stochastic regression

#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#define M 100
#define N 3
#define Max_it 1500

int main(void)
{
  int i, j, count, y[M];
  double x[M][N-1], theta[N], alpha;
  FILE *ifp;
  void stochastic(double x[][N-1], int *y, int m, double *theta, int n, double alpha, int tt);

  //read data from file
  count = 0;
  if((ifp = fopen("ex2data1.txt", "r")) == NULL)
  {
    printf("open error, check!\n");
    exit(1);
  }
  else
  {
    for(i=0;!feof(ifp);i++)
    {
      fscanf(ifp,"%lf,%lf,%d\n", &x[i][0],&x[i][1], &y[i]);
      count++;
    }
  }
  fclose(ifp);
  count;

  theta[0] = -25.0; theta[1] = 0.3; theta[2] = 0.2;
  alpha = 0.01;

  stochastic(x, y, count,theta, N, alpha, Max_it);
  printf("The regression parameters are:\n");
  for(i=0; i<N; i++)
  {
    printf("%10.2f \n", theta[i]);
  }
  exit(0);
}


//============================================================================================
void stochastic(double x[][N-1], int *y, int m, double *theta, int n, double alpha, int max_it)
//m: sample numbers, n: the dimesion of theta, max_it: max interation number.
{
  int i, j, k, l;
  double sum1, err, loss;

  loss = 1.0;
  for(k=0; k<max_it && loss>0.001; k++)//for max iteration numbers
  {
      err = 0.0;
      i = k%m;
      {
        sum1 = theta[0];
        for(j=1; j<n; j++)
        {
          
            sum1 += theta[j]*x[i][j-1];
        }
        sum1 = 1.0/(1.0 + exp(-sum1));
        err = y[i] - sum1;
        for(l=0;l<n;l++)
        {
          if(l == 0)
            theta[l] = theta[l] + alpha*err/m;
          else
            theta[l] = theta[l] + alpha*err*x[i][l-1]/m;
        }
      }
      loss = 0.0;
      for(l=0; l<m; l++)
      {
        sum1 = theta[0];
        for(j=1; j<n; j++ )
          sum1 += x[l][j-1]*theta[j];
        loss += (sum1 - y[l])*(sum1 - y[l]);
      }
  }
}
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