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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 = Skellam(10.0, 5.0)
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 = NumericalPoint(distribution.getDimension(), 12.0)
print("Point= ", point)
# Show PDF and CDF of point
eps = 1e-5
LPDF = distribution.computeLogPDF(point)
print("log pdf= %.12g" % LPDF)
PDF = distribution.computePDF(point)
print("pdf = %.12g" % PDF)
print("pdf (FD)= %.12g" % (distribution.computeCDF(
point + NumericalPoint(1, 0)) - distribution.computeCDF(point + NumericalPoint(1, -1))))
CDF = distribution.computeCDF(point)
print("cdf= %.12g" % CDF)
CCDF = distribution.computeComplementaryCDF(point)
print("ccdf= %.12g" % CCDF)
CF = distribution.computeCharacteristicFunction(point[0])
print("characteristic function= (%.12g%+.12gj)" % (CF.real, CF.imag))
LCF = distribution.computeLogCharacteristicFunction(point[0])
print("log characteristic function= (%.12g%+.12gj)" % (LCF.real, LCF.imag))
GF = distribution.computeGeneratingFunction(0.3 + 0.7j)
print("generating function= (%.12g%+.12gj)" % (GF.real, GF.imag))
LGF = distribution.computeLogGeneratingFunction(0.3 + 0.7j)
print("log generating function= (%.12g%+.12gj)" % (LGF.real, LGF.imag))
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)
parameters = distribution.getParametersCollection()
print("parameters=", parameters)
for i in range(6):
print("standard moment n=", i, ", value=",
distribution.getStandardMoment(i))
print("Standard representative=", distribution.getStandardRepresentative())
except:
import sys
print("t_Skellam_std.py", sys.exc_info()[0], sys.exc_info()[1])
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