import openturns as ot from time import time backends = ["MuParser", "ExprTk"] #backends = ["MuParser"] ot.ResourceMap.SetAsUnsignedInteger('SymbolicParserExprTk-MaxNodeDepth', 100000) size = 1000 N = 1000 X = ["x" + str(i) for i in range(N)] P = 10 nb_factors_generator = ot.Binomial(P - 1, 0.5) # Create the generators once and for all indices_generator = list() for k in range(P): indices_generator.append(ot.KPermutationsDistribution(k + 1, N)) for j in range(17): print("#" * 50) M = 2**j print("M=", M) t0 = time() print("Create formula...") formula = '' for i in range(M): K = int(nb_factors_generator.getRealization()[0]) factor = ''
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)
distributionCollection.add(triangular) continuousDistributionCollection.add(triangular) uniform = ot.Uniform(1.0, 2.0) 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)
import openturns as ot from matplotlib import pyplot as plt from openturns.viewer import View if (ot.Binomial().__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.Binomial().__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.Binomial() 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))
#! /usr/bin/env python from __future__ import print_function import openturns as ot observationsSize = 5 # Create a collection of distribution conditionedDistribution = ot.Normal() conditioningDistributionCollection = [] # First conditioning distribution: continuous/continuous atoms = [ot.Uniform(0.0, 1.0), ot.Uniform(1.0, 2.0)] conditioningDistributionCollection.append(ot.ComposedDistribution(atoms)) # Second conditioning distribution: discrete/continuous atoms = [ot.Binomial(3, 0.5), ot.Uniform(1.0, 2.0)] #conditioningDistributionCollection.append(ot.ComposedDistribution(atoms)) # Third conditioning distribution: dirac/continuous atoms = [ot.Dirac(0.0), ot.Uniform(1.0, 2.0)] conditioningDistributionCollection.append(ot.ComposedDistribution(atoms)) for conditioning in conditioningDistributionCollection: print("conditioning distribution=", conditioning) observationsDistribution = ot.Distribution(conditionedDistribution) observationsDistribution.setParameter(conditioning.getMean()) observations = observationsDistribution.getSample(observationsSize) distribution = ot.PosteriorDistribution(ot.ConditionalDistribution(conditionedDistribution, conditioning), observations) dim = distribution.getDimension() print("Distribution ", distribution) print("Distribution ", distribution) print("range=", distribution.getRange()) mean = distribution.getMean()
import openturns as ot from matplotlib import pyplot as plt from openturns.viewer import View if ot.Binomial().__class__.__name__ == 'Bernoulli': distribution = ot.Bernoulli(0.7) elif ot.Binomial().__class__.__name__ == 'Binomial': distribution = ot.Binomial(5, 0.2) elif ot.Binomial().__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.Binomial().__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.Binomial().__class__.__name__ == 'Histogram': distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15]) elif ot.Binomial().__class__.__name__ == 'KernelMixture': kernel = ot.Uniform() sample = ot.Normal().getSample(5) bandwith = [1.0] distribution = ot.KernelMixture(kernel, bandwith, sample) elif ot.Binomial().__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.Binomial().__class__.__name__ == 'Multinomial': distribution = ot.Multinomial(5, [0.2])
import openturns as ot from matplotlib import pyplot as plt from openturns.viewer import View if ot.Binomial().__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.Binomial().__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.Binomial().__class__.__name__ == 'Histogram': distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15]) else: distribution = ot.Binomial() 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()