def _rand_init(x_bounds, x_types, selection_num_starting_points): ''' Random sample some init seed within bounds. ''' return [ lib_data.rand(x_bounds, x_types) for i in range(0, selection_num_starting_points) ]
def selection_r(x_bounds, x_types, clusteringmodel_gmm_good, clusteringmodel_gmm_bad, num_starting_points=100, minimize_constraints_fun=None): ''' Call selection ''' minimize_starting_points = [lib_data.rand(x_bounds, x_types)\ for i in range(0, num_starting_points)] outputs = selection(x_bounds, x_types, clusteringmodel_gmm_good, clusteringmodel_gmm_bad, minimize_starting_points, minimize_constraints_fun) return outputs
def selection_r(acquisition_function, samples_y_aggregation, x_bounds, x_types, regressor_gp, num_starting_points=100, minimize_constraints_fun=None): ''' Selecte R value ''' minimize_starting_points = [lib_data.rand(x_bounds, x_types) \ for i in range(0, num_starting_points)] outputs = selection(acquisition_function, samples_y_aggregation, x_bounds, x_types, regressor_gp, minimize_starting_points, minimize_constraints_fun=minimize_constraints_fun) return outputs