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import openturns as ot
import unittest
import openturns.testing as ott
ot.TESTPREAMBLE()
class CheckKarhunenLoeveValidation(unittest.TestCase):
def test_KarhunenLoeveValidation(self):
# Create the KL result
numberOfVertices = 20
interval = ot.Interval(-1.0, 1.0)
mesh = ot.IntervalMesher([numberOfVertices - 1]).build(interval)
covariance = ot.SquaredExponential()
process = ot.GaussianProcess(covariance, mesh)
sampleSize = 100
processSample = process.getSample(sampleSize)
threshold = 1.0e-7
algo = ot.KarhunenLoeveSVDAlgorithm(processSample, threshold)
algo.run()
klresult = algo.getResult()
# Create validation
validation = ot.KarhunenLoeveValidation(processSample, klresult)
# Check residuals
residualProcessSample = validation.computeResidual()
assert type(residualProcessSample) is ot.ProcessSample
# Check standard deviation
residualSigmaField = validation.computeResidualStandardDeviation()
ot.Sample(numberOfVertices, 1)
# ott.assert_almost_equal(residualSigmaField, exact)
# Check mean
residualMean = validation.computeResidualMean()
ot.Sample(numberOfVertices, 1)
# ott.assert_almost_equal(residualMean, exact)
# Check graph
graph0 = validation.drawValidation()
graph1 = residualProcessSample.drawMarginal(0)
graph2 = residualMean.drawMarginal(0)
graph3 = residualSigmaField.drawMarginal(0)
graph4 = validation.drawObservationWeight(0)
graph5 = validation.drawObservationQuality()
if 0:
from openturns.viewer import View
View(graph0).save("validation1.png")
View(graph1).save("validation1-residual.png")
View(graph2).save("validation1-residual-mean.png")
View(graph3).save("validation1-residual-stddev.png")
View(graph4).save("validation1-indiv-weight.png")
View(graph5).save("validation1-indiv-quality.png")
def test_KarhunenLoeveValidationMultidimensional(self):
# Create the KL result
numberOfVertices = 20
interval = ot.Interval(-1.0, 1.0)
mesh = ot.IntervalMesher([numberOfVertices - 1]).build(interval)
outputDimension = 2
univariateCovariance = ot.SquaredExponential()
covarianceCollection = [univariateCovariance] * outputDimension
multivariateCovariance = ot.TensorizedCovarianceModel(covarianceCollection)
process = ot.GaussianProcess(multivariateCovariance, mesh)
sampleSize = 100
sampleSize = 10
processSample = process.getSample(sampleSize)
threshold = 1.0e-7
algo = ot.KarhunenLoeveSVDAlgorithm(processSample, threshold)
algo.run()
klresult = algo.getResult()
# Create the validation
validation = ot.KarhunenLoeveValidation(processSample, klresult)
# Check residuals
residualProcessSample = validation.computeResidual()
assert type(residualProcessSample) is ot.ProcessSample
# Check standard deviation
residualSigmaField = validation.computeResidualStandardDeviation()
zeroSample = ot.Sample(numberOfVertices, outputDimension)
ott.assert_almost_equal(residualSigmaField, zeroSample)
# Check graph
graph = validation.drawValidation()
if False:
from openturns.viewer import View
View(graph).save("validation2.png")
if __name__ == "__main__":
unittest.main()
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