ImportanceSamplingExperiment¶
(Source code, png, hires.png, pdf)

class
ImportanceSamplingExperiment
(*args)¶ Importance Sampling experiment.
 Available constructors:
ImportanceSamplingExperiment(distribution, size)
ImportanceSamplingExperiment(distribution, importanceDistribution, size)
Parameters: distribution :
Distribution
Distribution with an independent copula used to generate the set of input data.
size : positive int
Number of points that will be generated in the experiment.
importanceDistribution :
Distribution
Distribution according to which the points of the design of experiments will be generated with the Importance Sampling technique.
See also
Notes
ImportanceSamplingExperiment is a random weighted design of experiments. The
generate()
method generates points independently from the distribution . When thegenerate()
method is recalled, the generated sample changes. The weights associated to the points are all equal to:Examples
>>> import openturns as ot >>> ot.RandomGenerator.SetSeed(0) >>> distribution = ot.ComposedDistribution([ot.Uniform(0, 1)] * 2) >>> importanceDistribution = ot.ComposedDistribution([ot.Uniform(0, 1)] * 2) >>> myPlane = ot.ImportanceSamplingExperiment(distribution, importanceDistribution, 5) >>> print(myPlane.generate()) [ X0 X1 ] 0 : [ 0.629877 0.882805 ] 1 : [ 0.135276 0.0325028 ] 2 : [ 0.347057 0.969423 ] 3 : [ 0.92068 0.50304 ] 4 : [ 0.0632061 0.292757 ]
Methods
generate
()Generate points according to the type of the experiment. generateWithWeights
()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. getImportanceDistribution
()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. getWeight
()Accessor to the weights associated with the points. hasName
()Test if the object is named. 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)¶

generate
()¶ Generate points according to the type of the experiment.
Returns: sample :
NumericalSample
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
()¶ Generate points and their associated weight according to the type of the experiment.
Returns: sample :
NumericalSample
The points which constitute the design of experiments. The sampling method is defined by the nature of the experiment.
weights :
NumericalPoint
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.

getWeight
()¶ Accessor to the weights associated with the points.
Returns: weights :
NumericalPoint
of sizeWeights associated with the points. By default, all the weights are equal to .

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

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.