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])