ApproximationAlgorithm

class ApproximationAlgorithm(*args)

Approximation algorithm.

See also

LeastSquaresStrategy, ApproximationAlgorithmImplementationFactory

LeastSquaresMetaModelSelectionFactory

Notes

The ApproximationAlgorithm is built from an approximation algorithm implementation factory which is a ApproximationAlgorithmImplementationFactory or a LeastSquaresMetaModelSelectionFactory.

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

Methods

getClassName()	Accessor to the object's name.
getCoefficients()	Accessor to the coefficients.
getId()	Accessor to the object's id.
getImplementation()	Accessor to the underlying implementation.
getName()	Accessor to the object's name.
getPsi()	Accessor to the basis.
getRelativeError()	Accessor to the coefficients.
getResidual()	Accessor to the coefficients.
getWeight()	Accessor to the weights.
getX()	Accessor to the input sample.
getY()	Accessor to the output sample.
run()	Run the algorithm.
setName(name)	Accessor to the object's name.

getVerbose
setVerbose

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getCoefficients()

Accessor to the coefficients.

Returns:

coefficientsPoint

The coefficients

getId()

Accessor to the object’s id.

Returns:

idint

Internal unique identifier.

getImplementation()

Accessor to the underlying implementation.

Returns:

implImplementation

A copy of the underlying implementation object.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getPsi()

Accessor to the basis.

Returns:

coefficientsBasis

The basis

getRelativeError()

Accessor to the coefficients.

Returns:

relativeErrorfloat

The relative error

getResidual()

Accessor to the coefficients.

Returns:

coefficientsfloat

The residual

getWeight()

Accessor to the weights.

Returns:

weightPoint

Output weights

getX()

Accessor to the input sample.

Returns:

xSample

Input sample

getY()

Accessor to the output sample.

Returns:

ySample

Input sample

run()

Run the algorithm.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.