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Backpropagation Artificial Neural Network in C++

, 20 May 2008 GPL3 114K 7K 100
This article demonstrates a backpropagation artificial neural network console application with validation and test sets for performance estimation using uneven distribution metrics.
#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);

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

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


About the Author

Chesnokov Yuriy
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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