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:
distribution = ot.LogNormal()
dimension = distribution.getDimension()
title = str(distribution)[:100].split('\n')[0]
if dimension == 1:
distribution.setDescription(['$x$'])
pdf_graph = distribution.drawPDF()
cdf_graph = distribution.drawCDF()
fig = plt.figure(figsize=(10, 4))
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)
fig.suptitle(title)
elif dimension == 2:
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if (ot.ZipfMandelbrot().__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.ZipfMandelbrot().__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.ZipfMandelbrot().__class__.__name__ == 'Histogram'):
distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
else:
distribution = ot.ZipfMandelbrot()
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:
distributionCollection.add(uniform) continuousDistributionCollection.add(uniform) weibull = ot.WeibullMin(1.0, 1.0, 2.0) distributionCollection.add(weibull) continuousDistributionCollection.add(weibull) geometric = ot.Geometric(0.5) distributionCollection.add(geometric) discreteDistributionCollection.add(geometric) binomial = ot.Binomial(10, 0.25) distributionCollection.add(binomial) discreteDistributionCollection.add(binomial) zipf = ot.ZipfMandelbrot(20, 5.25, 2.5) distributionCollection.add(zipf) discreteDistributionCollection.add(zipf) poisson = ot.Poisson(5.0) distributionCollection.add(poisson) discreteDistributionCollection.add(poisson) x = [[1.0], [2.0], [3.0]] p = [0.3, 0.2, 0.5] userdefined = ot.UserDefined(x, p) distributionCollection.add(userdefined) discreteDistributionCollection.add(userdefined) size = 100
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.ZipfMandelbrot().__class__.__name__ == 'Bernoulli':
distribution = ot.Bernoulli(0.7)
elif ot.ZipfMandelbrot().__class__.__name__ == 'Binomial':
distribution = ot.Binomial(5, 0.2)
elif ot.ZipfMandelbrot().__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.ZipfMandelbrot().__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.ZipfMandelbrot().__class__.__name__ == 'Histogram':
distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
elif ot.ZipfMandelbrot().__class__.__name__ == 'KernelMixture':
kernel = ot.Uniform()
sample = ot.Normal().getSample(5)
bandwith = [1.0]
distribution = ot.KernelMixture(kernel, bandwith, sample)
elif ot.ZipfMandelbrot().__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.ZipfMandelbrot().__class__.__name__ == 'Multinomial':
distribution = ot.Multinomial(5, [0.2])
