GaussProductExperiment¶

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class GaussProductExperiment(*args)¶

Gauss product experiment.

Available constructors:

GaussProductExperiment(marginalDegrees)

GaussProductExperiment(distribution)

GaussProductExperiment(distribution, marginalDegrees)

Parameters

marginalDegreessequence of positive int: Numbers of points $N_i$ for each direction. Then, the total number of points generated is $cardI=\prod_{i=1}^n N_i$ . If not specified, each value $N_i$ is $5$ .
distribution :: Distribution $\mu$ of dimension $n$ with an independent copula.

See also

WeightedExperiment

Notes

The number of points for each direction doesn’t change and the generate() method always gives the same sample.

Examples

>>> import openturns as ot
>>> distribution = ot.ComposedDistribution([ot.Exponential(), ot.Triangular(-1.0, -0.5, 1.0)])
>>> marginalDegrees = [3, 2]
>>> experiment = ot.GaussProductExperiment(distribution, marginalDegrees)
>>> print(experiment.generate())
    [ X0        X1        ]
0 : [  0.415775 -0.511215 ]
1 : [  2.29428  -0.511215 ]
2 : [  6.28995  -0.511215 ]
3 : [  0.415775  0.357369 ]
4 : [  2.29428   0.357369 ]
5 : [  6.28995   0.357369 ]

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.
`getMarginalDegrees`()	Get the marginal degrees.
`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.
`setMarginalDegrees`(marginalDegrees)	Set the marginal degrees.
`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)¶: Initialize self. See help(type(self)) for accurate signature.

generate()¶

Generate points according to the type of the experiment.

Returns

sampleSample: Points $(\Xi_i)_{i \in I}$ which constitute the design of experiments with $card I = size$ . 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

sampleSample: The points which constitute the design of experiments. The sampling method is defined by the nature of the experiment.
weightsPoint of size $cardI$: Weights $(\omega_i)_{i \in I}$ associated with the points. By default, all the weights are equal to $1/cardI$ .

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_namestr: The object class name (object.__class__.__name__).

getDistribution()¶

Accessor to the distribution.

Returns

distributionDistribution: Distribution used to generate the set of input data.

getId()¶

Accessor to the object’s id.

Returns

idint: Internal unique identifier.

getMarginalDegrees()¶

Get the marginal degrees.

Returns

marginalDegreesIndices: Numbers of points $N_i$ for each direction.

getName()¶

Accessor to the object’s name.

Returns

namestr: The name of the object.

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns

idint: Internal unique identifier.

getSize()¶

Accessor to the size of the generated sample.

Returns

sizepositive int: Number $cardI$ of points constituting the design of experiments.

getVisibility()¶

Accessor to the object’s visibility state.

Returns

visiblebool: Visibility flag.

hasName()¶

Test if the object is named.

Returns

hasNamebool: True if the name is not empty.

hasUniformWeights()¶

Ask whether the experiment has uniform weights.

Returns

hasUniformWeightsbool: Whether the experiment has uniform weights.

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns

hasVisibleNamebool: True if the name is not empty and not the default one.

setDistribution(distribution)¶

Accessor to the distribution.

Parameters

distributionDistribution: Distribution used to generate the set of input data.

setMarginalDegrees(marginalDegrees)¶

Set the marginal degrees.

Parameters

marginalDegreessequence of positive int: Numbers of points $N_i$ for each direction.

setName(name)¶

Accessor to the object’s name.

Parameters

namestr: The name of the object.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters

idint: Internal unique identifier.

setSize(size)¶

Accessor to the size of the generated sample.

Parameters

sizepositive int: Number $cardI$ of points constituting the design of experiments.

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters

visiblebool: Visibility flag.

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