Bayesian Optimization is a pure Python implementation of bayesian global
optimization with gaussian processes.

This is a constrained global optimization package built upon bayesian inference
and gaussian process, 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, situations where the balance between
exploration and exploitation is important.