def testAnova(self):
# ----------------------------------------------------------
# define the learner
# ----------------------------------------------------------
# define UQ setting
builder = ASGCUQManagerBuilder()
builder.withParameters(self.params)\
.withTypesOfKnowledge([KnowledgeTypes.SIMPLE,
KnowledgeTypes.SQUARED,
KnowledgeTypes.EXPECTATIONVALUE])\
.useInterpolation()
builder.defineUQSetting().withSimulation(self.simulation)
samplerSpec = builder.defineSampler()
samplerSpec.withGrid().hasType(GridType_PolyBoundary)\
.withLevel(4)\
.withDegree(5)\
.withBoundaryLevel(1)
# ----------------------------------------------------------
# discretize the stochastic space with the ASGC method
# ----------------------------------------------------------
uqManager = builder.andGetResult()
# ----------------------------------------------
# first run
while uqManager.hasMoreSamples():
uqManager.runNextSamples()
# ----------------------------------------------------------
# specify ASGC estimator
# ----------------------------------------------------------
analysis = ASGCAnalysisBuilder().withUQManager(uqManager)\
.withAnalyticEstimationStrategy()\
.andGetResult()
# ----------------------------------------------------------
# expectation values and variances
_, _ = analysis.mean(), analysis.var()
# ----------------------------------------------------------
# estimated anova decomposition
anova = analysis.getAnovaDecomposition(nk=len(self.params))
# ----------------------------------------------------------
# check interpolation and decomposition
m = np.random.rand(100, self.params.getDim())
for i in range(m.shape[0]):
self.assertTrue(abs(analysis.eval(m[i, :]) - anova.eval(m[i, :])) < 1e-14)
# ----------------------------------------------------------
# main effects
me = anova.getSobolIndices()
print("-------------- Sobol Indices (t = %i) ------------------" % 1)
for (key, val) in sorted(me.items()):
print("%s: %s" % (key, val))
print(sum([val for val in list(me.values())]), "==", 1)
# ----------------------------------------------------------
# total effects
te = anova.getTotalEffects()
print("-------------- Total Effects (t = %i) -----------------" % 1)
for key, val in sorted(te.items()):
print("%s: %s" % (key, val))
print("---------------------------------------------")
print()
names = anova.getSortedPermutations(list(me.keys()))
values = [me[name] for name in names]
fig, _ = plotSobolIndices(values, legend=True, names=names)
fig.show()
plt.show()
"refinement_numSteps": 0,
"refinement_numPoints": 10,
"regularization_type": "Laplace",
"crossValidation_lambda": 3.16228e-06,
"crossValidation_enable": True,
"crossValidation_kfold": 5,
"crossValidation_silent": False
}
distSGDE = analysis.estimateDensity(dtype="sgde", config=config)
print("mean(u) = %g ~ %g (KDE) ~ %g (SGDE)" %
(analysis.mean()[0], distKDE.mean(), distSGDE.mean()))
print("var(u) = %g ~ %g (KDE) ~ %g (SGDE)" %
(analysis.var()[0], distKDE.var(), distSGDE.var()))
# ---------------------------------------------------------------------------
y = analysis.eval(analysis.generateUnitSamples())
fig = plt.figure()
plotDensity1d(distSGDE, label="SGDE", linewidth=3)
plt.vlines(distSGDE.mean(),
0,
distSGDE.pdf(distSGDE.mean()),
linewidth=3,
color="red")
plt.vlines(distSGDE.mean() - distSGDE.std(),
0,
distSGDE.pdf(distSGDE.mean() - distSGDE.std()),
linewidth=3,
color="red")
plt.vlines(distSGDE.mean() + distSGDE.std(),
0,
