HSICEstimator

class HSICEstimator(*args)

Base class of HSIC estimators.

See also

HSICEstimatorConditionalSensitivity, HSICEstimatorGlobalSensitivity, HSICEstimatorTargetSensitivity

Notes

HSIC-based analyses must be performed by using the derived classes: HSICEstimatorConditionalSensitivity, HSICEstimatorGlobalSensitivity and HSICEstimatorTargetSensitivity.

Methods

drawHSICIndices()	Draw the HSIC indices.
drawPValuesPermutation()	Draw the p-values obtained by permutation.
drawR2HSICIndices()	Draw the R2-HSIC indices.
getClassName()	Accessor to the object's name.
getCovarianceModelCollection()	Get the list of all covariance models used.
getDimension()	Get the dimension of the input sample.
getEstimator()	Get the estimator used for computations.
getHSICIndices()	Get the HSIC indices.
getId()	Accessor to the object's id.
getImplementation()	Accessor to the underlying implementation.
getInputSample()	Get the input sample.
getName()	Accessor to the object's name.
getOutputSample()	Get the output sample.
getPValuesPermutation()	Get the p-value estimated through permutations.
getPermutationSize()	Get the number of permutations.
getR2HSICIndices()	Get the R2-HSIC indices.
getSize()	Get the size of the input sample.
setCovarianceModelCollection(coll)	Set the covariance models.
setInputSample(inputSample)	Set the input sample.
setName(name)	Accessor to the object's name.
setOutputSample(outputSample)	Set the output sample.
setPermutationSize(B)	Set the number of permutations to be used for p-value estimate.

__init__(*args)

drawHSICIndices()

Draw the HSIC indices.

Returns:

graphGraph

The graph of all HSIC indices according to components.

drawPValuesPermutation()

Draw the p-values obtained by permutation.

Returns:

graphGraph

The graph of all p-values by permutation according to components.

drawR2HSICIndices()

Draw the R2-HSIC indices.

Returns:

graphGraph

The graph of all R2-HSIC indices according to components.

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getCovarianceModelCollection()

Get the list of all covariance models used.

Returns:

collCovarianceModelCollection

The list of all covariance models used. The last one is the output covariance model.

getDimension()

Get the dimension of the input sample.

Returns:

dimint

The dimension of the input sample.

getEstimator()

Get the estimator used for computations.

Returns:

estimatorHSICStat

The estimator used for internal computations.

getHSICIndices()

Get the HSIC indices.

Returns:

hsicIndicesPoint

The HSIC indices of all components.

getId()

Accessor to the object’s id.

Returns:

idint

Internal unique identifier.

getImplementation()

Accessor to the underlying implementation.

Returns:

implImplementation

A copy of the underlying implementation object.

getInputSample()

Get the input sample.

Returns:

inSampleSample

The input sample used for analysis.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getOutputSample()

Get the output sample.

Returns:

outSampleSample

The output sample used for analysis.

getPValuesPermutation()

Get the p-value estimated through permutations.

Returns:

pvalPoint

The p-values of all components estimated with permutations of the data.

getPermutationSize()

Get the number of permutations.

Returns:

permutationSizeint

The number of permutations.

getR2HSICIndices()

Get the R2-HSIC indices.

Returns:

r2hsicIndicesPoint

The R2-HSIC indices of all components.

getSize()

Get the size of the input sample.

Returns:

sizeint

The size of the input sample.

setCovarianceModelCollection(coll)

Set the covariance models.

Parameters:

collCovarianceModelCollection

The list of all covariance models.

setInputSample(inputSample)

Set the input sample.

Parameters:

inputSample2-d sequence of float

The input sample to be used.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

setOutputSample(outputSample)

Set the output sample.

Parameters:

outputSample2-d sequence of float

The output sample to be used.

setPermutationSize(B)

Set the number of permutations to be used for p-value estimate.

Parameters:

Bint

The number of permutation used for p-value estimates.