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TP3_prog1.py.ipynb 152KB

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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "code",
  5. "execution_count": 2,
  6. "id": "3eb7a65b",
  7. "metadata": {},
  8. "outputs": [],
  9. "source": [
  10. "####### Import #######\n",
  11. "from sklearn.datasets import fetch_openml\n",
  12. "import sklearn\n",
  13. "from matplotlib import pyplot as plt\n",
  14. "from sklearn import model_selection\n",
  15. "from sklearn import neural_network\n",
  16. "from sklearn import metrics\n",
  17. "from sklearn.svm import SVC\n",
  18. "import numpy as np\n",
  19. "import time\n",
  20. "import warnings\n",
  21. "warnings.filterwarnings(\"ignore\")"
  22. ]
  23. },
  24. {
  25. "cell_type": "code",
  26. "execution_count": 3,
  27. "id": "a8812842",
  28. "metadata": {},
  29. "outputs": [],
  30. "source": [
  31. "####### Loading data #######\n",
  32. "mnist = fetch_openml('mnist_784',as_frame=False)\n",
  33. "# images = mnist.data.reshape((-1, 28, 28))\n",
  34. "# plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation=\"nearest\")\n",
  35. "# plt.show()\n",
  36. "# print(\"Classe : \", mnist.target[0])"
  37. ]
  38. },
  39. {
  40. "cell_type": "code",
  41. "execution_count": 6,
  42. "id": "6ec263be",
  43. "metadata": {},
  44. "outputs": [
  45. {
  46. "name": "stdout",
  47. "output_type": "stream",
  48. "text": [
  49. "Dataset size : 5000\n",
  50. "Etiquettes size : 5000\n",
  51. "xtrain size : 4500\n",
  52. "xtest size : 500\n",
  53. "ytrain size : 4500\n",
  54. "ytest size : 500\n"
  55. ]
  56. }
  57. ],
  58. "source": [
  59. "### Create vector of 1000 random indexes\n",
  60. "rand_indexes = np.random.randint(70000, size=5000)\n",
  61. "### Load data with the previous vector\n",
  62. "data = mnist.data[rand_indexes]\n",
  63. "print(\"Dataset size : \", len(data))\n",
  64. "target = mnist.target[rand_indexes]\n",
  65. "print(\"Etiquettes size : \", len(target))\n",
  66. "\n",
  67. "### Split the dataset for training and testing\n",
  68. "# xtrain data set d'entraînement et ytrain étiquettes de xtrain\n",
  69. "# xtest dataset de prédiction et ytest étiquettes de xtest\n",
  70. "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
  71. "print(\"xtrain size : \", len(xtrain))\n",
  72. "print(\"xtest size : \", len(xtest))\n",
  73. "print(\"ytrain size : \", len(ytrain))\n",
  74. "print(\"ytest size : \", len(ytest))"
  75. ]
  76. },
  77. {
  78. "cell_type": "code",
  79. "execution_count": 7,
  80. "id": "3b1a54ef",
  81. "metadata": {},
  82. "outputs": [
  83. {
  84. "name": "stdout",
  85. "output_type": "stream",
  86. "text": [
  87. "Temps d'entraînement : 0.28424\n",
  88. "Score échantillon de test : 0.906\n",
  89. "Classe image 4 : 7\n",
  90. "Classe prédite image 4 : 9\n",
  91. "Précision pour chaque classe : \n",
  92. " [0.96078431 0.88235294 0.89830508 0.89361702 0.94117647 0.87179487\n",
  93. " 0.92592593 0.90243902 0.9 0.875 ]\n",
  94. "Matrice de confusion SVM:\n",
  95. " [[49 0 0 0 0 0 1 0 0 0]\n",
  96. " [ 0 60 0 0 0 0 0 0 0 1]\n",
  97. " [ 1 0 53 0 0 0 0 0 0 0]\n",
  98. " [ 0 1 2 42 0 2 0 0 1 0]\n",
  99. " [ 0 0 1 0 48 0 1 0 1 1]\n",
  100. " [ 1 2 0 2 0 34 2 0 2 1]\n",
  101. " [ 0 0 0 0 0 1 50 0 0 0]\n",
  102. " [ 0 2 1 0 0 0 0 37 1 2]\n",
  103. " [ 0 2 1 3 1 2 0 1 45 0]\n",
  104. " [ 0 1 1 0 2 0 0 3 0 35]]\n",
  105. "Zero-one classification loss :\n",
  106. " 0.09399999999999997\n"
  107. ]
  108. }
  109. ],
  110. "source": [
  111. "####### Premier modèle de Classifier #######\n",
  112. "\n",
  113. "#Entraîne le classifier\n",
  114. "clf = SVC(kernel=\"linear\")\n",
  115. "# print(\"Training...\")\n",
  116. "clf.fit(xtrain, ytrain)\n",
  117. "\n",
  118. "#Prédiction sur le jeu de tests\n",
  119. "# print(\"Predicting...\")\n",
  120. "t1 = time.time()\n",
  121. "pred = clf.predict(xtest)\n",
  122. "t2 = time.time()\n",
  123. "print(\"Temps d'entraînement : \", round(t2-t1,5))\n",
  124. "#print(\"Prédiction : \", pred)\n",
  125. "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
  126. "score = clf.score(xtest, ytest)\n",
  127. "print(\"Score échantillon de test : \", score)\n",
  128. "\n",
  129. "#Infos image 4\n",
  130. "print(\"Classe image 4 : \", ytest[3])\n",
  131. "print(\"Classe prédite image 4 : \", pred[3])\n",
  132. "\n",
  133. "#Calcul de différentes metrics\n",
  134. "print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
  135. "print(\"Erreur pour chaque classe :\\n\", metrics.zero_one_loss(ytest, pred))\n",
  136. "print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
  137. ]
  138. },
  139. {
  140. "cell_type": "code",
  141. "execution_count": 47,
  142. "id": "5a4a5485",
  143. "metadata": {},
  144. "outputs": [
  145. {
  146. "name": "stdout",
  147. "output_type": "stream",
  148. "text": [
  149. "Computing for kernel= poly ...\n",
  150. "Computing for kernel= rbf ...\n",
  151. "Computing for kernel= sigmoid ...\n",
  152. "Computing for kernel= precomputed ...\n",
  153. "Done\n"
  154. ]
  155. }
  156. ],
  157. "source": [
  158. "####### Variations de la fonction noyau #######\n",
  159. "\n",
  160. "list_training_times_kernel = []\n",
  161. "list_precision_scores_kernel = []\n",
  162. "list_zero_one_loss_kernel = []\n",
  163. "\n",
  164. "kernel_functions = [\"poly\",\"rbf\",\"sigmoid\",\"precomputed\"]\n",
  165. "kernel_train = xtrain\n",
  166. "kernel_test = xtest\n",
  167. "for i in kernel_functions:\n",
  168. " print(\"Computing for kernel=\", i, \"...\")\n",
  169. " if (i == \"precomputed\"):\n",
  170. " kernel_train=np.dot(xtrain,xtrain.T) # modified the train_set\n",
  171. " kernel_test=np.dot(xtest,xtrain.T) # modified the test_set\n",
  172. " \n",
  173. " #Entraîne le classifier\n",
  174. " clf = SVC(kernel=i)\n",
  175. " t1 = round(time.time(),5)\n",
  176. " clf.fit(kernel_train, ytrain)\n",
  177. " t2 = round(time.time(),5)\n",
  178. " #Prédiction sur le jeu de tests\n",
  179. " pred = clf.predict(kernel_test)\n",
  180. " # On sauvegarde le temps de calcul, la précision et \n",
  181. " # les taux d'erreurs par classe\n",
  182. " list_training_times_kernel.append(t2-t1)\n",
  183. " list_precision_scores_kernel.append(clf.score(kernel_test, ytest))\n",
  184. " list_zero_one_loss_kernel.append(metrics.zero_one_loss(ytest, pred))\n",
  185. "print(\"Done\")"
  186. ]
  187. },
  188. {
  189. "cell_type": "code",
  190. "execution_count": 48,
  191. "id": "9b961ed8",
  192. "metadata": {},
  193. "outputs": [
  194. {
  195. "data": {
  196. "text/plain": [
  197. "Text(36.0, 0.5, 'Zero-one loss')"
  198. ]
  199. },
  200. "execution_count": 48,
  201. "metadata": {},
  202. "output_type": "execute_result"
  203. },
  204. {
  205. "data": {
  206. 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\n",
  207. "text/plain": [
  208. "<Figure size 504x720 with 3 Axes>"
  209. ]
  210. },
  211. "metadata": {
  212. "needs_background": "light"
  213. },
  214. "output_type": "display_data"
  215. }
  216. ],
  217. "source": [
  218. "##### x coordinates\n",
  219. "x = kernel_functions\n",
  220. "training_times = list_training_times_kernel\n",
  221. "precision_scores = list_precision_scores_kernel\n",
  222. "zero_one_loss = list_zero_one_loss_kernel\n",
  223. " \n",
  224. "training_times = [round(i,2) for i in training_times]\n",
  225. "precision_scores = [round(i,2) for i in precision_scores]\n",
  226. "zero_one_loss = [round(i,2) for i in zero_one_loss]\n",
  227. "\n",
  228. "### Create plot\n",
  229. "fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(7,10))\n",
  230. "fig.tight_layout(pad=3.0)\n",
  231. "figs[0].plot(x,training_times, marker='o', color='r')\n",
  232. "figs[1].plot(x,precision_scores, marker='o', color='b')\n",
  233. "figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
  234. "\n",
  235. "### Add every x coordinates\n",
  236. "figs[0].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
  237. "figs[1].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
  238. "figs[2].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
  239. "\n",
  240. "for i in range(len(x)):\n",
  241. " figs[0].annotate(training_times[i], # this is the text\n",
  242. " (x[i],training_times[i]), # these are the coordinates to position the label\n",
  243. " textcoords=\"offset points\", # how to position the text\n",
  244. " xytext=(12,3), # distance from text to points (x,y)\n",
  245. " ha='center') # horizontal alignment can be left, right or center\n",
  246. " figs[1].annotate(precision_scores[i], # this is the text\n",
  247. " (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
  248. " textcoords=\"offset points\", # how to position the text\n",
  249. " xytext=(12,3), # distance from text to points (x,y)\n",
  250. " ha='center') # horizontal alignment can be left, right or center\n",
  251. " figs[2].annotate(zero_one_loss[i], # this is the text\n",
  252. " (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
  253. " textcoords=\"offset points\", # how to position the text\n",
  254. " xytext=(12,3), # distance from text to points (x,y)\n",
  255. " ha='center') # horizontal alignment can be left, right or center\n",
  256. "\n",
  257. "figs[0].set_xticks(x)\n",
  258. "figs[1].set_xticks(x)\n",
  259. "figs[2].set_xticks(x)\n",
  260. " \n",
  261. "### Add title and axis names\n",
  262. "figs[0].title.set_text('Training times for each kernel function')\n",
  263. "figs[1].title.set_text('Precision score for each kernel function')\n",
  264. "figs[2].title.set_text('Zero-one loss metrics for each kernel function')\n",
  265. "figs[0].set_xlabel('kernel')\n",
  266. "figs[1].set_xlabel('kernel')\n",
  267. "figs[2].set_xlabel('kernel')\n",
  268. "figs[0].set_ylabel('Training times (in seconds)')\n",
  269. "figs[1].set_ylabel('Precision score')\n",
  270. "figs[2].set_ylabel('Zero-one loss')"
  271. ]
  272. },
  273. {
  274. "cell_type": "code",
  275. "execution_count": 12,
  276. "id": "5726fcb1",
  277. "metadata": {
  278. "scrolled": true
  279. },
  280. "outputs": [
  281. {
  282. "name": "stdout",
  283. "output_type": "stream",
  284. "text": [
  285. "Computing for C= 0.1 ...\n",
  286. "Computing for C= 0.25 ...\n",
  287. "Computing for C= 0.5 ...\n",
  288. "Computing for C= 0.75 ...\n",
  289. "Computing for C= 1.0 ...\n",
  290. "Done\n"
  291. ]
  292. }
  293. ],
  294. "source": [
  295. "####### Variation du paramètre de tolérance aux erreurs C #######\n",
  296. "\n",
  297. "list_training_times_tol = []\n",
  298. "list_precision_scores_train_tol = []\n",
  299. "list_zero_one_loss_train_tol = []\n",
  300. "list_precision_scores_test_tol = []\n",
  301. "list_zero_one_loss_test_tol = []\n",
  302. "\n",
  303. "kernel_train = xtrain\n",
  304. "kernel_test = xtest\n",
  305. "tols = [0.1,0.25,0.5,0.75,1.0]\n",
  306. "\n",
  307. "for i in tols:\n",
  308. " print(\"Computing for C=\", i, \"...\")\n",
  309. " #Entraîne le classifier\n",
  310. " clf = SVC(C=i, kernel=\"rbf\")\n",
  311. " t1 = round(time.time(),5)\n",
  312. " clf.fit(kernel_train, ytrain)\n",
  313. " t2 = round(time.time(),5)\n",
  314. " #Prédiction sur le jeu de tests\n",
  315. " pred = clf.predict(kernel_test)\n",
  316. " pred_train = clf.predict(kernel_train)\n",
  317. " # On sauvegarde le temps de calcul, la précision et \n",
  318. " # les taux d'erreurs par classe\n",
  319. " list_training_times_tol.append(t2-t1)\n",
  320. " list_precision_scores_train_tol.append(clf.score(kernel_train, ytrain))\n",
  321. " list_zero_one_loss_train_tol.append(metrics.zero_one_loss(ytrain, pred_train))\n",
  322. " list_precision_scores_test_tol.append(clf.score(kernel_test, ytest))\n",
  323. " list_zero_one_loss_test_tol.append(metrics.zero_one_loss(ytest, pred))\n",
  324. "print(\"Done\")"
  325. ]
  326. },
  327. {
  328. "cell_type": "code",
  329. "execution_count": 11,
  330. "id": "741f82ca",
  331. "metadata": {},
  332. "outputs": [
  333. {
  334. "data": {
  335. "text/plain": [
  336. "Text(38.625, 0.5, 'Zero-one loss')"
  337. ]
  338. },
  339. "execution_count": 11,
  340. "metadata": {},
  341. "output_type": "execute_result"
  342. },
  343. {
  344. "data": {
  345. 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ya9aseK0OxoyBu+6CFSsgPR2GDYP+/eO3fmOMMaaUE5GYGw+Ld2BRMRBUAKjqr75Vv5JpzBgYNAgyfQvTy5e7YbDgwhhjjDkM8W55c5aIvCQivfznBVzLZyXTXXflBhUBmZluvDHGGGMKLN45FtcDg4GbcXUsvgCezW8hEfkDrknVZFxrcL+Lc7rCW7GiYOONMcYYE1W8cyySgSdVta+qngc8hes7JCpV/dJ3KjMRV/mzeKSnR0oQnHQSTJvm/jfGGGNMTOIdWEzFdZoUkAJ8WoDl/0yY1zxFZJCIzBKRWYE3NOJi2DBITc07LiXF1a/4+Wc48UTo2RM++cQCDGOMMSYG8Q4sqqjqrsCA/z81yvw5RCQd2K6qO0KnqeooVe2mqt3q1o3pbZfY9O8Po0ZBRgaIuL8vvABvvAFLl8KIEbBsGZx2Ghx3HEycaAGGMcYYE0W8A4vdItIlMCAiXYE9MS57NTA6zunJX//+LnjIznZ/A2+DVKkCgwfDb7/B88/Dhg1w9tnQtSu8+66b3xhjjDF5xDuwuAX4j4h8KSJfAm8BN8ayoKreq6pfxzk9hVe5snsF9ddfYfRo2LULzj8fOnaEt96CrKxEp9AYY4wpMeIaWKjqd0Ar3NshNwCtVbXkvm5aEBUrwpVXwoIFrv2LrCzo1w/atoXXX4eDBxOdQmOMMSbh4hpYiEgqcAcwRFV/BJqKSJ94biPhkpPhz3+G+fPhP/9xORqXXw6tWsFLL8H+/YlOoTHGGJMw8S4KGQ3sB473w6uAf8V5GyVDhQpwwQUwZw68/z7UrAkDB8LRR8Nzz8G+ffmtwRhjjClz4h1YNFfV4cABAFXdg2soq+yqUAHOPRe++w4mTYJGjeCGG6BZM3jyyUNb9jTGGGPKsHgHFvtFJAVQABFpDpSPn+4icMYZMGMGfPqpy7m45RY46ih49FFX6dMYY4wp4+IdWNwLTAaaiMgYXINZf4vzNko2ETj5ZNdq5/Tp0KED3H47NG0KDz4IOw5ppsMYY4wpM+L9VsgUoC9wJa4FzW6qOi2e2yhVevaEKVPgf/+DHj1c52YZGXDffbB1a6JTZ4wxxsRdvN8K+T2wV1U/BGoCfxeRjHhuo1Q67jj48EOYNQt69YL773cBxt//Dps2JTp1xhhjTNzEuyjkOSBTRDoCtwPLgdfivI3Sq2tXeO89+OEHVx/j4YddgHHbbbBuXaJTZ4wxxhRavAOLg6qqwLnAU6r6JFA9ztso/Tp0cK12/vQT9O0Ljz/uKnnefDOsWpXo1BljjDGHLd6BxU4RuRO4FPhQRJKAinHeRtnRurVrtfOXX1yjW889B82bw/XXw/LliU6dMcYYU2DxDiwuxr1eerWqrgMaAY/EeRtlT4sWrtXORYvgqqvc/y1awNVXu07QjDHGmFIi3m+FrFPVx1T1Sz+8QlWtjkWsmjaFkSNhyRKXa/Hmm9CyJVx2GSxcmOjUGWOMMfmKd46FiYfGjeGpp2DpUvjLX1w37W3awMUXw48/Jjp1xhhjTEQlIrAQkQoiMkxEnhaRKxKdnhLjyCNdq53LlsHQofDRR67iZ9++MHt2olNnjDHGHKJEBBa4t0ga4foYsdciQtWt61rtXLYM7r0XPvvMvbrapw98+22iU2eMMcbkiHsDWSIyRUR+FZElIrJURJbEsGhL4H+q+lfg+jDrHSQis0Rk1saNG+OZ5NKldm3Xaufy5fCvf7kWPY87Dk49Fb78MtGpM8YYY+KeY/ES8BhwAtAd6Ob/5mcVEGjjOit0oqqOUtVuqtqtbt268Upr6ZWW5poHX74chg93DW717AknnuhyM1QTnUJjjDHlVLwDi+2q+pGqblDVzYFPDMu9C5wmIk8DX8Q5TWVXtWqug7OlS+GJJ+DXX10HaCecAJMnF1mA0bRpU9q3b0+nTp3o1q3bIdPHjx9Phw4dcqZ/9dVXeaZnZWXRuXNn+vTpUyTpM8YYkziicfzyEZGHgSRcoJDTXbqqxq2mYbdu3XTWrFnxWl3ZsncvjB4NDz0EK1dCt27wj3/A2We7XlfjpGnTpsyaNYs6deqEnb5r1y6qVq2KiDBv3jwuuugiFga9LvvYY48xa9YsduzYwcSJE+OWLmOMMUVDRL5X1UN/SYYR7xyLHrjijweBf/vPo3HehomkShXX/sVvv8ELL8CWLXDuudC5M7zzDmRnF0syqlWrhvhAZvfu3Tn/A6xatYoPP/yQgQMHFktajDHGFK94N5B1YpjPSfHcholBpUowcKBrKvzVV2HPHrjwQmjf3jW6lXVINZYCERFOPfVUunbtyqhRo8LO895779GqVSvOOussXn755Zzxt9xyC8OHD6dChZLyQpIxxph4isvTXUQu9X//Gu4Tj22Yw5CcDJdfDgsWwNixrjikf3/XR8krr8CBA4e12hkzZjB79mw++ugjnnnmGb744tBqMeeddx4LFy7k/fff5x//+AcAEydOpF69enTt2rUwe2WMMaYEi9fPxqr+b/UIH5NISUnQrx/Mm+eKRKpWdX2StGzpikz27y/Q6ho2bAhAvXr1OO+885g5c2bEeXv27MnixYvZtGkTM2bM4IMPPqBp06b069ePzz77jEsvvbRQu2aMMaZkiWvlzeJglTfjQBUmToR//hO++w6aNIE77nCdnlWpEnXR3bt3k52dTfXq1dm9eze9e/fmnnvu4fTTT8+Z57fffqN58+aICLNnz+bss89m1apVeepaTJs2jUcffdQqbxpjTClQ7JU3ReRuEakdZfpJImLvFpYUIu5NkW+/da+lpqfDjTdCs2bw+OOQmRlx0fXr13PCCSfQsWNHjj32WM466yxOP/10Ro4cyciRIwH473//S7t27ejUqRODBw/mrbfeyhNUGGOMKbvikmMhIucCfwP2ArOBjUAV4GigE/Ap8KCqFrrZTMuxKAKqMG2ay8H4/HPXhPitt8INN8AHH7jGuFascAHIsGGunoYxxphyoyA5FvFux+Jo4PdAA2AP8DPwharuidc2LLAoYjNmuADj449dXYz9+/NW8kxNhVGjLLgwxphyJGGBRXGwwKKYzJwJvXq5V1VDZWS4DtGMMcaUC4lsIMuUFcce61ryDGf5cnj3Xdi5s3jTZIwxpsSzwMJElp4efrwInH8+HHEE9O4NTz4JixcXb9qMMcaUSBZYmMiGDXN1KoKlprrWPKdPh1tugdWr3d8WLVzDW7ff7qYdZuNbxhhjSre4BhYiMlxEaohIRRGZKiKbAq1ymlKof39XUTMjw+VSZGS44csuc920Dx/uWvX87TeXa9GkifvbqxfUqweXXAJjxsDmWDq4NcYYUxbE+62QuaraSUTOA/4E/AX4XFU7xmsbVnmzhNu5E6ZMcQ1wffghbNgAFSrA734HffrAWWdB27Zx7W3VGGNM0Upk5c2K/u+ZwFhV3RLLQiLSS0S+FJGRItIrzmkyxal6dejbF15+GdaudY1w3X23a3Rr6FDXEdpRR7kGuSZPjlxB1BhjTKkU78BigogsxHWdPlVE6uIazcqPArtwjWqtinOaTKJUqODeLrn/fvj+e1i1yhWldOzoAo8zznAVQP/0J3jxRVizJtEpNsYYU0hxb8dCRGoBO1Q1S0SqAtVVdV0+y1RQ1WwRqQ88pqr9Q6YPAgYBpKend12+fHlc02wSYM8e19rnxInus2KFG9+1qysy6dMHunRxwYkxxpiESmTLm6nAX4F0VR3kW+Jsqaox9TQlIpWAN1X1gkjzWB2LMkgV5s/PrZfxv/9BdjbUr+/qZPTp415rrVYt0Sk1xphyKZGBxVvA98DlqtpORFKA/6lqp3yW6wucBtQEnlPVaZHmtcCiHNi0ydW/mDjR/d2+HSpVcm+bBCqANmuW6FQaY0y5kcjAYpaqdhOROara2Y/7wd4KMYftwAHXf0mgyOSXX9z4Nm1yi0yOPx6SkxObTmOMKcMS+VbIfp9LoT4hzYF9cd6GKU8qVnQ5FY8+CgsXwq+/uq7dGzSAxx5z7WnUq+fa3HjzTdgS04tIxhhjiki8cyx6A3cDbYBPcD2dXhmtaKOgLMfC5NixI2+bGRs3usqev/99bm5G69bWZoYxxhRSQns3FZEjgOMAAb5R1U3xXL8FFias7Gz47rvcIpO5c934o47KDTL++EeoXDmhyTTGmNIo0YFFB6ApkFPorarvxmv9FliYmKxa5XIxJk6ETz91DXFVrQqnnuqCjDPPhCOPTHQqjTGmVEhk5c2XgQ7AT0C2H62qOiBe27DAwhRYZiZ8/nlukcnKlW58t265uRmdO1ubGcYYE0EiA4sFqtombisMwwILUyiq8OOPuUUm33zjxjVokNtmximnuNwNY4wxQGLfCvmfiBRpYGFMoYhAhw7w97/D11/D+vWuG/gTToC333bNix9xhGtu/JlnYNmyRKfYGGNKlXjnWPQEJgDrcK+ZCq4opEO8tmE5FqbI7N8PX32Vm5uxaJEb365dbm7GccdZmxnGmHInkUUhv+Ga9P6R3DoWqGrcOvewwMIUm19/za0A+sUXcPAg1K7tcjP69IHTToNatRKdSmOMKXKJDCw+U9WT4rbCMCywMAmxfTt88okLMiZNcs2OJyW5IpRABdCWLa3NDGNMmZTIwOJZXH8fEwhqcdNeNzVlSlYWzJyZW2Qyb54b37x5bpDRs6fr38SYEmjv3r307NmTffv2cfDgQS644ALuv//+PPM88sgjjBkzBoCDBw/y888/s3HjRmrXrs3jjz/Oiy++iIjQvn17Ro8eTZUqVRKxK6aYJDKwGB1mtL1uasq2FStcLsbEiTB1qmszo1q1vG1m1K+f6FQak0NV2b17N9WqVePAgQOccMIJPPnkkxx33HFh558wYQKPP/44n332GatXr+aEE05gwYIFpKSkcNFFF3HmmWdy5ZVXFu9OmGJVkMAirrXQVPWqeK7PmFIhPR2uu859MjPhs89yczPe9Zl1xx6bm5vRqZMVmZiEEhGqVasGwIEDBzhw4AAS5ZocO3Ysl1xySc7wwYMH2bNnDxUrViQzM5OGDRsWeZpN6RGX101F5G/+79Mi8lToJx7bMKZUSE11wcPIka4hrjlz4J//dIHEvfdCly7QuDFcey1MmOACEWMSICsri06dOlGvXj169+5Njx49ws6XmZnJ5MmTOf/88wFo1KgRt912G+np6TRo0IC0tDROPfXU4ky6KeHi1Y7Fz/7vLOD7MJ98iUhVEfleRPrEKU3GJJaIy524+27XENfatTB6NPzudzB2LJxzjmsz46yz4NlnYXncXp4qlL1793LsscfSsWNH2rZty7333nvIPNOmTSMtLY1OnTrRqVMnHnjggZxpTZs2pX379nTq1Ilu3WLKOTUJkJSUxNy5c1m1ahUzZ85k/vz5YeebMGECv//976lduzYAW7duZfz48SxdupQ1a9awe/du3njjjeJMuinh4lIUoqoT/L+Zqvqf4GkicmGMq7kDeDse6TGmRKpfH6680n3274cvv3TFJRMmuDoagwdD+/a5RSY9erg3T4pZ5cqV+eyzz/KUv59xxhmHlL//4Q9/YOLEiWHX8fnnn1OnTp3iSK4ppJo1a9KrVy8mT55Mu3btDpk+bty4PMUgn376KUcddRR169YFoG/fvnz99ddceumlxZZmU7LFu+XNO2Mcl4eInAIsANZHmD5IRGaJyKyNGzcWMonGlACVKsHJJ8Pjj7uGuBYuhEcfdTkYw4e7rt/r14fLL3ctgm7bVmxJK2j5uyl9Nm7cyDZ/Te3Zs4dPP/2UVq1aHTLf9u3bmT59Oueee27OuPT0dL755hsyMzNRVaZOnUrr1q2LK+mmFIhXHYszRORpoFFI/YpXgIMxrOJEXFfrfwauEZE86VLVUaraTVW7BaJkY8oMEdcGxq23us7SNm2CceNcQ1wffggXXwx168JJJ8Fjj7mGu4pYLOXv//vf/+jYsSNnnHEGP/30U9DuCKeeeipdu3Zl1KhRRZ5WE4MxY6BpU9fRXtOmrH3xRU488UQ6dOhA9+7d6d27N3369GHkyJGMHDkyZ7H33nuPU089lapBfef06NGDCy64gC5dutC+fXuys7MZNGhQAnbKlFRxed1URDoCnYAHgHuCJu0EPlfVrTGu50pgk6qGz1/FXjc15UxWlqufEWgB9Mcf3fgWLXKLTP7whyJrM2Pbtm2cd955PP3003myyXfs2EGFChWoVq0akyZNYsiQISzyTaCvWbOGhg0bsmHDBnr37s3TTz9Nz549iyR9JgZjxsCgQXkrCqemwqhR0L9/4tJlSpVEtmNRUVUPxG2FYVhgYcq15ctzg4zPPoN9+6B6dde8eJ8+LpejXr24bvL++++natWq3HbbbRHnadq0KbNmzTqkXsV9991HtWrVoi5r4kTVtaGSmZn72bMHTj/ddbYXKj29xFQYNiVfItuxKNKgwphyLyMDbrjBfXbvdg1yBdrMeOcdV6zSo0dubkaHDgVuM2Pjxo1UrFiRmjVr5pS/33HHHXnmWbduHfXr10dEmDlzJtnZ2RxxxBHs3r2b7Oxsqlevzu7du/nkk0+45557ImypnMjKcl/wgS/60C/+eA3v2VOwdK1Y4XK+GjaERo3c38AneDg1tWiOiymzrJtGY0qrqlXdK6vnnON+rc6Z4wKMDz90r7jefbdrMyPQM+tJJ8X0JbF27VquuOIKsrKyyM7O5qKLLsopfwe47rrreOedd3juuedITk4mJSWFcePGISKsX7+e8847D3CNKP35z3/m9NNPL9LDcNgOHCjaL/rA8L59+aclnJQUd75SU/P+n5rqKvkGD4dODx6+9lrYsOHQ9VevDt27w5o1MGsWrF4dPjipWTN8wBE8fOSRULHi4e2nKXPiWhRSHKwoxJgYrFsHH33kAo1PPoFdu6BKFfcmSp8+Ltho0sTNO2YM3HWX+wWbng7DhiWu7F3VvYpblF/0gf8PxlKvPESFCvl/kcdjuEqV+LXOGmsdC1XX2d6aNS7IWLMm9xM8vHbtocdOxBXBRcr1CPxfp447hqZIxdIXzMKFC7nqqquYPXs2w4YNy1NcuW3bNgYOHMj8+fMREV5++WV+97vfJayOxQQgdIXbcQ1nPa+qewu7DQssjCmgfftct++BNjOWLnXjO3Z0bwp8/LErmw8I96WTne3mKY5f+NnZBd/H5OSi/aIPDFeqVDqbY49n8JidDRs35h+AhMslqVgRGjTIPwCpUaN0HucSIpa+YDZs2MDy5ct5//33qVWrVp7A4oorruAPf/gDAwcOZP/+/WRmZlKrVq2EBRZPAnWBsX7UxcA6IAWooaqXFXYbFlgYUwiqrs2MQJHJ9Onh50tOdtnbwXUDDkflykX/Cz8lxbLhS6L9+13OWbigI/j/7dsPXTY1NXq9j0aNXICSklL8+1XKZGZmcsIJJ/Dcc8+FfW08tIL1jh076NixI0uWLMnTfk3CKm8CnVU1+L2yCSLyhar2FJGfIi5ljCkeItC6tfvcfrvLlg734+LgQejdu3Bf/CkpCWk51JQQlSq53JH09Ojz7d4dOddj9Wr49lv3/94wGd61akXO9Qj8X7++C5TLmaysLLp27cpvv/3G4MGDI/YFE2rJkiXUrVuXq666ih9++IGuXbvy5JNPFmjb8T7adUUkXVVXAIhIOhB4/2x/nLdljCmsSK8cZmTAyy8Xf3pM+VO1Khx9tPtEoupan42U67FmDfz0k8shycrKu6yICy7yC0COOKJMFb8E+oIJtEUzf/78sE22hzp48CCzZ8/m6aefpkePHgwZMoSHH364QNuOd2BxK/CViCwGBDgKuEFEqgKvxnlbxpjCGjYsfMW+YcMSlyZjQom43IlatSDal2NWlqv/Eanux4oV8L//udZtQ1WqlFv/I1oAUr160e1nEcivL5hQjRs3pnHjxjk5HBdccEFiAwtVnSQiRwOtcIHFwqAKm0/Ec1vGmDgIVOArKW+FGFMYSUmubtCRR0LXrpHn27fP5W5ECkDmz3eVmnfuPHTZatWiVzxt2NAFKFWqFN1+5iOWtmgiOfLII2nSpAm//PILLVu2ZOrUqbRp04ZJkybFvP24v24qIr8DmhIUtKjqa/Fav1XeNMYYUyx27nSv10arfLpmTfi2So44Iv8ApH79IqmHNG/evEPaornnnnvytEWzbt06unXrlqd5/gULFlCjRg3mzp2b80ZIs2bNGD16NLVr107YWyGvA82BuUCgoEtV9eZ4bcMCC2OMMSWGKmzZkv/bL+vWHfoqdYUKLnclvwCkdu38638UcXs0iXwrpBvQRktbq1vGGGPM4RBxuRNHHAHt20eeLyvL9dkSKddj6VKYMQM2bz502cqVo796+/338I9/5L4Wvny5qzsFCSnWjHeOxX+Am1V1bdxWGsJyLIwxxpRZe/fmFr9EaoRs9Wr3mm5+MjJg2bK4JCuRORZ1gAUiMhPIKXRS1XPivB1jjDGm7KlSBY46yn2i2bkzN+g4+eTw86xYEf/0xSDegcV9h7OQiLQGhuACk6mq+lw8E2WMMcaUKdWrQ6tW7pOREb49mvwaJysi8X7dNEL7wPku9zNwnYhUAF6IZ5qMMcaYMq2EtUcTl27mROQr/3eniOwI+uwUkR0xruMc4Ctgaphpg0RklojM2rhxYzySbIwxxpQN/fu7jgMzMlxl0oyMQzsSLEYlrtt0EflQVc+KMn0jECbPp9DqAGGaYzOeHZ+yz85x2WfnuOwrqnOcoap1Y5kx7j2ziEgSUJ+8DWRFrUEiIr2AvkBlIGrzXrHuWEGJyKxYa7yWR3Z8yj47x2WfneOyrySc47gGFiJyE3AvsB4ItASiQIdoy6nqNGBaPNNijDHGmOIX7xyLIUBLVQ3Twocxxhhjyrq4VN4MshLYHud1FpdRiU5ACWfHp+yzc1z22Tku+xJ+juPd8uZLQEvgQ/I2kPVY3DZijDHGmBIr3kUhK/ynkv8YY4wxphwpca+bFhURqQo8C+wHpqnqGD/+TOBGYJKqjvDjHgZSgUxVHZqgJBe7KMdoKK7X2iOB61V1lYgsBqYAs1U14VlvJjZRzvF9QGtgK/CAqq4pr/dBaRflHF8NdAeaAPNU9U67j0snEWkG3AWkqeoFQePbAXf6wYdUdX4i7uN4NZD1hP87QUQ+CP3EYxtx0Bd4R1WvAXL6LlHVScDwwLCIpAMVfVfvSSLSpNhTmjiRjtHDftzLwIl+9C4gBVevxpQeYc8xcBD3RXQA2FbO74PSLtJ9/JKqXgf8CrziR9t9XAqp6hJVvTrMpCHAYOAG4KZE3cfxKgp53f99NE7rKwqNgR/9/1lR5mtE7k22wi9XXm66iMdIRKoBFwG+L146A4KrT/NRcSXQFFqkc/ygqmb7FnAHAt9Rfu+D0i7afVwFOEpVf/Gj7D4uW9JUdRuAiFQnQd9nccmxUNXv/d/p4T7x2EYcrMIdVIi+36uD5mvilysvwh4jEakBPAf8TVV3AqhqtqpmAXt9Hy+mdAh7jlU10O7MBqAa5fs+KO2iPesuAN4NDNh9XOZsF5E0/8zeSYLu43i/FXI08BDQBqgSGK+qzeK2kcPkyx1HAHtxfZKcrqqXicjxuDKpWsATqvpfEXkI1wroPlW9M+JKy5gox+hdoCLuIn3b/73DLzZPVZ9IQHLNYYhyjv+Oe/DUAW5W1bXl9T4o7SKdYz9tPNBPVfeISEvsPi6VROQIYBjQG3gRaOPv43bA7bhcqOG+jkWx38fxDiy+wrW8+ThwNnCV38a9cduIMcYYY0qseGd9pajqVFwwsVxV7wNOivM2jDHGGFNCxbsdi0A53SIRuRGXZV4vztswxhhjTAkV76KQ7sDPQE3gn0AN4BFV/SZuGzHGGGNMiRW3wMJ3l/6wqt4elxUaY4wxptSJVwNZyf6Vpa4iIvFYpzGm+IlITRG5IZ95morI/OJKkzGmdIlXHYuZQBdgDjBeRP4D7A5MVNV3Iy1ojClRauJa7Xs2XisUkST/w8MYUw7E+62Q2sBm3JsgfXCvnPaJ8zaMMUXnYaC5iMwVkUf8Z76I/CgiF4fOLCJJfp7vRGSeiFzrx/cSkc9F5E18K5Ai8r6IfC8iP4nIoKB17BKRYSLyg4h8IyL1/fj6IvKeH/+DiPzOj79URGb6ND7vi2GNMSVEvHIs6onIX4H5gOIa5wgoH72cGVM2DAXaqWonETkfuA7oiGs46zsR+SJk/quB7araXUQqAzNE5BM/7Vi/rqV+eICqbhGRFL+u/6rqZqAq8I2q3iUiw4FrgH8BTwHTVfU8HzxUE5HWwMXA71X1gIg8C/QHXiui42GMKaB4BRZJuGaAw9WvsMDCmNLpBGCsL8ZYLyLTcb1jzgua51Sgg4gEelhMA47GdWg2MyioALhZRM7z/zfx8232807047/HtSYILufzcgCfhu0ichnQFReYgOtAa0N8dtcYEw/xCizWquoDcVqXMaZkiKUitgA3qerHeUaK9CKonpUfPgU4XlUzRWQauc3+H9Dc19OyiP5cEuBVa2LcmJIrXnUs7E0QY8qGnUB1//8XwMW+HkVdoCeuonawj4HrRaQigIgc4/uqCJUGbPVBRSvguBjSMhW43q83yXesNBW4QETq+fG1RSSjYLtojClK8QosTo7TeowxCeTrPMzwr5Mejyv2+AH4DNe77bqQRV4EFgCz/TLPEz7HYTKQLCLzcI3nxdJo3hDgRBH5EVdE0lZVFwB3A5/4dU0BGhRwN40xRSiuLW8aY4wxpnyL9+umxhhjjCnHLLAwxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjjDHGxI0FFsYYY4yJGwssjDHGGBM3FlgYY4wxJm4ssDhMItI/qHvoaPONFJF/FEeaEk1E/iUim0QktNnnhIv1fMV5m8tE5JQi3sYrIvKvw1z2PBFZKSK7RKRzHNKiItKisOtJJBGpKyK/iEgVPzxNRAaWgHTF5djmd72IyO9FZJG/Jv4kIu+KyOmF3W5xEZE/iMgv8Z63sETkPhF5ozi2VRKUycDCP9D3+JtjvYiMFpFq8dyGqo5R1VNjmO86Vf1nPLddEolIE+BWoI2qHpno9ISK9XyVM48CN6pqNVWdEzqxLAQKh2EoMFpV9yY6IQnyADDCXxPvAw8Dw4pjw/H48lXVL1W1ZbznLU6F+bFQUrZTJgML72xVrQZ0AbrjOi7KQ0Ti1W18mXMYxyYD2KyqG4phWyVq/aVYBvBTohNRUs6PiFQGrgDi+stSnIQ/a0UkKYbZ8lwTqjoTqCEi3YosYTEqKcfR5K/MnyRVXQ18BLSDnF9hg0VkEbDIj+sjInNFZJuIfC0iHQLLi0gTnx24UUQ2i8gIP/5KEfnK/y8i8riIbBCR7SIyT0QC28sTFYrINSLym4hsEZEPRKRh0DQVket8VuRWEXlGRMJ2SS8ix4rILBHZ4XNlHguadoLfj20+q/tKPz5NRF7z+7JcRO4O3Kh+f2b4/dgC3CcilUXkURFZ4bcxUkRSwqTlFFwvkw19LtErfvw5IvKTT8c0EWkdtMwyEblDXA+Vu0O/XPy2Hg0ZN15E/ur/Hyoii0Vkp4gsEJHzguYLty8558vP8zsR+c6fr+9E5HchaTslaDjnl5SIVBGRN/y1sM0vWz/cOQpJe4WgNG8WkbdFpLafNllEbgyZ/wcR6ev/byUiU/w184uIXJTf9oK2ebc/1xv8uU/z53UXkAT8ICKLwyz7hf/3B39OL/bjI16/IctHvHZEpJeIrPLnfx0wWkRqichEf21u9f83DlrfNBH5pz+vO0XkExGpEzQ90jUf0zXs9QC2qeqqCPvUQNy9fZsfPi5omz+ISK+Q9A4TkRlAJtBM8rm/RWSAiPzsp30shewOXtyz5zkRmSQiu4ET/aQ6/nraKSLTA9vx10EzYII/55X9/NOAs6Jsp9DPNHHFLX8HLvbb/sGPD3ccr/LHaaeILBGRa4PW00tEVgUNLxOR2/x52y4ib0luMVfM8/rpfxORtSKyRkQGSpQcPRE5yh/bnSIyBagTMv0/IrLOb+cLEWnrxw8C+gN/88dhgh8f7XnXwm9ru7ii6LeCpoV9dkTaTtyoapn7AMuAU/z/TXAR+D/9sOK+BGsDKbgcjQ24h0oS7hfLMqCyH/4BeByoClQBTvDruRL4yv9/Gq5b55qAAK2BBn7aK8C//P8nAZv8NisDTwNfBKVbgYl+PenARuD0CPv4P+Ay/3814Dj/fzqwE7gEqAgcAXTy014DxgPVgabAr8DVQftzELgJ1+11CvAE8IE/VtWBCcBDEdLTC1gVNHwMsBvo7dPxN+A3oFLQOZrrz09KmPX1BFaS2wNvLWAP0NAPXwg0xAXHF/ttNYiyL8HnqzawFbjMT7/EDx8Rev344fuAN/z/1/rjkIq7ProCNWK4Dm/BdRXe2J/754GxftrlwIyg5doA2/x8Vf1xuMqntQvuGmoben2F2f4Af8yb4a6Rd4HXQ663FlHuozzTie36beH/f4II1w7uWjkI/J9fTwruOj3fH9fqwH+A94PWPQ1YjLuuUvzwwzFc8xHTEWZ/BwMfhoybBgwk934Z5Mc3AjYDZ+Kuwd5+uG7QciuAtv68VSTK/Q38yZ+r1n7+u4GvIxzbof76CPsJWuYVYDvwe5/GKn7cTtz9VRl4En9fhLv2/bi/Au9GOGbxfKbdh7/PQo5/6HE8C2iOe9b+ERdwdInwHFoGzMQ9K2oDPwPXHca8pwPrfDpSgdeJcv/gns+P+WPS0x/zN4KmD8Bdj5Vx1+jckPP2r5D1RXvejQXuCjrHge+ow352FPaT8CCgSHbKXSC7cDfacuBZ/JeXvxhOCpr3OXzQETTuF3/BHu9vhOQw27iS3C+qk3APneOACiHz5Zw84CVgeNC0asABoGlQ2k4Imv42MDTCPn4B3A/UCRl/J/BemPmTgH24OhCBcdcC04L2Z0XQNPEXb/OgcccDSyOkpxd5b9J/AG8HDVcAVgO9gs7RgCjnUHAPlJ5++BrgsyjzzwXODbcvYc7XZcDMkOn/A64MSlukwGIA8DXQIcbrMBBY/AycHDStgT/3ybgHzG4gw08bBrzs/78Y+DJkvc8D94ZeX2G2PxW4IWi4ZWCbQddbQQKLWK7fFvldO/5a2Q9UibLtTsDWoOFpwN1BwzcAk/O55gt6Dd8FjAsZNw33BbEMuCRo/B0EBWl+3MfAFUHLPRDmeIa9v3G5qleH3C+ZQddE1HMVYX9eAV4LM25c0HA1IAtoEu7az+/ei/GaiPWZdh/hA4sHws0fNM/7wJCgays0WLg0aHg4MPIw5n2ZoIAUd52HPSe4AOogUDVo3Juh+xY0raZfV1rQOYr6hU/e591rwCigccg8h/3sKOynLBeF/ElVa6pqhqreoKp7gqatDPo/A7jVZ2duE5FtuF/RDf3f5ap6MNqGVPUzYATwDLBeREaJSI0wszbEBTqB5XbhfuU0Cpon+I2KTNyNGs7VuF9vC8Vlx/fx45vgftmFqgNUCt6+/z9428HHpS4uMv8+6LhM9uNjEbqv2X79kbaXh7orfxzuVyjAn4ExgekicrnkFl9twxV1BWc3Rlx3aNq80GMRyeu4L5BxPkt0uIhUjGG5DOC9oPT+jHug11fVncCHQD8/bz9y9zUD6BFyffYHYqkgG7qfy3GBTL5FN7GsL8L1C7FdOxs1qIKkiKSKyPPiim124ALnmpK3XkCkeyPSNV/Qa3grLsgL1R8XFL8TNC4DuDDkvJyACxgDwl2DkfYhA3gyaF1bcIFRLNdkNOHSkDPOn8MtuHMbSXXcj7Rw4vlMiyTPPojIGSLyjc/e34bLNaoTdsmCbz/SvA1D0pHf82Wrqu4OGpdzjEQkSUQe9kUbO3ABDUTZh3yed3/DXSszxRU9D/DjC/PsKJSyHFhEo0H/rwSG+SAk8ElV1bF+WrrEULlMVZ9S1a64rLJjgNvDzLYGd7IBEJGquGzb1QXeAdVFqnoJUA+XpfyOX99KXDZhqE24XxIZQePSQ7atIfPvwWWbBY5LmroKsbEI3VfBfQFE2l44Y4ELxJUB9wD+69eVAbwA3IgrvqgJzMfdXLGsO0/avOBjsRv3hRSQcyOq6gFVvV9V2wC/A/rgijLysxI4I+Q6q6KuDlBgXy8RkeNxWf2fBy03PWS5aqp6fQzbDN3PwC+p9TEsm+/6oly/sVw7oefnVlyOSg9VrYHLPoa85zSSaNd8Qa7hebh7N9R9fl1vBgU6K3E5FsHnpaqqPhy0XH7Xd+g+XBuyvhRV/Tp0RhH5uy8XD/sJmT1cGpoErasaLtt/TZS0tcYVCYcTt2cakY9Xznhx9T7+i3ujqb6/9ycR23VSGGtxxZgBTSLN6Oet5Y9FQHrQ/38GzgVOAdJwxWyQuw95jkN+zztVXaeq16hqQ1wu9LO+7kd+z46CXJ8FUl4Di2AvANeJSA9xqorIWSJSHVfethZ42I+vIiK/D12BiHT3y1fEfSntxf0aDfUmcJWIdPI3yIPAt6q6rKCJFpFLRaSuzwnY5kdn4X7pniIiF4lIsogcISKdVDULlw05TESq+4v1r0SoAe/X+wLwuIjU89tsJCKnxZjEt4GzRORkf1xuxRXFHPKgjETdK5AbgReBj1U1sJ9VcTfFRp+uq/CVc2M0CThGRP7sj9HFuHoNE/30uUA/Eakorjb8BYEFReREEWnvv2B24IK1cOc61Ejcsc/w66krIueGpCkD97rfW/7449N0jIhc5tNT0V9vrcnfWOAv4iqSVcNdb2/llwMXZD2ufkZATNfvYV471XFBwDZxlVrvjTGNEPmaL2g6ZuJySUJzCQ7gyrirAq+Lq/D8BnC2iJzmf4FWEVcZsDGHZyRwp+RW4ksTkQvDzaiqD/oviLCfGLZ1prjKrpWAf+LOYbRf4H/EFdWEE7dnGu56ayrR3/yohKuXsBE4KCJnAMXxGvnbuP1sLSKpwD2RZlTV5cAs4H4RqSQiJwBnB81SHfcs3Iz7AfNgyCpC77uozzsRuTDoutvq580i/2dH6HbiptwHFqo6C1eGOAJ3Un7Dlcfjv4zPxpWnrQBW4cqtQtXAPcC24rK8NuMi6tBtTcXVPfgvLmBpTm72d0GdDvzkf6E8CfRT1b2qugKXNXgrLotzLtDRL3MTLvBZAnyFeyi8HGUbd+COxzfisuw+xf2qzJeq/gJciqvMtQl3HM9W1f0F2EdwX46n+LQG1r0A+DeuXsR6oD0wI9YVqupmXE7Drbhz9Tegj6pu8rP8A3dutuLqsbwZtPiRuCzxHbjijOnE9nrik7hKhJ+IyE5cRc4eQWnah6tcGbqvO3EPzn64X4fryK30mJ+XcUU3XwBLcQHvTTEsF3Af8KrPRr2ogNdvQa+dJ3A5NZtwx2ZyrInM55qPOR3+2nwFd92Gm9YXl0P4Mu4X+bm4Nxk24n4d3s5hPlNV9T3ceR3n0zkfOONw1hWDN3GB2xZc5eP+kWYUke7AbnWvnR4izs+0//i/m0VkdoTt7QRuxn3Rb8X9+v/gMLcXM1X9CHgKl5P4G+7ZAy5ACOfPuPt7C+5YvxY07TXc98RqYAHueg/2EtDG33fvx/C86w58678LpuAq7E7BVUYOfXb8G3hG3Nt4ZwBdAtsRkb+IK0qZLyJjJe8bMTeJe6vkJxEZnt/xCtS4N8aYck9E6gJfAp1D6mWVSyLyX+AlVZ2U6LSUJP5X/3ygcgFyAIuUz0X9FfeG0irgO1yF4wVB8zwC7FLV+0WkFfCMqp7sc+m+wlXu3yMibwOTVPUVETkRV7H5LFXdJyL1NJ/2isp9joUxxgSo6kZVbWVBhaOq51tQ4YhrAr+SiNTC5S5NKClBhXcs8JuqLvE5bONwuWrB2uDeFkNVF+KKngKVuZOBFHF1ClPJrXdzPe7V7n1+uXwbQbTAwhhjjMnftbhir8W4OgyxVKAuTo3I+7bKKg59q+gHXJEeInIsrl5XY1+J/FFckf9aYLuqBvpWOgb4g4h8K64hru75JcQCC2OMMSYfqnq6f6uotqqep6prE52mEOHejAmt6/Aw7o2Vubj6VnNwlWBr4XI3jsK9LltVRAJ1jZJxDRQeh6tH9LZI+BahCVrAGGOMMaXbKvK+BtuYkNeIVXUHriXOQBMAS/3nNFzDcYE3T97FvU7/hl/vu+oqZM4UkWxcGxobIyXEciyMMcaY0u874Gj/enkl3Nsged6YEZGafhq4puq/8MHGCuA4cQ3VCXAy7q03cC2bnuSXPwb3yu8moih1b4XUqVNHmzZtmuhkGGOMMSXK9u3bWblyJapKnTp1aNCgARs3uoyFunXrsmvXLpYtWwZASkoKGRkZJCe7gos1a9awZcsWRITU1FQyMjKoUKEC2dnZLF++nC1btmThGpG7TV1r0xGVuqKQpk2bMmvWrEQnwxhjSq3JkyczZMgQsrKyGDhwIEOHDs0zfevWrQwYMIDFixdTpUoVXn75Zdq1c20yPf7447z44ouICO3bt2f06NFUqVIl3GZMMRozBu66C/bvh/R0GDYM+kdsoaTgRGSuqnaLZV4rCjHGmHIkKyuLwYMH89FHH7FgwQLGjh3LggUL8szz4IMP0qlTJ+bNm8drr73GkCFDAFi9ejVPPfUUs2bNYv78+WRlZTFu3LhE7IYJMmYMDBoEy5eDqvs7aJAbnwilLsfCGGPM4Zs5cyYtWrSgWTPXmnO/fv0YP348bdq0yZlnwYIF3HnnnQC0atWKZcuWsX6962Lm4MGD7Nmzh4oVK5KZmUnDhtH6LzMFdeAAbN8OW7fCtm2x/Z0zBw6GtKiRmelyMOKZaxErCyyMMaYcWb16NU2a5L480LhxY7799ts883Ts2JF3332XE044gZkzZ7J8+XJWrVpF165due2220hPTyclJYVTTz2VU08tjq46Sg9V2LUrejAQbdqu0G7kQlSsCLVqQc2a7m/t2ocGFQErVsRttwrEAgtjjClHwlXYD22WYOjQoQwZMoROnTrRvn17OnfuTHJyMlu3bmX8+PEsXbqUmjVrcuGFF/LGG29w6aWHdK9Sqh04EHtuQbi/Wfl0S1ijRt7goHnzvMPR/qakQGgrEk2buuKPUOnph44rDhZYGGNMOdK4cWNWrsxtoHHVqlWHFGfUqFGD0aNHAy4QOeqoozjqqKP4+OOPOeqoo6hbty4Affv25euvvy5xgYUq7Nx5+MFBZmb09Veq5L7oA1/2devC0UfnDkcLDNLSICkpvvs7bJirUxGc7tRUNz4RYg4sRKSqqu4uysQYY4wpWt27d2fRokUsXbqURo0aMW7cON58880882zbto3U1FQqVarEiy++SM+ePalRowbp6el88803ZGZmkpKSwtSpU+nWLaYXBQps//7Yg4Fw47Kzo68/LS3vl/4xxxQs16AkCdSjuOsuV/xRFG+FFES+gYWI/A54EagGpItIR+BaVb2hqBNnjDEmvpKTkxkxYgSnnXYaWVlZDBgwgLZt2zJy5EgArrvuOn7++Wcuv/xykpKSaNOmDS+99BIAPXr04IILLqBLly4kJyfTuXNnBg0aFHY72dku1yCWegXh/u7Jpxu4KlXyftnXrw8tW8YWHFSvHv9cg0Tr3z9xgUSofBvIEpFvgQuAD1S1sx83X1Xb5bNcVeBZYD8wTVXH+PEXA2cBB3CdnvwKvA7sxrXodZWqRow1u3XrptaOhTHGxC7QxkFBf83u23f49Qy2b4+eayASudggllwDazqjeInI97G2YxFTUYiqrgyp3JNP1RTA9aD2jqpOEJG3gMAbtecD/XGdmjwI3ALsU9VrROQZoCqwM5Z0GWOMie611+C663JzAJYvhwED4MMPoVmz6EULe/dGX3dKSt4v+wYNoHXr2HMNKlhLSmVSLIHFSl8cor6N8ZvJbUM8msbAj/7/4EDkUeBpYB0uuMj06/4QWKuqFlQYU8wOtyXGX375hYsvvjhnviVLlvDAAw9wyy23cPvttzNhwgQqVapE8+bNGT16NDVr1izmPSt7VN0X/4YNsX22bDl0Hfv3w9ix7os9NIegUaPoQUHw/5UrF9dem9IklqKQOsCTwCm4blk/AYao6uZ8lrsM2KqqE0VknKr2C5neArgR13vamar6gIjcDUxU1bkh8w4CBgGkp6d3XR7uvRpjzGHJysrimGOOYcqUKTRu3Jju3bszduzYPA0m3X777VSrVo17772XhQsXMnjwYKZOnXrIeho1asS3335LRkYGn3zyCSeddBLJycnccccdAPzf//1fse5baZGZCRs3xh4sRGq3oHZtqFcv7+fZZ8PPK+LWY7kGJhZxKwoRkSTgCVU9nCoh7wIjROQsYIKIvK6ql4nImcDZuMqgtwM7gFtF5FlcV6yPh65IVUcBo8DVsTiMtBhjIihMS4z169fPmWfq1Kk0b96cjIwMgDwNJx133HG88847xbE7JcLBg7BpU2xBwsaNkRtFqlo1N0Bo0gS6dj00cAh8jjjCNZ4U6sMPI7dxYEGFKQpRAwtVzRKRuiJSSVX3F2TF/tXUq4JGjfHjJwGTQma/pCDrNsbET2FaYgwOLMaNG8cll4S/lV9++eU8RSaljaqrjBhrjsLmCPm5SUl5g4EWLSIHCnXrusCisEpaGwem7IuljsUyYIaIfIB7cwMAVX2sqBJljCk+hWmJMWD//v188MEHPPTQQ4esa9iwYSQnJ9O/pLwL5+3ZU7DihwMHwq+nVq3cYKBNG+jVK3KwULNm8ecSlLQ2DkzZF0tgscZ/KgDVizY5xpjiVpiWGAM++ugjunTpkicHA+DVV19l4sSJTJ069ZBgJd4OHnQ5BfkVOwT+3xmhmnhKimsToV49V5Gxc+fcHITQQKFOHdcKY0lXkto4MGVfvoGFqt4PICLV3aDm00WKMaY0KUxLjAFjx449pBhk8uTJ/N///R/Tp08nNTW1wOlShR07Clb8EK4uelJS3qCgR4/IOQr16sWn+MGY8iyWljfb4Rqwqu2HNwGXq+pPRZw2Y0wxKExLjACZmZlMmTKF559/Ps96b7zxRvbt20fv3r0BV4HziSdGFqj4YX+Eml01a+YGAq1aQc+ekQOFWrWskqIxxSmW102/Bu5S1c/9cC/gQVX9XZGnLgxredOY+DrcVhnB9eIYKH6IJWDYsSP8eqpUiZ6LEFqpsTQUPxhTlsS75c2qgaACQFWn+ea6jTGl3Jgxed8YWL4crrkG1q+H447LP1DYtCl88UOFCnmLH7p3z7/4oYirYBhjikksgcUSEfkHrjgE4FJgadElyRhT1LKy4JdfYMiQQ7uI3rMHbr310GXS0nIDgWOOgRNOiBwo1K5txQ/GlFexBBYDgPtxDV4BfEHe9imMMSXYvn0wfz7Mng1z5ri/8+bl33vkRx/lLX6w5puNMbGI5a2Qrbj+QYwxJdzOnfDDD7kBxJw58NNPuU1A16jhXp+89lro0gXuuAPWrj10PRkZcPrpxZt2Y0zZEMtbIVOAC1V1mx+uBYxT1dOKOG3GmCg2bXKBQ3AQsWhRbp2HevVc8HDmme5v585w1FF5iygqVLBWGY0x8RVLUUidQFABLgdDROoVXZKMMcFUYfXqvEUZc+ZAUJtWZGS44OHSS3ODiAYN8q8Qaa0yGmPiLZbAIltE0lV1BYCIZADWEZgxRSA7GxYvPjSI2LTJTReBli3hD39wwUPgU7v24W/TWmU0xsRTLIHFXcBXIjLdD/fEd2FujDl8Bw7Azz/nBg9z5sDcublNTVesCO3awTnn5OZCdOgA1aolNNnGGBNVLJU3J4tIF+A4QIC/qOqmIk+ZMWXInj3uTYzgXIgff3RvbICr19CpE1x+eW4Q0batNQRljCl9Yqm8+XtgrqpOFJFLgb+LyJOqurzok2dM6bNtm8t5CA4ifv7ZFXOAa2K6Sxe46abcIOLoo12fFsYYU9rFUhTyHNBRRDoCtwMvA68BfyzKhBlTGqxff2h9iCVLcqc3bOgCh7593d8uXVwFSWtl0hhTVsUSWBxUVRWRc4GnVPUlEbmiqBNmTEmi6pq7Dg0igtuAaN7cBQ4DB+ZWqgzpRdwYY8q8WAKLnSJyJ64p754ikgRULNpkGZM4WVnw6695K1XOmQNbt7rpSUnQujWcckpuUUanTq7Ja2OMKe9iCSwuBv4MXK2q60QkHXikaJNlTPHYt8+1TBmcC/HDD7kNRlWu7N7EuPDC3CCifXtISUlsuo0xpqSK5a2QdcBjQcMrcHUsTDkxefJkhgwZQlZWFgMHDmTo0KF5pm/dupUBAwawePFiqlSpwssvv0y7du0AaNq0KdWrVycpKYnk5GRCu7x/9NFHuf3229m4cSN16tQp0v3YtSt8c9cHDrjp1au7wOGaa3LrQ7Rq5V77NMYYE5tYciwOi+9a/VlgPzBNVcf48RcDZwEHgEdV9WcRGQB0Brar6t1FlSZTcFlZWQwePJgpU6bQuHFjunfvzjnnnEObNm1y5nnwwQfp1KkT7733HgsXLmTw4MFMnTo1Z/rnn38eNmhYuXIlU6ZMIT09Pe7p3rz50Oauf/01t7nrunVd8HD66blBRLNm1iOnMcYUVpEFFkBf4B1VnSAibwFj/Pjzgf5ALeBBEfk7rrhlDhCmOySTSDNnzqRFixY0a9YMgH79+jF+/Pg8gcWCBQu48847AWjVqhXLli1j/fr11M+n5uJf/vIXhg8fzrnnnnvY6VOFNWsOrVS5YkXuPOnpLnj4859zg4iGDe3NDGOMKQpFGVg0Bn70/2cFjX8UeBpYhwsumgFbVHWoiAwXkeaqujh4RSIyCN/aZ1H8ujWRrV69miZNmuQMN27cmG+//TbPPB07duTdd9/lhBNOYObMmSxfvpxVq1ZRv359RIRTTz0VEeHaa69l0CDXaOsHH3xAo0aN6NixY8xpyc52r3IGV6qcPRs2bnTTReCYY+B3v4Mbb8x9M+OIIwp/HIwxxsQm1gay7gMy/PwCqKo2y2fRVbjgYi6Qk8GsqjOBmSLSArgRWA1s8ZO3AYc0WKyqo4BRAN26dbN+SoqR6qGHW0J+6g8dOpQhQ4bQqVMn2rdvT+fOnUlOdpfWjBkzaNiwIRs2bKB37960atWKbt26MWzYMD755JOI2z140DUqFZwLMWdObnPXycmuZco+fXJzITp2tOaujTEm0WLJsXgJ+AvwPXlzHvLzLjBCRM4CJojI66p6mYicCZyNCyBu92+abBGRx4BKqvpDAffBFKHGjRuzMqgbzVWrVtGwYcM889SoUYPRo0cDLhA56qijOOqoowBy5q1Xrx7nnXceM2fOpFatWixdujQnt2LVqlW0a9eFv/51JosXH5nT3PXevW79KSkuaLjsstwgom1b98aGMcaYkkXC/SLNM4PIt6rao5jSk69u3bpp6JsFpugcPHiQY445hqlTp9KoUSO6d+/Om2++Sdu2bXPm2bZtG6mpqVSqVIkXXniBL7/8ktdee43du3eTnZ1N9erV2b17N7179+bWW++hTp3T8xRlzJ/fFJgF1KFmzdzgIfD3mGOsuWtjjEkkEfleVbvFMm8sORafi8gjuByIfYGRqjr7MNNnSpHk5GRGjBjBaaedRlZWFgMGDKBt27aMHDkSgOuuu46ff/6Zyy+/nKSkJNq0acNLL70EwE8/radfv/PYswcyMw+SnPxnLrjg9Jx1N2jggoeVK+GJJ+CPf4SmTa1SpTHGlGax5Fh8Hma0qupJRZOk6CzHoniNGQN33eXeskhPh2HDoH//vPOouunB9SFmz3ZvawQ0a5ZbmTKQG3HkkcW7L8YYYw5PXHMsVPXEwifJlEZjxsCgQbmtUC5f7obXrIHGjfNWqtziq99WqOAalTrppNwgolMnqFkzUXthjDGmOMWSY5EG3Av09KOmAw+o6vYiTltYlmNRfJo2dcFEJJUqueatg+tDtG8PqanFlkRjjDHFIN51LF4G5gMX+eHLgNG4BrBMGRbcyFSouXOhTRtr7toYY0xesQQWzVX1/KDh+0VkbhGlx5QAP/0E99yT2/x1qIwM9/qnMcYYEyqWnhH2iMgJgQHfYNaeokuSSZTFi11bEe3bw5Qp0Lfvob14pqa6CpzGGGNMOLEEFtcDz4jIMhFZDowArivaZJnitHKlq5TZsiX8979w222u6ez//hdeeMHlUIi4v6NGHfpWiDHGGBOQb+XNnBlFagCo6o4iTVE+rPJm/KxfDw89BM8954o9rr0W/v53176EMcYYExCXypsicqmqviEifw0ZD4CqPlaoVJqE2bIFHnkEnnoK9u2DK65wdSoyMhKdMmOMMaVdtMqbVf3f6sWREFP0du50LVw++qj7v18/uO8+12S2McYYEw8RAwtVfd7/vb/4kmOKwp498Mwz8PDDsHkznHsu/POfrpKmMcYYE0/5Vt4UkeEiUkNEKorIVBHZJCKXFkfiTOHs3w/PPgvNm8Ptt0PXrjBzJrz/vgUVxhhjikYsb4Wc6its9gFWAccAtxdpqkyhHDwIo0e7Io7Bg11gMX06fPwxdO+e6NQZY4wpy2IJLAJtK54JjFXVLUWYHlMI2dnw1lvQrh0MGAB16sDkyfDFF9CzZ/7LG2OMMYUVS2AxQUQWAt2AqSJSF9hbtMkyBaEKH3zg+uvo1w+Sk+Hdd+G77+C006wbcmOMMcUn38BCVYcCxwPdVPUAsBs4t6gTZvKnCp9+Cscd5ypkZma6Hkl/+AHOO88CCmOMMcUvWjsWJ6nqZyLSN2hc8CzvFmXCTHQzZsBdd7m6E02auBYyr7jCOgUzxhiTWNHasfgj8BlwdphpigUWCfH99/CPf8BHH0H9+q6Rq0GDoHLlRKfMGGOMid6Oxb3+71XFlxwTSaDH0XffhVq1XJsUN94IVavmv6wxxhhTXGJpx+JBEakZNFxLRP4Vw3JVReRVEXlBRPoHjb9YRF4TkZdEpHXQ+JEi8uhh7EOZ9ttveXscvfdeWLoU7rjDggpjjDElTyxvhZyhqtsCA6q6FffqaX76Au+o6jXAOUHjzweuBu4EbgUQkQsA61ksSKDH0VatcnscXbrUNcGdlpbo1BljjDHhRatjEZAkIpVVdR+AiKQAsZToNwZ+9P9nBY1/FHgaWAfUEpH6QGfgBaBVuBWJyCBgEEB6enoMmy69Qnscvf5663HUGGNM6RFLYPEGrv2K0bhKmwOAV2NYbhUuuJhLUM6Iqs4EZopIC+BGXCXResA9QEcROUZVfw1ekaqOAkaB6zY9hm2XOqE9jl55paukaT2OGmOMKU3yDSxUdbiIzANOAQT4p6p+HMO63wVGiMhZuEa2XlfVy0TkTNybJtWA21V1HfC2iDQFbgwNKsq6nTvh8cfh3/+2HkeNMcaUfqKafwaAiGQAR6vqpyKSCiSp6s4iT10Y3bp101mzSn91jNAeR//0J3jgAesczBhjTMkjIt+rardY5o3lrZBrgHeA5/2oRsD7h526cm7/fhdQhPY4+t57FlQYY4wp/WJ5K2Qw8HtgB4CqLsLViTAFENzj6I03Wo+jxhhjyqZYAot9qro/MCAiybhKnCYGoT2O1q1rPY4aY4wpu2IJLKaLyN+BFBHpDfwHmFC0ySr9QnscrVjRFXfMnGk9jhpjjCm7Ygks7gA24tqkuBaYBNxdlIkqzVRdC5mhPY7OnesqaFpAYYwxpiyLGliISAXgR1V9QVUvVNUL/P9lqihk8uTJtGzZkhYtWvDwww8fMn3r1q2cd955dOjQgWOPPZb58+cDsHLlSk488URat25N27ZtGTLkSU48EU49FRYuvI+aNRuRmtqJ4cM78fHHk4p7t4wxxphiF7UdC1XNFpEfRCRdVVcUV6KKU1ZWFoMHD2bKlCk0btyY7t27c84559CmTZuceR588EE6derEe++9x8KFCxk8eDBTp04lOTmZf//736h24c47d/LUU1054ojePPVUG9avh5o1/8Jtt92WwL0zxhhjilcsRSENgJ9EZKqIfBD4FHXCisvMmTNp0aIFzZo1o1KlSvTr14/x48fnmWfBggWcfPLJALRq1Yply5axfv16tmxpwLBhXejWDWbNqk7r1q0ZPXo1N90EybG0aWqMMcaUMbEEFvcDfYAHgH8HfcqE1atX06RJk5zhxo0bs3r16jzzdOzYkXfffRdwgcjy5cu58spVeXocnTZtGbt2zeGPf+yRs9yIESPo0KEDAwYMYOvWrcWzQ8YYY0wCRQwsRKSKiNwCXIjrHGyGqk4PfIorgUUtXHURCalhOXToULZu3Urbtp3o3/9psrI68/nnydx+u+tx9LbbdnHVVefzxBNPUKNGDQCuv/56Fi9ezNy5c2nQoAG33nprseyPMcYYk0jRMuxfBQ4AXwJnAG2AIcWRqOLUuHFjVq5cmTO8atUqGjZsmGeePXtqkJY2mt9+g+xspXr1o5g16yiOOQYOHDhAnz7n079/f/r27ZuzTP369XP+v+aaa+jTp0/R74wxxhiTYNECizaq2h5ARF4CZhZPkopX9+7dWbRoEUuXLqVRo0aMGzeON998E8jtcfTJJ7exb18qV11ViRYtXmTBgp4cc0wNVJWrr76a1q1b89e//jXPeteuXUsD39f5e++9R7t27Yp934wxxpjiFi2wOBD4R1UPhhYPlBXJycmMGDGC0047jaysLAYMGECTJm05++yRfPop7Nt3Haec8jO//no5X32VxJYtbXjppZcAmDFjBq+//jrt27enU6dOgHuD5Mwzz+Rvf/sbc+fORURo2rQpzz//fJRUGGOMMWVDxN5NRSQL2B0YBFKATP+/qmqNYklhiHj3bjpmDNx1F6xYAY0bwwknwCefWI+jxhhjTEBBejeNmGOhqknxS1LJNGYMDBrkWscEWLkSxo51/Xp89JF1DmaMMcYUVCyvm5ZZd92VG1QE27nTggpjjDHmcJTrwGJFhLZEI403xhhjTHTlOrBITy/YeGOMMcZEV64Di2HDIDU177jUVDfeGGOMMQVXrgOL/v1h1CjIyHDdmWdkuOH+/ROdMmOMMaZ0ivi6aUklIhuB5UWw6jrApiJYb1lhx6fss3Nc9tk5LvuK6hxnqGrdWGYsdYFFURGRWbG+o1se2fEp++wcl312jsu+knCOy3VRiDHGGGPiywILY4wxxsSNBRa5RiU6ASWcHZ+yz85x2WfnuOxL+Dm2OhbGGGOMiRvLsTDGGGNM3ETrNr1MEZGqwLPAfmCaqo7x488EbgQmqeoIP+5hIBXIVNWhCUpysYtyjIYCzYEjgetVdZWILAamALNVNeFZbyY2Uc7xfUBrYCvwgKquKa/3QWkX5RxfDXQHmgDzVPVOu49LJxFpBtwFpKnqBUHj2wF3+sGHVHV+Iu7j8pRj0Rd4R1WvAc4JjFTVScDwwLCIpAMVVfVmIElEmhR7ShMn0jF62I97GTjRj94FpAAriz2VpjDCnmPgIO6L6ACwrZzfB6VdpPv4JVW9DvgVeMWPtvu4FFLVJap6dZhJQ4DBwA3ATYm6j8tNjgXQGPjR/58VZb5G5N5kK/xy5eWmi3iMRKQacBEwyI/qDAjwIfBRcSXQFFqkc/ygqmaLyDnAQOA7yu99UNpFu4+rAEep6i9+lN3HZUuaqm4DEJHqJOj7rDzlWKzCHVSIvt+rg+Zr4pcrL8IeIxGpATwH/E1VdwKoaraqZgF7RaQ8XUelXdhzrKrZ/t8NQDXK931Q2kV71l0AvBsYsPu4zNkuImn+mb2TBN3H5eatEF/uOALYC3wFnK6ql4nI8bgyqVrAE6r6XxF5CKgM7FPVOyOutIyJcozeBSriLtK3/d87/GLzVPWJBCTXHIYo5/jvuAdPHeBmVV1bXu+D0i7SOfbTxgP9VHWPiLTE7uNSSUSOAIYBvYEXgTb+Pm4H3I7LhRru61gU+31cbgILY4wxxhQ9y/oyxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjTA4RqSkiN+QzT1MRmV9caTLGlC4WWBhjgtXEtdoXNyKSFM/1GWNKNgssjDHBHgaai8hcEXnEf+aLyI8icnHozCKS5Of5TkTmici1fnwvEflcRN7EtwIpIu+LyPci8pOIDApaxy4RGSYiP4jINyJS34+vLyLv+fE/iMjv/PhLRWSmT+PzFrgYU7JYYGGMCTYUWKyqnYBvgE5AR+AU4BERaRAy/9XAdlXtjuvg6hoROcpPOxa4S1Xb+OEBqtoV6Abc7Bv5AagKfKOqHYEvgGv8+KeA6X58F+AnEWkNXAz83qcxC+gfr503xhReeeorxBhTMCcAY32Tz+tFZDoueJgXNM+pQAcRCfSwmAYcjevQbKaqLg2a92YROc//38TPt9nPO9GP/x7XmiDAScDlAD4N20XkMqAr8J2IgOtAa0N8dtcYEw8WWBhjIpEY57lJVT/OM1KkF7A7ZPgU4HhVzRSRaUAVP/mA5jYBnEX055IAr1oT48aUXFYUYowJthOo7v//ArjY16OoC/QEZobM/zFwvYhUBBCRY3xfFaHSgK0+qGgFHBdDWqYC1/v1JvmOlaYCF4hIPT++tohkFGwXjTFFyQILY0wOVd0MzPCvkx6PK/b4AfgM17vtupBFXgQWALP9Ms8TPsdhMpAsIvOAf+Lqb+RnCHCiiPyIKyJpq6oLgLuBT/y6pgCh9T6MMQlknZAZY4wxJm4sx8IYY4wxcWOBhTHGGGPixgILY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4FFCSMiV4rIV4lOR1ERkT+IyC+JTkckIvJ3EXmxmLepItKiiLcxTUQGHuay14vIehHZFdTV+eGmo6nf31LdAaKItBGRWUHDy0TklASnKW7HNr/rRUTOE5GV/proLCIzRaRtYbdbXESkv4h8Eu95C0tEXhGRfxXHtopSqQgs/IndFeajInJPotNnnFi+IFX1S1VtWVxpKihVfVBVD+sLuCzynYs9BpyqqtV8XyLB08tEoHAY/gk8muhEJNCjwI3+mpjjhx8ojg3H48tXVceo6qnxnrc4FebHQlFvp1QEFv7EVgv+ALcA64EXCrq+cvgQLBFK+nEv6elLkPq47s1/SnRCSsr5EZEGwInA+3Feb8L3T5xYvhcyyHtNfIDrMC7hHcKVhONY7qlqqfsAnXHdO/cKGpcGvASsBVYD/wKS/LQrgRnA48AWPy0NeA3YCCzH9ZhYIco2KwNPAGv85wmgsp/WC1gF3Aps8Gm4KmTZR4EVuGBoJJASYTtXAl8FDf8O+A7Y7v/+LmTeJf5YLAX6+/EtgOl+mU3AWxG21RRQ4CpgJbAVuA7ojuvVchswImSZAcDPft6PgQw//gu/rt3ALuDioONyB7AOeD0wLmh9TYB3/XnYHNheAfZhMu6XU/C4H4C+/v8n/b7twPWQ+Yeg+e4D3gHe8NMH+nFvBM1zDu4Bug2YBrQOmqZAi6DhV4B/+f/rABP9cluAL4lwfQWvJ9q14o97n6Dlkv2x6eKHjwO+9tv8gbz3xzRgYEGubeAYfz7Vn9PPwiy7Imj6LlyPqBVw99Ny3P3wGpAWcs0lH+Z92xzX0+pmv+9jgJpB6VkG3Ia7frcDbwFVgqafC8z153sxcHp+6Qizz5cDn4aMWwac4v9vhbsf+/nhPn6b2/z56RCy3B0+vftw170CV/hjuwm4K2j+CsBQn/bNwNtA7XDHtgDP02nAMH+s9/g0TAMeAmb64zgeqO2vi13k3uuLg9YzBbgiwjZiuSbC7nPIegYBB4D9Ph0TIhzH5KDjtBPXA+95UZ6zinv2LcI9254ht5POgsybBPzb78NS4MZo5wT3XTbbp/EtYBy5z5BauGfIRr+diUBjP20YkAXs9cch8NyM9rw7Fpjlp60HHguaFvbZEWk7+V5TBbkAS8IHqOkvljtCxr+P67K5KlAPd0NcG3RhHARu8hdcir+wxwPV/YX9K3B1lO0+gOvquR5Q15+Ef/ppvfz6HwAqAmcCmUAtP/0JXERf229vAvBQhO3kXMR+/q3AZT7dl/jhI/x+7gBa+nkb4LqVBhgL3IW7masAJ0TYVlN/0Y/0853qL6D3/X42wj0E/ujn/xPwG9Dap+du4OuQGy74izZwXP4P90BKISiwwN2EP+C+OKoGp7UA+3A5MCNouA3u5ggEfZf645WMC/zW4b9ocEHEAb9fFXz67sMHFuR+sfb25/Vvfv8rRdjfV8h9KDzkj2tF//kD/uETZh+CA4sniHCtAPcAY4KWOwtY6P9vhPuiOdPvS28/XNdPn0bkwCLatd2U6A/GQ6bjgs/fgGZANVzg+Hq4+Sn4fdvC71tln9YvgCeCtr3Mr6OhP4Y/A9f5acfiviR7+2PUCGiVXzrC7PMjwDMh45YBpwBdcF+Offz4Lrh7qAfuer/Cz1s5aLm5uAA7Jej4vOCHO+K+KFv7+W/x56qxPwbPA2MjHNtncfdCuM+8oLRP82lu649zRT9uNdDOH5P/kjfgznPt+3FPEfRlFTItlmsi7D6HWdcr+Pss5PjnHEc/7kLcdVAB90NnN9Ag9DkbtD8Tcd8v6bgv89MPY97rcEFMY1xg8CkR7h+gEi7Q+os/5hfgnkeBZ8gRwPlAKu5Z8B/g/ZDzNjBkndGed/8DLvP/VwOOK+yzI9In4YFCgRILggsGxhP0kMZl1+4jKBcA9yX8edCFsSJoWpKfv03QuGuBaVG2vRg4M2j4NGCZ/78XLtIPfrhuwEWB4i/o5kHTjgeWRthOzkWMCyhmhkz/n5+nKu4BcT4huR+4oGkUPrqNsk9N/UXfKGjcZuDioOH/Arf4/z8iKPjyF2EmubkW4QKL/eT9xdiL3MDieNxNGe6mi3UfqvvjG0jDMODlKPNvBTr6/+8DvgiZfh+5gcU/gLdD9nc1udF8tMDiAdx12iJa+oPXk9+14ufZCaT64THAPf7/O/AP6qBlP8b/giR6YBHt2g5cIwUJLKYCNwQNt8Q9MJOD56eA922E7f8JmBM0vAy4NGh4ODDS//888HiYdURNR5j5XwAeDhm3DLgfl0N3YtD45/BBWtC4X8gN1pcBA8Icz8ZB42aSm/vxM3By0LQG4Y5tftdcSHqmAQ+EGfdw0HAb3L0cyE0KF1hEvPdivCbC7nOYdb1C+MBiQKR99PPMBc4NurZCg4UTgobfBoYexryfERSQ4oLNSIFFT1wOYfB32deh+xY0rROwNeQcRf3CJ+/z7gt/jdYJmeewnx2RPqWijkWQO3AR9BXq99jLwEV8a0Vkm4hswz1E6gXNszLo/zrkRosBy3GRGyIyMqiC6N/99IZh5m8YNLxZVQ8GDWfiosK6uIjz+6C0Tfbj8xO6zZx0qupuXBR+nd/vD0WklZ/nb7gvqZki8pOIDMhnO+uD/t8TZria/z8DeDJoP7b47TSKsu6Nqro3wrQmwPKQ4xYQ0z6o6k7gQ6CfH9UP94ULgIjcKiI/i8h2n+Y03PkPCL4uQuU5/qqa7eePtr8Bj+B+oX0iIktEZGgMy0S9VlT1N9wXy9kikoorpnnTL5sBXBhYzi97Au6LJz/5XdsFFW59gUAiWEHvW0SknoiME5HVIrIDV4wVfD7B/UoLCNyH4K63xWHSG0s6gm3FBbShrsPl4H0esu5bQ85LE/Ie33DXYKR9yADeC1rXz7is6tBjW1Dh0hA8bjnuGIUe62DVcT92wonlmoi0z7EKvVYuF5G5QceqHdHTX5DtR5q3YUg68nu+rA75Lss5RiKSKiLPi8hyf61/AdQUkaRIK8zneXc1Lhd2oYh8JyJ9/PjCPDvCKjWBhYj0wmWNX6Cq20Imr8T94qijqjX9p4aqBr/+FHzyNuGi5Yygcem4X6Oo6nWaW1H0QT99TZj518SQ9E24L+e2QWlLU1cBNT+h2wxN58eq2ht3ASzEV2RV1XWqeo2qNsTlxDyb39saMVqJi8ZrBn1SVPXrKMtolGkrgfRwla0KuA9jgUtE5HhcVurn4F5txQWjF+GKpWrissIlxvTlOf4iIrgvhdV+VCYuEAg4Mij9O1X1VlVtBpwN/FVETo6yLYjtWhmL+zV9LrDABxvgjuXrIeemqqo+nM82D9lPYr+2IfzxC7e+g+QNWANpLsh9C66ISXH1FGrgsn6F2KzE1dEINz6/dASbh3tAh7oOdz0/HrLuYSHnJVVVxwbNE+0aDJfWM0LWV0VVV4fOGPIDKfQTWhk3XBqaBP2fjntmboqStta4os1wYr0mYhHpeOWMF5EM3PPwRuAIf+/PJ/Zr5XCtxRWDBDSJNKOft5F/rgSkB/1/Ky5np4e/1nv68YH58xyH/J53qrpIVS/BBcz/B7wjIlXJ/9lRkOsTKCWBha9pPA6XJT8ndLqqrgU+Af4tIjVEpIKINBeRP4Zbn6pm4bKvholIdX8R/hX36yeSscDdIlJXROrgyrujzR/YVjbuAn9cROr5/WkkIqfltywwCThGRP4sIskicjEuS3KiiNQXkXP8hbEPV7Emy6//QhEJXNxbcRdGVgzby89I4E7x76uLSJqIXBg0fT2uDDVWM3E318MiUlVEqojI7/26C7IPk3APrQdwlTyz/fjquIfXRiBZ3KvJNQqQvreBs0TkZHGvXd6KO9aBQGou8GcRSRKR04Gc601E+ohIC//Q2OHTHvUcxHitjMPVhbme3NwKcNfi2SJymk9PFRHpFXQMozmsa9vbCGST97yPBf4iIkeJSDXgQdx5yZMzVdD71quOu9a3iUgj4PYY0wmucuZV/nxW8Me21WGkYwrQRUSqhIzfCZwO9BSRwEP5BeA6EekhTlUROUtEwuV4xGIk7rmVAeDP2bnhZgz5gRT6iaXNiUvFtdeRiru33vHPzkOISGWgK+7YhBPTNRGjWJ4zVXHPjI0+fVfhciyK2tvAEH9t1cR90UfyP9zz6Wb/fO+LqwcUUB33Q2ObiNQG7g1ZPvQ4RH3eicilIlLXP2e2+dFZ5P/sKOhzvXQEFsA1uCyzJ8NE3iP9PJfjijcW4L6I3iF6Vs5NuPLsJbiasnWAoRImy9p/OdTCfamvwWU/zgbeEpG5wItAPRHZISK3+MVqAuP89F64i/wbcVlan+Ii0ajUtRnQB/eFthlXPNBHVTfhzt2tPj1bcF9qN/hFuwPfisguXEXAIaq6NL/txZCe93CR7ji/H/OBM4JmuQ94VVx22kUxrC8L92u+Ba7y2Cpc8U6B9kFV9+Eqg51C3i/bj3H1Qn7FZTHuJXrWZOh6f8H9In4a90vtbOBsVd3vZxnix20D+pP39cOjced5F+4B8qyqToths3fgilDCXiv+S/B/uLeF3goavxKXi/F33INlJe5LN5Z7/F+4e2Ae8CPu2o6pnQBVzcS/UeDP+3HAy7g3gL7A1Yzfi7vfwinofXs/rkLkdlwR2LuxpNOndSbuDajH/fLTyf0VHXM6VHU9riz9kC90n5vaGzhDRP6pqrNwz68Rfr2/4crsD9eTuPvhExHZiavI2aMQ64vmdVx9hnW4CtQ3R5n3HFwdtUg5XQW5JvLzEtDGX2/vh5tBVRfg3s74H+6LsT3urZei9gIuSJ0HzMH96DlImB8V/jnSF3c9bMU9+4Kv5ydwObCbcOd5csgqngQuEJGtIvIU+T/vTgd+8s/UJ3F1WPbG8OwI3U6+Aq/IlFviyqt+xT0MVuFe6bzEX5iBec7E3QRn4m7iJ1W1R5j1rMZlWy0XkfuAXapanhvRMaZMEpE2wKvAsVreH6KAiHyLq9g9P9FpKUlE5Axc5eHQIu0yrbTkWBSlY4HfVHWJjyDHcegvkXOB19T5BleBJvTXzMm4d7pDK1saY8oYVV2gqt0tqHBUtYcFFSAiKSJypi/aaIQrvngv0ekqbhZYuBr+wdlFqzi01n8s8/TDlSMGu1FE5onIyyJSKx6JNcYYU2IJrrhuK64o5GdcnaVyxQKL8LWEQ3+FRJ1HRCrhyhj/EzT9OVwN9E64Cor/LlQqjTHGlGiqmulzsqqraj1VvUpVdyQ6XcXNAguX+xD8SlBjDn3VLr95zgBm+0pdgKvgpapZQTX9g2v7GmOMMWWSBRausubR/jWoSrgijQ9C5vkAuNy/LnYcsN3Xzg+4hJBikJA6GOfh3qAwxhhjyrRS91ZInTp1tGnTpnFd5/bt21m5ciWqSp06dWjQoAEbN24EoG7duqgqK1euZPv27VSoUIGmTZtStWpVALKzs5k3bx7t27cnKSm3QbSlS5eSmZmJiFCpUiUyMjKoWLFiXNNtjDHGFIfvv/9+k6rG0mI0pa572aZNmzJr1qy4rW/Mj2O4a+pd7N++n/S0dIadPIz+7fvHbf3GGGNMaSciMb/xWOoCi3ga8+MYBk0YROaBTACWb1/OoAmDACy4MMYYYw5Dua5jcdfUu3KCioDMA5ncNfWuBKXIGGOMKd3KdWCxYvuKAo03xhhjTHTlOrBIT0sPO15RBn4w0AIMY4wxpoDKdWAx7ORhpFZMzTMuJTmF05qdxuvzXufop4/m5o9uZt2udQlKoTHGGFO6lOvAon/7/ow6exQZaRkIQkZaBi+c8wKTL5vMopsWcUXHK3j2u2dp9mQz7phyB5szNyc6ycYYY0yJVurasejWrZvG83XT/Py25Tfum3Yfb/74JtUrV+evx/2Vvxz/F2pUrpH/wsYYY0wZICLfq2q3WOYt1zkWsWhRuwVv9H2DH6//kVOancJ90+/jqCePYviM4Ye8UWKMMcaUdwUKLESkloh0KKrElGRt67Xlvxf9l1nXzOLYRsdyx6d30Pyp5oyYOYJ9B/clOnnGGGNMiZBvYCEi00SkhojUBn4ARovIY0WftJKpa8OufNT/I7686ktaHtGSmz66iWNGHMNLs1/iYPbBRCfPGGOMSahYcizSfLevfYHRqtoVOKVok1XynZB+Ap9f8TmfXPoJ9avWZ+CEgbR5pg1jfxxLtmYnOnnGGGNMQsQSWCT7njovAiYWcXpKFRGhd/PefDvwW96/+H2qJFfhz+/+mY4jO/L+wvcpbRVjjTHGmMKKJbB4APgY+E1VvxORZsCiok1W6SIinNvqXOZeN5ex549lf9Z+znvrPHq82INPFn9iAYYxxphyw143LQIHsw/y+g+vc//0+1m+fTl/SP8Dw04axh8y/pDopBljjDEFFtfXTUVkuK+8WVFEporIJhG5tPDJLLuSKyRzVeer+OXGXxhxxggWbVlEz1d6cvobpzNrTckOiowxxpjCiKUo5FRfebMPsAo4Bri9SFNVRlROrszgYwez+ObFPNL7EWatmUX3F7rT962+zN8wP9HJM8YYY+IulsCiov97JjBWVbcUYXrKpNSKqdz2u9tYMmQJ9/e6n6lLp9LhuQ70f7c/izZbdRVjjDFlRyyBxQQRWQh0A6aKSF1gb9Emq2yqUbkG9/zxHpYOWcodv7+D9xe+T+tnWltPqsYYY8qMmCpvikgtYIeqZolIKlBDVRPS5WdpqLwZq3W71vHQlw8x8vuRAFzb9Vr+/oe/c2S1IxOcMmOMMSZXvCtvVgQuA94SkXeAq4F8u/kUkaoi8qqIvCAi/YPGtxORMf7Tzo8bICJPi8i/Ykl0WXFktSN58ownrSdVY4wxZUYsRSHPAV2BZ/2nix+Xn77AO6p6DXBO0PghwGDgBuAmEakHXAzsBtbGnvSyIz0tnVFnj2LhjQvp27ovj3z9CM2easb90+5nx74diU6eMcYYE7NYAovuqnqFqn7mP1cB3WNYrjGw0v+fFTQ+TVW3qep2oDrQDNiiqkOBDBFpHroiERkkIrNEZNbGjRtj2HTpZD2pGmOMKe1iCSyygr/sfcubWVHmD1iFCy5Ct7NdRNJEpAawE1gNBN402QZUC12Rqo5S1W6q2q1u3boxbLp0s55UjTHGlFb5Vt4UkZOB0cASQIAM4CpV/Tyf5aoCI3BvkHwFnK6ql/l6Fbf7dQ1X1fki8k+gKlBJVW+Mtt6yVHkzVl+t+Iq7P7ub6cunk56Wzj097+GKTleQXCE50UkzxhhTDhSk8masb4VUBlrigoGFqpqwn83lMbAAUFU+XfIpd39+NzNXz+To2kdzf6/7ubjdxVSQWDKejDHGmMMTl8BCRPpGW1BV3z2MtBVaeQ0sAlSVCb9O4B+f/4N56+fRrl47/nniPzm35bmISKKTZ4wxpgyKV2AxOspyqqoDDidxhVXeA4uAbM3m7Z/e5t5p9/Lr5l/p3rA7/zrpX/Ru1tsCDGOMMXEV96KQksQCi7ysJ1VjjDFFLa4NZJmSzXpSNcYYU5JYYFFGFGVPqpMnT6Zly5a0aNGChx9++JDpqsrNN99MixYt6NChA7Nnz84zPSsri86dO9OnT588459++mlatmxJ27Zt+dvf/laoNBpjjCkZLLAoY6L1pPrblt8KvL6srCwGDx7MRx99xIIFCxg7diwLFizIM89HH33EokWLWLRoEaNGjeL666/PM/3JJ5+kdevWecZ9/vnnjB8/nnnz5vHTTz9x2223FXxnjTHGlDix9BWSKiL/EJEX/PDRItInv+VMYoXrSbXViFZc88E1BepJdebMmbRo0YJmzZpRqVIl+vXrx/jx4/PMM378eC6//HJEhOOOO45t27axdq1rnX3VqlV8+OGHDBw4MM8yzz33HEOHDqVy5coA1KtXr5B7bIwxpiSIJcdiNLAPON4PrwLKVWdhpVntlNo8dMpDLL55MTceeyOvzXuNo58+mps/upl1u/LvoHb16tU0adIkZ7hx48asXr065nluueUWhg8fToUKeS+1X3/9lS+//JIePXrwxz/+ke+++64wu2mMMaaEiCWwaK6qw4EDAKq6B9dQlilFjqx2JE+c/gS/3fRbnp5Uh346NGpPquHeGgp9nTXSPBMnTqRevXp07dr1kOkHDx5k69atfPPNNzzyyCNcdNFFYddjjDGmdIklsNgvIimAAvh+Q6zDilKqSVqTPD2pDp8xPGpPqo0bN2blypU5w6tWraJhw4YxzTNjxgw++OADmjZtSr9+/fjss8+49NJLc5bp27cvIsKxxx5LhQoV2LRpUxHttTHGmOISS2BxLzAZaCIiY4CpgFXhL+XC9aTa7MlmPDLjkTw9qXbv3p1FixaxdOlS9u/fz7hx4zjnnHPyrOucc87htddeQ1X55ptvSEtLo0GDBjz00EOsWrWKZcuWMW7cOE466STeeOMNAP70pz/x2WefAa5YZP/+/dSpU6f4DoAxxpgikW9goapTgL7AlcBYoJuqTivaZJniEtqT6t8+/VuenlTf+vktdp+ym2bdmlG1YVVa9nSvh44cOZKRI0cCcOaZZ9KsWTNatGjBNddcw7PPPpvvdgcMGMCSJUto164d/fr149VXX7UWQ40xpgyItROyRrheTXO601TVL4owXRFZy5tFK7gn1doptdm1fxf7s/bnTE+tmMqos0fRv33/BKbSGGNMcYprk94i8n/AxcBPQLYfrap6TuSlio4FFkUv0JNqn7F98gQVARlpGSy7ZVnxJ8wYY0xCFCSwSM5/Fv4EtExkV+mmeIkIvZv35kDWgbDTl29fzg0f3kCXBl3ofGRn2tVrR+XkysWcSmOMMSVRLIHFEqAi9iZIuZOels7y7csPGV85qTJjfhzDc7OeA1x/JW3rtqVzg850ObILnRt0pmP9jlSvXL24k2yMMSbBYgksMoG5IjKVoOBCVW8uslSZEmHYycMYNGFQnrdEAnUsLml3CUu3LmXOujnMXjubOevmMGnRJF6Z+woAgnD0EUfn5Gp0PrIznRt0pk6qvflhjDFlWSx1LK4IN15VXy2SFOXD6lgUrzE/juGuqXexYvsK0tPSGXbysIgVN1WVtbvWMmdtbrAxe+3sPLkeTWo0yQk2ujRwuRuNqjeyN0KMMaYEi2vlTb/CSsAxfvAXVQ1f+F4MLLAofbbs2cLcdXPzBBu/bPoFdW2uUTe1Lp0bdM4NNo7sTPPazakg1keeMcaUBPF+K6QX8CqwDNeUdxPgCnvd1BTGrv27mLd+Xp7cjfkb5nMg28Ws1StVp9ORnXKLUhp0pnWd1lRMqpjglBtjTPkT78Die+DPqvqLHz4GGKuqh3YAkXe5qsCzwH5gmqqO8ePbAXf62R5S1fl+/Ehgl6pG7T/bAouya3/Wfn7a8FOeehtz183NqeNROaky7eu3z6kg2qVBF9rXa09KxZQEp9wYY8q2eAcW81S1Q37jwix3GbBNVSeIyFuqerEf/wJwO67vkeGqeq2IXADUBFpZYGGCZWVnsWjLokPqbWzduxWAJEmiVZ1WeeptdDqyE2lV0hKccmOMKTvi3Y7FLBF5CXjdD/cHvo9hucbAj/7/rKDxaaq6zSe0uojUBzoDLwCtwq1IRAYBgwDS09Nj2LQpK5IquMChVZ1WXNL+EsBVEl2xfUWeQGPq0qm8Pu/1nOWa12p+SL2N+tXqJ2o3jDGm3Iglx6IyMBg4AVfH4gvg2fwazPI5FltVdaKIjFPVfn78C8BtuByLR3CdmvUGkoCOwCWq+muk9VqOhYlk/a71zFk3x+VurJvNnLVzWLx1cc70htUb5rz6GngjJSMtw95IMcaYfMT9rZCgFTdQ1bUxzlsVGAHsBb4CTlfVy3wdi9txQcrwoDoWTYEbrSjExNP2vduZu25unnobP2/8mSx1mWi1qtTK07BXlwZdOLr20SRVSEpwyo0xpuQoysBitqp2OeyUxYEFFqaw9hzYw48bfnSBxto5zFk3h3nr57Evy2XCpVZMpWP9jnnqbbSt15ZKSZUSnHJjjEmMogws5qhq58NOWRxYYGGKwoGsAyzctDBPvY256+ayc/9OACpWqEi7eu1yXn3t0qALHet3pGqlqglOuTHGFL2iDCxuUNVnDztlcWCBhSku2ZrNkq1LcnI2AvU2NmZuBFyz5S3rtMxTQbRzg87UTqmd4JQX3OTJkxkyZAhZWVkMHDiQoUOH5pmuqgwZMoRJkyaRmprKK6+8QpcuXVi5ciWXX34569ato0KFCgwaNIghQ4YAMHfuXK677jr27t1LcnIyzz77LMcee2wids8YU0hF0fJmfaC7H5ypqhsKkb5CscDCJJKqsnrn6jyvv85ZN4cV21fkzJORlnFIvY0G1RqU2EqiWVlZHHPMMUyZMoXGjRvTvXt3xo4dS5s2bXLmmTRpEk8//TSTJk3i22+/ZciQIXz77besXbuWtWvX0qVLF3bu3EnXrl15//33adOmDaeeeip/+ctfOOOMM5g0aRLDhw9n2rRpidtRY8xhi+vrpiJyEe7tjWm4CpdPi8jtqvpOoVJpTCkkIjSu0ZjGNRpzdsuzc8Zvytx0SLPl4xeOz2m2vF7VenlyNro06EKzWs1KRLAxc+ZMWrRoQbNmzQDo168f48ePzxNYjB8/nssvvxwR4bjjjmPbtm2sXbuWBg0a0KBBAwCqV69O69atWb16NW3atEFE2LFjBwDbt2+nYcOGxb9zxphiF0s7FncB3QO5FCJSF/gUsMDCGK9Oah1OaXYKpzQ7JWfczn07mbd+Xp5g45GvH+Fg9kEAalSukafn1y4NutCqTiuSK8RyW8bP6tWradKkSc5w48aN+fbbb/OdZ/Xq1TlBBcCyZcuYM2cOPXr0AOCJJ57gtNNO47bbbiM7O5uvv/66iPfEGFMSxPIEqxBS9LEZsN6hjMlH9crV+X367/l9+u9zxu07uI/5G+bnaW/j+e+fZ8/BPQBUSa5Ch/od8rS30b5+e6okVymydIYrDg3NSclvnl27dnH++efzxBNPUKNGDQCee+45Hn/8cc4//3zefvttrr76aj799NM4p94YU9LEElhMFpGPgbF++GJgUtElyZiyq3JyZbo27ErXhrld7WRlZ/HL5l/y1NsYN38cz3//POCaLW9Tt02eehudjuxEjco14pKmxo0bs3LlypzhVatWHVJsEW2eAwcOcP7559O/f3/69u2bM8+rr77Kk08+CcCFF17IwIED45JeY0zJFjWwEPeT5Clcxc1Ay5ujVPW9YkibMeVCUgUXOLSp24b+HfoDLodg2bZleRr2+mTxJ7z2w2s5y7Wo3eKQN1LqVa1X4O13796dRYsWsXTpUho1asS4ceN4880388xzzjnnMGLECPr168e3335LWloaDRo0QFW5+uqrad26NX/961/zLNOwYUOmT59Or169+Oyzzzj66KMP4+gYY0qbqIGFqqqIvO97Mn23mNJkTLknIhxV6yiOqnUUfVvn5gKs3bk2pxhlzro5zFozi/8s+E/O9EbVG+UJNLo06EKTGk2iVhJNTk5mxIgRnHbaaWRlZTFgwADatm3LyJEjAbjuuus488wzmTRpEi1atCA1NZXRo0cDMGPGDF5//XXat29Pp06dAHjwwQc588wzeeGFFxgyZAgHDx6kSpUqjBo1qgiOlDGmpImlr5BngFdU9bviSVJ09rqpMXlt3bP1kGbLF25aSLZmA1A7pXaet1E6H9mZo484mgriqkqN+XEMd029ixXbV5Cels6wk4fRv33/RO6SMaaEiXe36QuAlsAyYDeuOETz6za9qFhgYUz+Mg9kMm/9vDz1Nn7c8CP7s/YDULViVTod2YnU5FSmr5ieMx5ck+ajzh5lwYUxJke8A4uMcONVdflhpK3QLLAw5vDsz9rPzxt/ztOw14wVM3La2ghWOakyZxx9BvVS61GvavhP7ZTa1llbKVUULa2asq0oWt48AThaVUf7diyqqerSQqbzsFhgYUz8VLi/QtjAAqBdvXZs2L2BTZmbcopV8iwrFaiTWidvwBElEKlWqVqJaBCsvCuqllZN2RbvljfvBbrhikNGAxWBN4DfR1vOGFPypaels3z7oZmPGWkZ/Hj9j4B7HXbLni1s2L3hkM/GzI05/3+/5ns27N7A9n3bw26rclLliEFH6Kdual0qJ1cu0n0vr4qqpVVjAmJpx+I8oDMwG0BV14hI9SJNlTGmWAw7eRiDJgwi80BmzrjUiqkMO3lYznBShSTqVq1L3ap1aUvbfNe57+C+PAFHpM/8DfPZsHtDTnf1odIqp8UciNROqZ1TGdVEV1QtrRoTEEtgsd+/dqoAImL9RBtTRgQqaMbzrZDKyZVz+lPJj6qyc//OfIOQXzf/ylcrvmJT5qawRTcVpAJ1U+tGzQGxYhmnqFpaNSYglsDibRF5HqgpItcAA4AXizZZxpji0r99/4S9ASIi1KhcgxqVa9Cidot8549WLLNh9wY2ZLq/3635jg27N7Bj346w66mSXOXQACRC/ZC6VetSKalSvHc9YYqqpVVjAvINLFT1URHpDezA1bO4R1WnFHnKjDEmREGLZfYe3MvG3RGKZXwQsm7XOuatn8eG3RvyvHYbrGaVmjFXUq2VUqtEF8sUVUurxgREDCxE5DRV/RjABxJTgqZdqKr/ibSsMcaUBFWSq9AkrQlN0prkO6+qsmPfjnwrqf6y6Re+3P1lxGKZJPHBT5SimeBP1YpVi7VYpqhaWjUmIOLrpiKSBXwBXKqqq0OmzVbVLsWQvkPY66bGmJIgKzuLzXs251s/JPDZuX9n2PWkJKfEXEm1TmqdwyqWsdZVy4fDbZ8EYMCAAUycOJF69eoxf/78Q9YtIquAxkBdVd0ULR3RikLmAW8C34jIX0NyKPINr30lz2eB/cA0VR3jx7cD7vSzPQQcAO4AqgA/qOr/5bduY4xJtKQKSTlf+LHYc2BPvm/LrNm5hrnr5rJh9wYOZB8Iu55aVWrFXEm1Vkotxs4fm+fNn+XblzNowiAACy7KkKysLAYPHpynfZJzzjknz6vAH330EYsWLWLRokV8++23XH/99TlvBF155ZXceOONXH755Yes29e3qQGsiCUt0QILVdUXRGQ6MEZEzgQGq2omRGhRJ6++wDuqOkFE3gLG+PFDgMF+HcNV9VpchVBExDo6M8aUSSkVU0hPSyc9LT3feaMVywTXD/l5089MXz6dzZmbIxbLKHpIA2eZBzK5cdKN7N6/m1pValGzSk1qpfi/VWqRViWN5Aqx1O03JUVh2yfp2bMny5YtC7vuv/zlLwCrgJjeCo2l8uavInI88C9gjogcGs6E1xj40f+fFTQ+TVW3AQS3hyEi/YBPwq1IRAYBgwDS0/O/KY0xpjQTEdKqpJFWJY2jj8i/u/mD2QfZnBm+WObBrx4Mu8y2vdu4duK1EddZvVL1QwKOmlVq5vk/dFpguLjrjZj4tU8S6oMPPqBRo0YAe4hDYJFzVajqQWCoiEwGxgJ1Y1h3oDxmLhBcRXq7iKThcix2Qk5QkRGpGERVRwGjwNWxiGHbxhhTbiRXSKZ+tfrUr1b/kGljfhwTtnXVJjWa8PXVX7Nt7za27tnq/u51f3PG7cudtnTb0pzxkeqLBKcnagASYXzgUzGpYtyOTXkRj/ZJQmVmZjJs2DA++eQTnnrqqZjTEi2wuD90hKpOE5GuQOQwN9e7wAgROQuYICKvq+plwJPAU7jAZbiIdAYeBSaKyGOqau8wGWNMnERqXfWhUx6KuSGzUAezD7J97/Y8wUhocBIamCzftpyte7eydc/WiPVHAqpWrBo+N6Ry5FySwHB5bfyssO2ThLN48WKWLl1Kx44dAdoDScBsETlWVddFWi5iYKGq70cYvxV4OGJKcufbDVwVNGqMHz8fuCJk9oJf2cYYY/JVFK2rJldI5ojUIzgi9YgCL6uq7Dm4J3IwEjzs/67YvoJ56+exde/WiI2eBSRJUky5JJGCk9KaW1KY9kkiad++PRs2bABARH4E6gDdCvNWiDHGmDIgka2rhhIRUiumkloxlYbVI/9ajiQrO4vt+7bnCUTyC05W7ljJ1j1b2bp3a8RG0AJSK6YWuAgnMFy9UvWE5ZYUpn0SgEsuuYRp06axadMmGjduzP3338/VV199WGmJqdv0ksTasTDGGHO49hzYk38Rzt5teYpxAtO2790e9u2bgApS4dCKrjEW4dRKqVWopuOLuq2SuHabbowxxpQVKRVTSKmYQoPqkYsAIsnKzmLn/p0xF+Fs3bOVNTvX5AzvPbg3etqSU2KuWxIcnHy6+FOGfDykxLRVYjkWxhhjTDHYe3Bv3rduYgxOAstEyy0JJyMtg2W3LItL2i3HwhhjjClhqiRX4chqR3JktSMLvGy2ZrNz386wgcjVH4SvC7Fie0wNZcadBRbGGGNMCVdBKuQ0mhbqgekPhG2rJJZWXotCye3b1xhjjDH5GnbyMFIrpuYZl1oxlWEnD0tIeiywMMYYY0qx/u37M+rsUWSkZSAIGWkZjDp7VMJeMS51lTdFZCNwaJ5P4dUBojb6Uc7Z8Sn77ByXfXaOy76iOscZqhpLdx6lL7AoKiIyK9Yar+WRHZ+yz85x2WfnuOwrCefYikKMMcYYEzcWWBhjjDEmbiywyDUq0Qko4ez4lH12jss+O8dlX8LPsdWxMMYYY0zcWI6FMcYYY+Km3LS8KSJVgWeB/cA0VR3jx58J3AhMUtURftzDQCqQqapDE5TkYhflGA0FmgNHAter6ioRWQxMAWarasKz3kxsopzj+4DWwFbgAVVdU17vg9Iuyjm+GugONAHmqeqddh+XTiLSDLgLSFPVC4LGtwPu9IMPqer8RNzH5SnHoi/wjqpeA5wTGKmqk4DhgWERSQcqqurNQJKINCn2lCZOpGP0sB/3MnCiH70LSAFWFnsqTWGEPcfAQdwX0QFgWzm/D0q7SPfxS6p6HfAr8IofbfdxKaSqS1Q1XAchQ4DB/9/encdHVZ2PH/882ReysUUgEDYFwSoiIC7Ffa+2blWLWksV10pbv1atXdSKC/XXaqVqcV8QtNa6FVGLotUWIiogooiyJQhhMQl7SDLP749zMkyGyWQCk/15v155Ze6527nrPHPuuecAVwE/a6nruMOUWAAFwKf+c02U6Xqx6yJb5efrKBddvftIRDoBPwTG+6SDAQH+BbzeXBk0e62+Y3yHqgZE5AzgUuBDOu510NZFu47TgH6qusQn2XXcvuSoajmAiGTRQt9nHanEogS3UyH6dq8Oma63n6+jiLiPRCQbeBD4lapuBlDVgKrWADtEpCOdR21dxGOsqgH/cR3QiY59HbR10e515wAv1g7YddzuVIhIjr9nb6aFruMO81aIf+44GdgBvA+crKoXichhuGdSecC9qvoPEbkTSAUqVfWmehfazkTZRy8CybiT9Hn//wY/20JVvbcFsmv2QJRj/GvcjacrcK2qrumo10FbV98x9uNeBs5X1e0iMgi7jtskEekCTAROAB4Bhvjr+ADgelwp1CRfx6LZr+MOE1gYY4wxpulZ0Zcxxhhj4sYCC2OMMcbEjQUWxhhjjIkbCyyMMcYYEzcWWBhjgkQkV0SuamCaviKyqLnyZIxpWyywMMaEysW12hc3IpIYz+UZY1o3CyyMMaHuAgaIyHwR+aP/WyQin4rIeeETi0iin+ZDEVkoIpf79KNF5B0ReRbfCqSIvCQiH4nIZyIyPmQZW0RkoogsEJE5IpLv0/NF5J8+fYGIHO7TLxSRIp/Hv1ngYkzrYoGFMSbUjcDXqjoMmAMMAw4Cjgf+KCI9wqb/KVChqiNxHVxdJiL9/LhRwM2qOsQPj1PVQ4ARwLW+kR+ATGCOqh4EvAdc5tP/Arzr04cDn4nI/sB5wBE+jzXA2HhtvDFm73WkvkKMMY1zJDDNN/lcKiLv4oKHhSHTnAgcKCK1PSzmAPviOjQrUtXlIdNeKyJn+s+9/XQb/bSv+fSPcK0JAhwLXAzg81AhIhcBhwAfigi4DrTWxWdzjTHxYIGFMaY+EuM0P1PVN+okihwNbA0bPh44TFW3ichsIM2PrtJdTQDXEP2+JMCT1sS4Ma2XPQoxxoTaDGT5z+8B5/l6FN2AMUBR2PRvAFeKSDKAiOzn+6oIlwOU+aBiMDA6hrzMAq70y030HSvNAs4Rke4+vbOIFDZuE40xTckCC2NMkKpuBD7wr5MehnvssQB4G9e77dqwWR4BFgMf+3n+RuQSh5lAkogsBP6Aq7/RkAnAMSLyKe4RyVBVXQz8BnjTL+stILzehzGmBVknZMYYY4yJGyuxMMYYY0zcWGBhjDHGmLixwMIYY4wxcWOBhTHGGGPixgILY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxhhjTNxYYLGXRGSsiLwZw3QPichvmyNPLU1EbheRDSIS3vxzi4v1eMV5nStE5PgmXscTInL7Hs57pogUi8gWETk4DnlRERm4t8tpSSLSTUSWiEiaH54tIpe2gnzFZd82dL6IyBEistSfEz8QkRdF5OS9XW9LE5HvisiSZlrXLSLyTHOsq7Vp14GFv6Fv9xdHqYg8LiKd4rkOVZ2qqifGMN0VqvqHeK67NRKR3sB1wBBV3ael8xMu1uPVwdwDXKOqnVT1k/CR7SFQ2AM3Ao+r6o6WzkgLuQ2Y7M+Jl4C7gInNseJ4fiGHn7uq+h9VHRSPZcfT3vwwaI3radeBhXe6qnYChgMjcR0Y1SEi1n18PfZg3xQCG1V1XTOsq1Utvw0rBD5r6Uy0luMjIqnAj4G4/toUp8XvuSKSGMNkdc4JVS0CskVkRJNlzLQbLX6SNxdVXQ28DhwAwUj2ahFZCiz1ad8TkfkiUi4i/xWRA2vnF5HevjhwvYhsFJHJPv0SEXnffxYR+bOIrBORChFZKCK166sTKYrIZSLylYh8KyKviEjPkHEqIlf4osgyEfmriEik7RKRUSIyT0Q2+VKZP4WMO9JvR7kv6r7Ep+eIyFN+W1aKyG9qb3h+ez7w2/EtcIuIpIrIPSKyyq/jIRFJj5CX43G9Tfb0pURP+PQzROQzn4/ZIrJ/yDwrROQGcT1Vbg3/cvHruics7WUR+aX/fKOIfC0im0VksYicGTJdpG0JHi8/zeEi8qE/Xh+KyOFheTs+ZDj4S0pE0kTkGX8ulPt58yMdo7C8J4TkeaOIPC8inf24mSJyTdj0C0TkLP95sIi85c+ZJSLyw4bWF7LO3/hjvc4f+xx/XLcAicACEfk6wrzv+Y8L/DE9z6fXe/6GzV/vuSMiR4tIiT/+a4HHRSRPRF7z52aZ/1wQsrzZIvIHf1w3i8ibItI1ZHx953xM57B3KFCuqiX1bFMPcdf2//nh0SHrXCAiR4fld6KIfABsA/pLA9e3iIwTkc/9uDdkL7uFF3fveVBEZojIVuAYP6qrP582i8i7tevx50F/4FV/zFP99LOB06KsZ6/vaeIet/waOM+ve4FPzxGRR0VkjYisFve4NdGPG+jzXyHuEexzPn23c7f2nAtZ3woR+T9/PCtE5Dnxj7/8+F/5dX4jIpdKlNI7Eenn87FZRN4CuoaN/7uIrPXreU9Ehvr08cBY4Fc+n6/69Gj3tojb7MdFvE/Ut54moart9g9YARzvP/fGReB/8MOK+xLsDKTjSjTW4W4qibhfLCuAVD+8APgzkAmkAUf65VwCvO8/n4Tr3jkXEGB/oIcf9wRwu/98LLDBrzMVuB94LyTfCrzml9MHWA+cXM82/g+4yH/uBIz2n/sAm4ELgGSgCzDMj3sKeBnIAvoCXwI/DdmeauBnuO6v04F7gVf8vsoCXgXurCc/RwMlIcP7AVuBE3w+fgV8BaSEHKP5/vikR1jeGKCYXT3x5gHbgZ5++FygJy5IPs+vq0eUbQk9Xp2BMuAiP/4CP9wl/Pzxw7cAz/jPl/v9kIE7Pw4BsmM4D3+O6zK8wB/7vwHT/LiLgQ9C5hsClPvpMv1++InP63DcOTQ0/PyKsP5xfp/3x50jLwJPh51vA6NcR3XGE9v5O9B/vpd6zh3cuVIN3O2Xk447T8/2+zUL+DvwUsiyZwNf486rdD98VwznfL35iLC9VwP/CkubDVzKrutlvE/vBWwETsWdgyf44W4h860ChvrjlkyU6xv4gT9W+/vpfwP8t559e6M/PyL+hczzBFABHOHzmObTNuOur1TgPvx1Eenc92m/BF6sZ5/F8552C/46C0l7CXetZALdgSLgcj9uGnBzyLYdGeXcPZq696cVflk9cefG58AVftzJwFp/7DKAp8OXF+Fe/Ce//WP8/n0mZPw43LmXijsf54cdo9vDlhft3hZxm9mL+0Q8/1r8y79JN86dNFv8hbYSeAD/5eVPkGNDpn0QH3SEpC0BjgIO8xdCUoR1XMKuL6pjcTed0UBC2HTBAwo8CkwKGdcJqAL6huQt9OJ4Hrixnm18D7gV6BqWfhPwzwjTJwKVuDoQtWmXA7NDtmdVyDjxJ/SAkLTDgOX15Odo6l64vwWeDxlOAFYDR4cco3FRjqHgbsxj/PBlwNtRpp8PfD/StkQ4XhcBRWHj/wdcEpK3+gKLccB/gQNjPA9rA4vPgeNCxvXwxz4Jd9PZChT6cROBx/zn84D/hC33b8Dvw8+vCOufBVwVMjyodp0h51tjAotYzt+BDZ07/lzZCaRFWfcwoCxkeDbwm5Dhq4CZDZzzjT2Hbwamh6XNxn1prAAuCEm/gZAgzae9Afw4ZL7bIuzPiNc3rlT1p2HXy7aQcyLqsapne54AnoqQNj1kuBNQA/SOdO43dO3FeE7Eek+7hbpfyPm4e1Z6SNoFwDv+81PAFKAghnP3aHYPLC4MGZ4EPOQ/P0ZI8Ik7pyPuf1ywVA1khqQ9S1iAFDIu1y8rJ+R4RP3Cp+69LeI2sxf3iXj+dYRHIT9Q1VxVLVTVq1R1e8i44pDPhcB1vjizXETKcb+ie/r/K1W1OtqKVPVtYDLwV6BURKaISHaESXviAp3a+bbgfuX0Cpkm9I2KbbgLNZKf4n69fSGuOP57Pr037pdduK5ASuj6/efQdYful264aP2jkP0y06fHInxbA3759a2vDnVXw3TcjQTgR8DU2vEicrHsenxVjnvUFVoEWe+yw/Pmhe+L+jyN+wKZ7otJJ4lIcgzzFQL/DMnv57gber6qbgb+BZzvpz2fXdtaCBwadn6OBWKpIBu+nStxgUyDj25iWV495y/Edu6s15AKkiKSISJ/E/fYZhMucM6VuvUC6rs26jvnG3sOl+GCvHBjcUHxCyFphcC5YcflSFzAWCvSOVjfNhQC94Us61tcYBTLORlNpDwE0/wx/BZ3bOuThfuRFkk872nhCnElPWtC9svfcCUX4EpBBSgS98h1XIzLbShfPam73xq6l5Sp6taQtOD+EJFEEbnLP9rYhAtoIOxxSagG7m31bfPe3CfipiMEFtFoyOdiYKIPQmr/MlR1mh/XR2KoXKaqf1HVQ3DFZ/sB10eY7BvcCQCAiGTiim1XN3oDVJeq6gW4i+xu4AW/vGJgQIRZNuB+SRSGpPUJW7eGTb8dV5RWu19y1FWIjUX4tgruC6C+9UUyDThH3DPgQ4F/+GUVAg8D1+AeX+QCi3AXXCzLrpM3L3RfbMV9IdUKXpyqWqWqt6rqEOBw4Hu4RxkNKQZOCTvP0tTVAard1gtE5DBcUf87IfO9GzZfJ1W9MoZ1hm9n7a+r0hjmbXB5Uc7fWM6d8ONzHa5E5VBVzcYVKUPdY1qfaOd8Y87hhbhrN9wtflnPhgQ6xbgSi9Djkqmqd4XM19D5Hb4Nl4ctL11V/xs+oYj82j8rj/gXNnmkPPQOWVYn3KOAb6LkbX/cI+FI4nZPY/e8FuNKLLqG7JNsVR0KoKprVfUyVe2JK319oL56EI20BvfIslbv+ib00+b57a7VJ+Tzj4DvA8cDObhHarDrvK6zzQ3d26Jsc0P3icaci3usowcWoR4GrhCRQ8XJFJHTRCQL9wxuDXCXT08TkSPCFyAiI/38ybgvpR24X6PhngV+IiLDxFWMugOYq6orGptpEblQRLr5koByn1yD+6V7vIj8UESSRKSLiAxT1RpcMeREEcnyJ/AvqacGvF/uw8CfRaS7X2cvETkpxiw+D5wmIsf5/XId7iax242yPupegVwPPAK8oaq125mJu1DW+3z9BF85N0YzgP1E5Ed+H52Hq9fwmh8/HzhfRJLF1YY/p3ZGETlGRL7jv2A24YK1SMc63EO4fV/ol9NNRL4flqdC3Ot+z/n9j8/TfiJykc9Psj/f9qdh04BfiKtc1gl3vj3XUAlciFJc/YxaMZ2/e3juZOGCgHJxlVp/H2Meof5zvrH5KMKVkoSXElThnntnAk+Lq/D8DHC6iJzkf5WmiasgWMCeeQi4SXZV7MsRkXMjTaiqd/gvjYh/MazrVHGVXVOAP+COYbRf5UfhHtVEErd7Gu586+v3L6q6BngT+H8iki2uMvIAETkKQETODdnfZbh7Qk3IsvqzZ57327S/iGQAv6tvQlVdCcwDbhWRFBE5Ejg9ZJIs3H1vI+7Hyh0Rtjk0n1HvbVG2uaH7xN7sj5hZYOGp6jzcM8TJuAP1Fe55PP7L+HTcM7ZVQAnuWVa4bNwNrAxXDLYR10ZA+Lpm4eoe/AMXsAxgV/F3Y50MfOZ/odwHnK+qO1R1Fa5C2XW4Is75wEF+np/hAp9lwPu4m8JjUdZxA25/zBFXjPdv3K/KBqnqEuBCXGWuDbj9eLqq7mzENoL7cjze57V22YuB/4erF1EKfAf4INYFqupGXEnDdbhj9Svge6q6wU/yW9yxKcPVY3k2ZPZ9cEXim3CPM94lttcT78NVInxTRDbjKnIeGpKnSlzlyvBt3QyciDtPvsEV39ZWemzIY7hHN+8By3EB789imK/WLcCT4opWf9jI87ex5869uJKaDbh9MzPWTDZwzsecD39uPoE7byONOwtXQvgY7hf593FvMqzH/WK8nj28t6rqP3HHdbrP5yLglD1ZVgyexQVu3+IqH4+tb0IRGQlsVffa6W7ifE/7u/+/UUQ+9p8vxj3CXYy7Hl9g1+OmkcBcfw98BZigqsv9uFsIOXcbkwlVfR34C67U8CvcfQZcgBDJj3DX8re4/fpUyLincN8Jq/02zAmb91FgiM/nSzHc2yJuc9h9Yj3uWn8XuCLCev4lIv8U90ZMkfg3GCH4tsyn4h7FzGt4b9VVW9PeGGOMJyLdgP8AB2vdelkdkoj8A3hUVWe0dF5aiv/VvwhIbURpX4vwJalf4t5SKgE+xFU6XhwyzR+BLap6q4gMBv6qqsf5cSuAESE/shrFSiyMMSaMqq5X1cEWVDiqenZHDCrENXefIiJ5uJKkV1t7UOGNAr5S1WW+lG06rmQt1BDcG2Oo6he4x097WqG7DgssjDHGmMguxz1S+BpXhyGWytKtQS/qvsVSwu5vFi3APdZDREbh6nbV1ttQ3OPaj8Q1rNUoFlgYY0wHICIni2uJ8SsRuTHC+Lz6nrn78Yki8omIvBaSNkxE5tQ+i/dfUO2Gqp7s3yDqrKpn+oqkbUGkt6jC6z3chXuTZT6uztUnuLfFAI5Q1eG4+j1Xi8gYGsECC2OMaef8M/e/4r4ohuBeaR4SNtmvca1BHoirLHlf2PgJuIrKoSYBt6rqMNxbE5PinHWzZ0qo+3psAWGvEqvqJlX9iT92F+PadVnux33j/68D/ol7tBIzCyyMMab926tn7v7VxtNwr3yHUtzbcODaZ4jWDoZpPh8C+/pXzFNwb4m8EjqBiOT6ceCaq39PVTeJa1Ihy0+TiXvLZFFjVt7m3grp2rWr9u3bt6WzYYwxbUZZWRkVFRXU3js3btzI1q1b6dNnVxtOq1evJhAI0Lt3b7Zu3coXX3zB4MGDyczM5Ouvv2afffYhEAhQWlrKwIGu/ant27ezdOnS4DIGDRpEamosb0CbplZRUUFxcTGqSteuXenRowfr168HoFu3bmzZsoUVK1YAkJ6eTmFhIUlJSVRWVvL1164BW1Wlc+fO9OjRg48++miDqsbU4nKTdVPsI50HcH0BzFbVqT79PFzkWwXco6qfi+tN7y3gY1WdEm25ffv2Zd68Rr9Wa4wxHdbf//533njjDR55xBU4PP300xQVFXH//fcHp9m0aRMTJkzgk08+YcSIEXTq1IlHHnmE4uJiZsyYwQMPPMDs2bO55557eO01V83i2muv5bbbbuPss8/m+eefZ8qUKfz73/9ukW3s6EqnlrLs5mVUrqoktU8q/Sf2J39sXF7yAEBEwrs/qFeTBRa42qYvqOqr4rp0re3z4GxcQyx5uNbHLsV1FJZO9LbYjTHG7IGCggKKi3fdXktKSujZs263INnZ2Tz++OOA+6Xar18/+vXrx/Tp03nllVeYMWMGO3bsYNOmTVx44YU888wzPPnkk9x3n6uKce6553LppZc230aZoNKppSwZv4TANtdQb+XKSpaMXwIQ1+AiVk0ZWBQAn/rPoU0d34NrhXEtLrgAOBhXi/Vf1N9krDHGmD0wcuRIli5dyvLly+nVqxfTp0/n2WefrTNNeXk5GRkZpKSk8MgjjzBmzBiys7O58847ufPOOwGCJRbPPOMame3ZsyfvvvsuRx99NG+//Tb77rtvs29ba6QBJbAj4P62B4Kfa7bX7JYW/BxpunrGh0+7c81OCNTNQ2BbgGU3L2t3gUUJLriYT0glUd8kbJG4DlOu8WkBABHZISIJIf0j4NPHA+OBOs8EjTHGNCwpKYnJkydz0kknUVNTw7hx4xg6dCgPPfQQAFdccQWff/45F198MYmJiQwZMoRHH320weU+/PDDTJgwgerqatLS0pgyJeqT7GalqmiVxv7FHvI54hd7I4IA3bl3dRclRUhISyAhPcH9T0sgMT0xmJbcLbnO+LWPrY24nMpV9bU+3rSarPKmr2MxGddW+fvAyap6kYiciusvohOuTf0cXDv+AAtV9d5oyx0xYoRaHQtj4mvmzJlMmDCBmpoaLr30Um68sW4zB2VlZYwbN46vv/6atLQ0HnvsMQ44YFczBzU1NYwYMYJevXoFn7/Xuueee7j++utZv349XbvW20u0aUJN/fw9Gq3RPf4lvre/7veqL88Egl/cwS/1sC/74OdIAUA94xucNi0BSYilM99d/tf3f1Su3D2ISC1M5bAVh+3FTthFRD5S1RGxTNtkJRbq+qX/SUjSVJ8+A9eDY621wDiMMS2ipqaGq6++mrfeeouCggJGjhzJGWecwZAhu5o5uOOOOxg2bBj//Oc/+eKLL7j66quZNWtWcPx9993H/vvvz6ZNm+osu7i4mLfeestKGltQxOfvly1hxzc76Hx8570ujm/o171W7eWv91SJ+mWdkp1S7y/7qMFAA9NKkiDSuC/4ltJ/Yv86xxggISOB/hObvCPTiGIOLEQk0wcLxph2pKioiIEDB9K/v7sJnX/++bz88st1AovFixdz0003ATB48GBWrFhBaWkp+fn5lJSU8K9//Yubb76ZP/3pT3WW/Ytf/IJJkybx/e+HN5lg9kTN9hqqy6upLnN/VWVVdYary6qpLvfpfnjr4q11a7kBge0Blv9qOctZHnlFYSRp96L50M9JuUmx/bIP+RxTKUBK43+9d0S1pU8tVSoVrsHAQkQOxzWK0gnoIyIHAZer6lVNnTljTNNbvXo1vXvvaqSvoKCAuXPn1pnmoIMO4sUXX+TII4+kqKiIlStXUlJSQn5+Pj//+c+ZNGkSmzdvrjPPK6+8Qq9evTjooIMwjqpSs6WmUYFB7fiqsiq0Mvqv/8SsRJJyk0jKc3/pA9LZ+mk9vwcFDvjnATEV2SckWVuKrV3+2PwWCyTCxVJi8WfgJHyrXaq6oLHthhtjWq9I9azCi4BvvPFGJkyYwLBhw/jOd77DwQcfTFJSEq+99hrdu3fnkEMOYfbs2cHpt23bxsSJE3nzzTebOvvNTmuU6oo9Cwyqy6t3Kz2oQ6gTGCTnJZPaKzU4nJSXRFKuSw8drv0fKQCo9/l7n1S6ft/qvJj4i+lRiKoWh91ool0axpg2pCnaOLjhhhtYvnx5sLSipKSE4cOHU1RUxD777NN8G1ePwM7AngUGZdXUbIp++5NkqRsEdEkmfWB6nSAgGBiEBRGJWYlxL/pvbc/fTfsXS2BR7B+HqG9X/Fp274jGGNNGNVUbB+vWrQvOX9tibrzeClFVAtsCexQYVJdX1/mSjSQhI6FOEJDaO5XMAzMbDAyScpNIyEhoVZX+Wtvzd9P+xRJYXIHr5a4Xrm2KN4GrmzJTxpjm01RtHDREA0rN5poGg4D66iM09LZBYnZinSAgY7+M3YKAiI8YcpNISG1fdQpa0/N30/5FbcfCd7X7pKpe2HxZis7asTAmvvamjYNAdaBuiUEjAoPqiurdWgusI5G69QkilA7UW/cgJwlJbD2lBsa0dXFrx0JVa0Skm4ik+K52jTHtSKQ2Dr746ReUv19O5pDMBl9trNnSQH2DVKkTBKTkp5AxOKPeCoihaYmdElvVIwVjTGxieRSyAvhARF4Bgu8tqeqf6p3DGNOqBaoDbJ63mS+v/nK3+gZaqax5aE1wOLFT3VcY0/qlkXRwA4GBH05MT2zuTTPGtLBYAotv/F8CkNW02THGNAVVZetnWymfVU7ZrDLK3y2P/naDwOGlh7v6Bsntq76BMaZpNRhYqOqtACKS5QZ1S5Pnyhiz17av2B4MJMreLqOqtAqAtAFpdD+/O3nH5fH1dV9TWRK5jYOUbinNnWVjTDsQS8ubBwBPA5398AbgYlX9rInzZoxphJ3rd1L+tg8kZpWxY9kOAFL2SSHvuLzgX1phWnAerVJr48AYE1exPAqZAvxSVd8BEJGjgYeBw5suW8aYhlRvrqbivYpgILF1oasClZidSO7RuRRMKCDvuDwyhmTUWwnS2jgwxsRbLIFFZm1QAaCqs32X6MaYZhSoDLBpzqZgILG5aDNarUiqkHNEDv0m9iPvuDw6HdKpUX07WBsHxph4iiWwWCYiv8U9DgG4EBruEs8HHw8AO4HZqjrVp58HnAZUAfeo6uc+/SFgi6r+X6O3wph2SGuULfO3BAOJiv9UENgegATIGpFF71/1Ju+4PLIPzyYxzd6+MMa0DrEEFuOAW4EX/fB7wE9imO8s4AVVfVVEngOm+vSzgbFAHnAHcKmInAPMAwY3Iu/GtCuqyvYvtwcDifJ3yqkuqwYgY0gGPS7tQd5xeeQclUNybnIL59YYYyKL5a2QMlz/II1VAHzqP4e+13YPcD+wFsgTkXzgYFy9DQssTIdSuboyGEiUzSpj52rXDl1qn1S6/qArecflkXtsLqk9Uls4p8YYE5tY3gp5CzhXVcv9cB4wXVVPamDWElxwMR/XBgYAqloEFInIQOAa4CigO/A74CAR2U9VvwzLw3hgPECfPn1i2jBjWqOqb6son73rzY3tS7YDkNQlibxj3Vsbucflkj4g3VqdNMa0SVH7CgEQkU9U9eCG0iLMlwlMBnYA7wMnq+pFInIqcDrQCbheVdf66fsC1zRUx8L6CjFtSc22Gire3/XmxpaPt4BCQmYCuWNyg4FEpwM7xb27bGOMiZe49RXiBUSkj6qu8gsvBKJHI4CqbqVuXYypPn0GMCPC9CsAq7hp2rRAVYDNH24OBhKb/rcJ3alIspA9Opu+v+9L7nG5ZI/KJiHFWrQ0xrQ/sQQWNwPvi8i7fngM/rGEMR2dBpSti7buenPj3QrXMZdAp2GdKLi2gNzjcsn9bi6JmfbmhjGm/Yul8uZMERkOjAYE+IWqbmjynBnTSm1fFvLmxtvlVK13TWWn75tO/oX55B6XS94xeSR3sTc3jDEdTyyVN48A5qvqayJyIfBrEblPVVc2ffaMaXk7S3dS9rYPJGaVs2OFbyq7RwqdT+rsAonj8kjrndbAkowxpv2L5VHIg7i3NQ4CrgceA57Cvc1hTLtTvama8nfLg4HE1kWuqeyk3CTXVPZ1vqnswfU3lW2MMR1VLIFFtaqqiHwf+IuqPioiP27qjBnTXGp21LDpf5uCgcSmDzdBDSSkJZBzZA7dx7qeQLOGZyGJFkgYY0w0sQQWm0XkJlxT3mNEJBGwh8emzdIaZfPHm4OBRMX7FQR2BCARskdm0+fGPq6p7MOsqWxjjGmsWAKL84AfAT9V1bUi0gf4Y9Nmy5j4UVW2fbEtGEiUzy6nutw1lZ15QCY9LndNZecelUtSdiyXhDHGmPrE8lbIWuBPIcOrcHUsjGm1dhTvCAYSZbPK2LnGNZWd1jeNrme7prLzjs0jJT+lhXNqjDHti/08M+1C1cYqyt7ZFUhsX+qayk7ulkzuse6tjbzj8kjvn97COTXGmPbNmv4zDZo5cyaDBg1i4MCB3HXXXbuNLysr48wzz+TAAw9k1KhRLFq0CIDi4mKOOeYY9t9/f4YOHcp9990XnGf+/PmMHj2aYcOGMWLECIqKihqVp5qtNWycuZGvr/+aecPn8UG3D1h87mJKnyklfb90BvxpACMWjODwtYczdPpQel7W04IKY4xpBg32FdLaWF8hzaumpob99tuPt956i4KCAkaOHMm0adMYMmRIcJrrr7+eTp068fvf/54vvviCq6++mlmzZrFmzRrWrFnD8OHD2bx5M4cccggvvfQSQ4YM4cQTT+QXv/gFp5xyCjNmzGDSpEnMnj273nwEdgbYVLQpWCKxac4mtEqRFCH7sOxgiUTWyCwSki1eNsaYeIprXyG+gaxbgEI/vQCqqv33JpOmbSgqKmLgwIH07+8O9/nnn8/LL79cJ7BYvHgxN910EwCDBw9mxYoVlJaW0qNHD3r06AFAVlYW+++/P6tXr2bIkCGICJs2bQKgoqKCnj171lmvBpQtC7cEA4ny98oJbA24prKHd6LgF64tiZwjc0jMsDc3jDGmtYiljsWjwC+Aj4Caps2OaW1Wr15N7969g8MFBQXMnTu3zjQHHXQQL774IkceeSRFRUWsXLmSkpIS8vPzg9OsWLGCTz75hEMPPRSAe++9l5NOOon/+7//IxAI8MEHH7Dtq227Aol3yqna4JvKHpTOPj/ex725cXQuyZ3tbWdjjGmtYgksKlT19SbPiWmVIj0qC29t8sYbb2TChAkMGzaM73znOxx88MEkJe06tbZs2cLZZ5/NvffeS3Z2NgAPPvggk343iWPSj+HZR57l3MHn8sdK9xZzSq8UOp/a2QUSx+aSVmBNZRtjTFsRS2Dxjoj8EXgRqKxNVNWPo80kIpnAA8BOYLaqTvXp5wGnAVXAPcASXLPh6cA2Vb1iD7bDNJGCggKKi4uDwyUlJbs9tsjOzubxxx8HXCDSr18/+vXrB0BVVRVnn302Y8eO5fRjT2fDyxsom1XGY399jDMDZ/I5nzMidwS/rfkt+/51X/fmxn7p1lS2Mca0UbEEFof6/6GVNhQ4toH5zgJeUNVXReQ5YKpPPxsYC+QBd6jqpcDlACLymIgkqGog1g0wTWvkyJEsXbqU5cuX06tXL6ZPn86zzz5bZ5ry8nIyMjJISUnhkUceYcyYMWRnZ1O9rZqLvn8RPct7ctRzR/HB9R9AABLSE+ie3p11Y9dx6vhTmbtxLoNuGkSvq3q10FYaY4yJl1gayDpmD5ddAHzqP4fWzbgHuB9YiwsuEJEhwO3AmkhBhYiMB8YD9OnTZw+zY/ZEUlISkydP5qSTTqKmpoZx48YxdOhQHnroIQCuuOIKPv/8cy6++GISExPZd599mfjdicw/bj7/+c9/eK7qOfrTn/9m/JfEbonc8otbOOfn5/DUh08xYcIE7phzB2lpaUyZMqWFt9QYY0w8NPi6qYjkAL8Hxvikd4HbVLWigfkuAsp8d+vTVfX8sPEDgWtU9echaQ8Ad0frkt1eN21epVNLWXbzMipXVZLaJ5X+E/uTP9ZVylRVti12TWWXzSqjfHY5NZtcDJl5YGbwFdCcMTkkZVlbbMYY01bF9XVTXDfpi4Af+uGLgMdxjzqieRGYLCKnAa+KyNOqepGInAqcDnQCrheRnsBNuMa6qoHiepdomlXp1FKWjF9CYJsrRKpcWcmSy5ZQNruMwLYA5W+Xs3Otbyq7fxrdz3O9gOYek0tKd2sq2xhjOqJYSizmq+qwhtKai5VYNJ//9f0flSsrI45Lzk8m71hXIpF7XC7pfa1VS2OMaa/iXWKxXUSOVNX3/cKPALbvTQZN21C5KnJQgcDhaw63NzeMMcbsJpbA4krgSV/XQoBvgUuaMlOm5agqZbPKWPmHle7dnwhS+6RaUGGMMSaiWN4KmQ8cJCLZfnhTU2fKND9V5ds3vmXlbSvZ9L9NpPRKoftF3dnwwgYC23e9qJOQkUD/idaauzHGmMjqDSxE5EJVfUZEfhmWDoCq/qmJ82aagaqy8bWNrLxtJZvnbSa1Tyr7PrgvPX7Sg4TUBEpPqv+tEGOMMSZctBKLTP8/qzkyYpqXBpQNL21g5R9WsmX+FtL6pTHokUHkX5RPQsqu3kHzx+ZbIGGMMSZm9QYWqvo3///W5suOaWpao6x/YT0rb1/J1kVbSd83ncFPDKb7j7pbd+PGGGP2WoPfJCIySUSyRSRZRGaJyAYRubA5MmfiJ1AdoHRqKR8e8CGLz1+M1ij7T92fUZ+PYp8f72NBhTHGmLiI5dvkRF9h83tACbAfcH2T5srETaAqwJon1vDh/h/y+YWfI8nCkOeHMHLRSPJ/lI8k2tsdxhhj4ieW102T/f9TgWmq+q29atj6BXYGWPvkWlbduYody3fQ6eBODH1xKF2/3xVJsONnjDGmacQSWLwqIl/gGsW6SkS6ATuaNltmT9XsqGHtY2tZddcqKosryRqVxcC/DKTLaV2s7QljjDFNLpZ2LG4UkbuBTapaIyJbge83fdZMY9Rsr2HNw2tYdfcqdn6zk+zDsxn08CDyTsyzgMIYY0yzidaOxbGq+raInBWSFjrJi02ZMRObmq01fPPQN6z64yqqSqvIOSqH/Z/en9xjci2gMMYY0+yilVgcBbyN64k0nGKBRYuq3lzN6r+upuT/lVC1oYq84/MofL6Q3DG5LZ01Y4wxHVi0dix+7///pPmyYxpSXVFNyf0llPy5hOpvq+l8SmcKf1tIzmE5LZ01Y4wxpuE6FiJyBzBJVcv9cB5wnar+poH5MoEHgJ3AbFWd6tPPA04DqoB7gABwA5AGLFDVu/d4a9qxqm+rKLmvhJL7SqipqKHL6V0o/G0h2SOzWzprxhhjTFAsb4Wcoqq/rh1Q1TIRORWIGlgAZwEvqOqrIvIcMNWnnw2MBfKAO1T1UmAcgIjY45UwOzfspORPJayevJqazTV0Pasrhb8pJOtga2ndGGNM6xNLYJEoIqmqWgkgIulAagzzFQCf+s81Ien3APcDa3HBBX655wNvRlqQiIwHxgP06dMnhlW3fTtLd1J8TzGrH1xNYFuAbj/sRuHNhXT6TqeWzpoxxhhTr1gCi2eAWSLyOK7S5jjgyRjmK8EFF/MJaeFTVYuAIhEZCFwDwaCisL7HIKo6BZgCMGLECI1h3W1W5TeVFP+xmG/+9g2BygDdL+hO4c2FZO6f2fDMxhhjTAsT1Ya/p0XkZOB4QIA3VfWNGObJBCbjGtN6HzhZVS/yj1FOBzrhmgbvAbwKvAZsU9Vf1rNIwAUW8+bNazDPbc2O4h2sunsVax5Zg1Yr+1y0D31+3YeMfTNaOmvGGGM6OBH5SFVHxDJtLCUWAJ8D1ar6bxHJEJEsVd0cbQZV3QqEvlEy1afPAGaEpK/FlWx0SNtXbGfVnatY+/haAPa5ZB/63NiH9P7pLZwzY4wxpvFieSvkMlz9hs7AAKAX8BBwXNNmrX3b9tU2Vt25itKnSiEBelzWgz439CGtT1pLZ80YY4zZY7GUWFwNjALmAqjqUhHp3qS5ase2LdnGyokrKZ1aSkJKAj2v6kmfX/UhtVcs9WGNMcaY1i2WwKJSVXfWNg8tIkm4SpymEbZ+tpWVt69k3XPrSEhPoOAXBfT+v96k7mMBhTHGmPYjlsDiXRH5NZAuIicAV+EqW5oYbFmwhZW3r2T9C+tJ7JRInxv6UPDLAlK6pbR01owxxpi4iyWwuAG4FNcmxeW4ipePNGWm2oPNH21mxR9WsPHljSRmJ1L420IKJhSQ3CW5pbNmjDHGNJmEaCNFJAH4VFUfVtVzVfUc/7ndPgqZOXMmgwYNYuDAgdx11127jS8rK+PMM8/kwAMPZNSoUSxatCg4bty4cXTr3I2BWQP5aMRHVLxXQd9b+zJ65Wj63dbPggpjjDHtXtTAQlUDwAIR6RDNXdbU1HD11Vfz+uuvs3jxYqZNm8bixYvrTHPHHXcwbNgwFi5cyFNPPcWECRMAKH+/nCM/PZKJZRMJbA/Q745+jF4xmr6/60tyrgUUxhhjOoZYHoX0AD4TkSJga22iqp7RZLlqIUVFRQwcOJD+/fsDcP755/Pyyy8zZMiQ4DSLFy/mpptuAmDQoEEs+2IZs46cReIHiQzqPghugoyXMii8qbBFtsEYY4xpSbEEFrc2eS5aidWrV9O7d+/gcEFBAXPnzq0zzUEHHcQ//vEPhmwfwr+u/xervlnFyp0rOeHPJ9BzfE+K1xXDK82dc2OMMaZ1qDewEJE04ApgIK7i5qOqWt1cGWsJkaqO1L5mWzt+/PDxXHvltYy6dxQDMwbynT7fYdjfh9F7VO/d5jXGGGM6mmglFk8CVcB/gFOAIcCE5shUSykoKKC4uDg4XFJSQs+ePVFVNr6ykRV/WMGWj7ZwU+FNFE4sJP/ifAYMHsDAwQNbMNfGGGNM6xEtsBiiqt8BEJFHgaLmyVLLGTlyJEuXLmX58uX06tWL6dOnc/8l9zPv4HlsXbCVtAFp7DN5H/pe0pe0zDQefvhhxowZQ3Z2dktn3RhjjGkVor0VUlX7ob0/AqmVlJTE5MmTOemkk9iv934cvvZwAr8M8MLaF5h7yVxGfTGKsuFlfGfYdxg8eDCvv/469913X3D+Cy64gMMOO4wlS5ZQUFDAo48+2oJbY4wxxjS/ertNF5Eadr0FIkA6sM1/VlVtkZ/p8e42vXRqKctuXkblqkpSe6fS+dTOlL9TzvYl28kYkkHhbwvpfm53JFEaXpgxxhjTDsWl23RVTdzLTGQCDwA7gdmqOtWnnwechisRuUdVPxeRvwGH1z56aS6lU0tZMn4JgW0BACpXVbLmoTWk9E5h6AtD6XpmVyTBAgpjjDEmVlEbyNpLZwEvqOplQGibF2cDPwVuAq4DUNXLgSVNmJeIlt28LBhUhBIRup3dzYIKY4wxppFiacdiTxXgXlMFqAlJvwe4H1gL5MWyIBEZD4wH6NMnfo2AVq6qjJxeHDndGGOMMdE1ZYlFCS64qLMeVS1S1SuAZ4DiSDOGU9UpqjpCVUd069YtbhlM7RO5y/L60o0xxhgTXVMGFi8CZ4vIg8CrIvI0gIic6tN+D9zl0yYCB4vIQyLSbN/q/Sf2JyGj7i5IyEig/8T+zZUFY4wxpl2p962Q1qpJ3wrpk0r/if3JH5sft+UbY4wxbV1j3gppc4GFiKwHVjbBorsCG5pgue2F7Z/2z45x+2fHuP1rqmNcqKox1UVoc4FFUxGRebFGYx2R7Z/2z45x+2fHuP1rDce4KetYGGOMMaaDscDCGGOMMXFjgcUuU1o6A62c7Z/2z45x+2fHuP1r8WNsdSyMMcYYEzdWYmGMMcaYuGnKJr1blSidop0KXAPMUNXJPu0uIAPYpqo3tlCWm12UfXQjMADYB7hSVUtE5GvgLeBjVW3xojcTmyjH+BZgf6AMuE1Vv+mo10FbF+UY/xQYCfQGFqrqTXYdt00i0h+4GchR1XNC0g/A9cMFcKeqLmqJ67gjlVhE7BRNVWcAk2qHRaQPkKyq1wKJItK72XPacurbR3f5tMeAY3zyFiCdGJtlN61GfZ0DVuO+iKqA8g5+HbR19V3Hj/ruFL4EnvDJdh23Qaq6TFV/GmHUBOBq4CrgZy11HXeYEgvq7xQtXC92XWSr/Hwd5aKrdx+JSCfgh/jO4ICDAQH+BbzeXBk0e62+Y3yHqgZE5AzgUuBDOu510NZFu47TgH6qWtubtF3H7UuOqpYDiEgWLfR91pFKLCJ2ihbB6pDpevv5OoqI+0hEsoEHgV+p6mYAVQ2oag2wQ0Q60nnU1tXXOWDAf1wHdKJjXwdtXbR73Tm4fpwAu47boQoRyfH37M200HXcYd4K8c8dJwM7gPeBk1X1IhE5DPdMKg+4V1X/ISJ3AqlApareVO9C25ko++hFIBl3kj7v/9/gZ1uoqve2QHbNHohyjH+Nu/F0Ba5V1TUd9Tpo6+o7xn7cy8D5qrpdRAZh13GbJCJdgInACcAjwBB/HR8AXI8rhZrk61g0+3XcYQILY4wxxjQ9K/oyxhhjTNxYYGGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjTJCI5IrIVQ1M01dEFjVXnowxbYsFFsaYULm4VvviRkQS47k8Y0zrZoGFMSbUXcAAEZkvIn/0f4tE5FMROS98YhFJ9NN8KCILReRyn360iLwjIs/iW4EUkZdE5CMR+UxExocsY4uITBSRBSIyR0TyfXq+iPzTpy8QkcN9+oUiUuTz+DcLXIxpXSywMMaEuhH4WlWHAXOAYcBBwPHAH0WkR9j0PwUqVHUkroOry0Sknx83CrhZVYf44XGqeggwArjWN/IDkAnMUdWDgPeAy3z6X4B3ffpw4DMR2R84DzjC57EGGBuvjTfG7L2O1FeIMaZxjgSm+SafS0XkXVzwsDBkmhOBA0WktofFHGBfXIdmRaq6PGTaa0XkTP+5t59uo5/2NZ/+Ea41QYBjgYsBfB4qROQi4BDgQxEB14HWuvhsrjEmHiywMMbUR2Kc5meq+kadRJGjga1hw8cDh6nqNhGZDaT50VW6qwngGqLflwR40poYN6b1skchxphQm4Es//k94Dxfj6IbMAYoCpv+DeBKEUkGEJH9fF8V4XKAMh9UDAZGx5CXWcCVfrmJvmOlWcA5ItLdp3cWkcLGbaIxpilZYGGMCVLVjcAH/nXSw3CPPRYAb+N6t10bNssjwGLgYz/P34hc4jATSBKRhcAfcPU3GjIBOEZEPsU9IhmqqouB3wBv+mW9BYTX+zDGtCDrhMwYY4wxcWMlFsYYY4yJGwssjDHGGBM3FlgYY4wxJm4ssDDGGGNM3FhgYYwxxpi4scDCGGOMMXFjgYUxxhhj4sYCC2OMMcbEjQUWxhhjjIkbCyxaMRG5RETeb+l8NBUR+a6ILGnpfNRHRH4tIo808zpVRAY28Tpmi8ilezjvlSJSKiJbQro939N89PXb26Y7QxSRISIyL2R4hYgc38J5itu+beh8EZEzRaTYnxMHi0iRiAzd2/W2NBEZKyJvNtO6nhCR25tjXc2hzQUW/mBvifCnIvK7ls6fcWL5glTV/6jqoObKU2Op6h2qukdfwO2R72jsT8CJqtrJ9ysSOr5dBAp74A/APS2diRZ0D3CNPyc+8cO3NceK4/WFHOncVdWpqnri3i473vbmh0FzrafNBRb+YHcK/QN+DpQCDzd2eR3wJtgqtPb93trz10LycV2df9bSGWktx0dEegDHAC/Febktvn3ixPIdUUjdc+IVXOdx1jlcR6WqbfoPOBjX1fPRIWk5wKPAGmA1cDuQ6MddAnwA/Bn41o/LAZ4C1gMrcb0nJkRZZypwL/CN/7sXSPXjjgZKgOuAdT4PPwmb9x5gFS4YeghIr2c9lwDvhwwfDnwIVPj/h4dNu8zvi+XAWJ8+EHjXz7MBeK6edfUFFPgJUAyUAVcAI3E9XJYDk8PmGQd87qd9Ayj06e/5ZW0FtgDnheyXG4C1wNO1aSHL6w286I/Dxtr1NWIbZuJ+OYWmLQDO8p/v89u2Cddb5ndDprsFeAF4xo+/1Kc9EzLNGbgbaDkwG9g/ZJwCA0OGnwBu95+7Aq/5+b4F/kM951focqKdK36/fy9kviS/b4b74dHAf/06F1D3+pgNXNqYcxvYzx9P9cf07QjzrgoZvwXXO2oC7npaibsengJyws65pD28bgfgel3d6Ld9KpAbkp8VwP/hzt8K4DkgLWT894H5/nh/DZzcUD4ibPPFwL/D0lYAx/vPg3HX4/l++Ht+neX++BwYNt8NPr+VuPNegR/7fbsBuDlk+gTgRp/3jcDzQOdI+7YR99PZwES/r7f7PMwG7gSK/H58Gejsz4st7LrWvw5ZzlvAj+tZRyznRMRtDlvOeKAK2Onz8apP7wn8A3cfWQ5cGzLPKGCeP+alwJ+inLuXUPf+q7h74lLcPe+v7OrIMxH4fz6/y4Frou1/3PfWx7j79XPAdHbdL/Jw94v1fj2vAQV+3ESgBtjh81l7j4x2b4u4zdHuE/WtJ+bzqDETt7Y/IBd3Ud0Qlv4SrvvmTKC7vyAuD7lBVQM/w92M0/2J/TKQ5U/sL4GfRlnvbbhun7sD3fyB+YMfd7Rf/m1AMnAqsA3I8+PvxUX0nf36XgXurGc9wRPbT18GXOTzfYEf7uK3cxMwyE/bA9fFNMA04GbcxZwGHFnPuvr6C+EhP92J/qR6yW9nL9xN4Cg//Q+Ar4D9fX5+A/w37CIM/aKt3S93425I6YQEFrgLcwHuiyMzNK+N2IaLgQ9ChofgLpjaoO9Cv7+ScIHfWvwXDS6IqPLbleDzdws+sGDXF+sJ/rj+ym9/Sj3b+wS7bhR3+v2a7P++i78hRdiG0MDiXuo5V4DfAVND5jsN+MJ/7oX7ojnVb8sJfribHz+b+gOLaOd2X6LfLHcbjws+vwL6A51wgePTkaan8dftQL9tqT6v7wH3hqx7hV9GT78PPweu8ONG4b4kT/D7qBcwuKF8RNjmPwJ/DUtbARwPDMd9YX3Ppw/HXUOH4s73H/tpU0Pmm48LsNND9s/DfvggXMCxv5/+5/5YFfh98DdgWj379gHctRDpb2FI3mf7PA/1+znZp60GDvD75B/UDbjrnPs+7S+EfIGFjYvlnIi4zRGW9QT+OvPDCbgv1t8BKX4dy4CT/Pj/ARf5z52A0VHO3UvYPbB4Dfe90wf3xV8bjF4BLPbHIg/4d/jyQpaTgguqfuH37zm4e0/t/aILcDaQgbvu/w68FHaMLg1bZrR7W33bvMf3iYb+Wjw42NM/QHDBwMuE3KRxxbWVhJQC4L6E3wk5WVaFjEv00w8JSbscmB1l3V8Dp4YMnwSs8J+PxkX6oSfoOlxkKLgvpwEh4w4DlteznuCJjQsoisLG/89Pk4m7QZxNWOkHLmiago94o2xTX38h9ApJ2wicFzL8D+Dn/vPrhARf/sTcxq5Si0iBxU7q/mI8ml2BxWG4CzXShRjrNmT5/Vubh4nAY1GmLwMO8p9vAd4LG38LuwKL3wLPh23vanZF+NECi9tw5+nAaPkPXU5D54qfZjOQ4YenAr/zn2/A36hD5n0D/wuS6IFFtHO79hxpTGAxC7gqZHgQ7iaaFDo9jbxu61n/D4BPQoZXABeGDE8CHvKf/wb8OcIyouYjwvQPA3eFpa0AbsWV0B0Tkv4gPkgLSVvCrmB9BTAuwv4sCEkrYlfpx+fAcSHjekTatw2dc2H5mQ3cFiHtrpDhIbhrubY0KVJgUe+1F+M5EXGbIyzrCeoGFoeGnyfATcDj/vN7/th0jeHcvYTdA4sjQ4afB270n98mJPjEBZb1BRZjcKWBod9b/w3djrDphwFlYccj6hc+de9t9W3zHt8nGvprc3UsQtyAi6B/rH4veIW4KHCNiJSLSDnuJtI9ZJrikM9d2RVB1lqJi+YQkYdCKoj+2o/vGWH6niHDG1W1OmR4Gy5S7IaLQj8KydtMn96Q8HUG86mqW3GPG67w2/0vERnsp/kV7kuqSEQ+E5FxDaynNOTz9gjDnfznQuC+kO341q+nV5Rlr1fVHfWM6w2sDNtvtWLaBlXdDPwLON8nnY/7wgVARK4Tkc9FpMLnOQd3/GuFnhfh6ux/VQ346aNtb60/4n6hvSkiy0TkxhjmiXquqOpXuC+W00UkA/eY5lk/byFwbu18ft4jcV88DWno3G6sSMurDSRCNfa6RUS6i8h0EVktIptwj7FCjye4X261aq9DcOfb1xHyG0s+QpXhAtpwV+BK8N4JW/Z1YcelN3X3b6RzsL5tKAT+GbKsz3HF1+H7trEi5SE0bSVuH4Xv61BZuB87kcRyTtS3zQ0pBHqG7eNfhyz7p7jSxy9E5EMR+V6My20oXz2pu48aupesDvveCu4PEckQkb+JyEp/Xr8H5IpIYn0LbODeVt827819Iqo2GViIyNG4ovFzVLU8bHQx7hdHV1XN9X/Zqhr6+lPoAd2Ai5YLQ9L64H6NoqpX6K6Konf48d9EmP6bGLK+AfflPDQkbznqKqA2JHyd4fl8Q1VPwJ0UX+ArsqrqWlW9TFV74kpiHmjobY0YFeMi9NyQv3RV/W+UeTTKuGKgT6RKa43chmnABSJyGK4o9R1wr7bigtEf4h5L5eKKwiXG/NXZ/yIiuC+F1T5pGy4QqLVPSP43q+p1qtofOB34pYgcF2VdENu5Mg33a/r7wGIfbIDbl0+HHZtMVb2rgXXutp3Efm5D5P0XaXnV1A1Ya/PcmOsW3CMmxdVTyMYVBwuxKcbV0YiU3lA+Qi3E3bTDXYE7n/8ctuyJYcclQ1WnhUwT7RyMlNdTwpaXpqqrwycM+4EU/hdeGTdSHnqHfO6Du2duiJK3/XGPNiOJ9ZyIRXhei3GleqH7JEtVTwVQ1aWqegEuULwbeEFEMiMsp7HW4B6D1Opd34R+2l7+HlKrT8jn63ClOIf683qMT6+dvk5eG7q3Rdnmhu4Te7xP2lxg4WsaT8cVyX8SPl5V1wBvAv9PRLJFJEFEBojIUZGWp6o1uCKtiSKSJSKFwC9xv37qMw34jYh0E5GuuOd50aavXVcA94X/ZxHp7renl4ic1NC8wAxgPxH5kYgkich5uCLJ10QkX0TO8CdLJa6yTY1f/rkiUnvCl+FOlpoY1teQh4CbxL+vLiI5InJuyPhS3PPNWBXhLri7RCRTRNJE5Ai/7MZswwzcTes2XCXPgE/Pwt281gNJ4l5Nzm5E/p4HThOR48S9dnkdbl/XBlLzgR+JSKKInAwEzzcR+Z6IDPQ3kk0+71GPQYznynRcXZgr2VVaAe5cPF1ETvL5SRORo0P2YTR7dG5764EAdY/7NOAXItJPRDoBd+COS52SqcZet14W7lwvF5FewPUx5hNc5cyf+OOZ4Pft4D3Ix1vAcBFJC0vfDJwMjBGR2hv1w8AVInKoOJkicpqIRCrxiMVDuPtWIYA/Zt+PNGHYD6Twv1janLhQXHsdGbhr6wV/79yNiKQCh+D2TSQxnRMxCr/PFAGbROQGEUn35/8BIjLS5+1CEenmr69yP08Nkc/dxngemODPo1zcF319/oe7F13r7+Vn4er81MrC/agoF5HOwO/D5g/f5qj3tijb3NB9orH38KA2F1gAl+GKte6LEHk/5Ke5GPd4YzHui+gFohfv/Az3PHsZrvZsV+BGiVBk7b8c8nBf6t/gih8/Bm4XkQnA40C+iPy8nnXdgCsWnyOumOvfuOg0KnVtBnwP94W2Efd44HuqugF3HK/z+fkW96V2lZ91JDBXRLbgKgJOUNXlDa0vhvz8Exf9TvfbsQg4JWSSW4AnxRWx/TCG5dXgfs0PxFUeK8E93mnUNqhqJa4y2PHU/bJ9A1cv5EtcseMOohdXhi93Ce4X8f24X2qnA6er6k4/yQSfVg6Mpe7rh/vijvMW3E3lAVWdHcNqo54r/kvwf7i3hZ4LSS/GlWL8GnezKcZ96cZyvd+OuwYWAp/iz+0Y5kNVt+HfKPDHfTTwGO4NoPdwteV34K63SBp73d6KqxBZgXsE9mIs+fR5LcK9AfVnP/+77PoVHXM+VLUU93x9ty90X5p6AnCKiPxBVefh7l+T/XK/wj3H31P34a6HN0VkM64i56F7sbxonsbVZ1iLq0B9bZRpz8DVUauvpKsx50RDHgWG+PPtpZD7yDC/7A3AI7hHA+CCvc/8veQ+XN2NHfWcu43xMC4gXQh8gvuBU02EHxD+nnEW7tiX4e5zoefuvbjS1g24YzozbBH3AeeISJmI/IWG7231bXND94nw9cSs9lUZA4h7hvUl7mZQgnul8wJVXRwyzam4i+BU3EV8n6oeKiIH4H5BjsJVbJoJXKmqS5t3K4wxzUlEhgBPAqPUbqiIyFxcxe5FLZ2XliIip+AqCoc/vu4Q2mKJRVMaBXylqst8VDmd3X+JfB94Sp05uEo1PXDPFOeo6jZfpPcucGZzZt4Y0/xUdbGqjrSgwlHVQztaUOEfu5zqH230wj2++GdL56ulWGBRVy/qFiGVsHut//qmWYR7ntrFP4c8legVeIwxxrQPgns0V4Z7FPI5rn5Sh9Tizca2MpFqlIf/Cok4jap+LiJ34yosbcHViN6TykjGGGPaEF9HY2RL56O1sBKLukqoW8pQwO6v2tU7jao+qqrDVXUMrhKl1a8wxhjToVhgUdeHwL7+NagUXANLr4RN8wpwsX9dbDRQ4WvnE/JaYB9crd9pGGOMMR1Im3srpGvXrtq3b98mW35FRQXFxcWoKl27dqVHjx6sX78egG7duqGqFBcXU1FRQUJCAn379iUzMxOAJUuWUF1djYhQUFBAdnZjmkkwxhhjWqePPvpog6rG0kp009WxENdY0wO4Vy9nq+pUn34Aru12cC3nLcP1A7EJ+EZVo74z37dvX+bNmxe3fJaWTmXZspuprFxFamof+vefSH7+2Lgt3xhjjGnrRCS8S4l6NeWjkLNwrbNdhmswpdYE4GpcA04/w72muURVrwJ6iEizvUlRWjqVJUvGU1m5ElAqK1eyZMl4SkunNjivMcYYY3bXlIFFAbteywxtfSxHVctVtQLXFOknQKqI/AnXOUssnTrFxbJlNxMIbKuTFghsY9mym5srC8YYY0y70pSBRQm7OmUJXU+FuH4lsoHNqhpQ1d+o6i9x7wAvC1+QiIwXkXkiMq+2vkM8VFaualS6McYYY6JrysDiReBsEXkQeFVEnvbp9wF/wbWXfz+AiDwgIg8D81R1XfiCVHWKqo5Q1RHdusVUdyQmqal96hmjLFhwAuXl78VtXcYYY0xH0ObeChkxYoTGq/JmbR2L0MchCQnpdOnyA8rLZ1FVtY6cnKPo2/d35OYeg0iktrGMMcaY9k1EPlLVEbFM26HbscjPH8ugQVNITS0EhNTUQgYNepihQ59l9OjlDBx4L9u3f8mCBcfxySff5dtv36CtBWLGGGNMc+rQJRaxqKnZwdq1j7Fq1V1UVhaTlTWKvn1/R+fOp1oJhjHGmA7BSiziKDExjV69ruLQQ79iv/2mUFW1jk8//R4ffTSC9etfQjXQ0lk0xhhjWo1GBRYikiciBzZVZlqzhIQUeva8jFGjvmTQoMeoqdnEZ5+dybx5w1i37u8WYBhjjDHEEFiIyGwRyRaRzrgeOx/3bU50SAkJyfTo8RNGjvycwYOfJhDYyeLFP+TDD79DaemzqNY0vBBjjDGmnYqlxCJHVTfhWtJ8XFUPAY5v2my1fgkJSeyzz4WMGvUZ++8/DRA+/3wsRUVDWLv2KQIB6zHdGGNMxxNLYJEkIj2AHwKvNXF+2hyRRPLzz2fkyIUMHfoCCQnpfPHFjykqGsSaNY8SCOxs6SwaY4wxzSaWwOI24A3gK1X9UET6A0ubNlttj0gC3bqdzYgRn3DAAS+TlJTHkiWXMnfufqxe/RCBQGVLZ9EYY4xpcva6aRNRVb79diYrV97Gpk1zSEnpRZ8+N9Kjx6UkJqa1dPaMMcaYmMX1dVMRmeQrbyaLyCwR2SAiF+59Nts3EaFLl1M4+OD/cuCBb5Ge3o+vvvoZc+f2o7j4z9TUbGt4IcYYY0wbE8ujkBN95c3v4ToW2w+4vklz1Y6ICJ07H8+wYe9x0EHvkJGxP19//UvmzOnHqlWTqK7e0tJZNMYYY+ImlsAi2f8/FZimqt82YX7aLREhL+9ohg17m2HD/kOnTsNYtuwG5szpy8qVd1Bdvamls2iMMcbstVgCi1dF5AtgBDBLRLoBO5o2W+1bbu6RHHTQGwwfPofs7NEsX34zc+YUsmLFrVRVlbV09owxxpg9FlPlTRHJAzapao2IZADZqrq2yXMXQVupvNkYmzd/xMqVt7Nhw0skJmbTq9fP6N37FyQnd2nprBljjDFxr7yZDFwEPCciLwA/BTbGMF+miDwpIg+LyNiQ9ANEZKr/O0BEEkXkWT/dkyLS4fovyco6hAMO+CcjRsync+cTWbXqDubM6cvXX9/Azp3rWjp7xhhjTMxi+RJ/EDgEeMD/DfdpDTkLeEFVLwPOCEmfAFwNXAX8DEgHKv10W4DMmHPfznTqdBBDh/6dkSM/pUuX0yku/iNz5vTlq6+uo7JyTUtnzxhjjGlQLIHFSFX9saq+7f9+AoyMYb4CoNh/Du1AI0dVy1W1AsgCtgEqIv8CUlV1c2M2oD3KzBzKkCHPMmrU53Trdi4lJfcxZ04/li69lh07Slo6e8YYY0y9YgksakRkQO2Ab3kzlp62SnDBRfh6KkQkR0Sygc24EpAVqnoasEJEhoUvSETGi8g8EZm3fv36GFbdPmRkDGL//Z/k0EOXkJ9/Id988yBz5w7gyy+vZMeOlS2dPWOMMWY3DVbeFJHjgMeBZYAAhcBPVPWdBubLBCbj3iB5HzhZVS8SkQNw7WAIMMkv91GgDOjql721vuW2x8qbsdq+fQWrVt3F2rWPAco++1xCnz43kZ7ev6WzZowxph1rTOXNWN8KSQUG4YKBL1S1xTq+6MiBRa0dO4opLp7EN988jGo1+fkXUlj4azIy9mvprBljjGmH4hJYiMhZ0WZU1Rf3IG97zQKLXSorv6G4+B6++cZ1cta9+/kUFt5MZuaQls6aMcaYdiRegcXjUeZTVR23J5nbWxZY7G7nzlKKi/8fq1c/QCCwjW7dzqGw8Dd06nRgS2fNGGNMOxCXdixU9SdR/lokqDCRpaTkM2DAJEaPXkGfPjfx7bczmTfvIBYtOovNmz/Z6+XPnDmTQYMGMXDgQO66667dxqsq1157LQMHDuTAAw/k448/BmDJkiUMGzYs+Jednc29994LwPXXX8/gwYM58MADOfPMMykvL9/rfBpjjGl5Ha4xqvYsJaUr/ftPZPToFRQW/p6ysrf56KPhfPrp6WzaVLRHy6ypqeHqq6/m9ddfZ/HixUybNo3FixfXmeb1119n6dKlLF26lClTpnDllVcCMGjQIObPn8/8+fP56KOPyMjI4MwzzwTghBNOYNGiRSxcuJD99tuPO++8c+823hhjTKtggUU7lJzcmX79buGww1bSr9/tVFT8l48/PpQFC06mouK/jVpWUVERAwcOpH///qSkpHD++efz8ssv15nm5Zdf5uKLL0ZEGD16NOXl5axZU7dBr1mzZjFgwAAKCwsBOPHEE0lKSgJg9OjRlJRY+xzGGNMeWGDRjiUl5VBYeDOjR6+gf/+72LLlYz755Ajmzz+e8vJ3Y1rG6tWr6d27d3C4oKCA1atXN3qa6dOnc8EFF0Rcx2OPPcYpp5wS62YZY4xpxWLpKyRDRH4rIg/74X1F5HtNnzUTL0lJWfTpcwOjRy9nwID/x9ati5g//2g++eQoyspmEe2V40jjRKRR0+zcuZNXXnmFc889d7fpJk6cSFJSEmPHjt1tnDHGmLYnlhKLx4FK4DA/XALc3mQ5Mk0mMTGT3r1/yejRyxk48C9s3/4VCxYczyefHMHGjTMjBggFBQUUFxcHh0tKSujZs2ejpnn99dcZPnw4+fn5deZ78sknee2115g6depuwYoxxpi2KZbAYoCqTgKqAFR1O66hLNNGJSamU1DwMw499Gv23fcBKitX8+mnp/Dxx6PYsOHVOgHGyJEjWbp0KcuXL2fnzp1Mnz6dM844o87yzjjjDJ566ilUlTlz5pCTk0OPHj2C46dNm7bbY5CZM2dy991388orr5CRkdG0G2yMMabZxBJY7BSRdEABfL8hLdbypomfxMQ0evW6kkMPXcp++z1MVdVGFi06g48+OoT1619ENcDGjc9x9dVbGTOmP/36ZXLqqYMYOnQoDz30EA899BAAp556Kv3792fgwIFcdtllPPDAA8F1bNu2jbfeeouzzqrb3to111zD5s2bOeGEExg2bBhXXHFFs267McaYphFLXyEnAL8BhgBvAkcAl6jq7CbPXQTWQFbTCQSqWLfuWVaunMj27UtJSSmgqmodqjuD0yQkZDBo0BTy861OhDHGdBRN0VdIF2A07hHIHFXdsHdZ3HMWWDS9QKCa9euf54svfoxq9W7jU1MLOeywFc2fMWOMMS0iLi1vhknD9T66CRgiImP2NHOm9UtISCI//0eo1kQcX1m5kvXrX6Kqqrx5M2aMMabVS2poAhG5GzgP+AwI+GQF3mvCfJlWIDW1D5WVKyOMET777EwggaysQ8jLO47c3OPIyTmCxMT05s6mMcaYVqTBwAL4ATCosV2li0gm8ACwE5itqlN9+gHATX6yO4H1wK1++FTgAFXd1Jh1mabRv/9EliwZTyCwLZiWkJDBvvs+QHp6f8rKZlFePovi4ntYteouRFLJyTk8GGhkZY0gISGWU8wYY0x7EctdfxmQTOPfBDkLeEFVXxWR54CpPn0CcDWu1GOSql4OXCEi3YB0Cypaj9oKmsuW3Uxl5SpSU/vQv//EYHpu7neBW6iu3kJFxX+Cgcby5b8BfkNiYja5uUcFA43MzKHWXoUxxrRzsQQW24D5IjKLkOBCVa9tYL4C4FP/OfRhfY6qlgOISFZI+k+AJyMtSETGA+MB+vTpE0OWTbzk549t8A2QpKROdOlyCl26uGa5d+7cQHn5O8FAY+PGVwFITs4nL+/YYKCRnt63qbNvjDGmmcUSWLzi/xqrBBdczKduJdEKEcnBlVhsBhD3M/YY4I+RFqSqU4Ap4N4K2YO8mGaUktKV7t3PpXt314T3jh0rKSt7OxhorFs3DYC0tP7k5R3nA41jSUnp1pLZNsYYEwexvm6aAuznB5eoalUM82QCk4EdwPvAyap6ka9jcT3u1dVJqrpIRI4BDlPVOxparr1u2rapKtu2fU5Z2SwfaMympqYCgMzMA4OBRk7OGJKSshpYmjHGmOYQ13YsRORo3COKFbhgoDfwY1VtkbdCLLBoXwKBarZs+TgYaFRUvI9qJSJJZGWNCgYa2dmjSUhIbensGmNMhxTvwOIj4EequsQP7wdMU9VD9jqne8ACi/atpmYHmzb91wca/2bz5nlAgISEdHJyvhsMNDp1GoZIYktn1xhjOoTGBBax1LFIrg0qAFT1SxFJ3uPcGRNFYmKar+B5LDCRqqpyKireDZZoLFt2AwBJSXnk5h4TDDTS0/ezN06MMaYViCWwmCcijwJP++GxwEdNlyVjdklOzqVr1+/Ttev3AaisXEN5+dvBQGPDhhcBSEnpFQwy8vKOIzW1V0tm2xhjOqxYHoWk4tqdOBJXx+I94IHGNpgVL/YoxNRSVbZv/5ry8lk+0Hib6uqNAKSnDwp54+QYkpPzWji3xhjTdsW9E7KQBfdQ1TV7nLM4sMDC1Ec1wJYtC4OBRnn5ewQCWwGhU6fhIW+cHEliYkZLZ9cYY9qMpgwsPlbV4XucsziwwMLEKhDYyaZNRcFAY9OmOahWIZJCdvZhwUAjK2skCQlWbcgYY+rTlIHFJ6p68B7nLA4ssDB7qqZmK+Xl/wkGGlu2zAeUxMQscnLGBAONzMwDEIm1419jjGn/4v1WSKiH9yA/xrQKiYmZdOlyMl26nAxAVdVGysreCQYa3377LwCSk7uRm3tsyBsn/Vsy28YY06bEFFiISD4wElglIt1VdV3TZsuYppec3IXu3c+he/dzANixozjY7HhZ2SzWr38OgLS0vuTm1r5xciwpKfktmW1jjGnVYnkr5Ie4Pjxm494K+S5wvaq+0OS5i8AehZjm4Joe/yIYaJSXz6a6uhyAzMwDgoFGbu5RJCVlt2xmjTGmicW75c0FwAm1pRS+e/N/q+pBe53TPWCBhWkJqjVs3vxxMNCoqHifQGAHkEh29shgoJGTc7g1PW6MaXfiHVh8qqrfCRlOABaEpjUnCyxMa+CaHv9fMNDYtOlDoIaEhDRyco4MBhpZWcOt6XFjTJsX78qbM0XkDWCaHz4PmLGnmTOmPXBNjx9DXt4xwO1UV2+ivPzdYKCxfPlNLF8OSUm55OYeHQw0MjIGW9Pjxph2LWqJhbg7YAGu4maw5U1V/WfzZG93VmJh2oKdO0spK3s7GGjs2LECgJSUnuTlHRsMNNLSerdsRr2ZM2cyYcIEampquPTSS7nxxhvrjFdVJkyYwIwZM8jIyOCJJ55g+HDXpE3fvn3JysoiMTGRpKQkQq/P+++/n8mTJ5OUlMRpp53GpEmTmnW7jDHxEbcSC1VVEXnJ92T6YiMzkQk8AOwEZqvqVJ9+AHCTn+xOVV0kIuOAg4EKVf1NY9ZjTGuUkpJPfv4F5OdfAMD27cuC/Zt8++0blJY+A0B6+r6+Euhx5OUdQ3Jyl2bPa01NDVdffTVvvfUWBQUFjBw5kjPOOIMhQ4YEp3n99ddZunQpS5cuZe7cuVx55ZXMnTs3OP6dd96ha9eudZb7zjvv8PLLL7Nw4UJSU1NZt85eJjOmI4jlUcgcERmpqh82ctlnAS+o6qsi8hww1adPwPU9osAkEfkt7vHKJ0CLNhduTFNJT+9Penp/eva8DNUAW7cuCgYapaXP8M03D+GaHh8WDDRyc79LYmJmk+etqKiIgQMH0r+/a6/j/PPP5+WXX64TWLz88stcfPHFiAijR4+mvLycNWvW0KNHj3qX++CDD3LjjTeSmuoqs3bv3r1pN8QY0yrE0rzgMbjg4msRWSgin4rIwhjmKwCK/eeakPQcVS1X1QogC+gPfKuqNwKFIjIgfEEiMl5E5onIvPXr18ewamNaL5EEOnU6kN69f8GBB77GEUd8y8EHf0DfvreSmJhNSclf+PTTU3j//Tw++WQMK1bcSnn5+wQCVU2Sn9WrV9O7965HMgUFBaxevTrmaUSEE088kUMOOYQpU6YEp/nyyy/5z3/+w6GHHspRRx3Fhx829reJMaYtiqXE4pQ9XHYJLriYT90ApkJEcnAlFpuB1cC3flw50Cl8Qao6BZgCro7FHubHmFYpISGZnJzDyck5nL59f0tNzTYqKt4PlmisWHErcAsJCZnk5o4Jlmh06nRgXJoej1TPKryCabRpPvjgA3r27Mm6des44YQTGDx4MGPGjKG6upqysjLmzJnDhx9+yA9/+EOWLVtmlVeNaecaDCxUdaWIHAnsq6qP+3Ysdvvyj+BFYLKInAa8KiJPq+pFwH3AX3AVQSeparGIfCsifwJSVHXBnm+OMW1fYmIGnTufSOfOJwJQVfUt5eWzQ+povA5AcnJXcnOPCQYa6ekD9uhLu6CggOLi4uBwSUkJPXv2jHma2v/du3fnzDPPpKioiDFjxlBQUMBZZ52FiDBq1CgSEhLYsGED3bp1a3QejTFtR4OBhYj8HhgBDAIeB5KBZ4Ajos2nqluBn4QkTfXpi4Afh03720bl2pgOJDm5M926nUW3bmcBsGNHCeXlbwcDjfXr/w5AamqfYP8mubnHkZq6T0zLHzlyJEuXLmX58uX06tWL6dOn8+yzz9aZ5owzzmDy5Mmcf/75zJ07l5ycHHr06MHWrVsJBAJkZWWxdetW3nzzTX73u98B8IMf/IC3336bo48+mi+//JKdO3fuVsHTGNP+xPIo5EzcGxsfA6jqNyKS1aS5MsbUKy2tgH32uZh99rkYVWX79i+DQcaGDS+xdu3jAGRkDA0JNI4iKSkn4vKSkpKYPHkyJ510EjU1NYwbN46hQ4fy0EMPAXDFFVdw6qmnMmPGDAYOHEhGRgaPP+7WUVpayplnnglAdXU1P/rRjzj5ZNfJ27hx4xg3bhwHHHAAKSkpPPnkk/YYxJgOIJaWN4tUdZSIfKyqw/1rpP9T1QObJ4t1WTsWxtRPtYYtW+YHA42Kiv8QCGwHEsjKGhkMNLKzDycxMQ2A0tKpLFt2M5WVq0hN7UP//hPJzx/bshti4q4p2iq5/vrrefXVV0lJSWHAgAE8/vjj5ObmNvemmWYQ7ya9/w/YFzgBuBMYB0xT1b/sbUb3hAUWxsQuEKhk06Y5wUBj06a51DY9np19BElJndm48VVUdwTnSUjIYNCgKRZctCM1NTXst99+ddoqmTZtWp1XimfMmMH999/PjBkzmDt3LhMmTAi2VdK3b1/mzZu326OsN998k2OPPZakpCRuuOEGAO6+++7m2zDTbOLapLeq3iMiJwCbcPUsfqeqb+1lHo0xzSAhIZXc3KPIzT2Kfv1uo7p6MxUV7wUDjfLyWbvNEwhs48svr2T79uUkJ+eRlJRLUlJeyF8uycl51tlaG9JUbZWceOKJwc+jR4/mhRdapNNr08rUG1iIyEmq+gaADyTeChl3rqr+vRnyZ4yJo6SkLLp0OY0uXU4DYPbsBNyb33XV1GxmxYrodaoTEtKDgUZSUp4PQuoGIvUFJomJmVbfohlFaocktOXU+qZZvXo1PXr0CLZVIiJcfvnljB8/frd1PPbYY5x33nlNtxGmzYhWYjFDRN4DLlTV1WHjbgIssDCmjUtN7UNl5coI6YUceuiXVFeXU11dRlVVGdXVZcHhXWm7hisrS9i6dRFVVWXU1FREXa9IckjA0djAJDsu7Xd0JE3VVkmtiRMnkpSUxNix9vistWiq/n9iES2wWAg8i2t185dhJRT2U8OYdqB//4ksWTKeQGBbMC0hIYP+/SeSkJBCSkp3UlIa3xS3ag3V1RWNCEy+ZceOr6muLqeqqoy6jfWGE5KScuqUgMQemOSSkJDc+B3VxjVVWyUATz75JK+99hqzZs2yUqhWoqn6/4lVtMBCVfVhEXkXmCoipwJXq+o2IpWdGmPanNoKmvF+K0QkkeTkziQnd270vKpKTc2WOoFIQ4HJtm2Lg2mqlVGXn5CQGRKENC4wSUxM39Nd0qKaqq2SmTNncvfdd/Puu++SkZHREptmImiqOjWxiqXy5pcichhwO/CJiFy812s1xrQa+fljW9UbICJCUlIWSUlZQJ9Gz19TsyMYfNQGI7sCk92DlR07VlBd/QnV1WXU1GxpIG+pwcqrjQ1MEhOzWuwXfVO1VXLNNddQWVnJCSecALgKnLXLNC2nOerURBMtsAheAapaDdwoIjOBaYC1yWuMaZUSE9NITOxBamrjf3kFAlX+EU5sgcnOnWvZtu2LYHr0wtxEH2TsSWCSi0jinu4SSkunkpd3M1Om1JZK9QVcQFFLRPjrX/+627z9+/dnwYLIPS189dVXe5wn03Saok5NY0QLLG6NkNnZInIIcHmj1mKMMW1AQkIyKSldSUlp/LNl1QDV1ZvqPK5pKDDZsWNlcFg1eu+1iYnZdV73jTUw2bhxJkuXXhWsR1NZuZIlS9wv0NZUUmXipynq1DRGvYGFqr5UT3oZcFej1mKMMe2cSALJybkkJ+cCfRs1r6oSCGzb7U2baIHJtm1fBodDK9/GIhDYxhdf/JTS0mdISEgjISE97P+uz4mJu6fVN0/ttCIpVpGzBTVVnZpYxdJXiDHGmCYkIiQmZpKYmAkUNHr+QKAyJACpG5gsXXpNxHlUK6mq2kggsINAYDuBwA5qarYHhyGwN1sUNRjZk2CloQCodtq9eWTUXjRVnZpYNdikd2tjTXobY0zs/ve/vvW2VXLYYSvqnS8QqPJBxq7AY9f/ukHI7tNFClbqm7buMht6q6chIklxD1aijd/1ObVFS2maus+fuDbpvReZyAQeAHYCs1V1qk8/ANfAFsCdqrpIRL7Gtez5sapOaao8GWNMRxOtrZJoEhKSfZsfzduZtWqAQGBnxMAj9sAm0nQuvbq6jJ07v4m4TPeewp6Ld7ASawC0fv1zdY5xS9ejacpHIWcBL6jqqyLyHDDVp08ArsZVn56Eqwi6BUgHiiMtyBhjzJ5pqrZKmopIgn+zJ63Z1x0IVDdJKUxoUFPfPPHflm0sW3ZzuwssCoBP/efQZvRyVLUcQERqQ+GDca+3/gt4PXxBIjIeGA/Qp0/j32s3xpiOrLW1VdJaJSQkkZDQCejUrOt1lXcr9yBYcekrVkSuXFlZuapZt6NWUwYWJbjgYj4Q2rB/hYjk4EosNgOoagBARHaISELtcC3/eGQKuDoWTZhnY4wxplm5yrt7XkqzZs2j9dSjaZkf4k3Zk8+LwNki8iDwqog87dPvA/4CTAbuF5FBIvKYiDyGq4uxN1WRjTHGmA7F9e1Tt0n1WOrRNBV7K8QYY4xp41rTWyFtLrAQkfXA7mU+e68rsKEJltte2P5p/+wYt392jNu/pjrGhaoaU3cebS6waCoiMi/WaKwjsv3T/tkxbv/sGLd/reEYN2UdC2OMMcZ0MBZYGGOMMSZuLLDYxVr8jM72T/tnx7j9s2Pc/rX4MbY6FsYYY4yJGyuxMMYYY0zcdJhu06N0inYqcA0wQ1Un+7S7gAxgm6re2EJZbnZR9tGNwABgH+BKVS2xjuPapijH+BZgf6AMuE1Vv+mo10FbF+UY/xQYCfQGFqrqTXYdt00i0h+4GddFxjkh6ZE6+Wz267gjlVjUdop2GXBGbaKqzsB1hgaAiPQBklX1WiBRRHo3e05bTn376C6f9hhwjE+2juPapojHGKjGfRFVAeUd/Dpo6+q7jh9V1SuAL4EnfLJdx22Qqi5T1Z9GGFXbyedVwM9a6jruMCUW1N8pWrhe7LrIVvn5OspFV+8+EpFOwA/xncHRQMdxptWq7xjfoaoBETkDuBT4kI57HbR10a7jNKCfqi7xSXYdty/hnXy2yPdZRyqxqO0UDaJv9+qQ6Xr7+TqKiPtIRLKBB4FfqWqw4zhVrQF2iEhHOo/auojHOKSPnnW4rh078nXQ1kW7152D68cJsOu4HaoQkRx/z95MC13HHeatEP/ccTKwA3gfOFlVLxKRw3DPpPKAe1X1HyJyJ5AKVKrqTfUutJ2Jso9eBJJxJ+nz/v8NfraFqnpvC2TX7IEox/jXuBtPV+BaVV3TUa+Dtq6+Y+zHvQycr6rbRWQQdh23SSLSBZgInAA8Agzx1/EBwPW4UqhJvo5Fs1/HHSawMMYYY0zTs6IvY4wxxsSNBRbGGGOMiRsLLIwxxhgTNxZYGGOMMSZuLLAwxgSJSK6IXNXANH1FZFFz5ckY07ZYYGGMCZWLa7UvbkQkMZ7LM8a0bhZYGGNC3QUMEJH5IvJH/7dIRD4VkfPCJxaRRD/NhyKyUEQu9+lHi8g7IvIsvhVIEXlJRD4Skc9EZHzIMraIyEQRWSAic0Qk36fni8g/ffoCETncp18oIkU+j3+zwMWY1sUCC2NMqBuBr1V1GDAHGAYcBBwP/FFEeoRN/1OgQlVH4jq4ukxE+vlxo4CbVXWIHx6nqocAI4BrfSM/AJnAHFU9CHgPuMyn/wV416cPBz4Tkf2B84AjfB5rgLHx2nhjzN7rSH2FGGMa50hgmm/yuVRE3sUFDwtDpjkROFBEantYzAH2xXVoVqSqy0OmvVZEzvSfe/vpNvppX/PpH+FaEwQ4FrgYwOehQkQuAg4BPhQRcB1orYvP5hpj4sECC2NMfSTGaX6mqm/USRQ5GtgaNnw8cJiqbhOR2UCaH12lu5oAriH6fUmAJ62JcWNaL3sUYowJtRnI8p/fA87z9Si6AWOAorDp3wCuFJFkABHZz/dVES4HKPNBxWBgdAx5mQVc6Zeb6DtWmgWcIyLdfXpnESls3CYaY5qSBRbGmCBV3Qh84F8nPQz32GMB8Daud9u1YbM8AiwGPvbz/I3IJQ4zgSQRWQj8AVd/oyETgGNE5FPcI5KhqroY+A3wpl/WW0B4vQ9jTAuyTsiMMcYYEzdWYmGMMcaYuLHAwhhjjDFxY4GFMcYYY+LGAgtjjDHGxI0FFsYYY4yJGwssjDHGGBM3FlgYY4wxJm4ssDDGGGNM3Px/tC9z/qpuVx4AAAAASUVORK5CYII=\n",
  346. "text/plain": [
  347. "<Figure size 576x720 with 5 Axes>"
  348. ]
  349. },
  350. "metadata": {
  351. "needs_background": "light"
  352. },
  353. "output_type": "display_data"
  354. }
  355. ],
  356. "source": [
  357. "##### x coordinates\n",
  358. "x = tols\n",
  359. "training_times = list_training_times_tol\n",
  360. "precision_scores_train = list_precision_scores_train_tol\n",
  361. "zero_one_loss_train = list_zero_one_loss_train_tol\n",
  362. "precision_scores_test = list_precision_scores_test_tol\n",
  363. "zero_one_loss_test = list_zero_one_loss_test_tol\n",
  364. " \n",
  365. "training_times = [round(i,2) for i in training_times]\n",
  366. "precision_scores_train = [round(i,3) for i in precision_scores_train]\n",
  367. "zero_one_loss_train = [round(i,3) for i in zero_one_loss_train]\n",
  368. "precision_scores_test = [round(i,3) for i in precision_scores_test]\n",
  369. "zero_one_loss_test = [round(i,3) for i in zero_one_loss_test]\n",
  370. "\n",
  371. "### Create plot\n",
  372. "fig, figs = plt.subplots(nrows=5, ncols=1, figsize=(8,10))\n",
  373. "fig.tight_layout(pad=3.0)\n",
  374. "figs[0].plot(x,training_times, marker='o', color='r')\n",
  375. "figs[1].plot(x,precision_scores_train, marker='o', color='b')\n",
  376. "figs[2].plot(x,zero_one_loss_train, marker='o', color='g')\n",
  377. "figs[3].plot(x,precision_scores_test, marker='o', color='m')\n",
  378. "figs[4].plot(x,zero_one_loss_test, marker='o', color='y')\n",
  379. "\n",
  380. "### Add every x coordinates\n",
  381. "figs[0].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
  382. "figs[1].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
  383. "figs[2].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
  384. "figs[3].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
  385. "figs[4].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
  386. "\n",
  387. "for i in range(len(x)):\n",
  388. " figs[0].annotate(training_times[i], # this is the text\n",
  389. " (x[i],training_times[i]), # these are the coordinates to position the label\n",
  390. " textcoords=\"offset points\", # how to position the text\n",
  391. " xytext=(12,3), # distance from text to points (x,y)\n",
  392. " ha='center') # horizontal alignment can be left, right or center\n",
  393. " figs[1].annotate(precision_scores_train[i], # this is the text\n",
  394. " (x[i],precision_scores_train[i]), # these are the coordinates to position the label\n",
  395. " textcoords=\"offset points\", # how to position the text\n",
  396. " xytext=(12,3), # distance from text to points (x,y)\n",
  397. " ha='center') # horizontal alignment can be left, right or center\n",
  398. " figs[2].annotate(zero_one_loss_train[i], # this is the text\n",
  399. " (x[i],zero_one_loss_train[i]), # these are the coordinates to position the label\n",
  400. " textcoords=\"offset points\", # how to position the text\n",
  401. " xytext=(12,3), # distance from text to points (x,y)\n",
  402. " ha='center') # horizontal alignment can be left, right or center\n",
  403. " figs[3].annotate(precision_scores_test[i], # this is the text\n",
  404. " (x[i],precision_scores_test[i]), # these are the coordinates to position the label\n",
  405. " textcoords=\"offset points\", # how to position the text\n",
  406. " xytext=(12,3), # distance from text to points (x,y)\n",
  407. " ha='center') # horizontal alignment can be left, right or center\n",
  408. " figs[4].annotate(zero_one_loss_test[i], # this is the text\n",
  409. " (x[i],zero_one_loss_test[i]), # these are the coordinates to position the label\n",
  410. " textcoords=\"offset points\", # how to position the text\n",
  411. " xytext=(12,3), # distance from text to points (x,y)\n",
  412. " ha='center') # horizontal alignment can be left, right or center\n",
  413. "\n",
  414. "figs[0].set_xticks(x)\n",
  415. "figs[1].set_xticks(x)\n",
  416. "figs[2].set_xticks(x)\n",
  417. "figs[3].set_xticks(x)\n",
  418. "figs[4].set_xticks(x)\n",
  419. " \n",
  420. "### Add title and axis names\n",
  421. "figs[0].title.set_text('Training times for various level of tolerance (kernel=rbf)')\n",
  422. "figs[1].title.set_text('Precision score for various level of tolerance (kernel=rbf) on training dataset')\n",
  423. "figs[2].title.set_text('Zero-one loss metrics various level of tolerance (kernel=rbf) on training dataset')\n",
  424. "figs[3].title.set_text('Precision score for various level of tolerance (kernel=rbf) on testing dataset')\n",
  425. "figs[4].title.set_text('Zero-one loss metrics various level of tolerance (kernel=rbf) on testing dataset')\n",
  426. "figs[0].set_xlabel('tolerance')\n",
  427. "figs[1].set_xlabel('tolerance')\n",
  428. "figs[2].set_xlabel('tolerance')\n",
  429. "figs[3].set_xlabel('tolerance')\n",
  430. "figs[4].set_xlabel('tolerance')\n",
  431. "figs[0].set_ylabel('Training times (in seconds)')\n",
  432. "figs[1].set_ylabel('Precision score')\n",
  433. "figs[2].set_ylabel('Zero-one loss')\n",
  434. "figs[3].set_ylabel('Precision score')\n",
  435. "figs[4].set_ylabel('Zero-one loss')"
  436. ]
  437. },
  438. {
  439. "cell_type": "code",
  440. "execution_count": 5,
  441. "id": "62c7302a",
  442. "metadata": {},
  443. "outputs": [
  444. {
  445. "name": "stdout",
  446. "output_type": "stream",
  447. "text": [
  448. "Métriques pour SVM\n",
  449. "Paramètres : (C=1.0,kernel=\"rbf\")\n",
  450. "Taille de l'échantillon : 10000\n",
  451. "Proportion des datasets : 90%\n",
  452. "Temps d'entraînement (secondes) : 15.8407\n",
  453. "Temps de prédiction (secondes) : 3.68326\n",
  454. "Précision pour chaque classe : [0.98, 0.99, 0.958, 0.958, 0.955, 0.967, 0.949, 0.958, 0.94, 0.947]\n",
  455. "Précision : 0.96\n",
  456. "Erreur : 0.04\n",
  457. "Matrice de confusion :\n",
  458. " [[ 99 0 0 0 0 0 0 0 0 0]\n",
  459. " [ 0 98 1 0 0 0 0 1 0 0]\n",
  460. " [ 1 0 92 0 0 0 2 2 0 0]\n",
  461. " [ 0 0 2 91 1 0 0 0 3 0]\n",
  462. " [ 0 0 0 0 84 0 3 0 1 0]\n",
  463. " [ 1 0 1 2 0 87 0 0 2 1]\n",
  464. " [ 0 1 0 0 2 1 111 0 0 0]\n",
  465. " [ 0 0 0 0 0 0 0 114 0 1]\n",
  466. " [ 0 0 0 1 0 2 1 0 94 3]\n",
  467. " [ 0 0 0 1 1 0 0 2 0 90]]\n"
  468. ]
  469. }
  470. ],
  471. "source": [
  472. "####### Meilleur modèle de SVM #######\n",
  473. "\n",
  474. "### Create vector of 5000 random indexes\n",
  475. "rand_indexes = np.random.randint(70000, size=10000)\n",
  476. "### Load data with the previous vector\n",
  477. "data = mnist.data[rand_indexes]\n",
  478. "# print(\"Dataset : \", data)\n",
  479. "target = mnist.target[rand_indexes]\n",
  480. "\n",
  481. "# Split the dataset\n",
  482. "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
  483. "\n",
  484. "clf = SVC(C=1.0,kernel=\"rbf\")\n",
  485. "#Entraîne le classifier\n",
  486. "t1 = time.time()\n",
  487. "clf.fit(xtrain, ytrain)\n",
  488. "t2 = time.time()\n",
  489. "\n",
  490. "#Prédiction sur le jeu de tests\n",
  491. "pred = clf.predict(xtest)\n",
  492. "t3 = time.time()\n",
  493. "\n",
  494. "#Calcul de différentes metrics\n",
  495. "precisions = [round(i,3) for i in metrics.precision_score(ytest, pred,average=None)]\n",
  496. "\n",
  497. "print(\"Métriques pour SVM\")\n",
  498. "print(\"Paramètres : (C=1.0,kernel=\\\"rbf\\\")\")\n",
  499. "print(\"Taille de l'échantillon :\", 10000)\n",
  500. "print(\"Proportion des datasets :\", \"90%\")\n",
  501. "print(\"Temps d'entraînement (secondes) :\", round(t2-t1,5))\n",
  502. "print(\"Temps de prédiction (secondes) :\", round(t3-t2,5))\n",
  503. "print(\"Précision pour chaque classe :\", precisions)\n",
  504. "print(\"Précision :\", clf.score(xtest, ytest))\n",
  505. "print(\"Erreur :\", round(metrics.zero_one_loss(ytest, pred),5))\n",
  506. "print(\"Matrice de confusion :\\n\", metrics.confusion_matrix(ytest, pred))"
  507. ]
  508. },
  509. {
  510. "cell_type": "code",
  511. "execution_count": null,
  512. "id": "30a232d5",
  513. "metadata": {},
  514. "outputs": [],
  515. "source": []
  516. }
  517. ],
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  520. "display_name": "Python 3",
  521. "language": "python",
  522. "name": "python3"
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