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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 a FORM algorithm
# The first parameter is an OptimizationSolver
# The second parameter is an event
# The third parameter is a starting point for the design point research
myCobyla = Cobyla()
myCobyla.setMaximumIterationNumber(400)
myAlgo = FORM(myCobyla, myEvent, mean)
# Perform the simulation
myAlgo.run()
# Create a PostAnalyticalControlledImportanceSampling algorithm based on
# the previous FORM result
formResult = myAlgo.getResult()
mySamplingAlgo = PostAnalyticalControlledImportanceSampling(formResult)
print("FORM probability= %.11g" % formResult.getEventProbability())
mySamplingAlgo.setMaximumOuterSampling(250)
mySamplingAlgo.setBlockSize(4)
mySamplingAlgo.setMaximumCoefficientOfVariation(0.1)
print("PostAnalyticalControlledImportanceSampling=", mySamplingAlgo)
mySamplingAlgo.run()
# Stream out the result
print("PostAnalyticalControlledImportanceSampling result=",
mySamplingAlgo.getResult())
except:
import sys
print("t_PostAnalyticalControlledImportanceSampling_std.py",
sys.exc_info()[0], sys.exc_info()[1])
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