---

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

#include <MahalanobisClassifier.h>

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

List of all members.

---

Detailed Description

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

Mahalanobis classifier classifies unknown samples according to the Mahalanobis distance d²(s_i, m_j) = (s_i - m_j) C_j^-1 (s_i - m_j)^T, where s and m are sample and model feature vectors, respectively.

C_j^-1 is the inverse of the covariance matrix for class j.

Public Methods
	MahalanobisClassifier (util::List< Sample< T, I, C > > *trainingSamples, int classCount)
	Initialize a MahalanobisClassifier with the given set of training samples and number of classes.
C	getClassification (Sample< T, I, C > &sample) throw (ClassificationException&)
	Get classification for a sample.
void	setTrainingSamples (util::List< Sample< T, I, C > > *lst)
	Overridden to calculate covariance matrices from training data.

---

Constructor & Destructor Documentation

template<class T, class I, class C> prapi::MahalanobisClassifier< T, I, C >::MahalanobisClassifier (  util::List< Sample< T, I, C > > *    trainingSamples, int    classCount )

Initialize a MahalanobisClassifier with the given set of training samples and number of classes. No proximity measure is needed as the Mahalanobis distance is used in any case.

---

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

• MahalanobisClassifier.h

---

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