def by_samples(cls, samples, bounds=None):
nataf = NatafDensity()
nataf.build(samples)
return cls(nataf,
bounds=bounds,
params={
"name": "samples",
"samples": samples
})
def beta_marginals(cls,
lwr,
upr,
alpha,
beta,
covMatrix=None,
corrMatrix=None,
bounds=None):
range = upr - lwr
mean = lwr + alpha / (alpha + beta) * range
stddev = np.sqrt(alpha * beta /
(alpha + beta + 1.0)) / (alpha + beta) * range
if corrMatrix is None:
corrMatrix = Dist().corrcoeff(covMatrix=covMatrix)
num_dims = corrMatrix.shape[0]
nataf = NatafDensity()
nataf.initialize_random_variable_types([STD_BETA] * num_dims)
nataf.initialize_random_variable_parameters([mean] * num_dims,
[stddev] * num_dims,
[[alpha, beta]] * num_dims)
nataf.initialize_random_variable_correlations(corrMatrix)
return cls(nataf,
bounds=bounds,
params={
"name": "beta",
"lwr": lwr,
"upr": upr,
"alpha": alpha,
"beta": beta,
"corrMatrix": corrMatrix,
"covMatrix": covMatrix
})
def gamma_marginals(cls,
alpha,
beta,
covMatrix=None,
corrMatrix=None,
bounds=None):
mean = alpha * beta
stddev = np.sqrt(alpha) * beta
if corrMatrix is None:
corrMatrix = Dist().corrcoeff(covMatrix=covMatrix)
num_dims = corrMatrix.shape[0]
nataf = NatafDensity()
nataf.initialize_random_variable_types([GAMMA] * num_dims)
nataf.initialize_random_variable_parameters([mean] * num_dims,
[stddev] * num_dims,
[[alpha, beta]] * num_dims)
nataf.initialize_random_variable_correlations(corrMatrix)
return cls(nataf,
bounds=bounds,
params={
"name": "gamma",
"alpha": alpha,
"beta": beta,
"corrMatrix": corrMatrix,
"covMatrix": covMatrix
})
def normal_marginals(cls,
mean,
stddev,
covMatrix=None,
corrMatrix=None,
bounds=None):
if corrMatrix is None:
corrMatrix = Dist().corrcoeff(covMatrix=covMatrix)
num_dims = corrMatrix.shape[0]
nataf = NatafDensity()
nataf.initialize_random_variable_types([GAUSSIAN] * num_dims)
nataf.initialize_random_variable_parameters(
[mean] * num_dims, [stddev] * num_dims,
[[mean, stddev]] * num_dims)
nataf.initialize_random_variable_correlations(corrMatrix)
return cls(nataf,
bounds=bounds,
params={
"name": "normal",
"mean": mean,
"stddev": stddev,
"corrMatrix": corrMatrix,
"covMatrix": covMatrix
})