CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb3e015f",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48d70da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random \n",
    "import time\n",
    "import statistics\n",
    "import pandas as pd \n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from keras.models import Sequential  \n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.backend import clear_session\n",
    "\n",
    "from keras.layers import Dense, Dropout, Activation, Conv2D, MaxPooling2D, Flatten\n",
    "from tensorflow.keras import utils\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aee53c4",
   "metadata": {},
   "source": [
    "# Load Data, View, and Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "def76cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = np.load('kmnist-train-imgs.npz')\n",
    "labels_train = np.load('kmnist-train-labels.npz')\n",
    "data_test = np.load('kmnist-test-imgs.npz')\n",
    "labels_test = np.load('kmnist-test-labels.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7cfed9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "k_train_samples = data_train['arr_0']\n",
    "k_train_labels = labels_train['arr_0']\n",
    "k_test_samples = data_test['arr_0']\n",
    "k_test_labels = labels_test['arr_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ceba2dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(9):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    num = random.randint(0, len(k_train_samples))\n",
    "    plt.imshow(k_train_samples[num], cmap='gray', interpolation='none')\n",
    "    plt.title(\"Class {}\".format(k_train_labels[num]))\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2341f799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training matrix shape (60000, 28, 28, 1)\n",
      "Testing matrix shape (10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train = k_train_samples.reshape(60000, 784) \n",
    "X_test = k_test_samples.reshape(10000, 784)   \n",
    "\n",
    "X_train = X_train.astype('float32')   \n",
    "X_test = X_test.astype('float32')\n",
    "\n",
    "X_train /= 255                        \n",
    "X_test /= 255\n",
    "\n",
    "X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))\n",
    "X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))\n",
    "\n",
    "print(\"Training matrix shape\", X_train.shape)\n",
    "print(\"Testing matrix shape\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0c63772",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_classes = 10\n",
    "\n",
    "Y_train = utils.to_categorical(k_train_labels, no_classes)\n",
    "Y_test = utils.to_categorical(k_test_labels, no_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "741eb2b7",
   "metadata": {},
   "source": [
    "# Create Hyperparameter Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7ab4f30c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "\n",
    "#layer_widths = np.random.randint(1,100,20)\n",
    "#layer_heights = np.random.randint(1,10,20)\n",
    "#learning_rates = np.clip(np.random.exponential(scale=1,size=20),0,1)\n",
    "#momentums = np.clip(np.random.exponential(scale=1,size=20),0,1)\n",
    "#optimizer_choice = np.zeros(20)\n",
    "#for i in range(optimizer_choice.shape[0]):\n",
    "   # optimizer_choice[i] = random.randint(0, 1)\n",
    "#print(optimizer_choice)\n",
    "#trial = 0"
   ]
  },
  {
   "cell_type": "raw",
   "id": "22ec4e84",
   "metadata": {},
   "source": [
    "#clear_session()\n",
    "#models = []\n",
    "#for lw in layer_widths:\n",
    "        model = Sequential()\n",
    "        model.add(Conv2D(32,(3,3), activation='relu', kernel_initializer='he_uniform', input_shape=(28,28,1)))\n",
    "        model.add(MaxPooling2D((2,2)))\n",
    "        model.add(Flatten())\n",
    "        for lh in layer_heights:\n",
    "            model.add(Dense(lw, activation='relu', kernel_initializer='he_uniform'))\n",
    "        model.add(Dense(10, activation='softmax'))\n",
    "        model.summary()\n",
    "        models.append(model)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "4aedff2f",
   "metadata": {},
   "source": [
    "for model, lr, m in zip(models, learning_rates, momentums):\n",
    "            opt = SGD(learning_rate=lr, momentum=m)\n",
    "            model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ccc4c0a0",
   "metadata": {},
   "source": [
    "np.random.seed(0)\n",
    "callback = EarlyStopping(monitor='loss', patience=2, min_delta=0.01)\n",
    "histories = []\n",
    "for model in models:\n",
    "    idx = np.random.choice(X_train.shape[0], 5000, replace=True)\n",
    "    histories.append( model.fit(X_train[idx], Y_train[idx],\n",
    "              batch_size=128, epochs=20,\n",
    "              verbose=0)) #callbacks=[callback]))"
   ]
  },
  {
   "cell_type": "raw",
   "id": "25cf5b52",
   "metadata": {},
   "source": [
    "callback = EarlyStopping(monitor='loss', patience=2, min_delta=0.1)\n",
    "histories = []\n",
    "for model in models:\n",
    "    #idx = np.random.choice(X_train.shape[0], 5000, replace=True)\n",
    "    histories.append( model.fit(X_train, Y_train,\n",
    "              batch_size=128, epochs=10,\n",
    "              verbose=1,callbacks=[callback]))"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a81d4d1c",
   "metadata": {},
   "source": [
    "temp = np.random.choice(np.array([0.0001, 0.001, 0.01, 0.1, 1]), size=num_tries_per_trial, replace=True)\n",
    "print(temp)\n",
    "print(np.unique(temp))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "122059d1",
   "metadata": {},
   "source": [
    "## Making the Different CNN's to Test Out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c8cf768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "469/469 [==============================] - 6s 12ms/step - loss: 1.5321 - accuracy: 0.4093\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 6s 12ms/step - loss: 1.0198 - accuracy: 0.6329\n",
      "Epoch 3/5\n",
      "469/469 [==============================] - 5s 12ms/step - loss: 0.7379 - accuracy: 0.7716\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 12s 3ms/step - loss: 0.3939 - accuracy: 0.8794\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 11s 3ms/step - loss: 0.2019 - accuracy: 0.9390\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 24s 25ms/step - loss: 1.0946 - accuracy: 0.6855\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 23s 24ms/step - loss: 0.5967 - accuracy: 0.8297\n",
      "313/313 [==============================] - 1s 2ms/step - loss: 0.4349 - accuracy: 0.8722\n",
      "Trial: 1\n",
      "Chosen Model: 1\n",
      "Loss: 0.2018817663192749\n",
      "Training Accuracy: 0.9389500021934509\n",
      "Test Accuracy:  [0.43489131331443787, 0.8722000122070312]\n",
      "Learning Rate: 0.01\n",
      "Momentum: 0.2\n",
      "Layer Width: 49\n",
      "Layer Height: 1\n",
      "Batch Size: 16\n",
      "Convolutional Layer Height: 1\n",
      "Convolutional Layer Filters: 32\n",
      "\n",
      "Trial Time: 87.14621806144714\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 45s 24ms/step - loss: 1.6830 - accuracy: 0.4744\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 45s 24ms/step - loss: 0.9904 - accuracy: 0.7203\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 45s 24ms/step - loss: 0.7483 - accuracy: 0.7817\n",
      "Epoch 1/5\n",
      "235/235 [==============================] - 6s 24ms/step - loss: 0.7417 - accuracy: 0.7804\n",
      "Epoch 2/5\n",
      "235/235 [==============================] - 6s 24ms/step - loss: 0.4143 - accuracy: 0.8776\n",
      "Epoch 1/5\n",
      "469/469 [==============================] - 21s 44ms/step - loss: 2.4402 - accuracy: 0.1131\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 21s 44ms/step - loss: 2.2935 - accuracy: 0.1347\n",
      "313/313 [==============================] - 1s 2ms/step - loss: 0.7647 - accuracy: 0.7621\n",
      "Trial: 2\n",
      "Chosen Model: 1\n",
      "Loss: 0.4143127501010895\n",
      "Training Accuracy: 0.8776000142097473\n",
      "Test Accuracy:  [0.7646695971488953, 0.7620999813079834]\n",
      "Learning Rate: 0.01\n",
      "Momentum: 0.5\n",
      "Layer Width: 43\n",
      "Layer Height: 2\n",
      "Batch Size: 256\n",
      "Convolutional Layer Height: 1\n",
      "Convolutional Layer Filters: 32\n",
      "\n",
      "Trial Time: 189.03522372245789\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 25s 26ms/step - loss: 2.4073 - accuracy: 0.1007\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 24s 25ms/step - loss: 2.3145 - accuracy: 0.1029\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 31s 8ms/step - loss: 0.5624 - accuracy: 0.8307\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 30s 8ms/step - loss: 0.2965 - accuracy: 0.9123\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 44s 23ms/step - loss: 2.1594 - accuracy: 0.1941\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 44s 23ms/step - loss: 1.9704 - accuracy: 0.2500\n",
      "313/313 [==============================] - 2s 5ms/step - loss: 0.5564 - accuracy: 0.8350\n",
      "Trial: 3\n",
      "Chosen Model: 1\n",
      "Loss: 0.2964920997619629\n",
      "Training Accuracy: 0.9122999906539917\n",
      "Test Accuracy:  [0.5564072728157043, 0.8349999785423279]\n",
      "Learning Rate: 0.001\n",
      "Momentum: 0.5\n",
      "Layer Width: 27\n",
      "Layer Height: 2\n",
      "Batch Size: 16\n",
      "Convolutional Layer Height: 1\n",
      "Convolutional Layer Filters: 128\n",
      "\n",
      "Trial Time: 200.07841396331787\n",
      "Epoch 1/5\n",
      "469/469 [==============================] - 43s 90ms/step - loss: 2.1568 - accuracy: 0.2055\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 42s 91ms/step - loss: 1.9578 - accuracy: 0.3041\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 7s 8ms/step - loss: 0.3526 - accuracy: 0.8913\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 7s 8ms/step - loss: 0.1400 - accuracy: 0.9581\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 1.4396 - accuracy: 0.4658\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3403 - accuracy: 0.8993\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2133 - accuracy: 0.9370\n",
      "313/313 [==============================] - 1s 1ms/step - loss: 0.4209 - accuracy: 0.8905\n",
      "Trial: 4\n",
      "Chosen Model: 1\n",
      "Loss: 0.140022411942482\n",
      "Training Accuracy: 0.958050012588501\n",
      "Test Accuracy:  [0.4209364354610443, 0.890500009059906]\n",
      "Learning Rate: 0.1\n",
      "Momentum: 0.7\n",
      "Layer Width: 33\n",
      "Layer Height: 1\n",
      "Batch Size: 64\n",
      "Convolutional Layer Height: 1\n",
      "Convolutional Layer Filters: 32\n",
      "\n",
      "Trial Time: 122.64569115638733\n",
      "Epoch 1/5\n",
      "469/469 [==============================] - 6s 13ms/step - loss: 1.5612 - accuracy: 0.5203\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 6s 12ms/step - loss: 0.8635 - accuracy: 0.7560\n",
      "Epoch 3/5\n",
      "469/469 [==============================] - 6s 12ms/step - loss: 0.6710 - accuracy: 0.8081\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 106s 56ms/step - loss: 1.2213 - accuracy: 0.5877\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 102s 54ms/step - loss: 0.6037 - accuracy: 0.8205\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 100s 54ms/step - loss: 0.4128 - accuracy: 0.8793\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 7s 7ms/step - loss: 2.1950 - accuracy: 0.1731\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 6s 7ms/step - loss: 1.8201 - accuracy: 0.4144\n",
      "313/313 [==============================] - 5s 16ms/step - loss: 0.7381 - accuracy: 0.7758\n",
      "Trial: 5\n",
      "Chosen Model: 1\n",
      "Loss: 0.4127519726753235\n",
      "Training Accuracy: 0.8792833089828491\n",
      "Test Accuracy:  [0.7381150126457214, 0.7757999897003174]\n",
      "Learning Rate: 0.001\n",
      "Momentum: 0.6\n",
      "Layer Width: 6\n",
      "Layer Height: 1\n",
      "Batch Size: 32\n",
      "Convolutional Layer Height: 2\n",
      "Convolutional Layer Filters: 256\n",
      "\n",
      "Trial Time: 344.25976753234863\n",
      "Epoch 1/5\n",
      "469/469 [==============================] - 11s 23ms/step - loss: 1.0355 - accuracy: 0.6722\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 10s 22ms/step - loss: 0.5622 - accuracy: 0.8296\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 105s 111ms/step - loss: 2.0592 - accuracy: 0.2844\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 105s 112ms/step - loss: 1.5463 - accuracy: 0.5198\n",
      "Epoch 3/5\n",
      "938/938 [==============================] - 104s 111ms/step - loss: 1.1296 - accuracy: 0.6601\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 2.0507 - accuracy: 0.2931\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 1.6762 - accuracy: 0.4956\n",
      "313/313 [==============================] - 1s 2ms/step - loss: 0.8962 - accuracy: 0.7219\n",
      "Trial: 6\n",
      "Chosen Model: 0\n",
      "Loss: 0.5621627569198608\n",
      "Training Accuracy: 0.8295666575431824\n",
      "Test Accuracy:  [0.8961507081985474, 0.7218999862670898]\n",
      "Learning Rate: 0.01\n",
      "Momentum: 0.5\n",
      "Layer Width: 14\n",
      "Layer Height: 1\n",
      "Batch Size: 128\n",
      "Convolutional Layer Height: 2\n",
      "Convolutional Layer Filters: 32\n",
      "\n",
      "Trial Time: 360.5200366973877\n",
      "Epoch 1/5\n",
      "235/235 [==============================] - 10s 40ms/step - loss: nan - accuracy: 0.1001\n",
      "Epoch 2/5\n",
      "235/235 [==============================] - 9s 40ms/step - loss: nan - accuracy: 0.1000\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 9s 4ms/step - loss: 0.6896 - accuracy: 0.7693\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.3274 - accuracy: 0.8971\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 6s 6ms/step - loss: 0.9532 - accuracy: 0.6781\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 6s 6ms/step - loss: 0.4825 - accuracy: 0.8469\n",
      "313/313 [==============================] - 1s 2ms/step - loss: 0.6024 - accuracy: 0.8222\n",
      "Trial: 7\n",
      "Chosen Model: 1\n",
      "Loss: 0.3274460434913635\n",
      "Training Accuracy: 0.8971333503723145\n",
      "Test Accuracy:  [0.6024214625358582, 0.8222000002861023]\n",
      "Learning Rate: 0.1\n",
      "Momentum: 0.1\n",
      "Layer Width: 20\n",
      "Layer Height: 1\n",
      "Batch Size: 32\n",
      "Convolutional Layer Height: 3\n",
      "Convolutional Layer Filters: 16\n",
      "\n",
      "Trial Time: 49.416528940200806\n",
      "Epoch 1/5\n",
      "235/235 [==============================] - 10s 42ms/step - loss: 1.8496 - accuracy: 0.3528\n",
      "Epoch 2/5\n",
      "235/235 [==============================] - 10s 42ms/step - loss: 0.8971 - accuracy: 0.7283\n",
      "Epoch 3/5\n",
      "235/235 [==============================] - 10s 41ms/step - loss: 0.6431 - accuracy: 0.8089\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 13s 13ms/step - loss: 2.0163 - accuracy: 0.2469\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 12s 13ms/step - loss: 1.6867 - accuracy: 0.3976\n",
      "Epoch 1/5\n",
      "469/469 [==============================] - 25s 52ms/step - loss: 2.4063 - accuracy: 0.1002\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 25s 52ms/step - loss: 2.3124 - accuracy: 0.1021\n",
      "313/313 [==============================] - 1s 2ms/step - loss: 1.0113 - accuracy: 0.6889\n",
      "Trial: 8\n",
      "Chosen Model: 0\n",
      "Loss: 0.6430789232254028\n",
      "Training Accuracy: 0.8089333176612854\n",
      "Test Accuracy:  [1.0113441944122314, 0.6888999938964844]\n",
      "Learning Rate: 0.001\n",
      "Momentum: 0.9\n",
      "Layer Width: 30\n",
      "Layer Height: 2\n",
      "Batch Size: 256\n",
      "Convolutional Layer Height: 2\n",
      "Convolutional Layer Filters: 32\n",
      "\n",
      "Trial Time: 105.40647029876709\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 22s 23ms/step - loss: 0.6452 - accuracy: 0.8062\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 22s 23ms/step - loss: 0.4055 - accuracy: 0.8984\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 26s 28ms/step - loss: 0.7075 - accuracy: 0.7898\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 26s 28ms/step - loss: 0.3952 - accuracy: 0.8845\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 23s 24ms/step - loss: 2.1699 - accuracy: 0.1971\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 23s 24ms/step - loss: 1.9327 - accuracy: 0.2819\n",
      "313/313 [==============================] - 2s 5ms/step - loss: 0.7340 - accuracy: 0.7691\n",
      "Trial: 9\n",
      "Chosen Model: 1\n",
      "Loss: 0.3952218294143677\n",
      "Training Accuracy: 0.8845499753952026\n",
      "Test Accuracy:  [0.733985424041748, 0.76910001039505]\n",
      "Learning Rate: 0.001\n",
      "Momentum: 0.2\n",
      "Layer Width: 41\n",
      "Layer Height: 3\n",
      "Batch Size: 64\n",
      "Convolutional Layer Height: 1\n",
      "Convolutional Layer Filters: 128\n",
      "\n",
      "Trial Time: 144.30003213882446\n",
      "Epoch 1/5\n",
      "235/235 [==============================] - 11s 44ms/step - loss: 2.5030 - accuracy: 0.0827\n",
      "Epoch 2/5\n",
      "235/235 [==============================] - 10s 44ms/step - loss: 2.3401 - accuracy: 0.0915\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 7s 2ms/step - loss: 1.2874 - accuracy: 0.5340\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 6s 2ms/step - loss: 0.6757 - accuracy: 0.7941\n",
      "Epoch 3/5\n",
      "3750/3750 [==============================] - 6s 2ms/step - loss: 0.4658 - accuracy: 0.8641\n",
      "Epoch 1/5\n",
      "469/469 [==============================] - 6s 12ms/step - loss: 1.6089 - accuracy: 0.4485\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 6s 12ms/step - loss: 0.9692 - accuracy: 0.6825\n",
      "Epoch 3/5\n",
      "469/469 [==============================] - 6s 12ms/step - loss: 0.8142 - accuracy: 0.7360\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.9458 - accuracy: 0.7471\n",
      "Trial: 10\n",
      "Chosen Model: 1\n",
      "Loss: 0.465785950422287\n",
      "Training Accuracy: 0.8640666604042053\n",
      "Test Accuracy:  [0.9458010792732239, 0.7470999956130981]\n",
      "Learning Rate: 0.01\n",
      "Momentum: 0.6\n",
      "Layer Width: 6\n",
      "Layer Height: 3\n",
      "Batch Size: 16\n",
      "Convolutional Layer Height: 1\n",
      "Convolutional Layer Filters: 16\n",
      "\n",
      "Trial Time: 58.88945293426514\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 24s 6ms/step - loss: 0.9906 - accuracy: 0.6837\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 24s 7ms/step - loss: 0.4711 - accuracy: 0.8580\n",
      "Epoch 3/5\n",
      "3750/3750 [==============================] - 24s 6ms/step - loss: 0.3385 - accuracy: 0.8990\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 14s 4ms/step - loss: 1016.2856 - accuracy: 0.1017\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 14s 4ms/step - loss: 2.4490 - accuracy: 0.0997\n",
      "Epoch 3/5\n",
      "3750/3750 [==============================] - 13s 4ms/step - loss: 2.4557 - accuracy: 0.1008\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 46s 49ms/step - loss: 0.5599 - accuracy: 0.8157\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 47s 50ms/step - loss: 0.1740 - accuracy: 0.9467\n",
      "313/313 [==============================] - 2s 7ms/step - loss: 0.3834 - accuracy: 0.8829\n",
      "Trial: 11\n",
      "Chosen Model: 2\n",
      "Loss: 0.17396479845046997\n",
      "Training Accuracy: 0.9466833472251892\n",
      "Test Accuracy:  [0.383426308631897, 0.8828999996185303]\n",
      "Learning Rate: 0.01\n",
      "Momentum: 0.9\n",
      "Layer Width: 24\n",
      "Layer Height: 2\n",
      "Batch Size: 64\n",
      "Convolutional Layer Height: 3\n",
      "Convolutional Layer Filters: 128\n",
      "\n",
      "Trial Time: 209.02359056472778\n",
      "Epoch 1/5\n",
      "469/469 [==============================] - 7s 15ms/step - loss: 1951073042432.0000 - accuracy: 0.0986\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 7s 15ms/step - loss: 2.3054 - accuracy: 0.1000\n",
      "Epoch 3/5\n",
      "469/469 [==============================] - 6s 14ms/step - loss: 2.3054 - accuracy: 0.0986\n",
      "Epoch 1/5\n",
      "469/469 [==============================] - 7s 14ms/step - loss: 0.4131 - accuracy: 0.8691\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 7s 14ms/step - loss: 0.1372 - accuracy: 0.9586\n",
      "Epoch 1/5\n",
      "235/235 [==============================] - 11s 44ms/step - loss: 2.2291 - accuracy: 0.1958\n",
      "Epoch 2/5\n",
      "235/235 [==============================] - 10s 42ms/step - loss: 2.0743 - accuracy: 0.2566\n",
      "313/313 [==============================] - 1s 2ms/step - loss: 0.3313 - accuracy: 0.9073\n",
      "Trial: 12\n",
      "Chosen Model: 1\n",
      "Loss: 0.13715898990631104\n",
      "Training Accuracy: 0.9585833549499512\n",
      "Test Accuracy:  [0.3313342332839966, 0.9072999954223633]\n",
      "Learning Rate: 0.1\n",
      "Momentum: 0.7\n",
      "Layer Width: 50\n",
      "Layer Height: 3\n",
      "Batch Size: 128\n",
      "Convolutional Layer Height: 1\n",
      "Convolutional Layer Filters: 32\n",
      "\n",
      "Trial Time: 56.00532126426697\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 107s 57ms/step - loss: 2.3199 - accuracy: 0.0986\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 107s 57ms/step - loss: 2.3087 - accuracy: 0.0999\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 55s 15ms/step - loss: 2.0368 - accuracy: 0.2926\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 55s 15ms/step - loss: 1.7599 - accuracy: 0.4048\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 50s 26ms/step - loss: 1.5845 - accuracy: 0.5019\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 50s 27ms/step - loss: 0.8233 - accuracy: 0.7582\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 50s 27ms/step - loss: 0.6288 - accuracy: 0.8135\n",
      "313/313 [==============================] - 3s 8ms/step - loss: 0.9796 - accuracy: 0.6874\n",
      "Trial: 13\n",
      "Chosen Model: 2\n",
      "Loss: 0.6288301348686218\n",
      "Training Accuracy: 0.8134833574295044\n",
      "Test Accuracy:  [0.9795657992362976, 0.6873999834060669]\n",
      "Learning Rate: 0.0001\n",
      "Momentum: 0.7\n",
      "Layer Width: 38\n",
      "Layer Height: 3\n",
      "Batch Size: 32\n",
      "Convolutional Layer Height: 2\n",
      "Convolutional Layer Filters: 128\n",
      "\n",
      "Trial Time: 477.7331874370575\n",
      "Epoch 1/5\n",
      "235/235 [==============================] - 50s 210ms/step - loss: 1.7570 - accuracy: 0.4643\n",
      "Epoch 2/5\n",
      "235/235 [==============================] - 50s 211ms/step - loss: 1.2472 - accuracy: 0.6759\n",
      "Epoch 3/5\n",
      "235/235 [==============================] - 49s 209ms/step - loss: 1.0361 - accuracy: 0.7270\n",
      "Epoch 1/5\n",
      "235/235 [==============================] - 11s 45ms/step - loss: 1.6753 - accuracy: 0.3836\n",
      "Epoch 2/5\n",
      "235/235 [==============================] - 11s 46ms/step - loss: 0.6436 - accuracy: 0.7921\n",
      "Epoch 3/5\n",
      "235/235 [==============================] - 11s 45ms/step - loss: 0.3967 - accuracy: 0.8762\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 59s 16ms/step - loss: 0.5129 - accuracy: 0.8419\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 59s 16ms/step - loss: 0.2068 - accuracy: 0.9389\n",
      "313/313 [==============================] - 3s 8ms/step - loss: 0.4502 - accuracy: 0.8595\n",
      "Trial: 14\n",
      "Chosen Model: 2\n",
      "Loss: 0.20682856440544128\n",
      "Training Accuracy: 0.9388999938964844\n",
      "Test Accuracy:  [0.4502158761024475, 0.859499990940094]\n",
      "Learning Rate: 0.001\n",
      "Momentum: 0.8\n",
      "Layer Width: 22\n",
      "Layer Height: 2\n",
      "Batch Size: 16\n",
      "Convolutional Layer Height: 2\n",
      "Convolutional Layer Filters: 128\n",
      "\n",
      "Trial Time: 301.7687888145447\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 150s 40ms/step - loss: 0.6957 - accuracy: 0.7642\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 151s 40ms/step - loss: 0.1667 - accuracy: 0.9498\n",
      "Epoch 3/5\n",
      "3750/3750 [==============================] - 148s 39ms/step - loss: 0.0968 - accuracy: 0.9697\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 34s 9ms/step - loss: 1.5653 - accuracy: 0.4949\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 34s 9ms/step - loss: 2.1198 - accuracy: 0.2053\n",
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "469/469 [==============================] - 126s 267ms/step - loss: 9.0855 - accuracy: 0.0994\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 127s 271ms/step - loss: 2.3061 - accuracy: 0.1009\n",
      "Epoch 3/5\n",
      "469/469 [==============================] - 127s 272ms/step - loss: 2.3060 - accuracy: 0.1007\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.3008 - accuracy: 0.9151\n",
      "Trial: 15\n",
      "Chosen Model: 0\n",
      "Loss: 0.096806101500988\n",
      "Training Accuracy: 0.9696666598320007\n",
      "Test Accuracy:  [0.30078068375587463, 0.9150999784469604]\n",
      "Learning Rate: 0.01\n",
      "Momentum: 0.6\n",
      "Layer Width: 24\n",
      "Layer Height: 2\n",
      "Batch Size: 16\n",
      "Convolutional Layer Height: 3\n",
      "Convolutional Layer Filters: 256\n",
      "\n",
      "Trial Time: 904.7272064685822\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 12s 3ms/step - loss: 0.8954 - accuracy: 0.6956\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 12s 3ms/step - loss: 0.4401 - accuracy: 0.8591\n",
      "Epoch 1/5\n",
      "469/469 [==============================] - 52s 109ms/step - loss: 1.2426 - accuracy: 0.5748\n",
      "Epoch 2/5\n",
      "469/469 [==============================] - 51s 110ms/step - loss: 0.7197 - accuracy: 0.7685\n",
      "Epoch 3/5\n",
      "469/469 [==============================] - 52s 110ms/step - loss: 0.5694 - accuracy: 0.8247\n",
      "Epoch 1/5\n",
      "235/235 [==============================] - 13s 53ms/step - loss: 1.3262 - accuracy: 0.4900\n",
      "Epoch 2/5\n",
      "235/235 [==============================] - 12s 53ms/step - loss: 0.6106 - accuracy: 0.8018\n",
      "Epoch 3/5\n",
      "235/235 [==============================] - 12s 53ms/step - loss: 0.4685 - accuracy: 0.8535\n",
      "313/313 [==============================] - 1s 1ms/step - loss: 0.7242 - accuracy: 0.7714\n",
      "Trial: 16\n",
      "Chosen Model: 0\n",
      "Loss: 0.4400573670864105\n",
      "Training Accuracy: 0.859083354473114\n",
      "Test Accuracy:  [0.7241860032081604, 0.771399974822998]\n",
      "Learning Rate: 0.01\n",
      "Momentum: 0.6\n",
      "Layer Width: 30\n",
      "Layer Height: 3\n",
      "Batch Size: 16\n",
      "Convolutional Layer Height: 3\n",
      "Convolutional Layer Filters: 16\n",
      "\n",
      "Trial Time: 217.74034905433655\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3820 - accuracy: 0.8819\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1563 - accuracy: 0.9535\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 52s 27ms/step - loss: 0.6706 - accuracy: 0.7762\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 52s 28ms/step - loss: 0.1829 - accuracy: 0.9454\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 24s 13ms/step - loss: 0.3249 - accuracy: 0.8991\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 24s 13ms/step - loss: 0.1010 - accuracy: 0.9699\n",
      "313/313 [==============================] - 1s 4ms/step - loss: 0.2513 - accuracy: 0.9295\n",
      "Trial: 17\n",
      "Chosen Model: 2\n",
      "Loss: 0.1010039821267128\n",
      "Training Accuracy: 0.9698500037193298\n",
      "Test Accuracy:  [0.25132155418395996, 0.9294999837875366]\n",
      "Learning Rate: 0.1\n",
      "Momentum: 0.1\n",
      "Layer Width: 46\n",
      "Layer Height: 3\n",
      "Batch Size: 32\n",
      "Convolutional Layer Height: 2\n",
      "Convolutional Layer Filters: 64\n",
      "\n",
      "Trial Time: 166.5222508907318\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 47s 49ms/step - loss: 1.3887 - accuracy: 0.5521\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 46s 49ms/step - loss: 0.7109 - accuracy: 0.7848\n",
      "Epoch 3/5\n",
      "938/938 [==============================] - 46s 49ms/step - loss: 0.5246 - accuracy: 0.8378\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 23s 25ms/step - loss: 1.4780 - accuracy: 0.5101\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 23s 24ms/step - loss: 0.8392 - accuracy: 0.7290\n",
      "Epoch 3/5\n",
      "938/938 [==============================] - 23s 24ms/step - loss: 0.6463 - accuracy: 0.7911\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 15s 4ms/step - loss: 1.9282 - accuracy: 0.3854\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 14s 4ms/step - loss: 1.2662 - accuracy: 0.6431\n",
      "Epoch 3/5\n",
      "3750/3750 [==============================] - 14s 4ms/step - loss: 1.0004 - accuracy: 0.7112\n",
      "313/313 [==============================] - 2s 7ms/step - loss: 0.8962 - accuracy: 0.7215\n",
      "Trial: 18\n",
      "Chosen Model: 0\n",
      "Loss: 0.5245851278305054\n",
      "Training Accuracy: 0.8378000259399414\n",
      "Test Accuracy:  [0.8961812257766724, 0.7214999794960022]\n",
      "Learning Rate: 0.001\n",
      "Momentum: 0.6\n",
      "Layer Width: 22\n",
      "Layer Height: 2\n",
      "Batch Size: 64\n",
      "Convolutional Layer Height: 3\n",
      "Convolutional Layer Filters: 128\n",
      "\n",
      "Trial Time: 254.3032419681549\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 25s 27ms/step - loss: 1565627574546893635584.0000 - accuracy: 0.1005\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 25s 27ms/step - loss: 2.3145 - accuracy: 0.0996\n",
      "Epoch 3/5\n",
      "938/938 [==============================] - 25s 27ms/step - loss: 2.3153 - accuracy: 0.0978\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 8s 8ms/step - loss: 0.5131 - accuracy: 0.8387\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 7s 8ms/step - loss: 0.1937 - accuracy: 0.9422\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 15s 4ms/step - loss: 0.6021 - accuracy: 0.8054\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 14s 4ms/step - loss: 0.2259 - accuracy: 0.9314\n",
      "313/313 [==============================] - 1s 2ms/step - loss: 0.4258 - accuracy: 0.8752\n",
      "Trial: 19\n",
      "Chosen Model: 1\n",
      "Loss: 0.1936720758676529\n",
      "Training Accuracy: 0.9421833157539368\n",
      "Test Accuracy:  [0.42577865719795227, 0.8751999735832214]\n",
      "Learning Rate: 0.01\n",
      "Momentum: 0.9\n",
      "Layer Width: 15\n",
      "Layer Height: 2\n",
      "Batch Size: 64\n",
      "Convolutional Layer Height: 1\n",
      "Convolutional Layer Filters: 32\n",
      "\n",
      "Trial Time: 120.57645106315613\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 24s 6ms/step - loss: 0.5478 - accuracy: 0.8266\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 25s 7ms/step - loss: 0.1898 - accuracy: 0.9428\n",
      "Epoch 1/5\n",
      "3750/3750 [==============================] - 11s 3ms/step - loss: 2.3400 - accuracy: 0.1007\n",
      "Epoch 2/5\n",
      "3750/3750 [==============================] - 10s 3ms/step - loss: 2.3309 - accuracy: 0.1017\n",
      "Epoch 1/5\n",
      "938/938 [==============================] - 109s 116ms/step - loss: 2.0183 - accuracy: 0.2891\n",
      "Epoch 2/5\n",
      "938/938 [==============================] - 109s 116ms/step - loss: 1.6895 - accuracy: 0.4396\n",
      "313/313 [==============================] - 1s 3ms/step - loss: 0.3989 - accuracy: 0.8871\n",
      "Trial: 20\n",
      "Chosen Model: 0\n",
      "Loss: 0.1897684931755066\n",
      "Training Accuracy: 0.9428333044052124\n",
      "Test Accuracy:  [0.398891419172287, 0.8870999813079834]\n",
      "Learning Rate: 0.01\n",
      "Momentum: 0.4\n",
      "Layer Width: 15\n",
      "Layer Height: 2\n",
      "Batch Size: 16\n",
      "Convolutional Layer Height: 2\n",
      "Convolutional Layer Filters: 64\n",
      "\n",
      "Trial Time: 288.28462314605713\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "best_models = []\n",
    "best_histories = []\n",
    "best_accuracies = []\n",
    "best_loss = []\n",
    "best_lrs = []\n",
    "best_ms = []\n",
    "best_widths = []\n",
    "best_heights = []\n",
    "best_batch_sizes = []\n",
    "best_conv_heights = []\n",
    "best_conv_filters = []\n",
    "best_scores = []\n",
    "\n",
    "num_tries_per_trial = 3\n",
    "num_trials = 20\n",
    "\n",
    "for trial in range(1,1+num_trials): \n",
    "    t0_trial = time.time()\n",
    "    \n",
    "    # Hyperparameters\n",
    "    layer_widths = np.random.randint(5,51,num_tries_per_trial)\n",
    "    layer_heights = np.random.randint(1,4,num_tries_per_trial)\n",
    "    learning_rates = np.random.choice(np.array([0.0001, 0.001, 0.01, 0.1, 1]), size=num_tries_per_trial, replace=True)#np.random.randint(1,1000,num_tries_per_trial)/1000 #np.clip(np.random.exponential(scale=1,size=num_tries_per_trial),0,1)\n",
    "    momentums = np.random.randint(1,10,num_tries_per_trial)/10 #np.clip(np.random.exponential(scale=1,size=num_tries_per_trial),0,1)\n",
    "    batch_sizes = np.random.choice(np.array([16, 32, 64, 128, 256]), size=num_tries_per_trial, replace=True)\n",
    "    conv_heights = np.random.randint(1,4,num_tries_per_trial)\n",
    "    number_filters = np.random.choice(np.array([16, 32, 64, 128, 256]), size=num_tries_per_trial, replace=True)\n",
    "    \n",
    "    # Model Construction\n",
    "    clear_session()\n",
    "    models = []\n",
    "    count = 0\n",
    "    for lw, lh, ch, nf in zip(layer_widths, layer_heights, conv_heights, number_filters):\n",
    "            model = Sequential()\n",
    "            for i in range(ch):\n",
    "                model.add(Conv2D(nf,(3,3), activation='relu', kernel_initializer='he_uniform', input_shape=(28,28,1)))\n",
    "                model.add(MaxPooling2D((2,2)))\n",
    "            model.add(Flatten())\n",
    "            for i in range(lh):\n",
    "                model.add(Dense(lw, activation='relu', kernel_initializer='he_uniform'))\n",
    "            model.add(Dense(10, activation='softmax'))\n",
    "            #model.summary()\n",
    "            models.append(model)\n",
    "\n",
    "    # Model Compilation\n",
    "    for model, lr, m in zip(models, learning_rates, momentums):\n",
    "        opt = SGD(learning_rate=lr, momentum=m)\n",
    "        model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "    # Model Training\n",
    "    callback = EarlyStopping(monitor='loss', patience=1, min_delta=0.5)\n",
    "    histories = []\n",
    "    for model, bat_size in zip(models,batch_sizes):\n",
    "        histories.append( model.fit(X_train, Y_train,\n",
    "                  batch_size=bat_size, epochs=5,\n",
    "                  verbose=1,callbacks=[callback]))\n",
    "\n",
    "    # Identify best model from trial\n",
    "    find_best_model_in_trial = []\n",
    "    for history in histories:\n",
    "        find_best_model_in_trial.append(history.history['loss'][-1])\n",
    "\n",
    "    index = np.nanargmin(find_best_model_in_trial)\n",
    "    best_models.append(models[index])\n",
    "    best_loss.append(histories[index].history['loss'][-1])\n",
    "    best_histories.append(histories[index])\n",
    "    best_accuracies.append(histories[index].history['accuracy'][-1])\n",
    "    best_lrs.append(learning_rates[index])\n",
    "    best_ms.append(momentums[index])\n",
    "    best_widths.append(layer_widths[index])\n",
    "    best_heights.append(layer_heights[index])\n",
    "    best_batch_sizes.append(batch_sizes[index])\n",
    "    best_conv_heights.append(conv_heights[index])\n",
    "    best_conv_filters.append(number_filters[index])\n",
    "    best_scores.append(models[index].evaluate(X_test, Y_test))\n",
    "\n",
    "    print(\"Trial: \" + str(trial))\n",
    "    print(\"Chosen Model: \" + str(index))\n",
    "    print(\"Loss: \" + str(find_best_model_in_trial[index]))\n",
    "    print(\"Training Accuracy: \" + str(histories[index].history['accuracy'][-1]))\n",
    "    print('Test Accuracy: ', best_scores[trial-1][-1])\n",
    "    print(\"Learning Rate: \" + str(learning_rates[index]))\n",
    "    print(\"Momentum: \" + str(momentums[index]))\n",
    "    print(\"Layer Width: \" + str(layer_widths[index]))\n",
    "    print(\"Layer Height: \" + str(layer_heights[index]))\n",
    "    print(\"Batch Size: \" + str(batch_sizes[index]))\n",
    "    print(\"Convolutional Layer Height: \" + str(conv_heights[index]))\n",
    "    print(\"Convolutional Layer Filters: \" + str(number_filters[index]))\n",
    "    print()\n",
    "    t1_trial = time.time()\n",
    "    print(\"Trial Time: \" + str(t1_trial-t0_trial))\n",
    "t1 = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ed1099c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('best_models.pkl', 'wb') as f:\n",
    "    pickle.dump(best_models,f)\n",
    "\n",
    "with open('best_loss.pkl', 'wb') as f:\n",
    "    pickle.dump(best_loss,f)\n",
    "    \n",
    "with open('best_histories.pkl', 'wb') as f:\n",
    "    pickle.dump(best_histories,f)\n",
    "    \n",
    "with open('best_accuracies.pkl', 'wb') as f:\n",
    "    pickle.dump(best_accuracies,f)\n",
    "    \n",
    "with open('best_lrs.pkl', 'wb') as f:\n",
    "    pickle.dump(best_lrs,f)\n",
    "    \n",
    "with open('best_ms.pkl', 'wb') as f:\n",
    "    pickle.dump(best_ms,f)\n",
    "    \n",
    "with open('best_widths.pkl', 'wb') as f:\n",
    "    pickle.dump(best_widths,f)\n",
    "\n",
    "with open('best_heights.pkl', 'wb') as f:\n",
    "    pickle.dump(best_heights,f)\n",
    "    \n",
    "with open('best_batch_sizes.pkl', 'wb') as f:\n",
    "    pickle.dump(best_batch_sizes,f)\n",
    "    \n",
    "with open('best_conv_heights.pkl', 'wb') as f:\n",
    "    pickle.dump(best_conv_heights,f)\n",
    "    \n",
    "with open('best_conv_filters.pkl', 'wb') as f:\n",
    "    pickle.dump(best_conv_filters,f)\n",
    "    \n",
    "with open('best_scores.pkl', 'wb') as f:\n",
    "    pickle.dump(best_scores,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36f549e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#idx = np.nanargmin(best_loss)\n",
    "#print(\"Chosen Trial: \" + str(idx+1))\n",
    "#print(\"Final Loss: \" + str(best_loss[idx]))\n",
    "#print(\"Final Accuracy: \" + str(best_histories[idx].history['accuracy'][-1]))\n",
    "#print(\"Final Learning Rate: \" + str(best_lrs[idx]))\n",
    "#print(\"Final Momentum: \" + str(best_ms[idx]))\n",
    "#print(\"Layer Width: \" + str(best_widths[idx]))\n",
    "#print(\"Layer Height: \" + str(best_heights[idx]))\n",
    "#print()\n",
    "#print(\"Total Tuning Time: \" + str(t1-t0) + \"s\")\n",
    "#print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5771924c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Learning Rate</th>\n",
       "      <th>Momentum:</th>\n",
       "      <th>Hidden Layer Width</th>\n",
       "      <th>Hidden Layer Height</th>\n",
       "      <th>Batch Size</th>\n",
       "      <th>Convolutional Layer Height</th>\n",
       "      <th>Convolutional Layer Filters</th>\n",
       "      <th>Training Accuracy</th>\n",
       "      <th>Testing Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.2</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0.938950</td>\n",
       "      <td>0.8722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.5</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0.877600</td>\n",
       "      <td>0.7621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.5</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>0.912300</td>\n",
       "      <td>0.8350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0.958050</td>\n",
       "      <td>0.8905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.6</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>256</td>\n",
       "      <td>0.879283</td>\n",
       "      <td>0.7758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>0.829567</td>\n",
       "      <td>0.7219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0.897133</td>\n",
       "      <td>0.8222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.9</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>256</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>0.808933</td>\n",
       "      <td>0.6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.2</td>\n",
       "      <td>41</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>0.884550</td>\n",
       "      <td>0.7691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.864067</td>\n",
       "      <td>0.7471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.9</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>128</td>\n",
       "      <td>0.946683</td>\n",
       "      <td>0.8829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0.958583</td>\n",
       "      <td>0.9073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.7</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>0.813483</td>\n",
       "      <td>0.6874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.8</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "      <td>0.938900</td>\n",
       "      <td>0.8595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.6</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>256</td>\n",
       "      <td>0.969667</td>\n",
       "      <td>0.9151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.6</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0.859083</td>\n",
       "      <td>0.7714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.969850</td>\n",
       "      <td>0.9295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.6</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>128</td>\n",
       "      <td>0.837800</td>\n",
       "      <td>0.7215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.9</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0.942183</td>\n",
       "      <td>0.8752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.4</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.942833</td>\n",
       "      <td>0.8871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Learning Rate  Momentum:  Hidden Layer Width  Hidden Layer Height  \\\n",
       "0          0.0100        0.2                  49                    1   \n",
       "1          0.0100        0.5                  43                    2   \n",
       "2          0.0010        0.5                  27                    2   \n",
       "3          0.1000        0.7                  33                    1   \n",
       "4          0.0010        0.6                   6                    1   \n",
       "5          0.0100        0.5                  14                    1   \n",
       "6          0.1000        0.1                  20                    1   \n",
       "7          0.0010        0.9                  30                    2   \n",
       "8          0.0010        0.2                  41                    3   \n",
       "9          0.0100        0.6                   6                    3   \n",
       "10         0.0100        0.9                  24                    2   \n",
       "11         0.1000        0.7                  50                    3   \n",
       "12         0.0001        0.7                  38                    3   \n",
       "13         0.0010        0.8                  22                    2   \n",
       "14         0.0100        0.6                  24                    2   \n",
       "15         0.0100        0.6                  30                    3   \n",
       "16         0.1000        0.1                  46                    3   \n",
       "17         0.0010        0.6                  22                    2   \n",
       "18         0.0100        0.9                  15                    2   \n",
       "19         0.0100        0.4                  15                    2   \n",
       "\n",
       "    Batch Size  Convolutional Layer Height  Convolutional Layer Filters  \\\n",
       "0           16                           1                           32   \n",
       "1          256                           1                           32   \n",
       "2           16                           1                          128   \n",
       "3           64                           1                           32   \n",
       "4           32                           2                          256   \n",
       "5          128                           2                           32   \n",
       "6           32                           3                           16   \n",
       "7          256                           2                           32   \n",
       "8           64                           1                          128   \n",
       "9           16                           1                           16   \n",
       "10          64                           3                          128   \n",
       "11         128                           1                           32   \n",
       "12          32                           2                          128   \n",
       "13          16                           2                          128   \n",
       "14          16                           3                          256   \n",
       "15          16                           3                           16   \n",
       "16          32                           2                           64   \n",
       "17          64                           3                          128   \n",
       "18          64                           1                           32   \n",
       "19          16                           2                           64   \n",
       "\n",
       "    Training Accuracy  Testing Accuracy  \n",
       "0            0.938950            0.8722  \n",
       "1            0.877600            0.7621  \n",
       "2            0.912300            0.8350  \n",
       "3            0.958050            0.8905  \n",
       "4            0.879283            0.7758  \n",
       "5            0.829567            0.7219  \n",
       "6            0.897133            0.8222  \n",
       "7            0.808933            0.6889  \n",
       "8            0.884550            0.7691  \n",
       "9            0.864067            0.7471  \n",
       "10           0.946683            0.8829  \n",
       "11           0.958583            0.9073  \n",
       "12           0.813483            0.6874  \n",
       "13           0.938900            0.8595  \n",
       "14           0.969667            0.9151  \n",
       "15           0.859083            0.7714  \n",
       "16           0.969850            0.9295  \n",
       "17           0.837800            0.7215  \n",
       "18           0.942183            0.8752  \n",
       "19           0.942833            0.8871  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Learning Rate</th>\n",
       "      <th>Momentum:</th>\n",
       "      <th>Hidden Layer Width</th>\n",
       "      <th>Hidden Layer Height</th>\n",
       "      <th>Batch Size</th>\n",
       "      <th>Convolutional Layer Height</th>\n",
       "      <th>Convolutional Layer Filters</th>\n",
       "      <th>Training Accuracy</th>\n",
       "      <th>Testing Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Learning Rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.323486</td>\n",
       "      <td>0.351058</td>\n",
       "      <td>-0.044146</td>\n",
       "      <td>-0.017954</td>\n",
       "      <td>-0.061066</td>\n",
       "      <td>-0.372987</td>\n",
       "      <td>0.461918</td>\n",
       "      <td>0.499798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Momentum:</th>\n",
       "      <td>-0.323486</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.284671</td>\n",
       "      <td>0.067885</td>\n",
       "      <td>0.267989</td>\n",
       "      <td>0.042705</td>\n",
       "      <td>0.119193</td>\n",
       "      <td>-0.125795</td>\n",
       "      <td>-0.140051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hidden Layer Width</th>\n",
       "      <td>0.351058</td>\n",
       "      <td>-0.284671</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.315324</td>\n",
       "      <td>0.264319</td>\n",
       "      <td>-0.292134</td>\n",
       "      <td>-0.230949</td>\n",
       "      <td>0.205435</td>\n",
       "      <td>0.205144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hidden Layer Height</th>\n",
       "      <td>-0.044146</td>\n",
       "      <td>0.067885</td>\n",
       "      <td>0.315324</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.047683</td>\n",
       "      <td>-0.072509</td>\n",
       "      <td>-0.063604</td>\n",
       "      <td>-0.071176</td>\n",
       "      <td>-0.076111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Batch Size</th>\n",
       "      <td>-0.017954</td>\n",
       "      <td>0.267989</td>\n",
       "      <td>0.264319</td>\n",
       "      <td>-0.047683</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.205731</td>\n",
       "      <td>-0.321861</td>\n",
       "      <td>-0.367934</td>\n",
       "      <td>-0.379185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Layer Height</th>\n",
       "      <td>-0.061066</td>\n",
       "      <td>0.042705</td>\n",
       "      <td>-0.292134</td>\n",
       "      <td>-0.072509</td>\n",
       "      <td>-0.205731</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.318026</td>\n",
       "      <td>-0.147284</td>\n",
       "      <td>-0.079489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Convolutional Layer Filters</th>\n",
       "      <td>-0.372987</td>\n",
       "      <td>0.119193</td>\n",
       "      <td>-0.230949</td>\n",
       "      <td>-0.063604</td>\n",
       "      <td>-0.321861</td>\n",
       "      <td>0.318026</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105663</td>\n",
       "      <td>0.064882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Training Accuracy</th>\n",
       "      <td>0.461918</td>\n",
       "      <td>-0.125795</td>\n",
       "      <td>0.205435</td>\n",
       "      <td>-0.071176</td>\n",
       "      <td>-0.367934</td>\n",
       "      <td>-0.147284</td>\n",
       "      <td>0.105663</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Testing Accuracy</th>\n",
       "      <td>0.499798</td>\n",
       "      <td>-0.140051</td>\n",
       "      <td>0.205144</td>\n",
       "      <td>-0.076111</td>\n",
       "      <td>-0.379185</td>\n",
       "      <td>-0.079489</td>\n",
       "      <td>0.064882</td>\n",
       "      <td>0.990179</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Learning Rate  Momentum:  Hidden Layer Width  \\\n",
       "Learning Rate                     1.000000  -0.323486            0.351058   \n",
       "Momentum:                        -0.323486   1.000000           -0.284671   \n",
       "Hidden Layer Width                0.351058  -0.284671            1.000000   \n",
       "Hidden Layer Height              -0.044146   0.067885            0.315324   \n",
       "Batch Size                       -0.017954   0.267989            0.264319   \n",
       "Convolutional Layer Height       -0.061066   0.042705           -0.292134   \n",
       "Convolutional Layer Filters      -0.372987   0.119193           -0.230949   \n",
       "Training Accuracy                 0.461918  -0.125795            0.205435   \n",
       "Testing Accuracy                  0.499798  -0.140051            0.205144   \n",
       "\n",
       "                             Hidden Layer Height  Batch Size  \\\n",
       "Learning Rate                          -0.044146   -0.017954   \n",
       "Momentum:                               0.067885    0.267989   \n",
       "Hidden Layer Width                      0.315324    0.264319   \n",
       "Hidden Layer Height                     1.000000   -0.047683   \n",
       "Batch Size                             -0.047683    1.000000   \n",
       "Convolutional Layer Height             -0.072509   -0.205731   \n",
       "Convolutional Layer Filters            -0.063604   -0.321861   \n",
       "Training Accuracy                      -0.071176   -0.367934   \n",
       "Testing Accuracy                       -0.076111   -0.379185   \n",
       "\n",
       "                             Convolutional Layer Height  \\\n",
       "Learning Rate                                 -0.061066   \n",
       "Momentum:                                      0.042705   \n",
       "Hidden Layer Width                            -0.292134   \n",
       "Hidden Layer Height                           -0.072509   \n",
       "Batch Size                                    -0.205731   \n",
       "Convolutional Layer Height                     1.000000   \n",
       "Convolutional Layer Filters                    0.318026   \n",
       "Training Accuracy                             -0.147284   \n",
       "Testing Accuracy                              -0.079489   \n",
       "\n",
       "                             Convolutional Layer Filters  Training Accuracy  \\\n",
       "Learning Rate                                  -0.372987           0.461918   \n",
       "Momentum:                                       0.119193          -0.125795   \n",
       "Hidden Layer Width                             -0.230949           0.205435   \n",
       "Hidden Layer Height                            -0.063604          -0.071176   \n",
       "Batch Size                                     -0.321861          -0.367934   \n",
       "Convolutional Layer Height                      0.318026          -0.147284   \n",
       "Convolutional Layer Filters                     1.000000           0.105663   \n",
       "Training Accuracy                               0.105663           1.000000   \n",
       "Testing Accuracy                                0.064882           0.990179   \n",
       "\n",
       "                             Testing Accuracy  \n",
       "Learning Rate                        0.499798  \n",
       "Momentum:                           -0.140051  \n",
       "Hidden Layer Width                   0.205144  \n",
       "Hidden Layer Height                 -0.076111  \n",
       "Batch Size                          -0.379185  \n",
       "Convolutional Layer Height          -0.079489  \n",
       "Convolutional Layer Filters          0.064882  \n",
       "Training Accuracy                    0.990179  \n",
       "Testing Accuracy                     1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initialize data of lists. \n",
    "test_accuracies = []\n",
    "for score in best_scores:\n",
    "    test_accuracies.append(score[-1])\n",
    "data = {'Learning Rate': best_lrs,\n",
    "        'Momentum:': best_ms,\n",
    "        'Hidden Layer Width': best_widths,\n",
    "        'Hidden Layer Height': best_heights,\n",
    "        'Batch Size': best_batch_sizes,\n",
    "        'Convolutional Layer Height': best_conv_heights,\n",
    "        'Convolutional Layer Filters': best_conv_filters,\n",
    "        'Training Accuracy': best_accuracies, \n",
    "        'Testing Accuracy': test_accuracies} \n",
    "\n",
    "# Create DataFrame \n",
    "df = pd.DataFrame(data) \n",
    "\n",
    "with open('dataframe.pkl', 'wb') as f:\n",
    "    pickle.dump(df,f)\n",
    "  \n",
    "# Print the output. \n",
    "display(df)\n",
    "display(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6c55d6e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0EAAAHWCAYAAACxAYILAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABFM0lEQVR4nO3deXxMZ///8fdkJYkECSFEEvuSxF61U1WNraulVJVud1GUKtpaS7VoSxeK+y5dtNx3i1oqqoq2aoklrb2qUWrf10okuX5/+GW+RhIyTIw4r+fjMY+Huc51zvmcyZjMO+c617EZY4wAAAAAwCI83F0AAAAAANxKhCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAgD/vtt9/UrVs3RUVFKV++fAoICFCNGjU0duxYnThxwt3l5diePXtks9k0Y8aMG1r/jTfe0Lx58zK1r1ixQjabTStWrLip+lzlySefVGRkZI76BQQE5H5Bt0hkZKRat26d5bL169ff8M/+Zt83NptNvXr1um6/X375RcOHD9epU6duaD9ZadKkiZo0aeKSbUVGRspms133kd3rlNP3pavXzcqpU6cUEhKiWbNmuWybt6slS5aofv36yp8/v4KCgtSmTRtt3bo1y77ff/+96tatKz8/P4WEhOjJJ5/UkSNHHPosW7ZMAQEB2r9//60oH8jzCEFAHjVt2jTVrFlTCQkJGjBggOLj4zV37ly1a9dOH330kZ566il3l3jLZBeCatSoodWrV6tGjRq3vijkuuLFi2v16tVq1apVru7nl19+0YgRI1wagiZNmqRJkya5ZFtz587V6tWr7Y+M//vx8fEO7dm9TkOGDNHcuXNdUsvNGjFihMLCwtShQwd3l5KrvvnmG8XFxalo0aL6+uuv9dFHH2nXrl1q2LChdu/e7dB35cqViouLU2hoqL755htNnDhR33//vZo1a6bk5GR7v2bNmumuu+7SK6+8cqsPB8iTvNxdAADnrV69Ws8//7yaN2+uefPmydfX176sefPm6t+/v+Lj491Y4e0hMDBQd999t7vLuOOlpaUpNTXV4X14K/j6+ubZn2/lypVdtq3q1as7PM/4v1+zZk2FhIRku96FCxfk5+enMmXKuKyWm3HixAlNmTJF7777rmw2m7vLyWT37t2KiIiQl9fNf3UaOHCgYmJiNGfOHPux1qtXT+XLl9fQoUM1c+ZMe98BAwaofPny+uqrr+z7joqKUv369fXxxx/r+eeft/ft2bOnOnTooFGjRik8PPym6wTuZJwJAvKgN954QzabTVOnTs3yi6ePj4/atm1rf56enq6xY8eqYsWK8vX1VdGiRfXEE0/o77//dlivSZMmio6OVkJCgho2bCg/Pz+VLl1ab775ptLT0yVJR48elY+Pj4YMGZJpvzt27JDNZtN7771nb9uyZYseeOABFSpUSPny5VO1atX0ySefXPcYsxtmM3z4cIcvSDabTefPn9cnn3xiH/aTMcwou+Fw8+fPtw8tKVCggJo3b67Vq1dnuZ+tW7fqscceU1BQkEJDQ9W9e3edPn3aoe+HH36oRo0aqWjRovL391dMTIzGjh2rS5cuXfc4b9Qff/yhbt26qVy5cvLz81OJEiXUpk0bbd682d7n3LlzKliwoJ577rlM6+/Zs0eenp4aN26cve3QoUN67rnnVLJkSfn4+CgqKkojRoxQamqqw3o2m01jx47VqFGjFBUVJV9fXy1fvtylx7dr1y516tRJRYsWla+vrypVqqQPP/ww0zFkNczrm2++UWxsrHx9fVW6dGlNnDgx0/vmSp999pkqVaokPz8/Va1aVQsXLrQvGz58uAYMGCDp8hfPjPdYxnvqhx9+UJMmTRQcHKz8+fOrVKlSeuSRR3ThwoVrHt/Vw+EyjmX8+PF65513FBUVpYCAANWtW1dr1qzJ4auWvYwhlps3b9Z9992nAgUKqFmzZvZlV/9fu5n39P/+9z/VqVNHQUFB9s+Q7t27X3e9GTNmKDU1NdNZoPXr16tjx46KjIxU/vz5FRkZqccee0x//fVXpm3s379fzz77rMLDw+Xj46OwsDA9+uijOnz4sL3PqVOn1L9/f5UuXdr+ediyZUvt2LHjmvW9/vrrCgsLU8+ePfXzzz/LGHPdY8rK8ePHtXPnTsXFxTm8JyMiIhQdHa158+YpLS3NfjwJCQnq0qWLQ/jKCExXn8Fr06aNAgICNG3atBuqDbASzgQBeUxaWpp++OEH1axZM8d/6Xv++ec1depU9erVS61bt9aePXs0ZMgQrVixQhs3bnT4a/GhQ4fUuXNn9e/fX8OGDdPcuXM1ePBghYWF6YknnlCRIkXUunVrffLJJxoxYoQ8PP7vbynTp0+Xj4+POnfuLEnauXOn6tWrp6JFi+q9995TcHCwPv/8cz355JM6fPiwXn755Zt+PVavXq177rlHTZs2tQezwMDAbPt/8cUX6ty5s+677z59+eWXSk5O1tixY9WkSRMtW7ZMDRo0cOj/yCOPqEOHDnrqqae0efNmDR48WJL08ccf2/vs3r1bnTp1UlRUlHx8fPTrr79q9OjR2rFjh0M/Vzpw4ICCg4P15ptvqkiRIjpx4oQ++eQT1alTR5s2bVKFChUUEBCg7t27a+rUqRo7dqyCgoLs60+aNEk+Pj72L6eHDh3SXXfdJQ8PDw0dOlRlypTR6tWrNWrUKO3Zs0fTp0932P97772n8uXLa/z48QoMDFS5cuWuWa8xxiFMZcj4snelbdu2qV69eipVqpTefvttFStWTEuWLFHv3r117NgxDRs2LNv9xMfH6+GHH1ajRo00e/Zspaamavz48Q5fgq+0aNEiJSQkaOTIkQoICNDYsWP10EMPaefOnSpdurSefvppnThxQu+//77mzJmj4sWLS7p8JmfPnj1q1aqVGjZsqI8//lgFCxbU/v37FR8fr5SUFPn5+V3zNcnKhx9+qIoVK2rChAmSLg9Va9mypZKSkhx+fjciJSVFbdu21XPPPadBgwZl+fPIcKPv6dWrV6tDhw7q0KGDhg8frnz58umvv/7SDz/8cN36Fi1apOrVq6tgwYIO7Xv27FGFChXUsWNHFS5cWAcPHtTkyZNVu3Ztbdu2zf75tX//ftWuXVuXLl3SK6+8otjYWB0/flxLlizRyZMnFRoaqrNnz6pBgwbas2ePBg4cqDp16ujcuXP68ccfdfDgQVWsWDHb+oYPH65y5cpp1qxZmjRpksLDw9WxY0d16tRJ1apVu+7xZUhJSZGkLP+A5evrqwsXLmj37t0qX768tmzZIkmKjY3N1Dc2NlarVq1yaPPx8VG9evW0aNEijRw5Msc1AZZkAOQphw4dMpJMx44dc9R/+/btRpLp0aOHQ/vatWuNJPPKK6/Y2xo3bmwkmbVr1zr0rVy5smnRooX9+fz5840k891339nbUlNTTVhYmHnkkUfsbR07djS+vr5m7969DtuLi4szfn5+5tSpU8YYY5KSkowkM336dHufrl27moiIiEzHM2zYMHP1R5e/v7/p2rVrpr7Lly83kszy5cuNMcakpaWZsLAwExMTY9LS0uz9zp49a4oWLWrq1auXaT9jx4512GaPHj1Mvnz5THp6eqb9Zezj0qVL5tNPPzWenp7mxIkT1z2mq3Xt2tX4+/tft9+VUlNTTUpKiilXrpx58cUX7e27d+82Hh4e5t1337W3/fPPPyY4ONh069bN3vbcc8+ZgIAA89dffzlsd/z48UaS2bp1qzHm/35WZcqUMSkpKTmqLSIiwki65uPKn32LFi1MyZIlzenTpx2206tXL5MvXz77a5rV+6Z27domPDzcJCcn29vOnj1rgoODM71vJJnQ0FBz5swZe9uhQ4eMh4eHGTNmjL1t3LhxRpJJSkpyWP+rr74ykkxiYmKOXocrNW7c2DRu3Nj+PONYYmJiTGpqqr193bp1RpL58ssvc7ztjPfu0aNH7W1du3Y1kszHH3+cqf/13pfOvKcz3i8Z/7ed4efnZ/71r39dt19qaqo5d+6c8ff3NxMnTrS3d+/e3Xh7e5tt27Zlu+7IkSONJLN06VKn67vS1q1bzZAhQ0z58uWNJFOxYkUzYsQI8/vvv1933bS0NFO4cGHTrFkzh/aTJ0+aAgUKGEnml19+McYYM3PmTCPJrF69OtN2nn32WePj45Op/dVXXzUeHh7m3LlzN3h0gDUwHA64w2UMU3ryyScd2u+66y5VqlRJy5Ytc2gvVqyY7rrrLoe22NhYh6EncXFxKlasmMPZgSVLlujAgQMOw15++OEHNWvWLNMZqyeffFIXLlzINAQtt+3cuVMHDhxQly5dHM5gBQQE6JFHHtGaNWsyDWO6clihdPm1uHjxosPMTJs2bVLbtm0VHBwsT09PeXt764knnlBaWpp+//33XDmW1NRUvfHGG6pcubJ8fHzk5eUlHx8f7dq1S9u3b7f3K126tFq3bq1JkybZh+988cUXOn78uMPMaAsXLlTTpk0VFham1NRU+yMuLk7S5Yuzr9S2bVt5e3vnuN4GDRooISEh0+PTTz916Hfx4kUtW7ZMDz30kPz8/BxqadmypS5evJjt8LDz589r/fr1evDBB+Xj42NvDwgIUJs2bbJcp2nTpipQoID9eWhoqIoWLZrlUKurVatWTT4+Pnr22Wf1ySef6M8//8zJS3FNrVq1kqenp/15xhmAnNSTE4888kiO+t3oe7p27dqSpPbt2+u///1vjmcqO3XqlC5cuKCiRYtmWnbu3DkNHDhQZcuWlZeXl7y8vBQQEKDz5887vNcXL16spk2bqlKlStnuZ/HixSpfvrzuvffeHNWVncqVK2vkyJHauXOnNmzYoLZt2+rjjz9W+fLl9eijj15zXQ8PD/Xs2VPLli3T66+/riNHjuiPP/7Q448/bv/8ufLzSVK2Qzmzai9atKjS09N16NChGzw6wBoIQUAeExISIj8/PyUlJeWo//HjxyXJPoznSmFhYfblGYKDgzP18/X11T///GN/7uXlpS5dumju3Ln2GbNmzJih4sWLq0WLFg77zm6/V9Z2q1zvtUhPT9fJkycd2q9+PTKGsGS8Hnv37lXDhg21f/9+TZw4UT/99JMSEhLs169c+bq5Ur9+/TRkyBA9+OCDWrBggdauXauEhARVrVo10z779OmjXbt2aenSpZIuD7mqW7euw6x5hw8f1oIFC+Tt7e3wqFKliiTp2LFjDtvM6jW8lqCgINWqVSvT4+ovrMePH1dqaqref//9TLW0bNkyy1oynDx5UsYYhYaGZlqWVZuUs/d7dsqUKaPvv/9eRYsWVc+ePVWmTBmVKVNGEydOvO662bne++1m+Pn5XXOoaIabeU83atRI8+bNU2pqqp544gmVLFlS0dHR+vLLL6+5z4xt5suXL9OyTp066YMPPtDTTz+tJUuWaN26dUpISFCRIkUcajl69KhKlix5zf3kpI8z0tPTdfr0aZ06dUrnzp2Tl5dXpuF8WRk6dKhefPFFjRo1SqGhofbhpN26dZMklShRQtL/vR+y+qw8ceKEChcunKk94zXMrc8e4E7BNUFAHuPp6almzZpp8eLF+vvvv6/7Cz3jl+jBgwcz9T1w4MA1Z4+6lm7dumncuHGaNWuWOnTooPnz56tv374Of8UODg7WwYMHM6174MABSbrmvvPly+cw/WuG7L4A58SVr0VWNXl4eKhQoUJObXPevHk6f/685syZo4iICHt7YmLiDdeZE59//rmeeOIJvfHGGw7tx44dy/Ql7J577lF0dLQ++OADBQQEaOPGjfr8888d+oSEhCg2NlajR4/Ocn8ZwTVDbs3eVahQIXl6eqpLly7q2bNnln2ioqKyXddms2V5/U9u/VW8YcOGatiwodLS0rR+/Xq9//776tu3r0JDQ9WxY8dc2eeNyunP7Gbf0w888IAeeOABJScna82aNRozZow6deqkyMhI1a1bN8t1Mv5vXn1/s9OnT2vhwoUaNmyYBg0aZG9PTk7O1LdIkSKZJnu5Wk76XI8xRqtWrdLs2bP1v//9T0eOHFHdunU1fPhwdejQQUWKFLnuNry8vPTOO+9o5MiRSkpKUkhIiP2PSFFRUfbP6ujoaEnS5s2b7X8EyLB582b78itlvC43+tkOWAVngoA8aPDgwTLG6JlnnrFfZHulS5cuacGCBZIufwGWlOlLb0JCgrZv326fIcpZlSpVUp06dTR9+nR98cUXSk5Otv8VM0OzZs30ww8/2ENPhk8//VR+fn7XnN44MjJSR44ccfhCm5KSoiVLlmTqm9O/3FeoUEElSpTQF1984TCz0/nz5/X111/bZ4xzRsYXyysvcjbG5PrsTDabLdOF1YsWLcp2+FHv3r21aNEiDR48WKGhoWrXrp3D8tatW2vLli0qU6ZMlmdsrg5BucXPz09NmzbVpk2bFBsbm2UtWZ29kSR/f3/VqlVL8+bNc/h/ce7cOYcZ35yVk7Mxnp6eqlOnjv1sycaNG294f+7mqve0r6+vGjdurLfeekvS5SF22fHx8VHp0qUz3SPHZrPJGJPpvf7vf/8706QacXFxWr58uXbu3JntfuLi4vT777/naKKGq23ZskX9+/dXqVKl1LBhQ61cuVJ9+vRRUlKSVq1apV69euUoAF0pICBAMTExKl68uDZu3Khly5apT58+9uUlSpTQXXfdpc8//9zheNesWaOdO3fq4YcfzrTNP//8U8HBwdme/QRwGWeCgDyobt26mjx5snr06KGaNWvq+eefV5UqVXTp0iVt2rRJU6dOVXR0tNq0aaMKFSro2Wef1fvvvy8PDw/FxcXZZ4cLDw/Xiy++eMN1dO/eXc8995wOHDigevXqqUKFCg7Lhw0bZr/WZOjQoSpcuLBmzpypRYsWZZqt7GodOnTQ0KFD1bFjRw0YMEAXL17Ue++9l+VsYjExMVqxYoUWLFig4sWLq0CBAplqkS6Psx87dqw6d+6s1q1b67nnnlNycrLGjRunU6dO6c0333T6NWjevLl8fHz02GOP6eWXX9bFixc1efLkTMPqnJWWlqavvvoqU7u/v7/i4uLUunVrzZgxQxUrVlRsbKw2bNigcePGZXtm8PHHH9fgwYP1448/6rXXXnO4ZkaSRo4cqaVLl6pevXrq3bu3KlSooIsXL2rPnj369ttv9dFHH7l0GNG1TJw4UQ0aNFDDhg31/PPPKzIyUmfPntUff/yhBQsWXPML7MiRI9WqVSu1aNFCffr0UVpamsaNG6eAgIBMZw5yKiYmxl5X165d5e3trQoVKmjmzJn64Ycf1KpVK5UqVUoXL160z5x2s9ecuNPNvKeHDh2qv//+W82aNVPJkiV16tQpTZw4Ud7e3mrcuPE1123SpIkWL17s0BYYGKhGjRpp3LhxCgkJUWRkpFauXKn//Oc/mc54jhw5UosXL1ajRo30yiuvKCYmRqdOnVJ8fLz69eunihUrqm/fvpo9e7YeeOABDRo0SHfddZf++ecfrVy5Uq1bt1bTpk2zrW/8+PH68ccf1aVLF3Xu3Nk+VPRGrFixQgkJCYqNjZUxRuvWrdNbb72l+++/3+FaPUl666231Lx5c7Vr1049evTQkSNHNGjQIEVHR2f6w5N0OSA1btz4trzXEnBbcduUDABuWmJiounataspVaqU8fHxMf7+/qZ69epm6NCh5siRI/Z+aWlp5q233jLly5c33t7eJiQkxDz++ONm3759Dttr3LixqVKlSqb9ZDd71OnTp03+/PmNJDNt2rQsa9y8ebNp06aNCQoKMj4+PqZq1aoOs3kZk/UsX8YY8+2335pq1aqZ/Pnzm9KlS5sPPvggy9nhEhMTTf369Y2fn5+RZJ916+rZ4TLMmzfP1KlTx+TLl8/4+/ubZs2amVWrVjn0yWqGLWOMmT59eqaZwhYsWGCqVq1q8uXLZ0qUKGEGDBhgFi9enGnfzswOp2xmUctY/+TJk+app54yRYsWNX5+fqZBgwbmp59+yjTr2JWefPJJ4+XlZf7+++8slx89etT07t3bREVFGW9vb1O4cGFTs2ZN8+qrr9pnmsr4WY0bN+66x5EhIiLCtGrVKstlCQkJWf7sk5KSTPfu3U2JEiWMt7e3KVKkiKlXr54ZNWqUQ5+s1p07d66JiYkxPj4+plSpUubNN980vXv3NoUKFXLoJ8n07Nkzy3qvnm1w8ODBJiwszHh4eNh/rqtXrzYPPfSQiYiIML6+viY4ONg0btzYzJ8//7qvSXazw2X1ukoyw4YNu+42M2Q3O1x2Mw5m9b680ff0woULTVxcnClRooTx8fExRYsWNS1btjQ//fTTdetetmyZkWTWrVvn0P7333+bRx55xBQqVMgUKFDA3H///WbLli1Z/pz27dtnunfvbooVK2a8vb1NWFiYad++vTl8+LC9z8mTJ02fPn1MqVKljLe3tylatKhp1aqV2bFjxzXrO3DgwHWPIadWrVpl6tSpYwIDA42vr6+Jjo4248ePz3bGxe+++87cfffdJl++fKZw4cLmiSeecDimDH/88YeRZL7++muX1QrcqWzG3ODdvgAAeUZKSooiIyPVoEED/fe//3V3ObfUpUuXVK1aNZUoUULfffedu8vBNcTGxqp+/fqaPHmyu0vJk4YMGaJPP/1Uu3fvdri5KoDMCEEAcAc7evSodu7cqenTp2vGjBlKSEhwmBXuTvTUU0+pefPmKl68uA4dOqSPPvpIK1eu1HfffZenh6lZQXx8vB566CHt2rXrlg2/vFOcOnVKpUuX1vvvv2+/YTWA7PFnAgC4gy1atEjdunVT8eLFNWnSpDs+AEnS2bNn9dJLL+no0aPy9vZWjRo19O233xKA8oD7779f48aNU1JSEiHISUlJSRo8eLA6derk7lKAPIEzQQAAAAAshSmyAQAAAFgKIQgAAACApRCCAAAAAFhKnp4YIT09XQcOHFCBAgW4KRgAAABgYcYYnT17VmFhYfLwuPa5njwdgg4cOKDw8HB3lwEAAADgNrFv377rzjCZp0NQgQIFJF0+0MDAQDdXAwAAAMBdzpw5o/DwcHtGuJY8HYIyhsAFBgYSggAAAADk6DIZJkYAAAAAYCmEIAAAAACWQggCAAAAYCmEIAAAAACWQggCAAAAYCmEIAAAAACWQggCAAAAYCmEIAAAAACWQggCAAAAYCmEIAAAAACWQggCAAAAYCluDUHDhw+XzWZzeBQrVsydJQEAAAC4w3m5u4AqVaro+++/tz/39PR0YzUAAAAA7nRuD0FeXl6c/QEAAABwy7j9mqBdu3YpLCxMUVFR6tixo/78889s+yYnJ+vMmTMODwAAAABwhlvPBNWpU0effvqpypcvr8OHD2vUqFGqV6+etm7dquDg4Ez9x4wZoxEjRrih0pwZYbO5uwRJ0jBj3F0CAAAAcNuyGXP7fGM+f/68ypQpo5dffln9+vXLtDw5OVnJycn252fOnFF4eLhOnz6twMDAW1lqlghBAAAAgHucOXNGQUFBOcoGbr8m6Er+/v6KiYnRrl27slzu6+srX1/fW1wVAAAAgDuJ268JulJycrK2b9+u4sWLu7sUAAAAAHcot4agl156SStXrlRSUpLWrl2rRx99VGfOnFHXrl3dWRYAAACAO5hbh8P9/fffeuyxx3Ts2DEVKVJEd999t9asWaOIiAh3lgUAAADgDubWEDRr1ix37h4AAACABd1W1wQBAAAAQG4jBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEu5bULQmDFjZLPZ1LdvX3eXAgAAAOAOdluEoISEBE2dOlWxsbHuLgUAAADAHc7tIejcuXPq3Lmzpk2bpkKFCl2zb3Jyss6cOePwAAAAAABnuD0E9ezZU61atdK999573b5jxoxRUFCQ/REeHn4LKgQAAABwJ3FrCJo1a5Y2btyoMWPG5Kj/4MGDdfr0aftj3759uVwhAAAAgDuNl7t2vG/fPvXp00ffffed8uXLl6N1fH195evrm8uVAQAAALiTuS0EbdiwQUeOHFHNmjXtbWlpafrxxx/1wQcfKDk5WZ6enu4qDwAAAMAdym0hqFmzZtq8ebNDW7du3VSxYkUNHDiQAAQAAAAgV7gtBBUoUEDR0dEObf7+/goODs7UDgAAAACu4vbZ4QAAAADgVnLbmaCsrFixwt0lAAAAALjDcSYIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKUQggAAAABYCiEIAAAAgKXcdAhKS0tTYmKiTp486Yp6AAAAACBXOR2C+vbtq//85z+SLgegxo0bq0aNGgoPD9eKFStcXR8AAAAAuJTTIeirr75S1apVJUkLFixQUlKSduzYob59++rVV191eYEAAAAA4EpOh6Bjx46pWLFikqRvv/1W7dq1U/ny5fXUU09p8+bNLi8QAAAAAFzJ6RAUGhqqbdu2KS0tTfHx8br33nslSRcuXJCnp6fLCwQAAAAAV/JydoVu3bqpffv2Kl68uGw2m5o3by5JWrt2rSpWrOjyAgEAAADAlZwOQcOHD1d0dLT27dundu3aydfXV5Lk6empQYMGubxAAAAAAHAlp0OQJD366KOSpIsXL9rbunbt6pqKAAAAACAXOX1NUFpaml5//XWVKFFCAQEB+vPPPyVJQ4YMsU+dDQAAAAC3K6dD0OjRozVjxgyNHTtWPj4+9vaYmBj9+9//dmlxAAAAAOBqToegTz/9VFOnTlXnzp0dZoOLjY3Vjh07XFocAAAAALia0yFo//79Klu2bKb29PR0Xbp0ySVFAQAAAEBucToEValSRT/99FOm9v/973+qXr26S4oCAAAAgNzi9Oxww4YNU5cuXbR//36lp6drzpw52rlzpz799FMtXLgwN2oEAAAAAJdx+kxQmzZtNHv2bH377bey2WwaOnSotm/frgULFthvnAoAAAAAt6sbuk9QixYt1KJFC1fXAgAAAAC5zukzQQkJCVq7dm2m9rVr12r9+vUuKQoAAAAAcovTIahnz57at29fpvb9+/erZ8+eTm1r8uTJio2NVWBgoAIDA1W3bl0tXrzY2ZIAAAAAIMecDkHbtm1TjRo1MrVXr15d27Ztc2pbJUuW1Jtvvqn169dr/fr1uueee/TAAw9o69atzpYFAAAAADnidAjy9fXV4cOHM7UfPHhQXl7OXWLUpk0btWzZUuXLl1f58uU1evRoBQQEaM2aNc6WBQAAAAA54nQIat68uQYPHqzTp0/b206dOqVXXnnlpmaHS0tL06xZs3T+/HnVrVs3yz7Jyck6c+aMwwMAAAAAnOH07HBvv/22GjVqpIiICPvNURMTExUaGqrPPvvM6QI2b96sunXr6uLFiwoICNDcuXNVuXLlLPuOGTNGI0aMcHofAIDb2wibzd0laJgx7i4BAHCL2Ixx/lP//Pnzmjlzpn799Vflz59fsbGxeuyxx+Tt7e10ASkpKdq7d69OnTqlr7/+Wv/+97+1cuXKLINQcnKykpOT7c/PnDmj8PBwnT59WoGBgU7v29Vuh1/iEr/IAeQ9t8PnJ5+dAJC3nTlzRkFBQTnKBjd0nyB/f389++yzN1Tc1Xx8fFS2bFlJUq1atZSQkKCJEydqypQpmfr6+vrK19fXJfsFAAAAYE03FIJ+//13rVixQkeOHFF6errDsqFDh95UQcYYh7M9AAAAAOBKToegadOm6fnnn1dISIiKFSsm2xVDGGw2m1Mh6JVXXlFcXJzCw8N19uxZzZo1SytWrFB8fLyzZQEAAABAjjgdgkaNGqXRo0dr4MCBN73zw4cPq0uXLjp48KCCgoIUGxur+Pj4m5plDgAAAACuxekQdPLkSbVr184lO//Pf/7jku0AAAAAQE45fZ+gdu3a6bvvvsuNWgAAAAAg1zl9Jqhs2bIaMmSI1qxZo5iYmEzTYvfu3dtlxQEAAACAqzkdgqZOnaqAgACtXLlSK1eudFhms9kIQQAAAABua06HoKSkpNyoAwAAAABuCaevCQIAAACAvOyGbpb6999/a/78+dq7d69SUlIclr3zzjsuKQwAAAAAcoPTIWjZsmVq27atoqKitHPnTkVHR2vPnj0yxqhGjRq5USMAAAAAuIzTw+EGDx6s/v37a8uWLcqXL5++/vpr7du3T40bN3bZ/YMAAAAAILc4HYK2b9+url27SpK8vLz0zz//KCAgQCNHjtRbb73l8gIBAAAAwJWcDkH+/v5KTk6WJIWFhWn37t32ZceOHXNdZQAAAACQC5y+Jujuu+/WqlWrVLlyZbVq1Ur9+/fX5s2bNWfOHN199925USMAAAAAuIzTIeidd97RuXPnJEnDhw/XuXPnNHv2bJUtW1bvvvuuywsEAAAAAFdyOgSVLl3a/m8/Pz9NmjTJpQUBAAAAQG5y+pqg0qVL6/jx45naT5065RCQAAAAAOB25HQI2rNnj9LS0jK1Jycna//+/S4pCgAAAAByS46Hw82fP9/+7yVLligoKMj+PC0tTcuWLVNkZKRLiwMAAAAAV8txCHrwwQclSTabzX6foAze3t6KjIzU22+/7dLiAAAAAMDVchyC0tPTJUlRUVFKSEhQSEhIrhUFAAAAALnF6dnhkpKSMrWdOnVKBQsWdEU9AAAAAJCrnJ4Y4a233tLs2bPtz9u1a6fChQurRIkS+vXXX11aHAAAAAC4mtMhaMqUKQoPD5ckLV26VN9//73i4+MVFxenAQMGuLxAAAAAAHAlp4fDHTx40B6CFi5cqPbt2+u+++5TZGSk6tSp4/ICAQAAAMCVnD4TVKhQIe3bt0+SFB8fr3vvvVeSZIzJ8v5BAAAAAHA7cfpM0MMPP6xOnTqpXLlyOn78uOLi4iRJiYmJKlu2rMsLBAAAAABXcjoEvfvuu4qMjNS+ffs0duxYBQQESLo8TK5Hjx4uLxAAAAAAXMnpEOTt7a2XXnopU3vfvn1dUQ8AAAAA5CqnQ5Ak/f7771qxYoWOHDliv4lqhqFDh7qkMAAAAADIDU6HoGnTpun5559XSEiIihUrJpvNZl9ms9kIQQAAAABua06HoFGjRmn06NEaOHBgbtQDAAAAALnK6SmyT548qXbt2uVGLQAAAACQ65wOQe3atdN3332XG7UAAAAAQK5zejhc2bJlNWTIEK1Zs0YxMTHy9vZ2WN67d2+XFQcAAAAAruZ0CJo6daoCAgK0cuVKrVy50mGZzWYjBAEAAAC4rTkdgpKSknKjDgAAAAC4JZy+JggAAAAA8rIcnQnq16+fXn/9dfn7+6tfv37X7PvOO++4pDAAAAAAyA05CkGbNm3SpUuX7P/OzpU3TgUAAACA21GOQtDy5cuz/DcAAAAA5DVcEwQAAADAUghBAAAAACyFEAQAAADAUghBAAAAACwlRyGoRo0aOnnypCRp5MiRunDhQq4WBQAAAAC5JUchaPv27Tp//rwkacSIETp37lyuFgUAAAAAuSVHU2RXq1ZN3bp1U4MGDWSM0fjx4xUQEJBl36FDh7q0QAAAAABwpRyFoBkzZmjYsGFauHChbDabFi9eLC+vzKvabDZCEAAAAIDbWo5CUIUKFTRr1ixJkoeHh5YtW6aiRYvmamEAAAAAkBtyFIKulJ6enht1AAAAAMAt4XQIkqTdu3drwoQJ2r59u2w2mypVqqQ+ffqoTJkyrq4PAAAAAFzK6fsELVmyRJUrV9a6desUGxur6OhorV27VlWqVNHSpUtzo0YAAAAAcBmnzwQNGjRIL774ot58881M7QMHDlTz5s1dVhwAAAAAuJrTZ4K2b9+up556KlN79+7dtW3bNpcUBQAAAAC5xekQVKRIESUmJmZqT0xMZMY4AAAAALc9p4fDPfPMM3r22Wf1559/ql69erLZbPr555/11ltvqX///rlRIwAAAAC4jNMhaMiQISpQoIDefvttDR48WJIUFham4cOHq3fv3i4vEAAAAABcyekQZLPZ9OKLL+rFF1/U2bNnJUkFChRweWEAAAAAkBtu6D5BGQg/AAAAAPIapydGAAAAAIC8jBAEAAAAwFIIQQAAAAAshRAEAAAAwFJuKAT16tVLJ06ccHUtAAAAAJDrchyC/v77b/u/v/jiC507d06SFBMTo3379t3QzseMGaPatWurQIECKlq0qB588EHt3LnzhrYFAAAAADmR4xBUsWJFRUREqFOnTrp48aI9+OzZs0eXLl26oZ2vXLlSPXv21Jo1a7R06VKlpqbqvvvu0/nz529oewAAAABwPTm+T9Dp06e1YcMG/fTTT5ozZ45atmyp0NBQJScna8mSJXrooYdUrFgxp3YeHx/v8Hz69OkqWrSoNmzYoEaNGjm1LQAAAADIiRyfCbp06ZLuuusu9e/fX/nz59emTZs0ffp0eXp66uOPP1aZMmVUoUKFmyrm9OnTkqTChQtnuTw5OVlnzpxxeAAAAACAM3J8JigwMFDVq1dX/fr1lZKSogsXLqh+/fry8vLS7NmzVbJkSa1bt+6GCzHGqF+/fmrQoIGio6Oz7DNmzBiNGDHihvcBAAAA3ClG2GzuLkGSNMwYd5fgtByfCTpw4IBee+01+fr6KjU1VbVq1VLDhg2VkpKijRs3ymazqUGDBjdcSK9evfTbb7/pyy+/zLbP4MGDdfr0afvjRidkAAAAAGBdOQ5BISEhatOmjcaMGSM/Pz8lJCTohRdekM1m00svvaTAwEA1btz4hop44YUXNH/+fC1fvlwlS5bMtp+vr68CAwMdHgAAAADgjBu+WWpQUJDat28vb29v/fDDD0pKSlKPHj2c2oYxRr169dKcOXP0ww8/KCoq6kbLAQAAAIAcyfE1QVf67bffVKJECUlSRESEvL29VaxYMXXo0MGp7fTs2VNffPGFvvnmGxUoUECHDh2SdDlg5c+f/0ZKAwAAAIBruqEQFB4ebv/3li1bbnjnkydPliQ1adLEoX369Ol68sknb3i7AAAAAJCdGwpBrmLy4EwSAAAAAPK2G74mCAAAAADyIkIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFEIQAAAAAEshBAEAAACwFLeGoB9//FFt2rRRWFiYbDab5s2b585yAAAAAFiAW0PQ+fPnVbVqVX3wwQfuLAMAAACAhXi5c+dxcXGKi4vLcf/k5GQlJyfbn585cyY3ygIAAABwB8tT1wSNGTNGQUFB9kd4eLi7SwIAAACQx+SpEDR48GCdPn3a/ti3b5+7SwIAAACQx7h1OJyzfH195evr6+4yAAAAAORheepMEAAAAADcLEIQAAAAAEtx63C4c+fO6Y8//rA/T0pKUmJiogoXLqxSpUq5sTIAAAAAdyq3hqD169eradOm9uf9+vWTJHXt2lUzZsxwU1UAAAAA7mRuDUFNmjSRMcadJQAAAACwGK4JAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAluL2EDRp0iRFRUUpX758qlmzpn766Sd3lwQAAADgDubWEDR79mz17dtXr776qjZt2qSGDRsqLi5Oe/fudWdZAAAAAO5gbg1B77zzjp566ik9/fTTqlSpkiZMmKDw8HBNnjzZnWUBAAAAuIN5uWvHKSkp2rBhgwYNGuTQft999+mXX37Jcp3k5GQlJyfbn58+fVqSdObMmdwr1AkX3V3A/3e7vB4AkFO3w+cnn50A8prb4bNTun0+PzPqMMZct6/bQtCxY8eUlpam0NBQh/bQ0FAdOnQoy3XGjBmjESNGZGoPDw/PlRrzqjeDgtxdAgDkOXx2AsCNud0+P8+ePaug69TkthCUwWazOTw3xmRqyzB48GD169fP/jw9PV0nTpxQcHBwtuvkJWfOnFF4eLj27dunwMBAd5cDAHkGn58A4Lw77bPTGKOzZ88qLCzsun3dFoJCQkLk6emZ6azPkSNHMp0dyuDr6ytfX1+HtoIFC+ZWiW4TGBh4R7wRAeBW4/MTAJx3J312Xu8MUAa3TYzg4+OjmjVraunSpQ7tS5cuVb169dxUFQAAAIA7nVuHw/Xr109dunRRrVq1VLduXU2dOlV79+7Vv/71L3eWBQAAAOAO5tYQ1KFDBx0/flwjR47UwYMHFR0drW+//VYRERHuLMttfH19NWzYsExD/gAA18bnJwA4z8qfnTaTkznkAAAAAOAO4dabpQIAAADArUYIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgDkaUxyCgBwllvvEwQpLS1Nnp6e7i4DAPKU8+fPKz09XcYYBQYGurscAMgzTpw4oSNHjsjT01MRERHy8fFxd0luwZkgN/r99981YcIEHTx40N2lAECesW3bNj388MNq3LixKlWqpJkzZ0rijBAAXM+WLVt07733qn379oqJidHYsWOVlpbm7rLcgjNBbvLHH3+obt26OnnypI4fP65+/fopJCTE3WUBwG1t27ZtatSokZ544gnVrl1b69evV7du3VSlShVVq1bN3eUBwG1r27ZtatKkibp166Zu3bpp8eLFGjBggLp27arw8HB3l3fL2Qx/Orvlzp8/r969eys9PV21atXSCy+8oJdeekkvv/wyQQgAsnHixAk99thjqlixoiZOnGhvv+eeexQTE6OJEyfKGCObzebGKgHg9nPs2DE98sgjql69uiZMmCDp8tnzli1baujQocqfP7+Cg4MtFYY4E+QGHh4eqlmzpoKDg9WhQwcVKVJEHTt2lCSCEABk49KlSzp16pQeffRRSVJ6ero8PDxUunRpHT9+XJIIQACQBZvNpvvvv9/++SlJo0aN0pIlS3To0CEdO3ZMVapU0WuvvaYGDRq4sdJbhxDkBvnz51fXrl3l7+8vSWrfvr2MMXrsscdkjNGgQYMUHBys9PR0/fXXX4qKinJzxQDgfqGhofr8889Vrlw5SZcnlvHw8FCJEiWUlJTk0PfcuXMKCAhwR5kAcNsJDg5Wr169VKBAAUnSrFmzNGzYMH355Zdq3ry5tmzZogEDBmjZsmWEIOSujACU8Uu8Q4cOMsaoU6dOstls6tu3r8aPH6+//vpLn332mfz8/NxcMQC4X0YASk9Pl7e3t6TLn6OHDx+29xkzZox8fX3Vu3dveXnxaw4AJNkDkCTVrVtX69evV40aNSRJjRo1UmhoqDZs2OCu8m45fju4maenp4wxSk9PV8eOHWWz2dSlSxfNnz9fu3fvVkJCAgEIAK7i4eFhv/7HZrPZbzUwdOhQjRo1Sps2bSIAAUA2IiIiFBERIenytUEpKSkKCAhQdHS0myu7dZgi+zaQ8UvcGKMOHTqoYcOGOnr0qDZu3MhsRwCQjYx5fTw9PRUeHq7x48dr7NixWr9+vapWrerm6gAgb7DZbBo9erRWrVqldu3aubucW4Y/k90mbDab0tLSNGDAAC1fvlyJiYmKiYlxd1kAcNvy8Lj8dzxvb29NmzZNgYGB+vnnn+3DOwAA1/bVV19pxYoVmjVrlpYuXWofcmwFnAm6zVSpUkUbN25UbGysu0sBgDyhRYsWkqRffvlFtWrVcnM1AJB3VKpUSUePHtWPP/6o6tWru7ucW4r7BN1muMcFADjv/Pnz9glnAAA5d+nSJftEM1ZCCAIAAABgKQyHAwAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAgAAAGAphCAAAAAAlkIIAoA7xI4dO3T33XcrX758qlatmrvLcQmbzaZ58+bd9HYiIyM1YcKEm96OM/bs2SObzabExMRbul8AwPURggDgFjt69Ki8vb114cIFpaamyt/fX3v37r3p7Q4bNkz+/v7auXOnli1blm2/Q4cO6YUXXlDp0qXl6+ur8PBwtWnT5prr5BUzZsxQwYIFM7UnJCTo2WefvfUFXUeTJk3Ut29fd5cBAJbj5e4CAMBqVq9erWrVqsnPz09r165V4cKFVapUqZve7u7du9WqVStFRERk22fPnj2qX7++ChYsqLFjxyo2NlaXLl3SkiVL1LNnT+3YseOm67gdFSlSxN0l5BkpKSny8fFxdxkAkKs4EwQAt9gvv/yi+vXrS5J+/vln+7+vJT09XSNHjlTJkiXl6+uratWqKT4+3r7cZrNpw4YNGjlypGw2m4YPH57ldnr06CGbzaZ169bp0UcfVfny5VWlShX169dPa9assffbu3evHnjgAQUEBCgwMFDt27fX4cOH7cuHDx+uatWq6bPPPlNkZKSCgoLUsWNHnT17VpI0ZcoUlShRQunp6Q77b9u2rbp27Wp/PnnyZJUpU0Y+Pj6qUKGCPvvss2xfgxUrVshms+nUqVP2tsTERNlsNu3Zs0crVqxQt27ddPr0adlsNofX4erhcDd7fJIUHx+vBg0aqGDBggoODlbr1q21e/fubOu/EQMHDlT58uXl5+en0qVLa8iQIbp06ZKky4HWw8ND69evd1jn/fffV0REhIwxkqRt27apZcuWCggIUGhoqLp06aJjx47Z+zdp0kS9evVSv379FBISoubNm9tfg1KlSsnX11dhYWHq3bu3S48NANyJEAQAt8DevXtVsGBBFSxYUO+8846mTJmiggUL6pVXXtG8efNUsGBB9ejRI9v1J06cqLffflvjx4/Xb7/9phYtWqht27batWuXJOngwYOqUqWK+vfvr4MHD+qll17KtI0TJ04oPj5ePXv2lL+/f6blGcPIjDF68MEHdeLECa1cuVJLly7V7t271aFDB4f+u3fv1rx587Rw4UItXLhQK1eu1JtvvilJateunY4dO6bly5fb+588eVJLlixR586dJUlz585Vnz591L9/f23ZskXPPfecunXr5rCOM+rVq6cJEyYoMDBQBw8ezPZ1cMXxSdL58+fVr18/JSQkaNmyZfLw8NBDDz2UKfjdjAIFCmjGjBnatm2bJk6cqGnTpundd9+VdDnY3XvvvZo+fbrDOtOnT9eTTz4pm82mgwcPqnHjxqpWrZrWr1+v+Ph4HT58WO3bt3dY55NPPpGXl5dWrVqlKVOm6KuvvtK7776rKVOmaNeuXZo3b55iYmJcdlwA4HYGAJDrLl26ZJKSksyvv/5qvL29TWJiovnjjz9MQECAWblypUlKSjJHjx7Ndv2wsDAzevRoh7batWubHj162J9XrVrVDBs2LNttrF271kgyc+bMuWat3333nfH09DR79+61t23dutVIMuvWrTPGGDNs2DDj5+dnzpw5Y+8zYMAAU6dOHfvztm3bmu7du9ufT5kyxRQrVsykpqYaY4ypV6+eeeaZZxz23a5dO9OyZUv7c0lm7ty5xhhjli9fbiSZkydP2pdv2rTJSDJJSUnGGGOmT59ugoKCMh1TRESEeffdd116fFc7cuSIkWQ2b95sjDEmKSnJSDKbNm3Kdp3GjRubPn36ZLv8amPHjjU1a9a0P589e7YpVKiQuXjxojHGmMTERGOz2eyvx5AhQ8x9993nsI19+/YZSWbnzp32GqpVq+bQ5+233zbly5c3KSkpOa4NAPISzgQBwC3g5eWlyMhI7dixQ7Vr11bVqlV16NAhhYaGqlGjRoqMjFRISEiW6545c0YHDhzINGyufv362r59e45rMP9/eJTNZrtmv+3btys8PFzh4eH2tsqVK6tgwYIO+4uMjFSBAgXsz4sXL64jR47Yn3fu3Flff/21kpOTJUkzZ85Ux44d5enpad/PzR7TjXDV8e3evVudOnVS6dKlFRgYqKioKElyySQXGb766is1aNBAxYoVU0BAgIYMGeKw/QcffFBeXl6aO3euJOnjjz9W06ZNFRkZKUnasGGDli9froCAAPujYsWK9voz1KpVy2G/7dq10z///KPSpUvrmWee0dy5c5Wamuqy4wIAdyMEAcAtUKVKFQUEBKhLly5at26dAgIC1KxZM+3Zs0cBAQGqUqXKdbdxdXgxxlw30FypXLlystls1w0Z2W336nZvb+9M9V05FKxNmzZKT0/XokWLtG/fPv300096/PHHb/iYPDw87H0yZFwf4wxXHt/x48c1bdo0rV27VmvXrpV0eWIBV1izZo06duyouLg4LVy4UJs2bdKrr77qsH0fHx916dJF06dPV0pKir744gt1797dvjw9PV1t2rRRYmKiw2PXrl1q1KiRvd/VwyPDw8O1c+dOffjhh8qfP7969OihRo0a3dDrDQC3I0IQANwC3377rRITE1WsWDF9/vnnSkxMVHR0tCZMmKDExER9++232a4bGBiosLAw/fzzzw7tv/zyiypVqpTjGgoXLqwWLVroww8/1Pnz5zMtz5hwoHLlytq7d6/27dtnX7Zt2zadPn3aqf3lz59fDz/8sGbOnKkvv/xS5cuXV82aNe3LK1Wq5NQxZczwdvDgQXvb1ffg8fHxUVpa2jXrcsXxHT9+XNu3b9drr72mZs2aqVKlSjp58mSO1s2pVatWKSIiQq+++qpq1aqlcuXK6a+//srU7+mnn9b333+vSZMm6dKlS3r44Yfty2rUqKGtW7cqMjJSZcuWdXhkdV3YlfLnz6+2bdvqvffe04oVK7R69Wpt3rzZpccIAO7CFNkAcAtERETo0KFDOnz4sB544AF5eHho27ZtevjhhxUWFnbd9QcMGKBhw4apTJkyqlatmqZPn67ExETNnDnTqTomTZqkevXq6a677tLIkSMVGxur1NRULV26VJMnT9b27dt17733KjY2Vp07d9aECROUmpqqHj16qHHjxpmGTV1P586d1aZNG23dujXTWaABAwaoffv2qlGjhpo1a6YFCxZozpw5+v7777PcVtmyZRUeHq7hw4dr1KhR2rVrl95++22HPpGRkTp37pyWLVumqlWrys/PT35+fg59XHF8hQoVUnBwsKZOnarixYtr7969GjRokBOvzP85evRopjBXrFgxlS1bVnv37tWsWbNUu3ZtLVq0yD7s7UqVKlXS3XffrYEDB6p79+7Knz+/fVnPnj01bdo0PfbYYxowYIBCQkL0xx9/aNasWZo2bZp9aOLVZsyYobS0NNWpU0d+fn767LPPlD9//mtOvw4AeQlnggDgFlmxYoVq166tfPnyae3atSpRokSOApAk9e7dW/3791f//v0VExOj+Ph4zZ8/X+XKlXOqhqioKG3cuFFNmzZV//79FR0drebNm2vZsmWaPHmypMvDvubNm6dChQqpUaNGuvfee1W6dGnNnj3b6WO+5557VLhwYe3cuVOdOnVyWPbggw9q4sSJGjdunKpUqaIpU6Zo+vTpatKkSZbb8vb21pdffqkdO3aoatWqeuuttzRq1CiHPvXq1dO//vUvdejQQUWKFNHYsWMzbccVx+fh4aFZs2Zpw4YNio6O1osvvqhx48bleP0rffHFF6pevbrD46OPPtIDDzygF198Ub169VK1atX0yy+/aMiQIVlu46mnnlJKSorDUDhJCgsL06pVq5SWlqYWLVooOjpaffr0UVBQkH14YVYKFiyoadOmqX79+oqNjdWyZcu0YMECBQcH39AxAsDtxmauHFwNAADynNGjR2vWrFkMVwOAHOJMEAAAedS5c+eUkJCg999/n5uZAoATCEEAAORRvXr1UoMGDdS4ceNMQ+EAANljOBwAAAAAS+FMEAAAAABLIQQBAAAAsBRCEAAAAABLIQQBAAAAsBRCEAAAAABLIQQBAAAAsBRCEAAAAABLIQQBAAAAsJT/B4t+o+VdJmLHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chosen_idx = np.argwhere(np.array(best_accuracies) > 0.90)\n",
    "#print(chosen_idx)\n",
    "\n",
    "ab_ch = []\n",
    "for idx in chosen_idx:\n",
    "    ab_ch.append(best_conv_heights[idx[0]])\n",
    "\n",
    "\n",
    "ch_unique, ch_counts = np.unique(ab_ch, return_counts=True)\n",
    "ch_data = dict(zip(ch_unique, ch_counts))\n",
    "\n",
    "ch_x_values = list(ch_data.keys())\n",
    "ch_y_values = list(ch_data.values())\n",
    "ch_x_values = [str(r) for r in ch_x_values]\n",
    "  \n",
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.bar(ch_x_values, ch_y_values, color ='maroon', \n",
    "        width = 0.1)\n",
    "\n",
    " \n",
    "plt.xlabel(\"# of Convolutional Layers\")\n",
    "plt.ylabel(\"# of instances\")\n",
    "plt.title(\"Convolutional Layer Heights in Trials (acc > 90)\")\n",
    "plt.xticks(ch_x_values,rotation=45)\n",
    "plt.show()\n",
    "\n",
    "ch_select = statistics.mode(sorted(ab_ch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6c3c763a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "former_idx = chosen_idx\n",
    "chosen_idx = np.argwhere(np.array(best_conv_heights) == ch_select)\n",
    "\n",
    "chosen_idx = np.nonzero(np.in1d(chosen_idx, former_idx))[0]\n",
    "\n",
    "ab_hs = []\n",
    "for idx in chosen_idx:\n",
    "    ab_hs.append(best_heights[idx])\n",
    "\n",
    "hs_unique, hs_counts = np.unique(ab_hs, return_counts=True)\n",
    "hs_data = dict(zip(hs_unique, hs_counts))\n",
    "\n",
    "h_x_values = list(hs_data.keys())\n",
    "h_y_values = list(hs_data.values())\n",
    "h_x_values = [str(r) for r in h_x_values]\n",
    "  \n",
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.bar(h_x_values, h_y_values, color ='maroon', \n",
    "        width = 0.1)\n",
    "\n",
    " \n",
    "plt.xlabel(\"# of Hidden Layers\")\n",
    "plt.ylabel(\"# of instances\")\n",
    "plt.title(\"Hidden Layer Heights in Trials (acc > 90, momentum = 1)\")\n",
    "plt.xticks(h_x_values,rotation=45)\n",
    "plt.show()\n",
    "\n",
    "h_select = statistics.mode(sorted(ab_hs))\n",
    "print(h_select)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5a315b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen Trial: 1\n",
      "Final Training Loss: 0.2018817663192749\n",
      "Final Training Accuracy: 0.9389500021934509\n",
      "Final Testing Accuracy: 0.8722000122070312\n",
      "Final Learning Rate: 0.01\n",
      "Final Momentum: 0.2\n",
      "Final Hidden Layer Width: 49\n",
      "Final Hidden Layer Height: 1\n",
      "Final Convolutional Layer Height: 1\n",
      "Final Convolutional Layer Filters: 32\n",
      "Final Batch Size: 16\n",
      "\n"
     ]
    }
   ],
   "source": [
    "former_idx = chosen_idx\n",
    "chosen_idx = np.argwhere(np.array(best_heights) == h_select)\n",
    "\n",
    "chosen_idx = np.nonzero(np.in1d(chosen_idx, former_idx))[0]\n",
    "\n",
    "test_accuracies = []\n",
    "for score in best_scores:\n",
    "    test_accuracies.append(score[-1])\n",
    "\n",
    "#print(best_loss)\n",
    "idx = chosen_idx[0]\n",
    "print(\"Chosen Trial: \" + str(idx+1))\n",
    "print(\"Final Training Loss: \" + str(best_loss[idx]))\n",
    "print(\"Final Training Accuracy: \" + str(best_histories[idx].history['accuracy'][-1]))\n",
    "print(\"Final Testing Accuracy: \" + str(test_accuracies[idx]))\n",
    "print(\"Final Learning Rate: \" + str(best_lrs[idx]))\n",
    "print(\"Final Momentum: \" + str(best_ms[idx]))\n",
    "print(\"Final Hidden Layer Width: \" + str(best_widths[idx]))\n",
    "print(\"Final Hidden Layer Height: \" + str(best_heights[idx]))\n",
    "print(\"Final Convolutional Layer Height: \" + str(best_conv_heights[idx]))\n",
    "print(\"Final Convolutional Layer Filters: \" + str(best_conv_filters[idx]))\n",
    "print(\"Final Batch Size: \" + str(best_batch_sizes[idx]))\n",
    "print()\n",
    " \n",
    "#ab_lhs = []\n",
    "#for idx in chosen_idx:\n",
    "#    ab_lhs.append(best_heights[idx])\n",
    "#\n",
    "#lhs_unique, lhs_counts = np.unique(ab_lhs, return_counts=True)\n",
    "#lhs_data = dict(zip(lhs_unique, lhs_counts))\n",
    "#\n",
    "#lh_x_values = list(lhs_data.keys())\n",
    "#lh_y_values = list(lhs_data.values())\n",
    "#lh_x_values = [str(r) for r in lh_x_values]\n",
    "  \n",
    "#fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "#plt.bar(lh_x_values, lh_y_values, color ='maroon', \n",
    "#        width = 0.1)\n",
    "\n",
    " \n",
    "#plt.xlabel(\"Heights\")\n",
    "#plt.ylabel(\"# of instances\")\n",
    "#plt.title(\"Layer Heights in Trials\")\n",
    "#plt.xticks(lh_x_values,rotation=45)\n",
    "#plt.show()\n",
    "\n",
    "#lh_select = statistics.mode(sorted(ab_lhs))\n",
    "#print(lh_select)\n",
    "\n",
    "#print(lhs_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "133d3e27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "3750/3750 [==============================] - 13s 3ms/step - loss: 0.4141 - accuracy: 0.8741\n",
      "Epoch 2/3\n",
      "3750/3750 [==============================] - 13s 3ms/step - loss: 0.1903 - accuracy: 0.9441\n",
      "Epoch 3/3\n",
      "3750/3750 [==============================] - 13s 3ms/step - loss: 0.1321 - accuracy: 0.9625\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameters\n",
    "layer_widths = best_widths[idx]\n",
    "layer_heights = best_heights[idx]\n",
    "learning_rates = best_lrs[idx]\n",
    "momentums = best_ms[idx]\n",
    "batch_sizes = best_batch_sizes[idx]\n",
    "conv_heights = best_conv_heights[idx]\n",
    "number_filters = best_conv_filters[idx]\n",
    "    \n",
    "# Model Construction\n",
    "clear_session()\n",
    "count = 0\n",
    "model = Sequential()\n",
    "for i in range(conv_heights):\n",
    "    model.add(Conv2D(number_filters,(3,3), activation='relu', kernel_initializer='he_uniform', input_shape=(28,28,1)))\n",
    "    model.add(MaxPooling2D((2,2)))\n",
    "model.add(Flatten())\n",
    "for i in range(layer_heights):\n",
    "    model.add(Dense(layer_widths, activation='relu', kernel_initializer='he_uniform'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "#model.summary()\n",
    "\n",
    "# Model Compilation\n",
    "opt = SGD(learning_rate=learning_rates, momentum=momentums)\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Model Training\n",
    "callback = EarlyStopping(monitor='loss', patience=2, min_delta=0.01)\n",
    "history = model.fit(X_train, Y_train,\n",
    "                batch_size=batch_sizes, epochs=10,\n",
    "                verbose=1,callbacks=[callback])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5bf0b53a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='lower right')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "39a61736",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.3230 - accuracy: 0.9063\n",
      "Test Accuracy:  0.9063000082969666\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(X_test, Y_test)\n",
    "print(\"Test Accuracy: \", scores[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6b4e39d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step\n",
      "Accuracy: 0.9063\n",
      "Weighted Precision: 0.9070488773536727\n",
      "Weighted Recall: 0.9063\n",
      "Weighted F1 Score: 0.9062789793747337\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAGwCAYAAACuFMx9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACqSUlEQVR4nOzdd3wT9f/A8VeStmnTvReFttCyWhQBQVBBEfwJCMpXtgNEQZYiUxARUBkqQ0VBEYWvioADHF8VqgIuVhkyChTo3rtN2zRpxu+PSEtokZZmQT/PxyMPyOUu9+7d5d73GXcficFgMCAIgiAIwk1LausABEEQBEGwLJHsBUEQBOEmJ5K9IAiCINzkRLIXBEEQhJucSPaCIAiCcJMTyV4QBEEQbnIi2QuCIAjCTc7B1gE0hV6vJysrC3d3dyQSia3DEQRBEBrJYDCgVCoJCQlBKrVc+bOqqgqNRtPk73FycsLZ2dkMEVnXDZ3ss7KyCAsLs3UYgiAIQhOlp6fTokULi3x3VVUVEa3cyMnTNfm7goKCSE5OvuES/g2d7N3d3QFIiA/C3c1+WiQea9fN1iHUZY81H3b48EaJXG7rEOrS2992MlQ3vYRkbhJHJ1uHUIc9bid7o6WaP/ih5nxuCRqNhpw8HalHwvFwv/5cUabU06pLChqNRiR7a7pUde/uJm3SDjQ3B4mjrUOoyx6TPfaXxCR2ue/sbzsZ7DAme9x39rid7M4/m8gaTbFu7hLc3K9/PXrs8TzaMDd0shcEQRCEhtIZ9OiacP2lM+jNF4yViWQvCIIgNAt6DOibUKPYlGVtzX7qvgVBEARBsAhRshcEQRCaBT16mlIR37SlbUske0EQBKFZ0BkM6JpwF1BTlrU1UY0vCIIgCDc5UbIXBEEQmoXm3EFPJHtBEAShWdBjQNdMk72oxhcEQRCEm5wo2QuCIAjNgqjGv0mpyqV8/kYYB3/yoazAkYiYCp5cnEKbWysA2LayBX9860thlhMOTgYiYysYPSed6NvKTb7n3BE3tqwI4/wxN2SOBiI6VPLiJ2eQu5h/xw96vICBjxcSGGZ8pnbqOWc+Wx1I/B4Ps6/reo2YmsuT87LZ8aEf61+2zMAVDTXoiQKGTcrHJ6Ca1ERn1i8M4dQhN6use8SkLHrdX0yL1io0VVISjrrx0YowMpJcLpvLwKPPZfLAqHzcPLWcO+7GuwtbkXpeYZGYYm5X8sjEbKJiK/ENrGbx023Yv9u75vOZbybRb1ihyTJnjrry/MMdLBJPfUZMzaXXgFLC2qiN2y1ewcbXgsm4aL1njd8I28kezwX2sO+aojn3xr+pk/17s1uTds6FZ9+6gE+ght++9mfxqPas+fVvfIOrCYlU8dSryQS2NB64328I5pUx7Vj7x3E8fbWAMdG/+mg7Hp6SxfhXUnBwNJCaoMBSIzHmZzvy0dJgslKMA7L0G1bEoo9TmNI/mtRE2/+gom+pZMCYQpISbB9L78HFPLM4i7XzQzl9yJWBjxXy6mfJPN2nLfmZlh8UJba7ku8+CSDxhCtSBxg7M53X/nuOCf1iUatkAAybmM3D43NYNTuSjGRnRk3NYukn53iqbydUFTKzx+Ss0JF8RkHcF3689P7Feuc5vNeTVbMiat5Xa6z7vO9Od1Tw3SY/Eo8rkDkYGDs3m6WfJ/F077Y1283SboTtZI/nAnvYd8L1sXmb/XvvvUdERATOzs506dKF33//3Szfq1ZJOPCDD4+/mEbHHkqCI9SMmJlBQJiaXZ8EAnDXw4XcclcZQa3UtGyrYuzLqVQqHUg9U1vq+nhRKwY8mcPQqVm0bKsiJLKKOwYV4Si3zBXewThPDv/qQWaSnMwkOZtWBFNVIaVdlwqLrK8xnBU65q5NZc2cMJQltv9hD51QwK7Pffhpiy/pF5xZ/3Io+VmODHq88NoLm8GCsW2J+8qf1PMKks8oWDUnksBQDVGxl/aVgYefzGXruyH8ucuH1EQFK2dFInfRc89gy8QYv9eLzW+24M+ffK46T7VaQnG+Y82rvNS61/wvjokkbrsPqYnOJCW4sPL5lgS2qCaqk8pqMdwI28kezwX2sO+aQm+G143Kpsl+27ZtTJ8+nRdffJFjx45x11138cADD5CWltbk79brJOh1EhzlprvHyVnP2UN1q8GqNRLiPgtA4aElvEMlAKUFDpw/5o6nbzXzh3TkyVtv46X/dODMIcsNxXg5qdRA7yHFyBV6zsS7WmWd/2bq0gwO/eLBsd+t8/f/GwdHPVGdKjmyzzSWI/vc6dDVNidDhbtxrGxliTEpBIWp8Qmo5ujvnjXzVGuknDzoTvsuSpvECNCph5KtR47x4Z4TPLc8GU/fapvFAuDqcWm72f4C8nL2tJ3s7Vxwib3uu6vR/dMbvymvG5VNq/FXrVrF+PHjeeqppwBYs2YNu3btYt26dSxbtqxJ3+3ipqdtFyVfrmlBizbn8fSv5o+dfpw/5kZwRFXNfPE/e7F6chRqlRTvgGpe3nIGDx9jFX5uqrGqbNuqFjzxUhrhHSvY96U/i0a2Z/XPJwiJrKp33U0V3k7Fmu8u4CTXo6qQsmR8OGnnbVtt3ntwMW1iVEwbGG3TOC7x8NEhc4CSAtNDuCTfAe8ArQ0iMjBxQRqnDruRmmisGfL2NyaH4gLToVeLCxwJDFVbPUIwVk3//oMPuRlOBIVpeHxmBis+P8e0QR2o1tji2t/AhEVZnDroSuo5l2vPbiX2sp3s8VxQyz733b/RGWjiqHfmi8XabJbsNRoNR44c4YUXXjCZ3r9/f/766696l1Gr1ajVtSfJsrKyf13Hs29d4N2ZrXm6axekMgORMRXc9VABSadqr4xjepbx5q4TKIscidsSwMpJUSz/7hSeflr0/+zY/o/mce+IfAAiY1I58YcHv27z59F56dfzp19TxkU5k/tF4+qh486Bpcx6K43ZQ9vY7EfuH6Jh0pJM5o9uTbXa5i0/Jq7sLyORgC0uvqcsSSWiXSUzh9XTgaueGA0G24yL/dv3vjX/T01UcP6kgs1/nuD2e0v+tUrbUqYszSSivYqZD7Wx+rr/jb1sJ3s7F1zOXvedUD+bJfuCggJ0Oh2BgYEm0wMDA8nJyal3mWXLlrF48eIGryMoXM0rXyVQVSlFpZThHVjNyklRBITVXjA4K/QER6gJjlAT3aWcKXfewi9bAxg6NQvvAGPJrEWUaXtUi6gqCjLlDY6jsbTV0ppOOedPKGh7ayUPPZXP23PDLLbOf9MmthJvfy1rfzxXM03mALE9Khg8toBBEbeg11s3eZUVydBpwdvftBTv6aelON+6h/WkRSn06FvCrBHtKcip7RhYnG8s0Xv7V1OUXzvdy7ea4gL76BtblOdEXqYTIeHWr2mY/GoGd/QvY+bDrSnItnyHyqaw1Xayt3PBJTfSvrtcU9vdRZt9E0gkpknCYDDUmXbJvHnzKC0trXmlpzesZO2s0OMdWE15iYzj+zzp1r/46jMbJDWl14AwNT6BGrKSTK+is5Oc8W9h3R+9o5Pt6o+O/+HOhHvbMql/7evccRd+3eHNpP5trZ7owXgSPH9CwW13m7Z933a3kgSrtWkamLw4hV73FzN3TDtyM0wvAHPS5RTlOdL5rtoaKAdHPbHdlZw5Yvt+DwDuXlr8gzUU5Tlee2azMTDltQx6PVDKnGGtyU233IWzudhmO9XPlueCG3HfXU6PBF0TXnpsUyNnDjYrXvj5+SGTyeqU4vPy8uqU9i+Ry+XI5Q0/uI7t9QQDhLSuIifFmf++2pLQyCruHZFPVaWUr94OpVu/YrwCNZQXO/DT5iAKc5y4Y5Cxp7REAkMmZbFtZQvC21cS3rGCvV/6k3nBhVnvJ17/H/8vxr2QzeFf3cnPcsLFTUefISV06lnOgjGRFllfQ6gqZHXa5KoqpSiL6063pq8/8GP22+kknnDhTLwrAx4tJCC0mv/91/faC5vBlCWp3DOkkMUTolCVS/H2M94PXaF0QKOWAhJ2fBTIyMlZZCXLyUxxZuTkLNQqKXu+tUyMzgqdSekzKExNZIdKlCUylCUOPPp8Jn/+6ENRniOBLdSMnZNBabEDf+3y/pdvNa+pSzO55+FiFo2LMG63f/o2VChlaKqsU/64EbaTPZ4L7GHfCdfHZsneycmJLl26EBcXx8MPP1wzPS4ujiFDhphlHZVKGZ8tb0lhthNuXlp6PFDE6LnpODga0Osg84ILe7/wp6zYAXdvLW1uKefVr07Tsm1ttf2gp3LQVEn5eHErykscCO9QycLPzxBkoeo8L38ts99JwydAS6VSRvIZZxaMieTob/ZRErQn+771xt1bx5jnc/EJ0JJ6zpkFj0aQZ4V77AEefCwPgDe2njWZvnJWBHFf+QPwxfvByJ31TH0lFTdPLWePuzH/8bYWucceILpTBa9vq21umbjQWPsV94Uv77wYTkRbFfcNPY+rh46iPEdO7Hdn6ZTWFounPg+ONV5Mv/m16f3tb04PI267ddrDb4TtZI/nAnvYd02hN1DTF+t6l79RSQwG2z0SaNu2bTz22GOsX7+eO+64gw8++IANGzZw+vRpWrVqdc3ly8rK8PT0JP1sCB7u9nNV+Z8WPWwdQl1XaRqxKTt8GpWkETVHVmOHZxhDtcbWIdQhcbS/tmN73E72RmuoZi/fUFpaioeHZZ4OeClXHDwdhFsTckW5Uk/3jjkWjdVSbNpLaMSIERQWFrJkyRKys7OJiYnhhx9+aFCiFwRBEAShYWzeJXjy5MlMnjzZ1mEIgiAIN7lLHe2asvyNyubJXhAEQRCsQW+QoG/CMy6asqyt2U9DtyAIgiAIFiFK9oIgCEKzIKrxBUEQBOEmp0OKrgkV2jozxmJtItkLgiAIzYKhiW32thrTwhxEm70gCIIg3OREyV4QBEFoFkSbvSAIgiDc5HQGKTpDE9rs7e9hlg0mqvEFQRAE4SYnSvaCIAhCs6BHgr4JZVw9N27RXiR7QRAEoVkQbfY3uMfa346DxNHWYdRYkXzA1iHU8UK7u20dQh0GtWWGCW4Snf3dSWvQam0dQh0yb+uN695QupISW4dQlx2ONilxsq/RASUGKdjhqeBmc1Mke0EQBEG4lqZ30BPV+IIgCIJg14xt9k0YCOcGrsYXvfEFQRAE4SYnSvaCIAhCs6Bv4rPxRW98QRAEQbBzos1eEARBEG5yeqTN9j570WYvCIIgCDc5UbIXBEEQmgWdQYKuCcPUNmVZWxPJXhAEQWgWdE3soKcT1fiCIAiCINgrUbIXBEEQmgW9QYq+Cb3x9aI3viAIgiDYt+Zcjd/sk71vkIbx87Ppdm8ZTs56MpPkrJrZkgsnFWZfl04LP69pwbFvfFHmO+ERoKHLIwXcOzUT6T/Hn7pCyo8rwjgd50NlsQPeLdT0GpvDHY/m1fk+gwE+GteWxH1ePP5+Ih37F5slzhGTsuh1fzEtWqvQVElJOOrGRyvCyEhyuXztPPpcJg+MysfNU8u54268u7AVqefNv92uJqZ7OcMm5xMVW4lvkJZFT4az/ydP663/diWPPJNrXH9gNYufas3+3V4AyBwMPDE7k273lBLcUkOFUsaxP9z5aHkoRbnWG4hk0OMFDHy8kMAwDQCp55z5bHUg8Xs8rBbD5YY/lcrY55PZ+UkoHyyPAuD5187Q76Fck/nO/u3OjNFdbBEiACOm5vLkvGx2fOjH+pdb2CwOa56f6hNzexmPTMghKqbCeIxPiGJ/XO0gSF5+1Yyfm85td5Xi6qHj1CF33lvUiqwUZ6vEJzRcs072bp5aVu08z4m/3FnwaCQlBQ4Eh2uoKJNZZH371odwYEsAw99MIjC6kowTbnwxJxJndy13jjOe7L57pRVJBzwYufoC3i3UnP/Nk50LI/AIqK6TzP/4KMgig2rFdlfy3ScBJJ5wReoAY2em89p/zzGhXyxqlXHbDJuYzcPjc1g1O5KMZGdGTc1i6SfneKpvJ1QVltl+V3JW6Ek67czurd4s3JhqlXVeuf7kBBfitvvy0gdJJp/JXfS0ialky9vBJCcocPPUMvHlDBZtvMizg9pbLcb8bEc+WhpMVoocgH7Dilj0cQpT+keTmmjdE3JUTBn/NyybpHOudT6L/92H1Qva1ryvrrZdd6LoWyoZMKaQpATbJixrn5/q4+yiJ/mMgrgv/Hhp/YUrPjXw8vuJaLVSFk+IorJcxtDxOSz79KzJucKe6Glaj3q9+UKxOpsm+99++4033niDI0eOkJ2dzY4dO3jooYestv7hk/MoyHJi5YyWNdNyM+QWW1/qMTc69Cum/b0lAPi0KOLv73zJOOEGGJN92jE3bhuaT+seSgC6j87n4OeBZJx0NUn2WQkKft8YxLRvTvPq7beZNc4FY9uavF81J5JtR44RFVvBqUMegIGHn8xl67sh/LnLB4CVsyL5/PAx7hlcyA+fB5g1nquJ3+NxWQnV+sk+fq8n8Xvrr0moVMqYPybaZNq6hWG8/f1Z/EM05GdZp3R/MM40vk0rghn0eCHtulRYNdk7K7TMWXGGt1+OZuTEuvuqWiOhuMByv72GclbomLs2lTVzwhj1bI5NY7H2+ak+8fu8iN/nVe9noRFVtL+tgon9Y2pq9Na+FM7W+KPcM7iQn7ZZ5zzQGE1/qM6N26fdppFXVFRwyy23sHbtWpusv0f/UhJPKHjx/WS2/X2Kd3ed44HRhRZbX3hXJRf/9CQ/yXiSzUpQkHLYnXb3lJjMc+YXb0pzHDEY4OJ+D/KTnYm+u3YejUrK58+1YciiVNz9qy0W7yUKd+MY78oS47VhUJgan4Bqjv5em0iqNVJOHnSnfRelxeO5Ubl66NDrsWrJ7HJSqYHeQ4qRK/Scia9burakyQvOc+g3X44f8Kn389huJWz57U82/O8gzy4+h6ePxqrxXTJ1aQaHfvHg2O/uNln/5ax9fmosRydj+7VGXZtG9HoJ2mopHbuW2yos4SpsWrJ/4IEHeOCBB2y2/uCWGgY9VsDXG/zZ+nYgbTtXMmlJBtUaCT9/Wf9JqSn6PJNNldKBlfd1QiIzYNBJuH9WBrcOrv0BD345la/mRbD0jtuQOuiRSOGRZclEdKv98Xz3Skta3aY0Wxv9vzMwcUEapw67kZpovHr3/ucCo7jA0WTO4gJHAkPVVojpxuMo1zPuhUz27vShsty6yT68nYo1313ASa5HVSFlyfhw0s5br1R/9wO5tGlfznMj6q+BOvK7L3/sCiAvS05giyoem5bMso+O8+ywrmitWJ3fe3AxbWJUTBsYfe2ZrcDa56fGSr/oTG6GE+PmZPD2/HCqVFKGjs/BJ6AanwDbXKxdS9OfjX/jluxvqDZ7tVqNWl2bTMrKypr0fRIpnD/hwsfLQwC4eFpBq+gqBj5eYJEf09/f+3Bspy8j37pAYJSK7ARXvnulJR6BGrr8pwCAPzcFknbMjSc2nMM7VE3yIQ92LAzHPUBD1J1lJMR5cXG/J899f9Ls8dVnypJUItpVMnNYh7ofXtExVSIBww38hClLkTkYmLc2CanEwNoFLa+9gJllXJQzuV80rh467hxYyqy30pg9tI1VEr5fUBUTX7jAggm3UK2p/yLnt59qq3tTL7hx/pQ7m34+wO29C/nrZ3+LxwjgH6Jh0pJM5o9uTbXaPk7o1j4/NZZOK+WVSVE8vyKZL/8+ik4Lx/705NAe63WSbazmPJ79DZXsly1bxuLFi832fUV5DnXaLdMvOHPngFKzreNyPyxrSZ9nsrn1wSIAgtupKM50Ys97IXT5TwHVVRJ2vRnGY+vP17TrB7dXkZWg4LcNwUTdWcaF/R4UpcpZdEtXk+/+ZFIUEd2UTNx6xmzxTlqUQo++Jcwa0Z6CnNo25uJ8Y4ne27+aovza6V6+1RQX3FCHlMXJHAzMfy+JoDANc0dGW71UD6CtltZ00Dt/QkHbWyt56Kl83p4bZvF1R3VQ4u1Xzdvb42umyRwgpmspD47KZEjn3uj1pifQ4gI5eVnOhLRSWTy+S9rEVuLtr2Xtj+dM4oztUcHgsQUMirilTpyWZu3z0/W4cMqVKQNjULhrcXQ0UFrkyJodpzl/0rrNRA3VnEv2N1Tk8+bNo7S0tOaVnp7epO9LOOxKWGvTaufQSDV5mY5XWaJpqlVSJFLT4rBUBoZ/unjqqqXoquvOI5EZMPxzorlnUjbTfzzJc/+rfQE8uCCVYW+Y9gi/fgYmL06h1/3FzB3Trk6noJx0OUV5jnS+q7ZmxcFRT2x3JWeO2L6t015cSvShEVXMGx1V0+fBHlxqb7W04we8mTSkK1P/U/tKPOXO3u8DmfqfrvUmUHfPavyDqkwuJC0e5x/uTLi3LZP6177OHXfh1x3eTOrf1uqJHqx/fmqKSqUDpUWOhIRXERVbYXJ7XnOm1WpZsGABERERuLi4EBkZyZIlS9Dra/v1GwwGFi1aREhICC4uLvTp04fTp0+bfI9arWbatGn4+fnh6urK4MGDycjIaFQs9nP2aQC5XI5cbr7eqF9vCGD1N4mMnJbLb9950fZW4y03a+ZY5r7a9n1L+PXdULxCNARGV5J12pXfNwbRdVg+AM7uOiK7l/HDspY4OqfgHaoh6aA7R7/2Z9ACYw9md//qejvleYVq8AkzT3v5lCWp3DOkkMUTolCVS/H2M7a/VSgd/umMI2HHR4GMnJxFVrKczBRnRk7OQq2SsudbX7PE0BDOCh0hEbVtg0FhGiI7qlCWyMjPtHyicFboCAmv3eZBYWoiO1SiLHGgMNeRBesv0iamkoXj2iCV1fZ1UJbIrNYWPe6FbA7/6k5+lhMubjr6DCmhU89yFoyJtMr6VZUOpF5wM5lWVSmlrNQ43VmhZczkFP6M86co34nA0CqeeC6ZsmJH9v/sZ5UYAVQVMlLPuZhMq6qUoiyuO91arH1+qo+zQkdIq6qa90FhaiLbV6AsdSA/S85dA4ooLXQgL8uJ8HYqJi1MZf9ub5POu/ak6Q/VadyyK1asYP369WzevJmOHTsSHx/PuHHj8PT05LnnngPg9ddfZ9WqVWzatIno6GheffVV+vXrx7lz53B3Nxaepk+fznfffcfWrVvx9fVl5syZDBo0iCNHjiCTNay28IZK9uaW+LeCJU9FMO6FbMZMzyEn3Yn1L4eyZ4dl2sOGLEph16oW7HwpnPJCRzwCNXQflUffZzNr5hn9zgV+fD2MrdPbUFnigHeomvtnpdNjTN2H6ljKg48Z1/XG1rMm01fOiiDuK2Mb6hfvByN31jP1lVTcPLWcPe7G/MfbWu0ee4DoW1S88dXFmvfPLM4CYPc2b1Y+b/m28ehOlby+PbHm/cSXjVfacV/48unqYO7ob6xuXbfLtGllzvBoThywTg2Il7+W2e+k4ROgpVIpI/mMMwvGRHL0N/uogdHrJIRHV9B3cC6uHlqK8534+5AXy2d1QFXZrE9PVj8/1Sc6toLXLzsPTHwpDYC4L/1YOTsSnwANE15Mw8uvmqJ8R3752o8t74RYLb7G0hsk6Jtyn30jl92/fz9Dhgxh4MCBAISHh/P5558TH29s1jIYDKxZs4YXX3yRoUOHArB582YCAwPZsmULEydOpLS0lI0bN/LJJ59w3333AfDpp58SFhbGzz//zP3339+gWCQGg+0e9lteXs6FC8YHNXTu3JlVq1Zxzz334OPjQ8uW1z5Zl5WV4enpSR/JQzhI7Kdqa0XSAVuHUMcL7e62dQh1GNT213Nf4mB/Ccag1do6hDpk3vZXTasrKbF1CDcEiZP1mkcaQmuoZo96O6WlpXh4WObJjpdyxeuH78LF7fp/46pyLXO6/U56erpJrFerdV6+fDnr169n9+7dREdH8/fff9O/f3/WrFnDqFGjSEpKonXr1hw9epTOnTvXLDdkyBC8vLzYvHkzv/76K3379qWoqAjvy353t9xyCw899FCD+7HZ9MwWHx/PPffcU/N+xowZADzxxBNs2rTJRlEJgiAINyN9E6vxLz1UJyzMtHPryy+/zKJFi+rMP3fuXEpLS2nXrh0ymQydTsdrr73GqFGjAMjJMT64KTAw0GS5wMBAUlNTa+ZxcnIySfSX5rm0fEPYNNn36dMHG1YsCIIgCM1I00e9My5bX8m+Ptu2bePTTz9ly5YtdOzYkePHjzN9+nRCQkJ44oknauaTXPHcc4PBUGfalRoyz+Xsr85SEARBEOyYh4dHg5ocZs+ezQsvvMDIkSMBiI2NJTU1lWXLlvHEE08QFBQEGEvvwcHBNcvl5eXVlPaDgoLQaDQUFxeblO7z8vLo2bNng2O+oW69EwRBEITrpUPS5FdjVFZWIpWaplmZTFZz611ERARBQUHExcXVfK7RaNi3b19NIu/SpQuOjo4m82RnZ3Pq1KlGJXtRshcEQRCaBXNV4zfUgw8+yGuvvUbLli3p2LEjx44dY9WqVTz55JOAsfp++vTpLF26lKioKKKioli6dCkKhYLRo0cD4Onpyfjx45k5cya+vr74+Pgwa9YsYmNja3rnN4RI9oIgCIJgAe+88w4vvfQSkydPJi8vj5CQECZOnMjChQtr5pkzZw4qlYrJkydTXFxM9+7d2b17d8099gCrV6/GwcGB4cOHo1Kp6Nu3L5s2bWrwPfZg41vvmkrcetdw4ta7hhG33jWMuPXuxtWcb71bePA+nN2uP1dUlVezpPvPFo3VUuzvzCYIgiAIFmDtanx7IpK9IAiC0CyIgXAEQRAEQbhpiZK9IAiC0CwYmjievUGMZy8IgiAI9k1U4wuCIAiCcNO6OUr2EqnxZSfmRvawdQh1zLkQb+sQ6ljZ1f5uB9SVltk6hDrs8XZAfWWlrUOoy47OATUMeltHIFzG2kPc2hP7O4sIgiAIggXomjjqXVOWtbUbN3JBEARBEBpElOwFQRCEZkFU4wuCIAjCTU6PFH0TKrSbsqyt3biRC4IgCILQIKJkLwiCIDQLOoMEXROq4puyrK2JZC8IgiA0C6LNXhAEQRBucoYmjnpnEE/QEwRBEATBXomSvSAIgtAs6JCga8JgNk1Z1tZEshcEQRCaBb2hae3ueoMZg7EyUY0vCIIgCDe5Zl+yd3HV8cTsLHr+XyleftVcPKVg3cstSPzb1WYx+QZpGD8/m273luHkrCczSc6qmS25cFJh9nXptfDnW4EkfOtFRb4DrgFaYv5TTM8peSbjihRekLP39SDSD7piMIBflJoh76ThEVINgFYtYe+yIM5874W2SkrLnuX0X5yJe7DW7DEPfyqVsc8ns/OTUD5YHgXA86+dod9DuSbznf3bnRmju5h9/ZfEdFcy7JlcomJV+AZVs2h8JPt3eV02h4FHZ2QzYHQhbl5azh5z5d0Xw0hNdLFcTLcreeSZXKJiK/ENrGbxU63Zv9sYk8zBwBOzM+l2TynBLTVUKGUc+8Odj5aHUpTrZLGYRkzKotf9xbRorUJTJSXhqBsfrQgjI8nln7j0PDEzk259SghuqTbG9acHH60IoyjPMnHZ47670qMzsnlspukxXZTnwKjOMVZZ/7X2G0Cv+4sYMDqPNjGVePpomTygI0lnbHfuvBZ9EzvoNWVZW2v2yf75N1IJb1vF68+1oijXkXuHFrH88/M8fW8HCnMsdwK8GjdPLat2nufEX+4seDSSkgIHgsM1VJTJLLK+g+/7c/xzHwa8kYFfVBU5J134YW4L5G46uo4rBKA41YnPRkTSaVgxdz6Xi9xdR+FFZ2ROtSN6/fpqMBd+9eDBt9Jw8dKxZ2kwXz0dzuPfXEBqxtCjYsr4v2HZJJ2re0KJ/92H1Qva1ryvrrbsD9NZoScpQcHu7b4s3JBc5/Phk3MZ+nQeK2e0IiPJmdHP5rBsywXG9+6AqsIy+9NZoSc5wYW47b689EGSyWdyFz1tYirZ8nYwyQkK3Dy1THw5g0UbL/LsoPYWiQcgtruS7z4JIPGEK1IHGDszndf+e44J/WJRq2T/xFXBlrUhJJ9R4OahY+LCVBZtSOTZIZZJbPa47+qTctaZF0a2rnmv11mvzfha+w2M2/F0vDu//+DD9OUpVovteumRoG9Cu3tTlrU1myb7ZcuW8fXXX3P27FlcXFzo2bMnK1asoG3bttde2AycnPXcOaCERU+25tRBdwA+XRVCz/tLGfRYAZvfCLFKHJcbPjmPgiwnVs5oWTMtN0NusfVlHlPQ5r4yWt+jBMCzRTVnvisn51Tt1fvvKwOJ7KOkzws5NdO8WlbX/F+tlHLiC28GvplBeK8KAAauSmf9ne1I/dONiLvLzRKrs0LLnBVnePvlaEZOTK3zebVGQnGB5bbVleL3eBK/x/Ofd1cmDAMPjc9j6ztB/PmjNwBvPt+KrcdOcs9DRfzwmb9lYtrrSfxez3o/q1TKmD8m2mTauoVhvP39WfxDNORnWebidsFY09/zqjmRbDtyjKjYCk4d8qBS6cD8x9qZxrWoFW9/k4B/iJr8LPPvU3vcd/XR6aA439Fq67vctfYbwC87/AAIDFVbPT6hcWxaJ7Fv3z6mTJnCgQMHiIuLQ6vV0r9/fyoqKqyyfpnMgMwBNGrTqzV1lZSOt5snQTVWj/6lJJ5Q8OL7yWz7+xTv7jrHA6MLLba+Fl0rSf3LjaJk44k+74wzGfEKInsbk79BDxf3uuMTrmH72HDWdmvPJ0Nbc363R8135Jx0QV8tJeIuZc0090AtftFVZB41X9PD5AXnOfSbL8cP+NT7eWy3Erb89icb/neQZxefw9NHY7Z1N1ZQSw2+gVqO7KvdTtUaKScPuNGhq3WO74Zw9dCh12OxmqP6KNx1AChLrl7WcHW/FJf1yyP2tO9CIzRsOXKKzfsTmPdeCkEtbZdUG7Lf7N2lJ+g15XWjsule++mnn0zef/zxxwQEBHDkyBHuvvtui69fVSEjId6V0dNzSLvgTEm+I30eKqJd5woyk61XQrxccEsNgx4r4OsN/mx9O5C2nSuZtCSDao2En7+sP8k1RfeJ+aiVUj7sF41UBnod3D0zlw6DSwGoKHSgukLGwff9uXNGDr3n5JD8mzs7Jrdk5GfJtOxeQUWBAzInPc6eepPvdvXTUpFvnkPs7gdyadO+nOdG3Fbv50d+9+WPXQHkZckJbFHFY9OSWfbRcZ4d1hWthavz6+Pjb6z5KC4w/fuLCxwICLXdRcjlHOV6xr2Qyd6dPlSWWyvZG5i4II1Th91ITaz/QtDRSc+4Oens/dbXinHVspd9d/aYK28850JGkhxvfy2jns1h9TfnmXBvO5TF1j51X3u/3QhEm72dKC01Jhgfn/qTmlqtRq2uvbItKytr8jpffy6cGStT+fzIKXRauHBKwZ6d3rSJUTX5u6+HRArnT7jw8XJjE8LF0wpaRVcx8PECiyT7s997krDTiwdXp+MXXUVeggu/vBqMW0A1Mf8pwfBP/m5zXxndnjTWMAR2MJbYj2/xoWX3q5d0DAYwRxOXX1AVE1+4wIIJt1Ctqf/k/9tPATX/T73gxvlT7mz6+QC39y7kr5+tV+1axxUlAYkEzLJRmkjmYGDe2iSkEgNrF7S89gJmMmVJKhHtKpk5rMNV4tIz750LSKWw9qVwq8VVLxvvu/g9tTULKWchIV7Bpr/O0G9YEV9/EPAvS5rftfabYP/sJtkbDAZmzJjBnXfeSUxM/Z1yli1bxuLFi8263uxUObMfiUbuosPVXU9RniPz30siJ936nfPA2Ns2NdHZZFr6BWfuHFBqkfXtXR5E92fyaf+g8fv926opzXTkwHp/Yv5TgsJbh9TBgG+bKpPlfFuryYw3XuG7+mnRaaRUlUpNSveVhQ6E3lbZ5BijOijx9qvm7e3xNdNkDhDTtZQHR2UypHNv9HrTk3BxgZy8LGdCWtnmoq3on3ZWb/9qivJq21y9fLUUm6m243rJHAzMfy+JoDANc0dGW630PGlRCj36ljBrRHsK6un8KnPQM3/tRYLC1Mwd3c4mpXqw332nVslIOetMaIR1q/Kvtd9uJHqa+Gx8O7hQv152UycxdepUTpw4weeff37VeebNm0dpaWnNKz093WzrV6tkFOU54uappUtvZc3tStaWcNiVsNamP+bQSDV5mZbppFNdJTW5xQ5AKgPDP8lT5mQgKLaSoiuaNYqTnfAINVZ3BsWqkDrqSfnDvebz8jwHChKdzZLsjx/wZtKQrkz9T+0r8ZQ7e78PZOp/utZJ9ADuntX4B1VRlG+bk1NOmhOFuQ7cdndt7ZODo57YHuUkxNvu1qRLiT40oop5o6Os1P5qYPLiFHrdX8zcMe3q7XB6KdGHhlcx79F2KEts0ykN7HffOTrpCYtSU5RrrW1z7f12ozH80xv/el+GGzjZ20XJftq0aXz77bf89ttvtGjR4qrzyeVy5HLzHnBdepchkRhIv+hMaLiapxZkkpEkZ/c2X7Oup6G+3hDA6m8SGTktl9++86LtrZUMGFPImjlX3y5N0eZeJfvfC8AjpBq/qCpyT7tw+CM/Yh8prpnn9qcL+Pa5MMK6VdCyRwXJv7lz4VcPRm0x3told9fTaVgxe5YG4eKlxdlLx55lwfi3raJVr6Z3dFRVOpB6wc1kWlWllLJS43RnhZYxk1P4M86fonwnAkOreOK5ZMqKHdn/s1+T1381zgodIeG1F2ZBYWoiO1SiLHEgP8uJnRsDGDk1l8xkZzKT5YyaloNaJWXPTvM3xzQkpsJcRxasv0ibmEoWjmuDVGYsvQIoS2QW69swZUkq9wwpZPGEKFTlUrz9jO3eFUoHNGopUpmBBe9doE3HShY+FY1UaqiZR1nqYJG47HHfXenplzI5EOdJXqYjXn5aRj+Xi8JNR9wX1onhWvsNjLcKB4So8Q00HkctIo01gMX5jhQX2F8tQHMe9U5iMBhs9gBAg8HAtGnT2LFjB3v37iUqKqpRy5eVleHp6Ukf6VAcJNd3tXv3oGLGvZCJX3A1yhIZf/7ozccrQqhUNqEK0aC/9jz/ovt9pYx7IZvQCDU56U58/UEAP25p2sXHnAsn6p2uLpfyx+pAzu/2oLLQAbfAatoPKqXntDxkTrWHxokvvDmwzp/yHEd8ItX0ei6XqH61ve8vPVQn4TvjQ3Va9Syn3+Ksmofu1Gdl1+vvhLn842MknXPjg+VROMl1vPTOKVq3K8fVQ0txvhN/H/Lik3ciKMhxvvaXXUZX2vB+IJ3uUPLGF+frTN+93YeVM8KpeTDLmALcPXWcPe7K2hfDSD3XuAezSKQNP8F06qHk9e2JdabHfeHLp6uD2fzXqXqXmzM8mhMH3Ov9rF6yhv8+fko+VO/0lbMiiPvKn8BQNZv/+Lv+uEa248RBj3o/u5KhuuEPcLLWvmvKuWDeeynEdi/Hw0dHaaEDZ48q2PxGMGnnG3dMX0ni1LAkfK39BtDvP/nMfLPucwo+XRPCp281rICiNVSzR72d0tJSPDwatq8b61Ku+M/PT+Doev0XIdUVGr66b7NFY7UUmyb7yZMns2XLFr755huTe+s9PT1xcbn2j8ocyd4impjsLeFqyd6WmpLsLaUxyd5aGpPsraYRyd5aGpPsrcYOzwUNTfbWYs1k/3DcuCYn+x39Pr4hk71Nq/HXrVsHQJ8+fUymf/zxx4wdO9b6AQmCIAg3reZcjW/TZG/DSgVBEARBaDbsooOeIAiCIFiaeDa+IAiCINzkmnM1vt3cZy8IgiAIgmWIkr0gCILQLDTnkr1I9oIgCEKz0JyTvajGFwRBEISbnCjZC4IgCM1Ccy7Zi2QvCIIgNAsGmnb73I38ZBiR7AVBEIRmoTmX7EWbvSAIgiDc5ETJXhAEQWgWmnPJ/uZI9nodSEQlxb95o0NXW4dQx0/Je2wdQh33h3a2dQh1GLR22FKotcMR5oQGMWg0tg7BhMFw9WGwza05J3uRIQVBEAThJndzlOwFQRAE4Rqac8leJHtBEAShWTAYJBiakLCbsqytiWp8QRAEQbjJiZK9IAiC0CyI8ewFQRAE4SbXnNvsRTW+IAiCINzkRMleEARBaBaacwc9kewFQRCEZqE5V+OLZC8IgiA0C825ZC/a7AVBEAThJidK9oIgCEKzYGhiNf6NXLJv1sl+xNRceg0oJayNGk2VlIR4BRtfCybjorNN44rpXs6wyflExVbiG6Rl0ZPh7P/J02rrHzEpi173F9Oitcq4XY668dGKMDKSXACQOeh5YmYm3fqUENxSTYVSxrE/PfhoRRhFeU5miaGyXMrm14P560dPSgodaN1RxaRXMmh7qwqAN6e3JG67j8ky7W6r4K3vz9e8L8pz4MNXQjj6mzuV5VLCWqsZ+Wwudw0qNUuM1zJiai5Pzstmx4d+rH+5hVXWWR9bH09XGvR4AQMfLyQwzDggS+o5Zz5bHUj8Hg+bxWRv2wjsczs9OiObx2bmmkwrynNgVOcYG0XUOAbA0IRxpexwSKoGa9bJvtMdFXy3yY/E4wpkDgbGzs1m6edJPN27LWqVzGZxOSv0JJ12ZvdWbxZuTLX6+mO7K/nukwAST7gidYCxM9N57b/nmNAvFrVKhtxFT5uYCrasDSH5jAI3Dx0TF6ayaEMizw4xz49+9cwwUs45M+edVHwCq/n1Kx9eGNGGDXvP4hdsHCWr6z1lzFydVrOMg6PpT/H1aa2oUEpZtCkZTx8te3Z4s/SZcN75MZE2sSqzxHk10bdUMmBMIUkJtr1wBNsfT1fKz3bko6XBZKXIAeg3rIhFH6cwpX80qYm22V72to3APrcTQMpZZ14Y2brmvV5345Z2mxObJvt169axbt06UlJSAOjYsSMLFy7kgQcesMr6XxwTafJ+5fMt2X7qNFGdVJw66GaVGOoTv8fjsqt36594Foxta/J+1ZxIth05RlRsBacOeVCpdGD+Y+1M5lm3qBVvf5OAf4ia/Cx5k9avVkn44wcvFn2cTGyPCgAem5XDXz958v1/fRk7NwcARycDPgFXH2r1zBEF05Zn0K5zJQCjp+fy9QZ/Lpx0sWiyd1bomLs2lTVzwhj1bI7F1tNQtj6ernQwzrTEvGlFMIMeL6RdlwqbJTF720Zgn9sJQKeD4nxHm62/KfRIkDTTJ+jZtINeixYtWL58OfHx8cTHx3PvvfcyZMgQTp8+bZN4XD10AChLbFeqt0cK90vb5erXhq7uOvR6qChr+vWjTidBr5PgJNebTJe76Dl9qPYi7MR+N4bHduTJO9uxelYYJQWm6+54ewX7vvWirFiGXg97d3pRrZbQqWd5k2P8N1OXZnDoFw+O/e5u0fXcDKRSA72HFCNX6DkT72rrcOyWPW2n0AgNW46cYvP+BOa9l0JQS7VN42mMS73xm/K6Udm0ZP/ggw+avH/ttddYt24dBw4coGPHjnXmV6vVqNW1B1ZZWZkZozEwYVEWpw66knrOxYzfe6MzMHFBGqcOu5GaqKh3DkcnPePmpLP3W18qy5t+oaRw09O+SwVb1gTRMioFL38te3d6c/aogtAI4/7vek8Zdw0qIbCFhpw0Jza/HsycYa1Z+1MiTnJjdf6L61N47ZlwhnWMReZgQO6iZ+HGZELCNU2O8Wp6Dy6mTYyKaQOjLbaOm0F4OxVrvruAk1yPqkLKkvHhpJ23fZOHvbG37XT2mCtvPOdCRpIcb38to57NYfU355lwbzuUxc26Vdju2c3e0el0fPHFF1RUVHDHHXfUO8+yZctYvHixRdY/ZWkmEe1VzHyojUW+/0Y1ZUkqEe0qmTmsQ72fyxz0zHvnAlIprH0p3GzrnfNOKqtmtGT0bTFIZQbaxFZyz8PFXDhpvODoM6SkZt7wdlVE3VLJ47d34NAvHtw5wNgBb9OKYMpLZSzfdgEPHy37f/LktYkRrNxxnoj2VWaL9RL/EA2TlmQyf3RrqtXirtZ/k3FRzuR+0bh66LhzYCmz3kpj9tA2IuFfwd620+WdA1POQkK8gk1/naHfsCK+/iDAJjE1ht4gQSIeqmMbJ0+e5I477qCqqgo3Nzd27NhBhw71J5Z58+YxY8aMmvdlZWWEhYU1OYbJr2ZwR/8yZj7cmoJs8/QmvxlMWpRCj74lzBrRnoKcuttF5qBn/tqLBIWpmTu6nVlK9ZeEhGt48+sLVFVKqVBK8Q3U8trEVletMvQN1BLQoprMJGN/gawUJ7792J/395wlvK0xsbfuWMXJg258u8mP51ZkmC3WS9rEVuLtr2Xtj+dqpskcILZHBYPHFjAo4hb0+hv3ZGFO2mppTcez8ycUtL21koeeyuftuU3/Pd9M7H07qVUyUs4619S42TuDoYm98W/g7vg2T/Zt27bl+PHjlJSU8NVXX/HEE0+wb9++ehO+XC5HLm9a5y9TBqa8lknP/ytl9iNtyE0353ffyAxMXpxKz/7FzBnVntyMutvlUqIPDa9i7uh2KEss02HHWaHHWaFHWSLjyD4PnlqQVe98ZUUy8rMc8Qk09tRXq4wla6nU9Ncpkxkw6OssbhbH/3Bnwr2mnRtnrkoj/aIz298NEIn+GhydbuAzqRXZ03ZydNITFqW2aYdmoWFsnuydnJxo08ZYdd61a1cOHz7MW2+9xfvvv2/xdU9dmsk9DxezaFwEqnIp3v7GRFGhlKGpsl01rLNCR0hEbbtyUJiGyI4qlCUy8jMtX/MwZUkq9wwpZPGEKON28TPGUqF0QKOWIpUZWPDeBdp0rGThU9FIpYaaeZSlDmirm77t4ve6YzBAWGs1mclOfPhKKC1aV9F/RCGqCimfvBnEnQNL8AnUkpvuxMfLgvH00dLrAWMVflibKkIi1Lw1J4ynF2bh4a3lr588OfqbO0v+m9Tk+OqjqpDV6e9RVSlFWVx3ujXZ+ni60rgXsjn8qzv5WU64uOnoM6SETj3LWXDF3THWZG/bCOxzOz39UiYH4jzJy3TEy0/L6OdyUbjpiPvC59oL24Hm/Lhcmyf7KxkMBpNOeJb04NhCAN78+qLJ9Denh9V5YIs1Rd+i4o2vamN6ZrGxNLt7mzcrn29p8fU/+FgeAG9sPWsyfeWsCOK+8sc/SMMd/UoAWPfDKZN55oxsx4mDTX/oR0WZjI+XBVOQ7Yi7l45eA0oY90I2Do6g0xpIOevMz19GUFEmwydAyy29ypm/PgWFm7HY7uAIr35ykY1LQ3j5iQhUFVJCIjTMeiuN2/sqmxzfjcTWx9OVvPy1zH4nDZ8ALZVKGclnnFkwJpKjv9nu7gV720Zgn9vJL7iaee+m4OGjo7TQgbNHFUx/MJo8G10QNVZzTvYSg8F2rRDz58/ngQceICwsDKVSydatW1m+fDk//fQT/fr1u+byZWVleHp60ochOEhuzPs+rUVi1uYP8/gp+aCtQ6jj/tDOtg6hrhu5oVCwPxL7SlhaQzV7DTspLS3Fw8MyTwe8lCvabnkBmeL6z4W6SjXnRi+3aKyWYtMuw7m5uTz22GO0bduWvn37cvDgwQYnekEQBEGwd5mZmTz66KP4+vqiUCi49dZbOXLkSM3nBoOBRYsWERISgouLC3369KnzrBm1Ws20adPw8/PD1dWVwYMHk5HRuE7GNq3G37hxoy1XLwiCIDQj1u6NX1xcTK9evbjnnnv48ccfCQgI4OLFi3h5edXM8/rrr7Nq1So2bdpEdHQ0r776Kv369ePcuXO4uxubbKZPn853333H1q1b8fX1ZebMmQwaNIgjR44gkzXsLii7a7MXBEEQBEswJvumtNkb/73ygW5Xu1NsxYoVhIWF8fHHH9dMCw8Pv+z7DKxZs4YXX3yRoUOHArB582YCAwPZsmULEydOpLS0lI0bN/LJJ59w3333AfDpp58SFhbGzz//zP3339+g2MWTPwRBEAShEcLCwvD09Kx5LVu2rN75vv32W7p27cqwYcMICAigc+fObNiwoebz5ORkcnJy6N+/f800uVxO7969+euvvwA4cuQI1dXVJvOEhIQQExNTM09DiJK9IAiC0CyYqzd+enq6SQe9qz3/JSkpiXXr1jFjxgzmz5/PoUOHePbZZ5HL5Tz++OPk5BgHygoMDDRZLjAwkNRU44BMOTk5ODk54e3tXWeeS8s3hEj2giAIQrNgoGlj0l9a1sPDo0G98fV6PV27dmXp0qUAdO7cmdOnT7Nu3Toef/zxmvkkV9whYTAY6kyrE0sD5rmcqMYXBEEQBAsIDg6u8zTY9u3bk5aWBkBQUBBAnRJ6Xl5eTWk/KCgIjUZDcXHxVedpCJHsBUEQhGbB2kPc9urVi3PnzplMS0xMpFWrVgBEREQQFBREXFxczecajYZ9+/bRs2dPALp06YKjo6PJPNnZ2Zw6dapmnoYQ1fiCIAhC82CuevwGev755+nZsydLly5l+PDhHDp0iA8++IAPPvgAMFbfT58+naVLlxIVFUVUVBRLly5FoVAwevRoADw9PRk/fjwzZ87E19cXHx8fZs2aRWxsbE3v/IYQyV4QBEFoHprYQY9GLtutWzd27NjBvHnzWLJkCREREaxZs4YxY8bUzDNnzhxUKhWTJ0+muLiY7t27s3v37pp77AFWr16Ng4MDw4cPR6VS0bdvXzZt2tTge+zBxo/LbSrxuNyGE4/LbRjxuFzhpteMH5cbuelFpArn6/4efWUVSWNfuyEflytK9oIgCEKzIMazFwRBEISbXHMe9e6mSPZSVwVSif0MsaivrLR1CHUYrDRscGPc36KLrUOoY03y77YOoY7p4Q3vcWst9tgsZNBorj2TlUntcDvp7fBcIFjeTZHsBUEQBOGaDJJGd7Krs/wNSiR7QRAEoVlozm324qE6giAIgnCTEyV7QRAEoXmw8kN17IlI9oIgCEKzIHrjX8Pbb7/d4C989tlnrzsYQRAEQRDMr0HJfvXq1Q36MolEIpK9IAiCYL9u4Kr4pmhQsk9OTrZ0HIIgCIJgUc25Gv+6e+NrNBrOnTuHVqs1ZzyCIAiCYBkGM7xuUI1O9pWVlYwfPx6FQkHHjh1JS0sDjG31y5cvN3uAgiAIgiA0TaOT/bx58/j777/Zu3cvzs61owfdd999bNu2zazBCYIgCIL5SMzwujE1+ta7nTt3sm3bNnr06IHksqESO3TowMWLF80anCAIgiCYjbjPvuHy8/MJCAioM72iosIk+dujgaNzGDgqh8AWxoEgUs+7sGVtGPG/eQPQs38hA0bm0qZjOZ4+WqYMvoWkM65WjXHzgdMEhVXXmf7tJj/efbGFVWO50qAnChg2KR+fgGpSE51ZvzCEU4fcrLLumO5Khj2TS1SsCt+gahaNj2T/Lq+az3s9UMyAMQVEdarE00fHpP7tSEpQmG39Oi38tCaMIzv9UeY74hFQTbdH8ug/LQPpP/VjynxHvl3einO/e6Eqk9H69jL+szgZ/4gqk+9KPuLGD2+2IvW4G1IHA6EdKpi4+QxOznqzxXvJoMcLGPh4IYFhxkFiUs8589nqQOL3WG8s7pjby3hkQg5RMRX4BlazeEIU++O8az738qtm/Nx0brurFFcPHacOufPeolZkpVz/uONNNWJqLk/Oy2bHh36sf9nyv7vhkzLpdX8xLSJVaKqkJBx156MVYWQmu9Q7/7RXkxkwOo/3X2nJzo+DLR7fJY/OyOaxmbkm04ryHBjVOcZqMQjXp9HJvlu3bvzvf/9j2rRpADUJfsOGDdxxxx3mjc7MCnKc+PjNVmSlGk8i9z2cx8J1Z5k65BbSLihwdtGRcNSd33/0ZfpS29RSPDugLVJZ7eVjeLsqlm+9yO/fe9oknkt6Dy7mmcVZrJ0fyulDrgx8rJBXP0vm6T5tyc+0/IiDzgo9SQkKdm/3ZeGGuneHOCv0JMS78fv/vHn+jTSzr/+X9aH89VkQo1deICiqkvSTbnw+uw0u7jp6P5mNwQAfTmiHzFHPUxvOInfTsvfDEN57tCMvxB1DrjAm8uQjbrw/tgP3Tcpk6OIkHBwNZJ5xRSqxTJEhP9uRj5YGk5ViHH2t37AiFn2cwpT+0aQmWieZOrvoST6jIO4LP15af+GKTw28/H4iWq2UxROiqCyXMXR8Dss+PcuEfrGoVTKrxHi56FsqGTCmkKQE611sxN6u5LtPAkk84YpMZuCJWRm89t+zTOzfqc42uKNfEW1vLacgx9Fq8V0u5awzL4xsXfNer7PvQp4JUbJvuGXLlvF///d/JCQkoNVqeeuttzh9+jT79+9n37591x3IsmXLmD9/Ps899xxr1qy57u/5Nwd/9TF5v3l1KwaOzqXdrUrSLij49RtjjUVAaFV9i1tFaZHpLhkxNZesZCdO7LdOCfpqhk4oYNfnPvy0xReA9S+H0qWPkkGPF/LxMsuXLOL3eBK/59IFT91k/8tXxrgu1dqYW8pRd2L6FdHx3mIAfMPUHP3Wj/STxpqf/GRnUo+5M3f3MYKjVQAMezWJBV26cfRbP+4YmQfAzlciuHtsNvdNzqz57itL/uZ0MM70InHTimAGPV5Iuy4VVkv28fu8iN/nVe9noRFVtL+tgon9Y0g9b6yJWftSOFvjj3LP4EJ+2la3FtGSnBU65q5NZc2cMEY9m2O19b40rp3J+9VzItkaf5SomApOHa6thfEN1DB5UQovjm3Hko3nrBbf5XQ6KM63zYVGkzXjUe8a3UGvZ8+e/Pnnn1RWVtK6dWt2795NYGAg+/fvp0uX6xuf/PDhw3zwwQd06tTpupa/HlKpgd4DC3BW6Dh73N1q620MB0c99w4tZtc2X2zZMcTBUU9Up0qO7DPdTkf2udOha4WNorKuyK5KEv/0JC/JmCAzExQkxbvTvk8JAFqN8afkKK+tipfKwMHRQNI/J2tlgSOpx91x861mzdAYFnTtyjvDO5J02DrHn1RqoPeQYuQKPWfirds8dTWOTsaikkZdeyrS6yVoq6V07Fpu9XimLs3g0C8eHPvdtucEhbsOAGVp7cW/RGJg1sqLfLkhhLTz5muiaqzQCA1bjpxi8/4E5r2XQlBLy1xgC+Z1Xc/Gj42NZfPmzWYJoLy8nDFjxrBhwwZeffXVf51XrVajVtceWGVlZY1eX3h0Bau2n8RJrkdVKeOVye1Iu2C7H86/6fl/pbh56Ni93efaM1uQh48OmQOUFJgeLiX5DngHNI/nLPSdlIlKKWNZ385IZAYMOgkDZqXRZUgBAIGtVXiHVvH9660YvvQiTi569n4YQlm+E2V5xlJQYZqxKv2nNWEMmZ9KaIcKDn/tz7tjOvLCruMWK+GHt1Ox5rsLxmO+QsqS8eGknbdde/jl0i86k5vhxLg5Gbw9P5wqlZSh43PwCajGJ0Bj1Vh6Dy6mTYyKaQOjrbreugxMeDGVU4fdSU2sPTcNeyYLvQ6+2RRos8jOHnPljedcyEiS4+2vZdSzOaz+5jwT7m2Hstj+h1ppzkPcXtfe0el07NixgzNnziCRSGjfvj1DhgzBwaHxXzdlyhQGDhzIfffdd81kv2zZMhYvXnw9IdfISHZhyuBbcPPQ0ev+Qma+fp45Y2LsMuHfP7KIw3s8KMq1jyqzKw90iYQbug2rMY5958uRnf489lYiQdEqMhNc2bEkHM9ADbc/ko/M0cCT68/x+Zw2zL+lO1KZgeheJbTvU1zzHZeevtVzdC7dhxur9VvEVJD4lycHtgfw4Fzz9zUAyLgoZ3K/aFw9dNw5sJRZb6Uxe2gbu0j4Oq2UVyZF8fyKZL78+yg6LRz705NDe6zbR8U/RMOkJZnMH92aarVtR/6evDiFiHaVzBreoWZam5gKhozNZdqDMdiylu/yjp0pZyEhXsGmv87Qb1gRX39g3SaX6yLa7Bvu1KlTDBkyhJycHNq2bQtAYmIi/v7+fPvtt8TGxjb4u7Zu3crRo0c5fPhwg+afN28eM2bMqHlfVlZGWFhYo+LXVkvJTjP2cD1/yo3o2HKGPJHNOy+1vsaS1hUQqqHzXUpeeSrC1qFQViRDpwVvf9NSvKefluJ8+7+aN4dvl4XTd1Imtw0uBCCkXSXFmXJ+fi+U2x/JByAstoI5P/6NqkyGrlqCm6+WVUNiadnJWB3t8U9JNSiq0uS7A1urKMmSWyx2bbW0poPe+RMK2t5ayUNP5fP23Mb9dizlwilXpgyMQeGuxdHRQGmRI2t2nOb8Ses1NbSJrcTbX8vaH2vbwWUOENujgsFjCxgUcQt6veWT7KSXU+jRt4TZI9tTkFN7TMR0K8PLt5r//nHMJL6n5qfx0Lgcxt7d2eKx1UetkpFy1pnQCFGVb+8afaZ+6qmn6NixI/Hx8Xh7G2+fKS4uZuzYsUyYMIH9+/c36HvS09N57rnn2L17t8nDef6NXC5HLjfvSVEiAUcn89/y1FT9RxRSUuDAwV+sd4vU1WirpZw/oeC2u5X89VNtieu2u5Xs32XbuwSsRaOSIrmix7xEaqj3WdkuHsb21vxkZ9JPujFgprHE7tNCjWegmrwk09up8pOda9r+reVSW7k9qVQaT0ch4VVExVbw31XWu9X0+B/uTLi3rcm0mavSSL/ozPZ3A6yQ6A1MWpRKz/5FzB3dgdwM03PiLzv8OPan6W/t1U1n+XWnH7u/8LdwbFfn6KQnLErNqYO27UDcYM24g16jk/3ff/9tkugBvL29ee211+jWrVuDv+fIkSPk5eWZdOrT6XT89ttvrF27FrVajUxm3ttunpiRSvxv3uRnO6Fw1dF7YAGx3Ut5abyxuszNs5qAEA2+/5TAWkQYe1UX5ztSXGD528sukUgM9B9RxM9f+NjNbS1ff+DH7LfTSTzhwpl4VwY8WkhAaDX/+6+vVdbvrNAREl5beggKUxPZoRJliQP5WU64e2nxD9HgG2R8RkFYa2P7d3G+o1l6DnfsW0zcuy3wDtUQFFVJ5mlX9m4MofuwvJp5jv/PF1efarxD1WSfVfD14ghi+xfR7u5SwHhhec+ELH5aE0ZI+0pjm/1X/uRddGHcOsv0rB73QjaHf3UnP8sJFzcdfYaU0KlnOQvGRFpkffVxVugIaVXbHyEoTE1k+wqUpQ7kZ8m5a0ARpYUO5GU5Ed5OxaSFqezf7c3R3613IamqkJF6zvQirKpSirK47nRLmLIkhT6DC1kyIRpVuRRvP+M5qELpgEYtRVniiLLE9DjWaSUU5zte9V58S3j6pUwOxHmSl+mIl5+W0c/lonDTEfeFbfsVNZTEYHw1ZfkbVaOTfdu2bcnNzaVjx44m0/Py8mjTpk2Dv6dv376cPHnSZNq4ceNo164dc+fONXuiB/D2q2b2G+fxCdBQoZSRfNaVl8Z34NifXgD06FvMzBW19wHPeysRgE/fbsFn77Q0ezxX0/kuJYEtqtm1zX5+QPu+9cbdW8eY53PxCdCSes6ZBY9GkGeFe+zBeO/zG1+cr3n/zCLjrWu7t/uwckY4PfqVMmt1as3n89elAPDJqiA+XRXS5PX/Z3ESP6xsyZcvRVJe4IBHYDU9R+dw/7MZNfOU5jmy89VwlAX/PHRnqPGhO5frMz4brVrKzlfCqSxxIKR9BZM+TcCvlWWqQb38tcx+Jw2fAC2VShnJZ5xZMCaSo79Zr7d5dGwFr289W/N+4kvGmo64L/1YOTsSnwANE15Mw8uvmqJ8R3752o8t7zR9n91IBj1qvGh8fesZk+krZ0fy81e2K7lfyS+4mnnvpuDho6O00IGzRxVMfzDaaueBJmvGbfYSg+Ha/Qsv7/X+xx9/MGfOHBYtWkSPHj0AOHDgAEuWLGH58uUMGDDguoPp06cPt956a4Pvsy8rK8PT05N7XUfhILGfg01fWXntmazNHruRSq3/wJRrWZP0u61DqGN6eE9bh1CHxMzNaeZg0Fi3935DSO1wO+nV9tW+rjVUs9ewk9LSUjw8LNNseSlXhK1ZgtTl+jum6lVVpE9faNFYLaVBJXsvLy+TR+EaDAaGDx9eM+3S9cKDDz6ITqezQJiCIAiC0ESizf7f7dmzx9JxALB3716rrEcQBEFohppxNX6Dkn3v3r0tHYcgCIIgCBZy3TdJV1ZWkpaWhuaKdjJrPvJWEARBEBpMlOwbLj8/n3HjxvHjjz/W+7losxcEQRDsUjNO9o1+LuT06dMpLi7mwIEDuLi48NNPP7F582aioqL49ttvLRGjIAiCIAhN0OiS/a+//so333xDt27dkEqltGrVin79+uHh4cGyZcsYOHCgJeIUBEEQhKZpxr3xG12yr6ioICDAOOCBj48P+fnG54LHxsZy9OhR80YnCIIgCGZy6Ql6TXndqBqd7Nu2bcu5c8ZHe9566628//77ZGZmsn79eoKDg80eoCAIgiAITdPoavzp06eTnZ0NwMsvv8z999/PZ599hpOTE5s2bTJ3fIIgCIJgHs24g16jk/2YMWNq/t+5c2dSUlI4e/YsLVu2xM/Pz6zBCYIgCILQdE0ejFyhUHDbbbeZIxZBEARBsBgJTRz1zmyRWF+Dkv2MGTMa/IWrVq267mAEQRAEQTC/BiX7Y8eONejLLh8sx5r0lSr0Eq1N1l0fiQWG520qiYv1xrxuKH2F/Y0OOD2il61DqOO9VPsbiW9Km3ttHUJddjiyo76qytYh1GWj87RdaMa33tnVQDiCIAiCYDHNuINeo2+9EwRBEAThxtLkDnqCIAiCcENoxiV7kewFQRCEZqGpT8FrVk/QEwRBEAThxiJK9oIgCELz0Iyr8a+rZP/JJ5/Qq1cvQkJCSE1NBWDNmjV88803Zg1OEARBEMzGYIbXDarRyX7dunXMmDGDAQMGUFJSgk6nA8DLy4s1a9aYOz5BEARBEJqo0cn+nXfeYcOGDbz44ovILnt4TNeuXTl58qRZgxMEQRAEc2nOQ9w2us0+OTmZzp0715kul8upqKgwS1CCIAiCYHbN+Al6jS7ZR0REcPz48TrTf/zxRzp06GCOmARBEATB/Jpxm32jS/azZ89mypQpVFVVYTAYOHToEJ9//jnLli3jww8/tESMgiAIgiA0QaOT/bhx49BqtcyZM4fKykpGjx5NaGgob731FiNHjrREjBblG6Rh/Pxsut1bhpOznswkOatmtuTCSYVV1h9zu5JHnsklKrYS38BqFj/Vmv27vQCQORh4YnYm3e4pJbilhgqljGN/uPPR8lCKcp0sFtPAUdkMHJVNYKgagNTzCra8F0b8bz4A/Hjuj3qX+/D1cL7a2MIiMcV0VzLsmVyiYlX4BlWzaHwk+3d5XTaHgUdnZDNgdCFuXlrOHnPl3RfDSE207gBA1j6eqsplfLeyJX/v8kVZ4EiLjhUMW5RE+C3lgHFsmP+tacmfWwKpLHUgvHM5I165SEi0cRCiihIHvl/VkjO/e1GcJcfNp5pb+hfx4MxUXDx0Zokx5nYlj0zMrj3Gn27D/t3eNZ87K3Q8+UIGd/QvxsNbS26GnG8+DuR/nwaYZf0NMWJqLr0GlBLWRo2mSkpCvIKNrwWTcdHZajFcadDjBQx8vJDAMA0Aqeec+Wx1IPF7PGwW05VGTM3lyXnZ7PjQj/UvW+a3b07N+aE613Wf/dNPP83TTz9NQUEBer2egADr/SjNyc1Ty6qd5znxlzsLHo2kpMCB4HANFWXWG7XOWaEnOcGFuO2+vPRBkslnchc9bWIq2fJ2MMkJCtw8tUx8OYNFGy/y7KD2FoupIMeJj98MJyvNmCjveyiXhe+eYerDt5J2wZXRvW43mb/r3cVMf+08f+7ys1hMzgo9SQkKdm/3ZeGG5DqfD5+cy9Cn81g5oxUZSc6MfjaHZVsuML53B1QV1tmftjiePp3bhuxzCp5YnYhnoIZDOwJ4e0wMC38+ileQhrj1ofz6YQiPvXmewEgVP74TxjtjOvLynqM4u+kozXWiNNeJoS+mEBxVSVGGnM9fbENprhNPrz9rlhidFTqSzyiI+8KPl96/WOfziQvTuOUOJW9MjyQ3Q85td5Uy9dVUCnMdORDnXc83ml+nOyr4bpMficcVyBwMjJ2bzdLPk3i6d1vUKtuMYpmf7chHS4PJSpED0G9YEYs+TmFK/2hSE213EXJJ9C2VDBhTSFKC7WNpsGZ8n32THqrj59e0k/uiRYtYvHixybTAwEBycnKa9L0NNXxyHgVZTqyc0bJmWm6G3CrrviR+ryfxez3r/axSKWP+mGiTaesWhvH292fxD9GQn2WZ0v3BPb4m7zevCWfgqBza3aok7YIrxQWm6+3Rt5ATBz3JybDcjz5+jyfxey5tpyuTvYGHxuex9Z0g/vzRmBzefL4VW4+d5J6HivjhM3+LxXU5ax9Pmiopx3/0Y+KGBKK6lwEw6Pk0Tuz24bdPgnhwVhq/bgzl/6am0/mBQgAeX5nIC127c/gbf+4ak0NI20omvF+b1P1bVTF4dgqbprdFpwWZGR67Fb/Xi/i9Xv+8q5vs299Wwc9f+XHigLHE+uPnAQwYk090pwqrJfsXx0SavF/5fEu2nzpNVCcVpw66WSWGKx2MMz0vbFoRzKDHC2nXpcLmyd5ZoWPu2lTWzAlj1LPWOV8LTXNdHfQiIyOv+mqsjh07kp2dXfOy5u17PfqXknhCwYvvJ7Pt71O8u+scD4wutNr6r4erhw69HqvVPkilBnoPyMdZoePssbrVh16+Gm7vXcyuLwOtEk99glpq8A3UcmRfbXzVGiknD7jRoav17hCx9vGk10rQ6yQ4yvUm0x3lei7Ge1KYLqcs34n2d5Vc9pmBqO6lJB1xv+r3qsoccHbTmSXRN8Tpw270uK8Y30ANYKDTHWWERlRxZF/9F8HW4PpPE4ayxDal+itJpQZ6DylGrtBzJt7V1uEwdWkGh37x4NjvVz+O7FJTb7trTiX76dOnm7yvrq7m2LFj/PTTT8yePbvxATg4EBQU1KB51Wo1arW65n1ZWVmj13e54JYaBj1WwNcb/Nn6diBtO1cyaUkG1RoJP3/p06TvtgRHuZ5xL2Syd6cPleWWPQmFR1ewauvfOMn1qCplvDKlPWkX67Y73/dwHqoKGX/utlwV/rX4+FcDUFxgejgXFzgQEKqxWhzWPp6c3XRE3FbGj++0JCjqHB5+Gg5/40/KcXf8I1SU5hlrYNz/2T6XuPtVU5RZf41DebEDP74Txp2js80e79WsW9SS55an8Nmhv9FWS9Dr4a254ZyOt1UiMTBhURanDrqSes66fT6uFN5OxZrvLhh/hxVSlowPJ+28bUv1vQcX0yZGxbSB0dee2d6IavyGe+655+qd/u677xIfH9/oAM6fP09ISAhyuZzu3buzdOnSq9YQLFu2rE61f1NIpHD+hAsfLw8B4OJpBa2iqxj4eIHdJXuZg4F5a5OQSgysXdDy2gs0UUayC1Me6oybh5Ze/QuZuSKROY92qpPw+/8nlz3f+VOtsYMxla64B1YiAbDefbG2OJ7Grknkk9lRzL/9dqQyA2Ex5XQdkk/6qdqqZ8mVZyjDpW1jSqWU8d64DgS1qWTg9HSLxFufIeNyad+5gpefjCIv04mY7kqmvJpKUZ4jx/60ful+ytJMItqrmPlQG6uv+0oZF+VM7heNq4eOOweWMuutNGYPbWOzhO8fomHSkkzmj25NtdoOfvNCg5ltbz3wwAN89dVXjVqme/fu/Pe//2XXrl1s2LCBnJwcevbsSWFh/VWf8+bNo7S0tOaVnt60E1JRnkOdtq/0C84EhFRfZQnbkDkYmP9eEkFhGuaNibZ4qR5AWy0lO82F86fc2bQqnKSzrgx5PMtkno5dSgmLVPHTF7arwgcoyncEwPuKEqyXr5bifOuN9WSL48m/VRUztp9k9Zm/eG3/IeZ++zc6rQTfsCo8A4y1GmX5pn0slIWOuPuZ1nhUlctY+3hH5Ao9Ez84g8zROkUYJ7mesbMz+eDVMA7+4kXyWQXfbQ7kt+99+M8E67cFT341gzv6lzHnkdYUZFvujpeG0lZLyUqRc/6Ego+XBZOc4MJDT+XbLJ42sZV4+2tZ++M5fkg9zg+px7mlZwVDnizgh9TjSKV2XvQV99k33ZdffomPT+NKLw888EDN/2NjY7njjjto3bo1mzdvZsaMGXXml8vlyOXm6/CUcNiVsNZqk2mhkWryMh3Nto6mupToQyOqmDsiGmWJbQYqlEjA0cm0bfj+R3JJPOVG8jnbdGC6JCfNicJcB267u4yLp401Dw6OemJ7lLNxaYjV4rDl8SRX6JEr9FSWyjjzmzcPz0vGN0yNh7+GM394ERZj7Lug1Ug4f9CTh15IqVlWpZSx9rGOOMgNTNqYgKOz9c5oDo4GHJ0M6E0PLfQ6CRKrFhwNTHktk57/V8rsR9qQm27djrqN4ehku4xz/A93Jtzb1mTazFVppF90Zvu7Aej19v2EOXHrXSN07twZyWV1gAaDgZycHPLz83nvvfeaFIyrqyuxsbGcP3++Sd/TUF9vCGD1N4mMnJbLb9950fZW460ka+ZY735RZ4WOkPDaBBEUpiayQyXKEgcKcx1ZsP4ibWIqWTiuDVJZbelVWSJDW22Zs+ETz6cQ/5s3+TlyFK46eg/IJ/b2Ul56qmPNPApXLXf9XwEbVkRYJIYr/dt2ys9yYufGAEZOzSUz2ZnMZDmjpuWgVknZs9N6zTG2OJ4S9nlhMEBgpIr8VBd2LA0nMFLFHcPykEjg3vGZ7Ho3jIDwKgIiVPy0tgVOzjq6DTGWDqvKZbzzWEc0Khlj3zqDSilDpTTWHLn7ViM1QyXS1fedjPwsOSf2u/PU/Aw0VVJyM+V06q6k738K+OAVyzdXXTJ1aSb3PFzMonERqMqlNb+zCqUMTZVtqqvHvZDN4V/dyc9ywsVNR58hJXTqWc6CMY3vCG0uqgpZnX4MVZVSlMV1pwv2pdHJ/qGHHjJ5L5VK8ff3p0+fPrRr165JwajVas6cOcNdd93VpO9pqMS/FSx5KoJxL2QzZnoOOelOrH85lD07rJcgojtV8vr2xJr3E1/OACDuC18+XR3MHf1LAVi364zJcnOGR3PigGU6MHn7VTP79UR8AjRUKB1IPqfgpac6cuyv2tugeg8sAAns/d46t7VF31LJG1/UXgQ+sygTgN3bfVg5I5zt7wXi5Kxn6mtpuHvqOHvclXlj2ljtHnuwzfGkUjrwzYpWlOTIUXhq6fxAAYNnp9ZUw/d7JhNNlYytC1pTWeZA+K1Kpn16Gmc3Y2/ztJNupPxzl8XLd3c1+e5X/jiMb5hpTcX1iO5UwevbztW8n7jQ2PwW94UvK2dFsmxaa8bNyWDOW0m4e2nJy5Cz+Y0W/O9T6xxbAA+ONTYdvvm16a2Bb04PI267bfrvePlrmf1OGj4BWiqVMpLPOLNgTCRHf7vBesALdkFiMBgaXDGh1Wr57LPPuP/++xvcg/7fzJo1iwcffJCWLVuSl5fHq6++yr59+zh58iStWrW65vJlZWV4enrSR/IQDhL7qXqXyOzjdp3LSVzs76pbX1Fp6xDqMuivPY+VvZfyu61DqGNKm3ttHUIdhmrr3XlxQ6uvd6YNaQ3V7DXspLS0FA8Pyzwd8FKuaD1vKTLn6+/cqKuq4uKy+RaN1VIaVbJ3cHBg0qRJnDlz5tozN0BGRgajRo2ioKAAf39/evTowYEDBxqU6AVBEAShMUSbfSN0796dY8eOmSUhb926tcnfIQiCIAjCv2t0sp88eTIzZ84kIyODLl264Opq+jSnTp06mS04QRAEQTCrG7h03hQN7mb65JNPUlZWxogRI0hOTubZZ5+lV69e3HrrrXTu3LnmX0EQBEGwSza8z37ZsmVIJBKTp9AaDAYWLVpESEgILi4u9OnTh9OnT5ssp1armTZtGn5+fri6ujJ48GAyMjIavf4GJ/vNmzdTVVVFcnJynVdSUlLNv4IgCIIg1Dp8+DAffPBBnZrv119/nVWrVrF27VoOHz5MUFAQ/fr1Q6lU1swzffp0duzYwdatW/njjz8oLy9n0KBB6HSNG4K6wdX4lzrti85zgiAIwo3IXB30rhyX5d8e+FZeXs6YMWPYsGEDr776as10g8HAmjVrePHFFxk6dChgLFQHBgayZcsWJk6cSGlpKRs3buSTTz7hvvvuA+DTTz8lLCyMn3/+mfvvv7/BsTfqaRESO7tlQxAEQRAazEzV+GFhYXh6eta8li1bdtVVTpkyhYEDB9Yk60uSk5PJycmhf//+NdPkcjm9e/fmr7/+AuDIkSNUV1ebzBMSEkJMTEzNPA3VqA560dHR10z4RUVFjQpAEARBEG4k6enpJvfZX61Uv3XrVo4ePcrhw4frfJaTYxz7ITDQdGyRwMBAUlNTa+ZxcnLC29u7zjyXlm+oRiX7xYsX4+lpuzGmBUEQBOF6masa38PD45oP1UlPT+e5555j9+7dOP/Lg3yuLEAbDIZrFqobMs+VGpXsR44cSUBAQKNWIAiCIAh2wYrj2R85coS8vDy6dOlSM02n0/Hbb7+xdu1azp0zPkI6JyeH4ODgmnny8vJqSvtBQUFoNBqKi4tNSvd5eXn07NmzUaE3uM1etNcLgiAIQsP07duXkydPcvz48ZpX165dGTNmDMePHycyMpKgoCDi4uJqltFoNOzbt68mkXfp0gVHR0eTebKzszl16lSjk32je+MLgiAIwg3JiiV7d3d3YmJiTKa5urri6+tbM3369OksXbqUqKgooqKiWLp0KQqFgtGjRwPg6enJ+PHjmTlzJr6+vvj4+DBr1ixiY2PrdPi7lgYne/2VA04LgiAIwg3E3p6NP2fOHFQqFZMnT6a4uJju3buze/du3N1rRzZcvXo1Dg4ODB8+HJVKRd++fdm0aROyRg641qhR7+xNzah3DLGrUe/MMgi4mdnjSHz2OMKcQW+HPwc73E7b0xt32481DG95p61DqEvfuAefWIWdNclac9S7ttOXIpM3YdQ7dRXn1tyYo9416j57QRAEQRBuPI0eCEcQBEEQbkhWbLO3NyLZC4IgCM2CvbXZW5OoxhcEQRCEm5wo2QuCIAjNg6jGFwRBEISbm6jGFwRBEAThpiVK9oIgCELzIKrxBUEQBOEm14yTvajGFwRBEISbnCjZC4IgCM2C5J9XU5a/UYlkLwiCIDQPzbgav1kn+0GPFzDw8UICwzQApJ5z5rPVgcTvsd4ABzHdlQx7JpeoWBW+QdUsGh/J/l1eNZ/3eqCYAWMKiOpUiaePjkn925GUoLBsTLcreWRiNlGxlfgGVrP46Tbs3+1tMk9YGxXjX8ggtrsSidRAaqILS6e0Jj9LbrmYnsmtjemp1uzf7QWAzMHAE7Mz6XZPKcEtNVQoZRz7w52PlodSlOtkkXjqI5UZeGxGNvc+XIR3QDVFuY7EfeHLlreCMBjso0wwYmouT87LZseHfqx/uYVF1qEql7LtjZYc+smH0gJHImIqGLs4mTa3VgCwfWUL/vrWj8IsJxycDETGljNyTjpRt5UDUF7swPaVLfj7Ny8Ks5xw99HS7f4iRs5OR+FhmYFl7HHfjZiaS68BpYS1UaOpkpIQr2Dja8FkXLz+gVzMzRrHkzk151vvmnWyz8925KOlwWSlGBNUv2FFLPo4hSn9o0lNtM4PylmhJylBwe7tvizckFzv5wnxbvz+P2+efyPNSjHpSD6jIO4LP156/2Kdz4NbVrHyyzPs2ubPJ6tDqCiT0TKqCo3acl1AnBV6khNciNvuy0sfJJl8JnfR0yamki1vB5OcoMDNU8vElzNYtPEizw5qb7GYrjRicg4DH8vnzenhpCY6E3VLJTNXplKhlLFzY4DV4ria6FsqGTCmkKQEyx7b62e3Jv2cgqlvnccnsJrfvvbjlVEdWP3r3/gEawiJrOLJV5MJbFmFpkrK/zYE8+qY9rzzxzE8fLUU5TpSlOvEYy+l0iKqkoJMORteiKQ414mZHyRaJGZ73Hed7qjgu01+JB5XIHMwMHZuNks/T+Lp3m1Rq2w/iqW1jifBPGye7DMzM5k7dy4//vgjKpWK6OhoNm7cSJcuXSy+7oNxnibvN60IZtDjhbTrUmG1ZB+/x5P4PZfiqJvsf/nKF4DAFmqrxAMQv9eL+L1e/7yrm+yfmJ3J4T1ebFwWVjMtJ92y2yt+ryfxez3r/axSKWP+mGiTaesWhvH292fxD9GQn2Wd0n37LhXs3+3FoV+NceZmyLlnSDFRnSqtsv5/46zQMXdtKmvmhDHq2RyLrUejknLwB1/mfHSWDj2UAAyfmcHhXT7s/iSQkXPSufPhApNlHn85lV+3BpJ6RkHsnWW0bKdi1obapB4Urmbk3DTeeTYKnRZkFjhr2eO+e3FMpMn7lc+3ZPup00R1UnHqoJuNojKy1vFkds24Gt+mvfGLi4vp1asXjo6O/PjjjyQkJLBy5Uq8vLysHotUaqD3kGLkCj1n4l2tvv4bhURi4PZ7S8hMdua1/55j65FjrNmZwB39i20dmglXDx16PVSUWa8EdOqwG7f2UhIaUQVAZPtKOnYr5/Cvth/3eurSDA794sGx390tuh6dDvQ6CY5yvcl0J2c9Zw/VXbdWI+HnzwJQeGhp1eHqibWyzAEXN51FEj3Y9767xPWfJgxlie1L9dY6nizC0ITXDcymJfsVK1YQFhbGxx9/XDMtPDz8qvOr1WrU6toSbllZWZNjCG+nYs13F3CS61FVSFkyPpy086Ja6mq8/LQo3PQMn5TN5jdD2bg8jK69S3np/QvMHdmWkwdtf3J0lOsZ90Ime3f6UFluvRPj9ncDcXXX8eG+BPQ6kMpg04oQ9n7jY7UY6tN7cDFtYlRMGxh97ZmbyMVNT3QXJV+taUFom/N4+Vfzx04/LhxzI+ifRApw5Gcv1kyORqOS4hVQzYItCXj4aOv9TmWxA1+91YJ+j+ZaLG573Xe1DExYlMWpg66knnOxaSTWPJ4E87Fpyf7bb7+la9euDBs2jICAADp37syGDRuuOv+yZcvw9PSseYWFhV113obKuChncr9onhsUxff/9WPWW2m0jKq69oLNlOSfHir747zYsTGIpAQF29cFc+gXLwaOybdxdMbOevPWJiGVGFi7oKVV1917cDF9hxaxfGo4Ux5oz5vPt+KRZ3K575FCq8ZxOf8QDZOWZPL6s62otmCfistNfes8BoOEZ7p2ZXRkD378KJheDxUgldUWjTr2LOONXSd4Zecpbu1TwupJ0ZQW1C17VCplLH+8HS2iKnnk+QyLxWyP++5yU5ZmEtFexbLJ1j2mr2SL48mcLnXQa8rrRmXTkn1SUhLr1q1jxowZzJ8/n0OHDvHss88il8t5/PHH68w/b948ZsyYUfO+rKysyQlfWy2t6aB3/oSCtrdW8tBT+bw9t+kXEjejsmIHtNUS0s6bli7SLjjTsVu5jaIykjkYmP9eEkFhGuaOjLZqqR7g6QWZbHs3iH3fGkuDKWddCAjVMHJqDj9/6WvVWC5pE1uJt7+WtT+eq5kmc4DYHhUMHlvAoIhb0OvN29s8KFzN4q9OU1UpRaWU4R1YzepJUQSE1dbKOSv0BEVUERQB0V3KefbOW/l1awAPT82qmUdVLmXpo+1xdtUx68NzODha7kxrj/vuksmvZnBH/zJmPtyagmzr3V1SH1scT2bVjNvsbZrs9Xo9Xbt2ZenSpQB07tyZ06dPs27dunqTvVwuRy63zK1dl3N0uoH3qIVpq6UknlDQItK09iM0ooq8TNudiC4l+tCIKuaOiEZZYv1DW+6ix2DaVI1eJ0FiwwLQ8T/cmXBvW5NpM1elkX7Rme3vBlj0xOys0OOs0FNeIuPvfV48Oj/1qvMaDBKTkmKlUsZrY9rj6KRnzsfncHK27G/SHvcdGJjyWiY9/6+U2Y+0ITfd8ue+a7Hl8SQ0jU2TfXBwMB06dDCZ1r59e7766iurrH/cC9kc/tWd/CwnXNx09BlSQqee5Sy4ohesJTkrdISE15Z4gsLURHaoRFniQH6WE+5eWvxDNPgGVQMQ1tqYZIvzHSnOd7RyTDLys+R8+X4w89Ze5ORBd/7e707XPqX0uK+EOSPaWSSef4/JgcJcRxasv0ibmEoWjmuDVAbe/sbtpSyRoa22zhn7QJwnI5/NIS/TidREZ1rHqBg6IY/d22xXMlRVyOq08VZVSlEW151uLsf3eoJBQkhrFTkpznzyaitCIlX0GZFPVaWUr98OpWu/YrwDNSiLHdm9OZCiHCfuGGSsMleVS3ltdHvUKinT3j6PSilDpTTW0nj4ViO1QIWNPe67qUszuefhYhaNi0BVLq05piuUMjRVtrkKscXxZE7iPnsb6dWrF+fOnTOZlpiYSKtWrayyfi9/LbPfScMnQEulUkbyGWcWjInk6G/W62EafUslb3xxvub9M4syAdi93YeVM8Lp0a+UWatrS0Tz16UA8MmqID5dFWKZmDpV8Pq22v0ycWE6AHFf+LJyViR/7fLmnRdbMWJyNpMWp5Jx0ZlXnmnD6XjLbbfoTpW8vr32dqyJL2fUxPTp6mDu6F8KwLpdZ0yWmzM8mhMHrLM/33spjCdmZzF1aTpeftUU5jjyw6d+fLYmyCrrtxeVSgc+X96Swmwn3Ly0dH+giFFz03BwNKDXQdYFF1Z+EYCy2AF3by2tbyln8VenCGurAiDphBvnjxn32bN33mby3Wv3HzVpDjAXe9x3D441Xvy8+bXp7a9vTg8jbru9dBy8wTTjanyJwWCwWfiHDx+mZ8+eLF68mOHDh3Po0CGefvppPvjgA8aMGXPN5cvKyvD09KQPQ3CQWKaUe10sUfRoIonM/mKqU29qBwx6O/w12+F22p7+l61DqGN4yzttHUJdess88a9JJPZV1a41VLPXsJPS0lI8PCxzN8+lXBE7fikyp+u/20qnqeLkxvkWjdVSbNoi1a1bN3bs2MHnn39OTEwMr7zyCmvWrGlQohcEQRCExhC98W1o0KBBDBo0yNZhCIIgCDe7ZlyNb/NkLwiCIAhW0YyT/Y33VARBEARBEBpFlOwFQRCEZkHceicIgiAINztRjS8IgiAIws1KlOwFQRCEZkFiMCBpwqNlmrKsrYlkLwiCIDQPohpfEARBEISblSjZC4IgCM2C6I0vCIIgCDc7UY0vCIIgCMLN6qYo2UscHJBI7OdPMejsb6Qrg9b+Rk7DDnu2SuRyW4dQlx0eT/Y4wtyapN9tHUId0yN62TqEOqQKha1DMCE1aKDCOusS1fiCIAiCcLNrxtX4ItkLgiAIzUJzLtmLNntBEARBuMmJkr0gCILQPIhqfEEQBEG4+d3IVfFNIarxBUEQBOEmJ0r2giAIQvNgMDTtll87vF24oUSyFwRBEJoF0RtfEARBEISblijZC4IgCM2D6I0vCIIgCDc3id74asryNypRjS8IgiAIN7lmVbKPuV3JI8/kEhVbiW9gNYufas3+3V4AyBwMPDE7k273lBLcUkOFUsaxP9z5aHkoRblOVotx84HTBIVV15n+7SY/3n2xhdXiuJw9xnTJoCcKGDYpH5+AalITnVm/MIRTh9yssu4Rk7LodX8xLVqr0FRJSTjqxkcrwshIcrlsLgOPPpfJA6PycfPUcu64G+8ubEXqecsMRmKPx3hMdyXDnsklKlaFb1A1i8ZHsn+X12VzGHh0RjYDRhfi5qXl7DFX3n0xjNREl6t9ZaPotPDTmjCO7PRHme+IR0A13R7Jo/+0DKT/FHeU+Y58u7wV5373QlUmo/XtZfxncTL+EVUm35V8xI0f3mxF6nE3pA4GQjtUMHHzGZyczV/ke3RGNo/NzDWZVpTnwKjOMWZfV30Gjs5h4KgcAluoAUg978KWtWHE/+YNQM/+hQwYmUubjuV4+miZMvgWks64WiW26yaq8ZsHZ4We5AQX4rb78tIHSSafyV30tImpZMvbwSQnKHDz1DLx5QwWbbzIs4PaWy3GZwe0RSqrPaLC21WxfOtFfv/e02ox3AgxAfQeXMwzi7NYOz+U04dcGfhYIa9+lszTfdqSn2n5C7TY7kq++ySAxBOuSB1g7Mx0XvvvOSb0i0WtkgEwbGI2D4/PYdXsSDKSnRk1NYuln5zjqb6dUFXIzB6TPR7jzgo9SQkKdm/3ZeGG5DqfD5+cy9Cn81g5oxUZSc6MfjaHZVsuML53B7Nso1/Wh/LXZ0GMXnmBoKhK0k+68fnsNri46+j9ZDYGA3w4oR0yRz1PbTiL3E3L3g9DeO/RjrwQdwy5wpjIk4+48f7YDtw3KZOhi5NwcDSQecYVqQW7aKecdeaFka1r3ut1Eout60oFOU58/GYrslKdAbjv4TwWrjvL1CG3kHZBgbOLjoSj7vz+oy/Tl160WlxN0Zx749s02YeHh5Oamlpn+uTJk3n33XfNvr74vZ7E760/QVUqZcwfE20ybd3CMN7+/iz+IRrys6xTui8tMt0lI6bmkpXsxIn91imt1sceYwIYOqGAXZ/78NMWXwDWvxxKlz5KBj1eyMfLgi2+/gVj25q8XzUnkm1HjhEVW8GpQx6AgYefzGXruyH8ucsHgJWzIvn88DHuGVzID58HmD0mezzG4/d4Er/nUkxXJnsDD43PY+s7Qfz5o7HE+Obzrdh67CT3PFTED5/5N3n9KUfdielXRMd7iwHwDVNz9Fs/0k8aS6H5yc6kHnNn7u5jBEerABj2ahILunTj6Ld+3DEyD4Cdr0Rw99hs7pucWfPdV5b8zU2ng+J8R4uu42oO/upj8n7z6lYMHJ1Lu1uVpF1Q8Os3xuM3INSy28CsmvF99jZtsz98+DDZ2dk1r7i4OACGDRtmy7BquHro0Ouhosz8JbCGcHDUc+/QYnZt8wWsd0X/b+wlJgdHPVGdKjmyz91k+pF97nToaqXBsa+gcDeOO68sMV4cBYWp8Qmo5ujvtcm3WiPl5EF32ndR2iTGK9n6GA9qqcE3UMuRfR4106o1Uk4ecDPbfozsqiTxT0/ykowl1MwEBUnx7rTvUwKAVmM8DTrKa6vipTJwcDSQdNgYl7LAkdTj7rj5VrNmaAwLunblneEdSTpsevyZW2iEhi1HTrF5fwLz3kshqKXaouu7GqnUQO+BBTgrdJw9btm/WbAMm5bs/f1Nr9qXL19O69at6d27d73zq9Vq1Orag72srMxisTnK9Yx7IZO9O32oLLfNibDn/5Xi5qFj93afa89sJfYSk4ePDpkDlBSYHsIl+Q54B2htEJGBiQvSOHXYjdREY3u8t7+xn0NxgWnJrLjAkcBQ25y0L2cPx7hPzTYy3Y/FBQ4EhGrMso6+kzJRKWUs69sZicyAQSdhwKw0ugwpACCwtQrv0Cq+f70Vw5dexMlFz94PQyjLd6Isz7jvCtPkgLHtf8j8VEI7VHD4a3/eHdORF3Ydt0gJ/+wxV954zoWMJDne/lpGPZvD6m/OM+HediiLrXPqDo+uYNX2kzjJ9agqZbwyuR1pFyzT38QaRDW+HdBoNHz66afMmDEDiaT+EuOyZctYvHixxWORORiYtzYJqcTA2gUtLb6+q7l/ZBGH93hQlGubarz62FtMV9aqSSTYpBPNlCWpRLSrZOawDnU/rCdGg8G2NTX2cozXuGJ7GE8B5tlGx77z5chOfx57K5GgaBWZCa7sWBKOZ6CG2x/JR+Zo4Mn15/h8Thvm39IdqcxAdK8S2vcprg3vn/h6js6l+3BjtX6LmAoS//LkwPYAHpybZpZYLxe/p7a2I+UsJMQr2PTXGfoNK+LrD8zfBFSfjGQXpgy+BTcPHb3uL2Tm6+eZMybmxk34ooOe7e3cuZOSkhLGjh171XnmzZvHjBkzat6XlZURFhZm1jhkDgbmv5dEUJiGuSOjbVbiCQjV0PkuJa88FWGT9dfHnmIqK5Kh04K3v2kp3tNPS3G+dQ/rSYtS6NG3hFkj2lOQU9vufamt1du/mqL82ulevtV1SrLWZC/HOEDR5dsor/YC0svXfPvx22Xh9J2UyW2DCwEIaVdJcaacn98L5fZH8gEIi61gzo9/oyqToauW4OarZdWQWFp2KgfAI8BYyxAUVWny3YGtVZRkyc0S57WoVTJSzjoTGmG9WiFttZTsNONdEedPuREdW86QJ7J556XW11hSsDd2c5/9xo0beeCBBwgJCbnqPHK5HA8PD5OXOV06CYZGVDFvdFRN26st9B9RSEmBAwd/Me/f2BT2FJO2Wsr5Ewpuu9u07fu2u5UkxFvr9h8Dkxen0Ov+YuaOaUduhulJPyddTlGeI53vqm1ucnDUE9tdyZkjtmn3tKdjHCAnzYnCXAduu/uKbdSj3Gz7UaOSIrmi/lUiNdRbu+LiocPNV0t+sjPpJ92I6VcEgE8LNZ6BavKSTG8HzE92xttKTTKOTnrCotQ2rVWTSIxx3KguVeM35XWjsouSfWpqKj///DNff/21RdfjrNAREl77wwwKUxPZoRJliQOFuY4sWH+RNjGVLBzXBqmsts1VWSJDW2296yKJxED/EUX8/IWPVW+1+Tf2GNPXH/gx++10Ek+4cCbelQGPFhIQWs3//utrlfVPWZLKPUMKWTwhClW5FG8/Y+mvQumARi0FJOz4KJCRk7PISpaTmeLMyMlZqFVS9nxrmRjt8Rj/t5jys5zYuTGAkVNzyUx2JjNZzqhpOcZttNM8/UI69i0m7t0WeIdqCIqqJPO0K3s3htB9WF7NPMf/54urTzXeoWqyzyr4enEEsf2LaHd3KWBMcvdMyOKnNWGEtK80ttl/5U/eRRfGrTtnljiv9PRLmRyI8yQv0xEvPy2jn8tF4aYj7gvr9Jd5YkYq8b95k5/thMJVR++BBcR2L+Wl8camKjfPagJCNPj+U+vRIsJ4J0NxviPFBdZ7NkmjNOPe+HaR7D/++GMCAgIYOHCgRdcT3amS17cn1ryf+HIGAHFf+PLp6mDu6G/8Ya/bdcZkuTnDozlxwHolsc53KQlsUc2ubfbTMc8eY9r3rTfu3jrGPJ+LT4CW1HPOLHg0gjwr3GMP8OBjxmTxxtazJtNXzoog7itj59Mv3g9G7qxn6iupuHlqOXvcjfmPt7XIPfZgn8d49C2VvPHF+Zr3zywy3rq2e7sPK2eEs/29QJyc9Ux9LQ13Tx1nj7syb0wbs22j/yxO4oeVLfnypUjKCxzwCKym5+gc7n82o2ae0jxHdr4ajrLgn4fuDDU+dOdyfcZno1VL2flKOJUlDoS0r2DSpwn4tbJMyd4vuJp576bg4aOjtNCBs0cVTH8w2mrHt7dfNbPfOI9PgPEBTMlnXXlpfAeO/ekFQI++xcxccaFm/nlvGY+7T99uwWfv2EE/EMGExGCw7aWKXq8nIiKCUaNGsXz58kYtW1ZWhqenJ/c4/AcHiX10GAMw6HS2DuHGYIdXyRK5ddpfG8UOjyeD3v723Zqk320dQh3TI3rZOoQ6pAr76lynNWj4teJzSktLzd40e8mlXHHHA0twcHS+7u/RVlex/8eFFo3VUmxesv/5559JS0vjySeftHUogiAIws1M9Ma3nf79+2PjygVBEARBuKnZPNkLgiAIgjWIh+oIgiAIws1ObzC+mrL8DUoke0EQBKF5aMZt9nbzUB1BEARBECxDJHtBEAShWZDQxCfoNXJ9y5Yto1u3bri7uxMQEMBDDz3EuXOmD2EyGAwsWrSIkJAQXFxc6NOnD6dPnzaZR61WM23aNPz8/HB1dWXw4MFkZJg+B+JaRLIXBEEQmodLT9BryqsR9u3bx5QpUzhw4ABxcXFotVr69+9PRUXt8M2vv/46q1atYu3atRw+fJigoCD69euHUln7KPDp06ezY8cOtm7dyh9//EF5eTmDBg1C14hncIg2e0EQBEGwgJ9++snk/aWnxR45coS7774bg8HAmjVrePHFFxk6dCgAmzdvJjAwkC1btjBx4kRKS0vZuHEjn3zyCffddx8An376KWFhYfz888/cf//9DYpFlOwFQRCEZsFcA+GUlZWZvNTqhj0yubTU+LhqHx/jY8eTk5PJycmhf//+NfPI5XJ69+7NX3/9BcCRI0eorq42mSckJISYmJiaeRpCJHtBEASheTCY4QWEhYXh6elZ81q2bNm1V20wMGPGDO68805iYmIAyMnJASAwMNBk3sDAwJrPcnJycHJywtvb+6rzNISoxhcEQRCERkhPTzd5Nr68AWNqTJ06lRMnTvDHH3/U+UwiMe36ZzAY6ky7UkPmuZwo2QuCIAjNgsRgaPILwMPDw+R1rWQ/bdo0vv32W/bs2UOLFi1qpgcFBQHUKaHn5eXVlPaDgoLQaDQUFxdfdZ6GuClK9ga9AYMdPcdQ6uJi6xDqECPxNYxBo7F1CDcGif2VE6aH97R1CHW8kbLf1iHUMTvSvkbi0xuqrbiyf15NWb4RDAYD06ZNY8eOHezdu5eIiAiTzyMiIggKCiIuLo7OnTsDoNFo2LdvHytWrACgS5cuODo6EhcXx/DhwwHIzs7m1KlTvP766w2O5aZI9oIgCIJgb6ZMmcKWLVv45ptvcHd3rynBe3p64uLigkQiYfr06SxdupSoqCiioqJYunQpCoWC0aNH18w7fvx4Zs6cia+vLz4+PsyaNYvY2Nia3vkNIZK9IAiC0CxcXhV/vcs3xrp16wDo06ePyfSPP/6YsWPHAjBnzhxUKhWTJ0+muLiY7t27s3v3btzd3WvmX716NQ4ODgwfPhyVSkXfvn3ZtGkTMpmswbGIZC8IgiA0D1Z+Nn5Dhm+XSCQsWrSIRYsWXXUeZ2dn3nnnHd55553GBXAZkewFQRCE5uE6noJXZ/kblP31shEEQRAEwaxEyV4QBEFoFi5/Ct71Ln+jEsleEARBaB5ENb4gCIIgCDcrUbIXBEEQmgWJ3vhqyvI3KpHsBUEQhOZBVOMLgiAIgnCzEiV7QRAEoXmw8kN17EmzT/YurjqemJ1Fz/8rxcuvmounFKx7uQWJf7taZf3Dn8mkV/9CWkSq0KilJBx156PXW5GZXDuYjpevhifnpHHbnSW4eug4ddiddYsjyEq1zIA7MbeX8ciEHKJiKvANrGbxhCj2x9WOpezlV834uencdlepMZ5D7ry3qBVZKc4WiQdgxKQset1fTIvWKjRVUhKOuvHRijAykmq3Qa/7ixgwOo82MZV4+miZPKAjSWessx8BHp2RzWMzc02mFeU5MKpzjNViqI9vkIbx87Ppdm8ZTs56MpPkrJrZkgsnFTaJRyoz8NiMbO59uAjvgGqKch2J+8KXLW8FYTA0fMhOcxr0eAEDHy8kMMw4EFLqOWc+Wx1I/B6Payx5fXRaiFvTgqM7/VDmO+ERoKHrI/n0nZaJ9J/6VnWFlB9WtOT0bm8qih3xaaGm19gcej5We4wVpMr5/rVWpMS7o9VIaNu7lIcWpeDub57BZWK6Kxn2TC5RsSp8g6pZND6S/bu8aj7v9UAxA8YUENWpEk8fHZP6tyMpwTbHVUNY+3G59qTZJ/vn30glvG0Vrz/XiqJcR+4dWsTyz8/z9L0dKMxxsvj6Y28v5btPg0g86YZMZuCJGWm8timBif93K2qVDDCwcP05tNUSljzTjopyGUOfzGLpfy+fx7ycXfQkn1EQ94UfL62/cMWnBl5+PxGtVsriCVFUlssYOj6HZZ+eZUK/WIvEAxDbXcl3nwSQeMIVqQOMnZnOa/89Z7JOZ4We0/Hu/P6DD9OXp1gkjmtJOevMCyNb17zX62yTvC5x89Syaud5TvzlzoJHIykpcCA4XENFmWX2U0OMmJzDwMfyeXN6OKmJzkTdUsnMlalUKGXs3Bhgk5jysx35aGkwWSnGoUr7DSti0ccpTOkfTWqi+S9i964PYf9ngYxceZHAKBUZJ13ZPrs1zu467nrSOFjKt6+04uJ+T0atvoh3CzWJv3uy46UIPAI1xPQvRlMpZcNj7QlpX8nELQkA7FoZxsdPtWXqjlM1Fw1N4azQk5SgYPd2XxZuSK7384R4N37/nzfPv5HW9BUKFmPTZK/Valm0aBGfffYZOTk5BAcHM3bsWBYsWIDUHEfqNTg567lzQAmLnmzNqYPGQQc+XRVCz/tLGfRYAZvfCLF4DC892cHk/eoX2rD1UDxRMRWcOuxBaHgV7TuXM/GBW0g7b7xifvflSD4/GE+fBwvYtb3h4xk3VPw+L+L3edX7WWhEFe1vq2Bi/xhS/4ln7UvhbI0/yj2DC/lpm2VO1gvGtjV5v2pOJNuOHCMqtoJTh4ylr192+AEQGKq2SAwNodNBcb6jzdZ/peGT8yjIcmLljJY103Iz/n3sbUtr36WC/bu9OPSrZ0089wwpJqpTpc1iOhjnafJ+04pgBj1eSLsuFRZJ9qlH3enYr5j295YA4BOm5ti3vmScdDWZp8t/8ml9RxkAPUbncWBLABknXYnpX0xyvDvFGXKe/99JnN2NQ1gPf/MiL9/SjQt/eRB9Z1mT44zf40n8nkvbpm6y/+UrXwACW9juN9coooOebaxYsYL169ezdu1azpw5w+uvv84bb7zRpIf9N4ZMZkDmABq1aelLXSWl4+3lVonhSgp3LQDKEuN1mKOT8V6PanXtrtLrJWirJXTsorR6fI5OxoNdUyceKR27Wm+bKf45uV3aTvYiNELDliOn2Lw/gXnvpRDU0rYnwR79S0k8oeDF95PZ9vcp3t11jgdGF9o0plOH3bi1l5LQiCoAIttX0rFbOYd/tUyVeWNJpQZ6DylGrtBzJt4yzUDhXZVc+NOT/CTjhURWgoKUeHfa9SmpmSeiq5KEn70pzXHEYIALf3lQkOxC27tLAdBpJEgk4OBUez+Yo1yPRGog5bB9bEu7Y6B2TPvred24ud62Jfv9+/czZMgQBg4cCEB4eDiff/458fHx9c6vVqtRq2tPnmVlTbtyVVXISIh3ZfT0HNIuOFOS70ifh4po17mCzGRblH4MTJifyqnD7jWl5vQkF3Iz5IydlcY7CyKpUkl5+MlsfAKq8QnQWD3C9IvO5GY4MW5OBm/PD6dKJWXo+Bwrx2Ng4oI0Th12IzXRftoHzx5z5Y3nXMhIkuPtr2XUszms/uY8E+5th7LYNj+14JYaBj1WwNcb/Nn6diBtO1cyaUkG1RoJP3/pY5OYtr8biKu7jg/3JaDXgVQGm1aEsPcb28RzSXg7FWu+u4CTXI+qQsqS8eGknbdMP5R7JmVRpZTxRt9bkMgMGHQS/m9WOp2H1F6IDVmUwpcvRPJqjy5IHfRIpDBseRIR3YwX+S07l+Ok0PG/5S15YE46GOB/y1ti0Esoy7Of2iV7ItrsbeTOO+9k/fr1JCYmEh0dzd9//80ff/zBmjVr6p1/2bJlLF682KwxvP5cODNWpvL5kVPotHDhlII9O71pE6My63oaYvKiZCLaVjJrZMeaaTqtlFenRDN92UW+OHoYnRaO/eXJ4b1eVo/vUjyvTIri+RXJfPn3UWM8f3pyaI/ntRc2kylLUoloV8nMYR2uPbMVXd6ZK+UsJMQr2PTXGfoNK+LrD2zTFi2RwvkTLny83NgkdfG0glbRVQx8vMBmyb734GL6Di1i+dRwUhNdaN2xkmcWZVCY68jPX/raJCaAjItyJveLxtVDx50DS5n1Vhqzh7axSML/+ztfju70Y/RbFwiMriQrwZVvl7TCI1BD10cKAPhjUxBpx90Y9+FZvEI1JB9yZ8dLEbgHaIi+sww3Xy2PvnuerxdE8OemICRSuHVwAaEx5UhlN25SEizDpsl+7ty5lJaW0q5dO2QyGTqdjtdee41Ro0bVO/+8efOYMWNGzfuysjLCwsKaFEN2qpzZj0Qjd9Hh6q6nKM+R+e8lkZNu+c55l5u0MJkefYuZPaojBTmmtQoXTrsxdfAtKNy0ODoZKC1yZPWXJzl/yno9zU3iOeXKlIExKNy1ODoa41mz4zTnT1o+nkmLUujRt4RZI9pTYIUOlE2hVslIOetMaITtqvKL8hzqtDmnX3DmzgGlNooInl6QybZ3g9j3rfFiI+WsCwGhGkZOzbFpstdWS2s66J0/oaDtrZU89FQ+b89t2jmmPt8va8k9k7K4dbCxJB/cTkVxppxf3wul6yMFVFdJ+OmNMJ54P7GmXT+kvfGiYN8HITXt8W3vLmXeb8epKHJAKjPg4qljcdfb8Am7QdrQrc1AE9vszRaJ1dk02W/bto1PP/2ULVu20LFjR44fP8706dMJCQnhiSeeqDO/XC5HLrdM9bpaJUOtkuHmqaVLbyUfLg21yHrqMjDp5WR69iti7piO5GZcvRRRWW7cXSGtVETFlvPJGvOfhBqjUvlPPOFVRMVW8N9VLSy4NgOTF6fSs38xc0a1t3kns4ZwdNITFqXm1EE3m8WQcNiVsNamJ/7QSDV5mbar5pW76DFc8dhRvU6CxA4f8XWpj4q5VaukSK64UUMqNdTkIV21FF113Xkkl81zOVcfY1+fC395UFHoSIf7ii0Q9U2gGXfQs2mynz17Ni+88AIjR44EIDY2ltTUVJYtW1ZvsreELr3LkEgMpF90JjRczVMLMslIkrN7m3VKGFMWJ9PnwQKWPNMWVYUMbz9ju3eFUoZGbbw96s4HCiktciA/S05420qeWZDC/jgfjv7hZZGYnBU6QlpV1bwPClMT2b4CZakxhrsGFFFa6EBelhPh7VRMWpjK/t3eHP3dclX5U5akcs+QQhZPiEJVLr1sOznUdBZ089QSEKLGN9B4j3GLSOPfUJzvSHGB5WsBnn4pkwNxnuRlOuLlp2X0c7ko3HTEfWG7tuivNwSw+ptERk7L5bfv/r+9O4+P6d7/OP6arJNEFomsJCRBxL7EEnSl7lVVfu7PctFSUVdR3NTWqq0VGq2laBXtRbVquZZWf5RQS/VeJJZKJRVrECJBkonskzm/P1JhmiBpZM5IPs/HYx4P58yZc94mZ+Zzvud8z3xdCGqZzYuDbrFoUmUemD3c4ShnBoxNJiXJhsQELYFNc+gzIsVkn7nSvDblOtE/OpJ6zQa7GoU82yud5h3v8O6ggErZXnCXdH78xIeatfPwbJBD0ml7Dn7hTdu+qQBoHQsJaK/j+7l+WGsN1KyTx/nDThzb4k7PdxOL1xO90R2P+jk4uBWQeNyR72bV5amw63gE5j5o0+WitS/Ep969g0Uv3zwCGmeTmW5F6jUbHF30uPvk4+ZV9JnzDbzvM2dGd6UIlYt9dnZ2iVvsLC0tMRhMN9qAg2Mhr01JopZ3AZnplvy8syarIn0o1Jvm/uiXBhX9QMa83++TvWv+pED2bCm6zuvqns+Idy7h4lbA7VRr9m5155tPKu/LumGzLOat/614+h/Tiu6fjfp3LeZPDMDVI58RUy/jUuv3PFtqsW5J5d6m2POVFAA+vC8XwPwJ/kRtdgcgtGsab3107/agd5aeB+CrRT589XHlF7da3gW8/cklnFwLybhlxW/H7RnfsyEpSepdbkj4xZ73hvvz2pTrDBqfTPIVGz6bUZt9W9U7APl0mi9DJl5jzJwruNQq4FayNTu+qsXXi7xUy+Tirmfiksu4eujJzrTkYryWdwcFcPygY6Vsr/esi+ya78uWaf7cuWmNk2c+HQbeoOvYpOJlBi05y855vqwbX5/sdCtq1s7jrxMvEzr43o/qpF7QsmOeLzkZVtSsk8fzY5J4Oiz5seVs2CKbDzedLZ4eObMo3+6NrswPr0eHFzKYsPDewcc7yy4BsHaBF18tqPxbl8vNAFTkq/0JHghHoyjqnZcYOnQoe/bsYfny5TRp0oQTJ04wYsQIhg0bRmRk5CNfr9PpcHZ25lmLPlhpzOco0kJrfqeYlcJCtSM8EZR809/h8EQyx3PuBvPbxz+8dFjtCCVMDOikdgQjeqWA/YYtZGRk4ORUObcM3q0VXZpOwsryz38/6wvz2PvrvErNWllUbdkvWbKEadOmMWrUKFJSUvDx8eEf//gH06dPVzOWEEIIUaWoWuwdHR1ZtGjRA2+1E0IIIR4b6aAnhBBCVHHVuNib4YU3IYQQQjxO0rIXQghRPVTjlr0UeyGEENVDNb71Toq9EEKIaqE6D4Qj1+yFEEKIKk5a9kIIIaoHuWYvhBBCVHEGBTQVKNiGJ7fYy2l8IYQQooqTlr0QQojqQU7jCyGEEFVdBYs9UuxVpbG0RKOxVDtGMUNOjtoRSjLHUcoU87tp1cLW/EYsNOQ+nrHJHyvTjABdPhbm8x1wl7mNMAew8tIBtSMYycw00Lyx2imqvipR7IUQQohHktP4QgghRBVnUKjQqXjpjS+EEEIIcyUteyGEENWDYqhYXyEz7GdUVlLshRBCVA9yzV4IIYSo4uSavRBCCCGqKmnZCyGEqB7kNL4QQghRxSlUsNg/tiQmJ6fxhRBCiCpOWvZCCCGqBzmNL4QQQlRxBgNQgXvlDXKf/ROhabtM/vcf12nQLBs3zwJmvV6f/+6uabSMb/0cwqZcpVn7TDQWCokJdswZHUjqNdMNkOLmlU/YO9dp+7wOG62BpAu2LHjLj3Ox9ibZftP2mfQdeYMGzXJw8ypgZlgA/93lct8SCoPDr/PiwFvUcNHz2wkHPpnqS2KCnUnylab/mBsMe/s6Wz+vxWcz6phkm/3eSKLTX9KoE5BDfq4Fcccd+VekL0kX770P4fPO88L/3jR63W8nHPjn35qaJONLr96kx6u38PTNByDxjJavF3oSs8/JJNuHJ2N/srBUeCX8Os//z21qehRw+4Y1UZvcWPexF4qizqg/amTKvWPJto/8OLHLjcyb1vg1zaL/zAv4t7gDwPGdbhz42ovLsTW4k2bNtJ0n8GuSZbSOgjwNmyL8if7WnfxcC4I7pTMw4jyu3vmVklmUTbUq9lr7Qi7G2xO1qRbTlp8v8by3Xy7z/x3Prg3urF3oQ5bOEr8GueTnma5rQw1nPQu2neXUfxx5d3AA6Tet8K6XT5bOdCN6ae0NXIizZ/dGN6avvFji+X6jbtDn9RTmh9fl6gUtA8cmM3fdOcKeaUxOlulHHmvYIpsXB93iQpzWpNtt1i6T7Ws9STjlgKWlwpAJV4n48jf+0a05eTn33ofo/c4snBRQPF1QYLr9KfW6Nf+a4821S0UHqy/0vc3MVZcY3a0hiQmmeb+ehP2p/6hkerySykfj65GYoKVBi2zemp9IVqYl277wMEkGc8i0ZlJ9ks7YE7YoARfPfA5v8WDhwKbM2nucml755GVbUD9ER0iPm3w5uUGp69gwK4Bf9rjy+tLfqFFTz8bZ/ix5rTHT/u+k+gMTyml8dWRmZjJt2jS2bt1KSkoKrVq14uOPP6Zt27aVsr2Y/S7E7Hf5fapksR8yMYnofS58Mde3eF7yFdMWkH6jUrh5zYb54X7F825cNe2wqzH7nInZ5/z71B+/nBV6h6WwfokXP+8sOivy0T/rsv5ELM/1vs2Or91NmlVrX8jkpYksmuTL38cmm3Tb015rZDS9cFIA62OO06BpFr9G32s5F+RbkHbTxqTZ7joS5Ww0vTrSm5devUWjNlkmK/ZPwv4U3CaL/+524eiPRTlvXLXluV5pNGiebZLtm0Om/FwLju+sxejP42jYXgfAy+GXObHblf1rvfifiZcJ/VsqADevlP6dlK2z5NAGT8IWJtD4qQwAhi9KYFKHtsQdcqHpM+mVkr3MqnGxV7U3/vDhw4mKimLt2rXExsbSrVs3unbtSlJSksmzaDQK7Z5PJ+milogvz7D+2AkWbYsjtFuaSXN06JZBwil7pi6/yIZffuWTXWfoPvCWSTM8jJdfPm6eeo4dMC5msYdr0Dgk6yGvrBxj5lzl6F4nTvzkaPJt/5G9YyEAmRnGx9DNO+j45ugxVu79hbFzLuDsVqBGPCwsFJ7plYatvYH4GAdVMvyRuexPv0bXoGWnTGr75wIQEJxNk7Z3iP7RdJc71M5k0GswFGqwtjW+Lm2jNXAu2vkBrzKWGFuDwgILGj9973vTxSuf2kHZnI9R770UKrbsc3Jy2Lx5M99++y1PP/00ADNnzmTbtm0sW7aM2bNnl3hNXl4eeXl5xdM6ne6x5XGppce+hoF+b1xnzUe1+eIDX0KeyWDa8nNMHhBE7BHT7Kjefvm89MpNtqx0Z/1iT4JaZfPGe1cpyNew59+uJsnwMK7uRYUq7abxrpN20wqP2qa9JvfMy2nUb5rDmz0amnS7pVMYMTWRX6MdSUy417ci5oALP+10JSXJFq86ebwSfpUPvopnbK+mFOSb5li7XqMcFm0/h42tgZwsC94Lq8fls6Y9Y/Ug5rI/bfzEEwfHQj4/EIehECwsYXWkD/u/Ve8zZ+pM2hqFBLbR8f1iP7zrn8HJPZ+j37pz8YQjHv45ZVqHLtUGKxsDDi6FRvOdauWjS7WujNjlU41/Lle1Yq/X6yksLESrNf7SsbOz49ChQ6W+Zu7cucyaNatS8mg0RX/E/0a5sPULLwAuxNnTuM0degxKNVmx11jA2VN2rPrAB4Dzp+2p2zCXHq/eNItiX+wPHYQ0GgDTdWRy98nnjfeSeGdgIAUm7FPxIKNmXcK/UTYT+jU2mn/w/9yK/52YYE9CrANrfjpJ2+fS+c8u0/w9r563ZdQLDXFwKqRzjwwmfHyZiX3qm03BB1Tfn555OY0ufW7zwZh6JCbYEdgkm5Ezr3LrhjV7/u326BVUkUzDFiawZmIDJrZrh4Wlgl/TO7Trncrl2BoVWq+iYMo/5wMpigGlAiPXVeS1alOt2Ds6OhIaGsr7779PcHAwnp6efPPNNxw5coQGDUrv+PH2228THh5ePK3T6fD19S112fLSpVmhL9Bw+axxD+DL57Q0aXvnsWyjLG6nWJW4lnrlnJbOL2aYLMPD3P796LymewG3U+4dqbu46UlLNd3uVL9ZNjXd9SzdeaZ4nqUVNOuQxctDb/KSfwsMBtN8u7wx4xIduqQzcUAwN5Mf3r8iLdWGlGs21K6Xa5JsAPoCi+IOemdP2RPUMpvew1NZPPnxfHYqwlz2p9ffTWLDJ14c+K7oAOzSb3Z41M5nwJhk1Yq9Gpk86uUycVMsedkW5GRa4uJZwPJRQdTyK9v+6uSejz7fgqx0S6PWfeYtGwLbZFZK5nJRlIq1zuWa/Z+zdu1aFEWhdu3a2NrasnjxYgYOHIilZeldNm1tbXFycjJ6PC76AgsSTtlTJ8B4p67tn0tKkuk6V8VFO+AbmGc0r3ZAHilJZnAKDEi+bMOtG1a0fvreJRQrawPNOtwhzoTXgU8ecmTE80G80e3e48xJO37cWpM3ugWZqNArvDHzEh3/cpspg4O5cfXRLWVHlwLcvfONCpsarG3M40vLXPYnWztDiaHKDYUaNCp+Q6qZydbegItnAVnplpw+WJOWL5St31DdZnewtDYQ99O9W5rTb1iTdMaewJDHd9lVlJ+qvfEDAwM5cOAAWVlZ6HQ6vL296d+/P/7+/pWyPa19IT717hVSL988Ahpnk5luSeo1W/693Ju3l54n9ogjv/zXkZBnM+jQNZ1J/Rs9ZK2P15aVHiz8NoEBb97g4HYXgloW3Va2aJJp7h2Hh71PVqRes2HbFx4MGHODpItaki7a8vc3k8nLsWDfNtNdZsjJsiTxjPFZmNxsCzLTSs6vLKPfu8SzL9/ivRENybljQc1aRdeYszKtyM+zQGtfyOBxVzn0gyu3U2zwrJPH0AlX0N224j+7TfNevTblOtE/OpJ6zQa7GoU82yud5h3v8O6ggEe/+DF5Evanw1HODBibTEqSDYkJWgKb5tBnRAq7N6jTqlcr068HXEABz4AcUi/ZsWlOPbwCcujYLwWArHQrbiXZknGjqAF043zRZ83ZPR9njwLsnQrp3P8Gm2b7U6NmAQ4uejbN9qd2oywad06vtNxlplTwmv0T3LLXKIr5pE9LS8Pf35958+YxYsSIRy6v0+lwdnbmOeu+WGke3VJq3kHHvA1nSsyP2uTG/AlFX37d+qXSf9R1annnc/W8lrULa3M4qmaJ1zyMoq9Yb+v2XTN4bcp1avvnkXzFhi0rPNi5roIf8HI0B5qHZvLhprMl5u/e6Mr88HoU/wjKoJs4Ohfy20kHlk71LX+RfczXv+ZtOsuFOLsK/aiOhW3Zb3PceeFIqfPnTwxgz2Z3bGwNTF+eQGDjLBycCrmdas2p/zrx5cI63Lxe9u0Ycv/8Kf9/zr9Cy86ZuHroyc605GK8lo2feHD8YAXvXijHDdMm258qwM6hkCETr9Hxrxm41CrgVrI1+7915etFXuhN+LsIpsi08tKBBz4Xvb0WWyPrkpZsi4OzntYv3qT3xETsnYpOyf+8yYPVb5XsENtz/GVeDr8MQEGuhk1z/Dm6zZ2CXAsadcpgUMQ5XH1K73CZmWmgeeMUMjIyHuvZ2vvdrRVdHAdhpfnzZ2r1Sj57M7+u1KyVRdViv2vXLhRFISgoiHPnzjFx4kRsbW05dOgQ1taPLt7lLfamUtFiXynUPB/5IGbY2aU8xd5UKlLsK43qv44i/qyHFXs1SLE3DVVP42dkZPD2229z9epVXF1d+dvf/kZERESZCr0QQghRLtX4NL6qxb5fv37069dPzQhCCCGqCcVgQNFUz1vvzPDcrhBCCCEep2o1EI4QQohqTE7jCyGEEFWcQQFN9Sz2chpfCCGEqOKkZS+EEKJ6UBSgAp3snuCWvRR7IYQQ1YJiUFAqcBrfjH6Drtyk2AshhKgeFAMVa9nLrXdCCCGEKMWnn36Kv78/Wq2WNm3a8NNPP5k8gxR7IYQQ1YJiUCr8KK8NGzYwfvx4pk6dyokTJ3jqqafo3r07ly9froT/4YNJsRdCCFE9KIaKP8ppwYIFhIWFMXz4cIKDg1m0aBG+vr4sW7asEv6DD/ZEX7O/21lCr5jXwDOKmeUpYobHdWZ4/ctC0agdoQSDOe5PZvi3E2WTmWlef7s7d4rymKLzm56CCv2mjp6iz6JOpzOab2tri20pg2jl5+dz7NgxpkyZYjS/W7du/Oc///nzQf6EJ7rYZ2ZmAvCTfpu6QZ4ET24nUtMywwHmzJJ51QtRDs0bq52gdJmZmTg7O1fKum1sbPDy8uJQ8o4Kr6tGjRr4+voazZsxYwYzZ84ssezNmzcpLCzE09PTaL6npyfJyckVzlIeT3Sx9/Hx4cqVKzg6OqLRVKxFptPp8PX15cqVK2YzdKFkKhtzy2RueUAylZVkKpvHmUlRFDIzM/Hx8XlM6UrSarVcvHiR/Pz8Cq9LUZQS9aa0Vv39/rh8aeuobE90sbewsKBOnTqPdZ1OTk5m84G6SzKVjbllMrc8IJnKSjKVzePKVFkt+vtptVq0Wm2lb+d+tWrVwtLSskQrPiUlpURrv7KZ4YVcIYQQ4slnY2NDmzZtiIqKMpofFRVFx44dTZrliW7ZCyGEEOYsPDycV155hZCQEEJDQ1mxYgWXL19m5MiRJs0hxf53tra2zJgx45HXXkxJMpWNuWUytzwgmcpKMpWNOWYyV/379+fWrVu89957XL9+naZNm7Jjxw7q1q1r0hwa5Un+sV8hhBBCPJJcsxdCCCGqOCn2QgghRBUnxV4IIYSo4qTYCyGEEFWcFHvMY/jB+x08eJCePXvi4+ODRqNh27ZtquaZO3cubdu2xdHREQ8PD3r37s2ZM2dUzbRs2TKaN29e/KMeoaGh7Ny5U9VMfzR37lw0Gg3jx49XLcPMmTPRaDRGDy8vL9Xy3JWUlMTgwYNxc3PD3t6eli1bcuzYMdXy1KtXr8T7pNFoGD16tGqZ9Ho97777Lv7+/tjZ2REQEMB7772HwaDubxVnZmYyfvx46tati52dHR07diQ6OlrVTOLRqn2xN5fhB++XlZVFixYtWLp0qWoZ7nfgwAFGjx7N4cOHiYqKQq/X061bN7KyslTLVKdOHT744ANiYmKIiYnh+eefp1evXpw+fVq1TPeLjo5mxYoVNG/eXO0oNGnShOvXrxc/YmNjVc2TlpZGp06dsLa2ZufOncTFxTF//nxcXFxUyxQdHW30Ht39EZS+ffuqlikyMpLPPvuMpUuXEh8fz7x58/jwww9ZsmSJapkAhg8fTlRUFGvXriU2NpZu3brRtWtXkpKSVM0lHkGp5tq1a6eMHDnSaF6jRo2UKVOmqJTIGKBs3bpV7RhGUlJSFEA5cOCA2lGM1KxZU/n888/VjqFkZmYqDRo0UKKiopRnnnlGGTdunGpZZsyYobRo0UK17Zdm8uTJSufOndWO8VDjxo1TAgMDFYPBoFqGHj16KMOGDTOa16dPH2Xw4MEqJVKU7OxsxdLSUvn++++N5rdo0UKZOnWqSqlEWVTrlv3d4Qe7detmNF+N4QefJBkZGQC4urqqnKRIYWEh69evJysri9DQULXjMHr0aHr06EHXrl3VjgLA2bNn8fHxwd/fnwEDBnDhwgVV83z33XeEhITQt29fPDw8aNWqFStXrlQ10/3y8/P56quvGDZsmMkHK7lf586d2bt3LwkJCQD88ssvHDp0iBdffFG1THq9nsLCwhK/MW9nZ8ehQ4dUSiXKolr/gp45DT/4pFAUhfDwcDp37kzTpk1VzRIbG0toaCi5ubnUqFGDrVu30rixuuN3rl+/nuPHj5vNNcz27dvz5Zdf0rBhQ27cuMHs2bPp2LEjp0+fxs3NTZVMFy5cYNmyZYSHh/POO+9w9OhRxo4di62tLa+++qoqme63bds20tPTGTp0qKo5Jk+eTEZGBo0aNcLS0pLCwkIiIiL4+9//rlomR0dHQkNDef/99wkODsbT05NvvvmGI0eO0KBBA9VyiUer1sX+LnMYfvBJMWbMGE6dOmUWR/FBQUGcPHmS9PR0Nm/ezJAhQzhw4IBqBf/KlSuMGzeO3bt3m3x0rQfp3r178b+bNWtGaGgogYGBrFmzhvDwcFUyGQwGQkJCmDNnDgCtWrXi9OnTLFu2zCyK/RdffEH37t0rdcjVstiwYQNfffUV69ato0mTJpw8eZLx48fj4+PDkCFDVMu1du1ahg0bRu3atbG0tKR169YMHDiQ48ePq5ZJPFq1LvbmNPzgk+DNN9/ku+++4+DBg499aOE/w8bGhvr16wMQEhJCdHQ0H3/8McuXL1clz7Fjx0hJSaFNmzbF8woLCzl48CBLly4lLy8PS0tLVbLd5eDgQLNmzTh79qxqGby9vUsckAUHB7N582aVEt2TmJjInj172LJli9pRmDhxIlOmTGHAgAFA0cFaYmIic+fOVbXYBwYGcuDAAbKystDpdHh7e9O/f3/8/f1VyyQerVpfszen4QfNmaIojBkzhi1btvDjjz+a7YdaURTy8vJU236XLl2IjY3l5MmTxY+QkBAGDRrEyZMnVS/0AHl5ecTHx+Pt7a1ahk6dOpW4dTMhIcHkA4OUZtWqVXh4eNCjRw+1o5CdnY2FhfFXtKWlpeq33t3l4OCAt7c3aWlp7Nq1i169eqkdSTxEtW7Zg/kMP3i/O3fucO7cueLpixcvcvLkSVxdXfHz8zN5ntGjR7Nu3Tq+/fZbHB0di8+EODs7Y2dnZ/I8AO+88w7du3fH19eXzMxM1q9fz/79+/nhhx9UyQNF1zP/2I/BwcEBNzc31fo3TJgwgZ49e+Ln50dKSgqzZ89Gp9Op2jL85z//SceOHZkzZw79+vXj6NGjrFixghUrVqiWCYouL6xatYohQ4ZgZaX+V2PPnj2JiIjAz8+PJk2acOLECRYsWMCwYcNUzbVr1y4URSEoKIhz584xceJEgoKCeO2111TNJR5B1XsBzMQnn3yi1K1bV7GxsVFat26t+i1l+/btU4ASjyFDhqiSp7QsgLJq1SpV8iiKogwbNqz4b+bu7q506dJF2b17t2p5HkTtW+/69++veHt7K9bW1oqPj4/Sp08f5fTp06rluWv79u1K06ZNFVtbW6VRo0bKihUr1I6k7Nq1SwGUM2fOqB1FURRF0el0yrhx4xQ/Pz9Fq9UqAQEBytSpU5W8vDxVc23YsEEJCAhQbGxsFC8vL2X06NFKenq6qpnEo8kQt0IIIUQVV62v2QshhBDVgRR7IYQQooqTYi+EEEJUcVLshRBCiCpOir0QQghRxUmxF0IIIao4KfZCCCFEFSfFXgghhKjipNgLUUEzZ86kZcuWxdNDhw6ld+/eJs9x6dIlNBoNJ0+efOAy9erVY9GiRWVe5+rVq3FxcalwNo1Gw7Zt2yq8HiHEnyPFXlRJQ4cORaPRoNFosLa2JiAggAkTJpCVlVXp2/74449ZvXp1mZYtS4EWQoiKUn+0ByEqyV//+ldWrVpFQUEBP/30E8OHDycrK4tly5aVWLagoABra+vHsl1nZ+fHsh4hhHhcpGUvqixbW1u8vLzw9fVl4MCBDBo0qPhU8t1T7//6178ICAjA1tYWRVHIyMhgxIgReHh44OTkxPPPP88vv/xitN4PPvgAT09PHB0dCQsLIzc31+j5P57GNxgMREZGUr9+fWxtbfHz8yMiIgKgeLjgVq1aodFoePbZZ4tft2rVKoKDg9FqtTRq1IhPP/3UaDtHjx6lVatWaLVaQkJCOHHiRLnfowULFtCsWTMcHBzw9fVl1KhR3Llzp8Ry27Zto2HDhmi1Wl544QWuXLli9Pz27dtp06YNWq2WgIAAZs2ahV6vL3ceIUTlkGIvqg07OzsKCgqKp8+dO8fGjRvZvHlz8Wn0Hj16kJyczI4dOzh27BitW7emS5cu3L59G4CNGzcyY8YMIiIiiImJwdvbu0QR/qO3336byMhIpk2bRlxcHOvWrcPT0xMoKtgAe/bs4fr162zZsgWAlStXMnXqVCIiIoiPj2fOnDlMmzaNNWvWAJCVlcVLL71EUFAQx44dY+bMmUyYMKHc74mFhQWLFy/m119/Zc2aNfz4449MmjTJaJns7GwiIiJYs2YNP//8MzqdjgEDBhQ/v2vXLgYPHszYsWOJi4tj+fLlrF69uviARghhBlQedU+ISjFkyBClV69exdNHjhxR3NzclH79+imKoigzZsxQrK2tlZSUlOJl9u7dqzg5OSm5ublG6woMDFSWL1+uKIqihIaGKiNHjjR6vn379kqLFi1K3bZOp1NsbW2VlStXlprz4sWLCqCcOHHCaL6vr6+ybt06o3nvv/++EhoaqiiKoixfvlxxdXVVsrKyip9ftmxZqeu6X926dZWFCxc+8PmNGzcqbm5uxdOrVq1SAOXw4cPF8+Lj4xVAOXLkiKIoivLUU08pc+bMMVrP2rVrFW9v7+JpQNm6desDtyuEqFxyzV5UWd9//z01atRAr9dTUFBAr169WLJkSfHzdevWxd3dvXj62LFj3LlzBzc3N6P15OTkcP78eQDi4+MZOXKk0fOhoaHs27ev1Azx8fHk5eXRpUuXMudOTU3lypUrhIWF8frrrxfP1+v1xf0B4uPjadGiBfb29kY5ymvfvn3MmTOHuLg4dDoder2e3NxcsrKycHBwAMDKyoqQkJDi1zRq1AgXFxfi4+Np164dx44dIzo62qglX1hYSG5uLtnZ2UYZhRDqkGIvqqznnnuOZcuWYW1tjY+PT4kOeHeL2V0GgwFvb2/2799fYl1/9vYzOzu7cr/GYDAARafy27dvb/ScpaUlAIqi/Kk890tMTOTFF19k5MiRvP/++7i6unLo0CHCwsKMLndA0a1zf3R3nsFgYNasWfTp06fEMlqttsI5hRAVJ8VeVFkODg7Ur1+/zMu3bt2a5ORkrKysqFevXqnLBAcHc/jwYV599dXieYcPH37gOhs0aICdnR179+5l+PDhJZ63sbEBilrCd3l6elK7dm0uXLjAoEGDSl1v48aNWbt2LTk5OcUHFA/LUZqYmBj0ej3z58/HwqKo+87GjRtLLKfX64mJiaFdu3YAnDlzhvT0dBo1agQUvW9nzpwp13sthDAtKfZC/K5r166EhobSu3dvIiMjCQoK4tq1a+zYsYPevXsTEhLCuHHjGDJkCCEhIXTu3Jmvv/6a06dPExAQUOo6tVotkydPZtKkSdjY2NCpUydSU1M5ffo0YWFheHh4YGdnxw8//ECdOnXQarU4Ozszc+ZMxo4di5OTE927dycvL4+YmBjS0tIIDw9n4MCBTJ06lbCwMN59910uXbrERx99VK7/b2BgIHq9niVLltCzZ09+/vlnPvvssxLLWVtb8+abb7J48WKsra0ZM2YMHTp0KC7+06dP56WXXsLX15e+fftiYWHBqVOniI2NZfbs2eX/QwghHjvpjS/E7zQaDTt27ODpp59m2LBhNGzYkAEDBnDp0qXi3vP9+/dn+vTpTJ48mTZt2pCYmMgbb7zx0PVOmzaNt956i+nTpxMcHEz//v1JSUkBiq6HL168mOXLl+Pj40OvXr0AGD58OJ9//jmrV6+mWbNmPPPMM6xevbr4Vr0aNWqwfft24uLiaNWqFVOnTiUyMrJc/9+WLVuyYMECIiMjadq0KV9//TVz584tsZy9vT2TJ09m4MCBhIaGYmdnx/r164uf/8tf/sL3339PVFQUbdu2pUOHDixYsIC6deuWK48QovJolMdx8U8IIYQQZkta9kIIIUQVJ8VeCCGEqOKk2AshhBBVnBR7IYQQooqTYi+EEEJUcVLshRBCiCpOir0QQghRxUmxF0IIIao4KfZCCCFEFSfFXgghhKjipNgLIYQQVdz/A026Ge+8DVi0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_classes = model.predict(X_test)\n",
    "\n",
    "true_labels = np.argmax(Y_test,axis=1)\n",
    "\n",
    "predicted_labels = np.argmax(predicted_classes,axis=1)\n",
    "\n",
    "cm = confusion_matrix(true_labels, predicted_labels)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "\n",
    "print(\"Accuracy: \" + str(accuracy_score(true_labels, predicted_labels)))\n",
    "print(\"Weighted Precision: \" + str(precision_score(true_labels, predicted_labels, average='weighted')))\n",
    "print(\"Weighted Recall: \" + str(recall_score(true_labels, predicted_labels, average='weighted')))\n",
    "print(\"Weighted F1 Score: \" + str(f1_score(true_labels, predicted_labels, average='weighted')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "87ba23a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#chosen_idx = np.argwhere(np.array(best_heights) == lh_select)\n",
    "\n",
    "#ab_lws = []\n",
    "#for idx in chosen_idx:\n",
    "#    ab_lws.append(best_widths[idx[0]])\n",
    "\n",
    "#lws_unique, lws_counts = np.unique(ab_lws, return_counts=True)\n",
    "#lws_data = dict(zip(lws_unique, lws_counts))\n",
    "\n",
    "#lw_x_values = list(lws_data.keys())\n",
    "#lw_y_values = list(lws_data.values())\n",
    "#lw_x_values = [str(r) for r in lw_x_values]\n",
    "  \n",
    "#fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "#plt.bar(lw_x_values, lw_y_values, color ='maroon', \n",
    "#        width = 0.1)\n",
    "\n",
    " \n",
    "#plt.xlabel(\"Heights\")\n",
    "#plt.ylabel(\"# of instances\")\n",
    "#plt.title(\"Layer Heights in Trials\")\n",
    "#plt.xticks(lw_x_values,rotation=45)\n",
    "#plt.show()\n",
    "\n",
    "#lw_mode = statistics.mode(sorted(ab_lws))\n",
    "#print(lw_mode)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "596d333e",
   "metadata": {},
   "source": [
    "if trial == 0:\n",
    "    best_models = []\n",
    "    best_histories = []\n",
    "    best_accuracies = []\n",
    "    best_lrs = []\n",
    "    best_ms = []\n",
    "    best_widths = []\n",
    "    best_heights = []\n",
    "    trial = 0"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6bc66948",
   "metadata": {},
   "source": [
    "find_best_model_in_trial = []\n",
    "for history in histories:\n",
    "    find_best_model_in_trial.append(history.history['loss'][-1])\n",
    "\n",
    "index = np.nanargmin(find_best_model_in_trial)\n",
    "best_models.append(models[index])\n",
    "best_histories.append(histories[index])\n",
    "best_accuracies.append(histories[index].history['accuracy'][-1])\n",
    "best_lrs.append(learning_rates[index])\n",
    "best_ms.append(momentums[index])\n",
    "best_widths.append(layer_widths[index])\n",
    "best_heights.append(layer_heights[index])\n",
    "\n",
    "print(\"Chosen Model: \" + str(index))\n",
    "print(\"Final Loss: \" + str(find_best_model[index]))\n",
    "print(\"Final Accuracy: \" + str(histories[index].history['accuracy'][-1]))\n",
    "print(\"Final Learning Rate: \" + str(learning_rates[index]))\n",
    "print(\"Final Momentum: \" + str(momentums[index]))\n",
    "print(\"Layer Width: \" + str(layer_widths[index]))\n",
    "print(\"Layer Height: \" + str(layer_heights[index]))\n",
    "print(\"Trial: \" + str(trial))\n",
    "trial += 1"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9a69358b",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c64efa99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 1.9838 - accuracy: 0.2856\n",
      "Test accuracy: 0.2856000065803528\n"
     ]
    }
   ],
   "source": [
    "score = best_model.evaluate(X_test, Y_test)\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5b6835c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(best_histories[idx].history['accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='lower right')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(best_histories[idx].history['loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e3262dbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "results = best_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9002e9fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 4ms/step\n",
      "Accuracy: 0.7894\n",
      "Weighted Precision: 0.7933314405941213\n",
      "Weighted Recall: 0.7894\n",
      "Weighted F1 Score: 0.789827671440989\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_classes = best_model.predict(X_test)\n",
    "\n",
    "true_labels = np.argmax(Y_test,axis=1)\n",
    "\n",
    "predicted_labels = np.argmax(predicted_classes,axis=1)\n",
    "\n",
    "cm = confusion_matrix(true_labels, predicted_labels)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "\n",
    "print(\"Accuracy: \" + str(accuracy_score(true_labels, predicted_labels)))\n",
    "print(\"Weighted Precision: \" + str(precision_score(true_labels, predicted_labels, average='weighted')))\n",
    "print(\"Weighted Recall: \" + str(recall_score(true_labels, predicted_labels, average='weighted')))\n",
    "print(\"Weighted F1 Score: \" + str(f1_score(true_labels, predicted_labels, average='weighted')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e9f2ffe0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 5ms/step\n",
      "313/313 [==============================] - 1s 4ms/step\n",
      "313/313 [==============================] - 1s 5ms/step\n",
      "313/313 [==============================] - 2s 5ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred_ensemble = np.zeros((Y_test.shape))\n",
    "\n",
    "#print(y_pred_ensemble.shape)\n",
    "\n",
    "#count = 0\n",
    "#for i in range(y_pred_ensemble.shape[0])\n",
    "for model in models:\n",
    "    y_pred_ensemble += model.predict(X_test)\n",
    "\n",
    "y_pred_ensemble /= len(models)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ab475ba",
   "metadata": {},
   "source": [
    "### Extensions to the project\n",
    "1. Alter parameters of layers (Number of perceptrons, more Convolution and pooling layers) \n",
    "2. Preprocessing on the data (filter?)\n",
    "3. Ensemble learning\n",
    "4. Utilize Graphcore IPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a3eb4380",
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(y_pred_ensemble)\n",
    "#print(np.argmax(y_pred_ensemble, axis=1))\n",
    "ensemble_pred = np.argmax(y_pred_ensemble, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f8ac6685",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8404\n",
      "Weighted Precision: 0.8431929486961726\n",
      "Weighted Recall: 0.8404\n",
      "Weighted F1 Score: 0.8405224380378155\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#predicted_classes = ensemble_pred\n",
    "\n",
    "true_labels = np.argmax(Y_test,axis=1)\n",
    "\n",
    "predicted_labels = ensemble_pred\n",
    "\n",
    "cm = confusion_matrix(true_labels, predicted_labels)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "\n",
    "print(\"Accuracy: \" + str(accuracy_score(true_labels, predicted_labels)))\n",
    "print(\"Weighted Precision: \" + str(precision_score(true_labels, predicted_labels, average='weighted')))\n",
    "print(\"Weighted Recall: \" + str(recall_score(true_labels, predicted_labels, average='weighted')))\n",
    "print(\"Weighted F1 Score: \" + str(f1_score(true_labels, predicted_labels, average='weighted')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "095c6a69",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}