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import openturns as ot
from openturns.experimental import GaussianProcessFitter
from openturns.testing import assert_almost_equal
ot.TESTPREAMBLE()
# Set precision
ot.PlatformInfo.SetNumericalPrecision(3)
ot.ResourceMap.Set("GaussianProcessFitter-LinearAlgebra", "HMAT")
sampleSize = 6
inputDimension = 1
# Create the function to estimate
input_description = ["x0"]
formulas = ["x0"]
model = ot.SymbolicFunction(input_description, formulas)
X = ot.Sample(sampleSize, inputDimension)
X2 = ot.Sample(sampleSize, inputDimension)
for i in range(sampleSize):
X[i, 0] = 3.0 + i
X2[i, 0] = 2.5 + i
X[0, 0] = 1.0
X[1, 0] = 3.0
X2[0, 0] = 2.0
X2[1, 0] = 4.0
Y = model(X)
# Data validation
Y2 = model(X2)
for i in range(sampleSize):
# Add a small noise to data
Y[i, 0] += 0.01 * ot.DistFunc.rNormal()
basis = ot.LinearBasisFactory(inputDimension).build()
covarianceModel = ot.DiracCovarianceModel(inputDimension)
algo = GaussianProcessFitter(X, Y, covarianceModel, basis)
algo.setKeepCholeskyFactor(True)
algo.run()
# perform an evaluation
result = algo.getResult()
metaModel = result.getMetaModel()
conditionalCovariance = result.getCovarianceModel()
residual = metaModel(X) - Y
assert_almost_equal(residual.computeCentralMoment(2), [0.00013144], 1e-5, 1e-5)
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