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
