HSICUStat

class HSICUStat(*args)

Unbiased HSIC statistics.

See also

HSICVStat

Notes

This an unbiased estimator for the computation of an HSIC index.

Methods

computeHSICIndex(CovMat1, CovMat2, weights)	Compute the HSIC index between two samples.
computePValue(dist, n, HSIC_obs, mHSIC)	Compute the p-value of the statistic.
getClassName()	Accessor to the object's name.
getName()	Accessor to the object's name.
hasName()	Test if the object is named.
isCompatibleWithConditionalAnalysis()	Indicate the compatibility with a conditional HSIC estimator.
setName(name)	Accessor to the object's name.

__init__(*args)

computeHSICIndex(CovMat1, CovMat2, weights)

Compute the HSIC index between two samples.

Parameters:

covarianceMatrixXCovarianceMatrix

The xi-covariance model discretized over the input marginal sample xi.

covarianceMatrixYCovarianceMatrix

The covariance model associated with the output sample, discretized over the last one.

weightsPoint or Matrix (deprecated)

The weight argument used for the statistic.

Returns:

hsicIndexthe HSIC index of the two Sample.

computePValue(dist, n, HSIC_obs, mHSIC)

Compute the p-value of the statistic.

Parameters:

distGamma

A Gamma distribution to compute the p-value.

nint

The size of the samples.

HSIC_obsfloat

The previously computed HSIC index.

mHSICfloat

Bias-correcting term (only actually used by U-statistics).

Returns:

pvaluethe p-value of the statistic.

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

isCompatibleWithConditionalAnalysis()

Indicate the compatibility with a conditional HSIC estimator.

Returns:

isCompatiblebool

Indicate the compatibility with a conditional HSIC estimator (false).

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

Examples using the class

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