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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
try:
# We create a NumericalMathFunction
myFunc = NumericalMathFunction(['x1', 'x2', 'x3', 'x4'], ['y1', 'y2'], [
'(x1*x1+x2^3*x1)/(2*x3*x3+x4^4+1)', 'cos(x2*x2+x4)/(x1*x1+1+x3^4)'])
# We create a distribution
dim = myFunc.getInputDimension()
meanPoint = [0.2] * dim
sigma = [0.] * dim
for i in range(dim):
sigma[i] = 0.1 * (i + 1)
R = CorrelationMatrix(IdentityMatrix(dim))
for i in range(1, dim):
R[i, i - 1] = 0.25
distribution = Normal(meanPoint, sigma, R)
# We create a distribution-based RandomVector
X = RandomVector(distribution)
# We create a composite RandomVector Y from X and myFunction
Y = RandomVector(myFunc, X)
# We create a quadraticCumul algorithm
myQuadraticCumul = QuadraticCumul(Y)
# We test the attributes here
print("myQuadraticCumul=", myQuadraticCumul)
# We compute the several elements provided by the quadratic cumul algorithm
print("First order mean=", repr(myQuadraticCumul.getMeanFirstOrder()))
print("Second order mean=", repr(myQuadraticCumul.getMeanSecondOrder()))
print("Covariance=", repr(myQuadraticCumul.getCovariance()))
print("Value at mean=", repr(myQuadraticCumul.getValueAtMean()))
print("Gradient at mean=", repr(myQuadraticCumul.getGradientAtMean()))
print("Hessian at mean=", repr(myQuadraticCumul.getHessianAtMean()))
myQuadraticCumul_1 = QuadraticCumul(Y.getMarginal(0))
print("Importance factors=", repr(
myQuadraticCumul_1.getImportanceFactors()))
except:
import sys
print("t_QuadraticCumul_std.py", sys.exc_info()[0], sys.exc_info()[1])
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