PenalizedLeastSquaresAlgorithmFactory¶

class PenalizedLeastSquaresAlgorithmFactory(*args)¶

Penalized least squares algorithm factory.

See also

ApproximationAlgorithm, LeastSquaresMetaModelSelectionFactory

Notes

Implementation of an approximation algorithm implementation factory which builds an ApproximationAlgorithm.

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

Methods

`build`(x, y, weight, psi, indices)	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.

__init__(*args)¶

build(x, y, weight, psi, indices)¶

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: