Click here to Skip to main content
11,640,211 members (72,669 online)
Click here to Skip to main content
Add your own
alternative version

Kohonen's Self Organizing Maps in C++ with Application in Computer Vision Area

, 20 Nov 2007 GPL3 109.1K 5.1K 105
The article demonstrates the self organizing maps clustering approach for unsupervised AI classification tasks with application examples in computer vision area for faces clustering and recognition
som_demo-noexe.zip
som_demo.zip
bin
rgb.som
rgb_1.som
som.exe
som_src-noexe.zip
som_src.zip
src
Lib
LibSOM

#ifndef Som_h
#define Som_h


//class Node;  before inlined
#include "node.h"

class SOM
{
public:
        SOM(int dimensionality, int *dimensions, int weights_per_node,          //create/randomize nodes with weights        
            enum Node::DistanceMetric distance_metric, float *add = 0, float *mul = 0);     
        SOM(const wchar_t *fname);                                              //load SOM net, if empty, default norm=0
        ~SOM();

        enum Normalization {NONE, MNMX, ZSCR, SIGM, ENRG};
        enum TrainMode {SLOW, FAST};

        static const wchar_t g_distance[5][5];
        static const wchar_t g_normalization[5][5];
        static const wchar_t g_trainmode[2][5];

// Operators
        //const SOM& operator=(const SOM& som);

// Operations
        //training
        float R0() const;                               //calculate initial R0 for 1st epoch
        void train(const vector<float *> *vectors, 
                   float R, float learning_rule);       //train SOM for 1 epoch
        void vote_nodes_from(PREC rec);                 //voting scheme; get_bmu_node to a vector BMU
        void assign_nodes_to(PREC rec);                 //direct scheme; best vector to a node

        //testing
        const Node *classify(const float *vec);         //return BMU node

        int save(const wchar_t *fname) const;
        int save_2D_distance_map(const wchar_t *fname) const;

        void compute_normalization(PREC rec, enum Normalization norm);

// Access
// Inquiry
        inline int status(void) const; 
        inline int get_dimensionality(void) const;       
        inline int get_weights_per_node(void) const; 
        inline int get_nodes_number() const; 
        inline const Node* get_node(int n) const; 
        inline void set_distance_metric(enum Node::DistanceMetric distance_metric);                   
        inline void set_train_mode(enum TrainMode train_mode);                                              

private:
        SOM(const SOM& som);
        const SOM& operator=(const SOM& som);                

        int m_status;                   //SOM status after loading

        int m_dimensionality;           //dimension of SOM
        int *m_dimensions;              //dimensions sizes
        int m_weights_per_node;         //number of weights per node

        vector<Node *> m_nodes;         //array of nodes

        enum Normalization m_normalization;                     //normalization type
        enum Node::DistanceMetric m_distance_metric;            //distance metric used in training
        enum TrainMode m_train_mode;                            //slow/fast mode training

        float *m_data;                  //data vector to classify
        float *m_add;                   //normalization params for input data   (x+add)*mul
        float *m_mul;                   //normalization params for input data


        void create_nodes(const float *add = 0, const float *mul = 0);    //random weights for nodes init to m_nodes array
        const float* normalize(const float *vec);

        Node *get_bmu_node(const float *vec);                  //get best node (neuron that fire)
        Node *get_bmu_0node(const float *vec);                 //get best node if its m_class=0 (unclassified neuron)
};

inline int SOM::status(void) const
{
        return m_status;
}        

inline int SOM::get_dimensionality(void) const
{
        return m_dimensionality;
}          

inline int SOM::get_weights_per_node(void) const
{
        return m_weights_per_node;
}        

inline int SOM::get_nodes_number() const
{
        return (int)m_nodes.size();
}

inline const Node* SOM::get_node(int n) const
{
        return m_nodes[n];
}

inline void SOM::set_distance_metric(enum Node::DistanceMetric distance_metric) 
{
        m_distance_metric = distance_metric;
}

inline void SOM::set_train_mode(enum TrainMode train_mode) 
{
        m_train_mode = train_mode;
}

inline Node *SOM::get_bmu_node(const float *vec)
{
        Node *pbmu_node = m_nodes[0];
        float mindist = pbmu_node->get_distance(vec, m_distance_metric);
        float dist;

        for (int i = 1; i < get_nodes_number(); i++) {
                if ((dist = m_nodes[i]->get_distance(vec, m_distance_metric)) < mindist) {
                        mindist = dist;
                        pbmu_node = m_nodes[i];
                }
        }

        return pbmu_node;
}

inline Node *SOM::get_bmu_0node(const float *vec)
{
        Node *pbmu_0node = 0;
        int n;
        for (n = 0; n < get_nodes_number(); n++) {
                if (m_nodes[n]->get_class() == 0) {
                        pbmu_0node = m_nodes[n];
                        break;
                }
        }

        if (pbmu_0node != 0) { //there is 0class node m_nodes[n]
                float mindist = pbmu_0node->get_distance(vec, m_distance_metric), dist;
                for (int i = n + 1; i < get_nodes_number(); i++) {
                        if (m_nodes[i]->get_class() == 0 &&
                            (dist = m_nodes[i]->get_distance(vec, m_distance_metric)) < mindist) {
                                mindist = dist;
                                pbmu_0node = m_nodes[i];
                        }
                }
                return pbmu_0node;
        } else
                return 0;
}


#endif

By viewing downloads associated with this article you agree to the Terms of Service and the article's licence.

If a file you wish to view isn't highlighted, and is a text file (not binary), please let us know and we'll add colourisation support for it.

License

This article, along with any associated source code and files, is licensed under The GNU General Public License (GPLv3)

Share

About the Author

Chesnokov Yuriy
Engineer
Russian Federation Russian Federation
Highly skilled Engineer with 14 years of experience in academia, R&D and commercial product development supporting full software life-cycle from idea to implementation and further support. During my academic career I was able to succeed in MIT Computers in Cardiology 2006 international challenge, as a R&D and SW engineer gain CodeProject MVP, find algorithmic solutions to quickly resolve tough customer problems to pass product requirements in tight deadlines. My key areas of expertise involve Object-Oriented
Analysis and Design OOAD, OOP, machine learning, natural language processing, face recognition, computer vision and image processing, wavelet analysis, digital signal processing in cardiology.

You may also be interested in...

| Advertise | Privacy | Terms of Use | Mobile
Web02 | 2.8.150731.1 | Last Updated 20 Nov 2007
Article Copyright 2007 by Chesnokov Yuriy
Everything else Copyright © CodeProject, 1999-2015
Layout: fixed | fluid