GaussProductExperiment

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../../_images/GaussProductExperiment.png
class GaussProductExperiment(*args)

Gauss product experiment.

Available constructors:

GaussProductExperiment(marginalDegrees)

GaussProductExperiment(distribution)

GaussProductExperiment(distribution, marginalDegrees)

Parameters:

marginalDegrees : sequence 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.

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]
>>> myPlane = ot.GaussProductExperiment(distribution, marginalDegrees)
>>> print(myPlane.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)

x.__init__(…) initializes x; see help(type(x)) for signature

generate()

Generate points according to the type of the experiment.

Returns:

sample : Sample

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:

sample : Sample

The points which constitute the design of experiments. The sampling method is defined by the nature of the experiment.

weights : Point 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_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.

getMarginalDegrees()

Get the marginal degrees.

Returns:

marginalDegrees : Indices

Numbers of points N_i for each direction.

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 cardI 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.

setMarginalDegrees(marginalDegrees)

Set the marginal degrees.

Parameters:

marginalDegrees : sequence of positive int

Numbers of points N_i for each direction.

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 cardI of points constituting the design of experiments.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:

visible : bool

Visibility flag.