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#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
from openturns.testing import assert_almost_equal
ot.TESTPREAMBLE()
try:
# Set precision
ot.PlatformInfo.SetNumericalPrecision(3)
print("================")
print("Test using NLOpt")
print("================")
# Calibration of default optimizer
ot.ResourceMap.SetAsNumericalScalar('GeneralizedLinearModelAlgorithm-DefaultOptimizationLowerBound', 1.0e-5)
ot.ResourceMap.SetAsNumericalScalar('GeneralizedLinearModelAlgorithm-DefaultOptimizationUpperBound', 100)
# Data & estimation
spatialDimension = 1
X = ot.Normal().getSample(100)
X = X.sortAccordingToAComponent(0)
covarianceModel = ot.SquaredExponential( [1.0], [1.0])
model = ot.NumericalMathFunction(["x"], ["x - 0.6 * cos(x/3)"])
Y = model(X)
basis = ot.QuadraticBasisFactory(spatialDimension).build()
algo = ot.GeneralizedLinearModelAlgorithm(X, Y, covarianceModel, basis)
algo.setOptimizationSolver(ot.NelderMead())
algo.run()
# perform an evaluation
result = algo.getResult()
metaModel = result.getMetaModel()
conditionalCovariance = result.getCovarianceModel()
residual = metaModel(X) - Y
assert_almost_equal(residual.computeCenteredMoment(2), [1.06e-05], 1e-5, 1e-5)
assert_almost_equal(conditionalCovariance.getParameter(), [0.702138,0.00137], 2e-3, 1e-3)
print("Test Ok")
except:
import sys
print("t_GeneralizedLinearModelAlgorithm_nlopt.py", sys.exc_info()[0], sys.exc_info()[1])
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