#! /usr/bin/env python
import openturns as ot
parameters = ot.GammaMuSigma(0.1, 0.489898, -0.5)
distribution = ot.ParametrizedDistribution(parameters)
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=", oneRealization)
# Test for sampling
size = 10000
oneSample = distribution.getSample(size)
print("oneSample first=", oneSample[0], " last=", oneSample[size - 1])
print("mean=", oneSample.computeMean())
print("covariance=", oneSample.computeCovariance())
# Define a point
point = ot.Point(distribution.getDimension(), 1.0)
print("Point= ", point)
# Show PDF and CDF of point
algo.run()
tensorResult = algo.getResult()
myStudy.add('tensorResult', tensorResult)
tensorIn = [0.4]
tensorRef = tensorResult.getMetaModel()(tensorIn)
# Distribution parameters
# ArcsineMuSigma parameter ave
ams_parameters = ot.ArcsineMuSigma(8.4, 2.25)
myStudy.add('ams_parameters', ams_parameters)
# BetaMuSigma parameter save
bms_parameters = ot.BetaMuSigma(0.2, 0.6, -1, 2)
myStudy.add('bms_parameters', bms_parameters)
# GammaMuSigma parameter save
gmms_parameters = ot.GammaMuSigma(1.5, 2.5, -0.5)
myStudy.add('gmms_parameters', gmms_parameters)
# GumbelMuSigma parameter save
gms_parameters = ot.GumbelMuSigma(1.5, 1.3)
myStudy.add('gms_parameters', gms_parameters)
# LogNormalMuSigma parameter save
lnms_parameters = ot.LogNormalMuSigma(30000.0, 9000.0, 15000)
myStudy.add('lnms_parameters', lnms_parameters)
# LogNormalMuSigmaOverMu parameter save
lnmsm_parameters = ot.LogNormalMuSigmaOverMu(0.63, 5.24, -0.5)
myStudy.add('lnmsm_parameters', lnmsm_parameters)
# WeibullMinMuSigma parameter save
wms_parameters = ot.WeibullMinMuSigma(1.3, 1.23, -0.5)
myStudy.add('wms_parameters', wms_parameters)
# MemoizeFunction
#! /usr/bin/env python
import openturns as ot
distParams = []
distParams.append(ot.ArcsineMuSigma(8.4, 2.25))
distParams.append(ot.BetaMuSigma(0.2, 0.6, -1, 2))
distParams.append(ot.GammaMuSigma(1.5, 2.5, -0.5))
distParams.append(ot.GumbelLambdaGamma(0.6, 6.0))
distParams.append(ot.GumbelMuSigma(1.5, 1.3))
distParams.append(ot.LogNormalMuErrorFactor(0.63, 1.5, -0.5))
distParams.append(ot.LogNormalMuSigma(0.63, 3.3, -0.5))
distParams.append(ot.LogNormalMuSigmaOverMu(0.63, 5.24, -0.5))
distParams.append(ot.WeibullMaxMuSigma(1.3, 1.23, 3.1))
distParams.append(ot.WeibullMinMuSigma(1.3, 1.23, -0.5))
for distParam in distParams:
print('Distribution Parameters ', repr(distParam))
print('Distribution Parameters ', distParam)
non_native = distParam.getValues()
desc = distParam.getDescription()
print('non-native=', non_native, desc)
native = distParam.evaluate()
print('native=', native)
non_native = distParam.inverse(native)
print('non-native=', non_native)
print('built dist=', distParam.getDistribution())
# derivative of the native parameters with regards the parameters of the