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#! /usr/bin/env python
import openturns as ot
import openturns.testing as ott
ot.TESTPREAMBLE()
dimension = 5
indices = [2, 0, 1]
coll = [ot.Normal(dimension), ot.Multinomial(10, [1.0 / (dimension + 2)] * dimension)]
for nDistribution in range(len(coll)):
fullDistribution = coll[nDistribution]
distribution = ot.MarginalDistribution(fullDistribution, indices)
# To check if indices accessors are correct
otherDistribution = distribution
otherDistribution.setIndices(distribution.getIndices())
print("Test indices accessors", otherDistribution == distribution)
# To check if distribution accessors are correct
otherDistribution.setDistribution(distribution.getDistribution())
print("Test distribution accessors", otherDistribution == distribution)
print("Distribution ", repr(distribution))
print("Distribution ", distribution)
# Is this distribution elliptical ?
print("Elliptical = ", distribution.isElliptical())
# Is this distribution continuous ?
print("Continuous = ", distribution.isContinuous())
# Is this distribution discrete ?
print("Discrete = ", distribution.isDiscrete())
# Is this distribution integral ?
print("Integral = ", distribution.isIntegral())
# 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)
# Test the DDF/logDDF only in the continuous case
if distribution.isContinuous():
DDF = distribution.computeDDF(point)
print("ddf =", DDF)
LPDF = distribution.computeLogPDF(point)
print("log pdf=%.5e" % LPDF)
# Show PDF and CDF of point
PDF = distribution.computePDF(point)
print("pdf =%.5e" % PDF)
CDF = distribution.computeCDF(point)
print("cdf =%.5e" % CDF)
CCDF = distribution.computeComplementaryCDF(point)
print("ccdf =%.5e" % CCDF)
Survival = distribution.computeSurvivalFunction(point)
print("survival=%.5e" % Survival)
if distribution.isContinuous():
InverseSurvival = distribution.computeInverseSurvivalFunction(0.95)
print("Inverse survival=", InverseSurvival)
print(
"Survival(inverse survival)=%.5e"
% distribution.computeSurvivalFunction(InverseSurvival)
)
quantile = distribution.computeQuantile(0.95)
print("quantile=", quantile)
print("cdf(quantile)=%.5e" % distribution.computeCDF(quantile))
quantileTail = distribution.computeQuantile(0.95, True)
print("quantile (tail)=", quantileTail)
CDFTail = distribution.computeComplementaryCDF(quantileTail)
print("cdf (tail)=%.5e" % 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)
if distribution.isContinuous():
spearman = distribution.getSpearmanCorrelation()
print("spearman=", spearman)
kendall = distribution.getKendallTau()
print("kendall=", kendall)
print(
"Standard representative=", distribution.getStandardRepresentative().__str__()
)
ot.Log.Show(ot.Log.TRACE)
validation = ott.DistributionValidation(distribution)
validation.skipDependenceMeasures() # slow
validation.skipConditional() # FIXME
validation.skipTransformation() # FIXME
validation.run()
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