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#! /usr/bin/env python
import openturns as ot
import openturns.experimental as otexp
observationsSize = 5
# Create a collection of distribution
conditionedDistribution = ot.Normal()
conditioningDistributionCollection = []
# First conditioning distribution: continuous/continuous
atoms = [ot.Uniform(0.0, 1.0), ot.Uniform(1.0, 2.0)]
conditioningDistributionCollection.append(ot.JointDistribution(atoms))
# Second conditioning distribution: discrete/continuous
atoms = [ot.Binomial(3, 0.5), ot.Uniform(1.0, 2.0)]
# conditioningDistributionCollection.append(ot.JointDistribution(atoms))
# Third conditioning distribution: dirac/continuous
atoms = [ot.Dirac(0.0), ot.Uniform(1.0, 2.0)]
# conditioningDistributionCollection.append(ot.JointDistribution(atoms))
for conditioning in conditioningDistributionCollection:
print("\n" + "=" * 50 + "\n")
print("conditioning distribution=", conditioning)
observationsDistribution = ot.Distribution(conditionedDistribution)
observationsDistribution.setParameter(conditioning.getMean())
observations = observationsDistribution.getSample(observationsSize)
distribution = otexp.PosteriorDistribution(
ot.DeconditionedDistribution(conditionedDistribution, conditioning),
observations,
)
dim = distribution.getDimension()
print("Distribution ", distribution)
print("range=", distribution.getRange())
mean = distribution.getMean()
print("Mean ", mean)
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
# Sample anotherSample = distribution.getSample(size)
# print("anotherSample mean=", anotherSample.computeMean())
# print("anotherSample covariance=", anotherSample.computeCovariance())
# Show PDF and CDF of mean point
meanPDF = distribution.computePDF(mean)
meanCDF = distribution.computeCDF(mean)
print("Mean point= ", mean, " pdf=%.5g" % meanPDF, " cdf=%.5g" % meanCDF)
# Get 95% quantile
quantile = distribution.computeQuantile(0.95)
print("Quantile=", quantile)
# print("CDF(quantile)=", distribution.computeCDF(quantile))
# Extract the marginals
for i in range(dim):
margin = distribution.getMarginal(i)
print("margin=", margin)
print("margin PDF=", ot.Point(1, margin.computePDF(mean[i])))
print("margin CDF=", ot.Point(1, margin.computeCDF(mean[i])))
print("margin quantile=", margin.computeQuantile(0.95))
print("margin realization=", margin.getRealization())
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