GClasses

GClasses::SOM::TraditionalTraining Class Reference

Implments the traditional step-wise training of self-organized maps //TODO: finish this comment. More...

#include <GSelfOrganizingMap.h>

Inheritance diagram for GClasses::SOM::TraditionalTraining:
GClasses::SOM::TrainingAlgorithm

List of all members.

Public Member Functions

 TraditionalTraining (double initialWidth, double finalWidth, double initialRate, double finalRate, unsigned numIterations, NodeWeightInitialization *weightInitialization, NeighborhoodWindowFunction *windowFunc, Reporter *reporter)
 Create a traditional SOM training algorithm that starts its learning rate and neighborhood width at initialWidth and initialRate then decreases them exponentially so that they both reach finalWidth and finalRate after numIterations iterations. Each iteration consists of one presentation of an input datum to the network and one weight update of the neighbors of the winning neuron at the current learning rate.
virtual void train (GSelfOrganizingMap &map, GMatrix *pIn)
 Train the map.
virtual ~TraditionalTraining ()

Detailed Description

Implments the traditional step-wise training of self-organized maps //TODO: finish this comment.


Constructor & Destructor Documentation

GClasses::SOM::TraditionalTraining::TraditionalTraining ( double  initialWidth,
double  finalWidth,
double  initialRate,
double  finalRate,
unsigned  numIterations,
NodeWeightInitialization weightInitialization,
NeighborhoodWindowFunction windowFunc,
Reporter reporter 
)

Create a traditional SOM training algorithm that starts its learning rate and neighborhood width at initialWidth and initialRate then decreases them exponentially so that they both reach finalWidth and finalRate after numIterations iterations. Each iteration consists of one presentation of an input datum to the network and one weight update of the neighbors of the winning neuron at the current learning rate.

weightInitialization is the initialization function that will be used to initialize the node weights at the start of training. windowFunc is the window function used to determine the influence of neighbors on one another. reporter is the Reporter object that will be called to report progress during training.

The training object owns weightInialization, windowFunc, and reporter and so is responsible for deleting them.

virtual GClasses::SOM::TraditionalTraining::~TraditionalTraining ( ) [virtual]

Member Function Documentation

virtual void GClasses::SOM::TraditionalTraining::train ( GSelfOrganizingMap map,
GMatrix pIn 
) [virtual]

Train the map.

Implements GClasses::SOM::TrainingAlgorithm.