BasisFactory

class BasisFactory(*args)

Basis factory base class.

Parameters:

orthogUniVarPolFactoryOrthogonalUniVariatePolynomialFactory

Factory that builds particular univariate polynomial (e.g. Hermite, Legendre, Laguerre, …).

Methods

build()	Build the basis.
getClassName()	Accessor to the object's name.
getName()	Accessor to the object's name.
hasName()	Test if the object is named.
setName(name)	Accessor to the object's name.

See also

ConstantBasisFactory, LinearBasisFactory, QuadraticBasisFactory

Notes

BasisFactory is the interface of the OrthogonalUniVariatePolynomialFactory implementation. It represents the factory that allows the construction of any univariate orthonormal polynomial with any degree.

__init__(*args)

build()

Build the basis.

Returns:

basisBasis.

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.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

Examples using the class

Gaussian Process Regression vs KrigingAlgorithm

Gaussian Process Regression vs KrigingAlgorithm

Create a general linear model metamodel

Create a general linear model metamodel

Gaussian Process Regression: multiple input dimensions

Gaussian Process Regression: multiple input dimensions

Gaussian Process Regression : quick-start

Gaussian Process Regression : quick-start

Gaussian Process-based active learning for reliability

Gaussian Process-based active learning for reliability

Advanced Gaussian process regression

Advanced Gaussian process regression

Gaussian Process Regression: choose a polynomial trend on the beam model

Gaussian Process Regression: choose a polynomial trend on the beam model

Gaussian Process Regression : cantilever beam model

Gaussian Process Regression : cantilever beam model

Gaussian Process Regression: surrogate model with continuous and categorical variables

Gaussian Process Regression: surrogate model with continuous and categorical variables

Gaussian Process Regression: choose a polynomial trend

Gaussian Process Regression: choose a polynomial trend

Gaussian process fitter: configure the optimization solver

Gaussian process fitter: configure the optimization solver

Gaussian Process Regression: use an isotropic covariance kernel

Gaussian Process Regression: use an isotropic covariance kernel

Gaussian process regression: draw the likelihood

Gaussian process regression: draw the likelihood

Gaussian Process Regression : generate trajectories from the metamodel

Gaussian Process Regression : generate trajectories from the metamodel

Gaussian Process Regression: metamodel of the Branin-Hoo function

Gaussian Process Regression: metamodel of the Branin-Hoo function

Example of multi output Gaussian Process Regression on the fire satellite model

Example of multi output Gaussian Process Regression on the fire satellite model

Sequentially adding new points to a Gaussian Process metamodel

Sequentially adding new points to a Gaussian Process metamodel

Gaussian Process Regression: propagate uncertainties

Gaussian Process Regression: propagate uncertainties

EfficientGlobalOptimization examples

EfficientGlobalOptimization examples