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#! /usr/bin/env python
import openturns as ot
import openturns.testing as ott
ot.TESTPREAMBLE()
def fitKriging(covarianceModel):
"""
Fit the parameters of a kriging metamodel.
"""
coordinates = ot.Sample(
[
[1.0, 1.0],
[5.0, 1.0],
[9.0, 1.0],
[1.0, 3.5],
[5.0, 3.5],
[9.0, 3.5],
[1.0, 6.0],
[5.0, 6.0],
[9.0, 6.0],
]
)
observations = ot.Sample(
[[25.0], [25.0], [10.0], [20.0], [25.0], [20.0], [15.0], [25.0], [25.0]]
)
basis = ot.ConstantBasisFactory(2).build()
algo = ot.KrigingAlgorithm(coordinates, observations, covarianceModel, basis)
algo.run()
krigingResult = algo.getResult()
return krigingResult
# Isotropic covariance model
myIsotropicKernel = ot.IsotropicCovarianceModel(ot.SquaredExponential(), 2)
krigingFittedCovarianceModel = fitKriging(myIsotropicKernel).getCovarianceModel()
ott.assert_almost_equal(krigingFittedCovarianceModel.getScale()[0], 2.86427, 0.0, 1e-4)
ott.assert_almost_equal(
krigingFittedCovarianceModel.getAmplitude()[0], 6.65231, 0.0, 1e-4
)
with ott.assert_raises(TypeError):
ot.IsotropicCovarianceModel(ot.SquaredExponential(), 0)
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