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        "author": "Anton1o-I",
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        "description": "# thompson-sampling\nThompson Sampling Multi-Armed Bandit for Python\n\nThis project is an implementation of a Thompson Sampling approach to a Multi-Armed Bandit. The goal of this project is to easily create and maintain Thompson Sampling experiments.\n\nCurrently this project supports experiments where the response follows a Bernoulli or Poisson distribution. Further work will be done to allow for experiments that follow other distributions, with recommendations/collaboration welcome.\n\n## Usage\n\n### Setting up the experiment:\nThe following method will instantiate the experiment with default priors.\n```python\nfrom thompson_sampling.bernoulli import BernoulliExperiment\n\nexperiment = BernoulliExperiment(arms=2)\n```\n\nIf you want set your own priors using the Priors module:\n```python\n\nfrom thompson_sampling.bernoulli import BernoulliExperiment\nfrom thompson_sampling.priors import BetaPrior\n\npr = BetaPrior()\npr.add_one(mean=0.5, variance=0.2, effective_size=10, label=\"option1\")\npr.add_one(mean=0.6, variance=0.3, effective_size=30, label=\"option2\")\nexperiment = BernoulliExperiment(priors=pr)\n```\n\n### Getting an action:\nRandomly chooses which arm to \"pull\" in the multi-armed bandit:\n```python\nexperiment.choose_arm()\n```\n\n### Updating reward:\nUpdating the information about the different arms by adding reward information:\n\n```python\nrewards = [{\"label\":\"option1\", \"reward\":1}, {\"label\":\"option2\", \"reward\":0}]\nexperiment.add_rewards(rewards)\n```\n\n## Installation\n\n### Pip \n```\npip install thompson-sampling\n```\n\n\n",
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