Factorial¶

(Source code, 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 discretization 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.

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.
`getLevels`()	Get the levels of the stratified experiment.
`getName`()	Accessor to the object's name.
`hasName`()	Test if the object is named.
`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.

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

__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__).

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.

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

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: