TensorApproximationResult

class TensorApproximationResult(*args)

Functional chaos result.

Notes

Structure created by the method run() of TensorApproximationAlgorithm, and obtained thanks to the method getResult().

Methods

getClassName() Accessor to the object’s name.
getComposedMetaModel() Get the composed metamodel.
getComposedModel() Get the composed model.
getDistribution() Get the input distribution.
getId() Accessor to the object’s id.
getInverseTransformation() Get the inverse isoprobabilistic transformation.
getMetaModel() Accessor to the metamodel.
getModel() Accessor to the model.
getName() Accessor to the object’s name.
getRelativeErrors() Accessor to the relative errors.
getResiduals() Accessor to the residuals.
getShadowedId() Accessor to the object’s shadowed id.
getTensor([marginalIndex]) Accessor to the tensor.
getTransformation() Get the isoprobabilistic transformation.
getVisibility() Accessor to the object’s visibility state.
hasName() Test if the object is named.
hasVisibleName() Test if the object has a distinguishable name.
setMetaModel(metaModel) Accessor to the metamodel.
setModel(model) Accessor to the model.
setName(name) Accessor to the object’s name.
setRelativeErrors(relativeErrors) Accessor to the relative errors.
setResiduals(residuals) Accessor to the residuals.
setShadowedId(id) Accessor to the object’s shadowed id.
setVisibility(visible) Accessor to the object’s visibility state.
__init__(*args)
getClassName()

Accessor to the object’s name.

Returns:

class_name : str

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

getComposedMetaModel()

Get the composed metamodel.

Returns:

composedMetamodel : Function

\tilde{f} =  \sum_{k \in K} \vect{\alpha}_k \Psi_k

getComposedModel()

Get the composed model.

Returns:

composedModel : Function

f = g\circ T^{-1}.

getDistribution()

Get the input distribution.

Returns:

distribution : Distribution

Distribution of the input random vector \vect{X}.

getId()

Accessor to the object’s id.

Returns:

id : int

Internal unique identifier.

getInverseTransformation()

Get the inverse isoprobabilistic transformation.

Returns:

invTransf : Function

T^{-1} such that T(\vect{X}) = \vect{Z}.

getMetaModel()

Accessor to the metamodel.

Returns:

metaModel : Function

Metamodel.

getModel()

Accessor to the model.

Returns:

model : Function

Physical model approximated by a metamodel.

getName()

Accessor to the object’s name.

Returns:

name : str

The name of the object.

getRelativeErrors()

Accessor to the relative errors.

Returns:

relativeErrors : Point

The relative errors defined as follows for each output of the model: \displaystyle \frac{\sum_{i=1}^N (y_i - \hat{y_i})^2}{N \Var{\vect{Y}}} with \vect{Y} the vector of the N model’s values y_i and \hat{y_i} the metamodel’s values.

getResiduals()

Accessor to the residuals.

Returns:

residuals : Point

The residual values defined as follows for each output of the model: \displaystyle \frac{\sqrt{\sum_{i=1}^N (y_i - \hat{y_i})^2}}{N} with y_i the N model’s values and \hat{y_i} the metamodel’s values.

getShadowedId()

Accessor to the object’s shadowed id.

Returns:

id : int

Internal unique identifier.

getTensor(marginalIndex=0)

Accessor to the tensor.

Parameters:

marginalIndex : int

Index of the marginal

Returns:

tensor : CanonicalTensorEvaluation

Tensor data.

getTransformation()

Get the isoprobabilistic transformation.

Returns:

transformation : Function

Transformation T such that T(\vect{X}) = \vect{Z}.

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.

setMetaModel(metaModel)

Accessor to the metamodel.

Parameters:

metaModel : Function

Metamodel.

setModel(model)

Accessor to the model.

Parameters:

model : Function

Physical model approximated by a metamodel.

setName(name)

Accessor to the object’s name.

Parameters:

name : str

The name of the object.

setRelativeErrors(relativeErrors)

Accessor to the relative errors.

Parameters:

relativeErrors : sequence of float

The relative errors defined as follows for each output of the model: \displaystyle \frac{\sum_{i=1}^N (y_i - \hat{y_i})^2}{N \Var{\vect{Y}}} with \vect{Y} the vector of the N model’s values y_i and \hat{y_i} the metamodel’s values.

setResiduals(residuals)

Accessor to the residuals.

Parameters:

residuals : sequence of float

The residual values defined as follows for each output of the model: \displaystyle \frac{\sqrt{\sum_{i=1}^N (y_i - \hat{y_i})^2}}{N} with y_i the N model’s values and \hat{y_i} the metamodel’s values.

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.