{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing Bayesian Approaches to Disjoint Linear Bandits\n", "\n", "Linear bandits are useful for solving problems where the reward is a linear function of the context. In this notebook, we'll explore two different Bayesian approaches to linear contextual bandits by implementing variations of the disjoint LinUCB algorithm from [1]:\n", "\n", "1. **NormalInverseGammaRegressor**: Provides exact Bayesian inference with conjugate priors but assumes Gaussian outcomes\n", "2. **BayesianGLM**: More accurately models binary outcomes using logistic regression but relies on Laplace approximation for inference\n", "\n", "We'll simulate a situation very similar to the one described in the paper, where we have a set of users with some known context, and a set of articles that we want to recommend to them. We can represent what we know about the users as the context to a multi-armed bandit problem, and the articles as the arms. The reward for each arm is the click-through rate (CTR) of that article for that user.\n", "\n", "This comparison will highlight the trade-offs between **exact Bayesian inference** (NormalInverseGammaRegressor) and **more accurate likelihood modeling** (BayesianGLM) in contextual bandit settings.\n", "\n", "[1] Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. A contextual-bandit approach to personalized news article recommendation. Proceedings of the 19th international conference on World Wide Web, pages 661–670. ACM, 2010." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from numpy.typing import NDArray\n", "from scipy.special import expit\n", "\n", "rng = np.random.default_rng(8)\n", "\n", "\n", "class ClickthroughOracle:\n", " def __init__(self):\n", " self.context = None\n", " self.rewards = [] # bernoulli outcome of the clickthrough\n", " self.optimal_rewards = [] # p of the optimal article for the given context\n", " self.expected_rewards = [] # p of the article chosen for the given context\n", "\n", " self.article_coefs = np.array(\n", " [\n", " [-0.4, 0.005, 0.1],\n", " [-0.8, 0.0, 0.0],\n", " [-0.3, -0.005, 0.0],\n", " [0.1, 0.0, -0.5],\n", " ]\n", " )\n", "\n", " def set_context(self, context: NDArray[np.float64]):\n", " # the context will consist of a numerical age column,\n", " # a one-hot encoded gender column.\n", " self.context = context\n", "\n", " def generate_reward(self, _coef: NDArray[np.float64]) -> int:\n", " p = expit(np.dot(self.context, _coef))\n", " reward = rng.binomial(1, p)\n", " self.rewards.append(reward)\n", " self.expected_rewards.append(p)\n", " return reward\n", "\n", " def best_expected_reward(self):\n", " self.optimal_rewards.append(\n", " np.max(expit(np.dot(self.context, self.article_coefs.T)))\n", " )\n", "\n", " def article_1(self):\n", " # article that really appeals to older men\n", " return self.generate_reward(self.article_coefs[0])\n", "\n", " def article_2(self):\n", " # article that nobody really likes\n", " return self.generate_reward(self.article_coefs[1])\n", "\n", " def article_3(self):\n", " # article that younger people like, but is equally enjoyed by any gender\n", " return self.generate_reward(self.article_coefs[2])\n", "\n", " def article_4(self):\n", " # article that women of all ages like\n", " return self.generate_reward(self.article_coefs[3])\n", "\n", "\n", "oracle = ClickthroughOracle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Approach 1: NormalInverseGammaRegressor (Exact Bayesian Inference)\n", "\n", "We want to use the Bayesian version of LinUCB to recommend articles to users. This is done, in part, by setting the underlying model to be a Bayesian linear regression model. We'll use the `NormalInverseGammaRegressor` class to do this. \n", "\n", "The default constructor arguments for `NormalInverseGammaRegressor` will give us a prior that is a normal distribution with mean 0 and variance 1, which corresponds to a ridge regression model. This is actually a problem for our use case, as our rewards will be either 0 or 1. There are a number of ways we can fix this, but the simplest is to center the rewards at 0 by subtracting 0.5 from them.\n", "\n", "**Key characteristics:**\n", "- **Exact Bayesian inference**: Uses conjugate Normal-Inverse-Gamma priors for closed-form posterior updates\n", "- **Gaussian assumption**: Assumes rewards are normally distributed around the linear predictor\n", "- **Fast computation**: No iterative optimization required\n", "- **Trade-off**: Model mismatch since click-through is binary, not Gaussian\n", "\n", "We can also set the `lam` parameter to 4, which corresponds to the ridge regression parameter `alpha` in scikit-learn. This regularizes the model coefficients more strongly toward 0, which is useful as we know that they are unlikely to be greater than or less than 1.\n", "\n", "Additionally, we'll set our choice policy to be the `upper_confidence_bound` policy, which will choose the arm with the highest upper confidence bound. We'll also set the `alpha` parameter to be 0.80, somewhat arbitrarily. This parameter controls the trade-off between exploration and exploitation. A higher value of `alpha` will lead to more exploration, and a lower value will lead to more exploitation." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Setting up the NormalInverseGammaRegressor approach\n", "\n", "Next, we'll define our action space and reward function. The action space will be the set of articles, and the reward function will be whether or not the user clicked on the article." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'll define our action space and reward function. The action space will be the set of articles, and the reward function will be whether or not the user clicked on the article." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from enum import Enum\n", "\n", "\n", "class Article(Enum):\n", " article_1 = 1\n", " article_2 = 2\n", " article_3 = 3\n", " article_4 = 4\n", "\n", " def take_action(self, oracle: ClickthroughOracle):\n", " if self == Article.article_1:\n", " return oracle.article_1()\n", " elif self == Article.article_2:\n", " return oracle.article_2()\n", " elif self == Article.article_3:\n", " return oracle.article_3()\n", " elif self == Article.article_4:\n", " return oracle.article_4()\n", " else:\n", " raise ValueError(\"invalid param\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Next, we'll create our arms using NormalInverseGammaRegressor. Linear bandits are inherently contextual, so we'll need to use the `ContextualAgent` class." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from bayesianbandits import (\n", " Arm,\n", " ContextualAgent,\n", " NormalInverseGammaRegressor,\n", " UpperConfidenceBound,\n", ")\n", "\n", "# Create arms using NormalInverseGammaRegressor for exact Bayesian inference\n", "normal_arms = [\n", " Arm(Article.article_1, learner=NormalInverseGammaRegressor(lam=10.0)),\n", " Arm(Article.article_2, learner=NormalInverseGammaRegressor(lam=10.0)),\n", " Arm(Article.article_3, learner=NormalInverseGammaRegressor(lam=10.0)),\n", " Arm(Article.article_4, learner=NormalInverseGammaRegressor(lam=10.0)),\n", "]" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Running NormalInverseGammaRegressor with UCB\n", "\n", "Let's run the bandit for 10000 iterations, and see how well it does. We'll plot the cumulative reward over time to visualize how quickly the bandit learns to recommend the best article for each user.\n", "\n", "Note that this step can be slow, depending on the number of samples being used to estimate the posterior. If you want to speed things up, you can reduce the number of samples used to estimate the posterior by setting the `samples` parameter in the `UpperConfidenceBound` object. This highlights the connection between Thompson sampling and the UCB algorithm - both are strategies that bake the uncertainty of the posterior into the point estimate of the reward used to choose the arm. In the limiting case where the number of samples is 1, the UCB algorithm reduces to Thompson sampling." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "normal_ucb_agent = ContextualAgent(\n", " arms=normal_arms,\n", " policy=UpperConfidenceBound(0.95),\n", " random_seed=rng,\n", ")\n", "\n", "\n", "for _ in range(10000):\n", " user_gender = rng.binomial(1, 0.5) # 0 corresponds to female\n", " user_age = rng.integers(0, 47) # 0 corresponds to age 18\n", "\n", " context = np.array([1.0, user_age, user_gender])\n", "\n", " oracle.set_context(context)\n", " oracle.best_expected_reward()\n", "\n", " (action,) = normal_ucb_agent.pull(np.atleast_2d(context))\n", " action.take_action(oracle)\n", " normal_ucb_agent.update(\n", " np.atleast_2d(context), np.array([oracle.rewards[-1] - 0.5])\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Approach 2: BayesianGLM (Accurate Likelihood Modeling)\n", "\n", "While the NormalInverseGammaRegressor approach works well, it makes a fundamental assumption that doesn't match our problem: it assumes the outcomes are Gaussian when they're actually binary (0 or 1 for click/no-click).\n", "\n", "The `BayesianGLM` class provides a more principled approach by using **logistic regression** to model binary outcomes. However, this comes at the cost of exact Bayesian inference - we must rely on the **Laplace approximation** to approximate the posterior distribution of the coefficients.\n", "\n", "**Key characteristics:**\n", "- **Accurate likelihood**: Uses logistic regression for binary outcomes, matching the true data generating process\n", "- **Laplace approximation**: Approximates the posterior with a multivariate normal distribution centered at the MAP estimate\n", "- **Slower computation**: Requires iterative optimization to find the MAP estimate\n", "- **Trade-off**: More accurate model but approximate inference\n", "\n", "Let's compare how BayesianGLM performs on the same problem. We don't need to center the rewards since logistic regression naturally handles binary outcomes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from bayesianbandits import BayesianGLM\n", "\n", "# Create arms using BayesianGLM for more accurate likelihood modeling\n", "glm_arms_ucb = [\n", " Arm(Article.article_1, learner=BayesianGLM(alpha=100.0, link=\"logit\")),\n", " Arm(Article.article_2, learner=BayesianGLM(alpha=100.0, link=\"logit\")),\n", " Arm(Article.article_3, learner=BayesianGLM(alpha=100.0, link=\"logit\")),\n", " Arm(Article.article_4, learner=BayesianGLM(alpha=100.0, link=\"logit\")),\n", "]\n", "\n", "glm_arms_thompson = [\n", " Arm(Article.article_1, learner=BayesianGLM(alpha=100.0, link=\"logit\")),\n", " Arm(Article.article_2, learner=BayesianGLM(alpha=100.0, link=\"logit\")),\n", " Arm(Article.article_3, learner=BayesianGLM(alpha=100.0, link=\"logit\")),\n", " Arm(Article.article_4, learner=BayesianGLM(alpha=100.0, link=\"logit\")),\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Running BayesianGLM with UCB\n", "\n", "Let's run the BayesianGLM approach with UCB policy and compare its performance:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Create a new oracle for BayesianGLM UCB experiment\n", "glm_ucb_oracle = ClickthroughOracle()\n", "\n", "glm_ucb_agent = ContextualAgent(\n", " arms=glm_arms_ucb,\n", " policy=UpperConfidenceBound(0.95),\n", " random_seed=rng,\n", ")\n", "\n", "\n", "for _ in range(10000):\n", " user_gender = rng.binomial(1, 0.5) # 0 corresponds to female\n", " user_age = rng.integers(0, 47) # 0 corresponds to age 18\n", "\n", " context = np.array([1.0, user_age, user_gender])\n", "\n", " glm_ucb_oracle.set_context(context)\n", " glm_ucb_oracle.best_expected_reward()\n", "\n", " (action,) = glm_ucb_agent.pull(np.atleast_2d(context))\n", " action.take_action(glm_ucb_oracle)\n", " # Note: No need to center rewards for BayesianGLM\n", " glm_ucb_agent.update(np.atleast_2d(context), np.array([glm_ucb_oracle.rewards[-1]]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We can see that both agents quickly learn to recommend reasonably good articles per user, and clearly achieve sublinear regret, though in this specific instantiation, the BayesianGLM agent learns slightly faster than the NormalInverseGammaRegressor agent." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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9Lbwrlajbjcq1L+r2pi47GgibFXVxUXcidUEJHl+lLh4at5HVuDKN6QimMoZ/ZnVp0PFfXXNyQ920li9f7sZKZUVdBjWWSl2+1HXBo8xn6mLk7du9lTcS+zV4X2nMhpIGHHnkkW46q/2VEx07dnTdaYI/k7qjBb+vkjVofwVnaFKXP42T02uydu1a1/XxvPPOc12xVDY91CVOXTrU9Sq82+Y111yTaczEvuqO9oPWpW6TWrf3PurCd+KJJ9qoUaNcubzvp7pdhVPXGHXD8x7qora/2zg4g5c+i7ozqQ5eddVVIZ9JXa2y+o5qIHxwNxjtd/19eJcvzVf3OnVPy6qs+t6qrPqO6n2Cu7R5XS69z6vvmMYfdO3aNaQbkLqWattpH3rbVQ91IdPg8V9++cX//dW21/4L7oKjZB7q0pWVyy+/PKS8Od2POakT6hYo6oqXVRe7/Tn+qtud6u2+6LiqrnNav7p5ez7++GPXjcrr1qvP4CU/yWnX42A6pg0bNsw9ND5HGRe1j9X1SwlDPMHbWO+jbasu5nr/4Pqrfaxjr7qkebT9tA+8MSuqhxrvo4xreh5cJ7Rt9P7eOrX+//77z8aPH5+rzxVcXu+4oWO79qPqbDB9l4PH02i8kLqUBn+v9L3P6juvjHrB3/nghAe5pTqu7qhe90d181P3+NwkrPF+Y1XfRcmplHRB43bVRVb0v+qXV4f2R062WTTOhzzKPOnVa9U1jS1XN9q8HLOMnCOQQp7SyaZ38M8J9V3X4Gr9qAXTSYp+hPR6sOCT6uCTheDxHMHzFQQF03tp/EIwpX+W4D7q+mHXiaJ+VBUk6EdG4yzCT8S8k4ucUB9s9VNXWXXQVqASfOD2PqtOLsPpJC98W6hsKlf4j1n4Z95f6vOuPuTZjZXaW3kPPvhg/wlgTrbVgexXBSsac6c+5DoB0Tbx3ier/ZUT4eXJatt6Y1h0cujRc/3oe3VKiVV0sqV+8sEnLHpogLeEj8XJahvtq+7o5Ns7MQ9/n7ffftuNd9G28H7Qg09uPfqh1g+3xgKFy+02zmp/qr5qHEz4/Kzqa27qgwKL4DJoLJECYW/MnsqqMT9ZldU7EdfJshJs6CRfJ+DBFzK0bbUPFTSFb1uNBfT2n/d9CD+WKajK7iJG+L7O6X7MSZ3QunXipb/TdtdJvoLj4G2Q2+NvTo91oosJOm54Y15U5xRYKcDSibAowNUYLo2HUxk1fk5j3MLHZ2VH+1j7Wg99H1VH9X6zZ8+2p556yr+cyvHggw+6baUxR3ovbdP169eHbA99ZgVIwWN9FFSpXuiijqh+6O+UdTZ8H2lMjnh14p577nEn7do/qj86IQ4f65YVZbVVcgjVb/2mat3eiX94Hda4N297Znes0vc+q++8Ltp5J+z6bh8oBWJa16JFi9wY09wGZgqkdFFk3Lhxbh9qO2qefouCAyldeAu+eJdbOdlmB8Jbd27PhzyqK169Vsp0pT7XvtKFgunTp0ekjNh/jFRDntKBQwNkgwfa50T4QS072WU3ym5+eBKJnNCBWleldfDWyZUG5OvquH7gwwfTSk6zVulqp34UvvzyS5chSVe/lSFNV7z3Z5BvfmQU08m+Wip0ku1dPT4Q2W2rA9mv2q4aDK4MSApidOKik2udWO3v/YBy8r46IVOiD+1P1RNlxNJJ0pNPPulfxnt/JQHJ7kp++ElsVttoX3XHex/Nzy7dr7aL6rHqc1bfT6+1L6sbFud2G2e1/XLzHd3f+qCMhGq50YWHF154wZ0462qzTuCVRCC8rN6JuEcD8TWwXYHXyy+/7Obpb3R8UotHVu+f1ZX+nArf1zndjzk9niiZh1qMlbxAyyiJiNKF//777/5EJrk5/uYmQ58uRCmAVOIQnVB/++23LqDxWmu991Urrsqj1xXQqtVR5da8/dm2SgiiAEStdx6ljtbxW8k51PKt1/XeSrwRXifUGqrkDKrvSpSgwEwnsV42RW95BTYKeLOiFmzvgpICAl2YU7IaL0W4gjoFj1lRkKYAU7+lSoSklnEF9mqpUWAWXt6cfK/0fVBrp1q/FRR6dMHHu+gTiSyz+t3UcVHbRcGw6mhuqOVa5dC+08UUJVBR+VTPtd20Tv0+K8g8EAdyvqDPl10SJq/l19uW2u4SieBHxzUFVNo2qpeIHgIp5DlloNHVOl1VCu6ulRU1++uHQVdi9aPj0UmpflAifR8bvZeu2no/HqIMY+JdNdaPnQ6E+lEPvreHfogPlE5i9aOsh6626aRN96vRiY/3WfXD61399GheNO7pox90BVI6QdOPf7Dg8oZT9xNd9c3r9Oa6gqgU3jopCS6fd2U/r+mk8N1333VlUOuEfoiDTxS91k8FMMEn7JGuO143RJ187et91FVUrRR//vmnu1Ie69s4N3QyrpMtnfwGt2p53e/2RSfAOkHWhQMFv1qHtq32q1pjgo8b4bzvg1oh1ZLr0RV2BafeyfXe5GY/7qtOeHTSpYeyAyo4ULCo7rqPP/54nh9/dSKtG7Cri5Naa3WM9bqEBtM8PVR2XaxSd0hlNMzuJq/7oux2wS0wCtZ0ch+cJVJdVPUZw+nigFqA1BKliws6OQ6+t5JeUwuP3iMn+0jHQB0T9FAXcrUw6HP27Nkzy+BFXYXV9VABsS7meZRG/EB+k7U99ZnuvvtuyysKtHVxSa3aqoPhLdD74nWxU7Ck754CKNH/+l6r/KqbwdslKzm9MLA/9J3I6jdPvPne90bHC/XY0IUMfQ8O5KKL1305q5ZF5C+69iHP6UCtHw/9COqgF05XjXVQEe+mc2qyDqaryd5JX6Tpqo5HJ0ia1omurvh4V6t0IA5ONasTIXVV2F9aV3iXDF1tU+ud141FV+M0Tyc5wV1bdCVcJ+n7uy1ym/48u7FS4TdQ1kmcrporkAg+IVFrh65+e/s2L3lXFsOvJIbXp7yiEyl1MdFJoh46CQju/qT96bXoaT+ECx7HcSB1R1fhdRKutP1Z/dAGv4++n0r5qyv/WX0/w7dltLdxbmRVVm273FwE0fbRd8U7BunEV+tVIBm+DTTtpYDX91dptfv16xcyZksnfzntMpTT/ZiTOqHgJbgcooBKLSveMnl9/FXwoPfSMUItMuEtFNou4dvUa4nLafe+cAqate10k2mP9l/4+ygteVb3TVJXTKVoV0uaxsppmwUHwVqXd1+krFp2g79r4bcHUKCgbmkqS3bH46zqsAIwtcjsL213va9SqKulL1K9N7KiCxDqyaDuzPtDQZPG/mk/eoGUAjIF+rqg5y2zN94FvKwC5QOl74y2oVKzB9N76buu+uul4BcdN1QPdD4U/n0U/VaqxTInF4kkuF4jOmiRQp7TiYCuKupHVAc/dZXQfST0Y6Arouo2oe4m3kFBVwp1ku51adCVcv3w6spW8JXdSNAVQP2g6z11tVFBigZUqyuPN95IJw86kdCVSXVJ0ZVejS1QFyzd62J/qI+0utLofjj6zLoypeQWGoTsXSVVMKcfCvWz13bQj7lOdBV06kru7bffvl/vrSuf2p66opnbhBOisuiR1T2T1J1IVx7V8qhEAuryoBMUdZ0JvodWXtGVe12d1HgRnZio24p+mA7k6m1uaJ/pRFtXezUeTCfA4VR3NDBaJ2RKRKBWKu1XtdhqIPrUqVMPuO7o5FitTNoXumeN6pC2hbry6IRE28n7IVb/e30/Vb90tVRX/7VenUhpu+k1rc/r+hXtbZwbuheOTlY1zuW6665zJ9QKbBRkZBXIZkUnnDpZ0vbUyaCOZ2q90fdIF1R0XFKLhD6/utVpYLhOHvW+qvPqRqYWZZ28anmdjGsdOblKntP9mJM6oSQnSqSjMUm6Mq6TuEGDBvkDgfw4/qqFTMdNJTVQYBTcWit6HwUI6qqlbaTPpf2lz5mTCzEKJr0xffp8ahHQWFa1jNx7770hLTL67Douaf/qu6ftpcA3K/rNUtdObXPv5D2Yxl/pNf2G6DutdWocobrfab167tVHnVSrFVBjkHRBTBfu9BuTXQICJWjQeB3tF3XFVL1R2Q8k0NFxSnVV3Yt1LNIxy7v3n+qWWnA1rimrwFkBY3iCC1H5wscsenXqQE72VS612CmJTHDApGOQLkjpNyy4W2pWVJfUFV0XJbWd9Tm1r3Izxi87qlc6h1F5dIxR9z0lfNH3XMeY8Is2qvPq2qfPpHsg6rirFisFVzoXUWt/+JAB1SOvXus7oWW0H1Q3VKcQZVHMGIgCRvdQuOaaa9y9VpQWVffh0H06lEI6OIWrUgXrvglK71u4cGF3f4eePXuGLOOl3w1OS7y3dKReGlHdh8Oj9LW6h4nuy6B7yyiVbJUqVVxq1uD7Ekn//v1dGuIiRYq4FMlKo+qlcN3Xe2eV/lzpVZVaWfcy0XZQOfQ8/J5P8vHHH7t0tXpv3YPm4osvznT/D++zhMuqjPub/jyYlwo3q1TFP//8s9uvSmWt+3voXjYzZ87MslxZpb890P2qbXPWWWe59L5KPax7ruieMuHp53OT/jyrbaByBqdADr6Xlv5GqX4XL17sy4rqnNLBK8Wu6rhSQ+seZp999lmm8oVv39zUHaW8VrpgpeFW/VGZdT8a3V8nnNK7K6Wu7rmje5Jp/6mu/+9//3NpsYPldBtnt5+zq6/h2z+7lMvZbZus3u+bb75x913SZ9KxR/dXGjBgQI72vefXX3/N9Nk+//xzl5pen0MPbSvVk9mzZ4f8re4fpe2u7a9UzmPGjHG3JOjcufM+P2dO92NO6oRSgOv+NbqHlbaFjiW6pYG+r8EO9Pi7L7ovlD5rVvcuUyprpbbXveL0OXU7AX0vJkyYkOv05/r+6TPqfk4TJ04MWVap9q+44gqXxlz3f1N6eaW7z+47LaobupVBdvde0j2wtP+1vbTd9N3WrQ50P6XgdOq6Z5y3H7UvtN+C04xndVxSnTnyyCPdd1L3PLr77rv9t4QIvvVDdnVYnyn8tgCyfv16d2sG/b5oO+h3WeXXfa6+/fbbbI/5WT1+++23ff4GenKa/lw2btzo0p2rXgffBkL3R9M6dP+4cOHpz73U47pPm25REJwKPbfbLCuqE7plgI7jWr/qnert77//nu3f6LurdPiq4/ob3WNLv5XBadyzSn+uZXWbBNUb7x5diK4k/RPtYA6IBrWCqa88fYwB5BeNQVJrt1oB1NqC+NCqVSvXbVetAQDgYYwUAAB5QAkMwq9Vvvfee66rl8bKIT7onmDKcqcufgAQjDFSAADkAQ1C11hGjUvS+BuNdejfv78bI6p5iG1KHqEkAhpnpmQ64WO6AIBACgCAPKCB8BqAr0QFaoVS1zC1aig5gZJRILap67fu3aQkLIMHD47IvZUAJBbGSAEAAABALjFGCgAAAAByiUAKAAAAAHKJMVJ70tHqBmq6UVtObpIIAAAAIDEp46pugFy9enV3c/TsEEiZuSAqqztyAwAAACiYFi9ebDVq1Mj2dQIpM9cS5W2s0qVLW7Rbx1atWuVu2Li3CBigzoDjDPILv02gzqAgHWM2btzoGlm8GCE7BFJKXbinO5+CqFgIpHQTR5UjFioSYh91BtQZcJxBrOG3CYlQX/Y15Cd2SgoAAAAAcYJACgAAAAByiUAKAAAAAHKJMVI5lJ6ebrt27bL86COq91E/0VjqI4rYRZ3JG4UKFbKUlBRuiQAAALJEIJUDmzdvtv/++8/llM9reg+dGCt3Pfe0AnUmuooXL27VqlWz1NRUKiMAAAhBIJWDligFUTqhUkrGvA5uFEilpaVxJRzUmSjS93Dnzp0uFev8+fOtYcOGtBADAIAQBFL7oG52OqlSEFWsWDHLawRSoM7EBn3fCxcubAsXLnRBVdGiRaNdJAAAEEMYhJNDdLMDCh7GKQIAgOwQSAEAAABALhFIAQAAAEAuEUghpvz666+uG+X69eujXRQAAAAgWwRSCap79+4uIHnqqadC5n/11VdxNd4r0QIrJS149tlnrXXr1laiRAkrU6aMtWjRwh544AFbunRptIsXMxYsWOD2+5QpUzK9dtxxx9ltt90WMm/y5Ml27rnnWpUqVVxSCGXZu+aaa2zOnDkh6/MeSmfeoEEDe/zxx/PltgYAACDxEEglMJ1QPv3007Zu3bqIBwMFTSQ+844dO+ykk06yJ5980gW6o0aNsunTp9vLL79sq1evtldeecUSlRfI5IUhQ4bYkUce6bbvBx98YLNmzbL333/fBam9evUKWfbnn3+2ZcuW2T///GOPPPKIPfHEEzZgwIA8KRcAAEhsBFIJrGPHjla1alXr3bv3Xpf7/PPP7ZBDDrEiRYpYnTp17Pnnnw95XfMee+wxu+yyy6x06dJ27bXX2jvvvGNly5Z1J7GNGzd299k655xzbOvWrfbuu++6vylXrpzdcsst7l5cnkGDBtnhhx9upUqVcmW76KKLbOXKlTn+TN77/vjjj3bwwQdbyZIlrXPnzu7kWH766ScXQIa3YN166612wgkn+KdHjx5tRx99tEtxXbNmTVfOLVu27PUzK5i66aab3A1a9R61a9cO2bZ6z6uvvtqlytff6P2mTp3qf/3FF1907ztixAj3focddpjVqlXLjj32WHvjjTdcgOUZOnSodejQwX3WChUq2Omnn25z587NFJh88skndswxx7j3O+KII1wLzPjx49021rY55ZRT3L2QPArgzjzzTPdear3R+h999FF377K77rrLypcvbzVq1LCBAweGbL977rnHGjVq5PZzvXr1XICiWwNEm+rbFVdcYaeeeqp98803rs7XrVvX2rZta88995y9+eabIctrW6read9dfPHFdtRRR9mkSZOiVn4AABC/uI/UfujyymhbtWmH5RWf+SzJMl+9r1SqiH17c4ccr6dQoULuhFnBik7cdYIcbuLEiXbeeefZww8/bOeff76NHTvWbrjhBnfCqZNuj05KH3zwQXvooYfc9G+//eZOYtWa8tFHH9mmTZvs7LPPtrPOOsudnH///fc2b94869atmztZ1bpFJ98KUBR8KYDq0aOHex8tn1N6X5VHQZnSU19yySV25513utaIE0880b2/gsOrrrrKLa9A7uOPP3atD6KARMGXunWpNUKBhgIkPYIDiPDPrM+qk3UFLwqAFi9e7B4edS1TYPbDDz+41hCdxKs8Cm4UoAwePNi1SLVq1SrLzxXcYqOgTtvm0EMPtc2bN7tyaNuqq1twSm6VTQFa9erV7brrrnP7WkHqSy+95IIe7Vv9bd++ff1/o0BOdUEtYmPGjHHbSftdAdkff/zhtpXWpbJ6dUbrVBCr91ErmrrNad7dd99t0aSAWq152ZVDdSE7EyZMcPVfwTIAAEBuEUjtBwVRyzdut3igk++WLVu6E+7+/ftnev2FF15wJ/teFyi1OsycOdON4wkOpNS6cscdd/inFUgpKNIJev369d08tUgpuFmxYoVrDWnatKkdf/zx9ssvv/gDqSuvvNK/DrVsKDhp06aNCxb0Nzmh91ULjve+CoDUquIFjxdccIF9+OGH/kBq+PDhrrVIQZ2oFUmtEd44G42nUTnUMqTP4914NfwzL1q0yC2rliIFPWrV8Kil6c8//3TBoVr2vEBMY9I+++wz16KlgErje8L3z7Bhw9xzBU0KaMQrq0cBn1q6tG+aNWvmn68AslOnTq5FScGyAil9XgWvom2gACiYgjp9XgVkCmifeeYZF5zed9997vWePXu6sXX6TNqWojFcwa11el8F0NEOpNRFT5o0aZKj5du3b+8+t1oXVY+0XwikAADA/iCQ2g9qGcpLe2uR2h8aJ6WgQCe/4TSe5IwzzgiZp5PwPn36uJYcBSairmLh1OLhBTOirmI6yQ4OiDQvuOueWgDU+qUubxq7lZGR4Q9SFHjlRPj7qqtd8HsoSNKYGSVvUAuKWqpOO+00f+uE3nvatGluvkcJB1SW+fPnuy6DWX1mBZZqpVHwoRYtdbc7+eST/etUMKiWvGDbtm0L6ZIX7vXXX3etTwps1EIUHCCoJUktRGpxCd5OwYGUgq/gbS3NmzcPmRfedVLdOINbtbRM8Dq1z/U5gv9OrVQqoz6LPqcCN3Un3Bu9z8KFC91zL6FDcN1Q10q13h2I3CaK0OfQ/lUQNWPGDLv55ptdF9TwpCwAACAP+HxmGelmadvMdm03y9hllr7Tkv7oZ2VX/mNJVRqaddrdgygeEEjth9x0r9ufE0OdpKakpERscL66bKnVQi0Nwa1MuaEMc+EKFy4cMq3yZjXPCwIUMKgceiiIUQuLAgNN5yaZQ1bvEXxCrRYuBVpqMbn++uvtyy+/DGmVUSCgrmtqwQmnLnvZfWZl2lOgpZN/JS1QtzmNyVGLk9apgE5ZBsN5AZxas2bPnh3ymv7GayUK1qVLF9fi1a9fPxcMahsq2AnfTsHbwqsv4fO87b+/+23cuHEuOFVyBu0rdVvUtg0fSxdO3TW9cVRLlixxrXHBWfjUDTI7XpC2YcOGTK+pdVFl8FpQ5e+//7Z27drZvmg8nLL1iQIqBYZqjVVw77VEAgBQYOj8yZdhlpFmlr7LbMem3c996bsDHr02d4TZvJF7gp5dgWV3bd29jKb1mvs/ffdr7u81vWfd3jq1vizoDEa/wr4tSyyeEEgVELriri5+ak0JppNJjZMJpmmdoHqtUZGik901a9a4suiE1hunkhd04q9gTWN81PqiFqnggEhd5LwT6tzQCb66KeqhroxqmVq7dq1b5/Lly10ArFa5rFx44YWui5xSdWc3Tkq0jRRwKYhSq42om120qLuhgrr777/fP89radqb4K6P2i6S022uwLJixYquBVNdLj0bN260f//91x9AqUVQy6l7ogLmrIKuvY2TUh3XhQsFqARSAICYa7lR8LFrm9nOLWbpO8zSduyeVsCzba3Z+kW7n2t+2vY9y+1u5XHBi4IdvaZpPfT6js1m29cHApwYkrRihsUTAqkCQt29FFyoe1YwjQFSC44SQCg4UOvDq6++6rqcRZpae3T/HqX5/t///ue6Vul984I+q1oZlGBCAY83bsnLQKeufxpbpSx7anlSYKWxSvrs2dF4MrUgKQhScPbpp5+6DHA6UVfLlFpElBFPJ/U60VfXwu+++86Ng1I3wdtvv91Na0yaxqwpSFK3Mo2dUiuXF7hqnrrWvfXWW+791Gp37733WrSoJU1lUCuU6oo+Q1ZBS6Qp2YaXXVD7SwGm6otaMpXYRLTv3n77bZfoo2vXrq6VUcGaukMqKYhXbo/WoYBXwZOSZigph8bx7aubIgAALrhRkKKARcGMghEvQNm+MRDsqMva1tVmW1bvXk6vK9hxz3cEAp3tG/bMU2vOLrOdW3e/rgDK4uAeh8kpZoVSd/8f8ii057FnOqlQ6DxNp6SaFS5hVqiwe/hSitnmwuWtxNE3xFVKcQKpAkQJGTRGJJhaUnTCqfE4OknVibuW298ugHujE2B1sVNSAwV0em8lZNAJcKTpZFrpwJUAQuO9gmlc0ciRI10Li4IZdQtUV0AvIUZ2lKVOQZLGLynoUVCh7mveeCM91zqVjluZABVkqVulN3ZJLR5KBKHyKDugulqq+5zSdStNuQIt0fp08q+gQN351Iqo7RWeqCK/aP+obAo8da8mte553eHykhJZaEyVxvipC55aqTR+T8lLgrsFaoyfWs2URETJNtRqpRZPjQtUZsZgCnhF+091XWnTvWyOAIACRK0y//5stkMB0NbMLTdei47X0rN4vNmGRRbXFMQULm5WpLRZ0dJZBEHJZqkldwc3wQGQ/i9Zyaz1ZWbFK5glFw5aJikiRfNlZNiWlSutRMnKFk+SfLkdrZ2AdOKlMRcajxF+ZXr79u1uXIxOdvOj609ejJFCYqPO5J38/v7nFwXwSiZSuXLlkMQjAHUG+Xac8Y/NSQ8dk+O14HjjbbyxN2vnm/03fveJu/e3IY+weV4rkNf6o/V63dj0//Jp0d/ZKcXMUtUqk7o7MClczCylSGgAo//1WtEyZoWK7G7JSSkaCIZK1zArUWHPa0UCgZBbp5Ytsnu9Cn5i+HifEWO/S3uLDYLRIgUAAIC8o1adf4aZfXq5WVJyYOxPvKp3/O7gRMGNghoFLJouVjYQwKjlpkTF3YGNF8wo+AkOchQkIa4RSAEAACB3lk42W/xnIIObl7hg/iiznZstadd2q7hjsyUpzbWSIsQCBXHB1HLV/DyzhiftaQ3aExQp8PG6rmmeHnpdQVFq8WiVHjGIQAoAAKCgU5rqZVN2J0DwgiL/Y0/3O40V+ne42axv9rm6pL2dZCowqdIstPua99wFLkGBjHttz5gcje+pdqhZqeq7gyJ183OP5NCH3l1/V7Ts7q5w+ntvnUAEEUgBAADEepCzUymud+4OZpQgwaW8VrrrrYFucitnmc38ZncQ4Y05Cg6EvNYj/z2CfIHnajmKsPSi5S25aElLUiuPuvfVbGvW/iaz6tnfAgSIJwRSAAAA+0PprTcu2ROgeK03Sm6QZrZt3Z5U1mGtOgp8dm7ekwhhT6rr8NYfBUhpXva4HbsTLSh4ijXl65sde/fuliKXCntPK1Lx8pZRuZnLYKvkAUkxkDwAyAsEUgAAAKLWGmV3czc93bI7mNH9fNRyo+Bm/NuBrHLzfo3N4MbjkhkosAnOAKcxP6l7usB5873ne7rEFS9vVqtdoEtd8L2B1D1OmeaULa7O0XsfL6RWNCDBEUgBAIC8Me3T3QGHBvV7AUnw8339n5Nl9H9a0E1Ms0qHHfzeerh7Bu1Jse26tmXsGQO0bc/yMURBTLHyZlUO2Z3wQAGSUmYXKxea2tqjMUZNu5qVrh7NUgMFAoEUAADYP+pyNu7V3d3YgoMXBS6zvi04W/WIawOtP173NqW6VsATcsPTPckUiij7W8lAlzjX+hO0jMscpwxye9YJICYRSAEAgFCbV5ltXhEIiub/ZmX/HWVJqYVDW3b++TF2t1xwBjd/Rrc9z919fxTEeBnjUnZ3bytcdE+mN6W7Vva4YnvWlbQnE1yyWdVDzRqfEkiTrXsJASiQohpIjRo1yp599lmbOHGiLVu2zL788ks788wz/a/7fD576KGHrF+/frZ+/Xo76qijrG/fvtawYUP/MmvXrrWbb77Zvv32W3cn5G7dutlLL71kJUuWjNKnQm50797d7duvvvqKDQcAwRSwrFuwu9va7hl7urJl93zPdLbPc7COaR+bTXo3U/c2hSNFD2TvXPvr7hYYF9QkhQYn/v+Ts5gX9L8/KMri7wsV2RMMBaXEBoBEDqS2bNliLVq0sCuvvNLOPvvsTK8/88wz9vLLL9u7775rdevWtV69elmnTp1s5syZVrTo7kP6xRdf7IKwYcOG2a5du+yKK66wa6+91j788EMr6AGKtpunfPny1qZNG7dNDz30UIsVCnoVMEea1vn222/bgAED7K+//rKMjAyrXbu2dezY0QXeDRo0cMs9/PDDLoibMmVKlus57rjjbOTIkda7d2+79957Q1477bTT7Pvvv3fBvtaTF7Ir34IFC9x3YvLkye475H1mXXTo37+/+8wpKSnuc15yySXuO1G8eHG3vkceecS/ntKlS7v68Pjjj9uxxx6bJ58BwJ6B939/uzswcpnZMoLG5+zJ1KbEBd64HWV+++vL+Nl0R91q1ubq0IBI/6vFRmN5ACABRTWQOuWUU9wjKzop7NOnjz3wwAN2xhlnuHnvvfeeValSxZ1YXnDBBTZr1iwbOnSojR8/3g4//HC3zCuvvGKnnnqqPffcc1a9esEeaNm5c2cbOHCge758+XK3LU8//XRbtGiRxYoyZSLfJUJ156KLLnL15L777rMXX3zR1YWlS5e6Vk8FDe+8806O11ezZk23fHAgtWTJEhs+fLhVq1YtV2VTIKMgKDfvn1OXXXaZffHFF24/v/rqq1apUiWbOnWq+x7VqVPH39p7yCGH2M8//+xv0dV3RfXiv//+y5P9ASQ8BUVTPjAb32/39K7te+7vsyWQ0lrpruPRYVe4gMiXlGRbd2ZYsXZXW3LJSqFd5Vw3uD1d4ACgAInZMVLz5893J/9qQfDoJK9t27Y2btw4F0jp/7Jly/qDKNHy6uL3xx9/2FlnnZXlunfs2OEeno0bd6cvVauFHsE0rRNz75EfvPc50PcrUqSICzxF/99zzz12zDHH2MqVK91JtmieAg6dRFetWtUFIA8++KAVLlzYnfDXr1/fbcvgbawTcz3mzZvntvWMGTPs7rvvtt9++81KlChhJ598sr3wwgtWsWJFt/xnn31mjz76qP3777+uVaRVq1buPbWsWhDVtU8BjigwfuKJJ9w6CxUqZO3atXPvpXKIylSvXj23TgULKpu6eqrLp5aVjz76yD30Hl27dg0JiFR/gvdlTra1Wp4+/fRTGz16tOteKgqE9DkVlOambuR232a3fPh8le+DDz5w29G78CBqhevSpYur41451VIVXC/UQqWAe/bs2a7VEqHbWY+sjg3xzDuuJdJnyhP6fi2ftnuskNJd655BSn29pxUpadt6N0YoadXfeVuMEpXNGp60Zyqou5ubDPs/uOtblsuGvZ5peb1fJbPWl4eM/VFd2bhqlRWpWGlPmuww1CVkqhIcZxC/9SWn5YjZQEpBlHgnfB5Ne6/pf93oLZhOEtWNzVsmK+qmFdy9yaMbx23fvj1knroLamOmpaW5hxTqf6IlbVlpeaWwzo2Tsv4xTb9qeI7W4Z34eWXevHmzDRo0yHX1UkDqzVcwoy5wallR8HL99de7eXfeeafVqFHDTjzxRNc9rmXLlv5166T70ksvdetXi4aWUUCkboPbtm2z+++/38477zz76aefXLdLBWfa5jrB37Rpk40ZM8ZtV5UhvJw64b/lllusefPmrszaTwqIJ0yY4II2bzm9x9NPP+0+jwI/vYdaKLX/1a2zUaNGrmXSW35fX9zsltNrCiovvPBCtx0UiHmBlD7TY489FlL+3O6XnCyfVfm8af2vbel9ZgV9Wa1b+9Tb3sHr0wUFdQXUBQkFqzktV0HhbbM1a9a4epAo9Jk2bNjg6oK+Vwkpfaclb11tSQp6XHIE/Z9hSS5JQmBeUtp2K7xmthWZN9QdeINfT10xeb/eOqNwcfMVKmq+PWmpfUFZ2TKKlLOth1zopn1KbrDnXj567tPfqXUnOWX33yRp+dLRae3ZuMNs48qCVWcQUdQZxHN90flqXAdSealnz57Wo0cP/7RO3tVaoVYajRkJpsBKG1Mn6Ho4W1ZZ0qZl+V1s9yPvL8M+qBJq/E65cuX849EULCkpR2pqqn85BSEeBSVqNfr444/93diuvvpqF1ype5xauCZNmuQCrq+//tqV5Y033nAtTE899ZR/PQo4atWq5VqsFAzpZPScc85xrSOi5YPLqYf3uRSABVPQpmB5zpw51qxZM/9yCvS81ia1duk1tVY1adLEfQb9H7ytbrvtNhcwiIKGxYsX+98/KSn77arX9NA4PrXmacyekqOozigwVDfB4PLnZL/kdvmsyudN63+d4M+dOzfTZ85ufdp/Xr3YunWrlSpVyrXg6QIEQml7aptVqFDBPy4zUX6wVK90zIv6D5YCmvQdZjs27e4OpzFCGiukaXWJ0w1SFdjof40bUuIFtQ5pufSduy9qaZ5rJdrzWDnLkpZOzN+PodTX5eua7+LPzcrU8Np6MlEi63jsQBtTdQZxgTqDeK4vOf3Nj9lASt3MZMWKFSHjUDTttY5oGXVTC6aTdrWSeH+fFQUEemR3khs+zzuZ1sMpGdoKFkl7OmztvjIa9lqS3jcXmYiOP/541+VN1q1bZ6+//rprpfnzzz/9QY2CJgUHOhH3gh4Fk95nVWvQTTfd5B+XpgQWWq8SHci0adPsl19+cSfj4RRIqfubWqyU0ECJQjStoMo7kfd/tj3v988//7jgTl32Vq9e7W9aVeCjVipvOSVY8J57Y+HUonjwwQdnWqdo3JCSTGgM0ZNPPul/Lfz/rOg11Tl1Ifz888/d51WLnNdCEVI3wqi7Y/A4wJ07d7qrLeqa6HnzzTdd0pTs3jur8oXP97r47e1zeK83btzYvvnmGzetiwSqAwpg9bmCu3AisG+zOjbEuzz5XOlpZv/9abZ9Q9BNUcNujPrvz2azhuw+lilwUlAUy9RiVLyCWbsbdmedU+KE8Pv+VGtpSRo3lE3wlCgS9buAvEOdQbzWl5yWIWYDKZ2oKxjSgH4vcFIrgE6w1UIiGhOj8TVqITjssMPcvBEjRriTb68LVp64bmTerXtPtyvXsnCA6VvVncvLTifqwqdufcrsppYUjTHTCby6zynI0WtqmXj++ef9f6PWKyUxUMuQMiuqC5ky7XkUfGkMjrrZhVMArHFOyqg4duxY19VPyUDULU/70QvGgmldCvJURgVI2pdqbVIAEiy4m5UXPHhBlwIejfcJpisceoR3Bc0NtUq99tprLmukgtGcUGASnHFPQasSVQRvr/Duq8EU1KqpO5zqvXjJIfSZ//47Z+M0tE+D64U3Zk1j0d5///0crQPI0lvHma2YHlsbp/Fpu2+KqsQICn5cNrk99w5yWeX23PRU9xQ65CyzsrX2JFHQMtH/MQcAxK6oBlI6CVc3rOAEEzrpVBcjdQ1Tdyyd8Osk0Ut/rpNrL/uYWh+Ume6aa65xXcw0VkStJ2o5KegZ+/YW6Wsckyi4UdCiwMazcOHCTH+n7n0KZtSipSAvOFV969atXSuNssLtrXuckjToodYmvaeSIgR3rxSNQ1EApCDq6KOPdvOU4CG3NJ5JY6bU/TA48cKB0jrVpVCtYU2bNs3R3xQrViwkaFHd1gWB4Hl7o9YjJQJRS2xwwKUulmp21vdEVOeV5jyrz6zWKr3n3jLyKeD16gUQQl3sFowxWzTO7O8hu1tgvHsMBd+jaE3gWJ5jxcqbla+3O0V2kVK7gxo91//Fy+++4am/9afw7ue6Sarm675BKam7/05JEbwbqnqtRPqbsrUJhgAAiRlIKYGAuol5vBPryy+/3A3mVyY4je3RPXB0Bb5Dhw4uq1twv0VlKlPwpO5j3g15ddUfuxMJeEk31LVPWe68FiRRgKqsc2qFUra27777zp89L5gC1iOPPNJl+FOrjIIDz4033ugCHwUv2l8KFBQca51qAdM+VquiuvSpNUgtUeFd8Dzq7qexKG+99ZZrzVLZwu/dlBMKKtSFT/9rPJxa2xSEKEhUNzYFDcEUQITfp0ldFb1MgcHlU/KM/Ew6oLIrmNL21UUFtdIqiFJXxVtvvdV9FgVK5557rhv/puX0mra3WuCmT5/uxrepW6N3AULBsFcvvK59amXT/gX81i00+72v2R+7uwfn2okPBqXIDnqo85u6yjU5dXdLEQAAcSqqgZRudrq3NNBqyVAiAT2yoxP3gn7z3ewo6PTGlykwUDICpcnWdhcla7j99ttdIKqgSxnf1OqX1c1lr7rqKteCpUAqmFr+lIVPJ+E6edd61OKklkIFtuqaNmrUKNdtTK0iek1dB7O6f5iWVwCmrH1qAVMAoaDYK29Oqd4oOFCApy6Jyiao1kovC6FSswdTIovgBBii5bx7LQVToor8pFY+dYnU/bAUJCkIVeusgqjgFj19Zl1U0GdWsg+lkNffKlhW10wFZB7drNerF0pHr4BRY+m0HOCMeMJs1DPZbwxlkvOn1Pb/s7t1qkIDs8uHmKUWZ2MCABJaki+/bo4Uw7xuTxqLklXWPnU51MlrfmTt8lJT6yR4X4kD8pPSfCsIU3IJxJZYrTOJIL+///lF4wmVqEetxCEDav940+z3183WLcj8R03PMKt/wu4xR3sSK6DgyLbOANQZJOAxZm+xQVwkm0BsUFdApRVXt0B1LQOQoKZ8aPbD3Znnd37KrMWFZsXytzUWAIBYRyCFvVK3v8GDB7vxNeHd+gAkAN2P6bseZpMHZX7t1mlm5XbfKgEAAIQikMJeKemHHgASkG54+3gW3fTuXWxWNPuuDAAAwCz6nRABAPkuadtaS84qiDr7bYIoAABygBapHCInB1DwJOz3fu08q/Juu9B5FRqa/e83s8KB2xsAAIDs0SK1D949h3bu3LmvRQEkmK1bt7r/8/PeYXluxUxLfvWw0Hm6ke3NEwiiAADIBVqk9rWBUlLcvXZ0/x6dTOV1SkZSWYM6E336HiqIUipW3Tss/CbOcevXp8x+7R06r+31Zp2eiFaJAACIWwRS+6D78ujmpbqXzMKFC/PlBE659BWwcU8gUGeiS0FU1apVE6cihgVRGac+Z8lHXBO14gAAEM8IpHIgNTXVGjZsmC/d+xRErVmzxipUqBATNyRD7KPO5A21QCdMS5RMGBAyufa0AVb2sLOiVhwAAOIdgVQOKagpWrRovpwU6wRO70UgBeoMIiI9zWzI7f5JX7ubbWfNo9i4AAAcAJo8ACDR9Q3N0Ofr0CNqRQEAIFEQSAFAIls4zmz1nMD0yY+bFSsbzRIBAJAQCKQAIFGtnW82sHPovHY3Ras0AAAkFAIpAEhEaTvMXm4ZOu/2mUpFGq0SAQCQUAikACARvXF06PRJj5mVOShapQEAIOGQtQ8AEsn2DWZ9DjXbvj4w74hrzY66JZqlAgAg4RBIAUCi8PnMnqqVef6pz0ajNAAAJDS69gFAohj3WuZ5D6yKRkkAAEh4BFIAkAiWTDT76f7AdKNTzB7eYJaSGs1SAQCQsAikACDebVtv1u+E0HkXDo5WaQAAKBAIpAAg3j1dO3T6wo9Jcw4AQB4jkAKAePbPz6HTHR8xaxx2E14AABBxBFIAEK+WTTP7oFtgunhFsw63RbNEAAAUGARSABCPJr9v9mbYTXf/NzpapQEAoMAhkAKAeDPqObOvbwyd162/Welq0SoRAAAFDjfkBYB4MuJxs1FhN9g9Z6BZs7OjVSIAAAokAikAiBdbVmcOom6aYFaxYbRKBABAgUUgBQDxYM6PZh+eFzrvrrlmJSpGq0QAABRoBFIAEKt8PrMFo80GX2i2c1Poa5d+RRAFAEAUEUgBQCzaudXsyWySRxx0uFn94/O7RAAAIAiBFADEkvRdZv/8ZPbRRVm/fu1Is2ot8rtUAAAgDIEUAMSKH+4x++ONrF+7bbpZ2Vr5XSIAAJANAikAiKaVs8x+esDs35+zfr16K7Orh5slF8rvkgEAgL0gkAKAaBnf3+y7Hlm/dvhVZod1N6t2aH6XCgAA5ACBFABEyw93Z55Xq53ZRR+bFS0TjRIBAIAcIpACgGiY/5tZRlpguuurZoeeb5aSyv4AACAOEEgBQH5KTzNbNsXs3dMD81pdYtb6UvYDAABxhEAKAPLjxrqbV5r92NNsxueZXz+hF/sAAIA4QyAFAJGUkWH2y+Nma+ftvifU1jVmi8Zlv/zx95uVqso+AAAgzhBIAUAkW55ePMRs09J9L9viIrODu5g1PoXtDwBAHCKQAoBIWDvf7PV2Zmnbsl+mSjOzQ84ya3eTWeGibHcAAOIYgRQARMLLLUOnCxc3u/FPs9QSZoVSd/+flMS2BgAgQRBIAcCBSNu5uztfsNaXmZ3exyy5ENsWAIAERSAFAPsjI91s+CNmY14KnV/veLOur7BNAQBIcARSAJBbK2aa9W2X9WsXf8r2BACgAEiOdgEAIO5aorIKohp2MntglVmhwtEoFQAAyGe0SAFAbtKbP103dN7Rd5od3WN3MgkAAFBgEEgBQE7s2Gw26CyzHRsC80580OzoO9h+AAAUQHTtA4Cc+ORSs//+DEzXO44gCgCAAoxACgD2ZWhPs7kjAtPH3GV22ddsNwAACjACKQDYm0mDzH5/PTDdqLPZCQ+wzQAAKOAIpAAgO+sWmn1zU+i8cwayvQAAAIEUAGTr40tCp3suMUstzgYDAAAEUgCQpUW/my2fFpg+b5BZkZJsLAAA4NC1DwCyul/UF9cGpltdata0K9sJAAD4EUgBQLj5o8zWLwxMH38f2wgAAIQgkAKAcL89H3je4Xaz0tXZRgAAIASBFAAEm/uL2fyRgel2N7N9AABAJgRSAODZuNRs0JmB7VGthVmJCmwfAACQCYEUAHi+Drtn1IUfs20AAECWCKQAQP7+zmzu8MC2OLufWelqbBsAAJAlAikAyMgw++iiwHZoeqbZoeexXQAAQLYIpADg21tCt8EZr7JNAADAXqXs/WUASHAfX2o265vA9GnPmxUpFc0SAQCAOECLFICCa8KA0CCq+Xlmba6OZokAAECcIJACUDBt32g25PbQeWe8Fq3SAACAOEMgBaBg+uTS0On7V5ilpEarNAAAIM4QSAEomDfenfdrYPrSL80KF41miQAAQJwhkAJQ8Ay9N/C8fH2z+idEszQAACAOxXQglZ6ebr169bK6detasWLFrH79+vbYY4+Zz+fzL6PnDz74oFWrVs0t07FjR/vnn3+iWm4AMWztfLOZXwemL/ksmqUBAABxKqYDqaefftr69u1rr776qs2aNctNP/PMM/bKK6/4l9H0yy+/bG+88Yb98ccfVqJECevUqZNt3749qmUHEIN0EWZA58B03WPMyteLZokAAECciun7SI0dO9bOOOMMO+2009x0nTp1bPDgwfbnn3/6W6P69OljDzzwgFtO3nvvPatSpYp99dVXdsEFF0S1/ABiyLb1Zn3bm21eHph39tvRLBEAAIhjMR1ItW/f3t566y2bM2eONWrUyKZOnWqjR4+2F154wb0+f/58W758uevO5ylTpoy1bdvWxo0bl20gtWPHDvfwbNy40f2fkZHhHtGk91eAGO1yIH5QZ3KykdIt6ZXDLGnrav8sX4c7zFeikjagFTTUGVBnwHEGsSQjxs5/c1qOmA6k7r33XhfkNGnSxAoVKuTGTD3xxBN28cUXu9cVRIlaoIJp2nstK71797ZHHnkk0/xVq1ZFvUugdtyGDRtcZUpOjumel4gR1Jl9KzH5LSsVFERtr9fJ1je71mzlSiuIqDOgzoDjDGJJRoyd/27atCn+A6lPPvnEPvjgA/vwww/tkEMOsSlTpthtt91m1atXt8svv3y/19uzZ0/r0aOHf1rBWs2aNa1SpUpWunRpi3ZFSkpKcmWJhYqE2Eed2dcGSrekvz7wT/oOPd9Sz+hrlZOSrKCizoA6A44ziCUZMXb+W7Ro0fgPpO666y7XKuV10WvevLktXLjQtSgpkKpataqbv2LFCpe1z6Ppli1bZrveIkWKuEc47bhY2HmqSLFSFsQH6sxekkt8eU1gXFTV5pZ01ptuexV01BlQZ8BxBrEkKYbOf3NahuiXdC+2bt2a6YOoi5/Xb1Fp0RVMDR8+PKR1Sdn72rVrl+/lBRBjRj5t9teXgemj79SROpolAgAACSKmW6S6dOnixkTVqlXLde2bPHmySzRx5ZVX+iNXdfV7/PHHrWHDhi6w0n2n1PXvzDPPjHbxAUTTpuVmv/YOTNc+yuzgLtEsEQAASCAxHUjpflEKjG644QZbuXKlC5Cuu+46dwNez913321btmyxa6+91tavX28dOnSwoUOH5rhvI4AE9dHupDR+3b+jNQoAAERMkk/pMQo4dQdU2nRlC4mFZBMKGitXrhwTfUQR+6gzWVg62eyt4wLTl31jVu/YfNwrsY06A+oMOM4glmTE2PlvTmOD6JcUACJp7KuhQVTFRgRRAAAg4gikACSOlbPMfro/dN6570arNAAAIIERSAFIDMrm+fqRofMu+9qsStNolQgAACSwmE42AQA5snWtWZ9DQ+fd8LtZ5YPZgAAAIE/QIgUg/v3e12znpsB0x4cJogAAQJ4ikAIQ33ZsNhv1TGD6yBvNjrotmiUCAAAFAF37AMS34CCq+blmnZ+MZmkAAEABQYsUgPg1+X2zMS8FptteH83SAACAAoRACkB8mj/K7OsbA9NNTjercVg0SwQAAAoQAikA8endLoHnKUXNugS1TAEAAOQxxkgBiD+j+4RO3/mPWdHS0SoNAAAogGiRAhBfNq0w+/mhwPQJvQiiAABAviOQAhA/dmwye75R6LwOPaJVGgAAUIARSAGID9M/M+tdI3TeNSPMkjmMAQCA/McZCIDYt/hPs8+vDp133iCzg8jSBwAAooNkEwBi2+p/zfqfFDqvW3+zpl2jVSIAAAACKQAx7N+fzd7vFpiu0szs8m/NipePZqkAAADo2gcgRq2ZGxpEyaVfEUQBAICYwBgpALFp3Kuh0xd9alayUrRKAwAAEIIxUgBiT9oOswkDAtPXjjSr3jKaJQIAAAhBixSA2PPFtYHnzboRRAEAgJhDIAUg9m66O/OrwHSbsLTnAAAAMYBACkBsGfVc6HTt9tEqCQAAQLYIpADEjpHPmo3pE5i+8KNolgYAACBbBFIAYsNvz5v98nhgOjnFrFHnaJYIAAAgWwRSAKJv41Kz4Y+Gzuv+nVlSUrRKBAAAsFekPwcQXVvXmr1wcOi8XqvNChWOVokAAAD2iRYpANGTkW72TN3QeVf9TBAFAABiHoEUgOj5tXfo9HmDzGq2iVZpAAAAcoxACkB0/DPMbNSzgemjbjVr2pW9AQAA4gKBFID8t3OL2ZfXBaZLVTfr+Ah7AgAAxA0CKQD57+dHzLauCUxfM4IMfQAAIK4QSAHIX9vWm/35ZmD66hFmpauxFwAAQFwhkAKQv359KvC88WlmNQ5jDwAAgLhDIAUg//x4v9kffQPTba5k6wMAgLhEIAUgfwx70Gzcq4HpklXN6p/I1gcAAHGJQApA3ls2zWzMS6Hzug8hwQQAAIhbKdEuAIAEt2ub2aAzQ+fdv8KscNFolQgAAOCA0SIFIG8N6RGa6vzakQRRAAAg7hFIAcg7a+eZTf0wMN31FbPqLdniAAAg7hFIAcgbW9eavdwqMF2thVnry9jaAACgYAZSo0aNsrS0tEzzNU+vAYCl7zJ7pm7ohrjoEzYMAAAouIHU8ccfb2vXrs00f8OGDe41ALCxL4duhGPuNitVlQ0DAAAKbiDl8/ksKSkp0/w1a9ZYiRIlIlUuAPEqI8Ns+KOB6fY3m51wfzRLBAAAEL3052effbb7X0FU9+7drUiRIv7X0tPTbdq0ada+ffvIlxBAfPnqf4HnZWuZnfx4NEsDAAAQ3UCqTJky/hapUqVKWbFixfyvpaam2pFHHmnXXHNN3pQSQHz45mazaR8HpltcFM3SAAAARD+QGjhwoPu/Tp06duedd9KND0Cosa+YTXovMJ2cYnb0HWwlAACQkHI9Ruqhhx5y3fp+/vlne/PNN23Tpk1u/tKlS23z5s15UUYA8ZClb8QTofPuWWCWkhqtEgEAAMRGi5Rn4cKF1rlzZ1u0aJHt2LHDTjrpJNfV7+mnn3bTb7zxRt6UFEBs2rnV7KUWZmnbAvMeWq8BldEsFQAAQGy1SN166612+OGH27p160LGSZ111lk2fPjwSJcPQKx7r6vZlpWB6e7fEUQBAICEl+sWqd9++83Gjh3rEkwE09ipJUuWRLJsAGLZru27k0v8Nz4wr801ZnU6RLNUAAAAsRlIZWRkuHTn4f777z/XxQ9AAbDhP7MXDwmdV+MIs9Oei1aJAAAAYrtr38knn2x9+vTxT+u+UkoyoSQUp556aqTLByDWbF6VOYiSCz6IRmkAAADio0Xqueeec8kmmjZtatu3b7eLLrrI/vnnH6tYsaINHjw4b0oJIDZkpJs91yB0Xr3jzc4fZFaEFmkAAFBw5DqQqlmzpk2dOtU+/vhj979ao6666iq7+OKLQ5JPAEhAzzUMnb74M7OGJ0WrNAAAAPERSO3atcuaNGliQ4YMcYGTHgAKiFHPmm1dE5ju0IMgCgAAFFi5GiNVuHBh150PQAGzY5PZ6JeCZiSZdXwoigUCAACIs2QTN954o7v5blpaWt6UCEDseaa+2c5Ngen7lkazNAAAAPE3Rmr8+PHuxrs//fSTNW/e3EqUKBHy+hdffBHJ8gGItjEvm6XvCExfN8ostXg0SwQAABB/gVTZsmWtW7dueVMaALElbYfZmMDtDqxiI7NqLaJZIgAAgPgMpAYOHJg3JQEQeya+E5pg4sY/o1kaAACA+B0jBaAA+fH+wPOLPtEduKNZGgAAgPhtkWrVqpUlZXEypXlFixa1Bg0aWPfu3e3444+PVBkBRMOf/cwydgWmG57MfgAAANjfFqnOnTvbvHnzXJIJBUt6lCxZ0ubOnWtt2rSxZcuWWceOHe3rr7/O7aoBxJK/vgw8P+ZuWqMAAAAOpEVq9erVdscdd1ivXr1C5j/++OO2cOFCl83voYcesscee8zOOOOM3K4eQKwkmVg4JjB9zF3RLA0AAED8t0h98sknduGFF2aaf8EFF7jXRK/Pnj07MiUEkP++uyPwvNk5Zimp7AUAAIADCaQ0Dmrs2LGZ5mueXpOMjAz/cwBxZvpnZpMHBaZrt49maQAAABKja9/NN99s//vf/2zixIluTJR3k963337b7rvvPjf9448/WsuWLSNfWgB5a8VfZp9fFTqv9WVsdQAAgAMNpB544AGrW7euvfrqqzZo0O6r1o0bN7Z+/frZRRdd5KYVaF1//fW5XTWAaEpPM+vbPvTmu1f8YFaocDRLBQAAkDj3kbr44ott3LhxtnbtWvfQcy+IkmLFikWsa9+SJUvskksusQoVKrj1Nm/e3CZMmOB/3efz2YMPPmjVqlVzrytj4D///BOR9wYKjIx0s+cbh8678kezEhWjVSIAAIDEC6TWr1/v78qnQEomTZrkgp5IWrdunR111FFWuHBh++GHH2zmzJn2/PPPW7ly5fzLPPPMM/byyy/bG2+8YX/88YdLy96pUyfbvn17RMsCJKzNK836HmW2dXVgXoceZsXLR7NUAAAAidW1b9q0aa7Vp0yZMrZgwQK7+uqrrXz58vbFF1/YokWL7L333otY4Z5++mmrWbOmDRw40D9P3QqDW6P69Onjuht6qdb1/lWqVLGvvvrKZRIEsA+DzjZbNSu0S1/Hh9hsAAAAkQykevToYd27d3ctQaVKlfLPP/XUU0O690XCN99841qXzj33XBs5cqQddNBBdsMNN9g111zjXp8/f74tX77cBXYeBXht27Z13Q2zC6R27NjhHp6NGzf6sw3qEU16fwWI0S4H4scB1ZnZ31vyiunuqa9wCfN1esKs1WVaaeQLipjBcQbUGXCcQSzJiLHz35yWI9eBlDL0vfnmm5nmK8hRUBNJ8+bNs759+7rgTd0I9d633HKLpaam2uWXX+5/P7VABdP03srSu3dve+SRRzLNX7VqVdS7BGrHbdiwwVWm5OT96nmJAma/64zPZxW/v9vfv3dr47NsU41T9EXIq6IiRnCcAXUGHGcQSzJi7Px306ZNeRNIFSlSxN+CE2zOnDlWqVIli/RGPfzww+3JJ590061atbIZM2a48VAKpPZXz549XXDm0edRF0KVv3Tp0hZN+sxJSUmuLLFQkRD79qvO+DIsqW87S9oUGNdYrOuzViyF+78VBBxnQJ0BxxnEkowYO//NadK8XAdSXbt2tUcffdQ++eQTN60PrbFR99xzj3Xr1s0iSZn4mjZtGjLv4IMPts8//9w9r1q1qvt/xYoVblmPpvd2HysFg3qE046LhZ2nbRorZUF8yFWd2brW7JnAWEPnmLstObV4npUPsYfjDKgz4DiDWJIUQ+e/OS1DrkuqrHmbN2+2ypUr27Zt2+zYY4+1Bg0aWMmSJe2JJ56wSFLGvtmzZ2dq+apdu7Y/8YSCqeHDh4e0Lil7X7t27SJaFiAhqM/vc41C51VvZXbs3dEqEQAAQFzKdYuUkjkMGzbMRo8e7TL4Kahq3bp1SMKHSLn99tutffv2rmvfeeedZ3/++ae99dZb7uFFrrfddps9/vjj1rBhQxdY9erVy6pXr25nnnlmxMsDxDWfz2xYL7OMXYF5DU4yu/hTfZmiWTIAAIDED6Q8HTp0cA+P7iOlG+MOGTIkUmWzNm3a2JdffunGNKk7oQIlpTvXDYE9d999t23ZssWuvfZad38rlWno0KERuyEwkDBB1Ifnmf3zU2DeKc+Ytb0umqUCAAAoGIHUjz/+6FqjlDVP94+qV6+e/f3333bvvffat99+61KVR9rpp5/uHtlRq5SCLD0AZBNEfXCu2b/DAvPqHkMQBQAAkB+BVP/+/d39m3Tz3XXr1tnbb79tL7zwgt188812/vnnu2x6SgQBIMbGRPU+yGzX1sC8UtXMug2IZqkAAADiXo6TTbz00kv29NNP2+rVq13GPv3/+uuv2/Tp0106coIoIAZNHRwaRMntf5mVjOytCgAAAAqaHAdSc+fOtXPPPdc9P/vssy0lJcWeffZZq1GjRl6WD8D+ykg3G9MnMF2hgdm9i8ySC7FNAQAA8qtrn1KdFy9e3D8uSfdhCr53E4AY82l3s9VzAinOr/012iUCAAAomMkmNC5K94uStLQ0e+edd6xixYohy9xyyy2RLSGA3Bt8kdns7wLTJz7EVgQAAIhGIFWrVi3r16+ff1o3wh00aFDIMmqpIpAComzCgNAgqt5xZvWPj2aJAAAACm4gtWDBgrwtCYADt36x2ZDbA9OFS5hd+hVbFgAAIFrJJgDEOF+GWZ9mofN6/qem4miVCAAAIGERSAGJYnJoV1s77z2zZL7iAAAAeYGzLCARZKRb0tiXA9NNTjdrekY0SwQAAJDQCKSABFBk/jBLWjtv90TximbnDIh2kQAAABIagRQQ7zYutXLDbg1Md33ZLKVINEsEAACQ8PYrkJo7d6498MADduGFF9rKlSvdvB9++MH++uuvSJcPwD4kfXp5YKJUdbOGndhmAAAAsRZIjRw50po3b25//PGHffHFF7Z582Y3f+rUqfbQQ9z0E8hXUz60pCUTAtNnvWFWKFf32QYAAEB+BFL33nuvPf744zZs2DBLTU31zz/hhBPs999/358yANgf80aafXW9fzKj4yNm9Y5lWwIAAMRiIDV9+nQ766yzMs2vXLmyrV69OlLlArA3aTvN3uvqn8xIKW7WNhBUAQAAIMYCqbJly9qyZcsyzZ88ebIddNBBkSoXgL35/MqQydUXDDUrVJhtBgAAEKuB1AUXXGD33HOPLV++3JKSkiwjI8PGjBljd955p1122WV5U0oAAUpzPutb/2TGZd9aRskqbCEAAIBYDqSefPJJa9KkidWsWdMlmmjatKkdc8wx1r59e5fJD0AeG9oz8LxKc7M6HdjkAAAA+SzX6b2UYKJfv37Wq1cvmzFjhgumWrVqZQ0bNsybEgII+OdnszlDA9PnvcvWAQAAiIdAavTo0dahQwerVauWewDIJxuXmn3QLTBdrq5ZhfpmGRnsAgAAgFjv2qc053Xr1rX77rvPZs6cmTelAhDK5zMb0Dl03sWfsZUAAADiJZBaunSp3XHHHe7GvM2aNbOWLVvas88+a//991/elBCA2dSPzNYvDGyJK4aaVWzAlgEAAIiXQKpixYp20003uUx9c+fOtXPPPdfeffddq1OnjmutAhBhO7eaffW/wHTDk81qt2MzAwAAxFMgFUxd/O6991576qmnrHnz5q6VCkCETR0cOn1B2DQAAADiJ5BSi9QNN9xg1apVs4suush18/vuu+8iWzoAZt/1CGyFTk+aFcp1jhgAAABEWK7PyHr27GkfffSRGyt10kkn2UsvvWRnnHGGFS9ePNJlAwq29F1m73YJTBevYNb2+miWCAAAAPsbSI0aNcruuusuO++889x4KQB5ZNiDZovGBaabdTNLPqDeuAAAAIhWIKUufQDy2PrFZr+/HphudIrZKc+w2QEAAOIpkPrmm2/slFNOscKFC7vne9O1a9dIlQ0omHZsNuvTLDBd91iziz6KZokAAACwP4HUmWeeacuXL7fKlSu759lJSkqy9PT0nKwSQHY33u19UOi8Ln3YVgksLT3DNu9Ic7u+5xfTbezc1ebLh/f1ZfgsKTkpH94JiYI6A+oM8voY06Raafvs+vaJFUhlZGRk+RxAhI17NXT62HvMytdjMyeoKYvX25mv0V0aAADZsjPNEnqM1HvvvWfnn3++FSlSJGT+zp07XTa/yy67LJLlAwqOf342++mBwHTz88yOvy+aJUIeWrB6y16DqHqVSuTdm/vM0tPTrJBS6dMoBeoMOM4g2ny7f5dqlC1m8STJ51OHkpwrVKiQLVu2zHXzC7ZmzRo3Lx679m3cuNHKlCljGzZssNKlS0e1LGrxW7lypduWyWRoKzj+7Gf2/Z2B6YMON7tmeI7+lDoTf3amZdhZr4+xv5Zu9M87tEYZq1Ai1epXKml3dmpsRQsXyrP3p86AOoO8xnEG8Vxfchob5LpFSnGXxkKF+++//9wbAsilDUtCg6hCqWbnDmQzJrD3xi0ICaJ6nNTIbjmxYVTLBAAAcifHgVSrVq1cAKXHiSeeaCkpgT9VK9T8+fOtc+fOuXx7oIDbvtHsxaah826ZYlYmLOEEEkr/0fP9z3Vd6pqjGQcHAEDCBlJetr4pU6ZYp06drGTJkv7XUlNTrU6dOtatW7e8KSWQiDLSzZ6qGTrvpgkEUQls1JxV9uT3s2zZhu3+eRMfOMmKpeZdNz4AABDlQOqhhx5y/ytgUrKJokWL5lGRgAJAQxMfLR8676THzCrSvSsRrdy03R7+5i/7fvryTK+VL5EalTIBAIADk+sxUpdffvkBviUA+/C80I3Q/Fyzo25hwySYTdt3WfOHf8rytUZVStrDXQ7J9zIBAIAoBVIaD/Xiiy/aJ598YosWLXJpz4OtXbs2QkUDEtSwh8z+CTq5PriLWbe3o1ki5JGjnhqR5fxP/9fO2tQJa5EEAABxJdf5BR955BF74YUXXPc+pQTs0aOHnX322S5V4cMPP5w3pQQSxahnzcb0CZqRZHb++1EsEPLCwDHzrc6939nG7aE3FvzfsfVt4gMdCaIAACiILVIffPCB9evXz0477TQXOF144YVWv359O/TQQ+3333+3W26hexKQpYXjzEY8Hjrv3oVsrDi3dWea/fjXclu+YYf9MGOZTftvQ6ZlKpUqYuPv7xiV8gEAgBgJpJYvX27Nmzd3z5W5T61Scvrpp1uvXr0iX0IgEezcYjYw7PYAd801K8q91+KV7qk3fNZKu/q9CXtd7rjGlazvxYflW7kAAECMBlI1atSwZcuWWa1atVxL1E8//WStW7e28ePHW5EiRfKmlEC8pzl/snpgulARs57/maWQrS1eTf9vg3V5dfRel6lXsYQNuaWDFU/N9WEWAADEgVz/wp911lk2fPhwa9u2rd188812ySWXWP/+/V3iidtvvz1vSgnEs1HPhU6f9QZBVBz7avISu+3jKZnmlyySYg93PcRa1ixjdSqUsJRCuR6CCgAAEjmQeuqpp/zPlXBCLVPjxo2zhg0bWpcuXSJdPiC+rfzb7NcnA9OtLjFrdnY0S4QDsHjt1iyDqA+vbmvtG1Rk2wIAUIAccJ+Tdu3auQeAMLu2m73eNjBd4wizM15jM8Wxp4f+HTI9sHsbO75J5aiVBwAAxHgg9c033+R4hV27dj2Q8gCJ48f7Qqcv+jhaJcEBSs/w2Zh/V9uQacv882iFAgCgYMtRIHXmmWfmaGVJSUnuhr1AgfffBLMJ/QOb4ay3zIpzA9Z4o7Tm1w2amGl+/Uol6MoHAEABl6NAKiMjI+9LAiQKn8/s7RMD01UPNWtxfjRLhBzKyPDZg9/MsBGzVtrSDduzXe7xM3ffAgIAABRc5OUFIu3lVqHTF33CNo5x301bZu+MnW/jF6zb63LNDypjvc9ubs0O4v5fAAAUdLkOpB599NG9vv7ggw8eSHmA+DbnR7N18wPTx99vVrpaNEuEvVi1aYed8erobFufyhUvbFt2pNv3tx5tDSqXZFsCAID9D6S+/PLLkOldu3bZ/PnzLSUlxd2gl0AKBda2dWYfnhd6491j7opmiZCFDVt32ZXvjrdJi9a5XphZaV2rrH14zZFWtHAhtiEAAIhMIDV58uRM8zZu3Gjdu3d3N+sFCqzXw24DcPc8ZWCJVmkQlnVvyuL19u3UpfbO2AVZbptSRVLsnSuPcEGUEucAAADk+Rip0qVL2yOPPOJuyHvppZdGYpVA/MhIN+t3gtmmQGps69bfrAhdwaJhw7Zd9uR3s+zjCYtdcCSbdqRlu3yTqqWse/s6dsERtfKxlAAAIN5FLNnEhg0b3AMocL652WzZlMB09VZmzc+JZokKlB+mL7Onhv5tpYqmmBKMzly20f/a3gKoM1pWtz7nt6T1CQAA5E8g9fLLL4dM+3w+W7ZsmQ0aNMhOOeWU/SsFEK92bDab8kHovCt/ilZpCoRd6Rk2c+lG27IzzS7q98c+ly9fItU90tIzrFqZYnZCk8rWtWV1q1K6aL6UFwAAJKZcB1IvvvhiyHRycrJVqlTJLr/8cuvZs2ckywbEvk/CurI+TKtsXvlr6Qa79r2JtmT9tr0uV7hQku1K99kNx9W3W05sSMIIAAAQG4GUMvQB2JPqfO6IwKa44EM2S4SpxXvItGV28+DMSW7Cjbn3BDuobDH2AQAAyBfckBfYHzu3hKY6r3O0WZPT2JYRtH1XujXpNTTb149uWNGaVittdSuWsPPb1GSsEwAAiO1Aavv27fbKK6/YL7/8YitXrrQMje4OMmnSpEiWD4g9qvPP1Audd9570SpNwpmzYpNdN2iizV+9JcvX+17c2k5pzk2OAQBAnAVSV111lf300092zjnn2BFHHMFVYBQ8X1xtlrY9MH1mX7Pi5aNZooTowjdp0Xq79/Np9s/KzVku88udx7nWJwAAgLgMpIYMGWLff/+9HXXUUXlTIiCWbV1rNuPzwPSx95q1vCiaJYp7H49fZPd8Pj3b12/v2Mhu7dgwX8sEAAAQ8UDqoIMOslKlSuX2z4AE6dJXN3Te8WSqPJBWqKvfnWDD/16Z5evPn9vCzmp1kCUnJ+33ewAAAMRMIPX888/bPffcY2+88YbVrl07b0oFxKJhvUKnr/01WiWJ+wDqns+n2ScT/sv02hF1y9ttHRtau3oV6DYMAAASK5A6/PDDXcKJevXqWfHixa1w4cIhr69duzaS5QNiw5bVZuNeDUwfcZ1Z9VbRLFFc0k1xG9z/Q5avjbzrOKtdgTFQAAAgQQOpCy+80JYsWWJPPvmkValShavGKBje7hg6feoz0SpJwqUzP75xJXv78jZWiC58AAAgkQOpsWPH2rhx46xFixZ5UyIg1qydZ7Yu6EbU3b+PZmnixph/V9vMpRt3P5+72n6dvSrk9VJFUmzawydzMQYAABSMQKpJkya2bdu2vCkNEIvePinwvFg5szpkrNyXwX8usp5fZJ+Jr23d8jb4miMJogAAQMEJpJ566im744477IknnrDmzZtnGiNVunTpSJYPiJ70NLPHKoTOu/KnaJUm5q3atMNG/L3CRv2z2r6btizb5a47pp71PPXgfC0bAABA1AOpzp07u/9PPPHETJm4kpKSLD093fKKgriePXvarbfean369HHzlPhCgd1HH31kO3bssE6dOtnrr7/uxm8BB+S9M0KnD7vCrFIjNmrYuKdHh8y0D/9YlO12eemClla4ULKVKJJi7etXcM8BAAAKXCD1yy+/WDSMHz/e3nzzTTv00END5t9+++323Xff2aeffmplypSxm266yc4++2wbM2ZMVMqJBLF8htnC0YHpio3MTnshmiWKOWu37LTWjw3b6zJDbzvamlSllRoAACSeXAdSxx57rOW3zZs328UXX2z9+vWzxx9/3D9/w4YN1r9/f/vwww/thBNOcPMGDhxoBx98sP3+++925JFH5ntZkSBGBwVNRUqb3TQ+mqWJGRu27rLvpi+z+77MevyTMu9demRta127nB3bqJKVKRba9RcAAKDABlKjRo3a6+vHHHOMRdqNN95op512mnXs2DEkkJo4caLt2rXLzQ9OhlGrVi2XWTC7QEpdAPXwbNy4O7NYRkaGe0ST3l/dJKNdjgJt6xpLnvG5fzLjht+1YyxW5WWd2bwjzTo8/YsVTy1kKzftsAxf1ssdUr20Db6mrZUsEnpIoR7HJo4zoM6A4wxiSUaMnf/mtBy5DqSOO+64TPM0NsoT6TFSGvs0adIk17Uv3PLlyy01NdXKli0bMl/jo/Radnr37m2PPPJIpvmrVq1yY66ivePU0qbKlJzMWJJoKPXbI+bdFnZb/VNtw7ZCZttWWqyKdJ3ZvCPdvvlrtf3091r7e+VWN2/j9rQsl61eOtW6Nqtol7epals3rLXdSyPWcZwBdQYcZxBLMmLs/HfTpk15E0itW7cuZFotQpMnT7ZevXq5TH6RtHjxYpdYYtiwYVa0aNGIrVcJK3r06BHSIlWzZk2rVKlS1LMOqiIpMFVZYqEiFTgT37Hkvz70TxY55larXLmyxbJI1ZkN23bZVe9OsEmL1mf5uu6XW7VMUdu0Pc06HVLFrjyqrjWpWuoASo5o4TgD6gw4ziCWZMTY+W9O445cB1JK6BDupJNOci1DCk7U3S5StK6VK1da69atQ1q81L3w1VdftR9//NF27txp69evD2mVWrFihVWtWjXb9RYpUsQ9wmnHxcLOU0WKlbIUKNs3mn0XCLCt+XmWXDs+xtkdSJ1Zun6btX9qxF6XufXEhnb7SWQsTCQcZ0CdAccZxJKkGDr/zWkZch1IZUfd6WbPnm2RpBTr06eHDmq/4oor3Dioe+65x7Ui6T5Ww4cPt27durnXVYZFixZZu3btIloWFAAjNP4uaBDQac9ZItu6M80Of/xn27oz6+645x9e085qfZAdUae8Jas5CgAAAPsfSE2bNi1kWn0Zly1b5u7x1LJlS4ukUqVKWbNmzULmlShRwipUqOCff9VVV7mWsPLly7tueTfffLMLosjYh1xJ22k2JdClz64aZlY0c+trvFq0Zqu9MuIfSym0OyCauWyTTV2cdRe+G46rbz1OamQp3O8JAAAgcoGUgiU1vSmACqbAZcCAAZbfXnzxRdf8phap4BvyArny/Z1mO/cMLFQAVfOIuN+AC1ZvsdkrNtmwmSvss4n/7XP5589tYd0Oq5EvZQMAAChwgdT8+fNDphXEaGBYJJNB7M2vv/4aMq33fe2119wD2C/zR5lNejcw3a1/3N/rqdVjP2WbqjzctcfUs56nNAnJvgkAAIAIB1K1a9fO7Z8AsSsj3ezdLqHzGgTuSxaPTn35t2yDqIe7NLUj61dwzwslJVndiiXowgcAALAfcpwWY8SIEda0aVP/zWuDKe/7IYccYr/99tv+lAGIno8vCZ2+Y47Sxli8mvbfeluyflvIvNs6NnQtTpN6nWTdXcry0u7RsEopgigAAIC8bpHq06ePXXPNNVneZ0kp0a+77jp74YUX7Oijj97fsgD5H0TN/j4w3am3WakqcbkXtqdl2L8rN1vXV8f45zWsXNKG9Tg2quUCAACwgh5ITZ061Z5++ulsXz/55JPtuecSO100EshnV5rN+jZ0Xrsb8uWtN+9Is4ywZC37468lG+2FYbNt2n8bbEdaRqbX+1/e5oDfAwAAAAcYSOkmt7pnU3ZSUlJs1apVOV0dEB3b1pl9eb3ZnB9C518/NiKr/3flJpu8KOu04gqe7vk89L5oeeWw2uWsVoXi+fJeAAAABVGOA6mDDjrIZsyYYQ0aNMj2/lLVqlWLZNmAyAdRT9fJPP/BtWbJhQ549T/+tdyuGzTRoum4xpWsy6HVSWMOAAAQK4HUqaeear169bLOnTtnSnW+bds2e+ihh+z000/PizICkfF6u8zzevwdkSBq0LgF1uvrv3L1N8c0qnTA77t1R5rd1amRVS68w2ofVM3djgAAAAAxFEg98MAD9sUXX1ijRo3spptussaNG7v5f//9t7uHU3p6ut1///15WVZg/00YYLZpWWD60AvMurxkVvjA7382+p/VmYIoZckrWTTrr5dSjrerVyFi923KyMiwlStXRmRdAAAAiHAgVaVKFRs7dqxdf/311rNnT/PtGSyvk8FOnTq5YErLADFnySSzIbcHpsvXNzv7zYisWt+DS/r/ETLv9Ytb26nN6eYKAACQyFJyezPe77//3tatW2f//vuvO4ls2LChlStXLu9KCByIjAyzfseHzvvf6Iht0+Of+zVk+o/7TrQqpQ+8lQsAAAAJFEh5FDi1aUNqZcSBGZ9lzs6XGplsdlMXr7cFa7b6p7u2qE4QBQAAUEAwMh2Ja9t6sy+uCUwfcZ1ZlUMitvozXgvc/FZevrBVxNYNAACA2EYghcT1TlAWycLFzU5+PGKrfnHYnJDpobcdHbF1AwAAIPYRSCExTf3IbEXQzW+7vGyWkhqRVQ+ftcJeGv6Pf7p2heLWpGrpiKwbAAAA8YFAColn51azL68LTNc73uzQcyO2+oe/DU11/t0ttEYBAAAUNARSSDw/PRA6fd67EVu1MlUuXrvNP/3jbcdYySL7lbMFAAAAcYxAComX7nxC/8D0BYPNipaJ2OofGzLL/7x1rbLWuGqpiK0bAAAA8YNAConl6xtCp5ucGrFVZ2T4bMCY+f5pgigAAICCi0AKiWPVbLOpg0MTTETQ1P/Wh0w/0rVZRNcPAACA+EEghcQx+f3A89RSZq0vi9iqJy5ca2e9PtY/fWrzqpaawtcHAACgoGKUPBKDz2c2NqgF6oaxZklJEVitz859Y5xNWLguZP4lR9Y+4HUDAAAgfnFJHYnhxUMCzw86zKxsrYis9o5Pp2YKoi5qW8va168YkfUDAAAgPtEihfg3pIfZxiWB6YO7RmS1o/9ZbV9MClqvmX32v3Z2eJ3yEVk/AAAA4heBFOLbhiVmEweGzutw2wGvduP2XXZJ/z9C5i146rQDXi8AAAASA137EN9mfWvmywhM91pzwKscNWeVHfrwTyHzxtx7wgGvFwAAAImDFinEr4x0s6H3BKavH2dWaP+r9DdTl9otgydnmv/WpYfZQWWL7fd6AQAAkHgIpBC/Xj088LxiI7PKB+/XatIzfHbhW7/bnwvWZnrtoS5N7eRDqh5IKQEAAJCACKQQnya9Z7Z2XmC65cX7le58yfptdtRTI7J87esbj7IWNcseSCkBAACQoAikEJ/3jPrm5sB0asn9SjDxyLd/2cAxCzLNH37HsVa/UskDLSUAAAASGIEU4s9fX4ZO35M5GMrOmyPn2ldTltrS9dtsw7ZdIa/Vq1TChvc41pIicCNfAAAAJDYCKcSfz64IPG93k1mhwjn6s3mrNlvvH/7O8rUXzmthZ7euEakSAgAAIMERSCG+/Ptz6PRJj+b4T+/+bFrIdJGUZCueWshG3HGclSuRGqkSAgAAoAAgkEJ8jY369MrAdLNzzJIL5ehPe34x3SYsXOef/vXO46xOxRJ5UUoAAAAUANyQF/FjzEtmOzYEps/sm6M/e/L7WTb4z0Uh8wiiAAAAcCAIpBAfZv9g9vNDgekLBpulpOboHlFvjQpKk25mA7oH3X8KAAAA2A907UPs27rWbPAFgen6J5g1OXWvf7J47Vb7ZupSe/bH2SHz5z55qhVKJisfAAAADgyBFGLf8EdCp88ZsNfFt+1Mt6Of+SXT/PtPPZggCgAAABFB1z7Eti1rzCa+E5ju1t+sWLm9/knrx4ZlmndI9dJ2VYe6eVFCAAAAFEC0SCG2fXF16HSzbntdfMDo+bZtV7p/+vRDq1mPkxpZvUol86qEAAAAKIAIpBC7lk4xmzsiMH3eILOk0PFNyzdst8mL1lmGz8xnPnt0yMyQ11+9qHV+lRYAAAAFCIEUYtOq2WZvHRuYrnO0WdOuIYus3Ljdjuw9PNtVTH/45LwsIQAAAAowxkgh9uzaZvbaEaHzOvfOtNiF/X7PdhXHNa5kpYoWzovSAQAAALRIIQaNfjF0+ozXzKo2z9Slb+6qLf7pk5tWsSPqlnfPSxZJsc7NquZPWQEAAFAg0bUPsWXzSrORT4cGUa0uybTYMc+Gpjd/6zJusgsAAID8Q9c+xJaRzwSe1z4qyyDqvXELbGdahn96QHeCKAAAAOQvAinEjk0rzMb3C0x3fDjTIh+PX2QPfv2Xf7pm+WJ2QpMq+VVCAAAAwKFrH2LHp5cHnjc7x6xmIOHEjrR0O+TBHy1Nec6DfHJdu/wsIQAAAODQIoXYsGCM2aJxgel2N4a8fOU74zMFUT/dfoxVK1Msv0oIAAAA+NEihejbsMTsnVMD0406mx0UuJHurGUbbcy/a0L+5Le7j7ea5YvnZykBAAAAPwIpRN+Yl7K9Z9T2Xel2yku/+aeTk8zm9T4tP0sHAAAAZELXPkTXnJ/M/nwzMH3pl2bl67mnPp/POjwdmub86xs75HcJAQAAgExokUL0bFtv9uG5genKTc3qn+CeLl671Y5+JjSIuqhtLWteo0x+lxIAAADIhBYpRM/vr4dOn/uO+29XekamIOrw2uXsybOa52fpAAAAgGzRIoXoWL/IbOTTgelrfrHxWyrZS1//YaP/XR2y6OmHVrNXLmyV/2UEAAAAskEghXyzatMOW715h6Wl+6zGz/dYuT3zp1U8zbq+sszM9Ah1avOq9upFgQx+AAAAQCwgkMJ+y8jw2bQlG2zdlp3+eT4L3OvJF3Tbp88n/WffT1/unrdOmmNfFPne/9otS3aPiwp34RG17MmzmrGHAAAAEHMIpLBfRs1ZZZcN+DPXf3dU8nT7IDWQ3nxUenNb4KsWssyA7ofb0Q0rWeFCDOEDAABAbCKQQq4t27Btv4KoGkmrQoIomdnmcbshtaod06iSVStT1GpXKMEeAQAAQMwjkEKu6N5O7XqPCJlXu0Jx69a6RqZlk8KmL5twj9m2oBnnf2D/O/g49gAAAADiDoEUcuXsvmNDpgde0caOb1x53384e6jZb4sD01cMNavdjq0PAACAuMQgFOTY38s32uRF6/3TRzWokLMgSlknBp8fmG58GkEUAAAA4hqBFHLsioHjQ6bfv6ptzv7w54dCp7v1Y6sDAAAgrtG1D/u0eUeadXlltC3bsN0/77e7j7ekpPBRUGG2rDEbdKbZ8mmBeac8Y5ZKQgkAAADENwIp7NWvs1da97CWqKKFk61m+eL77s73bL3QedVbm7W9ji0OAACAuEfXPmRr4/ZdmYIoGXb7sfvealM+CJ0uVs7s8m/Z2gAAAEgItEghSxu27rIWj/4UMu+WExpYj5Mb73uL7dpu9vWNgekmp5tdEBZYAQAAAHGMQAqZbNuZnimIerpbczu/Ta2cba33zgg8L1/f7LxBbGUAAAAkFAIp+IOnXl/PsLmrNoekOJdTm1fNeRC1YqbZ4t8D08fcZZZMD1IAAAAkFgKpAmjDtl324rA59t30ZbZ2y05Lz/Blu+zZrQ+yF85rmfOV9w27yW7LCw+gpAAAAEBsiummgt69e1ubNm2sVKlSVrlyZTvzzDNt9uzZIcts377dbrzxRqtQoYKVLFnSunXrZitWrIhamWOdz+ezVo/+ZO+MXWCrNu3YaxDVpUX13AVREwaETt869QBKCgAAAMSumG6RGjlypAuSFEylpaXZfffdZyeffLLNnDnTSpTYfS+i22+/3b777jv79NNPrUyZMnbTTTfZ2WefbWPGjIl28WPSKyP+taxip9a1yrr7Qh1ao4z1POVgK1woad/3iQq2c6vZkNsD0/WONytXJzKFBgAAAGJMTAdSQ4cODZl+5513XMvUxIkT7ZhjjrENGzZY//797cMPP7QTTjjBLTNw4EA7+OCD7ffff7cjjzwySiWPTd9NW2YvDJvjn1bQ9NG1R1rx1AhUg6H3hk5f/NmBrxMAAACIUTEdSIVT4CTly5d3/yug2rVrl3Xs2NG/TJMmTaxWrVo2bty4bAOpHTt2uIdn48aN7v+MjAz3iCa9v7rf5UU5Bo1bEDL9wVVHWNGU5AN/r51bLHnSu/7JjNP7mCUl68Mc2HoR9TqDxESdAXUGHGcQSzJi7Fwmp+WIm0BKH+i2226zo446ypo1a+bmLV++3FJTU61s2bIhy1apUsW9trexV4888kim+atWrXJjrqL9ORUwqjIlRzDb3Zj5G+z3+Wv9019e2cy2bFhrWyKw7pJ/vmglg6ZXHtTZbOXKCKwZ0awzSFzUGVBnwHEGsSQjxs5lNm3alFiBlMZKzZgxw0aPHn3A6+rZs6f16NEjpEWqZs2aVqlSJStdurRFuyJpbJLKEqmKtHDNFrvj64n+6TNbVrcWDWpGZN2WtsOSJ73hn8zoNsAqV6kSmXUjanUGiY06A+oMOM4glmTE2LlM0aJFEyeQUgKJIUOG2KhRo6xGjRr++VWrVrWdO3fa+vXrQ1qllLVPr2WnSJEi7hFOOy4Wdp4qUiTL0n9MaJe+G49vELnPOW1w4HlyiiU37xaZ9SKqdQaJjzoD6gw4ziCWJMXQuUxOyxD9ku6FmvcURH355Zc2YsQIq1u3bsjrhx12mBUuXNiGDx/un6f06IsWLbJ27cLuZ1RAaRu+//si//QHV7e1hlVKRWblaTtDM/Wd8Vpk1gsAAADEuJRY786njHxff/21u5eUN+5Jac6LFSvm/r/qqqtcNz0loFC3vJtvvtkFUWTs2+3XOatCUpwf1aBi5HbQuFcDz6u3MmtxQeTWDQAAAMSwmA6k+vbt6/4/7rjjQuYrxXn37t3d8xdffNE1v+lGvMrE16lTJ3v99dejUt5YNHJ2IJBqVatcZFc+eVDg+WFXRHbdAAAAQAxLifVuaTkZDPbaa6+5B0KlZ/js4/GL3fPkpN1joyJm+QyztfMC060vY/MDAACgwIjpMVI4ML/PW2PbdqW75yc1rWLlS6TmTbe+tv/TCMHIrRsAAACIcQRSCWxk0PioTodkn8Uw19bMNZsalK2v/S2RWzcAAAAQBwikEthbo+b5u/Ud37hyZFa6faPZK61Dx0aVOSgy6wYAAADiBIFUgpqzInBH5uYHlbFykerW98mlgefJKWbtb47MegEAAIA4QiCVoEb8vdL/vHmNMpFZ6ZJJZvN+DUyf9rxZhfqRWTcAAAAQRwikEtQne7L1ySVH1o7MSn+4O7Q16rDdKegBAACAgoZAKgGlpWfYvNVb3PPChZKsfqWSB77Sf382+298YPrWqQe+TgAAACBOEUgloM8n/RcyXbjQAe7mtJ1m73cLTJ/0qFmZGge2TgAAACCOEUgloCmLN/if39ax0YGv8Jl6geclKpu1vf7A1wkAAADEMQKpBDT4z0X+55e1O8DxUfNHme0MZAC0Tk+YpUTwxr4AAABAHCKQSjBbd6b5n9coV8xKFS28/yvLSDd7t0tgunQNs0PPO8ASAgAAAPGPQCrBTP8v0K2vWOFCB7ayMX1Cp0kwAQAAADgEUgnmi0lL/M+vPrru/q9o+mdmwx8NTB93n1mhlAMsHQAAAJAYCKQSzMcTAvePalmz3P6tZMqHZp9fFTqvw20HWDIAAAAgcRBIJZBVm3aETDeqUnL/7hf1VVhWvhvHm6UUOcDSAQAAAImDvloJmq3vyHrlLSkpKXcr2Lg09H5Rctt0s7K1IlRCAAAAIDEQSCWQF4bN8T+/qkPQvZ9ywucze/GQ0Hm3zTArWzNCpQMAAAASB137EsSyDdtCpk9sUjl3Kxh8gZkvIzB9zgCCKAAAACAbBFIJYtScVf7nh1QvbcnJuejWN+51szlDA9OHXWHWLKyLHwAAAAA/AqkEMfrfNf7nj57RLHd/PPXDwPNCqWadnoxgyQAAAIDEwxipBJCR4bMx/652z0sVSbEWNcrk/I+H3me2fHpg+v7lZskHeCNfAAAAIMHRIpUAZi3faGu37HTPj6xfwVIK5XC3/vKk2e+vBaZPfJAgCgAAAMgBAqkE8PH4wE14OzSomLM/Sk8zG/l06DyNjQIAAACwT3TtSwBfTV7if35UTgOpt44NnX5gJTfdBQAAAHKIFqk4t3DNFtu4Pc0/Xb9SiX3/0V9fmq2YEZi+7BuCKAAAACAXCKTi3FM//O1/3r19HUtK2kfacyWW+LR76Lx6Ya1TAAAAAPaKQCrO/bcucCPek5tW2fvCSyaavdEhdN59S/OoZAAAAEDiIpCK87Tn/67c7J9uv7fxUT6fWb8TQued/75Zag66AgIAAAAIQbKJOLZk/TbbtivdPe94cOW9L/x+t9DpK38yq9U2D0sHAAAAJC5apOLYnBWb/M8bVSmV/YJf32g2d3hguv0tBFEAAADAASCQimOzgwKpxlWzCaRGPms2+f3QeSc9msclAwAAABIbXfvi2Jzle2mR0pioz67Yneo82L2LzfaV2Q8AAADAXtEiFcdmr9idaKJQcpLVC79/1A/3ZB1EFS2djyUEAAAAEhOBVJxKS8+wuXsy9tWtWMKKpBQKvLhqttmfb4b+wY1/EkQBAAAAEULXvji1YM1W25me4Z43Du/W99oRodMPrjVLDgq0AAAAABwQWqTi1OzsxkdNGBC64HWjCKIAAACACCOQilNfTVnif35wtVKBBBNDbg8sVLiEWbUWUSgdAAAAkNgIpOLQ4rVbbdjMFe551dJF7djGlXa/8G6X0AXvmR+F0gEAAACJj0AqDs1cttH//JzDauxONLHiL7MFvwUWOvYes5Qi0SkgAAAAkOAIpOK0RcpTv/KetOdTB4cudFzPfC4VAAAAUHAQSMWhRUGBVK3yxc0y0s3GvhJYoMcsbroLAAAA5CECqTi0cE0gkKqpQOqZeoEXq7c2K109OgUDAAAACggCqTi0YM0W93+J1EJWaf10s+3rAy826Bi9ggEAAAAFBIFUnNmZluEfI1W3UglL6h8WOB1/X3QKBgAAABQgBFJxZtHaLZbh2/38osKjQl+8fSZjowAAAIB8QCAVZ/5dubtbX2FLs4uWPxN4oemZZmUOil7BAAAAgAKEQCrOjF+w1v3/SMo7oS90DcraBwAAACBPEUjFkS070qz/6Pnuef3kpYEXTn7CrGjp6BUMAAAAKGAIpOKIF0QlWYa1Tf478EL7m6JXKAAAAKAAIpCKI3NXbXb/t0/+KzCz0SnRKxAAAABQQBFIxZHpSza4/29O+SowkwQTAAAAQL4jkIoTm7bvsnmrtliH5Ol2ZPKswAutLo1msQAAAIACiUAqTvy1dKO1T55h76f2Dsys0MCs6qHRLBYAAABQIBFIxYkZSzZY38J9Qmde+aNZMrsQAAAAyG+chceJ1b8NsDJJWwMzLvvGrETFaBYJAAAAKLAIpOLAwn+m2707AzfczWjSxazesVEtEwAAAFCQEUjFgfVf3RMynXzOgKiVBQAAAACBVMxbuXattdgyxj/9d6fBZimpUS0TAAAAUNDRIhXjlr5+hv/5j+mHW+MjuQEvAAAAEG0EUjHs3xl/WMu0af7pUsfeaElJSVEtEwAAAAACqZi1butOW/BxYGzUGl8pa9/x7KiWCQAAAMButEjFqFsG/W4dC032Ty++bFxUywMAAAAggEAqBqWl+6z28p/80ytLN7OW9WtGtUwAAAAAAgikYtDsVVutU8Zv/unKHbpHtTwAAAAAQhFIxaJ5v9ixhQJJJqzlRdEsDQAAAIAwBFIx6Ljpd/uf/3XInWapJaJaHgAAAAChCKRizYq/rJAv3T+5rfU1US0OAAAAgMwIpGLNvz/7n36SdqzVrVIuqsUBAAAAkBmBVIzZPvYt//N+6adZhZJFoloeAAAAAJkRSMUSn89St610T7f7CtuRR7SNdokAAAAAJHIg9dprr1mdOnWsaNGi1rZtW/vzzz8t3vi2rbMUS3PPiybtsvtOax7tIgEAAABI1EDq448/th49ethDDz1kkyZNshYtWlinTp1s5crdrTvx4r+50/3Ph5c41YqlFopqeQAAAAAkcCD1wgsv2DXXXGNXXHGFNW3a1N544w0rXry4DRgwwOLJpr9/9T9fWbReVMsCAAAAIHspFud27txpEydOtJ49e/rnJScnW8eOHW3cuHFZ/s2OHTvcw7Nx40b3f0ZGhntES+qyif7n1Q47PaplQfxQPfH5fNQXUGfAcQYxg98mxHN9yWk54j6QWr16taWnp1uVKlVC5mv677//zvJvevfubY888kim+atWrbLt27dbtLxS7j77d1kna5P8t3UsVTnuuiYiel/2DRs2uAOQLiIA1BlwnEG08duEeK4vmzZtKhiB1P5Q65XGVAW3SNWsWdMqVapkpUuXjlq5Xry4sq3dfKhN/neptW5Y04qmFsjdg/04+CQlJbn6GwsHH8Q+6gyoM+A4g1iSEWPnMkpelxNxf6ZesWJFK1SokK1YsSJkvqarVq2a5d8UKVLEPcJpx0V755UvWdSaVy/pgqholwXxQwefWKi/iB/UGVBnwHEGsSQphs5lclqG6Jf0AKWmptphhx1mw4cPD4lqNd2uXbuolg0AAABAYor7FilRN73LL7/cDj/8cDviiCOsT58+tmXLFpfFDwAAAAAiLSECqfPPP98linjwwQdt+fLl1rJlSxs6dGimBBQAAAAAEAkJEUjJTTfd5B4AAAAAkNfifowUAAAAAOQ3AikAAAAAyCUCKQAAAADIJQIpAAAAAMglAikAAAAAyCUCKQAAAADIJQIpAAAAAMglAikAAAAAyCUCKQAAAADIJQIpAAAAAMglAikAAAAAyCUCKQAAAADIJQIpAAAAAMillNz+QSLy+Xzu/40bN0a7KJaRkWGbNm2yokWLWnIycS6oM+A4g+jjtwnUGRSkY8zGPTGBFyNkh0DKzO04qVmzZn7sGwAAAABxECOUKVMm29eTfPsKtQpIFLx06VIrVaqUJSUlRT0CVkC3ePFiK126dFTLgvhAnQF1BhxnEGv4bUI81xeFRwqiqlevvtcWMlqkNFAsOdlq1KhhsUSVKBYqEuIHdQbUGXCcQazhtwnxWl/21hLliX4nRAAAAACIMwRSAAAAAJBLBFIxpkiRIvbQQw+5/wHqDDjOIBbw2wTqDDjGZEayCQAAAADIJVqkAAAAACCXCKQAAAAAIJcIpAAAAAAglwikAAAAACCXCKRiyGuvvWZ16tSxokWLWtu2be3PP/+MdpGQD3r37m1t2rSxUqVKWeXKle3MM8+02bNnhyyzfft2u/HGG61ChQpWsmRJ69atm61YsSJkmUWLFtlpp51mxYsXd+u56667LC0tLWSZX3/91Vq3bu0ycDVo0MDeeecd9nECeOqppywpKcluu+02/zzqDMItWbLELrnkEnccKVasmDVv3twmTJjgf93n89mDDz5o1apVc6937NjR/vnnn5B1rF271i6++GJ3w8yyZcvaVVddZZs3bw5ZZtq0aXb00Ue737KaNWvaM888w86IQ+np6darVy+rW7euqw/169e3xx57zNUTD3WmYBs1apR16dLFqlev7n6Dvvrqq5DX87N+fPrpp9akSRO3jI5t33//veULH2LCRx995EtNTfUNGDDA99dff/muueYaX9myZX0rVqyIdtGQxzp16uQbOHCgb8aMGb4pU6b4Tj31VF+tWrV8mzdv9i/zv//9z1ezZk3f8OHDfRMmTPAdeeSRvvbt2/tfT0tL8zVr1szXsWNH3+TJk33ff/+9r2LFir6ePXv6l5k3b56vePHivh49evhmzpzpe+WVV3yFChXyDR06lH0cx/78809fnTp1fIceeqjv1ltv9c+nziDY2rVrfbVr1/Z1797d98cff7jjwY8//uj7999//cs89dRTvjJlyvi++uor39SpU31du3b11a1b17dt2zb/Mp07d/a1aNHC9/vvv/t+++03X4MGDXwXXnih//UNGzb4qlSp4rv44ovdMW3w4MG+YsWK+d588012SJx54oknfBUqVPANGTLEN3/+fN+nn37qK1mypO+ll17yL0OdKdi+//573/333+/74osvFF37vvzyy5DX86t+jBkzxp3PPPPMM+785oEHHvAVLlzYN3369DzfBgRSMeKII47w3Xjjjf7p9PR0X/Xq1X29e/eOarmQ/1auXOkOSCNHjnTT69evdwcE/Yh5Zs2a5ZYZN26c/2CWnJzsW758uX+Zvn37+kqXLu3bsWOHm7777rt9hxxySMh7nX/++S6QQ3zatGmTr2HDhr5hw4b5jj32WH8gRZ1BuHvuucfXoUOHbDdMRkaGr2rVqr5nn33WP0/1qEiRIu7ERXSCouPO+PHj/cv88MMPvqSkJN+SJUvc9Ouvv+4rV66c/7jjvXfjxo3ZKXHmtNNO81155ZUh884++2x3QivUGQQLD6Tys36cd955rr4Ga9u2re+6667z5TW69sWAnTt32sSJE12Tpyc5OdlNjxs3LqplQ/7bsGGD+798+fLuf9WNXbt2hdQPNV/XqlXLXz/0v5qyq1Sp4l+mU6dOtnHjRvvrr7/8ywSvw1uGOha/1N1T3TnD9yt1BuG++eYbO/zww+3cc891XX9btWpl/fr1878+f/58W758eUhdKlOmjOtmHnycUdcbrcej5fV79ccff/iXOeaYYyw1NTXkOKPuyuvWrWPHxJH27dvb8OHDbc6cOW566tSpNnr0aDvllFPcNHUGe5Of9SOa5zcEUjFg9erVri9y8EmwaFqVEAVHRkaGG+dy1FFHWbNmzdw81QEdQHSwya5+6P+s6o/32t6WUbC1bdu2PP1ciLyPPvrIJk2a5MbYhaPOINy8efOsb9++1rBhQ/vxxx/t+uuvt1tuucXeffddf52Rvf0O6X8FYcFSUlLcRZ/cHIsQH+6991674IIL3IW7woULu+Bbv08azyLUGexNftaP7JbJj2NOSp6/A4BctTDMmDHDXfUDsrN48WK79dZbbdiwYW5gLZCTizS66vvkk0+6aZ0U61jzxhtv2OWXX84GRCaffPKJffDBB/bhhx/aIYccYlOmTHGBlBILUGeA3WiRigEVK1a0QoUKZcrCpumqVatGrVzIXzfddJMNGTLEfvnlF6tRo4Z/vuqAun+uX78+2/qh/7OqP95re1tGmXKUTQfxQ133Vq5c6TIw6uqdHiNHjrSXX37ZPdeVOOoMgilrVtOmTUPmHXzwwS7bZ/BxYm+/Q/pf9S6YMoMq61ZujkWID8r86rVKqev4pZdearfffru/FZw6g73Jz/qR3TL5ccwhkIoB6rZ12GGHub7IwVcPNd2uXbuolg15T2M0FUR9+eWXNmLECJdqNpjqhrpVBNcP9Q3WCZBXP/T/9OnTQw5Iaq1QkOSdPGmZ4HV4y1DH4s+JJ57o9reuEHsPtTaoy433nDqDYOouHH5bBY19qV27tnuu445OOoKPEer2q3EKwccZXdBRIO/RMUu/Vxr34C2jlMga1xl8nGncuLGVK1eOnRJHtm7d6saqBNNFX+1voc5gb/KzfkT1/CbP01kgx+nPlcnknXfecVlMrr32Wpf+PDgLGxLT9ddf79KD/vrrr75ly5b5H1u3bg1JZa2U6CNGjHDpz9u1a+ce4enPTz75ZJdCXSnNK1WqlGX687vuustl/XvttddIf55AgrP2CXUG4WnyU1JSXErrf/75x/fBBx+448H7778fkqpYvztff/21b9q0ab4zzjgjy1TFrVq1cinUR48e7bJGBqcqVlYupSq+9NJLXapi/bbpfUh/Hn8uv/xy30EHHeRPf64U17qthjLAeqgzBdumTZvcLVf0UEjxwgsvuOcLFy7M1/qh9Oc6vj333HPu/Oahhx4i/XlBpPv66GRZ95NSOnTl1Efi08Enq4fuLeXRQeeGG25wKUB1ADnrrLNcsBVswYIFvlNOOcXdX0E/dnfccYdv165dIcv88ssvvpYtW7o6Vq9evZD3QGIFUtQZhPv222/dBRddtGvSpInvrbfeCnld6Yp79erlTlq0zIknnuibPXt2yDJr1qxxJzm6n5Bur3DFFVe4k6lgul+MUq1rHToR18kU4s/GjRvdMUXnJUWLFnW/GbpnUHAaaupMwfbLL79kef6iIDy/68cnn3zia9SokTu/0a1evvvuO19+SNI/ed/uBQAAAACJgzFSAAAAAJBLBFIAAAAAkEsEUgAAAACQSwRSAAAAAJBLBFIAAAAAkEsEUgAAAACQSwRSAAAAAJBLBFIAAAAAkEsEUgAA5IHjjjvObrvtNrYtACQoAikAQNzq3r27JSUluUfhwoWtbt26dvfdd9v27dujXTQAQIJLiXYBAAA4EJ07d7aBAwfarl27bOLEiXb55Ze7wOrpp59mwwIA8gwtUgCAuFakSBGrWrWq1axZ084880zr2LGjDRs2zL22Y8cOu+WWW6xy5cpWtGhR69Chg40fP97/t++8846VLVs2ZH1fffWVC8Q8Dz/8sLVs2dIGDRpkderUsTJlytgFF1xgmzZt8i+zZcsWu+yyy6xkyZJWrVo1e/755/PlswMAoodACgCQMGbMmGFjx4611NRUN61ufp9//rm9++67NmnSJGvQoIF16tTJ1q5dm6v1zp071wVYQ4YMcY+RI0faU0895X/9rrvucvO+/vpr++mnn+zXX3917wcASFwEUgCAuKbARi1BanFq3ry5rVy50gU2aiXq27evPfvss3bKKadY06ZNrV+/flasWDHr379/rt4jIyPDtV41a9bMjj76aLv00ktt+PDh7rXNmze79T333HN24oknujIocEtLS8ujTwwAiAWMkQIAxLXjjz/eBUwKnF588UVLSUmxbt262bRp09y4qaOOOsq/rBJSHHHEETZr1qxcvYe69JUqVco/re57Cti81qqdO3da27Zt/a+XL1/eGjduHJHPBwCITQRSAIC4VqJECddlTwYMGGAtWrRwLURt2rTZ598mJyebz+cLmafgK5wCsGAaQ6VWKgBAwUXXPgBAwlBgdN9999kDDzxg9evXd2OlxowZExIkKdmEuvlJpUqVXNIItWZ5pkyZkqv31Pso0Prjjz/889atW2dz5syJyGcCAMQmAikAQEI599xzrVChQq673/XXX+/GSw0dOtRmzpxp11xzjW3dutWuuuoqt6y64xUvXtwFX+qi9+GHH7qxULmh8Vlan95nxIgRLuGF7m+loA4AkLjo2gcASCgaI3XTTTfZM888Y/Pnz3dd8JQcQi1Phx9+uP34449Wrlw5/1im999/3wVBSkShZBFKd37ttdfm6j2V0EJJJ7p06eLGUt1xxx22YcOGPPqEAIBYkOQL7xwOAAAAANgr+h0AAAAAQC4RSAEAAABALhFIAQAAAEAuEUgBAAAAQC4RSAEAAABALhFIAQAAAEAuEUgBAAAAQC4RSAEAAABALhFIAQAAAEAuEUgBAAAAQC4RSAEAAACA5c7/AV8v14eubhfEAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Calculate regrets for both approaches\n", "normal_ucb_regret = np.cumsum(\n", " np.array(oracle.optimal_rewards) - np.array(oracle.expected_rewards)\n", ")\n", "\n", "glm_ucb_regret = np.cumsum(\n", " np.array(glm_ucb_oracle.optimal_rewards) - np.array(glm_ucb_oracle.expected_rewards)\n", ")\n", "\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(normal_ucb_regret, label=\"NormalInverseGamma + UCB\", linewidth=2)\n", "plt.plot(glm_ucb_regret, label=\"BayesianGLM + UCB\", linewidth=2)\n", "plt.xlabel(\"Round\")\n", "plt.ylabel(\"Cumulative Regret\")\n", "plt.title(\"Comparison: NormalInverseGammaRegressor vs BayesianGLM with UCB\")\n", "plt.legend()\n", "plt.grid(True, alpha=0.3)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing Thompson Sampling Approaches\n", "\n", "`upper_confidence_bound` is not guaranteed to do well, and can often involve fine-tuning the `alpha` parameter. Instead, we may want to use the `thompson_sampling` policy, which does not require any tuning. Let's compare both approaches with Thompson sampling as well." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from bayesianbandits import ThompsonSampling\n", "\n", "# Create oracles for Thompson sampling experiments\n", "normal_thompson_oracle = ClickthroughOracle()\n", "glm_thompson_oracle = ClickthroughOracle()\n", "\n", "# NormalInverseGammaRegressor with Thompson Sampling\n", "normal_thompson_agent = ContextualAgent(\n", " arms=[\n", " Arm(Article.article_1, learner=NormalInverseGammaRegressor(lam=10.0)),\n", " Arm(Article.article_2, learner=NormalInverseGammaRegressor(lam=10.0)),\n", " Arm(Article.article_3, learner=NormalInverseGammaRegressor(lam=10.0)),\n", " Arm(Article.article_4, learner=NormalInverseGammaRegressor(lam=10.0)),\n", " ],\n", " policy=ThompsonSampling(),\n", " random_seed=rng,\n", ")\n", "\n", "# BayesianGLM with Thompson Sampling\n", "glm_thompson_agent = ContextualAgent(\n", " arms=glm_arms_thompson,\n", " policy=ThompsonSampling(),\n", " random_seed=rng,\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Much as we did with the UCB algorithm, we'll run both bandits for 10000 iterations, and plot the cumulative regret over time." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Run NormalInverseGammaRegressor with Thompson Sampling\n", "\n", "for _ in range(10000):\n", " user_gender = rng.binomial(1, 0.5) # 0 corresponds to female\n", " user_age = rng.integers(0, 47) # 0 corresponds to age 18\n", "\n", " context = np.array([1.0, user_age, user_gender])\n", "\n", " normal_thompson_oracle.set_context(context)\n", " normal_thompson_oracle.best_expected_reward()\n", "\n", " (action,) = normal_thompson_agent.pull(np.atleast_2d(context))\n", " action.take_action(normal_thompson_oracle)\n", " normal_thompson_agent.update(\n", " np.atleast_2d(context), np.array([normal_thompson_oracle.rewards[-1] - 0.5])\n", " )\n", "\n", "# Run BayesianGLM with Thompson Sampling\n", "\n", "for _ in range(10000):\n", " user_gender = rng.binomial(1, 0.5) # 0 corresponds to female\n", " user_age = rng.integers(0, 47) # 0 corresponds to age 18\n", "\n", " context = np.array([1.0, user_age, user_gender])\n", "\n", " glm_thompson_oracle.set_context(context)\n", " glm_thompson_oracle.best_expected_reward()\n", "\n", " (action,) = glm_thompson_agent.pull(np.atleast_2d(context))\n", " action.take_action(glm_thompson_oracle)\n", " glm_thompson_agent.update(\n", " np.atleast_2d(context), np.array([glm_thompson_oracle.rewards[-1]])\n", " )" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Comprehensive Comparison\n", "\n", "We can see that both approaches achieve sublinear regret, but with some interesting differences. Let's compare all four combinations:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Final Regret Results (10000 rounds):\n", "UCB - NormalInverseGamma: 50.51\n", "UCB - BayesianGLM: 104.37\n", "Thompson - NormalInverseGamma: 108.34\n", "Thompson - BayesianGLM: 155.22\n", "Random selection (linear): 863.73\n", "\n", "Regret rates (final_regret/rounds):\n", "UCB - NormalInverseGamma: 0.0051\n", "UCB - BayesianGLM: 0.0104\n", "Thompson - NormalInverseGamma: 0.0108\n", "Thompson - BayesianGLM: 0.0155\n", "Random selection: 0.0864\n" ] } ], "source": [ "# Calculate regrets for all approaches\n", "normal_thompson_regret = np.cumsum(\n", " np.array(normal_thompson_oracle.optimal_rewards)\n", " - np.array(normal_thompson_oracle.expected_rewards)\n", ")\n", "\n", "glm_thompson_regret = np.cumsum(\n", " np.array(glm_thompson_oracle.optimal_rewards)\n", " - np.array(glm_thompson_oracle.expected_rewards)\n", ")\n", "\n", "# Calculate proper linear regret baseline: random arm selection\n", "# Recreate the context sequence and calculate true baseline\n", "np.random.seed(123) # Reset to get same context sequence\n", "rng_baseline = np.random.default_rng(123)\n", "\n", "article_coefs = np.array(\n", " [\n", " [-0.4, 0.005, 0.1],\n", " [-0.8, 0.0, 0.0],\n", " [-0.3, -0.005, 0.0],\n", " [0.1, 0.0, -0.5],\n", " ]\n", ")\n", "\n", "optimal_rewards_baseline = []\n", "random_expected_rewards = []\n", "\n", "for _ in range(10000):\n", " user_gender = rng_baseline.binomial(1, 0.5)\n", " user_age = rng_baseline.integers(0, 47)\n", " context = np.array([1.0, user_age, user_gender])\n", "\n", " # Calculate expected rewards for all arms for this context\n", " all_arm_rewards = expit(np.dot(context, article_coefs.T))\n", "\n", " # Optimal reward\n", " optimal_reward = np.max(all_arm_rewards)\n", " optimal_rewards_baseline.append(optimal_reward)\n", "\n", " # Random selection: expected reward is average of all arms\n", " random_expected = np.mean(all_arm_rewards)\n", " random_expected_rewards.append(random_expected)\n", "\n", "# Calculate cumulative regret for random baseline\n", "random_regret = np.cumsum(\n", " np.array(optimal_rewards_baseline) - np.array(random_expected_rewards)\n", ")\n", "\n", "# Create comprehensive comparison plot\n", "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))\n", "\n", "rounds = np.arange(1, len(normal_ucb_regret) + 1)\n", "\n", "# UCB Policy - Linear scale\n", "ax1.plot(normal_ucb_regret, label=\"NormalInverseGamma\", linewidth=2, color=\"blue\")\n", "ax1.plot(glm_ucb_regret, label=\"BayesianGLM\", linewidth=2, color=\"red\")\n", "ax1.plot(\n", " random_regret,\n", " label=\"Random Selection\",\n", " linewidth=1,\n", " color=\"gray\",\n", " linestyle=\"--\",\n", " alpha=0.8,\n", ")\n", "ax1.set_xlabel(\"Round\")\n", "ax1.set_ylabel(\"Cumulative Regret\")\n", "ax1.set_title(\"Upper Confidence Bound Policy\")\n", "ax1.legend()\n", "ax1.grid(True, alpha=0.3)\n", "\n", "# UCB Policy - Log-log scale\n", "ax2.loglog(\n", " rounds, normal_ucb_regret, label=\"NormalInverseGamma\", linewidth=2, color=\"blue\"\n", ")\n", "ax2.loglog(rounds, glm_ucb_regret, label=\"BayesianGLM\", linewidth=2, color=\"red\")\n", "ax2.loglog(\n", " rounds,\n", " random_regret,\n", " label=\"Random Selection\",\n", " linewidth=1,\n", " color=\"gray\",\n", " linestyle=\"--\",\n", " alpha=0.8,\n", ")\n", "ax2.set_xlabel(\"Round (log scale)\")\n", "ax2.set_ylabel(\"Cumulative Regret (log scale)\")\n", "ax2.set_title(\"Upper Confidence Bound Policy (Log-Log)\")\n", "ax2.legend()\n", "ax2.grid(True, alpha=0.3)\n", "\n", "# Thompson Sampling - Linear scale\n", "ax3.plot(normal_thompson_regret, label=\"NormalInverseGamma\", linewidth=2, color=\"blue\")\n", "ax3.plot(glm_thompson_regret, label=\"BayesianGLM\", linewidth=2, color=\"red\")\n", "ax3.plot(\n", " random_regret,\n", " label=\"Random Selection\",\n", " linewidth=1,\n", " color=\"gray\",\n", " linestyle=\"--\",\n", " alpha=0.8,\n", ")\n", "ax3.set_xlabel(\"Round\")\n", "ax3.set_ylabel(\"Cumulative Regret\")\n", "ax3.set_title(\"Thompson Sampling Policy\")\n", "ax3.legend()\n", "ax3.grid(True, alpha=0.3)\n", "\n", "# Thompson Sampling - Log-log scale\n", "ax4.loglog(\n", " rounds,\n", " normal_thompson_regret,\n", " label=\"NormalInverseGamma\",\n", " linewidth=2,\n", " color=\"blue\",\n", ")\n", "ax4.loglog(rounds, glm_thompson_regret, label=\"BayesianGLM\", linewidth=2, color=\"red\")\n", "ax4.loglog(\n", " rounds,\n", " random_regret,\n", " label=\"Random Selection\",\n", " linewidth=1,\n", " color=\"gray\",\n", " linestyle=\"--\",\n", " alpha=0.8,\n", ")\n", "ax4.set_xlabel(\"Round (log scale)\")\n", "ax4.set_ylabel(\"Cumulative Regret (log scale)\")\n", "ax4.set_title(\"Thompson Sampling Policy (Log-Log)\")\n", "ax4.legend()\n", "ax4.grid(True, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Print final regret values for analysis\n", "print(\"Final Regret Results (10000 rounds):\")\n", "print(f\"UCB - NormalInverseGamma: {normal_ucb_regret[-1]:.2f}\")\n", "print(f\"UCB - BayesianGLM: {glm_ucb_regret[-1]:.2f}\")\n", "print(f\"Thompson - NormalInverseGamma: {normal_thompson_regret[-1]:.2f}\")\n", "print(f\"Thompson - BayesianGLM: {glm_thompson_regret[-1]:.2f}\")\n", "print(f\"Random selection (linear): {random_regret[-1]:.2f}\")\n", "\n", "# Show the regret rates to demonstrate sublinear behavior\n", "print(\"\\nRegret rates (final_regret/rounds):\")\n", "print(f\"UCB - NormalInverseGamma: {normal_ucb_regret[-1] / 10000:.4f}\")\n", "print(f\"UCB - BayesianGLM: {glm_ucb_regret[-1] / 10000:.4f}\")\n", "print(f\"Thompson - NormalInverseGamma: {normal_thompson_regret[-1] / 10000:.4f}\")\n", "print(f\"Thompson - BayesianGLM: {glm_thompson_regret[-1] / 10000:.4f}\")\n", "print(f\"Random selection: {random_regret[-1] / 10000:.4f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis: The Power of Misspecified Models\n", "\n", "The experimental results illustrate why NormalInverseGammaRegressor performs well despite assuming Gaussian outcomes for binary data. Two factors combine here: the Bernstein-von Mises theorem and the fact that contextual bandits seek to model the **mean reward** E[Y|X], not individual outcomes.\n", "\n", "Even though individual clicks are binary, the conditional expectation E[Y|X] = p(click|context) is continuous and bounded in [0,1]. NormalInverseGammaRegressor's Gaussian assumption is therefore less egregious than it initially appears—it's modeling a continuous quantity (the click probability) rather than discrete outcomes. The model misspecification manifests primarily in the variance structure, not the mean function.\n", "\n", "The Bernstein-von Mises theorem ensures that as data accumulates, the posterior distribution converges to a normal distribution centered at the MLE, regardless of the likelihood specification. Combined with the fact that we're estimating a continuous mean function, this explains why NormalInverseGammaRegressor's exact conjugate inference competes effectively with BayesianGLM's theoretically correct but approximately computed posterior.\n", "\n", "In sequential decision problems, this has important implications. When the quantity of interest is a conditional expectation rather than individual outcomes, and when exact posterior computation is available through conjugacy, the benefits of computational efficiency can outweigh the costs of likelihood misspecification. The convergence guarantees of Bernstein-von Mises provide the theoretical foundation, while the continuous nature of the estimation target reduces the practical impact of the distributional mismatch." ] } ], "metadata": { "kernelspec": { "display_name": "bayesianbandits", "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": "3.10.13" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }