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Backpropagation Artificial Neural Network in C++

, 20 May 2008 GPL3
This article demonstrates a backpropagation artificial neural network console application with validation and test sets for performance estimation using uneven distribution metrics.
// stdafx.h : include file for standard system include files,
// or project specific include files that are used frequently, but
// are changed infrequently

#pragma once
#pragma warning(disable : 4996)

#include <iostream>
#include <tchar.h>

// TODO: reference additional headers your program requires here
#include <windows.h>
#include <stdio.h>
#include <math.h>
#include <wchar.h>
#include <conio.h>
#include <time.h>

#include <vector>
#include <algorithm>

using namespace std;

typedef struct _entry {
        float *vec;                //vector
        int size;                  //vector size
        wchar_t fname[_MAX_PATH];  //vector file name
        int cls;                   //vector class

typedef struct _rec {
        vector<PENTRY> entries;         //rec entries
        vector< vector<int> > indices;  //2D array of classes indices
        vector<int> clsnum;             //classes numbers of indeces columns

   2D type array

    [entry ... vec ...]   (+ size,fname,class type)
    [entry ... vec ...]
    [entry ... vec ...]
    [entry ... vec ...]
    N = rec.size()

    individual point - rec.entries[y].vec[x]

    indices to y axis of entries vector
     vector<int> x0;  vector<int> x1; vector<int> x2; ... vector<int> xClassesNum

    clsnum[0],clsnum[1], ... clsnum[N]
      [x0][x1][x2][x3] ... [xN]    N different classes
      [x0][x1][x2][x3] ... [xN]
      [x0][x1][x2][x3] ... [xN]

      rec->clsnum[] =   3, 1, 2    <--  1D vector

                        0  1  8    <--  rec->indices[x].at(y)  2D array
                       10  5  9
                        3  2
                        4  6


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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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