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
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
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.
- __init__(*args)¶
- build(index)¶
Build the element of the given index.
- Parameters:
- indexint,
Index of an element of the Basis.
- 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¶
Estimate a GEV on the Port Pirie sea-levels data
Estimate a GPD on the daily rainfall data
Estimate a GEV on race times data
Estimate a GEV on the Fremantle sea-levels data
Create a functional basis process
Create a process from random vectors and processes
Create a general linear model metamodel
Kriging: propagate uncertainties
Kriging : multiple input dimensions
Kriging : cantilever beam model
Kriging: choose an arbitrary trend
Kriging the cantilever beam model using HMAT
Example of multi output Kriging on the fire satellite model
Kriging : generate trajectories from a metamodel
Kriging: choose a polynomial trend on the beam model
Kriging with an isotropic covariance function
Kriging: metamodel of the Branin-Hoo function
Kriging: configure the optimization solver
Kriging: choose a polynomial trend
Kriging: metamodel with continuous and categorical variables
Estimate Sobol indices on a field to point function
Create a multivariate basis of functions from scalar multivariable functions
Compute leave-one-out error of a polynomial chaos expansion
Compute confidence intervals of a regression model from data
EfficientGlobalOptimization examples