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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
distribution = Multinomial(5, NumericalPoint(3, 0.25))
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=", repr(oneSample[0]), " last=", repr(oneSample[1]))
print("mean=", repr(oneSample.computeMean()))
print("covariance=", repr(oneSample.computeCovariance()))
print("support=\n" + str(distribution.getSupport()))
interval = Interval(NumericalPoint(distribution.getDimension(), 1.0),
NumericalPoint(distribution.getDimension(), 3.0))
print("support restricted to the interval=\n" + str(interval) +
" gives=\n" + str(distribution.getSupport(interval)))
# Define a point
point = NumericalPoint(distribution.getDimension(), 1.0)
print("Point= ", repr(point))
# Show PDF and CDF at point
LPDF = distribution.computeLogPDF(point)
print("log pdf=%.6f" % LPDF)
PDF = distribution.computePDF(point)
print("pdf =%.6f" % PDF)
CDF = distribution.computeCDF(point)
print("cdf=%.5f" % CDF)
quantile = distribution.computeQuantile(0.95)
print("quantile=", repr(quantile))
print("cdf(quantile)= %.6f" % distribution.computeCDF(quantile))
mean = distribution.getMean()
print("mean=", repr(mean))
covariance = distribution.getCovariance()
print("covariance=", repr(covariance))
parameters = distribution.getParametersCollection()
print("parameters=", repr(parameters))
except:
import sys
print("t_Multinomial.py", sys.exc_info()[0], sys.exc_info()[1])
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