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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
print("begin histo comp test")
collection = HistogramPairCollection(4)
collection[0] = HistogramPair(1.0, 0.5)
collection[1] = HistogramPair(0.7, 1.5)
collection[2] = HistogramPair(1.2, 3.5)
collection[3] = HistogramPair(0.9, 2.5)
print("collection=", collection)
collectionSize = len(collection)
print("collection = ", repr(collection))
distribution = Histogram(-1.5, collection)
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=", repr(oneRealization))
# Test for sampling
size = 10000
oneSample = distribution.getSample(size)
print("oneSample first=", repr(oneSample[0]), " last=", repr(oneSample[1]))
print("mean=", repr(oneSample.computeMean()))
print("covariance=", repr(oneSample.computeCovariance()))
size = 100
for i in range(2):
msg = ''
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= ", repr(point))
# Show PDF and CDF at point
eps = 1e-5
# derivative of PDF with regards its arguments
DDF = distribution.computeDDF(point)
print("ddf =", repr(DDF))
# by the finite difference technique
print("ddf (FD)=", repr(NumericalPoint(1, (distribution.computePDF(
point + NumericalPoint(1, eps)) - distribution.computePDF(point + NumericalPoint(1, -eps))) / (2.0 * eps))))
# PDF value
LPDF = distribution.computeLogPDF(point)
print("log pdf=%.6f" % LPDF)
PDF = distribution.computePDF(point)
print("pdf =%.6f" % PDF)
# by the finite difference technique from CDF
print("pdf (FD)=%.6f" % ((distribution.computeCDF(point + NumericalPoint(1, eps)) -
distribution.computeCDF(point + NumericalPoint(1, -eps))) / (2.0 * eps)))
# derivative of the PDF with regards the parameters of the distribution
CDF = distribution.computeCDF(point)
print("cdf=%.6f" % CDF)
CCDF = distribution.computeComplementaryCDF(point)
print("ccdf=%.6f" % CCDF)
# quantile
quantile = distribution.computeQuantile(0.95)
print("quantile=", repr(quantile))
print("cdf(quantile)=%.6f" % distribution.computeCDF(quantile))
mean = distribution.getMean()
print("mean=", repr(mean))
covariance = distribution.getCovariance()
print("covariance=", repr(covariance))
parameters = distribution.getParametersCollection()
print("parameters=", repr(parameters))
for i in range(6):
print("standard moment n=", i, " value=",
distribution.getStandardMoment(i))
print("Standard representative=", distribution.getStandardRepresentative())
testSize = 0
for i in range(testSize):
q = RandomGenerator().Generate()
if (fabs(q - distribution.computeCDF(distribution.computeQuantile(q))) > eps):
print("q=%.6f" % q, " quantile=%.6f" % distribution.computeQuantile(q)[
0], " CDF(quantile)=%.6f" % distribution.computeCDF(distribution.computeQuantile(q)))
except:
import sys
print("t_Histogram.py", sys.exc_info()[0], sys.exc_info()[1])
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