ConstantBasisFactory

class ConstantBasisFactory(*args)

Constant basis factory.

Parameters:

dimensionint

Input dimension of the basis.

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

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]

__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 : multiple input dimensions

Kriging : multiple input dimensions

Kriging: propagate uncertainties

Kriging: propagate uncertainties

Kriging : draw the likelihood

Kriging : draw the likelihood

Kriging : cantilever beam model

Kriging : cantilever beam model

Kriging: choose an arbitrary trend

Kriging: choose an arbitrary trend

Gaussian Process Regression : cantilever beam model

Gaussian Process Regression : cantilever beam model

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

Gaussian Process Regression : quick-start

Gaussian Process Regression : 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