Metadata-Version: 2.1
Name: thompson-sampling
Version: 0.0.4
Summary: Thompson Sampling
Home-page: https://github.com/Anton1o-I/thompson-sampling
Author: Anton1o-I
Author-email: a.iniguez21@gmail.com
License: LICENSE.txt
Description: # thompson-sampling
        Thompson Sampling Multi-Armed Bandit for Python
        
        This 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.
        
        Currently 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.
        
        ## Usage
        
        ### Setting up the experiment:
        The following method will instantiate the experiment with default priors.
        ```python
        from thompson_sampling.bernoulli import BernoulliExperiment
        
        experiment = BernoulliExperiment(arms=2)
        ```
        
        If you want set your own priors using the Priors module:
        ```python
        
        from thompson_sampling.bernoulli import BernoulliExperiment
        from thompson_sampling.priors import BetaPrior
        
        pr = BetaPrior()
        pr.add_one(mean=0.5, variance=0.2, effective_size=10, label="option1")
        pr.add_one(mean=0.6, variance=0.3, effective_size=30, label="option2")
        experiment = BernoulliExperiment(priors=pr)
        ```
        
        ### Getting an action:
        Randomly chooses which arm to "pull" in the multi-armed bandit:
        ```python
        experiment.choose_arm()
        ```
        
        ### Updating reward:
        Updating the information about the different arms by adding reward information:
        
        ```python
        rewards = [{"label":"option1", "reward":1}, {"label":"option2", "reward":0}]
        experiment.add_rewards(rewards)
        ```
        
        ## Installation
        
        ### Pip 
        ```
        pip install thompson-sampling
        ```
        
Platform: UNKNOWN
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