# GeneralizedExponential¶

class GeneralizedExponential(*args)

Absolute exponential covariance function.

Available constructors:

GeneralizedExponential(spatialDim=1)

GeneralizedExponential(scale, p)

GeneralizedExponential(scale, amplitude, p)

Parameters:
spatialDimint

Spatial dimension . When not fulfilled, the input dimension is equal to the size of the parameter . By default, equal to 1.

scalesequence of floats

Scale coefficient . The size of is the input dimension.

amplitudesequence of positive floats

Amplitude of the process . Must be of size equal to 1. By default, equal to .

pfloat,

Define the exponent of the euclidean norm that is used within the model.

Notes

The generalized exponential function is a stationary covariance function with dimension .

We consider the scalar stochastic process , where is an event, is a domain of .

The generalized exponential function is defined by:

The correlation function writes:

Examples

Create a standard generalized exponential covariance function:

>>> import openturns as ot
>>> covModel = ot.GeneralizedExponential(2)
>>> t = [0.1, 0.3]
>>> s = [0.2, 0.4]
>>> print(covModel(s, t))
[[ 0.868123 ]]
>>> tau = [0.1, 0.3]
>>> print(covModel(tau))
[[ 0.728893 ]]


Create a generalized exponential covariance function specifying the scale vector and p:

>>> covModel2 = ot.GeneralizedExponential([1.5, 2.5], 1.5)
>>> covModel2bis = ot.GeneralizedExponential([1.5] * 2, 1.5)


Create a generalized exponential covariance function specifying the scale vector, the amplitude and p:

>>> covModel3 = ot.GeneralizedExponential([1.5, 2.5], [3.5], 1.5)


Methods

 __call__(*args) Evaluate the covariance function. computeAsScalar(*args) Compute the covariance function for scalar model. computeCrossCovariance the covariance function on a given mesh. discretize(*args) Discretize the covariance function on a given mesh. Discretize and factorize the covariance function on a given mesh. Discretize and factorize the covariance function on a given mesh. discretizeHMatrix(*args) Discretize the covariance function on a given mesh using HMatrix result. discretizeRow(vertices, p) (TODO) draw(*args) Draw a specific component of the covariance model with input dimension 1. Accessor to the active parameter set. Get the amplitude parameter of the covariance function. Accessor to the object's name. Get the full parameters of the covariance function. Get the description full parameters of the covariance function. Accessor to the object's id. Get the input dimension of the covariance function. getMarginal(*args) Get the ith marginal of the model. Accessor to the object's name. Accessor to the nugget factor. Get the spatial correlation matrix of the covariance function. Get the dimension of the covariance function. P accessor. Get the parameters of the covariance function. Get the description of the covariance function parameters. Get the scale parameter of the covariance function. Accessor to the object's shadowed id. Accessor to the object's visibility state. Test if the object is named. Test if the object has a distinguishable name. Test whether the model is diagonal or not. Test whether the model is stationary or not. Compute the gradient according to the parameters. Compute the gradient of the covariance function. setActiveParameter(active) Accessor to the active parameter set. setAmplitude(amplitude) Set the amplitude parameter of the covariance function. setFullParameter(parameter) Set the full parameters of the covariance function. setName(name) Accessor to the object's name. setNuggetFactor(nuggetFactor) Set the nugget factor for the variance of the observation error. setOutputCorrelation(correlation) Set the spatial correlation matrix of the covariance function. P accessor. setParameter(parameter) Set the parameters of the covariance function. setScale(scale) Set the scale parameter of the covariance function. Accessor to the object's shadowed id. setVisibility(visible) Accessor to the object's visibility state.
__init__(*args)
computeAsScalar(*args)

Compute the covariance function for scalar model.

Available usages:

computeAsScalar(s, t)

computeAsScalar(tau)

Parameters:
s, tfloats (if ) or sequences of floats (any )

Multivariate index

taufloat (if ) or sequence of floats (any )

Multivariate index

Returns:
covariancefloat

Covariance.

Notes

The method makes sense only if the dimension of the process is . It evaluates .

In the second usage, the covariance model must be stationary. Then we note for as this quantity does not depend on .

computeCrossCovariance(*args)

computeCrossCovariance the covariance function on a given mesh.

Parameters:
firstVertices

Container of the first discretization vertices

secondVertices

Container of the second discretization vertices

Returns:
MatrixMatrix

Container of the cross covariance

Notes

This method computes a cross-covariance matrix. The cross-covariance is the evaluation of the covariance model on both firstVertices and secondVertices.

If contains points and contains points, the method returns an matrix ( being the output dimension).

To make things easier, let us focus on the case. Let be the points of firstVertices and let be the points of secondVertices. The result is the matrix such that for any nonnegative integers and , .

discretize(*args)

Discretize the covariance function on a given mesh.

Parameters:
where

Container of the discretization vertices

Returns:
covarianceMatrixCovarianceMatrix

Covariance matrix (if the process is of dimension )

Notes

This method makes a discretization of the model on the given Mesh, RegularGrid or Sample composed of the vertices and returns the covariance matrix:

discretizeAndFactorize(*args)

Discretize and factorize the covariance function on a given mesh.

Parameters:
where

Container of the discretization vertices

Returns:
CholeskyMatrixTriangularMatrix

Cholesky factor of the covariance matrix (if the process is of dimension )

Notes

This method makes a discretization of the model on the given Mesh, RegularGrid or Sample composed of the vertices thanks to the discretize() method and returns its Cholesky factor.

discretizeAndFactorizeHMatrix(*args)

Discretize and factorize the covariance function on a given mesh.

This uses HMatrix.

Parameters:
where

Container of the discretization vertices

hmatParamHMatrixParameters

Parameter values for the HMatrix

Returns:
HMatrixHMatrix

Cholesk matrix (if the process is of dimension ), stored in hierarchical format (H-Matrix)

Notes

This method is similar to the discretizeAndFactorize() method. This method requires that requires that OpenTURNS has been compiled with the hmat library. The method is helpful for very large parameters (Mesh, grid, Sample) because it compresses data.

discretizeHMatrix(*args)

Discretize the covariance function on a given mesh using HMatrix result.

Parameters:
where

Container of the discretization vertices

hmatParamHMatrixParameters

Parameter values for the HMatrix

Returns:
HMatrixHMatrix

Covariance matrix (if the process is of dimension ), stored in hierarchical format (H-Matrix)

Notes

This method is similar to the discretize() method. This method requires that OpenTURNS has been compiled with the hmat library. The method is helpful for very large parameters (Mesh, grid, Sample) because it compresses data.

discretizeRow(vertices, p)

(TODO)

draw(*args)

Draw a specific component of the covariance model with input dimension 1.

Parameters:
rowIndexint,

The row index of the component to draw. Default value is 0.

columnIndex: int, :math:0 leq columnIndex < dimension

The column index of the component to draw. Default value is 0.

tMinfloat

The lower bound of the range over which the model is plotted. Default value is CovarianceModel-DefaultTMin in ResourceMap.

tMaxfloat

The upper bound of the range over which the model is plotted. Default value is CovarianceModel-DefaultTMax in ResourceMap.

pointNumberint,

The discretization of the range over which the model is plotted. Default value is CovarianceModel-DefaultPointNumber in class:~openturns.ResourceMap.

asStationarybool

Flag to tell if the model has to be plotted as a stationary model, ie as a function of the lag if equals to True, or as a non-stationary model, ie as a function of if equals to False. Default value is True.

correlationFlagbool

Flag to tell if the model has to be plotted as a correlation function if equals to True or as a covariance function if equals to False. Default value is False.

Returns:
graphGraph

A graph containing a unique curve if asStationary=True and if the model is actually a stationary model, or containing the iso-values of the model if asStationary=False or if the model is nonstationary.

getActiveParameter()

Accessor to the active parameter set.

In the context of kriging, it allows one to choose which hyperparameters are tuned.

Returns:
activeIndices

Indices of the active parameters.

getAmplitude()

Get the amplitude parameter of the covariance function.

Returns:
amplitudePoint

The amplitude parameter of the covariance function.

getClassName()

Accessor to the object’s name.

Returns:
class_namestr

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

getFullParameter()

Get the full parameters of the covariance function.

Returns:
parameterPoint

List the full parameter of the covariance function i.e. scale parameter , the the amplitude parameter , the Spatial correlation parameter ; and potential other parameter depending on the model;

getFullParameterDescription()

Get the description full parameters of the covariance function.

Returns:
descriptionDescription

Description of the full parameter of the covariance function.

getId()

Accessor to the object’s id.

Returns:
idint

Internal unique identifier.

getInputDimension()

Get the input dimension of the covariance function.

Returns:
inputDimensionint

Spatial dimension of the covariance function.

getMarginal(*args)

Get the ith marginal of the model.

Returns:
marginalint or sequence of int

index of marginal of the model.

getName()

Accessor to the object’s name.

Returns:
namestr

The name of the object.

getNuggetFactor()

Accessor to the nugget factor.

This parameter allows smooth predictions from noisy data. The nugget is added to the diagonal of the assumed training covariance (thanks to discretize) and acts as a Tikhonov regularization in the problem.

Returns:
nuggetFactorfloat

Nugget factor used to model the observation error variance.

getOutputCorrelation()

Get the spatial correlation matrix of the covariance function.

Returns:
spatialCorrelationCorrelationMatrix

Correlation matrix .

getOutputDimension()

Get the dimension of the covariance function.

Returns:
dint

Dimension such that This is the dimension of the process .

getP()

P accessor.

Returns:
pint,

Define the norm that is used within the model.

getParameter()

Get the parameters of the covariance function.

Returns:
parametersPoint

List of the scale parameter and the amplitude parameter of the covariance function.

The other specific parameters are not included.

getParameterDescription()

Get the description of the covariance function parameters.

Returns:
descriptionParamDescription

Description of the components of the parameters obtained with the getParameter method..

getScale()

Get the scale parameter of the covariance function.

Returns:
scalePoint

The scale parameter used in the covariance function.

Accessor to the object’s shadowed id.

Returns:
idint

Internal unique identifier.

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.

isDiagonal()

Test whether the model is diagonal or not.

Returns:
isDiagonalbool

True if the model is diagonal.

isStationary()

Test whether the model is stationary or not.

Returns:
isStationarybool

True if the model is stationary.

Notes

The covariance function is stationary when it is invariant by translation:

We note for .

Compute the gradient according to the parameters.

Parameters:
s, tsequences of float

Multivariate index .

Returns:
gradientMatrix

Gradient of the function according to the parameters.

Compute the gradient of the covariance function.

Parameters:
s, tfloats or sequences of float

Multivariate index .

Returns:
gradientMatrix

setActiveParameter(active)

Accessor to the active parameter set.

In the context of kriging, it allows one to choose which hyperparameters are tuned.

Parameters:
activesequence of int

Indices of the active parameters.

setAmplitude(amplitude)

Set the amplitude parameter of the covariance function.

Parameters:
amplitudePoint

The amplitude parameter to be used in the covariance function. Its size must be equal to the dimension of the covariance function.

setFullParameter(parameter)

Set the full parameters of the covariance function.

Parameters:
parameterPoint

List the full parameter of the covariance function i.e. scale parameter , the the amplitude parameter , the Spatial correlation parameter ; and potential other parameter depending on the model;

Must be at least of dimension .

setName(name)

Accessor to the object’s name.

Parameters:
namestr

The name of the object.

setNuggetFactor(nuggetFactor)

Set the nugget factor for the variance of the observation error.

Acts on the discretized covariance matrix.

Parameters:
nuggetFactorfloat

nugget factor to be used to model the variance of the observation error.

setOutputCorrelation(correlation)

Set the spatial correlation matrix of the covariance function.

Parameters:
spatialCorrelationCorrelationMatrix

Correlation matrix .

setP(p)

P accessor.

Parameters:
pint,

Define the norm that is used within the model.

setParameter(parameter)

Set the parameters of the covariance function.

Parameters:
parametersPoint

List of the scale parameter and the amplitude parameter of the covariance function.

Must be of dimension .

setScale(scale)

Set the scale parameter of the covariance function.

Parameters:
scalePoint

The scale parameter to be used in the covariance function. Its size must be equal to the input dimension of the covariance function.

Accessor to the object’s shadowed id.

Parameters:
idint

Internal unique identifier.

setVisibility(visible)

Accessor to the object’s visibility state.

Parameters:
visiblebool

Visibility flag.

## Examples using the class¶

Create a gaussian process from a cov. model using HMatrix

Create a gaussian process from a cov. model using HMatrix

Kriging : draw covariance models

Kriging : draw covariance models