def test_dist_sub(distribution):
"""Test distribution subtraction."""
dist1_e = cp.E(distribution() - 3.0)
dist2_e = cp.E(3.0 - distribution())
base_e = cp.E(distribution()) - 3.0
np.testing.assert_allclose(dist1_e, -dist2_e, rtol=1e-05, atol=1e-08)
np.testing.assert_allclose(dist2_e, -base_e, rtol=1e-05, atol=1e-08)
def test_dist_add(distribution):
"""Test distribution addition."""
dist1_e = cp.E(distribution() + 2.0)
dist2_e = cp.E(2.0 + distribution())
base_e = cp.E(distribution()) + 2.0
np.testing.assert_allclose(dist1_e, dist2_e, rtol=1e-05, atol=1e-08)
np.testing.assert_allclose(dist2_e, base_e, rtol=1e-05, atol=1e-08)
def sens(xdict, funcs):
dv = xdict['xvars'] # Get the design variable out
UQObj.QoI.p['oas_example1.wing.twist_cp'] = dv
obj_func = UQObj.QoI.eval_ObjGradient
con_func = UQObj.QoI.eval_ConstraintQoIGradient
funcsSens = {}
# Objective function
# Full integration
g_mu_j = collocation_grad_obj.normal.mean(cp.E(UQObj.jdist),
cp.Std(UQObj.jdist), obj_func)
g_var_j = collocation_grad_obj.normal.variance(obj_func, UQObj.jdist,
g_mu_j)
# # Reduced integration
# g_mu_j = collocation_grad_obj.normal.reduced_mean(obj_func, UQObj.jdist, UQObj.dominant_space)
# g_var_j = collocation_grad_obj.normal.reduced_variance(obj_func, UQObj.jdist, UQObj.dominant_space, g_mu_j)
funcsSens['obj', 'xvars'] = g_mu_j + 2 * np.sqrt(
g_var_j
) # collocation_grad_obj.normal.reduced_mean(obj_func, UQObj.jdist, UQObj.dominant_space)
# Constraint function
# Full integration
funcsSens['con', 'xvars'] = collocation_grad_con.normal.mean(
cp.E(UQObj.jdist), cp.Std(UQObj.jdist), con_func)
# # Reduced integration
# funcsSens['con', 'xvars'] = collocation_grad_con.normal.reduced_mean(con_func, UQObj.jdist, UQObj.dominant_space)
fail = False
return funcsSens, fail
def objfunc(xdict):
dv = xdict['xvars'] # Get the design variable out
UQObj.QoI.p['oas_example1.wing.twist_cp'] = dv
obj_func = UQObj.QoI.eval_QoI
con_func = UQObj.QoI.eval_ConstraintQoI
funcs = {}
# Objective function
# Full integration
mu_j = collocation_obj.normal.mean(cp.E(UQObj.jdist), cp.Std(UQObj.jdist),
obj_func)
var_j = collocation_obj.normal.variance(obj_func, UQObj.jdist, mu_j)
# # Reduced integration
# mu_j = collocation_obj.normal.reduced_mean(obj_func, UQObj.jdist, UQObj.dominant_space)
# var_j = collocation_obj.normal.reduced_variance(obj_func, UQObj.jdist, UQObj.dominant_space, mu_j)
funcs['obj'] = mu_j + 2 * np.sqrt(var_j)
# Constraint function
# Full integration
funcs['con'] = collocation_con.normal.mean(cp.E(UQObj.jdist),
cp.Std(UQObj.jdist), con_func)
# # Reduced integration
# funcs['con'] = collocation_con.normal.reduced_mean(con_func, UQObj.jdist, UQObj.dominant_space)
fail = False
return funcs, fail
def test_constant_expected():
"""Test if polynomial constant behave as expected."""
distribution = chaospy.J(chaospy.Uniform(-1.2, 1.2),
chaospy.Uniform(-2.0, 2.0))
const = chaospy.polynomial(7.)
assert chaospy.E(const, distribution[0]) == const
assert chaospy.E(const, distribution) == const
assert chaospy.Var(const, distribution) == 0.
def test_dist_add():
for name, dist in zip(dist_names, dists):
dist1_e = cp.E(dist() + 2.0)
dist2_e = cp.E(2.0 + dist())
base_e = cp.E(dist()) + 2.0
np.testing.assert_allclose(dist1_e, dist2_e, rtol=1e-05, atol=1e-08)
np.testing.assert_allclose(dist2_e, base_e, rtol=1e-05, atol=1e-08)
def test_dist_sub():
for name, dist in zip(dist_names, dists):
dist1_e = cp.E(dist() - 3.0)
dist2_e = cp.E(3.0 - dist())
base_e = cp.E(dist()) - 3.0
np.testing.assert_allclose(dist1_e, -dist2_e, rtol=1e-05, atol=1e-08)
np.testing.assert_allclose(dist2_e, -base_e, rtol=1e-05, atol=1e-08)
def test_analytical_stieltjes(analytical_distribution):
"""Assert that Analytical Stieltjes produces orthogonality."""
coeffs, [orth], norms = chaospy.analytical_stieltjes(
order=4, dist=analytical_distribution)
assert orth[0] == 1
assert numpy.allclose(chaospy.E(orth[1:], analytical_distribution), 0)
covariance = chaospy.E(
numpoly.outer(orth[1:], orth[1:]), analytical_distribution)
assert numpy.allclose(numpy.diag(numpy.diag(covariance)), covariance)
assert numpy.allclose(numpoly.lead_coefficient(orth), 1)
def OptCostRS(gain, pdf):
polynomials = cp.orth_ttr(order=2, dist=pdf)
samples, weights = cp.generate_quadrature(order=2,
domain=pdf,
rule="Gaussian")
stateTensor = [OptCost(s, gain) for s in samples.T]
# stateTensor = pool.map(OptCost,samples.T)
PCE = cp.fit_quadrature(polynomials, samples, weights, stateTensor)
print "\nGain = {}".format(gain)
print "PCE Expectation: {} ".format(cp.E(poly=PCE, dist=pdf))
return cp.E(poly=PCE, dist=pdf)
def test_distribution_subtraction(distribution):
"""Test distribution subtraction."""
right_subtraction = chaospy.E(distribution() - 3.0)
left_subtraction = chaospy.E(3.0 - distribution())
reference = chaospy.E(distribution()) - 3.0
numpy.testing.assert_allclose(right_subtraction,
-left_subtraction,
rtol=1e-05,
atol=1e-08)
numpy.testing.assert_allclose(left_subtraction,
-reference,
rtol=1e-05,
atol=1e-08)
def test_distribution_addition(distribution):
"""Assert adding."""
right_addition = chaospy.E(distribution() + 2.0)
left_addition = chaospy.E(2.0 + distribution())
reference = chaospy.E(distribution()) + 2.0
numpy.testing.assert_allclose(right_addition,
left_addition,
rtol=1e-05,
atol=1e-08)
numpy.testing.assert_allclose(left_addition,
reference,
rtol=1e-05,
atol=1e-08)
def Kurt(poly, dist=None, fisher=True, **kws):
"""
Kurtosis operator.
Element by element 4rd order statistics of a distribution or polynomial.
Args:
poly (Poly, Dist) : Input to take kurtosis on.
dist (Dist) : Defines the space the skewness is taken on.
It is ignored if `poly` is a distribution.
fisher (bool) : If True, Fisher's definition is used (Normal -> 0.0).
If False, Pearson's definition is used (normal -> 3.0)
**kws (optional) : Extra keywords passed to dist.mom.
Returns:
(ndarray) : Element for element variance along `poly`, where
`skewness.shape==poly.shape`.
Examples:
>>> x = cp.variable()
>>> Z = cp.Uniform()
>>> print(np.around(cp.Kurt(Z), 8))
-1.2
>>> Z = cp.Normal()
>>> print(np.around(cp.Kurt(x, Z), 8))
0.0
"""
if isinstance(poly, cp.dist.Dist):
x = cp.poly.variable(len(poly))
poly, dist = x, poly
else:
poly = cp.poly.Poly(poly)
if fisher:
adjust = 3
else:
adjust = 0
shape = poly.shape
poly = cp.poly.flatten(poly)
m1 = cp.E(poly, dist)
m2 = cp.E(poly**2, dist)
m3 = cp.E(poly**3, dist)
m4 = cp.E(poly**4, dist)
out = (m4-4*m3*m1 + 6*m2*m1**2 - 3*m1**4) /\
(m2**2-2*m2*m1**2+m1**4) - adjust
out = np.reshape(out, shape)
return out
def solve_nonlinear(self, params, unknowns, resids):
power = params['power']
method_dict = params['method_dict']
dist = method_dict['distribution']
rule = method_dict['rule']
n = len(power)
if rule != 'rectangle':
points, weights = cp.generate_quadrature(order=n - 1,
domain=dist,
rule=rule)
# else:
# points, weights = quadrature_rules.rectangle(n, method_dict['distribution'])
poly = cp.orth_chol(n - 1, dist)
# poly = cp.orth_bert(n-1, dist)
# double check this is giving me good orthogonal polynomials.
# print poly, '\n'
p2 = cp.outer(poly, poly)
# print 'chol', cp.E(p2, dist)
norms = np.diagonal(cp.E(p2, dist))
print 'diag', norms
expansion, coeff = cp.fit_quadrature(poly,
points,
weights,
power,
retall=True,
norms=norms)
# expansion, coeff = cp.fit_quadrature(poly, points, weights, power, retall=True)
mean = cp.E(expansion, dist)
print 'mean cp.E =', mean
# mean = sum(power*weights)
print 'mean sum =', sum(power * weights)
print 'mean coeff =', coeff[0]
std = cp.Std(expansion, dist)
print mean
print std
print np.sqrt(np.sum(coeff[1:]**2 * cp.E(poly**2, dist)[1:]))
# std = np.sqrt(np.sum(coeff[1:]**2 * cp.E(poly**2, dist)[1:]))
# number of hours in a year
hours = 8760.0
# promote statistics to class attribute
unknowns['mean'] = mean * hours
unknowns['std'] = std * hours
print 'In ChaospyStatistics'
def __init__(self, jdist, distribution_type, QoI_dict, data_type=np.float):
self.data_type = data_type
self.n_rv = cp.E(jdist).size
self.collocation_dict = {} # Dictionary that will hold all the
# information pertaining to the different QoI
if distribution_type == "Normal" or distribution_type == "MvNormal":
self.collocation_dict = copy.copy(QoI_dict)
for i in self.collocation_dict:
quadrature_degree = self.collocation_dict[i][
'quadrature_degree']
q, w = np.polynomial.hermite.hermgauss(quadrature_degree)
if self.collocation_dict[i]['reduced_collocation'] == False:
points, weights = self.getQuadratureInfo(
jdist, quadrature_degree, q, w)
else:
print('i = ', i)
print('dominant_dir = ',
self.collocation_dict[i]['dominant_dir'])
dominant_dir = self.collocation_dict[i]['dominant_dir']
points, weights = self.getReducedQuadratureInfo(
jdist, dominant_dir, quadrature_degree, q, w)
self.collocation_dict[i]['points'] = points
self.collocation_dict[i]['quadrature_weights'] = weights
self.allocateQoISpace(self.collocation_dict[i])
else:
raise NotImplementedError
def __init__(self,
jdist,
quadrature_degree,
distribution_type,
QoI_dict,
include_derivs=False,
reduced_collocation=False,
dominant_dir=None,
data_type=np.float):
assert quadrature_degree > 0, "Need at least 1 collocation point for \
uncertainty propagation"
self.n_rv = cp.E(jdist).size
self.QoI_dict = utils.copy_qoi_dict(
QoI_dict) # copy.copy(QoI_dict) # We don't
self.distribution_type = distribution_type
self.data_type = data_type
# Get the 1D quadrature points based on the distribution being
# approximated
if distribution_type == "Normal" or distribution_type == "MvNormal":
self.q, self.w = np.polynomial.hermite.hermgauss(quadrature_degree)
if reduced_collocation == False:
self.getQuadratureInfo(jdist, quadrature_degree, self.q,
self.w)
else:
self.getReducedQuadratureInfo(jdist, dominant_dir,
quadrature_degree, self.q,
self.w)
self.allocateQoISpace(include_derivs)
else:
raise NotImplementedError
def _gc_correlation_pairwise(
distributions,
rho,
order=15,
force_calc=False,
):
assert len(distributions) == 2
# Check if this is special combination
special_dist_result = _special_dist(distributions)
if type(special_dist_result) is bool:
check_success = False
else:
f = special_dist_result
check_success = True
# If not force_calc and is special combination
if force_calc is False and check_success is True:
result = rho * f
else:
arg_1 = np.prod(cp.E(distributions))
arg_2 = np.sqrt(np.prod(cp.Var(distributions)))
arg = rho * arg_2 + arg_1
kwargs = dict()
kwargs["distributions"] = distributions
kwargs["order"] = order
kwargs["arg"] = arg
grid = np.linspace(-0.99, 0.99, num=199, endpoint=True)
v_p_criterion = np.vectorize(partial(_criterion, **kwargs))
result = grid[np.argmin(v_p_criterion(grid))]
return result
def reduced_dVariance(self, QoI_func, jdist, dominant_space, mu_j,
dQoI_func, dmu_j):
"""
same as dVariance but in the reduced stochastic space.
"""
x = cp.E(jdist)
rv_covariance = cp.Cov(jdist)
n_quadrature_loops = len(dominant_space.dominant_indices)
dominant_dir = dominant_space.iso_eigenvecs[:, dominant_space.
dominant_indices]
ref_collocation_pts = self.q
ref_collocation_w = self.w
idx = 0
colloc_xi_arr = np.zeros(n_quadrature_loops)
colloc_w_arr = np.zeros(n_quadrature_loops)
dvariance_j = np.zeros([self.QoI_dimensions, self.QoI_dimensions])
idx = self.doReduced_dVariance(x, rv_covariance, dominant_dir, mu_j,
dmu_j, dvariance_j, ref_collocation_pts,
ref_collocation_w, QoI_func, dQoI_func,
colloc_xi_arr, colloc_w_arr, idx)
assert idx == -1
dvariance_j[:] = dvariance_j[:] / (np.sqrt(np.pi)**n_quadrature_loops)
# TODO: figure out a better way of handling arrays and matrices for this routine
return dvariance_j.diagonal()
def calcMarginals(self, jdist):
"""
Compute the marginal density object for the dominant space. The current
implementation is only for Gaussian distribution.
"""
marginal_size = len(self.dominant_indices)
orig_mean = cp.E(jdist)
orig_covariance = cp.Cov(jdist)
# Step 1: Rotate the mean & covariance matrix of the the original joint
# distribution along the eigenve
dominant_vecs = self.iso_eigenvecs[:, self.dominant_indices]
marginal_mean = np.dot(dominant_vecs.T, orig_mean)
marginal_covariance = np.matmul(
dominant_vecs.T, np.matmul(orig_covariance, dominant_vecs))
# Step 2: Create the new marginal distribution
if marginal_size == 1: # Univariate distributions have to be treated separately
marginal_std_dev = np.sqrt(np.asscalar(marginal_covariance))
self.marginal_distribution = cp.Normal(np.asscalar(marginal_mean),
marginal_std_dev)
else:
self.marginal_distribution = cp.MvNormal(marginal_mean,
marginal_covariance)
def reduced_mean(self, QoI_func, jdist, dominant_space):
x = cp.E(jdist)
covariance = cp.Cov(jdist)
n_quadrature_loops = len(dominant_space.dominant_indices)
dominant_dir = dominant_space.iso_eigen_vectors[:, dominant_space.
dominant_indices]
ref_collocation_pts = self.q
ref_collocation_w = self.w
idx = 0
colloc_xi_arr = np.zeros(n_quadrature_loops)
colloc_w_arr = np.zeros(n_quadrature_loops)
mu_j = np.zeros(self.QoI_dimensions)
idx = self.doReducedUniformMean(x, covariance, dominant_dir, mu_j,
ref_collocation_pts, ref_collocation_w,
QoI_func, colloc_xi_arr, colloc_w_arr,
idx)
assert idx == -1
# TODO: The following scaling doesnt look right, INVESTIGATE
mu_j[:] = mu_j[:] * (0.5**n_quadrature_loops)
if len(mu_j) == 1:
return mu_j[0]
else:
return mu_j
def KG(z, evls, pnts, gp, kernel, NSAMPS=30, DEG=3, sampling=False):
# Find initial minimum value from GP model
min_val = 1e100
X_sample = pnts
Y_sample = evls
#for x0 in [np.random.uniform(XL, XU, size=DIM) for oo in range(20)]:
x0 = np.random.uniform(XL, XU, size=DIM)
res = mini(gp, x0=x0,
bounds=[(XL, XU)
for ss in range(DIM)]) #, method='Nelder-Mead')
#res = mini(expected_improvement, x0=x0[0], bounds=[(XL, XU) for ss in range(DIM)], args=(X_sample, Y_sample, gp))#, callback=callb)
# if res.fun < min_val:
min_val = res.fun
min_x = res.x
# estimate min(f^{n+1}) with MC simulation
MEAN = 0
points = np.atleast_2d(np.append(X_sample, z)).T
m, s = gp(z, return_std=True)
distribution = cp.J(cp.Normal(0, s))
samples = distribution.sample(NSAMPS, rule='Halton')
PCEevals = []
for pp in range(NSAMPS):
# construct future GP, using z as the next point
evals = np.append(evls, m + samples[pp])
#evals = np.append(evls, m + np.random.normal(0, s))
gpnxt = GaussianProcessRegressor(kernel=kernel,
n_restarts_optimizer=35,
random_state=98765,
normalize_y=True)
gpnxt.fit(points, evals)
# convinience function
def gpf_next(x, return_std=False):
alph, astd = gpnxt.predict(np.atleast_2d(x), return_std=True)
alph = alph[0]
if return_std:
return (alph, astd)
else:
return alph
res = mini(gpf_next, x0=x0, bounds=[(XL, XU) for ss in range(DIM)])
min_next_val = res.fun
min_next_x = res.x
#print('+++++++++ ', res.fun)
#MEAN += min_next_val
PCEevals.append(min_next_val)
if not sampling:
polynomial_expansion = cp.orth_ttr(DEG, distribution)
foo_approx = cp.fit_regression(polynomial_expansion, samples, PCEevals)
MEAN = cp.E(foo_approx, distribution)
else:
MEAN = np.mean(PCEevals)
#print(PCEevals, '...', MEAN)
#hey
#MEAN /= NSAMPS
return min_val - MEAN
def dVariance(self, QoI_func, jdist, mu_j, dQoI_func, dmu_j):
"""
Computes the partial derivative of the variance
This implementation is CURRENTLY ONLY for QoI's that return scalar values.
"""
x = cp.E(jdist)
rv_covariance = cp.Cov(jdist)
systemsize = x.size
ref_collocation_pts = self.q
ref_collocation_w = self.w
idx = 0
colloc_xi_arr = np.zeros(systemsize)
colloc_w_arr = np.zeros(systemsize)
dvariance_j = np.zeros([self.QoI_dimensions, self.QoI_dimensions])
# dvariance_j = np.zeros(self.QoI_dimensions)
idx = self.do_dVariance(x, rv_covariance, mu_j, dmu_j, dvariance_j,
ref_collocation_pts, ref_collocation_w,
QoI_func, dQoI_func, colloc_xi_arr,
colloc_w_arr, idx)
assert idx == -1
dvariance_j[:] = dvariance_j[:] / (np.sqrt(np.pi)**systemsize)
# TODO: figure out a better way of handling arrays and matrices for this routine
return dvariance_j.diagonal()
def EHI(x,
gp1,
gp2,
xi=0.,
x2=None,
MD=None,
NSAMPS=200,
PCE=False,
ORDER=2,
PAR_RES=100):
mu1, std1 = gp1(x, return_std=True)
mu2, std2 = gp2(x, return_std=True)
a, b, c = parEI(gp1, gp2, x2, '', EI=False, MD=MD, PAR_RES=PAR_RES)
par = b.T[c, :]
par += xi
MEAN = 0 # running sum for observed hypervolume improvement
if not PCE: # Monte Carlo Sampling
for ii in range(NSAMPS):
# add new point to Pareto Front
evl = [np.random.normal(mu1, std1), np.random.normal(mu2, std2)]
pears = np.append(par.T, evl, 1).T
idx = is_pareto_efficient_simple(pears)
newPar = pears[idx, :]
# check if Pareto front improvemed from this point
if idx[-1]:
MEAN += H(newPar) - H(par)
return (MEAN / NSAMPS)
else:
# Polynomial Chaos
# (assumes 2 objective functions)
distribution = cp.J(cp.Normal(0, std1), cp.Normal(0, std2))
# sparse grid samples
samples = distribution.sample(NSAMPS, rule='Halton')
PCEevals = []
for pp in range(NSAMPS):
# add new point to Pareto Front
evl = [np.random.normal(mu1, std1), np.random.normal(mu2, std2)]
pears = np.append(par.T, evl, 1).T
idx = is_pareto_efficient_simple(pears)
newPar = pears[idx, :]
# check if Pareto front improvemes
if idx[-1]:
PCEevals.append(H(newPar) - H(par))
else:
PCEevals.append(0)
polynomial_expansion = cp.orth_ttr(ORDER, distribution)
foo_approx = cp.fit_regression(polynomial_expansion, samples, PCEevals)
MEAN = cp.E(foo_approx, distribution)
return (MEAN)
def expect(self):
"""Return the expected value of the distribution.
Returns
-------
float
The expected value of the distribution.
"""
return float(chaos.E(self.dist))
def galerkin_approx(coordinates, joint, expansion_small, norms_small):
alpha, beta = chaospy.variable(2)
e_alpha_phi = chaospy.E(alpha*expansion_small, joint)
initial_condition = e_alpha_phi/norms_small
phi_phi = chaospy.outer(expansion_small, expansion_small)
e_beta_phi_phi = chaospy.E(beta*phi_phi, joint)
def right_hand_side(c, t):
return -numpy.sum(c*e_beta_phi_phi, -1)/norms_small
coefficients = odeint(
func=right_hand_side,
y0=initial_condition,
t=coordinates,
)
return chaospy.sum(expansion_small*coefficients, -1)
def getMCResults4Web(self):
'''
Use surrogate model to perform Monte Carlo simulation
@param nmc, number of Monte Carlo realizations
'''
if (self.co2Model is None):
self.getMetaModels()
meanCO2 = cp.E(self.co2Model, self.jointDist)
stdCO2 = cp.Std(self.co2Model, self.jointDist)
meanBrine = cp.E(self.brineModel, self.jointDist)
stdBrine = cp.Std(self.brineModel, self.jointDist)
return [
meanCO2[self.nlw:], stdCO2[self.nlw:], meanBrine[self.nlw:],
stdBrine[self.nlw:]
]
def test_orth_ttr():
dist = cp.Normal(0, 1)
orth = cp.orth_ttr(5, dist)
outer = cp.outer(orth, orth)
Cov1 = cp.E(outer, dist)
Diatoric = Cov1 - np.diag(np.diag(Cov1))
assert np.allclose(Diatoric, 0)
Cov2 = cp.Cov(orth[1:], dist)
assert np.allclose(Cov1[1:,1:], Cov2)
def Skew(poly, dist=None, **kws):
"""
Skewness operator.
Element by element 3rd order statistics of a distribution or polynomial.
Args:
poly (Poly, Dist) : Input to take skewness on.
dist (Dist) : Defines the space the skewness is taken on.
It is ignored if `poly` is a distribution.
**kws (optional) : Extra keywords passed to dist.mom.
Returns:
(ndarray) : Element for element variance along `poly`, where
`skewness.shape==poly.shape`.
Examples:
>>> x = cp.variable()
>>> Z = cp.Gamma()
>>> print(cp.Skew(Z))
2.0
"""
if isinstance(poly, cp.dist.Dist):
x = cp.poly.variable(len(poly))
poly, dist = x, poly
else:
poly = cp.poly.Poly(poly)
if poly.dim < len(dist):
cp.poly.setdim(poly, len(dist))
shape = poly.shape
poly = cp.poly.flatten(poly)
m1 = cp.E(poly, dist)
m2 = cp.E(poly**2, dist)
m3 = cp.E(poly**3, dist)
out = (m3-3*m2*m1+2*m1**3)/(m2-m1**2)**1.5
out = np.reshape(out, shape)
return out
def getSamples(self, jdist, include_derivs=False):
n_rv = cp.E(jdist).shape
pert = np.zeros(n_rv, dtype=self.data_type)
# Get the all the function values for the given set of samples
for i in range(0,self.num_samples):
for j in self.QoI_dict:
QoI_func = self.QoI_dict[j]['QoI_func']
self.QoI_dict[j]['fvals'][i,:] = QoI_func(self.samples[:,i], pert)
if include_derivs == True:
for k in self.QoI_dict[j]['deriv_dict']:
dQoI_func = self.QoI_dict[j]['deriv_dict'][k]['dQoI_func']
# print('\n j = ', j)
# print('self.num_samples = ', self.num_samples)
# print('dQoI = ', dQoI_func(self.samples[:,i], pert))
self.QoI_dict[j]['deriv_dict'][k]['fvals'][i,:] = dQoI_func(self.samples[:,i], pert)
def _gc_correlation_pairwise(distributions, rho, seed=123, num_draws=100000):
assert len(distributions) == 2
arg_1 = np.prod(cp.E(distributions))
arg_2 = np.sqrt(np.prod(cp.Var(distributions)))
arg = (rho * arg_2 + arg_1)
kwargs = dict()
kwargs["args"] = (arg, distributions, seed, num_draws)
kwargs["bounds"] = (-0.99, 0.99)
kwargs["method"] = "bounded"
out = optimize.minimize_scalar(_criterion, **kwargs)
assert out["success"]
return out["x"]
def calculate_uqsa_measures(joint_dist, polynomial, alpha=5):
""" Use chaospy to calculate appropriate indices of uq and sa"""
dists = joint_dist
mean = cp.E(polynomial, dists)
var = cp.Var(polynomial, dists)
std = cp.Std(polynomial, dists)
conInt = cp.Perc(polynomial, [alpha / 2., 100 - alpha / 2.], joint_dist)
sens_m = cp.Sens_m(polynomial, dists)
sens_m2 = cp.Sens_m2(polynomial, dists)
sens_t = cp.Sens_t(polynomial, dists)
return dict(mean=mean,
var=var,
std=std,
conInt=conInt,
sens_m=sens_m,
sens_m2=sens_m2,
sens_t=sens_t)