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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# We create a numerical math function
myFunction = NumericalMathFunction(
('E', 'F', 'L', 'I'), ('d',), ('-F*L^3/(3.*E*I)',))
dim = myFunction.getInputDimension()
# We create a normal distribution point of dimension 1
mean = NumericalPoint(dim, 0.0)
# E
mean[0] = 50.0
# F
mean[1] = 1.0
# L
mean[2] = 10.0
# I
mean[3] = 5.0
sigma = NumericalPoint(dim, 1.0)
R = IdentityMatrix(dim)
myDistribution = Normal(mean, sigma, R)
# We create a 'usual' RandomVector from the Distribution
vect = RandomVector(myDistribution)
# We create a composite random vector
output = RandomVector(myFunction, vect)
# We create an Event from this RandomVector
myEvent = Event(output, Less(), -3)
# We create an importance sampling Carlo algorithm */
mean[0] = 4.99689645939288809018e+01
mean[1] = 1.84194175946153282375e+00
mean[2] = 1.04454036676956398821e+01
mean[3] = 4.66776215562709406726e+00
myImportance = Normal(mean, sigma, R)
myAlgo = ImportanceSampling(myEvent, myImportance)
myAlgo.setMaximumOuterSampling(250)
myAlgo.setBlockSize(4)
myAlgo.setMaximumCoefficientOfVariation(0.1)
print("ImportanceSampling=", myAlgo)
# Perform the simulation
myAlgo.run()
# Stream out the result
print("ImportanceSampling result=", myAlgo.getResult())
except:
import sys
print("t_ImportanceSampling_std.py", sys.exc_info()[0], sys.exc_info()[1])
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