdbooQJK4EQRA2QKFYBEEQNkDiShAEYQMkrgRBEDZA4koQBGEDJK4EQRA2QOJKEARhAySuBEEQNkDiSqQFjz76KPLz85Gfn4+LLroIV199NZ544gk0NTXFjtmxYwd+8pOf4PLLL8cPf/hDXHvttXj44YfxySefJJRXXl6OCy+8EM888wyzvpaWFjz22GO49NJLMWLECJSWlqK+vt626yNSDxJXIm0YNWoUtmzZgj//+c8oKyvDe++9h7lz5wKI7Jpw++23IyMjA0uXLsXGjRuxfPly9OnTB4sWLUooa+3atbjnnnuwfv16BIPBhO8feeQRvP/++3j++edRWVkJRVEwffp0fPfdd7ZfJ5EiuJqqmyAkMXfuXOXOO++M++zXv/61csEFFygHDx5Uhg4dqjz++OPMc48ePRr3//fff1+57LLLlLa2NuWGG25QNmzYEPf93r17lby8POWvf/1rXBlDhgxRqqqq5FwQkfKQ5UqkLZ06dUI4HMZbb72FYDCI+++/n3lc9+7d4/7/5ptvYuLEicjIyMAtt9yCtWvXxn3/4YcfIjMzE5dddllcGcOGDcMHH3wg/0KIlITElUhLPv/8c7z++usYPnw4GhoakJ2djXPOOcf0vCNHjmDz5s2YPHkyAGDSpEn48MMPsXfv3tgxDQ0N6NGjR0JWqzPPPDOW9YogSFyJtGH79u24+OKLMWzYMNx0003o168fli1bBsVCbqKqqioMHjwYF1xwAQDgrLPOwuWXX45169Zxne/z+YTaTqQflM+VSBuGDRuG5557DoFAAGeddRaysrIAAOeddx5aWlpw8OBBQ+tVURS89dZbqKurw0UXXRT7PBwO43//938xZ84cZGVlITc3F0ePHkUoFIqzXhsbGzFw4EDbro9ILchyJdKGTp06YcCAAejbt29MWAFgwoQJyMrKwq9//WvmeceOHQMAbN26FV9//TXeeOMNrF+/Pu5Pe3s7Nm/eDAAYOXIk2trasHXr1lgZx48fR21tLS655BIbr5BIJchyJdKes88+G0888QSeeOIJNDc3Y9q0aejXrx+OHTuGmpoabNu2Da+//jrWrFmD0aNH4+KLL04o45prrsGbb76JG2+8Eeeddx4KCgqwYMECLFq0CF27dsW///u/4+yzz8YNN9zgwhUSXoQsV6JDMHXqVFRUVOD777/Hgw8+iOuvvx6zZ8/GN998g8ceewyNjY2oqanBhAkTmOffcMMN2L59O7766isAwJIlSzBmzBj85Cc/wa233opwOIzVq1fHbWpJdGxoJwKCIAgbIMuVIAjCBkhcCYIgbIDElSAIwgZIXAmCIGyAxJUgCMIGSFwJgiBsgMSVIAjCBkhcCYIgbIDElSAIwgb+PwssLc7leEGHAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 320, 13\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.267\n", "LR cohens kappa score: 0.234\n", "LR average precision score: 0.242\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 1, 12\n", "GB f1 score: 0.960\n", "GB cohens kappa score: 0.959\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 2\n", "KNN fn, tp: 10, 3\n", "KNN f1 score: 0.333\n", "KNN cohens kappa score: 0.319\n", "\n", "\n", "------ Step 4/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1280 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 317, 14\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.258\n", "LR cohens kappa score: 0.224\n", "LR average precision score: 0.220\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 331, 0\n", "GB fn, tp: 1, 12\n", "GB f1 score: 0.960\n", "GB cohens kappa score: 0.958\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 329, 2\n", "KNN fn, tp: 9, 4\n", "KNN f1 score: 0.421\n", "KNN cohens kappa score: 0.407\n", "\n", "\n", "====== Step 5/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 5/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 310, 23\n", "LR fn, tp: 7, 6\n", "LR f1 score: 0.286\n", "LR cohens kappa score: 0.247\n", "LR average precision score: 0.225\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 332, 1\n", "GB fn, tp: 0, 13\n", "GB f1 score: 0.963\n", "GB cohens kappa score: 0.961\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 330, 3\n", "KNN fn, tp: 6, 7\n", "KNN f1 score: 0.609\n", "KNN cohens kappa score: 0.595\n", "\n", "\n", "------ Step 5/5: Slice 2/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 320, 13\n", "LR fn, tp: 11, 2\n", "LR f1 score: 0.143\n", "LR cohens kappa score: 0.107\n", "LR average precision score: 0.173\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 3, 10\n", "GB f1 score: 0.870\n", "GB cohens kappa score: 0.865\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 2\n", "KNN fn, tp: 10, 3\n", "KNN f1 score: 0.333\n", "KNN cohens kappa score: 0.319\n", "\n", "\n", "------ Step 5/5: Slice 3/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.4142135623730951\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 321, 12\n", "LR fn, tp: 8, 5\n", "LR f1 score: 0.333\n", "LR cohens kappa score: 0.304\n", "LR average precision score: 0.286\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 0, 13\n", "GB f1 score: 1.000\n", "GB cohens kappa score: 1.000\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 333, 0\n", "KNN fn, tp: 9, 4\n", "KNN f1 score: 0.471\n", "KNN cohens kappa score: 0.461\n", "\n", "\n", "------ Step 5/5: Slice 4/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1278 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 314, 19\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.222\n", "LR cohens kappa score: 0.183\n", "LR average precision score: 0.194\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 333, 0\n", "GB fn, tp: 2, 11\n", "GB f1 score: 0.917\n", "GB cohens kappa score: 0.914\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 331, 2\n", "KNN fn, tp: 7, 6\n", "KNN f1 score: 0.571\n", "KNN cohens kappa score: 0.559\n", "\n", "\n", "------ Step 5/5: Slice 5/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n", "trained 52 points min:1.0 max:1.0\n", "-> create 1280 synthetic samples\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-> test with 'LR'\n", "LR tn, fp: 317, 14\n", "LR fn, tp: 9, 4\n", "LR f1 score: 0.258\n", "LR cohens kappa score: 0.224\n", "LR average precision score: 0.203\n", "\n", "-> test with 'GB'\n", "GB tn, fp: 329, 2\n", "GB fn, tp: 2, 11\n", "GB f1 score: 0.846\n", "GB cohens kappa score: 0.840\n", "\n", "-> test with 'KNN'\n", "KNN tn, fp: 327, 4\n", "KNN fn, tp: 10, 3\n", "KNN f1 score: 0.300\n", "KNN cohens kappa score: 0.281\n", "\n", "### Exercise is done.\n", "\n", "-----[ LR ]-----\n", "maximum:\n", "LR tn, fp: 322, 25\n", "LR fn, tp: 12, 7\n", "LR f1 score: 0.345\n", "LR cohens kappa score: 0.316\n", "LR average precision score: 0.326\n", "\n", "\n", "average:\n", "LR tn, fp: 316.44, 16.16\n", "LR fn, tp: 9.08, 3.92\n", "LR f1 score: 0.236\n", "LR cohens kappa score: 0.200\n", "LR average precision score: 0.219\n", "\n", "\n", "minimum:\n", "LR tn, fp: 308, 9\n", "LR fn, tp: 6, 1\n", "LR f1 score: 0.059\n", "LR cohens kappa score: 0.013\n", "LR average precision score: 0.158\n", "\n", "\n", "-----[ GB ]-----\n", "maximum:\n", "GB tn, fp: 333, 2\n", "GB fn, tp: 3, 13\n", "GB f1 score: 1.000\n", "GB cohens kappa score: 1.000\n", "\n", "\n", "average:\n", "GB tn, fp: 331.96, 0.64\n", "GB fn, tp: 0.84, 12.16\n", "GB f1 score: 0.942\n", "GB cohens kappa score: 0.940\n", "\n", "\n", "minimum:\n", "GB tn, fp: 329, 0\n", "GB fn, tp: 0, 10\n", "GB f1 score: 0.833\n", "GB cohens kappa score: 0.827\n", "\n", "\n", "-----[ KNN ]-----\n", "maximum:\n", "KNN tn, fp: 333, 6\n", "KNN fn, tp: 13, 8\n", "KNN f1 score: 0.727\n", "KNN cohens kappa score: 0.719\n", "\n", "\n", "average:\n", "KNN tn, fp: 330.48, 2.12\n", "KNN fn, tp: 8.48, 4.52\n", "KNN f1 score: 0.448\n", "KNN cohens kappa score: 0.435\n", "\n", "\n", "minimum:\n", "KNN tn, fp: 327, 0\n", "KNN fn, tp: 5, 0\n", "KNN f1 score: 0.000\n", "KNN cohens kappa score: 0.000\n", "\n" ] } ], "source": [ "runExerciseForSpheredNoise(\"folding_car-vgood\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "sustainable-firmware", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "///////////////////////////////////////////\n", "// Running SpheredNoise on folding_flare-F\n", "///////////////////////////////////////////\n", "\n", "-> Shuffling data\n", "### Start exercise for synthetic point generator\n", "\n", "====== Step 1/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 1/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_18860/947557640.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrunExerciseForSpheredNoise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"folding_flare-F\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/analysis.py\u001b[0m in \u001b[0;36mrunExerciseForSpheredNoise\u001b[0;34m(datasetName)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mshuffler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenShuffler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0mexercise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExercise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshuffleFunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumOfShuffles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/exercise.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, gan, dataset)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0msliceTitle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Slice {sliceNr + 1}/{self.numOfSlices}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"\\n------ {stepTitle}: {sliceTitle} -------\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exerciseWithDataSlice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msliceData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"### Exercise is done.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/exercise.py\u001b[0m in \u001b[0;36m_exerciseWithDataSlice\u001b[0;34m(self, gan, dataSlice)\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;31m# Train the gan so it can produce synthetic samples.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-> Train generator for synthetic samples\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSlice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;31m# Count how many syhthetic samples are needed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/SpheredNoise.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Train: Expected at least one feature.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisc\u001b[0m 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noise.extend(list(filter(\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m np.random.normal(0, 1, noiseDimension))))\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/SpheredNoise.py\u001b[0m in \u001b[0;36m\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0mnoiseDimension\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnPointsToAdd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoiseSize\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m noise.extend(list(filter(\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m np.random.normal(0, 1, noiseDimension))))\n\u001b[1;32m 54\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoise\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mpointCount\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "runExerciseForSpheredNoise(\"folding_flare-F\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "original-twist", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "///////////////////////////////////////////\n", "// Running SpheredNoise on folding_hypothyroid\n", "///////////////////////////////////////////\n", "\n", "-> Shuffling data\n", "### Start exercise for synthetic point generator\n", "\n", "====== Step 1/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 1/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_18860/4033420787.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrunExerciseForSpheredNoise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"folding_hypothyroid\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/analysis.py\u001b[0m in \u001b[0;36mrunExerciseForSpheredNoise\u001b[0;34m(datasetName)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mshuffler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenShuffler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0mexercise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExercise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshuffleFunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumOfShuffles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumOfSlices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m \u001b[0mexercise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 102\u001b[0m \u001b[0mexercise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaveResultsTo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"data_result/{datasetName}-{ganName}.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exerciseWithDataSlice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msliceData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"### Exercise is done.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/exercise.py\u001b[0m in \u001b[0;36m_exerciseWithDataSlice\u001b[0;34m(self, gan, dataSlice)\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;31m# Train the gan so it can produce synthetic samples.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-> Train generator for synthetic samples\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSlice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;31m# Count how many syhthetic samples are needed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/SpheredNoise.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Train: Expected at least one feature.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreateDisc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnoiseSize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminoritySet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 88\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpointDists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminDistPointToSet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajoritySet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m 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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_18860/1355106185.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrunExerciseForSpheredNoise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"folding_kddcup-guess_passwd_vs_satan\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/analysis.py\u001b[0m in \u001b[0;36mrunExerciseForSpheredNoise\u001b[0;34m(datasetName)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mshuffler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenShuffler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0mexercise\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mexercise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaveResultsTo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"data_result/{ganName}-{datasetName}.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/exercise.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, gan, dataset)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0msliceTitle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Slice {sliceNr + 1}/{self.numOfSlices}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"\\n------ {stepTitle}: {sliceTitle} -------\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exerciseWithDataSlice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msliceData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"### Exercise is done.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/exercise.py\u001b[0m in \u001b[0;36m_exerciseWithDataSlice\u001b[0;34m(self, gan, dataSlice)\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;31m# Train the gan so it can produce synthetic samples.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-> Train generator for synthetic samples\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSlice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;31m# Count how many syhthetic samples are needed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/SpheredNoise.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Train: Expected at least one feature.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreateDisc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnoiseSize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminoritySet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 88\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpointDists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminDistPointToSet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajoritySet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m 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"runExerciseForSpheredNoise(\"folding_kddcup-guess_passwd_vs_satan\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "thorough-impression", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "///////////////////////////////////////////\n", "// Running SpheredNoise on folding_kr-vs-k-three_vs_eleven\n", "///////////////////////////////////////////\n", "\n", "-> Shuffling data\n", "### Start exercise for synthetic point generator\n", "\n", "====== Step 1/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 1/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_18860/24679254.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrunExerciseForSpheredNoise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"folding_kr-vs-k-three_vs_eleven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/analysis.py\u001b[0m in \u001b[0;36mrunExerciseForSpheredNoise\u001b[0;34m(datasetName)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mshuffler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenShuffler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0mexercise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExercise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshuffleFunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffler\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exerciseWithDataSlice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msliceData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"### Exercise is done.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/exercise.py\u001b[0m in \u001b[0;36m_exerciseWithDataSlice\u001b[0;34m(self, gan, dataSlice)\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;31m# Train the gan so it can produce synthetic samples.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-> Train generator for synthetic samples\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSlice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;31m# Count how many syhthetic samples are needed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/SpheredNoise.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisc\u001b[0m \u001b[0;34m=\u001b[0m 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"runExerciseForSpheredNoise(\"folding_kr-vs-k-three_vs_eleven\")" ] }, { "cell_type": "code", "execution_count": null, "id": "working-poland", "metadata": { "scrolled": false }, "outputs": [], "source": [ "runExerciseForSpheredNoise(\"folding_kr-vs-k-zero-one_vs_draw\")" ] }, { "cell_type": "code", "execution_count": null, "id": "complimentary-observer", "metadata": { "scrolled": false }, "outputs": [], "source": [ "runExerciseForSpheredNoise(\"folding_shuttle-2_vs_5\")" ] }, { "cell_type": "code", "execution_count": null, "id": "included-pound", "metadata": { "scrolled": false }, "outputs": [], "source": [ "runExerciseForSpheredNoise(\"folding_winequality-red-4\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "primary-rendering", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "///////////////////////////////////////////\n", "// Running SpheredNoise on folding_yeast4\n", "///////////////////////////////////////////\n", "\n", "-> Shuffling data\n", "### Start exercise for synthetic point generator\n", "\n", "====== Step 1/5 =======\n", "-> Shuffling data\n", "-> Spliting data to slices\n", "\n", "------ Step 1/5: Slice 1/5 -------\n", "-> Reset the GAN\n", "-> Train generator for synthetic samples\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_18860/3285141280.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrunExerciseForSpheredNoise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"folding_yeast4\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/analysis.py\u001b[0m in \u001b[0;36mrunExerciseForSpheredNoise\u001b[0;34m(datasetName)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mshuffler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenShuffler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0mexercise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExercise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshuffleFunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumOfShuffles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumOfSlices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m \u001b[0mexercise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 102\u001b[0m \u001b[0mexercise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaveResultsTo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"data_result/{datasetName}-{ganName}.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mexercise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaveResultsTo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"data_result/{ganName}-{datasetName}.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/exercise.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, gan, dataset)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0msliceTitle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Slice {sliceNr + 1}/{self.numOfSlices}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"\\n------ {stepTitle}: {sliceTitle} -------\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exerciseWithDataSlice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msliceData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"### Exercise is done.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/exercise.py\u001b[0m in \u001b[0;36m_exerciseWithDataSlice\u001b[0;34m(self, gan, dataSlice)\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;31m# Train the gan so it can produce synthetic samples.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-> Train generator for synthetic samples\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSlice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0;31m# Count how many syhthetic samples are needed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/SBI/Saptarshi/LoGAN/library/SpheredNoise.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreateDisc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnoiseSize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminoritySet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpointDists\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminDistPointToSet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajoritySet\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m 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axis))\n\u001b[1;32m 2295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/tensorflow/python/ops/math_ops.py\u001b[0m in \u001b[0;36mreduce_sum_with_dims\u001b[0;34m(input_tensor, axis, keepdims, name, dims)\u001b[0m\n\u001b[1;32m 2303\u001b[0m return _may_reduce_to_scalar(\n\u001b[1;32m 2304\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2305\u001b[0;31m gen_math_ops._sum(input_tensor, dims, keepdims, name=name))\n\u001b[0m\u001b[1;32m 2306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/tensorflow/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(input, axis, keep_dims, name)\u001b[0m\n\u001b[1;32m 11186\u001b[0m \u001b[0mA\u001b[0m\u001b[0;31m 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11190\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_eager\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "runExerciseForSpheredNoise(\"folding_yeast4\")" ] }, { "cell_type": "code", "execution_count": null, "id": "painful-liechtenstein", "metadata": { "scrolled": false }, "outputs": [], "source": [ "runExerciseForSpheredNoise(\"folding_yeast5\")" ] }, { "cell_type": "code", "execution_count": null, "id": "dutch-chocolate", "metadata": { "scrolled": false }, "outputs": [], "source": [ "runExerciseForSpheredNoise(\"folding_yeast6\")" ] } ], "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }