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#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.RandomGenerator.SetSeed(0)
try:
# Instanciate one distribution object
graph = ot.BipartiteGraph([[0, 1], [0, 1]])
distribution = ot.CumulativeDistributionNetwork([ot.Normal(2)]*2, graph)
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())
# Define a point
point = [1.0]*distribution.getDimension()
print("Point= ", point)
# Show PDF and CDF of point
LPDF = distribution.computeLogPDF( point )
print("log pdf =%.4e" % LPDF)
PDF = distribution.computePDF( point )
print("pdf =%.4e" % PDF)
CDF = distribution.computeCDF( point )
print("cdf =%.4e" % CDF)
CCDF = distribution.computeComplementaryCDF( point )
print("ccdf =%.4e" % CCDF)
Survival = distribution.computeSurvivalFunction( point )
print("survival=%.4e" % Survival)
quantile = distribution.computeQuantile( 0.95 )
print("quantile=", quantile)
print("cdf(quantile)= %.12g" % distribution.computeCDF(quantile))
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)
except:
import sys
print("t_CumulativeDistributionNetwork_std.py", sys.exc_info()[0], sys.exc_info()[1])
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