Metadata-Version: 1.2
Name: dillinger
Version: 1.0.0.dev1
Summary: Bayesian optimization for iterated multi-armed bandit     experiments.
Home-page: https://github.com/chipfranzen/dillinger
Author: Charles Franzen
Author-email: chip.franzen@gmail.com
License: MIT
Description: # Dillinger: Deadly accurate multi-armed bandits
        
        Dillinger is a guide to using Bayesian optimization to select new actions for multi-armed bandits. The core of the project is a **Gaussian Process** class that can be fit to observations from multi-armed bandits. To facilitate demonstration, the package also has the following features: a data generator that simulates LTV of customers based on a price sensitivity curve, and an implementation of the Softmax bandit algorithm.
        
        This project is still very much under construction, as I'm adapting an existing project to make it more useable and accessible to those interested in applying Bayesian optimization to A/B tests or multi-armed bandit experiments.
        
        See `demos\` for examples of how to use this package.
Keywords: mutli-armed-bandits bayesian-optimization gaussian-processes
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3
