def setUp(self):
"""
Set up problem.
"""
super(Test_prob_with_emulated_volumes_3to1, self).setUp()
calcP.prob_with_emulated_volumes(self.disc)
self.P_ref = np.loadtxt(data_path + "/3to1_prob_mc.txt.gz")
def setUp(self):
"""
Set up problem.
"""
super(Test_prob_with_emulated_volumes_3to1, self).setUp()
calcP.prob_with_emulated_volumes(self.disc)
self.P_ref = np.loadtxt(data_path + "/3to1_prob_mc.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")
def setUp(self):
"""
Set up problem.
"""
super(Test_prob_with_emulated_volumes_1to1, self).setUp()
calcP.prob_with_emulated_volumes(self.disc)
def setUp(self):
"""
Set up problem.
"""
super(Test_prob_with_emulated_volumes_1to1, self).setUp()
calcP.prob_with_emulated_volumes(self.disc)
emulated_inputs2 = bsam.random_sample_set('r',
my_disc._input_sample_set._domain,
num_samples = 10001,
globalize=False)
# Make exact discretization
my_disc_exact = my_disc.copy()
my_disc_exact._output_sample_set._values_local = my_disc._output_sample_set._values_local + my_disc._output_sample_set._error_estimates_local
my_disc_exact._output_sample_set._error_estimates_local = np.zeros(my_disc_exact._output_sample_set._error_estimates_local.shape)
my_disc_exact._output_sample_set.local_to_global()
my_disc_exact.set_emulated_input_sample_set(emulated_inputs2)
# Solve stochastic inverse problems
my_disc.set_emulated_input_sample_set(emulated_inputs)
calculateP.prob_with_emulated_volumes(my_disc)
calculateP.prob_with_emulated_volumes(my_disc_exact)
# Set in input space to calculate errors for
s_set = samp.rectangle_sample_set(3)
s_set.setup(maxes=[[0.75, 0.75, 0.75]], mins=[[0.25, 0.25, 0.25]])
s_set.set_region(np.array([0,1]))
# Calculate true probabilty using linear surrogate on true data
sur = surrogates.piecewise_polynomial_surrogate(my_disc_exact)
sur_disc_lin = sur.generate_for_input_set(emulated_inputs, order=1)
(Pt, _) = sur.calculate_prob_for_sample_set_region(s_set,
regions=[0])
P_true = Pt[0]
if comm.rank == 0:
print "True probability: ", P_true
emulated_inputs2 = bsam.random_sample_set('r',
my_disc._input_sample_set._domain,
num_samples=10001,
globalize=False)
# Make exact discretization
my_disc_exact = my_disc.copy()
my_disc_exact._output_sample_set._values_local = my_disc._output_sample_set._values_local + my_disc._output_sample_set._error_estimates_local
my_disc_exact._output_sample_set._error_estimates_local = np.zeros(
my_disc_exact._output_sample_set._error_estimates_local.shape)
my_disc_exact._output_sample_set.local_to_global()
my_disc_exact.set_emulated_input_sample_set(emulated_inputs2)
# Solve stochastic inverse problems
my_disc.set_emulated_input_sample_set(emulated_inputs)
calculateP.prob_with_emulated_volumes(my_disc)
calculateP.prob_with_emulated_volumes(my_disc_exact)
# Set in input space to calculate errors for
s_set = samp.rectangle_sample_set(3)
s_set.setup(maxes=[[0.75, 0.75, 0.75]], mins=[[0.25, 0.25, 0.25]])
s_set.set_region(np.array([0, 1]))
# Calculate true probabilty using linear surrogate on true data
sur = surrogates.piecewise_polynomial_surrogate(my_disc_exact)
sur_disc_lin = sur.generate_for_input_set(emulated_inputs, order=1)
(Pt, _) = sur.calculate_prob_for_sample_set_region(s_set, regions=[0])
P_true = Pt[0]
if comm.rank == 0:
print "True probability: ", P_true