diff --git a/.ipynb_checkpoints/TP1_prog2.py-checkpoint.ipynb b/.ipynb_checkpoints/TP1_prog2.py-checkpoint.ipynb
index 8750677..5398024 100644
--- a/.ipynb_checkpoints/TP1_prog2.py-checkpoint.ipynb
+++ b/.ipynb_checkpoints/TP1_prog2.py-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "530f620c",
    "metadata": {},
    "outputs": [],
@@ -16,12 +16,13 @@
     "from matplotlib import pyplot as plt\n",
     "from sklearn.model_selection import KFold\n",
     "import time\n",
-    "import statistics"
+    "import statistics\n",
+    "from sklearn import metrics"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "68b6a517",
    "metadata": {},
    "outputs": [],
@@ -711,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 5,
    "id": "24b641ef",
    "metadata": {},
    "outputs": [
@@ -774,7 +775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 7,
    "id": "343e5d14",
    "metadata": {},
    "outputs": [
@@ -782,9 +783,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "names_distances :  ['manhattan', 'euclidean', 'minkowski']\n",
-      "training_times_ms :  [49.86, 44.88]\n",
-      "prediction_times_ms :  [1762.26, 1307.53]\n"
+      "training_times_ms :  [39.89, 48.87]\n",
+      "prediction_times_ms :  [1778.06, 1312.49]\n"
      ]
     },
     {
@@ -793,13 +793,13 @@
        "Text(36.0, 0.5, 'Times (in ms)')"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n"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\n",
       "text/plain": [
        "<Figure size 504x720 with 3 Axes>"
       ]
@@ -813,7 +813,6 @@
    "source": [
     "##### x coordinates\n",
     "x = nb_jobs\n",
-    "print(\"names_distances : \", names_distances)\n",
     "training_times_ms = [round(i*1000,2) for i in training_times]\n",
     "print(\"training_times_ms : \", training_times_ms)\n",
     "prediction_times_ms = [round(i*1000,2) for i in prediction_times]\n",
@@ -852,9 +851,9 @@
     "figs[2].set_xticks(x)\n",
     "    \n",
     "### Add title and axis names\n",
-    "figs[0].title.set_text('Scores for various n_jobs (k=3,train_size=0.9,dataset_size=25000,p=2)')\n",
-    "figs[1].title.set_text('Training times for various n_jobs (k=3,train_size=0.9,dataset_size=25000,p=2)')\n",
-    "figs[2].title.set_text('Prediction times for various n_jobs (k=3,train_size=0.9,dataset_size=25000,p=2)')\n",
+    "figs[0].title.set_text('Scores for various n_jobs (k=3,train_size=0.9,dataset_size=25000,distance=euclidean)')\n",
+    "figs[1].title.set_text('Training times for various n_jobs (k=3,train_size=0.9,dataset_size=25000,distance=euclidean)')\n",
+    "figs[2].title.set_text('Prediction times for various n_jobs (k=3,train_size=0.9,dataset_size=25000,distance=euclidean)')\n",
     "figs[0].set_xlabel('n_jobs')\n",
     "figs[1].set_xlabel('n_jobs')\n",
     "figs[2].set_xlabel('n_jobs')\n",
@@ -865,11 +864,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "98107e41",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrice de confusion K-NN :\n",
+      " [[51  0  0  0  0  1  0  0  0  0]\n",
+      " [ 0 56  0  0  0  0  0  0  0  0]\n",
+      " [ 3  1 45  1  0  0  1  1  0  0]\n",
+      " [ 0  1  1 35  0  1  0  1  1  1]\n",
+      " [ 0  3  0  0 48  0  0  0  0  2]\n",
+      " [ 0  1  0  1  0 38  0  0  0  0]\n",
+      " [ 0  0  0  0  0  2 44  0  0  0]\n",
+      " [ 0  2  0  0  3  0  0 47  0  0]\n",
+      " [ 2  0  0  0  0  3  1  0 42  2]\n",
+      " [ 0  0  0  0  4  1  0  1  2 50]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Create vector of 5000 random indexes\n",
+    "rand_indexes = np.random.randint(70000, size=5000)\n",
+    "### Load data with the previous vector\n",
+    "data = mnist.data[rand_indexes]\n",
+    "# print(\"Dataset : \", data)\n",
+    "target = mnist.target[rand_indexes]\n",
+    "\n",
+    "# Split the dataset\n",
+    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
+    "\n",
+    "# Training on xtrain,ytrain\n",
+    "clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n",
+    "clf.fit(xtrain, ytrain)\n",
+    "# Predicting on xtest\n",
+    "pred = clf.predict(xtest)\n",
+    "print(\"Matrice de confusion K-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/.ipynb_checkpoints/TP2_prog1.py-checkpoint.ipynb b/.ipynb_checkpoints/TP2_prog1.py-checkpoint.ipynb
index 2178f20..a895d5b 100644
--- a/.ipynb_checkpoints/TP2_prog1.py-checkpoint.ipynb
+++ b/.ipynb_checkpoints/TP2_prog1.py-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "3eb7a65b",
    "metadata": {},
    "outputs": [],
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "a8812842",
    "metadata": {},
    "outputs": [],
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "6ec263be",
    "metadata": {},
    "outputs": [],
@@ -612,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 7,
    "id": "b5c53e81",
    "metadata": {},
    "outputs": [
@@ -621,9 +621,247 @@
      "output_type": "stream",
      "text": [
       "Computing for activations =  identity ...\n",
+      "Iteration 1, loss = 3.11224909\n",
+      "Iteration 2, loss = 0.70854568\n",
+      "Iteration 3, loss = 0.52376870\n",
+      "Iteration 4, loss = 0.43536739\n",
+      "Iteration 5, loss = 0.39083398\n",
+      "Iteration 6, loss = 0.36212214\n",
+      "Iteration 7, loss = 0.34711959\n",
+      "Iteration 8, loss = 0.33529051\n",
+      "Iteration 9, loss = 0.32645959\n",
+      "Iteration 10, loss = 0.31210041\n",
+      "Iteration 11, loss = 0.30752402\n",
+      "Iteration 12, loss = 0.30330625\n",
+      "Iteration 13, loss = 0.29551273\n",
+      "Iteration 14, loss = 0.29731196\n",
+      "Iteration 15, loss = 0.29336812\n",
+      "Iteration 16, loss = 0.28803723\n",
+      "Iteration 17, loss = 0.29129394\n",
+      "Iteration 18, loss = 0.28711416\n",
+      "Iteration 19, loss = 0.28879544\n",
+      "Iteration 20, loss = 0.28476943\n",
+      "Iteration 21, loss = 0.28758300\n",
+      "Iteration 22, loss = 0.29131686\n",
+      "Iteration 23, loss = 0.28409160\n",
+      "Iteration 24, loss = 0.28754863\n",
+      "Iteration 25, loss = 0.28786580\n",
+      "Iteration 26, loss = 0.28697806\n",
+      "Iteration 27, loss = 0.28521567\n",
+      "Iteration 28, loss = 0.28971331\n",
+      "Iteration 29, loss = 0.28413101\n",
+      "Iteration 30, loss = 0.28784417\n",
+      "Iteration 31, loss = 0.28586074\n",
+      "Iteration 32, loss = 0.29405241\n",
+      "Iteration 33, loss = 0.28681966\n",
+      "Iteration 34, loss = 0.28580153\n",
+      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
       "Computing for activations =  logistic ...\n",
+      "Iteration 1, loss = 2.30769833\n",
+      "Iteration 2, loss = 2.30291040\n",
+      "Iteration 3, loss = 2.30244898\n",
+      "Iteration 4, loss = 2.30268244\n",
+      "Iteration 5, loss = 2.30239578\n",
+      "Iteration 6, loss = 2.30232797\n",
+      "Iteration 7, loss = 2.30225384\n",
+      "Iteration 8, loss = 2.30250679\n",
+      "Iteration 9, loss = 2.30217561\n",
+      "Iteration 10, loss = 2.30191053\n",
+      "Iteration 11, loss = 2.30200626\n",
+      "Iteration 12, loss = 2.30190266\n",
+      "Iteration 13, loss = 2.30192900\n",
+      "Iteration 14, loss = 2.30175066\n",
+      "Iteration 15, loss = 2.30167723\n",
+      "Iteration 16, loss = 2.30164233\n",
+      "Iteration 17, loss = 2.30159434\n",
+      "Iteration 18, loss = 2.30156877\n",
+      "Iteration 19, loss = 2.30152622\n",
+      "Iteration 20, loss = 2.30154849\n",
+      "Iteration 21, loss = 2.30150245\n",
+      "Iteration 22, loss = 2.30148105\n",
+      "Iteration 23, loss = 2.30142407\n",
+      "Iteration 24, loss = 2.30137533\n",
+      "Iteration 25, loss = 2.30152046\n",
+      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
       "Computing for activations =  tanh ...\n",
+      "Iteration 1, loss = 0.90876300\n",
+      "Iteration 2, loss = 0.54644437\n",
+      "Iteration 3, loss = 0.46970528\n",
+      "Iteration 4, loss = 0.41632441\n",
+      "Iteration 5, loss = 0.40556014\n",
+      "Iteration 6, loss = 0.39304098\n",
+      "Iteration 7, loss = 0.34769367\n",
+      "Iteration 8, loss = 0.33746557\n",
+      "Iteration 9, loss = 0.31966629\n",
+      "Iteration 10, loss = 0.30298759\n",
+      "Iteration 11, loss = 0.31346061\n",
+      "Iteration 12, loss = 0.31918136\n",
+      "Iteration 13, loss = 0.33433773\n",
+      "Iteration 14, loss = 0.31077716\n",
+      "Iteration 15, loss = 0.31443855\n",
+      "Iteration 16, loss = 0.29622440\n",
+      "Iteration 17, loss = 0.29625351\n",
+      "Iteration 18, loss = 0.28699997\n",
+      "Iteration 19, loss = 0.30362581\n",
+      "Iteration 20, loss = 0.30475983\n",
+      "Iteration 21, loss = 0.28076258\n",
+      "Iteration 22, loss = 0.28172739\n",
+      "Iteration 23, loss = 0.28994075\n",
+      "Iteration 24, loss = 0.27289827\n",
+      "Iteration 25, loss = 0.26683419\n",
+      "Iteration 26, loss = 0.25457182\n",
+      "Iteration 27, loss = 0.25186019\n",
+      "Iteration 28, loss = 0.24638283\n",
+      "Iteration 29, loss = 0.24811218\n",
+      "Iteration 30, loss = 0.24545728\n",
+      "Iteration 31, loss = 0.23533057\n",
+      "Iteration 32, loss = 0.23147359\n",
+      "Iteration 33, loss = 0.23779321\n",
+      "Iteration 34, loss = 0.23702320\n",
+      "Iteration 35, loss = 0.23076596\n",
+      "Iteration 36, loss = 0.23144501\n",
+      "Iteration 37, loss = 0.23887763\n",
+      "Iteration 38, loss = 0.24003808\n",
+      "Iteration 39, loss = 0.23919613\n",
+      "Iteration 40, loss = 0.24155683\n",
+      "Iteration 41, loss = 0.23472365\n",
+      "Iteration 42, loss = 0.24448571\n",
+      "Iteration 43, loss = 0.23936428\n",
+      "Iteration 44, loss = 0.24179928\n",
+      "Iteration 45, loss = 0.22778094\n",
+      "Iteration 46, loss = 0.22838648\n",
+      "Iteration 47, loss = 0.22280802\n",
+      "Iteration 48, loss = 0.22293833\n",
+      "Iteration 49, loss = 0.21873897\n",
+      "Iteration 50, loss = 0.22255140\n",
+      "Iteration 51, loss = 0.22840063\n",
+      "Iteration 52, loss = 0.21844829\n",
+      "Iteration 53, loss = 0.20518849\n",
+      "Iteration 54, loss = 0.21499200\n",
+      "Iteration 55, loss = 0.20790650\n",
+      "Iteration 56, loss = 0.21104213\n",
+      "Iteration 57, loss = 0.21095536\n",
+      "Iteration 58, loss = 0.21809221\n",
+      "Iteration 59, loss = 0.21593479\n",
+      "Iteration 60, loss = 0.21037508\n",
+      "Iteration 61, loss = 0.20032046\n",
+      "Iteration 62, loss = 0.20149654\n",
+      "Iteration 63, loss = 0.20030395\n",
+      "Iteration 64, loss = 0.19530838\n",
+      "Iteration 65, loss = 0.20230686\n",
+      "Iteration 66, loss = 0.18727727\n",
+      "Iteration 67, loss = 0.18870452\n",
+      "Iteration 68, loss = 0.19615807\n",
+      "Iteration 69, loss = 0.20960995\n",
+      "Iteration 70, loss = 0.20044529\n",
+      "Iteration 71, loss = 0.19594940\n",
+      "Iteration 72, loss = 0.21108270\n",
+      "Iteration 73, loss = 0.20002339\n",
+      "Iteration 74, loss = 0.19700233\n",
+      "Iteration 75, loss = 0.18615323\n",
+      "Iteration 76, loss = 0.18832837\n",
+      "Iteration 77, loss = 0.19898041\n",
+      "Iteration 78, loss = 0.20418693\n",
+      "Iteration 79, loss = 0.19368883\n",
+      "Iteration 80, loss = 0.18468801\n",
+      "Iteration 81, loss = 0.18402290\n",
+      "Iteration 82, loss = 0.18080945\n",
+      "Iteration 83, loss = 0.18818585\n",
+      "Iteration 84, loss = 0.18884275\n",
+      "Iteration 85, loss = 0.19623093\n",
+      "Iteration 86, loss = 0.18967099\n",
+      "Iteration 87, loss = 0.18097206\n",
+      "Iteration 88, loss = 0.18184130\n",
+      "Iteration 89, loss = 0.18070406\n",
+      "Iteration 90, loss = 0.18038370\n",
+      "Iteration 91, loss = 0.17927189\n",
+      "Iteration 92, loss = 0.18369377\n",
+      "Iteration 93, loss = 0.17680643\n",
+      "Iteration 94, loss = 0.17613015\n",
+      "Iteration 95, loss = 0.18412622\n",
+      "Iteration 96, loss = 0.18176041\n",
+      "Iteration 97, loss = 0.17843724\n",
+      "Iteration 98, loss = 0.17323722\n",
+      "Iteration 99, loss = 0.15801558\n",
+      "Iteration 100, loss = 0.16873188\n",
       "Computing for activations =  relu ...\n",
+      "Iteration 1, loss = 0.84846145\n",
+      "Iteration 2, loss = 0.25873061\n",
+      "Iteration 3, loss = 0.19454472\n",
+      "Iteration 4, loss = 0.16307308\n",
+      "Iteration 5, loss = 0.14511879\n",
+      "Iteration 6, loss = 0.12548967\n",
+      "Iteration 7, loss = 0.11394716\n",
+      "Iteration 8, loss = 0.10319434\n",
+      "Iteration 9, loss = 0.09340300\n",
+      "Iteration 10, loss = 0.08821125\n",
+      "Iteration 11, loss = 0.08199792\n",
+      "Iteration 12, loss = 0.07829423\n",
+      "Iteration 13, loss = 0.07068076\n",
+      "Iteration 14, loss = 0.06754605\n",
+      "Iteration 15, loss = 0.06630167\n",
+      "Iteration 16, loss = 0.05935105\n",
+      "Iteration 17, loss = 0.05915074\n",
+      "Iteration 18, loss = 0.05527145\n",
+      "Iteration 19, loss = 0.05223425\n",
+      "Iteration 20, loss = 0.05321723\n",
+      "Iteration 21, loss = 0.04984139\n",
+      "Iteration 22, loss = 0.04480437\n",
+      "Iteration 23, loss = 0.04665587\n",
+      "Iteration 24, loss = 0.04289580\n",
+      "Iteration 25, loss = 0.03622594\n",
+      "Iteration 26, loss = 0.03530255\n",
+      "Iteration 27, loss = 0.04065954\n",
+      "Iteration 28, loss = 0.04278904\n",
+      "Iteration 29, loss = 0.03540820\n",
+      "Iteration 30, loss = 0.03756356\n",
+      "Iteration 31, loss = 0.02881830\n",
+      "Iteration 32, loss = 0.03197044\n",
+      "Iteration 33, loss = 0.03519754\n",
+      "Iteration 34, loss = 0.03226369\n",
+      "Iteration 35, loss = 0.03484656\n",
+      "Iteration 36, loss = 0.02892709\n",
+      "Iteration 37, loss = 0.02495425\n",
+      "Iteration 38, loss = 0.02849610\n",
+      "Iteration 39, loss = 0.02382546\n",
+      "Iteration 40, loss = 0.02260332\n",
+      "Iteration 41, loss = 0.02632971\n",
+      "Iteration 42, loss = 0.03400246\n",
+      "Iteration 43, loss = 0.02602642\n",
+      "Iteration 44, loss = 0.02628543\n",
+      "Iteration 45, loss = 0.02091241\n",
+      "Iteration 46, loss = 0.02698089\n",
+      "Iteration 47, loss = 0.02287099\n",
+      "Iteration 48, loss = 0.01953821\n",
+      "Iteration 49, loss = 0.02287816\n",
+      "Iteration 50, loss = 0.01786992\n",
+      "Iteration 51, loss = 0.01614434\n",
+      "Iteration 52, loss = 0.02288893\n",
+      "Iteration 53, loss = 0.01831700\n",
+      "Iteration 54, loss = 0.02733430\n",
+      "Iteration 55, loss = 0.01667890\n",
+      "Iteration 56, loss = 0.01619622\n",
+      "Iteration 57, loss = 0.02049603\n",
+      "Iteration 58, loss = 0.02224142\n",
+      "Iteration 59, loss = 0.01578972\n",
+      "Iteration 60, loss = 0.01984688\n",
+      "Iteration 61, loss = 0.02051338\n",
+      "Iteration 62, loss = 0.01760285\n",
+      "Iteration 63, loss = 0.02048740\n",
+      "Iteration 64, loss = 0.01581426\n",
+      "Iteration 65, loss = 0.01006159\n",
+      "Iteration 66, loss = 0.01263444\n",
+      "Iteration 67, loss = 0.01597941\n",
+      "Iteration 68, loss = 0.01564173\n",
+      "Iteration 69, loss = 0.01664765\n",
+      "Iteration 70, loss = 0.01238414\n",
+      "Iteration 71, loss = 0.01315483\n",
+      "Iteration 72, loss = 0.01670207\n",
+      "Iteration 73, loss = 0.01893887\n",
+      "Iteration 74, loss = 0.01745178\n",
+      "Iteration 75, loss = 0.01212984\n",
+      "Iteration 76, loss = 0.01667099\n",
+      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
       "Done\n"
      ]
     }
@@ -640,7 +878,7 @@
     "for i in activations:\n",
     "    print(\"Computing for activations = \", i, \"...\")\n",
     "    #Entraîne le classifier\n",
-    "    clf = neural_network.MLPClassifier(random_state=1, max_iter=25, hidden_layer_sizes=(50,)*10, verbose=False, activation=i)\n",
+    "    clf = neural_network.MLPClassifier(random_state=1, max_iter=100, hidden_layer_sizes=(50,)*10, verbose=True, activation=i)\n",
     "    t1 = round(time.time(),5)\n",
     "    clf.fit(xtrain, ytrain)\n",
     "    t2 = round(time.time(),5)\n",
@@ -658,7 +896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 9,
    "id": "c7afbbdc",
    "metadata": {},
    "outputs": [
@@ -667,9 +905,9 @@
      "output_type": "stream",
      "text": [
       "x :  ['identity', 'logistic', 'tanh', 'relu']\n",
-      "training_times :  [11.6006600856781, 16.919300079345703, 26.391479969024658, 18.122960090637207]\n",
-      "precision_scores :  [0.8994761904761904, 0.11223809523809523, 0.9152857142857143, 0.9637619047619047]\n",
-      "zero_one_loss :  [0.10052380952380957, 0.8877619047619048, 0.08471428571428574, 0.03623809523809529]\n"
+      "training_times :  [34.288330078125, 33.1211998462677, 171.96871995925903, 106.40883994102478]\n",
+      "precision_scores :  [0.9074285714285715, 0.11142857142857143, 0.9351904761904762, 0.9683333333333334]\n",
+      "zero_one_loss :  [0.09257142857142853, 0.8885714285714286, 0.06480952380952376, 0.03166666666666662]\n"
      ]
     },
     {
@@ -678,13 +916,13 @@
        "Text(36.0, 0.5, 'Zero-one loss')"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 504x720 with 3 Axes>"
       ]
@@ -995,7 +1233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 7,
    "id": "abb0fcf1",
    "metadata": {},
    "outputs": [
@@ -1003,79 +1241,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 1, loss = 0.58722635\n",
-      "Iteration 2, loss = 0.19664437\n",
-      "Iteration 3, loss = 0.14644059\n",
-      "Iteration 4, loss = 0.12219867\n",
-      "Iteration 5, loss = 0.10425279\n",
-      "Iteration 6, loss = 0.09855065\n",
-      "Iteration 7, loss = 0.07754340\n",
-      "Iteration 8, loss = 0.07198762\n",
-      "Iteration 9, loss = 0.06616934\n",
-      "Iteration 10, loss = 0.06035184\n",
-      "Iteration 11, loss = 0.05569499\n",
-      "Iteration 12, loss = 0.05829348\n",
-      "Iteration 13, loss = 0.05370925\n",
-      "Iteration 14, loss = 0.04997678\n",
-      "Iteration 15, loss = 0.04527340\n",
-      "Iteration 16, loss = 0.03983840\n",
-      "Iteration 17, loss = 0.04076422\n",
-      "Iteration 18, loss = 0.04029400\n",
-      "Iteration 19, loss = 0.03321192\n",
-      "Iteration 20, loss = 0.03882352\n",
-      "Iteration 21, loss = 0.03363780\n",
-      "Iteration 22, loss = 0.03320547\n",
-      "Iteration 23, loss = 0.02775446\n",
-      "Iteration 24, loss = 0.04253825\n",
-      "Iteration 25, loss = 0.03002649\n",
-      "Iteration 26, loss = 0.02438176\n",
-      "Iteration 27, loss = 0.02810122\n",
-      "Iteration 28, loss = 0.03876961\n",
-      "Iteration 29, loss = 0.02501501\n",
-      "Iteration 30, loss = 0.02376453\n",
-      "Iteration 31, loss = 0.02010948\n",
-      "Iteration 32, loss = 0.02460232\n",
-      "Iteration 33, loss = 0.02330741\n",
-      "Iteration 34, loss = 0.01953304\n",
-      "Iteration 35, loss = 0.02254089\n",
-      "Iteration 36, loss = 0.02653422\n",
-      "Iteration 37, loss = 0.03004069\n",
-      "Iteration 38, loss = 0.02443066\n",
-      "Iteration 39, loss = 0.01923374\n",
-      "Iteration 40, loss = 0.02801464\n",
-      "Iteration 41, loss = 0.01522026\n",
-      "Iteration 42, loss = 0.01749346\n",
-      "Iteration 43, loss = 0.02286608\n",
-      "Iteration 44, loss = 0.02714804\n",
-      "Iteration 45, loss = 0.01312122\n",
-      "Iteration 46, loss = 0.01681842\n",
-      "Iteration 47, loss = 0.01937897\n",
-      "Iteration 48, loss = 0.02501177\n",
-      "Iteration 49, loss = 0.02581483\n",
-      "Iteration 50, loss = 0.01928808\n",
-      "Iteration 51, loss = 0.02221606\n",
-      "Iteration 52, loss = 0.01724194\n",
-      "Iteration 53, loss = 0.02403539\n",
-      "Iteration 54, loss = 0.01944278\n",
-      "Iteration 55, loss = 0.01724867\n",
-      "Iteration 56, loss = 0.01351808\n",
-      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
-      "Paramètre :\n",
-      "\n",
-      "random_state =  1\n",
-      "max_iter =  300\n",
-      "nb_hidden_layer =  15\n",
-      "hidden_layer_size =  85\n",
-      "solver =  adam\n",
-      "activation =  relu\n",
-      "alpha =  1e-07\n",
-      "Temps d'entraînement :  78.07823991775513\n",
-      "Score :  0.9738095238095238\n",
-      "Zero-one loss :  0.02619047619047621\n"
+      "Matrice de confusion A-NN :\n",
+      " [[59  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 60  0  0  0  0  0  1  0  0]\n",
+      " [ 0  0 42  0  0  1  2  1  2  0]\n",
+      " [ 0  0  1 44  0  1  0  0  0  0]\n",
+      " [ 0  0  0  0 46  0  1  0  0  4]\n",
+      " [ 0  0  0  0  0 31  0  0  1  0]\n",
+      " [ 0  0  0  0  0  0 48  0  0  0]\n",
+      " [ 1  0  0  1  0  0  0 49  0  0]\n",
+      " [ 0  1  1  5  0  1  0  0 48  0]\n",
+      " [ 2  0  0  1  1  2  0  0  2 40]]\n"
      ]
     }
    ],
    "source": [
+    "### Create vector of 5000 random indexes\n",
+    "rand_indexes = np.random.randint(70000, size=5000)\n",
+    "### Load data with the previous vector\n",
+    "data = mnist.data[rand_indexes]\n",
+    "# print(\"Dataset : \", data)\n",
+    "target = mnist.target[rand_indexes]\n",
+    "\n",
+    "# Split the dataset\n",
+    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
+    "\n",
     "best_training_time = 0\n",
     "best_precision_score = 0\n",
     "best_zero_one_loss = 0\n",
@@ -1090,7 +1280,7 @@
     "a = 0.0000001\n",
     "\n",
     "#Entraîne le classifier\n",
-    "clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=True)\n",
+    "clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=False)\n",
     "t1 = round(time.time(),5)\n",
     "clf.fit(xtrain, ytrain)\n",
     "t2 = round(time.time(),5)\n",
@@ -1104,17 +1294,18 @@
     "best_precision_score = clf.score(xtest, ytest)\n",
     "best_zero_one_loss = metrics.zero_one_loss(ytest, pred)\n",
     "\n",
-    "print(\"Paramètre :\\n\")\n",
-    "print(\"random_state = \", r)\n",
-    "print(\"max_iter = \", max_i)\n",
-    "print(\"nb_hidden_layer = \", nb_hl)\n",
-    "print(\"hidden_layer_size = \", hl_size)\n",
-    "print(\"solver = \", sol)\n",
-    "print(\"activation = \", act)\n",
-    "print(\"alpha = \", a)\n",
-    "print(\"Temps d'entraînement : \", best_training_time)\n",
-    "print(\"Score : \", best_precision_score)\n",
-    "print(\"Zero-one loss : \", best_zero_one_loss)"
+    "# print(\"Paramètre :\\n\")\n",
+    "# print(\"random_state = \", r)\n",
+    "# print(\"max_iter = \", max_i)\n",
+    "# print(\"nb_hidden_layer = \", nb_hl)\n",
+    "# print(\"hidden_layer_size = \", hl_size)\n",
+    "# print(\"solver = \", sol)\n",
+    "# print(\"activation = \", act)\n",
+    "# print(\"alpha = \", a)\n",
+    "# print(\"Temps d'entraînement : \", best_training_time)\n",
+    "# print(\"Score : \", best_precision_score)\n",
+    "# print(\"Zero-one loss : \", best_zero_one_loss)\n",
+    "print(\"Matrice de confusion A-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
    ]
   },
   {
@@ -1142,7 +1333,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/.ipynb_checkpoints/TP3_prog1.py-checkpoint.ipynb b/.ipynb_checkpoints/TP3_prog1.py-checkpoint.ipynb
index 7b0e827..fcbea2b 100644
--- a/.ipynb_checkpoints/TP3_prog1.py-checkpoint.ipynb
+++ b/.ipynb_checkpoints/TP3_prog1.py-checkpoint.ipynb
@@ -2,329 +2,451 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "530f620c",
+   "execution_count": 1,
+   "id": "3eb7a65b",
    "metadata": {},
    "outputs": [],
    "source": [
+    "####### Import #######\n",
     "from sklearn.datasets import fetch_openml\n",
-    "from sklearn import model_selection\n",
-    "from sklearn import neighbors\n",
-    "from sklearn.svm import SVC\n",
     "import sklearn\n",
+    "from matplotlib import pyplot as plt\n",
+    "from sklearn import model_selection\n",
+    "from sklearn import neural_network\n",
+    "from sklearn import metrics\n",
+    "from sklearn.svm import SVC\n",
     "import numpy as np\n",
-    "\n",
-    "mnist = fetch_openml('mnist_784',as_frame=False)"
+    "import time\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a8812842",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "####### Loading data #######\n",
+    "mnist = fetch_openml('mnist_784',as_frame=False)\n",
+    "# images = mnist.data.reshape((-1, 28, 28))\n",
+    "# plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation=\"nearest\")\n",
+    "# plt.show()\n",
+    "# print(\"Classe : \", mnist.target[0])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "eb2c4496",
+   "id": "6ec263be",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset :  [[0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " ...\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]]\n",
-      "Etiquettes :  ['1' '3' '4' ... '5' '1' '2']\n",
-      "Prédiction :  ['6' '7' '1' '4' '2' '7' '6' '6' '4' '9' '8' '4' '0' '0' '6' '8' '5' '0'\n",
-      " '9' '6' '5' '0' '7' '7' '0' '7' '6' '1' '0' '1' '6' '6' '5' '8' '5' '6'\n",
-      " '6' '5' '0' '7' '7' '5' '2' '7' '3' '2' '2' '6' '0' '0' '5' '8' '2' '4'\n",
-      " '1' '0' '9' '6' '3' '7' '6' '3' '9' '4' '0' '0' '8' '8' '0' '6' '7' '1'\n",
-      " '8' '3' '1' '6' '9' '1' '8' '0' '2' '0' '4' '5' '9' '3' '4' '3' '6' '3'\n",
-      " '2' '3' '8' '0' '8' '6' '1' '7' '3' '8' '4' '2' '0' '7' '9' '4' '0' '2'\n",
-      " '2' '0' '2' '2' '3' '0' '0' '0' '6' '8' '2' '4' '3' '7' '2' '6' '8' '4'\n",
-      " '3' '8' '8' '0' '4' '6' '1' '0' '4' '6' '6' '0' '0' '6' '1' '6' '5' '5'\n",
-      " '1' '5' '8' '2' '6' '4' '7' '5' '3' '2' '5' '8' '5' '2' '2' '3' '0' '3'\n",
-      " '6' '1' '4' '8' '1' '7' '7' '5' '9' '1' '3' '5' '0' '7' '8' '6' '5' '0'\n",
-      " '6' '6' '8' '5' '9' '5' '3' '9' '7' '4' '9' '0' '1' '5' '3' '3' '6' '1'\n",
-      " '1' '1' '8' '7' '7' '1' '7' '4' '1' '1' '3' '8' '4' '4' '3' '9' '8' '4'\n",
-      " '0' '4' '4' '9' '6' '0' '6' '0' '3' '8' '8' '0' '9' '1' '4' '4' '2' '1'\n",
-      " '5' '7' '5' '0' '7' '6' '0' '4' '5' '7' '5' '9' '4' '3' '4' '4' '0' '5'\n",
-      " '0' '0' '1' '9' '1' '7' '3' '4' '6' '0' '5' '9' '6' '1' '1' '5' '6' '5'\n",
-      " '2' '9' '4' '3' '4' '1' '0' '0' '4' '2' '1' '7' '1' '4' '1' '3' '9' '2'\n",
-      " '0' '8' '7' '7' '4' '4' '7' '1' '8' '7' '1' '4' '6' '9' '2' '7' '1' '4'\n",
-      " '5' '1' '1' '4' '2' '7' '3' '8' '5' '8' '3' '3' '4' '7' '2' '1' '4' '9'\n",
-      " '9' '4' '7' '9' '3' '4' '9' '7' '1' '0' '7' '7' '3' '8' '4' '6' '1' '3'\n",
-      " '5' '5' '4' '9' '6' '0' '1' '1' '0' '0' '0' '3' '2' '7' '9' '8' '0' '3'\n",
-      " '6' '1' '9' '4' '0' '1' '0' '0' '1' '6' '9' '6' '3' '8' '2' '5' '9' '5'\n",
-      " '1' '3' '7' '0' '9' '3' '2' '6' '8' '5' '1' '5' '4' '1' '4' '1' '1' '3'\n",
-      " '1' '5' '7' '2' '3' '2' '6' '1' '2' '6' '3' '8' '7' '3' '3' '9' '8' '0'\n",
-      " '4' '3' '7' '7' '9' '3' '9' '8' '7' '8' '0' '4' '8' '8' '0' '4' '1' '5'\n",
-      " '1' '2' '1' '3' '5' '4' '9' '8' '1' '3' '1' '5' '8' '4' '8' '2' '9' '8'\n",
-      " '2' '3' '6' '3' '5' '2' '4' '0' '1' '0' '1' '8' '9' '9' '6' '2' '4' '1'\n",
-      " '5' '6' '7' '7' '1' '5' '0' '2' '6' '5' '0' '3' '2' '8' '8' '9' '7' '9'\n",
-      " '4' '4' '1' '9' '7' '8' '2' '1' '9' '6' '2' '4' '8' '7' '8' '9' '9' '4'\n",
-      " '6' '9' '9' '5' '6' '9' '9' '8' '5' '5' '6' '4' '6' '8' '8' '7' '6' '0'\n",
-      " '0' '9' '2' '3' '7' '7' '1' '5' '9' '1' '9' '9' '1' '4' '1' '9' '6' '9'\n",
-      " '0' '9' '4' '6' '1' '0' '7' '0' '8' '9' '7' '3' '8' '2' '3' '0' '2' '8'\n",
-      " '3' '1' '7' '0' '2' '1' '0' '4' '2' '0' '8' '1' '5' '2' '4' '5' '0' '9'\n",
-      " '8' '1' '3' '9' '8' '7' '2' '4' '6' '2' '3' '9' '1' '8' '2' '1' '9' '0'\n",
-      " '2' '4' '0' '9' '1' '4' '1' '3' '2' '4' '9' '5' '0' '2' '2' '1' '1' '7'\n",
-      " '6' '8' '4' '9' '7' '7' '9' '4' '2' '3' '8' '1' '3' '5' '7' '9' '2' '0'\n",
-      " '4' '8' '1' '6' '1' '7' '9' '6' '3' '6' '0' '0' '4' '7' '1' '1' '1' '4'\n",
-      " '5' '6' '6' '1' '7' '6' '1' '7' '6' '1' '1' '2' '0' '8' '6' '1' '4' '3'\n",
-      " '3' '6' '8' '7' '1' '1' '1' '4' '3' '3' '2' '6' '3' '3' '8' '8' '3' '1'\n",
-      " '8' '6' '6' '8' '8' '9' '6' '7' '6' '7' '8' '9' '1' '8' '3' '9' '5' '0'\n",
-      " '6' '6' '9' '3' '1' '2' '5' '5' '0' '9' '5' '9' '0' '0' '6' '1' '8' '5'\n",
-      " '0' '2' '2' '8' '3' '9' '7' '2' '7' '6' '2' '8' '6' '8' '8' '0' '2' '0'\n",
-      " '6' '2' '7' '7' '3' '7' '2' '7' '1' '7' '9' '3' '4' '7' '7' '9' '9' '2'\n",
-      " '5' '8' '3' '7' '7' '2' '1' '7' '1' '1' '9' '9' '3' '0' '9' '4' '9' '0'\n",
-      " '7' '6' '7' '7' '7' '7' '9' '7' '8' '1' '1' '6' '2' '6' '3' '8' '2' '8'\n",
-      " '1' '5' '7' '0' '8' '3' '2' '7' '5' '1' '5' '3' '5' '2' '1' '7' '6' '0'\n",
-      " '2' '6' '3' '2' '6' '0' '6' '2' '3' '9' '8' '6' '4' '9' '1' '3' '0' '4'\n",
-      " '2' '3' '8' '1' '9' '0' '3' '5' '4' '5' '3' '2' '5' '0' '1' '1' '8' '3'\n",
-      " '5' '6' '2' '1' '9' '3' '0' '4' '5' '9' '7' '2' '2' '1' '2' '1' '1' '5'\n",
-      " '0' '9' '3' '7' '1' '9' '6' '5' '1' '6' '0' '1' '1' '6' '5' '8' '2' '2'\n",
-      " '1' '8' '9' '7' '6' '8' '4' '5' '2' '3' '0' '7' '6' '0' '6' '6' '6' '0'\n",
-      " '8' '8' '3' '4' '0' '9' '7' '5' '1' '1' '1' '4' '6' '7' '9' '6' '3' '9'\n",
-      " '3' '9' '1' '9' '6' '4' '5' '4' '7' '0' '1' '9' '4' '8' '4' '6' '1' '8'\n",
-      " '5' '6' '5' '1' '2' '7' '9' '5' '8' '0' '8' '8' '3' '2' '9' '4' '4' '8'\n",
-      " '3' '0' '6' '5' '9' '7' '0' '0' '9' '7' '0' '3' '2' '1' '0' '5' '6' '4'\n",
-      " '0' '4' '6' '9' '3' '0' '4' '1' '5' '6' '3' '6' '9' '1' '5' '6' '3' '0'\n",
-      " '1' '6' '1' '0' '6' '2' '1' '7' '1' '9']\n",
-      "Probabilités :  [[0.  0.  0.  ... 0.  0.  0. ]\n",
-      " [0.  0.  0.  ... 1.  0.  0. ]\n",
-      " [0.  1.  0.  ... 0.  0.  0. ]\n",
-      " ...\n",
-      " [0.  0.  0.  ... 1.  0.  0. ]\n",
-      " [0.  0.4 0.  ... 0.1 0.  0.3]\n",
-      " [0.  0.  0.  ... 0.1 0.  0.9]]\n",
-      "Classe image 4 :  9\n",
-      "Classe prédite image 4 :  4\n",
-      "Score échantillon de test :  0.912\n",
-      "Score données apprentissage :  0.94325\n"
+      "Dataset size :  5000\n",
+      "Etiquettes size :  5000\n",
+      "xtrain size :  4500\n",
+      "xtest size :  500\n",
+      "ytrain size :  4500\n",
+      "ytest size :  500\n"
      ]
     }
    ],
    "source": [
+    "### Create vector of 1000 random indexes\n",
     "rand_indexes = np.random.randint(70000, size=5000)\n",
-    "\n",
+    "### Load data with the previous vector\n",
     "data = mnist.data[rand_indexes]\n",
-    "print(\"Dataset : \", data)\n",
+    "print(\"Dataset size : \", len(data))\n",
     "target = mnist.target[rand_indexes]\n",
-    "print(\"Etiquettes : \", target)\n",
+    "print(\"Etiquettes size : \", len(target))\n",
     "\n",
+    "### Split the dataset for training and testing\n",
     "# xtrain data set d'entraînement et ytrain étiquettes de xtrain\n",
     "# xtest dataset de prédiction et ytest étiquettes de xtest\n",
-    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.7)\n",
-    "\n",
-    "n_neighbors = 10\n",
-    "clf = svm.SVC(kernel=\"linear\")\n",
-    "# On entraîne l'algorithme sur xtrain et ytrain\n",
-    "clf.fit(xtrain, ytrain)\n",
-    "# On prédit sur xtest\n",
-    "pred = clf.predict(xtest)\n",
-    "print(\"Prédiction : \", pred)\n",
-    "# Probabilités des prédictions sur xtest\n",
-    "pred_proba = clf.predict_proba(xtest)\n",
-    "print(\"Probabilités : \", pred_proba)\n",
-    "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
-    "score = clf.score(xtest, ytest)\n",
-    "print(\"Classe image 4 : \", target[3])\n",
-    "print(\"Classe prédite image 4 : \", pred[3])\n",
-    "print(\"Score échantillon de test : \", score)\n",
-    "\n",
-    "scoreApp = clf.score(xtrain, ytrain)\n",
-    "print(\"Score données apprentissage : \", scoreApp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "90db6e29",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset :  [[0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " ...\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]]\n",
-      "Etiquettes :  ['9' '9' '8' ... '9' '4' '6']\n",
-      "[0.92, 0.922, 0.93, 0.966, 0.924, 0.922, 0.922, 0.896, 0.92, 0.91, 0.916, 0.94, 0.938, 0.938, 0.926, 0.936, 0.932, 0.932, 0.934, 0.938, 0.922, 0.934, 0.96, 0.926, 0.942, 0.934, 0.908, 0.926, 0.92, 0.936, 0.932, 0.924, 0.922, 0.938, 0.938, 0.916, 0.932, 0.96, 0.942, 0.922, 0.926, 0.938, 0.936, 0.924, 0.938, 0.946, 0.922, 0.928, 0.912, 0.908, 0.916, 0.932, 0.932, 0.93, 0.92, 0.928, 0.908, 0.932, 0.918, 0.938, 0.92, 0.93, 0.938, 0.924, 0.924, 0.932, 0.916, 0.916, 0.934, 0.928, 0.924, 0.94, 0.942, 0.926, 0.924, 0.912, 0.93, 0.906, 0.894, 0.922, 0.924, 0.912, 0.906, 0.942, 0.95, 0.924, 0.926, 0.92, 0.92, 0.9, 0.918, 0.908, 0.93, 0.942, 0.916, 0.934, 0.916, 0.92, 0.91, 0.918, 0.93, 0.918, 0.916, 0.894, 0.934, 0.926, 0.934, 0.91, 0.9, 0.914, 0.928, 0.918, 0.924, 0.916, 0.908, 0.904, 0.922, 0.912, 0.92, 0.914, 0.926, 0.906, 0.902, 0.914, 0.9, 0.936, 0.906, 0.942, 0.922, 0.906]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for k in range(2,15):\n",
-    "    \n",
-    "    for train_index, test_index in kf.split(data):\n",
-    "#         print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
-    "        X_train, X_test = data[train_index], data[test_index]\n",
-    "        y_train, y_test = target[train_index], target[test_index]\n",
-    "        \n",
-    "        clf = neighbors.KNeighborsClassifier(k)\n",
-    "        # On entraîne l'algorithme sur xtrain et ytrain\n",
-    "        clf.fit(X_train, y_train)\n",
-    "        # On prédit sur xtest\n",
-    "        pred = clf.predict(X_test)\n",
-    "#         print(\"Prédiction : \", pred)\n",
-    "        # Probabilités des prédictions sur xtest\n",
-    "        pred_proba = clf.predict_proba(X_test)\n",
-    "#         print(\"Probabilités : \", pred_proba)\n",
-    "        # On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
-    "        score = clf.score(X_test, y_test)\n",
-    "        scores += [score]\n",
-    "#         print(\"Classe image 4 : \", target[3])\n",
-    "#         print(\"Classe prédite image 4 : \", pred[3])\n",
-    "#         print(\"Score échantillon de test : \", score)\n",
-    "        scoreApp = clf.score(X_train, y_train)\n",
-    "#         print(\"Score données apprentissage : \", scoreApp)\n",
-    "print(scores)"
+    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
+    "print(\"xtrain size : \", len(xtrain))\n",
+    "print(\"xtest size : \", len(xtest))\n",
+    "print(\"ytrain size : \", len(ytrain))\n",
+    "print(\"ytest size : \", len(ytest))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "bf91b914",
+   "id": "3b1a54ef",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2  :  0.9232000000000001\n",
-      "3  :  0.933\n",
-      "4  :  0.9308\n",
-      "5  :  0.9326000000000001\n",
-      "6  :  0.9300000000000002\n",
-      "7  :  0.922888888888889\n",
-      "8  :  0.9266666666666666\n",
-      "9  :  0.9273333333333333\n",
-      "10  :  0.9206666666666666\n",
-      "11  :  0.9208888888888889\n",
-      "12  :  0.9197777777777778\n",
-      "13  :  0.9175555555555555\n",
-      "14  :  0.9162222222222223\n",
-      "15  :  0.9148888888888889\n"
+      "Matrice de confusion SVM:\n",
+      " [[44  0  2  0  0  0  0  0  0  0]\n",
+      " [ 0 56  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0 46  0  0  0  0  1  0  0]\n",
+      " [ 0  1  2 48  0  2  0  0  2  0]\n",
+      " [ 0  1  0  0 42  0  0  0  1  3]\n",
+      " [ 0  0  0  0  0 36  0  0  1  1]\n",
+      " [ 0  0  1  0  0  1 54  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 54  0  2]\n",
+      " [ 0  1  1  5  1  0  0  0 43  0]\n",
+      " [ 0  0  0  0  1  0  0  1  1 45]]\n"
      ]
     }
    ],
    "source": [
-    "nice_scores = np.array_split(scores, 14)\n",
-    "for i in range (0,14):\n",
-    "    print (i+2, \" : \", nice_scores[i].mean())\n"
+    "####### Premier modèle de Classifier #######\n",
+    "\n",
+    "#Entraîne le classifier\n",
+    "clf = SVC(kernel=\"linear\")\n",
+    "# print(\"Training...\")\n",
+    "clf.fit(xtrain, ytrain)\n",
+    "\n",
+    "#Prédiction sur le jeu de tests\n",
+    "# print(\"Predicting...\")\n",
+    "pred = clf.predict(xtest)\n",
+    "#print(\"Prédiction : \", pred)\n",
+    "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
+    "# score = clf.score(xtest, ytest)\n",
+    "# print(\"Score échantillon de test : \", score)\n",
+    "\n",
+    "#Infos image 4\n",
+    "# print(\"Classe image 4 : \", ytest[3])\n",
+    "# print(\"Classe prédite image 4 : \", pred[3])\n",
+    "\n",
+    "#Calcul de différentes metrics\n",
+    "# print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
+    "print(\"Matrice de confusion SVM:\\n\", metrics.confusion_matrix(ytest, pred))\n",
+    "# print(\"Zero-one classification loss :\\n\", metrics.zero_one_loss(ytest, pred))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "cc24e898",
+   "execution_count": 47,
+   "id": "5a4a5485",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset :  [[0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " ...\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]]\n",
-      "Etiquettes :  ['0' '0' '5' ... '9' '8' '6']\n",
-      "Temps d'entraînement :  0.002\n",
-      "Temps de prédiction :  0.338\n",
-      "Temps total :  0.34\n",
-      "Temps d'entraînement :  0.003\n",
-      "Temps de prédiction :  0.31\n",
-      "Temps total :  0.313\n",
-      "Temps d'entraînement :  0.002\n",
-      "Temps de prédiction :  0.328\n",
-      "Temps total :  0.33\n",
-      "Temps d'entraînement :  0.003\n",
-      "Temps de prédiction :  0.305\n",
-      "Temps total :  0.308\n",
-      "Temps d'entraînement :  0.003\n",
-      "Temps de prédiction :  0.254\n",
-      "Temps total :  0.257\n",
-      "Temps d'entraînement :  0.003\n",
-      "Temps de prédiction :  0.244\n",
-      "Temps total :  0.247\n",
-      "Temps d'entraînement :  0.004\n",
-      "Temps de prédiction :  0.203\n",
-      "Temps total :  0.207\n",
-      "3  :  0.9045714285714286\n",
-      "4  :  0.91\n",
-      "5  :  0.9168\n",
-      "6  :  0.925\n",
-      "7  :  0.934\n",
-      "8  :  0.922\n",
-      "9  :  0.952\n"
+      "Computing for kernel= poly ...\n",
+      "Computing for kernel= rbf ...\n",
+      "Computing for kernel= sigmoid ...\n",
+      "Computing for kernel= precomputed ...\n",
+      "Done\n"
      ]
     }
    ],
    "source": [
-    "from sklearn.model_selection import KFold\n",
-    "import time\n",
+    "####### Variations de la fonction noyau #######\n",
     "\n",
-    "rand_indexes = np.random.randint(70000, size=5000)\n",
+    "list_training_times_kernel = []\n",
+    "list_precision_scores_kernel = []\n",
+    "list_zero_one_loss_kernel = []\n",
     "\n",
-    "data = mnist.data[rand_indexes]\n",
-    "print(\"Dataset : \", data)\n",
-    "target = mnist.target[rand_indexes]\n",
-    "print(\"Etiquettes : \", target)\n",
-    "\n",
-    "# xtrain data set d'entraînement et ytrain étiquettes de xtrain\n",
-    "# xtest dataset de prédiction et ytest étiquettes de xtest\n",
-    "\n",
-    "scores = []\n",
-    "\n",
-    "for j in range (3, 10):\n",
-    "    xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=(j/10))\n",
+    "kernel_functions = [\"poly\",\"rbf\",\"sigmoid\",\"precomputed\"]\n",
+    "kernel_train = xtrain\n",
+    "kernel_test = xtest\n",
+    "for i in kernel_functions:\n",
+    "    print(\"Computing for kernel=\", i, \"...\")\n",
+    "    if (i == \"precomputed\"):\n",
+    "        kernel_train=np.dot(xtrain,xtrain.T) # modified the train_set\n",
+    "        kernel_test=np.dot(xtest,xtrain.T) # modified the test_set\n",
     "    \n",
-    "    t1 = round(time.time(),3)\n",
-    "    clf = neighbors.KNeighborsClassifier(n_neighbors=3,p = 2, n_jobs=-1)\n",
-    "    # On entraîne l'algorithme sur xtrain et ytrain\n",
-    "    clf.fit(xtrain, ytrain)\n",
-    "    t2 = round(time.time(),3)\n",
-    "    # On prédit sur xtest\n",
-    "    pred = clf.predict(xtest)\n",
-    "    t3 = round(time.time(),3)\n",
-    "    \n",
-    "    print(\"Temps d'entraînement : \", round(t2-t1,3))\n",
-    "    print(\"Temps de prédiction : \", round(t3-t2,3))\n",
-    "    print(\"Temps total : \", round(t3-t1,3))\n",
-    "#         print(\"Prédiction : \", pred)\n",
-    "    # Probabilités des prédictions sur xtest\n",
-    "    pred_proba = clf.predict_proba(xtest)\n",
-    "#         print(\"Probabilités : \", pred_proba)\n",
-    "    # On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
-    "    score = clf.score(xtest, ytest)\n",
-    "    scores += [score]\n",
-    "#         print(\"Classe image 4 : \", target[3])\n",
-    "#         print(\"Classe prédite image 4 : \", pred[3])\n",
-    "#         print(\"Score échantillon de test : \", score)\n",
-    "    scoreApp = clf.score(xtrain, ytrain)\n",
-    "#         print(\"Score données apprentissage : \", scoreApp)\n",
+    "    #Entraîne le classifier\n",
+    "    clf = SVC(kernel=i)\n",
+    "    t1 = round(time.time(),5)\n",
+    "    clf.fit(kernel_train, ytrain)\n",
+    "    t2 = round(time.time(),5)\n",
+    "    #Prédiction sur le jeu de tests\n",
+    "    pred = clf.predict(kernel_test)\n",
+    "    # On sauvegarde le temps de calcul, la précision et \n",
+    "    # les taux d'erreurs par classe\n",
+    "    list_training_times_kernel.append(t2-t1)\n",
+    "    list_precision_scores_kernel.append(clf.score(kernel_test, ytest))\n",
+    "    list_zero_one_loss_kernel.append(metrics.zero_one_loss(ytest, pred))\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "9b961ed8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(36.0, 0.5, 'Zero-one loss')"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "##### x coordinates\n",
+    "x = kernel_functions\n",
+    "training_times = list_training_times_kernel\n",
+    "precision_scores = list_precision_scores_kernel\n",
+    "zero_one_loss = list_zero_one_loss_kernel\n",
+    "        \n",
+    "training_times = [round(i,2) for i in training_times]\n",
+    "precision_scores = [round(i,2) for i in precision_scores]\n",
+    "zero_one_loss = [round(i,2) for i in zero_one_loss]\n",
     "\n",
-    "# nice_scores = np.array_split(scores, 7)\n",
-    "# print(scores)\n",
-    "n = 3\n",
-    "for i in scores:\n",
-    "    print (n, \" : \", i)\n",
-    "    n += 1"
+    "### Create plot\n",
+    "fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(7,10))\n",
+    "fig.tight_layout(pad=3.0)\n",
+    "figs[0].plot(x,training_times, marker='o', color='r')\n",
+    "figs[1].plot(x,precision_scores, marker='o', color='b')\n",
+    "figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
+    "\n",
+    "### Add every x coordinates\n",
+    "figs[0].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[1].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[2].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "\n",
+    "for i in range(len(x)):\n",
+    "    figs[0].annotate(training_times[i], # this is the text\n",
+    "                 (x[i],training_times[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[1].annotate(precision_scores[i], # this is the text\n",
+    "                 (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[2].annotate(zero_one_loss[i], # this is the text\n",
+    "                 (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "\n",
+    "figs[0].set_xticks(x)\n",
+    "figs[1].set_xticks(x)\n",
+    "figs[2].set_xticks(x)\n",
+    "    \n",
+    "### Add title and axis names\n",
+    "figs[0].title.set_text('Training times for each kernel function')\n",
+    "figs[1].title.set_text('Precision score for each kernel function')\n",
+    "figs[2].title.set_text('Zero-one loss metrics for each kernel function')\n",
+    "figs[0].set_xlabel('kernel')\n",
+    "figs[1].set_xlabel('kernel')\n",
+    "figs[2].set_xlabel('kernel')\n",
+    "figs[0].set_ylabel('Training times (in seconds)')\n",
+    "figs[1].set_ylabel('Precision score')\n",
+    "figs[2].set_ylabel('Zero-one loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "5726fcb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computing for C= 0.1 ...\n",
+      "Computing for C= 0.25 ...\n",
+      "Computing for C= 0.5 ...\n",
+      "Computing for C= 0.75 ...\n",
+      "Computing for C= 1.0 ...\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "####### Variation du paramètre de tolérance aux erreurs C #######\n",
+    "\n",
+    "list_training_times_tol = []\n",
+    "list_precision_scores_tol = []\n",
+    "list_zero_one_loss_tol = []\n",
+    "\n",
+    "kernel_train = xtrain\n",
+    "kernel_test = xtest\n",
+    "tols = [0.1,0.25,0.5,0.75,1.0]\n",
+    "\n",
+    "for i in tols:\n",
+    "    print(\"Computing for C=\", i, \"...\")\n",
+    "    #Entraîne le classifier\n",
+    "    clf = SVC(C=i, kernel=\"rbf\")\n",
+    "    t1 = round(time.time(),5)\n",
+    "    clf.fit(kernel_train, ytrain)\n",
+    "    t2 = round(time.time(),5)\n",
+    "    #Prédiction sur le jeu de tests\n",
+    "    pred = clf.predict(kernel_test)\n",
+    "    # On sauvegarde le temps de calcul, la précision et \n",
+    "    # les taux d'erreurs par classe\n",
+    "    list_training_times_tol.append(t2-t1)\n",
+    "    list_precision_scores_tol.append(clf.score(kernel_test, ytest))\n",
+    "    list_zero_one_loss_tol.append(metrics.zero_one_loss(ytest, pred))\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "741f82ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(36.0, 0.5, 'Zero-one loss')"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "##### x coordinates\n",
+    "x = tols\n",
+    "training_times = list_training_times_tol\n",
+    "precision_scores = list_precision_scores_tol\n",
+    "zero_one_loss = list_zero_one_loss_tol\n",
+    "        \n",
+    "training_times = [round(i,2) for i in training_times]\n",
+    "precision_scores = [round(i,3) for i in precision_scores]\n",
+    "zero_one_loss = [round(i,3) for i in zero_one_loss]\n",
+    "\n",
+    "### Create plot\n",
+    "fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(7,10))\n",
+    "fig.tight_layout(pad=3.0)\n",
+    "figs[0].plot(x,training_times, marker='o', color='r')\n",
+    "figs[1].plot(x,precision_scores, marker='o', color='b')\n",
+    "figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
+    "\n",
+    "### Add every x coordinates\n",
+    "figs[0].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[1].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[2].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "\n",
+    "for i in range(len(x)):\n",
+    "    figs[0].annotate(training_times[i], # this is the text\n",
+    "                 (x[i],training_times[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[1].annotate(precision_scores[i], # this is the text\n",
+    "                 (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[2].annotate(zero_one_loss[i], # this is the text\n",
+    "                 (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "\n",
+    "figs[0].set_xticks(x)\n",
+    "figs[1].set_xticks(x)\n",
+    "figs[2].set_xticks(x)\n",
+    "    \n",
+    "### Add title and axis names\n",
+    "figs[0].title.set_text('Training times for various level of tolerance (kernel=rbf)')\n",
+    "figs[1].title.set_text('Precision score for various level of tolerance (kernel=rbf)')\n",
+    "figs[2].title.set_text('Zero-one loss metrics various level of tolerance (kernel=rbf)')\n",
+    "figs[0].set_xlabel('tolerance')\n",
+    "figs[1].set_xlabel('tolerance')\n",
+    "figs[2].set_xlabel('tolerance')\n",
+    "figs[0].set_ylabel('Training times (in seconds)')\n",
+    "figs[1].set_ylabel('Precision score')\n",
+    "figs[2].set_ylabel('Zero-one loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "62c7302a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training...\n",
+      "Predicting...\n",
+      "Score échantillon de test :  0.9506666666666667\n",
+      "Précision pour chaque classe : \n",
+      " [0.99324324 0.97633136 0.93377483 0.95375723 0.92546584 0.91549296\n",
+      " 0.96527778 0.95833333 0.96402878 0.91472868]\n",
+      "Matrice de confusion pour C=1.0 et kernel=rbf :\n",
+      " [[147   0   0   0   1   0   0   0   1   0]\n",
+      " [  0 165   0   0   0   0   0   0   1   0]\n",
+      " [  1   2 141   0   0   1   1   1   0   0]\n",
+      " [  0   0   3 165   0   3   0   1   1   1]\n",
+      " [  0   0   1   0 149   0   1   0   0   5]\n",
+      " [  0   0   0   4   2 130   3   1   1   1]\n",
+      " [  0   0   1   0   0   3 139   0   0   0]\n",
+      " [  0   1   3   0   4   0   0 138   0   4]\n",
+      " [  0   1   1   3   1   4   0   0 134   0]\n",
+      " [  0   0   1   1   4   1   0   3   1 118]]\n",
+      "Zero-one classification loss :\n",
+      " 0.04933333333333334\n"
+     ]
+    }
+   ],
+   "source": [
+    "####### Meilleur modèle de SVM #######\n",
+    "\n",
+    "#Entraîne le classifier\n",
+    "clf = SVC(C=1.0,kernel=\"rbf\")\n",
+    "print(\"Training...\")\n",
+    "clf.fit(xtrain, ytrain)\n",
+    "\n",
+    "#Prédiction sur le jeu de tests\n",
+    "print(\"Predicting...\")\n",
+    "pred = clf.predict(xtest)\n",
+    "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
+    "score = clf.score(xtest, ytest)\n",
+    "print(\"Score échantillon de test : \", score)\n",
+    "\n",
+    "#Calcul de différentes metrics\n",
+    "print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
+    "print(\"Matrice de confusion pour C=1.0 et kernel=rbf :\\n\", metrics.confusion_matrix(ytest, pred))\n",
+    "print(\"Zero-one classification loss :\\n\", metrics.zero_one_loss(ytest, pred))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbb5eda6",
+   "id": "30a232d5",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -346,7 +468,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/TP1_prog2.py.ipynb b/TP1_prog2.py.ipynb
index 888409e..5398024 100644
--- a/TP1_prog2.py.ipynb
+++ b/TP1_prog2.py.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "530f620c",
    "metadata": {},
    "outputs": [],
@@ -16,12 +16,13 @@
     "from matplotlib import pyplot as plt\n",
     "from sklearn.model_selection import KFold\n",
     "import time\n",
-    "import statistics"
+    "import statistics\n",
+    "from sklearn import metrics"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "68b6a517",
    "metadata": {},
    "outputs": [],
@@ -863,11 +864,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "98107e41",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrice de confusion K-NN :\n",
+      " [[51  0  0  0  0  1  0  0  0  0]\n",
+      " [ 0 56  0  0  0  0  0  0  0  0]\n",
+      " [ 3  1 45  1  0  0  1  1  0  0]\n",
+      " [ 0  1  1 35  0  1  0  1  1  1]\n",
+      " [ 0  3  0  0 48  0  0  0  0  2]\n",
+      " [ 0  1  0  1  0 38  0  0  0  0]\n",
+      " [ 0  0  0  0  0  2 44  0  0  0]\n",
+      " [ 0  2  0  0  3  0  0 47  0  0]\n",
+      " [ 2  0  0  0  0  3  1  0 42  2]\n",
+      " [ 0  0  0  0  4  1  0  1  2 50]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Create vector of 5000 random indexes\n",
+    "rand_indexes = np.random.randint(70000, size=5000)\n",
+    "### Load data with the previous vector\n",
+    "data = mnist.data[rand_indexes]\n",
+    "# print(\"Dataset : \", data)\n",
+    "target = mnist.target[rand_indexes]\n",
+    "\n",
+    "# Split the dataset\n",
+    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
+    "\n",
+    "# Training on xtrain,ytrain\n",
+    "clf = neighbors.KNeighborsClassifier(n_neighbors=3,p=2,n_jobs=1)\n",
+    "clf.fit(xtrain, ytrain)\n",
+    "# Predicting on xtest\n",
+    "pred = clf.predict(xtest)\n",
+    "print(\"Matrice de confusion K-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
+   ]
   },
   {
    "cell_type": "code",
@@ -894,7 +930,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/TP2_prog1.py.ipynb b/TP2_prog1.py.ipynb
index 2178f20..a895d5b 100644
--- a/TP2_prog1.py.ipynb
+++ b/TP2_prog1.py.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "3eb7a65b",
    "metadata": {},
    "outputs": [],
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "a8812842",
    "metadata": {},
    "outputs": [],
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "6ec263be",
    "metadata": {},
    "outputs": [],
@@ -612,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 7,
    "id": "b5c53e81",
    "metadata": {},
    "outputs": [
@@ -621,9 +621,247 @@
      "output_type": "stream",
      "text": [
       "Computing for activations =  identity ...\n",
+      "Iteration 1, loss = 3.11224909\n",
+      "Iteration 2, loss = 0.70854568\n",
+      "Iteration 3, loss = 0.52376870\n",
+      "Iteration 4, loss = 0.43536739\n",
+      "Iteration 5, loss = 0.39083398\n",
+      "Iteration 6, loss = 0.36212214\n",
+      "Iteration 7, loss = 0.34711959\n",
+      "Iteration 8, loss = 0.33529051\n",
+      "Iteration 9, loss = 0.32645959\n",
+      "Iteration 10, loss = 0.31210041\n",
+      "Iteration 11, loss = 0.30752402\n",
+      "Iteration 12, loss = 0.30330625\n",
+      "Iteration 13, loss = 0.29551273\n",
+      "Iteration 14, loss = 0.29731196\n",
+      "Iteration 15, loss = 0.29336812\n",
+      "Iteration 16, loss = 0.28803723\n",
+      "Iteration 17, loss = 0.29129394\n",
+      "Iteration 18, loss = 0.28711416\n",
+      "Iteration 19, loss = 0.28879544\n",
+      "Iteration 20, loss = 0.28476943\n",
+      "Iteration 21, loss = 0.28758300\n",
+      "Iteration 22, loss = 0.29131686\n",
+      "Iteration 23, loss = 0.28409160\n",
+      "Iteration 24, loss = 0.28754863\n",
+      "Iteration 25, loss = 0.28786580\n",
+      "Iteration 26, loss = 0.28697806\n",
+      "Iteration 27, loss = 0.28521567\n",
+      "Iteration 28, loss = 0.28971331\n",
+      "Iteration 29, loss = 0.28413101\n",
+      "Iteration 30, loss = 0.28784417\n",
+      "Iteration 31, loss = 0.28586074\n",
+      "Iteration 32, loss = 0.29405241\n",
+      "Iteration 33, loss = 0.28681966\n",
+      "Iteration 34, loss = 0.28580153\n",
+      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
       "Computing for activations =  logistic ...\n",
+      "Iteration 1, loss = 2.30769833\n",
+      "Iteration 2, loss = 2.30291040\n",
+      "Iteration 3, loss = 2.30244898\n",
+      "Iteration 4, loss = 2.30268244\n",
+      "Iteration 5, loss = 2.30239578\n",
+      "Iteration 6, loss = 2.30232797\n",
+      "Iteration 7, loss = 2.30225384\n",
+      "Iteration 8, loss = 2.30250679\n",
+      "Iteration 9, loss = 2.30217561\n",
+      "Iteration 10, loss = 2.30191053\n",
+      "Iteration 11, loss = 2.30200626\n",
+      "Iteration 12, loss = 2.30190266\n",
+      "Iteration 13, loss = 2.30192900\n",
+      "Iteration 14, loss = 2.30175066\n",
+      "Iteration 15, loss = 2.30167723\n",
+      "Iteration 16, loss = 2.30164233\n",
+      "Iteration 17, loss = 2.30159434\n",
+      "Iteration 18, loss = 2.30156877\n",
+      "Iteration 19, loss = 2.30152622\n",
+      "Iteration 20, loss = 2.30154849\n",
+      "Iteration 21, loss = 2.30150245\n",
+      "Iteration 22, loss = 2.30148105\n",
+      "Iteration 23, loss = 2.30142407\n",
+      "Iteration 24, loss = 2.30137533\n",
+      "Iteration 25, loss = 2.30152046\n",
+      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
       "Computing for activations =  tanh ...\n",
+      "Iteration 1, loss = 0.90876300\n",
+      "Iteration 2, loss = 0.54644437\n",
+      "Iteration 3, loss = 0.46970528\n",
+      "Iteration 4, loss = 0.41632441\n",
+      "Iteration 5, loss = 0.40556014\n",
+      "Iteration 6, loss = 0.39304098\n",
+      "Iteration 7, loss = 0.34769367\n",
+      "Iteration 8, loss = 0.33746557\n",
+      "Iteration 9, loss = 0.31966629\n",
+      "Iteration 10, loss = 0.30298759\n",
+      "Iteration 11, loss = 0.31346061\n",
+      "Iteration 12, loss = 0.31918136\n",
+      "Iteration 13, loss = 0.33433773\n",
+      "Iteration 14, loss = 0.31077716\n",
+      "Iteration 15, loss = 0.31443855\n",
+      "Iteration 16, loss = 0.29622440\n",
+      "Iteration 17, loss = 0.29625351\n",
+      "Iteration 18, loss = 0.28699997\n",
+      "Iteration 19, loss = 0.30362581\n",
+      "Iteration 20, loss = 0.30475983\n",
+      "Iteration 21, loss = 0.28076258\n",
+      "Iteration 22, loss = 0.28172739\n",
+      "Iteration 23, loss = 0.28994075\n",
+      "Iteration 24, loss = 0.27289827\n",
+      "Iteration 25, loss = 0.26683419\n",
+      "Iteration 26, loss = 0.25457182\n",
+      "Iteration 27, loss = 0.25186019\n",
+      "Iteration 28, loss = 0.24638283\n",
+      "Iteration 29, loss = 0.24811218\n",
+      "Iteration 30, loss = 0.24545728\n",
+      "Iteration 31, loss = 0.23533057\n",
+      "Iteration 32, loss = 0.23147359\n",
+      "Iteration 33, loss = 0.23779321\n",
+      "Iteration 34, loss = 0.23702320\n",
+      "Iteration 35, loss = 0.23076596\n",
+      "Iteration 36, loss = 0.23144501\n",
+      "Iteration 37, loss = 0.23887763\n",
+      "Iteration 38, loss = 0.24003808\n",
+      "Iteration 39, loss = 0.23919613\n",
+      "Iteration 40, loss = 0.24155683\n",
+      "Iteration 41, loss = 0.23472365\n",
+      "Iteration 42, loss = 0.24448571\n",
+      "Iteration 43, loss = 0.23936428\n",
+      "Iteration 44, loss = 0.24179928\n",
+      "Iteration 45, loss = 0.22778094\n",
+      "Iteration 46, loss = 0.22838648\n",
+      "Iteration 47, loss = 0.22280802\n",
+      "Iteration 48, loss = 0.22293833\n",
+      "Iteration 49, loss = 0.21873897\n",
+      "Iteration 50, loss = 0.22255140\n",
+      "Iteration 51, loss = 0.22840063\n",
+      "Iteration 52, loss = 0.21844829\n",
+      "Iteration 53, loss = 0.20518849\n",
+      "Iteration 54, loss = 0.21499200\n",
+      "Iteration 55, loss = 0.20790650\n",
+      "Iteration 56, loss = 0.21104213\n",
+      "Iteration 57, loss = 0.21095536\n",
+      "Iteration 58, loss = 0.21809221\n",
+      "Iteration 59, loss = 0.21593479\n",
+      "Iteration 60, loss = 0.21037508\n",
+      "Iteration 61, loss = 0.20032046\n",
+      "Iteration 62, loss = 0.20149654\n",
+      "Iteration 63, loss = 0.20030395\n",
+      "Iteration 64, loss = 0.19530838\n",
+      "Iteration 65, loss = 0.20230686\n",
+      "Iteration 66, loss = 0.18727727\n",
+      "Iteration 67, loss = 0.18870452\n",
+      "Iteration 68, loss = 0.19615807\n",
+      "Iteration 69, loss = 0.20960995\n",
+      "Iteration 70, loss = 0.20044529\n",
+      "Iteration 71, loss = 0.19594940\n",
+      "Iteration 72, loss = 0.21108270\n",
+      "Iteration 73, loss = 0.20002339\n",
+      "Iteration 74, loss = 0.19700233\n",
+      "Iteration 75, loss = 0.18615323\n",
+      "Iteration 76, loss = 0.18832837\n",
+      "Iteration 77, loss = 0.19898041\n",
+      "Iteration 78, loss = 0.20418693\n",
+      "Iteration 79, loss = 0.19368883\n",
+      "Iteration 80, loss = 0.18468801\n",
+      "Iteration 81, loss = 0.18402290\n",
+      "Iteration 82, loss = 0.18080945\n",
+      "Iteration 83, loss = 0.18818585\n",
+      "Iteration 84, loss = 0.18884275\n",
+      "Iteration 85, loss = 0.19623093\n",
+      "Iteration 86, loss = 0.18967099\n",
+      "Iteration 87, loss = 0.18097206\n",
+      "Iteration 88, loss = 0.18184130\n",
+      "Iteration 89, loss = 0.18070406\n",
+      "Iteration 90, loss = 0.18038370\n",
+      "Iteration 91, loss = 0.17927189\n",
+      "Iteration 92, loss = 0.18369377\n",
+      "Iteration 93, loss = 0.17680643\n",
+      "Iteration 94, loss = 0.17613015\n",
+      "Iteration 95, loss = 0.18412622\n",
+      "Iteration 96, loss = 0.18176041\n",
+      "Iteration 97, loss = 0.17843724\n",
+      "Iteration 98, loss = 0.17323722\n",
+      "Iteration 99, loss = 0.15801558\n",
+      "Iteration 100, loss = 0.16873188\n",
       "Computing for activations =  relu ...\n",
+      "Iteration 1, loss = 0.84846145\n",
+      "Iteration 2, loss = 0.25873061\n",
+      "Iteration 3, loss = 0.19454472\n",
+      "Iteration 4, loss = 0.16307308\n",
+      "Iteration 5, loss = 0.14511879\n",
+      "Iteration 6, loss = 0.12548967\n",
+      "Iteration 7, loss = 0.11394716\n",
+      "Iteration 8, loss = 0.10319434\n",
+      "Iteration 9, loss = 0.09340300\n",
+      "Iteration 10, loss = 0.08821125\n",
+      "Iteration 11, loss = 0.08199792\n",
+      "Iteration 12, loss = 0.07829423\n",
+      "Iteration 13, loss = 0.07068076\n",
+      "Iteration 14, loss = 0.06754605\n",
+      "Iteration 15, loss = 0.06630167\n",
+      "Iteration 16, loss = 0.05935105\n",
+      "Iteration 17, loss = 0.05915074\n",
+      "Iteration 18, loss = 0.05527145\n",
+      "Iteration 19, loss = 0.05223425\n",
+      "Iteration 20, loss = 0.05321723\n",
+      "Iteration 21, loss = 0.04984139\n",
+      "Iteration 22, loss = 0.04480437\n",
+      "Iteration 23, loss = 0.04665587\n",
+      "Iteration 24, loss = 0.04289580\n",
+      "Iteration 25, loss = 0.03622594\n",
+      "Iteration 26, loss = 0.03530255\n",
+      "Iteration 27, loss = 0.04065954\n",
+      "Iteration 28, loss = 0.04278904\n",
+      "Iteration 29, loss = 0.03540820\n",
+      "Iteration 30, loss = 0.03756356\n",
+      "Iteration 31, loss = 0.02881830\n",
+      "Iteration 32, loss = 0.03197044\n",
+      "Iteration 33, loss = 0.03519754\n",
+      "Iteration 34, loss = 0.03226369\n",
+      "Iteration 35, loss = 0.03484656\n",
+      "Iteration 36, loss = 0.02892709\n",
+      "Iteration 37, loss = 0.02495425\n",
+      "Iteration 38, loss = 0.02849610\n",
+      "Iteration 39, loss = 0.02382546\n",
+      "Iteration 40, loss = 0.02260332\n",
+      "Iteration 41, loss = 0.02632971\n",
+      "Iteration 42, loss = 0.03400246\n",
+      "Iteration 43, loss = 0.02602642\n",
+      "Iteration 44, loss = 0.02628543\n",
+      "Iteration 45, loss = 0.02091241\n",
+      "Iteration 46, loss = 0.02698089\n",
+      "Iteration 47, loss = 0.02287099\n",
+      "Iteration 48, loss = 0.01953821\n",
+      "Iteration 49, loss = 0.02287816\n",
+      "Iteration 50, loss = 0.01786992\n",
+      "Iteration 51, loss = 0.01614434\n",
+      "Iteration 52, loss = 0.02288893\n",
+      "Iteration 53, loss = 0.01831700\n",
+      "Iteration 54, loss = 0.02733430\n",
+      "Iteration 55, loss = 0.01667890\n",
+      "Iteration 56, loss = 0.01619622\n",
+      "Iteration 57, loss = 0.02049603\n",
+      "Iteration 58, loss = 0.02224142\n",
+      "Iteration 59, loss = 0.01578972\n",
+      "Iteration 60, loss = 0.01984688\n",
+      "Iteration 61, loss = 0.02051338\n",
+      "Iteration 62, loss = 0.01760285\n",
+      "Iteration 63, loss = 0.02048740\n",
+      "Iteration 64, loss = 0.01581426\n",
+      "Iteration 65, loss = 0.01006159\n",
+      "Iteration 66, loss = 0.01263444\n",
+      "Iteration 67, loss = 0.01597941\n",
+      "Iteration 68, loss = 0.01564173\n",
+      "Iteration 69, loss = 0.01664765\n",
+      "Iteration 70, loss = 0.01238414\n",
+      "Iteration 71, loss = 0.01315483\n",
+      "Iteration 72, loss = 0.01670207\n",
+      "Iteration 73, loss = 0.01893887\n",
+      "Iteration 74, loss = 0.01745178\n",
+      "Iteration 75, loss = 0.01212984\n",
+      "Iteration 76, loss = 0.01667099\n",
+      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
       "Done\n"
      ]
     }
@@ -640,7 +878,7 @@
     "for i in activations:\n",
     "    print(\"Computing for activations = \", i, \"...\")\n",
     "    #Entraîne le classifier\n",
-    "    clf = neural_network.MLPClassifier(random_state=1, max_iter=25, hidden_layer_sizes=(50,)*10, verbose=False, activation=i)\n",
+    "    clf = neural_network.MLPClassifier(random_state=1, max_iter=100, hidden_layer_sizes=(50,)*10, verbose=True, activation=i)\n",
     "    t1 = round(time.time(),5)\n",
     "    clf.fit(xtrain, ytrain)\n",
     "    t2 = round(time.time(),5)\n",
@@ -658,7 +896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 9,
    "id": "c7afbbdc",
    "metadata": {},
    "outputs": [
@@ -667,9 +905,9 @@
      "output_type": "stream",
      "text": [
       "x :  ['identity', 'logistic', 'tanh', 'relu']\n",
-      "training_times :  [11.6006600856781, 16.919300079345703, 26.391479969024658, 18.122960090637207]\n",
-      "precision_scores :  [0.8994761904761904, 0.11223809523809523, 0.9152857142857143, 0.9637619047619047]\n",
-      "zero_one_loss :  [0.10052380952380957, 0.8877619047619048, 0.08471428571428574, 0.03623809523809529]\n"
+      "training_times :  [34.288330078125, 33.1211998462677, 171.96871995925903, 106.40883994102478]\n",
+      "precision_scores :  [0.9074285714285715, 0.11142857142857143, 0.9351904761904762, 0.9683333333333334]\n",
+      "zero_one_loss :  [0.09257142857142853, 0.8885714285714286, 0.06480952380952376, 0.03166666666666662]\n"
      ]
     },
     {
@@ -678,13 +916,13 @@
        "Text(36.0, 0.5, 'Zero-one loss')"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 504x720 with 3 Axes>"
       ]
@@ -995,7 +1233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 7,
    "id": "abb0fcf1",
    "metadata": {},
    "outputs": [
@@ -1003,79 +1241,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration 1, loss = 0.58722635\n",
-      "Iteration 2, loss = 0.19664437\n",
-      "Iteration 3, loss = 0.14644059\n",
-      "Iteration 4, loss = 0.12219867\n",
-      "Iteration 5, loss = 0.10425279\n",
-      "Iteration 6, loss = 0.09855065\n",
-      "Iteration 7, loss = 0.07754340\n",
-      "Iteration 8, loss = 0.07198762\n",
-      "Iteration 9, loss = 0.06616934\n",
-      "Iteration 10, loss = 0.06035184\n",
-      "Iteration 11, loss = 0.05569499\n",
-      "Iteration 12, loss = 0.05829348\n",
-      "Iteration 13, loss = 0.05370925\n",
-      "Iteration 14, loss = 0.04997678\n",
-      "Iteration 15, loss = 0.04527340\n",
-      "Iteration 16, loss = 0.03983840\n",
-      "Iteration 17, loss = 0.04076422\n",
-      "Iteration 18, loss = 0.04029400\n",
-      "Iteration 19, loss = 0.03321192\n",
-      "Iteration 20, loss = 0.03882352\n",
-      "Iteration 21, loss = 0.03363780\n",
-      "Iteration 22, loss = 0.03320547\n",
-      "Iteration 23, loss = 0.02775446\n",
-      "Iteration 24, loss = 0.04253825\n",
-      "Iteration 25, loss = 0.03002649\n",
-      "Iteration 26, loss = 0.02438176\n",
-      "Iteration 27, loss = 0.02810122\n",
-      "Iteration 28, loss = 0.03876961\n",
-      "Iteration 29, loss = 0.02501501\n",
-      "Iteration 30, loss = 0.02376453\n",
-      "Iteration 31, loss = 0.02010948\n",
-      "Iteration 32, loss = 0.02460232\n",
-      "Iteration 33, loss = 0.02330741\n",
-      "Iteration 34, loss = 0.01953304\n",
-      "Iteration 35, loss = 0.02254089\n",
-      "Iteration 36, loss = 0.02653422\n",
-      "Iteration 37, loss = 0.03004069\n",
-      "Iteration 38, loss = 0.02443066\n",
-      "Iteration 39, loss = 0.01923374\n",
-      "Iteration 40, loss = 0.02801464\n",
-      "Iteration 41, loss = 0.01522026\n",
-      "Iteration 42, loss = 0.01749346\n",
-      "Iteration 43, loss = 0.02286608\n",
-      "Iteration 44, loss = 0.02714804\n",
-      "Iteration 45, loss = 0.01312122\n",
-      "Iteration 46, loss = 0.01681842\n",
-      "Iteration 47, loss = 0.01937897\n",
-      "Iteration 48, loss = 0.02501177\n",
-      "Iteration 49, loss = 0.02581483\n",
-      "Iteration 50, loss = 0.01928808\n",
-      "Iteration 51, loss = 0.02221606\n",
-      "Iteration 52, loss = 0.01724194\n",
-      "Iteration 53, loss = 0.02403539\n",
-      "Iteration 54, loss = 0.01944278\n",
-      "Iteration 55, loss = 0.01724867\n",
-      "Iteration 56, loss = 0.01351808\n",
-      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n",
-      "Paramètre :\n",
-      "\n",
-      "random_state =  1\n",
-      "max_iter =  300\n",
-      "nb_hidden_layer =  15\n",
-      "hidden_layer_size =  85\n",
-      "solver =  adam\n",
-      "activation =  relu\n",
-      "alpha =  1e-07\n",
-      "Temps d'entraînement :  78.07823991775513\n",
-      "Score :  0.9738095238095238\n",
-      "Zero-one loss :  0.02619047619047621\n"
+      "Matrice de confusion A-NN :\n",
+      " [[59  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 60  0  0  0  0  0  1  0  0]\n",
+      " [ 0  0 42  0  0  1  2  1  2  0]\n",
+      " [ 0  0  1 44  0  1  0  0  0  0]\n",
+      " [ 0  0  0  0 46  0  1  0  0  4]\n",
+      " [ 0  0  0  0  0 31  0  0  1  0]\n",
+      " [ 0  0  0  0  0  0 48  0  0  0]\n",
+      " [ 1  0  0  1  0  0  0 49  0  0]\n",
+      " [ 0  1  1  5  0  1  0  0 48  0]\n",
+      " [ 2  0  0  1  1  2  0  0  2 40]]\n"
      ]
     }
    ],
    "source": [
+    "### Create vector of 5000 random indexes\n",
+    "rand_indexes = np.random.randint(70000, size=5000)\n",
+    "### Load data with the previous vector\n",
+    "data = mnist.data[rand_indexes]\n",
+    "# print(\"Dataset : \", data)\n",
+    "target = mnist.target[rand_indexes]\n",
+    "\n",
+    "# Split the dataset\n",
+    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
+    "\n",
     "best_training_time = 0\n",
     "best_precision_score = 0\n",
     "best_zero_one_loss = 0\n",
@@ -1090,7 +1280,7 @@
     "a = 0.0000001\n",
     "\n",
     "#Entraîne le classifier\n",
-    "clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=True)\n",
+    "clf = neural_network.MLPClassifier(random_state=r, max_iter=max_i, hidden_layer_sizes=hl, solver=sol, activation=act, alpha=a, verbose=False)\n",
     "t1 = round(time.time(),5)\n",
     "clf.fit(xtrain, ytrain)\n",
     "t2 = round(time.time(),5)\n",
@@ -1104,17 +1294,18 @@
     "best_precision_score = clf.score(xtest, ytest)\n",
     "best_zero_one_loss = metrics.zero_one_loss(ytest, pred)\n",
     "\n",
-    "print(\"Paramètre :\\n\")\n",
-    "print(\"random_state = \", r)\n",
-    "print(\"max_iter = \", max_i)\n",
-    "print(\"nb_hidden_layer = \", nb_hl)\n",
-    "print(\"hidden_layer_size = \", hl_size)\n",
-    "print(\"solver = \", sol)\n",
-    "print(\"activation = \", act)\n",
-    "print(\"alpha = \", a)\n",
-    "print(\"Temps d'entraînement : \", best_training_time)\n",
-    "print(\"Score : \", best_precision_score)\n",
-    "print(\"Zero-one loss : \", best_zero_one_loss)"
+    "# print(\"Paramètre :\\n\")\n",
+    "# print(\"random_state = \", r)\n",
+    "# print(\"max_iter = \", max_i)\n",
+    "# print(\"nb_hidden_layer = \", nb_hl)\n",
+    "# print(\"hidden_layer_size = \", hl_size)\n",
+    "# print(\"solver = \", sol)\n",
+    "# print(\"activation = \", act)\n",
+    "# print(\"alpha = \", a)\n",
+    "# print(\"Temps d'entraînement : \", best_training_time)\n",
+    "# print(\"Score : \", best_precision_score)\n",
+    "# print(\"Zero-one loss : \", best_zero_one_loss)\n",
+    "print(\"Matrice de confusion A-NN :\\n\", metrics.confusion_matrix(ytest, pred))"
    ]
   },
   {
@@ -1142,7 +1333,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,
diff --git a/TP3_prog1.py.ipynb b/TP3_prog1.py.ipynb
index 7b0e827..e992c42 100644
--- a/TP3_prog1.py.ipynb
+++ b/TP3_prog1.py.ipynb
@@ -2,329 +2,463 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "530f620c",
+   "execution_count": 1,
+   "id": "3eb7a65b",
    "metadata": {},
    "outputs": [],
    "source": [
+    "####### Import #######\n",
     "from sklearn.datasets import fetch_openml\n",
-    "from sklearn import model_selection\n",
-    "from sklearn import neighbors\n",
-    "from sklearn.svm import SVC\n",
     "import sklearn\n",
+    "from matplotlib import pyplot as plt\n",
+    "from sklearn import model_selection\n",
+    "from sklearn import neural_network\n",
+    "from sklearn import metrics\n",
+    "from sklearn.svm import SVC\n",
     "import numpy as np\n",
-    "\n",
-    "mnist = fetch_openml('mnist_784',as_frame=False)"
+    "import time\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "eb2c4496",
+   "id": "a8812842",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset :  [[0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " ...\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]]\n",
-      "Etiquettes :  ['1' '3' '4' ... '5' '1' '2']\n",
-      "Prédiction :  ['6' '7' '1' '4' '2' '7' '6' '6' '4' '9' '8' '4' '0' '0' '6' '8' '5' '0'\n",
-      " '9' '6' '5' '0' '7' '7' '0' '7' '6' '1' '0' '1' '6' '6' '5' '8' '5' '6'\n",
-      " '6' '5' '0' '7' '7' '5' '2' '7' '3' '2' '2' '6' '0' '0' '5' '8' '2' '4'\n",
-      " '1' '0' '9' '6' '3' '7' '6' '3' '9' '4' '0' '0' '8' '8' '0' '6' '7' '1'\n",
-      " '8' '3' '1' '6' '9' '1' '8' '0' '2' '0' '4' '5' '9' '3' '4' '3' '6' '3'\n",
-      " '2' '3' '8' '0' '8' '6' '1' '7' '3' '8' '4' '2' '0' '7' '9' '4' '0' '2'\n",
-      " '2' '0' '2' '2' '3' '0' '0' '0' '6' '8' '2' '4' '3' '7' '2' '6' '8' '4'\n",
-      " '3' '8' '8' '0' '4' '6' '1' '0' '4' '6' '6' '0' '0' '6' '1' '6' '5' '5'\n",
-      " '1' '5' '8' '2' '6' '4' '7' '5' '3' '2' '5' '8' '5' '2' '2' '3' '0' '3'\n",
-      " '6' '1' '4' '8' '1' '7' '7' '5' '9' '1' '3' '5' '0' '7' '8' '6' '5' '0'\n",
-      " '6' '6' '8' '5' '9' '5' '3' '9' '7' '4' '9' '0' '1' '5' '3' '3' '6' '1'\n",
-      " '1' '1' '8' '7' '7' '1' '7' '4' '1' '1' '3' '8' '4' '4' '3' '9' '8' '4'\n",
-      " '0' '4' '4' '9' '6' '0' '6' '0' '3' '8' '8' '0' '9' '1' '4' '4' '2' '1'\n",
-      " '5' '7' '5' '0' '7' '6' '0' '4' '5' '7' '5' '9' '4' '3' '4' '4' '0' '5'\n",
-      " '0' '0' '1' '9' '1' '7' '3' '4' '6' '0' '5' '9' '6' '1' '1' '5' '6' '5'\n",
-      " '2' '9' '4' '3' '4' '1' '0' '0' '4' '2' '1' '7' '1' '4' '1' '3' '9' '2'\n",
-      " '0' '8' '7' '7' '4' '4' '7' '1' '8' '7' '1' '4' '6' '9' '2' '7' '1' '4'\n",
-      " '5' '1' '1' '4' '2' '7' '3' '8' '5' '8' '3' '3' '4' '7' '2' '1' '4' '9'\n",
-      " '9' '4' '7' '9' '3' '4' '9' '7' '1' '0' '7' '7' '3' '8' '4' '6' '1' '3'\n",
-      " '5' '5' '4' '9' '6' '0' '1' '1' '0' '0' '0' '3' '2' '7' '9' '8' '0' '3'\n",
-      " '6' '1' '9' '4' '0' '1' '0' '0' '1' '6' '9' '6' '3' '8' '2' '5' '9' '5'\n",
-      " '1' '3' '7' '0' '9' '3' '2' '6' '8' '5' '1' '5' '4' '1' '4' '1' '1' '3'\n",
-      " '1' '5' '7' '2' '3' '2' '6' '1' '2' '6' '3' '8' '7' '3' '3' '9' '8' '0'\n",
-      " '4' '3' '7' '7' '9' '3' '9' '8' '7' '8' '0' '4' '8' '8' '0' '4' '1' '5'\n",
-      " '1' '2' '1' '3' '5' '4' '9' '8' '1' '3' '1' '5' '8' '4' '8' '2' '9' '8'\n",
-      " '2' '3' '6' '3' '5' '2' '4' '0' '1' '0' '1' '8' '9' '9' '6' '2' '4' '1'\n",
-      " '5' '6' '7' '7' '1' '5' '0' '2' '6' '5' '0' '3' '2' '8' '8' '9' '7' '9'\n",
-      " '4' '4' '1' '9' '7' '8' '2' '1' '9' '6' '2' '4' '8' '7' '8' '9' '9' '4'\n",
-      " '6' '9' '9' '5' '6' '9' '9' '8' '5' '5' '6' '4' '6' '8' '8' '7' '6' '0'\n",
-      " '0' '9' '2' '3' '7' '7' '1' '5' '9' '1' '9' '9' '1' '4' '1' '9' '6' '9'\n",
-      " '0' '9' '4' '6' '1' '0' '7' '0' '8' '9' '7' '3' '8' '2' '3' '0' '2' '8'\n",
-      " '3' '1' '7' '0' '2' '1' '0' '4' '2' '0' '8' '1' '5' '2' '4' '5' '0' '9'\n",
-      " '8' '1' '3' '9' '8' '7' '2' '4' '6' '2' '3' '9' '1' '8' '2' '1' '9' '0'\n",
-      " '2' '4' '0' '9' '1' '4' '1' '3' '2' '4' '9' '5' '0' '2' '2' '1' '1' '7'\n",
-      " '6' '8' '4' '9' '7' '7' '9' '4' '2' '3' '8' '1' '3' '5' '7' '9' '2' '0'\n",
-      " '4' '8' '1' '6' '1' '7' '9' '6' '3' '6' '0' '0' '4' '7' '1' '1' '1' '4'\n",
-      " '5' '6' '6' '1' '7' '6' '1' '7' '6' '1' '1' '2' '0' '8' '6' '1' '4' '3'\n",
-      " '3' '6' '8' '7' '1' '1' '1' '4' '3' '3' '2' '6' '3' '3' '8' '8' '3' '1'\n",
-      " '8' '6' '6' '8' '8' '9' '6' '7' '6' '7' '8' '9' '1' '8' '3' '9' '5' '0'\n",
-      " '6' '6' '9' '3' '1' '2' '5' '5' '0' '9' '5' '9' '0' '0' '6' '1' '8' '5'\n",
-      " '0' '2' '2' '8' '3' '9' '7' '2' '7' '6' '2' '8' '6' '8' '8' '0' '2' '0'\n",
-      " '6' '2' '7' '7' '3' '7' '2' '7' '1' '7' '9' '3' '4' '7' '7' '9' '9' '2'\n",
-      " '5' '8' '3' '7' '7' '2' '1' '7' '1' '1' '9' '9' '3' '0' '9' '4' '9' '0'\n",
-      " '7' '6' '7' '7' '7' '7' '9' '7' '8' '1' '1' '6' '2' '6' '3' '8' '2' '8'\n",
-      " '1' '5' '7' '0' '8' '3' '2' '7' '5' '1' '5' '3' '5' '2' '1' '7' '6' '0'\n",
-      " '2' '6' '3' '2' '6' '0' '6' '2' '3' '9' '8' '6' '4' '9' '1' '3' '0' '4'\n",
-      " '2' '3' '8' '1' '9' '0' '3' '5' '4' '5' '3' '2' '5' '0' '1' '1' '8' '3'\n",
-      " '5' '6' '2' '1' '9' '3' '0' '4' '5' '9' '7' '2' '2' '1' '2' '1' '1' '5'\n",
-      " '0' '9' '3' '7' '1' '9' '6' '5' '1' '6' '0' '1' '1' '6' '5' '8' '2' '2'\n",
-      " '1' '8' '9' '7' '6' '8' '4' '5' '2' '3' '0' '7' '6' '0' '6' '6' '6' '0'\n",
-      " '8' '8' '3' '4' '0' '9' '7' '5' '1' '1' '1' '4' '6' '7' '9' '6' '3' '9'\n",
-      " '3' '9' '1' '9' '6' '4' '5' '4' '7' '0' '1' '9' '4' '8' '4' '6' '1' '8'\n",
-      " '5' '6' '5' '1' '2' '7' '9' '5' '8' '0' '8' '8' '3' '2' '9' '4' '4' '8'\n",
-      " '3' '0' '6' '5' '9' '7' '0' '0' '9' '7' '0' '3' '2' '1' '0' '5' '6' '4'\n",
-      " '0' '4' '6' '9' '3' '0' '4' '1' '5' '6' '3' '6' '9' '1' '5' '6' '3' '0'\n",
-      " '1' '6' '1' '0' '6' '2' '1' '7' '1' '9']\n",
-      "Probabilités :  [[0.  0.  0.  ... 0.  0.  0. ]\n",
-      " [0.  0.  0.  ... 1.  0.  0. ]\n",
-      " [0.  1.  0.  ... 0.  0.  0. ]\n",
-      " ...\n",
-      " [0.  0.  0.  ... 1.  0.  0. ]\n",
-      " [0.  0.4 0.  ... 0.1 0.  0.3]\n",
-      " [0.  0.  0.  ... 0.1 0.  0.9]]\n",
-      "Classe image 4 :  9\n",
-      "Classe prédite image 4 :  4\n",
-      "Score échantillon de test :  0.912\n",
-      "Score données apprentissage :  0.94325\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "rand_indexes = np.random.randint(70000, size=5000)\n",
-    "\n",
-    "data = mnist.data[rand_indexes]\n",
-    "print(\"Dataset : \", data)\n",
-    "target = mnist.target[rand_indexes]\n",
-    "print(\"Etiquettes : \", target)\n",
-    "\n",
-    "# xtrain data set d'entraînement et ytrain étiquettes de xtrain\n",
-    "# xtest dataset de prédiction et ytest étiquettes de xtest\n",
-    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.7)\n",
-    "\n",
-    "n_neighbors = 10\n",
-    "clf = svm.SVC(kernel=\"linear\")\n",
-    "# On entraîne l'algorithme sur xtrain et ytrain\n",
-    "clf.fit(xtrain, ytrain)\n",
-    "# On prédit sur xtest\n",
-    "pred = clf.predict(xtest)\n",
-    "print(\"Prédiction : \", pred)\n",
-    "# Probabilités des prédictions sur xtest\n",
-    "pred_proba = clf.predict_proba(xtest)\n",
-    "print(\"Probabilités : \", pred_proba)\n",
-    "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
-    "score = clf.score(xtest, ytest)\n",
-    "print(\"Classe image 4 : \", target[3])\n",
-    "print(\"Classe prédite image 4 : \", pred[3])\n",
-    "print(\"Score échantillon de test : \", score)\n",
-    "\n",
-    "scoreApp = clf.score(xtrain, ytrain)\n",
-    "print(\"Score données apprentissage : \", scoreApp)"
+    "####### Loading data #######\n",
+    "mnist = fetch_openml('mnist_784',as_frame=False)\n",
+    "# images = mnist.data.reshape((-1, 28, 28))\n",
+    "# plt.imshow(images[0],cmap=plt.cm.gray_r,interpolation=\"nearest\")\n",
+    "# plt.show()\n",
+    "# print(\"Classe : \", mnist.target[0])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "90db6e29",
+   "id": "6ec263be",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dataset :  [[0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " ...\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]]\n",
-      "Etiquettes :  ['9' '9' '8' ... '9' '4' '6']\n",
-      "[0.92, 0.922, 0.93, 0.966, 0.924, 0.922, 0.922, 0.896, 0.92, 0.91, 0.916, 0.94, 0.938, 0.938, 0.926, 0.936, 0.932, 0.932, 0.934, 0.938, 0.922, 0.934, 0.96, 0.926, 0.942, 0.934, 0.908, 0.926, 0.92, 0.936, 0.932, 0.924, 0.922, 0.938, 0.938, 0.916, 0.932, 0.96, 0.942, 0.922, 0.926, 0.938, 0.936, 0.924, 0.938, 0.946, 0.922, 0.928, 0.912, 0.908, 0.916, 0.932, 0.932, 0.93, 0.92, 0.928, 0.908, 0.932, 0.918, 0.938, 0.92, 0.93, 0.938, 0.924, 0.924, 0.932, 0.916, 0.916, 0.934, 0.928, 0.924, 0.94, 0.942, 0.926, 0.924, 0.912, 0.93, 0.906, 0.894, 0.922, 0.924, 0.912, 0.906, 0.942, 0.95, 0.924, 0.926, 0.92, 0.92, 0.9, 0.918, 0.908, 0.93, 0.942, 0.916, 0.934, 0.916, 0.92, 0.91, 0.918, 0.93, 0.918, 0.916, 0.894, 0.934, 0.926, 0.934, 0.91, 0.9, 0.914, 0.928, 0.918, 0.924, 0.916, 0.908, 0.904, 0.922, 0.912, 0.92, 0.914, 0.926, 0.906, 0.902, 0.914, 0.9, 0.936, 0.906, 0.942, 0.922, 0.906]\n"
+      "Dataset size :  5000\n",
+      "Etiquettes size :  5000\n",
+      "xtrain size :  4500\n",
+      "xtest size :  500\n",
+      "ytrain size :  4500\n",
+      "ytest size :  500\n"
      ]
     }
    ],
    "source": [
-    "for k in range(2,15):\n",
-    "    \n",
-    "    for train_index, test_index in kf.split(data):\n",
-    "#         print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
-    "        X_train, X_test = data[train_index], data[test_index]\n",
-    "        y_train, y_test = target[train_index], target[test_index]\n",
-    "        \n",
-    "        clf = neighbors.KNeighborsClassifier(k)\n",
-    "        # On entraîne l'algorithme sur xtrain et ytrain\n",
-    "        clf.fit(X_train, y_train)\n",
-    "        # On prédit sur xtest\n",
-    "        pred = clf.predict(X_test)\n",
-    "#         print(\"Prédiction : \", pred)\n",
-    "        # Probabilités des prédictions sur xtest\n",
-    "        pred_proba = clf.predict_proba(X_test)\n",
-    "#         print(\"Probabilités : \", pred_proba)\n",
-    "        # On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
-    "        score = clf.score(X_test, y_test)\n",
-    "        scores += [score]\n",
-    "#         print(\"Classe image 4 : \", target[3])\n",
-    "#         print(\"Classe prédite image 4 : \", pred[3])\n",
-    "#         print(\"Score échantillon de test : \", score)\n",
-    "        scoreApp = clf.score(X_train, y_train)\n",
-    "#         print(\"Score données apprentissage : \", scoreApp)\n",
-    "print(scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "bf91b914",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2  :  0.9232000000000001\n",
-      "3  :  0.933\n",
-      "4  :  0.9308\n",
-      "5  :  0.9326000000000001\n",
-      "6  :  0.9300000000000002\n",
-      "7  :  0.922888888888889\n",
-      "8  :  0.9266666666666666\n",
-      "9  :  0.9273333333333333\n",
-      "10  :  0.9206666666666666\n",
-      "11  :  0.9208888888888889\n",
-      "12  :  0.9197777777777778\n",
-      "13  :  0.9175555555555555\n",
-      "14  :  0.9162222222222223\n",
-      "15  :  0.9148888888888889\n"
-     ]
-    }
-   ],
-   "source": [
-    "nice_scores = np.array_split(scores, 14)\n",
-    "for i in range (0,14):\n",
-    "    print (i+2, \" : \", nice_scores[i].mean())\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "cc24e898",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset :  [[0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " ...\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]\n",
-      " [0. 0. 0. ... 0. 0. 0.]]\n",
-      "Etiquettes :  ['0' '0' '5' ... '9' '8' '6']\n",
-      "Temps d'entraînement :  0.002\n",
-      "Temps de prédiction :  0.338\n",
-      "Temps total :  0.34\n",
-      "Temps d'entraînement :  0.003\n",
-      "Temps de prédiction :  0.31\n",
-      "Temps total :  0.313\n",
-      "Temps d'entraînement :  0.002\n",
-      "Temps de prédiction :  0.328\n",
-      "Temps total :  0.33\n",
-      "Temps d'entraînement :  0.003\n",
-      "Temps de prédiction :  0.305\n",
-      "Temps total :  0.308\n",
-      "Temps d'entraînement :  0.003\n",
-      "Temps de prédiction :  0.254\n",
-      "Temps total :  0.257\n",
-      "Temps d'entraînement :  0.003\n",
-      "Temps de prédiction :  0.244\n",
-      "Temps total :  0.247\n",
-      "Temps d'entraînement :  0.004\n",
-      "Temps de prédiction :  0.203\n",
-      "Temps total :  0.207\n",
-      "3  :  0.9045714285714286\n",
-      "4  :  0.91\n",
-      "5  :  0.9168\n",
-      "6  :  0.925\n",
-      "7  :  0.934\n",
-      "8  :  0.922\n",
-      "9  :  0.952\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import KFold\n",
-    "import time\n",
-    "\n",
+    "### Create vector of 1000 random indexes\n",
     "rand_indexes = np.random.randint(70000, size=5000)\n",
-    "\n",
+    "### Load data with the previous vector\n",
     "data = mnist.data[rand_indexes]\n",
-    "print(\"Dataset : \", data)\n",
+    "print(\"Dataset size : \", len(data))\n",
     "target = mnist.target[rand_indexes]\n",
-    "print(\"Etiquettes : \", target)\n",
+    "print(\"Etiquettes size : \", len(target))\n",
     "\n",
+    "### Split the dataset for training and testing\n",
     "# xtrain data set d'entraînement et ytrain étiquettes de xtrain\n",
     "# xtest dataset de prédiction et ytest étiquettes de xtest\n",
+    "xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=0.9)\n",
+    "print(\"xtrain size : \", len(xtrain))\n",
+    "print(\"xtest size : \", len(xtest))\n",
+    "print(\"ytrain size : \", len(ytrain))\n",
+    "print(\"ytest size : \", len(ytest))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3b1a54ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Temps d'entraînement :  0.28424\n",
+      "Score échantillon de test :  0.906\n",
+      "Classe image 4 :  7\n",
+      "Classe prédite image 4 :  9\n",
+      "Précision pour chaque classe : \n",
+      " [0.96078431 0.88235294 0.89830508 0.89361702 0.94117647 0.87179487\n",
+      " 0.92592593 0.90243902 0.9        0.875     ]\n",
+      "Matrice de confusion SVM:\n",
+      " [[49  0  0  0  0  0  1  0  0  0]\n",
+      " [ 0 60  0  0  0  0  0  0  0  1]\n",
+      " [ 1  0 53  0  0  0  0  0  0  0]\n",
+      " [ 0  1  2 42  0  2  0  0  1  0]\n",
+      " [ 0  0  1  0 48  0  1  0  1  1]\n",
+      " [ 1  2  0  2  0 34  2  0  2  1]\n",
+      " [ 0  0  0  0  0  1 50  0  0  0]\n",
+      " [ 0  2  1  0  0  0  0 37  1  2]\n",
+      " [ 0  2  1  3  1  2  0  1 45  0]\n",
+      " [ 0  1  1  0  2  0  0  3  0 35]]\n",
+      "Zero-one classification loss :\n",
+      " 0.09399999999999997\n"
+     ]
+    }
+   ],
+   "source": [
+    "####### Premier modèle de Classifier #######\n",
     "\n",
-    "scores = []\n",
+    "#Entraîne le classifier\n",
+    "clf = SVC(kernel=\"linear\")\n",
+    "# print(\"Training...\")\n",
+    "clf.fit(xtrain, ytrain)\n",
     "\n",
-    "for j in range (3, 10):\n",
-    "    xtrain, xtest, ytrain, ytest = model_selection.train_test_split(data, target,train_size=(j/10))\n",
+    "#Prédiction sur le jeu de tests\n",
+    "# print(\"Predicting...\")\n",
+    "t1 = time.time()\n",
+    "pred = clf.predict(xtest)\n",
+    "t2 = time.time()\n",
+    "print(\"Temps d'entraînement : \", round(t2-t1,5))\n",
+    "#print(\"Prédiction : \", pred)\n",
+    "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
+    "score = clf.score(xtest, ytest)\n",
+    "print(\"Score échantillon de test : \", score)\n",
+    "\n",
+    "#Infos image 4\n",
+    "print(\"Classe image 4 : \", ytest[3])\n",
+    "print(\"Classe prédite image 4 : \", pred[3])\n",
+    "\n",
+    "#Calcul de différentes metrics\n",
+    "print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
+    "print(\"Matrice de confusion SVM:\\n\", metrics.confusion_matrix(ytest, pred))\n",
+    "print(\"Zero-one classification loss :\\n\", metrics.zero_one_loss(ytest, pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "5a4a5485",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computing for kernel= poly ...\n",
+      "Computing for kernel= rbf ...\n",
+      "Computing for kernel= sigmoid ...\n",
+      "Computing for kernel= precomputed ...\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "####### Variations de la fonction noyau #######\n",
+    "\n",
+    "list_training_times_kernel = []\n",
+    "list_precision_scores_kernel = []\n",
+    "list_zero_one_loss_kernel = []\n",
+    "\n",
+    "kernel_functions = [\"poly\",\"rbf\",\"sigmoid\",\"precomputed\"]\n",
+    "kernel_train = xtrain\n",
+    "kernel_test = xtest\n",
+    "for i in kernel_functions:\n",
+    "    print(\"Computing for kernel=\", i, \"...\")\n",
+    "    if (i == \"precomputed\"):\n",
+    "        kernel_train=np.dot(xtrain,xtrain.T) # modified the train_set\n",
+    "        kernel_test=np.dot(xtest,xtrain.T) # modified the test_set\n",
     "    \n",
-    "    t1 = round(time.time(),3)\n",
-    "    clf = neighbors.KNeighborsClassifier(n_neighbors=3,p = 2, n_jobs=-1)\n",
-    "    # On entraîne l'algorithme sur xtrain et ytrain\n",
-    "    clf.fit(xtrain, ytrain)\n",
-    "    t2 = round(time.time(),3)\n",
-    "    # On prédit sur xtest\n",
-    "    pred = clf.predict(xtest)\n",
-    "    t3 = round(time.time(),3)\n",
-    "    \n",
-    "    print(\"Temps d'entraînement : \", round(t2-t1,3))\n",
-    "    print(\"Temps de prédiction : \", round(t3-t2,3))\n",
-    "    print(\"Temps total : \", round(t3-t1,3))\n",
-    "#         print(\"Prédiction : \", pred)\n",
-    "    # Probabilités des prédictions sur xtest\n",
-    "    pred_proba = clf.predict_proba(xtest)\n",
-    "#         print(\"Probabilités : \", pred_proba)\n",
-    "    # On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
-    "    score = clf.score(xtest, ytest)\n",
-    "    scores += [score]\n",
-    "#         print(\"Classe image 4 : \", target[3])\n",
-    "#         print(\"Classe prédite image 4 : \", pred[3])\n",
-    "#         print(\"Score échantillon de test : \", score)\n",
-    "    scoreApp = clf.score(xtrain, ytrain)\n",
-    "#         print(\"Score données apprentissage : \", scoreApp)\n",
+    "    #Entraîne le classifier\n",
+    "    clf = SVC(kernel=i)\n",
+    "    t1 = round(time.time(),5)\n",
+    "    clf.fit(kernel_train, ytrain)\n",
+    "    t2 = round(time.time(),5)\n",
+    "    #Prédiction sur le jeu de tests\n",
+    "    pred = clf.predict(kernel_test)\n",
+    "    # On sauvegarde le temps de calcul, la précision et \n",
+    "    # les taux d'erreurs par classe\n",
+    "    list_training_times_kernel.append(t2-t1)\n",
+    "    list_precision_scores_kernel.append(clf.score(kernel_test, ytest))\n",
+    "    list_zero_one_loss_kernel.append(metrics.zero_one_loss(ytest, pred))\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "9b961ed8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(36.0, 0.5, 'Zero-one loss')"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "##### x coordinates\n",
+    "x = kernel_functions\n",
+    "training_times = list_training_times_kernel\n",
+    "precision_scores = list_precision_scores_kernel\n",
+    "zero_one_loss = list_zero_one_loss_kernel\n",
+    "        \n",
+    "training_times = [round(i,2) for i in training_times]\n",
+    "precision_scores = [round(i,2) for i in precision_scores]\n",
+    "zero_one_loss = [round(i,2) for i in zero_one_loss]\n",
     "\n",
-    "# nice_scores = np.array_split(scores, 7)\n",
-    "# print(scores)\n",
-    "n = 3\n",
-    "for i in scores:\n",
-    "    print (n, \" : \", i)\n",
-    "    n += 1"
+    "### Create plot\n",
+    "fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(7,10))\n",
+    "fig.tight_layout(pad=3.0)\n",
+    "figs[0].plot(x,training_times, marker='o', color='r')\n",
+    "figs[1].plot(x,precision_scores, marker='o', color='b')\n",
+    "figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
+    "\n",
+    "### Add every x coordinates\n",
+    "figs[0].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[1].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[2].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "\n",
+    "for i in range(len(x)):\n",
+    "    figs[0].annotate(training_times[i], # this is the text\n",
+    "                 (x[i],training_times[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[1].annotate(precision_scores[i], # this is the text\n",
+    "                 (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[2].annotate(zero_one_loss[i], # this is the text\n",
+    "                 (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "\n",
+    "figs[0].set_xticks(x)\n",
+    "figs[1].set_xticks(x)\n",
+    "figs[2].set_xticks(x)\n",
+    "    \n",
+    "### Add title and axis names\n",
+    "figs[0].title.set_text('Training times for each kernel function')\n",
+    "figs[1].title.set_text('Precision score for each kernel function')\n",
+    "figs[2].title.set_text('Zero-one loss metrics for each kernel function')\n",
+    "figs[0].set_xlabel('kernel')\n",
+    "figs[1].set_xlabel('kernel')\n",
+    "figs[2].set_xlabel('kernel')\n",
+    "figs[0].set_ylabel('Training times (in seconds)')\n",
+    "figs[1].set_ylabel('Precision score')\n",
+    "figs[2].set_ylabel('Zero-one loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "5726fcb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computing for C= 0.1 ...\n",
+      "Computing for C= 0.25 ...\n",
+      "Computing for C= 0.5 ...\n",
+      "Computing for C= 0.75 ...\n",
+      "Computing for C= 1.0 ...\n",
+      "Done\n"
+     ]
+    }
+   ],
+   "source": [
+    "####### Variation du paramètre de tolérance aux erreurs C #######\n",
+    "\n",
+    "list_training_times_tol = []\n",
+    "list_precision_scores_tol = []\n",
+    "list_zero_one_loss_tol = []\n",
+    "\n",
+    "kernel_train = xtrain\n",
+    "kernel_test = xtest\n",
+    "tols = [0.1,0.25,0.5,0.75,1.0]\n",
+    "\n",
+    "for i in tols:\n",
+    "    print(\"Computing for C=\", i, \"...\")\n",
+    "    #Entraîne le classifier\n",
+    "    clf = SVC(C=i, kernel=\"rbf\")\n",
+    "    t1 = round(time.time(),5)\n",
+    "    clf.fit(kernel_train, ytrain)\n",
+    "    t2 = round(time.time(),5)\n",
+    "    #Prédiction sur le jeu de tests\n",
+    "    pred = clf.predict(kernel_test)\n",
+    "    # On sauvegarde le temps de calcul, la précision et \n",
+    "    # les taux d'erreurs par classe\n",
+    "    list_training_times_tol.append(t2-t1)\n",
+    "    list_precision_scores_tol.append(clf.score(kernel_test, ytest))\n",
+    "    list_zero_one_loss_tol.append(metrics.zero_one_loss(ytest, pred))\n",
+    "print(\"Done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "741f82ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(36.0, 0.5, 'Zero-one loss')"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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w0UWg7gtFRGQbpSy5mlkrMxtuZi+b2cUx5QPM7FEze93MGkVlP0XT9klVPPmqWBFeeAF22QV69IDffktLGCIiUjakLLm6+7fu3hc4BegUUz7Y3S8EngD+HhWvBqoC81MVT4Hq1IFXX4Vly6BnT1i/Pm2hiIhI6ZbSamEz6wZ8CLwTV16DkHRfjYo6AL2Bfnksp4+ZTTOzaYsXL05dwO3awZNPwn//C5dfnrr1iIhImZbS5Orur7n7AcDmfgXNrBbwMPBPd18VTbfJ3bOAdWa2VUzuPsLdO7l7p5Q/jPyUU2DAgNCiOOq8X0REpDBS1rewmR0K9AC2Ayaa2Uh3Pwt4CqgEDDSzF4GFwDXRbFPcfVOqYkrYrbfCl1+Gs9c2beBvf0t3RCIiUoqUz6fiJGL5cthnH1ixAqZNg8aNU79OEREpUYr6VBzdipOX7beHcePgzz+he/fwV0REJAFKrvlp1QqefTb03tSnD5Sys3wREUkPJdeCdOsGN98ckux996U7GhERKQWUXBMxcGCoGv7HP+Dtt9MdjYiIlHD5Jlcza2Rm/zCzcWb2qZm9b2YPmdlxud0yU2ZVqBD6HW7VCk49FX7+Od0RiYhICZZngjSzJwm9KK0H7gBOBy4B3gaOBj40s4OLI8gSoWbN0IPTpk1w4omwenW6IxIRkRIqv/tc73b3r3Mp/xoYY2aVgfL1hPEWLUIfxMccA+edBy++GB5bJyIiEiPPM9fcEquZ7WBme0Xj17v7j6kMrkQ66ii44w54+WW4/fZ0RyMiIiVQgddNzWyKmdUysx2BL4Enzeye1IdWgl11FZx+Olx/PUyYkO5oRESkhEmkUVJtd19J6MrwSXfvCByR2rBKODN47DFo3x7OOAO+/z7dEYmISAmSSHKtaGa7EJ5iMz7F8ZQe1aqFBk7bbQcnnBC6SRQRESGx5Hoz8Cbwo7t/ambNgVmpDauUaNIEXnoJfvoJzjwztCQWEZFyr8Dk6u4vufte7n5JNPyzu/dMfWilxCGHhJ6bxo+HG25IdzQiIlIC5Hkrjpk9AOTZma67X5GSiEqjSy6Bzz4Lj6pr3x566n8PEZHyLL8z12nAdKAKsDehKngW0B7ISnlkpYkZPPQQ7LcfnHMOzJiR7ohERCSNCnyeq5m9Bxzl7hui4UrAW+7+92KIbyvF9jzXovjlF+jUCapWhU8/hR13THdEIiKyDVL5PNcGQM2Y4RpRWUEBtTKz4Wb2spldHFPe1sxGRa+2UdlgM7vfzAYXdgNKlAYNYMwYWLAg9EG8cWO6IxIRkTRIJLkOBj43s6fM7CngM+C2gmZy92/dvS/hFp7YrN8PuJTQT/HlZtYEqBRdw80ws8aF3IaSZb/94OGHw9NzBgxIdzQiIpIGibQWfhLYFxgbvfZ396cTWbiZdQM+BN6JKa7t7svdfQXhjLghMD8aNw9olMty+pjZNDObtnjx4kRWnV69e8Nll8Hdd8OoUemORkREilmij43LABYDy4DdE30ajru/5u4HAL1iileYWW0zqwWsAhayJaE2BhbkspwR7t7J3TvVrVs3wZDT7J57wm06F1wA06enOxoRESlG+T0VBwAzuwM4FfgGyO4lwYH3C5jvUEKXidsBE81spLufBQwF7gcMGOLu88xsQ9Rf8V/uPj+vZZYqlSqFDiY6dQqPqJs2DerXT3dUIiJSDBJpLfw9sJe7/1U8IeWvRLcWzs1nn8FBB4Uk+/bbULlyuiMSEZEEpbK18M9ApcKHJADsvTc8/jh88AH83/+lOxoRESkGBVYLA2uBL8zsHWDz2at6aCqE00+Hzz+HO++EDh3CdVgRESmzEkmur0Uv2Ra33w5ffhm6SmzdGg44IN0RiYhIihSYXN39aTOrDOweFX2f3VuTFEJGBjz/POyzT+h7eNo0aNgw3VGJiEgKFHjNNWr1Owt4EHgI+CHRW3Ekzo47hmfArloFPXrAunXpjkhERFIgkQZNdxP6Fj7E3Q8GugD3pjasMqxtWxg5EqZOhYsvhgJaa4uISOmTSHKt5O7fZw+4+w+o9fC26d4d/v1veOopGDYs3dGIiEiSJdKgaZqZPQ6MjIZ7ER5FJ9vihhvgiy/C7Tlt28Lf0/KQIRERSYFEzlwvJvTOdAWh0/2ZQN9UBlUuVKgQqod33x1OPhnmzEl3RCIikiSJJNeKwFB37+Hu3QldF2akNqxyolat0MBp48bQReLatemOSEREkiCR5PoOUDVmuCrwdmrCKYd23x2eew6++io8TUcNnERESr1EkmsVd1+dPRC9r5a6kMqhY4+F226DF14IvTiJiEiplkhyXWNme2cPmFlH4M/UhVROXXMNnHJKeMD6pEnpjkZERLZBIq2F+wMvmdkv0fAuhEfQSTKZwRNPwPffw2mnwaefwm67pTsqEREpgkS6P/zUzFoCexCewfqduj9MkerVQwOnTp3ghBPgk0+gZs10RyUiIoWUSPeH1YBrgH7uPgNoamZdE5jvRDN71MzGmdlRMeXnm9lwM5tgZrdHZT9FZX22YVvKhqZN4cUX4Ycf4KyzYNOmAmcREZGSJZFrrk8C64H9o+EFwK0FzeTur7r7hcC5xFQju/vj7t4X+AF4KipeTWiFPD/RwMu0ww6Du++GcePg5pvTHY2IiBRSIsl1V3cfAmwAcPc/CdXDibqe0On/ZmZWBWgW061iB6A3oZOKrZhZHzObZmbTFi9eXIhVl2JXXAHnnAM33RSqikVEpNRIJLmuN7OqgAOY2a7EPDQ9LxbcAbzh7p/FjT4JGJM94O6b3D0LWGdmW8Xk7iPcvZO7d6pbt24CIZcBZjB8OHTuHKqHv/km3RGJiEiCEkmuNwCTgMZmNorQqcQ/E5jvcuAI4CQz62tmI2PGnQy8BGBme5jZE2b2BDDF3XWRMVuVKjBmTGjodOKJsGxZuiMSEZEEmCfQI5CZ7QTsR6gO/tjdl6Q6sLx06tTJp02blq7Vp8dHH4WO/Q87DCZMCA9eFxGRlDOz6e7eqbDzJdJa+EBgnbtPALYHrjOzzMKHKEV24IHh0XRvvgnXXZfuaEREpACJVAs/DKw1s3bA1cBc4JmURiVb69MH+vaFIUNg9Oh0RyMiIvlIJLlu9FB3fAJwv7sPBdSzQToMHQoHHRQ6+P/883RHIyIieUgkua4ys2uBM4EJZpYBVEptWJKrypXh5Zdhp51CA6fycluSiEgpk0hyPZVw68357v4b0BDQo1vSpX59GDsWFi0KHf1vUE+UIiIlTYHJ1d1/c/d73P2DaHieu+uaazp16gSPPgpTpsBVV6U7GhERiZPIU3GkJDrrrHDd9d57oUMHOO+8dEckIiKRRKqFpaQaMgQOPzy0Iv7kk3RHIyIiESXX0qxiRXjhBWjYEHr0gF9/TXdEIiJCgp1ImNlkM/vBzH42s9lm9nNxBCcJ2Gmn0LH/8uXQsyf8VWC3zyIikmKJnLk+DtwDHAR0BjpFf6Wk2GsveOop+N//4LLLIIEuLUVEJHUSSa4r3P0Nd//d3Zdmv1IemRTOySeHrhEfewwefjjd0YiIlGuJJNf3zOxOM9vfzPbOfqU8Mim8m2+G446Dfv3g/fcLnHz+/Pn8/e9/p1WrVrRp04ahQ4fmOe2nn35KRkYGL7/8co7yrKwsOnToQNeuXbc5fBGRsiKRW3H2jf7GPhXAgcOSH45sk4wMGDUK9tkHTjoJpk2DJk3ynLxixYrcfffd7L333qxatYqOHTty5JFH0rp16xzTZWVlcc0119ClS5etljF06FBatWrFypUrk745IiKlVSKdSPw9l5cSa0lVuzaMGwfr1kH37rB2bZ6T7rLLLuy9d6iEqFmzJq1atWLhwoVbTffAAw/Qs2dP6tWrl6N8wYIFTJgwgQsuuCC52yAiUsrleeZqZme6+7NmdmVu4939nvwWbGYnAscB9YAH3f2tqPxGoBWwDLjZ3X8xs8FANWCtuw8oyoZIjJYtwxnsCSeEp+mMHAlm+c4yZ84cPv/8c/bdd98c5QsXLmTs2LG8++67fPrppznG9e/fnyFDhrBq1aqkb4KISGmW35lr9ehvzTxe+XL3V939QuBcQv/E2TYC64ENwHIzawJUcvcrgAwza1zYjZBcHH98uAY7ahTck+//QaxevZqePXty3333UatWrRzj+vfvzx133EFG3APax48fT7169ejYsWPSQxcRKe3MU3zbhpndDYxy98+i4QruvsnMugFNgU+Bfd39PjO7HJjm7v+LW0YfoA9AkyZNOs6dOzelMZcZ7qEV8dix8MYbcNRRW02yYcMGunbtSpcuXbjyyq0rKZo1a0b2MbJkyRKqVavGiBEj+OSTTxg5ciQVK1Zk3bp1rFy5kh49evDss8+mfLNERIqLmU13904FTxk3X17J1cyuBx5y9z/yGH8YUM3dx+cx3oDBwGR3fzuX8fsRGkU9C1zh7v8wsyHAA+4+P6+AO3Xq5NOmTStgs2Sz1ath//1h4UL49FPYddfNo9ydc845hx133JH77ruvwEWde+65dO3alZNOOilH+ZQpU7jrrrsYPz7XQ0FEpNQqanLNr7XwDOB1M1sHfAYsBqoAuwHtgbeB2/KZ/3LgCKC2mbUADnT3s8zsOqAxUIeQVH81sw1mdg/wV36JVYqgRo3Qg1PnznDooeHa64IF0KQJH519NiNHjmTPPfekffv2ANx2223MmzcPgL59+6YtbBGR0qzAamEz2w04ENgF+BP4Fnjf3f9MfXhb05lrEQ0YAHfckbOsWjUYMQJ69UpPTCIiJVzSq4VLKiXXImraFHK7Vp2ZCXPmFHc0IiKlQlGTq56KU15EVb1bmTs373EiIlIkSq7lRT49NZGZCW3awD/+Ae+8A+vXF19cIiJlkJJreTFoULjGGqtatfDA9bvugl12gfvvhyOOCI+xO/FEeOQRndWKiBRBIs9zHWJmtcyskpm9Y2ZLzOzM4ghOkqhXr9B4KTMztBjOzAzDV18NV10Fb78Nf/wRuk4880z4/HPo23fLWe3VV8O77+qsVkQkAYm0Fv7C3dubWXfgROD/gPfcvV0xxLcVNWgqJu7w7beh84k33ghP2dmwIdzac/jhcMwx4ZVfdbOISCmXivtcs1WK/h4LPO/uf1gB/dRKGWAGrVuH11VXwapV4cw1O9mOGxema9NmS6I96CCoXDm9cYuIlACJnLkOJpyx/gnsA2wPjHf3ffOZLWV05loCxJ7VTpwIH3yw5az2iCO2JNvG6iZaREq3lN7namY7ACvdPcvMqgM13f23IsS5zZRcS6D4s9rsRlDZZ7XHHgsHHqizWhEpdVKWXM2sGnAl0MTd+0Q9Nu2RV5/CqabkWsK5w8yZWxKtzmpFpBRLZXJ9AZgOnO3ubc2sKvA/d29fpEi3kZJrKZN9VjtxYki286Ouo9u23ZJodVYrIiVUKpPrNHfvZGafu3uHqOxLtRaWQsvrrLZmzdAC+dhjQ7Jt1CjdkYqIAKltLbw+Olv1aEW7An8VdkUimIXrsNm9Qa1aFXqEyk62r74aptNZrYiUcomcuR4JXA+0Bt4iPCHnXHefkvLocqEz1zIq+6w2u/r4ww+3nNXGXqvVWa2IFKNUtxbeCdgPMOBjd19S+BCTQ8m1nIg9q504MTyDFsJZbXb18YEHQqVK+S9HRGQbpDq57gU0JaYa2d3HFHZlyaDkWg65wzff5LxWu3GjzmpFJOVS2aDpCWAv4BtgU1Ts7t67gPlOBI4D6gEPuvtbUfkAYFdgZ+Bid19gZj8Bk4HP3H1EfstVchVWrsx5rTb7rHbPPXNeq9VZrYhso1Qm15nu3nobAtsBuMvdz48r7w7UcPeRZvYl8AUw2t3fyG95Sq6SQ+xZ7cSJ4Vpt9lntkUeGRHv00TqrFZEiSWVyfRy4291nFjGwu4FR7v5ZTFkN4FGgj7uvMrMKhOu5E9z96FyW0QfoA9CkSZOOc+fOLUooUh7orFZEkiiVyfVg4HXgN8ItOEaoFt6rgPkMGAxMdve3Y8prAQ8C17n7/Lh5XgV6uPsm8qAzV0mYO3z99ZZEm31WW6tWzmu1DRumO1IRKaFSmVx/JHR/OIMt11xx93xPH83sCuAc4FNCle+B7n6WmY0hPGlnIfBi9PeaaLav3P2+/Jar5CpFln1Wm327z8KFoXyvvbYk2gMO0FmtiGyWyuT6rrsfVuTIkkzJVZKiFJzVzp8/n7PPPpvffvuNChUq0KdPH/r165djGnenX79+TJw4kWrVqvHUU0+x9957AzB06FAeffRR3J0LL7yQ/v37p2ErREq3VPbQ9J2ZPUeoGt7cM1O6bsURSQqzcB12zz3hn/8MZ7Vvv70l2Y6JDu80ntVWrFiRu+++m7333ptVq1bRsWNHjjzySFq33tK+8I033mDWrFnMmjWLTz75hIsvvphPPvmEr7/+mkcffZSpU6dSuXJljj76aI477jh22223YotfpDyrkMA0VQlJ9Sjg+OjVNZVBiRS7WrWgRw949NHwcIGvvoLBg2GHHeDuu+HQQ6FOHTjpJHj88S1Vyim0yy67bD4LrVmzJq1atWJh3HrHjRvH2WefjZmx3377sXz5cn799Ve+/fZb9ttvP6pVq0bFihU55JBDGDt2bMpjFpGgwDNXdz+vOAIRKTFiz2qvuQZWrMjZAvmVV8J02We1xx4L+++f0rPaOXPm8Pnnn7PvvvvmKF+4cCGNYx7f16hRIxYuXEjbtm0ZOHAgS5cupWrVqkycOJFOnQpdsyUiRZRncjWzf7r7EDN7gKjT/ljufkVKIxMpKWrXDme1PXqEa7UzZmxJtHffDXfcEc58s++rPeYYaNAgaatfvXo1PXv25L777qNWrVo5xuXWZsLMaNWqFddccw1HHnkkNWrUoF27dlSsmMhVIBFJhvy+bd9Gf9V6SCSbWThj3WuvLWe1sddqs89q27Xbkmi34ax2w4YN9OzZk169etGjR4+txjdq1Ij587fc0bZgwQIaRIn9/PPP5/zzQ98t1113HY3UkYZIscnzmqu7vx69XevuT8e+gLXFE55ICVe7NvTsCY89Fjqs+PLLcK22dm2480445JAt12qfeAJ++SXhRbs7559/Pq1ateLKK6/MdZpu3brxzDPP4O58/PHH1K5dm1122QWA33//HYB58+YxZswYTj/99G3fXhFJSCK34nzm7nsXVFZcdCuOlBrxZ7XZiTW/s9pRo2DgQJg3jw/r1eNvixax5557UqFC+D/4tttuY968eQD07dsXd+eyyy5j0qRJVKtWjSeffHLztdW//e1vLF26lEqVKnHPPfdw+OGHF+vmi5QFSb/P1cyOAY4FTgFeiBlVC2jt7vsUJdBtpeQqpZJ7aIGcnWg/+giyssIZbva12rVrQ1Xz2piKoWrVYMQI6NUrfbGLlGOpSK7tgPbAzcC/Y0atAt5z92VFiHObKblKmZB9VjtxIkyalH91cWYmzJlTbKGJyBap7KGpkrtvKHJkSabkKmVO9llt+/a5jzeDTXl2ty0iKVTU5FpgJxIlKbGKlElm4TpsZmbu42vUKFRDKBFJv0R6aBKR4jBoULjGGqtiRVi9Gpo3h/794bff0hKaiBSOkqtISdGrV2i8lJkZzmYzM+Gpp+Cnn8K4YcNCkr3qKli0KN3Rikg+Ernm+jpb99C0gtC5xCPuvi5FseVK11yl3PrxR7j1Vhg5EqpUgcsug6uvDvfRikhKpOyaK/AzsBp4NHqtBBYBu0fDIlIcWrQIZ7Lffgvdu4dOKpo2heuug6VL0x2diMRIJLl2cPcz3P316HUmsI+7Xwrk2ZGEmZ1oZo+a2TgzOyqmvK2ZjYpebaOywWZ2v5kN3uYtEinrdt8dnn0WvvkGjj8+9AjVrBn861+wLC13yIlInESSa10za5I9EL3Prodan9dM7v6qu18InAucGjOqH3ApcAlwebS8StGDADLMrPFWCxORrbVqBc8/Hx4kcPTRocq4aVO48UZYvjzNwYmUb4kk16uAD83sPTObAnwAXG1m1YGnE5j/euDBmOHa7r7c3VcANYGGQHbP4/OArXoXN7M+ZjbNzKYtXrw4gVWKlCNt2sCLL4Z+jY84Am66KZzJ3npreAi8iBS7RO5znQjsBvSPXnu4+wR3X+Pu9+U1nwV3AG+4+2cxo1aYWW0zq0Xo7WkhWxJqY2BBLjGMcPdO7t6pbt26iW2ZSHmz117hqTyffQYHHxyqiZs1g9tvD7fziEixSfRWnI5AG2Av4BQzOzuBeS4HjgBOMrO+ZjYyKh8K3A8MAx5w93nABjO7B8hy9/m5L05EEtKhA4wbB59+Gh4McN11IckOGQJr1qQ7OpFyIZFbcUYCuwJfAFlRsafrYem6FUekkD75JFyHnTQJ6tULDwfo23frDitEZCup7Fv4W8JTcPKfsJgouYoU0X//CzfcEB4YsPPOMGAA9OkDVaumOzKREiuV97l+Dexc+JBEpEQ54ACYPBnefz+0NO7fP9w7O2wY/PVXuqMTKVMSSa51gJlm9qaZvZb9SnVgIpIif/sbvPtueO26K1x+eUiyw4fD+jzvrhORQkikWviQ3Mrd/T8piagAqhYWSSL3kGT//e9QbdykCVx/PZx7LlSqlO7oRNIulY+c+09ur6KFKSIlihkcfjh8+GFo8LTLLuE67B57wJNPwsaN6Y5QpFTKM7ma2YfR31VmtjLmtcrMdGe6SFliBl26wP/+BxMmwE47Qe/e0LIlPPOMkqxIIeWZXN39oOhvTXevFfOq6e61ii9EESk2ZnDssTB1Krz2GtSqBeecA61bw6hRkJVV8DJEJLFOJMwsw8wamFmT7FeqAxORNDILDwWYPh3Gjg2365x5JrRtC6NHw6ZN6Y5QpEQrMLma2eWER8xNBiZEr/EpjktESgIzOPFE+PxzeOklyMiA008PXS2+/LKSrEgeEjlz7UfoT7iNu+8ZvfZKdWAiUoJUqAAnnQRffRXOXLOy4OSTQ1eLY8eGVscislkiyXU+sCLVgYhIKVChApx6Knz9dbgGu24d9OgBHTuGa7RKsiJAYsn1Z2CKmV1rZldmv1IdmIiUYBkZcMYZ4YHtzzwTHm13wgmwzz6htbGSrJRziSTXeYTrrZUJz1/NfolIeVexIpx1Fnz3HTzxBCxdCl27wn77wZtvKslKuVVgD00ljXpoEinBNmyAp5+GW26BefNCf8Y33RQ6qjBLd3QihZb0HprM7L7o7+uxfQqrb2ERyVOlSnDBBTBrVuireN48OPJIOOQQmDIl3dGJFJs8z1zNrKO7Ty9q38Jm1hwYCNR295Niys8HOgONga/c/Voz+4lQ9fyZu4/Ib7k6cxUpRf76Cx57DG67DX75BQ49FG6+OTw8QKQUSPqZq7tPj/4WqW9hd//Z3c/Ppfxxd+8L/AA8FRWvBqoSWiaLSFmx3XZw6aXw008wdGi4NnvwweFs9r//TXd0IimTSCcSu5nZy2Y208x+zn5ty0rNrArQzN2/j4o6AL0J99TmNn0fM5tmZtMWL168LasWkXSoUgWuuCIk2bvvDvfLHnggHH00fPJJuqMTSbpEWgs/CTwMbAT+DjwDjNzG9Z4EjMkecPdN7p4FrDOzrWJy9xHu3sndO9WtW3cbVy0iaVOtGlx5Jfz8MwwZErpX3G8/OO440OUeKUMSSa5V3f0dwvXZue5+I3BYQTOZ2U5mNhzoEN0jG5uQTwZeiqbbw8yeMLMngCnurv7URMq66tXh6qth9my4/Xb4+GPo3Bm6dQtdLYqUcok8LP0j4G/Ay8C7wEJgsLvvkfrwtqYGTSJl0MqV8MADcNddsHw5dO8ON94Y+jAWSaOUPSwd6A9UA64AOgJnAucUdkUiInmqVQsGDoQ5c0JSffddaNcu9F/89dfpjk6k0PJNrmaWAZzi7qvdfYG7n+fuPd3942KKT0TKk9q14YYbQnXxv/4Vennaa6/wJJ5vv013dCIJy68TiYpRI6OOZupaRUSK0Q47hPthZ8+Ga6+F11+HNm3CM2V/+CHd0YkUKL8z16nR38+BcWZ2lpn1yH4VQ2wiUt7ttBMMGhSqi//5z/B4u1at4Jxz4Mcf0x2dSJ4Suea6I7CU0EK4K3B89FdEpHjUqQODB4cz2f/7v/Dg9pYtoXfvcFuPSAmTX3KtFz1a7mtgRvT3m+ivWhiISPGrVy+0KP75Z7j8cnjuOdhjD+jTB+bOTXd0Ipvll1wzgBrRq2bM++yXiEh67Lwz3HtvSLIXXxyexLPbbuH9fPWiKumXX8f9n7n73sUcT4F0n6uIbGXBgvBwgMceC4+2u/DC0BCqYcN0RyalXCruc1ULYREpHRo1goceCo+6O/dceOQR2HVX6N8ffvst3dFJOZRfcj282KIQEUmGzMyQWH/4AXr1gmHDoHlzuOoqWLQo3dFJOZLfI+f+KM5ARESSplkzePxx+P57OOUUuO++kGSvuQaWLEnaanr37k29evVo27ZtvtN9+umnZGRk8PLLLwPw/fff0759+82vWrVqcd999yUtLkm/RG7FEREpnXbdFZ56KvTu1KNHaGnctClcdx0sXbrNiz/33HOZNGlSvtNkZWVxzTXX0KVLl81le+yxB1988QVffPEF06dPp1q1anTv3n2b45GSQ8lVRMq+3XeHkSPhm2/Ck3cGDw5nt//6FyxbVuTFHnzwwey44475TvPAAw/Qs2dP6tWrl+v4d955h1133ZXMzMwixyG5K2rNQrasrCyA1mY2vrDrVnIVkfKjZctwb+yMGeFB7bfeGs5kb7wxPI0nyRYuXMjYsWPp27dvntOMHj2a008/PenrlqLXLGQbOnQowJ9FWbeSq4iUP23awIsvwpdfwhFHwE03hTPZW28Nj79Lkv79+3PHHXeQkZGR6/j169fz2muvcfLJJydtnbLFttQsLFiwgAkTJgAU6SJ9ypKrmTU3s8fN7OW48hvN7AUzG25mDaKywWZ2v5kNTlU8IiJb2WsveOWV8ID2Qw4J1cTNmoUHuK9atc2LnzZtGqeddhpNmzbl5Zdf5pJLLuHVV1/dPP6NN95g7733pn79+tu8Lim8/GoW+vfvz5AhQ4q87JQlV3f/2d3Pz2XURmA9sAFYbmZNgErufgWQYWaNUxWTiEiu2reHV1+FadNg//1Dg6fmzWHIEFizpsiLnT17NnPmzGHOnDmcdNJJPPTQQ5x44ombxz///POqEk6jvGoWxo8fT7169ejYsWORl11xW4MrgtvcfZOZdQMuAD4Fsvsrmwc0ihkWESk+HTvC+PEwdWp4ruw118Ddd4e/ffuGp/IMHAjz5kGTJpy+yy5MmTOHJUuW0KhRI2666SY2bNgAkO91VoC1a9cyefJkHnnkkeLYMslFds0CwJIlS5g4cSIVK1bkk08+4bXXXmPixIkAzYHGZvasu5+Z6LLz7P4wWczsZXc/KZfy/QhP2nkWuMLd/2FmQ4AH3H1+3LR9gD4ATZo06ThXHXSLSHH43/9Ckp08GWrVgj//hCh5AlCtGowYETqskBJpzpw5dO3ala+/zv95M+eeey5du3blpJNypisz+wGY5e6Fehpcys5czWwnYBDQwcyuBVq7+1lmdh3QGKhDSKq/mtkGM7sH+Cs+sQK4+whgBIS+hVMVs4hIDvvvD2+9BR9+GBo+xSZWgLVrw8MCZsyAypVhu+3C3/j3BQ3nNS4jI/SVLEVy+umnM2XKlCLVLGyrlJ+5Jps67heRtKhQAfL6vaxcGdavT/46zVKTtJO5nDxaQqfFqFE5qu0ZNGibaxWK2nF/Oq65ioiUPk2a5P7M2MxMmDMnJN6NG+Gvv0KizX7lN5zMaVesKHja+DPvZKhQITVJu7DTvvUW/Pvfoeoewr7q0ye8T0O1vZKriEgiBg0KP9Zr124pq1YtlEM4y6xUKbxKKveQYFOV4Auads2agufbuDF527t2bTiTVXIVESmhsn+gk1ztWKyyq5krV053JHnbtCnxBB47fNJW7WaDefOKN/6IkquISKJ69SpdybQ0qlABqlQJr8LIzMy92r5Jk+TEVUjq/lBEREq/QYNCNX2s2Gr7YqbkKiIipV+vXuGe48zMUP2dmZnWe5BVLSwiImVDCaq215mriIhIkim5ioiIJJmSq4iISJKVuu4PzWwxkKye++tQxAfhlnLldbtLE+2jkk37p2RL5v7JdPe6hZ2p1CXXZDKzaUXpM7K0K6/bXZpoH5Vs2j8lW0nYP6oWFhERSTIlVxERkSQr78l1RLoDSJPyut2lifZRyab9U7Klff+U62uuIiIiqVDez1xFRESSrkx3f2hm1YGHgPXAFHcfFZUfC1wGTHT3YVHZYKAasNbdB6Qp5KTJZ9sHALsCOwMXu/sCM/sJmAx85u5pr04pD/LZPzcCrYBlwM3u/ktZOzZLi3z20flAZ6Ax8JW7X6vvUPEzs+bAQKC2u58UU94WuDYavN3dv07Hd6isn7n2AF529wuBbtmF7j4RGJI9bGZNgErufgWQYWaNiz3S5Mtr2wdHZU8Af4+KVwNVgfnFHmX5lev+ATYSfsw3AMvL6LFZWuT1HXrc3fsCPwBPRcX6DhUzd//Z3c/PZVQ/4FLgEuDydH2HyvSZK9AImBG9z8pnuoZs+VLMi+Yr7V+SPLfdzGoApwB9oqIOgAETgDeKK8ByLq/9c5u7bzKzbsAFwKeUvWOztMjvO1QFaObu30dF+g6VHLXdfTmAmdUkTb/vZf3MdQHhg4T8t3VhzHSNo/lKu1y33cxqAQ8D/3T3VQDuvsnds4B1ZlbWj4mSItf94+6bore/AzUom8dmaZHf78dJwJjsAX2HSpQVZlY7+q1bRZq+Q2W6tXB0zWQYsA74EDja3c8ys/0JdfI7APe5+ytmdjuwHfCXu1+b50JLiXy2fQxQiXDAvRj9vSaa7St3vy8N4ZY7+eyf6wg/AHWAK9z917J2bJYWee2jaNw44DR3/9PM9kDfoWJnZjsBg4AjgceA1tF3qC1wNaEmYUh0zbXYv0NlOrmKiIikg6ovREREkkzJVUREJMmUXEVERJJMyVVERCTJlFxFSggz297MLilgmqZm9nVxxSQiRaPkKlJybE/oVSZpzCwjmcsTkcQouYqUHIOBXc3sCzO7M3p9bWYzzOzU+InNLCOa5lMz+8rMLorKDzWz98zsOaIehszsVTObbmbfmFmfmGWsNrNBZvalmX1sZvWj8vpmNjYq/9LMDojKzzSzqVGMjyh5i+ROyVWk5BgA/OTu7YGPgfZAO+AI4E4z2yVu+vOBFe7emdCR/IVm1iwatw8w0N1bR8O93b0j0Am4IroBH6A68LG7twPeBy6Myu8H/hOV7w18Y2atgFOBA6MYs4Beydp4kbKkrPctLFJaHQQ8H3Wpt8jM/kNIoF/FTHMUsJeZZT8RpDawG6Hj/6nuPjtm2ivMrHv0vnE03dJo2vFR+XRCbzcAhwFnA0QxrDCzs4COwKdmBqGj+t+Ts7kiZYuSq0jJZAlOc7m7v5mj0OxQYE3c8BHA/u6+1symAFWi0Rt8SzdtWeT/m2DA0+qCUaRgqhYWKTlWATWj9+8Dp0bXVesCBwNT46Z/E7jYzCoBmNnuUX+48WoDy6LE2hLYL4FY3gEujpabEXWC/g5wkpnVi8p3NLPMwm2iSPmg5CpSQrj7UuCj6Fab/QlVwF8C7xKeYvRb3CyPATOBz6J5HiH3M89JQEUz+wq4hXA9tyD9gL+b2QxCdXEbd58JXA+8FS1rMhB/HVhEUMf9IiIiSaczVxERkSRTchUREUkyJVcREZEkU3IVERFJMiVXERGRJFNyFRERSTIlVxERkSRTchUREUkyJVcREZEkU3ItBcysl5m9lcB0w83sX8URU7qZ2a1mtsTM4rsETLtE91eS1znHzI5I8TqeMrNbizhvdzObHz0/tkMSYnEza7Gty0knM6trZt+bWZVoeIqZXVAC4krKZ1vQ8WJmB5rZrOiYONHMxpjZ0du63pJCT8XZRmY2B6hPeKLIGmAi4Uklq5O1DncfBYxKYLq+yVpnSWZmjYGrgEx3L3GPPEt0f5UzdwGXufu43EaamQO7ufuPxRtWWg0AnnT3dekOJE1uBoa5+1AAM/sFeJjQF3appzPX5Dje3WsQHirdmdC5eQ5mpn9k8lCEzyYTWFqUxJrq/aD9nKdM4Jt0B1FS9o+ZbQecAzyb5OWamaX9d93MMhKYLMcx4e5TgVpm1illgRWjtO+EssTdFwJvAG1hc/XKpWY2C5gVlXU1sy/MbLmZ/dfM9sqe38waR1Uji81sqZkNi8rPNbMPo/dmZvea2e9mtsLMvjKz7PXlqIYxswvN7Ecz+8PMXjOzBjHj3Mz6RtUyy8zsQYuegB3PzPYxs2lmttLMFpnZPTHjDoq2Y3lU7XduVF7bzJ6JtmWumV2f/aWPtuejaDv+AG40s+3M7C4zmxetY7iZVc0lliMIT2NpEFUnPRWVdzOzb6I4pphZq5h55pjZNRae5LIm/gc2WtddcWXjzOzK6P0AM/vJzFaZ2Uzb8tDxvLZl8/6KpjnAzD6N9tenZnZAXGxHxAzfaGbPRu+rmNmz0bGwPJq3fm77KC72CjExLzWzF81sx2jcJDO7LG76L82sR/S+pZlNjo6Z783slILWF7PO66N9/Xu072tH+3U1kAF8aWY/5TLv+9HbL6N9empUnufxGzd/nseOmR1qZgui/f8b8KSZ7WBm46Njc1n0vlHM8qaY2S3Rfl1lZm+ZWZ2Y8Xkd8wkdw5F9geXuviCPbdrFwnf7H9HwfjHr/NLCM3pj4x1kZh8Ba4HmVsD328x6m9m30bg3bRsfHWjht+dhM5toZmuAv0ej6kTH0yoz+0/2eqLjoDnwerTPt4umnwIcty2xlBjurtc2vIA5wBHR+8aE/8RuiYadkAh2BKoSzmx/J3yxMgj/uc4BtouGvwTuBaoTHmZ9ULScc4EPo/ddCI8A257w8OpWwC7RuKeAW6P3hwFLonVuBzwAvB8TtwPjo+U0ARYDR+exjf8Dzore1wD2i943ITyD9HSgErAT0D4a9wwwjvB80qbAD8D5MduzEbiccGmiKnAf8Fr0WdUEXgduzyOeQ4EFMcO7E6rkj4zi+CfwI1A5Zh99Ee2fqrks72BgPlueErUD8CfQIBo+GWhA+Gf01Ghdu+SzLbH7a0dgGXBWNP70aHin+OMnGr4ReDZ6f1H0OVQjHB8dgVoJHIf9CY+VaxTt+0eA56NxZwMfxczXGlgeTVc9+hzOi2Ldm3AMtYk/vnJZf+/oM29OOEbGACPjjrcW+XyPcownseO3RfT+PvI4dgjHykbgjmg5VQnHac/oc60JvAS8GrPsKcBPhOOqajQ8OIFjPs84ctneS4EJcWVTgAvY8n3pE5U3BJYCxxKOwSOj4box880D2kT7rRL5fL+BE6N91Sqa/nrgv3l8tgOi4yPXV8w8TwErgAOjGKtEZasI36/tgKFE34vcjv2o7EpgTLp/15PxSnsApf0VHSCro4NtLvAQ0Q94dJAeFjPtw0SJN6bse+AQwvM7FwMVc1nHuWz5sT4s+uLtB1SIm+4ptiTXx4EhMeNqABuApjGxHRQz/kVgQB7b+D5wE1AnrvxaYGwu02cAfwGtY8ouAqbEbM+8mHFGSFi7xpTtD8zOI55DyZlc/wW8GDNcAVgIHBqzj3rnsw+N8ON0cDR8IfBuPtN/AZyQ27bksr/OAqbGjf8fcG5MbHkl197Af4G9EjwOs5Prt8DhMeN2ifZ9RcKP/hrC9WqAQcAT0ftTgQ/ilvsIcEP88ZXL+t8BLokZ3iN7nTHHW2GSayLHb4uCjp3oWFkPVMln3e0JD5PPHp4CXB8zfAkwqYBjvrDH8EBgdFzZFOCeaF+eHlN+DTH/qERlbwLnxMx3cy6fZ67fb0Lt2vlx35e1McdEvvsqj+15Cngml7LRMcM1CG1TGud27Cfy3StNL1ULJ8eJ7r69u2e6+yXu/mfMuPkx7zOBq6KqneVmtpxwNtUg+jvX3TfmtyJ3fxcYBjwILDKzEWZWK5dJGxCSffZ8qwn/7TaMmSa2pe1awsGfm/MJ/8V/F1VNdo3KGxP+w49XB6gcu/7ofey6Yz+XuoSziOkxn8ukqDwR8du6KVp+XuvLwcO3ejThbATgDGIaJJnZ2balKn85odq/Tswi8lx2fGyR+M8iLyMJP6KjzewXMxtiZpUSmC8TGBsT77eEH7X67r4KmACcFk17Glu2NRPYN+747AXsnMA647dzLiGZF1iNncjy8jh+IbFjZ7HHNBoys2pm9khUhb2S8M/j9pbzOmFe3428jvnCHsPLCP/oxOtF+Mfw5ZiyTODkuP1yEDkfVJ/bMZjXNmQCQ2OW9Qfhn4NEjsn85BbD5rJoH/5B2Ld5qUk4USn1lFxTz2PezwcGRYk4+1XN3Z+PxjWxBBpcuPv97t6RUA20O3B1LpP9QvgSAWBm1QlVWAsLvQHus9z9dKAeoXrt5Wh584Fdc5llCeEsIzOmrEncuj1u+j8J1Y/Zn0ttD43EEhG/rUb4Ecxrfbl5Hjgpuia0L/BKtKxM4FHgMkJV7vbA14Qfo0SWnSO2SOxnsYbwo5xtcyJz9w3ufpO7twYOALoSqnULMh84Ju44q+KhTUD2tp5uZvsTqj3fi5nvP3Hz1XD3ixNYZ/x2NiFUxy5KYN4Cl5fP8ZvIsRO/f64inFnv6+61CNWWkHOf5iW/Y74wx/BXhO9uvBujZT0Xk+znE85cY/dLdXcfHDNfQcd3/DZcFLe8qu7+3/gJzey66Jporq+4yXOLoXHMsmoQqsx/ySe2VoTLY6WekmvxehToa2b7WlDdzI4zs5rAVOBXYHBUXsXMDoxfgJl1juavRPhhXkc4K4n3HHCembWPGgvcBnzi7nMKG7SZnWlmdaMzwuVRcRbhjOcIMzvFzCqa2U5m1t7dswjVUIPMrGaUoK4kj5aR0XIfBe41s3rROhuaWZcEQ3wROM7MDo8+l6sI1dJb/Vjkxd0/J1TLPwa86e7Z21md8KOxOIrrPKIGawmaCOxuZmdEn9GphOuc46PxXwCnmVklC60kT8qe0cz+bmZ7Rj+yKwn/sOS2r+MNJ3z2mdFy6prZCXExZRJuhXgh+vyJYtrdzM6K4qkUHW+tKNjzwP+ZWbPoR/S2aNn51sTEWES4XpstoeO3iMdOTUIiXG6hodcNCcYIeR/zhY1jKuFsOf5scQPhGn91YKSFRoDPAsebWRczy4h+Gw61mEZYhTQcuNbM2kRx1jazk3Ob0N1vi/7ByvWVwLqOtdAArDJwC2Ef5lfTcwih2rrUU3ItRu4+jXBNYRihWuhHwvU5ooR0POE60jxgAeEaWLxahC/xMkK12VLCPYTx63qHcC3yFULS3pUtVYGFdTTwTfSf6lDgNHdf5+7zCI0sriJU93wBtIvmuZyQ/H8GPiT8WD6RzzquIXweH0dVdW8Tzi4K5O7fA2cSGr0sIXyOx7v7+kJsI4QEcUQUa/ayZwJ3E66TLgL2BD5KdIHuvpRwxnkVYV/9E+jq7kuiSf5F2DfLCNe1n4uZfWdC9eBKQtXuf0js1o2hhIY1b5nZKkLjpn1jYvqL0OAofltXAUcRjpNfCNWK2Q2BCvIEoRr7fWA24Z++yxOYL9uNwNNRVeUphTx+C3vs3Ec4Y19C+GwSvq+ygGM+4TiiY/MpwnGb27gehJqiJwhn6ycA1xH+yZtPqK0q0u+3u48l7NfRUZxfA8cUZVkJeI7wz8sfhAZ5vfKa0Mw6A2s83JJT6mW3jhQRkWJkZnWBD4AOce00yiUzewV43N0npjuWZFByFRERSTJVC4uIiCSZkquIiJQZZna0hd7FfjSzAbmM38HMxlroAWuqRT3cRePmmNkMC7feTYspP9lCD3CbLMHuGZVcRUSkTIha1j9IaKDVmnDLWeu4ya4DvnD3vQi3tg2NG/93d2/v7rFJ9GtCI7P3SZCSq4iIlBX7AD+6+89Rq+vRhJbWsVoTehTD3b8DmloBfXa7+7fRXQkJK3UNmurUqeNNmzZNdxgiIlLCLFu2jBUrVpCdI5YuXcqaNWto0qTJ5mkWLlzIpk2baNy4MWvWrOG7776jZcuWVK9enRkzZpCRkYGZUadOHerWrcv06dOXuHtdCA9JAP4R3VaZrxLx+KXCaNq0KdOmFbhdIiJSzrz00ku8+eabPPbYYwCMHDmSqVOn8sADD2yeZuXKlfTr14/PP/+cTp06UaNGDR577DHatWvHL7/8QoMGDfj999858sgjueeeezjkkEPiuy9NSKlLriIiIrlp1KgR8+dv6QBqwYIFNGiQsyvjWrVq8eSTTwLhwTXNmjWjWbNmAJunrVevHt27d2fq1KL3Z6FrriIiUiZ07tyZWbNmMXv2bNavX8/o0aPp1q1bjmmWL1/O+vWh87bHHnuMgw8+mFq1arFmzRpWrVoFwJo1a3jrrbdo27YwPZ3mpDNXEREpEypWrMiwYcPo0qULWVlZ9O7dmzZt2jB8+HAA+vbty7fffsvZZ59NRkYGrVu35vHHHwdg0aJFdO/eHYCNGzdyxhlncPTRRwNgZt0J3avWBSaY2Rfunm/f56WuQVOnTp1c11xFJF0mTZpEv379yMrK4oILLmDAgJy3Ui5btozevXvz008/UaVKFZ544okcZ0BZWVl06tSJhg0bMn58eH7Dl19+Sd++fVm9ejVNmzZl1KhR1KqV25MkJT+jRsHAgTBvHjRpAoMGQa88ezNOjJlNj7stJyGqFhYRSVBWVhaXXnopb7zxBjNnzuT5559n5syZOaa57bbbaN++PV999RXPPPMM/fr1yzF+6NChtGqV80FDF1xwAYMHD2bGjBl0796dO++8M+XbUtaMGgV9+sDcueAe/vbpE8rTQclVRCRBU6dOpUWLFjRv3pzKlStz2mmnMW7cuBzTzJw5k8MPPxyAli1bMmfOHBYtCo+1XbBgARMmTOCCCy7IMc/333/PwQeHx8oeeeSRvPLKK8WwNaWHO6xYAbNnw/Tp8NZbMHo0PPQQ3HorXHklXHQRrF2bc761a8OZbDromquISIIWLlxI48abn/9No0aN+OSTT3JM065dO8aMGcNBBx3E1KlTmTt3LgsWLKB+/fr079+fIUOGbG44k61t27a89tprnHDCCbz00ks5WryWJZs2hST5xx9bXkuX5hzOrXzZMsjK50nGNWrAmjW5j5s3LzXbUhAlVxGRBOXWRsXMcgwPGDCAfv360b59e/bcc086dOhAxYoVGT9+PPXq1aNjx45MmTIlxzxPPPEEV1xxBTfffDPdunWjcuXKqdyMbZaVBcuX558QcytftiychealVi3Yccctr8zMnMPZr5122vJ+hx2gcmVo2jRUBceL6T+iWKUsuZpZdeAhYD0wxd1HReWnAscBG4C73P1bM/sJmAx85u4jUhWTiMi22Jb7KEePHs1rr73GxIkTWbduHStXruTMM8/k2WefpWXLlrz11lsA/PDDD0yYMKFYtmfjxpDwCjpzjH8tX55/ktx++5zJsFmznAkxt0S5/fZQqVLRt2XQoHCNNbZquFq1UJ4OqTxz7QG87O6vm9kLQPZl5Z6Ep9HvANwGXACsBqoCZbMuRETKhNj7KBs2bMjo0aN57rnnckyzfPlyqlWrRuXKlXPcR3n77bdz++23AzBlyhTuuusunn32WQB+//136tWrx6ZNm7j11lvp27dvoeLasCEkyUSqWGNfK1bkvUyzcFYYmwR3223rM8f4RLn99pCRUajwkyK7VXCyWwsXVSqTayNgRvQ+trb8LsL9Qr8REixAB8CACcAb8Qsysz5AHyBHH5EiIsXphRcqsmbNMJo370LFill07574fZT5ef7553nwwQdxh6OP7sG++57HBx8knijjLuHmUKFCzgRYvz60apV7FWvsa/vtw7ylSa9e6Uum8VJ2n6uZnQUsc/fxZjba3U+LG98CuMzd+8eUvQr0cPdNeS1X97mKSDpk3+oRX+04YsTWP+jr1hW+0c4ff+TdKAfC2WB+yTCvcbVqlb4kWZIU9T7XVCbX6sAwYB3wIXC0u59lZscCxwM1gKuB2sA10Wxfuft9+S1XyVVE0iEzM/eWp1Wrwr775kyWf/6Z93IqVSr4rDG3cTVrhqpaKV5FTa4pqxZ29zXAeTFFo6LyicDEmPLfgN6pikNEpCgWL4apU+GTT8LfvG7p+PPP0Hq2eXPo1KngRFm9upJkeaBbcUSk3PvzT/jss5zJdPbsMK5CBWjTJtxLuXr11vNmZsL77xdvvFLyKbmKSLmyaRN8992WJPrJJ/DVV1s6KWjcOFTzXnwx7LMPdOwYEmte11zTdauHlGxKriJSpv3yy5ZEOnUqfPrplta1tWpB585wzTUhke6zD+yyS+7LKWm3ekjJpqfiiEiZsWpV6Hs29qx04cIwrmJFaNcuJNB99w1/99hDLWklfyWuQZOISCpt3Ahff53zOunMmaHaF0IDo4MP3pJM27cPLXtFioOSq4iUeO6hKjb2jHT69C23vOy4Y0iiPXtuqd6tUye9MUv5puQqIiXOsmXh2mj2ddJPPoHffw/jttsOOnQIjYuyz0qbN9ftLVKyKLmKSFr99VdorRt7VvrDD1vGt2wJRx+95TrpXnuFp6CIlGRKriJSbNzhxx9zXif9/HNYvz6Mr18/JNGzzw5/O3UKfdyKlDZKriKSMtm9HMUm02XLwrhq1ULyvOKKLWeljRurelfKBiVXEUmKRHo5ym5wtO++0Lp1uD1GpCzSoS1SwkyaNIl+/fqRlZXFBRdcwIABA3KMX7ZsGb179+ann36iSpUqPPHEE7Rt23bz+KysLDp16kTDhg0ZP348AP/6178YN24cFSpUoF69ejz11FNbPeS7MHLr5WjGjHB7DIQz0H322bqXI5HyQp1IiJQgWVlZ7L777kyePJlGjRrRuXNnnn/+eVq3br15mquvvpoaNWpwww038N1333HppZfyzjvvbB5/zz33MG3aNFauXLk5ua5cuZJatWoBcP/99zNz5szNzyBNxC+/5Dwjza2Xo9jOGfLq5UiktFEnEiJlwNSpU2nRogXNmzcH4LTTTmPcuHE5kuvMmTO59tprAWjZsiVz5sxh0aJF1K9fnwULFjBhwgQGDhzIPffcs3me7MQKsGbNGiyfC5urV8O0aTmT6YIFYVx2L0dnnqlejkTyo+QqUoIsXLiQxo0bbx5u1KgRn3zySY5p2rVrx5gxYzjooIOYOnUqc+fOZcGCBdSvX5/+/fszZMgQVmWfVsYYOHAgzzzzDLVr1+a9994DEuvl6G9/Uy9HIoWl5CpSguR2mSb+LHPAgAH069eP9u3bs+eee9KhQwcqVqzI+PHjqVevHh07dmTKlClbLefWWwfRp88gBgy4nW7dhlGx4k3q5UgkRZRcRUqQRo0aMX/+/M3DCxYs2KrhUa1atXjyySeBkIybNWtGs2bNGD16NK+99hoTJ05k3bp1rFy5ksMPP5O///3ZuF6OzsDsOPbd9yb1ciSSIkquIiVI586dmTVrFrNnz6Zhw4aMHj2a5557Lsc0y5cvp1q1alSuXJnHHnuMgw8+mCpVatGjx+00bHg7U6fCe+9N4c8/7+Ldd5/l3XehefNZHH30buy7L8ye/Ro//dSSMWPStJEi5YCSq0gJUrFiRYYNG0aXLl3Iysqid+/etGnTZnPL3r59+zJz5recccbZrF+fQdWqrdlhh8epWTNnL0e77grVq8OwYaGjhvPPH8D06d/z+ecVyMzMLFRLYREpPN2KI1KCjBq19cO4jzoq/16OOnbc0nJ3333Vy5FIMulWHJFSbtSo8KSXtWvD8Ny5cNZZoT9eUC9HIqWJvpYiabZ+Pbz7LvTtuyWxZnMPHde/+qp6ORIpTZRcRdJgzRqYNAnGjoXx42HFirynXbECDjmk+GITkW2nflVEismyZfDMM9C9e7h/9KSTQoLt0QNefz1cK81NkybFG6eIbDuduYqk0K+/hirdsWPhvfdCj0gNG8IFF4Sk+re/bblmumJFzmuuEBosDRqUltBFZBsouYok2c8/w5gxIaH+73/huuluu8FVV4WE2qlT7n3x9uoV/sa3Fs4uF5HSQ7fiiGwj99A/79ixIal++WUo79AhVAH36BFa9er2GJHSR7fiiBSjTZvC/abZCfXHH0PyPOAAuPvukFSbNUt3lCKSLkquIgnauBHef39Lle8vv4TrpYcdBv/4B5xwAuy8c7qjFJGSQMlVJB/r1sHkySGhvvYa/PFHeOTa0UeH6t7jjoMddkh3lCJS0ii5isRZuRImTgwJdeLEcE9q7dpw/PEhoXbpElrxiojkJWXJ1cyqAw8B64Ep7j4qKj8VOA7YANzl7t9G5cOB1e7+j1TFJJKXxYvDmemYMfD226HXpPr14cwzQ0I99FCoXDndUYpIaZHKM9cewMvu/rqZvQCMisp7Ar2AHYDbgAvM7CRgGtAyhfGI5DB//pYGSR98EBopNW0Kl10WEup++0FGRrqjFJHSKJXJtREwI3qfFVN+F/AA8Buwg5nVBzoAj5JHcjWzPkAfgCbqrka2wfffh2Q6Zgxk39HVpk24t7RHD2jXTrfMiMi2S2VyXUBIsF8Q082iu08FpppZC+Ay4BCgHvBvoJ2Z7e7uP8QuyN1HACMg3OeawpiljHGHzz/fklC//TaU77MPDB4cbpnZfff0xigiZU8qk+sYYJiZHQe8bmYj3f0sMzsWOB6oAVzt7r8BL5pZU+Cy+MQqUlhZWfDf/265ZWbu3NAj0iGHwCWXwIknQqNG6Y5SRMoy9dAkZUL2Y9vGjIFx4+D330MDpKOOCtW9xx8fOssXESmMlPfQZGbV3X1NYVcgkiq5PbatRo1w72mPHnDMMVCzZrqjFJHyqMDkamYHAI8RqnGbmFk74CJ3vyTVwYnEW7YsPJ5t7NiQWNetg512gp49w/XTI46AKlXSHaWIlHeJnLneC3QBXgNw9y/N7OCURiUS49dfQ1XvmDE5H9t24YUhocY+tk1EpCRI6CfJ3edbzvsTsvKaViQZfv55yz2osY9t+8c/QkLN67FtIiIlQSLJdX5UNexmVhm4Avg2tWFJeZPfY9tuvjkkVD22TURKi0SSa19gKNCQcO/qW8ClqQxKyoe8Htt24IF6bJuIlG75JlczywDuc/dexRSPlHF5Pbbt8MP12DYRKTvyvWrl7llA3ag6WMqISZMmsccee9CiRQsGDx681fhly5bRvXt39tprL/bZZx++/vprANatW8c+++xDu3btaNOmDTfccMNW8951112YGUuWLNlctm5daOF73nmhM/zDD4cnngh9944cGe5JnTQJLrpIiVVEyoZEqoXnAB+Z2WvA5vtc3f2eVAUlqZOVlcWll17K5MmTadSoEZ07d6Zbt260bt168zS33XYb7du3Z+zYsXz33XdceumlvPPOO2y33Xa8++671KhRgw0bNnDQQQdxzDHHsN9++wEwf/58Jk+eTJMmTVi1KjxdJv6xbd26hepePbZNRMqyRJLrL9GrAqBb8ku5qVOn0qJFC5o3bw7Aaaedxrhx43Ik15kzZ3LttdcC0LJlS+bMmcOiRYuoX78+NWrUAGDDhg1s2LCB2Fbkl1zyf+y//xA++OAE9tgDNmzQY9tEpHwqMLm6+00AZlYzDPrqlEclKbNw4UIaN268ebhRo0Z88sknOaZp164dY8aM4aCDDmLq1KnMnTuXBQsWUL9+fbKysujYsSM//vgjl156KQ0a7Mv998OIEa/xzTcNGT++HRkZ0KcP9Oqlx7aJSPlU4J2CZtbWzD4Hvga+MbPpZtYm9aFJKuTWl3TcPcwMGDCAZcuW0b59ex544AE6dOhAxaiXhoyMDF544QuuvHIBDz88lSZNvqZfv7X8/PMgrr76Zj7/PHSKf/PNodWvEquIlEeJVAuPAK509/cAzOxQwrNXD0hdWJIqjRo1Yv78+ZuHFyxYQIMGDXJMU6tWLZ588kkgJONmzZqxYkUzrr8+9rFt29Ow4aH87W+T6Nu3C+efP5sXX2zHiy+GZe69995MnTqVndVCSUTKoUT6uKmenVgB3H0KUD1lEUlKde7cmVmzZjF79mzWr1/P6NGj6datW45pli9fzp9/rueDD+DIIx9jyZKDOeSQWtx++2J22mk5DzwAP/zwJ82avc3FF7fk+OP35Pfff2fOnDnMmTOHRo0a8dlnnymxiki5lciZ689m9i9gZDR8JjA7dSFJKlWsWJFhw4bRpUsXsrKy6N27N23atGH48OFs3AgtWvTl4Ye/ZcKEs8nKyqBChdYcccTjnHYaNG/+K/37n8OIEVkMH76JU045ha5du6Z7k0RESpwCn+dqZjsANwEHRUXvAze5+7IUx5YrPc+16EaNgoEDYd48aNIEBg0KDw7XY9tERHKXsue5Rkn0iiJFJSXGqFGhBe/atWF47lw455zQ3eDGjVse29ajR+jkQY9tExEpukSe5zoZONndl0fDOwCj3b1LimOTJBo4cEtizZaVFc5KX3sNDjpIj20TEUmWRH5O62QnVghnsmZWL3UhSSrMm5d7+erVoXMHERFJnkRaC28ysybZA2aWCeR/oVZKnCZNClcuIiJFl0hyHQh8aGYjzWwkoUHTtakNS5Lt1FO3LqtWLTRqEhGR5EqkQdMkM9sb2A8w4P/cfUkBs0kJsnYtvPRSeOJM5cowf/6W1sK99DBBEZGkS6RB04HAF+4+3szOBK4zs6HuPjf14Uky3HILzJ4NU6bAIYekOxoRkbIvkWrhh4G1ZtYOuBqYCzyT0qgkaWbMgLvuCs9SVWIVESkeiSTXjR56mjgBuN/dh6JHz5UKmzaFe1u33x7uvDPd0YiIlB+J3IqzysyuJXR7eLCZZQCVUhuWJMMjj8DHH8Mzz4ROIkREpHgkcuZ6KvAXcL67/wY0BHQeVML9+isMGBB6WzrzzHRHIyJSviTSWvg34J6Y4XnommuJ178//PUXPPxw6OJQRESKjzq8K4MmToQXX4Rbb4Xddkt3NCIi5U8i1cJSiqxZA5dcAq1awdVXpzsaEZHySWeuZcyNN4Yn3nzwQegwQkREil+inUjcCGRG0xvg7t68gPmqAw8B64Ep7j4qKj8VOA7YANwFfE+4l7YqsNbd+xZ1Y8q7L76Ae++FCy8MT7kREZH0SOTM9XHg/4DpQFYhlt0DeNndXzezF4BRUXlPoBewA3Cbu18AXARgZk+YWQV331SI9Qjh8XF9+oRbbu64I93RiIiUb4kk1xXu/kYRlt0ImBG9j03KdwEPAL8REixm1hq4Ffg1t8RqZn2APgBN9BiXXD38MHz6KTz3HOywQ7qjEREp3xJp0PSemd1pZvub2d7ZrwTmW0BIsDnW4+5To6rfZ4H5UdlMd+8BWPRIuxzcfYS7d3L3TnXr1k1g1eXLwoVw3XVw1FFw2mnpjkZERBI5c903+tsppsyBwwqYbwwwzMyOA143s5HufpaZHQscD9QArjazBoRH2FUANhIlXEncFVfAhg3w0EO6p1VEpCRIpBOJvxdlwe6+BjgvpmhUVD4RmBg3+eVFWYfAa6/BmDFw++2w667pjkZERCCBamEzq21m95jZtOh1t5nVLo7gJH+rV8Nll0HbtnDVVemORkREsiVyzfUJYBVwSvRaCTyZyqAkMf/+d3jw+YgRUEmPUhARKTESuea6q7v3jBm+ycy+SFE8kqDp02HoUOjbF/bfP93RiIhIrETOXP80s81dEkSdSvyZupCkIBs3hnta69UL11pFRKRkSeTM9WLg6eg6qwF/AOemMijJ34MPwmefwQsvhAehi4hIyZJIa+EvgHZmVisaXpnqoCRv8+fD9dfDMcfAySenOxoREclNnsnVzM5092fN7Mq4cgDc/Z5cZ5SUuvzy0NWh7mkVESm58jtzrR79rVkcgUjBxo6FceNgyBBo2jTd0YiISF7M3dMdQ6F06tTJp02blu4wit3KldC6NdSpE/oQ1q03IiKpZ2bT3b1TwVPmlEgnEkPMrJaZVTKzd8xsiZmdWbQwpaiuvx5++UX3tIqIlAaJ3IpzVNSIqSuhM/7dgatTGpXk8OmnMGwYXHop7LNPuqMREZGCJJJcs8+TjgWed/c/UhiPxMm+p3WXXeDWW9MdjYiIJCKR+1xfN7PvCB1HXGJmdYF1qQ1Lsg0dCl98AS+/DLXVo7OISKmQUIMmM9sBWOnuWWZWDajl7r+lPLpclKcGTXPnhkZMhx8eWgnr1hsRkeJV1AZN+d3nepi7v2tmPWLKYicZU9iVSeLcwzVWs3C9VYlVRKT0yK9a+BDgXcKDzeM5Sq4p9corMGEC3HMPNGmS7mhERKQwdJ9rCbRiBbRqBTvvDFOnQsVEroyLiEjSpfI+19vMbPuY4R3MTO1WU+i662DRonBPqxKriEjpk8itOMe4+/LsAXdfRrgtR1Lg44/h4YdDH8KdCv2/koiIlASJJNcMM9sue8DMqgLb5TO9FNGGDeGe1oYN4ZZb0h2NiIgUVSKVjs8C75jZk4SGTL2Bp1MaVTl1770wYwa8+irU1OMSRERKrUSe5zrEzL4CjiA8LP0Wd38z5ZGVM7Nnw403woknwgknpDsaERHZFok2l/kW2Ojub5tZNTOr6e6rUhlYeeIOF18MGRnwwAPpjkZERLZVIq2FLwReBh6JihoCr6YwpnLnhRfgzTdh0CBo1Cjd0YiIyLZKpEHTpcCBwEoAd58F1EtlUOXJsmXQv39oGXzppemORkREkiGRauG/3H19dteHZlaR0LBJkuDaa2HxYnjjjVAtLCIipV8iZ67/MbPrgKpmdiTwEvB6asMqHz76CB55JJy5duiQ7mhERCRZEkmu1wCLgRnARcBE4PpUBlUerF8PF10U+g2+6aZ0RyMiIsmUb7WwmVUAvnL3tsCjxRNS+XDXXfDNN/D661CjRrqjERGRZMr3zNXdNwFfmpmey5JEP/4YemDq2RO6dk13NCIikmyJNGjaBfjGzKYCa7IL3b1bfjOZWXXgIWA9MMXdR0XlpwLHARuAu4BNhKrnKsCX7n5HEbaj1Mi+p7VSJbj//nRHIyIiqZBIci3qFcEewMvu/rqZvQCMisp7Ar2AHYDb3P0CQpeKmFmZf0bsc8/B22+HB6A3aJDuaEREJBXyTK5mVgXoC7QgNGZ63N03FmLZjaL5ALJiyu8CHgB+IyTY7PWdBrxViOWXOn/8Af/3f7DvvtC3b7qjERGRVMnvzPVpQtXtB8AxQGugXyGWvYCQYL8g5tquu08FpppZC+Ay2JxYM/OqEjazPkAfgCZNSu/l33/+MyTYt9/WPa0iImWZuefeH4SZzXD3PaP3FYGp7r53wgsO11yHAeuAD4Gj3f0sMzsWOB6oAVxNuKb7OjAeWOvuV+a33E6dOvm0adMSDaPEeP99OOSQkGDvKNNXlUVEyg4zm+7uhX66dn7J9bPYZBo/nC6lMbn+9Re0bw/r1sHXX0P16umOSEREElHU5JpftXA7M1uZvXxCD00ro/fu7rWKEGe5NGQIfPcdTJyoxCoiUh7kmVzdXVcFk+CHH8LTbk49FY45Jt3RiIhIcUik+0MpIvfQKrhKFbjvvnRHIyIixSXRh6VLETzzDLz3HgwfDjvvnO5oRESkuOjMNUWWLIGrroIDDoALL0x3NCIiUpyUXFPk6qthxYrwSLkK+pRFRMoV/eynwHvvwVNPhQTbtm26oxERkeKm5Jpk69aFRkzNm8O//pXuaEREJB3UoCnJbr893H7z5ptQtWq6oxERkXTQmWsSffddSK5nnAFHHZXuaEREJF2UXJNk0ya46CKoUQPuvTfd0YiISDqpWjhJnnoqdM7/6KNQr166oxERkXTSmWsS/P47/OMfcNBB0Lt3uqMREZF0U3JNgquugtWrdU+riIgESgXb6O234dln4ZproHXrdEcjIiIlgZLrNvjzz3BPa4sWMHBguqMREZGSQg2atsGgQfDTT+HstUqVdEcjIiIlhc5ci+ibb+COO+Dss+Hww9MdjYiIlCRKrkWQfU9rrVpw113pjkZEREoaVQsXweOPw0cfwRNPQN266Y5GRERKGp25FtJvv8E//wmHHALnnpvuaEREpCRSci2kK6+EtWvDPa1m6Y5GRERKIiXXQpg0CZ5/Hq67DvbYI93RiIhISaXkmqC1a+GSS0JSHTAg3dGIiEhJpgZNCbr5Zpg9G6ZMge22S3c0IiJSkunMNQEzZsDdd8N554WGTCIiIvlRci3Apk3Qpw9svz3ceWe6oxERkdJA1cIFeOQR+PhjeOYZ2GmndEcjIiKlgc5c8/Hrr6Hx0uGHw5lnpjsaEREpLZRc89GvH/z1Fzz8sO5pFRGRxCm55mHCBHjpJbj+ethtt3RHIyIipUnKkquZVTezp83sUTPrFVN+qpk9Y2aPm1mrqOwRM5uRqlgKa80auPRSaNUqdHUoIiJSGKk8c+0BvOzuFwLdYsp7AucD1wJXAbj7RcD3KYylUG68EebODY2ZKldOdzQiIlLapLK1cCMg+2w0K6b8LuAB4DdghxSuv0i++ALuvRcuuAD+9rd0RyMiIqVRKs9cFxASbI71uPtUd+8LPAvMT2RBZtbHzKaZ2bTFixcnJbhJkyaxxx570KJFCwYPHgxAVla4p3WnneDaa5fRvXt39tprL/bZZx++/vrrfOcFOPXUU2nfvj3t27enadOmtG/fPimxiohI6WLunpoFm1UHhgHrgA+Bo939LDM7FjgeqAFc7e6/mdkg4DRgMtDP3f/Ka7mdOnXyadOmbVNsWVlZ7L777kyePJlGjRrRuXNnnn/+ed59tzWXXw6jRsHnn19NjRo1uOGGG/juu++49NJLeeedd/Kct3Xr1jnWcdVVV1G7dm3+/e9/b1OsIiKSPmY23d07FXa+lFULu/sa4LyYolFR+URgYty0A4GBqYol3tSpU2nRogXNmzcH4LTTTmPkyHE8+GBrjjoKTj8dRo2aybXXXgtAy5YtmTNnDosWLeLnn3/eat5x48blSK7uzosvvsi7775bXJskIiIlSLm8FWfhwoU0btx483CjRo148cWFbNgADz0U7mlt164dY8aMAUIynjt3LgsWLMh13oULF+ZY/gcffED9+vXZTffwiIiUS+UyucZXhX/2Gfz8s/Hvf8Ouu4ayAQMGsGzZMtq3b88DDzxAhw4dqFix4lbzAlhcDxPPP/88p59+esriFxGRkq1c9i3cqFEj5s8PbalWrYInnlhA/foN+Mc/tkxTq1YtnnzySSAk42bNmtGsWTPWrl27eV6ABQsW0KBBg83DGzduZMyYMUyfPr14NkZEREqccnnm2rlzZ2bNmsXs2bO5/vr1rFw5mrvu6kalSlumWb58OevXrwfgscce4+CDD6ZWrVo55l2/fj2jR4+mW7ctt/G+/fbbtGzZkkaNGsWvVkREyolyeeZasWJFhg0bxqGHdmHevCw6d+7NmWe2Yfjw4QD07duXb7/9lrPPPpuMjAxat27N448/nmPeLl26kJWVRe/evWnTps3mZY8ePVpVwiIi5VzKbsVJlWTcijNqFFx3HcybBxUqwPDhcOGFSQpQRETKjBJ3K05JNWpU6Chi7dowvGkT9O8P1apBr175zioiIpKQcnfNdeDALYk129q1oVxERCQZyl1ynTevcOUiIiKFVe6Sa5MmhSsXEREprHKXXAcNCtdXY1WrFspFRESSodwl1169YMQIyMwM3RxmZoZhNWYSEZFkKXethSEkUiVTERFJlXJ35ioiIpJqSq4iIiJJpuQqIiKSZKWu+0MzWwzMTdLi6gBLkrSs0qS8bndpon1Usmn/lGzJ3D+Z7l63sDOVuuSaTGY2rSh9RpZ25XW7SxPto5JN+6dkKwn7R9XCIiIiSabkKiIikmTlPbmOSHcAaVJet7s00T4q2bR/Sra0759yfc1VREQkFcr7mauIiEjSlenuD82sOvAQsB6Y4u6jovJjgcuAie4+LCobDFQD1rr7gDSFnDT5bPsAYFdgZ+Bid19gZj8Bk4HP3D3t1SnlQT7750agFbAMuNndfylrx2Zpkc8+Oh/oDDQGvnL3a/UdKn5m1hwYCNR295NiytsC10aDt7v71+n4DpX1M9cewMvufiHQLbvQ3ScCQ7KHzawJUMndrwAyzKxxsUeafHlt++Co7Ang71HxaqAqML/Yoyy/ct0/wEbCj/kGYHkZPTZLi7y+Q4+7e1/gB+CpqFjfoWLm7j+7+/m5jOoHXApcAlyeru9QmT5zBRoBM6L3WflM15AtX4p50Xyl/UuS57abWQ3gFKBPVNQBMGAC8EZxBVjO5bV/bnP3TWbWDbgA+JSyd2yWFvl9h6oAzdz9+6hI36GSo7a7Lwcws5qk6fe9rJ+5LiB8kJD/ti6Mma5xNF9pl+u2m1kt4GHgn+6+CsDdN7l7FrDOzMr6MVFS5Lp/3H1T9PZ3oAZl89gsLfL7/TgJGJM9oO9QibLCzGpHv3WrSNN3qEy3Fo6umQwD1gEfAke7+1lmtj+hTn4H4D53f8XMbge2A/5y92vzXGgpkc+2jwEqEQ64F6O/10SzfeXu96Uh3HInn/1zHeEHoA5whbv/WtaOzdIir30UjRsHnObuf5rZHug7VOzMbCdgEHAk8BjQOvoOtQWuJtQkDImuuRb7d6hMJ1cREZF0UPWFiIhIkim5ioiIJJmSq4iISJIpuYqIiCSZkqtICWFm25vZJQVM09TMvi6umESkaJRcRUqO7Qm9yiSNmWUkc3kikhglV5GSYzCwq5l9YWZ3Rq+vzWyGmZ0aP7GZZUTTfGpmX5nZRVH5oWb2npk9R9TDkJm9ambTzewbM+sTs4zVZjbIzL40s4/NrH5UXt/MxkblX5rZAVH5mWY2NYrxESVvkdwpuYqUHAOAn9y9PfAx0B5oBxwB3Glmu8RNfz6wwt07EzqSv9DMmkXj9gEGunvraLi3u3cEOgFXRDfgA1QHPnb3dsD7wIVR+f3Af6LyvYFvzKwVcCpwYBRjFtArWRsvUpaU9b6FRUqrg4Dnoy71FpnZfwgJ9KuYaY4C9jKz7CeC1AZ2I3T8P9XdZ8dMe4WZdY/eN46mWxpNOz4qn07o7QbgMOBsgCiGFWZ2FtAR+NTMIHRU/3tyNlekbFFyFSmZLMFpLnf3N3MUmh0KrIkbPgLY393XmtkUoEo0eoNv6aYti/x/Ewx4Wl0wihRM1cIiJccqoGb0/n3g1Oi6al3gYGBq3PRvAhebWSUAM9s96g83Xm1gWZRYWwL7JRDLO8DF0XIzok7Q3wFOMrN6UfmOZpZZuE0UKR+UXEVKCHdfCnwU3WqzP6EK+EvgXcJTjH6Lm+UxYCbwWTTPI+R+5jkJqGhmXwG3EK7nFqQf8Hczm0GoLm7j7jOB64G3omVNBuKvA4sI6rhfREQk6XTmKiIikmRKriIiIkmm5CoiIpJkSq4iIiJJpuQqIiKSZEquIiIiSabkKiIikmRKriIiIkmm5CoiIpJkSq7lmJmda2YfpjuOVDGzv5nZ9+mOIy9mdp2ZPVbM63Qza5HidUwxswuKOO/FZrYoes7sTgXPke+ymkbbW6ofUGJmrc1sWszwHDM7Is0xJe2zLeh4MbPuZjY/OiY6RM8TbrOt6001Jdc4ZtYr2onxLzezf6c7PgkSSRLu/oG771FcMRWWu9/m7kVKQmVR9ACCe4Cj3L1G1Ndy7PgykSyL4BbgrnQHkUZ3AZdFx8Tn0fDNaY6pQEqucdx9VLQTN7+A/sAi4NHCLq8c/hCUCCX9cy/p8aVJfcKj8L5JdyAlZf+Y2S7A34FXk7zctG+fBYnkoExyHhOvER4qUaIfGqHkWgAz6wDcC5zm7r9GZbXN7HEz+9XMFprZrWaWEY0718w+MrN7zewP4MZo+mfMbLGZzTWz6/M7qMxsOzO7z8x+iV73mdl20bhDzWyBmV1lZr9HMZwXN+9dZjYvql4bbmZVE9zWA8zsUzNbEf09IGbcuWb2s5mtMrPZZtYrKm9hZv+J5lliZi/ksezss47zoiqeZWbW18w6m9lXZrbczIbFzdPbzL6Npn3Tosebmdn70SRfRrUKp8Z8LteY2W/Ak9llMctrbGZjov2wNHt9hdiGSWZ2WVzZl2bWI3o/NNq2lWY23cz+FjPdjWb2spk9a2YrgXOjsmdjpulmZt9En8UUM2sVMy7HmbqZPWVmt0bv65jZ+Gi+P8zsg0R+tPI7VqLPvWvMtBWjz2bvaHg/M/tvtM4vLTwztkB5HdtmtjuQXYW/3MzezWX292PGrzaz/c2sgoXv09zo+/CMmdXOY92F/d7uambvRsfKEjMbZWbbxyxvjpn9Izp+V5jZC2ZWJWb8CWb2RXQ8/GRmRxcURy6OBD5z93V5bFNLC9/H06LhrtE6l0f7Z6+4eK+x8ESjNdFx72Z2TnQMLDGzgTHTVzCzAVHsS83sRTPbMY84ExId14PM7CNgLdA8GrWrhereFWY2zsLjDLczs9VABuG7/hNA9FlMB47allhSzt31yuMFbA/8BFwTV/4q4fFe1YF6hOdsXhSNOxfYCFxOePxXVeAZYBzhWZ1NgR+A8/NZ782Ex4LVA+oC/wVuicYdGi3/ZqAScCzhIN0hGn8f4T+7HaP1vQ7cnsd6zgU+jN7vCCwDzoriPj0a3inazpXAHtG0uxAeQQbwPDCQ8I9aFeCgPNbVFHBgeDTdUcC66LOsBzQEfgcOiaY/EfgRaBXFcz3w35jlOdAiZjj7c7kD2C763A8FFkTjMwiPb7s32p7NsRZiG84GPooZbg0sB7aLhs+MPq+KwFXAb0CVaNyNwIZouypE8d0IPBuN353wgPMjo/36z2j7K+exvU8Bt0bvb48+10rR629ET7zKZRs2L4d8jhXg38ComPmOA76L3jcElhKOvQpRzEuButH4KcAFRTi2m0bxVSzgGKoYU9Y7+pyaAzWAMcDI3Kan8N/bFtG2bRfF+j5wX8y650TLaBB9ht8CfaNx+wArovkrRJ9Zy4LiyGWb7wQejCubAxwB7A3MA7pG5XsTvkP7Eo73c6Jpt4uZ7wugcbR92Z/Po9FwO+AvoFU0ff9oXzWKPoNHgOfz+GwfInwXcnt9FRP7lCjmNtHnXCkqWwi0jT6TV4i+F7kd+1HZ/cA96coNibzSHkBJfQFGSIjjiPmhIlRd/QVUjSk7HXgven8uMC9mXEY0feuYsouAKfms+yfg2JjhLsCc6P2hwJ/k/IH5nfAAbCP8QO8aM25/YHYe6zmXLcn1LGBq3Pj/RdNUj74kPWO3O5rmGWAE0KiAzzP7y9gwpmwpcGrM8CtA/+j9G8T8A0L4gVoLZEbDuSXX9UTJLKYsO7nuDywmlx/uQmxDzejzzY5hEPBEPtMvA9pF728E3o8bfyNbkuu/gBfjtnchcGge2/sUW5LrzYTjtEV+8ccup6BjJZpmFVAtGh4F/Dt6fw1RAouZ903gnOj9FPJOrvkd29nHSGGS6zvAJTHDexD+iakYOz2F/N7msf4Tgc9jhucAZ8YMDwGGR+8fAe7NZRn5xpHL9I8Cg+PK5gA3AQuAv8eUP0z0j0pM2fds+Yd1DtA7l8+zUUzZVEItHYR/Fg6PGbdLbp9tQcdcXDxTgJtzKRscM9ya8F3OyO3YT+S7VxJeqhbO2zWE/6TO8WhvRjIJ/239GlW9LCd8kerFTDM/5n0doDIwN6ZsLuE/WaKquOxGU9dF4xvkMn2DmOGl7r4xZngt4b/2ukA1YHpMbJOi8oLEr3NznO6+BjgV6Btt9wQzaxlN80/CD/XUqEqzdwHrWRTz/s9chmtE7zOBoTHb8Ue0nob5LHux51F9RvhvfW7c55YtoW1w91XABOC0qOg0QtIBwEJV/bdR1dZyoDZh/2eLPS7i5fj83X1TNH1+25vtTsLZ21sWqu4HJDBPvseKu/9I+HE93syqAd2A56J5M4GTs+eL5j2IxB6cXtCxXVi5LS87mcYq7PcWM6tnZqOjqtuVwLPk3J8QaieyZX8PIRxvP+USbyJxxFpG+KcuXl9CTc57ccu+Km6/NCbn55vbMZjXNmQCY2OW9S2QxdafbWHlFkNs2VzCZxT/WceqSfiHv8RScs2FhetHA4GT3H153Oj5hP8867j79tGrlrvHNg2PTcZLCP/tZcaUNSGcleDufX1L46nbovG/5DL9LwmEvoSQoNrExFbbQ6OsgsSvMz7ON939SMIP6HdEjbvc/Td3v9DdGxDOyB+y5NzqMZ9QVbZ9zKuqu/83n3k8n3HzgSaWS0OOQm7D88DpZrY/oSrtPQi3/RD+ITuFUEW/PaFa0BKML8fnb2ZG+GFcGBWtJSTDbDvHxL/K3a9y9+bA8cCVZnZ4PuuCxI6V5wlnVScAM6OEC+GzHBm3b6q7++AC1rnVdpL4sQ25f365LW8jOf9py465MN9bCNXtDuzl7rUI1f5GYuYDu+ZRXlAcsb4iXDKI15dwPN8bt+xBcfulmrs/HzNNfsdgbrEeE7e8Ku6+MH7CuJOE+Fd8A7XcYmgc874J4TdzST6xtSJc5imxlFzjWGiBNppQPfl5/HgPjZreAu42s1rRRf9dzeyQ3Jbn7lnAi8AgM6tpoVHOlYT/gvPyPHC9mdU1szqE61/5TZ+9rk2EpHevmdWLtqehmXUpaF5gIrC7mZ1hofHKqYTqmfFmVt9CY5vqhB+G1YT/YDGzk82sUbSMZYQvTlYC6yvIcOBai+5nixqBnBwzfhFbGkMkYirwKzDYzKqbWRUzOzBadmG2YSLhx/xm4IXoM4fwn/RGoqpnC7dt1SpEfC8Cx5nZ4RZuSbmK8Fln/zPxBXCGmWVYaBiz+XiLGrG0iBLyyij2fPdBgsfKaMK18YvZctYK4Vg83sy6RPFUsdB4rBEFK9KxHVkMbCLnfn8e+D8za2ZmNYDbCPslRw1FYb+3kZqEY325mTUErk4wToDHgfOi/Vkh+mxbFiGOycDeFtNQKrIKOBo42Myy/6l5FOhrZvtaUN3MjjOz3M58EzGc8LuVCRDtsxNymzDuJCH+lcg9qWdauJ+3GuG79XL027kVC407OxI+mxJLyXVrFxKqPYbm8h/Y8GiaswlVvTMJP8Yvk3+V2OWE61s/Ax8SfqieyGf6W4FphP9aZwCfRWWJuIZQRfhxVJX1NuE6VL483FPYlfCjvpRQVdrV3ZcQjpOrCGcJfxB+2C+JZu0MfGKhVd9rQD93n51grPnFM5bQOGl0tB1fA8fETHIj8HRUZXVKAsvLIpzVtSA0qFhAqOou1Da4+1+ERjNHkDPhvEm4TvwDoVprHflXA8cv93vCmdEDhP/YjweOd/f10ST9orLlQC9y3pqxG2E/ryZcJ3/I3acksNp8j5UoEfwPOAB4IaZ8PuFs9jpCwptPSDyJ/J4U+dh297WEa20fRft9P8L3aCShsdFswud+eR6LKOz39iZCI6EVhMsBYxKJM4p1KnAeoQHdCuA/bDnDTjgOd18EvEv4vOPHLSc0mDrGzG5x92mE369h0XJ/JFxLLqqhhO/DW2a2itC4ad9tWF5+RhLaEfxGaFR4RT7TdiO0WUm0xiMtLOflRBERKUnMrDXwNLCP6wcbM/uE0Njx63THkh8lVxERkSRTtbCIiEiSldvkamZHm9n3Zvaj5XLrQtQg4P5o/FcW9UwTjetnZl9buG2jf0z5LdG0X5jZW2a2LbcYiIhIKVUuk6uFrsYeJDSQaU24taJ13GTHEBqK7Ab0IdygjZm1JTQa2IfQo0lXM9stmudOd9/L3dsD4wktIUVEpJwpl8mVkBh/dPefo9aYo9m6Nd4JwDMefAxsH92m0wr42N3XRs39/wN0B3D3lTHzV6dw95SJiEgZUeoaNNWpU8ebNm26TctYtmwZK1asIHs5S5cuZc2aNTRp0mTzND/++CM777wzNWqEe+p/+OEHGjZsSIUKFfjpp59o2bIlFSpU4IcffqBatWqb5124cCFLly4lIyOD3XffnUqVKm1TrCIikj7Tp09f4u6J9HKXQ6lLrp06dfJp06YVPGE+XnrpJd58800eeyw8p3rkyJFMnTqVBx54YPM0xx13HNdeey0HHXQQAIcffjhDhgyhY8eOPP744zz44IPUqFGD1q1bU7VqVe69994c67j99ttZt24dN9100zbFKiIi6WNm0929U2HnK5fVwo0aNWL+/C339y9YsIAGDRokPM3555/PZ599xvvvv8+OO+7IbrvtRrwzzjiDV155JUVbICIiJVm5TK6dO3dm1qxZzJ49m/Xr1zN69Gi6deuWY5pu3brxzDPP4O58/PHH1K5dm112CZ2o/P777wDMmzePMWPGcPrppwMwa9aszfO/9tprtGzZEhERKX/S/jT6dKhYsSLDhg2jS5cuZGVl0bt3b9q0acPw4aF3w759+3LssccyceJEWrRoQbVq1XjyySc3z9+zZ0+WLl1KpUqVePDBB9lhhx0AGDBgAN9//z0VKlQgMzNz8/JERKR8KZfXXEfNGMXAdwYyb8U8mtRuwqDDB9Frz15JilBERMqKol5zLXdnrqNmjKLP631Yu2EtAHNXzKXP630AlGBFRCQpyt0114HvDNycWLOt3bCWge8MTFNEIiJS1pS75DpvxbxClYuIiBRWuUuuTWo3KVS5iIhIYZW75Dro8EFUq1QtR9l2Gdsx6PBBaYpIRETKmnKXXHvt2YsRx48gs3YmhpFhGdStVpdTWp+S7tBERKSMKHfJFUKCndN/Dptu2MQrp7zCglULuPt/d6c7LBERKSPKZXKNdULLE+jesjs3/ecmfl72c7rDERGRMqDcJ1eA+4+5n0oVKnHxhIspbZ1qiIhIyaPkCjSq1YhBhw3irZ/eYvTXo9MdjoiIlHJKrpFLOl9C5wad6f9mf5b9uSzd4YiISCmWsuRqZtXN7Gkze9TMesWUtzWzUdGrrZlVM7NnzewhM7s+VfEUJKNCBiOOH8HStUu55u1r0hWGiIiUAak8c+0BvOzuFwKxz3PrB1wKXAJcDrQCvnf3S4BdzKxxCmPKV/ud29N/v/48+tmjfDjvw3SFISIipVwqk2sjIPtp41kx5bXdfbm7rwBqAp8D25nZPUADoGH8gsysj5lNM7NpixcvTmHIcOOhN9KkdhMuGn8R67PWp3RdIiJSNqUyuS4gJNj49awws9pmVgtY5e6b3P16d78SWAZsdT+Mu49w907u3qlu3bopDBlqVK7Bg8c+yMzFM7nzoztTui4RESmbUplcxwA9zexh4HUzGxmVDwXuB4YBDwBE11sfBaa5++8pjCkhXXfvykmtT+KW929h1tJZ6Q5HRERKmXL5sPRE/LLqF1o92IrODToz+azJmFnK1ykiIiVLUR+Wrltx8tCgZgNuP/x23pn9DqNmjEp3OCIiUoooueajb6e+7NtwX/7vzf9j6dql6Q5HRERKCSXXfFSwCow4fgTL1y3nn5P/me5wRESklFByLcBe9ffiqv2v4okvnuA/c/6T7nBERKQUUHJNwL8P+TdNt2/KReMv4q+Nf6U7HBERKeGUXBNQrVI1Hj7uYb5f+j2DPxyc7nBERKSEU3JN0NEtjua0tqdx24e38f2S79MdjoiIlGBKroVwb5d7qVapGn0n9NVzX0VEJE9KroWwc42dueOIO5gyZwpPf/l0usMREZESSsm1kC7Y+wIObHwg/3jrHyxZuyTd4YiISAmk5FpIFawCj3R9hBV/reCqt65KdzgiIlICKbkWQZt6bfjnAf/kmS+f4d3Z76Y7HBERKWGUXIvo+oOvZ9cddqXv+L6s27gu3eGIiEgJouRaRFUrVeXh4x5m1h+zuO2D29IdjoiIlCBKrtvgyF2PpNeevRj84WC+XfxtusMREZESQsl1G93T5R5qVK7BReMvYpNvSnc4IiJSAii5bqN61etx55F38sG8D3jy8yfTHY6IiJQASq5J0LtDbw7OPJirJ1/N72t+T3c4IiKSZkquSWBmDD9uOKvXr+bKN69MdzgiIpJmSq5J0qpuKwYcNIBRM0Yx+afJ6Q5HRETSSMk1ia7723XstuNu9J3Qlz83/JnucEREJE2UXJOoSsUqDO86nJ+X/cyt79+a7nBERCRNlFyT7LBmh3FOu3MY8t8hfP371+kOR0RE0kDJNQXuOuouam9XW/e+ioiUU0quKVCnWh3uPupu/jv/vzw6/dF0hyMiIsVMyTVFzm53Noc2PZRr3r6G31b/lu5wRESkGCm5pkj2va9/bvyT/pP6pzscEREpRkquKbRHnT0Y+LeBvPDNC7wx6410hyMiIsVEyTXFrjnwGlrWacklEy9h7Ya16Q5HRESKgZJrim1XcTse6foIc5bP4aYpN6U7HBERKQZKrsXg4MyD6d2+N3f/726+WvRVusMREZEUS1lyNbPqZva0mT1qZr1iytua2ajo1dbMMszsuWi6p82sTCb8O4+6kx2r7kif1/uQtSkr3eGIiEgKpTKR9QBedvcLgW4x5f2AS4FLgMuBqsBf0XSrgeopjCltdqy6I/d0uYdPFn7CI9MfSXc4IiKSQqlMro2A+dH72FO12u6+3N1XADWBtYCb2QRgO3dfFb8gM+tjZtPMbNrixYtTGHJq9dqzF4c3O5xr37mWX1b9ku5wREQkRQqVXM1sBzPbK8HJFxASbPx6VphZbTOrBawC9gbmuPtxwBwzax+/IHcf4e6d3L1T3bp1CxNyiWJmDO86nL82/kW/Sf3SHY6IiKRIgcnVzKaYWS0z2xH4EnjSzO5JYNljgJ5m9jDwupmNjMqHAvcDw4AHgJlAKzN7CNgLmFWE7Sg1WuzYgn8d/C9envky438Yn+5wREQkBczd85/A7HN372BmFwCN3f0GM/vK3RM9g02qTp06+bRp09Kx6qRZn7WeDo90YPX61XxzyTfUqFwj3SGJiEguzGy6u3cq7HyJVAtXNLNdgFMAnWolQeWMyjzS9RHmrZjHjVNuTHc4IiKSZIkk15uBN4Ef3f1TM2tOGa+6LQ4HNTmIPnv34b6P7+PzXz9PdzgiIpJEBVYLlzRloVo427I/l9HywZY0qd2Ej8//mIwKGekOSUREYqSsWtjMhkQNmiqZ2TtmtsTMzixamBJrh6o7cF+X+5j2yzQe+vShdIcjIiJJkki18FHuvhLoSri9Znfg6pRGVY6c1vY0uuzahevevY4FKxekOxwREUmCRJJrpejvscDz7v5HCuMpd8yMh457iKxNWVzxxhXpDkdERJIgkeT6upl9B3QC3jGzusC61IZVvjTfoTk3HHIDY78by7jvxqU7HBER2UYJNWgysx2Ale6eZWbVgFru/lvKo8tFWWrQFGtD1gY6jujIsnXLmHnJTGpuVzPdIYmIlHupbNBUCTgLeMHMXgbOB5YWPkTJT6WMSjzS9REWrlzIv977V7rDERGRbZBItfDDQEfgoei1d1QmSbZ/4/3p26kvD0x9gGm/lL2zcxGR8iKR5NrZ3c9x93ej13lA51QHVl7ddvht1Ktej4vGX8TGTRvTHY6IiBRBIsk1y8x2zR6IemjS075TZPsq23P/0ffz2a+f8cAnD6Q7HBERKYJEkuvVwHvR03H+A7wLXJXasMq3k1qfxLG7Hcu/3vsX81bMS3c4IiJSSAUmV3d/B9gNuCJ67eHu76U6sPLMzHjw2AdxnMsmXkZp66JSRKS8q5jXCDPrkceoXc0Mdx+TopgEaLp9U2469Caunnw1Y78bS49Wee0OEREpafK8z9XMnsxnPnf33qkJKX9l9T7X3GzctJFOIzqxeO1iZl4yk9pVaqc7JBGRcqWo97nmeeYatQqWNKpYoSIjjh/Bfo/9f3v3Hh5Vee59/HvnBAmHyJkkk4AxkHBGIIB2V6mIUCyR5LUSpbIrWiClgnurrZaqbd9SLbS+KogUuqUeEKzuIAgRRA6KVhIQ5CRgiigQEBAI0BAISe73jxmGJCRhCDOZSXJ/riuXM8+stXIvJjM/11rPep6B/Gb1b5gx3Do4GWNMXeBJhybjR/1j+jMxeSIvbniRnLwcf5djjDHGAxaudcDUwVOJahbFuHfHcb7kvL/LMcYYcxkWrnVA80bNmfHDGWw5vIXns5/3dznGGGMuw5OxhSNE5AkRmet63klEfuT70kxZqUmppCSm8NTap/g6/2t/l2OMMaYanhy5zgPOATe4nh8A/uCzikylRIQZP5yBIEzMmmj3vhpjTADzJFyvU9VpwHkAVS0ExKdVmUrFRcbxh1v+QFZuFm998Za/yzHGGFMFT8K1SETCAQVwjTN8zqdVmSr9ov8v6BPVh8nLJ5N/Nt/f5RhjjKmEJ+H6FLAciBWR+cAq4Jc+rcpUKSQohDk/msORgiP8etWv/V2OMcaYSngytvBKIA34KbAA6Keqa31blqlO3+i+TOo/idkbZ/Pp/k/9XY4xxpgKPL0VpzFwAjgFdBWRm3xXkvHE73/we2KaxzBuqd37aowxgcaTW3H+BHwCTME5/dyjwCM+rstcRrNGzXhx+ItsP7Kdv3z6F3+XY4wxpowqxxYuYyTOaeasE1OASUlMITUpld99+Dvu6nYX8S3i/V2SMcYYPDst/BUQ6utCTM3M+OEMQoNCyViWYfe+GmNMgPAkXM8An4vIX0XkhQs/vi7MeCameQxTb5nK+3veZ+H2hf4uxxhjDNXM5+peQOQ/K2tX1Vcus14TYBZQBKxV1fmu9u7A467FngaOAr9zPR8OdFfVU1VttyHN5+qpktISbvifG/jm5DfsmriLFuEt/F2SMcbUCzWdz9WTW3FewXkLzmeunzcuF6wuacDbqvozIKVM+2RgIvBz4EFVPayqE4AngDXVBaupXHBQMHNGzOHYmWP86oNf+bscY4xp8DzpLTwIyAVexHkk+qWHt+I4gP2uxyVl2iNVNV9VTwLNyrTfB1Qa2iIyTkQ2isjGo0ePevCrG57e7Xvz0MCHmLtpLh/v+9jf5RhjTIPmyTXXvwC3qerNqnoTMBT4fx6sdwBnwFb8PSdFJFJEmgOnAUREgB8AayrbkKrOUdV+qtqvTZs2Hvzqhul3g35HXGQc45eOp6ikyN/lGGNMg+VJuIaq6u4LT1T1SzzrPZwJ/B8ReQl4V0Rec7U/D7wAzARmuNoGAevUurtelSZhTZg1fBZfHP2C6Z9M93c5xhjTYHnSoellnIP2XwjH0UCIqt7n49oqZR2aLu+ut+5iye4lbMvYRqdWnfxdjjHG1Fk+69AEZAA7gEk4OyN9AUy40l9kas9zw56jUUijKu99Xb58OYmJiSQkJPDMM89c8rqqMmnSJBISEujZsyebNm1yv5afn8+dd95JUlISXbp04dNPL45tPGPGDBITE+nWrRu//KXN7WCMacBU1eMfIOpKlvfFT9++fdVc3os5Lyq/RV/b8lq59uLiYo2Pj9c9e/bouXPntGfPnrpjx45yyyxbtkyHDRumpaWl+umnn2r//v3dr40ZM0bnzp2rqqrnzp3TEydOqKrq6tWrdfDgwXr27FlVVT18+LAP984YY2oHsFFrkFWeDtx/wTLvx7vxhQn9JjAgZgD/teK/OHbmmLs9JyeHhIQE4uPjCQsLIz09ncWLF5dbd/HixYwZMwYRYeDAgeTn53Po0CFOnTrFRx99xP333w9AWFgY11xzDQAvvfQSjz32GI0aNQKgbdu2tbOjxhgTgK40XMUnVRivC5Ig5oyYQ/7ZfH658uIp2ry8PGJjY93PHQ4HeXl55datapmvvvqKNm3acN9993H99dfzwAMPUFBQAMCXX37JunXrGDBgADfffDMbNmzw8R4aY0zgutJwneuTKoxP9GzXk4dveJiXP3+ZD7/+EKDSa7DOO6EuqmqZ4uJiNm3aREZGBps3b6ZJkybua7bFxcWcOHGC9evXM336dO666y4b69gY02B5FK4i0k5EfgTsExE731eHPHnzk1x7zbWMXzqec8XncDgc7N+/3/36gQMHiI6OLrdOVcs4HA4cDgcDBgwA4M4773R3dnI4HKSlpSEi9O/fn6CgIL777rta2ENjjAk8nozQdBeQA/wYuAvIFpE7fV2Y8Y6I0Ahm3T6L3cd288zHz5CcnExubi579+6lqKiIhQsXkpKSUm6dlJQUXn31VVSV9evXExkZSVRUFO3btyc2Npbdu523Pa9atYquXbsCMHLkSFavXg04TxEXFRXRunXr2t1ZY4wJEJ7M5zoFSFbVIwAi0gb4AHjbl4UZ7xmWMIz07un88eM/kt49nZkzZzJ06FBKSkoYO3Ys3bp1Y/bs2QBMmDCB4cOHk5WVRUJCAhEREcybN8+9rRkzZjB69GiKioqIj493vzZ27FjGjh1L9+7dCQsL45VXXrnkdLMxxjQUngwisU1Ve5R5HgRsKdtWm2wQiZr59t/fEv98PKVaSlFJEXGRcUwdPJXRPUb7uzRjjAlYNR1EwpMj1+UisgLnzDgAo4CsK/1Fxr9W7V1FiZa4xxz+5uQ3jHt3HIAFrDHGeFm111xdA+q/APwV6An0Auaoqs1rVsdMWTXlksH8z5w/w+MfPF7FGsYYY2qq2iNXVVUReUdV++IciN/UUftO7qu0ff+p/aS/nU5qUirDOw2nWaNmlS5njDHGc57cirNeRJJ9XonxqbjIuErbm4Y2ZfXe1aT/bzptprdhxIIRzNs8j+/O2G00xhhTU56E6w9wBuweEdkqIttEZKuvCzPeNXXwVCJCI8q1RYRGMHvEbA49fIgPf/ohGf0y2Hp4K2OXjKXdn9txyyu3MDNnJgdOHfBT1cYYUzd50lu4Q2XtqvqNTyq6DOstXHPzt81nyqop7Du5r8rewqrK5m83k7kzk8ydmez8bicA/WP6k5aURmqXVDq36uyP8o0xptbVtLfwZcPVtfH/ADqp6jzXfa5NVXVvDeq8ahautWvXd7tYtHMRmbsy2XjQ+e/erU030rqkkZqUSu/2ve1+VmNMveWzcBWRp4B+QKKqdhaRaOAtVf1ezUq9Ohau/rPv5D7e2fUOmTszWbdvHaVaSsdrOpKWlEZalzRuiL2BILnS4aqNMSZw+TJcPweuBzap6vWutq2q2rMmhV4tC9fAcKTgCEt2L2HRrkWs3LOS86XnadekHSOTRpLWJY1BHQcRFhzm7zKNMeaq+DJcc1S1v4hsUtU+ItIE+NTC1Vxw8uxJsnKzWLRrEVm5WRScLyCyUSQjEkeQlpTG0IShl3SmMsaYusCX4foI0AkYAjwNjAUWqOoLNSn0alm4BrbC84Ws/GolmTszWbJ7CSfOniA8JJxhCcNI65LGjzr/iGsaX+PvMo0xxiO+7tA0BLgN52TpK1R15ZWX6B0WrnXH+ZLzfPTNR2TuzOSd3e9w8PRBQoJCuOXaW0hLSuOOpDto37S9v8s0xpgqeT1cRWSoqq6o4rUfq+pbV/rLvMHCtW4q1VJy8nLct/jsObEHQfhe3PdITUolNSmVa1tc6+8yjTGmHF+EawnwEfATVc2r8NomVe1To0qvkoVr3aeqbD+yncydmSzatYgth7cAcH376923+HRt09Vu8THG+J0vwnUzMAt4EvjvskeqIrL5Qs/h2mbhWv/sOb6HRbsWkbkzk08PfApA51adSU1KJa1LGsnRyRa0xhi/8EW4Xugd3BmYD2wHJqrqGTtyNb5y8PRBFu9aTOauTNbsXUOJluBo7mBkovMWn+93+D4hQZ7MlGiMMVfPZ+HqehwC/AFIBcYAL1m4Gl87XnicpV8uJXNnJiv2rOBs8VlahbfijsQ7SO2Syq3xt9I4pLG/yzTG1GM+OS1c8dSviAwCXgbaqKpf5iazcG2YCooKWP6v5WTuymTpl0s5de4UTcOacnun2226PGOMz/giXEeq6juVtLcAxqvqM1dcpRdYuJpzxedY8/Ua5y0+u97h6JmjNApuxJDrhpCWlMaIxBG0jmjt7zKNMfWAT+9zDSQWrqasktISPtn/iXtygX0n9xEkQdzc4WbSuqQxMmkkjuYOf5dpjKmjLFxNg6eqbDq0yd3z2KbLM8ZcLQtXYyqobrq8tC5p9GrXy27xMcZUK+DC1TXA/yygCFirqvNd7d2Bx12LPa2q20VkLM6Zd06q6m+q266Fq6mJyqbLu/aaa9330tp0ecaYygRiuN4L5KvquyLypqqOcrXPBR4FFJgGPAG8BmwG9qvqi9Vt18LVXK3Kpstr37Q9dyTeYdPlGWPKqWm4+vJ/1R3AftfjkjLtkaqar6ongWZAPHBcVR8DOojIdRU3JCLjRGSjiGw8evSoD0s2DUHbJm15oM8DLLtnGUcfPcobaW/w/bjv8/rW1xn6+lDa/bkd9y66l0U7F3Hm/Jlar2/58uUkJiaSkJDAM89c2ilfVZk0aRIJCQn07NmTTZs2uV/r2LEjPXr0oHfv3vTrd/H7YNSoUfTu3ZvevXvTsWNHevfuXRu7YkyD5cuhbg7gDNjPKR/iJ0UkEueR62kgDzjuei0faFpxQ6o6B5gDziNXn1VsGpzIxpHc3eNu7u5x9yXT5b2+9fVany6vpKSEiRMnsnLlShwOB8nJyaSkpNC1a1f3Mu+99x65ubnk5uaSnZ1NRkYG2dnZ7tfXrFlD69blb0V688033Y8ffvhhIiMjfbofxjR0vgzXTGCmiNwOvCsir6nqvcDzwAs4p6+bpqr7ReS4iDwLhKnqFh/WZEyVwkPDSUlMISUx5ZLp8hbtWlQr0+Xl5OSQkJBAfHw8AOnp6SxevLhcuC5evJgxY8YgIgwcOJD8/HwOHTpEVFTUZbevqvzjH/9g9erVXq/dGHORz8JVVQuA+8o0zXe1bwf+s8KyT/iqDmNqIjQ4lMHxgxkcP5gZw2eUmy5vwrIJZCzL8Ml0eXl5ecTGxrqfOxyOckelVS2Tl5dHVFQUIsJtt92GiDB+/HjGjRtXbt1169bRrl07OnXq5JV6jTGVsxHQjbmMIAlioGMgAx0D+dOtfyo3Xd7D7z/Mw+8/7LXp8irrYFhxW9Ut88knnxAdHc2RI0cYMmQISUlJ3HTTTe7lFixYwN13312j2owxnrN7D4y5AiJCj3Y9eGrQU3w+4XP+9eC/mD5kOo1DGvPEmifo/lJ3kl5M4vEPHicnL6fSIKyOw+Fg//797ucHDhwgOjra42Uu/Ldt27akpqaSk5PjXq64uJjMzExGjRp1xfttjLkyFq7GXIXrWl7HIzc+wj/v/yd5/53HrOGziIuMY/o/pzPgbwOIey6OSe9NYu3XaykuLb7s9pKTk8nNzWXv3r0UFRWxcOFCUlJSyi2TkpLCq6++iqqyfv16IiMjiYqKoqCggNOnTwNQUFDA+++/T/fu3d3rffDBByQlJeFw2HCQxvianRY2xkuim0WTkZxBRnJGueny5m6ay4ycGe7p8tK6pDE4fnCl0+WFhIQwc+ZMhg4dSklJCWPHjqVbt27Mnj0bgAkTJjB8+HCysrJISEggIiKCefPmAXD48GFSU1MB51HqPffcw7Bhw9zbXrhwoZ0SNqaW2PCHxvhYddPlpXVJ44cJP3RPlzd/23ymrJrCvpP7iIuMY+rgqYzuMdrPe2BMwxVwIzT5ioWrqcuqmy4vqmkUr299ncLiQvfyEaERzBkxxwLWGD+xcDWmjqlsurzKdIjswNcPfV27xRljAAtXY+o0VSX498EolX8e7+5+NwNiBtA/pj/XR11f6fVaY4z31TRcrUOTMQFARIiLjOObk99c8lp4SDjr9q1jwfYFAIQEhdCrXS932PaP6U9i60Sb1ceYAGLhakyAmDp4KuPeHVdusoCy11wPnj5ITl4OOXk5ZOdl89rW15i1cRYAzRs1Jzk62R24AxwDfDI8ozHGM3Za2JgAciW9hUu1lF3f7XKG7YFscg7msPXwVvf9tLHNYxngGED/aGfY9onqQ9OwS+bFMMZUw665GmMoPF/I5m83u8M2Jy+Hr058BTiHcezetrs7bPvH9Kdbm24EBwX7uWpjApeFqzGmUkcLjrLh4IZygXu80DnLY5PQJvSN7lvu+m1s89gaj41sTH1j4WqM8YiqsufEHmfYuq7fbv52M0UlRQC0b9reed3WFbjJ0clENrb5X03DZOFqjKmxopIitny7xdlh6qDzGu7uY7vdrye1TrrYWSpmAD3a9SAsOMyPFRtTOyxcjTFelX82nw15G9xHt9l52RwpOAJAo+BG9InqU+4IN75FvJ1ONvWOhasxxqdUlX0n97nDNicvh88Ofea+dahVeCv3ddsBMQNIjkmmdURrP1dtzNWxcDXG1Lri0mJ2HNnhDtvsvGx2HNnhHmnquhbXlTu6tdGlTF1j4WqMCQinz53ms0OflTvCPXDqAHDp6FIDHAPo3KqzjS5lApaFqzEmYFUcXWpD3gZOFzkndo9sFElyTDL9o/vXidGlli9fzuTJkykpKeGBBx7gscceK/e6qjJ58mSysrKIiIjg73//O3369AGgY8eONGvWjODgYEJCQrjwXfbWW2/x29/+lp07d5KTk0O/flf8XW58xMYWNsYErOhm0YxMGsnIpJFA5aNLTfvntIAfXaqkpISJEyeycuVKHA4HycnJpKSk0LVrV/cy7733Hrm5ueTm5pKdnU1GRgbZ2dnu19esWUPr1uWvRXfv3p3MzEzGjx9fa/tifMvC1RhT64IkiK5tutK1TVd+2vunQOWjS739xdvu5QNhdKmcnBwSEhKIj48HID09ncWLF5cL18WLFzNmzBhEhIEDB5Kfn8+hQ4eIioqqcrtdunTxee2mdlm4GmMCQnhoODfG3siNsTe62yqOLpW5K5O/bf4bcOnoUgNiBuBo7vDp7UB5eXnExsa6nzscjnJHpVUtk5eXR1RUFCLCbbfdhogwfvx4xo0b57NajX9ZuBpjAlabJm0Y3mk4wzsNByofXer57OdrbXSpyvqoVAzz6pb55JNPiI6O5siRIwwZMoSkpCRuuukmr9VnAoeFqzGmzhARElomkNAygdE9nbMFlR1d6kLv5CW7l7jX8eboUg6Hg/3797ufHzhwgOjoaI+XufDftm3bkpqaSk5OjoVrPWXhaoyp08KCw0iOSSY5JpmJTATgROEJNh7c6A7b9/71Hq9seQW4utGlkpOTyc3NZe/evcTExLBw4ULeeOONcsukpKQwc+ZM0tPTyc7OJjIykqioKAoKCigtLaVZs2YUFBTw/vvv8+STT3r/H8QEBAtXY0y90yK8BUOuG8KQ64YAlY8uNeezOTyf/TxwcXQp9+nkKkaXenPnmxTcWkB8v3hCJITUu1Pp1q0bs2fPBmDChAkMHz6crKwsEhISiIiIYN68eQAcPnyY1NRUAIqLi7nnnnsYNmwYAIsWLeLBBx/k6NGj3H777fTu3ZsVK1b4/N+pPrqaW6XA2SO8X79+xMTEsHTpUgBEpBcwG2gKfA2MVtVT1dVh97kaYxqkKx1davex3UzMmuge7hEgIjSCOSPmVDmhvaldJSUldO7cudytUgsWLCjXmzsrK4sZM2aQlZVFdnY2kydPLtcp7dlnn2Xjxo2cOnWKpUuXIiKfAQo8oqofishY4FpVfaK6WuzI1RjTIIUEhdCrfS96te/FuL7OXrsVR5dat28dC7YvqHIbZ86f4eEVD9OnfR9aRbSiReMWhAaH1tYumAqu9lapAwcOsGzZMqZMmcKzzz5bdtOJwEeuxyuBFYCFqzHGeKJZo2YM6jiIQR0HudsujC6V+mZqpescLjhM11kXv7ybhTWjZXjLcj+twltd0tYyvCWtIpztLRq3oFFII1/vXr13tbdKPfTQQ0ybNo3Tp09X3PR2IAVYDPwYiK24QEUWrsYYU40Lo0t1iOzANye/ueT1tk3a8vyw5zleeJxjZ45xvPA4x88ed/638Djbjmxzt5doSZW/p0lok0uDt3HVgXzhxyZCuOhqbpVaunQpbdu2pW/fvqxdu7biImOBF0TkSWAJUHS5WnwWriLSBJjlKmKtqs53tXcHHnct9rSqbheRPTgPtTep6hxf1WSMMTU1dfBUxr077pJrrs8OfZb07umXXV9VOV102h265cL4wk+ZUN5xZIf78fnS81VuNzwk/NLgbVx9ILcMb0l4SHi9m3/3am6Vevvtt1myZAlZWVmcPXuWU6dO8ZOf/AQAVd0F3AYgIp2B2y9Xi886NInIvUC+qr4rIm+q6ihX+1zgUZwXiKep6ngR2QJ8DixU1feq2651aDLG+Mv8bfOZsmoK+07uIy4yjqmDp/q8M5OqUnC+oOpALhPMZV87VnjMPbhGZRoFN7o0eBtXH8gtw1vSJLRJwIZycXExnTt3ZtWqVcTExJCcnMwbb7xBt27d3MssW7aMmTNnujs0TZo0iZycnHLbWbt2LX/+85/LdmgarqpHRCQI+DvOA8aXq6vFl6eFHcA21+Oy50IiVTUfQESaudquBwRYBlwSriIyDhgHEBcX56NyjTGmeqN7jK71nsEiQtOwpjQNa0pcpOfff6pKYXFhtYF8rPBi+57je9hQuIHjhccpLC6scrthwWGVhm7LxlUHcqvwVjQNa+rzUA4JCWHmzJkMHTqUkpISxo4d6/GtUpdxt4hMdD3OBC67kq+PXE+o6lIRWaiq6a72ucAjOI9cp6vq+DLrvAOkqWppVdu1I1djjPGtwvOFlR8dVwjkij8F5wuq3GZIUEiVwVtde/NGzT0OZV+cWQi4+Vxd11xnAmeBj4Fhqnqv65rroziPVKcB54FfuVbbqqrPVbddC1djjAlMZ4vPcqLwxBUF8rHCY/y76N9VbjNYgmkR3uKygfz5t58zI3sGZ0vOutf1xn3IAReuvmLhaowx9UtRSdEloXy5QD5eeJxT56odJAmADpEd+Pqhr2tcm02Wbowxpk4KCw6jXdN2tGva7orWO19ynvyz+RwvPE6XF7u4R9cqa9/Jfd4q84oE+eW3GmOMMVcpNDiUNk3akNg6scrOXlfSCcybLFyNMcbUeVMHTyUiNKJcW0RoBFMHT/VLPRauxhhj6rzRPUYzZ8QcOkR2QBA6RHbw66QK1qHJGGOMqUJNOzTZkasxxhjjZRauxhhjjJdZuBpjjDFeVueuuYrIUeDSeZ9qpjXwnZe2VZc01P2uS+w9Cmz2/gQ2b74/HVS1zZWuVOfC1ZtEZGNNLlTXdQ11v+sSe48Cm70/gS0Q3h87LWyMMcZ4mYWrMcYY42UNPVzn+LsAP2mo+12X2HsU2Oz9CWx+f38a9DVXY4wxxhca+pGrMcYY43X1eso514Tts4AiYK2qzne1Dwd+AWSp6kxX2zNABHBGVR/zU8leU82+PwZcB7QHMlT1gIjsAVYCm1TV76dTGoJq3p/fAl2AE8DvVfVgffvbrCuqeY/uB5KBWGCrqj5un6HaJyLxwBQgUlXvLNPeHXjc9fRpVd3uj89QfT9yTQPeVtWfASkXGlU1C5h24bmIxAGhqjoJCBaR2Fqv1Puq2vdnXG0vAz9wNf8bCAf213qVDVel7w9QjPPL/DyQX0//NuuKqj5D/6OqE4Avgb+7mu0zVMtU9StVvb+SlyYDE4GfAw/66zNUr49cAQewzfW4pJrlYrj4odjnWq+uf0iq3HcRaQrcBYxzNV0PCLAMeK+2Cmzgqnp//qiqpSKSAjwAbKD+/W3WFdV9hhoD16rqbleTfYYCR6Sq5gOISDP89P1e349cD+D8h4Tq9zWvzHKxrvXqukr3XUSaAy8Bv1TV0wCqWqqqJcBZEanvfxOBotL3R1VLXQ+PAE2pn3+bdUV13x93ApkXnthnKKCcFJFI13fdafz0GarXvYVd10xmAmeBj4FhqnqviNyA85x8C+A5Vf1fEXkaaAScU9XHq9xoHVHNvmcCoTj/4P7h+u+vXKttVdXn/FBug1PN+/NrnF8ArYFJqnqovv1t1hVVvUeu1xYD6apaKCKJ2Geo1olIK2AqMAT4G9DV9RnqDjyK80zCNNc111r/DNXrcDXGGGP8wU5fGGOMMV5m4WqMMcZ4mYWrMcYY42UWrsYYY4yXWbgaEyBE5BoR+flllukoIttrqyZjTM1YuBoTOK7BOaqM14hIsDe3Z4zxjIWrMYHjGeA6EflcRKa7fraLyDYRGVVxYREJdi2zQUS2ish4V/sgEVkjIm/gGmFIRN4Rkc9EZIeIjCuzjX+LyFQR2SIi60Wknau9nYgscrVvEZEbXe0/EZEcV41/tfA2pnIWrsYEjseAParaG1gP9AZ6AbcC00UkqsLy9wMnVTUZ50DyPxORa12v9QemqGpX1/OxqtoX6AdMct2AD9AEWK+qvYCPgJ+52l8APnS19wF2iEgXYBTwPVeNJcBob+28MfVJfR9b2Ji66j+ABa4h9Q6LyIc4A3RrmWVuA3qKyIUZQSKBTjgH/s9R1b1llp0kIqmux7Gu5Y65ll3qav8M52g3ALcAYwBcNZwUkXuBvsAGEQHnQPVHvLO7xtQvFq7GBCbxcJkHVXVFuUaRQUBBhee3Ajeo6hkRWQs0dr18Xi8O01ZC9d8JArxiQzAac3l2WtiYwHEaaOZ6/BEwynVdtQ1wE5BTYfkVQIaIhAKISGfXeLgVRQInXMGaBAz0oJZVQIZru8GuQdBXAXeKSFtXe0sR6XBlu2hMw2DhakyAUNVjwCeuW21uwHkKeAuwGucsRt9WWOVvwBfAJtc6f6XyI8/lQIiIbAX+L87ruZczGfiBiGzDebq4m6p+AfwGeN+1rZVAxevAxhhs4H5jjDHG6+zI1RhjjPEyC1djjDHGyyxcjTHGGC+zcDXGGGO8zMLVGGOM8TILV2OMMcbLLFyNMcYYL7NwNcYYY7zs/wOOfZkhniseHQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 504x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "##### x coordinates\n",
+    "x = tols\n",
+    "training_times = list_training_times_tol\n",
+    "precision_scores = list_precision_scores_tol\n",
+    "zero_one_loss = list_zero_one_loss_tol\n",
+    "        \n",
+    "training_times = [round(i,2) for i in training_times]\n",
+    "precision_scores = [round(i,3) for i in precision_scores]\n",
+    "zero_one_loss = [round(i,3) for i in zero_one_loss]\n",
+    "\n",
+    "### Create plot\n",
+    "fig, figs = plt.subplots(nrows=3, ncols=1, figsize=(7,10))\n",
+    "fig.tight_layout(pad=3.0)\n",
+    "figs[0].plot(x,training_times, marker='o', color='r')\n",
+    "figs[1].plot(x,precision_scores, marker='o', color='b')\n",
+    "figs[2].plot(x,zero_one_loss, marker='o', color='g')\n",
+    "\n",
+    "### Add every x coordinates\n",
+    "figs[0].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[1].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "figs[2].tick_params(axis='both', which='both', labelsize=7, labelbottom=True)\n",
+    "\n",
+    "for i in range(len(x)):\n",
+    "    figs[0].annotate(training_times[i], # this is the text\n",
+    "                 (x[i],training_times[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[1].annotate(precision_scores[i], # this is the text\n",
+    "                 (x[i],precision_scores[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "    figs[2].annotate(zero_one_loss[i], # this is the text\n",
+    "                 (x[i],zero_one_loss[i]), # these are the coordinates to position the label\n",
+    "                 textcoords=\"offset points\", # how to position the text\n",
+    "                 xytext=(12,3), # distance from text to points (x,y)\n",
+    "                 ha='center') # horizontal alignment can be left, right or center\n",
+    "\n",
+    "figs[0].set_xticks(x)\n",
+    "figs[1].set_xticks(x)\n",
+    "figs[2].set_xticks(x)\n",
+    "    \n",
+    "### Add title and axis names\n",
+    "figs[0].title.set_text('Training times for various level of tolerance (kernel=rbf)')\n",
+    "figs[1].title.set_text('Precision score for various level of tolerance (kernel=rbf)')\n",
+    "figs[2].title.set_text('Zero-one loss metrics various level of tolerance (kernel=rbf)')\n",
+    "figs[0].set_xlabel('tolerance')\n",
+    "figs[1].set_xlabel('tolerance')\n",
+    "figs[2].set_xlabel('tolerance')\n",
+    "figs[0].set_ylabel('Training times (in seconds)')\n",
+    "figs[1].set_ylabel('Precision score')\n",
+    "figs[2].set_ylabel('Zero-one loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "62c7302a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training...\n",
+      "Predicting...\n",
+      "Score échantillon de test :  0.9506666666666667\n",
+      "Précision pour chaque classe : \n",
+      " [0.99324324 0.97633136 0.93377483 0.95375723 0.92546584 0.91549296\n",
+      " 0.96527778 0.95833333 0.96402878 0.91472868]\n",
+      "Matrice de confusion pour C=1.0 et kernel=rbf :\n",
+      " [[147   0   0   0   1   0   0   0   1   0]\n",
+      " [  0 165   0   0   0   0   0   0   1   0]\n",
+      " [  1   2 141   0   0   1   1   1   0   0]\n",
+      " [  0   0   3 165   0   3   0   1   1   1]\n",
+      " [  0   0   1   0 149   0   1   0   0   5]\n",
+      " [  0   0   0   4   2 130   3   1   1   1]\n",
+      " [  0   0   1   0   0   3 139   0   0   0]\n",
+      " [  0   1   3   0   4   0   0 138   0   4]\n",
+      " [  0   1   1   3   1   4   0   0 134   0]\n",
+      " [  0   0   1   1   4   1   0   3   1 118]]\n",
+      "Zero-one classification loss :\n",
+      " 0.04933333333333334\n"
+     ]
+    }
+   ],
+   "source": [
+    "####### Meilleur modèle de SVM #######\n",
+    "\n",
+    "#Entraîne le classifier\n",
+    "clf = SVC(C=1.0,kernel=\"rbf\")\n",
+    "print(\"Training...\")\n",
+    "clf.fit(xtrain, ytrain)\n",
+    "\n",
+    "#Prédiction sur le jeu de tests\n",
+    "print(\"Predicting...\")\n",
+    "pred = clf.predict(xtest)\n",
+    "# On calcule le score obtenu sur xtest avec les étiquettes ytest\n",
+    "score = clf.score(xtest, ytest)\n",
+    "print(\"Score échantillon de test : \", score)\n",
+    "\n",
+    "#Calcul de différentes metrics\n",
+    "print(\"Précision pour chaque classe : \\n\", metrics.precision_score(ytest, pred,average=None))\n",
+    "print(\"Matrice de confusion pour C=1.0 et kernel=rbf :\\n\", metrics.confusion_matrix(ytest, pred))\n",
+    "print(\"Zero-one classification loss :\\n\", metrics.zero_one_loss(ytest, pred))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cbb5eda6",
+   "id": "30a232d5",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -346,7 +480,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,