Factorial

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../../_images/openturns-Factorial-1.png
class Factorial(*args)

Factorial design of experiments.

Available constructor:

Factorial(center, levels)

Factorial(dimension, levels)

Parameters:
centersequence of float

Center of the design of experiments. If not specified, the design of experiments is centered on \vect{0} \in \Rset^n.

levelssequence of float of dimension n_{level}

The discretisation of directions (the same for each one), without any consideration of unit.

dimensionpositive int

Dimension n of the space where the design of experiments is created.

See also

StratifiedExperiment

Notes

Factorial is a stratified design of experiments enabling to generate a pattern with points only on diagonals. It is not convenient to model influences of single input variables. The factoriel pattern is obtained by discretizing each principal diagonal according to the specified levels, symmetrically with respect to the center of the design of experiments.

The number of points generated is 1 + 2^n n_{level}.

In order to scale each direction and translate the grid structure onto the proper center, use the operator *= and += of Sample.

Examples

>>> import openturns as ot
>>> levels = [4.0, 2.0, 7.0]
>>> myCenteredReductedGrid = ot.Factorial(2, levels)
>>> mySample = myCenteredReductedGrid.generate()
>>> # Translate the grid
>>> mySample+=4
>>> # Scale each direction
>>> mySample*=2

Methods

generate()

Generate points according to the type of the experiment.

getCenter()

Get the center of the stratified experiment.

getClassName()

Accessor to the object's name.

getId()

Accessor to the object's id.

getLevels()

Get the levels of the stratified experiment.

getName()

Accessor to the object's name.

getShadowedId()

Accessor to the object's shadowed id.

getVisibility()

Accessor to the object's visibility state.

hasName()

Test if the object is named.

hasVisibleName()

Test if the object has a distinguishable name.

setCenter(center)

Set the center of the stratified experiment.

setLevels(levels)

Set the levels of the stratified experiment.

setName(name)

Accessor to the object's name.

setShadowedId(id)

Accessor to the object's shadowed id.

setVisibility(visible)

Accessor to the object's visibility state.
__init__(*args)
generate()

Generate points 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.

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)
>>> myExperiment = ot.Experiment(ot.MonteCarloExperiment(ot.Normal(2),5))
>>> print(myExperiment.generate())
    [ 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 ]
getCenter()

Get the center of the stratified experiment.

Returns:
centerPoint

Sequence which has different meanings according to the nature of the stratified experiment: Axial, Composite, Factorial or Box (see corresponding documentation).

getClassName()

Accessor to the object’s name.

Returns:
class_namestr

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

getId()

Accessor to the object’s id.

Returns:
idint

Internal unique identifier.

getLevels()

Get the levels of the stratified experiment.

Returns:
levelsPoint

Sequence which has different meanings according to the nature of the stratified experiment: Axial, Composite, Factorial or Box (see corresponding documentation).

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.

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.

hasVisibleName()

Test if the object has a distinguishable name.

Returns:
hasVisibleNamebool

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

setCenter(center)

Set the center of the stratified experiment.

Parameters:
centersequence of float

Sequence which has different meanings according to the nature of the stratified experiment: Axial, Composite, Factorial or Box (see corresponding documentation).

setLevels(levels)

Set the levels of the stratified experiment.

Parameters:
levelssequence of float

Sequence which has different meanings according to the nature of the stratified experiment: Axial, Composite, Factorial or Box (see corresponding documentation).

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.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:
visiblebool

Visibility flag.

Examples using the class

Deterministic design of experiments

Deterministic design of experiments

Create a deterministic design of experiments

Create a deterministic design of experiments