#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.TESTPREAMBLE()
ot.RandomGenerator.SetSeed(0)
# Instanciate one distribution object
distribution = ot.MaximumEntropyOrderStatisticsDistribution([
ot.Trapezoidal(-2.0, -1.1, -1.0, 1.0),
ot.LogUniform(1.0, 1.2),
ot.Triangular(3.0, 4.5, 5.0),
ot.Beta(2.5, 3.5, 4.7, 5.2)
])
dim = distribution.getDimension()
print("Distribution ", distribution)
# Is this distribution elliptical ?
print("Elliptical = ", distribution.isElliptical())
# 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[size - 1]))
elif ot.LogNormal().__class__.__name__ == 'ComposedDistribution':
copula = ot.IndependentCopula(2)
marginals = [ot.Uniform(1.0, 2.0), ot.Normal(2.0, 3.0)]
distribution = ot.ComposedDistribution(marginals, copula)
elif ot.LogNormal().__class__.__name__ == 'CumulativeDistributionNetwork':
coll = [ot.Normal(2),ot.Dirichlet([0.5, 1.0, 1.5])]
distribution = ot.CumulativeDistributionNetwork(coll, ot.BipartiteGraph([[0,1], [0,1]]))
elif ot.LogNormal().__class__.__name__ == 'Histogram':
distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
elif ot.LogNormal().__class__.__name__ == 'KernelMixture':
kernel = ot.Uniform()
sample = ot.Normal().getSample(5)
bandwith = [1.0]
distribution = ot.KernelMixture(kernel, bandwith, sample)
elif ot.LogNormal().__class__.__name__ == 'MaximumDistribution':
coll = [ot.Uniform(2.5, 3.5), ot.LogUniform(1.0, 1.2), ot.Triangular(2.0, 3.0, 4.0)]
distribution = ot.MaximumDistribution(coll)
elif ot.LogNormal().__class__.__name__ == 'Multinomial':
distribution = ot.Multinomial(5, [0.2])
elif ot.LogNormal().__class__.__name__ == 'RandomMixture':
coll = [ot.Triangular(0.0, 1.0, 5.0), ot.Uniform(-2.0, 2.0)]
weights = [0.8, 0.2]
cst = 3.0
distribution = ot.RandomMixture(coll, weights, cst)
elif ot.LogNormal().__class__.__name__ == 'TruncatedDistribution':
distribution = ot.TruncatedDistribution(ot.Normal(2.0, 1.5), 1.0, 4.0)
elif ot.LogNormal().__class__.__name__ == 'UserDefined':
distribution = ot.UserDefined([[0.0], [1.0], [2.0]], [0.2, 0.7, 0.1])
elif ot.LogNormal().__class__.__name__ == 'ZipfMandelbrot':
distribution = ot.ZipfMandelbrot(10, 2.5, 0.3)
else:
print('dist1-dist2:', result)
graph = result.drawPDF()
result = dist1 * dist2
print('dist1*dist2:', result)
graph = result.drawPDF()
result = dist1 / dist2
print('dist1/dist2:', result)
# graph = result.drawPDF()
result = ot.LogNormal() * ot.LogNormal()
print('logn*logn:', result)
graph = result.drawPDF()
result = ot.LogUniform() * ot.LogUniform()
print('logu*logu:', result)
graph = result.drawPDF()
result = ot.LogUniform() * ot.LogNormal()
print('logu*logn:', result)
graph = result.drawPDF()
result = ot.LogNormal() * ot.LogUniform()
print('logn*logu:', result)
graph = result.drawPDF()
# For ticket #917
result = ot.Weibull() + ot.Exponential()
print('Weibull+Exponential:', result)
print('result.CDF(1.0)=%.6f' % result.computeCDF(1.0))
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.LogUniform().__class__.__name__ == 'ComposedDistribution':
correlation = ot.CorrelationMatrix(2)
correlation[1, 0] = 0.25
aCopula = ot.NormalCopula(correlation)
marginals = [ot.Normal(1.0, 2.0), ot.Normal(2.0, 3.0)]
distribution = ot.ComposedDistribution(marginals, aCopula)
elif ot.LogUniform().__class__.__name__ == 'CumulativeDistributionNetwork':
distribution = ot.CumulativeDistributionNetwork(
[ot.Normal(2), ot.Dirichlet([0.5, 1.0, 1.5])],
ot.BipartiteGraph([[0, 1], [0, 1]]))
elif ot.LogUniform().__class__.__name__ == 'Histogram':
distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
else:
distribution = ot.LogUniform()
dimension = distribution.getDimension()
if dimension == 1:
distribution.setDescription(['$x$'])
pdf_graph = distribution.drawPDF()
cdf_graph = distribution.drawCDF()
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
pdf_axis = fig.add_subplot(121)
cdf_axis = fig.add_subplot(122)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=False)
elif dimension == 2:
distribution.setDescription(['$x_1$', '$x_2$'])
pdf_graph = distribution.drawPDF()
import openturns as ot
import numpy as np
import matplotlib.pyplot as plt
import math
distribution = ot.LogUniform(-1.0, 1.0)
sample = distribution.getSample(5)
#print(distribution.drawLogPDF(-1.0, 1.0, 10000 ))
Graph.viewer.View(distribution.drawLogPDF(-1.0, 1.0, 10000))
"""
plt.xscale("log")
plt.plot(distribution)
plt.show()
"""
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.LogUniform().__class__.__name__ == 'Bernoulli':
distribution = ot.Bernoulli(0.7)
elif ot.LogUniform().__class__.__name__ == 'Binomial':
distribution = ot.Binomial(5, 0.2)
elif ot.LogUniform().__class__.__name__ == 'ComposedDistribution':
copula = ot.IndependentCopula(2)
marginals = [ot.Uniform(1.0, 2.0), ot.Normal(2.0, 3.0)]
distribution = ot.ComposedDistribution(marginals, copula)
elif ot.LogUniform().__class__.__name__ == 'CumulativeDistributionNetwork':
coll = [ot.Normal(2), ot.Dirichlet([0.5, 1.0, 1.5])]
distribution = ot.CumulativeDistributionNetwork(
coll, ot.BipartiteGraph([[0, 1], [0, 1]]))
elif ot.LogUniform().__class__.__name__ == 'Histogram':
distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
elif ot.LogUniform().__class__.__name__ == 'KernelMixture':
kernel = ot.Uniform()
sample = ot.Normal().getSample(5)
bandwith = [1.0]
distribution = ot.KernelMixture(kernel, bandwith, sample)
elif ot.LogUniform().__class__.__name__ == 'MaximumDistribution':
coll = [
ot.Uniform(2.5, 3.5),
ot.LogUniform(1.0, 1.2),
ot.Triangular(2.0, 3.0, 4.0)
]
distribution = ot.MaximumDistribution(coll)
elif ot.LogUniform().__class__.__name__ == 'Multinomial':
distribution = ot.Multinomial(5, [0.2])
#! /usr/bin/env python
from __future__ import print_function
import openturns as ot
ot.TESTPREAMBLE()
ot.RandomGenerator.SetSeed(0)
def checkMarginals(coll):
osmc = ot.OrderStatisticsMarginalChecker(coll)
print("marginals=", coll)
print("isCompatible=", osmc.isCompatible())
print("partition=", osmc.buildPartition())
coll = [ot.Uniform(-1.0, 1.0), ot.LogUniform(1.0, 1.2),
ot.Triangular(3.0, 4.0, 5.), ot.Uniform(5.0, 6.0), ot.Uniform(5.5, 6.5)]
checkMarginals(coll)
coll.append(ot.Uniform(0.0, 1.0))
checkMarginals(coll)
instrumental)
RWMHsampler.setLikelihood(conditional, y_obs, model, x_obs)
print("Log-likelihood of thetaTrue = {!r}".format(
RWMHsampler.computeLogLikelihood(thetaTrue)))
# produces an error with current master branch
real_504 = RWMHsampler.getRealization()
print("With 504 observations, getRealization() produces {!r}".format(
real_504[0]))
ott.assert_almost_equal(real_504[0], 2.0)
# this example throws in ot 1.19 as it tries to evaluate the likelihood outside the support of the prior
# see MetropolisHastingsImplementation::computeLogPosterior
obs = ot.TruncatedNormal(0.5, 0.5, 0.0, 10.0).getSample(50)
likelihood = ot.GeneralizedPareto()
prior = ot.ComposedDistribution(
[ot.LogUniform(-1.40, 4.0),
ot.Normal(), ot.Normal()])
proposals = [
ot.Uniform(-prior.getMarginal(k).getStandardDeviation()[0],
+prior.getMarginal(k).getStandardDeviation()[0])
for k in range(prior.getDimension())
]
initialState = prior.getMean()
mh_coll = [
ot.RandomWalkMetropolisHastings(prior, initialState, proposals[i], [i])
for i in range(2)
]
for mh in mh_coll:
mh.setLikelihood(likelihood, obs)
sampler = ot.Gibbs(mh_coll)
parameters_sample = sampler.getSample(200)
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if (ot.LogUniform().__class__.__name__ == 'ComposedDistribution'):
correlation = ot.CorrelationMatrix(2)
correlation[1, 0] = 0.25
aCopula = ot.NormalCopula(correlation)
marginals = [ot.Normal(1.0, 2.0), ot.Normal(2.0, 3.0)]
distribution = ot.ComposedDistribution(marginals, aCopula)
elif (ot.LogUniform().__class__.__name__ == 'CumulativeDistributionNetwork'):
distribution = ot.CumulativeDistributionNetwork(
[ot.Normal(2), ot.Dirichlet([0.5, 1.0, 1.5])],
ot.BipartiteGraph([[0, 1], [0, 1]]))
else:
distribution = ot.LogUniform()
dimension = distribution.getDimension()
if dimension <= 2:
if distribution.getDimension() == 1:
distribution.setDescription(['$x$'])
pdf_graph = distribution.drawPDF()
cdf_graph = distribution.drawCDF()
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
pdf_axis = fig.add_subplot(121)
cdf_axis = fig.add_subplot(122)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=False)
else:
distribution.setDescription(['$x_1$', '$x_2$'])
pdf_graph = distribution.drawPDF()
fig = plt.figure(figsize=(10, 5))
