ML_DM1_SupervisedLearning_KNN Dataset2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "77c31795",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         0.98924731 1.         1.         1.         1.\n",
      " 1.        ]\n",
      "Accuracy score = 0.9984639016897081\n",
      "time for training and predicting the test sets = 0.14321852400462376 s\n",
      "Test score = 0.9923076923076923\n",
      "time for the learning curve = 8.425135347999458 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f971a7ac940>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dataset 2 : Wines\n",
    "# Output : Type of wine : red or white\n",
    "# KNeighborsClassifier() --> weights = 'uniform'\n",
    "\n",
    "import numpy as np\n",
    "import time\n",
    "import pandas as pd\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# split your datas between training and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "# comparing datas\n",
    "from sklearn.metrics import accuracy_score\n",
    "# model \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# validation curve\n",
    "from yellowbrick.model_selection import validation_curve\n",
    "# learning curve\n",
    "from sklearn.model_selection import learning_curve\n",
    "# will be used to convert string into float\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "labelencoder_X = LabelEncoder()\n",
    "\n",
    "#dataset\n",
    "a = io.open('wine-quality-white-and-red.csv', encoding='latin-1')\n",
    "df = pd.read_csv(a)\n",
    "df = df.take([10*i for i in range(650)], axis = 0)\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "#input\n",
    "X = df.drop(columns=['type'])\n",
    "\n",
    "#output\n",
    "y = df['type']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "#time before training the model\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "model = KNeighborsClassifier(weights = 'uniform')\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "validation = cross_val_score(model, X, y, cv=7)\n",
    "print(validation)\n",
    "print('Accuracy score =', validation.mean())\n",
    "\n",
    "#Testing the test set\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "\n",
    "#time after predicting the test sets\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time for training and predicting the test sets =',timeA,'s')\n",
    "\n",
    "print('Test score =', score)\n",
    "\n",
    "#Plot the learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(KNeighborsClassifier(weights = 'uniform') , X , y , train_sizes=np.linspace(0.01,1,50), cv=7, scoring = 'accuracy', n_jobs= -1)\n",
    "\n",
    "#time after doing the learning curve\n",
    "time3 = time.perf_counter()\n",
    "timeB = time3 - time2\n",
    "print('time for the learning curve =',timeB,'s')\n",
    "\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc ='best')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4ec70f2f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         0.98924731 1.         1.         1.         1.\n",
      " 1.        ]\n",
      "Accuracy score = 0.9984639016897081\n",
      "time for training and predicting the test sets = 0.07064245799847413 s\n",
      "Test score = 0.9923076923076923\n",
      "time for the learning curve = 7.446920772999874 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f971a5c5eb0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dataset 2 : Wines\n",
    "# Output : Type of wine : red or white\n",
    "# KNeighborsClassifier() --> weights = 'distance'\n",
    "\n",
    "import numpy as np\n",
    "import time\n",
    "import pandas as pd\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# split your datas between training and test\n",
    "from sklearn.model_selection import train_test_split\n",
    "# comparing datas\n",
    "from sklearn.metrics import accuracy_score\n",
    "# model \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# validation curve\n",
    "from yellowbrick.model_selection import validation_curve\n",
    "# learning curve\n",
    "from sklearn.model_selection import learning_curve\n",
    "# will be used to convert string into float\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "labelencoder_X = LabelEncoder()\n",
    "\n",
    "#dataset\n",
    "a = io.open('wine-quality-white-and-red.csv', encoding='latin-1')\n",
    "df = pd.read_csv(a)\n",
    "df = df.take([10*i for i in range(649)], axis = 0)\n",
    "df = df.apply(LabelEncoder().fit_transform)\n",
    "\n",
    "#input\n",
    "X = df.drop(columns=['type'])\n",
    "\n",
    "#output\n",
    "y = df['type']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)\n",
    "\n",
    "model = KNeighborsClassifier(weights = 'distance')\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "\n",
    "validation = cross_val_score(model, X, y, cv=7)\n",
    "\n",
    "print(validation)\n",
    "print('Accuracy score =', validation.mean())\n",
    "\n",
    "#Testing the test set\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "\n",
    "#time after training and predicting the test sets\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time for training and predicting the test sets =',timeA,'s')\n",
    "\n",
    "print('Test score =', score)\n",
    "\n",
    "#Plot the learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(KNeighborsClassifier(weights = 'uniform') , X , y , train_sizes=np.linspace(0.01,1,50), cv=7, scoring = 'accuracy', n_jobs= -1)\n",
    "\n",
    "#time after doing the learning curve\n",
    "time3 = time.perf_counter()\n",
    "timeB = time3 - time2\n",
    "print('time for the learning curve =',timeB,'s')\n",
    "\n",
    "#standard deviation\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "#means\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc ='best')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6539222f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hQ9M+z4Z58+ZRVVXFBRdcgKIoDB06NPnekoMLRaa4lUgk+xNvvvkmXq+XE088EcuyuPrqqzn22GOTpmqJRNIeqewlEsl+xaZNm7jzzjsJh8PE43HmzJnDbbfdlnQolEgk7ZHKXiKRSCSSAxzpoCeRSCQSyQGOVPYSiUQikRzgHHDe+JZlEQwGsdlsWV3HkUgkEolkf0cIQTwex+PxtIspAQegsg8Gg+2yakkkEolEcjAwceLEtIGzDjhl3+KRO3HixJT7rZ2xdu3atPeAJV1HzmXvIeey95Bz2XvIuew9enMuY7EYmzZt6vBWygGn7FtM93a7vUspNLtSV9I5ci57DzmXvYecy95DzmXv0dtz2dHxtXTQk0gkEonkAOeA29lLJBLJ/o5hGFln0esPYrFYf4twwNDVuVRVNSXZU9btutxCIpFIJDnD7/cPaGU6fvz4/hbhgKE7cxmLxfD7/V1ul9Od/apVq3jkkUeSecNb+Pe//81vfvMbdF3nvPPO44ILLsCyLO6++242btyI3W5n4cKFjB49mnXr1vGDH/yAMWPGAHDxxRcns5NJJBLJgYRhGGiaNqBTz8bj8aydnyWd0525tNvthEIhDMPo0g4/Z8r+97//PW+88UYyL3UL8Xic+++/n5dffhmXy8XFF1/MySefzBdffEEsFuPFF19k5cqVPPDAAzzxxBOsX7+eyy+/nO9+97u5ElUikUgGBJZldctEu3TdbhRF4YzDRuRAKslAoyVLZlfImRl/1KhR/OpXv2r3fOvWrYwaNYr8/HzsdjuzZ8/ms88+Y8WKFRx//PFAIjd3S77mtWvX8t5773HppZdy2223dZjCVCKRSA5GInGT65Z8xnVLPiUSN/tbHEkf0J2AcTnb2X/9619PyUHdQiAQSLnw7/F4CAQCBAIBvF5v8rmmaclc2vPmzWPq1Kk88cQT/OY3v+Hmm2/OOH7LYiFbVqxY0aX6ko6Rc9l7yLnsPfaXuRw/fjzxeDzr+vf9ez3b6xOboIX//IJbTz60R+M/+uijfPnll9TV1RGJRBg+fDiFhYU89NBDyTrBYDBt2//3//4fRxxxRId3xx9++GG+/e1vM3To0G7JZlkWv/jFL9iyZQuKomCz2bjxxhsZMWL/tWh0NJedEY/H2bp1a5fa9Lk3vtfrTXm5YDCIz+dr97zFnDV37lzy8vIAmDt3LgsWLMhqnKlTp2Z9f3HFihXMnj27C28h6Qg5l72HnMveY3+ZyxbHvGzPcbfV+Xnsv5uTn3/xn01ccfQkxha3j6CWLXfccQcAr776Ktu2beP//u//UsqDwSAejydt2x//+Med9n333Xd3Wy6A999/n4aGBv74xz8C8Pbbb/PYY4/xxBNP9Kjf/qKzueyMWCzGtGnTUn5PotFop5vcPlf248ePZ+fOnTQ2NuJ2u/nss8+44oorUBSFd999l9NPP52VK1cyceJEAK644gruuOMOpk+fzvLly5kyZUpfiyyRSCT9wk1LV/Dyqp0dllcHIkSMVtN9xDCZ9vBSBnudHbY5f8ZoHjqz6wufW265hcbGRurq6njyySd55JFHqKyspKGhgRNOOIFrr72WW265hdNPP53a2lref/99IpEIu3bt4nvf+x7nnnsul112GXfffTd/+9vfKCsro66ujvLycm699VaOP/543n33XX75y1/i9XrJz89n0qRJXH311UkZSktLWbt2LX/729846qij+OpXv8oJJ5wAwLvvvsuvf/1rAA477DB+9rOfsXz5ch577DEcDgcFBQXcd999fPnllzzyyCPYbDYuuOAChg0bxi9+8Qs0TWPkyJHcc889HUah25/pM2W/dOlSQqEQF154IbfccgtXXHEFQgjOO+88hgwZwty5c1m2bBkXXXQRQgjuu+8+ILESXLBgATabjZKSkqx39hKJRCLpXY466ijmzZtHQ0MDM2fOZN68eUSj0aSyb0sgEOCpp55ix44d/OAHP+Dcc89NKbfb7fzhD39g2bJlPP300xxzzDEsXLiQF198kZKSEm644YZ240+aNIkFCxawePFiFi5cSGlpKbfccguzZs1iwYIFvPTSSxQXF/PrX/+aiooK7rjjDv7yl78wZMgQnn32WZ544glOOukkotEoL730EkIIvvGNb/DnP/+Z4uJiHnvsMV577TUuuOCCXE5jv5BTZT9ixAgWL14MwJlnnpl8fsopp3DKKaek1FVVlXvuuaddH1OmTOGFF17IpZi9gmWZqKrWvzIIE1XpXxkkEknv8dCZszvdhW+r8zPtoaXJ3b1T11hz45k9MuN3xtixYwEoKChgzZo1fPTRR3i93rRxASZPngzA0KFD05YfemjCt6C0tJRYLEZ9fT1er5eSkhIADj/8cGpra1PabNiwgbFjx/Loo48ihGDZsmVce+21LFmyhLy8PIqLi4HEcUJLf0OGDAHgiCOO4NFHH+Wkk05Kvkd9fT3V1dXJhUokEuHYY4/t6TQNSGRQnV4gGg9R0bQNS/RvxKvq5p0YZvaOPRKJZP9mXLGPm05pPdq8+ZQpOVP00OoF/uqrr+Lz+fj5z3/Od7/7XSKRCEKItHUz9dVCcXExwWCQ+vp6IBGnZV+WL1/Oo48+immaKIrChAkTcLlclJSU0NzcTGNjIwALFy5k9+7dBAIBqqurAfjkk0+S8VpaUsAWFhZSWlrK448/znPPPccPfvAD5syZ07VJ2U+Q4XJ7iGHGqQ+Wo6AQiDSQ5yruFznCsQCWZdIcqaPIU9ovMkgkkr7n5lOm8txn21AUuOmUvslGd/TRR3P99dezYsUKXC4Xo0ePTirV7qKqKnfccQff+9738Pl8WJbF6NGjU+pcdtllPPjgg5x99tl4vV5UVeWhhx5CVVXuuusurrzySlRV5bDDDmP69OksXLiQq6++GkVRyM/P5/7772fz5s0pY/70pz/l+9//PkIIPB5Pyq2DAwlF7Lsc289p8UjsC298ISyqm3dhiYQJTUFhSP7Ybt2B7Cm1/t3EzRhCWJQWjOs3c/7+4vW8PyDnsvfYX+ayq974LfRlUJ3uepBnw+9+9zsuv/xy7HY7//d//8dxxx3H2WefnZOxBgI98cYH0nrjd6T75M6+B9QHKzAtI6ncTWESijXjceT3qRwxI0I0HtlrmlIIhBvIc5f0qQwSiaT/OHPKyP4WoVfweDxccMEFOJ1Ohg8fLkOj9yJS2XeT5nAtUSOcsotXFZVgpLHPlX0g0pA8g1IUhWCsCZ+ruF8sDBKJRNJdvv3tb/Ptb3+7v8U4IJEOet0gFPUTiDSg0F6Zxq0okXjXIyJ1F8OME46nhhAWwiIYbewzGSQSiUQysJE7+y4SM8I0hqtQlPTrJFXRCEQacNpyc6a1L/5IPeo+siiKSjDahNdZ2Ccy9DZ94UYirR4SieRgQir7LmBaBnWB8rQ7+rZEjTAxI4pdz85BsLtYwiIcb0ZJY6AxrTjhWACX3Zum5cDDskwC0UaiRpCYEQVyq/AVRcOle3DZfThsbqn8Jfs1u+vWg6IwsqhncfElBy5S2WeJEIK6wJ6s6qqKSiBST5G3e8kesiUQaQChkG7toSgqgUjDgFb2ljAJRZoJGwFiRgQFBUVR2lkqckXECBKO+1FUDYfuwmPPx2EbuHnEJZJ0GFacj7f9FUWBoQWHoKsHXqhXSc+RZ/ZZ0hiq7FLAmogRwDSNnMkjhCAUbep0Rxozw0SNcM5k6A5CCAKRRmr9ZVQ2bqU5UodhxlAVtV9214qighBE4yFqA2VUNm6jIVhJzIj0uSwDEdHPgaIkmVmz+z0C0Xr8kXrWlr3fK31u3ryZ73//+1x22WWcd955/PKXv8zp8dpNN93Eyy+/nPLsmWee4Re/+EXa+pdddhlbt27l1Vdf5Z133mlXnikK3ltvvUVVVRU1NTU9Ts6zc+dOvv/973PFFVfwne98h4cffrjLueb7AqnssyQcD3ZNGQkFf7QuZ/IEY82YovPc1aqiEYw05EyGrtJiHWkO1xA3oyiKNqDM56qiIRBE4kFqA7tzuljbHxBCUNW8g1DU39+iSDrAH6lj7Z5WBb+m7D38kfoe9dnc3Mz111/PbbfdxnPPPcfixYvZtGlTTsOWX3DBBbz++uspz1577TXmzZvXabtzzz2Xr371q10e749//COBQIBBgwb1WNk/+uijfPvb3+app57imWeeYceOHWkXIP2NNONnSVdVkqIohGN+8l2DOnTm6wmhSGNW5u5wPIBhxtG1/jXtCWFRF9hDzIjmZD56GwWVhlAVJb7h/S1Kv9EcrsWyTJrCVTh0F5omvy76mk+3/40dtas7LI/EA5hW66LUtAyWfP4oTlvHx3djSqZzxNiO76+/8847zJkzJxlaVtM0HnzwQWw2Gx9//DEPPfQQDoeDCy64gEGDBrXLKmcYBtdeey1CCOLxOD/72c8YM2YM11xzDYFAgEgkwo033pgSlvbwww+nvr6ePXv2MHz4cFavXk1JSQkFBQVcc801+P1+GhoamDdvHpdcckmy3a9+9StKSkq44IILuOOOO9iyZQsjR45MBp3ZtGkTDzzwAJZl0dzczO23305zczNffvklN998Mw8//DA333wzixcvZtmyZWkz5P3+97/HZrNRVlbG6aefzlVXXZUyX8OGDeO1117D4/Ewffp0HnvsMXRdx7IsFi5cyOrVq4nH41x99dWceuqpPPDAA6xYsQKAr33ta3zve99LZhRsbGzkd7/7HX/4wx/49NNPEUIwf/58TjvttA5/Xtki/3pziBDgz0EI3XAsQNyKZhUlT1W0fg+hK4RFjb8Mw4wNqJ18JmJGiFC0CXcfx00YCMSMCIFoU3JBWR8qZ5BvVD9LJekLqqurGTkyNUhP2yhv0WiUV155BSEEX/3qV9tllZszZ04ybv6WLVsIBALs2rWL2tpannnmGerq6tixY0e7cc8//3zeeOMNrrrqKl599VUuuugidu7cyTe/+U2+9rWvUVVVxWWXXZai7Fv44IMPiEajLF68mPLycv75z38CsGXLFm6++WYmTZrE0qVLefXVV1m4cCGHHnood999dzKVrRCiwwx55eXlvPHGG8RiMY4//vh2yv66667jz3/+M48++iibNm3ixBNP5M477+Tjjz+moaGBl19+mZqaGp5//nk0TaOsrIzFixdjGAYXXXRRMkXvUUcdxfz583n//fcpKyvjhRdeIBqNcsEFF3DssceSl5fXo5+rVPY5RFEUQtEmfM6iXlVywWhjl8LhRmIBLLfVZ45vbRHCojaw/yl6SJznN4VrcOieg2pXK4SgIVSJ2ubnFTdj+MP1+FxF/SjZwccRY0/vdBfuj9Sx5PNfJHf3mqpz9qzr8Tm7/3MaNmwY69evT3m2e/duKisrAZI7/oaGhrRZ5W688UZ27NjBD3/4Q3Rd56qrrmLChAlceumlXH/99RiGwWWXXdZu3LPOOov58+fz3e9+l08++YTbb7+duro6nn32Wf71r3/h9XoxjPRHa5s3b2b69OlJ+YcOTThHDx48mMcffxyn00kwGMTrTW/x6OhdTjrpJCZOnIiu6+i6jtPpbNf2o48+Yv78+cyfP59gMMiDDz7I448/TlFRETNnzgRg0KBBXHfddfzhD3/g8MMPR1EUbDYb06ZNY+vWrUBrRsFNmzaxbt265BwZhkF5eXmPlf3At6fu57SE0O0tYkaUaLzrTneBcM/O8bpDi6KPG/ufom9FoTFc1d9C9Cn+SD3mPs6oCgkflMS1SMlAwecsZurwE5Ofp404qUeKHuDkk0/mP//5D7t27QIgHo/zwAMPsGnTJqA1RkVhYWHarHIff/wxgwcP5umnn+aqq67i0UcfZePGjQSDQZ588kkeeOABFixY0G7coqIixo8fz+OPP87cuXPRdZ2nn36amTNn8sgjj/CNb3yjQyfBcePGsXLlSgCqqqqoqkr8zd5777385Cc/4cEHH2TixInJ9oqipPTV0bu0fd+OePjhh1m2bBmQsICMHTsWu93OuHHjWLNmDQB+v58rrriC8ePHJ0348XicVatWJZP9tIwzbtw45syZw3PPPcezzz7LaaedxogRPc95cPBsV/oJVVEJRnsvhG4gUp8MjZst/RFCN6HodxM34vuxok8QiQcJRf24HblLHTpQiBlRAtH6tH4VCioNwQoG543e73+mBxLTRp7E1uovUBSYOuLEzA0y4PV6eeCBB7j99tsRQhAMBjn55JO55JJL+OSTT5L1FEVJm1VOURSuu+46nn32WVRV5Uc/+hFjxozhN7/5DUuWLMFms/GTn/wk7dgXXHAB3/ve9/jHP/4BJBYed999N0uXLqWgoABN05Ln8W059dRTWbFiBfPmzWPYsGEUFiYCin3rW9/ihz/8IcXFxZSWltLQkHBY/spXvsJNN92UXHR09C5tM+R1xGOPPcbChQv5+c9/jt1uZ8SIEdx99914PB6WL1/OxRdfjGma/OhHP+LEE0/kk08+4cILLyQej3PqqacyZcqUlP5OOeUUPvnkEy655BJCoRCnnnpqhxaJriCz3pFdRqyKxi103U0vgSVMir3DexxVzzDjVDXv6JY5XgiLPFdJzqPqrVixgq/M+gq1/t0Y5v6v6FsQQGn+mD7NJtjXmdqEENT4d6U4fKWr43bkUeAe3Gdy9QYHeta7vgyqk8usdwcbfZn1Tprx+4CWELo9JV1o3GxpCaGba4QQB5yiB0AIGoIHtjk/EGnAsDqPJdFiJerL/A+SzIwsPkxGz5N0ijTj9xExI8yehswmoc5QoEfX1kxhsKdhE51ZKFRFxWX3kecq7tIu1hImzeE6glYNhllwYCl6EkouGg8Sivlx2w88c75hRvFH6zOGggZQUWkIVjIkf2y/OH1KJJKuI5V9H6EoajcPAXpRBhSULBR4KNpMKNqE0+bF6yzErrf3QG0hZkQIRBqIxAO0LCIONEXfgqKoNIWqcdo8B5ySqw9WZaXoWxBC0BCspNg7LIdSSSSSdAghuvw9K5W9pB2JXyKFqBEi3BzArjvxOgtT4uyHYs0EI03EjAiqqu4XgXJ6AyEEjaEqijy5zXvQl/jD9RhWrEvKXlEUIvEAwWhTrzmfSkBVVWKxWJfP7CUHF6Zpdvl3RCp7SaeoqophxWgIVtAU0nHY3ETjISxhoChql28G7O8oikIkFiBiD3bZ4VIIC9MyMcwYhhXHEiadZfeLWgGaw7WdSYOKiqbp6KodTbN12eJgmDH8kbpuLdZURaMpVIOZ4ZxfVXTcjrxuOpYKQnE/uqrj0LuXpMgw41nMZe7RVVvGAE26rhMOhwmFQmjawAon3UI8Hk/rES/pOl2dSyEEpmlimia63jX1LZW9JCsURUVg7TXX98x3YH9HUVQaglU4bK5O6wnLwsLEtEyEMLEsC6GIhIrOYv7iIpSVU6UQFoLW+8OqoqMqKqqiJfwuOtEX8R6GL1YUJaOMQgia9wYncjt8OG3ejEosGk+8e8QI7r0PLdAUG067B6+jAF3rfFdjCZNgpImIESBmRLOey1xiCQun3ZvRF8bn82EYxoBMpgKwdetWpk2b1t9iHBB0dS4VRcFut3dZ0YNU9hJJN0lkysseBVXNzbW9ff1BhLAwhYXJwEjk03IsFDPDRIJBFEXFaXPjceSn7NbjRpRAtJGoEcKyjL3vpSQXBgKLcMxPMNqITXfg1L14nQVJ5SmEIBTzE441EzNCgNqnKZMzoaAQiDRmFT67O1/mfYk8Zug9+mouB/ZvlEQiOaBoUbzReIhwzI+m2rHrTuJGBMOMJ4+FOrM2qIqGaRoEjAb84TocNjeqohE1glhC7E2X3HfxELKlJTlWb+fKkEiyQSp7iUTSL6iKhhAm0b139rsTGVJRNOJmSwhfJSWe/0DEtGJEjTAOvfMjIImktxkY9i2JRCI5CFAUjWCksb/FkByEyJ29RCKR9CERI4AQ1kHt5NoZhhkjEGmks5sqBwICgWmZfTaeVPYSiUTSlwgIRptynqdif0QIi7pgOVYfKsH+wrJMRB860cqlpUQikfQhiqISjvn7W4wBSX2wEtMcGLdIDjSkspdIJJI+JmZEiRvRzBUPIvzheqLx4IAMJHQgIJV9Fixdt5u3N3U/a91bm+p5e1N9L0okkUj2Z1RVJRDteSbMA4VIPIg/Ui/9GHKIPLPPQCRuct2Sz7BEnOPGFeDUu/bLGDEs7vrndhSFbrWXSCQHJuF4kIJuJDQ50DDMRDjug30eck1ONc+qVau47LLL2j3/97//zXnnnceFF17I4sWLAbAsizvvvJMLL7yQyy67jJ07dwKwc+dOLr74Yi655BLuuuuuPg8h+eC/17K9PsDOhiiPL9vT5fa/WVbGrsZot9tLJJIDEyEsQgf52b0QFnWBcjqN6SzpFXKm7H//+99z++23E42mnkvF43Huv/9+nn76aZ577jlefPFFampqePvtt4nFYrz44ovccMMNPPDAAwDcf//9XHvttfz5z39GCME777yTK5Hbsa3Oz0P/Xpf8/JsP97CrIZJ1+50NER7/sLzb7XuTmGGyrrKRzTXNiAP7RotEsl+gKirhWP/G6+9v6oOVmJZ0yOsLcqbsR40axa9+9at2z7du3cqoUaPIz8/Hbrcze/ZsPvvsM1asWMHxxx8PwMyZM1m7di0A69at48gjjwTghBNO4MMPP8yVyO24dsmnRIzWKyBRw+LOf27Puv1d/9xO1Gi1RHS1fW9RF4yyrqqJuGkRjBmsq2ogZhz4V1skkoFOdG+Y4IMR6ZDXt+TszP7rX/86ZWVl7Z4HAgF8Pl/ys8fjIRAIEAgE8Hpb86VrmoZhGIg2Z1oejwe/PzuzV8tiIVtWrFjR7llTY/tVdyAYYNOmTVn1GQgG2vfpz759TxFCUB6M0xwz0PZxfNm+QzAyz4E7Bz4EffV+BwNyLnuPgTqXO5QynGpef4vRJdJ9X3aFuBUhYjUe1A55Qli41MIez2W29LmDntfrJRgMJj8Hg0F8Pl+755Zloet6SrzsYDBIXl52fxRTp07F4XBkVXfFihXMnj273fP/N2Yi0x5amrK7P3vmUAaPHEyBK3Omou9ZtXz0UuoXTFy1YS8eyvACNzYtd8k6onGTzXXNFHgFHYXusAQUFrgZ5HX22ribNm1i4sSJvdbfwYycy95jIM+loqgMyRvTox2uJSyEsPYGasktK7/4gplf+Uq321vCpCFYDpT2nlD7IZZlUra1Nq3u6Q7RaLTTTW6fK/vx48ezc+dOGhsbcbvdfPbZZ1xxxRUoisK7777L6aefzsqVK5N/mIcddhgff/wxc+bM4YMPPuCoo47qM1nHFfu46ZQp3POv1clnv/xvBaMKHRS7bQzxuSjxtF9Q1AWj7GgIcc9bO1KeD8uzs7I8xO8/ruTc6UUUuhwMz3PhsPWu0q8LRtnZEERVOnd7URXY1RgkYpiMLPD0qgwSiSQ7LMsgEg/isns7rReMNhGO+REIRItyFyZCCMReRxzRByFmg1Yt1c07etSHpsqLYH1Nn8340qVLCYVCXHjhhdxyyy1cccUVCCE477zzGDJkCHPnzmXZsmVcdNFFCCG47777ALj55pu54447ePTRRxk3bhxf//rX+0rkxPinTOW5z7ZhiTjHjsnjz1/UsOg/Ffz0q8PZ1RCgvCnIYJ+LwV4nNYEI1YEIMcPi5++Xs7Mxxjcm5bO6IoxNU1h82RRO/8Nqnvu8hvElTmYNV2gIRclz2hia58LrsAFgWoK4aRE1TKKmiWmBYVlYlkBTFXRNRVMUnLqGw6aiKSqaqiAE7KgPUB+Oou2zS/h4lx8FhSNHpX6haIpCbTBKOG5ySIlvwGcNk0gONBRFJRRt6lDZh2MBmsO1mMJAabd8V/Zm/8u9nC2oiiaV9X5ITn9iI0aMSF6tO/PMM5PPTznlFE455ZSUuqqqcs8997TrY+zYsTz//PO5FLNTnDaNX5x9OI3BSk4YX8C6yiAf7wqweFUdF80swRJQ0RymrCGIqqqoCixd38AH2/0cOtjF944qpTEEmqowPN/Jz88cx/++tJmH39vDY98awxCfnWDMYGN1M6qqYJoWAgVIKPbOlK9pCSwBCmLvH3zij35fRR8zLJ78qBpFgZnD3Nj3OadXgFDMYF1lEyPy3cQtk7gpMEyLuCUwLAvDtDAsgRBQ6LYzPN+V02MIieRgImKEsCwTVW39m4rGQzSFa4ibUVRFS6PoJZLsOXi9I7rAmVNGcurEQuyayv3fHMsgj86fPq/ls90JB7xPdvn5fE8IVYHVFUGe/rSaQpfGLacMo9Tr4OuTizl1YhEAX51Qwg+OGoI/anHfv/ckvfU/Kwvw6a4AuqZi0xRsmppxl62pCjYtsdP/rCzAZ7sDab8OXlpdR1UgTqU/zstrOo7kZ1oW2+v9lDeFqQlEaAjHCETjROImhpUwDyoKNIZjrK5oZFudn2hcevVLJD1FQUlG1IsZUWr9ZdQF9mBaBqoiF9WSniOVfReZMiSPW746HF1TeOT9cnY3RHjyo2qe/LiK8uYoD71bjgLcfPJwClw2hue7U9orClw2u5S5E/PZWhfl8Q8ricbNZB8xo+tBg1p27unaVzbHeKWNgn95dR2V/liHfWVrxtcUheZInLWVjWyp8ROOybuyEkl3URSFUNRPXaCcWv8u4mb0oPZUl/Q+8repi2iqwtGjCrjq6CEEYha3/n13ctd869920xgx+d85g5la6qbIbUfXVKqbd1LdvDPZx7B8N1cdNYQJJU7e2dLM/e+WU+qtZ7CnvsOddzRWTjRWkbass5377z6uImYKZpT6mV7qJ2YKfvdRVZfH6Gw+ArE466ua+KLsSxpiNV1qL5FIEljCJGaEpZKX5ATpZdENhuW5+OqEAlbuCfLB9tZ7/3Uhg6NGeTnj0EJMIRiR78K0DDZULAeg2DscTdVRFBhW4OLWU4bzkyXbWVnuZ+GpFQgBC97zcsoheZT6Wq/2CWFS418JwPCiwShtzHrpdu6zh3uo8MdYUxFidUUIXbW4eHqi//XVHsw0xgMhTPzhVYCC3ZY6RjaoikVd8+fELZPG0CwK3K4utZdIDnZkcBlJLpFLyG6gayrFbjuheHutGbcSznKFLjs2TWN7zSrCcT/huJ/ttauS9YbluSnx2Bieb+f0ibUM8sQZ7I1z6viadjvv5tAGbGoYmxqmObQhpaxl595CzBTc8NedPPJ+Bf/c1IQCfHNSa/+nT6ylIRQnGEs9aw9GNmJaIUwrSDCysctz0tJeVaOsr/hchuSVSCSSAYRU9t1keL4r7XUXVQFTwPA8N6FYc4qC316zilCseW89hRKPg+F5cU6bWJusc9rEWqqam3j+8xpWVwQJx/wEI62BeYKRTTSGmvlwh5/fLq9kTUWonQz5To3LjxjEL741mj9fMpxvTqxLlp0+sRZ/NMBd/yojtNe5zjADKQo+GNmIaQbb9dsR+7b3hzewrS79UYFEIpFI+h5pxu8mNk3jhhNG8D8vbErurO2awpVHDaHAacNh0/h853Is0bqDtoTJFzvfYnTxlMQDIbhk+naUNoEw7Jrgu7N38M5WP3saIDa+jpH5rRYETbX4eMdyfvXR6ER9lb0X9VraK/z8zNHJY4CGwDI0tbW9TRPcfMIe3vgyyCsra/jWlCLi8a1AWyuFRXN4JYXeY7Oai4T5P7X9ztpPGJZ3Gi67/BWTSCSS/kZ+E/eAI0cVcO7UIl5Yldg5nz+9mEEeG8PyOz6vDkTrWVf+n+TndKd0I/OjzJ9VnqYkga4qXDqrhOmlbiYOcrF4VS1/WdkqQ9vz/nQUuULMn5WwCESiOzut210UBbbXBzistCAn/UskEokke6Sy7wF2XePyI4fw7tZmFAXOn1ZEntOGy5aY1slDj6bWX4bYu+tVUJlYegR2vXUxEI6F2FL9Ka17cwWvcxqqmgjD+/cNlcwZsZuWODimBauqxnH9iSXJPuZNL+bfW1plaIvPNYO6eDWtO+9E/yh23vyygU21ESYWK5w4dk+KDHmumVnPQ2KMqnbto6ZFZXOY0rzsnfVMS9AYjlGcJgyxRCKRSLqHVPY9ZHyxj/+dMxhNUfZGyWu9V6+kGNhh3OCZjCmZ3q6P+lCI+kAigYHHORmPa0Ky7Jixpby5PsIZkxJX2jQVLpmZenffrqt8/6jBKCjtouMlFg0qLcq+bf/nzhjFQ+/u4Y8rAxhWnK+OT5yzW1b6GNsdhdzdF0XRUVU3ClDeHKbIbceuZ/buj5smG6qbiZkWPoeeVRuJRCKRZEY66PUQp03j1AmFHDnKi9eh4957Ri2EYN2e/yAQ2DQnLpuPsSUz0vYxdfgsVNWNpnrwOCellJXm2XE7JlETtNEQ1hECdFYjRGoQmzmjfGmVcCC0GjBQFFu7/nVV4caThjNzmJvFa4uoCdpojmioqqDW/1kyuQZ0HLhHCEFzaAUgUBQHQqgIEccwE+mB1b3m/EyE4wbrK5sxLYGmKFm1kUgkEkl2SGXfC4zIdxM1rZRd/Z6GjdQF91DiHcmU4cczeejRHSaPcNkcDMmfjc81I+399vOmDeLvm0awdONInPYJmFaQQHhdRrmi8SrCsR3oWj557vT92zQFXVUwLJW/rB7K//tiGCsrfCiijq01G4nvdT7sKHBPOLqNuFGLwzaMyuChVDYN2jt2WbJOIGZSE4h0KGcgGmdDdXOKNSEQNWgIdxzpTyKRSCTZI834vYDLrjOmyJPMWheJB9lY+RG6amPK8ONw2jo3ewNMGjyBdVWNacvsusoJh0xAQSHf46KuuYJQdAsO+wjsejEAlhDoqoIlIG5aaKpFc+hzQCHPPRub3lFWe5JXCFdV+gDY1ehiYnEQXfmSq16JU+T2sbGmVVm/vLqOUw7JY5A7TiC8BkWx4bTPYNGyClRRzMKv1RKJ7cHjnIKiKGgK7GkKUeR2oKmpLokN4Rjb6/ztwvRqqsLuhgAFzqI+zeglkUgkByJyZ99LDPEmnNCEEKwv/w+GFWdi6VFZKXoAh01jxrAi8l12TKv9eXmLmV5RNPI8swFoDn6GECaCRJCe6cOKmDm8iGlDC7GMDVhWCI9zIigFnY595Zwh2LVWjRqM26kOT8Rls7h8diXrq8O0idtDzBT87F+7qWj8DIGJzzWDV9YGqQrEqQgqVAWLMK0AhtWcMs6+pvmaQIRtte0VfQumgLKm7O/7SyQSiSQ9Utn3MhVNW6jx76bIM4wRhZMyN2iDpiqMLfJy6JB8HLqWomDbYtdLcDsOwbQCxI2NTC8tSPF4D8eqqQtsxOMo4OhxxzBzeCHD8l3kOe1pQ+WW5tk5r40X/7zpxRw5+jDs+hAOKWrm4unhdm1GF1RjU2tZV+3lnn8LFq9qDdzzxpdOAKKxspQ2TeEYTXtN83uaQuxuDLXb6bdFIbEgkJn1JBKJpGdIZd8LtCS6icZDfFmxHE3VmTr8hG7HunbbdQ4dks+YQg9AO794U0ChbxpOm4+GwAYC0bqkDKZlsHbPB4DC1OEnoKk6Nk1lsNfFuGIvpXlO0hgOmDe9mCFeG6U+G+dPK0JRFPI8s1DQ+er4Mga5jWQynUEeg8tmVhEzNd7cNIo1FZGUhckX5R4MSyES25MyhqYq7GgIsKM+QJU/Qid6PomqJNpIJBKJpPvIM/se0jbRjddZiGFGOXToMbjsvh73XexxUOR2sLsxSE0wgrZ38TCqwM0gr5M67wl8tuNN1pS9hyUMQGGQbxShWDNjiqdR4B7Srs/h+W7CcRN/NJ4S0Cfd9T1NdeN1T8Mf+oIbjqsGAggBqupFV0187ln8/Myx/PTvu1jVJmxv1NTY1VTAuMIGDLMZXctLlgkB9aFYVoq+hRZnvUJX58GCACJxk821zdg1lQmD8rJO2SuRSCQHMnJn30PaJrqp8e+i0F3KyKLDeq1/RYFRhR6mlRYwyOtk+rBCBnkTZvJi7zBGFh1KMNZIOB4gHPezq34dbnsehww5vMM+xxf7sGvtf/Tpru+57GOx6YMY5GlIJtMpcTdg1wfjso8B4OpjS1PO/AHiVilAu9090CVFDwmLwK6GQMbkOnXBKF9WN2FagnDcZG1FA6GY0XkjiUQiOQiQyr4H7JvoBmD84Fk5SVVp1zWG57vb7VRHFh7aru6EwYd3eM0PEguISYPyOixPravgcU5u99zjnJx8z33P/HVV4anPFEBNuYLXE6wMzno76gPsaAikWCssARuqmzq99ifpmKZwjPWVTVQ2t/fZkEgk+xdS2feADRWpiW4Adtat7VMZNld/1u5ZedOWjO10TWXioDysLHLRhqKb2z0LRjelfG458y9xKXz3iEHUhRR2NOZhmM0Ypj/jGJlQgOo0znoxw2RdZSMN4VjymKMtqqKwqyHY50F6TEvgj8QJxQyihknMtDBMC9MS7SwUlhCYliBuWsRMk0jcJBiLE4ybBKJxwjGTmGkS76SP3iQcN9hY08yWOj8x06TCH2Z9VaN0lJRI9mPkmf1BjNuuM6bIy/a6IGms+l2i5cy/rq6ObxxWyIc7/by9xcP/Ht5IJLYHr6u9daCraIrCjsZg0irRGI6xvT6xm+/MlqKpCo3hGOsqG5lQ4stZGN5ANE59KEYgmlDyiqIgRCIzQkK+RPhkZW8U5ZZy0SassthbS1EU9vhjmNXNeyMZKm1KE320+Fd4HTZ8Dp0it6NHPgqGabG7KUh9MIamKsnFk0IiguK66iaG5bko9WWf60AikQwMpLLvAYcMnk2Nf1fys6poTB56dJ/KMHno0dQF9iQtDF2VocjtIBw3O/WOb59MR02bKGfOKB9lahOqovCT44Zy49IghlVOKFqWtbLPFH8/EIlTH4oSiBrUBKJZL1IUEsGG1lc1MbbYS74zs7NfJiwhqAtGaY7E8UfjiVC/eydRTwq276R29Ln95GtKIrph+zqtdU1L0BSO0RCKsaM+iNum4bXr5Lvs5Gfh0AgJp8k9zUGq/JFkjod0qEB5U5iGUIzxxV6Zu0Ai2Y+Qyr4H7GuyHztoBm57dmfhvYXbnsfYkhlsrfm82zJ05KHfgq558TgnEYx8CYDHOQlN83Ta57A8O/NmlLKuejczSpswzAC61nmAoZb4+4oCM4e52yX1gcQuPbGbV7ptjdhS68fn0FE7sQe0WMktRPKDIKEYhUgkCorETRRFSS6SOosZkGtUJXFkETMt6sMxaoJRFEXBqauoqoK+V4mre/+rqSqamlgsVPkjCEh7DJJunKhhsrZK7vIlkv0Jqey7SY1/N+WNm/E5ijGsRKCYjhLd5Jqxg2ZQ3ri5RzKML/axrqoRI13UHRIKPhLbCSjtkvV0xJmHFfLHz0qYURpgU/U2DhvaPuNfW1ri7wO8vKaeS75SkrZeNkqpMzRFIRTr+flzfyr3TLTIFjMtyOJVu/omLbv8xlCMcTnc5VtCsK0uQKHLLtMeSyQ9QCr7bhA3Y6zf8x8URWXayBMJxxLOX515wOcSTdWTpvvuyqAoMHlQHl9WNxE3RTuTvqJo+FwzACVtsp70cimcOmEiprWDULQMf3QK66tCac30lc0xXmmTYKcl/n6pr2vm9mzT8Ep6jqpAxDBZW9nEiHwXg3t5lx+IGWytbUaIxM0AS4jktVOJRNI1pLLvBpsqPyZiBBk/eBY+ZzE+Z3F/i8TgvNE97kPXVKYNLaSyOUx5c7idwnfYh3W5z5GFPr4sL2RkfgN/WrGDT8tIa6b/3cdVxNqE4YuZgt99VMVdc0dmPVY2xwCS3kdVoGzvWf7YXtrllzeFqfSHU45IdjWEsIRgiDw6kEi6jPw27CJ1gT2UNWzA6yhiXMnM/hYnJ5TmuZg2NB+vQ0+blKerjCoaC0DcqGhNk7u6jormGP/c2MjD75Wzck8oQy+Z6SgNryT3qAqEDZN1VU1UB7p/L98wLTZWN6Uo+hY0FcqaQvLev0TSDeTOvgsYZpx1ez5AQWHqiBNQ1QPXG9mmaRxSkkdTJMau+iBxq71pP1tc9mH4Q19w+PBm/rklcQ7/55V1/Hlla/Icn13FjIuUuP1fn5if9Ri9dQwg6RkKsLsxscsfV+zFpmX/N9IUibG9LnEk1tHvmqYo7GkOYQrB8Hx3L0gskRwcyJ19FuyuW0918y42V31KOB5gTMkM8l2D+lusPiHfaWfasEKG+Jykd93LjKo62OP3Ma4ozHGj6plemgiyU+jS+MFRQ3j8nLH8+dIJXDij9ThEAX7xn0q21GYX/S7dMcCCt8uIp0kd+PEuP5/s6jjIzuo9W1mzZ1uWb5eeTGNk0351Tf+G+o3GyonGKrrcTlMgHDdZU5l99MLdjUG21GYXfElTFCr9EXY3yvTHEkm2yJ19Bgwrzsfb/krcjBA1QngcBYwf/JX+FqvPGZ7vZrDXQVlTGMO0EAiEUPb+N3EdzaLjyG5b64sYkdfMvKnVhOIq66s9HFLi4YzDCpN15k0v5t9bmlEUuHBGMb/8byW3/2MX9502inHFnTtm1QbbK8adDTG+//JWLppZwlcn5KOrSsZz/Wg8jo31CCAaH4nDZuvyXPXUd6ClvWkanDrN6hffAyFM/OFVgILdNjhrp8y2qMCuxhBV/jCq0vE7GJaJYXXtloWmQHUgimUJRhd17IwZM0waIzEaIjJHguTgRir7DKzZ/R6BaKt5uCVt7MGITdMY28kXK4CtqYqoQrs0uieMm4gwd+B1mHgdJmdOquObUw9JqdM2896Ro7yoisJj/6ng9n/s5t7TRjK2yNnO2z5iWPx2eRXb66P7yKpw4jgfH2zz86tllSxeVcfFXymmsjne6fW+z3evZFxhbO//r+Loce0TCmXy+M/mCmFnffS0fVfqdEQwshHTCiX/3+vqXnInTQHDEmS6/9edIyJNgbpQDEsEGFPkJRI3aYrECBuJkMMRw8QwLWyaSmUoTllTkBH5nceHkEgOVA5OrZUl/kgda/e8n/ycCE8qPYE7Q1EUxhZ52VTjT7mHXuJVqG1qrXfaxFoGueNA6pn6nFGtqYG/OiEfUwh++d9KfvqP3fzsayNSdsw1QYP7/72HHQ1RDil2cugQJ0vXNwIJK8ElXynhstlxXl5dz983NPLYfypTxnppVR2HDnZS4kns3utDTYzI250sH5G3i8rmQyjNK0g+y7Rrz8Z3oLM+etq+K3U6wjADBCMbk5+DkY247KMzBlLqD1QFGsJR6suiCNgbcTCBAtj2Rl7SFIUqfxQFRZ71Sw5K5Jl9J3y8dSmm1Wr+E4hk7npJx/icdko8Dtpu7v3hVbS10mqqRXN4Zca+vjaxgB8fU0pzxOSWN3clve1//kEF176+gx0NUb45uYCHvjmKyw8fzBCvjVKfjfP3ZuErdtu48qgh/P78cQz2pK5t45bgjn+WcdWr27nq1e1UNn6BXWuV2q4JNlR+yie7AskgPOk8/oUQ7GyIsnR9Aze+ubOd78DP3ipjW10kmXQoXR/huMWKsgC3/3N3u/a3/W0X/93eTNNeU3Q2tw6yqdORX0HCfN/WQyO7n1V/0RIVUM9gHtAUqPRH2NPU85sfEsn+Rs529pZlcffdd7Nx40bsdjsLFy5k9OjWu+BLlizhqaeewufzcc455zBv3jxisRi33noru3fvxuv1cueddzJmzBjWrVvHD37wA8aMGQPAxRdfzOmnn54r0SW9wKhCL82RBsxeSM/2jckFNITj/OmLVu/9ZTv82DW48cRhnDi+NTxwyzHAvjvZQV4bo4scVO9ztj/Io3P4yISZ26HvZl+ihsUjb5ehKjCqwM6uxliy7MVVtWyuCbO5NkJjpGMz9e7GGD95fQc+h8ohxU5WV7QqmxdX1vLRLj8766Ok8SUEoDpo8MC75QAMz7NT4W+V4aXVdRw92stgb6tvQdU+Cr6r1oFoXLSLqBc1cphmrw/RFJJX9+QOX3IwkTNl//bbbxOLxXjxxRdZuXIlDzzwAE888QQA9fX1LFq0iNdee428vDzmz5/P0UcfzXvvvYfb7Wbx4sVs27aNBQsW8NRTT7F+/Xouv/xyvvvd7+ZK3LTMGX8mFZ9vSe7u+yPRzf6KosDYEh8bq5rQVCVNMh3wOTsPn9uWTWm88icNcqUoekg9BtiXK+cMYVX59uTO2a4p3H/6qKQSrGz0g1id0qbEM4YLZxSxpiLE+urU+92mBZ+WBSly65w0Po/pQ90M9dq4662y5Bg2TeGyWSXsaoyxuiLIF+Wpu0pTwLa6KJMGOZk21M2IfAePf1iZIuMNJwylrCnG6ooQqytCKRaTuCm4esmOjieOhHXg3nf2cMepI5KLgnR+AUIIypvj/HFFPpfPqkpaYiwBz60s5aaT2/e9P0Ys1FSFSn8YBYVh+fJYTnJwkDNlv2LFCo4//ngAZs6cydq1rUljysrKmDx5MgUFBQBMmzaNVatWsWXLFk444QQAxo0bx9atWwFYu3Yt27dv55133mH06NHcdttteL25/3LxOYuZOvxEVu1+B+ifRDf7M167ziCvk7pQtF0yHYBofA+63v3Ut05b106hSvPsnDetiL/svd9//vTipKK3rBgamzAFSSUnBIwu2M6sUeNRlEHc9c/drNiTet1rWqmb+04bidLmjKLtGPOmF3PutNYrhbf9fVfKzh5g1nAP93y9NVJglT+WIuOxYxO/cxfOhLv/tZvPylJlKHBqTBrcqrQ2VofbWRq210f57uKtlPpsHFLs5KNdrdfcXlxVy9a6CJtrI9SH4lx9VAX7Osavrgjz/Oc1nD2lCK8j4Zm/P0cs1BSFCn8IRYGheVLhSw58cqbsA4FAikLWNA3DMNB1ndGjR7NlyxZqa2vxeDwsX76cMWPGcOihh/Luu+9y6qmnsmrVKqqqqjBNk+nTpzNv3jymTp3KE088wW9+8xtuvvnmTsdvu7jIhhUrVqR9bol8bIoHIUyMBg+bGjd1qd+DkU2bWudICEFlU4uJ2ovucpDIH2fhD6+noV4Fkdmc+q3RgpV7IL7XMGBT4azRgrKysi7Jdkyx4C1XQpMdXRROttfs21BtEeLRYfitGgDytCIURxUVNR9jxUZz9ljB6opUGS48BPbs2ZPVGADzxsOXVal9nDs29T06a3/WGMGq8tT2/3e4g5I2U1gzysE9H4aSdXQFvjbGRlnAYnNDnP/uiKfIa1rw8a4APpvCRVMCzBzqZ2ONmyJ3HKdu4XOYzBnh54WVNl5fW8epo+2cMsrG2ztjSevA08t2cMb49gGMVlcboMD0Qem/anpa3hXS/a7sKROUuGyUuKSvcldo+zcu6R5CWLjUwg51T2+Ts99wr9dLMNi6A7EsC11PDJefn8+tt97K1VdfTWlpKVOmTKGwsJCTTjqJrVu38j//8z/MmjWLKVOmoGkac+fOJS8vsbuZO3cuCxYsyDj+1KlTcTiyy5K1YsUKZs+e3WF5aZ2X+mBlr8SfP9DZtGkTEydOTHk2MmbwZXUzmgLRmA4k7uc3BZfj8pVR5Ds5ZWecjhHA+YGa1h3zjBJmTkyfFS8TVykJ0/PYvabnaLyKxkANupbP4NI5rCnfnvDaHjKKuua3wVZJSdFkRujFWcuw7xhdfY+rFD91dXWMHT2yy+33rXPBzJLk9T3TEtz2912sq0o9kpha6uLerw+mzv82QmjURqbwzy1VeGwGVxxezsUzDYYXDuLlNfUs3RrjnV3xlHP8f+2Ic/asEe38Al75cDuKAqdOG9Vu59/T8q5QVlbGiBEj0paZQuDzuSn2dh5t0d6FaIAHMun+xiVdx7JMyrbWdqp7ukI0Gu10k5szu9usWbP44IMPAFi5cmXKL4dhGKxatYo//elPPPjgg2zbto1Zs2axZs0aZs+ezXPPPcepp57KyJGJL7orrriC1asTZ6nLly9nypQpuRI7LSOLD2Nw3qge9SGEQAirx/96JAPZydDT99wXt11niM+BJRLJdBz2oTjtw3DaRmKYDYSim7Pqe9704nbe9t1hzihf8ozZEnGaQ58DCnnu2SiKyvTh45k2fByKopPnTvwhNgdXIISZtQxtx+jOe8wZ5etwN5tN+47qaKrCdccPxa61Lq7smsK1xw/FH16FEDF8rqmccdgIKgNFbGscjKYWYZo1nD3Fy1PzxvOd2YOImiLFoTBmJq5Itv35Z7oR0NPy3kJTFMqbQ6zc09DpvzXlDTRHYpk7lEgGIDnb2c+dO5dly5Zx0UUXIYTgvvvuY+nSpYRCIS688EJsNhvnnnsuDoeDyy+/nKKixBfSokWLePrpp/H5fNx7770A3H333SxYsACbzUZJSUlWO/uBhqrqlOaP6VEftf4y4mY0c8UOEQwtOKTTXXStfzdxs/tfaEIRCCHajTEi30NTOE7cbOOg555BtLmaQHgdDttQdK1j5zpIDbrTW+fDgfBaLCuExzkZm17YrtxuG4TLMZ5wdCvByJd4XVN7LENP3yOb9p3VSee7UOCopim4B5tWjMsxHkVRku1dDpNAuJ5IfA9uxzjmzShmTWWIz/fxX1hdEeK7i7cyfaibUQWOTm8EZIon0Ne5DjRVQWt3ByEVUwg21wQocNkYU+RNiSMhkQx0FJFuK7Yf02LK6E0zPkBF4xbI8GXQGXbNSbFveLfbAzSFagjFmrvdXlP1jEcRwWgzjaGqTsObdoQlLHZuKWfUIaVoSvt1ZDhu8GVVc0q0tEisjKbgx9j0Ygq9JxKLVwAKDvvQtGNEY+U9Km9bR1F0GgIfoKk+ivO+2mFIWEsY1DW/hWWFKfKdjGWFsx6jszqZ6Mz0nA2dyRAzLBZ9sBIUuPrYQ/GH3sEScYrzTm236DLNILXN/8CuD6bQl3C6rWyO8cPXWm82aApMH+pmS10EfzS9dchrVxldmPib3NkQJRCzulR+xEhPl1Iet6Wnc7kvAhiR72aQt/Mwzgci0ozfO7SY8ecccUyv9JdJ90mvlD5C13q+I9F1ByJqoXRDEQPoamYZ3HYfTeGabvWvKCp2xYPSwemQy6ZT6nOlpC912kcQiZURje8hFNlMOLaVjuKxZ4rX3lIuUCjSB6UNa9xaB1rusOV7Du809ruq6OS5Z9EY+C9Nwc8QGIkdcwcx43sjrnxPySSDTRNcPCMRUTASi2OJKF7XtLTWFU3zoGuFxIwaLCuKqjraWQcu3OsXYAnBjvooj7xfnhKTACAQs1i/11cg3Q4jU3mso0AE/YBCIu5/XTDK2CIvDlv6n7EQEIjGaY4mrFqjC73tbjpIJH2BVPZ9gCUs7HrPdwAu3UMDgu6qjmwWHIqi4LR5iMa7nlHMZfOgKI1oqt7h2f+wfBcOXaU2GMEfiaNrKj73TGLNNQQia2n5mk8Xjz1TvPa25Q51B3muwwjFTUJxE0Fi99m2DoDbMRGbnvn832Ebgss+hnBsR8p46WLG91Zc+Z6QzVzZ1IRijcbL0LVC3I4JHfbntA8nEG4gGq/A5RgDpCYuavELUBWFccVO7jx1RMrO364pPH7u2BQzfVfKE8/i1IcMitwD42tLUyBimKyramSwz8mwPDehmEFzxCAcT/yLGiagJE3+wVgjkwfnyyMASZ+z/1yM3a8ROGw9j9alqhpaN3eJljCzlsHrKMASnScuade/ZeF1JM68MyUKKvY4mDQ4n+nDCylyO9BVF27HJNru54KRjZhm64IjXbz2zsorGtdS4rWYPCSfWSOKmFjiI88RJxRJvTKk6yOJm1a7f4bVfrHicoxP+byvDNnI2Rd0da4AvK4pnfpyOGyJI6hIrPX6WotfwPfnDOnQL6CFtjENulN+2BAXVYE4t/19Fw2hgZXBTlUUqv1RPi+rY0N1M9WBMP5oHMMSaKqaotjjpsX6qkYMs2eOsBJJVxkYS+QDHFXVUXvJlKtrdoxuOdAp2LXsfBjsuhOb6sAU2X+p2nQ7Nj3Rv6bqWclo1zRGFXoYVejho60N+5Ra1Pvfw24bDEBsn+h7bcuFgLhZk1JuCZNPtv2VYu+w5LO6QDlin+xrdmUTM0d8vZ1sMcOirClIczSeTL0aiKzrVMaO5GwOr6TQe2wnM9G7pItt3/lcQii6BYdtSId96poXXSsgZlRjWTHUvUdCnUUsTLfz7275gq+N4E9f1PHq2np+ujftccEAuhuvKmTt52JagvVVTUwenIddl9f5JH3DwPlrOYCxqV3Pid5xX91T9rpm69JZv8vuwx+pz3j/HRLX7dy21siCumKjq3cG0nmVWyJCJLarwzaZyqNGkPLGzq/1KYqS1qTqsmtMGJRHMGZQ1hgkEEu/8Mkkw0ChN+R02oYTMBv3mvIzx5zIdGugK+UOm8blRwzCFILX1zXw03/s5r5vjCTfpWcM2fvxLj91dQa96J/XYyyRUPiHDs7v8LxfIulNpLLvA3Q1ux11Vn1pDkTMn5USTmmndM1B0OMswB+pI7sbCAK3Mz/5SVNtaa/fdcbkoUdTF9iTPD5QFY3Dhn2NYNyGPxIjZoZoDv6X1h2pSr73OEYXlVDgtBOOBVix8+8p7WePOQ2XrVUBpKuTKdeBx64zaXA+/kicHeosyuv/kSJDofc4NLX1eCRmhWgOtJVTIc81M+t5aMG0BLqiYFqiy+e7TvtoYvG26XxT5TStEA2B1LnMRkaHfTiByDoi8bKslD10vvPvarmiKPzvkYOxBCxdn1D4d++T9jhdYJ4nP6rGNA1OnWYNuJC+X1Y3MWlQHi67/Co+mAjHTBrCYWJG3x3nyN+wHCOEwJal+TwbnDY3jWETrYs/OlsXbwOoippw1DPCGes6bd4UE6ZddyGwULrgSui25zG2ZAZbaz4HEnkIhhe2bsXqgzE2V9fRGEyY0vPck5k14pCkGdRl97VrX+RJvXKWrk62uQ58ThvThg1HEdPY07AqIbNzEqpWgl1Xcdl03DadfJdORUMgOYaCgqqqWEKgZrH4MS2Bz2ljWJ4LX7iO0aUF7GkO0RCOJY8TOkMIk2B4fcozj3MSdtug5GdN86TkKfA4J2WVq17XfOhaPrF4NZaIoyq9Z7HKFkVR+P6cwViW4M0NjVz7+o5kHoCWhD5tSZfwZ6CxoaaZCYPy8EqFf8BhWoLmaJxQzCAcN4kaJhHDBJEIl+vsQ98N+duVY4Qwcdozf5Fmi67Zu+ykl7gN0PVkHy57HpF4sFPzvyVMPI78lGe61r1fq7GDZiTN7mNLZqSUFXnsHD76SJZt3oUpBEeOmYOmalm370qdzjh06CzqA9uwhOCwobMpcDnb7bxbxjCsOHEzgmmsYnTJKVQHIkQMCy2NzjYtQb7TzvB8V8ouz2HTGFfsI26a7GkKUxeKpW3fQiDyJablx2UfR9ysQkHBaW9/J9rjnEQkthNQ8DgnZf3+DttwDHM90VgFLkfPokp2F0VRuPLoIfijJh9sb03os3hVLW6bQv7es/zGsMFLq1vTIuc6ME93UYDNNc0cUuzF5xxYskm6TjhmUBOI0BSJEzFMNFVJWehrigIKGF3zge4xUtnnGEXVMnqndxVNs2GaXfFIFthtXb/657J7aQppiLS3nvfKotjaefkrioqq6olLxl1AU/WkWT3dnPW0PNs62cpY7Em/gGpbZ3f9emoDZQzN38mU0kk0hGNUNocJxQw0VcEUgiKXg2F5rk7Pbm2axpgiLyPyLcqbw9QGo8lYBS2mfl1pJhTZhNPm5ZhDTqA+UA5AkaeE8uYw9aEolkg4kymKhs81g0RwoewXj077cIKR9UTjZf2m7CHhAR/exwRqWPCHTzqOEREzBb/7qKrbgXlyiQJsrgvg1DQUJZF5UUFJ/KxIvK+iKthUlUK3DY+9760qko6JGSbVgShNkRjhuIGuJjZINm3gHBtJZZ9jumo+z6pPxYFJ9speU23dvg3gtHsJRZvTnr8LIfA40p+56qqOYcbTlnVGpgh/PS3Ptk5vjeFzFrNsy8tsqPiIYu8ICl0eCl12miMxGsNxhuY5sXUhwYquqYwq9DCiwE1FcxghoNBtw6WrLN/6LiCYOvwEdNWWIueoQg8jCzxUB8LUBKNEDQuHfVjHA3U4fh66mkc0XtVvpvzOGFPo4IzDEldA/7q+gR0Nqa6i2+ujRAwL5wA7u4fEPeiYmXm7V+UPo6kKbruG126nyG3HKZ38eh1LiE73K6awqA3EaAxHCcfNpIWvRdEPNKSyzzHZRK3rKppmowu6Hr0HtwG8jkKC0ca05+8CC/c+JvwWNNXWLWV/oOGye5lUOof15f9lffl/+cqor6EoCnlOO3k9MNmqisLw/FaLypaqFQSi9YwonEyxN31YZkWBIT4XQ3wuGsMxqvxhmiPxZH+KopCNL6DDPhwj8iWxeCVOe//tkq+cM4RV5amBeW4/dXjSTD9zqDslMI8C1AYN/m/pTm49ZTjD8xP1svHm76y8r2lRKqGYSSgWprw5iE3V8DoSfiOZLt2oCuiqhl1L3IJQUdBU9YCJ7OePxNhaF0BTFDwOHa9Dp8jlQM+wy44ZJvWhGIGYQTBqELestIm9WlBQ0Peeqe0PQZKkss8hve2c14LT5sEfqct6t94T64Ku2XDorrTJcRy6G11Lv5DozvW7A5URhZOpbNpGjX8XFU1bGVZwSK/23xyuY1vNFzhtHiaVzsmqTYHLToHLjmFaGJbAMC1ilsC0LCwhMC2S/++PGYRjRtIk6bSPIBj5kkhsT78q+3QJfdIF5kmm+p1RTCBqJh37fnJcKXNGebPy5u+ofCCgqyoCgT8axx/NboFticT3k7VXlymKQFXUvUc8nbcta4xgVjVh11R0VcGmadh1BY/Nhl1PBBEyLUHMsAjFDaKGRdw0MSyRDFqlqglFadc0dEXBpqm4bBouu469B6bvan+YsqZEOG5TCJojcZoicXbWB3HaNLwOG167TpHbgSWshHKPGgRjBjHDSipvAF1V6Ek+lIGGVPY5RGDh6IZjXCZsmgMly19CISz0HobqddnziIaqUzzuLWHh6sSTvTvX7w5UFEVhyvDj+XDzK2yo+JBi7zCaQomz5Y6OBKqbdxKy6tKWtZQDlPhGsnbP+wgEhw07vss5GHRNRdeADGbgSNykOhChKRLDML1oqo9ovJJIrAwFLW2yHQGEIntQALut44RAgo7PNjMlFMomME95425My+TCGROx6yqHDXHzq2UVPPheORNLnJR66xHAy2vy03rzd1aerZy9kRipN0nosbb2uta/00yuNkJA1DD3hgLe+wwwTQtBwkpk7e1E19S031SmKYibEG4T5CqxyBSoCrjtNkYWuPE6srdKbq8PUB+MtttlKyR+v0xL0BSO0RiOsaM+ACjY2ir3zjxfDwCkss8hCTNP7+/sFUVB0+xYVmZbviUETr1noXrd9rx2yXEURcVt7/iOtF13dvn63YGM257HhNIj2FCxnPV7/os/kkjfWuwd3s5R0LQMNlQsJ27EMa0jOiwHGBauxR+pY1jBRAb5crfLdtoS0Q7BQyhmsL58FHWBdTSHVqIoOvlqyV4rkIpDU3HoGnYNNgTWoqAwZ+yhHTpExk2TrbUBQnEjxWs5m4RCmQLztCT8sUwTm5ZQQCeOz2NcsYMFb5WxrT7EwlMrEALuec/LMaO9DPYlFEyVP86SdbXcdXKifMF73rTe/NkmaOrPxEi5RoEUM3mmdMHpUBUFda/CjRomG6ubyXfaGFXo6TTSoGlabKzxJz3fs5F1IDnO9RVS2ecQTbPnbGdrU21Es1D2ai/cBkgkx/GmJMdx2jydvltH5v2DmVFFU6hs2ka1f2fy2fbaVRwyODW98vaaVYTj/qzKt9Z8gUN3M3noUTmWvhW3XWdS6SQ+3LIOIaIIESXfsZtJpYen1NtStYJIPJCQOc17tGDTNCYPyWdPU4jK5nBr0pgsEwp1FpgnmfBHTe1jZIGDoXl2Dh++h0GehOl77vgafrwk9Xf6W5NrkuWnjq9h0X/c3H/66HZjZJugqb8SI+2PaKpCIGawprKREreDkYWedrEqQjGDTTWJtN/7wbF5v3LwLW/6EFsOnPNayNZca+slpds2OY4lLLyOgk7rK4ra61cO93cUReGQwbNSnm2vWYU/Uo9hxTGsOP5IPdtrV2VdDoLxg2flxDekM1Ql9We7q251xvcIxZo77XN4vpuJgxKKuzcSCqXrwzCaEMJACINid4jTJtYmy0+bWMvEkjhHj3Zy9Ggnk0ri7cprgw0s+s9uKppCCGFgGE2djpGuvK8TI+3vaIpCQzjGqvIGKptbg3zVBaNsqG7qR8n2L+S3cQ7pjRz2HWHXXfjD9ahq5ybB3grV2zY5jk2zZ5WyN+GR352kPQcuO+tSk+lYwuTDLa90WD9TOUCNfxcjiw7tFfmyZWPlRymfs3mPDRXLmTW6fdKhtvicdqYNLWTZluX0NKFQuoRAdf63k58unJpa364Jbj4+NSvivuULT90KbAULqhvT1UodI115uvcYaB7/AxEFKG8OUxOM4LbrNGYZVVKSQCr7HGFZJo4enpV3RjYR8Xr7NkAiOU4dLlvn8cxb0BQdA6nsM2HTnOS7EuFsm8I1xM1Il8oHCr0lp6YqeB064X1/dboWoyktiuLApiXu4cfNBoSIdrHcjj+WR3lzjEjcYkxBmDxn6t14gR2HnnAWjBr1KPv8DUSN1BfZHzz+Bwqqkggi5Y/EpaLvIlLZ5woF7HruTKuqoqJpNoToOLayRe8uODzOAprCNXicBVnVl2b89qRL+HPU+LOSMfpDsWaWbX65S+WZkvkMhPcAmDD48LR9pWNS6Rxq/KlZ+jS1a2GnnbZR7RICFftOTuYBMMwAdc1v0TYhUObyUxiseRhTIvjP9mYeWbaL20/ajH2v81/MVFjw7hhQWv7u8rijTblpwR9WDOLmUywce5V6tvH7exoPQFoPDm7kEjJHaKq9Syllu0OmYDmqovaqo5yqqAzyjco6b7e+9/qdpJWWhD8t7JuMp6flfUVX5QTYVZ+aoKcz9lX0iqITjm0jbmR3RiuEQTCSemSyb8IfXfOm5AXoSrmmKpw0Pp9heQX8fVOrcv77phJqgnYicUEkLqgJ2FPKQ3GNT8osLvrTZm75206e/KiKl1bXJ8tfXl1Hpb+9Naxl9//kx1VpM6X1tFxy4COVfY7IpXNecowMJnpd7f3bAA5b9nEDbHuv30lSGTtoBi6bD5fNlzYZT0u5jrPT8o7a9xXZvofL5sNjL6CsYQN1gT0Z+w1Gm9hc9Rk21YnT5sVl8zFt+EmAoDn0WafWrBYC4fWYVhCXfTya6kZYjrQJfzzOSWiqG031dKv8yqOG8PbWQdQEbVQHbLy9dRBPnDeWP158CH+8+BCeOHdssjxiKPgcJudP1RlVYGddZZg31jdgWK0L4pgp+Pn75SnPoHX3X+mP8/Ka+n3F6HE5JHb+n+wKpC1rW2d1TVfycnR9DElukHbWHJGLmPjtxtCdiGhjhxaEXITq7Qo2zZ4I0CGP1lLINqFPeXl5txP+9AVdSUzksLn5eOvrrNvzH4455LwOLU5CCNbt+QBLmEwbcVIySuTgvNFU+ydQ2bSZUGQTHtfkDuWKG/WEopvRVA8+91Ri8cHU1tWlvd+eKSFQpvLSPDtnTSnhL6sDCODsqYPaRfFrKT+kOMjpE+s4f1qc+UdOwB81ueOfu9lSm+rb8GV1hIue38RhQ9xMH+pmmM/GK2tSd/9t7/tXNsd6VA7Z+Q201DFNg1OnWV32LZC+Cf2LVPY5wBIWthye17fg1N1YWGgdGGhyeRsgGxLX7w68ACK9QTYJfRorOw443NNkPr1FVxITjSmZzvbaVWyu/pRDhx6Ttv6u+vU0hCoZkjeW0vxxKWWHDTua+kAZgciXOOzD0LX2xxdCmDQFPwMgzz0bRdFx2IchOskbnikhUKbyedOLuSpDFL+rtjTTEMnn9IkNRGJleF2H4XNo3HLSsJT4/boKx4/xsaU+yud7gny+p/01vZgpuOnNXUwrTfgFrKkMJdtnW/7rZZUs/EZr1sJs/AayqdOZX0Bf+SZkYqD4LnyyO4AjZDC3j8aTyj4niHZpX3OBqmpoSvofoSXMPpEhE4l0vDIhjgTGD55FtX8nu+rWUZo3jkJPaUp5KNbM5qpPsGkODh3WfjFg0xxMGX48X+z6F03BFRT5Tmp3TBWMfIlp+XE5xmG3Dcrp+7SQKYpf23KHrY5ovBzDbEbX8tLE7y9JKsGGkMGayhD/79NqaoKppvP6kMH72zqOW5CpfGV5iB++uo1pQ92MLLCnmPbT7fx7ah3Ipn2mPrIpz8RAsS7EDIunPqlBFfB/Z5t9krVQ2lFygKrq3U4p21U63r0r2Ps40Eo6epJxT3Jgoak6U4efAMDaPe9jWgbVzTupbt6513z/H0zLYPLQYzq8RTI4bzSl+eMxzHqCkc1AIu58NFZB3GggGNmEqrrxuqb12XtBIopfZzvFlnKHLZGRMBJr9V2YN72YIV4bpT5bimWg0K1zwrg87j9tFHZNYUapn+mlfmyawiNnjOLZi8bz7EXjefiMUSkx3m2awsPfHMUzF47nmQvH89A3U8s1BQ4b4qIqEOfNLxv57fJq4vvs/G//x26e/qQ6+e/2f+5uZx343UdVKe/YmV/A7z6uatf+ieWp7TP1kU15JnravrdIyGFQETR46N9r+2RMubPPAX3hnNd2rHSBa3TNlvPbANnQkeVBcnBS4B7C6OKp7Kxby6aqT6nZm9AnHA9QHyxnkG8UQ/PHd9rHoUOPoT6wh1B0PTZ9MP7wKgTK3jwMgjz3rHYR/gYKDvtQCKlE42V4XYlASJksA6V5duZNL2DaoI0IAevqRjN5cOtiqNidWCS0WAfmTS/m0CGt5SWe1PILZyasB3FTsLk2zKL/VLCnOdX6VumP8+razpXhqvIQj/2ngmmlbkrT+BXMGu6mwh9nTUWI1eWhdu0/Lwty05s7mT404ZtQ4NR67HsAHZvps23fWR+9Ub6vHA/+ex2XHT6OscXZxS/pLgPzL2I/py+c81rHchCK+duZM3Wlf8/rW5DZ7yT7MmHIEVT7d7GrrnVHs7HyI3TVzmHDjsv4u2LXnRw67FhW7X6HYHh5Mu48gMs+BodtSM5k7ymqYsNuG0IsXoFh+tG1xBd8Z/H9AU6fWEskllDIo4pqgMEp5dlk/9u33KYpHDbEzc++NnIfvwGFm08eSrG71SpXF4zz4HsVyVsCipLwL3h7cxNvb25/HTJmCv7vr63XJ102BcVqzainKjCqwM6G6jDrq8K8sLIOhdS4STFT8LO3yvj6xAIA/rmpMa114a65rQmgOjPTp7Mu/G55FXd9LTWBVE+PEjKV/+6jVDkihsk1Sz7ljStOaTePvYlU9r2MEKLXQtRmg8PmRoSsdp7CNn1gKHuZ/U6yL5qqc8jg2awpezf5TAiLsYNn4LRlFzhnSN5Yir3D213lczk6twoMBJy24cTiFURje9A7uVXQgmEGiMRaw/hGYpvwOsekxAToit/AvuXt/QaKOXr0Ps6Pg1zMmx5N1rl4ZgkXzSxmR32U1RUhXl5dR2MkNZJgvlPjvGlFTB/qYWyRgxdW1ibbX7TXuhCMmaytDLG6IsRbm5oIxVMdKXc3xvjDJ9Udzs26yjB/+LiK6UM9TCl1sWRtfYoT4AUzitlcG2FNRZCN1eF27T/fE+Set8qS1oUxRY6MjoRdLb9oZjHb987T6oogK9I4XfYFUtn3MkJYOO1di/TVE3TNjqKm/vFawsKuZX8fPpfomj2xXJcbe0kbKpu2tnvWEKyELH3qFEVJGz43Ht9Asfd4InGTiGFhCYE+wNKhOWxDAYVIfE+nVwhbSBfjP118/UzWgc7KM1kG2tYxTYPzpxWhKgrjip2MK3Zy1ChvinXArin8/MzRKSbydGN47BpzRvmYM8rHmYcWctVr25P+A7qaiGNQ6EqoqYawwe8+qqIlJpACRA2LJesaWLKuod1XzF++qOWV1XVE2+yi21oPVCXhF/HJ7gCf7A7slUchFG+tv3hVLV67SsFeGRrDBi+trksp93RS/peVtSxZW5fS5yCvTl3QoCWUglPXWHT2EWnnvDeRyr6XUdS+z/amazZMs423rhDYuxD8Jpeoe6/fyTh6kkx09agnXRIop01jXJuzz3DMpDkao15XMC2RVb7zXKOqduz6YGJGFYYZQNf6P3xtJstA2zp1dXUZrQPnTy9udxaejW/C+fvcTDhtcmFKnfqQ0Wpd+EoJ500rYkN1mNUVId78soFArHVRJEgcN5w+uYDpQ91MLXXz5pcN7awL1YG9fgUVQf6z3U/boJ+GBU9+3LFlwbDg952UC5Goc+qEfKYPdTOt1M0gr40/fV6TlOPmU6bk/LwepLLvdfryvD45pupIUfaaZss6pG1foMrrd5J9SBdbv6sx/rPpw2XXcNldNPkcjC4tYHdjkKZIvN+VvtM+gphRRTS+B11rH5mvLS7HuHYx/vNcM3tdpkyWgZY6ZWr6kMXZWAcyjdFV3wO7rjJjmIcZwzxsq4/w6e5UE/m0oW5+eExph+0BBnttfHVCPl+dkE9zdHe7PkYX2vnm3kXHmxsa2NkQa19+6N7yL9uXzxjm5trjh6Z5jyZUATedsk/6xRwhlX0v05fn9a1j2hEikNwZDbTrbroqlb0klZbY+VtrPge6F+O/q304bBqHDMojHDcoawzRFI6ja/2j9B22YcDnRGJ70obhbUEIi2A4NaeAyz4q5bx+oJCNdaCnfXRWfuWcIawqTz1KuPKoIVm376iPO04dkbRSzBruaXdckVI+rH35vjK0yHHFkYNwhIw+uWMP8p59r5JIKdv3O3uHzZ0Sg74309r2BvL6nSQdvRHjvzt9uGw6EwblcVhpPi67jmn1/SFTwpQ/CMNswDQ7dtgKRTdjmI04bCNRlZa/64Hr7Jop3kBv9NFRectRQgvpjhIy9Z+pj56Wt+XIkV7mDO27wGc5+xa2LIu7776bjRs3YrfbWbhwIaNHt4bOXLJkCU899RQ+n49zzjmHefPmEYvFuPXWW9m9ezder5c777yTMWPGsHPnTm655RYURWHChAncddddqOrAW6cIrJzmsO8Im+ZA2eueIoSF3geheruCvH4nSUdvxPjvSR9uu86kQXkEonHKm8MYZsdKXyCIGCaqovSar6nDPoKYUZ1w1NMmtis3zGYC4fWoioM890xiRjVNwU+JGhUIMUP+PaUhm6OEnvbR0/L+ImfK/u233yYWi/Hiiy+ycuVKHnjgAZ544gkA6uvrWbRoEa+99hp5eXnMnz+fo48+mvfeew+3283ixYvZtm0bCxYs4KmnnuL+++/n2muvZc6cOdx555288847zJ3bVxGFs0dB6Zd49IqioGl2LMvAEgJnPyw4OsOuO7CEKXf4knb0Roz/nvbhddiYOCjz0ZclBNWBCPXBKKG4gd7DDYfTNgw/XxCN7cHjnIgpEh7iCgkrYXNwBWDhc38FVbXjtI8gGi8nEtuNYTZi0wszDXHQ0d9HCb0lQy7I2bfvihUrOP744wGYOXMma9e2BtAoKytj8uTJFBQUADBt2jRWrVrFli1bOOGERDjNcePGsXVr4nrOunXrOPLIIwE44YQTWLZs2YBU9rrW+ylls8Wm2olaRiJefj9mQkuH3sbyIJHsr6iKQqnPRanPRThmUBmI0BSOYe1V0l3uT3Vg10uIGTU4tBjDC4oJxQyqAmEC4c3EzXocthE47cOTbRy2EURiu4nEyqSy74BsHA172kdPy/uDnGmFQCCA19t6LqJpGoZhoOs6o0ePZsuWLdTW1uLxeFi+fDljxozh0EMP5d133+XUU09l1apVVFVVYZpmignY4/Hg9/szjt92cZENK1as6Px9zCoyXRbXFAe71doujdtbRC0/cRFGRadCa+wXGVpIN5cBs+PrKZKO2bRpU+ZKkqzIxVy6hKApatIYNQntNfOrWSz4xd77XW6nF/QaAg3rqWweAYBmhQjG1yHQ8TcMItBY1qalhe5WCYZ30lRfAMnjO4EpElHxOhtdAIYl0Hq4KSkrK8tcSdIppjAZ7x2cUff0FjlT9l6vl2Cw1fHEsix0PTFcfn4+t956K1dffTWlpaVMmTKFwsJCTjrpJLZu3cr//M//MGvWLKZMmYKmaSnn88FgkLy8zF67U6dOxeHI7ux6xYoVzJ49u9M6FY1byKTsPY4C8lzFWY3Z20TiQer8e/A48inw9F+40I7msrp5F6YlPfK7wqZNm5g4sf1ZrqTr9MVcmpbAH40TiBqE4wbhuEnUMNFUNbnzN0XCQ7vE7WRInpO4Gea9DduJanUUlI5nkG8Un27/KxgWM0aehMM2gsrmMGHDouXiQGOggmi8jJLBXtyOIrwOHZ9Dp8jtzOpKoT8Sp8Ifxt/NK4hlZWWMGDGiy+0kqRimCc2xjLonW6LRaKeb3Jwp+1mzZvHuu+9y+umns3LlypQ/NMMwWLVqFX/6058wDIPLL7+c6667jjVr1jB79mxuu+021qxZw65didjKhx12GB9//DFz5szhgw8+4KijjsqV2N3Gssx+cc5rwa67ECRM5gMRTdExkcpecuCiqQoFLjsFrla/HdMSNEfjBCJxYqZFodtBkbu13KG7KXCV0hiu5MuKDwnF/DSEKhmcN4bSvHEoikKR20FjOEalP0wkblLgHkVVUxkFzlomD+16eGCf04bPaSMaNyn3h6kPRXu805cMfHKm7OfOncuyZcu46KKLEEJw3333sXTpUkKhEBdeeCE2m41zzz0Xh8PB5ZdfTlFRwmtx0aJFPP300/h8Pu69914Abr75Zu644w4effRRxo0bx9e//vVcid19lIQjWn+hKiqqovbrgqMzdE0nZmauJ5EcSGiqQqHLTqGrY8ddXUt8DUfiATZVfYyuOThs6LEp/j9tFxGmlUdt8yfU+HcwqfTIbvsJOWwaY4u8jCrwUN4coi4Y7bb/QV9hWgKvQ0dXVZoi8QEt60AjZ8peVVXuueeelGfjx7euQn/84x/z4x//OKW8qKiIZ555pl1fY8eO5fnnn8+JnL1Fwjmvfz0v7bpzwCTA2Rd5/U4iaU8o1kx9sCL5WQiL8SUzcdg6XrRrqk6JbyRVzdsJRBvwOXt2vUtTFUYWeBhZ4KE6ECacYVVeqymYonvn/i3KOmZaxAwr62MESwhsmsboIndy4bS+somYKXcQ2TKw3Lb3U4QQ2DVnf4tBgXvgpva06U6EaJ+dTyI5mNlQsTwZ7reFumA5YwZN77Rdaf5Yqpq3U9m0rcfKvi2DvZlzakRrHYwbWkhVIEJ9KErMFHQWiNAUYFOh0O2g1OfEpiW+A5ojcar9EZqi8U7bAwzPdzPElyrbxEE+1lY2ZpRXkiCrrWhZWRnvvfcepmmye/fuXMu0X5LnLMlcKcfo2sAKk9sWm2aXme8kkizIxvpV4h2FqmhUNW/vA4nao2sqw/PdTBtayCHFXrx2W7tIhKYQeOw644u9TB9WxMgCT1LRA+Q5bRwyyMfMYYUM9rrQNTWlD1MIitwOpg8rbKfoW2QYW+ztlwiI+yMZd/Z/+9vfeOKJJwiHw7z44otcdNFF3HTTTZx11ll9Id+ARwiBx5GPpkkjSWeoijagkvNIJAOB7iYE0jUbJb6RVDfvIBBpwOvsvzv3+S47+S47piWo8kdojsbwOWyU+lxZmek1VWFYvoth+S4C0TjVgSgCwagCd8riIO3YTjuleS6qA5Eu7SVMS4DCQeWYmPHb9/e//z1/+ctf8Hq9FBcX89prr/Hkk0/2hWz7BYqi9Nt1u/0NTR2Y/gQSSX/Rksynha4kBCrNGwtAZT/t7velRWlPHpzP8Hx3t671eR02xhV7GV/sy6joWxie78bdxWQyEwf5GFvsPahSb2dU9qqqpgTHGTx48ICMS98fCAQ+Z1G/O+btL2gDLBufRDIQ6G5CoEG+vab8pm05lG7/4JAs8sEnQolrTCstwOe0U+RycNjgfDRVOSiUfkbb84QJE3j++ecxDIMvv/ySP//5z0yePLkvZBvwaIqOx1HQ32LsN+iqRixzNYnkoKK7yXx0zU6xdwQ1/p0Eoo14D+LvIk1TGV/sZVONP61FwRSCIT4nI/JTUwM7bBpTSwvZUR9IxBs4gO/yZdyS3nnnnVRVVeFwOLjtttvwer3cddddfSHbgMYSJj5XsbxK1gXkzl4iSc/gvNHdSujTYsqvahoYpvz+xOe0MzTPzb7+egI4pMTXTtG3oCgwttjLqELPAb3Dz7iMXLBgAffffz833HBDX8iz32DXnbjtAy/ZwUDGpjmxLBNVldfvJJLeYFDeKJRylarm7Ywf/JX+FqffGZbvIhCLE4oZWAKcusqEQdmd/w/yOvE6dDbX+LHEgaf2M+7sN23alBLjXgKWsMhzDepvMfY7bLq8fieR9CY2zUGJdzj+SB3BaFN/izMgGL/3/L7Ybeew0oKsHf0AXDadqUML8DpsxE2r03/GfnblL+POXlVVTj75ZMaOHZuSWOaPf/xjTgUbyDhtHhx65uATklTk9TuJpPcZkjeOGv9uttZ8QWne2A6PA6qbdwJ0u7ylTsiq67S8p2P0FE1VmDGsiO6esKqKwiElPmJmerN/C6YpqAlGaIrEuhQN0LQEdl2lwGnHDBndE7IbZFT2N954Y1/Isd8gsMh39X8Anf0VTbVhWn33Cy6RHOgM8o0CoLJpK43BSoq9w9s5+pmWwYaK5QDdKm9bJ27EMa0jcjJGb9EbrlR2LcPGRINRdg/gIRQzqAlEaIzEMCzaRQQ0TIHLruFz2BjkceCy61iWSVl9pOeCZknGbdaRRx5JOBzm3Xff5a233qK5uZkjjzyyL2QbcAghcNny0DV5X7y7SCc9iaR3setOXDYfQliE4362165qV2d7zSrCcX+3y9vWMYjkbIz9FbddZ3SRlxnDiphQ4iN/b/x+u6ZS4nEybWgBU0oLGFXowWXvnwBsGUf9/e9/z7/+9S/OPPNMhBD89re/ZfPmzVx11VV9Id/AQhHku+Wuvidoiow0KJH0JqFYM5F4q1/VtpqVOHVvMplONB5iW+3Kbpf3Rh/7lm+vWcWwgglZBxDan8hz2shzDrxNTcZv3jfeeIOXXnoJpzOR6OWCCy7g3HPPPeiUvRDgdRahykQuPUKXO3uJpFfZULEcgZX8LITFuvIPOqzf0/Le6MMSJhsqljNr9ABMV36AklHZCyGSih7A4XCg6wff7kxVlF7NLnWwout2rLC8fieR5BKPPZ9hhRMBKG/YRDDW1O3y3ugjXbklLCR9R0atfdRRR3H11VdzzjnnAPDaa68xZ86cnAs20CjyDpcBdHoBu+7sHe+ZHGNYcTRFP+h/5qYVl34WA5x0yXRmjflG0kRemj+OZZtf7nZ5b/SxbzlIK19fk9FB76c//SlHH300S5Ys4bXXXuOoo47illtu6QvZBhR2vf/z1R8IqIqWuG8/gLGERaG7dL9YlOQSISwK3KXt8q1LBhaZkun0tDwXY9h1F1XN26kNlHX5fSXdI6OyD4VCCCH45S9/ye23305tbS3xeLwvZJMcoDg0d+ZK/YgCeBx5FLgGHbSKTgiB25GPx5GPIiMhDXgyJdPpaXnbOjrOHo/xlZFzUVBYt+c/GKbMmNEXZFT2N9xwA9XV1QB4PB4sy+Kmm27KuWCSAxe33Teglahdd6IoKm5HHk5b54E1DlQ0VSffNQhFUdAHuCVG0ppMZ/LQo9PeX+9peds6xfohPR6jwDOEsYNmEIkH2FT1aTfeWNJVMp7Zl5eX89vf/hYAr9fLddddx1lnnZVzwSQHLjbdiabaEAPQQUcIgV1vtTwUuIZQ7d/BwRTnVwhBgWdI0l/BpjoxzUA/SyXJRKaodD0tb6nTWBntlTHGD5pFVfMOdtevpzR/HEWeoRnHl3SfjDt7RVHYuHFj8vPWrVsPSm98Se9i1wamD4SFlXLWqGmJHe5AXJjkgoT53ofD1hoO2qY5EAdgYhBJ/6KqGlOHnwgorN3zgYysmWMyau2bb76Z7373uwwZkljp19fX8/DDD/eFbJIDGKfdQzgeGHCx8m2aHV1L9RJ2O/IJxQLEzb4LbdlfqIpG/j5Jnpw2D02hahQZY0LSyxS4BzOmZBo7alezuepTJg89usfx9XsrB0Cux6jx78JvNndY3ttkVPZer5f58+cza9YsFi1aRFlZGaFQqC9kkxzAuGxeGqnqbzHa4dDSJzgqdA+hyr/jgHZWs7Ao9pSi7LMA0zWbjIsgyRmHDJ5NdfMOdtatZbBvdI/i6/dmDoBcj7Gx8iMMw8KwzuyTa4gZt1ULFy7k0EMPpby8HK/Xy5IlS1i0aFHOBZMc2CiKOuCuMwph4bR705Zpmk6eq+SANecLIXDbfB06JNo0R9rnEklP0VR9rzkfVu5+u0fx9XszB0DuxwgQJ8TasvfT1ultMip7y7I47rjjeO+99/ja177G0KFDMc2B60kt2X+w6+6BdRasKDj0jq8Feh0F2AbYAqW3UBWVAvfgDsulspfkkkJPKcPyJxA3W53/ttesIhRrNXOHYs0pyrO3y/trjDVl7+GP1Lebk94moxnf5XLx9NNP8/HHH3PnnXfyxz/+EY/n4LyOJOld3PY8miN1aAwME7FDc2eMmFfoLqW6eUc7U/f+jCUsir3DOn0nu+4kEG0ccD4WkgOHmBlO+WwJk4+3vk7+3kVoU6g65cpub5f31ximZfDx1jc4dcr8bKeqW2RU9o888ggvvfQSv/zlL8nPz6eqqoqf//znORVKcnCgazZsmgNrAHjhCiFSsnx1hK7ZyHOV0BypOyDO74UQuOzejPEEHDZPIhvU/v/KkgFKusVmzIxQ49/VYZtcl/fVGH1BRmU/ZMgQfvzjHyc/33jjjTkVSHJw4dBchC1/f4uBEBZuhy+rul5nIeG4n6gR7rSeitrvFgDTMkHp+KhEU3QK3EMy9qMqKppmRwzgYEiS/Zt0Mf6PGncWLnvi7zIc8/PRttdzVt5fY2iqzpzx3+rVuUyHvDAv6Vdcdi+hWFO/K0W7zdml9MUl3hGd+hsIBNXNO3pBsu5jWSaD80aiq51EwFOUrE3zds2ecYEjkXSXlvj5W2s+BxLhdX2u4mS5z1Wc0/L+GmPaiJP6JKOqPICT9Ct23QUD4By4q/H6FUVFVbUO/2mqjr2fcwDouh277upUzq6cwevSSU+SY3Id478rOQByP4YXG26mjjgxbZ3eRu7sJf2Koig4dVe/7hgtYaaY2XoLp91LNBTsN6tFbycccto8+CN1XbKASCRdoSV+fsv/93V5X44xqfQoaiua+yzVr1T2kn7HobuJxEP9ljteU23Y9N7ftbrtXppC/RM4yBImrix9ELLFpjkOCKdEycAm1zH+s80BkOsxBvlGEa2uzVivt+h/+6nkoMflyAP6L1iNQ08fNa+nJAIH5abvTGiqrdffS2bAk0j2X3K2s7csi7vvvpuNGzdit9tZuHAho0e3rnaWLFnCU089hc/n45xzzmHevHnE43FuueUW9uzZg6qqLFiwgPHjx7Nu3Tp+8IMfMGbMGAAuvvhiTj/99FyJLuljVEXFpjsxzHifj20JK6dpbJ02DzEj0udWi1wtYGQGPIlk/yRnyv7tt98mFovx4osvsnLlSh544AGeeOIJAOrr61m0aBGvvfYaeXl5zJ8/n6OPPpoNGzZgGAYvvPACy5Yt47HHHuNXv/oV69ev5/LLL+e73/1ursSV9DMOzY1hNvX5uIoCTlv6ELm9gduRR1Oopk+TyAhh4bL1vg8CgF1zEI75++3IRSKRdI+cmfFXrFjB8ccfD8DMmTNZu3ZtsqysrIzJkydTUFCAqqpMmzaNVatWMXbsWEzTxLIsAoFAMpXu2rVree+997j00ku57bbbCATkzuJAw2X3pUSV6ivsmiuniktVNOy2Pg6xq6hZBQjqDk6794DNDyCRHMjkbGcfCATwelt3TJqmYRgGuq4zevRotmzZQm1tLR6Ph+XLlzNmzBjcbjd79uzhtNNOo6Ghgd/+9rcATJ8+nXnz5jF16lSeeOIJfvOb33DzzTd3On7bxUU2rFixousvKUlLd+cyaNYg6LtY+UII7KqXnWpuneiiVoCYFezWomLTpk1dbqMpdirUxi63y5aAWZ2zvnNJd+ZSkh45lz1HCAuXWthnuidnyt7r9RIMBpOfLctK7tTz8/O59dZbufrqqyktLWXKlCkUFhbyzDPPcNxxx3HDDTdQUVHBd77zHZYuXcrcuXPJy8sDYO7cuSxYsCDj+FOnTsXhyM7DesWKFcyePbsbbynZl57MZUOwkkg8mLliL2EJk6H543OevtUw41Q1b+/ylbVNmzYxceLELrWxhEWRpzQnVwlbqPWXpSQs2R/ozlxK0iPnsnewLJOyrbW9pnui0Winm9ycmfFnzZrFBx98AMDKlStTfjkMw2DVqlX86U9/4sEHH2Tbtm3MmjWLvLw8fL7El1R+fj6GYWCaJldccQWrV68GYPny5UyZMiVXYkv6EafNi9WHJmJdc/RJnnZdy83VvnTk2gcBZAY8iWR/JGc7+7lz57Js2TIuuugihBDcd999LF26lFAoxIUXXojNZuPcc8/F4XBw+eWXU1RUxPz587ntttu45JJLiMfjXHfddbjdbu6++24WLFiAzWajpKQkq529ZP/DafPQl35fzj68FufUPAT7wAHRoXly7jxn110yA55Esp+RM2Wvqir33HNPyrPx48cn///HP/5xSoIdAI/Hw6JFi9r1NWXKFF544YXcCCoZMCiKgl1z9YmJ2BImzhyauvfF7cjDH6nPqSVBCAtnjhzz2uKwuWUGPIlkP0MuzSUDCqfu6TTBTG+hKlrO7qKnQ9fs6Dk25QtaAhTlFlVRZXAdiWQ/Qyp7yYDC7chD9EE0PYfe90lqcn1s4NBdfWZat3WWSU8ikQw4ZGx8yYBCVTV8zmIsy8jpOG577nfA++Ky5+096+59U74QAqeeu0iA+6JrDpnuViLZj5DKXjLgyNsn//OBgl13oqm2nASlEcLC7ey7BYzMgCeR7F9IM75E0ofk6vjAbnP2qeK1aQ4ZMlci2Y+Qyl4i6UPcOQgLLITA0YcmfNibAU+T5/YSyf6CVPYSSR/isLl7fQcusPrFB8Gm9nHMf4lE0m2kspdI+pjedqSzaQ50zdarfWaDXXP0yTVJiUTSc6Syl0j6GKe9d8MC98c1Quj995BIJLlDKnuJpI9x2jyoveTcZlkWnj4IpJMOTdXR+iC3gEQi6TlS2UskfYyiKL22G9d1G3o/JqaRSXEkkv0Dqewlkn7AYfP2yn17Zz+Z8FuQyl4i2T+QQXUkkn7AbffSGKro9MxbCAtTdBJJUAic/eCF3xaH7sYfqe80KY6KitKDML6WsHocBjjjXPYBKgqKDEIk6SekspdI+gFFURlacEgie00HVGrNDMs/pJNO6PcIdk67h2EFncgI1AcrepTJUFNVTKv7Cl8IgV31dj6XfUBzpI5wzN+vMkgOXqSyl0j6CVXROt0RK4qa05S4vUUmGd2OPBqCVd1S1kIIPI4CovEQhhXvpoQCu+Lp97l02/MI5ig3gkSSCXlmL5FIcorL5uu2GV9g4bEX4LL7uu3j4LR5BkRoX7vuRFXl/krSP0hlL5FIcoqiKDht3Qsk5NDdaJqOx5HfrfZCWLj62a+hLQ4tt2mOJZKOkMpeIpHkHI+jANHFnACWsHDZfUDiSMNp83Z5XFXRur3QyAUO3ZWTrIcSSSakspdIJDnHoTvR1K4lzlEUJSXmv9ue12VF6RggJvwWXI48GWJY0i9IZS+RSPoEl93bJUXn1FMVtdPu6ZJzm2WZ+JwFXREx56iKis0mYxNI+h6p7CUSSZ/gdRQiOrtr2AZLmHic7c/pnfbsTfI23dGv0QU7wqH1byAkycGJVPYSiaRPUFUNh56dg5qu2tKGFPY6CrCyOPsXQuCy+bosY1/gtPuyeod9sYTZrXYSCUhlL5FI+hCXPS9jpjwhRIfOeLrmwK45M44jEHgGmAm/BYfu7NZde121o6t9n8pYcmAglb1EIukz3HZfRoc5gYXHUdBhucvmy3j279Q9PQ6xm0uytXC0IITAZfehyXv6km4ycP8aJBLJAUc2d+4dugtd63gH63bm01mcYSEs3P2U9jdb7Lq7i175Aq+zsMs3GiSSFqSyl0gkfYqnk3N3IayMZ+2qona6YFDUgXW3Ph1uR9ciAjptXlRFlWZ8SbeRyl4ikfQpDt2F3tEOVSGrXbmrkzv3Tt09oO7Wp0NVtKyv4FnCTEYQtOsuLEs66Um6jlT2Eomkz+nozr1T92YVR99p86SNM29ZFl5HYa/ImGuyvYKnq3YctkRdmz7wrhJK9g+kspdIJH2Ox16AIHVn3nYHmwlFUdJezbNp9v1GITrt3oxX6Voc81pQFRVNk056kq4jlb1EIulzNE1v55GuKbbkDjYbfM6CFJO2EAKnvevx8/sLh+5CyXAFTyDw7nOFUHrkS7qDVPYSiaRf2PfOfVei40Hizn3qLt5qpxgHOpmy4Lls3nZ38jv0d5BIOkEqe4lE0i+47T7UvY50lujeWXvbO/cOvWux8wcCDlvHV/A6OtaQyl7SHaSyl0gk/YKiqDj0xG7erjk7vVvfER5nAQKRSIfrGJjhcTvD7fC1811ooa1jXltsmiNjFEKJZF9ydvhjWRZ33303GzduxG63s3DhQkaPHp0sX7JkCU899RQ+n49zzjmHefPmEY/HueWWW9izZw+qqrJgwQLGjx/Pzp07ueWWW1AUhQkTJnDXXXehqnKdIpHs73ic+YT8zfjsxd1qryoqTt1DzAwP2Fj4naEqGjbNgWkZKc+FEB0uXuw2J50FFZJI0pEzjfn2228Ti8V48cUXueGGG3jggQeSZfX19SxatIjnnnuO559/nqVLl1JWVsb777+PYRi88MIL/OhHP+Kxxx4D4P777+faa6/lz3/+M0II3nnnnVyJLZFI+hCH7kZTbHh6EPHO7cjDsR/cre+I9KFz2zvmtaAq2n53XCHpf3Km7FesWMHxxx8PwMyZM1m7dm2yrKysjMmTJ1NQUICqqkybNo1Vq1YxduxYTNPEsiwCgQC6njA8rFu3jiOPPBKAE044gQ8//DBXYkskkj5mSN7orO7Wd4TT5qHAPbgXJepbnLb2V/Ccts79D7pz5CE5uMmZGT8QCOD1tl6D0TQNwzDQdZ3Ro0ezZcsWamtr8Xg8LF++nDFjxuB2u9mzZw+nnXYaDQ0N/Pa3vwUSJq2WVbvH48Hv92ccv+3iIhtWrFjRpfqSjpFz2XvIuew9BupcCiEIWtWAsvezhUstRFcrOmwTtpowRbSPJGzPpk2b+m3sA4WWn3Nf/V7mTNl7vV6CwWDys2VZyZ16fn4+t956K1dffTWlpaVMmTKFwsJCnnnmGY477jhuuOEGKioq+M53vsPSpUtTzueDwSB5eZlNflOnTsXhyC64xooVK5g9e3YX31CSDjmXvYecy95joM9lnb+cmBkGEn4IQ/LHdlq/OVxLMNrUF6K1Y9OmTUycOLFfxj6QsCyTsq21vfZ7GY1GO93k5syMP2vWLD744AMAVq5cmfLLYRgGq1at4k9/+hMPPvgg27ZtY9asWeTl5eHzJZxS8vP/f3t3GhtV+bdx/DrLzHSjC0HkMShL2aRISBEXVKL8QZS4USWtC4aCJhoqikJqgWKxtbURjQ0ag4pvCu4CipFocEljIYgNFBBcKnWpGEOtLG2l28zzon/moQ9QpsMsnen386oz58ycuz+mXHPO3HP/ktTe3q6Ojg6NHTtWO3bskCRVVFTo8ssvD9awASDkXI5YeTye/66Yd+6TGWbko6eCdmY/ffp0VVZWKisrSx6PR8XFxdq8ebOam5uVmZkph8OhjIwMuVwuZWdnq3///po7d66WLl2qe+65R21tbVq0aJHi4uKUm5ur/Px8vfDCCxo+fLhmzJgRrGEDQMjFORN19N/DMmT4tDCQy46Thxn56IGghb1pmnr66ae73Jeamur9OScnRzk5OV22x8fHq6ys7LTnGjZsmNatWxecgQJAmJmm1bkioOnwaaa9aVqyzmNSI/oeXi0A0As4rRjFOX1rBCRJFr3t0QN0VACAXiApbkCPvj9vm87TFuMBzoYzewDoBXq6UI7NmT16gLAHgAhk2U55mJEPHxH2ABCBYuw4wh4+I+wBIAJZpi2DhmDwEa8UAIhQFr3t4SPCHgAilG0wSQ++IewBIELR/Q6+IuwBIELZplMeD8vm4twIewCIUC5HrNzqCPcwEAEIewCIUJbpkMl/4/ABrxIAiFCGYcjic3v4gLAHgAjGjHz4grAHgAjGjHz4grAHgAhmMSMfPiDsASCCuexYecQa+egeYQ8AEcy2nJKMcA8DvRxhDwARzDAMWfS2xzkQ9gAQ4WzTDvcQ0MsR9gAQ4Wy63+EcCHsAiHC25WBGPrpF2ANAhHMyIx/nQNgDQIRzWFzGR/cIewCIcIZhMiMf3SLsASAKMCMf3SHsASAKWMzIRzcIewCIAhZn9ugGYQ8AUcBlx8nt7gj3MNBLEfYAEAUctivcQ0AvRtgDQBQwDVOmxaV8nBmvDACIEkmxF8jd0R7UYziMePVz9ff78W0dLfq37bgMg3PNUCLsASBKxDn7Bf0YLjNeCbEpfj/e7elQ85FjNOUNsaCFvdvtVkFBgX744Qc5nU4VFRVpyJAh3u2bNm3S2rVr1a9fP82aNUuzZ8/Whg0btHHjRklSS0uLDhw4oMrKSv3+++966KGHNHToUEnS3XffrZkzZwZr6ACAIDENS7blZDJhiAUt7Ldu3arW1la988472r17t5599lm98sorkqSGhgaVlZVp48aNSkxM1Ny5c3X11VcrIyNDGRkZkqSVK1fqzjvvVGJiovbv36/s7GzNmzcvWMMFAISIw3Spxd0c7mH0KUH70KSqqkrXXXedJGnChAnat2+fd1tdXZ3GjBmj5ORkmaapyy67TNXV1d7te/fuVU1NjTIzMyVJ+/bt01dffaV7771XS5cuVWNjY7CGDQAIMqcVQ5e+EAvamX1jY6MSEhK8ty3LUnt7u2zb1pAhQ1RTU6P6+nrFx8dr+/bt3kv0krRmzRotWLDAe3v8+PGaPXu2xo0bp1deeUUvv/yycnNzuz3+qW8ufFFVVdWj/XF21DJwqGXgUMvAOd9adrjb1ew+LMOwAjSiyOPxuBVrpoTsdRm0sE9ISFBTU5P3ttvtlm13Hi4pKUl5eXl65JFHNGjQIKWlpSklpXPCx7Fjx3Tw4EFdddVV3sdOnz5diYmJ3p8LCwvPefxx48bJ5fLte6dVVVWaOHGiz78bzo5aBg61DBxqGTiBquWfR34OwGgil9vdobqf6wP2umxpaen2JDdol/HT09NVUVEhSdq9e7dGjRrl3dbe3q7q6mqtX79epaWlOnjwoNLT0yVJO3fu1OTJk7s81/z587Vnzx5J0vbt25WWlhasYQMAQsBhsQhQKAXtzH769OmqrKxUVlaWPB6PiouLtXnzZjU3NyszM1MOh0MZGRlyuVzKzs5W//6d39usra3V4MGDuzxXQUGBCgsL5XA4NGDAAJ/O7AEAvZfTjlFbR0u4h9FnBC3sTdPU008/3eW+1NRU7885OTnKyck57XEPPPDAafelpaXp7bffDvwgAQBh4XLE6/iJBpl9+HP7UGIJIwBAyDmtGBkGS+uECmEPAAg5wzD43D6ECHsAQFg4rZhwD6HPIOwBAGHhtGPl9rjDPYw+gbAHAISFyxEvQ6ykFwqEPQAgLEzDlMXn9iFB2AMAwsZJ2IcEYQ8ACBsHTXFCgrAHAIRNrDNBbjFJL9gIewBA2FimLdt0hHsYUY+wBwCElcPkc/tgI+wBAGHlsAn7YCPsAQBhFeuIl9vTEe5hRDXCHgAQVg47RoZBHAUT1QUAhB3ftw8uwh4AEHYOmuIEFWEPAAg7lyNObjef2wcLYQ8ACDuXHSvDMMI9jKhF2AMAws4wTNm2M9zDiFqEPQCgV3CafG4fLIQ9AKBXcDhi5fGwTn4wEPYAgF4h1hEvj+iAFwyEPQCgVzANS5bJ5/bBQNgDAHoNFtcJDsIeANBrOK0YeTxcyg80O9wDAADgpBhngo63NET9d+4N05QRwvNtwh4A0GvYlkP/k5wa7mGExJ/m0ZAdi8v4AABEOcIeAIAoR9gDABDlCHsAAKIcYQ8AQJQj7AEAiHJB++qd2+1WQUGBfvjhBzmdThUVFWnIkCHe7Zs2bdLatWvVr18/zZo1S7Nnz9aGDRu0ceNGSVJLS4sOHDigyspK/fPPP3ryySdlGIZGjhypp556SqbJ+xQAAHwRtMTcunWrWltb9c477+iJJ57Qs88+693W0NCgsrIylZeXa926ddq8ebPq6uqUkZGh8vJylZeXKy0tTcuXL1diYqJKSkr02GOP6c0335TH49Hnn38erGEDABB1ghb2VVVVuu666yRJEyZM0L59+7zb6urqNGbMGCUnJ8s0TV122WWqrq72bt+7d69qamqUmZkpSfruu+90xRVXSJKmTJmibdu2BWvYAABEnaBdxm9sbFRCQoL3tmVZam9vl23bGjJkiGpqalRfX6/4+Hht375dQ4cO9e67Zs0aLViwwHvb4/F4l06Mj4/X8ePHz3n8U99c+KKqqqpH++PsqGXgUMvAoZaBQy0DJ1S1DFrYJyQkqKmpyXvb7XbLtjsPl5SUpLy8PD3yyCMaNGiQ0tLSlJKSIkk6duyYDh48qKuuusr72FM/n29qalJiYuI5jz9u3Di5XL51T6qqqtLEiRN92hfdo5aBQy0Dh1oGDrUMnEDWsqWlpduT3KCFfXp6ur788kvNnDlTu3fv1qhRo7zb2tvbVV1drfXr16u9vV3Z2dlatGiRJGnnzp2aPHlyl+caO3asduzYoSuvvFIVFRVd3gj8fye7JbW2tvZovC0tLT3aH2dHLQOHWgYOtQwcahk4garlycw7W8dAwxOkXoInZ+P/+OOP8ng8Ki4u1v79+9Xc3KzMzEy99NJL2rp1q1wul7Kzs3XTTTdJkl5//XXZtq25c+d6n6u2tlb5+flqa2vT8OHDVVRUJMuyznjc48eP68cffwzGrwQAQK82atQo9evX77T7gxb24eJ2u9XU1CSHwxH1LRIBAJA6z+jb2toUHx9/xq+mR13YAwCArliZBgCAKEfYAwAQ5Qh7AACiHGEPAECUC9r37CPBuZr1wDfV1dVatWqVysvL9euvv9K0yA9tbW1aunSp/vjjD7W2turhhx/WiBEjqKUfOjo6tHz5ctXW1sqyLJWUlMjj8VBLP/3999/KyMjQG2+8Idu2qaOf7rjjDu9X4gYPHqyHHnoopLXs0/9K3TXrgW9ee+01LV++3LswBE2L/PPRRx8pOTlZb775pl577TUVFhZSSz99+eWXkqS3335bCxcuVElJCbX0U1tbm1asWKGYmBhJ/H376+T/jycbvYXjNdmnw767Zj3wzSWXXKLVq1d7b9O0yD833XSTHn30Ue9ty7KopZ+mTZumwsJCSdKhQ4c0YMAAaumn0tJSZWVlaeDAgZL4+/bX999/r3///Vfz5s3T/fffr927d4e8ln067M/WrAe+mzFjhrfngeRf0yJ01iohIUGNjY1auHChHnvsMWp5HmzbVm5urgoLCzVjxgxq6YcNGzaof//+3hMiib9vf8XExGj+/Plau3atVq5cqcWLF4e8ln067Ltr1gP/+NO0CJ3+/PNP3X///br99tt16623UsvzVFpaqk8//VT5+fld1h+nlr754IMPtG3bNs2ZM0cHDhxQbm6uGhoavNupo++GDRum2267TYZhaNiwYUpOTtbff//t3R6KWvbpsE9PT1dFRYUkndasB/452bRIkioqKnT55ZeHeUSRob6+XvPmzdOSJUt01113SaKW/tq0aZPWrFkjSYqNjZVhGBo3bhy17KH169dr3bp1Ki8v16WXXqrS0lJNmTKFOvrh/fff984J++uvv9TY2KhrrrkmpLXs08vlnqlZT2pqariHFXHq6ur0+OOP69133+1R0yL8n6KiIm3ZskXDhw/33rds2TIVFRVRyx5qbm5WXl6e6uvr1d7ergcffFCpqam8Ls/DnDlzVFBQINM0qaMfWltblZeXp0OHDskwDC1evFgpKSkhrWWfDnsAAPqCPn0ZHwCAvoCwBwAgyhH2AABEOcIeAIAoR9gDABDlCHsAflm2bJn27t171u11dXWaOnXqGbeNHj06WMMCcAYsFwfAL88880y4hwDAR4Q9EKV27NihNWvWKCYmRj///LNGjx6tVatWyel0nnH/a6+9VjNmzFBVVZUsy9KLL76oiy++WHv27FFJSYlOnDihlJQUrVy5UhdffLHmzJmjnJwcXXnllXr++ef16aefKiUlRRdccIGmTp2qK664QidOnNCiRYv0008/KTExUS+//LJSUlIkSfn5+dqzZ49SUlJUXFysiy66SLW1tVqxYoWOHDmiuLg4LVu2TOPHj9eTTz6pI0eO6Ndff9WSJUu0c+dOVVZWyjRNTZs2TTk5OaEsLRBxuIwPRLFdu3ZpxYoV2rJliw4dOqSvv/76rPsePnxYV199tTZt2qRJkyZp/fr1am1t1fLly/X8889r48aNys7OVn5+fpfHffHFF6qqqtLHH3+sV199Vfv37/dua2hoUHZ2tj7++GMNGDBAn3zyiXfbpEmT9OGHH2r69OneqwRLlizRnDlztHnzZuXl5enRRx9Va2urJCk5OVlbtmzR6NGjVVFRoY8++khvvfWWampquqx9D+B0nNkDUWzkyJEaNGiQJCk1NVVHjx7tdv+THc5Gjhypb7/9Vr/88ot+//13Pfzww959Ghsbuzxm27Ztuvnmm+V0OuV0OjVt2jTvtoEDB2r8+PGSpBEjRuiff/6R1NkF7LbbbpMk3X777XrxxRfV1NSk3377TTfeeKOkzrbTSUlJOnjwoCR5n+fCCy+Uy+VSVlaWbrjhBi1evFgul8u/AgF9BGEPRLFTQ9AwDJ1rdeyT+5/c1+12a/Dgwfrwww8lSR0dHaqvr+/yGNM05Xa7z/h8p3aRPPX4p3b083g8sm37jGPzeDzq6OiQ1PkG4eRzvvfee/rmm29UUVGhrKwslZeXa9iwYd3+bkBfxmV8AGc1fPhwHT16VN9++62kzranixcv7rLP5MmT9dlnn6m1tVWNjY366quvvH26z6a5uVmff/659zknT56shIQEDR48WJ999pmkzk6U9fX1GjlyZJfH7t+/X/fdd58mTZqk3Nxcpaamqra2NlC/MhCVOLMHcFZOp1NlZWV65pln1NLSooSEBJWWlnbZ5/rrr9euXbs0a9YsJSUlaeDAgee8rJ6YmKitW7eqrKxMF154oUpKSiRJzz33nAoKCrR69Wo5HA6tXr36tAmFY8eO1YQJE3TLLbcoNjZW6enpmjJlSmB/cSDK0PUOwHnZtWuXfvnlF82aNUttbW3KzMxUcXGxxowZE+6hAfgvwh7oI06cOKHMzMwzblu4cKH+85//+PW8R44c0RNPPKHDhw/L4/Hojjvu0Pz5889nqAACjLAHACDKMUEPAIAoR9gDABDlCHsAAKIcYQ8AQJQj7AEAiHKEPQAAUe5/ARvqa214475qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValidationCurve(ax=<AxesSubplot:title={'center':'Validation Curve for KNeighborsClassifier'}, xlabel='n_neighbors', ylabel='score'>,\n",
      "                cv=7, estimator=KNeighborsClassifier(),\n",
      "                param_name='n_neighbors',\n",
      "                param_range=array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
      "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
      "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]))\n",
      "time for doing the validation and learning curve = 14.821728969996911 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f971a3d1430>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hyperparameters : weights = 'uniform'\n",
    "# Vary : n_neighbors\n",
    "\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "#validation curve\n",
    "print(validation_curve(KNeighborsClassifier(weights = 'uniform') , \n",
    "                       X , y , \n",
    "                       param_name ='n_neighbors', \n",
    "                       param_range=np.linspace(1,50,dtype=\"int\"), \n",
    "                       cv=7, \n",
    "                       score='accuracy'))\n",
    "\n",
    "#learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(KNeighborsClassifier(weights = 'uniform') , \n",
    "                                                         X , y , \n",
    "                                                         train_sizes=np.linspace(0.01,1,50), \n",
    "                                                         cv=7, \n",
    "                                                         scoring = 'accuracy', \n",
    "                                                         n_jobs= -1)\n",
    "\n",
    "time2 = time.perf_counter()\n",
    "timeA = time2 - time1\n",
    "print('time for doing the validation and learning curve =',timeA,'s')\n",
    "\n",
    "#means\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "#standard deviation\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "# plot the figure\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa414566",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValidationCurve(ax=<AxesSubplot:title={'center':'Validation Curve for KNeighborsClassifier'}, xlabel='leaf_size', ylabel='score'>,\n",
      "                cv=7, estimator=KNeighborsClassifier(n_neighbors=3),\n",
      "                param_name='leaf_size',\n",
      "                param_range=array([ 2,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
      "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
      "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]))\n",
      "time for doing the validation and learning curve = 11.727090766000401 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9718d699d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hyperparameters : n_neighbors = 3, weights = 'uniform'\n",
    "# Vary : leaf_size\n",
    "\n",
    "import time\n",
    "time1 = time.perf_counter()\n",
    "\n",
    "#validation curve\n",
    "print(validation_curve(KNeighborsClassifier(n_neighbors = 3) , \n",
    "                       X , y , \n",
    "                       param_name ='leaf_size', \n",
    "                       param_range=np.linspace(2,50,dtype=\"int\"), \n",
    "                       cv=7, \n",
    "                       score='accuracy'))\n",
    "\n",
    "#learning curve\n",
    "train_sizes, train_scores, valid_scores = learning_curve(KNeighborsClassifier(n_neighbors = 3) , \n",
    "                                                         X , y , \n",
    "                                                         train_sizes=np.linspace(0.01,1,50), \n",
    "                                                         cv=7, \n",
    "                                                         scoring = 'accuracy', \n",
    "                                                         n_jobs= -1)\n",
    "\n",
    "time2 = time.perf_counter()\n",
    "time = time2 - time1\n",
    "print('time for doing the validation and learning curve =',time,'s')\n",
    "\n",
    "#means\n",
    "train_mean = np.mean(train_scores, axis = 1)\n",
    "valid_mean = np.mean(valid_scores, axis = 1)\n",
    "\n",
    "#standard deviation\n",
    "train_std = np.std(train_scores, axis = 1)\n",
    "valid_std = np.std(valid_scores, axis = 1)\n",
    "\n",
    "# plot the figure\n",
    "plt.plot(train_sizes,train_mean, label = 'Training score')\n",
    "plt.plot(train_sizes,valid_mean, label = 'Cross-validation score')\n",
    "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color = '#DDDDDD')\n",
    "plt.fill_between(train_sizes, valid_mean - valid_std, valid_mean + valid_std, color = '#DDDDDD')\n",
    "\n",
    "plt.title('Learning curve')\n",
    "plt.xlabel('Training size')\n",
    "plt.ylabel('Accuracy score')\n",
    "plt.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "70be0c87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         0.98924731 1.         1.         1.         1.\n",
      " 1.        ]\n",
      "accuracy score for upgraded model=  0.9984639016897081\n",
      "Test score = 0.9923076923076923\n",
      "Test score upgraded model = 0.9923076923076923\n",
      "----------\n",
      "[0.98924731 1.         1.         1.         1.         1.        ]\n",
      "accuracy score default model =  0.9982078853046595\n",
      "Test score default model = 0.9923076923076923\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameters : n_neighbors = 3, weights = 'uniform', leaf_size = default\n",
    "\n",
    "import time\n",
    "time1 = time.perf_counter()\n",
    "model = KNeighborsClassifier(n_neighbors = 3, \n",
    "                             weights = 'uniform')\n",
    "\n",
    "model.fit(X_train,y_train)\n",
    "validation = cross_val_score(model, X, y, cv=7)\n",
    "print(validation)\n",
    "print('accuracy score for upgraded model= ', validation.mean())\n",
    "\n",
    "#Testing the test set\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "print('Test score =', score)\n",
    "predictions = model.predict(X_test)\n",
    "score = accuracy_score(y_test,predictions)\n",
    "print('Test score upgraded model =', score)\n",
    "\n",
    "\n",
    "print('----------')\n",
    "\n",
    "#default model\n",
    "model2 = KNeighborsClassifier()\n",
    "model2.fit(X_train,y_train)\n",
    "predictions = model2.predict(X_test)\n",
    "validation2 = cross_val_score(model2, X, y, cv=7)\n",
    "predictions2 = model2.predict(X_test)\n",
    "score2 = accuracy_score(y_test,predictions2)\n",
    "print(validation2[1:])\n",
    "print('accuracy score default model = ', validation2[1:].mean())\n",
    "print('Test score default model =', score2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db853a19",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}