{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5bcc5b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from library.exercise import Exercise\n",
    "from library.dataset import DataSet, TrainTestData\n",
    "from library.GanExamples import StupidToyListGan\n",
    "from library.SimpleGan import SimpleGan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e08d9447",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "import random\n",
    "from imblearn.datasets import fetch_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cc212ea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "       [1, 1, 3, 1, 2, 0],\n",
      "       [1, 1, 3, 1, 1, 1],\n",
      "       [1, 1, 3, 1, 1, 2]], dtype=int64), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
      "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,\n",
      "       0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1], dtype=int64)), (array([[3, 3, 0, 0, 2, 1],\n",
      "       [3, 3, 0, 0, 2, 2],\n",
      "       [3, 3, 0, 0, 2, 0],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 1, 2],\n",
      "       [1, 1, 3, 2, 1, 0],\n",
      "       [1, 1, 3, 2, 0, 1]], dtype=int64), array([0, 0, 0, ..., 1, 0, 0], dtype=int64), array([[3, 3, 0, 1, 0, 0],\n",
      "       [3, 3, 1, 0, 2, 2],\n",
      "       [3, 3, 1, 0, 0, 2],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 2, 0],\n",
      "       [1, 1, 3, 2, 0, 2],\n",
      "       [1, 1, 3, 2, 0, 0]], dtype=int64), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
      "       1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
      "       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0], dtype=int64)), (array([[3, 3, 0, 0, 2, 1],\n",
      "       [3, 3, 0, 0, 2, 2],\n",
      "       [3, 3, 0, 0, 2, 0],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 1, 1],\n",
      "       [1, 1, 3, 2, 0, 2],\n",
      "       [1, 1, 3, 2, 0, 0]], dtype=int64), array([0, 0, 0, ..., 0, 1, 0], dtype=int64), array([[3, 3, 0, 0, 1, 0],\n",
      "       [3, 3, 0, 2, 2, 2],\n",
      "       [3, 3, 0, 2, 0, 2],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 1, 2],\n",
      "       [1, 1, 3, 2, 1, 0],\n",
      "       [1, 1, 3, 2, 0, 1]], dtype=int64), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
      "       0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0], dtype=int64)), (array([[3, 3, 0, 0, 2, 1],\n",
      "       [3, 3, 0, 0, 2, 2],\n",
      "       [3, 3, 0, 0, 2, 0],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 0, 1],\n",
      "       [1, 1, 3, 2, 0, 2],\n",
      "       [1, 1, 3, 2, 0, 0]], dtype=int64), array([0, 0, 0, ..., 0, 1, 0], dtype=int64), array([[3, 3, 0, 1, 2, 1],\n",
      "       [3, 3, 1, 0, 1, 0],\n",
      "       [3, 3, 1, 0, 0, 2],\n",
      "       ...,\n",
      "       [1, 1, 3, 1, 2, 1],\n",
      "       [1, 1, 3, 1, 2, 0],\n",
      "       [1, 1, 3, 1, 1, 1]], dtype=int64), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
      "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0], dtype=int64)), (array([[3, 3, 0, 0, 2, 1],\n",
      "       [3, 3, 0, 0, 2, 2],\n",
      "       [3, 3, 0, 0, 2, 0],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 0, 1],\n",
      "       [1, 1, 3, 2, 0, 2],\n",
      "       [1, 1, 3, 2, 0, 0]], dtype=int64), array([0, 0, 0, ..., 0, 1, 0], dtype=int64), array([[3, 3, 0, 0, 1, 1],\n",
      "       [3, 3, 0, 0, 0, 2],\n",
      "       [3, 3, 0, 1, 2, 2],\n",
      "       ...,\n",
      "       [1, 1, 3, 0, 1, 1],\n",
      "       [1, 1, 3, 1, 0, 1],\n",
      "       [1, 1, 3, 1, 0, 2]], dtype=int64), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0,\n",
      "       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
      "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1], dtype=int64)), (array([[3, 3, 0, 0, 2, 1],\n",
      "       [3, 3, 0, 0, 1, 1],\n",
      "       [3, 3, 0, 0, 1, 0],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 1, 1],\n",
      "       [1, 1, 3, 2, 1, 0],\n",
      "       [1, 1, 3, 2, 0, 2]], dtype=int64), array([0, 0, 0, ..., 0, 0, 1], dtype=int64), array([[3, 3, 0, 0, 2, 2],\n",
      "       [3, 3, 0, 0, 2, 0],\n",
      "       [3, 3, 0, 0, 1, 2],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 1, 2],\n",
      "       [1, 1, 3, 2, 0, 1],\n",
      "       [1, 1, 3, 2, 0, 0]], dtype=int64), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
      "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0], dtype=int64)), (array([[3, 3, 0, 0, 2, 2],\n",
      "       [3, 3, 0, 0, 2, 0],\n",
      "       [3, 3, 0, 0, 1, 1],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 1, 2],\n",
      "       [1, 1, 3, 2, 0, 1],\n",
      "       [1, 1, 3, 2, 0, 0]], dtype=int64), array([0, 0, 0, ..., 1, 0, 0], dtype=int64), array([[3, 3, 0, 0, 2, 1],\n",
      "       [3, 3, 0, 0, 1, 0],\n",
      "       [3, 3, 0, 0, 0, 1],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 2, 0],\n",
      "       [1, 1, 3, 2, 1, 0],\n",
      "       [1, 1, 3, 2, 0, 2]], dtype=int64), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
      "       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
      "       0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1], dtype=int64)), (array([[3, 3, 0, 0, 2, 1],\n",
      "       [3, 3, 0, 0, 2, 2],\n",
      "       [3, 3, 0, 0, 2, 0],\n",
      "       ...,\n",
      "       [1, 1, 3, 2, 0, 1],\n",
      "       [1, 1, 3, 2, 0, 2],\n",
      "       [1, 1, 3, 2, 0, 0]], dtype=int64), array([0, 0, 0, ..., 0, 1, 0], dtype=int64), array([[3, 3, 0, 1, 1, 1],\n",
      "       [3, 3, 0, 1, 1, 2],\n",
      "       [3, 3, 0, 1, 0, 0],\n",
      "       ...,\n",
      "       [1, 1, 3, 1, 1, 2],\n",
      "       [1, 1, 3, 2, 2, 1],\n",
      "       [1, 1, 3, 2, 1, 1]], dtype=int64), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
      "       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0], dtype=int64))], 'db_size': 1728, 'db_n_attr': 6, 'imbalanced_ratio': 24.043478260869566}\n"
     ]
    }
   ],
   "source": [
    "datasetName = \"folding_car_good\"\n",
    "pickle_in = open(f\"{datasetName}.pickle\",\"rb\")\n",
    "example_dict1 = pickle.load(pickle_in)\n",
    "\n",
    "print(example_dict1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6a016d8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['folding', 'db_size', 'db_n_attr', 'imbalanced_ratio'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_dict1.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "34d2aea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "myData = example_dict1[\"folding\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8760a565",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[3, 3, 0, 0, 2, 1],\n",
       "        [3, 3, 0, 0, 2, 2],\n",
       "        [3, 3, 0, 0, 1, 2],\n",
       "        ...,\n",
       "        [1, 1, 3, 2, 1, 0],\n",
       "        [1, 1, 3, 2, 0, 1],\n",
       "        [1, 1, 3, 2, 0, 2]], dtype=int64),\n",
       " array([0, 0, 0, ..., 0, 0, 1], dtype=int64),\n",
       " array([[3, 3, 0, 0, 2, 0],\n",
       "        [3, 3, 0, 0, 1, 1],\n",
       "        [3, 3, 0, 1, 1, 0],\n",
       "        ...,\n",
       "        [1, 1, 3, 1, 2, 1],\n",
       "        [1, 1, 3, 2, 2, 1],\n",
       "        [1, 1, 3, 2, 0, 0]], dtype=int64),\n",
       " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "        0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "        0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0], dtype=int64))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61b5e0fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = myData[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3be9d114",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_con = np.concatenate((k[0],k[2]),axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca8b05f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = np.concatenate((k[1],k[3]),axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67db0cbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = target.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f087cf7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1728,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dddfa82b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_con = data_con.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d0f1248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1728, 6)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_con.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8631ccf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDataset():\n",
    "    labels = target\n",
    "    features = data_con\n",
    "    label_1 = list(np.where(labels == 1)[0])\n",
    "    label_0 = list(np.where(labels == 0)[0])\n",
    "    features_1 = features[label_1]\n",
    "    features_0 = features[label_0]\n",
    "    \n",
    "    data0 = np.array(range(100)).reshape((25,4))\n",
    "    return DataSet(data0=features_0, data1=features_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1383de03",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = loadDataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3111b09e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getRandGen(initValue, incValue=257, multValue=101, modulus=65537):\n",
    "    value = initValue\n",
    "    while True:\n",
    "        value = ((multValue * value) + incValue) % modulus\n",
    "        yield value\n",
    "            \n",
    "randGen = getRandGen(2021)\n",
    "random.seed(2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9f9c9709",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shuffler(data):\n",
    "    data = list(data)\n",
    "    size = len(data)\n",
    "    shuffled = []\n",
    "    while size > 0:\n",
    "        p = next(randGen) % size\n",
    "        size -= 1\n",
    "        shuffled.append(data[p])\n",
    "        data = data[0:p] + data[(p + 1):]\n",
    "    return np.array(shuffled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c60583c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ganName = \"SimpleGAN\"\n",
    "gan = SimpleGan(numOfFeatures=k[2].shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6818aa73",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> 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",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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C8CGD4dq1m/t42nHts38NWZIgyTKk6Ghh94/RmMpIHzt2DDNmzED//v3x8ssvX3GZzkBV8MyIreQLnJ/7IgBAysrmHjrlmjIJ8qGDgCzD+sfnuW/X1EpwcXHshVMngfAISAMGAGDbcR7V4ZKSOuHU089CXrUCUt4ElIz7JdbuqeR+WKl67ympLOwPzAsD+OmYWh1cGsA69/BcV395TvmMRVT5064vykp9ild11AJPV1IFzzRu6s6dOzFhwgSMHDkS8+fPD+o6ytGjRqlhafKqFfwjLFJSAVmGlDdBaKQBAMDhAK7uCWnAAK7bcffmTajKu1fNvpO3bcU/V2zByTMX+EodaI42sKSmsUzDmbNgzcnlGoXQrIPLhqSii9z++6f5d3QDfaWYwkhXVFRg+vTpmDVrFn79619DkiSjp8Qd5UsuJEusrBSQJMirVgjRwRXjJGVlqzp4ydPz8Ex1D27Zhp5lS9F09BgAqA+IMfs24+oLp7nGZQOaUK0F88UlWWh0cPmjZcDx4+o/iSheBYgLL9QSaoksgEmM9AcffIC6ujq88sorSE9PV3/99a9/NXpq3FDieUV4BZb8AuZxecPCeNOipkRZKdbuqeR7gJeSCrmBPRAs+QVAbS2GfbsDf446jszju4SmZwvxZpUDvJJiwOkEPG6fufAuXuVeMF9YHLFZDvCMwlSadHvpKJq0ovEZoev5xwzzeDhoNWkpIQGW/AIU9x7CVR92TZkE6UQFPLZOLE65vh6wWCClDQbAT68FANe8lw3Rwd0L5gO1tewFm42bDq7VpJ3jx7JU+17JLPVe0Pc32OiQmrQRaD0Bo4vG8MQ/xlRETQn10NCb2Zh5fBdeGJ/CzXBZ8gsQ1tcbgy1ZgPBwoBffsDRDdgxeVAN8/hxgs+Hrv77DVU4yokY3wQhpT1rryQJ8vS0topNZ/D12EYkBakRJXR0QGwtpwEAh63p6+WphSQ/+OwakpOKrCgdLFLptEPeYcK1X++z/9wJOXmhAz/hovDA+RddxkpI64dS4eyAfOgTIHhYDHmKHd3pDnnQbEZoE4EWJQjAikUUpnymq1x9kGYiMBGRZ2LoCAg+0NAlCSi3pYf/dij+tfwkZc2dz/Yy1JVmtM2dhXFoPrsWr2OfpUQ+gQ+3wzkhC2khrQ6REFW1RohCEfsEFls9UsObkMo/LahWSfWf0ugK+oYe811hbkpW33g+wz9P6x+chDRgorG41wQhpI20I3igEEUXhFYzYMQAQXlvZEh8P2W4XljasbZnlE5Y2cxb3NdZ+jiJ18PDF7yF89uOGhN+FKiGtSRuBEoUgX9sLgDgd3AhERpQAAB7+JZyHjzJpJypa2LpqNf+Sp+dx9WoDyVXbj5zjOqYRBcGCHdKkTYwShSDcqxWMcAMNsBoWcXFArM2wllm8vdpATRt4R84QxkJGWjDWnFwkrVopVAc3IrxQa0xE6eBqur3bzX6HmOw7bQge7wM8I5o2EMZCRjoE0GaJicIwHVygEfMvySrb7fy9Wo3OH8w7MaIZUxT9J4IPZaeg/bsQ/IwYz84z2uYCANR2UrxDG7XSER3eBT/kSYcA2toHwY424oK3Dq5tsguIqdshSiIjzAMZ6RDAiBvbCB28oaio2XMuKxXWZFdKZgklZDwJHpCRJrhgxKFW7aLF6pii9FrybAnekJEm+OCXii7Cq7ZNnaIaZjKeRLBAB4cEH7TZhhrpgafRjB41CrUZt3B7f4IwAvKkCS4YFoJHEEEGedIEFwwLwQtBRFU2JIyBPGmC6OBQ5mFwQ0aaIHTEiNBDIw5pCXGQkSYIHTHEqzWgXjghDjLSRNASMl6tZkzyqoMPMtJE0CLSqwxUEU/Y+NoOMYK8anoAiIOMNCEM0Te20rVcttu5j6lWxKurC+jV8qS1cEeeoY/ayopksPlCRpoQhmjPVh3HW52OJ8oDARfsQJObNWvVeNWiMKpvJ+ng/CAjTYjD61nKneK4e16qZ1tb22JMHp6fWhEvIgJwNgISu7VEJPEE6tbC26uVsrKBqCj2O+ngXCEjTXCnhV5bUszf81IMhySxX59/BvnQQb6dYlJSAVkGbr0NSEpSX1OMJy+0skYw6+ChChlpgjv+Xi0yMlWtWO/O3i0eCLGxzHBGRgKyzFer9RorqcbBvOqqKshL34F86BDXwztP6Z7mFwV5tUbo4KEKpYUT/ElJBQ4eYA1ivUYMCQmQy8shr1oBREXrVnxJfSBYLMDFBkijxwIA5FUrIOVNUH+GRwq1fycY97NzmPwhe7gYLc+ypZAPHQJ27wIiItl4V3UTXthKVAkAI5obmwEy0iGGIXUelO1wbCykhASfmwwpqWqrK13QPhAkC3sIXNVN/bN721Zuba4UY6UeWmZkAiXFkPImcFlrS35B84PA2QiE2wD46uC82odpZQ1R3yP3gvlARTncdjt7QfPnYDbYJHeEGEY0pVW24FJWtuptAkD44vcQPvtxfSMQNA8EyB6gyQ2cOgm4nEybBv/DPObhHgQ+/4wdIHKK7rDm5LLdgSwDnToBMTHsH7w6OAB+0R0mOiwM9nhw8qQJrrg3b2LerNvN3ZMF0OxJp9zANPCqs0yPjusM1NdByspG+OzH9R8XmrA/ZQ4RkdykDpWyUsDtZvHZbre6znpKSFqUa5TtdlVWcW/bKsSrtc6cFVDu8P+znhixY/CHPOkQQvUGErv4/h38PAbPsqXMg21sFOPJKp50STFw/hzzZGUZcFSz8TnGLCsasbxqBZM6wqxARibX6A5LfgH7PMPC1ANZxNqAuDiuRks9BPZL1BEV3SEsHtwEOwbTGenS0lKMGDHC6GkEJYqmB0d1iwQPvW8uNcqiUxzgcjFv1uWClJWNkqfn4ZnqHth+5JwuY2lRu4VnZDLD1bUr0PMaIDxCje7ghSW/gMkq9fXAV/8RF25YXweER7ADWQA49736T3obFTVpx1ENNDWxA9msbLUbPc/ojlAtXmUaIy3LMlauXIkHH3wQLpfL6Olwx1CtK65zixtJ75tL0b6x/UvA6QIaGgCnC/K2rfjnii04eeYC1u6p1GWsgOzfBzQ1ATYbS7iQPZDyJqC49xA8tbpM1weE8lkCAK7uCUBiY1utQEwsN68W0OxUAsgqPIyKmrQjScDFi0BdHeRtW1ucNfA6KFW+o0YXrxKJaYz0/Pnz8eGHH+KRRx4xeipCEHmApxqRXsksS2zUaPVG2n7kHJ5aXYbi3kP43FzxCUBEOPuz9/cx+zbj6gunMS6th75jQbMdv2AHPB7g5Hc+6dlr91Ti5IUGXR8QypjuBfOZDCB7gLBwoPoC8P33wKmT8JTu4WNUvAk0SnghAKBXMlev1pJfwB5AAJOSIMbL1coaRiftiMQ0Rvr+++/H6tWrkZKSYvRUgg7Vq/3qP4AksQM8r7HgYbQAdsgjpQ2G9JO72aFdeDj7PToaw/Zuw5/OfIYR/broOiag2Y7He1O0IyIBNOvg49J6oGd8tK4PCFViAZgOLsssJC4iEvC4AaeT1fLgYVTKSpvDC196gclZAFetVo0qiYwEunWHlJXNDhI57hgA392nqKQZMyTqSLLsfRSahB07dmDmzJkoKSkxeipcaCgqQu2ixUB8Z7i+3I6YgomInzOnxb/bpk5B9KhRuox5+vY74D52HJauXSHX1gIWC8Kvvx5Jq1bis32n8dFXFbhveC/cNqi7LuNpqcq7F86vdzFdOjycRSDIMiSbDVcf2KfrWMraucorIJ89y4xlRARiH5yMb+6ZwvU6lfEdL72MphMnsKP3EKz/cQ7GfleCEecPI3LkHXDt+ga2qQ/q9rkCwIXnn0fd62+qGZVwu2Ht0wfdt2zWbQx/GoqKYJ/9a/V7BMhoOnoMYX37ImnVSm7jVuXdC9f+A4DHg4R5f9N1Hc1MUIXgnTtXC4/HVM+cFjjnvgicqGAZcRGRqHvvfVzsd73q7bgWvgm5vBz2hW+iNuMWXcb0jMgCTlXCM/B6YN9ewFEN18BBOL18NQYsW4pn8wtgTboeVVU1uoynxTXwx8Dub4DoGCYDDBgIlB8Hxv9c//EWLYbz8FGgtoY9DADA6UT9jhK83/UOnLzQgPe3HcOPk2J1HdYnQWjFP2HdvAmFxdWojO+OwkEpsKT1wNo9lRj3xESM6NcFtTpet2tHCfPYGxtZzZCKcrib3Di9fLVuHnRSUiefz8q18E3IAHDxIpxnq5iMVl+vfqd4heC5f54P+dk5gCTpen8YQVJSpzb/rGnkjpAjPoEZLUny2QJz2VL5h6U1NQkrCi8XrWcG0+NmnnT5cez89V/w++Sf6B7dYZs6BVJyMqT78tlYAGCxcJM5FLSatHKAOO66RFx94TTGSGe5SUoAmCbtbAQiI5ujOyrK+Z51KIWk4hMAh4N9rwREQPgk75ggiUYUIW+klQ9a70I/rWGdOQvWPn1YRlxGZosvHKD/6XiLsLTELkBKKlcdUT3Ac1SzF+IT2LVKFqw9cJ6L0YoeNQrhi9+DJTWNHWxZrMzLA5Axdzae61yJzOO79P+clVP/ujrVQGUWvoM/bX4VmYXvcHlA+BSS8haRku12ltTi9zO6f6e1af7JyZDyJojTbU0QEieaoJI7fgiqMTl4gFuWlhZrTi6wchnch4/6eiAiiuHs26uGpcneLDH0Suab+Tf8Jkg1Dp/MsHHXJaJQ5uPVKmMAEgAZUla2T80HyVvYSa91VjMqJYk9hLwhWpbUNHXbP6JfF90PSdXvrdXKxu0cjx2x16Aw/VaMOb8fN43J4pct5/1spdFj1etUUtEt+QUIX/yefmP54V/EilemoZkwnZEeNmyY0ENDNb4zJRXytq1q+Uyl6A8PA2abOgX2hW+2KC7EO7UVtfprzq0hb9vK4mj37QWuvRZAc8TBzQBu5jl4Siqwexd29M1A4cXeGNP1FIZ5ox78b/L2wuKULUy6stmAhgZmtFPThBgr2W5nclJsLAoH5aAyvjs++fENyMpJUeenW+x7gJRwxbkQ6eSIqLhnJkxnpEWj/dBd3i8cz9oHWiypabBq6khwG0vj1SohWlJWtr7V51rDUQ253COk9kFDURE71LLbgVgbCgfdgcr47lg/7B4g1ob1Kbn4ae9BGLGYg1eZNwFy+XHg6BEgPBzuBfO5Vmbzr7hnyS/AOIcV6w6cxpjuTmw/cg5rq3tg3NPzdPPi1Yd9XFyLSnu6VzMkVEJek9ai6GlajY0HtYsWi9XSNMXoAQAV5T5ZYjx0eCVOGsNvElb7QFlX1NUBsgdjEl24OkLG6LJNWJ+Si1NSlP6Hd961lbdtZVXvACYpnfxO7QTDA+0ZhpJm7/n8M1UH53FYqdwfSqKMcm2W/AKuO89Qh4y0BiX4X/fymX6ED0kX1usPaD1iRMjBS0W5sIMeZV3ZAaWEYf/3Bv60/iUM++9WjC7bxCW6wyeRRSEqioVY1tVxeTi5N2+C+9k5ascXxSCvT8nlmrTTWsZfqBzgGUXIyx1G4Nq1u7n4jmSB+9k5rAUSJ2/EX8cTWeIRVqtvvYODB7jVPlDXVZKYgYxszja8aUyWqtPqiVZ2cD8xGzuS01F4Qy7GfLsNw06VcTkUVut1OBsh2+0YI51FYfxVGJfWFyW3zWMx2QBeGM8vezcUD/CMgoy0AYQPSYezbK/auUNJ1eZdA1h5AAg5eFF08JjY5uvTtHbigbquMbEsJbyxkWv9aMB3baWCySi0X4XKpF5Y36cfhv/nLT5GTFlbb5xyZuE7uNl7SPnU6jJV5uCRdq8Qigd4RkFyhwEoHp9U44D1j89DGjCQqw5uaIlHm02Njwb41pJWPWmbjcWDx9q4F8PxKa5UVopx6dfg6hgrRpdt8nko8uo+47+ePJN2CGMgT9oAlBC8QJ4tDyz5BazIkt0O9+ZNQrweZUwAzb3+srKb42qhv/elXVdA0BZc8WqtVsherzYTYA9Fx3GuMejS6LEtdgk8YrIJYzFdgaX20BFqdwAtayGIwDVlEuTyckjJyVzjdwONiYsNbOfgPWDjNY/LrSuPJrzqNcbFtWiyyyvaQfRnacT3Ndih2h1ESwK0AeKdCh8opJFn2nBDUZF6PYEiKrjIPq002eUZ3mhUyUzCGMiTNgCjPWkALTxcvT0yHl7rZXn4l3AePtqqx87Tk/YfM9D4HRXypPXnSjxp0qRDhEAhUzyzxJSmrLzDC7VoNWlP6Z4W4X489P9LhaJRWBqhB+RJG0AoeCZKwoVaeIiTx65Fu65GaPDBSih8X0VDmjRhOD61f5N7C0sNVyDdlggWSO4g+KHE85YfF1uSFXykDYIwAvKkCW6Iju4giGCEPGmCG615s+ThEkTbISNNEEGAEl6ojdihh2FwQEaaIIIAterg3jKgqQluu119nQx2x4Y0aYLQGUO6WCsZpd7yrIBBhbUI3SEjTRA6E8g4cjfcSiRNl66Q0gbDOnOWTykAouNCRpoIagz1arVx4Qvmc/Vqta2t1LKoiuHmXK7VkDUOIchIE0FNa1t+roZFYxzdC+ZD3vMNUFvLNfQwUM1qUeGOJKvw5ZJGuqGhoc2/COJyaA2jMO8rkFfrjYTgZVgCGkebjWvfzEDrqXuzgVbGlDvFCc8oDSUuGd0xYsQINDY2tumN9u/fr8uEiODF3zBq/8wtCkHr1W7bClSUw223Q8rK1r3forbKnlJ4ScrK5lbESot2bUVFcqgRJd5qiv5rTBEl+nBJI71u3To89NBDiImJwW9/+1tRcyKCFaWLiV9TWp4GRlulTu0UA3DRa31aaZ06CTidwIkTgM2mjs29p6TXmxUReqfWz05JhbxtK2S7Hait5TaeIeVvTcAljfS1116LJUuWIC8vD6dOncLPfvYzQdMigpKy0sBNaf2Nt04Euql5lhJVjJZstwNOF+DxAI5q4Pw5dTxeTYZlu11dW/e2rYDDwX2Xos0odXlrsiAuDtK113Lv1RlKRvqyB4dXX3015syZg6+++krEfIggxpJfELgprc5erU9Exd4yuB//FZx33g5P6R7W59Hr1eqt1yoasJSVDUSEswfR8JtYU9zELlwNF4Dmta2tVTVintq7Vn8OGF2iM1qt35DzDYOgetIGEIr1ebVpy/K2rQAA68xZsObk6raNVdZV23cQx48xjxZgXcQvXgQASGmDuXWjke12oKoKkD3MUDsc3Opau+a9DHnVClYWFmC7lFgbUF8PyB72ejvTxFv7vhpZs7u1jjgdpXa40HrS1dXVeP/999v7NkSQ4uPVHjrEjAgAOBxwL5gP15RJAHT2ajV9B3H9IPZadDQzWL2S2a+UVN29L1+v1sMaHmi8Wi4en3cXIm/bytZWsjCPuq4WaGoCykr5RXgY0Dcz0NjBnrTzg430F198gdmzZyMrKwtz587Vc04EZ1rbKvIwIgENF+DjAem+FdcYLuzby15TPOiEBJaNp6lrrReW/ALmvQNARiYzlgAgWZhWzCGhRZEA2DgSW2ObjXnTYWF8o0rKSltcm7xqhZiYaa1ExjFpxwxSyhUVWPruu++wevVqrFmzBqdPn0ZMTAwmTJiAiRMn8pofoSP+23H3s3OAmFjg/Dm1II/e4VPa6ApP6R62Nc/KhiU1jWnDcXG6GxKfAzzFUMoy5OUfAG43lxA8Vc4BmNRx/BgzlJLEvNqICADQPblEObwLJHsof3ZNmcTl4NCSX9DcIq2ujnmzGZmQahzcQw4v1VtST/w1/UBj8j7EvKyRdjqd+PTTT7Fq1Srs3LkTFosFmZmZOHPmDD744ANcd911XCdI6Ifq1cbFNXu1jmpu42l1aPeC+cDJ74CICMjbtsLt3ZpLAwZwizpwzXsZ8qmTQEQkEBUF2M9jxzVpKEy/G2Mq9mOYjt6Xz9o6G5kOXlsDREezokeNjZCyshE++3FdxmuBvzcpoBOONSdXffCqh5YlxbD88XmuCTRqSVbNPHiNp4RuqgfOmqgZUZEml5Q7/vCHP+Dmm2/G73//e8TGxuL555/Hl19+iSVLlkCSJISF6Vfp9MCBA7jvvvswePBgjB07FqWlfOsNmAHROl7A7fjwm5hGC0DKym4uzqMDiuGSV60AKsoBV5NGAmBbcx7ej5oJt20rEB4BeNxMAoiKQmFqLiq7Xov1Kbn6erVaHTw+gb0myyz87uJF9eGk9+esXCtSUpuvR6ReqzwcbDb1wc9T6tB+p+RDB+F+dg7X+8eakwspIQFwOADAkA5Dl7Syy5cvR+/evfHwww8jOzsbCQkJXCbhdDoxY8YMTJo0Ce+//z42btyIKVOmYMuWLbDZbFzGNAOq93XwABAVzfWprHogtbXMcFR4dWLN7/K2rYhYvU63Mf2THQD2IJCL1gMuFzD8JvWG1vO6A+4YAEgDBmJMogvrL5zG2OsS9Y0E0HqysbHNXdLdbuZheh0avb0vz7KlkA8dBA4egJQ3oVnmCeBV64178ybIJ06wHUPKDey75ajme4CnxNRnZAIlxaomzvP+0Uor2vcXFat9SU/6vffew4033oi5c+filltuQUFBAZYuXYrTp0/rOoni4mK4XC5MnjwZ4eHhGD16NPr3749PPvlE13FMh+LlCOimrRouRd7wGi6eWHNy1ThoKSsbACAvWwqcPcOSPUqK+RwyabxapWO5lJWN8MXvYfh/t+JPm19FZuE7ug6pHuClpDZn3YWFNR/mZWSyNdDZq7XkF7AHXn0D5I+WqYe0Ijw+z7Kl7IHf1AR89R/2uV5sZAeJOn9/fXZHXllFypsAacAAZrA57hZ410C5HJf0pDMzM5GZmYnf/e532LJlC9atW4eXXnpJjebYuHEjunfv3m5v9/Dhw+jXr5/Pa3379sWhQ4fa9b5mpaGoCK6FbzZ7PJpu2txqHygeyPCbINU4fNosAfwPXnDwANv2NzWxf/AaLlSUM71v86Z2X2+LddV4kvK2rXB5sxslQPdrVXRR5/ixzFgBTJe2WLCj5w0ojMvEmIrDuurg6u4orjMzlhGRXmN1A+QKlp6uJJdwISUVKPsvk3dkmaXBe7Vpvb3aFpq/ywV521ZYZ87yHl5auJdkNYo2icoRERG46667cNddd8HhcOCTTz7BunXr8Oqrr+Ktt97C3Xffjeeff/4HT6K+vh5RUVE+r0VHR19xdb0uXTqGNFK1aDGkExWwxMfD2r8fwocMhmvXN7BNfRDVL/0VbkmCNcx6RQHvlx3z4F40xcQg7GI9ktauQUNREWoP7kX4kf1w7dqNzo9MQ/SoUbqNp9DwyDTULlqC8CGDMem6bYDFAng8mPxaZ3zS43aMC/8vbtzzGawrP0TS/ePbNZayrtb4eFi863rxX5shJSZAhgzP4W+BQweRMO9vXK4VAM6EWdCk/CUyEpaEeBSm3YXTV/XChn79kNXwHWxTH0S0Dp/tmTf+AfnYcViSukLq2xfuExVAeASkr3dCVkIO31gArFwG29Qp7bpm7XexoagItYsWw3L+POTOnRHWpzfChwxG/dIPEH7zCLiPHodUUw1byRe6rfOFYRmoP3QQMT/JQ+O/NqPp2HGEhVlhWbkMbosFkiwj4ZFpuqyr2bjik7+4uDjcf//9uP/++3Hq1Cl8/PHHKCwsbNckYmJiWlTba2hoQExMzBW9zw/JONRmuwFiwmpsU6fAvvBNSPkFcANwecesrm6Au8kDXNsLmD5T16xE98/zIS9bCvfPH2BZeQvfhFxeDmfZXiAqGvaFb6I24xbdxgP81jYnF/jiPwBkwGJBYdpPUBnXDR93vQoZ54+q82oPyrrK3nV1Pv0kkwK6JrEfcDiAyEgu16ogT/8f4Nk5TIu2WiH9+rcY57Bi3YHvMapnIvD6EtQCqNXhs21q8gCyDE9UDNDkBhpdgAzIw29i8kNcZzQ1uYHDR9t1zf4Zh8p3B3FxgK0TnGer4PyUyRvOL/6jZlnquc6uHSWQI6NQv6MEGJEFnKqEe8QtcANA2V4gbwJqM27RZV1FoHvG4YULF+BRUmu97N27F9HR0XjkkUewfv36K5uhH/369cOxY8d8Xjt69Cj69+/frvdtC9o4SFHFy6NHjVI1Lu2Y7gXz1YM8HuFSyrbXpwZwRiYQF6dKDnrSYj2Vr5BHwrjrEnH1hdMYm+jSTe/zX1e4mnyLHHkP8HieyFtzcpkObrVCypuA4t5DsPbAeYzet1l3Hdw6c1ZzNE5tLYtiaWoCdu9i0pIksZ/RW5PWav6ayIeAdVl0wkdbF5TIYhYua6SXLFmC22+/HXv27PF5/cUXX0R2drYuKeHDhg2DLMt455134HK5sH79ehw8eBC5uQKEek2IUmsFXESNLwI1GuDzzwBJglTjUG80Xgd4yoHowqePYuHjh7Hw6aO4cek8Lgd4gDfqwG5Xw+7Qz/uwt1p8Ejy49xssK8XaPZWojO+O9YNyuOjg6gNOORfyyED1BexITsfvbpyE4t5DdD30cm/exKIp3G4WBud92EtZ2bD+8XkW986hyJL2WrX3qchQOKO4pJEuLCzEK6+8ghkzZmDgwIE+//baa6/h0UcfxYsvvogNGza0axIRERF46623sGHDBmRmZuL111/HggULkJiY2K73bROaG0r7RVDaHvnUIOY9vtYz4oRaiS4yEpDllnG1eqK5Ns+ypczLcjb6pIZzizpwOJhnGR4B7N8HeDzYcW0qnrnYG/8p3Ma1Mpz83XdAjQNypzjc/ekS9Pj+BMZel8hVQrPOnAV06w6EhwE/TkFh2l2o7NYba/dU6joO+xwloNH7OZYUN5efhf6VBQOhvU+NjrwQwSWN9DvvvIMnnnhCLfyvxWaz4aGHHsKMGTOwZMmSdk9kwIAB+PDDD7F7926sW7cOI0aMaPd7tgUjvOeGoqLmcTQGkucXTk16AHy244C3cpok6b5lVJJnZLudeVwuJ3s4uFxqSBzPkpYs/M7Doh7Cw1GYfjfzavVOZNHgWbYUOPc9k1o+/wzDSjbhz2uex41/+y23sDT1fetq2cFs+XGMS78GPXt2xbi0HrqOackvgDRgIKSCyUDSVaysgMulRnQQ+nPJg8OjR4/i9ttvv+Qb3HnnnVi0aJGukxKJT+Fyb/lDz7KlsM6cxS0srXbRYl9PToCm1kIf1qQNs8pp+mf/qZr7oUPekqEylINDJSRO70Nan7ThslI16UHKewDjenfDugMcElm0pKQCe8u8oYaSeoDIMyxN/UwVDzc2FpmF7+BmDteovV+c27YyrT8mlu1agrQKndFc0khHR0ejvr7+km/gdrsRGRmp66SMwj+ziNcWKnxIOouq8GsjxRP/QkfKmJbUNK4RLWoRnogIZkAgqcWGeNQ+UA9f95YB4eGs8E9EBORVK3DjVVtxo8MB6XAycM8duo2pjq3otZBYIouSbdilK6RrrtH/gR+oHdmttwkpcOSDsxGwdQrqwzsjuaSRzsjIwMcff3zJIkpr1qzBoEGDdJ+YEfA0zFpcu3YLSdvVor02z7KlzTr47Me5p6JLeRNYplhtLfP2YmNZvZCSYn4Pp7jOQH2dWtwI4Xyq0Glp1t3r2TXKMnZcm4rC9Lvxs/w7MaJfF13G8alm6P0cZbudRXRUlMPi3QUCfFOXld2mf2IUoS+X1KSnT5+ODz/8EK+99loLj7qurg7/+Mc/8P7772P69OlcJxls2KZOCVgMR5QmLupEXM0SUx5A588BF7xdSz7/jEuWmHXmLPYAsNmY1OHxAJ3igKQkrq2dAEWvHQCpYDLTbe+fiMIbf4rKrtfqeoCnrdEd6HMUFUqqnKGEz3486A/vjOSSnvSgQYPw97//HU899RQWLlyIPn36oFOnTnA4HDh27BgSExMxb9483HjjjaLmGxREjxqlBvlrvVqeZSX9W1QJuaG023FvgSXEJzTXV+apg5eXM0/d7QbqaiH17y+0fCYAWFLT8LPeQ7B2T6WuB3jazueimuwSxtGmHof19fXYsmUL9u/fD4fDgYSEBKSmpuKWW24xlR7dEXscisp41PaEa62ql9749KFLSQ1YlF7P+srKuvr0UyxaDziqId2Xz62Ws9pT8WIDe+B6f+fZc0/bx1FKSOD6WYZiT07eXEnG4SU96YaGBsydOxcbNmxAREQEcnJy8OSTTwZ1+VDR+Hu13IymxqvVbod5Gmmfw0rNjgEAVx1eWVP35k3e5gIsjtedmsav15+3fKZSvEretlW3wlGB0HafEVV8njCGS2rS8+fPx5YtWzB16lRMnjwZn3/+OZ555hlRcyP0RJs+K0gHNzpLzD/xgptG611bNaqirJRFlXgr0fFAXVsead+EqbikJ71hwwa8/PLLGD58OABWunTixIlwuVwIDw8XMkFCH1rzao3SwXnrw+qh7MEDrGN4+XFukST+ayuXlwM1Di5j+SPsfIEwjEsa6TNnzqBv377q32+44QbIsoxz586he/fu3CdH6If/zSy6iacoQ6IdUw1LO/wtEBHJTfIItLYUlkboxSWNtNvthtVqVf8uSRLCw8Phcrm4T4zghxE6uKgDUq1Xq0oNSkSJV/LgGjtMni2hM/p1kiUIf7QFljSyimy38+k+A2N2DATBk8sa6dWrV/sUV3K73fj4449bNKWdOHGi/rMjOjStpaKrMdOcEbZjIAiOXDJO+o472lbfQJIkbN68WbdJ/VA6Ypx0qCAiTjsU11UEtK76o1uc9L///e92T4YgAHHFqwgi2CBNmhACGWa+tHYwq/0zrX/HhIw0QQQB/kWVAv2ZjHTHpE2NaAmC+GFoMzp5Znf6ZHFq26EJ7qFJ6A950gTBkdY8XJ6hhyLrpBD8ISNNBD3+LbWEarWBuqcI7MIDiIkPF5WsFIq0qVRpR4FC8EKb1tbVv5SolJwMAGpIIK9you7Nm1jrMEmCNGCgkDF50Nq6ag2zsmMQtbYdnSsJwSNNmhBKII3WNe9lrh1pfLqHa7uXc9Zq1XZastxSKxYETx1cK+W0qokT7YaMNCEU7Y2t/FletYJLuyfFQAFoLiEKcG3f5UNKKiB7gIxMdq3btgrRh7WGmWcrLa1h1pal9SmLS7Qb0qQJoQTSS3lVjAt0aIe4OGY4IyIAl5NrYX7VWJUUQ46KBiwWIR6m0jHdbbdDysrmpoO3Gvvur8MT7YI8aUIoSgMAxXAqHi6vinj+W3ApK5vJHmFhrKO4w8HFy3Rv3sQKSVksQKyNPRxsNnEepkcGTp0U5r37wNGTbi2kUVQTZyMgT5oQTiAP171gvq4RAdqIDrXynreNFq7qxjq2OKqBq3tyq6UNh6P5sDIhQe3zqLeH6R9ZAQCICAfCw4HaWuH6sP9uSU9aC2nkWVnRaMiTDnGM8ECKx0zG73IeRfGYyc2HTLW1umqnPnr3oYPAqZOA0wk0uYETFUBNDYu6SEjg1oNQOazUNuLloYNrDZcidaBzPIsoEeS9i/oeheIBJRnpEEW9qRbM53aw1Nq4aw+cR6WtK9YdON+8HbfZdO3Vp9zMyMhk/QYlCegcDzReBFwu9nePR9Wk9UaRdVStvayUjSl7dPcwA/aMjI0V2gMx0IEwj+9UaweUUlY2EBXFfg8yyEiHKGovPkANS+MVCucfbTBm32b0qKrA6H2b1e24lJXdfPPpgHIzSzUOIDYWsFqZVyl5v/IREUDPa7hp0gA7wJP3fMO825RUQJYh5U3gkm3oE4+c2AUA4Jr3MpuD3a7reAExIBXd5+EkKKLEiJ0nGekQxZJfwA6zAGYoK8ohf7SMiwfkH087PKIOzyWcxfCIOq7bcfXwLukqdlhYVwdYJHZg2PMaoFeyuO2yACOi6uD1dYDDweSVinKggl94o3Jop5Vy5G1bgYsX2e8caa0bPU947hJag4x0iKGNHZYSEthN7ahm/xgRycVoKQ8ExaPziejguB1XjJaUkICvKhz43Y2TsKNXGuBsZMbrq//oajj9vSzrzFnsQVBXB/nwt8AFO+QTJ4Qk7ajJO4ldWCSLzp+pv7zBS8rxp7WIDh8ZhCeaXYIwHZ7ruxNtRlQ4kfbmUg7wdtw/E1LaYKBrVy7enjUnlz0QqqrgfnYO3C+9wGSAl17guh3XelfrU3JRmdADhel3M08aAOI66/qAUNbWvWC++iAEAJw5DVRXA01NwPlz3LwwrQ6uxJ4DAJqadPdqWxzgybKatCNlZUNKG8weUjojSvtuFW3fTkHjm85Iv/POO5gxY4bR0xBCa5lhvLPEFK923flwVHZLxie9hvE/ZEpJZR27m5qAc983/378GHD0SHNnb53wD8Ebm+hCzz498bP8OyGNGs0OmUaN1tX7Ug8r4VvPGZLEfu/cGejFd0vun8Wp7pJ0RquDy6tWAG438PlnkPftZbKZgAevIV6tAdElpjHSdXV1ePHFF/GXv/zF6KkIo7XaBzz0tUAyx+iyTegZH41xaT0AgO+WsayUJXWEhTVvwRO7sAM8i1X34bTG6itnLNYeOI8x0llkzJ3NLcFD9WQBpvenpDK9H2DX63ZDyspmoXK8jIliODIy2Rw6xwO9krl4tQCapY7GRvZZNjayUEdOOrhPLL03YkZetUJYlJIR6e+mMdLTp0/HyZMncd999xk9FWH4a7XKh8/DWPp4594beXivOLwwPgUj+nXRbZxANB/gJTGdFGj2LoffBMRE6x46pdVnCwfloDK+Owv580a0iNDBUVYKnD/HDtQ8HkCy6FqnxF8ic44fC/mjZSwW/Kv/MN39zBkWI84JS34Bi8e+9Tb2ILr1NqBrEncdXBkbsszW16CkHRGHlcKMtNPpRFVVVYtf33//PQDgb3/7G+bPn4+uXbuKmpKhqNtxoEUYGI+tmxHhSgoBDZfLBXyvFDqSdD+8A9g1y9u2Ysyx/6BHbRVGf72OjVtbK247brWyh1PBZEgDBqiHenrc2C0O7yrKmRfrbAScLiYpQQacLv7Fq0qKAckCqcYB6dprAVsn3b9f2rVV75+MTFaLRZKEp7+LOqwUlha+e/duTJo0qcXrVqsV+/btQ7du3do9Rpcutna/h0JDURFqFy2GbeoURI8apdv7KlhXLoN8ogLW+HjIiQmQaqoR/uZ8uHbtBs6fh3ShGtaVHyLp/vH6DHj/eOD+8WgoKoKjphpyYgKihmXA9fAvYZs6BQC4XW/DI9NQu2gJbFMfBABUv/Qy5NoaeM6z2hYSgIRHpiH6Cmrstoa6ris/BCDDfaICw05UYFjxp80/FB4O2M/ru77wfmdWLmPrunIZpPPn0RQZCam+DjGxEXCFW2HLGo7ouX/UZ7wA6yoBsPTtDefmLQBkICoKkD2IGZaB+Hasr3/94yr/dbZYIMkyEh6ZBgDqvPT4TFW832EAqMq7F/KJCqChAVJMDKTOcbAmJuo/pgkwXdH/V199Ffv378drr712xf/3hxb9D9RVQrbbmffHoXB5UlInnF6+ukXBdKXOA+LiICUkcCk6pBTA9y/OrtQ+QK9kRKxep8tYl+tgra1+p8d1tljX0j1s+x/XGaitAerrgZgY5uU5qiHdl4/w2Y+3e1wF/+YCiIsDzp7x1pT2qA0H9P4++a+z++knmTYMicWFx8ZCGjDwB48bqOi/T8H/0j2QV62AlDcBltQ0IV1ZAnXb6Ug1O66k6D8VWILvtlE1Vl26qnoxj1KW/mUePcuWskL0JcWQRo/V1Xho0Ra/8ZTuaS4pySHx4HLFcGC36/ZAUFDWVU2wCI9gCTPnz7EfcDrZ3z0eLttxf8OhGDBkZEKqcQgpOgSXi/0ugcW+O/Uryao1zqqDYbc3h6WVlQrpTt5qmdQgxDQHh0bSWu0DJQpC1ImxVOPgqqu1qAy3bat6mIVeybrXPuAdsXIptAkW7AXf3/Wci1af1Va685TugfzuEpbpuG+vLmMFooUOHh4OdOsOZN8OhFmBeP2+x4FCRQEID0sLJciTRmCvVmQTTwWeJR4BTb2Ogweat+PORuZ5bf8S6BSn6wNC69X6XFdtLTv975UM15RJ3GpJ+2zHT37HrjkyUv13vcb02YkdPMAyDFetaP4BWQYc1ZDLPbp7mAFLskZEsJj0inJ2zV6HQ4/vVaCdmDR6rCpzaL1qQh9Mp0m3h47UiFarnQJ8NFp/Aul4amnLLl0hXXONGB18zzfsH6KidNVpW1tX2W5nLbNkD6slrfNZg/Yh5F73MYtYufU2oOossLeM6eCZw5mUlTdBnA4eEwPU1bZ7zMs1+NWeb/A8TwkmSJPuAATSaxUPl5ee15qOx/ugx3+H4FbC33ols9AtHbfGAddVaZnljc3WW3bR7hhQUsySdirKgWNH2Q80NDR7tQJ0cEDzcHK7uXm1gVqhkXHWHzLSBtFarz9521a+fff8EHEAo7y/ehPPnMWMCAfD1dp2HGDpy1JWNrdD2RY6uJa6Ol212kAHeK1F0fCSz/y/O2Sc+UByhwG0tn0EAOf4sbqHwpmBgFtjq1WX7biC/7qqUoAqAUjtCkW7HC1C4RbMZ/q7JAEXmFas1/ithVJq/6zXdV7q+0r8MK5E7qDojhDCyGadAQvTyDLXQyZtwSMWqyxzPQT2b7JrnTmLZd/FxrJQOB3HbzVyhqIrgg7ypA3gUp5JiyIyOqL1vnh5k1cyD70PmVpb19aSanhsz0XsGNo6PnnS5oUODjswPDVirXZpaMZWSqqqFfM2WoCvJq5kkvI6nA2YLBQTKy4szbu25EkHD2SkQwjtA0D1ZjlHlASEc4GnQLsRNUbcYuEqB2jX2LNsaXOTXZ3ilC+L4OJZBH9Ikw5RtKU8tenvPFE0cblTHFdD6d8hxb15U7NWCwgzYsoaW2fOEtPaCa1kzxIdGtKkDcBsGh9PrdqnEI9fISm9x1PWVe10oymSBVCyxQ/FbN/XYICiO4grgqf3FajZADIyuXp7ap1fbTsw79hSVrYwr5Yg9IA0aULYYaWi0Uo1DiHRJQH1YdJqiQ4GedIEV7TdK/w7a4iM2SatluiokCZtAKTxUTxvR4LWVX9IkyZMD3m2BNE2SJMmDCGUOmsQRHsgI00QQUhH7wFINENyB0EEIUroo7xqhW//Q6LDQUaaIDijjWQRFdWizSgl7b9jQ3IHQXAmUDd6t91O3bSJNkGeNBGyiPZqg92bVdbTNe9lw+qWByPkSRMhi9bDFenViuxALxL/bvRCKysGMeRJE4ZhhFarRaSHq1wfACG1Q4zUwZGRSd1hdIQ8acIwRGu1gcLSRHWoEeW1BxoPgNAdg2vKJMhUJ0U3yJMmDEO0VquGpX20DPKeb1ijWAG4N29iD6G4OCAlVbgOLnqdjdihBLMOTkaaMAyl+JJKL1YgnxfqdjyuM7cxAuFZtpTVtU5IAMpKhcQt+zfFFVmeVVtUizehEA9ORpowHK0R470dt+QXADYb9weCFqO6eftLHiIhHVw/yEgThh/gFY+ZjN/lPIriMZO5jyXqgaCg1cE9y5ZC3rZVbFNagwyXqAeE4rVLNY6grRdORprwuaF431zbj5zDU6vLsP3IOfW1QvkqVHZLRqF8FZcxtYiuae2/HQcgRK91b94EedUKQLJA3rZVvF4r+AFhRCy6sN0C13cnOgQit+Nr91Ti5IUGrN1Tqb42Lq0HesZHY1xaDy5jKmw/cg7PVPdAydPzYM3JFeLt+W/HRbXv8ixbCkgSIHsAgOt1BtqJCd0xQKwOrl7vgvlCdgtkpEMYxast7j2kuVEs55tLa5CV8QHghfEpGNGvC5cxFfwfEDy9L21ctCW/ACgpZkZTkNGy5BdAGjAQ1j8+DykrW0h3du1ODBCzYzAC9Rpra4XsFshIhzBaoyXi5nJv3oSMubPxp/JPMfSJyfjnso04eeYCPv5sn5Bto/YBoe1izsP78jdckCyALAuRObQPBxEP3kA7MVE7BiNC8NTdkc0mZLdAySwmhLcBURiX1gNr91RiXFoPFEuTsfbAeYy7LhE333MHl/F80oYvXsQYfIL1N/4Uo8+Wck222H7knHqdz3WuhGfuS3Db7YDDwW1Mnwa8pXuAgwcg5U0AwFqHiXg4AEzmQFwc9+7sRjX89flOSRa4n52jzokXyvVq71OemMaTfvfdd5GTk4OhQ4fi3nvvRUlJidFTMgwRWqni1T7XuRIj+nURcninLZ+JxC4Ydmov/hx1HMN7xQnTwUVtxxWNtLj3EDxzsTd2XJMKedUK7jpmoCQW68xZwrxao5J2pLwJTH+XJGEhh6J0cFMY6Y0bN2LRokV44403sHPnTjzwwAOYPn06zp8/b/TUDMGSXwDExUG227l9yf0fBCIO76w5uSh5eh5+n/wT7PhxFhAezqIeBOrgorfja/dUojK+OwoH3Mo0afCVk7Q7MJGHaYBveKO8bSvXrE7/Wijhsx9n+vuAgUGng5vCSFdVVeHhhx9G//79YbFYkJeXB6vVioMHDxo9NUOw5uSy7DTvdpwH/qFoWq+aJ4pXuz4lF5BlptWCr1c7ol8XvDA+BZnHdwkPSxuX1gM9u8VjXPo1QBLbpYiQOowssCTCSAa6VkBsZqUohBlpp9OJqqqqFr++//57TJw4ERMnTlR/dufOnaivr8ePfvQjUdMzHby/8NqUbPezcyDvLYP72TnCDu9+etsgdYsajGFpCiP6dcFznSuRWfgOe0HUg3fBfNWT5S2fBTqslLKyIaUN5pbVqb1WIzMrRSDJsiyLGGjHjh2YNGlSi9etViv27dun/v3QoUN48MEH8Ytf/AIPPfSQiKn50FBUhNpFi2GbOgUA1D9HjxolfC4iqMq7F679ByDX1EDq1Anh11+PpFUruY/bUFQE++xfQ5YkRAwaxH3MhqIiOF76K2RI6Pzk42jctQv1Sz9ATMFExM+Zw3Xsqrx70XT0GKT4eFgTE2Gb+iD379Pp2++A+9hxWPv0QecnH0ftoiXcxlWuL6xvX3jOn0PTseMI69MH3bZs1n2sQLB7lt/1GY0wI90WtmzZgieffBIPPfQQpk2bdsX//9y5Wng87bsc15RJkMvLWYgNoP5Zz5KWSUmdUFVVo9v7tQdt2rK8bSsAwDpzFnev1jVlEuRDhwDZA+sfn9dlvEutq/K5Ii6Oaabe6A69P9uAY897GfKqFZDyJiB89uNcx1LQM0Loct9X7VjuBfOBinKgVzIiVq9r17jBTFJSpzb/rCk0aYBFdzz22GN47rnnfpCB1gujiuGIpkWyhXJox3E77kNKKpM68iZwfyBsP3IOv7vpIey4IRsAhCVbGJV9B/jKWbz1aNHVDEMNU8RJf/LJJ5g3bx7effddpKWlGToXI2M+RWJEPK0PZaWAJLGwtNQ0roZ67Z5KnJKi8EnWBAzvFce82tFjuXm1imepeOvKuiphabzj37WIbDag9aKD7fDOSEzhSb/11ltwOp2YPHky0tPT1V9btmwxdF6iTqsNP4k3IEtMttsBlwuQLNw993FpPXC1fBF3b+Mf7ge0TBtW1pV3WJq2eJW6U0pJDdr07FDBFJ70mjVrjJ5CQPwbiPLCyFZHyviis8SYh9kZqK/jKie5N29CxrKlGOrn1fI0WmrEgd0OeDzC1lWbtDP0fa9Xa7cL04atM2cFbZNdIzGFJx3qGK2DG9FeCXFxgKOaxUmL8GoBNib4xSkrqEk7o59kOrhX5uAdliaqmmBriE6eCRXISJsAny93Walhh0yiiuEAYMk64eGA7OHu1Sqp0bwThLRodXClZRbKSrmss7Kumcd34bnOlciYO5v7A4EQhynkDqIZbWGeYEMr62ivU1QxHKWuhIi11RavsnTm+5kG6rouUuYg+EKetMkQ7dWK7NahlVWMrCshYkwl0zBj7mwAfNOVjehKQoiDPOkQxf+wUqRXawRq52yBYXCiwt/81zVYd2KhCnnSIUqo1D7wT9qRV62AfOiQkGs1wsOlw7vggzzpECVUvK8WDyBvgSUR12rkzoEIHsiTJgzRwQ1L2pFlIanoBKEX5EkTwgi1pB2C0APypAlhBGrtJFJioSgIoiNiqlKl7UWPUqUiMFOp0mCC1pUPtK760yFLlRIEQRAtISNNEARhYshIEwRBmBgy0gRBECaGjDRBEISJISNNEAIxorAV0bEhI00QAgnmOikEH8hIE4RAQiWhhnYM+kFp4QQhkFApuiSyS3mwQ540YRrI++KD0Q0eiPZBRpowDSL12lB6IBihg1Nda/0gI02YBpHel5EHeKIfEOTVdmzISBOGo+2eIsr7MtJwid4xiGiNZgaCdXdERpowHCNkDoA9EAAEtV4bSiF/wXqtZKQJH4L9kClQA16jHhDBvGMI9u+RSMhIEz6I9kZEb8f9b+Rg92qNOsALpWvlDRlpwgfR3ojom9n/RhZ5Y5NXG3yIWFsy0oSKEYdMRt3M24+cw1Ory7D9yDlhY4aKVxtKh5Ui1paMNKES7PqslrV7KnHyQgPW7qnkPpa/txXsIXhGHeAF646BjLRJCdYvnILRJ/Hj0nqgZ3w0xqX14D6W0YeVobI7ClYdnIy0SQl2r9ZIzXL7kXNYu6cS49J6YES/LtzHC8XDSkt+ATzLlgatkyES03QLf/PNN7Fs2TJUV1ejX79++O1vf4uMjIwreo9g6hYu0gNyTZkEubwcUnKyGjvcEWlrV+unVpfh5IUG9IyPxgvjUwTMzDj0+B79kG7hwfKd4kWH6xb+6aef4oMPPsC7776LXbt2IS8vDzNmzIDb7TZ6aoZgdFiaSIw4wBMpdRiF0Uk7werVGoEpSpXeddddyMrKQmxsLC5evIjq6mp07twZFospniHCEV3m0cjymdoDPN7Sg/Lwy8wvwIjxoRN1YM3JDanvVLAhzAo6nU5UVVW1+PX9999DkiTExsbi888/R3p6Ov7xj3/gN7/5DSRJEjW9Vgn2AzwtRlyrkQd4IhG9YzBSByf0RZgmvWPHDkyaNKnF61arFfv27QPADLkkSfjkk0/wzDPP4J///Cf69esnYnqtUpV3L5qOHkNY375IWrXS0LnwRuS1NhQVoXbRYtimTkH0qFFcx/IdcwlsUx8UNqbCI0uKUXGuDr26xGLhg5nCxv1s32l89FUF7hveC7cN6i5sXEI/THNw6M8DDzyAO++8E7/85S/b/H94HBzy0Id/yEGMCDr6YWVb11V0dIdRYwL6HJK2dV1DKYmlvXS4g8O33noLzz77rM9rTqcTnTq1/UJ4Eaz1AAIRCinSgDGJLJnHd+GF8SlCDTQQOnJSMGMKIz106FB8/PHHKCkpQVNTEz766CNUVlbijjvuMHpqBCeUBwIgvlRoKBguRQMHIOzhQLo3H0xhpIcMGYLnnnsOc+bMwfDhw1FYWIi3334biYmJRk/NEIK1eHkgjEjaEenVGmW4RO4WFEJp1ykS02rSP4RgSWYJpUQAPXVMM6+raE1az/HMeobSkelwmjThSyhsG0MtFV20Dp4xdzae68w/9pzgDxlpExIK20Yji+EApIMTHQcy0gSA4C+fqYV0cKIjQZq0AZhR4wsGHdyM8byhtK5E2yFNmrhiQsH7CjUdnAgOyJM2APJM+GDm6I6ODH1f9Yc8aaJDEEo6OEH8UExRqpQITah8JkFcHvKkCcMgz5YgLg950oRhkGdLEJeHPGmCIAgTQ0aaIAjCxJCRJgiCMDFkpAmCIEwMGWmCIAgTQ0aaIAjCxJCRJgiCMDFkpAmCIEwMGWmCIAgTQ0aaIAjCxARVWrjFIhk9hTbTkebakaB15QOtq3EEVT1pgiCIYIPkDoIgCBNDRpogCMLEkJEmCIIwMWSkCYIgTAwZaYIgCBNDRpogCMLEkJEmCIIwMWSkCYIgTAwZaYIgCBNDRtog3n33XeTk5GDo0KG49957UVJSYvSUOiwHDhzAfffdh8GDB2Ps2LEoLS01ekodni+//BLjx4/HkCFDkJubi+XLlxs9paDC4XDgtttuw+rVqy//wzIhnA0bNsi33HKL/O2338put1v+v//7P3nIkCHyuXPnjJ5ah6OxsVG+/fbb5bffflt2Op1yYWGhnJGRIdfU1Bg9tQ7LqVOn5PT0dHnjxo2y2+2W9+zZI994443y559/bvTUgoZf/epX8nXXXSevWrXqsj9LnrQBVFVV4eGHH0b//v1hsViQl5cHq9WKgwcPGj21DkdxcTFcLhcmT56M8PBwjB49Gv3798cnn3xi9NQ6LCdPnsSYMWOQm5sLi8WC1NRUZGZmYteuXUZPLShYs2YNamtrMWDAgDb9fFBVwTMTTqcT1dXVLV6XJAkTJ070eW3nzp2or6/Hj370I1HTCxoOHz6Mfv36+bzWt29fHDp0yKAZdXwyMjKQkZGh/v3ChQsoKSnBT3/6UwNnFRycOHEC//jHP7B8+XJMnTq1Tf+HjDQndu/ejUmTJrV43Wq1Yt++ferfDx06hNmzZ+N///d/0bVrV5FTDArq6+sRFRXl81p0dDQaGhoMmlFwUVNTg0ceeQRpaWnIyckxejodGrfbjSeeeAK/+c1vkJSU1Ob/R0aaE8OGDbusfLFlyxY8+eSTeOihh/DQQw8JmllwERMTg8bGRp/XGhoaEBMTY9CMgodjx45hxowZ6N+/P15++WVYLKSOtofXXnsNffr0wZ133nlF/49W3SDeffddPPbYY3juuecwbdo0o6fTYenXrx+OHTvm89rRo0fRv39/g2YUHOzcuRMTJkzAyJEjMX/+fERGRho9pQ7P+vXrsWHDBlVOOnToEP74xz/iD3/4w6X/o4CDTMKP9evXy2lpafI333xj9FQ6PI2NjfKtt97qE92Rnp5OkTLtoLy8XE5PT5ffe+89o6cS1IwbN46iO8zKW2+9BafTicmTJyM9PV39tWXLFqOn1uGIiIjAW2+9hQ0bNiAzMxOvv/46FixYgMTERKOn1mH54IMPUFdXh1deecXn+/nXv/7V6KmFJNQ+iyAIwsSQJ00QBGFiyEgTBEGYGDLSBEEQJoaMNEEQhIkhI00QBGFiyEgTBEGYGDLSRNByxx13YODAgeqvQYMG4fbbb8eLL76I+vp69ef27duHWbNm4aabbkJ6ejry8vKwbt26gO9ZXFyMgQMH4ve//33Afz9y5AgKCgowePBg/OQnP8HGjRu5XBsROpCRJoKaxx57DF988QW++OILbNmyBXPnzsXatWvx/PPPAwC2bt2KBx54AD169MCiRYuwZs0ajB07Fk899RQWL17c4v3Wrl2L3r17Y/369bh48aLPvzU2NmLq1Kno06cPVq1ahQkTJuCxxx7D/v37hVwrEaRwz30kCIO4/fbb5aVLl7Z4/fXXX5eHDh0q19bWysOHD5dfffXVFj/zxhtvyKmpqT7p5Y2NjXJGRoa8evVq+YYbbpDXrFnj83/WrFkj33TTTbLL5VJf+5//+R/5qaee0u+iiJCDPGki5LBarYiIiMCWLVtQU1ODyZMnt/iZ/Px8LFmyBHFxceprW7ZsQW1tLbKzs3HzzTdj1apVPv9n165dGDx4MMLCmotL3njjjfj666+5XQsR/JCRJkIGj8eD0tJSvP/++xg5ciT279+PPn36wGaztfhZm82GoUOH+hjctWvXYsiQIUhMTERubi527tyJEydOqP9+9uxZdOvWzed9kpKScObMGX4XRQQ9ZKSJoOYvf/mLWiDohhtuQH5+PlJTU/H444/D4XAENNCBqK6uxtatW5GbmwuAHUparVYfb7qhoQERERE+/y8iIgJOp1O/CyJCDir6TwQ106dPx7hx4wAA4eHh6Nq1q2pIExIS4HA42vQ+RUVFcLlcasH2+Ph4ZGZmYs2aNZg1axYsFguioqJaGGSn09micwxBXAnkSRNBTUJCApKTk5GcnIyrr77ax9NNTU3FsWPHUFtb2+L/1dTUoKCgAP/9738BMKkDAEaOHIlBgwZh0KBB2L59O06fPo0vvvgCANCtWzdUVVX5vE9VVVULCYQgrgQy0kTIcssttyAxMRFvv/12i39bvnw5du/ejZ49e+LkyZPYtWsXHn30Ufzzn/9Uf61evRqxsbGq5DFkyBDs3r0bTU1N6vvs3LkT6enpwq6JCD5I7iBClqioKPzhD3/Ar371K9TV1eGee+5BWFgYNm7ciAULFuCJJ55AYmIiFi5ciMjISEyaNMkn2gMA7rnnHnz00Uew2+2488478corr+CZZ57B1KlTsW3bNnz22WdYuXKlQVdIBAPkSRMhzciRI7F48WJ8++23+MUvfoF7770X//73v/HSSy/hF7/4BQBg3bp1uPvuu1sYaACYOHEimpqasG7dOsTExODNN9/E8ePHcc8992DFihX4f//v/+G6664TfVlEEEGdWQiCIEwMedIEQRAmhow0QRCEiSEjTRAEYWLISBMEQZgYMtIEQRAmhow0QRCEiSEjTRAEYWLISBMEQZgYMtIEQRAm5v8HcdKYtxNUMPQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 211, 121\n",
      "LR fn, tp: 9, 5\n",
      "LR f1 score: 0.071\n",
      "LR cohens kappa score: -0.002\n",
      "LR average precision score: 0.045\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 328, 4\n",
      "GB fn, tp: 7, 7\n",
      "GB f1 score: 0.560\n",
      "GB cohens kappa score: 0.544\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 1/5: Slice 2/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 322, 10\n",
      "LR fn, tp: 14, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.035\n",
      "LR average precision score: 0.072\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: 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 1/5: Slice 3/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 316, 16\n",
      "LR fn, tp: 13, 1\n",
      "LR f1 score: 0.065\n",
      "LR cohens kappa score: 0.021\n",
      "LR average precision score: 0.052\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: 332, 0\n",
      "KNN fn, tp: 11, 3\n",
      "KNN f1 score: 0.353\n",
      "KNN cohens kappa score: 0.344\n",
      "\n",
      "\n",
      "------ Step 1/5: Slice 4/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 321, 11\n",
      "LR fn, tp: 14, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.037\n",
      "LR average precision score: 0.041\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: 14, 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",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 321, 10\n",
      "LR fn, tp: 13, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.034\n",
      "LR average precision score: 0.053\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: 331, 0\n",
      "KNN fn, tp: 13, 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",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 324, 8\n",
      "LR fn, tp: 14, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.030\n",
      "LR average precision score: 0.074\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: 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 2/5: Slice 2/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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qRT8bUrdbI3RRKpnFKV6mhYtOfonWRAK7SSJm48uT0lzYPRXIywNq1kTC5m3c/eldg5MEE9Gp+qKoV+8qnFyyXPlkJlWJumQWv0NWRSR61qhbbgYeCz2QMRSoVg2oUZPpazfqT+8anCSYuF2fWuYszyuiOfqDlLQg/PIwy0Skr9XMzaCn6Ixe3PhJjyBwU2ugtJTpa+d1a9hNMHG7XnQsREZE8zWSko4xeC0OJ9ECvMrErC/ehUCn/lTeEELRCSa84X5WNtwVaXGa+fZVDS31G+STVhQnPmmRBXgKBw8Ajh4Brm+EhOWrIo6L9KeKLg50cslylMyZDQAITZjou1kO73g4LVVaFbNn0My373WRMq8hnzRhiMwVeZWjBXhdFlYtP9EWohUL2ApW49pFFWAyi2uPZutWJqSkJSEzYYHXLcBMcJgwEYF27RGaMFG3L5H+VDf8+jy+6oofENaYiE6tN2rPTtYjAODokfIZRFVEfrjM4tppnUYMpKQlITNhgfflYLUn+kUTmfxi5TweX3VFhS7K4rdy391IkGEhWv6KRHOUhVeQkpaEygkmKlRNk2ndBjOHA8EgtL17Ki16VVLojAQT7tR6nb6qticqQcZs5mNH/liLJFHtQ0NKWhIqp8i6VTWN52EXbd0WrF3LrFGB/DzgyhVbpUr1YLogdPqqej5vJImRUud1Z1npy83wQBVR7UNDSjrKkO37FpVgYlSq1K51m/vWPHbfDkqV6sG6Vr0Ftqrnu6kMZSodt/zQshZrAfU+NKSkowzZvm9RCSZWSpXyWLdJo0cxPyB2S5UawXqx9RbYnCoCN10WKiL6QyNzHcYppKQloJJ1y4MbpUp5LU6rfVUlsV8/YR8QK4isP+0EvQSTin3JfBZFoso6jBdQMosE7AT1GwXCi05YAOQlmABlSkRbthSBjKHCSpLqYac4kBvJF6LbNCuuJDLBxGwMZV+b36BkFgHIsgRkR3aonGBi5LIwuhfh34tmzRRWqpSVPBKeOdjpTw+RMxvAwrRfoq9d9LOh2oKdKsS0Ja1y2mrSjs04/9pcKVaFTAvGqC+jexH+HZcLgOqJlu8Vy4ph3Xe7/bFmCCKfM7N75efUerKk9YlpS1plv5RpdIJA3Eow4enL6F6Efw9kDJXia6/Yn6jdx2UUV2L15fR+uZFab9QXz8wnVohpS1plZFrSvNi11pxaSrz+1BN/+H9SrFsAKHzod8Cn/wB+2QsJL73iuD2R8tltK2wJ8tw3P/j1ZUGWdJST2K+fUmFAFbFb+lJmOjMgz7oFzEP6eNqyK58RboX76V2DG7NTlWe8MiBLWlGMvsJWrBqRvj2RPtCSDR+jZMZzQM5FBO7KjLBuRcpdr95VOD7sXmnWLa/sbliHon26FN3hDLKkYxQr1qjIKm1mbfGmM7PSsfX6MpNPFevWTvKLyKgPwJ3oCIruKMNLvzgpaQFYuYGibrLoEDynSphXObESVvT6MpNPpBIJt1UyZ7aQHVFY8C7IVcRoRxTZrgbRCTkquzW8/ICQu0MAbiSYyFo4LNnwMXeIlcxpqVnyi5Esdna2CbelnT8P5OQI3RGF1R/vOFrZ7ZynDzfGMFZCR1mQu0Mh3EgwkRWCZ8eic8OqMLRGKyS/6J1jx5ozDW+bMJHLFWHmuhCdGm6WPl8R0e4xEf2JIhoKR1mBlDQHTl423pucNHqUtKkf7wfEjWmp01KlVbkwbZrhJql2C0cxi0MBhjuiiPa1B9u2Q6B5CwTbttP9v5XOdVh/hRc3fO2svlR1j4iElDQHMr/cMkPwnBQHctvXbrVUaVXyFy12FH5nxz8usi3WcR6lb2WmJHo2YtfXzotq1ercgpQ0B7Hy5Q4jeqpstOAFWHzhOEqV1hg+DAiFgCtXIvqzPfNh1bOeMBG4vhEAWJppmT1LrOO8Sl92JInd9yTWMwuNICXNQax8ucOI9rWzkkvsymP0YteeMgWoXh0oLrbcH+sjAoD5kdCzIEs2fIzCwQNQOHiAcYjjV7u5XWhGSt9wSy0XrFs3oj5UDsHzEuWU9FdffYWuXbt6LUZMIcvXHsgYClSrBtSoKay2g5P61FUx+4jwWr+l7y4Cjh4BjkbKF5ZbW7ZUyEavdjZgsILRh8sNgyXWZqpWUUZJa5qGDz74APfddx+Kioq8FiemEJn8wiJ+0iMI3NQaKC21rJTMrFuWBam3IwrzGkzKfJoppqrHg5nDy1wgyXUj5LNbyMmum8GuUuWd/ThZEJVZ6MtPKKOkZ8+ejffeew8PPPCA16IoAWsjVdGITH4R2RdgriREhhDyZiiGYVmbCctXIXDddRHyhRVS/KRH+DMVDXYfd8O65Z2NOFkQ5SVW3CPKKOm7774by5cvR+vWrb0WRQl446RlWTBOF6GcZCiyivXzhhDqXYPd6Tavm8SKBfjF92cxefkefPH92Uq/m6XW62E2G2HJYzQbMcQkAkelwlG+QVOML7/8UuvYsaPXYnhOflaWdnrwEC0/K8vS+acHZ2gn2nfQTg8eYus4D3bbKrumDN1rYh2z2y+rTZHjcX7qVO1Yo8basUY3aOenTjU930rf98/bqt3+wifa/fO26vZ3vHlLS31pmqYdb95SO3btddrx5i1ty1MRWePqJlaeN1VQLi1869atmDBhAnbs2MH9f1WrguckbZU3JdesL5mV8YywsiMKK7Wap9969a7CiYGDLFets9L2F9+fxcrd2RjYrgG6Nq1b6RhvqjarGiAAbFnxCVbtP4cBLZLRbVAfZntmmNW65k0LF1kN0Ctk1KimtHAfoFLaqp3iQKKTWVj+VCtTVzuV5qwupFm5Vyt3Z+P4hQKs3J0dcUzPd8vMUDRxWaSuXog/bngZqasXWrlUZrifma/dzXHlQeZCoJ9cJaSkXcQPDwJvZpudc8KwlJPRy23mTzXy3bLa1IP1AQkzsF0DNNQu4/bPllqKJHESHmingp9RuJ/o59CtfAFeo8aJUvdTzgMpaRfxw4PAm9lm55yKiI5dZlm3ejixbrs2rYtnP38TXb7eZE2RtG4LXMqBduyY5fDAivJwR31c3wi43iS1nhOVrVuK7vCILl262PJHRxMlGz7GmYwhQl8MlnIKFy+yUz9Z7xyWdcsbLWCm1Ae2a4BraydiYLsGltpzmvzCk/WIPV8BJSXA2Z+4FImdSJ1wuF/C8lWV7oXTWueirVuRcdJ+mKmKQLmFQyeotnBol6JRIxD48Si0666Xspkn7yJK4eABZVPr6xshYfmqiOOTl+/B8QsFuLZ2Ip4fbB5S6cZik5Ptx3gxGr+SDfy1ulnthTEbf562WOeYbZ9lpz+/biprB1o4jGKCmcMR16SJUAvBqVuDByPr1siKcmPaapQM5MbU3yju2si6NUN26VCzuh8irdtYsX5FQpa0oogOweOF1Z7oEDxXwrbu/y0Kvztk2WKzIsNnk57C6qtvRP+LB9Fj1h8rHeOxEM12m7EjK0t+K5a3nvwyZyPRCFnSRCXsWqN2rFvb1qhBNpobC6xJo0dxZUZaGb81rdORfXV9rGkdKacb1QB5Cl/JTNeXujOPIngpHynpKMHuNNLKjig8MB9mjnrQpm2ZnJPYrx9XTY9g5nBsbdIRT9zQD1tWfKJ7zoDkIjS8fBEDkiMLgOkpTiPZrFYD5FGGTJfFhIkItGtfVvPaAN5wRVk786iCl/KRklYQO9Eddq1Rlj9VdE1g3rofTuO0eWpmhNLSsbrxLciuXger9p/T7Ys3wcRINqvVAHkiSViFpkT74WOxTKmX8pGSVpDSdxeh+NBhYV9tM+XEY3GaWbcsi5S3LyvWLXO3FM6swv6JF9Hg0mnckXjRUB5Re0HazbDkLVUqOwTPLnYUv2gXhMjwQJGQkraI7KB+kdEdZi+aSH+kmUXK05cV69bIhaIX3WHW981fb8Kzn8/DzV9vMpSHdy9I3hh0M3gjMUTPRlRC9AdEVZcLKWmLyFwsCaWlo96yD4QVPw9mDsfWNj3x5C1jxKRPm7gsBrZIRsPiXAxokeyoL7O2wvLoKRG9Uq9mfbMUkl1rU++Y6TZdKCuw9PjzfxUyGzF1MXHMRsxQOUNRdnuiICVtEZUXS6xYt1k9huJEoLqQ9GkzJdFtUB9MnzwkonqbUZusF9uorYry6CmR+A4plncWN2sLsO9r13turER3rNx/DtlxSZZmI47dWQIXdEUbMzJdEKqWcSAlXQWesCeniFL8VtoRmT4tOpJEdPggABTt3CU0ksSur13vuQlkDAXi4oDLl42LOXHMRkzHT3AhfpnuEVVdEDKhZJYqqJK2ypvMIho3EhaM0qRFJ8cAQNKOzTj/2lxh6cwA8Pjzf0V2XBIaFudi+uQhEdfGlWDCUX/aDLPxs/tMG42hzGQWPyfOiEpmISVdBVUeCllKWvT1mmXT8SiMLSs+wcr95zDQoPB9yYaP8fn7a7H6us4Y2Lp+pXPsZGya1dn47KV5WH0uHv2Ti9DjoVGmbTLrpTjMOuTB9j3mzNp0VRaX23IDyjh0CVX9Um4husqZyP3+WH5ZwGL0RxVYm8aa+W7Noj8iYLgZuPcOtIBoV13S6FHKrsOIbktlSEnHCKziQCJr+AYyhmLrjTfjyaHPWIokYbVnFtlh9ZyKsD4idooDlWww3hHFaEHOSTlPFnpj6TRrU3TssujiUSpGY4iG3B2KwjNVsjLt43EzmBVX+nz1Z1jTOh2/7tUqYq8/gK9UacmGj1HywvPAhfMI3D1MmGVpNH5m+/3xUjRqBLTd/wIABNq1t1Q4yq1ynnr9OS1VyltgSfSajuouDRbk7iDKsTLtE5WwYiWcjyeSJJSWDuTlAoWFpgWHrFKy4WOc6t3H1n5/rDYNy5Ty7ogiONqC1Z+Vtii6Q21ISbuE03RcHuymGNttz0wJd21aF88Pbq1rZevBu6WWGaXvLkLx4R+49vszSzApmTMb2u5/lS8uhrFTM1r7bBNw+XLZ3zqwMhWNYPnaze47t89aYPKLGbHi0mBB7g6XcDLNBPSnSn6e+lmJnhDZV+CNOSi+cBFISrLUn1lIHM9uKGbYre/sRP6KWHmOSjZ8jNAH76LkzkzXXRrRCrk7FEf2NFMVjGYHpe8a72bN25bZOaG0dNTfuAGB666znD69dcg4PNn/MWxt0lk/u23CRKDuNcDxY8x0bivyhyZMLHOR/PscPXifDZ7ZiNV6HkZFvlSwbmWmn3sNKWmHOAl74p0auvFyiH7YWfWpjXy3vG1ZPafqeLFcGlnXd0F2ckOsbtLV0Bev5ztnjR/LRaIX7lexLd5iTjwhfVYNCKMiXyqEqfrBYBEFKWmHyHxY7L4cImstmPlug5nDgWAQ2t49lc6x47s1KwwFgMs/ygq/G9iuARrWqo7+Fw8aKi89a9Xu/ddTlE4/SnoYzTTC4yKqyBdgrXCUzHWYaIGppAsKCiz/iVX88LCIXI03S1YJpaUD+XnAlSuWojVYccaWCkNxFAdiuQS6Nq2L6aN6oMesPxoqJl1rtXVbIPcStB9/5HJrqBCJYeU4D1YKR4nqTwVrXhZMJd21a1d06NDB0p9YxQ8PC28NYhZWfJ+8/lGWr9osksQowURvZxsjlwDTZfHvY0WzZuqfs+croLgYOHdWt7iSmVuj6vnhhTjWh4vnfmlX1QLOnS37WwdLpUwtYuW++8GoUQ1mdMePP/6IMWPGoEaNGvjf//1fZkOpqanCheNFpegOp4iu3cG7Ii8rksSNZJaiUSOgfXsQKC1F6JlppvIza2z8+xguFwDVE3V3OmdFrdhJMGElyOjhtJATbzKLzLojfkZKdMd1112H+fPnIzs7GydOnEBqaqrhH0JtZEWS8NaMDqWlI/Bf/wVcVcty+rSZdRvMHA4UFgL5BRGLdnoY+dHDxwKNGpVZhzZ87UZuDYRC0PZ8reu/DWYOB5LrlpUztVAPm3Wv3LBure52HsbvkRhey2+6cNiwYUNMmTIFX375pQx5CAZOHhZZkSR2akYb9WXWlrZsqe7xUFo64q6/DggGLMnM8qOHxy1+0iNcvnbWIppZlmUoLb0sfDDpKku+dpbLwkrUhxsur4r4PRLDa/ktRXf0798f06dPd1sWwgQ3iuMbYdvXbhBtYcsvzmir3Lo1aLPWY48i0K592eKdBUT72s2szaptVb1Heh+uL74/i8nL90REuljafaUKVoo8Ge1Yz+vbD18LWrcVZpHKtG699qM7zji8ePEiVq1ahd/85jeiZLJNtPuk7fqJZWaIWfHvVjzmtFiPyOJAIimaNRPaksVA7ToIPTbZkV88DKtwFe91WfGLB348Cu266y0/M24VjrLTlwp4nnG4efNmTJo0CT169MBzzz1ntxmCA7vWrUxLgNUXb2ywnZC0sIVVsHat7vk8sxEn1lr8pEcQaN0GKCmx1FfFmYaRr31guwZoqF3G7Z8ttTwbsVKiVu8cVjKLlWvQGzMqU2oPLkv62LFjWL58OVasWIGTJ0+iRo0aGDRoEIYNG4bGjRu7Kacl/GRJm1k+okuVipLNjb5ERneELayEZk2B1+fr9mdVfqfWmlFfZiVMjSJJ7MjkpE6IndkclSr9D9Is6cLCQqxcuRL33nsvbr31VsydOxeNGzdGIBDA4sWL8cQTTyihoP2GyjtUsNoT7Re3U6qUZeGGsxSf6DrK0qYDLJzGEBv1xUqdN/O127IgS0uBE8eF1Akxu/9elyq14mv3W6QJU0k//fTT6NatG5566inUrFkT06ZNw5YtWzB//nwEAgHExcUJE2T//v2466670L59ewwYMABffWVtl2eR+HUxQvSLweuysAJvmJiVVPaSObN1U56zegzFMa2acZaiDkbp0+EFOb2+eNsLY5RgUjGShGf3cRahCROBGjWA+ARDRSfyw+V1qVKZGZayYCrpJUuWoG7dunj66acxbdo0DBo0CFdffbVwIQoLCzF+/Hj069cP27dvx/33349Ro0YhNzdXeF8sVK7DwXrpRdf0YNUz5vV9htnWfySeTHsQ2/qPjDimFy1gxVcNQPecge0a4Pq6NS1tOmDWn1lfRhgVVwIA7NgGaFrZ3wYY+dp5LcRQWjpCz0xD4MYbLbcVPkcvusNOJIkZIp9tM6XuR182U0n/5S9/QefOnfHcc8+he/fuGD58OBYtWoSTJ08KFWLbtm0oKirCyJEjER8fjzvuuAPNmjVDVlaW0H7MUPkGuvEBEenWYFm3ALBa+xmy6zfCau1nltqzErIX6NFTN0Sva9O6eO2+VMubDgDm1m1owkSp6dNVlZPZ/WB9FHjbCp8jq1SpnWfbLLXeSKn7oYxDVZhKOjU1Fc8++yw2b96MF198EVdffTVeeOEF9O7dG6WlpVi/fr0Qa/e7775D06ZNK/3WpEkTHDx40HHbPKh8A934gIh0a1ixbvVqcDAzEc0sNo7iSqy+AJhat7wWZGjCxLKswRPHIxJa9GYOZtatyBobVqNmgrVrC6n3IkKeqvjRbWEbjZOLFy9q7733npaZmam1aNFCa9++vfaHP/yBt5lKzJkzRxs3blyl35599lnH7cYC+VlZ2unBGVp+VpYS/eVnZWnZvfpoJ3v1tiTT6cEZ2on2HbTTg4dEHDs/dap2vHlL7fzUqQxZhli+dt6+ql6rXn8b92Zr98/bqm3cmx3R5vHmLbVj116nHW/e0pFsVs4xGgsr98vouBWZePoSCe+99zOOkllOnDiBjz76CKtXr8aaNWtsfygWLlyITZs2YcGCBeW/TZkyBTVq1MCUKVMst+OnEDwzrIbvuBHU71aCiR6jPr27bD5XCsz75RLb7VTFKHysZMZzQM5FBO7KNA33c5pgwlOIyKxQk9VzeK+BdTxpx2acf22usAQZXlQPvxMZRus4meXChQsoLS2t9NvevXuRmJiIBx54wJGCBoCmTZvi8OHDlX47dOgQmjVr5qhdlREVSeKGG0R0ggmTEIDAv/+20I6Vcduy4hOMfmQBtqz4pHJXBjU6WAkfZgkm/QOn0fDUEfQPnI6Qg2e3FCuRJLYW7RzsTJ7Yr5+nOwep7tKQJZ+pkp4/fz569+6N3bt3V/p9xowZ6NmzJ9555x3HQnTp0gWapmHhwoUoKirCmjVrcODAAaSnq/f1FIWo+E83/OhO609zy6QBKLFWGN/KuK3cfw7HQ0lYtf9cxDGe3VUqKkWjYk6pqxfijxteRurqhZV+t/MRthJJoqcImTuimPjt7Tw/sp5FlRfyAXnyMZX06tWr8eKLL2L8+PFo3rx5pWOvvvoqHnzwQcyYMQPr1q1zJERCQgLefPNNrFu3DqmpqXj99dcxZ84cJCcnO2pXZWSVDrUDr8Vm1bp9/Pm/Rli387otwesLEvDaH7MtFcYPJ6s8ecsYbFnxib51m3gRDXNO447EixFy6Fm3rI+SWYIJbwU/1rVVjCQxejb0FCGrmJPd2QirwJLo1HqRSl9mroOsQAOmkl64cCEeffTR8sL/FUlKSsKYMWMwfvx4zJ8fmX7Ly4033oj33nsPu3btwqpVq9C1a1fHbaqMrNKhdhGZhQaUWbfZcfrWLU9Nj3CyyolAdazaf073nJu/3oSpX87HzV9vqvS7nUgSswQT3gp+rGszbdOAQMZQICEBqJkkbDYiKgTPal+qZt+qAFNJHzp0CL1792Y2cOutt+K7774TKlQsY1QgSGTySxij0pe8/ZkV6wGAgS2S0bA4FwNaRM6ODAvjG1i34XC+AS2SDa1bveJAbhVz0h1jhpuB22VhgpNiTobPR+u20AqsbfAbxqhQk1lfKmffqgAzuqNbt26YN28eWrRoYdjAwYMHMXLkSHz++eeuCMhDNER3mBUI4m3HbmSCm/2KbIunOJDobZ9Y8vFGYljZ5opF1UgDp4WQRJYqtftMqB7dYYaU6I5OnTrho48+Yja+YsUKtGrVypIgfsWLmh5Jo+8T0g7LojDb5NWtfkW2VfWcsEV6Ydq0yJM5k1/MrNtg5nBsbdIRT9zQTzeShMevH8gYClSrBtSoaas4kFFWoWHNEQtRH3ZKlerdLzfqvcQSTEt63759yMzMxNixYzFy5MhKfum8vDwsWLAAb7zxBubPn4/OnTtLEZiFW5a0FwXGRZYq5bVIZFswIvsLW6SBpCTEf7bVUT9WrNvHn/8rsuOS0LA4F9MnDzHtj2XNl5cqrVULgTp1IuQ0eg71+gn/pp0/D+Tk2LJuZcxGWJAlXQbTkm7VqhX+/Oc/45133kGXLl0wcOBADBs2DAMGDECXLl2wdOlSzJo1SwkF7Saq+7lEV/5iFgfSO99heUge+cys23CIXY3hwyKOiVyQCzOwRTIaXD6POw5/bmnRzkokBqAfgscTSWIWKWL3mXZjE1qZ4aV+xFLGYX5+PjZu3IhvvvkGOTk5qFOnDtq2bYvu3bujWrVqMuS0RDT4pMN4aUlbKRRfEafbJvHIZ9V3K2r87PrFjdq0sq0W7/1yy7oVYUmLzlD1E1Is6YKCAjz55JPo1asXnn/+eVy6dAmPPfYYJk2ahLS0NKUUdLTjpJwjb3nR0ISJXJu4Voxd1osUMTvOYzHx7lQdhjV+bmRYGs1GjCIxKsrHbUGKLDRlAk8WJeBChmoMwrSkZ8yYgVWrVmHEiBEIBoNYvHgx2rVrh5deekmiiNaJZkua19qwYo3xWswszCJFeCNJrMjPOodn/OzUxDCjcPAA4IfDQHw8Qs+9UKlNXV+1A2uS2/K22BePJeh33PB/S7Gk161bh5kzZ2Ls2LEYPXo0/vznP2PDhg0oKirik5ZwjMoZioB5pAhvqVLRSRCs8bNbyJ4ZZz5hIhAfDxQVR1jTupaySbQFIM53K7LsabSgciQJU0mfOnUKTZo0Kf93mzZtoGkazp6NfCgJc3gWUZwms1hR6rxujYryVb2Grk3r4vnBrZH6w07m8aqF+M12ROF2M9gcPzvp0x/9Yx+OH8nGR//YF3EslJYOXPtfQDBgqT3ts03A5ctlfxsgSpG4sbuK31HZ5cJU0iUlJQiF/lOeLBAIID4+nixpm/BYh7lvOUu1d1IIya7/1srxCAwsSLvym40fjzVqdi137PkYDc5n4471C/XXCqp8BJ0qWRUy82TmDPD2Fa2RJJZKlRJi4LEOnSazOMHJQhq3RSp40cts/LgUpYkL4pb+PfDs+pfQ5dutlratKnczHDuGwsEDKl1DaMJE4PpG5deoh0hFYrctmW4B3r5k1wmRhamSXr58ORYvXlz+p6SkBB999FGl3xYvXixDVt/DYx0m9usnUbIqMJSTnUgSp9ETFbFanMho/ETV3wj3hYbXAsHKr5FpIaezPwFHI+Oa7bognNT94EWmP5v32YjWSBJmdEefPn2sNRIIYMOGDcKEsks0R3fIhDfSwCx2tmQD344oLKxGfYQ+eBcld2aaWoqsLD+7ESZ2I0nsRhjw1v344vuzWLk7GwPbNdDdrDcsR50HxiK3U/eI4zzPh5W4atUzYu0iKrrD0fZZqkFKWgxupE/rncPqx8mLaFQcyCh9umTObODEcSA+AYEbb3ScUOFGSB8L3gQTs3BIsyJfopOPuI0CnyS/SN0+i5CDyEUZmYsoVhJMeHZEMTtmBk+p0nI3Q3w8oJVac4P8G1O3Bofrwkp/Rm4N3gST249uRYMfvsHtR7fqHjfz64tOPnLDrRFNkCWtEBUthIYrVziypEWnM7uBW5Y0oG/FFD70O+DTfwC/7IWEl15h9uU0ndmNBBMetwarf9mp9Xbxi1vDCLKkoxCvy3wC7qx+27HY7YQHAv/Zpmv94rWRB3dsAzSt7G+TvlhlSCueg2AQ2t49EdatrQQTg7bK6ZQKBAJlf5vAuo92U+vt9qdSm36ElLRCyA6xMlJOolfvWS8br4vH7MUNb9O1dNepiGN6yon1AVnd+BZkV6+ju+VX+By93cftYKWtwKUcILlu2d8V0LsG1n3kdY+Y9RXuT7QLItbcGkaQkvYBbiQQiPSnmqFdVQs4d7bs7yrwWkvb+o/Ek2kPYlv/kbrHw9t0DU2pH3FMTzmx+meVIQ0j0io1a4u3VKnd+2iUtWnUV7g/OzMfJ4XDeNryM6SkfYDsqaRwC8bAzWDUF+tlW639DNn1G2G19jPdrroN6oPpk4fg1mHW4sxZ19ptUB9M/WEtuhz6p+HY81qlrGsza8tQaRnEtdu9j6ysTdFx7SKfbZmuOpmQknaArBvo1lTSaDosOkVW5G7Wdrf8shvJwhp7O/ff6NpYxZpMjxsk3di1blnRHaKLOamQ6s5CBb84KWkHiL6BIusKmL2IvNNhVntmfYnczdqoUJMZdu8Va+zttGmkSFbuzsbxCwVYuTtb9/+xjvPORsx23uHNejVzWbCeMxVS3Vmo4BcnJe0A0TdQ9tSPR36nMc08isQNv7gr6cwWyotWxUiR9A+cRsNTR9A/cFr3/w1s1wANtcu4/bOlQopDobQUOH5MyFiY9aWCorOLCoWXSEmbIHJhwwzZUz8e+Vnt2e2L1y/Ok/BRdbdwNxT/l0dz8GTfifjyaI75ySZ0XjQLf1z2R3ReNEv3eNemdfHs52+iy9ebHM9GQhMmAjVqAAnVhIyF2f1XQdH5GUpmMcGrFFQrgfBuBfvLSiLgTZ/mSfgQsVu4Gf877zOcyLmMhrWqY/qoHo7asrJLTlX5za7HabKNjNIEfk9YYUHJLJJQearm1qIGT7sy06eDmey9EgGUJ3zEd+sacUjPonPia/91r1ZoWKs67tjzsWO3gZUNGKrKb3qfHFQzlIXIZ1iFSAw3ICVtgioPs1HCghsfEFG+aivn8Pqqs3oMxYlAdcMFtnDCBy5Yc0GwFtHMrs3IBWGndKid58z0PnHW6vYCkc+wCpEYbkBK2icYJSzYCbEyO4fXV21m3fKG+7FeNrMFNrPiQDzYTa3Xli0F8vIcZyFWRO9+2Q0fNAv3YyWziEakEaTyrNcJ5JNWlKr+LB7fndPiQLxY2Qmcpz/Wtdrd6dqoTTd8okWzZkJ7/12g1tUIPf4HIe3KvF9mpUq9xi9+bPJJ+xBZKbJ2LUC7WEkw4emPda12k0tEpzMD7NKhgZtaA6Wl4qbeNsL9jJA5G3GDaHVrGEGWtETMrCHeUqWyLQrVy1Gyxs/NSBK9kp961+XkWkXulmN3NsJClRK3KhG1lvTChQsxfvx4r8VwBTNrUnRdBNF4XY7SSjozQiFoe752HCdtxdceyBgKVKsG1Kjp2NduNVXb6rPB8ovbjUG3M1NxA7t+bL9GfyijpPPy8jBjxgxMnz7da1Ec4cRlYacugsyFEq/LUZopglBaOpCXCxQWIn9R5ObIoiNJeN0aTqoB8rplWBX1bGUompzjh0U7v7pJlFHS48aNw/Hjx3HXXXd5LYoj/GBRAPasCrv+W1ZImuhIkrByqjF8mKW+nBZzqqqcmGPBWQ3QCkby81bnc7qO4UapUtH44UOihzSfdGFhIS5evBgpQCCAa665BqdOnUL9+vXx8ssv45tvvsGrr77K3YcKPmlR/jK3s71ERguYtcW7mzXLn2olkgSwPn6i/ZussXAj6sNN/6yIZ5BnHcbKc+gXfzQgzicNTRJffvmlduONN0b8admyZaXzZs+erT3wwAOyxIpZ8rOytNODh2j5WVmut3V+6lTtePOW2vmpUy21d7x5S+3Ytddpx5u3jDi2cW+2dv+8rdrGvdmc8mUIvFbjtvKzsrTsXn20Ex07ayd79Y447/TgDO1E+w7a6cFDuPrlHUMWVsZD1JiZPRu8z6Hd8fMzykV3+N2SdkJFK+Hndw92ZMWYWRy8FolMC6Zo1kxoSxYDtesg9NhkW/1VtGJkzhrKz9n9LwBAoF37SufZjfrgnY2IuIbAj0ehXXe96ZixZj6iiUVLWhmfNCG3VClvXzJ3veCtP20p6qNWLWjHjqFw8ABH/s+Kfk2jfoOZw4HrGwHXR/o/7S7asSJJnFwD65y4Jk0s+W95Myyd1ntRoUyDTEhJK4TMUqW8fbmx6CIqWqBiO3opzeXhd2d/Ao5a/9CYpWIb1f0IpaUjYfkqJCxfZXlB1KzWNW8kidPF2lBaOuot+8CS/Lz7PDqNJIk1SEkrhMxdKnhX493a9ULEFl4VFXr45a66P5+edWtm0YlWFLzVAKuez/Phkmndyo4kiTWUU9IPPvigLX80YR3ZSQmiSpUatVVRoYdjkVG7ciyynnXrdEcRK+VFK8I7c6h6Ps+HS2XrttJsRGG3hirJL8opacJ9ZCcliOzPVFH8Oxa5aMsXpm0FM4cDwSC0vXtsuQX0jrMqzPHGGVsZG79btyq7NVSRjZS0IsgO6he5U7ip7AKLz5spCr1kFpa1hvw84MoVYWVFWRvG8l6rFYvTDevWyTm8qOzWUEU2UtKKIDtTUeR+f6aycxafd5JaH7Yga0+ZYkk+O1ETLPlYG8Y6wegaVFAkTgwMXsXP21c0RJKQkjZBloXr5zocdiJJRPrFw20VzZppGBJnlE5up6xoWL6SObMj+uPdMLbqNfDORlRQJDINDDdCR1VxaxhBStoEWTdQ9svmdSSJG35qbdlSw5rRrGJJdsMRAXBZt44+TJyzETtbeNnFSgihyL5Eh46qMBthQUraBNVvoEjcmDWwEj54i/UYEW4rkDG0XFlU3fqJVSzJbjhioEdPLuvWyYeJdzbCG4JndUstIxcUj/tMpgvCK1+7SEhJm6D6DRSJzHrRdqum6RFuK37SI+XKomqcdNemdfH84Nbo2rSu5f5EW7dOFlB5ZyOBjKFAQgJQM8nSWLIWPM36AuRuXhxrkJJWHLd84nrtulUvmmcqbPZyWkkB59n6ice6tZJcsmXFJ3j8+b9iy4pPIjvjVOpm1i1rbHlT661sqcW6j3aTj5ycEyuQklYctywKvXZFWrcV2+TdEYWlDMzGI3wNif36WROQw7q1klyycv85ZMclYdX+c7rXxqN4zKxbs7Hl6S919UL8ccPLSF290FZfPDhxQaiSYCITUtKK45ZFIXp6KqovO4rHUWF5DuuWFSUSZmCLZDS4fB53HP7csT+1YjgfK3JFhG/fL9atym4Qtz4gypUqdQKVKnWHkg18m7iK6I+nHKVe6U2rpUp5+7Ky6YAbpVFxuQConii1OL7bG0/YQeVSpVXvO5UqjUJ4rASZ0z5RNTYqwvLdci/WMlwWgNhIEjtbajmhYuSKl5sUi3zeZCa/yMStmUac0NYIR4SruPG6IGQ8sDyyAebyVfTddnMq3L9dFtpnm1C056syGe8eXH44lJYubIy6Nq1bHiFiZNWJ7M92W63bAgf2G364eBH5vMl+dmUh8r5XhCxpwciyEtz6aouKTzWTb2CLZDQszsWAFsmOZTZLLjHCqXWoZ62y2uTtzyy6Q2QkiZWoGVEJKzKTX6IBUtKCkVmD2I1pn6xylN0G9cH0yUPQbVAfpyL/p68JE6W6BKyUF3XSn1l0B28kiZOYcNHRHSJrx0Q7pKQFI9rClb2a7XU5SjdmIqysRycWnV5/X7bpiSdvGYUv2/SM/A8mvvOqmMUu80aSmMWEm42FzHovxH8gJW0DJ1XaeFHdrWEFmeF+PG26YdGtLrga2Vf9DGsKro48yOmCCMcud140S/dedRvUB1N/WIsuh/5pOQadtYBqNhYy670Q/4GUtA1klxVV2a1hBV5fu2h/JatN0R9Blq9ddCEn3jbN7oPdsRAdaRSLCSssKE7aBjJiNd2OUfU63rRo1kxoy5YikDE0YvcQEXHGFcfvi+/P4sOlG9F/3wbcnJDnKHa54rgB0B1D0WPr1b2y+gyKjAsX3Z6XzznFSXuIKlM1P8ebsqq0ibZuV+7ORnbtn2NNqzTHbVacgRjNRnijPsywc69kWqOi75fI9lTOULQKKWkfw/sAmr24ImsQm/XF2hHFyQck3G/FUqUD2zXAtfVr49dDezv+KFVUIEbKhDfqww14+zML92M9G7z3y+zZEGlARMMCJbk7FMXKVElE+nRFCrunAnl5QM2aSNi8zbbsVvqyek4Yq9cabjOhWVPg9fmG5/G26xTVU+v10t0rPoO8zwarf9HuEVUhd0eMIDKSxMyqCG/iGsgY6kjmqn3ZKfxfFauWYdVSpWZWm+jwQCOL004kiUzr1izdnffZcLLBAVEZsqQVJfwVjgarg/ca9KwwXsvQ6vixFjDtXAfL4uS9LrNiTiKtWz2cLF57vTCtAmRJxwjRYHXwXoPIWtemIX2CS5UGMoYCcXHA5cu61jRPgolZMotI61Y0Xi9MRxOkpBUnGh520W6ZijhNZ+ZJnw6lsTe0Bcp2REG1akBJibX9BRlZiGaF+OMnPYKEzdsiZgAi3EuEOpCSJsoRWRzISSSJ6OQX3lKlLMVvpVQpl4XLsOTtKlVWhqXTqBmvS5XGIqSkiXJEFgcyO593N2uWdSs6nZml+I02tK2IkYVr1JeoWtdW2rSLSFeJyrWuVYSUtOJIfQAZU2+7Kc2iIklkRgu4VaVN7146sW5ZkSSik19ULq4UDQkrLCi6Q1HciO4wW3EXEYXhFjIjE+z0ZwXRkTpuxy77JbpD1UgSiu6IEWSmyIqIwnALtxZQRVcDZFmkoi1IVtamHjJnI9HwbKgCKWnFkZki62YURkVk+xCdFLvnacusPb3xdbJYGz/pEQRuag2UlgopVeqHEruxiDJK+u2330ZaWho6duyIIUOGYMeOHV6LFHWIfhHttmemGEUrcR4L0mmGosjZiJUPiMhSpSKJdutWJkoo6fXr1+Ott97CG2+8ge3bt+Oee+7BuHHjcO5c5LZAhP8xC5sTbd2yFkSrKhPTvk12VxEZSWJFAZMylIsXkSRKKOkzZ87g/vvvR7NmzRAMBpGRkYFQKIQDBw54LRrhAiITTAALipUzq1BUhqIVWGNBClg9vIgkkaakCwsLcebMmYg/P/30E4YNG4Zhw4aVn7t9+3bk5+fjF7/4hSzxCEhOWOCwbgFni168LgHeD4hTvPbtW2lH5VhkP9fOtoK0ELytW7dixIgREb+HQiHs27ev/N8HDx7Efffdh3vvvRdjxoyRIRrxb85kDEHxocOIa9IE9ZZ9YHp+wdq1yH1rHpJGj0Jiv35cbfH2dWHaNOQvWowaw4eh9pQp1i/KBmXXNR9Jo++LuC6V4B1DJ+2IfDZEI2ocVCVOVkddunQxdV9s3LgRjz32GMaMGWNLQdP2Wc4ouTMT2ruLUHLnPda2TXptLrQjR3D+tbnI7dSdqy3uvrbugFatOvK37kCRhfPD42frXnXqDnTqjlwAuZz3QOSzYRrX3vwmaHv2oqh5K2cx4Qb3ouIzKPLZEA2vbLIQFSetTDLL22+/jZdeegnTp0/HbbfdZqsNWUpaRvlQu0o6WpMI3CpVKhqR/ZmWWXU5+chpMovMTQ5UJKqSWbKysjBr1iwsXLjQtoKWicoxoH5IIrDjQ3RSx0Lk7uMqpU8LX2AViFup9bGIEkr6zTffRGFhIUaOHImUlJTyPxs3bvRaNF1UXnVX+QMSRniIHQM7ysKJohO5359ZW3YWWEV+sMzww7PoB5RQ0itWrMC+ffuwa9euSn969+7ttWi+Q5VylCx4lYXT5BeRCSZ2FI9M69Ysq9CudStz9kNURgklTaiBrOkwr7JwmvzCrSw4wwPNkF3BjyWf6BrVKqByeKAISEkT5cicnioduyw4YcWtmhmyffuqui5U/oCIQJnoDhFEY6lSVcswAv6I7pDRl9P/ZxfVoztUfTZkEVXRHYQxMhfZeJG5w4YfigPJtui8Li2rUiRJNPu+SUkrjtcvIgs/1yB2AzvRE04jV0T52u0g09cey5CSloDMF1Hmy0E1iCtj5DtXxeL0i6+dqAwpaQnQ1M8asndfcQM9xcW8/4KtW17ZnODnZ81PkJKWgN8tRK9xqmT1lKRbiltXcbEUsWDrlls2QnlISUvALy+HqvGmTmci3NataBiK2OsypYT6kJImylE1ksTpTETvIykzRdoN363qi6yin41Y/iiRkibKUTWSxI2ZiFsFgPSUiRvyq+5CE/1sqP5RchNS0kQ5KkeSuIEb8vMoE6nhd5IRPbZ+f9acQBmHiuJF0X89VM3mMsOr8eMZLzdqXYu8X2Zj6NdnQxaUcUhIIRanmbIsXDf84jLvVyw+G15ASppg4tY008uFILO+ZaVPu+EXV7VIFmEfUtIEE7d8n15aYaZ9+zh92g81Tgg+SEkTnsC79ZPbfVfChfRpI7cGKTr1UC3cj5Q04Qm8Wz+53XdF3LBuvd7vTzXFozKq+dpJSRNMpL7cEutYsIjGuGY3FI/MZ8Pr+iteQkqaYKJK+rTf8dqt4XVMuJ/68vpeVYWUtA+JVqtCNQtGFKz7JSt92q3ZgQqp9dEOJbMoCisQ3o0kiGhDlWQggH2/RN9Lke1ZGUN6Fo2hZJYYJpatChkIn6kwfO1upE/Lsm7D/dGz6C5kSSuKSpagDESnGDsZP5WtW5n9xdozKBqypImowmxhyM9+eNnWZjRat7EcQkiWtKKoYMXILKBTsuFjlMyZDQAITZgY0R+vdUjFgZyjwjMYxo++b7KkCdeRHfbESvigncljm2icHViFlLTPkD3tV2URinYmt0a0ugVUi12WCbk7FMVoquTXRSjZqDRVl4nsEDzCGHJ3KIYyxYF83p8MZFubsZhaT4iDlLQgVCkO5Pf+ZCDbH02p9YQTSEkLIhotTplEs69dpnVLz2H0oYySnjt3Lnr16oWUlBQMGTIEO3bs8FokLqLR4pSJSpEkwpFo3dJzGH0ooaT/9re/YfHixXj77bexc+dOZGRkYPz48SgpKfFatJhHpq9dlUgSP/clk2iNJFENJZT0bbfdhqysLDRq1AhXrlzBxYsXcfXVVyMYVEK8mEamr12mdUvbTDmHYs3lIE0LFhYW4syZMxF/fvrpJwQCAdSsWROffvopUlJS8Morr+Dxxx9HIBCQJR5hAFmc/sLP6fOEPtLipLdu3YoRI0ZE/B4KhbBv3z4AZYo8EAggKysLTzzxBD788EM0bdrUch+xECcNUEqzFWI1xpfipNVBVJy0ssks99xzD2699Vb89re/9VoU5TiTMQTFhw4jrkkT1Fv2gdfiECYUrF2L3LfmIWn0KCT26yehr/lIGn2f630RcojzWgAAePPNN3Hs2DE888wz5b8VFhbiqquMvy56xIwlfWcmtHcXoeTOe8jSMUClAktFr82FduQIzr82F7mdurvaFzp1Bzp1Ry6AXIfPBlnSzoiqjMOOHTvio48+wo4dO1BcXIz3338f2dnZ6NOnj9eiKUm0LkTJROaiF/luCScoYUl36NABU6dOxZQpU3D27Fm0bNkSCxYsQHJysteiERKR6mtv3RY4sF9KgkkoLZ0+qIRtlFDSANC/f3/079/fazEID6lo3bqu1Ch9mvAJSrg7CHWJ1pCuaHRBUHJJdKJsdIcdYmXhUCZUqtQ/iL5XsTiGIomqhUNCXaLR4pRNtJaxJeRAlrSikBXjDJXGj2YjsQlZ0gThgGj1tRPRhzLRHQQhE5mRJBSCRziBLGlCGci6JYhIyJImlIGsW4KIhCxpQhnIuiWISMiSJpSBrFuCiIQsaYIgCIUhJU0QBKEwpKQJgiAUhpQ0QRCEwpCSJgiCUBhS0gRBEApDSpogCEJhSEkTBEEoDClpgiAIhSElTRAEoTBRlRYeDAa8FkEo0XY9sqHxcw6NoTNEjF9U7cxCEAQRbZC7gyAIQmFISRMEQSgMKWmCIAiFISVNEAShMKSkCYIgFIaUNEEQhMKQkiYIglAYUtIEQRAKQ0qaIAhCYUhJK87bb7+NtLQ0dOzYEUOGDMGOHTu8Fklp9u/fj7vuugvt27fHgAED8NVXX3ktkq/YsmULBg8ejA4dOiA9PR1LlizxWiRfkpOTg169emH58uXOG9MIZVm3bp3WvXt37dtvv9VKSkq0v/71r1qHDh20s2fPei2akly5ckXr3bu3tmDBAq2wsFBbvXq11qlTJ+3SpUtei+YLTpw4oaWkpGjr16/XSkpKtN27d2udO3fWPv30U69F8x0PPfSQ1qJFC23ZsmWO2yJLWmHOnDmD+++/H82aNUMwGERGRgZCoRAOHDjgtWhKsm3bNhQVFWHkyJGIj4/HHXfcgWbNmiErK8tr0XzB8ePH0b9/f6SnpyMYDKJt27ZITU3Fzp07vRbNV6xYsQK5ubm48cYbhbQXVVXw/EhhYSEuXrwY8XsgEMCwYcMq/bZ9+3bk5+fjF7/4hSzxfMV3332Hpk2bVvqtSZMmOHjwoEcS+YtOnTqhU6dO5f++cOECduzYgV//+tceSuUvfvzxR7zyyitYsmQJRo8eLaRNUtIes2vXLowYMSLi91AohH379pX/++DBg5g0aRJ+//vf45prrpEpom/Iz89H9erVK/2WmJiIgoICjyTyL5cuXcIDDzyAdu3aIS0tzWtxfEFJSQkeffRRPP7446hXr56wdklJe0yXLl1M3RcbN27EY489hjFjxmDMmDGSJPMfNWrUwJUrVyr9VlBQgBo1angkkT85fPgwxo8fj2bNmmHmzJkIBskraoVXX30VjRs3xq233iq0XRp9xXn77bfx8MMPY+rUqRg7dqzX4ihN06ZNcfjw4Uq/HTp0CM2aNfNIIv+xfft2DB06FH379sXs2bNRrVo1r0XyDWvWrMG6devK3UYHDx7EM888g6efftpZwwIWMgmXWLNmjdauXTvtX//6l9ei+IIrV65ov/zlLytFd6SkpFA0jEWOHDmipaSkaH/5y1+8FiUqGDhwIEV3RDtvvvkmCgsLMXLkSKSkpJT/2bhxo9eiKUlCQgLefPNNrFu3DqmpqXj99dcxZ84cJCcney2aL1i8eDHy8vLw4osvVnre/vSnP3ktWkxD22cRBEEoDFnSBEEQCkNKmiAIQmFISRMEQSgMKWmCIAiFISVNEAShMKSkCYIgFIaUNBHV9OnTB82bNy//06pVK/Tu3RszZsxAfn5++Xn79u3DxIkTccsttyAlJQUZGRlYtWqVbpvbtm1D8+bN8dRTT+ke//777zF8+HC0b98ev/rVr7B+/XpXro2IDUhJE1HPww8/jM2bN2Pz5s3YuHEjnnvuOaxcuRLTpk0DAGzatAn33HMPGjRogLfeegsrVqzAgAEDMHnyZMybNy+ivZUrV+KGG27AmjVrcPny5UrHrly5gtGjR6Nx48ZYtmwZhg4diocffhjffPONlGslohDHOYsEoTC9e/fWFi1aFPH766+/rnXs2FHLzc3Vbr75Zu3ll1+OOOeNN97Q2rZtWymt/MqVK1qnTp205cuXa23atNFWrFhR6f+sWLFCu+WWW7SioqLy3373u99pkydPFndRRExBljQRk4RCISQkJGDjxo24dOkSRo4cGXFOZmYm5s+fj1q1apX/tnHjRuTm5qJnz57o1q0bli1bVun/7Ny5E+3bt0dc3H8KTHbu3Bn//Oc/XbsWIrohJU3EFKWlpfjqq6/wzjvvoG/fvvjmm2/QuHFjJCUlRZyblJSEjh07VlK4K1euRIcOHZCcnIz09HRs374dP/74Y/nx06dPo379+pXaqVevHk6dOuXeRRFRDSlpIuqZPn16ebGgNm3aIDMzE23btsUjjzyCnJwcXQWtx8WLF7Fp0yakp6cDKFuUDIVClazpgoICJCQkVPp/CQkJKCwsFHdBRExBRf+JqGfcuHEYOHAgACA+Ph7XXHNNuSKtU6cOcnJyLLWzdu1aFBUVlRd1r127NlJTU7FixQpMnDgRwWAQ1atXj1DIhYWFETvGEIRVyJImop46deqgUaNGaNSoERo2bFjJ0m3bti0OHz6M3NzciP936dIlDB8+HF9//TWAMlcHAPTt2xetWrVCq1at8MUXX+DkyZPYvHkzAKB+/fo4c+ZMpXbOnDkT4QIhCKuQkiZimu7duyM5ORkLFiyIOLZkyRLs2rUL1157LY4fP46dO3fiwQcfxIcfflj+Z/ny5ahZs2a5y6NDhw7YtWsXiouLy9vZvn07UlJSpF0TEV2Qu4OIaapXr46nn34aDz30EPLy8jBo0CDExcVh/fr1mDNnDh599FEkJyfjtddeQ7Vq1TBixIhK0R4AMGjQILz//vs4f/48br31Vrz44ot44oknMHr0aHz22Wf4xz/+gQ8++MCjKyT8DlnSRMzTt29fzJs3D99++y3uvfdeDBkyBJ988gleeOEF3HvvvQCAVatW4fbbb49Q0AAwbNgwFBcXY9WqVahRowbmzp2LH374AYMGDcLSpUvx0ksvoUWLFrIvi4gSaGcWgiAIhSFLmiAIQmFISRMEQSgMKWmCIAiFISVNEAShMKSkCYIgFIaUNEEQhMKQkiYIglAYUtIEQRAKQ0qaIAhCYf4/ZB4M8NNMG4cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 196, 136\n",
      "LR fn, tp: 8, 6\n",
      "LR f1 score: 0.077\n",
      "LR cohens kappa score: 0.004\n",
      "LR average precision score: 0.041\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 324, 8\n",
      "GB fn, tp: 5, 9\n",
      "GB f1 score: 0.581\n",
      "GB cohens kappa score: 0.561\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 327, 5\n",
      "KNN fn, tp: 12, 2\n",
      "KNN f1 score: 0.190\n",
      "KNN cohens kappa score: 0.168\n",
      "\n",
      "\n",
      "------ Step 2/5: Slice 3/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 212, 120\n",
      "LR fn, tp: 5, 9\n",
      "LR f1 score: 0.126\n",
      "LR cohens kappa score: 0.057\n",
      "LR average precision score: 0.057\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 331, 1\n",
      "GB fn, tp: 9, 5\n",
      "GB f1 score: 0.500\n",
      "GB cohens kappa score: 0.488\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 2/5: Slice 4/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 223, 109\n",
      "LR fn, tp: 11, 3\n",
      "LR f1 score: 0.048\n",
      "LR cohens kappa score: -0.026\n",
      "LR average precision score: 0.048\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 326, 6\n",
      "GB fn, tp: 5, 9\n",
      "GB f1 score: 0.621\n",
      "GB cohens kappa score: 0.604\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 5/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 211, 120\n",
      "LR fn, tp: 7, 6\n",
      "LR f1 score: 0.086\n",
      "LR cohens kappa score: 0.019\n",
      "LR average precision score: 0.047\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 325, 6\n",
      "GB fn, tp: 4, 9\n",
      "GB f1 score: 0.643\n",
      "GB cohens kappa score: 0.628\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 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",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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89PpgQ2iNPVvMslc/XmnrUREiTkxanzo1aZH3iVyc9GuvvYaTJ09i6tSp6NatW+rPr371K91DIwIgIkpCVMzyle3Ox/O398RVg64OHGkRKmbZIyTNK21dBKzx5kvquWgnLA+6HItGaNKiIE06uojWUngy5MK8LvOYekTBk+0YBvsZmt01DuU9eqe1RdV0E4a81qTzjXwIR+NFtDbEo/WE0XR5PisqTI11T9HHZQFn57B85uyMNtPC1FSgq2QqCWkN6F7o+bBJ8AhPGT8+UTHLQcfrdfBtGOw5LBp7e0Zbvhbg1wEJaQ3oXui6NwkZuDeegn4lKaedzM3Iay55sgzDrAfWhitqE7Y3A2fMcL4X4NcBCWkN6F7oojcJL6eVyiOzeOtjiMJrLlmbhIz0dF3PDeTHG5kJkJA2ENmLX/Qm4VV9TeaRWW7cwnLjriP4yVV34N1L+wSO5AgCj8aua5PI9zTquEFC2kB0L35eQebltGK1i361B+oE8uTjLbDlx0+lNp5l2w5gf6IRVl19Y9pmFHZueTRXFWYML41dhalHp9kun7R4EtIGomrxey10XkHm5bQS7czyGhdLY/faOMIKTuvoUaBJE1+N3TbzbGrdXYkZQ5fJQ9VmwEK3IqMSEtIGojs4X2byhfOevE4vr3GxBLKMaIfaeXPr6o40a+arsYsovh/W8SjjjcWkwwB0O99VkvfJLCYE5+tKZlH17M7kC1vz8kqXFlGHwW8+edK53XPC+jfWkVUyjrEKem8RuOfInk+ewkphiEsRJT8omYWDfHptYjm9VGjsTq3HeU9ZGmDF6tXM9pp1a7nSud0wT2NhFNq/st35eOS8A+gxZZKw0DjWuEQdl+XG/R1UrF6NqjGjgc5dPA8D0BVjng/kvZDWbf+tWbcWpcOGK7Hp6QrN8rJdyiqG78yQc7bXzpub9YQR3vXgZb7xEzQ106dxC2ueLMOwsdPu76B85qy6A3A/fF/5pu7XJgMTHZJ5L6R1239r581F9e49SgSn2+kV15hlZ4acsz3Ij553PTj7yVZUKaXFg32QLC9eGrvo+S8aO0ap/Tcfiyj5kfdCOhuiBJmf06de27ZKYlrdTi9VMctebWHxMt+4T9XgSSzhxct84ytoPA6S9SNbZIdTO2eZdPJZE+XBRIdk3jsOs6GioLgKx6FoZ4yM4v28eH03rPlUVRheltMr23yzTrwR9sx33obKT3YbVTs76o5EchwKRLcGIroOg+wqcypfF4N8N/b8sZxeQeGx8coy9WRNKWdo56LWbjZzRzYzjwxMNEvIgoR0FnTbwmQuxmwxy37IKBbEi/u7YUV32M8YxukVNstQRcyyTJNO4cCBvv1kM/PIQLfypBIS0oagSuixwtHcTq8g8BQLkv2WYMOK7hAxf+4+/JywAKRErJiSZchCR8Ew3cqTSkhIayJozLJMM0W2mOUwBMkyzBaSlqt274zu8Ar/CxOSptsJ69UuyznrDhE1QbtUhQmORHIcasLp1Gm5bImyjEOTsgxTf2c4vdx98Dih3E4ZvxOm0aQJEs2acc1HPmTE2VSNGY3E55/B+spXI306t2mQ4zAC6NqhTcoyzBaSJsqW6xeSBoiJWfYi6gfEskJETdAu8wnSpA2ADqIVS3Hxudg/eEiguiC5aMB+mrn7zWHy8RbCD4hlIbO2B61P8ZAmTeQ9QW22ubxZZOvbaZ8WfUAsT+ifrNoehFpIkzYA0lSCE8QerHs+ZdqnvezB9j3RuQvw4ftIjhiFTa27C9ek88keLxPSpCNIPnnMRVeD0x0mFbSglAiy1RxxFo6SVVPbtPmPOySkDUFXhbps7TLuGeZZVYWesVCdZcgim3lG9lyYOP9xh4S0IehOp1WV2Qiwk0NyjVn2avPqV7QWz3oer7YwBB237OgdqlCnHhLSMGOH1p1OqyqzkWW/lPXjYyXLiNbiecl1reWrgHKSr6F/5DiE/gppBQvnoeaGEbFyunjNqYojmIq2vIOjz89IOdHsZJl3L+2DlZ1LcN2Ha3HVoKtR0K9EyL2rxoyGtXMnYNWi4OePoqBfCdfzB0Gnc063IzaOkOOQE52mhtp5aor+q0aHxm6TcZLImWSZlZ1LMg6NzUVDdd8zOWIUYNUCiYSnSccm1+fnfdMy4e2QEANp0grx1qTno+aGm4VpSDxhUiaET4V5k2F91tak3c/ESu4Ik8ziTCkHoGQeg463TrvfAVhWSrvPFS9N2uQ1ZTo8mjQJaQMQ/TqZLSNOxOu3CFixvSIOH5AtVPwK7cvGK7PRnWU4KHEYV/z6/wcSSSQ6dAg1Pq/59Kp/QrU9skPmjpBE/VWRK9qgcxfgi4q6/3ISdp5E1HrmMQOIimwJcwyWmzB1vP2yDFdYX0LBzx9FokMH4VXxstU/URXZEvXfaVBIk2agWhNw76oqXxdFmxp4CPOcflqx2xGbTWMXPd885qYwcyizXocT5/pUZepxPltqM9ekscv4PZImHRLdoT4yw61YTq9cn5Xns7IK/LO0YrcjNpvGLsopl2qfPi3USS5BYY3bK8tQ1PyztGcZ4aPO+VKlsQcZiw6ME9Lvv/8+rrzySq1j0HHShBOZr4vuAvwsjS9MYgnrnsCZhb5zJ2p+9qC0zEO7PWhpTdExy6nNAAhsbopaIkgupp5c5tk5X173lDV3IhUZERgjpC3LwsKFC3H77bejqqpK93C0IvM0lmw2TVE/ZlaWYbYwNREbREG/EhQvWhjKRp2NbOF1BXdPlJqVF1boyYQ1tlzm2aTMRu1Km5a7Mpg2bRrmz5+Pu+66S/dQYgdTK/E4MivMj9mv0D6AM46sjkIzD1lCoWL1amatEPcxXTpjlmWkp4scc65jYLXL2CBEz6kT3ZqzG2Mch4cOHcKFF16Id999F3fffTe2bNnC3QeF4IUnjJMkbOifqJjlgoXzUPnJ7rOp6NOnnf1AWVlOR2blPBbbwRUwIzEIquOQWetTd4w0z1ozkUg6Di+88ELdQyCgsEJdiNA/1j2Bs06lorFj0kw6qUNjz9g0ndcGJYjm5ufgCpqRmCuqw9T8zE26atCYpgGLwhhN2iaMJk2Ep2L1apTPnI2isbejcOBAafcpHTYc1bv3oF7btihetNCzLShe42a15/KM2cZbNPZ2lM+chaKxYwAA5TNno373rqja+l5am4h5te+bbNoUyebNUDR2DMpnzkL17j1ING2KgjNthQMHhppTFnVzNyvVf7Z2Ijz1dA9AJFE3dxiRTtujN9CjN8oBlEs0wdTcMALWvLmoueHms6d+MNoC4xh3IXC2z+MVqK2qxvHjFannYbWFHe/R52fA2rsXR5+fUfeq3aM3Tp15/Xa2iZhX+77VR48Cn+zGf6Y8UfcPRecC1TWo+WQ3jj4/A+U9eoeb0zOkxUk7nrO8R+/UNV7tBJtImjvyEftVtGL1agB64zFVZ2+p8t6LimDJNt6wr985RbY4zDduk46sMLVskS06okfiDglpjdjConzmbADRC5OKAlGxXYbaOByCWUfRf7/2sDDj7WO4Tv0wTkj36tUrb+zRtrAoGns7AMULfeeOtMQS3ZqQKA2pZt1alA4bnuonKhp71MLUVMGKtzdtg5WNcUI6n7CFhWxHCzuxxAISSaEB+0GEglfMssgkGq/63DIFVFiNPcz888QsR00Tdc+h7sQSHZCQNhhZwfkF/UqkVEgLIhRk12Gw08LtJBpVJh3bLm2n23u1yYDHThw1jT0fhbIb40LwwhD16A43UQrOB9QdNpDtPl/+/lDsHzzEs1pbmPrVfmOxNwGvBAv7mrD3VTXPNtmSraKeWKIDiu6ICTrtxLloQibZf1kau63ZWovegLVzR+paESnaNdOn1R0C0KRJWi0SZ5usuihebX7tIomKczaqkJD2wAQHi84KabJ/3F62aRu/+Q8iFLwqp9XOmwskkoBlpbUFfVa/LMNUGJyjFomzTaRJx1kXxVkrRWRonDtE1Nnm/F68TD35bqYQBQlpD6LmYAmDDtull23axm/+w2jsyRGjgOJi4EsXprUFfVbPCmmM0p1B+uVVBpzmC3z4fmqj8zJpiHBI2iGizjZbILNK4MrEBOVJNSSkz6Crhqw7ZIw1FtnoMFNkq0ssav7dGjsAJJo1A8rKUoKGZd/l0eJzHUsuws15fbaNzkmY8qZ2iKif+UZVLZJ8Up5sSEifwVNDUmBqqNz+sbBi+DyoLsrD+iHLTo5gCTLnOHhNPV7jytaPU8B5jYXneZzXJ67uk7WPMMkydoion/lGVSlU3fH8OiAhfQZdjo7kiFFIuGKWVYVJyY5Zdt9TVUiak2xZeaJMPdnsxGkCTlCGoP09WW9vCDy+MGsqjPmGV+h53Uunn0YXFIJnAEVb3sHR52cICQmzcb/Gs2oap163jx6tEyBnwqVEhcf5haR51XTO5d7uzxQXn4uDCxYHDlMTiVfomah7s0L6nN+fqDA/J9lCRJ3fJes7d487CofGyl4rFIInAZmvP4UDB0oPU0uOyKxpnM0unPN9z6Sco3OXjOOx3K/8IrR4P1ODl/1XpUkHEG++SdPCHd+fSg0wmynJa9wyEfX8JkWnkJAOiEmvP0FghaQ5j6/yI2xImp1y7j6dm/XKD4TPPHSbGipWr87JFs3znF7tsk06uVTjk0VGNb7OXaREmdgEieePY3w2CemARO3L54nY8EoEyTUkzSvl3K0Fpv7dkQDCg/0DBVD3Cn0mJK3sl0+eFRYeAoT3+8yWLBImYkMkOu3+9vzLjq33c/KapAGLgoR0QOL25TNjlh2HxjqflTckLehc1c6bC5SWAocPpbUF/aF7bS4WrIxQvyAChPc5vcw37sxDEfCYZ3RtErIVGVFO3qhBjkPN1Kxbi4KF81BzwwilG0DYQ2PD3NN2NKFzF1iL3gASCSQ6dGQ6Lf0cOF7/xnLEMh2pAZ1eQeaQ5cQT6SDL9h2kJbkAQp1eXo5DFY7YuEKOwwhRO8+7tKZMeLSSMNqKrQFWPfVkhtaJD9/PsJO7tfBcYpaDwOP0ytaHX8wyT10UHk3efS0zUSgmYWomxSzrgDRpzdRp0vNRc8PNUn9UPJXTRJLSWL+oABoVcoeJ5TTGO29D5Se707TkmunTgM/2Al9thYK7J2ZonWEr43mFFvq9sQS5Ntv9/MImRX2/ujXpOFbUI006QhT0K0HxooWxLKIEnNUAE8NuzHAaBnFu8WqFNevWovY//8nI7HPC0jrDOr1Ymrk7jdoeHyvz0NlHEE0+yLVeyUqiUKWx54Pd2Q8S0nmCCqeLX4U0t5Yqa4OonTcXtceOZ2b23T0Ricu6ouDuiVLmwhZYiav71L01nIkmQVlZaly25inj0FiWQGZtHDKQXV4gbk57XkhIG47Mha6q3gKrXYSdmyUUrKNHkWzaNENjB85GrLA0eWFz8eH7QKPC1KbkZ/cWIZTdWrh9L7syHjp3qfuAwGgTL3u4rPICvOOJGySkDUfnQhdVbyFMmB4Lv80AZWVINm+eVWOXNa/ZYsFFJXW4zSjuewF1wtpa9EZGneuwZNsgwiQmiRhP3CAhbTiyFjpTG3KdIM67+GVXtLPJthnYpTWDXCtKk3cn1jCzLAUKSVafLFOK0xfAi/1MzqL/ADublZWYJMtMkQ9Zhk4ousMAsp0hJwNWdEDNzx4EEkkkOnQIXWiJhVd/ou/jnE8ZEQg8MeYy5rBm+jQAQMHdE9MiOVjtYbCfqUH7dsALswNdqyICIw7RHhTdQWSFpQ2507lF11vIZrsUFYVQsXp14KJKImKWvdq8CGNDDaRFC35jcb6ZZLtWtjbLMvXEHdKkDcCrtGYcCJKV51UyNWfOxEm7Y7Jtgc3KMgwTswx4a8yiT9JW9TbiRPabCQ9x0KIBgZp0RUVF4D9EcFiaVBydH26thxWmBogpmeqc06KxY3wL/AOZ0RbONoBfM/TS2FP9exwGIDMyQUbfutdp3O3PLHw16a5du+L06dOBOvr444+FDSpXoqJJu7UBGZo0j8YjSzvirQUSZhzOPlsuW+Jr4xednee2B7NqgIjIMnQ/J2tOvWqR8GR5uslVk9atdZuMME16+fLlaNWqFS655BK8/PLLvn+I4IgOSWMRppqcKHgjK8KMQ6eG5bYH+9msgXAae7Y5dfbvvFamBuylsevWuuOCr5D+yle+gtmzZ+PAgQPYv38/evbs6fknjsh6Fc1FIIuKWQ57bdBx+WlRXs8fZhw8c5prirzXs3qFpLmTZUSEqbmvzwj9E1Q/24tsc8cy9chKosnWHheyRne0bNkSDz74IP7617+qGI9R6NIERNiseX78PLU03IhMFpGd/psSaK5jvQC+WhjuZ2KNuy7ufGdG3Lm7frZ7bGHn3/4urUVvwNq5I61d9PFd9ridfgdWXRQdGa1xIlAI3qBBg/D444/LHotxqHqFdoaMAewfuOyx+IXB8YajebXr1oRSkRyuY72AYEKM9+3EfaYkq809Ni9B4zlHnbukOWHtvlBTA5w8mdbu1Q/P/LNKyWYz9YhCRmJSUHRq66FD8I4fP47ly5fjv//7v0WNKWei4jjMwBEylp5YcrYYvixSRX/OlOq0w+DCFMP3gsfpFSa8TFVpzWzhcKzyp7mG0PnOHeMUeK/1k0tIoF+yVb44B0WH/ilJZnnnnXcwadIkXH311ZgyZUqu3eQ9NevOltbMTCzJfmgs7728zCgp7ZJxQKwom7VX6JmXU809xqCvs+43k6Dj4yXb6zePxp5Nk/d6M7GOHgWqKjNOgfdaP2ETcYKg+41JBjod01ya9L/+9S8sXrwYS5YswcGDB9G4cWMMGTIEI0eORJs2bWSOMxBR1KSrxoxG4vPPYH3lq9I1ZpZ2VfXUk7AWvYHEsBtRf9IP064PE5Lml0DiFXomLFEjh2SWXMLURIxXRNih1xuIKPw0aRXHsPHOURS0e6GadGVlJZYtW4ZbbrkFAwYMwIwZM9CmTRskEgm89tprmDx5shECOqokR4xCvbZtpe/QtfPmAokkYFnp93KU1hR5r1ySRUQVaHImszht7dkOjdXhgBIRdlhw90Rt9ZZ5NPNctVHeOYqbI9FXSD/00EP4xje+gZ/+9Kc455xz8Oijj+LPf/4zZs+ejUQigXr16gkbyD/+8Q/cdNNN6Nq1K66//nq8/744oWEyBf3UnMySHDEKiQ4dUnbLtHaBMcusLEMRoWc89y+fOSsj9AxARoW4tLYcnF7ZzB1etUjiFKbG+h5FV0MUFUsuGlXmG19zx8UXX4zWrVvjzjvvRJ8+fdCsWbPUv33ta1/D0qVL0b59+9CDqKysxLe//W2MHj0aI0eOxJtvvomHHnoI69evR1FRUeB+omjuAPRUwctGrq+MImpVhDUBuM1H7oxAEVmGrM8HccLmkmXo96yyTQ2AmevTBETOac7mjldeeQVXXHEFpkyZgt69e2PUqFGYO3cuDh48yDWgbGzatAlVVVW49dZbUb9+fVx33XVo3749Vq1aJfQ+UcAU54pIrUeUhurEL7HEbT6qi08+zIxP5unbDSscLZsT1h6jCE1PhanBC1PWqU5UaeyBHIeVlZVYv349li9fjg0bNqC6uhoAcM8992D06NFc2i6LOXPm4K233sKcOXNSbQ8++CAKCwsxefLkwP3EQZOOS5WvIIRxvPnNk1tLOes0PVsr26+PbN+B1/hYTlgvLZ53ToIgy2HG0qTzaZ3KgEeThsXJ8ePHrfnz51sjRoywLr74Yqtr167Wj3/8Y95u0pg+fbo1fvz4tLaHH344dL9R5NSqVdbhocOtU6tWabz/MCn3d/d9eOgwa3/X7tbhocNz7CvYPJ1atco6cE1f6+A116Zd79VHtr69xs1qPzx0mPWviztZ+zpeEmisuc7JqVWrrH0dL7H+dXGnnOaTF93rNJ8Ilcyyf/9+LF26FCtWrMDKlStz7QZz5szBhg0b8NJLL6XaHnzwQTRu3BgPPvhg4H7CatK6QndMsvmJ1pBY4XiJVq1w5y2VQNICamsxo2qcVM1PdKgazxuAlxbP23c2WEktvGP1wqT1GReEJ7McO3YMtbW1aW0fffQRCgsLcdddd4US0ADQrl077NmzJ61t9+7dQpySPMQtdCcXRNvZWOF4yRGjzq68ZIHwDdGdzJItWYYXnuiFs4klHdLm1MumG8YXgOJi4EsXprWLrndBtmj1ZBXSs2fPRt++fbFt27a09ieeeAJ9+vTBq6++GnoQvXr1gmVZmDNnDqqqqrBy5Urs2LEDJSVq4z51ZhX5ofKHITo0zjmnaX3XAkDizH/D45yj8pmz0qqypTTGLIcK8M5z0HA3VgErKQrB4UNAaWl6n4zaHu4wSR68xh3HLENT8BXSK1aswNSpUzFhwgR07Ngx7d+ee+453HPPPXjiiSewZs2aUINo0KABXnzxRaxZswY9e/bECy+8gOnTp6N58+ah+uVFdhW2XBGp9ZgQRwsAs765ALN6L8Csby4QMgbnHLGSWYJUZROVNMGKk3ZeG0ZIAt7p/UgkAKuWmaxkvb0hbSy5nofIG1dPb6fh8RXSc+bMwX333Yc77rgDjRs3Tvu3oqIi3HHHHZgwYQJmz/Y/STgIHTp0wPz58/Hee+9h+fLluPLKK0P3GRdEZmr5/phclfcAtZqQiOy75IhRKBw4MOPIrEBzx9A6g94zWy0S57VhhCTA3gTqkpU6eiYreY2FF8+NTrDGTpzFV0jv3r0bffv29e1gwIAB+OSTT4QOikhHRcxycgS7jGauMcs8wt2+llXnOdeY5WztTDhT5J19MzV2h3nFeW1Ys5pb8NZMn8Z0WLIOA8gwOwWAVbAq43thzF3YzSgocTep+ArpwsJCnDp1yreDmpoaNGzYUOigTCHqXz5Pyi6AOqdT8Ze4E1F4NHYgc17rtPgdsBa9kRFtIPJ1Odv3GUZ48tQiyWVsTtybAJDpEHXPWxhTntPG7+7f1uZzPUiBF79KjnE1qfgK6R49emDp0qW+HSxZsgSdOnUSOihTUPXle5XWlLlJsAQlS+vJtRi+3w/UPa91WrwFJJIZcx3mh87cDHy+T/ez5iQ4AwhB3k0t6z0ZDlHeefN7VqeN390/gFAHKfCOy97QVR6I4TUWVfgK6fHjx2P+/Pl47rnnMjTqkydP4tlnn8Wrr76K8ePHSx2kLlR9+SxNBZC7SbAEpftYpDACyu8H6r6XV5hatn7CPiPv51nkYuoRkTqfDVZEid/Y/J7VaeN39u+1QYgkyIauyuGvS2P3FdKdOnXCb37zG7z66qvo1asXBg8ejJEjR+L6669Hr1698MYbb+Cpp57CFVdcoWq8SlH15bM0FUBuvQW308tt0xS1IFlCgSVAZMx10PnzqwOSs6nHxwkLIENjD5NExRNZ4XVtrmuNZzPIRRPl2dBloytEN1DG4alTp7B+/Xp8/PHHKCsrQ7NmzdClSxf07t3bKHt0HGp3yISnVoWoGhLOLEP77+52nvoYQXDOJ2ssIqvS+WcZug5YkFQM331Ml187Tx0Rm2zrM2g1Pqr3cRZhGYcVFRX4yU9+gmuuuQaPPfYYTpw4gfvvvx+TJk1Cv379jBLQqoiyM9EvskNFlqFn9mGWPoIQ5DXeK/NQdEjaWW3PdXyVR4hf2BBLtz3Yr11GtEVQ803cbcey8K3aP23aNKxfvx5jx45FMpnEa6+9huPHj+Ppp59WNDzzcIdaRYmCfiXMMbPawzynU2N29+3V7tdHEJzjrTivEFXPz0ByxKizccmdu6T15/z/bGNxElTDTxOM9t89Qvx47p9G5y7Ajn9kxnV7tPPOaVDc4/eao5yfkxNVv9GwZqqg+ArpNWvW4Mknn8TXv/51AEDPnj0xcuRIVFVVoX79+tIGZTKyFrpphHlOns1AFM7xOh2xtiZpv2o723IhmwBgmVfsOGZ07oLEmbEKwSuu+8P3gUQS1qI3UNPlstQ44yYkvVD1G1X1nL7mjkOHDqFt27apv1966aWwLAtHjhyRNiDTUeFMVPm65uXgYWkIJmcfOr8Xv5CxID9cv+fkMdMECVPjhZnZ2LlLRkEpVmKSaLzWju4sQ1UOf1XmG18hXVNTg4KCgtTfE4kE6tevj6qqKqmDyndUhvrwRACoGJdX9iHPBsEKGct2v6DJEdkEgPOHKyNMza291Z/1St1RXc7EEoBtEw8JO2Y5PYpFlt0721h0oGwzkNo7kROqduiNu47gJ1fdgXcv7ZMmDL00IZGJJV5kc4blukHwbDyqYpa94NXkWQ5RGQKEHbOcrrGrWru6YpZ1kPW478WLF6cVV6qpqcHSpUvTDqUFgJEjR4ofXZ4iy3boNmMs23YA+xONsLJzCb4+70UADk2oVStmHQxZtlwbL3tiGDsja+Nxhqk57cQiY5a90tuz9et3Les7sNucY89GLs/p/g6cG2guTlge3OPNF98QkCVO+tprrw3WSSKBdevWCRtUrlCctD/uONWNu45g2bYD+M7bb6DXBxsyYplF/dg27jqCpW9tx3UfrsVVg67OECiif9TFxefi4ILFvvHRXnHaYWN5/ez5QZ9XVIy6X7IMz3OacDJL3GKseeKkQx2fZRokpP0RIUBy4YHFH2LfsQq0tL7Aw3950TexJAz2czS7axyOPj8j1X8aZ94S0LlL6ENjecbECv2TMedBk0h4vvOg6zPoBpELqsLdVEFCOmKYoKnIxE9jt44eTQnOsBqSLYwKmjdDTXXN2X8oK0sT1gV3Tzzj9NoBWFaqBrMMbc3ZJwDm/4vUDGUIyqDrk7IMgyP8jEMiP/ByWIX1pF/Z7nw8NrQzrhp0tdDIB/e4bKdVAtbZCANHOU9n1EGd0yu9UI8Ux6gjy9DZP++9gtbC8KqLwuO4zBXKMpQDadIGYIomLbq2hGy8xlW05R0cPZNx6KdNutvC2I29TiT3OsVbxLNm+76C2OCDwLM+dZklTF2jXpAmLZA479BuvLQe0doQz5zmklgSNE7arWHyhHWxwtG86oK4w9RyeWMJoqW6Y8ydY4l7aJyq59MBadIuvDQkmTu0OxpBtNPFNHjmNJf5Z2l+QbROmyBOw6BOWJYzUsQbS5CIDVHrJ5sm7eUcFe14Venslg1p0iHw0pB0aCBxDdjnmVNRduJsbwkAuDLlgiaL2HHn9v9nS+cO+qzutcGKBfcaY9haz8zMQ0lJNDqzX02BhLQL9w9FdR0A5w9XxgYhytQQ5p48jqww8x9EeIhO3bbv6T77D8g8NJaVzh30Wd1ro3beXKD0MHD4UMa17HTuHRnp3GFNPUGTaHjWlCoTnBcmmDtJSLtQJZS97mv/cEVpJV5ajy0Y/LQpUdpKtreEsJqdFzw/ZFHfu9s2nUpvZxwa62XHzvW+XudEsjaOMJEtQa/lrYvCIuvmKvl3aoLGTkKaE9k7q2gNIaiDy+9aUaYGr7cEUaYe+54Vq1cD8NfYZX2PXpo5qz2MFu+en4J+6cdKMecfZzcO9xFUIt5Y3Bt/nca+U+qhsbLCRm1McEiS45ATGY5EmSF4PI4X2SFpXmFgohIwnMkstec2ybin/fewIWlBnl/0swW9n43II9Hc69PLQWknJtnfuZ3V6T5GLCzO5/DKXDU9JI8chxLRbQvj1RDCvC6KDkmTPXd2/wlYKc3O6UxzjlH2G4tfu4hXaJ6SqUE/ExRPv43LpHNWYxdbMpX1Pdr3VO3wVwFp0gaQS8iYDA3QrXXy1LEIEyoVNvnCfQ87mcWdci4zbCtbjQ7nobDOfxdRQMmrXVUIXtAxikLms+Zy71wgTToGqPJqe2mXduhY2JA0VrtXOjeQm9bt1kxTySwBbL0bdx3BA4s/xMZd6acN8UbBOH+4zugJAEIdwqo19iCwondkOvVY/cfZkUhC2hCCLnTRi9EpDJ19y3TwuCM7UgLOfl12HBrr1JRyPdLKhuXgWrbtAPYdq8DSt7bnHNnCMvVkRFp4nBTO2iR4ntWdZeicA1VOOhMiIFShw4xC5g4DKC4+F/sHD9Hm6FDxqug002Rz4IWtpma/SgZxcL3bqhtWdi7BdR+uRa8PNqQ5vWzzRC6mnqCZqw8s/hD7Dh1Dy2MH8WjP87idXqoyYlnz6ff8Moh6lqETMndEEJ0OSRWaEI/GHrQt6D3t0D8AGQ6uXh9swMN/eTFVoQ8Qd2is31js+R98WQu0PHYQ121fJyRmmcdM42XqCXovmzibGkyANGkDUFkFT2Rolh9hwvfCki1kzO++YUL/vDR/1puD00kKiHMkelXjY11rH8ZwUdNCPDa0c+D55EXU90uaNGE0MoPzZWhCYWy6ovHTAG3BGcbplU3zzxYyJtKR6BWSxrp28GUtcFHTQgy+rAX3fcOMMVd0ZQPrhjRpA2BpKkFtmqaiSpNmfZZVT9rrWhXzKjNkLFtInojQP1M06ThBmnQM8NKQdNisc9Hiddopy2fOChymFmZegyYcidLYWWSLAhJdC0bkGIlgGCek58yZgwkTJugehnY8s7okJGFkK8AkwxzhFZIXZIxOWEK2aOyYwDHmcYtZdhOnzLt8xRghffLkSTzxxBN4/PHHdQ9FehGlIOjSRMMmlnDd1074YMT4+o3RCWuegp7MEoRc4rNFRafkMi7ViSWEfIwR0uPHj8e+fftw00036R5KXoX6eGrsrGptIW2n7ixDO+EjW7ibKAFnf69/WfE2M/SMFZLGu0F4tXtdm6tCwKPFy8AERSZfUCakKysrUVpamvHn3//+NwDg17/+NaZNm4YLLrhA1ZA8UW3/tUtrOttULX5VmY1u4VHQrySjXCbvGFnUrFuL0mHDmTZ1W2Nf2bmEmWW4bNsB7Dt0DEvfWC+ttCaQvhnkKlR5tHgZ5TzzSZHRjbLojnfffRejR4/OaC8oKMD27dtTf3/mmWfw8ccf47nnnlMxLK2UDhuO6t17UK9tWxQvWujZFgcqVq9G+czZKBp7OwoHDpR2nyBz+tb2g3j9r59h4J9exxXb3kprf3X2alz30R/Ru/EXoee/7plnoWjsmLRnvmv2Juzd/x+0PHYATzbbj6qtf89pXlj9s9q81lSYtabq+yQMDMELI6RVheCJDp9qdtc4lPfoLbTvbOONa1hUzbq1KFg4HzU33Jz1WUXPS9DEko27jmDpG+tx3fZ1+HqDkzmH/vkl0Xgly2xq3R3Lth3A4MtaoOenWwM9K4XgiYdC8CQjOjjfqYmoSiyRHbHh18Z7LQ8F/UpQvGihERXSvBJLrmx3Ph7teR6+3uBkKDMKKyXcrp9t39N5pmJBv5JUQall2w5InRee6B3CHxLSAfAqrakjZjkXVEQbAHybgc4wtdS8Tp8mdAxBnLCBxxbgO3cL2dp5jvKyjDMVAT1ZhroiW+ICCekAsJxeumKWc0Fk/V3ekDRVYWo8mnmqfgYgdAw8TlgZ0RnOsXttEFe2Ox+PDe2MK9udz92/H36KjGwndLZ20ajeDIwT0vfcc49xTkNVmjNPHQaR5CLggoakef1AN7Xujp9edz82te4u5Bl4NHN7XgvunqiteDzP5iUD0YJGlSID6J871eYb4xyHYYh67Q4eB5dIqsaMhrVzB2BZKPj5o75OL1FjsSuwtbS+wMN/eTGtEFEudTSc4/ry94f6zmeQPqLo4OKpw51LfW4/dK1dHYh4Jh7HIQlpA1BZqpRFzbq1dZl/iSQSHTowC+SLLkC0cdcRLNt2AN95+41UsX0b+0zFXH8Mzvnk6SNqRaxsWMWUcols8SLM+ozqnMqGojsILliJJbk4vdz4mVF6froVjw3tnFZs332moojXSj+7vkiHcFDzgczEElbmpqpkJS+iVhjMREiTNgDdmrQseF6/RcYsFxefi4MLFqdpl84js9wnkrMK5It4VtazOU069t9FmnpkCN0orM+wR66phjRpwgh4IjhkRQCktEtGSJpIx6yfxujU5u04ZqdgDhumxpq7uGiRQVEVZqoD0qQNIAqaig5y1YScmrTsLEMvnH0CYNr3ZWrAXsd35XIfv/WZTw5DkZAmHRPyTRtyIyNmmQdRBYi87PuqwtRkhozpjlnOB0hIG4yqhc57arQqdKfIi8w+9EOG08s5dzJf+3XHLOeDIkNC2mBULXRnPYe4EzYrMgy6UuRlauy6o0fyQWMnIW0wqha6qnoOLI1dthYf5KQSGYKGJTx0psh7tUddE42Lc9APEtKEtHoOblgau2wt3haWrNNYZBbqYQkP2/RQO29uWt8ia6sAhhe1ErwZqFJkdEJCOkJEXethaeyytXhbWNqnsTg3A5lOLy/hoUIoqtLYvWCt03wwS8iChHSE0LnQRWwQLI3dS4sXtSHZwvK713TK2Axk2KKzmRRUCEVVGrsXPKYe0URdkWFBQjpC6FzoqjcI0fdjbQY6okd03FN5aU0PUw85EnODhHRATNihdS50Va/Fdpt9aKwOh5Do0Luw4XhB74nOXZgau0zBxRqvlyavgjg6EinjMCAy6wCYlnGoKltMVr2FqJzJx7pPmOfnqYvCg998emU2hqlFwiJumY2UcSiBOFfzChKmJgOdjiy/yA5VP3rRbywiNHbetealseuMMY8bJKQDotPUIGMhOn+MPOU8RcJ6LY5yliELP2HoLLZkt8nYIGQ+v1dmo+jvUXdmo05ISBuGKu2SpfUAmdXgRAqtXBySsmKWTcgyFF0/2+vzOp/fC97vVXdmo05ISOeIyuB8GQuRqfUwCvyLDEdjaezZ+ucVXDXr1qJ02PCsjiyeMLUwqIxZZn2et+qfjCSasNcGQWZikm7IcZgjIh2JpjkO3eT6Gu6eo5RpheNILt57V40ZjcTnn8H6yleZjixW0X/ZReF1Or1EOGeDrk/38/g9n+hnF3GWo0rIcaiAODsS3eSq9bjnKJcjuXg1u+SIUajXtq2nIwvILPovsj4GC51OL1XmMyDzu/J7PrJZB4c0aQMIGuKk61ggHg0pDPbhtIMvaxGqjojf6euixs7zHTjvCYD5/6pD/XjI9U0vquFxKiBNOkaYEKYGILCGFAbRxZZ4tDVWNT4/bZnnO3COQ3b2IWvsusLU8sGppwIS0oYRprRmGHjCtESbCey2QYnDWYsthTX1eD0Ta4OQ8bouO/vQRmbMcpyddCZCQtowdGk9PBq76AgAu63nijlZS6aGnR+vZ2JV41Nls/aqqS0qPV30pq47sSTfNgMS0oahyyGpKrEkrPkm7Px4PZPoAkw8gszLzKNKA+Y9eEG3ky4fsgydkOPQAHSE4JngkJSFCSGNQcPuatatxV9WvI2VnUvw3Ws6CTt4gef7fWDxh9h3rAIXNS3EY0M7Z/Rlwnw6iYNDkhyHRFZUOCTz7bXUSdAMudp5c9Hrgw14+C8vCj0Zh+f7FX3wguzvPd8ckqRJG4BpmoooVGnmbs3KLwQvaB+q0FlxLyi861P3G1kUNG3SpInQ8IaksdARyRCknacP0eiqOKjSjqs7gSRuNmsS0gQT3pA0FjwCyNm3qJhlVQ5JEdmHslEpOHWbI3RvEqIhIU0wER2SxsIpjJ19i4pZZrXLsJfyCF5dMcu6BadK4vasJKR9yGfHl4ozAZ3C2Nm3zA0im2nkLyve5gpH8xobz9rRbeohzIaEtA8qFjqrtGa+4BVVIHODyGYaWdm5hDs1nTU2HuGZzdQT5nl0FAAjxGKMkH755ZfRr18/XH755Rg+fDi2bNmie0hKFnrtvLmo3r0nLzUeljCWTTbTyHev6SQkHM25dpyCzG7fNOjWwKYeUchI5yaNXT5GhOC9+eabePjhh/HSSy+hbdu2WLJkCaZMmYK1a9eiefPmgfuJYghezbq1KFg4HzU33GyMDS0KIUx+mBbSyApJ80og4a0EmK2Oslf97C0/fip1nx5TJvmGzPnNJ0+lwaivK5FELgSvtLQUd955J9q3b49kMolhw4ahoKAAO3bs0D006RT0K0HxooVGLVoZ2pEqG62JsN7IeEw9vH072wF2/Wynxi76jZHs5GJRpklXVlbi+PHjmQNIJHDBBRektW3evBm33XYb3nrrrYx/8yOKmjTAr6n4tYsgTN9en+VJcAibDGGaJq0Tr++DR2PnrXeuqn62zHrhsuHRpGEp4q9//avVoUOHjD+XXHJJ2nU7duywvvGNb1gzZsxQNTSjOTx0mLW/a3fr8NDhgdpFcGrVKuvw0GHWqVWruD/rNa66PocH6pPnWvZng409zHMSdWT7rmSuU1bfMu+nCyNs0jbr16/H/fffjzvuuAPjxo3j/jxp0rnj7M9pu7T/HlRbETGuMH04zziUrbEHJSranRdh3kxUv/FFIc0e4NOkjRHSL7/8Mp5++mk8/vjj+Na3vpVTH3EU0qpwCiynYHYKbFXV8rL1n22TCOqIlbnR5WrqMRET1qdpiDbJGe84XLVqFZ566inMmTMnZwFNhMOrSLyKanlAurMwW//Zsg/djlhVjkgZJ9kQZqLyOzVCkx4yZAh27NiBhg0bprVPnToVffv2DdwPadLRJdeDXVkasHs+s70lOMPUwpiURJimTDSN0PoUTyTNHSIgIR1dRAon93xms7dbR48CZWWescZhzRQ85huWeUk3tD7FEzlzB0HILIrjZb5Jtd89UWpqdSpmuXMXptlF5qGxXsQtFj3OkCZtAKSpiMXU+fTSqFWYONz34HlLMHU+owxp0jGCNJ744KUlqyit6XZqkjMzOpCQNhzdqbS0SYhDZ51jt1COU83luK9REtKGo9tGqXuTIMQQJ6HsJu5rlIS04eg+A0/XSSJ+1xKEk7ibbkhIEwDU2UtzLYavA9okokGc3xIAEtLEGVQtdJ6TRGQcDhuFQ2MJwgkJaUIpPIfGhkFETWPRm4SMk1GI+ENCmjCWMJosj8buJSTDbBw85hvS2Ak/SEgTxhJGk+XR2GUISR7zjQrnLGnr0YWENGEsuu3kYeAZO+ta0YfDqtLWaTMQDwnpCEE/ADnoDnMMe60bHi1etNZNphvxkJCOEDq1IdogwsOjsYvW7u3iUrXz5qZ9h6K17rjHLOuAhHSEUJVYYmLMchwIawIJCo+DUtWhDixo4w8GCekIoSpMTWfMMoWphYfHQclaUzI2iLDX5jMkpPOYKGUZqvhBx2UjUBWLzkKnSQeIp6mOhHQew/OjVRWzLCNMLeiPlDS78Kgy6XgRx8gWEtJEIFTFLMsg6I+UNLvoo9PGLmszoJNZDIBOvjiL14khYU4S0XkQLGvcos5OVAWtz2DwrB06mYWILDLMHarMOix0anaEWmS9GdYT2htBhKSgXwlzkXu1i8aOJZYpQO3nsDcC++8sTUzF+YeE2ZAmTRAOTItsibrTiwgPCWmCkEjYyBYVUS0ARbaYDDkODYAcM2Kh+ayDx0HpZ1bJdT7JfOMNOQ4JguDSwuMWsxwnSEgTnlCKdrTRffYf1QURA5k7DMDU13MRMcs6MHU+o0oU5zNqa5TMHURO6DxJxKs9ThoSIY84xaKTJm0AUdRURMKjsQfRkHjnM6iDK1+dXvm+PmVAmjQRKUQUYApC2PrZ5PQidEBCmtAOTwEmGbWOdcYsk0mHyAaZOwyAXifF4jWfOs0Vok06vIQx39D6FA+ZOxRCWk900BmSpsqk4wWZb6KLMUJ6xowZuOaaa9CtWzcMHz4cW7Zs0T2kQNBCJ4KgyqTjBcUsRxcjhPQf/vAHvPbaa3j55ZexdetWDBs2DBMmTEBNTY3uoWWFFjoRBVRtBixIkQmHEUL6W9/6FlatWoVWrVrh9OnTOH78OM477zwkk0YMz5e4LXRycBGiiVPMsg6UScHKykqUlpZm/Pn3v/+NRCKBc845B3/605/QrVs3PPvss/jRj36ERCKhanjGo/sIINNsmrRBRAfd6elRR1l0x7vvvovRo0dntBcUFGD79u0A6gR5IpHAqlWrMHnyZPz+979Hu3btVAyPOEPF6tUonzkbRWNvR+HAgb7tXteGu/csFI0dk7W/0mHDUb17D+q1bYviRQtD35sgTMXYELybb74ZAwYMwG233Rb4MxSCF21UldbUeeZhFKH1KZ7IheC9+OKL+NnPfpbWVllZiXPP9R44ET9UldbkMdOQ04vQjRFC+vLLL8fSpUuxZcsWVFdX4/XXX8eBAwdw7bXX6h4aoRBVtkuezUCGL4CcsAQPxpg7VqxYgWeeeQZHjhzBJZdcgsmTJ6Njx45cfZC5gwDMn09VWYYscjHfmD6fUYTH3GHMaeGDBg3CoEGDdA+DIKTDOpFcxSnlQLr5Jl9t7FHDGCFNEPlCQb8SZuahCqGpajMgxEFCmiDyCFWbASEOIxyHBEEQBBsS0gRBEAZDQpogCMJgSEgTBEEYDAlpgiAIgyEhTRAEYTAkpAmCIAyGhDRBEITBkJAmCIIwGBLSBEEQBhOrtPBkMrrHbUV57CZC8ykWmk/xBJ1TY0qVEgRBEJmQuYMgCMJgSEgTBEEYDAlpgiAIgyEhTRAEYTAkpAmCIAyGhDRBEITBkJAmCIIwGBLSBEEQBkNCmiAIwmBISBvCyy+/jH79+uHyyy/H8OHDsWXLFt1Dihz/+Mc/cNNNN6Fr1664/vrr8f777+seUqT585//jKFDh6J79+4oKSnBggULdA8pFpSVleGaa67B4sWLg33AIrSzZs0aq3fv3tY///lPq6amxvrd735nde/e3Tpy5IjuoUWG06dPW3379rVeeuklq7Ky0lqxYoXVo0cP68SJE7qHFkn2799vdevWzXrzzTetmpoaa9u2bdYVV1xh/elPf9I9tMjzgx/8wLr44outRYsWBbqeNGkDKC0txZ133on27dsjmUxi2LBhKCgowI4dO3QPLTJs2rQJVVVVuPXWW1G/fn1cd911aN++PVatWqV7aJFk3759GDRoEEpKSpBMJtGlSxf07NkTW7du1T20SLNkyRKUl5ejQ4cOgT8Tqyp4JlNZWYnjx49ntCcSCYwcOTKtbfPmzTh16hT+67/+S9XwIs8nn3yCdu3apbW1bdsWO3fu1DSiaNOjRw/06NEj9fdjx45hy5Yt+O53v6txVNHm888/x7PPPosFCxZg7NixgT9HQloR7733HkaPHp3RXlBQgO3bt6f+vnPnTkyaNAn/+7//iwsuuEDlECPNqVOn0KhRo7S2wsJCVFRUaBpRfDhx4gTuuusuXHbZZejXr5/u4USSmpoa3HffffjRj36E4uJirs+SkFZEr169spov1q9fj/vvvx933HEH7rjjDkUjiweNGzfG6dOn09oqKirQuHFjTSOKB3v27MGECRPQvn17PPnkk0gmyUKaC8899xzatGmDAQMGcH+WZtwQXn75Zdx777145JFHMG7cON3DiRzt2rXDnj170tp2796N9u3baxpR9Nm8eTNuvPFG9O/fH9OmTUPDhg11DymyrFy5EmvWrEmZkXbu3Imf//zneOihh7J/WLIjkwjAypUrrcsuu8z6+9//rnsokeX06dPWN7/5zbTojm7dulGETI7s3bvX6tatm/XKK6/oHkosGTx4MEV3RIkXX3wRlZWVuPXWW9GtW7fUn/Xr1+seWmRo0KABXnzxRaxZswY9e/bECy+8gOnTp6N58+a6hxZJXnvtNZw8eRJTp05NW5O/+tWvdA8t76DjswiCIAyGNGmCIAiDISFNEARhMCSkCYIgDIaENEEQhMGQkCYIgjAYEtIEQRAGQ0KaiDXXXnstOnbsmPrTqVMn9O3bF0888QROnTqVum779u2YOHEirrrqKnTr1g3Dhg3D8uXLmX1u2rQJHTt2xE9/+lPmv+/atQujRo1C165d8e1vfxtvvvmmlGcj8gMS0kTsuffee/HOO+/gnXfewfr16zFlyhQsW7YMjz76KABgw4YNuPnmm9GiRQvMnDkTS5YswfXXX48HHngAs2bNyuhv2bJlaN26NVauXIkvvvgi7d9Onz6NsWPHok2bNli0aBFuvPFG3Hvvvfj444+VPCsRQ6TmPhKEZvr27WvNnTs3o/2FF16wLr/8cqu8vNz6+te/bj3zzDMZ1/z2t7+1unTpkpZafvr0aatHjx7W4sWLrUsvvdRasmRJ2meWLFliXXXVVVZVVVWq7X/+53+sBx54QNxDEXkFadJEXlJQUIAGDRpg/fr1OHHiBG699daMa0aMGIHZs2ejSZMmqbb169ejvLwcffr0wTe+8Q0sWrQo7TNbt25F165dUa/e2QKTV1xxBf72t79JexYi3pCQJvKK2tpavP/++3j11VfRv39/fPzxx2jTpg2Kiooyri0qKsLll1+eJnCXLVuG7t27o3nz5igpKcHmzZvx+eefp/798OHDuPDCC9P6KS4uxqFDh+Q9FBFrSEgTsefxxx9PFQi69NJLMWLECHTp0gU//OEPUVZWxhTQLI4fP44NGzagpKQEQJ1TsqCgIE2brqioQIMGDdI+16BBA1RWVop7ICKvoKL/ROwZP348Bg8eDACoX78+LrjggpQgbdasGcrKygL1s3r1alRVVaUKtzdt2hQ9e/bEkiVLMHHiRCSTSTRq1ChDIFdWVmacGkMQQSFNmog9zZo1Q6tWrdCqVSu0bNkyTdPt0qUL9uzZg/Ly8ozPnThxAqNGjcIHH3wAoM7UAQD9+/dHp06d0KlTJ2zcuBEHDx7EO++8AwC48MILUVpamtZPaWlphgmEIIJCQprIa3r37o3mzZvjpZdeyvi3BQsW4L333sNFF12Effv2YevWrbjnnnvw+9//PvVn8eLFOOecc1Imj+7du+O9995DdXV1qp/NmzejW7duyp6JiBdk7iDymkaNGuGhhx7CD37wA5w8eRJDhgxBvXr18Oabb2L69Om477770Lx5czz//PNo2LAhRo8enRbtAQBDhgzB66+/jqNHj2LAgAGYOnUqJk+ejLFjx+Ltt9/GW2+9hYULF2p6QiLqkCZN5D39+/fHrFmz8M9//hO33HILhg8fjj/+8Y/45S9/iVtuuQUAsHz5cnznO9/JENAAMHLkSFRXV2P58uVo3LgxZsyYgU8//RRDhgzBG2+8gaeffhoXX3yx6sciYgKdzEIQBGEwpEkTBEEYDAlpgiAIgyEhTRAEYTAkpAmCIAyGhDRBEITBkJAmCIIwGBLSBEEQBkNCmiAIwmBISBMEQRjM/wNWsRzrVUyohwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 203, 129\n",
      "LR fn, tp: 6, 8\n",
      "LR f1 score: 0.106\n",
      "LR cohens kappa score: 0.035\n",
      "LR average precision score: 0.053\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 328, 4\n",
      "GB fn, tp: 4, 10\n",
      "GB f1 score: 0.714\n",
      "GB cohens kappa score: 0.702\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 329, 3\n",
      "KNN fn, tp: 13, 1\n",
      "KNN f1 score: 0.111\n",
      "KNN cohens kappa score: 0.095\n",
      "\n",
      "\n",
      "------ Step 3/5: Slice 2/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 222, 110\n",
      "LR fn, tp: 8, 6\n",
      "LR f1 score: 0.092\n",
      "LR cohens kappa score: 0.022\n",
      "LR average precision score: 0.054\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 327, 5\n",
      "GB fn, tp: 4, 10\n",
      "GB f1 score: 0.690\n",
      "GB cohens kappa score: 0.676\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 3/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 324, 8\n",
      "LR fn, tp: 14, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.030\n",
      "LR average precision score: 0.047\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: 332, 0\n",
      "KNN fn, tp: 14, 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",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 326, 6\n",
      "LR fn, tp: 14, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.025\n",
      "LR average precision score: 0.054\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 331, 1\n",
      "GB fn, tp: 0, 14\n",
      "GB f1 score: 0.966\n",
      "GB cohens kappa score: 0.964\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 3/5: Slice 5/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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cTGlRkg2JdD2iVSxZsKbvrLrVLAhxiUngQkMlrU7W9eap08FF9oR5qvxDdFmCaFm1BpbvT8CyStwnI0/tlcROy6Kkkliqmr3IwBonzox04EIucMHzYkqLkmxIpGuh59RLq1iyYE3fmUKqYkEoMGUm2pw6ISmIrOuNvNikFK6mJZzNmZEO/vgxOF5IEV2v5sfYiGJpSrYC7SOA9p4XUyOPEyNAIl0LPadeWsWSBcuyNPJNoMXqVBJSR1YmbA8Pga1vL9E5/No1QEkJ+E+XS16vJZzNlGwFbDbA6RRdr+bHWItYqpm9yKH0g2ueOh1cs2Z1rtfb+LrPm0S6FnpOvYwsliwcWZm4mjjabbFk1r10MfgjR+CY86KofmdGOvgzZ+B4Za7keysJqTMjHSi4DFRUiM6pOeOQul5LOJs5Lh6cdYLgM691vZoxoCSWlnUbYVm3UbbclGyFMyPdp3zH3prB+gIk0rXwVSEFtPtC5XBmpMN2NEdy+g54YBOF0wHY7aKbyJRsBXgnwHGSN5iSkJqSrUD4XUBQkOicwJSZ4MY/ISmkVeVafN6s67XuDFT6LrWIEqtuvrwc+PWqcHQDPddkWPi6z5tEup7R02JgLRppuQlMyVagokJy+g5otDqnTgc6dQY6dBTdROa4eJj/Nh/cPV0lbzAlITXHxcOydTssB47UWWxZrpKa57C+S7lyrT9qrBhx24xp4L87DBReB7r3qHO7mQuPJ0+4HuuInmsyLHzZ8AJIpOsdb029tNwE5rh4hDw9SRer0xwXD27gIOBKAZw5R0XlzpyjwiLcwgUiUVETFsa0DBUiYuRcJdV1v/2mIJZvvykqs82YBsfzfxHKawteRAeA54WjDMwfAFaMeFW8ucMBHMuRrNvtMahxI46eazL+DIl0PaOrxcBYNNLzJmCJpRohrV7ES18hbXWWlwsiXktU1EQ6MAUpOkYQS3OAuG3dewCBgUBAgPwM4dqvrseasLZ9554HOE44ysBqNzNG/KHBQt1NmsiOMXfDJrXGjyuNQV9d4NO73STS9YzeFgP/yy9wzJ1V992BCgPtVvoqWSFliaWqkLEaFqXonKqyBg3EotK9BxAQADRvwRQkNG4MvrBQ1G7uZrFwfWmJ+H2P5QBNmoKL7Ck/Q2jR0vVYkyprs0lTkeCpcQ+xhFRxI86R47Ds2ic7xpQWJlkLj3vXb8esN/+Dveu3y7bdXbTOMsu2bHEr6ZVW9J4dk0jXM4qbETQu8OFKAVBeXudNLUoDzdS5k+v71KR7D8BsBlq0FImKqvjaGhal6Jwqq9NsFovKsRwgtBG4du2YgsQ1awYUF0svTMq0Tc2Mx/zibHC9o2F+cbaozLJoibARZ9e3orapcQ8piSVLdFbsPY8xHx3Air3nZetnwRoLG05dR35AKDaeul7nepVmXPyPPwLXfgXfqLFb7S75eJl7Sa80ovfCJIm0DHpNYZQsS80LfK3CgeDgOm9qURpozrM/u75PTY7lAI0ag7v7bpEgKYWMAbcty9BQcNYJonPc3aSj5jxW28xx8Tg8Jw0vF7XGvrPXJOs1x8UD3XvA8crcOqcl3bt+O2a9/jm+mfScW1EaLNHZcrwAZTYHthwvkKxXaUYlJ5aOrEwMz/4arW/k49G8w5J1s1CccRUXCe6nwwfrXDcAhD450b2kVxrRe3Z8Rzwt3B1q3iCe/PBNyVY4Cgur/y9V7sxId3uBTyn+lmVxsq5taB2L0pWfgUtMEp2npc2AYFlCxqpklZnj4nGwQxQ2HM3HyLPX0L9zC8nznDlHwZ8+BWfOUVHbbTOmCf7jhwYDqz51KUvPOolL5TyKfr2B/p0HSNbtIpa128lIHrXhWAHyLU2wKewB9JMYY46sTPA//QQU3QDf/QHxGzdrBpSUCMdaPBxahq1XeDzcnJNsM2tsV4slIBJLZ0Y6+v54GH1/PCAsIOItyfrlYI19U7IVjl9+AYqL3BbSBo88gpJo6e+JNY6MDom0DFqFRw4tQupNms6dC/sk6V1sSm12ZGVWf5Zy59UUy9oLU6zrNxzNR96NMmw4mi8r0u4KKX+jEAhqAty4IdsvVNgE609qJ17btsAvvwjHWgz/cQ82tYvG8JPbYZo6TlTuzEgHim6/r5RlmZfneqyBNWs5/jc3F1xEBJA8WFTOGtsssTQlW+H46UeguNitCA8tRoJW1IxBo0LuDhkoJEgMa/rO8pGqcuEwxJJ1/cjI1mjbtAFGRraWrNaRlSmEo8mFvN3XzfVYg//9XQjuvXUFY38XIlm3MyMdsNuEPyTEEoWFgj/9tvVYk74lF/D61jT0xQ3JMcbaiAOAGQ6n5AZS3NG4bQcs+7NFPnNzXDzM814DF9Ub5hF/lKxbCb02XCnhy7sOSaT9DF3DgVhWJ8NHqsp3XGVtSlid6N4DKC+T3JzRv3MLvJnQXd7VkZEOlJUJf0iEvHHBwUDLMOFYiwdHDcFbs0fjwVFDJOs2JVuBJk0EIZYQS5YfVCnHhtJGHKVwOL6wEI6li92KH2ct8Dkz0rG/1II5u/LdivDQsnitBV/edUgi7QV81mJgWG9MQVIzK2FYnTiWAwQ3kN2cwULLYqoS5rh4WHbtE0LeJMTS1CMSXNd7YeoRKXn9fnNLzPn2V1mxU4w/l9kR6cxIB86fB34+K5uy1N1Me6ZkKzbd8xDyG4W5FeGhZfFaC748MyaR9gK+ajGwrDetuS48EcUhBWv6XkWV1Vm2ZYuoTCkuWHGLdfZ3cDz/F0kh3dSqB/KDmsiKnWI0hMyOSFOyFbAE3l7ck8bdTHvmuHiM7HU32lSWYsS9zWXrl8NXU9t6E8OJdE5ODvr37+/tZugKWQxiWFanUsywqpSmfSJhi+7BtDqL3xb705XighV/cHleOEgI6fArOWhdUSQrdkpx3HI+a3NcPMzzF4LrFcV0p7ibaU/JDWRU9NzQoufsmOP526PIy/A8j//85z9YuHAhAODw4brHYV67VgKn0zvdCQtrhKtXb3rlvesDvftnnzgO/InjQEUFOOsEkdVrnzgO/O2IhcBlK1WXAYBtQIwQrgYAoaGw7PktWsKRlQnH3FmAzQZzly4wr/nS5dq967dj46nrGHFvc0lRYkUNOLIyhbweNwrBPT7W60+RMcoYZX1mrCgfJcLCGuHy6nXydQ+IAUpLgZAQlzHgCZTGoBJhYY1kywxjSS9evBiff/45pkyZ4u2m+Dy6ZtrTqW6lTHtafJlcYpJsHo6aVmeTF8UiqmQ1Klqkbmbh8zYsq9M2Yxpsve6H7aH+7i1MsjLtsR5MrAJmzhMdN7ToOTs2jCVdUFCA8PBwHDhwAFOnTiVLWgNaf9WlqOqfHnVXYU9LBb92DbjEJK+ImpG+wyrctTrtaangV68CmjaD+cXZMMfFi/rHrJthddqiugtCCoDrHS05DljjxJYwQvC1t4+AZd1G1X1SQsmSNjIsSxq8wdi/fz/fu3dvbzfDp7m1eTN/JWE0f2vzZp+q21cRPpNEyc/k1ubNfP7gWP7y4FhR+a3Nm/nLg2P5fImyKvJ69+Evtrmbz+vdR1R28e72/MU2d/MX29wtuj6v633VZVcSRkvWfSUhkb/UM0qyvPCNN/i8rvfxhW+8Ib7uiSf4i23b8Re7dZdtN2uc0BiqG4axpKsgS9qY+Hr/7Gmp4L/IABo3gXnWHBcrq8qCDxn3v6Jdlap2SyaMAM6fAwIDYV7wtmvdE8eBP/o9AAgZ9WpYlayy6rr7RQmpWoODYdmf7Vo2YxqwczsQEAguMtK1bo2WtC/iy2PUJ3zSBMHyg2r1hbPyUldtxLmVvkp0nepwSacTsFfWOdOeUoZAVu5oy6IlML/7d3CRkaLrA1NmwnLgCCxbt7uVspQwDpS7gzAMrBwbWhNecYlJ1ZZ0bUHjEpPAr12DhtaxsNe6Tk0OF/PU6dWLYJKPANOQr0IpMZBRc70QnoNEmjAMVWIpt6FF70x7TSWmy2pEkISS0BPD+aS1QD5p/fD3/gH+30fqn3EhnzRBEISPQiJNEARhYEikCYIgDAyJNEEQhIEhkSYIgjAwJNIEQRAGhkSaIAjCwJBIEwRBGBgSaYIgCANDIk0QBGFgSKQJgiAMDIk0QRCEgSGRJgiCMDAk0gRBEAaGRJogCMLAkEgTBEEYGBJpgiAIA0MiTRAEYWBIpAmCIAwMiTRBEISBIZEmCIIwMCTSBEEQBoZEmiAIwsCQSBMEQRgYEmmCIAgDQyJNEARhYEikCYJQxJGVCfvEcXBkZXq7KXccJNIE4UOwxNKelgrbgBjY01I9/r7OjHTwublwZqR7vG6CDYk0QbiBnFg6sjJhSxgBW8II6bKHh8DWt5fbQsoSS37tGqC0VDh6GFOyFVxEBEzJVo/XTbAhkSZ8EjXTb9Y5rDLbjGmwRXWHbcY02brlxNKZkQ5cyAUuyJQVXAYqKtwWUpZYcolJQEiIcPQw5rh4BC5bCXNcvMfrJtiQSBOa0DL9VrQ6h8XC1i9K8nrH0sXgj34Px9LFsm1jWZ3M6fvunYDTKRxlkBNLU7IVaB8BtJcpC78LCApyW0hZYhmYMhOWPQcRmDLTrboJY0IifQfgrkVZVW5LGIGC2CHS1y9cAP67w3AsXCAqU5p+K1qdVwqA8nJdrE7m9P2hwYDJJBxlkBNLc1w8LOs2wrJuo3TZ1u2wHDhCQloDWpRkQyJtELQOVNYUnWV1Ki0IVQlp5bnz0ucUF7kea6A0/Va0OluFA8HBktebp04HF9kT5qnTJesG2FYnq8yyaAks2cdgWbREtm4jonXhUMuPuRZoUZINiXQ9wrqJNA9UFVN0KZQWhKqENKBjR2k/6JhkIDRUONZCafqtaHVu2wHL/mzJ643sI9UageGuWPJr1wAlJeDTV7glpm67hzRCi5JsmCJdVlam+p+/oKfFwJr+ax6ojCk6y+pUErsqIQ3fkeVXflCWWGpelFy6GPyRI3DMnSUqVyOk7ooll5gkjIGgILfE1G33kEaM/INrBDie53m5wp49e6KiokJVRSdPnvRYo9zl2rUSOJ2y3VGFfeI48Lm54CIiELhsperrwsIa4erVm+y601LBr10DLjHJ50RNTf/cwZGVCWdGOkzJVtFN6sjKrHbRmKdOdylnlamp29YvCigvBzgO5tRFMMfFV/dRzRhgnWNLGAH8fBYwmcD1inIpt6elgk9fAViCwN1/v2T9Sp+JXJlSuV7foVGQ65/SZ2YEwsIayZYFsC7cuHEjnnrqKTRs2BAvvfSSxxtmREzJ1uov1NMEpswEfEycAWGQX/13BhyPJddJSKvKmaKydDFwIReOwkJRefXC4u3/1yxnldU8p8rqFJU3biKINM+LytWMAdY55qnTqz+T2uWBKTPh6BHJrN8cF8+c2VT1rebf1X3OOQr+9Ck4c44aTpBYY0FPA4Y5DnwAprujXbt2WL58OfLz83Hp0iXExMTI/vMXfHnqpZerxpmRDvvJU3C8MldUNytCo6rcXV+m4sKiTFnNc+Sm6OZZc4BOnYFOnUXlasaA0qKkeep0cM2aSV7r2PgV+CPZcGz8SrZ+FnpuaNEaP86CNuK4h+LCYZs2bTB37lzs37+/PtpDaECvxR1TslVYlOQ4Ud1KYql0gyj5y5kLizJlNc9hCSnaRwDnz0mKpZ6CpGaRl7lA2KgxcP2acKx1DRwOgOeBiA5eaTcL2ojjHkx3RxXDhw/H8OHD9W4LoRG9XDXmuHg0adIAhR98JGl1KlmcSuWA/PTdNmMasGsH0LgxzPNecym3p6WCX70KaNoM5hdn19lHyxQdlUIqVbcjKxP8ieNAWRn48HDxhW3bAr/8Ihzl6ma4gXD4oCDEhw+6vOzMSAeqFvFzz8vWzYI5hh4aLHwejPhxFqyx4KuuwPpAcwheUVERPvvsM0+0hdCInhZDyX/+4/YUXckNo2i98TxQVCQq59euASoqgILLsrMH1mYbVLkjpNwSVQKqIKRS8ecuYnnyhPjCwkKA44SjG8hZnUqx5WrwZvy4L25qqY82uy3Se/bsQUpKCgYOHIgFCyRuAKLesaelwta3F2wPi3cHat3oYNv2X7enukpuGLnpOwDgvm7CsUEDsXUXfXstpElT+dkDY7NNtUhKiaUGITUlW4WFSY6TtDrVTO1ZbiC5sEel2HK1sIRn7/rtmPXmf7B3/Xa365dDi7vOkZWJq4mjZWPL5dIPaKU+NuLUSaQvXryIxYsXIzY2Fk899RR2796NpKQkfP3113q1j6gDLMtS68IM176d8B8Jy1LJmlBcuJGZvgMAFxwMtAwD1+1+kXXH3SwWyrp0kZ09sDbbsGLLtQipOS4elt37YDlyXNLqNPWIBNf1Xph6RMrXHRdf7Xqo66aYFXvPY8xHB7Bi73nZ+lnICY8jKxMbjlxEfkAINp66Lnmtuxt57Gmp4HOOAjcKge493Gpz5c/nZBevWYvbWqiPRUlFkbbZbNiwYQPGjx+PYcOG4aOPPkLHjh3BcRxWrVqFl19+GR07dtStgd7AF6ddAATLkuOAJk1Eg0bzwsy167KWpZI1oeSGYbVN6wYL1mYb1vQ9MGUmzH+bDxzLkR0H5rh4oHsPOF6ZKxIeVoIoZ0Y69pdaMGdXPtMidScawpGViS2HzqHMVoktxwsk63VXLJ0Z6Rh+Ygda37iMRxtIzEzcbHN1md0OVFYCx3Ik62ZhSrYioFMn2XGiFAnkLvWxKMkU6VdffRUPPvgg5s2bh5CQEMyfPx979+7F8uXLwXEcAgJUrTuq4tSpUxgzZgx69uyJESNGICen7l+Up/DVXALczWKgRUtwXX4nGjRadwY2tI51S0jVwGqbUoQGy9oE2GLpyMqEbdDvYevZrc45T6qQEx5WgihTshWb7nkI+Y3CmBYp/8svQMlNkVja01KBW7eE2Ue0a/irMyMdw07tRIOKMgwrO1+nNleXyYilKdmKvpeP4/Xd/0S/H3ZJ1u1uBAeXmAQEBwOtwt0aR+a4eISt/bfsOFGKBDIyTJFevXo1WrRogVdffRXz58/HqFGj0KRJE483wmaz4ZlnnsEjjzyCQ4cO4emnn8bEiRNRUlLi8fdSg6/GVerZ7qZz57olpIC6THvfjJ+KWS+vwDeLlonKWeFwqhJEyYilMyMdKLoh/CHlay8pEcRKZhza01IF95LZLBae7j0EVwogElJzXDyGN7ej9c2rTIsU169JiiW/do2wPoDbP8w1MCVbYT24Funp02H997uSdbsrlua4eGF2EdYKfGGh5PfpbirVwJSZsOzPhmXbDp8UUj1hivTKlSvRp08fLFiwAAMGDIDVakV6ejouX77s0UYcPHgQdrsdEyZMQGBgIB599FF06dIFmzdv9uj7qMVX4yr1bjdr0UhL8ihnRjo23R2N/EatsOl6oPgERjic4g9T9x6A2Sz8v5ZYughp1QJlTUJDgYAA4SgBv3aNIKLBwWLhOZYj1B0QIBJSAOi7+0u8vjkVfXd/KVk3a4rOJSYBQUFAk6YisTTHxf/ma6/d39toEUtzXLywSae42O0FPm88/suXYYp0TEwMXn/9dezZswfvvfcemjRpgrfffhuxsbFwOp3Ytm2bR6zdn376CZ07d3Z5rVOnTjhz5ozmuu809p29htnrjmHf2Wser3vDqevIDwiVnKJrSh7VvQeGH89C6+ICDK+8JC6vElAJIVX8YTqWI/jSpcSyppAGB4vrnjpdEEpAUlSqxbJhiKjclGwFWrQEAPC//CI9i3A6gUt5shap3BQ9MGUmLAeOgOvSRVIsuZvFOHD/QPw1fLBb40Ap+dR+c0v8ddDTODh8Qp3r9tauQ19GVXSHxWLBww8/jCVLlmDv3r145ZVXEBUVhX/84x8YOHAg5s6dq6kRt27dQnCtm6RBgwZ+lV2vJnouTG44mo+8G2XYcDTf43UPb1CE1jevSE7RWVNoNULa92IOXt+ahn4FEnHFZWWCRSszHliiwhLLamu1eQvwhYUo27JF1G4AwqYSCb90YMpMcPd3B5xOkeiY4+KBkBDB0v71qlhIBw4SfMoVFbI+b3cX+EzJVmzqFof8pne5NQ5YYunMSMemNlHID22JTXyrOtftrV2HPg2vgby8PP7999/n//CHP2iphv/kk0/4CRMmuLw2Z84c/o033tBUrxIj1v+h+l99ciUhkb/UM4q/kjDa43XvOJ7PP73sAL/jeL7H684fHMtfjOjI5w8eIll+a/Nm/kpCIn9r8+Y6l13q3Ye/2C6Cv9i+A19Y63vPHxzLX2zbjr/Ytp2ojOd5Pq/rffzFtu34vK73Sbbr8uBY/mKbu/mL7SJEn/mtzZuF6+/tJll2sX0H/mKbu/m83n0k6y584w3+YucufF7vPqK+XR4cy1+8u71ku68kJAplbe6W/TxZ/crrep/QpzZ3S44jLeOg8I03+Lyu90l+1rc2b+a/Gv88P+mdLbqMMdY4MTJ6tltVeMaNGzfQuHFjmEy/Gd7Hjx9HmzZtMGXKFEyZMkXTD0Xnzp2xYsUKl9d+/vln/OlPf6pTPVpSlWpN4ViXNJCOx5LBZ6TD8difPZ468t6cb/HXL9NhamjF1bC6bZNWSnPpqHQCdjscZ84gb+x4UeiabcFC4EIurl/5FZboAS5l9g8+Ap+bi8IPPkJJrTJED4CzQUMh7wSA0pWfwT7pt9hjZ/+BwJkfhbLlK1zKAIDv3QfYvRN87z6Sn2fl5Su3O+iAvWs3l3PsH3wE3l4J2CoQGNVTVFa1QMc3aChZt/3AYcBeCf7qVVHf+MnTgFfmAhyHWwcOw17jesdjycCVX4U/Jk+VHgcJjwFr1wAJj4nLEx4DvsgAGjeRHEf3h4XgtRH3AfhtbKseo5OmI3DSdNghcV9ED4DzFxvsp/JxY08FroYNUa6vDjDHiQJK/dMz056WdgPsVKWK7o7ly5cjNjYWR48edXl94cKFGDRokEe2hPft2xc8z2PFihWw2+34+uuvcfr0acTH+9bCnVq0RkOwcGakgz9zWjJjXXW5m0/fOGRNwV8ffQEHInpJR0OUlgrT+9JSUVGdIk9qL3hV+ZUBYSdfLbibxUDzFpILdAAAW42c6BJhZQgwAyGhsGd/Ly7r0BHo1Fn2MV2sBb6qaAjunq6SZUphYUoLfOb5C8G1ayd5rZ47A1lrE2pwJ3mUR+r20Ux7TJHetGkT3nvvPTzzzDPo2rWrS9n777+PZ599FgsXLsTWrVs1NcJiseBf//oXtm7dipiYGPzzn//E0qVL0bx5c031KrHswdXV/6TQ03fMqltzek+eBziT5PVaNods4lshP6wdNnUfKp1kJyRE8B2HhIiKFJ8AM3W6EEXRqLFkWBk6dhLEctacOrebG5MsLPCF38UQ0nsQ+uQTojI1mfZY59TM71yb5Rk7MSZtO5bNTGWGJ7ozTrQKKcsfPjzvMFrfyMejeYdlr1d8co1c/Dlj96kaFJ9c44OZ9phPZhk9ejRGjBiB8ePHy1bwwQcfYOfOnfjiiy883ri64okns9SkLk9pqetTL1h1a32ShB5PoggLa4QN+89jw9F8jIxsjf6dW3j8fb31BA3bjGnAzu3gmjWD6eVXXd5739lrzD4DKp8AExwMy/5sl7Ixi3agzBSIBpXl+Oz4SskxZksYIWxpbh8By7qNrmVjk4Djx4D7u8OyytU6/GbaS9jUpCuGF53GwCVvARCPUWa7B8QIM6KQEFj2uAqmLUpYLIXJBEv2McnPRPHJNTJ90uKSCAtrhMur1xn+KSxSuO3u+PnnnxEbG8usfNiwYfjpp5/ca5nB0XUKw0pIr/FXWa9f9f6dW+DNhO6yYqX1fVnJ8O1pqbD1i4JtWKzIOtt39hpeWvYNvkmZJ2uRMmNwb7tu+MJCkQWmJlrG8do8IdPea/MkCp2uxxoMK/oRDezlGPbTPvkxxtpQU5VhTyLTXt9vN+H1r99B3283ybbbbauTkfOkCub4diN5lFr0dCV6C6ZIN2jQALdu3WJW4HA4EBQU5NFGGQU9pzC+umGGJZasbGOqbo6qTSs7t0s/wLW8HLhSICmkl4rLsanJPfKZ9j77VHgA7Gefigur4q9DQkSiMjKyNdo2bYCRka3l211c7HqsSaXd9VgDa+bHSP/sL7DmMLYrszbUsMRSo5C6m/OkCi3b+fUUUl9M+cAU6ejoaHz1FTt/8Pr169Gtm8RuLcKr6DXQWWLJyjam6uaokWFPdF5V8qjG4uRRIyNbo03jYAwvOiNvkd6OHKk+1qAq057lgQdEoqI0ewAADIoVBHGQxKyTUaY1ZSlLLNUKqbuZ9r5ZtAyz5q2U3MavBi0L2FrwxZQPTJGePHkyPv/8c7z//vsii7q0tBRLlizBZ599hsmTJ+vaSH9E72mXbgOdIZZKzyRUvDmqMuyZTOKt0FXJo34nTh7Vv3MLvDVxIAamvSZvkQ4eIojlYHHIWFXbai8cqsVdsVQztWdl2gPk85oo5UOpwt0dgJuuByI/pKX0Nn4VaM1u6C6+OINlLhwCwK5duzB79mzcvHkTHTt2RKNGjVBcXIxz586hefPmmDdvHoYOHVpf7WXi6YXDuuDJhUNP4OlFuKr+6dlu1qJRfSwq1vU7rAnrUVqO114REjkNHiIS628WLcOm64EY3tyOgTMmStbtziKefeI4vHxXLPIbh6N16a9Y+No4yf65+wRvNe2ub7R8f96GtXCoKNKAsG17x44dOHnyJIqLi9GsWTP06NEDAwYMMJQ/2pdE2luRDO5S1T+jtlupXY6sTOERWsVF4MYki0THNmNa9fP7agupmj7L/XjZJ44D/93tUDWJaIhZ81YiP6RltZBK1s0QS7l2O7Iy8e3KL7GpXR8Mb+nAwBkTfVrE1ODt/mm5N9wW6bKyMixYsABbt26FxWJBXFwcXnjhBYTKZAXzNr4k0r5GffWPJZYssVKy8F3EMjS0TmFlamYPelrSnsJXxqi7YqfGkNB116GGWabbIXiLFy/Gjh078OSTT2LChAnYvXs3Xn755Tq9OeF/KGVJ0+RrZ6Ql1ZJpT/EhrYxoCDU+UjlfpzkuHpZd38Ly/QlJv/TAGROx8LVxhnEZqEVNWlF305I6li4Gf+QIHHNedH/XrRcy7enlS2eK9NatW5GamopJkybhySefxN///ndkZWXBbheHExF3DkpZ0jQtWLr53EHFHY0KD2m1LFqCtr/kSgqpkRebmDv7VPxgKl0vF1LJr10jhDR+ulxWqDWJpdMB2O1u77r1RqY9vcYJU6QLCgrQqVOn6r8feOAB8DyPa9c8n6uY8B3cfSahGrRGQxgRd4WwClZsujMjHfzxY3C8kCL5LEWlH0ylUDi5kMqa372c0Lorluap04FOnYEOHd1+lJY7T4cxKswseA6HA+aqp1oA4DgOgYGBZEnf4QSmzARkBrk5Lt6QFqcSjqxMXP5wCRyVTpinTq9zH+xpqeBvZ6Uzz5rj6pdeuhg4fw6O44Kvu2ZZtRDe/r/U+1bHppeXi84xJVvheCEFcDqF82p8L1Ux0CyhY51jSrbCcTsssnZ5YMpM2G+3Tc4qZY0FfxxDeuG5J8kShMGxp6WCX70KaNoM5hdni8SSP3ce4HlZsWTBElLhDZyAzS4tsjJCWAWXmFT9AyCVIMppnSAplmrEjnWO0vUsoSU8h6JIr1u3Dg0bNqz+2+Fw4KuvvkKzZs1czhs7dqznW0cYFj1D8fSqm1+7RnhwbMFlSbHEzWI4Kh1uTbGZQjp1enXGN8ksfAp9VBJDEkv/hhmCN2SIuoTeHMchKyvLY41yFwrB04/a/dN1U4tOdbMsaeDO+w79DV/uHysEj2lJb9/u+YThhH+gxt9ptLrJ4iR8EfJJE26h5+IOLRwRxG+oelo4QRAE4R1IpAmCIAwMiTRBEISBIZEmCIIwMCTSBEEQBoZEmiAIwsCQSBMEQRgYEmmCIAgDQyJNEARhYEikCYIgDAyJNEEQhIEhkSYIgjAwJNIEQRAGhkSaIAjCwJBIEwRBGBgSaYIgCANDIk0QBGFgSKQJgiAMDIk0QRCEgSGRJgiCMDAk0gRBEAaGRJogCMLAkEgTBEEYGBJpgiAIA0MiTRAEYWBIpAmCUIUjKxP2iePgyMr0dlPuKEikCcLHYImlnkLqzEgHn5sLZ0a6x+sm5CGRJggdsKelwjYgBva0VFGZbcY02KK6wzZjmlt1s8RSTyE1JVvBRUTAlGz1eN2EPCTShF/Dsiy1Cimrbn7tGqC0VDjWZvdOwOkUjm7AEks9hdQcF4/AZSthjov3eN2EPIYT6RUrVuCZZ57xdjOIeoQliKwylshW4Vi6GPzR7+FYulhUplVIWVYrl5gEhIQIx9o8NBgwmYSjG7DEkoTU/zCMSJeWlmLhwoV46623vN0UQoKrEye6JaSqfKQsQWSUMUVWBVqFlGW1BqbMhGXPQQSmzBSVWRYtgSX7GCyLlrjVbn+EFiXlMYxIT548GXl5eRgzZoy3m+Kz6LmgZNv2X7eEVJWPlCWIjDKmyN7GPHU6uMieME+dLirTKqT+aLUqjRO9xJQWJeWpN5G22Wy4evWq6N+vv/4KAHj33XexePFitGzZsr6a5BVYU3StN4CeC0qWYUPdElI1PlKWILLKWCJbhRGFVM337K5YOrIyYUsYAVvCCLfGkdI40UtMaVGSAV9P7N+/n7/nnntE/+677z6X8xYvXsxPmTKlvpolya3Nm/krCYn8rc2bPV53Xtf7+Itt2/F5Xe8TlV1JSOQv9YzirySMdqtuod2jJdvNKiPEqBkDtzZv5r8a/xw/6Z0t/I7j+S5lhW+8wV/s1IW/FN1HVMflwbH8xYiO/OXBQ2TrVhoLcuVXEhL5ixEd+YsRHd0aR0rjhMZR/RNQXz8Gffv2xenTp3V9j2vXSuB08prrsX/wEfjcXBR+8BFKogeouiYsrBGuXr2pfGLCY8DaNUDCY6LzHY8lg89Ih+OxP6urqzbRA4DoASgBUFL7elaZClT3r56xp6WCX7sGXGKSpEW9d/12fPbTLXCNm8A6tBv6d25RXebIyqxeUDRPnY67Hk+o7qP9g4/AHz+O65OeBmedIFm3/YOP8GW7Yci/fguffXMO94eFVJfZVn4GlJfDeSlfNI4qK50Az6Oy0iH7mSqNBblyx2PJwJVfb//ftUzVd6g0TjSOIz0x6hhVQ1hYI9kyw/ikjYSeUy/WFN2IU3O1aPGHO7Iy8U3KPLy07BvsO3tNVGYbFgtbvyhJF5HS4uGGU9eRb2mMSxXAhqP5LmXOjHTgQi5wQTx9NyVbAVsF4HTK1m1KtmJ40Rm0aRyMkZGtXcq4xCQgKAgIv0s0jlh+8upzFMaCXLk5Ll6ov1kz2bq1oOcCn6/WrTck0hL4sliy0DpQ967fjllv/gd7128X180IdVPj59zU5B5cKi6XFtIrBUB5uaRYKi0ejry3OdrYitEmCCIhNSVbgfYRQHvxD7I5Lh6cdQIQGipbtzkuHgPTXsNbEwe6WOjA7R/jA0dg2bpdUkhNyVY4M9Ld/i6YP4qM70IrjoULwH93GI6FC9y7ntFuPRcPfXlhkkTaj9h39hpeWvYNvkmZp8tNsOHUdeQHhGLjqet1uk5pZsKySE3JVqBVOBAcLCmWSouHD44agvdfGI73J4uFVJXVWVkJfsvXbkU7KArSmdNwvDLXLaFmCvGvvwKVlcJR7np3Zz6F112PdYQ1BvWcwfrywqThRPrZZ5/F+++/7+1m6IpeU68NR/Nxqbgcm5rco8tNMPLe5mhTWYIR9zYXlbGm8Gqm7nIWqTkuHuZZc8Dd3x2mHpGia9VEMyiL5Rk4XpmLsi1bXMr4tWuA8nLgSoGsVcoSHcfbbwpW59tvisr48nLg5k3gVpnsjyZzs861a4IQX7smLrNVCMebxbKfiduRQM2aux7rCGsM6jmD9eXZseFE+k5Ar6nXyMjWaNM4GMOLzuhyE/Rr7MDrP21Av8YOUZkz5yj406fgzDkqKlMrpHLnODPSwR8/BscLKSLBYvmVq+tm7Tps1Bi4WQyUV6Dk4+WuhREdJOuryf7wbvhr3wnYH95NXHijUDheKRD3++SJ2x1wyP5oMv3t5WWuxxpwY5KFkMjgYNnPxN2t5eZZc8D1joZ51hzJepVQGoO+6jvWs90k0l5Ar6lX/84t8NbEgRiY9prkTaA1htaxdDH4776DY+YMkViyBEWtkOLns8D5c6Jz+EaNBYtWYhGP5VdWxf5vbzegEqFPPuFalnv+9puYZBf5NgW0QX7jcGwKaCMq4x4fKy+WVbHlg4fIChbL3849Plbwlz8+VlQWmDIT5nfSwHW7X/YzcXdruTkuHgeHT8Ccg0WSaxNa0cuA0Tr2ldA1sZXHayQU0XPqpWSRKokliwOh7fHXR2fiQPueIrFkCYoaIT3QqD3++ugLONAhSnzO4YOu71MDc1w8LOs2wrJuo7w7hRVN0biJcAxrhQaPPCLuU2goOOsE2bqHN7ejdemvGN7cLipjiaWaHY0sf3tgykxwiUng166RdId8WhCI/33g/+HTgkDZ+lmwLEN31yaq6pUbn46sTPAXLwozm+49PNpurWNfCV396R6vkfAqv1mk56XDyjRYnZt+N0CwGrsPFYmlUmihkpBu6jIA+U3CsanXI6JzqsVy/BPM3YVyMC1DxvRdzY7GgTMmYsGg1uj3wy7pRbiNX4E/kg3Hxq9EZcszdmLMoh1YnrFTtn53M+1tvcKjzByIrVek9w0oLRw6Zj0vGcXhyMrE8Oyv0fpGPh7NO1znulli6cxIB679CjgcwLEcybqVkLNoNc+4FNDT8Kq3zSxEPWIyAZZAybAyLYNoZPdwbDx1HSOiIxA46v9pbaV03d3DRWWBKTMBN8S5JnKbXsxx8XAs/xccz/8Fl9OXw7zic5fr9q7fjg2nrmPkvc3x4KghknU7li4GLuTCUVgo/nwZeU1+E1I7nhCV3hbLubMAm11UtyMrE6iwATwP1IpOcWRlYti5w9gW0QfDzBILi3CNLqn6HGqWobJS+KO4SHRd3x8Po++PB4RxBnFCtJpCWfvzMCVb4SgsrP6/2jK1VIU2enrsexOypP0M89Tp4HpFwTx/occH5YOjhuCt2aNlxUpr3a8Hn0efd19iph6VQs2iDf9FBlBSIhxrc/yYUM/34kVPpam9IytTsAwrK4GSEnFZUJDwx33ihcVhRT+iga0cw4p+lKzbmZEOlFcAToeobmdGOmC3CX/k5YnKrHs/R3rGcxiX87Vk3aZkqyDwnEna6qwKexyTLC5r3ATgONkMgUoRHHKzKjUzLiVYFq2vLkqSJe1n6G0x7Dt7DRuO5mNkZGtRuBxri7YjK7PawpFrH/9FhrBp5YuMOlnOLMut6r1RXi78wXHiClq0EELZwsTJvYY3KMKm67fwqITPueq9USmOdqkus9uBgABwwcGicmvmx7CWlgIhIcArk0TlpmQrHMd+EOoIDRWX/fQjUFwsEks1FmnV51RXq1PN+FI6hzUWam/V9+RYVhonRoUsaT+EuTNQozWx4Wg+8m6UiXYGAsoRHoqr31UCKiGkrHYrLdq4vCcv4aOtqAA4Dlx5haio3w+78Pq3y9Dvh12SdZuSrUBwEGAySwopyw+qtFvSHBcP84K3hZlRrUVPc1w8LLv2wXLkuGjx0RMWqZ7REEox2not8PnqhhYSaS+h59SLNUXXGio0nLuCNgW5GM5dEZUpRXgo3iBVAiohpKx2Ky3asKbvNdvd0CoOZ1NqtzkuHub5C8H16iUtpAyxNPWIBNf1XslNOjXhCwvhWLpYOi2pTF4TR1Ymvpn0HGa9/rlsqJwRxVLPBT5f3dBC7g4voefUa+S9zYVFOImdgXILK2qJ2bQCfXJzwf0UAdTyTbMW+NRMk7kxydXuEk+2W+m9q9rdVCKLWk23QM2/1eLIyhR2HN4oBPf4WBc3UM3djnJ1V4vl7f+LFviuFAC4PYupVfem1kOQH9QEG09dx4MSbWN9pp5YxJNDqzvlToPjean5n2/iqVSl7lDXNIlqfLRGoqp/Rm23UrvsaangV60U3BrJVhexdGRlCqFmN4vAJSWL/Om2hBGCULaPgGXdRnHdE8eBz80FFxGBwGUrxWXf3Q5VCw2FZc9vMd+OrExBoDkO3D1dRddWnyPjo61ud3ERuDHJoj59+8UWbGrXByO7h+PBUUN8OpWnGrzdPy33BitVKYm0h/D2ANGb+ugfS3QA9sIkSygBwDYg5rcIiVpiyRJSQFmkFRfCZCxppWs9ja+MUXc/EzWGhJ6ft9IYZEH5pAmPsu/sNcxed0yU+xnQ5mtXSkvKWphU8h1ziUlAQAAQGChyp1T7rBs0kHS1KOV/VtpGbdm6HZYDR/wqh7iqJ7UzxgLzMXIaU63q+Rg5FnotTJJI+ym6LkwyIjy03ARKaUlZC5NKYheYMhOWwzmwHDoqEktzXDws23ag7U9nfE5IFYWwXxRsw2JlxwFLLFlPga+OO1/5iVuZ9rQ+6Z2Fu8mjtKLXOCGR9lP0tBhGRrZG26YNRLmfAW03QZVYWvZny+arUNqmbUS0PnyYeX1VEv5Zz4vqqJlqVW4cMMWSsVuyOucJD9m6WWOB+YOr4sk1LNxNHmVUSKT9FD0thv6dW+DNhO6izSyAsW8CpXwVVxNHu/X4L+C21dnrftge6i8tliUl4NNXSKdhVfgxZQpp1bbtykpRHVxiEhAcDLQKZ7uB5GK1GU+BN8+aA3TqDHTq5FamPX99jJwe0MKhh/CVRRl3qa/+6bnoYxsWK/i8W4XDsm2HS5l94jhwv1wA3669dISGwoKQLaq7YHUC4HpHu5xnT0sFn74CCAoC1+1+lzI1fWIumKalCq6Hxk1gnjWH+bnQGDUuFN1RD/jyAFFDzf7puULOiqTQsnoOALZ+UcL0PzgYlv3ZLmWOrEyY//05HI/92a0fB9uMacCuHUCjxjC/Is7nbYTQxTtpjPoaLJGmzSxEnfFWDgStG3FYm2XMcfEIezxB8iZXs8GClRdabR0EIQWJNFFntIolC/PU6bJ1axU6T6Q8JYj6hkSaqDN6WoVkcRKEKxTdQRAEYWBIpAmCIAwMiTRBEISBIZEmCIIwMCTSBEEQBoZEmiAIwsCQSBMEQRgYEmmCIAgDQyJNEARhYEikCYIgDAyJNEEQhIEhkSYIgjAwJNIEQRAGhkSaIAjCwJBIEwRBGBgSaYIgCANDIk0QBGFgSKQJgiAMDIk0QRCEgSGRJgiCMDAk0gRBEAaGRJogCMLAkEgTBEEYGMOI9Keffoq4uDj07t0bo0ePxuHDh73dJIIgCK9jCJHetm0bPv74Y3z44Yc4dOgQ/vznP2Py5Mm4fv26t5tGEAThVQwh0levXsXTTz+NLl26wGQyITExEWazGadPn/Z20wiCILxKQH29kc1mQ1FRkeh1juMwduxYl9cOHTqEW7du4Xe/+119NY8gCMKQcDzP8/XxRgcOHMC4ceNEr5vNZpw4caL67zNnzuCJJ57A+PHj8dRTT9VH0wiCIAxLvYm0Gnbs2IEXX3wRTz31FCZNmlTn669dK4HT6Z3uhIU1wtWrN73y3vWBv/cP8P8+Uv+MS1hYI9myenN3KPHpp59i0aJFeOutt/Dwww97uzkEQRCGwBAivXnzZqSlpeHTTz9FZGSkt5tDEARhGAwh0v/6179gs9kwYcIEl9ffe+89xMbGeqdRBEEQBsAQIr1+/XpvN4EgCMKQGCJOmiAIgpCGRJogCMLAkEgTBEEYGBJpgvAC9rRU2AbEwJ6W6tb1jqxM2CeOgyMr08MtI4wGiTRxR8MSO3taKmx9e8H28BBZMVS8XkaI+dWrgJIS4egGzox08Lm5cGaku3U94TuQSBOGxzZjGmxR3WGbMU1U5sjKhC1hBGwJI9wSUsfCBeC/OwzHwgWiMn7tGqCiAii4LCuGLLHk164BSkuFY22aNnM91hFTshVcRARMyVa3rid8BxJpQjUsy9CelgpbvyjYhsWKxFDN1J55zu6dgNMJ7NwuqtuZkQ5cyAUuyFuVTKuzuMj1WAMuMQkICgLC75IVQ5ZYcolJQEiIcKyF+cXZ4HpHw/zibMl6lTDHxSNw2UqY4+Ldut6okBtHDIn0HQTTolRxc/Br1whT9PQVovP4tWuA8nLgSoFIDJkWpZpzHhosHAMtorpNyVagfQTQXt6qZArpmGQgNFQ41iIwZSYsB47AsnW7rBiyxDIwZSYsew4iMGVmna4zAu66cbRCbhwxJNJegDXItVoSrOtZN4Cam4NLTAJMJsAiFksuMQkIDgZahYvEkGVRqjnHsmgJzO/+HVyPHqK6zXHxsKzbCMu6jR4XUiPDcvOocQEp4bYbRyPkxhFjqCx4WvFkFjxHViacGekwJVtVWTt1ycBlGxADlJYCISGw7DnoUmafOA58bi64iAgELltZ53azrmf1Sam/Vf2r6+fiS7iTRU3pM3UsXQwAME+dLiq3zZgmuHIeGgzLoiV1el/7xHHgj34PAOAie7p813Jldekfq1/2tFTwa9eAS0wy1I+bv2bBI0taBj2nXSyrUaslwbqeZVGqnX4bfZouhSMrE9+kzMNLy77BvrPXROVVM5sb8+fLlslN7Z0Z6dhvC8Gcg0WiuhX95VW+9t0769wnlptHjQtICX+cffgqZEnLoKcl7Yvo3T9HViZWbDmKbW2j8HC4CU8kD3YpZ1lvSpadfeI4TO/8J+SHhqFti1AsHdvLpbxqZsOFhiLwmwOSZVKznqp2zzlYhPymd6FteFO8mdDdpUwvS9od7vQxauRZIFnSbuCLFqPeOLIy8c2k5zDr9c+xd/12UbmWBSVnRjq2temJMrMFW6+If2hZflAlH6kp2QoEBAImE6R+wqtmNg2tY2XL5Pzp5rh4/CkpFm3Dm2JkZGtRGctfblm0BJbsY/Ui0EZC6wK2u/jqoiSJtB+i103gzEjHplY9kB/UBBtPiZ/krmVByZRsxbBL36OBw4aHW3GicpZYqhHScYm/x31tmsDar72ovGr63nTuXNky1tS+f+cWeDOhO/p3bsHqYr2i5wK0mk0+LLQuYLuLry5KkrvDQ9R1Kqnn1Iu1eOjuwmRYWCNcXr0O336xBZva9cHI7uF4cNQQ17oNuqCklvp2B6gZA6zPlHW9bVgscKUACAiAeeG7MMfF16l/rHFiGxADlJQAALje0XVe4NaygM3Cl905LHcHibSHqOsA0RrFwUKPm8CXbwC1SPVRrZDu338Smx4cjT89GuNiUdvTUsF/kQE0bgLzrDkuddgSRgDnzwEWC8zzF0rW724kkK1flBC3jt+E1KPRHatXAU2bwfzibMO4BH15jJJP2oDoOfXyRBSHv7F3/XbMevM/8r70flHIj46R3NGoNP3m167Bpi4DkF8BbDiaLyqT2+QjvIETsNll63c3Eogbkywbt64GxegOhU0+dyJ6+dNJpL2Ev4qlXr5QrRs0Npy6jvyAUHlfenk5nPn5kjsalX5MucQkDP9pD9oEQbR4yNrkY546HejUGejQQbZ+d3ctBqbMhGV/NizbdvjcGNMzQ6Av7pYkd4eH8OWplhpqbmZhhZXptRmHtXlDDXvXb8fGU9cx4t7m0r70LzJgatYM3MyXfE7U1OKpdRNHVqaQkKq4CNyYZNn1B3c3xNj69hISWwUFwXLgSJ37p+hPZ4RUakGrP10OsqT9EL3DmFgbNPTajKN1g8aDo4bgrdmjRQIN/GZ1tj580KcEWml2oTWxlVyGQGdGurAoWV7OjOQxYoZANSkK3EWv2TGJtB+idxgTSyy1DlS569Xk6PAWSj+KWkMi5VK1OjPSgXM/Az+fhePtN0XXaU5sJZMh0JRsBVqFA8HBTLEzYoZAX9wtSSLth+i9KGlUsVRCyVd56d5ubiW9cmakg8/JgeP5v0jnvF66GPzR76vdRKJrlX5QZbaPm5KtQJW38kah6DKtia3kMgSa4+Jh2bYDlv3ZTLHz1wyB9Q35pD3EneKT1hu9YmiBGrHDrcJh2bbDtUxD0itHViYcz/9F+MNkgiX7mGvdCSMEF1H7CFjWbaxzn1jbx+sSm05j1LiQT5rwKHr6vDVbnSwUEvxzoaFuJb0yx8UDg4cIaVyrcl/XLJ86HVxkTyGaQ+JaJauRtX3cF6fvRN0I8HYDCN+jpljW55TUlGyttjrdgRuTXG111iYwZSbCFvxN0hIzx8Ur9pOVf0PN9QQhB4k0UWe0iiUL89TpsnVrFbvAlJkAWZyEj0EiTdQZPS1DsjoJwhXySRMEQRgYEmmCIAgDQyJNEARhYEikCYIgDAyJNEEQhIEhkSYIgjAwJNIEQRAGhkSaIAjCwJBIEwRBGBgSaYIgCAPjV9vCTSbujn5/vfH3/gH+30fqn+/hV/mkCYIg/A1ydxAEQRgYEmmCIAgDQyJNEARhYEikCYIgDAyJNEEQhIEhkSYIgjAwJNIEQRAGhkSaIAjCwJBIEwRBGBgSaR1YsWIFnnnmGW83wyOcOnUKY8aMQc+ePTFixAjk5OR4u0m6kJOTg/79+3u7Gbqwd+9eJCQkICoqCvHx8Vi9erW3m+RRduzYgREjRqBXr14YOnSo3/UPPOExSkpK+Lfeeovv2rUrP2XKFG83RzMVFRV8bGws/8knn/A2m43ftGkTHx0dzd+8edPbTfMYTqeTX7NmDd+7d2++d+/e3m6Ox7l06RLfq1cvftu2bbzD4eCPHj3K9+nTh9+9e7e3m+YRCgoK+O7du/M7d+7keZ7njx07xj/wwAP8sWPHvNwyz0GWtAeZPHky8vLyMGbMGG83xSMcPHgQdrsdEyZMQGBgIB599FF06dIFmzdv9nbTPMbixYvx+eefY8qUKd5uii7k5eVh+PDhiI+Ph8lkQo8ePRATE4Ps7GxvN80jtGrVCvv27cOgQYPgdDpx48YNmM1mhISEeLtpHsOvsuDpjc1mQ1FRkeh1juPQsmVLvPvuuwgPD8c//vEPXL161Qst9Cw//fQTOnfu7PJap06dcObMGS+1yPM8/vjj+Mtf/oIDBw54uym6EB0djejo6Oq/b9y4gcOHD+OPf/yjF1vlWUJDQ1FWVobo6GhUVlbiqaeeQocOHbzdLI9BIl0Hjhw5gnHjxoleN5vNOHHiBMLDw73QKv24desWgoODXV5r0KABysrKvNQiz+Nv3xmLmzdvYsqUKYiMjERcXJy3m+NRgoKCcOTIEZw+fRqTJk1CREQEHnvsMW83yyOQSNeBvn374vTp095uRr3RsGFDVFRUuLxWVlaGhg0beqlFhLucO3cOzzzzDLp06YLU1FSYTP7l6TSZTLBYLHjggQeQlJSErKwsvxFp//qmCI/SuXNnnDt3zuW1n3/+GV26dPFSiwh3OHToEJKSkjB06FAsXrwYQUFB3m6Sxzh48CASEhJcXrPZbGjcuLGXWuR5SKQJWfr27Que57FixQrY7XZ8/fXXOH36NOLj473dNEIlFy5cwOTJkzF9+nQ8//zz4Dj/enLJfffdh4KCAnzyySdwOBzIzs7G2rVrMXr0aG83zWOQSBOyWCwW/Otf/8LWrVsRExODf/7zn1i6dCmaN2/u7aYRKlm1ahVKS0vx3nvvoVevXtX/3nnnHW83zSM0atQIH330EbZt24aYmBjMmzcPb7zxBmJiYrzdNI9Bj88iCIIwMGRJEwRBGBgSaYIgCANDIk0QBGFgSKQJgiAMDIk0QRCEgSGRJgiCMDAk0oRfM2TIEHTt2rX6X7du3RAbG4uFCxfi1q1b1eedOHEC06dPx+9//3v06tULiYmJ2Lhxo2SdBw8eRNeuXTFv3jzJ8rNnz8JqtaJnz574n//5H2zbtk2XvhF3BiTShN/z3HPPYc+ePdizZw927NiBBQsWYMOGDZg/fz4AYNeuXfjzn/+M1q1b4+OPP8b69esxYsQIzJ49G8uWLRPVt2HDBnTo0AFff/01ysvLXcoqKirw5JNPomPHjli7di2SkpLw3HPP4eTJk/XSV8IP8XI+a4LQldjYWD49PV30+j//+U++d+/efElJCd+vXz/+H//4h+icDz/8kO/Rowd/7dq16tcqKir46Ohoft26dfwDDzzAr1+/3uWa9evX87///e95u91e/dq0adP42bNne65TxB0FWdLEHYnZbIbFYsGOHTtw8+ZNTJgwQXROcnIyli9f7pKsZ8eOHSgpKcGgQYPw4IMPYu3atS7XZGdno2fPnggI+C3BZJ8+ffDdd9/p1hfCvyGRJu4onE4ncnJy8Nlnn2Ho0KE4efIkOnbsiNDQUNG5oaGh6N27t4vgbtiwAVFRUWjevDni4+Nx6NAh/PLLL9XlV65cEeWoDgsLQ0FBgX6dIvwaEmnC73nrrbeqEws98MADSE5ORo8ePTBz5kwUFxdLCrQURUVF2LVrV3UWwCFDhsBsNrtY02VlZbBYLC7XWSwW2Gw2z3WIuKOgpP+E3zN58mSMHDkSABAYGIiWLVtWC2mzZs1QXFysqp4tW7bAbrdj2LBhAICmTZsiJiYG69evx/Tp02EymRAcHCwSZJvNJnrCDUGohSxpwu9p1qwZIiIiEBERgTZt2rhYuj169MC5c+dQUlIiuu7mzZuwWq344YcfAAiuDgAYOnQounXrhm7dumHfvn24fPky9uzZA0B4HFft51tevXr1jnpMF+FZSKSJO5oBAwagefPm+OSTT0Rlq1evxpEjR9C2bVvk5eUhOzsbzz77LL788svqf+vWrUNISEi1yyMqKgpHjhxBZWVldT2HDh1Cr1696q1PhH9B7g7ijiY4OBivvvoqZsyYgdLSUowaNQoBAQHYtm0bli5dihdeeAHNmzfHBx98gKCgIIwbN070aKZRo0bhiy++QGFhIYYNG4b33nsPL7/8Mp588kl888032LlzJ/797397qYeEr0OWNHHHM3ToUCxbtgw//vgjxo8fj9GjR2P79u14++23MX78eADAxo0b8Yc//EHy2Xljx45FZWUlNm7ciIYNG+Kjjz7C+fPnMWrUKKxZswaLFi3CvffeW9/dIvwEejILQRCEgSFLmiAIwsCQSBMEQRgYEmmCIAgDQyJNEARhYEikCYIgDAyJNEEQhIEhkSYIgjAwJNIEQRAGhkSaIAjCwPx/BT5yZzVJn+AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 313, 18\n",
      "LR fn, tp: 13, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.046\n",
      "LR average precision score: 0.050\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 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",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 323, 9\n",
      "LR fn, tp: 14, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.033\n",
      "LR average precision score: 0.042\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 331, 1\n",
      "GB fn, tp: 1, 13\n",
      "GB f1 score: 0.929\n",
      "GB cohens kappa score: 0.926\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 2/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 201, 131\n",
      "LR fn, tp: 8, 6\n",
      "LR f1 score: 0.079\n",
      "LR cohens kappa score: 0.007\n",
      "LR average precision score: 0.041\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 327, 5\n",
      "GB fn, tp: 9, 5\n",
      "GB f1 score: 0.417\n",
      "GB cohens kappa score: 0.396\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",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 205, 127\n",
      "LR fn, tp: 9, 5\n",
      "LR f1 score: 0.068\n",
      "LR cohens kappa score: -0.005\n",
      "LR average precision score: 0.046\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 324, 8\n",
      "GB fn, tp: 7, 7\n",
      "GB f1 score: 0.483\n",
      "GB cohens kappa score: 0.460\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 328, 4\n",
      "KNN fn, tp: 12, 2\n",
      "KNN f1 score: 0.200\n",
      "KNN cohens kappa score: 0.180\n",
      "\n",
      "\n",
      "------ Step 4/5: Slice 4/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 202, 130\n",
      "LR fn, tp: 11, 3\n",
      "LR f1 score: 0.041\n",
      "LR cohens kappa score: -0.035\n",
      "LR average precision score: 0.040\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 328, 4\n",
      "GB fn, tp: 3, 11\n",
      "GB f1 score: 0.759\n",
      "GB cohens kappa score: 0.748\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 327, 5\n",
      "KNN fn, tp: 12, 2\n",
      "KNN f1 score: 0.190\n",
      "KNN cohens kappa score: 0.168\n",
      "\n",
      "\n",
      "------ Step 4/5: Slice 5/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 215, 116\n",
      "LR fn, tp: 5, 8\n",
      "LR f1 score: 0.117\n",
      "LR cohens kappa score: 0.052\n",
      "LR average precision score: 0.060\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 327, 4\n",
      "GB fn, tp: 7, 6\n",
      "GB f1 score: 0.522\n",
      "GB cohens kappa score: 0.505\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 330, 1\n",
      "KNN fn, tp: 12, 1\n",
      "KNN f1 score: 0.133\n",
      "KNN cohens kappa score: 0.125\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",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 209, 123\n",
      "LR fn, tp: 10, 4\n",
      "LR f1 score: 0.057\n",
      "LR cohens kappa score: -0.017\n",
      "LR average precision score: 0.038\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 329, 3\n",
      "GB fn, tp: 10, 4\n",
      "GB f1 score: 0.381\n",
      "GB cohens kappa score: 0.364\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",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 323, 9\n",
      "LR fn, tp: 14, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.033\n",
      "LR average precision score: 0.094\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: 332, 0\n",
      "KNN fn, tp: 11, 3\n",
      "KNN f1 score: 0.353\n",
      "KNN cohens kappa score: 0.344\n",
      "\n",
      "\n",
      "------ Step 5/5: Slice 3/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 195, 137\n",
      "LR fn, tp: 7, 7\n",
      "LR f1 score: 0.089\n",
      "LR cohens kappa score: 0.016\n",
      "LR average precision score: 0.048\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 327, 5\n",
      "GB fn, tp: 5, 9\n",
      "GB f1 score: 0.643\n",
      "GB cohens kappa score: 0.628\n",
      "\n",
      "-> test with 'KNN'\n",
      "KNN tn, fp: 332, 0\n",
      "KNN fn, tp: 11, 3\n",
      "KNN f1 score: 0.353\n",
      "KNN cohens kappa score: 0.344\n",
      "\n",
      "\n",
      "------ Step 5/5: Slice 4/5 -------\n",
      "-> Reset the GAN\n",
      "-> Train generator for synthetic samples\n",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 324, 8\n",
      "LR fn, tp: 13, 1\n",
      "LR f1 score: 0.087\n",
      "LR cohens kappa score: 0.057\n",
      "LR average precision score: 0.065\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: 332, 0\n",
      "KNN fn, tp: 14, 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",
      "Epoch 1/3\n",
      "Epoch 2/3\n",
      "Epoch 3/3\n",
      "-> create 1272 synthetic samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> test with 'LR'\n",
      "LR tn, fp: 321, 10\n",
      "LR fn, tp: 13, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.034\n",
      "LR average precision score: 0.048\n",
      "\n",
      "-> test with 'GB'\n",
      "GB tn, fp: 330, 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, 0\n",
      "KNN fn, tp: 12, 1\n",
      "KNN f1 score: 0.143\n",
      "KNN cohens kappa score: 0.138\n",
      "\n",
      "### Exercise is done.\n",
      "\n",
      "-----[ LR ]-----\n",
      "maximum:\n",
      "LR tn, fp: 326, 137\n",
      "LR fn, tp: 14, 9\n",
      "LR f1 score: 0.126\n",
      "LR cohens kappa score: 0.057\n",
      "LR average precision score: 0.094\n",
      "\n",
      "\n",
      "average:\n",
      "LR tn, fp: 262.52, 69.28\n",
      "LR fn, tp: 10.68, 3.12\n",
      "LR f1 score: 0.048\n",
      "LR cohens kappa score: -0.005\n",
      "LR average precision score: 0.052\n",
      "\n",
      "\n",
      "minimum:\n",
      "LR tn, fp: 195, 6\n",
      "LR fn, tp: 5, 0\n",
      "LR f1 score: 0.000\n",
      "LR cohens kappa score: -0.046\n",
      "LR average precision score: 0.038\n",
      "\n",
      "\n",
      "-----[ GB ]-----\n",
      "maximum:\n",
      "GB tn, fp: 332, 8\n",
      "GB fn, tp: 10, 14\n",
      "GB f1 score: 0.966\n",
      "GB cohens kappa score: 0.964\n",
      "\n",
      "\n",
      "average:\n",
      "GB tn, fp: 328.84, 2.96\n",
      "GB fn, tp: 5.24, 8.56\n",
      "GB f1 score: 0.670\n",
      "GB cohens kappa score: 0.659\n",
      "\n",
      "\n",
      "minimum:\n",
      "GB tn, fp: 324, 0\n",
      "GB fn, tp: 0, 4\n",
      "GB f1 score: 0.381\n",
      "GB cohens kappa score: 0.364\n",
      "\n",
      "\n",
      "-----[ KNN ]-----\n",
      "maximum:\n",
      "KNN tn, fp: 332, 5\n",
      "KNN fn, tp: 14, 3\n",
      "KNN f1 score: 0.353\n",
      "KNN cohens kappa score: 0.344\n",
      "\n",
      "\n",
      "average:\n",
      "KNN tn, fp: 330.64, 1.16\n",
      "KNN fn, tp: 12.44, 1.36\n",
      "KNN f1 score: 0.160\n",
      "KNN cohens kappa score: 0.151\n",
      "\n",
      "\n",
      "minimum:\n",
      "KNN tn, fp: 327, 0\n",
      "KNN fn, tp: 11, 0\n",
      "KNN f1 score: 0.000\n",
      "KNN cohens kappa score: -0.014\n",
      "\n"
     ]
    }
   ],
   "source": [
    "exercise = Exercise(shuffleFunction=shuffler, numOfShuffles=5, numOfSlices=5)\n",
    "exercise.run(gan, data)\n",
    "exercise.saveResultsTo(f\"{datasetName}-{ganName}.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}