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#! /usr/bin/env python
import openturns as ot
import openturns.testing as ott
ot.TESTPREAMBLE()
# Instantiate one distribution object
x = [[1.0], [2.0], [3.0], [3.0]]
p = [0.3, 0.1, 0.6, 0.6]
distribution = ot.UserDefined(x, p)
print("Distribution ", repr(distribution))
print("Distribution ", distribution)
# Is this distribution elliptical ?
print("Elliptical = ", distribution.isElliptical())
# Is this distribution continuous ?
print("Continuous = ", distribution.isContinuous())
# Has this distribution an independent copula ?
print("Has independent copula = ", distribution.hasIndependentCopula())
# Test for realization of distribution
oneRealization = distribution.getRealization()
print("oneRealization=", repr(oneRealization))
# Test for sampling
size = 10
oneSample = distribution.getSample(size)
print("oneSample=Ok", repr(oneSample))
# Define a point
point = ot.Point(distribution.getDimension(), 2.0)
# Show PDF and CDF of a point
pointPDF = distribution.computePDF(point)
pointCDF = distribution.computeCDF(point)
print("point= ", repr(point), " pdf= %.12g" % pointPDF, " cdf=", pointCDF)
# Get 95% quantile
quantile = distribution.computeQuantile(0.95)
print("Quantile=", repr(quantile))
print("entropy=%.6f" % distribution.computeEntropy())
print("Standard representative=", distribution.getStandardRepresentative())
print("parameter=", distribution.getParameter())
print("parameterDescription=", distribution.getParameterDescription())
parameter = distribution.getParameter()
parameter[-1] = 0.3
distribution.setParameter(parameter)
print("parameter=", distribution.getParameter())
# To prevent automatic compaction
ot.ResourceMap.SetAsUnsignedInteger("UserDefined-SmallSize", 5)
sample = ot.Sample(40, 3)
for i in range(4):
for j in range(3):
sample[i, j] = 10 * (i // 3 + 1) + 0.1 * (j + 1)
multivariateUserDefined = ot.UserDefined(sample)
print("Multivariate UserDefined=", multivariateUserDefined)
# Has this distribution an independent copula ?
print("Has independent copula = ", multivariateUserDefined.hasIndependentCopula())
print("Marginal 0=", multivariateUserDefined.getMarginal(0))
print("Marginal (2, 0)=", multivariateUserDefined.getMarginal([2, 0]))
# cdf bug
loi_UD = ot.UserDefined([[350], [358], [360], [353], [364], [355], [349], [351]])
assert loi_UD.computeCDF([349]) == 0.125, "wrong cdf at min"
assert loi_UD.computeCDF([364]) == 1.0, "wrong cdf at max"
ot.Log.Show(ot.Log.TRACE)
validation = ott.DistributionValidation(distribution)
validation.skipParameters() # probabilities are renormalized so not independent
validation.run()
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