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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
from math import fabs
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
continuousDistributionCollection = DistributionCollection()
discreteDistributionCollection = DistributionCollection()
distributionCollection = DistributionCollection()
beta = Beta(2.0, 3.0, 0.0, 1.0)
distributionCollection.add(beta)
continuousDistributionCollection.add(beta)
gamma = Gamma(1.0, 2.0, 3.0)
distributionCollection.add(gamma)
continuousDistributionCollection.add(gamma)
gumbel = Gumbel(1.0, 2.0)
distributionCollection.add(gumbel)
continuousDistributionCollection.add(gumbel)
lognormal = LogNormal(1.0, 1.0, 2.0)
distributionCollection.add(lognormal)
continuousDistributionCollection.add(lognormal)
logistic = Logistic(1.0, 1.0)
distributionCollection.add(logistic)
continuousDistributionCollection.add(logistic)
normal = Normal(1.0, 2.0)
distributionCollection.add(normal)
continuousDistributionCollection.add(normal)
truncatednormal = TruncatedNormal(1.0, 1.0, 0.0, 3.0)
distributionCollection.add(truncatednormal)
continuousDistributionCollection.add(truncatednormal)
student = Student(10.0, 10.0)
distributionCollection.add(student)
continuousDistributionCollection.add(student)
triangular = Triangular(-1.0, 2.0, 4.0)
distributionCollection.add(triangular)
continuousDistributionCollection.add(triangular)
uniform = Uniform(1.0, 2.0)
distributionCollection.add(uniform)
continuousDistributionCollection.add(uniform)
weibull = Weibull(1.0, 1.0, 2.0)
distributionCollection.add(weibull)
continuousDistributionCollection.add(weibull)
geometric = Geometric(0.5)
distributionCollection.add(geometric)
discreteDistributionCollection.add(geometric)
poisson = Poisson(2.0)
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 = 100
# 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]
factoryCollection = DistributionFactoryCollection(3)
factoryCollection[0] = UniformFactory()
factoryCollection[1] = BetaFactory()
factoryCollection[2] = NormalFactory()
aSample = Uniform(-1.5, 2.5).getSample(size)
model = FittingTest.BestModelBIC(aSample, factoryCollection)
print("best model BIC=", repr(model))
model, best_result = FittingTest.BestModelKolmogorov(
aSample, factoryCollection)
print("best model Kolmogorov=", repr(model))
# BIC ranking
resultBIC = SquareMatrix(distributionNumber)
for i in range(distributionNumber):
for j in range(distributionNumber):
value = FittingTest.BIC(
sampleCollection[i], distributionCollection[j], 0)
resultBIC[i, j] = value
print("resultBIC=", repr(resultBIC))
# Kolmogorov ranking
resultKolmogorov = SquareMatrix(continuousDistributionNumber)
for i in range(continuousDistributionNumber):
for j in range(continuousDistributionNumber):
value = FittingTest.Kolmogorov(continuousSampleCollection[
i], continuousDistributionCollection[j], 0.95, 0).getPValue()
if (fabs(value) < 1.0e-6):
value = 0.0
resultKolmogorov[i, j] = value
print("resultKolmogorov=", repr(resultKolmogorov))
# ChiSquared ranking
resultChiSquared = SquareMatrix(discreteDistributionNumber - 1)
for i in range(discreteDistributionNumber - 1):
for j in range(discreteDistributionNumber - 1):
value = FittingTest.ChiSquared(discreteSampleCollection[
i], discreteDistributionCollection[j], 0.95, 0).getPValue()
if (fabs(value) < 1.0e-6):
value = 0.0
resultChiSquared[i, j] = value
print("resultChiSquared=", repr(resultChiSquared))
except:
import sys
print("t_FittingTest_std.py", sys.exc_info()[0], sys.exc_info()[1])
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