heron.training — Heron 0.1.0+65.g49fbb43.dirty documentation


These are functions designed to be used for training a Gaussian process made using heron.


cross_validation(p, gp) Calculate the cross-validation factor between the training set and the test set.
ln_likelihood(p, gp) Returns to log-likelihood of the Gaussian process, which can be used to learn the hyperparameters of the GP.
run_sampler(sampler, initial, iterations) Run the MCMC sampler for some number of iterations, but output a progress bar so you can keep track of what’s going on
run_training_map(gp[, metric, repeats]) Find the maximum a posteriori training values for the Gaussian Process.
run_training_mcmc(gp[, walkers, burn, …]) Train a Gaussian process using an MCMC process to find the maximum evidence.