ConstantBasisFactory

class ConstantBasisFactory(*args)

Constant basis factory.

Parameters:

dimensionint

Input dimension of the basis.

See also

BasisFactory, LinearBasisFactory, QuadraticBasisFactory

Notes

A factory for constant basis of input dimension dimension.

Examples

>>> import openturns as ot
>>> basis = ot.ConstantBasisFactory(2).build()
>>> f = ot.AggregatedFunction(basis)
>>> x = [2, 3]
>>> print(f(x))
[1]

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.

__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

Kriging: propagate uncertainties

Kriging: propagate uncertainties

Kriging : multiple input dimensions

Kriging : multiple input dimensions

Kriging : draw the likelihood

Kriging : draw the likelihood

Kriging : cantilever beam model

Kriging : cantilever beam model

Kriging the cantilever beam model using HMAT

Kriging the cantilever beam model using HMAT

Kriging : generate trajectories from a metamodel

Kriging : generate trajectories from a metamodel

Kriging: choose a polynomial trend on the beam model

Kriging: choose a polynomial trend on the beam model

Kriging with an isotropic covariance function

Kriging with an isotropic covariance function

Kriging: metamodel of the Branin-Hoo function

Kriging: metamodel of the Branin-Hoo function

Kriging : quick-start

Kriging : quick-start

Sequentially adding new points to a kriging

Sequentially adding new points to a kriging

Kriging: configure the optimization solver

Kriging: configure the optimization solver

Kriging: choose a polynomial trend

Kriging: choose a polynomial trend

Advanced Kriging

Advanced Kriging

Kriging: metamodel with continuous and categorical variables

Kriging: metamodel with continuous and categorical variables

EfficientGlobalOptimization examples

EfficientGlobalOptimization examples