lti::supervisedInstanceClassifier Class Reference

Abstract class, parent of all supervised instance classifiers. More...

#include <ltiSupervisedInstanceClassifier.h>

Inheritance diagram for lti::supervisedInstanceClassifier:

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Collaboration diagram for lti::supervisedInstanceClassifier:

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List of all members.

Public Member Functions
	supervisedInstanceClassifier ()
	supervisedInstanceClassifier (const supervisedInstanceClassifier &other)
virtual const char *	getTypeName () const
supervisedInstanceClassifier &	copy (const supervisedInstanceClassifier &other)
supervisedInstanceClassifier &	operator= (const supervisedInstanceClassifier &other)
const parameters &	getParameters () const
virtual bool	train (const dmatrix &input, const ivector &ids)=0
virtual bool	classify (const dvector &feature, outputVector &result) const =0
Protected Member Functions
bool	makeOutputTemplate (const int &outSize, const dmatrix &data, const ivector &ids)

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Detailed Description

Abstract class, parent of all supervised instance classifiers.

This class defines the interface for all supervised train methods which are not dependant on time.

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Constructor & Destructor Documentation

lti::supervisedInstanceClassifier::supervisedInstanceClassifier

(

)

default constructor

lti::supervisedInstanceClassifier::supervisedInstanceClassifier

(

const supervisedInstanceClassifier &

other

)

copy constructor

Parameters:

other

the object to be copied

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Member Function Documentation

virtual bool lti::supervisedInstanceClassifier::classify	(	const dvector &	feature,
		outputVector &	result
	)			const [pure virtual]

Classification.

Classifies the feature and returns the outputVector with the classification result.

Parameters:

	feature	the vector to be classified
	result	the result of the classification

Returns:

false if an error occurred during classification else true

Implemented in lti::crispDecisionTree, lti::kNNClassifier, lti::lvq, lti::MLP, lti::rbf, lti::shClassifier, and lti::svm.

supervisedInstanceClassifier& lti::supervisedInstanceClassifier::copy

(

const supervisedInstanceClassifier &

other

)

copy data of "other" functor.

Parameters:

other

the functor to be copied

Returns:

a reference to this functor object

Reimplemented from lti::classifier.

Reimplemented in lti::crispDecisionTree, lti::decisionTree, lti::kNNClassifier, lti::lvq, lti::manualCrispDecisionTree, lti::MLP, lti::rbf, lti::shClassifier, and lti::svm.

Referenced by operator=().

const parameters& lti::supervisedInstanceClassifier::getParameters

(

)

const

returns used parameters

Reimplemented from lti::classifier.

Reimplemented in lti::crispDecisionTree, lti::decisionTree, lti::kNNClassifier, lti::lvq, lti::manualCrispDecisionTree, lti::MLP, lti::rbf, lti::shClassifier, and lti::svm.

virtual const char* lti::supervisedInstanceClassifier::getTypeName

(

)

const [virtual]

returns the name of this type ("supervisedInstanceClassifier")

Reimplemented from lti::classifier.

Reimplemented in lti::crispDecisionTree, lti::decisionTree, lti::kNNClassifier, lti::manualCrispDecisionTree, lti::MLP, lti::rbf, lti::shClassifier, and lti::svm.

bool lti::supervisedInstanceClassifier::makeOutputTemplate	(	const int &	outSize,
		const dmatrix &	data,
		const ivector &	ids
	)			[protected]

Sets the outputTemplate probability distributions according to the classification of the given data.

The distributions are built by the follwing rule:

1. Classify next data vector
2. For the position in the output with the highest probability increase the count for the actual id by one.
3. While there is more data go back to 1
4. For each position: divide each count by total number of counts

This results in a distribution over the ids that caused highest probability for each position of the output.

Parameters:

	outSize	size of the outputTemplate
	data	train of validation data
	ids	ids of the data-vectors

Returns:

false upon error

supervisedInstanceClassifier& lti::supervisedInstanceClassifier::operator=

(

const supervisedInstanceClassifier &

other

)

[inline]

Alias for "copy".

Parameters:

other

the functor to be copied

Returns:

a reference to this functor object

Reimplemented from lti::classifier.

Reimplemented in lti::crispDecisionTree, lti::decisionTree, lti::kNNClassifier, lti::lvq, lti::manualCrispDecisionTree, lti::MLP, lti::rbf, lti::shClassifier, and lti::svm.

References copy().

virtual bool lti::supervisedInstanceClassifier::train	(	const dmatrix &	input,
		const ivector &	ids
	)			[pure virtual]

Supervised training.

The vectors in the input matrix must be trained using as "known" classes the values given in ids.

Parameters:

	input	the matrix with input vectors (each row is a training vector)
	ids	the output classes ids for the input vectors.

Returns:

true if successful, false otherwise. (if false you can check the error message with getStatusString())

Implemented in lti::kNNClassifier, lti::lvq, lti::manualCrispDecisionTree, lti::MLP, lti::rbf, lti::shClassifier, and lti::svm.

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The documentation for this class was generated from the following file:

• ltiSupervisedInstanceClassifier.h