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Diffstat (limited to 'scripts/tensorflow/prediction-v2.ipynb')
| -rw-r--r-- | scripts/tensorflow/prediction-v2.ipynb | 11361 |
1 files changed, 0 insertions, 11361 deletions
diff --git a/scripts/tensorflow/prediction-v2.ipynb b/scripts/tensorflow/prediction-v2.ipynb deleted file mode 100644 index e8a809d..0000000 --- a/scripts/tensorflow/prediction-v2.ipynb +++ /dev/null @@ -1,11361 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "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": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/fast_data/drewe/software/envs/tf_gpu/lib/python3.6/site-packages/sklearn/preprocessing/_data.py:2357: UserWarning: n_quantiles (1000) is greater than the total number of samples (517). n_quantiles is set to n_samples.\n", - " % (self.n_quantiles, n_samples))\n" - ] - } - ], - "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=1, 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": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.6014 - acc: 0.6687\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5998 - acc: 0.6709\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.6020 - acc: 0.6608\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.6017 - acc: 0.6603\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5958 - acc: 0.6696\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5955 - acc: 0.6694\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5917 - acc: 0.6742\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5897 - acc: 0.6785\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5990 - acc: 0.6642\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5970 - acc: 0.6683\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5895 - acc: 0.6760\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5870 - acc: 0.6784\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5849 - acc: 0.6797\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5849 - acc: 0.6821\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5788 - acc: 0.6867\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5958 - acc: 0.6685\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5857 - acc: 0.6777\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5829 - acc: 0.6779\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5847 - acc: 0.6786\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5830 - acc: 0.6799\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5799 - acc: 0.6830\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 47us/step - loss: 0.5717 - acc: 0.6913\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5741 - acc: 0.6883\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5704 - acc: 0.6942\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5741 - acc: 0.6845\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5686 - acc: 0.6938\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5645 - acc: 0.6988\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5689 - acc: 0.6928\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5630 - acc: 0.6983\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5635 - acc: 0.6975\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5664 - acc: 0.6876\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5717 - acc: 0.6863\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5586 - acc: 0.6977\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5570 - acc: 0.6988\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5565 - acc: 0.6960\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5493 - acc: 0.7130\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5520 - acc: 0.7027\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5499 - acc: 0.7082\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5515 - acc: 0.7043\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5508 - acc: 0.7057\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5442 - acc: 0.7114\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5430 - acc: 0.7092\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5443 - acc: 0.7046\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5479 - acc: 0.7072\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5442 - acc: 0.7096\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5308 - acc: 0.7204\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5374 - acc: 0.7158\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5446 - acc: 0.7093\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5398 - acc: 0.7119\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5358 - acc: 0.7144\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.6079 - acc: 0.6537\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.6001 - acc: 0.6654\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5989 - acc: 0.6658\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 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[==============================] - 0s 35us/step - loss: 0.5862 - acc: 0.6788\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5933 - acc: 0.6647\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5859 - acc: 0.6819\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5881 - acc: 0.6751\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 34us/step - loss: 0.5812 - acc: 0.6778\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 35us/step - loss: 0.5753 - acc: 0.6804\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 46us/step - loss: 0.5823 - acc: 0.6784\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 54us/step - loss: 0.5767 - acc: 0.6789\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5751 - acc: 0.6894\n", - "Epoch 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[==============================] - 0s 42us/step - loss: 0.5912 - acc: 0.6696\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5780 - acc: 0.6867\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5729 - acc: 0.6899\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 49us/step - loss: 0.5684 - acc: 0.6949\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5672 - acc: 0.6899\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5804 - acc: 0.6760\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 55us/step - loss: 0.5712 - acc: 0.6955\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 46us/step - loss: 0.5657 - acc: 0.6986\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5634 - acc: 0.6913\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 43us/step - loss: 0.5764 - acc: 0.6874\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5671 - acc: 0.6942\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5633 - acc: 0.7016\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5651 - acc: 0.6951\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5516 - acc: 0.7130\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5518 - acc: 0.7072\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5469 - acc: 0.7075\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5480 - acc: 0.7122\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5480 - acc: 0.7074\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5572 - acc: 0.7048\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5449 - acc: 0.7137\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5411 - acc: 0.7138\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5416 - acc: 0.7196\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5405 - acc: 0.7123\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5310 - acc: 0.7208\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5333 - acc: 0.7210\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5315 - acc: 0.7290\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 37us/step - 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"stream", - "text": [ - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5149 - acc: 0.7433\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5255 - acc: 0.7325\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5142 - acc: 0.7379\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5122 - acc: 0.7373\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5135 - acc: 0.7389\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5175 - acc: 0.7408\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 36us/step - loss: 0.5212 - acc: 0.7404\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.5050 - acc: 0.7464\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 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[==============================] - 0s 40us/step - loss: 0.5940 - acc: 0.6664\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5987 - acc: 0.6642\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5958 - acc: 0.6613\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5965 - acc: 0.6608\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5917 - acc: 0.6656\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 39us/step - loss: 0.5918 - acc: 0.6656\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5937 - acc: 0.6650\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.6041 - acc: 0.6605\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 40us/step - loss: 0.5911 - acc: 0.6724\n", - "Epoch 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loss: 0.5482 - acc: 0.6997\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5460 - acc: 0.7015\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5412 - acc: 0.7061\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5358 - acc: 0.7122\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5340 - acc: 0.7114\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5449 - acc: 0.7064\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5313 - acc: 0.7206\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 42us/step - loss: 0.5514 - acc: 0.6954\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 45us/step - loss: 0.5431 - acc: 0.7104\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 38us/step - loss: 0.5316 - acc: 0.7170\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 41us/step - loss: 0.5386 - acc: 0.7109\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.5257 - acc: 0.7230\n", - " 0.7064249695172389\n" - ] - }, - { - "data": { - "image/png": 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KS0v58Y9/TMuWLWnfvj29evVi/vz5NeanNtQ2D8lkkttvv502bdrQtm1bpk2bRnmsnLvuvovOnTtzTrdz6NypM01ym2JJgE2FO+nf/3J2bNHYvkFj7w6N2D4dO6mjOYogNpmBBPWjZZyeU8x36u2lScMYjU8zyKwXJZidRSCaiR7JQQtlIoFwZYOgFBQXw7p1rkHQNGjcGJo3h2Dwa92TI+Wkk8728TlRiUQiLFmyBIBbbrmF8ePH8/DDDxOLxbjqqquYMGEC/fv3p7y8nGuvvZY//OEPjBgxgmXLlnHXXXfx3nvv0bZtWyzL4qWXXqoS/+LFizFNM3WNo8WsWbMoKCigoKCA+fPn85Of/KTaQnrSpEkMHjwYPa32mp+fT/fu3Zk+fTrDhw+v1fV++MMf0qJFCwoKCtA0jfXr17Ny5cpvJA9PPPEEjRo1YtWq1Vimxaatm0hYSX77u6dRuCOExj//Mp9/vpRdGwMkzVzqZOfy6cf/oUuX891uoHCSsJ4gICYYBsFIGE030LQguhFADxi178LZtg327nV/Z2RAbu5xMwYV+DIXPj7HgJ49e1JYWAjAm2++yQUXXED//v0BiEajvPjii/z2t78F4He/+x0PP/wwbdu2BcAwDO68885K8e3YsYObb76ZJUuWkJeXx7p165g9ezZdunShY8eO3HrrrSQSiSrpmDx5Mm3atKF3797Mmzev2rTOmDGDYcOGISL06NGDoqIitm6t6t574403GDRoUGp73bp1lJaWMnr0aPLz82t1X9atW8f8+fMZPXo0mtePfuaZZ3L55ZfX6vyaqG0eJk2axAP3P0hJSSkl5aXUz6qHnjCIFcHuHQZfbQrx1tRp9Os3hKSpAJv+/QbywT+mkFu/iNzsnTSMlJITsYjmZJPdoB7R7CwimZmEolGMYODw+vSzstwuotNPh2bNjrtBAL+l4HOK8uGK7Uc9zn7tGtcqnG3bzJ49m9tuuw1wu47OOeecSmFatmxJaWkp+/btY9myZdx///0HjbNRo0ZMnDiRMWPG8Pe//514PE6fPn2YPXs2bdq0YdiwYUyYMIF77703dc7WrVt57LHHWLRoETk5OfTt25cuXbpUibuwsJCmTfcPBGzSpAmFhYXk5uam9iWTSdavX0/6qof5+fnceOONXHTRRaxevZodO3bQqFGjg+Zj+fLl5OXlVWpt1MSQIUNYvXp1lf333Xcfw4YNqzEPjqM444wmbNr0JdnZdVCOAlEU7d2BUg6/eOTn/Pvf/6Vpk5aMfOgF6tbLpmKE/NbCDRQWbuSyfheSkxkjJHH0XmczfsKvyMwOoouBFs5EE32/c/hwSCSgvBzq1nW3MzOhVavj4juoCd8o+JyS1LYAP5rEYjHy8vLYuHEj55xzDpdeeing9nfXVHs80pEiq1evpkWLFrRp0wbY312VbhTmz59Pnz59aNjQXYFxyJAhrFmzpkpc6b6NmtK1a9cu6tSpU2nf1KlTmT59OpqmMXjwYKZNm8aIESOOWl4P5jc5kIo8JC2TRCyJ7djEk3ESZhzDMFAkKU/EKSzcQuvWF3PHHeN5bcpzjHnmIX779KuEAhYhPck7c/+P6wZ9l9ysMgBsTaPxGbls27aNYDCAaDroRzApTCnYvRt27nR/h0IQjbrHTiCDAH73kY/PUaPCp7Bp0yaSySTjx48HoH379lUcv+vXryczM5OsrCzat2/PokWLDuta1RXk1VGbgrhJkyZ89dV+cYHNmzdz+umnVwoTiUQqzRZfunQpBQUFXHrppTRv3pypU6emupDq16/P3op+co89e/bQoEED2rdvzxdffIHjOIdM15AhQ8jLy6vyN2XKFEzbJGknSdpJ4lacRqc1YlXBKkrLS7Edi61bttD0jCYEjRDJeIg92zKIx1oRDke5pO81BAPC9dcPZu3az2nVPE7TBsU0yCjhL+/9hcE33EwsnEMiox5WtC62BIhEokgwCkbokOmuQjwOGzbAjh2uQcjJOSG6iWrCNwo+PkeZnJwcnn/+ecaMGYNpmnz/+9/nk08+4cMPPwTcFsU999zDz3/+cwAefPBBnnzyyVQt3nEcxo4de9BrtG3blo0bN7J27VoAXnvtNXr37l0pzHnnncfcuXPZvXs3pmkybdq0auO66qqrmDJlCkopPv30U3Jycip1HQHUrVsX27ZThiE/P5/HH3+cjRs3snHjRrZs2UJhYSGbNm2ie/fuzJs3j23btgGwcOFCEokETZs2pWXLlnTr1o3HHnssZdgKCgqYMWNGlXS99dZbLFmyhCVLlrDw84X8+7N/88lnnzD4hsGUJ8sxLRPTMrFMi4EDBvL2m28TkghLFn1BZkY2QhM2bQywbatQFtNBhIsvHsjqNf+kceNyFsyfSduWLTD3FWMmHZZv2sre4n2cf9FFhMNBMsMBMsMGX25YR4cOHQ76PKrFcVxDsGGDaxgCAddvcMYZYJy4nTQnbsp8fE5iunTpQufOnZk6dSpDhw5lxowZ3H333YwYMQLbthk6dGhq2GanTp147rnnuPHGGykvL0dEDul4DYfDTJ48meuuuw7LsujevTt33HFHpTC5ubk8/vjj9OzZk9zcXLp27VppOGkFAwcOZObMmbRq1YpoNMrkyZOrvWb//v355JNP6NevH1OnTmXWrFmVjl9zzTVMnTqVkSNHMm7cOAYOHIjjOGRmZpKfn59yLE+cOJH7778/db369evzzDPPAGA5Fo7a34qIW3F3KoBSaGjo6CCgo2NoBo7tYDsOA/oP5P33Z9OuQ3tCoSi/+fXLlJVrKITrrz+Hme//h3DE5KknHuXOO+/g178aScP69Zg4YTyRhqchCH/7wwRuvOEGoqHK3UNz5sw5Mkf4jh2wZ4/7u149aNjwhOsqqg6pbTP0RKFbt26qujHYPt9uVq5cydlnn328k3FKs3jxYsaOHctrr712xHEopTAds9pjlmNhKxtNNHTRcRwH5XjGQHMLUxFB0zSUUti2Q+k+h7JyjfIyHdv2JhPjoAdtIhk2WWELQwMMA0NThAxB13U0EbASEMwA7eAFda9evZgxYwZ1K5zDtcWy4Kuv3HkHFf6Db4jqvgcRWaSU6naoc/2Wgo+PT63o0qULffv2xbbtWo0eqkApRdyOYzlWal9Aq+qsFYQgQXRx47aVjWiCA5iAZZqgFJYtlJfplO7TSZoGoBAcNN0hFE2SGYkTMHSCmjdCyDCIBATdjrujhtDcvn09AHLwHvSdO3dy33331c4glJZCUZHbPSTidhE1b1519vIJjm8UfHx8as2tt95aq3DlZjm2qtxVFdbD6JqOIIiIV9t3w1iWhSAoXKlp27axLIs4gqMEOyEkyjUScQPTFFCuNJ2hOWSFTDKiJuGQg2bogIEhNpohaAIiJjgK9CAED6/G3rBhQ66++uqDB7Jt2L7dNQjgTkKrMCInmUEA3yj4+PgcAxzlEDWiqdFPFYYgnYqCX9d1dF1HRDCTSWKmSWkCEjEDM2Fgxd2KvRuRoIkiGlZkRi0iEZuAVY6ENEQ0NN1BE0G0EAQyDhDoPwYF9L597qxky3INQMOGcMDQ3ZMN3yj4+Ph8LRzlVPETKNy5GVo13TOO42CaJo7joOu6u/iM42BZiqI9Qml5GNMSNCqG3gqhIETDioyITdiIgWWSREMcV3BOj2QjRgCpKPiPdQ39QAG7aNSVqAgdwZDVEwzfKPj4+BwxFd1EglTyEwS14P4C2sO2bRKJBI5S7tICaMRsC9tUFBcLpaUBr0Ug6JpDZsgmElFkRBWBgEIlkyjl4CQTOOEwmh4gEgq4o5qOZP7A16GkZL+AXaNGbnfRSdhVVB2+UfDx8TlsKloHtrKJGlE00ap0DzmO4xoBx8FxFEnHwdHEHe1jC/EyobxESCQ8xy8amRFF3YxyopniFrJev5HjKJKio0RhRKMY4Ux3FJH+DU61cpz90hZ16kAy6RqDE3gi2pHgT17z8TlKnKzS2atWraJnz56EQiHGjBlTY7xKKfpe3JfCXYWUmWWYtsmsv87C0I1KGkVz587liiuuIJlMYlkWtgg/vOMOpk2bQdkenZ0bHB6695dccF4HLv9uV266sScLF7xH09MSnN44TiRkkRQN07Gw7DhJK47lJNE1i4yQTkZmNuFggMABBuGpp56iVatWnHXWWfzjH/+oMQ8PP/wwbdq04eyzz+b555+vdHzBggXous6f//xnwB19NGDAAHe+wdq1riEA12A1bnzKGQTwjYKPz1GjQuZi2bJl1KtXLyVzUSGd/dBDD7FmzRq++OIL/vOf//CHP/wBICWd/frrr7Ny5UqWLVvGmWeeWSX+dOnsIUOGHLV016tXj+eff75aQ5TO39/7O+07tqdOVh3CEiYkId6e+jYXXHABb7zxBolEgkQiQTKZxLZtYkmTMsdg9x6DRJlQVhSkdF+I34/9NTt3bWPme0v4+F8LmfGXfNB2ATHiiQQJxwE7QUAX9Ggdwtn1yazTgIyc+hjROtXOK1ixYgVTp05l+fLlvP/++9x5553VTtR79dVX+eqrr1i1ahUrV67khhtuSB2zbZuRI0dy2WWXpfY1zM4mNyuLeTNnun6ECh/CKYxvFHx8jgEnk3R2o0aN6N69O7qhYzkWCStR6S9uxYlbcV57/TUuv+JyNOV295TsKeY///kP48e9yNtvvYUVMzFjJmbcJmkKO7aH2fFlgLI9Go7j+gkCRgnvTp/I+BeepEULmzp1y2lYvw6DB15JKKCRmREgOztKVjRKKJJZbYugOmbMmMENN9xAKBSiRYsWtGrVis8++6xKuAkTJvDLX/4yNbs6XdX1hRde4Nprr3X3KQW7dsH69VzduzdvzJwJTZpAgwaHTMvJju9T8Dk1WT3r0GEOl7O+W6tgJ5t0NriziU3HdCUmKlwDyp0/UCFe9+l/PuX5cc+jTAeFMOOvf6X/pf1p3eFscurWZ+5nK2jd6hy27w5imYKZAA2bSNQkEk5Sv24Je3ctpGmTXOpkGDhlJSjHAnTCoSDBkCCiwAiCZvCzn/2MOXPmVEnrDTfcwEMPPVRpX2FhIT169EhtV8h/H8i6det46623mD59Og0bNuT555+ndevWFBYWMn36dD766CMWfPqpK1GxYwcA3Xr25JE//Qmysw/6jE4VfKPgc2pSywL8aHKySWc7yiFpu33kpmOiiUZACxDU9i9Or1CEgiFwFHv37qVetI47hDQAb787jVtuvYuvvtK5+OIbmPbmWzxwf1cEhSYO9eskiIZsAgYERIElWJaOUoJIAAIBjHCEYCicqrmn8+yzz9b6ftRG/htcv0w4HGbhwoW8++673Hrrrfz73//m3nvv5emnn94/U9s0XQG73FwahUJs2bKl1mk52fGNgo/PUaLCp1BcXMwVV1zB+PHjueeee2jfvj0ff/xxpbDVSWd37ty51tf6OtLZFYJzZWaZKy2hBwnrYQJawB02Gk/g2A7K0x5CcxAEwzCwtARowoZ1RcyZM5cvvliBiOA4NiLw9FOP0r5NkHj5buqF3TUJHMthd9Fe6p6WS7vOnSncsgVLN8jOzj6oUTyclkJt5L8rwl177bWAK+D3gx/8AICFCxak/Au7du1i5vvvYzRrxtWtWxMvKSESiRz0Pp9K+D4FH5+jzIkonR2Px3n77bcxLZN9sX2UxEpQtiIkIcQRcNxuL8dxwFaI7c4j0HUdpQm2nqR1q5Ys/d8GNn4ZZerb73HlVUP5aM56Fi1YxZqVyzmzRTOWLfiIts2asnX7dhav20gsEKZg226WrVhFj/PPp27dutx2223ce++9mKY74W3r1q28/vrrVfL47LPPpqSz0/8ONAjgyn9PnTqVRCLBhg0bKCgo4Nxzz60S7uqrr+ajjz4C4F//+pfb0tq2jQ0zZ7Jx6VI2btzI9773Pf4wYQJXDx4MwJo1a45MOvskxTcKPj7HgHTp7EgkwowZMxg9ejRnnXUWHTt2pHv37tVKZ5999tl06NCh2vWF00mXzu7YsSOaplUrnf3YY4/Ro0cP+l3aj06dO6FQRAIRMoIZRAOuDMW2bdto3rw548aN43dP/46WZ7WiaF8JSlOUxiy27oYvCyOc2+NK3v/HZ9imxgfvT+W67w3ijCYmRihBUNqTAAAgAElEQVSGCVz13QHkvzMDq85p/GnSq9x99z306d2b2279Aa+8MpH69Vw9oNGjR9OwYUPatWtHhw4duPrqq1NdXEdK+/btuf7662nXrh0DBgxg/Pjxqa6ggQMHprp/HnroId555x06duzIqJ//nImPPuoONxVxu4yq4Yils09SfOlsn1MCXzrbpUJkTimFUoqklSTpJNENnYAeIKAFMDTDk5ZwnciOaYPtdTUFhERCKC7WKCvXMW3Am5u8c+dWHnn4Ft6ZPotohttd5CTjaFackLvoAXqdhmiGccS+km8E23YlKoqL3e1wGE4/3f2/Go5YOvs44ktn+/h8i0l1+3i/AUQT4nYcBKKhMEE9hFI2lhUj7iiUsgGFiAa2wlYQSxjs2xEkFnOLBQUYQUWdHEVGRNGqZUNGjLiVpLWXTJWBJEqRRBJERyJR9IxMtBN4RTHAXQHtyy8rC9jVr1+jRMVhSWefIpzgT9DHx+dAKloB4I0gSibRNLf/Xzd0NNFIOkkMXRHSDBzHJJFMohwH0NC0IJoEQAkqCaVlGnvKDBJJT9FUg2iWQ0a2TU7IRnNMLFthm4orLh+IQmFYZdi2gxGMYmRlYZwsM3uDQVeqopYCdrWSzj7F8I2Cj89JgKsi6i5SY9s2DvuHk2qau1JZ+iI2jrIJiWDbguME0ETHdhQocGywHSgv1Sgu0UhaGkrclSLr5tjkZNvomo3YJmZ5kqRoOBIg4NjogC6A466GFszIQAJVF8w5YVDKnYWcmelmUNPgO99xF8A5kbu4jiO+UfDxOUFRSmFZVkpaGtzZznEnjmhCyAgRNsKV5Kldn0ICyzJxHEFEw7G8xWsUFJdplJXpJOPi9g8JiK6I5tjUybIJODEc08EBVNLGUUIgoKEpGxENCYXQNME2LcTQ0Wrohz8hSCZh61YoK3OF63Jz3f0nshE7AfCNgo/PcUYplRqeeSCO42AYRmohGgDN0YgGoiljsH89giSOk0ApB00LoOshHMt1ACdsne07NEzTdRqLQCTskJHtEMmwAZOoskBXOIFMHNMCO4lEI2i6jnIc17Fs21i2g+g6xom6doBSsHevOyPZcdwWwje8RvLJjG8UfHyOMxWFesCrwVbMJAZSK5IB2I7rRFa4PgXbsSsZAtdxrKNrUZQIScshHoO9xQbxmIZSEDAUOXUsIlGFZijAQjk2AjhGCEcJmBZimhiRMBIKYZtJREDT9dSfHGJt4+NGIuG2DsrL3e3sbDjtNLe7yKdWnKBP1sfn5ONwpbMt2yJmxohbcd6b9R7dz+1O+/btade+Hfc/eD9J5Q4njdtx4nac4vJi+vXrx/ndzufNN94kmSzBceKI6GgSRlkRlB3m0kv788l/F7Jjl87WbUESMQ0NxYcfTGLc2BHUrQPhkEbIiRFSNu9Oe5cLel7EOd170LdPX5Z98QUYBramkYyVoxwHTTfQdINL+19GSUlpKl/Tp09HRFi1alVqX4V0djrDhw9PyVGbpslDDz1E69at6dChA+eeey6zZn19raqnRo+mVevWnNW3L//473+haVNXxC7NINQknb13716uueYaOnXqxLnnnsuyZcsASCaT9OrVK+XP+TbgGwUfn6PE4Uhnz5s3j3EvjMM0TZZ9sYwH73+Q/5vyf/xv+f/4YukXtGnVhoxARuovakRZsWQFlmmx4LMFXH/99xBx0PQwmhZE2RqgUxbTSZrC7h0Bykt0NCA70+H005JEMxSiC7om6AIGCjEiNG/egg//+U8WL1jAwz//OXfeey9iGOiGQTAcIRiJohsGM2fOpHPnzmSnCcPl5+dz4YUXMnXq1Frfp0cffZStW7eybNkyli1bxt/+9jdKSkq+1r1fsWIFU6dNY/ncubyfn8+dTz6JXU2XUU3S2U8++SR5eXksXbqUKVOm8NOf/hSAYDDIJZdcctD1K041jqlREJEBIrJaRNaKSJW56SLSTETmiMhiEVkqIgOPZXp8fL4pDiadHQgF+O3Y3zJ2zFiCepAXnn+Bhx9+mI7tOxLQA0RCEUbcOQLHcbBtG9M02bx5M7fccgtLly7l3HO7s6pgJR98+AnduvWkU+dzuHn4Haxa67Btu45tu07kSNBh9ocTufiSs7lyUH8+m/8pFcOPlG1iJk1sM0nPrl3IDgaxyss5p9s5FBYWYoRC6IaBpu9fu+CNN95g0KBBqe3S0lLmzZvHK6+8UmujUF5ezssvv8wLL7xAyPNJNG7cmOuvv/7wb7LjuH6DWGy/dHaLFrTo0eOwpbNXrFjBJZdcAuyXENm+fTvgSmO88cYbh5++k5Rj1tEmIjowHrgU2AwsEJG/KqVWpAV7BHhbKTVBRNoBM4HmxypNPt8e5n4196jH2adpn1qFq0k623Zs4lYc27Zp+Z2WlJeVk0wkWb58OQ888IC3bOX+vwpEhEaNGjFuwnieH/sck9+YRulexY9/NIiXX/4nzZu34RejbmHa23/k9h/fQzCoOP10B6Vv5pkxv+HTTz8lJyuT/pcNoFOHs7HK9rmFougERUPhoEWjaLrO66+8wncHDqxWtXTevHn86U9/Sm3/5S9/YcCAAbRp04Z69erx+eef07Vr14Pem7Vr19KsWbNKrY2aOKgg3j33wJYt7gij0lIKN2+mR8+eqWGmhyud3blzZ959910uvPBCPvvsMzZt2sTmzZtp3LgxHTp0YMGCBYdM76nCsfS+nAusVUqtBxCRqcAgIN0oKKDi7cgBvj36tD7HlNoW4EeTmqSzbdsmaSddETqlCBkhAkHXqVzhRDZNk6S31KOIpApl20liOjYJpYjHLcyExt4tUVavWcoZp7egRYs2RCKK4cNv5v+mTKDJGSMwDDAMxYIFC7jool40ql8HyksZfMV3KVi3Hk0PouuGOwoJ0KNRJBBgzpw5TJo0iU8++aTa/O3Zs4esrKzUdn5+fkqq+4YbbiA/P5+uXbseNZnwaqWzbdttHWzc6G6HQnDaaVQn1nM40tkPPfQQP/3pT8nLy6Njx4506dIFw/NF6LpOMBikpKSkUv5PVY6lUTgD+CptezNw3gFhHgc+EJG7gQygX3URicjtwO0AzZo1O+oJ9fE5GlT4FIqKirjiiisYN24cP7rjR7Rs05L/zvsvgTsChMNhdF1PSWeHQiHatm3LokWL6NSpE46ysBQoO4mNYCpABSjbo1OyO+KuYKYLmRk2wZCiZUsLTYM1a9T+uVhKIY6NZsfRlIUqKcZBQwJh9HCUcJ2qkg1Lly7lhz/8IbNmzaJ+/frV5s8wDBzHQdM0du/ezUcffcSyZcsQEWzbRkT43e9+R/369dm7d2+lc/fs2UODBg1o1aoVX375Za0K2CotBccB0+SGAQN46Pbb3VXQ6tcHTfva0tnZ2dlMnjzZu32KFi1a0KJFi9R5FcbkW0HFlPmj/QdcB0xM2x4KvHBAmPuA+73fPXFbEdrB4j3nnHOUj8+BrFix4ngnQWVkZCjTNlVJrET9a96/VJMmTdTOfTtV0b4i1aJFC/XBBx8opZQqKytTAwcOVL///e9VPB5XCxcuVGeeeab6fMl8tbdkj9pZtFeNfnK02r4rpjZsSKrlyyz1v6W2mjRptrr44oGqvDyhiouLVdOmTdXy5ctVIpFQQ4cOVWPGjFGJeFz1uvAC9fHsD9SapV+opk2aqMJ161RpcbE6v2dPdeedd1ZJ96ZNm1TLli3VvHnzDpq/8847TxUUFCillPrjH/+obr/99krHe/XqpT7++GMVj8dV8+bNU89k48aNqlmzZqqoqEgppdSDDz6ohg8frhKJhFJKqS1btqjXXnvt4DfXspRatUqp5cuVWr9eqVis0uFly5apTp06qXg8rtavX69atGihLMuqEs3IkSPVK6+8opRSas6cOapbt25KKaX27t2bSs9LL72khg4dmjpn165dqm3btgdP3wlGdd8DsFDVouw+lo7mzUDTtO0mVO0eug14G0Ap9V8gDJz6i6D6nLKUJ8uxbZtzu59L586dmTl9JjlZOcyYMYMnnngiJZ3dtWtXRvzkTrAVbc86iyeeeoLhw2/n3K49Ob/7+axfu5ed24KUlek4SohGFQ0bOoRCCl13pbNfeuklbrrpJrp27YpScMv3byS5r8idYGbB6U2a8cjDD9P7sssYMHBgjf39v/71r9m9ezd33nkneXl5dOtWvZDm5Zdfzty5cwG36+iaa66pdPzaa6/lzTffJBQK8frrr/ODH/yAvLw8vve97zFx4kRycnKAw5DOVu7EO8CdgHbaadC4MTRvXkXR9Iiks0eNYuLEiYCrKtq+fXvatm3LrFmzGDduXCruOXPmMHDgt2cMzDGTzhYRA1gDXAIUAguAm5RSy9PCzALeUkq9KiJnA7OBM9RBEuVLZ/tUx/GQzq5wHIu4MhJmwkTTNMJGGMMwUn6BCsdxhaS1iGA5No6VQIkiiYadDFCyN0Qivr+epuuQGbXJznYIhcGpdHEbTJOE5aB5k9oMlUDpQTB09IArUKdpOrphILperfP4cNi6dSvDhg3jn//859eKp1aYpitvHY26XUTHkcGDB/PUU09x1llnHdd0HA4npHS2UsoSkbuAfwA6MEkptVxEfo3bjPkrcD/wsoj8DNfpPPxgBsHH50Sg3Cz3tITcZSpDuju0UnQhHHa1iJS3rkGFdhG4wnVKhBIrDraDIQaJZIiSIp1EzHUI6DpkZDhEwzaRiDuvWTMEC0hYDroIEo+hbAulHJSmYQTdWcbYDloow5topkOaw/pokJuby49+9CP27dtXq9FDR4RS7joH27e7hi8Wc3WLjmI+DodkMsnVV199UhmEr8sxnfutlJqJO8w0fd8v036vAC44lmnw8Tna2MomYkQQBE00t6WgFJpoaKJVNgYi2KKRUAochVLgJBVWSZTd5QYVE2U1DerWdcjJduUqUKAZGmWmjW4pUA6GbaFZCUgm0EMBNF1DDwb3F/zBKFowckzVP49oPkFtSRewA1fZNDf3uBkEcCevDRs27Lhd/3jgC4L4+BwGCSsBgC56pSGPFfMKTNN0J5whmAhKgaAwbIVZpijeJ8TiGe6iBbiCndnZDplRCwFsyzUkooGVSCCxOLqGO6IIcYebZmWgR7PdwvJE1SA6HJRyl8TcuXO/gN1pp7m6Rb689TeObxR8fGqJoxwsZRHWwymDoDwxO9M0sRWYlo1ogq0gJII4sG+3YkeJgV2xtKUIGZkO2VmKcMhBOQpbKRCHhGmiiSBKQTKBoWvo4SC6rmFgu62CQAT0U+zTLSlxDUJOjutM9gXsjhv+nffxqQGlFJayMG0TRzn7lUs1Hcdx3DWQk0nXMChFUjQ0TaEphWMKu/dCSUkA5QgIBAKKjAyLcFShawrlOMQSgOY61HRcmexQ0EDFY2BoGJEgugaa7s5ARg+dGq0Dx3H/Kha7yc11u4++BZPDTnR8o+Djk4ZSCsuxMB0TW7mjenTRCRkhNLTUGgZJM5nyGxiGgetydiBhsbcoRGmp4Y6mVBCNONSpaxOJ2JTFEgQ0DV1A6QpN87SFRBDLQSwLzDi6stDDIfSAAXoA9JNkucvaEIu5EhWBgKtkKuLOTD5R12f4lnEKVDl8fL4+pmNSmiyl1CwlbscxNIOoEXUVSgNRAloAXXP9CHHTpCyZxBIN0whQ6iQpN+M0yMikx3kX0a/fOYwYMQgnuZMmp5XTuGE56woWc9mA73Leed05p9s5PDN2LMFQmEAoRCAUYvaHH3JRnz506dmTvJ7nM2r0k2jRLAhmpAxCIpGgX79+5OXlHVS1s0+fPlQ3bPvVV1/lrrvuqrJ/xowZdOrUKTVHoSaZi1gsRu/evbHdfjDAlaIIh8MUFxcf9Dp9+vRh4WefwfbtlC5fzo9HjaLlhRfSvkMHevXqxfz58w/+gA6BUop77rmHVq1a0alTJz7//PNqwyWTSW6//XbatGlD27Zteeedd1LH3n77bdq1a0f79u256aabANi5cycDBgz4Wmk72fBbCj4+uIWKrukEtaCrPZTWReM4TkqXyHYcyiwbQwfRbDQnSXyfTtHeCKFQhD9PW0RW1OShUbfx7rsTGPWLUZTGY1x/0038/rnn6X/ppViJGEOGDOGPf/oTd9xxB8uWLePe+37GX99+i/Yd2mJbFi9NmYpolT/PxYsXY5omS5YsOap5v+SSS7jqqqsQEZYuXcr1119faX2ECiZNmsTgwYNTk8LAncTWvXt3pk+fzvDhw2u+iG1DYSFkZvLDX/6SFq1aUbB+PZphsH79elauXPm18jBr1iwKCgooKChg/vz5/OQnP6nW0DzxxBM0atSINWvW4DgOe/bsAaCgoICnnnqKefPmUbduXXbs2AFAw4YNyc3NZd68eVxwwbdjoKTfUvD5VmM7NgkrQcJOoOEuRp9uECzLIpFIuM5kpUgiBAI6AeVQsktny8You3eGsS1Xe+j0hkka1FP0vKAHW3ZsI+Yo3sifSo8ePflu/0sxRBEOh3n22WcZ88wzaMrm2TG/Y9RP76Fd6zMRzcCI1uHOO++slM4dO3Zw8803s2TJEvLy8li3bh2zZ8+mS5cudOzYkVtvvZVEIlElf5MnT6ZNmzb07t2befPmVXsPMjMzU47zsrKyGoXrDpTOXrduHaWlpYwePZr8/Pzqb7BS7iS0RAJMk3XbtjF/xQpGP/ssmudMPvPMM7n88strfki1YMaMGQwbNgwRoUePHhQVFbF169Yq4SZNmsSoUaMAd95IgwaugMLLL7/MiBEjqFvX1YWqkNQGXzrbx+eUoOSjqpLL6SgcElbCXbteBF10lGaQpGLpS4ekbZOwLETcEUPhPn0IiFCyV1G0NwOl3LChoEN2prukZSBDMJXDnH/NZeiw4ejisG7Vcrp1bodhlaFp7ryGs888g9KyUmKl+1ixYiX3//RnSFa9GsfkN2rUiIkTJzJmzBj+/ve/E4/H6dOnD7Nnz6ZNmzYMGzaMCRMmpFRLwZ2B/Nhjj7Fo0SJycnLo27cvXbp0qTb+6dOnM2rUKHbs2MF7771X5XgymWT9+vU0b948tS8/P58bb7yRiy66iNWrV7Njx45KhSng+gsq8lS3Lsv37SOvS5dKrY2aGDJkCKtXr66y/7777qsyd6CwsJCmTfer6lRIZ+fm5qb2VayE9+ijjzJ37lxatmzJiy++SOPGjVmzZg0AF1xwAbZt8/jjj6e6jbp168YjjzxyyPSeKvhGweeUJOvivgc9bjs2mhUjGoiiiUbCW5Desixs5ZC0HTQRMlEElQMIpcUJtu8NYVua50C2yM5yMAwHpQmxeIwLL+zBpk2b6NKlC9/t1xvdLkcpBz0QxojmIAjKsVG2DQiO5QpYSyiEHMYkrdWrV9OiRQvatGkDwC233ML48eMrGYX58+fTp0+flK7QkCFDUoXfgVxzzTVcc801fPzxxzz66KN8+OGHlY7v2rWLOnXqVNo3depUpk+fjqZpDB48mGnTpjFixAjEcSB9+coGDSAcRurVc53MteRwVjurTgjhwBaPZVls3ryZCy64gLFjxzJ27FgeeOABXnvtNSzLoqCggLlz57J582Yuuugili1bRp06dWjUqFFKO+nbQK3eQhEJikirY50YH59vCltB0oGYaVOaTFKSSGJarnSEphQZuka2oRFCKIuFKNweZueuKI4VIBzUaHq6zem5ikgUgmGdzEyDSCTCgs/mU7ByGWYixh//MB49nEXHzl1Y9PlinLIYdlk5TnmMdWsKyMzIIDsnm/YdOvD54sWHlf7aqsEc7hoGvXr1Yt26dezatavS/kgkQjweT20vXbqUgoICLr30Upo3b87UqVPdLqTiYuonk+wtLIQKh7SmsaeoiAYNGtC+fXu++OKLSosI1cSQIUPIy8ur8jdlypQqYWsjnV2/fn2i0WhKyO+6665LOaSbNGnCoEGDCAQCtGjRgrPOOouCggIA4vE4kUikFnfv1OCQRkFELgf+B/zT284TkenHOmE+PscKy7EoTpZjWTbKsXFsmwCKIIoQioDjoCvYu1dj0+YwO3YGScQ1dE1oUN+hyRkWgaArWWGIRZAyJFkCKEiUUDc7g3HPjeXZF/+IIzo333wz8+bNY/a/P0bLiJLQdX42ahQ/HzkSLRLhwZEjefLJJ1O1eMdxGDt27EHzULFk5Nq1awF47bXX6N27d6Uw5513HnPnzmX37t2Ypsm0adOqjWvt2rUpI/P555+TTCarrKlQt25dbNtOGYb8/Hwef/xxNm7cyMaNG9myaROFX37JpgUL6N6uHfMWL2abV7teuHAhiUSCpk2b0rJlS7p168Zjjz2WumZBQQEzZsyokq633nqLJUuWVPmrTnbiqquuYsqUKSil3JXmcnIqdR2BayCvvPLKlNLr7NmzadeuHeD6DSrWbti1axdr1qzhzDPPBGDNmjV06NCh2nt3KlKb7qNf4y6OMwdAKbXEbzX4nIyYnrO4JBFHOYoIOpploQmuLpHjoERjX1mYomID23InlQUNh+zMONk5gDJA2RhioYkFSuFoIUQPAkIgsz4iQpe8bnTq0IE3J0/m5htv5N38fO4dOZK7f/YzbNtm6NChqWGbnTp14rnnnuPGG2+kvLwcETmk4zUcDjN58mSuu+46LMuie/fu3HHHHZXC5Obm8vjjj9OzZ09yc3Pp2rVrpeGkFbzzzjtMmTKFQCBAJBLhrbfeqraF0b9/fz755BP69evH1KlTmTVrlutILiqC7du5pm9fps6axchHHmHciy8ycNAgHMchMzOT/Pz8lEbTxIkTuf/++2nVqhXRaJT69evzzDPPHMkjTTFw4EBmzpyZirNiwRyAvLy81Iitp59+mqFDh3LvvffSsGHDVLjLLruMDz74gHbt2qHrOs8880zKMM6ZM+drO8JPJg4pnS0inyqleojIYqVUF2/fUqVUp28khQfgS2f7VMfBpLMdpSi3PQkJ5eA4JmHdANNGBMSbQGbZOtt3BEjEBUcpggGHullxMqJxJKAjEkbTdXSrFEcCOKKhGQEMI1BJjdQpL0dZFhIMIoaRcrQejs/gRGTx4sWMHTuW1157bf/OLVtcowDubOTTTnMnpZ1C9OrVixkzZqRGJp0MHGvp7JUicj2giUgL4KfAp0eUUh+fb4Ck47haQt5IoqTXfx0R0AM6CdNCFCgUesB18BYXO+zcaWDbDrrh0KBunOwMG00UWiCKSACUg7Ld4anoBoZlIXYSlUhSqf6tFFpGBlKLETYnE126dKFv377Ytr1/9FBODpSWunpFp6CA3c6dO7nvvvtOKoPwdamNUbgL+CXuGh/v4q6PMOpYJsrH53BRgK0UMds1CAFNMLzyKaJriG2TcCySpoWmNLBtRDQSSZ2dOzXKY+6IokjU4YzTFLrjSS4YAiI4joNYMXRNQ4tkgqOBoSPB6uUnDtfBe7Jw6003uS2DCp9DRga0anVc5a2PJQ0bNuTqq68+3sn4RqmNUbhMKTUSGFmxQ0QG4xoIH5/jglKKcq8FELMVCceh3HZQQKaho6cVyqZjUmaWoVAYyiBkhIgl4uwtDlJWprkL2WgODesrsrNBswHNQVQMZbrdqwFNQ9M1JJSBQkOZcdD1U7bwr4LjwO7dsGuX60eIRNxV0eCUNQjfVmpjFB6hqgF4uJp9Pj7HFOX5BhSwpKScctshqmuYjqI+QpZRtbvGUQ5xK46OjqY0DF0jVpZky7YQjqMjAnXqWEQyTSK6gVjgep6TOOho4Qx0AMe9rlMWcwtFEbRvi4BbhYBdxYzpunV98bpTmBqNgohcBgwAzhCR9PFx2RywXKyPz7FGKcX84jL2WTZRXcNSip51MlOGYOWO6mvsMTPmvq2WhZIkJeUBduzIxFEa0ajQsJGFqRwMdDAVaIKjK7SkjaGHEMdBmabrH9D+n70zj4+quhv3c+7s2fcQQgIhYZEtEQRFBXdqQayIVVqKtdbaKla7aKWvr339tVattdpSl9ZCteor4a2K1oraFlfcN8CwhS2B7PtMZrlz5957fn/cZEhICEEIEjLP5zOfZGbO3Dk3y/3OOed7nq9iLR47HIN+0bhfmCY0NFgFcACcTktxHR//5fYrxoDS10ihASgDVGBzl8fbgWUD2akYMQ7EBHy6wekpCST0MiLojaAWJBwJ4zBMTFMjFIqnuSUB6Chyk6KhaRKbAnaENRJQNOwGKIaBsFlTR8LhGDqBoCudAUEISEuDzMzYVNEQ4KC/YSnlZ1LKlcA4KeXKLrf/k1I2Hex1MWIcLVTD5BNvgE99ATb6gpZ24hABQUqJYRgEAgGC/gCoYUwjTCCQTHNLIiBIiNdJTNRwCIh3KsQ5Few2UOwSuxpE0SJgcyI8cSguF0o/FRQ2m42SkhImTZrE/Pnzo64dgM2bN3PuuecyduxYxowZw69+9atuu5JffvllTjnlFE466STGjx/PzTff3OP4A6XO7uSjjz7CZrPxzDPPWA9kZFijglGjIDubUDh8ZOrsjj75/X6+//3vU1hYyMSJE48LdXZlZSXnnXceU6ZM4eyzz6aqqgoYmurs/oT9XCFEqRBikxCivPM24D2LMaTw6wZtEZ22iE5tWOMjb4C3WttpjuiMcDkZ4XYyLSnuoK+XUkaNpmrIj6b5UISK020jqKbT2mZpCtLSDBJTDeLibLhtwlo+iAQh7MOuelGEQElJw5aUEt2/0F88Hg8bNmygrKyMtLQ0HnroIcCqQ3DxxRezbNkyysvL2bhxI++++y4PP/wwAGVlZdxwww089dRTbN26lbKysuhu2q50VWdfccUVh9W3Q2EYBrfefDNfmT3bWjMBqyrayJHWojKHVmf3l2uuuYa0tDR27NjB5s2befzxx3toNQ6XrursRx99lOuuu67Xdl3V2Vu2bInuAr/55pu58sor2bRpE7/4xS+iJtWu6uyhQn+CwuPAY1hJ318F/g8oHcA+xRgibGoP8ok3wCfeAA9iYD8AACAASURBVO+2+dkWUNkWUNkdDGMXgqlJ8ZyfnkSWy0GWy0GqY/9sp2maqKpKff1Gtpe/QiQSQlW9RLR29EgAgUDYEmlpTqOlxYkpITktgtupYTMlNlOCABMDwiEUhxslMQ0lNfOoZBTNnDmT6upqAJ5++mnOOOMM5syZA0BcXBwPPvgg99xzDwD33nsvt912G+PHjwfAbrcfU3U2us4ff/UrFs6aRVZyMgQCvTb7QursA9i1axcffPABd955Z3TD3/Ggzt6yZQvnnXceAOecc0437UZMnd2TOCnlq0KI+6SUu4D/FkK8PdAdi3HiUxeOMDXJWrQsjHOR4uj7z1FKSSgUoq2tjZaGekLtPgKhTdhsGcQnp6FrJggwFDu7PwsTCgiktIrjJCYZBPwmAROEQ0Fg6bMxDGxSR4lzgIj0+f4FUzL6dV6GYbBu3Tq++93vAtbU0bRp07q1KSwsxO/34/P5KCsr46c//WmfxxwQdbaU4PNRvXEja156idcef5yP7ryz14XkL6zOPoDNmzdTUlJy3Kmzi4uLefbZZ7nppptYs2YN7e3tNDc3k56eHlNn90JYWB+ddgkhfgBUA33/5mPEOAS7gpZYLcN56D9B0zTZt2M7Lc37iOhNKIqKvz1A2NARDo2IMIiz2TCcdoJ+BwGvi7hMG3EZ4HEbJCdFcNmsTFPFpYANiESsjWhSIoigxCeDOLJF1FAoRElJCRUVFUybNo0LLrgAsILZwUYfX3RUcsTq7EgEamvB7+dHv/41v/mv/8I2ZoyVatpLnw5LnX2UzvVYqrPvu+8+brjhBh5//HFmz55Nbm4u9o4iQENNnd2foPBjIAG4Efg1kAxcPZCdinHi0xIxmJBwaB2xqqrU7NtL9Y6tJGQ143EnIs1MFC2dnPR0muyt+No8hFUnDfuSME3rQuCwSVJSdFweEwWB3aGg2CwvkQBsmChOByhHb/dx55qC1+vloosu4qGHHuLGG29k4sSJvPXWW93a7t69m4SEBBITE5k4cSKffPIJxcXF/X6vI1Zn+/3WzWbj423bWPTjH8OPf0xTUxNr167Fbrd328nblzobrJHE6NGjWbp0Kenp6bS2tnZ7u5aWFjIyMkhJSYmqs5VDLN4fzkjhi6qzV65cCcDw4cN57rnnOn40fp599lmSk5OBmDq7B1LKD6SU7VLKvVLKJVLKi4HKY9C3GCc4nj4uCpFIhMbGRvbt20dT1eckZYeIT0knLXsWmi2HiDuB8hZ4+o9F3P2DmYSCNkxD4HZKstMiZGWo2JwGCpafTehhTFXFpig4k5KwexwoLieK04VwxB3xKKErycnJLF++nPvuu49IJMLixYtZv359tHBNKBTixhtv5Gc/+xkAt9xyy7FXZ6ekWCmmo0ezp7IyqsC+7LLLePjhh3uoHQ6pzq6pobq6msrKSqZPn84777xDXV0dMDjU2U1NTdEaD3fffTdXX73/c29Mnd0FIcR0IBdYL6VsEkJMxNJdnAuMOAb9i3EC0BrRqQtHaI7o2DtrARumlflzAOFwGK/Xi9frRVEU3G6QohaHpwjTzGHrvi3U76tl44bx/OuFcegRBzaHDZdLMny4jtslMQyJhkK8y4YiwAyFkFLicLtQpI4ww4AEm/OoBoOunHzyyRQXF1NaWsqSJUt44YUX+OEPf8jSpUu/HHV2RgZTi4r2p5MKYQWFw6BXdXYXFixYQGlpKbfeeit/+MMfmDt37qBRZ7/xxhv8/Oc/RwjB7Nmzo5ljEFNn739CiLuBhcBGoABYg2VI/Q3wiJQyeKw62ZWYOntwEDRMNrYHkRL8hkG6w06qw066w1pgFEKQaFOi0xuqqtLS0kIgEMCUTQgaEMKOr66epqpGwq4ChM1BfZWTf744jT1VqUhg+rQgX7+0neEjvRQVjkEqoJsSU0C804ap6whVxRHvRpERQIDdbeXS2XqX2Z1QqKqlqOic+snO3i+zO0x6VWcPAWLq7P18DSiWUoaEEGlATcf9npN8MWIcQNg0kRImJXqwCYg/SLaJpmk0NDQQCoWw2+243ZJ23x4wM2nc00JbQxjVkUJNIIPP3prAxx8mIoSNlBSDKy9v4pSSEMnD3AQjIOwCzbB2KDsEmOEwih7BJnQUqVtBwDFE5oZN05LXNTdbWUYOh6WoSEj4wofsVZ19ghNTZ3dHlVKGAKSULUKIbbGAEKO/bPGHyHI6SOpjB7Lf76eurg6bzYbdESKkBgjU76BxZzPtfgPdH8JrjuTFdVOprEyxPtwrkvPPD/HtK9tJTJC4ExMQQhBqBi2iIyIR7NJAsSnYbDYUm0S448E5hHw9qgrV1fsFdp2KiqNwIe861z4UiKmzuzNaCNFpQhXAqC73kVJeeqiDCyEuBP6AlQS4Qkp5Ty9tLgfuwFLib5RSfrP/3Y9xvKEaJp/7QwQMk8K43k2apmnS0tJCS0sLHo8HI+Jnx8638IZtoEdo15zEZToo313MS6+NJxKBtAzJvHkh5s9XSUsz6fqnKw2DSFhDhoK4XA6cTgeK3YYwdavdUBkddCIEaJolsBs+fL/iOkaMftBXUFh4wP0HD+fAQggb8BBwAVAFfCSE+IeUckuXNmOwCvacIaVsFULE9j8McmrDEcKmyfTkeJQD0iGllLS0tNDU1IRhGCg4aNhXQSSyC1P3MCJpHJ6QjXf3qax9ppjyvfFgg/POC/ODH/hJSOi5/mX4/fg/+ADGjiM+KR67DYQZAcUONof1dYAWk48rQiFwu62A4HJBfr6lp4gJ7GIcJgcNClLKdUd47BnATinlbgAhRCnWOsWWLm2+BzwkpWzteM+GI3zPGF8ijVoEr26Q5eyupOgU1NXW1lJbU4vD4SLo1WgKtmB31mO3mYj2DLZ8oPHKa6PYW5eB0+0mJd3kxhvbmTlT6/FeUtehejPeTWWEImGcjvHYhIGQgCMebP3ZgnMCYBhQX29VQ8vNtcpjQkxvHeMLM5D/ObnAvi73q4BTD2gzFkAI8Q7WFNMdUspXDjyQEOJa4FqA/Pz8AelsjCNngy/IMJeD9I6AoGka9bWNtDa2Ew7qCCQRn53EpDRCoWoy492kujOpqTRY9fwMPtuait3hIC3TxoIFQebPD5GY2HN0YDY10bxpC2pVGXrmaFJOm4ZwOFDcicf6lL9cfD6oqwNdt0YIhnHo18SIcQgGcmzZ21bKA//D7cAY4GzgG8AKIURKjxdJ+aiU8hQp5SmZh5lbHePYsNUfAmBigod0p51gMMTuHZXU7GjFVBUSEuJwJ9lJSHaQniDA7sXjqOL5lxL47/vO4fOd2SQmO7jmeypPPNHCN78Z7BYQpGki/X5Mr5fA1u3UpOaQPHMqky69hInFxcdFWcxjps4uLmb1ww9DVZUVEOLiYPRoa0GZw1dnv/HGGyQnJ1NSUkJJSQm//OUvez0/KSXnnnsuPp8v+tiaNWsQQrBt27Zux7vooou6vfaqq66KKrkjkQjLli1jzJgxTJo0iRkzZvTY8/BFuPvuuykqKmLcuHG8+uqrBz2H2267jbFjx3LSSSexfPlyAH77299Gz3/SpEnYbDZaWlrQNI3Zs2ej6/oR92+w0O+RghDCJaXsqWE8OFVAXpf7I7DSWg9s876UMgLsEUJsxwoSHx3G+8Q4DggZJlMS41CEwO/3s6NsL4aqEJfoIW2Em5AWoraihjgv7N67h7KmFl765/nUNYxAsds556ww114b6FhE7o6UEmPjRsxgCM3h5POAQsakQoYrJklxji/hbHunU3MB+11Et912W1Sd/cgjjzBnzhyCwSALFy7k4YcfZunSpVF19ksvvcT48ePRdZ1HH320x/E/++wzIuEwG/7v/6xRgaJAVpZVHvMIg+KsWbP45z//2WebtWvXUlxcTFJSUvSxVatWceaZZ1JaWsodd9zRr/e6/fbbqa2tpaysDJfLRX19PW+++eaRdJ8tW7ZQWlrK5s2bqamp4fzzz6e8vLxH6uzjjz/Ovn372LZtG4qi0NBgzVjfcsst3HLLLQC8+OKLPPDAA6R1BNnzzjuP1atXs3jx4iPq42DhkCMFIcQMIcTnwI6O+8VCiD/249gfAWOEEAVCCCewCPjHAW2eB87pOG4G1nTS7sPof4wvmYBh8Hqzj6aIjk1K9m6v5/P3d6MHBKnDPMRlQF1dDRt3raMt+A5N4ff533dS+Mtjl1HflMfwXMFdd3lZtqy994Cg6xjl5ai+dvbkFLB7xEkkzziNsyeOIMl9/K4bDJg6e9MmSi65hF3NzayrqODk885j8pQpR6bO7icHqrP9fj/vvPMOK1eupLS0fzb9YDDIX/7yF/74xz/i6qjznJ2dzeWXX35EfXvhhRdYtGgRLpeLgoICioqK+PDDD3u0e+SRR/jFL34R3V3dm9W10/7aSUyd3ZPlwEVYF3CklBuFEOcc6kVSSl0IcQPwKtZ6wV+llJuFEL8EPpZS/qPjuTlCiC2AAdwipWz+gucS4xgSMAw2+kIETZMkm0Jeu0nDrjpaW1rJGJGIIw6q1I9p3LcVRQ9iD+nUfz6Tp18+g2Z/Ag67jYULVRYvDuB2H/x99L372LurCm/+OOxJwygalkxW0qGLxu/65MgqefVG4bQDl8R656iqs6WEtjay0tP3q7Offx41EuHssWOPTJ3dhffee4/i4mKGDx/Offfdx8SJE3u0eeedd/jzn/8cvf/8889z4YUXMnbsWNLS0vj000+ZOnVqnz+bnTt3kp+f3220cTB+/OMf8/rrr/d4fNGiRSxb1r0icHV1Naeddlr0fqc6+0B27drF6tWrWbNmDZmZmSxfvpwxY8ZEnw8Gg7zyyis8+OD+ZMtJkybx0UdDZ/KiP0FBkVJWHjBn268VLSnlWmDtAY/9osv3EvhJxy3GIKJR00m0K0zBibc+SHNDG9ITYsRJqQTws7n5E2RwD7Q6MfwzePnfU3hvYw6Kw0bRGJ0f/cjLmDG9z9NqukkwYqKFQnhrmgnlFlIyfRIZCS7cDltHQfmtoPnpfemq/xfwo8lRV2eHw5beOhi0Uk47sdvZvnnzkamzuzB16lQqKytJSEhg7dq1XHLJJezYsaNHu5aWFhIT9y/mr1q1Kvp+ixYtYtWqVUydOvWoqbMfeOCBfrftjzobrHUZt9vNxx9/zHPPPcfVV1/N22/vLw/z4osvcsYZZ0SnjsBaK3I6nbS3t3c7/xOV/gSFfUKIGYDs2HvwQyBWjnMIY0hJS0Qn3KDSooEaCSASVFKSExCtOlX1O3EE91G5tYC33z6NbXtyQbHh8sC3lgS49NIQik1S0RKmNaRj6/K/qwT8BIIqdkzcukayCXlF+eSmeKx/ctUHlR3TIJnjjqudykdNnS2lpadobLS+t9t76CmOWJ3dha6f2ufOncv1119PU1NTtCpZJ3a7Paq8bm5u5rXXXqOsrAwhBIZhIITg3nvv7VOdXVRUxN69e/t1gT2ckUJ/1Nmd7RYutLZgLViwgO985zvdni8tLe02ddRJZzAZCvQn++g6rE/y+UA9cFrHYzGGKHtCYXy6gcsbImi0gDtMUnI84d1tNNU0UNfuo3TV6Tz02Hy27svD5rFx7lc0HvlTGwsWBtjRHOSDSh9VbWHS4uyMSnMzMtFGXv1usvZsZrzexukpNs4YkUbJGSczesyI/Re3uk3gToHR50Da6CNeYB0IjkidraqYu3Zx/733WgEhJQUKC+GA6ZYjVmd3oa6uLhpkPvzwQ0zTJL0Xad64cePYvdta8nvmmWe48sorqezQbu/bt4+CggLWr1/PmDFjqKmpYevWrQBUVlayceNGSkpKiIuL47vf/S433ngjmmbtP6mtreWpp57q8X4PPPBAr+rsAwMCWOrs0tJSwuEwe/bsYceOHcyYMaNHu0suuYTXXnsNgDfffDM60gLwer28+eab3dZNAJqbm8nMzMThOH6SGgaS/owUdCnlogHvSYzjFlNKqlSNsCmp1yL4whFyIyECTc148twoNhv+Ji9VDRXs8vp48n+n0VyXSmKKm8suU5k3L4QnUScUMdnTEsGn6hRleEhy23HZrc8lavVe9ICP4efMJiU/v+cnXNOAYAuYOgyfAo7j+1PbF1JnL1pE0OtFAPPOOcfalXwQgd1hq7Nzcpg6dep+dXYXnnnmGR555BHsdjsej4fS0tJeRxjz5s3jjTfeoKioiFWrVvW4OC9cuJCnn36aWbNm8dRTT/Gd73wHVVVxOBysWLEiWrTmzjvv5L//+7+ZMGECbreb+Pj4g6bB9peJEydy+eWXM2HCBOx2Ow899FA082ju3LmsWLGC4cOHs2zZMhYvXswDDzxAQkICK1asiB5jzZo1zJkzh/gDNv69/vrrzJ0794j6N5g4qDo72kCIXcB2YDXwnJSy/Vh07GDE1NnHjr2hMDXhCD7dupAMVxTMFh9acyNawMDb5CMp3U4cTrSgyvryFp56eiqqmsSwrAC//KWdwkIro2hzXYCIIXHYBDlJTtI6UkmllAQrK7FVV5N98snEFxV174S3GlorQAuANCAhC7Ing7279ro3VfCgpKbGSjU9SgK7o0ltbS1XXnkl//73v7/srhxTLr30Uu6++27GjRv3ZXel3wyUOhsAKWWhEOJ0rJTS/yeE2ACUSin7l4MWY9BhSImUsDMYJs/tZEKCB1tY5aP1Gwm2+HHq4HZ6SLPbyYhLRxMa721S+cvKEnTdzfjJAb59XSU+VwafVVnHVHWTk7LjSfFYf3JSSsLhMLquE9fQQPrkybi7FIWPEm4HTypkT7TEdvZDZx4NGgzDWjdIStovrcvJOS6nxMAaeXzve9/D5/P1K3voREDTNC655JJBFRCOlH4leksp3wXeFULcAfwe+F8gFhROQAK6wTttfsxmFZtuMtxloAabqKirxWwOkp+UTuKwJIwUgVf3sjG8jc/fdfP0nydh4uKMcxSW3SKx2brrSAQQ57SmijqDQXJyMskOB1pSMp6OHH2gY3Swx/peVyGtEDw9NroPbvx+K7MoErGyiwoKrGBwnAaETo50P8Fgw+l09lr+80TmkEFBCJGAJbJbBJwEvACcPsD9ivElUKNqlPlDJCMoFk5sIzw0tTXTbLSgxSnY4uPRR9rZYH6O9Nahh128/8oE/vX8WBQTzrtI54pv+0iO611VbbS2ovr9OJ0ucjMzUJqb0WrrsKV0ueCH26FxKySNgKSO7BHnFy8Mc9xhGJavyOu17rvdlt76OA8GMYYO/RkplAEvAvdKKd8+VOMYg4+wafKRN0DQMMl12ClqMzDtUOdtpNXbSkgLIbGRmJnIPrmLRFQ2vFPMmr9Pwuu1Awqzz9nG7IvtpMd3n1bQN28GTcMwTTSvl8T8fFLi4hBtXqSUuMeNxd6ZOhhqA3+9FQQyxp542ucDBXaZmVZpzFhAiHEc0Z+gMFpK2dM/EGPQo5kmpoR32/wIYGZKAnEBHTUUosURoLXNSzgcxuPx4E5IxB8MUbPbx4srZlFRmYWqC/IKNC6+opV0ewWn5J1MnGd/2p4MBDC8XiJFRdgcDnKyskg6mNDQWwV1n4M7GZJHnHgBwTCs6SLDsNYPcnKsugcxYhxnHDQoCCF+J6X8KfCsEKJHilJ/Kq/FOP6QUqKakp1BldpwBKUljNB0Zrjc2Ft9BMMazSEvRqoNRVFwuVyEApKG9kb+8R8H/375AhwiicQ0yTcvb+ayuWBTbDRsj8PlULq9T6B8B0pCAlmjRpGUlBT1zXQj1GplFrXXQ2oBZI3v2Waw0pnZJ4SVSTRsmBUUjoLALkaMgaKvj2OrO74+iFVB7cBbjEGGbkreafPzdms71T6V8U0RzjBsnF2QiSc/iVCypE7xQpoDr9dLe3s7LVobG3bXcN/Dw3n1pREo0k3Jme3c8D/7mD/HJMFlw6aH6RIP0HWdQCBAYmIC+VOnkpKS0jMgmAZUfwp734e2fVaVtITBXXivmzp73jzaPv/c2plMhzp7wQLGnnYaY8aOPTJ1dkkJq1ev7vF8J4erzgZLd11SUsLEiRN7bILr5ERWZ7e2trJgwQKmTJnCjBkzKCsrA4ips7sipexUDJ4kpexWirNDdHekldliHCPadYOKUBhdQkQ3OaXdxBOW2BJcOIbFE5E6TU2N+P1+pCJpaWtFVVW0JI1PPjRZ9afTaPU6SEtq5htfr6Y4aRcjvUkYjSEaQkFCwQBOpwsjoKJGIgghyEhLwyEEiv0gf2KtFRBqgZwSSMo5pj+PgcLj8bDhs8+gtZVvX301D61cyW033EDI4zl66uxIJKrnPlq0tbVx/fXX88orr5Cfnx/VSR/IiazOvuuuuygpKWHNmjVs27aNpUuXsm7dOpxOZ0yd3QtX9/LYd492R2IcPcKmSUA3orcGLYJqSjKlYFyTRrzdhmt0CsowD83eFiorK1FVlV3+XXxU+RE7Ajuotlfzr9edPPa7U2htFIzN2c3/XPk4M4eVk5Ci0BbvxJsYj62oiIzTz2TY+XOIjBlD3CmnMPKrXyXl9NOJnzkTexexmDU6+AS2vwJN5ZAx7oQJCFEqK6GujpnFxVS3tkJBAU+Xlh49dfaGDZSUlLBr1y7WrVvHySefzOTJk49Inf30009z6aWXRqsa9qaThhNbnb1lyxbOO+88YL9CpL6+Hoips6MIIa7ASkMtEEI81+WpRKCt91fFOB74yBtASmvaWoYNIo0hRthspNvs2BLdOHIT8Pv9NDY2IqVEURSamppoaWthTNYY0pKyWfFoMi/904Xmb2fmlI384DsfkZ51Kgl5+3dJCkWg2OzWruRgkMS0NLKysnpfO4CO0UErDC+BuHRrymiACG05+gZ2z4SePiBgv8DONCEYxBCCdZs28d3vfx/s9iNTZ3eQlZW1X539z3+iqipnn332UVFnl5eXE4lEOPvss2lvb+emm27qNTf/RFZnFxcX89xzz3HmmWfy4YcfUllZSVVVFdnZ2TF1dhc+BJqxKqZ1XUNoBz4byE7F+OLUhjVUU3JGSgJuIQjvakNJScCRY/lcdNOgvr4en8+Hx+PBZrMRCATwGl7cmW4UOYzbbk1l82aIGG187cI3OGeuyYiSr+F0pPV4P03TiEQiJCcnk5mZ2beVs3knZE2AxGEDdfpRDnoBHyj8fkLhMCVf/zoV1dVHR53dB9u3bz9q6mxd1/nkk09Yt24doVCImTNnctppp3WTxcGJrc5etmwZN910EyUlJUyePJmTTz4Ze8fUZ0yd3YGUcg+wB/jPsetOjCOhRtUoD6rkuZ14bArSlEhD4shNAEG30UF8fLy1QOjdRouvBVUNU/NWIfesjsfr1YhPaOPb39zE2SPcDB85GuWAgBBVVMTFkZ2djdvt7v2f3lcDehjaKkGakJR7jH4aA4xpWje73RqS5eRYawqbNx+5OrsfHE119ogRI8jIyCA+Pp74+Hhmz57Nxo0bewSFE1mdnZSUxGOPPQZYP9uCggIKCgqir4upswEhxJsdX1uFEC1dbq1CiJZj18UYfWFKSVtE5xNvgDJ/iDSHnUKPNVcbqfEjFIEW0aiurqa2tjZqwhRCoIWCNG3bjrndxpoHprPiL0W0+ExGj23gv65/kwVFOlkpNkSXf14pJYFAAIfDQX5+Prm5udHjdcNXAzv+DbUbIRKEhGwoOOvE2H8QCMDu3VBdvT/ttMuegyNSZwOmaXL//ff32YWjqc7+2te+xttvv42u6wSDQT744INe5YInsjq7ra0t2p8VK1Ywe/bs6BRXTJ29n86Smxl9tInxJWJIyfpWP2HTxKMoTEuKJ91pxwzphCp9ICWRTBv1e/dit9tJ6KJhNvQIVRW7efut8ax/fRqhkILDE2TRJVUs/kY6nt3Z2MaNg7i46AXfMAxCoRCpqamkp6f3vnYQbIH2WmjbazmL0otOjEAA1h6Dhgbo/BQshPVYLxlWX0id/Y1vEAwGEUIwb968PrtyNNXZJ510EhdeeCFTpkxBURSuueYaJk2a1KPdiazO3rp1K1deeSU2m40JEyawcuXK6LFj6uwDGwgxCqiRUmpCiDOBKcBTUkpfny8cIGLq7P2EDJP32/yclpKAx6agt6hEGoIgJbYEJ2qypK6ujri4uOgFPKIaqH6d915v5c9PpFDXlIDdZmPc2BoWLnifqaMLSEtOwqypwT55MsJjeYwikQiapjFs2LC+h/3Vn1hf47MgJW+gfwRRBlyd3VVgJwRkZFiKihMl4PWDmDp78JhSB1SdDTwPTBdCFAJPAC8BTwMX9fmqGANOyDRRhMBWGyDUroEER3YctlQ3/oCf+rp6PB4PelhFbbfKYPibNda+pPHkM6PQkaSlN/D1yzYzeWILo9NySXAmgGmidNEwhMNhpJTk5eX1b141aQQkZg/kqR87pLSCQVtHwp3HYykqhsj8cldi6uyhQX+CgimljAghLgV+L6VcLoSIZR8dB2zxh0h32JCGjjM/CSXO+nV6vV4aGhrweDx4a6sJtbXi8MThTkjg3fUunniuEFMJMu2CCm5aKijIOB1F6VnQRUpJKBTC4XCQk5Nz6DnVsN9SVpxICAEOh/U1KwvS0oa0oiKmzj7x6Vc5TiHE14ElwCUdjw2NFZfjmH2qRsgwmZoUDy1+wEotbGy0dibHxcXRsreCsL+d9JEFuBIS+cc/JMv/Go+Jxpz5H/LDn44h1dW7oE7XdcLhMImJiWRmZvbYGdoDLQgVb1uG08Fe+yASsW6dhW/S0yE5GZzOvl8XI8YJQH+CwtXA9Vjq7N1CiAJg1cB2K0ZftEV0dgZURmrgaFYxNINgMEhDXRNCiOiCsmkYZI4uYm9NMn/6hYP33ldQpMaChduZ9c3WXgNC5+hAURRyc3OJ67ww9kV7PQQawJUIo8482qd77JDSqnNQX2+NBkaPthaRFSUWEGIMGfpTjrNMCHEjUCSEGA/slFL+euC7FuNAgobJpvYgIcMk2W4jty4E2U5Up0FDcwOeu3tBxgAAIABJREFU+LjoJ/r2xgba28I8+mQa699yEQyruFw+rrj4Y875Vjtees4Jd+49SE1NJTU19dCjA9ivvE7IsjKNBiuaZq0dBDqmv4bAJqUYMXqjP5XXZgFPAtVYVRWHCSGWSCl7F6nEGBAatQh7Qxq6lBQnxeGsDWJz2Ag6NBrCLd0CAoCuG/zl8XF8+KmOUFROPfUzzvjKNnKL0tGdeYxy7x8ldKaaxsXFMXz48KiTpk+kBM0PdWVWQZz0woE47YFHSmhpsWolm+Z+xXVS0pBeO4gxdOlPPt0DwFwp5RlSytOBecAfBrZbMboSNk02+IIowOQ4N8maxB42CCdBfX09cXHdA0IkHObRR+J572MnitvPkhte4ZwlTZx86tlML/wKE1ImkOnOjE4VRSIRcnJyyM3NPXRAaK2Ahq2wcx1UrLccRoM1IADU1FjTRaZprRsUFlpfv0BA6KbOnj+ftrb9irDNmzdz7rnnMnbsWMaMGXNcqbN/+9vfUlJSEu27zWajpaXn/tSYOnto0J+g4JRSbum8I6XcCsQmWI8xNs1kfI2Kc5cPrdKHLgwavI14PB4URcHQI3hra6jbUcFf7m9i7aupRJBcsfgtTj1jKnNPnsPIlP3b/k3T7Kh5kEh+fj6JiYmHViL4G6CxHBS7FQgKz4W86QN85gNMSoqVXZSXB7m5vW5E6y8ej4cNGzZQVlZGWloaDz1kKcNCoRAXX3wxy5Yto7y8nI0bN/Luu+/y8MMPA0TV2U899RRbt26lrKyM0aNH9zh+V3X2FVdc8YX7eSC33HJLdLfw3XffzVlnnUVaWk/P1aHU2f2lqzq7rKyMF198kfaOlOkvSld19iuvvML111/f60a9rursrVu3smjRImC/OnvTpk088cQT3HTTTQDd1NlDhf4EhU+FEH8WQpzZcXuEmBDvmFAeUHmt2cd7jT6M+iBKnAP3uFSc41JodgRwuJzYbDakKQkHQjTUNfK3/03lmX8WogvBvMu3cNFX3Iwb3j0bqNNqmp2dTVZWVlT8dUh8NZbqOr0I0grAPgjLSYZC0NS0/358vDU6OMprCDNnzoxaOp9++unjWp3dlVWrVvGNb3yj1+di6uyhQX+uBj8AbgR+hrWm8Bbwx4HsVAz41BugKaIzNt5NhiGQyeAamYSUkubGJiKRCE67m+YqP831LeypUPnbqvHUNmRiI8RZC8v46hXxJCb33DcQCoVISko6vA1IugbSAE/moJhr3759e/cHTBN8Puj8RJqVddg1kvu7gckwDNatW8d3v2uVHTne1dmdBINBXnnlFR588MFen4+ps4cGfQYFIcRkoBBYI6W899h0aWgS0A0+8QWJSIldCMKmycyUBBIUhUhtANlxIfb5fLS1tSF0J9W7W2ho2M0nGxNZ81IxuulieFqQJQs+wjtDJzchG2Rdt/fRNA2Hw3FozXUn7fXgq7KmjuwuSBk1AGd/9Ol2AQ8ErMyixERrATktDTIzj7qiIhQKUVJSQkVFxaBSZ3fy4osvcsYZZ/Q6dQQxdfaQV2cLIf4Lq8Lap1iai19KKf96zHo2xNirajgVwdSEOOxCIAS4FAWtqh3Dp+EcmUR7ezv19fUohhNvfRCfv4Z/v5bPq+vHIFGYOqGGC858Cd/YZHIT0ogTW1CUeIRwIKUkHA5jmiZ5eXn9SzeVEnzVoDhgxHSIH2RuxAMFdi4XDB9uqSoGgM41hcGmzu6ktLT0oFNHEFNnDxV1NlLKXm/AZiC+4/tM4KODte3jGBcC24GdwLI+2l0GSOCUQx1z2rRp8kRkS3tQVgTVbo/p3rAMbW+Rui8s/X6/3L59u9xVvkd+/No2ufZPL8hLztksTxrZIMcXNMl7b1wvX131e/nCf1bKnXvKZUXFp3LnrtdkVVWV3LlzpywvL5e1tbVSVdWD9KALvjopazZIuesNKbetldLfNEBnffTYsmVLzwdra6XcvFnKLVukbGiQ0jAGtA/x8fHR7z/99FOZl5cnNU2TwWBQFhQUyH//+99SSimDwaCcN2+eXL58uZRSyo0bN8rCwkK5fft2KaWUhmHI3/3udz2O//rrr8t58+ZJKaUMhUIyLy9P7tixQ0op5be//W35+9//Xkop5VlnnSU/+ugjWVNTI/Pz82VTU5PUNE2eeeaZcunSpb32va2tTaampkq/33/Q8zv11FOj7/enP/1JXnvttd2enz17tnzrrbekqqpy1KhR0d9JRUWFzM/Pl21tbVJKKW+55RZ51VVXyXA4LKWUsqamRj755JMHfd/+UFZWJqdMmSJVVZW7d++WBQUFUtf1Hu1uvfVWuXLlSiml9fM85ZRTpJRStra2Rvvz6KOPyiVLlkRf09TUJMePH39E/TvW9Pb/AHws+3Hd7mv8HJZSBjoCRyP9W5SOIoSwYVVs+yowAfiGEGJCL+0SsdYsPjic459ItEZ0qlQNW5dPdWZYR6tqR/HYUUWEmpoaXE430hCghVj5zDi2Vg4jKcvB4v/ey9ir/OiTxjChcDq6thFV3YkQDoLBIElJSYwaNYphw4b1nXLavAtqNkDjVhCKlWE06kyIP8ZVzI6Erp+gMzKsKaPRowdkuqgvuqqzPR4PL7zwAnfeeSfjxo1j8uTJTJ8+vVd19kknncSkSZOora3t8/hd1dmTJ09GUZQ+1dnnn39+n/P9a9asYc6cOcTHxx+0Tac6G6ypowULFnR7vlOd7XK5ourskpISLrvssh7q7MzMTCZMmMCkSZO45JJLolNcX5Su6uwLL7ywhzq7pqYGgGXLlvHss88yefJkfv7zn3dTZ0+cOJHx48fz8ssv84c/7M+6j6mzO58Qog14rfMuVn2FzvtIKS/t88BCzATukFJ+peP+zzted/cB7X6PVd3tZuBmKWWfXuwTTZ1tSMlbLe2kO+1MSdyvlDBVnUhNAC1Toa6uDqfTia9Op6Wxgacft7Puw3zcySYL/+tzkrJamTpsBFkOL3YZwGZLwO0+CU0zcblcDB8+/ODTCKYJDZvBNCDQaAUCmwviM8E+eDKPt27dyknDh1s207y8IaW0PlbE1NmDx5Q6UOrshQfc7z0l4eDkAvu63K8CTu3aQAhxMpAnpfynEKLnbp397a4FrgXIz88/zG4c3zRoESQwOaH7PLcZMWn3t9OiB9EDNvztYfSIzrsfe3jz4xEoLhuzv7+eaSc5KEiaQLIjDjVUj9szHrs9vWOzjUl2dnbvAUFKaN1jFcUJtUL2RKt2ckL2oMgu6kZDg7UrufMDjs9n7T+IcVSJqbOHBn3VaF53hMfu7coSHZYIIRSs3dJXHepAUspHgUfBGikcYb+OK/YENfI9zuiFW5oSLaDSvLkGXySEKz4RvzeMO1Xy6Ucajz09EsM0WHhFDSfPDDMt08r20LRahLBjt6djmibhcJgRI0b0vgchooK/Dhq3Q0o+pI2GuN4zTo5rTBOefx5+/3u44w5LUZGdbe1IjjEgxNTZJz5ffPvmoakCupbeGgHUdLmfCEwC3ui4IA4D/iGEuPhQU0gnCt6IjiZNsp0OpG6iVvvwNbTh97Vj2mwEpJ1QrUp7uJX31qfy54cKkKrGnNnVfOWre/HarSkSw/ChaXtxOvPQNI1IJEJ2djaeg2XZ1HwKRmRwO4v27YM774RPOiq9ud3W2sEQqaMbI8ZAMZBB4SNgTIdquxpYBHyz80kppZcu9Z+FEG/QjzWFEwXVMNkWUEmy2/AIQbCyjcZ9dRipdhyjkohEwNEawfT4eWNdOqtLh6OpQcafvp3zrqrAq0FBwkSEEEQi9Si2JCKRJBwOa4rtoAvK7XVWIZy8U8E9iKcAPvvMCghpafCzn0FqaiwgxIhxFOh3UBBCuKSUPffRHwQppS6EuAF4FbABf5VSbhZC/BIrNeofh9/dEwNTSsqDKhEpmRTvIVLvp6m6AZntwuZw0rg3iFAEuHT+9ng8/3p5GOFgO2ed/jYXLQiRb2aSmjuSuI5iNoZhEIm4yMhIIT09ve89CKFWSMyxah8MNtrb9+so5s+3FpW/9jVrumjr1i+3bzFinCD0R509A1gJJAP5Qohi4Bop5Q8P9Vop5Vpg7QGP/eIgbc/uT4cHO1JKPvQG8OkG05Licfg1andUY6bacSV5CLRqKB4brnQ7j62Afz6fhkOJsPiKDcyeZ2N0zikodhNsrahqK2pYBekjJ2cKqalZ/euEM35wLSZrGjz2GDz9NDz5JOTnW/0fYnO9MWIcC/qTt7ccuAhoBpBSbsRKT41xmKiGyfpWPz7d4HSXm7hqP3UbKzE9AnuKhzavhtcfYV97mCf/pvF8aRpOm+DmZV7OujSCYndg2vegGVuJ6C2oqk5iQgp5+cUkJY04dAdME3R14E/0aPL55/Ctb8Ff/mLpKt5//8vu0UEZrOpsr9fL/PnzKS4uZuLEidGdvQcSCoU466yzutlHH3jgAdxuN16vt8/36donv9/P97//fQoLC5k4cSKzZ8/mgw+ObJuSlJIbb7yRoqIipkyZwqefftprO03TuPbaaxk7dizjx4/n2WefBWDv3r1RN9SUKVNYu9b6LPv5559z1VVXHVHfBhv9mT5SpJSVB6Q19nTSxjgoUkr2qhrNER1FwBlJ8dj2tdPia2O7ESYScaKVteIPGnjcNjZ+nszzT8dhcyh887oaUqZuwlvXTk6aG2kmER8/nVAoQkZGct8OIy1gbUZTOn7NWru1wJyc13v744lQCB55BFatslJN8/Ph9tuhD6Hbl02n5gL2u4huu+22qDr7kUceYc6cOQSDQRYuXMjDDz/M0qVLo+rsl156ifHjx6PrOo8++miP43dVZx9NHnroISZMmMCLL75IY2Mj48aNY/HixTgPKEH617/+lUsvvbTb9OSqVauYPn06a9as6ffF85prrqGgoIAdO3agKAq7d+9m6xFO/7388svs2LGDHTt28MEHH3Ddddf1Gmh+/etfk5WVRXl5OaZpRutG3HnnnVx++eVcd911bNmyhblz51JRUcHkyZOpqqpi7969J1w6/MHoz0hhX8cUkhRC2IQQPwIObtWK0YNdoTDbAypxNoUJCR7cmklLi5dPG7zU1ZskaoLsBCfFE1LY+3kSzz3mwUTlW9/ZwoxZL5PauocRCWHSUuNxu8egqjoJCQmHltrVbgJpWqrr9CLIORlGn338O4zKymDRImu6SAj49ret4HAcB4QDGUzqbCEE7e3tSCnx+/2kpaX1msp8oDp7165d+P1+7rzzTlat6l/Z9l27dvHBBx9w5513RvXVo0ePZt68ef16/cF44YUXuPLKKxFCcNppp9HW1tbrrvC//vWv/PznPwdAURQyMqz/BSFEtHiQ1+vt5k2aP3/+YdWLGOz0Z6RwHdYUUj5Qj7X7+LqB7NSJgm5KdCnRTMnYeDejPFZGUOvWGiqa2vD5oXCYQp7bi9HcwvK/5rL27SwkES69eBunzWilaZ+HvIRx5IycjM3pRFVV3G7XwTelaQFrZ3LbPoiEIG8GeAbZRq7ERKs85tix1ujggJ2Z/aGx6Ui32fQkM+O8frUbbOrsG264gYsvvpjhw4fT3t7O6tWroxfsTjRNY/fu3YwaNSr6WGfthVmzZrF9+3YaGhqi9QkOxubNmykpKemXkPGKK67oqUAHfvKTn/TYO1BdXU1e3v4RcKc6OycnJ/pY53Te7bffzhtvvEFhYSEPPvgg2dnZ3HHHHcyZM4c//vGPBAIB/vOf/0Rfd8opp3DPPffws5/97JB9PhE4ZFCQUjZgpZPGOAxMKfnEFyBomChCMC7ejW6Y7NreQEtZLW0JDtLNNoY3NtAQSeOh54t5d2M8dmeQ735rJ2fPstOWmMLwrDRGpY/D7CiMk5iYSGZmZo9/2ih1n4OpW4Fg2CRwD5KNXBs2QHGxNTIYORL+9CeYMOELV0Lr7wX8aDJY1dmvvvoqJSUlvPbaa+zatYsLLriAWbNmddu13NTURMoBu8RLS0tZs2YNiqJw6aWX8ve//52lS5cetXM9nGpnvel6Dnw/XdepqqrijDPO4P777+f+++/n5ptv5sknn2TVqlVcddVV/PSnP+W9995jyZIllJWVoSgKWVlZUXfSUKA/2Ud/octO5E6klNcOSI9OAMKmye5gmKBhckpiHGrYoKK2nc98QYIbakhxO1AynGQ0aby8uYTH1uQSCEicDj83XruOmedOJOyR+AL7GJ04moiuo+s6w4YNIyEhofd/LtOEYDMYGmRPGjw7lFta4L774F//gv/3/6BzGmHKlC+3X1+AwarOfuyxx1i2bBlCCIqKiigoKGDbtm3MmDGj27mp6v4khU2bNrFjx45o4NM0jdGjR7N06dI+1dkpKSls3LgxquDui8MZKfRHnZ2enk5cXFxU5Pf1r3+dlStXArBy5UpeeeUVwJr6U1WVpqYmsrKyUFX14BtBT0D6s6bwH2Bdx+0dIAvo936FoUbYNHmzpZ0mTWdCggd/KMK2Oh+mYTDMHqQo08OowiQKM538bsUolj+WRXu7wbRp7fz6to8pnq1RLRppVBvJ8eSQbEvGNM2D11HWw1YhnB2vQvXHVrqpI673zh1PSAlr18Jll1kBwe2GSOTL7tVRITk5meXLl3PfffcRiURYvHgx69evj05JhEIhbrzxxuh0xC233MJdd90V/RRvmib3339/n+/RWTJy586dADz55JOcddZZ3dqceuqpvPHGGzQ3NxOJRPj73//e67Hy8/NZt86abquvr2f79u09akSnpqZiGEY0MKxatYo77riDiooKKioqqKmpobq6msrKSqZPn84777xDXZ1V4Onjjz8mHA6Tl5dHYWEhp5xyCv/zP/8TDWw7duzghRde6NGv1atXR2tHd731pp24+OKLeeKJJ5BS8v7775OcnNxt6gisADl//vyo6XXdunVMmDChx89g69atqKoaHWGVl5czadKkXn92JyL9mT7qNoYTQjwJDC1NYj8IGSY7gip14QgeRWFWmrXJakebSrLbTopsJyLthBUDKeGRh+PZsNlJShrc+JMAs2drbPu8iTqtngRXAfnx+bgUF8FgkLy8vB6ZIFHa9lmFcOIzYdjkwVE3ua4O7roL3n3Xun/qqXDbbVYBnBOErursJUuW8MILL/DDH/6QpUuXYhgGS5Ys6VWdHQwGEUIccuG1qzpb13WmT5/epzo7JyeHqVOn9lrM/vbbb+eqq65i8uTJSCn5zW9+E12A7cqcOXNYv349559/PqWlpbz88svdnl+wYAGlpaXceuut/OEPf2Du3LmYpklCQgKrVq2KjgxWrFjBT3/6U4qKioiLiyM9PZ3f/va3h/XzPZC5c+eydu3a6DG7ptWWlJREM7Z+85vfsGTJEn70ox+RmZkZbfe73/2O733vezzwwAMIIXj88cejH8Bef/31I14IH0wcVJ190BcIUQi8KqUsGpgu9c3xqs5u0nTKAyqj41xkOe0oQqBGDN7Z2USmPYwjrELEjl6j8vK/TR57YRhOh+Tu31SQc1IVLaFKWqoqyMmbyuis85FSEggEyMrK6jGXC1gyO38DGGFILRg8DqOyMrj+eggGrQXln/wELrroiDfT9aYKjnF0+eyzz7j//vt58sknv+yuHDPC4TBnnXUW69ev710ueZwyUOrszgO1sn9NQQFagGUHf8XQQ+0YJbgUwTCX5d8JajrNfo1wIAAijBqwYbdF2PCZjSf/kYNul3zjRxsw8z6lMphEknMkmc5cRqZZdvFQKERKSkq0MEmUtr3QVG6tIWRPsBaSB8N0USdjx1om01Gj4NZbrUI4MQYFJ598Mueccw6GYfSvnOsJwN69e7nnnnsGVUA4Uvo8U2GNn4qxhHYApjzcocUQYFN7ENUwmZxsVa1q8KlU1Ptp2tOC5mtHS4/HLUKUfyB54G8jkXaFCy55jQvObCDJVYTLMZZgXQth0Y4QoKoqLpeLjIyM/WsI7R2q60gQUkdZG9AGg67CMGD1ams0kJQETiesXGl9H2PQcfXVV3/ZXTimjBkzhjFjxnzZ3Tim9BkUpJRSCLFGSjmtr3ZDmUYtgt8wmZoUR4LdRlg3+HxfG462MAkiTO6UNOytjWx4KcSvS0vQ7YJZ52/lvK9sItmYg1obJKhVghBkjBqN2RFyhw0bZs3Bql7wN0Lzjo7aB4XgGCQFxMvL4Ze/hG3brO/vuMN6PBYQYsQ4bunPmOhDIcRUKWXvMpEhTlvEIMtpJ8luQ1N1aur9BCv9ZLs0UkfE4arysfEtlV/9fSphh4PzL9jHqXPfJjt7JmYAEtIycCcmoXRon0OhECNGjMDRqYFu2mntO0grtHYlD4Yyk5oGK1bA3/5mjRSGDYOvfOXL7lWMGDH6wUGDghDCLqXUgTOB7wkhdgEBrIpqUkp58CrgQ4w4m42dDX52lrcQUXXi4iKk5Tpx22xUbWrmzjUnE7bZOPW0Pcz/+n+obQhjr9NR8eFOSkYqCuFwGNM0yczM3J8T3V4HwSbInXb8qyk62bTJGh1UVFhTW5dfDjfcAHGDaN0jRowhTF8jhQ+BqcAlx6gvg5aIYVLdGiQ/LQ6XEga7SVx8HGp9kHtWj8Nv2hlXsp1zv/4i3rCTFNswsgvGIIQNTdex2+2kpKTgdru7p55GgpCUO3gCwr59cM011iL4yJHwi19Yu5RjxIgxaOhrLkIASCl39XY7Rv07btkRUPmwzU9NOIIAbIpCqlsQ9PvwuDxIX4Q/3wu7G+JJS21j3rx/k+gbzQijmPFFp+FwedB0nZycHHJzc0lOTsblcvXcnKYMoiyPvDxYsACuvtoS2A2xgDBY1dmtra0sWLCAKVOmMGPGDMrKyno9rpSSc889NyqOA1izZg1CCLZt2xZ97I033uCiiy7q9tqrrrqKZ555BoBIJMKyZcsYM2YMkyZNYsaMGT32PHwR7r77boqKihg3bhyvvvpqr21mzZpFSUkJJSUlDB8+nEsusT7zbtu2jZkzZ+Jyubjvvvui7TVNY/bs2ei6fsT9Gyz0NVLIFEL85GBPSin73nJ5gtMa0RnmciB0E1U1kKZJc1sbTpcToZq8/oqDlz9MQUnQWPSd58gcnk26czQ5ubkoNnt0D0Ji4iCsgNaJzwe//71VBa1TtLZs2fGfETVADFZ19l133UVJSQlr1qxh27ZtLF26NLq7tytr166luLi4mxNp1apVnHnmmZSWlnJHZyLBIbj99tupra2lrKwMl8tFfX09b7755hGdw5YtWygtLWXz5s3U1NRw/vnnU15e3iN19u23345+v3Dhwqj1NS0tjeXLl/P88893a+90OjnvvPNYvXo1ixcvPqI+Dhb6GinYgAQg8SC3IU+i3UYwoOMNaKQ5TYxQBGdDhJoygz8+kQaK5PJF75OWIkj8/+2dd3hUVf7/XyeTNumFAIEhEFqAFEJ3FRHFRcCGgBgeXFRA1raiLn6xrLvoshYsKIhdf64CCZoVYcXyRcpXRekghkgNCUkoIQkJSSbJtPP7406GkMYEMmlzXs8zzzP3zOfe8zkzyf3c094facDLy9sREMLDw+velFaFyQgFR8/nQmhtbNwIt98Oa9fCokWabAW4bUCoSVuSzk5PT2fMGE1AsEo+4/Tp07Xsakpnl5aWsmXLFj788EOnpaWNRiPvv/8+S5cudeQR79SpE1OnTnXq/PpYs2YNSUlJ+Pj4EB0dTe/evdm+fXu99iUlJWzcuNHRU+jYsSPDhg07v8CjGhMnTmTFihWX5V9boqE7zkkp5XPN5kkbosJqwyQlxeVmcouM9A7XU5l5GlOJwOrnwSspHSkvt3Bl/HEGDN+G3iMRvV8g4eHhlJWVERYWRlhYHYJ1NhsUH4ezWdp8gj5UW3HUmigogJde0oICQGKiJm/dyoLB/+YXX9yokYzt4JzibFuTzh44cCBffPEFI0eOZPv27WRlZZGTk0OnTp0usNuyZQvvvvuu4/jLL79k3Lhx9O3bl7CwMHbv3s3gwQ2vPzly5AhRUVEX9Dbq49FHH2XTpk21ypOSknjiiQv3z+bm5nLFFVc4jquks+tj9erVjBkzxik/4uLi2LFjx0Xt2gsNBYXW9V/eSiizWNlSVIqnEOiEJEjnSfGWbCxllehCfPlqSxi/7zYTFmRk0oyNFJcI+nSNotQmKS8vp3PnzoSGhtatXlmUCYUZ2uRysAG8WtHmNClh3Tp47TVt2MjPD/7yF5g8uVUuk3X2Bt6UtFXp7CeeeIK5c+eSmJhIfHw8gwYNqnMHb2Fh4QXDncnJyY76kpKSSE5OZvDgwU3W1sWLFztt64x0dnWSk5OZPXu2U9fW6XR4e3tTUlLStod7naShoND8gvRtgDNmC0GeOoYF+1NQUkll5lm8TJWEjwjnaLYvyalBQDn3zztOie4UEeFXERzZhYKMDHr27Fl3D6GKwgzo0FfbpNbaKCmBxYu1gHDllfDkk1BDhdLdaavS2UFBQQ5hOCkl0dHRREdH17Lz9PR0SF4XFBSwceNG0tLSEEJgtVoRQrBo0aIGpbN79+7N8ePHnbrBNqan4Ix0dhUFBQVs376d1atXN1h/dSorK/H1bSObRi+Teh/xpJSFzelIa6fcamNrUSmHyiro4uOFB2DJKcVy6hzeXf0wlZbz0pMWbKVljBl2AJ3fenRenegeGcvp06cJCAiorWNUFwGdXd4Wp7HZtM1noO1CfvppbQ/CG2+ogNAAbU06u6ioCJPJBGgKpqNGjapzWCUmJoaMjAwAUlNTmTFjBllZWWRmZpKdnU10dDQ//fQTffr04cSJE468y1lZWfz6668kJibi5+fHrFmzePjhhx11njx5kuXLl9eqb/HixXVKZ9cMCKBJZ6ekpFBZWcmxY8c4fPjwBfkgqvP5559z0003OX2TLygoICIios75hvZI6+v3t0KsUnKwrAKLlFwREkCU3gebxcaZ9DOc1dnw7xrAh/8vmNzCACKjJaOmrkcfoaO75Rm6AAAgAElEQVRfn2Hk5xdgtVqJioqqW1SrOEfbtXx0I1jNIFrJT5KZCffeCx9/fL7suutgwoTWM6TViqkuna3X61mzZg0LFy4kJiaG+Ph4hg0bVqd0dv/+/YmLi6szv3B1qktnx8fH4+Hh0aB09vXXX1/veP/vv/9ObGws/fr145tvvuGNN96o0+7GG2905CJITk52JKupYvLkyaxcuRIfHx+WL1/OPffcQ2JiIlOmTOGDDz5wPBQtXLiQiIgIBgwYQFxcHBMnTnQMcV0qsbGxTJ06lQEDBjBu3DiWLVvmWHk0YcKECzKnpaSkMG3atAvOP3XqFAaDgddee42FCxdiMBgcS283bdrEhAkTLsu/tkSjpbNbmpaQzi40W9hXYiQh0I8wL+3GnnPoLPt+PIp3gj+lOeE88z8+ICv58/wf6NpnL32jJqMniJycXAYOHFi7lyAl5KVrqqehPbRVRsHdWl7XyGKBTz6B99/Xkt506QKpqZqQXStGSWe7npMnTzJjxgzWr3evdCqTJk3ihRdeICYmpqVdcRqXSme7OzYpKTRb8NfpHAGh6LSRc4Wl6DzK6Lg/l+cWX4G0mJk2NY9eQwsw+MbhK4MoKCwkLCysdkAwV0DGZu29YVjr2bF88KCWErNqMvLWW2Hu3FYfEBTNQ2RkJPfeey/nzp1zatVOe8BkMjFx4sQ2FRAuFxUULsL+0nLKrTa6+nojpURKsFps4FWBb/5p3tx5JcWE0m9IIcP+uA6T1YqP7wjOFpzF19eXqMgITfK6OoUZ4BMIPUa2TKNqYrHAu+9qAnY2m9Y7+NvfoJ4xWYX7crn7Cdoa3t7edab/bM+ooNAAZVYrxRYrXY2gN5rJzNfGGMuNZVQeTWf3MQPbDnTEx9fIjJnfUi4tdA2+Hl/PMIrlabp06UKQzgglZyCw2sRsRD9tyKi1oNNpGdGkhGnT4P77lYCdQuGmqKDQAKcqzXgC5vwKdF38CYv0R+dZyYl1u7Ge9SR5ayxS2pjxp8N06tSRgmIfIv0MnDx5Ej+9Hp3NDPn7IawVpss0GqGsDCIitInjZ56B/HxISGhpzxQKRQvSSpa6tD5KLVaOlZuI8PLE20MQHOGHX4gnp48ewWzTsTQtkXOVXkRHn+a6605QcqoID09vyo2lWAsziTRl4n9mF+hDIKKVjUf+8osmaf23v52Xp+jSRQUEhUKhegp1kW+ykGcyE6jzwD/PhMVbh9Vq5eSJE5jzrBzMDuSnLR3w1sGMWYf5vcJEuSmfnp2G4pm7k2APG2E9hmhDRi29mqg6xcXajuR167Tj0FCtrCENJoVC4Va4tKcghBgnhDgohDgihKi140QI8ZgQIl0IsU8IsUEI0d2V/jhLZnklZinprvfBZpN0MARQUFBARb6RnHwL/94UjQdmrr32CBHdcwkwehITFENnkyeBft54dh2kDRm1loAgJWzYoAnYrVunrSZ6+GFtD4IKCE3KqVOnSEpKolevXgwYMIAJEybw3nvv1ZKSVihaKy4LCkIIHbAMGA8MAKYJIQbUMNsDDJVSJgCpwCJX+dNYInU6PM9UYqm0UlFRQfHps/iU6/hhmy/HssPw05dz8zVHOZcDnDVS6eFJRdFpKn0jwNOnpd0/j5TaMNH8+VBYCIMHQ0oKzJihTTArmgwpJbfddhujR4/m6NGjpKen8/zzz9epOKpQtFZc2VMYDhyRUmZIKU1ACnBrdQMp5SYppdF+uBUwuNAfp5FAZbmFijIzwZ30FOafxMtoZWu65POvoxBe3ky/fTeB3bwxG4II7xWD8PSkU4dQ/IIj6NyaJCCEgJ49tdVETz4J77wDUa1QW6mpGTq0/tcXX5y3++KLhm0bwaZNm/Dy8rpgZ3FiYiJXX301paWlTJkyhX79+jF9+nSHdtFzzz3HsGHDiIuLY86cOY7y0aNHM3/+fIYPH07fvn0deQCsVivz5s0jPj6ehIQEli5dCsCuXbu45pprGDJkCDfccMNFd0QrFPXhyqDQFciudpxjL6uPWUCd6ZeEEHOEEDuFEDvPnDnThC7WJuNcOdl5pRRll6EP8MJkLsayayd5e3J49fUQhM6LmyfmMWL4AbKteXTUBREiPelgziHc20xIeAQBAQEu9fGinDgB1bXk77pL25XcShVN2wtpaWm1JLKr2LNnD6+//jrp6elkZGQ4chs89NBD7Nixg7S0NMrLy/nqq68c51gsFrZv387rr7/Os88+C8B7773HsWPH2LNnD/v27WP69OmYzWb+8pe/kJqayq5du5g5cyZPP/206xusaJe4cqK5LoGcOjU1hBB3AkOBa+r6XEr5HvAeaDIXTeVgTU5lFJNTbCQMiDKE4BMoyPpuO1mVehZ9MZQiq57EoYUkJW2l/LgP3pW59DBaEAElGPWh0OtaV7nmHDYbrFoFy5aBjw98/jmEhYGnJ3Ts2LK+NTfOSqFMmqS9XMzw4cMxGLSOcJW89siRI9m0aROLFi3CaDRSWFhIbGwsN998s901za8hQ4aQmZkJwPfff899993n0NEKCwsjLS2NtLQ0h1S31WolsjX1VhVtClcGhRygW7VjA3CippEQ4nrgaeAaKWXt1FHNSHmpmQ5RgXj5ehKo9yYnJwedh46UHYmczvGjWycbf310B7qzEo/iMoxh/uh6X4PRoiMiPLwlXYeMDFi4EPbt045HjVK9gmYmNjbWkYe4JlVZxkDT57dYLFRUVPDAAw+wc+dOunXrxoIFC6ioqKh1TpU91J2bQUpJbGwsv/zyS1M3SeGGuPKusQPoI4SIFkJ4A0nA2uoGQohBwLvALVLKPBf64hQHLSZypBUfD0FRUREVRUVs+z2AH74PRycszL1vPaGVezGfyKbAKxCzCMVo0SGEwK+ldgBbLPDhhzB9uhYQIiK0ZafPP69WFjUz1113HZWVlbz//vuOsh07dtSbf7gqAHTo0IHS0tJ6A0p1xo4dyzvvvOMIEoWFhcTExHDmzBlHUDCbzezfv/9ym6NwU1wWFKSUFuAh4Dvgd+AzKeV+IcRzQohb7GYvo+WB/lwIsVcIsbaeyzULZ2xWBgf501knyD92DGtaBstSYvDyMHLbtT8Q1/EY+zNhp0WS5e1B35h+9OzZk+jo6JbTWn/6aXj7bU3R9LbbtCGjUaNaxhc3RwjB6tWrWb9+Pb169SI2NpYFCxbUm+wlJCSEe++9l/j4eCZOnMiwYcMuWsfs2bOJiooiISGBgQMHsnLlSry9vUlNTWX+/PkMHDiQxMREfv7556ZunsJNUNLZdg6eOMe2E8VMH9yVUydPUpGXx1tvBLF2Wxf6ds3hrYeOcdTPxJHcA/TuO5CYXgPo0qnLJadTbDL27tWUTZ96Cpy4qbRXlHS2QnEeJZ19GUgpOZdfweHTpSREBmEsK6OsrIy9e0NZvyUIX8+zzJ36A3n+PhzLK6Bzpx4kxAyiQ4cWkrvevVubRJ0zRztOTNRWFqk9BwqFoglw+6BQabRwLLcEm78nXUJ9yT99gqIifxYvCcJmK+H2cbvo1K87WaZCArz9iO0d33CeZVdRVgZLlsB//qMdDx2qbUQDFRAUCkWT4dZBwWK2UlpmIsfTRrfOQXhWGqkoLmLRs50pPWdkWL8jDEg8zfYsEzpZQQf/ULp0NeDR3Kt6tmyBf/0L8vK05aUzZ0J8fPP6oFAo3AK3DQrlJSZOHTtHqZDo/HT08fUi/8BeVryj59ART8KD8hly0w4OGs1E9ehAr/AB9O8dh97fv/mcLCqCV1+Fb+x7+mJj4e9/h16tTIZboVC0G9wyKEgpKSs2ERDiQ0gnX4zllZjLStmxoYi1m/vhoTMza9Iv5HcOZxC+REXGEGnoRlhYM88jvP++FhB8fOCBB7QEOGrvgUKhcCFuGRRM5RbKiirp0C0As4fAarVy5rff+GRlT2SllVtH78DmXYh/RSD+wREEBAU13zyClJpeEcCf/6yJ2D34IBhahSyUQqFo57jlY6eU4OWrwz9Y2zFaWlbG1i2eZJ0NIjSigthxp/EaHkWn7p0JjuyCoVuU6+cRpITVq7X5ApNJKwsKghdeUAFBoVA0G27ZU6giq7ySExUmSktK+GJ9NyzSylVDszDrIrB6lxLhaSAiIsL1G9NycjSJiqr9F+vXw403urZOhUKhqAO37CmYK62ctVrJMFYSYK7E+HMRR7MEAfpyRl5dQb+43uh0ki4du6DX613niM0GK1bAHXdoASE0VJOnmDDBdXUqXIoQgj/96U+OY4vFQkREhMuT7Oh0OhITE4mLi+Pmm2+mqKjI8VlOTg633norffr0oVevXsydOxdTVW+UuhMDHTp0qFYd5eXlXHPNNVitVkfZ6tWrEUJw4MABR1lmZiZxcXEXnLtgwQJeeeWVRtXXWL799ltiYmLo3bs3L774Yp02RUVFDgnz/v37X6AXtXjxYmJjY4mLi2PatGkX6FC50qf6bLKzs7n22mvp378/sbGxvPHGGwCYTCZGjRrlkDppatwqKFgtNk5nnuPQ8WIyPG2EleUS+Nt/+XJVEGYbXDkkh4AONg4c30MIIS770gFNwO6ee2DxYqishPHjNYmKsWPPzyko2hz+/v4OGWyA9evX07VrQ4rxTYNer2fv3r2kpaURFhbGsmXLAG1RxaRJk5g4cSKHDx/m0KFDlJaWOqS1G5MY6KOPPmLSpEnoqu2LSU5OZuTIkaSkpDjlp6sSEVmtVh588EG++eYb0tPTSU5OJj09vZbd3LlzGTduHAcOHODXX3917PrNzc1lyZIl7Ny5k7S0NKxWa4Nt2rx5M3ffffdl+9SQjaenJ6+++iq///47W7duZdmyZaSnp+Pt7c2YMWNYtWpVI78l53Cr4aPSsxWYjBZOhHliCNFjyN7H6m3hpB3piF5/jsTri9EHd6VTgAfxXeMJ8g66QN2ySTlwAPbv1yStn3oKRo50TT1uSCNz4ziNs+oq48ePZ926dUyZMoXk5GSmTZvmSJKzfPlylixZgslkYsSIEbz11lvodDomTpxIdnY2FRUVzJ07lzlz5pCZmcn48eMZOXIkP//8M127dmXNmjUX7b3+4Q9/YJ9dLXfjxo34+vpyzz33AFqPYvHixURHR/Pss8+ydevWOhMD1cWKFStYuXKl47i0tJQtW7awadMmbrnlFhYsWHDR76a+RESXy/bt2+nduzc9e/YEICkpiTVr1jBgwPlkj+fOneOHH37g448/BsDb2xtvb2/H5xaLhfLycry8vDAajfVqVjWlTw3ZREZGOiTQAwMD6d+/P7m5uQwYMICJEyfy5JNPMn369MvysS7cpqdgMVk5d6aCgDAffP08SQjUk5eVzY87BiCEJ1ddfRTvHt5ke2TjGeCJr68ver2+aSeYz549/378ePif/4HPPlMBoZ2RlJRESkoKFRUV7Nu3jxEjRgCaHs2qVavYsmULe/fuRafTsWLFCkB7Ct+1axc7d+5kyZIlFBQUAHD48GEefPBB9u/fT0hICP+p2tFeD1arlQ0bNnDLLZrm5P79+2sl/gkKCiIqKoojR440mBioOiaTiYyMDHr06OEo+/LLLxk3bhx9+/YlLCyM3bt3X/Q6ztYHcPXVV5OYmFjr9f3339eyzc3NpVu380r9BoOB3NzcC2wyMjKIiIjgnnvuYdCgQcyePZuysjIAunbtyrx584iKiiIyMpLg4GDGjh1bq54RI0aQmJjI7NmzWbt2rcOn77777pJ8csYGtCG5PXv2OP6W4uLi2LFjR53f2+XiNj0Fq8WGh04QHKGHsyWYy0soypHs+70T6EyMHn0Is7c/PSLi6BfZDz/PJpTCrqjQ0mCmpsKnn0J0tDZENHVq09WhcOACvcRGkZCQQGZmJsnJyUyoNj+0YcMGdu3a5VBDLS8vp6M9+dGSJUtYvXo1oI0lHz58mM6dOxMdHe14kq6ebKcm5eXljuQ9Q4YMcSTcqSv/QkPl9ZGfn09IDSn25ORkHnnkEUALhMnJyQwePLje6zZWPLKqd+UMdQl71qzPYrGwe/duli5dyogRI5g7dy4vvvgi//znPzl79ixr1qzh2LFjhISEcPvtt7N8+XLuvPPOC66xbds2QBs++vjjjx29jkv1yRmb0tJSJk+ezOuvv05QUBCg9fi8vb0pKSkhMDCwXh8uBbcJCgDCQ+Ch0578zx3/nZ/3dsMsPOjR+zT+/jrO4sXArgMdWa2ahJ07tZVFOTnaxrM9e7SgoGjX3HLLLcybN4/Nmzc7nvqllNx111288MILF9hu3ryZ77//nl9++QU/Pz9Gjx7tmOSsmZynaq6iJlVzCsXFxdx0000sW7aMhx9+mNjY2Fq9i3PnzpGdnU2vXr3Iy8tzKo+DXq+/YOK1oKCAjRs3kpaWhhDaXh8hBIsWLSI8PJyz1XvFaHkfoqOjMRgMTtUHWk+hpKSkVvkrr7zC9ddff0GZwWAgO/t89t+cnJxawz8GgwGDweB42p4yZYpjYvf7778nOjqaiIgIQMt69/PPP9cKCo3BWZ8asjGbzUyePJnp06c7MvFVUVlZia+v7yX7Vx9uM3xUk8IzZ9i4rwdmq43Y+IOUWHKJDIlsuoBQWqqtJLrvPi0g9O4NH3/cLKkfFS3PzJkz+fvf/058NY2qMWPGkJqaSl6elk+qsLCQrKwsiouLCQ0Nxc/PjwMHDrB169ZLrjc4OJglS5bwyiuvYDabGTNmDEajkU8++QTQhpf++te/cvfdd+Pn5+d0YqDQ0FCsVqsjMKSmpjJjxgyysrLIzMwkOzub6OhofvrpJwICAoiMjGTDhg2Odn777beMHDmyUYmIfvzxR/bu3VvrVTMgAAwbNozDhw9z7NgxTCYTKSkpjiG0Kjp37ky3bt04ePAgoPXcqsb3o6Ki2Lp1K0ajESklGzZsaFCKffTo0Q32Epz1qSEbKSWzZs2if//+PPbYYxecV1BQ4LLl8m4XFA6XVSCBw5mSI7mh+PrAsOiDhPuHcWWPK5umkr17taGhL77QBOzuu08bNqo2waRo3xgMBubOnXtB2YABA1i4cCFjx44lISGBP/7xj5w8eZJx48ZhsVhISEjgmWee4YorrrisugcNGsTAgQNJSUlxJP75/PPP6dOnD3379sXX15fnn38eaFxioLFjx/LTTz8B2tDRbbfddsHnkydPdkxEf/LJJyxcuJDExESuu+46/vGPf9CrV69GJyJyFk9PT958801uuOEG+vfvz9SpU4mNjQVgwoQJnDihZQJeunQp06dPJyEhgb179/LUU08B2lzBlClTGDx4MPHx8dhsNuZUydNXo2pOoearrjkFZ3xqyGbLli18+umnbNy40VHP119/DWgT9hNctHTdbZLsVBrNFOSWcaqjFxFeOt58/FfWftOHgSNKmDnxXQJih3Bl3OSmcTI7W9t7EBMDzzwD9pUFCtehkuy4nj179vDaa6/x6aeftrQrbs+kSZN44YUXiImJqfNzlWSnkXhYbfyyqws2KRkWl4lOZyOkQ91frlNICdu2wYgR2gRyt25a3uSYGCVgp2g3DBo0iGuvvRar1XrBXgVF82IymZg4cWK9AeFyccs71q+7Kjl5yhs/z7PEdFrNOX8vIoMvcYPR6dPw6KPw0EPw3/+eL+/fXwUERbtj5syZKiC0MN7e3syYMcNl13fLnsKGr63YTCYGxR8lIqYfkdFjCdWHNu4iNht8+SW8/joYjRAQAK7WSFIoFAoX45ZBIT3NEzwEiVflodf7ExYS0bgLHD+uLTOt2qwzejTMnw8RjbyOQqFQtDLcLiiUG+F4hsADK1GG03h7XoVe34iNavv2aauJTCYIC9N2JY8Zo/SKFApFu8DtgkLmIQ+sFSa6dDoH/r7ogwIbN0bavz9ERWmTyI89BsHBrnNWoVAomhm3CwqH9msbeCK7FGL1tBIYcpEt4iYTLF+ubToLCdHmDT76CPyaUAZDoVAoWglutzzm8G4zNpuFXr2PE+hTjl7vX7/xb7/BnXfCW2/Bq6+eL1cBQaFQtFPcqqdQeCSLX7d0wKazEdH3NB0NI9HrO9Q2LC+Ht9+G5GRtD0JUlJKnUCgUboHbBIXKykp+2HeEYmMn/IMr6DtoOD0NCbUNt2/XVhadOKHtM7jrLpgzB6rpritaP9nZ2VRWVjbZ9Xx8fC6QOG4KZs6cyVdffUXHjh1JS0tz+ryioiJWrlzJAw88UOfnCxYsICAggHnz5jl1vcbaK9o3bjN8VFKUT1ZWAD46T3r2MtIzNAK/msNAx4/Dgw9qAaFvX/jkE21TmgoIbY7Kykr8/Pya7NXYAONMZq67776bb7/9ttFtKyoq4q233mr0eQqFM7hNUMBUQe6JQISHwNC9gICAgNqKqFFRMG0aPPCAFhD69WsZXxVuwahRowgLC2vQpqysjBtvvJGBAwcSFxfHqlWreOKJJzh69CiJiYk8/vjjAPzrX/8iJiaG66+/3qEC2hAN2S9fvpzhw4eTmJjIn//8Z6xWK/Pnz78gEC1YsIBXq8+zKdoNbjN8hM1C1slwrNjo1qOIkJBQKCyEl1+GyZPP53CsIVGrUDSGESNGUFlZSWlpKYWFhY4EOS+99BI33HBDo6/37bff0qVLF9atWwdAcXExI0aMIC0tjb179wKwa9cuUlJS2LNnDxaLhcGDBzeY3awh++rZ4by8vHjggQdYsWIFSUlJPPLII44hq88+++ySejmK1o/bBAWzGU6eCUTobBj6RKLfuBkWvwbnzkFWFqxYoTagKS6bxmTmcob4+HjmzZvH/Pnzuemmm7j66qtrJbD58ccfue222xzDoTU1+2vSkH192eFmzJhBXl4eJ06c4MyZM4SGhhIVFXVZbVO0TlwaFIQQ44A3AB3wgZTyxRqf+wCfAEOAAuAOKWWmK3w5kmnDYpWEBpUwZsXbeBy2T+xdcQU89ZQKCIpWSd++fdm1axdff/01Tz75JGPHjq1TDK2xqS7rs68vOxxomcpSU1M5deoUSUlJjapP0XZw2ZyCEEIHLAPGAwOAaUKImllmZgFnpZS9gcXAS67yZ/c+TzyNFYzJ/V+iDqchgoJgwQJYuhQuM8GHQlETZzJzOcOJEyfw8/PjzjvvZN68eezevZvAwMAL0lSOGjWK1atXU15eTklJCf+trtZbBw3Z15cdDrQ8zCkpKaSmpjJlypTLbpuideLKnsJw4IiUMgNACJEC3AqkV7O5FVhgf58KvCmEENIFmX/S0zzQlxoZEnEIcd11moBdeHhTV6NoJfj4+GA0Gpv0es5QNadQk7rmFKZNm8bmzZvJz8/HYDDw7LPPMmvWrAtsfvvtNx5//HE8PDzw8vLi7bffJjw8nKuuuoq4uDjGjx/Pyy+/zB133EFiYiLdu3fn6quvdpw/YcIEPvjggwsymw0ePLhe++rZ4Ww2G15eXixbtozu3bsTGxtLSUkJXbt2JTIyssE6FG0Xl2VeE0JMAcZJKWfbj/8EjJBSPlTNJs1uk2M/Pmq3ya9xrTnAHICoqKghVU8ujeHB+/PZ/FUpq/5xnLjZoy61WYpWisq8plCcp7VmXqtr0LJmBHLGBinle8B7oKXjvBRnlr3dAcvSDnh49LiU0xUKhcItcOU+hRyg+hZQA3CiPhshhCcQDBS6yiFPT5UMTaFQKBrClbfIHUAfIUS0EMIbSALW1rBZC9xlfz8F2OiK+QSFe6D+dBSKy/8/cFlQkFJagIeA74Dfgc+klPuFEM8JIaoWRn8IhAshjgCPAU+4yh9F+8bX15eCggIVGBRujZSSgoICfH19L/kaLptodhVDhw6VO3fubGk3FK0Ms9lMTk4OFRUVLe2KQtGi+Pr6YjAY8KqRM741TDQrFM2Gl5cX0dHRLe2GQtHmUdOuCoVCoXCggoJCoVAoHKigoFAoFAoHbW6iWQhxBmj8lmaNDkD+Ra3aF6rN7oFqs3twOW3uLqWMuJhRmwsKl4MQYqczs+/tCdVm90C12T1ojjar4SOFQqFQOFBBQaFQKBQO3C0ovNfSDrQAqs3ugWqze+DyNrvVnIJCoVAoGsbdegoKhUKhaAAVFBQKhULhoF0GBSHEOCHEQSHEESFELeVVIYSPEGKV/fNtQogeze9l0+JEmx8TQqQLIfYJITYIIbq3hJ9NycXaXM1uihBCCiHa/PJFZ9oshJhq/633CyFWNrePTY0Tf9tRQohNQog99r/vCS3hZ1MhhPhICJFnz0xZ1+dCCLHE/n3sE0IMblIHpJTt6gXogKNAT8Ab+BUYUMPmAeAd+/skYFVL+90Mbb4W8LO/v98d2my3CwR+ALYCQ1va72b4nfsAe4BQ+3HHlva7Gdr8HnC//f0AILOl/b7MNo8CBgNp9Xw+AfgGLXPlFcC2pqy/PfYUhgNHpJQZUkoTkALcWsPmVuDf9vepwBghRF2pQdsKF22zlHKTlLIqk/1WtEx4bRlnfmeAfwKLgPagqe1Mm+8FlkkpzwJIKfOa2cemxpk2SyDI/j6Y2hke2xRSyh9oOAPlrcAnUmMrECKEiGyq+ttjUOgKZFc7zrGX1WkjtWRAxUB4s3jnGpxpc3VmoT1ptGUu2mYhxCCgm5Tyq+Z0zIU48zv3BfoKIbYIIbYKIcY1m3euwZk2LwDuFELkAF8Df2ke11qMxv6/N4r2mE+hrif+mutunbFpSzjdHiHEncBQ4BqXeuR6GmyzEMIDWAzc3VwONQPO/M6eaENIo9F6gz8KIeKklEUu9s1VONPmacDHUspXhRB/AD61t9nmevdaBJfev9pjTyEH6Fbt2EDt7qTDRgjhidblbKi71tpxps0IIa4HngZukVJWNpNvruJibQ4E4oDNQohMtLHXtW18stnZv+01UkqzlPIYcBAtSLRVnGnzLOAzACnlL4AvmnBce8Wp//dLpT0GhR1AHyFEtBDCG20ieW0Nm7XAXfb3U4CN0j6D00a5aJvtQynvogWEtj7ODBdps5SyWErZQUrZQ0rZA52NPXIAAATSSURBVG0e5RYpZVvO5erM3/aXaIsKEEJ0QBtOymhWL5sWZ9p8HBgDIITojxYUzjSrl83LWmCGfRXSFUCxlPJkU1283Q0fSSktQoiHgO/QVi58JKXcL4R4DtgppVwLfIjWxTyC1kNIajmPLx8n2/wyEAB8bp9TPy6lvKXFnL5MnGxzu8LJNn8HjBVCpANW4HEpZUHLeX15ONnmvwLvCyEeRRtGubstP+QJIZLRhv862OdJ/gF4AUgp30GbN5kAHAGMwD1NWn8b/u4UCoVC0cS0x+EjhUKhUFwiKigoFAqFwoEKCgqFQqFwoIKCQqFQKByooKBQKBQKByooKFodQgirEGJvtVePBmx71Kcm2cg6N9uVOH+1S0TEXMI17hNCzLC/v1sI0aXaZx8IIQY0sZ87hBCJTpzziBDC73LrVrgHKigoWiPlUsrEaq/MZqp3upRyIJpY4suNPVlK+Y6U8hP74d1Al2qfzZZSpjeJl+f9fAvn/HwEUEFB4RQqKCjaBPYewY9CiN3215V12MQKIbbbexf7hBB97OV3Vit/Vwihu0h1PwC97eeOsev0/2bXufexl78ozueneMVetkAIMU8IMQVNX2qFvU69/Ql/qBDifiHEomo+3y2EWHqJfv5CNSE0IcTbQoidQsuj8Ky97GG04LRJCLHJXjZWCPGL/Xv8XAgRcJF6FG6ECgqK1oi+2tDRantZHvBHKeVg4A5gSR3n3Qe8IaVMRLsp59hlD+4ArrKXW4HpF6n/ZuA3IYQv8DFwh5QyHk0B4H4hRBhwGxArpUwAFlY/WUqZCuxEe6JPlFKWV/s4FZhU7fgOYNUl+jkOTdaiiqellEOBBOAaIUSClHIJmi7OtVLKa+3SF38Drrd/lzuBxy5Sj8KNaHcyF4p2Qbn9xlgdL+BN+xi6FU3Tpya/AE8LIQzAF1LKw0KIMcAQYIdd3kOPFmDqYoUQohzIRJNfjgGOSSkP2T//N/Ag8CZafoYPhBDrAKeluaWUZ4QQGXbNmsP2OrbYr9sYP/3RZB+qZ92aKoSYg/Z/HYmWcGZfjXOvsJdvsdfjjfa9KRSACgqKtsOjwGlgIFoPt1bSHCnlSiHENuBG4DshxGw0meF/SymfdKKO6dUF84QQdebYsOvxDEcTYUsCHgKua0RbVgFTgQPAaimlFNod2mk/0TKQvQgsAyYJIaKBecAwKeVZIcTHaMJwNRHAeinltEb4q3Aj1PCRoq0QDJy0a+T/Ce0p+QKEED2BDPuQyVq0YZQNwBQhREe7TZhwPj/1AaCHEKK3/fhPwP/Zx+CDpZRfo03i1rUCqARNvrsuvgAmouUBWGUva5SfUkoz2jDQFfahpyCgDCgWQnQCxtfjy1bgqqo2CSH8hBB19boUbooKCoq2wlvAXUKIrWhDR2V12NwBpAkh9gL90FIWpqPdPP9XCLEPWI82tHJRpJQVaAqUnwshfgNswDtoN9iv7Nf7P7ReTE0+Bt6pmmiucd2zQDrQXUq53V7WaD/tcxWvAvOklL+i5WbeD3yENiRVxXvAN0KITVLKM2gro5Lt9WxF+64UCkCppCoUCoWiGqqnoFAoFAoHKigoFAqFwoEKCgqFQqFwoIKCQqFQKByooKBQKBQKByooKBQKhcKBCgoKhUKhcPD/ASZ50cAwIuazAAAAAElFTkSuQmCC\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "array([0.43307701, 0.66932142, 0.69341809, ..., 0.47402373, 0.34038126,\n", - " 0.57002372])" - ] - }, - "execution_count": 5, - "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": 6, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.nn.v3.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "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": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[<matplotlib.lines.Line2D at 0x7fa445a36ac8>]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "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": [ - "results[test] = probas_\n", - "plt.plot(results)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6326 - acc: 0.6410\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6339 - acc: 0.6419\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6298 - acc: 0.6427\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.6340 - acc: 0.6348\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 30us/step - loss: 0.6302 - acc: 0.6462\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6294 - acc: 0.6448\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6339 - acc: 0.6377\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 32us/step - loss: 0.6309 - acc: 0.6418\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.6338 - acc: 0.6352\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6317 - acc: 0.6415\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6317 - acc: 0.6408\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6305 - acc: 0.6445\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6302 - acc: 0.6434\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6290 - acc: 0.6415\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6300 - acc: 0.6415\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6299 - acc: 0.6443\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6285 - acc: 0.6488\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6297 - acc: 0.6471\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6298 - acc: 0.6441\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6283 - acc: 0.6438\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6291 - acc: 0.6448\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6290 - acc: 0.6437\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6279 - acc: 0.6434\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6282 - acc: 0.6396\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6290 - acc: 0.6476\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6312 - acc: 0.6405\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6271 - acc: 0.6425\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6283 - acc: 0.6471\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6281 - acc: 0.6444\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6281 - acc: 0.6477\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6273 - acc: 0.6463\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6285 - acc: 0.6440\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6259 - acc: 0.6495\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6279 - acc: 0.6493\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6296 - acc: 0.6440\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6286 - acc: 0.6403\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6263 - acc: 0.6481\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6265 - acc: 0.6496\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6276 - acc: 0.6474\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6271 - acc: 0.6518\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6282 - acc: 0.6455\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6285 - acc: 0.6448\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6287 - acc: 0.6434\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6262 - acc: 0.6459\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6251 - acc: 0.6495\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6274 - acc: 0.6466\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 33us/step - loss: 0.6270 - acc: 0.6469\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 32us/step - loss: 0.6294 - acc: 0.6476\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6266 - acc: 0.6462\n", - "Epoch 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[==============================] - 0s 29us/step - loss: 0.6284 - acc: 0.6484\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 31us/step - loss: 0.6259 - acc: 0.6449\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6275 - acc: 0.6448\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6269 - acc: 0.6470\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6270 - acc: 0.6470\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 28us/step - loss: 0.6271 - acc: 0.6467\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6276 - acc: 0.6460\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6286 - acc: 0.6433\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6265 - acc: 0.6469\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6263 - acc: 0.6466\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6276 - acc: 0.6465\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6271 - acc: 0.6503\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6270 - acc: 0.6476\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6293 - acc: 0.6415\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6253 - acc: 0.6517\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6270 - acc: 0.6449\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6259 - acc: 0.6482\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6249 - acc: 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[==============================] - 0s 24us/step - loss: 0.6303 - acc: 0.6436\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6308 - acc: 0.6415\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6296 - acc: 0.6393\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6310 - acc: 0.6415\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6307 - acc: 0.6429\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6297 - acc: 0.6438\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6316 - acc: 0.6385\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6298 - acc: 0.6416\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6298 - acc: 0.6414\n", - "Epoch 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- "7272/7272 [==============================] - 0s 25us/step - loss: 0.6298 - acc: 0.6430\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6315 - acc: 0.6396\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6309 - acc: 0.6415\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6281 - acc: 0.6441\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6299 - acc: 0.6444\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6320 - acc: 0.6392\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6290 - acc: 0.6433\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6313 - acc: 0.6427\n", - "Epoch 22/50\n", - "7272/7272 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[==============================] - 0s 24us/step - loss: 0.6314 - acc: 0.6429\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6317 - acc: 0.6407\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6297 - acc: 0.6429\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6298 - acc: 0.6393\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6327 - acc: 0.6378\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 29us/step - loss: 0.6303 - acc: 0.6469\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6299 - acc: 0.6451\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6305 - acc: 0.6425\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6312 - acc: 0.6419\n", - "Epoch 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[==============================] - 0s 25us/step - loss: 0.6314 - acc: 0.6427\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6290 - acc: 0.6458\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6282 - acc: 0.6449\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6281 - acc: 0.6434\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6312 - acc: 0.6445\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6270 - acc: 0.6462\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6296 - acc: 0.6459\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6289 - acc: 0.6433\n", - "Epoch 42/50\n", - "7272/7272 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[==============================] - 0s 59us/step - loss: 0.6316 - acc: 0.6397\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 44us/step - loss: 0.6330 - acc: 0.6382\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 27us/step - loss: 0.6299 - acc: 0.6397\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 32us/step - loss: 0.6307 - acc: 0.6462\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6305 - acc: 0.6390\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6305 - acc: 0.6408\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 29us/step - loss: 0.6320 - acc: 0.6388\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 37us/step - loss: 0.6294 - acc: 0.6392\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 36/50\n", - "7272/7272 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[==============================] - 0s 23us/step - loss: 0.6332 - acc: 0.6407\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6332 - acc: 0.6355\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6398 - acc: 0.6331\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6317 - acc: 0.6425\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6334 - acc: 0.6385\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6339 - acc: 0.6386\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6327 - acc: 0.6371\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6322 - acc: 0.6458\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6333 - acc: 0.6416\n", - "Epoch 12/50\n", - 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"Epoch 21/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6322 - acc: 0.6396\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6306 - acc: 0.6458\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6309 - acc: 0.6434\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6329 - acc: 0.6443\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6303 - acc: 0.6427\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6296 - acc: 0.6418\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6309 - acc: 0.6423\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6300 - acc: 0.6459\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6314 - 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[==============================] - 0s 22us/step - loss: 0.6283 - acc: 0.6441\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6292 - acc: 0.6399\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6285 - acc: 0.6425\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6302 - acc: 0.6421\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6341 - acc: 0.6405\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6326 - acc: 0.6381\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6317 - acc: 0.6403\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6344 - acc: 0.6359\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6331 - acc: 0.6360\n", - 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- 0s 21us/step - loss: 0.6293 - acc: 0.6426\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6296 - acc: 0.6415\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6293 - acc: 0.6465\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6298 - acc: 0.6471\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6288 - acc: 0.6465\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6283 - acc: 0.6422\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6282 - acc: 0.6456\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6280 - acc: 0.6452\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6301 - acc: 0.6449\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6275 - acc: 0.6482\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6274 - acc: 0.6444\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6273 - acc: 0.6434\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6272 - acc: 0.6471\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6322 - acc: 0.6432\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6291 - acc: 0.6471\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6280 - acc: 0.6425\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6268 - acc: 0.6474\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6273 - acc: 0.6467\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6271 - acc: 0.6491\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6331 - acc: 0.6361\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6322 - acc: 0.6394\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6333 - acc: 0.6407\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6342 - acc: 0.6385\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6345 - acc: 0.6374\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6336 - acc: 0.6419\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6327 - acc: 0.6370\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6315 - acc: 0.6422\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6335 - acc: 0.6386\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6318 - acc: 0.6418\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6322 - acc: 0.6403\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6329 - acc: 0.6379\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6330 - acc: 0.6415\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6316 - acc: 0.6429\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6316 - acc: 0.6401\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6322 - acc: 0.6383\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 22us/step 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[==============================] - 0s 23us/step - loss: 0.6308 - acc: 0.6455\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6287 - acc: 0.6437\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6301 - acc: 0.6421\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6293 - acc: 0.6447\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6291 - acc: 0.6415\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6316 - acc: 0.6449\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6292 - acc: 0.6419\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6282 - acc: 0.6460\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6300 - acc: 0.6436\n", - "Epoch 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[==============================] - 0s 23us/step - loss: 0.6319 - acc: 0.6429\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6326 - acc: 0.6407\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6357 - acc: 0.6381\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6324 - acc: 0.6401\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6319 - acc: 0.6418\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6319 - acc: 0.6427\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6307 - acc: 0.6379\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6310 - acc: 0.6411\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6319 - acc: 0.6421\n", - "Epoch 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0.6427\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6348 - acc: 0.6429\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6300 - acc: 0.6469\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6306 - acc: 0.6465\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6322 - acc: 0.6415\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6304 - acc: 0.6447\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6305 - acc: 0.6463\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6314 - acc: 0.6436\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6298 - acc: 0.6484\n", - "Epoch 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[==============================] - 0s 25us/step - loss: 0.6342 - acc: 0.6346\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6334 - acc: 0.6410\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6322 - acc: 0.6366\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 26us/step - loss: 0.6336 - acc: 0.6377\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6317 - acc: 0.6422\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6307 - acc: 0.6418\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6318 - acc: 0.6411\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6334 - acc: 0.6374\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 22us/step - loss: 0.6324 - acc: 0.6393\n", - "Epoch 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loss: 0.6297 - acc: 0.6440\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6299 - acc: 0.6407\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6304 - acc: 0.6361\n", - ">>\n", - ".\n", - "Epoch 1/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6352 - acc: 0.6366\n", - "Epoch 2/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6350 - acc: 0.6381\n", - "Epoch 3/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6340 - acc: 0.6381\n", - "Epoch 4/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6333 - acc: 0.6360\n", - "Epoch 5/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6351 - acc: 0.6327\n", - "Epoch 6/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6350 - acc: 0.6399\n", - "Epoch 7/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6351 - acc: 0.6364\n", - "Epoch 8/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6336 - acc: 0.6370\n", - "Epoch 9/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6324 - acc: 0.6390\n", - "Epoch 10/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6338 - acc: 0.6416\n", - "Epoch 11/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6363 - acc: 0.6375\n", - "Epoch 12/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6329 - acc: 0.6363\n", - "Epoch 13/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6346 - acc: 0.6381\n", - "Epoch 14/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6333 - acc: 0.6361\n", - "Epoch 15/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6329 - acc: 0.6374\n", - "Epoch 16/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6317 - acc: 0.6381\n", - "Epoch 17/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6332 - acc: 0.6366\n", - "Epoch 18/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6321 - acc: 0.6404\n", - "Epoch 19/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6326 - acc: 0.6456\n", - "Epoch 20/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6315 - acc: 0.6447\n", - "Epoch 21/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6307 - acc: 0.6419\n", - "Epoch 22/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6343 - acc: 0.6356\n", - "Epoch 23/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6301 - acc: 0.6463\n", - "Epoch 24/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6334 - acc: 0.6367\n", - "Epoch 25/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6302 - acc: 0.6437\n", - "Epoch 26/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6337 - acc: 0.6411\n", - "Epoch 27/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6331 - acc: 0.6361\n", - "Epoch 28/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6317 - acc: 0.6410\n", - "Epoch 29/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6334 - acc: 0.6381\n", - "Epoch 30/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6306 - acc: 0.6443\n", - "Epoch 31/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6295 - acc: 0.6454\n", - "Epoch 32/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6305 - acc: 0.6449\n", - "Epoch 33/50\n", - "7272/7272 [==============================] - 0s 25us/step - loss: 0.6301 - acc: 0.6388\n", - "Epoch 34/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6309 - acc: 0.6394\n", - "Epoch 35/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6318 - acc: 0.6466\n", - "Epoch 36/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6305 - acc: 0.6414\n", - "Epoch 37/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6329 - acc: 0.6360\n", - "Epoch 38/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6288 - acc: 0.6485\n", - "Epoch 39/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6298 - acc: 0.6438\n", - "Epoch 40/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6306 - acc: 0.6448\n", - "Epoch 41/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6288 - acc: 0.6436\n", - "Epoch 42/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6308 - acc: 0.6389\n", - "Epoch 43/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6289 - acc: 0.6465\n", - "Epoch 44/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6288 - acc: 0.6419\n", - "Epoch 45/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6295 - acc: 0.6485\n", - "Epoch 46/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6294 - acc: 0.6470\n", - "Epoch 47/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6310 - acc: 0.6408\n", - "Epoch 48/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6326 - acc: 0.6432\n", - "Epoch 49/50\n", - "7272/7272 [==============================] - 0s 24us/step - loss: 0.6289 - acc: 0.6410\n", - "Epoch 50/50\n", - "7272/7272 [==============================] - 0s 23us/step - loss: 0.6302 - acc: 0.6397\n", - " 0.6936436549902885\n" - ] - }, - { - "data": 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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([0.32885039, 0.69790697, 0.65291864, ..., 0.55279464, 0.39190483,\n", - " 0.36502624])" - ] - }, - "execution_count": 9, - "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": 10, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr.v3.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "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": 12, - "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": [ - " 0.6600275735294119\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": [ - " 0.6410049019607843\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": [ - " 0.693688725490196\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": [ - " 0.6756525735294118\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": [ - " 0.6942279411764706\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": [ - " 0.6694975490196078\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": [ - " 0.6883976715686274\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": [ - " 0.7092647058823529\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": [ - " 0.6774096699283731\n", - " 0.7044642693021745\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" - ] - }, - { - "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([0.24498057, 0.76407456, 0.60673071, ..., 0.66584657, 0.42254475,\n", - " 0.40462401])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Logistic regression (scikit)\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", - " 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": 13, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.lr2.v3.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "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": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ">>\n", - " 0.6918474264705883\n", - ">>\n", - " 0.6687438725490196\n", - ">>\n", - " 0.7036397058823529\n", - ">>\n", - " 0.6899785539215686\n", - ">>\n", - " 0.7006372549019608\n", - ">>\n", - " 0.6927267156862745\n", - ">>\n", - " 0.7107383578431373\n", - ">>\n", - " 0.7054718137254903\n", - ">>\n", - " 0.696354935756432\n", - ">>\n", - " 0.7062564718425067\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([0.40430639, 0.58317981, 0.54512806, ..., 0.45729833, 0.39574646,\n", - " 0.39546193])" - ] - }, - "execution_count": 15, - "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": 16, - "metadata": {}, - "outputs": [], - "source": [ - "df_results = pd.DataFrame(data={\"name\": names, 'pred': results})\n", - "df_results.to_csv('/home/drewe/notebooks/genotox/pred.rf.v3.csv', index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "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": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} |