#! /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]))
#! /usr/bin/env python from __future__ import print_function import openturns as ot ot.RandomGenerator.SetSeed(0) ot.ResourceMap.SetAsBool('MaximumLikelihoodFactory-Parallel', False) distribution = ot.Trapezoidal(1.0, 2.3, 4.5, 5.0) size = 10000 sample = distribution.getSample(size) factory = ot.TrapezoidalFactory() estimatedDistribution = factory.build(sample) print("distribution=", repr(distribution)) oldPrecision = ot.PlatformInfo.GetNumericalPrecision() ot.PlatformInfo.SetNumericalPrecision(4) print("Estimated distribution=", repr(estimatedDistribution)) ot.PlatformInfo.SetNumericalPrecision(oldPrecision) estimatedDistribution = factory.build() print("Default distribution=", estimatedDistribution) estimatedDistribution = factory.build(distribution.getParameter()) print("Distribution from parameters=", estimatedDistribution) estimatedTrapezoidal = factory.buildAsTrapezoidal(sample) print("Trapezoidal =", distribution) oldPrecision = ot.PlatformInfo.GetNumericalPrecision() ot.PlatformInfo.SetNumericalPrecision(4) print("Estimated trapezoidal=", estimatedTrapezoidal) ot.PlatformInfo.SetNumericalPrecision(oldPrecision) estimatedTrapezoidal = factory.buildAsTrapezoidal() print("Default trapezoidal=", estimatedTrapezoidal)
import openturns as ot from matplotlib import pyplot as plt from openturns.viewer import View if ot.Trapezoidal().__class__.__name__ == 'Bernoulli': distribution = ot.Bernoulli(0.7) elif ot.Trapezoidal().__class__.__name__ == 'Binomial': distribution = ot.Binomial(5, 0.2) elif ot.Trapezoidal().__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.Trapezoidal().__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.Trapezoidal().__class__.__name__ == 'Histogram': distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15]) elif ot.Trapezoidal().__class__.__name__ == 'KernelMixture': kernel = ot.Uniform() sample = ot.Normal().getSample(5) bandwith = [1.0] distribution = ot.KernelMixture(kernel, bandwith, sample) elif ot.Trapezoidal().__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.Trapezoidal().__class__.__name__ == 'Multinomial': distribution = ot.Multinomial(5, [0.2]) elif ot.Trapezoidal().__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)
import openturns as ot from matplotlib import pyplot as plt from openturns.viewer import View if (ot.Trapezoidal().__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.Trapezoidal().__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.Trapezoidal() 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))
import openturns as ot from matplotlib import pyplot as plt from openturns.viewer import View if ot.Trapezoidal().__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.Trapezoidal().__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.Trapezoidal().__class__.__name__ == 'Histogram': distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15]) else: distribution = ot.Trapezoidal() 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()