Home
last modified time | relevance | path

Searched refs:myDistribution (Results 1 – 25 of 86) sorted by relevance

1234

/dports/math/openturns/openturns-1.18/python/doc/examples/meta_modeling/polynomial_chaos_metamodel/
H A Dplot_chaos_beam_sensitivity_degree.py46 myDistribution = ot.ComposedDistribution([dist_E, dist_F, dist_L, dist_I]) variable
59 g.setInputDescription(myDistribution.getDescription())
67 inputTrain, outputTrain, multivariateBasis, totalDegree, myDistribution argument
104 inputTrain, outputTrain, myDistribution, adaptiveStrategy, projectionStrategy
179 inputTrain = myDistribution.getSample(N)
181 inputTest = myDistribution.getSample(n_valid)
186 inputTrain, outputTrain, multivariateBasis, totalDegree, myDistribution
220 inputTrain = myDistribution.getSample(N)
222 inputTest = myDistribution.getSample(n_valid)
225 inputTrain, outputTrain, multivariateBasis, totalDegree, myDistribution
H A Dplot_chaos_cantilever_beam_integration.py28 myDistribution = cb.independentDistribution variable
54 inputTrain = myDistribution.getSample(N)
100 g, myDistribution, adaptiveStrategy, projectionStrategy)
125 inputTest = myDistribution.getSample(n_valid)
/dports/math/openturns/openturns-1.18/python/doc/examples/probabilistic_modeling/distributions/
H A Dplot_create_extreme_value_distribution.py27 myDistribution = ot.GeneralizedExtremeValue(0.0, 1.0, 0.0) variable
31 print(myDistribution.getActualDistribution())
39 graphPDF = myDistribution.drawPDF()
44 graphCDF = myDistribution.drawCDF()
/dports/math/openturns/openturns-1.18/python/doc/examples/meta_modeling/low_rank_tensors_metamodel/
H A Dplot_tensor_cantilever_beam.py88 myDistribution = ot.ComposedDistribution([E, F, L, I]) variable
99 X_train = myDistribution.getSample(sampleSize_train)
146 X_train, Y_train, myDistribution, functionFactory, nk, maxRank)
158 for j in range(myDistribution.getDimension()):
173 X_test = myDistribution.getSample(sampleSize_test)
/dports/math/openturns/openturns-1.18/python/doc/examples/data_analysis/distribution_fitting/
H A Dplot_fit_extreme_value_distribution.py53 myDistribution = ot.GeneralizedExtremeValueFactory().buildAsGeneralizedExtremeValue( variable
59 print(myDistribution)
63 print(myDistribution.getActualDistribution())
70 graph = myDistribution.drawPDF()
90 graph = myDistribution.drawPDF()
/dports/math/openturns/openturns-1.18/python/doc/examples/meta_modeling/kriging_metamodel/
H A Dplot_kriging_hyperparameters_optimization.py73 myDistribution = ot.ComposedDistribution([E, F, L, I], myCopula) variable
84 X_train = myDistribution.getSample(sampleSize_train)
95 dimension = myDistribution.getDimension()
229 dimension = myDistribution.getDimension()
242 X_new = myDistribution.getSample(20)
290 X_train = myDistribution.getSample(sampleSize_train)
H A Dplot_kriging_cantilever_beam.py31 myDistribution = cb.distribution variable
42 X_train = myDistribution.getSample(sampleSize_train)
126 X_test = myDistribution.getSample(sampleSize_test)
H A Dplot_kriging_cantilever_beam_hmat.py31 myDistribution = cb.distribution variable
42 X_train = myDistribution.getSample(sampleSize_train)
135 X_test = myDistribution.getSample(sampleSize_test)
H A Dplot_kriging_beam_trend.py36 myDistribution = cb.distribution variable
47 X_train = myDistribution.getSample(sampleSize_train)
174 X_test = myDistribution.getSample(sampleSize_test)
/dports/math/openturns/openturns-1.18/python/test/
H A Dt_Waarts_concave.py29 myDistribution = Normal(mean, sigma, R) variable
31 start = myDistribution.getMean()
32 Covariance = myDistribution.getCovariance()
38 vect = RandomVector(myDistribution)
H A Dt_FORM_draw.py52 myDistribution = Normal(mean, sigma, R) variable
58 myDistribution.setDescription(componentDescription)
61 vect = RandomVector(myDistribution)
H A Dt_FORM_sensitivity.py64 myDistribution = testDistributions[i] variable
69 myDistribution.setDescription(componentDescription)
72 vect = RandomVector(myDistribution)
H A Dt_Waarts_convex.py45 myDistribution = Normal(mean, sigma, R) variable
47 start = myDistribution.getMean()
48 Covariance = myDistribution.getCovariance()
54 vect = RandomVector(myDistribution)
H A Dt_LHS_std.py29 myDistribution = Normal(mean, sigma, R) variable
32 vect = RandomVector(myDistribution)
H A Dt_ProbabilitySimulationAlgorithm_draw.py29 myDistribution = Normal(mean, sigma, R) variable
32 vect = RandomVector(myDistribution)
H A Dt_Waarts_noisy_lsf.py114 myDistribution = ComposedDistribution(aCollection, aCopula) variable
115 myDistribution.setName("myDist")
117 start = myDistribution.getMean()
118 Covariance = myDistribution.getCovariance()
124 vect = RandomVector(myDistribution)
H A Dt_ProbabilitySimulationAlgorithm_sensitivity.py29 myDistribution = Normal(mean, sigma, R) variable
32 vect = RandomVector(myDistribution)
H A Dt_PostAnalyticalControlledImportanceSampling_std.py29 myDistribution = Normal(mean, sigma, R) variable
32 vect = RandomVector(myDistribution)
H A Dt_Analytical_std.py23 myDistribution = Normal(mean, sigma, R) variable
26 vect = RandomVector(myDistribution)
/dports/math/openturns/openturns-1.18/python/doc/examples/reliability_sensitivity/reliability/
H A Dplot_axial_stressed_beam.py53 myDistribution = sm.distribution variable
60 inputRandomVector = ot.RandomVector(myDistribution)
127 algoFORM = ot.FORM(myCobyla, myEvent, myDistribution.getMean())
201 dimension = myDistribution.getDimension()
/dports/math/openturns/openturns-1.18/lib/test/
H A Dt_FORM_draw.cxx67 Normal myDistribution(mean, sigma, R); in main() local
73 myDistribution.setDescription(componentDescription); in main()
76 RandomVector vect(myDistribution); in main()
H A Dt_DickeyFullerTest_std.cxx39 Normal myDistribution; in main() local
40 WhiteNoise whiteNoise (myDistribution, timeGrid); in main()
H A Dt_FORM_sensitivity.cxx75 Distribution myDistribution(testDistributions[i]); in main() local
80 myDistribution.setDescription(componentDescription); in main()
83 RandomVector vect(myDistribution); in main()
H A Dt_DirectionalSampling_std.cxx53 Normal myDistribution(mean, sigma, R); in main() local
56 RandomVector vect(myDistribution); in main()
H A Dt_LHS_std.cxx53 Normal myDistribution(mean, sigma, R); in main() local
56 RandomVector vect(myDistribution); in main()

1234