def setUp(self):
"""
Set up problem.
"""
super(Test_prob_on_emulated_samples_10to4, self).setUp()
calcP.prob_on_emulated_samples(self.disc)
def setUp(self):
"""
Set up problem.
"""
super(Test_prob_on_emulated_samples_10to4, self).setUp()
calcP.prob_on_emulated_samples(self.disc)
def setUp(self):
"""
Set up problem.
"""
super(Test_prob_on_emulated_samples_3to1, self).setUp()
calcP.prob_on_emulated_samples(self.disc)
self.P_emulate_ref = np.loadtxt(data_path+"/3to1_prob_emulated.txt.gz")
def postprocess(station_nums, ref_num):
filename = 'P_q' + str(station_nums[0] + 1) + \
'_q' + str(station_nums[1] + 1)
if len(station_nums) == 3:
filename += '_q' + str(station_nums[2] + 1)
filename += '_ref_' + str(ref_num + 1)
data = Q[:, station_nums]
output_sample_set = sample.sample_set(data.shape[1])
output_sample_set.set_values(data)
q_ref = Q_ref[ref_num, station_nums]
# Create Simple function approximation
# Save points used to parition D for simple function approximation and the
# approximation itself (this can be used to make close comparisions...)
output_probability_set = sfun.regular_partition_uniform_distribution_rectangle_scaled(
output_sample_set,
q_ref,
rect_scale=0.15,
cells_per_dimension=np.ones((data.shape[1], )))
num_l_emulate = 1e4
set_emulated = bsam.random_sample_set('r', lam_domain, num_l_emulate)
my_disc = sample.discretization(input_sample_set,
output_sample_set,
output_probability_set,
emulated_input_sample_set=set_emulated)
print("Finished emulating lambda samples")
# Calculate P on lambda emulate
print("Calculating prob_on_emulated_samples")
calcP.prob_on_emulated_samples(my_disc)
sample.save_discretization(my_disc, filename,
"prob_on_emulated_samples_solution")
# Calclate P on the actual samples with assumption that voronoi cells have
# equal size
input_sample_set.estimate_volume_mc()
print("Calculating prob")
calcP.prob(my_disc)
sample.save_discretization(my_disc, filename, "prob_solution")
# Calculate P on the actual samples estimating voronoi cell volume with MC
# integration
calcP.prob_with_emulated_volumes(my_disc)
print("Calculating prob_with_emulated_volumes")
sample.save_discretization(my_disc, filename,
"prob_with_emulated_volumes_solution")
def postprocess(station_nums, ref_num):
filename = 'P_q'+str(station_nums[0]+1)+'_q'
if len(station_nums) == 3:
filename += '_q'+str(station_nums[2]+1)
filename += '_ref_'+str(ref_num+1)
data = Q[:, station_nums]
output_sample_set = sample.sample_set(data.shape[1])
output_sample_set.set_values(data)
q_ref = Q_ref[ref_num, station_nums]
# Create Simple function approximation
# Save points used to parition D for simple function approximation and the
# approximation itself (this can be used to make close comparisions...)
output_probability_set = sfun.regular_partition_uniform_distribution_rectangle_scaled(\
output_sample_set, q_ref, rect_scale=0.15,
cells_per_dimension=np.ones((data.shape[1],)))
num_l_emulate = 1e4
set_emulated = bsam.random_sample_set('r', lam_domain, num_l_emulate)
my_disc = sample.discretization(input_sample_set, output_sample_set,
output_probability_set, emulated_input_sample_set=set_emulated)
print "Finished emulating lambda samples"
# Calculate P on lambda emulate
print "Calculating prob_on_emulated_samples"
calcP.prob_on_emulated_samples(my_disc)
sample.save_discretization(my_disc, filename, "prob_on_emulated_samples_solution")
# Calclate P on the actual samples with assumption that voronoi cells have
# equal size
input_sample_set.estimate_volume_mc()
print "Calculating prob"
calcP.prob(my_disc)
sample.save_discretization(my_disc, filename, "prob_solution")
# Calculate P on the actual samples estimating voronoi cell volume with MC
# integration
calcP.prob_with_emulated_volumes(my_disc)
print "Calculating prob_with_emulated_volumes"
sample.save_discretization(my_disc, filename, "prob_with_emulated_volumes_solution")
