{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.cm import rainbow\n",
    "%matplotlib inline\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as st\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "# Sklearn library for implementing Machine Learning models and processing of data\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cancer data set dimensions : (569, 31)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>radius.mean</th>\n",
       "      <th>texture.mean</th>\n",
       "      <th>perimeter.mean</th>\n",
       "      <th>area.mean</th>\n",
       "      <th>smoothness.mean</th>\n",
       "      <th>compactness.mean</th>\n",
       "      <th>concavity.mean</th>\n",
       "      <th>concave points.mean</th>\n",
       "      <th>symmetry.mean</th>\n",
       "      <th>fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>texture.w</th>\n",
       "      <th>perimeter.w</th>\n",
       "      <th>area.w</th>\n",
       "      <th>smoothness.w</th>\n",
       "      <th>compactness.w</th>\n",
       "      <th>concavity.w</th>\n",
       "      <th>concave points.w</th>\n",
       "      <th>symmetry.w</th>\n",
       "      <th>fractal dimension.w</th>\n",
       "      <th>Diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   radius.mean  texture.mean  perimeter.mean  area.mean  smoothness.mean  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   compactness.mean  concavity.mean  concave points.mean  symmetry.mean  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   fractal dimension    ...      texture.w  perimeter.w  area.w  smoothness.w  \\\n",
       "0            0.07871    ...          17.33       184.60  2019.0        0.1622   \n",
       "1            0.05667    ...          23.41       158.80  1956.0        0.1238   \n",
       "2            0.05999    ...          25.53       152.50  1709.0        0.1444   \n",
       "3            0.09744    ...          26.50        98.87   567.7        0.2098   \n",
       "4            0.05883    ...          16.67       152.20  1575.0        0.1374   \n",
       "\n",
       "   compactness.w  concavity.w  concave points.w  symmetry.w  \\\n",
       "0         0.6656       0.7119            0.2654      0.4601   \n",
       "1         0.1866       0.2416            0.1860      0.2750   \n",
       "2         0.4245       0.4504            0.2430      0.3613   \n",
       "3         0.8663       0.6869            0.2575      0.6638   \n",
       "4         0.2050       0.4000            0.1625      0.2364   \n",
       "\n",
       "   fractal dimension.w  Diagnosis  \n",
       "0              0.11890          M  \n",
       "1              0.08902          M  \n",
       "2              0.08758          M  \n",
       "3              0.17300          M  \n",
       "4              0.07678          M  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the dataset\n",
    "dataset = pd.read_csv('/home/user/data/Breast_Cancer_Data_CSV.csv')\n",
    "dataset.drop(['ID number'], axis=1, inplace=True)\n",
    "\n",
    "print(\"Cancer data set dimensions : {}\".format(dataset.shape))\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
   ],
   "source": [
    "# split dataframe into two based on diagnosis\n",
    "dataset = shuffle(dataset)\n",
    "\n",
    "# X = dataset.iloc[:, :-1].values\n",
    "Y = dataset.pop('Diagnosis')\n",
    "X = dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['M' 'B']\n",
      "[1 0]\n"
     ]
    }
   ],
   "source": [
    "print(Y.unique())\n",
    "Y = Y.map({'M': 1, 'B': 0})  #Encoding categorical data values\n",
    "print(Y.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-9.46200126e-16  7.23039719e-16  3.19119382e-16 -1.56212285e-16\n",
      " -2.00844366e-17  5.13268936e-17 -5.13268936e-17  0.00000000e+00\n",
      "  1.16266572e-15  1.78528326e-16 -4.68636855e-17 -7.14113302e-17\n",
      "  2.25392011e-16  4.46320814e-17 -3.12424570e-16  9.37273709e-17\n",
      "  3.57056651e-17 -1.78528326e-16 -3.43667027e-16 -2.23160407e-18\n",
      "  1.85223138e-16 -5.75753850e-16  2.18697199e-16  1.22738224e-16\n",
      "  2.38781635e-16  1.33896244e-17 -7.81061424e-17 -1.76296721e-16\n",
      " -2.14233991e-16 -1.08009637e-15]\n",
      "[ 0.09042209 -0.09017492  0.08870921  0.12261548 -0.03214772  0.00590519\n",
      " -0.01806404 -0.01493757  0.04392402 -0.03657289  0.15955937 -0.08822509\n",
      "  0.14820771  0.23762433 -0.12130755 -0.00697142 -0.05599851 -0.13639054\n",
      "  0.0279137   0.04923451  0.08896116 -0.13465217  0.08081482  0.11978937\n",
      " -0.12920354  0.02132954 -0.00780344 -0.05244179  0.02996088  0.0527163 ]\n"
     ]
    }
   ],
   "source": [
    "# split our dataset into training and testing datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, Y, test_size=0.3, random_state=0)\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "print(X_train.mean(axis=0))\n",
    "X_test = scaler.transform(X_test)\n",
    "print(X_test.mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def draw_confusion_matrix(y_test, y_pred):\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    conf_matrix = pd.DataFrame(\n",
    "        data=cm,\n",
    "        columns=['Predicted:0', 'Predicted:1'],\n",
    "        index=['Actual:0', 'Actual:1'])\n",
    "    TN = cm[0, 0]\n",
    "    TP = cm[1, 1]\n",
    "    FN = cm[1, 0]\n",
    "    FP = cm[0, 1]\n",
    "    Acuuracy = round((TN + TP) / float(TN + TP + FN + FP), 3)\n",
    "    Misclassification = 1 - Acuuracy\n",
    "    Sensitivity = round(TP / (float(TP + FN)), 3)\n",
    "    Specifity = round(TN / (float(TN + FP)), 3)\n",
    "    Precision = round(TP / (TP + FP), 3)\n",
    "    print('Acuuracy = ', Acuuracy, 'Sensitivity =', Sensitivity, 'Specifity =',\n",
    "          Specifity, ' Precision =', Precision)\n",
    "    plt.figure(figsize=(8, 5))\n",
    "    sn.heatmap(conf_matrix, annot=True, fmt='d', cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "# LogisticRegression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def logistic_reg():\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    logistic = LogisticRegression()\n",
    "    logistic.fit(X_train,y_train)\n",
    "    y_pred = logistic.predict(X_train)\n",
    "    draw_confusion_matrix(y_train,y_pred)\n",
    "    y_pred = logistic.predict(X_test)\n",
    "    draw_confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuuracy =  0.987 Sensitivity = 0.974 Specifity = 0.996  Precision = 0.993\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuuracy =  0.988 Sensitivity = 0.983 Specifity = 0.991  Precision = 0.983\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 195,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 195,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_reg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#K Neighbors Classifier\n",
    "# The classification score varies based on different values of neighbors that we choose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def find_max_knn_score():\n",
    "    knn_scores = []\n",
    "    for k in range(1,21):\n",
    "        knn_classifier = KNeighborsClassifier(n_neighbors = k)\n",
    "        knn_classifier.fit(X_train, y_train)\n",
    "        knn_scores.append(knn_classifier.score(X_test, y_test))\n",
    "    return np.argmax(knn_scores) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def knn_model():\n",
    "    m = find_max_knn_score()\n",
    "    knn = KNeighborsClassifier(n_neighbors = m)\n",
    "    knn.fit(X_train,y_train)\n",
    "    y_pred=knn.predict(X_test)\n",
    "    draw_confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuuracy =  0.977 Sensitivity = 0.966 Specifity = 0.982  Precision = 0.966\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 199,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#Support Vector Classifier (SVC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def SVC_model():\n",
    "    from sklearn import svm\n",
    "    clf = svm.SVC(gamma='scale')\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    draw_confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuuracy =  0.977 Sensitivity = 0.966 Specifity = 0.982  Precision = 0.966\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 202,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVC_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def svc_scores():\n",
    "    svc_scores = []\n",
    "    kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "    for i in range(len(kernels)):\n",
    "        svc_classifier = SVC(kernel = kernels[i])\n",
    "        svc_classifier.fit(X_train, y_train)\n",
    "        svc_scores.append(svc_classifier.score(X_test, y_test))\n",
    "    colors = rainbow(np.linspace(0, 1, len(kernels)))\n",
    "    plt.bar(kernels, svc_scores, color = colors)\n",
    "    for i in range(len(kernels)):\n",
    "        plt.text(i, svc_scores[i], round(svc_scores[i],3))\n",
    "        plt.xlabel('Kernels')\n",
    "        plt.ylabel('Scores')\n",
    "        plt.title('Support Vector Classifier scores for different kernels')\n",
    "    best_kernel = kernels[np.argmax(svc_scores)]\n",
    "    return(best_kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def svc_model(kernel):\n",
    "    print(\"SVC using kernel {}\".format(kernel))\n",
    "    svc_classifier = SVC(kernel=kernel)\n",
    "    svc_classifier.fit(X_train,y_train)\n",
    "    y_pred=svc_classifier.predict(X_test)\n",
    "    draw_confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJtUIujqitD7vaSHP3RV5K/25COC6pYgYUPQW+2BEvBIRS4teLRNwY5HtnRWT3VYMR0fEryPiMy0Ew6SU3qDpzVV3RcQpEbFLJ+2jrSlta99sYTt7qMjTj+r9oXTkOy2tuxMj4pqIOCAiNmgh/xByU6LyaevR2dvubuQrwQl4tIV1eWKRv3yfbdP201ER0YemN9V1ZH105DhaKvPeFvK0lNYpImIbmoL1m1v4Hi8o8mxVvaQW101pn9uqVvnFPD7YyjwerTH+hWK4cQt1aIR9yA/0DiA3ExrTwv5RWuYvtLC8/yTf9Yfqy/xUSqmei0K11gtUWTcRsS4wovj3ohbqVzr+1/o+KrX12Nat2MFZD1dc8flp8SEiNiO3uz2NvBF/DphMfptCI71QKyGltCgiXiX/iFeL/lucviytfNpNK9KqzfetiHiZ3Pa31nxXVQ+XV5PfmrI1+aHE0g/dQeS2kUvJ7VUrla4wrE1ejtasX/Z3KX9ndF5TOiFr6Xt7vhh2xrp/uRh2yklTROxEbo43qGx0qV15It+V25jcpG2FlNLdRd8Kp5K/24OK8p4Efg9clvJrbMuNJT/Iuj35VvRZwIKIuJcc4P/vKgqgSidB9XaAuD4r37HpyHd6Ffmk4UjyXbTDgeUR8Tj5TSWXpbLXMNN8f2jLNt7Z225pnw3auM+2c/vpiAFFPaHr9uVSmS+2kKelunWW8qu7g2rmarJ+jfH1bCu9afvxvWRZSunNGvnfKobr1FF2R5Tuzk9MKY1rJW9pmTcoPq2ptsz1bm8LWkirtm4Glv0/gNbV+s4rtfXY1q14x2A1k1J6KaU0gXxV5qVi9NguqEpHmjC1NG3vDpTLqrpiXdxKLt2OL29OVPr7zpTSS6ystE8+klKKOj5HlU3bmc3GSjqy/juy7ku35ocWVz0b7SryycCj5GZr/VJKG6aUNkspvYP8Ckyoso5TSqeTHyj7L3Lb0gXkq7LfBJ6MiEMr8k8n387+LPA/5LdXbEB+CPPnwIMR0SwA6SSlbe2TdW5rz1cpo93facqOIrfx/3/k4HkJ+VWu44FnImJk2SQd3b47a9strcd/17keP1o+cVu3nwbqqn25HqviWFap/Hyobx3f47trlFPPtnJjndvKWY1ZtIYrdci5f0R8tZW8pWU+vs5lvr9KGZ21vZV/5zvWUbe6Lqa349jWrRgYrKZSSvOAUq+XQyuSS81t1muhiP6tzKKyScUKRXOZ0lXIWpF+zelpusJQPm3p761bmW8p6l9VdwZaUmoqdEhE9ImIQTR1TFerXXYpWNg+InrVyFNL6QpEzXXUAfPqKHvLYtgZ677U7GFt8lseGiYitiXfTl5KfiDwrpTSwopsLV7dSynNTCmdm1L6GPnOwkhyk6F1gMsiYmBF/qUppVtSSkenlIaR94dvkx9c3I18BbmzldqXv2cVzKumlNLfUkqnpZT2IR83Pk3us6IfcG3ZflB+ha0t23hnb7ulfXbTGu2jW9XW7acDSu9/h67bl0tl1vMbsCqVX6jprH2iNI8u3eca4FLyHfEAfhoRX2khb3de5nnkB6OhE+rXhmNbt2JgsHorndxUPlBTetBwS6oorla21mPy1hExuEbah8ntBRP5dXjVVH2oqmiHV2qDOrUsqfT3dhGxRY0yP0JT87ipNfK0ZMVDyQ16aPt68rrfkNxE4FBy/V4j306sptQ+dUPyg2ptUWoPPjIi2nI1sLTcLS1zaX3u20Ke0hWQ9qz71vyJprsG36n3gFrn91jaD+a0cHv3ozXGrySltCyldA/5DsBS8o9ArbbapWn+lVL6Hrm9LtR+6LCRStvaZ1fBvOqSUlqc8kOZXyhGbUF+tgDyqw5LJ/mfaEOxnb3t/pmmK5qfacf0zbRn+2lD2Yto2o+6al8ulfmRFvKsiu2/0nSatq/O2idK+9yOxYsmVqV6jvN1Syn9N/kCRgBXRsQXa2QtLfOnI6JbNV9PKb1F0zONnXocbOXY1q0YGPRAEbFNRLS4QUXE+uROm2Dlk/O/FcNRxVX2SidS323m/6wy3yC/oxfyQ7C1Hhg6uXjwp9IJ5DsZ88m31UsmFuPWIfd3UDnfXuReOAH+1M72e/PL/q633XVNxbL/rvi31M4Q4JepxsPBKaW/0/QA1feK77GqiFi/Yh3eQG5HOYC2XXEuLXdLy1x6S8iBRXv8yrrsTNNJ0Q1tmHddUkqJpu1qN+BHLT2wG9kJ1H6rSrnXi+E7I2KldqYRsStNB/PKtGrbcMkSmn6Mexf5W2v7u6g8fye7uhh+KCK+1FLGiGj4w4ytrLtFZX/3hhXbwHXFuP8bEfVeVe7UbTel9DpNb4M5raW7BhGxduS3GJX+b9P20yCl9TG2eCatmYj4BHkfg07Yl2l6kPPDEVH5li8iYjuaXq6wyhTbV+lO7tdbOnEvji+t3VWvZiJNz09c2NIFjk7Y5+o5zrdJSulsclOZtchXwEdXyXY1+SLhVuS7DDV1xnGmDlcXw89HREvBat31a+uxrbsxMOiZdgSeioibI+Lz5T+QEdE3Ij5FvsK6TTG68sHjW8kb56bknXlQMW3/iDiF3JfA67RsPnB0RJxTOkBGfkXoNeQr3Yn8vuxa3gXcUrrrUJzknkRufwdwfip7wKpo2lF6Peq4yG9z6VdMuwX56vyHaer8rM1SfmVj6YG4Me0po4rSD83HaHpDU2uvd/wa+aRgF+C+iBhZ+gGJiLUi4r0RcRr5CuqKh+RSfhC91Cb11Ii4OCK2Kptu64j4ZvEdl3uiGH4iar/m9RdFvgB+GxH7FuVGROxPflBybXIHTr+sUUaHpJR+S9M2cCxwf0QcVHGiNSgiDidfBfoh9T2E93dyvxBrkd/itW1R1joRcQj51bC1Hmr7eURcERGjouytE0UZ15IfWl5I05uIdomIv0XEuIjYrnRHIyLWjYjPAd8o8t1JJ0sp/Y6m5obXRH4j04rvPyI2iYiDI+JWqrwiuAHuiYiLImKv8gsUEfFeck/DkB+C/UfZNOeSv6tBwJ8i4lOlYCsiNij2lRsqgoZVse1+i9y50hbkZ0QOirK7dsV3fRL5eZLyN7W1dftphIvJzTv6AndExPBinr2KbbD0asY7Ukr3NXC+AKSU/khuGhjAryLiE2X7wV7kd/G/VbuETnUOuXOyfuRj75crji/vioijyceXT7W18JRfh1l6WPcA8vr/QNnyrxMR74+I75HfMthIpeP8l6L6BcF2SSmdBpxP3oeuj4hPV6T/naY7oWcXv0ulcxMiol+x/f+MplfBrkqXk58H7AXcFhFfLw8AImKzyK9+vw84vs4y23Ns6z5SN+hMwU/bPjTvQKv0eZOm3iJLn6VU6UG4KGNcRd5Xaep1+DRa7+DsjzT1CLiUpi7KS+V9s8o8B5elj6apU6FXy/5O5KtvK/WgSd5xyzt2q5zvMuC4luZbx7o9o6z8N8g/ErOo6MymDd/V2uS23KUyn6pzugNp3iX7YvJt7iU0/94qe+Jdi6aeekuf12jqMKhaZyyDiu+gtA7/VSzz9Ip8Q2nqFbS0fhaW/f8sZT18lk1XtROYDmz/x1Wsm9IyLqwY9zhlHQ3Rcs/Hh9C8u/r5ZevsWfLdnlRlnfyubJrlxXosr8fbwBfL8r+/oo5vkdt9l8/7IYqeQcum66yej/uRm7VVHgterxj3PxXTdfg7pak34tJ290qxPsq3r32qTLcLTR0tJfI+8WpFfbdc1dsu+RWT/6L5dz+vYpkSsGd7t58612uLHZwVeT5Usc7mV9RzKlU6yqSOzhDrrOMWNPWWm8i/XwuKv+eQ++mpugwd3Bda7OCsyLMdOYAr3zbnFXUs/x4Pbcu8q9Sj/Fi+iHwcWFr+3VdM02oHjS199+Tn20plLyZ3tDYL+FkbvreanaTR1PngYuCAirS1aeokrHybe5Xm5w13tfX7qnc/bWnbJT9D9mBZPZaTj0dvVNS5soPLWh2ctevY1l0+3jHogVJKd5JfdfhN8kn09CKpH/kEaSq5M5BdUkordUJWlHExuXnEQ+QD3lrk15p+JqV0Zp31OJEcKEwh7/hvkK8EfTyl9P1Wpv0VuY3r78k7zlLyidzXgc+mlJZWmWZZSukr5JO4icWy9iP/GF8PfCCl9ON66t6CM8kPgf6VfEVr6+LTrtuvxXL8omzUtXVO9zvyD9Q55KtTbxV1mE/+nr4L7JBSeqFiuuUpvz5uH3IzgBeBPuQTvSnkjs/Oq5hmLvm7uIX8A7gpeZkHV+R7mvyWhbPIB75SW9W/kwOqXVJ+406nKr7jbchNi+4hn0j0IR9wnyY3NzkQeF+q3dFQZZk3kX9UJ5FPUNYh/2j+N/lNErVerfgt8vZyJzCTfIW3F/mk50pgeEqp/CrY38mvEL6c3MTvdfLzJK+T7/IdT+4N9o166t1RKaU3UkqfJj8Udwt5OdcnL/8z5LckjabpKmcjjSHfnfwj+aS9D/kH+UlycPvelK8uV9b5cfJd0++St+m3yLfkZxTL8H9o/qDyKtl2U0oPk4/L/0k+yXiDvM8uIjcPPBcYkVKaXDZZW7efhkgpPUh+2PIi8ve8LjkIeRQ4GfhQSqnTXuBQHLfeT/6dKr3D/jXyW7qGkwO1LpFSeoZ8V+dr5G3zVZpeMf04edv8CB24up3ymwN3IK//fxRlb0g+/t4NnARs297ya8xzIvm38z7yNrkF+TjfkI4WU0onkR9KXpfcGmC/srSlKaWjyevt58DsIl8f8vd/M7nzwmpNkTpdym8I/HBRh9vJD8iX7uA9CUwg3+E5v84i23Vs6y6iiG6kukTEEeRXO96b8pP2bZl2MMUBP6XUFa+jkyRJUg3eMZAkSZJkYCBJkiTJwECSJEkSBgaSJEmS8OFjSZIkSXjHQJIkSRIGBpIkSZIwMJAkSZKEgYEkSZIkDAwkSZIkAWt3dQVWVxHxLLAhMKuLqyJJkqTV22Bgfkppm44UYmDQeTbs06fPJsOGDdukqysiSZKk1deTTz7JokWLOlyOgUHnmTVs2LBNpkyZ0tX1kCRJ0mpsxIgRTJ06dVZHy/EZA0mSJEkGBpIkSZIMDCRJkiTRAwODiDgkIi6JiD9FxPyISBHxs3aWtWVEXBkRL0bE4oiYFREXRsTGja63JEmS1J31xIePTwV2Ad4Angd2aE8hETEEeAAYBPwGmAZ8APgGcEBE7JlSerkhNZYkSZK6uR53xwA4ERhK7iPgPzpQzo/JQcG4lNLBKaXvpJRGAj8EtgfO7nBN1S08//zzjB07lne+85307t2bwYMHc8IJJ/Dqq6+2qZxbbrmFkSNHstFGG7HeeusxbNgwzjzzTN56662V8p5++ulERIufIUOGNJtmn332aXWaI488skPrQtKax2OgpHr1uDsGKaV7Sn9HRLvKiIhtgVHkzscurUgeDxwNfDkiTk4pLWxfTdUdzJgxgz322IO5c+dy0EEHscMOO/DnP/+Ziy66iDvuuIPJkyczYMCAVsv57ne/y1lnnUW/fv0YPXo0AwYM4P7772f8+PFMnDiRu+66iz59+qzIv88++9Qs69Zbb2Xq1Kl8/OMfbzb+iCOOqDndJZdcwiuvvLLSNJLUEo+BktokpdRjP8A+QAJ+1sbpjiqm+2mN9DuL9P06ULcpw4cPT+pao0aNSkC6+OKLm40/8cQTE5COOeaYVsuYOnVqioi00UYbpRkzZqwYv3z58vS1r30tAWn8+PF11Wfp0qVpyy23TEB6/PHH65pm2rRpCUibbbZZWrJkSV3TSFJKHgOlNcXw4cMTMCV18Ny6JzYlaoTti+HTNdKfKYZDWysoIqZU+9DOZx/UODNnzmTixIkMHjyY448/vlnaGWecQd++fbnuuutYuLDlm0K33HILKSWOOuoott122xXjI4JzzjmHiOAnP/kJy5Yta7VOt912G88//zy77747O++8c13LcfnllwMwZswY1llnnbqmkSSPgZLaak0NDPoXw9drpJfGb7QK6qJOcvfddwMwatQo1lqr+aa+wQYbsOeee/Lmm2/y0EMPtVjOnDlzAJr9IJaXM3DgQObOncvf/va3VutU+oE7+uij61qGJUuWcO211xIRfPWrX61rGkkCj4GS2m5NDQxaU3p4IbWWMaUAY51XAAAgAElEQVQ0otqH/JYjdaGnnnoKgKFDq9/42W677QB4+ulaN46ygQMHAvDss8+ulLZgwQLmzZsHwLRpLX/lL7zwArfffjv9+/fnC1/4QsuVL/zqV79i3rx5fPSjH636oyxJtXgMlNRWa2pgULoj0L9G+oYV+dQDvf56/vr696/+NZfGv/baay2Wc+CBBwIwYcIEZs2a1Szt1FNPLT1T0uobPiZMmMCyZcs47LDDWH/99VutP7T96poklXgMlNRWPe6tRA3yVDGs9QzBdsWw5cso6tFKP2atvd1qjz324JhjjuGnP/0pO++8M6NHj2aTTTZh8uTJPPLII+y444488cQT9OrVq2YZy5cv58orrwTq/4F75plnuPfee9lss8046KCD6lwqSaqPx0BJldbUOwalV56Oiohm6yAiNgD2BBYBLTe8VLdWuhpWumpWaf78+c3yteSyyy7jiiuu4D3veQ833HADl112Geuuuy533nknO+20EwCDBg2qOf3tt9/OP//5zzY/cJdS8oE7Se3iMVDdQVf0owG02B/G7rvvXnWauXPn8o1vfIMhQ4bQu3dvBg4cyKc+9alWn8NZnazWdwwiYh1gCPB2SmlGaXxKaUZETCT3ZXA8cEnZZGcAfcmvMrUPgx5s++3zy6dqtZ995pn88qla7W8rjR07lrFjx640/qijjgJgt912qzlt6Xb4McccU9e8lixZwjXXXOMDd5LazWOgulpX9aNRsvXWW3PEEUesNH7LLbdcadzs2bPZc889eeGFF/jABz7AwQcfzLx587j55pu5/fbbufHGG/nMZz7TrvXQo3T0faer+gMcDFxdfO4gPyA8o2zc98vyDi7SZ1UpZwjwUpH+a+Bc4O7i/6eAAR2sp/0YdLHp06cnIA0ePDgtW7asWdr8+fNT3759U58+fdIbb7zR7nnceeedCUh77713zTwvvPBC6tWrV+rfv39auHBhXeX+8pe/TEDaf//92103SWs2j4Hqal3Zj0Zr22Wlgw46KAFp3Lhxafny5SvGP/PMM2nDDTdMm2yySXr55ZfrLm9Va1Q/Bl1+ot/mCsPpxcl7rc+ssrw1A4MifSvgKuBfwBJgNnARsEkD6mlg0A209aD05JNPpieffHKlcl5//fWVxk2fPj1tvfXWqVevXunBBx+sWYczzzwzAelrX/ta3fUeOXJkAtJNN91U9zSSVMljoLrKjBkzWg1M119//VYD0+9+97sJSN/85jdXSps/f36KiDRo0KC0dOnSZmltCQwWLVqU1llnnbTWWmul+fPnr5R+8sknV92PupNGBQY9rilRSul0cnBQT95ZNL16tFr6c8CYRtRL3dOPf/xj9thjD8aNG8ekSZMYNmwYDz/8MPfccw9Dhw7l7LPPbpZ/2LBhQNNDeSVHHnkks2fPZsSIEWy88cZMnz6dW2+9lbfffpsJEybUbK+4fPlyrrjiCqD+B+6mT5/OPffcw2abbcanP/3pti6yJK3gMVBdpZ5+NCZOnMhDDz3EfvvtV7OctvSjseuuuzZLf+2117jyyiuZM2cO/fv3Z8SIEVW31VdeeYW3336bQYMGscEGG6yUXpr3pEmT+PrXv97KkvdsPS4wUH3OaPklE2uQIRzKo9zDaUz6zR3c+pvb2IDN+SDj2Pvp8fxo4CZVp6pcf0s5kDlczjWP3MBiFtCXQWzHaPbkW8weszNn1Agvn+FOZjObLdmdm3feiZvrqPFd/A+JxHYvjeGcddv3wN34VnvgkFZfT8w8o6ur0H0E/PxXh3Lphfdw/32T+P1tt7Lpphtw6Fc+yHHj9mbO6z9iTpVnkyvX4fs+sJTpM+dw/S+vYeHCxQwY0Jf9PrYdY4/ek+13mF1znf/pj88we/ZsdnnflqzV92aemNn6UfCC/76LlBIHfmY7nn7unDYv8o7bjm/zNGq8evrRmDhxIk8//XSLgUFb+tGoDAwef/xxjjzyyGbjdtllF6677roVD80DbLzxxvTq1Yt58+bxxhtv0K9fv2bTzJw5c8U8VncGBlrt9WcrDuaquvKeXqNPu135CrvylTbPezs+XrPMWvbnfPbn/DbPS5Kq2fyd/TnrewfXlffvM06vOv6g0bty0Ohdq6a1ZK99tqtZZi0nfXt/Tvr2/m2el7qXRvajce655zJhwgSOO+44Bg8evCKtpX40TjrpJEaPHs3QoUNZb731mDZtGueffz433XQTI0eO5LHHHmOLLbYAoE+fPowcOZK77rqL0047jQsuuGBFOTNnzmTChAlV57E6WlNfVypJkqQuUjqhr7cfjVdffZWdd96ZMWPGcPLJJ7P77rvzox/9iB133BFgpX40fvCDH7DHHnswcOBA+vXrx/vf/35uvPFGRo8ezbx58/j+97/fLP+FF17IxhtvzA9/+EM+9KEPcfLJJ3PEEUew6667svXWW1edx+rIwECSJEkN1Z360Sh37LHHAnDfffc1G/+e97yHKVOmMGbMGJ577jkuueQS/vCHP3DUUUetuGNQ7zx6MpsSSZIkqaG6Uz8a5TbddFMAFi5cuauqbbbZZkUP3eWuuuqqNs2jJ/OOgSRJkhpq3333BWDixIksX768WdqCBQuYPHkyffr0qflGq3pMnDiR2bNns/fee694XqA1pV6Mq73lqJbSHYNDDz207ZXsYQwMJEmS1FBDhgxh1KhRzJo1i0svvbRZ2vjx41m4cCGHH344ffv2XTF+2rRpVd/8U2p2VG7GjBkcffTR9OrVi/POO69Z2tSpU6veEfjrX//KKaecAsBhhx3WLG3x4sUsXry42biUEqeddhoPPPAAn/zkJ9lnn31aXujVgE2JJEmS1HBd1Y/GxRdfzM0338zIkSPZaqut6N27N9OmTeOOO+5g2bJlfPWrX+WLX/xis2meeeYZ9tprL/bff38GDx7MkiVLuOuuu/jHP/7BbrvtxrXXXtsJa6j7icqVr8aIiCnDhw8fPmXKlC6Zv/0YrNnsx0BrMvsxWLN1eT8GrbxlZ03zHHAacAfwMrA5cDAwHqjsSai05ip/wq4BLgemAQuAQcA+wLeAnavM89fAtcBfgbnAW8AA4P3AV4Fq3eb9GzgBeACYA6wD7AB8CTgOWLf1RW3SBefWI0aMYOrUqVNTSiM6Uo53DCRJktQptoI6exJaOSAo+UrxqdfBxactNgV+3sZpVkc+YyBJkiTJwECSJEmSgYEkSZIkDAwkSZIkYWAgSZIkCQMDSZIkSRgYSJIkScLAQJIkSRIGBpIkSZIwMJCkTvX8888zduxY3vnOd9K7d28GDx7MCSecwKuvvtqmcm655RZGjhzJRhttxHrrrcewYcM488wzeeutt2pO849//IPPf/7zDBo0iPXWW4/tt9+e8ePHs2jRopXyvv3221x00UWMGTOGXXfdlXXXXZeIYMKECW1eZklSz7R2V1dAklZXM2bMYI899mDu3LkcdNBB7LDDDvz5z3/moosu4o477mDy5MkMGDCg1XK++93vctZZZ9GvXz9Gjx7NgAEDuP/++xk/fjwTJ07krrvuok+fPs2mefjhhxk5ciRvv/02hxxyCFtttRV33303Z555JpMmTWLSpEn07t17Rf6FCxdywgknALDZZpvxjne8g+eee66xK0SS1K15x0CSOslxxx3H3Llzufjii/n1r3/Neeedx913382JJ57IU089xSmnnNJqGX/5y184++yz2WijjXj88ce5+uqr+cEPfsBDDz3E1772NSZPnsz555/fbJply5YxZswY3nzzTW666SZ+8YtfcP755/Pwww8zevRoJk+ezA9/+MNm06y//vrcdtttvPjii8yZM4exY8c2dF1Ikro/AwNJ6gQzZ85k4sSJDB48mOOPP75Z2hlnnEHfvn257rrrWLhwYYvl3HLLLaSUOOqoo9h2221XjI8IzjnnHCKCn/zkJyxbtmxF2r333suTTz7JRz7yET796U+vGL/WWmvxve99D4DLLruMlNKKtHXXXZePf/zjbL755h1abklSz2VgIEmd4O677wZg1KhRrLVW80PtBhtswJ577smbb77JQw891GI5c+bMAWgWFJSXM3DgQObOncvf/va3leZ9wAEHrDTNtttuy9ChQ5k9ezYzZ85s20JJklZrBgaS1AmeeuopAIYOHVo1fbvttgPg6aefbrGcgQMHAvDss8+ulLZgwQLmzZsHwLRp0xo+b0nSmsXAQJI6weuvvw5A//79q6aXxr/22mstlnPggQcCMGHCBGbNmtUs7dRTT13RHKj8LUeNmrckac3iW4kkqQuUTugjosV8e+yxB8cccww//elP2XnnnRk9ejSbbLIJkydP5pFHHmHHHXfkiSeeoFevXg2ftyRpzeIdA0nqBKWr8qWr95Xmz5/fLF9LLrvsMq644gre8573cMMNN3DZZZex7rrrcuedd7LTTjsBMGjQoE6ZtyRpzeEdA0nqBNtvvz1Qux3/M888A9R+DqDS2LFjq75C9KijjgJgt91267R5S5LWDN4xkKROsO+++wIwceJEli9f3ixtwYIFTJ48mT59+rD77ru3ex4TJ05k9uzZ7L333myxxRYrxo8cORKAO+64Y6VpZs6cydNPP83WW29d9U1HkqQ1l4GBJHWCIUOGMGrUKGbNmsWll17aLG38+PEsXLiQww8/nL59+64YP23atGZvFyopNf0pN2PGDI4++mh69erFeeed1yxt7733ZtiwYdx333389re/XTF++fLlfPvb3wbg2GOP9RkDSVIzNiWSpE7y4x//mD322INx48YxadIkhg0bxsMPP8w999zD0KFDOfvss5vlHzZsGECzjscAjjzySGbPns2IESPYeOONmT59Orfeeitvv/02EyZMWOmuQ69evbjqqqsYOXIkhxxyCIcccgjvete7mDRpEo8++ih77rknJ5544kr1Pe+881YEJo899hgAV111Fffffz8AH/7wh1c0XZIkrX4MDCSpkwwZMoRHH32U0047jTvuuIPbbruNzTffnHHjxjF+/Hg22WSTuso58MADufzyy7nhhhtYsGABgwYNYvTo0XzrW99i5513rjrNBz/4QR555BHGjx/PxIkTWbBgAVtvvTWnnXYa3/nOd+jdu/dK09xxxx3ce++9zcY98MADPPDAAyv+NzCQpNVXVF6ZUmNExJThw4cPnzJlSpfM/wxbCKzRxnfxbr2jPequ0Z7o4mcXnph5RpfOX11rx23Hd20FbKKnLji3HjFiBFOnTp2aUhrRkXJ8xkCSJEmSgYEkSZIkAwNJkiRJGBhIkiRJwsBAkiRJEgYGkiRJkjAwkCRJkoSBgSRJkiQMDCRJkiRhYCBJkiQJAwNJkiRJGBhIkiRJwsBAkiRJEgYGkiRJkjAwkCRJkoSBgSRJkiQMDCRJkiRhYCBJkiQJAwNJkiRJGBhIkiRJwsBAkiRJEgYGkiRJkjAwkCRJkoSBgSRJkiQMDCRJkiRhYCBJkiQJAwNJkiRJGBhIkiRJwsBAkiRJEgYGkiRJkjAwkCRJkoSBgSRJkiQMDCRJkiRhYCBJkiQJAwNJkiRJGBhIkiRJoocGBhGxZURcGREvRsTiiJgVERdGxMZtLOfDEfGbYvq3IuKfEXFbRBzQWXWXJEmSuqMeFxhExBBgCjAG+DPwQ2Am8A3gwYgYUGc5/wH8CdivGP4QuBfYG7g9Ik5pfO0lSZKk7mntrq5AO/wYGASMSyldUhoZERcAJwJnA8e2VEBErAOcC7wFjEgpPVWWdg7wF+CUiPh+Smlx4xdBkiRJ6l561B2DiNgWGAXMAi6tSB4PLAS+HBF9WylqE6A/8HR5UACQUnoSeBroA/RrQLUlSZKkbq+n3TEYWQwnppSWlyeklBZExGRy4LA7MKmFcuYC/waGRsR2KaVnSgkRMRTYDngspfRyaxWKiCk1knZobVpJkiSpu+hRdwyA7Yvh0zXSSyf4Q1sqJKWUgOPJyz8lIq6JiHMj4lry8wtPAJ9rQH0lSZKkHqGn3THoXwxfr5FeGr9RawWllG6MiBeB64HDy5JeAq4iP9DcqpTSiGrjizsJw+spQ5IkSepqPe2OQWuiGKZWM0YcBvyB/EaiYcD6xXAS8CPgl51UR0mSJKnb6WmBQemOQP8a6RtW5KuqeI7gSnKToS+nlKallBallKYBXyY3J/pcROzT8SpLkiRJ3V9PCwxKbxCq9QzBdsWw1jMIJaOAdYB7qzzEvBy4r/i3ajMhSZIkaXXT0wKDe4rhqIhoVveI2ADYE1gEPNRKOb2L4aY10kvjl7SnkpIkSVJP06MCg5TSDGAiMJj8VqFyZwB9gWtTSgtLIyNih4iofHXon4rhIRGxc3lCROwKHEJ+TuHuxtVekiRJ6r562luJAI4DHgAujoj9gCeBDwL7kpsQnVKR/8liWHowmZTSnyPiKmAM8EhE3ALMJgccBwPrAhemlJ7oxOWQJEmSuo0eFxiklGZExPuBM4EDgE8A/wIuBs5IKb1SZ1FHkp8lOAL4GLABMB+4H/iflJJvJZIkSdIao8cFBgAppefIV/vryRs1xifg6uIjSZIkrdF61DMGkiRJkjqHgYEkSZIkAwNJkiRJBgaSJEmSMDCQJEmShIGBJEmSJAwMJEmSJGFgIEmSJAkDA0mSJEkYGEiSJEnCwECSJEkSBgaSJEmSMDCQJEmShIGBJEmSJAwMJEmSJGFgIEmSJAkDA0mSJEkYGEiSJEnCwECSJEkSBgaSJEmSMDCQJEmShIGBJEmSJAwMJEmSJGFgIEmSJAkDA0mSJEkYGEiSJEnCwECSJEkSBgaSJEmSMDCQJEmShIGBJEmSJAwMJEmSJGFgIEmSJAkDA0mSJEkYGEiSJEnCwECSJEkSBgaSJEmSMDCQJEmShIGBJEmSJAwMJEmSJGFgIEmSJAkDA0mSJEkYGEiSJEnCwECSJEkSBgaSJEmSMDCQJEmShIGBJEmSJAwMJEmSJGFgIEmSJAkDA0mSJEkYGEiSJEnCwECSJEkSBgaSJEmSMDCQJEmShIGBJEmSJAwMJEmSJGFgIEmSJAkDA0mSJEkYGEiSJEnCwECSJEkSBgaSJEmSMDCQJEmShIGBJEmSJAwMJEmSJGFgIEmSJAkDA0mSJEkYGEiSJEnCwECSJEkSBgaSJEmSMDCQJEmSRA8NDCJiy4i4MiJejIjFETErIi6MiI3bUdZOEXFtRDxXlDU3Iu6NiMM7o+6SJElSd7R2V1egrSJiCPAAMAj4DTAN+ADwDeCAiNgzpfRynWUdAUwA3gR+B8wCNgLeC3wCuLbB1ZckSZK6pR4XGAA/JgcF41JKl5RGRsQFwInA2cCxrRUSEbuTg4K/AweklOZUpK/TyEpLkiRJ3VmPakoUEdsCo8hX9i+tSB4PLAS+HBF96yjue0Av4LDKoAAgpfR2x2orSZIk9Rw97Y7ByGI4MaW0vDwhpbQgIiaTA4fdgUm1ComILYG9gEeBJyJiX2AEkIDHgHsqy5ckSZJWZz0tMNi+GD5dI/0ZcmAwlBYCA2C3svx3A/tUpP8tIj6bUpreWoUiYkqNpB1am1aSJEnqLnpUUyKgfzF8vUZ6afxGrZQzqBh+HhgGfLYo+93AdcBOwO8jYt32V1WSJEnqOXraHYPWRDFMreTrVTY8KqX0u+L/+RHxFXKw8H5gNHB9SwWllEZUrUi+kzC8nkpLkiRJXa2n3TEo3RHoXyN9w4p8tbxaDBcDt5UnpJQS+TWokF+DKkmSJK32elpg8FQxHFojfbtiWOsZhMpyFtR4yLgUOPRpQ90kSZKkHqunBQb3FMNREdGs7hGxAbAnsAh4qJVy/grMAwZGxGZV0t9bDGe1v6qSJElSz9GjAoOU0gxgIjAYOL4i+QygL3BtSmlhaWRE7BARzd4QlFJaCvy0+Pd75UFGROwEHAEsBW5q8CJIkiRJ3VJPfPj4OOAB4OKI2A94EvggsC+5CdEpFfmfLIZRMf4cYD/gcGCniPgjsCn5geP1gJPreV2pJEmStDpYZXcMImLjOnskblFx1+D9wNXkgOBkYAhwMfChlNLLdZbzJjkwOANYn3wH4tPkoOMTKaULOlpXSZIkqado6B2D4gr+x4BzU0qvFuMGATcCHwaWRsSlKaWTOjKflNJzwJg681beKShPexM4vfhIkiRJa6xG3zH4OvDZUlBQ+D6wFzAdeBn4RkR8vsHzlSRJktQBjQ4MdgHuL/0TEX2AQ4C7UkrbA9sDzwHHNni+kiRJkjqg0YHBIODFsv8/SH6Q92qAlNIC4HfkAEGSJElSN9HowGAxzTsF2wtIwH1l4+YDmzR4vpIkSZI6oNGBwbPAyLL/RwPPpJReKBu3FblzMUmSJEndRKMDg2vIfQI8HBF/AnYCflGRZzjwVIPnK0mSJKkDGt3B2U+A3YEvkDsUuxU4v5QYER8AhgHXN3i+kiRJkjqgoYFBSult4EsRcWz+Ny2oyDITeB8wq5HzlSRJktQxjb5jAEBKaX6N8fPw+QJJkiSp2+mUwCAiNiU/eDwM6JtSOqps/DbA31JKizpj3pIkSZLaruGBQUQcCVxM7r8gyK8rPapI3gx4EDgauKLR85YkSZLUPg19K1FE7A9cDjwNfIb8MPIKKaW/A08ABzdyvpIkSZI6ptF3DL4N/AvYO6U0PyLeVyXPX4EPNXi+kiRJkjqg0f0YvB/4Xa2HjwvPA+9o8HwlSZIkdUCjA4N1gYWt5NkIWNbg+UqSJEnqgEYHBrOAEa3k+SD2fCxJkiR1K40ODH4D7BURn6uWGBFjgJ2BXzV4vpIkSZI6oNEPH38P+D/A9RFxCNAfICK+BuwFfBZ4BrikwfOVJEmS1AENDQxSSq9GxN7AtUD5XYOLi+GfgC+llFp7DkGSJEnSKtTwDs5SSv8E9omIncmvJR0AvA48lFKa0uj5SZIkSeq4hgYGEfERYH5K6bGU0l/JfRZIkiRJ6uYa/fDxPcDRDS5TkiRJUidrdGAwD1jU4DIlSZIkdbJGBwZ/BPZocJmSJEmSOlmjA4NTge0j4v9FxDoNLluSJElSJ2n0W4n+E/g78F/AkRHxODAHSBX5UkrpyAbPW5IkSVI7NTowOKLs73cUn2oSYGAgSZIkdRONDgy2aXB5kiRJklaBRvd8PLuR5UmSJElaNRr98LEkSZKkHqjRTYkAiIjdgaOA9wEbAa8DU4CrUkoPdMY8JUmSJLVfwwODiDiL/HaiqEjaFRgbEeenlP6r0fOVJEmS1H4NbUoUEZ8jv6r0n+Q7BtsCfYrhUcX4b0fE5xs5X0mSJEkd0+hnDL4OvATsllK6MqU0K6W0uBheCewG/Bs4vsHzlSRJktQBjQ4MdgFuSinNq5ZYjL+R3KxIkiRJUjfR6MBgbeDNVvK8SSc99CxJkiSpfRodGEwHDoyIquUW4z8BzGjwfCVJkiR1QKMDg+uBYcBvImK78oSIGALcBLwH+EWD5ytJkiSpAxrdpOcC4ADgk8DHI+JF4F/AO4AtyIHI/UU+SZIkSd1EQ+8YpJSWAPsDpwDPAluS30S0VfH/KcB+RT5JkiRJ3UTDHwJOKb0NnAucGxH9gP7A6ymlNxo9L0mSJEmN0alvByqCAQMCSZIkqZtrdM/HIyLitIjYrEb6O4p0+zGQJEmSupFGv5XoZOAoYG6N9JeAI4GTGjxfSZIkSR3Q6MDgQ8A9KaVULbEYfzewZ4PnK0mSJKkDGh0YvAN4vpU8LwKbN3i+kiRJkjqg0YHBm8CmreTZFFjc4PlKkiRJ6oBGBwaPAQcVryldSURsCBxU5JMkSZLUTTQ6MLicfEfgrojYuTwhInYBJgIDi3ySJEmSuomG9mOQUvrfiPg4cDjwl4h4CXgB2ALYDAjgmpTS9Y2cryRJkqSOafQdA1JKRwDHAv8gP4w8ohg+ARydUhrT6HlKkiRJ6phO6fk4pXQ5cHlErA9sBLyWUnqzM+YlSZIkqeM6JTAoSSm9GREfBUZGRAD3ppRu7sx5SpIkSWq7DjcliohPRcR9EbF3lbSrgVuAccDXgRsj4lcdnackSZKkxmrEMwafBoYDD5ePjIgDyQ8hvwmcBXwbmAkcHBFfbMB8JUmSJDVII5oSfQB4MKX0VsX4sUACxqSUbgKIiOuAGcChgG8mkiRJkrqJRtwxeAf5ZL/SR4DXgBVNh1JKc4DfA+9rwHwlSZIkNUgjAoONgVfKR0TEu4BNgPtTSqki/7PAgAbMV5IkSVKDNCIwWABsWTFuRDH8S41pKpsdSZIkSepCjQgM/gZ8MiL6lY37DPn5gvur5N8G+FcD5itJkiSpQRoRGPyc3Jzo3ogYFxE/Ij9cPAe4pzxj0ZfBh8m9IkuSJEnqJhrxVqIrgM8CHwN2BQJ4G/hGSmlZRd79yA8r/6EB85UkSZLUIB0ODFJKyyPik8AXgT2Al4GbU0qPVck+ELgI+G1H5ytJkiSpcRpxx4CU0nJyk6Kft5Lvl8AvGzFPSZIkSY3TiGcMJEmSJPVwBgaSJEmSDAwkSZIkGRhIkiRJwsBAkiRJEgYGkiRJkuihgUFEbBkRV0bEixGxOCJmRcSFEbFxB8r8SEQsi4gUEWc1sr6SJElSd9eQfgxWpYgYAjwADAJ+A0wDPgB8AzggIvZMKb3cxjI3AK4B3gT6NbbGkiRJUvfXE+8Y/JgcFIxLKR2cUvpOSmkk8ENge+DsdpR5EdAfOLdx1ZQkSZJ6jh4VGETEtsAoYBZwaUXyeGAh8OWI6NuGMg8CxgDjgBcbU1NJkiSpZ+lRgQEwshhOTCktL09IKS0AJgPrA7vXU1hEDAL+B/h1SulnjayoJEmS1JP0tGcMti+GT9dIf4Z8R2EoMKmO8i4nB0fHtrdCETGlRtIO7S1TkiRJWtV6WmDQvxi+XiO9NH6j1gqKiLHAQcAXUkovNaBukiRJUo/V0wKD1kQxTC1mihgMXAjcmFK6oSMzTCmNqDGPKcDwjpQtSZIkrSo97RmD0nnZPiAAABmoSURBVB2B/jXSN6zIV8uVwCLguEZUSpIkSerpelpg8FQxHFojfbtiWOsZhJLh5Fee/rvo0CxFRAKuKtJPKcb9umPVlSRJknqGntaU6J5iOCoi1ip/M1HRSdme5DsBD7VSzrXktxdV2g74CPAYMAX4S4drLEmSJPUAPSowSCnNiIiJ5DcPHQ9cUpZ8BtAX+GlKaWFpZETsUEw7rayccdXKj4gjyIHB71NKpzZ8ASRJkqRuqkcFBoXjgAeAiyNiP+BJ4IPAvuQmRKdU5H+yGAaSJEmSquppzxiQUpoBvB+4mhwQnAwMAS4GPpRSernraidJkiT1TD3xjgEppeeAMXXmrftOQUrpanLAIUmSJK1RetwdA0mS/n97dx4tWVmfe/z7oIKK2kzBCUM7MMUVGRoZRBQQAUUGYzQOIQLLqwZEHOKSKAo4xUSDaASjVwky5IJEA2apgag0KIRwBdGrtxEu0BoFFAQaZGgZfvePdxddVNcZmj6nq+v097PWWZvz7ne/+60+m1311H7fvSVJM89gIEmSJMlgIEmSJMlgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSGNNgkGSTJCcnuSHJ0iSLk5yQZP1pbr9ukjck+eckVyW5K8mdSX6Q5N1J1p7t1yBJkiStTh496g6sqCTPBi4BNgbOBa4CdgCOBPZJsktV/XaKZnYFTgduBS4AzgE2APYDPgn8SZKXVNW9s/MqJEmSpNXL2AUD4CRaKHh7Vf1DrzDJ8cA7gY8Cb52ijZuAPwfOrqrf97XxRGAh8ALgcODvZ7TnkiRJ0mpqrIYSJXkWsBewGDhxYPUxwF3AQUnWnaydqrqyqs7oDwVd+Z0sCwO7zUSfJUmSpHEwVsEA2KNbnl9VD/av6D7UXww8HthpJfZxX7e8fyXakCRJksbKuA0l2qJbXj3B+mtoVxQ2B77zCPdxaLf89+lUTnL5BKu2fIT7lyRJkla5cbtiMK9bLplgfa98vUfSeJK3AfsAVwInP5I2JEmSpHE0blcMppJuWSu8YfInwAm0icmvqqr7ptik7ahqwQTtXQ5st6L9kCRJkkZh3K4Y9K4IzJtg/ZMG6k1LkgOBM4HfALtV1XWPrHuSJEnSeBq3YPCzbrn5BOs365YTzUFYTpJXA2cDvwZeXFU/m2ITSZIkac4Zt2BwQbfcK8nD+t49g2AX4B7g0uk0luT1wP8CbqCFgmtmsK+SJEnS2BirYFBV1wLnA/NpDyDrdxywLnBqVd3VK0yyZZLl7hCU5I3AacAvgBc5fEiSJElrsnGcfHwYcAnwmSQvARYBOwK704YQvX+g/qJu2ZuYTJLdaXcdWot2FeKQJAObcXtVnTDjvZckSZJWQ2MXDKrq2iTbAx+i3Vr05cCNwGeA46rq1mk0synLrpYcOkGdn9PuUiRJkiTNeWMXDACq6r+BQ6ZZd7lLAVV1CnDKzPZKkiRJGl9jNcdAkiRJ0uwwGEiSJEkyGEiSJEkyGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRJkiQMBpIkSZIwGEiSJEnCYCBJkiQJg4EkSZIkDAaSJEmSMBhIkiRJwmAgSZIkCYOBJEmSJMY0GCTZJMnJSW5IsjTJ4iQnJFl/BdvZoNtucdfODV27m8xW3yVJkqTV0aNH3YEVleTZwCXAxsC5wFXADsCRwD5Jdqmq306jnQ27djYHvgucCWwJHALsm2Tnqrpudl6FJEmStHoZxysGJ9FCwdur6sCqOqqq9gA+BWwBfHSa7XyMFgo+VVUv6do5kBYwNu72I0mSJK0RxioYJHkWsBewGDhxYPUxwF3AQUnWnaKddYGDuvrHDKz+bNf+3t3+JEmSpDlvrIIBsEe3PL+qHuxfUVV3AhcDjwd2mqKdnYHHARd32/W38yBwfvfr7ivdY0mSJGkMjNscgy265dUTrL+GdkVhc+A7K9kOXTuTSnL5BKu2XrRoEQsWLJiqiVlx40j2qtXF10dz2D3kuqVLR9sBjdSCddYZ6f7vXeoZcE322HW+PuouaE03gs9+ixYtApi/su2MWzCY1y2XTLC+V77eKmpnMg/cc889S6644orFK9GGHpktu+VVI+3FCN14xah7sEZb448/D7+RWuOPP78aGymPP4ArRnIWnA/csbKNjFswmEq6Za2qdqpqxN/NalDvKo5/G42Cx59GyeNPo+TxN/7GbY5B75v8eROsf9JAvdluR5IkSZoTxi0Y/KxbTjT2f7NuOdHcgZluR5IkSZoTxi0YXNAt90rysL4neSKwC3APcOkU7Vza1dul266/nbVoE5j79ydJkiTNaWMVDKrqWtqtROcDhw+sPg5YFzi1qu7qFSbZMsmW/RWr6nfAaV39YwfaeVvX/nk++ViSJElrinGcfHwYcAnwmSQvARYBO9KeOXA18P6B+ou6ZQbK3wfsBrwryTbAZcBWwAHAb1g+eEiSJElzVqpW9gY+q16SZwAfAvYBNqTdm+wc4LiqunWgbgFU1WAwIMkGtCcfHwg8Ffgt8C3gg1X1y9l8DZIkSdLqZCyDgSRJkqSZNVZzDCRJkiTNDoOBJEmSJIOBJEmSJIOBJEmSJAwGkiRJkjAYaBVKMj9JJTmlr+yUrmz+yDomrQLDjn9pJiRZ2Ls19wpu97okP0xyZ3dsnjAb/dNoPdLjY3XzSD4vJFmcZPGsdWoOGscHnEmSpJWQZGfgDOA64HPA3cClI+2UpJEzGGjU/hr4OPCrUXdEktYg+wIB/qKqLhl1ZzSr/gJ4/Kg7MQP8vLAKGAw0UlV1I+3J1ZKkVedp3fKGkfZCs66qfjHqPswEPy+sGs4x0EgNGzPYPxa7++8zk9yS5N4kP0jyiknae12SC5Lc1tVflOToJOsMqXtgktOTXJ3kriS/S3J5krcnWe7/jb6+PivJEUl+nOSeJAtn6J9DIzRw3G2Z5Jwkt3bHxveT7DVkm3WSHNUdC3cnuSPJ95K8Zpr7PLPb54smWP+n3fp/WNnXp/E1cGxunuSsJL9J8mCS3frqrZPkI0muT7I0ybVJjkmydl+dg7vx5od0Rdd3bTvXawwl2T/Jd5Lc2P3Nb0hyYZLD+uoMnWPQHS/HJrmu2/b67vhZpzseFg7UP7Yr3617r728O+/dkOT43vtskj26fd7RvReflmTDCfq/IMlXu+N5aZKfJzkpyVOH1B06xyDN25L8tHvf/1WSzyaZ94j+UddwXjHQ6mxT4DLaGNjTgA2APwPOTbJnVV3QXznJl4BDgV8CXwNuB3YCPgy8JMlLq+r+vk0+DjwI/Bft0uQ8YA/g08DzgYMm6NengV2BbwDfBB5Y6Veq1ckzgf8EfgJ8Hngq7bj7VpLXV9VZAN2HrfOAFwNXASfSLtf/KXBWkm2q6n1T7Oukru23ABcNWf/mbvmFlXpFmiueTTtfXU2bH/A44I6+9V+hnbv+BbgPOAA4Ftg+yf5VVcCVwHHAgcDWtPPZ7d32t6OxkeTNtHPUTcC/AbcAGwPPowW/kybZNsBXaUPKrgE+CzwGOBh47hS7PgJ4GXAOsBDYC3gnsEGSc4Ezae+PXwBeAPw5sFG3TX8fXtH1IbRj9ufAAuAvgQOS7FJVi6foC8AJwNtpVxO+wLJjf0dgbeD302hDPVXljz+r5AeYDxRwSl/ZKV3Z/CH1CjhmoI29u/JvDpQf3JV/DXjcwLpju3VHDpQ/e0gf1wK+3NXfcWBdr6+/Ap456n9Pf2b2Z+C4+8TAuu1pbza3AU/qyv66dywCj+6ruzGwuFv3giHtnzLQ9k+Ae4GNBsqfSQuuF4/638af0f4MHJsfG7J+YbfuamD9vvLH0kJuAQcNbLPcudef8foBLgeWAhsPWbdR338vbB/3Hrb+oO7vfxGwdl/5erQvOgpYOLBN7710CbBVX/k6wE9pX5L9Fnhx37q1gP/ottumr/wJtCDzALDrwH7e29U/f6B8uWOWFjwK+H/ABn3l/cf+4lH/rcbpx6FEWp39HPhIf0FVnQf8AthhoO6RwP3AoVV1z8C6D9NOVm8YaOvawR1W1YO0b9CghZBh/q6qrp/OC9BYWgJ8qL+gqn5A+4Z2PeCVXfGhtDedd1Xflaiq+g3tmAN40zT29znaG+sbB8rfTPsm7fMr2H/NXb+mfds/kQ9X1W29X6rqXlqAhXa8au65n/alxcNU1S1TbNc73xxdVQ99o15Vt7Ps/DWRz1TVor5tlgJn0ULAN6rqwr51DwKnd79u3dfGAcCGwFlV9b2B9v+e9uXKS5P84RR96Q2J+2hV3dq33/5jXyvAoURanV1ZVcOG6fw3sHPvlySPp51wbgHe0a6QLmcpsFV/QTfm8T3Ay4FnAesObPP0Cfp12XQ6r7F1RVXdOaR8Ie3NdNskXwOeA/yqqq4aUve73XLbaezvVNqwtjfT3hBJ0rukfxtteIgE8KPuQ9hELhxS9j3ah8fpHIsaL2fQzhk/TXIW7e9/cVXdPI1tt6VdkRx2R6rvT7HtD4aU9SaxXz5kXe8uQpv0lW3XLb87UJequj/JRbQrZdvSvgycSK+dyY59rQCDgVZnE413vZ+HT5xfn/bN6h8Ax0yn4STrAf+bNlzjMtqHs1u7ttejXYFYbsJy56bp7ENj69cTlPf+7vO6H5j4Dhm98vWm2llV3ZnkdOCtSXavNnfmAOApwAndN18STH3uWe7YraoHkvyWNsRNc0hVHZ/kFuAw2hj7dwCV5ELgPd2VzonMA26th8+765noHNizZEjZ/dNY95iB/cPKn0N77Ux27GsFOJRIc0HvRPTDqspkP33bvIkWCo6rqh2r6rCqOrqqjqVdEp3M2D9BUpN68gTlT+mWS1h2zD1lgrpP7as7HZ/rlm/plk461jBTnXuWO3aTPIo2ZOOO5atr3FXVqVW1E+1vvC/wJeBFwHlJJguDd9AmCw/7gniic+BMmqlzaG/9ZMe+VoDBQGOvqn5Hm/j03CQbTHOz53TLrw5Z9+IZ6ZjG1XZJnjikfLdu+cNuqNG1wNOTbDak7u7d8orp7LCqfgxcDLwyyY7AnsBF/eN4pWkYdu7alTY64IeruC9aharq9qr6ZlX9D9ok3Q1of/uJ/JD2GfAFQ9a9cOZ7OHT/sOy8+pAurPT6MNU5tLd+smNfK8BgoLnieNptyU7uhgk9TJL1k2zXV7S4W+42UG9bnLC0ppsHfLC/IMn2tMnrS4B/7YpPpg1h+0T3zVSv7kbAB/rqTNfnaMdw7/Z9//hIOq812geSrN/7Jcljgb/pfv2n0XRJsyXJPhN849+7UnD3JJuf2i0/MvCci3ksO3/NpnNow3dfl2SngXXvoM37+3ZN/XC2U7rl+/u/GBw49rUCTFKaE6rq5CQLaGMtr03Su3vRBrQhQy+ivTG+tdvkVNrE4xOS7E67j/NmwCtotzz9s1X7CrQauQh4U/fN/cUse47BWsBbqqo3JOOTtPtyHwD8KMk3ac8xeDXtjfnvqmqqSXz9zgY+RZv0fgvtOJRWxCLaRNT+5xg8m3ZP+dNG2THNijOBe5N8n/ZlV2jfkj+fNgn425NseyrwWmAf4CdJvk6bA/Aq2uTiLWiTk2dFVf0uyaG0896FSc6mvWcvoD0X4SaWDa2crJ2L0x4AeQTtdfQf+7fhk5JXmFcMNGdU1eHAfrR7F+8JvAvYn/YN8CdoD0Hp1b2BZQ8peyHwNtoD1Q4DjlqlHdfq5nra5fXbaEHyNbTL1S+v7uFmAN0t/l4KvL8rOoJ216JrgNdX1XtXZKdde2d0v54yxd1npGFeQ7tKtR/tnLYW7d7zr6oq50bNPUfR3u+2o713HUL7cP9eYPeqWu42pj3d8fBK2q1JH0M7fx1Ae47P4V21WZ2XUlXnArvQngWzN/BXtLsH/iOwoKqum2ZTR9L6v4QWJl5He/jknvhwsxUWzxWSBEnm00LBl6vq4BH1YSHt6tYWVXXNKPogac2W5KXA+cDHq8qhtWsYrxhI0mogyQ60CXTnGQokzbYkTxtStiHtuSqwbD6V1iDOMZCkEUryl7R5BYfQxvRO61kckrSSjk+yNe0hZzfTHkD2MtrcvM9XlQ/zXAMZDCRptN5Le0O+DjjIN2NJq8jXaPf/34/2ILF7abf+Phn44gj7pRFyjoEkSZIk5xhIkiRJMhhIkiRJwmAgSZIkCYOBJEmSJAwGkiRJkjAYSJIkScJgIEmSJAmDgSRpDZLkOUkqiQ9wkqQBBgNJEt2H5aFPvOw+TF/b1fnYqu6bJGnVePSoOyBJWn0lWQB8E9gIOKKqPjviLkmSZonBQJI0VJI9gX8F1gZeW1Vnj7hLkqRZ5FAiSdJykrwW+AbwILDPRKEgyR8lOTXJL5P8PslNSc5IstmQuqd3w5H+MMmRSf5PknuSfLtbv2e3/ugk2yb5VpIlSe5KsjDJjhP04dFJ3pbkv5LckeTuJFckOSxJpvl6n5Lk+CQ/6/Z3e5KrkvxTkvnT/GeTpLHmFQNJ0sMkORL4FPBr4GVVdeUE9fYF/gV4FPBvwLXAM4BXAfsmeXFV/WjIpicCL6QNUfoG8PuB9TsA7we+D/xPYNOuze8m2aaqrunrw9pdG3sCVwFnAEuBPbr97AAcPMXrXRe4BJgP/Afw9e41bQq8EjgLWDxZG5I0FxgMJEkPSfI3wFHANcDeVXX9BPU2pH0I/x2wa1Vd1bfuecB/Al8Enj9k822Abarq5xN0Yz/goKo6va/Nw4HPAkcAb++r+0FaKPg08O6qeqCr/yjgS8Abk5xdVd+Y5GXvBTwT+GRVvWfgda5DG0olSXOeQ4kkSf2OAu6jDR8aGgo6BwPzgKP7QwFAVf0YOBnYPsnmQ7b9+CShAODC/lDQ+SJtWNMOvYLuw//hwK/oCwVdHx4A3t39+oZJ9tXvnsGCqlpaVXdOc3tJGmteMZAk9TsP2Bv45yT7VNXtE9TbuVtum+TYIeuf0y23Aq4eWHfZFH34wWBBVS1NcjOwfl/xVsB6tCFPH5hgOsG9Xb3JXADcCByd5Pm0IU4XA1dW1YNTbCtJc4bBQJLU7wDgK8D+tDH9e1XVLUPqbdgt3zJFe08YUnbTFNtMFEbup439H+zDFsAxK9iHh1TV7Ul2Ao6lDWPap1t1c5ITgY9W1f1T9FmSxp5DiSRJD6mqpbSJvl8BtgUWJnnKkKpLuuVzqyqT/JwxbDcz1N1eH86eog/L3SFpuQ5V/aKqDgWeDPwxcCQtoBxLmwgtSXOewUCS9DDdt+OvB74MPBe4MMkmA9Uu7Za7rsq+DfgpcCewc5IZuQJeVQ9W1U+q6jO0IVUAB85E25K0ujMYSJKW003ePQT4PLA5cNHA/fy/BNwBfCjJ9oPbJ3lUkt1muY/30e5UtAlwQpLHDunH05JMOscgyR8n2XTIqid3y7tXurOSNAacYyBJGqqqCnhrknuAdwDfS7JHVV1TVTcneTXwVeCy7iFl/5c2TOgZwC7AE5lifP8MOAZ4Hu3uRAck+S5wA+1D/WbAC4D3AosmaWNv4G+TXEJ7FsLNtNdwAO1OSJ+Ytd5L0mrEYCBJmlRVvTPJ3cD7aFcO9qyqn1bV+Um2Bv6K9iyAF9EeLnYjcD4tNMx23+5Lsj9wEPBG2uThJ9A+3F8HHA2cOUUz3wKeThsWdSAt0NwE/DtwfFVdOsm2kjRnpH0hJEmSJGlN5hwDSZIkSQYDSZIkSQYDSZIkSRgMJEmSJGEwkCRJkoTBQJIkSRIGA0mSJEkYDCRJkiRhMJAkSZKEwUCSJEkSBgNJkiRJGAwkSZIkYTCQJEmShMFAkiRJEgYDSZIkSRgMJEmSJGEwkCRJkgT8f97WFKdgVGCqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 205,
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 387
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_kernel = svc_scores()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC using kernel linear\n",
      "Acuuracy =  0.977 Sensitivity = 0.949 Specifity = 0.991  Precision = 0.982\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 206,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model(best_kernel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#Decision Tree Classifier\n",
    "# use the Decision Tree Classifier between a set of max_features and see which returns the best accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def dt_model_index_max():\n",
    "    dt_scores = []\n",
    "    for i in range(1, (X.shape[1]) + 1):\n",
    "        dt_classifier = DecisionTreeClassifier(max_features = i, random_state = 0)\n",
    "        dt_classifier.fit(X_train, y_train)\n",
    "        dt_scores.append(dt_classifier.score(X_test, y_test))\n",
    "    best_num_features = np.argmax(dt_scores)+1\n",
    "    plt.plot([i for i in range(1, (X.shape[1]) + 1)], dt_scores, color =     'green')\n",
    "    for i in range(1, (X.shape[1]) + 1):\n",
    "        plt.text(i, dt_scores[i-1],  (i))\n",
    "        plt.xticks([i for i in range(1, X.shape[1] + 1)])\n",
    "        plt.xlabel('Max features')\n",
    "        plt.ylabel('Scores')\n",
    "        plt.title('Decision Tree Classifier scores for different number of maximum features')\n",
    "    print(\"max score is {} - at {} features\".format(dt_scores[best_num_features-1], best_num_features))\n",
    "    return(best_num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def dt_model(num_features):\n",
    "    dt_classifier = DecisionTreeClassifier(max_features=num_features, random_state = 0)\n",
    "    dt=dt_classifier\n",
    "    dt.fit(X_train, y_train)\n",
    "    y_pred=dt.predict(X_train)\n",
    "    draw_confusion_matrix(y_train,y_pred)\n",
    "    y_pred=dt.predict(X_test)\n",
    "    draw_confusion_matrix(y_test,y_pred)\n",
    "    return dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max score is 0.9590643274853801 - at 5 features\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 210,
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 456
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_num_features = dt_model_index_max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuuracy =  1.0 Sensitivity = 1.0 Specifity = 1.0  Precision = 1.0\n",
      "Acuuracy =  0.959 Sensitivity = 0.966 Specifity = 0.955  Precision = 0.919\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'splitter': 'best',\n",
       " 'max_depth': None,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'max_features': 5,\n",
       " 'random_state': 0,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'class_weight': None,\n",
       " 'presort': False,\n",
       " 'n_features_': 30,\n",
       " 'n_outputs_': 1,\n",
       " 'classes_': array([0, 1]),\n",
       " 'n_classes_': 2,\n",
       " 'max_features_': 5,\n",
       " 'tree_': <sklearn.tree._tree.Tree at 0x7f1637239578>}"
      ]
     },
     "execution_count": 211,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 211,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 211,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = dt_model(best_num_features)\n",
    "dt.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def find_features_importance(model):\n",
    "    from sklearn.feature_selection import SelectFromModel\n",
    "    sel = SelectFromModel(model)\n",
    "    sel.fit(X_train, y_train)\n",
    "    selected_feat= X.columns[sel.get_support()]\n",
    "    print(selected_feat, len(selected_feat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['concave points.mean', 'concave points.std', 'texture.w', 'perimeter.w',\n",
      "       'area.w'],\n",
      "      dtype='object') 5\n"
     ]
    }
   ],
   "source": [
    "find_features_importance(DecisionTreeClassifier(max_features=10, random_state = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['perimeter.mean', 'area.mean', 'concave points.mean', 'area.std',\n",
      "       'radius.w', 'perimeter.w', 'area.w', 'concavity.w', 'concave points.w'],\n",
      "      dtype='object') 9\n"
     ]
    }
   ],
   "source": [
    "find_features_importance(RandomForestClassifier(n_estimators=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "def rf_model():\n",
    "    rf_classifier = RandomForestClassifier(n_estimators = 100)\n",
    "    rf=rf_classifier\n",
    "    rf.fit(X_train, y_train)\n",
    "    y_pred=rf.predict(X_test)\n",
    "    draw_confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuuracy =  0.971 Sensitivity = 0.966 Specifity = 0.973  Precision = 0.95\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 216,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "hidden_1 (Dense)             (None, 32)                992       \n",
      "_________________________________________________________________\n",
      "hidden_2 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "output (Dense)               (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 1,537\n",
      "Trainable params: 1,537\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING\n",
      "Acuuracy =  0.975 Sensitivity = 0.941 Specifity = 0.996  Precision = 0.993\n",
      "VALIDATION\n",
      "Acuuracy =  0.982 Sensitivity = 0.966 Specifity = 0.991  Precision = 0.983\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Loss: 0.096       ||   Train Accuarcy: 0.975\n",
      "Validation Loss: 0.095  ||   Validation Accuarcy: 0.982 \n",
      "\n",
      "{'batch_size': 32, 'epochs': 48, 'steps': None, 'samples': 398, 'verbose': 0, 'do_validation': True, 'metrics': ['loss', 'acc', 'val_loss', 'val_acc']}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 217,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 217,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "execution_count": 217,
     "metadata": {
      "image/png": {
       "height": 603,
       "width": 610
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "execution_count": 217,
     "metadata": {
      "image/png": {
       "height": 603,
       "width": 610
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KERAS MODEL\n",
    "#NOTE: WE CAN ADD CHANGES TO IMPROVE THE VALIDATION, SPLIT AND SHUFFLE PROCCESSES\n",
    "\n",
    "def plot_history(history):\n",
    "    # Plot training & validation accuracy values\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.plot(history.history['acc'],'b-', label='Validate')\n",
    "    plt.plot(history.history['val_acc'], 'r-', label='Train')\n",
    "    plt.title('Model accuracy')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['Train', 'Test'], loc='upper left')\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "    # Plot training & validation loss values\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.plot(history.history['loss'],'b-', label='Val')\n",
    "    plt.plot(history.history['val_loss'], 'r-', label='Train')\n",
    "    plt.title('Model loss')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(['Train', 'Test'], loc='upper left')\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "def deep_learning_model(epochs_to_run=48):\n",
    "    import keras\n",
    "    #`Sequential` from `keras.models`\n",
    "    #`Dense` from `keras.layers`\n",
    "    n=X_train.shape[1]\n",
    "\n",
    "    # Initialize the constructor\n",
    "    model = keras.models.Sequential()\n",
    "    # Add first hidden layer\n",
    "    model.add(keras.layers.Dense(input_shape=(n,), units=32, activation=\"relu\", name=\"hidden_1\"))\n",
    "    # Add second hidden layer\n",
    "    model.add(keras.layers.Dense(units=16, activation='relu', name=\"hidden_2\"))\n",
    "    # Add output layer\n",
    "    model.add(keras.layers.Dense(units=1, activation='sigmoid', name=\"output\"))\n",
    "    model.summary()\n",
    "\n",
    "    model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['acc'])  # sample_weigths=True\n",
    "    initial_weights = model.get_weights()\n",
    "    history = model.fit(X_train, y_train, batch_size=32, epochs=epochs_to_run, validation_data=[X_test, y_test], shuffle=True, verbose=0)\n",
    "    print(\"TRAINING\")\n",
    "    y_pred = np.round(model.predict(X_train))\n",
    "    draw_confusion_matrix(y_train,y_pred)\n",
    "    print(\"VALIDATION\")\n",
    "    y_pred = np.round(model.predict(X_test))\n",
    "    draw_confusion_matrix(y_test,y_pred)\n",
    "    score1 = model.evaluate(X_train, y_train, batch_size=32, verbose=0)\n",
    "    score2 = model.evaluate(X_test, y_test, batch_size=32, verbose=0)\n",
    "    print(\"Train Loss: %.3f       ||   Train Accuarcy: %.3f\" % (score1[0],score1[1]))\n",
    "    print(\"Validation Loss: %.3f  ||   Validation Accuarcy: %.3f \\n\" % (score2[0],score2[1]))\n",
    "    print(history.params)\n",
    "    plot_history(history)\n",
    "    return model, initial_weights\n",
    "\n",
    "model, initial_weights = deep_learning_model(epochs_to_run=48)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Our Implementation of Neural Network\n",
    "-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
    "#MODEL 1\n",
    "#GENERIC NEURAL NETWORK WITH BACKPROP AND BINARY CROSSENTROPY LOSS FUNCTION + SGD OPTIMIZER\n",
    "#THERE WERE MADE CHANGES IN THE WHOLE CODE, THAT WITHOUT THEM, THIS MODEL IS GONNA WORK DIFFERENTLY\n",
    "# symbols (A,Z,W,b) follow this article: https://towardsdatascience.com/lets-code-a-neural-network-in-plain-numpy-ae7e74410795\n",
    "\n",
    "class Layer:\n",
    "    def __init__(self, input_shape, units, activation, seed = 2):\n",
    "        np.random.seed(seed)\n",
    "        num_features = input_shape[-1]\n",
    "        limit = np.sqrt(6/(num_features+units))\n",
    "        self.W = np.random.uniform(low=-limit, high=limit, size=[num_features, units])\n",
    "        self.b = np.zeros(shape=[units])  # np.random.randn(1, units)\n",
    "        if activation == \"relu\":\n",
    "            self.activation = lambda x: np.maximum(x, 0)\n",
    "            self.derivative = lambda x: x > 0\n",
    "        elif activation == \"sigmoid\":\n",
    "            self.activation = lambda x: 1/(1+np.exp(-x))\n",
    "            self.derivative = lambda x: self.activation(x)*(1-self.activation(x)) # (1/(1+np.exp(-x)))*(1-(1/(1+np.exp(-x))))\n",
    "        else:\n",
    "            # if you want to add another activation function, you must implement both self.activation and self.derivative\n",
    "            raise Exception(\"unknown activation\")\n",
    "\n",
    "    def forward(self, input):\n",
    "        # the function calculates, saves and returns the output of the layer given an input\n",
    "        self.A = input\n",
    "        self.Z = np.dot(input, self.W) + self.b\n",
    "        return self.activation(self.Z)\n",
    "\n",
    "    def backward(self, dA):\n",
    "        # the function calculates the changes in bias and weights of layer, given error dA\n",
    "        dZ = dA * self.derivative(self.Z)\n",
    "        self.db = np.mean(dZ, axis=0)\n",
    "        self.dW = np.mean(np.dot(self.A.T, dZ), axis=0)\n",
    "        dA = np.dot(self.W, dZ.T)  # this is the error we send to the previous layer\n",
    "        return dA.T\n",
    "\n",
    "    def update(self, learning_rate):\n",
    "        # we use the simplest update rule gradient descent (no momentum, no adaptive learning rate)\n",
    "        self.W -= learning_rate * self.dW\n",
    "        self.b -= learning_rate * self.db\n",
    "\n",
    "\n",
    "class Network:\n",
    "    def __init__(self):\n",
    "        # contains the layers\n",
    "        self.layers = []\n",
    "\n",
    "    def add_layer(self, layer):\n",
    "        if len(self.layers) != 0:\n",
    "            assert(self.layers[-1].W.shape[-1] == layer.W.shape[0]) # check compatabillity of last layer's output with the new layer's input\n",
    "        self.layers.append(layer)\n",
    "\n",
    "    def forward(self, input):\n",
    "        #forward passing the input through the layers\n",
    "        output = input\n",
    "        for layer in self.layers:\n",
    "            output = layer.forward(output)\n",
    "        return output\n",
    "\n",
    "    def loss(self, y_true, y_pred):\n",
    "        y_pred = np.clip(y_pred, 1E-7, 1-(1E-7)) # clip values to avoid undefined log(0)\n",
    "        return np.mean(-y_true * np.log(y_pred) - (1-y_true) * np.log(1-y_pred)) #loss function of binary_crossentropy\n",
    "\n",
    "    def loss_derivative(self, y_true, y_pred):\n",
    "        y_pred = np.clip(y_pred, 1E-7, 1-(1E-7)) # clip values to avoid undefined log(0)\n",
    "        return np.mean(-y_true/y_pred + (1-y_true)/(1-y_pred)) # derivative of binary_crossentropy\n",
    "\n",
    "    def backward(self, y_true, y_pred, learning_rate):\n",
    "        # the initial error is given by the derivative of the loss\n",
    "        dA = self.loss_derivative(y_true, y_pred)\n",
    "        for layer in reversed(self.layers):  # moving in backward direction fron the output to input\n",
    "            # each layer is responsible for backward propogating throuth itself - it only needs the error and it returns error for the next layer\n",
    "            dA = layer.backward(dA)\n",
    "            # each layer update itself using the learning rate and its calculated corrections\n",
    "            layer.update(learning_rate)\n",
    "\n",
    "    def train_step(self, input, y_true, learning_rate):\n",
    "        # train the network given an input, ground_truth and learning rate\n",
    "        # returns the loss at this step (before the changes of the weights)\n",
    "        y_pred = self.forward(input)\n",
    "        loss = self.loss(y_true, y_pred)\n",
    "        self.backward(y_true, y_pred, learning_rate)\n",
    "        return y_pred, loss\n",
    "\n",
    "    def train(self, X, Y, epochs, learning_rate=0.01 ,batch_size=32, validation_data=None, shuffle = True):\n",
    "        # main training loop\n",
    "        # train with X and Y for given number of epochs\n",
    "        # can handle validation data for displaying validation loss\n",
    "        # training data (X) and targets (Y) can be shuffled at each epoch\n",
    "        losses = []\n",
    "        val_loss = []\n",
    "        if Y.ndim == 1:\n",
    "            Y = np.expand_dims(Y, axis=-1)\n",
    "        if validation_data is not None:\n",
    "            if validation_data[1].ndim == 1:\n",
    "                validation_data[1] = np.expand_dims(validation_data[1], axis=-1)\n",
    "        num_batches = np.ceil(X.shape[0] / batch_size).astype(int)\n",
    "        indices = list(range(len(X)))\n",
    "        for k in range(epochs):\n",
    "            epoch_loss = []\n",
    "            for batch in range(num_batches):\n",
    "                x = X[batch*batch_size : (batch+1)*batch_size]\n",
    "                y = Y[batch*batch_size : (batch+1)*batch_size]\n",
    "                _, loss = self.train_step(x, y, learning_rate)\n",
    "                epoch_loss.append(loss)\n",
    "            # print(self.layers[-1].b)\n",
    "            losses.append(epoch_loss[-1])\n",
    "            if k % (max(1, epochs//100)) == 0:\n",
    "                if validation_data is not None:\n",
    "                    val_loss.append(self.loss(validation_data[1], self.predict(validation_data[0])))\n",
    "                    print(k, loss, val_loss[-1])\n",
    "                else:\n",
    "                    print(k, loss)\n",
    "            if shuffle:\n",
    "                random.shuffle(indices)\n",
    "                X = X[indices]\n",
    "                Y = Y[indices]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.forward(X)\n",
    "\n",
    "    def display_weigths(self):\n",
    "        for index, layer in enumerate(self.layers):\n",
    "            print(\"W_{}\".format(index))\n",
    "            print(layer.W)\n",
    "            print(\"b_{}\".format(index))\n",
    "            print(layer.b)\n",
    "\n",
    "\n",
    "#The model is overfitting"
   ]
  },
  {
   "cell_type": "code",
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      "297 8.059047875742342 10.556881396878854\n",
      "Acuuracy =  0.345 Sensitivity = 1.0 Specifity = 0.0  Precision = 0.345\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "execution_count": 220,
     "metadata": {
      "image/png": {
       "height": 304,
       "width": 450
      },
      "needs_background": "light"
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "network = Network()\n",
    "network.add_layer(Layer(input_shape=[30], units=32, activation='relu', seed = 2))\n",
    "network.add_layer(Layer(input_shape=[32], units=16, activation='sigmoid', seed = 2))\n",
    "network.add_layer(Layer(input_shape=[16], units=1, activation='sigmoid', seed = 2))\n",
    "for layer, w, b in zip(network.layers, initial_weights[::2], initial_weights[1::2]): #intializes weights the same as keras\n",
    "    layer.W = w\n",
    "    layer.b = b\n",
    "network.train(X_train, y_train, epochs=300, learning_rate=0.13, validation_data=[X_test, y_test])\n",
    "\n",
    "y_pred = network.predict(X_test)\n",
    "draw_confusion_matrix(y_test, np.round(y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#MODEL 1\n",
    "#GENERIC NEURAL NETWORK WITH BACKPROP AND BINARY CROSSENTROPY LOSS FUNCTION + SGD OPTIMIZER\n",
    "#THERE WERE MADE CHANGES IN THE WHOLE CODE, THAT WITHOUT THEM, THIS MODEL IS GONNA WORK DIFFERENTLY\n",
    "# symbols (A,Z,W,b) follow this article: https://towardsdatascience.com/lets-code-a-neural-network-in-plain-numpy-ae7e74410795\n",
    "\n",
    "\n",
    "class Layer:\n",
    "    def __init__(self, input_shape, units, activation, seed = 2):\n",
    "        np.random.seed(seed)\n",
    "        num_features = input_shape[-1]\n",
    "        limit = np.sqrt(6/(num_features+units))\n",
    "        self.W = np.random.uniform(low=-limit, high=limit, size=[num_features, units])\n",
    "        self.b = np.zeros(shape=[units])  # np.random.randn(1, units)\n",
    "        if activation == \"relu\":\n",
    "            self.activation = lambda x: np.maximum(x, 0)\n",
    "            self.derivative = lambda x: x > 0\n",
    "        elif activation == \"sigmoid\":\n",
    "            self.activation = lambda x: 1/(1+np.exp(-x))\n",
    "            self.derivative = lambda x: self.activation(x)*(1-self.activation(x)) # (1/(1+np.exp(-x)))*(1-(1/(1+np.exp(-x))))\n",
    "        else:\n",
    "            # if you want to add another activation function, you must implement both self.activation and self.derivative\n",
    "            raise Exception(\"unknown activation\")\n",
    "        self.db = np.zeros_like(self.b)\n",
    "        self.dW = np.zeros_like(self.W)\n",
    "\n",
    "    def forward(self, input):\n",
    "        # the function calculates, saves and returns the output of the layer given an input\n",
    "        self.A = input\n",
    "        self.Z = np.dot(input, self.W) + self.b\n",
    "        return self.activation(self.Z)\n",
    "\n",
    "    def backward(self, dA, optimizer):\n",
    "        # the function calculates the changes in bias and weights of layer, given error dA\n",
    "        self.dZ = dA * self.derivative(self.Z)\n",
    "        dA = np.dot(self.dZ, self.W.T)  # this is the error we send to the previous layer\n",
    "        self.db = np.mean(self.dZ, axis=0) * optimizer.learning_rate\n",
    "        self.dW = np.mean(np.dot(self.A.T, self.dZ), axis=0) * optimizer.learning_rate\n",
    "        # self.db = optimizer(last_value=self.db, change=np.mean(self.dZ, axis=0)) if with optimizer\n",
    "        # self.dW = optimizer(last_value=self.dW, change=np.mean(np.dot(self.A.T, self.dZ), axis=0)) if with optimizer\n",
    "        return dA\n",
    "\n",
    "    def update(self):\n",
    "        # we use the simplest update rule gradient descent (no momentum, no adaptive learning rate)\n",
    "        self.W -= self.dW\n",
    "        self.b -= self.db\n",
    "\n",
    "\n",
    "class Optimizer:\n",
    "    # gradient descent with momentum\n",
    "    def __init__(self, learning_rate, momentum=0):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.momentum = momentum\n",
    "\n",
    "    def __call__(self, last_value, change):\n",
    "        x = self.momentum * last_value + self.learning_rate * change\n",
    "        return x\n",
    "\n",
    "\n",
    "class Network:\n",
    "    def __init__(self):\n",
    "        # contains the layers\n",
    "        self.layers = []\n",
    "\n",
    "    def add_layer(self, layer):\n",
    "        if len(self.layers) != 0:\n",
    "            assert(self.layers[-1].W.shape[-1] == layer.W.shape[0]) # check compatabillity of last layer's output with the new layer's input\n",
    "        self.layers.append(layer)\n",
    "\n",
    "    def forward(self, input):\n",
    "        #forward passing the input through the layers\n",
    "        output = input\n",
    "        for index, layer in enumerate(self.layers):\n",
    "            output = layer.forward(output)\n",
    "        return output\n",
    "\n",
    "    def loss(self, y_true, y_pred):\n",
    "        y_pred = np.clip(y_pred, 1E-7, 1-(1E-7)) # clip values to avoid undefined log(0)\n",
    "        return np.mean(-y_true * np.log(y_pred) - (1-y_true) * np.log(1-y_pred)) #loss function of binary_crossentropy\n",
    "\n",
    "    def loss_derivative(self, y_true, y_pred):\n",
    "        y_pred = np.clip(y_pred, 1E-7, 1-(1E-7)) # clip values to avoid undefined log(0)\n",
    "        return np.mean(-y_true/y_pred + (1-y_true)/(1-y_pred)) # derivative of binary_crossentropy\n",
    "\n",
    "    def backward(self, y_true, y_pred, optimizer):\n",
    "        # the initial error is given by the derivative of the loss\n",
    "        dA = self.loss_derivative(y_true, y_pred)\n",
    "        for layer in reversed(self.layers):  # moving in backward direction fron the output to input\n",
    "            # each layer is responsible for backward propogating throuth itself - it only needs the error and it returns error for the next layer\n",
    "            dA = layer.backward(dA, optimizer)\n",
    "            # each layer update itself using the learning rate and its calculated corrections\n",
    "            # the optimizer tells the layer what should be the changes applied to its weights\n",
    "            layer.update()\n",
    "\n",
    "    def train_step(self, input, y_true, optimizer):\n",
    "        # train the network given an input, ground_truth and learning rate\n",
    "        # returns the loss at this step (before the changes of the weights)\n",
    "        y_pred = self.forward(input)\n",
    "        loss = self.loss(y_true, y_pred)\n",
    "        self.backward(y_true, y_pred, optimizer)\n",
    "        return y_pred, loss\n",
    "\n",
    "    def train(self, X, Y, epochs, optimizer ,batch_size=32, validation_data=None, shuffle = True, verbose=0):\n",
    "        # main training loop\n",
    "        # train with X and Y for given number of epochs\n",
    "        # can handle validation data for displaying validation loss\n",
    "        # training data (X) and targets (Y) can be shuffled at each epoch\n",
    "        losses = []\n",
    "        val_loss = []\n",
    "        if Y.ndim == 1:\n",
    "            Y = np.expand_dims(Y, axis=-1)\n",
    "        if validation_data is not None:\n",
    "            if validation_data[1].ndim == 1:\n",
    "                validation_data[1] = np.expand_dims(validation_data[1], axis=-1)\n",
    "        num_batches = np.ceil(X.shape[0] / batch_size).astype(int)\n",
    "        print(\"num_batches = \", num_batches)\n",
    "        indices = list(range(len(X)))\n",
    "        for k in range(epochs):\n",
    "            batch_loss = []\n",
    "            for batch in range(num_batches):\n",
    "                x = X[batch*batch_size : (batch+1)*batch_size]\n",
    "                y = Y[batch*batch_size : (batch+1)*batch_size]\n",
    "                _, loss = self.train_step(x, y, optimizer)\n",
    "                batch_loss.append(loss)\n",
    "            # print(self.layers[-1].b)\n",
    "            losses.append(np.mean(batch_loss))\n",
    "            if verbose > 0:\n",
    "                if k % (max(1, epochs//100)) == 0:\n",
    "                    if validation_data is not None:\n",
    "                        val_loss.append(self.loss(validation_data[1], self.predict(validation_data[0])))\n",
    "                        print(k, losses[-1], val_loss[-1])\n",
    "                    else:\n",
    "                        print(k, losses[-1])\n",
    "            if shuffle:\n",
    "                random.shuffle(indices)\n",
    "                X = X[indices]\n",
    "                Y = Y[indices]\n",
    "\n",
    "    def predict(self, X):\n",
    "        return self.forward(X)\n",
    "\n",
    "    def display_weigths(self):\n",
    "        for index, layer in enumerate(self.layers):\n",
    "            print(\"W_{}\".format(index))\n",
    "            print(layer.W)\n",
    "            print(\"b_{}\".format(index))\n",
    "            print(layer.b)\n",
    "\n",
    "\n",
    "#The model is overfitting\n",
    "\n",
    "import random\n",
    "for k in range(2):\n",
    "    network = Network()\n",
    "    network.add_layer(Layer(input_shape=[30], units=32, activation='relu', seed = 2))\n",
    "    network.add_layer(Layer(input_shape=[32], units=16, activation='sigmoid', seed = 2))\n",
    "    network.add_layer(Layer(input_shape=[16], units=1, activation='sigmoid', seed = 2))\n",
    "#     for layer, w, b in zip(network.layers, initial_weights[::2], initial_weights[1::2]): #intializes weights the same as keras\n",
    "#         layer.W = w\n",
    "#         layer.b = b\n",
    "    optimizer = Optimizer(learning_rate=0.1, momentum=0.0)\n",
    "    network.train(X_train, y_train, epochs=300, optimizer=optimizer, validation_data=[X_test, y_test])\n",
    "    y_pred = network.predict(X_test)\n",
    "    draw_confusion_matrix(y_test, np.round(y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#MODEL 2\n",
    "#THIS MODEL WAS MODIFIED (EDITED) BY ME\n",
    "#IT'S SUPPOSED TO WORK AND I DON'T FIND A MISTAKE YET\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(100)\n",
    "class Layer:\n",
    "    \"\"\"\n",
    "    Represents a layer (hidden or output) in our neural network.\n",
    "    \"\"\"\n",
    "    def __init__(self, n_input, n_neurons, activation=None, weights=None, bias=None):\n",
    "        \"\"\"\n",
    "        :param int n_input: The input size (coming from the input layer or a previous hidden layer)\n",
    "        :param int n_neurons: The number of neurons in this layer.\n",
    "        :param str activation: The activation function to use (if any).\n",
    "        :param weights: The layer's weights.\n",
    "        :param bias: The layer's bias.\n",
    "        \"\"\"\n",
    "        self.weights = weights if weights is not None else np.random.rand(n_input, n_neurons) * 0.01\n",
    "        self.activation = activation\n",
    "        self.bias = bias if bias is not None else np.random.rand(n_neurons)\n",
    "        self.last_activation = None\n",
    "        self.error = None\n",
    "        self.delta = None\n",
    "    def activate(self, x):\n",
    "        \"\"\"\n",
    "        Calculates the dot product of this layer.\n",
    "        :param x: The input.\n",
    "        :return: The result.\n",
    "        \"\"\"\n",
    "        r = np.dot(x, self.weights) + self.bias\n",
    "        self.last_activation = self._apply_activation(r)\n",
    "        return self.last_activation\n",
    "    def _apply_activation(self, r):\n",
    "        \"\"\"\n",
    "        Applies the chosen activation function (if any).\n",
    "        :param r: The normal value.\n",
    "        :return: The \"activated\" value.\n",
    "        \"\"\"\n",
    "        # In case no activation function was chosen\n",
    "        if self.activation is None:\n",
    "            return r\n",
    "        # tanh\n",
    "        if self.activation == 'tanh':\n",
    "            return np.tanh(r)\n",
    "        # sigmoid\n",
    "        if self.activation == 'sigmoid':\n",
    "            return 1 / (1 + np.exp(-r))\n",
    "        #relu\n",
    "        if self.activation == 'relu':\n",
    "            return np.maximum(r, 0)\n",
    "        return r\n",
    "    def apply_activation_derivative(self, r):\n",
    "        \"\"\"\n",
    "        Applies the derivative of the activation function (if any).\n",
    "        :param r: The normal value.\n",
    "        :return: The \"derived\" value.\n",
    "        \"\"\"\n",
    "        # We use 'r' directly here because its already activated, the only values that are used in this function are the last activations that were saved.\n",
    "        if self.activation is None:\n",
    "            return r\n",
    "        if self.activation == 'tanh':\n",
    "            return 1 - r ** 2\n",
    "        if self.activation == 'sigmoid':\n",
    "            return r * (1 - r)\n",
    "        if self.activation == 'relu':\n",
    "            return r > 0\n",
    "        return r\n",
    "\n",
    "\n",
    "class NeuralNetwork:\n",
    "    \"\"\"\n",
    "    Represents a neural network.\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self._layers = []\n",
    "    def add_layer(self, layer):\n",
    "        \"\"\"\n",
    "        Adds a layer to the neural network.\n",
    "        :param Layer layer: The layer to add.\n",
    "        \"\"\"\n",
    "        self._layers.append(layer)\n",
    "    def loss(self, y_true, y_pred):\n",
    "        y_pred = np.clip(y_pred, 1E-7, 1-(1E-7))\n",
    "        return np.mean((-y_true * np.log(y_pred) - (1-y_true) * np.log(1-y_pred)))\n",
    "    def loss_derivative(self, y_true, y_pred):\n",
    "        y_pred = np.clip(y_pred, 1E-7, 1-(1E-7))\n",
    "        return np.mean((-y_true/y_pred + (1-y_true)/(1-y_pred)))\n",
    "    def feed_forward(self, X):\n",
    "        \"\"\"\n",
    "        Feed forward the input through the layers.\n",
    "        :param X: The input values.\n",
    "        :return: The result.\n",
    "        \"\"\"\n",
    "        for layer in self._layers:\n",
    "            X = layer.activate(X)\n",
    "        return X\n",
    "    def predict(self, X):\n",
    "        \"\"\"\n",
    "        Predicts a class (or classes).\n",
    "        :param X: The input values.\n",
    "        :return: The predictions.\n",
    "        \"\"\"\n",
    "        ff = self.feed_forward(X)\n",
    "        # One row\n",
    "        if ff.ndim == 1:\n",
    "            return np.argmax(ff)\n",
    "        # Multiple rows\n",
    "        return np.argmax(ff, axis=1)\n",
    "    def backpropagation(self, X, y, learning_rate):\n",
    "        \"\"\"\n",
    "        Performs the backward propagation algorithm and updates the layers weights.\n",
    "        :param X: The input values.\n",
    "        :param y: The target values.\n",
    "        :param float learning_rate: The learning rate (between 0 and 1).\n",
    "        \"\"\"\n",
    "        # Feed forward for the output\n",
    "        output = self.feed_forward(X)\n",
    "        # Loop over the layers backward\n",
    "        for i in reversed(range(len(self._layers))):\n",
    "            layer = self._layers[i]\n",
    "            # If this is the output layer\n",
    "            if layer == self._layers[-1]:\n",
    "                layer.error = y - output\n",
    "                # The output = layer.last_activation in this case\n",
    "                layer.delta = layer.error * layer.apply_activation_derivative(output)\n",
    "            else:\n",
    "                next_layer = self._layers[i + 1]\n",
    "                layer.error = np.dot(next_layer.weights, next_layer.delta)\n",
    "                layer.delta = layer.error * layer.apply_activation_derivative(layer.last_activation)\n",
    "        # Update the weights\n",
    "        for i in range(len(self._layers)):\n",
    "            layer = self._layers[i]\n",
    "            # The input is either the previous layers output or X itself (for the first hidden layer)\n",
    "            input_to_use = np.atleast_2d(X if i == 0 else self._layers[i - 1].last_activation)\n",
    "            layer.weights += layer.delta * input_to_use.T * learning_rate\n",
    "\n",
    "    def train(self, X, y, learning_rate, max_epochs):\n",
    "        \"\"\"\n",
    "        Trains the neural network using backpropagation.\n",
    "        :param X: The input values.\n",
    "        :param y: The target values.\n",
    "        :param float learning_rate: The learning rate (between 0 and 1).\n",
    "        :param int max_epochs: The maximum number of epochs (cycles).\n",
    "        :return: The list of calculated loss errors.\n",
    "        \"\"\"\n",
    "        losses = []\n",
    "        for i in range(max_epochs):\n",
    "            for j in range(len(X)):\n",
    "                self.backpropagation(X[j], y[j], learning_rate)\n",
    "            if i == 0 or (i + 1) % 10 == 0:\n",
    "                lossnum = self.loss(y_true=y, y_pred = nn.feed_forward(X))\n",
    "                losses.append(lossnum)\n",
    "                print('Epoch: #%s, Loss: %f' % ((i + 1), float(lossnum)))\n",
    "        return losses\n",
    "\n",
    "    @staticmethod\n",
    "    def accuracy(y_pred, y_true):\n",
    "        \"\"\"\n",
    "        Calculates the accuracy between the predicted labels and true labels.\n",
    "        :param y_pred: The predicted labels.\n",
    "        :param y_true: The true labels.\n",
    "        :return: The calculated accuracy.\n",
    "        \"\"\"\n",
    "        print(len(y_pred == y_true))\n",
    "        return (y_pred == y_true).mean()\n",
    "\n",
    "nn = NeuralNetwork()\n",
    "nn.add_layer(Layer(30, 32, activation='relu', weights=initial_weights[0], bias=initial_weights[1]))\n",
    "nn.add_layer(Layer(32, 16, activation='relu', weights=initial_weights[2], bias=initial_weights[3]))\n",
    "nn.add_layer(Layer(16, 1, activation='sigmoid', weights=initial_weights[4], bias=initial_weights[5]))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def plot_history(X):\n",
    "    # Plot training & validation accuracy values\n",
    "    plt.figure(figsize=(10,10))\n",
    "    plt.plot(X,'b-')\n",
    "    plt.title('Model Loss')\n",
    "    plt.title('Changes in Loss')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epoch (every 10th)')\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "# Define train data\n",
    "X1 = np.array(X_train)\n",
    "y1 = np.array(y_train)\n",
    "# Train the neural network\n",
    "losses1 = nn.train(X1, y1, 0.01, 10000)\n",
    "print('Accuracy: %.2f%%' % (nn.accuracy(nn.predict(X1), y1.flatten()) * 100))\n",
    "# Plot changes in loss\n",
    "plot_history(losses1)\n",
    "\n",
    "\n",
    "# Define test data\n",
    "X2 = np.array(X_test)\n",
    "y2 = np.array(y_test)\n",
    "# Train the neural network\n",
    "# losses2 = nn.train(X2, y2, 0.01, 100)\n",
    "# print('Accuracy: %.2f%%' % (nn.accuracy(nn.predict(X2), y2.flatten()) * 100))\n",
    "# Plot changes in loss\n",
    "# plot_history(losses2)"
   ]
  }
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