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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 = MeixnerDistribution(1.5, 0.5, 2.5, -0.5)
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())
size = 100
for i in range(2):
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(), 1.0)
print("Point= ", point)
# Show PDF and CDF of point
LPDF = distribution.computeLogPDF(point)
print("log pdf=%.6f" % LPDF)
eps = 1.0e-5
PDF = distribution.computePDF(point)
print("pdf =%.6f" % PDF)
print("pdf (FD)=%.6f" % ((distribution.computeCDF(point + NumericalPoint(1, eps)) -
distribution.computeCDF(point + NumericalPoint(1, -eps))) / (2.0 * eps)))
CDF = distribution.computeCDF(point)
print("cdf=%.6f" % CDF)
CCDF = distribution.computeComplementaryCDF(point)
print("ccdf=%.6f" % 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))
quantile = distribution.computeQuantile(0.95)
print("quantile=", quantile)
print("cdf(quantile)=%.6f" % distribution.computeCDF(quantile))
mean = distribution.getMean()
print("mean=", mean)
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())
# Specific to this distribution
alpha = distribution.getAlpha()
print("alpha=%.6f" % alpha)
beta = distribution.getBeta()
print("beta=%.6f" % beta)
delta = distribution.getDelta()
print("delta=%.6f" % delta)
mu = distribution.getMu()
print("mu=%.6f" % mu)
standardDeviation = distribution.getStandardDeviation()
print("standard deviation=", standardDeviation)
skewness = distribution.getSkewness()
print("skewness=", skewness)
kurtosis = distribution.getKurtosis()
print("kurtosis=", kurtosis)
except:
import sys
print("t_MeixnerDistribution_std.py", sys.exc_info()[0], sys.exc_info()[1])
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