FunctionalChaosResult¶

class FunctionalChaosResult(*args)¶

Functional chaos result.

Returned by functional chaos algorithms, see FunctionalChaosAlgorithm.

Parameters:

sampleX2-d sequence of float: Input sample of $\vect{X} \in \mathbb{R}^{n_X}$ .
sampleY2-d sequence of float: Output sample of $\vect{Y} \in \mathbb{R}^{n_Y}$ .
distributionDistribution: Distribution of the random vector $\vect{X}$
transformationFunction: The function that maps the physical input $\vect{X}$ to the standardized input $\vect{\xi}$ .
inverseTransformationFunction: The function that maps standardized input $\vect{\xi}$ to the the physical input $\vect{X}$ .
orthogonalBasisOrthogonalBasis: The multivariate orthogonal basis.
indicessequence of int: The indices of the selected basis function within the orthogonal basis.
alpha_k2-d sequence of float: The coefficients of the functional chaos expansion.
Psi_ksequence of Function: The functions of the multivariate basis selected by the algorithm.
residualssequence of float, $\hat{\vect{r}} \in \mathbb{R}^{n_Y}$: For each output component, the residual is the square root of the sum of squared differences between the model and the meta model, divided by the sample size.
relativeErrorssequence of float, $\widehat{\vect{re}} \in \mathbb{R}^{n_Y}$: The relative error is the empirical error divided by the sample variance of the output.

Notes

Let $n \in \mathbb{N}$ be the sample size. Let $n_Y \in \mathbb{N}$ be the dimension of the output of the physical model. For any $j = 1, ..., n$ and any $i = 1, ..., n_Y$ , let $y_{j, i} \in \mathbb{R}$ be the output of the physical model and let $\hat{y}_{j, i} \in \mathbb{R}$ be the output of the metamodel. For any $i = 1, ..., n_Y$ , let $\vect{y}_i \in \mathbb{R}^n$ be the sample output and let $\hat{\vect{y}}_i \in \mathbb{R}^n$ be the output predicted by the metamodel. The marginal residual is:

$\hat{r}_i = \frac{\sqrt{SS_i}}{n}$

for $i = 1, ..., n_Y$ , where $SS_i$ is the marginal sum of squares:

$SS_i = \sum_{j = 1}^n (y_{j, i} - \hat{y}_{j, i})^2.$

The marginal relative error is:

$\widehat{re}_i = \frac{\hat{r}_i / n}{\hat{s}_{Y, i}^2}$

for $i = 1, ..., n_Y$ , where $\hat{s}_{Y, i}^2$ is the unbiased sample variance of the $i$ -th output.

This structure is created by the method run() of FunctionalChaosAlgorithm, and obtained thanks to the getResult() method.

Methods

`getClassName`()	Accessor to the object's name.
`getCoefficients`()	Get the coefficients.
`getComposedMetaModel`()	Get the composed metamodel.
`getDistribution`()	Get the input distribution.
`getId`()	Accessor to the object's id.
`getIndices`()	Get the indices of the final basis.
`getInputSample`()	Accessor to the input sample.
`getInverseTransformation`()	Get the inverse isoprobabilistic transformation.
`getMetaModel`()	Accessor to the metamodel.
`getName`()	Accessor to the object's name.
`getOrthogonalBasis`()	Get the orthogonal basis.
`getOutputSample`()	Accessor to the output sample.
`getReducedBasis`()	Get the reduced basis.
`getRelativeErrors`()	Accessor to the relative errors.
`getResiduals`()	Accessor to the residuals.
`getShadowedId`()	Accessor to the object's shadowed id.
`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.
`setInputSample`(sampleX)	Accessor to the input sample.
`setMetaModel`(metaModel)	Accessor to the metamodel.
`setName`(name)	Accessor to the object's name.
`setOutputSample`(sampleY)	Accessor to the output sample.
`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_namestr: The object class name (object.__class__.__name__).

getCoefficients()¶

Get the coefficients.

Returns:

coefficients2-d sequence of float: Coefficients $(\vect{\alpha_k})_{k \in K}$ .

getComposedMetaModel()¶

Get the composed metamodel.

Returns:

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

getDistribution()¶

Get the input distribution.

Returns:

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

getId()¶

Accessor to the object’s id.

Returns:

idint: Internal unique identifier.

getIndices()¶

Get the indices of the final basis.

Returns:

indicesIndices: Indices of the elements of the multivariate basis used in the decomposition.

getInputSample()¶

Accessor to the input sample.

Returns:

inputSampleSample: The input sample.

getInverseTransformation()¶

Get the inverse isoprobabilistic transformation.

Returns:

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

getMetaModel()¶

Accessor to the metamodel.

Returns:

metaModelFunction: Metamodel.

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getOrthogonalBasis()¶

Get the orthogonal basis.

Returns:

basisOrthogonalBasis: Factory of the orthogonal basis.

getOutputSample()¶

Accessor to the output sample.

Returns:

outputSampleSample: The output sample.

getReducedBasis()¶

Get the reduced basis.

Returns:

basislist of Function: Collection of the K functions $(\Psi_k)_{k\in K}$ used in the decomposition.

getRelativeErrors()¶

Accessor to the relative errors.

Returns:

relativeErrorsPoint: 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:

residualsPoint: 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:

idint: Internal unique identifier.

getTransformation()¶

Get the isoprobabilistic transformation.

Returns:

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

getVisibility()¶

Accessor to the object’s visibility state.

Returns:

visiblebool: Visibility flag.

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns:

hasVisibleNamebool: True if the name is not empty and not the default one.

setInputSample(sampleX)¶

Accessor to the input sample.

Parameters:

inputSampleSample: The input sample.

setMetaModel(metaModel)¶

Accessor to the metamodel.

Parameters:

metaModelFunction: Metamodel.

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

setOutputSample(sampleY)¶

Accessor to the output sample.

Parameters:

outputSampleSample: The output sample.

setRelativeErrors(relativeErrors)¶

Accessor to the relative errors.

Parameters:

relativeErrorssequence 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:

residualssequence 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: