GPyOpt.experiment_design package¶

Submodules¶

class GPyOpt.experiment_design.base.ExperimentDesign(space)¶

Bases: object

Base class for all experiment designs

class GPyOpt.experiment_design.grid_design.GridDesign(space)¶

Grid experiment design. Uses random design for non-continuous variables, and square grid for continuous ones

get_samples(init_points_count)¶: This method may return less points than requested. The total number of generated points is the smallest closest integer of n^d to the selected amount of points.

GPyOpt.experiment_design.grid_design.multigrid(bounds, points_count)¶: Generates a multidimensional lattice :param bounds: box constraints :param points_count: number of points per dimension.

class GPyOpt.experiment_design.latin_design.LatinDesign(space)¶

Latin experiment design. Uses random design for non-continuous variables, and latin hypercube for continuous ones

class GPyOpt.experiment_design.random_design.RandomDesign(space)¶

Random experiment design. Random values for all variables within the given bounds.

fill_noncontinous_variables(samples)¶: Fill sample values to non-continuous variables in place

get_samples_with_constraints(init_points_count)¶: Draw random samples and only save those that satisfy constraints Finish when required number of samples is generated

GPyOpt.experiment_design.random_design.samples_multidimensional_uniform(bounds, points_count)¶: Generates a multidimensional grid uniformly distributed. :param bounds: tuple defining the box constraints. :points_count: number of data points to generate.

class GPyOpt.experiment_design.sobol_design.SobolDesign(space)¶

Sobol experiment design. Uses random design for non-continuous variables, and Sobol sequence for continuous ones

GPyOpt.experiment_design.initial_design(design_name, space, init_points_count)¶