BatchSOM Class Reference

#include <batch_som.h>

Inheritance diagram for BatchSOM:

Collaboration diagram for BatchSOM:

Public Member Functions
	BatchSOM (Descent &_radius, unsigned long _nSteps)
	BatchSOM (std::istream &_is)
virtual	~BatchSOM ()
virtual void	train (ClassificationMap &_som, const ClassicTrainingVectors &_ts) const
Public Member Functions inherited from SOM
	SOM (Descent &_alpha, Descent &_radius, neighType _neighType, unsigned long _nSteps)
	SOM (std::istream &_is)
virtual	~SOM ()
	SOM (const SOM &)=delete
void	alpha (Descent _alpha)
void	radius (Descent _radius)
void	nSteps (const unsigned long &_nSteps)
virtual void	train (ClassificationMap &_som, ClassicTrainingVectors &_ts) const
virtual double	test (const ClassificationMap &_som, const TS &_examples) const
virtual void	clear ()
virtual void	printSelf (std::ostream &_os) const
virtual void	readSelf (std::istream &_is)
virtual void	saveObject (std::ostream &_os) const
virtual void	loadObject (std::istream &_is)
SOM &	operator= (const SOM &op1)
Public Member Functions inherited from ClassificationAlgorithm< ClassificationMap >
	ClassificationAlgorithm (const std::string &_ID="")
virtual	~ClassificationAlgorithm ()
virtual void	train (DS &_ds, const TS &_examples) const
virtual void	train (DS &_ds, TS &_examples) const
virtual double	test (const DS &_ds, const TS &_examples) const=0
virtual const std::string &	setID () const
virtual std::string &	setID ()
void	setListener (BaseListener *_listener)

Additional Inherited Members
Public Types inherited from SOM
enum	neighType { GAUSSIAN = 0, BUBBLE = 1 }
	Type of neighborhood function. More...
Public Types inherited from ClassificationAlgorithm< ClassificationMap >
typedef ClassificationMap	DS
typedef DS::TS	TS
Protected Attributes inherited from SOM
Descent	somAlpha
Descent	somRadius
	alpha(t) More...
neighType	somNeigh
	radius(t) More...
unsigned long	somNSteps
	Neighborhood type for training (Bubble or Gaussian) More...
Protected Attributes inherited from ClassificationAlgorithm< ClassificationMap >
std::string	ID
BaseListener *	listener

Detailed Description

This class trains a Kohonen's Self Organizing Map using Batch SOM.

Definition at line 44 of file batch_som.h.

Constructor & Destructor Documentation

BatchSOM() [1/2]

BatchSOM::BatchSOM ( Descent &  _radius, unsigned long  _nSteps  )

inline

Constructs the algorithm Parameter: _radius How is gonna decrease the radius of neighborhood Parameter: _nSteps Number of training steps

Definition at line 54 of file batch_som.h.

            : SOM(_radius, _radius, BUBBLE, _nSteps)
    {};

BatchSOM() [2/2]

BatchSOM::BatchSOM

(

std::istream &

_is

)

Construct a BatchSOM from the code vectors in a stream Parameter: _is The stream

Definition at line 37 of file batch_som.cpp.

                                 : SOM(_is)
{
    readSelf(_is);
}

~BatchSOM()

virtual BatchSOM::~BatchSOM ( )

inlinevirtual

Virtual destructor

Definition at line 68 of file batch_som.h.

    {};

Member Function Documentation

train()

void BatchSOM::train ( ClassificationMap &  _som, const ClassicTrainingVectors &  _ts  ) const

virtual

Trains the SOM Parameter: _som The som to train Parameter: _ts The training set

Definition at line 47 of file batch_som.cpp.

{


    unsigned long t = 0;

    int verbosity = listener->getVerbosity();
    if (verbosity)
        listener->OnReportOperation((std::string) "Batch Training Kohonen SOM....\n");
    if (verbosity == 1 || verbosity == 3)
        listener->OnInitOperation(somNSteps);

    SomIn aveVector(_som.theItems[0].size());
    std::vector<unsigned> tmpVector;

    while (t < somNSteps)
    {
        _som.classify(&_ts);
        // Check for each SOM unit
        for (unsigned it = 0; it < _som.size(); it++)
        {
            for (unsigned a = 0; a < aveVector.size(); a++)
                aveVector[a] = 0.;
            long total = 0;
            // Collects a list of the input vectors assigned to the neighborhood
            std::vector<unsigned> neig = _som.neighborhood(_som.indexToPos(it), ceil(somRadius(t, somNSteps)));
            for (std::vector<unsigned>::iterator itt = neig.begin();itt < neig.end();itt++)
            {
                tmpVector =  _som.classifAt(*itt);
                for (unsigned j = 0 ; j < tmpVector.size() ; j++)
                {
                    SomIn v = _ts.theItems[tmpVector[j]];
                    for (unsigned a = 0; a < v.size(); a++)
                        aveVector[a] += v[a];
                    total++;
                }

            }
            if (total != 0)
            {
                for (unsigned a = 0; a < aveVector.size(); a++)
                    aveVector[a] /= (floatFeature) total;
                _som.theItems[it] = aveVector;
            }
        }

        if (verbosity == 1 || verbosity == 3)
            listener->OnProgress(t);
        if (verbosity >= 2)
        {
            char s[100];
            sprintf(s, "Iteration %d of %d.\n", (int)(t + 1), (int)somNSteps);
            listener->OnReportOperation((std::string) s);
        }
        t++;

    } // while t < somSteps

    if (verbosity == 1 || verbosity == 3)
        listener->OnProgress(somNSteps);
}

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

• xmipp/libraries/classification/batch_som.h
• xmipp/libraries/classification/batch_som.cpp