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prapi::kNNClassifier< T, I, C > Class Template Reference

#include <Classifier.h>

Inheritance diagram for prapi::kNNClassifier< T, I, C >:

List of all members.

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Detailed Description

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

A k nearest neighbors classifier.

This classifier takes k nearest neighbors to a sample, according to a given proximity measure. The classification result is the class that has the majority in the k nearest neighbors. If there is a draw between two classes, then the closest one wins. If k equals to one, the classifier functions exactly as a nearest neighbor classifier does.

Public Methods
	kNNClassifier (util::List< Sample< T, I, C > > *trainingSamples, ProximityMeasure< T > *measure, int classCount, int k=1)
	Create a new kNN classifier with the given training samples, proximity measure, class count and k.
	kNNClassifier (util::List< Sample< T, I, C > > &trainingSamples, ProximityMeasure< T > *measure, int classCount, int k=1)
	Create a new kNN classifier with the given training samples, proximity measure, class count and k.
	kNNClassifier (util::List< Sample< T, I, C > > &trainingSamples, ProximityMeasure< T > &measure, int classCount, int k=1)
	Create a new kNN classifier with the given training samples, proximity measure, class count and k.
C	getClassification (Sample< T, I, C > &sample) throw (ClassificationException&)
	Get the classification for a single sample.
void	setk (int newk)
	Set a new value for k.
int	getk (void)
	Get the value of k.

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Constructor & Destructor Documentation

template<class T, class I, class C> prapi::kNNClassifier< T, I, C >::kNNClassifier (  util::List< Sample< T, I, C > > *    trainingSamples, ProximityMeasure< T > *    measure, int    classCount, int    k = 1 )

Create a new kNN classifier with the given training samples, proximity measure, class count and k. (Autorelease measure.)

template<class T, class I, class C> prapi::kNNClassifier< T, I, C >::kNNClassifier (  util::List< Sample< T, I, C > > &    trainingSamples, ProximityMeasure< T > *    measure, int    classCount, int    k = 1 )

Create a new kNN classifier with the given training samples, proximity measure, class count and k. (Autorelease measure.)

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Member Function Documentation

template<class T, class I, class C> C prapi::kNNClassifier< T, I, C >::getClassification (  Sample< T, I, C > &    sample )  throw (ClassificationException&) [virtual]

Get the classification for a single sample. This method is used by holdOut and leaveOneOut to classify each sample. Subclasses must override this method. Parameters: sample  the sample to be classified Returns: the classification. Simple classfiers (like NN or kNN) use integers. More sophisticated ones may use any classification type. See also: Sample for more information. Implements prapi::Classifier< T, I, C >.

template<class T, class I, class C> void prapi::kNNClassifier< T, I, C >::setk (  int    newk )

Set a new value for k. K is always ensured to be odd. That is, the least significant bit of k will always be turned on.

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

• Classifier.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.