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prapi::OLVQ1< I, C > Class Template Reference

#include <VectorQuantizer.h>

Inheritance diagram for prapi::OLVQ1< I, C >:

List of all members.

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Detailed Description

template<class I = std::string, class C = int>
class prapi::OLVQ1< I, C >

OLVQ1 implements the optimized-learning-rate learning vector quantization algorithm for training the code book of a vector quantizer.

Public Methods
	OLVQ1 (ProximityMeasure< double > *measure=new EuclideanDistance< double >, int classCount=1)
	Create a new OLVQ1 vector quantizer.
	OLVQ1 (ProximityMeasure< double > &measure, int classCount=1)
	Create a new OLVQ1 vector quantizer.
void	train (const util::List< Sample< double, I, C > > &trainingSamples, const util::List< Sample< double, I, C > > &initialCodeBook, double initialAlpha=0.3, unsigned int iterations=0)
	Train the code book.

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Constructor & Destructor Documentation

template<class I = std::string, class C = int> prapi::OLVQ1< I, C >::OLVQ1 (  ProximityMeasure< double > *    measure = new EuclideanDistance<double>, int    classCount = 1 )  [inline]

Create a new OLVQ1 vector quantizer. There are no training samples as the internal code book is used as such. Parameters: measure  the proximity measure to be used in measuring distances between vectors (autorelease). classCount  the number of classes in the data

template<class I = std::string, class C = int> prapi::OLVQ1< I, C >::OLVQ1 (  ProximityMeasure< double > &    measure, int    classCount = 1 )  [inline]

Create a new OLVQ1 vector quantizer. There are no training samples as the internal code book is used as such. Parameters: measure  the proximity measure to be used in measuring distances between vectors. classCount  the number of classes in the data

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Member Function Documentation

template<class I, class C> void prapi::OLVQ1< I, C >::train (  const util::List< Sample< double, I, C > > &    trainingSamples, const util::List< Sample< double, I, C > > &    initialCodeBook, double    initialAlpha = 0.3, unsigned int    iterations = 0 )

Train the code book. Parameters: trainingSamples  the samples to use in training initialCodeBook  the code book to start with. Typically randomly selected training samples. initialAlpha  the learning rate parameter, [0,1]. iterations  the number of iterations to run. If set to zero, 40 times the number of code vectors is used.

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

• VectorQuantizer.h

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Documentation generated on 11.09.2003 with Doxygen.
The documentation is copyrighted material.
Copyright © Topi Mäenpää 2003. All rights reserved.