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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# We create a NumericalMathFunction
myFunction = 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 = myFunction.getInputDimension()
R = CorrelationMatrix(dim)
for i in range(dim):
R[i, i] = 1.0
for i in range(1, dim):
R[i, i - 1] = 0.5
m = NumericalPoint(dim, 1.0)
s = NumericalPoint(dim, 2.0)
distribution = Normal(m, s, R)
ref_distribution = distribution
print("distribution = ", repr(ref_distribution))
# We create a distribution-based RandomVector
X = RandomVector(distribution)
print("X=", X)
print("is composite? ", X.isComposite())
# Check standard methods of class RandomVector
print("X dimension=", X.getDimension())
print("X realization (first )=", repr(X.getRealization()))
print("X realization (second)=", repr(X.getRealization()))
print("X realization (third )=", repr(X.getRealization()))
print("X sample =", repr(X.getSample(5)))
# We create a composite RandomVector Y from X and myFunction
Y = RandomVector(CompositeRandomVector(myFunction, X))
print("Y=", Y)
print("is composite? ", Y.isComposite())
# Check standard methods of class RandomVector
print("Y dimension=", Y.getDimension())
print("Y realization (first )=", repr(Y.getRealization()))
print("Y realization (second)=", repr(Y.getRealization()))
print("Y realization (third )=", repr(Y.getRealization()))
print("Y sample =", repr(Y.getSample(5)))
except:
import sys
print("t_RandomVector_function.py", sys.exc_info()[0], sys.exc_info()[1])
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