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Diffstat (limited to 'scripts/tensorflow/prediction-v2-norm.ipynb')
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diff --git a/scripts/tensorflow/prediction-v2-norm.ipynb b/scripts/tensorflow/prediction-v2-norm.ipynb new file mode 100644 index 0000000..a013912 --- /dev/null +++ b/scripts/tensorflow/prediction-v2-norm.ipynb @@ -0,0 +1,11720 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras import optimizers, regularizers\n", + "from keras.layers import Dense, Dropout, Input\n", + "from keras.models import Model, Sequential\n", + "from random import shuffle\n", + "from scipy import interp\n", + "from sklearn.linear_model import LogisticRegression\n", + "from scipy.stats.mstats import gmean\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import roc_curve, auc\n", + "from sklearn.model_selection import StratifiedKFold, train_test_split\n", + "from sklearn.preprocessing import QuantileTransformer\n", + "import contextlib\n", + "import glob\n", + "import gzip\n", + "import h5py\n", + "import keras\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "import pylab as plt\n", + "import random\n", + "import scipy\n", + "import sklearn\n", + "import tensorflow as tf\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import make_classification\n", + "\n", + "\n", + "\n", + "random_state = np.random.RandomState(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "X_f = '/home/drewe/notebooks/genotox/GenoTox-database.csv'\n", + "y_f = '/home/drewe/notebooks/genotox/outcome-mod-2.csv'\n", + "\n", + "X = pd.read_csv(X_f).values[:,:-1]\n", + "y = pd.read_csv(y_f).values\n", + "\n", + "\n", + "ix = [i for i in range(y.shape[0])]\n", + "shuffle(ix)\n", + "X = X[ix, :]\n", + "y = y[ix]\n", + "names = pd.read_csv(X_f)['Unnamed: 0'][ix].values\n", + "X = sklearn.preprocessing.quantile_transform(X, axis=0, output_distribution='uniform', copy=True)\n", + "y = y[: ,0]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.4109 - acc: 0.8058\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3981 - acc: 0.8133\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.4038 - acc: 0.8046\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3869 - acc: 0.8196\n", + "Epoch 5/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3724 - acc: 0.8258\n", + "Epoch 6/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3895 - acc: 0.8194\n", + "Epoch 7/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3761 - acc: 0.8219\n", + "Epoch 8/50\n", + "7272/7272 [==============================] - 0s 39us/step - loss: 0.3636 - acc: 0.8332\n", + "Epoch 9/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3596 - acc: 0.8288\n", + "Epoch 10/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.3411 - acc: 0.8392\n", + "Epoch 11/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.3469 - acc: 0.8391\n", + "Epoch 12/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.3447 - acc: 0.8441\n", + "Epoch 13/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3425 - acc: 0.8409\n", + "Epoch 14/50\n", + "7272/7272 [==============================] - 0s 39us/step - loss: 0.3313 - acc: 0.8436\n", + "Epoch 15/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3159 - acc: 0.8545\n", + "Epoch 16/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3230 - acc: 0.8584\n", + "Epoch 17/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.3212 - acc: 0.8553\n", + "Epoch 18/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2947 - acc: 0.8688\n", + "Epoch 19/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.3099 - acc: 0.8623\n", + "Epoch 20/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3064 - acc: 0.8628\n", + "Epoch 21/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2890 - acc: 0.8711\n", + "Epoch 22/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3001 - acc: 0.8674\n", + "Epoch 23/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.3063 - acc: 0.8599\n", + "Epoch 24/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2808 - acc: 0.8740\n", + "Epoch 25/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2679 - acc: 0.8782\n", + "Epoch 26/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2628 - acc: 0.8859\n", + "Epoch 27/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2655 - acc: 0.8805\n", + "Epoch 28/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2608 - acc: 0.8827\n", + "Epoch 29/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2845 - acc: 0.8700\n", + "Epoch 30/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2455 - acc: 0.8903\n", + "Epoch 31/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2503 - acc: 0.8893\n", + "Epoch 32/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2449 - acc: 0.8860\n", + "Epoch 33/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2559 - acc: 0.8886\n", + "Epoch 34/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2434 - acc: 0.8908\n", + "Epoch 35/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2328 - acc: 0.8958\n", + "Epoch 36/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2326 - acc: 0.8981\n", + "Epoch 37/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2380 - acc: 0.8944\n", + "Epoch 38/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2162 - acc: 0.9064\n", + "Epoch 39/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2256 - acc: 0.9006\n", + "Epoch 40/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2275 - acc: 0.8959\n", + "Epoch 41/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2220 - acc: 0.9025\n", + "Epoch 42/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2103 - acc: 0.9076\n", + "Epoch 43/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2416 - acc: 0.8926\n", + "Epoch 44/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2079 - acc: 0.9051\n", + "Epoch 45/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.2303 - acc: 0.8951\n", + "Epoch 46/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2055 - acc: 0.9103\n", + "Epoch 47/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2086 - acc: 0.9109\n", + "Epoch 48/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.2075 - acc: 0.9102\n", + "Epoch 49/50\n", + "7272/7272 [==============================] - 0s 33us/step - loss: 0.1959 - acc: 0.9116\n", + "Epoch 50/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2170 - acc: 0.9040\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7272/7272 [==============================] - 0s 44us/step - loss: 0.3747 - acc: 0.8229\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.3632 - acc: 0.8311\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.3711 - acc: 0.8265\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.3593 - acc: 0.8317\n", + "Epoch 5/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.3483 - acc: 0.8355\n", + "Epoch 6/50\n", + "7272/7272 [==============================] - 0s 41us/step - loss: 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33/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2248 - acc: 0.9015\n", + "Epoch 34/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2191 - acc: 0.9028\n", + "Epoch 35/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2245 - acc: 0.9006\n", + "Epoch 36/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2254 - acc: 0.9026\n", + "Epoch 37/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2263 - acc: 0.8959\n", + "Epoch 38/50\n", + "7272/7272 [==============================] - 0s 34us/step - loss: 0.2261 - acc: 0.8998\n", + "Epoch 39/50\n", + "7272/7272 [==============================] - 0s 36us/step - loss: 0.2013 - acc: 0.9120\n", + "Epoch 40/50\n", + "7272/7272 [==============================] - 0s 35us/step - loss: 0.1990 - acc: 0.9149\n", + "Epoch 41/50\n", + "7272/7272 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loss: 0.2579 - acc: 0.8845\n", + "Epoch 34/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2486 - acc: 0.8861\n", + "Epoch 35/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2573 - acc: 0.8833\n", + "Epoch 36/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2371 - acc: 0.8934\n", + "Epoch 37/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2364 - acc: 0.8930\n", + "Epoch 38/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2241 - acc: 0.8985\n", + "Epoch 39/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2326 - acc: 0.8988\n", + "Epoch 40/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2256 - acc: 0.8989\n", + "Epoch 41/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2235 - acc: 0.8977\n", + "Epoch 42/50\n", + "7272/7272 [==============================] 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"7272/7272 [==============================] - 0s 38us/step - loss: 0.3869 - acc: 0.8211\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3745 - acc: 0.8229\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3641 - acc: 0.8273\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3596 - acc: 0.8324\n", + "Epoch 5/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3581 - acc: 0.8322\n", + "Epoch 6/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.3583 - acc: 0.8351\n", + "Epoch 7/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3404 - acc: 0.8442\n", + "Epoch 8/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3326 - acc: 0.8480\n", + "Epoch 9/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3256 - acc: 0.8549\n", + "Epoch 10/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.3261 - acc: 0.8476\n", + "Epoch 11/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3244 - acc: 0.8511\n", + "Epoch 12/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3177 - acc: 0.8567\n", + "Epoch 13/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3123 - acc: 0.8574\n", + "Epoch 14/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.3115 - acc: 0.8560\n", + "Epoch 15/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2961 - acc: 0.8665\n", + "Epoch 16/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2873 - acc: 0.8659\n", + "Epoch 17/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2867 - acc: 0.8735\n", + "Epoch 18/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2890 - acc: 0.8684\n", + "Epoch 19/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2697 - acc: 0.8783\n", + "Epoch 20/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2793 - acc: 0.8735\n", + "Epoch 21/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2671 - acc: 0.8800\n", + "Epoch 22/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2686 - acc: 0.8797\n", + "Epoch 23/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2633 - acc: 0.8839\n", + "Epoch 24/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2598 - acc: 0.8823\n", + "Epoch 25/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2733 - acc: 0.8761\n", + "Epoch 26/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2393 - acc: 0.8914\n", + "Epoch 27/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2345 - acc: 0.8926\n", + "Epoch 28/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2338 - acc: 0.8967\n", + "Epoch 29/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2364 - acc: 0.8938\n", + "Epoch 30/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2410 - acc: 0.8940\n", + "Epoch 31/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2403 - acc: 0.8915\n", + "Epoch 32/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2193 - acc: 0.9026\n", + "Epoch 33/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2267 - acc: 0.8992\n", + "Epoch 34/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2047 - acc: 0.9097\n", + "Epoch 35/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2197 - acc: 0.9042\n", + "Epoch 36/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2153 - acc: 0.9028\n", + "Epoch 37/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2000 - acc: 0.9098\n", + "Epoch 38/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2106 - acc: 0.9044\n", + "Epoch 39/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2156 - acc: 0.9039\n", + "Epoch 40/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2004 - acc: 0.9110\n", + "Epoch 41/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.1857 - acc: 0.9186\n", + "Epoch 42/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.1993 - acc: 0.9097\n", + "Epoch 43/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.2016 - acc: 0.9131\n", + "Epoch 44/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.2030 - acc: 0.9058\n", + "Epoch 45/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.1882 - acc: 0.9152\n", + "Epoch 46/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.1992 - acc: 0.9079\n", + "Epoch 47/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.1865 - acc: 0.9213\n", + "Epoch 48/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.1716 - acc: 0.9241\n", + "Epoch 49/50\n", + "7272/7272 [==============================] - 0s 37us/step - loss: 0.1760 - acc: 0.9216\n", + "Epoch 50/50\n", + "7272/7272 [==============================] - 0s 38us/step - loss: 0.1768 - acc: 0.9183\n", + " 0.6616299019607843\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.40160879, 0.23865981, 0.0192348 , ..., 0.20569094, 0.98110449,\n", + " 0.58497733])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(64, input_dim=X.shape[1], activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(64, activation='relu'))\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + " \n", + " # Fit the model\n", + " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", + " \n", + " # evaluate the model\n", + " probas_ = model.predict(X[test,:])\n", + " prbs.append(probas_)\n", + " # Average the predictions\n", + " probas_ = np.mean(np.hstack(prbs), axis=1)\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v3.norm.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7fe47ca5b470>]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WAS8459aY2YNmdpXXbD6wz8zWAguBu51z+9qqaBGReHbNmD5tvo6QH4UEcM7NA+Y1mvZAvWEH3OX9iIhIM5IS2/76UV2hKiLiQwp3EREfirtwD/QAiYjEr2h8oDvuwl1EREKLu3DXRUwiIqHFXbiLiEhoCncRER9SuIuI+JDCXUTEhxTuIiJRFo0PdCvcRUSiLBqX6yjcRUR8SOEuIuJDCncRER9SuIuI+JDCXUTEhxTuIiJRFo2vyFK4i4j4kMJdRMSHFO4iIlGmm3WIiPiQ+txFRKRVFO4iIj6kcBcR8SGFu4iIDyncRUSiTF/5KyLiQ7pZh4iItIrCXUTEhxTuIiI+FFa4m9k0Mys0syIzu6eZdp83M2dm+ZErUUREWipkuJtZIjALuAIYAcwwsxFB2qUD3wQWR7pIERFpmXDO3McBRc65Tc65KmA2cHWQdj8BHgGORrC+Uxw4crwtFy8i0ubayxeH9QW21xsv9qbVMbOxQD/n3NwI1iYiIq0UTrgHO8jUfUzTzBKAR4HvhFyQ2UwzKzCzgtLS0vCrFBGRFgkn3IuBfvXGc4Cd9cbTgVHAO2a2BTgfmBPsTVXn3DPOuXznXH5WVlbrqxYRkWaFE+5LgSFmlmdmycD1wJzamc65A865TOdcrnMuF/gQuMo5V9AmFYuIxLl28X3uzrlq4A5gPrAOeME5t8bMHjSzq9q6QBERabmkcBo55+YB8xpNe6CJthNPvywRETkdukJVRMSHFO4iIj6kcBcR8SGFu4hIlOlmHSIi0ioKdxERH1K4i4j4kMJdRMSHFO4iIj6kcBcR8SGFu4hIlLWLLw4TEZH4o3AXEfEhhbuIiA8p3EVEfEjhLiLiQwp3EREfUriLiESZvhVSRMSHopDtCncRET9SuIuIRJm6ZUREpFUU7iIiPqRwFxGJMheFt1QV7iIiPqRwFxHxIYW7iIgPKdxFRKJNH4UUEfEfXaEqIuJDLgpXMSncRUR8KKxwN7NpZlZoZkVmdk+Q+XeZ2VozW2VmC8xsQORLFRGRcIUMdzNLBGYBVwAjgBlmNqJRs+VAvnPuTOAl4JFIFyoi4hftpc99HFDknNvknKsCZgNX12/gnFvonDvsjX4I5ES2TBER/2gvXxzWF9heb7zYm9aUW4HXg80ws5lmVmBmBaWlpeFXKSLiI+3lzN2CTAtam5l9CcgHfhlsvnPuGedcvnMuPysrK/wqRUSkRZLCaFMM9Ks3ngPsbNzIzCYD9wOXOOeORaY8ERFpjXDO3JcCQ8wsz8ySgeuBOfUbmNlY4GngKudcSeTLFBHxj3bxOXfnXDVwBzAfWAe84JxbY2YPmtlVXrNfAmnAi2a2wszmNLE4EZFPvWj0uYfTLYNzbh4wr9G0B+oNT45wXSIi/tVOPi0jIiJxRuEuIuJDCncRkSgb1iu9zdehcBcRibKUpLaPXoW7iIgPKdxFRHxI4S4i4kMKdxERH1K4i4hEWXv5VkgREYkzCncRER9SuIuI+JDCXUTEhxTuIiJR1l7uoSoiInFG4S4i4kMKdxERH1K4i4j4kMJdRCTKXBSuUVW4i4j4kMJdRMSHFO4iIj6kcBcR8SGFu4hIlOkKVRERHzJr+3Uo3EVEoiwxCumucJfT8sSMsbEuQSTuJCjcpb3Ly+wc6xJE4k5CgsJdRIBOyYmxLkEi6IZx/dt8HQp3kUaSonBWJZ9uqVE4WIcV7mY2zcwKzazIzO4JMj/FzP7qzV9sZrmRLrTWLz43uq0WLY0Myjq9LpcF37kkQpXEL70nIbESMtzNLBGYBVwBjABmmNmIRs1uBfY75wYDjwK/iHShtb54btu/nPk0ePaWcSHbjMvLqBu+ZkyfFq9jUFZai//Gbz57VssfN2lbi+6eFOsSoiKcM/dxQJFzbpNzrgqYDVzdqM3VwLPe8EvAZWbR+CRny/3Pv5/dZsu+eEhmyDZvfHtCm62/KWkpSadMG5TVmXG5GUFan3T/9JPH8OG9uwRtk9hEF8ZfZ57fYPyG8z4dB+WbLshtk+UmJ0W/B3XisKyILCejc3JElhMp7TOZIi+cPaYvsL3eeLE3LWgb51w1cADoEYkCg/nGpYNPmTaqb8PwmXxGz6B/m9M9Nej0p750auifl9d8+DX2nxMG0SExsOec4YXh3VOHNWgzOCuNLQ9P5+z+3UIu75KhgSdXekoSL3/9gibbffWSQadM+/70M+qGX7n9Atb/ZFqD+YkJxgtfHc+ZOV2DLvPS4T0bHBQ6JCbQI8iTdHivdL49eSjXnZPTYPp5Axtu/p9fO/qUOr8//Qz+ePO5AHxv2jC2PDyd9I6nHoiaM7Z/t9PuPqr1h5vPpVunDvzkmlENlh+u704ZSkozIdytU4cG4/0ygu+LwTx+/cnunW9eNoRrxvRp0YnK7EYH28bTgz2nJg0L/hxqTmqHU/uSa7dxJKR3TOIHnxnB1JHZXDQ49MlUYxOHZZGUePrp3vh53RLn5nY/7fWHw1yI62DN7DpgqnPuK974fwDjnHPfqNdmjdem2Bvf6LXZ12hZM4GZAP379z9n69atrS78SFUNT767kdU7DvD2+hKW3H8Zew4c47klW6k54fjZtaPpkJjAul0Heerdjdw5eSirdxzgs2f1Cfz+zXvMuuFszhnQnYpj1QzKSmPNzgOs33WIrWWHycvsxLVjc3hjzW7u/OsK7rh0MAMyOpOYYKzffbDubGTaqF78+B9rSUlK4P9cdxb7KquoOFpNVnoKxfuPMKxXOsu37efo8RNsK6us61Y6eryGgi372X3wKJ8/J4f7Xv6Y/hmd2Fl+hL8tK+bvX7+QYb3SeWbRRi4d3pPBPdPZfeAozy3eygWDM3l1xU4SLPBkuu/KM5j78S4GZ6WR3SWFZVv3M2VkL4pKKninsISvXDwQgMLdh3hlxQ4y01K49aK8usfSOcfTizZxuKqGy8/IZtGGUm4Y15/unZOpOFbNo29+wvemDSMlKZH3Nuzlp6+tpWeXjvzsmlH0y+gEQM0Jx/Jt+znhYOu+Sq7L7wfAI/9cT2ZaCrdclEd1zQneXLuHxZvLuGRoFpOGnxoeZZVV/Pdbn9AltQOThvfkSFUNWekp/OmDLUwb2ZvNeytISkygV5eOLN1SxnemDCMxwVi8aR9DstMpOXSU1TsO8o+VO+nTLZUpI7JZsqWMmy7I5d1PSklJSuCNNXuYMjKbb81ewWXDe/Kl8QNIMKs7mALsqzgGQPdOyTy1aCPdUpPZWlbJ0+9u4vZJgzgvrwclh47x2qqdlB0+zq+/cBZ5PTpz+HgNTyzYwIShWSSYMX5QD/70wRYWbyrjsevH8E5hKYW7D9IjLYVpI3sx9idvktE5mS/k9yN/QHf2H67i7pdWMf3M3uQP6E5uj86UVhzjC/n9WFVcjmGM9g7INSccv5xfyKi+XaiqPsHCwlLO7t+N7WVH6JScyJSR2ezYf4Rpo3phZryxZjfLtu3nO5cP45M9hyg5dJRLh2fz3oa9nD8wg90Hj3LLH5fyH+NzyUpL5tLh2cxaWMTXJg7iXxv2ktE5mb8s3krn5CRSkhK46cJc/rFyFxOHZXH7Xz7iaxMHMaZfN94pLGXRhlL+tWEvL351POfmZvBOYQl//2gHuw4c4fnbzufQ0WpeKNjOyuJyrjqrL51TEqk54XAOFqzfw+FjNQzM6kx6xw4kGDz61gb+ePO5DM1Op6N3AKk54ag54SgqqeCh19dx8ZBMzsvrwdWz3q/bjm/dNYE/fbCV5MQExvbvzvQzewNwyS8XYsCLX72AZ/93CxOGZlF+uIrROV3p3TWV6poTDL7/de67cjiXnZHNquJyrhjVm9dX72JQVhoj+3Tl4dfX0TO9I5OG9+S/F2xgTL9uvFiwndsuHsjSLWXMXrqdK0b1qjvg3z1tOFv3VnJBKw5K9ZnZMudcfsh2YYT7eOBHzrmp3vi9AM65h+q1me+1+cDMkoDdQJZrZuH5+fmuoKAgrH9GREQCwg33cLpllgJDzCzPzJKB64E5jdrMAW70hj8PvN1csIuISNsK2cHpnKs2szuA+UAi8Hvn3BozexAocM7NAf4v8GczKwLKCBwAREQkRsJ698o5Nw+Y12jaA/WGjwLXRbY0ERFpLV2hKiLiQwp3EREfUriLiPiQwl1ExIcU7iIiPhTyIqY2W7FZKdDaS1Qzgb0RLCdSVFf42mNNoLpaoj3WBP6va4BzLuQX/8Qs3E+HmRWEc4VWtKmu8LXHmkB1tUR7rAlUVy11y4iI+JDCXUTEh+I13J+JdQFNUF3ha481gepqifZYE6guIE773EVEpHnxeuYuIiLNiLtwD3Wz7jZY3+/NrMTMVteblmFmb5rZBu93d2+6mdnjXm2rzOzsen9zo9d+g5ndGGxdLaipn5ktNLN1ZrbGzL7VTurqaGZLzGylV9ePvel53o3TN3g3Uk/2pjd5Y3Uzu9ebXmhmU0+nLm95iWa23MzmtqOatpjZx2a2wswKvGkx3Ybe8rqZ2Utmtt7bx8bHsi4zG+Y9RrU/B83sznbyWH3b29dXm9nz3nMg5vsWELgLT7z8EPjK4Y3AQCAZWAmMaON1TgDOBlbXm/YIcI83fA/wC2/4SuB1wIDzgcXe9Axgk/e7uzfc/TRq6g2c7Q2nA58QuHl5rOsyIM0b7gAs9tb3AnC9N/0p4Gve8NeBp7zh64G/esMjvG2bAuR52zzxNLfjXcBzwFxvvD3UtAXIbDQtptvQW+azwFe84WSgW3uoy1tuIoGbAQ2IdU0Ebi+6GUitt0/d1B72Ledc3IX7eGB+vfF7gXujsN5cGoZ7IdDbG+4NFHrDTwMzGrcDZgBP15veoF0E6nsVuLw91QV0Aj4CziNw4UZS421I4B4B473hJK+dNd6u9du1spYcYAFwKTDXW0dMa/KWsYVTwz2m2xDoQiCwrD3VVW85U4D320NNnLx3dIa3r8wFpraHfcs5F3fdMuHcrDsasp1zuwC837U3A22qvjar23tpN5bAWXLM6/K6P1YAJcCbBM5Cyl3gxumN19HUjdUjXddjwPeAE954j3ZQE4AD3jCzZRa4vzDEfhsOBEqBP3jdWL8zs87toK5a1wPPe8Mxrck5twP4FbAN2EVgX1lG+9i34i7cg922vD193Kep+tqkbjNLA/4G3OmcO9ge6nLO1TjnxhA4Wx4HnNHMOtq8LjP7DFDinFtWf3Isa6rnQufc2cAVwO1mNqGZttGqK4lAN+STzrmxQCWBLo9Y14XXd30V8GKoptGoyevjv5pAV0ofoDOBbdnUOqKaD/EW7sVAv3rjOcDOGNSxx8x6A3i/S7zpTdUX8brNrAOBYP+Lc+7v7aWuWs65cuAdAn2e3Sxw4/TG66hbvze/K4HbNEayrguBq8xsCzCbQNfMYzGuCQDn3E7vdwnwMoGDYay3YTFQ7Jxb7I2/RCDsY10XBILzI+fcHm881jVNBjY750qdc8eBvwMX0A72LYi/cA/nZt3RUP+G4DcS6POunf5l793684ED3svF+cAUM+vuHe2neNNaxcyMwH1r1znnft2O6soys27ecCqBnX8dsJDAjdOD1VVbb/0bq88Brvc+XZAHDAGWtKYm59y9zrkc51wugf3lbefcv8eyJgAz62xm6bXDBB771cR4GzrndgPbzWyYN+kyYG2s6/LM4GSXTO26Y1nTNuB8M+vkPSdrH6uY7lt1TrfTPto/BN4J/4RAX+79UVjf8wT6044TOMLeSqCfbAGwwfud4bU1YJZX28dAfr3l3AIUeT83n2ZNFxF42bYKWOH9XNkO6joTWO7VtRp4wJs+0NtZiwi8pE7xpnf0xou8+QPrLet+r95C4IoIbcuJnPy0TExr8ta/0vtZU7svx3obessbAxR42/EVAp8sifW+1QnYB3StN609PFY/BtZ7+/ufCXzipV3s77pCVUTEh+KtW0ZERMKgcBcR8SGFu4iIDyncRUR8SOEuIuJDCncRER9SuIuI+JDCXUTEh/4/+aCgyO7lq7cAAAAASUVORK5CYII=\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "results[test] = probas_\n", + "plt.plot(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + ".\n", + "Epoch 1/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6205 - acc: 0.6522\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6192 - acc: 0.6506\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6182 - acc: 0.6555\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6197 - acc: 0.6536\n", + "Epoch 5/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6192 - acc: 0.6562\n", + "Epoch 6/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6194 - acc: 0.6528\n", + "Epoch 7/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6172 - acc: 0.6550\n", + "Epoch 8/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6179 - acc: 0.6526\n", + "Epoch 9/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6167 - acc: 0.6568\n", + "Epoch 10/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6200 - acc: 0.6535\n", + "Epoch 11/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6179 - acc: 0.6602\n", + "Epoch 12/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6176 - acc: 0.6591\n", + "Epoch 13/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6201 - acc: 0.6528\n", + "Epoch 14/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6199 - acc: 0.6536\n", + "Epoch 15/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6160 - acc: 0.6562\n", + "Epoch 16/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6169 - acc: 0.6566\n", + "Epoch 17/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6169 - acc: 0.6577\n", + "Epoch 18/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6164 - acc: 0.6561\n", + "Epoch 19/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6156 - acc: 0.6555\n", + "Epoch 20/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6155 - acc: 0.6568\n", + "Epoch 21/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6168 - acc: 0.6558\n", + "Epoch 22/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6167 - acc: 0.6587\n", + "Epoch 23/50\n", + "7272/7272 [==============================] - 0s 27us/step - loss: 0.6151 - acc: 0.6586\n", + "Epoch 24/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6167 - acc: 0.6568\n", + "Epoch 25/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6168 - acc: 0.6581\n", + "Epoch 26/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6176 - acc: 0.6575\n", + "Epoch 27/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6145 - acc: 0.6624\n", + "Epoch 28/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6168 - acc: 0.6536\n", + "Epoch 29/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6193 - acc: 0.6531\n", + "Epoch 30/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6149 - acc: 0.6581\n", + "Epoch 31/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6155 - acc: 0.6594\n", + "Epoch 32/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6169 - acc: 0.6565\n", + "Epoch 33/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6153 - acc: 0.6614\n", + "Epoch 34/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6143 - acc: 0.6612\n", + "Epoch 35/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6150 - acc: 0.6598\n", + "Epoch 36/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6142 - acc: 0.6565\n", + "Epoch 37/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6152 - acc: 0.6631\n", + "Epoch 38/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6145 - acc: 0.6555\n", + "Epoch 39/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6144 - acc: 0.6632\n", + "Epoch 40/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6145 - acc: 0.6588\n", + "Epoch 41/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6137 - acc: 0.6586\n", + "Epoch 42/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6130 - acc: 0.6624\n", + "Epoch 43/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6134 - acc: 0.6630\n", + "Epoch 44/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6151 - acc: 0.6572\n", + "Epoch 45/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6139 - acc: 0.6594\n", + "Epoch 46/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6159 - acc: 0.6612\n", + "Epoch 47/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6150 - acc: 0.6597\n", + "Epoch 48/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6130 - acc: 0.6632\n", + "Epoch 49/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6127 - acc: 0.6581\n", + "Epoch 50/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6137 - acc: 0.6580\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6192 - acc: 0.6510\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6200 - acc: 0.6528\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6193 - acc: 0.6526\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6184 - acc: 0.6517\n", + "Epoch 5/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6207 - acc: 0.6522\n", + "Epoch 6/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6202 - acc: 0.6533\n", + "Epoch 7/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6184 - acc: 0.6575\n", + "Epoch 8/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6192 - acc: 0.6546\n", + "Epoch 9/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6182 - acc: 0.6558\n", + "Epoch 10/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6189 - acc: 0.6542\n", + "Epoch 11/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6189 - acc: 0.6590\n", + "Epoch 12/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6187 - acc: 0.6573\n", + "Epoch 13/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6188 - acc: 0.6531\n", + "Epoch 14/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6196 - acc: 0.6507\n", + "Epoch 15/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6198 - acc: 0.6528\n", + "Epoch 16/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6190 - acc: 0.6587\n", + "Epoch 17/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6183 - acc: 0.6575\n", + "Epoch 18/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6169 - acc: 0.6566\n", + "Epoch 19/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6176 - acc: 0.6587\n", + "Epoch 20/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6166 - acc: 0.6564\n", + "Epoch 21/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6196 - acc: 0.6576\n", + "Epoch 22/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6182 - acc: 0.6584\n", + "Epoch 23/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6183 - acc: 0.6518\n", + "Epoch 24/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6173 - acc: 0.6565\n", + "Epoch 25/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6189 - acc: 0.6515\n", + "Epoch 26/50\n", + "7272/7272 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0.6561\n", + "Epoch 46/50\n", + "7272/7272 [==============================] - 0s 24us/step - loss: 0.6136 - acc: 0.6609\n", + "Epoch 47/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6137 - acc: 0.6620\n", + "Epoch 48/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6118 - acc: 0.6588\n", + "Epoch 49/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6114 - acc: 0.6610\n", + "Epoch 50/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6117 - acc: 0.6614\n", + ">>\n", + ".\n", + "Epoch 1/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6182 - acc: 0.6562\n", + "Epoch 2/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6173 - acc: 0.6553\n", + "Epoch 3/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6186 - acc: 0.6575\n", + "Epoch 4/50\n", + "7272/7272 [==============================] - 0s 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[==============================] - 0s 22us/step - loss: 0.6163 - acc: 0.6558\n", + "Epoch 14/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6153 - acc: 0.6592\n", + "Epoch 15/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6165 - acc: 0.6554\n", + "Epoch 16/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6161 - acc: 0.6565\n", + "Epoch 17/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6143 - acc: 0.6566\n", + "Epoch 18/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6147 - acc: 0.6616\n", + "Epoch 19/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6166 - acc: 0.6551\n", + "Epoch 20/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6139 - acc: 0.6544\n", + "Epoch 21/50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6150 - acc: 0.6535\n", + "Epoch 22/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6143 - acc: 0.6564\n", + "Epoch 23/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6140 - acc: 0.6587\n", + "Epoch 24/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6146 - acc: 0.6558\n", + "Epoch 25/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6150 - acc: 0.6595\n", + "Epoch 26/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6181 - acc: 0.6553\n", + "Epoch 27/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6136 - acc: 0.6532\n", + "Epoch 28/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6172 - acc: 0.6526\n", + "Epoch 29/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6141 - acc: 0.6597\n", + "Epoch 30/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6126 - acc: 0.6588\n", + "Epoch 31/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6135 - acc: 0.6591\n", + "Epoch 32/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6140 - acc: 0.6586\n", + "Epoch 33/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6144 - acc: 0.6612\n", + "Epoch 34/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6127 - acc: 0.6566\n", + "Epoch 35/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6130 - acc: 0.6612\n", + "Epoch 36/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6132 - acc: 0.6581\n", + "Epoch 37/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6175 - acc: 0.6586\n", + "Epoch 38/50\n", + "7272/7272 [==============================] - 0s 21us/step - loss: 0.6142 - acc: 0.6577\n", + "Epoch 39/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6125 - acc: 0.6608\n", + "Epoch 40/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6148 - acc: 0.6564\n", + "Epoch 41/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6126 - acc: 0.6588\n", + "Epoch 42/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6118 - acc: 0.6638\n", + "Epoch 43/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6130 - acc: 0.6562\n", + "Epoch 44/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6135 - acc: 0.6612\n", + "Epoch 45/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6138 - acc: 0.6581\n", + "Epoch 46/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6117 - acc: 0.6605\n", + "Epoch 47/50\n", + "7272/7272 [==============================] - 0s 23us/step - loss: 0.6113 - acc: 0.6609\n", + "Epoch 48/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6128 - acc: 0.6584\n", + "Epoch 49/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6122 - acc: 0.6642\n", + "Epoch 50/50\n", + "7272/7272 [==============================] - 0s 22us/step - loss: 0.6123 - acc: 0.6586\n", + " 0.6677879901960784\n" + ] + }, + { + "data": { + "image/png": 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kQAxwanFSPFFyMoIc1ypEagsDLTuK3spJNNdHSk42bVvl0iIrk5yMdLKcDjKDATLWrzMNgqZB69bQqRO43Qd0TfYXe0azjc1BwufzUVxcTElJCTk5OUycOBEwK9MLL7yQ8ePHs3LlShYvXsxXX33FK6+8AkBJSQm333477777LsuXL6ekpIQuXbrUi3/RokXEYjGKi4sZPXr0Qcv3zJkzKS0tpbS0lNdff51bb721wXBvvfUWl1xyCY6kt9fCwkIGDhzItGnTmp3ejTfeSE5ODqWlpSxdupQpU6awc+fOQ1qGcDiMy+Vi/vz5LF68mMWLF3P88cczatQoPB4P706eRIusdFYs/oY7fnUj99/zG+KBcnp3bc/G9RtZungzW8pS2LA+g507UqiuchGNChqKFHeUtOwQ6W2qSGkVIiVHx5spKI+GzyW0cnlpm5JBG186Ga46nTNbt5pjTnUdUlOhSxdo0QIOY1eobRRsbA4BgwcPZtOmTQC8//77nHrqqZx99tkApKSk8PLLL/OHP/wBgD/96U/89re/pWfPngA4nU5uu+22WvFt376dq6++muLiYvLz81m9ejWzZs2ioKCAE088kbFjxxKJROrlY/LkyfTo0YOhQ4cyb968BvM6ffp0xowZg4hwyimnUFFRwZYt9d177733HhdddFFie/Xq1fj9fp544gkKCwubdV1Wr17N/PnzeeKJJ9A0s/rp0qUL5513XrPOb4zGyhAOhxPdQh6PB5fLhR7YzfLib9i+bSunFPQm5t/F3/7xCVdefS0hlcrZ513NrDlfsmlnKms2ZTBo8Ejem/oJoagbQ4HLGSM9JUTbHD+d2oRo1TpOeq6Ltm4nbd0uct2a2RLIzCArIx1HenrjGU9PN7uI2rWDjh0PW+sgmaNukR0bm+bw2bJtBz3O4b1bNyucruvMmjWLG264ATC7jk466aRaYbp27Yrf76eqqoqSkhLuueeeJuNs1aoVkyZN4tlnn+WTTz4hHA4zbNgwZs2aRY8ePRgzZgyvvvoqd911V+KcLVu28Mgjj7Bw4UIyMzM544wzKCgoqBf3pk2b6NBhz0DA9u3bs2nTJtq2bZvYF41GWbNmDcmrHhYWFnLllVdy+umns2LFCrZv306rVq2aLMfSpUvJz8+v1dpojNGjR7NixYp6+++++27GjBnTaBnC4TDt2rVjzZo1nHTSSbhcLoxQBbFodeINvPDjT7j0slGEVQahkMb6jTsw9M6sW+dAKSE1NZMNGyrIzs6lT+/+TJ78J7Lv+BUuVwxfmgunxwPBICIGQXHgiMXQNK1pAwAQiUAwCNnZ5nZaGnTrdlh8B41hGwWbY5LmVuAHk1AoRH5+PuvWreOkk07irLPOAsz+7sZGhOzvSJEVK1bQuXNnevToAcC1117LxIkTaxmF+fPnM2zYMFq2NFdgHD16NCtXrqwXV7Jvo7F87dy5k6ysrFr7ioqKmDZtGpqmcckll/DBBx8wbty4g1bWpvwmddF1nUgkQnVlBUoBSqHicWLhMPHq3URiLqIqlUhYiMU13nvvA556cgrr12koFLpuoBtxkBhOl0LTIKelonVbnXgkh/LdW0jNcBJ3unB7nTgcDqqdLsTnwyFChnMvlbpSsGsX7Nhhfvd4ICXFPHYEGQSwjYKNzUGjxqdQWVnJ+eefz8SJE7nzzjvp06cPX3zxRa2wa9asIS0tjfT0dPr06cPChQvp169fs9NqqCJviOZUxO3bt2fjxj3iAmVlZbRr165WGJ/PV2t0zpIlSygtLU0Yvmg0SpcuXRg3bhwtWrRg9+7dtc4vLy8nNzeXrKwsFi9ejGEYie6jxmispXDHHXdw1VVXAXuuQ5vWrdmwfh2DBpyEy+tl06YysrJbUr7bSSDYgpi+J60VKxcT13V69u2POHU87igdOrRFZA09uncgHo8TCFTRo0M2oNgYj5CS4sOX5iIYjhAIm452ESGzro+gIcJh2LzZ/A+QmXlEdBM1hu1TsLE5yGRmZvLiiy/y7LPPEovF+OUvf8ncuXP57LPPALNFceedd3L//fcDcN999/HUU08l3uINw2DChAlNptGzZ0/WrVvHqlWrAHjnnXcYOnRorTCDBg1izpw57Nq1i1gsxgcffNBgXBdeeCFvv/02Sim+/vprMjMza3UdAWRnZ6PresIwFBYW8uijj7Ju3TrWrVvH5s2b2bRpE+vXr2fgwIHMmzePrVu3ArBgwQIikQgdOnSga9euDBgwgEceeSRRoZeWljJ9+vR6+Zo6dSrFxcUUFxezaNEiFi5cyLx58xg1ahQigh6NoEcjqGAl5599Ju+//z6xaIxP/zUPry8bpfXEH0xB4cTtNsjJFtq0hq/mFnHVlaPo0dVPt04BOraLM3LkhUyd+lcMw+DDDz9k6NChqHAYYiFWlS7nhJ69CIQjIEJ2RjrZGelkpac1+RthGLB9O6xdaxoEl8v0Gxx3HDiP3PfxIzdnNjZHMQUFBfTr14+ioiKuueYapk+fzh133MG4cePQdZ1rrrkmMWwzLy+P559/niuvvJJgMIiI7NXx6vV6mTx5MpdffjnxeJyBAwfyq1/9qlaYtm3b8uijjzJ48GDatm1L//79aw0nrWHEiBHMmDGDbt26kZKSwuTJkxtM8+yzz2bu3LkMHz6coqIiZs6cWev4yJEjKSoq4oEHHuCFF15gxIgRGIZBWloahYWFiZbBpEmTuOeeexLptWjRgmeeeSYRT81cAAAiVeyuCia1jASngG41gBwuF4bDxc/OupyPZ35FXv9T8Hp9PP74JDTR8aXGufD8QXw591N8XvPtfPr0D/jb34oQAZFUlFJce+213DDmOvr06k1WdjZvTJpM0OXEkepj1tdfc86FF5CdsRd/QV22b4fycvN7Tg60bHnEdRU1hDS3GXqkMGDAANXQGGybnzbLly+nV69ehzsbxzSLFi1iwoQJvPPOOwc13nAgQCRYjVKG6Q9IkkDTlWA43GRlZgAQjAetucEQj2lEwg6C1S4iYbOyFQGvN05mmkZaRgARZcYZV2jK7MNXkTAoc3ZxwOEwTxKzO0hL9SJJPoJIJMLQoUOZO3cuzn19u4/HYeNGc95Bjf/gR6Kh50FEFiqlBuztXLulYGNj0ywKCgo444wz0HW9ydFDkWCgWT6PUFTHMAwMpfA6NNyp2QkfSHWoOlH5u1wO/LEAsaiDaNiFinkJhYR4vCYmA6dDkZERJTMzCoaO062hAhHQvQigaRriigIgHgeO9HREhHAs3qRfYMOGDfzhD39onkHw+6GiwuweEjG7iDp1OqxzDvYH2yjY2Ng0m7Fjxya+N1b5iwgxh4e6h2q6hZRSGMoAh0a60+wui8aF6nD1njgQMnwZRKNC5W6hulrD7PkyMFsSCk1T+HxxfD6dVK8TEQeEBNGdEFZomg9ni3TTINSpmKviepOjwmro3r073bt3b/qi6Dps22YaBDAnodUMOT3KDALYRsHGxmY/iAQDAHhT6ztbq8MxUJDmcZgtAetTI5YvIog4CcaDVIejKIcbRFC40OKpRKMQjQrl24VodE+8Llccr1cnJcWF16sQpYMSwIkIOOIhFKCl+XCmuGuNbqoxAjU0e+TQ3qiqMmclx+OmAWjZEuoM3T3asI2CjY1NozTVGvCkpFIdjtVqEZgtAQOfU4hG9wzd1DQNLeonYISTun1A071UVWcSCgkN+MDRNEhPV2RmGjidfhyONMAgHjEAcGqYUtThMArBmZqCM8V0KCcbgoNmBGqIx01jUFVlbqekQNu25vyDoxzbKNjY2DRZ+TfWGoiEYojsaRHUKIfWnFfTNaNF/UTiOmEjhnK48bq9aJqHXbs0qqr2dK84HOD1KjwehdsNbrfC7VaJHhjDwDQGMQMBHC4NpUC5QGIGkfQMIgCxeCIPB9UQ1LoA1aZB0DRo1crsLjoKu4oawjYKNjY/UZINQWOVf2MopUh1O4jH44kWQU3ffTSpz0fiIQACykFc96FCPipCEImIlS5kZhpkZSkcDj8qZtRKx7CiMuIGIGjKQHNp4BYMyyfgCIcJuNxozZlZfCAYhmkEwOwiikZNY3AET0TbH+zJazY2B4mjTTpbKYU3NY11G8sYduZwPB4Pzz77bINxVodjVAajDBl2Bhu27CAejxOLxZg+fbo5k/v7YqpCVVSFqpg1ZxaXXn4poXCQHZVuNm7P5Kbr7mLquzOoqBD8/jjPPfcg559/Apdd1peLLjyZ//zrY4gZEPdhqNR6HxxpOHxpSIppEDRNwxmN4oxECGgONJ+Pic/8iW7dunHCCSfw73//u8FyKKX47W9/S48ePejVqxcvvvgiALt372bkyJHk5eVx8sknU1JSApgztYcMGUJ8+3ZYtYqEk0PEHGp6jBkEsI2Cjc1B40iXzo5FIsRjMcIBP+GAP9G9k5OTw4svvtigIapBKcV/P/sX+Xkn0io7nTSPk7AR5v2p7zNo8CA+/uhjMnwZpHsz0KIuYmEH27fmEPCno8c0RMDtjpOTE2LKlPGEQutZ+PVsvp33BR+89xFV1TF0lYpyari85sfpERxucLjB5XWYRsAyBFooZLYSMjIQr5eylSsoKipi6dKl/Otf/+K2225rcKLelClT2LhxIz/88APLly/niiuuAOCpp54iPz+fJUuW8Pbbb/PrX/8aALdSnHnSSUydMsX0I9T4EI5hbKNgY3MIOBKksyPBAK+/9irdu3fj9NNO439ff43T5cKbmoY3NQ1PSipgKrAOHDgQl8uVOFcplfATmK2COG+/+zbDRwwnZIQIxAP4/X6++d83vDrxVf728XR27NBYs0bYWelCNwTRFBkZYdq1CpCeqmiRAW49xDtvv82fn3wenzsHV2oGx3U5jtFXXo7TI2guEqOVRASn04kzGsURDpvbGRk4MjIIpKTi9/qojMUREaZPn84VV1yBx+Ohc+fOdOvWjW+++abe7/Lqq6/y8MMPJ0Ym1ai6Llu2jDPPPBPYIyGybdkyWLOGi08/nff++U9o3x5ycw/sxjgKsH0KNscmK2buPcy+csK5zQp2uKSzX3rhBW4fdxuGrhMJBdmyZStPPPX0XqWza6gxBLFYrNZbdiBmVtDzv57PX179C6nOVKLRKDP+9neGDj2T9PTepKTk8NW8hfTuXYDbaTqKu3RRaHEN8CAOQQdWbVxLh44dyGmXk4i/xgBompbwS4gId999N7NnzTIDJQ0vveKKK7j1nntrOZE3bdrEKaecktiukf+uy+rVq5k6dSrTpk2jZcuWvPjii3Tv3p1+/frx8ccfc9ppp/HNl1+yfv16ypYupXWfPvQdOJBvly2DjIwmf6NjBdso2BybNLMCP5gcLunsSDDAlaNG8Zc3Xufe++9Hczjw+FIo/v77JqWzExPJrDdzXdfRdT2hYBqI6igFmgiiRajYvRufT6OqKkJVlcZ7733C1Vf/mkjExYgRVzBrViHnnNufLVs1XJb8NArwaBi6eQ2cbq1e+i6XC03TMPx+U/LaOu3Z3/8eHnuMgC+l3sioutetOfLfYPplvF4vCxYs4OOPP2bs2LF8+eWXjB8/nl//+tfk5+dzYu/eFPTqZa6Z0LEjjrQ03G431dXVpO9tvYRjANso2NgcJH4M6WxD102fQDCQ+C4iuH0+NEf9x7mhijHZCNQdQhozIBA1EIIICp/LrMSD0Sia5mL16jRENCoqdjF//mxWrSrBoQlKmd04L7z4R1q0aEFF+W50fxxraQN2795NqzYt6dy5Mxs3bqSyspKsrCwcDgeapqFbC9Y7kt7Gx/3613wxZw6CoCUV44orrmD8+PG1ytQc+e+acJdeeilgCvhdf/31AGR4PEx+6y1zEp1SdO7Uic6nn24ugsMeY/JTwPYp2NgcZA6tdLY5YqhfQX82bNxI2ZateFJS9yqdHY1G+eCDD9B1nWg0SmUwij9qEIwpgjFFIGoQjUcQorilGic6TvESiXjYusPF5g3ZdDq+B2UbV+Nzx/l6XhG/vGI0a9euYtXqUlavWUunzl2YN28e3Tp2YfOWLSxfsxJHqoOyLev5vmQJeXl5pKenM3bsWO6//36UUvgNxQ+lq5hUNDXhI6j5PP3nCXy/eDFLFhcnJLSLi4vrGQQw5b9bk2qqAAAgAElEQVSLioqIRCKsXbuW0tJSTj755HrhLr74Yj7//HMA/vvf/5qLFG3dSsWiRUR37QJMFdchQ4eSYUlV7Nq1i5YtW9byuRzL2C0FG5tDwKGQzo6GQ9RoRTRHOrtNmzY8/PDDDB48mNatW5OXl0ckphOMKRwSwusy49q6dRunnnom1dXVaKLxysuv889/LsDjcWEY1gQ00Thz+AhWr/kvZw7vwj9mfLRntFLUAAUXn38RRe8XcdrJP2PSG2/xq9tuJhKJ4Ha7eeONNxJO3cfGj+ehxx6jd8+euLxe0lNSePzJJw9oolmfPn0YNWoUvXv3xul0MnHixIRo34gRI5g0aRLt2rVj/Pjx/PKXv+S5554jzedj0kMPQXk5y9euZcxVV+Fwu+nduzdvvvlmIu7Zs2czYsSI/c7b0YYtnW1zTHCsS2fXaA3VjBhqimT/QA1KBYnEzYlhPomCAod4MBQEwxCNOYhEPFQGHCg0NDE7ETQNPB5FTo5B5c4yxt58IzOn/7N2ggJYvoJYREcZCkTwqChaQz4TkcRaxpV7USk9JOi6KVFRWWlue73Qrp35vwEuueQSnn76aU444YQfMZMHhi2dbWNzDNNcg1BjDGKxKlNxQYRIzLAEqAWXDh6nEImmUhH0EgxqRKNmZV4jU41ARpqQkaHj8YDTueelMaV1W2648QaqIn4y6ozEiYV1jHAYATxuQURDGlnIviquo5KkKH5UwmHYsKG2gF2LFo1KVESjUS6++OKjyiAcKLZRsLE5gtmbQUieT2AYBkoFraGdZnhHvAqfS4jrQnmVm6qgj3jcrAAVCkThdBq4XAZpKU4yMsztBFbXEAACl112GfGIQSxsSlgnHNWahtej4czIaFSmuoZDqkm0N9xus/nTTAE7t9vNmDFjfqTMHRnYRsHG5gimRooiGXMuQZVlBGpGD+0ZQbQ7auCI7wAUsaiLreXphEMulNmrg+Yw8KXG8aXEyUn3WC/JGmBA1ED3GyR3KiunNR5FgQrFQcDp1hCx5hWEQogCNI1q3Whw+OhhMwJKmbOQ09JMxT1Ng+OPNxfAOUYE7A42tlGwsTkCqRGrExHi8ep6cwoAHI40RCQxnwAgYgRx6mFc8XR2VPqIRMwK3aEJqammBHVKigIc1seipkUgYFhSE8nUpO9wuHA4HKhAgESiIom5BAKHzwDUJRqFLVsgEDCF69q2Nff/REYR7S9HyK9nY2NTQ02XkdOjktYt9mEYuiX9sOcNNxDVccUD6FoUBbjiDvwVLfCHzIrP4RBLhdSo5R8A6nUN4dGIRwxE9hiBGjRNw+VyoQKWsqqlOwRm9xAcQcbAnBgB27ebyqYOx4++RvLRzBHyK9rY2NSgG0FcHk/CGJijiPTEQjXJC9s7Ywa6I46upRCpTmP3bnONAU0zyM2FjAwdTTPXIbB8u0h8j88guWsIy0/gcJvdUA6HI9ElJSIYfj/VCOLzmeckOYsPqWT1vhCJmK2DYNDczsiANm3M7iKbZmFPXrOxOUgciHT236dNo3//fHqe0J2C/oN54J4HUaEIKlyJMx7AGQ8Qq97JWeePpOBnZzDpg2ls16EKBxXVmWzbmEF5uWkQbrzxDLZv/x9ZKXG0mAHWwjSF77/DvffehdOt4Uxz4kh14HDDXz8q5JRTB3LKqQMZ/oufs3z5clwuFxIKQSCA8vsxqqupUnDhhRcgoSCZLieZLieff/IPMl1Ofvjhh0RZ5syZw/nnn1/r2lx33XV8+OGHAMRiMcaPH0/37t3p27cvJ598MjNnHrhW1dNPPEG37t054Ywz+Pf//gcdOpgidkkGYb+ls5OXizvGsY2Cjc1BYr+ks8NVfPfVf7nr7t/wf5NepGThfBZ+V0ynbicQ0NLwk0qVSqFKpTC3uJRo3GDBtwu48PzRxCuzqNyShb/Ci2GAz6do0yaM02mgaSqhO4RHM1sETkFpYDjNyrGmS6hbt27MmTOHxYsX89t77+WWm27CSJKdqFEl/WzObE7Kz681HLWwsJDTTjuNoqKiZl+nhx56iC1btlBSUkJJSQn/+Mc/qLbS21+WLVtG0QcfsHTOHP5VWMhtTz2F3kCX0T5LZ7vdnHnmmU2uX3GscUiNgoicIyIrRGSViNSbmy4iHUVktogsEpElIvLTmTZoc0zTHOnsp59+kmCkij+/PJHxD95Lt159qVJeosrB1dffBJhLXWqOCDt3beTWm6/n+yVL6NdvIMULNvOf/3zOpZeexKWX9uMPfxhLy5ZBPB4jUdkD/N///R99+vThF+eexVdffYWmabjdbtxuN1o4DIEAP+vXjyyXC+X303fgyZRt2YLfl1JLdgJgWlERF110UaKMfr+fefPm8eabbzbbKASDQd544w1eeuklPNZw0NatWzNq1Kh9v8iGYfoNQqE90tmdO9P5lFMOjnT2tm2AKY3x3nvv7Xv+jlIOWUebiDiAicBZQBnwrYj8XSm1LCnY74C/KqVeFZHewAyg06HKk81Phzkb5xz0OId1GNascHuTzo4EA7RtnUHAHyRGOsuWl/Lru+4lFPeZQ0u1SGIyWSAOSgnZqV149JFJvPnmn3nllX8gEuKmG4czY8a/6NWrO2PHjuUvf/lLLamLso2beOyxx/jiv/NokZ3DOecOpyAvD+X3U4WAJPkHLN597TVGnHtug07jefPm8Ze//CWx/be//Y1zzjmHHj16kJOTw3fffUf//v2bvDarVq2iY8eO9Sa/NcRvfvMbZs+eXW//FVdcwfg774TNm80RRn4/m8rKOGXw4MQw0wOSzv7mG1M6u6yM1q1b07dvX7799tu95vdY4VB6X04GViml1gCISBFwEZBsFBRQc3dkApsPYX5sfkI0twI/mOxNOjsU2JH47tIMELN/HUi8uaa6HQTiilRnKpGIUFVlLm5vGKY6g8MBHTrorFy5nC5dOtGrV3cArr76al577TV+9atfoXSFHtD5ZtW3DB06lE6dO1DpD3DRJZeyet1a/L6UBtcznj17Nu9NnszcuXMbLF95eXkt6ejCwkLuuusuwKyoCwsL6d+//0GTCX/uuefq79R1s3Wwbp257fFAmzao+iH3Xzr7xBMpKCjAafkiHA6HLZ19kDgO2Ji0XQYMqhPmUeBTEbkDSAWGNxSRiNwM3AzQsWPHg55RG5uDQVPS2bM/+xc3XXsJXncayjAoXbWB1NQ0MjIy6N27N9999x3Hde8McY1wwEV5lYNweE+l5vEoWrQw8HoVXu+eOQtRa83geDxuzmoOm8NDtRQN3etENwwqqk15bZ/Xg1vTGmwFLFmyhBtvvJGZM2fSokWLBsvndDoTay3s2rWLzz//nJKSEkQEXTeHy/7pT3+iRYsW7N69u9a55eXl5Obm0q1bNzZs2NCsCrZeS8EwIBbjinPOYfzNN5uroLVoAZp24NLZGRlMnjwZMI14586d6dy5c+K8n5J0dmI88sH+AJcDk5K2rwFeqhPmbuAe6/tgzFaE1lS8J510krKxqcuyZcsOdxZUampq4vs333ypOnRorwKBnWrXlpWq0/Ed1YwZM1QoFFLl5eXq3HPPVRMmTFCRSEQtWLBAde7SWc36cqFasUJXS5bE1H33PauWLdNVWVlMVVVFVSQSUZ9++qk699xzVSQSUZWVlap9+/Zq6dKlKlIdUldf8Uv1p6f+pEKVATVkyBA1+3//U8tXlqqOHTqonTt3qmg0qk477TQ1bty4evlev3696tq1q5o3b16T5Rs0aJAqLS1VSin12muvqZtvvrnW8SFDhqgvvvhChcNh1alTp8Rvsm7dOtWxY0dVUVGhlFLqvvvuU9ddd52KRCJKKaU2b96s3nnnnaYvbjyu1A8/KLV0qVJr1igVCtU6XFJSovLy8lQ4HFZr1qxRnTt3VvF4vF40DzzwgHrzzTeVUkrNnj1bDRgwQCml1O7duxP5ef3119U111yTOGfnzp2qZ8+eTefvCKOh5wFYoJpRdx9KR3MZ0CFpuz31u4duAP4KoJT6H+AFjv1FUG2OWWKxKmKxKgr6dKffiX34qOhDfJ4MPvjwI55++mny8vIYMGAAeQX9GXPDLewKVdO6c2fuG/8Mt998E+ec04eRI/MIBDbTuXOcli0NPJ49nSM1rQNN05j40itcecVVFJx8Ejg1bhl3C2GvCx1QkSjd27Tm0d//nsGDBzN8+PBG+/sfe+wxdu3axW233UZ+fj4DBjQspHneeecxZ84cwOw6GjlyZK3jl156Ke+//z4ej4d3332X66+/nvz8fC677DImTZpEZmYmAE888QQtW7akd+/e9O3bl4svvjixOlwtlNoza9rhMOcbtG4NnTrVUzRNls4+55xz6klnb95sVj3jx4/no48+4sQTT+TBBx9k0qRJgKkq2qdPH3r27MnMmTN54YUXEnHb0tkHK2IRJ7ASOBPYBHwLXKWUWpoUZiYwVSk1RUR6AbOA41QTmbKls20a4nBJZ9dIUADWbON0CFeZK4650wj5/RiGgafO8MhdoWo8To1gtRt/hQ9dN2V5cnMNMjONeulEo1GImek4lCPRh67pEfO/y1zjuBohA1VLnvpgsWXLFsaMGcN//vOfgxpvg8Riprx1SorZRXQYsaWzDxJKqbiI3A78G1Nk5S2l1FIReQyzGfN34B7gDRH5DabT+bqmDIKNzZFCjTEQEVyuDAhXAQYqVoGhFHFHCioaRSUZhGA8SChm6hSFA26q/KnUzIlKTVW0alVfiiIUiFjpgEs5UU4Nw5p1TDCIw+1IKJNWxXVT0egQzS5u27YtN910E1VVVc0aPbRfKGWuc7Btm+lUDoVM3SLt8EypsqWzDzJKqRmYw0yT9z2c9H0ZcOqhzIONzcGknjFIoDDc6cTjcVPKOhIGBZK0uHAsLkSqMggHHNSsf+PxQE6OTlpabWNgylJEASElzeoqiRgYLkx10kiEgMuNeL1gaQ/9GHIT+zWfoLkkC9iBqWzatu1hMwhgS2fb2Ng0QTxuzrqtZQzCVeZIIKWIRaPEI2EEIWxE0DzmBLJgKET5TjfhUCoaGprsWc2srjFIjCaKGTjdGm6cqIhuKZAKTqcpP1EtGprPd+RoDh0ISkF5OezYsUfArk0bU7fIlrf+0bGNgo3NXkhuHTide/rplVIoQyfuTCUSDCISxh8LY7hciObAFfNRVeEgUG1W3C4N0tIUWVnm0FJzsZqkdHTTIDgdblxOhcMAJQrxOHA6HFQHgqhgHBE5dgxCDdXVpkHIzDSdybaA3WHDvvI2Nk3QYOsAMEIV6Lr5Bq8cCk2EmEth4Marp1NdrbE7IKZiqUBGhjnPINlnoBS4vFqideCokadWCi0uaJoD0cNUh+PmUgciZKfXXnDnqMUwzE/NYjdt25rdRz+ByWFHOrZRsLFphBqDkNw6MAzDVMyMxVCeDOKRMOFABVFdEQmlENydjjLMLg8R04Gcm2vgdpvGIB4xEqMs43oMFTXDumIxlGFANEpIrEVgHDGzVZByjLUKQiFTosLlMpVMRUznyl6WxrT5cbBVUm1sLOLx6sQ8g1isCthjEIxQJXH/LkLl24hV7cIfDeMPVBDWwyinF39VLl2Py+aSkScxcmQev/nNhWRn76JdOx23W7GkuISzhp9Nv4IT6de/L89M+AOa03RkOp1O/vbpLAafdTZ5P/sZg04ZwJNP/p7sjHSy0tMSBiESiTB8+HDy8/ObVO0cNmwYDQ3bnjJlCrfffnu9/dOnTycvLy8xR6ExmYtQKMTQoUOt9R1MnnvuObxeL5WVlU2mM2zYMBZ88w1s24Z/6VJuefBBup52Gn369mXIkCHMnz+/0fI0B6UUd955J926dSMvL4/vvvuuwXDRaJSbb76ZHj160LNnTz766KPEsb/+9a/07t2bPn36cNVVVwGwY8cOzjnnnAPK29GG3VKwsaF+N1EkGEAZiqgy10KWWBDlTEHcKcSc5qxPr5ZCeblG+W5BKcHj8fHFFwvIyjK44YYbeOONV7n3N/cTDIYYNfoyXnr5JYYOHUqgvJyrr7+edK+XcTfdzPIVPzB+/P3MnDmDnj17Eo/Hef311+vlcdGiRcRiMYqLiw9q2c8880wuvPBCRIQlS5YwatSoWusj1PDWW29xySWXJCaFgTmJbeDAgUybNo3rrruu8UR0HTZtgrQ0bnz4YTp360bpmjVoTidr1qxh+fLlB1SGmTNnUlpaSmlpKfPnz+fWW29t0NA8+eSTtGrVipUrV2IYBuXl5QCUlpby9NNPM2/ePLKzs9m+fTsALVu2pG3btsybN49TT/1pDJS0Wwo2NphvmnpUIxzwE6quNoXqnE40lxtlxDAcbqIOD1UqTjBqUF2Rwuo1DnaVayglZGUqNA2yssyJZ4MKCti4dj0qFObDqe8w+OSBDBk0CBUKkZmWxosvv8zzEyfiycnmjy+/wj33j6dnz56AqTF022231crf9u3bufrqqykuLiY/P5/Vq1cza9YsCgoKOPHEExk7diyRSKReuSZPnkyPHj0YOnQo8+bNa7DsaWlpCfG4QCDQqHDde++9V0s6e/Xq1fj9fp544gkKCwsbu7DmJLRIBGIxVm/dyvxly3jiuefQLGdyly5dOO+885r4dfbO9OnTGTNmDCLCKaecQkVFBVu2bKkX7q233uLBBx8ETBHC3FxTQOGNN95g3LhxZGdnA3sktcGWzraxOSao/ry+5HJddCNUa/F5Iwaa00UwquPU9wwLEhF0p4bz1NOJVPsI+72JeQapKabPwOs141F+P5GIYtacL7j2urG4slIoWbmSvP79wePB4/HgdDo5Lj2Dar+fjdt2sXzZUh584L4m89qqVSsmTZrEs88+yyeffEI4HGbYsGHMmjWLHj16MGbMGF599dWEaimYM5AfeeQRFi5cSGZmJmeccQYFBQUNxj9t2jQefPBBtm/fzj//+c96x6PRKGvWrKFTp06JfYWFhVx55ZWcfvrprFixgu3bt9eqTGuua2KeQXY2S6uqyC8oqNXaaIzRo0ezYsWKevvvvvvuenMHNm3aRIcOe1R1aqSz27Ztm9hXsxLeQw89xJw5c+jatSsvv/wyrVu3ZuXKlQCceuqp6LrOo48+mug2GjBgAL/73e/2mt9jBdso2ByTpP/8jL2GicWqcLkyUEoRDgTQdR23FsMRV/hcDgy3OdKnKhwiWOEmXOZJ2BCvV5GdXXueQSgUYuCwM9iwYT0FBQUM+/kQwuEwSincbjeG10cIUOE4AgiQnerBoe37WPwVK1bQuXNnevToAcC1117LxIkTaxmF+fPnM2zYsISu0OjRoxOVX11GjhzJyJEj+eKLL3jooYf47LPPah3fuXMnWVlZtfYVFRUxbdo0NE3jkksu4YMPPmDcuHGIYUDy8pW5ueD1Ijk5ppO5mezLamcNCSHUbfHE43HKyso49dRTmTBhAhMmTODee+/lnXfeIR6PU1paypw5cygrK+P000+npKSErKwsWrVqldBO+inQrO4jEXGLSLdDnRkbmx8bwzCIxWLo4Wo8WoyoDn6HRpUI5dVhNm2Ns3VjKqFqc2RMerqiQwedDh3MWcjRaJRoNEooEMbn9TJv3ly+//57IpEIb7zxBmlpafTrk8d3CxaiYgbpccjQNHZtLSMtLY309HT69OnDwoUL9ynfzVWD2dc1DIYMGcLq1avZuXNnrf0+n49weE/racmSJZSWlnLWWWfRqVMnioqKzC6kykpaRKPs3rSJRHNK0yivqCA3N5c+ffqwePFiDKO+vlNdRo8eTX5+fr3P22+/XS9sc6SzW7RoQUpKSkLI7/LLL084pNu3b89FF12Ey+Wic+fOnHDCCZSWlgIQDofx1VmM6Fhmr0ZBRM4Dvgf+Y23ni8i0Q50xG5tDQc0Io2i0kkgoRKCqkmDFTuIGVJNK0EjDvzOVbRsy2bU1jWC1F0EjPV3RsaNOmzZ6oqsIQI8ZSCiOI6aDCG63m3bt2vHin5/npRdforw6xAWjr+DL/33Ff7+cjeZzElEx7rzzTu6//34A7rvvPp566qnEW7xhGEyYMKHJctQsGblq1SoA3nnnHYYOHVorzKBBg5gzZw67du0iFovxwQcfNBjXqlWrEkbmu+++IxqN1ltTITs7G13XE4ahsLCQRx99lHXr1rFu3To2r1/Ppg0bWP/ttwzs3Zt5ixax1Xq7XrBgAZFIhA4dOtC1a1cGDBjAI488kkiztLSU6dOn18vX1KlTKS4urvdpSHbiwgsv5O2330Ypxddff01mZmatriMwDeQFF1yQUHqdNWsWvXv3Bky/Qc3aDTt37mTlypV06dIFgJUrV9K3b98Gr92xSHO6jx7DXBxnNoBSqthuNdgcrSilqK420A0dcOF16DjdbpzeDMrLNSoqNHTDsKQoICXFID1d1ZKvhqT5BrrCoRTiM/WJnIaGRBX9C/rTp18eH0/7kJuvu5Z/TJ/OHXfcwf133omu61xzzTWJYZt5eXk8//zzXHnllQSDQURkr45Xr9fL5MmTufzyy4nH4wwcOLDWUpxgCtg9+uijDB48mLZt29K/f/9aw0lr+Oijj3j77bdxuVz4fD6mTp3aYAvj7LPPZu7cuQwfPpyioiJmzpxp+mQqKmDbNkaecQZFM2fywO9+xwsvv8yIiy7CMAzS0tIoLCxMrC43adIk7rnnHrp160ZKSgotWrTgmWeeae5P2CAjRoxgxowZiThrFswByM/PT4zY+uMf/8g111zDXXfdRcuWLRPhfvGLX/Dpp5/Su3dvHA4HzzzzTMIwzp49+4Ad4UcTe5XOFpGvlVKniMgipVSBtW+JUirvR8lhHWzpbJuGaEo6e4+8taK6OohheMj0gqDw61H8oXSqKtzocQ0E0jJitG7hrKdYqgfie/zSgKZHiRpxvF5XLaXSZCntY2nS2aJFi5gwYQLvvPPOnp2bN5tGAczZyG3amJPSjiGGDBnC9OnTEyOTjgYOtXT2chEZBWgi0hn4NfD1fuXUxuYwEIuEEeUzl6w0PGR7QTeErdVe/FUZYJgVt8+raN3awCNi9ofXealWChypDlQgQEzXCTmcaKmpGC5XLaXShpa7PBYoKCjgjDPOQNf1PaOHMjPB7zf1io5BAbsdO3Zw9913H1UG4UBpzt17O/AwYAAfY66P8OChzJSNzcEgEgwklhiM6FVEDQMDjU3lqVT7U1CGoImGx6PIzlakpxsQNdBjBoajrrtNoeIRHAGFw+HA7/XhdbuPqZZAcxh71VVmy6DG55CaCt26HVZ560NJy5Ytufjiiw93Nn5UmmMUfqGUegB4oGaHiFyCaSBsbI5IIsEARIO4XA6q9QBR3U000orqCieGYXb/pKUpsrN1fA4dFOh+w1zRzKWhxBxSWWNURISwSwh7TN+BwE/LIBgG7NoFO3eaTSafz1wVDY5Zg/BTpTlG4XfUNwC/bWCfjc2RQbgKFQnjdDmpBDSHl0BlLsFqByhI9RlkpsbxuA3QzZ4f5dQQl4bTo1mzmxUulwtN0wiEzJnCLqeTrJSfztDEBDUCdjUzprOzbfG6Y5hGjYKI/AI4BzhORJLHx2VgdiXZ2BwxxKNRwgE/hgpBPISuuajSYyiE6l3ZhAMaIpCbFSMtw1ze0unZc/tHo1FzGc2wQtM0nE4nHo+HSn8A0YSsY0Wyel8wDNi+3VwAB8DtNiWuU1MPb75sDilNtRS2AyVAGFiatL8aGH8oM2Vj01zWFi/E2LgQjsvHIX4ieoSw4UB3uIgH3ISqUonFzDUNWrWKkeJTOD17ujui0WhitFDM6UJzODAiEWKxOOGYH5GfqEGAPQZBBHJyoGVLu6voJ0Cjv7BSapFS6k3gBKXUm0mfvyqldjZ2no3Nj4mxcSFdenZG86QSMAx0RzqalkusIpuq8jTiuuB2QYfjDTKyJGEQlFIYhkHAUMRcbnSPFy0eJz0cIh1FdkZ6Qrq6uTgcDvLz8+nbty8XXHBBQmsHYOnSpfz85z+nR48edO/enccff7zWrOSZM2cyYMAAevXqRc+ePbn33nvrxX+opLNr+Pbbb3E4HHz44Yfmjtxcs1XQqRO0bk0oEjkw6WwrT36/n1tuuYWuXbvSp0+fI0I6e8OGDQltqLy8PGbMMJeW//7775tWfz0GaY7ZP05EikRkiYisrPkc8pzZ2DRF6WewYiZ+Sllh7CYeD6IAFc5g22YX1VWgocjN0jn+uFhi8lm1blAV1/EbimrdwOF0khmLJoyBIyMDx36u/uXz+SguLqakpIScnBwmTpwImJpIF154IePHj2flypUsXryYr776ildeeQWAkpISbr/9dt59912WL19OSUlJYjZtMsnS2aNHj96/69YIuq7zwL338oshQ/aIBDqdcPzxplOZvUtnN5cbb7yRnJwcSktLWbp0KVOmTKknq7GvJEtnv/7669x6660NhkuWzl62bFliFvgTTzzBqFGjWLRoEUVFRQmV2hNPPJGysjI2bNhwQPk7mmiOUZgCTMYccHEu8Feg6BDmycamaUo/Y5t/MfM3L2d5LMriakVMaZTvzGHr1v/P3pnHRV3nj//5Hm4QERAVFRQBL07PMs+0XL9aptlhXzc1S9vV1n61HbZW6/a12u3SLLNMq01XcbXI9ajWUNOs1DxD5BCFFFC5r2EYZub1++MjI8ghisg1z8fj88jPzHs+8/qMOa95X8+XDjGDm7OF7j0sePoI6lLvoNy346YuHcZSPE1lAPVKBtUxZMgQ0tLSAFi3bh1Dhw5l7NixALi6uvL+++/z97//HYA33niDhQsXNpo6G5OJ9/7v/5gyfDgdPDyguLjaZtelzr6C5ORk9u/fz+LFi607nJuCOlspRUGBVlgpPz+/kjfp7rvvJiqq9Xzl1SUpuIrItwAikiwiLwJXV1DasNEAZGbu5lDsZuIzjCRk6XAJHI1z2miK8lwwFDsiIrRrZ8KUk0naySxSf80m9dds4o9lknzJLokAACAASURBVBSby7nYbM6eyCXtWCYZiUX8lmLktzOlnDmeddWjrpjNZmJiYpg4cSKgDR0NGDCgUpvAwECKioooKCggNja2yvNXUq7OHj58OEePHqVLly7MnDmTDRs28Ouvv2IymVixYkWl15Srs/ft28eOHTuIi4urfFERyM8n7ccfid62jT889JDWK6hmIrmu6uyrceLECSIjI+uszq6rEK8mdXZFKqqz+/fvz/3338+FCxcAWLRoEWvXrqVr166MHz+e9957z/q6gQMHsnfv3qvG21Koy5LUUqWJUJKVUn8A0oAOV3mNDRs3HIvFQvKvh7Av8CUvsi8Oxfbs+XwE333nxDvvZOJkZ6Gdh5G2bXTg6QWOOuu8gc7OjnaODpTk5eFkZwdK3dCeAWjDRJGRkaSkpDBgwADuvPNOAOs+h+q4VotpOfVWZ5eVQUYGFBXx/159lX/85S/YBQdrS02riema1Nk36F5vpjp7/fr1zJw5kz//+c/89NNPPPzww8TGxqLT6VqdOrsuSeEpoA0wH3gV8ABmNWRQNmxcyZYdn2HQH4Oi8zh36cvxA+2J+dcAzp0XHCnDxcmMcwcjRgdFoYNCLAIm86VSmmW4iIWSYrRk0LZtg8RYPqeQn5/PXXfdxfLly5k/fz4hISHs2bOnUtvTp09XUWdHRETU+b3qrc4uKtIOOzt+iY9n6lNPwVNPkZWVxfbt27G3t6+0k7c2dTZoPYkePXowb948vL29yc3NrfR2OTk5tG/fnnbt2lnV2bqrrGS6liI716vOXr16NQCrV6/mm2++AbShP4PBQFZWFh06dGh16mzrjs1rOYCu1/O6G3EMGDBAbLQO9p7bK7t+2yXfnflO1m5cKsePr5Gtn30ud0/Nkh699NKjl17GjcmW5RuTZP/hY5JdYpDCwkLJz8+XoqIi0ev1UlJSIqb8/JsSr5ubm/XPhw8fFj8/PzEajaLX6yUgIEB27NghIiJ6vV4mTJggy5YtExGRY8eOSWBgoCQkJIiIiNlslrfffrvK9Xft2iUTJkwQEZGSkhLx8/OTpKQkERGZMWOGLF26VERERo4cKQcPHpT09HTx9/eXrKwsMRqNMmzYMJk3b552MYtF5OJFEaOx0nvMmDFDNm7cWO39de3aVUpKSkREZMGCBfLaa69Ver579+6SkpIi58+fl27duklGRoaIiBw8eFB69uwpZrNZRETuv/9+efHFF8VisYiISGJionz11VdX/XxrY+vWrTJu3DixWCzy008/yaBBg6pt9+CDD0pMTIyIiHz66ady3333iYjIuHHj5NNPPxURkbi4OPH19bXGt2nTJnn88cfrFd/NJi4urspjwC9Sh+/YWnsKSqlBQBfgBxHJUkqFoOkuRgNdGzph2WjdmCwmOqU7kp2VSTd3I/t2FLHqnxMpwx2fdsIjjxTTrtcFdF2ccSrSJo+NIri5uVnHrM2FhY0iaevXrx8RERFERUXx8MMPs/mSOnvevHmNo85u357+QUGXl5Mqpe07uAaqVWdXYPLkyURFRfH888/z7rvvMn78+Gajzn777beZPXs2S5YsQSnFZ599Zu1l2dTZ5U8o9TowBTgGBADRaIbUfwArRER/s4KsiE2d3bL5Ie0HTBYTIkLWsQR8db6ktw1i7/ZcYjb1pNTZg8BgPfdOz8TDy4y9TnFbgDuZF7LoG9oHs9mMi4uLlgxEGmTuoFlhMGiKivKhn44dL8vsrpFq1dktnNJLezN++OEH7O2bj/22odTZ9wARIlKilPIC0i+dVx3ks2HjBpEdl0Tvtr3Iy8ujs64znZ2NvPdONnEngzDaOTLmgUJeGpmLXVAbLpwqoqzUTMrxLFw6WDCbzSiltIQADTZ30CywWDR5XXa2lhwdHDRFRZvr351drTq7hfPbb7/x97//vVklhPpS250aRKQEQERylFLxtoRgo6E4c/QQFrMJo7EUo9tvuLmZSUzswMLFvTmX600HrzZMfug8j/czknvBQKnZgtlion2ACyUlWkJwNJlsvQPQegVpaZcFduWKihvwRT5rVutaYxIcHExwcHBjh3FTqS0p9FBKlZtQFdC9wjkicu/VLq6UGge8C9gBq0Tk79W0eQBYBAhwTET+t+7h22junDl6CLPJRJnJRJsu/pSZknHMOsnWr8fzry2B6I0Q0EEYN+sUnt4mzpc5o9o70bazwsXFg+LiYixJSdh16wa08t5BOUqB0agJ7Dp3vqy4tmGjDtSWFKZccf7+tVxYKWUHLAfuBM4BB5VS/xGRuAptgtEK9gwVkVyllG3/QyvDUFKCc6cuiMnEgcwDlBZ58fQ/JnAyORR7szD2tiwi7s+nXag7Q92cMJlMGAwGPD09yc3NxXjyJAHe3px1dm7dvYOSEnB21hKCkxP4+2sb0WwCOxvXSI1JQURi6nntwcApETkNoJSKQpunqLitcjawXERyL73n1bdE2mgxJB78mZ+TMnAyepBT+iUJhwLYumEEZYWCR3sL//toJj1Ggb1yZ4iLA3q9Hnt7e4xGI9nZ2daE0Gb4cDh5srFvp3Ewm+HCBa0aWpcuWnlMsOmtbVw3DTl70gU4W+H8HHDLFW16Aiil9qENMS0SkW+uvJBSag4wB8Df379BgrXR8OxOuIjJrK12M5lMpMWm4B4USlJaOtvW3E9aQnd0FhN3BmXy3NOFtG0rqDauGI1GSkpK8Pb2JisrCzl1is5tPVDlCaG1UlAA58+DyaT1EMzmq7/Gho2r0JB9y+oWh1+5/tUeCAZGAQ8Bq5RS7aq8SGSliAwUkYE+17i22kbTwWQW7ujbkWE9POjZppTg9m3YtTWNj/4aSXZSF7q2LeKvs39l8acueIS7QDcXiouL0el0lJWVkZmZiVKK7m09cB99e5NLCDdNnR0RwYYPPoBz57SE4OoKPXpoE8pcuzp79+7deHh4WN1Cr7zySrX3JyKMHj3aKo4DiI6ORilFfHx8pevdddddlV47c+ZMq5K7rKyMBQsWEBwcTGhoKIMHD66y5+F6eP311wkKCqJXr158++231bYZPny49T47d+5s3bUtNai3MzMzGTduXL1ja07UuaeglHISkaoaxpo5B/hVOO+Ktqz1yjY/i0gZcEYplYCWJA5ew/vYaMJU7B3olJCZmUlubi5fJ6ax4UN/ziZ1xs7iysTJJh6/43tcw4YCWOcOvLy8yMnJwcHBwboKpPDsuUa7n9oo11zAZRfRwoULrersFStWMHbsWPR6PVOmTOGDDz5g3rx5VnX2tm3b6N27NyaTiZUrV1a5/pEjRygrLeXov/+t9Qp0OujQQSuPWc8NesOHD2fr1q21ttm+fTsRERG0rTCZv379eoYNG0ZUVBSLFi2q03u99NJLZGRkEBsbi5OTExcuXOD777+vT/jExcURFRXFiRMnSE9P54477iAxMbHK0tmKYrspU6ZYra8V1dv79+/nj3/8I/v378fHxwdfX1/27dvH0KFD6xVjc+GqPQWl1GCl1K9A0qXzCKXUe1d5GWhf7MFKqQCllCMwFfjPFW2+4pJxVSnVHm046fQ1xG+jibE74SLfxV2wHgBj+nTgFj9XAi7msOPX82z7OYsNb/UhO8mH7h3defW5iyy472s821kQEUpKSjCbzZSVlZGdnY1SiuDgYIr27qVw5y6UQ9NfM95g6uzjx4mcNInk7GxiUlLoN2YMYeHh9VNn15Er1dlFRUXs27eP1atX11ktrdfr+fjjj3nvvfdwulTnuWPHjjzwwAP1im3z5s1MnToVJycnAgICCAoK4sCBAzW2LywsZOfOndaeQm3q7UmTJvGvf/2rXvE1J+ryr2sZcBfaFzgickwpdVV1toiYlFJPAN+izRd8IiInlFKvoDk4/nPpubFKqTjADDwrItnXeS82GpHyHoG9neKOvh2tjxsMBs6dO8f3eUWcTU/gVLoDR9f2IbfQGW/vTF7832N08SoCoKTLUEr0etzd3fHx8eHUqVP06tXLei0pM+E+um7W9uRD9avkVR2BA66cEquecnX2o48+CtRNnf3nP/+5+ouJQF4eHby9WbVqFW+99RZbv/oKQ1kZo3r2JCYmhp49ezJ9+nRWrFhRyZJars4+dOgQHh4e1spi1fHTTz8RERFB586deeuttwgJCanSZt++fXz00UfW86+++opx48bRs2dPvLy8OHz4MP3796/1szl16hT+/v6Vehs18dRTT7Fr164qj0+dOpUFCypXBE5LS+PWW2+1nlenzq5IdHQ0Y8aMscZRk3rb19eXgQMH8uKLL1413pZCXZKCTkRSr7At1mlGS0S2A9uveOzlCn8W4OlLh41mSE3JwGKxkJ2dTW5uLjlnTrH3lIkL/+5GYpovIIQG/MZfn/yG7j7dKPUbTqHRSFlJCR07dsTd3d3qnSnauxcpMwFcUw+hrl/gN5Ibrs4uLdX01nq9tuS0HHt7Ek6cqJ86uwL9+/cnNTWVNm3asH37diZNmkRSUlKVdjk5ObhXWPa7fv166/tNnTqV9evX079//xumzl6yZEmd21an66nt/davX89jjz1Wp9fb1NlVOauUGgzIpb0HfwJs5ThbOTUlA9AmRDMyMjiQeQAL9qxdG8jxn7rSxlKGk7uOaQ87Mnlye1xcfo9BhBK9HgcHB/z9/a1DCklJSeh0umvqHTQ2N0ydLaLpKTIztT/b21fRU9TkLLuSunwRV/zVPn78eObOnUtWVpa1Klk59vb2VuV1dnY2O3fuJDY2VlOLXFKMvPHGG7Wqs4OCgvjtt98oLCyslGCq41p6CnVRZ5eTnZ3NgQMHKpUQre31NnV2VU12B7Tym1mXjiigfV0UrA1x2NTZTYMdJ85XecxisUhubq4kJibKpkObZOmHn8mg2zPEv3u+hARmy5NzD0hcXLqkpaVJWlqanD17VhISEuT8+fMSHx9f6TgeHS0FMTulcM+eOsVTnSr4ZnND1NklJWJOSpK3n31W5MQJkbQ0EZNJRG6wOrsCGRkZVk30/v37xc/Pz3pekVtuucX6fh9++KHMmTOn0vMjRoyQPXv2iMFgkO7du1v/TlJSUsTf31/y8vJEROTZZ5+VmTNnSmlpqYiIpKeny5o1a675865IbGyshIeHi8FgkNOnT0tAQICYLn1uV7JixQqZPn16pcdqU2//8ssv8rvf/a5e8d1sGkydfQmTiExtsKxko9lQcSWRvV3lX6AWi4WsrCzy8vJwdXVl646u7H2vN2UmC918cnlh3hYCh3ShTZsugLYs0Wg00qlTJy5cuIBOp7NOJkuZqdnvQbgudfbUqejz81HAhNtv13Yl1yCwu2Z1tq8v/fv3v6zOrsCmTZtYsWIF9vb2uLi4EBUVVW0PY8KECezevZugoCDWr19f5df6lClTWLduHcOHD2ft2rU88sgjGAwGHBwcWLVqFR6XNtYtXryYF198kb59++Ls7Iybm1uNy2DrSkhICA888AB9+/bF3t6e5cuXW1cejR8/nlWrVll/+UdFRVWJvTb1tk2dfWUDpZKBBGAD8KWIFN6MwGrCps5uPL6Lu1BlqAi05aPnz5+npKQEV1dX/rYyj23rvHA3wZBh2SyalUAbVxOlfsMxmUyUlpbi4OCAx+nT2KNIzs4i0PtSAXUH++tKBtWpgpsl6enaUtMbJLC7kWRkZDB9+nR27NjR2KHcVEaMGMHmzZvx9PRs7FDqTEOpswEQkUCl1G1oS0r/ppQ6CkSJSN3WoNlo9lScP6iIxWIhPz+fnJwclFLEpKaxd607e3Z3w00ZeHhUJo/PjkPZ2VPsO4TS4mLs7e3p2LEjbdq0ofj0GdxH345LQgLuFVYZtRrMZm3eoG3by9I6X99GKQpUF3x9fZk9ezYFBQV1Wj3UEsjMzOTpp59uVgmhvtRpOYeI/Aj8qJRaBCwF/oU2t2CjhbM7QdNRXbmyqKioiKysLCwWC87OzuzKMrHl3Q4kx7XH2amM2dOTmfZIMEY6YjQaKfvpZ9q1dcfJyQl1JoVirm01UYujqEhbWVRWpq0uCgjQkkETTQjl1Hc/QXPDx8enUq3q1sBV/1UqpdqgieymAn2AzcBtDRyXjZtMxfmCitjbKUb10uS1VyYDJycncn9MYH1GW778vAvFWV44+Oh48uHD3PNAOAD6fftQZjM+HTrSrppVROWrjFoNZrPmK8rP186dnTW9dRNPBjZaD3X5qRYLbAHeEJG9V2tso/lRXW/gSsqXmZaVleHs7IydnR0Xvo9j6x5fPv8pELfSYnzDinnluaM4mbX9h8V7f8DBwYGukydXqVyVlJRkXd7YaoqYXCmw8/HRSmPaEoKNJkRdkkIPEbE0eCQ2bjoV5wrKewNXIiLkHk0jNycXezt7XO3tyTmXQGq6Ex9Gh3AyzwuDvohbBsVz19SfcTJ3wsl+kLbqRKfwu2diJf9MxWTQqzXNI5jN2nCR2azNH/j6anUPbNhoYtSYFJRSb4vIn4EvlFJVxhWkDpXXbDRtyq2lV2JIyEHMgtlspqCwgJJSA069PDmfEEdpkfDBzwHs/bYdZjO4uWcy776fmTn7NgoL03F3H0ppaSly6DCePj7WhNAqk0H5yj6ltJVEnTppSeEGCOxs2GgoahvM3XDpv++jVVC78rDRAjEk5GC2WCjppDjvUoChkw7nnlpC+PeJ9jy5ZBi7/9sRJ7OJGfc58+liPTNn30ZR0X6Uske/bx/mn/fj5e2Fx6hRAFZlQq9evVr0UFEldfaECeT9+qu2M5lL6uzJk+l5660E9+xZP3V2ZCQbNmyo8nw516rOBk13HRkZSUhICCNHjqy2jbQAdbaIsHDhQnr27EmfPn1YtmwZAPn5+dx9991EREQQEhJi3adgU2dXQETKFYN9RKRSKc5Lorv6Vmaz0QQpLSklx8OAOcfMYbMOs0WReiSFmH/35syxtihLPoGdc3nl0XP0HBNGRoL2xWaxGFFHBZ2DA12n3IOjo6P1mhaLpVX0DlxcXDh65Ajk5jJj1iyWr17NwieeoMTF5caps8vKrHruG0VeXh5z587lm2++wd/fn4sXqy+A2BLU2Z999hlnz54lPj4enU5nvdfly5fTt29ftmzZQmZmJr169WLatGk2dXYNzKrmsUdvdCA2GhcRITc3l+TjRzkff4Lc00lcSEnhzD/z+Pff+nImti1ODvncNTODtZ970nNMGBdOFaGzU+Tm7sVotNDW1Y3ukydXSgitbnVRaiqcP8+QiAjScnMhIIB1UVE3Tp199CiRkZEkJycTExNDv379CAsLq5c6e926ddx7773WqoYdOlQ/v9QS1NkrVqzg5Zdftv4/WX6vSikKCwsREYqKivDy8rIujrCpsy+hlHoQbRlqgFLqywpPuQN51b/KRnPgys1oFouF8/tPU1Jcgs5eR7eICLKydEQvdiM7wYlis56Q/ln8feoxPPoNASBt2yHEbKGdezHG7CT83Ibg4OlSKQGUDxs1xpBRSdyNN7C79PWu/olygZ3FAno9ZqWIOX6cRx9/HOzt66fOvkSHDh0uq7O3bsVgMDBq1Kgbos5OTEykrKyMUaNGUVhYyJNPPsn06dOrtGsJ6uzk5GQ2bNhAdHQ0Pj4+LFu2jODgYJ544gkmTpxI586dKSwsZMOGDdb/l23q7MscALLRKqZVnEMoBI40ZFA2GpbyCWZDQg76E1nk5uZSWlZKrsqAjopP9kLUux7k5hnp4JnLzEdO8cj/BOD8m4GiAwfAZMJidsJzdB90upN09BiJp8+oKu/TmMNGNX6BNxRFRZSUlhJ5//2kpKXdGHV2LSQkJNwwdbbJZOLQoUPExMRQUlLCkCFDuPXWW63XLqclqLNLS0txdnbml19+4csvv2TWrFns3buXb7/9lsjISHbu3ElycjJ33nknw4cPp23btjZ1djkicgY4A3x388KxcTOxmCzkeRoxOOtwcfHCfPwsqw4MYudGd9roFBERWbz1kivt2gVQduAAvyVkUubpgalHIWaPfBwdhTZtPGjfflSVa7f4YSOLRTvs7bWVRL6+2pzCiRP1V2fXgas5y8qpyxdx165dad++PW5ubri5uTFixAiOHTtWJSm0BHV2165dmTJlCgCTJ0/mkUceAbRhtgULFqCUIigoiICAAOLj4xk8eHCrU2fX+K9WKfX9pf/mKqVyKhy5SqmcmxeijRvJ7oSLtM3Qaz2E/Dz0ej0uLi7syjby4id92bnRnbb2ihkzipn13DEsJ38iY+2XZCWewz7QH5chhbTr7MSg4dPo3n0iubldSEhIqHJA4wwb3RSKi+H0aUhLu7zstMKeAw8PD5YtW8Zbb71FWVkZ06ZN44cffuC777TfVyUlJcyfP5/nnnsOgGeffZbXXnvN+iveYrHwzjvv1BpC7969SUlJ4dSpUwCsWbOmyqqhW265hd27d5OdnU1ZWRkbN26s9lr33HMPe/fuxWQyodfr2b9/f7VywV69enH6tFYtd9OmTUyfPp3U1FRSUlI4e/YsAQEB/PDDDwQHB5Oens7JkycBSE1N5dixY0RGRuLq6sqjjz7K/PnzMRqNgDbMtXbt2irvt2TJEo4ePVrluDIhAEycOJGoqChKS0s5c+YMSUlJDB48uEq7SZMmsXPnTgC+//57a+Lz9/cnJkZbO3PhwgUSEhLo0aMHoA2vhYaGVvvZtURqGz4qdxK0r6WNjWZE+c7lvh3dyPEwUOqscHVxYftvZaxc6ELROWc6esNf/pKPIf/fOG7PpsyhHe17+iORkRiNRnS6XwkImGRd1dFaVhYB2h6Dixeh/FewUtpj9lX/GV2XOvuhh9Dr9SilrqpqvpHq7D59+jBu3DjCw8PR6XQ89thj1X4JtgR19oIFC5g2bRpLliyhTZs2rFq1CtBWRM2cOZOwsDBEhH/84x/WIkM2dfaVDZTqDqSLiFEpNQwIB9aKSEGtL2wgbOrs66M8IQwwQU52NuauTjg7O3PggCMvLNahCu3o1sORhZP3knvxRwDCQyORyEjKyspwdXXFy8uLouIf8Wk/xnrdhISEJpEUGlydXVFgpxS0b68pKlryENkV2NTZzceU2qDqbOArYJBSKhD4HNgGrAPuqvVVNpoM1oRQJmRlZ4O/C9vjz7JjUxeO7G2DpbSMW7vlMPfOODL15zB39+a20VPIydmLKv6Rdu08cXRypKgYMtIvkpOdYL12i543AG14KCMD8i4tuHNx0RQVzs6NG1cjYFNntw7qkhQsIlKmlLoXWCoiy5RSttVHzYTyhDCkuwdf/JiIuaMzKT+dJ3pJN0w57niIifvvSGXW3zpxKMeEjk6MKC4iPWcPDvb2BAVNqSSzy8luGj2Dm4ZS4OCg/bdDB/DyatWKCps6u+VTp3KcSqn7gYeB8k/HoeFCsnEjMZmF4YHt+OKnJIpzspDDLkR/3ouSPB1dutvxxJwsBt/eCQCzmOlXdIKsMgOOXv0JCppUrbuoxVNWph3lhW+8vcHDAypsyrNho6VSl6QwC5iLps4+rZQKANY3bFg26kv5BrWfTseReMKCzqI4d6AjW2OCEWBQr1ReXXgMV8dS9sedxixmnJUdpfYltO35BEVFRdaVLUDrENmJaHUOLlzQegM9emiTyDqdLSHYaDXUpRxnrFJqPhCklOoNnBKRVxs+NBv1wWQWBnd14deUUsJP27Ps2x7Ep3qgVBG//10yI8Iv4OpYSmm32zFkunBL+1vQ6/VkZn6H5Odjb29fZZ16i8Zo1OYOiou186usobdho6VSl8prw4E1QBqggE5KqYdFpHqRio1GJyYrn5j4Yxw7bSFumxv/+i6EMtc2uLcv47GHswgPaYtTzln261MxZP4MFiguLsbT05OiYteGXcXT1BCBnBytVrLFcllx3bZtq547sNF6qcsA8RJgvIgMFZHbgAnAuw0blo3rJSYrn58T0wnIziPps378vDUcQ5mOW4NP8fa8HUwc8APdnX/ht3Yn0Xv0JMw1jE4lndDr9aSlfYudrpUNk6Sna8NFFos2bxAYqP33OhJCJXX23XeTl3dZEXbixAlGjx5Nz549CQ4OblLq7DfffJPIyEhr7HZ2duTkVN2f2pLV2TV9BkajkREjRmAymeodX3OhLknBUUTiyk9E5CTQyr45mgc7swvYn5SB2+5Yotfewt4j7ri1KeP//vwLf13WDv87BlPa7XYyPZ3QOxmI8IDCwn24tUmmS9ds/Pz8CA9/qLFv4+bSrp22usjPD7p0qXYjWl1xcXHh6NGjxMbG4uXlxfLlmjKspKSEiRMnsmDBAhITEzl27Bg//vgjH3zwAYBVnb127VpOnjxJbGysdTdtRSqqsx988MHrjvNKnn32Wetu4ddff52RI0fi5eVVpd3V1Nl1paI6OzY2li1btlBYWFive6iozv7mm2+YO3dutRv1KqqzT548ydSpU4GaPwNHR0fGjBlTaxJuadQlKRxWSn2klBp26ViBTYjX5NiZXcCeHw7g+GM8X24YTvqFjnR0yuSthzYwYrBmDC0q2k9O7m5O5p7Eo+MIIAJ396FEhD+MT/sxeHtXX1ylRVFSAllZl8/d3LTewQ2eQxgyZIjV0rlu3bomrc6uyPr163nooep/GLRkdXZFrvwMbOrsqvwBmA88hzansAd4ryGDsnFt7Mwu4FxSFo4H8tm8/U5yiuzpE3SBl8dspP3v/5dik4n87O8RgdPiiqfvcLqUdQFarp+o3L9kxWKBggIo/0XaocM110iu6+ors9lMTEwMjz6qlR1p6urscvR6Pd988w3vv/9+tc+3ZHV2OdV9BqGhoRw8ePCq8bYUak0KSqkwIBCIFpE3bk5INq6F1BPZpBYWovbEs37LMHRGR8IHm/nH7J+QnO5kZ3+PTie0bduOM+JKewd7hnUZ1mT0FA1FpXsrLtZWFrm7axPIXl7g43PDFRUlJSVERkaSkpLSrNTZ5WzZsoWhQ4dWO3QELVudXU51n4GdnR2O3qTAqgAAIABJREFUjo51Mru2BGqzpP4FTXExDdihlKquApuNRmZvcREFhRbe/XQgRr07ob1K+eukndjHx2EwmSgoyEWn64fBEEhWWhY+RT4kJCS0jk1oZrOWDFJTtSWnTk7QvTt07NggzqLyOYXU1FSMRqN1TiEkJKTKxG916uxr4Uaqs8uJioqqcegILquzAas6+7HHHqN79+68+eabbNiwARGpszr7ajz11FPWyd+KR/mwW0WuV519/PjxOn0G5cmkVSAi1R7ACcDt0p99gIM1ta3lGuOABOAUsKCWdvcBAgy82jUHDBggrZmU2Cw5fSxT/nXwrPxzf6r844t4GTWqWIL8c2X69CJJTk6Tcx8skvidC2Xbodcl+vv/k12/7ZJdv+2Svef2Nnb4DUZcXFzVBzMyRE6cEImLE7l4UcRsbtAY3NzcrH8+fPiw+Pn5idFoFL1eLwEBAbJjxw4REdHr9TJhwgRZtmyZiIgcO3ZMAgMDJSEhQUREzGazvP3221Wuv2vXLpkwYYKIiJSUlIifn58kJSWJiMiMGTNk6dKlIiIycuRIOXjwoKSnp4u/v79kZWWJ0WiUYcOGybx586qNPS8vTzw9PaWoqKjG+7vlllus7/fhhx/KnDlzKj0/YsQI2bNnjxgMBunevbv17yQlJUX8/f0lLy9PRESeffZZmTlzppSWloqISHp6uqxZs6bG960LsbGxEh4eLgaDQU6fPi0BAQFiMpmqtHv++edl9erVIqJ9ngMHDrQ+V9NnkJWVJb17965XfDeb6v49AL9IHb63a/u5VCoixZcSRyZ1m5S2opSyQ6vY9j9AX+AhpVTfatq5o81Z7L+W67dWLGbhVCc7XMwGwjL0bP57B86dNhLQtZBnnvkOo3EvJ3WHOOXhjnuHofTpeD+j/EYxym8Uw7oMa+zwG56Kv6Dbt9eGjHr0aJDhotqoqM52cXFh8+bNLF68mF69ehEWFsagQYOqVWf36dOH0NBQMjIyar1+RXV2WFgYOp2uVnX2HXfcUet4f3R0NGPHjsXNza3GNuXqbNCGjiZPnlzp+XJ1tpOTk1WdHRkZyX333VdFne3j40Pfvn0JDQ1l0qRJ1iGu66WiOnvcuHFV1NnlldMWLFjAF198QVhYGC+88IJVnV3bZ7Br1y7Gjx9fr/iaEzWqs5VSecDO8lO0+grl54jIvbVeWKkhwCIR+d2l8xcuve71K9otRavu9gzwjIjU6sVuzers1BPZGE5lsr+NkUiDPYtWd+PosQKCuuXx2MvbcHQx4LzvN9xcXBj+R20rSUufOyjn5MmT9OncWbOZ+vm1KqX1zaK1qrPvvfdeXn/99Wb176ih1NlTrjivfklCzXQBzlY4PwfcUrGBUqof4CciW5VSVXfrXG43B5gDWoWk1oiIkJ+bz69tjBzPief9t3uTnZWNp9sF5kzaQ5skA31LjWS7BeI6apJ19U2rmDu4eFHblVz+A6egQNt/YOOG0hrV2UajkUmTJjWrhFBfaqvRHFPPa1c3w2XtliildGi7pWde7UIishJYCVpPoZ5xNTtEhM0paaQUF5AZ68XW1QPRmb0JD3PgvkE/0/+uyeh0OjoX/0qJfc/W8z+wxQJffQVLl8KiRZqiomNHbUeyjQahtamzHR0dmT59emOHcVO5/u2bV+cc4FfhvCuQXuHcHQgFdl9aIdEJ+I9SauLVhpBaCzuzCyizWEiJv4A55zh5MR3ZuLU9ZjsH+g8r457fZ9Dj58O4penJM9qTrHNA17UV9AwAzp6FxYuhfNWOs7M2d+Bgs7rbsFEfGjIpHASCL6m204CpwP+WPyki+VSo/6yU2k0d5hRaE2UWC76J2XQ4V8SmY0788z89QcFj95zkzqGnCHRwwOTpjPeQ35OXnNx6eggAR45oCcHLC557Djw9bQnBho0bQJ2TglLKSUSq7qOvARExKaWeAL4F7IBPROSEUuoVtKVR/7n2cFsP+vhsziak0aG4jM3HvVj77/Y46px46SVhYu90Cn3vxADkXThIcXJy65g7KCy8rKO4+25tUvmee7ThopMnGzc2GzZaCHVRZw8GVgMegL9SKgJ4TET+dLXXish2YPsVj71cQ9tRdQm4NVB8Mou83DyKXIRNqSbWRjtir7PwzJ+F3/3OgOW0mbL9B2jv2Y58O7uW30MwGuHTT2HdOlizBvz9NYtpKxvrtWHjZlCXn5fLgLuAbAAROYa2PNVGA1AUl0l2djbflxaz9asC1i71xd6k4/f35nPPPQYcUr/HaBYKxcLFbt1wDQtr7JAbll9/hd//Hj7+WNNV/PxzY0dUI81VnZ2fn8/dd99NREQEISEhfPrpp9Vet6SkhJEjR1ayjy5ZsgRnZ2fy8/NrfZ+KMRUVFfH4448TGBhISEgII0aMYP/++m1TEhHmz59PUFAQ4eHhHD58uNp2RqOROXPm0LNnT3r37s0XX3wBQGpqKmPGjCE8PJxRo0Zx7tw5ADIzMxk3bly9Ymtu1CUp6EQk9YrHqjppbdQbg8HA7t0/seLMTj5aYSDu22DaOHrw3HNOPP7/vLSEYDSS7dkPe3sHevXq1WKFdpSUwDvvwKxZcPq01jv4+GNowqtfmqs6e/ny5fTt25djx46xe/du/vznP2M0Gqu0++STT7j33nutm8JA28Q2aNAgoqOj6/x+jz32GF5eXiQlJXHixAk+++wzsiqaa6+Dr7/+mqSkJJKSkli5ciV//OMfq2336quv0qFDBxITE4mLi2PkSM0M/MwzzzB9+nSOHz/Oyy+/zAsvvACAj48Pvr6+dTLMthTqkhTOXhpCEqWUnVLq/wE1W7VsXDOGhBxObd7HiQ0xHLDL4ccvxpGX0I+27g688moZd99twPG3PZSWluIaPhE5dYoeHasqf1sMsbEwdao2XKQUzJgB69dDLYbPpkZzUmcrpSgsLEREKCoqwsvLC/tq6kpcqc5OTk6mqKiIxYsXs3593cq2Jycns3//fhYvXmydB+vRowcTJkyo0+trYvPmzUyfPh2lFLfeeit5eXnV7gr/5JNPrF/4Op2O9u21tS5xcXGMGTMGgNtvv53NmzdbX2NTZ1flj2hDSP7ABbTdx9WnYRvXRdwPR/iuXTIFdGPjJ2NxNrRHuebxx2diGTKkN2z9hCKDAQf/QdgfOQpAm+HDGznqBsTdXSuP2bMnvPQSXEd50Mys+m6zqYpP+zF1atfc1NlPPPEEEydOpHPnzhQWFrJhw4YqCxeMRiOnT5+me/fu1sfK6w4MHz6chIQELl68WG19goqcOHGCyMjISr2NmnjwwQerKtCBp59+usregbS0NPz8Lq+AL1dn+/r6Wh8rH8576aWX2L17N4GBgbz//vt07NiRiIgIvvjiC5588kmio6MpLCwkOzsbb29vBg4cyIsvvnjVeFsKV00KInIRbTmpjQbg9PZT/ICJrOxQtiyPxFyqCAoxMWHeQcYUlCGbP8ZksZA55B7c3d0pUgpXv66NHfaN5+hRiIjQegbdusGHH0LfvtddCa2uX+A3kuaqzv7222+JjIxk586dJCcnc+eddzJ8+PBKu5azsrJod8Uu8aioKKKjo9HpdNx7771s3LiRefPm3bB7vZZqZ9Xpeq58P5PJxLlz5xg6dCjvvPMO77zzDs888wxr1qzhrbfe4oknnuCzzz5jxIgRdOnSxdpb6tChg9Wd1Bqoy+qjj6mwE7kcEZnTIBG1IkwmE+eSTrI/vR37N/cCFHf/j4n58ws5UlCKXcJxpE93XEMm0CYryzq80KLIyYG33oL//hf+9jcoH0YID2/cuK6D8jmF/Px87rrrLpYvX878+fMJCQlhz549ldpWp86OiIio83vV5Cy7krp8EX/66acsWLAApRRBQUEEBAQQHx/P4MGDK92bwWCwnh8/fpykpCRr4jMajfTo0YN58+bVqs5u164dx44dw2KxXHUZ9bX0FOqizvb29sbV1dUq8rv//vtZvXo1AJ07d+bLL78EtInwL774wirwMxgMuLi41BprS6IucwrfATGXjn1AB6DO+xVsVM+Zo4eI+ddW1u5ty+6vwgBXnviDgYUPbsdpxyrafrcdk8VC2/734uPj0/L2IYjA9u1w331aQnB2hrKyxo7qhuDh4cGyZct46623KCsrY9q0afzwww989913gNajmD9/Ps899xyg1Qd+7bXXrL/iLRYL77zzTq3v0bt3b1JSUjh16hQAa9assU6alnPLLbewe/dusrOzKSsrY+PGjdVey9/fn5gYbbjtwoULJCQkVJno9vT0xGw2WxPD+vXrWbRoESkpKaSkpJCenk5aWhqpqakMGjSIffv2cf78eQB++eUXSktL8fPzIzAwkIEDB/LXv/7VmtiSkpIqjeGXs2HDBmvd5IpHddqJiRMn8vnnnyMi/Pzzz3h4eFQaOgItQd59991W02tMTAx9+2ri5qysLGutiNdff51Zsy6Xj0lMTCQ0NLTaz65FUhe/dsUDLZHEXOvrbtTRUuopHPt+t8y4K0NCemVJ15Bi2fTuLsn6ca3sfHeubF31f7Lx4EbR6/XW9vHx8Y0Y7Q0mI0PkT38SGTBAO+bOFUlLq9clq62ncJOpWE9BROSuu+6Szz//XEREjh8/LiNHjpSePXtKYGCgLFq0SCwWi7Xtli1bpH///tK7d2/p06ePPPPMM1WuX7GegojId999J5GRkRIaGiqPPPKIGAwGEblcT0FE5JNPPpHg4GAZMWKEzJ8/v9p6CmlpaXLnnXdKaGiohISE1FjbYNasWdaaEN27d5eTJ09Wev6pp56Sv//97yIi8tVXX0m/fv0kIiJChg4dKocOHbK2y8/Pl8cee0x69OghoaGhMnLkSDlw4EANn2rdsFgsMnfuXOs1y+9fRCQiIsL655SUFBk+fLiEhYXJ6NGjJTU1VURENm7cKEFBQRIcHCyPPvqo9bMUEXnzzTettS+aC/Wpp1CjOrsmlFKBwLciEnTjU9TVaQnq7OTDB3n/fcW3e7rj7i10fiSOWYG7cDjnhNlspt/4WXTo0KHSCpAWo8COjYW5c0Gv1yaUn34a7rpLm0uoB9Wpgm3cWI4cOcI777zDmjVrGjuUm8qIESPYvHkznp6ejR1KnWkodXb5hXK5PKegA3KABTW/wkZtxGTl880POqL2BqEw0veePYRZYumXBMVeAfiMHYunp+d1T0A2eXr21Eym3bvD889rhXBsNAv69evH7bffjtlsrtPqoZZAZmYmTz/9dLNKCPWl1qSgtG+mCDShHYBFrrVrYcPKjou5JMcXsPUjH9xNMOXWGP7klUixZx+Mgwbi7+tbZUIrKSmpTpNyTRazGTZs0HoDbduCoyOsXq392Uazo+JYe2vAx8eHSZMmNXYYN5Vak4KIiFIqWkQG1NbOxtVJOPATP58uZffyYIylFiK6JzJh2H8pHvMCbdq0wcfHp8qGoaSkJIDmO2yUmAivvALx8dqfFy3SHrclBBs2mix1WQR+QCnVX0Sql4nYuConf97HhpMWNn82iMLzhXToVMQfBpwkIHQkTp6eeHt7Vxouqtg7aJYaC6MRVq2Cf/5T6yl06gS/+11jR2XDho06UGNSUErZi4gJGAbMVkolA8VoFdVERGquAm7DSsKhNKKiU/j8m9vR6QsI6HiOd2anUXbmIo59Z1SbEKAZ9w6OH9d6Bykp2uTxAw/AE0+Aq2tjR2bDho06UFtP4QDQH2hdA2o3kLif9vHqv/V8//XtONi5cXuPbF4Yc4QcozP2gf60b9/emhCafe8AtGpojz2mlcns1g1eflnbpWzDho1mQ22zlwpARJKrO25SfM2SM0cP8eOOlTy51JXdWyNxEcW03gd5eezX/BYopIcPIfL391dKCEDzt576+cHkyZrZdP36VpcQmqs6Ozc3l8mTJxMeHs7gwYOJjY2t9roiwujRoykoKLA+Fh0djVKK+Ph462O7d+/mrrvuqvTamTNnsmnTJgDKyspYsGABwcHBhIaGMnjwYL7++usa76euvP766wQFBdGrVy++/fbbGu9h4cKF9OzZkz59+rBs2TIA3nzzTSIjI61/f3Z2duTk5GA0GhkxYgQmk6ne8TUXausp+Cilnq7pSRGpfctlK+XM0UPsKhA+eGMCeWfdaKsr5v/mJTGocx5x5lswdG3LPYODOHXqlHUHZbPtHRQUwNKlWhW0ctHaggX13nPQXCnXXMBlF9HChQut6uwVK1YwduxY9Ho9U6ZM4YMPPmDevHlWdfa2bdvo3bs3JpOJlStXVrl+RXX2jeS1114jMjKS6Oho4uPjmTdvnnWHc0W2b99OREREJSfS+vXrGTZsGFFRUSwqX0hwFV566SUyMjKIjY3FycmJCxcu8P3339frHuLi4oiKiuLEiROkp6dzxx13kJiYWGXp7GeffcbZs2eJj49Hp9Nx8eJFQNtV/uyzzwKwZcsWlixZgpeXFwBjxoxhw4YNTJs2rV4xNhdq6ynYAW0A9xoOG9VgNpmI2e5K8RmFd5scZkz8Lx2MFzl92oKDsyMhXsqqM+jVq1fz7R3s3An33w//+Q+88YamrYBWmxCupDmpsytqo8v1GRcuXKjS7kp1dlFREfv27WP16tVERUXV6XPR6/V8/PHHvPfeezg5OQHQsWNHHqhnnYzNmzczdepUnJycCAgIICgoiAMHDlRpt2LFCl5++WXrEu/qrK7l9tdybOrsy2SIyCs3LZIWwrr4NL6PHoaTRXhi7CH82rnRZUBfPMI64e3tTWJiYvOdRAbIzoZ//ENLCgCRkZreuoklg/9m5V+90TUytr1Hndo1N3V2REQEX375JcOGDePAgQOkpqZy7tw5OnbsWKndvn37+Oijj6znX331FePGjaNnz554eXlx+PBh+vevff3JqVOn8Pf3r9TbqImnnnqKXbt2VXl86tSpLFhQef9sWloat956q/W8XJ19JcnJyWzYsIHo6Gh8fHxYtmxZpR9ler2eb775hvfff9/6WGhoKAcPHrxqvC2F2pJC0/pX3sQ5cWA1en0RW9f8D45GmBCcRJd2FvJH9Wdov+44ODiQlJTUfDehicC2bVo1tIICbTXRn/4EU6ZAE7ynun6B30iaqzp7wYIFPPnkk0RGRhIWFka/fv2qLbKTk5ODu/vlQYL169db32/q1KmsX7+e/v3737B7XbJkSZ3bVrentrr3Ky0txdnZmV9++YUvv/ySWbNmsXfvXuvzW7ZsYejQodahI9DmihwdHSksLKx0/y2V2pLCzRfSN2Oy0nPYd+h+zp72ootLIeOGZdBv9thK2+MtFkvz7SUUFsKSJVpCuO02eOEFuMJC2dpprurstm3bWusyiwgBAQEEBARUaWdvb29dIZednc3OnTuJjY1FKYXZbEYpxRtvvFGrOjsoKIjffvutTl+w19JTqIs6u7zdlClTAJg8eTKPPPJIpeejoqIqDR2VU55MWgV1seY1paMpWlLPn4+RZ5etE9/wi9I+OFeWT98kW9//p5hMJmubxMRESUxMbMQorwOzWaTCPUhMjMi2bSIV7J5NhaZmST18+LD4+fmJ0WgUvV4vAQEBVsOoXq+XCRMmWM2bx44dk8DAQElISBAREbPZLG+//XaV61e0pJaUlIifn58kJSWJiMiMGTNk6dKlInLZkpqeni7+/v6SlZUlRqNRhg0bVq0lNTc3V0pLS0VEZOXKlfLwww9Xe3+33HKL9f0+/PBDmTNnTqXnR4wYIXv27BGDwSDdu3e3/p2kpKSIv7+/5OXliYjIs88+KzNnzrS+Z3p6eo1m1roSGxsr4eHhYjAY5PTp0xIQEFDp3185zz//vKxevVpEtM9z4MCB1ufy8vLE09NTioqKKr0mKytLevfuXa/4bjb1saQ2vX5/MyL1RDb/OpjKu1+b+Nf7o3AqtOf+vpmM73uWLkPDOXXqFAkJCdZCIc1qQjklBWbPhs8+u/zY6NEwfnyTmz9oivTr14+IiAiioqJwcXFh8+bNLF68mF69ehEWFsagQYOsy0PDw8NZunQpDz30EH369CE0NLTa+sIVcXZ25tNPP+X+++8nLCwMnU7HH/7wh0ptfH19WbRoEUOGDOGOO+6ocbz/5MmThISE0Lt3b77++mvefffdattNmDDBWotg/fr11mI15UyZMoV169bh5OTE2rVreeSRR4iMjOS+++5j1apV1qI1ixcvxsfHh759+xIaGsqkSZOsQ1zXS0hICA888AB9+/Zl3LhxLF++3LryaPz48dbKaQsWLOCLL74gLCyMF154gVWrVlmvER0dzdixY3Fzc6t07V27djF+/Ph6xdecuGZ1dmPTVNTZZ44e4mzK92zP7MaWd8Mx5LXhjv5FLBj3b8ztgykLC2ueKmeTCT7/HD7+WCt607kzbNqkieyaMDZ1dsOTkZHB9OnT2bFjR2OHclO59957ef3115vV0G+DqrNtVCb1RDYWs7D1TAHZpX3ZsGIQdmLHkJAM/vKeB4YfuuM7btxVf+k1SRIStJKY5ZOR99wDTz7Z5BOCjZuDr68vs2fPpqCgoE6rh1oCRqORSZMmNauEUF9sSeEaSU88hncXZ8zOioOreuFYaMftfbJ4dMB/KfuxIw5Ozpw/f755rTIymeCjjzSBncWi9Q5efBEq1Oi1YQOo936C5oajo2O15T9bMrakcA3s37yLw/YOtDO045ctnqQmO+LaVs8DA9Zw3jsA3+HD6NSpE8nJydalgs0COzutIpoIPPQQ/PGPNoGdDRutFFtSqAPlQ0bmMhPtOnTE/pgrv+7zxqTSmPy/m+nhYMbtwQl4e3uTnJzcPHoJej0UF4OPjzZx/NJLkJUF4eGNHZkNGzYaEVtSqAMWsyCmMxxvZ49kwpp/CpbSTMbfs5uBDu1wCuppXT3RLPYi/PQTvPoqdOkCH36oJYXOnbXDhg0brRpbUqgD55OPc8LLARfxZvsnXckqyCFogIHbHvBA90MeHceNA2j6O5bz87Udydu2aeeentpj7do1blw2bNhoMjToN5hSapxSKkEpdUoptaCa559WSsUppY4rpWKUUt0aMp7r4czRQxzS2eHs1Z7EL305fKaYdl4FvP2yKxFtwzDoLCQlJTXtvQgiEBOjCey2bdNWE82fr+1BsCWEG8r58+eZOnUqgYGB9O3bl/Hjx7Ny5coqKmkbNpoqDdZTUErZAcuBO4FzwEGl1H9EJK5CsyPAQBHRK6X+CLwBPNhQMV0rZ44e4mJqAfbtfJGoLLbt6IS9YwFL/18aLi5t+XnzLlxdXZv2cJGItpKo3C/fv7927u/fuHG1QESEyZMnM2PGDKs19OjRo2zZsqWRI7Nho+40ZE9hMHBKRE6LiBGIAu6p2EBEdomI/tLpz0DXBoznmti/eRfnz+Tj7d+bohId733Rm2JHA5NnHqPb8HD2nc7F0d6eibN/39ih1o5S0KOHtprohRe0OYTWkBAGDqz5+PLLy+2+/LL2ttfArl27cHBwqLSzODIykuHDh1NUVMR9991H7969mTZtmtVd9MorrzBo0CBCQ0OZM2eO9fFRo0bx/PPPM3jwYHr27GmVtpnNZp555hnCwsIIDw/nvffeA+DQoUOMHDmSAQMG8Lvf/a557pOx0SRoyKTQBThb4fzcpcdq4lGg2vJLSqk5SqlflFK/ZGZm3sAQa8ZcZuLWSbfj7GNm77/akW8qYcCQ08ybdisGgwHvUwkE+jbRoZf0dKjokp8xQ9uV3ESNpi2F2NjYKorsco4cOcLSpUuJi4vj9OnT1toGTzzxBAcPHiQ2NpaSkhK2bt1qfY3JZOLAgQMsXbqUv/3tbwCsXLmSM2fOcOTIEY4fP860adMoKyvjT3/6E5s2beLQoUPMmjWLhQsXNvwN22iRNOREc3WCnGqdGkqp3wMDgZHVPS8iK4GVoGkublSANZF6IhulU5SUlLAx1sCR/U64OpSx4E+B/PbbGUrj4ii22OMaVv0XQKNhscCGDbB8OTg5wcaN4OUF9vZQTTGRFk1dVSj33qsdDczgwYPp2lXrCJfrtYcNG8auXbt444030Ov15OTkEBISwt13330pNC2uAQMGkJKSAsB3333HH/7wB6va2svLi9jYWGJjY62qbrPZjK/NYGvjOmnIpHAO8Ktw3hVIv7KRUuoOYCEwUkSqlo66yaSeyAagQzd3srOz+X6zJy5mPfeNKaK0NBOj0Qi0wfv22wkObkJftKdPw+LFcPy4dj5ihK1XcJMJCQmx1iG+kvIqY6D5+U0mEwaDgblz5/LLL7/g5+fHokWLMBgMVV5T3h6qr80gIoSEhPDTTz/d6Fuy0QppyG+Ng0CwUipAKeUITAX+U7GBUqof8BEwUUQuNmAsdcZiFrqFeGM0lhEXd5jje+3Q6YR7Z3fAYDCQ7dgRRwdHRvVqIgnBZILVq2HaNC0h+Phoy05fe822sugmM3r0aEpLS/n444+tjx08eLDG+sPlCaB9+/YUFRXVmFAqMnbsWD788ENrksjJyaFXr15kZmZak0JZWRknTpyo7+3YaKU0WFIQERPwBPAtcBL4t4icUEq9opSaeKnZm2h1oDcqpY4qpf5Tw+VuOkVFRXzwZS+UcuG2yHRKS8+glMIr6SThAe0bO7zLLFwIK1ZoRtPJk7UhoxEjGjuqVolSiujoaHbs2EFgYCAhISEsWrSo2mIvAO3atWP27NmEhYUxadIkBg0adNX3eOyxx/D39yc8PJyIiAjWrVuHo6MjmzZt4vnnnyciIoLIyEh+/PHHG317NloJNnX2FZw5nkWX3m35etVqnnpvKm2VC3Mf+Yzet/QlIiKCX7/aybAZk69+oZvF0aOa2fQvf4E6fKm0VGzqbBs2LmNTZ99gco+msWVXGNi5cse4Upz9MuiU4QnGMuRS4Y5G4/BhbRJ1zhztPDJSW1nU2HHZsGGjRWBLCldgNptJPFXKj7/2QOckPPSQnl/jzbi5OeNxxxhK4y40TmB3CmLhAAAZvElEQVTFxbBsGXzxhXY+cKC2EQ1sCcGGDRs3DFtSqMCZo4dIjT3Huo0hFIkjQ8fo6Va2m8Rz+ZzpAifjLmBv1wilKPft0wR2Fy9qy0tnzYKwsJsfhw0bNlo8tqRQgYxTOeTRm92JXXBQBUz1+474H2LRefhi6H8rd/TteHMDysuDt9+Gry/t6QsJgZdfhsDAmxuHDRs2Wg22pFCB0tJSPtvtDcCd/Q9T3PUUZW6KC21D6NgYPYSPP9YSgpMTzJ2rFcCx7T2wYcNGA2JLCpcQETJOOnPiBze8HGH8oP9i8fBgQJ85JBTexH0JIpqvCODxxyEnB+bNg65NRgtlw4aNFoztZ+clkg5nsO1nf3DQcdvgXXi3Laa4bTDe3t43JwARiI7W5guMRu2xtm3h9ddtCcGGDRs3DVtSQFNb/Lw7lv/f3rnHRVmte/z7iCCQimC4JRElb6moaBa2UzM1Eys1Y6d8NG95rLSTXfBUdtrR7mZl270xrd0pj9tU0DiZ7i62U6PUIq+oSCaKKGilouINuQzr/PG+jAMMMCMMCLO+n898PjPrfd53PWsG5pm1nvU+vy2ZrfHgDHcP3sl5SwhNfZviWxtaxdnZhi7ya6/B3r3wzTeu71Oj0WjsoIMCkJ+Xz5bN7WjU2JMhN+/i4jkPLI0g8qZI13ZcXAzLl8PYsca9B/7+RnmKESNc26/GZYgIDz30kPV1UVERgYGBLhfZ8fDwIDw8nLCwMO677z7Onj1rPZadnc2oUaPo1KkTHTp0YNasWWYNLwN7wkAHDhwo10deXh533HEHFovF2rZ69WpEhP3791vbMjMzCQsLK3VubGws8+bNc6o/Z1m3bh1dunShY8eOzJ07167N2bNnrSXMu3btWqpe1Pz58+nevTthYWFER0eXqkPlSp8qs5k6dSqtWrUq9X4WFBQwcOBAa6mTmkYHBWDnj3v5d2prilUuw3sl4etZRMToR/nh8FnWu2obakYGTJkC8+dDfj5ERholKoYNu5JT0NQ7rrvuOmsZbIBvvvmGNm0qqxhfM/j4+JCSkkJqaioBAQEsXLgQMHJlY8aMYfTo0aSnp3PgwAEuXLhgLa1dIgw0aNAgDh06RFpaGq+//jq//17+fpzFixczZswYPGzui4mPj6d///5WUaGqcKY/Z7BYLMycOZOvvvqKtLQ04uPjSUtLK2c3a9Yshg8fzv79+9m9e7f1rt9jx44RFxfH9u3bSU1NxWKxVDqmpKQkJk+eXG2fqrKZPHky69atK3WOl5cXQ4YMYeXKlVW9LVeF2yeaCwsLWZoUgFDAoN5phLftS3J7C7tPFOLh4eG6baj798O+fUZJ6zlzoH9/1/TjhjipjeMwjlZXiYyM5IsvviAqKor4+Hiio6OtIjnLli0jLi6OgoICIiIiWLRoER4eHowePZqsrCwuX77MrFmzmD59OpmZmURGRtK/f39++OEH2rRpw5o1a/Dx8am0/9tuu409ZrXcjRs34u3tzZQpUwBjRjF//nxCQ0N5+eWXSU5OtisMZI/ly5ezYsUK6+sLFy6wZcsWvv32W0aOHElsbGyV701FQkTVZevWrXTs2JEbb7wRgHHjxrFmzRq6detmtTl37hzff/89S5YsAYwvVy8vL+vxoqIi8vLy8PT05NKlSxXWrKpJn6qyGThwoLVsui2jR4/m+eefZ/z48dXy0R5uPVM4si+Hzz/ayM8pofh6NOKeqBb8fHo//n7+KBrV/I6jM2euPI+MhP/6L1i1SgeEBsa4ceNISEjg8uXL7Nmzh4iICMCoR7Ny5Uq2bNlCSkoKHh4eLF++HDB+he/YsYPt27cTFxdHTo5Rwj09PZ2ZM2eyb98+WrRowf+V3NFeARaLhQ0bNjBypFFzct++feWEf5o3b05ISAgHDx6sVBjIloKCAjIyMmjfvr217bPPPmP48OF07tyZgIAAdu7cWeV1HO0PYMCAAYSHh5d7rF+/vpztsWPHaNv2SqX+4OBgjh07VsomIyODwMBApkyZQu/evZk2bRoXL14EoE2bNsTExBASEkJQUBB+fn4MGzasXD8RERGEh4czbdo01q5da/Xp6xK5Wyd9csTGHmFhYWzbtq1Ku6vBbWcKR/blUGwp5rtdQdBIEXH7GYKC8sg9rfAqDkdqcsno8mVDBjMxET7+GEJDjSWiBx+suT40VlxYL9EhevbsSWZmJvHx8YywyQ9t2LCBHTt2WKuh5uXl0coUP4qLi2P16tUAZGVlkZ6eTuvWrQkNDbX+krYV2ylLXl6eVbzn5ptvtgru2NNfqKy9Ik6dOkWLMqXY4+PjefLJJwEjEMbHx9OnT58Kr+tMf4B1duUI9gp7lu2vqKiInTt3smDBAiIiIpg1axZz587llVde4cyZM6xZs4bDhw/TokUL/vSnP7Fs2TImTCgtt/vTTz8BxvLRkiVLrLOOq/XJERt7eHh44OXlxfnz52nWrFmV9s7gtkGh2KLwDvTiux9uABQTJvhRUJCNj48PlmIY2q2GZgnbtxviN9nZxo1nu3YZQUHToBk5ciQxMTEkJSVZf/UrpZg0aRJvvPFGKdukpCTWr1/Pjz/+iK+vL4MGDbImOcuK85TkKspSklPIzc3l3nvvZeHChTzxxBN079693Ozi3LlzZGVl0aFDB06cOOGQjoOPj0+pxGtOTg4bN24kNTUVEcFisSAivPXWW7Rs2ZIztrNiDN2H0NBQgoODHeoPjJnC+fPny7XPmzePoUOHlmoLDg4mK+uK+m92dna55Z/g4GCCg4OtM7eoqChrYnf9+vWEhoYSGBgIGKp3P/zwQ7mg4AyO+lSVTUXk5+fj7e191f5VhPsuHynFqlX5XM5vTEiHU3TocBk5fJjjecU1k1i+cMHYSfToo0ZA6NgRliypFelHTd0zdepU/vznP9PDpkbVkCFDSExM5MQJQ0/q9OnTHDlyhNzcXPz9/fH19WX//v0kJydfdb9+fn7ExcUxb948CgsLGTJkCJcuXWLp0qWAsbz0zDPPMHnyZHx9fR0WBvL398disVgDQ2JiIhMnTuTIkSNkZmaSlZVFaGgomzdvpmnTpgQFBbFhwwbrONetW0f//v2dEiLatGkTKSkp5R5lAwLALbfcQnp6OocPH6agoICEhATrEloJrVu3pm3btvzyyy+AMXMrWbsPCQkhOTmZS5cuoZRiw4YNlZZiHzRoUKWzBEd9csTGHjk5OQQGBuLp6VmlrbO4bVDITNvJyhW5UHyOP96ZQWZmJmeLTnG6R7fq5xJSUoyloU8/NQrYPfqosWxkk2DSNGyCg4OZNWtWqbZu3brx6quvMmzYMHr27Mldd93Fr7/+yvDhwykqKqJnz568+OKL9OvXr1p99+7dm169epGQkGAV/vnkk0/o1KkTnTt3xtvbm9dffx1wThho2LBhbN68GTCWju6/v7SuyAMPPGBNRC9dupRXX32V8PBwBg8ezEsvvUSHDh2cFiJylMaNG/Puu+9y991307VrVx588EG6d+8OwIgRIzh+3FACXrBgAePHj6dnz56kpKQwZ84cwMgVREVF0adPH3r06EFxcTHTS8rT21CSUyj7sJdTcMSnymwAoqOjue222/jll18IDg7mo48+AoyE/QgXbV13W5GdubO3sGx1KO2uh6FzMrglALL2biJo8MPVDwpZWca9B126wIsvgrmzQOM6tMiO69m1axd//etf+fjjj+vaFbdnzJgxvPHGG3Tp0sXucS2y4yRKQdJ3IYjA6Ie9yW0Gea3zYO9V7jhSCn76CSIijARy27aGbnKXLrqAnabB0Lt3b+68804sFkupexU0tUtBQQGjR4+uMCBUF7f8xvrykzNk/i4088vnjjsu8PPpY5zYuJt2La5ieef33+Gpp+Dxx+Ff/7rS3rWrDgiaBsfUqVN1QKhjvLy8mDhxosuu75YzhcSEHTRqdBMjo/wROcl1Z84yqmsETQcMcPwixcXw2Wfwt7/BpUvQtCm4IOmj0Wg0tYnbBYXDKTs4dLQVHl4B9Ot3jkaNGtEYcS4gHD1qbDMtuVln0CB49lkwt7NpNBpNfcXtgkJuruJUbiDeXtC69QU2f7UKuc6JUhZ79hi7iQoKICDAuCt5yBBdr0ij0TQI3C4o7N22lULLaNq2usRb+7bh7xtI775/dPwCXbtCSIiRRH76afDzc52zGo1GU8u4V1BIX8+e1NZgaU7HkGM09/Nhjn9rmvWoZCtjQQEsW2bcdNaihZE3WLwYakNnQaPRaGoZ99oeU1zIwV/b49GkEWcC02lz7hziWUlc3LsXJkyARYvgnXeutOuAoNFoGihuNVNQCjKPtQQFLYNOM/P+h+wXn8rLg/feg/h446SQEF2eQqPRuAVuFRTWpxRw8YIXft7n8AsssB8Qtm41dhYdP27cZzBpEkyfDjZ11zXXPllZWeTn59fY9Zo0aVKqxHFNMHXqVD7//HNatWpFamqqw+edPXuWFStWMGPGDLvHY2Njadq0KTExMQ5dz1l7TcPGrZaP1mQ0Jk888Ao9QeOQ1uUNjh6FmTONgNC5MyxdatyUpgNCvSM/Px9fX98aezgbYBxR5rKnquUIZ8+eZdGiRU6fp9E4glsFhezMAHwuQ4+bLzP2Dx3KG4SEQHQ0zJhhBISbbqp9JzVuw8CBAwkICKjU5uLFi9xzzz306tWLsLAwVq5cyXPPPcehQ4cIDw9n9uzZALz22mt06dKFoUOHWquAVkZl9suWLePWW28lPDycRx55BIvFwrPPPlsqEMXGxvKObZ5N02Bwq+Wj3w62RKG49UZfGjduDKdPw9tvwwMPXNFwfPrpunVSU6+JiIggPz+fCxcucPr0aatAzptvvsndd9/t9PXWrVvHDTfcwBdffAFAbm4uERERpKamkpKSAsCOHTtISEhg165dFBUV0adPn0rVzSqzt1WH8/T0ZMaMGSxfvpxx48bx5JNPWpesVq1adVWzHM21j9sEhYICOHHcD4sU0vWmYjqlp8Mjj8C5c3DkCCxfrm9A01QbZ5S5HKFHjx7ExMTw7LPPcu+99zJgwIByAjabNm3i/vvvx9fcFVdVPf7K7CtSh5s4cSInTpzg+PHjnDx5En9/f0JCQqo1Ns21iUuDgogMB/4OeAAfKqXmljneBFgK3AzkAGOVUpmu8OXAASgohKDrTnHrig+hRMikXz+YM0cHBM01SefOndmxYwdffvklzz//PMOGDbNbDM1ZqcuK7CtShwNDqSwxMZHffvuNcePGOdWfpv7gspyCiHgAC4FIoBsQLSJly5A+DJxRSnUE5gNvusqf1D3FNDl3kUEnN+KRnAzNm0NsLCxYANUU+NBoyuKIMpcjHD9+HF9fXyZMmEBMTAw7d+6kWbNmpWQqBw4cyOrVq8nLy+P8+fP8y7Zarx0qs69IHQ4MHeaEhAQSExOJioqq9tg01yaunCncChxUSmUAiEgCMApIs7EZBcSazxOBd0VElAuUf1J3FeJ74SI9AjNg8GCjgF3LljXdjeYaoUmTJly6dKlGr+cIJTmFstjLKURHR5OUlMSpU6cIDg7m5Zdf5uGHHy5ls3fvXmbPnk2jRo3w9PTkvffeo2XLltx+++2EhYURGRnJ22+/zdixYwkPD6ddu3YMsCnuOGLECD788MNSymZ9+vSp0N5WHa64uBhPT08WLlxIu3bt6N69O+fPn6dNmzYEBQVV2oem/uIy5TURiQKGK6Wmma8fAiKUUo/b2KSaNtnm60Omzaky15oOTAcICQm5ueSXizO88AKs/N+DfPaXXwmb5kRFVE29QCuvaTRXqI7ymiu3pNpbtCwbgRyxQSn1gVKqr1Kqb+BVlqd+7TXYf7Qj3abqgKDRaDQV4cqgkA3Y3gIaDByvyEZEGgN+wGlXOdS4sRZD02g0mspw5VfkNqCTiISKiBcwDlhbxmYtMMl8HgVsdEU+QeMe6D8djab6/wcuCwpKqSLgceBr4GdglVJqn4j8RURKNkZ/BLQUkYPA08BzrvJH07Dx9vYmJydHBwaNW6OUIicnB29v76u+hssSza6ib9++avv27XXthuYao7CwkOzsbC5fvlzXrmg0dYq3tzfBwcF4ltGMdzTR7DZ3NGsaNp6enoSGhta1GxpNvUenXTUajUZjRQcFjUaj0VjRQUGj0Wg0VupdollETgLO39JscD1wqkqrhoUes3ugx+weVGfM7ZRSVd79W++CQnUQke2OZN8bEnrM7oEes3tQG2PWy0cajUajsaKDgkaj0WisuFtQ+KCuHagD9JjdAz1m98DlY3arnIJGo9FoKsfdZgoajUajqQQdFDQajUZjpUEGBREZLiK/iMhBESlXeVVEmojISvP4TyLSvva9rFkcGPPTIpImIntEZIOItKsLP2uSqsZsYxclIkpE6v32RUfGLCIPmp/1PhFZUds+1jQO/G2HiMi3IrLL/PseURd+1hQislhETpjKlPaOi4jEme/HHhHpU6MOKKUa1APwAA4BNwJewG6gWxmbGcD75vNxwMq69rsWxnwn4Gs+f8wdxmzaNQO+B5KBvnXtdy18zp2AXYC/+bpVXftdC2P+AHjMfN4NyKxrv6s55oFAHyC1guMjgK8wlCv7AT/VZP8NcaZwK3BQKZWhlCoAEoBRZWxGAf80nycCQ0TEnjRofaHKMSulvlVKlSjZJ2Mo4dVnHPmcAV4B3gIaQk1tR8b8H8BCpdQZAKXUiVr2saZxZMwKaG4+96O8wmO9Qin1PZUrUI4CliqDZKCFiATVVP8NMSi0AbJsXmebbXZtlCEGlAu0rBXvXIMjY7blYYxfGvWZKscsIr2Btkqpz2vTMRfiyOfcGegsIltEJFlEhtead67BkTHHAhNEJBv4EvjP2nGtznD2/90pGqKegr1f/GX33TpiU59weDwiMgHoC9zhUo9cT6VjFpFGwHxgcm05VAs48jk3xlhCGoQxG9wkImFKqbMu9s1VODLmaGCJUuodEbkN+Ngcc7Hr3asTXPr91RBnCtlAW5vXwZSfTlptRKQxxpSzsunatY4jY0ZEhgIvACOVUvm15JurqGrMzYAwIElEMjHWXtfW82Szo3/ba5RShUqpw8AvGEGivuLImB8GVgEopX4EvDEKxzVUHPp/v1oaYlDYBnQSkVAR8cJIJK8tY7MWmGQ+jwI2KjODU0+pcszmUso/MAJCfV9nhirGrJTKVUpdr5Rqr5Rqj5FHGamUqs9aro78bX+GsakAEbkeYzkpo1a9rFkcGfNRYAiAiHTFCAona9XL2mUtMNHchdQPyFVK/VpTF29wy0dKqSIReRz4GmPnwmKl1D4R+QuwXSm1FvgIY4p5EGOGMK7uPK4+Do75baAp8ImZUz+qlBpZZ05XEwfH3KBwcMxfA8NEJA2wALOVUjl153X1cHDMzwD/IyJPYSyjTK7PP/JEJB5j+e96M0/yEuAJoJR6HyNvMgI4CFwCptRo//X4vdNoNBpNDdMQl480Go1Gc5XooKDRaDQaKzooaDQajcaKDgoajUajsaKDgkaj0Wis6KCgueYQEYuIpNg82ldi276iapJO9plkVuLcbZaI6HIV13hURCaazyeLyA02xz4UkW417Oc2EQl34JwnRcS3un1r3AMdFDTXInlKqXCbR2Yt9TteKdULo1ji286erJR6Xym11Hw5GbjB5tg0pVRajXh5xc9FOObnk4AOChqH0EFBUy8wZwSbRGSn+fijHZvuIrLVnF3sEZFOZvsEm/Z/iIhHFd19D3Q0zx1i1unfa9a5b2K2z5Ur+hTzzLZYEYkRkSiM+lLLzT59zF/4fUXkMRF5y8bnySKy4Cr9/BGbQmgi8p6IbBdDR+Fls+0JjOD0rYh8a7YNE5EfzffxExFpWkU/GjdCBwXNtYiPzdLRarPtBHCXUqoPMBaIs3Peo8DflVLhGF/K2WbZg7HA7Wa7BRhfRf/3AXtFxBtYAoxVSvXAqADwmIgEAPcD3ZVSPYFXbU9WSiUC2zF+0YcrpfJsDicCY2xejwVWXqWfwzHKWpTwglKqL9ATuENEeiql4jDq4typlLrTLH3x38BQ873cDjxdRT8aN6LBlbnQNAjyzC9GWzyBd801dAtGTZ+y/Ai8ICLBwKdKqXQRGQLcDGwzy3v4YAQYeywXkTwgE6P8chfgsFLqgHn8n8BM4F0MfYYPReQLwOHS3EqpkyKSYdasSTf72GJe1xk/r8Mo+2CruvWgiEzH+L8OwhCc2VPm3H5m+xazHy+M902jAXRQ0NQfngJ+B3phzHDLieYopVaIyE/APcDXIjINo8zwP5VSzzvQx3jbgnkiYldjw6zHcytGEbZxwOPAYCfGshJ4ENgPrFZKKTG+oR32E0OBbC6wEBgjIqFADHCLUuqMiCzBKAxXFgG+UUpFO+Gvxo3Qy0ea+oIf8KtZI/8hjF/JpRCRG4EMc8lkLcYyygYgSkRamTYB4rg+9X6gvYh0NF8/BHxnrsH7KaW+xEji2tsBdB6jfLc9PgVGY+gArDTbnPJTKVWIsQzUz1x6ag5cBHJF5A9AZAW+JAO3l4xJRHxFxN6sS+Om6KCgqS8sAiaJSDLG0tFFOzZjgVQRSQFuwpAsTMP48vy3iOwBvsFYWqkSpdRljAqUn4jIXqAYeB/jC/Zz83rfYcxiyrIEeL8k0VzmumeANKCdUmqr2ea0n2au4h0gRim1G0ObeR+wGGNJqoQPgK9E5Ful1EmMnVHxZj/JGO+VRgPoKqkajUajsUHPFDQajUZjRQcFjUaj0VjRQUGj0Wg0VnRQ0Gg0Go0VHRQ0Go1GY0UHBY1Go9FY0UFBo9FoNFb+H5WTKT78fqljAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.70463479, 0.57834029, 0.35220766, ..., 0.52625859, 0.25496623,\n", + " 0.43516079])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Logistic regression (SGD)\n", + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " for mod in range(5):\n", + " print('>>')\n", + " curr_try = 0\n", + " while curr_try <10:\n", + " print('.')\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(1, activation='sigmoid'))\n", + " # Compile model\n", + " opt = keras.optimizers.Adam(epsilon=None, amsgrad=True)\n", + " model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n", + " \n", + " # Fit the model\n", + " history = model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=0)\n", + " if history.history['acc'][-1] > 0.53:\n", + " break\n", + " else:\n", + " curr_try += 1\n", + "\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train], epochs=50, batch_size=64, verbose=1)\n", + " \n", + " # evaluate the model\n", + " probas_ = model.predict(X[test,:])\n", + " prbs.append(probas_)\n", + " # Average the predictions\n", + " probas_ = np.mean(np.hstack(prbs), axis=1)\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v3.norm.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6685537828100875\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.7202411587383281\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.7168627450980394\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6643872549019608\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.671936274509804\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6846323529411765\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6668872549019609\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6213235294117647\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6297058823529411\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6967401960784314\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6637254901960785\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.713872549019608\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6824999999999999\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6579166666666668\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.640735294117647\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6849264705882354\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6687990196078433\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6941176470588236\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.6903676470588236\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.73577705, 0.50272183, 0.19845793, ..., 0.3937505 , 0.3072859 ,\n", + " 0.41628284])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Logistic regression (scikit)\n", + "cv = StratifiedKFold(n_splits=20)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " model = LogisticRegression(random_state=0)\n", + " model.fit(X[train,:], y[train])\n", + " probas_ = model.predict_proba(X[test,:])[:, 1]\n", + " results[test] = probas_\n", + " \n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v3.norm.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>\n", + " 0.6996023454876323\n", + ">>\n", + " 0.6807397966998965\n", + ">>\n", + " 0.706390931372549\n", + ">>\n", + " 0.6830269607843136\n", + ">>\n", + " 0.6797181372549019\n", + ">>\n", + " 0.6988848039215687\n", + ">>\n", + " 0.7022120098039215\n", + ">>\n", + " 0.6979289215686274\n", + ">>\n", + " 0.7029473039215686\n", + ">>\n", + " 0.7025183823529413\n" + ] + }, + { + "data": { + "image/png": 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Ki4vpkp9Pl/x8OnfJp0H9evzP/9xHWTJJcfE+brnhOvLbt+fS3v3YtmEjYilWLv2CH//wbnbvDbN1SyZbtmSyf2+QWKmGshWG7hANJ8nJjlG3XjHnNLRoWE+nboNMatetRWZuNpHamQQyQuiRQGWFoBQcPAjr17sKQdOgQQNo3hyCwa9VJseLv6LZx+cEEYlEWLZsGStWrKBOnTo8++yzgFuZXn311Tz00EOsWbOG5cuX89///pc///nPAKxYsYJ7772XV155hVWrVrFixQpatmxZKf6lS5dimibLli1j2LBhJ0zuWbNmsXbtWtauXcsLL7zAXXfdVWW4SZMmMXToUPS01mtBQQE9evRgxowZNU7v9ttvp06dOqxdu5aVK1cyZcoU9u7de9LzkJWVxbxFi5n3yUcs+GQezZo2YcjVl2M4JRT+bTI5ObVZ8PEKRt48ioce+DVbNkfJzu7G5k072bhmJ5hCAEVW0KRedpxm9WI0a2DRqK5Qv06AullRMqNhwtkRjEAATdcR7ShV7M6dsG2b2yvIyHBNRrm5rvnoFOErBR+fk0CvXr3Ytm0bAK+99hoXXXQRl112GQDRaJRnnnmGxx9/HIA//OEPPPLII7Rv3x4AwzC4++67K8S3e/dubrrpJpYtW0Z+fj7r169n9uzZdO3alU6dOnHrrbeSSCQqyTF58mTatm1L3759mT9/fpWyzpw5k5EjRyIiXHjhhRQVFbFjR+XhvVdffZVrrrkmdb5+/XpKSkoYN24cBQUFNSqX9evX8/HHHzNu3Dg0r8Js2bIlV155ZY2er46ZM2cyYsQIkskkXfK7sv/AAdZ8+SX7YnH2px2JZBzNLGX96rXs2bOXiy7qS2k8yJtvvkf//j9k544gvS8axoIFc1CWIiA2l17yPT54/zUa14/RrGGM2vUsMmvrBHMiBDLDhGtFCWdHCWaF0SPHOA00K8s1ETVuDM2anbLeQTpn3CY7Pj414b3Pd53wOAd0aFCjcLZtM3v2bG677TbANR2df/75FcK0atWKkpISDh06xIoVKxgzZswR46xfvz4vvvgi48eP51//+hfxeJx+/foxe/Zs2rZty8iRI3nuuee47777Us/s2LGDxx57jMWLF5OTk0P//v3p2rVrpbi3bdtG06ZfTQRs0qQJ27Zto1GjRqlryWSSDRs2kL7rYUFBAcOHD6d3796sXr2a3bt3U79+/SPmY+XKleTn51fobVTHsGHDWL16daXr999/PzfeeGMF89vWrVtp2LgxxbaDYRg0btKEnbt20KRJY3SVRKFQCpRjgTKY/vc3ufKqa9myJUAiobFj+3YaNWhCxEgQiZrUys4iM7yJeg3q0ad3JyY89SSi34NIgHA0SDAcPL4B3UQCysqgdm33PDMTWrc+JWMH1eErBZ+zkppW4CeSWCxGfn4+Gzdu5Pzzz+fSSy8FXHt3dRXI8c4UWb16NS1atKBt27YA3HzzzTz77LMVlMLHH39Mv379qFfP3YFx2LBhrFmzplJc6ZVrdXLt3buXWrVqVbhWWFjIjBkz0DSNoUOHMn36dO65554Tlteqxk2UckgkikkkitA0tyJNKiFpW5RZCQwxiYqNpmwCKoHmDRqLBEAJiXiI0lKd1157g9/+ZjJmUkPXFZrm0LRxkqYNk4CBaEIgEELsAA3rNmLXzl1EQxkYAd2dJXSsKAX79sGePe7/oRBEvSmmp5FCAF8p+PicMMrHFA4ePMhVV13Fs88+y+jRo8nLy+PDDz+sEHbDhg1kZmaSlZVFXl4eixcvpkuXLjVOq6qKvCpqUhE3adKELVu+ci6wdetWGjduXCFMJBKpsFr8008/Ze3atSnFl0wmadmyJffccw+5ubkcOHCgwvP79++nbt261KpVi+XLl+M4Tsp8VB1V9RSUchg16i5uueU2dF3HchSO7dCsaVMO7NhNVA/h2A5bt+2gbv1mlMY0YjGdeEwnHtdwbPhi9TIsy6Rzfkeys0uJRkzOPacROzZupFmDRjgCh4oPkVu/Abqho4IQzcwgmB06allWSTwO27e7fwFyck4LM1F1+GMKPj4nmJycHCZOnMj48eMxTZMbb7yRefPm8d577wFuj2L06NE8+OCDADzwwAP89re/TbXiHcdhwoQJR0yjffv2bNy4kXXr1gEwdepU+vbtWyFMz549mTt3Lvv27cM0TaZPn15lXFdffTUvv/wySik++ugjcnJyKpiOAGrXro1t2ynFUFBQwNixY9m4cSMbN25k+/btbNu2jU2bNtGjRw/mz5/Pzp07AVi0aBGJRIKmTZvSqlUrunfvzmOPPZZSbGvXrmXmzJmV5Jo2bRrLli1jyZIlLFq0iAUL5rFgwVxuvPEm4pZDSdykOGkitsNVA6/klVdewbYVH8xfSEZGDspuwvatIQ7sM4iVCcpRBIIWc+cUMPy6wbTMLSI3bBO0NAZefiWvvPk6RlYGM2b9k379+hMMBwiEdNauXUvHjh2P+HtUiePA7t3w5ZeuQggE3HGDc84B4/Rtj5++kvn4nMF07dqVLl26UFhYyIgRI5g5cyajRo3innvuwbZtRowYkZq22blzZ5566imGDx9OWVkZInLUgddwOMzkyZO57rrrsCyLHj16cOedd1YI06hRI8aOHUuvXr1o1KgR3bp1qzCdtJyBAwfy9ttv07p1a6LRKJMnT64yzcsuu4x58+YxYMAACgsLmTVrVoX7Q4YMobCwkJ/97Gf86U9/YuDAgTiOQ2ZmJgUFBamewYsvvsiYMWNS6eXm5vLHP/4xFY9SCqVMwFWQxUmTpGODckACgA6iCIgAAgou6ncFb818h3btOhAKRfnVr18kkdBAE66/7nzmzFlEViROQBLMevsNZr5WCHoWaDoaGrfffge33nUb5+W1o06dOhQWFqIbrrxz5sw5voHw3bth/373/zp1oF69085UVBVS027o6UL37t1VVXOwfb7drFq1ivPOO+9Ui3FWs3TpUiZMmMDUqVNPSHxKKUylSDoKO23cRTkmSlkoNGzbxnZssC0MLYB44wjKgWRMIxEX4jGNZFJDcB1KaCJEoorsbEVmBuhYSDKGMhVKAijHwQnoiB5E03U0XSNQjT+hRCJB3759mTdvHsaxtu4tC7ZscdcdRI/uouJEUtX3ICKLlVLdj/as31Pw8fGpEV27dqV///7Ytl2j2UPV4ShFme1gew3SoKYREhtBEbfimGYZtq3hKB0HcFCI6CjLoKxUiJUZJJNaypMoArpAJOSQGYWsTBtRCuUoVJnCSibdcHoAAjpaIIhuGO6gsS5Vu+b02Lx5M48//njNFEJJCRQVueYhEddE1Lz5KV1zcDz4SsHHx6fG3Hrrrcf1nKMcSi0TU7mOJQSIaIIoCzNZSlLZOErHthw0FSCghwEhFgMzblBWomHbnis5EUQgHHaIBhXRMEQiDhJyzT12wnEVl2Yito0W1TGCASScVUGmmgzCt2nThjZt2hw5kG3Drl2uQgB3EVr5lNMzTCGArxR8fHxOMnEryUEzASJkaKBhgWPhOO6YgW0LyoliOeBYkEhoJJI6ibiObeupejUQhIwMRUbQJhJ20LwhBYIaoKEchWOaKDOBroFuO2ihIFoggBjGyamgDx1yVyVblht/vXpw2NTdMw1fKfj4+JwQXO+iZsrJHIBlWyQck4BmEAWsZAxb6ShlABoiQjIBsUSQRJlGMi4gkmrFGwHIithkZTiEy/cd8BSBUqBsB4mXYVs2ylZooggEg2iiIeEAWjh8cjJrWa4yKHdgF41Co0bu+oMzHF8p+Pj4nBDKrDIc5RDUgihl4jhxbBssRyOIRdKxMYwgmhYmmRRKSjQOlYCZ1NxWv1KI5lpfIhGHaFQRCilUzMbSBLM8IQUkHHdRoHLQlI0ejqLZNrqhuy12EeRkrgUoLv7KgV39+q656Aw0FVWFrxR8fHyOimmbxO2jb3UawsCxyrBtC4cgSWWgA4IQTxrEijRKSyU1PgAKXVOEgybRiEVmpiDy1SC2VeL6ptYCgmiAYyHKBsdCsy3EdtwZSZarMiQYQU7WtE/HcZUAuCaiZNJVBqfxQrTjwV+85uNzgjhTXWd/8cUX9OrVi1AoxPjx4711Au6RtJMkrAQJO0FAAlxzxTU4MYeMQCYBPYN/zfw32aFsvvh8PY6lU5o0eXfOfIZ8fyQldoCymMGBvUFuvukuXnn5LYoPCYm4ycQ/PcTVg9py3bWdGHHTBSxd8jY5tQ2MgIEeEIwQ6AGFZoAWETQddA2CYhMwdHQLND2MllUHrVZdtKwsfv/MM7Rp14527drxzjvvVFk2SikeeeQR2rZty3nnncfEiRMBOHjwIIMGDaJLly7k5eWl1mrs2bOHK664wl1vsG6dqwjA7RU0aHDWKQTwlYKPzwnjdHednV7Zpx9ZOVmMnzCe++6/D8uxKE6WpI64lUABAS3AO2+/Q17HTuiRCIfiCUricaYVFNDrO9/hzenTUSpOPK4oKQ6STGjs3Rzm0O4A8RLX/m/oDjlZCaZMeoTSkq18suAjlnzyCTNmvEnCjKFpIJaNFo+hlcUw4gkCyiQkSUIqScBJIJqOBCKIHkDPzkELhRBNY9WqVUybNo2VK1fy73//m7vvvrvKhXpTpkxhy5YtfPHFF6xatYobbrgBgGeffZYOHTqwfPly5s6dy5gxY0gmk9TLzqZRVhbz337bHUcoH0M4i/GVgo/PSeB0cZ09adIk2rZty3f7fJcP5n2A6ZiUmCUVjuzcbLr16EYgEMBWClOCKC2SOuKOxqGEzd9efZVLvncFiWQMZScwSw6w8KOPmDD+Kf5eOJ3tm7LYuyOTWInuLj5GEQo61MpOkJFhUzfXJhwq5ZVXJ/P0U38kJ1PDUGU0rJPBtYMuR4vF0K04uu6gRyJokQgSDqMkjCMhHCeAk3BwSkq/MuN4zJw5kxtuuIFQKESLFi1o3bo1CxcurFQezz33HL/4xS9Sq6vLvbqKCMXFxSilKCkpoU6dOhhFRbBhA4P79uXVt9+GJk2gbt2v81qcEfhjCj5nJ6tnHT3MsdLuezUKdjq4zlZKsWXbFh4b+xgfLviQ7JxsBl0+iG5du5EVzKpgurJtG9O2SdqKgA2G5WAoKxVGKYUhwqKPPuIvzzxBhiHE4wZ/L3yXXr0uJyOzK1lZuXz22TK6dOlKVqZNKGjTvPEh1yOo46ApB2WZfLnuc5qecw6ZehSnzEHEcFv/AiCIoZBAAES4/4EHmPvBB5XK4obhw3nooYcqXNu2bRsXXnhh6rzc/ffhrF+/nmnTpjFjxgzq1avHxIkTadOmDffeey9XX301jRs3pri4mGl/+hOat/FP9169+Pnzz0N29hF/o7MFXyn4nJ3UsAI/kZxq19lPP/M0d957J7ayKbPKmPffefTu05tzG5+LJhrDrnddZ1uWhWVZFeIzEXTRCGlCSBPgq4oabJSKc+DAAUyzLps2BTEt4Y03/s5NN40mYNgM/f51zJv3Gld9L4+N6x10UWjuQgTXLIRCAzSlgQhGVtTdrP4INvmnPHt/TaiJ+29wx2XC4TCLFi3izTff5NZbb+U///kP77zzDvn5+bz//vus//xzLr38cnr/859kt2lD/VCI7du311iWMx3ffOTjc4IoH1PYtGkTyWQyNaaQl5dXaeC3KtfZx0JVlaCjHLeiRSNiRIgEIgT0AIKQSCSwLAvbtrEsC8MwCIfDBIJBHE0n6TiIcgDlWWbiKFWGUmWYZoK9e6OIGOzda5BMwqGiXSxcOIdf/+p2Lr+sNS/85QneenM6UlZE/cwgRUX70dEJRLIIRGtRdKiYho2b0b5DF7Zs20ZZMnlEhQDwk5/8hPz8/EpHudktnZq4/y4P9/3vfx9wHfh9+umnAEx+6SWGDhmCiNA6L48WrVrxRTwOmZnE43EikUjNf5wzHF8p+PicYL5J19lr164lbsWZ8rcpXNT7IoKauyOYJlrKdfbOnTsxTZO33noLXdcJBoMopSiLJzgQj1FqxQlKHE0slDIpK0tw4ECQvXuz2bEjm82bsjlwwKB587bs272KxnUOsnjBVEbccB2rP/+CVV+sYc2adbRo0YKFn35B+84XsHP3XtZt3UIgK8zWPdv5dMVndOt5PpnIVqgBAAAgAElEQVS52dx2222MHj2apDeTZ8eOHbzyyiuV8vjkk0+ybNmySsfhpiNw3X8XFhaSSCT48ssvWbt2LRdccEGlcIMHD+b9998H4IMPPnB7Wjt30iwnh9me19ddu3axet06WnruLdasWXN8rrPPUHyl4ONzEkh3nR2JRJg5cybjxo2jXbt2dOrUiR49elTpOvu8886jY8eOVe6RnE666+zz889H13XuuavirmcNGzbkkUceoU+fPgwcOJCuXbtiOw6JZJK4bWPioItN6e6ddGjTkYkTn+N3v5tAu3Z5bNoYp2i/oqTYwbYdIiGLqwZeyuovPiAnR/H6zH9w9ZAhYAmGI4TE4PtDhjJtxnSitTN55dVXuO3OO8jPz+faa6/lxRdfJCcnB4Bx48ZRr149OnToQMeOHRk8eHBqd7jjJS8vj+uvv54OHTpwxRVX8Oyzz6ac9g0cODBl/nnooYd444036NSpEw8/+CAvPvoo7N/Po3fdxX8/+ohOnTpxySWX8Pvf/5663qDycbvOPkPxXWf7nBV8G11nK6UoMUsIG2ECWsUN4x3HSbXEdV3HcRzXO6kCwcHBIYCFOEK8NMrBQxrxuKCUg4ZDZsRED0IgIERCEAkpduzcya133MH/vjkdR0XQPHcUgawQmnYGrea1bddFxcGD7nk4DI0bu3+roE+fPsycOZPa5U7uzgB819k+Pt8ClFIo3AVlAKbjruI1xKgYRikSiYTnTVSwbRtHIKZsUDYRsSgrC3GgOIPSUh2Uu82lJjY50Th1ciyMzGilaZ8Nz2nCLT8YwcEDMWrXzUSPuIrojPLuEI/D5s0VHdjl5labiT179nD//fefUQrh6+IrBR+fM4CEncC0v3I2F9SDhLQQuqZXWIhmWRamaaKUIhAwUIASh7idRFkQPxhkT0kUyxaU4wA24UCSjEiCrBydUCgAeghwEKsMTM1TGgrbguuHDCVYKwPRz9CqIxh0lV0NHdjVq1ePwYMHf0PCnR6cob+sj8+3A8uxMB0Ty7EI6SF00dFEI5GI4yiFIxaOk8RxnNSMJNF1NE0odRI4DpgJoexQiHhpwHUmJ0JAs4lG42TX1olEDW+bS0A5iJ2AhO0qFE2DINiWoId0QiEdtNN/S8kUSrmrkDMz3a0wNQ3OPdfdAOeM6uJ8c/hKwcfnNEQphaUs4lYcXXTCehhD03EcE8s2cZwEum7gOLa3yVcIRDAVJBSgIFGmU1YkJOJuJS5AZoZFNBwnZFhoGVGCgfIK3vU4ilmGMnWUHkSLBFPjBiIKI6TDmTR2kEzCjh1QWuo6rmvUyL0eCBz5uW85vlLw8TlFKKVwlFPxGirVMwAI6UF0HBwnQdxMgsLzMBpAtCBoriJAwFRgJ4VkiVBarGGaChRo4pCdaZERTqCHNRw0AqEMdN0bM1AOUlbmbm8pBhKMEIgGKsxksqXiYrfTGqXgwAHYvdv1bKrr3/geyWcyvlLw8fmGcJRTYdFZzIqhUGjpM8MFDM3AwEAcE8eJ4eC6mlbKQNMMNA0sTScBrpJICMlSoaxUI5nArRRRrgO6jCSZURMnGsFWIQKGm5ZuKfDGHlxHRRqSGcUwApVWAtuWwxkzSTGRcHsHZWXueXY2NGzomot8aoS/TsHH5wRxJNfZyz5bRv+L+9POc+089ldjUUoRNaJkBDOY++5c+n6nL907d6dzh848/OCD7kY1SsdydJIYlNmKgVddRc9evSgseJ3Yfp29mwz2bNEp2u/uYKZpijvuuJhd2+bTqF4JkVrgRMPomvBGwauMue9/MOJxMMtAYugBm7/P+Dvde/fm/PO7c9FFF7Hok8WYCTt12KbjbnCP4uKLL+ZQmqfQGTNmICJ88cUXqWtz587lqquuqlA2t9xyC6+//joApmny0EMP0aZNGzp27MgFF1zArFlf31fV78aNo3WbNrTr3593FiyApk1dJ3ZpCqF3796pldGNGzdODSIf7j68nGQySZ8+fSq5BTmb8ZWCj88JojrX2WVlZQwdPJQHHnyAtWvW8unyT1n08SJefvFldE3ns88+Y9SoUUya9BKLFy9g4cIPOLd5MxzC2BIkqQVQus5nSz4lEbN4840lfKfHDRTtAzOp0DSHrCyHRo0dmjeJY+gOWlChRyNEI0EiAZ2gphBslO3gABKNokdqoYWzadHmPN5/fy6LP1nCQw88zN1334WmS+rQAxpGQGPWrFl06dKF7DTHcAUFBXz3u9+lsLCwxuX06KOPsmPHDlasWMGKFSv45z//SXFx8dcq+88//5zC6dNZOXcu/y4o4O7f/ha7CpPRf/7zn9TK6F69ejF06FAA6tSpw8SJEyvtYxEMBrnkkkuOuH/F2cZJVQoicoWIrBaRdSJSaW26iDQTkTkislREPhWRgSdTHh+fb4py19lKKaZMnULPXj254vIrEJEKrrNt2+bxxx/ngQfGcG7rpsSVgxXIYuSPR2GKQdKGxCGNdZ/u54c3/5AVK5Yz6KpubNmyjqXL3uWGH3Tn+9d15eeP3oGjSnCSDiAENIOgo/jbX18ir0MHBlx8CQv+Mx8R0MJhHMfAtsBK2lzQvSfZmTmA8J3e32Hb9m3ohlbhEBFeffVVrrnmmlQeS0pKmD9/Pi+99FKNlUJZWRl//etfefrppwl500EbNGjA9ddff+yF7DjuuEEs9pXr7BYtaHHhhdW6zi6nuLiY999/P9VTqF+/Pj169CBQxSD04MGDefXVV49dvjOUk2ZoE3dPvWeBS4GtwCci8g+l1OdpwX4O/F0p9ZyIdADeBpqfLJl8vj3M3TL3hMfZr2m/GoWzbZv33nuPm265iZgV4/PPP+eC7hegbEXSSqCUwznnNKKkpIRdu3awcuVn3Dnqx5gECBlBdIR4mXCgSCgrc9tt0Wh9fvnL55k6dQKvFc5ASRk98i9h1j/fpm2r1tz6o9v423N/YdRdd7st/IDG9p2b+NXjv2HBhx+SU7sel33vcvK75KPQMQJuZX84U56ezPe+V7WH2fnz5/P888+nzt966y2uuOIK2rZtS506dViyZAndunU7YtmsW7eOZs2aVehtVMdPfvIT5syZU+n6DTfcwEOjR8P27e4Mo5IStm3dyoW9eqWmmVbnOrucGTNmcMkll9RIjo4dO/LJJ58cNdzZwskcfbkAWKeU2gAgIoXANUC6UlBA+a+SA3x7/NP6nFRqWoGfSNJdZ+d3zefiARcT1INoaChHYZomIg6OE8ftpCt03cZBgBAhCWCWws69QjKhoVAIikjYJhJKUq+2O5U0Wy9l5ecraHFuM9q3awpmKSNu+D5/mTSF0ff9GJSDE0/wycIl9O3dl/qNmiEi3DBsGGvWriEYrvqznzNnDi+99BLz5s2r8v7+/fvJyspKnRcUFKT2b7jhhhsoKCigW7duJ8xN+JNPPln5om27vYONG93zUAgaNqSqcfAjpVdQUMDtt99eIznKnQgWFxdXyP/ZyslUCucAW9LOtwI9DwszFvg/ERkFZAADqopIRH4E/AigWbNmJ1xQH58TQSQSYcnSJezcu5Nrh1zL5OcnM2rUKNq3a8+8efNQKoEI6HqQjRu3k5GRSTCrHm3aduDjeUupX6sbpqUhyl0flpFtk5Gt0MUmYMcJhhSaLhiZEQgGQBMcR0fZNsoIojQdpUdBNLRgCCMSQQ8YKSUgmlRbUX766afcfvvtzJo1i9zc3CrDGIaB4zhomsa+fft4//33WbFiRcqVhojwhz/8gdzcXA4cOFDh2f3791O3bl1at27N5s2ba1TBVuopOA6YJjdccQUP/ehH7i5oubmgaTV2nQ2wb98+Fi5cyIwZM46Yfjrl+zB8GziZYwpVvX2HK/ThwBSlVBNgIDBVRCrJpJR6QSnVXSnV/et6U/TxOZmYtkl2TjZPT3yaJ554gtLSUq6//noWLPgvc+fOQQgQKzO57777uf3HY9i9xeAHwx5k4sTxrFu3noAOdWpZvP7WeGrXUWQaJlFJEAwYiBFCiYajoE3LVmzcuIn169YjwSgFf3+dPr37gtIBQQ8YXNirF3PnzmXfvn2Ypsn06dOrlHnz5s0MHTqUqVOnpjbtqYp27dqxYcMGAF5//XVGjhzJpk2b2LhxI1u2bKFFixbMmzePNm3asH37dlatWgXApk2bWL58Ofn5+USj0eNznb14McumT2fZG2+4pqMWLVy/RZ5/ppq6zgaYPn06V111VY0r+X379lGvXr0qxxvORk6mUtgKNE07b0Jl89BtwN8BlFILgDBw9m+C6nPW4uCgKY28Dnl07NiRgoICohGNaYV/4/HfPUHHjt3I79qTli17MOSaUVhJnQ7tO/PLXz7BIz//AdcMyePiy/LZs2MrYacMzUmitCAWQZx4AmwHFTMJaWFe+PPz/OCHP+T8Xheg6Tp3330XgbCBaIJuaDRq1IixY8fSq1cvBgwYUK29/1e/+hX79u3j7rvvJj8/n+7dq3akeeWVVzJ37lzANb8MGTKkwv3vf//7vPbaa4RCIV555RV++MMffj3X2UqRWiCh6+56gwYNoHnzSh5Na+o6G6CwsJDhw4dXeH7nzp00adKECRMmMG7cOJo0aZKaejtnzhwGDvz2zIE5aa6zRcQA1gCXANuAT4AfKKVWpoWZBUxTSk0RkfOA2cA56ghC+a6zfariVLvOdhybkuQhlA3YCl10HBGUkyShFLGDmZSUBHActx2m6YqMDEVmhkM4oijvHyvLRFlxNBR6KMO95uiIoxDLREIhlAIjIGi6jujf3KzyHTt2MHLkSN59992Tn5hpuu6to1HXRHQKGTp0KL/73e9o167dKZXjWDgtXWcrpSwRuRd4B9CBSUqplSLyK2CRUuofwBjgryLyE1zT0i1HUgg+PqcbSikSZhmmXYpt2RgESCpFQgAlWLEABw9EsU0dRyAQdMjMsolmOAQN18KqHAfKYgg2mmOBZqDpBpK0AA2UgyEOWjiAFjnyFpYnk0aNGnHHHXdw6NChGs3aOS6Ucvc52LXLHVSOxVy/RdqpWVKVTCYZPHjwGaUQvi7+Jjs+ZwXfZE/Btm0cx8G2LWwnie3EQAxwgiAacUdBLEhZiU5pTEMBRkCRW9cmO9OLREAcB5VIgG0jyoKAjhIdjCCaGNiWQtO8BWSGhuhnkHfS4yHdgR24nk0bNfId2B0Hp2VPwcfnbCWejKOJBiQwVQIRHU3CoBvs3QVlZQFQAgJKFFk5NvVyFYbuzb1IJNwZQ44NKHcmESCBCLoRxDYVjgIjJBiBs1wRgNs72L8f9uz5yoFdw4au3yLfvfU3jq8UfHyOkaSdJBgMYplJhCAhI8rBmLB/h4ZludM+QyFFKGqTkemQGdGQ8sl4loWyLJSho2kWmhFAMwKIBBE9lPJsGgjp3676sLjYVQg5Oe5gsu/A7pThl7yPTw1QSmHF3CmUTtLBthOATjwRYHcpxEp0RCmMkEOdujbRiPtcUEA8Z2rKNFGWCY6FEQghehBHi2DZbq8C20E5ikDYOLP2PD4eHMc9yje7adTINR99CxaHne74SsHH5zCcuFVhRU35/gYKRRmKuDjYpWFixSGspOsXSBNFrYw4dWvbiChIlkfmoDQdR7njpppmoIVCqEDES0LQDUHSlMBZrxBiMddFRSDgejIVcVcmH2VrTJ9vBt9Lqo/P4SjQIgZaxICwhhV0MCJBlBEkkdAo25dF6f4ojqkT0IVaUZNm9Uo5t00OPfv1odtFFzH0xhspisdRkQhOMMjKdRsYdO1g8nt2p9P5XXn8D7/DCOoEQjq6ofHOO//mggt6kJfXgfbt21fy1gnuqtoBAwaQn59/RK+d/fr1o6rJGFOmTOHee++tdH3mzJl07tw5tUahOjcXsViMvn37Ytt26tqTTz5JOBzm4MGDR0ynX79+LFq4EHbtomTlSn788MO0+u53yevYkT59+vDxxx9Xm5+aoJRi9OjRtG7dms6dO7NkyZIqwyWTSX70ox/Rtm1b2rdvzxtvvAG4C/j69+9P165d6dy5M2+//TYAn332GbfccsvXku1Mw1cKPj6HoVCYtknCSlCcKCFuWsSSNnuKFEW7QpgxDV1zaFA7RouGxdTPTRLOCRGJRFi4cCGLFy+mdu3aPP/CCxiBAJZtM2zYtTz8wE9Ys2IZy5cu47///S9//vOfAVixYgX33nsvr7zyCqtWrWLFihW0bNmyklxLly7FNE2WLVvGsGHDTlh+L7nkEpYvX86yZcuYNGlStT6BJk2axNChQ1OLwsBdxNajR4+ju4ywbdi2Dfbt4/Zf/II6DRuydsMGVq5cyZQpU9i7d+/XysOsWbNYu3Yta9eu5YUXXuCuu+6qMtxvfvMb6tevz5o1a/j888/p27cv4C6ou/7661m6dCmFhYXcfffdAHTq1ImtW7eyefPmryXfmYSvFHx8cE1GTsw9LMeiLF5KPB4jVqrYtzvA1o0BivcYYENGxOHcxglyshVaJISEQxXm0QcCAS76Ti92bNuKU1bKq1Mm06tnDy67pC8EwkQzM1OuswH+8Ic/8Mgjj9C+fXvA9TFUXimVs3v3bm666SaWLVtGfn4+69evZ/bs2XTt2pVOnTpx6623kkgkKuVr8uTJtG3blr59+zJ//vwq856ZmZnyiVRaWlqtf6TDXWevX7+ekpISxo0bR0FBQdUFq5S7CC2RANNk/c6dfPz554x78kk0bzC5ZcuWXHnllVU/X0NmzpzJyJEjEREuvPBCioqK2LFjR6VwkyZN4uGHHwZA0zTq1nUdKIhIagXzwYMHK/hNGjRo0DHtF3Gm448p+JyVFL9f2eVydThJC9MxUYZbhyUTJsmYjpkMkLS8Te+VQ/DC3tTOSlC7XtjdHxlwHKeiOwZHIxkzee/d97hlxEiUHmL1hg1073khhHNSabZq1YqSkhIOHTrEihUrGDNmzBFlrF+/Pi+++CLjx4/nX//6F/F4nH79+jF79mzatm3LyJEjee6551JeS8FdgfzYY4+xePFicnJyUuaRqpgxYwYPP/wwu3fv5n//938r3U8mk2zYsIHmzZunrhUUFDB8+HB69+7N6tWr2b17N/Xr16/4oMhXCrN2bVYeOkR+164VehvVMWzYMFavXl3p+v3338/IkSMrXNu2bRtNm37lVafcdXajRo1S18p3wnv00UeZO3curVq14plnnqFBgwaMHTuWyy67jKeffprS0lLee++91HPdu3fn8ccf58EHHzyqzGcDvlLwOSvJurh/jcNaZUniRhLD0TlwQFG0N5AaZ1YCGRGTOtlJIpk2mh5FKYXjOIgIhmGgaRq26RCLxejZsxsbN2/m/K75fO/K76EH3cFTrZoVucfqTrqc1atX06JFi5QDu5tvvplnn322glL4+OOP6devX8qv0LBhw1izZk2V8Q0ZMoQhQ4bw4Ycf8uijj1aoFAH27t1LrVq1KlwrLCxkxowZaJrG0KFDmT59Ovfccw/iOJC+fWXduhAOI3XquIPMNeRYdjurahHu4WVrWRZbt27loosuYsKECUyYMIGf/vSnTJ06lYKCAm655RbGjBnDggULGDFiBCtWrEDTNOrXr1/Bd9LZTo3MRyISFJHWJ1sYH59vCqUUtmlimSaJWBmJkjJ273TYtzeArcAIWWTXTtC0aYKmTXUysiOIGDiODUoRMAwCumtOMuM2jmW723EuXcymTZtJWg5/fv4FRIS8vLxKA78bNmwgMzOTrKws8vLyWLx48THLXxOOVen06dOH9evXV7LxRyIR4vF46vzTTz9l7dq1XHrppTRv3pzCwkLXhHTwILnJJAe2bXPHEQA0jf1FRdStW5e8vDyWL1/u9rCOwrBhw1L7KacfL7/8cqWwNXGdnZubSzQaTTnyu+6661ID0i+99FJq97devXoRj8dTZRCPx4lEIkeV92zhqEpBRK4EPgPe9c7zRaTmjsh9fE5DbMvCLI0TLzlI3EmyvyibouIgDoqcWiWc26CYxjkJMrQ4JIqRZAm6XYZhJyCRwCorwywtxUmWYagYQc2rMDWDnJwcJk6cyPjx4zFNkxtvvJF58+alWt+xWIzRo0enzBEPPPAAv/3tb1OteMdxmDBhwhHlb9++PRs3bmTdunUATJ06NTVoWk7Pnj1r5Dp73bp1KSWzZMkSkslkpT0VateujW3bKcVQUFDA2LFj2bhxIxs3bmT7pk1s27yZTZ98Qo8OHZi/dCk7vdb1okWLSCQSNG3alFatWtG9e3cee+yxVJpr165l5syZleSaNm3aV66z047DTUfgus5++eWXUUrx0UcfkZOTU8F0BK6CHDRoUMrT6+zZs+nQoQPg7tMye/ZswHUREY/HUz2sNWvW0LFjxyrL7mykJuajX+FujjMHQCm1zO81+JxJmI5J3PIqbUe5G8LELExlcbA0SmlpBkqBrjk0ql1KIMMAI4ytaeia7q0hcFvcjuWghQUjoLn+iw5viXvuTrt27UqXLl0oLCxkxIgRzJw5k1GjRnHPPfdg2zYjRoxITdvs3LkzTz31FMOHD6esrAwROerAazgcZvLkyVx33XVYlkWPHj248847K4RJd53dqFEjunXrVmE6aTlvvPEGL7/8MoFAgEgkwrRp06rsYVx22WXMmzePAQMGUFhYyKxZs9yxlKIi2LWLIf37UzhrFj/7+c/50zPPMPCaa3Ach8zMTAoKClImtBdffJExY8bQunVrotEoubm5/PGPfzzq73gkBg4cyNtvv52Kc/Lkyal7+fn5LFu2DIDf//73jBgxgvvuu4969eqlwj3xxBPccccdPPnkk4gIU6ZMSZXBnDlzvvZA+JnEUR3iichHSqkLRWSpUqqrd+1TpVTnb0TCw/Ad4vlURboDMNuyUMo1T9iOjWklIaHQRWE7NklbOFQS5GBZBPE6y1lRk+yMMnRsJBAimhGp1vSiG1qFxWbfFpYuXcqECROYOnXqVxe3b3eVArirkRs2PKsc2CUSCfr27cu8efMwziDXGyfbId4qEbke0ESkBfA/wEfHJamPzwkmvno/ylYoy8aJuYObVjKBpmlYjju9VMOtxJO6RXFJmENFYZSjoYkiO5qkVlYMTUyU0gmIEMqJop3tHkmPg65du9K/f39s2/5q9lBODpSUuP6KzkIHdps3b+bxxx8/oxTC16UmOb0X+AXgAG/i7o/w8MkUysenJsRX7wcg0iEXWbUbLWJ4M4MUSncd12kiWLZFcXGA4kPZ2JYOShEMmWTVilE7bKG0ILapoTmKYEYYOUW++88Ebv3BD9yeQfmYQ0YGtG59yvY7ONm0adOGNm3anGoxvlFqohQuV0r9DPhZ+QURGYqrIHx8vnHiq/djmoewpIhQixzKyg7iOCax0v2YZhxTWYihk0iESJRGKCkJoBzXT6lh2NSuFcfIBM1SqKS36bwmBCIh9G/J5uzHjOPAvn2wd687jhCJuLuiwVmrEL6t1EQp/JzKCuCRKq75+JxUTPMQRQc/IVlaTLBZFoaeTWlpCXv37SWZzKQsEUfpOqUl2ZQcDOLYnilDIBq2qZVjIhmCUhpa0iQaMCAYxDB0jKB+3GsGznrKHdiVr5iuXdt3XncWU61SEJHLgSuAc0QkfX5cNq4pycfnG6Psix0Uxz9HMKgT/Q5aVg579uzhQPEBNu08SIPsDGwrzMF9mST+P3vnHV9VfTf+9/fO5GbfLBJIIJMRRmSKMixVyoOogFTx10e0Clr3o63Vp2prLdVqW1cf3Kh1QKhUxIFaBXGgMmQZNoSEDLLn3eN8f38ccknIIBBCEnLer9d9kXPv9577OUDO537X++NSb+5mE4RZfISYPZiNfhQETo8g2O9HChNSH4Tg2MSxlhBaoihQXq4WwAEwmVTFdUhI98al0aW011MoB3IBF7CryfMNwP1dGZSGhjo34MblKsZ9uBqXrxhzUjRhYdnY7T7KC/I5YD+AW7ipLGkgyjSIyooItfSlQSE6ThJucuOVIPVG7DozipQYdAI9enRCh8GkJYN2aUwIQoDVCrGx2lBRH6DNf2Ep5TYp5VJgsJRyaZPHv6SUnVMaamichPr6bVTXbKD+4D7ASEjKSHy+TH44kMuGwxv40f4jR/a7+eTvmTzz8H/hcqqTzKFBLmJjGjBix+5XcOl0KDodZr2OSLOJcJMZgVr3+EwnBL1eT3Z2NsOHD+eyyy4LuHYAdu3axbRp08jMzCQjI4M//elPzXYlf/zxx4wdO5ahQ4eedXV2I5s3b0av17Ny5Ur1iZgYtVcwaBDEx+M8tjzztNXZx2Ky2WzcfPPNpKWlkZWVpamzexgdmVPoL4T4MzAMCMzCSSkzuywqjT6Jy12Kz9eAx12BX3ESXD4Yn9BTF+4hLz+PWl8til7BYEvmq3/35z9rzEi/H53fj8ngJ95qR2f04TcZMej1GPV6zAY9ep0e6ZfgB79Ub2hdUcgmODg4sEmq0UX0wAMP4HQ6ufzyy3n++eeZPn06DoeDK6+8kueee47bbrstoM7+6KOPGDJkCD6fj5deeqnF+Zuqs880fr+f+37zG342ZcpxuZ/BAAMHBtqcTJ3d0ZvnwoULSUlJ4cCBA+h0OvLy8tizZ0+n4m+qzt64cSO33HJLq4mmqTpbURSqjw2NNaqzb7nlFnbv3s3MmTPJz89vps5OTk7uVIy9hY70BV8HXkPd0vlfwL+AvuOR1ThruJxFIBWM5n7U+uPZc+QQW5RdfF/6HZX2Qmq3Wfj8HyN54OY0PvtAj+J2cfGQ3Tx0+2ZCIsETZsIXEoLRZCTYZMRiNCEUHT63H0WR6g5knVAnlbt489nEiRMpLi4GYNmyZVx44YVMnz4dAIvF0qPU2fh8/ONPf+LKyZOJi4gAu73VZqelzj6BQ4cOsXHjRhYvXhzY4ayps3sWHekpWKSUnwoh/ialPAQ8KIT4uqsD0+hbONyVuL0NHLa7qM2tx+1wEG0ORV9p58ePIvhuwyCKK6OQQo/f7/nyOPYAACAASURBVGVM6l6mX3iY/uMHURiXiV94CDIILAL0egNFu2vx+9T1EEIn0Ok7PxaeMjKmQ+38fj9r167lxhtvBNShozFjxjRr0yPU2VJCfT3FO3aw6qOPWPf662xevLjVieTTVmefwK5du8jOztbU2T2YjiQFt1AHXw8JIX4FFAPt/8traJwEuz0PKb1UOiupdFZS7yzFZ7Oh2xdEhrk/5hg7X26J5MV3xlFcEYwfPSGRMGp8HVMz9zMhxUP4qCk4jSbcbh9GhxezX4JeD1JH0lAriiLRG3ToDWdnctTpdJKdnU1+fj5jxozhkksuAdTx7rbmL7pNne31wtGjYLPxP3/+M4//7nfoMzLUpaatxHRK6uwzdK2aOrt76EhSuBsIBe4E/gxEADd0ZVAa5yaK4sHhOIxE4nIVExqSSZXbhlGEEi3TsXhsuBx2trkNPPvMJA4XRiKlIDamgfmzSpg+pAyD4sFs1GEeNoJtio46mwujz4sO0As9xiY6Ar1edMncQVs0zinU1dUxa9YslixZwp133klWVhZfffVVs7atqbNHjRrV4c/qtDrbZlMfej1b9u5l/t13w913U1lZyZo1azAYDMyePbvZtbWlzga1J5Gamsptt91GdHQ0NTU1zT6uurqamJgYIiMjA+rstmpMNHIqPYXTVWcvXboUUNXZn3zyCdBcnR0XF6eps09ESrlRStkgpTwipbxWSnk5UHAWYtM4x/D5GvB4KjHoQwkLy8KjC6faqUPssuD+3knVpnK+3h/HA89OpeBoHLH9DNxxj40X7tvK5ZdILBnJ1IwbQ/7osfyoCGqdLkYHG7k0NRmDQY9Op0NvbP7oDnFdr1BnR0aqS0xTUzlcUBBQYM+bN4/nnnuuWUKADqizS0ooLi6moKCAcePGsWHDBkpLSwFNnd3baLenIIQYB/QHvpFSVgohslB1F9OAAWchPo1ziPqGHzGb4ggOVv/r7C/ciWG7HT8G9nlq2HVoBO+vywZFYcJ4O/f+ph5/w1H21FvQhUaiNxlxOj0kG/UkhIcxOCyM5FAL+h64dr7HqbNjYhidnn58OakQalI4BVpVZzdhzpw55OTkcN999/HMM88wc+ZMTZ3dC2lTnS2EeAy4EtgBpACrUA2pjwPPSykdZyvIpmjq7N6FonjweKvxeqpwuUuJif4JQugoqyxj1faNmL6xs2FvElt/TEEREaA3MmvWUUbOs6OXfrzlFUREhJEaFU54eDgREREEBwc3G3pw1HvIKzhAZvpgTMF9x2bZIVwuVVHROPQTH39cZneKtKrOPsfR1NnNuQIYJaV0CiGsQMmx45aDfBoax1AUH1J6AHC7y7A78hDoMJmiCQ8biWNvFftL8sgtOsyuz6NY+9UE9CEWdAYjWcP9zJ1bRr+BlVRaokiz1WPyOghNHkJsbCwmkwm/V6Gy0NZsrNzt8AJgMPW8HkO3oSiqvK6qSl1lZDSqiorQ0NM+Zavq7HMcTZ3dHJeU0gkgpawWQuzVEoLGybDZ9uD11iKEHim9hFhSsVhScHn97DxSTemPuWwy55P72Vj2fRuLMdTC9JmSyy+vIyXFy6eHCqmqcmGtbYCKUmKHZxGVmIgQAkWRHM2rw+9TiE48vmwyOMxI/dEzs+z0nMDlguLi4wK7RkXFGbiR33BD31pjoqmzm5MqhGg0oQpgUJNjpJRzT3ZyIcQM4BlAD7wipfxLK22uAh4GJLBDSvn/Oh6+Rs9DEho6BLP5+Hh1RYOLok2FuOsb0LsEP7w2kfx9UQQF67j7ATfnT3GhKAp1Hj92r4+L3A5CE/uRMOInBPfrh5SSyqIGbDVupISkoVEYjM1vcKJUcxgFEAI8HlVgl5h4XHGtodEB2ksKV55w/H+ncmIhhB5YAlwCFAGbhRDvSyl3N2mTgVqw50IpZY0QQtv/0Ivxeuvw+uoxm9VVH2X1LrYVFrOndDfJ5Tp2lEax+tVkXI4g4mMFDy924xrk4euj9fjdLhSvlzCfl1CTgaTsbIxGI4pfofqoA1uNm5gBYQSHGtEbtR5BC5xOCApSE4LZDMnJas2DHjgJr9GzaTMpSCnXdvLc44GDUso8ACFEDuo8xe4mbRYBS6SUNcc+s7yTn6nRTSiKh6KKXPzShFMEsy+vDLfLRV7lOtKLInj33RFs2h+NosDwkU5uubkMp7OEQ7mSFLeDJK8bExAcFUV8ZiY69HicPuqrnNiq3cQkhRIapTn8W+D3Q1mZWg2tf3+1PCZoemuN06YrZ0/6A4VNjouACSe0yQQQQmxAHWJ6WEr5yYknEkLcBNwE9BkpVW9DUdzkVdQQZx1LXY0dQ/V+FFmEfa/gr69eRK0nGLPFz1VXHmb02DKORkTjKteRZA1jTEwKoSGhBMXGYLRYEEJw9KCqqRA6QdygcCzhpu6+xJ5HfT2UloLPp/YQmthLNTROl67sW7Y2yHvi+lcDkAFcBFwDvCKEiGzxJilfklKOlVKOjT3FtdUaZwdFcYM0EGMUBJd+i7PsO1a/msgrT8+m0BZMbKaLhYvzSL/YS2G4FX91DRMMkhnjx5AwbBjmuESkMOFx+qgtc+B2+ohNDqN/ZlSvSQhnTZ09ahQrnnsOiorUhGCxQGqqOqHMqauz169fT0REBNnZ2WRnZ/PII4+0en1SSqZNmxYQxwGsWrUKIQR79+5tdr5Zs2Y1e+/1118fUHJ7vV7uv/9+MjIyGD58OOPHj2+x5+F0eOyxx0hPT2fw4MF8+umnrbaZPHly4DoTExMDm/TaUm9XVFQwY8aMTsfWm+hwT0EIYZZSttQwtk0RkNTkeADqstYT23wvpfQCh4UQ+1CTxOZT+ByNbsbpLMJWvYWg4t249tawscDHmx/O42hZHP5gPdMvq2T+pUfwOBoQRTZibQ4GxkQTM24i9ioflfU1+HwK5mN7DKQiCY8OxhTUu5YBnhV1ttvN9n/9S+0V6HQQF6eWx+xkbYjJkyfz4YcftttmzZo1jBo1ivDw8MBzy5cvZ9KkSeTk5PDwww936LMeeughjh49Sm5uLmazmbKyMr788svOhM/u3bvJyclh165dlJSUcPHFF7N///4WS2e//vq4y/PKK68MWF/bUm/HxsaSkJDAhg0buPDCCzsVY2/hpD0FIcR4IcSPwIFjx6OEEP/owLk3AxlCiBQhhAmYD7x/Qpv3gJ8cO28M6nBS3inEr9GN+Hx2HOWbqc37kMqNJZQVDOCxT4bwp1dnUFbdj7BEPYvuP8qi6ftIio4itrwea5WOpMwJ6NMnUlGm4LL7iIgLZsDgKBIzIknMiKT/4CisiSHdoqg4U3SZOnvnTrJnz+ZQVRVr8/M576c/ZcTIkZ1TZ3eQE9XZNpuNDRs2sHTp0g6rpR0OBy+//DL/+Mc/MB+r8xwfH89VV13VqdhWr17N/PnzMZvNpKSkkJ6ezqZNm9ps39DQwLp16wI9hfbU27Nnz+btt9/uVHy9iY58FXsWmIV6A0dKuUMI8ZOTvUlK6RNC3A58ijpf8KqUcpcQ4hFgi5Ty/WOvTRdC7Ab8wL1SyqrTvBaNs4jD46Og5DucW7fjVmL5sdLAP9+fSNlhKxaTiblzvJw3t5LQyjwMxmBcB4sQ7nASJ2cTlaQOcxhMeozmrtkEdeiHzlXyao20MSdOibXOGVVnSwm1tcRFRx9XZ7/3Hi6vl4syMzunzm7Cd999x6hRo0hMTORvf/sbWVlZLdps2LCBF198MXD83nvvMWPGDDIzM7FarWzdupXRo0e3+3dz8OBBkpOTm/U22uLuu+/miy++aPH8/Pnzuf/+5hWBi4uLOf/88wPHjerstli1ahU//elPA3G0p94eO3YsDz744EnjPVfoSFLQSSkLTrAtdmhGS0q5BlhzwnO/b/KzBO459tDoJdjdLr47UECQu4x+3iTKk7J542ULtcUx9O/n5Q9/qCcywcHWQ4XIwjIsfgsR/foRP2EQCUPjz0pd5I7ewM8kZ1yd7XaremuHQ11y2ojBwL5duzqnzm7C6NGjKSgoIDQ0lDVr1jB79mwOHDjQol11dTVhYWGB4+XLlwc+b/78+SxfvpzRo0efMXX2U0891eG2HVFnN2X58uUsXLiwQ+/X1NktKRRCjAfksb0HdwAt/2dpnJM4ncW43c0rWO3bepBIv4tol43ihiHc96CVkmIdg/o7uPd3ZZhig/jmYCn62hr6B5sZPHUCofHRGCxBbXzKucEZU2dLqeopKirUnw2GFnqKTquzm9D0W/vMmTO59dZbqaysDFQla8RgMASU11VVVaxbt47c3FyEEPj9foQQPPHEE+2qs9PT0zly5AgNDQ3NEkxrnEpPoSPq7EaqqqrYtGkTq1at6tD7NXV2S25B/SafDJQB5x97TuMcRlF81Nb9gMNxCKMxipCQdHxHItm/2YDDmYrV2p+3t6dw03OjKC7WEZ3s5b9/X0GhGfZWu4h31DDN4GD4eZlEDEo85xNCUzqlzna5UA4d4sknnlATQmQkpKXBCcMtnVZnN6G0tDSQZDZt2oSiKES3Is0bPHgweXnqlN/KlStZsGABBce024WFhaSkpPDNN9+QkZFBSUlJoO5yQUEBO3bsIDs7G4vFwo033sidd96Jx6M6so4ePcpbb73V4vOeeuqpVtXZJyYEUNXZOTk5uN1uDh8+zIEDBxg/fnyr1/vOO+8wa9YsgoKCmr2/LfW2ps5uiU9KOb/LI9HoUfh8dfh9DsLDR1LjNFO4uYryOj/2YBuRipNH/pbEN1ut6IWZrDF1XDK7hMx6L76aGiIMdvReB5GDBxNyCoVjziVOS509fz6OujoEcOlPfqLuSm5DYHfK6uyEBEaPHn1cnd2ElStX8vzzz2MwGAgODiYnJ6fVHsall17K+vXrSU9PZ/ny5S1uzldeeSXLli1j8uTJvPXWW/zyl7/E5XJhNBp55ZVXiDi2sW7x4sU8+OCDDBs2jKCgIEJCQtpcBttRsrKyuOqqqxg2bBgGg4ElS5YEVh7NnDmTV155JfDNPycnp0Xs7am3NXX2iQ2EOATsA1YA70opG85GYG2hqbO7HqezCJt9H2ZTPA1KGoe/L0KvuCFRx5HNpfz9lSFU1pkwmRUW3FBLWtoRwip99A+uhyAjQRHhhFmjicsYjOEsdbtbUwX3SkpK1KWmZ0hgdyY5evQoCxYs4LPPPuvuUM4qU6ZMYfXq1URFRXV3KB2mq9TZAEgp04QQF6AuKf2jEGI7kCOl7NgaNI1ehcdTicdTTogllT0bob7+ECHCTXh6GDlvR7BspRWfzsyA4V5+fn0hSWFOKvYcVZcXxkcTk5hIZFw/QiJ7zy9Qt+H3q/MG4eHHpXUJCZ3ec9BVJCQksGjRIurr6zu0euhcoKKignvuuadXJYTO0qHdQVLKb4FvhRAPA08DbwNaUjjHcHud1NTuwGSMpm63j4o6BxED6jm8q5qX7xhJUbEel07hp1eUc8no3QxpcFK/v4TImHSGTDmfmEQrBlPv2H3c7dhs6soir1ddXZSSoiaDHpoQGunsfoLeRmxsbIvSpOc6J00KQohQVJHdfGAosBq4oIvj0jiLHKlyUFzrxO6yo/c5Ca4Io7Chgb0Reg4sT+bLNUMROjPWRDdXXLePi8KrCHPYsZuNWLMnEh6XgbWfFYOpd+1A7hb8ftVXVFenHgcFqXrrHp4MNPoOHfktzgU+AJ6QUn59ssYavY/8KjsDooIJtztx+8Af5GOrCOOTP4dSUaRg1ivMn+dm7iV5FG/ZgqlMwRmZyICUUYRExhISae51Sopu4USBXWysWhpTSwgaPYiO/CanSimVLo9E46wjpcTtUwgqsmFxKxR5DlFoVNhVInj7b1ZcdS4GDPDyv7dW0C+ijPyte/G4TcSOPJ/EtAHEJlkRQmAw9awJ0R6J368OF/n96vxBQoJa90BDo4fRZlIQQvxdSvlr4N9CiBZLlDpSeU2j59Hg8lJYre6OrXd5cfsUwiTsC6+gsKKKXV8P5Z0XosDrYVh2HX+5vpwQ4aC4shZEMEMumETGmDSMQQZ0vdhNdFZoXNknhLqSqF8/NSmcAYGdhkZX0d7mtRXH/vw/1ApqJz40ehl5FTb2ljbg9PqIsBhJiw1lSkYMZksd3+zZx7+fzWblPyIweBV+PsvOUw94UMrzOFJbRXGDm8gBGaSMSMFsMWoJoRWaqbMvvZTaH39UdyZzTJ09Zw6Z559PRmZm59TZ2dmsWLGixeuNnKo6G1TddXZ2NllZWS02wTWiqbP7Bu1VXmtUDA6VUjYrxXlMdNfZymwaZ5niWicDrSGEH7VjtjuQSLZWlrDiKyerVl2A3m4kLNjDbTe7mT6+lvK8Ig5W2DDFDSC+fwKjxg3HEqGtLmqL4OBgtm/bBjU1XHfDDSxZupQHbr8dZ3DwmVNne70BPfeZora2lltvvZVPPvmE5ORkystbL4CoqbM1dXYjN7Ty3I1nOhCNrsPp8XO40o6xoIGIEjtBRj0yLYzdIU7+8rnkX2+lonPomTKqhlcey2OUWM/WL7exe3ceoaGRTLl4EhdcMoaI2JCzIrPr1RQUQGkpE0eNorimBlJSWJaTc+bU2du3k52dzaFDh1i7di3nnXceI0aM6JQ6e9myZcydOzdQ1TAurvVS6Zo6u2/Q3pzC1ajLUFOEEO82eSkMqG39XRo9gYPlNhweHwBev0KN3YvJoKOfxUjVIAt1NdUc3XOEd9+JZPO/47AoPhbNKWPa+UXUHKig3OnBlBTDxBETSB06FJ2h900kO3efeQN78LCWPiDguMBOUcDhwC8Ea3fu5MabbwaDoXPq7GPExcUdV2d/+CEul4uLLrrojKiz9+/fj9fr5aKLLqKhoYG77rqLBQsWtGinqbP7Bu2tPtoEVKFWTGs6h9AAbOvKoDQ6R2mdi4HRFkwGtSMYW1vHHrebnXoPjvxCEusL+Gh5Il/9x4RZcXHN1FyGDaqhoAK8ihtrZjJjfzKJyMgWlVF7DW3ewLsKmw2n2032z39OfnHxmVFnt8O+ffvOmDrb5/Pxww8/sHbtWpxOJxMnTuT8888PnLsRTZ3dN2hvTuEwcBj4/OyFo9EZFEXS4PahSElMqBmzUcc3NTZqfG5kuEKG34fPEcYTjw/hx/1B6HCzYN52RmQohKUOIMLjon/KBPqnpaPrYd6dHoeiqA+DQV1JlJCgzins2tV5dXYHOJPq7AEDBhATE0NISAghISFMmTKFHTt2tEgKmjq7b9DmnIIQ4stjf9YIIaqbPGqEENVnL0SNk+HxKVTbPazfX87mw9VYTHoMeoEE6hwOhtXXM8ogObQ7mltvi2TbHgshwdX86qpPmTIFULyEGnSkZw2nf7qWEE6K3Q55eVBcfHzZaZM9B51SZwOKovDkk0+2G8KZVGdfccUVfP311/h8PhwOBxs3bmxVLqips/sG7Q0fNZbcjGmnjUY3oyiSPUfrqXd5CQ8yMrx/BEFGPXUeL2s35VNvs1Nus7PiqVA+/Y8Jj99OVvIebrvqQ6LS/gt7tY1+SSmMmDRem0Q+GX4/lJdD47dgIdTnDC1/jU5LnX3NNTgcDoQQJ1U1n0l19tChQ5kxYwYjR45Ep9OxcOHCVm+Cmjq7b9ARdfYgoERK6RFCTAJGAm9JKevbfWMXoamzj1Pv8rIpT+20jR0URaRFXS5qd7n57Lv92F0OInWhLHkmnsLCYCR2LrtsPb8cugHvwJ9QXhGOJUhh+KQJhES035Xv6XS5OrupwE4IiIlRFRW6jizgOzfQ1Nm9x5Tapeps4D1gnBAiDXgD+AhYBsxq910aXUatw4NPkWw/UktokIHspEj0eh2H7C7q6usp21tGjduJo9jAE8/F4nJJYmPLuXrO5/RrKOJw/gA8dicxCcFknJdFcJiluy+p5yKlmgxqjy24Cw5WFRVBfaeSXCOaOrtv0JGkoEgpvUKIucDTUspnhRDa6qNuotbhYeuRGiKCTWTGhxEVYeabOjtur5eG2lpiFB+hOiP7frDw3rIwfIpkzKhS5oz+HmNtDbGRySQMjyRmzLQ+9R/9tBECjEb1z7g4sFr7tKJCU2ef+3SoHKcQ4ufAtUDj346x60LSaIviWid7SuqJCzczcoC6XPSQw4XJ4yZ5bylmYcAv9Ty1VM/HG4NAeJk9fiezsvMwmkOJnDyZEEM9MdZIQrWE0DZer/poLHwTHQ0REaDVitDoA3QkKdwA3Iqqzs4TQqQAy7s2LI0TqXN6OVhuIzU2hNRYtW6v3+ejckchPpudoKAgSvUm/vgHE/sOmdCZXdx0XRljTRUEpY0kJrSCYN9hrJZggmNGdPPV9FCkVOsclJWpvYHUVHUSWafTEoJGn6Ej5ThzhRB3AulCiCHAQSnln7s+NI2mOD1+woIMDIoOoWxPJaUeL4X791Kj+Ai1wr83KvzzzZE4nGbCYqu57Y6dZCcMJag4jLAUgdkbhDVjMqbgUDD2nTXXHcbjUecO7Hb1+CRr6DU0zlU6UnltMvAmUAwIoJ8Q4lopZesiFY0uQ68T1Pv9bKirw0M1+mA76cNGsuFDCy+8pseAgcHDy1mw4EMGhV9AaH0RwuLHYgkh2hKGLjy2uy+h5yElVFertZIV5bjiOjy8T88daPRdOrKe7ilgppTyQinlBcClwDNdG5ZGUzx+hW+P1rLZ7uT7ihpESQlpzjpGDhzIh0sjePEFI9LvZ/Zl2/jdvesZEB5PkNOLcFRgHT+emJhodH1o6eQpUVKiDhcpijpvkJam/nkaCaGZOvuyy6itPa4I27VrF9OmTSMzM5OMjIwepc7+61//GtBJDx8+HL1eT3V1y/2pmjq7b9CRO4VJSrm78UBKuQfQBljPEoqUrKuo4WBZKaOwEfXjZgY5HIT3S+Nvzw7i3XcNCLzcdsthrphZimgYj36/BVN9IdbzziMmMR5RdQiElhRaJTJSXV2UlAT9+7e6Ea2jBAcHs337dnJzc7FarSxZoirDnE4nl19+Offffz/79+9nx44dfPvttzz33HMAAXX2W2+9xZ49e8jNzSU1NbXF+Zuqs6+++urTjvNE7r333sBu4ccee4ypU6ditVpbtDuZOrujNFVn5+bm8sEHH9DQ0NCpa2iqzv7kk0+49dZbW92o9/XXXweudeLEicydq9YKa6rOfumll7jlllsAmqmz+woduVNsFUK8KISYdOzxPJoQ76yxqbiUgwcLGOKoxer3EOaNICIpib88Ec133xvQBTWw6P4vGTt0O/oqL2FH9xEe7iR++jRiE8MQdUWAhJjB3X0pPQOnEyorjx+HhKi9gzM8hzBx4sSApXPZsmU9Wp3dlOXLl3PNNde0+pqmzu4bdORr0a+AO4Hfos4pfAX8oyuD0lDxeb3U11aTWuFncHgsHFUQYUf4a04Mm7f4CQ6u54b/eY/zkkMx2PoRrZN406NJSE0gzFMKdTUQHAmRA0F/+t+AeyP79u1r/oSiQH09NH4jjYs75RrJgwd3LLH6/X7Wrl3LjTeqZUd6ujq7EYfDwSeffML//d//tfq6ps7uG7R7pxBCjADSgFVSyifOTkgaAB6ng6J1O/HpDEiDCVs/PdbwCF78RxbffptCsNnHrxbuJE4Jw1gyCIu7FF9CNMmDIgmq3wehcRA7BMLiu/tSuoVmN3C7XV1ZFBamTiBbrRAbe8YVFU6nk+zsbPLz83uVOruRDz74gAsvvLDVoSPQ1Nl9hfaK7PwOtcLaVlTNxSNSylfPWmR9GEXxU7L1KD9KQX5cJPqaegZbLCxbFsyaNQMwmSXXzfmcGNcR9EooQZlmjKGpJI0aibl6H8QNg6iB3X0Z3c+JAjuzGRITVVVFF9A4p9Db1NmN5OTktDl0BJo6u88gpWz1AewCQo79HAtsbqttO+eYAewDDgL3t9NuHiCBsSc755gxY+S5jKIo8tON2+SSt9fLxz/5Wr797U65c/9hufjxYpk5rEIOGVYq//jQu/LNp5+QH65YI7fu2CEPHTok3W63lLYKKfd9ImVdSXdfxlln9+7dLZ88elTKXbuk3L1byvJyKf3+Lo0hJCQk8PPWrVtlUlKS9Hg80uFwyJSUFPnZZ59JKaV0OBzy0ksvlc8++6yUUsodO3bItLQ0uW/fPimllH6/X/79739vcf4vvvhCXnrppVJKKZ1Op0xKSpIHDhyQUkp53XXXyaefflpKKeXUqVPl5s2bZUlJiUxOTpaVlZXS4/HISZMmydtuu63V2Gtra2VUVJS02WxtXt+ECRMCn/fCCy/Im266qdnrU6ZMkV999ZV0uVxy0KBBgX+T/Px8mZycLGtra6WUUt57773y+uuvV//PSilLSkrkm2++2ebndoTc3Fw5cuRI6XK5ZF5enkxJSZE+n6/Vts8//7xcsGBBs+c+/PBDOWPGDKkoivzuu+/kuHHjAq9t2bJF/uxnP+tUfGeb1n4fgC2yA/ft9vrPbiml/VjiqKBjk9IBhBB61Ipt/wUMA64RQgxrpV0Y6pzFxlM5/7mI3+fjyH9+oKKggURrBLNT45mabGXdmiDe/KcR8HHrzUVcMLCB+LhsBqamEBkWxoCYMEx1+VC0GcITITyhuy+l+2j6DTomRh0ySk3tkuGi9miqzg4ODmb16tUsXryYwYMHM2LECMaNG9eqOnvo0KEMHz48MMnZFk3V2SNGjECn07Wrzr744ovbHe9ftWoV06dPJyQkpM02jepsUIdf5syZ0+z1RnW22WwOqLOzs7OZN29eC3V2bGwsw4YNY/jw4cyePTswxHW6NFVnz5gxo4U6u+nwT2s9opkzZ5Kamkp6ejqLFi0KrAwDTZ19/AUhaoF1jYeo9RUaj5FSzm33xEJMBB6WUv7s2PH/HnvfYye0exq1uttvgN9IKdv1Yp/L6my3w0Hp+l18mxRDiMvOmEQrK1YE8+IrHvxIbrqhimkZDuyVJViGJhEb5iExLgqTqwp0RgiJhqiUPrnpas+ePQxNTFRtpklJkuI48AAAIABJREFUfUppfbbQ1Nm9xxfWVersK084bn1JQtv0BwqbHBcBE5o2EEKcByRJKT8UQrTcrXO83U3ATQDJycmnGEbvoL6yAlt1FUKnw+Z0EqSY+POfw/j6a4EiXdyxqIGpEfsx1oGpvwWPSSHB2IBJhkCwFSKTwNT2t7xzmvJydVdy4xec+np1/4HGGUVTZ/cN2qvRvLaT527t62qgWyKE0KHulr7+ZCeSUr4EvARqT6GTcfUo/D4flUfycdoaCI2yolgExUU+3n4pjsoiH8HBPq65+Qdmj4yhtiCCoOGDqLFXY7VaMWMHa2rfTQaKAu+9B08/DQ8/rCoq4uPVHckaXYKmzj736crF60VAUpPjAUDTdV1hwHBg/bEVEv2A94UQl59sCOlcwe/zUp6fh9vhICZpIL5CD1sKK3lvaTLOMoVBA9w89OtaKgx2qovD8GNElmwlWmcnVAmFyFAQfbSecmEhLF4MP/ygHgcFqXMHRs3qrqHRGboyKWwGMo6ptouB+cD/a3xRSllHk/rPQoj1dGBO4VxBKgrlh/Nw2uqJ1iVSvtfBobJq3vkyAXupiaREN7+/ayv2vBIUv0QJD8NohZAgPyJ5MgSFqROpfXD+AIBt29SEYLXCb38LUVFaQtDQOAN0OCkIIcxSypb76NtASukTQtwOfArogVellLuEEI+gLo16/9TDPTfw+3wU7tqJ3+el/5As6vfZ2EY9ZblOflwXjM7g4rp5G6itPoIhLp3UtP5E2/NwO2w4DXHofYIos7nvJYSGhuM6issuUyeVr7hCHS7as6d7Y9PQOEfoiDp7PLAUiACShRCjgIVSyjtO9l4p5RpgzQnP/b6Nthd1JODejs/rxdVQjzjqJz5xIEqBi5q6OtwV9byWMwibHsZevReZ7URfMpAQVy3eA4V4rKHQbwTxKYPbXTZ4TuLxwGuvwbJl8OabkJysJsQFC7o7Mg2Nc46OrNt7FpgFVAFIKXegLk/VOA2qiwupLinCHByCZXgc9dEKH+7bxXN/DeOoS8+AkeXMu6yMEWIAorYSEW0h+fyZJEyYS2LK4L61sxLgxx/hv/8bXn5Z1VV8/313R9QmvVWdXVdXx2WXXcaoUaPIysritddea/W8TqeTqVOnNrOPPvXUUwQFBVFXV9fu5zSNyWazcfPNN5OWlkZWVhZTpkxh48bObVOSbaivm9LQ0BDQZmdnZxMTExPQdLjdbq6++mrS09OZMGEC+fn5APz4449cf/31nYqtt9GRpKCTUhac8FxLJ63GSXHZbDjqaolK7I8lysrB/Xt4/5O9LF0ympLqfkQn6PjN/B3EVERQkl9PVEQQYy+6lH4DBhISEkJISEjfqYvgdMKTT8INN0Bento7ePll6MGrX3qrOnvJkiUMGzaMHTt2sH79en7961/j8XhatHv11VeZO3duYFMYqJvYxo0b10wZcTIWLlyI1WrlwIED7Nq1i9dff53Kpuba06At9XVTwsLCAtrs7du3M3DgwIA6e+nSpURFRXHw4EHuvvtu7rvvPgBGjBhBUVERR44c6VR8vYmO3GEKjw0hSSGEXgjxP0DbVi2NVnHU11FRkIep2oDzgI3yigr+824+f/1TGnZbOAMGe7n/97uwxtkJDRGMG1DHuPOSAnrhPkVuLsyfrw4XCQHXXQfLl0M7hs+eRm9SZwshaGhoQEqJzWbDarViaKWuxInq7EOHDmGz2Vi8eDHLl3esbPuhQ4fYuHEjixcvDnzBSU1N7fSO4fbU161x4MABysvLmTx5cuD91113HQDz5s1j7dq1gZ7cZZdddkr1Ino7HZlovgV1CCkZKEPdfdwyDWu0SX1FORVHDhPlj8NrgUqLix8O+1jy6ljsMpgxkzzcencDoY49WCyJDDCaiI3OQvTruCDtnCIsTC2PmZkJDz0EJ+zM7AgVlZ3dZtOS2Jifdqhdb1Nn33777Vx++eUkJibS0NDAihUrWvRIPR4PeXl5DBo0KPBcY+2FyZMns2/fPsrLy4mLi2v3Onbt2kV2dnaz3kZbXH311S0V6MA999zDghPmk9pTX7fG8uXLufrqqwPCwKbvNxgMREREUFVVRUxMDGPHjuUvf/kLv/3tb08a87nASZOClLIcdTmpxmlQU1pCTUkx4R4rXp2efY5ajpbo+MuTCdgUE5kTKvnNbd9jcvtxehzEDhxOjKEBERTet1QN27fDqFFqz2DgQHjhBRg27LQroXX0Bn4m6a3q7E8//ZTs7GzWrVvHoUOHuOSSS5g8eXKzXcuVlZVEnrBLPCcnh1WrVqHT6Zg7dy7vvPMOt9122xm71vbmTU6kNV1Pe5+Xk5PDm2++2aH39zV19knvOkKIl4UQL534OBvB9XZ8Hg/VxYWEOsPRm4M53FDLPp+Ox5dGUV1pICmxljt++TXC48fksZAZlUp/fR06V83JT36uUF0Nv/sdLFwIa5osVBs5slOlMbuDxjmFgoICPB5PYE4hKyurxcRva+rsU6EtZ9mJdORG/NprrzF37lyEEKSnp5OSktKs5jKo1+ZyuQLHO3fu5MCBA1xyySUMGjSInJycwBBSe+rsrKwsduzYgaIoJ43r6quvbjYx3Ph44403WrQ9FXX2jh078Pl8zXpvTd/v8/moq6sL1JXoa+rsjnwV/RxYe+yxAYgDOrxfoa/idbtw1NeiN5oICougIthBodPBa69DVa6elFAXf/vf70mgnKBaF8MiBQP6DUQXEq2qK8L6dfcldC1Sqklg3jz4z3/UHcleb3dHdUaIiIjg2Wef5W9/+xter5df/OIXfPPNN3z++eeA2qO48847A8MR9957L48++mjgW7yiKDz55JPtfsaQIUPIz8/n4MGDALz55ptMnTq1WZsJEyawfv16qqqq8Hq9vPPOO62eKzk5mbVr1eG2srIy9u3b12KiOyoqCr/fH0gMy5cv5+GHHyY/P5/8/HxKSkooLi6moKCAcePGsWHDBkpLSwHYsmULbrebpKQk0tLSGDt2LH/4wx8Cie3AgQOsXr26RVwrVqxoNjHc+Dhx6Ajg8ssv54033kBKyffff09ERES7Q0cnWlIvv/xy/vnPfwKwcuVKpk2bFkio+/fvZ/jw4a2e61ykI8NHzfpwQog3gb6lSTwFXHYb9toaaktL0OsNBEVGUXW4Chlr5qNVFgq/jSfGoueBB3Zj0uUinfEMG3MBwTFJENQ3JGOUlsKjj8K336rHEybAAw+oBXDOEZqqs6+99lpWr17NHXfcwW233Ybf7+faa69tVZ3tcDgQQpx04rWpOtvn8zFu3Lh21dkJCQmMHj261WL2Dz30ENdffz0jRoxASsnjjz9OTExMi3bTp0/nm2++4eKLLyYnJ4ePP/642etz5swhJyeH++67j2eeeYaZM2eiKAqhoaEsX748ME/xyiuv8Otf/5r09HQsFgvR0dH89a9/PaW/3xOZOXMma9asCZyz6bLa7Oxstm/fHjj+17/+xZo1zbZPceONN3LttdeSnp6O1WptNrGsqbNP9gYh0oBPpZTpXRNS+/RkdXZdeRmVhfmYLSFYIiIJj42nqLiYgk1HePPLID5+fxAGi4nH/lDBoH7vYvQaSR17HdHR0d0d+tkjNxduvRUcDnVC+Z57YNasTu/Obk0VrHFm2bZtG08++WSzsfhzHbfbzdSpU/nmm29aXZHVU+kqdXbjiWo4bjfVAdXA/W2/o+/icTqIShiANbE/iqJQUlJC1Y81fLohmM8/zcBgVrj97krOG7mR+sMu+g+a0GY93HOWzEzVZDpoENx3n+pv0ugVnHfeefzkJz/B7/d3aPXQucCRI0f4y1/+0qsSQmdp90qFOqg2ClVoB6DIU+1a9DH0x/7z1NTUcGD7Lr7cruOtj0ejE17m/Xw3c6criNIapBhEVPqE01590mvw+2HFCrU3EB4OJhMsXar+rNHruOGGG7o7hLNKRkYGGRkZ3R3GWaXdpCCllEKIVVLKMe210ziO2+fniz2FfH9kK/48E8tWTkAv9Uy9aA+3LYwlKCgSj68MY3T/c39Fw/798MgjsHev+vPDD6vPawlBQ6PH0pE+0SYhxGgpZUuZiAYAiuLnwI4dVNXZqTBEsq20DtkQwefvDEdxGRk9uIJ7f7kbk2kKPp8PiSQ8PKy7w+46PB545RX45z/VnkK/fvCzn3V3VBoaGh2gzaQghDBIKX3AJGCREOIQYEetqCallG1XAe9D1JaVUnO0hD3FtcSmD6HeZSfWV8/XaxJoKNGTYq3ivl99gik8DkXR4XbY6GeNouJcHZPduVPtHeTnq5PHV10Ft98OFkt3R6ahodEB2uspbAJGA32rFt0p4LLZqC4pJLJff8KM8RgtVRw8XIz8NpIdW62Eh+p46PFaogdmYjJl4HQ66R/korrMi4g+B51GhYXqJjRFUXcl//736i5lDQ2NXkN7m9cEgJTyUGuPsxRfj0VR/JTmHcAYEk65KZyKvEq2bisjujiK//wnHaMxiFvu9jEwTQEETqeTfv36odfrcRmj6J88qLsv4cyTlARz5qhm0+XL+1xC6K3q7JqaGubMmcPIkSMZP348ubm5rZ5XSsm0adOor68PPLdq1SqEEM12QK9fv55Zs2Y1e+/111/PypUrAfB6vdx///1kZGQwfPhwxo8f32LPw+nw2GOPkZ6ezuDBg/n0009bbTN58uTAzujExMRA/eW21NsVFRXMmDGj07H1JtpLCrFCiHvaepy1CHsozvp6pN9PVUg8K/cfpKyuAqdO8vWnKdh9BsZeABdf7EJxlaGU7CDOdQj7we8oOLQPk8l4bixxq69Xh4q2bTv+3P33q/sQTKbui6ub6K3q7EcffZTs7Gx27tzJG2+8wV133dVquzVr1jBq1KhmTqTly5czadKkU7KIPvTQQxw9epTc3Fxyc3P54IMPaGho6NQ17N69m5ycHHbt2sUnn3zCrbfe2upGva+//jqwM3rixIkBdXZb6u3Y2FgSEhLaNMyei7SXFPRAKBDWxqNPU11SRJ0ljDcOb6PGfpDhig5dQRr7docSEuTjf/7HjqFmN97yjYQEx2EZOAabKY6EYRMZOGxs76+LsG4d/Pzn8P778MQTqrYC+l6J0DboTers3bt389OfqgLBRn1GWVlZi3YnqrNtNhsbNmxg6dKlHU4KDoeDl19+mX/84x8BLXx8fDxXdbJOxurVq5k/fz5ms5mUlBTS09PZtGlTm+0bGhpYt25doKfQnnp79uzZvP32252KrzfR3tfVo1LKR85aJL0It8OB3+ejRAh0zjquTRpC7WE9K5b1B4+TRTdVERMThiyswW+JJy7rCkrKqzCERBIel3TyD+jJVFXB44+rSQEgO1vVW/ewZPCfyrqTNzpFpsdEdKhdb1Nnjxo1infffZdJkyaxadMmCgoKKCoqIj4+vlm7DRs28OKLLwaO33vvPWbMmEFmZiZWq5WtW7cyenT7608OHjxIcnJys95GW9x999188cUXLZ6fP38+99/ffP9scXEx559/fuC4UZ3dFqtWreKnP/1pII721Ntjx47lwQcfPGm85wrtJYWe9VveA5CKQsGP2/H7vLiFgQ0lu4gNjSaq3Myf/hWN3S4YO6yKC2dsp6FBj9+1B1dwMnlHipFStjok0GuQEj76SK2GVl+vria64w648soeqfju6A38TNJb1dn3338/d911F9nZ2YwYMYLzzjuv1eHN6upqwsKODxIsX7488Hnz589n+fLljB49+oxd61NPPdXhtqeqzl6+fDkLFy7s0Pv7mjq7vaRw9oX0PRwpJYrix52WxYo9+6gxBDGNAaz4KJzdhRZiIx3cOvdbdLogPN5MgnVgCkkjNV3VRPXqIaOGBnjqKTUhXHAB/O//QhsWyr5K45xCXV0ds2bNYsmSJdx5551kZWXx1VdfNWvbmjp71ClMzJ9JdXZ4eHhAICelJCUlhZSUlBbtDAYDiqKg0+moqqpi3bp15ObmIoTA7/cjhOCJJ55oV52dnp7OkSNHaGhoaJZgWuNUegqnos6uqqpi06ZNzUqItvd+TZ19DCll9dkMpLdQ64cvi6uJPlLC9f4YlEPBvP1RGHg83H3FVsKGgd0Tjs3mwGgOJiYmGp1O1zsTgqKom89A3YX8wAPqxPIzz2gJoR16mzq7trY2UJP5lVdeYcqUKa0O7wwePJi8vDxA1UsvWLCAgoIC8vPzKSwsJCUlhW+++YaMjAxKSkrYs2cPAAUFBezYsYPs7GwsFgs33ngjd955Z+Azjx49yltvvdXi85566qlW1dknJgRQ1dc5OTm43W4OHz7MgQMHGD9+fKvX+8477zBr1iyCgoKavb8t9XZfU2f3wjtV91Lv8WM4XEtmkECfEMfil6Nx27xcOrWcsZfH4zKbqa2VDBgwgH7WMCJ7q9IhPx8WLYLXXz/+3LRpMHNmj5s/6Ik0VWcHBwezevVqFi9ezODBgxkxYgTjxo1rVZ09dOhQhg8f3m59YWiuzh4xYgQ6na5ddfbFF1/c5nj/nj17yMrKYsiQIXz88cc888wzrba79NJLWb9+PaAOv8yZM6fZ61deeSXLli3DbDbz1ltv8ctf/pLs7GzmzZvHK6+8QkSEOqS3ePFiYmNjGTZsGMOHD2f27NmBIa7TJSsri6uuuophw4YxY8YMlixZEpD2zZw5s9nwT05OTot6CjNnziQ1NZX09HQWLVoUWBkGmjq7x9Nd6my3w8uWHbnkHa6jTmeiX78qPnj1fLZsCiMzoZInlzgJChHkHVxNXFgmGZHHlmQmT+xddRJ8PnjjDXj5ZbXoTWIirFzZ45eYaursrufo0aMsWLCAzz7rW+VUpkyZwurVq4mKiuruUDpMl6qzNcDn9ZO/8xCHj9iwy2CCo21s/3YQP/wQhsXs4bfX/IhZPwBD4Tb0rhoSUqIgMhHCEsEYdPIP6Cns2wd//KMqrwO44gq4664enxA0zg4JCQksWrSI+vr6Dq0eOheoqKjgnnvu6VUJobNoSeEk2GpcVG4sxVvTABGhYJKEi0g+WJmC3qDjzgWFJI2JQ+gltbIeY7909AkjIbgX/dL4fPDii6rATlHU3sGDD0IbY7IafZfO7ifobcTGxgb2MvQVtDmFk1Bf5SI41EhtejBFeicRwZKnHo/H59Vx8fgypg47glR81BX/mypnJUFBmRgMvWylgl6vVkSTEq65BnJytISgodFH0XoKHUAxCArqGggLEuxcH09VSTipkfXcdNFmREwyRFpwV7uxDrqelNQ0jEZjd4d8chwOsNshNladOH7oIaishJEjuzsyDQ2NbkTrKbSDY28VyuFqPD4PlQ4HIa5QPn5XHVu8/aqDhI4fgb5/Iu7K73H69URGWXtHQvjuO1Vp/eCDx/UUiYlaQtDQ0NB6Cu1RnldCoauM7yoc+MxGtnwYj9ejY0xWAcOTKkCfiN/vp6LmCFEDJhMXF9fdIbdPXZ26I/mjj9TjqCj1ucjI7o1LQ0Ojx9ClPQUhxAwhxD4hxEEhRIsdJ8eMq7uFEDuFEGuFEAO7Mp5ToeZoFYfKKznQP57q+CQGWrLZ/n0ERoOfKy/NhcREvB4bRZtXYND5GDgwq7tDbhspYe1aVWD30UfqaqI771T3IGgJ4YxSWlrK/PnzSUtLY9iwYcycOZOXXnqphUpaQ6On0mU9BSGEHlgCXAIUAZuFEO9LKXc3abYNGCuldAghbgGeAM6cE/g02VNcT25+JXURFmJDDIxw6Vj1fAS4XEw+fyNRVFBSF4lSeZigMCMpY64kKLx/d4fdOlKqw0SNfvnRo9Xj5OTujescRErJnDlzuO666wLW0O3bt/PBBx90c2QaGh2nK4ePxgMHpZR5AEKIHOAKIJAUpJRNxSbfA//dhfGcFI/LR02Zg33VDYQoVSQLhX5hFp5caeDAAQPBlkouWegiwTwYQ10+llAv5tQ0goNjEKKHTs8IAampqsDurrvUIji9UblxqoxtZ4/O734Hxzz6vPsuPPpo221PYaPkF198gdFobLazODs7m9raWtauXcu8efPIzc1lzJgxvPXWWwgheOSRR/jggw9wOp1ccMEFvPjiiwghuOiii5gwYQJffPEFtbW1LF26lMmTJ+P3+7nvvvv49NNPEUKwaNEi7rjjDn744QfuuecebDYbMTExvP766wFNg4bGqdCVd4f+QGGT46Jjz7XFjUCr5ZeEEDcJIbYIIbZUVFScwRCbU7y/ljy7C0OUmfgyNzERkeTlBbHmHSvS52f+f21kcPQAFK8Nm1kiMgZhCOuHydTD5hJKSqCpS/6669RdyT3UaHqu0HjDb41t27bx9NNPs3v3bvLy8gK1DW6//XY2b95Mbm4uTqeTDz/8MPAen8/Hpk2bePrpp/njH/8IwEsvvcThw4fZtm0bO3fu5Be/+AVer5c77riDlStX8sMPP3DDDTfwwAMPdP0Fa5yTdGVPoTVBTqtODSHEfwNjgamtvS6lfAl4CVTNxZkKsBGXzUtDtQuAyOQwrIofu8OJd3gUv7s5BOmXTJ18gKwJFfj0Rmye3YSFhWG1jiIoqHUTY7egKLBiBSxZAmYzvPMOWK1gMEBPnwQ/03T0G/7cucd7DV3I+PHjGTBgAEBArz1p0iS++OILnnjiCRwOB9XV1WRlZXHZZZcdC02Na8yYMeTn5wPw+eef86tf/SqgtrZarYEKZo2qbr/fr/USNE6brkwKRUDTijIDgBZSciHExcADwFQpZcvSUV1MVbENl82LwazHPCCEgy4PcQcqUIA/PxFCeZme4bHlXDNtG4b4FDzOCoSSQnzU4J6VEPLyYPFi2LlTPZ4yResVnGWysrICdYhPpLHKGKi1nH0+Hy6Xi1tvvZUtW7aQlJTEww8/jMvlavGexvbQem0GKSVZWVl89913Z/qSNPogXXnX2AxkCCFShBAmYD7wftMGQojzgBeBy6WU5V0YS5s0VLmIiAsmOjEEc6gRw5E6DBVlrD3Yjy0bg4kwe/jfa3NxDkuisu4g9cV7iTFIIiKt3RFuS3w+WLoUfvELNSHExqrLTh99VFtZdJaZNm0abrebl19+OfDc5s2b+fLLL1tt35gAYmJisNlsbSaUpkyfPp0XXnghkCSqq6sZPHgwFRUVgaTg9XrZtWtXZy9Ho4/SZUlBSukDbgc+BfYA/5JS7hJCPCKEuPxYs7+i1oF+RwixXQjxfhun61JCIs0YTHqklNgbbBT6FFZ+lIlf+rjiZ19RNbCSopL9hPqLyBw0gdTxMzDEDOqOUFvywAPw/POq0XTOHHXIaMqU7o6qTyKEYNWqVXz22WekpaWRlZXFww8/3Gaxl8jISBYtWsSIESOYPXs248aNO+lnLFy4kOTkZEaOHMmoUaNYtmwZJpOJlStXct999zFq1Ciys7P59ttvz/TlafQR+rQ6u77SSVWJnUEjonHvr6GoroEfykpY9WoiuQdiScsu44aL1xAUn0C4OERkqCRt7M2Ygs5+qcc22b5dNZv+7nfQgZvKuYqmztbQOI6mzj4NnDYPtWUOrAkhajlBr59yQzXfbQvix70xiFAb06Z8id5rJFxvJC7ISET61O5PCFu3qpOoN92kHmdnqyuLjhUU0dDQ0OgMfTYpuO0+gkKNhEer9Q5sdht5eeV8/v4FKAa4fPZOUhQdEVYLVuUwwdZYLOHdOI9gt8Ozz8K//60ejx2rbkQDLSFoaGicMfpsUgAwmvUInaAht4wGu52lKwdjd0iGZxUxLWYnLvP/b+9Mo6uqskX9TUJ6CE1oBJJAgNATI6EIXAFRFOkKARkCQ1REHlaBr7B4eG3q1RXvtSvBoUUJUjxloCUkILdASgWL9mqhgIAREqQnkhAlEkgIpD+Z78feOYSQ5qQ5CclZ3xh7jHPWnmutOU+z517NnrOQ5s2zadK+Lf6dImjaNLB+FN2zB155BdLSrO2ls2ZB//71o4vBYGjUeKRTUFUchUU08bK29mVeuszuU804daQVLXyymTVyO02CM/ELbE6zVp0I6TUJP/82da9oRga8+SZssZ/p69sX/uM/oFu3utfFYDB4BB7pFPJzCrl6OY82oc3Izc3lXFImf13eBlFl0oh9hHW8jF+zDlzJVEK734dvfTgEsPIkb9liPYg2d66VAMc8e2AwGNyIRzqFgrwivP28CGzhS9KZs6xc356sK0WERvzEsMiv8fXzJ9e7HYXhvfGt662nqla8IoAnn4RLl2DePLCfhjUYDAZ34pG3nRdTsvBv5s21S5dYu/YY33wfQNOmuYx/5HtadY3kckAXkpqE0v62Oox8qgobN1rrBfn5VllQELz2mnEIBoOhzvC4kULGhWxQaNkugKS4vfxzQ3e8pAkjJqUQ2FI4hy+Sk0loeDA9bguqG6VSUqwQFcXPX2zbBuPG1U3fBoPBUAKPGynkZhfQPDef5H/sZfuXuZzLCCSobTadfpVKiN9Fgpumckf3aH7VLdz9yhQVwZo1MHWq5RBatbLCU4wd6/6+DW5BRHjkkUec7wsLC2nbtq3bk+x4eXkRFRVFv379+PWvf01GRobzXEpKCg888AARERF069aN+fPnk188GqXsxEAnTpy4qY+cnBzuuusuHA6Hs2zjxo2ICMeOHXOWJSUl0a9fvxvqLlq0iCVLllSpv6qydetWevbsSffu3Xn99ddvOn/8+HGioqKcR1BQEG+//bbL9d2hU2Uys2bNol27djd8nvn5+QwfPtwZ6qS28TinUJCXS17WFdLyfuSzE33A15t/G3meZq3bExzgQ0TEBMLCBt4UdKzWOXMGHn8c3noL8vJgzBgrRMWoUdfXFAwNjsDAQGcYbIBt27bRqZP7pyH9/f2Jj48nISGB1q1bs2zZMsDaaTd58mQmTpzIyZMnOXHiBFevXnWG1i5ODDRixAhOnz7N0aNHefXVV7lw4cJNfaxatYrJkyfjVeK5mNjYWIYOHepMKlQZVemvKjgcDuZGsbfQAAAV7UlEQVTNm8eWLVs4evQosbGxHD169AaZnj17Eh8fT3x8PAcPHiQgIIBJkya5XL8ku3fvZubMmTXWqTKZmTNnsnXr1hvq+Pj4MHLkSNatW+fKR1NlPG76KPNCCgHXlOOp7Un8sTm+zfK4a0Qqv3gpLVqG0bFjZ/c7BIBjxyAx0Qpp/cILMHSo+/v0ECrKr1MTXI2uMmbMGD777DOmTJlCbGws06dP56uvvgLgo48+YunSpeTn5xMTE8Py5cvx8vJi4sSJJCcnk5uby/z585kzZw5JSUmMGTOGoUOH8vXXX9OpUyc++eQT/P39K+x/yJAhHLaj5e7cuRM/Pz8ef/xxwBpRvPXWW4SHh/PSSy+xd+/eMhMDlcWaNWtYu3at8/3Vq1fZs2cPu3btYsKECSxatKjSz6a8REQ1Zf/+/XTv3p2uXbsCMG3aND755BP69OlTpvyOHTvo1q0bnTt3rlb92tKpMpnhw4c7w6aXZOLEiTz//PM8/PDD1davPDxupAAQ0LIVn+7uAlLEPSMTONs0jYKiAjp0HOReh3D58vXXY8bAv/87rF9vHEIjY9q0acTFxZGbm8vhw4eJiYkBrHg069atY8+ePcTHx+Pl5cWaNWsA6y784MGDHDhwgKVLl5Keng7AyZMnmTdvHomJibRs2ZL/Ln6ivRwcDgc7duxgwgQr5mRiYuJNiX+CgoIICwvj1KlTFSYGKkl+fj5nzpyhS5cuzrJNmzYxevRoevToQevWrTl06FCl7bjaH8CwYcNumO4pPrZv336T7Pnz5wkNvR6pPyQkhPPnz5fbdlxcHNOnT69y/ZiYGKKiopg9ezabN2926vRFcbrbKupUVb2L6devH99++22lctXB40YKAMfPKoeOBKG+uXQefo6mTfswvn9/2rVwU6jp3FxYscKKUfS3v0F4uDVF9NBD7unPw6mleInVJjIykqSkJGJjYxlbYn1ox44dHDx40BkNNScnh3Z28qOlS5eyceNGAJKTkzl58iS33XYb4eHhzjvpksl2SpOTk+NM3hMdHe1MuFNW/oWKysvj4sWLtCwVij02Npann34asBxhbGwsAwYMKLfdqt5wFY+uXKGswJ7l9Zefn8/mzZt57bXXqlx/3759gDV9tHr1alavXl0jnaqid0m8vLzw8fEhKyuL5s2bVypfFTzSKfz3Dm+KpIDIO8/QpaiQTsG3ERLcyj2jhAMHrJ1FKSnWg2fffWc5BUOjZsKECSxcuJDdu3c77/pVlccee+yGixFYF5jt27fzzTffEBAQwIgRI5y5Fkon5yleqyhN8ZpCZmYm48ePZ9myZfzud7+jb9++N40urly5QnJyMt26dSMtLc2lPA7+/v43JABKT09n586dJCQkWAElHQ5EhDfeeIPg4GAulxwVY+V9CA8PJyQkxKX+wBopZGVl3VS+ZMkS7r333hvKQkJCSE6+nv03JSWl3JDlW7ZsYcCAAbRv375a9V3FlTZr0m9eXh5+fn410rFMVLVBHdHR0Vpd8vPzddemf+rYESnatVuqvrD0U920foVmZFyudpvlkpWl+sorqtHR1jF1qmpiYu33Y1BV1aNHj9a3CqqqGhgYqKqqycnJ+vbbb6uq6q5du3TcuHGamJio3bt31wsXLqiqanp6uiYlJemmTZt0/Pjxqqr6ww8/qK+vr+7atUvPnj2rffv2dba9ePFiffHFFyvsV1X10KFDGhoaqvn5+VpUVKTR0dH6wQcfqKpqYWGhzp49WxcsWKCqqkVFRTpo0CBduXKls/7+/ft19+7dN/UREhKiOTk5qqq6YsUKnTNnzg3nhw8frl9++aWqqkZHR+v27duddkZEROipU6eq1F9VKCgo0PDwcD1z5ozm5eVpZGSkJiQklCk7depUXbVqVbXr16ZOrsiU/h2oql68eFF79epVbt9l/R+AA+rCNdaj1hSuXLmCXC4g7VIgeepFSJtMmvi2IDCgWe12FB9vTQ39/e9WALvf/MaaNqrBopWhYRESEsL8+fNvKOvTpw8vv/wyo0aNIjIykvvuu4+ffvqJ0aNHU1hYSGRkJH/84x8ZPHhwjfq+4447uP3224mLi3Mm/vn444+JiIigR48e+Pn58eqrrwJVSww0atQo/vWvfwHW1FHxzp1iHnzwQedC9IcffsjLL79MVFQU99xzDy+++CLdunWrciIiV2natCnvvPMO999/P7179+ahhx6ib9++AIwdO5bUVCsTcHZ2Ntu2bXPmv3alfkmK1xRKH2WtKbiiU2X9Tp8+nSFDhnD8+HFCQkJ4//33AWvBfqy7tq674jlupaMmI4ULP6fptmU7tVevLO3a67we271ct/xrvzocRdVus0zOnVMdMkR15kzV06drt21DmdwqI4XGzKFDh3TGjBn1rYZBVSdNmqTHjh0r93xNRgoetaZw5eI1zqcXkasO2rTNIKcgF7/AWpiTU4V9+yAmxlpADg218ib37GkC2BkaDXfccQd33303DofjhmcVDHVLfn4+EydOpGfPnm5p32OuWPn5+aT9nExaViEOHIQGpZBTGESH29rV7FmxCxfg97+Hp56Cf/zjennv3sYhGBods2bNMg6hnvHx8eHRRx91W/seM1JwOBxIk0wuZ7WmUJWwwCsEd+pPj5D2lVcui6Ii2LQJ3n4bsrOhWTPw9q5dpQ0Gg6GO8RinoOoAMriQ1gtV6NAyj1ZhNy8kucS5c9Y20+KHdUaMgGefhbZta0tdg8FgqBc8xik4HNdQrpH6cwsocNCp/TXatqhGes3Dh63dRPn50Lq19VTyyJEmXpHBYGgUeIxTAMjNLCA5uQkqhfQeWs1sar17Q1iYtYi8YAG0aFG7ShoMBkM94jFOoUjhx+QmFKrQrF023UNdzJWQnw8ffQSTJ0PLlta6wapVEBDgXoUNBoOhHvCY7THX8gr5+ScfvCiiRYc8vDrHVF7pyBGYMQOWL4c337xebhyCwWBopHjMSAHg4qWWOFBahCjePr7lC+bkwLvvQmys9QxCWJg1UjAYDIZGjsc4BS0q4kKaNWXUtYs/3k3L2T66f7+1syg11XrO4LHHYM4c8PGpQ20NNSU5OZm8vLxaa8/X1/eGEMe1waxZs/j0009p164dCQkJLtfLyMhg7dq1zJ07t8zzixYtolmzZixcuNCl9qoqb2jceMz0UUFeLmkXW+AQoV1oUdlC587BvHmWQ+jRAz780HoozTiEBkdeXh4BAQG1dlTVwbiSmausrFqukJGRwfLly6tcz2BwBY9xCgAXLrUmv0kToiK8aFLWFtKwMJg+HebOtRxCr151r6TBYxg+fDitW7euUObatWuMGzeO22+/nX79+rFu3Tqee+45Tp8+TVRUFM888wwAr7zyCj179uTee+/l+PHjlfZdkfxHH33EoEGDiIqK4sknn8ThcPDss8/e4IgWLVrEmyXX2QyNBo+ZPsrOFjKzAmnq7eBX4bbZly7B4sXw4IPXczguWFB/ShoaPDExMeTl5XH16lUuXbrkTJDzpz/9ifvvv7/K7W3dupWOHTvy2WefAZCZmUlMTAwJCQnEx8cDcPDgQeLi4vjuu+8oLCxkwIABFWY3q0i+ZHY4b29v5s6dy5o1a5g2bRpPP/20c8pq/fr11RrlGG59PMYpnE/xRoHWba7QIagjfP45LFkCV67Ajz/CmjXmATRDjalKZi5X6N+/PwsXLuTZZ59l/PjxDBs27KYENl999RWTJk0iwN4VV5yKszwqki8vO9yjjz5KWloaqamp/PLLL7Rq1YqwsLAa2Wa4NXGrUxCR0cCfAS/gPVV9vdR5X+BDIBpIB6aqapI7dDlxqpBC/AgNuozfwj/D119bJwYPhhdeMA7BcEvSo0cPDh48yOeff87zzz/PqFGjygyGVtWsgeXJaznZ4QCmTJnChg0b+Pnnn5k2bVqV+jM0HNy2piAiXsAyYAzQB5guIqWzzDwBXFbV7sBbwJ/cpU/KmUL8s68x9IetlkMICoJFi+Avf4EaJvgwGEozYsSIGo8SAFJTUwkICGDGjBksXLiQQ4cO0bx58xvSVA4fPpyNGzeSk5NDVlYW/ygZrbcMKpIfOXIkGzZsIC0tDbDSaP7444+AlYc5Li6ODRs2MGXKlBrbZrg1cedIYRBwSlXPAIhIHPAAcLSEzAPAIvv1BuAdERE7IUSt8lOyF/5Xr9E5MBXuuccKYBccXNvdGG4RfH19yc7OrtX2XKF4TaE0Za0pTJ8+nd27d3Px4kVCQkJ46aWXeOKJJ26QOXLkCM888wxNmjTB29ubd999l+DgYO6880769evHmDFjWLx4MVOnTiUqKorOnTszbNgwZ/2xY8fy3nvv3ZDZbMCAAeXKl8wOV1RUhLe3N8uWLaNz58707duXrKwsOnXqRIcOHSrsw9BwETdcf62GRaYAo1V1tv3+ESBGVZ8qIZNgy6TY70/bMhdLtTUHmAMQFhYWXXznUhXmzUvny81ZvPfcWWLm3V1dswy3KD/88AO9e/eubzUMhluCsv4PInJQVQdWVtedI4WyJi1LeyBXZFDVlcBKgIEDB1bLiy1bFoy+E4xql+pUNxgMBo/Anc8ppAAlHwENAVLLkxGRpkAL4JK7FBIxydAMBoOhItx5ifwWiBCRcBHxAaYBm0vJbAYes19PAXa6Yz3B4BmYn47BUPP/gducgqoWAk8BXwA/AOtVNVFE/lNEijdGvw8Ei8gpYAHwnLv0MTRu/Pz8SE9PN47B4NGoKunp6fj5+VW7DbctNLuLgQMH6oEDB+pbDcMtRkFBASkpKeTm5ta3KgZDveLn50dISAjepXLG3woLzQZDneHt7U14eHh9q2EwNHjMsqvBYDAYnBinYDAYDAYnxikYDAaDwUmDW2gWkV+Aqj/SbNEGuFipVOPC2OwZGJs9g5rY3FlV21Ym1OCcQk0QkQOurL43JozNnoGx2TOoC5vN9JHBYDAYnBinYDAYDAYnnuYUVta3AvWAsdkzMDZ7Bm632aPWFAwGg8FQMZ42UjAYDAZDBRinYDAYDAYnjdIpiMhoETkuIqdE5KbIqyLiKyLr7PP7RKRL3WtZu7hg8wIROSoih0Vkh4h0rg89a5PKbC4hN0VEVEQa/PZFV2wWkYfs7zpRRNbWtY61jQu/7TAR2SUi39m/77H1oWdtISKrRCTNzkxZ1nkRkaX253FYRAbUqgKq2qgOwAs4DXQFfIDvgT6lZOYCK+zX04B19a13Hdh8NxBgv/6tJ9hsyzUHvgT2AgPrW+86+J4jgO+AVvb7dvWtdx3YvBL4rf26D5BU33rX0ObhwAAgoZzzY4EtWJkrBwP7arP/xjhSGAScUtUzqpoPxAEPlJJ5APjAfr0BGCkiZaUGbShUarOq7lLV4kz2e7Ey4TVkXPmeAf4LeANoDDG1XbH5fwHLVPUygKqm1bGOtY0rNisQZL9uwc0ZHhsUqvolFWegfAD4UC32Ai1FpENt9d8YnUInILnE+xS7rEwZtZIBZQLBdaKde3DF5pI8gXWn0ZCp1GYRuQMIVdVP61IxN+LK99wD6CEie0Rkr4iMrjPt3IMrNi8CZohICvA58L/rRrV6o6r/9yrRGPMplHXHX3rfrSsyDQmX7RGRGcBA4C63auR+KrRZRJoAbwEz60qhOsCV77kp1hTSCKzR4Fci0k9VM9ysm7twxebpwGpVfVNEhgB/s20ucr969YJbr1+NcaSQAoSWeB/CzcNJp4yINMUaclY0XLvVccVmRORe4A/ABFXNqyPd3EVlNjcH+gG7RSQJa+51cwNfbHb1t/2Jqhao6lngOJaTaKi4YvMTwHoAVf0G8MMKHNdYcen/Xl0ao1P4FogQkXAR8cFaSN5cSmYz8Jj9egqwU+0VnAZKpTbbUyl/xXIIDX2eGSqxWVUzVbWNqnZR1S5Y6ygTVLUh53J15be9CWtTASLSBms66Uydalm7uGLzOWAkgIj0xnIKv9SplnXLZuBRexfSYCBTVX+qrcYb3fSRqhaKyFPAF1g7F1apaqKI/CdwQFU3A+9jDTFPYY0QptWfxjXHRZsXA82Aj+019XOqOqHelK4hLtrcqHDR5i+AUSJyFHAAz6hqev1pXTNctPn/AP9PRH6PNY0ysyHf5IlILNb0Xxt7neRFwBtAVVdgrZuMBU4B2cDjtdp/A/7sDAaDwVDLNMbpI4PBYDBUE+MUDAaDweDEOAWDwWAwODFOwWAwGAxOjFMwGAwGgxPjFAy3HCLiEJH4EkeXCmS7lBdNsop97rYjcX5vh4joWY02fiMij9qvZ4pIxxLn3hORPrWs57ciEuVCnadFJKCmfRs8A+MUDLciOaoaVeJIqqN+H1bV27GCJS6uamVVXaGqH9pvZwIdS5ybrapHa0XL63ouxzU9nwaMUzC4hHEKhgaBPSL4SkQO2ce/lSHTV0T226OLwyISYZfPKFH+VxHxqqS7L4Hudt2Rdpz+I3ace1+7/HW5np9iiV22SEQWisgUrPhSa+w+/e07/IEi8lsReaOEzjNF5C/V1PMbSgRCE5F3ReSAWHkUXrLLfoflnHaJyC67bJSIfGN/jh+LSLNK+jF4EMYpGG5F/EtMHW20y9KA+1R1ADAVWFpGvd8Af1bVKKyLcood9mAqcKdd7gAerqT/XwNHRMQPWA1MVdX+WBEAfisirYFJQF9VjQReLllZVTcAB7Du6KNUNafE6Q3A5BLvpwLrqqnnaKywFsX8QVUHApHAXSISqapLseLi3K2qd9uhL/4vcK/9WR4AFlTSj8GDaHRhLgyNghz7wlgSb+Adew7dgRXTpzTfAH8QkRDg76p6UkRGAtHAt3Z4D38sB1MWa0QkB0jCCr/cEzirqifs8x8A84B3sPIzvCcinwEuh+ZW1V9E5Iwds+ak3cceu92q6BmIFfahZNath0RkDtb/ugNWwpnDpeoOtsv32P34YH1uBgNgnIKh4fB74AJwO9YI96akOaq6VkT2AeOAL0RkNlaY4Q9U9XkX+ni4ZMA8ESkzx4Ydj2cQVhC2acBTwD1VsGUd8BBwDNioqirWFdplPbEykL0OLAMmi0g4sBD4lapeFpHVWIHhSiPANlWdXgV9DR6EmT4yNBRaAD/ZMfIfwbpLvgER6QqcsadMNmNNo+wApohIO1umtbien/oY0EVEutvvHwH+x56Db6Gqn2Mt4pa1AygLK3x3WfwdmIiVB2CdXVYlPVW1AGsaaLA99RQEXAMyRaQ9MKYcXfYCdxbbJCIBIlLWqMvgoRinYGgoLAceE5G9WFNH18qQmQokiEg80AsrZeFRrIvnP0XkMLANa2qlUlQ1FysC5ccicgQoAlZgXWA/tdv7H6xRTGlWAyuKF5pLtXsZOAp0VtX9dlmV9bTXKt4EFqrq91i5mROBVVhTUsWsBLaIyC5V/QVrZ1Ss3c9erM/KYABMlFSDwWAwlMCMFAwGg8HgxDgFg8FgMDgxTsFgMBgMToxTMBgMBoMT4xQMBoPB4MQ4BYPBYDA4MU7BYDAYDE7+P7z5Cj2Jo60SAAAAAElFTkSuQmCC\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([0.54703285, 0.55016726, 0.34399391, ..., 0.4632062 , 0.38298533,\n", + " 0.47006439])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv = StratifiedKFold(n_splits=10)\n", + "results = np.zeros_like(y, dtype=float)\n", + "\n", + "tprs = []\n", + "aucs = []\n", + "mean_fpr = np.linspace(0, 1, 100)\n", + "\n", + "i = 0\n", + "for train, test in cv.split(X, y):\n", + " print('>>')\n", + " keras.backend.clear_session()\n", + " prbs=[]\n", + " model = RandomForestClassifier(n_estimators=1000, random_state=0, max_leaf_nodes=200)\n", + " # Fit the model\n", + " model.fit(X[train,:], y[train])\n", + "\n", + " \n", + " probas_ = model.predict_proba(X[test,:])[:, 1]\n", + " results[test] = probas_\n", + "\n", + " # Compute ROC curve and area the curve\n", + " fpr, tpr, thresholds = roc_curve(y[test], probas_[ :])\n", + " print(' ' + str(auc(fpr, tpr)))\n", + " tprs.append(interp(mean_fpr, fpr, tpr))\n", + " tprs[-1][0] = 0.0\n", + " roc_auc = auc(fpr, tpr)\n", + " aucs.append(roc_auc)\n", + " plt.plot(fpr, tpr, lw=1, alpha=0.3,\n", + " label='ROC fold %d (AUC = %0.2f)' % (i, roc_auc))\n", + "\n", + " i += 1\n", + "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n", + " label='Chance', alpha=.8)\n", + "\n", + "mean_tpr = np.mean(tprs, axis=0)\n", + "mean_tpr[-1] = 1.0\n", + "mean_auc = auc(mean_fpr, mean_tpr)\n", + "std_auc = np.std(aucs)\n", + "plt.plot(mean_fpr, mean_tpr, color='b',\n", + " label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n", + " lw=2, alpha=.8)\n", + "\n", + "std_tpr = np.std(tprs, axis=0)\n", + "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n", + "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n", + "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n", + " label=r'$\\pm$ 1 std. dev.')\n", + "\n", + "plt.xlim([-0.05, 1.05])\n", + "plt.ylim([-0.05, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver operating characteristic example')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n", + "results" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", + "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v3.norm.csv', index=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(results[y==0],100, color='red', alpha=0.5)\n", + "plt.hist(results[y==1],100, color='blue', alpha=0.5)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} |