3.TS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a1601067",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import random\n",
    "import math\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "454ff80f",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd4414fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select bandit type --OPTIONS {'bernoulli', 'normal'}\n",
    "bandit_type = 'bernoulli' \n",
    "num_sims= 5000 # Number of simulation\n",
    "horizon = 250 # The number of times the algorithm has been able to pull on any of the arms that are available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b17b88a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def ind_max(x):\n",
    "    max_value = max(x) # obtain max value from list\n",
    "    max_keys = [k for k, v in enumerate(x) if v == max_value]\n",
    "    return random.choice(max_keys)\n",
    "\n",
    "\n",
    "class ThompsonSampling():\n",
    "    def __init__(self, initial_alpha, initial_beta, counts, values, s_counts):\n",
    "        self.counts = counts\n",
    "        self.s_counts = s_counts\n",
    "        self.values = values\n",
    "        self.alpha = initial_alpha\n",
    "        self.beta = initial_beta\n",
    "        return\n",
    "\n",
    "    def initialize(self, n_arms):\n",
    "        self.counts = [0 for col in range(n_arms)]\n",
    "        self.values = [0.0 for col in range(n_arms)]\n",
    "        self.s_counts = [0 for col in range(n_arms)]\n",
    "        return\n",
    "\n",
    "    def select_arm(self):\n",
    "        rho = lambda i: random.betavariate(self.alpha + self.s_counts[i], self.beta + self.counts[i] - self.s_counts[i])\n",
    "        temp = len(self.counts)\n",
    "        mu = [rho(i) for i in range(temp)]\n",
    "        #mu = map(rho, list(range(temp)))\n",
    "        return ind_max(mu)\n",
    "\n",
    "    def update(self, chosen_arm, reward):\n",
    "        self.counts[chosen_arm] = self.counts[chosen_arm] + 1\n",
    "        if reward == 1:\n",
    "            self.s_counts[chosen_arm] += 1\n",
    "\n",
    "        n = self.counts[chosen_arm]\n",
    "        value = self.values[chosen_arm]\n",
    "        new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\n",
    "        self.values[chosen_arm] = new_value\n",
    "        return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2791f5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BernoulliArm():\n",
    "    def __init__(self, p):\n",
    "        self.p = p\n",
    "    def draw(self):\n",
    "        if random.random() > self.p:\n",
    "            return 0.0\n",
    "        else:\n",
    "            return 1.0\n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "class NormalArm():\n",
    "    def __init__(self, mu, sigma):\n",
    "        self.mu = mu\n",
    "        self.sigma = sigma\n",
    "  \n",
    "    def draw(self):\n",
    "        return random.gauss(self.mu, self.sigma)\n",
    "\n",
    "  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2edb1e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_algorithm(algo, arms, num_sims, horizon):\n",
    "    chosen_arms = [0.0 for i in range(num_sims * horizon)]\n",
    "    rewards = [0.0 for i in range(num_sims * horizon)]\n",
    "    cumulative_rewards = [0.0 for i in range(num_sims * horizon)]\n",
    "    sim_nums = [0.0 for i in range(num_sims * horizon)]\n",
    "    times = [0.0 for i in range(num_sims * horizon)]\n",
    "  \n",
    "    for sim in range(num_sims):\n",
    "        sim = sim + 1\n",
    "        algo.initialize(len(arms))\n",
    "    \n",
    "        for t in range(horizon):\n",
    "            t = t + 1\n",
    "            index = (sim - 1) * horizon + t - 1\n",
    "            sim_nums[index] = sim\n",
    "            times[index] = t\n",
    "            \n",
    "            chosen_arm = algo.select_arm()\n",
    "            chosen_arms[index] = chosen_arm\n",
    "            \n",
    "            reward = arms[chosen_arms[index]].draw()\n",
    "            rewards[index] = reward\n",
    "            \n",
    "            if t == 1:\n",
    "                cumulative_rewards[index] = reward\n",
    "            else:\n",
    "                cumulative_rewards[index] = cumulative_rewards[index - 1] + reward\n",
    "      \n",
    "            algo.update(chosen_arm, reward)\n",
    "\n",
    "    return [sim_nums, times, chosen_arms, rewards, cumulative_rewards]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c9af6950",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_tests_bernoulli(means=[0.7, 0.2],\n",
    "                   filename=\"bern_thompson_sampling_results.csv\",\n",
    "                   num_sims= num_sims, horizon=horizon):\n",
    "\n",
    "    \n",
    "    n_arms = len(means)\n",
    "    arms = [BernoulliArm(mu) for mu in means]\n",
    "    best_arm = ind_max(means)\n",
    "    \n",
    "    print(\"Best arm is at index \" + str(ind_max(means)))\n",
    "\n",
    "    f = open(filename, \"w\")\n",
    "\n",
    "    algo = ThompsonSampling(1,1, [], [], [])\n",
    "    algo.initialize(n_arms)\n",
    "    results = test_algorithm(algo, arms, num_sims, horizon)\n",
    "    \n",
    "    \n",
    "    \n",
    "    for i in range(len(results[0])):\n",
    "        f.write(\",\".join([str(results[j][i]) for j in range(len(results))]) + \"\\n\")\n",
    "\n",
    "    f.close()\n",
    "    # Read this \n",
    "    file_location = './' + filename\n",
    "    df = pd.read_csv(filename, header=None, \n",
    "                     names=[\"Sim\", \"T\", \"ChosenArm\", \"Reward\", \"CumulativeReward\"])\n",
    "    \n",
    "    return df, best_arm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3db49a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_tests_normal(means = [250, 290],\n",
    "                          sigmas=[70, 90],\n",
    "                          filename=\"normal_thompson_sampling_results.csv\",\n",
    "                          num_sims= num_sims, horizon=horizon):\n",
    "\n",
    "\n",
    "    n_arms = len(means)\n",
    "    \n",
    "    hyp = list(zip(means, sigmas))\n",
    "    arms = [NormalArm(mu, sig) for mu in means for sig in sigmas]\n",
    "    best_arm = ind_max(means)\n",
    "    \n",
    "    print(\"Best arm is at index \" + str(ind_max(means)))\n",
    "\n",
    "    f = open(filename, \"w\")\n",
    "\n",
    "    algo = ThompsonSampling(1, 1, [], [], [])\n",
    "    algo.initialize(n_arms)\n",
    "    results = test_algorithm(algo, arms, num_sims, horizon)\n",
    "    \n",
    "    \n",
    "    for i in range(len(results[0])):\n",
    "        f.write(\",\".join([str(results[j][i]) for j in range(len(results))]) + \"\\n\")\n",
    "\n",
    "    f.close()\n",
    "    \n",
    "    # Read this \n",
    "    file_location = './' + filename\n",
    "    df = pd.read_csv(filename, header=None, \n",
    "                     names=[\"Sim\", \"T\", \"ChosenArm\", \"Reward\", \"CumulativeReward\"])\n",
    "    \n",
    "    return df, best_arm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6295e95",
   "metadata": {},
   "source": [
    "# Performance evaluation\n",
    "\n",
    "## Probability of choosing best arm over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "596b2e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_prob_best_arm(df, best_arm):\n",
    "    df = df.copy()\n",
    "    xseries = df[df['ChosenArm']==best_arm].groupby(['T'])['ChosenArm'].count().to_frame()\n",
    "    xseries['ChosenArm'] = xseries['ChosenArm']/num_sims\n",
    "    xseries.plot(ylabel='Probability of selecting best arm', xlabel='Time', figsize=(15,8))\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749e5748",
   "metadata": {},
   "source": [
    "## Average reward over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c1fff096",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_avg_reward(df):\n",
    "    df = df.copy()\n",
    "    plt.figure(figsize=(15,8))\n",
    "    xseries = df.groupby(['T'])['Reward'].mean()\n",
    "    xseries.plot()\n",
    "    plt.ylabel(\"Average reward\")\n",
    "    plt.title(\"Plot of average reward across \" + str(num_sims) + \" simulations\")\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b12a66",
   "metadata": {},
   "source": [
    "## Cumulative reward over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3819783f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cumulative_reward(df):\n",
    "    \"\"\" total amount of reward that the algorithm has won for us up until some fixed point in time. \"\"\"\n",
    "    df = df.copy()\n",
    "    plt.figure(figsize=(15,8))\n",
    "    xseries = df.groupby(['T'])['CumulativeReward'].mean()\n",
    "    xseries.plot()\n",
    "    plt.title(\"Plot of cumulative reward over time\")\n",
    "    plt.ylabel(\"Cumulative reward\")\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56845169",
   "metadata": {},
   "source": [
    "# Example\n",
    "\n",
    "With only two arms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6bb2e8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "if bandit_type == 'normal':\n",
    "    df, best_arm = simulate_tests_normal()\n",
    "    plot_prob_best_arm(df, best_arm)\n",
    "    plot_avg_reward(df)\n",
    "    plot_cumulative_reward(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aa3a9634",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best arm is at index 0\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JklRvYozMKlnL+MIi3l+2iWE92vHzq49i3GE9HRC9kVn2JEmSJNWLtxdv4Pbni3h1/jr6dmrNhEtHcNGovmQ7IHoiLHuSJEmSDkrxqi1MKCxi8ker6No2l++ffxifPTaPljnZSUdr1ix7kiRJkg7IkvXbuXNqCX98eyntcnO4ZWw+XzppEO1aWjOaAn8LkiRJkvZL7QHRQwh8+aRBfO20oXRxQPQmxbInSZIkqU5qD4heXlnNpUf146Yzh9GnkwOiN0WWPUmSJEl75YDo6cmyJ0mSJGm3HBA9vVn2JEmSJP0DB0TPDJY9SZIkScDuB0R/6OqjGOuA6GnJsidJkiTpnwZEn3jpCC50QPS0ZtmTJEmSmrFdB0T/wfmHcaUDomcEy54kSZLUDO06IPqtqQHR2zogesbwNylJkiQ1I7sOiP6VkwfztVOH0NkB0TOOZU+SJElqBnYdEP2yMTUDovfu6IDomcqyJ0mSJGWwTwZEv296KRu37+RTR/bm1rH5DHZA9Ixn2ZMkSZIy0K4Dop+S351vjitwQPRmxLInSZIkZZBdB0QfldeJSZeP5PghXZOOpkZm2ZMkSZIywK4Douf3dED05q7Byl4I4VHgPGB1jPHw1LQfAF8B1qQe9t0Y419T874DXAtUATfFGAsbKpskSZKUSWoPiN6vswOiq0ZD7tl7HLgX+OUu0yfFGCfUnhBCOAy4AhgO9AGmhhDyY4xVDZhPkiRJSmu1B0Tv1s4B0fWPGqzsxRhnhhAG1vHhFwC/jTGWAwtCCKXAMcCrDZVPkiRJSldL1m9n0tRinn17mQOia4+S2BpuDCF8HpgD3Bpj3AD0BV6r9ZilqWmSJEmSUmoPiJ7lgOjah8Yuew8APwJi6udE4EvA7g4mjrt7ghDCdcB1AHl5eQ2TUpIkSWpCHBBdB6JRy16McdUnt0MIDwN/Sd1dCvSv9dB+wPI9PMdDwEMAY8aM2W0hlCRJkjKBA6LrYDRq2Qsh9I4xrkjdvQj4IHX7z8CTIYQ7qLlAyzDgjcbMJkmSJDUVuxsQ/VtnF3B4XwdEV9015NALTwGnAd1CCEuB7wOnhRBGUnOI5kLgqwAxxg9DCM8AHwGVwA1eiVOSJEnNjQOiqz6FGNP3SMgxY8bEOXPmJB1DkiRJOii7GxD9tnEFDoiufQohvBljHLO7eV6bVZIkSUrQrgOi33HZCC4Y6YDoOniWPUmSJCkBuw6I/p+fHs4Vx/R3QHTVG8ueJEmS1IgcEF2NxS1KkiRJagS7Doh+3cmDud4B0dWALHuSJElSA/rnAdH7829nDqNXx1ZJR1OGs+xJkiRJDaBsZxW/fHUh90+f54DoSoRlT5IkSapHO6uq+d2cpdw9rWZA9FPzu/NNB0RXAix7kiRJUj2oro785f0VTJpSzIK12zhqQGfuumIkxw52QHQlw7InSZIkHYQYI9OL1jC+sIiPVmzmkF7teeQLYzjjkB4OiK5EWfYkSZKkAzR74Xpuf34usxduIK9LG+68fCSfHtGHLAdEVxNg2ZMkSZL200fLNzNhchEvzF1Nj/Yt+dGFh3P5mP7k5mQlHU36X5Y9SZIkqY4Wrt3GHVOK+fO7y+nYugX/fs4hXHPCQFrnZicdTfonlj1JkiRpH1ZtLuOuaSU8M3sJLbKzuOH0IVx3yhA6tm6RdDRpjyx7kiRJ0h5s2FbBgzPm8fgrC6mOkauOzeOGM4bSo70Doqvps+xJkiRJu9hWXsmjLy3goZnz2VpRyUWj+vKNs/Lp36VN0tGkOrPsSZIkSSnllVU8+fpi7nuxlLVbKxh7WE9uG1dAQa/2SUeT9ptlT5IkSc1eVXXk2beXMWlKMcs27uD4wV156PMFjM7rnHQ06YBZ9iRJktRsxRgp/HAVEycXUbJ6K0f268hPP3MEJw3t5oDoSnuWPUmSJDVLL5eu5fbCIt5dspEh3dvywFWjOefwXpY8ZQzLniRJkpqVd5ZsZHzhXF4uXUffTq25/ZIjuXhUX3KyHRBdmcWyJ0mSpGahZNUWJkwuovDDVXRtm8t/nHcYVx2XR8scB0RXZrLsSZIkKaMtWb+dO6eW8OzbS2mTm8M3zsrn2pMH0a6lX4WV2dzCJUmSlJHWbCnnvhdL+c3riwghcO1Jg/jaaUPp0jY36WhSo7DsSZIkKaNsLtvJwzPn88hLCyivrOayMf246cxh9O7YOuloUqOy7EmSJCkjlO2s4olXFvLAjHls3L6T847szS1j8xncvV3S0aREWPYkSZKU1nZWVfPMnCXcPa2EVZvLOa2gO7eNK+Dwvh2TjiYlyrInSZKktFRdHXnuveVMmlLMwnXbGTOgM/dcOZpjBnVJOprUJFj2JEmSlFZijLxYtJrxhcV8vGIzh/Rqz6PXjOH0gh4OiC7VYtmTJElS2nhjwXrGF85l9sINDOjahruuGMn5R/YhK8uSJ+3KsidJkqQm78Plm5hQWMSLRWvo0b4lP7nocC4b058W2VlJR5OaLMueJEmSmqwFa7dxx5Rinnt3OR1bt+Db5x7CF44fSOvc7KSjSU2eZU+SJElNzspNZdw1rYRn5iwhNzuLG08fyldOGUzH1i2SjialDcueJEmSmowN2yp4YMY8nnhlIdUxcvVxA7jh9KF0b98y6WhS2rHsSZIkKXHbyit55KUFPDxzPlsrKrl4VD9uPmsY/bu0STqalLYse5IkSUpMeWUVv3ltMfe9WMq6bRWcPbwnt44rIL9n+6SjSWnPsidJkqRGV1Ud+eNbS7lzagnLNu7gxKFd+ebZhzCyf6eko0kZw7InSZKkRhNjpPDDlUyYXEzp6q2M6NeRn33mSE4a1i3paFLGsexJkiSpUbxUspbxhXN5d+kmhvZox4OfG83Zw3sRggOiSw3BsidJkqQG9fbiDYwvLOKVeevo26k14y85kotH9yM7y5InNSTLniRJkhpE8aotTCgsYvJHq+jaNpfvn38Ynz02j5Y5DoguNQbLniRJkurVkvXbmTS1mGffXka73BxuHZvPl04aRNuWfvWUGpP/x0mSJKlerNlSzr0vlPDkG4vJCoHrTh7M9acOoXPb3KSjSc2SZU+SJEkHZdOOnTw0cx6PvrSQiqpqLj+6PzedMYxeHVslHU1q1ix7kiRJOiA7Kqp4/JWFPDhjHpt27OT8EX24ZWw+g7q1TTqaJCx7kiRJ2k87q6p5evYS7p5Wwuot5Zxe0J3bzi5geJ+OSUeTVItlT5IkSXVSXR157r3l3DGlmEXrtnP0wM7c+9nRHDOoS9LRJO2GZU+SJEl7FWPkhbmrGV9YxNyVWzi0dwceu+ZoTivo7oDoUhNm2ZMkSdIevT5/HeMLi5izaAMDu7bh7itHcd4RvclyQHSpybPsSZIk6Z98sGwT4wuLmFG8hp4dWvJfFx3BpWP60SI7K+lokuqowcpeCOFR4DxgdYzx8NS08cD5QAUwD/hijHFjCGEg8DFQlFr8tRjj9Q2VTZIkSbs3f81WJk4p5n/eW0GnNi34zrmH8IUTBtKqRXbS0STtp4bcs/c4cC/wy1rTpgDfiTFWhhB+BnwH+PfUvHkxxpENmEeSJEl7sGLTDu6aWsLv3lxKy5wsvn7GUL5yymA6tGqRdDRJB6jByl6McWZqj13taZNr3X0NuKSh1i9JkqR9W7+tggeml/LEq4sgwtXHDeCG04fSvX3LpKNJOkhJnrP3JeDpWvcHhRDeBjYD34sxzkomliRJUubbWl7JI7MW8PCs+WyvqOTi0f24+axh9OvcJulokupJImUvhPB/gErgN6lJK4C8GOO6EMJRwJ9CCMNjjJt3s+x1wHUAeXl5jRVZkiQpI5TtrOI3ry/m/hdLWbetgnOG9+LWcfkM69k+6WiS6lmjl70QwheouXDLmTHGCBBjLAfKU7ffDCHMA/KBObsuH2N8CHgIYMyYMbGxckuSJKWzyqpq/vjWMu6cWszyTWWcNLQb3zy7gBH9OyUdTVIDadSyF0I4h5oLspwaY9xea3p3YH2MsSqEMBgYBsxvzGySJEmZqLo68vyHK5k4uYh5a7Yxon8nxl86ghOHdks6mqQG1pBDLzwFnAZ0CyEsBb5PzdU3WwJTQgjw9yEWTgF+GEKoBKqA62OM6xsqmyRJUqaLMTKzZC3jC+fywbLNDOvRjgc/dxRnD+9J6nuYpAzXkFfjvHI3kx/Zw2P/APyhobJIkiQ1J28uWs/Pni/ijQXr6de5NXdcNoILRvYlO8uSJzUnSV6NU5IkSfXoo+WbmTC5iBfmrqZbu5b88ILhXHF0Hrk5WUlHk5QAy54kSVKaW7B2G5OmFPPnd5fToVUO3zqngGtOGEibXL/qSc2ZnwCSJElpasWmHdw9rZRn5iwhNzuLG04fwnUnD6FjmxZJR5PUBFj2JEmS0sz6bRU8ML2UJ15dRIyRq48bwL+ePoQe7VslHU1SE2LZkyRJShNbyyv5xaz5/GLWArZXVHLRqH7cfNYw+ndpk3Q0SU2QZU+SJKmJK9tZxa9fW8T90+exflsF5wzvxa3j8hnWs33S0SQ1YZY9SZKkJqqyqprfvbmUu6eVsGJTGScP68Zt4woY0b9T0tEkpQHLniRJUhNTXR35y/srmDSlmAVrtzGyfycmXjaCE4Z0SzqapDRi2ZMkSWoiYoxML17D+OeL+GjFZgp6tufhz4/hrEN7EIIDokvaP5Y9SZKkJmDOwvXc/nwRbyxcT/8urZl0+Qg+PaIv2VmWPEkHxrInSZKUoI9XbGZCYRHT5q6mW7uW/PCC4VxxdB65OVlJR5OU5ix7kiRJCVi8bjt3TCniv99dTruWOXzz7AK+eOJA2uT69UxS/fDTRJIkqRGt3lzGPS+U8tQbi8nOCnz1lCFcf+pgOrXJTTqapAxj2ZMkSWoEm3bs5Ocz5vHYywvZWVXN5Uf356Yzh9GzQ6uko0nKUJY9SZKkBrSjoorHX1nIgzPmsWnHTj49og+3jM1nYLe2SUeTlOEse5IkSQ1gZ1U1T89ewt3TSli9pZzTC7pz29kFDO/TMelokpoJy54kSVI9qq6OPPfecu6YUsyiddsZM6Az9352NMcM6pJ0NEnNjGVPkiSpHsQYmV60htsLi/h4xWYO6dWeR68Zw+kFDoguKRmWPUmSpIM0e+F6bn9+LrMXbiCvSxvuumIk5x/ZhywHRJeUIMueJEnSAfpw+SYmFBbxYtEaerRvyY8vPJzLxvR3QHRJTYJlT5IkaT8tWLuNO6YU89y7y+nYugXfPvcQvnD8QFrnZicdTZL+l2VPkiSpjlZs2sHd00p4Zs5SWuZkcePpQ/nKKYPp2LpF0tEk6Z9Y9iRJkvZh/bYKHpheyhOvLoIIVx83gBtOH0r39i2TjiZJe2TZkyRJ2oOt5ZU8MmsBD8+az/aKSi4e3Y+bzxpGv85tko4mSftk2ZMkSdpF2c4qfv3aIu6fPo/12yo4Z3gvbh2Xz7Ce7ZOOJkl1ZtmTJElKqayq5g9vLeWuqSUs31TGycO6cdu4Akb075R0NEnab5Y9SZLU7FVXR/72wUomTili/pptjOzfiQmXjeCEId2SjiZJB8yyJ0mSmq0YIzOK1zBhchEfLNtMfs92PHT1UYw9rCchOCC6pPRm2ZMkSc3Sm4vW87Pni3hjwXr6d2nNHZeN4IKRfcnOsuRJygyWPUmS1Kx8vGIzEwqLmDZ3Nd3bt+RHFwzn8qPzyM3JSjqaJNUry54kSWoWFq3bxh1Tivnzu8tp3zKHb51TwDUnDKRNrl+HJGUmP90kSVJGW7W5jLumlfDM7CW0yM7ia6cO4aunDKFjmxZJR5OkBmXZkyRJGWnDtgoenDGPx19ZSHWMfPbYPG48Yyg92rdKOpokNQrLniRJyijbyit59KUFPDRzPlsrKrloZF++MTaf/l3aJB1NkhqVZU+SJGWE8soqnnx9Mfe9WMrarRWMO6wnt51dQH7P9klHk6REWPYkSVJaq6yq5o9vL+OuqSUs27iDE4Z05eHPFzAqr3PS0SQpUZY9SZKUlmKMPP/BSiZMLmLemm2M6NeRn33mSE4a1i3paJLUJFj2JElSWokx8lLpWsYXFvHe0k0M7dGOBz83mrOH9yIEB0SXpE9Y9iRJUtp4a/EGbn9+Lq/NX0/fTq2ZcOkILhrVl+wsS54k7cqyJ0mSmryilVsYX1jE1I9X0a1dLj84/zCuPDaPljnZSUeTpCZrj2UvhPA+EPc0P8Z4ZIMkkiRJSlm8bjuTphbzp3eW0a5lDreNy+eLJw6ibUv/Xi1J+7K3T8rzUj9vSP38VernVcD2BkskSZKavdWby7jnhVKeemMxOdmBr54yhOtPHUynNrlJR5OktLHHshdjXAQQQjgxxnhirVnfDiG8DPywocNJkqTmZdP2nTwwYx6Pv7KAyqrIFcf05+tnDKNnh1ZJR5OktFOXYyDahhBOijG+BBBCOAFo27CxJElSc7K9opLHXl7IgzPmsbW8kgtG9OEbY/MZ0NWvHJJ0oOpS9r4EPBZC6EjNOXybUtMkSZIOSnllFb99Ywn3vFDK2q3lnHVoT247O59DenVIOpokpb29lr0QQjZwaoxxRAihAxBijJsaJ5okScpUVdWRZ99exp1Ti1m6YQfHDe7Cz68+iqMGdE46miRljL2WvRhjVQjhAmBSjHFzI2WSJEkZKsZI4YermDi5iJLVWzmib0f+66IjOHlYNwdEl6R6VpfDOF8OIdwLPA1s+2RijPGtBkslSZIyzsula7m9sIh3l2xkcPe23H/VaM49vJclT5IaSF3K3gmpn7WvvhmBM+o/jiRJyjTvLtnI7YVzebl0HX07teb2S47k4lF9ycnOSjqaJGW0fZa9GOPpB/LEIYRHqRmrb3WM8fDUtC7U7CEcCCwELosxbkjN+w5wLVAF3BRjLDyQ9UqSpKahZNUWJkwuovDDVXRtm8t/nHcYVx2XR8uc7KSjSVKzUJc9e4QQPgUMB/53kJsY477G2XscuBf4Za1p3wamxRh/GkL4dur+v4cQDgOuSK2jDzA1hJAfY6yq6wuRJElNw9IN27lzagl/fGspbXJz+MZZ+Vx78iDatazT1w5JUj3Z56duCOFBoA1wOvAL4BLgjX0tF2OcGUIYuMvkC4DTUrefAKYD/56a/tsYYzmwIIRQChwDvFqXFyFJkpK3dms5971Yym9eWwwBrj1pEF87bShd2uYmHU2SmqU6nbMXYzwyhPBejPE/QwgTgT8e4Pp6xhhXAMQYV4QQeqSm9wVeq/W4palpkiSpidtStpOHZy3gkVnzKaus5tKj+nHTmcPo06l10tEkqVmrS9nbkfq5PYTQB1gHDKrnHLu7DFfc7QNDuA64DiAvL6+eY0iSpLoq21nFr15dxP3TS9mwfSefOqI3t4zLZ0j3dklHkyRRt7L3lxBCJ2A88BY1JezhA1zfqhBC79Revd7A6tT0pUD/Wo/rByzf3RPEGB8CHgIYM2bMbguhJElqOJVV1fz+zaXcObWElZvLOCW/O98cV8AR/TomHU2SVEtdrsb5o9TNP4QQ/gK0ijFuOsD1/Rn4AvDT1M//rjX9yRDCHdRcoGUYdTgvUJIkNZ7q6sjfPljJxMlFzF+7jVF5nZh0+UiOH9I16WiSpN2oywVaZgEzgVnAy3UteiGEp6i5GEu3EMJS4PvUlLxnQgjXAouBSwFijB+GEJ4BPgIqgRu8EqckSU1DjJGZJWsZXziXD5ZtpqBnex7+/BjOOrSHA6JLUhMWYtz7kZAhhMHAScDJwHFAOTArxviNho+3d2PGjIlz5sxJOoYkSRnrrcUbuP35ubw2fz39OrfmlrH5XDCyL9lZljxJagpCCG/GGMfsbl5dDuOcH0LYAVSk/jsdOLR+I0qSpKakaOUWxhcWMfXjVXRr15L//PRwrjwmj9ycrKSjSZLqqC6Hcc4D1gJPAo8AX48xVjd0MEmS1PiWrN/OpCnFPPvOMtq1zOGbZxfwxRMH0ibXAdElKd3U5ZP7bmoO47wSGAXMCCHMjDHOa9BkkiSp0azeXMa9L5by1BuLyQqB604ZzNdOHUKnNg6ILknpqi6Hcd4F3BVCaAd8EfgBNUMjZDdsNEmS1NA2bd/JgzPn8djLC6isilx+dH9uOnMYPTu0SjqaJOkg1eUwzonU7NlrB7wK/Ac1V+aUJElpantFJY+9vJCfz5jHlvJKPj2iD984K5+B3domHU2SVE/qchjna8DtMcZVDR1GkiQ1rIrKap56YzH3vFDK2q3lnHVoD24dV8ChvTskHU2SVM/qUvb+AHw2hDAoxvijEEIe0CvG6KDnkiSliarqyJ/eXsakqcUs3bCDYwd14edXj+aoAV2SjiZJaiB1KXv3AdXAGcCPgC3UFMCjGzCXJEmqBzFGJn+0iomTiyhetZXD+3bgJxcdwSnDujkguiRluLqUvWNjjKNDCG8DxBg3hBC8NJckSU3cy6Vrub2wiHeXbGRw97bcf9Vozj28lyVPkpqJupS9nSGEbCAChBC6U7OnT5IkNUHvLNnI+MK5vFy6jj4dW3H7Z47k4tF9ycl2QHRJak7qOs7es0CPEMJPgEuA7zVoKkmStN+KVm5h4uQiJn+0ii5tc/m/5x3GVcfm0aqFoyVJUnO017IXQsgCFgDfAs4EAnBhjPHjRsgmSZLqYNG6bdw5tYQ/vbOMdrk53Do2ny+eNIh2LevyN11JUqba678CMcbqEMLEGOPxwNxGyiRJkupg5aYy7nmhhKdnLyEnO/DVU4Zw/amD6dTGU+slSXU7jHNyCOEzwB9jjLGhA0mSpL3bsK2CB2bM44lXFlJVHbnymDy+fsZQenRolXQ0SVITUpeydwvQFqgMIZRRcyhnjDE6+qokSY1oa3klj8xawMOz5rOtopKLRvXl5jPzyevaJulokqQmaJ9lL8bYvjGCSJKk3SvbWcWvX1vE/dPnsX5bBecM78Ut4/LJ7+k/0ZKkPfPMbUmSmqidVdX8bs5S7p5WwsrNZZw8rBu3jStgRP9OSUeTJKUBy54kSU1MdXXkufeWc8eUYhat287ovE5Munwkxw/pmnQ0SVIasexJktRExBiZ9vFqJkwuYu7KLRzauwOPXjOG0wt6EEJIOp4kKc3UqeyFEE4ChsUYHwshdAfaxRgXNGw0SZKaj1fmrWV8YRFvL97IwK5tuPvKUZx3RG+ysix5kqQDs8+yF0L4PjAGKAAeA1oAvwZObNhokiRlvneWbGRCYREvla6ld8dW/PTiI/jMUf1okZ2VdDRJUpqry569i4BRwFsAMcblIQQv/yVJ0kEoWrmFiZOLmPzRKrq0zeX/nncYVx2bR6sW2UlHkyRliLqUvYoYYwwhRIAQQtsGziRJUsZavG47k6YW86d3ltEuN4dbxubzpZMG0a6lp9FLkupXXf5leSaE8HOgUwjhK8CXgIcbNpYkSZll1eYy7nmhhN++sYSc7MB1pwzm+lOG0LltbtLRJEkZqi6Dqk8IIYwFNlNz3t5/xBinNHgySZIywIZtFTw4Yx6Pv7KQqurIlcfkceMZQ+nZoVXS0SRJGa4uF2j5BvA7C54kSXW3tbySR2Yt4OFZ89lWUclFo/py85n55HVtk3Q0SVIzUZfDODsAhSGE9cBvgd/HGFc1bCxJktJT2c4qfv3aIu6fPo/12yo4Z3gvbhmXT35Pr20mSWpcdTmM8z+B/wwhHAlcDswIISyNMZ7V4OkkSUoTO6uq+d2cpdw9rYSVm8s4eVg3bhtXwIj+nZKOJklqpvbn0l+rgZXAOqBHw8SRJCm9VFdHnntvOZOmFLNw3XZG53Vi0uUjOX5I16SjSZKaubqcs/c1avbodQd+D3wlxvhRQweTJKkpizEy7ePVTJhcxNyVWzikV3se+cIYzjikByGEpONJklSnPXsDgJtjjO80cBZJktLCK/PWMr6wiLcXb2Rg1zbcfeUozjuiN1lZljxJUtOxx7IXQugQY9wM3J6636X2/Bjj+gbOJklSk/LOko1MKCzipdK19O7Yip9efASfOaofLbKzko4mSdI/2duevSeB84A3gQjU/nNlBAY3YC5JkpqM4lVbmDi5iMIPV9GlbS7f+9ShfO64AbRqkZ10NEmS9miPZS/GeF7q56DGiyNJUtOxeN127pxazLPvLKNdbg63jM3nSycNol3L/bm+mSRJyajLBVqmxRjP3Nc0SZIyxarNZdzzQgm/fWMJ2VmB604ZzPWnDKFz29yko0mSVGd7O2evFdAG6BZC6MzfD+PsAPRphGySJDWqDdsqeHDGPB5/ZSFV1ZErj8njxjOG0rNDq6SjSZK03/a2Z++rwM3UFLs3+XvZ2wzc17CxJElqPFvLK3lk1gJ+MWs+WysquWhUX24+M5+8rm2SjiZJ0gHb2zl7dwF3hRC+HmO8pxEzSZLUKMp2VvHr1xZx//R5rN9WwdnDe3LruALye7ZPOpokSQdtn+fsxRjvCSEcDhwGtKo1/ZcNGUySpIZSWVXNH95ayl1TS1i+qYyThnbjtrMLGNm/U9LRJEmqN3W5QMv3gdOoKXt/Bc4FXgIse5KktFJdHfnbByuZOKWI+Wu2MaJ/JyZcOoIThnZLOpokSfWuLteOvgQYAbwdY/xiCKEn8IuGjSVJUv2JMTKjeA0TJhfxwbLNDOvRjp9ffRTjDutJCGHfTyBJUhqqS9nbEWOsDiFUhhA6AKtxQHVJUpp4c9F6fvZ8EW8sWE+/zq2ZeOkILhzVl+wsS54kKbPVpezNCSF0Ah6m5qqcW4E3GjKUJEkH6+MVm5lQWMS0uavp1q4lP7xgOFccnUduTlbS0SRJahR1uUDLv6ZuPhhCeB7oEGN8r2FjSZJ0YBat28YdU4r587vLad8yh2+eXcAXTxxIm9y6/H1TkqTMsbdB1UfvbV6M8a2GiSRJ0v5btbmMu6aV8MzsJeRkB64/dQjXnzKEjm1aJB1NkqRE7O3PnBP3Mi8CZ9RzFkmS9tuGbRU8OGMej7+ykKrqyJXH5PH1M4bSo0OrfS8sSVIG29ug6qc3ZhBJkvbHtvJKHn1pAQ/NnM/WikouHNmXb5yVT17XNklHkySpSajLOHuf3910B1WXJCWhvLKKJ19fzH0vlrJ2awVnHdqT287O55BeHZKOJklSk1KXs9WPrnW7FXAm8BYOqi5JakRV1ZE/vrWUO6eWsGzjDo4b3IWHPn8Io/M6Jx1NkqQmqS5X4/x67fshhI7Arw50hSGEAuDpWpMGA/8BdAK+AqxJTf9ujPGvB7oeSVJmiDFS+OFKJkwupnT1Vo7o25H/d/ERnDysmwOiS5K0FwdyHertwLADXWGMsQgYCRBCyAaWAc8CXwQmxRgnHOhzS5Iyy8ula7n9+bm8u3QTg7u35f6rRnPu4b0seZIk1UFdztl7jpqrbwJkAYcBz9TT+s8E5sUYF/kPtyTpE+8s2cj4wrm8XLqOPh1bcftnjuTi0X3JyXZAdEmS6qoue/Zq72mrBBbFGJfW0/qvAJ6qdf/G1AVh5gC3xhg37LpACOE64DqAvLy8eoohSWoKSlZtYcLkIgo/XEWXtrn83/MO46pj82jVIjvpaJIkpZ0QY9z3o4AQQgdqlcMY4/qDWnEIucByYHiMcVUIoSewlpq9iD8CescYv7S35xgzZkycM2fOwcSQJDUBS9Zv586pJTz79lLa5ObwlZMHc+3Jg2jX8kDONpAkqfkIIbwZYxyzu3l1OYzzOmrK1w6gGgjUFLLBB5nrXOCtGOMqgE9+ptb5MPCXg3x+SVITt2ZLOfe9WMpvXl9ECIFrTxrE104bSpe2uUlHkyQp7dXlT6bfpGbv29p6XveV1DqEM4TQO8a4InX3IuCDel6fJKmJ2Fy2k4dmzOfRlxdQXlnNZWP6cdOZw+jdsXXS0SRJyhh1KXvzqLkCZ70JIbQBxgJfrTX59hDCSGr2Gi7cZZ4kKQPsqKjiiVcX8sD0eWzasZPzjuzNLWPzGdy9XdLRJEnKOHUpe98BXgkhvA6UfzIxxnjTga40xrgd6LrLtKsP9PkkSU3bzqpqnp69hLunlbB6Szmn5nfnm2cXcHjfjklHkyQpY9Wl7P0ceAF4n5pz9iRJqpPq6shz7y3njinFLFq3naMGdOaeK0dx7OCu+15YkiQdlLqUvcoY4y0NnkSSlDFijLxYtJrxhcV8vGIzh/Rqz6PXjOH0gh4OiC5JUiOpS9l7MXVFzuf4x8M4D2roBUlSZnpjwXpuf34ucxZtYEDXNtx1xUjOP7IPWVmWPEmSGlNdyt5nUz+/U2tafQy9IEnKIB8s28T4wiJmFK+hR/uW/PjCw7n86P60yM5KOpokSc3SPstejHFQYwSRJKWnBWu3MXFyEX95bwUdW7fg2+cewheOH0jr3Oyko0mS1KzVZVD1z+9ueozxl/UfR5KULlZs2sHd00p4Zs5ScrOzuPH0oXzllMF0bN0i6WiSJIm6HcZ5dK3brYAzgbcAy54kNUPrt1XwwPRSnnh1ETFGrj5uADecPpTu7VsmHU2SJNVSl8M4v177fgihI/CrBkskSWqStpZX8sisBTw8az7bKiq5eFQ/bj5rGP27tEk6miRJ2o267Nnb1XZgWH0HkSQ1TWU7q/jN64u5/8VS1m2r4OzhPbl1XAH5PdsnHU2SJO1FXc7Ze46aq28CZAGHAc80ZChJUvIqq6r541vLuHNqMcs3lXHi0K588+xDGNm/U9LRJElSHdRlz96EWrcrgUUxxqUNlEeSlLAYI3/7YCUTJxcxb802RvTryO2XjOCkYd2SjiZJkvbDHsteCGEo0DPGOGOX6SeHEFrGGOc1eDpJUqOJMTKrZC3jC4t4f9kmhvZox4OfG83Zw3sRggOiS5KUbva2Z+9O4Lu7mb4jNe/8BsgjSUrAW4s3MP75Il6dv46+nVoz/pIjuXh0P7KzLHmSJKWrvZW9gTHG93adGGOcE0IY2HCRJEmNZe7KzUwoLGbqx6vo2jaX759/GJ89No+WOQ6ILklSuttb2Wu1l3mt6zuIJKnxLFq3jUlTivnvd5fTrmUOt43L54snDqJtywO5SLMkSWqK9vav+uwQwldijA/XnhhCuBZ4s2FjSZIawspNZdz9QgnPzF5CTnbgq6cM4fpTB9OpTW7S0SRJUj3bW9m7GXg2hHAVfy93Y4Bc4KIGziVJqkfrt1Xw4Ix5PPHKQqpj5LPH5nHj6UPp0WFvB3FIkqR0tseyF2NcBZwQQjgdODw1+X9ijC80SjJJ0kHbWl7JL2bN5xezFrCtopKLRvXlG2fl079Lm6SjSZKkBrbPkzNijC8CLzZCFklSPSnbWcWvX1vE/dPnsX5bBecM78Ut4/LJ79k+6WiSJKmReCa+JGWQnVXV/P7Npdw1tYSVm8s4eVg3bhtXwIj+nZKOJkmSGpllT5IyQHV15Ln3ljNpSjEL121nVF4n7rh8BCcM6ZZ0NEmSlBDLniSlsRgjL8xdzfjCIuau3MIhvdrzi8+P4cxDexCCA6JLktScWfYkKU29Om8d4wvn8tbijQzo2oa7rhjJ+Uf2ISvLkidJkix7kpR23l2ykQmTi5hVspZeHVrxXxcdwaVj+tEiOyvpaJIkqQmx7ElSmihZtYWJk4t5/sOVdG7Tgu996lA+d9wAWrXITjqaJElqgix7ktTELVm/nUlTi/nT28tok5vDzWcN49qTBtG+VYuko0mSpCbMsidJTdTqzWXc+2IpT72xmKwQ+PLJg7n+1CF0aZubdDRJkpQGLHuS1MRs3F7Bz2fO57GXF1BZFbns6P7cdMYwenVslXQ0SZKURix7ktREbCuv5LGXF/DzmfPZWl7JBSP6cPNZ+Qzs1jbpaJIkKQ1Z9iQpYeWVVTz5+mLue7GUtVsrOOvQntw6Lp9De3dIOpokSUpjlj1JSkhlVTV/fGsZd00rYdnGHRw/uCsPfb6A0Xmdk44mSZIygGVPkhpZdXXkbx+sZOKUIuav2caIfh352WeO5MShXQnBAdElSVL9sOxJUiOJMTK9eA0TCov4cPlmhvVox8+vPopxh/W05EmSpHpn2ZOkRjB74XrGP1/EGwvX079La+64bAQXjOxLdpYlT5IkNQzLniQ1oA+WbWLC5CKmF62hR/uW/OjCw7l8TH9yc7KSjiZJkjKcZU+SGsC8NVu5Y0ox//PeCjq2bsG3zz2ELxw/kNa52UlHkyRJzYRlT5Lq0bKNO7hrajG/f3MprVpkc9MZQ/nyKYPp0KpF0tEkSVIzY9mTpHqwdms5971Yym9eWwzANScM4l9PH0K3di0TTiZJkpory54kHYRNO3by8Mz5PPryAsorq7n0qH58/cxh9O3UOulokiSpmbPsSdIB2FFRxeOvLOTBGfPYtGMn5x3Zm2+MzWdI93ZJR5MkSQIse5K0Xyoqq/nt7MXc80Ipa7aUc3pBd247u4DhfTomHU2SJOkfWPYkqQ6qqiN/ensZk6YWs3TDDo4Z2IX7rxrN0QO7JB1NkiRptyx7krQXMUYKP1zFxMlFlKzeyvA+HfjxhYdzan53QnBAdEmS1HRZ9iRpN2KMvFS6lvGFRby3dBODu7fl/qtGc87wXmRlWfIkSVLTZ9mTpF28uWgD4wvn8tr89fTt1JrbLzmSi0f1JSc7K+lokiRJdWbZk6SUj1dsZuLkIqZ+vJpu7XL5wfmHceWxebTMyU46miRJ0n6z7Elq9hau3cakqcX8+d3ltG+ZwzfPLuCLJw6kTa4fkZIkKX35TUZSs7VyUxl3TSvhmTlLyM3O4munDuGrpwyhY5sWSUeTJEk6aJY9Sc3O+m0VPDC9lCdeXUSMkc8dm8cNZwylR/tWSUeTJEmqN4mUvRDCQmALUAVUxhjHhBC6AE8DA4GFwGUxxg1J5JOUmbaU7eQXsxbwyEsL2F5RycWj+/FvZw6jf5c2SUeTJEmqd0nu2Ts9xri21v1vA9NijD8NIXw7df/fk4kmKZOU7aziV68u4v7ppWzYvpNzD+/FrePyGdqjfdLRJEmSGkxTOozzAuC01O0ngOlY9iQdhJ1V1TwzZwl3Tyth1eZyTsnvzm3j8jmyX6eko0mSJDW4pMpeBCaHECLw8xjjQ0DPGOMKgBjjihBCj90tGEK4DrgOIC8vr7HySkoj1dWR595bzh1Tilm0bjtHDejMXVeM4rjBXZOOJkmS1GiSKnsnxhiXpwrdlBDC3LoumCqGDwGMGTMmNlRASeknxsjUj1czcXIRc1du4dDeHXj0mjGcXtCDEELS8SRJkhpVImUvxrg89XN1COFZ4BhgVQihd2qvXm9gdRLZJKWnV+atZXxhEW8v3sjArm24+8pRnHdEb7KyLHmSJKl5avSyF0JoC2TFGLekbo8Dfgj8GfgC8NPUz/9u7GyS0s87SzYyobCIl0rX0rtjK3568RF85qh+tMjOSjqaJElSopLYs9cTeDZ1SFUO8GSM8fkQwmzgmRDCtcBi4NIEsklKE8WrtjBxchGFH66iS9tcvvepQ/nccQNo1SI76WiSJElNQqOXvRjjfGDEbqavA85s7DyS0suS9duZNKWYZ99ZRrvcHG4Zm8+XThpEu5ZN6eLCkiRJyfPbkaS0sHpzGfe8UMpvZy8mKwSuO3kw1586hM5tc5OOJkmS1CRZ9iQ1aRu3V/DAjHk88cpCKqsiVxzTn6+fMYyeHVolHU2SJKlJs+xJapK2lVfy6EsLeGjmfLZWVHLhyL7cfNYwBnRtm3Q0SZKktGDZk9SklO2s4snXF3Pfi6Ws21bBuMN6cuu4Agp6tU86miRJUlqx7ElqEiqrqvnDW0u5a2oJyzeVceLQrtw2roBReZ2TjiZJkpSWLHuSElVdHfnrByu4Y3Ix89duY0T/Toy/dAQnDu2WdDRJkqS0ZtmTlIgYI9OL1jC+sIiPVmwmv2c7Hrr6KMYe1pPUOJySJEk6CJY9SY3ujQXrGV84l9kLN5DXpQ13Xj6S80f0ITvLkidJklRfLHuSGs0HyzYxvrCIGcVr6NG+JT++8HAuG9Of3JyspKNJkiRlHMuepAZXunork6YU8z/vr6BTmxZ8918O4erjBtI6NzvpaJIkSRnLsiepwSxZv527ppXwx7eW0rpFNjedMZQvnzKYDq1aJB1NkiQp41n2JNW71ZvLuPfFUp56YzEhBK49aRDXnzqEru1aJh1NkiSp2bDsSao3G7dX8OCM+Tz+ygIqqyKXHd2fr58xlN4dWycdTZIkqdmx7Ek6aFvLK3n0pQU8PHM+WysquXBkX24+axgDurZNOpokSVKzZdmTdMDKdlbx69cWcf/0eazfVsHZw3tyy9gCCnq1TzqaJElSs2fZk7TfdlZV88ycJdwzrZSVm8s4eVg3bhtXwIj+nZKOJkmSpBTLnqQ6q6qOPPfuciZNLWbRuu0cNaAzky4fyfFDuiYdTZIkSbuw7Enapxgjkz9axcTJRRSv2sphvTvw2DVHc1pBd0IISceTJEnSblj2JO1RjJGXStcyobCId5duYnC3ttz72VH8y+G9ycqy5EmSJDVllj1Ju/XmovWMLyzitfnr6dupNbdfciQXj+pLTnZW0tEkSZJUB5Y9Sf/gw+WbmDi5mBfmrqZbu5b856eHc8Ux/WmZk510NEmSJO0Hy54kAOat2codU4r5n/dW0KFVDt86p4BrThhIm1w/JiRJktKR3+KkZm7phu3cNbWEP7y1lFYtsvn6GUP58smD6di6RdLRJEmSdBAse1IztXpLGfe9UMqTbywmhMAXTxzE104bQrd2LZOOJkmSpHpg2ZOamY3bK3hwxnwef2UBlVWRS8f056Yzh9K7Y+uko0mSJKkeWfakZmJreSWPvrSAh2fOZ2tFJReO7MvNZw1jQNe2SUeTJElSA7DsSRmubGcVv35tEfdPn8f6bRWcPbwnt4wtoKBX+6SjSZIkqQFZ9qQMtbOqmmfmLOGeaaWs3FzGycO6cdu4Akb075R0NEmSJDUCy56UYaqqI39+dxmTppSweP12xgzozJ1XjOS4wV2TjiZJkqRGZNmTMkSMkcIPV3HHlCKKV21leJ8OPHbN0ZxW0J0QQtLxJEmS1Mgse1KaizEyq2QtEyYX8d7STQzp3pb7Pjuacw/vRVaWJU+SJKm5suxJaWz2wvWMLyzijQXr6dupNeMvOZKLRvUlJzsr6WiSJElKmGVPSkMfLNvEhMlFTC9aQ/f2LfnhBcO5/Oj+tMzJTjqaJEmSmgjLnpRGSldv4Y4pxfz1/ZV0atOCb597CF84fiCtcy15kiRJ+keWPSkNLF63nTunFfOnt5fRukU2N505jC+fPIgOrVokHU2SJElNlGVPasJWbirjnhdKeHr2ErKzAl8+eTDXnzqELm1zk44mSZKkJs6yJzVB67aW88D0efzqtUVUx8iVx+Rx4xlD6dmhVdLRJEmSlCYse1ITsmnHTn4xaz6PvrSAHTuruHh0P/7tzGH079Im6WiSJElKM5Y9qQnYXlHJYy8v5KGZ89m0YyefOrI33zgrn6E92iUdTZIkSWnKsiclqGxnFU++vpj7p5eydmsFZx7Sg1vG5TO8T8eko0mSJCnNWfakBOysqub3by7l7mklrNhUxvGDu/Lzqws4akDnpKNJkiQpQ1j2pEZUVR157t3l3Dm1mIXrtjOyfycmXDqCE4d2SzqaJEmSMoxlT2oEMUYKP1zFHVOKKF61lUN6tecXnx/DmYf2IISQdDxJkiRlIMue1IBijMwoXsPEycW8v2wTg7u15Z4rR/GpI3qTlWXJkyRJUsOx7EkN5PX565g4uZg3Fq6nb6fW3H7JkVw8qi852VlJR5MkSVIzYNmT6tm7SzYyYXIRs0rW0r19S354wXAuP7o/LXOyk44mSZKkZsSyJ9WTopVbmDi5iMkfraJTmxZ859xD+PzxA2mda8mTJElS47PsSQdp4dptTJpazJ/fXU7b3BxuPmsY1540iPatWiQdTZIkSc2YZU86QMs37uDuaSX87s2ltMgOXHfKYK4/ZQid2+YmHU2SJElq/LIXQugP/BLoBVQDD8UY7woh/AD4CrAm9dDvxhj/2tj5pH1Zs6Wc+14s5cnXFwNw9XED+NfTh9CjfauEk0mSJEl/l8SevUrg1hjjWyGE9sCbIYQpqXmTYowTEsgk7dPG7RX8fOZ8Hn95IRVV1Vwyuh9fP3Mo/Tq3STqaJEmS9E8avezFGFcAK1K3t4QQPgb6NnYOqa42l+3kkVkLeOSlBWyrqOT8I/tw81nDGNy9XdLRJEmSpD1K9Jy9EMJAYBTwOnAicGMI4fPAHGr2/m3YzTLXAdcB5OXlNV5YNTvbyit5/JWFPDRzPpt27OTs4T35xth8DunVIelokiRJ0j6FGGMyKw6hHTAD+EmM8Y8hhJ7AWiACPwJ6xxi/tLfnGDNmTJwzZ07Dh1WzsqOiil+9tpAHZ8xn/bYKzjikB7eMzefwvh2TjiZJkiT9gxDCmzHGMbubl8ievRBCC+APwG9ijH8EiDGuqjX/YeAvSWRT81W2s4qn3ljM/dPnsWZLOScP68Y3xuYzOq9z0tEkSZKk/ZbE1TgD8AjwcYzxjlrTe6fO5wO4CPigsbOpeaqorOZ3by7h3hdKWbGpjGMHdeG+z47mmEFdko4mSZIkHbAk9uydCFwNvB9CeCc17bvAlSGEkdQcxrkQ+GoC2dSMVFZV88e3l3H3tBKWbtjB6LxOTLh0BCcM6UrN3yQkSZKk9JXE1ThfAnb3Tdox9dQoqqojz727nLumlbBg7TaO6NuRH114OKfld7fkSZIkKWMkejVOqTFVV0f+9sFK7pxaTMnqrRzSqz0PXX0UYw/racmTJElSxrHsKePFGJny0SomTS3h4xWbGdqjHfd9djTnHt6LrCxLniRJkjKTZU8ZK8bI9OI1TJpSzHtLNzGwaxsmXT6CT4/oS7YlT5IkSRnOsqeM9ErpWiZOKebNRRvo26k1t3/mSC4e3Zec7Kyko0mSJEmNwrKnjDJ74XomTi7itfnr6dWhFT++8HAuG9Of3BxLniRJkpoXy54ywtuLN3DHlGJmlaylW7uWfP/8w7jymDxatchOOpokSZKUCMue0tr7SzcxaWoxL8xdTZe2uXz3Xw7h6uMG0jrXkidJkqTmzbKntPTR8s1MmlrMlI9W0bF1C755dgHXnDCQti3dpCVJkiSw7CnNFK/awp1Ti/nr+ytp3yqHW8bm88UTB9K+VYuko0mSJElNimVPaWHemq3cNbWE595bTtvcHG46YyjXnjSYjm0seZIkSdLuWPbUpC1cu427XyjhT28vo1WLbK4/dQjXnTyYzm1zk44mSZIkNWmWPTVJS9Zv554XSvjDW8vIyQpce9IgvnrqELq1a5l0NEmSJCktWPbUpCzfuIN7XyzlmdlLyMoKfP74AXzt1CH06NAq6WiSJElSWrHsqUlYvnEHD0yfx9OzlxCJXHlMHjecPpReHS15kiRJ0oGw7ClRKzbt4P4X/17yLh3Tn389bQj9OrdJOpokSZKU1ix7SsSKTTV78n77xhKqY03Ju+F0S54kSZJUXyx7alQrN5XxwPRSnqpV8v71tCH072LJkyRJkuqTZU+N4p9LXj/+9bShljxJkiSpgVj21KBWbS7jgenzePKNxVRXRy45qh83nG7JkyRJkhqaZU8NonbJq6qOXDK6HzeeYcmTJEmSGotlT/Vq9eYyHpgxjydfX0xlquTdcPpQ8rpa8iRJkqTGZNlTvdi15H1mdF9uPH2YJU+SJElKiGVPB2X15jIenDGf37y+iMrqyMWj+vL1Myx5kiRJUtIsezogq7eU8fMZ8/n1a38veTeeMZQBXdsmHU2SJEkSlj3tp9Vbynhoxnx+/foidlZFLhrVlxtPH8rAbpY8SZIkqSmx7KlO1mwp5+cz5vHr1xdRUVnNRaP68fUzLHmSJElSU2XZ016t2VLOQzPn8avXakrehalz8gZZ8iRJkqQmzbKn3Vq7tZyHZs7nl68utORJkiRJaciyp3+wdms5D8+czy9fXUR5ZRUXjqy58Mrg7u2SjiZJkiRpP1j2BMC6/92TV1PyLhjZl69b8iRJkqS0Zdlr5tZtLeehWfP55St/L3k3njGUIZY8SZIkKa1Z9pqp1ZvL+MVLC/j1a4vYsbOKC0b04cYzhjG0hyVPkiRJygSWvWZm8brtPDhzHr+fs5TK6mrOH9GHr1vyJEmSpIxj2Wsm5q7czAPT5/Hcu8vJycrikjH9+OopgxnQ1atrSpIkSZnIspfh3ly0nvtfnMe0uatpm5vNl08ezLUnDaJnh1ZJR5MkSZLUgCx7GSjGyMyStdz/YimvL1hP5zYtuGVsPp8/fgCd2uQmHU+SJElSI7DsZZCq6kjhhyu5f3opHyzbTK8Orfi/5x3Glcf0p02uv2pJkiSpObEBZICKymr+9PYyHpwxj/lrtzGoW1t+9pkjuHBUX1rmZCcdT5IkSVICLHtpbGt5JU/PXsIvZs1nxaYyDuvdgXs/O4pzD+9NdlZIOp4kSZKkBFn20tDKTWU8/spCnnx9EZvLKjlmYBf+38VHcGp+d0Kw5EmSJEmy7KWVj1ds5uFZ83nu3eVUVUfOPbw3Xz55EKPyOicdTZIkSVITY9lr4mKMzCpZy8Oz5jOrZC1tcrO56tgBfOnEQeR1bZN0PEmSJElNlGWviaqorOa5d5fz8Kz5zF25hR7tW/LNswu46tg8h0+QJEmStE+WvSZm046dPPn6Yh5/ZQGrNpdT0LM94y85kk+P7OOVNSVJkiTVmWWviViyfjuPvryAZ2YvYVtFFScN7cbtl4zglGHdvOiKJEmSpP1m2UtQjJE5izbw+CsL+dv7K8gKgU+P6MOXTx7MYX06JB1PkiRJUhqz7CVgW3klf3pnGb96dRFzV26hfascvnLKYK45YSC9O7ZOOp4kSZKkDGDZa0Slq7fy69cW8Yc3l7KlvJLDenfgpxcfwadH9qFNrr8KSZIkSfWnyTWMEMI5wF1ANvCLGONPE450ULZXVPLX91fy9OzFzF64gdzsLP7liF5cffxARud18nw8SZIkSQ2iSZW9EEI2cB8wFlgKzA4h/DnG+FGyyfZPjJF3l27i6dlLeO7d5Wwtr2RQt7Z865wCLhvTn27tWiYdUZIkSVKGa1JlDzgGKI0xzgcIIfwWuABIm7I3o3gN//U/H1O0agutWmTxqSP6cPnR/Tl6YGf34kmSJElqNE2t7PUFltS6vxQ4NqEsB6RFVqBVbjb/ddERnD+iN+1btUg6kiRJkqRmqKmVvd3t+or/8IAQrgOuA8jLy2uMTPvl+CFd+e8bTkw6hiRJkqRmLivpALtYCvSvdb8fsLz2A2KMD8UYx8QYx3Tv3r1Rw9WFh2pKkiRJagqaWtmbDQwLIQwKIeQCVwB/TjiTJEmSJKWdJnUYZ4yxMoRwI1BIzdALj8YYP0w4liRJkiSlnSZV9gBijH8F/pp0DkmSJElKZ03tME5JkiRJUj2w7EmSJElSBrLsSZIkSVIGsuxJkiRJUgay7EmSJElSBrLsSZIkSVIGsuxJkiRJUgay7EmSJElSBrLsSZIkSVIGsuxJkiRJUgay7EmSJElSBrLsSZIkSVIGsuxJkiRJUgay7EmSJElSBgoxxqQzHLAQwhpgUdI5UroBa5MOoYzmNqaG5jamhuY2pobk9qWG1lS3sQExxu67m5HWZa8pCSHMiTGOSTqHMpfbmBqa25gamtuYGpLblxpaOm5jHsYpSZIkSRnIsidJkiRJGciyV38eSjqAMp7bmBqa25gamtuYGpLblxpa2m1jnrMnSZIkSRnIPXuSJEmSlIEse/UghHBOCKEohFAaQvh20nmUGUIIC0MI74cQ3gkhzElN6xJCmBJCKEn97Jx0TqWHEMKjIYTVIYQPak3b4/YUQvhO6jOtKIRwdjKplU72sI39IISwLPU59k4I4V9qzXMb034JIfQPIbwYQvg4hPBhCOHfUtP9LNNB28v2ldafYx7GeZBCCNlAMTAWWArMBq6MMX6UaDClvRDCQmBMjHFtrWm3A+tjjD9N/WGhc4zx35PKqPQRQjgF2Ar8MsZ4eGrabrenEMJhwFPAMUAfYCqQH2OsSii+0sAetrEfAFtjjBN2eazbmPZbCKE30DvG+FYIoT3wJnAhcA1+lukg7WX7uow0/hxzz97BOwYojTHOjzFWAL8FLkg4kzLXBcATqdtPUPMhJO1TjHEmsH6XyXvani4AfhtjLI8xLgBKqfmsk/ZoD9vYnriNab/FGFfEGN9K3d4CfAz0xc8y1YO9bF97khbbl2Xv4PUFltS6v5S9bxhSXUVgcgjhzRDCdalpPWOMK6DmQwnokVg6ZYI9bU9+rqk+3RhCeC91mOcnh9e5jemghBAGAqOA1/GzTPVsl+0L0vhzzLJ38MJupnlsrOrDiTHG0cC5wA2pQ6SkxuDnmurLA8AQYCSwApiYmu42pgMWQmgH/AG4Oca4eW8P3c00tzPt1W62r7T+HLPsHbylQP9a9/sByxPKogwSY1ye+rkaeJaaQwNWpY4p/+TY8tXJJVQG2NP25Oea6kWMcVWMsSrGWA08zN8PcXIb0wEJIbSg5ov4b2KMf0xN9rNM9WJ321e6f45Z9g7ebGBYCGFQCCEXuAL4c8KZlOZCCG1TJwcTQmgLjAM+oGbb+kLqYV8A/juZhMoQe9qe/gxcEUJoGUIYBAwD3kggn9LcJ1/AUy6i5nMM3MZ0AEIIAXgE+DjGeEetWX6W6aDtaftK98+xnKQDpLsYY2UI4UagEMgGHo0xfphwLKW/nsCzNZ875ABPxhifDyHMBp4JIVwLLAYuTTCj0kgI4SngNKBbCGEp8H3gp+xme4oxfhhCeAb4CKgEbmhqVxdT07OHbey0EMJIag5tWgh8FdzGdMBOBK4G3g8hvJOa9l38LFP92NP2dWU6f4459IIkSZIkZSAP45QkSZKkDGTZkyRJkqQMZNmTJEmSpAxk2ZMkSZKkDGTZkyRJkqQMZNmTJKmWEELXEMI7qf9WhhCWpW5vDSHcn3Q+SZLqyqEXJEnagxDCD4CtMcYJSWeRJGl/uWdPkqQ6CCGcFkL4S+r2D0IIT4QQJocQFoYQLg4h3B5CeD+E8HwIoUXqcUeFEGaEEN4MIRSGEHon+yokSc2JZU+SpAMzBPgUcAHwa+DFGOMRwA7gU6nCdw9wSYzxKOBR4CdJhZUkNT85SQeQJClN/S3GuDOE8D6QDTyfmv4+MBAoAA4HpoQQSD1mRQI5JUnNlGVPkqQDUw4QY6wOIeyMfz8Jvpqaf18D8GGM8fikAkqSmjcP45QkqWEUAd1DCMcDhBBahBCGJ5xJktSMWPYkSWoAMcYK4BLgZyGEd4F3gBMSDSVJalYcekGSJEmSMpB79iRJkiQpA1n2JEmSJCkDWfYkSZIkKQNZ9iRJkiQpA1n2JEmSJCkDWfYkSZIkKQNZ9iRJkiQpA1n2JEmSJCkD/X9wRKnRbJgZZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if bandit_type == 'bernoulli':\n",
    "    df, best_arm = simulate_tests_bernoulli()\n",
    "    plot_prob_best_arm(df, best_arm)\n",
    "    plot_avg_reward(df)\n",
    "    plot_cumulative_reward(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "05401301",
   "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>Sim</th>\n",
       "      <th>T</th>\n",
       "      <th>ChosenArm</th>\n",
       "      <th>Reward</th>\n",
       "      <th>CumulativeReward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</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>1249995</th>\n",
       "      <td>5000</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249996</th>\n",
       "      <td>5000</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249997</th>\n",
       "      <td>5000</td>\n",
       "      <td>248</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249998</th>\n",
       "      <td>5000</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249999</th>\n",
       "      <td>5000</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1250000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Sim    T  ChosenArm  Reward  CumulativeReward\n",
       "0           1    1          1     0.0               0.0\n",
       "1           1    2          1     1.0               1.0\n",
       "2           1    3          1     1.0               2.0\n",
       "3           1    4          0     1.0               3.0\n",
       "4           1    5          0     1.0               4.0\n",
       "...       ...  ...        ...     ...               ...\n",
       "1249995  5000  246          0     1.0             160.0\n",
       "1249996  5000  247          0     0.0             160.0\n",
       "1249997  5000  248          0     1.0             161.0\n",
       "1249998  5000  249          0     1.0             162.0\n",
       "1249999  5000  250          0     1.0             163.0\n",
       "\n",
       "[1250000 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c92836e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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