diff --git a/.gitignore b/.gitignore index 4b3946acfa2f4829672a8fa7a4c9909fedee1964..cf2eba712c173fc25f7c3462929893eccf2cade5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,14 +1,14 @@ +# *.pyc + +# data file data/ +# vscode setting .vscode -<<<<<<< HEAD -hw1/cs285.egg-info -hw2/cs285.egg-info -hw3/cs285.egg-info -hw4/cs285.egg-info -hw5/cs285.egg-info -======= -cs285.egg-info/ ->>>>>>> 659479d5570010905d208d07bd9a3d904a0e187e +# cs285 py env things +cs285.egg-info + +# jupyter notebook +.ipynb_checkpoints diff --git a/README.md b/README.md index 413ac86b7949b7da6db73b301e91c50ec88b0447..2c23686f5d0e3af5fbe0f8da734358b6a4b4107a 100644 --- a/README.md +++ b/README.md @@ -12,16 +12,21 @@ Assignments for [Berkeley CS 285: Deep Reinforcement Learning, Decision Making, [对应CSDN博客地址 点击此处](https://blog.csdn.net/qq_39537898/article/details/116905668) 大概训练了总长500步,迭代次数90,修改了done,所以左边走到快摔倒的时候就会自动停止掉了,倍速了1.2: -
- +
## hw2 +2021/5/27 完成了第二次作业的solution.md + 2021/5/19 完成代码,等待对应PDF分析结果等 [详情请点击此处见solution.md](hw2/solution.md) [对应CSDN博客地址 点击此处](https://blog.csdn.net/qq_39537898/article/details/117064479) + + + + diff --git a/hw2/DataViz.ipynb b/hw2/DataViz.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..fc389b96335e436f032480e869ab78d959f7c03a --- /dev/null +++ b/hw2/DataViz.ipynb @@ -0,0 +1,1046 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "import os\n", + "import tensorflow as tf\n", + "import glob\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "figsize=(5.7, 3)\n", + "export_dir = os.path.join('../image')\n", + "\n", + "sns.set_theme()\n", + "sns.set_context(\"paper\")\n", + "\n", + "def get_section_results(file):\n", + " \"\"\"\n", + " requires tensorflow==1.12.0\n", + " \"\"\"\n", + " X = []\n", + " Y = []\n", + " Z = []\n", + " for e in tf.train.summary_iterator(file):\n", + " for v in e.summary.value:\n", + " if v.tag == 'Train_EnvstepsSoFar':\n", + " X.append(v.simple_value)\n", + " elif v.tag == 'Eval_AverageReturn':\n", + " Y.append(v.simple_value)\n", + " elif v.tag == 'Eval_StdReturn':\n", + " Z.append(v.simple_value)\n", + " return X, Y, Z" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Experiemnt 1" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def read_q1_data(batch):\n", + " full_data = pd.DataFrame()\n", + "\n", + " for folder in os.listdir('data'):\n", + " split = folder.split('_')\n", + " if 'CartPole-v0' in split and batch in split:\n", + " config_list = split[split.index(batch):split.index('CartPole-v0')]\n", + " config = '_'.join(config_list)\n", + "\n", + " logdir = os.path.join('data', folder, 'events*')\n", + " eventfile = glob.glob(logdir)[0]\n", + "\n", + " X, Y, Z = get_section_results(eventfile)\n", + " data = pd.DataFrame({'Iteration': range(len(X)), \n", + " 'Config': np.repeat(config, len(X)), \n", + " 'Train_EnvstepsSoFar': X, \n", + " 'Eval_AverageReturn': Y,\n", + " 'Eval_StdReturn': Z})\n", + " data['Eval_AverageReturn_Smooth'] = data['Eval_AverageReturn'].ewm(alpha=0.6).mean()\n", + " data['Eval_StdReturn'] = data['Eval_StdReturn'].ewm(alpha=0.6).mean()\n", + " full_data = pd.concat([full_data, data], axis=0, ignore_index=True)\n", + " \n", + " return full_data\n", + "\n", + "data_lb = read_q1_data('lb')\n", + "data_sb = read_q1_data('sb')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=figsize)\n", + "sns.lineplot(data=data_lb, x='Iteration', y='Eval_AverageReturn_Smooth', hue='Config')\n", + "plt.savefig(os.path.join(export_dir, 'q1_lb.png'),dpi=200, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=figsize)\n", + "sns.lineplot(data=data_sb, x='Iteration', y='Eval_AverageReturn_Smooth', hue='Config')\n", + "plt.savefig(os.path.join(export_dir, 'q1_sb.png'),dpi=200, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=figsize)\n", + "df = data_lb\n", + "df = df[~df['Config'].isin(['lb_rtg_na'])]\n", + "sns.lineplot(data=df, x='Iteration', y='Eval_StdReturn', hue='Config')\n", + "plt.savefig(os.path.join(export_dir, 'q1_a1.png'),dpi=200, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=figsize)\n", + "df = data_sb\n", + "df = df[~df['Config'].isin(['sb_no_rtg_dsa'])]\n", + "sns.lineplot(data=df, x='Iteration', y='Eval_AverageReturn_Smooth', hue='Config')\n", + "plt.savefig(os.path.join(export_dir, 'q1_a2.png'),dpi=100, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=figsize)\n", + "df = data_sb\n", + "df = df[~df['Config'].isin(['sb_no_rtg_dsa'])]\n", + "sns.lineplot(data=df, x='Iteration', y='Eval_StdReturn', hue='Config')\n", + "plt.savefig(os.path.join(export_dir, 'q1_a3.png'),dpi=100, bbox_inches='tight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Experiment 2" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 1000 -lr 0.05 -rtg --exp_name q2_b1000_r0.05 -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 1000 -lr 0.01 -rtg --exp_name q2_b1000_r0.01 -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 1000 -lr 0.1 -rtg --exp_name q2_b1000_r0.1 -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 3000 -lr 0.05 -rtg --exp_name q2_b3000_r0.05 -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 3000 -lr 0.01 -rtg --exp_name q2_b3000_r0.01 -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 3000 -lr 0.1 -rtg --exp_name q2_b3000_r0.1 -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 5000 -lr 0.05 -rtg --exp_name q2_b5000_r0.05 -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 5000 -lr 0.01 -rtg --exp_name q2_b5000_r0.01 -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 5000 -lr 0.1 -rtg --exp_name q2_b5000_r0.1 -ngpu\n" + ] + } + ], + "source": [ + "# 打印命令行指令\n", + "# Experiment 2使用\n", + "for b in [1000, 3000, 5000]:\n", + " for lr in [5e-2, 1e-2, 1e-1]:\n", + " print(f'''python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b {b} -lr {lr} -rtg --exp_name q2_b{b}_r{lr} -ngpu''')" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IterationConfigTrain_EnvstepsSoFarEval_AverageReturnEval_AverageReturn_Smooth
00b5000_r0.55007.01.01.0
11b5000_r0.510007.01.01.0
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900 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Iteration Config Train_EnvstepsSoFar Eval_AverageReturn \\\n", + "0 0 b5000_r0.5 5007.0 1.0 \n", + "1 1 b5000_r0.5 10007.0 1.0 \n", + "2 2 b5000_r0.5 15007.0 1.0 \n", + "3 3 b5000_r0.5 20007.0 1.0 \n", + "4 4 b5000_r0.5 25007.0 1.0 \n", + ".. ... ... ... ... \n", + "895 95 b3000_r0.5 288005.0 1.0 \n", + "896 96 b3000_r0.5 291005.0 1.0 \n", + "897 97 b3000_r0.5 294005.0 1.0 \n", + "898 98 b3000_r0.5 297005.0 1.0 \n", + "899 99 b3000_r0.5 300005.0 1.0 \n", + "\n", + " Eval_AverageReturn_Smooth \n", + "0 1.0 \n", + "1 1.0 \n", + "2 1.0 \n", + "3 1.0 \n", + "4 1.0 \n", + ".. ... \n", + "895 1.0 \n", + "896 1.0 \n", + "897 1.0 \n", + "898 1.0 \n", + "899 1.0 \n", + "\n", + "[900 rows x 5 columns]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def read_q2_data():\n", + " full_data = pd.DataFrame()\n", + "\n", + " for folder in os.listdir('data'):\n", + " split = folder.split('_')\n", + " if 'q2' in split and 'InvertedPendulum-v2' in split:\n", + " config_list = split[split.index('q2')+1:split.index('InvertedPendulum-v2')]\n", + " # print('_'.join(config))\n", + " config = '_'.join(config_list)\n", + "\n", + " logdir = os.path.join('data', folder, 'events*')\n", + " eventfile = glob.glob(logdir)[0]\n", + "\n", + " X, Y, Z = get_section_results(eventfile)\n", + " data = pd.DataFrame({'Iteration': range(len(X)), \n", + " 'Config': np.repeat(config, len(X)), \n", + " 'Train_EnvstepsSoFar': X, \n", + " 'Eval_AverageReturn': Y})\n", + " data['Eval_AverageReturn_Smooth'] = data['Eval_AverageReturn'].ewm(alpha=0.6).mean()\n", + " full_data = pd.concat([full_data, data], axis=0, ignore_index=True)\n", + " \n", + " return full_data\n", + "\n", + "data_q2 = read_q2_data()\n", + "data_q2" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=figsize)\n", + "df = data_q2\n", + "# df = df[~df.Config.str.contains(\"r0.5\")]\n", + "df = df[~df.Config.str.contains(\"b1000\")]\n", + "df = df[~df.Config.str.contains(\"b3000\")]\n", + "sns.lineplot(data=df, x='Iteration', y='Eval_AverageReturn', hue='Config')\n", + "\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "\n", + "plt.savefig(os.path.join(export_dir, 'q2_a1.png'), dpi=200, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=figsize)\n", + "df = data_q2\n", + "df = df[~df.Config.str.contains(\"r0.5\")]\n", + "df = df[~df.Config.str.contains(\"r0.05\")]\n", + "# df = df[~df.Config.str.contains(\"b3000\")]\n", + "sns.lineplot(data=df, x='Iteration', y='Eval_AverageReturn', hue='Config')\n", + "\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "\n", + "plt.savefig(os.path.join(export_dir, 'q2_a2.png'), dpi=200, bbox_inches='tight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Experiemnt 3" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IterationConfigTrain_EnvstepsSoFarEval_AverageReturnEval_AverageReturn_Smooth
00b40000_r0.00540006.0-233.006180-233.006180
11b40000_r0.00580063.0-150.115143-173.798296
22b40000_r0.005120085.0-112.425797-134.456950
33b40000_r0.005160231.0-90.130249-107.162183
44b40000_r0.005200308.0-124.554543-117.705563
..................
9595b40000_r0.0053871074.0126.622177147.023098
9696b40000_r0.0053911888.0188.888428172.142296
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9898b40000_r0.0053992449.0155.035599149.018515
9999b40000_r0.0054032935.0132.000137138.807488
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100 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Iteration Config Train_EnvstepsSoFar Eval_AverageReturn \\\n", + "0 0 b40000_r0.005 40006.0 -233.006180 \n", + "1 1 b40000_r0.005 80063.0 -150.115143 \n", + "2 2 b40000_r0.005 120085.0 -112.425797 \n", + "3 3 b40000_r0.005 160231.0 -90.130249 \n", + "4 4 b40000_r0.005 200308.0 -124.554543 \n", + ".. ... ... ... ... \n", + "95 95 b40000_r0.005 3871074.0 126.622177 \n", + "96 96 b40000_r0.005 3911888.0 188.888428 \n", + "97 97 b40000_r0.005 3951937.0 118.559952 \n", + "98 98 b40000_r0.005 3992449.0 155.035599 \n", + "99 99 b40000_r0.005 4032935.0 132.000137 \n", + "\n", + " Eval_AverageReturn_Smooth \n", + "0 -233.006180 \n", + "1 -173.798296 \n", + "2 -134.456950 \n", + "3 -107.162183 \n", + "4 -117.705563 \n", + ".. ... \n", + "95 147.023098 \n", + "96 172.142296 \n", + "97 139.992889 \n", + "98 149.018515 \n", + "99 138.807488 \n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def read_q3_data():\n", + " full_data = pd.DataFrame()\n", + "\n", + " for folder in os.listdir('data'):\n", + " split = folder.split('_')\n", + " if 'LunarLanderContinuous-v2' in split:\n", + " config_list = split[split.index('q3')+1:split.index('LunarLanderContinuous-v2')]\n", + " config = '_'.join(config_list)\n", + "\n", + " logdir = os.path.join('data', folder, 'events*')\n", + " eventfile = glob.glob(logdir)[0]\n", + "\n", + " X, Y, Z = get_section_results(eventfile)\n", + " data = pd.DataFrame({'Iteration': range(len(X)), \n", + " 'Config': np.repeat(config, len(X)), \n", + " 'Train_EnvstepsSoFar': X, \n", + " 'Eval_AverageReturn': Y})\n", + " data['Eval_AverageReturn_Smooth'] = data['Eval_AverageReturn'].ewm(alpha=0.6).mean()\n", + " full_data = pd.concat([full_data, data], axis=0, ignore_index=True)\n", + " \n", + " return full_data\n", + "\n", + "data_q3 = read_q3_data()\n", + "data_q3" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=figsize)\n", + "sns.lineplot(data=data_q3, x='Iteration', y='Eval_AverageReturn_Smooth', hue='Config')\n", + "\n", + "plt.savefig(os.path.join(export_dir, 'q3.png'), dpi=200, bbox_inches='tight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Experiment 4" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 10000 -lr 0.005 -rtg --nn_baseline --exp_name q4_search_b10000_r0.005_rtg_nnbaseline -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 10000 -lr 0.01 -rtg --nn_baseline --exp_name q4_search_b10000_r0.01_rtg_nnbaseline -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 10000 -lr 0.02 -rtg --nn_baseline --exp_name q4_search_b10000_r0.02_rtg_nnbaseline -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 30000 -lr 0.005 -rtg --nn_baseline --exp_name q4_search_b30000_r0.005_rtg_nnbaseline -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 30000 -lr 0.01 -rtg --nn_baseline --exp_name q4_search_b30000_r0.01_rtg_nnbaseline -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 30000 -lr 0.02 -rtg --nn_baseline --exp_name q4_search_b30000_r0.02_rtg_nnbaseline -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 50000 -lr 0.005 -rtg --nn_baseline --exp_name q4_search_b50000_r0.005_rtg_nnbaseline -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 50000 -lr 0.01 -rtg --nn_baseline --exp_name q4_search_b50000_r0.01_rtg_nnbaseline -ngpu\n", + "python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 50000 -lr 0.02 -rtg --nn_baseline --exp_name q4_search_b50000_r0.02_rtg_nnbaseline -ngpu\n" + ] + } + ], + "source": [ + "# 打印命令行指令\n", + "# Experiment 4使用\n", + "for b in [10000, 30000, 50000]:\n", + " for lr in [5e-3, 1e-2, 2e-2]:\n", + " print(f'''python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b {b} -lr {lr} -rtg --nn_baseline --exp_name q4_search_b{b}_r{lr}_rtg_nnbaseline -ngpu''')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IterationConfigTrain_EnvstepsSoFarEval_AverageReturnEval_AverageReturn_Smooth
00b10000_r0.0110050.0-60.946136-60.946136
11b10000_r0.0120100.0-104.890411-92.334904
22b10000_r0.0130150.0-35.839565-56.119943
33b10000_r0.0140200.0-92.547371-78.550625
44b10000_r0.0150250.0-62.816284-69.012349
..................
89595b50000_r0.024809600.0265.139038284.867040
89696b50000_r0.024859700.0296.879120292.074288
89797b50000_r0.024909800.0290.738983291.273105
89898b50000_r0.024959900.0304.936066299.470881
89999b50000_r0.025010000.0243.145752265.675804
\n", + "

900 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Iteration Config Train_EnvstepsSoFar Eval_AverageReturn \\\n", + "0 0 b10000_r0.01 10050.0 -60.946136 \n", + "1 1 b10000_r0.01 20100.0 -104.890411 \n", + "2 2 b10000_r0.01 30150.0 -35.839565 \n", + "3 3 b10000_r0.01 40200.0 -92.547371 \n", + "4 4 b10000_r0.01 50250.0 -62.816284 \n", + ".. ... ... ... ... \n", + "895 95 b50000_r0.02 4809600.0 265.139038 \n", + "896 96 b50000_r0.02 4859700.0 296.879120 \n", + "897 97 b50000_r0.02 4909800.0 290.738983 \n", + "898 98 b50000_r0.02 4959900.0 304.936066 \n", + "899 99 b50000_r0.02 5010000.0 243.145752 \n", + "\n", + " Eval_AverageReturn_Smooth \n", + "0 -60.946136 \n", + "1 -92.334904 \n", + "2 -56.119943 \n", + "3 -78.550625 \n", + "4 -69.012349 \n", + ".. ... \n", + "895 284.867040 \n", + "896 292.074288 \n", + "897 291.273105 \n", + "898 299.470881 \n", + "899 265.675804 \n", + "\n", + "[900 rows x 5 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def read_q4_data():\n", + " full_data = pd.DataFrame()\n", + "\n", + " for folder in os.listdir('data'):\n", + " split = folder.split('_')\n", + " if 'HalfCheetah-v2' in split and 'search' in split:\n", + " config_list = split[split.index('search')+1:split.index('rtg')]\n", + " # print('_'.join(config))\n", + " config = '_'.join(config_list)\n", + "\n", + " logdir = os.path.join('data', folder, 'events*')\n", + " eventfile = glob.glob(logdir)[0]\n", + "\n", + " X, Y, Z = get_section_results(eventfile)\n", + " data = pd.DataFrame({'Iteration': range(len(X)), \n", + " 'Config': np.repeat(config, len(X)), \n", + " 'Train_EnvstepsSoFar': X, \n", + " 'Eval_AverageReturn': Y})\n", + " data['Eval_AverageReturn_Smooth'] = data['Eval_AverageReturn'].ewm(alpha=0.6).mean()\n", + "\n", + " full_data = pd.concat([full_data, data], axis=0, ignore_index=True)\n", + " \n", + " return full_data\n", + "\n", + "data_q4 = read_q4_data()\n", + "data_q4" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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lyz/0Dhw4MBoIBJtvbCgUkjMyMhMdK3MdfJgLBAKy1+tNGIZBewpj7TFihBJscrTtKZC1rHs41bF33nk7Y+vWLa7f/e6Jw07Jp0KnT1Kqqr6lqupNwMWqqt7U4jVXVdVVvWWIQCAQdAXDMGioXM32z+YTCezDbMskWLOu0/N0LYq/ei0NlZ93qZ1E3A+SjEl2dFjHYssB9GaJz1ioHKszr0vXbw972mAigb3dPv94pa06WDQalQBkWcZud+h2u00fOnR4pLS0xBYMBk0VFeVmWZYNu91udKTMlZ+fH9m4cb1T0zQ2b97gnjTppGBHCmPt0XJ03J4CWcu6HamOrVq10v3WW2/kPPLIo3sO95CQKqmsce9RFGUhB9eaPwF+qarqia9tJxAIjho1+94EIGPAJZhka6tjhmHgK/0Qf81aBoycCbaRhOq3Urf/36Tnn3fISBdA1yLUHXifUP0WJMmMrsdwZ09pt25LtLgf2eI+7DS8yWzH5iqkYsdzODPGEAkU4+nX/XAcm3swdSXvoSVCyOa+s3e7SR2spqba+sMf3rn/kUceLCwrK7EnEpo0atSYQNOa9403ziqdM+fmkZIkcccd9+yHpDLXJ5/8N3327OuUjIzM+K9//b97AebMubvkN795aLCmJaQLLri4OiMjUwNoUhiTZbNx//2/2tsV+6666pqaBx742eDZs69XBg4sjDQpkP3ylz8d/P/+36N7W6qOORwO/aGHfrMHYP783w5yOBz63Lm3jQT47W9/vzs7O6fHwdVdVgdTFOU/wNvAy41F1wIzVVW9qKdGHEGEOthh6Cv9hL7T1+O9n4lYA6VbnkC2epNJTIZc3Wq92Fe+goaKT+k37DpyByjU14cw9AQlmx8jc+A3cWaMOeSavvKVBGvWkzHwYmyuQg5smk+/4ddjTys8rC0NlZ8Rrt9G7sibDlvPMAwi/iKCNesI+VTylJuxOnK71X/DMCjZ/PtkXxpzoAt1sL5Jb6mDFaiq+kyL939SFOVHPTNNIBAIDhKs3YDFkUvuiB9Qu/8dytW/4PCMRLZ6klHONevIGfY9bGkDm8+RTGacmeMJ1Kw7xHEbhkGwZj3ufqfh8AwHwOYsIBrY16nj7iyivLl9ScLhGYbDM6zHgXJN69wR/95mxy04srz55j8y33nnrZyWZY8++viuphH615FUHPcBRVHu5uCI+7tASe+bJBAI+iKGYRCs3YA751RMspWsQTMJ1W8lFjxAIlaPHg+SPeRq7O4hh5ybljmJcvVZEjEfZqu3uTwa2Eci3oAzY3xzmS2tkGig+JBrtEWL+bvkuFvSG9HtaVkndSheIuh9Zs68qnbmzKt6JTHK0SIVx30D8BDwQeP7T4Ebe90igUDQJ4kGi0nEfDgzxgFJJ+jKGIsrY2yn51qdeVgceQRrN7YSAwnUrMPpHYVsPhhgZksrxF+1BsPQm/dUt4cW92N15vegR92jafQuEHRElx23qqrVwJxOKwoEAkE3CNasx+lVuh2UlZY1CX/l53hypyFJMnoiQrh+GzlDv9uqns01EEOPEw+XH9YxNwWnCQRfN1LR4x4OPAM05f/bCsxRVVXkDRcIBD1C16KE6reSPeTqbl/DlTGehopVVO1+hezBVxOs24zJ4sLWZmrdJNuwOvsTCRR36LgNw0g6bqtw3IKvH6lMlT8P/EZV1f8AKIpyUWPZmYc9SyAQCDohVL8Vk2zH7u6WZhEAJrODPGU2VUWvUr7jOSRJIi1zUrvrzjZXIdHAPujXbtAuuhbBMBJixH0UaE9k5Mkn/5D/6acrMjweTyI9PTM+f/7jRXD8iYzU1FSbH3jg50PC4bBp0qST/HPnzisFuOKKS8ZlZ2fHAS644JLqq6/+bk0q9yyVHeHeJqcNoKrquyTTngoEAkGPCNVvw5kx7rBrzl1BtrjpN+LGxoxm1biyJrVbz5Y2iGigmI62wzYpeAnHfey44YZZpQsXvqQ2OW1N03j++YUFCxY8u+ORR+bvXrDgsYEALUVGRo4cFXz99VezAZpERv7yl79uf/fdpdl1dbVyXV2t/O67S7MXLnxp++2337X/qaceG9CZHS1FRhYtelk1mUwsX/6ht2WdliIjF198afVzzz2bB7Bo0Z/zku9fVnfs2O5U1W12SOYuX7jwJXXhwpfUVJ02pDbi3qwoyuMcjCq/BtiSaoMCgUDQEsPQiQaKceec2ivXM5ksZA/+NlqbCPOW2NIGomth4pGqdlOlanE/JtmByWTpFZuOVwKxoBzVep6r3Cbb9DSrq8PtVW1FRgD+9rcX+7/xxmv9LrvsyqrLL7+y9ngUGdm6dXPanXfOKwGYOnWa78sv17gVZXQkkUhIt9xyg+J0uhLz5t23f9CgwZ2O+luSiuOeDdwB/KKpb8ADqTQmEAgEbYmHKzD0ODbXwM4rdxFJkjDb0js8LpudWOw5RIPFHTruvj7a1nSNB1b/dnxUi/VYGcMmW7X5Zz60Xja1f6m2IiOPP/70jrlz55X6fD55zpybR06Zcor/eBQZiUajJofDYSTb9WilpSVWgGeffWF7VlZ2YuXK/7offfSRQX/848KdXb6ZpBZVHgbmp3JxgUAg6IxIoLhZGetokhT02Ic7++RDjgnHDbJJ5ten/3xTb424O3LacKjISGZmlgbg9Xq1iRMn+3fs2O4YMGBg7HgTGbFabXrjeyMQ8Msej0cDyMrKTgCceebZ/qeeeuzwmYDaocsfiKIoFyuKskpRlP2KopQqilKmKEppqg0KBAJBS7qSxexI4EwfTci3nUS07pBj3Um+ciKSZnVpWY7MeE9fh5smh0NFRnw+nwyQSCTYvn2rq7BwcPR4FBkZO3ZcYPnyZV6ANWs+80yZcoo/Go1KkUhEAti2bYu97UNAV0hlqnwBcAWwWVXVriU4FwgEgsNgGAbRYDGuzEuPett29xDsaYOoL1tO9uArWx1LxBt6pPIlSI22IiO///1vB+zfX+zQdZ1vfGNG7fDhI6Jw/ImMzJp1a/mvfnX/kCVLFudOnDjJP2rUmEhZWanlxz+eO8Jud2gA8+bd13kavzakIjKyjKS0ZzzVRo4hQmTkMPSVfkLf6evx1s94pIqybc9QMO5eZIsrpXN7o6+xUDnl6kJyldnYWuzpLtv+LGnZU3BnT+nR9XsDITLSN+ktkZH7gP8qirIKaI6AU1X1/h7aJxAI+ijRQDFme3bKTru3sDrzcGVOoL7kA/oNv6F5z3dyjTvtmNgkOLqc6CIjT5PMT74F6DgUTyAQCLpIJFCM3TXomNrg7f8Nyrb+kUjDThzekRiGhp4IYraINBV9gRNdZMSiquq8I2aJQCDoUxiGQTSwj/T8846pHWarF3e/06gr+QC7eyhaIgiI5CuCry+pOO6PFEX5GbCU1lPlO3rdKoFAcMKjxXxo8QZsxyCivC2e3DMJ1m6moXJVUjZUMmEyH5vpe4GgM1Jx3E1RGhe2KDOAc3vPHIFA0FeIBouRrekdZjc7mphkKxkDL6J6z+uACdns7hVtbYHgSJBKApZzjqQhAoGg72AYBsHajcnR7dcEp1fB4RmOr/y/WB1iK5jg60unjltRlG8BG1VV3df4/iGS+7mLgbmqqhYdWRMFAsGJRqRhJ5HAPvJHzznWprQiY8BFRLY9jWwVgWlHi/bUwZ5++sm8tWu/8CYScWnChEn+e+/9WQkcf+pgS5b8PWvx4r/1N5vNxquvvtlr2h5dyZz2CFAFoCjKTOC7wE3AG8CzvWWIQCDoGxi6Rl3J+3j6TTtsPvFjgdnqJXPgt3BljDvWpnwt0Px+OV5dbenpS/P7U8p3fvPNP6xYtOiv6osvLt6+ffvWtKKi3bbjUR3snHNm+BYv/mevi3F1ZapcV1W1aff/FcBfVFVdC6xVFOWO7jasKMrpwB9IBroFgGsb7fkr4AaWqar6YGPdS0mKmxjAXaqqftHddgUCwbHFX/U5hh7Hkzv9WJvSLq5M4bQBjESCop/9eLwRjfZYZESy2bThT/xxvWRu3+W0VQebPv0sP0A8HpccDqeWk9Mvfjyqg2Vn56SczrQrdMVxG4qiZAENwAySzraJdjvdRfYB56mqGlIU5YfAHCAdeE5V1SWKoryjKMoYQAUeBs4iqf/9KnBGD9oVCATHCC0ewFe+gsyB38QkW4+1OYLDIJnNDP3f323Soz0XGTHZbHpHThsOVQebPv2sbfPn/2bAJ5+syJg4cVKDy+XSj0d1sCNFVxz3g8BaknfhA1VVNwAoinImsLe7Dauq2lKgJAYkSDrkJqnQpSSdtQ7sUFXVD/gVRbEoimJXVbXD5PACgeDria/sv1jsOTjFVPRxgex2a3ILR3ekaKsOBvCTn9x/YN68+w7cd989Q5cv/9A7cODA6PGmDnak6NRxq6r6lqIo7wBuVVVbyuh8BXyn6Y2iKFNVVf0sVQMaR/O3AxcB1zbKhwLUA0OATKBlu/WNZV1SJktPd6ZqErJs6tZ5xxt9pZ/Qd/r6de5nNFxDoHYdQyfdTFp6z/dIf5372pv0hX42qYNVV1eZLRarEY1GJZvNZsiyjN3u0O12m95SHSwQ8JvaqoOdc86MhvbUwcaOHR/avHmDe/bsW8sBXnrpuXxN09iyZVPK6mAjRiiRVas+8Z522rSGlnVPPvlU/zPPLCi49tobq1qqgx0purQdTFXVBK2dJ6qqBttUexo4KZXGFUVxAktIRqdXK4oSajGa9gK1je22jGBpKu8S3REhON6EGrpLX+kn9J2+ttdPLR7A0OOYbRnHyKok1XvfxZ42mAR5vfJZ9OXP9HA0iowcV7RVB3vkkQcLy8pK7ImEJo0aNSbQtOZ9vKmDLVv2nnfJksW51dVV1ttuu2nk3Xf/uHj06LE9ni3usjpYZyiKsk5V1ckp1DeTjEx/XlXVfzaWzQdWq6r6hqIobwH3k1zjXgOcSTJo7XVVVbu6xi3UwQ5DX+kn9J2+ttfP2uKlJGI++g2/9hhZBbFwJeXb/3yICldP6Muf6eEQ6mAnBr2lDtYZqT4BfI/GgDNFUe4C3gEeBV5SFGUe8JGqqlsAFEV5EFjW2MY9vWaxQNAHiIZK0LVjGxLiK/sYh1fpNactEPQWJ7o6WGek9ISnqupfSW79asvF7dR9G3i7m3YJBH0WXYsRD1cCBroex2SyHLG2EtE6avcvJbPwslZpTCOBYsI+lbxRtx2xtgWC7nI8qoP1Zsj6p714LYFA0AvEwmUgJf/NE5GaI9pWJLCPiH8PlTtfIhFLxu5EGoqo2v0K7n6nY3X0O6LtCwR9hS6PuBVFSQOuAQYDzeF2qqre3/iz28lYBALBkSEWLMHq7I8WDxCPVGN1Hrkc3IlINXb3MJAkKne9hDvnVOpKPsCbdxaeXJF6QSDoLVKZKl8KbATWkdxbLRAIvuZEQyXYnAXEI9XEo9VHtK14tAarIxdv/29QVfQqdQfeI7PwUtKyuhyzKhAIukAqjjtdVdW5R8wSgUDQ68SCJTjzRwPJEfGRJB6pweFVkExmcoZeQyJWj8WefUTbFBz/tCcy0pE4R3sCIR0JgKxbt9b55JO/LzQMg+uu+0HZjBkX+qB9MZDObKypqTY/8MDPh4TDYdOkSSf5586d1yqPSKpCJ+eeO33ysGHDQgDf//4NZeecM6OhvXY7IpU17jcVRfm+oihuRVGsTa9UGhMIBEcPLe5HizdgcxVgsWcTP4Jr3IahkYjWYrFnASCZzMJpnwCEQ3G5wRex9PQVDsVTynfenjhHRwIhHQmALFjw2MBHHpm/+6mnFu54/vmFBZqmdSgGcjg0TWPRoj/nJeu/rO7Ysd2pqttapftORegEICcnJ7Zw4UvqwoUvqak6bUhtxH1j489HSG7Lkhp/Dk21UYFAcOSJBksxmZ3I1nTM9mzi0WoMQ0eSej+NciJaD+iYbcJZnyhoms7Lz3w2PhHXeywyYraYtFl3T18vy+3/7XUkMtKS9eu/crUnELJx4wZ3WwGQ6dPPbNA0TcrPL4gD5OcXRIqKdtk7EgNpy5Ili7PWrFntjcVi0rRpZ/i2bt2cdued80oApk6d5vvyyzVuRRndvMcyFaGTc86Z0VBbW2OZPft6JTs7J3bffb8ozszMSmnrWZcct6IoJuA6VVV7JnxwAAAgAElEQVRF5LhAcJwQCx3A6sxHkiQstmwwNLSY74hkUItHqzGZXchmR+eV+zBGXCNRFcKS//XPbibLJq770dRN8bjW4yc9i0XWO3La0L7ISNs6Pp/P3J5ASCDgl9sKgNTV1ZpdLldzXZcrTauvr5dTEQOJx+PSE088sxvgzTf/0c/hcBgAbrdHKy0taTXbnKrQyeLF/9yclZWdeO21V7IWLPhDwa9+9UgxKdDVlKe6oigLSDGlqUAgOHZEgyXY0wYBYDI7MckO4pHqI+K4E5FqLLasXr/uiUb4i1JiO2vxfn8ckqXHA9kjjsNp0RxYjonISFs8Hk+iPYGQ9gRA0tMzEsFgsPkGB4NBOT09XUtFDGT06LHNab2tVpveWN8IBPyyx+NpdU9SEToBaFpXv/TSmXXvvPN2q+QvXSGVJ6l3FUW5RVGUdLHGLRB8/YgG9lO85e8k4n4MwyAWKsXqKgBAkiTM9qwjFlkej9RgFmvahyVe7CO2uw4k0OqFuGFLmkRGKisrzBaLtd0snJMnTwlu3rzBrWkaGzeubxYIaRIAAVi16hPvpEknBRwOhyHLslFeXmYJBoOmsrIS25AhwyInn3yqf82azzwAnYmByLLcbMfYseMCy5cv8wKsWfOZZ8qUU1pN5TcJnQC0J3SiaRqbN29wT5p0UjAYDJo0LenXP/vs07T+/Quiqd6vVNa4v9f48/4WZWKNWyD4muArX0EsXEJD7U48udMx9BhWZ0HzcYstm3gnkeWGoeOvWoMzfXSr7GedEY9W40wf3W3bT3T0cJzQ6gPYJ+cR212HVhfBnNNzhbQThbYiIx2Jc7QnENKRAMicOXcd+PnP7x1mGAY33DCr1Gw205EYSGfMmnVr+a9+df+QJUsW506cOMk/atSYCBwUGUlF6GTDhnXORx99ZLDD4dQsFov+i1/8al+q96vXREa+pgiRkcPQV/oJJ35f45Eayrb9kRGn3E3lgY34ypZjtmWQP+ZgXqSGilWEfSq5I29q9xq6Hqdm7xuEfSpp2aeQOfCQ7MMdcmDjfLIGXY7DO7LHfekqx8tnahgGwY/2YsQ00i4cRmhlMZLDjPPUgs5PRoiM9FV6RWREUZRb2ytXVfXZ7homEAh6h0D1l9jSBuFIy8Ob58HuGYqeCLeqY7Fn01C5qt3ztUSIqqLF6Ikw6fnn0VCxioyC85FMnX9FaIkQuhYWU+UdkCjxkyjz4545CskkIWfYSZQdUblmQQosWvTn3M8++7RZOtpstujPPLNo57G0qTNSebLq3+J3G3ABsB0QjlsgOAIk16lLsDoLkKSOB1C6FiNQu56swsuay9pT4TLbs9ETIbRECNnsbHWses9rSEjkjrwJk8mKr+JTwg07uzT9HY9UgyRjtqZ3WrcvEt1ShXV4JnJaMiRITrcT3XZkk+EIus7s2bdVzJ59W8WxtiMVuuy4VVV9qOV7RVEeAt7tdYsEAgEAseB+Kna+QHrBhXj6ndZhvVDdJkwmGw6vctjrma3pIMnEI9XIaYXN5fFIFdFAMflj7mx26K70sQRrN3TJcScjyjOPyP7w451ETYhERQD3tAHNZaYMO0YkgR6OY3IcObU2wYlLT/7TsoCBvWWIQCBoTUPV55itGfjKPiIRrWu3jmEY+Ku+IC17SqeOU5JMWGxZh6Q+DdSsx5Y2qNU2MVfWRMK+XWjxYNvLHEI8euJGlOuRBEa8+7uhopursBR6kd225jJTmhXMJhFZLug2XXbciqKUKYpS2vQCPgYeOGKWCQR9CC3ub04oAZCI1ROu307WkKuwpw2hpngp7QWSRgN7iUeruyzkYbZnEQuVNb83DJ1g7SZcmZNa1bM6CzDbMgjWbU7W0zVC9WorG5uIfw33cOvhOLHdtejRTtNQd4gR0/Av3UH4i9LOK7eD5o8S31ePbVxrOVNJkpDT7eh1wnELukcqU+X9O68lEAhSJRaupEL9C66sSWQOvAQAf9UX2FwDsDnzyRh4CWXbniFYs4607IM5kAzDoL70I9KyJiNb0rrUVlrWSVTt/jtp2VOwOvOINOzC0GOHTIlLkoQrcwLB2g2YrenUl35AIlpL1uArcWWMa1U3Ea3BlTG2h3ehdzA0nejWaiKbGpcsPyvBNjIL6/AMjLiOHooj2WQs/TvPXBb6vAQjphEv9mFMHYBkSi1QO7q1GrmfC3P2wXiCQH0VO75YxghpONEtRSTUz7AMn4plxHQke9c+wxON9kRGnnzyD/mffroiw+PxJNLTM+Pz5z9eBLBs2Xvel19+ob8kScyde2/x5MlTQqkKfLQnVNKZjR0JmbSkPfGSjsRSekoqI+7/dqVMIBB0HUNPULP3DWxphQRrN+Cv+iIZbFbzFe6c5Lq22eoho+B86ko+aCUUEvapxCOVePPO7nJ7Ds8wXJnjqS3+F4ahE6hZjzNjLCb50FxKrswJxMPl1Oz9B86McbgyJxGs3XSI/YloHeZeHHHrET9GPOWcFCQqg/jfVImq1ThPH4D3u+NwnlGYLH97B4H3dhP+opTgsj3ogcN/V8f21BHfV0/aBcMwEjqJiiBGIoZWs5/EvvXEtn5EbNvHxIvWkDiwBT3cWiciURMitqsWe+NoW6vaQ+TLf/LZK//Lzk2raSj/CkNzYx58ErEtHxH42z2E319A9Ms3iO9chR6qT7n/Jxo33DCrdOHCl9Qmp61pGs8/v7BgwYJndzzyyPzdCxY8NhBSE/joSKjkcGia1qGQSRMdiZe0J5bSG3Q64lYUJRPIAbIVRRnBwf2BHqBfhycKBIJOqSv5AEOPkz3kaiL+Iqr3/oNocD8mkx1H+qjmeq6syUT8e6gqWkzuyFmYZBv1pR/i7je1y6PtJtILLqBs29PUl35IuGEHucNvaLee2eole+g1WB15mK1eosH9VOx4sVVUeixUChi9pgSm1e4nvPRRAKyTvollzLlI5sMnaDQMg+imSiLry7GNzsE+OQ/JnByTWAd5sRR6MGIakjWZATPw3m4iGytwTms/REfzRwmtPoBjSj7mbCfmfDfxfXXEVi9Cr96L5PAguTLAgFgkwI76OPmmABmZ/TD3H4Vp8CWEPqnAOjwTc4GbeNEXRD58hlJnAb64Tv7AYZQbbobF3VgnnY510jfRSraRKN6AVrWX+M7VWGoPYDvtO71yT3tCNByQE7Foj6MOzVabbnOkdRgs0FZkBOBvf3ux/xtvvNbvssuurLr88itri4p22fPzCyJut1t3u926piWkSCQipSLw0fR7W6GStqxatdL9yit/zbVarcaIEUqwqGi3s62QyXnnne9rqt+ReEl2dk7312oOQ1emyr8F/AAopPXWrwbgF0fAJoHghELX48RCpcRD5cTCFUgmCxZ7DqATqFlL7oibMMk2nOmj8eadha/sY9LzZ7QKNpMkicxBl1G58yWq9yzBmT4aXQvj6TctZXtks4PMgZdQvWcJZlsWVlfHMabOFpHqVucAzFYvobqtuHNOBsBX8SnOjHGYZFtHl+gQrbIIvb4M89CTkcw2tJpiwu/MxzxoMqbcYcS+epvYxndxnH8Hcu7wdq9h6AbBj/agVYdwnTsEywAPAInynURX/x3nN3+MZHUi2Q5+1TlO6k/g3V1YR2VgsicwOdPxF5US21GLJSyjN8QwD/BgHZWcRbAO8hJeU4wUqSDt+icxOZJt1O/bT+VH6xjhGkDUCBFxaFhL96DvOYBtQn/sk/IwogGin7wEky5j6xefMeHsK8kZMIzlf32CYZlj0QMxZLcN84CxmAd8PZYbmtA1jaV/eXB8Ih7rBXUwq3bF7f+33iS3f6m2IiOPP/70jrlz55X6fD55zpybR06Zcoq/vr5eTktLa3aETqdLq6urNacq8NGeUEl71NfXWV58cfE2WZb50Y9mj2grZNKybiriJb1Bp45bVdUXgRcVRZmpquqbR9IYgeBEIx6ppmr339HifiyOflgcuehahEDNWhLRWtLzZ2BzHcyg5ck9E7M1o92tXSaThZyh11CuLqLuwH/IKLiwWw4TwJk+mrScU7E6+h92j3hLJEnCmTGOUN0m3DknEw2WEGnYRf/RP0q5fSMRI/zh0xihBlj1NyzDpxLf/TmWoadiO+P6ZAT8iGlEP/krkZUv4Lzy10ht1hT1WITI58tJlGTgPMvZ7LSNaJDIR3/CCNYR2/IhtsnfanWeuZ8Lc34aofc/Ae19qvp9i/QSLxXxvdQnKpCzHKR7C+lXbSU9uz/m/DSMmIF1+GWYHB4M3aDm/U3I5ToueybOaYOp3bQOKqJk2SZjYjXxSCbhyqnEvvgHkj2Tosp63Bn9GD5xOpJkIr2gAC2aQK+LILtt+L9cQ/3Hy+n3veswp2Wh+2PN/TlWmGSZS29+cFNvjbg7ctpwqMhIk8yl1+vVJk6c7N+xY7tjwICBsUAg2OyzQqGQnJGRmUhF4MMwDNoTKmmPESOUoNxoc3tCJi3rpiJe0hukkoBli6Io/wJyVVU9VVGUccAlqqo+eoRsEwiOayL+PVTtWYLDM4Kswm8dkoXMMIxDnGYyKGx8h9eULWnkDPsu/srVpGVP6ZF9mQMuSvkcV+Z4GipWkojW4Sv/L86MsUjl+4mFN0MihmRzYR5x+iFb09r2NbbpfTAM0q5/gsSBzcTVlVhHn4P1lKua60myBdvUawguvo+4ugLr6G80XksntuHflG18l4R0GpJNJvrxn5FdP8WUM4TIyheR7GlYJ3+L2JrXsY67AMnS+gHHJG8kHhnMgcgECuJe4sNklNO/RXXpHsr3bWefupb1K97C5nAxerhCLlkY1uTnUv/JDrSyEPXDIww742wkSWJY4bls/ORfrP/qJSQJEqvjsPqT5vZkc5AZ35/XfF9GTD6bhhXl2GvykNIldix5ES3DQ+ThX5F93g+wZBUcc8cNYHOkaYeb4u4tmkRGqqurzBaL1fD5fLLX69USiQTbt291XXHF1VWDBw+JlpaW2ILBoCkQ8JtkWTbsdrvRJPBxzjkzGtoT+Bg7dnxo8+YN7tmzby0HeOml5/I1TWPLlk3NQiXtIbd40GgSMhkxQomsWvWJ97TTprUKajj55FP9zzyzoODaa2+s6ky8pDdIxXH/BbgXWNj4fguwGBCOWyBoQ7BuMzX73sSbeyaevLPaHdV2daTbFqsjl6xBM3tqYrew2LOxOPpTV/I+kYbd5DinEv7kaUzp+WC2JKe+936F/ZxbkSw29Iif6MoX0Wr247zobkzp/dFD9cTW/Qv72bOQrA4sQ0/BMvSUdtuTbC6sU2YS+/INLMNOA4ud8Cd/ZfX6L8keMpZB8ZOwj8zB8J3Fgbd+RyhnJAU1W0m76tdInmxi65cS3/YR1gkH865Ht69k25b3cVkvZIBjIibnRjLPvBGA3MKR5BYm861Hw0H27/iKrz5cwhlDZ2A6ECKcXolRFKQyp5zxZx78DCRJYsIZ36LfwBFIkglryQbkTf/Gesq3MY89D8lkwmyxYeg60QP7yR82jm0rd1FfVMz2FUup6SeDHCbn25cT3VSGHo2TxpAj+El+vWgrMvL73/92wP79xQ5d1/nGN2bUDh8+Igpw442zSufMuXmkJEncccc9+wFSEfgAaE+opDM6EjJpEhnpSLykI7GUnt6vLouMKIqypnGkvU5V1cmNZetVVZ3U2bnHECEychj6Sj/h6PY14t9L5e6/kTnwki7vr+4tjkY/GypXU1/yAc6M8Ti++ALzkFOwnXwFAHpDJeF3HwezFev4C4h+9iqSOxtTWiZayTYcF91NbNvHGA2VOL718y49vBh6gtDr/4N58EkYsQgl2z5jXdhOQX+F0aHT2GJbQ1XdHmKRIJKhc/LkqQw551oAYts+JvblG7i+9zuQLdRt+ojPP/oncauLUy+4iSxLGpH3/gfnVb9Gzkqu9euahhFugGANib1fsWP9SopiHs7yXA3AbmMDk679HmZLx0FzhqGj7d+IPGA8kungyK1h1aeUP7eQtJNPITpwNOZyE5tKl3Da1T+kpHovxdvXclb/7yLn2HCdmnTcQmSkb9IrIiNAqaIok2hc8VcU5RagqBfsEwhOGOKRaqr3vIYnd/pRd9qpElVrwDCwKlkpjf5dGeMI1qzHpWWRCNRiHXd+8zGTpx/Oy39BeNnTRD5ehHXK5VgnfRMkiejnSwgt/T/QNZwzHyC2vRokCduow0ekSyYztqnXJB8IrE7Ks8Yy0Oll2tjLqf5sL+7+eRSedDK5hQrbVvyDHQcOMLhxat4ycjqxr94m+uUbFO/ZzsayGvrnFnDKVXdhtScj4+OFE4hv+QD5rFnEK0o48OjDyKYIGSNlJHc2yrnfJ7p7D5XFewgmfNiK11HzmonMiy/Bktn+NjhJMmEuPHRMU7/8QzynTydWWYF0YDWuYRdx6vgZZI0aT3piFAd2bSRS7yd9VG6XPw9Bz3jzzX9kvvPOWzktyx599PFdTSP0ryOpOO5bgceA/oqilAArgR8eEasEguMQLR6kavffsXuGp7S3+miSOLAZZAtyvxFEvirDiGskKoM4Tx+AZOla8LBscZM36oeE3vw1ltHnHJI4RLK5cFw8DyPcgMl1MI2qfeo1mDw5GJEAejST8Jo9mNKsnTpuAHngRKwnX4meN5LSJc9y1hU/JLSnDvugTCacejAmQDljJjsXPkj53m30HzIGSbYgj7+Qrz7+JyWajclnXMqwUy5AkiRiFRWEtm7BVnAK8TUvontGUbrwL5hdViI+GWnqXaSNnwDAwLV72FL3CYPPn0n25BH4VvyX/b99hIK778VW0DV5zsjePUT27qH/D+dgzsig7j8fYKo24z0vOY0vmy2cev53kT6ooyFURTYZnVxR0BvMnHlV7cyZV9UeaztSIZXMaZXAtS3LFEVpdw9cV1AUxUIybepY4GZVVV9XFCUb+CvgBpapqvpgY91LSW49M4C7VFX9orvtCgTdQYsHaKhcjRbzkYg3gKFjdw/F4R2JJJkJVH9JsG4TVmd/sgov6/b69ZEkvnctkQ+eBrMF62k/B8B96UiCK/bh/88u0s4bgsl1+D3TTWglW9Br9+O48K52j0smObnXuQ3WMeei+aMElu7EOjqb2LZqtIYosicZPBaLhln74WvkFAxj+MQzDl5PkrCddBm7N36K3eUhp/9Q/Cu34TxncKvr251uhoybyrY1H9B/yBg0LcEXu/fQYM9hxszbyOiXzLdhJBKU/ekp4rW16MEgZpeZxOfPkjY0h7x7HqLmX0upfm0xrtFjSNTV0fDxR5x25924xiazxrlPnUrFSy+w//9+Q8Hcu3EMH9HpPatf/hGuiZOwZCVH6RkXX4Dv5Y0YET35jQdkZg+iQQqw98AmsscfPW1zwfFFlxy3oigFQD6wQVXVmKIoucCdwI10X2gkAXwbuK1F2X3Ac6qqLlEU5R1FUcYAKvAwcBbJpC+vAme0vZhAcKTQ9ThVRa9iGBp29xCsrgFg6IQbdtFQsQrQsXtGkD3429g9w7+WTjuxbz2RZc9gm/Y9tDKVyFdbsQwdg5zpwP3NEQSWFRHZWInz9PYTSekhH4mdn4JkQnJ4iG35EItyNibnoVKekZCf2vJ91Fbsx19bQUNtBS5PFtMvmw2aQejjfZhzXThOySdRFiBR4kf22PDXVbLyrYVgGOzfsQ6nO4P8oa33Nu/ZuobBo09BqwiBBOZc1yHtjzr5XN557mEq9+9kx7r/0lBbwbnfm4fTffBBoubtN9GCQYb8dj5aMID/43eQtAAZ35mDZDKRdem3aFj9KfUff0RkTxGOkUqz0waQZJncH8xC9ng48If5DPjxfTiGDuvw/muBAP41n5F/x8EHHckkITkt6MEY5CSn7Y1QHIAJMy7v8FoCQVcyp/2YpEPdBTgVRfkL8CDwHNB+KGgXUFXVAMoUpdV+1TM4KFyylKSz1oEdqqr6Ab+iKBZFUeyqqooM/YIjjmEY1O57G12LkDdyFiZz8xZQPLnT0LUIuh7HbOk893Wq6L5ywssXYnJlIOePwpQ5EL12P1r5ToxQPbbTvoPcL+ksDF0nvuk9yveuQZftSDYXktUBjYFR8R2fYDvtaqxjZ6AVnkb0jV0Q/BwYhGSRsY3pR2hVMebMYuJb3keyp2EeOB45dzjxXZ8T37YckzcXyepMpvfU4lgnXtzK3qqSIr54/xX8dZVYbU4y8gbiycxjSP8hbFj5JpX7d+D1ZWDENJwXDkuuQee7iZc2UO+q5dN/LaL/4NGccsH32bVhJav//QLnffce0rOT2uL+ukpqSvdw2oXXElcbcBSmI8mH7sN1ebMoHDWFFf/8MzaHi3O/M7eV0w7v3kXte/9hwN33IjudyE4nWVff1OoaJruD7CuvpvKVv2LEYhT+z4OHtCNJEjlXXY0eCVPx/CIKH3gIk6V9mU7fpysxZ2biHD2mdTsua6v0q3owmUvdZBHxYYKO6cpfx2xAUVW1VlGUgcAO4FRVVTd1cl53cKmqGm78vR4YAmQCLTUN6xvLuiTZk57u7LxSG2TZ1K3zjjf6Sj+h+32t2LOMaKCI4VPmYHO2F4h0ZO6fFmqg8r3HsWYVYPZmE1X/S7TmAJbsQuwDFAyXi+Dbv8Fz+lW4xp5J7XvPEK/ej3fqTHRdQw8H0GNh0DUMXSPt/JtJm3AuAA37fMhpFhLFH5D4uAbZnYXV7CAUyyX62So8U07G0BJEdq8iuvrvWPuPIPuKH2MfMqnD2YSa8v188tafGTnxdMaceg7pWbmtEqZEQ7WoX3zAaVnfwj06h4x+yQcdm5JN5b9Uvtj9OmNPOZtTz0/u484671Ii/mpWvf0XzrrsetwZ2ZTu/JLcgcMYMGgQB5Z/hff0QlwdfKanzbiMWNjH2TN/gDfzYGZmLRql+MVF9LvgfPJPP/mwn4H34hkEV63EXpBP3oSOdcnds25k0z3zCH30HgOuOTRNqaHp7Fv5Mf0vvoiMzNbxAPFMB6a43vy36T/gJ+a2tfpb7Uv/p4Ku0RXHHVZVtRZAVdX9iqJsP0JOGyDUYjTtBWpJOu2W83FN5V2iO1tj+so2qb7ST+heX6PBA1Ts/Yh+w68jHHMSjh2de2UkYoTeeRTJ7sF89m1IZiv2k8Gma81biyTAUTAJ/8eLaPh0CfLA8Tiuepi0/PzmfrYciyY4+L/g31yJZVg25uy7SBRvIO6rxYgEkDMzkBwzMcYl04vaJ85E8tcSCoeo8wfR1n5O3uDRyObWo8pAfTUfvvo4A0ZMZsy0y5EkCV9D6wmxoRPO5t/PPUwoUo9peEazLYbLjKYlyLD2Z8SUC/H5ws3njD/rKgL+F3jv70+TaBQdOXnGd6lYXYwe13EMzejwM5UsXs668g4MWn8HVL22GC2h47n0ii79PeTd/WMkWe60bs61N1Ly1BNYxk7ENuDg6mG8ro7yRc+SCIaxTD71kOskrCa02nBzebg6iGFv3V43t4MdN7SnDtaRqlZ7yl4dKXetW7fW+eSTvy80DIPrrvtB2YwZF/qgfRWvzmysqak2P/DAz4eEw2HTpEkn+efOnddq4NiRQtkdd9w6PBgMmnVdZ9asW0rPPvvcho7aSIWuOO7BiqK80vi7BAxq8R5VVb/fG4Y0shK4BHgDuBi4H9gJjFQUxUUyhCMhpskFRwN/1Rqc6aOxuwcftTYNPUFk+bMYET/Oy/+nlcBGy/3AAOaBE3B++2H0yt3IhR2Phlui+SJo1SGcZxUiu/MwDzi4bpuoChL4zy70SAKT3Yy/rooPXvkd8WgYi9UOkoQzLZ3TLr6ejH4DMAyDin3b+fLD18jOH8qU877ToQ0uTyYjR56JVAVyzsHRY9BfS02shJFDptM2JaYsmznjspsxDIN4NEw46MNlzSTw9g6cpw/AZDNDuFNFxmYi+/ZSt+x9Bsz7CSZb11LFdjT1fUj/xk/AffKplL/wHDnf/g6SxUK8uprKv7+MfeAgBv3qIWTXoevxJpeV+P6D3+VGKI7J2bUAwaOBHknIJPSe5902m3ST3dzl7VXnnDPDN3Pmt2uvu+7q5rWFJmWvRYte3r5p0wbnU089NuB3v3uyqEm565ZbflTx8MMPFC5f/qH3vPPO9y1Y8NjARx6Zv9vj8Wq33faDUeecM8O3c6farOK1dOlbGc8992zeT35y/4HD2aJpGosW/Tnv4osvrb700svr7rjj1uGqus2uKKOb/VBLhbKFC5/Jff31V7Ovv/4HVffc89P9w4YNj9bW1shz5tyqnH32uVu7dwNb0xXHfUWb93/qjYYBFEV5DTgZCCiKcirJLGwvKYoyD/hIVdUtjfUeBJaRjCq/p7faFwg6IhH3E6rbSu6I9pWzjgRGLEx42R/R60pxXnpfh/rMmi9C8MM9yDkuLAVuzAXjuxwQF9tTj9zPiew+1HHJ2U5MbivxPfWYR6az+t8vkFs4kqkX34BsthCPRVj38Rsse+X3DJ0wncr9OwnWVzNk3FQmnT3zsHmfAYYWTMFXvpd41T5yBiTX5teveJMMdza2QMfOSpIkrHYnVruT4Md7MWc7sQw5NCgOkkFgctqh983QNCpefB7PtDNwjup42rsn9Pvu99n/u/+jZMETGIl4csp/5pVkXHDRIXnWmzClJde4m1LC6sE4eqad8toQeZnHdnrc0A0a/rFtPAm9xyIjmE2a93vj1nekad5WHWz69LMOyZy1fv1XrvaUvTZu3OBuq9w1ffqZDZqmSfn5BXGA/PyCSFHRLntHKl5tWbJkcdaaNau9sVhMmjbtDN/WrZvT7rxzXgnA1KnTfF9+ucbd0nF3pFA2bNjwKIDNZjdMKeq5H46uiIw0a243RpMPU1V1laIoNlLQ8+7g2u3p1l3cTr23gbd70pZAcDiiwf1YnQXNuaQD1WuxOHIPq5zVm+iBWsLvPpYc1c78n1b7n9sS31MPsgnJJBH+shRpg4znilEd1m+JVhvGnJuGruvs3fI5ssVKoTIZSULWEmwAACAASURBVDIlHeTQTGK7a9lasYJoOMg3rprTPDVusdo59YLvUzBsPNvWfEChMpnhE8/A5uiarKjsl9DSDFa+9SyZeYW4PFmUFm1m/JU/QV9ejR6MY3IdHOFGS0rwf/E5rgkTcQwdRrykgfj+BtyXjWz3QcX3yQoqXnye/DvvJm3CxFbH6pa9T6K+jgH3/rRLtnYH2e1m8EP/r/m9oesdOuwmTC4rxPWk7KjNjB6KsbzCh6/azw0XHio0czSRTBKeq0Zv6q0Rd0dOGw5VB5s+/axtbev4fD5ze8pegYBfbqvcVVdXa3a5XM11Xa40rb6+Xk5FxSsej0tPPPHMboA33/xHP4fDYQC43R6ttLSk1ZNmxwplSR577P+zd+bhUdX3/n+dc2ZfMtn3kD1DSNh3WRQQBVREROtWtVK12lZ7bb329ra92lbbW3vbam1tq6Ki1h0FcUFURHaQACEhTEIWsu/b7Ms55/dHIBCSQCJo5Wdez8PzkJmzfM+ZmfP5fj/b+/fJV111TdPgN2h4DDl10W63rwRup0ebOxNIBf4GXHyuBjPCCP8OXG37aa9ehzlyPJGjloIq42rdS3jigmGVdvW0D1b7CWycCcXVhmftw4iRyRgX3NWTDX4agjXd6DIjMOTHInd4ca4rRQ3KQ2qgoroDBMJVtr36Z1ydLSiKQsnujYybfTnRiRko8RLyfi8NFYXMXHFLb3exk0nKHEtS5uBCKIMRavEQN34M0zVRdDTX0tFUw7jZVxCWkojT0o3vQCNimB5/dTW+yhpUbxCNNRL3hqP4w90QAn1eDJKtf/sI96Fiml54HvPYcTQ9+zSGB3+NxtazKvfX1tC29i3ib105oLv6y+JMRhtAtPRMVBR3EFGvIegMcKTDw3eWfjlegeFyzL39pXcQO1UdbKBtwsLCQgMpew2k3BUeHhFyu929Pwi32y2Fh4fLw1Hxys3Ncx//v06nV45tr7pcTiksLKzPPRlcoQyeeeYfcaIoqitWfKtt2DdmEIbzhPkhMIceHW4cDkcpMNKXb4TzmoCnkY6a9wiLn4u3q5SOug14Og8BKuaI/DPufxy5uRzP27/Gs+YhVL970O1UVemjAax4u/G++yhi1CiMl95zRqOtuAPI7V60KTagx9UKoLiCZxyjoigEuzzs3bEGc1gUi275GZfd9ksS0nLZ/s6zvPW3n/LOS7+iI9jE2KxLiU48dyIXii+E0u1HGx9GUuZY8mcuZs6yOxg9pWdypMuNJtjUhWtHEf4jjWjDY7BMm4Rl1jiEqBBdxe+jGa3BMCG+37H9dbU0PPkEUUuXkfiDe9ElJtG46mlURcFddJCa3z1M2IwLsEydds6uZzBKqtpx+878WRxHkEQEowbVFcDZ7UOSVaZNTCIy7Av3tjovOa4O1tzcpNFqdQMKaEycONldVHTAKssyhYX7e5W9jit3AWzfvtU2YcIkl9FoVCVJUhsbG7Rut1tsaKjTp6dn+qZMmebcvXtnGMCZVLwkSeodR15evmvTpo9sALt37wybPHlqH1f+cYUygJMVyt56643IkpJi809/+ouas7tDfRlOsWDA4XAE7Xb78V7lek5WKh9hhPMMJeSltfJ1zJHjCE+4CKMth+ay1bjb9mONmdpPhvNUVDmE0lZN4NDHhMp2oLXPQW6rwfP+HzFddj+Ctu/DV249infDYyBK6PIXosmYinfDYwgmG8aL7z7j+aBntS2G6ZFsPTFqQSsh6CUUVwApYvCHvbOjmT3vv8wk+SLscxaSdJIrefzcKxk99WICPg+SRot6xIVSd24z6OUWD4JOQgzrH1uXvV66Cz+k85OPsU6bTszN30ITfiKGrc9biKpz0vDcYyRZf9QnRh1sbaHusT9hmTyVyCWXIwgC8Svv4OhDv6D+b3/BfbCQ6GXLiVi05EtvjFNS1c6jr+zHqNdw6dQUFk5Nwag/82d6vJb7g/11LAQumDpwE5z/nzlVHWwwVa2BlL0GU+76/vfvrf2v//pxpqqq3HzzbfUajYbBVLzOxG233dH4P//zs/TXX38lbvz4Cc7Ro8f44IQ62EAKZbIs89hjf0hLTU333HnnrXZRFNV//OO50nNxv4ajDvYzetzklwO/oKe+e4PD4fjDuRjIl8SIOthp+KZcJ/S/VkUO0Fr1BkrIQ1z2rb1G0+esor3mPWKzbkSjsw14LLmzHv9nzyG3VIIiIyXY0c/4FlJ0GqrPhWf97xCMYRgv/VFvVniwci++Tf9AmzMb0RZHoOgjVGcLYtQoTFf8FEF3wiUdanIhxZgZKCbo2liOFGnEODmx9zXn+lJ0mRHoc2MG/EzrK4rZvn4VaaMmk9WVh+2G/NO61eUuH863HYStyB1yC9Qz4S1oQG73op8Yhre0FF18PLqEBNyFhTS/+jKS2UzsTTdjyhk4rquqKm3r3qbjg/dIvPuHmMeOw+Dr5tAvH0KfmkrinXcjaE4YSde+AhpXPUXcrbdhnXyiT5TLG6SmyUluWuSwxh8MKby1pQKdRuTiKSlYjH2zzUOywkPP7iE3LYKMhDDWbq3E7Qvxk+smMCru9OVZ7s1VdMsqqw/UckesjYgbx/V5f0Qd7JvJOVEHczgcj9jt9kvpiXdMBf7ocDjeP0djHGGEr4yAp57WqrcAgdisG/qsdA3WNBLH3H36/QveAUHEuPjHSDHpCNoTq0jBYMG45Cd41v0W90v3IViiEAxm5HoH+pnXo8vvSQnR5i1Eri1CjE3vY7QVbxDXB+UYZyajz+nb8EUNyIQa3RjG93UXi9a+3bdO5dDuD8kcN4u8rIvxbq0+YyxcshkQw/QEa7qHJAAyFELNbjQJFhqffQZ/9VEUrxdUFUGnI2rpMiIuvqSP4T0VQRCIvvIqRIOB+r8+TvTV19C5cQOGjEwSvntnv30tEyeR+dhf+8SZVVXl6fWHKCxv49bFo5k7PrHPe52uAM0dHpo6vIRb9OSnRyKKAi5vkCfeLKTD5Uenldiwu4Z5k5JYMiO114B/UlCH0xtk2ewMTAYNU3NjeWFDKY+/Wcgvbp6CzTJ4+Zlo0dFyuIVJKRFIp7kHI3w5PPPMP+J27tzW6+LRaLTKk08+U/bvHNOZGNa3xOFwbAA2fEljGWGELxVVlXE276SzYROWqImEJ12CKA6tTvc4iqeLUMVujJf9J5qEgVeHoikc87JfIDeWoXg6UT2d6CZegSbxhItXEEU0o8b12zdU3xNy85e0osuO7OPeDdY7EXQSUnTfhDHRokPp8g84Fnd3O231lUy75AbUlr5Z26dDmxJ2zgy3qqjIrR4Ic+M7Ukbaw/+LFBZGsLkJyWJFYxvYszEQkZcuRtTpaX5pNVGzZxP57e8gSANPRE5NDtta2EBpTSffmp/FCxsc6LQiM8bEU1bbycsflVHV6ESrEYkJN9La5cVm1jF3fCLbDjZiNmj475unYDFq2V/WyrqtlWw/2MCNl9jJSQln7dYKbrg4B5Oh55EqiSI3XZLDn147wONvHuSBGyaiG2TC5JNEBG+IvNRwRO8Ze4GMcI5ZufLOppUr7zxnGd9fBcPJKm8AYoHjPhsTUEOPJve9X2I3tRFGGDbtNe8hB7uxRE3GEJaFu7OKxsNrkENuotOuwRT+xUptgoc3I4YnIsWfXrlJMFjQpA1fjztY140mJYxQnZNQowttwgk3a7CmC22ytZ8LXbToCdYNHBKqdhQQHpNEWGQc3qoGhCG6vrWjbPhLWnvKlHRnV8Yrt3tBUWn7cA3hCxb2qmPpk75YLDd83nxMY8YQk5VGl3NovZhaO728/HEZ1y3IZu74REwGDc+sL2FHURPFle1cOCGRu5flE2kzIAoCHl+QbQcb+exAPWnxVm5dPLrX8E7KiWF8VhQb99Ty9PpDmA0aEqPNzMzv6wnRSCJ3X5XPb1bvZdV7Jdy5NG/AOHtRUzdZGgmbJMLXqPnKCF9fhrPiXgesczgc7wLY7fYlwBXAS8BTwIC++BFG+KoJeBpwte7FFJFHa9UbiKIeWfZgiZpMeOI8ROmLZeyqikywZBO6SVcO+ABWVRVUBoxND+34KqF6J8aZyYh6Cf+h1l7DrcoKoVonxln968pPbuJxKtWH9zJq9GTgWMnREA23FG1C0EkE653o0gZudjJU5FYPaGVC7a1ELrn8rI51HF1c/IACIwOhKCqr3ishJyWcOeMSAJgzLhFZUSk80saD35lKcmzfWnSTQcvCYwlmAyGJIoumj2JiTjRrt1SyeEYq4gDfCbNByz1Xj+W/n9rF0lnpJEb3LUeTFYVtFW2MNZlQugNIA6idjTDCqQzHcF/gcDh6JTgdDsd7drv9fx0Ox11no8s9wgjnmq6GzZjCxxCdthwl5MPTVUJkbBoBefCmJkMhVFWAGvShzZrZ7z1VVfFsrQYFzBemfqHjy21e1ICMNsGKZNXjfKcUuduPaNbi2XwUNCLahP7NTkTriSYeJ9Pd3kRnSx2zr7wdAMUVQJsSNqSxCKKANtlKsKbr7A13l5dASw2Rl13+ldVRh2SFncVNFFW2UVzZjigKPHTbtD4TrosmJHHRhKSzOk9chIk7luaddpuEKDMpsRaKq9r7Ge79ZW00egNgMhFq9QzaEW6EEU5mOIb7iN1u/z09etgA1wLldrtdR4+GwQgjfGkEfS0EPE2YI09fW+131+HtLiMh9y4ARI0BS9RETFYTgbPMoA8Wf4zWPpdQsx/vnkrM89J6m4EEDrcRrOhEGGIMeSBCdd1IseaeOHakESnOjL+oGcUVQHEFsCzOGjCx7HjcWnEF+nRWqD68l6jEdMxhPRnUijvQW/c9FDQpNrzbalAV9Qt7EQB8FTXIQTfR8/s1RTxr3L4gT71ziMn2GGblJyCKAk0dHv6+tphOp59JOTF8Z0kuuakRQyrN+rLIS4ukuLKdhVP6ruA/3VfLlPx4hPYgql9GNH3x78/5ykAiI3/72+Pxe/fusYVCQWHcuAnOH//4p3UAH320wfbii88lCILAPff8uHrixMmewQQ+Kisr9I888mBaKBQSLrlkSev119/UCgMLlZxpjIMJmZzMQOIlg4mTXHXVkvzo6OggwCWXLGm95prrhtWcZTgNWG4EWoCf01MO1nbsNRlYMJyTjjDCcAh4GmkqfY62o2vwdpWd8l4Dno5DvU1Nuho+xRw5Fq3hHCRVqSpyczmB4o/wbV6F3OBAN2YegYpOFE9P9neozUOo1YP383oME+JQ3cF+K9+hEqx3ok06EdPW58YQKGtH8YYwLkyl7PBWWusre13iHmcHB7asY9v6VWCU+mSWq6pKtaOAUfZJPX8rao+AxTAmFtoES4+LvmHwkkpvWSn1Tz5B966dqHL/6/bX1SG3OTFPGIOoPffx21c/OUJDm5vXPjnCg8/u4Z3tVTz47B5ibAYevn06377UzqScmH+r0QbIS4/EUd1JSD7RfKepw8Ohqg7mTUzqDWF8Ew33QHz3u99reuaZFxzPP//K4cOHD1kqKsr1sizz7LNPJf3lL/8sffjhR8v/8pc/pUBfgY+cnNHuN954NRrgiSf+lHT33ffWPP30C4c/+GB9dEdHu3RcqOSpp1Yfvvvue2ueeOJPZ0y0kGWZ40ImzzzzokMURTZt+rhPRuXhw4d6xUsWL768ddWqf8YDveIkq1a96CgtPWxyOEoMADqdTn3qqdWOp55a7Riu0YbhlYN5gEcHebtruCceYYShEPA00nzkBUwRY5C0Vtqq1xI/+k40Wit+VzXN5f8CVMS6jZjCR+NzVpAw5vvDOoca8OIvWIcufyGi5UR9b7DoQ/w7X0OMTEaMSsGw4HsI1jhCdcWYLkgh1OjCtaEcQSuhy4lCPzYOX2EzcqcPTezwXMKKL4Tc4sE448RzRJsShmFKIrrMCCpLd1O0/V0URcZoCccWlUBjVQkRcSlotHo63Y0o9Tpij5U4tTcexdXZwqicngQ51RsElWHVZQtaCV1aOAFHG9qkvi52VVXp2PA+rW+9iXnceJpfWk3rmteJuHQx4RfOQ5AkVEWh6flnsCYuxpj9xbqwKao6YOwY4EBZCzuKGvnFLVOIshl4d/tRPt1Xx7fmZ3Hh+MQvveHKcMhOtqGoKuV1XdhH9YRsNhXUkZ0STlKMBbdZi9zuPSuPzZeBz+eTQqHgWfcq12i0isFgGHRGO5jISDAYFIxGkxwTExusqDhiSExM8lmtVsVqtSqyHBJ8Pp8wmMBHfX29YeLEyR6AsWPHO/fvLzAf//+pQiWnsn37Fuu//vVCnE6nU7Oz7e6KinLTqUImCxYs7LV7g4mXDCZOEgqFhNtvv9luMplD9933QE1qatrQZe4YXla5HfgtkAv0FiU6HI6M4ZxwhBGGStDXQvOR1Zgi8olIXgyo+JxHaat6C1vChbSUv4w1ZhphcbNxt+/H2bwLS/RktPqhN9dQgz687/8RubWK0NF9mK74L0STjVDdIfw7X8O48Ad9ssNDzW7UoII20Yo21Yag1yC3ejBOSUAQBUSbHrljcMPt6mylo7mGUCiIKsskZuZjMFkJNTgRjJo+3c8EUcCQF4OqKjj2fkLejEVkjL2A2iMH6GyuY/637iU6MR1Fkalbs526g/toEkqpqyylreEoKTkTMJh7DK7iDoIoIBiHt/LUZtlwf1iF7PQjWnQEW1vwVVTQvWM7vopyEu/6AZYJE1F8Prq2bqF9/Tq6t24h7tbb8B4+TLC1HSFJd1oXvaKoPPNuCeX1XSTHWEiOMeP2hahs6Ka6ycmKi7K45JQkMX9A5u9vHWTR9FG9DU6unZ/FtfOzhnV9XxU6rUROso2iynbsoyLwBUJsKWzgO4t7xGFEiw4k4awz+M8liqKwZs2/xoZCobMelEajka+77tb9gynIDSQy8uijjyRv3fpZxPjxE7rNZrPS2dkpWSyW3rCsyWSWOzraNYMLfJxI1rRYrHJXV5cGYCChkoHo7OzQPv/8KyWSJHHXXSuzTxUyOXnbwcRL/H6/OJA4yT//+dzhqKjo0JYtm62///3DqX/961PDqhsfzq94FfAjejLIZwPfAUZSIEf4UlAVmdaqtzCEZRGRvPjY6kkgOm0ZDYf/QXPZasLiZmFLmIcgCFhjpmGNGV4vajXox/vBn1CDPszX/R7fpn/iWf8kiv5ylM4jaEbdBNa+ZWPB2m40cebeB6xxYt8SICnCgNzhHfB8fq+bj17+I6IkodUZCAb8lO3/jPnX3kOwzok20TrgKrG+vAivq4vMcbPQGUxkjr2gz/uiKBGdlY2+woSjfieJGXlMufg6bNEJvdsorgCiWTvkVaji99P+wXt0bHif8NzlND6+B0/9HlS/H01kJMasbFJ/+SDa6JieMRgMRFy8kLCZF9Dy+itUP/wrBEEg7sY7UCpAOI3hfvWTIxRXtbN8bgaNbR4qGrox6jRMHR3L+Mwo1m6tYMaYOMJO8ha8taUCSRRYOittSNfzdSAvPYrdJU1cfWEmO4qbMOgkJub0hHREi25Yn89XgSiKLF9+w8FzteI+nezrQCIj99//s9r77nug9oEH/iNj06aPbSkpKX6Xy91rszwejxQRERkaXODjxL10uVySzWYLqarKQEIlA5GdbXdLx3oEDCRkcvK2g4mXDCZOEhUVHQKYM+dC5xNP/GnUUO7hyQzHcOsdDsceu90uORyONuAPdru9AHhouCcdYYQz0dW0BSXkJjL55j4PM0lrJSb9WgLeJizRU77wg05VFbwfPo7qc2G8/AFEYxiG+T+k+80CcJUhhelQ5WRc75ZhuSwbTVRP05NgbTe6zMFX9FK4YdCa6v2b38IcFsmC6+9DFEWCfi+fvPY429atYqJ8IcbpA2c4H/78EzLGzhxQqav3vBYdBtHMFbfeP2B7TMUdGLKb3Pn5HppfeQlRqyN+5e2IaiRaRwLhy2aiT0pEEz54dr5kNhN/60rCps/EV1mBIWM0npqjiIOsJD/eW8vm/XU8cOMk0hP6Z7wrqsqB8jbe3lLBzYt6Vqd7Djfz8d5aHrp9BlrN12eFeiby0iN5fdMRnJ4An+ytZd7EJKRjhkObEYEUdXqBmX8HPe7twV3c54rjIiOtrS0arVan+v1+Qa/Xq5IkYTAYFYNBr2RkZPnq6+v0brdbdLmcoiRJqsFgUI8LfMybd3H3yQIfiYmJvsLC/aa8vLGeoqID1pUr72gEWL16VaIsyxQXH+wVKhkI6aTGPseFTLKz7b7t27fapk+/oPvkbadMmeZ88sm/JN144y0tJ4uXHBcnWbz48s7du3eG3XXXD+v8fr+gqioGg0EtKSk2nDoJGArDMdxeu92uBQrtdvsvgMZh7j/CCEPC766ju3ErMZnXI2r6h6D0llHoLaNQ3B0ESrch6Axox8zvldNUVZVQ+S7EyCSkyIHrcEOOrcgtlZivfQTRGIaqqnh3NyNao9AltaEfPx9BZ8S9tRrv7nosizJRPUGUDt9pS6qkCCP+4hZUVe0zqWg8epijhz9n4Y33987ytXojc5ffxb5XXiakDaBJ7t/TurW+kraGKmYsufm09+x0tdxAr9a1GgoRbG1FlWVUOYQuIaFP0pi3rJSGp/5O9LLlhF98CaJWiyordJeXoDUknNZon4wpdwym3DH4S9v6uMkDQZmK+m7aun00tHnYsLua712ZP6DRBhAFgesXZPPbFwuYNykZf0Dm6fWHuHFhDmPSo86rXvvJMWasZh1vfVZBU4eXuRNOtFwVDRpEw9B0zf9/5FSRkYcffnBUQ0OdIRSShdGjx7iOx7xvueW2+u9//7s5giDwgx/8Rw3AQAIfAN///o/qHnnkoTRZDgmXXLK4NSIiUgYYSKjkTAwmZHJcZGQw8ZKBxEkaGuq1P/nJPdkGg1EGuO++B6pPd+6BGI7ISAZQDxjocZmHAX8/Ju/5dWVEZOQ0fB2vU1GCNB5+CoM1jciUJQNuE6o/TODAe8i1BxEjk1FdHYjRqRjm9dQr+zavQq4pRIxJx7TslwiC0Oda1YAH96s/RTfxCnT5C3v2KWrGV9iE9fIcpJMUrBR3kO63D2OanYLqk/EfaibsqsG1khVXgO43Swi7ZkxvhnAo6OeD1b9jlH0y42b3b0DS+cFh6muLcSZ7mbrwesSTZvpb1z2NRqM7o+FW3AG63ygh+buTcQX7T+BdH1UgRZvo3PM2zl07e1/Xj0ol+b77kSwWZLebow/9EuvUqcRcc12f/b0FDYQaXFgvyz7tOE7FW9CA3OnDMj+dpnYPT6w5SHOnl6gwA1E2AzPGxDFrbMIZj/OPdcU0d3hp6fQye1wC187L+lp+f8/EU+8Us6O4iVn58ay8fMyA2xx/Jh+f+I2IjHwzOWuREbvdLgK/cjgcNwE+4MFzN7wRRjhBd+NnoMqEJ17c7z3F04l/5yuEyvegtc9Bf9WDSNGpKO6Onvj0G78AVUWMTMa07Jd41v8voaoCtOmT+xzHX7AOwWBBO2Zez3HdAXwFDZgvSutjtKGnRtqQF4Pv8wbEMB3a5NM3MBHMWtCKyB2+XsNdtP19RFEib8alA1xTEJr9JM2bxmefPM2Wtf/kgstvI+B1Ubj1Heorilh44/1nvG+CUQuiQKjLDyYJV+EBVJ8P67Tpx64xiBgTxLlnN8n/+V8Y0tNRA0Hq/vJnah79Hcn33U/zyy8iWa1EX7Wi3/H1OVH4i5qR271IkUN36Squntrx/UdaeeqdQ+SnR/LfN0/GoBuebVhxYSY/e2on4zOjWHFR5rD2/TqRlx7JjuImLpwUz2e1OyhuK0En6TBqjCiqQqO7mUZPM9PiJ3FtzpX/7uF+I3j77Tcj3313bczJr/3+938+cnyF/nVkSL8eh8Oh2O32ZLvdbjpWFjbCCOecgKeB7qYdxGbdgCj1jceGaovwbnwCKWoUpqsf7OMCF80RGJfcT7BoA4gatHkLEAQRXf5CAp+vQZN6Iitc6WwgWLQR46L7elXBAuUdiOEGtKMGFrvQ58XgL2snVO9Cnx972msQBAEp3IDc6UObZKW9qZrSgk+5aMX3kTT9S30C5e2I4QasGcksiP0PPlvzJB+++Hs8zg5ikjO55Mb7CY85c3cvQRQQzVp89a3Uf/gargP7EQQBQ2YW2qgoFHcAb1kl+pRRGLNzelZzWh3JP/oxdU88xtEHf44SCJD6y4cGVOkSLTqkaBPBuu7hGW53gFp/kL9+WsrVF2Zy6bSUL5SXEGUz8OuV04gMMwxaHnY+kJ9pY9Y8L6sq/goITIufhIKCN+hDFAQmxo4l3hxHhm3Y+UojfEGWLbu6fdmyq9v/3eMYDsOZ9rYABXa7/QNOCI3gcDh+ds5HNcI3DlVVaKtejzlyHAZr3wpDxduNb9M/0eUvRDdl+YAPfkEU0Y3r25lLN34xgUMfEyrfCZEXo3i68H32LJpR49Ek5x07r0rgSDv63MEbtghaCeOUBLyfNwypPlsKN6B0elEUmT0bXyE9bzqxKf1dzKqqEihrRz86GkEQMFnDmf+tezm4bT2JGfnEp+UO2cjJHg+yt4u6V7egGrykPfQwTaufpe2dt4m94VYIKnQf3E7kkgV9jikaDCTd8x80vfAclvET0MXFD3oObaKVYJ0Tw9i4Qbc5lUCXn43Nndx2WS4z8wY/9lCIjRg8Oe+rZl/zQfyyn9SwFOJMMYjC6ROvO3ydfFq7jW31uzBpjCxOv5gZ8VPQSl+vuu0Rzg+GY7jXH/s3wgjnHGfzTuRgNxFZN/V5XVVV/FueQwiLRTd52bBWa4LejG7cYvx738YpBnFveQUxIgnDBSfOITe7UdxBtOmnT7rSpUegTQ0fUutPKcJAoLyD0r2f4nN1MX7FDwbcTm46du6MCIItLahyCNFoZOLcZUPuMqYEAnRs3EDHhg+wZs4nfMI0TEvG9+hXX30NNf/7CLaZPXF82d2OdVr/kJmo05Gw8o4znkuTZMVX2DRkxbBOpx/RFyIrO+asjfbXCWfALkBxPQAAIABJREFUxXPF/8Kqs9Lh78QgGbgi41IuTL6g3/ezO+DkvcqP2Fa/i1RrCpeFz8db28nMaVPRSAM/fmtqjhIKBUlP/3rWpI/w72c4ndOet9vtcUCmw+HYbrfb9QyvZeoII/QSCjppr34HJehGVWWC/jaiU69C1PR1w4ZKtxKqLcZ89a8QxOGX/ujyFxIs2kj39jfQTbyRUHcKslOHeCyBN3CkHW1KGKLhzD+FofbrFsONyJ0NFFW8x7RLb0ToVAiJ7j6rdVVV8RbWoWhcVP/2QQJ1tX2PYTKjiYhAExGJLjYGbUwsuoQEjPZcRG3PKs1fU0PDU0+iBALEXn8DWlMmQqu313gYM7MwjxtP16db0emyCLvgAkR93xj+cJCiehTDQo2u3rDCkbouXv6olLuXjSXKdqICQFYUXl5bzPWCxMK5X6xr2pdNUA7SHXARZTwxaZMVmd2NBUQYwhkdOXAi3o76PUSbovn5tPtwhzzsKf+cbQc+o8pRRn7kaHLt+XSpLvY2H+CTmi0kmuO5Z8Lt6LoFtmz5BEmSKCsrITd3bJ/jqqrK/v2fU1x8gAsuuOjLvPQRznOG0zltJXA7EANkAqnA34D+WUQjjHAaFNlPS/m/ECUj5uhJCIKEpLVgsPZNOpI76vFtfwn9zOsRbUN3z56MoDNivPLnaFpEOnY1Ilp8uDdVYlmUhWTVEajq+sJqXoMR1AVAVklJGkeCNQv3xgpQVfS5MRgmxaN6Q7i3VhOs68LTuhvbrDlYJk9BNJlQvB4Ut4dQVyehjnaC7e0EW5rx7dlNoL4OJAnrtBlowsNpf2ct1hkzib3uRkSDgVCTC1dhM1KDs1cONPqqq2n+x+tIsVGEL553VtcliAKahB53uXaUjfZuH0+sOQiqyt/XFfHADZPQSCKqqrL6AwchVwAsJjRfw/7bqqry3KFX2N9ykNzIHC5MvgBFVVlb/j5tvnaMGgMPzngAg+bEREeWZRBgS/1OLh51IYIgEHD6qN1TypiwDJo729jXVkBhyX4ckfVERkfx7dxrmRgzlurqSrZs+YSpUy9AVRWKig6QkzOmt1Y4EAiwZcsntLU1c/HFS4iPTxxs6COMMCxX+Q+BqcBuAIfDUXpsBT7CCENGVWVaK18HBGIyvoUoDbwCDNUU4v34STTpU9GOvnDY5wm1eQjVOpE7fchtHggqmGYmo00Px7urDvdHFehyohC0IprE/vXTp8Pr7qaxqoTU3Kn9Oi+5utr49O0nmK5dQn7qAtybjmIYF4cm3oxnaw3B2m4UbxB0QTpLXiftkYcRdSfc4pLRCJFR6FP6158rwQDuA/vp3rYV9/4C4lfegXXqiW5xmjgLtqlJdG8+iuWybCSrHm14LOaM6SjBbnTxZy67OhPaJCu+A034/CEef7OQtHgr31mSy6+e28Nbn1Vwzbws3thcTkFpC/89Nwuxuvvf0g1MVVU8IS9m7cBx8e31uylpd3DXuO9Q1HaYVcX/QlVVFqTMYd6oOfzh8yf4uOYzLks/FmaQQ7z77luIZi1uyc20+EmoqsrOnVtIT89i9ux5+EI+PqneglLrQVutYWLKFEL1AT7Yu47W1mamTZuF3T6GUChEYeE+ystLycnJRVEUNm/eiM/nZcmS5Vgs36x67oHUwR5//I+J27Z9FhEWFhYKD48MPvronyvg/FMH++Uv/yu1urrKqKoqV165vHn58mvPSRLccAx3wOFwBO12uwpwzFU+tCLwEb4RBGu6kDtONCLSZUX2UTtSVZX26ncJ+tqIs982oNFWVZXgwQ34d72OburV6MYv7n3wn9rUZCBUVSVQ0or383o0cRbECAP6/Fii8+NxBoIAGKcloXpD+A80oc+PHbZkZcEnb1Bbtp+qQ3uYsfjbGC02VFWhqbqUXR+8SHRCOgZ9BMHiNnSjo9GPi+1py7o0B9/+JsQwPU1v/g3rjKl9jPaZELU6rFOmYZ0yeGvX8OnJeOq7cW+qwjgpAc+2GnRJcRhnThrWNQ6GJtGKsq2G19eXEAwp3HFFHiaDhjuX5vHoy/vocPkpKG3hJ9dNxNbiRR6GjOi5IigHecXxFnua9vHTqfeSaOkbX290N/F62Tquty8nPzqX/OhcrsxcjKIqvYZ+aeZiXih5jTlJMwjTWSks3EcgEMDd3cHkxNEYNQYcjkN0d3cxf35PmZ9BY2BJxkLIgKqqcvbs2UFYmI3k5FFMnz6LyMieBEiNRsOYMWMpLj5AVpad3bu30dXVyZIlV2EyfX0S8ADkkEdS5cBZh0QFSadIGtOwyqtuvvm2+ssuW9rRO5Zj6mB///uzh53ObunnP38gY9WqFx0nq4M99dSTcW+88Wr0t799a8txdbBx4yZ4brvtxtGLFi3pAPjgg/XRzzzz4uGDBw+YnnjiT8l/+MPjFacbx8nqYLffflfTr3/9y1GbNn1sO1lk5GR1sPXr10asWvXP+Pvv/1ntLbesbMzMzPL7/X7hppuuGbN06fJ2zQBVG8NlOEd42263/wmw2u3264CVwOqzHsEI5z1qSMG7u45ARUdPHFcQULrcBBw1aBMc4G5Fkz4Fjw08nSXE5XwHjbb/KlcNBfBtfZ5Q5V6Ml/wQTeqEntdlBc+OWkI13WgzwtHbo5HC+3dUU0M92wVrujBdlIbupPIuyaSFY4ZbEAVMc0fhK2hEPzpqWNfaXHuEuiOFXLTiB5Ts/vBYY5VJ1JUfxO91kTluFhMuXEawtAPZZsA47YRKlaCVME5NxFdZga+ygoQ77xrWuYeCIAiY5ozC+W4Z7o8r0Y+LxTA+/qz0tE9GNGnxGjUITd3cc914TMdyA3JSwlk2J523t1Ryz4pxZCXZcFd2DUv/+1zQ7uvgqYOr8YZ85ERk8mrpW/xo4vd6P4OgHGRV8b+YEJPPtPgTkxnjKR36JsaM5aPqzbxf+RGXxM6lqGg/k2fN4sXDr5Fbl8Thw8Xs27eHKVNmYDD0L49LS8skLW3wevOcnDEUFe1n06YNNDU1sGjR0q+d0VZVmfrix8aqSvCs+8oKolZOHvef+wVh4EOdqg4G8NJLzyesWfNa7NKly1uuvHJ5+/moDpaZmeWHHhlPURTPmfdpOMlpj9jt9kvp0d+eCvzR4XC8f05GMQTsdvvt9AibBIDbHA7HaWdJI3w1yN1+3J9WgaxgXZKNYAjg3/kKSscBFP1NBJui0CWA8+DruNIjiIyci9YQ0+84iqezt3e4adkvkCJ6apcVXwj3pipUTxDDlESClR041zoQIwyI5h5hBmQVucOL3OlDNOuwLske0LCfjCCJGKcOL46oKAr7Pl1D5vjZxI3KITYlC8fnm2iuPUL+BUtIzh6PTt/zENePHry8rOOTj7BMnIQ26uw1wwdC0EpYFmaiOP1o4s+t27XD6Wd3u4u58TbiIvsamiUzUpk9LhHbsZ7oiivQTw70XBMI+HE4DhGREsvetkK21e0i3TaKH064g6AS5Fc7H2V3YwHTEyYTlIM8U/wSfjnAt+xX0dhYT1VVOYIg9vSODwZwuZw4nd2YzRYuTJvKS3VvI5Z4sMZHssW1h5j4OKZmXMDu3duIjY0nK8t+5kEOgE6nY/TofAoLC5g375Le1TjAoQMNeFwBpsw6t7kXw0UQJBLz7j14rlbcgxlt6K8O9uc//630nnvuq+/q6pK+//3v5kyePNV5PqqDHeeZZ/4RN3v2he0n9z8/G4aTnHYTsM7hcGw4J2ceBna7PRL4LjALmAj8Drj2qx7HNxk1pKAG5F7Xt6qqBErb8X5ejzY5DOOMJOSqHfh2vIwYkYj56v9CVaJwvX8EJTYbt7kGs8+MuvEF3Lo3EaNTESMSIehH8XajtFQgRqZgWnQfwrGezYo7iOvDIwh6DZbLshENGvTZkchdPkKNbhRPAMUdRBAFdJkRSBFGpGgTguaLP2ecHc3s/fh1/F4XshxCkjRkjZ9NWt50jh7ag6ernfyre/S+BUFk9NQFjJ664Az3LoQaCiIajIS6u3Ht2U3Sj3487LG1dHpp7/bR5Q4QDClkp4QTGz5wMxTRrAWThr2OFt7ZVkmXJ8D03Dhm5MVhM+upbOimqtFJTrKN/IyheR1UVeWFDQ4irDrCPCHUkNLnXguC0Gu0AVR3ENHyxRPTXC4nBw7sxWoNIzc3H1GSEIUTq5bm5ibeXvc2Xo+HltJupHQLV2VfzvT4Scfqqo1cnnEpbx15F3tkFi+WvE6Lt40fjPsuhw8epKhoP6mpGUiSRCikotFoSEpKwWyy0traTNm+Q0wWMnHK3dTF1GMJWbgyczEZtjR6lKx6vCk+bxC9QTPs1dTYsRNITh5FdPSJpj5tLW62fljGxUsHb6v7VSJpTDLDdHF/EU5VB4uMjJIBbDabPH78RGdp6WFjcnJK4HxTBwN49911EYcOFZv/8IfHztlicziu8rHAf9vt9nLgDeBth8PRea4GcgamAZ86HI4QsOeYNvgIXyG+ggb8Ja1IMSa0qeGEGpyEmt2YZiShSbUQ2L6a4JGd6GfegHb0XARBRFGCqBPbaWvfgCEim8hxy1HHdqG0VCK3HkXpbEDQGpGiUtCmTUKTM6tPyZd3XwOCQYvlkgwE6cQPTLIZkGynX1EfJxQMULL7Q7LyJ2K0nb4DWXd7E5++/gSR8amk5ExE0mjwuro4uP09Du36EDkUIO+CxeiNQ1ezlT0ean73GwKNjRjSMxANBrRx8Rjto/H4Qhj00hk7gR2p62LtlgqKqzrQakRsZh2CAC2dPmLCDUzOiWXZnHR02hP3rrSmk5c2ltLc4WXB5GQSokzsOtTEr5//HFUFq0lLcoyF93ce5bKZqSydnX7GcewqaeLQ0XZ+detUWH8Epds/aBc1VVF7FMm+gKtcURQOHSqksLCA8KgojtZXUXDwc+os7ZjDLORHjCZSsFFUuI8mcxeJY1KIKxGZYZnP3nfbSbyog8TUMGprj5KjT2W3aOM32/9ItGjjW0mXsXfLNpzObhYuvKxP9rbXE6Robx17Pq4jOT2S5ctvoKyshKioGBITk/uMMTu7R6msrdnFmtX7mDEvg7GTz9zh7mQkSdPHaMshhY/fKSErN5YMe3+v1P/PnKoO1tXVJdlsNjkUCnH48CHzVVdd05KWlu4/39TBtm/fYl27dk3MY489WXa6ScJwGY6r/AHgAbvdPglYAWyz2+3VDodj8Rl2PRdEAh0n/T3kOxAePvy4kSSJX2i/843hXKer0YVtShKCJOA+0obGrCP+pgkguGl7+1EUTxdxNz2MLjYVRQnRWrOFlpqtCIKEWcnDVDea8CkmhAgzJCbS4zwZnECbh86KDhKuHYs+6ou5e1vqqvjkzafxeVyUH9jKsjt+hlJWhXVMLlpb3/amHS0NbH7jCZIyRzPvqtv6CH1MuWgRJZ9vpqWuislzFyKd1DijfcdO2rZsJWruHMInT0bUamjp9PLs+mKc3T4WV27AqteT+6uHcBYX011cTOIN11Hf4eO3q/eQMyqCH98wCbOh78o0GFIocDSzYddRCo+0cuHEJO5aMZ6EKHPvyq6108uBshbWbC6nrL6Ln357CqIosK24ieffO8SlM9K4Zn52r471ZXMy6XT6CQRlYiKMCILAwSOt/N/LBdS2eviP6yZiNvZfIauqypYD9bz0YRk3XTqanMwYqo1HMSJiGuT7E+r206VCeKINzUmr8OJmB58e3UGrp53Ork6yzKl8b96tSKKEqqpUVFSwfftWAoEAMy+czT8rX8EcbiZPSMdcZ8Lf6ONoQxlHkHHGBbj+ouvIj7Wzy7qTPXu2EvJnsmHdXsKTWkEIEQqFSAgZSaDHqJa0FpKUlMjSpX1jyoWf17JhbTERUWYmz0xlx6cVLL4qnzlzBv+e+nxBNq4twRZpZO+2o0y9IA3DAPdvMLo6vXR3eomJt2IwaPn0AwfBgMySFWMxnPR9+CY8j05VB/u///ttck1NtVFRFC666OL2rKxsP5x/6mCPPvrbVKPRqNxzz505AL/97f+VR0fHDFvG81SGrA52nGMqYSuA5YDscDhO/wQ+B9jt9sXAhQ6H46fH/t7vcDgmDGHXb6Q6mKoq+N016EyJiOLgD5IzXWfQ347fdRS9lIZ7bTXW5aORrHpUJQSCgOrpxrPuEcSwWIwL7kIwWPC763oaq8g+bAkXYY4Yi+IO4VzrwDw3ddB+4Kfi/qQSBAHzvLRhXn2PoXF8/gkHt60nY+xMxs+9kgOb36D+cBGZhQ3Yxk4g8Qf39hrAuvKD7Nn4CvGpo5l26Y2ndZ+djKfUQd0fH8U8djyew4dAkuhMy+ddVxSWrCxm1u7AeKSIl9IvZ9yETBZMTiYp2sxeRwv/WFfM/ElJHK7uQJZV7l0xjogwPaU1XRQ4WthV0oSsqEzLjWXRtFH94sl97pUvyD/WFlPT7GJMRhQFh5tZeVkuU0afvq/6cdq6euqxRRF+ct1EjPoTE5O6VjcvfeigstHJstnpLJyagigIONeXosuKJJisZ9++PUyfPhvdSRnyoUYXro0V2G4a23ufP6vdwetla5kWN4l4QwytBdUEPX4ErUhORi4dHW20tbUwZsxYRufm89eiZzFo9Nw9/rY+LUWDcpCyzgomjsrF7+7xSPr9QV556WWsNiNuTxchbxipyflctGg0Pp8Xv9+HxRKGVtv/99Da5OLN1QXMWZhN7vieDPTXVu0le0wsk2YO3DNcVVU2vHUIZ6ePZTdN4M3VBaRlRTHjoowBt+/9rFx+dm6qpL6mE1e3H1EUUBSV8CgT3R1errhuHImjwvvsM6IO9s3krNXBAOx2+8+BqwA3Pa7yqx0OR925GeIZ2QX80m63S8B4oOwrOu95hSIHcLXtw9myEznQhcYQTXTqVehMJ+p3h1JSdfxYLRWvIAecqEoATXY0cvtRAnUNBL3NSBoLxto29LZYjJf+iFCwC2fNFlytn2OJmkR40sW95V6SVcKQH4t3Tz2aROsZY9ChFjfB2m6sS4cfEZFDQT7/6FXqyg8ya+lKEjPyARgbk03Dru3UTk5HLj6EYdc2zGPHse/TNdQeOUDejEWMnrqw12irikKwqZGQ03lClOMkAg311D/xOBGLlhC9bDn1jR189OJ7xB8t4Tr3bjSdYShuN4n/+VNuUmx8UlDLL5/eRUZSGFUNTq6/OJv5k5LxBUL8c90hHnpuT89xQwp5aZHcuDCHidnRfdzfg2E2aPnRNeN5Y3M5h6s7+fnNk0mKGbqXIspm4P7rJ/D7l/fx59cPcN+1E5AkgfXbq3h3x1Em22P4zXenIer8vdZAtOiQnX527NhFQ0MdGo2GmTPn9h7zuCqYIAgElRBvH3mXbfW7uS3vRibE5LNlyyeYdWbsc2by6uevY2yzkByZxNy5CzCZzLxRuo5Ofxc/HXdvvz7gWknLmCg7Rq0B/zHZhPKSVgRfCkJEM7NnX0SYOZ43nitgdH43iaPCMZkGDm8EAzIb15Vgz49jzIQTv5MxExI4sLuWiTP6i6Koqsq+nTXUHe1gxa2T0eokZs7LYMOaYvImJmK1GfB5g1Q4WsgeE4f2WHvYUEjhgzXFCILA9AvTSUi2Ybbq6Wh109TgxGjU9jPaI3z5nI/qYMPR4/4h8KbD4aj/coc06Pm/B9wMBIGVDofjyBB2O+9W3KoiE/DUoaohVCWE1hiPRnfmzFyfs5LWqjUIopawmOmYIvLobPgUd/t+wmJnoapBfM4qgr5mjDY71ugpxCWPoavL238Mqkrb0bcIepuIy1mJc3sRAUM1YpyEzpiAVhuBa++LeMMFNOZ4REmH31WN3pKGLX4OBmv/FpdqSMG51oE2MwLjhMH7VquqivvDCkSzFtPs4Skked3dbH9nFT53N3OW3UFYVM953MVF1P/lT9iuu47PK3fh7Gju3ScqIY1pl9zQu21ZQQmtr75MRHcjYjDQ06lsyjQir7+R+pYm0tMz+fSjfZjffAZv3CgM195MpyvAm59VMDE7mhsX5mBSAjgLPkcbHYN5TF7vuRra3GwpbCAnOZwJ2SeyiBVFZUthPRajjvz0SPRD6AM+GGfz3XV6Avz+X/vQhHXhVroIyEGmjInEp22ltOMI7qCHRanzuSJzEd499VQ0V1HY7WDWrHls3ryRBQsW98aCW3eV09bQyJsJuxEbAoQFTcwYfwETsydx5IiDPXu2c9lly7HZwiloLuS54peZnzIHi86MN+jlo+rN/GjSXaSfRiXr+LWqqsprq/aSOTqmTyb2x+8cJhiUWbQ8b9BjfPq+g4bablbcMqnXwAL4fSFWP7GDS5fnMSojsvf1znYPWzceoaGmi4XLxpCW1ZPYp6oq77xSiMGoJSrWzP5dNciySkSUicUr8jFbdGx6z0FDTRdX3zJpWC71r3bFfXBHQkLa16/V3TeQhoaq4LhxY2cO9N6wXeUAdrt9ND1Z3SscDse4sxvel8p5Z7jbjq7F3X4QQdIiIIAgEpd9y4AlVNDzwOhu2kpXw2bC4mdji+9JDDuOp72EjuqP0FpiMISlozVE4ekowt1RjN4YhTl6OuaIsb0SlwCu1r101H1IvP27aPTRdL9ajHFmMpLFSbBsG6EjOxEMVvSL78HZuQ8QsERNQms4fXZysLoL9+ajhF01etCkpcDRTjyfVZ92GzkUpLOlDjkURJFDdLY2UF9RRGtdBdFJGcy6YmVvAlmgqYnq3zxI4lXLMM2/tKfBi8dNxaOP4EuMIvzaFSSFJSEisuXF9URvWUdbYg4HTOkcUa1MTzUzo+hdGqIjqIiKIKfLR/iBfbjTRnNwwmXsq+hAVeGmS3KYljtwI8GAHEQUBDTiufFAhpQQHb4uoo2RfVaD1c5avIKLbHPOGdWqoKexhCAIvV6GTn8Xr5Ss5WBbMQbCiDAbMWh0pFiTGR2ZhUbU8vTB1VyaNp8ZnTm8f3Ajk6bNwG4fQ0HBbqqqyrniihVUuapp/rgEWStQr7RBUCU5OZWjleXYbBF0dXUwY8YcMjNzeseyvX43B1qK8MsB/HKAi5JnMT1h8mBDB078TuuOdrL+tUK+ffcMTCfF01sanbz5fAE33DmNsAGy7yvLWtn49iGW3zyJ6Lj+HopN7zrw+0MsWp5HMCizb2cN+3dWk5IRyawFWYSdUnLY2uTi9Wf3YrUZmDY3jdTMSDauLaG91U2mPYaSwkaWf3sikTFDT248+TqHytkY7v37D6yKiIiZbjJZ+8/oR/jK8Hicxo6Oll0TJoy/baD3h7PitgPfoie+nQX8HFjrcDjKz9VgvwTOK8Md8DbRePifxNlXojclHus0tg5fdzmx2bf0M4yKHKC16k0C7lqi0pZjDOvb8EFVVTzbagiWd2CclYI+68TKQQ55CLkKaanZhoCAKXLssWP6cbcfIGrUUsyRYwm1eXC9W0Yg8QCess+IHZWDNmsmmoypCNqhZXafPB7X+0fQxJoxTulfQ624AjjfKUU/Lg5D3sATlVAwwGdrnqS1vgJJo0UUNZhskSSm55GYmU9kXErvxEXx+6n+7W/QxceT+8BP+ngXGooL6Prz4zTEaOmyaNAEtWTWulAXXU3u8iWoQGl1J29+Vo6708kU02ECSghZFbGFjyM7Nxv72HgURUVRVTTSwIYyqIT4v71/xRv0cp19OblROQNudyZCSoii1hL2tRykuO0w3pCPFEsi45iGu0yi1VKHQ78fyQSJ5nhusF9NvDl2wLCIz+ejpKSQkpIiJIOW8LwEvFKALXU7SbEmcp39KuLNA09CDreX8eSBZ5nbkY/OLVCV56Te00SOLQNTqYLGpKPcV80YTzKdipu09AymTp2FXq/H6/VQXFyIJIlMnDh497eTURQVcZDmMcd/px+sKUark1hw+eh+27z90n5i4izMuriv0paqqry+ai+p2VFMH0QEpam+m7de2MfcS3Mo2H4UQRSYtSCTtOzB6+/bWtyERxqRjn0fFEVl60dHKC6oZ9HyPNJzhl+7/1Ua7r1795okSfOEKIq5cB4Ln5/XqKqiKCWyHPrB5MmTB/zgz2i4j8W2VwDd9MS23wI+czgcX0/Jn76cV4a7ufxlRFFLdPqK3tdUVaGt6i387hpiM29Ea+wxaHLQRXP5y6DKxGRej0ZnQ1UUhJMSq3xFzfgKm9AmWFEDMuaF6YSq9iLF5yCabISHm2irrqTrwIv41U40yWMRNUZ0pkSs0T2rHd/BJlwlNWyqfR5EiQuv/v6A2tJDJVDViWd7DbZrxiCcFL9VFRXXhnIErYh5QfqABkeRZbauexpXVyvzr70Hg2nwHuOqqtK06mm8leWk/vx/iIyP6vOZvlb6Nt7SUpaIoykuPITP2cyeiQJTpyzhktR5vSvWkKzw6saDyM27OOocRUpYA+GGFBrKtVxx3TiS004vB/pm2TscaCliUux4Pq75jEmx41ictoA408BG9VTcQQ9b63ayuXY7QSXIuJg8JsTkk2CO45Odu3CVdiIaXKBqUEMajCYdftH5/9q77/A2r/vQ419sEAABcIOb4no5RZEiNahlyZZlyfKK5e3YqZ2kSd0kTZMm96Zp6zY3N/emT3vvrZ20aZaTNJ7ykjUtWZYlak9O6eXee4EgARDz/gFK0aCsYcky6fN5Hj/G+wLvi3P0gvjhnPec3wFPAHW4jlWr1xJv+lMQbmg4w9GjBzCYjNRqWrG6jBidGjxJKoqkYhbYSs6Vy+/30dLShEajwWqNJCzMQFNTPZU1x3G7XdwRKOZk+QjxZhvycAP1XfXEDYZjs9pIGDCROC+T5MIr/1DxTPro6bBjiQzDet4gvLFRF+++XEl+ccK0g8SsVgMH9jSyd0cDDz5dQozt0s9DszzI7i1neOq5RWjPG3TX2jjEznfrePLriwi7zCIowWCQjb89weiwk5LFKRQtTEZ9HTkCgsEgzgkPRtP1rcz2aQZuYWa4mr6754AWQiuBvSfL8sTZfOXCjeMeb8c91kh87l9csF+hUBLKrHzwAAAgAElEQVSVdj9Dbe/Sc+bf0RmTCbPm4Rg4jFprJbw/yGT1/8blssPkBKqUeegXPYLfYcB9ogfjqjkojRocm+pxH96Mr+ZtQIEquRBsqThPbEcfm4F2ZAxtbBza9D8t6BEMBrHL7XQO1ZCfGE8geR4Vm37F7Y/8FZbo+HP5uU2WaEzWC1sS3f/xM1SmcKLW34vaGhpw4/d5GfC1Y9Ko8DSNnMsuFgwGmTjQSMDhIbjUSt3h90nLLcVoiTqvLAGO7Pgj9sFuVn7hOZTjLoJhpmmDn39igqbtW3DXVSN95/soL0pJ6fQ6Odl6ikWaQppjM/ljTTjf+3oxyWF9vH7yTTpPNZJVlE96dDoJxjjmJgY4PW7FYkigOCuJUycPkZBaxN73G1i4IY6akToM6jAMmjASjDbSzCkoFApqh2T2dO7n2yVfJ92SygJbCa/Xv8OPDv8LVq0FyZnEkrwlZCRd2Bq0j7gwGLXU2k/z+7pXseqsrJ1zBwttJWhVWkZGhjnwwX5cQz1ExsRRML8UdVDFQN8IVcc7KZqfi8c6Qf2xan7x4S/Rx5lYnrSEAlM2R47sp2T+QjaN7SZSF8tXC5+isUHm6NGDjE520zhuJCVlDh0drZw6dWwqs5QClysUOIxGE0UF85mTlI77rQbWxa1EZdZRGjcPX7aPzq4hVE4l4X3dTIZ9/K2TztYRjla00t/tQKkKXcdVd+eQkRPD+Ngkm14J3TM+tr+NzNyYC7q7g8Ege7bLHNnXwur78qYN2gBpWVHowzScruqlqCzp3LEnDrSRVxR/2aANoYQydz2Yj0KpwBR+/cuhKhSK6w7agjCdq2lxK4DlhO5prwVOAkuBTFmWr705++maES3uYDBIf8NLaPQxRKasv+zrvO5BJoarcI7UojOlYhxW4K3ajn7RoyiMEaDS4K3ajrfDSUB7F/qiGPRFoS+rsTcrCY7ux3jXbSg0erz1FShG2lHmr0GVNh/PyffwNx/FsOFH54Jhzb6tJDTZGPNuJrp0DV67k3p3F/0dDWTNW05T9QHGRwewRCew+onvnpvfPNnZQds//j36tDlMdnVivX0141mJ1B55H9e4nXTjPFLDC4nYMBeVWs3Y7mP425R0TB5BdssYLVGM2wdJyy0jMXMufe31dDdV4/N6WLb6izh+/wc83d2oLFYMOTlo4xPwBeFQXR+GwW4i/EOcnptHeJiRex8KTe86e00DgQDvVLyNo22Q2Jh4+vt6UYen8Nj9dyLLdRw/foiAMsi4wUOduZ1wrYmFQ9lkZeaSnz+Xip0NtPYcISXNxplTKnoiGokqDK3jPOFz0jPRR6LRRnnCQra27GRF0hLWzrn93HVuqOtHbQpS3XiA0e4BUCl46P4nMBiMBINBju5r5fiBdlAGcYQPkJUTxz3Ll6BWqXC5nJw6dYzGRhmfy0ROThGLl+dd8Bk5tq+N1qYhHny6GFmu41TlMczzE9jdtY/coSSSzPEMJLhpsDfz/dJvYtCEguHY2CgNDTItLY04nRNotToKC4vJyclDpVIzOenG4XAQGRmFUhlautP+cg3GVWnnlhCtOdFNxc4GbAYty8PD2Ng/RvntGRRMk5jE7w/wyn8eJSHZQn5JAjG2cOpOdbN/VxNzyxJpbRjCbA3jrgfz2fFWLQBrNxSgUCjwef3s2VZPR+sIdz2QT3zyx08xrDzaSeXhDtY/MpfIGGPonvhrVTzxtYWYzJ/9gCpa3MLFrmlw2lQQXwE8BNwDnJZlec1NKtuNMCMCt8tez2DLRuLzvzHt4hvT8fXIuDb/lLA130CdMrUYRzCI+2QvkzX9BBTHUAerCFv2FKrYdMY3vk5QV4L5kdJzgVkVdFJ9pILh040k+NMJ+naRuv4Z1Il5NJzcS9eBYxQZl6GIOs3Alr2gVGIomkdjFDjsA2TNW0ZS1jw+ePX/kDF3KfmLQh+Fgddfxd3eRtJ3vkf3gT0cObITX9BJ3uI1pBYup6/+FNYqPWcCRzEYrKS6s2mYOEqXq460cQ05j32Z4bF+6msPMjzaS0x8Okl584kkjKFf/xqDlEPMhkdwtzQzWHMc78AwvQNuFIEA2qQETkfq6XWZSDS7KS4qpqCgCKvVQH//KG+++zbOyWH80TF4nRIjw/1Ilh4giM/no7x8BSaTma1b32bFqtW80vQ28a1GHnzwcVRKHX/4+SGKb4uktq6CgTA/ppZ8Hn227FwX7+iknYquw1R0H8JmiOWbxV9FqVDisLvZs02mt9OOytiHzjzO/BWL2bl/OymmRNasuo8Pt9TT22XHN7ebjvFOFqiW0Se7KV+VgS1Fx/bt72KxROAbj0OnMbPmgUtHS2vUKn7+v/dwxz25pGZGsnnzmyQkJBNmMXLs0EFq4zvwqAP8Telfkmi6dJnPQCDA0NAAZrMVne7jg9rYO2fQFcSiy4xk0u3j5V8cYcHyNLLMetxV/QzlR7Pz3TpKylOZX55yQe/ImapeDn7YzJNfX3jBaO6utlHef6eOyBgDdz9UiFqjYmzUxau/Osbqe3OJijWx4+1avF4/j/xZKSrNlbuufb4Ae7fX0yQPsHxNNvU1fYRbdNy2dmYkYBSBW7jYdY0qB5AkSQksl2V5z9T2X8qy/OINLNuNMCMCd2/9b9AZEolIuvA30Nlrc3F3cMBpx/nWP6DJXoJuwUPnXuvc04avf4LBnEh+sq2W56RuMof3otCEoYjIwDuyEtPdWWBWUVWxmYZTe4mMSaFYtRJFAHyeSYZUBzEtvI22D/ZTYCwn6G9k6Oguou7/AqaiYrp/9m8EVUoiV9+Ff8yOb2SYieQ4jp3Yhif9EeakJhH30k8Zu+MOWt0TOMYdQBCrY4KYXie/MS7CFh3OE1FGIpxuNF4VHf0H2BJUkJa3iFUjVYzt3QNKJarwcIIKCIzaUer1BLxeIteuI+reB3AHPGxpfp+POg+AT0dc/zq+9UARByp2AhCTtoA3dhwmP2qA++97hLi4CF76wysMOEZpiO8gfng9Pq+SJ1ZnE2fVUFtbSVZWDmZzqPV29OhBurs7UEXo6Ohp55kNf07VwW6azgwwWlZHoM9JxJAes7qQgF/L+kcKL7hO/kBoCqhKqUKu7mXfzkZsSWaik8dpbDqDeyiJtPRkmvx16MZGUXqs6JWpaMuGOWQ/xHdKniPGEEXVsU5OHmwjIrWHyMgostLn8/YfTvLws6VERl86OtlqNbDj3VpaGgZ5+JlSenu72L17O1qtjry8QpzRAUwaI3lR0wetYDDI8f1tJM2JwJb48S3Z8V3NqKMN6OfZOPhhM21NQzz8TCmTlX34h5yY7kins3WE7W/Vkltko3xVBgpFKOHIq788Ss5c27T3rifdPtQa5bkBXgDHKlqpO9WD3x/Almhh1foc4mzmq/47DQaDnKkKXYeAP8BjX12AJWL6dK2fNSJwCxe77vkpsiwHgD3n7XoG+KwF7s8893g7noluotMevOS5yX0v4euoRpOzAk1u6N6zr+EAntMfobTEoS39AoFAAKVSyWRNP76+ccLWZvLbNypJS7DyiyY1P3n87wlr/ghtyb0E9w9hr2zlUMtGFEol9z37fYKNCrwto4Tfm83gzhPE9i9naG8n+cZygt5jDJ04TsTd97BNmcGhV+vRWG9nZfcBUl7biDUpDqVSSeDYUYJ56Uw07GDP0TTWKALUDvaicQ0QbYthl76N/EAaSvMk31ZW055zDx/Wn8Gi6iNx1IMlKYHHVt7Ov752Cl/OIh5/4WFUOj1BhYLugQnkM5301dYz5glgNuVhqD5CxcgO/B417qYSjJkyuaWDbN+0A7d3mHXrHiAq2sqR03NwjjnZe3A345PjDE+M0ZzYxx3pS7g7veyCf+v58xdesD1v3nza2pqxt44yEeNlX9shOo6rSCrTc3BE5gdLvk3dkROMO9rpqY+mtXGIOeeNNj6bwvPEwXaOVrSy8LZEeodqae+ws3r1WrQqM7u3yIT7EulSjBOnHcaabuS9gT18o/jLxBhC94fzixOorDzOuGOCu+66h53v1JOZFztt0D5X9oVJ1Jzo4lhFKyq1Eo3SStDvITe3kCutTnTyUAcnDrZz8lAHdz6QR2rG5e9TK41aAhMexkZdVB3rZO2DBaFMYI5JlFNd0ElpEdz72Fzee7UaglB+ewaNdf24XV4KSqZfnU2nv/Rrad6iFDpbR0meE0HJRa33q6FQKMgtiic2Ppyh/okZE7QFYTqq559//oac6MUXX/zaN77xjV/ckJPdOM87nZ5rPkiv1+B2e29CcS413LEVnTERU9SFGVz9g21MVvwObcGd+NpO4DmyEW/1doITowQzFtIfkU3t4fc5+v4r+AecGFs0GFeksbtliNNtI/zd06XI7SN0jEJqTioNNYfpb5cJHzWjTY4gf/WjHD1pJ6ZhBMOKVFRWPYbMBFzyXsL84TC5l+GTdVjX3s27iixONgzw1JocygoSMM2fzxuuRGotmSx5/G6GDx5ieCBIWNQEVnU7/XExoNJgjU9lq1YmoFBhjLSiwQTjdiaGm1GHeUlWaulUewnGWcnLzqA428bGj5o50+mgorqXl3c1sPNYJ/bJIDEZycRnJFMzdpwqz278PXMoM67hyduKKUhIpmr/SVSeSUzqHKqPDBJjMzG/MJ43T9Vjcg3g9LhpNytYP3cpK5LKUSkvDGAXZ5RTqVSYTOF0dXWQkpqPvGsCizGco5G7WJWyjHmxBSQkJFF3ugpLlIqmGjf58xLOTV0KBoMc3N1M5dFOSm+L4EzjQcxmK3fcsQ6rNYIwg5a8efEUliRxRlPDhG4SZ/sIcwIJlGTMR6cLTbUbHh6gseUE7uF4IiIiOXW4gzvvz7tsAg+9XoPPF0CpVHDqSCc+r5/YmCR6W5VMjPtISY+8bNBraxpi7/YG1m4oICrGyJ5t9YRb9CiAlvpBzlT3EggEsUzlOfePuvAPuznYMIgxXEvZ0jQAJqv70djCUceEbh8Yw3UkpVk5sLsJt9PH6aoe8uYlkDwnctpyTEepVJAz10ZCivVc+a/n79Rg1BIVe2OXOr3ZrrWeRqMO4B9vWoGEW+66u8ovJknSCVmWS678yk/VTekq902OoNKEX5C05Hp4XP30nvkPbDlfQxsWyis95nEwNukg8qM/oDBYCVv156GBQC3VDPR30dvbQU9rHWFGC0lZRUTHzkFz2IXL5MKyppy//dURnlojsTjfRkvPGC/91xvka2qIT8vBlpRDVJsR5biGAa0Cz4SXESVkPZBLXISBtl4HWzbtorSnAnPnCGNL1nIiIhe5Y5S/ebSYhPNaeU63l5+9VYXBfRrvhJ+11R9hffoput54mTOlizg9amYo7TRxpijuz1vB706/wrfSn2Xvnl3E6sJIraxC2deP9dmvcMoxysjIEMuWrSKgsbLjcDtxkQbSbOGkxZsxhWnwBny81bCZw13HKB+6m+C4lrUP5mMM17G/4iOamhpxmsL56gMPcfxAOycPtpO9zMLbrtfQdabhHU7nh19aQtDtwxplQHPeVLTxsUneeOk46dnRlC1LO5fEw+v1U3O8gyP72rFHd+HN7sOv8vHfF/wVmqlrPzjYzwcfbGdyQk2utICyJZlMun3s2SbT3T7KvKXh1J4+wvz5i8jJyZ82aPZM9PHjw/9KrjmT/PFUuro6sFis6HR67PZRkpJS6GsOp6fDjlRgY+Xdl783e7nP7kCvg/derSIrL5alqzMvKYd9xMXGl05QsjiZ4kWh7mu5upcPt8oEgxAZY8QaGUZHywj6MA3Z+bFoBpzEjnnYPODgoT+bT2RMaICd/ZUajCtSL1mLu697jM2vVQHw5NcXTduyvhYzfU2BqyW6yoWL3cjAfVKW5eIbcrIb54YHbo+zh77636I1xBOT/hhK9ZWTkJydXx3wuXAMHiPMIqENi2Wo7R38PhexGY+de+1/Vv2OlpEm/qapB/OGHzMwNEjr6WO09rtAoSAjzkL63AVExacRnPDiPNCBz+lmT88rjCji8PjTuN86grOmhomCdGr6TtMbtYC/uO9u+n7xc1wN9ejve5rKDgPl8RbeCXo51T7CksJ4PjjeyT3R4+QcfoeeJfeyeTw0Z/y7j867ZJGLYDDIvn27ae3oBL+Hcp2RwO4PGE9MoC49jcF0GA0O82zqMwz1TPDH4d/zTOnDpBmS0OvDwO/H3dKCPjM0Faq6+iSVlcdJkTKoCJzCrAsnzhhLmEpHk72V1rF2jJiZ274SnxMsEWEMD06QVjBJe3sLSs8cDqfvZEXSYiL1EQw3++g5FCDx9gD93TYWzU3A7Auw/c0acubaLhiY9MHmM4wOOUEBo0NO8ubFMzLopLNtFI1GxfI1WTToq3mveTt/VfznZEVcmOjG6Zxgx7ZtjI2NkZ9byukTDvRhenLm6zhVeZDy8hUXZAmbTu2QTJo5GYM6jN7ebhwOO263m2AwSF7eXEaH3Gx/q5b7Hi+aNgvYWR/32T0bvM3WMKLjjFgjDbjdXvq6HPT3jJGaEcXq+3IvCOoOuxutTn0uyE66fcg1vbTIgySYdGTaPSjvycISEfp8BNw+xl6rJfyBHFTTjNge7BvH5fRcU2v7euo6m4jALVzsRgbuL8my/NINOdmNc92Be2R4DKddRq21oDOGplT5fU565V+iN6XhdQ8QDPiJzXwClebyXW++3npcO/4fhru/x4S/E3vvXoIBL1pDAh5nL7FZX0SriiA4PsSQWsU/nXiR2DE/ReMGAo5QSk9r3DwGXB5MSgNBAqwqWYXSFWDy9CD28AAf+oKcbGpjqXI3ymCAGI0Viy0JubuOhHEVJ5L93HbKT0RSEmE5ufRs28HhVc/w3a+sYGRkgrf31NLadJqclEh0dVUoLSks/uLdvNW4BbdvkmcKHmds1IXL6SUuIdSKqq4+SU1NJctXr6bp9BnG7XZyDh6muTCfvnAFHYNKYkZS8U76CbfoGXM4MRa4+eJdd122q7azq52dH25DoVKjM+nw+Cbx6gIk5KSRrEmhZrsdrVbN2g0F6MM0bHt3H4NjMpNDKdz/WDlNyBzpOY434MUT8JLeVYK3R8eGL82HAPzXLw6RXRDHmcpe1j9SSGJqxLm0mA8+HUp72VDXT92pHmJsJtIyo7AlWVCplHj9XlrG2siOyJy27H6/n9f/uBlPoB+FEiIjIhkZHWbJkttIT7/+hDXnu5oFYq70JT867KS1YYiRISejw060WjVxiWZsiWYSUqyXzVI2ncCEh7GNpzE/nIdyquveNzDB+LZGLE/ORXEN57oeInBPTwTu2e9q5nG/Alz2RbIsP36jC3UDXXPg9vuceMdOMNh1CIJBAn43puhSrPErGWh5g2DQS1zm0wSDPgaaX8fnGUFnTCEY8ABBrImr8fs1HN72B3LK7sB08nUCoz0o9OGMFSZjii7BYMllvP8I3v4GwtoHCQ60AkE2RZnwDqkImwhij9Bz5/wvEBWbzpYdW9B6Yjnpt1AS0Y5pUkuKIZ4mdT+D9gECSj1zo2wYtryJ5kuP09Vxhv6OerLmr6StYgc5DQ6qUqKYmP9nFGbEMP7iT0mdl0vRd79Jd/cAm7ZsZDgwRmRAjd+uwhcGXXFOfFYfA65B/rrkL6jZNkpvl50NX5rPuLOfPXt2kllWwJt9O9D61eT1xlOUP4+TVScYnjRgcaeyYrVEYmoEWp2KrRWHaD3sJCkhijvuycU4TUKLrac/pHGXA4PSiy3RTPKcSGprKyktWcqxj0axRIRx5/15aLQqhocH2bbtXeJjcomyJlO0IPnSa+kP8O7LlaiUCuwjLtKlGJbckcGhPc00y4M8/GwpW9+oxmTWT5su81qNjbpxjLnQGbz09fVgtUaSmHhpuW6mTzOYBQNB7P9VhWltJuqp/NuephHcVb2YH8i96e8vAvf0ROCe/a4mcK/4uOdlWf7ohpboxrrmwO0YOMLkWC16awnGiHw8rh6G2zfj84yiVOqw5XwF1dRc62DAh72vgoDPhVKlxePsweex09cfQ/uZSiZd42RpPcx94h9wfPQCY8k6Egv/GuyDuN5/AYXOgDpjMeqUQlxhZn736j9ic6lZsfYx/rlzE0+e0jKqTaRXDyOODJwTPlJyzfjc1fh8PrzqGPrcVlbmqjlTV0WqSkNUcRmtzZ2Mjo4wEaPEETPE3dHl/Lz5dZSnVzMxruCBTDWZ239L+t/9kK0nj9PjGUCbbSb/1eMcN69HE9mLxuDjsUcf44/yRjx2BYG9CSSmWpmYmEBhakCTaOLg2GmkpqUEAwomomWiAuALKAiOSWz4YtkFI3cdnnH+bvdPWTJ0D65RH2sfLLgg21VTZxdbXq8mOsbMsqU5bH+rlnkLklHo+6mrPY1VV8S6h+aiViuZnHSzZcvbxMcnXrCU5HTO3r9OSYtk5XoJpVKB1+vn9V8fw2jSMdDr4LGvLpgRiTiuxqcdzOwb6wgrTUCbFsqO5zrVi38wNBXsZhOBe3oicM9+Vxwd8hkPzDdceMwCkrNuO/eHojMmY5O+wkcHthIVnUWiJpyupmqibKlogkqoB8v8ZahMJgIBL71nfo06UM2CNY/j3vc7To6qGHnnd+SV5aDpr8Vz4C18jfvQSMvRLX4MhUpNMBhk1zv/F6sjwO1Lvoa6IchXfGtQuGUqzZOYhvTg95OQaqW7bpTIqExySpP5+fuN/OCLRSRMDkJ1Hc0lZbQcPgmBMJQ6L8omI0siV5Odkom1awcLV6sYarax8rYMhp0tbNm6GXeUhZaYAb4XKKPS243BpCd7XjH1dQdpamjgztSVvPTaLnJSjNz1hXxee3kjfg/UjnSQ3bicDCmWuAQzxw9q8GsbCfosPPhk6SXTbcK1JpKiYzFlO4hrSOadP55ixV3ZBIOh1JfymW40cT4efmwharWKux8qZMvr1Wh1CpSWABlzVVNBe5IPPtiGXh/GggVLrng9TWYdj32ljNjYcMYcbgA0GhUr7srmvVermF+eMmuC9q2gNGkJjP9p5kZgbBLlJ0gPKgjClV31sE5JksqBfwXO5lg0Ar2yLF+az3AGO9JdiW5ESVFE4bl9AZS8dthCapyTzOQx9m/6NXEaKwm1XaBQMFaxl6TvfA+lXk9Xp54oqw7/0AEiAxMsmrOSysrdOPNdBJ2Z6AckRlJUdA+7GPrV8/i9Hnw+LwGFgsXpj4A8gSonmsguNSctQRQBBSMTFgK2VkbzwDFswtbr4eh2Fwsyo0lPMNP5H3+g31bCUHcCwxmNDFsbsDh0ZKiTqPqoB1xqFiTOp2akku+vWcHQ0CBHDBqCXi25H1aQviCP8a7t9MYvIm9eIiXzU6g/0cKJ44dZZ3uQiKEkxhZ00tFpJqh24OxPIbEnjZKlf8qIlTPXRrOcQWy8+bJzZAui86gerOO7d67EGhXG7s1nMIbrUEV76J1TyzfXPoFaHRrtnZBiZe2GAuTqXuLTrVTXnCAlJZU9e3aiUqm4/fa1V5yTfJY+TIPyotW7ktIiuO/xImITrrzWuXB5Z+dynxUYm0SbYfiYIwRB+KSuZT7GC8C9wGZZloslSdpAKGf5rHJIbqclcIJ/WZV/boWo9r5xPD4/9Z12Tm16BaXPT19wmPwvP0tETiEdP/0J3f/+IqbHH6Gl9iTm4iUEDQ14s/MJvnsAKd6AMqgkbLCAIFqG2jz4ojWEWwvIX1xMdfdpkpsjiPJZMd09B5VFT9Pe1+my6hhRGzHqdCw5c5wuVTRLK5toiwgnoJnPeKOHNz44RYw8RFtcHrErfJRnlhOm0qMNqKnYtpPb1qeyf2c3iioDLquej3x7aW+sRxWMwjFio690gjVnuhmYUGBP1JI714ZKpSQ3L4/6Fjs739+KMVJHi6OO/YcG6LAOsnreMqK10aRL5yUcUSnJypt+KcizCqNz2dy8g56JPuaWJpFbFE+zo4UXK3/Fs/lPEG24cKRxUloESWkRBAIB6htq2bTpDaKjY1m1ag0azfRrdV+LhBTrJz7H553SqME/4j63HXB4RItbEG6yawncQVmWuyRJUgHIsrxxasnPWaU0rhi5+wA1g2dQv7kdBgYZySgnLTaGnJ5KetpriIorxqsZocPRTbRxEUnf/i5t//N56n73z8TpfXRUjdOjnMfC+TVobzOgTUjA3+xGHx5O0KYlsaOMfae2EakZp9HZg96YgcVkxXxXNgqtiqHBMXocw6gi0zD0pnLb+mzUr+0jq2YAxZOPstdZj9nTR/IpI679Fs7ELuO2dRJ5cy/MRNUQF8+4q58nv76Qhtp+Jg8N0Fovo5xMwZIaSauyjsjuUpRfymGsrodknwqTOTS9Lb84gZNH4vCa7ETEKkh1xDCsdXDf4gcoiL6+gVwJRhulccX89NgL3J+xjsLoXH5T+0fWpK5kXmzhZY9TKpUsWrSM5uYGFixYglr9yeb/CjeO0qTF2zkGhKaCBT3+c1nTBEG4Oa7lG9AuSZIRqJAk6ZdAP+C+wjEzztw5cfyuKp7djftIdrSgNKrJ2L+ZdSoTHRFxuM1qzvSmkJ6ZSHP1TryRJRQoWxhJnMTuVVMUTGGPNxOLRsXxwyUsXliFLzCIwbOcUf9pXh2wM88dzmSahe6AFeOkm8WKOKJXZaLQqvD6/Gx6aTO6hFgiwtMxqS1k5MYR+P4PUCgVKPVh/IS7AHAtc/DOrw8yL8t8SdAGSE1Np66uiuLiMqTCWCob7HQpx+lNO0AAP+XlpWQOprNjk4xSqeCOe/40EtgYriM9K572Zh13r1/MkHcQ+6QDKXL66VBXQ6FQ8HTeIxT0SbxW/w7vNm0lOyKTdXNWX/FYmy0Bm236FJnCraM0agiMe3BPpdxFEQrmgiDcPNcSuO8HJoFvAU8AZuDya1DOUJouB2sDJTQ1bEQRCGKIjaMCJSZFKTrlUYa8yRQsy+TMgTYijUaGPvwZsnqClqCOxXc8Svsb+8i1qckPD0OBkV55OXFJTvxKHZX1ze9MEKgAAA3hSURBVNiSI3AF/WRPpnHSrsYS18teqijYeJS8p57h7cPdWD1dTKotjPTrKFkUi0KhQGW49L5hmDWcx75z52XrkpIyhyNH9jM8PERfXzdqVGQUZLE6Mo3cSInYKAujo07U6tBCGCkZF3ZVl6/KIL84AY1WhU0bh8348V3hV0OhUFBqKyYzIp2KrsPcnrLs3C0JYeZRWcNQqJV4O+yoogwYV0Xf9PnbgvB5dy2B+z5gkyzLY8BLN6c4t55Co2SpPxzPsJpIl5+kpQ8z9M6/4fY1o9N0Y0x/kJq+McJNGlQuMw5GsPu1rHjgqzgjDPSkzWOlJoyx2nepS4slTllKYDCK1z1HMaVYibIryB9oJ0LKZ1znoZgIauIGONnfQucvf0b9WCLZMVpS41I4dWqSpLSI665LWFgYcXEJyHItbW0tLFhQPm0Gr4KShGkXfDCG66adb30jWHUW1qdf/keHMDMojRosD1+6vKggCDfPtTR1CoHDkiRtliTpS5IkzcqRPer4MILqAGmaHCb00OnQMqSei1FdRURsEksWzKWvuZE83y4GvEVExGdS6V9AZ9DAvx96iVJlFDU+O8GCeJpK/HxAGxtHazAFfcR7TWRXVRBxZzkKvZoStZYWt49uTRpr77oXuyGctCQ7Hp0OU7SEJSKMcMuVU6p+nLS0dBobZcxmyw3L4CUIgiDcOlcduGVZ/r4sy7nA3wPZwH5JkrbdtJLdIt6qrXSNHyZNX0BPlJYP+rfgMptxa+IZTY7AYnbxqPUQ2tgIouIiMMbeTUrWXLbX7+fB4RUoFNCbEs+cp/+c5xY9R1KygjC9g/iUQlY9vJ7oL2zAsmQJuuwolEDduJuqmj7cinB2DWczJyqJosQUervdJKZ98t9GKSlphIUZKCtbfM1LIQqCIAifPddzc3F06j8Hofvcs8pYxBwaHKdRBfykha/E7p7A6DKjz1hMt8nHj4/8P45q0zk1qUNtbqH+TB0LvH6e7MskyxPBe6PjFE9Ni+pob8U/MsKqVWu4c+Ui1GYzkevWo1Aq0eXHYFqXSWx6JKbJAC+8WUVxVgxL7r2HgtVr6WwdISn1+rvJz9Lrw9iw4QliY22f+FyCIAjCrXctCVh+CDwATAAbgQdlWe66WQW7Vfr6uok3hePsPk5GylJcrQbUSjXLC4uwJS5l76s/oUkdxJulJm8ijsnxRs44+9H6TQxPRNCq1pORaGFoaICKig8pKysnOSXlkvdRqJSoowzkFtloaRyi1e7m2w8XATA8MIHb6SUx9cbcjRAtbUEQhNnjmqaDAffIstx9swrzWZC36C7G+h2MNn2IgSWsMBuYmPAQEx+Ot76CEZ+FvJx8Rjv6yBg3k1eyjldHtjHeY0evslOogLfe7MDr9ZKZKZGT8/EDd5LnRKLTqbg/x0Z8VGihhs7WEWJs4einVlwSBEEQhLOu2FUuSdIXAGRZfgGwXfTcMzepXLdMMAATTW0YsrNR+I9yu0FPgi0clRJ6Tu7CgZ6M/kjmeTJ4OWk/k5k6Kn0yZUuWc7h/DnnFt1NWtpiFC5dQVlZ+xfdTKhVIBXG4esbxevwAdLaOknQD7m8LgiAIs8/V3OM+Pzvary567i+v940lSfqBJEltkiRtvmj/85IkVUiStE2SQjk1JUmKntqukCTp+et9z6tx4kAr+xypGBatRqlpwuufpJBJPFXbaPaEkWCIRedVEXlvHv1hDv7t1H8SobdSnCDx468tp7Qwg9TUdNLTs1Aqr24IQfGiFPz+AJtercQ57qG7Y/QTTQMTBEEQZq+riSyKyzyebvta/AZYdf4OSZLygTJZlpdOPf+9qae+D/xman+ZJEl53CTpUX4cGiuNY+FYH/g+h8YmsQR0OI6dpEsRTbI7CuPKOWgNeh7Ovp9+5yCL48tQKBREXOecZ4NRy/1PFKFQKHj9N8cIBsGWZLnBNRMEQRBmg6sJ3MHLPJ5u+6rJstwL+C/avQw42wLfPLUNocVMzt//8YswfwLKrmaKlM0c2d9BQ6uLkYALV5aRZv189GhJXVaIyjqVzztK4tmCJ1medOUu8SvR6TXc88hcYuLDSZ4TgVotsokJgiAIl7qawWmFkiR1E2pdR009Zmo78vKHXZdIoBlAlmWXJEmmqf1GWZZdU49HgTlXe0Kr9dqWGHSb9BQtzWZ0xMCRE+8TFuNnV2MbAPNT5hJXdGGGsVXWRdd0/it54isLCQSCKD+FtJEqlfKa/31mqs9LXT8v9YTPT10/L/UUrt4VA7csy9c9tFmSpEhg6zRP/UiW5S3T7B8BrFPH6oHxqf1OSZL0siy7AQswfLVlGB11XlOZ9eUrmJgYZujdd8FrIS2lkPLbM/D7/Wi12ms+32eZ1WqYVfX5OJ+Xun5e6gmfn7peaz1jYsJvYmmEz4Kbuj6iLMvDwLU0SfcC/xP4D2AdUDG1f9/U9lvAWuAHN7CYF+jt7eaDD7aRn19Expx8tDoNarVaLCUpCIIgfCbcsmgkSdKzwJ+FHkq7gEdlWa6VJKlSkqQKQpnZnpp6+U+B30uS9NfAblmWa29WuXw+HytXriIh4ap74wVBEAThU6MIBq97fNlMEBwYcFzzQaILbvb5vNT181JP+PzU9Tq7ykW6xFlMDF0WBEEQhBlEBG5BEARBmEFE4BYEQRCEGUQEbkEQBEGYQUTgFgRBEIQZRARuQRAEQZhBZv10sFtdAEEQhFtATAebxWZ7OjDx4RUEQRBmFdFVLgiCIAgziAjcgiAIgjCDiMAtCIIgCDOICNyCIAiCMIOIwC0IgiAIM4gI3IIgCIIwg8z26WDXTJKkrxBaJ9wDPCPLcvMtLtINIUnSYuBfCdVrHHiC0PX/AxAO7JJl+flbVsCbQJKkpcA+IGZq16yrqyRJC4D/AWiArcBvmYX1BJAk6UWghFCD478Bh4GXgASgBnhOluXALSvgJyBJkgbYA+QDX5ZleaMkSdFMcy0lSVoP/C2hPBXfkmX56C0ptHDLiBb3eSRJigS+DCwH/gb4X7e2RDdUG3C7LMsrgPeA54DvA7+RZXkpUCZJUt6tLOBN8G3g2NTjWVdXSZJ0wD8A98uyvFKW5X9mFtYTQJKkLCBXluVy4CHgn4BngGOyLC8DAsBdt7CIn5QP2AD83/P2XXItJUlSAT8C7iT07/B/PvWSCrecCNwXWgDskWXZN/UrVrrVBbpRZFnulmXZObXpIfRFsRTYPLVvM6EfLLPCVKukApiY2jUb67oIcAEbJUl6X5KkuczOegL0AS5JktSAFRgAljFL6irLclCW5Z6Ldk93LbOAelmWHbIsdwEaSZL0n2JRhc8AEbgvFAmMnLc96/59JEmKAv4C+DVglGXZNfXUKKH6z3iSJCkJ1fHfz9s9G+uaAOQRanl9C/g5s7OeAA5CvUYy8D7wz1z49zqb6nrWdNfy4u+o2Vhv4QpmXWD6hEYI/Zo/y3+rCnIzSJJkAN4AvinL8iDgPO/XugUYvmWFu7EeBzbJsuw+b99srOsIsF+W5QlZlk8TqtdsrCfAaiCKUIuzBPgZF/69zqa6njXdtbz4O2o21lu4AhG4L3QYWCFJkkqSpBKg4VYX6EaZ6mJ8FXhBluUDU7v3AeumHq+d2p4NCoENkiRtB+YCLzM763oYkCRJUkqSZAPczM56Qui7anhq8NkYYAT28qe6rmP21PWs6a5lA5AtSZJx6pr7LvqBKnwOzPbVwa6ZJElfA54CvMCzsiw33uIi3RCSJH0ReAE4NbVrC6ERub8nNGp1tyzLf39rSnfzSJK0h9CgHwWzsK6SJD1DaBaEBvgecJrZWU8Voc9rGqAnNIjr7al9NkL1/vpMHVUOIEnS60ApoVkf2wndDrjkWkqSdC/w3wmNKv+2LMuHb02JhVtFBG5BEARBmEFEV7kgCIIgzCAicAuCIAjCDCICtyAIgiDMICJwC4IgCMIMIgK3IAiCIMwgInALAiBJUu/U/9MkSdpwA897vyRJmedtb5UkSXujzi8IwuePCNyCcKE0QvO+r9rUHOPLuR84F7hlWV4ny7Ln+oomCIIg5nELAhBqccuybJMkaT+h/N9thBJgvEso53nO1Eu/IcvyIUmSXgKcQBmhjHRNhJJi6KaOfYLQEo3bCKWpdBBaFKMayJFl2S1J0g+BRwmtbPW3siy/J0nSbVPn8Uy950uyLP/4JldfEIQZRLS4BeFCfwvskGV5nizLfwR+CLwpy3IZ8AChHNlnWYEFsiz/C/CRLMsLZVmeB+wnlHXvMLCJ0DrR82RZdpw9cGod7XuB+cAa4AVJkkxTTxcDXwKKgK9JkmS8ifUVBGGGUd/qAgjCZ9xqYK0kSc9PbUdN5X0H2CjL8tkuqxRJkt4A4gADsOsK5y2fOn4S6JEk6QShFjqEFg4ZApAkqYXQKmCzJm++IAifjAjcgvDxFMBaWZa7z98pSRKEusrP+jfgH2RZ3jM1uG39J3jPyfMeB4CPu4cuCMLnjOgqF4QLOQgt6nDWLuC5sxuSJBVd5jgzoZazEvjix5zvrAPAA5IkaaZWeSoGaj9JwQVB+HwQgVsQLlQFhEmSdEqSpCeAfwISJEmqkiSpDvjyZY77EaGBaIeBlvP2vwr849T5zgVwWZaPEFqh7STwPqE10sdvfHUEQZhtxKhyQRAEQZhBRItbEARBEGYQEbgFQRAEYQYRgVsQBEEQZhARuAVBEARhBhGBWxAEQRBmEBG4BUEQBGEGEYFbEARBEGYQEbgFQRAEYQb5/9kxz9lZ7qMHAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=figsize)\n", + "sns.lineplot(data=data_q4, x='Iteration', y='Eval_AverageReturn_Smooth', hue='Config')\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "\n", + "plt.savefig(os.path.join(export_dir, 'q4.png'), dpi=200, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "def read_q4_optimal_data():\n", + " full_data = pd.DataFrame()\n", + "\n", + " for folder in os.listdir('data'):\n", + " split = folder.split('_')\n", + " if 'HalfCheetah-v2' in split and 'search' not in split:\n", + " config_list = split[split.index('q4')+1:split.index('HalfCheetah-v2')]\n", + " # print('_'.join(config))\n", + " config = '_'.join(config_list)\n", + "\n", + " logdir = os.path.join('data', folder, 'events*')\n", + " eventfile = glob.glob(logdir)[0]\n", + "\n", + " X, Y, Z = get_section_results(eventfile)\n", + " data = pd.DataFrame({'Iteration': range(len(X)), \n", + " 'Config': np.repeat(config, len(X)), \n", + " 'Train_EnvstepsSoFar': X, \n", + " 'Eval_AverageReturn': Y})\n", + " data['Eval_AverageReturn_Smooth'] = data['Eval_AverageReturn'].ewm(alpha=0.6).mean()\n", + "\n", + " full_data = pd.concat([full_data, data], axis=0, ignore_index=True)\n", + " \n", + " return full_data\n", + "\n", + "data_q4_optimal = read_q4_optimal_data()\n", + "data_q4_optimal\n", + "\n", + "plt.figure(figsize=figsize)\n", + "sns.lineplot(data=data_q4_optimal, x='Iteration', y='Eval_AverageReturn_Smooth', hue='Config')\n", + "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "\n", + "plt.savefig(os.path.join(export_dir, 'q4_optimal.png'),dpi=200, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cs285", + "language": "python", + "name": "cs285" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/hw2/cs285/infrastructure/rl_trainer.py b/hw2/cs285/infrastructure/rl_trainer.py index 7470f8549edaecefd11d85fcec19c5322a989e78..0de2ccfead1cbeb49deeac19cc7c4afa8c826317 100644 --- a/hw2/cs285/infrastructure/rl_trainer.py +++ b/hw2/cs285/infrastructure/rl_trainer.py @@ -152,8 +152,7 @@ class RL_Trainer(object): def collect_training_trajectories(self, itr, load_initial_expertdata, collect_policy, batch_size): # TODO: get this from hw1 # if your load_initial_expertdata is None, then you need to collect new trajectories at *every* iteration - # if at itr = 0 and load inital expertdata return directly - if itr == 0 and load_initial_expertdata is not None: + if itr==0 and load_initial_expertdata is not None: with open(load_initial_expertdata,'rb') as f: loaded_paths = pickle.loads(f.read()) return loaded_paths,0,None diff --git a/hw2/cs285/policies/MLP_policy.py b/hw2/cs285/policies/MLP_policy.py index 5cb39172e23c3589caef0ab0b0f7cada4fa703ac..3938187190364f9aa1f35567ff11f02a1d0b6820 100644 --- a/hw2/cs285/policies/MLP_policy.py +++ b/hw2/cs285/policies/MLP_policy.py @@ -117,8 +117,8 @@ class MLPPolicy(BasePolicy, nn.Module, metaclass=abc.ABCMeta): return distributions.Categorical(logits = prob_action) else: mean_prob = self.mean_net(observation) - std_prob = self.logstd(observation) - return distributions.Normal(loc=mean_prob, std=std_prob) + std_prob = torch.exp(self.logstd) + return distributions.MultivariateNormal(mean_prob, scale_tril = torch.diag(std_prob)) ##################################################### ##################################################### @@ -143,10 +143,8 @@ class MLPPolicyPG(MLPPolicy): # HINT3: don't forget that `optimizer.step()` MINIMIZES a loss log_pi = self.forward(observations).log_prob(actions) - # mul for Multiplies each elements, mean for expectation, neg for minimize the neg expectation function = maximize the pos expectation loss = torch.neg(torch.mean(torch.mul(log_pi, advantages))) - # TODO: optimize `loss` using `self.optimizer` # HINT: remember to `zero_grad` first self.optimizer.zero_grad() @@ -173,9 +171,9 @@ class MLPPolicyPG(MLPPolicy): # TODO: optimize `baseline_loss` using `self.baseline_optimizer` # HINT: remember to `zero_grad` first - self.baseline.zero_grad() + self.baseline_optimizer.zero_grad() baseline_loss.backward() - self.baseline_loss.step() + self.baseline_optimizer.step() train_log = { 'Training Loss': ptu.to_numpy(loss), diff --git a/hw2/solution.md b/hw2/solution.md index 215a30e15f11067ec292bf8711b91648bf6c6fb2..eded00f868269ee2f8cd735a4a419c8fd8b99fad 100644 --- a/hw2/solution.md +++ b/hw2/solution.md @@ -1,11 +1,30 @@ # 前言 -详细写的记录见博客:[CSDN 链接:]() +详细写的记录见博客:[CSDN 链接:https://blog.csdn.net/qq_39537898/article/details/117064479](https://blog.csdn.net/qq_39537898/article/details/117064479) 参考: -1. [https://github.com/welkin-feng/cs285-homework-2020/tree/master/hw2](https://github.com/welkin-feng/cs285-homework-2020/tree/master/hw2) 有bug 做了pull request +1. [https://github.com/welkin-feng/cs285-homework-2020/tree/master/hw2](https://github.com/welkin-feng/cs285-homework-2020/tree/master/hw2) 2. [https://github.com/welkin-feng/cs285-homework-2020/tree/master/hw2](https://github.com/welkin-feng/cs285-homework-2020/tree/master/hw2) -# +## 编译运行前 +使得cs285用在 +``` +cd +$ pip install -e . +``` +### Requirement +运行Notebook里的代码时要添加conda环境,进入cs285环境 +```bash +conda install ipykernel +python -m ipykernel install --user --name=cs285 +``` +```bash +conda install tensorflow seaborn +``` +# PDF问题 + +## Experiment 1 + +### 运行指令 ```bash python cs285/scripts/run_hw2.py --env_name CartPole-v0 -n 100 -b 1000 \ -dsa --exp_name q1_sb_no_rtg_dsa -ngpu @@ -19,4 +38,127 @@ python cs285/scripts/run_hw2.py --env_name CartPole-v0 -n 100 -b 5000 \ -rtg -dsa --exp_name q1_lb_rtg_dsa -ngpu python cs285/scripts/run_hw2.py --env_name CartPole-v0 -n 100 -b 5000 \ -rtg --exp_name q1_lb_rtg_na -ngpu -``` \ No newline at end of file +``` + +### 输出图片 +首先第一个是输出两幅图,第一幅是对比small batch也就是batch_size给了1000,迭代次数100次, +`-dsa` 表示关掉了standardize_advantages,也就是不对advantage做归一化处理 +`-rtg` 表示reward_to_go,也就是设置reward的求和时间是从$t$开始到$T-1$ +然后对比图如下: + +![运行结果示意图](../image/q1_sb.png) + +第二幅图是对比大batch size下,batch size给到了5000,迭代还是100次,对比图如下: + +![运行结果示意图](../image/q1_lb.png) + + +### 问题回答 +1. 当不采用归一化的时候,对比trajectory-centric one和reward-to-go,哪种estimator表现的更好? + + 暂且觉得,reward-to-go更好,首先是每一次能坚持下来的ep_len就比较长,虽然最后几次迭代不知道为啥降下来了; + 通过平均return下看下来在50次之前的迭代,reward-to-go的平均return都比ntrajectory-centric one高 + 以上的现象在大的batch size下体现的更加明显;但是其实reward-to-go主要是减小了方差,这一点当我们打开return方差的时候非常明显 + ![运行结果示意图](../image/q1_a1.png) + 比如此图表中rtg就是有reward to go的 + +2. 归一化advantage是否有用? + + 首先肯定有用呀,在上一题我们提到的50次迭代后reward-to-go突然平均return下降了,通过归一化advantage就基本没有了,主要是因为方差的突然增大,而归一化可以使好的reward的trajectory更被下一次选取保存 + ![运行结果示意图](../image/q1_a2.png) + + 方差方面归一化后,基本也没有了无standardization的突变了: + ![运行结果示意图](../image/q1_a3.png) + +3. batch size是否产生了影响? + + 你的训练集越多计算迭代次数少也能学到更多吧,前提是能坚持下去的ep len更长,所以主要体现在后半段较为明显 + 具体我们可以直观的看一下平均return方面,5000的batch size比1000的平均return基本在每次迭代都高 + ![运行结果示意图](../image/hw2_q6.png) + +剩下就没有问题了,只是说确认一下你的最大的收敛是否到了200这个点,从平均return的图里可以看到大batch size(3000)下是到了的,小batch size(1000)下在归一化和reward-to-go都开启的情况下可以保持后续稳定收敛 + + +## Experiment 2 +首先这里是要你找到在迭代次数100以内 把分数打到1000上的batch size和learning rate的参数应该对应是多少 + +### 运行指令 +对比的范围是batch size在1000,3000,5000之间,学习率在0.05,0.01,0.1之间: +```bash +python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 1000 -lr 0.05 -rtg --exp_name q2_b1000_r0.05 -ngpu +python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 1000 -lr 0.01 -rtg --exp_name q2_b1000_r0.01 -ngpu +python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 1000 -lr 0.5 -rtg --exp_name q2_b1000_r0.5 -ngpu +python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 3000 -lr 0.05 -rtg --exp_name q2_b3000_r0.05 -ngpu +python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 3000 -lr 0.01 -rtg --exp_name q2_b3000_r0.01 -ngpu +python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 3000 -lr 0.5 -rtg --exp_name q2_b3000_r0.5 -ngpu +python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 5000 -lr 0.05 -rtg --exp_name q2_b5000_r0.05 -ngpu +python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 5000 -lr 0.01 -rtg --exp_name q2_b5000_r0.01 -ngpu +python cs285/scripts/run_hw2.py --env_name InvertedPendulum-v2 --ep_len 1000 --discount 0.9 -n 100 -l 2 -s 64 -b 5000 -lr 0.5 -rtg --exp_name q2_b5000_r0.5 -ngpu +``` + +运行指令是直接拿python生成的,详情见[Notebook里的](DataViz.ipynb) + +首先主要是要找到合适的batch size和learning rate使得迭代在100以内就能获得平均1000多的return,运行以上命令后,我们可以直接输出一下图看一下各自的平均return是多少: + +从图里就很明显可以看出来了,在`batch_size=5000`&`learning_rate=0.01`可以在迭代100以内获得基本快、稳定的平均1000多return + +![运行结果示意图](../image/q2_a1.png) +![运行结果示意图](../image/q2_a2.png) + +主要第一幅大batch size下对比learning rate的有效性,可以大概知道0.01比较好,然后在对比batchsize 一般来说... 越大当然越好就是有点慢 + +## Experiment 3 +```bash +python cs285/scripts/run_hw2.py \ +--env_name LunarLanderContinuous-v2 --ep_len 1000 \ +--discount 0.99 -n 100 -l 2 -s 64 -b 40000 -lr 0.005 \ +--reward_to_go --nn_baseline --exp_name q3_b40000_r0.005 -ngpu +``` +有一说一 这个真的好慢好慢好慢啊... +主要给画出整个学习过程的return变化: +![运行结果示意图](../image/q3.png) + +## Experiment 4 + +```bash +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 10000 -lr 0.005 -rtg --nn_baseline --exp_name q4_search_b10000_r0.005_rtg_nnbaseline -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 10000 -lr 0.01 -rtg --nn_baseline --exp_name q4_search_b10000_r0.01_rtg_nnbaseline -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 10000 -lr 0.02 -rtg --nn_baseline --exp_name q4_search_b10000_r0.02_rtg_nnbaseline -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 30000 -lr 0.005 -rtg --nn_baseline --exp_name q4_search_b30000_r0.005_rtg_nnbaseline -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 30000 -lr 0.01 -rtg --nn_baseline --exp_name q4_search_b30000_r0.01_rtg_nnbaseline -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 30000 -lr 0.02 -rtg --nn_baseline --exp_name q4_search_b30000_r0.02_rtg_nnbaseline -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 50000 -lr 0.005 -rtg --nn_baseline --exp_name q4_search_b50000_r0.005_rtg_nnbaseline -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 50000 -lr 0.01 -rtg --nn_baseline --exp_name q4_search_b50000_r0.01_rtg_nnbaseline -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 --discount 0.95 -n 100 -l 2 -s 32 -b 50000 -lr 0.02 -rtg --nn_baseline --exp_name q4_search_b50000_r0.02_rtg_nnbaseline -ngpu +``` + + +第四个是另一个环境下的,已经给了测试的范围是batch size在[10000,30000,50000],然后learning rate在[0.005,0.01,0.02]里选 +不过问题里面只需要画出一幅图并说明这个是怎么影响的整个任务的表现的... emmm 其实这个问题在第二个图里能看出来? +首先是learning rate不能太大,也不能太小,太大了 基本没法找到最优的reward [emmm 这么一说真的很像做DL或者ML的作业哦] + +![运行结果示意图](../image/q4.png) + +可以比较明显的开出来batch size=50000,然后learning rate=0.02时最优 + +然后在找到optimal后对比一下各个方法下的效果: + +```bash +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 \ +--discount 0.95 -n 100 -l 2 -s 32 -b 50000 -lr 0.02 \ +--exp_name q4_b50000_r0.02 -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 \ +--discount 0.95 -n 100 -l 2 -s 32 -b 50000 -lr 0.02 -rtg \ +--exp_name q4_b50000_r0.02_rtg -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 \ +--discount 0.95 -n 100 -l 2 -s 32 -b 50000 -lr 0.02 --nn_baseline \ +--exp_name q4_b50000_r0.02_nnbaseline -ngpu +python cs285/scripts/run_hw2.py --env_name HalfCheetah-v2 --ep_len 150 \ +--discount 0.95 -n 100 -l 2 -s 32 -b 50000 -lr 0.02 -rtg --nn_baseline \ +--exp_name q4_b50000_r0.02_rtg_nnbaseline -ngpu +``` +然后就可以直接输出图啦: + +![运行结果示意图](../image/q4_optimal.png) + +结果很明显具有reward to go和baseline用学习方法做的可以有较快的上升平均return值 \ No newline at end of file diff --git a/image/hw2_q6.png b/image/hw2_q6.png new file mode 100644 index 0000000000000000000000000000000000000000..807d9f2fcb2279140664b193ca215338d3835df7 Binary files /dev/null and b/image/hw2_q6.png differ diff --git a/image/q1_a1.png b/image/q1_a1.png new file mode 100644 index 0000000000000000000000000000000000000000..af51d29448c111bd4ee7276ebd843900f29cbc7b Binary files 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