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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
continuousDistributionCollection = DistributionCollection()
discreteDistributionCollection = DistributionCollection()
distributionCollection = DistributionCollection()
beta = Beta(2., 3., 0., 1.)
distributionCollection.add(beta)
continuousDistributionCollection.add(beta)
gamma = Gamma(1., 2., 3.)
distributionCollection.add(gamma)
continuousDistributionCollection.add(gamma)
gumbel = Gumbel(1., 2.)
distributionCollection.add(gumbel)
continuousDistributionCollection.add(gumbel)
lognormal = LogNormal(1., 1., 2.)
distributionCollection.add(lognormal)
continuousDistributionCollection.add(lognormal)
logistic = Logistic(1., 1.)
distributionCollection.add(logistic)
continuousDistributionCollection.add(logistic)
normal = Normal(1., 2.)
distributionCollection.add(normal)
continuousDistributionCollection.add(normal)
truncatednormal = TruncatedNormal(1., 1., 0., 3.)
distributionCollection.add(truncatednormal)
continuousDistributionCollection.add(truncatednormal)
student = Student(10., 10.)
distributionCollection.add(student)
continuousDistributionCollection.add(student)
triangular = Triangular(-1., 2., 4.)
distributionCollection.add(triangular)
continuousDistributionCollection.add(triangular)
uniform = Uniform(1., 2.)
distributionCollection.add(uniform)
continuousDistributionCollection.add(uniform)
weibull = Weibull(1., 1., 2.)
distributionCollection.add(weibull)
continuousDistributionCollection.add(weibull)
geometric = Geometric(.5)
distributionCollection.add(geometric)
discreteDistributionCollection.add(geometric)
poisson = Poisson(2.)
distributionCollection.add(poisson)
discreteDistributionCollection.add(poisson)
collection = UserDefinedPairCollection(
3, UserDefinedPair(NumericalPoint(1), 0.0))
point = NumericalPoint(1)
point[0] = 1.0
collection[0] = UserDefinedPair(point, 0.3)
point[0] = 2.0
collection[1] = UserDefinedPair(point, 0.2)
point[0] = 3.0
collection[2] = UserDefinedPair(point, 0.5)
userdefined = UserDefined(collection)
distributionCollection.add(userdefined)
discreteDistributionCollection.add(userdefined)
size = 10000
# Number of continuous distributions
continuousDistributionNumber = continuousDistributionCollection.getSize()
# Number of discrete distributions
discreteDistributionNumber = discreteDistributionCollection.getSize()
# Number of distributions
distributionNumber = continuousDistributionNumber + \
discreteDistributionNumber
# We create a collection of NumericalSample of size "size" and of
# dimension 1 (scalar values) : the collection has distributionNumber
# NumericalSamples
sampleCollection = [NumericalSample(size, 1)
for i in range(distributionNumber)]
# We create a collection of NumericalSample of size "size" and of
# dimension 1 (scalar values) : the collection has
# continuousDistributionNumber NumericalSamples
continuousSampleCollection = [NumericalSample(size, 1)
for i in range(continuousDistributionNumber)]
# We create a collection of NumericalSample of size "size" and of
# dimension 1 (scalar values) : the collection has
# discreteDistributionNumber NumericalSamples
discreteSampleCollection = [NumericalSample(size, 1)
for i in range(discreteDistributionNumber)]
for i in range(continuousDistributionNumber):
continuousSampleCollection[
i] = continuousDistributionCollection[i].getSample(size)
continuousSampleCollection[i].setName(
continuousDistributionCollection[i].getName())
sampleCollection[i] = continuousSampleCollection[i]
for i in range(discreteDistributionNumber):
discreteSampleCollection[
i] = discreteDistributionCollection[i].getSample(size)
discreteSampleCollection[i].setName(
discreteDistributionCollection[i].getName())
sampleCollection[
continuousDistributionNumber + i] = discreteSampleCollection[i]
# Test the normality of several samples using the Anderson Darling test
andersonDarlingResult = NumericalPoint(distributionNumber)
for i in range(distributionNumber):
result = NormalityTest.AndersonDarlingNormal(sampleCollection[i])
andersonDarlingResult[i] = result.getBinaryQualityMeasure()
print("sample ", sampleCollection[
i].getName(), " result=", andersonDarlingResult[i])
print("andersonDarlingResult=", repr(andersonDarlingResult))
# Test the normality of several samples using the Cramer Von Mises test
cramerVonMisesResult = NumericalPoint(distributionNumber)
for i in range(distributionNumber):
result = NormalityTest.CramerVonMisesNormal(sampleCollection[i])
cramerVonMisesResult[i] = result.getBinaryQualityMeasure()
print("sample ", sampleCollection[
i].getName(), " result=", cramerVonMisesResult[i])
print("cramerVonMisesResult=", repr(cramerVonMisesResult))
except:
import sys
print("t_NormalityTest_std.py", sys.exc_info()[0], sys.exc_info()[1])
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