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result.c
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//Analysis Type - Classification
#include <stdio.h>
#include <conio.h>
#include <math.h>
#include <stdlib.h>
#include <windows.h>
#define DLLEXPORT __declspec(dllexport)
/* THIS FUNCTION IS EXPORTED . Эта функция экспортируется */
DLLEXPORT void StatNeuroResults(int* ind, double* m);
double k_fdm_2_MLP_7_5_4_input_hidden_weights[5][7]=
{
{-2.83527958159302e-1, 2.89934657209722e-1, 4.93363880849622e-1, -8.75724651080467e-1, 3.86000525771560e-1, 1.82146865118403e-1, -2.05279873575766e-2 },
{2.97812861471928e-1, 1.42301799139965e-1, -7.58626265889516e-1, -7.82614296791588e-1, -1.02063543314337, 6.38774255373127e-1, 4.20075850130804e-1 },
{3.43415056829368e-1, 3.84781629695125e-3, -6.33110359044437e-2, 5.02148106000819e-1, -1.27475623675377e-1, 1.60987105441913e-1, 4.26474307659468e-1 },
{-4.36753073296557e-1, 1.76403838052590e-1, 1.71362386921746, -2.27483908688549e-1, 2.11207987348687, -8.89472360928051e-1, -5.05838802786523e-1 },
{-2.02800528826114e-1, 1.48588755981540e-1, 4.63190700047228e-1, -1.70823353038845e-1, 2.77028658832428e-1, -2.42203737060510e-1, -2.03067848177509e-1 }
};
double k_fdm_2_MLP_7_5_4_hidden_bias[5]={ -6.73686816815229e-2, 7.22496824891226e-2, 4.54283879143130e-1, -4.08285902648488e-1, -9.38365269570421e-2 };
double k_fdm_2_MLP_7_5_4_hidden_output_wts[4][5]=
{
{-3.30540876410893e-1, 8.03483723644652e-1, 6.22202553803680e-2, -3.75709843965768e-1, 9.71766437797953e-2 },
{-9.12979067767539e-2, -1.14577174142916, -1.63050195202422e-1, 8.56127075917471e-1, 4.10048928154471e-2 },
{-4.03744658113511e-1, 1.71055867370482, -6.75479951013817e-2, -1.90478492220575, -3.15517074957689e-1 },
{6.50730589902806e-1, 6.09347120302843e-2, -8.06634675624268e-1, 4.55050895033275e-1, 2.67965376280203e-1 }
};
double k_fdm_2_MLP_7_5_4_output_bias[4]={ -4.05667237897579, -2.27344473043056, 6.65589660176576e-1, -7.41574398847519e-1 };
double k_fdm_2_MLP_7_5_4_max_input[7]={ 9.70000000000000e+1, 3.10000000000000e+1, 1.00000000000000e+2, 2.50000000000000e+1, 6.70000000000000e+1, 1.87000000000000e+2, 1.05000000000000e+2 };
double k_fdm_2_MLP_7_5_4_min_input[7]={ 6.10000000000000e+1, 1.30000000000000e+1, 6.40000000000000e+1, 7.00000000000000, 1.80000000000000e+1, 1.60000000000000e+2, 5.00000000000000e+1 };
double k_fdm_2_MLP_7_5_4_input[7];
double k_fdm_2_MLP_7_5_4_hidden[5];
double k_fdm_2_MLP_7_5_4_output[4];
void k_fdm_2_MLP_7_5_4_ScaleInputs(double* input, double minimum, double maximum, int size)
{
double delta;
long i;
for(i=0; i<size; i++)
{
delta = (maximum-minimum)/(k_fdm_2_MLP_7_5_4_max_input[i]-k_fdm_2_MLP_7_5_4_min_input[i]);
input[i] = minimum - delta*k_fdm_2_MLP_7_5_4_min_input[i]+ delta*input[i];
}
}
double k_fdm_2_MLP_7_5_4_logistic(double x)
{
if(x > 100.0) x = 1.0;
else if (x < -100.0) x = 0.0;
else x = 1.0/(1.0+exp(-x));
return x;
}
void k_fdm_2_MLP_7_5_4_Normalise(double out[],long length)
{
long i, j;
double sum = 0.0;
for(i=0; i<length; i++)
{
if(out[i]>100)
{
out[i] = 1.0;
j = i;
for(i=0; i<length; i++)
{
if(i!=j) out[i] = 0.0;
}
break;
}
else out[i] = exp(out[i]);
}
for(i=0; i<length; i++)
{
sum += out[i];
}
for(i=0; i<length; i++)
{
out[i] = out[i]/sum;
}
}
void k_fdm_2_MLP_7_5_4_ComputeFeedForwardSignals(double* MAT_INOUT,double* V_IN,double* V_OUT, double* V_BIAS,int size1,int size2,int layer)
{
int row,col;
for(row=0;row < size2; row++)
{
V_OUT[row]=0.0;
for(col=0;col<size1;col++)V_OUT[row]+=(*(MAT_INOUT+(row*size1)+col)*V_IN[col]);
V_OUT[row]+=V_BIAS[row];
if(layer==1) V_OUT[row] = k_fdm_2_MLP_7_5_4_logistic(V_OUT[row]);
}
}
void k_fdm_2_MLP_7_5_4_RunNeuralNet_Classification ()
{
k_fdm_2_MLP_7_5_4_ComputeFeedForwardSignals((double*)k_fdm_2_MLP_7_5_4_input_hidden_weights,k_fdm_2_MLP_7_5_4_input,k_fdm_2_MLP_7_5_4_hidden,k_fdm_2_MLP_7_5_4_hidden_bias,7, 5,0);
k_fdm_2_MLP_7_5_4_ComputeFeedForwardSignals((double*)k_fdm_2_MLP_7_5_4_hidden_output_wts,k_fdm_2_MLP_7_5_4_hidden,k_fdm_2_MLP_7_5_4_output,k_fdm_2_MLP_7_5_4_output_bias,5, 4,1);
}
/*
The body (realization) of the above function prototype. Тело (реализация) заявленного выше прототипа функции.
Performs certain actions and returns result. Производит некие действия и возвращает результат
*/
DLLEXPORT void StatNeuroResults(int* ind, double* m)
{
int i=0;
*m=3.e-300;
printf("\n%s\n","Enter values for Continuous inputs");
printf("%s","Cont. Input-0(Isx. CHSS, sokr/min): ");
scanf("%lg",&k_fdm_2_MLP_7_5_4_input[0]);
printf("%s","Cont. Input-1(Isx. CHD, dblx/min): ");
scanf("%lg",&k_fdm_2_MLP_7_5_4_input[1]);
printf("%s","Cont. Input-2(Min gr, krc/min): ");
scanf("%lg",&k_fdm_2_MLP_7_5_4_input[2]);
printf("%s","Cont. Input-3(DlR min gr, kc): ");
scanf("%lg",&k_fdm_2_MLP_7_5_4_input[3]);
printf("%s","Cont. Input-4(Age): ");
scanf("%lg",&k_fdm_2_MLP_7_5_4_input[4]);
printf("%s","Cont. Input-5(Height): ");
scanf("%lg",&k_fdm_2_MLP_7_5_4_input[5]);
printf("%s","Cont. Input-6(Weight): ");
scanf("%lg",&k_fdm_2_MLP_7_5_4_input[6]);
k_fdm_2_MLP_7_5_4_ScaleInputs(k_fdm_2_MLP_7_5_4_input,0,1,7);
k_fdm_2_MLP_7_5_4_RunNeuralNet_Classification();
//Normalise output if output activation is not Softmax;
k_fdm_2_MLP_7_5_4_Normalise(k_fdm_2_MLP_7_5_4_output,4);
for(i=0;i<4;i++)
{
if(*m<k_fdm_2_MLP_7_5_4_output[i])
{
*m=k_fdm_2_MLP_7_5_4_output[i];
*ind=i+1;
}
}
return;
}
/*
int main(int argc, char** argv)
{
int i;
printf("argc = %d\n", argc);
for (i = 0; i < argc; i++)
{
printf("argv [%d]:%s\n", i, argv[i]);
}
return 0;
int index;
int keyin=1;
double max;
while(1)
{
StatNeuroResults(&index,&max); /////////////////////
printf("\n%s","Predicted category = ");
switch(index)
{
case 1: printf("%s\n","2"); break;
case 2: printf("%s\n","3"); break;
case 3: printf("%s\n","4"); break;
case 4: printf("%s\n","5"); break;
default: break;
}
printf("\n%s%.14f","Confidence level = ",max);
printf("\n\n%s\n","Press any key to make another prediction or enter 0 to quit the program.");
keyin=getch();
if(keyin==48)break;
}
return 0;
}
*/