# DualLinearCombinationEvaluation¶

class DualLinearCombinationEvaluation(*args)

Dual linear combination evaluation implementation.

Available constructors:

DualLinearCombinationEvaluation(scalarFctColl, vectCoefColl)
Parameters: scalarFctColl : sequence of Function A collection of functions of size , such that , . vectCoefColl : 2-d sequence of float Sample of size and dimension .

Notes

It returns a Function which is the function defined as the linear combination of the functions with vector coefficients in :

Methods

 draw(*args) Draw the output of function as a Graph. getCallsNumber() Accessor to the number of times the function has been called. getClassName() Accessor to the object’s name. getCoefficients() Accessor to the coefficients. getDescription() Accessor to the description of the inputs and outputs. getFunctionsCollection() Accessor to the collection of functions. getId() Accessor to the object’s id. getInputDescription() Accessor to the description of the inputs. getInputDimension() Accessor to the number of the inputs. getMarginal(*args) Accessor to marginal. getName() Accessor to the object’s name. getOutputDescription() Accessor to the description of the outputs. getOutputDimension() Accessor to the number of the outputs. getParameter() Accessor to the parameter values. getParameterDescription() Accessor to the parameter description. getParameterDimension() Accessor to the dimension of the parameter. getShadowedId() Accessor to the object’s shadowed id. getVisibility() Accessor to the object’s visibility state. hasName() Test if the object is named. hasVisibleName() Test if the object has a distinguishable name. isActualImplementation() Accessor to the validity flag. parameterGradient(inP) Gradient against the parameters. setDescription(description) Accessor to the description of the inputs and outputs. setFunctionsCollectionAndCoefficients(…) Accessor to the coefficients and the collection of functions. setInputDescription(inputDescription) Accessor to the description of the inputs. setName(name) Accessor to the object’s name. setOutputDescription(outputDescription) Accessor to the description of the outputs. setParameter(parameter) Accessor to the parameter values. setParameterDescription(description) Accessor to the parameter description. setShadowedId(id) Accessor to the object’s shadowed id. setVisibility(visible) Accessor to the object’s visibility state.
 __call__
__init__(*args)

Initialize self. See help(type(self)) for accurate signature.

draw(*args)

Draw the output of function as a Graph.

Available usages:

draw(inputMarg, outputMarg, CP, xiMin, xiMax, ptNb)

draw(firstInputMarg, secondInputMarg, outputMarg, CP, xiMin_xjMin, xiMax_xjMax, ptNbs)

draw(xiMin, xiMax, ptNb)

draw(xiMin_xjMin, xiMax_xjMax, ptNbs)

Parameters: outputMarg, inputMarg : int, outputMarg is the index of the marginal to draw as a function of the marginal with index inputMarg. firstInputMarg, secondInputMarg : int, In the 2D case, the marginal outputMarg is drawn as a function of the two marginals with indexes firstInputMarg and secondInputMarg. CP : sequence of float Central point. xiMin, xiMax : float Define the interval where the curve is plotted. xiMin_xjMin, xiMax_xjMax : sequence of float of dimension 2. In the 2D case, define the intervals where the curves are plotted. ptNb : int or list of ints of dimension 2 The number of points to draw the curves.

Notes

We note where and , with and .

• In the first usage:

Draws graph of the given 1D outputMarg marginal as a function of the given 1D inputMarg marginal with respect to the variation of in the interval , when all the other components of are fixed to the corresponding ones of the central point CP. Then it draws the graph: .

• In the second usage:

Draws the iso-curves of the given outputMarg marginal as a function of the given 2D firstInputMarg and secondInputMarg marginals with respect to the variation of in the interval , when all the other components of are fixed to the corresponding ones of the central point CP. Then it draws the graph: .

• In the third usage:

The same as the first usage but only for function .

• In the fourth usage:

The same as the second usage but only for function .

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> f = ot.SymbolicFunction(['x'], ['sin(2*pi_*x)*exp(-x^2/2)'])
>>> graph = f.draw(-1.2, 1.2, 100)
>>> View(graph).show()

getCallsNumber()

Accessor to the number of times the function has been called.

Returns: calls_number : int Integer that counts the number of times the function has been called since its creation.
getClassName()

Accessor to the object’s name.

Returns: class_name : str The object class name (object.__class__.__name__).
getCoefficients()

Accessor to the coefficients.

Returns: coef : Sample The vectorial coefficients which define the linear combination of .
getDescription()

Accessor to the description of the inputs and outputs.

Returns: description : Description Description of the inputs and the outputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getDescription())
[x1,x2,y0]

getFunctionsCollection()

Accessor to the collection of functions.

Returns: fctColl : FunctionCollection The collection of scalar functions which defines the linear combination of .
getId()

Accessor to the object’s id.

Returns: id : int Internal unique identifier.
getInputDescription()

Accessor to the description of the inputs.

Returns: description : Description Description of the inputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getInputDescription())
[x1,x2]

getInputDimension()

Accessor to the number of the inputs.

Returns: number_inputs : int Number of inputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getInputDimension())
2

getMarginal(*args)

Accessor to marginal.

Parameters: indices : int or list of ints Set of indices for which the marginal is extracted. marginal : Function Function corresponding to either or , with and .
getName()

Accessor to the object’s name.

Returns: name : str The name of the object.
getOutputDescription()

Accessor to the description of the outputs.

Returns: description : Description Description of the outputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getOutputDescription())
[y0]

getOutputDimension()

Accessor to the number of the outputs.

Returns: number_outputs : int Number of outputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getOutputDimension())
1

getParameter()

Accessor to the parameter values.

Returns: parameter : Point The parameter values.
getParameterDescription()

Accessor to the parameter description.

Returns: parameter : Description The parameter description.
getParameterDimension()

Accessor to the dimension of the parameter.

Returns: parameter_dimension : int Dimension of the parameter.
getShadowedId()

Accessor to the object’s shadowed id.

Returns: id : int Internal unique identifier.
getVisibility()

Accessor to the object’s visibility state.

Returns: visible : bool Visibility flag.
hasName()

Test if the object is named.

Returns: hasName : bool True if the name is not empty.
hasVisibleName()

Test if the object has a distinguishable name.

Returns: hasVisibleName : bool True if the name is not empty and not the default one.
isActualImplementation()

Accessor to the validity flag.

Returns: is_impl : bool Whether the implementation is valid.
parameterGradient(inP)

Parameters: x : sequence of float Input point parameter_gradient : Matrix The parameters gradient computed at x.
setDescription(description)

Accessor to the description of the inputs and outputs.

Parameters: description : sequence of str Description of the inputs and the outputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getDescription())
[x1,x2,y0]
>>> f.setDescription(['a','b','y'])
>>> print(f.getDescription())
[a,b,y]

setFunctionsCollectionAndCoefficients(functionsCollection, coefficients)

Accessor to the coefficients and the collection of functions.

Parameters: scalarFctColl : sequence of Function The collection of functions . vectCoefColl : 2-d sequence of float The sample of coefficients .
setInputDescription(inputDescription)

Accessor to the description of the inputs.

Returns: description : Description Description of the inputs.
setName(name)

Accessor to the object’s name.

Parameters: name : str The name of the object.
setOutputDescription(outputDescription)

Accessor to the description of the outputs.

Returns: description : Description Description of the outputs.
setParameter(parameter)

Accessor to the parameter values.

Parameters: parameter : sequence of float The parameter values.
setParameterDescription(description)

Accessor to the parameter description.

Parameters: parameter : Description The parameter description.
setShadowedId(id)

Accessor to the object’s shadowed id.

Parameters: id : int Internal unique identifier.
setVisibility(visible)

Accessor to the object’s visibility state.

Parameters: visible : bool Visibility flag.