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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# Create a collection of distribution
aCollection = DistributionCollection()
aCollection.add(Normal(0., 4))
aCollection.add(Uniform(5., 7.))
aCollection.add(Triangular(7., 8., 9.))
# Instanciate one distribution object
distribution = Mixture(
aCollection, NumericalPoint(aCollection.getSize(), 1.0))
print("mixture=", distribution)
classifier = Classifier(MixtureClassifier(distribution))
inS = NumericalSample()
inS.add(NumericalPoint(1, 2.))
inS.add(NumericalPoint(1, 4.))
inS.add(NumericalPoint(1, 6.))
inS.add(NumericalPoint(1, 8.))
for i in range(inS.getSize()):
print("inP=", inS[i], " class=", classifier.classify(inS[i]))
print("classes=", classifier.classify(inS))
for i in range(inS.getSize()):
for j in range(aCollection.getSize()):
print("inP=", inS[i], " grade|", j, "= %g" %
classifier.grade(inS[i], j))
for j in range(aCollection.getSize()):
print("grades|", j, "=", classifier.grade(
inS, Indices(inS.getSize(), j)))
except:
import sys
print("t_MixtureClassifier_std.py", sys.exc_info()[0], sys.exc_info()[1])
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