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#! /usr/bin/env python
from __future__ import print_function
import sys
import openturns as ot
ot.TESTPREAMBLE()
ot.RandomGenerator.SetSeed(0)
def progress(percent):
sys.stderr.write('-- progress=' + str(percent) + '%\n')
def stop():
sys.stderr.write('-- stop?\n')
return False
# We create a numerical math function
myFunction = ot.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 = [0.0] * dim
# E
mean[0] = 50.0
# F
mean[1] = 1.0
# L
mean[2] = 10.0
# I
mean[3] = 5.0
sigma = [1.0] * dim
R = ot.IdentityMatrix(dim)
myDistribution = ot.Normal(mean, sigma, R)
# We create a 'usual' RandomVector from the Distribution
vect = ot.RandomVector(myDistribution)
# We create a composite random vector
output = ot.RandomVector(myFunction, vect)
# We create an Event from this RandomVector
myEvent = ot.Event(output, ot.Less(), -3.0)
# We create a Monte Carlo algorithm
myAlgo = ot.MonteCarlo(myEvent)
myAlgo.setMaximumOuterSampling(250)
myAlgo.setBlockSize(4)
myAlgo.setMaximumCoefficientOfVariation(0.1)
print("MonteCarlo=", myAlgo)
# Perform the simulation
myAlgo.run()
# Stream out the result
print("MonteCarlo result=", myAlgo.getResult())
# Use the standard deviation as a stoping rule
myAlgo = ot.MonteCarlo(myEvent)
myAlgo.setMaximumOuterSampling(250)
myAlgo.setBlockSize(4)
myAlgo.setMaximumCoefficientOfVariation(0.0)
myAlgo.setMaximumStandardDeviation(0.1)
myAlgo.setProgressCallback(progress)
myAlgo.setStopCallback(stop)
print("MonteCarlo=", myAlgo)
# Perform the simulation
myAlgo.run()
# Stream out the result
print("MonteCarlo result=", myAlgo.getResult())
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