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Keras_BayesOpt.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Keras & Bayesian Optimization"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"np.random.seed(71)\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import random"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The algorithm & data are same as \n",
"- http://tjo.hatenablog.com/entry/2016/07/21/190000 in R MXnet& rBayesianOptimization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://archive.ics.uci.edu/ml/datasets/Online+News+Popularity"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>url</th>\n",
" <th>timedelta</th>\n",
" <th>n_tokens_title</th>\n",
" <th>n_tokens_content</th>\n",
" <th>n_unique_tokens</th>\n",
" <th>n_non_stop_words</th>\n",
" <th>n_non_stop_unique_tokens</th>\n",
" <th>num_hrefs</th>\n",
" <th>num_self_hrefs</th>\n",
" <th>num_imgs</th>\n",
" <th>...</th>\n",
" <th>min_positive_polarity</th>\n",
" <th>max_positive_polarity</th>\n",
" <th>avg_negative_polarity</th>\n",
" <th>min_negative_polarity</th>\n",
" <th>max_negative_polarity</th>\n",
" <th>title_subjectivity</th>\n",
" <th>title_sentiment_polarity</th>\n",
" <th>abs_title_subjectivity</th>\n",
" <th>abs_title_sentiment_polarity</th>\n",
" <th>shares</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>http://mashable.com/2013/01/07/amazon-instant-...</td>\n",
" <td>731.0</td>\n",
" <td>12.0</td>\n",
" <td>219.0</td>\n",
" <td>0.663594</td>\n",
" <td>1.0</td>\n",
" <td>0.815385</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>0.100000</td>\n",
" <td>0.7</td>\n",
" <td>-0.350000</td>\n",
" <td>-0.600</td>\n",
" <td>-0.200000</td>\n",
" <td>0.500000</td>\n",
" <td>-0.187500</td>\n",
" <td>0.000000</td>\n",
" <td>0.187500</td>\n",
" <td>593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>http://mashable.com/2013/01/07/ap-samsung-spon...</td>\n",
" <td>731.0</td>\n",
" <td>9.0</td>\n",
" <td>255.0</td>\n",
" <td>0.604743</td>\n",
" <td>1.0</td>\n",
" <td>0.791946</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>0.033333</td>\n",
" <td>0.7</td>\n",
" <td>-0.118750</td>\n",
" <td>-0.125</td>\n",
" <td>-0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" <td>711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>http://mashable.com/2013/01/07/apple-40-billio...</td>\n",
" <td>731.0</td>\n",
" <td>9.0</td>\n",
" <td>211.0</td>\n",
" <td>0.575130</td>\n",
" <td>1.0</td>\n",
" <td>0.663866</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>0.100000</td>\n",
" <td>1.0</td>\n",
" <td>-0.466667</td>\n",
" <td>-0.800</td>\n",
" <td>-0.133333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" <td>1500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>http://mashable.com/2013/01/07/astronaut-notre...</td>\n",
" <td>731.0</td>\n",
" <td>9.0</td>\n",
" <td>531.0</td>\n",
" <td>0.503788</td>\n",
" <td>1.0</td>\n",
" <td>0.665635</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>0.136364</td>\n",
" <td>0.8</td>\n",
" <td>-0.369697</td>\n",
" <td>-0.600</td>\n",
" <td>-0.166667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" <td>1200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>http://mashable.com/2013/01/07/att-u-verse-apps/</td>\n",
" <td>731.0</td>\n",
" <td>13.0</td>\n",
" <td>1072.0</td>\n",
" <td>0.415646</td>\n",
" <td>1.0</td>\n",
" <td>0.540890</td>\n",
" <td>19.0</td>\n",
" <td>19.0</td>\n",
" <td>20.0</td>\n",
" <td>...</td>\n",
" <td>0.033333</td>\n",
" <td>1.0</td>\n",
" <td>-0.220192</td>\n",
" <td>-0.500</td>\n",
" <td>-0.050000</td>\n",
" <td>0.454545</td>\n",
" <td>0.136364</td>\n",
" <td>0.045455</td>\n",
" <td>0.136364</td>\n",
" <td>505</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 61 columns</p>\n",
"</div>"
],
"text/plain": [
" url timedelta \\\n",
"0 http://mashable.com/2013/01/07/amazon-instant-... 731.0 \n",
"1 http://mashable.com/2013/01/07/ap-samsung-spon... 731.0 \n",
"2 http://mashable.com/2013/01/07/apple-40-billio... 731.0 \n",
"3 http://mashable.com/2013/01/07/astronaut-notre... 731.0 \n",
"4 http://mashable.com/2013/01/07/att-u-verse-apps/ 731.0 \n",
"\n",
" n_tokens_title n_tokens_content n_unique_tokens n_non_stop_words \\\n",
"0 12.0 219.0 0.663594 1.0 \n",
"1 9.0 255.0 0.604743 1.0 \n",
"2 9.0 211.0 0.575130 1.0 \n",
"3 9.0 531.0 0.503788 1.0 \n",
"4 13.0 1072.0 0.415646 1.0 \n",
"\n",
" n_non_stop_unique_tokens num_hrefs num_self_hrefs num_imgs ... \\\n",
"0 0.815385 4.0 2.0 1.0 ... \n",
"1 0.791946 3.0 1.0 1.0 ... \n",
"2 0.663866 3.0 1.0 1.0 ... \n",
"3 0.665635 9.0 0.0 1.0 ... \n",
"4 0.540890 19.0 19.0 20.0 ... \n",
"\n",
" min_positive_polarity max_positive_polarity avg_negative_polarity \\\n",
"0 0.100000 0.7 -0.350000 \n",
"1 0.033333 0.7 -0.118750 \n",
"2 0.100000 1.0 -0.466667 \n",
"3 0.136364 0.8 -0.369697 \n",
"4 0.033333 1.0 -0.220192 \n",
"\n",
" min_negative_polarity max_negative_polarity title_subjectivity \\\n",
"0 -0.600 -0.200000 0.500000 \n",
"1 -0.125 -0.100000 0.000000 \n",
"2 -0.800 -0.133333 0.000000 \n",
"3 -0.600 -0.166667 0.000000 \n",
"4 -0.500 -0.050000 0.454545 \n",
"\n",
" title_sentiment_polarity abs_title_subjectivity \\\n",
"0 -0.187500 0.000000 \n",
"1 0.000000 0.500000 \n",
"2 0.000000 0.500000 \n",
"3 0.000000 0.500000 \n",
"4 0.136364 0.045455 \n",
"\n",
" abs_title_sentiment_polarity shares \n",
"0 0.187500 593 \n",
"1 0.000000 711 \n",
"2 0.000000 1500 \n",
"3 0.000000 1200 \n",
"4 0.136364 505 \n",
"\n",
"[5 rows x 61 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data=pd.read_csv(\"OnlineNewsPopularity/OnlineNewsPopularity.csv\")\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index([u' timedelta', u' n_tokens_title', u' n_tokens_content',\n",
" u' n_unique_tokens', u' n_non_stop_words', u' n_non_stop_unique_tokens',\n",
" u' num_hrefs', u' num_self_hrefs', u' num_imgs', u' num_videos',\n",
" u' average_token_length', u' num_keywords',\n",
" u' data_channel_is_lifestyle', u' data_channel_is_entertainment',\n",
" u' data_channel_is_bus', u' data_channel_is_socmed',\n",
" u' data_channel_is_tech', u' data_channel_is_world', u' kw_min_min',\n",
" u' kw_max_min', u' kw_avg_min', u' kw_min_max', u' kw_max_max',\n",
" u' kw_avg_max', u' kw_min_avg', u' kw_max_avg', u' kw_avg_avg',\n",
" u' self_reference_min_shares', u' self_reference_max_shares',\n",
" u' self_reference_avg_sharess', u' weekday_is_monday',\n",
" u' weekday_is_tuesday', u' weekday_is_wednesday',\n",
" u' weekday_is_thursday', u' weekday_is_friday', u' weekday_is_saturday',\n",
" u' weekday_is_sunday', u' is_weekend', u' LDA_00', u' LDA_01',\n",
" u' LDA_02', u' LDA_03', u' LDA_04', u' global_subjectivity',\n",
" u' global_sentiment_polarity', u' global_rate_positive_words',\n",
" u' global_rate_negative_words', u' rate_positive_words',\n",
" u' rate_negative_words', u' avg_positive_polarity',\n",
" u' min_positive_polarity', u' max_positive_polarity',\n",
" u' avg_negative_polarity', u' min_negative_polarity',\n",
" u' max_negative_polarity', u' title_subjectivity',\n",
" u' title_sentiment_polarity', u' abs_title_subjectivity',\n",
" u' abs_title_sentiment_polarity', u' shares'],\n",
" dtype='object')\n"
]
}
],
"source": [
"print data.columns"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>n_tokens_title</th>\n",
" <th>n_tokens_content</th>\n",
" <th>n_unique_tokens</th>\n",
" <th>n_non_stop_words</th>\n",
" <th>n_non_stop_unique_tokens</th>\n",
" <th>num_hrefs</th>\n",
" <th>num_self_hrefs</th>\n",
" <th>num_imgs</th>\n",
" <th>num_videos</th>\n",
" <th>average_token_length</th>\n",
" <th>...</th>\n",
" <th>min_positive_polarity</th>\n",
" <th>max_positive_polarity</th>\n",
" <th>avg_negative_polarity</th>\n",
" <th>min_negative_polarity</th>\n",
" <th>max_negative_polarity</th>\n",
" <th>title_subjectivity</th>\n",
" <th>title_sentiment_polarity</th>\n",
" <th>abs_title_subjectivity</th>\n",
" <th>abs_title_sentiment_polarity</th>\n",
" <th>shares</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>12.0</td>\n",
" <td>219.0</td>\n",
" <td>0.663594</td>\n",
" <td>1.0</td>\n",
" <td>0.815385</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>4.680365</td>\n",
" <td>...</td>\n",
" <td>0.100000</td>\n",
" <td>0.7</td>\n",
" <td>-0.350000</td>\n",
" <td>-0.600</td>\n",
" <td>-0.200000</td>\n",
" <td>0.500000</td>\n",
" <td>-0.187500</td>\n",
" <td>0.000000</td>\n",
" <td>0.187500</td>\n",
" <td>593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9.0</td>\n",
" <td>255.0</td>\n",
" <td>0.604743</td>\n",
" <td>1.0</td>\n",
" <td>0.791946</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>4.913725</td>\n",
" <td>...</td>\n",
" <td>0.033333</td>\n",
" <td>0.7</td>\n",
" <td>-0.118750</td>\n",
" <td>-0.125</td>\n",
" <td>-0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" <td>711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9.0</td>\n",
" <td>211.0</td>\n",
" <td>0.575130</td>\n",
" <td>1.0</td>\n",
" <td>0.663866</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>4.393365</td>\n",
" <td>...</td>\n",
" <td>0.100000</td>\n",
" <td>1.0</td>\n",
" <td>-0.466667</td>\n",
" <td>-0.800</td>\n",
" <td>-0.133333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" <td>1500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>9.0</td>\n",
" <td>531.0</td>\n",
" <td>0.503788</td>\n",
" <td>1.0</td>\n",
" <td>0.665635</td>\n",
" <td>9.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>4.404896</td>\n",
" <td>...</td>\n",
" <td>0.136364</td>\n",
" <td>0.8</td>\n",
" <td>-0.369697</td>\n",
" <td>-0.600</td>\n",
" <td>-0.166667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" <td>1200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>13.0</td>\n",
" <td>1072.0</td>\n",
" <td>0.415646</td>\n",
" <td>1.0</td>\n",
" <td>0.540890</td>\n",
" <td>19.0</td>\n",
" <td>19.0</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>4.682836</td>\n",
" <td>...</td>\n",
" <td>0.033333</td>\n",
" <td>1.0</td>\n",
" <td>-0.220192</td>\n",
" <td>-0.500</td>\n",
" <td>-0.050000</td>\n",
" <td>0.454545</td>\n",
" <td>0.136364</td>\n",
" <td>0.045455</td>\n",
" <td>0.136364</td>\n",
" <td>505</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 59 columns</p>\n",
"</div>"
],
"text/plain": [
" n_tokens_title n_tokens_content n_unique_tokens n_non_stop_words \\\n",
"0 12.0 219.0 0.663594 1.0 \n",
"1 9.0 255.0 0.604743 1.0 \n",
"2 9.0 211.0 0.575130 1.0 \n",
"3 9.0 531.0 0.503788 1.0 \n",
"4 13.0 1072.0 0.415646 1.0 \n",
"\n",
" n_non_stop_unique_tokens num_hrefs num_self_hrefs num_imgs \\\n",
"0 0.815385 4.0 2.0 1.0 \n",
"1 0.791946 3.0 1.0 1.0 \n",
"2 0.663866 3.0 1.0 1.0 \n",
"3 0.665635 9.0 0.0 1.0 \n",
"4 0.540890 19.0 19.0 20.0 \n",
"\n",
" num_videos average_token_length ... min_positive_polarity \\\n",
"0 0.0 4.680365 ... 0.100000 \n",
"1 0.0 4.913725 ... 0.033333 \n",
"2 0.0 4.393365 ... 0.100000 \n",
"3 0.0 4.404896 ... 0.136364 \n",
"4 0.0 4.682836 ... 0.033333 \n",
"\n",
" max_positive_polarity avg_negative_polarity min_negative_polarity \\\n",
"0 0.7 -0.350000 -0.600 \n",
"1 0.7 -0.118750 -0.125 \n",
"2 1.0 -0.466667 -0.800 \n",
"3 0.8 -0.369697 -0.600 \n",
"4 1.0 -0.220192 -0.500 \n",
"\n",
" max_negative_polarity title_subjectivity title_sentiment_polarity \\\n",
"0 -0.200000 0.500000 -0.187500 \n",
"1 -0.100000 0.000000 0.000000 \n",
"2 -0.133333 0.000000 0.000000 \n",
"3 -0.166667 0.000000 0.000000 \n",
"4 -0.050000 0.454545 0.136364 \n",
"\n",
" abs_title_subjectivity abs_title_sentiment_polarity shares \n",
"0 0.000000 0.187500 593 \n",
"1 0.500000 0.000000 711 \n",
"2 0.500000 0.000000 1500 \n",
"3 0.500000 0.000000 1200 \n",
"4 0.045455 0.136364 505 \n",
"\n",
"[5 rows x 59 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del data['url']\n",
"del data[' timedelta']\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#l=len(data)/10\n",
"l=5000\n",
"data.reindex(np.random.permutation(data.index))\n",
"data_test=data[:l]\n",
"data_train=data[l:]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x7f2c237e9050>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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LUcqXcwgr7sKxoAOb13lwsIUzbbnXU+8uslRyCPXHIe3E8uhnt9vpnnNtX/G4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MMft6KhzckGIVGCFZo02LSqBi2dLpQV6mER3RNRZqq0GmDrGRlCRKEQLmx4rsUBfY4q0ksokw2u\n1N/p8KDSYoSt240N189Gr8sLXY4SeQYNOu0ebLyxGt2OfnQ6hOt4ZNsq0ZINwh07duD+++/HvHnz\nAAA9PT34z//8T/z85z9PeOZSLdxd3dbtRrFZN2KBAKkhpVKrjFYUGwSLtlRYhU+zOu39+NOhi5Ht\nlYurBK97+oOCIaN3rhL2MHb0DA/tlYm2AaAwLwd7/3Qh6nzhePuWTpfgiXmeQQ1ELYQqDgoqnkM4\n2SG3kz2fiGisIvGw2p147cB53HbDHAwEggBk6OnrR1GeFn99rxlX1VoFw5Y2raxBr3vknMLo5dAr\ni7naKFGiddi9eCFqDuGdq2pwWdUoJ1BGqK3Kg1wOdPb2o9xqQEe3ByWFevgG/PjgpC0yRLTSaoRc\nhshw0LCldWX4/dtnsGrJJfD5g3j9cAOW1pXh1ffOC44Rdzdk2yrRkg3CxsZGPP3003jyySdx6NAh\nfPOb38SXv/zlZOQt5cLd1cvmV6Kjo2/E61JDQqVWGR3wBwWFdkZpruB1c+7oQzabO52jbheatdj7\n2oXI9p2rhQ3Grt7+UbdNRrWgwWoyCHs420VPbMTbp0VhK0432qd0yC0R0WSIR3lcVpWH0412uPr9\naOt2IxAMjbix8IhGQpxpdowYPRF9jFqlQCAIBBHkPEKiBBKPsmrpZGD6dCM1si4WGWSoqcjH8Ys9\ngk6C5VeVY+XiGZGOkaMnbLj1c7OhEy2w6PH64er3Y8AfRMvQfbK4Hlcp5TAbNbj9xmqcbXKgzGrI\nulBqkg3CLVu24Mc//jHWr1+PgYEB7NixA3PmzJE6LStIDSmNxPnrcKG8aGScP/EiMeJttUou6GFU\nq4Sls0w0x7BMNMfQIYpD6BDFITSbhGElCkVzBhUyueBmSDzH0WQQDYnVC7f1WtWo21IY+J6IEinW\nKITqijzsz1FCJpPB4x0c1REegqRSylFelIcT57oicwz1OUoYdcKHZdqoeYe+gQCef/U4FPK5uLrG\nMu6bISIaG/FCecUF2jhHUqqMdeRXrIajuJPA6RmA0zMgWEhGJpMhT3RvWlpowMb6PHT39kemA4jr\n7GAwhJf3n8HSujKUWQzYd/ACblpySVaFUovbIDx06FDk//Pnz0dDQwMGBgbQ1dWFQ4cOYfHixUnJ\nYDqLDDGyOVEx9HQ52gefduJcS+9gkMyBAHLUCiystkReLy4QDhEVb9u6h8czy0Tbg4RzDCETzjE0\n6FT4w7vnItviwPdymUzUQyls8LV1uUfddvT5BO/vcApXdNKoFIIGrUY1vsctUkN2iYgmI9YohPoF\nZdjwudk439IbWVBGPARp3fJZaLD1QatRoqxID6VCjttvrEZrlwslhXqEgiHcuKgSPn8QR463Dabd\n7kSuTs1h8EQJolUrBPckOo1C+iRKqrGO/IrVcBR3Emg1SsggrJ+PnrBhxVB8QqVCDv9QgPpFl5fg\n8MetAML3wiFhh8tQsHqP14+WThdc/f6s64SI2yDcsWNH3P0ymWzSDcJTp07hoYcewl133YXbb78d\nbW1tePTRRxEKhVBUVIStW7dCpVJhz5492LVrFxQKBTZs2ID169fD7/fjscceQ0tLCxQKBbZs2YLy\n8vJJ5WciwkNK4/1B7+r1Cm4iLKJVP7scHkGB7BJNaK2wGtDnGogEWTWJhiX5fAFoVAoo5DJo1Ar4\nfMIgrD293lG3m9qdo25bC7SCHkTxKqUmowa79g6vpHfnauEcxOL8HJxvHV4Zypo/zqd1UWvg8Bk6\nEU018Q1GVbEBh0914EJrH9RKOeRyGdYumzUiZE93Xz/y9Gr4gyE0tbsQAmDJywEgQ5PNifdPDi5I\nc8u1M7Ho8hIAgN8fxMmLPYJ0OAyeaOo0dbjg9EQtlOf1A7XS51HyjHXkV6PNKej5s9k9KC/U4os3\nzEGv04c8owat3S5Y87SwizojnO4BaHOUUCpkyNXnYMkVJQhhuOF4utEOo06Nd481R97DHwzh+vkV\nkMsGR98tubw46zoh4jYIX3jhhYS9qcfjwfe//31cc801kX0/+tGPsGnTJtTX12P79u3YvXs31qxZ\ngx07dmD37t1QKpVYv3496uvrsX//fphMJjz77LM4cOAAtm3bhu3btycsv/FIjYUOBAOCp1WBoLDB\nVmDKwWsHLkS2xQ0q70AATR1OeLx++ANBaMqEcwy1WjXONjkir88sFw5JFc/5E29XWo2ibeGFqZAL\nh4zOKr9M8Lp9xJBU4UU5pyIP/iAiPXzVFeO7uLioDBElUvQoj6piA+wunyB27NK6Mnh8HlhED7PM\nxhx02D2C+nHT6hoU5ObA4fJi7bJZaOty4TdvDi+6teH62dBoFJGAyACHwRNNpaJ8LfYevBDZ/tJN\nbA2mG6mRdYFgCMcv9sAfDGDN0pmRMBBHT9gii3WtWz4rsh8A7lgpvHcusxgi8wr/f/buPDquo8wb\n/7f3fVUvklqbLcmyrDhGyEscJ3JsvGbBZBwbkowDhMmcGd7D+b0kwwBJIAwDBGbInAOT4bwwLAOZ\nJJ6EwBADsQMkcYLtOLZxnMSWN9mWZC0t9b7v/fuj3VddJVltrS1Zz+ecnLi6+t6u7r66fZ9bVU9p\nlFJsWV2HYCQBu1mNz3x0CeKJNFy+GNa2OiAWi/D6scvCtvdvbkImm5mX15pF5xB2dnbin/7pn/DB\nBx9AJBLhQx/6EJ544gnU1NRM+EUVCgV++MMf4kc/+pHw2DvvvIOvf/3rAIB169bhpz/9Kerq6nDj\njTdCo8kNpfzwhz+MY8eO4dChQ/jYxz4GALj55pvx6KOPTrgtk1EsYNGq+B409uQkFYuYHkKpmO0H\nC0dSbA+jie1hDEeTTL2dW1jeoJXjvk1NuR5GsxoGDdvDKOXXIeRm0PKLuvKLvuo1Mrz81vCQ1L/e\nwg5JLZaUpxhKKkMImU6FozxOdnnx7jkXUy+TipHN5u44F56rBzxhJJLsEP1AKInfvNkplPms0JcG\nAjhyyomHtrXAH0yMejFECJm4QU9kzDIpvWIj6945OYCnnj9+5XzLXnPmE8H0cqPZXL7caLtEIo0F\nlQZmtF1bs53Jxr99XQNT3nwTe54e8kVRXqZCJptBR7d/Xs33LhoQ/vM//zMefPBBrFy5EtlsFgcP\nHsQTTzyBn/3sZxN+UbFYDLmc7a2KRqOQyXIBS1lZGQYHB+F2u2E2m4XnmM1mDA0NweVyCY+LRCKI\nxWKkUilIpUXfzpQqFrDwARVf9gTjsBpVQkDm4ZK+hKJsj1uQKwdCY5eD4SRzF4WfQ+gLJZg/jB0f\naWTq+Um3OhVbdvvjXJnNUjrZdQQpqQwhZKb0OEMjFqHXquTwBWNQauTY+/bwEkDtrQ6oFewNNH5Y\nqVLO7iufaGbAHYFKLr3OLy0ImXla7pqFL5PZr6vfDwDCyLNC+XOoQs7ODVXIpcL5ma/js4ny16kq\n7jViiTSyWRF+9dYlGHUKDPkjcHojEItx3SeYKRpBZbNZ3HbbbUJ548aN0zqcNP+a43k8k8mM+jjP\natUVf9I4tm2sYQ+OhhoT87xKLuNVRZmaqderFfjFKwU9iFubmfoKC5tkptKiYeqt3DAmq0nJ1PNJ\naJyeKFMf4i5ggpEEU19mUIzoYWTazw1B1WvkTP2h9/uZHtRHP7USq5dWCOV0Jot3Tg6gq9+PugoD\nVraUQ1zQS3prmRZyhQxd/X7UVhiwiqu/VpP53ktlutqs1SqLPwm5VPlT/fcyVab7+5zr+y+FqX5P\npdhfY40Jvzt4UegJbGuyIZXJ4MCJy9h2Wz22r2uAP5SAzaTC5cEgZDIJ7t/chLM9PtTYdfDyN/Qi\nCXx8QyO8wThUChki0QQ0SikUcil+d/AiwrEUc06c7Z/hdO1zps3Hz3m+tFGjkjI9+Rrl5H7HSm2+\nfG+F6ipyU5/UCimOdjiF7/PDTTZIxCKolVLU2LSotGjgDsSgUcqg08iEofhHO5y465YFuPu2BkRj\nSZgNShw55RT2X8Zl0w9dWS8837vYPRhAMJKEWilF72AIFWVq7P7jOVRatOh1RUa9Vp0Ks+E4LRoQ\nJpNJnDx5Ei0tuUXH33vvPaTT6SJbjZ9Go0EikYBcLofT6YTdbofNZsPQ0JDwHKfTidbWVthsNrhc\nLjQ1NSGVykX/19I7OJFhi0Duixpt24XlGmYsdH25hnmeVi3Dri2L0ecOo9KigUEjY+r73dyyE+4w\nU5/NZJiTWzaTYeqlEjABm1QiYuodVi6gtLLtM3HLTpTpFUx9NJ5mehg/dUczU69SsFlE1UoJU3+x\n18fs/1KvDw0FCzTza8qMNkewoVyL1UsrMDQUhNvN9shei6t9d+PZvhQm0+axhEIxAMWDwkQyPeV/\nL1NhOvd9vey/FKbyPU31Z3St+1tYrsHf3b2UmdtyuseHDStqkExkmNEU7a0O/PFID7a1L8TiWhPc\n/hjqq3VwWBejxxlCmUGJ/X/pwZbVC/Dq4W5hu11bF+OVgxeF5Abnu71oKNeW7D2Xcp90rM7MPudT\nG6US9iJdJhVPWTtLcbzOl++t0MqWcjxybyv6XWEs2twkDK+XiIF/eTZ3vahRSrF9XQNzbr1vcxPc\nvijsZRoMeSOQy6RIpzPQqCT4+MZF8PhjsJvV8AZjeOD2xXD7YzBqFfjf/Z0Ix1Job3Uwc77v3diE\nox1ObLm5DgDg9Ebw2tEeAFOfz2K2nFuLRlFf+tKX8Mgjj8Dj8Vx5ISu+853vTOjFxrJ69Wrs27cP\nd911F/bt24dbb70VN954Ix5//HGEQiGIRCIcP34cjz32GILBIPbu3Ys1a9bgtddew6pVq6a8Pdek\nSBbMYDiJZ/aeFsq7trITX0eumcOW+z1s0gK1kh3rLJNJEAjHkc5kEU+moR+xDqCECUj1KrYrXS4Z\ne51BfpmJfq6cTLI3Bvg5NTq1jNn/325rYeppjiAhZLYYbW5LZ68fCplsRHbQ9JWbdfFEGt3BIE5e\ncGGTrg7P7TsjZK1bvqQcyVSGSSJzusuLJQstwjAmGgZPyNTxBmLsNJxArPhGZFYRi0eeh7PZLF57\ntw8rltihVkghk4pHnJPP9fhw5JQT29c14JVDXdAopVi9tAKhSArIZiGRiOB0h5HKZDHo9aKp1gSl\nXIzt6xtxtss7Ynjq+V4f2prtCIYTQhvyrtdr1aIBoUajwd69exEMBiESiaDVavHuu+9O6kVPnDiB\nxx9/HB6PBxKJBLt378ZPfvITfOlLX8L//M//oLKyEnfffTckEgkeeeQRPPjggxCLxfjc5z4HrVaL\n22+/HQcOHMB9990HhUKBb3/725Nqz0QVSyrjDyWYHrKRc/wSTA8bP4STX1zToGGHaIYjKeauNT9H\nMBRNMQEpn8XUyU245ssjXp8bIhpLpJmTb4xb9oIPIPkyzREkhMwm2WwWp3t86HNHEAgnYNIr0Nnr\nH3GxUFeux7P7CrPcNcHtj6G91QGTTok/vtOFtmY73P4Ytqyuw95DlxCO5c7z0XgKCyv1+FCjFc21\nBhBCpoZeo0Bnb0BYdqKey8xO5pZ8HoqOLi/C0SROXXAjHEthW3s9kim2A6LGroNcJoZWLcWO9Y0I\nRBKwGFVweSOIJtKIxlOoseuEa+Z81lKHRYMFlQaIxGCu12VSMfzhBMrL1MLIjrzr9Vr1qgFhIBCA\nz+fDo48+iu9+97vC44ODg/jiF7+Iffv2TfhFly1bhj179ox4/Kc//emIxzZt2oRNmzYxj4nFYjz5\n5JMTfv2pUqyHy6CVM1k4+R7CMpMKYrFYCKjK9GwAZizMEjrKOoSjzREs1OeKjFk26dkhoybu9bXc\neHytin19tVKGZ165eg8o32Op5wLaYumHCSFkJp3q9uHI6cERi9DvPXQJ29c1wBeKI5nKoPOyn1kj\nK5nKwmZW4WJfEP5QfMRCydva6+ENxnCsw4mtNy9AJgs8t+809Oql1+WdZkJKIRxLjZl5ncwtfKdL\nftmJVDoNmVSMbe31SGcyMGjkGPBEUFuuRzyexqWBANQKKf73jfPYevMC7Dt8DodmLB4AACAASURB\nVBqldESixGg8BXcghgMn+nD7zQuYY2ddWxVq7Dr87s+5+d757NANNSbUl7PTsa4XVw0Ijx8/jp//\n/Ofo6OjAJz/5SeFxsViMW265ZUYaN9sZdFwPmo492Ir1wEVjXA8ft2xDMDJ2ltDqci0T0PE9bhVl\nfFIbNgmNVCxi/gAe4AI6d5HhF8V6AKssKiagdHBDZIulHyaEkJnU4wyNyErXOxgSevvEYhGOdThx\n+80LUGXX4ZevnQOQC/p2rG/Em8d7sfbKOa9QPJk7R7c12/HKlYQy7a0OfHDBAxFyCbQIIZMTCCfG\nLJO5he90yZ9XlQqpsIb3urYq/PbPw71369qqhCQy7a0OBMO5ZF9tzXak02yvYl25Hgq5GKtuqICb\nu76VScUIhIYThfmDCWxZWT3teQBK6aoB4dq1a7F27Vo8//zzuPfee0d9zp///Od5HRyGI+yQz3Ak\nydSXl7F3EcrNbJlf2N3HZalzeqNjluOJNBPQOWzsRYVcKmbaJ5OycwiHfLExyzq1fMweQH7Oo53L\netpYZYAnlEDPYAjVNi0WVdPwKELI7FVj18LpZW9s3dBQht7BMMx6JcKxJNqa7XjxtXNYscTOPM9/\n5cLjaIcTW1bXMZntTFcWsi+ca56/uPnu88cRiqWwosly3a9zRch04qe58GUyt/CdHHXleiyuNWGo\n4FpYJGLPmYVlqUQMqzl3nRqNp3Dqglu4Jq60aOENxnDo/X60Ndth1rPJ9vQaBXzBGFbdUIF0OgOV\nUoLXT/RhYYUeNTbNdXmuLjqH8GrBIAD86Ec/mtcBYaVFg+f+cFYoP3JvK1OfSSexa+ti9LlySV0y\nGTZgLDOqhLscwMiF62vK2T+GWu6Pw8vd0eDLPc4QEzAq5RKgIK+LkevRNGrH18OpkImYLKcqbl2u\nc5f98ATiiMXT8ATiON/rxyIH9QYSQman5lojxGKgyqZFMJyAvUyNaDyFVw5dgkYpxV+taxB6Hfh5\nhfkbZOFYKjfEdH0DfME4Ki0a/Or180JimV1bF8MTiKFMr8Tvr8xLOdvjQzqTwU3NtuvyQoOQmaDm\nMp+rFJLiG5GSyc8R7HGGYNApEI4ksMBhxMLyXMCVn1Z0tscHg1YOrUqGYCTJjMDgpyIVDgtNpTPo\n7g+gvdUBsz63/ET+mvjejUYkkilheL9GmZsipVZKoVPJoVJIoJKr8Ks3LiAcS8FsaMCv3ziPHesb\nEYomr8uRbZNayf1q6wLOF+KCSagqhRQSMV8vG7HOYKFBbojlIBdwiUW5+Sv5IZtibv/lFg3Tw8iv\nW1hdMfaQUpWSnSOoVrGHA7+/Cq7HM5MFM6T1wTvZ99fvjTJDYh+4fTETEGYyGRw+M4TugRBqynVY\n1WyBGNybJISQGSKCCIurTWiqMuLt04N495wLDqtWyBQ64AoL58GjHU5sWFENo06JUCSBQU8UD2xd\njN4rNwAH3RFU27UIRpJYsrBMWFfrdJcXqitDnvJzYgwaOd4954JeLb8uLzQImQnpbBq15Tr0ucK5\nZbeyU79EGpk6hXMENUoptqyuw6vvdKF1kRVGjQyX+kOosWvxsVvqcKrLh//36/exYWUtUpmMMKdb\nJWdHwpn1cmxftxBqpVw4Dn5/4CKi8TTWtVVBJhVDpZDB5YugyqZF71BY2F6E3Hrcew/lFrnfvq5B\nCBjzQ0/94Th6nLguz9OTCgj5rtr5pmdweB1BEYDLg2Esrh4+SHpd7PhnvmwoksXTH0wyAdWO9Y1M\nfSzOTqCu5NYdTCYzTH1tBbs2yVBBEhoRgEEuKU0qlWb+0FLc+pMDLvb5/W627OWGxPILNx8+M4T/\n/M3JgkdasLqZHYZFSiOTyaCz81zR59XVLYREQndhydxUeIe6xq5Fc60RIohwqtsnnJuOwCkEbgff\n78fm1TW4f3MThnxRlOlVI9aushnVwlD7/HZ57a0OWI0qDHhyvx0yqRjb1zWgTC9HPJG6btOZEzIT\nshkJnhnjJjyZOVc7txYqnCPY1mwfkQE0f+78wn2tGPBEsGFlLX7zZqewzbb2emaqU5VVixf+eA7b\n2utHTHcKhJOQy8R48U/nmO11GjleLbjOvm9zE8rNGuz/Sw/c/hhkUjFWLLGj0qrBmmUVsBrVUMhF\nONPjxaLqke9pLptUQDjfabl19h7i1tlzcD1slVxZo+IXdme/jnCMHWIajrJlXzDBldmAq28oMmbZ\npFfAXzDp2sxlGfUGE8z7u311HVOv5bKe8llIjVoF9rw1PNmXn4PYPRAaUZ6qgDB/Mho43osKs3rU\nkxG5upDfjf/vX1+G2mC76nMi/kF87wsfRX1941WfQ8hsxmexu39zEyrM6hHJDJRyCVYssUOrkkEl\nl+Fsjw8ARtzkutjnh6bgvMgnl1HKJdh76BK2tdfjXeUQys1q9LsjMOnksBhV8IcTONThhD+YuOpF\nFCFkdMVuwpOZU2xZtmw2yyRm5M+VheU+dwTP7jszYt52PJlCKJLE2ycHAAAapQzhWGrE9KY+Vxip\nVAZxbmm0YCSBCHddfeGyH2+fHMB9m5rg8kehU8vx9vv9uTUO1zeg3x2GxaBEKh2FSIzrahoUBYST\n4OcCMr6sVLBz7JRy9ofdxSVxcfvZssXITnIt48o6PiDjUuqOSPpiZpO+8ElpysvYC/syA//63DIS\nahkT0Oo0/LIYY89BrCnXcWV2SOtkgrpiJyNSnNpgg9bkKHUzCJk2fOB3tseHZ/edwUPbbmAeN+oU\nwsXEgCcyYv5gHp/Yi3+eSpG7YDl32Ydta+sRjCRg0ikgEmURiSWgvzJ0VK2Q4ncHL+L+zU1YRfMK\nCbkmDuvYN+HJzCm2LNupbh+e23ca7a0OJBJpNNWamERcNXadsO5gPgMofz4VA1hUY4JGLYPNpIZE\nLIJGKR0xWs6oVeCl189j+7oGoGBQWjabhULOjnCSXyn3ucJ44y+XAeQyl75+7DKC4QQkIuB3By6i\nrdkOQIR6RwbirKhob+hcQHMIx1AsICm27EQqBVweyqUxT6YzaOAWSTXpFXi5oAftk3ewPWiFWUhV\nCil8IfZutFbNrxPIfp0alZiZg6hVswd+sRTNUimYgFYuZQ9wmVSEKqtWWCdRwdWXm/mAlC2X6WVM\n+8r07Oc3maCu2MmIEEIK51VrlFLU2HM3qWKJFP7x/lZ0dPngDyeEtag231SLZCqDox1OrGurQplR\nCZu5EcFIAjajCp19fpy55BHOa7XlWsik1fCHE7lM1NHcOValkOJcjw+N1Ub8Zn8nbl+zACplLqtz\nfn3DWz/kQCCSxJkeHzMVgRAyukw6hV1bFqPPHUZlmQaZTKr4RmRa8Dkrasu1ONnlFYKmfleYWTdy\nYaUeD21rwbvnXFAppNh76BLuvGUB+t0RJJK55SKOdjhxz/oGJFNZBCMJGPVKvPT6OSFh16dub8Jf\nrWvAG0e7c9eu3giMWgX2/6UHADDgyc0XlEnFqLSo0e+KIJFKY/u6BoRjSURiKRzryAWlhR0iYrEI\na1sdyGSySKQywvqzlwdDOPA+UKZXXhcdEEUDwng8jrfeegt+v58JAO+55x78+Mc/ntbGlVqxgKTY\nshOF6/KJMHKdvvz8kXxAJJeyCVVUChkTMH58A9uDF40lhwMysxqxOPv6mSygkEkgEYtyd0G4+L3c\nomaT0nA9iun0cECbSmdQx81B9EeS6B0KC/WwsXdlqmyq4ZOzRYNaO9tDeaE3xMyR3Lm+kel+n0xQ\nx5+Mqu20zhchhFWYxU4ukzBzWB7a1gKdSoY9BWtcqRVSvPpuF9qa7ZBIJPj574bnqXz6rmbUlutx\n4ES/sJ97Ny2C1aiCTi3PnWdVMmxYUY1D7/fj9psXoGsggI+trYc7EINUosC9Gxchlc7CHYghkcpg\nz1sX8LG19ejo8qGp2jhn7zwTMhPUagXcvjjS6Swi8RQsRnnxjci0yJ9be5whVNu1yGTBXE9/6o5m\nIVkXkEti2OMMMb2EgXACcqkYRp0COz7SCLc/BrlMgl++Njxve1t7PSLRBFKZLPrcUaTSWfQMRfDc\nq2ewttXBXGNaDGp4gzE4LBoM+qLCkhND3ggWOAxweiK4fc0CaFVS7HnrgrBdJpOFWCyCSimFTS2H\n2xeF3ayGKJvF5aEwYok0817magdE0YDwb/7mbyASieBwsEPH7rnnHigU1/caL32uMBMw9bvCzJdc\nZdXgsiuXHEAEoIoLiKxGFV4ZYx2/cCSFIV8U0XgK2Wx2RECoUbE9gBolOyRTqZChszcgBGT1XA9k\nPJEZM6BLcUln+CAqGmeHlNrMbECazYCp/8TGRUx9vzuGZ/YOv/8H72rGwvLh+mJB21j1xSYsL64x\n4KFtLcIaiM21tAYiIYQlgggttSYsqTXif/98ial795wLH2q0CGWNMjfP+5YPOWDUKhAcMcIiib0H\nLwnn7EXVRshlYiSSGfx6/3AihJ0facTqpRWQy8RQyqXIZIGjpwawZKEFcqkYiVRG+M1ZvbQCQ74o\nLl72Qi6T4ESnG1U2LdKpNGwmmhtNSKEMd9M7m6W/jVLJn1vz18x73+lh6k9e9OC+zU3wBxOotmux\npNYIESCMkIjGU7CaVHB5oxj0RpFIpOGwaeELcLkyXCGoFFLhWnRt63CscrTDifs2NWHQF0UimcaB\nE5exZKEFPc4QHDYtVraU4/Vjl9He6sDPfzecjGjH+kYsbbAikUqj1q6H0xtBKpVBOpXB5cHcNXUs\nkUa9Qw+LSYkhX240XzYLvPVu75ztgCgaECaTSezevXsm2jLrFEsa44+ySVea64xMfSiaYALK/HCh\nvEw2y2zP9wCGInFmSGYoyv4hxLiAjR8vn0xlmHKCK7v88THLoUhizHIklmLeXyTGDs/od42d1CYf\ntOWXneCDtvwdpgFPBOVmNZbUDn++xXpvO7r9TAZTvXpuduETQqafCCI0VRuxp+AxlUIKfzCBv93W\ngvO9AVRaNPjvghtc96xvYPYRjiaZIVAmnQLeYBxmnYK5yBGJRFDKJei5cmGRSmVw160Lcak/CKNO\niV++dq7gNRrhDcawelkVfn/gApYstOCDC2401Zgw6I/izAEfrAYVVt9gg4SW7CHzXDKZZm6y69SU\nJmO2GLHs2ZXz6+YVVTjV7cO+dy7DbFBgy+o6ZqTG/Zub8PtDV3oET47sWFEppEwCmqMduW3O9vig\nUkjxmzc7sX1dA156/Ty2rK5DtzMInVqOAXdYGKXH39zzBGOQiEWQiMXM0mr3bixoCwCzQQUDcvkx\n8tfBH1u7EFkAWWTn3M26on8tDQ0N8Hq9MJnm38V0KJJkAzpuSGj3QJgpdw2EsbJpuKxVKfCrN4a7\nnfkUyKl0mhkyyi/roFbJcaGgB3Ah1wPoCyXGLGcyYwec/DIXfJlfd5BPUmPQyvFyQbf6rq1NTL3Z\nwPYgm7gspsWCtvwdptuW12BoKMhsW2w4Kc0hJISMR3OtkZnDcqzDib+7eykA4LWjPbjtw1XM86Px\nFO7f3AR3IIZILDViBIdGJUc8kYZeqxDWsgKGL3J+e+CS8Fy7WY1oLIWAlL0pFwjHkUpl8NJr50Zc\nKLW3OiARi+APJdDnDsOoVaDWpsGiavbGJCHzBT+qyV5GGbBni6udXwtv7vNZRAHAxSVbPNftw/2b\nm9DvjiCVzuBYhxPLC7LTh2MpuP0xZujpgDvCnD8Bdkmg7evYm3smnRIapRSXh9jryCE/u7RaLJ6C\nVCJijrmdH2lE31AI8UQKH260zKmgsGhAODAwgE2bNqG+vp5Zb+zZZ5+d1obNBgq5hPmiP3UHG9AV\nC6jya01drazTKISAL5vNjgj4orEUlwWUDcj0Gvb1+CyffJIYP1c26uTDSWPK1DBwWUtjyRQzZDWe\nZANWPkuqy8dezJj0CuzaulhYHNTItW865wjSHEJCyHiIIMJNzTbo1XL0OEP4u7uXornGgNff7ceK\nJfYRGUSlEgl6h8JIptMoN2sgFoE5XwbDcRztcOITGxfBH2ITejkL1nzVKHPDRtPZLAxahTAXRaOU\nQqeRwxsMYsvquhF3sWUSMcotGnj9Uew/3ovwlREbqQxgs7K/JYTMB6kUd5M9RQvTzxbFzq86tRw2\no3JErg0jt163XC6B2x9DPJlCuVmDpfUWLHTosaBSj96hMGwm1Yg10u1lapzr9jGPFfYqDnkj2L6u\nAf5QAgatHG8c64bLHx/RG2m8co2fH/GRyWSFYa75+YPuQAzJVAZSqRh//sCJcCQ5ZzKPFg0I//Zv\n/3Ym2jErDXAHJl9WKdh1BFUK9kd/5LIPbLlYwFcsC6hYzF6ASMTswWbSs8tGmLlyOJpiusP5g98T\niDPt27K6lqkvtg5hIJQcsThoIYtJyfTA2szsH34mk8HhM0Po2d+JapsOq5otEF8ZFsVPWC4cTlpY\nP9pwU0IIGQ0/7+VklxfP7sudI09dcOO+TU3oc4VhNargC+YuOl2+KPYeuoS1rQ44LFq4/FGUl6nh\n8cew5eY6uAMxWIxsQq3Cc31bs10YJnrklBP3bmyCJxBFmUElnJ+PnHLi4xsacd+mJlzsD0AuFUMq\nEaHHGYRaKcWdtyzEb/98AYlEGud6fFAoZDjf7Z0zFyKETAWtSoFf0ML0s9ZY51cgt7yDTCrGtvZ6\nRONJJFMZqJQSbF/XgG5nUOhZbGu248CJfgC53r0X/pjLNLp9XQPiiSQ0agU231QLo1YBtUKMcDSJ\nxhqjsF4hkBtqmmc1qUftPbw8GMT2dQ3w+GOosmsx6Alj+7oGZDJZZl74vRtzGU3lUjFsZhV6BkNI\nJrP49RtnhUBx50caYdAqsHKxBae7/Uz+i9miaEC4cuXKmWjHrFRt13Fl9g5x71CYCZiU3Hom4WiS\nuVsV4RaaD4THLuu5OyN8j6BWLYPVqBL2r+MCMqVMzASMShk7x2TIFx2zbOBe38D3SPLLXnDj9ftc\n4THL0TgbEPM9pO+cGWKGlIrQgpvyQwMKJo+Pdqkz1nBTQgi5FoWjGPLrBx455cTaVgeOduT+X2PX\nwWZSAyLg+T8MX9y0tzrwu4OXsOMjjXj17Uu5uSqRBGKJNPYeuojt6xpGLGwPAE5vBGa9Eucus3e0\nh3wxvHZ0ODHDA1ub0dnnQzSWwlvHe3NLVygkEImAAyd6cbTDiXAsNWdToBMyXv3u8JhlMrvwo8RC\n0aQw1HPFEvuVbM9LIJeKUVehRyCUEG5+5XU7g8O9c/4YKq0aJkHMfZua8MJrnVizrEK4XtWqZCgz\nKHFTSzkcNi2CXH6MfO+hWa9iAsUNK6ox5ItCxiWAPN/rE4bx/+L3bCdIKpWGyxeDPxyH0xNBOpPB\nT/ecEp7zyL2ts2ZEx5ydcfvkk0/ixIkTEIlEePTRR7F06dIpf42Viy1IpppxeSiMKqsGK5utTP3I\nOXZsWaWQ4r/3Dl8g/PUWdo4dH+DpNXxAx/ZA8gFnNJZiDlZ+/92DbMAq5wLCYgGfVi1l1zHk1jkU\nc93yfA9llZUNoB1cmR8awJe7uZNFtzMkBIS08DwhZLqNlggByCUu2PGRRlweDCGTzebmeWfYpF35\niwqpGFiy0AKPPwaReHi+yd5Dl7BmWaWw4H1eNptFKJIYsQizTs2enzu6PKh3GCCCCGs/LINCLkE2\nk0UinYVBKxeWs3AHYth3pAtKhRyVZUo0OKjHkFyfbNwoLJtJdZVnktngaudXAKgt16PKpsOv3+jE\nplW1cPtjMGgVkEnFQgDIb1NmUMLJXUc6vRGsX14Nk07BXC/fv7kJapUMqXR2xOi9SosWD2w142y3\nl3lcp1Hg12+cZzKZFrahcBgqkOtkqbZpEE+moVUr4PJFgSywa0sTYokMvME4nL4ofvmnM7AaVCUf\nzTEnA8IjR46gq6sLu3fvRmdnJx577LFpyYR6utuP/yq401CmVzJBh0LOriOolLNfpIdLj8uX+SGn\nfMDnDbJz9Dzc3WSPf+xypYU9yPkAVs29vlrJHg7pVJb5A+LnUPZwAadKIQWWDNfLpCJm//zC9vwF\nDl/mA1Z9wRxNShpDCJlu/NB0iRgoN6nRUGPCpV4f/nikB2tbHdh/vBcfW1vPbFtp0aK9VYo/vNON\nLasXIBRNwhsYPqeHYymUGVR4+c1O4TzZ4DAimU5DIhbjrXd70d7qgFQiRiqdQYTLUq1SSOEJxPDq\n4W4AuXks29rr0dnrR41dxwzFun9zE8QSwOmL4djZTpSXaeDyRWE2KFFtUTFBYrElfQiZrVQjrsko\n8+5sxp9fw7GkcL34ysGLaGu2w+WPQyQSC6MjNEopdm1dnBvRlsnCqFdg48oa2M1qhKMJOLjl3+wm\nNX7zZidWL60QsjaLAEgkw/vctLIG69qqIBKJoFPLoVKIkUylcvPGhwepCSsFHO1wCgvcm3RKvHIw\nt1YtfxMvEkvBE0hAJBLjF78fjiW2r2/AS6+xQ1R//vvTJe/YmJMB4aFDh7BhwwYAQH19PQKBAMLh\nMDQaTZEtx6dY0NHvjsAfSghJYRLcBGZ+zp5JxwY4fPYkNxcwWgwqZmH6T9/JBmQmA7d/LounLxhn\nTo7+ELt/d8Hri7gyAPQMhsYs85N9jVxSncvckFo+4OTXWVRzczANGhlTbyjoQaWkMYSQ6cbPeQGA\nxdUmWK06JOK5If75iwPtlfNZIpFGtV0HuUyEaFyCDStrgWwGaqUEKrkGH9/QCH8oAZtZDZc3gq03\nL4AnEINWJcPLb3WirdkOh0WDlS3lkEnFEAEo06sRiiawa+tinOv2QS6X4FiHE1turhPa1dZsZ+aE\nF+odCkMkAl4/dllIiCCTitEzEIRcKsZgYADJRC7xWXmZGi5fDBajEv2eMM5e9sFsUMHtv4hys4aZ\ny03IbJLKZKCQSSARi6CQS5DJZotvREqGP79mkYXZoMb754fQ1mzHsY7c8FGHRTUiZ0RHlw/fvTJK\nTKOU4s5bFgAQQYyskCyx0qqBTAqsWVYJo04BpVwCTwD409EebFxRLVxfWkwqPFdwAy2XYCZ3vbxj\nfSMC4TgsRpWwlFt+eaG719ZDKoawpJBMKsb9W5pwttsnzHdcuaQc/FHou9K5kz8Xi0UirG11oMfp\np4BwvFwuF2644QahbDKZ4HK5pjwgLBZ06DVy7CkI2O7nhmz6Q3EmoOGTwpTplfjtn4e3f4BLumLQ\nyIQsnZWWkVk6A2Fu/xE+i6iCGc/M79+oVTABJ1/vsLKfJ7/OoVHLBWwjsq6OPSRVIRUzcyAVXA9p\niFvmIxwZ7o4vllSGEDK3RKMZHD7rRK/rHBxWLVbdaIdqFgce/DmoudaAji4/05uYTGZRY9PAF0pg\n0BuFXqOAPxwfTkpjVMHli6LapsWQN4o7blkAjz+Gl9+6gHAshXvWNyCbzc1HLzOqMOAKQy6XIBpP\n5YK6gmH6hcOV+DvVZQalMF+xcAkMIJdQQS5l19tqb3Xg9wcvManZ21sdV37vWrC6eWR6+Plkrh2r\n80UmI8Jzr179mme+mivHqwgirF5agfpyDU51+VBuUqParkVTtVEIHvP48y8A/L9fv4+1rQ4YtEpI\nJCKkM1l09QQBkQh6tQy+YAxVVg12bmhEOJpEuUWD011e+AIx7FjfCG8oDrtJBW8ghkPv9wuJajzB\nOLRqOeRcXo5gJIHL4QSzxMWmVTVMucKiGZGhP9+Zwp+L+cSLM21OBoS87DTdBSqWqZJPA86XjToF\n9hQEfPwcP7VSPLzsg1kNtZINiAY8UTz/h7NC+d6Ni7B0wXC91aBiAtJPcT2I0Rib1CYaZwOskctG\nsGWjRs60z8QFfCq5BLXlOiFgVXEBndWgYP54LNy6hG2Lrfjze074QnFoVDIsX8zO0ay0aPBcwft/\n5N5W4d+j3bknhMxdh886mQyByAK3LasoXYOKGO0cNFpvIi+LLE51+ZBOZ4R518l0Bg1VeoSiSQBK\n3NrqgM2khi8Yg04tRxYZaBQSVFo1iMTSCEYTMOkUSCRzayF6g7k72PkLkaMdTmFYlV4tx2tHu3HL\nh3LrKPLzXALhONJp9jc0/5zC5+b/3T0QmvcB4Vw7VucLD7dOnDsQu8oz55e5drxey/XdaL2Lf3f3\nUvQ4Q6i0qPGRtgp0dPkhApjr91NdPvS7wnBYNHD5o1hca4I/FIdGJYVamVtKSKOSY82ySpSXaeD2\nR9FYbcRv9ndCpZBgXVsNgpEE9Fo5JCIRzHolEwDqNHJsX9eAUCQBrVqOWCIJsUiEHesbcWkgAJVC\niv1/6cH29Q1CT2Een0djps3JgNBms8HlcgnlwcFBWK3WMbbIsVp1RZ8z4rXGyP5zQ72F6WG7od7C\nvIZCxo5nV8jETH3/oUvwBOKIxlNIpjMwxxVMPZ+BzhuMM/WJZDfTg5hKJpl6nVqBn/52OJvRp+9s\nZuv5JDIaGVPf904X5NLhQ6TPHcbGm4Yj0t53uuH0RnPtT2UQjauwYdXw9sH3+pj9h6KpEd/Bjo8Y\ncDW3lmkhV8jQ1e9HbYUBq1rKIRaPfy7LRL73UpuuNmu1yuJPAkZk0boas1k7alun8zOf7u9zru+/\nFKbiPfW6znHl0JR9VtPxmU9mn8WyymUyWRw+OYB0OoN6hwFufwxD/ty51qRVIhCOC0PjXP4Y7CYl\n1EoJ83ugUUoAgxwQSbBskQ0mnRztrY7cBQyGL2AMGgVkXMKxfJKEwoQN+X/XVern9DE8249VYOqP\n19l2/E/X/ux8oj+zet4fq8D8Obfy51W7deT15Vjn3vx5t6vfj5aFZVjRbMc7J/tx8qIHd99Wn8sD\nIgJqyzWIJ7Pod0dQZlDgwTub0eeKwGxQIhjOTSPL9zDu2roYyKYBkYQJHIe8UdRVsO+zskxT0uN1\nTgaEa9aswdNPP42dO3fi5MmTsNvtUKvVRbeb6PIDVqtu1G3ry7VMl3V9uZZ5nkImBTDca6iUS5n6\nhZUG/O/+4UyZD9/bytRXWPgspmqmXiyRMRNVH9jazNQbteyQU5NGztSPR0xABQAAIABJREFUNkev\nsF6tlI+5/1q7Di8WTIx9hGu/1aDCzwuGrK5Y3Dru76ChXIvVSyswNBSE2x0qvgHnat/deLYvhela\nKiMUigEoHhQmU5lrOjt4PKERbZ3sZz6W6dz39bL/UpiK98RnIXZYtFOy3+n4zKd6n6Ptr6Fci4by\n3GdSZ7u2OdL53scBTwQapRxtTVa80zEk9ETWlmsRCCexa+tiOD0RWI0qSCS54fsPbG3GgCcMu1kN\ntz+GB25fDI1SinKTGmqVFO5ADA9ta8GKprIp+15KYTYfq8DMHFuzbZ9Ttb8PL7EimWxGrysEh0WL\nD7fYpvR7mWlT1XY6t177/grPu15vGI2VejRWDgeRo+0vgwwOdwyhayAIh1WDdDqD9tYqaNUyiEWA\nXC7DoCeGT97RjCFvFBqVDFqVFI1VSjywdfh4XbXMXtJz65wMCFtbW9HS0oJPfOITkEgk+OpXv1qS\ndhTr1m5tNONwRxrhWBJmvQKtjWVMfUuReXArb7Ahnc4OHyxL2WE6q2+0A1kI9auXsfVNdSa4TjiF\ntjYtYNu5vNmKWDKD3qEQHFYtVjTbmPo13P7XcPunxeEJIVNlFXe+WbVsfg9LnIjR1l9d3WyfkiGe\n030zYy6hY3V2UkGM25ZV0LHKoeN1eokhnvB5ttw4e86tczIgBICHH3641E0oKn+QfLS9YdQvu1hA\nqShycsvXX42syPZSiHHbjRPfvlj7aXF4Qsi1oos5MlfQsUrmEjpeybWYfWmGCCGEEEIIIYTMCAoI\nCSGEEEIIIWSeooCQEEIIIYQQQuYpCggJIYQQQgghZJ6as0llCCGllc1k0N3dNeJxr1cLj4ddIqSu\nbiEkEslMNY0QQgghhFwjCggJIRMSDQ7hqf9xQW3oH/N5Ef8gvveFj6K+vnGGWkYIIYQQQq4VBYSE\nkAlTG2zQmhylbgYhhBBCCJkgmkNICCGEEEIIIfMUBYSEEEIIIYQQMk9RQEgIIYQQQggh8xQFhIQQ\nQgghhBAyT1FASAghhBBCCCHzFAWEhBBCCCGEEDJPUUBICCGEEEIIIfMUBYSEEEIIIYQQMk+VZGH6\nw4cP4/Of/zyefPJJrF27FgBw+vRpfO1rX4NYLEZTUxOeeOIJAMCPf/xj7Nu3D2KxGJ/97Gexdu1a\nhEIhPPLIIwgGg9BoNHjqqaeg1+tL8VYIIUVkMxl0d3dd03Pr6hZCIpFMc4sIIYQQQkjejAeE3d3d\neOaZZ7B8+XLm8W9961v4yle+gpaWFjzyyCN46623sGDBArzyyit44YUX4Pf7cf/996O9vR3/9V//\nhVWrVuHBBx/ECy+8gB/96Ef4h3/4h5l+K4SQaxANDuGp/3FBbegf83kR/yC+94WPor6+cYZaRggh\nhBBCZnzIaHl5OZ5++mloNBrhsWQyid7eXrS0tAAA1q9fj4MHD+Lw4cNob2+HRCKB2WyGw+HAuXPn\n8Pbbb2Pjxo0AgHXr1uHgwYMz/TYIIeOgNtigNTnG/E9tsJW6mYQQQggh886M9xDK5fIRj3m9XhgM\nBqFsNpsxODgIk8kEs9ksPF5WVoahoSG4XC6YTCbhMZfLNf0NJ4RMq2sZWur1auHxhGhoKSGEEELI\nFJnWgPDFF1/EL3/5S4hEImSzWYhEInzuc5/DmjVrJrS/TCYz4rFsNjvZZhJCRhHxD45ZHw16AIiK\n7udan+fpO4Nv/OcpKLXmMZ8XC3nw+EMbUVNTW3Sf45UPOKdL4f5paCwhhBBCZoNpDQh37NiBHTt2\nFH2e2WyG1+sVyk6nE3a7HTabDRcuXBj1cZfLBa1WC6fTCZvt2oaaWa268b+JKdiWti/t9pN97VKY\nrjb/3//zSfzfa3rmtml5fXJ9murjdbbvbzr2Odv3N137nGnz8XOej22kY3Vm9jkf2zgX3vNElHTZ\niXzvnlQqxcKFC/GXv/wFAPDqq6/i1ltvxapVq7B//36kUik4nU4MDg6ioaEBa9aswSuvvMI8lxBC\nCCGEEELI+IiyMzzm8g9/+AO+//3vY3BwEBqNBiaTCS+99BI6Ozvx1a9+FdlsFsuWLcMXv/hFAMCz\nzz6Ll19+GSKRCJ///OexatUqRCIRfOELX4DP54Ner8e//uu/QqvVzuTbIIQQQgghhJA5b8YDQkII\nIYQQQgghs0NJh4wSQgghhBBCCCkdCggJIYQQQgghZJ6igJAQQgghhBBC5ikKCAkhhBBCCCFknqKA\nkBBCCCGEEELmKQoICSGEEEIIIWSeooCQEEIIIYQQQuYpCggJIYQQQgghZJ6igJAQQgghhBBC5ikK\nCAkhhBBCCCFknqKAkBBCCCGEEELmKQoICSGEEEIIIWSeooCQEEIIIYQQQuYpCggJIYQQQgghZJ6i\ngJAQQgghhBBC5ikKCAkhhBBCCCFknqKAkBBCCCGEEELmKQoICSGEEEIIIWSemrUB4enTp7Fx40Y8\n++yzI+refvttfPzjH8d9992Hxx57rAStI4QQQgghhJC5b1YGhNFoFN/5znewZs2aUeufeOIJfP/7\n38dzzz2HUCiEN998c4ZbSAghhBBCCCFz36wMCBUKBX74wx/CYrGMWv/SSy/BbrcDAMxmM3w+30w2\njxBCCCGEEEKuC7MyIBSLxZDL5Vet12q1AIDBwUEcPHgQa9eunammEUIIIYQQQsh1Y1YGhNfC7Xbj\n7//+7/G1r30NBoOh1M0hhBBCCCGEkDlnTgaEoVAIDz30EB5++GGsXr36mrbJZrPT3CpCpgYdq2Qu\noeOVzBV0rJK5go5VMtOkpW7ARHz729/Gpz/96asmnRmNSCTC0FBwQq9nteomvC1tX9rtp+K1Z9pk\njtVrMdnPpJT7n8ttn6n9z7SpPl6n+jOajs98trdxrrznmTbbj9Xp2Od8bON0veeZNB3XAXPlc57N\nbZwr73kiZmVAeOLECTz++OPweDyQSCTYvXs3tm/fjqqqKtxyyy14+eWX0d3djRdeeAEikQh33XUX\nduzYUepmE0IIIYQQQsicMisDwmXLlmHPnj1XrX/vvfdmsDWEEEIIIYQQcn2ak3MICSGEEEIIIYRM\nHgWEhBBCCCGEEDJPUUBICCGEEEIIIfMUBYSEEEIIIYQQMk9RQEgIIYQQQggh8xQFhIQQQgghhBAy\nT1FASAghhBBCCCHzFAWEhBBCCCGEEDJPUUBICCGEEEIIIfMUBYSEEEIIIYQQMk9RQEgIIYQQQggh\n8xQFhIQQQgghhBAyT83agPD06dPYuHEjnn322RF1Bw8exI4dO/CJT3wCP/jBD0rQOkIIIYQQQgiZ\n+2ZlQBiNRvGd73wHa9asGbX+m9/8Jp5++mk8//zzOHDgADo7O2e4hYQQQgghhBAy983KgFChUOCH\nP/whLBbLiLqenh4YjUbY7XaIRCKsXbsWb7/9dglaSQghhBBCCCFzm7TUDRiNWCyGXC4ftc7lcsFs\nNgtls9mMnp6emWralMpmszjV7UOPM4QauxbNtUaIIBpRP3C8FxVm9Yh6QsYjfzydP9gFrUqGKosK\ni6rpmCKEEDL3ZTIZHD4zhJ79nai26bCq2QLx7Oz3IGTWHa+zMiAcj2w2W+omTNipbh+eev64UH7k\n3la01JquuZ6Q8eCPp/ZWB1IZ0DFFCCFkzjt8Zgj/+ZuTBY+0YHWzvWTtIWQss+14nXMBoc1mw9DQ\nkFB2Op2w2WzXtK3Vqpvw605m26ttP3C8ly17Irhtec0110/29dOZLN45OYCufj/qKgxY2VIOsXj0\n3qLpeP8ztf1kX7sUpqPNA8d7oVFK0dZsRzSeglmvhMsfhdU6sWNqLNP5mU/39znX918KU/2eZvv+\npmOfs31/07XPmTYfP+f50saBAxfR3upANJ6CWiHFgCcyp4/Z+fK9Tfc+Z+v+ZtvxOucCQofDgXA4\njL6+PthsNrzxxht46qmnrmnboaHghF7TatVNeNuxtq8wq5lyuVnNPK9Y/WRf/2SX95p6IKfr/c/E\n9lPx2qUwmTZfTYVZjbZmO968cqPhCJx4aFvLlL/WZD/zUu37etl/KUzle5rqz2g6PvPZ3sa58p5L\nYT5+zvOljWV6Jfa8dVEof+qO5ilrZymO1/nyvU3nPmfb/gqnipl003O8TvRYnZUB4YkTJ/D444/D\n4/FAIpFg9+7d2L59O6qqqrBhwwY88cQTePjhhwEAd955J2pra0vc4okRiyHcHVAppJBwQ4eba414\n5N5WDHgiKDersaTWOK79F5uD2OMMMc/vcYZo+OB1rKnagJOXvMxjTk+kRK0hhBBCpo43GBuzTEip\nFU7dWXNjBVMXiaZK0STBrAwIly1bhj179ly1fvny5di9e/cMtmh6nO/1C/8WXSkvrh4OyLKZLAKR\nBNyBGNRKGbLIjisBSLE5iDV2LfP8aq5Mri/Hzrlg1MmZIQoWg7LUzSKEEEImrcyoYn7frEZVqZtE\n5jk+eWS/KyzU1VboIZGIhePVqB89meZMmZUB4XyhUcnxqzcuCOVdWxcz9ZOdcNrnCjMnx35XmAkI\nJRJg+7oGuP0xlBmUkNHRcF3zBOLwh+LMY1ebM0oIIYTMJXyOwczczTlIrhP5jpl8/gaNUoaPra1H\nOJKARCwSpvAAQG15aee7UghQQvzFOV/uHgiNKI8nINSqZczB9tC2Fqa+1xXBS6+fF8q7tjRhkYOG\njF6vQtEk1EoZ9h3uFh6zcfNUCSGEkLkoFEky1zz0+0ZKLT81qzB/A5DrjBlws1N2egt6D0uBFmgp\noUVV7JzARq5cYdWwZcv4Tm78/DC+7AmwAaibK5PrS5VNi3AsyTwWjCRK1BpCCCFk6oS43zP6fSOl\nlp+aFY2z8wO7ncER1/QOa2mnbVEP4TQqltQlnzSmxxlCtV07ImmMVJzFri2L0ecOo9KigVwyvtfX\nqRVcmR2fbNIpuTL7fHJ9CYUTMOu571xL3zkhhJC5z8hdw9DvGymVwrmDu7YuRjiWwpFTTqFepZDC\nH0oMX+OXaaAscURGAeE0KrqwfMH49tFmcqXSwDN7TwvlT93RPK7Xr7KomCymDu5uRLF6cn2ptGjg\nCcWY71yloFMAIYSQuU+lkNLvG5kV+Ov/DSuqcc/6RnQNBKBSSHGsw4mPra1nrvH5aV0zjf5aJoHP\nHjTeZR1O9/hw5PQgovEUnN4IxGIwWUZdvhiTFMblG18K5cYqAzyhBHoGQ6i26bCo2jBqffdACDXl\nI+uL9XCSuaW51ojX372M2nId+lxhOKwaJFPJ4hsSQgghs1wimaTfNzLj+FhgcY0BA54IViyxQ62Q\n4uQFF0x6JUKRJJpqTbjY50dbsx1ON5v40ROIlvR9TElAePnyZQwMDGD58uV44YUX8O677+Izn/kM\n6uvrp2L3s9Zkl3Xo90SYSaY1di0TEFqMSvz2ALto5Xh0dPuZLKV6Ndu+YvVFezjJ3JIFxCIpfvFK\nh/DQA1vHd0wRQgghsxH9vpFS4Dt3QrEknt13Rqjfvq4BL/7pnFBub3XgzeO92L6ugU3syK00MNOm\nJKnMl7/8Zcjlcpw6dQovvvgiNm/ejG984xtTsetZbbQewEKLawx4aFsLtqyuxUPbWtBcy/bAFUvq\n4gnExixns1mc7PJi7zs9ONXlRRZsjuVi7Ztsmcwtp7p96OOyWPFlQgghZC6i3zdSCn3uXOfOkVNO\n7D/eC7efvZb3cSsIGDRy7FzfiFiCTTTDrzQw06akh1AkEuHGG2/E9773Pdx///1Yu3Ytfvazn03F\nrme1Yj2AxXrgiiV1KZYUptiQ07pyLdMdXVfBtq9Y+2nh+utLjzM0IqtVhUWNDDIQU8JhQgghc1il\nlf19q6S8CGQGBMIJYZ3BRCINuVyCNcsqIBWLcbTDCTu3/MmiaiNaak041eXF7w5cEh7nVxqYaVMS\nEEYiEbz33nvYt28f/vu//xuJRAKBQGAqdj2rFcsSWmwOoUop4SZAs2lEq6wqZuH4aht7UOXvSgjP\nt7FDTlMZMPVti23jan++fsATQblZPaK+2BxKMrvU2LVwB2PYvq4B3c4gVAopfvX6eagUEty0+NrX\ntySEEEJmG6lYxFxTSSR0PUKmX1O1Ef5wQrjefvvkANpbHdh/ZVhoLJHE9vUNCIWTqLCocabbB3cw\nDnE2g4e2tSASS416jT3TpiQgfPDBB/GVr3wFO3fuhNlsxlNPPYU777xzKnY9q4kgQkut6arz6or1\nsA242HUBB9zshNJBX4wZX/zpO5uxyDFc7w2y3ct8+UyPly13e3HDOLKc5t/fbctrMDQUHFFfbI7h\nZJPSUFKbqdVca8SL+y/A7Y8x6Y8vD4aB0g5dJ4QQQialezDM3ASXy8a5VhchE9Bca8SpLvZ6O7/u\noDcYh0WvQDyVhVYtw4t/OodwLFd3z/pGDHmDuPVDDiycBSPwpiQgvP3223H77bcL5YcffhgiEV24\nF+thq6nQwemOCD2AFWYVU395MDxm2WJUjlk2cWvOmbkhqcWGnBZTrAd0sklpKKnNFMsC5WVqRGPs\nuHX+uCGEEELmGodFw5QruTIh00EE0YgpYPklT1LpDNQqGQb7g8hms1i9tAKH3u9HW7MdLl+uE6h3\nKHj9BIS//e1v8eMf/xh+vx/Z7HC30xtvvDHhfT755JM4ceIERCIRHn30USxdulSoe/bZZ7Fnzx5I\nJBLccMMN+PKXvzyZ5k+bYj1skWiS6QHks4jyJ7MKrpzNZpnhEYWfPQCIRVlmyKmYmybWM8TeTauw\naMYVEBbrAS0WMBYz2e0J61S3D5l0BpVWDba11yMYSSCbzcIbjCOLLPW+EkIImbNC0QRzzROOJkrd\nJDKHjTYt6mqPV1tVuG9TE/o9uUXmLw0E0N7qwLEOJ5RyCd74y2UAwN1r69HWbGeuvSstpQ8GgSkK\nCP/93/8d3/jGN1BZWTkVu8ORI0fQ1dWF3bt3o7OzE4899hh2794NAAiFQvjJT36CP/3pTxCJRPjM\nZz6D9957DzfeeOOUvPZMGvREmaQvgx52yGgimcR9m5rg9ERgL1MjmWTX1BnyxZiDasvqWvYFuF5a\nvtfWxw0x5cvFhmwWm4M42aQ0lNRmavU4QwhEEzDp5AiE4xCJRDBoFTBo5TjX68MiBwXbhBBC5iaV\nUopoLC2U1UpaaptM3Gij1MrKdHj79CDePeeCUSPHkD+KMz0+lBmUeO7V3FIT93ykAQdO9AvblemV\n2L6uAb2DIchlEmiuHJf5RDTuQBSnurwlnxY1JX8ttbW1WLFixVTsCgBw6NAhbNiwAQBQX1+PQCCA\ncDgMjUYDuVwOhUKBUCgElUqFWCwGg8FQZI+lkclkcPjMEHr2d6LapsOqZguTzbHcqsb5Hr9QruAy\nZEmlUjzzymmhzK9RoueyjvJZSNPpLNMDef+WJqa+mstCWlPO9kAWDim9PBgaOaS0yBzEYkNmi5ns\n9oRVY9eiazAEuUyK149dFh5f11aFTCaLVAolPyERQgghEyERi/HS68Prv4137WZCCo02Su2dkwN4\nbt8ZtDXboVbJ8Zs3OwEAK5bkEvNplFLoVDK0tzqQzmRQbtYgnR2+Fn/75AB2bV2M9lYHpBIx0ukM\nDpzow6uHu0s+LWpKAsLW1lb827/9G1auXAmJZHgS7+rVqye0P5fLhRtuuEEom0wmuFwuISD83Oc+\nhw0bNkCpVOKjH/0oamtrx9hb6Rw958KZbh+i8RSisRQkYmBl03Cmz3AkyfTwlZexAaHbH2MCNref\nXYfQbJAzPYgGNft1DnmjY5YTiQzz+nUVeqa+38NmMa2xs1lMT3b58G+7h++ePHxvK5O0JpvJIhBJ\nwB2IQa2UjXtYYrEht2R8mmuN8IbiI9ZmEotFGPJG8cwrp0t+QiKEEEImwunhEvVxZULGY7RRal39\nfrQ123Gsw4kNK2uxYokdaoUUVqMCjVVNcHojSGcAg1oGpUKGF187JwSLeUPeKHNtnV+ovtTToqYk\nIDx48CAA4Pjx4eBAJBJNOCDkFc6NC4VC+MEPfoBXX30VGo0Gn/zkJ3H27FksWrRoSl5rKvnD7Pj1\nAFf2h8YuWwwq/P7gJaH8wFb2blcilcXloRCi8VRu4mo121PKB5j8Wii+UJwZb88vnlksi+npbjar\n0ukuNovpO2eGmHUYRWjBTc20vEGpiCCCWimBxcgmLyo3q5FMZwDQPE1CCCFzk5X7bbNSwjQyCaNN\ni1IqZLg0EMCW1XV46fXz0CilWL20AqFYGlFfHEc7nAjHUti5oRGJZAYapRRqBRtqabnRfPmMpKWe\nFjUlAeEzzzwDIBe4TUV2UZvNBpfLJZQHBwdhtVoBABcuXEB1dbUwTLStrQ0ffPDBNQWEVqtuwm0a\nbdt0Jot3Tg6gq9+PugoDVraUQywefv+ZTJa5C/DxDY3Mfmwm7uRlUjH1Lt8Fpt7lizL1oUgPs397\nmZqpD4TZCdbBSIKpN2gU+MUrHUL5ga3NTL1ZPzJraWG91axiejDtZra+e38ns333YAh3tTcI5WKf\nH/PZTPF3N9tNV5sHjvRAKmHXasoiC6U8N5S5ocY0Ja89nZ/5dH+fc33/pTDV72m272869jnb9zdd\n+5xp8/Fzni9tzGQyzG9bJpOd08fsfPnepnufk9mfzaoXrlX/dLwPOo0cdeU6nL0y3aut2Y4/HukR\nnp/v7bvYF8CRU05sX9eA/X/pQXurA0p57ob8ENdzvbjWhI0ra7FqjGvgmTAlAeHp06fx6KOPIhKJ\nYO/evfiP//gP3HLLLVi2bNmE9rdmzRo8/fTT2LlzJ06ePAm73Q61Ote75XA4cOHCBSQSCcjlcnzw\nwQdob2+/pv1OdNih1aobdduObi8OdwzPsUsmk8yQygiX3j8SSzH7USslTMCmUUqYehvXo2c1q5h6\nvscxEE4w9Xr1yICvsL7XxY6P7nWFRuy/sH38/pUKMaqsWmHIqkrBtt+oZZe5MGoVTP3JLu81LSsx\n2uc/Wpan0YajXu27u1al+jGZriGyoUgS6Qw7VHjzTbWotKixa2sTPugcQiKenNRcwsl+5qXa9/Wy\n/1KYyvc01Z/RdHzms72Nc+U9l8J8/JznSxvd/jibaO+m2ilrZymO1/nyvU3nPie7v2w2i7dPDzKj\n3e7d2IS6ch1UCinEXCdYvrcvv+xEtzOIdctrgCygUkjQ6wrj7ff7hRsXi6qNuGf9IrjdIbjd7DX5\nRE30WJ2SgPDrX/86vvWtb+Gb3/wmgNy6hF/+8peFzKDj1draipaWFnziE5+ARCLBV7/6Vfz617+G\nTqfDhg0b8JnPfAa7du2CVCpFa2srli9fPhVvY9z63OwcuyobO8eOXyaighvC2TMYxu8OXBLKd6yp\nw8qCvC8GrRS7ti5GnysMh1WDMq2M2X60gKvQoC8yZtlhZbunHVzqW6tJBW9geJionevRjETSQlYl\nYGTSmzKDkgkoLXq2fWd7fCPK1zpckdYonJjmOhMuD7InHZtJBbc/DoVMgkFPFP5QYtxrUhJCCCGl\nVG5hr7H4aTOEjEc+GDxXkPwRAM73+lCmU+DN471Yv7yaqVtQqYdKIcWxDieAXGA45I2ixq5Fz2AQ\nlVYdwrGUEDu0L6ssaa9goSkJCKVSKRYvHg4GFixYAKl0crt++OGHmXJT03CktHPnTuzcuXNS+58K\no/XQFQqFE8zwhVCEXTZCq2LHEWtUbMDnCSTGzDLKD/2TStiDymJgh3yWcUNA44nEcFIasxrxJNv+\nWDzFZCn9ay5LKZ+chC/HEuz7jSfZHlO9RsGV2c9jLLRG4cS01BrhC8awa+tinO7yQqWQ4levn8df\nrWvAG0e70TOUu2nA39wghBBCZjOpeOxrIkLG41S3D//5m5NY2+oQHtMopWisNsLjj2H98mrIuOtw\nlVyC2nKdUD7W4cQdaxagzxXB68d6oVHmOnri8fSoy7WV0pQFhD09PcL8wf37949YJP161FRtxJ6C\n8qJq9outtGjw3B/OCuVH7m1l6pUKMdODplZImPp+d2TMskolQa1dhz53GJUWDdQK9uQnl0qYA1Uh\nY/cvl8nGDDhdvtiY5WI9jMl0FkO+KKLxFLLZLLRcFlSdWsa0T6dmA+Kx0BqFEyOCCFnkgvcjp5zC\n4+cv+7G2rQY9ziCOdjjhCdBi9YQQQuYOftSWdhzXFITw8h0PRzucaG91QCWXQquW4bl9wyPj1i+v\nZo45mbQaZQYlTDoFDFoFVi+tgEQsgt2cG2EXjqXgC8Zx9y0LZvbNXIMpCQj/8R//EZ/97Gdx8eJF\ntLW1weFw4F/+5V+mYtez2uIaAx7a1oLugRBqynVormWzfDZVG/CpO5pxeSic63Hh6sViERMwGbTs\nsg+1FTomaUtdORv0xONZPLN3OKB74HY2C2m3M8gcqHKpGLihXCjzKZr5skHH9uAZtGwPXjjKZikN\nx9gspNFYmnl9m7mRqQ9wWU0DXJbVsYyW/Ylcm1A0hVQqwzxWadHA7Y+ibzCAtmY7wrEkTnVd+xBe\nQggh/z97Zx7eVnXn/e/VvlqSbUmW5SWJ43hLSD1ZjAk1JA0kYWhThjYTYGjLTNNOlxlmYNq3hGeg\nzzBM6MLQtw8zz7Sd5X3hpaQLZRIoS0thEkIWEppSGjshq+14kS1b+34lvX/IuvY5Tiwvcizbv89f\nPj73Hp2reyWd7/ltxFxiMXKJ8LgwGoKYClnDQ9bF89aWKnS52HhE3hPPbFDjp2+cldptzU4MeCOZ\n9fcIvPGoUMiLIKyoqMBLL72E4eFhqFQqGAwGdHd35z5xntPR5WMCTYt0bBzbe2fduNDrRyQmIpFI\nQqOUMXUIwxFxwjqEiQQrqKodbKCo2xthBKPby9YZLOcseOWcBa/cqmfOd1rZmEejTsEIviI9u9um\nUirQMxiSBG05d34owrqMhjiX2QqrHpdH3EwFABU29vyJyNYoJMEydYLhBE5dcGPn5lokUmmEIgkI\nQsZlufU6JwY8Ebz9ux6UWXT0/hIEQRDzAjGRYGozi6KY+ySCuApGdKSgAAAgAElEQVQN1Wbs2t6E\nzv4AjDoVTEUqlJq00GuUKDFpcOC33dCqM5548XgSFXYj+rjEMJGYCKvZCEEQsK7RjvpqS8EaMPIi\nCP/8z/8c//Zv/4bi4mIAwP79+/G9730Pb775Zj6GL1hyJUUZ8sc4Cxkr+Pg6hXx70MMKPr6wfIlJ\nw9Qp5F0+tWqBSUrDu5TKBDDzq6lgLZSBUIKJIfzTzayFT62SM+d/7nbWQsnXPbSXsElpcjkVZzOJ\n9p/sgaNYN6PMl0SGdDoNm0WDVcutcPuiiIspRGIiIlERFTY9Ojo9qK204IZVDpSY1OQ2ShAEQcwL\ndDo1zl/2SbWZaypMuU8iiCuQSqVw4qwbvlAcyZGNc71GMS6RossTgdWsRTyRhFYtR5WtCPHGFHRq\nBU50uLCi0gyPPwpLkRpVdiOcpdqCXVPlRRDef//9+PznP489e/bgP/7jP9DX14cf//jH+Ri6oMmV\nFCUQjk/YLuZcMnn3BqtZi1cniPEb8kUnbIejqQljBHvd4QnbfJIcXrC6uJhGvh1LiEyMYCyRZI/n\nLJouT4RJZEKZRPNPe5cXgUgcapUcaqUCbxwcrRX5qU21ON7uwvF2F7a31SAQFXGm20vJZQiCIIiC\nJxhOTLgJTxCT5diZQZzp8uK9DhdaVzlgMqjQwyVO7HYF8T+/vQwAuHPjcnT2s2Fad25aDpVShriY\ngkIug0ErR42zcDcp8iIIN2zYgNLSUnzxi19EW1ubVKh+oVNRqmUEj5NLebzUUcRY+JZyLp96vZJx\nyTRyLpmDnGAa4tpGToDySVlyCTYTJ0BN3Hg2rswE3+YFMT8fvibQlpZqpj+eYOvhOUpXMP2USTT/\ndLuCMGhUkMuS4zYQgpE49BoFQlERve4gjh904Z4tdSQICYIgiIKHz+QeDE8+LwFBpFIpHDsziK7+\nIOylOug1CmxtXYIuVwCWIs04r7cSswab11VCr1UhGI5DOSZOEAA8/hji8STeON6N65vKcPRUP1RK\nGVob7NfysibNjATh1772NSmzKABUVVXhwIED+PrXvw4ACz6xTG2FCcPBuJRUZkUlq/zTOZwig+EE\nk1RGrWIfJquFffhKzawg06pkjCDVqNgsovYS3mWTbevUbBZSnYZ9HHxc2QzeYqhWsVlStdz8x9Vh\n5ATzuBhDrk2ZRPNPld2AU5eGYTaqUc7dn3BUxJoGOw6e7JGKqvJWbYIgCIIoRPhNa34NRRATcezM\nIH78+hmsabBjcDiMUrNW8rI73u7Cn3+8QVrzllv18PozYTdZT6ux5SkAQEympEL1qpH1eVd/cGEK\nwhtuuCFf8yhIcsWw5UoqM8zFEPJJY2QC60cscO1BT5gRZLzFUCFnBRi/O+ENxpjzvVxWT9dwhE3R\nzNVBLNKp8N8HLkht3uU0nQYTY3jvNrZOYUuDDalkGj3uIJxWA1pWsR8CPtMS385mce0eCKLSNj6L\nKzF1GqrNGPBF8co7F9B6XTm2t9XA7QujrFiPnoEgKsuM2Lm5Fm8c7wIAFHO1KwmCIAiiEPEG2czn\nPm7NQxBZsuv7blcQVXYDGqrN6OwLSJvibc1OeLoyeUL0GgXWNNjR5QrBpFdBENIQAMTEFIqLNJJn\n1YkOFz69qRb+cBxmgwp6rVJK/pgtVF9VVriGjRkJwjvuuEP6+/Lly2hvb4cgCGhqakJ5efmMJzfX\n5Iphy5VUJhBiLV5+rp1MphlBtvMW1mXSatHi1VfGlJXgBNnlwRBzPm/hKzZq8NLbF0fP58pSODiB\nWsYlfUmIIu7dWi/VOeQzdvECdZCrU3jy3CBC0QSSyUxA7vtnBrG+blQUymVgBCunb3MKbmLqCBDg\nC8aw7YalSKbSiMWTkMtkkrA/eqofbc1O3LSmEjq1Ah5/NMeIBEEQBDH3mPRqPPNqh9Tm10wEAWRc\nQw+dcuHUxWHo1Aq8+V4Xbr9xGSxFGvQPh7C1pQqQCVjqKMLRU/1oXeVAXEzBF4whkUjCZtHhuTG1\nCNuanTh4sgehqAi3L7Mu/skbZ3HvtnqYDCoUF2mgVSlQVWZAS4N1ri47J3mJIXz++efxox/9CKtW\nrUI6ncYTTzyBr371q4xgnI/kimHLlVSm1Kzh2uzxvMWOb6uVrEsm71JqyvH6vhBrIQyE2PEtRvVo\nFtJSPaycNUivV8PjiyGZTCMcFVFqYl/Pxrlj2Mxc2Qyu1h3f7h0Kw2rWStfXPxRm4tUohnB2MBlU\nkMsFyFMC9h04j5aVDqY/Hk9KGbUSYgpHOgbQ0lAKGWRXGZEgCIIg5pZBb5hrR65yJLFYSafTOHTK\nhf/zy8zGgV6jwPa2Gpy6OAyrSYNquxEdnR4YdSoAKey8pRZymYwRgHw+DI1Kjk1rK+G06iCKaem5\nO9vlxdFT/Xjwrma0rWLXWYVIXgThvn378Oqrr0KtzgiGcDiM++67b94LwlwxbM4cSWUSCZGticNl\n2TTnSOrS6w4jEE5IMYbROGuhyxXDZ9CyLp/3bGFdOocCMXT2BzIpmsUUFAoZxh4RiYiMS+i4830R\n5vqH/OyXbzyemvB8hVyGF94a/ZB97o9ZC+aSMgOTlGeJo3BN7fMJmSAgnQb63CGEoiKSSVaoV5YZ\nIRME9A2GcPiDPoSiIgQ04foC9XsnCIIgCAu3qc0XqieI091eeAJxbFjtQFmxHt5gDP3DYVRa9dBq\nlHjhrXNY02BHIBxHKq2HaziC4Eh+i1KTGjf9USVkMja8y2xUQymXIRRJoHtgNBNpNm5wvhgz8iII\nFQqFJAYBQKfTQalUTnDG/CBXDFsknkSF1SAJvmicFXwqtRLP/HLUfeGznODJldTFUaqHxh+TBF9J\nESsg5TKBEVz3cXUAw7E4Ixgj8fFJXMa6nPJJZ1zDkQnbtmItAiNZvQQA9mLW5dTlGV9WYix9XJkL\nvp1Ms3US19bbQMycYCQOQZBJz+uJDhfamp3QqOSIxpN45Z2LCEUzJUPWN5Xhrfcuo3sghPUNKbIS\nEgRBEAWJWskm2uO9qgjC5Y3AE4hCrVTA7Y3gRIcLoaiInZtrEY4l0bLSAbkAyGVKnO70wGJUQzeS\nZO+mP6rEC2+dg16jQFuzEzqNAnqNEl5/DLFEEhqVHFq1AmXFOmjVCilucL4kRMyLICwrK8Njjz0m\nJZk5dOgQHI6ZmUf37NmD999/H4IgYPfu3Vi1apXU19/fjwceeACiKKKxsRHf/OY3Z/RaVyNXDJsn\nEGOLVG5lLWADOco+9OcQXJHoxBa6XIJNq1biuddG53cPNz8+ayjfLreyWSj5rJTgBFuNky1szyfR\nKeMEo5Mf38oenytGk5gedVXF+O2ZAZzocOHOjcsRiiYQjorwB+M4eqpfOi4SE0fcJoBQNIFjHYMF\nmx2LIAiCWNxcHmDzKqiV8gmOJhYjY8ud6TUK3HbDUgz5o5DLZXjxjcx6ORsTCGQyh2Y3zb2BTNhV\nKCri4MkebF5fhdeOXEIomvHeu3dbHQQI0Kjl0GsVKLPoUGk3oLHaPH4iBUheBOFjjz2GZ599Fr/4\nxS8gCAJWr16Nz3zmM9Me7/jx4+js7MTevXtx/vx5PPzww9i7d6/U/8QTT+Av/uIv8LGPfQyPPfYY\n+vv7UVZWlo9LYcgVw+bmkqjwSVWKTRrG5bGYi8GzFGnwyuFLUpsXfJ5AjDnfE2BjAEtMGq7Njp+r\ncL3Vws7PysU8BiNsxq5glH39/qHIhG2DRj7qMlusg0HLPm7JVIrZzUul2DIduWI0iemRBiAIQOsq\nB5QKAVaLBqkk4PZFpS+/UDRzT0qKNFKGLI1SjusbbEymXYIgCIIoBJw2PbOmqbBS2QmCJZ4QpWek\nym7EK4cvotiogtOqx9brq1Fi1sIbiGLT2koc+0MfTnS4cNsNSzEciI6rQ1hWrEPLSgeSyRROdLjg\n9kZhtejQ1R9A09IS3LjSPK/WS3kRhD/5yU/whS98gfnf97//ffz1X//1tMY7cuQINm/eDACoqamB\n3+9HKBSCXq9HOp3Ge++9h6eeegoA8Pd///czm/wEmDkXTbORSxqTQ1DJZQKzW8WXZTBoOcGk4+oI\nFusQ7PFJ7TLuYUzlEFR2PukL15YLMmZ+fAyfUavG/31lTMYuLktpMSdILdz7FRfBWFD/4uNNTP+A\nhy17odewgboVOWI0ienR2edHJJaEXCYgIaYhJlP4+Ztnpf4dm2uRTgP+YAwqpYD3RgSiUa9CeydZ\naQmCIIjCQwDrtbTM2XD1g4kFDV82Ti4HetxhRGIpCADaLwzheHvG8ldhNUj1BoFRC+HmdZWIiykE\nInE4rXq8dbxLMpJU2g149fBFuH0x6ZziIg2eGVkzv3a0a1xlgkJnRoLw6NGjOHr0KPbv3w+fb1S4\niKKIX/ziF9MWhG63GytXrpTaFosFbrcber0ew8PD0Ol0ePzxx9He3o61a9figQcemMllXJVYXGQE\nSYxLCsMLqs9ygolP2e/xj6+JE0skM+n/E0kUcbdjXIwfJwiH/XGmf1vrEvb1AlwdQs7C2Me5sPLt\nXOeHo2yMYjjGxij2D4cmbJeYWBdSvubdikozxBTQPxxGWbEOdZXzw+xe6FQ7ivBhtxflpQa88NY5\nrGtk3UAHhiP4n99exp2bliOZTGN72zIIgoDXj15CuNGBM11eOEr1lHmUIAiCKBh6uTwEfJtYuKRS\nKRw7M4iu/iCqyowo0imZsnH3bqvH/3ttfKkIhVwGl4d9TrLF5C1GDX42ZrP8M9sa0OMOwlGih9sb\nkcQgkMk06uPCruZLMpksMxKEy5Ytw+DgIABALh+1bikUCvzzP//zzGY2hnQ6zfw9MDCAz33ucygv\nL8cXvvAFHDhwADfddFPOcaxW45Re1xtkBZnFWMOM0es+xxzf6w4x/SVmVvCUmLVMf/Bkz7gYwbH9\n0XiSsUBG40mm32phBZTVomH69drRYvTCSHts/7gspwbVlM436tTSbgiQsSCO7S/lLIjFRez8YvEE\nIyjjCXHcPbJZ2bjE6TDV+14IzOacL7iCqK00o39kAyAbMC29tlmLm5qdCEcScPuiWFpehGAwiqZl\npTDplXD7ovjRvj9AJluF229cds3nP9v3c76PPxfk+5oKfbzZGLPQx5utMa81i/F9XixztBjV49rz\n+ZldLPctH2O+fOgCk/Pjtg1LpL8rrTp4g2wIVjqdRluzE0q5MC6honZkTRSIsAKvfziEUDiBuCEJ\ng5ZNnFlq1o4La1peZZn0tRTCczojQWiz2fDxj38czc3NqKiouOIxe/bswUMPPTTlcd1ut9QeGBiA\n1Zop5mixWOB0OqXXa21txblz5yYlCAcHA1OaRy2XJGW5s4gZY5y/uk3P9KeSnEtnMsX0XykL59h+\nS5Ea+98eLRvx2T9uYPoFsIXdBe4a1Ur5OJfQsf16LZvl1KBRMP0GnQrAqFWvSKdi+t1cjR+3l53/\noIctS8H3m4wa/PuYD/Dntzdd8R5ZrcYp37t8nJs9fy6YyZxzcbHXh1QyJSX1yQZM6zQKhKMiXjmc\nyTJ677Y6xBIpCBBQatEi4Q5DJhNgKdJg09pK+IIxDAz6x/nIz/Q9n4jZHHuhjD8X5POa8v0ezcZ7\nXuhznC/XPBcsxvd5scxRr1Uwaw69VpG3ec7F87pY7ttMx0yn0/D4o1jXaIdZr4KYSiOVTEs5ET7a\nXIlYIsmFcNXjFyMlJvzBOD5zWwN6BoOwmXXwBKL4s611EGQCs8ZPpdKZxHungI1rKnDvtnpcHgyi\nwmqAs1SL5U4THryrGd2uICrtBtSU6Sd1LYXy3ZqXGMKriUEA6OjouGrf1diwYQOefvpp7NixA6dO\nnYLdbodOl1HwcrkcFRUV6OrqQlVVFU6dOoXbb7992nOfiIZqMx68q1lyWeQzBaVTrL/6EgcrIAd9\nUaZ/aysbI+fgdiX4tmuYNWMPDI93h2AzalUx/f3DfFZTti2XCUxheLmcXdj7gzFmfD5rqJHbDTHq\n2B0Tq0WHV8daELfxLrURNmmOf/JFZLP+4d2uIKrsBjRUz6/g3blkicOE3xzvwoolZtx1Sx0GvGEk\nxBQSYoq5325vFIFwHMlkCo4SHWJiCmIqjZ/9ZtSFwlmql1wieJ/9+ioTOrp8dI8IgiCIWaeHyzKq\nUyuAxjmcEHFNaO/y4qcj65LbWqtRpFFiyBdFqVmLOzfWoMcdRijMhjT1uUPY3laDs5e90Nv0cHsi\n+M3xbqn/Y+sqkUqlmedp45pRraNSyqUYwq2t1di4uhwA0FRtmVduomPJiyDMN83NzWhqasLOnTsh\nl8vxyCOP4MUXX4TRaMTmzZuxe/dufOMb30A6ncaKFSuwadOmWZmHAAFN1RbcvLbqiuq9byg0YduU\no/C8ViXnCsvLuePZ83kB5uDKQDhK2DZ/Pm/OvtQXxK/f7ZLat6yvQusYzeYLsuZyL9fOVUdRLksz\nSXPkcjbpjU6jwsGToxbQe7fVY7K0d3kZ//D5Frw7l6xvKkP3gB9ICXj+16dx04gv/SdvqmGOM2hV\nOHiyB6GoiLtuWYFKmwHD/ijuuHk5NEoBnkAcXQNBhKIi1taVoKPLx9yTXdubGBcOukcEQRDEbMGv\nuYoMlJl8MTC2IoDJoMHzvx5TDm5bPUqKNEizy084SvVSIpkquxGhKCsYS0waXOz1M//LFqgHMiFR\n2RjCKtv8qDOYi4IUhADGJYqpqxvN0FlVVYUf//jH13pK46guK+LarJk2l2C62BcYJ8iuH1PnTakQ\nmPNVCjaBRzDMlYWIsElfFNz5Ss4CWFVmYCx0VWXsQ221sDGQNi5mMVcdxEgshb2//lBq77xlBdPv\nC8YmbE9ErpIgvLWKrFOjyGQCBj0RJEey0mZdRrXc82rQKbB5XSU8wTiG/TFE46JUkmLjmgokU2m8\ndrQTd25cjhNn3Bj2sfevq3/ie0QQBEEQ+SLXmolYmCwZWcsmUykM+th1aLcriOPt/fj4jUuxeV0l\nZDIBJr0ayWQKeo0CoaiIIV9UWgdFYiKWOIqgUyvG5VdYUWlGcZEGRToVZLKM11+VzYD1DdZrebmz\nRsEKwvlASZGSEWQlRexuVO8g677AWwCryw3M+cXc+QpOwPFtrUaJblcQkZiIdDqNSjsr6PgvQxVX\npFXg9JGMaxt0cty7tR69QyGUl+pRxJXFcFh18Prj0vwtRtZlNBITGcGZzdyUpbbCPGF7Iqq4a+Wv\nnSyIE2Mv0SM9IgizRVbvunUFqsuM6B0MwV6iw6GTl3H9dU68cbwbaxrsiMREfKKtBm5PGHK5DEMj\nWXR7BoLQqOQIRhO4ZV0lxFQawUgCDqte+sIFxt8jgiAIgsgXBq2SCYMxammJu5DJbvx/2O2FAECl\nkKOEy1ZvNWuhVcshCDIpa6heo8CaBjs++hEnDDolIjERLSsdkAlAJAZo1XK8fOgCPrLChu1tNYgl\nRMTiSfz3gfMIRUXcuXE5Uqk03j7Zg7+8Y9WCybY+65+WNG+nXUB09ocw6I1IgkytkGGFc1R08C6d\nZVw7nkgx5xt07O1wDUe5On1LmH6FbOKyF31uTpCqWUEXDItMPz/faCyNTlcAkZgIUUyhxmli+hNi\nmsmS+lmujmGRXoV9B6/uEiqXsUlx5FP4TGXjO7PBu3x8Zy4L4mJnw0obXn+3G3+6uRZdriBqnEWQ\nCQKeGVOL5zPb6nG+14fWVQ7ExRRUShniiSRSABwWLWQCoNcosMxpwofdXujUCshkAt567zIA4Hi7\nC7u2N8EXiF/xHhEEQRBEvogmksya5L7bqQ7hQobf+N+0thIygV1XKuQCNq+rwvO/PiOV2FrfVCat\nUwDg05tqoZDLEUuIqLQb0TcYwkdW2PDGSEzhxj+qwFu/HT0+FE3AH0wBWFhry1kXhOvXr5/tl5gz\nDDolI6h2bWcLr6eRZiyAMrDiOBJlsx7ZimuZft7/vUjPWuByxTDailmBZ7ewbS/nosm3I7GJBeMA\nV7fQxbeHJ25f6guySWssOtRXTs7tMxvfebUPYi4L4mJHDhl0aiViiRSUChn+32tncPMfscmhOjo9\nqLIbIQjAgCcCtTKTKTbrNnrP1jrcdsNS7Dt4Hmsa7AjHRCwrN6HUpEbjslJEYiJ8wTiKjCoMesLo\nAMh1lyAIgpgV+nPUViYWFh92e5l2SZEaQ37WkLKu0Y4lI+FdWRdQgXOP84Vi+NWx0fCttmYnLMZR\nS2O5lV37imIKbxzvRluzc0GtLfMiCE+fPo3du3cjHA7jtddew7/8y7/gxhtvxOrVq/FXf/VX+XiJ\ngqTXHZqwHRdZC6BaxT5UQS7rEd82cCmUDVwWT75QPd8e8rFlH4a4LJ65ks7wMX28YCw2aRiX0GIT\nOx6flZSf3xIuhnGJI39un7kyxBJAOJaAWqmQau6UcHUjtWoFegaCqCwzMl+w2YKuw74ojHoVWlY6\nkEym0H5hCMfbXbhz43Jpl/Z4uwuf3lQLXyiGTlcQMhkY0U8QBEEQ+aC4iFuTFKlzn0TMW/RcLUC1\nSo6YJ8n8T6tWwB/OJETMxgmOX+soJTfSSExEcZEGyWQKW1qqUaRX4fWjl6SyFMVGDfyhzFrYpFct\nqLVlXgThP/zDP+Cf/umf8PjjjwMAbrvtNjz00EPYu3dvPoYvWPQa9mHUce1kkk1Zu2MzawE0cRZA\nvh2PJyds+0NxRvD5Q2wW0BKTBr9855LU5l02xwlOLulNpg7hKEYt21YpZOPqHI4lGmULz8dirOBN\nptmyHWvrbUz/TNw+c2WIJYAqexECkTgcyoxQP/Dbbty7rR6nOz3QqhV4r8OF7W01GB6JFcySjQUt\nNWulLF3AqFDkNw6G/FEc+0Mf1jTY8fsLwxjwxeD2ROAo1aOloXTB+N8TBEEQc4dcJnC15uomOJqY\nL4wtM1Zs0iAUicMbjKPUrMHmdZXwheIjta4zyWHu3LgcXa6AtI75k43Lcc+WOrg8EZSX6qCSy7Bx\nTQXUSjmKDCp4fFGsabBLz85xuHDPljqolDKcv+zDxjVV8AWi+M3xbqxrtMM4sjZeUbmwPJ7yIggV\nCgXq60fFxtKlS6FQLPxgXr4IqoELYA6EElybK9ugYbM68jF+vjHHC1wbyKRYHhujx9f5c1i0uHdb\nPXrdITitejhL2KyhaqUMFVaDVBZCzSW9MeqUzPz4OoO8ewbf1mpV+L+/HK1DyMcY9rpDzG5enzvE\nCL6ZuH1SltHcKGSZZ9TtzViS1Uo5lAoB9VVm9A1H8MmbaqBRyVDGPTf1VWbUVpow5GWFokIug16j\ngJlL/W3UqZgvWyAjHl/edxFAE1rHZNYlCIIgiOnAb14O+yefuZwoXMZ6i2U3nrPcdUudtDa2Fmsz\nheZDMdRXW9DrDuHOTctx5P0enO0JoNSkhqNkKc73+FFhN+DVwxexaW0VDn/Qh5aVDuY1h3xRiMkU\nDv6uF3+2tR6vHL4IIGNxLDVp8OBdzQvKOgjkURB2d3dLfrkHDhxY0MlkspRZNPCHEpIFzM6VZbBw\n7gpjfZIBIBiKM4IsFB4v+Pa/fVFq8xY+jz/KCDZPgP0y7PNEGQvOZ29rQK1ztN8fTiCWSCKZSiOW\nSCLIudtHouz8IjF2fmajesL2uJhCrp0rBnMmbp+UZTQ3l/qC8IXj8IXiON7uws5banHusl8S6GqV\nDNF4CmmkmeesbyiMuJgaJ6/FZAp/snE5fMEY7t1WjyFfFFqNAkqZANdVrN1d/UG0NtiZHUAqYk8Q\nBEFMFd4VkM84Scw/0uk0+ofDWNdoh06tgJhKMf3eYAzVZcaM+B/xOmtrdjJr3x2ba7FqhR16jYL5\n/50bl+PNE13Y2roEMi7Nvr1Yh+FAFHfdsgJqpYCWlQ4YdSqEInEsKTMsyNCXvAjCr3/96/jyl7+M\nixcvYs2aNXA6nfjWt76Vj6ELmuVOM+IiIBcEVNoNqHWygqXIoGAsdEYta4Ez6NU4d9mXyeKZTKGm\ngq1rODxG8OnUinG7X0a9StoZETA+6cygJzxhW0yyWUL5OoEKBfvh4QUpX2eRt3CaOIHIu8T6AvEJ\n22Nz8ExVGlCW0dwsdRjg9sdgGPHDl8lkaL/gRuOyUsTiSSTENJ57PZOZ63i7Szpvw2oHbBY9ItEE\ndmyuxcVev+SakRAzSWr6hsKorTTD64/ihSOduGdLPY6e6pfGqHYYcfRUP+wlOpy57EHvUASnOz3Q\nqRX45eGL+Ms7VtH9IgiCICaN28tukrt90dwnEQVNe5cXz70+Wmj+U5tGQ6/0mkx972A4AYtRjcuu\nIDatrYScE3cXe/1ovzCEGz/iZP4/5IvC7YvhhbfOYUtLFXZsroUvGEckJuJcjxfvvN+HOzcuh88d\nQ42zCL5AHE1NDtSUsflAFgp5EYT19fV46aWXMDw8DJVKBYNhYWTdyeV2mCvTZTicYgQV79KZENks\no3xh+JKiiWMAeRdPvs0Xli8xs21fMD5hO5dLqMszmqRGQCYT5Vg0KlYwarj5zWYtQcoympvhYBw9\nA0G8NpJdi08Ikxyx8vPFWcuK9dIxNzU7GbG41FGEH//qjDTen22tG6lFGGfiSY26zGbJxV4vxGQR\n9o3U9wEyLiEk4AmCIIipUGrW4JXDl6Q2v2Yi5h/85n4oEsf2thr0uoOoshvxwlvn0NbsxGtHO6Vj\nPsPdd606kzAmytXCLi/VY12jHVq1AslUGgkxDV8ghqOn+tHWnBGPgXAcjhIdrm+wQYAAq9W4YPNS\n5EUQfu1rXxuXxhUAvv3tb+dj+Dljpm6HPe7ghO1hf4yzALL+7nzKZF6QBcMJrmwFm8Vz0MNmGXV7\nWcHGZxU1chZGPkso3y4p0jKClRe8w9zu3BB3fbIcdQhnYuWjLKO56eoPYjjAJYAZc8+yQvBEhwsb\n11RAqZBBq1YiFBmNjT0xknjGF4whlU6Pe8YGPZlgba1Kied/PbrL9+lNtRjyR6FWKnC224utrUtw\n4LfdaFxWCpkgwGRUI400kAbjSvrREhL2BEEQxHh8wYkT7Ux5u7UAACAASURBVBGFTTqdxpEP+nCu\ny4MquwEyGaDiPM9KzVpEYyLaLwyhwmbEprWVUMhZ/REXk2hrdkIhl0FMpvBehwuNy0rQfmFIej6W\nOorwq2OX4PZl1kCb1lYiEIqhymGEVqPAuyMeTdF4EqUm7aIIYcmLILzhhhukvxOJBI4dO4aKiooJ\nzpgfzNTt0MnV7Svn2iUmTlBxheX54/k6gEEu5pBvl5XqcP6yH0DGgscLOrVKNqEFz2xQ4t6t9egd\nCqG8RA+zgUsqMxyasG216PDSodEYyD//eCPTn6sO4UysfJRlNDdVZUZE4+yO2dj3PJutKxRJQK9V\nomcgiFg8iWXOUdfmUFSUYlcPnuwZtyNbpFchjTQGOaE45I8iEI5jiSMzVv9wCNtuWIqzXV6oVXL8\n+PXTSIhJeIMxaFVKDAWicPuj0GiUWEbWXoIgCILDoFPCM7LJKQDjEuERhU17lxf/9uIHWNNgx8V+\nP6rsRgTDcSZr6C/eOofWVQ5sbV3CeCqNJTBSwi0cScBpM2DDdeUw6lU43u6S1pz11RZEYqO5DcqK\ndVArZXjp0AU0LitF47IS1Fdb4CjWoq5ycRgU8iII77jjDqa9Y8cOfPGLX8zH0HPKTN0OdRoZ7r61\nTkrKotewJrABLqaPb2vVMsbNTqdmz7dxLqG8iyi4sg5jF/JAJsuo1ayVxlcr2fFDsSQ6XYFMjKOY\ngkpl4l6PFZhWM5/FVGAEJz9+rve3vsqEXdub0D0QRKXNiIZq9vWJmbG+vhThaBx3b6nL1BTUqeAJ\nxLBxTQVkMgE2sw7BSBxWC1teQq9V4DPb6tE/HIZJr4ZWI8fAcMYarVFl0jkLggCjTgWtRg6dRjfu\ntWUCUGU3YtATQblVjyFvZFwJi1MXh6FVK3Dw5Ggm3RKTBq7h8LhyFZSUhiAIYnGjUsiZNc99tzdM\ncDRRaHS7gmz5h3YXtrZWo8sVkEJT9BoFzEYN3N4Ibmp24tQFN2QyAbe2VMFi1GDIF4Feqxw1tpzK\neK8NB6K4Z0sd3L4oinQqvHr4Iv7k5uUY9EVg1KqgVsnQOxiE2xeTXn9pWdGCTB5zNfIiCFNc1p++\nvj5cunRpRmPu2bMH77//PgRBwO7du7Fq1apxxzz55JP43e9+h2effXZGr3U1crkd5lqEiikwWTyT\nKS7pSo7C8JcHw/CH4lJh+1giifVjyuoMB2JcllHW/c81HJmw7Q0kmKQyn97E1kmMRFjrUZhr84JV\nywnWi70B5stZp1Zg3YrRWoPZ97fbFUSl3TDu/e3o8uFH+05J7SIdZQrNJ6e7fLg8EILZqIFWo4RC\nIUPILyKZSuOt9y5Lx93aUsWcl0wCz4wRbztvWQGTXgUxmcKQP4riIg3zXG1cU4Equ0Fy4TDqVDBq\n5egaCCESE9EzEBxnDc8+0xHO5/9Sn3/kh4EtV0FZZQmCIBY34zObR65yJFGIVNkNuNjvl9p6jQI2\nixaR6Og6YE2DHT9/86zU/vSmWvyMa/u4tfCAJwyNWoF4IolDv+uR8hVcdgchEwTp/D/byno4Lbbc\nE3kRhI2NjRAEQSo1YTQasWvXrmmPd/z4cXR2dmLv3r04f/48Hn744XFF7s+fP48TJ05AqZw9l4Bc\nboe5FqGBECu4/pQrTG82KhlBVWzkCtXrVXh5jMvlPVvYIqs6jRIvjSlLwY8/LgWziRWggUh8wnYq\nnWYEHT++Ui6DWimHXCZArZJDqWAFoZ2LaeRjHHMl5aFMobNLtyuIEx0ubFhdDgCIxQTYzJpxcYUm\nw8TZYge9EfzmeDfuvrUOMrmAM50epl8mCEgLAnQqORKpNAw6JSJxkXm2Pv0x9tmqrTBj38HzWMvV\nKKyyG9F+YUgqVzH2Wvhro2eFIAhi8WDkNtUNenIZnU80VJsRiImSNXBNgx1DvigcJTpsb6tBIBwH\nn66EjxP1hWLj1iwWowbneryoshvRusqBN453AwAcxXoM+UY3DQY9Ydy7rR6+YAy1FeZFl3siL4Lw\n9OnTuQ+aAkeOHMHmzZsBADU1NfD7/QiFQtDrR60I3/rWt/Dggw/i+9//fl5feyqc7fay7cteZhHK\nZ+30cu1wNMkIRj7+asjPJ2Vh2xaDihGUFk5QJlMpxoKYTLG1ISushgnbPv6Dxs0/EktKGSUB4HNc\n4flhvk6if2opoClT6OxSZTcgFBUhJtMw6VX41bFOrGmwo5ir3aTXKtjyIhr2a8Nmzgh9ty8ChUyG\nuioLk3k0lU7jUq+fEYBbW6uZMQIjcQKhaCZeMRCOSzUN7761Dmcve6FVK/DakUtY02CXMvKm02mc\n7vZCrZZjw2oHyor16BkISklppuI2yge0k9spQRDE/EHDl8Li8iIQhY0AAbe1LkU4ksDpTg8UclnG\n8HHoItY02BGJiajl4vnG1cM2qJk1S5XdiP1vZ7KYH2934VObarFpbSXEZAr7Dp7H+qYy6dxyqx43\nrixbtL/7eRGE3/ve966YZTTL/fffP6Xx3G43Vq5cKbUtFgvcbrckCF988UW0trbC4XBMb8J5Ynyd\nPbbNx/TZuML1fBZRvj0uRpCL0QtFxQkFpccfZxbh21qXMP3jBSPr+lvKvV6pmZ1/rrIUjlI9k1Tm\nC1zh+Vwut5QpdHbJvr8DnjCUSjlaVzmg16oQjYm4Z0sdPP4YlEo5Brj7Ok7ojySVicaTOHiyC5vX\nVXLZYwUEx2QmBca7S1tNGnS6glApZHjhzXPYtLYSFqMa0VgSCTHGCEyDVomWBiuAjJX+tx8O4q33\nLqOt2Sl9Ho6e6kc4Vge1Uo7+oTAsRg0qSrVYUXl1kUdupwRBEPMXD5fJnA+jIQqX7Obu4B/6EE8k\nM15zRRoEQnFsXl+NXncQWrUCvzp6CXfdUodzPZlNYl8wNi454ktvX8CtLUvg9kXgCcQkF1EACEbi\nOPaHPul/giBga2s1qmwGrG+wLloxCORJELpcLpw5cwYtLS2QyWQ4dOgQamtrsWTJknwML7miAoDP\n58O+ffvwn//5n+jt7WX6cmG1Gqc9hyudG4mJzIMYiYnMcUvKjIwFb0mZkennXSrtFi3Tr9MqmKQ0\nBq2C6b+SoBzbb+YshmaDiunvHzov/S0gI+jG9ht17OsX6ZRMPy8YS0wapt/mCjDvj61Yx/Qf+aCP\nWYDv/tx6tK5iRb7NyibCmQ4zue9zxWzPOTu+zVqEwx/04vt7T+KTN9UwBWD/dHMt+ofCONHhwpoR\n90yrWQuFXMa4Kt+5cTnu2VKHS/1+3NTsRDyRBAQBqpGkRcOBqOTqmf0SHvJFcO+2evS6Q7CZddCo\n5dCpFYiLKeg1CjhLM7UOQ1FxXAYxg06JEx8OYUvLEvSf7JE2o/h4ww+7vCgvNSAUSeDAby9jTYMd\ncqUSaxrseOPdTgz5IghFEli93IqWlQ70j9k8AYD+4TBuXsvGT+aD+fg85iLf11To483GmIU+3myN\nea1ZjO/zYpljiUmN/W+PJiG77/aGef3MLpb7BmTWg8c6BgCwyRDv3VaPVArMprAnEIVBqxxJXqeE\nzaKFyxOByZDZ0I7Ekjh72Yv2C0PYdsMS5nWMWhUjECtsBtxx03IouJCniZgP92U65EUQejwe/PSn\nP4VCkRnu/vvvx1e+8hV89atfndZ4NpsNbrdbag8MDMBqzVgEjh49iqGhIdx9992IxWLo7u7GE088\ngW984xs5x51u+YGrFaJcYjfi52+OWugevKuZOa7apkf/cBihaALFRWpU2w1Mv1GrGFese2x/IJhg\nXDLv2VLH9PNlJOwWLdOv5dwnNGo50281a/HqmOQg926rZ/qDYfb1793Gvn4snmDmH0+ITP+Hnd5x\nZSWW2Ucf+nNdbKzZuS4PlpeNdwudSSHQmRYRnasP6WyWyuDfkz+ccyMUFdEzyJYN8QXjONHhwvqm\nMshkAiptZvjDcUSiCdy7rR6nOz3SDt0LIz75AHD3rXX48a/OMBY7ANKunkmvQjKVRp87DFFMSe4c\nbc1OHDzZg8/+cYNUz7Ct2QkxlcI9W+ow7I8hFE3gl4cuIhQVkUym4CjWSV/u2bqJWbRqBXrdQRxv\nd0mfg3NdHgx6QjjTNfps/vKdS3jwrmY4uA2asmIdBgcDec1gOttFbRfC85rv92g23vNCn+N8uea5\nYDG+z4tljgoFm+hOqZDlbZ5z8bwuhvuW/X39w4XhcZu6AHC604PlThN2bK6F2xuFmEzBoFPhtTfP\nQq/JFJzXqOSIxUcTxrQ1OyEg40X36uFLTEK7t97rkspYNCwpRtt1dng8ofETm8Vrnu0xp/us5kUQ\nDgwMSGIQAFQqFYaGhqY93oYNG/D0009jx44dOHXqFOx2O3S6zGJty5Yt2LJlCwCgp6cHDz300KTE\n4Gww0yyZyTTropnirJ0uT2TCtkEjHycoxyImRVSXGdHrDqG8VI9Uiv2w8WUuBj28C2hkwrZWrWSy\nTfIuq7liAClGcO5Jp9MwG9VY12hHuZWvk6nBrS3ViMZF6DRK9LpDePdUf8aXfyCAhmoLulxByGSs\nOBoYeU75L/cBbxi6kVjWsVlMs0Iwe7xrOIxIVGTST7/zfh+2tlYzGwxd/UHs2LgMwZEfADGVwt23\n1uFCjw8qlRzvjbFsZjdFKu2GK/7wdLuCuHWdE1+8YxUu9fpRVTZa5oRcSQmCIAofbyDOZk7nkpUR\nhUU6ncbR0wP43Vk3quxGyGWAVqNkrIHlpQYAaWjVCsQSIqrtRdKG8dg1AjC6ltCqFFApZdLGNQAc\n+0Mf/vjGpdh2w1J4A1F8pLYULQ1WpnzVYicvgrCpqQk7duzAmjVrAAAnT57EihUrpj1ec3Mzmpqa\nsHPnTsjlcjzyyCN48cUXYTQapWQzhUCuLJm97pBkmdCpFehzh7gspGwM4M5b2PesnLMAOrh2IJzA\noDeCSExEKp2GRsU+2DKZAhd6/FIdQb4O4bg6gly7wq5n5l9pYwVDb44YyFwxgNk6g139QWYBT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VKC4uhtfrneEVzA6z7fpGC1diIZLv53quXVAJgiCI2Ye+6wufK92jresrqW5gAZAXQXj69Omr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Y6CsvppGtXsJSsKYTAYxJtvvom3334bX//61+FyueDxeCYtb+/evVi9ejUAoKqqCh6PB36/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\nDAbjDeX/AZ1p4/BapUw0AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2c2963f550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"plt.rcParams[\"figure.figsize\"] = (10, 6)\n",
"sns.pairplot(data_test[data_test.columns[:5]])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y_train=data_train[' shares'].as_matrix()\n",
"y_test=data_test[' shares'].as_matrix()\n",
"data_train.drop(' shares',axis=1)\n",
"data_test.drop(' shares',axis=1)\n",
"x_train=data_train.as_matrix()\n",
"x_test=data_test.as_matrix()\n",
"#for GPU\n",
"x_train = x_train.astype(\"float32\")\n",
"x_test = x_test.astype(\"float32\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5000, 59)\n",
"(34644, 59)\n"
]
}
],
"source": [
"print x_test.shape\n",
"print x_train.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RandomForest"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.metrics import mean_squared_error\n",
"from math import sqrt\n",
"rms=lambda x,y:sqrt(mean_squared_error(x,y))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"logy_train=np.log(y_train)\n",
"logy_test=np.log(y_test)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(34644, 5000)"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(logy_train),len(logy_test)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
" max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_split=1e-07, min_samples_leaf=1,\n",
" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
" n_estimators=100, n_jobs=1, oob_score=False, random_state=0,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf=RandomForestRegressor(n_estimators=100, random_state=0)\n",
"rf.fit(x_train,np.log(y_train))"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.002460366584112899"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logy_predict=rf.predict(x_test)\n",
"rms(logy_predict,logy_test)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.005392327405477055"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf200=RandomForestRegressor(n_estimators=200, random_state=0)\n",
"rf200.fit(x_train,logy_train)\n",
"logy_predict=rf200.predict(x_test)\n",
"rsm(logy_predict,logy_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NeuralNet (Keras)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def norm(x):\n",
" return (x-np.mean(x,axis=0))/np.var(x,axis=0)\n",
"x_train=norm(x_train)\n",
"x_test=norm(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n",
"/home/xiangze/src/scikit-learn/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation\n",
"from keras.optimizers import SGD\n",
"from keras.layers import Convolution2D, MaxPooling2D, Flatten,Dropout"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"59\n"
]
}
],
"source": [
"inlen=len(x_train[0])\n",
"print inlen"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/2\n",
"34644/34644 [==============================] - 156s - loss: 4.6784 \n",
"Epoch 2/2\n",
"34644/34644 [==============================] - 183s - loss: 5.0914 \n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fa3e8223b90>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model=Sequential()\n",
"model.add(Dense(360, input_dim=inlen))\n",
"model.add(Activation('tanh'))\n",
"model.add(Dropout(0.2))\n",
"model.add(Dense(30))\n",
"model.add(Activation('tanh'))\n",
"model.add(Dropout(0.2))\n",
"model.add(Dense(10))\n",
"model.add(Activation('tanh'))\n",
"model.add(Dropout(0.2))\n",
"model.add(Dense(1,activation='linear'))\n",
"sgd = SGD(lr='0.01', momentum=0.9, nesterov=True)\n",
"model.compile(loss='mean_squared_error', optimizer=sgd)\n",
"model.fit(x_train, logy_train, nb_epoch=2, batch_size=1, shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"logy_predict_keras0=model.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 8.23036289],\n",
" [ 8.97610378],\n",
" [ 8.23036289],\n",
" ..., \n",
" [ 5.15600491],\n",
" [ 4.41026402],\n",
" [ 5.15600491]])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logy_predict_keras0"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.223524868997211"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rms(logy_predict_keras0,logy_test)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/2\n",
"34644/34644 [==============================] - 185s - loss: 6.0240 \n",
"Epoch 2/2\n",
"34644/34644 [==============================] - 173s - loss: 5.3703 \n"
]
}
],
"source": [
"model_r=Sequential()\n",
"model_r.add(Dense(360, input_dim=inlen))\n",
"model_r.add(Activation('tanh'))\n",
"model_r.add(Dropout(0.2))\n",
"model_r.add(Dense(50))\n",
"model_r.add(Activation('tanh'))\n",
"model_r.add(Dropout(0.2))\n",
"model_r.add(Dense(20))\n",
"model_r.add(Activation('tanh'))\n",
"model_r.add(Dropout(0.2))\n",
"model_r.add(Dense(10))\n",
"model_r.add(Activation('tanh'))\n",
"model_r.add(Dropout(0.2))\n",
"model_r.add(Dense(1,activation='linear'))\n",
"sgd = SGD(lr='0.01', momentum=0.9, nesterov=True)\n",
"model_r.compile(loss='mean_squared_error', optimizer=sgd)\n",
"\n",
"model_r.fit(x_train, logy_train, nb_epoch=2, batch_size=1, shuffle=False)\n",
"\n",
"logy_predict_kerasr=model_r.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.9448095431504773"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rms(logy_predict_kerasr,logy_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bayesian Optimization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://scikit-optimize.github.io/notebooks/bayesian-optimization.html"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def f(params):\n",
" model=Sequential()\n",
" model.add(Dense(params[0], input_dim=inlen))\n",
" model.add(Activation('tanh'))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(params[1]))\n",
" model.add(Activation('tanh'))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(params[2]))\n",
" model.add(Activation('tanh'))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(1,activation='linear'))\n",
" sgd = SGD(lr='0.01', momentum=0.9, nesterov=True)\n",
" model.compile(loss='mean_squared_error', optimizer=sgd)\n",
" model.fit(x_train, logy_train, nb_epoch=2, batch_size=1, shuffle=False)\n",
" \n",
" _logy_predict=model_r.predict(x_test)\n",
" \n",
" if(np.isnan(np.array(_logy_predict)).sum()>0):\n",
" return 1e12\n",
" else:\n",
" return rms(_logy_predict,logy_test)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/2\n",
"34644/34644 [==============================] - 170s - loss: nan \n",
"Epoch 2/2\n",
"34644/34644 [==============================] - 144s - loss: nan \n",
"Epoch 1/2\n",
"34644/34644 [==============================] - 164s - loss: 7.6721 \n",
"Epoch 2/2\n",
"34644/34644 [==============================] - 237s - loss: 6.7560 \n",
"Epoch 1/2\n",
"34644/34644 [==============================] - 232s - loss: 7.7396 \n",
"Epoch 2/2\n",
"34644/34644 [==============================] - 230s - loss: 8.7367 \n",
"Epoch 1/2\n",
"34644/34644 [==============================] - 225s - loss: nan \n",
"Epoch 2/2\n",
"34644/34644 [==============================] - 224s - loss: nan \n",
"Epoch 1/2\n",
"34644/34644 [==============================] - 219s - loss: nan \n",
"Epoch 2/2\n",
"34644/34644 [==============================] - 271s - loss: nan \n",
"Epoch 1/2\n",
"34644/34644 [==============================] - 310s - loss: nan \n",
"Epoch 2/2\n",
"34644/34644 [==============================] - 312s - loss: nan \n",
"Epoch 1/2\n",
"34644/34644 [==============================] - 311s - loss: nan \n",
"Epoch 2/2\n",
" 7621/34644 [=====>........................] - ETA: 245s - loss: nan"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-42-4da33c3b2295>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mn_calls\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m# the number of evaluations of f including at x0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[0mn_random_starts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m# the number of random initialization points\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m random_state=None)\n\u001b[0m",
"\u001b[1;32m/home/xiangze/Work/scikit-optimize/skopt/gp_opt.pyc\u001b[0m in \u001b[0;36mgp_minimize\u001b[1;34m(func, dimensions, base_estimator, acq, xi, kappa, search, n_calls, n_points, n_random_starts, n_restarts_optimizer, random_state)\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[1;31m# First points\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[0mXi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mspace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrvs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_random_starts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrng\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 156\u001b[1;33m \u001b[0myi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mXi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 157\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 158\u001b[0m raise ValueError(\n",
"\u001b[1;32m<ipython-input-41-9c8ed3ad4f63>\u001b[0m in \u001b[0;36mf\u001b[1;34m(params)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[0msgd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSGD\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'0.01'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmomentum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.9\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnesterov\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msgd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogy_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnb_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[0m_logy_predict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel_r\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, **kwargs)\u001b[0m\n\u001b[0;32m 400\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 401\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 402\u001b[1;33m sample_weight=sample_weight)\n\u001b[0m\u001b[0;32m 403\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 404\u001b[0m def evaluate(self, x, y, batch_size=32, verbose=1,\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight)\u001b[0m\n\u001b[0;32m 1028\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1029\u001b[0m \u001b[0mval_f\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1030\u001b[1;33m callback_metrics=callback_metrics)\n\u001b[0m\u001b[0;32m 1031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1032\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36m_fit_loop\u001b[1;34m(self, f, ins, out_labels, batch_size, nb_epoch, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics)\u001b[0m\n\u001b[0;32m 766\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'size'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 767\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 768\u001b[1;33m \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 769\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 770\u001b[0m \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m 595\u001b[0m \u001b[0mfeed_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 596\u001b[0m \u001b[0msession\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 597\u001b[1;33m \u001b[0mupdated\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 598\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 599\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 338\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 339\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 340\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 341\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 342\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 562\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 563\u001b[0m results = self._do_run(handle, target_list, unique_fetches,\n\u001b[1;32m--> 564\u001b[1;33m feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[0;32m 565\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 566\u001b[0m \u001b[1;31m# The movers are no longer used. Delete them.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 635\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 636\u001b[0m return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[1;32m--> 637\u001b[1;33m target_list, options, run_metadata)\n\u001b[0m\u001b[0;32m 638\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 639\u001b[0m return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 642\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 643\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 644\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 645\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStatusNotOK\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 646\u001b[0m \u001b[0merror_message\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror_message\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m 626\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 627\u001b[0m return tf_session.TF_Run(\n\u001b[1;32m--> 628\u001b[1;33m session, None, feed_dict, fetch_list, target_list, None)\n\u001b[0m\u001b[0;32m 629\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 630\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"from skopt import gp_minimize\n",
"from skopt.acquisition import gaussian_lcb\n",
"from sklearn.gaussian_process import GaussianProcessRegressor\n",
"from sklearn.gaussian_process.kernels import Matern\n",
"\n",
"noise_level=0.1\n",
"\n",
"gp = GaussianProcessRegressor(kernel=Matern(length_scale_bounds=\"fixed\"), \n",
" alpha=noise_level**2, random_state=0)\n",
"\n",
"res = gp_minimize(f, # the function to minimize\n",
" [(inlen,500),(10,100),(10,20)], # the bounds on each dimension of x\n",
" base_estimator=gp, # a GP estimator (optional)\n",
" acq=\"LCB\", # the acquisition function (optional)\n",
" n_calls=10, # the number of evaluations of f including at x0\n",
" n_random_starts=10, # the number of random initialization points\n",
" random_state=None)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'res' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-43-0aeca8884591>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mskopt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplots\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplot_convergence\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplot_convergence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"gp_optimize\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'res' is not defined"
]
}
],
"source": [
"from skopt.plots import plot_convergence\n",
"plot_convergence((\"gp_optimize\", res))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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