MixtureClassifier

(Source code, png, hires.png, pdf)

../../../_images/MixtureClassifier.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(self, \*args)

Classify points according to the classifier.

getClassName(self)

Accessor to the object’s name.

getDimension(self)

Accessor to the dimension.

getId(self)

Accessor to the object’s id.

getMixture(self)

Accessor to the mixture distribution.

getName(self)

Accessor to the object’s name.

getNumberOfClasses(self)

Accessor to the number of classes.

getShadowedId(self)

Accessor to the object’s shadowed id.

getVerbose(self)

Accessor to the verbosity.

getVisibility(self)

Accessor to the object’s visibility state.

grade(self, inP, outC)

Grade points according to the classifier.

hasName(self)

Test if the object is named.

hasVisibleName(self)

Test if the object has a distinguishable name.

isParallel(self)

Accessor to the parallel flag.

setMixture(self, mixture)

Accessor to the mixture distribution.

setName(self, name)

Accessor to the object’s name.

setParallel(self, flag)

Accessor to the parallel flag.

setShadowedId(self, id)

Accessor to the object’s shadowed id.

setVerbose(self, verbose)

Accessor to the verbosity.

setVisibility(self, visible)

Accessor to the object’s visibility state.
__init__(self, \*args)

Initialize self. See help(type(self)) for accurate signature.

classify(self, \*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(self)

Accessor to the object’s name.

Returns
class_namestr

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

getDimension(self)

Accessor to the dimension.

Returns
dimint

The dimension of the classifier.

getId(self)

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getMixture(self)

Accessor to the mixture distribution.

Returns
mixtDistMixture

The mixture distribution.

getName(self)

Accessor to the object’s name.

Returns
namestr

The name of the object.

getNumberOfClasses(self)

Accessor to the number of classes.

Returns
n_classesint

The number of classes

getShadowedId(self)

Accessor to the object’s shadowed id.

Returns
idint

Internal unique identifier.

getVerbose(self)

Accessor to the verbosity.

Returns
verbbool

Logical value telling if the verbose mode has been activated.

getVisibility(self)

Accessor to the object’s visibility state.

Returns
visiblebool

Visibility flag.

grade(self, inP, outC)

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 that grades inputPoint with respect to the class k. The greatest, the best.

In the second usage, it returns an Indices that collects the grades of the i^{th} point of inputSample with respect to the i^{th} class of classList.

hasName(self)

Test if the object is named.

Returns
hasNamebool

True if the name is not empty.

hasVisibleName(self)

Test if the object has a distinguishable name.

Returns
hasVisibleNamebool

True if the name is not empty and not the default one.

isParallel(self)

Accessor to the parallel flag.

Returns
flagbool

Logical value telling if the parallel mode has been activated.

setMixture(self, mixture)

Accessor to the mixture distribution.

Parameters
mixtDistMixture

The mixture distribution.

setName(self, name)

Accessor to the object’s name.

Parameters
namestr

The name of the object.

setParallel(self, flag)

Accessor to the parallel flag.

Parameters
flagbool

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

setShadowedId(self, id)

Accessor to the object’s shadowed id.

Parameters
idint

Internal unique identifier.

setVerbose(self, verbose)

Accessor to the verbosity.

Parameters
verbbool

Logical value telling if the verbose mode has been activated.

setVisibility(self, visible)

Accessor to the object’s visibility state.

Parameters
visiblebool

Visibility flag.