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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
meanPoint = NumericalPoint(1)
meanPoint[0] = 1.0
sigma = NumericalPoint(1)
sigma[0] = 3.0
R = CorrelationMatrix(1)
R[0, 0] = 1.0
# Create a collection of distribution
dimension = 2000
print("Creating a composed distribution of dimension ", dimension)
aCollection = DistributionCollection(
dimension, Normal(meanPoint, sigma, R))
for i in range(dimension):
aCollection[i] = Normal(meanPoint, sigma, R)
# Create a a copula
aCopula = IndependentCopula(dimension)
# Instanciate one distribution object
distribution = ComposedDistribution(aCollection, aCopula)
print("Distribution created.")
# Is this distribution elliptical ?
print("Elliptical = ", 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 sampling
size = 10
anotherSample = distribution.getSample(size)
# Define a point
zero = NumericalPoint(dimension, 0.0)
# Show PDF and CDF of zero point
zeroPDF = distribution.computePDF(zero)
zeroCDF = distribution.computeCDF(zero)
print(" pdf=%.6f" % zeroPDF, " cdf=%.6f" % zeroCDF)
# Get 95% quantile
quantile = distribution.computeQuantile(0.95)
print("Quantile=", repr(quantile))
print("CDF(quantile)=%.6f" % distribution.computeCDF(quantile))
# Extract a 2-D marginal
indices = Indices(2, 0)
indices[0] = 1
indices[1] = 0
print("indices=", repr(indices))
margins = distribution.getMarginal(indices)
print("margins=", repr(margins))
print("margins PDF=%.6f" % margins.computePDF(NumericalPoint(2)))
print("margins CDF=%.6f" % margins.computeCDF(NumericalPoint(2)))
quantile = NumericalPoint(margins.computeQuantile(0.5))
print("margins quantile=", repr(quantile))
print("margins CDF(qantile)=%.6f" % margins.computeCDF(quantile))
print("margins realization=", repr(margins.getRealization()))
sample = margins.getSample(1000)
print("margins sample mean=", repr(sample.computeMean()))
print("margins sample covariance=", repr(sample.computeCovariance()))
except:
import sys
print("t_ComposedDistribution_large.py",
sys.exc_info()[0], sys.exc_info()[1])
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