def setUp(self):
"""
Set up problem.
"""
if isinstance(self.data, samp.sample_set_base):
if self.data._reference_value is not None:
Q_ref = None
else:
Q_ref = self.Q_ref
else:
Q_ref = self.Q_ref
self.data_prob = sFun.uniform_partition_uniform_distribution_rectangle_scaled(
self.data, Q_ref, rect_scale=0.1, M=67, num_d_emulate=1E3)
self.d_distr_samples = self.data_prob.get_values()
self.rho_D_M = self.data_prob.get_probabilities()
if type(self.Q_ref) != np.array:
self.Q_ref = np.array([self.Q_ref])
if len(self.data_domain.shape) == 1:
self.data_domain = np.expand_dims(self.data_domain, axis=0)
self.rect_domain = np.zeros((self.data_domain.shape[0], 2))
r_width = 0.1*self.data_domain[:, 1]
self.rect_domain[:, 0] = self.Q_ref - .5*r_width
self.rect_domain[:, 1] = self.Q_ref + .5*r_width
'''
Suggested changes for user:
Try different ways of discretizing the probability measure on D defined as a uniform
probability measure on a rectangle (since D is 2-dimensional) centered at Q_ref whose
size is determined by scaling the circumscribing box of D.
'''
randomDataDiscretization = False
if randomDataDiscretization is False:
simpleFunP.regular_partition_uniform_distribution_rectangle_scaled(
data_set=my_discretization, Q_ref=Q_ref, rect_scale=0.25,
cells_per_dimension = 3)
else:
simpleFunP.uniform_partition_uniform_distribution_rectangle_scaled(
data_set=my_discretization, Q_ref=Q_ref, rect_scale=0.25,
M=50, num_d_emulate=1E5)
# calculate probablities
calculateP.prob(my_discretization)
########################################
# Post-process the results
########################################
'''
Suggested changes for user:
At this point, the only thing that should change in the plotP.* inputs
should be either the nbins values or sigma (which influences the kernel
density estimation with smaller values implying a density estimate that
looks more like a histogram and larger values smoothing out the values
Try different ways of discretizing the probability measure on D defined
as a uniform probability measure on a rectangle or interval depending
on choice of QoI_num in myModel.py.
'''
randomDataDiscretization = False
if randomDataDiscretization is False:
simpleFunP.regular_partition_uniform_distribution_rectangle_scaled(
data_set=my_discretization,
Q_ref=Q_ref,
rect_scale=0.25,
cells_per_dimension=3)
else:
simpleFunP.uniform_partition_uniform_distribution_rectangle_scaled(
data_set=my_discretization,
Q_ref=Q_ref,
rect_scale=0.25,
M=50,
num_d_emulate=1E5)
# calculate probabilities
calculateP.prob(my_discretization)
########################################
# Post-process the results
########################################
'''
Suggested changes for user:
At this point, the only thing that should change in the plotP.* inputs
should be either the nbins values or sigma (which influences the kernel
density estimation with smaller values implying a density estimate that
