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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# Multivariate case
coll2 = DistributionCollection(0)
coll2.add(Dirac(1))
coll2.add(Dirac(2))
coll2.add(Bernoulli(0.7))
coll2.add(Uniform(3.0, 4.0))
d2 = ComposedDistribution(coll2)
coll1 = DistributionCollection(0)
coll1.add(Uniform())
coll1.add(Uniform())
d1 = ComposedDistribution(coll1)
distribution = ConditionalDistribution(d1, d2)
dim = distribution.getDimension()
print("distribution=", distribution)
print("Parameters ", distribution.getParametersCollection())
print("Mean ", distribution.getMean())
print("Covariance ", distribution.getCovariance().clean(1e-10))
# Is this distribution an elliptical distribution?
print("Elliptical distribution= ", distribution.isElliptical())
# Has this distribution an elliptical copula?
print("Elliptical copula= ", distribution.hasEllipticalCopula())
# Has this distribution an independent copula?
print("Independent copula= ", distribution.hasIndependentCopula())
# Test for realization of distribution
oneRealization = distribution.getRealization()
print("oneRealization=", oneRealization)
# Test for sampling
size = 10
oneSample = distribution.getSample(size)
print("oneSample=", oneSample)
# Test for sampling
size = 10000
anotherSample = distribution.getSample(size)
print("anotherSample mean=", anotherSample.computeMean())
print("anotherSample covariance=", anotherSample.computeCovariance())
# Define a point
zero = NumericalPoint(dim, 0.0)
# Show PDF and CDF of zero point
zeroPDF = distribution.computePDF(zero)
zeroCDF = distribution.computeCDF(zero)
print("Zero point= ", zero, " pdf=", zeroPDF, " cdf=", zeroCDF)
# Get 95% quantile
quantile = distribution.computeQuantile(0.95)
print("Quantile=", quantile)
print("CDF(quantile)= %.12g" % distribution.computeCDF(quantile))
# "
# 1D tests
# Create a collection of distribution
conditionedDistribution = Normal()
conditioningDistributionCollection = DistributionCollection(0)
# First conditioning distribution: continuous/continuous
atoms = DistributionCollection(0)
atoms.add(Uniform(0.0, 1.0))
atoms.add(Uniform(1.0, 2.0))
conditioningDistributionCollection.add(ComposedDistribution(atoms))
# Second conditioning distribution: discrete/continuous
atoms = DistributionCollection(0)
atoms.add(Binomial(3, 0.5))
atoms.add(Uniform(1.0, 2.0))
conditioningDistributionCollection.add(ComposedDistribution(atoms))
# Third conditioning distribution: dirac/continuous
atoms = DistributionCollection(0)
atoms.add(Dirac(0.5))
atoms.add(Uniform(1.0, 2.0))
conditioningDistributionCollection.add(ComposedDistribution(atoms))
for i in range(conditioningDistributionCollection.getSize()):
print("conditioning distribution=",
conditioningDistributionCollection[i])
distribution = ConditionalDistribution(
conditionedDistribution, conditioningDistributionCollection[i])
dim = distribution.getDimension()
print("Distribution ", distribution)
print("Parameters ", distribution.getParametersCollection())
print("Mean ", distribution.getMean())
print("Covariance ", distribution.getCovariance())
# Is this distribution an elliptical distribution?
print("Elliptical distribution= ", distribution.isElliptical())
# Has this distribution an elliptical copula?
print("Elliptical copula= ", distribution.hasEllipticalCopula())
# Has this distribution an independent copula?
print("Independent copula= ", distribution.hasIndependentCopula())
# Test for realization of distribution
oneRealization = distribution.getRealization()
print("oneRealization=", oneRealization)
# Test for sampling
size = 10
oneSample = distribution.getSample(size)
print("oneSample=", oneSample)
# Test for sampling
size = 10000
anotherSample = distribution.getSample(size)
print("anotherSample mean=", anotherSample.computeMean())
print("anotherSample covariance=", anotherSample.computeCovariance())
# Define a point
zero = NumericalPoint(dim, 0.0)
# Show PDF and CDF of zero point
zeroPDF = distribution.computePDF(zero)
zeroCDF = distribution.computeCDF(zero)
print("Zero point= ", zero, " pdf=%.6f" %
zeroPDF, " cdf=%.6f" % zeroCDF)
# Get 95% quantile
quantile = distribution.computeQuantile(0.95)
print("Quantile=", quantile)
print("CDF(quantile)= %.12g" % distribution.computeCDF(quantile))
except:
import sys
print("t_ConditionalDistribution_std.py",
sys.exc_info()[0], sys.exc_info()[1])
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