examples/quickstart_ja/rtb/rtb_synthetic_customize_env_ja.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickstart: 入札オークションのシミュレーション (環境設定)\n",
    "\n",
    "このノートブックでは入札オークション (Real-Time Bidding; RTB) のシミュレーション環境をカスタマイズする例を紹介します．\n",
    "\n",
    "特に、以下の4つの順番で解説していきます: \n",
    "\n",
    "1. 入札シミュレーションとオンライン強化学習\n",
    "2. 標準的な環境設定\n",
    "3. 環境設定のカスタマイズ\n",
    "4. 入札設定のカスタマイズ\n",
    "\n",
    "\\* このライブラリは，オンラインとオフラインの方策・モデルベース評価で [d3rlpy](https://github.com/takuseno/d3rlpy)のアルゴリズムを利用しています．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SCOPE-RL モジュールのインポート\n",
    "import scope_rl\n",
    "from scope_rl.policy import OnlineHead\n",
    "\n",
    "# import rtbgym modules\n",
    "from rtbgym import RTBEnv, CustomizedRTBEnv\n",
    "from rtbgym.utils import NormalDistribution\n",
    "\n",
    "# d3rlpy アルゴリズムのインポート\n",
    "from d3rlpy.algos import DiscreteRandomPolicyConfig\n",
    "from d3rlpy.algos import RandomPolicyConfig as ContinuousRandomPolicyConfig\n",
    "from d3rlpy.preprocessing import MinMaxActionScaler\n",
    "\n",
    "# その他のライブラリのインポート\n",
    "import gym\n",
    "import torch\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.utils import check_random_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Tuple, Union, Optional\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1.2\n"
     ]
    }
   ],
   "source": [
    "# バージョン\n",
    "print(scope_rl.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random stateの設定\n",
    "random_state = 12345\n",
    "random_ = check_random_state(random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# warnings\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. セットアップ，人工データ生成，オフライン強化学習\n",
    "まず初めに，今回用いるRTB環境を紹介します．\n",
    "\n",
    "#### 強化学習でのRTB環境のセットアップ\n",
    "RTBにおいて強化学習エージェントの目的は，予算制約が与えられもとで，一つのエピソード内でのKPI(クリックやコンバージョン)を最大化することです. KPIを最大にするために，エージェントは入札価格関数パラメータ $\\alpha$ を選択します．\n",
    "入札価格関数パラメータ $\\alpha$ を利用することで以下の入札価格を決定します．\\\n",
    "$bid_{t，i} = \\alpha \\cdot r^{\\ast}$，\n",
    "ここで$r^{\\ast}$はKPIの予測値または期待値を表します.\n",
    "\n",
    "この問題を制約付きマルコフ決定過程(CMDP)として定式化します.\n",
    "\n",
    "- `タイムステップ`: 一つのエピソード(一日，一週間)に含まれるタイムステップ(24時間，数日など).\n",
    "- `状態`: それぞれのタイムステップで，環境からフィードバックが返ってきます.\n",
    "  - タイムステップ\n",
    "  - 残予算\n",
    "  - 一つ前のタイムステップでのインプレッション単位の特徴(予算消費率，一つのインプレッションあたりのコスト，オークション落札率，報酬)\n",
    "  - 一つ前のタイムステップでの入札関数パラメータ(強化学習エージェントが選ぶ行動) \n",
    "- `行動`: エージェントはKPIを最大化するために入札価格パラメータ$\\alpha$を選択します.\n",
    "- `報酬`: 一つのタイムステップあたりに得られたクリックまたはコンバージョンの合計数.\n",
    "- `制約`: あらかじめ決められた、一つのエピソードあたりの予算．\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 環境のセットアップ\n",
    "env = RTBEnv(random_state=random_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ランダムに行動を選択するエージェントを定義\n",
    "agent = OnlineHead(\n",
    "    ContinuousRandomPolicyConfig(\n",
    "        action_scaler=MinMaxActionScaler(\n",
    "            minimum=0.1,  # 方策が取りうる最小の値\n",
    "            maximum=10,  # 方策が取りうる最大の値\n",
    "        )\n",
    "    ).create(device=device),\n",
    "    name=\"random\",\n",
    ")\n",
    "agent.build_with_env(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 環境とエージェントの相互作用\n",
    "# 6行で強化学習の相互作用を記述できる\n",
    "# only 6 lines are needed for RL interaction\n",
    "for episode in range(10):\n",
    "    obs, info = env.reset()\n",
    "    done = False\n",
    "\n",
    "    while not done:\n",
    "        action = agent.predict_online(obs)\n",
    "        obs, reward, done, truncated, info = env.step(action)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['timestep', 'remaining_budget', 'budget_consumption_rate', 'cost_per_mille_of_impression', 'winning_rate', 'reward', 'adjust_rate']\n",
      "(7,)\n"
     ]
    }
   ],
   "source": [
    "# 環境からのフィードバック\n",
    "print(env.obs_keys)\n",
    "print(obs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 累積報酬と残予算の変遷を可視化しています\n",
    "# 目的は予算制約の中で強化学習を利用して, 累積報酬を最大化する方策を得ることです\n",
    "\n",
    "def visualize_interaction_on_environment(env, agent):\n",
    "    obs, info = env.reset()\n",
    "    done = False\n",
    "    remaining_budget = [obs[1]]\n",
    "    cumulative_reward = [0]\n",
    "\n",
    "    while not done:\n",
    "        action = agent.predict_online(obs)\n",
    "        obs, reward, done, truncated, info = env.step(action)\n",
    "        remaining_budget.append(obs[1])\n",
    "        cumulative_reward.append(cumulative_reward[-1] + reward)\n",
    "\n",
    "    # プロット\n",
    "    fig = plt.figure()\n",
    "    ax1 = fig.add_subplot(111)\n",
    "    ax1.plot(remaining_budget[:-1], label='remaining budget')\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.plot(cumulative_reward[:-1], label='cumulative reward', color='tab:orange')\n",
    "    ax1.set_xlabel('timestep')\n",
    "    ax1.set_ylabel('remainig budget')\n",
    "    ax1.set_ylim(0, env.initial_budget + 100)\n",
    "    ax2.set_ylabel('reward (coversion)')\n",
    "    ax1.legend(loc='upper left')\n",
    "    ax2.legend(loc='upper right')\n",
    "    plt.show()\n",
    "    \n",
    "visualize_interaction_on_environment(env, agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 標準的な環境設定\n",
    "\n",
    "SCOPE-RLでは，離散行動空間と連続行動空間の両方で標準的な環境を設定しています．\n",
    "\n",
    "これらの環境は報酬予測器として`sklearn.linear_model.LogisticRegression()`を利用しており、以下の行動空間を持ちます．"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.1         0.16681005  0.27825594  0.46415888  0.77426368  1.29154967\n",
      "  2.15443469  3.59381366  5.9948425  10.        ]\n"
     ]
    }
   ],
   "source": [
    "# 離散行動空間\n",
    "env = gym.make(\"RTBEnv-discrete-v0\")\n",
    "print(env.action_meaning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Box(0.1, 10.0, (1,), float64)\n"
     ]
    }
   ],
   "source": [
    "# 連続行動空間\n",
    "env = gym.make(\"RTBEnv-continuous-v0\")\n",
    "print(env.action_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 環境設定のカスタマイズ\n",
    "以下では、シミュレーション環境をカスタマイズする方法を解説します．\n",
    "\n",
    "環境には以下の変数があります．\n",
    "- `objective`: RTBの重要性能指標 (KPI, \"click\"または\"conversion\")\n",
    "- `cost_indicator`:コストが発生するタイミング(\"impression\", \"click\", \"conversion\")\n",
    "- `step_per_episode`: 一エピソードでの意思決定の数\n",
    "- `initial_budget`: 一エピソードでの初期予算 (制約)\n",
    "- `n_ads`: 入札オークション環境上の広告の数\n",
    "- `n_users`: 入札オークション環境上のユーザーの数\n",
    "- `ad_feature_dim`: 広告の特徴ベクトルの次元\n",
    "- `user_feature_dim`: ユーザーの特徴ベクトルの次元\n",
    "- `ad_feature_vector`: 広告特徴量 (ベクトル)\n",
    "- `user_feature_vector`: ユーザー特徴量 (ベクトル)\n",
    "- `ad_sampling_rate`: 入札オークションでどの広告を選ぶかを決めるサンプリング確率\n",
    "- `user_sampling_rate`: 入札オークションでどのユーザーが対象となるかを決めるサンプリング確率\n",
    "- `WinningPriceDistribution`: 入札オークションの落札価格分布\n",
    "- `ClickTroughRate`: クリック率 (クリック/ インプレッション)\n",
    "- `ConversionRate`: コンバージョン率 (コンバージョン/ クリック)\n",
    "- `standard_bid_price_distribution`: 平均インプレッション確率が0.5と予想される時の入札価格の分布\n",
    "- `minimum_standard_bid_price`: 標準入札価格の最低値\n",
    "- `search_volume_distribution`: タイムステップごとの検索ボリューム分布\n",
    "- `minimum_search_volume`: タイムステップごとの最小の検索ボリューム\n",
    "- `random_state`: ランダムシード"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ケース 1. 一般的な環境パラメタのカスタマイズ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnwAAAGwCAYAAAAzL7gEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACKdElEQVR4nOzdd3yN5//H8dfJ3pMsEoIYIbFixB6xSlVpq61apUOjVlu66NIa/dqUokp/pWiLlraUIIogQqzYQmIkZqbsc35/HDmVxsg5TnJnfJ6Px3m4z33f5zrvc6r3+bju+74ulUaj0SCEEEIIIcotE6UDCCGEEEKI4iUFnxBCCCFEOScFnxBCCCFEOScFnxBCCCFEOScFnxBCCCFEOScFnxBCCCFEOScFnxBCCCFEOWemdICyIDc3l8OHD+Pu7o6JidTIQgghRFmgVqtJTEykcePGmJlV7JKnYn/6Ijp8+DDNmzdXOoYQQgghDHDgwAGaNWumdAxFScFXBO7u7oD2L4ynp6fCaYQQQghRFNeuXaN58+a63/GKTAq+Isg/jevp6UnVqlUVTiOEEEIIfcjlWHLThhBCCCFEuadowffpp5+iUqkKPOrWravbnpmZSWhoKK6urtjZ2dGvXz8SExMLtBEXF0fPnj2xsbHBzc2N9957j9zc3AL77Ny5kyZNmmBpaUmtWrVYvnx5SXw8IYQQQohSQfEevvr163Pt2jXdY/fu3bptY8eOZePGjfz888+Eh4dz9epV+vbtq9uel5dHz549yc7OZu/evaxYsYLly5czadIk3T6xsbH07NmTjh07Eh0dzZgxYxg+fDhbtmwp0c/5KBqNRukIQgghhCjHFL+Gz8zMDA8Pj0Lrk5OT+e6771i1ahWdOnUC4Pvvv6devXrs27ePli1b8vfffxMTE8O2bdtwd3enUaNGfPHFF0yYMIFPP/0UCwsLFi1ahK+vLzNmzACgXr167N69m1mzZtGtW7cHZsrKyiIrK0v3PDU1tUifJS8vj5ycnCJ/9lvpWazad4mUzFw+6ulf5NcJ8aQsLCzkmhYB6H/cEqK0keNZ0She8J09exYvLy+srKwIDg5mypQp+Pj4EBUVRU5ODiEhIbp969ati4+PDxEREbRs2ZKIiAgCAgIK3H3TrVs3RowYwYkTJ2jcuDEREREF2sjfZ8yYMQ/NNGXKFD777LMifwaNRkNCQgJJSUlFfg1ATp6axs65aIAz585jbip/YUXJMDExwdfXFwsLC6WjCIUYetwSorSR41nRKFrwtWjRguXLl1OnTh2uXbvGZ599Rtu2bTl+/DgJCQlYWFjg5ORU4DXu7u4kJCQAkJCQUOhW6/znj9snJSWFjIwMrK2tC+X64IMPGDdunO75lStX8Pd/eA9c/kHTzc0NGxsbVCpVkb8DuzsZpGblYGtpRhVnmyK/TghDqdVqrl69yrVr1/Dx8dHr76soP57kuCVEaSHHs6JTtODr0aOHbjkwMJAWLVpQrVo11q5d+8BCrKRYWlpiaWmpe56SkvLQffPy8nQHTVdXV73fy6uSOWcTU0nPA7WJGTYWine6igqgcuXKXL16ldzcXMzNzZWOI0rYkx63hChN5HhWNKXqHKKTkxO1a9fm3LlzeHh4kJ2dXeh0Q2Jiou6aPw8Pj0J37eY/f9w+Dg4ORikq8699sbExrHfOytwUJxttN3RiStZj9hbCOPJPfeTl5SmcRCjhSY9bQpQmcjwrmlJV8KWlpXH+/Hk8PT1p2rQp5ubmhIWF6bafPn2auLg4goODAQgODubYsWNcv35dt8/WrVtxcHDQnYINDg4u0Eb+PvltGMuTdCO7OViiQkVqZg7pWbmPf4EQT0hOewiQvweifJC/x0WjaMH37rvvEh4ezsWLF9m7dy/PPvsspqamvPTSSzg6OjJs2DDGjRvHjh07iIqKYujQoQQHB9OyZUsAunbtir+/PwMHDuTIkSNs2bKFjz/+mNDQUN0p2TfffJMLFy4wfvx4Tp06xTfffMPatWsZO3askh+9AEszU5xttd3QiSmZCqcRQgghRHmj6AVjly9f5qWXXuLWrVtUrlyZNm3asG/fPipXrgzArFmzMDExoV+/fmRlZdGtWze++eYb3etNTU3ZtGkTI0aMIDg4GFtbWwYPHsznn3+u28fX15c//viDsWPHMmfOHKpWrcrSpUsfOiSLUtzsLblzN4e0rFzSsnKxs5Rr+YQQQghhHIpWFatXr37kdisrKxYsWMCCBQseuk+1atX4888/H9lOhw4dOHz4sEEZS4qFmSkuNubcSs8mMSUT20q2Fa6besiQISQlJbFhw4Yiv0alUrF+/Xr69OlTbLkuXryIr68vhw8fplGjRkZt+9NPP2XDhg1ER0cbtV0hRMky1nGiOI83ZZkhvw+iIOlGKkUq21tx+672Or60rFzsrSrW3UZz5szRe9aRa9eu4ezsXEyJyqadO3fSsWNH7ty5U2hYIyFE6fGgIsbb25tr165RqVIl5YKVNI0G1LmgyQMzK6XTlFtS8JUiFmYmuNpacDMti8SULOwszUq0ly87O1vRgSsdHR31fs2DZmkRQoiyytTUVJHjWk5OjnGHNNFoQKOGvBxQ52gLurz7/8yBvNx/twGYWpDjUluGVikmpeou3fJCo9FwNzvXoIetpSnZuWpup2eRmJqp9+v16SHr0KEDI0eOZMyYMVSqVEl3XePx48fp0aMHdnZ2uLu7M3DgQG7evFngdW+//TZjxozB2dkZd3d3lixZQnp6OkOHDsXe3p5atWrx119/6V6Tl5fHsGHD8PX1xdramjp16jBnzpwCeYYMGVLg1GyHDh0YNWoU48ePx8XFBQ8PDz799NMCr1GpVLp/HV+8eBGVSsW6devo2LEjNjY2NGzYkIiIiAKvWbJkCd7e3tjY2PDss88yc+bMIvWEnTp1ilatWmFlZUWDBg0IDw/XbVu+fHmhNjZs2FCoYJ86dSru7u7Y29szbNgwMjML3qSTm5vLqFGjcHJywtXVlQkTJjB48OAC34tarWbKlCm677Jhw4b88ssvuu+gY8eOADg7O6NSqRgyZMhjP5sQaDSQna7MQ4/jllqtZvr06dSqVQtLS0t8fHz48ssvAW3vtkqlKjCcV3R0NCqViosXLwL//r+6adMm6tSpg42NDc899xx3795lxYoVVK9eHWdnZ0aNGlVgmI/7jzX5nJycWL58+QNzPu6Y9+mnn7JixQp+++03VCoVKpWKnTt36o5j0dHRqNVqqlatysKFCwu0ffjwYUxMTLh06RIASUlJDB8+nMqVK+Pg4ECnTp04cuTIQ7/D/PdYs2YN7du3x8rKipUrVwKwdOlS6tWrh5WVFXXr1i1w3fxzzz3HyNBQyM2G7HTGjByBSqXiVNQuSIonO+EUtrY2bPt5KVw7yuafFtKmdTBObl64evnSq08/zh8/CBm3ISuVi7HnUXkGsua3LbTvNxyrak1YuXIleXl5jBs3TnccHD9+vMw5bwTSw1cMMnLy8J+0RZH3jvm8m16DN69YsYIRI0awZ88eQHvg6NSpE8OHD2fWrFlkZGQwYcIEXnjhBbZv317gdePHj+fAgQOsWbOGESNGsH79ep599lk+/PBDZs2axcCBA4mLi8PGxkZ34Pr5559xdXVl7969vP7663h6evLCCy88Mt+4cePYv38/ERERDBkyhNatW9OlS5eHvuajjz7if//7H35+fnz00Ue89NJLnDt3DjMzM/bs2cObb77JtGnT6N27N9u2bWPixIlF+q7ee+89Zs+ejb+/PzNnzuTpp58mNja2yAPXrl27lk8//ZQFCxbQpk0b/u///o+5c+dSo0YN3T7Tpk1j5cqVunmj58yZw4YNG3RFHGin/vvxxx9ZtGgRfn5+7Nq1i1deeUV349Ovv/5Kv379OH36tNHGmxQVQM5d+MpLmff+8CpY2BZp1w8++IAlS5Ywa9Ys2rRpw7Vr1zh16pReb3f37l3mzp3L6tWrSU1NpW/fvjz77LM4OTnx559/cuHCBfr160fr1q3p37+/IZ/osce8d999l5MnT5KSksL3338PgIuLC1evXtW1YWJiwksvvcSqVasYMWKEbv3KlStp3bo11apVA+D555/H2tqav/76C0dHR7799ls6d+7MmTNncHFxeWjG999/nxkzZtC4USOsLMxYueJ7Jk2cyPyZU2kcUI/Dh4/w2ujx2KpTGfxCb9o3qsW3P/4C108AEL5zB5VcnNi5dQt1PZ8jMjKanJwcWjXxB9Sk381k3BsDCaxfj7SMbCZNn8ezr00gem8YJuaWkKr9rXp/+rfM+N//aNykCVZWVsyYMYPly5ezbNky6tWrx4wZM1i/fj2dOnUy6L+F0JKCr4Lz8/Nj+vTpuueTJ0+mcePGfPXVV7p1y5Ytw9vbmzNnzlC7dm0AGjZsyMcffwxoD8BTp06lUqVKvPbaawBMmjSJhQsXcvToUVq2bIm5uXmB+Yl9fX2JiIhg7dq1jyz4AgMD+eSTT3RZ58+fT1hY2CMLvnfffZeePXsC8Nlnn1G/fn3OnTtH3bp1mTdvHj169ODdd98FoHbt2uzdu5dNmzY99rsaOXIk/fr1A2DhwoVs3ryZ7777jvHjxz/2tQCzZ89m2LBhDBs2DNB+19u2bSvQyzdv3jw++OADnn32WQDmz59f4KakrKwsvvrqK7Zt26YbS7JGjRrs3r2bb7/9lvbt2+sO8G5ubnINn7Go8+DubUhLgLRESLv+gD+va09PlQXWHhD4HtzMA/N7J3pyMpTLc/0UmD/+HyapaenMmTOb+V99xODuzYAsavq50MavFSTGwO2L/7aX5aBdvnVe++eNs2B9F5KvkJOTw8IvxlHTyxKw5Lmenfm/nzeSeDwcO1vwd61Bx9bN2PHnOvp3CPg3QFK89n3yadSQfEW77saVf98v0QJz4LO38ovFDHxDGhPR/xnW/rCUF9o3wA6wJosssvFQ3dbudud2oXYGdG/JjBkziIvahk9VL9RqNatX/cjHY96AxBh274/iwP59XD/+D5aWFkAO/3vvVTb8upZfls3l9YEPOL7ee48xw16kb6vaoE4G4JNPJzFj4ij6tg8EwLdzEDHDX+bb5asY3LcrHYKbMHrSdG7cuoOZuSUxZy8w8Z2R7DxwjDffGsnOQ2dp1rQJNt4NwcSMfsMCwcRU97bLAlpTuXJlYuJv06BBA7BM1eYYM4a+946toD1WfvDBB/Tt2xeARYsWsWWLMp0o5YkUfMXA2tyUmM+fbNiXxORMbqRlYWluSq3KRb9j19rc9PE73adp06YFnh85coQdO3ZgZ2dXaN/z58/rCr7AwEDdelNTU1xdXQkI+PfAmD9/8f2DYi9YsIBly5YRFxdHRkYG2dnZj70L7f73AfD09CzQ5uNe4+npqctRt25dTp8+rSum8jVv3rxIBd/9g3WbmZkRFBTEyZMnH/u6fCdPnuTNN98s1OaOHTsASE5OJjExkebNm+u2m5qa0rRpU9RqNQDnzp3j7t27hQre7OxsGjduXOQsAu1pxKzU+4q2BxVy95bTb2gvKC8v7PKvq8qGvHvHFpUKhv716NcVF5UK8h4/09DJU6fIysqmc6smD94/v+DOy/53e969dep769S52FhbUdPbXbePu6sj1b09sbMyu2+dM9dv3Cj4Puqc/7zvvZsN8rK07ee/3719Fixfw7LVvxF3JYGMzCyyc3JoVL/Ov21o1Peuc7v/PQq206heDer5+bLq1995f+RQwvdEcv3mLZ5/qgPkZXHk+AnS0u/iWq9Vga8iIzOL87EXH/I9ad8jKKC27jtLv5vB+YuXGfbu57z23mS499ciNzcPRwcHcK1Fg7Z1cHFxIfzULSwsLWncuAm9XhjEgh49wMGT8IiDdOgUouutPXv2LJMmTWL//v3cvHlTdxyLi4vTFnz3BAUF6ZaTk5O5du0aLVq00K3LP97Kad0nIwVfMVCpVE88J663iw13s/PI02jIzdPgaFM8F7Ha2hY8jZKWlsbTTz/NtGnTCu2bXzwBhS6qValUBdblF6j5/4OvXr2ad999lxkzZhAcHIy9vT1ff/01+/fvf2S+B71PfptFec1/cxQXExOTQgej/OmrjCktLQ2AP/74gypVqhTYdv/8zxVabjakP6gH7gFFXc5dPRpWgW0lsHMHO7f//OkOtpXLzh2GuUC6JTh5Qxn6e2Ptca8YcqoOrtULbTdx0k6jqXH2hXt37+fYptz3mmpg5465hSW4+ulep7KphLmVbcF1Vg6oszS6dSqVCo2dR4F9cnLV2v/2rn6Qeu+GNycfcPVj9dpfePeLOcyY9hXBLZpjb2/H1zPnsD/y4L9tWNqDhbpAm/9tB2DAgFdY9fOvvP/JV6z6aw7du3bB1a8ZAGkaGzw9Pdj5d+HhyZwcHcH1AXf73nsPW886UKkOmJqRduMWAEuWLC1QbIH2H55Y2qMC2rVrx87wcCwtLenQoQOBgYFkZWVx/Phx9u7dqzt7AvD0009TrVo1lixZgpeXtneyQYMGZGdnF2j/v79DonhIwVdKmZma4GpvyfWUTBJTs3CwNi+RO3abNGnCr7/+SvXq1TEzM95fjz179tCqVSveeust3brz588brf2iqlOnDpGRkQXW/ff5w+zbt4927doB2psroqKiGDlyJKCdvDs1NZX09HTdweu/Y+vVq1eP/fv3M2jQoAJt5nN0dMTd3Z3IyEjd++Tl5XHo0CFdT6i/vz+WlpbExcXRvn37B+asUPNKHvsFzmwuWMhl3NGvDQv7fws3e/fChVz+sk0lMC0nh8zMTIiN1fbEWJaRIhXwq98Qa2trwnbvY3jdBoW2V66ivabt2u1UnD28AYiOOaPdaGkLlnZgfu/zWt53FsPMAlQmBdeZmoOJmW5d5cqVuXYzSff87Nmz3L17V9uepZ22fQALG7C0Y8+BKO0xb9S/szqdvxhX4H0srG3JS0op+L7/aQfg5UFD+fjTL4g6fppf1v/GokWLdNuaNA8m4ZPPMbNxpHr16kX7Iu9/DwvtfMruHp54eXlx4cIFBgwY8NCXtm/fniVLlmBpacmXX36JiYkJ7dq14+uvvyYrK4vWrVsDcOvWLU6fPs2SJUto27YtALt3735sNEdHRzw9Pdm/f3+h422TJk2K9vnEA5WTo1f5VMnOgltpWWTm5JGckYOTTfEPmRIaGsqSJUt46aWXdHfHnjt3jtWrV7N06VLtv/QM4Ofnxw8//MCWLVvw9fXl//7v/4iMjMTX19fIn+DR3n77bdq1a6e76WL79u389ddfRSqmFyxYgJ+fH/Xq1WPWrFncuXOHV199FYAWLVpgY2PDhx9+yKhRo9i/f3+hu/dGjx7NkCFDCAoKonXr1qxcuZITJ04UuGnj7bffZsqUKdSqVUt3zeGdO3d0+ezt7Xn33XcZO3YsarWaNm3akJyczJ49e3BwcGDw4MFUq1YNlUrFpk2beOqpp7C2tn7gKfoy7/opWPea9pTYf5mYP6Qn7r+FnFuRbxYQyrOysmLChAmMHz8eCwsLWrduzY0bNzhx4gTDhg2jVq1aeHt78+mnn/Lll19y5swZZsyYYZT37tSpE/Pnzyc4OJi8vDwmTJjwyOFDinLMq169Olu2bOH06dO4uro+dGiq6tWr06pVK4YNG0ZeXh69e/fWbQsJCSE4OJg+ffowffp0ateuzdWrV/njjz949tlnC5wufZzPPvuMUaNG4ejoSPfu3cnKyuLgwYPcuXOHcePGAdrRE8aOHYuFhQVt2rTRrXv33Xdp1qyZ7h+8zs7OuLq6snjxYjw9PYmLi+P9998vUo7Ro0czdepU/Pz8qFu3LjNnzixw53Vx27VrF19//TVRUVFcu3at0OD+Go2GTz75hCVLlpCUlETr1q1ZuHAhfn7/9tTevn2bt99+m40bN+pmDJszZ46ix2IZlqUUMzMxoZKd9nRLYkpWiVy/4OXlxZ49e8jLy6Nr164EBAQwZswYnJycMDEx/K/LG2+8Qd++fenfvz8tWrTg1q1bBXr7Skrr1q1ZtGgRM2fOpGHDhmzevJmxY8diZfX4Xo6pU6cydepUGjZsyO7du/n99991g6O6uLjw448/8ueffxIQEMBPP/1UaAiZ/v37M3HiRMaPH0/Tpk25dOlSgTvvACZMmMBLL73EoEGDCA4Oxs7Ojm7duhXI98UXXzBx4kSmTJlCvXr16N69O3/88Yfuh6RKlSp89tlnvP/++7i7u+t6Icud7V9oi71qbaDvEhj0G7y1D8bHwsQbMO4EvL4DXl4NvedCp4+g+Wvg3xt8WoCLrxR7ZdDEiRN55513mDRpEvXq1aN///6663rNzc356aefOHXqFIGBgUybNo3Jkycb5X1nzJiBt7c3bdu25eWXX+bdd9/FxsbmofsX5Zj32muvUadOHYKCgqhcubJutIQHGTBgAEeOHOHZZ58tcOe9SqXizz//pF27dgwdOpTatWvz4osvcunSJd211EU1fPhwli5dyvfff09AQADt27dn+fLlBYrUgIAAnJycaNSoka546dChA3l5eXTo0EG3n4mJCatXryYqKooGDRowduxYvv766yLleOeddxg4cCCDBw/WXQL032uvi1N6ejoNGzZ86Cxf06dPZ+7cuSxatIj9+/dja2tLt27dCtyAN2DAAE6cOMHWrVvZtGkTu3bt4vXXXy+pj/BgGvFY8fHxGkATHx9faFtGRoYmJiZGk5GRUSzvnZun1hy/kqQ5En9Hczstq1jeo6IbPny4pk2bNkrHeKC8vDxN7dq1NR9//LFR2y3uv7fFLu6ARvOJg0bzqZNGc/2U0mnKnDL/31+I+zzq7/Ojfr+LAtCsX79e91ytVms8PDw0X3/9tW5dUlKSxtLSUvPTTz9pNBqNJiYmRgNoIiMjdfv89ddfGpVKpbly5YpBOYxBevhKOVMTFZXt7/XypWailruUntj//vc/jhw5wrlz55g3bx4rVqxg8ODBSscC4NKlSyxZsoQzZ85w7NgxRowYQWxsLC+//LLS0UoPjQa2fapdbvQyVK6jaBwhROmXmppKSkqK7pGV9fi7wh8kNjaWhIQEQkJCdOscHR1p0aKFbpD/iIgInJycCpxODwkJwcTE5LE3KhYnKfjKAFdbS8xMTMjOVZN0N/vxLxCPdODAAbp06UJAQACLFi1i7ty5DB8+XOlYgPY0yPLly2nWrBmtW7fm2LFjbNu2jXr16ikdrfQ4HwaXdoOpJbQv2jVBQoiKzd/fH0dHR91jypQpBrWTkJAAUOh0ubu7u25bQkICbm5uBbabmZnh4uKi20cJctNGGZDfy3ctOYPrKVk42VhgUoJz7JY3a9euVTrCQ3l7ez/yOp4KT62GbfcG8G7+mnZYESGEeIyYmJgCQ1lVxGGspIfPSDTFfKrV1dYCc1MTsvPU3EmXXj7xZIr772uxiVkPCUfB0gHajFM6TZlXZv8eCHGfovw9tre3x8HBQfcwtODz8PAAIDExscD6xMRE3TYPD49CEwTk5uZy+/Zt3T5KkILvCeXfln/3rj4DuOrP5L5r+a6nZqFWy4FaGC5/4FNDh9lRRF4ObL93x2WrUWBbtDmMRWElddwSoiSU5PHM19cXDw8PwsLCdOtSUlLYv3+/bjam4OBgkpKSiIqK0u2zfft21Gp1oUGtS5Kc0n1CpqamODk56ap5GxubYhsg2cZUg6k6l+xcNYl3wNm2+MflE+WPWq3mxo0b2NjYGHVw7WJ3+P/g9gXtjBYtRzx+f/FQJXncEqI4FcfxLC0tjXPnzumex8bGEh0djYuLCz4+PowZM4bJkyfj5+eHr68vEydOxMvLSzdWX/5wWa+99hqLFi0iJyeHkSNH8uKLL+Ll5WWUjIYoQ0f70iu/i/Zxc7waQ0ZWLnfu5nDLRIWHg6UcpIVBTExM8PHxKTt/f7Lvws570/21G19wZgJhkJI8bglRnIx9PDt48CAdO3bUPc8fdHrw4MEsX76c8ePHk56ezuuvv05SUhJt2rRh8+bNBcZLXblyJSNHjqRz5866gZfnzp1rlHyGUmnkIo7Hunz5Mt7e3sTHx1O1atWH7peXl1cs86feLztXzZDvD5CYkskb7WryQjO5aF3oz8LC4okG0i5xu2dph2Jx8oGRUdqpsIRRlMRxS4ji9KjjWVF/vysC6eEzIlNT02K/hsAKeLFlTcb/epTZOy/Sr7kvtpbyn1GUYxl3tAUfQMePpdgzspI4bgkhlFeG/okv8vVtUoXqrjbcTs9mRcRFpeMIUbx2z4bMZHCrDwHPKZ1GCCHKJCn4yiAzUxNGh2gnaV686wKpmXI6RpRTKVdh/yLtcudJYCI9UUIIYQgp+Mqo3g2rULOyLUl3c/h+z0Wl4whRPMKnQ24meLeE2t2UTiOEEGWWFHxllKmJijEhtQFY8s8Fku9KL58oZ26dh0M/aJdDPoWyckexEEKUQlLwlWE9Azyp425PamYuS3dfUDqOEMa1fTJo8sCvG1QLVjqNEEKUaVLwlWEmJirGdtFey7dsd6xMuSbKj6vRcGIdoNJeuyeEEOKJSMFXxnWr70F9LwfSs/P4dpf08olyIuxz7Z+BL4BHA2WzCCFEOSAFXxmnUqkYe+9avhV7L3IzLUvhREI8odhdcD4MTMyhwwdKpxFCiHJBCr5yoHM9Nxp6O5GRk8eineeVjiOE4TQa7YwaAEFDwcVX0ThCCFFeSMFXDqhUKsZ10fby/d++SySmZCqcSAgDndoEV6LA3Bbavad0GiGEKDek4Csn2vlVomk1Z7Jy1SyUXj5RFuXlQtgX2uXgt8DOTdk8QghRjkjBV06oVCreudfLt2p/HFeTMhROJISejq6Gm6fB2hlava10GiGEKFek4CtHWtWqRMsaLmTnqZm/45zScYQoupxM2DFFu9z2HbByVDaPEEKUM1LwlTPjutQBYG1kPPG37yqcRogiOvgdpFwGhyrQbLjSaYQQotyRgq+cae7rQlu/SuSqNczbflbpOEI8XmYK7PqfdrnD+2BurWweIYQoh6TgK4fG3ruW79dDV7h4M13hNEI8RsR8yLgNlWpDw5eVTiOEEOWSFHzlUBMfZzrWqUyeWsPcMOnlE6VY2g3YO1+73GkimJopm0cIIcopKfjKqfxr+TZEX+Hc9TSF0wjxEP/8D3LSwasJ1Hta6TRCCFFuScFXTgVUdaSLvztqDcyRXj5RGt25CJHfaZdDPgWVSsk0QghRrknBV47lz7G76ehVTiekKpxGiP/YMQXUOVCjI9Ror3QaIYQo16TgK8f8vRx4KsADjQZmbT2jdBwh/pV4Ao6u0S53nqRsFiGEqACk4CvnxoTURqWCzScSOH4lWek4QmiFfQFowL8PVGmidBohhCj3pOAr52q729O7oRcAs7dJL58oBeL2wZm/QGUKnT5WOo0QQlQIUvBVAKM6+2Gigm0nr3MkPknpOKIi02hg26fa5cavQCU/ReMIIURFIQVfBVCzsh3PNq4KwEy5lk8o6exWiIsAMytoP0HpNEIIUWFIwVdBjO7sh6mJivAzN4i6dFvpOKIiUqsh7DPtcvPXwbGKsnmEEKICkYKvgvBxteH5ptLLJxR0/FdIPA6WjtBmrNJphBCiQpGCrwIZ2akW5qYq9py7xb4Lt5SOIyqS3GzYMVm73GY02Lgom0cIISoYKfgqkKrONvRv5g3AzL/PoNFoFE4kKoxDK7Qza9i5Q4s3lU4jhBAVjhR8FczIjn5YmJlw4OJt9pyTXj5RArLSIHy6drn9eLCwVTaPEEJUQFLwVTAejlYMaOEDwIytp6WXTxS//Qsh/To4+0KTwUqnEUKICkkKvgpoRIeaWJmbcDguiZ1nbigdR5Rnd2/Dnrna5U4fg6m5snmEEKKCkoKvAnKzt2JQcHVAO8eu9PKJYrN7JmSlgHsA1O+rdBohhKiwpOCroN5oVwMbC1OOXk5ma0yi0nFEeZR8BfYv1i6HfAImcrgRQgilyBG4gnK1s2RIq+oAzNp2FrVaevmEkYVPhbwsqNYaaoUonUYIISo0KfgqsNfb1cDO0oyT11LYfCJB6TiiPLlxBg7/qF3u/AmoVMrmEUKICk4KvgrMycaCV9v4Atpr+fKkl08Yy47JoFFDnafAp4XSaYQQosKTgq+CG9bGFwcrM85eT2PT0atKxxHlwZUoiPkNUEGniUqnEUIIgRR8FZ6jtTmvt6sBwJxtZ8nNUyucSJR52z7T/tnwJXD3VzaLEEIIQAo+AQxp7YuzjTkXbqazIVp6+cQTOL8DYsPB1AI6vK90GiGEEPdIwSewszTjjfY1AZi3XXr5hIE0Gtj2qXY5aBg4V1M0jhBCiH+VmoJv6tSpqFQqxowZo1uXmZlJaGgorq6u2NnZ0a9fPxITC44ZFxcXR8+ePbGxscHNzY333nuP3NzcAvvs3LmTJk2aYGlpSa1atVi+fHkJfKKyZVBwNVxsLbh06y6/SS+fMETMb3AtGizsoO07SqcRQghxn1JR8EVGRvLtt98SGBhYYP3YsWPZuHEjP//8M+Hh4Vy9epW+ff8drT8vL4+ePXuSnZ3N3r17WbFiBcuXL2fSpEm6fWJjY+nZsycdO3YkOjqaMWPGMHz4cLZs2VJin68ssLEw47W22mv55u84J718Qj95ubD9C+1y8Eiwq6xsHiGEEAUoXvClpaUxYMAAlixZgrOzs259cnIy3333HTNnzqRTp040bdqU77//nr1797Jv3z4A/v77b2JiYvjxxx9p1KgRPXr04IsvvmDBggVkZ2cDsGjRInx9fZkxYwb16tVj5MiRPPfcc8yaNUuRz1uaDQquhrONObE309kod+wKfUSvhFvnwMYVgkOVTiOEEOI/FC/4QkND6dmzJyEhBUfij4qKIicnp8D6unXr4uPjQ0REBAAREREEBATg7u6u26dbt26kpKRw4sQJ3T7/bbtbt266Nh4kKyuLlJQU3SM1NfWJP2dZYGtpxvB7vXzztp+TcflE0eRkwM6p2uW274KVg7J5hBBCFKJowbd69WoOHTrElClTCm1LSEjAwsICJyenAuvd3d1JSEjQ7XN/sZe/PX/bo/ZJSUkhIyPjgbmmTJmCo6Oj7uHvX3GGlhjcqjpONuZcuJEu4/KJojmwBFKvgqM3BL2qdBohhBAPoFjBFx8fz+jRo1m5ciVWVlZKxXigDz74gOTkZN0jJiZG6Uglxs7SjOH3Zt+YG3ZWevnEo2UkwT8ztMsdPgDz0vX/shBCCC3FCr6oqCiuX79OkyZNMDMzw8zMjPDwcObOnYuZmRnu7u5kZ2eTlJRU4HWJiYl4eHgA4OHhUeiu3fznj9vHwcEBa2vrB2aztLTEwcFB97C3tzfGRy4zBreqjqO1OedvpPPHsWtKxxGl2d65kJkEletCwxeVTiOEEOIhFCv4OnfuzLFjx4iOjtY9goKCGDBggG7Z3NycsLAw3WtOnz5NXFwcwcHBAAQHB3Ps2DGuX7+u22fr1q04ODjoTsMGBwcXaCN/n/w2RGH2VuYMu9fLNy/sLGrp5RMPkpoA+xZqlztNBBNTZfMIIYR4KDOl3tje3p4GDRoUWGdra4urq6tu/bBhwxg3bhwuLi44ODjw9ttvExwcTMuWLQHo2rUr/v7+DBw4kOnTp5OQkMDHH39MaGgolpaWALz55pvMnz+f8ePH8+qrr7J9+3bWrl3LH3/8UbIfuIwZ0ro6S/+5wNnrafx5/Bq9Ar2UjiRKm11fQ85dqNoM6vZUOo0QQohHUPwu3UeZNWsWvXr1ol+/frRr1w4PDw/WrVun225qasqmTZswNTUlODiYV155hUGDBvH555/r9vH19eWPP/5g69atNGzYkBkzZrB06VK6deumxEcqMxyszHn1vmv5pJdPFHD7AkQt1y6HfAoqlZJphBBCPIZKo9HIL/ljXL58GW9vb+Lj46latarScUpMckYObaZuJzUrl28GNOGpAE+lI4nS4tfhcOxnqBUCr/yqdBohhHigivr7/SCluodPKMvR2pyhrasD0ssn7pNwTFvsAXSe9Oh9hRBClApS8IlHerWNL3aWZpxKSOXvmASl4wilxe2DVf21yw36gWdDZfMIIYQR5eXlMXHiRHx9fbG2tqZmzZp88cUX3H8yVKPRMGnSJDw9PbG2tiYkJISzZ88qmLpopOATj+RkY8GQVtUBmBN2Tnr5Kiq1GvbMge+fgpQr4FITunz++NcJIUQZMm3aNBYuXMj8+fM5efIk06ZNY/r06cybN0+3z/Tp05k7dy6LFi1i//792Nra0q1bNzIzMxVM/nhS8InHGtbGF1sLU05eS2HrycTHv0CUL3dvw+qXYOsk0ORpe/beCAfHin09jBCi/Nm7dy/PPPMMPXv2pHr16jz33HN07dqVAwcOANrevdmzZ/Pxxx/zzDPPEBgYyA8//MDVq1fZsGGDsuEfQwo+8VjOthYMvtfLNzfsLHKfTwUSHwmL2sKZzWBqCb1mQb/vwLJiDUYuhCjbUlNTSUlJ0T2ysrIeuF+rVq0ICwvjzJkzABw5coTdu3fTo0cPAGJjY0lISCAkJET3GkdHR1q0aEFERETxf5AnIAWfKJLhbWtgY2HKiaspbDt5/fEvEGWbRgN758H33SHlMrjUgOFbtXPlyhAsQogyxt/fH0dHR91jypQpD9zv/fff58UXX6Ru3bqYm5vTuHFjxowZw4ABAwBISNBey+7u7l7gde7u7rptpZViAy+LssXF1oJBwdVZFH6eOWFnCKnnhkp++MunjDuw4S04/af2ef1n4em5YOWgbC4hhDBQTEwMVapU0T3Pn5zhv9auXcvKlStZtWoV9evXJzo6mjFjxuDl5cXgwYNLKm6xkB4+UWSvtfXF2tyU41dS2H5KevnKpcsHYVE7bbFnagE9Z8Bz30uxJ4Qo0+zt7XFwcNA9Hlbwvffee7pevoCAAAYOHMjYsWN1PYIeHh4AJCYWvJ49MTFRt620koJPFJmrnSWDgqsBMEeu5StfNBqI+AaWdYfkOHCuDsO2QrPhcgpXCFFh3L17FxOTgqWRqakparUa0M7e5eHhQVhYmG57SkoK+/fvJzg4uESz6ktO6Qq9vNauBj9EXOLo5WR2nr5Bx7puSkcSTyrjDvw2Ek5t0j73fwZ6zwMrR2VzCSFECXv66af58ssv8fHxoX79+hw+fJiZM2fy6quvAqBSqRgzZgyTJ0/Gz88PX19fJk6ciJeXF3369FE2/GNIwSf0UsnOklda+rDkn1hmh52lQ53Kci1fWXYlCn4eAklx2lO4Xb+E5q9Jr54QokKaN28eEydO5K233uL69et4eXnxxhtvMGnSv7MKjR8/nvT0dF5//XWSkpJo06YNmzdvxsrKSsHkjydz6RaBzMVX0I3ULNpO305mjprlQ5vRoY708pU5Gg0cWAxbPgJ1DjhVg+eXQ5UmSicTQgijkd/vf8k1fEJvle0tGdBCruUrszKSYO1A+Gu8ttir2wve2CXFnhBClGNS8AmDvNG+BpZmJhyOS2L3uZtKxxFFdfUwLG4PJzeCiTl0nwb9fwRrJ6WTCSGEKEZS8AmDuNlb8XILHwDmbJNevlJPo4EDS+C7rnDnIjj5wKtboOWbcr2eEEJUAFLwCYO92b4mFmYmHLx0h73nbykdRzxMZrL2xow/34W8bKjTU3sKt2pTpZMJIYQoIVLwCYO5O1jxcnPp5SvVrkbDt+0hZgOYmEG3r+DFlWDtrHQyIYQQJUgKPvFE3mxfEwtTEw5cvE3EBenlKzU0GohcCt91gTux4OitPYUbHCqncIUQogKSgk88EQ9HK15s7g1oe/lEKZCZAr+8Cn+8oz2FW7vHvVO4QUonE0IIoRAp+MQTG9FB28u3P/Y2+6SXT1nXjsLiDnBinfYUbtfJ8NJPYOOidDIhhBAKkoJPPDFPR2teaKYd0FJ6+RSi0cDBZbA0BG6fB4eqMPQvaPW2nMIVQgghBZ8wjhEdamFuqiLiwi0OxN5WOk7FkpUKvw6HTWMhLwv8usGb/4B3c6WTCSGEKCWk4BNGUcXJmueD7l3LF3ZG4TQVSMJx7Snc47+AyhS6fA4vrZZTuEIIIQqQgk8YzVsdamJuqmLPuVscvCi9fMVKo4GoFbC0M9w6B/ZeMPRPaD0aTOR/ayGEEAXJL4MwmqrONjzX9N61fGFyLV+xyUqD9W/AxlGQmwm1usCbu8GnpdLJhBBClFJS8AmjeqtDLcxMVPxz9iZRl+4oHaf8SYyBJR3h6BrtKdzOn8DLa8HWVelkQgghSjEp+IRRebvY0K+J9PIVi8M/wpJOcPMM2HvCkE3QdpycwhVCCPFY8kshjC60Yy1MTVTsOnODw3HSy/fEstNh/Qj4LRRyM6BmZ+0p3GqtlE4mhBCijJCCTxidj6sNfRtXAaSX74ldPwmLO8KRVaAygU4fw4BfwLaS0smEEEKUIVLwiWIxspO2l2/n6RtExycpHadsil517xTuabDzgMEbod17cgpXCCGE3uSXQxSLaq629Gmk7eWbK718+sm+CxtCYcMIyLkLNTpqT+FWb6N0MiGEEGWUFHyi2IzsVAsTFWw/dZ2jl5OUjlM23Dit7dWL/lF7CrfjR/DKr2BXWelkQgghyjAp+ESx8a0kvXx6ObJaO2vGjZNg5w6DfoP248HEVOlkQgghyjgp+ESxCr3Xy7ft5HWOX0lWOk7plJMBv43UDqaccxd822tP4fq2UzqZEEKIckIKPlGsala24+mGXoDcsftAN8/Cks5w+P8AFXT4AAauBzs3pZMJIYQoR8yUDiDKv7c71eL3I1fZGpPIiavJ1PdyVDpS6XD0Z9g4GnLSwbYy9FsKNToonUoIIUQpEBcXx6VLl7h79y6VK1emfv36WFpaGtye9PCJYlfLzZ5egdpePrmWD+0p3I2jYd1wbbFXva32FK4Ue0IIUaFdvHiRCRMmUK1aNXx9fWnfvj09evQgKCgIR0dHunTpws8//4xarda7bSn4RIkY1akWKhVsOZHIyWspSsdRzs1zsLQLRC0HVNB+gvbmDHsPpZMJIYRQ0KhRo2jYsCGxsbFMnjyZmJgYkpOTyc7OJiEhgT///JM2bdowadIkAgMDiYyM1Kt9OaUrSoSfuz1PBXjyx9FrzA07y8JXmiodqeQd/xV+HwXZaWBTCfotgZqdlE4lhBCiFLC1teXChQu4uroW2ubm5kanTp3o1KkTn3zyCZs3byY+Pp5mzZoVuX0p+ESJGdXJjz+OXuOv4wmcSkihroeD0pFKRk4mbPkADi7TPq/WGvp9Bw6eyuYSQghRakyZMqXI+3bv3l3v9uWUrigxdTzseSpAe+pyXtg5hdOUkFvn4buQf4u9tu/CoN+l2BNCCFGipOATJWpUZz8A/jx+jTOJqQqnKWYn1sO37SHhGNi4amfM6DwRTKVjXQghxMMlJiYycOBAvLy8MDMzw9TUtMDDEHr/8tSoUYPIyMhC55iTkpJo0qQJFy5cMCiIqBjqejjQvb4Hm08kMDfsLPNfbqJ0JOPLzYItH0HkEu1zn2DtKVzHKsrmEkIIUSYMGTKEuLg4Jk6ciKenJyqV6onb1Lvgu3jxInl5eYXWZ2VlceXKlScOJMq/UZ392HwigT+OXWN0Yip+7vZKRzKe2xfg5yFw7Yj2eZux0PFj6dUTQghRZLt37+aff/6hUaNGRmuzyL9Cv//+u255y5YtODr+O3huXl4eYWFhVK9e3WjBRPnl7+VAV393/o5JZN72c8x9qbHSkYwj5jftFGlZKWDtAn0Xg18XpVMJIYQoY7y9vdFoNEZts8gFX58+fQBQqVQMHjy4wDZzc3OqV6/OjBkzjBpOlF+jOvvxd0wiG49eZVRnP2q52SkdyXC5WbB1EuxfpH3u3QKeWwaOVZXNJYQQokyaPXs277//Pt9++63ROtOKXPDlj+rs6+tLZGQklSpVMkoAUTE1qOJISD13tp1MZP72s8x+sYz28t25qD2Fe/Ww9nmrUdB5EpiaK5lKCCFEGda/f3/u3r1LzZo1sbGxwdy84G/K7du39W5T7wuLYmNjdcuZmZlYWVnp/aZCAIzu7Me2k4n8fkTby1ejchnr5Tu5ETaEQlYyWDtDn0VQR/+xkYQQQoj7zZ492+ht6l3wqdVqvvzySxYtWkRiYiJnzpyhRo0aTJw4kerVqzNs2DCjhxTlU0BVRzrXdSPs1HXmbz/HzP6NlI5UNLnZsO0T2PeN9nnV5tpTuE7eyuYSQghRLvz30jlj0HscvsmTJ7N8+XKmT5+OhYWFbn2DBg1YunSpUcOJ8m90iHZcvg3RV4i9ma5wmiK4cwm+7/5vsRc8Eob+KcWeEEIIo8rLy+PXX39l8uTJTJ48mfXr1z9wlJSi0rvg++GHH1i8eDEDBgwoMPhfw4YNOXXqlMFBRMUUWNWJjnUqo9bA/O2lfPaNU3/At23hShRYOcKLP0G3L+V6PSGEEEZ17tw56tWrx6BBg1i3bh3r1q3jlVdeoX79+pw/f96gNvUu+K5cuUKtWrUKrVer1eTk5BgUQlRso0NqA9pevku3SmEvX16OdiDl1S9DZjJUaQpv/AN1n1I6mRBCiHJo1KhR1KxZk/j4eA4dOsShQ4eIi4vD19eXUaNGGdSm3gWfv78///zzT6H1v/zyC40bl9E7LYWiGnk70b52ZfLUmtLXy5cUD9/3gIj52uctQ2HoZnCupmwuIYQQ5VZ4eDjTp0/HxcVFt87V1ZWpU6cSHh5uUJt637QxadIkBg8ezJUrV1Cr1axbt47Tp0/zww8/sGnTJoNCCDE6xI/wMzdYd/gKb3fyw8fVRulIkJ0O33WF1Ktg6Qh9voF6vZROJYQQopyztLQkNbXwfPNpaWkF7p/Qh949fM888wwbN25k27Zt2NraMmnSJE6ePMnGjRvp0kVmFRCGaeLjTFu/SuSpNSzYUUp6+aJXaYs9R294c5cUe0IIIUpEr169eP3119m/fz8ajQaNRsO+fft488036d27t0FtGjTBZ9u2bdm6datBbyjEw4wJ8eOfszf59dBlRnaqhbeLgr18ajXsW6hdDh4JztWVyyKEEKJCmTt3LoMHDyY4OFg36HJubi69e/dmzpw5BrUpM7qLUqNpNRfa1KrE7nM3+WbnOab0DVQuzNm/4fZ57ancxgOUyyGEEKLCcXJy4rfffuPs2bO6EVDq1av3wJtmi0rvgs/Z2RmVSlVovUqlwsrKilq1ajFkyBCGDh1qcChRcY0O8WP3uZv8fPAyoR1rUdVZoV6+/Js0mg4CS3tlMgghhKjQ/Pz88PPzM0pbBt208eWXX9KjRw+aN28OwIEDB9i8eTOhoaHExsYyYsQIcnNzee2114wSUlQczaq7EFzDlYgLt/jpQBzvdatb8iGuHYWL/4DKFJq/UfLvL4QQosIZN24cX3zxBba2towbN+6R+86cOVPv9vUu+Hbv3s3kyZN58803C6z/9ttv+fvvv/n1118JDAxk7ty5UvAJg7zSshoRF27xS9RlxobUxsxU73uLnkz+LBr+z8gMGkIIIUrE4cOHdeMZHz58+KH7Pegsa1GoNBqNRp8X2NnZER0dXeg88rlz52jUqBFpaWmcP3+ewMBA0tNL4SC6Brh8+TLe3t7Ex8dTtWpVpeOUe1m5eQRP2c7t9GyWDQmiU133knvz1ASY1QDUOTB8O1RtWnLvLYQQwqjk9/tfeneduLi4sHHjxkLrN27cqBsgMD09HXt7ue5JGMbSzJRnG1cBYE1kfMm+eeRSbbHn3UKKPSGEEKVCSkoKGzZseKIpbPUu+CZOnMh7771H7969dRP6PvPMM4wfP55PPvkEgK1bt9K+ffvHtrVw4UICAwNxcHDAwcGB4OBg/vrrL932zMxMQkNDcXV1xc7Ojn79+pGYmFigjbi4OHr27ImNjQ1ubm6899575ObmFthn586dNGnSBEtLS2rVqsXy5cv1/diihPVvpj2VGnbyOjdSs0rmTXMyIPI77XLLt0rmPYUQQoj/eOGFF5g/X3vzYEZGBkFBQbzwwgsEBATw66+/GtSm3gXfa6+9Rnh4OLa2troJfW1sbAgPD2fYsGEAvPPOO6xZs+axbVWtWpWpU6cSFRXFwYMH6dSpE8888wwnTpwAYOzYsWzcuJGff/6Z8PBwrl69St++fXWvz8vLo2fPnmRnZ7N3715WrFjB8uXLmTRpkm6f2NhYevbsSceOHYmOjmbMmDEMHz6cLVu26PvRRQmq7W5PI28nctUa1h++XDJvemQ1ZNwGJx+oK4MsCyGEUMauXbto27YtAOvXr0ej0ZCUlMTcuXOZPHmyYY1qShlnZ2fN0qVLNUlJSRpzc3PNzz//rNt28uRJDaCJiIjQaDQazZ9//qkxMTHRJCQk6PZZuHChxsHBQZOVlaXRaDSa8ePHa+rXr1/gPfr376/p1q3bQzNkZmZqkpOTdY+YmBgNoImPjzfmRxWPsWr/JU21CZs0Hf+3Q6NWq4v3zfLyNJp5QRrNJw4azd75xfteQgghSkR8fHyZ/P22srLSxMXFaTQajWbgwIGaCRMmaDQajebSpUsaW1tbg9osUg9fSkpKkR+GysvLY/Xq1aSnpxMcHExUVBQ5OTmEhITo9qlbty4+Pj5EREQAEBERQUBAAO7u/17U361bN1JSUnS9hBEREQXayN8nv40HmTJlCo6OjrqHv7+/wZ9LGK5XoCfW5qZcuJFO1KU7xftm58Pg5hmwsIfGA4v3vYQQQohH8Pb2JiIigvT0dDZv3kzXrl0BuHPnDlZWVga1WaRhWZycnIp8G3BeXp5eAY4dO0ZwcDCZmZnY2dmxfv16/P39iY6OxsLCAicnpwL7u7u7k5CQAEBCQkKBYi9/e/62R+2TkpJCRkYG1tbWhTJ98MEHBcbAuXLlihR9CrC3MqdnoCe/RF1mTWQ8QdVdiu/N8gdabjIIrByK732EEEKIxxgzZgwDBgzAzs6OatWq0aFDB0B7qjcgIMCgNovUw7djxw62b9/O9u3bWbZsGW5ubowfP57169ezfv16xo8fj7u7O8uWLdM7QJ06dYiOjmb//v2MGDGCwYMHExMTo3c7xmRpaam7kcTBwUHuOFZQ/s0bfxy7RlpW7mP2NlDiCbiwE1Qm0EIGWhZCiIrsypUrvPLKK7i6umJtbU1AQAAHDx7UbddoNEyaNAlPT0+sra0JCQnh7NmzRs3w1ltvsW/fPpYtW8bu3bsxMdGWazVq1DD4Gr4i9fDdf8ft559/zsyZM3nppZd063r37k1AQACLFy9m8ODBegWwsLDQjenXtGlTIiMjmTNnDv379yc7O5ukpKQCvXyJiYl4eHgA4OHhwYEDBwq0l38X7/37/PfO3sTERBwcHB7YuydKl6BqztSobMuFG+lsOnKVF5v7GP9N8gdarvc0OFczfvtCCCHKhDt37tC6dWs6duzIX3/9ReXKlTl79izOzs66faZPn87cuXNZsWIFvr6+TJw4kW7duhETE2Pw6db75eTkULduXTZt2sSzzz5bYFvPnj0Nblfvu3QjIiIICgoqtD4oKKhQ8WUItVpNVlYWTZs2xdzcnLCwMN2206dPExcXR3BwMADBwcEcO3aM69ev6/bZunUrDg4OulOwwcHBBdrI3ye/DVG6qVQq+gdpe/nWHCyGMfnSrsPRtdrllqHGb18IIYTiUlNTC9xvkJX14OG+pk2bhre3N99//z3NmzfH19eXrl27UrNmTUDbuzd79mw+/vhjnnnmGQIDA/nhhx+4evUqGzZsMEpWc3NzMjMzjdLW/fQu+Ly9vVmyZEmh9UuXLsXbW79pqD744AN27drFxYsXOXbsGB988AE7d+5kwIABODo6MmzYMMaNG8eOHTuIiopi6NChBAcH07JlSwC6du2Kv78/AwcO5MiRI2zZsoWPP/6Y0NBQLC0tAXjzzTe5cOEC48eP59SpU3zzzTesXbuWsWPH6vvRhUL6NqmKmYmKw3FJnElMNW7jkd9BXjZUCQLv5sZtWwghRKng7+9f4GbMKVOmPHC/33//naCgIJ5//nnc3Nxo3LhxgZonNjaWhISEAjeDOjo60qJFi0feDKqv0NBQpk2bVmhc4Seh91y6s2bNol+/fvz111+0aNECgAMHDnD27Fm9BwO8fv06gwYN4tq1azg6OhIYGMiWLVvo0qWL7r1MTEzo168fWVlZdOvWjW+++Ub3elNTUzZt2sSIESMIDg7G1taWwYMH8/nnn+v28fX15Y8//mDs2LHMmTOHqlWrsnTpUrp166bvRxcKqWxvSae6bvwdk8iayHgm9jLSDTQ5mdqZNQCC3wID5ycUQghRusXExFClShXd8/xOof+6cOECCxcuZNy4cXz44YdERkYyatQoLCwsGDx4sO6G0AfdDJq/zRgiIyMJCwvj77//JiAgAFtb2wLb161bp3ebes+lCxAfH8/ChQt1U3zUq1ePN998U+8evrJC5uJTXtjJRIatOIiLrQX7PuiMhZnendOFHfoBfn8bHKrC6CNgqve/f4QQQpRi+v5+W1hYEBQUxN69e3XrRo0aRWRkJBEREezdu5fWrVtz9epVPD09dfu88MILqFSqIk06URRDhw595Pbvv/9e7zYN+oXz9vbmq6++MuSlQhikfe3KuNlbcj01i20nE3kqwPPxL3oUjQYi7vUWt3hDij0hhBB4enoWGoatXr16ujOY+TeEJiYmFij4EhMTadSokdFyGFLQPY7ev3K7du165PZ27doZHEaIhzEzNeG5plX5Zud51kTGP3nBd3473DgJ5rbasfeEEEJUeK1bt+b06dMF1p05c4Zq1bQjOPj6+uLh4UFYWJiuwEtJSdENLWdMubm57Ny5k/Pnz/Pyyy9jb2/P1atXcXBwwM7OTu/29C748gf/u9/9gzLrO/CyEEX1QpA33+w8z66zN7ialIGX0xMMq5M/FEuTgWDtZJR8QgghyraxY8fSqlUrvvrqK1544QUOHDjA4sWLWbx4MaCtd8aMGcPkyZPx8/PTDcvi5eVFnz59jJbj0qVLdO/enbi4OLKysujSpQv29vZMmzaNrKwsFi1apHebel8IdefOnQKP69evs3nzZpo1a8bff/+tdwAhiqp6JVta1nBBo4Ffoi4b3tD1U3BuG6CSgZaFEELoNGvWjPXr1/PTTz/RoEEDvvjiC2bPns2AAQN0+4wfP563336b119/nWbNmpGWlsbmzZuNMgZfvtGjRxMUFMSdO3cKjBn87LPPFhpqrqj07uFzdHQstK5Lly5YWFgwbtw4oqKiDAoiRFH0b+bNvgu3WXswnpEda2FiYsCdtfm9e3V7gksN4wYUQghRpvXq1YtevXo9dLtKpeLzzz8vMCKIsf3zzz/s3bsXCwuLAuurV6/OlStXDGrTCLc6arm7uxc67y2EsfVo4Im9lRmX72Sw9/wt/RtIvwlH791FFSwDLQshhCh91Gr1Ay+Ru3z5ssHTverdw3f06NECzzUaDdeuXWPq1KlGvUNFiAexMjflmUZe/LgvjjUH42njV0m/Bg4ug9xM8GwEPjLbihBCiNKna9euzJ49u8C1g2lpaXzyySc89dRTBrWpd8HXqFEjVCoV/x2+r2XLlixbtsygEELoo3+QDz/ui2PLiQSS7mbjZGPx+BcB5GbBgXsjpgePlIGWhRBClEozZsygW7du+Pv7k5mZycsvv8zZs2epVKkSP/30k0Ft6l3wxcbGFnhuYmJC5cqVjXqxohCP0qCKA/U8HTh5LYUNh68wpLVv0V547BdIvw72XlC/T7FmFEIIIQxVtWpVjhw5wurVqzl69ChpaWkMGzaMAQMGFLiJQx96F3z5Y9EIoRSVSkX/oKp8ujGGNQcvM7hV9QJDAz2QRvPvzRotXgdT8+IPKoQQQhggMzMTKysrXnnlFaO1adBNG2FhYfTq1YuaNWtSs2ZNevXqxbZt24wWSojH6dO4ChZmJpy8lsLxKymPf0HsLkg8DuY20HRIsecTQgghDOXm5sbgwYPZunUrarXaKG3qXfB98803dO/eHXt7e0aPHs3o0aNxcHDgqaeeYsGCBUYJJcTjONlY0L2+doqbNQfjHv+CiHt/Nxu9DNbOxZhMCCGEeDIrVqzg7t27PPPMM1SpUoUxY8Zw8ODBJ2pTpfnv3RePUbVqVd5//31GjhxZYP2CBQv46quvDB4fpjTTd/JlUTL2nLvJgKX7sbcy48CHIVhbmD54x5tnYX4QoIKRB6FSrRLNKYQQQhll/fc7NTWVX375hZ9++ont27dTo0YNXnnlFSZNmqR3W3r38CUlJdG9e/dC67t27UpycrLeAYQwVHANV7xdrEnNzOWv49cevmP+tXu1u0uxJ4QQosywt7dn6NCh/P333xw9ehRbW1s+++wzg9rSu+Dr3bs369evL7T+t99+e+TI1EIYm4mJiuebegOwJjL+wTvdvQ3R925hl4GWhRBClCGZmZmsXbuWPn360KRJE27fvs17771nUFtFukt37ty5umV/f3++/PJLdu7cSXCwduDaffv2sWfPHt555x2DQghhqOeaVmXWtjPsj73NxZvpVK9kW3CHg8sgNwM8AqB6G2VCCiGEEHrYsmULq1atYsOGDZiZmfHcc8/x999/065dO4PbLNI1fL6+RRvnTKVSceHCBYPDlFZl/RqA8m7wsgOEn7nBWx1qMr573X835GbD7ABIS4Bnv4WGLyoXUgghRIkrq7/fNjY29OrViwEDBvDUU09hbv7kQ4kVqYfvv4MtC1Ga9G/mTfiZG/wSdZlxXWpjZnrvSoUT67XFnp0H1O+rbEghhBCiiBITEw2eM/dh9B54WYjSJqSeOy62FlxPzSL8zA0613PXDrQcMV+7Q/PhYFbE6deEEEIIhdnb25OXl8eGDRs4efIkoL2k7plnnsHU9CEjUjyGQQMvC1GaWJiZ0LdxFeC+mzcu7YGEo2BmDU1fVTCdEEIIoZ9z585Rr149Bg0axLp161i3bh0DBw6kfv36nD9/3qA2peAT5UL/Ztq7dcNOXed6aua/Ay03fBFsXRVMJoQQQuhn1KhR1KxZk/j4eA4dOsShQ4eIi4vD19eXUaNGGdSmnNIV5YKfuz2NfZw4HJfEtt0RvHz6L+2Glm8pG0wIIYTQU3h4OPv27cPFxUW3ztXVlalTp9K6dWuD2pQePlFu9A/S9vJZH1oCaMCvK1SurWwoIYQQQk+WlpakpqYWWp+WloaFhWHXpOvdw3f06NEHrlepVFhZWeHj44OlpaVBYYR4Er0aejF70wG6ZW8DFdK7J4QQokzq1asXr7/+Ot999x3NmzcHYP/+/bz55pv07t3boDb1LvgaNWqESqV66HZzc3P69+/Pt99+i5WVlUGhhDCEnaUZEz0jsUnM4qplDbxqdFA6khBCCKG3uXPnMnjwYIKDg3Vj8OXm5tK7d2/mzJljUJt6n9Jdv349fn5+LF68mOjoaKKjo1m8eDF16tRh1apVfPfdd2zfvp2PP/7YoEBCGCwvhy6pGwCYn9GV1KxcZfMIIYQQBnBycuK3337jzJkz/PLLL/zyyy+cPn2a9evX4+joaFCbevfwffnll8yZM4du3brp1gUEBFC1alUmTpzIgQMHsLW15Z133uF///ufQaGEMEjMb1jcTeC2yolfsoMJOHqNl5r7KJ1KCCGEMEitWrWoVauWUdrSu4fv2LFjVKtWrdD6atWqcezYMUB72vfatWtPnk6IorpvoOUL1V8kG/N/x+QTQgghypB+/foxbdq0QuunT5/O888/b1Cbehd8devWZerUqWRnZ+vW5eTkMHXqVOrW1c5jeuXKFdzd3Q0KJIRB4vbB1cNgaolvj1GYmaiIjk/idELhu5yEEEKI0mzXrl089dRThdb36NGDXbt2GdSm3qd0FyxYQO/evalatSqBgYGAttcvLy+PTZs2AXDhwgXeekvukBQlaF/+QMv9cXWrQud619hyIpE1kfFMetpf2WxCCCGEHh42/Iq5uTkpKSkGtal3D1+rVq2IjY3l888/JzAwkMDAQD7//HNiY2Np2bIlAAMHDuS9994zKJAQersdCye1/9jIH4olf+aN9Ycvk5Wbp1QyIYQQQm8BAQGsWbOm0PrVq1fj729YJ4ZBM23Y29vz5ptvGvSGQhjd/m8BDdTsDG71AGjnVxl3B0sSU7LYFnOdnoGeymYUQgghimjixIn07duX8+fP06lTJwDCwsL46aef+Pnnnw1qs0gF3++//06PHj0wNzfn999/f+S+hg4IKIRBMpPh8P9pl4P/vYzAzNSE55pWZcGO86w5GC8FnxBCiDLj6aefZsOGDXz11Vf88ssvWFtbExgYyLZt22jfvr1BbRap4OvTpw8JCQm4ubnRp0+fh+6nUqnIy5PTZ6IEHfoBstOgcl1tD999XgjyZsGO8/xz9gZXkjKo4mStUEghhBBCPz179qRnz55Ga69I1/Cp1Wrc3Nx0yw97SLEnSlRe7r3TuWiv3fvPDDDVXG0JruGKRgO/HLysQEAhhBCiaDQaTbG2r/dNG0KUGid/h+R4sHGFwBceuEv+zRtrD8ajVhfv/0xCCCGEoerXr8/q1asLDHv3IGfPnmXEiBFMnTpVr/YNumkjLCyMsLAwrl+/jlqtLrBt2bJlhjQphP72faP9s9lwMH/w6druDTyw/82MK0kZ7Dl/k7Z+lUswoBBCCFE08+bNY8KECbz11lt06dKFoKAgvLy8sLKy4s6dO8TExLB7925OnDjByJEjGTFihF7t613wffbZZ3z++ecEBQXh6emJ6j+n0YQoEfEH4HIkmFpoC76HsDI3pU+jKvzfvkusiYyXgk8IIUSp1LlzZw4ePMju3btZs2YNK1eu5NKlS2RkZFCpUiUaN27MoEGDGDBgAM7Oznq3r3fBt2jRIpYvX87AgQP1fjMhjCbi3kDLAS+Andsjd+3fzJv/23eJv08kcic9G2fbwoNZCiGEEKVBmzZtaNOmjdHb1fsavuzsbFq1amX0IEIU2Z1L2uv3oMBQLA/ToIoj/p4OZOep2RB9pZjDCSGEEKWP3gXf8OHDWbVqVXFkEaJoDiwGjRpqdAD3+kV6Sf7NG2si44v9TighhBCitNH7lG5mZiaLFy9m27ZtBAYGYm5uXmD7zJkzjRZOiEIyU7Rj7wG0DC3yy/o0qsKXf57kVEIqx64kE1jVqXjyCSGEEKWQ3gXf0aNHadSoEQDHjx8vsE1u4BDF7vCPkJUCrn5QK6TIL3O0MadHAw9+i77K6sh4KfiEEEJUKHoXfDt27CiOHEI8njoP9i/SLge/BSb6XZHQP8ib36KvsjH6KhN7+mNtYVoMIYUQQojSx6Bx+IRQxKk/IOkSWDtD4It6v7xlDVe8XayJv53Bn8eu0a9p1WIIKYQQQugvJSWlyPs6ODjo3X6RCr6+ffuyfPlyHBwc6Nu37yP3Xbdund4hhCiS/KFYgoaBhY3eLzcxUfFCU29mbD3DmoPxUvAJIYQoNZycnIp8aZwhU9kWqeBzdHTUhXB0dNT7TYR4YpejIH4fmJhD89cMbua5oKrM2naGA7G3ib2Zjm8lWyOGFEIIIQxz/yVzFy9e5P3332fIkCEEBwcDEBERwYoVK5gyZYpB7as0MkbFY12+fBlvb2/i4+OpWlV6hRTxy6tw/Fftqdy+3z5RU0O+P8DO0zcY0aEmE7rXNVJAIYQQpU1Z/f3u3Lkzw4cP56WXXiqwftWqVSxevJidO3fq3abe4/AJUeKSL8OJDdrlIgy0/Dj9g7Rj8v0adZncPPVj9hZCCCFKVkREBEFBQYXWBwUFceDAAYPaNOimjV9++YW1a9cSFxdHdnZ2gW2HDh0yKIgQD3VgMWjyoHpb8Gz4xM11rueOq60F11Oz2Hn6BiH+7kYIKYQQQhiHt7c3S5YsYfr06QXWL126FG9vb4Pa1LuHb+7cuQwdOhR3d3cOHz5M8+bNcXV15cKFC/To0cOgEEI8VFYaHFyuXQ4u+kDLj2JhZkLfJlUAWB0Zb5Q2hRBCCGOZNWsW8+bNIyAggOHDhzN8+HACAwOZN28es2bNMqhNvQu+b775hsWLFzNv3jwsLCwYP348W7duZdSoUSQnJxsUQoiHil4FWcngUhP8uhmt2fyp1nacvs71lEyjtSuEEEI8qaeeeoqzZ8/Su3dvbt++ze3bt3n66ac5c+YMTz31lEFt6n1KNy4ujlatWgFgbW1NamoqAAMHDqRly5bMnz/foCBCFKLOg33faJdbjtB7oOVHqeVmTxMfJw7FJfHroSuM6FDTaG0LIYQQhsrJyaF79+4sWrSIL7/80mjt6v0L6uHhwe3btwHw8fFh3759AMTGxsqk9MK4zmyGO7Fg5QSNXjZ68/m9fD8fjJe/u0IIIUoFc3Nzjh49avR29S74OnXqxO+//w7A0KFDGTt2LF26dKF///48++yzRg8oKrCIe717TYeAhfHHy+sZ6IWNhSkXbqYTefGO0dsXQgghDPHKK6/w3XffGbVNvU/pLl68GLVaO5RFaGgorq6u7N27l969e/PGG28YNZyowK5Gw6XdYGIGzV8vlrewszSjV6Anaw9eZk1kPM19XYrlfYQQQgh95ObmsmzZMrZt20bTpk2xtS3Y6TFz5ky929S74DMxMcHkvmupXnzxRV58Uf95TYV4pPxr9+o/C45Viu1t+jfzZu3By/x57Bqf9PbHwcq82N5LCCGEKIrjx4/TpEkTAM6cOVNgW1GnX/svg8bhy8zM5OjRo1y/fl3X25evd+/eBgURQiflqnZWDYCWTz7Q8qM08XGmZmVbzt9IZ+ORqwxoUa1Y308IIYR4nPunWTMWvQu+zZs3M2jQIG7evFlom0qlMmhCXyEKOLAY1Lng0wqqNCnWt1KpVLzYzIcv/zzJ2sh4KfiEEEKUS3rftPH222/z/PPPc+3aNdRqdYGHFHviid29DQeWapeNNNDy4zzbpApmJiqOXE7mVEJKibynEEKI0m/q1KmoVCrGjBmjW5eZmam7h8HOzo5+/fqRmJho9Pc+ePAg48eP58UXX6Rv374FHobQu+BLTExk3LhxuLvLdFSiGOydB9mp4B4AdQwbXFJflewsCamn/fu8RmbeEEIIAURGRvLtt98SGBhYYP3YsWPZuHEjP//8M+Hh4Vy9etXgIuxhVq9eTatWrTh58iTr168nJyeHEydOsH37dhwdHQ1qU++C77nnnmPnzp0Gvdl/TZkyhWbNmmFvb4+bmxt9+vTh9OnTBfYpSiUdFxdHz549sbGxwc3Njffee4/c3NwC++zcuZMmTZpgaWlJrVq1WL58uVE+gzCi9Fuw/1vtcscPjDrQ8uPkj8m3/vAVsnKlp1oIIcqT1NRUUlJSdI+srKxH7p+WlsaAAQNYsmQJzs7OuvXJycl89913zJw5k06dOtG0aVO+//579u7dqxuX2Bi++uorZs2axcaNG7GwsGDOnDmcOnWKF154AR8fH4Pa1PsXdf78+axbt44hQ4YwY8YM5s6dW+Chj/DwcEJDQ9m3bx9bt24lJyeHrl27kp6ertvncZV0Xl4ePXv2JDs7m71797JixQqWL1/OpEmTdPvExsbSs2dPOnbsSHR0NGPGjGH48OFs2bJF348vitPeOZCTDp4NS6x3L1+72pXxcLAi6W4OW2OM3zUvhBBCOf7+/jg6OuoeU6ZMeeT+oaGh9OzZk5CQkALro6KiyMnJKbC+bt26+Pj4EBERYbS858+fp2fPngBYWFiQnp6OSqVi7NixLF682KA29b5p46effuLvv//GysqKnTt3Frg9WKVSMWrUqCK3tXnz5gLPly9fjpubG1FRUbRr105XSa9atYpOnToB8P3331OvXj327dtHy5Yt+fvvv4mJiWHbtm24u7vTqFEjvvjiCyZMmMCnn36KhYUFixYtwtfXlxkzZgBQr149du/ezaxZs+jWzXjzs4onkHYDDizRLnf4EAy87dxQpiYqnmtalfk7zrEmMp5egV4l+v5CCCGKT0xMDFWq/DvEl6Wl5UP3Xb16NYcOHSIyMrLQtoSEBCwsLHByciqw3t3dnYSEBKPldXZ21k1dW6VKFY4fP05AQABJSUncvXvXoDb17uH76KOP+Oyzz0hOTubixYvExsbqHhcuXDAoRL7k5GQAXFy0A+AWpZKOiIggICCgwDWF3bp1IyUlhRMnTuj2+W+V3q1bt4dW41lZWQW6fvO/dFGM9syGnLvg1QRqK1OEvxCkPa27+9xNLt8x7H8oIYQQpY+9vT0ODg66x8MKvvj4eEaPHs3KlSuxsrIq4ZT/ateuHVu3bgXg+eefZ/To0bz22mu89NJLdO7c2aA29S74srOz6d+/f4HBl41BrVYzZswYWrduTYMGDYCiVdIJCQmFbiDJf/64fVJSUsjIyCiUZcqUKQW6fv39/Y3yGcVDpCZC5L0pZDqWfO9ePh9XG1rVdEWjgZ8PXlYkgxBCCOVERUVx/fp1mjRpgpmZGWZmZoSHhzN37lzMzMxwd3cnOzubpKSkAq9LTEzEw8PDaDnmz5+vm9Tio48+Yty4cSQmJtKvXz+Dp1zTu2obPHgwa9asMejNHiU0NJTjx4+zevVqo7etrw8++IDk5GTdIyYmRulI5due2ZCbAVWbQa2Qx+5enPJv3vgl6jJ5ao2iWYQQQpSszp07c+zYMaKjo3WPoKAgBgwYoFs2NzcnLCxM95rTp08TFxdHcHCw0XK4uLjg5aW9tMjExIT333+f33//nRkzZhS4iUQfel/Dl5eXx/Tp09myZQuBgYGYmxecisqQ+d1GjhzJpk2b2LVrF1WrVtWt9/Dw0FXS9/fy3V9Je3h4cODAgQLt5d/Fe/8+/72zNzExEQcHB6ytrQvlsbS0LNDdm5IiY7MVm5RrpaJ3L1+3+h44WJlxJSmDPedu0q52ZUXzCCGEKDn29va6s4z5bG1tcXV11a0fNmwY48aNw8XFBQcHB95++22Cg4Np2bKl0XIMGjSIjh070q5dO2rWrGmUNvXu4Tt27BiNGzfGxMSE48ePc/jwYd0jOjpar7Y0Gg0jR45k/fr1bN++HV9f3wLbmzZt+thKOjg4mGPHjnH9+nXdPlu3bsXBwUF3KjY4OLhAG/n7GLMaFwbaPRPyssC7JdToqHQarMxN6dNYe2HvmoMyJp8QQoiCZs2aRa9evejXrx/t2rXDw8ODdevWGfU9LCwsmDJlCn5+fnh7e/PKK6+wdOlSzp49a3CbKo1Go9h5q7feeotVq1bx22+/UadOHd16R0dHXc/biBEj+PPPP1m+fLmukgbYu3cvoO1xbNSoEV5eXkyfPp2EhAQGDhzI8OHD+eqrrwDtsCwNGjQgNDSUV199le3btzNq1Cj++OOPIt2le/nyZby9vYmPjy/QAymeUPIVmNsI8rJh0O9Qo73SiQA4fiWZXvN2Y2Fqwv4PO+Nsa6F0JCGEEAYo67/fV65cYdeuXYSHhxMeHs6ZM2fw9PTk8mX9rzM3+M6Lc+fOsWXLFt1ND4bUjQsXLiQ5OZkOHTrg6empe9x/jeDjKmlTU1M2bdqEqakpwcHBvPLKKwwaNIjPP/9ct4+vry9//PEHW7dupWHDhsyYMYOlS5fKkCxK+2eGttir1hp82ymdRqdBFUfqezmQnadm/eErSscRQghRQTk7O+Pq6oqzszNOTk6YmZlRubJhlxrp3cN369YtXnjhBXbs2IFKpeLs2bPUqFGDV199FWdnZ91Yd+VJWf8XQqmUFA9zG4M6B4b8AdXbKJ2ogB8iLjLptxPUcbdn85i2BcabFEIIUTaU1d/vDz/8kJ07d3L48GHq1atH+/bt6dChA+3atTP4pg29e/jGjh2Lubk5cXFx2NjY6Nb379+/0EDKQjzUP//TFnu+7UpdsQfwTMMqWJiZcDoxlSOXk5WOI4QQogKZOnUq58+f55NPPmH16tXMmjWLZ555xuBiDwwo+P7++2+mTZtWqFL28/Pj0qVLBgcRFcidi3D4R+1yhw8VjfIwjjbmPNVAe5f3mki5eUMIIUTJOXz4MB999BEHDhygdevWVKlShZdffpnFixdz5swZg9rUu+BLT08v0LOX7/bt24+cqkQInV3/A3Wu9q7caqX3TukX7o3Jt+7QZf5v3yXUMi6fEEKIEtCwYUNGjRrFunXruHHjBn/++ScWFhaEhoZSr149g9rUu+Br27YtP/zwg+65SqVCrVYzffp0OnZUflgNUcrdvgDRq7TLHUtn716+lr6udKhTmaxcNRM3HOflpfu4dCtd6VhCCCHKOY1Gw6FDh5g5cya9e/emY8eO/PjjjwQEBDBq1CiD2tR74OXp06fTuXNnDh48SHZ2NuPHj+fEiRPcvn2bPXv2GBRCVCDhX4MmTzujhndzpdM8komJimWDm7Ei4iLTN59m34XbdJ/9D+91q8PgVtUxNZEbOYQQQhifi4sLaWlpNGzYkPbt2/Paa6/Rtm3bQlPN6sOgcfiSk5OZP38+R44cIS0tjSZNmhAaGoqnp6fBQUqzsnqXT6lz6zzMDwKNGoZvh6pNlU5UZHG37jLh16NEXLgFQNNqzkx/LpCale0UTiaEEOJhyurv9x9//EHbtm1xcHAwWpt6FXw5OTl0796dRYsW4efnZ7QQpV1Z/QtT6qx7HY6uAb9uMGCt0mn0plZr+Ckyjil/niItKxcLMxPGdanN8Da+mJkaPKSlEEKIYlLWf7/PnTvH+fPnadeuHdbW1mg0GoOHCdPrV8rc3JyjR48a9EaigrtxBo79rF3u+IGyWQxkYqJiQItqbBnbjna1K5Odq2bqX6fot3AvpxNSlY4nhBCinLh16xadO3emdu3aPPXUU1y7dg3QzuP7zjvvGNSm3t0Sr7zyCt99951BbyYqsPBp2lO5dXqCV2Ol0zyRKk7WrBjajK+fC8Teyowjl5PpNe8f5oWdJSdPrXQ8IYQQZVxxjHms900bubm5LFu2jG3bttG0aVNsbW0LbJ85c6ZBQUQ5dv0kHP9Vu9zhfWWzGIlKpeL5IG/a1a7MR+uPse3kdWZsPcNfxxOY/lwgDao4Kh1RCCFEGfX333+zZcsWo455rHfBd/z4cZo0aQJQaPA/mX5KPFD4NEAD9Z4Gz0Cl0xiVu4MVSwYF8fuRq3zy+wlirqXQZ8EeRnSoychOtbA0M1U6ohBCiDKmOMY81rvg27Fjh0FvJCqoxBNwYr12uX356N37L5VKxTONqtCqZiU++f04fx5LYN72c2w5kcD05xrSyNtJ6YhCCCHKkPwxj7/44gvAOGMe613wCaGXnVO0f/r3AY8GikYpbpXtLflmQFP+PHaNSb8d50xiGn2/2cNrbWswtkttrMylt08IIcTjFceYxzKWhCg+147CyY2Aqtxcu1cUTwV48vfY9jzTyAu1Br7ddYGn5vzDwYu3lY4mhBCiDGjQoAFnzpyhTZs2PPPMM6Snp9O3b18OHz5MzZo1DWpTevhE8dk5Vftng77gZtjcf2WVi60Fc15sTK9ALz5af4wLN9N5/tsIhrSqznvd6mBjIf/rCSGEKOz+MY8/+ugjo7UrPXyieFw9DKf/AJVJub12ryi6+LuzdWx7nm9aFY0Gvt9zke6z/2Hv+ZtKRxNCCFEKFdeYx1LwieKR37sX8DxUrq1sFoU52pjz9fMNWT60GV6OVsTdvsvLS/bz0fpjpGXlKh1PCCFEKVMcYx7LeSVhfJej4Mxmbe9eu/FKpyk1OtRxY8vYdkz56xSr9sexcn8cO0/f4Ku+AbSvXVnpeEIIIUqJ4hjzWAo+YXz5d+YGvgiVaimbpZSxtzLnq2cD6BXgyYR1R4m/ncHgZQd4IagqH/X0x9HaXOmIQgghFFYcYx5LwSeMK/4AnNsKKlNo/57SaUqtVrUqsXl0O77ecpoVERdZe/Ay4Wdu8NWzAXSu5650PCGEEAoqjjGP5Ro+YVw7vtL+2eglcKmhbJZSztbSjE9712ftG8H4VrIlMSWLYSsOMmb1Ye6kZysdTwghRDkiBZ8wnksRcGEHmJhBO+ndK6pm1V34a3RbXm9XAxMVbIi+SpdZ4fx17JrS0YQQQpQTUvAJ49mZ37s3AJyrKxqlrLEyN+XDp+rx64hW+LnZcTMtmxErD/HWyihupmUpHU8IIUQZJwWfMI6LuyF2F5iYQ7t3lU5TZjX2cWbTqDaM7FgLUxMVfx5LoMvMcH6LvoJGo1E6nhBCiDJKCj7x5DSaf6/dazIInHyUzVPGWZqZ8m63OvwW2pq6HvbcuZvD6NXRvP5/UVxPyVQ6nhBCiDJICj7x5GJ3waU9YGoBbd9ROk250aCKI7+PbMPYkNqYm6rYGpNIz3m7ibokc/IKIYTQjxR84sloNP+Ou9d0CDhWUTROeWNhZsLoED82vt2G2u523EjN4sXF+1i1P07paEIIIcoQKfjEk7mwA+IiwNQS2oxTOk25VdfDgfVvtaZHAw9y8jR8uP4YH64/RnauWuloQgghygAp+ITh7r92L+hVcPBUNk85Z2tpxjcDmvBetzqoVLBqfxwvLdkn1/UJIYR4LCn4hOHOhcHlSDCzhjZjlU5TIahUKkI71mLZkGbYW5kRdekOT8/fzaG4O0pHE0IIUYpJwScMo9HAji+1y82Ggb1MB1aSOtZx4/eRbfBzsyMxJYsXv93Hmki5rk8IIcSDScEnDHNmC1w9BOY20Hq00mkqJN9KtqwPbU23+u5k56mZ8OsxJm44Ltf1CSGEKEQKPqE/jebfWTWaDQc7N2XzVGB2lmYsHNCUd7rURqWC/9t3iQFL93EjVWbnEEII8S8p+IT+Tv8J146Aua307pUCJiYq3u7sx9JBQdhbmhF58Q5Pz9tNdHyS0tGEEEKUElLwCf2o1bDj3rh7Ld4A20rK5hE6neu5s2Fka2pWtiUhJZMXvo1g7cF4pWMJIYQoBaTgE/o5tQkSj4GFPbR6W+k04j9qVrZjQ2hrQuq5k52rZvwvR/nkt+Pk5Ml1fUIIUZFJwSeKTq3+d1aNlm+CjYuyecQD2VuZs3hgU8aE+AGwIuISA5bu52aaXNcnhBAVlRR8ouhO/gbXY8DSAYJDlU4jHsHERMWYkNosGRSEnaUZB2Jv03vebo5dTlY6mhBCCAVIwSeKRp0HO6dql1u+BdbOyuYRRdLF350Noa2pUcmWq8mZ9Fu0l1+jLisdSwghRAmTgk8UzYn1cOMUWDlCyxFKpxF6qOVmx4aRrelc143sXDXv/HyEzzaekOv6hBCiApGCTzze/b17wSPB2knROEJ/DlbmLBkUxKhOtQD4fs9FBn13gFtyXZ8QQlQIUvCJxzv2C9w6C1ZO0OJNpdMIA5mYqBjXtQ6LXmmKrYUpERdu0Xv+Ho5fkev6hBCivJOCTzxaXi6E3+vdaz0KrByUzSOeWPcGHmwIbU11VxuuJGXw3KK9/BZ9RelYQgghipEUfOLRjq2F2xfA2gWav650GmEkfu72/DayDR3rVCYzR83o1dFM3hRDrlzXJ4QQ5ZIUfOLh8nIgfJp2ufVosLRXNo8wKkdrc5YObsbIjtrr+pbujmXw9we4nZ6tcDIhhBDGJgWfeLgjq+HORbCpBM1fUzqNKAamJire7VaHhQOaYGNhyp5zt+g9fzcxV1OUjiaEEMKIpOATD5abDbuma5fbjAELW0XjiOLVI8CT9W+1ppqrDZfvZNB34R5+P3JV6VhCCCGMRAo+8WDRKyEpDmzdIGiY0mlECajjYc/voW1oV1t7Xd+onw4z5c+T5Kk1SkcTQgjxhKTgE4XlZsE/M7TLbcaChY2yeUSJcbQx5/shzRjRoSYA3+66wJDvD5B0V67rE0KIskwKPlHY4f+D5Hiw84CgoUqnESXM1ETFhO51mf9yY6zNTfnn7E16z9/DqQS5rk8IIcoqKfhEQTmZsOte717bd8DcWtk8QjG9Ar1Y91YrvF2sibt9l2cX7OWPo9eUjiWEEMIAUvCJgg79AKlXwd4LmgxSOo1QWD1PBzaObENbv0pk5OQRuuoQ0zafkuv6hBCijJGCT/wrJ+Pfa/favQPmVsrmEaWCk40F3w9pxhvtagCwcOd5Xl0eSXpWrsLJhBDCuKZMmUKzZs2wt7fHzc2NPn36cPr06QL7ZGZmEhoaiqurK3Z2dvTr14/ExESFEhedFHziX1HLIS0BHL2h8UCl04hSxMzUhA+eqsfclxpjZW5C+JkbLNx5XulYQghhVOHh4YSGhrJv3z62bt1KTk4OXbt2JT09XbfP2LFj2bhxIz///DPh4eFcvXqVvn37Kpi6aFQajUbOzTzG5cuX8fb2Jj4+nqpVqyodp3hk34U5DSH9OvSaLTdriIf64+g1QlcdwtHanIgPOmFjYaZ0JCGEeKAn/f2+ceMGbm5uhIeH065dO5KTk6lcuTKrVq3iueeeA+DUqVPUq1ePiIgIWrZsaeyPYDTSwye0Ipdoiz0nH2g0QOk0ohTr3sCD6q42JGfksDYyXuk4QgjxWKmpqaSkpOgeWVlZRXpdcnIyAC4uLgBERUWRk5NDSEiIbp+6devi4+NDRESE8YMbkRR8AjKS4J+Z2uX2E8DMQtE4onQzNVExrK32er7v9sSSm6dWOJEQQjyav78/jo6OuseUKVMe+xq1Ws2YMWNo3bo1DRo0ACAhIQELCwucnJwK7Ovu7k5CQkJxRDcaORcjYO9cyEyCynWh4UtKpxFlwHNNqjJr6xnib2ew+UQCvQK9lI4khBAPFRMTQ5UqVXTPLS0tH/ua0NBQjh8/zu7du4szWomRHr6KLjUB9i3ULneaCCamyuYRZYK1hSkDW1YDYMmuC8ilwEKI0sze3h4HBwfd43EF38iRI9m0aRM7duwocO2fh4cH2dnZJCUlFdg/MTERDw+P4ohuNFLwVXS7voacu1AlCOr2VDqNKEMGBVfD0syEI5eT2R97W+k4QgjxxDQaDSNHjmT9+vVs374dX1/fAtubNm2Kubk5YWFhunWnT58mLi6O4ODgko6rFyn4KrLbF7RDsQCEfAoqlZJpRBnjamfJc021//JdsuuCwmmEEOLJhYaG8uOPP7Jq1Srs7e1JSEggISGBjIwMABwdHRk2bBjjxo1jx44dREVFMXToUIKDg0v1HbogBV/FtuMrUOdCzc7g21bpNKIMGt62BioVhJ26ztnEVKXjCCHEE1m4cCHJycl06NABT09P3WPNmjW6fWbNmkWvXr3o168f7dq1w8PDg3Xr1imYumgULfh27drF008/jZeXFyqVig0bNhTYrtFomDRpEp6enlhbWxMSEsLZs2cL7HP79m0GDBiAg4MDTk5ODBs2jLS0tAL7HD16lLZt22JlZYW3tzfTp08v7o9W+l07Csd+1i53nqRsFlFm+Vaypau/OwBL/4lVOI0QQjwZjUbzwMeQIUN0+1hZWbFgwQJu375Neno669atK/XX74HCBV96ejoNGzZkwYIFD9w+ffp05s6dy6JFi9i/fz+2trZ069aNzMxM3T4DBgzgxIkTbN26lU2bNrFr1y5ef/113faUlBS6du1KtWrViIqK4uuvv+bTTz9l8eLFxf75SrXtX2j/rN8XvBopGkWUba/fm3Jt/eErXE/NfMzeQgghlKDosCw9evSgR48eD9ym0WiYPXs2H3/8Mc888wwAP/zwA+7u7mzYsIEXX3yRkydPsnnzZiIjIwkKCgJg3rx5PPXUU/zvf//Dy8uLlStXkp2dzbJly7CwsKB+/fpER0czc+bMAoXh/bKysgoMypiaWs5OVV3cA2f/BhMz6PSx0mlEGde0mgtNqzkTdekOK/Ze5L1udZWOJIQQ4j9K7TV8sbGxJCQkFBjN2tHRkRYtWuhGs46IiMDJyUlX7AGEhIRgYmLC/v37dfu0a9cOC4t/BxPu1q0bp0+f5s6dOw987ylTphQYoNHf3784PqIyNBoI+0y73GQQuNZUNo8oF167NxDzj/viSM/KVTiNEEKI/yq1BV/+iNXu7u4F1t8/mnVCQgJubm4FtpuZmeHi4lJgnwe1cf97/NcHH3xAcnKy7hETE/PkH6i0OP0XxO8HM2toN17pNKKc6OLv/u90awdlujUhhChtSm3BpyRLS8sCAzTa29srHck41HkQ9rl2ueWb4OCpbB5RbpiaqBieP93abpluTQghSptSW/Dl3/GSmJhYYP39o1l7eHhw/fr1Attzc3O5fft2gX0e1Mb971FhHF0LN06ClSO0Hq10GlHOPNe0Ki62Fly+k8Ffx0v3nJJCCFHRlNqCz9fXFw8PjwKjWaekpLB//37daNbBwcEkJSURFRWl22f79u2o1WpatGih22fXrl3k5OTo9tm6dSt16tTB2dm5hD5NKZCbpR13D6DNWLCuQJ9dlAgrc1MGBWunW1ss060JIUSpomjBl5aWRnR0NNHR0YD2Ro3o6Gji4uJQqVSMGTOGyZMn8/vvv3Ps2DEGDRqEl5cXffr0AaBevXp0796d1157jQMHDrBnzx5GjhzJiy++iJeXdjL3l19+GQsLC4YNG8aJEydYs2YNc+bMYdy4cQp9aoUc/B6S48DOA5q/oXQaUU4NbKmdbu3YlWT2XZDp1oQQorRQdFiWgwcP0rFjR93z/CJs8ODBLF++nPHjx5Oens7rr79OUlISbdq0YfPmzVhZWeles3LlSkaOHEnnzp0xMTGhX79+zJ07V7fd0dGRv//+m9DQUJo2bUqlSpWYNGnSQ4dkKZeyUrVz5gJ0mAAWNsrmEeWWq50lzwdV5cd9cSz55wLBNV2VjiSEEAJQaeS8y2NdvnwZb29v4uPjqVq1qtJx9LdzGuz8ClxqQOgBMDVXOpEox2JvptNpxk40Gtg6th1+7uXkpichRJlT5n+/jajUXsMnjCT9Juydp13u9LEUe6LY+VaypZu/9oaoJf9cUDiNEEIIkIKv/PtnJmSngmdD8H9W6TSignjt3nRrGw5f5XqKTLcmhBBKk4KvPEuKh8gl2uXOn4CJ/OcWJaNpNWeCqjmTnadm+d6LSscRQogKTyqA8mznVMjLhuptoWYnpdOICia/l+/HfZdIk+nWhBBCUVLwlVfXT8GRVdrlkE9BpVI0jqh4utRzx7eSLSmZuayNlOnWhBBCSVLwlVfbvwCNGur2gqpBSqcRFZCJiYrhbX0BmW5NCCGUJgVfeRQfCac2gcoEOk1UOo2owPo1qYqrrQVXkjL4U6ZbE0IIxUjBV95oNLDtU+1yw5fBra6icUTFpp1urToAi3edl+nWhBBCIVLwlTfnw+DSbjC1gA7vK51GCAYGV8PK3ITjV1KIuHBL6ThCCFEhScFXnqjVsO0z7XKz18DJW9k8QgAuthY831T7d3HJLhmIWQghlCAFX3kSsx4SjoKFPbR9R+k0QugMa+OLSgU7Tt/gTGKq0nGEEKLCkYKvvMjLge2TtcutR4GtTFovSo/qlWzpXv/edGvSyyeEECVOCr7y4vD/we0LYFMJWr6ldBohCtFNtxZ9hUSZbk0IIUqUFHzlQfZd2DlNu9x+PFjaKZtHiAdo4uNMs+rO5ORpZLo1IYQoYVLwlQf7F0FaAjj5QNMhSqcR4qFeayvTrQkhhBKk4CvrMu7Antna5Y4fgZmlonGEeJSQeu7UqGRLamYua2S6NSGEKDFS8JV1u2dDZjK4+UPA80qnEeKRtNOtaXv5lu2OJUemWxNCiBIhBV9ZlnJVezoXoPMkMDFVNo8QRdC3SRUq2d2bbu3YNaXjCCFEhSAFX1kWPh1yM8G7BdTurnQaIYrk/unWlvxzQaZbE0KIEiAFX1l16zwc+kG7HPIpqFSKxhFCHwNb3jfd2nmZbk0IIYqbFHxl1fbJoMkDv65QrZXSaYTQi7OtBS8EaadbW/yPDMQshBDFTQq+suhqNJxYB6ig8ydKpxHCIMPa+GKigp2nb3A6QaZbE0KI4iQFX1kU9rn2z4DnwaOBslmEMFA1V1u6N7g33Zr08gkhRLGSgq+sid0F58PAxAw6fqh0GiGeSP5AzL9FXyEhWaZbE0KI4iIFX1mi0cC2z7TLTYeCi6+yeYR4Qo19nGle3UWmWxNCiGImBV9ZcmoTXDkI5jbQ7j2l0whhFK+10/byrdwv060JIURxkYKvrMjLhbAvtMst3wJ7d2XzCGEkneu6UaOydrq11QfilI4jhBDlkhR8ZcXR1XDzNFg7Q+tRSqcRwmhMTFS6a/lkujUhhCgeUvCVBTmZsGOKdrnNOLByVDaPEEb2bGPtdGtXkzNlujUhhCgGUvCVBQe/g5TLYO8FzV9TOo0QRmdlbsrge9OtfRsu060JIYSxScFX2mWmwK7/aZc7vA/m1srmEaKYvNKyGtbmpsRcS2GvTLcmhBBGJQVfaRcxHzJug6sfNBqgdBohio12urWqACzeJQMxCyGEMUnBV5ql3YC987XLnSeCqZmyeYQoZsPa1MBEBeFnbnAqIUXpOEIIUW5IwVea/fM/yEkHr8ZQr7fSaYQodj6uNvRo4AnAkl2xCqcRQojyQwq+0urOJYj8Trsc8imoVIrGEaKk5A/E/PsRmW5NCCGMRQq+0mrHV6DOgRodtA8hKohG3k4099VOt/b9XunlE0IIY5CCrzRKPAFH12iXO09SNosQCnj93kDMq/bFkZqZo3AaIYQo+6TgK43CvgA04P8MVGmqdBohSlynum7UrGxLalYuayLjlY4jhBBlnhR8pU3cPjjzF6hModNEpdMIoQiZbk0IIYxLCr7SRKOBbZ9qlxsPgEp+isYRQkl9Glehkp0lV5Mz+eOoTLcmhBBPQgq+0uTsVoiLADMraP++0mmEUJSVuSlDWlUD4NtdMt2aEEI8CSn4Sgu1GsI+0y43fx0cqyibR4hSIH+6tZPXUthzTqZbE0IIQ0nBV1oc/xUSj4OlI7QZq3QaIUoFJxsL+jfzBuDbXecVTiOEEGWXFHylQW427JisXW49CmxclM0jRCkyrI0vJir45+xNTl6T6daEEMIQUvCVBodWwJ2LYOsGLUconUaIUsXbxYYeAfnTrV1QOI0QoiJYsGAB1atXx8rKihYtWnDgwAGlIz0xKfiUlp0O4dO1y+3Hg4WtsnmEKIXe0E23dpVryRkKpxFClGdr1qxh3LhxfPLJJxw6dIiGDRvSrVs3rl+/rnS0JyIFn9L2fQPp18G5OjQZrHQaIUqlwKpOtPB1IVetYfmei0rHEUKUYzNnzuS1115j6NCh+Pv7s2jRImxsbFi2bJnS0Z6IFHxKunsb9szVLnf8GMwslM0jRCn2+r1evlX7Zbo1IYR+UlNTSUlJ0T2ysrIeuF92djZRUVGEhITo1pmYmBASEkJERERJxS0WUvApKfky2LmBewNo0E/pNEKUah3ruFHLzY7UrFxWH5Dp1oQQRefv74+jo6PuMWXKlAfud/PmTfLy8nB3dy+w3t3dnYSEhJKIWmzMlA5QoXkGwlv7IDUBTKT2FuJRtNOt+TLh12Ms2xPLkNbVMTeV/2+EEI8XExNDlSr/jm9raWmpYBplyNFSaabm4OStdAohyoT86dauJWey6ehVpeMIIcoIe3t7HBwcdI+HFXyVKlXC1NSUxMTEAusTExPx8PAoiajFRgo+IUSZYWlmytDW1QH4NlymWxNCGJeFhQVNmzYlLCxMt06tVhMWFkZwcLCCyZ6cFHxCiDJlQAsfbCxMOZWQyu5zN5WOI4QoZ8aNG8eSJUtYsWIFJ0+eZMSIEaSnpzN06FCloz0RuYZPCFGmONlY8EKQN8v3XmTxrgu09ausdCQhRDnSv39/bty4waRJk0hISKBRo0Zs3ry50I0cZY308Akhypz7p1uLuSrTrQkhjGvkyJFcunSJrKws9u/fT4sWLZSO9MSk4BNClDneLjY8dW+6tXnbz3I1KYM8tVzPJ4QQDyOndIUQZdLr7Wqw6eg1/jqewF/HE7AwNaGKszXeLjb4uFjj42KDt7ON9rmrDQ5W5kpHFkIIxUjBJ4QokwKrOjGiQ03+PHaNK3cyyM5TE3szndib6Q/c39HaHB8XG3xcbKh6ryDMLwq9nKyxMJMTHkKI8ksKPiFEmTWhe10mdK9Lbp6ahJRM4m7fJf72XeJvZxB3+y5xt+9y+c5dbqZlk5yRw7EryRy7klyoHRMVeDre6xXM7x289/BxscHV1gKVSqXAJxRCCOOQgk8IUeaZmZpQ1dmGqs42ULPw9vSsXOLv/FsIxt975BeFWblqriRlcCUpg4gLhV9vY2H67+nh+4pCHxfte1pbmBb/hxRCiCcgBZ8QotyztTSjrocDdT0cCm3TaDTcSM0i/s69AvBWhm45/vZdElIyuZudx+nEVE4npj6w/cr2lng7W2NrKYdUUbaYmqhwsDLHwdrs3p/mOFqbF1rnYGWGvZW5XPpQhlWoo9OCBQv4+uuvSUhIoGHDhsybN4/mzZsrHUsIoSCVSoWbgxVuDlY0reZSaHtWbh5X7tzrGbyToe0ZvHVXWxTeuktqVi43UrO4kZqlQHohSpa1uWmhQtDhgQWi9vm/xaM59lZmMv+1gipMwbdmzRrGjRvHokWLaNGiBbNnz6Zbt26cPn0aNzc3peMJIUopSzNTalS2o0Zlu0LbNBoNyRk5xN/W9gpm5eYpkFAIw+XkakjJzCElM5eUjBztckbuvT/vPTJzScvKBSAjJ4+MnDwSUwz7B46NhekDew8drM2pZGfJqM5+xvx44j4qTQWZjLJFixY0a9aM+fPnA9q58by9vXn77bd5//33H/nay5cv4+3tTXx8PFWrVi2JuEIIIUSpkZunJi0rt2Ax+N/i8GFF430F46O42Vty4KMQo+aW3+9/VYgevuzsbKKiovjggw9060xMTAgJCSEiIqLQ/llZWWRl/fuvl+Rk7V19165dK/6wQgghRCllAjgBTlaAFeCkAizuPR4uN09NenYeaZm5pGXlkJqRS2p2LmkZuaRm5ZCWmYuZqQmXL182at783221Wm3UdsuiClHw3bx5k7y8vELz4Lm7u3Pq1KlC+0+ZMoXPPvus0Hq53k8IIYQoPp8UU7uJiYn4+PgUU+tlQ4Uo+PT1wQcfMG7cON3z3NxcTp48ibe3NyYmxr3gNDU1FX9/f2JiYrC3tzdq22WVfCcPJt9LYfKdFCbfyYPJ91JYRfhO1Go1iYmJNG7cWOkoiqsQBV+lSpUwNTUlMTGxwPrExEQ8PDwK7W9paYmlpWWBda1bty6WbCkp2onfq1SpgoND4SEjKiL5Th5MvpfC5DspTL6TB5PvpbCK8p1U9J69fBXi/mgLCwuaNm1KWFiYbp1arSYsLIzg4GAFkwkhhBBCFL8K0cMHMG7cOAYPHkxQUBDNmzdn9uzZpKenM3ToUKWjCSGEEEIUqwpT8PXv358bN24wadIkEhISaNSoEZs3by50I0dJs7S05JNPPil0Crkik+/kweR7KUy+k8LkO3kw+V4Kk++kYqkw4/AJIYQQQlRUFeIaPiGEEEKIikwKPiGEEEKIck4KPiGEEEKIck4KPiGEEEKIck4KPgUtWLCA6tWrY2VlRYsWLThw4IDSkRQ1ZcoUmjVrhr29PW5ubvTp04fTp08rHatUmTp1KiqVijFjxigdRXFXrlzhlVdewdXVFWtrawICAjh48KDSsRSTl5fHxIkT8fX1xdrampo1a/LFF19Q0e7L27VrF08//TReXl6oVCo2bNhQYLtGo2HSpEl4enpibW1NSEgIZ8+eVSZsCXnUd5KTk8OECRMICAjA1tYWLy8vBg0axNWrV5ULLIqFFHwKWbNmDePGjeOTTz7h0KFDNGzYkG7dunH9+nWloykmPDyc0NBQ9u3bx9atW8nJyaFr166kp6crHa1UiIyM5NtvvyUwMFDpKIq7c+cOrVu3xtzcnL/++ouYmBhmzJiBs7Oz0tEUM23aNBYuXMj8+fM5efIk06ZNY/r06cybN0/paCUqPT2dhg0bsmDBggdunz59OnPnzmXRokXs378fW1tbunXrRmZmZgknLTmP+k7u3r3LoUOHmDhxIocOHWLdunWcPn2a3r17K5BUFCuNUETz5s01oaGhuud5eXkaLy8vzZQpUxRMVbpcv35dA2jCw8OVjqK41NRUjZ+fn2br1q2a9u3ba0aPHq10JEVNmDBB06ZNG6VjlCo9e/bUvPrqqwXW9e3bVzNgwACFEikP0Kxfv173XK1Wazw8PDRff/21bl1SUpLG0tJS89NPPymQsOT99zt5kAMHDmgAzaVLl0omlCgR0sOngOzsbKKioggJCdGtMzExISQkhIiICAWTlS7JyckAuLi4KJxEeaGhofTs2bPA35mK7PfffycoKIjnn38eNzc3GjduzJIlS5SOpahWrVoRFhbGmTNnADhy5Ai7d++mR48eCicrPWJjY0lISCjw/5GjoyMtWrSQY+99kpOTUalUODk5KR1FGFGFmWmjNLl58yZ5eXmFZvlwd3fn1KlTCqUqXdRqNWPGjKF169Y0aNBA6TiKWr16NYcOHSIyMlLpKKXGhQsXWLhwIePGjePDDz8kMjKSUaNGYWFhweDBg5WOp4j333+flJQU6tati6mpKXl5eXz55ZcMGDBA6WilRkJCAsADj7352yq6zMxMJkyYwEsvvYSDg4PScYQRScEnSqXQ0FCOHz/O7t27lY6iqPj4eEaPHs3WrVuxsrJSOk6poVarCQoK4quvvgKgcePGHD9+nEWLFlXYgm/t2rWsXLmSVatWUb9+faKjoxkzZgxeXl4V9jsR+snJyeGFF15Ao9GwcOFCpeMII5NTugqoVKkSpqamJCYmFlifmJiIh4eHQqlKj5EjR7Jp0yZ27NhB1apVlY6jqKioKK5fv06TJk0wMzPDzMyM8PBw5s6di5mZGXl5eUpHVISnpyf+/v4F1tWrV4+4uDiFEinvvffe4/333+fFF18kICCAgQMHMnbsWKZMmaJ0tFIj//gqx97C8ou9S5cusXXrVundK4ek4FOAhYUFTZs2JSwsTLdOrVYTFhZGcHCwgsmUpdFoGDlyJOvXr2f79u34+voqHUlxnTt35tixY0RHR+seQUFBDBgwgOjoaExNTZWOqIjWrVsXGrLnzJkzVKtWTaFEyrt79y4mJgUP6aampqjVaoUSlT6+vr54eHgUOPampKSwf//+Cn3szS/2zp49y7Zt23B1dVU6kigGckpXIePGjWPw4MEEBQXRvHlzZs+eTXp6OkOHDlU6mmJCQ0NZtWoVv/32G/b29rprahwdHbG2tlY4nTLs7e0LXcNoa2uLq6trhb62cezYsbRq1YqvvvqKF154gQMHDrB48WIWL16sdDTFPP3003z55Zf4+PhQv359Dh8+zMyZM3n11VeVjlai0tLSOHfunO55bGws0dHRuLi44OPjw5gxY5g8eTJ+fn74+voyceJEvLy86NOnj3Khi9mjvhNPT0+ee+45Dh06xKZNm8jLy9Mde11cXLCwsFAqtjA2pW8TrsjmzZun8fHx0VhYWGiaN2+u2bdvn9KRFAU88PH9998rHa1UkWFZtDZu3Khp0KCBxtLSUlO3bl3N4sWLlY6kqJSUFM3o0aM1Pj4+GisrK02NGjU0H330kSYrK0vpaCVqx44dDzyODB48WKPRaIdmmThxosbd3V1jaWmp6dy5s+b06dPKhi5mj/pOYmNjH3rs3bFjh9LRhRGpNJoKNgy7EEIIIUQFI9fwCSGEEEKUc1LwCSGEEEKUc1LwCSH+v537CYlq7+M4/j7MTZs/mZPJSIM1Es6g1YS06D8mFVohWAYRhQkFGv6NDLIIyhbTxoUtImihQYsJihY5LoTQhKKIVpbRIKFCFEZMoQwpjL9ndYdn7tN96Ll1rzrP57Wa8zu/8zvf31l9+J6ZERGRNKfAJyIiIpLmFPhERERE0pwCn4iIiEiaU+ATERERSXMKfCIiIiJpToFPRBa0wcFBLMviy5cv812KiMiipcAnIgvKrl27aG1tTR5v27aNDx8+sHz58nmrSaFTRBa73+a7ABGR/yYjI4O8vLz5LkNEZFFTh09EFoza2loeP35MV1cXlmVhWRY9PT0p3bWenh6ys7Pp7e0lEAjgcDg4fPgw8Xic27dv4/P5cLvdNDc3k0gkkmvPzMzQ1taG1+vF6XSyefNmBgcHk+fHx8eprKzE7XbjdDpZt24dfX19jI2NUVZWBoDb7cayLGprawGYm5sjFApRUFCA3W5n48aN3Lt3L7nm753BSCRCMBhk6dKlbNmyhVevXv3tz1JE5N+pwyciC0ZXVxfRaJT169fT0dEBwOvXr/9jXjwe5/r164TDYaampjh06BAHDx4kOzubvr4+3r17R3V1Ndu3b+fIkSMANDY2MjIyQjgcZtWqVTx48ICKigqGh4cpLCykoaGB2dlZhoaGcDqdjIyM4HK5yM/P5/79+1RXV/P27VuysrKw2+0AhEIh7ty5w82bNyksLGRoaIjjx4+Tm5tLaWlpst5z587R1dVFXl4eFy5coLKykmg0ypIlS/6BpyoiAhgRkQWktLTUtLS0JI8HBgYMYGKxmDHGmO7ubgOY0dHR5Jy6ujrjcDjM1NRUcqy8vNzU1dUZY4wZHx83NpvNvH//PuVeu3fvNu3t7cYYYzZs2GAuX7783Zr+WIMxxnz79s04HA7z9OnTlLknT540R48eTbkuHA4nz3/+/NnY7XZz9+7dH3wiIiI/Tx0+EVl0HA4Ha9euTR57PB58Ph8ulytlbHJyEoDh4WESiQR+vz9lnZmZGXJycgBobm7m9OnT9Pf3s2fPHqqrqwkGg39aw+joKPF4nL1796aMz87OUlJSkjK2devW5OcVK1YQCAR48+bN/7hrEZG/ToFPRBadP74KtSzru2Nzc3MATE9PY7PZePnyJTabLWXe7yHx1KlTlJeXE4lE6O/vJxQK0dnZSVNT03drmJ6eBiASieD1elPOZWZm/vXNiYj8DRT4RGRBycjISPmxxa9QUlJCIpFgcnKSnTt3/um8/Px86uvrqa+vp729nVu3btHU1ERGRgZASl3FxcVkZmYyMTGR8n2973n27BmrV68GIBaLEY1GKSoq+gU7ExH5MQp8IrKg+Hw+nj9/ztjYGC6XK9ml+xl+v59jx45RU1NDZ2cnJSUlfPr0iUePHhEMBjlw4ACtra3s27cPv99PLBZjYGAgGcrWrFmDZVn09vayf/9+7HY7y5Yto62tjTNnzjA3N8eOHTv4+vUrT548ISsrixMnTiTv39HRQU5ODh6Ph4sXL7Jy5Uqqqqp+el8iIj9Kf8siIgtKW1sbNpuN4uJicnNzmZiY+CXrdnd3U1NTw9mzZwkEAlRVVfHixYtk5y2RSNDQ0EBRUREVFRX4/X5u3LgBgNfr5cqVK5w/fx6Px0NjYyMAV69e5dKlS4RCoeR1kUiEgoKClHtfu3aNlpYWNm3axMePH3n48GGyaygi8k+wjDFmvosQEUlHg4ODlJWVEYvFyM7Onu9yROT/mDp8IiIiImlOgU9EREQkzemVroiIiEiaU4dPREREJM0p8ImIiIikOQU+ERERkTSnwCciIiKS5hT4RERERNKcAp+IiIhImlPgExEREUlzCnwiIiIiae5f7JPRcjIS72oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "env = RTBEnv(\n",
    "    objective=\"click\",  # クリック数の合計を最大化\n",
    "    cost_indicator=\"click\",  # クリックするごとにコストが発生\n",
    "    step_per_episode=14,  # 1エピソード14日とする14 days as an episode\n",
    "    initial_budget=5000,  # 14日で予算は5000\n",
    "    random_state=random_state,\n",
    ")\n",
    "visualize_interaction_on_environment(env, agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ケース 2. 広告とユーザのカスタマイズ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>feature3</th>\n",
       "      <th>feature4</th>\n",
       "      <th>feature5</th>\n",
       "      <th>feature6</th>\n",
       "      <th>feature7</th>\n",
       "      <th>feature8</th>\n",
       "      <th>feature9</th>\n",
       "      <th>feature10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ad1</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ad2</th>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ad3</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ad4</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ad5</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     feature1  feature2  feature3  feature4  feature5  feature6  feature7  \\\n",
       "ad1        -1         1        -1        -1         1         1         1   \n",
       "ad2         1        -1         1         1         1         1        -1   \n",
       "ad3        -1         1         1        -1        -1         1         1   \n",
       "ad4        -1        -1        -1        -1        -1         1        -1   \n",
       "ad5        -1        -1        -1        -1        -1         1         1   \n",
       "\n",
       "     feature8  feature9  feature10  \n",
       "ad1         1         1          1  \n",
       "ad2        -1         1         -1  \n",
       "ad3         1         1          1  \n",
       "ad4         1        -1          1  \n",
       "ad5        -1         1          1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 入札には以下の広告を利用します\n",
    "ad_features = np.sign(random_.normal(size=(5, 10))).astype(int)\n",
    "ad_names, feature_names = [f\"ad{i+1}\" for i in range(5)], [f\"feature{i+1}\" for i in range(10)]\n",
    "ad_df = pd.DataFrame(ad_features, columns=feature_names, index=ad_names)\n",
    "ad_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>feature3</th>\n",
       "      <th>feature4</th>\n",
       "      <th>feature5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user1</th>\n",
       "      <td>0.069877</td>\n",
       "      <td>0.246674</td>\n",
       "      <td>-0.011862</td>\n",
       "      <td>1.004812</td>\n",
       "      <td>1.327195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user2</th>\n",
       "      <td>-0.919262</td>\n",
       "      <td>-1.549106</td>\n",
       "      <td>0.022185</td>\n",
       "      <td>0.758363</td>\n",
       "      <td>-0.660524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user3</th>\n",
       "      <td>0.862580</td>\n",
       "      <td>-0.010032</td>\n",
       "      <td>0.050009</td>\n",
       "      <td>0.670216</td>\n",
       "      <td>0.852965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user4</th>\n",
       "      <td>-0.955869</td>\n",
       "      <td>-0.023493</td>\n",
       "      <td>-2.304234</td>\n",
       "      <td>-0.652469</td>\n",
       "      <td>-1.218302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user5</th>\n",
       "      <td>-1.332610</td>\n",
       "      <td>1.074623</td>\n",
       "      <td>0.723642</td>\n",
       "      <td>0.690002</td>\n",
       "      <td>1.001543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user96</th>\n",
       "      <td>-1.686933</td>\n",
       "      <td>1.089539</td>\n",
       "      <td>2.060882</td>\n",
       "      <td>-0.241235</td>\n",
       "      <td>-0.947872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user97</th>\n",
       "      <td>0.676294</td>\n",
       "      <td>-0.653356</td>\n",
       "      <td>-0.652295</td>\n",
       "      <td>0.528828</td>\n",
       "      <td>0.357793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user98</th>\n",
       "      <td>0.188649</td>\n",
       "      <td>0.869417</td>\n",
       "      <td>-0.050667</td>\n",
       "      <td>-0.716365</td>\n",
       "      <td>-0.103259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user99</th>\n",
       "      <td>-1.141037</td>\n",
       "      <td>-0.500777</td>\n",
       "      <td>-0.389301</td>\n",
       "      <td>-0.473851</td>\n",
       "      <td>0.128664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user100</th>\n",
       "      <td>0.153694</td>\n",
       "      <td>0.444790</td>\n",
       "      <td>0.128532</td>\n",
       "      <td>0.252530</td>\n",
       "      <td>-0.940639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         feature1  feature2  feature3  feature4  feature5\n",
       "user1    0.069877  0.246674 -0.011862  1.004812  1.327195\n",
       "user2   -0.919262 -1.549106  0.022185  0.758363 -0.660524\n",
       "user3    0.862580 -0.010032  0.050009  0.670216  0.852965\n",
       "user4   -0.955869 -0.023493 -2.304234 -0.652469 -1.218302\n",
       "user5   -1.332610  1.074623  0.723642  0.690002  1.001543\n",
       "...           ...       ...       ...       ...       ...\n",
       "user96  -1.686933  1.089539  2.060882 -0.241235 -0.947872\n",
       "user97   0.676294 -0.653356 -0.652295  0.528828  0.357793\n",
       "user98   0.188649  0.869417 -0.050667 -0.716365 -0.103259\n",
       "user99  -1.141037 -0.500777 -0.389301 -0.473851  0.128664\n",
       "user100  0.153694  0.444790  0.128532  0.252530 -0.940639\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 以下の100人のユーザーがランダムに検索クエリを投げると仮定します\n",
    "user_features = random_.normal(size=(100, 5))\n",
    "user_names, feature_names = [f\"user{i+1}\" for i in range(100)], [f\"feature{i+1}\" for i in range(5)]\n",
    "user_df = pd.DataFrame(user_features, columns=feature_names, index=user_names)\n",
    "user_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 上記のデータを使って，以下のようにオークションをシミュレーションできる\n",
    "# (timestep, n_ads/users)または(n_ads/users, )の配列を与えることでad_sampling_rateとuser_feature_vectorは,\n",
    "# タイムステップごとに定義することも，タイムステップをまたいで定義することもできる\n",
    "env = RTBEnv(\n",
    "    step_per_episode=7,  # 週7日\n",
    "    n_ads=5,  # 5つの広告を利用\n",
    "    n_users=100,  # 100人のユーザーが存在\n",
    "    ad_feature_dim=10,  # それぞれの広告は10次元の特徴を持つ\n",
    "    user_feature_dim=5,  # それぞれのユーザーは5次元の特徴を持つ\n",
    "    ad_feature_vector=ad_features,  # 上で定義した広告の特徴を利用\n",
    "    user_feature_vector=user_features,  # 上で定義したユーザーの特徴を利用\n",
    "    ad_sampling_rate=np.ones(5) / 5,  # シミュレーションで全ての広告を一様にサンプルリングする\n",
    "    user_sampling_rate=np.ones((7, 100)) / 100,  #シミュレーションの全てのタイムステップで全てのユーザーは一様にサンプルリングする\n",
    "    random_state=random_state,\n",
    ")\n",
    "visualize_interaction_on_environment(env, agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ケース 3. オークションの落札価格分布，クリック率，コンバージョン率のカスタマイズ\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 入札価格を使ってオークションの落札価格分布を定義しています.\n",
    "# デフォルトでは, ガンマ分布を使ってオークションの価格分布をシミュレーションしていることに注意してください.\n",
    "# 詳しくはrtbgym.WinningPriceDistributionを参照してください.\n",
    "\n",
    "from rtbgym import BaseWinningPriceDistribution\n",
    "\n",
    "@dataclass\n",
    "class CustomizedWinningPriceDistribution(BaseWinningPriceDistribution):\n",
    "    \"\"\"初期化.\"\"\"\n",
    "    n_ads: int\n",
    "    n_users: int\n",
    "    ad_feature_dim: int\n",
    "    user_feature_dim: int\n",
    "    step_per_episode: int\n",
    "    standard_bid_price_distribution: NormalDistribution = NormalDistribution(\n",
    "        mean=50,\n",
    "        std=5,\n",
    "        random_state=12345,\n",
    "    )\n",
    "    minimum_standard_bid_price: Optional[Union[int, float]] = None\n",
    "    random_state: Optional[int] = None\n",
    "    \n",
    "    def __post_init__(self):\n",
    "        self.random_ = check_random_state(self.random_state)\n",
    "    \n",
    "    def sample_outcome(\n",
    "        self,\n",
    "        bid_prices: np.ndarray,\n",
    "        **kwargs,\n",
    "    ) -> Tuple[np.ndarray]:\n",
    "        \"\"\"各オークションのインプレッションとセカンドプライスを確率的に決定する．\"\"\"\n",
    "        #  単純な正規分布からの落札価格のサンプリング\n",
    "        winning_prices = self.random_.normal(\n",
    "            loc=self.standard_bid_price,\n",
    "            scale=self.standard_bid_price / 5,\n",
    "            size=bid_prices.shape,\n",
    "        )\n",
    "        impressions = winning_prices < bid_prices\n",
    "        return impressions.astype(int), winning_prices.astype(int)\n",
    "    \n",
    "    @property\n",
    "    def standard_bid_price(self):\n",
    "        return self.standard_bid_price_distribution.mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ここでは, クリック率とコンバージョン率もユーザーと広告の両方の特徴ベクトルを使ってカスタマイズしています.\n",
    "# rtbgym.ClickThroughRate，rtbgym.ConversionRateも合わせて参照してください.\n",
    "from rtbgym import BaseClickAndConversionRate\n",
    "from rtbgym.utils import sigmoid\n",
    "\n",
    "@dataclass\n",
    "class CustomizedClickThroughRate(BaseClickAndConversionRate):\n",
    "    \"\"\"初期化.\"\"\"\n",
    "    n_ads: int\n",
    "    n_users: int\n",
    "    ad_feature_dim: int\n",
    "    user_feature_dim: int\n",
    "    step_per_episode: int\n",
    "    random_state: Optional[int] = None\n",
    "    \n",
    "    def __post_init__(self):\n",
    "        self.random_ = check_random_state(self.random_state)\n",
    "        self.ad_coef = self.random_.normal(\n",
    "            loc=0.0, \n",
    "            scale=0.5, \n",
    "            size=(self.ad_feature_dim, 10),\n",
    "        )\n",
    "        self.user_coef = self.random_.normal(\n",
    "            loc=0.0, \n",
    "            scale=0.5, \n",
    "            size=(self.user_feature_dim, 10),\n",
    "        )\n",
    "    \n",
    "    def calc_prob(\n",
    "        self,\n",
    "        ad_ids: np.ndarray,\n",
    "        user_ids: np.ndarray,\n",
    "        ad_feature_vector: np.ndarray,\n",
    "        user_feature_vector: np.ndarray,\n",
    "        timestep: Union[int, np.ndarray],\n",
    "    ) -> np.ndarray:\n",
    "        \"\"\"CTRの計算 (インプレッションあたりのクリック).\"\"\"\n",
    "        ad_latent = ad_feature_vector @ self.ad_coef\n",
    "        user_latent = user_feature_vector @ self.user_coef\n",
    "        ctrs = sigmoid((ad_latent * user_latent).mean(axis=1))\n",
    "        return ctrs\n",
    "    \n",
    "    def sample_outcome(\n",
    "        self,\n",
    "        ad_ids: np.ndarray,\n",
    "        user_ids: np.ndarray,\n",
    "        ad_feature_vector: np.ndarray,\n",
    "        user_feature_vector: np.ndarray,\n",
    "        timestep: Union[int, np.ndarray],\n",
    "    ) -> np.ndarray:\n",
    "        \"\"\"impression=Trueの場合にクリックが発生するかどうかを確率的に決定します.\"\"\"\n",
    "        ctrs = self.calc_prob(\n",
    "            timestep=timestep,\n",
    "            ad_ids=ad_ids,\n",
    "            user_ids=user_ids,\n",
    "            ad_feature_vector=ad_feature_vector,\n",
    "            user_feature_vector=user_feature_vector,\n",
    "        )\n",
    "        clicks = self.random_.rand(len(ad_ids)) < ctrs\n",
    "        return clicks.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class CustomizedConversionRate(BaseClickAndConversionRate):\n",
    "    \"\"\"初期化.\"\"\"\n",
    "    n_ads: int\n",
    "    n_users: int\n",
    "    ad_feature_dim: int\n",
    "    user_feature_dim: int\n",
    "    step_per_episode: int\n",
    "    random_state: Optional[int] = None\n",
    "\n",
    "    def __post_init__(self):\n",
    "        self.random_ = check_random_state(self.random_state + 1)\n",
    "        self.ad_coef = self.random_.normal(\n",
    "            loc=0.0, \n",
    "            scale=0.5, \n",
    "            size=(self.ad_feature_dim, 10),\n",
    "        )\n",
    "        self.user_coef = self.random_.normal(\n",
    "            loc=0.0, \n",
    "            scale=0.5, \n",
    "            size=(self.user_feature_dim, 10),\n",
    "        )\n",
    "    \n",
    "    def calc_prob(\n",
    "        self,\n",
    "        ad_ids: np.ndarray,\n",
    "        user_ids: np.ndarray,\n",
    "        ad_feature_vector: np.ndarray,\n",
    "        user_feature_vector: np.ndarray,\n",
    "        timestep: Union[int, np.ndarray],\n",
    "    ) -> np.ndarray:\n",
    "        \"\"\"CVRの計算 (クリックあたりのコンバージョン).\"\"\"\n",
    "        ad_latent = ad_feature_vector @ self.ad_coef\n",
    "        user_latent = user_feature_vector @ self.user_coef\n",
    "        cvrs = sigmoid((ad_latent * user_latent).mean(axis=1))\n",
    "        return cvrs\n",
    "    \n",
    "    def sample_outcome(\n",
    "        self,\n",
    "        ad_ids: np.ndarray,\n",
    "        user_ids: np.ndarray,\n",
    "        ad_feature_vector: np.ndarray,\n",
    "        user_feature_vector: np.ndarray,\n",
    "        timestep: Union[int, np.ndarray],\n",
    "    ) -> np.ndarray:\n",
    "        \"\"\"click=Trueの場合にクリックが発生するかどうかを確率的に決定します.\"\"\"\n",
    "        cvrs = self.calc_prob(\n",
    "            timestep=timestep,\n",
    "            ad_ids=ad_ids,\n",
    "            user_ids=user_ids,\n",
    "            ad_feature_vector=ad_feature_vector,\n",
    "            user_feature_vector=user_feature_vector,\n",
    "        )\n",
    "        conversions = self.random_.rand(len(ad_ids)) < cvrs\n",
    "        return conversions.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "env = RTBEnv(\n",
    "    WinningPriceDistribution=CustomizedWinningPriceDistribution,\n",
    "    ClickThroughRate=CustomizedClickThroughRate,\n",
    "    ConversionRate=CustomizedConversionRate,\n",
    "    random_state=random_state,\n",
    ")\n",
    "visualize_interaction_on_environment(env, agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ケース 4. 標準入札価格と検索ボリューム分布のカスタマイズ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "env = RTBEnv(\n",
    "    step_per_episode=5,  # 週5日\n",
    "    standard_bid_price_distribution=NormalDistribution(\n",
    "        mean=100, \n",
    "        std=10, \n",
    "        random_state=random_state\n",
    "    ),\n",
    "    minimum_standard_bid_price=70,  # 最低標準落札価格は70\n",
    "    search_volume_distribution=NormalDistribution(\n",
    "        mean=np.array([100, 200, 100, 200, 300]),  # 検索ボリュームは日によって異なる（タイムステップに依存）\n",
    "        std=np.ones(5) * 20,\n",
    "        random_state=random_state,\n",
    "    ),\n",
    "    minimum_search_volume=50,  # 毎日少なくとも50のクエリが検索される\n",
    "    random_state=random_state,\n",
    ")\n",
    "visualize_interaction_on_environment(env, agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 入札設定のカスタマイズ\n",
    "ここでは，意思決定者が入札設定をカスタマイズする方法を説明します．\n",
    "具体的には，以下のモジュールを定義することで，独自の行動空間と入札価格計算規則を設定することができます．\n",
    "- `reward_predictor` \n",
    "    入札価格を計算するために，予測報酬を使います．\\\n",
    "        入札価格 = 調整レート * 予測報酬 ( * 定数)\\\n",
    "    None の場合，予測報酬の代わりに真の報酬を使用します．\n",
    " \n",
    "\n",
    "- `scaler` \n",
    "    スケーラーは入札価格計算における定数を定義します．\\\n",
    "        入札価格 = 調整レート * 予測報酬 (* 定数)\\\n",
    "        定数 = スケーラー * 標準入札価格\\\n",
    "    ここで標準入札価格は入札価格の平均 (インプレッション確率が約50%の入札価格) を示します．\n",
    "\n",
    "\n",
    "- `action_space`  \n",
    "    連続調整率空間 $[0, \\infty)$ をエージェント行動空間 $[0.1, 10]$ に変換します．\n",
    "    離散的なアクションでも連続的なアクションでも構いません．\n",
    "    (調整率のスケールはスケーラーで調整できます)\n",
    "\n",
    "    \n",
    "引数は以下のように与えられる：\n",
    "- `original_env`: ベースとなるRTB環境\n",
    "- `reward_predictor`: 入札価格を決定するための報酬を予測する機械学習モデル\n",
    "- `scaler`: 入札価格決定に使用するスケーリング係数 (定数)\n",
    "- `action_min`: 調整率の最小値\n",
    "- `action_max`: 調整率の最大値\n",
    "- `action_type`: 強化学習エージェントの行動タイプ (\"discrete\" または \"continuous\")\n",
    "- `n_actions`: 離散行動の数\n",
    "- `action_meaning`: エージェントの行動インデックスと実際の\"離散\"行動のマッピング関数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 環境の初期化\n",
    "env = RTBEnv(random_state=random_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ケース 1. 連続行動空間を定義"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's customize the continuous action space\n",
    "custom_env = CustomizedRTBEnv(\n",
    "    original_env=env,\n",
    "    action_type=\"continuous\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Box(0.1, 10.0, (1,), float64)\n"
     ]
    }
   ],
   "source": [
    "print(custom_env.action_space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# another example\n",
    "custom_env = CustomizedRTBEnv(\n",
    "    original_env=env,\n",
    "    action_min=0.1,\n",
    "    action_max=0.5,\n",
    "    action_type=\"continuous\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Box(0.1, 0.5, (1,), float64)\n"
     ]
    }
   ],
   "source": [
    "print(custom_env.action_space)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ケース 2. 離散行動空間を定義"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's customize the environment and discretize the action space\n",
    "custom_env = CustomizedRTBEnv(\n",
    "    original_env=env,\n",
    "    action_type=\"discrete\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete(10)\n",
      "[ 0.1         0.16681005  0.27825594  0.46415888  0.77426368  1.29154967\n",
      "  2.15443469  3.59381366  5.9948425  10.        ]\n"
     ]
    }
   ],
   "source": [
    "print(custom_env.action_space)\n",
    "print(custom_env.action_meaning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# another example\n",
    "custom_env = CustomizedRTBEnv(\n",
    "    original_env=env,\n",
    "    action_type=\"discrete\",\n",
    "    n_actions=5,\n",
    "    action_meaning=np.arange(1, 6)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete(5)\n",
      "[1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "print(custom_env.action_space)\n",
    "print(custom_env.action_meaning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ケース 3. 報酬予測器をカスタマイズ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_env = CustomizedRTBEnv(\n",
    "    original_env=env,\n",
    "    reward_predictor=LogisticRegression(),  # 報酬予測器としてロジスティック回帰を利用\n",
    "    action_type=\"discrete\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_env = CustomizedRTBEnv(\n",
    "    original_env=env,\n",
    "    reward_predictor=None,  # 真の報酬予測器として真の(期待)報酬を利用\n",
    "    action_type=\"discrete\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "データ生成や可視化は以下を参照してください．  [examples/quickstart_ja/rtb/rtb_synthetic_data_collection_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rtb/rtb_synthetic_data_collection_ja.ipynb). \n",
    "\n",
    "オフライン強化学習やオフ方策評価 (OPE) に関しては以下を参照してください． [examples/quickstart_ja/rtb/rtb_synthetic_discrete_basic_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rtb/rtb_synthetic_discrete_basic_ja.ipynb) (離散行動空間)， [examples/quickstart_ja/rtb/rtb_synthetic_continuous_basic_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rtb/rtb_synthetic_continuous_basic_ja.ipynb) (連続行動空間).\n",
    "\n",
    "OPEのより発展的なトピック(累積分布関数の推定など) やオフ方策選択 (OPS) は，以下を参照してください. [examples/quickstart_ja/rtb/rtb_synthetic_discrete_advanced_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rtb/rtb_synthetic_discrete_advanced_ja.ipynb) (離散行動空間)， [examples/quickstart_ja/rtb/rtb_synthetic_continuous_advanced_ja.ipynb](https://github.com/hakuhodo-technologies/scope-rl/blob/main/examples/quickstart_ja/rtb/rtb_synthetic_continuous_advanced_ja.ipynb) (連続行動空間)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考文献\n",
    "\n",
    "- Takuma Seno and Michita Imai. \\\n",
    "\"d3rlpy: An Offline Deep Reinforcement Library.\", 2021.\n",
    "\n",
    "- Di Wu, Xiujun Chen, Xun Yang, Hao Wang, Qing Tan, Xiaoxun Zhang, Jian Xu, and Kun Gai. \\\n",
    "\"Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising.\", 2018.\n",
    "\n",
    "- Jun Zhao, Guang Qiu, Ziyu Guan, Wei Zhao, and Xiaofei He. \\\n",
    "\"Deep Reinforcement Learning for Sponsored Search Real-time Bidding.\", 2018.\n",
    "\n",
    "- Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. \\\n",
    "\"OpenAI Gym.\", 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "70404ee114725fce8ed9e697d67827f8546c678889944e6d695790702cbfe1f5"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}