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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# Instanciate one distribution object
for dim in range(1, 5):
theta = NumericalPoint(dim + 1)
for i in range(dim + 1):
theta[i] = (i + 1.0) / 4.0
distribution = Dirichlet(theta)
description = [''] * dim
for j in range(1, dim + 1):
oss = 'Marginal ' + str(j)
description[j - 1] = oss
distribution.setDescription(description)
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=", repr(oneRealization))
# Test for sampling
size = 10000
oneSample = distribution.getSample(size)
print("oneSample first=", repr(
oneSample[0]), " last=", repr(oneSample[size - 1]))
print("mean=", repr(oneSample.computeMean()))
print("covariance=", repr(oneSample.computeCovariance()))
if (dim == 1):
size = 100
for i in range(2):
RandomGenerator.SetSeed(0)
msg = ''
if FittingTest.Kolmogorov(distribution.getSample(size), distribution).getBinaryQualityMeasure():
msg = "accepted"
else:
msg = "rejected"
print(
"Kolmogorov test for the generator, sample size=", size, " is", msg)
size *= 10
# Define a point
point = NumericalPoint(
distribution.getDimension(), 0.5 / distribution.getDimension())
print("Point= ", repr(point))
# Show PDF and CDF of point
LPDF = distribution.computeLogPDF(point)
print("log pdf= %.8g" % LPDF)
PDF = distribution.computePDF(point)
print("pdf = %.8g" % PDF)
CDF = distribution.computeCDF(point)
print("cdf= %.8g" % CDF)
quantile = distribution.computeQuantile(0.95)
print("quantile=", repr(quantile))
print("cdf(quantile)= %.6f" % distribution.computeCDF(quantile))
mean = distribution.getMean()
print("mean=", repr(mean))
standardDeviation = distribution.getStandardDeviation()
print("standard deviation=", repr(standardDeviation))
skewness = distribution.getSkewness()
print("skewness=", repr(skewness))
kurtosis = distribution.getKurtosis()
print("kurtosis=", repr(kurtosis))
covariance = distribution.getCovariance()
print("covariance=", repr(covariance))
# Extract the marginals
for i in range(dim):
margin = distribution.getMarginal(i)
print("margin=", margin)
print("margin PDF= %.8g" %
margin.computePDF(NumericalPoint(1, 0.5)))
print("margin CDF= %.8g" %
margin.computeCDF(NumericalPoint(1, 0.5)))
print("margin quantile=", repr(margin.computeQuantile(0.95)))
print("margin realization=", repr(margin.getRealization()))
if (dim >= 2):
# Extract a 2-D marginal
indices = Indices(2, 0)
indices[0] = 1
indices[1] = 0
print("indices=", indices)
margins = distribution.getMarginal(indices)
print("margins=", margins)
print("margins PDF=", margins.computePDF(NumericalPoint(2, 0.5)))
print("margins CDF= %.8g" %
margins.computeCDF(NumericalPoint(2, 0.5)))
quantile = margins.computeQuantile(0.95)
print("margins quantile=", repr(quantile))
print("margins CDF(quantile)= %.6f" % margins.computeCDF(quantile))
print("margins realization=", repr(margins.getRealization()))
except:
import sys
print("t_Dirichlet_std.py", sys.exc_info()[0], sys.exc_info()[1])
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