def setPolynomialChaosResult(self, chaosResult):
"""
Accessor to the polynomial chaos result.
Parameters
----------
chaosResult : :py:class:`openturns.FunctionalChaosResult`
The polynomial chaos result.
"""
try:
ot.FunctionalChaosResult(chaosResult)
except NotImplementedError:
raise Exception('The given parameter is not an FunctionalChaosResult.')
self._chaosResult = chaosResult
self._userChaos = True
for i in range(len(x_quadq)):
print("Quad10 (49) y_quad1 study#" + str(i))
x_c, y, q = fl(x_quadq[i])
y_quadq.append(y)
pc_predictor_r.fit(x_trainr, y_trainr)
pc_predictor_q.fit(x_quadq, y_quadq)
pc_predictor_ls1.fit(x_train5, y_train5)
pc_predictor_ls2.fit(x_train3, y_train3)
surror = pc_predictor_r.predictor.pc_result
surroq = pc_predictor_q.predictor.pc_result
surrols1 = pc_predictor_ls1.predictor.pc_result
surrols2 = pc_predictor_ls2.predictor.pc_result
Surror = ot.FunctionalChaosResult(surror)
Coeffsr = Surror.getCoefficients()
Surroq = ot.FunctionalChaosResult(surroq)
Coeffsq = Surroq.getCoefficients()
Surrols1 = ot.FunctionalChaosResult(surrols1)
Coeffls1 = Surrols1.getCoefficients()
Surrols2 = ot.FunctionalChaosResult(surrols2)
Coeffls2 = Surrols2.getCoefficients()
g_ref = np.array(Coeffsr)
(n_pc, k) = np.shape(g_ref)
gq = np.array(Coeffsq)
gls1 = np.array(Coeffls1)
gls2 = np.array(Coeffls2)
LC_pc_q = np.zeros(n_pc)
LC_pc_ls1 = np.zeros(n_pc)
distribution_corr = ot.ComposedDistribution([ot.Normal()] * 2, copula)
# %%
# ANCOVA needs a functional decomposition of the model
enumerateFunction = ot.LinearEnumerateFunction(2)
productBasis = ot.OrthogonalProductPolynomialFactory([ot.HermiteFactory()] * 2,
enumerateFunction)
adaptiveStrategy = ot.FixedStrategy(
productBasis, enumerateFunction.getStrataCumulatedCardinal(4))
samplingSize = 250
projectionStrategy = ot.LeastSquaresStrategy(
ot.MonteCarloExperiment(samplingSize))
algo = ot.FunctionalChaosAlgorithm(model, distribution, adaptiveStrategy,
projectionStrategy)
algo.run()
result = ot.FunctionalChaosResult(algo.getResult())
# %%
# Create the input sample taking account the correlation
size = 2000
sample = distribution_corr.getSample(size)
# %%
# Perform the decomposition
ancova = ot.ANCOVA(result, sample)
# Compute the ANCOVA indices (first order and uncorrelated indices are computed together)
indices = ancova.getIndices()
# Retrieve uncorrelated indices
uncorrelatedIndices = ancova.getUncorrelatedIndices()
# Retrieve correlated indices:
correlatedIndices = indices - uncorrelatedIndices
