Basis

class Basis(*args)

Basis.

Available constructors:

Basis(functionsColl)

Basis(size)

Parameters:
functionsColllist of Function

Functions constituting the Basis.

sizeint

Size of the Basis.

Examples

>>> import openturns as ot
>>> dimension = 3
>>> input = ['x0', 'x1', 'x2']
>>> functions = []
>>> for i in range(dimension):
...     functions.append(ot.SymbolicFunction(input, [input[i]]))
>>> basis = ot.Basis(functions)

Methods

build(index)

Build the element of the given index.

getClassName()

Accessor to the object's name.

getDimension()

Get the dimension of the Basis.

getId()

Accessor to the object's id.

getImplementation()

Accessor to the underlying implementation.

getName()

Accessor to the object's name.

getSize()

Get the size of the Basis.

getSubBasis(indices)

Get a sub-basis of the Basis.

isFinite()

Tell whether the basis is finite.

isOrthogonal()

Tell whether the basis is orthogonal.

setName(name)

Accessor to the object's name.

add

__init__(*args)
build(index)

Build the element of the given index.

Parameters:
indexint, index \geq 0

Index of an element of the Basis.

Returns:
functionFunction

The function at the index index of the Basis.

Examples

>>> import openturns as ot
>>> dimension = 3
>>> input = ['x0', 'x1', 'x2']
>>> functions = []
>>> for i in range(dimension):
...     functions.append(ot.SymbolicFunction(input, [input[i]]))
>>> basis = ot.Basis(functions)
>>> print(basis.build(0).getEvaluation())
[x0,x1,x2]->[x0]
getClassName()

Accessor to the object’s name.

Returns:
class_namestr

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

getDimension()

Get the dimension of the Basis.

Returns:
dimensionint

Dimension of the Basis.

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.

getSize()

Get the size of the Basis.

Returns:
sizeint

Size of the Basis.

getSubBasis(indices)

Get a sub-basis of the Basis.

Parameters:
indiceslist of int

Indices of the terms of the Basis put in the sub-basis.

Returns:
subBasislist of Function

Functions defining a sub-basis.

Examples

>>> import openturns as ot
>>> dimension = 3
>>> input = ['x0', 'x1', 'x2']
>>> functions = []
>>> for i in range(dimension):
...     functions.append(ot.SymbolicFunction(input, [input[i]]))
>>> basis = ot.Basis(functions)
>>> subbasis = basis.getSubBasis([1])
>>> print(subbasis[0].getEvaluation())
[x0,x1,x2]->[x1]
isFinite()

Tell whether the basis is finite.

Returns:
isFinitebool

True if the basis is finite.

isOrthogonal()

Tell whether the basis is orthogonal.

Returns:
isOrthogonalbool

True if the basis is orthogonal.

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

Examples using the class

Create a functional basis process

Create a functional basis process

Create a process from random vectors and processes

Create a process from random vectors and processes

Trend computation

Trend computation

Create a general linear model metamodel

Create a general linear model metamodel

Mixture of experts

Mixture of experts

Perfom stepwise regression

Perfom stepwise regression

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

Configuring an arbitrary trend in Kriging

Configuring an arbitrary trend in Kriging

Example of multi output Kriging on the fire satellite model

Example of multi output Kriging on the fire satellite 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

Choose the trend basis of a kriging metamodel

Choose the trend basis of a kriging metamodel

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

Advanced kriging

Advanced kriging

Kriging :configure the optimization solver

Kriging :configure the optimization solver

Kriging : choose a trend vector space

Kriging : choose a trend vector space

Estimate Sobol indices on a field to point function

Estimate Sobol indices on a field to point function

EfficientGlobalOptimization examples

EfficientGlobalOptimization examples