MixtureClassifier¶

(Source code, png)

class MixtureClassifier(*args)¶

Particular classifier based on a mixture distribution.

Available constructors:: MixtureClassifier(mixtDist)

Parameters:

mixtDistMixture: A mixture distribution.

See also

Classifier, ExpertMixture

Notes

This implements a mixture classifier which is a particular classifier based on a mixture distribution:

$p( \vect{x} ) = \sum_{i=1}^N w_i p_i ( \vect{x} )$

The classifier proposes $N$ classes. The rule to assign a point $\vect{x}$ to a class $i$ is defined as follows:

$i = \argmax_k \log w_k p_k( \vect{x} )$

See useful methods classify() and grade().

Methods

`classify`(*args)	Classify points according to the classifier.
`getClassName`()	Accessor to the object's name.
`getDimension`()	Accessor to the dimension.
`getId`()	Accessor to the object's id.
`getMixture`()	Accessor to the mixture distribution.
`getName`()	Accessor to the object's name.
`getNumberOfClasses`()	Accessor to the number of classes.
`getShadowedId`()	Accessor to the object's shadowed id.
`getVerbose`()	Accessor to the verbosity.
`getVisibility`()	Accessor to the object's visibility state.
`grade`(*args)	Grade points according to the classifier.
`hasName`()	Test if the object is named.
`hasVisibleName`()	Test if the object has a distinguishable name.
`isParallel`()	Accessor to the parallel flag.
`setMixture`(mixture)	Accessor to the mixture distribution.
`setName`(name)	Accessor to the object's name.
`setParallel`(flag)	Accessor to the parallel flag.
`setShadowedId`(id)	Accessor to the object's shadowed id.
`setVerbose`(verbose)	Accessor to the verbosity.
`setVisibility`(visible)	Accessor to the object's visibility state.

__init__(*args)¶

classify(*args)¶

Classify points according to the classifier.

Available usages:

classify(inputPoint)

classify(inputSample)

Parameters:

inputPointsequence of float: A point to classify.
inputSample2-d a sequence of float: A set of point to classify.

Notes

The classifier proposes $N$ classes where $N$ is the dimension of the mixture distribution mixtDist. The rule to assign a point $\vect{x}$ to a class $i$ is defined as follows:

$i = \argmax_k \log w_k p_k( \vect{x} )$

In the first usage, it returns an integer which corresponds to the class where inputPoint has been assigned.

In the second usage, it returns an Indices that collects the class of each point of inputSample.

getClassName()¶

Accessor to the object’s name.

Returns:

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

getDimension()¶

Accessor to the dimension.

Returns:

dimint: The dimension of the classifier.

getId()¶

Accessor to the object’s id.

Returns:

idint: Internal unique identifier.

getMixture()¶

Accessor to the mixture distribution.

Returns:

mixtDistMixture: The mixture distribution.

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getNumberOfClasses()¶

Accessor to the number of classes.

Returns:

n_classesint: The number of classes

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns:

idint: Internal unique identifier.

getVerbose()¶

Accessor to the verbosity.

Returns:

verbbool: Logical value telling if the verbose mode has been activated.

getVisibility()¶

Accessor to the object’s visibility state.

Returns:

visiblebool: Visibility flag.

grade(*args)¶

Grade points according to the classifier.

Available usages:

grade(inputPoint, k)

grade(inputSample, classList)

Parameters:

inputPointsequence of float: A point to grade.
inputSample2-d a sequence of float: A set of point to grade.
kinteger: The class number.
classListsequence of integer: The list of class number.

Notes

The grade of $\vect{x}$ with respect to the class k is $log w_k p_k ( \vect{x} )$ .

In the first usage, it returns a real value that grades inputPoint with respect to the class k. The larger, the better.

In the second usage, it returns a Point that collects the grades of the $i^{th}$ element of inputSample with respect to the $i^{th}$ class of classList.

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.

isParallel()¶

Accessor to the parallel flag.

Returns:

flagbool: Logical value telling if the parallel mode has been activated.

setMixture(mixture)¶

Accessor to the mixture distribution.

Parameters:

mixtDistMixture: The mixture distribution.

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

setParallel(flag)¶

Accessor to the parallel flag.

Parameters:

flagbool: Logical value telling if the classification and grading are done in parallel.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters:

idint: Internal unique identifier.

setVerbose(verbose)¶

Accessor to the verbosity.

Parameters:

verbbool: Logical value telling if the verbose mode has been activated.

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters:

visiblebool: Visibility flag.

Examples using the class¶

Mixture of experts

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An Open source initiative for the Treatment of Uncertainties, Risks'N Statistics

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