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Python is a thin wrapper around a C++ core. This makes usage a bit odd in some cases. For example: params = {
'n_iterations' : 50,
'n_init_samples' : 20,
's_name' : "sGaussianProcessNormal",
'c_name' : "cHedge(cEI,cLCB,cExpReturn,cOptimisticSampling)"
} You have a C++ function call listed in a python str: "cHedge(cEI,cLCB,cExpReturn,cOptimisticSampling)" See More
The Bayesian optimization implementation needs the user to specify a kernel width that determines how smooth the system assumes functions will be with no data. Other implementations do not require this assumption and integrate out the smoothness assumption using Markov Chain Monte Carlo (MCMC) sampling. The choice here is the width of a Matern kernel which, if you have to have one, is a more flexible kernel than the more commonly found Squared Exponential kernel. See More
Chocolate uses no master process for distributing tasks. Every task is completely independent and only gets its information from a database. Chocolate is thus ideal in controlled computing environments where it is hard to maintain a master process for the duration of the optimisation. See More
The Bayesian optimization implementation needs the user to specify a kernel width that determines how smooth the system assumes functions will be with no data. Other implementations do not require this assumption and integrate out the smoothness assumption using Markov Chain Monte Carlo (MCMC) sampling. See More