heron.regression¶

Functions and classes for contructing regression surrogate models.

Functions

`clear_output`
`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.
`load`(filename)	Load a pickled heron Gaussian Process.
`logp`(x)
`minimize`(fun, x0[, args, method, jac, hess, …])	Minimization of scalar function of one or more variables.
`prior_transform`(x)
`run_nested`(gp[, metric])
`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.
`run_training_nested`(gp[, method, maxiter, …])	Train the Gaussian Process model using nested sampling.
`train_cv`(gp)

Classes

MultiTaskGP(training_data, kernel[, tikh, …]) An implementation of a co-trained set of Gaussian processes which share the same hyperparameters, but which model differing data.

Regressor(training_data, kernel[, tikh, …])

Attributes:

SingleTaskGP(training_data, kernel[, tikh, …]) This is an implementaion of a Single task Gaussian process regressor.

partial partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.