idmtools_calibra.algorithms.gpc module#

class idmtools_calibra.algorithms.gpc.GPC(x_cols, y_col, training_data, param_info, kernel_mode='RBF', kernel_params=None, verbose=False, debug=False, **kwargs)[source]#

Bases: object

classmethod from_config(config_fn)[source]#

classmethod from_dict(config)[source]#

set_training_data(new_training_data)[source]#

save(save_to=None)[source]#

define_kernel(params)[source]#

kernel_xx(x, theta)[source]#

static kernel_xp(x, p, theta)[source]#

kxx_gpu_wrapper(x, theta, deriv=-1)[source]#

kxp_gpu_wrapper(x, p, theta)[source]#

assign_rep(sample)[source]#

expectation_propagation(theta)[source]#

find_posterior_mode(theta, f_guess=None, tol_grad=1e-06, max_iter=100)[source]#

negative_log_marginal_likelihood(theta)[source]#

negative_log_marginal_likelihood_and_gradient(theta, f_guess=None)[source]#

static func_wrapper(f, cache_size=100)[source]#

laplace_predict(theta, f_hat, p)[source]#

ep_predict(theta, p)[source]#

optimize_hyperparameters(x0, bounds=(), k=-1, eps=0.01, disp=True, maxiter=15000)[source]#

evaluate(data)[source]#

plot_data(samples_to_circle=None)[source]#

plot_histogram()[source]#

plot(x_center, res=10)[source]#

plot_errors(train, test, mean_col, var_predictive_col, truth_col=None, figsize=(16, 10))[source]#