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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
dimension = 5
indices = [2, 0, 1]
fullDistribution = ComposedDistribution([Normal(), Uniform(), Exponential(
), Weibull()], ComposedCopula([GumbelCopula(), ClaytonCopula()]))
distribution = MarginalDistribution(fullDistribution, indices)
print('Distribution ', repr(distribution))
print('Distribution ', distribution)
# Is this distribution elliptical ?
print('Elliptical = ', distribution.isElliptical())
# Is this distribution continuous ?
print('Continuous = ', distribution.isContinuous())
# Test for realization of distribution
oneRealization = distribution.getRealization()
print('oneRealization=', oneRealization)
# Test for sampling
size = 10000
oneSample = distribution.getSample(size)
print('oneSample first=', oneSample[0], ' last=', oneSample[size - 1])
print('mean=', oneSample.computeMean())
print('covariance=', oneSample.computeCovariance())
if (distribution.getDimension() == 1):
size = 100
for i in range(2):
print('Kolmogorov test for the generator, sample size=', size, ' is ', FittingTest.Kolmogorov(
distribution.getSample(size), distribution).getBinaryQualityMeasure())
size *= 10
# Define a point
point = [1.0] * distribution.getDimension()
print('Point= ', point)
# Show PDF and CDF of point
DDF = distribution.computeDDF(point)
print('ddf =', DDF)
LPDF = distribution.computeLogPDF(point)
print('log pdf=', LPDF)
PDF = distribution.computePDF(point)
print('pdf =', PDF)
CDF = distribution.computeCDF(point)
print('cdf=', CDF)
CCDF = distribution.computeComplementaryCDF(point)
print('ccdf=', CCDF)
Survival = distribution.computeSurvivalFunction(point)
print('survival=', Survival)
quantile = distribution.computeQuantile(0.95)
print('quantile=', quantile)
print('cdf(quantile)=', distribution.computeCDF(quantile))
quantileTail = distribution.computeQuantile(0.95, True)
print('quantile (tail)=', quantileTail)
CDFTail = distribution.computeComplementaryCDF(quantileTail)
print('cdf (tail)=', CDFTail)
mean = distribution.getMean()
print('mean=', mean)
standardDeviation = distribution.getStandardDeviation()
print('standard deviation=', standardDeviation)
skewness = distribution.getSkewness()
print('skewness=', skewness)
kurtosis = distribution.getKurtosis()
print('kurtosis=', kurtosis)
covariance = distribution.getCovariance()
print('covariance=', covariance)
correlation = distribution.getCorrelation()
print('correlation=', correlation)
spearman = distribution.getSpearmanCorrelation()
print('spearman=', spearman)
kendall = distribution.getKendallTau()
print('kendall=', kendall)
for i in range(6):
print('standard moment n=', i, ', value=',
distribution.getStandardMoment(i))
print('Standard representative=', distribution.getStandardRepresentative())
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