Python CumulativeDistributionNetwork示例

示例#1

import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.LogNormal().__class__.__name__ == 'Bernoulli':
    distribution = ot.Bernoulli(0.7)
elif ot.LogNormal().__class__.__name__ == 'Binomial':
    distribution = ot.Binomial(5, 0.2)
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)

示例#2

文件： openturns-Beta-1.py 项目： ThibaultDelage/openturns.github.io

import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.Beta().__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.Beta().__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.Beta().__class__.__name__ == 'Histogram':
    distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
else:
    distribution = ot.Beta()
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()
    fig = plt.figure(figsize=(10, 5))
    plt.suptitle(str(distribution))

示例#3

文件： openturns-CumulativeDistributionNetwork-1.py 项目： ThibaultDelage/openturns.github.io

import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if (ot.CumulativeDistributionNetwork().__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.CumulativeDistributionNetwork().__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.CumulativeDistributionNetwork()
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(['$x_1$', '$x_2$'])

示例#4

#! /usr/bin/env python

from __future__ import print_function
import openturns as ot
import openturns.testing as ott

ot.RandomGenerator.SetSeed(0)

try:
    # Instanciate one distribution object
    graph = ot.BipartiteGraph([[0, 1], [0, 1]])
    distribution = ot.CumulativeDistributionNetwork([ot.Normal(2)] * 2, graph)
    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=", 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())

示例#5

文件： openturns-CumulativeDistributionNetwork-1.py 项目： ThibaultDelage/openturns.github.io

import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.CumulativeDistributionNetwork(
).__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.CumulativeDistributionNetwork(
).__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.CumulativeDistributionNetwork().__class__.__name__ == 'Histogram':
    distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
else:
    distribution = ot.CumulativeDistributionNetwork()
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:

示例#6

文件： openturns-CumulativeDistributionNetwork-1.py 项目： ThibaultDelage/openturns.github.io

import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
if ot.CumulativeDistributionNetwork().__class__.__name__ == 'Bernoulli':
    distribution = ot.Bernoulli(0.7)
elif ot.CumulativeDistributionNetwork().__class__.__name__ == 'Binomial':
    distribution = ot.Binomial(5, 0.2)
elif ot.CumulativeDistributionNetwork().__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.CumulativeDistributionNetwork().__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.CumulativeDistributionNetwork().__class__.__name__ == 'Histogram':
    distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
elif ot.CumulativeDistributionNetwork().__class__.__name__ == 'KernelMixture':
    kernel = ot.Uniform()
    sample = ot.Normal().getSample(5)
    bandwith = [1.0]
    distribution = ot.KernelMixture(kernel, bandwith, sample)
elif ot.CumulativeDistributionNetwork().__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.CumulativeDistributionNetwork().__class__.__name__ == 'Multinomial':
    distribution = ot.Multinomial(5, [0.2])
elif ot.CumulativeDistributionNetwork().__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)

示例#7

文件： t_CumulativeDistributionNetwork_std.py 项目： dubourg/openturns

#! /usr/bin/env python

from __future__ import print_function
import openturns as ot

ot.RandomGenerator.SetSeed(0)

try:
    # Instanciate one distribution object
    graph = ot.BipartiteGraph([[0, 1], [0, 1]])
    distribution = ot.CumulativeDistributionNetwork([ot.Normal(2)] * 2, graph)
    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=", 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())