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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
from math import *
TESTPREAMBLE()
try:
for dim in [2, 5, 10]:
print("dimension = ", dim)
# We create a numerical math function
inputVar = list(['X' + str(i) for i in range(dim)])
myFunction = NumericalMathFunction(inputVar, ['y'], ['0'])
# We create a normal distribution point of dimension 1
mean = [0.] * dim
sigma = [1.0] * dim
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 a StandardEvent from this RandomVector
myStandardEvent = StandardEvent(
output, Less(), 2.0)
std = Normal()
beta = NumericalPoint(3)
beta[0] = round(-std.computeQuantile(1e-3)[0])
beta[1] = round(-std.computeQuantile(1e-5)[0])
beta[2] = round(-std.computeQuantile(1e-7)[0])
importanceLevel = NumericalPoint(3)
importanceLevel[0] = 0.01
importanceLevel[1] = 0.05
importanceLevel[2] = 0.10
accuracyLevel = NumericalPoint(3)
accuracyLevel[0] = 1.5
accuracyLevel[1] = 2.0
accuracyLevel[2] = 4.0
confidenceLevel = NumericalPoint(3)
confidenceLevel[0] = 0.90
confidenceLevel[1] = 0.95
confidenceLevel[2] = 0.99
pointNumber = UnsignedIntegerCollection(3)
pointNumber[0] = 10
pointNumber[1] = 100
pointNumber[2] = 1000
# TABLE 1 : we impose beta, the importance level, the accuracy level,
# tne confidence level and we calculate the corresponding deltaEpsilon
# and pointNumber N
print("beta ", "importanceLevel ", "accuracyLevel ",
"confidenceLevel ", "deltaEpsilon ", "pointNumber")
# loop on beta
for indexBeta in range(beta.getDimension()):
# We create the design point
designPoint = NumericalPoint(dim, 0.0)
designPoint[0] = beta[indexBeta]
# loop on the importance level epsilon
for indexImportanceLevel in range(importanceLevel.getDimension()):
# loop on the accuracy level tau
for indexAccuracyLevel in range(accuracyLevel.getDimension()):
# loop on the confidence level (1-q)
for indexConfidenceLevel in range(confidenceLevel.getDimension()):
# we calculate the corresponding deltaEpsilon and
# pointNumber N
myTest = StrongMaximumTest(myStandardEvent, designPoint, importanceLevel[
indexImportanceLevel], accuracyLevel[indexAccuracyLevel], confidenceLevel[indexConfidenceLevel])
print("%.6f" % beta[indexBeta], " %.6f" % importanceLevel[indexImportanceLevel], " %.6f" % accuracyLevel[
indexAccuracyLevel], " %.6f" % confidenceLevel[indexConfidenceLevel], " %.6f" % myTest.getDeltaEpsilon(), " %.6f" % myTest.getPointNumber())
# TABLE 2 : we impose beta, the importance level, the accuracy level, the pointNumber N and we calculate the corresponding deltaEpsilon and confidence level
# std::cout << std::right
# << std::setw(10) << "beta "
# << std::setw(16) << "importanceLevel "
# << "accuracyLevel " << "pointNumber " << "deltaEpsilon " << "confidenceLevel" << std::endl
print("beta ", "importanceLevel ", "accuracyLevel ",
"pointNumber", "deltaEpsilon ", "confidenceLevel")
# loop on beta
for indexBeta in range(beta.getDimension()):
# We create the design point
designPoint = NumericalPoint(dim, 0.0)
designPoint[0] = beta[indexBeta]
# loop on the importance level epsilon
for indexImportanceLevel in range(importanceLevel.getDimension()):
# loop on the accuracy level tau
for indexAccuracyLevel in range(accuracyLevel.getDimension()):
# loop on the pointNumber N
for indexPointNumber in range(pointNumber.getSize()):
# we calculate the corresponding deltaEpsilon and
# confidenceLevel
myTest = StrongMaximumTest(myStandardEvent, designPoint, importanceLevel[
indexImportanceLevel], accuracyLevel[indexAccuracyLevel], pointNumber[indexPointNumber])
print("%.6f" % beta[indexBeta], " %.6f" % importanceLevel[indexImportanceLevel], " %.6f" % accuracyLevel[
indexAccuracyLevel], " %.6f" % pointNumber[indexPointNumber], " %.6f" % myTest.getDeltaEpsilon(), " %.6f" % myTest.getConfidenceLevel())
except:
import sys
print("t_strongMaximumTest_tabulatedValues.py",
sys.exc_info()[0], sys.exc_info()[1])
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