ApproximationAlgorithmImplementationFactory¶

class ApproximationAlgorithmImplementationFactory(*args)¶

Approximation algorithm factory base class.

Methods

`build`(*args)	Build the approximation.
`getClassName`()	Accessor to the object's name.
`getName`()	Accessor to the object's name.
`hasName`()	Test if the object is named.
`involvesModelSelection`()	Get the model selection flag.
`setName`(name)	Accessor to the object's name.

See also

PenalizedLeastSquaresAlgorithmFactory, LeastSquaresMetaModelSelectionFactory

Notes

It represents a generic class (virtual) for different factories like PenalizedLeastSquaresAlgorithmFactory or LeastSquaresMetaModelSelectionFactory

This class is not usable because it operates only within the class FunctionalChaosAlgorithm.

__init__(*args)¶

build(*args)¶

Build the approximation.

Parameters:

x2-d sequence of float: The input random observations $\left\{\vect{X}^{(1)}, ..., \vect{X}^{(n)}\right\}$ where $\vect{X}=(X_1, \dots, X_{n_X})^T$ is the input of the physical model, $n_X$ is the input dimension and $n$ is the sample size.
y2-d sequence of float: The output random observations $\left\{\vect{Y}^{(1)}, ..., \vect{Y}^{(n)}\right\}$ where $\vect{Y}=(Y_1, \dots, Y_{n_Y})^T$ is the output of the physical model, $n_Y$ is the output dimension and $n$ is the sample size.
weightsequence of float: Weights associated to the input sample points such that the corresponding weighted experiment is a good approximation of $\mu$ , where $\mu$ is the distribution of the standard random vector $\vect{Z}$ associated with the physical input random vector $\vect{X}$ . If unspecified, all weights are equal to $\frac{1}{n}$ , where $n$ is the size of the sample.
psisequence of Function: The functional basis.
indicessequence of int: Indices in the basis.

Returns:

algorithm: ApproximationAlgorithm: The estimation algorithm.

getClassName()¶

Accessor to the object’s name.

Returns:

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

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.

involvesModelSelection()¶

Get the model selection flag.

A model selection method can be used to select the coefficients of the decomposition which enable to best predict the output. Model selection leads to a sparse functional chaos expansion.

Returns: