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

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20 Nov 2007GPL318 min read 175K   7.1K   109  
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
#pragma once



class CSignal
{

        int format;

        HANDLE fp, fpmap;
        LPVOID lpMap;


        bool read11(wchar_t *fname);           //obsolete
        //void read12(wchar_t *fname);         //obsolete
        bool read13(wchar_t *fname);

        void changeext(wchar_t *path, wchar_t *ext);


public:
        CSignal(wchar_t *fname);                  //open existing file
        CSignal(wchar_t *fname, int n, int m);    //create new
        ~CSignal();


        int N, M;                             //NxM size of mapped array
        vector<float *> data;                 //N array of pointers to filemapping
        wchar_t name[_MAX_PATH];              //file name


        void dump(wchar_t *fname);            //dump contents to text file

        void minmax(float *buff, int len, float &min, float &max);
        void nminmax(float *buff, int len, float a, float b);
        void nenergy(float *buff, int len, int L = 2);


};



/*
    reads data from list file

	1.      file1  1
                file2  2
		file3  1
		....

     files in separate files on disk    1.1 - simple text file
	                                1.2 - ecg like data (header in this file)
				        1.3 - mitbih like format (header in separate file *.hea  [N M])


    AI file format
    2.          file1  1
	        x1 x2 x3 ... xn
		file2  2
		x1 x2 x3 ... xn
		file3  1
		x1 x2 x3 ... xn
		...

     files data in this list file

*/

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License

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


Written By
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.

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