Basics

Tutorials

Models

heron.regression

Functions and classes for contructing regression surrogate models.

Functions

clear_output([wait])

Clear the output of the current cell receiving 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.