lasdi.gp

Functions

fit_gps(X, Y)	Trains each GP given the interpolation dataset.
eval_gp(gp_dictionnary, param_grid)	Computes the GPs predictive mean and standard deviation for points of the parameter space grid
sample_coefs(gp_dictionnary, param, n_samples)	Generates sample sets of ODEs for one given parameter.

Module Contents

lasdi.gp.fit_gps(X, Y)

Trains each GP given the interpolation dataset. X: (n_train, n_param) numpy 2d array Y: (n_train, n_coef) numpy 2d array We assume each target coefficient is independent with each other. gp_dictionnary is a dataset containing the trained GPs (as sklearn objects)

lasdi.gp.eval_gp(gp_dictionnary, param_grid)

Computes the GPs predictive mean and standard deviation for points of the parameter space grid

lasdi.gp.sample_coefs(gp_dictionnary, param, n_samples)

Generates sample sets of ODEs for one given parameter. coef_samples is a list of length n_samples, where each terms is a matrix of SINDy coefficients sampled from the GP predictive distributions