SobolIndicesExperiment¶

class
SobolIndicesExperiment
(*args)¶ Experiment to computeSobol’ indices.
 Available constructors:
SobolIndicesExperiment(distribution, size, computeSecondOrder=True)
SobolIndicesExperiment(experiment, computeSecondOrder=True)
Parameters: distribution :
Distribution
Distribution with an independent copula used to generate the set of input data.
size : positive int
Size of each of the two independent initial samples. For the total size of the experiment see notes below.
experiment :
WeightedExperiment
Design of experiment
computeSecondOrder : bool, defaults to True
Whether to add points to compute second order indices
See also
Notes
Sensitivity algorithms rely on the definition of specific designs. The method generates design for the Saltelli method. Such designs could be used for Jansen, Martinez and MauntzKucherenko methods. This precomputes such input designs using distribution or experiment by generating two independent samples and mixing columns of these ones to define the huge sample (design). If computeSecondOrder is disabled, result design is of size where p is the input dimension. If computeSecondOrder is enabled, design’s size is , see [Saltelli2002]. Model’s answer could be evaluated outside the platform.
Examples
>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> formula = ['sin(_pi*X1)+7*sin(_pi*X2)*sin(_pi*X2)+' + \ ... '0.1*((_pi*X3)*(_pi*X3)*(_pi*X3)*(_pi*X3))*sin(_pi*X1)'] >>> model = ot.SymbolicFunction(['X1', 'X2', 'X3'], formula) >>> distribution = ot.ComposedDistribution([ot.Uniform(1.0, 1.0)] * 3, \ ... ot.IndependentCopula(3)) >>> size = 10 >>> experiment = ot.SobolIndicesExperiment(distribution, size, True) >>> sample = experiment.generate()
Methods
generate
()Generate points according to the type of the experiment. generateWithWeights
(weights)Generate points and their associated weight according to the type of the experiment. getClassName
()Accessor to the object’s name. getDistribution
()Accessor to the distribution. getId
()Accessor to the object’s id. getName
()Accessor to the object’s name. getShadowedId
()Accessor to the object’s shadowed id. getSize
()Accessor to the size of the generated sample. getVisibility
()Accessor to the object’s visibility state. hasName
()Test if the object is named. hasUniformWeights
()Ask whether the experiment has uniform weights. hasVisibleName
()Test if the object has a distinguishable name. setDistribution
(distribution)Accessor to the distribution. setName
(name)Accessor to the object’s name. setShadowedId
(id)Accessor to the object’s shadowed id. setSize
(size)Accessor to the size of the generated sample. setVisibility
(visible)Accessor to the object’s visibility state. 
__init__
(*args)¶ x.__init__(…) initializes x; see help(type(x)) for signature

generate
()¶ Generate points according to the type of the experiment.
Returns: sample :
Sample
Points which constitute the design of experiments with . The sampling method is defined by the nature of the weighted experiment.
Examples
>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> myExperiment = ot.MonteCarloExperiment(ot.Normal(2), 5) >>> sample = myExperiment.generate() >>> print(sample) [ X0 X1 ] 0 : [ 0.608202 1.26617 ] 1 : [ 0.438266 1.20548 ] 2 : [ 2.18139 0.350042 ] 3 : [ 0.355007 1.43725 ] 4 : [ 0.810668 0.793156 ]

generateWithWeights
(weights)¶ Generate points and their associated weight according to the type of the experiment.
Returns: sample :
Sample
The points which constitute the design of experiments. The sampling method is defined by the nature of the experiment.
weights :
Point
of sizeWeights associated with the points. By default, all the weights are equal to .
Examples
>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> myExperiment = ot.MonteCarloExperiment(ot.Normal(2), 5) >>> sample, weights = myExperiment.generateWithWeights() >>> print(sample) [ X0 X1 ] 0 : [ 0.608202 1.26617 ] 1 : [ 0.438266 1.20548 ] 2 : [ 2.18139 0.350042 ] 3 : [ 0.355007 1.43725 ] 4 : [ 0.810668 0.793156 ] >>> print(weights) [0.2,0.2,0.2,0.2,0.2]

getClassName
()¶ Accessor to the object’s name.
Returns: class_name : str
The object class name (object.__class__.__name__).

getDistribution
()¶ Accessor to the distribution.
Returns: distribution :
Distribution
Distribution used to generate the set of input data.

getId
()¶ Accessor to the object’s id.
Returns: id : int
Internal unique identifier.

getName
()¶ Accessor to the object’s name.
Returns: name : str
The name of the object.

getShadowedId
()¶ Accessor to the object’s shadowed id.
Returns: id : int
Internal unique identifier.

getSize
()¶ Accessor to the size of the generated sample.
Returns: size : positive int
Number of points constituting the design of experiments.

getVisibility
()¶ Accessor to the object’s visibility state.
Returns: visible : bool
Visibility flag.

hasName
()¶ Test if the object is named.
Returns: hasName : bool
True if the name is not empty.

hasUniformWeights
()¶ Ask whether the experiment has uniform weights.
Returns: hasUniformWeights : bool
Whether the experiment has uniform weights.

hasVisibleName
()¶ Test if the object has a distinguishable name.
Returns: hasVisibleName : bool
True if the name is not empty and not the default one.

setDistribution
(distribution)¶ Accessor to the distribution.
Parameters: distribution :
Distribution
Distribution used to generate the set of input data.

setName
(name)¶ Accessor to the object’s name.
Parameters: name : str
The name of the object.

setShadowedId
(id)¶ Accessor to the object’s shadowed id.
Parameters: id : int
Internal unique identifier.

setSize
(size)¶ Accessor to the size of the generated sample.
Parameters: size : positive int
Number of points constituting the design of experiments.

setVisibility
(visible)¶ Accessor to the object’s visibility state.
Parameters: visible : bool
Visibility flag.