{"repo":"bayesian-optimization/BayesianOptimization","free":true,"listed":false,"github":"https://github.com/bayesian-optimization/BayesianOptimization","clone":"git clone https://github.com/bayesian-optimization/BayesianOptimization.git","description":"A Python implementation of global optimization with gaussian processes.","language":"Python","stars":8692,"topics":["optimization","gaussian-processes","bayesian-optimization","python","simple"],"license":"MIT","category":"machine-learning","readme_excerpt":"Bayesian Optimization Pure Python implementation of bayesian global optimization with gaussian processes. This is a constrained global optimization package built upon bayesian inference and gaussian processes, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions and situations where the balance between exploration and exploitation is important. Installation pip (via PyPI): Conda (via conda-forge): How does it work? See the documentation for how to use this package. Bayesian optimization works by constructing a posterior distribution of functions (gaussian process) that best describes the function you want to optimize. As the number of observations grows, the posterior distribution improves, and the algorithm becomes more certain of which regions in parameter space are worth exploring and which are not, as seen in the picture below. As you iterate over and over, the algorithm balances its needs of exploration and exploitation taking into account what it knows about the target function. At each step a Gaussian Process is fitted to the known samples (points previously explored), and the posterior distribution, combined with a exploration strategy (such as UCB (Upper Confidence Bound), or EI (Expected Improvement)), are used to determine the next point that should be explored (see the gif below). This process is designed to minimize the number of steps require","default_branch":null,"files":null,"tree":[],"storefront":"/r/bayesian-optimization","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/bayesian-optimization/BayesianOptimization/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}