example_simplified

#pragma hdrstop
#pragma argsused

#ifdef _WIN32
#include <tchar.h>
#else
  typedef char _TCHAR;
  #define _tmain main
#endif

#include <algorithm>
#include <stdio.h>
#include <math.h>
#include <iostream>

using std::cin;
using std::cout;
using std::endl;

    double training_set_inputs[4][3];
    double training_set_outputs[4];
    double SynapticWeight[4], new_situation[3];
    double i,j,l, Output5, DOToutput5;
    double Boutput[5], BsigmoidPart[5], Bsigmoid_DerivativePart[5], BerrorPart[5], BadjustmentPart[5], BadjustSynapticWeight[4];
    int q;


 int _tmain(int argc, _TCHAR* argv[])
{

    training_set_inputs[0][0]=0;training_set_inputs[0][1]=0;training_set_inputs[0][2]=1;
    training_set_inputs[1][0]=1;training_set_inputs[1][1]=1;training_set_inputs[1][2]=1;
    training_set_inputs[2][0]=1;training_set_inputs[2][1]=0;training_set_inputs[2][2]=1;
    training_set_inputs[3][0]=0;training_set_inputs[3][1]=1;training_set_inputs[3][2]=1;

    training_set_outputs[0]=0;
    training_set_outputs[1]=1;
    training_set_outputs[2]=1;
    training_set_outputs[3]=0;



    // Should be a random number between -1 and +1 ; fixed values b/c of example.
    SynapticWeight[1]=-0.16595599;
    SynapticWeight[2]=0.44064899;
    SynapticWeight[3]=-0.99977125;


    for( i = 1; i <= 10000; i++ ){

        //4 number of training sets
        for( q = 1; q <= 4; q++) {

        Boutput[q] = training_set_inputs[q-1][0]*SynapticWeight[1]+training_set_inputs[q-1][1]*SynapticWeight[2]+training_set_inputs[q-1][2]*SynapticWeight[3];

        BsigmoidPart[q] = 1 / (1 + exp(-(Boutput[q])));

        Bsigmoid_DerivativePart[q] = BsigmoidPart[q] * (1 - BsigmoidPart[q]);

        BerrorPart[q] = (training_set_outputs[q-1] - BsigmoidPart[q]);

        BadjustmentPart[q] =  BerrorPart[q] * Bsigmoid_DerivativePart[q];

        }

        //3 number of synapses
        for( q = 1; q <= 3; q++) {

        BadjustSynapticWeight[q] = training_set_inputs[0][q-1]*BadjustmentPart[1]+training_set_inputs[1][q-1]*BadjustmentPart[2]+training_set_inputs[2][q-1]*BadjustmentPart[3]+training_set_inputs[3][q-1]*BadjustmentPart[4];

        SynapticWeight[q]=SynapticWeight[q]+BadjustSynapticWeight[q];

        }


       printf("\nTEST %.14f %.14f %.14f ADJUSTMENTS %.14f %.14f %.14f", SynapticWeight[1], SynapticWeight[2], SynapticWeight[3], BadjustSynapticWeight[1], BadjustSynapticWeight[2], BadjustSynapticWeight[3]);
    }

    new_situation[0]=1;new_situation[1]=0;new_situation[2]=0;

    Output5=new_situation[0]*SynapticWeight[1]+new_situation[1]*SynapticWeight[2]+new_situation[2]*SynapticWeight[3];

    DOToutput5 = 1 / (1 + exp(-(Output5)));


    printf("\n The output is currently %lf \n", DOToutput5);





    cout << "Press enter to exit..." << endl;
    cin.get();





    return 0;
}

// python and c++
// ref:   medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1
// A.I. / Neural Network crude example