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#!/usr/bin/env python
import openturns as ot
from openturns.testing import assert_almost_equal
from openturns.usecases import ishigami_function
ot.RandomGenerator.SetSeed(0)
im = ishigami_function.IshigamiModel()
input_names = im.distribution.getDescription()
n = 250
sampleX = im.distribution.getSample(n)
sampleY = im.model(sampleX)
ot.ResourceMap.SetAsUnsignedInteger("SobolIndicesAlgorithm-DefaultBootstrapSize", 100)
ot.RandomGenerator.SetSeed(42) # Set seed for bootstrap interval computation algorithm
mySobol = ot.RankSobolSensitivityAlgorithm(sampleX, sampleY)
indices = mySobol.getFirstOrderIndices()
assert_almost_equal(indices, [0.208654, 0.493591, -0.0669488], 1.0e-4, 1.0e-2)
interval = mySobol.getFirstOrderIndicesInterval()
assert_almost_equal(
interval.getLowerBound(), [0.107531, 0.406036, -0.195595], 1.0e-4, 1.0e-2
)
assert_almost_equal(
interval.getUpperBound(), [0.328238, 0.569699, 0.0936075], 1.0e-4, 1.0e-2
)
# Testing that we still get the same results when using setDesign
normal = ot.Normal()
mySobol2 = ot.RankSobolSensitivityAlgorithm(normal.getSample(10),
normal.getSample(10))
ot.RandomGenerator.SetSeed(42)
mySobol2.setDesign(sampleX, sampleY, sampleX.getSize())
indices2 = mySobol2.getFirstOrderIndices()
interval2 = mySobol2.getFirstOrderIndicesInterval()
assert_almost_equal(indices2, indices, 0, ot.SpecFunc.ScalarEpsilon)
assert_almost_equal(
interval2.getLowerBound(), interval.getLowerBound(), 0,
ot.SpecFunc.ScalarEpsilon)
assert_almost_equal(
interval2.getUpperBound(), interval.getUpperBound(), 0,
ot.SpecFunc.ScalarEpsilon)
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