LinearBasisFactory

class LinearBasisFactory(*args)

Linear basis factory.

The linear basis is the collection of functions \psi_k: \mathbb{R}^d \mapsto \mathbb{R} for 0 \leq k \leq d defined:

\psi_0(x_1, \dots, x_d) & = 1 \\
\psi_k(x_1, \dots, x_d) & = x_k \mbox{for } 1 \leq k \leq d

Parameters:
dimensionint

Input dimension d 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.

Examples

>>> import openturns as ot
>>> basis = ot.LinearBasisFactory(2).build()
>>> psi_1 = basis.build(1)
>>> print(psi_1)
class=LinearEvaluation name=Unnamed center=[0,0] constant=[0] linear=[[ 1 ]
 [ 0 ]]
__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: choose a polynomial trend on the beam model

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

Gaussian Process Regression: choose a polynomial trend

Gaussian Process Regression: choose a polynomial trend

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

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