SquaredExponential

(Source code, png)

../../_images/openturns-SquaredExponential-1.png
class SquaredExponential(*args)

Squared exponential covariance function.

Available constructors:

SquaredExponential(spatialDim)

SquaredExponential(scale)

SquaredExponential(scale, amplitude)

Parameters:
spatialDimint

Spatial dimension n. When not fulfilled, the input dimension is equal to the size of the parameter \vect{\theta}. By default, equal to 1.

scalesequence of floats

Scale coefficient \vect{\theta}\in \Rset^n. The size of \vect{\theta} is the input dimension.

amplitudesequence of positive floats

Amplitude of the process \vect{\sigma}\in \Rset^d. Must be of size equal to 1. By default, equal to [1].

Methods

activateAmplitude(isAmplitudeActive)

Activate/deactivate the amplitude parameter(s).

activateNuggetFactor(isNuggetFactorActive)

Activate/deactivate the nugget factor.

activateScale(isScaleActive)

Activate/deactivate the scale parameter(s).

computeAsScalar(*args)

Compute the covariance function for scalar model.

computeCrossCovariance(*args)

Compute the covariance function on a given mesh.

discretize(*args)

Discretize the covariance function on a given mesh.

discretizeAndFactorize(*args)

Discretize and factorize the covariance function on a given mesh.

discretizeAndFactorizeHMatrix(*args)

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.

getActiveParameter()

Accessor to the active parameter set.

getAmplitude()

Get the amplitude parameter \vect{\sigma} of the covariance function.

getClassName()

Accessor to the object's name.

getFullParameter()

Get the full parameters of the covariance function.

getFullParameterDescription()

Get the description full parameters of the covariance function.

getInputDimension()

Get the input dimension n of the covariance function.

getMarginal(*args)

Get the ith marginal of the model.

getName()

Accessor to the object's name.

getNuggetFactor()

Accessor to the nugget factor.

getOutputCorrelation()

Get the spatial correlation matrix \mat{R} of the covariance function.

getOutputDimension()

Get the dimension d of the covariance function.

getParameter()

Get the parameters of the covariance function.

getParameterDescription()

Get the description of the covariance function parameters.

getScale()

Get the scale parameter \vect{\theta} of the covariance function.

hasName()

Test if the object is named.

isDiagonal()

Test whether the model is diagonal or not.

isStationary()

Test whether the model is stationary or not.

parameterGradient(s, t)

Compute the gradient according to the parameters.

partialGradient(s, t)

Compute the gradient of the covariance function.

setActiveParameter(active)

Accessor to the active parameter set.

setAmplitude(amplitude)

Set the amplitude parameter \vect{\sigma} 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 \mat{R} of the covariance function.

setParameter(parameter)

Set the parameters of the covariance function.

setScale(scale)

Set the scale parameter \vect{\theta} of the covariance function.

See also

CovarianceModel

Notes

The squared exponential function is a stationary covariance function with dimension d=1.

We consider the scalar stochastic process X: \Omega \times\cD \mapsto \Rset, where \omega \in \Omega is an event, \cD is a domain of \Rset^n.

The squared exponential function is defined by:

C(\vect{s}, \vect{t}) = \sigma^2 e^{-\frac{1}{2}  \left\|\dfrac{\vect{s}-\vect{t}}{\vect{\theta}}\right\|_{2}^{2}}, \quad \forall (\vect{s}, \vect{t}) \in \cD

The correlation function \rho writes:

\rho(\vect{s}, \vect{t}) = e^{-\frac{1}{2}  \left\| \vect{s}- \vect{t} \right\|_{2}^{2}}, \quad \forall (\vect{s}, \vect{t}) \in \cD

Examples

Create a standard squared exponential covariance function:

>>> import openturns as ot
>>> covModel = ot.SquaredExponential(2)
>>> t = [0.1, 0.3]
>>> s = [0.2, 0.4]
>>> print(covModel(s, t))
[[ 0.99005 ]]
>>> tau = [0.1, 0.3]
>>> print(covModel(tau))
[[ 0.951229 ]]

Create a squared exponential covariance function specifying the scale vector (amplitude is fixed to 1):

>>> covModel2 = ot.SquaredExponential([1.5, 2.5])
>>> covModel2bis = ot.SquaredExponential([1.5] * 3)

Create a squared exponential covariance function specifying the scale vector and the amplitude :

>>> covModel3 = ot.SquaredExponential([1.5, 2.5], [3.5])
__init__(*args)
activateAmplitude(isAmplitudeActive)

Activate/deactivate the amplitude parameter(s).

In the context of Kriging, defines whether amplitude parameters should be tuned.

Parameters:
isAmplitudeActivebool

If True, the amplitude parameters are all tuned. If False, none of them is tuned.

activateNuggetFactor(isNuggetFactorActive)

Activate/deactivate the nugget factor.

In the context of Kriging, defines whether the nugget factor should be tuned.

Parameters:
isNuggetFactorActivebool

If True (resp. False), the nugget factor is (resp. is not) tuned.

activateScale(isScaleActive)

Activate/deactivate the scale parameter(s).

In the context of Kriging, defines whether scale parameters should be tuned.

Parameters:
isScaleActivebool

If True, the scale parameters are all tuned. If False, none of them is tuned.

computeAsScalar(*args)

Compute the covariance function for scalar model.

Available usages:

computeAsScalar(s, t)

computeAsScalar(tau)

Parameters:
s, tfloats (if n=1) or sequences of floats (any n)

Multivariate index (\vect{s}, \vect{t}) \in \cD \times \cD

taufloat (if n=1) or sequence of floats (any n)

Multivariate index \vect{\tau} \in \cD

Returns:
covariancefloat

Covariance.

Notes

The method makes sense only if the dimension of the process is d=1. It evaluates C(\vect{s}, \vect{t}).

In the second usage, the covariance model must be stationary. Then we note C^{stat}(\vect{\tau}) for C(\vect{s}, \vect{s}+\vect{\tau}) as this quantity does not depend on \vect{s}.

computeCrossCovariance(*args)

Compute the covariance function on a given mesh.

Parameters:
firstVerticesSample or Point

Container of the first discretization vertices

secondVerticesSample or Point

Container of the second discretization vertices

Returns:
MatrixMatrix

Cross covariance matrix

Notes

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

If firstVertices contains n_1 points and secondVertices contains n_2 points, the method returns an n_1 d \times n_2 d matrix (d being the output dimension).

To make things easier, let us focus on the d=1 case. Let \vect{x}_0, \dots, \vect{x}_{n_1-1} be the points of firstVertices and let \vect{y}_0, \dots, \vect{y}_{n_2-1} be the points of secondVertices. The result is the n_1 \times n_2 matrix \mat{M} such that for any nonnegative integers i < n_1 and j < n_2, \mat{M}_{i,j} = \mathcal{C}(\vect{x}_i, \vect{y}_j).

discretize(*args)

Discretize the covariance function on a given mesh.

Parameters:
whereMesh or RegularGrid or Sample

Container of the discretization vertices

Returns:
covarianceMatrixCovarianceMatrix

Covariance matrix \in \cS_{nd}^+(\Rset) (if the process is of dimension d)

Notes

This method makes a discretization of the model on the given Mesh, RegularGrid or Sample composed of the vertices (\vect{t}_1, \dots, \vect{t}_{N-1}) and returns the covariance matrix:

\mat{C}_{1,\dots,k} = \left(
    \begin{array}{cccc}
    C(\vect{t}_1, \vect{t}_1) &C(\vect{t}_1, \vect{t}_2) & \dots &
    C(\vect{t}_1, \vect{t}_{k}) \\
    \dots & C(\vect{t}_2, \vect{t}_2)  & \dots &
    C(\vect{t}_2, \vect{t}_{k}) \\
    \dots & \dots & \dots & \dots \\
    \dots & \dots & \dots & C(\vect{t}_{k}, \vect{t}_{k})
    \end{array} \right)

discretizeAndFactorize(*args)

Discretize and factorize the covariance function on a given mesh.

Parameters:
whereMesh or RegularGrid or Sample

Container of the discretization vertices

Returns:
CholeskyMatrixTriangularMatrix

Cholesky factor of the covariance matrix \in \cM_{nd\times nd}(\Rset) (if the process is of dimension d)

Notes

This method makes a discretization of the model on the given Mesh, RegularGrid or Sample composed of the vertices (\vect{t}_1, \dots, \vect{t}_{N-1}) 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:
whereMesh or RegularGrid or Sample

Container of the discretization vertices

hmatParamHMatrixParameters

Parameter values for the HMatrix

Returns:
HMatrixHMatrix

Cholesk matrix \in \cS_{nd}^+(\Rset) (if the process is of dimension d), 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:
whereMesh or RegularGrid or Sample

Container of the discretization vertices

hmatParamHMatrixParameters

Parameter values for the HMatrix

Returns:
HMatrixHMatrix

Covariance matrix \in\cS_{nd}^+(\Rset) (if the process is of dimension d), 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, 0 \leq rowIndex < dimension

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, pointNumber \geq 2

The discretization of the range [tMin,tMax] 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 \tau=t-s if equals to True, or as a non-stationary model, ie as a function of (s,t) 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 \vect{\sigma} of the covariance function.

Returns:
amplitudePoint

The amplitude parameter \vect{\sigma} \in \Rset^d 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 \vect{\theta} \in \Rset^n, the the amplitude parameter \vect{\sigma} \in \Rset^d, the Spatial correlation parameter \mat{R} \in \cS_d^+([-1,1]); 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.

getInputDimension()

Get the input dimension n of the covariance function.

Returns:
inputDimensionint

Spatial dimension n 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 \mat{R} of the covariance function.

Returns:
spatialCorrelationCorrelationMatrix

Correlation matrix \mat{R} \in \cS_d^+(\Rset).

getOutputDimension()

Get the dimension d of the covariance function.

Returns:
dint

Dimension d such that C : \cD \times \cD \mapsto \cS_d^+(\Rset). This is the dimension of the process X.

getParameter()

Get the parameters of the covariance function.

Returns:
parametersPoint

List of the scale parameter \vect{\theta} \in \Rset^n and the amplitude parameter \vect{\sigma} \in \Rset^d 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 \vect{\theta} of the covariance function.

Returns:
scalePoint

The scale parameter \vect{\theta} \in \Rset^n used in the covariance function.

hasName()

Test if the object is named.

Returns:
hasNamebool

True if the name is not empty.

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 C is stationary when it is invariant by translation:

\forall(\vect{s},\vect{t},\vect{h}) \in \cD \times \cD, & \, \quad
C(\vect{s}, \vect{s}+\vect{h}) = C(\vect{t}, \vect{t}+\vect{h})

We note C^{stat}(\vect{\tau}) for C(\vect{s}, \vect{s}+\vect{\tau}).

parameterGradient(s, t)

Compute the gradient according to the parameters.

Parameters:
s, tsequences of float

Multivariate index (\vect{s}, \vect{t}) \in \cD \times \cD.

Returns:
gradientMatrix

Gradient of the function according to the parameters.

partialGradient(s, t)

Compute the gradient of the covariance function.

Parameters:
s, tfloats or sequences of float

Multivariate index (\vect{s}, \vect{t}) \in \cD \times \cD.

Returns:
gradientMatrix

Gradient of the covariance function.

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 \vect{\sigma} of the covariance function.

Parameters:
amplitudePoint

The amplitude parameter \vect{\sigma} \in \Rset^d 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 \vect{\theta} \in \Rset^n, the the amplitude parameter \vect{\sigma} \in \Rset^d, the Spatial correlation parameter \mat{R} \in \cS_d^+([-1,1]); and potential other parameter depending on the model;

Must be at least of dimension n+\frac{d(d+1)}{2}.

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 \mat{R} of the covariance function.

Parameters:
spatialCorrelationCorrelationMatrix

Correlation matrix \mat{R} \in \cS_d^+([-1,1]).

setParameter(parameter)

Set the parameters of the covariance function.

Parameters:
parametersPoint

List of the scale parameter \vect{\theta} \in \Rset^n and the amplitude parameter \vect{\sigma} \in \Rset^d of the covariance function.

Must be of dimension n+d.

setScale(scale)

Set the scale parameter \vect{\theta} of the covariance function.

Parameters:
scalePoint

The scale parameter \vect{\theta} \in \Rset^n to be used in the covariance function. Its size must be equal to the input dimension of the covariance function.

Examples using the class

Create a process from random vectors and processes

Create a process from random vectors and processes

Compare covariance models

Compare covariance models

Create a general linear model metamodel

Create a general linear model metamodel

Kriging : multiple input dimensions

Kriging : multiple input dimensions

Kriging: propagate uncertainties

Kriging: propagate uncertainties

Kriging : draw the likelihood

Kriging : draw the likelihood

Kriging : cantilever beam model

Kriging : cantilever beam model

Kriging: choose an arbitrary trend

Kriging: choose an arbitrary trend

Gaussian Process Regression : cantilever beam model

Gaussian Process Regression : cantilever beam model

Example of multi output Kriging on the fire satellite model

Example of multi output Kriging on the fire satellite model

Kriging: choose a polynomial trend on the beam model

Kriging: choose a polynomial trend on the beam model

Kriging with an isotropic covariance function

Kriging with an isotropic covariance function

Kriging: metamodel of the Branin-Hoo function

Kriging: metamodel of the Branin-Hoo function

Kriging: configure the optimization solver

Kriging: configure the optimization solver

Kriging: choose a polynomial trend

Kriging: choose a polynomial trend

Kriging: metamodel with continuous and categorical variables

Kriging: metamodel with continuous and categorical variables

Validation of a Karhunen-Loeve decomposition

Validation of a Karhunen-Loeve decomposition

Time variant system reliability problem

Time variant system reliability problem

Estimate Sobol indices on a field to point function

Estimate Sobol indices on a field to point function

The HSIC sensitivity indices: the Ishigami model

The HSIC sensitivity indices: the Ishigami model

Example of sensitivity analyses on the wing weight model

Example of sensitivity analyses on the wing weight model