text: /*
   ******************************************************************
   * HISTORY
   * 15-Oct-94  Jeff Shufelt (js), Carnegie Mellon University
   *	Prepared for 15-681, Fall 1994.
   * Modified by Shuai Che
   ******************************************************************
   */
  #ifdef OPEN
  #include <omp.h>
  #endif
  #include <stdio.h>
  #include <stdlib.h>
  #include "backprop.h"
  #include <math.h>
  //#define OPEN
  
  #define ABS(x)          (((x) > 0.0) ? (x) : (-(x)))
  
  #define fastcopy(to,from,len)\
  {\
    register char *_to,*_from;\
    register int _i,_l;\
    _to = (char *)(to);\
    _from = (char *)(from);\
    _l = (len);\
    for (_i = 0; _i < _l; _i++) *_to++ = *_from++;\
  }
  
  /*** Return random number between 0.0 and 1.0 ***/
  float drnd()
  {
    return ((float) rand() / (float) BIGRND);
  }
  
  /*** Return random number between -1.0 and 1.0 ***/
  float dpn1()
  {
    return ((drnd() * 2.0) - 1.0);
  }
  
  /*** The squashing function.  Currently, it's a sigmoid. ***/
  
  float squash(x)
  float x;
  {
    float m;
    //x = -x;
    //m = 1 + x + x*x/2 + x*x*x/6 + x*x*x*x/24 + x*x*x*x*x/120;
    //return(1.0 / (1.0 + m));
    return (1.0 / (1.0 + exp(-x)));
  }
  
  
  /*** Allocate 1d array of floats ***/
  
  float *alloc_1d_dbl(n)
  int n;
  {
    float *new;
  
    new = (float *) malloc ((unsigned) (n * sizeof (float)));
    if (new == NULL) {
      printf("ALLOC_1D_DBL: Couldn't allocate array of floats\n");
      return (NULL);
    }
    return (new);
  }
  
  
  /*** Allocate 2d array of floats ***/
  
  float **alloc_2d_dbl(m, n)
  int m, n;
  {
    int i;
    float **new;
  
    new = (float **) malloc ((unsigned) (m * sizeof (float *)));
    if (new == NULL) {
      printf("ALLOC_2D_DBL: Couldn't allocate array of dbl ptrs\n");
      return (NULL);
    }
  
    for (i = 0; i < m; i++) {
      new[i] = alloc_1d_dbl(n);
    }
  
    return (new);
  }
  
  
  bpnn_randomize_weights(w, m, n)
  float **w;
  int m, n;
  {
    int i, j;
  
    for (i = 0; i <= m; i++) {
      for (j = 0; j <= n; j++) {
       w[i][j] = (float) rand()/RAND_MAX;
      //  w[i][j] = dpn1();
      }
    }
  }
  
  bpnn_randomize_row(w, m)
  float *w;
  int m;
  {
  	int i;
  	for (i = 0; i <= m; i++) {
       //w[i] = (float) rand()/RAND_MAX;
  	 w[i] = 0.1;
      }
  }
  
  
  bpnn_zero_weights(w, m, n)
  float **w;
  int m, n;
  {
    int i, j;
  
    for (i = 0; i <= m; i++) {
      for (j = 0; j <= n; j++) {
        w[i][j] = 0.0;
      }
    }
  }
  
  
  void bpnn_initialize(seed)
  {
    printf("Random number generator seed: %d\n", seed);
    srand(seed);
  }
  
  
  BPNN *bpnn_internal_create(n_in, n_hidden, n_out)
  int n_in, n_hidden, n_out;
  {
    BPNN *newnet;
  
    newnet = (BPNN *) malloc (sizeof (BPNN));
    if (newnet == NULL) {
      printf("BPNN_CREATE: Couldn't allocate neural network\n");
      return (NULL);
    }
  
    newnet->input_n = n_in;
    newnet->hidden_n = n_hidden;
    newnet->output_n = n_out;
    newnet->input_units = alloc_1d_dbl(n_in + 1);
    newnet->hidden_units = alloc_1d_dbl(n_hidden + 1);
    newnet->output_units = alloc_1d_dbl(n_out + 1);
  
    newnet->hidden_delta = alloc_1d_dbl(n_hidden + 1);
    newnet->output_delta = alloc_1d_dbl(n_out + 1);
    newnet->target = alloc_1d_dbl(n_out + 1);
  
    newnet->input_weights = alloc_2d_dbl(n_in + 1, n_hidden + 1);
    newnet->hidden_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);
  
    newnet->input_prev_weights = alloc_2d_dbl(n_in + 1, n_hidden + 1);
    newnet->hidden_prev_weights = alloc_2d_dbl(n_hidden + 1, n_out + 1);
  
    return (newnet);
  }
  
  
  void bpnn_free(net)
  BPNN *net;
  {
    int n1, n2, i;
  
    n1 = net->input_n;
    n2 = net->hidden_n;
  
    free((char *) net->input_units);
    free((char *) net->hidden_units);
    free((char *) net->output_units);
  
    free((char *) net->hidden_delta);
    free((char *) net->output_delta);
    free((char *) net->target);
  
    for (i = 0; i <= n1; i++) {
      free((char *) net->input_weights[i]);
      free((char *) net->input_prev_weights[i]);
    }
    free((char *) net->input_weights);
    free((char *) net->input_prev_weights);
  
    for (i = 0; i <= n2; i++) {
      free((char *) net->hidden_weights[i]);
      free((char *) net->hidden_prev_weights[i]);
    }
    free((char *) net->hidden_weights);
    free((char *) net->hidden_prev_weights);
  
    free((char *) net);
  }
  
  
  /*** Creates a new fully-connected network from scratch,
       with the given numbers of input, hidden, and output units.
       Threshold units are automatically included.  All weights are
       randomly initialized.
  
       Space is also allocated for temporary storage (momentum weights,
       error computations, etc).
  ***/
  
  BPNN *bpnn_create(n_in, n_hidden, n_out)
  int n_in, n_hidden, n_out;
  {
  
    BPNN *newnet;
  
    newnet = bpnn_internal_create(n_in, n_hidden, n_out);
  
  #ifdef INITZERO
    bpnn_zero_weights(newnet->input_weights, n_in, n_hidden);
  #else
    bpnn_randomize_weights(newnet->input_weights, n_in, n_hidden);
  #endif
    bpnn_randomize_weights(newnet->hidden_weights, n_hidden, n_out);
    bpnn_zero_weights(newnet->input_prev_weights, n_in, n_hidden);
    bpnn_zero_weights(newnet->hidden_prev_weights, n_hidden, n_out);
    bpnn_randomize_row(newnet->target, n_out);
    return (newnet);
  }
  
  
  void bpnn_layerforward(l1, l2, conn, n1, n2)
  float *l1, *l2, **conn;
  int n1, n2;
  {
    float sum;
    int j, k;
  
    /*** Set up thresholding unit ***/
    l1[0] = 1.0;
  #ifdef OPEN
    omp_set_num_threads(NUM_THREAD);
    #pragma omp parallel for shared(conn, n1, n2, l1) private(k, j) reduction(+: sum) schedule(static)
  #endif 
    /*** For each unit in second layer ***/
    for (j = 1; j <= n2; j++) {
  
      /*** Compute weighted sum of its inputs ***/
      sum = 0.0;
      for (k = 0; k <= n1; k++) {	
        sum += conn[k][j] * l1[k]; 
      }
      l2[j] = squash(sum);
    }
  }
  
  //extern "C"
  void bpnn_output_error(delta, target, output, nj, err)  
  float *delta, *target, *output, *err;
  int nj;
  {
    int j;
    float o, t, errsum;
    errsum = 0.0;
    for (j = 1; j <= nj; j++) {
      o = output[j];
      t = target[j];
      delta[j] = o * (1.0 - o) * (t - o);
      errsum += ABS(delta[j]);
    }
    *err = errsum;
  }
  
  
  void bpnn_hidden_error(delta_h,   
  					   nh, 
  					   delta_o, 
  					   no, 
  					   who, 
  					   hidden, 
  					   err)
  float *delta_h, *delta_o, *hidden, **who, *err;
  int nh, no;
  {
    int j, k;
    float h, sum, errsum;
  
    errsum = 0.0;
    for (j = 1; j <= nh; j++) {
      h = hidden[j];
      sum = 0.0;
      for (k = 1; k <= no; k++) {
        sum += delta_o[k] * who[j][k];
      }
      delta_h[j] = h * (1.0 - h) * sum;
      errsum += ABS(delta_h[j]);
    }
    *err = errsum;
  }
  
  
  void bpnn_adjust_weights(delta, ndelta, ly, nly, w, oldw)
  float *delta, *ly, **w, **oldw;
  {
    float new_dw;
    int k, j;
    ly[0] = 1.0;
    //eta = 0.3;
    //momentum = 0.3;
  
  #ifdef OPEN
    omp_set_num_threads(NUM_THREAD);
    #pragma omp parallel for  \
        shared(oldw, w, delta) \
  	  private(j, k, new_dw) \
  	  firstprivate(ndelta, nly, momentum) 
  #endif 
    for (j = 1; j <= ndelta; j++) {
      for (k = 0; k <= nly; k++) {
        new_dw = ((ETA * delta[j] * ly[k]) + (MOMENTUM * oldw[k][j]));
  	  w[k][j] += new_dw;
  	  oldw[k][j] = new_dw;
      }
    }
  }
  
  
  void bpnn_feedforward(net)
  BPNN *net;
  {
    int in, hid, out;
  
    in = net->input_n;
    hid = net->hidden_n;
    out = net->output_n;
  
    /*** Feed forward input activations. ***/
    bpnn_layerforward(net->input_units, net->hidden_units,
        net->input_weights, in, hid);
    bpnn_layerforward(net->hidden_units, net->output_units,
        net->hidden_weights, hid, out);
  
  }
  
  
  void bpnn_train(net, eo, eh)
  BPNN *net;
  float *eo, *eh;
  {
    int in, hid, out;
    float out_err, hid_err;
  
    in = net->input_n;
    hid = net->hidden_n;
    out = net->output_n;
  
    /*** Feed forward input activations. ***/
    bpnn_layerforward(net->input_units, net->hidden_units,
        net->input_weights, in, hid);
    bpnn_layerforward(net->hidden_units, net->output_units,
        net->hidden_weights, hid, out);
  
    /*** Compute error on output and hidden units. ***/
    bpnn_output_error(net->output_delta, net->target, net->output_units,
        out, &out_err);
    bpnn_hidden_error(net->hidden_delta, hid, net->output_delta, out,
        net->hidden_weights, net->hidden_units, &hid_err);
    *eo = out_err;
    *eh = hid_err;
  
    /*** Adjust input and hidden weights. ***/
    bpnn_adjust_weights(net->output_delta, out, net->hidden_units, hid,
        net->hidden_weights, net->hidden_prev_weights);
    bpnn_adjust_weights(net->hidden_delta, hid, net->input_units, in,
        net->input_weights, net->input_prev_weights);
  
  }
  
  
  
  
  void bpnn_save(net, filename)
  BPNN *net;
  char *filename;
  {
    int n1, n2, n3, i, j, memcnt;
    float dvalue, **w;
    char *mem;
    ///add//
    FILE *pFile;
    pFile = fopen( filename, "w+" );
    ///////
    /*
    if ((fd = creat(filename, 0644)) == -1) {
      printf("BPNN_SAVE: Cannot create '%s'\n", filename);
      return;
    }
    */
  
    n1 = net->input_n;  n2 = net->hidden_n;  n3 = net->output_n;
    printf("Saving %dx%dx%d network to '%s'\n", n1, n2, n3, filename);
    //fflush(stdout);
  
    //write(fd, (char *) &n1, sizeof(int));
    //write(fd, (char *) &n2, sizeof(int));
    //write(fd, (char *) &n3, sizeof(int));
  
    fwrite( (char *) &n1 , sizeof(char), sizeof(char), pFile);
    fwrite( (char *) &n2 , sizeof(char), sizeof(char), pFile);
    fwrite( (char *) &n3 , sizeof(char), sizeof(char), pFile);
  
    
  
    memcnt = 0;
    w = net->input_weights;
    mem = (char *) malloc ((unsigned) ((n1+1) * (n2+1) * sizeof(float)));
    for (i = 0; i <= n1; i++) {
      for (j = 0; j <= n2; j++) {
        dvalue = w[i][j];
        fastcopy(&mem[memcnt], &dvalue, sizeof(float));
        memcnt += sizeof(float);
      }
    }
    //write(fd, mem, (n1+1) * (n2+1) * sizeof(float));
    fwrite( mem , (unsigned)(sizeof(float)), (unsigned) ((n1+1) * (n2+1) * sizeof(float)) , pFile);
    free(mem);
  
    memcnt = 0;
    w = net->hidden_weights;
    mem = (char *) malloc ((unsigned) ((n2+1) * (n3+1) * sizeof(float)));
    for (i = 0; i <= n2; i++) {
      for (j = 0; j <= n3; j++) {
        dvalue = w[i][j];
        fastcopy(&mem[memcnt], &dvalue, sizeof(float));
        memcnt += sizeof(float);
      }
    }
    //write(fd, mem, (n2+1) * (n3+1) * sizeof(float));
    fwrite( mem , sizeof(float), (unsigned) ((n2+1) * (n3+1) * sizeof(float)) , pFile);
    free(mem);
  
    fclose(pFile);
    return;
  }
  
  
  BPNN *bpnn_read(filename)
  char *filename;
  {
    char *mem;
    BPNN *new;
    int fd, n1, n2, n3, i, j, memcnt;
  
    if ((fd = open(filename, 0, 0644)) == -1) {
      return (NULL);
    }
  
    printf("Reading '%s'\n", filename);  //fflush(stdout);
  
    read(fd, (char *) &n1, sizeof(int));
    read(fd, (char *) &n2, sizeof(int));
    read(fd, (char *) &n3, sizeof(int));
    new = bpnn_internal_create(n1, n2, n3);
  
    printf("'%s' contains a %dx%dx%d network\n", filename, n1, n2, n3);
    printf("Reading input weights...");  //fflush(stdout);
  
    memcnt = 0;
    mem = (char *) malloc ((unsigned) ((n1+1) * (n2+1) * sizeof(float)));
    read(fd, mem, (n1+1) * (n2+1) * sizeof(float));
    for (i = 0; i <= n1; i++) {
      for (j = 0; j <= n2; j++) {
        fastcopy(&(new->input_weights[i][j]), &mem[memcnt], sizeof(float));
        memcnt += sizeof(float);
      }
    }
    free(mem);
  
    printf("Done\nReading hidden weights...");  //fflush(stdout);
  
    memcnt = 0;
    mem = (char *) malloc ((unsigned) ((n2+1) * (n3+1) * sizeof(float)));
    read(fd, mem, (n2+1) * (n3+1) * sizeof(float));
    for (i = 0; i <= n2; i++) {
      for (j = 0; j <= n3; j++) {
        fastcopy(&(new->hidden_weights[i][j]), &mem[memcnt], sizeof(float));
        memcnt += sizeof(float);
      }
    }
    free(mem);
    close(fd);
  
    printf("Done\n");  //fflush(stdout);
  
    bpnn_zero_weights(new->input_prev_weights, n1, n2);
    bpnn_zero_weights(new->hidden_prev_weights, n2, n3);
  
    return (new);
  }
  