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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)
ot.ResourceMap.Set("GeneralizedLinearModelAlgorithm-LinearAlgebra", "HMAT")
# Test 1
print("========================")
print("Test standard using HMat")
print("========================")
sampleSize = 6
spatialDimension = 1
# Create the function to estimate
input_description = ["x0"]
foutput = ["f0"]
formulas = ["x0"]
model = ot.NumericalMathFunction(input_description, foutput, formulas)
X = ot.NumericalSample(sampleSize, spatialDimension)
X2 = ot.NumericalSample(sampleSize, spatialDimension)
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(spatialDimension).build()
covarianceModel = ot.DiracCovarianceModel(spatialDimension)
algo = ot.GeneralizedLinearModelAlgorithm(X, Y, covarianceModel, basis)
algo.run()
# perform an evaluation
result = algo.getResult()
metaModel = result.getMetaModel()
conditionalCovariance = result.getCovarianceModel()
residual = metaModel(X) - Y
assert_almost_equal(residual.computeCenteredMoment(2), [0.00013144], 1e-5, 1e-5)
assert_almost_equal(conditionalCovariance.getParameter(), [0.011464782674211804], 1e-5, 1e-3)
print("Test Ok")
except:
import sys
print("t_GeneralizedLinearModelAlgorithm_std_hmat.py", sys.exc_info()[0], sys.exc_info()[1])
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