SOM Class Reference

#include <som.h>

Inheritance diagram for SOM:

Collaboration diagram for SOM:

Public Types
enum	neighType { GAUSSIAN = 0, BUBBLE = 1 }
	Type of neighborhood function. More...
Public Types inherited from ClassificationAlgorithm< ClassificationMap >
typedef ClassificationMap	DS
typedef DS::TS	TS

Public Member Functions
	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)

Protected Attributes
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

Friends
std::ostream &	operator<< (std::ostream &_os, const SOM &_som)
std::istream &	operator>> (std::istream &_is, SOM &_som)

Detailed Description

This class trains a Kohonen's Self Organizing Feature Map

Definition at line 136 of file som.h.

Member Enumeration Documentation

neighType

enum SOM::neighType

Type of neighborhood function.

Enumerator
GAUSSIAN
BUBBLE

Definition at line 140 of file som.h.

{ GAUSSIAN = 0, BUBBLE = 1} neighType;

Constructor & Destructor Documentation

SOM() [1/3]

SOM::SOM ( Descent &  _alpha, Descent &  _radius, neighType  _neighType, unsigned long  _nSteps  )

inline

Constructs the algorithm Parameter: _alpha How is gonna decrease alpha Parameter: _radius How is gonna decrease the radius of neighborhood Parameter: _radius How is gonna decrease the radius of neighborhood Parameter: _neighType Type of neighborhood function Parameter: _nSteps Number of training steps

Definition at line 150 of file som.h.

            : ClassificationAlgorithm<ClassificationMap>(), somAlpha(_alpha),
              somRadius(_radius), somNeigh(_neighType), somNSteps(_nSteps)
    {}

SOM() [2/3]

SOM::SOM

(

std::istream &

_is

)

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

Definition at line 37 of file som.cpp.

                       : ClassificationAlgorithm<ClassificationMap>()
{
    readSelf(_is);
}

~SOM()

virtual SOM::~SOM ( )

inlinevirtual

Virtual destructor

Definition at line 165 of file som.h.

    {}

SOM() [3/3]

SOM::SOM ( const SOM &  )

delete

Member Function Documentation

alpha()

void SOM::alpha

(

Descent

_alpha

)

Sets the alpha descent function Parameter: _alpha alpha(t)

Sets the alpha function Parameter: _alpha alpha(t)

Definition at line 47 of file som.cpp.

{
    somAlpha = _alpha;
}

clear()

void SOM::clear ( )

virtual

Clears the Algorithm

Definition at line 186 of file som.cpp.

{
    somNeigh = GAUSSIAN;
    somNSteps = 0;
    listener = NULL; // it can not be deleted here
}

loadObject()

void SOM::loadObject ( std::istream &  _is )

virtual

Loads the SOM class from a stream. this method can be used to load the status of the class. Parameter: _is The output stream

Definition at line 248 of file som.cpp.

{
    readSelf(_is);
}

nSteps()

void SOM::nSteps

(

const unsigned long &

_nSteps

)

Sets the number of training steps Parameter: _nSteps Number of training steps

Definition at line 65 of file som.cpp.

{
    somNSteps = _nSteps;
}

operator=()

SOM& SOM::operator= ( const SOM &  op1 )

inline

Operator "=" Parameter: op1 SOM

Definition at line 265 of file som.h.

    {
        std::stringstream _str;
        op1.printSelf(_str);
        readSelf(_str);
        return *this;
    }

printSelf()

void SOM::printSelf ( std::ostream &  _os ) const

virtual

Standard output for a SOM algorithm Parameter: _os The output stream

Reimplemented from ClassificationAlgorithm< ClassificationMap >.

Definition at line 197 of file som.cpp.

{
    _os << (int) somNeigh << std::endl;
    _os << somNSteps << std::endl;
    somAlpha.printSelf(_os);
    somRadius.printSelf(_os);
}

radius()

void SOM::radius

(

Descent

_radius

)

Sets the radius descent function Parameter: _radius radius(t)

Sets the radius function Parameter: _radius radius(t)

Definition at line 56 of file som.cpp.

{
    somRadius = _radius;
}

readSelf()

void SOM::readSelf ( std::istream &  _is )

virtual

Standard input for a SOM algorithm Parameter: _is The input stream

Standard input for a som algorithm Parameter: _is The input stream

Definition at line 209 of file som.cpp.

{
    clear();
    try
    {
        if (_is)
            _is >> (int&) somNeigh;
        if (_is)
            _is >> somNSteps;
        somAlpha.readSelf(_is);
        somRadius.readSelf(_is);

    }
    catch (std::exception& e)
    {
        std::stringstream msg;
        msg << e.what() << std::endl << "Error reading the SOM algorithm";
        throw std::runtime_error(msg.str());
    }

}

saveObject()

void SOM::saveObject ( std::ostream &  _os ) const

virtual

Saves the SOM class into a stream. this method can be used to save the status of the class. Parameter: _os The output stream

Definition at line 237 of file som.cpp.

{
    printSelf(_os);
}

test()

double SOM::test ( const ClassificationMap &  _som, const TS &  _examples  ) const

virtual

Tests the SOM Parameter: _som The som to test Parameter: _examples The training set of examples returns the quantization error

Tests the SOM Parameter: _som The som to test Parameter: _examples The training set of examples

Definition at line 148 of file som.cpp.

{

    // Defines verbosity level
    int verbosity = listener->getVerbosity();
    if (verbosity)
    {
        listener->OnReportOperation((std::string) "Estimating quantization error....\n");
        listener->OnInitOperation(_examples.size());
    }


    /* Scan all data entries */
    double qerror = 0.0;
    for (size_t i = 0; i < _examples.size(); i++)
    {
        SomIn& theBest = _som.test(_examples.theItems[i]); // get the best
        qerror += euclideanDistance(theBest, _examples.theItems[i]);
        if (verbosity)
        {
            auto tmp = (int)((_examples.size() * 5) / 100);
            if ((tmp == 0) && (i != 0))
                tmp = i;
            else
                tmp = 1;
            if ((i % tmp) == 0)
                listener->OnProgress(i);
        }
    }
    if (verbosity)
        listener->OnProgress(_examples.size());
    return (qerror / (double) _examples.size());
}

train()

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

virtual

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

Definition at line 76 of file som.cpp.

{
    unsigned long t = 0;

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


    while (t < somNSteps)
    {
        for (std::vector<SomIn>::iterator i = _ts.theItems.begin();
             t < somNSteps && i < _ts.theItems.end() ; i++, t++)
        {
            // get the best matching.
            SomIn& theBest = _som.test(*i)
                             ;
            if (somNeigh == BUBBLE)
            { // Bubble
                // update the neighborhood around the best one
                std::vector<unsigned> neig = _som.neighborhood(_som.codVecPos(theBest),
                                             ceil(somRadius(t, somNSteps)));
                for (std::vector<unsigned>::iterator it = neig.begin();it < neig.end();it++)
                {
                    SomIn& v = _som.theItems[*it]
                               ;
                    for (unsigned j = 0; j < v.size(); j++)
                        v[j] += ((*i)[j] - v[j]) * somAlpha(t, somNSteps);
                }
            }
            else
            { // Gaussian
                // update all neighborhood convoluted by a gaussian
                double radius = somRadius(t, somNSteps);
                double alpha = somAlpha(t, somNSteps);
                for (unsigned it = 0 ; it < _som.size(); it++)
                {
                    double dist = _som.neighDist(_som.codVecPos(theBest), _som.indexToPos(it));
                    double alp = alpha * (double) exp((double)(-dist * dist / (2.0 * radius * radius)));
                    SomIn& v = _som.theItems[it];
                    for (unsigned j = 0; j < v.size(); j++)
                        v[j] += ((*i)[j] - v[j]) * alp;
                }
            } // else

        } // for examples

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


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

}

Friends And Related Function Documentation

operator<<

std::ostream& operator<< ( std::ostream &  _os, const SOM &  _som  )

friend

Standard output for a som algorithm Parameter: _os The output stream Parameter: _som The som algorithm to be printed

Definition at line 243 of file som.h.

    {
        _som.printSelf(_os);
        return _os;
    };

operator>>

std::istream& operator>> ( std::istream &  _is, SOM &  _som  )

friend

Standard input for a som algorithm Parameter: _is The input stream Parameter: _som The som algorithm

Definition at line 254 of file som.h.

    {
        _som.readSelf(_is);
        return _is;
    };

Member Data Documentation

somAlpha

Descent SOM::somAlpha

protected

Definition at line 275 of file som.h.

somNeigh

neighType SOM::somNeigh

protected

radius(t)

Definition at line 277 of file som.h.

somNSteps

unsigned long SOM::somNSteps

protected

Neighborhood type for training (Bubble or Gaussian)

Definition at line 278 of file som.h.

somRadius

Descent SOM::somRadius

protected

alpha(t)

Definition at line 276 of file som.h.

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

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