{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Classifying primary tumor with microRNA markers\n", "\n", "### Raw data from TCGA is processed in R \n", "- `mirna_data_processing.R` \n", "- `raw_data.R`\n", "- `nano_count.R`\n", "\n", "### Some parameter tuning is computed on the UIUC nano cluster: \n", "- `nn_loop_miRNA.py`\n", "- `nn_miRNA.py` \n", "\n", "Data structures from the cluster are saved using cPickle, and loaded into this jupyter notebook for further visualization and analysis. \n", "\n", "Files generated by R can be found under the `tumor-origin/mirna` folder" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy\n", "import pandas as pd \n", "from tensorflow.keras import layers \n", "from tensorflow.keras.utils import to_categorical\n", "\n", "from numpy import random\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn import svm\n", "\n", "from sklearn import metrics\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import classification_report\n", "from sklearn.metrics import accuracy_score\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "import cPickle as pickle" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('mirna/raw.txt', sep='\\t')\n", "types = pd.read_csv('mirna/types-numeric.txt', sep='\\t')\n", "labels = pd.read_csv('mirna/types-labels.txt', sep='\\t')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "# train test split \n", "random.seed(69)\n", "ii = numpy.random.rand(len(data)) < 0.7 \n", "\n", "np_data = data.values\n", "np_types = types.values\n", "np_labels = labels.values\n", "\n", "# scaling data \n", "from sklearn import preprocessing\n", "min_max_scaler = preprocessing.MinMaxScaler()\n", "np_data_min_max = min_max_scaler.fit_transform(np_data)\n", "\n", "train = np_data_min_max[ii]\n", "test = np_data_min_max[~ii]\n", "\n", "# train = np_data[ii]\n", "# test = np_data[~ii]\n", "\n", "pand_train = data[ii]\n", "pand_test = data[~ii]\n", "\n", "# types = numbers assigned (0-16)\n", "train_types = np_types[ii]\n", "test_types = np_types[~ii]\n", "\n", "# labels = string values assigned (then one-hot encoded later)\n", "train_labels = np_labels[ii]\n", "test_labels = np_labels[~ii] \n", "\n", "# ravel \n", "r_train_types = train_types.ravel()\n", "r_test_types = test_types.ravel()\n", "\n", "r_train_labels = train_labels.ravel()\n", "r_test_labels = test_labels.ravel()\n", "\n", "# One hot encoding of string labels for keras model \n", "encoded_train = to_categorical(r_train_types)\n", "encoded_test = to_categorical(r_test_types)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Baseline models \n", "- Random Forest \n", "- KNN\n", "- SVM (computed but not used) " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Accuracy: ', 0.9537037037037037)\n" ] }, { "data": { "text/html": [ "
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PredictedblcabrcacholcoadescahnsckichkirclichluadovpaadpradskcmstadthcaucecAll
True
blca1284000000000000000132
brca1372000000000000000373
chol02100000000000000012
coad0201520000000000000154
esca243053300000000010066
hnsc1100015400000000001157
kich00000028100000000130
kirc1100001175000000000178
lich0000100011800001000120
luad1700010001571000105173
ov0000000000140000004144
paad00000101020390061050
prad0200000000001680000170
skcm0000000000000123000123
stad1008010003011013100146
thca0100000001000001670169
ucec0700000001000000171179
All157402101653415729177118164141401691241391681822376
\n", "
" ], "text/plain": [ "Predicted blca brca chol coad esca hnsc kich kirc lich luad ov \\\n", "True \n", "blca 128 4 0 0 0 0 0 0 0 0 0 \n", "brca 1 372 0 0 0 0 0 0 0 0 0 \n", "chol 0 2 10 0 0 0 0 0 0 0 0 \n", "coad 0 2 0 152 0 0 0 0 0 0 0 \n", "esca 24 3 0 5 33 0 0 0 0 0 0 \n", "hnsc 1 1 0 0 0 154 0 0 0 0 0 \n", "kich 0 0 0 0 0 0 28 1 0 0 0 \n", "kirc 1 1 0 0 0 0 1 175 0 0 0 \n", "lich 0 0 0 0 1 0 0 0 118 0 0 \n", "luad 1 7 0 0 0 1 0 0 0 157 1 \n", "ov 0 0 0 0 0 0 0 0 0 0 140 \n", "paad 0 0 0 0 0 1 0 1 0 2 0 \n", "prad 0 2 0 0 0 0 0 0 0 0 0 \n", "skcm 0 0 0 0 0 0 0 0 0 0 0 \n", "stad 1 0 0 8 0 1 0 0 0 3 0 \n", "thca 0 1 0 0 0 0 0 0 0 1 0 \n", "ucec 0 7 0 0 0 0 0 0 0 1 0 \n", "All 157 402 10 165 34 157 29 177 118 164 141 \n", "\n", "Predicted paad prad skcm stad thca ucec All \n", "True \n", "blca 0 0 0 0 0 0 132 \n", "brca 0 0 0 0 0 0 373 \n", "chol 0 0 0 0 0 0 12 \n", "coad 0 0 0 0 0 0 154 \n", "esca 0 0 0 1 0 0 66 \n", "hnsc 0 0 0 0 0 1 157 \n", "kich 0 0 0 0 0 1 30 \n", "kirc 0 0 0 0 0 0 178 \n", "lich 0 0 1 0 0 0 120 \n", "luad 0 0 0 1 0 5 173 \n", "ov 0 0 0 0 0 4 144 \n", "paad 39 0 0 6 1 0 50 \n", "prad 0 168 0 0 0 0 170 \n", "skcm 0 0 123 0 0 0 123 \n", "stad 1 1 0 131 0 0 146 \n", "thca 0 0 0 0 167 0 169 \n", "ucec 0 0 0 0 0 171 179 \n", "All 40 169 124 139 168 182 2376 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# random forest baseline \n", "\n", "rfmodel = RandomForestClassifier(n_estimators=100)\n", "rf = rfmodel.fit(train, r_train_labels)\n", "\n", "rf_pred = rf.predict(test)\n", "\n", "# Model Accuracy, how often is the classifier correct\n", "print(\"Accuracy: \", metrics.accuracy_score(r_test_labels, rf_pred))\n", "rf_cm = confusion_matrix(r_test_labels, rf_pred,)\n", "\n", "y_true = pd.Series(r_test_labels)\n", "rf_pred = pd.Series(rf_pred)\n", "\n", "pd.crosstab(y_true, rf_pred, rownames=['True'], colnames=['Predicted'], margins=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cm_labels = ['brca', 'blca', 'chol', 'coad', 'esca', 'hnsc', 'kich', 'kirc', 'lich', 'luad', 'ov', 'paad', 'prad', 'skcm', 'stad', 'thca', 'ucec']\n", "labels = r_test_labels\n", "plt.matshow(rf_cm, cmap='binary')\n", "plt.xticks(range(len(labels)), labels, rotation=45)\n", "plt.yticks(range(len(labels)), labels)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "labels = ['blca', 'brca', 'chol', 'coad', 'esca', 'hnsc', 'kich', 'kirc', 'lich', 'luad', 'ov', 'paad', 'prad', 'skcm', 'stad', 'thca', 'ucec']\n", "label_values = True\n", "# plt.figure(figsize=(8,8))\n", "# plt.matshow(rf_cm)\n", "matfig = plt.figure(figsize=(12,12))\n", "plt.matshow(rf_cm, fignum=matfig.number)\n", "\n", "plt.title('True label')\n", "\n", "plt.colorbar()\n", "plt.xticks(range(len(labels)), labels, rotation=90)\n", "plt.yticks(range(len(labels)), labels)\n", "plt.ylabel(\"Clustering label\")\n", "if label_values:\n", " for (i, j), z in numpy.ndenumerate(rf_cm):\n", " plt.text(j, i, z, ha='center', va='center',\n", " bbox=dict(boxstyle='round', facecolor='white', edgecolor='0.3'))" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[139, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0], [0, 123, 0, 0, 1, 7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 4, 369, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 145, 5, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 24, 0, 0, 6, 33, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0], [0, 3, 0, 0, 0, 5, 144, 0, 0, 0, 0, 0, 3, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 30, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 176, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 116, 1, 0, 1, 0, 0, 1, 0], [0, 3, 4, 0, 1, 1, 0, 0, 0, 0, 159, 0, 3, 0, 2, 0, 0], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 168, 0, 0, 0, 0, 0], [0, 0, 0, 0, 7, 7, 2, 0, 0, 0, 1, 0, 126, 0, 0, 3, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 165, 0, 0, 0], [0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 172, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 6, 0, 0, 42, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 122]]\n" ] } ], "source": [ "nn_cm = [[139,0,1,0,0,0,0,0,0,0,0,0,0,0,4,0,0],\n", " [0,123,0,0,1,7,1,0,0,0,0,0,0,0,0,0,0],\n", " [0,4,369,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\n", " [0,2,0, 10,0,0,0,0,0,0,0,0,0,0,0,0,0],\n", " [0,2,0,0,145,5,0,0,0,0,0,0,2,0,0,0,0],\n", " [0 ,24,0,0,6, 33,1,0,0,0,0,0,2,0,0,0,0],\n", " [0,3,0,0,0,5,144,0,0,0,0,0,3,1,1,0,0],\n", " [0,0,0,0,0,0,0, 30,0,0,0,0,0,0,0,0,0],\n", " [0,0,2,0,0,0,0,0,176,0,0,0,0,0,0,0,0],\n", " [0,0,0,1,0,0,0,0,0,116,1,0,1,0,0,1,0],\n", " [0,3,4,0,1,1,0,0,0,0,159,0,3,0,2,0,0],\n", " [0,0,2,0,0,0,0,0,0,0,0,168,0,0,0,0,0],\n", " [0,0,0,0,7,7,2,0,0,0,1,0,126,0,0,3,0],\n", " [0,0,0,0,0,0,0,1,0,0,3,0,0,165,0,0,0],\n", " [0,1,3,0,0,0,0,0,0,0,1,0,2,0,172,0,0],\n", " [0,0,0,0,0,0,1,0,1,0,0,0,6,0,0, 42,0],\n", " [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,122]]\n", "print(nn_cm)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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LGxQQ5Ei82g4VwHrcyaMDQVX/D/i/oOwdYPs579/duBGaIG1/Boz3ZoygdrQmCETkBOAW9g04qslRDepHJtlTobT3S6qaEJEeIpJR+0lEEKi3olzI1DhPZd6Anx2keUGCA6iJzN2AG5nLwY3SBcEeVXVEpFpE8nAXgOkWkO0fA/8QkY2419rRuJHxQPCeuLxV47yKSGsR6RnU4FLP5t6Ulpp9QaS0hD31mJE+zJHdn1zgJRHZiTv45x+qGtRE2dcD80TkDfaN3L8qCMMiEgFGqWooDpU30OgO3IiMEOyAH0SkK24k9kTcm4e3cAcFrA/A9rW4cwgXAw+JyEjgF6r6Urpte/wZN/q8kHDmcf1ARO4Bfu9tX+MdSxB8DrwtIrOpNdWcqt4ThHEJd/qv57xUprtwB/4o8Kf6P+IPXn9TpO7qUm8SfGTuA0/7Q7htrQR4NwjDqvq+iAzEjYYCrFLVqvo+4zOhDi71UlqycAMGf8KNji8IyPYVqvpn3HSGmn2zVDWouZONNGHTbyVBRIbi3iWfD6xX1a8GZLc97upaAPNVNbBcSRH5IKwpNETkU+AcVQ1ysEdt+y8D/8u+3NBvAZeo6ukB2F6sqsNE5AzcqPgNwOOqOjLdtj3776lqILmJddjPxo3E1VxjLwPTVTXti0J4KQwHoarT0m3bsx/q9F+1bGbi5m4GNnNBmP3NAcfRE8hT1SUB2jyeg1eyC2KgWV1tbrHuv8paOu0vqZXSMtQLnMxR1ZMCsP0C7o3jE97273Hb/RXptp0OBg3N1MeeC/IhSnLG9vjCpt9qomzFzdnaQZrztrwIXG1qlkbt7qUaBDWv479F5Ge4keja0amdAdjeEpYT69FBVR+utf2IiPw4INs1SU5n4eZsLpOaIeTB8JqI3IU7KXjt1W4CaXeewxpKRCQoh7UeQpv+S0QOysMOcOQ+hNjfiMgrqnqaZ2/tgfvSbPtxoA+wiH1PQBQI6ryHNhbCI8yUlvOB2SLiAGcChc3ViTX2xxzZWojI1cBFQAfcxy3fCSCH5le1/q8dHhdvO+2To3tc7Nm7+oD9aXvs56UUgPuo72+4A3zCWDpwh4h8C3jS255KMPPnAiwUd3aK3rjLhOYCTkC2Yd9I8VEH7E9ruxORX6vqj0XkWZLkgqtq2kaRh2n7AOYS3vRftR8ltwJOw00xCMqhCqO/aYX7WLu9l8ZRc8OYB3RJl90DGA0cU3PzEgKhjoVgX0rLnexLIUprSssBM4Fcifs78zbuPL4FAQVrjDRijuz+dMNdmGACbicbxHyap8DeFZ2uZv88zSDnmDwmif10j+aumbdUgTLCWzrwctwc2Xs9u+8AlwVk+wrcdILlqlomIt1xB4QExddxIxU92dcfBPEjW5PG8QZw4Jy96Z5jNkzbtfk5rvP6fW/7ZQLKU1XVH9be9pyLp4Kw7RFGf/Nd3GurM64TVRMsKMa9/oNgKe4Ar00B2TuQS3Bnp6hp58XevqDmD74bt72fhJuXHMTvXM0sITUI7hOwmtULg87R9g2bR9bFcmRr4Q28uRLXgRLcVVAeVNW0d3Ii8nfcKVGe8HZ9E8hX1YvSbTts+xLyiiue/R97g09q7uDvDsK+iNyPG4E9VVUHedpfUtWgBl/MBQpxo3F7H3UGOODpQ+DbqrrU256KWxdpz9sN03ZTQ0TiwFINbknsMPubm4Bfq2qRiNwIjARuS2c6Ta3ofy4wHHeAU+2nT4E8BRCRn9babIW7muGKAPvav+M6zzXTnQVZ7xcBc4Os93QyaGimPvJs57APg/E911qObBPjCmB8zUATEbkD964xiLv1Iap6TK3t10QkyKlBwrQf9oorQ2ucWM/+zgDtj1PVkeItC+tpD3Jlr66qemaA9g7kAuCfIvJN3CjNt9k/Mn/E2RaRv6vqRSLyMclTG4YGcAy10yoiuBHSv6fbbi3C7G8uUNVbReRE3BSau3Gjgum8gbkbNzhyB1B7wY+afYGgqrVT2RCRu3FXuAuKMOv9BlX9e8D1njZsHtl9mCO7P8L+UxAlILCW8qGIjFfV+QAiMg74ICDbYduPiEjbAyKiQbbNMO1XeYN8agb8dCDYHNl3RORYVf04QJt7UdXPRWQKbt7al8DXVDWQCeJDtH2t9/fsAGzVxd21/q8GvghiurlahNnf1PTxZwEPqerzIjI9nQbVXfoaEYnX/F+Dl1YWFllA1wDttah6N4LBHNn9eRh4T0RqVhr6Bu48m2mjVlQmjutUfOlt98BdizythG3fI9mKKzMCsh22/d8CTwMdRWQGbpTwhnQbrVXvMeA/ReRz3EedNXP4pjUqmCQaWQBEca+/tEYlw7QNoKqbvL9fpNNOA8fwRsOl0soo9vU3AN2BVTV1k+Y62OANsDsduEPc6cfSuly7iHwfNye4t4jUnuorF3fgUSAc0PajuAObg8qPhRZW70YwWI7sAXjTYZ3obb6lqh+l2V6965un+8cubPu1juMY9o2UfzWA2SKajH1xJ0g/DdeJfCWIqcjCrvcw7TcB7cUkH1CX9oVAwrR9wHGEWf9ZuNMvfayqq0WkE3CspnEREnGXwG4LzGT/6eaKgxw1f8B5r8ad+rA6JPsHcaTVezoZOLSVPjQ7yGB6cib0+iz0HFlzZA3DMAzDMJoR5sjuw1ILDMMwDMMwmhEKOJYZAVh+SJ2IyFUt1b5pDw/T3vJsh23ftIeHaW95tg3/MUe2bsJu6GHaN+0t075pb5n2TXvLtG/ajSMCSy0wDMMwDMNoZtg8si5HrCMba52t8fyChgvWQTy3La2P7nbII+HiW0oP2TZAK7LIk4JQRuKFaTts+6bdtLc0+6bdtLc0+4dru5hd21W1g5/HZBw6R6wjG88voO8lPwnN/tH3vhOabcMwDOMIJxIN176TaLjMEcq/9Z+hzQFtHMwR68gahmEYhmEciagKCbVhTmCDvQzDMAzDMIxmikVkDcMwDMMwmhmODfYCLCJrGIZhGIZhNFPMkTUMwzAMwzCaJS0mtUAS1eQWb6a1U0HUp2i8KpQRoyT3KBLxVof2JZlKrEuCjDZxJNkgVIWKwioSmyNoid/3HUrkaId4OyGWlXwEbKLSoWqXQ2JdBNTnxxgZSqxrgow2MSRZpShU7K4msVnQYv/vuSIdEsTaC/GcOrRXeNrXp0F7XIl2TZDZNiztDrH2hKo9o02MSKwe7VsELUqD9vYOsQ5KPDtGsidzaW3zKWivLKqmeougu9OgvZ2nPSe5dqfSobKm3h2ftcc87W1D0l7gEO2oZNSnvdCr97Rod8hoEyUSr0f7VkEL06C9bYJoB4eM3Dq0VymVuxIk1sf81x5Vot0a0F5cTWKr4OxKh3av3uvUXqvNJ5rPo3oFEhaLBFqIIyuJanptW8bw/r0ZOXwY8Xjcl+9NJBJ8+vnnvPXue6zpOITqeOvGfUGmkv9VYdxJJzBgYH9isYOrw3EcNm7YxEtzXqLoDcfHDl5pNRg6jGzDGRPPIDc3N2mp8vJy3n7zHdZ89CXF8xz/ftgzlfzTIow5YSyDBg+sU/vmTZuZ+8KLrnYfO/h4b4eCMdlMPGci+fl5iBysa8+ePbz52lt8uWgjJe/6qD3D1T7qhNEcM3hQ0vboOA5bNm9hzvNzKX7T8bWDj/V0KBjbmrMmTSQ/Pz+p9vLyct56fR5rF62n5G0ftcdd7SOOG8GQoUPq1L51y1bmPD+HojcdnJ0+au/u0HZcK86edBb5berWnpY2H1fyT40wbPwwhg4fmlS7qrJ1y1ZeeO4Fit5ycHb4pz3a1aHt+AzOnnQ2bdq2Saq9oqKCd+a9y2cfrqX4Lcc/pybmah8y9lhGjBpet/at23jh2RcompfA2e6j9i4ObcZncPaks2hb0LZO7fPfns+nH66h6E1/teedEmHI2MGMGDWCjIyMg4qoKtu2bef5Z5+n6O1qnK3+Ta0VObqaNuNjnD1pEgXtCurUvmD++3yy8DOK3lT/tEdd7YNGD2L02FF1at+xfQfPzX6e3e9U4WzxU3uC/OPjnD3pLNq1a5dUe2VlJR+89wErPviEotedZuXMGi4twpHtVvgZp4wbxbU//EHShny49Or5DI/+7f/4tNOIRn0u/ytRzp/6DaZ+c2qDZYcOO5Y79S6Kn/enk4l0cugwsg33/OYe2rRpU2/Zc845h1tumsbHu1exZ8lhmwYgf0KUcy86h0u//a0Gyw4bPoxZziyK/NJe4NBmdCt+9eu7Oeqoo+ote84553DDL29g+a7PqVhx2KYByD8pylnnnclll1/WYHscMXIE06fNoHiOQrUP2ts6tBmdyd333kWnTp3qLXvOOedw4/U3sazwU8qXHrZpAPJPjPK1c7/Kd666skHto0aP4tabb6VojkLV4WuXfIf8MRncdc+ddOnSpd6y55xzDtNumsYSH9t83vFRTj37ZL5/9fca1D5m7BhuvuEWiuYqVPqgPc8hf2yMu+65i65du9Zb9pxzzuG2W25j0e4VlC3yZ776vOOjTPj6ifzwRw33wePGjeXG629ytVf4oD3XIW9sjDt/dQfdu3evt+ykSZOYcdsMPixeSulCf7Tnjoty4teO49r/+hGRSP3O+fjx47j+lzdQ/KKi5T5oz3bIGxdh1l0z6dWrV71lJ02axKzb7+D9kiWUfnDYpgHIHRtl/Gmj+el1P21Q+3HHH8cvf/4/FL2o6B4ftGcpueOjzLzjdvr06VNv2UmTJnHXHXczv3QhJfOdw7ZtBEuLiEu3qijmwvPPS4sTC3DO2Weje4qRRk0QrVRnVnDe+eelVHr8+PHk5OQgWf50rvEC4YyJZzToxALE43HOu2AyrTr5dd+jJFpVcv4FqWkfM2YMeXn5SLY/2iNtHE486cQGnViAjIwMvnH+N2jdyZ8oPoCTVcX5F56fUnscOXIkBW0LfNMu+Q7jxo9r0ImFffXe2rd6B81NcEGK2ocNG0aH9h2J5PhU7/nK6NGjGnRiwdN+4Xk+tnn33F940QUpaR8yZAhHH3U0kVz/tA8fPrxBJxYgFotx3oXnkXm0j5GxNpqy9mOOOYaunbv6pz1PGTJkSINOLEA0GuX8C88n4yj/tEcL4IKLzm/QkQMYOHAgPbv3RPL8caYieQ4DBwxs0IkFV/sFF51PRkf/fidj7YULLrogJe39+vWjT68+vmmXPIf+ffs16MQCRCIRLrjofOLtm1M01p1HNuxXU6BpHEWa0epqcnJy9tt3+eWX07FjR4YMGbJ334033sjQoUMZPnw4X/va19i4cSMAu3btYvLkyQwdOpSxY8eydOn+4aloNEo8I7NxjmwERITMzMz9ds+dO5cBAwbQt29fZs2atd97Wa2zfIuhx7KiB6UTJDsnNeTm5iI+xu8ddcjKytpvX33as7OzEb98yRi0LTjYga/Lfk5ODpEM/zq4aj24PTao3adzL4egXZLltR0iiUZqz8nJ8a3NS1xp01jtPrb5hCbIzs5OyTZATm4uRH1awTOm5LfJP2h3Xfb9vt4TJBpV77m5ORDzSzuN1u7beQdUnMZpz/Px3McgPz8v6VuJRIIRI0Zw9tln77OdmwsR/yKSjdWe56N2iUFu3sHa67ve1UftRnC0CEc2GZdddhlz587db991113HkiVLWLRoEWeffTa33norALfffjvDhw9nyZIlPPbYY1x77bVpOaZEIsE111zDnDlzWL58OU8++STLly9Pi61kJDsnQRG29jDtm3bTbtpNe9D85je/YdCgQYHaDFt72Pb9RAGHSOivpkDTOIoQmDBhAgUFBfvty6t191ZaWrr3Mdjy5cs59dRTAffRz9q1a9myZYvvx7RgwQL69u1L7969ycjIYMqUKTzzzDO+26mLZOckKMLWHqZ9027aTbtpD5L169fz/PPPc+WVVwZqN2ztYds30kOTdGRF5CcistR7/VhEZonINbXev0VEfpYO29dffz3dunXjiSee2BuRHTZsGP/6178A90L44osvWL9+ve+2N2zYQLdu3fZud+3alQ0bNvhupykStvYw7Zt20x607bDtm/Zw+/kf//jH3HnnnSnlrvpJ2NrDtm+khybnyIrIKOA/gXHAeOA7wN+Ai2oVu8jb5zszZsxg3bp1XHLJJdx3330A/OK+sPGjAAAgAElEQVQXv6CwsJDhw4fzu9/9jhEjRhCN+jcYwDAMwzCC4LnnnqNjx46MGjUq7EMxDpOESuivpkBTnH7rROBpVS0FEJF/AScBHUWkM9AB2KWq6w78oIhcBVwFEM9te1gHcckllzBx4kSmTZtGXl4eDz/8MODOederVy969+59WN+fjC5durBu3T5Z69evT2mE9ZFA2NrDtG/aTXvQtsO2b9rD0/72228ze/ZsXnjhBcrLyykqKuJb3/oWf/3rX9NuO2ztYds30kOTi8jWwz+AC4CLqSMaq6oPqupoVR0dzcpOVqReVq9evff/Z555hoEDBwJQWFhIZWUlAH/605+YMGHCfvm0fjFmzBhWr17NmjVrqKys5KmnnmLSpEm+22mKhK09TPum3bSbdtMeFDNnzmT9+vWsXbuWp556ilNPPTUQJxbC1x62fT9RhASR0F9NgaYYkX0LeEREZuEuKDcZuBSoBB4C2gNfOVwjU6dO5fXXX2f79u107dqVadOm8cILL7Bq1SoikQg9evTggQceAGDFihX8x3/8ByLC4MGD+fOf/3y45pMSi8W47777OOOMM0gkElx++eUMHjw4LbaSkeycXHHFFYHYDlt7mPZNu2k37aa9JRC29rDtG+mhyTmyqvqhiDwCLPB2/UlVPwIQkVxgg6puOlw7Tz755EH76nLajjvuOD755JPDNZkSEydOZOLEiYHYOpBk5yRIwtQetn3Tbtpbmn3THp72Gk4++WROPvnkQG2GrT1s+4b/NDlHFkBV7wHuSbL/2MP4zsM6pga//1A+09gP+alBG3dO/D5/iqKqKa+25rv9ELWn6zvTZvsIOvdh13ujCFm73+obZz9M2yFf70dQmw/zJ879vmZ0vR8CThNZWStsWsRZkIxMdu/enbbvr6qqorqyAifaiPsCx82bKC0tTfkjxaUlvqy7DlBVUs2unbtSLl9YWIhW+GIagFgkRnFxccrli4uLfLOvlcK2LdtSLr97924S5f51cvFIRqPa4+6i3WilP7a1Uti2dXvK5QsLC3H8rHeJN077bn+1b9+6o3G2fdUeo7CwMOXyhYWFvl3vVAo7tu0MxzYQ01ij6t1X+1Wwc3vjtEuVfz+NEY02SvvOnbtQn7RrJezc0bh+Xqp9XJ7XiTaqze/aucvH651G/8ZFEjYbUXOkRTiyJZn5/P6PD+4dsOUnqsrv738AJ7cdSGNOpxApyuC+3/6eRKLhpW3/8fd/UF5Sge459GOtTfU24bnZz/Pll182WLaoqIi/PPQwpV9U+WMcQXbFue8396Wk/el/PU1p8R60zJ/O3dkhzHvrbVauXNlg2V27dvHoXx6jdI1/bUd3RFOu99nPPEtxYQla6pP2ncKC995LaTWb3bt38/CfHqFsrV/1DoltEX7/299TXV3dYNk5L8xh185CtNg/7QsXLuTjjz9usGxxcbHPbR6qNwt/+N39VFU1/J0vvfgS27dtx/FL+64ISz52Vy1siJKSEv784F8o81F75SblD7+7P6U++JVXXmXz5s04Rf5pX758GQsXLmywbGlpKQ898CdftVdscLj/vgeoqGj4rujNN99kw4YNOLt90r47wierP+H9999vsGxZWRkP3v8Qe9Y1Yqn1Bihfn+CB3/+R8vLyBsvOmzePtV+sxdntj1vi7I7w+ZrPmT9/foNly8vL+eMfHvRVuxEc0hzD6anQ+uhu2veSn7gb6tB1x2o6Zyr9+vUnMzPDFxuJ6mrWfPEF63fsZk37Y9BaEdmj732n4S+IKnknRziqdwd69+5FLB4/qIjjOGzcsJG1n65l9ysK5f5FSaJdHPLGxhgyeDB5SdYiB9hTVsby5SvYvbyMPUvAjSP7QETJ/UqEo3q3p0/f3nVq37RxE2s+XUPRK4ru8U97pIND7gkRBg8aTJuCNklTHMpKy1i2bBlFS8spX+7jfHkRJXdChI69C+jTty/xjOTat2zawmerP6PoVfXNiQeItPe0H1O39j1lZSxbtpzdy8opX6r4Wu8nRmjfpy39+vUlnnHwtaiOw5bNW/j0kzRob+eQe2KEY445hrYFbevUnpY2L0rOCRE69G1D3379yEjSD7nat7J69acUv+qgpT5GBgscck+KMGjQIAraFdShfQ8rVqygcEUZexb5WO+i5BwfoV3ffPr3759cuyrbtmxl1apPKH5N0RIftbdxyJ0gDBw0iHbt29WpfeXKlRSuLKXsQ5+1j49Q0C+P/v37k9kq86AirvZtfLJqFUWvK1qcgvZIatFDyU+Qe5IyaOAg2nVIrr18TzkrV65k1yellC0UUtLupOL0KdnjIhT0z2XAgAF1at++dTsrV62k+HVFi/yrd8lzyD1ZGDhgIO07tk+qvaK8wtNeQukCh1S0/1v/uVBVR/t2oIdA72OzdfrTQ8I8BAAu6bcg9HPRMhxZAHVotaeQeFW5r4k41fFWlLdugx7QqaTkyAJElEg7RVprndePloOzMwJV/k8+LDkOkXytO1vaAS0RnF0pdm6NIWzt2Z72g/1IlwQ4JYIWpuHBhXjasxrQvivi6yPeveazlEgbp27tDmix4BSmod5T0V7h1XtY2tPV5sPW3trTXte9fLq1FyiS3YD2XRGoCEl7qeDsTIN2vHr3U3uKjiyAtHKQNg6SUcdvnyNombjtLlXtKTmysFd7ltb5DFgrPe0+Bmr20kqJtHWQ+uq9THB2pF7v5sjuoyk4sk1ysFdakAjlWQU0/IAjYBzB2Rbe6hhaEiFREpLxsLWXRkiknqLss3HB2R6i9jIhURZSPphpD8c2oHuExJ4Qte8QSD1N2V/zYWonZO3lEXRzWJmE4WqnXHA2HXm5r0rTWVkrbFpEjqxhGIZhGIZx5GGOrGEYhmEYhtEsaTmpBYZhGIZhGEcIjsUiAYvIGoZhGIZhGM0Ui8gahmEYhmE0I1QhYSt7AUewIxvfUpr6FFhpYPtVx4Vmu/2D74Zmu6UjcX/mKD4UtMr/BT8Mw2iipDz9lWEc2Zg7bxiGYRiGYTRLjtiIrGEYhmEYxpGJ4Pi+cEfzxCKyhmEYhmEYRrPEHFnDMAzDMAyjWWKpBYZhGIZhGM0IxWYtqMHOgmEYhmEYhtEssYhsDRGFaB3vJQDHh6RqdYiof1OmOBIBqeugG0F92h0gkc6EcnVbYV0mqqjnTR8w7ck50rWL1t37mfb02QaIqWlPRjWgpj0thK09TSQsFgmYIwuZSv7JUaozKojH4kmLVCeqiSZiFL3loEWNbzjiVNOt7BMo2UE8nuFbP11dVYm0ymVd9gASsdaN/4JUtTue9t3+XjTxXg6thihEhGjk4O9WVaoTCWRLBiULEvj5AyetlLyTI1THq+rVHtEoJe8ozg5/tWf0qSber4pIRIgk1Q7ViSqiu1tTPF/87eRTrPeIRil5V3G2+6s91t2h9TAgQr31HtmRQfG7CX+1x5X8r0Spbl1BLBpHknx1daKaiOPV+05/tUe7OmSNAESJRg++i6ip98jOTIrf8Vl7zNOe1YD2NLX5aBeHrJE0rH2Xp92P4EENMSVvQpREdgra09DmI50S5IwWtAHt0d2ZFM3zWXvU055Tn/YEEY1Q8p6Ds9WH4EgtIkclyBkbwRGHWH3aizIpeisN2k+KkshNQfsCB2eLv9qNYGjZjmyGkn9ahAu+OZkLLrqAWCz56XAch3nz5vHb6O8oes1Bi1Pv5EQTdC9ZwVfGjeAHV3+feDy543AoJBIJZj/7LI8/+Xe+zBtCItoq9Q972r9x8TlMmTqlXu3z58/nnsi9FL1+aI58MuK9HNqNy+KOu++gc+fOdZYrKSnhlhun8WnkC0rnO/jizGYqeacJF196Ieedf17SHxZwHarFixcz/dYZFL/p+ObUZPSppv2oLO64exZHHXVUneXKy8u54/Y7WBxZQcnbEXzRnmKbV1U+/vhjbrtlOkVvJXxzamLdHNqOb8WsO2fSrVs3JNkvC1BWVsb0adNZEfmUknk+1XtMyT81wlcnncoVV15e57Woqnz00UfMnD6TojccnF3+aI92cWg7PpPb75hBz54969S+Z88ebp8+k2WRlRS/5ZP2qJJ3aoRTzprAVd+7ql7tixcvZsatMyjysc1HOzu0OS6DGTOn07t37zq1723z0WUUv+GT9oiSd0qECWeewNU/qLsPVlWWLl3KrTff5mubjxydoM3xGUy//Tb69u1bp/aKigrumnUXH0WWUPSG489NjKf9+NPH8cNrf0BGRvIFW1SV5cuXM+2maex+K+GbIx/pmKDNCXFunXEr/fv3r1f7PXffywfRDyl6zUftJ0cYe+oo/uunP65X+6pVq7jphpvZPa/Kd0feSD8tOi4d7eQwcvwIpnyzbkcOIBKJMGHCBC6Ycj6t+zfulGVWFNK5XR7X/vAHvjqxANFolMnf+AYnHT+OrLKtjfvs0Q7HjhrCty79VoPajz/+eKZeOoWsgf5d4FnHRrjt9tvqdWIBcnJymDFrOhmdgUb46fUR7Zxg3IljufCiC+t0YgFEhOHDh/Pd719F7jD/VuzK6F/NzDtvr9eJBWjVqhXX33Q9OZ1bIbmOL7ZTbfMiwtChQ7n6h98nb7h/2nOHx7jplhvp3r17nT9qAFlZWUybPo3WneJItvpiO9LBoefAHny3HkcOXO0jR47kiquuIPdY/67ZvBFxrr/pf+jVq1e92lu3bs1Nt9xIVudMJNcn7e0duvXrwtU/uLpB7cOHD+e7V3+XPB/bfN6IDH55/S/o06dPvdpr2nxulywkzz/tnXsfzQ+vrb8PFhGOPfZY39t8/shM/vsX19GvX796tWdmZvLLG35JXtccJN8n7QVKx57t63XkwNU+ePBgfvRfPyJ/pL/af3LdTxgwYECD2v/7F9dR0C2fSFuftLdV2nVry8/++6cNah84cCD/9dMfkz8i0xfbQaAIjob/agq0aEc2liP0G9A35fI9e/Yko03jgtjRRAW9evZM+vjYL/r16UNWpLpRn4lkQf+B/VIu36NHDzLy/QrgK5VOJd26dUupdGZmJu3atifS2p8OLiMvSt/+jav3SJYvpiGqKA5HH310SsVjsRidO3VGWvvjyB5KmxefzjtAJZX06NEjpbLxeJyjOh6FHELWTDIirZW+/ep3pGrTo0cPojn+ddTVUknPnj1TKhuPxzn6qE6+nXtprfTuU3ck9EB69OjhX5sHEtGqlOs9FovR6Wg/tUPP3qn3wX63eSdWnbL2aDRK1y5dfevrpLW6dZmi9h49ekCmf9o1I9E47d26+VfvrZTu3bvXG6yoTY8ePdAMf/pZI1hatCMrEUnayOfOncuAAQPo27cvs2bN2rs/Go2m/EOwDyUW29/G5ZdfTseOHRkyZMjefddddx0DBw5k6NChTJ48mcLCQgAWLFjA8OHDGT58OMOGDePpp58+yEIkEmn0A7hD0e7nGAyp+c4UbPttX6IHa09WJzVEIpGkuVWHSkQi+7WjVatW7a3j4cOHk5eXx69//eu97yfLKztUktV7fdr9rnc4OEcwqHpHIBY/+GYsqDavcJBDUa/2mI+POIWD+qH67PuvvXH1Hov5az/Z04egtO/9zhRsg1fvfrb5Rte7f47s3u9MwTb429chya+hILUbwRCoIysiPUVkaZL9r4vI6CCPpS4SiQTXXHMNc+bMYfny5Tz55JMsX77cVxuXXXYZc+fO3W/f6aefztKlS1myZAn9+/dn5syZAAwZMoQPPviARYsWMXfuXL773e9SXd246GuqBKG9KdqG5HUSFAMGDGDRokUsWrSIhQsXkpWVxeTJkwOzH6b2sOu9Jbd5027aTXuw9v0mQST0V1OgaRxFE2LBggX07duX3r17k5GRwZQpU3jmmWd8tTFhwgQKCgr22/e1r31tb9Rg/PjxrF+/HnBzBWv2l5eXH0JEOHWC0N4UbUPyOgmDV155hT59+qT8OM4PwtQedr235DZv2k27aQ/WvpEewnBkYyLyhIisEJF/ish+mVgicqaIfCgii0XkFW/fWBF5V0Q+EpF3RGRAug5uw4YN++Vudu3alQ0bNqTLXFL+8pe/8PWvf33v9nvvvcfgwYM59thjeeCBB+odpHM4hKm9KZz3psBTTz3F1KlTwz6MwAi73ltymzftpj1o+y1Zu98o4Ggk9FdTIIyjGAD8QVUHAUXA1TVviEgH4CHgfFUdBlzovbUSOElVRwA3Abcn+2IRuUpEPhCRD6qoSKeGtDFjxgxisRiXXHLJ3n3jxo1j2bJlvP/++8ycOZPy8vIQj9BIF5WVlcyePZsLL7yw4cKGYRiGYYTiyK5T1be9//8KnFjrvfHAm6q6BkBVd3r784F/ePm19wKDk32xqj6oqqNVdXScQ5tGo0uXLqxbt27v9vr16+nSpcshfVdjeeSRR3juued44oknkqYQDBo0iJycHJYuPSjN2BfC1B6m7abCnDlzGDlyZIPTch1JhF3vLbnNm3bTHrT9lqzdSB9hOLIHDgtMZZjgbcBrqjoEOAffZhQ9mDFjxrB69WrWrFlDZWUlTz31FJMmTUqXub3MnTuXO++8k9mzZ5OVtS/bYs2aNXsHd33xxResXLky5Sl8GktY2sO23VR48sknW1RaAYRf7y25zZt2027am/PvjJBoAq+mQBgre3UXkeNU9V3gm8A8XOcUYD7wBxHppaprRKTAi8rmAzWJLJel8+BisRj33XcfZ5xxBolEgssvv5zBg5MGgA+ZqVOn8vrrr7N9+3a6du3KtGnTmDlzJhUVFZx++umAO+DrgQceYN68ecyaNYt4PE4kEuEPf/gD7du39/V4aghCe1O0Dcnr5IorrgjMfmlpKS+//DJ//OMfA7NZQ5jaw673ltzmTbtpN+3B2jfSQxiO7CrgGhH5C7AcuB/PkVXVbSJyFfAvEYkAW4HTgTuBR0XkBuD5dB/gxIkTmThxYtq+/8knnzxoX12Ow6WXXsqll16atmM5kHRrb6q2k9VJkGRnZ7Njx45QbIetPcx6D9u+aTftLc1+S9buJzWDvYyAHVlVXQsMTPLWybXKzAHmHPC5d4H+tXbd4MfxOAmlsrIy5fJVVVWo09gJk4WKRtg4FKqqqkg08jPaSO2VlZXQaO31U11dnfIMDO6598euU61UVVWlXL6qqgr8WvBFIeE4qGrKU6lVVlb6s/Y4jW/zftoGECJUVVXVu2TkQfZ9OvfqQEV56oNAfa133KUwGzMHdJWf594RKioaW+/+mAYQpFHXXGWlv9dcZUXq9e53m6ex2isqfevrcNzvS9m239r1EOrdr3anNPr3XVTwteEbgdCi3flEsbLow8U4Tmq9xsdLPqZiR+oXJUB1rDWrP1ndqAuqMagqi5Z8zB5t3JrwTiks/nAxiURqLvDSj5dSsdOvhRiEDDJZsWJFSqV37drF9l3b0TJ/Otiq3Q6LFqZe70uXLqO62C8vWohKjNWrV6dUvLS0lHUb1vmmvbFtfunSpTjF/nXscScj5QnIi4qK2Lx1s2/atUxYvnR5ys7k0o+XUrXbP082Vh1n2bJlKZUtKSlh46YNaJk/trUMVq1YlbJTsXTpMqqL/Kv3aGU85XovLS1l/Yb1vtb7qlWfpNwHL1u2zNc2LxXRlLWXlZXx5bovfNX+6WefUpGiI7906VKcUh8d2fJIytrLy8tZs3YNjl/aS4XPP/885Vl+li9fju5pGjmfRuOI3nLLLWEfQ1qYOe2OW7pK73rLaKlQ2rqQ9Zu/pEdPdwL6ioqKg16FhYU88/+e4fmnX6DkfaC64cZeNsqdqy4RzUTKdrHswwUM6N8PEUlq41BeO3bs4NHHHuedDz9me04vEHc5vqyF6xs8Pi0RyrKK+GLjWnr26lmv9ueee57Z/5xN6XuakvZUqCxM8N7n8+jZuyetW7emqqrqINvl5eWsW7eOm66/id0fV5DY7FMHVyIUtdrBpm0b6dGzO6qaVHtRURGvvfYajz/8V4rfdaCyYfuSwhKL1cUO76x6nb79+tCqVauk2isqKti4cSO33nwbO1aUULUuSoPrVjoN35Sk2uaLiop48803efTPj1H0biIl7alQuSvBB1++Q7fu3cjOzq6z3jds2MAtN06jcFkZVev9+1GvzN7Dys9X0LdfnzqvxaKiIl759ys8+fhTFM9PQJVP2ncmWLh+Pl26diE7O5vq6uqk2jdu3Mi0m25lx9ISqtaBH+uV6h6hKruc5Z8to2//vvVqf+211/jrw09Q7Ge976jmo40L6NSlEzk5OUm1127z25fupuoL8Ec7JLIrWbp6Cf0H1N0H17T5R/70qL9tfkc1izd/wFGdjiI3N7de7dNvncHWpYVUrQG/tDvZVSxetYj+A/sTiUSS2i4uLuatt97iLw8+TNE71VDhj/aKHdV8vHUh7Tu2Jy8vr07tmzZtYsatt7Nl6U4qPwNftJeDk13NRysWMnDQgHq1v/vuu/zx9w9S9E4iJe2fs3zTLbfc8uBhH+RhcM8fZt0y+sLuKBLq65U/fBr6uRDVIzOMnicFOk5Oa7hgVMkdHyVaAI4kj9QIUSgRdr+TgPLULrDtVx23b0MdjtrzBTlVhThV/s1vK7E4lfFcNrTuhUb2RWTbP/hual8QVXLHRYm2q097BEqjFL1T7fvdaqRjgvzRmSRiVdT1OCfqxClbnaBila+mIebVe1vFkeQOoBBB9kTZ/W4VWpLawwuJp/bIPNKxirzhURLRuiNkUY1Rvg7Kl0VIpWPXqhSj/im1+QhS7mkv9vfBTaS9Q/6YOIl4NfXVe/maBHuWKb4ufB9RcsZEiXeERH1tfk+Uoneq0FKftbdzyB/bgHb1tC/1Wbso2aOjZBxdv/bGtvlUibR1yB8Xx8lIoHXkDUSdOOVfOuxZ4uC79lERMjpJ/drT1ebbOOSNj6P1adc4FV86lC1Og/aRETI6p6B9fhVa5K92yXfIHx9HMxvQvt6h7COftaNkjYyQ2aUB7RWe9t2paf+3/nOhqo728UAbTdch+XrN308I8xAA+J/Bc+o9FyLSCngTyMRNZ/2nqt4sIr2Ap4B2wELgUlWtFJFM4DFgFLADuNhLS60Tc2TTxH6ObMCk7MgavpOqI5sOUnZkDcMwjEPGHNl9pODICpCtqiUiEsedqepa4CfAv1T1KRF5AFisqveLyNXAUFX9nohMASar6sX1HUOLzpE1DMMwDMNobqhK6MvTpjJrgrqUeJtx76XAqcA/vf2PAt/w/j/X28Z7/zRpYGS0ObKGYRiGYRjGodBeRD6o9brqwAIiEhWRRbhTqr4MfAYUqmpNvsd6oGaJtS7AOgDv/d246Qd1EsY8soZhGIZhGMZhkGga88hubyjNQlUTwHARaQM8TfJpWA+ZJnEWDMMwDMMwjCMXVS0EXgOOA9qISE0wtSv7Vm/dAHQD8N7Pxx30VSfmyBqGYRiGYRi+IyIdvEgsItIad7XWFbgO7QVesf8AnvH+n+1t473/qjYwK4GlFhiGYRiGYTQjFHB8naosbXQCHhWRKG7w9O+q+pyILAeeEpHpwEfAn73yfwYeF5FPgZ3AlIYMmCObJtr/aUFotl/cuCg02wBndB4eqv0wsSmwDMMwDMNFVZcAI5Ls/xwYm2R/OXBhY2yYI2sYhmEYhtGskKYy2Ct07CwYhmEYhmEYzRJzZA3DMAzDMIxmiaUWGIZhGIZhNCMUcLRZDPZKOxaRNQzDMAzDMJol5sgahmEYhmEYzRJLLQAiHRJECyCWFU36fqLCIVGoJDZGwO952zKUaOdq4rkRJJrkuxWqihMktkTR0kO779hTLrzxXj6bt7WmOpFcY2OJiNK+7R5OGF1Eu7bVDX8gGXEl2iVBPC9ah3b1tEfQEv/vuSLtHaLttM56dyocqncriQ1pqPe4Eu3iEM+rr96r06e9nac9uw7tlbW0+/34KlXtWyNocXq0R9op8axo0mpNq/ZYjXZBYkm0pVt7gac9ux7tRUpifRq0R5Vo1xS0b4ugRWnQ3tYh0r4e7VW16t1Jg/YuDvH8erSXeNp3p0F7G4dIhwa019S739ojbr3H8oRIvB7t2yNoof/apY1DtL1DPCcWvPY0k7BYJGCOLBl9lYLROXztzNPJy89LWqasrIw3X3uTjYu2U/aB4ptTk6Hkn6qMOm40AwYNIBo92KlQVTas38hr/36NojedRnfwe8qF6ff1p1XOEAYPGU08Hvfl0B3H4csvP+O5u97mtp+s4qj2VY37ggwl/7QIw8eN4Jghg+rUvnHDJl59+VWK3nB87eBjPR0KxrbijK+fQX6b/KRl9uzZw7w33mb94i2Uznfwrd7jrvZh44ZxzJBjiMUOvgxVlc2bNvPvl15xtfvYwce6O7Qdl8kZE8+kTR3ay8vLefvNd1i3eBMl7zr+OTVxJf/UCMeOOZYhw4bUqX3L5i28/OK/KX7Twdnln/ZoV4e24zM4c+KZtGnbJmmZ8vJy3p33Ll8s3kjJ2z5qjyl5p0YYPHoww0YMTaodYMvmLbw092WK33JwdvqovYtDm/EZnDnxDNoWtE1apry8nPfeWcDaResonuez9lMiHDNqEMNHDa9T+9YtW3lp7ksUveXg7PBPe6RTgjbHZ3Dm18+goF1B0jKVlZW89857fL7oS4rfcvxzaqKu9gEjBzBqzMg6tW/buo0X57xE0bwEznYftR+dIP+4OGdMPIP27dslLVNZWcn789/ns4/WUvSmz9pPjtBvRD9Gjx1V5+/P9m3bmTvnRYrersbZ6k+wBSDSMUH+CTHOmHhWndqrqqp4/70P+PSjzyl63UftRmC0aEc20sGh7ags7vnNr+jQoUO9Zc8991x+cd0v+HTnBqo+96eh508Qzr3oHC799rcaLDts+FDu0XspniONutAefLIXR3WdwI9+9DMiEf/v3mbP7sP0+x7ld7csa9Tn8idEOev8r3PZf/4HIvXrGTlqBHfpXRQ9r750MpG2Dpaz0FwAACAASURBVG3HZHL3vXfTqVOnestOnjyZ//n59azctZbKVYdtGoD8k6Kc8Y2vcuV3rmxQ+6jRo5jpzKL4BYXE4WuXNg75YzO4+9676dy5c71lJ0+ezA2/vIEVuz+nvHHVWyd5J0Q57exT+N7V321Q+9hxY5k+bTpFLyhU+6A93yF/XJy77rmLrl271lv2vPPO46brb2bZ7tXs+fiwTQOQd3yUk79+Ij/40Q8a1D5u/DhuvflWiuYoVPmgPdchb1yMO391B927d6+37Hnnnce0m29lSdFK9iw+bNMA5I2PcuLXjuPa//pRg/3Qcccfx8033ELRXIVKH7TnOOSNjzHrzpn06tWr3rKTJ0/mtmnTWVS8nLIP610VM2Vyx0U57qtj+MnPftKg9uNPOJ6brr+J3S8qlPugPdshb3yUmXfeTp8+feote95553H79JksLF1C6fv+aM8ZE2XMKSO57uc/SxqsqM2JJ53I9b+4gaKXFN3jg/YsJfe4KDNmzaBfv371lp08eTJ3zLyT90sXUfKec9i2g0ARG+zl0aLj0pE2ymmnn9qgEwuQlZXF5Asm07qLPxFNUKozK7jo4tQWsDjxxBPJyc5BWjeug/lkbTaTJ1+cFicW4Oyzz2XLdjfymzpKonUlF0+5qMEfdIDx48eTl5ePZPvTuUbaOJx40okNOrEAmZmZnH/ReWT5Vu/g5FRx8ZSLU9I+ZswYCtoWIDl+aVfGjx/foBMLkJGRwQUXX0DrTv5pJy/BxVNTq/cRI0bQoX1HIn5pz1dGjx7doBMLEI/HueDi82nVyb97fWnjcPHU1Op92LBhHH1UJyK5/tX7iOHDG3RiwdV+oc/1HilQLp56UUr90JAhQ+jSuQuRPP/q/djBxzboxALEYjEumnIhmUf7FxWMtYOLp6bWBx9zzDH06NaTSJ4/zlQkTxk4cFCDTixANBp1tXf0T3u8g3Dx1IsadGIBBgwYQO9evRGftEueQ7++/Rp0YsHVfvHUi4h3MMewOdKiHdlYq0jSx8pz585lwIAB9O3bl1mzZu3dn5OTQzTTp1MWARHXUTqQRCLBiBEjOPvss/fbn5WVBbHGde5le9zjrqG8vJyxY8cybNgwBg8ezM033wy4j3Ovv/56+vfvz6BBg/jtb38LwK5du5g8eTJDhw5l7NixLF26dH8ZkQjZ2ZnsKW9c5+eo4+qpRV3nHSA7Oxvx63c1Dm3bHfxotb56j2T418FVO9X71Ul9tsHT7pM/JTGlbcHBj9Tr0y4+ak9o47Tn5OSAT/Uu8cZpz83N9a/NAQlNNE57bm6jr/c6iWmj+rpcP20DCRLud6ZgGyAvz0/tkN+2kdqj/ml3xGlUvefm5/p2vROH/CQpc/Vp16h/EUltpPb8/Dwf+zqSpgvW19dppHlEY439CcyRFZGeIrK04ZLhkkgkuOaaa5gzZw7Lly/nySefZPny5YEew29+8xsGDRqUlu/OzMzk1VdfZfHixSxatIi5c+cyf/58HnnkEdatW8fKlStZsWIFU6ZMAeD2229n+PDhLFmyhMcee4xrr702LccV9nkP075pN+2m3bQHhWkP9/fdTxwiob+aAk3jKDxExL9nGofIggUL6Nu3L7179yYjI4MpU6bwzDPPBGZ//fr1PP/881x55ZVp+X4R2XuHXFVVRVVVFSLC/fffz0033bT38VfHjh0BWL58OaeeeioAAwcO/P/snXl8VOW9uJ/3nJlJgGQSAgQwASFsCVvCJoiIInUpvddaUerWqtDt1m7azVtbBQRELS4/tbYuVWvvxd7b1mpvlVoXQNCILGFJACkSJGEJISSTySSZmXPe3x8TKJJZkzNzEvM+n898IDPvnO95zvc973znnbNQWVnJsWPHLF8vu7e7nfGVu3JX7so9VSh3++IrkkOqC1mHEOK/hBC7hRB/FEL0FkJUCiHuF0JsBa4VQowUQrwphNguhNgqhBghhMgQQrzV9vdOIcQXk7WC1dXVDBky5PTf+fn5VFdXJytcO37wgx/wwAMPJO2YVgh9Ky0pKSE3N5dLL72U6dOns3//fv7whz8wdepUPv/5z7Nv3z4gdKzen//8ZyA0CBw8eJCqqirL18nu7W5nfOWu3FMd2+74yl25pzp2V4ivSA6pLmTHAL+SUhYBHuDbbc+fkFJOllK+BPwX8ISUshiYCRwBWoAvSSknA3OAVSKeMya6Gf/3f/9Hbm4uU6ZMSWocXdcpKyujqqqKTZs2sWvXLlpbW0lPT2fz5s18/etfZ+HChQDceeed1NfXU1JSwmOPPcakSZPiOnBfoVAoFApFcpASDClsf3QFUn35rUNSyo1t//898L22//8BQAiRCeRJKV8GkFK2tD3vBFYIIWYDJpAHDASOnrlwIcQ3gG8ApPPpE4niJS8vj0OHDp3+u6qqiry8vA4tK1E2btzIq6++ymuvvUZLSwsej4ebbrqJ3//+90mJl52dzZw5c1izZg35+flcffXVQOhSJLfeeisAbreb5557DgidEDZ8+HAKCgosXxc7t7vd8ZW7ck91bLvjK3flnurYXSG+Ijmkekb27FNBT/3dFON9NwIDgClSyhLgGJDebuFSPiWlnCqlnOqk/dUA4mHatGns27ePAwcO4Pf7eemll7jyyis7tKxEue+++6iqqqKyspKXXnqJSy65xPIi9vjx49TX1wOhC/7/4x//oLCwkKuuuop33nkHgHXr1jF69GgA6uvr8fv9ADzzzDPMnj0btzv8jSM6g53b3e74yl25K3flniqUu33xrcaUwvZHVyDVM7JDhRDnSynfB24ANgCTTr0opWwUQlQJIa6SUv5FCJEG6EAWUCOlDAgh5gDnJmsFHQ4Hjz/+OJdffjmGYbBw4ULGjRuXrHAp58iRI9x8880YhoFpmixYsIB/+7d/Y9asWdx44408/PDDZGRk8MwzzwCwe/dubr45dNOCcePG8eyzzyZlveze7nbGV+7KXbkr91Sh3D+7n+89lVQXsnuB24QQvwUqgCeB757V5ivAb4QQS4EAcC2h42b/KoTYCWwG9iRzJefNm8e8efOSGSImF198MRdffLHly504cSLbtm1r93x2djZ/+9vf2j1//vnn89FHH1m+HuGwe7vbGV+5K/eeFl+5K/eeGF9hPSkrZKWUlUBhmJeGndVuH3BJmHbnJ2GtkDLRC19bd6HshEN3VTogIhPcjonnybrlWR07WctMWuzP0La3O+8JYbO71faJxbczts37u9193sLwCX+6fsbynkxCt6jtUldQtY0evRWCPpO6Eyfjbl9fX4/RbFFnN0Eg8Pl8cb/F621M+L7rmX2goaEh0bWLG8Mw8Db56dPbSOh9DuGgsbEx7vaN3kakP9G1C4/0Q21NbdztPR4PRot1g5xTc+LxeBKKb5l7QHDi+Im429fX12NaFBvAIWx09wtOHK+zJTaE+nwi+2JDQ0PC+3tE/CKhsa6hoQFhVWzAIR0J5b2hvgH8FsUPwMkT8ee9vr7euu0OaFJPyP3kyXqkRe7SDyfr6uNu39DQgDCsKws0U0+oz588eRIZsCa2DED9yUTd1RV5uiM9upA1T2i8+cY/OHDgQMy2dXV1/PeLq2k6aNFehkBrdPHkE09imrFvi/fyn1+muakV2ZzYADexsIHnn//16RO2rERKyQvPP83IYSZprkQKPYFocPKrx5/EMGIXwK++8leaPD5kkzWDu1mn8e67G+I6ZKKhoYHfPfcivoPWbT9Z5+DJON1f+9treBo8FroLSks/YM+e2EfneDweXvjt7/BZ1ufBrNXidn/j729Qd6IO6bXOfcuWzZSXl8ds6/V6ee6Z5y3c3yFYI/j1E78hGAzGbPvWW29z/PhxzEaL3OsF27dvZ+fOnTHbNjU18dunn8P3iXXugWPw6yd+QyAQe5nr16/nyNEj1rmf1CivqKCsrCxmW5/Px7NP/5aWQ7FzFC/+w5Lf/OqpuMbgDRs2UFVVhemxqJCt1/joo71s2bIlZtuWlhae/vUzNFclNikRjdZqg6eefIrW1taYbUtLS6k8WInZYE1ZYtZr7P94P5s2bYrZtrW1lad//Qyt1da5pwIDYfujKyC623R6vLhFjpwu5sZspw02yJrhpLi4OOx92AG8jU1s376dhh0ttO6JM3FaHN/sdIn7Ijhn5EBGjhqJ09n+SA/TlFQfqmbf3n143hbIltg7+d+r/jVoGQY8/NsCqmqGMGbMWFyujl3N4WwMM8ihTw7Q1HiAJT/YQ2affxXjl59TEnsBusR9scbgNneXq/1N7U1Tcriqmo/27MPzlky4iI+Glmvgnulg4sSJ5PTPCbs7Nnl97Nixg/pdzbTskmDVTqtJ3BdpDBqVy6jRoyK6H6k+wt7de/G8LZE+C90HmLgv0Jg4sZicfn0Jd0nmpiYfO3fspL7cR/MOa90zZ2sMGjmA0YWjw7tLydHqo+zZvQfP2yayycIZov4mmW3u/fqHd/c1+di5cxcnK5poLrPYfZZG7qh+jCkcQ1qaq10TU0qOHT7G7ordeN4xkV4L3fuZZM7SmDhhIv0G5IR1b/Y1h9z3ePFtsdBdhNwHjMqhsKgwrLuUkmNHaqgoL8ezViIbLXTva5I5W2PChAn0H9AvovuuXeXU7WnEt9la94yZGgNG96WoqJC09PZjsJSSmqM1lJdX4FlrIj0WumebZM4WjJ8wgQG5/cO6tzS3Ur5rFyf2NtK0ycQydyQZ52v0H51F0bixpEdyP3ac8l278KyTSIsKWQDR5j5h/AQGDIzsXlFRQe3eBppK43N/U/5xi5RyqmUr2gFyx/aT1/z+83auAgBPTvkv27dFjy9kAUSmiZYlwRlhWxgC6RGY9QnsYPEUsgCaROtvIHrJiPuPbBGYdXrcP7WdWchCqJjdsacPR467iGMyKC40Af1zAhQX+eiV/ukZ5bgKWWhzNxG9ARF+28sWgXlCs+5nxjMQGSZadoy8ewVmncC6gb0Nu937tLlHmkm32Z0WgVGnQatN7k0C80QS3MUZ7poN7r0lWl/TRneJ6C0ju7e2ubfY4G629Xkb3c06DZkM915t7mlR3JsEZm0S3Glz7xPD/aRm6WTFKZLhrgrZf9EVCtlUX7WgSyIbNYz4D9e0FlNg1iQ3DboOk8Y1MSnm5XpTjCkwa+w7Jkl6NQyvTcHtdm/SMOzqDj3ZXQrM4za6+wSGz6b4UmAet++nyB7t3iwwmu3qd21FYvynJViKve7JQ0KXuY6r3fToY2QVCoVCoVAoFN0XVcgqFAqFQqFQKLol6tAChUKhUCgUim6Fuo7sKdRWUCgUCoVCoVB0S9SMrEKhUCgUCkU3w+wi13G1GzUjq1AoFAqFQqHolqgZ2WQhY9+tK1nEfR3XJKGlp9sW22xpsS22QqFQKBSK1KIKWYVCoVAoFIpuhJRgqOvIAurQAoVCoVAoFApFN0XNyCoUCoVCoVB0M9Tlt0KoraBQKBQKhUKh6JaoQlahUCgUCoVC0S1RhxacQkjQI7xmAMk+qFqXRLwkXBAiv2gB0dxNwLQ6dpR40TDA+u0gI+8FSXE/C01G/jqZ7Lwr9/Ao9yTGRrkr9/Z81t2TgERgqpO9AFXIQpok6yKdYForuha+ujJMA4fpwrPeQDZaO4mdNsbENVoiMdFE+2VLKZFSwnEX3k2mtQW1q809PQ73dw2kp7Pukl4TDLTBrQAIEb+LBEzTxOFLp7FUB6OT28HZ5t6rFU3Tww6hp903GMgGa/PuGGrSu0RgYkTNuzzmpGmTiaWDvFOSNVsn2DuGu2xzr7fWXc836TNJYIro7hx34f3AsLbPO9ry3tuPpmlR3J00bjQxT1rsnmfSZ7LAwEDXwrgD0jQRtS4aS613d8/WMfrE4f6eiVlnrbs22CBzqk4wlnudi8b3LHbXJe4LdYzMONzfNzFPWOw+yCBzWnR30zTRTra5W1lY6RL3LB3DHcOdNvdai91zDTKnx5H3eheNGy1210LuZpYfEdHdxIGDxg8MzJqOzLAo7KZnF7IuSdZcjau+/O98+bov43Q6wzYzDIN169bxK/1JPG+bSK81O3raGJPcaVmsuH85AwcOjFjYeb1eli1Zxl7tY7zvSywpapwh93+b/3luuOkGXC5X2GaGYbBhwwYe0x/H847ZqUK+10SDIZMHsHT5Evr27Zvw+1tbW3nisV/xnv4BjRscHR/w2tyv+NJlfPXmr0R1Ly0t5eEHH8Gz1rSgkA/hGGLSd0Y6K1Yu59xzz42Y98bGRpbcvZR/agdpKrWomHVIsi7R+NyVl3DrwltIS0sL28wwDD788EN+ef+qkLtFhbyeZ9J3RhrLVy5j+PDh8fX59yxy10PuF39hNl//xteium/dupX7VzyAZ51pWSGvn2OSfb6LZSvuZcSIEVHd71t2HxX6PrwbrHN3z9GYfcUFfOvb34zqXlZWxsrlK/Gss66Q1wYZZM90ce/ypYwaNSqiu8/n477lKynX9tD4rkXuWsh95qXT+c73bovobpom27dvZ8W9K/C8a10xqw00yJrpZOmyJYwePRotTDEHIfcHVz7Idr2cxvUWTVpoEvfFGuddMoXv3/490iNc49s0TXbu3MmypcvxrDescx9gkHWBg3uW3kNRUVFE9+bmZlY9+BDb9O141lnkLiTuizSmXFzM7T+6nV69eoVtZpomFRUVLL1nKQ0bDMzj6ojL7kaPzpg+2KTkvInc9JWbIhaxALquc8kll3D1gi/Ra7RVm0ziHGXw4EMPMGjQoKizkxkZGSxdvpT0PAeij7Qkuj7YZPzksdx8680RCzkIuV900UVce/019C7sxLdVp4k+2M/9v1zZoSIWIC0tjR/c8X0GF+Si9Q12eFX0gSZjikez6GsLY7pfcMEF3HjzDfQZa913vowSnSX3LmbYsGFR856ZmcnylctIy9MQ4cfghNEHmowYV8A3v/WNiB/oEHKfMWMGX731K2SOj7xvJErmJAe/WPxzCgoKUt7ntVyTcwuHctt3vh3Tfdq0aSz8+q24J1rn7p7s5K5f/IyRI0fGdF9872L65LkQmRa59zfJH3UO3/3+d2K6T5kyha9/6+u4SyLvG4mSNcXFnT/7KaNHj47q3rt3b+5e/Asy8nsh3Na5DyrI5fYf/iCqu6ZpTJo0iW/d9i1r3Sen8eOf/ojCwsKIhRyE3O+6+y6yhmQgsixyz5EMGNaPH/3khxGLWAi5FxcX853v3UbWZCvznsbtP7qdcePGRXXv1asX/3nXnWQPdaNlW+eec242P/3ZTyMWsRByHz9+PN+/4/uWuqcCE2H7oyvQowtZR4ZgdOHouNsXFBTg6mtRQeMCh8NJv3794mvucjFwwEBEukU7eW8YXRT9Q+VMCgoKcGV33F2kS7Ldfendu3eHlwGhQaegoAB6dfzOaaKXZEwC7sOGDcOVZd1PTn7ZyrBhw+Jqm5aWxoB+AxC9rMm7SJeMGhN5Ruxshg8fjsNt3TAREP643UN9fpBlRbzWSzJydOSZ0LMZNmwYeoZ1A3UwAXen08mggYOty3svScGIgqjFxJkMHz4crXO76qcw9GBC7ucMPsdCdygoGJ6Qu1WxAUxn/O4Oh4O8vDxL837uueei6/GNX8OHDweLPmMApMsILTMOdF1n6JChlo51Q4cOTchdptl3R05Fx+nRhazQBA5H++JszZo1jBkzhpEjR7Jy5crTz+u6ntBxndGD025gPXToEHPmzGHs2LGMGzeORx999FOv67pu2aGSibo7HI5Oxz57QFm4cCG5ubmMHz/+9HN1dXVceumljBo1iksvvZSTJ0+2W47D2cl1EYSdgY+Wdyu/eEpku20RKbbl8QU4nYn1eWu/dMt2/S6au8NhtXv8ebeiz5+JpP0+EDXvYfbPDmNz3qWdeQccNub99DLjiH2qrVUfM4nmPeRuXSELifV5q/f3RD/fQ3tp90ACphS2P7oCtheyQojnhRDXJNB+mBBiV7LWxzAMbrvtNl5//XUqKipYvXo1FRUVyQr3KRwOB6tWraKiooLS0lKeeOKJlMWG1LvfcsstrFmz5lPPrVy5krlz57Jv3z7mzp3bbqBLFnbm3c7YdsdX7spduSv3VGF3fEVysL2Q7Wps2rSJkSNHhn5Kd7m47rrreOWVV1ISe/DgwUyePBkIHR9ZVFREdXV1SmJD6t1nz55NTk7Op5575ZVXuPnmmwG4+eab+ctf/pK0+GdiZ97tjG13fOWu3JW7ck8VdsdXJIeUF7JCiK8KIXYIIbYLIV5se3q2EOI9IcTHp2ZnRYgHhRC7hBA7hRBfTsX6VVdXM2TIkNN/5+fnp7SYPEVlZSXbtm1j+vTpKYvZFdyPHTvG4MGDARg0aBDHjh1LSVw73e3e7spduac6vnJX7qmO3RXiW40pNdsfXYGUXn5LCDEO+DkwU0pZK4TIAR4CBgOzgELgVeCPwNVACVAM9Ac+FEKsj7H8bwDfAEjHwjMVUozX62X+/Pk88sgjuN1uu1fHNoQQ1h2TrFAoFAqF4jNHqsvpS4D/lVLWAkgp69qe/4uU0pRSVgAD256bBayWUhpSymPAOmBatIVLKZ+SUk6VUk51EvkyK9HIy8vj0KFDp/+uqqoiLy+vQ8vqCIFAgPnz53PjjTdy9dVXpywu2O8OMHDgQI4cOQLAkSNHyM3NTUlcO93t3u7KXbmnOr5yV+6pjt0V4iuSQ9eYF4bWM/5v6xTctGnT2LdvHwcOHMDv9/PSSy9x5ZVXpiS2lJJFixZRVFTEHXfckZKYZ2Kn+ymuvPJKXnjhBQBeeOEFvvjFL6Ykrp3udm935a7clbty/6zH7grxLaULXLGgq1y1INV39nobeFkI8ZCU8kTboQWReBf4phDiBSAHmA38GIh8VWcLcDgcPP7441x++eUYhsHChQsZN25cMkOeZuPGjbz44otMmDCBkpISAFasWMG8efNSEj/V7tdffz1r166ltraW/Px8lixZwp133smCBQt49tlnOffcc/mf//mfpMU/Ezvzbmdsu+Mrd+Wu3JV7qrA7viI5pLSQlVKWCyGWA+uEEAawLUrzl4Hzge2ELpn2EynlUSHEsGSv57x581JWPJ7JrFmzQveYt5FUuq9evTrs82+99VZK4p+NXXm3O7bd8ZW7cu9p8ZV7z3S3Egld5s5adpPqGVmklC8AL0R5PaPtX0loBvbHZ71eCYxv/87EMQ2J3++Pu30gEECaFhWaZuiadokQCAQsu16zNCR+fyCx2J1xlxAMdvy2smfi9/uhMzdgMaG1pTV2uzZCee9EvLMQQiMQCES9XeaZBINB667TLaG1NTH3Tm3rsxAIAoFA1FsDJyu+NKG1NbH93VJ3IRLaB4J+6/Z3pEjc3cLv1KfyHi9+S93Bb6P76WXG29YfwLL5DJPEx3lLfy62N++BBD/fQ+7d56YIihBd5RhZWzC8kh1lOzHN+D6tyneV01pnTTFGIFTInnngeTS8Xi9Ha44ifdYMMmYT7Ni2I273XbvKaT3ZcXfZrFHvOUldXV3sxlEIBALs3bMX2dzxriubBTu374r7i0RFeQX+TrifjUumsWfPnrjaejweamqPYVqUd+kTlO+sSMy9IbEvXNFwmK643b1eL0ePHbGsz0ufYHf57vjdK3YT8FhXyTqCTnbv3h1X26amJqqPVlvoDnv37I27kK6oqCDYaN0Huh5wxJ13n89H1eEqS/O+76N9cRdU5eXlmF5LQgOgtepx572lpYWDnxxENlnk3izYv39/3BM2u3fvRjZZEjpEsxZ33ltbWzlQecDSse7jAwfi/uK+e/du6MTnisI+9MWLF9u9DknhviX3L84XBVHbSK/A2+skR2sPM7ztXtyBQKDdw+Px8Ne//h+v/umvNG2SEIxjR4t52SiB2SLZULGWorFFpKenYxhGu9itra0cOXKExb9YQl2Fl0CVNTuabBL4ejdQdfQQBSMKTs8WhXN//bXXefkPL8ftLsLdWlOGLqW1cftaxk8cj8vlChsv0sPv91NbW8uqBx7i4M7D+Pc7CHdeoIzjg1o2CZr7eDh4uJKRo0ZGdX/j72/wv//9R7ybJASsGWD99UE2HXiPgpEFZGRkRMz74cOHufuue6gv9xGstm5wb+nj5cCh/YwaMyqie2NjI2/+401Wv/gS3lLDQneDDw++x/CCYWRmZsbu8+VeAlXWfaj7M3zsq9zL6MLRaJoW0f2dd9by4nMv4i01wW+R+0mDLYdKOXfYUDIzMzFNM6z70aNHWXL3Uk6Uewh8Alac/yp9gmBGC3s+rmBM4ZiI7l6vl/Xr1/P8My/Q+L5hqfvW6g8YMnQIbrc7rLvf7+fYsWPcu3gZx3fVE6gES9ybIZjRSsX+cgrHFkZ137BhA7996jk87xvQapF7nUHZkQ/JG5IX1b2mpoZ7Fy+jpvwk/o/BEvcWMDL87Nq3g6Kxhei6HtG9tLSU3zzxlKXurXVBdhzbwqBzBpGVlRXVfcWy+ziyqxb/P8Eqd5kRZPveMorGFUV0b2pq4oMPPuBXjz2J5/0gtMSO/TEVRxYvXvxUp1eyE9z/+IOLz71yHBJh62PPbz+0fVsIu4/JTBZukSOni7mxGzokmefraH0lJuGLIA0dfDqejUFkc5w7WJzXP3XkG2QWOwlqAcL9pCElOHHSUilp3iWJawePN6d6yF3PkRgR3AUawudIyF1Lj3w+nqsgSK8REBR+Ev0JR8eJUavRtE2P+POX2dIS58IkmTN09H4x3Jt1PBsNy2aHTqHlGmRNcxHUY+T9Y5Pm8jjzHnfwNvf+kqAMhu2qAg2txUHDxgCyydpZCm2ASdY0J0FHrD5v0rzTeveM6TqOAXG4vxdAei1272+SdV4c7gclzTtMLHc/T8eRG8O9tc290WL3fiF3wxlARnB34MR/SOIrs9hdSDKm6TgGQpBAdPf3A0iPxe45Ju7pTswo7k6ctFZLfFutd+8zVcM5SERxMKZvawAAIABJREFUF2h+Z8i9wWL3bBP3+dHdHTgJHJY0bUmC+2Qd5znR8t7mXhpA1sfn/qb84xYp5VTrVjRx+hbmyoufvdbOVQDgL7N+Zfu2UIVssrDzQv425zRaIZts4i5kFQqFQqHoAF2hkM0uzJUXPbPAzlUA4NULn7B9W6gDQhQKhUKhUCgU3RJVyCoUCoVCoVAouiUpv/yWQqFQKBQKhaJzdJU7a9mNmpFVKBQKhUKhUHRLVCGrUCgUCoVCoeiWqEMLFAqFQqFQKLoREqEOLWhDFbIKy7HzEliHfzzTttgAeQ9vsi12PDeDUCg+U9h5mUOw/VKHCoVCFbIKhUKhUCgU3Q7TyptHdGPUMbIKhUKhUCgUim6JKmQVCoVCoVAoFN0SdWiBQqFQKBQKRXdCquvInkLNyCoUCoVCoVAouiVqRlahUCgUCoWiGyFRM7KnUIUsoPU30fpKHOnhJ6gNv4nhAfOIBlafJZgm0QcZODI0RLjwEoJNJsZxDem1fgJd62ei9TVx9NLDvm4ETMwGMJLh7pTo55g4MgRCa79sKSXBJhOzRkM2dcxdmAYZnhrSAi3oWHOpHBNo1V00ZQ7AcKZ1bCFOiT44iN4HNK29m0QSbJKYtXpy8p5jouXEyLsHjMNJyLsjlHc9Q6Dp4ZYdyrtRk6Q+39dE62fi6BXezQiYmI1t7lZ/UOgSPc9G9+w2997h3c1gaKxLmvs5JnpmDPfjGrIxSXnPOeXeHjMoMbwSo1oH02J3rS3vsdxrNaTHeneRZaL3j5H3U30+We4ZoDnCf8gFfW15/6y5K1JCjy9knQUmfaf24nOXzSUrOytsmyZvE++ue5djO07i2yKx6oNdpEvcl8CUGecxcvQIdL19USGl5Ej1Eda+sw7PehNZb92O7hhm0ve8dOZeOpe+fbPDtvE1+diwfgNHtp/At9k6d5ySrLka46dOYOz4IhyO9l1RSkn1oWrWr30Xz1oz4UFOmAbDavYwZmA/pk6aSVqay5JVNwyD/ZUH+WDLVj4ePJ6gq1diC3CZuC+WlJw3idGFoyK6HztyjLfffofGjRJ5MnzB2RH0fJO+M1zMvfRz9M3JRoS5Fmezr5n3NrxH9Y7jNJWaWJZ3h8R9iUbR5CImlkyI6H66z6+zts/reSbZM5x87rLP0Tenb0T30vdKqdpxDO/7pnUFnUPinqMxZtIYJpZMwOVq3x+llBw9fIx33n6HxvUm5knr3LXBBtnnh9xz+uWEdW9paaF04wcc2nkE70YL3fWQ+6iSUZRMLo7ofuzIMd5+6x0a3zUx66zMuxGzz7e2trJt8zb2bz+IZ51pXVGjS9wXa4woHsGkKSWkpbX/8iul5NjRGt5+6+2Q+wkL855rkHWBg7mXXk7/Af0i5L2VzR9spnLHIRrftdBdk7gv0igoHs7kaZMjuh+vqeWtf7yFZ4OBWWuh+wAT9wU6cy+9jAG5/SPmfcumrRzYfhDPegvdFSmjRxeyWn+TnGm9WPXIKgYOHBi17dXzr+anP/op++uOEDxgTUd3Xyy47qsLuObaa2K2nTFzBveZK/GskRDsfHytn0nfaemseviXDB48OGrbq+dfzZ0/vpP99Yfx/7PToQHImq1zxVWXsujri8IOLmcyeepkHpaP4PmbTGiQyaurZProAn5250/DfknoLK+88irPv/Q/fDR0SkLvc18ouOrL/85NX7kpZtsLLryApffcS+ObGvg7n3eRbZI93cmDDz1Ifn5+1LbXXHsNd915F3saKmnd3enQALgv0Jkz70Ju++5tMfN+us+/bk2fF26TrOkOHlj1AEOHDo3a9toF1/KLn91NhWc/Lbs6HRqAzPN1Lrz8fL73g++FnYU/k5mzzmfZkuUh94AF7pkmWTOcrHxwJcOHD4/a9tprr+Xun99DueefNO+w5leMzBk653/uPO740e0x3U/1ec8aaU2fzzLJnu6Kq88vWLCA++97gA99ZXg/sMj9PJ3zLpnMj37yo5jj0OyLLuSeXywOubda4N5b4p6ps3zlckaNGhW17YIF17JsyXLKvBU0bTE7HRsgY5rO5IuLufNnscfg2RddyN133Y3n7xLZYoF7L0nmTI17VyylsLAwatsFCxawYtl9bPHtoGlT97nJhTq0IESPPtlL6yuZe+ncmEUsQJ8+fbj62qvpk++0JrguMZwBrp5/dVzNp06dSv/+/REZ1uxkWrbJxZdcFLOIBejduzfzF8ynl1XuSIzefq6/8fqYxQzArFmzyHRnIvok5p7h93LDdV9OShELcOWV/47wt6AZgfjfJCRBVwtfvu7LcTUvLi4m75w8tAxrPli0bMn0GTNifqADpKWlce1119I7z6q8A1lG3Hm3vs9Lpk6dFrOIBXC5XCy4/lp6nWOdu9bX5IabbohZyAFMmjSJgbkD0TItcs+SlJSUxCxiAZxOJ9fd8GV6nWPdPIfeD2646fq43E/3eavcs02mz5geV5/XdZ3rb7wO5wDrCgRHf7j+xuvjGofGjx/PuUPORXNbtL9nmYwZUxiziAVwOBxcd+OXSRtkXVngHCC44ab43MeOHUvB8BGILGvcRZbJqJGjYhax8K+8u3J7dEnUbenRWXOka2EPJ1izZg1jxoxh5MiRrFy58vTzmZmZaGkWbTIHuJxpnxrYDx06xJw5cxg7dizjxo3j0Ucf/dRbMjIyEA6LBvc0jewwhxNEcne73Wgu6wZ3U5r06dMnrtgAGX0yEInWFEE/mZmZn3pq4cKF5ObmMn78+HbNV61ahRCC2tpaANauXUtWVhYlJSWUlJSwdOnST7UXQpDWuzeaacS/Thromo7T+WmZYcOGMWHCBEpKSpg6deqnXnO73eC0Ju/CIcnp17fd89H6vLAw74YMtstJtLxnZmYmnvcICKekb7/4+3wotnWzM4Y0yMjIiCs2QKbbDRbt7zgl2X0TG+ssiw0YJJZ3tzvTuj7vpF2fjzbWut1uTJHAPh0DU5iJuWe5LevzOCE7gc84t9uN1K0pJAGkZibU57Oy3AiLvj8JR2hbnk20Pi8169wVqaNbFrJCiEohRP9kLNswDG677TZef/11KioqWL16NRUVFckI1Q6Hw8GqVauoqKigtLSUJ554ImWxwV73VMW+5ZZbWLNmTbvnDx06xBtvvNFutu7CCy+krKyMsrIy7r77bsvX50zeeecdysrK2Lx5c1LjnE1PyHtXjK/c7XO3c6y1270n593u+FYiEZjS/kdXoFsWsslk06ZNjBw5koKCAlwuF9dddx2vvPJKSmIPHjyYyZMnA6Fvh0VFRVRXV6ckNtjrnqrYs2fPJicnp93zt99+Ow888EBcP3l/1ugJee+K8ZW7fe52jrV2u/fkvNsdX5EcUlLICiG+KoTYIYTYLoR4UQgxTAjxdttzbwkhhra1+3chxAdCiG1CiDeFEAPbnu8nhHhDCFEuhHgGy68H9C+qq6sZMmTI6b/z8/NTWkyeorKykm3btjF9+vSUxbTT3c7Yr7zyCnl5eRQXF7d77f3336e4uJjPf/7zlJeXJ20dhBBcdtllTJkyhaeeeippccLRU/Nud3zlbv84C6kfa+1278l5tzu+1UgpbH90BZJ+1QIhxDjg58BMKWWtECIHeAF4QUr5ghBiIfD/gKuADcAMKaUUQnwN+AnwQ+AeYIOUcqkQ4gvAomSvt514vV7mz5/PI488Ejo+UpE0fD4fK1as4I033mj32uTJkzl48CAZGRm89tprXHXVVezbty8p67Fhwwby8vKoqanh0ksvpbCwkNmzZycllkKhCKHGWoWi+5OKGdlLgP+VUtYCSCnrgPOB/257/UVgVtv/84G/CyF2Aj8GxrU9Pxv4fdv7/wacDBdICPENIcRmIcTmAK0dWtm8vDwOHTp0+u+qqiry8vI6tKyOEAgEmD9/PjfeeCNXXx3fFQ2swk53u2Lv37+fAwcOUFxczLBhw6iqqmLy5MkcPXoUt9t9+kSFefPmEQgETp8IZjWnXHNzc/nSl77Epk2bkhInUuyelveuEF+52+cO9o21drv35LzbHV+RHLraMbKPAY9LKScA3wTSE3mzlPIpKeVUKeVUJx2749K0adPYt28fBw4cwO/389JLL3HllVd2aFmJIqVk0aJFFBUVcccdd6Qk5pnY6W5X7AkTJlBTU0NlZSWVlZXk5+ezdetWBg0axNGjR5EydOb0pk2bME2Tfv36Wb4OTU1NNDY2nv7/G2+8EfaqCsmiJ+a9K8RX7va52znW2u3ek/Nud3yrMRG2P7oCqbghwtvAy0KIh6SUJ9oOLXgPuI7QbOyNwLttbbOAUwes3HzGMtYDNwDLhBCfB9pfP8giHA4Hjz/+OJdffjmGYbBw4ULGjRsX+40WsHHjRl588cXTl2ECWLFiBfPmzUtJfDvdUxX7+uuvZ+3atdTW1pKfn8+SJUtYtCj8kSp//OMfefLJJ3E4HPTq1YuXXnopKSeDHTt2jC996UsABINBbrjhBq644grL40SiJ+S9K8ZX7va52znW2u3ek/Nud3xFckh6ISulLBdCLAfWCSEMYBvwXeA5IcSPgePArW3NFwP/K4Q4SagAPnX17iXAaiFEOaEi+JNkrvO8efNSVjyeyaxZs07PANqFXe6pir169eqor1dWVp7+/3e+8x2+853vJHV9AAoKCti+fXvS40Tjs573rhpfudsT2+6xVuW9Z7orkkNKblErpXyB0AleZ3JJmHavAO2uhSGlPAFcloQ168BgZt/gZ+nAm+CyrB70ZYLbsaPxk/5h1YHlJ9zjrN72iS7Pxvh2utv9pdLu7W61fWLx7Yxtfd4TWqbdfd7Kj5lE23/G8p5MpOwet6gVQgwBfgcMJNQlnpJSPiqEWAx8ndBkJsDPpJSvtb3nPwmd1G8A35NS/j1ajK52jGxKCfpMTtbVx92+oaEBo9mizh4Av78Vw4j/DjKNjY1IC+67DmC2Sk7Unoi7fUNDA2aLdXc9cQjH6eNC48Hr9SL9iQZJw+PxJPim+JFS0uLzYWgJfB80wDQNWlvjPxmxoaEBLMq7DAhO1NYlFNtMdLtHwSGcCeXd4/EknvcISL/gxPH43a2MDaE+n0h/tDLv+AUnT8Q/1nk8HutiAw5pn7v0w4njiY11mmndba01qSfU5+vrG5B+69zrTzbE3b6hoQFhWFcWaKaeUN7r6+uRCdzxOxoyAPUnE+vzwkjO7cx7OEHgh1LKscAM4DYhxNi21x6WUpa0PU4VsWMJHXo6DrgC+JUQImpienQha9ZpvPnGP/jkk9hHKpw8eZLVv1+N7xOLPtlMgd7q4rlnn4vrm+Bbb71NXV0d0mvNAGfUaax9ey0HDhyI2bahoYH/fnE1TZ9YNMIgEB4nT/36aUwzdnH82t9ew9vYhPQl5u5Jy+Tp557H77ewGmlDSsnvXvw9sncmUk/khw2Bw5fO0089E1feN2zYwJEjRzAbrdlVzTpBaWlpXJcR83q9/O75F/EdtCrvYJ7QeOrJp+L6Amd1nzdPCrZs2cKePXtitm1qauKF3/4On2V9HozjIm739evXc/z4ccxGi9zrBWXby+K6i5HP5+P5Z1+g+ZB17oEaePrXzxAMBmO2Pd3nPVblXaP0gw/46KOPYrYNBAI8+9Sz+I9YNzvnP2ry9K+fIRCIvT1LS0s5VHXIMnfZoLH3oz2UlZXFbNva2spvn36O5mrrbs/rP2zwzG+ejWsM/vDDDzlQeQCzwaKxrkFj/8f72bJlS8y2fr+fZ5/6La2HrXNPBXZfQzae68hKKY9IKbe2/b8R2A1Eu1TEF4GXpJStUsoDwD+B86LFEN1tOj1e3CJHThdzY7bT80yypjuYPGVK2HvQA3g9XrZu3Ub9Dh8tFXEOMPGcFOSUZF0iGD5mGAUjh+Nwti+ITMPkSPURdu7cReNakN44dvI4c6oPNnDPcDJ5ymRy+vUNeyKT1+Nl27YyTu700bJLYtm9KHSJe45G/uhzGF04Gper/c3FTcOk6lA1u3ftxvO2jKuQPfzjmf/6Q5oMrdlHfi+NCePGkZ7esStZnI0RNDhw8CAfHTzEx4PHYzhcp1/LeziOy2Y5JO6LJENH5zNy9EicYfIupeRo9TF27NhB47sC2Rh7pkDGUSQAaLkG7pkOJk+eTL8BOWHz3uT1UbatjLpyL81lFuZdk2RepHHO6IEUjS3EleZq18Q0JUeqDof6/Dsyvj4fb/gBJu4L9Jju28u2U1fRiG+rxe6zNQaPzqVoXBFpEdyPHj7Kzh078bxjIi36AgOg9TfJnKUxedJk+uf2C+vua2pmR9l2and78G222H2WxqAxAxg7fmxY9zP7vGetifTE4R7nyZdaroH7/Oh5b2lupXxXOcf2nsD7noR4fraNZ6wVkowLNAaN6cfY8eNI79V+HJJScvRwDTu2l+FZJ5EWFXMAWo5J5oWCkpJJ5A4aEN7d18KOHTs5vqeeplITy/IuJBkzNXLH5DB+wviI7seO1LC9rAzPeomst9C9r0nmbI2SkpKo7rt27qJm70m875tx5f1N+cctUsqplq1oB8gYPUgWP3Fz7IZJ5r3LHjgInHldyqeklGHv6iOEGEbo5P3xwB3ALYAH2Exo1vakEOJxoFRK+fu29zwLvC6l/GOkdejxhSyAcJto2RLhCL8tpCGQjQKzLoEdLN6z2x0SfYCJSJcRxw7ZKjBOaNAS5zITyKnINNH6RnE329xPCCy/oZom0XJNtF4x3Gs1aI0v9qcKWQBp0ttbhzPQjLCor0sg6EynuU9fTP3TBXhchSyALtH6G4hekkhHkkm/wKzToSW+fhdvIQttec+WCGeUvHsFZm2S8j7ARPSWEXeThPt8AoiMtj7fhd3NExoyGe59TLScaO4gmzTM40lwF6H9PaZ7nYZstnDC4FTTDBOtr4lo/505FNsE6ROYx7X4iliIf6wVbXnvY6F7Aog+bX3eFSHvEmSTwKzRSEreY7n729wT/NUtrvC9JVqOGds9gbyrQvZfvHfZA3FtCyFEBrAOWC6l/HPbnVtrCX0A3gsMllIu7Eghm5KTvbo60qNhJO9QyugEBcYR+47LkY0aRvyHb1mLKTCP6lh35G0YhIYvs38yI3QMQ2Aes2/3sz3vx2zs814Nw2tTcLvdmzSMJruCd4W823Q0nRSYNT0473a6+wSG77N47KvoFid7AQghnMCfgP+SUv4ZQEp57IzXnwb+r+3PamDIGW/P51+XZQ1Ljz5GVqFQKBQKhUKRHEToeI5ngd1SyofOeH7wGc2+BOxq+/+rwHVCiDQhxHBgFBD1p041I6tQKBQKhULRzYjnZKsuwAXAV4CdQohTZx3+DLheCFFC6NCCSkJ3cz1174H/ASoIXfHgNill1LPwVCGrUCgUCoVCobAcKeUGwh94/VqU9ywHlscbQx1aoFAoFAqFQqHolqgZWYVCoVAoFIpuhKR73NkrFagZWYVCoVAoFApFt0TNyCYJPXeAbbGNYzW2xbabvEc22xpfzxscu1GSCB48ZFtshUKhUCjsQBWyCoVCoVAoFN0JmdC9jz7TqEMLFAqFQqFQKBTdEjUjq1AoFAqFQtHNMK2+nXA3Rc3IKhQKhUKhUCi6JaqQVSgUCoVCoVB0S9ShBaeRkct6E8LfmKKTiA4cqS3B+nWJ4i6BZF+rTsjISmayfzqxwV3ItoUngBT0rLxDUva50yj3SC8p9ySi3MPzWXdPApJuc4vapKMKWZcka7ZOsJc/SiOJI+jC866B9HZ2EluSPrIJrX8TphkEkUBHlBIhNDRfH5rKM8Ds5Lo4JVkXRXeXSJxGm3ujtRP4jqEmfYo1AgQQYUcZiSZ0jCod31YTSwc6l4n7AkkwrTViE4nEKdNo/ABkfed3FecgD85BDQTNACKhvIMQGnpLJk0fZYPsZB7i6PMhdxeN7xmYddbmXc8zyZgcK+8a5lEHTZsszrtD4r5Qx8zwI5Fhly2ROE0Xno0Gst5ad22wQeZUPbo7GrLGiXeTYe0HrN7mnhnDXbpo3GhgnrTYfaBB5nlxuNc68ZZa7O6QuC/UMPoEIKK7iZM0GjcZmDW6dbEBLdcgc7qDAP6I7gINccJJ4/sWu2uSzAt0ZHYceX/fwDxhcd4HmGTO0GO717W5Wzl5oUkyZ+rIvgEk4ccSiRka50sNzFr1I3V3pGcXsk5J1lyNL8z/PNffcB3p6elhmwUCAd5++x2edjyN520T2dTxzp4+ysuQCdksXvoQOTk5iRU0gNfr5bFHH2ezvpOm7VkdH/Da3C+/6nPc9JWb6NWrV9hmgUCA9evX86Tj1yH3ThfyIRxDTfrOSGfZinsZNmwYmtZ+uVJK6urqWHLPUg5qh/FZdYlYl4n7IoMvLriSaxdcEzHvwWCQzZs388v7V9G40UA2dPzDzTmogf5jBMtWPMrgwYM7lPdHHnqUbdoefHv6dzrvsfp8MBhk27Zt3L/iARrXm5YVNfo5Jtnnu1i6bAkjRoxA19tvUyklDQ0NLFuynH9qB2kqtaiY1SXuORqzr5jJ17/5dXr37h22WSAQ4MMPP+ShBx/Gs9ZENljjrg0yyJ7pYsm9ixk1alRYd4CGhgbuW3Yfe/WP8b5nkbsWcp956XS+9e1v0qdPn7DNzuzznnWmZYW8NtAg6wIni5few5gxYyK6ezweVi5fyW79n3g3WJl3wZwvzGbholsj5t0wDMrLy7l3yTI8Gw3M49YUs1puyP3uJb+gqKgoqvuDK39Jub6Hxnety3vmRRpTLy7me7d/j4yMjLDNDMOgrKyMlctX4nnXtKyY1fqbuGfp/Pzuuxg/fnxE98bGRh568CG2O8ppXG9aU8gLSeZsjZILx3HHj++I6r5z506WL12BZ0N3KmaFurNXG90lY0lBP8dk4rQJ3HLrzRE/0AGcTieXX34ZX7zmi6SP7sQmcxg4BjRz/4Mr6devX8LFDEBGRgY/ufPHDBiShciMPJsYC32wSdGkQr729a9FLGIh5D537lyuuW4+vYusm6XoU6xx7/KlFBQUhC1iAYQQ9OvXj5UP3IdrCJBmzUXz9EFBppw/ia989aaoeXc4HMyYMYOvfXMRmRM6s6tItNw6Hlx1P+ecc06H8/6fd91JzuDeiN7NHV6TePu8w+Fg2rRpfPPb38A9ydXheGeTOdnBL+75OaNHj474oSaEIDs7m+Url9ErX0f0sSbvWq7J0DH5fPf7341YzECoz8+cOZNbFt1M5kSnJbEBsqa4+NnP/5PCwsKI7gBZWVksXb6U3vkuRIZF7gNM8kYO4vYf/iBiEQuf7vPuEmvdf/qfP2Hs2LFR3d1uN0uWLSEjPx3htmh/H2hSMHYYt33n21Hzrus6EydO5I4f3U7WZOv6fNaUNH700x9GLeQg5H73kl/gHtobkW1R3nMkA4bn8NOf/TRiIQch9ylTpvDt736bLAv396wpLm7/4Q8oLi6O6p6Zmcldd99F36GZaFa595XknJvFXXffFdO9pKSE79/xPUvzrkgdPbqQdWQICseOibuwGDVqJGk5HZ/EFmlB+vXtH3UwjQdd1xk+fDjCFezwMrReUDSuMG73ESNG4Mq2qpCV+KWfYcOGxdW6d+/e9M8ZgNbbmgHO6dYoHFsYd/uCggL0yJ/9sXEYpKel069fv04sJJT3YcOGgbPjeU+0z48YMQJh0XYHCAo/w4cPj6ttWloaA3MHISJ/z0oIrZdkdOGoiF+czqagoABHpnUzHkE9QEFBQVxtXS4XgwcNtmzbi14wYtTIuN1HjBiB1sc6d8MRjNvd6XRyzjl5iF5WuUvGFMXf5wsKCpAuw5LYAKYzGHefdzqd5OcPsdR92LBhUYvIMxkxYgT0Mi2JDSDTjLjz7nA4GDJ0qKXu5557btzuBQUFyDTr3BWpo0cXskITOBztC9M1a9YwZswYRo4cycqVK08/r+t6h2bTzuTsnerQoUPMmTOHsWPHMm7cOB599NFPvb5q1SqEENTW1n7qeafT0alfnoSemLvD4UjseN5Y8Wm/LSLF5lRbi8ILvX1sgPr6eq655hoKCwspKiri/ffftya2kGjap+O1tLRw3nnnUVxczLhx47jnnnsAWLRoEcXFxUycOJFrrrkGr9f7qfeF8tCJVQnT5xcuXEhubi7jx49v176z8c5GItvFj5Z3S+OLUKFwNtH7vEWxCR0ykVCfD7N/dhghQ2PGWUQf66wLn2jenQ7r9vdQ3j8dO9q463A42o4ltY5E+7xl2z6Me7T4Vvd5SGycd1q8vyf6+d6hE7BtREr7H12BHl3IhsMwDG677TZef/11KioqWL16NRUVFUmL53A4WLVqFRUVFZSWlvLEE0+cjnfo0CHeeOMNhg4dmrT4Z5Jq964S+xTf//73ueKKK9izZw/bt2+nqKgoabHS0tJ4++232b59O2VlZaxZs4bS0lIefvhhtm/fzo4dOxg6dCiPP/540tbhFLfccgtr1qxJepxw2J33ntzne7J7tHE32djt3pPzbnd8RXJQhexZbNq0iZEjR1JQUIDL5eK6667jlVdeSVq8wYMHM3nyZCB0nFBRURHV1dUA3H777TzwwAOdngWOl1S7d5XYEDrBZv369SxatAgI/bSbnZ2dtHhCiNPHbQUCAQKB0JUM3G43EJq9a25uTknuZ8+eTU5OTtLjhMPuvPfkPt+T3aONu8nGbveenHe74yuSQ8oKWSHETUKITUKIMiHEb4QQuhDieSHELiHETiHE7W3tRgoh3hRCbBdCbBVCjBBCZAgh3mr7e6cQ4ovJWs/q6mqGDBly+u/8/PyUDXCVlZVs27aN6dOn88orr5CXl0dxcXFKYoO97nbGBjhw4AADBgzg1ltvZdKkSXzta1+jqakpqTENw6CkpITc3FwuvfRSpk+fDsCtt97KoEGD2LNnD9/97neTug52Y3fee3Kf78nuZ3LmuJsK7HbvyXm3O77VSClsf3QFUlLICiGKgC8DF0gpSwDtGiZ3AAAgAElEQVQD+DmQJ6UcL6WcADzX1vy/gCeklMXATOAI0AJ8SUo5GZgDrBJhpqqEEN8QQmwWQmwO0PEz+u3A6/Uyf/58HnnkERwOBytWrGDp0qV2r1aPIRgMsnXrVv7jP/6Dbdu20adPn3bHb1mNruuUlZVRVVXFpk2b2LVrFwDPPfcchw8fpqioiD/84Q9JXQeFoidz5rh76tcQhULRvUjVjOxcYArwoRCirO3vHKBACPGYEOIKwCOEyCRU3L4MIKVskVL6CB3+vUIIsQN4E8gDBp4dREr5lJRyqpRyqpO0Dq1oXl4ehw4dOv13VVUVeXl5HVpWvAQCAebPn8+NN97I1Vdfzf79+zlw4ADFxcUMGzaMqqoqJk+ezNGjR5O6Hna4d4XYEPpmnp+ff3pW5pprrmHr1q0piZ2dnc2cOXM+dZyqrutcd911/OlPf0rJOtiF3XnvyX2+J7tD+3E3Vdjt3pPzbnd8KwmdbKVmZCF1hawAXpBSlrQ9xkgpvw8UA2uBbwHPRHn/jcAAYErbjO4xIPJFMDvBtGnT2LdvHwcOHMDv9/PSSy9x5ZVXJiMUEDoWctGiRRQVFXHHHXcAMGHCBGpqaqisrKSyspL8/Hy2bt3KoEGDkrYekHr3rhIbYNCgQQwZMoS9e/cC8NZbbzF27NikxTt+/Dj19fUANDc3849//IMxY8bwz3/+Ewj1i1dffZXCwvgvE9YdsTvvPbnP92T3cONuqrDbvSfn3e74iuSQqjt7vQW8IoR4WEpZI4TIATKBk1LKPwkh9gK/l1I2CiGqhBBXSSn/IoRIA3QgC6iRUgaEEHOAc5O1og6Hg8cff5zLL78cwzBYuHAh48aNS1Y4Nm7cyIsvvsiECRMoKSkBYMWKFcybNy9pMSORaveuEvsUjz32GDfeeCN+v5+CggKee+652G/qIEeOHOHmm2/GMAxM02TBggV84Qtf4MILL8Tj8SClpLi4mCeffDJp63CK66+/nrVr11JbW0t+fj5Lliw5fdJbsrE77z25z/dkdzvHXbvde3Le7Y6vSA4pKWSllBVCiJ8DbwghNCAA3AG83PY3wH+2/fsV4DdCiKVt7a4ldNzsX4UQO4HNwJ5kru+8efNSVkjOmjULGeNibJWVlSlZF0ite1eKDVBSUsLmzVbdBzc6EydOZNu2be2e37hxY0rin8nq1atTHvNM7M57T+7zPdU9nnE3mai890x3q1G3qA2RqhlZpJR/AM4+c2VymHb7gEvCLOJ8q9fJNCTBYPx3SQoGg0izc4NfIvGi4fcH6Mw1u6UhCQQCcbcPBoPQSfdwywx3werI8a2JK4OJ571T10eXAsO05k5BgUDn8p5on+9svLMRCILBIC5XfLeCtDS+Cf5Wf9zNrexzELrkWkL9zkp3KUJjRryxO9vnz+JU3uMlELAwvgl+f/x5DwQCCIvvCpBo3i2rsU06kHf73C3d3yWJf8ZJgaUdX5ESevR1ZA2vZEfZzri/mVdU7Ka1ruOFqGx1cOJkLR6Pp8PLgNDOuX//fmRrx7+HmD7YuX0XphnfJ/Xuit20nrSmCAeBS6Sxb9++uFp7PB5q645jNlszwAYaJTu374o773v27MHwxm4XkaBOq7+l0yfrBQIBPv74Y/C3vztVvCTa5/fu3Ytp4VXInNJ1+jjkWDQ1NXG05ijSZ03ezWbB7oo9GEZ8Xyr27NlDoNG6StZhOON29/l8HD5SbZm79MFHez+K23337t0YjdZ9oOsBR9zuLS0tVFVXWefeLKjYWRG3+969e6HVqttxg+bX43ZvbW3l4CefWOr+8ccfx13Q7dmzx7LYAKIlfne/30/lwUpMy/q8OH0sbDzs3bsXWnp0SdRt0RcvXmz3OiSF+5bcvzhfRL/Hs2wSNKbXcfxkDSNGjsDhcGAYRruH1+vlb397jZf/8DJNH0oIxt7RtIw+7Z80NYQD3tu2luKSiaSnp2OaZtiY4R7BYJATJ07w4P2/5JPdNfir+hDufn4yjuufyiZBU+96jtYeZuSokei6HnZdvF4vf1/zd/539R9p2iQhYM0g4280KP3nBsYUjiYzMxMpZVjfw4cP84uf3U1DeTPBw7Fjizjuqy2bNBqcx6hrqKVgREHEvPt8Pt59911++9RzNH4owB97kNMyM8KtFZg6727+OxMmjqd3795hfaPlvba2lvvve5CqfScJHM0kXN7NhthfkOLt8z6fj/fee4+nfvU0nvcNaLUo7/VBPqx8n5GjR5KVlRUx70ePHmXxL5ZQV+4l8IkM65so0ifwZzTx8aH9FI4txOFwhO3zTU1NrFu3juefeYHGUgP8VrkbbP7kfUaMLCArKwsgrHtNTQ1L7l7K8fIGAgfBEvdmQSCzmX0HP6JobFFE9zP7vOd9C91PGmyt+oDhBcNO32gkkvvSe+6lpvwk/gMQ0z2OG4ZIn6Alo5GDhysZUzgmontzczNbtmzh0YcexfO+gWyxyL3OoOzwJoaeOzSq+/Hjx1m2ZDlHy0/g3w+W5L0FjAw/FfvLGTt+LE6nM6J7aWkpv3rsSTzvB8Ey9yDbj24mf2g+ffv2BcK719bWct+y+6jeWYN/H1jlbmYG2LVvB+MnjIvq/uGHH/LYI4/T+F4wrrx/TMWRxYsXP9XplewEy//fqsXZl02xcxUAqP3DOtu3hbDzOKFk4hY5crqYG7uhQ+K+QIcsA0OGn3HUhI7e7KBhg4GMc1ZQH5gb4RVJ2lAfaee04jdb4lrWp1ZXc2E2pOPb0yfiT0DGsZo4FyZxz3Qgsg2CMvw3dk3o6C1OGjYELf2mDqANMsia6iIgWiPe29yJi9aPJc3l8RUzwhnfT9Y4JJnnS8gIRMm7hh5w4fkAZGN8MzT6Oe2uCvev1/p76D3ER8BsTvinQ6eehtHQi+aPs4m0HYIHD4V9vh3x9Hk09KCLhvcCyAZrZym0XIOsaU4Cmj9y3oUL/0HwbTex9ObvmiTzfB2tnxmlz2vofhcNGwPIRovdB5hkneck8P/ZO/P4KKt74X/PM0vWmWwQlgTZAoSEJexLIgpq8aVeW6+9Koji8tYu3rZqrfuGKy50td5uVq222F5vb+17b6UuqIAQQ4CAQBEUEAhrgGRmss7Mc94/JlAwszyTnMkQc76fz3wgT06e3/k+5zzP85vzLMeI0ueFk7a90FSTAPfpNow+UdwJ9fmGNX6kR7F7H5OsaSfbPfxIt0M4adsPTRssulud+e5ku+dGPtYJDOxBJ54PA5jHLbpb3JGNvHZ3W3R3f62gcX0Qpe0uJJlTbdj6SQIy/OikwMAecNKw1o+sV9zuOSbuGQ4CsdwPChqrg2pvbRCSzCk2bP1juAedeNb6MU9Yc39bvrZeSjlZXUXjJ61ooBz6zE3JrAIA/7hscdK3hU5kE0TkRDbxWE5kv4BYTmQTRLRENtFYTmQ1mi8K3TR9d0S+oOdPTXTOlkR2yNPfSGYVANj+rw8lfVvoG0I0Go1Go9FoND0SnchqNBqNRqPRaHok3fb6LY1Go9FoNBpN15GcPVPEJhs9IqvRaDQajUaj6ZHoEVmNRqPRaDSaHoZ+1DCEHpHVaDQajUaj0fRI9IhsgggePZbsKvRKpN/6VJSJIFh7MGmxD9w+M2mxAQY+syap8TW9EJHksRipZuppjUbTeXQiq9FoNBqNRtOTkOiHvdrRtxZoNBqNRqPRaHokOpHVaDQajUaj0fRI9K0FGo1Go9FoND0N/doCQI/IajQajUaj0Wh6KHpEVqPRaDQajaaHoR/2CqETWcDINTFyTAxn+E5h+iWmV2AeNgDFHccusfUPYqRLhNFx3VJKzBYwj9iQzeoH0C25+wTmoQS5DzCjuIPZDOZRA9mkfoc1ctrdU5Lg7pDY+gUw0iSEcUeC2QpmnQ3Z2Ml2l5IM71GcbY3YulbbU5gS/I4UGjP7YNqdnVuJ7bR2t0Vo9xYwj4rOu0dBZJvYck2EUyDCbHozIJE+QfBgAtrdkrsM9flEuGeZ2PJiuDe2u6s+SRrt7hkx3OsMpC8B7m4TW14Q4QQRRt4Mtrf7IZt6dwvtLlslwWMG0tP9fV4GJebJdjeT0O6t7e3uTYC7y8ToEzrOh+3zQZCNJMZd0y30+kTWPsQkZ0oK551/Hjl5OWHL+Lw+Pli1hqObG2iukSg7uTkkWXMko8tKKC4ZhcPh6FDENE32fbafyjUf4n3fVHqAt59jkj01hfNnR3Zv9DWyZvVajmw+QdMG1e4GxWXFjB4zOqL7/r21VK6pxPOuqfQgZys0yZ7mPOUe7sTW6Gtk7QeVHP7oOE3VCt2dEvd5JqUTxzCyeERYdyklhw4cZtXKVXg/kMj6OFNRKRl0dAdDM51MmzGJ9LQ0JVUPBILs+PRTNv1jC5/2LyVoT4lvBXaJe7bBiHEjGDN+DE5n+HY/WHuQD1atwfO+iWxQ1+7GgCDZMxycP/t8cvvkhm33psYmqtZWcWBLHY2VJsra3RZyHz52OGPLxpCS0nHbSSk5WHuI1atWh9zrFbr3D5I1w8H5s88jr29eePemZtZVruPAliP41prqEjqbxH2+wdCxQxk/YVxE90MHDrFq5Wq8K03MEwrd84NkzRDMOn8OffP7hHVvbm6hunId+7cewbdWqnO30OellBw9Usf7776PZ00A84iqr57W+nxrayubNmxi90d78bxnqkvoDIlrlsHgMecwYXIZqampHYpIKTly6Agr31+JZ5WJeUxhu/cxcVcYzDpvNvn9+4Z1b2lpYcO6jezdUot3lUJ3TbfRqxNZI88kZ2oqS3/0DAMGDIha9oorr+AH37+DPfWHCexR09GzzhVc/JWLuPHrN4bdwU7nnXdW8Jx8Ds8bQskB1sgNJbFLf/wMAwcOjFr2iiuv4I7b72R3w0H8n6pyt3Hhv8zmG9/6Rkz3lStX8hPzp3j+JpUcZER2KIl95kdPU1hYGLXslVddyV0/uJtPPftp26HG3V0Bl1w+j0XXL4rpfu55FTz28ON43zLAbz1+/om9lPbL5vGHF4dNGrrKK7//A6+98SY7B46P6+/cM22cO3cm373lOxhG9BPWjPIZPC2fwfOGhICCdneZZE13sOTpJQwdOjRq2fkL5nPvXffxsXc3LVu7HBoIuc+4cCq33X5rTPfyc2eyRD4Zco+j3SMhMk3cM+w88dTjDB8+PGrZBQvmc98997Pdu4vmj7ocGgDXNBtT50zk9jtux2aLnqTNOn8Wjy5+DM9yCW0K3DNM3NMFjy15jBEjRkQtu2DBfB6490G2Nn5K86Yuhwbi6/MXXnQB991zP563TCUj8vH0+QULFvDkE09R3VyDd63Z5dgAmZNtjD+3lPseuDdmu8++YDYP3v8Q3r9LZIuCdk+TuMoFix9dTElJSdSyCxYs4NHFj1HTtJXGdT3nCSrZc6qaUHr1w15GjsmFF10YM4kFyMzM5GtXXE56Ycdv051DEkxrZeG1C2MmMwAXXDCH9Mx0RLqanmvkmJw/5/yYSSxARkYGV1z1b6QXdvJScgckwQy/ZfdZs2bhcrsQGercK2ZVxExiAdLT07lywRWkFyhyF5JASgsLFi6w5D5x4kQGDhiI4YrvxJIdaOTqK69ISBILMP+qK6GxAWHGObNRdpBrFi2MeUIHmD59On365CEyVbW7ZPLkyTFP6AApKSnMX3gVaQWq9ncQOaZl9ylTptAvvx+GS5F7tmTC+LKYSSyA0+lkwcL5St1teXDNomtiJjMAEyZMoGBAgTr3LJMxpWNiJrEADoeDq69dQNoAdSOi8fT54uJiSopLMLJU9vlJlvq8zWbjmkULsfdREhoAR77gmkULLbX7mDFjGD50GMKtJokWWSZFw0fETGIB7HY7V1+7AGc/he2u6TZ6dSJrT7ORnZPVYfny5csZNWoURUVFLFmy5NRyt9uNLVXRJmu//e70Sy0tLS1MnTqV8ePHU1payoMPPnjGn2SkZ4Bd0QEuxSAnN7vD8kjuLpcr4r2kncGUQTIzMy3FBsjMyEQoOq8KB+T1ye2wPFL8zMxMde4G2AwbTueZifGQIUMYO3YsZWVlTJ48+Yzfud1ucMTX7oYZxOVynbHshhtuID8/nzFjxnQov3TpUoQQ1NXVnbF83bp12O12XnvttTOW22w27M6UuBPZoAx0qFfUds90KWx3SW6fjrfQRGt3VbEBgrJjm0Rzd7lcyvZ3HJLsOPd3ZbGBoIiv3V1uV9x9PiJh3KMda10uF9KmJpmC8H0+2r6YnZOt7FqpsMsOt41Fi+1yuQgKddPuSsOMq93dWVnq9nc7ZGVbP7+7XC6k0XOmHJaEHvZK9udsoFsSWSHEECHElu6I1VWCwSA333wzb7zxBtu2bWPZsmVs27atW2KnpKSwYsUKNm3aRE1NDcuXL6eysrJbYkN0dyujh4mKHapAQsMn1f0k7777LjU1NVRXV5+xXFX86667juXLl3dYvm/fPt58803OOeecM5YHg0HuvPNOvvSlL4Vdn4paxWr3ZPa75Pf53uue6F0u2rG2O/b3SPtiqALJi530dk9o9LPjOK9RT68ekQ1HVVUVRUVFDBs2DKfTyVVXXcXrr7/eLbGFEKdGKf1+P36/v1t3rmS6JzP22RC/O5g1axa5uR1Hom+99VaeeuqpDn3tZz/7GZdffjn5+fkJq1Oyt7vu873TPdnH2kj74hc9drLbPdnxNYmhOxNZmxDi10KIrUKIN4UQaUKI94QQTwohqoQQO4QQ5wIIIUrbl9UIITYLIUa0L7+2/edNQoiXE1HJ2tpaBg0adOrnwsJCamtrExEqLMFgkLKyMvLz87nooouYNm1at8VOpnuyt3uy4wsh+NKXvsSkSZP41a9+1W1xX3/9dQoKChg//syHtmpra/nv//5vvvWtbyU0frK3u+7zvdMdknus7a0ku92THV8pktCD38n+nAV051sLRgDzpZRfF0L8Cbj8ZB2klFOFEPOAB4ELgW8CP5FS/l4I4SSUBJcC9wEzpZR1QojkfKVMMDabjZqaGurr67nsssvYsmVL2HuZNF8sVq9eTUFBAUeOHOGiiy6iuLiYWbNmJTRmU1MTjz/+OG+++WaH391yyy08+eSTlh5Q0Wh6IvpYq9F8MejOs9RuKWVN+//XA0Pa///nMMvWAvcIIe4EBkspm4E5wH9KKesApJTHPx9ACHGTEKJaCFHtp7VTlSwoKGDfvn2nft6/fz8FBQWdWldXyM7OZvbs2ZHvo0oAyXRP9nY/G+ID5Ofnc9lll1FVVZXwmJ9++im7d+9m/PjxDBkyhP379zNx4kQOHTpEdXU1V111FUOGDOG1117j29/+Nn/5y1+U1+Fs2O66z3d//GS7n04yjrW9lWS3e7LjaxJDdyayp2eWQf45Gtz6+WVSyj8AlwLNwN+EEHOsBJBS/kpKOVlKOdlB5147NGXKFHbu3Mnu3btpa2vj1Vdf5dJLL+3UuuLl6NGj1NfXA9Dc3Mxbb71FcXFxt8SG5LonM3ay4zc2NuL1ek/9/8033+yWkaGxY8dy5MgR9uzZw549eygsLGTDhg3079+f3bt3n1r+ta99jeeee46vfvWryuvQm9tduyfPPdnH2t5Ksts92fFVI2XyP2cDZ+WECEKIYcAuKeVPhRDnAOOAt4D/FkL8UEp5TAiRG25UtqvY7XaeffZZ5s6dSzAY5IYbbqC0tFR1mLAcPHiQRYsWEQwGMU2TK664gksuuaRbYkNy3ZMZO9nxDx8+zGWXXQZAIBBgwYIFXHzxxcrjzJ8/n/fee4+6ujoKCwtZvHgxN954o/I48dCb2127J8892cfaZO6LyYyd7HZPdnxNYjgrE1ngCuAaIYQfOAQ8LqU8LoR4DHhfCBEENgLXJSL4vHnzmDdvXiJWHZVx48axcePGbo97OslyT3bsZMYfNmwYmzYpmkYoCsuWLYv6+z179oRd/uKLL6qvzGn01nZPduxkx09m7GQfa2Pti1/U2NC7+7xyzpIR0WTTLYmslHIPMOa0n58JU6aO9ntkpZRLgCVhyrwEvKS4bvH+hcLYylaVcOLfTjHWF+d2VB4/jvWpd4+zfCfjq653h/V39u+Sue2TGDtuktjvpIx3D7W2TutlkxlbfbvrPm81vvI1xhG7B52QNWfQqx9JDjQHOXG83nJ5j8dDsEVRZ2+fOKa5udnyn/gafUrmnAcwW0yO11m/M8Pj8WC2qJvtxibsp+4LtYKv0YdsUxNb+uHYUevuXq8Xs1Vdu5tmkNZW6w8jNjR4wB9fuwdt8W3feAkEAvhbWzGN+L4L24Ujrnp5vV6kP97ahUe2CY7XnbBc3uPxKOtzAHZhx+PxxBU/3naPiF9w4lh87spiAzYZX39saGhQ6l4f53GegLpTY7x9/sTxE6Cqz/vj7/M2qW6aVmEacfX5+vp6dfu7H+pPNFgu7/V6EcFenRL1WHp1q5nHDd556232798fs2xDQwOv/v6PNO1VdWYT2JtSeP43v7X0TfCNv71Bc2MLsklRInvc4N133+Ozzz6LWdbj8bDslVdp3KfoCIPA8Dr47W9+i2nGTo7f/Pub+Dw+pe6rV61i9+7dMcv6fD5+//IfaNqr6ugqsLek8sJvX7TU7pWVlRw8dADTG9+uesKewUuv/J6mpqbO1jQiUkpe/N3L4MqBOF/PZR43eP7XzxMMxp4KcuXKlRw7dgzpVdTu9YLq6mp27NgRs2xTUxOvvPR7mpT1eQjWCZ7/9W8tua9evZojR45gqnI/Idi0qYbt27fHLNvS0sLvXniZ5v3q3ANHsewe6vMHMT2q2t1gy9YtbN26NWbZ1tZWXvrt72ipVTdVaTx9fsuWLWzfvh2zQc2p2awXrF+/3lKfDwQCvPCbF/AfURIagLaDkt/+5gX8/th9aePGjezes1uZu2ww+OTTT6ipqYlZ1u/388JvXqDtoLrBmsST/Olpz5YpasUXdTjdLXLlNHFBzHK2QpOsaQ6mT59Obl7HedgBvF4f6z5cx/HNjbRskViaSM+w8K3WLsmaDUNHD2bU6FE4HB1Ht0zTZN9n+9lcsxnPuwLZZGEnN60dhG0FIfdp06eR2ycHEcbL6/VRXVXN8Y98NG+26G4Fu8Q922DI6HMoLhmF0+nsUMQ0TfZ/tp9NNZvxrDCRjeq+dxkDgmTNcDBt2jTy+uaGdff5GqmuqubYFi/NNdbchd3CCKVd4j5fMrxkKCNGjQjb7lJKDtYeYn31eryrBdITuz/V3jL19BVQUPcpgxwBJk2YQHp6Wux6WSAQDLLzk0/5+LN9fNqvFNP+z3Yb+Mya2CuwSdznGwwqLqB0bEn4dpeSA/tq2bihBs+7JjLOJD4aRn4Qd7mdadOm0advXtjZnBobm9hQvYGjW+tpqlbY5w2J+zyDwtEDKR1bQkpKxzermFJycP9BNqzfgOc9E+lR6N7XxF1uY+q0qfTN7xPWvampiQ3VGzm6rZ7GKhOV7q5ZBgXF/Rk7fkxY93/2+Wo870uklYTGynEWMPKCuMsFU6ZOIb9f34juNRs2cfgfJ2j8ECy5WznWWujzUkqOHD5K1YdVeFebmMcU9/mZdqZNm0qfCO3e0tLKR5s+4sD2w3hXmWAqanchcVUY9C/uy/iycaSmpXYoIqXk0MHDVK+rxrvSxDyh0D3XxHWuwZQpU+g3ID+se3NzC5s3buLg9jp8H5iWXvL/tnxtvZRysrKKdoKUYYVy4CM3J7MKAOxZeE/St0WvT2QBRJaJkS0RjvDbQgZBeg3MOoHlA7vFAyw2iS0/iEiTEVctWwTBOgNaLe7gFhNZSJC7VWwSW76JSLfg3qL+m59wmxg5kd0JgukzMI9ad7eUyALYJEZ+EJFqRl51q8A8bkM2W2v3MxJZAClJbzyGo7UJIRWNNAhBwJ5KU2Yups1xxq8sJbIAhsToZ2LEaHfzmIFsTkC7u9rb3Rml3RsF5hED5X3einurwKxLkrsJpi+B7vkmRkYM92OG9asvVo+zgMg0MXLM6O6NAvOIDcvuVo+1Vtq9TWAeF0if+gulMdtdntbnVSWxp4Kf5h5BTba2uyscrDgVPsPEyIvR50+6Wxxh1InsPzkbEtmz9a0F3YpsMAhav5VGLUFB8GDymiH57urux4oX6TEIWr99Sy1BgZnodheCpsw+kJnYMHFjCsyDNpJ1EU96DYKJu304Or3d/VAS3X0GwQQkiZboze0uk9zujQbBxiQFTzRfzHHIuOnV98hqNBqNRqPRaHouekRWo9FoNBqNpichOWsetko2ekRWo9FoNBqNRtMj0YmsRqPRaDQajaZHom8t0Gg0Go1Go+lp6Ie9AD0iq9FoNBqNRqPpoegR2UQRx7tcNV8cZCCQtNiW3+OaIOyFBUmLHdhfm7TYmiSij7OaXo1+2Av0iKxGo9FoNBqNpoeiE1mNRqPRaDQaTY9E31qg0Wg0Go1G09PQD3sBekRWo9FoNBqNRtND0YmsRqPRaDQajaZHom8tOEWsMfpEPx0YLX4yYyc7vnZPTuxExe/stTDVddHtfnbG1+5f3Pi92T1B6FsLAJ3IgkOSNctGMN2PKcO/ykUIA4ffScOqALJR7SC2fZBJZpmNNtkW9vcSidNIoW2PoHmzidIdziHJOtdGMCOau8ARSAm5+9S62wpNXBNiu/v3CppqFLvb290z/QRlABFm3afcVweQXsXuA01ck6K7Owwngf0GTRvUu7sr7Eh3GwEzinuw3d3TdXd7Py+pg3y0BVri+juJxG5zIpoyadruBtnFutgk7gobMitAwPRHd/8ggGxQ2+5GvyDuqfaY7R48aKNxXRCl7W6TuMttyOwY7ma7e71i9/wg7mkO2mT4PnDS3Txkw7cuCCrnkTfa3XMiuyMETjMFz5oA5gnF7n1N3NPstBHD/YgN34fq3V0zbJAXw122ux9X7N7HxD09lrsDedSOtzIB7tNtiD4B/LHc1wYwj+mL1D2R3p3IOiRZFxjM/eqFzO+l+Q8AACAASURBVF8wn8zMzLDFWlpaWPHOCn7reAHPClNZMms/xyRneioPP7qYoUOHYrPZOpSRUnLkyBEWP/Aw+40jNNcoCR1K5OYYXHjpHBZec3VE99bWVt577z1+bf+NUndboUnO9BQWP/oQw4cPj+h+9OhRHn7wEfYZh2naoOjrZ7v7+fPO5bobrovqvmrVKn7h+GXIXVEibxtokj3TyUMPP8iIESMiutfV1fHo4sfYY9TSVC1RktTYJO7ZBrMunsEN//cGXC5X2GKtra2sWbOGn9ufw/Ou2aVE3t7PQ06xySOPPkVhYWFY30hIKfF4PPzsJ8+y0dhO07bczp/ojJD7zIumctM3b4rqXlVVxU+W/hTPe6aSRB5CSWxWuYMHHrqf4uJi7PaOh18pJcePH+eJR5fwie0zGisVfYkxJO7zDabOmcS3bv4mbrc7bLG2tjbWrVvHj575cchdUSJv5AfJKrdz34P3UlJSEtH9xIkTPPXEU3xs34VvjUL38wwmnT+ef//ev+NyuRCi43r9fj/r16/nmSeX4n3fxFSUyBt9TdwVNu69/25KS0txOBwdykgpqa+v5+klT/MP+yf4VityFxLXLIOyc0v53m3fw+12R3TfuHEjTy95Gs/7prJE3sgLud917x2MGzcuontDQwNLn/4hW+3b8a421SSzQuKqMBhbMZpbb7+FrKysiO6bN29myWNP4lkV7DnJrERt0t+D6SEtlhhsA03GTC7l/379/0ZMZgBSU1OZ9+V5XHr5v5A6Ut0myygzePTxRygqKop4chdC0K9fP55a+iSOwSY41CRztgEmxWWj+MY3b4rqnpKSwty5c/nXKy8jfbT1BCQWmRNsPPzYYkaOHBnVPT8/nyefWYJzsIQURe79TIrGDufm79wc0/3CCy/kigX/RkapOnfXJNupZCaae9++fXniqcdJOcdApKmJbetnMrRkMN/53nciJnIQcp89ezYLrp1P5tiufN+V2AvqefqZJxk8eHBcSSyEtkNWVhZ333sXfQozERnNna6JkW8yaFQBt37/1pju5557LotuvBbX+I4n3s6SNdnJ3ffexZgxY8ImchDyzcvL47Elj5I+yIHIVNPnjb4mA4r6cfsd34+YxAI4nU7Ky8u54evX4y5T6/6Du37AuHHjorrn5uby8GMPk3lOCsKlyD3PJH94H+68586IiRyAw+Fg+vTp3PStr+Oa6FQSG0Lu3//BbZSVlYVN5CDknpOTw+JHF+M+Jx2Rpcg9V9JnaA73PnBvxEQOQu5Tp07lW//+LdyT1Lp/77bvMmnSpKju2dnZPPDQ/WQPzsTIVuSeI8kZ4ub+h+4jOzs7qvukSZP49+/dTJZCd033EfEMJYT4GVHuwJBSfjchNepG7JmCkjGjI3bwzzNixAhScx004+96cCEJyABDhgyxVDwzM5Pc7Dy86ceQDV3/FmakQ8nY+NydOTYaUTFzlaRNtjFs2DBLpTMyMsjL6YMn7Qhma9fdRbqM292RZQMl7tAmWxkxYoSlsunp6eTn5dOQdgDZrMA9TVJcMgrDsPaFrKioCLvbAMzOBbSZ2G12+vXr17m/P7kam41hw4exb90/Or0OI00yqnikZffhw4djd6kb8QjY2igqKrJUNiUlhQH9B3AsfRfS1/XYIg1GjAw/+h+OoqIibJnq3IP2gGV3p9PJwIGFHEn/GOntemyRBkVF4a/6hGP48OEY6epuPjQdAYYPH26prMPhYFDhIA6lb0U2dD22SJcRr/aFo6ioCJGmzl2mBC23u8Ph4JzBgzmQtglOdD22SJMMGTwk4henz1NUVIRM6eRxTpNUoh3Rq4H1UT49HmGIsJ18+fLljBo1iqKiIpYsWXJqud1uV3fLmgh9E/38STVSbAidzFXFF7bOuKs7sQnocHCN5m6329VdPxCEHR2IFN9msyEMde4SGZe7za6u3eN1t9vtXXMXEuNzri0tLUydOpXx48dTWlrKgw8+CMDu3buZNm0aRUVFXHnllbS1nXkfqcPh6FoXNMDhjM9d5S2qUsbZ5yOMYHUKIXE4knSso/1e588db6K5OxT3+XDbMlJ8h8Oh/NmfeNztDru6Q62gE+2u9gmiePq8Q/E51p5k90QjZfI/ZwMRUwMp5Uunf4D//NzPnUIIMUQIseVzyyYLIX4a4+8UjEvEJhgMcvPNN/PGG2+wbds2li1bxrZt27ojdFJjJzu+du897ikpKaxYsYJNmzZRU1PD8uXLqays5M477+TWW2/lk08+IScnh+effz5hdTiJbnftrt21u6ZnE3OMSwgxQwixDdje/vN4IcRzKishpaw+W25VqKqqoqioiGHDhuF0Ornqqqt4/fXXv/Cxkx1fu/cedyHEqXuT/X4/fr8fIQQrVqzga1/7GgCLFi3iL3/5S8LqcBLd7tpdu2v3Hos8Cz5nAVYu1v4YmAscA5BSbgJmqQguhBgmhNgohPiBEOJ/2pdlCiFeEEJ8JITYLIS4/LTyjwkhNgkhKoUQXbvpLgK1tbUMGjTo1M+FhYXU1tYmItRZFTvZ8bV773IPBoOUlZWRn5/PRRddxPDhw8nOzj51Cba7toFud+3e3fG1e+901yQOS3cdSin3fW5R+JeOxoEQYhTwX8B1wLrTfnU/0CClHCulHAesaF+eAVRKKccDK4Gvh1nnTUKIaiFEtZ/WrlZRo9EkCJvNRk1NDfv376eqqort27cnu0oajUajUYwQYpAQ4l0hxDYhxFYhxPfal+cKId4SQuxs/zenfbkQQvxUCPFJ+2DmxFgxrCSy+4QQMwEphHAIIW4HOv/ocIi+wOvA1e0jvKdzIfDzkz9IKU8+v9gG/E/7/9cDQz6/Uinlr6SUk6WUkx2kdKpiBQUF7Nv3z7x9//79FBQUdGpdPSl2suNr997pnp2dzezZs1m7di319fUEAoFurYNud+3e3fG1e+90TwhSJP8TmwDwfSllCTAduFkIUQLcBbwjpRwBvNP+M8D/AUa0f24C/iNWACuJ7DeBm4EC4ABQ1v5zV2gA9gIVcfyNX8pTz8gFSdBkDlOmTGHnzp3s3r2btrY2Xn31VS699NJEhDqrYic7vnbvPe5Hjx6lvr4egObmZt566y1Gjx7N7Nmzee211wB46aWX+MpXvpKwOpxEt7t21+7aXZM4pJQHpZQb2v/vJTQQWgB8BTj54oCXgK+2//8rwO9kiEogWwgxIFqMmMmglLIOuLpzChFpAy4D/t7+NoIDp/3uLUKJ8i0AQoic00ZlE47dbufZZ59l7ty5BINBbrjhBkpLS7/wsZMdX7v3HveDBw+yaNEigsEgpmlyxRVXcMkll1BSUsJVV13Ffffdx4QJE7jxxhsTVoeT6HbX7tpdu2u6ByHEEGAC8CHQT0p5sP1Xh4CTzz0VAKffzrq/fdlBIhAzkRVCDAN+QmhIWAJrgVullLviMvgcUspGIcQlhBLXR0771aPAz9tf0RUEFgN/7kqseJk3bx7z5s3rzpBnRexkx9fuvcN93LhxbNy4scPyYcOGUVVV1S11OB3d7tq9N8VOdvze7K6as+S1t32EENWn/fwrKeWvPl9ICJFJ6LmoW6SUntMnJJJSSiE6b2Pl8vwfCN2zeln7z1cBy4BpnQkopdwDjGn/fz0wpf1Xf21f5gMWhfm7zNP+/xrwWmfin7HOoDx1T54VAoGAutdNSJBSYpqm5ZmGgsGgsvgyKPH7rc9QptS9nWAwaHnGmUAg0OnJpTogoa21LXa502JLhRO+CGEQCARwOq1NhxgMqGt3JB0mGohGl7e7FJjBLj8bCoRe1dWlF3Cb0NaWvD4vhIjveOP3K2x3kVx34nP3+9Uea/1x9Hm/yu3eTnztHlD3onmTThzn1c4GkdR275T72ZEd9iDqpJSToxUQQjgIJbG/l1KeHJg8LIQYIKU82H7rwJH25bXAoNP+vLB9WUSsZFDpUsqXpZSB9s8rQKqFvzvrCfgkWzZvRVo8any8/WNajimYnhZACuw42LXL2sC21+vleP0xZJOag4zZBFvjcN++fTutJxS5I3CKFHbu3GmptM/no+74UUwFU7QCyCbB1o+2YprWMrQdO3bgb1CTjAE4se7e2NjIkWOHlbW7bBL8Y+v2+Nw9XXAPGgSCAQ4ejHhVyBKBQIBPP/kU2jo/25XZLPh42/bQF0IL7Nixg4BH3UnNHnRYbveWlhYOHFIzLTGAbIKdO3bG5R70qXO3BRzs2LHDUtnW1lZqD+xX1+ebBZ/s/NSy+86dOzEblYQGwGizW273trY29u7bq9R916e7LSeTO3bsUBYbQLTYLLe73+/ns72fKT3W7dmzx3Iyu2PHDmhRNX1kN5Ds98dafI+sCA29Pg/8Q0r5w9N+9Vf+OWi5iNALAE4uv7b97QXTCb3FKuoJJGKrtb8aIRd4QwhxV/uMXIOFEHcAf4td/bOf4AGDbev/wS+e+yUNDQ0Eg8GwH5/Px19f/yv/85f/pWWHuoO7b6PJ/fc8wPbt22lrawsbOxAIUFtbyw9uuwP/bgF+NTt58KDBx5s+4efPPkd9fX1U9//93//l9df+SvM/1A1LetcHeeiBxWzbti2q+4EDB7jj+3fStkdAqyL3QwafbtnDz37yM06cOBHRvbGxkeXL/85/LnuNxi3WRxVi4an28/BDj7B161ZaW1sjuh86dIg7b7+L1j0S2aLI/YjBZ//Yx4+W/jiqe1NTE2+99RavvvJHfJu74i7w78/mjtvv5NNPP8Xv90eMGelz7NgxHnvkcY7tb0T6Ov8d2jxisH/HQZ55amlM9xUrVvDKi7/Hu0nVlzdoWNfGk48/xaZNm6K2++HDh7n7jnto/iyA9Cn64lpncHDnEZ58/EmOHz8e1f3999/nxd+8hGejWvdnnlrKxo0bI7oHg0GOHj3KvXffh++zNqRXlbvgyK72PnTsWFT31atX85tfPq/c/UfP/Jj169fT0tIS1f2Bex/A+1kzskGR+3HBsd31PPLQI1Hdm5ubWbNmDb/4+S/xrLc+eh2Lhuo2fvaTZ6mqqorqXldXx4P3P0T9Hh9mvSL3esGJ3V4WP/AwdXV1EWO3tLTw4Ycf8tzPnlPqrjlFOXANMEcIUdP+mQcsAS4SQuwk9Laqk/MF/w3YBXwC/Br4dqwAItKInBBiN6F8O1yvklLKYXHKdCtukSuniQtiF3RIsipsmK4AATP8CdsmbNjbHDSsCir9tgpgKzRxTbDTRmvY0VEBOI0UWndJmrdEao5OYpe4K2zgDuCP6G5gb3PSsDqAbFT7bdU20MQ1KbI7hNzb9kDzZhPl7uU2yIrh7m9396l1N/oHcU920Caiu/v3QlONYndbu3u2BfcPAkivNXd7YeTX2Nj6ekkb3ERbsCnuy6ZOWwrSl07TjqyIlz0D+y2+1Nwmcc+0QU4Qvxk+WbEJA3ug3d2juN3zg7inOvCLVsyI7e4kUCtoXK+43Q2Ja6YNkRvZ3RAGjoCThjUBZINi974m7mn22O4HBI3VCXCfYUPkxXAPtrvXK3bv0+5uRHcPHjLwrQuqvbxvSFzTbBh9TdrM8ImaIQSOYAqetQHME4rd80zc0y24HzbwVSl2F5LMaTaM/GjtLnCY7e7Hrbm/LV9bH+tyeqJJGVwoB9z7vWRWAYDPvnFH0rdFxES2p2M5kdVoNEqIlsgmGsuJrEaj0XSRsyORHSQH3HMWJLLf/EHSt4Wld7EKIcYAJZx2b6yU8neJqpRGo9FoNBqNRhMLK6/fehA4n1Ai+zdCsy6sBnQiq9FoNBqNRqNJGlZuCPkacAFwSEp5PTAeyEporTQajUaj0Wg0kUn2GwvOkjtTrSSyzVJKEwgIIdyE3vU1KMbfaDQajUaj0Wg0CcXKPbLVQohsQq9BWA/4CM3updFoNBqNRqNJBmfJiGiyiZnISilPvsPrF0KI5YBbSrk5sdXSaDQajUaj0WiiEzGRFUJMjPY7KeWGxFRJ02WE2nfdxs0X9JVumugk8xVYu56ckbTYAMPu1BepNBqNJhlEG5FdGuV3EpijuC4ajUaj0Wg0GivoMSMgSiIrpZzdnRXRaDQajUaj0WjiwdKECBqNRqPRaDSaswSJ2ul8ezBqJ1XWaDQajUaj0Wi6CZ3IajQajUaj0Wh6JFamqA339oIG4DMpZUB9lTQajUaj0Wg00RD6YS/A2j2yzwETgc2AAMYAW4EsIcS3pJRvJrB+3YKRYyKyTYQjQoEgmF6BecQgtAkUYpfY+puINBl+1RJkayi2bFF/P4yRa2JkmWCPsEeYAtMnMA8nyL2fiUiP4A7IFoF51EA2q3cX2Wao7aO1e6Lcbe3tniz3LBMjN8nuaTLiNSHZIjDrDGRT52IL08RVdxRnSwtCml2o7JkEnCn4cnLxp6Z2bgWGxDYghntre7t30j0awt3e7s4IBYJgNgrMQwlod+O0Ph/NvU4gG9VfLBQuEyNPIpyRjnWnuau+97A3u4t294wY7scE0pcA98x295TI7rJREEyEu6ZbsJLIHgBulFJuBRBClAAPA3cAfwZ6dCJrP8cke6qT8nMryM3LCVvG6/FRuaaSYx95ad4kUXaAd0iy5hiMGDuC4tJiHI6OWYVpmuzdvY8N1RvwrDCVHuQcw0yyJ6VQXlFOTm522DKNvkaqq6o5vOUEjesUutsl7jkGRWOKKBkzGoczvPv+z/ZTXbUez7um0oOcrcAka7qDiopy8vrmhS3j8/r4cG0VdVsaaNqg2H22wfAxwygZW4LT2TGrkFKy/7P9rKuqDrl7FboPCJI1w0FFRQV5+eHdG32NVFWu48iWEzRVK3S3hdyHlg5lzLhSnCnh3Wv3HaCqsgrPeybSE5+7ME1G7vyY4W4XZRPLSEtJUVJ10zTZe+AA6zZsZPvoEtrSM+JbgU3iPt9gcMlgxpaNJSWMO0DtvgN8uPZDPO+byAZ17W7kB3GX26momEXffn3DlmlqbKa6qppDW4/RWGmirN0Nifs8g3NKBzFuwvgY7pV4VprIeoXufU3c5TbKK8rJ7x/evbmpmfVV6zm4rQ7fWlNdUmNIXLMMBpUUMn7COFLTwn8JOrD/IJVr1+JdaWKeUOjex8RdYTCzvJx+A/LDlmluamZD9UYObDuC7wPF7hUGhaUFlE0cH9H90IHDrPlgDd5VJuZxhe55Jq4Kg5nlM+k/sF/YMi3NLWxcX0Pt1kN4Vyt013QbVhLZkSeTWAAp5TYhRLGUcpdI9ov3u4iRZ5I9NYWlP36GgQMHRi07f8FV/OC2O9hbf5TAZ2q8s2bZuPDSOXzjmzcRa1u+9eZb/EL+Es//SiU7mtE3SO7kdJb++Bn69Qu/g5/kmmuv4e477uET3z5a/tHl0ABknWvj/C/P4uZ//3ZM93feWcFz8jk8f5Ngdt1dZIeS2KeXPsU555wTtezVC6/mztvvYrfnIG2fdDk0AO5yG7Munsl3vvcdDCP6QXvlypX8RP405B5U4O42cc9wsOTpJQwdOjRq2asXXs1dd9zNLm8trR93OTQArpk2Zlw4ldtuvzWm++rVq/mR/DGeNyQErLsP2bubKYPP4f6778ZuV/9ilrffeYdnf/1rtkyYjIzhcDqu6TamzpnI7Xfcjs1mi1q2srKSp+UzeJZL8Cto90wT90w7jy95jBEjRkQte821C7n3rvvY4dtD85YuhwbANc3GpPPHc+c9d8Z0r66u5olHl4TaXYV7homr3OCRxx+muLg4atlrrr2G++95gH/4PqVZ0fyVmVNslM0aw7333xPTfcOGDTz28ON4/i6hVYF7msRVbrD40cWUlJRELXvtIj8P3vcgWxs/oWmjmmvWGZMMxlaUcP9D98XcFy+4aA6PPPQIDX+XoODqo0iVuCoEDz78AGPHjo1a1n+tn4cffISPmrfTWN2Drtf3oKomEitH4a1CiP8QQpzX/nkO2CaESAH8Ca5fQjFyTC6ce2HMJBbA7XZzxfx/I31QpGux8SIJprex6LprYyZyABd96SLSMtJCl6YUYO8D8/5lXswkFiA1NZUF18wnrVBVUiAJZvq57vpFltwvuGAOme7M0KUpBRg5JueeWxEziQXIyMjgqquvVNjuILMCLLp+UcxEDmDWrFlkZWUpdJfMmD49ZhILkJ6ezoKF80lT6G7kmFx/43WW3CsqKsjLy0Nkxufu9nq5buHChCSxABdecAGZ6ek4m5vj+jsjT7Lo+kUxkxmA6dOnk5+fj+FS1O7ZkrKysphJLEBKSgpXX7uA1AJ128/WFxbdYM198uTJDBwwEMOtzn1M6ZiYSSyA0+lk4aKrlbo78uE6i+4TJ07knEHnKHMX2SbFo4pjJrEADoeDa667htQBsetpFWc/g2uvv8bSvjh+/HiGDhmG4VZzK5DIMhk+rChmEgsh92uvvwZnf/38e0/ESqtdB3wC3NL+2dW+zA/06EkT7Gk2srOzOixfvnw5o0aNoqioiCVLlpxa7na7saUq6uhG6MtUWlqapdgAmRmZoCincGTYycpyn7Fs3759zJ49m5KSEkpLS/nJT35y6ndutzvy/ZSdICgDZGZmnrEsmrsrw6UsvnBAbp/cDssjxXe5XBgp6q4+BEw/LpfLUuyT8dW5S3L6dLyFJlqfN5wK3aU/vnbvjHubH7f7zL59ww03kJ+fz5gxY04tu/LKKykrK6OsrIwhQ4ZQVlZ26nebN29mxowZlJaWMnbsWFpaWs5YX0ZmJkYwvmddgzLQoV7R3N1ud+R71+PFIckOc/tQtHZXur8TiKvPu91ucChyt0N2TnzuyrY7YAozrj6flZWFUOQuHJAV5zlO2tXdUy4NM64+n5WdpfQ4n51j3d3lciENde6a7iNmVialbJZSLpVSXtb+eUZK2SSlNKWUvu6oZCL5/IhgMBjk5ptv5o033mDbtm0sW7aMbdu2JSj2mT93Z+xQ/DMrYLfbWbp0Kdu2baOyspKf//znp+Krvo1EEOd2V/38QRztnohbaE5fZ7e3e7zbXnX8ONxVbfvrrruO5cuXn7Hsj3/8IzU1NdTU1HD55Zfzr//6rwAEAgEWLlzIL37xC7Zu3cp7773X4f51FbWK3edV73PW4ye7z6sO36P2d+XuZ/7c3e5WY4Ny9Q5rTKa7JnHETGSFEOVCiLeEEDuEELtOfroaWAgxRAix5XPLJgshftrVdXeFqqoqioqKGDZsGE6nk6uuuorXX3/9Cx8bYMCAAUycGHrbmsvlYvTo0dTW1nZL7GS79+Z27w3us2bNIje34yg8hB4u+9Of/sT8+fMBePPNNxk3bhzjx48HIC8vz9Jl4XjR7a7dtXvvcdckDivXyZ8HfghUAFNO+yhHSlktpfzu55cLIbptKt3a2loGDRp06ufCwsJuS+aSGfvz7Nmzh40bNzJt2rRuiZds997c7r3ZHWDVqlX069fv1P2jO3bsQAjB3LlzmThxIk899VRC4ibbvTe3u3bX7smIrxohk/85G7CSIDZIKd9IZCWEEMOA/wL+AJwnpbxECPEQMBwYBuwVQiwEngQuBkzg11LKnyWyXr0Vn8/H5Zdfzo9//OMO9zdpNF80li1bdmo0FkK3FqxevZp169aRnp7OBRdcwKRJk7jggguSWEuNRqPRhMNKIvuuEOJpQu+MbT25UEq5QUUFhBCjgFcJPUCWA5x32q9LgAopZbMQ4lvAEKBMShkQQnS4TiiEuAm4CSCV9E7Vp6CggH379p36ef/+/RQUFHRqXT0p9kn8fj+XX345V1999al7BruDZLv35nbvze6BQIA///nPrF+//tSywsJCZs2aRZ8+fQCYN28eGzZsUJ7IJtu9N7e7dtfuyYivHP3OW8DarQXTgMnA48DS9s8ziuL3BV4HrpZSbgrz+79KKU++4+ZC4Jcnp8WVUh7/fGEp5a+klJOllJMddO4l6FOmTGHnzp3s3r2btrY2Xn31VS699NJOrasnxYbQvYI33ngjo0eP5rbbbuu2uJB8997c7r3Z/e2336a4uJjCwsJTy+bOnctHH31EU1MTgUCA999/39Lri+Il2e69ud21u3ZPRnxNYog5IiulTOQrthqAvYTuvw33mHRjAmOHxW638+yzzzJ37lyCwSA33HADpaWlX/jYAB988AEvv/wyY8eOPfUqoscff5x58+YlPHay3Xtzu/cG9/nz5/Pee+9RV1dHYWEhixcv5sYbb+TVV18947YCgJycHG677TamTJmCEIJ58+bx5S9/WXmddLtrd+3ee9w1iSNiIiuEWCilfEUIEXZoTkr5QwXx24DLgL8LIXyEpsONxFvAN4QQ7568tSDcqKwK5s2b1y3J29kWu6KiAimTd/d2Mt2THV+7Jzb2smXLwi5/8cUXwy5fuHAhCxcuTGCNQuh21+69LX5vdleKRM/s1U60WwtOTiTuivBRgpSyEbgEuBWI9mTRbwiN3m4WQmwCFiiKr2I1nYydtNDt8a1XQPV2kvHugYq3VTLdE7VOy7GTfPRL9rZXQbfUSvk+F0/o5PZ51eGT3efiWqdy93jKJvnYkMQ1Jttd03kijshKKX8phLABHinlj1QHllLuAca0/7+ef77S66/tyx76XPkAcFv7RwmB5iD19Q2Wy3s8HoItimb+MEOvam5ubu4wu1ckfI0+ZZMC+xsDNDR4LJf3eDxIhRMS24Qdn8/XYcabSHgbvcriSz8cO2p9MN/r9WK2qjvI2Q0HXq+X7OyOsw1Fiq/OXXCi7oTl8h6PB7NNobtw4PP5SEmxdg97p9ydDjweD3l5efFX0CKNPh9mbt+4/sYm7Hg8HtLTrT2I6vF4IKDoYQ6/oP54veXiyvd37Hi9XstvQfF4POBX5B6A+hPxuSvb7oAhDXw+X8T3GH+ehoYGpCJ36YeGOM9xImAQejFQ1xGmgcfjoW9fa/tKQ32D0uN8/Qnr7l6vF2EaQFBNBTTdRtSHvaSUQWB+tDI9GfOEwdt/f5sDB6Ld0RDC4/Hwp2X/K2ylegAAIABJREFUSdM+VUd3ga3JyUsv/s7SN8E3//4mzY3NyCY1B7hAHfzt//2Nw4cPxyzb0tLCH15eRvP++KbkjIzA5nPw4gsvWXJ/550V+Dw+ZKMad/OEwerVq9m7d2/Mso2Njbz6+z8qbHcQDXZeeuElTDP2yWLlypWhE5syd8Haykp2794ds2xTUxN/eGUZzQrdzRMGLzz/oiX31atXc+zYMaQ3PnePy8WLr7xCIKCqv57J2++8g6+piTaLX0BPYh4TvPTCSwSDsU+UlZWVHDlyBDNO94ix6wU1NTXs3LkzZtnW1lZ+/7s/0FKrbvsFj8JLv7Xmvm7dOg4cPIDpUee+ZesWtm/fHrNsW1sbr7z0e6Xu/iPwokX3DRs2sHffXmXust5g+8fbLc3U5/f7efnFl2k5qC6Razts8rsXXra0L27atInde3ZhetRMAy8bDD7d9QkfffRRzLJ+v5/fvfAybYd62BS18iz4nAWIWImEEOJHgAP4I6c9fKXq9VuJwi1y5TQR+3U59nNMsqc6Ka8oJ7dP+G/MXo+PyjWVHPvIS/MmibKJ9BySrDkGI8YOp7i0uMM0mACmabJ39z42VG/As8JENlrYyS1OtecYZpI9KYXyinJywszDDtDoa6S6qprDW07QuM6iu5VLNHaJe45B0ZhhjB5TjNPp7FDENE327dnH+nUb8LxrIn1qDnAAtgKTrOkOystn0ie/T9gyPq+PD9dWUbelgaYNCtvdLnHPNhg+ZgglY0siuu/fW0t1VXXI3avQfUCQrBkOZpaX07dfJPdG1n24jiNbTtBUrdDdFnIfWjqYMeNKcaZ0dJdSsn9vLes+XIfnPRNp4cS268kZp/4vTJOROz9muNtF2dixpFkc/Y2FaZp8VltL9cYato8uoS0949Tvht25NvYKbBL3+QaDSwYxtmwsKRHcD+w7wIeVVXjeN5EN6trdyA/iLrdTXl5Ofv/wI2RNjc1UV1VzaOsxGitNlLW7IXGfZ3BOaQHjJowP6w5Qu+8AH66txLNSIusVuvc1cZfbmFk+k34D8sOWaWpsZsO69RzcVodvranu1UaGxDXLYFDJQMZPGEdqWmrYYgf2H6Ry7Vq8KyXmCYXufUxcFQYzZ86k/8B+Ycs0NzWzoXojB7YdwfeBYvcKg4KS/kyYVBbR/WDtIdauWYt3lYl5XKF73mnuBZHda9bXULvtMN7V1tzflq+tl1JOVlbRTpAyaJAsuO3WZFYBgN23fT/p28JKIvtumMVSSjknMVVSg9VEFsDIMRHZJqJjHhkiCKZXYB4xUD4btF1i62ci0mX4VUuQraHYssVi7DjmjDZyTYwsE+wR+oEpMH0C83Ac7lbvNYrlDsgWgXnUQDYnYBbubDPU9tHaPV53q9gktv5JdM8yMXKT7J4mI14Tki0Cs86wfAXi9EQWQsms61gdzuZmhFQ1yiIIOJ34cnLxp555QraUyAIYp7W7Ivd4EG4TI1cinBH20SCYjQLzUALa3Yp7q8CsE9a+sMeJcJkYeTHcm9rdVb+f82xwz5WIlEjH+dPaXbW7aHfPiOF+TCgdrDgVPrO93aO4y0ZBMA73syWRLbw1+Ynsru8nP5FN9uu3zgrMEwYo/AYcFwFBsFb9PO5WMY8bSr8Bx0WS3WW9QVDhqE9cBJPs3mAQVDjaFxfd4C4NA0/f8CNvScUUBA8ksd09BkHrt8arJdnuXoOgN0nBe7O7FAQPJtHdZxD0JS28phuIeSYTQvQTQjwvhHij/ecSIcSNia+aRqPRaDQajUYTGStDMi8CfwcGtv+8A7glURXSaDQajUaj0cQg2Q96nSUPe1lJZPtIKf9E+/s42l+Dpd9PodFoNBqNRqNJKlYS2UYhRB7tubcQYjqhqWU1Go1Go9FoNJqkEfNhL0ITEPwVGC6E+ADoC/xbQmul0Wg0Go1Go4nMWXJpP9lYSWS3AucBowi9j+VjrI3kajQajUaj0Wg0CcNKIrtWSjmRUEILgBBiAzAxYbXSdA09Z7Sml2H5Pa4J4vj1M2IXShC5LyTXXaPRdD9Chj6aKImsEKI/UACkCSEm8M+3Y7sBa5OFazQajUaj0Wg0CSLaiOxc4DqgEFjKPxNZL3BPYqul0Wg0Go1Go9FEJ2IiK6V8CXhJCHG5lPK/urFOGo1Go9FoNJpoqJ5OuIdi5aGtQiGEW4T4jRBigxDiSwmvmUaj0Wg0Go1GEwUriewNUkoP8CUgD7gGWJLQWmk0Go1Go9FoIpPsWb3OkofNrCSyJ8eu5wG/k1JuPW2ZRqPRaDQajUaTFKy8fmu9EOJNYChwtxDCRft0tV8IHJKsChumK0DADIQtYhM27G0OGlYFkU1qc3hboYlrgp02WpFhXpslAIeRQtsuSfMWidLvEHaJu8KGdEdzN7C3OWlYHUA2qn19sH2wSeY4G22yNeIXO6fhJFAraFxvoty93AZZAfzR3P3t7j617kb/IO4pjojtDiF3/15BU41id1u7e7YF9w8CSK9i934hd79oxYzmvk/QtDEB7jNtkBPDPdDu7umEuzTp37qLlLZjBNpauljhf2LY7BgpLmqdwwnYM+JfgYU+bwiBw0zBszaAeVxxu/c1cU+zx2z3wAFBY3X39/lT7pUBzGOK3fu0uxvR3YOHDHzrgmrvfTQkrmk2RN8gftMfvogQOILt7X5CsXt+EPfU6Pu7w3Agj9nwrg2CqdBdSDKn2bDlm7SZbeHrl8A+r+kerCSyNwJlwC4pZVP7dLXXJ7Za3YRdkjXH4IJLZrNg4XwyMzPDFmtubmbFOyv4nfNlPO9IZcmsrdAkZ3oKDz78AMOGDcNms3UoI6XkyJEjPPzgIxwwjtK8WUno0EltjsHsL8/immsXRnRvaWnhvffe4wXni3hWmMqSWfsQk9xpaSx+5CEGDx6MYYRf77Fjx3ji0SXstu2nsUrRyc0mcc82mHXxTBZdvwiXyxW2WEtLC6tWrebXjl/jVehuDAiSPdPJAw/dT1FREXZ7x91QSkldXR2PLn6MfcYhmjYoCX3KfeZF07jx6zdEdG9tbeWDDz7gl85fhdpdUSJv9AuSVe7g/gfvY+TIkVHdn3h0CXtstTRVK/oCZ0jc5xtMnTOJm775ddxud9hira2tVFZW8pzjP/C8a8aXyEuTgS07KBtRwC3ffYTs7Oyu17udlpYWVq5axa9/+xK1aaUE7HG8BbG93c+dO5Prbojc5/1+PzU1NSx9aimelaayE7vR18RdYeOe++6ipKQkbLsDHD9+nCefeIpP7J/RWKl2f4/V5/1+Px999BFPPvEU3lWmsmTWyAu533XvHYwZMyaq+9KnlvKxfRe+NYrchcRVYTC2opjv3vrdiP2xra2NjRs38sOnf4T3fROzXpF7fpCscjv33H83o0ePjuju8Xj4+c+eY6NjE56VpppkVkgyyw1Ky0dy6+23RHT3+/1s2rSJZ558Rmmf7w70e2RDWGmxCiATGCeEmAWUAuqOzknENtCkZNJovvntb5CVlYXNZgv7yczM5NKvXMolX/0yqSPVfVvMnGDwyOMPU1xcjNPpDBvbbrczcOBAnv7hUziGSnCo6bm2ASajxhdx879/O6p7RkYGX/7yl/nK1y4lbbSqHVySNhaefHoJRUVFOByOiPHz8/N54qnHSRtsR2Qocu9vMnzMEL7zve+QnZ0d1f3ii+fyb/O/RsYYK9/5rOGe7OCBh+6ntLSUlJSUiO3ev39/nnxmCSlDBCJVkXu+yeDRg7j1+7dEdU9PT+eiiy7iqoVXkjlOnXvWFCf33n8PY8eOjekeancbQtFbq418k8KRA7j9ju+Tk5MT1X3OnDksvO5qXOMdccVw+j3kZwgeuO8e8vLyIsbozCcjI4P/c/HFfO2r/0Ju4GBc9TrZ5797S/Q+n5qayvTp0/n2d75N1iRnXDGikTXFye13fJ8JEyZEbHebzUbfvn157IlHyRzsRLhU9vnCmH0+NTWVKVOm8L1bv0vWZLXut95+C5MmTYrp/vBjD+ManIbIUuNu5EryhmZz/0P3R+2PaWlpzJw5k2/e/A3citv9znvupKysLKp7Tk4Od997F/lFfTDy1FzwNbIlOUNdPPjwA1HdU1NTmTZtGt/+zreVumu6DyuZyQ9O+9wP/D/goQTWqduwZwrGjCtFCGvJ6ajiUaTmxXdii4iQBPAzbNgwS8VdLhe52XmIdEUHuHQojcO9uLiYlBxF7nbAgMLCQkvFU1NTGdh/oDJ3kS4pHVsacRT484wcORJHVsfR8s7SRisjRoywVDYjI4P8vH5K3UeXFsfn7lbn7hfW3dPT0+nfb4C6Pp8mGVVSHPbKRzhGjhyJ3R3fF1e72cLQoUNxOBTtK2EYNWoUaUb4S8SREGmSkrElcbW7TFV3B1nQ7mfkyJGWyqakpFAwsFBxn///7L17fFTVvff/XnsugZBMLkAECRS5hRAukTuICFJLS3t8TrX10UefKqC2PZ6e01rrlXqrt6JUPcVW62lPrRdobetPn1axXkAuEiFAuEUQECQJ10CSuSWTmdnr90cCDTCZ2QNrZidkvV+veSWZrJnv+uzvd6393WuvvVaxZe1Dhw5FZkSV2AYw3RHLMe92uxnQf4A67d0lgwZf1O5I6OkMGzZMmW2AiCNsWbvD4WDo0KHKLlxFpmTgwIGW2+KwYcNAYcxr0kfCli2l/Jc2ryuAkUBd6quWeoRDxGzgy5Yto6ioiCFDhvDEE/9coMHpdKqbtiVACHFG59qebWhp6KrsC4eI2cDTpd0R46Qyb948CgoKGDly5Bn/c7pcSu27M8688o6nXSi82ySleUbcxfW7U53fES0ny9OJ63eV2pFJaVcadwa43SmOeSnPaFex4vr111+npKTlYqq8vPyU8lu2bGHKlCmUlJQwatQomppOnWfrdDoRyT4ubJzp97jtzelUet8yWb+7XGr7m2S0u1wupOLHsZOKeZcTi+MLiTE4i35eod/lmX6Pe+zdavt5O7WnBbtXLOggh+tsTlHVQLEK40IIf+vPC4UQf05Qdp8QopcKu/GIRqPcdtttvPPOO1RWVrJkyRIqKytTbdZ22x3B/k033cSyZcvSZq8t2u9ae6rsx4rrkSNH8te//pXp06ef8n4kEuGGG27g+eefZ/v27axYsSJlo7tdtb1B19Zut/2ufOw1qSFhIiuE+KUQ4r9aX4uBVYCqR08AkFIekFJ+S+V3ni3r1q1jyJAhDBo0CLfbzbXXXsubb7553tvuCPanT59Ofn5+2uy1Rftda0+V/VhxXVxcTFFR0Rll//GPfzB69GjGjBkDcHJuXyroqu0NurZ2u+135WOvFNkygGz3qyNgZUS2HNjQ+loL3CWlvEFlJYQQA4UQ21p/dwghnhJCbBNCbBFC/KBN0R+07iy2VQgxXGUdTlBTU0P//v1P/l1YWEhNTU0qTHUo2x3Bvp1ov2vtdtg/nc8++wwhBLNnz2bs2LEsXLjQtrqkko523NOJ3drttm8nXVn7+UzCGeBSypfSUZE23AoMBEqllBEhRNtLt1op5VghxL8BdwA3p7luGo1GkzIikQirV69m/fr1ZGZmMmvWLMaNG8esWbPsrppGo9F0SNpNZIUQW4k9lVcAUko5OkV1+jLwvJQyQouh423+99fWnxuAq86omBC30pII042ze/SxX79+VFVVnfy7urqafv36ndV3dSbbHcG+nWi/a+122D+dwsJCpk+fTq9eLY8DzJkzh40bN553iWxHO+7pxG7tdtu3k/NOewe5tW838aYWfAP4lxivE+/bQaj1Z5QYSbiU8jdSyvFSyvEuMs7KwIQJE9i1axd79+6lubmZpUuXcuWVV55DlTuH7Y5g306037X2jhDzs2fPZuvWrQSDQSKRCB999BEjRoywrT6poqMd93Rit3a77dtJV9Z+PhMvkXUBhVLKL9q+gEKs7Qh2trwHfFcI4QQ4bWpBynE6nSxevJjZs2dTXFzMNddcQ0lJyXlvuyPYv+6665gyZQo7d+6ksLCQ3/72t2mzrf2utafKfqy4fuONNygsLGTt2rV8/etfZ/bs2QDk5eVx++23M2HCBEpLSxk7dixf//rXldYnXr3Shd1+78ra7bbflY+9JjXES0ifAe6J8b639X+pGpX9b2AYsEUIEQZeBBanyFZM5syZw5w5c9JpskPYttv+kiVLbLF7Au13rT0VtBfX3/zmN2O+f8MNN3DDDUqfp41JV25vXVm73fa7+rFXip5aAMRPZC+QUm49/U0p5VYhxEAVxqWUWa0/99Gy0QKtc2Nvb321LTuwze/lwIxztm9KIpGI5fKRSERd4MiWxaJN07S840w0GlVmX0bPRrsq42Caye2gEgmHlR77cNj6zkjRaBRpqusxBIJoNGrd7xF1fk9WeyQSUa49EonE3JShPfvKtJsQbk5Oe9K2BYSTaFdnQyQSQcokV42XZ6NdoOrgn/C7VcI2xnw4HEagTjuQXF8bjijralu0J9vPq9uGXYiWvs4qYcX9fOSstOvssLMR70yaG+d/3VVXxA4ifknltk+RFnuNXbt20XQ8ua0h20UKnMLFvn37LBX3+/0crz+GDKrpZMwgbN9amZT25jpF2zZGWi4iDhw4YKl4KBTi4KGDyEY12mVQULk1Ob+HG9RtWekWGezatctS2WAwyJFjR9RpbxTsqNxp+UJi9+7dRLzqtm10STd79uyxVLapqYlDRw6pi/lGwc4dn1nWvmfPHiK+5E5qEaMb+/buTSpxSZZdu3bTJJPbJEEGBZ9utx7zu3fvRoTUbenmiDjZvXu3pbLNzc0cOFCtzO+yUfDp9h1J+V00q1u71wg7Lcd8OBymqrpKnfagYO/evZaTyd27dyvrawCcUZdlv0ejUXbv2o1sVGNbNgr2fbHPcltUHfPpwO41ZDvDOrLlQohbTn9TCHEzLasGdHqiBwy2lW/nv1/8b/x+f7vlmpqaePvvb/PWX/4fTZ+pO6kHKkwW3PtTdu/e3W5HY5omhw8f5s4f30X4CwPCajqZ6EGDnRWf8cLzv4mrPRQK8e677/LXP75B8FNVyZygaZvgrjvuitvJSik5duwY9929gGBVGOlXpP2wwe6te3jul8/h8/naLRcKhXj//ff502uvE9iuLpH1bYjy8IM/Y8eOHXG1Hz16lLvvvIfQflNZ5x49bLC38gt++ewvE2r/8MMPee0PS/BvVZeUNZSHeeThR6msrGz3BCOlpLa2lnvuvJem/VFkUI1t84hB1c4anl70NF6vt91yoVCIVatW8dJv/4Bvc3IXrs2uHI74ozzy2BPU19dbThytEAqFeO+993j9r29wzNknqc9GDxvs2vp5wpgPh8OUl5ez+NnFNGxoPtcqn6ShvJknn3iSLVu2tDs6KqXk+PHj3H/f/fj3h5A+de1936f7E8Z8OBxm06ZNPLPoGRrK1Wpf9OQvqKioiKu9rq6OBxY8gHd/ENmg6OLtuKB2bx2PPvxo3HgMh8OsW7eOXy/+NV7Ffn/80SfYtm1b3Pbu9XpZ+MSTHN5zFLNWTTJp1gnq9nn52YOPJNS+YcMG5TGvSR+iPecKIS4A3gCa+WfiOh5wA9+UUh5KSw3PEo/Il5OEhSVrXJKc6Q6imWFMGTupEMLAFXbTsCqCDKi9YnP2N8kqddAsQzH/L5G4jQya9wkat5io24iaFu2XOoj2CBOVkdbbaacihIEr0qrdr1a76yKTHqMchMxQTFUSictwEz3oILA+ilLtzlbtWfG0C1yRDBpWR5A+tdodF5pkj0vs93C1ILhRsd+dEs80JzK7mUg87dFW7V612o2+UTzjnXG1uww30RoHgQ2K/e6QeKY5kDkRImY4vvY1EWSDNe3H50755x/SpG9oD+7QMSLNTSDU1N8wHBjdsqlxDyHi7HHy/fz/WWvtCyzEPELglhl410Ywjyn2e0EUzyQXzbIp5v9P+N085MC/Pqr0FreVmD+pvSyiLJk6gdHbxDPJSTMJtB9x4P9EsXZDkj3FgegZIdxOzCMEbrPV78cVa78gimdi/PbuNFyIOifeNVEwFWuf7ED0SqA9yZh/X/55g5RyvLqKJk+3fv3ll753e+KCKeaz+2+3/Vi0m8ieLCDETFrnrwLbpZQfprxWCrCcyJ4k0ciJwsaVtH07bdttX2u3x7bd9juX9lMS2VPMKL73FiMptpzInkT7vWPaT7Vtu+2fP9p1IvtPOkIia2Vnr+XA8jTUxWbS0ZA6qn2tvWva19pTb8buYxwLu+vUBfyu7Xcw2x3BviZVpHI9WI1Go9FoNBpNKuggD1vZTed6RE+j0Wg0Go1Go2lFJ7IajUaj0Wg0mk6Jnlqg0Wg0Go1G05noQOu42o0ekdVoNBqNRqPRdEr0iGyqMNTtDNPpMNVtHqDRdAaSXwJLHfvvn2qbbYABD39sn3G7+1k7+zq7V8RQvbScRnOW6ERWo9FoNBqNprOhryUAPbVAo9FoNBqNRtNJ0SOyGo1Go9FoNJ0NPSIL6BFZjUaj0Wg0Gk0nRSeyGo1Go9FoNJpOiZ5aoNFoNBqNRtOJEOh1ZE+gE1lA5JgYeRLhjB0VMgrSZ2DWClrCRyEOiaMgiugu2/1q2SSI1hoQUj+ALnKiGLkmop1IkFGQfgOz1iAl2i8wrWlvUr/UjMg1MXLb9zsmmH6BeSQF2p0SR0EC7SGBecxABlOg3dMa8652tEdbtR9NgXaj1e+ZCbTXGsjGFGjPNjHy42hPpd8NiXGBiZFK7dIkq+EYrlAjwjTPvq6nEXFnEMzOI+LudnZfYEiMAhOjh00xn2Vi5EUR7nYKmGAGBOYRB8r9Ltr4vZ1uPKXas02MPDO+9mBrzJs2aG8WmMcEMpCCc1wPE6OnRLjjtPdAq3Zp85JmmrOiyyeyjkKT3EkuJk2eTH7PvJhlfD4/6z9Zz/EtAZq2SZR1ck5Jzky4qHgQRcVFuFxnusM0Taq+qGZLxRa8y01kUF1Dd1wYJWeig8mTp5LfKz9mGb8/0KJ9W4DGraBSu2emwcDigRSXDMflcp1RxDRNqvfXsHnTZrwfmko7OecAk9yJbiZNnkxefm7MMsFgkI3lmzhaWU9gnYky7S5JzuUGg0uGMLRoaEy/Syk5WHOIDeXleD8ykQ3qtBt9ouRMcTJ58hTye+chYugKBIJsWL+B2u0NBDcojHmHxDPDYMDw/owYNQK3+8wzqyklB6sPsHHDppaY9ynUXhDFc4mTyZMm0bN3T0SMtTgDgSAbyzdydHs9wXKF2g2J5zKDwuJCSkaNICMj44wiLX4/yIbyjXhXmEhvktqlyaCqzxjYw82YsaPpFuP4ng1SSqoOHKRiyxZ2DSyhuVuP5L7AkGRPNygcfiEjx4yMoz1FMd8ziucSwcRJU+hd0Cum3xuDjWzaWMHhT+sIfKLW79nTDC4s7suoMaPo1i229kMHDlO+fj3elSayXnHMT3UwedKUdmM+FAqxdfM2aj49jG+1qS6ZFS3a+xZfwOjSMe1qP3zoCOvXrce30sSsU6g93yT7UoMJEydwQZ+CmNqbmkJs3ljBwR21+NeYnSuZ1SOyQBdPZI18k7zJbp78xZMUFhbGLdvwnQbu+NFPqPbWEtmvZhHunEsFM+Zcym3//m8xG1hb3nn7Hf5b/g7vO1JJQzNyo+RMcvDkooUMGDAgblnvd7z85Md3st93jMg+Ndo90xxM/+pUfvCfP8Aw4ndc/3j3H7wgf4P3bamkgzV6muROyuCpXzxJv3794pZtamrinjvvZXegiqbt52wagJzLHHz5ysv57vduTej3srIynpRP4V0mIXzu2oXHJGeKk8cXPs7gwYPjlg0EAtx1x93s9R2k+bNzNg1A9hQHEy8fyx133oHDET+WVq5cybPyv1r8HlWgPdvEM9XJo48/wrBhw+KWDQaD3HPnvezxVxPacc6mAcie7GDcjDHcde9dCbWvXr2ap+UzLe09Yl17/8P7KO1XwMP33x/z4vBcef+DD3juxf/m02HjkElsRpA10UHp9JHc99N7E2ovKyvjSZ5q0a4i5jNNsqfCQ488yIgRI+KWDYVC3Hf3Aj4L7Kdx2zmbBiBrvINR04r56YMLcDrjn3LXr1/PE/y8pb03K9CeZeKZ4rAU85FIhEcffpSKpkr869RkSD3GOiieOpQHH34gYTxu2rSJRx56FO+7EkIKtHeTZE0TLHjgPkpLS+OWDYfDPLDgASqDuwls1NlhZ6NLP+xl5JvMuuLLCZNYgJycHK69/n+TOUDNCAdIIpkh5t88L2EyA/C1OV+je49uLbdjFWDkmcy8fGbCJBbA4/Fw3fXX0qO/quseiZkdZv4t8xMmsQBfmf0VsjxZyrQ7ekq+NuerCZNYgG7duvGduf+X7oWKtAtJpHuIefPnWvL75MmT6du3L4ZHld8lkydPSZjEAvTo0YMbbryezP7qEiIj32T+LfMTJjMA06dPp2fPnohsRdpzJePHj094QgfIzMxUrt3RSzLvlnmWtE+bNo2CggKMJLV7gj7mfec7KUliAb48axaerCzcTY1Jfc7ZG+Zb1D558mT69lEY87kmI0tGJkxiATIyMrhx3nfo1k/djmGuCwTzbpmbMIkFmDBhAv0LB2DkqOvnx40bZynmnU4nc2+ei6tAiWkA3H0F826eaykeL774Yi4aeBFGjprpMCLHZMjgIQmTWACXy8Xcm+fi7tulU6JOS5f2mrO7o93byrHweDw4uqm63dTyo3v37pY/ktUjC9qbz5ms+W6i3akUsfB4PBiqtANRGSErK8ty+aweWe3P70oSV5aDnNwcy+U9Hg9CVV7gAMNwxLyl3h45OTnQ3nzOJBEuSX7vJP2u6toNiMgw2dnZlstnZ2crO/bCLcnvlZx2VTEHEJERPB5PUvaT9buIJHd8z4bs7GyMaDipz0RFJKl6qYx5XJLcJPt5nOrmFpsimpT23FxP+/O3k0S4SDrmo0LdtrvSMJOIWKiJAAAgAElEQVSK+dzcXHXt3QW5edb7+ezsbKRDnd9Tjmx52MvuV0egSyeyQMxRsWXLllFUVMSQIUN44oknTv+EQtun/t3U1MTEiRMZM2YMJSUlPPDAA8psWSUajXLxxRfzjW9845T3W46TQu0xvivecbcyepmU/dO+b968eRQUFDBy5EildmLajvFePPuqt1RP9tirrsDpx95Ov8ezr9p2LFJx3K3E9oMPPki/fv0oLS2ltLSUt99+G4Dm5mbmzp3LqFGjGDNmDCtWrIhlQEm94vv9rExYth2vr02F35PRnur2VlVVxcyZMxkxYgQlJSU8++yz7ZZNBen0e6ze1s723hURQvxOCHFECLGtzXsPCiFqhBAVra85bf53jxBitxBipxBithUbaU1khRB+Rd8zsO1BUUk0GuW2227jnXfeobKykiVLllBZWZkKU2eQkZHBhx9+yObNm6moqGDZsmWUlZWlxfYJnn32WYqLi9NqE+w97gA33XQTy5YtS5u9jmTfzmNvt9+7gvb2YutHP/oRFRUVVFRUMGdOy3nkxRdfBGDr1q289957/PjHP8ZUuPLBCez2u519rd3anU4nixYtorKykrKyMp577rnzLuY7qv0uyu+Br8Z4/2kpZWnr620AIcQI4FqgpPUzvxJCJJzn0+VHZE9n3bp1DBkyhEGDBuF2u7n22mt5880302JbCHHydns4HCYcDqf1KrG6upq///3v3HzzzWmzeQI7jzu0zMfMz4+9csP5bt/OY2+337uC9mRiq7KykssvvxyAgoICcnNzKS8vV14nu/1uZ19rt/a+ffsyduxYoOV2enFxMTU1NWmxbbd2u+0rR3aAV6IqSrkSOG5R0f8ClkopQ1LKvcBuYGKiD9mSyAohZggh/tbm78VCiJtaf79fCLFeCLFNCPEb0dq7CCHGCSE2CyE2A7elqm41NTX079//5N+FhYVpa+TQcsVYWlpKQUEBV1xxBZMmTUqb7R/+8IcsXLjQ0gNYqrH7uHdl7Dz2dvu9K2tfvHgxo0ePZt68edTV1QEwZswY3nrrLSKRCHv37mXDhg1UVVUpt223drCvr+0I2k+wb98+Nm3a1GW0221fcwr/LoTY0jr14MRE7n5A2w6nuvW9uHTEEdnFUsoJUsqRQHfgxGTN/wF+IKUc094HhRC3CiHKhRDlYULpqKtyHA4HFRUVVFdXs27dOrZtS8kMijP429/+RkFBAePGjUuLPY1GYx/f//732bNnDxUVFfTt25cf//jHQMt82sLCQsaPH88Pf/hDpk6dammlgc6IXX1tR8Hv93P11VfzzDPPJPVAlqYDYfdobMuIbK8TeVfr61YLNf81MBgoBQ4Ci87+IHTMdWRnCiHuBDKBfGC7EGIVkNs6RA3wMvC10z8opfwN8BsAj8g/q+fp+vXrd8oIRHV1taVlmlSTm5vLzJkzWbZsWVoeQFqzZg1vvfUWb7/9Nk1NTXi9Xm644QZeeeWVlNuGjnPcuyJ2Hnu7/d5VtV9wwQUnf7/llltOPtzpdDp5+umnT/5v6tSplpZuSha7/d6WdPe1HUF7OBzm6quv5vrrr+eqq65Km127tdtt/zylVko5PpkPSCkPn/hdCPEicOIOfQ3Qv03Rwtb34mLXiGzkNNvdAIQQ3YBfAd+SUo4CXjzxv3QxYcIEdu3axd69e2lubmbp0qVceeWVabF99OhR6uvrAWhsbOS9995j+PDhabH9+OOPU11dzb59+1i6dCmXX3552pJYsPe4d3XsPPZ2+72raj948ODJ3994442TCVwwGCQQCADw3nvv4XQ6La2/mix2+93OvtZu7VJK5s+fT3FxMbfffnva7IL92u22r2lBCNG3zZ/fBE7cDnkLuFYIkSGEuAgYCqxL9H12jch+AYwQQmTQMn1gFrCafyattUKILOBbwJ+llPVCiHohxDQp5Wrg+lRVzOl0snjxYmbPnk00GmXevHmUlJSkytwpHDx4kBtvvJFoNIppmlxzzTVnLIN1vmLncQe47rrrWLFiBbW1tRQWFvLQQw8xf/78LmHfzmNvt9+7gvZYsbVixQoqKioQQjBw4EBeeOEFAI4cOcLs2bMxDIN+/frx8ssvK68P2O93O/tau7WvWbOGl19+mVGjRp3cLOCxxx47uXJFKrFbu932VdNR1nGNhxBiCTCDlikI1cADwAwhRCktkxP2Ad8FkFJuF0L8CaikZcDzNillwoWNbUlkpZRVrZXdBuwFNrW+X986zLwNOASsb/OxucDvhBAS+Ecq6zdnzpy0NOrTGT16NJs2bUq73dOZMWMGM2bMOOU9KVPfYuIe9xSbX7JkSWoNnIP9NBx622I+kW07487+mFdjP1ZstXeRNHDgQHbu3Bn/C6VUsqR0fL+f+/fHI15fa7/fU2t72rRp7Wq0W3s68jI723tXREp5XYy3fxun/KPAo8nYSGsiK6XMavP7ncCdMcosABbEeH8D0PZBrzM+myyRxij1dQ2Wy3u9XqJNitZUNAHZsjB3t27WZk8EgoGk9l2Paz4kqTteb7m8z+fDDKlr6IZw4Pf7Le/u5Q/4kcltJtQu4UCUhnrrfvf5fMpsE4WoGaW5udny7l5er1fJnvMAMiI4fqzOcnmv14tsVud3h3Di8/no2bOnpfJ+v7pjL8OC47XWtfv96mIOwCEc+Hw+MjMzLZX3+XxJt3fpdOH3+0+ZA6sav9+PmWd9tygAh2zxu9Udrnxen7KYJyyoT7KvE1GDlk763DGkgc/nI8/iMWtoaECqau9hqEuivft8PhzSgaqUUpgt2nv37m2pvLehQV17j5B0Py9MBy0DgZrOREdctSBtmHUG77/3/inzxdrD7/fz5z/9hWC1qjObwNGYwSt/eMXSleAHH3xI0B9EBhUlsnUGKz5cwYEDBxKWDQQC/Gnp62q1B1yWta9cuRKf14cMqNEePS549513OXz4cMKyzc3NvPbyEpoOKOrcpMDZlMGrr7xmSfvGjRs5cPAApk+V3wVla8vYv39/wrJNTU388bU/KvQ7UO/g5ZdesbTIfllZGbW1x5B+ddrLy8vZu3dvwrKhUIglryylsUaddlln8PJLL1vSvn79eg4fOZy0333ds/jDa68RiaTmZLxq9WoaGhpo7mZ9a22A6DF4+aWXiUYTb3+6adMmag7WqIv5BoNt27eya9euhGXD4TCv/uE1Gg+q26Y1fBRefukVS9o3b97M/qr9mF5F2usNyss3WIr5aDTKyy+9QuSYEtMAhI9Iy9q3bdvGnr2fI71q0hLZYLB7zy62b9+esGwkEuHVP7xG82F1fk8Ldq9Y0EEGscX5OpzuEflykpiVsJxzoEnehAwum3EZeT1jXzH7fX7WrPqYo1saaKyQWLqvZlhYssYlyblcUjxmOMNLinC5ztxk2jRNqvZVUbZ2Hb6PBNKv7trD2T9C7gQ3l82YTl7PvJgLgvt9ftauKePI1nqCmyxuU2ta6AxckpzLDYrGDGPEqOJ2tVfvr6Hs4zK8y02kT5121yCTvPHduWzmdHLzYu/DHgw2sr5sPQe2HcG/1gSpaITILcmZZVBycTHDiofF1C6l5GDNIVavWoVvpcSsU6fd0c8kZ7KLGTMuI79Xfky/BwJBPllTxqFtxwmsM1G2PbFT4plpMGTUIEaVjsLtju33A9UH+Xj1x3g/MpEN6rQbfaPkTnFx2cwZ9Iyjff3adRzYVkugTKF2R4v2waMuYlTpSDIyMs4oYpomB2sOsWbVarwrJbI+sfb99089+bswTQZV7WBovodxpaV0i2HjbDBNk6oDB1n7ySfs/lIJocx/3kkZ8PDHib/AIfHMMLho1JcYc/HomNr/GfOr8a00rcW8lX4WMAqi5EwRTJ9xGb0LesX0ezDYyIZPyqnefgT/Wqy1dyt9nSHJvszgolEDKB07hoxusbUfOnCYVStX4VtlYh63oN3iBg5Gnyi5U5xcNuMyevbuGVN7qClExcbN7NtWhfcjE0wL320ldzAk2dMNvjSykIvHl8a8+yil5MjBI6xcuRLvKhPzmML23svEM81g+vTpFPQtiKm9qamJjes3sX9bDb5V1rS/L/+8Idkn9VXTvW9/OejG9D6sF4vKn99u+7Ho8oksgJFvYuSZGO7YAWyGJaZPYB42sHxSs9jB4pQ4+kQxMiXCOPO7pZSYjWAedSAb1Q+gG3lRjFwTIyOOdr/APOzAsnYrnTu0aO9rxtFOq3ZD2Uh0W4yeZov29vwekUi/IHrIUJfEnsAlcfQxMTJBxHCrlGA2ScxaQ+nFywmMXBMj30S4RczzoRlp9fvBJGLeKo42fne04/cmMI8KZEC9dpFr4kigXfoF0U6ivW0iCy3JbFb9EdyhRhyKuneJJOxyE/D0JJxx6mispUQWwGjV3iOe9iRj3mo/CwiPiZEfxcggZkJjRk+0d4f19m61r0uF9iR2IhM5rTGfASJGTEtTYgZaY95KEnui0lZo1S56xHaXlC1T3cxaQ+lgxQlEtonRq+UcF7O9R0EGSEp7h0hk+3SQRHah/YlsR1xHNu2Yxw1rV8CpICKIVjux64aGWefArLNpwfOIIFrlsE/7MUPp1X9ShG3WXm9gWhjtSwlRQbTaPu2y3iByHmuXhoEvv08KLZwlpiBaY6PfvQZRr2GPfbu1NxhEFN7ZSIpW7XYhfQZRn01+16SFLj1HVqPRaDQajUbTedEjshqNRqPRaDSdjM6wjmw60COyGo1Go9FoNJpOiR6R1Wg0Go1Go+ls6BFZQI/IajQajUaj0Wg6KTqR1Wg0Go1Go9F0SvTUglRhdX3BVJDE+oIajaZzY3kd1xSx75EpttkeuGCtbbZt5zxdA15jHf2wVwt6RFaj0Wg0Go1G0ynRiaxGo9FoNBqNplOipxZoNBqNRqPRdDb01AJAj8hqNBqNRqPRaDopekRWo9FoNBqNpjMh0SOyregRWY1Go9FoNBpNp0SPyDolnksckBMlKiMxixjCgaPRScPqKLJR7dJWjgtNPGOdhEUzsp3LKxdumvZImioloNC+S5JziQOZHYmvvdlFw5oI0q/2usfoEyVnvCuB9gxCn5s0bles3SHJnurAyDOJyHDs+gkHjpCLhtVhZECx9gui5ExwExah9rULN6G90LjVRLn2KQ6M/HjajRbtqfB7QZSciQn8Ltw0fwHBzYq1G63aeybQ3uymYU0Y6VOsvbeJZ6KTiJFA+34IVqRA+2QHRq842jFwhN00fBxGes9Cu5T0P/AFuXW1RBsbz7HCberldBLN8rC7/yDC3bon/wVCkjXJgaOgfe0CA2fERcPHEWSDYr/3NMmZ5CLsaEZixizjEm7CNYLAhihK/S4kWRMdOC5IpN1Nw9owsl6x9jwTzxQXkTjancJN5KAgUB4FqVj7BAeOPpKIbI5dBANn1I13bRizTo/tdUa6diLrlHguN5g551Kuu/46srOzYxZrbGzk/fc/YEnGErwfSGXJrONCk9ypbhbcfx+DBg3C6TzTHVJKDh8+zM8efIRDRh1N25SYbkliLxd85X/N4lvfvpqsrKyYxZqamigrK+M37hfxLjeVJTVG3+hJ7YMHD25X+5EjR3jkoUc56DhG4xYlpsEh8cw0mHrFRG6ceyPZ2dmIGGvvNjU1sfKjlfze/RLeD01lyaxxQZTcS1zc+9N7GDp0aLvajx49ymM/e5xqxxEaK5SYBkPimWEw8fJxzL9lXlzta9as4bfu36nVXhAl5xIX9yy4m6Kiona119bW8vgjT1DlOExwo6KEzpB4LjMYN2MMt3zvFjweT7vaT4l5Rcms0dvEM83B3ffeyfDhw3G5XGeUkVJy7Ngxfv7YQvY5awiWK7qAMyTZ0w1Kp4/k+7d9r13toVCITz75hOfdLySvXUq+VLWHEZndufupp+jZs2dMG2dDY2Mj/3j/fV7781/YMWwkkYxu1j8sJNmXGoyaVsxt//Fv5OTkxKxXc3Mz5eXlLH7mObwfmcqSWSPfxHOpwe13/pCRI0fidrvPKCOl5Pjx4yxa+At2u74gUKYo5oUk6xKDkkuG8YMf/ju5ubntat+4cSP/9fQvW7QrSmaNPJPs6QY/+sl/MHr06Ha119XV8cyiZ9jp+hz/x6ou4CRZUw2GTxnMf97+H+Tl5bWrffPmzTz91DP4PjIxFSfyqUKg9HKnU9OlE1nHhSbFY4u57Qe3xe1w3W43V199FfX19bxZ/Y6ypCJrnMHPHn2YoUOHxi03YMAAnnr6SebdNI+mnQLC5x6+zsIoF08Zzy233pxQ+1e+8hX8fj+vHP8TvjWxr6iTxTPeyYMPP0BxcXHccv379+fJXyxk3k3zaNwJhM5du6OPyUUjL+JHP/4RhtF+p+V2u/nGv3wDr9fLnw7/fwTWqZmQ5JngYsEDCxg1alTccoWFhSxc9HPm3jiPph0mskmB9gtMBhQP4I47f4zD4Wi3nNvt5mtf+xper5c/1v4V31o1fs+Z2JLEXnzxxXHL9evXj58/9QTzbppP4w4TGTx320aByYVFF3LXvXcl1P7PmP8jvtXqtP/k7h8zbty4uOUuvPBCHl/4GPNvmk/jp83IwLn73ehl0mfoBdz303sTav/yl79MIBDg5folNKy0vrGLuylIL7+XJ37xFJmZmedc59Pr9e2rr6a+vp668k3U9B9k+bNGL0nvIfn89MEFMS+c2tqYMWMGwWCQ3/n+QMNyNZva5Ex08R+3/zuTJ0+OW65v3748+sQj3Dz3FoKfBpANCvyeL+k5OIcHHr4/5oXTCdxuN9OnT6epqYkX/f9D/Qex79AlS84EN//2n99l6tSpccv16dOHhx99mFvn30qg0oesV6A9T5J7URYPPfJgzAT6BG63m0suuYRQKMSvgy9S/54a7Zr00TkuPVKEI0swunSU5VGDESOKychXlPsLSYQwgwcPtlQ8JyeHvJx8RKaaZMqV62Dk6BLL2ocPH47Lo+r6T9IsQwkT+BN4PB565ffG6K5Gu8iUjBo9Mm4S25bhxcNx56q75msWIYqKiiyVzcrKoqBXgTK/i0zJiJHFcZOZthQXF+PKsVbWCmHRbFl7jx496FPQR5l2o7ukeMRwy9qHDx+OK1tdFxlxhC1rz8zM5MK+/RT6HYYVDUvK747s5Nq7q6mJCwsLlSexbRkxfDhZ0eQSDdFdMnTokLhJbFuKioowepxN7WJjuqOW/Z6RkcGXBgxQ5/fukkGDBsVNYtsyfPhwZbYBZDfr2t1uNwO/NBBDYV930UUXxU1i21JUVATd1Fy0pg3ZAV4dgC6dyBoOEbNzW7ZsGUVFRQwZMoQnnnji5PtOpxNhKErmBAghzkim2rN9wr6qewmGwzijc6uqqmLmzJmMGDGCkpISnn322ZTYPuU725BQu6poFeDOOLNzi+93RbYBKc3ktas69oKYHXs87Sp7CYlMSrvL5VKn3Uje70q1y+S0O1VqFxK3+8xkJq72JG0LKXE5T7Uxb948CgoKGDly5Cnv//KXv2T48OGUlJRw5513AhAOh7nxxhsZNWoUxcXFPP7442fYcDqdybtEgCuJmFcac60k63dlu4wbnIXf1WYntrV3QcwEPp3aNemhUyayQgh/qr47Go1y22238c4771BZWcmSJUuorKxMlbkOYxtaGvKiRYuorKykrKyM5557rsto78p+19q19lTZv+mmm1i2bNkp7y1fvpw333yTzZs3s337du644w4AXn/9dUKhEFu3bmXDhg288MIL7Nu3T2l9TqD9rrXbYV+TGjpMIiuEUHf/8hxYt24dQ4YMYdCgQbjdbq699lrefPPN8942tMzRGjt2LADZ2dkUFxdTU1OTFtt2a+/KftfatfZU2Z8+fTr5+fmnvPfrX/+au+++m4yMDAAKCgqAljtUgUCASCRCY2Mjbrcbj8ejtD4n0H7X2u2wrxoh7X91BNKSyAohBgohdgghXhVCfCqE+LMQIlMIsU8I8XMhxEbg20KIW4QQ64UQm4UQfxFCZLZ+/iIhxFohxFYhxCOprGtNTQ39+/c/+XdhYWHakjk7bZ/Ovn372LRpE5MmTUqLPbu1d2W/a+1aezrtf/bZZ6xatYpJkyZx2WWXsX79egC+9a1v0aNHD/r27cuAAQO44447zkiCVaH9rrXbYV+TGtI5IlsE/EpKWQx4gX9rff+YlHKslHIp8Fcp5QQp5RjgU2B+a5lngV9LKUcBB9NY5y6J3+/n6quv5plnnknZiIhGo+maRCIRjh8/TllZGU8++STXXHMNUkrWrVuHw+HgwIED7N27l0WLFvH555/bXV2NRtPBSWciWyWlXNP6+yvAtNbf/9imzEghxCohxFbgeqCk9f1LgCWtv7/cngEhxK1CiHIhRHmY0FlVsl+/flRVVZ38u7q6mn79+p3Vd3Um2ycIh8NcffXVXH/99Vx11VVps2u39q7sd61da0+n/cLCQq666iqEEEycOBHDMKitreW1117jq1/9Ki6Xi4KCAi655BLKy8tTUgftd63dDvvKsXvFgq40taCV0yWf+DvQ5r3fA//eOvL6ENAtRvn2DUj5GynleCnleBcZZ1XJCRMmsGvXLvbu3UtzczNLly7lyiuvPKvv6ky2oeWp6vnz51NcXMztt9+eNrtgv/au7HetXWtPp/1//dd/Zfny5UDLNIPm5mZ69erFgAED+PDDDwEIBAKUlZUxfPjwlNRB+11rt8O+JjWkc0OEAUKIKVLKtcD/AVYDp6+Kng0cFEK4aBmRPTF5ZQ1wLS0judenspJOp5PFixcze/ZsotEo8+bNo6SkJPEHO7ltgDVr1vDyyy8zatQoSktLAXjssceYM2dOym3brb0r+11r19pTZf+6665jxYoV1NbWUlhYyEMPPcS8efOYN2/eyV2uXnrpJYQQ3HbbbcydO5eSkhKklMydO5fRo0crrc8JtN+1djvsK6eDjIjaTToT2Z3AbUKI3wGVwK+BH5xW5qfAJ8DR1p8n9oz9T+A1IcRdQMofMZwzZ05akreOZnvatGlIaV/LsFO73fa1dq39fLS/ZMmSmO+/8sorZ7yXlZXF66+/nrK6nI72u9auOT9IZyIbkVLecNp7A9v+IaX8NS0JLqe9vxeY0uatBSoqJE1JJGJ9l5hoNKo00ZNSYpqm5R2motGosiuwZLVHIhGlV3+SFj1WdxpSqR3ZMhfYKtFoFGmqEy8QRKNRW/zeoj3JmFesPRKJWN5tR2ncJel31TEvRIvfrRJNon0mRIrk/Z6sdiGIJLnrVrJEo9HkXSIhYqPfT35nEmWVnWaSbO+p0J5MzIcVt/dkz+9IgR7m7Hx0mHVk7SDil+yo3Gk5Od21azeh44o6alPgFC72799vqXggEOB4w3Fko5ptT5rrI+zc8Znl8p9//jkRn6oGLnALt+XFzoPBILXHj2IG1WiXjYJPt+2w7Pc9e/YQblCz7zqAW2SwZ88eS2Wbmpo4WntEmd9lo2Dnp9Zjfs+ePUR86rZtdEo3e/futVQ2FApx+MghZKMa22aj4LMduzBNa3rUxjw4oy7LT+E3Nzdz8NBBpLKYhz27dlvWvmfPHsxActrDGRkcrDlAc3Pz2VTRErv37MHvSG78RTbBnj2fW06oPv/8c2XHHcAIOy3HfDgcprq6Sml737dvn2Xte/bsgUZ1aYEIOSzHfCQSoWr/fqXav/jii6T8LkKdKCXqAGvIdql1ZKWU+6SUIxOXTC/RAwZb1m/l9//zEo2N7Z8tm5ubeffdf/Dmn9+k6TN1J/XAZpP77lnA3r172z3BSCk5duwYd91xN5H9AprVNPJIjYNPVn3CkteWEAq1v8JDOBzm448/5ve/fQnfVnWjLYHNJj+97/6WE2YC7XffeQ/NVUBIjfboIYNdW3fzwvO/IRgMtlsuHA6zfPlylr7yRwKV6hJZ36YoD93/MLt3t59YSCmpq6vjnjvvJVQtlSVz0cMGn1fu47nFv0qofdWqVbzy+1fxK/S7b2OEnz30CJ999lm7J5gT2u+7ewGN1VFkQI3fzSMG+3dW88tnf5lQ+8mY32J9JC8RDRuaeeyRx9mxY0dc7Q0NDdx/3/0Eq5uRfkXajxrU7D7E04ueIRAItFsuEolQVlbGf7/wW7wVyWkPde/BsYzuLHjwIRoaGpTevWpubmbZu+/yxt/f5kivPkl91qw1OPz5UZ5auCih9vXr1/P8cy/g3aQuGW/YEOKpnz/Ftm3b4iZVXq+Xhx/4Gd79QWS9Ir8fFxzde5yfP/Zz/P72N8SMRCJs2LCBX/3yVzQo1d7M04ueYfPmzXG1+3w+Hn34Uer2+zBVaa8THP+igUcffjSu9mg0SkVFBc/+4r9o2Ji6izBN6hB2zolMJR6RLyeJWYkLuiU50x1EuscLYIkz4sa7Kor0q839nV8y6THGICybETGvKyQO4SCy30GwwsTSRtRWN+rOkORMN4hktK9dInGZbrwfm8h6i9otxpRzQKt22tduCAfRagfBjRa1W8XV6vfMZlpuJZ353RKJK+rGuzqK9Kr1u6PQJOvixNrNAw4C5Yq1OyWe6Q7MHs3IeNrNVu0NirX3M8kam0A7BuZhJ4F1KdB+qQMzy4L2NVHrMW8Ro2+U7PGOBH43kIdd+NeduNWpCEer9uwE2qUb35ooZp017fseaTPrS5pcVLWXHrVHkAqnGUhAZGWza+BQmrv3OPn+wAVrrX2BQ+KZ5sD0WND+cRTzuGK/F0TJnuRs9Xssn0oEBuKYC99axX43JNmXOJC5YSSx29NJ7WujmMcUa+9tkj3ZQViGECJ2zAsMxPFW7aZi7VMdyDwL2suimLXWtL8v/7xBSjleXUWTJ7Ogvxz2v9O7ulAsNi++3fZjoRPZk8j2x6dNUHoyjYWQ7ZtItmFbTWRPEke7JPlONdmYUqk9aRRrTxatPTYpb3Nae3v/Slb7KYnsye+RCKnu7pUURsx+zXIi+89v0n5v519au3U6TCJ7TccmVtMAACAASURBVAdIZJ+zP5FN58NeHRzR2phsQgob55hr7bahtduE1p5aEwIprD3ImV60322jK2vXpBSdyGo0Go1Go9F0MjrKw1Z204ke0dNoNBqNRqPRaP6JTmQ1Go1Go9FoNJ0SPbVAo9FoNBqNprOhpxYAekRWo9FoNBqNRtNJ0SOy5yPn6ZJqVhAua1ufno/IiLrF+8+uAl037royyS+BpY6j34ux9Fca6f28fdptx7BxVQpT3QY1nRn9sFcLekRWo9FoNBqNRtMp0YmsRqPRaDQajaZToqcWaDQajUaj0XQmJPphr1b0iKxGo9FoNBqNplOiE1mNRqPRaDQaTadETy3QaDQajUaj6WzoqQWATmQBEB4TI1cinLGjQkYF0icwj6dgANshMQpMjG4SROwiMiSI1hoQaqfAOSCyTYy8ONrNVu3HBO1W8GwxWrV3t0m7J4rhibbfCkyQAQPzmAPl2h0So1cEEc/vzQLzuAOa1Med8JgYOSbC1Y5tE2RAYB41SJXfRXeJiOf3YwY0pcDvWa0x74oT836BWZuimO9tIjLjazePGchUaE/U3mUbv0vF9kWr3xNpP24gG8/StpR0azqOK9IImGdd1dOJGm5CGblEnd3O7gtEq997xNHe3Or3s9Uez3yPVr+7E/j9SAra+wntmWZ87XUGMpiCvi5TYuSbibWnIuY1aaHLJ7KOfiY5k5yMGzeevJ65Mcv4vX42btxE/ZYgTZUKA90p8cw0GFA0gKFFQ3G5znSHGTWprqrh022f4v1QIoPq7Dv6RvFMdjFu3Djye+XFLOP3Bdi0cRN1W4M0bZMo6+QcLdoLhxVSNHwYLveZGZUZNampPkDl1kr12i8M4xlvMH7cJHLzc2KWCQYa2bJ5C8d2+AluUtjBOyWe6VEGDOvPkGFDYvpdSsmhA4fZsnkLvtUOpE/dmo2OflFyJjoZN25SuzHfGGxiy+Yt1H7aQGC9Qr8bkuzLDPoVXcjw4iLcGWeu+2uakkM1B9myZSu+5SbSr+7kZvQ28VziYOzYsfTsnY+IcWYN+INsrtjM8UofwY2KtU836DusD8UlxWTE0C6l5GDNIbZu2Yp3uYn0KdTeJ4pnirOlvffMi6m9qTHEti1bObKzDv9aU92J3ZBkTzPoU3QBI0aOaFf7iZj3rjCR3iS1S8mF/l1cmCkYPXYk3btlKKm6KSUHDh5i6/at7M8ZQcTVI7kvEJKsSwz6FPVmxMgSunU/s15SSg4fPMLmigq8H5nIBoV+zzfJvtTg4osvpvcFvWL7PdjEli1bObqjnkCZibKYF5KsKVAwrCcjR41sV/uRQ0ep2FSBd3UUWa+urzPyTLKnG5SWjqOgT+/0xnyKEeh1ZE/QpRNZI88kd7KLhYsWMmDAgLhl6+rquONHP+GA7ziRKjUNzXOpg+lfncoP/vMHGEb8juv/vfX/+L38A963pZKGJnJNcqa4eOLJJ7jooovilm1oaOCOH/2Eav8xIvvUNHLPNAdTr5jEj378w4Ta3/772/yW/2nRbp67fSMvQu4EB0/+4kkKCwvjlg0Gg9xz573sDR6iaaciv081mTFnOrf9+7/F7Fjbsnr1ap6Wz+D7wICwCu0muZPcLFz084Qx39jYyL133ceeYBWN28/ZNADZUxyMnzGGu+69C4cj/vH84IMP+RW/avF7VEHMZ7UksT977GGGDx8et2wgEOCuO+5mX+AgoZ3nbBqA7EkOSi8t4b7770uofeXKlTwr/wvvOxIiCrTntLT3xxc+xuDBg+OWDYVC3H/fA+xo/JzgJjVnyqwJDkZNK+anDy7A6Yx/2jkR895lMqmYLwjuZ0TfXB792UNkZKhJYtvy/gcf8NwLL7Ivfxwyic0Aeow3KLmkiAcevh+Xq51bIK2UlZXxJE+1aG9W4PfukuxLBQseuI/S0tK4ZUOhEAvuWcDOxi9o3KzG7z1KBcMnD+KhRx7E7Y6/WU15eTmPP/IE3vekmjtw3SRZlwruWXA348aNi1u0ubmZn957v9KY16SPLv2wl5Fv8uWvXJHwhA6Ql5fHdTdcS+YAVTtHScysZm757i0JEzmAf7nyX+ie1Q2RqaaROfJNZlw+I2ESC5CTk8P/+b/X0WNA/E7YOhLpCfPd799qSfucr88hK7uHMu1GT5Ovfv1rCZNYgMzMTG6afyPdClWNEkgimU3ccuvNCZNYgGnTptG3b1+MLDU72bTE/JctxXz37t2Ze/NNdOuv7nrX6Gly6/dvTZjIAcyadTn5+fmILEV+z5OMGzcuYRIL0KNHD26c9x0ylcU8OHpLy9qnT59O7969MbJVaTeZdum0hEksQEZGBvNvnUfGhepGxlwFcMv3bk6YxEKbmPckpz076mP+3BtTksQCfHnWLPJycnBGgkl9zt3H4Jbv3ZwwiQWYPHky/Qv7J629PUSOSdGw4QmTWGjx+9yb59K9nzq/u/sKbv7u/IRJLMD48eO5aOBFGB41U0KMHJPBgwYnTGIB3G638pjXpI8uncg6Mw3y8mPfWo1FTk4Oju6qbrW1zNPOzMy0/JGszB6g6LxqZAh69uppuXxOTg5GN3XhEpERsrKyLJfPyspCKLqGcGUa5ObGnk4QC4/H0+6cwqRxgGE4kjrZ5uTkQDvzOZPF2T35mEeVdiAiw2RnZ1su7/F4lPlduCU9e+fbYhtaYt7j8Vgur9Lvwg29Cqy3d4/Hg3So2wY0IlKvXUSbk7JxNng8HhxmcltBmyKaVMzn5ua0O58zWYQbcvOs93U5OTlIh7q5xaYRTconubm57c5dTxbhgtw8632d6phPC7IDvDoAXTqRBRFzVGzZsmUUFRUxZMgQnnjiiTM+o8x6jK+Ka9vCCN45GY9j38roYVLmYxzHeNqV2hdnft/OnTspLS09+fJ4PDzzzDOnfEah+aRsp1p7VVUVM2fOZMSIEZSUlPDss8+mxnY735k2v7fzfemK+Vikrb3HYN68eRQUFDBy5Mgz/me/39XYiKXxJz/5CcOHD2f06NF885vfpL6+/uT/tmzZwpQpUygpKWHUqFE0NTWdbkBJvdLp96RjPoV9XVNTExMnTmTMmDGUlJTwwAMPJKzrOdnvYO1dkxpsTWSFED8UQlgfkmz5zAwhxN9SVadoNMptt93GO++8Q2VlJUuWLKGysjJV5jqMbbvt2629qKiIiooKKioq2LBhA5mZmXzzm988720DOJ1OFi1aRGVlJWVlZTz33HNdxu9dOeZvuukmli1bljZ7bUmX9lgar7jiCrZt28aWLVsYNmwYjz/+OACRSIQbbriB559/nu3bt7NixQpL0wGSxW6/22k/IyODDz/8kM2bN1NRUcGyZcsoKytLi22w/9irRkhp+6sjYPeI7A+BpBLZVLNu3TqGDBnCoEGDcLvdXHvttbz55pvnvW277dutvS0ffPABgwcP5ktf+lKXsN23b1/Gjh0LQHZ2NsXFxdTU1KTFtt1+78oxP336dPLzrU+1UEm6tMfS+JWvfOXkXN3JkydTXV0NwD/+8Q9Gjx7NmDFjAOjZs6el+czJYrff7bQvhDg5pSwcDhMOh9M6Emr3sdekhrQlskKIHkKIvwshNgshtgkhHgAuBJYLIZa3lvm1EKJcCLFdCPFQm89+VQixQwixEbgqlfWsqamhf//+J/8uLCxM20ndTtt227dbe1uWLl3Kdddd1+VsA+zbt49NmzYxadKktNiz2+865u2ho2j/3e9+x9e+9jUAPvvsM4QQzJ49m7Fjx7Jw4cKU2LRbu932o9EopaWlFBQUcMUVV6StrwH7tWtSQzpHZL8KHJBSjpFSjgSeAQ4AM6WUM1vL3CelHA+MBi4TQowWQnQDXgT+BRgH9GnPgBDi1tZEuDxMKKViNOcnzc3NvPXWW3z729/uUrYB/H4/V199Nc8880zKH5rRaOzm0Ucfxel0cv311wMtUwtWr17Nq6++yurVq3njjTf44IMPbK7l+YfD4aCiooLq6mrWrVvHtm3b7K5S58Tuh7y66MNeW4ErhBA/F0JcKqVsiFHmmtZR101ACTACGA7slVLuklJK4JX2DEgpfyOlHC+lHO/i7JZg6devH1VVVSf/rq6upl+/fmf1XZ3Jtt327dZ+gnfeeYexY8dywQUXdCnb4XCYq6++muuvv56rrkrpTY9TsNvvOubtwW7tv//97/nb3/7Gq6++evLWdmFhIdOnT6dXr15kZmYyZ84cNm7cqNy23drttn+C3NxcZs6cmdZ52h1Fu0YtaUtkpZSfAWNpSWgfEULc3/b/QoiLgDuAWVLK0cDfgbPcD/DsmTBhArt27WLv3r00NzezdOlSrrzyyvPett327dZ+giVLlth2a98u21JK5s+fT3FxMbfffntabdvtdx3z9mCn9mXLlrFw4ULeeuutU5Y/nD17Nlu3biUYDBKJRPjoo48YMWKEcvt2+91O+0ePHj25SkRjYyPvvfeepXWdVWH3sdekhrTt7CWEuBA4LqV8RQhRD9wM+IBsoBbwAAGgQQhxAfA1YAWwAxgohBgspdwDpPRM73Q6Wbx4MbNnzyYajTJv3jxKSkpSabJD2Lbbvt3aoWU3p/fee48XXnghrXbttr1mzRpefvllRo0adXLh9Mcee4w5c+ak3Lbdfu/KMX/dddexYsUKamtrKSws5KGHHmL+/PlpsZ0u7bE0Pv7444RCIa644gqg5YGv559/nry8PG6//XYmTJiAEII5c+bw9a9/XXmd7Pa7nfYPHjzIjTfeSDQaxTRNrrnmGr7xjW+kxTbYf+xVo7eobSGdW9SOAp4UQphAGPg+MAVYJoQ4IKWcKYTYREviWgWsAZBSNgkhbgX+LoQIAqtoSX5Txpw5c9JyEu9otu22b7f2Hj16cOzYsS5ne9q0aUgbl1Gx2+9dNeaXLFlii90TpEN7LI3xkvUbbriBG264IZVVArpuzI8ePZpNmzal3W5b7D72GvWkLZGVUr4LvHva2+XAL9uUuamdzy6jZa6sUiJNJg31sabqxsbn82GGFO16YoKULQtEd+tmbQZFMBhouQRQYT5kUl9Xn7hgK16vF7NZXbJjCINAIECPHj0slfcH/EhF2iNBE2+D13J5v9+vZL97AEyImlHC4bDlNSq9Xq8y+5HG5GLe6/UiouqWx3EIJz6fj549re0y5fP5lPldhgV1x6zHfIttldod+P1+y7v5+RT6XYbh+LE6y+V9Ph/CdAARJfYdtPjd6g5XXq8Pkj32Dhc+n+8samcdv8+PaRQk9RlDGvh8PvLy8iyV9zZ4lcU8YahPur0bgJrznJAtMd+7d29L5RsaGpBqQg4ZIal+viXmDaAT7e6lR2QB+9eRtRWzTvDBex9w+PDhhGUDgQB/ff2vBKpV9TACZ5Obpa8ttTQatnLlSgL+IDKo5sRm1hus+PAjDh48mLBsMBjkL3/6C40KtTuCbpa8usSS9tWrV+Pz+pABNdqjdQbvvvMPjh49mrBsOBxm6Wt/pPGQos5NCpzN3fjj0j9aKr5582ZqDtRg+tU01WRi/oT2JlXaARoclv2+fv16amtrkX5VMS8oL1/P/v37E5Ztbm7mj0v+ROMBVTEPZp3Ba6+8hmkmThI2bdrE4SOHMX2KEtl6gzWr1nDgwIGEZaPRKEteXUrzYXVblUaPwWuvLLGk/WTMJ6ndb2Tx2tI/EYkoyoROY/369Rw/VkvYmdzS55FaWPLqEqLRxO1o27ZtfFH1BaZXUXtvMNi5cwe7du1KWDYSibD01T8SOqTO7+Gjktdesaa9srKSz/d+jlSkXTYY7Nq9ix07diQsezLmj6jTrkkfws5biqnEI/LlJDErYTnXIJO88d2ZdcXl7e7LHPAHWPXRKg5vqSO4QaJsDz+3JGeWwegJIxk+YvjJRbrbIqWk6otqVq9cjXe5ifSpu/ZwDjTJm9iNWV9u0R5rYeqAP8CalWs4uOUYwXKF2l0t2kvGjaBk1Ih2tVfvr2bVR6vxrjAtdXDC5bZk3j0oQt7Fbi7/8uXk5Mbei7ypqYmyNZ9Qve0w/k8MkKr8buK5LErpxDEMGz60Xe2HDx7mww+X4/vYQNYlvnkiI9aSLtcgk7xx3eLGfFNTE+vWrmP/1oP4VpvWtFvpS5wSz+UGxWOLGDVmZMxRaSklB2oO8tHyj/B+ZCLr1cW8o59J7mQXs66YRX7P/Jgx3xhspOzjMqq3HMa/1qJ2KzglnpkGRRcPZXTpKNzuM2NVSsmhA4dZ/uFyfCtNzDqF7X2ASe7EDGZdcTl5+XkxtYdCITas28DezfvxrjTBVKTd0aJ96JjBlI4b0672wwcP8+EHy/H9/+ydeXxU1fn/3+fOEsgyk40gghuENQHCDoIIWrWltt9aW6tfa1VcqtLWWhfcF1ygVlttrbV2pdqC1rY/228rVUFFKQgqASGyySKBQBIgyySZZObe8/tjBkzNLHfCmVxCzvv1mlcmd87c53zu89xzn3vumXPetrAOJtdec92UdjuwOKlxM8V9c5kwfiy9Mjo3c02seu2t2sebby1nt384bd5Pp6br88zK5DtwSXwzDAaNPo0x48rIiFEvKSX791WzbOmyiPYD6vxuFJn4p7o563Nn06eoMKbfg8FW3nt3DTvXV9L4tk2/GzYWizAkvukwcNTJjJ0wNq726v01LHt9GQ3/kVi1NvZr2bu5NvpY+Ka6OPuc+No7E/Ovy5fej04V6hhZhSfJEV+6yckqAPDe7292/Fj0+EQWwCi0MPIk7l6xGw+zzcJsAKvKQOlC1AAeietEC3e2QBgd9y2lJNxkYe13KeuNbY9RYGHkWbh7x248zJCFVQ9murT3s3DnJNFebSCb7DXsdhNZAKMgjJFrxfd7yMJsFFj73OqSmcN4LFwnhHFlgeHqaF9KSbg50qjLgL3VhewmsgBGgRmJ+d6xtVshidkI5t4UEni7bYk7EvOubIHhirVvSThgYdYYSEU90e0x8iyMfAt3ZuyYNkMWVqra7eKSuPon0d5kYVanSXuS890KW5/6XVUSexhX1O85SbTXGLZv2P8rkQWQFlnN+/GaQWI0KZ2mDTctvfIJe/57KJStRBbAiPpdofZUEH4LV2H8mO+U3+0kshDRfqKJKxsMd+x9h5sszAMu+72xNhNZSI/2YyWRLTnf+UR2zULnE9mu/LHXMYtVa2DVJhoNpn6ZwiOEBOYuV4JROYJ0jgCxDhhYBwzntH/ipHY31gFVowBTJGRg7vY6NhrLOuByTnvYYb8fMrAOORTzptMx7+D5bgrM3WnWLgyasvrRdHR7UY/VBdoTIOsNwvUO+d0SmJXuJG1d+uw7ql3TJfToMbIajUaj0Wg0mu6L7pHVaDQajUaj6W4cnyNDU0b3yGo0Go1Go9FouiW6R1aj0Wg0Go2mOyH1yl6H0T2yGo1Go9FoNJpuiU5kNRqNRqPRaDTdEj20QHNcIUNtTldBo+la7M7nmS5SmNNTNbbncU0Tu+ZNSV4oTZxyr7PanfS7JooeWgDoHlmNRqPRaDQaTTdF98hqNBqNRqPRdCME+sdeh9E9shqNRqPRaDSabolOZDUajUaj0Wg03RI9tECj0Wg0Go2muyH12ALQPbIajUaj0Wg0mm6K7pF1S3KmuDDyJBbhmEUMXNDsomFFGNkilJo3TjDxj/MSdoWIN5eGW3po3mLSulmtbVxR7fnxtQsMRLM7Pdr7mvjHx9cuJXjw0LLNIviRJDK8XREuSc5kF64CiZlIe0tUe7Ni7UUm/gnJtQe3W7RsVKzdiGovTKzdCLqpXxFCNqm9300W81KCGw9teyTNH1io1p49yYW7jyQsw4gYuz6i/T8hZECx9kIL/0QPYXcSv++StKxPg/bxAneRRZhwzD0LDIxWN/WrLGSjYu0FEe2mJ4RMoL11t6S5XLF2Icme4MJ9QiK/C4xWD/UrQ8iGTmiXkpOqdpFbfxArGDz6Oh+ul8uFmZXNxyeeSlvvrE7sQJI13oXnBAgTiq+9Laq9XrHfcy18UzxYCfzuxkNor6TpffV+zxrrwnNiIu0GRpub+lUhZJ3u2+uO9OxE1iXxzTQ447wpXHLpJeTk5MQs1tLSwuuvL+XPvf5Mw+sSGVRzohn9THJP93Ln3Xdw2mmn4XZ3dIdlWVRXV/PwvEfY76qjtULRSR7VPuVzE7ns8m8m1P7mG2+yqNdiGpZKZcms0dfEP9XDHXfNZdCgQXG119TU8MiD89nnOkRwgxLTYEh8MwwmnjWWy6+8nJycHESMFq6lpYW3l7/N8xl/pGGZVJbMGkUR7XPvvJXBgwfH1V5bW8v8hxZQ5aqlZb0S05Ek9kyD8TNGM/ua2XG1B4NBVqxYwcKMP9CwzFKWzNqJeSklhw4d4vFHf8wOdyVNqxVd3AxJznSDsjNKuPb6a+Nqb21tZeXKVfwm4zc0vmEpS2aNQgvfNBc3z72JYcOG4fF4OpSRUnLgwAEenf8jdrv30fyBopsYIcmZCiOnDuO6Od/G5/PF1b569Wp+9Ytf0/CmQu0FFjlnGNx0642MGDEirvaDBw/y2A8fZ6d7D83vqdOePdWgZOoQbvju9fj9/rja33vvPX7582cj2lNJZqXklN3bKPFlc8udC8jLy4tpozMEg0FWrlzJr597nq2DRqSWzApJ9ukGw6YM4js3ziE3Nzeu9vLycn7+5NM0vGUpS2ZFrkXOmQbfvekGRo0ahdfr7VDm8Pn+xI+fZJtnF02rVCWzkuzJBoMnn8qNP/heXO1tbW2sW7eOnz3xVER7N0pmu8OsBUKI3wLnA9VSytLotnzgBeBUYCdwkZTykIg46ElgFtAMXCGl/CCZjR6dyLr6WwwdM5Tvff97GEb84M3MzOQb37iIhvp6/lH5Ki3lauznjHfxwIP3M2zYsITlsrOzeewnP+Kq2VfRutWA0NGf5K5+FoNGDuIHt9yUVPuFX7uQ+oYGXq76F83vH7VpAHwT3dx7/z2UlpYmLHdY++wrryK4BWhTo/3UklO55bZbcLniTyafmZnJVy74Co2Njfxl/z9oWqOm1fBN9HDXvXcyevTohOWys7P50Y8f5aorr6JliwQFN1CuvhYnDR/A3DvnJtX+pS99iUBjgBdr/x+BldZR2wb7MZ+VlcX8Rx/h6iuvoXlTM7Lh6LUbfSxOHNqPu+69K6n2WbO+QFNTE4sO/pnGFWq0+yd6uHnuTUyaNClhuaysLH742AKuuvJqWja3IZsUaC+0KCou5J77745583CYzMxMzjvvPJqbm3m+7kUa3lET8/6JHr5/y/eYMiXxAgJH/D77Glo2B5GNCrQXSPoU53HfvHtjJtCHyczM5JxzzqGlpYU/NPyJ+jftT/if0RygqK2FBQ/9hF69eh11nT9bry9+8YsEAgF+t/RNtp882PZ3jXxJ/iAfDzx0f8wksr2NmTNn0traym8Cv6dumZrFDvwTvFz/3WuYPn16wnJZWVk8PP8hrpl9Lc2bAsg6BX7Pk/gHZvHgI/PIyMiIWy4zM5MzzzyTtrY2nm36LXWvx35Kpek0vweeAv7QbtvtwFIp5QIhxO3R/+cCXwAGR1+TgF9E/yak+9x6pAFXtmBU2ciEiVx7SkpLyMhXlPsLSZgQgwfba5Ty8vLI9xcgMtVcWIwsGDVmlG3tpaUlZOSpuu+RtMnWpMnMYfx+P33y+yjTLnpLRpaVJkxm2jOiZAReZdqhTdjX7vP5KCrsi6FKe6akZOSI1LT7Fa0cJSQh2mzHfK9evTjl5FPU+T1TMrxkuH3tI4bj8alrIsPuEMOHD7dVNisri/4n9Feo3WLosKEJk9j2jBgxAnfshzSdwvSEbcd8ZmYmA04coNTvg4cMTpjEtqekpAQjOzUb3tYWTj75FOVJbHtKSkroHU5t5ULRWzJo0KCESWx7hg8fjujE6IW49LZs+z0jI4PTTj1NaVs38LTTEiax7Rk+fDj0VnPT2iXIY+SVrJpSLgcOfmbz/wALo+8XAl9pt/0PMsIqIFcI0S+ZjR6dyBouEfMEX7JkCUOHDqW4uJgFCxYc2e7xeBCGokf7AoQQHS6q8WwDkYuQKvMugdfbsWFPpB1V2qN89qKaTLtQFa0GMRu3xH5XZBuQ0upwUe0qvyNS166slRCR8WifjfnZs2dTVFQUs3fe41V37IUBGRmpne8qW0gpZWox7/Uo9vt/aw8Gg0ycOJHRo0dTUlLCfffdd+Qzj0ehbUAiU4p5r2Lt3lT9nqptKTu0p/Hi+mc/+xnDhg2jpKSE2267DYCdO3fSu3dvysrKKCsr47rrrutgwu12I1L9lbpB6u280ufVzvrdk+L1vVs8qz/2KBRCvNfuda2N7/SVUlZF3+8D+kbf9wd2tytXGd2WkC5LZIUQuUKIG6LvZwgh/q+rbKeCaZrMmTOHV155hYqKChYtWkRFRcVxb9tp+1p7z9QOcMUVV7BkyZIus9eenuz3jIwMli1bxrp16ygvL2fJkiWsWrWqS2w7rb0r7MeK6zfeeIOXX36ZdevWsXHjRm655ZYjnw0aNIjy8nLKy8t55plnlNalPT055p22f5xSK6Uc3+71bCpfllLa7NuNT1f2yOYCN3ShvU6xevVqiouLGThwIF6vl4svvpiXX375uLfttH2tvWdqB5g+fTr5+fldZq89PdnvQgiysyPP0EOhEKFQSNkPlJLhtPausB8rrn/xi19w++23H3kqUlRUpNSmHXpyzDttXzXCcv7VSfYfHjIQ/Vsd3b4HOKlduQHRbQnpykR2ATBICFEO/AjIFkK8JITYJIT4Y/TXagghJggh/iOEWCeEWC2EyBFCnCqEeFsI8UH0dXq6Krlnzx5OOunT4zhgwAD27El6HLu9bafta+09U7vT9GS/Q6SHqqysjKKiIs4555ykP0RThdPanbK/ZcsW3n77bSZNmsSZZ57JmjVrjny2Y8cOxowZw5lnnsnbb7+dtjr05Jh32r7mCH8HLo++vxx4ud32b4kIGT6gPQAAIABJREFUk4H6dkMQ4tKVsxbcDpRKKcuEEDOIVLwE2AusAKYKIVYTmZLhG1LKNUIIH9BCJFs/R0oZFEIMBhYB47uw7hqNRnPc4XK5KC8vp66ujgsuuIANGzYknUlE03nC4TAHDx5k1apVrFmzhosuuojt27fTr18/PvnkEwoKCnj//ff5yle+wsaNG/H5fE5XWXMs0w2G9AohFgEziIylrQTuI9Kx+aIQ4ipgF3BRtPi/iEy9tY3I9FtX2rHh5PRbq6WUlQDRXtpTgXqgSkq5BkBK2RD9PAt4SghRBpjAkFg7jA4yvhagF5mdqlT//v3ZvfvTscaVlZX07590rLESnLTttH2tvWdqd5qe7Pf25ObmMnPmTJYsWdIliazT2p2yP2DAAL761a8ihGDixIkYhkFtbS19+vQ5Mtxg3LhxDBo0iC1btjB+vPr+mp4c807b74lIKS+J89HZMcpKYE6qNpyctaC13XuTxEn1TcB+YDSRntiYc4lIKZ89PODYg70pNz7LhAkT2Lp1Kzt27KCtrY3Fixfz5S9/uVP76k62nbavtfdM7U7Tk/1eU1NDXV0dEFn847XXXrM9VdLR4rR2p+x/5Stf4Y033gAiwwza2tooLCykpqYG04zM3bp9+3a2bt3KwIED01KHnhzzTtvXpIeu7JFtBJLNTLgZ6CeEmBAdWpBDZGiBH6iUUlpCiMsBRRNbdsTtdvPUU09x3nnnYZoms2fPpqSkJF3mjhnbTtvX2numdoBLLrmEN998k9raWgYMGMADDzzAVVdd1SW2e7Lfq6qquPzyyzFNE8uyuOiiizj//PO7xLbT2rvCfqy4nj17NrNnz6a0tBSv18vChQsRQrB8+XLuvTeyYINhGDzzzDNp+wFkT455p+2rRs8WFqHLElkp5QEhxAohxAYiyen+GGXahBDfAH4mhOgdLfc54GngL0KIbwFLgKZ01nXWrFnMmjUrnSaOSdtO29fae6b2RYsWOWL3MD3V76NGjWLt2rWO2IbjP+bjxfXzzz/fYduFF17IhRdemLa6fJaeGvPHgn2Nerp0jKyU8n/jbP9Ou/drgMmfKbIVGNXu/7lK6mNJwmH7y9GZpolMdULqRPalREppe8ob0zSVDe5OVXs4HFY6sFwS0WN3lSWV2pGR6YbsotQ2IBCYpml7VTX12lOLeZW2JRaWZdnWHg6HUXbKpeh31TEv4MjjYzuYKZyfSemM3xUiECm2N2rPuXDa/S4idU4jEZ+kOD1ain6PaFc7BVsqsaTU75KUr++R46u7ObsbPXplr3BAsmXTFtvlt2/fTtshRRcXS+AS7v8aeJ6I5uZmDtUfRLaoaWSsZthcscV2Yr59+3ba6lRdWAVe4WXXrl22SgeDQWoP1mIp0i5bBJs/sq99586dtNWru0h5RQY7d+60Vba1tZWaAzXK/C6Dgq2bt9rWvmPHDsINipZtlAK38FBZWWmreDgcZs+ePepivkWwbcvHKfndDKi7qLml17bfQ6EQ+/ZXKfX79o+3Y1n2fLljxw6sJnUJjct0p6R9b9VeddpbYPv2HSlpT9V2KCODysrdKSVOqbJjxw6CHntLzR5Gtgh27dplO5ncsWMHBNX5XbS5Ivu0gWmafLL7E6Ux/8knn6SkXbR2o5RIAlI6/zoG6EZeU49ZZVC+ej3PP/c8bW3x17A2TZNly5bx1xf/RssWdWsxt3wouev2u6msrEx4ca2vr+eO2+4kVCmgTc1JblYZbFxbwcLfLUyq/a233uLPi16ieZO6ZK5pvcU9d93LJ598klB7Q0NDRPseIKjGtrnfYPO6Lfzm17+ltbU1fjnTZMWKFfxx4Z9oqlB3gQqUm9x3z/3s3LkzofbGxkbuuv1uWvdYyBY1ts39Bh9v3M4vn3k2qfaVK1fyh989R+MG+71ZyYjE/F1JY765uZmH5z1M/e4Asl5RIlttsGvTJ/z8qaeTal+zZg2//dXvaFivTnvDByEefvARtm3bllB7IBDgvrvvo2lPG7JRkfZaF5Vbq/jZk08l1f7+++/zq2d+pVR7/fttLHjkh2zZsiVhQtnc3My8+x8kUNmCbFCl3WDf9mp+8vgTCbVblsXatWt55ufP0FAev02MRTArhxpL8ND8BTQ3Nx9tlf8L0zR59913+d1zz1GZ3zf5F9phHRTU7DzAY48+TjAYvwG1LIt169bx1E9/Tv0HqWlPRP37rfzksZ+wcePGhH5vaWlh/sMLqPukAatOkd8PCg7uquOHj/yQlpb4DahlWWzYsIEnf/ykUu2arkOofFR+LOET+XKS6DC7Q0cyJP4zXYQzWnEZsR9zm5aJ2/LSsNxENqrN/T2nWfQeKbAwMWIsKh8ZfgDWHjfNH1goXQDdG9XeqxXDcMXc8xHtb5vIBrXa3adaZI5Krl1WuWl6T7F2T1R7bxva3zGR9Yq1n2yRWWZD+343TavToH26i3BmG4ZhxNcuo9rrujjmAWlZiENeGv9jgqVQuzvq96TaPTSusLAOqdXu6m+RPVYQxsQVY3jFkeFGtV4aV5n2HvPGabc64Jb4pgnMrNYE2q2I9pUS65DN/Vr2bnCNfiY5412ECcfWTtTvB6N+V/mI2yXxneHCzLHh95UW1gF7ft81b8qR98KyGLh7GxkHqm0PnbGDaZoYvTP5+KRigjn+I9tPuXelvR24JL5pLixfGyKRdqLaa9XGvFFkkjPJRZhQzGusJJJMGnVeGlcoPt+NiHbTn8TveGh818Sqthfzr8uX3pdSOjqXfXbeSbLsrBudrAIAK/56q+PHQieyhxEy/lwIJsrHDXXAJWPnKjJqX2Ui81kSabdQ27DEwkntyPgjxbtCuyFjPxfpCdrj+R0gDMe19nh+h9S1201kj6BYu81E9ggqtaeMWu3tE9lPTUiMVI9JAqQwkDESY9uJ7Kd7On5iPmXUateJ7KccC4mskwsiHFtIET2ZHMLsmjXOY9KTteOwdktEGlJH0H53jJ7s9+NduxBYrmPx0qr9rjk+ORbPNo1Go9FoNBpNIo7PB+op06N/7KXRaDQajUaj6b7oRFaj0Wg0Go1G0y3RQws0Go1Go9FouhECvUTtYXSPrEaj0Wg0Go2mW6J7ZI9HUp6ORzEKp57RaDRJ6Mnnm83lvdNF6lNgqWPH/BhTf3Uhp93hnHYNx9TKWk6je2Q1Go1Go9FoNN0SnchqNBqNRqPRaLolemiBRqPRaDQaTTdD/9grgu6R1Wg0Go1Go9F0S3SPrEaj0Wg0Gk13Q/fIArpHVqPRaDQajUbTTdE9soDIsTD8Ejxxbm9MgWwQWHVpyPtdEqPQQvSWkRmOYxEUmAcNaFU/1YzItjD8VmLtAYF1KA1TehlR7ZnEHewjgwLrgAFtadKem8TvAYF1UBDfOZ3Ehva0+j0rqt2rtXfAFMgmgXUgDdqFxOhjIXoDRhz7rQLzQBrP90Qxb0W116ZJe6FEZMrE2g8aEEyj3+Npl0T9boB0Rrt10EB2VruUZNXX4W1pRkir83X9DGFPBs0+P+GMjE7uIao9K4n2QwayJQ1+7y0x8izIcCDmNV1Cj09kjX4m/skeysrKyM3zxywTaGxi3bp11K8P0rpJYaB7JP6zDE4afDIDB56Gy90xWbSkZN/efWyq2ETDMgvZpC6ZNvqG8U02KBs9lrz83JhlmgLNrFu3jroNrbR+pDCRd0l8Mwz6Ffdj8JDBeDwdQ9GyJHv37GXLR1toWCqVNnJGkYnvdDejR48mryAvZvPVFGhm/fr11G1oIbhBoqyRMyS+Mw1OGHwCg4cMxuv1dChiSUnVnio2V2ymYZlENivU3sfCN9Vg9OiyuNqbm5pZv/5D6jY207Jerfac6QYnDO7LkKFD4mpPW8wXWuRMNRg9ejT5BXmIGPOQNjc18+GHGzhU0URLuWLt0wyKBvdh6LChZGR4OxSRUrKvaj8fbfyIhjcsZEChdhsx3xpsZePGCmo319O0ykKZdhHR3mdwPsOGD4urfX9VNRUbN9LwpoVsVKi9j4nvdIPRo8ck1F5R8RG1W+oIrJLqklkhyT7doM+QPIYPH0ZGr44JoZSS6n01bDysvSFF7VJyys6POckMUVpSQu9evZRU3bIs9lVXU1Gxns1DR9CWmZXiHiTZUwwKh/gZUTIirvaa6lo2fPghDW9ZyHp1fhe5FjnTBSNHjqZPUWHM8z1tMd8F6B97RejRiayRa5E7xcOCHy3gtNNOS1j24MGD3Pz9W6gK1GNWqjnR/DNcfO7LZ/Ht666NeYK159V/v8ov5bM0vCLBPPoTTfhN/JNdzH/0EQYNGpSw7KFDh7jlplvZG6gjvFtNyPimuph41jhuue1mXK7Evb1/f/kfLJR/oOFfai4uwmfhm+rm4fkPMWTIkIRl6+vrueWmW9nTdIDQDjUNXM7pLsbNGM3cO+cm1f6vf/6L3/DbiHZLgfYcC99UFw8+Mo9hw4YlLNvQ0MCtP7iNyuZa2rYdtWkAcia7KDujhLvuvSupduUxn2Xhm2Zw/4P3U1JSkrBsIBDgtpvnsqt5P21bjto0ANkTXYycNpx77r8btzvxebR06TKe5umI9nDXxnxrayt33X43W4O7aC5Xc6XMnuBixOlDuG/evXg8HW9e2rN8+XKe5Kc0LJEQUqA928J3ur2Yb21t5d677mNTcDvNa4/aNABZY10MmzKIBx66H6+3YwLfnnfeeYefyCdo+LdM6SnUiXt3U5LZiwUPPUZmZuZR1rgjry9dys9/9Ss2jhyLlSR225M5xmDw5FN58JEHyUjSo7tq1Sp+xGMR7SqeRmRIcqYLbr9rLhMmTEhYNB0xr+k6evQYWaPA4nPnnpM0iQXIz8/nfy+7hKxTEjfC9o1Lwr1auebaq5MmsQDnnncu+QX5iGw1J5kr32LGWTOSJrEAeXl5fPPyS9VpRyL9IW74zvVJkxmAL//Pl8jyZUYeTSnAyLc444xpSS/oAH6/n29deRmZpyS+AKWCyA9zvU3ts744C5/fp1C7ZPLkSUkv6AA+n4/LZ3+LTGV+j/SI2tWuOuaNfMm4ceOTJrEA2dnZXHn1FQpjHtxFkuvmfDtpEgtw9tln0adPH4wcdTE/bZq9mM/IyOCa666mV391w4k8feG6Od9OmsQCTJ8+nX4n9FOqfZLNmM/IyODa668h40R1l0bviYJv33Bt0iQWYNq0aQwYMADDl5r2gqYA11x5ZVqSWIDPnX02hfkFZLQ0p/S9jP4urr3+2qRJLMDkyZM59ZRTI0PdFGDkWgwaOChpEgufxnyGwpjXdB09OpF1ZxrkF+TZLp+bm4urt6LHDh7I8PaydUE/TE5ODkJRPmVkCAr7FNgu7/f7MVRpB8IyTE5Oju3yOdnqtAsvFBYV2i7v8/lw9VKnPWSF8Pl8KdlXpt0jKUjB77m5uRjqcnjC0kHtXklBn3xHbEMk5v3+2MOXYuH3++OP50wR4YU+fe3HvN/vx3KpW/o2LMIp+d2f648/hjlFhJeU2zrLUKfdEmZK2vPychEpajdCbSnZ6Aw+vx9XKJTSdyzDTCnm8/LyEIruHYUHcvNiD5mLhd/vRyqM+bQjAUs6/zoG6NGJLIiYvaFLlixh6NChFBcXs2DBgg7fSSezZ8+mqKiI0tLSDp/Z6bm1TYxdBYNBJk6cyOjRoykpKeG+++77jG119kWMfSU67kq1x9lfPPuqbcfaZ+KYS6/tpPbTfOx7kt/t2o5WIK22u6ytibPPxH5Pr+3du3czc+ZMRowYQUlJCU8++WTcsumwnw6/f9ZGLP9+4xvfoKysjLKyMk499VTKysoAeO211xg3bhwjR45k3LhxLFu2LMb+O1GnGNuc9Hsi+11xvmvSQw9PZDtimiZz5szhlVdeoaKigkWLFlFRUdFl9q+44gqWLFnSZfbak5GRwbJly1i3bh3l5eUsWbKEVatWdYltp4+7k/a1dq29p7U1Tmt3u908/vjjVFRUsGrVKn7+858fd36P5d8XXniB8vJyysvLufDCC/nqV78KQGFhIf/4xz/48MMPWbhwIZdddpny+oDzfnfavnLkMfA6BtCJ7GdYvXo1xcXFDBw4EK/Xy8UXX8zLL7/cZfanT59Ofr79x58qEUKQnZ0NQCgUIhQKddldqtPH3Un7WrvW3tPaGqe19+vXj7FjxwKRIVvDhw9nz549XWK7q7Qn8q+UkhdffJFLLrkEgDFjxnDiiScCUFJSQktLC62trcrr5LTfnbavSQ9dksgKIU4VQmxo9/8tQoj7hRDFQojXhRDrhBAfCCEGRT+/VQixRgixXgjxQLvvfSu6bZ0Q4rl01HXPnj2cdNJJR/4fMGBAlzVwxwKmaVJWVkZRURHnnHMOkyZN6hK7Th93J+1r7Vp7V9t2mmNJ+86dO1m7dm2PaesA3n77bfr27cvgwYM7fPaXv/yFsWPH2vqBVqo4rd1p+5r04PT0W38EFkgp/yaE6AUYQohzgcHARCJDbP4uhJgOHADuBk6XUtYKITrcagohrgWuBehFen69ebzjcrkoLy+nrq6OCy64gA0bNsQcQ6fRaDTdnUAgwIUXXsgTTzyR9h9LHUssWrToSG9sezZu3MjcuXN59dVXHaiVJlX0PLIRnExkc4D+Usq/AUgpgwDRRPZc4PAsftlEEtvRwJ+llLXR8gc/u0Mp5bPAswA+kd8pF/fv35/du3cf+b+yspL+/ft3ZlfdmtzcXGbOnMmSJUu6JJF1+rg7aV9r19q72rbTHAvaQ6EQF154IZdeeumRsaJdgdPaw+Ewf/3rX3n//ff/a3tlZSUXXHABf/jDH2xNy9gZnNbutH1NeuiqMbLhz9hKtOyIAOZLKcuir2Ip5W/SW71PmTBhAlu3bmXHjh20tbWxePFivvzlL3eVeUepqamhrq4OgJaWFl577TVbcy+qwOnj7qR9rV1r72ltjdPapZRcddVVDB8+nB/84AddZhec1/76668zbNgwBgwYcGRbXV0dX/ziF1mwYAFTp05Nm22ntTttXzlSOv86BuiqRHY/UCSEKBBCZADnA41ApRDiKwBCiAwhRCbwb2C2ECI7ur2/EKIIWAZ8XQhREN2ell8puN1unnrqKc477zyGDx/ORRddZGsCdVVccsklTJkyhc2bNzNgwAB+85suy+Gpqqpi5syZjBo1igkTJnDOOedw/vnnd4ltp4+7k/a1dq29p7U1TmtfsWIFzz33HMuWLTsyHdW//vWvLrHdVdrj+Xfx4sUdhhU89dRTbNu2jXnz5h05HtXV1crr5LTfnbavSQ9dMrRAShkSQswDVgN7gE3Rjy4Dfhn9LAR8XUr5qhBiOLAy+ov5APBNKeVGIcTDwFtCCJPI0IMr0lHfWbNmMWvWrHTsOimLFi1yxC7AqFGjWLtW0bqMncDJ4+60fa1da+9qnGxrwFnt06ZNQzrYm9QV2uP59/e//32HbXfffTd33313WutzmJ58vmvSQ5eNkZVS/hT4aYyPzopR9kngyRjbFwILVdUpHLSor6u3XT4QCGC2qlk+jzC0hVqxLAvDsNcxHggEIum+AqxWSd2hOtvlGxsbsVRpBwxh0NzcbHtJxaamJqQi7YTg4IFDtosHAgGsNnUXPbfhJhAI2P5xSVNTEzKsxrYMCw4dtO/3QCCAVKjdJSLa7U77pDLmZSg17Y2NjepiDnAJF4FAwHbMBxobIaxo+rsQKftdWAagZqUjFy4aGxttr+bX0KBOuwynpr2xsRFDugBFS6VKg0AgQF6evVUkG+sbUz7fLXfkvEongUAAM8f+SlkAIqq9T58+tsrX1zcobOugob7BdnnVMd8V6B97RejR88hadYKlry2jpqYmadnm5mb+9tLfaNmj6MpmClwhDy+//HdbxdeuXUvtgRqsgJrG3ao3eOuN5ezfvz9p2WAwyF///Dda9ipqYRC4Wry8sPhFW70iq1atoqGhHtmkSHudwYq336Gqqipp2dbWVv7y4l9pVuV3wAh4WLzoBVva16xZw8FDB5Gq/F4nWLVqFXv37k1atq2tjZdeeImWKoXZXIOLFxbZ8/vatWupqa1WGPOC9957j8rKyqRlQ6EQL73wF4JVqmIeZJ3BCzb9vm7dOvbtr8Jq7PqYN02TFxa9SKha3VXSOih4YdGLWFby5HDDhg3s2bsHq0Gddrsxb1kWLyx6gVCtOu3hA/DCohdsaa+oqGDX7p1YDaldmut7Z/HCX/5COKwuXttTXl7O/n37aO2dldL3QjWSFxa9iGkmTw43b97M9h3bkSlqj4dsMNi6bStbt25NWvZIzNfozLA7Ipx8vJJOfCJfThJnJy3nLZbkj8/k3M+fg88fu4esubmZ5W8sZ295Lc3vSVQt1Sp6S3xnCyafMZlBxQNj9sxKKdlftZ/XXn2dxuUW1iEbJ7nhsmXfc5pJ/vhenPv5cyJrm8egubmZt996h73ramhaY3OZWsvGHa1X4j/boGzSaEaUDsfl6lhnKSV791Sx7LVlNLxlIevV3Xe5T7XIm9iL875wHrlxtLe0tPDOWyuoXLefplUWypbo9US0j544khEjR+B2d3wwIqWkau8+lr62NKK9TqH2ky3yJmVEtMdZizwYDLJi+Qp2r9tHYKUFUqH2swxKJ5QwcnRpXO0px7xNXAMs8iZ7Oe8L55GXH7uHLBgMsvKdlexat5fACoXa3RLfWQYl44czesyo+Nr3VfPav1+j8W0L62DXxnwoFGLNu++xbe12Gt60wFKofabBiHFDKRtXFlM7QPX+al5d8ioNb1tYB2xot7lgi/tkk7yJiWM+FArx/ur32bL2YxrekmDa2Led66cron3o2MGMHT8Gj8cTs1hNdQ3/fuVVGt4xsWqTa98xf8qR98KyGLxtM8MK8pg0fjy9FM0BK6VkX00Nr72+lK2DhtDs//TYnXbHyuQ7cEl8MwwGjxnE+InjEmiv5d9L/k3DijBWtb3rlx2MIhP/VDfnfeE8CvsUxizTmZh/Xb70vpRyvLKKdoIc/wA5fvJ3nawCAG++ervjx6LHJ7IARh8TVz64M2OfQGarhVknMfcaKEtmDtNL4upn4vG5EEaMfUtJqNHErDaQjTYvajYTWQCj0MSVb8XVbrVahOvB3OvCtnY7iSyAR+Lqb+HxGQhXAu37DWRA/cMDo9DCVSCTaJeYe9Lgd6e1F7TTHsO81dZOu6pE7jAeietEC48/nnYINYZTi/kUMAosjHyJJ8sB7e7DfhcIdwxt6daeLOZDFuEGiVlpqEtiD+OSuAbY0F5j2O+VS2HlQaPATKw9HNW+x2UviQX7v9p2Rf3uT6K91rB9w94+kYVIMuuv3kfv1lbcitYOlUCLy0Ugr4DWrOz/+sxWIgtgRPzu9gkMTxztgah2hTfshxG5Fq5CC0+2O/b53omYPyYSWd8xksi+5nwi6/SCCMcEVo0LqybRULw0jsAICswd7gSjcgTpdJNV68KqdTmjPSQwd7oc1G5g1Trkd6e1HzCwDjiofVci7dAV2uM/hE2j9rDD2p2MedNpv7uSxLy6nsAOmALzk/Rql4ZB3QknYn80cBdhpV97ImSdQbjOcOZ813QJOpHVaDQajUaj6UYIQBynT9RTRd+KaDQajUaj0Wi6JTqR1Wg0Go1Go9F0S/TQAo1Go9FoNJruhrqp3bs1ukdWo9FoNBqNRtMt0YmsRqPRaDQajaZboocWHI/YncdVo9FoujHCHXuC/a5Chtocs217Htc04Rpa7Jhtc/M2x2wfS+hZCyLoHlmNRqPRaDQaTbdE98hqNBqNRqPRdCdk9KXRPbIajUaj0Wg0mu6JTmQ1Go1Go9FoNN0SPbRAo9FoNBqNplshQf/YC9A9shqNRqPRaDSaborukXVJcia7cOWDJcIxiwhcEBDU/8eEoFBq3uhr4h/nxXSHiT1yW+Cy3DRtDtO2Va1tXJKcSS5cBRJLxJ6yS2BAk4uG/4SRLYq1F5n4xyfSDi7LQ/NWk9bNSk2DEdVemMjvUe0rw8hmxdr7WPgneDHdIRJpb/nYJPiRBBTatxXzBiLoon5lCNmo9n7XKLTwT/BgehL4XXoIbjdp2ZgG7TZiXgRd1K8KIRsUay+w8E+0oX2HScsGxdqFJGu8C+8JYCbye0vU7wHF2vMs/JM8WF4TGWdJIpf0ENxl0bLeQql2Q5I1TuIutLCI53eBaHPT8J5E1rvU2QaMXAvfZA8yifbWTyya1ynWLiRZYw28J4rEflcY80ZOAzkDGgkTTLnX0DDcWC29adpZCPJo/SDJHGuQ0T+J9tao9vru1bcndIcs0NMTWZfEN9Ngyucm8L/f/F+ys7NjFmtpaeH1117nb73/H/VLpbJk1uhr4p/qYe4dtzJw4EBcro4nrZSS/fv3M/+hBdS4GmjdpKiBMyS+GQYTzxrLN7/1TXJycmIWCwaDLFv2Bi/1fomG1yVSlfaiiPZbb7+Z4uLiuNqrq6tZ8PAC9rvqaa1QqP1Mg7EzRnP5ld/C5/PFLBYMBnnrreW80PsFGpZKZYm80cfCN83Fzbd9n8GDB+N2dzwNpZTU1NTww0ceZZ/rEMENSkzbjvm2tjbWrFnDb3r9loY3LGXJrFEY0X7TrTcydOjQuNpra2t5dP6PqHIfoGWdEtNHYn7CzLFcdnn8mG9ra+P999/nV7/4NQ1vWsqSWaPAIucMgxtv/i7Dhg3D44k9B2ptbS2P/fBxKt3VtJQrSmaFJHuqQcnUIXz7hmvx+/0xi7W2tvLuu+/y+14LI35XlMwaeRY50w2++4M5jBgxIq72AwcO8PijP2a3ex/NH6jTnnO6xehpJcy+5sq453soFGL9+vX8/KdP07gCZcmskWuRc6bBd75/PaWlpQm1/+TxJ9jl2Uvze6puYiTZkw0GTz6N79w4h9zc3JilVMa8kVOP77Q6br/jNk455ZSYbXsiAoEAi/60mJXL1xLY1u8okllJ1mSDQRNP5ns3fZe8vLyYpUKhEOXl5fziZ89c3H+bAAAgAElEQVTQ8JbV7ZJZTQ9PZF0nWgwuG8wPbvkBhhE/eH0+H5d+81ICgQD/rHxN2YU1Z6Kb++fdx4gRIxKW8/v9PP7EY1w9+2paPzYgdPQNnOtEi4EjT+OW225J2ND4fD4uvvgbNAUC/H3vEpo/OGrTkf1OdHPP/XczcuTIhOX8fj+P/eQxrr7qGlq3SWhToP0Ei5NLTub2O+cm1f71r3+NpqYm/t/+f9K0Rs3tr2+Smzvvvp0xY8YkLBfR/iOuuvJqgtvU3EDZjXmAL3zhC7S2tvJ8/WLq31KzyIZ/kodb77iZCRMmJC7n9/OjHz/K1bOvIbjFVHIT4eoXiflb5yaOeYDzzjuPUCjE7wPPU79Mnfabbr2RKVOmJCzn8/l49PEfcvWVVxPcGkI2Hb12o9Ci75A+3Dfv3riJ1GHOP/98gsFWFtX9mYa3VWn38r2b5zBt2rSE5Xw+Hz98bAHXzL6Wli1BZECB9qIw/Yf1465770zq95kzZ2JZFs8Ef0P9sqM2DYBvkpc5N36bM888M3E5n48Fj87n2quupWVLC7JBgfZ8SV5xNg8+Mo+MjIyEZT+N+T9Svyx276Udevev4eFHFjBo0KBOfd/n83HTD77PgQP3Ur6/Bqshv1P7MXIl/oGZPLzgIXr16pWw7Nlnn41pmvyq+bfUva4XFOpu9OhbD1eOoGzs6KQX9MOMHDWSjAJFK8kISZg2hg4daqt4fn4++bkFiN5qkikjC0aPHW37brl0ZCkZ+arueyRttDJ8+HBbpfPy8ijMK0RkqtEuMiWjx4yyrX3kyFK8eeoeNYZEa9Kbl8P4/X6KCoswFGlPNeZLS0sxctQ95gwZbba1+3w+Tig6QZnfjewUY760FCNLiWkAwu4QJSUltspmZ2dzYr/+iEw1tkUmDB0+NGkSe5jS0hLcPnV+N70h237PyspiQP8B6s733halo0ps+33EiBGQoS6ZkRmmbe2ZmZmcfNIpStu64kHFSZPYw0Ri/ihsG5GhEwMHDuz8PgDDMCgbMwpXr84n1CJLMnDgwKRJ7GFGjBih7PraZUjp/OsYoEcnsoZL4PV6O2xfsmQJQ4cOpbi4mAULFhzZ7vF4EIaixl2AEKJD4xrPNhB5DKvIYyJF7V6vF1Rpj/LZx8qJtEeOvSLDBmT06tiwd4nfAUtaHRKKZNpVDZmLFfOzZ8+mqKiI0tLSDuW9Xq/SgViS1LRH4k6N7Vgxn0h7pJ4KtUuZWsyrPPaGJCMjxfNd4ekukSn63aPu6mQQM5Grq6vja1/7GsOGDWP48OGsXLkyatuLVDrTfIraM7xK2zpvyn4/Gu0Sl8uFEB2DxzRNxowZw/nnnw/ApZdeytChQyktLWX27NmEQqH/Ku/xeLB5vx0bQcrXd6kHnXZLumUiK4QIpGvfpmkyZ84cXnnlFSoqKli0aBEVFRXpMnfM2HbavtbunPYrrriCJUuWdJm99mjtOuad0A5w44038vnPf55Nmzaxbt0620+IjhantTtl/8knn/yvY3zppZeyadMmPvzwQ1paWvj1r3+d9jo4few16aFbJrLpZPXq1RQXFzNw4EC8Xi8XX3wxL7/88nFv22n7Wrtz2qdPn05+fufGoR0tWruOeSe019fXs3z5cq666iog0nMX74dQqnFauxP2Kysr+ec//8nVV199ZNusWbMQQiCEYOLEiVRWVqa1DuD8sVeKBGE5/zoWSGsiK4Q4VQixSQjxRyHER0KIl4QQmUKIe4UQa4QQG4QQz4rocwghxDXR7euEEH8RIjJCTAhxmhBipRDiQyHEQ+ms8549ezjppJOO/D9gwAD27NmTTpPHhG2n7Wvtzml3Eq1dx3xX2wbYsWMHffr04corr2TMmDFcffXVNDU1dYltp7U7Yf/73/8+jz76aMyx+aFQiOeee47Pf/7zaa0DOH/sNemhK3pkhwJPSymHAw3ADcBTUsoJUspSoDdwfrTsX6PbRwMfAVdFtz8J/EJKORKo6oI6azQajeY4JRwO88EHH3D99dezdu1asrKyOoxV1ajh//7v/ygqKmLcuHExP7/hhhuYPn06Z5xxRhfX7DjA6R969aAfe+2WUq6Ivn8emAbMFEK8K4T4EDgLOPxT3lIhxNvR7Ze22z4VWBR9/1w8Q0KIa4UQ7wkh3gvR2qnK9u/fn927dx/5v7Kykv79+3dqX93JttP2tXbntDuJ1q5jvqttQ6QnbsCAAUyaNAmAr33ta3zwgaK5BZPgtPautr9ixQr+/ve/c+qpp3LxxRezbNkyvvnNbwLwwAMPUFNTw49//OO02W+P08dekx66IpH9bMougaeBr0V7WH8FHJ4f4/fAd6LbH2i3PdZ+OhqS8lkp5Xgp5XgP9qYb+SwTJkxg69at7Nixg7a2NhYvXsyXv/zlTu2rO9l22r7W7px2J9Hadcw7of2EE07gpJNOYvPmyJKBS5cutT1F1tHitPautj9//nwqKyvZuXMnixcv5qyzzuL555/n17/+Nf/+979ZtGiR7ekAjxanj70mPXTFgggnCyGmSClXAv8LvAOcDtQKIbKBrwEvRcvmAFVCCA+RHtnDg1dWABcT6dG9NJ2VdbvdPPXUU5x33nmYpsns2bNtz/3YnW07bV9rd077JZdcwptvvkltbS0DBgzggQceOPIjmHSjteuYd0I7wM9+9jMuvfRS2traGDhwIL/73e+6xK7T2p22f5jrrruOU0455cgCIV/96le5995702rzWNGujGPjyb7jdEUiuxmYI4T4LVAB/ALIAzYA+4A17creA7wL1ET/Hl5D8kbgT0KIuUDaf2I4a9YsZs2alW4zx5xtp+1r7c7YXrRoUfJCaURr1zHvBGVlZbz33nuO2HZau1P2Z8yYwYwZM4DIOGUncPrYa9TTFYlsWEr5zc9suzv6+i+klL8gkuh+dvsOoP26jh2+2xmkJTFN+yu4mKaJVDi4WUqJlDLm5NGxsCxL2R1YZ7SrvPuT0X3aXW1HqX2ZWiMa8bsi24BAYJqm7cdplqVujhOn/U5UuxP2pdkJ7QoRpOZLM6zQvoRwCvtT7XeRot/DYWfPd9WkFHfKtafq96NZCUMoa68syzq6dlemdg59qr37dHOKY+THVk7To+eRDQckWzdvs11+586dtNUpuou0BC7htj31RzAY5GDdASVrzgNYzbBl01bb5Xft2kVbvao7aIHX8P7XoPtEtLa2cuBQLZYi7bJFsOWjLbZvSnbt2kWoQd3FzSsy2LVrl62ybW1t1NRWK/N7Z2JelW0AL17b2kOhEPur9yNb1Ni2WlKPeYLqmki39LJz505bZUOhEPv2Vyk79rJFsP3j7SnFvNWsxDQALtNj2+/hcJiqfQq1Bw22bt6WknYjrK6Pxwi7bWs3TZPKPZVK27pdu3bZTi537doFrUdh2zKwpKSmpqbz+4iyZfM2wsHOLw0ug4JPPvnE9k3Erl27EG09OiXqtqTVa1LKndEpto5JzCqDD1atZfGfFie8Y7csi+XLl/PS4r/QskVd71hwI9w59y6qqhLPKBYIBLjjtjsJ7zWgTU0DZ+4z+PD9DTz/3PNJtf/nP/9h0XOLad6kLplr/tDinjvvYc+ePQkvMIFAgLtuv5u2vUBQjW1zv8FH6zaz8HcLOyyL2B7Lsli1ahXPL/wjTRXqHoMF1pvcd8/9VFZWJtTe3NzMvXfdS2uVhVSUVNiNeSkl69ev5+mf/YKG8jY1xoHG8jDz7n+QTz75JKn2++6+j5aqELJJUcxXRWL+uT8kjnkpJRs3buSnT/yM+rXqtDesDfHwvEfYsWNHQu0tLS3Mu/9Bmve2IhsV3bjWGuzeuoenn3o6YcxLKSkvL+eXT/+ShnUqtbcx/+EFfPzxxwm1B4NBHp73MI17mpENirTXuNlWsZ3f/Oq3SbVv2bKFxx59nIZyded7/QetPLrgR2zdujWh9tbWVuY/NJ+GygCyXpH2g4LqnbX85PEnaGuL788jMf+Tn1L/wdH4XWDWFjL31juorq7u1B7C4TAvvPAi768pxwr4Ol0T65DgwO5DPPbo40m1b9q0iZ88/gT1azs325HGWYTKR+XHEj6RLyeJs5MXzJD4Z7gIe1vxuD0xi4TNMC7TTcPbFrJBbe7vLZZkDLcQhoj5qFlKSdg0ocpD03sWShdAz5D4z3QRzmjF7fIQa4RD2AzjsqLa69Vq95xm0atUgiFwxdUeRuzPILDaRKl2b9TvdrS/YyHr1Gp3n2LRexRgkEC7iajxEnj3aB/3fQabMW9IF4GVEqtWsfaTLXqPJql244CXxpVp0J405k0MaRB418Kq7nyPUCxcAywyxwBCxhxWc0T7QS+N/1Gs3R3Vnpk45g3pIvAfiXVArd9d/S0yx5JAO4TNEMahjIh2K7l24fHaM+6R+KZZhDOCSf3e9IGBVR37vOhQ55C9pM/oZ5I9XiCTaHfVZ9Dwjj3ttnFJfNNdmNlqY941tDjuZ97Cg7gKajHiXNcSEQqH8JBF47YTwIztB9PuUyWXxHeGCzPHhvbVFtZ+e9pfly+9L6Ucb68S6cGX3V9OLv22k1UA4LV373P8WOhE9jCGhHgxbKK2YemAjIxWjmciRIIPFZBIuwWYx7F2IeOPFD/etfdkvzuqHXDLHhrzKNVuO5E98gW12u0mskc4jvyeKJGNIMGwSHnMqTQirwTYTmQPo1i7TmQ/5VhIZLvix17dA0tEAtoRBDjzA84IPVm7FNELiBNovzuGo9qBcJqTxUQ4GvNo7U7R5doFWGqfaHQap/2uSSs6kdVoNBqNRqPpTkicvRk/htA/0dNoNBqNRqPRdEt0j6xGo9FoNBpNN0Ig9TyyUXSPrEaj0Wg0Go2mW6ITWY1Go9FoNBpNt0QPLdBoNBpNtyTl6a80ykh5CiyFHLhmSvJC6eTZl5y1fxg9tADQPbIajUaj0Wg0mm6KTmQ1Go1Go9FoNN0SPbRAo9FoNBqNpruhhxYAukdWo9FoNBqNRtNN0T2yGo1Go9FoNN2JbrSylxBiJ9AImEBYSjleCJEPvACcCuwELpJSHurM/nWPrEaj0Wg0Go0mncyUUpZJKcdH/78dWCqlHAwsjf7fKXSPLCCyLIxcGf9omGA1CmR9GvJ+Q2IUWIjegIhdRAbBOmhAKE6Bo8CW9oBA1qVLu0T0ls5p90vwxCmQTu0iqj0zgfbWqPa2NGjPlBi5VnztFshGgVUniFvBThvvBtoDAuvQcai9t8TIS6K9SWAdTJP2fInISqL9kAGtadKea4E3ToF0aifq90Ta26J+T4f2XlG/O6HdRszTBladgWzppG0p8YbqcYdbEFJdN6VleGn1+rBcGcr2qTnC/wAzou8XAm8Cczuzox6fyBonmPgmuyktKcWf549ZpinQxIaNG2hY10rbFoVJjUvim2HQd2BfBg48Dben49XFsiyq9lSxY9sO6pdKCKprZIwiE9/pLkpKSsnNy41ZprmpOaJ9fSutmxQ2cIYk50yDvgMLGVQ8MK72fXur2L5tBw1LZecbuVjm+1jkTDUoHRHxuxAd993c1MzGjRtp2BAkWKFY+3SDokEFDBo0CI+3o3ZpWeyr2s/HWz+mYZlENivUXhjVXjIKf15uTO0tzc1s3FhB/cYgwQ0SZRc3Q5IzzaBPcT7FxYPweDteWaVlsX9fNdu2bFOvvcAiZ5pByYhR5ObH095CRUUF9RXNtKxXqF1Isqca9CnOpXjwYLwZMbRLSfW+arZs2UrjMgvZpK69MfItcs4wGDFiJHn5eXG1f/TRR9R91ExLuWLtpxsUFPsZMmRIXO01+6vZvHkLjW9YyIBC7bkWOdMFw4eXkl+YH1N7sCUY0b6pieYPFGufbJA/2MfQoUPwZnRMiqSU1FTXsGXTZhretJCN6rSLw9qHlVDQpyCu9k2bNnFoc4Dm97o+5g8dOERFxUYa37YiNzKpICV9mnfQ193M8NLh9O7VS0nVJZL91TVs2bqOypwSwu4sJftVyTGyRG2hEOK9dv8/K6V89jNlJPCqEEICv4x+3ldKWRX9fB/Qt7MV6NGJrMi18E/xMP/RRxg0aFDCsrW1tdx80y3sb2rE3KOmkfFNczHxrHHcctvNuFyuhGX//OKfWSxfpOEVCfLoGxnht/BPdfPwgocZPHhwwrIHDx7k5u/fwr6mesK7FWmf6mLcjNHMvXNuUu1/++vfeF7+iYZ/KdKeY+Gb6uLBR+YxbNiwhGUPHTrELTfdyt6mQ4R3qdGeM8VF2fRS7rrnzqTa//7yP1go/xDxu6VAe7aFb5rBAw89wIgRIxKWra+vj2hvPkjb9qM2DUD2JBcjpw3jnvvvwe1O3Py88q9X+DW/jfhdhfasSBJ737x7GTlyZMKyjY2N3PqD29jdUkPb1qM2DUD2BBclU4dw37x78cS4cWvPq/9+lV/KZyN+NxVoz5TknGFw9313UVZWlrBsIBDgtpvn8klLNa2bj9o0AFnjDIZNKeaBh+7HG+PmpT1Lly7jaZ6OaA8r0N5bkjNdcMc9dzBu3LiEZZuamph7y+3satlH8KOjNg1A5hiDwZNP48FH5pERI4ltz/Lly3mSn9KwRKp5CtUrov32u+YyYcKEhEWbm5u547Y72RHcQ8uGozcNqcX82rVreXjeIzS8mlqnRV5wDwP9gh/98KdkZ2cfbZU78PrSpTz9y1+xyz8WafTolCkete2GC8RjmpRyjxCiCHhNCLGp/YdSShlNcjtFjx4j6yqw+Ny5ZydNYgEKCwu59LL/JfOUxCejfSSWr43v3vidpMkMwNcv+jq9sjMiQxAU4CqwOHPmjKRJLEB+fj6XXfFNMk9Vp13mhfiOTe0XfPUCsnJ6Rx5NKcAokEw7Y2rSJBYgLy+Py2d/i6zTEl98U0EUmHzne3Nsaf/y/3yJnNzsyCNJBRj5komTJiVNYgH8fj9XXn2FQr+Dq4/FnO/NSZrEAnxh1hfIy89F5KjTPm7cuKRJLEBOTg6zr7mSLGXnO7hPkNzw3euTXtABzj3vXAr7FGKo0p5nMWrkqKRJLEB2djZXXTtbYVsH3n6CG757fdIkFuDss8/ihBNOwPCp0z5iREnSJBYgKyuLa667Wqn2jP4G13/nuqRJLMD06dPp379/ZMiTAoxciyGDhyRNYgEyMzO59vpr6H2yumQtlZgfM2YMJSNKIkMgUsAvG7nmqivTksQCfO7ssynq0wdPOJCW/R8VUjr/slVNuSf6txr4GzAR2C+E6AcQ/Vvd2cPQoxNZd6ZBQWGB7fJ5eXm4M1U9Yo30tWdmZtr+Sk5WNngVNXAZgj5FhbbL5+bm4uqtLlzCVpicnBzb5XNyfAhFw5SEV9Knbx/b5f1+P65e6h5vh6w2/P7Yw1hi2vf5EYryaOGVKfvdUDg8LCxDqWn3q9VeWGT/fPf7/cpiDiAsw+Tmxh7CE4vc3Fxl5zteSUGffGdsA2ERTsnvSu17IL8wNe3So3CcpTBT0p6fn4dQpF14Ib8gz3b53NxcpEud9lRjvqBPQfxxvHEwrNTalM6Qm5uHYYXSauN4RQiRJYTIOfweOBfYAPwduDxa7HLg5c7a6NGJLIiY44WWLFnC0KFDKS4uZsGCBR2+o8x6jF0ltB3rCyqNJ7Af6zgdlfkYxz6RduX2U/C7atux9pk45tJrO6n9NB/7nuR3u7ajFVBqK1XtqtWn5vf02k5k3/Hz3emYT3PYz549m6KiIkpLSzt81tlj/9nvxbJx6623MmzYMEaNGsUFF1xAXV0dAK+99tqRJzXjxo1j2bJlMfbfqWppIvQF3hFCrANWA/+UUi4BFgDnCCG2Ap+L/t8pujSRFULsFELY7w5yANM0mTNnDq+88goVFRUsWrSIioqK49620/a1dq1da9fauwqt3Tn7V1xxBUuWLOlyG+eccw4bNmxg/fr1DBkyhPnz5wORYYP/+Mc/+PDDD1m4cCGXXXZZWuumjmNgWIGNoQVSyu1SytHRV4mU8uHo9gNSyrOllIOllJ+TUh7s7JHo4T2yHVm9ejXFxcUMHDgQr9fLxRdfzMsvd7rHu9vYdtq+1q61a+1ae1ehtTtnf/r06eTn2x/qocrGueeee2Rs/uTJk6msrAQiY3NPPPFEAEpKSmhpaaG1tTWt9dOoJW2JbHRcxD+FEOv+f3t3FxtXmd9x/PefGU+A0DhOAu2uTUur7rJLsiwkhADRSu3uIhK7ZMXVFlFVFLor7apSLyqVXkQrERXBUrVXWRXlIs2mRUTENhAScCIKSUh2zQZCgISGJYtDYifxS/w245d5O08vZsw6YMfj5IyPH/v7kSJ5xifzPz+fY/uZv5/nHDM7bmY/HPe5a83sNTP7Uenx35rZB6Vt/7v03DYz+08zazWzT83sL8xsq5n9n5ltq9R+d3R06Kabbvr8cV1dnTo6OipVbtbUjro+2ck+07Wjrk92ss907dlQfzbYunWr1q9f/6Xnm5qatHLlyrIW5mH2qOS1JNZJOueca5AkM6uW9HNJ10vaIWm7c267mS2XtFHSvc65ntJty8bUSLpH0gYVJwavlfT3ko6Y2e3OuWPjC5rZjyX9WJKuUfmLqAAAwNz35JNPKpFI6OGHH77k+RMnTujxxx/Xvn37ItqzaXIq+6oBc10lpxZ8qOJE3p+b2XeccwOl51+W9F/Oue2lx9+VtNM51yNJX5gn8YpzzpVeq9M596FzLpB0QsX7817CObfFOXenc+7OKl3ZO6ra2lqdPXv288ft7e2qra29otfyqXbU9clO9pmuHXV9spN9pmvPhvpR2rZtm3bv3q3nnnvukgVi7e3tevDBB7V9+/ayLseJ2aViA1nn3G8lrVRxEPqvZvaz0qcOS1pn5S1PHJuoEoz7eOxxRbrJq1ev1ieffKK2tjZls1nt2LFDGzZsqESpWVU76vpkJzvZyT5TyB5d/ai0tLTomWee0a5duy657GV/f78aGhr09NNPa+3atRHu4RUIZsG/WaBiUwvM7KuSep1z/2Nm/SpOCZCkn5X+/ULSTyW9IelFM/sP59xFM1tyNavXrlYikdDmzZt1//33q1Ao6NFHH9Xy5cvnfO2o65Od7GQn+0whe3T1H3roIe3fv189PT2qq6vTE088occee6ziNZ566illMhndd999kooLvp599llt3rxZp06d0qZNm7Rp0yZJ0r59+3TjjTeGuk+onErOkf2WpH8zs0BSTtJPJDWWPvePkraa2TPOuX82syclHTCzgqT3JD1Swf2aUn19verr6+dd7ajrk53s860+2ck+3+o///zzkdSYbLC8ceNGbdy4sdK7hAqq2EDWObdX0t4vPH3zuI//bty2v5T0yy/8/0fGfXxa0oqJPnc18qOBBgYGy94+nU4ryIbUSw+K87QzmUzZKySHR4alfEjlM4EG+gem3rAklUopyIY3sTxmMQ0PD5d9Z7OhoSG5sG6skpf6evvL3rx43MPLnrCE0um0Fi1aVNb2Q0NDciEdd3cF2V0uvOzxUvZyL7+TTqdDO+ddztQ/3ewh1ZakuMU1NDRU9jmfTqWkQkhXYs/btL/fQ82uuNLpdNl380ul0lI+rOyadvbQvu6SzMWUTqdVU1PeHbZSgyF+7fOa1u+4YvaYwvqb8XTP+YH+gWl/vzsrnluVlEql5Kwyt8C9GsZiL0nz/DqyQb/05utv6OLFi1NuOzo6qpebd2m4I6zRlCmRSaq5qbmsrVtbW4u/WIfD+QEbDMR04M0D6u7unnLbTCajl5pf1si5bCi1JVN8NKmmxvKyHzlyRIODA3JDIWXvj+nQW4fU2dk55bbZbFYvNb2kkfPh3Z4wNlylpp1NcmX8EDp69Kh6+3pDy+4GYnq79W2dP39+ym1zuZyaG1/UyPnwRjSWiquxzOzvv/++unu6FKTDOudN77zzblmXGsrlcmre2azRELO7gZh2vtBYVvbjx4/rQucFBanwsh87duzza2deTj6fV/POZmUuFEKpLUlBv5Wd/aOPPlL7ufbwsg+ajh8/rs8++2zKbQuFgpp2NinbGV72Qq/U+EKTgmDqweHJkyd1+sxpucFwfjUHA6aTH59UW1vblNsWCgU1vtCkbFd4Ex+nc86fOXNGH3z4gYKB6R33tF2nxuYXlc+H+M5rnBMnTuhcR4dyiYUVeX1cPSvnBPPRIlvi1tj3ptwueYvT0lULtb5h3aT3ax4ZGdEbr7+pjve6NPSbQKHdw2+BU/X3TWu+s0a3fOPrn1+sebwgCHSu47z2vbZPgwcCuYHw3nskv+a0ZNV1Wv9X61VdvWjC2wOOjIxo//8e0NljFzTUGnL278W0eu2d+satt6iqqupLmwRBoPPnzmvvWPb+8LJX/VmgJauvU/0D61VdXT1p9oNvvqUzx84p/etAciFlTxazr1q7Urcu/+ak2TsvdOq1PS1KHQwU9IWXPXFzoCV3XaOGBxpUvXjy7IcOHNLpYx1KHw4xe1Ux+x33fFsrbltx2ewtr7Zo8GCgoDfE7H8cqGbNNWp4oF6LaxZPmH10dFSHD/5Kbe+dUepQyNm/G9O37/6Wbrv9tkmzd3d1a8/uV5V6K1BwMbzs8bpANXcn1bChQTU1NRNmz2Qy+tWhX+t3R08r9VYgBSFlTxSzr7hrue5YdfuE2Z1z6urq1quvvKrBQwUFPSFmrw20+O6kGh6o15KlSybN3nq4VaeOtmnwYLjZF/1lTCvu+qbuWHWHksnklzZxzqm7u0d7XtmjwcN5BV3xcGpLin2loMX3VKlhQ4OWLl06afbftB7Rb9/9nQb3F8LLXsY575xTX2+fdu/ao/63syq0T33cL/7onnEvEKhu6GN9vW6p1t69JrRrwAZBUDwmr7Wo47o/V2bB7/+K9N6Wf3rXOXdnKIWuUPW1X3H33vxIlLsgSWo5+XTkX4t5P5CVpNgfFlS11JRYOPEPj8JooFxfUPoGC/mmywucErUFJRdXySYq76RMf06FCzG5dPgN9ILPWDUAAAUuSURBVNiNBVUtu0z2TKBcb4WyJ0vZaxKy+ASv7aTMQF6FCyaXqkD2GwpKLDNVXX+Z7GPHPazBzJgqp3hdQQsiyx4osUyqWhif8LDORPbk4oRiictk77TQOlPjxZYFSixzqro+MXn2/kCFs3Mw+9JAiRsmzx5kA2XHjntYg5kxiVL2msmzZwfzyndaqG/Yx8SWBIrf4JT8g8tkHzvuFckeKLk4rljVZbJ3Wahv2MdMmT3nlO0rVCZ7Ged8Lp1XvtvKfuN2yUBWklyghcMXdK1GNdGP0ys1EsQ1kqxRLnnpNDAGsr83GwaylVzs5Y2gM65Mp5TRZIN6kxTeO+RLZEz5TxPKT1pbquRhCrriynRFlD1ryrcllG+TNGn9CtWWFHTHle2WslFkz5kKbQkNR5Y9Fnn2EUmRZO+JKdszT7NfjCl7MaLseVPhdEIjp6VIsvfGFPRKuciyx6M77lNmr2D9mTjnLaahhV/V0NW9CjzFQBYAAMAnTlIwN/+iPl3zerEXAAAA/MVAFgAAAF5iagEAAIBXXPFi9KAjCwAAAD/RkQUAAPANHVlJc3ggm1Jfz+uucepbuUxumaSesPbHs/pkjw7Z51/tqOuTPTpkvxJbGqOrXfQnV7sDCM+cHcg65264mv9vZu9EeZHfKOuTnezzrT7ZyT7f6pM92ov4IzxzdiALAAAwZzG1QBKLvQAAAOApBrKT2zKP65N9ftYn+/ysT/b5WZ/smBPM0ZoGAADwRvWCP3L31v5N1LuhlrZ/fzfq+cZ0ZAEAAOAlFnsBAAB4xUkuiHonZgU6sgAAAPASA1kAAAB4iakFAAAAvmGxviQ6sgAAAPAUHVkAAACfOEkBHVmJjiwAAAA8xUAWAAAAXmJqAQAAgG9Y7CWJjiwAAAA8xUAWAAAAXmJqAQAAgG+YWiCJjiwAAAA8RUcWAADAK46ObAkdWQAAAHiJgSwAAAC8xNQCAAAAnzhJQRD1XswKdGQBAADgJTqyAAAAvmGxlyQ6sgAAAPAUA1kAAAB4iakFAAAAvmFqgSQ6sgAAAPAUA1kAAAB4iakFAAAAXnFSwNQCiY4sAAAAPEVHFgAAwCdOco47e0l0ZAEAAOApBrIAAADwElMLAAAAfMNiL0l0ZAEAAOApBrIAAADwElMLAAAAfMMtaiXRkQUAAICn6MgCAAD4xDkp4DqyEh1ZAAAAeIqBLAAAALzE1AIAAADfsNhLEh1ZAAAAeIqOLAAAgGcci70k0ZEFAACApxjIAgAAwEtMLQAAAPCKY7FXCR1ZAAAAeImBLAAAALzE1AIAAACfOEkBUwskOrIAAADwFB1ZAAAA3ziuIyvRkQUAAICnGMgCAADAS0wtAAAA8IiT5FjsJYmOLAAAADxFRxYAAMAnzrHYq4SOLAAAALzEQBYAAABeYmoBAACAZ1jsVURHFgAAABVhZuvM7GMzO2Vm/xL26zOQBQAAQOjMLC7pF5LWS7pV0kNmdmuYNZhaAAAA4Bs/rlpwl6RTzrlPJcnMdkj6gaSPwipARxYAAACVUCvp7LjH7aXnQkNHFgAAwCMp9e193TUui3o/JF1jZu+Me7zFObdlJneAgSwAAIBHnHProt6HMnVIumnc47rSc6FhagEAAAAq4Yikr5nZn5pZUtJfS9oVZgE6sgAAAAidcy5vZv8gaa+kuKStzrkTYdYw57igLgAAAPzD1AIAAAB4iYEsAAAAvMRAFgAAAF5iIAsAAAAvMZAFAACAlxjIAgAAwEsMZAEAAOAlBrIAAADw0v8DURUaMGUGNEwAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "labels = ['ov','blca', 'brca', 'chol', 'coad', 'esca', 'hnsc', 'kich', 'kirc', 'lich', 'luad', 'prad', 'stad', 'thca', 'ucec', 'paad','skcm']\n", "label_values = True\n", "# plt.figure(figsize=(8,8))\n", "# plt.matshow(rf_cm)\n", "matfig = plt.figure(figsize=(12,12))\n", "plt.matshow(nn_cm, fignum=matfig.number)\n", "\n", "plt.title('True label')\n", "\n", "plt.colorbar()\n", "plt.xticks(range(len(labels)), labels, rotation=90)\n", "plt.yticks(range(len(labels)), labels)\n", "plt.ylabel(\"Clustering label\")\n", "if label_values:\n", " for (i, j), z in numpy.ndenumerate(nn_cm):\n", " plt.text(j, i, z, ha='center', va='center',\n", " bbox=dict(boxstyle='round', facecolor='white', edgecolor='0.3'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ShuffleSplit(n_splits=10, random_state=69, test_size=0.3, train_size=None)\n", "Features sorted by their score:\n", "[(0.1688, 'hsa_miR_140_5p'), (0.0628, 'hsa_miR_944'), (0.0278, 'hsa_miR_532_5p'), (0.0247, 'hsa_miR_584_5p'), (0.0221, 'hsa_miR_146a_5p'), (0.0209, 'hsa_miR_362_3p'), (0.0199, 'hsa_miR_23b_3p'), (0.0178, 'hsa_miR_211_5p'), (0.0153, 'hsa_let_7i_5p'), (0.0149, 'hsa_miR_92b_3p'), (0.0127, 'hsa_miR_561_5p'), (0.0114, 'hsa_miR_577'), (0.0113, 'hsa_let_7g_5p'), (0.011, 'hsa_miR_206'), (0.0104, 'hsa_miR_141_5p'), (0.0103, 'hsa_miR_375'), (0.0091, 'hsa_miR_135b_5p'), (0.009, 'hsa_miR_28_5p'), (0.0081, 'hsa_miR_192_5p'), (0.008, 'hsa_miR_99b_5p'), (0.0075, 'hsa_miR_194_5p'), (0.0072, 'hsa_miR_205_5p'), (0.007, 'hsa_miR_200a_3p'), (0.0068, 'hsa_miR_29a_5p'), (0.0066, 'hsa_miR_181c_3p'), (0.0061, 'hsa_miR_135a_3p'), (0.0058, 'hsa_let_7e_3p'), (0.0051, 'hsa_miR_197_3p'), (0.005, 'hsa_miR_22_3p'), (0.005, 'hsa_miR_135a_5p'), (0.0047, 'hsa_miR_628_5p'), (0.0046, 'hsa_miR_361_5p'), (0.0043, 'hsa_miR_30e_5p'), (0.0042, 'hsa_miR_199b_5p'), (0.0037, 'hsa_miR_30d_5p'), (0.0037, 'hsa_miR_10b_5p'), (0.0034, 'hsa_miR_6510_3p'), (0.0033, 'hsa_miR_934'), (0.0033, 'hsa_miR_671_3p'), (0.0032, 'hsa_miR_27b_3p'), (0.0032, 'hsa_miR_196b_5p'), (0.0029, 'hsa_miR_5680'), (0.0029, 'hsa_miR_132_3p'), (0.0028, 'hsa_miR_374a_3p'), (0.0027, 'hsa_miR_194_3p'), (0.0024, 'hsa_miR_1301_3p'), (0.0023, 'hsa_miR_200c_3p'), (0.0022, 'hsa_miR_885_5p'), (0.0021, 'hsa_miR_505_3p'), (0.002, 'hsa_miR_122_5p'), (0.0018, 'hsa_miR_21_5p'), (0.0018, 'hsa_miR_192_3p'), (0.0018, 'hsa_miR_101_3p'), (0.0017, 'hsa_miR_30d_3p'), (0.0017, 'hsa_miR_29c_3p'), (0.0017, 'hsa_miR_29a_3p'), (0.0017, 'hsa_miR_28_3p'), (0.0017, 'hsa_miR_27b_5p'), (0.0017, 'hsa_miR_205_3p'), (0.0017, 'hsa_miR_1262'), (0.0016, 'hsa_miR_429'), (0.0016, 'hsa_miR_26b_5p'), (0.0016, 'hsa_miR_26a_5p'), 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'hsa_miR_105_3p'), (0.0, 'hsa_miR_103b'), (0.0, 'hsa_miR_103a_2_5p'), (0.0, 'hsa_let_7f_2_3p'), (0.0, 'hsa_let_7d_3p'), (-0.0001, 'hsa_miR_7705'), (-0.0001, 'hsa_miR_7156_5p'), (-0.0001, 'hsa_miR_7106_5p'), (-0.0001, 'hsa_miR_6891_5p'), (-0.0001, 'hsa_miR_627_3p'), (-0.0001, 'hsa_miR_5699_5p'), (-0.0001, 'hsa_miR_543'), (-0.0001, 'hsa_miR_495_3p'), (-0.0001, 'hsa_miR_4687_3p'), (-0.0001, 'hsa_miR_4677_3p'), (-0.0001, 'hsa_miR_431_3p'), (-0.0001, 'hsa_miR_409_3p'), (-0.0001, 'hsa_miR_3942_5p'), (-0.0001, 'hsa_miR_3137'), (-0.0001, 'hsa_miR_30b_5p'), (-0.0001, 'hsa_miR_3074_5p'), (-0.0001, 'hsa_miR_144_5p'), (-0.0001, 'hsa_miR_134_5p'), (-0.0001, 'hsa_miR_1304_3p'), (-0.0001, 'hsa_miR_1277_3p'), (-0.0003, 'hsa_miR_522_3p')]\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.model_selection import ShuffleSplit\n", "from sklearn.metrics import r2_score\n", "from collections import defaultdict\n", " \n", "X = train\n", "Y = r_train_types\n", "names = pand_train.columns.values\n", "rf = RandomForestRegressor()\n", "scores = defaultdict(list)\n", "# start with 10 splits\n", "rs = ShuffleSplit(n_splits=10, test_size=.30, random_state=69)\n", "rs.get_n_splits(X)\n", "\n", "print(rs)\n", "\n", "#crossvalidate the scores on a number of different random splits of the data\n", "for train_idx, test_idx in rs.split(X):\n", " X_train, X_test = X[train_idx], X[test_idx]\n", " Y_train, Y_test = Y[train_idx], Y[test_idx]\n", " r = rf.fit(X_train, Y_train)\n", " acc = r2_score(Y_test, rf.predict(X_test))\n", " for i in range(X.shape[1]):\n", " X_t = X_test.copy()\n", " numpy.random.shuffle(X_t[:, i])\n", " shuff_acc = r2_score(Y_test, rf.predict(X_t))\n", " scores[names[i]].append((acc-shuff_acc)/acc)\n", "print \"Features sorted by their score:\"\n", "print sorted([(round(numpy.mean(score), 4), feat) for\n", " feat, score in scores.items()], reverse=True)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " blca 0.82 0.97 0.89 132\n", " brca 0.93 1.00 0.96 373\n", " chol 1.00 0.83 0.91 12\n", " coad 0.92 0.99 0.95 154\n", " esca 0.97 0.50 0.66 66\n", " hnsc 0.98 0.98 0.98 157\n", " kich 0.97 0.93 0.95 30\n", " kirc 0.99 0.98 0.99 178\n", " lich 1.00 0.98 0.99 120\n", " luad 0.96 0.91 0.93 173\n", " ov 0.99 0.97 0.98 144\n", " paad 0.97 0.78 0.87 50\n", " prad 0.99 0.99 0.99 170\n", " skcm 0.99 1.00 1.00 123\n", " stad 0.94 0.90 0.92 146\n", " thca 0.99 0.99 0.99 169\n", " ucec 0.94 0.96 0.95 179\n", "\n", " micro avg 0.95 0.95 0.95 2376\n", " macro avg 0.96 0.92 0.94 2376\n", "weighted avg 0.96 0.95 0.95 2376\n", "\n", "0.9537037037037037\n" ] } ], "source": [ "# Random Forest Classification report \n", "print(classification_report(y_true, rf_pred))\n", "\n", "print(accuracy_score(y_true, rf_pred, normalize=True, sample_weight=None))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def getClassAccuracy(rf_cm):\n", " for i in range (len(rf_cm)): \n", " print(\"\\n\" + str(i) + \": \" + cancers[i])\n", "\n", " # TP\n", " tp = rf_cm[i][i]\n", " print(\"TP: \" + str(rf_cm[i][i]))\n", "\n", " # FP, columns...things that are classified as XXX cancer, but are not\n", " fp = 0\n", " for col in range(len(rf_cm[0])):\n", " if col != i:\n", " fp += rf_cm[col][i]\n", " print(\"FP: \" + str(fp))\n", "\n", " # FN, rows...\n", " fn = 0\n", " for row in range(len(rf_cm)):\n", " if row != i:\n", " fn += rf_cm[i][row]\n", " print(\"FN: \" + str(fn))\n", "\n", " # TN, ... everything else\n", " tn = 0\n", " for row in range(len(rf_cm)):\n", " if row != i:\n", " for col in range(len(rf_cm[0])):\n", " if col != i: \n", " tn += rf_cm[row][col]\n", " print(\"TN: \" + str(tn))\n", " \n", " accuracy = (tp+tn)/float(tp+tn+fp+fn)\n", " \n", " print(\"Accuracy: \" + str(accuracy))\n", "\n", "# getClassAccuracy(rf_cm)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# KNN \n", "# n_neighbors rule of thumb... num_features^(1/2)\n", "knnmodel = KNeighborsClassifier(n_neighbors=3)\n", "knn = knnmodel.fit(train, r_train_labels) \n", "\n", "knn_pred = knn.predict(test)\n", "\n", "# 3 n_neighbors got accuracy of 83% \n", "# 49 neighbors got accuracy of 75% \n", "# 25 neighbors got 79% \n", "# 10 neighbors got 81% " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Accuracy: ', 0.8320707070707071)\n" ] }, { "data": { "text/html": [ "
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PredictedblcabrcacholcoadescahnsckichkirclichluadovpaadpradskcmstadthcaucecAll
True
blca100150313100000000000132
brca10358001000000000004373
chol1190100000000000012
coad4311451000000000000154
esca313062500000000010066
hnsc2000213601290020300157
kich00000027300000000030
kirc1200012170010000001178
lich110004029270130531120
luad37010802411411131765173
ov0100000001138000004144
paad010001021110272050050
prad0101020001011580303170
skcm640004055300093120123
stad60060500810021309600146
thca02000101210102001500169
ucec212000411005003210139179
All16741110162431673019611417214042186961221611572376
\n", "
" ], "text/plain": [ "Predicted blca brca chol coad esca hnsc kich kirc lich luad ov \\\n", "True \n", "blca 100 15 0 3 13 1 0 0 0 0 0 \n", "brca 10 358 0 0 1 0 0 0 0 0 0 \n", "chol 1 1 9 0 1 0 0 0 0 0 0 \n", "coad 4 3 1 145 1 0 0 0 0 0 0 \n", "esca 31 3 0 6 25 0 0 0 0 0 0 \n", "hnsc 2 0 0 0 2 136 0 1 2 9 0 \n", "kich 0 0 0 0 0 0 27 3 0 0 0 \n", "kirc 1 2 0 0 0 1 2 170 0 1 0 \n", "lich 1 1 0 0 0 4 0 2 92 7 0 \n", "luad 3 7 0 1 0 8 0 2 4 114 1 \n", "ov 0 1 0 0 0 0 0 0 0 1 138 \n", "paad 0 1 0 0 0 1 0 2 1 11 0 \n", "prad 0 1 0 1 0 2 0 0 0 1 0 \n", "skcm 6 4 0 0 0 4 0 5 5 3 0 \n", "stad 6 0 0 6 0 5 0 0 8 10 0 \n", "thca 0 2 0 0 0 1 0 1 2 10 1 \n", "ucec 2 12 0 0 0 4 1 10 0 5 0 \n", "All 167 411 10 162 43 167 30 196 114 172 140 \n", "\n", "Predicted paad prad skcm stad thca ucec All \n", "True \n", "blca 0 0 0 0 0 0 132 \n", "brca 0 0 0 0 0 4 373 \n", "chol 0 0 0 0 0 0 12 \n", "coad 0 0 0 0 0 0 154 \n", "esca 0 0 0 1 0 0 66 \n", "hnsc 0 2 0 3 0 0 157 \n", "kich 0 0 0 0 0 0 30 \n", "kirc 0 0 0 0 0 1 178 \n", "lich 1 3 0 5 3 1 120 \n", "luad 11 3 1 7 6 5 173 \n", "ov 0 0 0 0 0 4 144 \n", "paad 27 2 0 5 0 0 50 \n", "prad 1 158 0 3 0 3 170 \n", "skcm 0 0 93 1 2 0 123 \n", "stad 2 13 0 96 0 0 146 \n", "thca 0 2 0 0 150 0 169 \n", "ucec 0 3 2 1 0 139 179 \n", "All 42 186 96 122 161 157 2376 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Model Accuracy, how often is the classifier correct\n", "print(\"Accuracy: \", metrics.accuracy_score(r_test_labels, knn_pred))\n", "\n", "knn_cm = confusion_matrix(r_test_labels, knn_pred,)\n", "\n", "y_true = pd.Series(r_test_labels)\n", "knn_pred = pd.Series(knn_pred)\n", "\n", "pd.crosstab(y_true, knn_pred, rownames=['True'], colnames=['Predicted'], margins=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " blca 0.60 0.76 0.67 132\n", " brca 0.87 0.96 0.91 373\n", " chol 0.90 0.75 0.82 12\n", " coad 0.90 0.94 0.92 154\n", " esca 0.58 0.38 0.46 66\n", " hnsc 0.81 0.87 0.84 157\n", " kich 0.90 0.90 0.90 30\n", " kirc 0.87 0.96 0.91 178\n", " lich 0.81 0.77 0.79 120\n", " luad 0.66 0.66 0.66 173\n", " ov 0.99 0.96 0.97 144\n", " paad 0.64 0.54 0.59 50\n", " prad 0.85 0.93 0.89 170\n", " skcm 0.97 0.76 0.85 123\n", " stad 0.79 0.66 0.72 146\n", " thca 0.93 0.89 0.91 169\n", " ucec 0.89 0.78 0.83 179\n", "\n", " micro avg 0.83 0.83 0.83 2376\n", " macro avg 0.82 0.79 0.80 2376\n", "weighted avg 0.83 0.83 0.83 2376\n", "\n", "0.8320707070707071\n" ] } ], "source": [ "# KNN classification report \n", "print(classification_report(y_true, knn_pred))\n", "\n", "print(accuracy_score(y_true, knn_pred, normalize=True, sample_weight=None))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('Accuracy: ', 0.15698653198653198)\n" ] }, { "data": { "text/html": [ "
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PredictedbrcaAll
True
blca132132
brca373373
chol1212
coad154154
esca6666
hnsc157157
kich3030
kirc178178
lich120120
luad173173
ov144144
paad5050
prad170170
skcm123123
stad146146
thca169169
ucec179179
All23762376
\n", "
" ], "text/plain": [ "Predicted brca All\n", "True \n", "blca 132 132\n", "brca 373 373\n", "chol 12 12\n", "coad 154 154\n", "esca 66 66\n", "hnsc 157 157\n", "kich 30 30\n", "kirc 178 178\n", "lich 120 120\n", "luad 173 173\n", "ov 144 144\n", "paad 50 50\n", "prad 170 170\n", "skcm 123 123\n", "stad 146 146\n", "thca 169 169\n", "ucec 179 179\n", "All 2376 2376" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## SVM \n", "\n", "svmmodel = svm.SVC(gamma='scale', decision_function_shape='ovo')\n", "svm = svmmodel.fit(train, r_train_labels)\n", "\n", "svm_pred = svm.predict(test)\n", "\n", "# Model Accuracy, how often is the classifier correct\n", "print(\"Accuracy: \", metrics.accuracy_score(r_test_labels, svm_pred))\n", "\n", "svm_cm = confusion_matrix(r_test_labels, svm_pred,)\n", "\n", "y_true = pd.Series(r_test_labels)\n", "knn_pred = pd.Series(svm_pred)\n", "\n", "pd.crosstab(y_true, svm_pred, rownames=['True'], colnames=['Predicted'], margins=True)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot sample expression distribution\n", "values = test[100]\n", "times = numpy.arange(len(test[0])) * 10 \n", "plt.plot(times, values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extracting feature importance" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. feature hsa_miR_205_5p (0.011021)\n", "2. feature hsa_miR_135a_5p (0.010891)\n", "3. feature hsa_miR_194_5p (0.010295)\n", "4. feature hsa_miR_192_5p (0.010200)\n", "5. feature hsa_miR_375 (0.009484)\n", "6. feature hsa_miR_205_3p (0.009425)\n", "7. feature hsa_miR_944 (0.008933)\n", "8. feature hsa_miR_200c_5p (0.008926)\n", "9. feature hsa_miR_122_5p (0.008564)\n", "10. feature hsa_miR_194_3p (0.008348)\n" ] } ], "source": [ "# Top 10 most important features, ranked by random forest \n", "importances = rfmodel.feature_importances_\n", "indices = numpy.argsort(importances)[::-1]\n", "#for f in range(train.shape[1]):\n", "for f in range(0,10):\n", " print(\"%d. feature %s (%f)\" % (f + 1 , data.columns[indices[f]], importances[indices[f]]))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuning the learning rate\n", "\n", "The output `400epoch50learn` contains dictionaries for accuracy results at different learning rates, and accuracy results for different epochs. \n", "\n", "Learning rates were generated using `numpy.geomspace` for logarithmically spaced values. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#### CAREFUL\n", "#### You are about to load in new variables from the cluster \n", "#### To use for further visualization \n", "filename='db/400epoch50learn'\n", "with open(filename, 'rb') as fp:\n", " learnloss = pickle.load(fp)\n", " histories = pickle.load(fp)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## First plot accuracy vs learning rate to decide on a good range \n", "\n", "def visualizeLearnLossRange(learnloss): \n", " plt.title('model test accuracy')\n", " plt.xscale('log')\n", " plt.ylabel('accuracy')\n", " plt.xlabel('learning rate')\n", " plt.scatter(list(learnloss.keys()),list(learnloss.values()))\n", " plt.gca().invert_xaxis()\n", " #plt.axvspan(2.8e-04, 2e-05, color='yellow', alpha=0.5)\n", " plt.savefig(\"mirna/accuracy_learningrate_range.pdf\")\n", " plt.show()\n", "\n", "visualizeLearnLossRange(learnloss)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Max test accuracy: \n", "(0.7946127946127947, [5.1794746792312125e-05])\n", "Rate: 2.4420530945486548e-05\tAccuracy: 0.7727272727272727\n", "Rate: 5.1794746792312125e-05\tAccuracy: 0.7946127946127947\n", "Rate: 6.250551925273976e-05\tAccuracy: 0.773989898989899\n", "Rate: 9.102981779915228e-05\tAccuracy: 0.7815656565656566\n", "Rate: 0.00013257113655901095\tAccuracy: 0.7849326599326599\n", "Rate: 0.00023299518105153718\tAccuracy: 0.7781986531986532\n", "6\n" ] } ], "source": [ "## Look at rates and decide lower/upper bound \n", "max_acc = max(learnloss.values()) # maximum value\n", "max_lr = [k for k, v in learnloss.items() if v == max_acc] # getting all keys containing the `maximum`\n", "print(\"Max test accuracy: \")\n", "print(max_acc, max_lr)\n", "\n", "# print(ll)\n", "# print(\"Mean Accuracy: \" + str(numpy.mean(histories[ll]['acc'])))\n", "# print(\"Max Accuracy: \" + str(max(histories[ll]['acc'])))\n", "orderedkeys = []\n", "for ll in learnloss:\n", " orderedkeys.append(ll)\n", "orderedkeys = sorted(orderedkeys)\n", "\n", "# get numbers for plotting\n", "# good rates are above 0.77 when training \n", "counter = 0\n", "goodrates = []\n", "for ll in orderedkeys: \n", "# if ll < 3e-04 and ll > 2.5e-05: \n", " if learnloss[ll] > 0.77: \n", " counter+=1\n", " goodrates.append(ll)\n", " print(\"Rate: \" + str(ll) + \"\\tAccuracy: \"+ str(learnloss[ll]))\n", "print(counter)\n", "\n", "\n", "filename='goodrates'\n", "with open(filename, 'wb') as fp:\n", " pickle.dump(goodrates, fp)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def visualizeEpochs(learnloss, histories):\n", " fig = plt.figure(figsize=(10,3))\n", " counter = 0\n", " nrRows = 4\n", " nrCols = 3\n", " for lr in goodrates: \n", " # generate subplots \n", " ax = fig.add_subplot(nrRows, nrCols, counter+1)\n", " ax.plot(histories[lr]['acc'])\n", " ax.plot(histories[lr]['val_acc'])\n", " plt.title('learning rate: ' + '{:.3g}'.format(lr))\n", " ax.set_ylabel('accuracy')\n", " ax.set_xlabel('epoch')\n", " ax.legend(['train', 'test'], loc='upper left')\n", " counter +=1\n", " fig.set_figheight(12)\n", " fig.set_figwidth(15)\n", " plt.tight_layout()\n", " plt.savefig(\"mirna/6-accuracy_epochs.pdf\")\n", " plt.show()\n", " \n", "\n", "visualizeEpochs(learnloss, histories)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "# Learning rate: 0.01 to 0.000001\n", "# encoded labels are one-hot encoded \n", "# Test labels are treated with ravel\n", "learnloss = {} \n", "histories = {}\n", "def learnLoss(learningRate, epochs, train, encoded_train, test, encoded_test, test_labels):\n", " model = tf.keras.Sequential()\n", " model.add(layers.Dense(128, activation='sigmoid'))\n", " model.add(layers.Dense(128, activation='sigmoid'))\n", " model.add(layers.Dense(17, activation='softmax'))\n", " model.compile(optimizer=tf.train.RMSPropOptimizer(learningRate),\n", " loss='categorical_crossentropy',\n", " metrics=['accuracy'])\n", " \n", " model.fit(train, encoded_train, validation_data=(test, encoded_test), epochs=epochs, batch_size=32)\n", " \n", " \n", " # test \n", " pred_y = model.predict_classes(test)\n", " nnyhat = confusion_matrix(test_types, pred_y)\n", " accuracy = metrics.accuracy_score(test_labels, pred_y)\n", " print(\"Accuracy: \", accuracy)\n", " learnloss[learningRate] = accuracy \n", " histories[learningRate] = model.history.history" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "learningRates = numpy.geomspace(0.01, 0.000001, num=50)\n", "print(learningRates)\n", "for lr in learningRates:\n", " learnLoss(lr, 500, train, encoded_train, test, encoded_test, r_test_types)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "### CAREFUL\n", "### You are about to DUMP and replace the current saved variables\n", "\n", "filename='500epoch50learn'\n", "with open(filename, 'wb')as fp:\n", " pickle.dump(learnloss, fp)\n", " pickle.dump(histories, fp)\n", " \n", "filename='500epoch50learn'\n", "with open(filename, 'rb') as fp:\n", " learnloss = pickle.load(fp)\n", " histories = pickle.load(fp)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tuning the batch size\n", "Batch sizes were chosen from 100 to 2000, and then narrowed down from 1 to 100 " ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "filename='200epoch-10batch'\n", "with open(filename, 'rb') as fp:\n", " batch_acc = pickle.load(fp)\n", " batch_hist = pickle.load(fp)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch: 1\tAccuracy: 0.7373737373737373\n", "Batch: 11\tAccuracy: 0.7672558922558923\n", "Batch: 21\tAccuracy: 0.7243265993265994\n", "Batch: 31\tAccuracy: 0.742003367003367\n", "Batch: 41\tAccuracy: 0.7617845117845118\n", "Batch: 51\tAccuracy: 0.7441077441077442\n", "Batch: 61\tAccuracy: 0.7310606060606061\n", "Batch: 71\tAccuracy: 0.7184343434343434\n", "Batch: 81\tAccuracy: 0.7268518518518519\n", "Batch: 91\tAccuracy: 0.7264309764309764\n", "Batch: 101\tAccuracy: 0.7251683501683501\n", "Batch: 111\tAccuracy: 0.7344276094276094\n", "Batch: 121\tAccuracy: 0.7117003367003367\n", "Batch: 131\tAccuracy: 0.7293771043771043\n", "Batch: 141\tAccuracy: 0.7024410774410774\n", "Batch: 151\tAccuracy: 0.70496632996633\n", "Batch: 161\tAccuracy: 0.7175925925925926\n", "Batch: 171\tAccuracy: 0.7184343434343434\n", "Batch: 181\tAccuracy: 0.6746632996632996\n", "Batch: 191\tAccuracy: 0.6898148148148148\n", "Batch: 201\tAccuracy: 0.6712962962962963\n", "Batch: 211\tAccuracy: 0.6708754208754208\n", "Batch: 221\tAccuracy: 0.6944444444444444\n", "Batch: 231\tAccuracy: 0.6553030303030303\n", "Batch: 241\tAccuracy: 0.6632996632996633\n", "Batch: 251\tAccuracy: 0.6498316498316499\n", "Batch: 261\tAccuracy: 0.6561447811447811\n", "Batch: 271\tAccuracy: 0.6485690235690236\n", "Batch: 281\tAccuracy: 0.6590909090909091\n", "Batch: 291\tAccuracy: 0.6599326599326599\n", "0\n" ] } ], "source": [ "orderedkeys = []\n", "for b in batch_acc:\n", " orderedkeys.append(b)\n", "orderedkeys = sorted(orderedkeys)\n", "\n", "counter = 0\n", "goodrates = []\n", "for b in orderedkeys: \n", "# if learnloss[ll] > 0.77: \n", "# counter+=1\n", "# goodrates.append(ll)\n", " print(\"Batch: \" + str(b) + \"\\tAccuracy: \"+ str(batch_acc[b]))\n", "print(counter)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## First plot accuracy vs learning rate to decide on a good range \n", "\n", "def visualizeMetricAcc(dic): \n", " plt.title('model test accuracy')\n", " plt.xscale('log')\n", " plt.ylabel('accuracy')\n", " plt.xlabel('batch')\n", " plt.scatter(list(dic.keys()),list(dic.values()))\n", " plt.gca().invert_xaxis()\n", "# plt.axvspan(2.8e-04, 2e-05, color='yellow', alpha=0.5)\n", "# plt.savefig(\"accuracy_learningrate_range.pdf\")\n", " plt.show()\n", "\n", "visualizeMetricAcc(batch_acc)\n" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Max test accuracy: \n", "(0.7672558922558923, [11])\n" ] } ], "source": [ "## Look at rates and decide lower/upper bound \n", "max_acc = max(batch_acc.values()) # maximum value\n", "max_lr = [k for k, v in batch_acc.items() if v == max_acc] # getting all keys containing the `maximum`\n", "print(\"Max test accuracy: \")\n", "print(max_acc, max_lr)\n" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def visualizeEpochsGeneral(learnloss, histories):\n", " fig = plt.figure(figsize=(10,3))\n", " counter = 0\n", " nrRows = 2\n", " nrCols = 3\n", " for lr in learnloss: \n", " if lr < 100 and lr < 50:\n", " ax = fig.add_subplot(nrRows, nrCols, counter+1)\n", " ax.plot(histories[lr]['acc'])\n", " ax.plot(histories[lr]['val_acc'])\n", " plt.title('learning rate: ' + '{:.3g}'.format(lr))\n", " ax.set_ylabel('accuracy')\n", " ax.set_xlabel('epoch')\n", " ax.legend(['train', 'test'], loc='upper left')\n", " counter +=1\n", " fig.set_figheight(8)\n", " fig.set_figwidth(15)\n", " plt.tight_layout()\n", " #plt.savefig(\"accuracy_epochs.pdf\")\n", " plt.show()\n", " \n", "\n", "visualizeEpochsGeneral(batch_acc, batch_hist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Code beyond this point was for debugging purposes. " ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5574, 2403)\n" ] } ], "source": [ "model = tf.keras.Sequential()\n", "print(train.shape)\n", "#model.add(layers.InputLayer(input_tensor=train, input_shape=train.shape))\n", "model.add(layers.Dense(128, activation='sigmoid'))\n", "#model.add(layers.Dense(128, activation='sigmoid'))\n", "model.add(layers.Dense(128, activation='sigmoid'))\n", "model.add(layers.Dense(17, activation='softmax'))\n", "model.compile(optimizer=tf.train.RMSPropOptimizer(5.1794746792312125e-05),\n", " loss='categorical_crossentropy',\n", " metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 5574 samples, validate on 2376 samples\n", "Epoch 1/200\n", "5574/5574 [==============================] - 4s 736us/step - loss: 2.8503 - acc: 0.0673 - val_loss: 2.7450 - val_acc: 0.1570\n", "Epoch 2/200\n", "5574/5574 [==============================] - 2s 383us/step - loss: 2.6926 - acc: 0.1496 - val_loss: 2.6553 - val_acc: 0.1570\n", "Epoch 3/200\n", "5574/5574 [==============================] - 2s 288us/step - loss: 2.6412 - acc: 0.1498 - val_loss: 2.6215 - val_acc: 0.1591\n", "Epoch 4/200\n", "5574/5574 [==============================] - 1s 213us/step - loss: 2.6082 - acc: 0.1552 - val_loss: 2.5906 - val_acc: 0.1742\n", "Epoch 5/200\n", "5574/5574 [==============================] - 1s 248us/step - loss: 2.5755 - acc: 0.1760 - val_loss: 2.5571 - val_acc: 0.1848\n", "Epoch 6/200\n", "5574/5574 [==============================] - 3s 465us/step - loss: 2.5404 - acc: 0.2015 - val_loss: 2.5210 - val_acc: 0.2348\n", "Epoch 7/200\n", "5574/5574 [==============================] - 2s 335us/step - loss: 2.5024 - acc: 0.2408 - val_loss: 2.4831 - val_acc: 0.2723\n", "Epoch 8/200\n", "5574/5574 [==============================] - 1s 260us/step - loss: 2.4621 - acc: 0.2722 - val_loss: 2.4441 - val_acc: 0.3056\n", "Epoch 9/200\n", "5574/5574 [==============================] - 3s 475us/step - loss: 2.4195 - acc: 0.3256 - val_loss: 2.4002 - val_acc: 0.3245\n", "Epoch 10/200\n", "5574/5574 [==============================] - 2s 349us/step - loss: 2.3751 - acc: 0.3470 - val_loss: 2.3568 - val_acc: 0.3552\n", "Epoch 11/200\n", "5574/5574 [==============================] - 1s 207us/step - loss: 2.3301 - acc: 0.3789 - val_loss: 2.3132 - val_acc: 0.3716\n", "Epoch 12/200\n", "5574/5574 [==============================] - 1s 212us/step - loss: 2.2842 - acc: 0.4013 - val_loss: 2.2688 - val_acc: 0.4007\n", "Epoch 13/200\n", "5574/5574 [==============================] - 1s 244us/step - loss: 2.2370 - acc: 0.4255 - val_loss: 2.2238 - val_acc: 0.4175\n", "Epoch 14/200\n", "5574/5574 [==============================] - 1s 215us/step - loss: 2.1898 - acc: 0.4392 - val_loss: 2.1778 - val_acc: 0.4327\n", "Epoch 15/200\n", "5574/5574 [==============================] - 1s 212us/step - loss: 2.1434 - acc: 0.4582 - val_loss: 2.1337 - val_acc: 0.4419\n", "Epoch 16/200\n", "5574/5574 [==============================] - 1s 192us/step - loss: 2.0974 - acc: 0.4781 - val_loss: 2.0913 - val_acc: 0.4423\n", "Epoch 17/200\n", "5574/5574 [==============================] - 1s 226us/step - loss: 2.0521 - acc: 0.4770 - val_loss: 2.0472 - val_acc: 0.4579\n", "Epoch 18/200\n", "5574/5574 [==============================] - 1s 216us/step - loss: 2.0066 - acc: 0.4916 - val_loss: 2.0049 - val_acc: 0.4760\n", "Epoch 19/200\n", "5574/5574 [==============================] - 1s 235us/step - loss: 1.9626 - acc: 0.5039 - val_loss: 1.9618 - val_acc: 0.4870\n", "Epoch 20/200\n", "5574/5574 [==============================] - 2s 282us/step - loss: 1.9182 - acc: 0.5099 - val_loss: 1.9205 - val_acc: 0.5147\n", "Epoch 21/200\n", "5574/5574 [==============================] - 2s 376us/step - loss: 1.8755 - acc: 0.5305 - val_loss: 1.8798 - val_acc: 0.5177\n", "Epoch 22/200\n", "5574/5574 [==============================] - 1s 219us/step - loss: 1.8322 - acc: 0.5355 - val_loss: 1.8389 - val_acc: 0.5265\n", "Epoch 23/200\n", "5574/5574 [==============================] - 1s 198us/step - loss: 1.7903 - acc: 0.5499 - val_loss: 1.8005 - val_acc: 0.5236\n", "Epoch 24/200\n", "5574/5574 [==============================] - 1s 227us/step - loss: 1.7498 - acc: 0.5527 - val_loss: 1.7623 - val_acc: 0.5417\n", "Epoch 25/200\n", "5574/5574 [==============================] - 1s 222us/step - loss: 1.7095 - acc: 0.5694 - val_loss: 1.7245 - val_acc: 0.5497\n", "Epoch 26/200\n", "5574/5574 [==============================] - 1s 229us/step - loss: 1.6705 - acc: 0.5743 - val_loss: 1.6877 - val_acc: 0.5640\n", "Epoch 27/200\n", "5574/5574 [==============================] - 2s 311us/step - loss: 1.6320 - acc: 0.5849 - val_loss: 1.6520 - val_acc: 0.5715\n", "Epoch 28/200\n", "5574/5574 [==============================] - 1s 244us/step - loss: 1.5946 - acc: 0.5940 - val_loss: 1.6166 - val_acc: 0.5850\n", "Epoch 29/200\n", "5574/5574 [==============================] - 1s 211us/step - loss: 1.5576 - acc: 0.6039 - val_loss: 1.5817 - val_acc: 0.5964\n", "Epoch 30/200\n", "5574/5574 [==============================] - 1s 249us/step - loss: 1.5216 - acc: 0.6173 - val_loss: 1.5490 - val_acc: 0.6065\n", "Epoch 31/200\n", "5574/5574 [==============================] - 1s 238us/step - loss: 1.4872 - acc: 0.6240 - val_loss: 1.5162 - val_acc: 0.6162\n", "Epoch 32/200\n", "5574/5574 [==============================] - 1s 224us/step - loss: 1.4533 - acc: 0.6381 - val_loss: 1.4856 - val_acc: 0.6153\n", "Epoch 33/200\n", "5574/5574 [==============================] - 1s 253us/step - loss: 1.4204 - acc: 0.6457 - val_loss: 1.4526 - val_acc: 0.6359\n", "Epoch 34/200\n", "5574/5574 [==============================] - 2s 283us/step - loss: 1.3879 - acc: 0.6577 - val_loss: 1.4217 - val_acc: 0.6448\n", "Epoch 35/200\n", "5574/5574 [==============================] - 2s 272us/step - loss: 1.3566 - acc: 0.6647 - val_loss: 1.3912 - val_acc: 0.6540\n", "Epoch 36/200\n", "5574/5574 [==============================] - 2s 273us/step - loss: 1.3258 - acc: 0.6726 - val_loss: 1.3622 - val_acc: 0.6599\n", "Epoch 37/200\n", "5574/5574 [==============================] - 2s 278us/step - loss: 1.2962 - acc: 0.6773 - val_loss: 1.3331 - val_acc: 0.6633\n", "Epoch 38/200\n", "5574/5574 [==============================] - 1s 235us/step - loss: 1.2666 - acc: 0.6825 - val_loss: 1.3047 - val_acc: 0.6776\n", "Epoch 39/200\n", "5574/5574 [==============================] - 1s 217us/step - loss: 1.2383 - acc: 0.6923 - val_loss: 1.2788 - val_acc: 0.6738\n", "Epoch 40/200\n", "5574/5574 [==============================] - 1s 261us/step - loss: 1.2108 - acc: 0.6959 - val_loss: 1.2524 - val_acc: 0.6835\n", "Epoch 41/200\n", "5574/5574 [==============================] - 1s 205us/step - loss: 1.1834 - acc: 0.7020 - val_loss: 1.2243 - val_acc: 0.6932\n", "Epoch 42/200\n", "5574/5574 [==============================] - 1s 199us/step - loss: 1.1566 - acc: 0.7104 - val_loss: 1.1988 - val_acc: 0.6970\n", "Epoch 43/200\n", "5574/5574 [==============================] - 2s 293us/step - loss: 1.1308 - acc: 0.7142 - val_loss: 1.1737 - val_acc: 0.7050\n", "Epoch 44/200\n", "5574/5574 [==============================] - 1s 256us/step - loss: 1.1056 - acc: 0.7214 - val_loss: 1.1495 - val_acc: 0.7050\n", "Epoch 45/200\n", "5574/5574 [==============================] - 1s 210us/step - loss: 1.0810 - acc: 0.7237 - val_loss: 1.1250 - val_acc: 0.7151\n", "Epoch 46/200\n", "5574/5574 [==============================] - 1s 224us/step - loss: 1.0571 - acc: 0.7309 - val_loss: 1.1024 - val_acc: 0.7205\n", "Epoch 47/200\n", "5574/5574 [==============================] - 1s 241us/step - loss: 1.0337 - acc: 0.7348 - val_loss: 1.0793 - val_acc: 0.7264\n", "Epoch 48/200\n", "5574/5574 [==============================] - 1s 189us/step - loss: 1.0113 - acc: 0.7382 - val_loss: 1.0581 - val_acc: 0.7285\n", "Epoch 49/200\n", "5574/5574 [==============================] - 1s 264us/step - loss: 0.9890 - acc: 0.7447 - val_loss: 1.0356 - val_acc: 0.7311\n", "Epoch 50/200\n", "5574/5574 [==============================] - 1s 265us/step - loss: 0.9671 - acc: 0.7490 - val_loss: 1.0134 - val_acc: 0.7391\n", "Epoch 51/200\n", "5574/5574 [==============================] - 3s 459us/step - loss: 0.9463 - acc: 0.7571 - val_loss: 0.9941 - val_acc: 0.7386\n", "Epoch 52/200\n", "5574/5574 [==============================] - 3s 468us/step - loss: 0.9261 - acc: 0.7585 - val_loss: 0.9738 - val_acc: 0.7403\n", "Epoch 53/200\n", "5574/5574 [==============================] - 4s 719us/step - loss: 0.9062 - acc: 0.7609 - val_loss: 0.9537 - val_acc: 0.7483\n", "Epoch 54/200\n", "5574/5574 [==============================] - 3s 593us/step - loss: 0.8868 - acc: 0.7653 - val_loss: 0.9350 - val_acc: 0.7508\n", "Epoch 55/200\n", "5574/5574 [==============================] - 2s 335us/step - loss: 0.8679 - acc: 0.7702 - val_loss: 0.9164 - val_acc: 0.7551\n", "Epoch 56/200\n", "5574/5574 [==============================] - 5s 892us/step - loss: 0.8497 - acc: 0.7747 - val_loss: 0.8979 - val_acc: 0.7618\n", "Epoch 57/200\n", "5574/5574 [==============================] - 4s 704us/step - loss: 0.8319 - acc: 0.7770 - val_loss: 0.8807 - val_acc: 0.7635\n", "Epoch 58/200\n", "5574/5574 [==============================] - 2s 407us/step - loss: 0.8148 - acc: 0.7829 - val_loss: 0.8643 - val_acc: 0.7647\n", "Epoch 59/200\n", "5574/5574 [==============================] - 2s 289us/step - loss: 0.7982 - acc: 0.7851 - val_loss: 0.8476 - val_acc: 0.7656\n", "Epoch 60/200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "5574/5574 [==============================] - 3s 534us/step - loss: 0.7819 - acc: 0.7874 - val_loss: 0.8318 - val_acc: 0.7702\n", "Epoch 61/200\n", "5574/5574 [==============================] - 3s 460us/step - loss: 0.7664 - acc: 0.7912 - val_loss: 0.8157 - val_acc: 0.7710\n", "Epoch 62/200\n", "5574/5574 [==============================] - 2s 355us/step - loss: 0.7511 - acc: 0.7933 - val_loss: 0.8005 - val_acc: 0.7811\n", "Epoch 63/200\n", "5574/5574 [==============================] - 2s 396us/step - loss: 0.7362 - acc: 0.7973 - val_loss: 0.7859 - val_acc: 0.7799\n", "Epoch 64/200\n", "5574/5574 [==============================] - 2s 307us/step - loss: 0.7218 - acc: 0.8009 - val_loss: 0.7722 - val_acc: 0.7811\n", "Epoch 65/200\n", "5574/5574 [==============================] - 3s 452us/step - loss: 0.7079 - acc: 0.8041 - val_loss: 0.7579 - val_acc: 0.7854\n", "Epoch 66/200\n", "5574/5574 [==============================] - 2s 363us/step - loss: 0.6943 - acc: 0.8062 - val_loss: 0.7439 - val_acc: 0.7934\n", "Epoch 67/200\n", "5574/5574 [==============================] - 2s 282us/step - loss: 0.6809 - acc: 0.8114 - val_loss: 0.7311 - val_acc: 0.7925\n", "Epoch 68/200\n", "5574/5574 [==============================] - 2s 284us/step - loss: 0.6681 - acc: 0.8150 - val_loss: 0.7180 - val_acc: 0.8030\n", "Epoch 69/200\n", "5574/5574 [==============================] - 1s 257us/step - loss: 0.6560 - acc: 0.8181 - val_loss: 0.7062 - val_acc: 0.8085\n", "Epoch 70/200\n", "5574/5574 [==============================] - 1s 216us/step - loss: 0.6439 - acc: 0.8208 - val_loss: 0.6935 - val_acc: 0.8199\n", "Epoch 71/200\n", "5574/5574 [==============================] - 1s 192us/step - loss: 0.6324 - acc: 0.8249 - val_loss: 0.6817 - val_acc: 0.8224\n", "Epoch 72/200\n", "5574/5574 [==============================] - 1s 201us/step - loss: 0.6209 - acc: 0.8301 - val_loss: 0.6706 - val_acc: 0.8211\n", "Epoch 73/200\n", "5574/5574 [==============================] - 1s 201us/step - loss: 0.6099 - acc: 0.8319 - val_loss: 0.6593 - val_acc: 0.8266\n", "Epoch 74/200\n", "5574/5574 [==============================] - 2s 270us/step - loss: 0.5991 - acc: 0.8376 - val_loss: 0.6496 - val_acc: 0.8253\n", "Epoch 75/200\n", "5574/5574 [==============================] - 2s 284us/step - loss: 0.5888 - acc: 0.8394 - val_loss: 0.6386 - val_acc: 0.8295\n", "Epoch 76/200\n", "5574/5574 [==============================] - 2s 334us/step - loss: 0.5783 - acc: 0.8448 - val_loss: 0.6276 - val_acc: 0.8418\n", "Epoch 77/200\n", "5574/5574 [==============================] - 2s 383us/step - loss: 0.5683 - acc: 0.8495 - val_loss: 0.6187 - val_acc: 0.8354\n", "Epoch 78/200\n", "5574/5574 [==============================] - 2s 330us/step - loss: 0.5587 - acc: 0.8518 - val_loss: 0.6080 - val_acc: 0.8430\n", "Epoch 79/200\n", "5574/5574 [==============================] - 2s 314us/step - loss: 0.5492 - acc: 0.8567 - val_loss: 0.5987 - val_acc: 0.8451\n", "Epoch 80/200\n", "5574/5574 [==============================] - 1s 236us/step - loss: 0.5399 - acc: 0.8599 - val_loss: 0.5901 - val_acc: 0.8464\n", "Epoch 81/200\n", "5574/5574 [==============================] - 2s 296us/step - loss: 0.5308 - acc: 0.8656 - val_loss: 0.5808 - val_acc: 0.8464\n", "Epoch 82/200\n", "5574/5574 [==============================] - 1s 267us/step - loss: 0.5222 - acc: 0.8671 - val_loss: 0.5720 - val_acc: 0.8485\n", "Epoch 83/200\n", "5574/5574 [==============================] - 3s 457us/step - loss: 0.5132 - acc: 0.8705 - val_loss: 0.5623 - val_acc: 0.8565\n", "Epoch 84/200\n", "5574/5574 [==============================] - 2s 310us/step - loss: 0.5050 - acc: 0.8735 - val_loss: 0.5545 - val_acc: 0.8540\n", "Epoch 85/200\n", "5574/5574 [==============================] - 2s 408us/step - loss: 0.4969 - acc: 0.8742 - val_loss: 0.5469 - val_acc: 0.8573\n", "Epoch 86/200\n", "5574/5574 [==============================] - 2s 280us/step - loss: 0.4889 - acc: 0.8760 - val_loss: 0.5387 - val_acc: 0.8594\n", "Epoch 87/200\n", "5574/5574 [==============================] - 2s 286us/step - loss: 0.4811 - acc: 0.8793 - val_loss: 0.5309 - val_acc: 0.8615\n", "Epoch 88/200\n", "5574/5574 [==============================] - 1s 269us/step - loss: 0.4735 - acc: 0.8812 - val_loss: 0.5242 - val_acc: 0.8598\n", "Epoch 89/200\n", "5574/5574 [==============================] - 2s 355us/step - loss: 0.4660 - acc: 0.8837 - val_loss: 0.5157 - val_acc: 0.8666\n", "Epoch 90/200\n", "5574/5574 [==============================] - 2s 277us/step - loss: 0.4584 - acc: 0.8863 - val_loss: 0.5096 - val_acc: 0.8649\n", "Epoch 91/200\n", "5574/5574 [==============================] - 1s 265us/step - loss: 0.4515 - acc: 0.8861 - val_loss: 0.5014 - val_acc: 0.8721\n", "Epoch 92/200\n", "5574/5574 [==============================] - 2s 271us/step - loss: 0.4446 - acc: 0.8900 - val_loss: 0.4944 - val_acc: 0.8725\n", "Epoch 93/200\n", "5574/5574 [==============================] - 2s 349us/step - loss: 0.4377 - acc: 0.8902 - val_loss: 0.4872 - val_acc: 0.8754\n", "Epoch 94/200\n", "5574/5574 [==============================] - 2s 277us/step - loss: 0.4309 - acc: 0.8931 - val_loss: 0.4811 - val_acc: 0.8779\n", "Epoch 95/200\n", "5574/5574 [==============================] - 2s 276us/step - loss: 0.4243 - acc: 0.8958 - val_loss: 0.4757 - val_acc: 0.8758\n", "Epoch 96/200\n", "5574/5574 [==============================] - 1s 192us/step - loss: 0.4180 - acc: 0.8952 - val_loss: 0.4682 - val_acc: 0.8792\n", "Epoch 97/200\n", "5574/5574 [==============================] - 1s 193us/step - loss: 0.4117 - acc: 0.8979 - val_loss: 0.4634 - val_acc: 0.8775\n", "Epoch 98/200\n", "5574/5574 [==============================] - 1s 225us/step - loss: 0.4055 - acc: 0.8983 - val_loss: 0.4562 - val_acc: 0.8855\n", "Epoch 99/200\n", "5574/5574 [==============================] - 1s 180us/step - loss: 0.3996 - acc: 0.9011 - val_loss: 0.4505 - val_acc: 0.8826\n", "Epoch 100/200\n", "5574/5574 [==============================] - 1s 201us/step - loss: 0.3937 - acc: 0.9003 - val_loss: 0.4453 - val_acc: 0.8834\n", "Epoch 101/200\n", "5574/5574 [==============================] - 1s 260us/step - loss: 0.3878 - acc: 0.9024 - val_loss: 0.4390 - val_acc: 0.8893\n", "Epoch 102/200\n", "5574/5574 [==============================] - 1s 216us/step - loss: 0.3823 - acc: 0.9058 - val_loss: 0.4334 - val_acc: 0.8906\n", "Epoch 103/200\n", "5574/5574 [==============================] - 1s 235us/step - loss: 0.3769 - acc: 0.9064 - val_loss: 0.4291 - val_acc: 0.8889\n", "Epoch 104/200\n", "5574/5574 [==============================] - 1s 192us/step - loss: 0.3718 - acc: 0.9069 - val_loss: 0.4244 - val_acc: 0.8914\n", "Epoch 105/200\n", "5574/5574 [==============================] - 1s 250us/step - loss: 0.3666 - acc: 0.9089 - val_loss: 0.4195 - val_acc: 0.8914\n", "Epoch 106/200\n", "5574/5574 [==============================] - 1s 211us/step - loss: 0.3615 - acc: 0.9099 - val_loss: 0.4132 - val_acc: 0.8935\n", "Epoch 107/200\n", "5574/5574 [==============================] - 1s 250us/step - loss: 0.3566 - acc: 0.9116 - val_loss: 0.4088 - val_acc: 0.8952\n", "Epoch 108/200\n", "5574/5574 [==============================] - 1s 197us/step - loss: 0.3517 - acc: 0.9128 - val_loss: 0.4043 - val_acc: 0.8931\n", "Epoch 109/200\n", "5574/5574 [==============================] - 1s 188us/step - loss: 0.3468 - acc: 0.9139 - val_loss: 0.3996 - val_acc: 0.8960\n", "Epoch 110/200\n", "5574/5574 [==============================] - 2s 342us/step - loss: 0.3420 - acc: 0.9144 - val_loss: 0.3951 - val_acc: 0.8973\n", "Epoch 111/200\n", "5574/5574 [==============================] - 2s 362us/step - loss: 0.3374 - acc: 0.9160 - val_loss: 0.3917 - val_acc: 0.8948\n", "Epoch 112/200\n", "5574/5574 [==============================] - 2s 325us/step - loss: 0.3330 - acc: 0.9173 - val_loss: 0.3864 - val_acc: 0.9007\n", "Epoch 113/200\n", "5574/5574 [==============================] - 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1s 221us/step - loss: 0.2998 - acc: 0.9268 - val_loss: 0.3562 - val_acc: 0.9045\n", "Epoch 121/200\n", "5574/5574 [==============================] - 1s 200us/step - loss: 0.2963 - acc: 0.9257 - val_loss: 0.3521 - val_acc: 0.9070\n", "Epoch 122/200\n", "5574/5574 [==============================] - 1s 184us/step - loss: 0.2924 - acc: 0.9277 - val_loss: 0.3486 - val_acc: 0.9082\n", "Epoch 123/200\n", "5574/5574 [==============================] - 1s 183us/step - loss: 0.2889 - acc: 0.9290 - val_loss: 0.3455 - val_acc: 0.9091\n", "Epoch 124/200\n", "5574/5574 [==============================] - 1s 183us/step - loss: 0.2852 - acc: 0.9309 - val_loss: 0.3422 - val_acc: 0.9095\n", "Epoch 125/200\n", "5574/5574 [==============================] - 1s 190us/step - loss: 0.2818 - acc: 0.9299 - val_loss: 0.3392 - val_acc: 0.9091\n", "Epoch 126/200\n", "5574/5574 [==============================] - 1s 190us/step - loss: 0.2784 - acc: 0.9315 - val_loss: 0.3354 - val_acc: 0.9104\n", "Epoch 127/200\n", "5574/5574 [==============================] - 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val_acc: 0.9259\n", "Epoch 183/200\n", "5574/5574 [==============================] - 2s 312us/step - loss: 0.1572 - acc: 0.9562 - val_loss: 0.2378 - val_acc: 0.9268\n", "Epoch 184/200\n", "5574/5574 [==============================] - 2s 282us/step - loss: 0.1558 - acc: 0.9555 - val_loss: 0.2375 - val_acc: 0.9259\n", "Epoch 185/200\n", "5574/5574 [==============================] - 2s 273us/step - loss: 0.1545 - acc: 0.9566 - val_loss: 0.2360 - val_acc: 0.9268\n", "Epoch 186/200\n", "5574/5574 [==============================] - 2s 307us/step - loss: 0.1531 - acc: 0.9566 - val_loss: 0.2350 - val_acc: 0.9263\n", "Epoch 187/200\n", "5574/5574 [==============================] - 2s 307us/step - loss: 0.1519 - acc: 0.9569 - val_loss: 0.2345 - val_acc: 0.9259\n", "Epoch 188/200\n", "5574/5574 [==============================] - 2s 298us/step - loss: 0.1507 - acc: 0.9571 - val_loss: 0.2336 - val_acc: 0.9263\n", "Epoch 189/200\n", "5574/5574 [==============================] - 2s 282us/step - loss: 0.1493 - 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Predicted012345678910111213141516All
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chol02010000000000000012
coad0200145500000020000154
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kich00000003000000000030
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lich0001000001161010010120
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skcm0000000000000001122123
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thca0000000100300165000169
ucec0130000000102017200179
All139162381111605814931177116165168145166179471222376
\n", "
" ], "text/plain": [ "Predicted 0 1 2 3 4 5 6 7 8 9 10 11 12 13 \\\n", "True \n", "blca 0 123 0 0 1 7 1 0 0 0 0 0 0 0 \n", "brca 0 4 369 0 0 0 0 0 0 0 0 0 0 0 \n", "chol 0 2 0 10 0 0 0 0 0 0 0 0 0 0 \n", "coad 0 2 0 0 145 5 0 0 0 0 0 0 2 0 \n", "esca 0 24 0 0 6 33 1 0 0 0 0 0 2 0 \n", "hnsc 0 3 0 0 0 5 144 0 0 0 0 0 3 1 \n", "kich 0 0 0 0 0 0 0 30 0 0 0 0 0 0 \n", "kirc 0 0 2 0 0 0 0 0 176 0 0 0 0 0 \n", "lich 0 0 0 1 0 0 0 0 0 116 1 0 1 0 \n", "luad 0 3 4 0 1 1 0 0 0 0 159 0 3 0 \n", "ov 139 0 1 0 0 0 0 0 0 0 0 0 0 0 \n", "paad 0 0 0 0 0 0 1 0 1 0 0 0 6 0 \n", "prad 0 0 2 0 0 0 0 0 0 0 0 168 0 0 \n", "skcm 0 0 0 0 0 0 0 0 0 0 0 0 0 0 \n", "stad 0 0 0 0 7 7 2 0 0 0 1 0 126 0 \n", "thca 0 0 0 0 0 0 0 1 0 0 3 0 0 165 \n", "ucec 0 1 3 0 0 0 0 0 0 0 1 0 2 0 \n", "All 139 162 381 11 160 58 149 31 177 116 165 168 145 166 \n", "\n", "Predicted 14 15 16 All \n", "True \n", "blca 0 0 0 132 \n", "brca 0 0 0 373 \n", "chol 0 0 0 12 \n", "coad 0 0 0 154 \n", "esca 0 0 0 66 \n", "hnsc 1 0 0 157 \n", "kich 0 0 0 30 \n", "kirc 0 0 0 178 \n", "lich 0 1 0 120 \n", "luad 2 0 0 173 \n", "ov 4 0 0 144 \n", "paad 0 42 0 50 \n", "prad 0 0 0 170 \n", "skcm 0 1 122 123 \n", "stad 0 3 0 146 \n", "thca 0 0 0 169 \n", "ucec 172 0 0 179 \n", "All 179 47 122 2376 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nn_pred = model.predict_classes(test)\n", "\n", "nnyhat = confusion_matrix(test_types, nn_pred)\n", "print(nnyhat)\n", "print(\"Accuracy: \", metrics.accuracy_score(r_test_types, nn_pred))\n", "# for i in range(len(r_test_labels)):\n", "# print(\"X=%s, Predicted=%s\" % (r_test_labels[i], ynew[i]))\n", "\n", "y_true = pd.Series(r_test_labels)\n", "y_pred = pd.Series(nn_pred)\n", "\n", "pd.crosstab(y_true, y_pred, rownames=['True'], colnames=['Predicted'], margins=True)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_15 (Dense) (None, 128) 307712 \n", "_________________________________________________________________\n", "dense_16 (Dense) (None, 128) 16512 \n", "_________________________________________________________________\n", "dense_17 (Dense) (None, 17) 2193 \n", "=================================================================\n", "Total params: 326,417\n", "Trainable params: 326,417\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.99 0.97 0.98 144\n", " 1 0.78 0.94 0.86 132\n", " 2 0.96 1.00 0.98 373\n", " 3 1.00 0.83 0.91 12\n", " 4 0.90 0.94 0.92 154\n", " 5 0.66 0.53 0.59 66\n", " 6 0.96 0.95 0.96 157\n", " 7 1.00 0.97 0.98 30\n", " 8 0.98 0.99 0.99 178\n", " 9 1.00 0.97 0.99 120\n", " 10 0.97 0.93 0.95 173\n", " 11 1.00 0.98 0.99 170\n", " 12 0.89 0.87 0.88 146\n", " 13 1.00 0.99 0.99 169\n", " 14 0.97 0.96 0.96 179\n", " 15 0.93 0.86 0.90 50\n", " 16 0.99 0.98 0.99 123\n", "\n", " micro avg 0.95 0.95 0.95 2376\n", " macro avg 0.94 0.92 0.93 2376\n", "weighted avg 0.95 0.95 0.95 2376\n", "\n", "0.9482323232323232\n" ] } ], "source": [ "\n", "print(classification_report(test_types, nn_pred))\n", "\n", "print(accuracy_score(test_types, nn_pred, normalize=True, sample_weight=None))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 0.97 0.98 144\n", " 1 0.76 0.93 0.84 132\n", " 2 0.97 0.99 0.98 373\n", " 3 0.91 0.83 0.87 12\n", " 4 0.91 0.94 0.92 154\n", " 5 0.57 0.50 0.53 66\n", " 6 0.97 0.92 0.94 157\n", " 7 0.97 1.00 0.98 30\n", " 8 0.99 0.99 0.99 178\n", " 9 1.00 0.97 0.98 120\n", " 10 0.96 0.92 0.94 173\n", " 11 1.00 0.99 0.99 170\n", " 12 0.87 0.86 0.87 146\n", " 13 0.99 0.98 0.99 169\n", " 14 0.96 0.96 0.96 179\n", " 15 0.89 0.84 0.87 50\n", " 16 1.00 0.99 1.00 123\n", "\n", " micro avg 0.94 0.94 0.94 2376\n", " macro avg 0.92 0.92 0.92 2376\n", "weighted avg 0.94 0.94 0.94 2376\n", "\n", "0.9423400673400674\n" ] } ], "source": [ "\n", "print(classification_report(test_types, nn_pred))\n", "\n", "print(accuracy_score(test_types, nn_pred, normalize=True, sample_weight=None))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5574, 2403)\n" ] } ], "source": [ "model = tf.keras.Sequential()\n", "print(train.shape)\n", "#model.add(layers.InputLayer(input_tensor=train, input_shape=train.shape))\n", "model.add(layers.Dense(128, activation='sigmoid', input_dim=2403))\n", "#model.add(layers.Dense(128, activation='sigmoid'))\n", "#model.add(layers.Dense(128, activation='sigmoid'))\n", "model.add(layers.Dense(128, activation='sigmoid'))\n", "model.add(layers.Dense(17, activation='softmax'))\n", "model.compile(optimizer=tf.train.RMSPropOptimizer(5.1794746792312125e-05),\n", " loss='categorical_crossentropy',\n", " metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 5574 samples, validate on 2376 samples\n", "Epoch 1/200\n", "5574/5574 [==============================] - 2s 311us/step - loss: 2.9108 - acc: 0.0646 - val_loss: 2.7692 - val_acc: 0.1578\n", "Epoch 2/200\n", "5574/5574 [==============================] - 1s 206us/step - loss: 2.6696 - acc: 0.1502 - val_loss: 2.5982 - val_acc: 0.1574\n", "Epoch 3/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 2.5494 - acc: 0.1620 - val_loss: 2.5057 - val_acc: 0.1936\n", "Epoch 4/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 2.4710 - acc: 0.2269 - val_loss: 2.4383 - val_acc: 0.2744\n", "Epoch 5/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 2.4032 - acc: 0.2784 - val_loss: 2.3725 - val_acc: 0.2997\n", "Epoch 6/200\n", "5574/5574 [==============================] - 1s 157us/step - loss: 2.3370 - acc: 0.2982 - val_loss: 2.3110 - val_acc: 0.3228\n", "Epoch 7/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 2.2743 - acc: 0.3204 - val_loss: 2.2530 - val_acc: 0.3350\n", "Epoch 8/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 2.2148 - acc: 0.3428 - val_loss: 2.1938 - val_acc: 0.3468\n", "Epoch 9/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 2.1573 - acc: 0.3590 - val_loss: 2.1400 - val_acc: 0.3775\n", "Epoch 10/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 2.1038 - acc: 0.3850 - val_loss: 2.0897 - val_acc: 0.4053\n", "Epoch 11/200\n", "5574/5574 [==============================] - 1s 155us/step - loss: 2.0528 - acc: 0.4123 - val_loss: 2.0400 - val_acc: 0.4137\n", "Epoch 12/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 2.0036 - acc: 0.4309 - val_loss: 1.9905 - val_acc: 0.4390\n", "Epoch 13/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.9563 - acc: 0.4519 - val_loss: 1.9450 - val_acc: 0.4545\n", "Epoch 14/200\n", "5574/5574 [==============================] - 1s 156us/step - loss: 1.9116 - acc: 0.4700 - val_loss: 1.9043 - val_acc: 0.4621\n", "Epoch 15/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.8695 - acc: 0.4787 - val_loss: 1.8636 - val_acc: 0.4844\n", "Epoch 16/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.8314 - acc: 0.4937 - val_loss: 1.8284 - val_acc: 0.4920\n", "Epoch 17/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.7946 - acc: 0.5077 - val_loss: 1.7914 - val_acc: 0.5042\n", "Epoch 18/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.7605 - acc: 0.5201 - val_loss: 1.7566 - val_acc: 0.5093\n", "Epoch 19/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.7267 - acc: 0.5337 - val_loss: 1.7250 - val_acc: 0.5181\n", "Epoch 20/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.6960 - acc: 0.5368 - val_loss: 1.6971 - val_acc: 0.5366\n", "Epoch 21/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.6661 - acc: 0.5513 - val_loss: 1.6682 - val_acc: 0.5429\n", "Epoch 22/200\n", "5574/5574 [==============================] - 1s 157us/step - loss: 1.6366 - acc: 0.5590 - val_loss: 1.6400 - val_acc: 0.5551\n", "Epoch 23/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.6086 - acc: 0.5633 - val_loss: 1.6136 - val_acc: 0.5627\n", "Epoch 24/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.5818 - acc: 0.5721 - val_loss: 1.5861 - val_acc: 0.5686\n", "Epoch 25/200\n", "5574/5574 [==============================] - 1s 157us/step - loss: 1.5563 - acc: 0.5780 - val_loss: 1.5619 - val_acc: 0.5842\n", "Epoch 26/200\n", "5574/5574 [==============================] - 1s 157us/step - loss: 1.5324 - acc: 0.5861 - val_loss: 1.5418 - val_acc: 0.5880\n", "Epoch 27/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.5108 - acc: 0.5883 - val_loss: 1.5180 - val_acc: 0.5930\n", "Epoch 28/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.4888 - acc: 0.5949 - val_loss: 1.4993 - val_acc: 0.5913\n", "Epoch 29/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.4687 - acc: 0.5992 - val_loss: 1.4763 - val_acc: 0.5997\n", "Epoch 30/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.4460 - acc: 0.6042 - val_loss: 1.4563 - val_acc: 0.6002\n", "Epoch 31/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.4265 - acc: 0.6078 - val_loss: 1.4370 - val_acc: 0.6065\n", "Epoch 32/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.4088 - acc: 0.6116 - val_loss: 1.4209 - val_acc: 0.6111\n", "Epoch 33/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.3914 - acc: 0.6139 - val_loss: 1.4045 - val_acc: 0.6136\n", "Epoch 34/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.3769 - acc: 0.6172 - val_loss: 1.3874 - val_acc: 0.6128\n", "Epoch 35/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.3592 - acc: 0.6195 - val_loss: 1.3703 - val_acc: 0.6183\n", "Epoch 36/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.3425 - acc: 0.6250 - val_loss: 1.3556 - val_acc: 0.6258\n", "Epoch 37/200\n", "5574/5574 [==============================] - 1s 156us/step - loss: 1.3248 - acc: 0.6331 - val_loss: 1.3399 - val_acc: 0.6254\n", "Epoch 38/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.3141 - acc: 0.6301 - val_loss: 1.3292 - val_acc: 0.6237\n", "Epoch 39/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.3026 - acc: 0.6304 - val_loss: 1.3178 - val_acc: 0.6279\n", "Epoch 40/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.2883 - acc: 0.6329 - val_loss: 1.3037 - val_acc: 0.6313\n", "Epoch 41/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.2718 - acc: 0.6380 - val_loss: 1.2888 - val_acc: 0.6317\n", "Epoch 42/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.2600 - acc: 0.6401 - val_loss: 1.2751 - val_acc: 0.6359\n", "Epoch 43/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.2462 - acc: 0.6457 - val_loss: 1.2641 - val_acc: 0.6338\n", "Epoch 44/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.2369 - acc: 0.6448 - val_loss: 1.2505 - val_acc: 0.6406\n", "Epoch 45/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.2246 - acc: 0.6485 - val_loss: 1.2406 - val_acc: 0.6469\n", "Epoch 46/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.2092 - acc: 0.6514 - val_loss: 1.2264 - val_acc: 0.6448\n", "Epoch 47/200\n", "5574/5574 [==============================] - 1s 157us/step - loss: 1.1984 - acc: 0.6570 - val_loss: 1.2197 - val_acc: 0.6473\n", "Epoch 48/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.1897 - acc: 0.6568 - val_loss: 1.2102 - val_acc: 0.6486\n", "Epoch 49/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.1826 - acc: 0.6581 - val_loss: 1.2014 - val_acc: 0.6511\n", "Epoch 50/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.1761 - acc: 0.6568 - val_loss: 1.1919 - val_acc: 0.6524\n", "Epoch 51/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.1641 - acc: 0.6572 - val_loss: 1.1782 - val_acc: 0.6582\n", "Epoch 52/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.1525 - acc: 0.6618 - val_loss: 1.1711 - val_acc: 0.6553\n", "Epoch 53/200\n", "5574/5574 [==============================] - 1s 172us/step - loss: 1.1426 - acc: 0.6688 - val_loss: 1.1599 - val_acc: 0.6599\n", "Epoch 54/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.1311 - acc: 0.6713 - val_loss: 1.1525 - val_acc: 0.6599\n", "Epoch 55/200\n", "5574/5574 [==============================] - ETA: 0s - loss: 1.1240 - acc: 0.671 - 1s 158us/step - loss: 1.1242 - acc: 0.6710 - val_loss: 1.1486 - val_acc: 0.6604\n", "Epoch 56/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.1193 - acc: 0.6719 - val_loss: 1.1409 - val_acc: 0.6633\n", "Epoch 57/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.1129 - acc: 0.6769 - val_loss: 1.1358 - val_acc: 0.6625\n", "Epoch 58/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 1.1063 - acc: 0.6764 - val_loss: 1.1293 - val_acc: 0.6675\n", "Epoch 59/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.0978 - acc: 0.6781 - val_loss: 1.1228 - val_acc: 0.6650\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 60/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.0917 - acc: 0.6781 - val_loss: 1.1192 - val_acc: 0.6671\n", "Epoch 61/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.0843 - acc: 0.6807 - val_loss: 1.1102 - val_acc: 0.6709\n", "Epoch 62/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 1.0773 - acc: 0.6864 - val_loss: 1.0999 - val_acc: 0.6734\n", "Epoch 63/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.0694 - acc: 0.6868 - val_loss: 1.0976 - val_acc: 0.6742\n", "Epoch 64/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.0663 - acc: 0.6875 - val_loss: 1.0883 - val_acc: 0.6738\n", "Epoch 65/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 1.0599 - acc: 0.6871 - val_loss: 1.0819 - val_acc: 0.6785\n", "Epoch 66/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 1.0559 - acc: 0.6907 - val_loss: 1.0777 - val_acc: 0.6797\n", "Epoch 67/200\n", "5574/5574 [==============================] - 1s 166us/step - loss: 1.0479 - acc: 0.6921 - val_loss: 1.0683 - val_acc: 0.6818\n", "Epoch 68/200\n", "5574/5574 [==============================] - 1s 180us/step - loss: 1.0409 - acc: 0.6973 - val_loss: 1.0651 - val_acc: 0.6852\n", "Epoch 69/200\n", "5574/5574 [==============================] - 1s 169us/step - loss: 1.0348 - acc: 0.6990 - val_loss: 1.0573 - val_acc: 0.6860\n", "Epoch 70/200\n", "5574/5574 [==============================] - 1s 162us/step - loss: 1.0285 - acc: 0.6990 - val_loss: 1.0505 - val_acc: 0.6932\n", "Epoch 71/200\n", "5574/5574 [==============================] - 1s 175us/step - loss: 1.0230 - acc: 0.7017 - val_loss: 1.0433 - val_acc: 0.6902\n", "Epoch 72/200\n", "5574/5574 [==============================] - 1s 164us/step - loss: 1.0175 - acc: 0.7070 - val_loss: 1.0382 - val_acc: 0.6932\n", "Epoch 73/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 1.0110 - acc: 0.7106 - val_loss: 1.0326 - val_acc: 0.6949\n", "Epoch 74/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 1.0076 - acc: 0.7094 - val_loss: 1.0290 - val_acc: 0.6944\n", "Epoch 75/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9980 - acc: 0.7124 - val_loss: 1.0237 - val_acc: 0.6940\n", "Epoch 76/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9927 - acc: 0.7131 - val_loss: 1.0159 - val_acc: 0.6978\n", "Epoch 77/200\n", "5574/5574 [==============================] - 1s 164us/step - loss: 0.9839 - acc: 0.7151 - val_loss: 1.0112 - val_acc: 0.6923\n", "Epoch 78/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.9856 - acc: 0.7130 - val_loss: 1.0073 - val_acc: 0.6982\n", "Epoch 79/200\n", "5574/5574 [==============================] - 1s 162us/step - loss: 0.9805 - acc: 0.7158 - val_loss: 1.0040 - val_acc: 0.7033\n", "Epoch 80/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9767 - acc: 0.7142 - val_loss: 1.0000 - val_acc: 0.7050\n", "Epoch 81/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.9661 - acc: 0.7137 - val_loss: 0.9884 - val_acc: 0.7062\n", "Epoch 82/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 0.9601 - acc: 0.7178 - val_loss: 0.9877 - val_acc: 0.7071\n", "Epoch 83/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.9545 - acc: 0.7180 - val_loss: 0.9844 - val_acc: 0.7050\n", "Epoch 84/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9486 - acc: 0.7182 - val_loss: 0.9775 - val_acc: 0.7100\n", "Epoch 85/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.9484 - acc: 0.7174 - val_loss: 0.9782 - val_acc: 0.7075\n", "Epoch 86/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.9449 - acc: 0.7201 - val_loss: 0.9745 - val_acc: 0.7054\n", "Epoch 87/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9396 - acc: 0.7191 - val_loss: 0.9723 - val_acc: 0.7083\n", "Epoch 88/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.9402 - acc: 0.7187 - val_loss: 0.9687 - val_acc: 0.7100\n", "Epoch 89/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 0.9439 - acc: 0.7131 - val_loss: 0.9674 - val_acc: 0.7113\n", "Epoch 90/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9372 - acc: 0.7173 - val_loss: 0.9646 - val_acc: 0.7125\n", "Epoch 91/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.9318 - acc: 0.7194 - val_loss: 0.9530 - val_acc: 0.7205\n", "Epoch 92/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9232 - acc: 0.7207 - val_loss: 0.9476 - val_acc: 0.7163\n", "Epoch 93/200\n", "5574/5574 [==============================] - 1s 157us/step - loss: 0.9168 - acc: 0.7232 - val_loss: 0.9464 - val_acc: 0.7184\n", "Epoch 94/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9130 - acc: 0.7230 - val_loss: 0.9430 - val_acc: 0.7155\n", "Epoch 95/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9104 - acc: 0.7246 - val_loss: 0.9405 - val_acc: 0.7138\n", "Epoch 96/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.9066 - acc: 0.7286 - val_loss: 0.9406 - val_acc: 0.7151\n", "Epoch 97/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9026 - acc: 0.7259 - val_loss: 0.9370 - val_acc: 0.7134\n", "Epoch 98/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.9001 - acc: 0.7273 - val_loss: 0.9352 - val_acc: 0.7117\n", "Epoch 99/200\n", "5574/5574 [==============================] - 1s 162us/step - loss: 0.8978 - acc: 0.7277 - val_loss: 0.9276 - val_acc: 0.7146\n", "Epoch 100/200\n", "5574/5574 [==============================] - 1s 165us/step - loss: 0.8924 - acc: 0.7278 - val_loss: 0.9241 - val_acc: 0.7151\n", "Epoch 101/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 0.8905 - acc: 0.7271 - val_loss: 0.9262 - val_acc: 0.7159\n", "Epoch 102/200\n", "5574/5574 [==============================] - 1s 167us/step - loss: 0.8889 - acc: 0.7302 - val_loss: 0.9211 - val_acc: 0.7159\n", "Epoch 103/200\n", "5574/5574 [==============================] - 1s 164us/step - loss: 0.8851 - acc: 0.7282 - val_loss: 0.9173 - val_acc: 0.7201\n", "Epoch 104/200\n", "5574/5574 [==============================] - 1s 163us/step - loss: 0.8793 - acc: 0.7314 - val_loss: 0.9157 - val_acc: 0.7168\n", "Epoch 105/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8772 - acc: 0.7336 - val_loss: 0.9110 - val_acc: 0.7231\n", "Epoch 106/200\n", "5574/5574 [==============================] - 1s 162us/step - loss: 0.8759 - acc: 0.7336 - val_loss: 0.9070 - val_acc: 0.7210\n", "Epoch 107/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8711 - acc: 0.7350 - val_loss: 0.9075 - val_acc: 0.7247\n", "Epoch 108/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8698 - acc: 0.7347 - val_loss: 0.9076 - val_acc: 0.7235\n", "Epoch 109/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.8638 - acc: 0.7352 - val_loss: 0.9051 - val_acc: 0.7193\n", "Epoch 110/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.8620 - acc: 0.7345 - val_loss: 0.9022 - val_acc: 0.7222\n", "Epoch 111/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8602 - acc: 0.7366 - val_loss: 0.8932 - val_acc: 0.7273\n", "Epoch 112/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.8568 - acc: 0.7390 - val_loss: 0.8911 - val_acc: 0.7306\n", "Epoch 113/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 0.8591 - acc: 0.7399 - val_loss: 0.8899 - val_acc: 0.7269\n", "Epoch 114/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.8590 - acc: 0.7400 - val_loss: 0.8927 - val_acc: 0.7252\n", "Epoch 115/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8589 - acc: 0.7400 - val_loss: 0.8880 - val_acc: 0.7340\n", "Epoch 116/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8563 - acc: 0.7418 - val_loss: 0.8895 - val_acc: 0.7315\n", "Epoch 117/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.8523 - acc: 0.7399 - val_loss: 0.8890 - val_acc: 0.7285\n", "Epoch 118/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8505 - acc: 0.7422 - val_loss: 0.8869 - val_acc: 0.7285\n", "Epoch 119/200\n", "5574/5574 [==============================] - 1s 157us/step - loss: 0.8507 - acc: 0.7427 - val_loss: 0.8806 - val_acc: 0.7336\n", "Epoch 120/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8429 - acc: 0.7452 - val_loss: 0.8744 - val_acc: 0.7353\n", "Epoch 121/200\n", "5574/5574 [==============================] - 1s 157us/step - loss: 0.8390 - acc: 0.7460 - val_loss: 0.8733 - val_acc: 0.7336\n", "Epoch 122/200\n", "5574/5574 [==============================] - 1s 162us/step - loss: 0.8346 - acc: 0.7504 - val_loss: 0.8728 - val_acc: 0.7336\n", "Epoch 123/200\n", "5574/5574 [==============================] - 1s 166us/step - loss: 0.8349 - acc: 0.7481 - val_loss: 0.8689 - val_acc: 0.7344\n", "Epoch 124/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 0.8323 - acc: 0.7487 - val_loss: 0.8649 - val_acc: 0.7386\n", "Epoch 125/200\n", "5574/5574 [==============================] - 1s 163us/step - loss: 0.8275 - acc: 0.7519 - val_loss: 0.8664 - val_acc: 0.7370\n", "Epoch 126/200\n", "5574/5574 [==============================] - 1s 164us/step - loss: 0.8284 - acc: 0.7499 - val_loss: 0.8627 - val_acc: 0.7374\n", "Epoch 127/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.8226 - acc: 0.7476 - val_loss: 0.8652 - val_acc: 0.7273\n", "Epoch 128/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.8189 - acc: 0.7474 - val_loss: 0.8606 - val_acc: 0.7315\n", "Epoch 129/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.8145 - acc: 0.7504 - val_loss: 0.8591 - val_acc: 0.7332\n", "Epoch 130/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8124 - acc: 0.7519 - val_loss: 0.8511 - val_acc: 0.7332\n", "Epoch 131/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.8097 - acc: 0.7522 - val_loss: 0.8480 - val_acc: 0.7348\n", "Epoch 132/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.8069 - acc: 0.7522 - val_loss: 0.8472 - val_acc: 0.7374\n", "Epoch 133/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 0.8049 - acc: 0.7555 - val_loss: 0.8457 - val_acc: 0.7407\n", "Epoch 134/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8057 - acc: 0.7539 - val_loss: 0.8407 - val_acc: 0.7412\n", "Epoch 135/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.8005 - acc: 0.7557 - val_loss: 0.8451 - val_acc: 0.7353\n", "Epoch 136/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7990 - acc: 0.7578 - val_loss: 0.8446 - val_acc: 0.7332\n", "Epoch 137/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.7969 - acc: 0.7573 - val_loss: 0.8331 - val_acc: 0.7361\n", "Epoch 138/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7970 - acc: 0.7569 - val_loss: 0.8352 - val_acc: 0.7370\n", "Epoch 139/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.7923 - acc: 0.7594 - val_loss: 0.8332 - val_acc: 0.7407\n", "Epoch 140/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7924 - acc: 0.7571 - val_loss: 0.8336 - val_acc: 0.7344\n", "Epoch 141/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7870 - acc: 0.7605 - val_loss: 0.8298 - val_acc: 0.7378\n", "Epoch 142/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7864 - acc: 0.7569 - val_loss: 0.8311 - val_acc: 0.7391\n", "Epoch 143/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.7871 - acc: 0.7567 - val_loss: 0.8304 - val_acc: 0.7433\n", "Epoch 144/200\n", "5574/5574 [==============================] - 1s 163us/step - loss: 0.7867 - acc: 0.7583 - val_loss: 0.8294 - val_acc: 0.7433\n", "Epoch 145/200\n", "5574/5574 [==============================] - 1s 220us/step - loss: 0.7823 - acc: 0.7621 - val_loss: 0.8304 - val_acc: 0.7391\n", "Epoch 146/200\n", "5574/5574 [==============================] - 2s 422us/step - loss: 0.7806 - acc: 0.7634 - val_loss: 0.8230 - val_acc: 0.7479\n", "Epoch 147/200\n", "5574/5574 [==============================] - 1s 228us/step - loss: 0.7777 - acc: 0.7610 - val_loss: 0.8154 - val_acc: 0.7475\n", "Epoch 148/200\n", "5574/5574 [==============================] - 2s 292us/step - loss: 0.7751 - acc: 0.7623 - val_loss: 0.8084 - val_acc: 0.7504\n", "Epoch 149/200\n", "5574/5574 [==============================] - 1s 212us/step - loss: 0.7732 - acc: 0.7635 - val_loss: 0.8112 - val_acc: 0.7483\n", "Epoch 150/200\n", "5574/5574 [==============================] - 1s 197us/step - loss: 0.7760 - acc: 0.7628 - val_loss: 0.8133 - val_acc: 0.7420\n", "Epoch 151/200\n", "5574/5574 [==============================] - 1s 197us/step - loss: 0.7720 - acc: 0.7637 - val_loss: 0.8133 - val_acc: 0.7424\n", "Epoch 152/200\n", "5574/5574 [==============================] - 1s 165us/step - loss: 0.7686 - acc: 0.7630 - val_loss: 0.8135 - val_acc: 0.7424\n", "Epoch 153/200\n", "5574/5574 [==============================] - 1s 196us/step - loss: 0.7700 - acc: 0.7625 - val_loss: 0.8138 - val_acc: 0.7420\n", "Epoch 154/200\n", "5574/5574 [==============================] - 1s 249us/step - loss: 0.7710 - acc: 0.7594 - val_loss: 0.8134 - val_acc: 0.7424\n", "Epoch 155/200\n", "5574/5574 [==============================] - 1s 222us/step - loss: 0.7686 - acc: 0.7601 - val_loss: 0.8128 - val_acc: 0.7458\n", "Epoch 156/200\n", "5574/5574 [==============================] - 1s 203us/step - loss: 0.7654 - acc: 0.7635 - val_loss: 0.8083 - val_acc: 0.7462\n", "Epoch 157/200\n", "5574/5574 [==============================] - 1s 164us/step - loss: 0.7593 - acc: 0.7657 - val_loss: 0.8086 - val_acc: 0.7466\n", "Epoch 158/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.7608 - acc: 0.7644 - val_loss: 0.8074 - val_acc: 0.7449\n", "Epoch 159/200\n", "5574/5574 [==============================] - 1s 164us/step - loss: 0.7590 - acc: 0.7675 - val_loss: 0.8088 - val_acc: 0.7445\n", "Epoch 160/200\n", "5574/5574 [==============================] - 1s 163us/step - loss: 0.7598 - acc: 0.7655 - val_loss: 0.8081 - val_acc: 0.7433\n", "Epoch 161/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.7601 - acc: 0.7650 - val_loss: 0.8036 - val_acc: 0.7458\n", "Epoch 162/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 0.7596 - acc: 0.7646 - val_loss: 0.8073 - val_acc: 0.7416\n", "Epoch 163/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7577 - acc: 0.7666 - val_loss: 0.8060 - val_acc: 0.7391\n", "Epoch 164/200\n", "5574/5574 [==============================] - 1s 221us/step - loss: 0.7520 - acc: 0.7652 - val_loss: 0.7993 - val_acc: 0.7420\n", "Epoch 165/200\n", "5574/5574 [==============================] - 1s 182us/step - loss: 0.7521 - acc: 0.7679 - val_loss: 0.8015 - val_acc: 0.7504\n", "Epoch 166/200\n", "5574/5574 [==============================] - 1s 168us/step - loss: 0.7513 - acc: 0.7679 - val_loss: 0.8032 - val_acc: 0.7454\n", "Epoch 167/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 0.7503 - acc: 0.7670 - val_loss: 0.8009 - val_acc: 0.7462\n", "Epoch 168/200\n", "5574/5574 [==============================] - 1s 161us/step - loss: 0.7489 - acc: 0.7657 - val_loss: 0.8024 - val_acc: 0.7496\n", "Epoch 169/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.7502 - acc: 0.7677 - val_loss: 0.7998 - val_acc: 0.7479\n", "Epoch 170/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7480 - acc: 0.7679 - val_loss: 0.7956 - val_acc: 0.7487\n", "Epoch 171/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7449 - acc: 0.7686 - val_loss: 0.7971 - val_acc: 0.7487\n", "Epoch 172/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.7436 - acc: 0.7689 - val_loss: 0.7962 - val_acc: 0.7504\n", "Epoch 173/200\n", "5574/5574 [==============================] - 1s 192us/step - loss: 0.7453 - acc: 0.7702 - val_loss: 0.7953 - val_acc: 0.7487\n", "Epoch 174/200\n", "5574/5574 [==============================] - 1s 165us/step - loss: 0.7424 - acc: 0.7695 - val_loss: 0.7979 - val_acc: 0.7462\n", "Epoch 175/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7361 - acc: 0.7731 - val_loss: 0.7866 - val_acc: 0.7534\n", "Epoch 176/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.7341 - acc: 0.7748 - val_loss: 0.7885 - val_acc: 0.7513\n", "Epoch 177/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.7344 - acc: 0.7716 - val_loss: 0.7884 - val_acc: 0.7492\n", "Epoch 178/200\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "5574/5574 [==============================] - 1s 166us/step - loss: 0.7364 - acc: 0.7716 - val_loss: 0.7910 - val_acc: 0.7462\n", "Epoch 179/200\n", "5574/5574 [==============================] - 1s 164us/step - loss: 0.7344 - acc: 0.7740 - val_loss: 0.7891 - val_acc: 0.7517\n", "Epoch 180/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7330 - acc: 0.7756 - val_loss: 0.7933 - val_acc: 0.7471\n", "Epoch 181/200\n", "5574/5574 [==============================] - 1s 159us/step - loss: 0.7315 - acc: 0.7731 - val_loss: 0.7905 - val_acc: 0.7496\n", "Epoch 182/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.7359 - acc: 0.7711 - val_loss: 0.7861 - val_acc: 0.7492\n", "Epoch 183/200\n", "5574/5574 [==============================] - 1s 158us/step - loss: 0.7306 - acc: 0.7752 - val_loss: 0.7880 - val_acc: 0.7479\n", "Epoch 184/200\n", "5574/5574 [==============================] - 1s 160us/step - loss: 0.7303 - acc: 0.7752 - val_loss: 0.7850 - val_acc: 0.7521\n", "Epoch 185/200\n", "5574/5574 [==============================] - ETA: 0s - loss: 0.7274 - acc: 0.776 - 1s 165us/step - loss: 0.7258 - acc: 0.7775 - val_loss: 0.7846 - val_acc: 0.7551\n", "Epoch 186/200\n", "5574/5574 [==============================] - 1s 207us/step - loss: 0.7236 - acc: 0.7836 - val_loss: 0.7883 - val_acc: 0.7508\n", "Epoch 187/200\n", "5574/5574 [==============================] - 1s 207us/step - loss: 0.7234 - acc: 0.7802 - val_loss: 0.7811 - val_acc: 0.7546\n", "Epoch 188/200\n", "5574/5574 [==============================] - 1s 174us/step - loss: 0.7224 - acc: 0.7817 - val_loss: 0.7828 - val_acc: 0.7517\n", "Epoch 189/200\n", "5574/5574 [==============================] - 1s 169us/step - loss: 0.7207 - acc: 0.7813 - val_loss: 0.7825 - val_acc: 0.7555\n", "Epoch 190/200\n", "5574/5574 [==============================] - 1s 177us/step - loss: 0.7259 - acc: 0.7777 - val_loss: 0.7835 - val_acc: 0.7529\n", "Epoch 191/200\n", "5574/5574 [==============================] - 1s 190us/step - loss: 0.7209 - acc: 0.7815 - val_loss: 0.7743 - val_acc: 0.7601\n", "Epoch 192/200\n", "5574/5574 [==============================] - 1s 205us/step - loss: 0.7166 - acc: 0.7838 - val_loss: 0.7747 - val_acc: 0.7563\n", "Epoch 193/200\n", "5574/5574 [==============================] - 1s 207us/step - loss: 0.7154 - acc: 0.7817 - val_loss: 0.7769 - val_acc: 0.7525\n", "Epoch 194/200\n", "5574/5574 [==============================] - 1s 182us/step - loss: 0.7156 - acc: 0.7795 - val_loss: 0.7720 - val_acc: 0.7559\n", "Epoch 195/200\n", "5574/5574 [==============================] - 1s 197us/step - loss: 0.7118 - acc: 0.7804 - val_loss: 0.7641 - val_acc: 0.7597\n", "Epoch 196/200\n", "5574/5574 [==============================] - 1s 205us/step - loss: 0.7136 - acc: 0.7820 - val_loss: 0.7681 - val_acc: 0.7551\n", "Epoch 197/200\n", "5574/5574 [==============================] - 1s 194us/step - loss: 0.7143 - acc: 0.7804 - val_loss: 0.7648 - val_acc: 0.7588\n", "Epoch 198/200\n", "5574/5574 [==============================] - 1s 204us/step - loss: 0.7118 - acc: 0.7813 - val_loss: 0.7596 - val_acc: 0.7593\n", "Epoch 199/200\n", "5574/5574 [==============================] - 1s 205us/step - loss: 0.7091 - acc: 0.7844 - val_loss: 0.7595 - val_acc: 0.7580\n", "Epoch 200/200\n", "5574/5574 [==============================] - 1s 207us/step - loss: 0.7071 - acc: 0.7824 - val_loss: 0.7602 - val_acc: 0.7601\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(train, encoded_train, validation_data=(test, encoded_test), epochs=200, batch_size=32)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 3 sigmoid (+ input) and 1 output: 0.7441077441077442\n", "\n", "\n", "nn_pred = model.predict_classes(test)\n", "\n", "nnyhat = confusion_matrix(test_types, nn_pred)\n", "print(nnyhat)\n", "print(\"Accuracy: \", metrics.accuracy_score(r_test_types, nn_pred))\n", "# for i in range(len(r_test_labels)):\n", "# print(\"X=%s, Predicted=%s\" % (r_test_labels[i], ynew[i]))\n", "\n", "y_true = pd.Series(r_test_labels)\n", "y_pred = pd.Series(nn_pred)\n", "\n", "pd.crosstab(y_true, y_pred, rownames=['True'], colnames=['Predicted'], margins=True)" ] }, { "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": 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": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Cross-validation (in-progress)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "## Try cross validation \n", "\n", "# Load libraries\n", "import numpy as np\n", "from tensorflow.keras import models\n", "from tensorflow.keras import layers\n", "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.datasets import make_classification" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "# Create function returning a compiled network\n", "def create_network():\n", " model = tf.keras.Sequential()\n", " model.add(layers.Dense(128, activation='sigmoid'))\n", " model.add(layers.Dense(128, activation='sigmoid'))\n", " model.add(layers.Dense(17, activation='softmax'))\n", " model.compile(optimizer=tf.train.RMSPropOptimizer(5.1794746792312125e-05),\n", " loss='categorical_crossentropy',\n", " metrics=['accuracy'])\n", " # Return compiled network\n", " return model" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "# Wrap Keras model so it can be used by scikit-learn\n", "neural_network = KerasClassifier(build_fn=create_network, \n", " epochs=100, \n", " batch_size=32, \n", " verbose=0)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.65285253, 0.66469322, 0.66953714])" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Evaluate neural network using three-fold cross-validation\n", "cross_val_score(neural_network, train, encoded_train, cv=3)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }