Basis¶
- class Basis(*args)¶
Basis.
- Available constructors:
Basis(functionsColl)
Basis(size)
- Parameters:
- functionsColllist of
Function
Functions constituting the Basis.
- sizeint
Size of the Basis.
- functionsColllist of
Methods
add
(elt)Add a function.
build
(index)Build the element of the given index.
Accessor to the object's name.
getId
()Accessor to the object's id.
Accessor to the underlying implementation.
Get the input dimension of the Basis.
getName
()Accessor to the object's name.
Get the output dimension of the Basis.
getSize
()Get the size of the Basis.
getSubBasis
(indices)Get a sub-basis of the Basis.
isFinite
()Tell whether the basis is finite.
Tell whether the basis is orthogonal.
setName
(name)Accessor to the object's name.
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)
- __init__(*args)¶
- build(index)¶
Build the element of the given index.
- Parameters:
- indexint,
Index of an element of the Basis.
- indexint,
- Returns:
- function
Function
The function at the index index of the Basis.
- function
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__).
- 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.
- getInputDimension()¶
Get the input dimension of the Basis.
- Returns:
- inDimint
Input dimension of the functions.
- getName()¶
Accessor to the object’s name.
- Returns:
- namestr
The name of the object.
- getOutputDimension()¶
Get the output dimension of the Basis.
- Returns:
- outDimint
Output dimension of the functions.
- 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.
- subBasislist of
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 multivariate basis of functions from scalar multivariable functions
Gaussian Process Regression: multiple input dimensions

Gaussian Process Regression: choose an arbitrary trend
Gaussian Process Regression: choose a polynomial trend on the beam model
Gaussian Process Regression : cantilever beam model
Gaussian Process Regression: surrogate model with continuous and categorical variables
Gaussian Process Regression: choose a polynomial trend

Gaussian process fitter: configure the optimization solver
Gaussian Process Regression: use an isotropic covariance kernel
Gaussian Process Regression : generate trajectories from the metamodel
Gaussian Process Regression: metamodel of the Branin-Hoo function
Example of multi output Gaussian Process Regression on the fire satellite model
Gaussian Process Regression: propagate uncertainties
Compute leave-one-out error of a polynomial chaos expansion
Compute confidence intervals of a regression model from data
Create a process from random vectors and processes
Estimate Sobol indices on a field to point function