/* 
 ****************************************************************** 
 * HISTORY 
 * 15-Oct-94  Jeff Shufelt (js), Carnegie Mellon University 
 *      Prepared for 15-681, Fall 1994. 
 * 
 ****************************************************************** 
 */ 
 
#ifndef _BACKPROP_H_ 
 
#define _BACKPROP_H_ 
 
#define BIGRND 0x7fffffff 
 
/*** The neural network data structure.  The network is assumed to 
     be a fully-connected feedforward three-layer network or four-layer
     network with a recurrent context layer between the input an hidden layers. 
     Unit 0 in each layer of units is the threshold unit; this means 
     that the remaining units are indexed from 1 to n, inclusive. 
 ***/ 
 
typedef struct { 
  int input_n;                  /* number of input units */ 
  int context_n;                /* number of context units */ 
  int hidden_n;                 /* number of hidden units */ 
  int output_n;                 /* number of output units */ 
 
  double *input_units;          /* the input units */ 
  double *context_units;        /* the context units */ 
  double *hidden_units;         /* the hidden units */ 
  double *output_units;         /* the output units */ 
 
  double *context_delta;        /* storage for context unit error */ 
  double *hidden_delta;         /* storage for hidden unit error */ 
  double *output_delta;         /* storage for output unit error */ 
 
  double *target;               /* storage for target vector */ 
 
  double **input_weights;       /* weights from input to hidden layer */ 
  double **context_weights;     /* weights from context to hidden layer */ 
  double **hidden_weights;      /* weights from hidden to output layer */ 
 
                                /*** The next three are for momentum ***/ 
  double **input_prev_weights;  /* previous change on input to hidden wgt */ 
  double **context_prev_weights;/* previous change on context to hidden wgt */ 
  double **hidden_prev_weights; /* previous change on hidden to output wgt */ 
} BPNN; 
 
 
/*** User-level functions ***/ 
void fastcopy(char *, double *, int);
void fastcopy(double *, double *, int);
void fastcopy(double *,char *,unsigned long);

 
void bpnn_initialize(unsigned int); 
BPNN *bpnn_internal_create(int,int,int,int); 
 
BPNN *bpnn_create(int,int,int); 
BPNN *bpnn_create(int,int,int,int); //with recurrent context layer
void bpnn_free(BPNN *); 
 
void bpnn_train(BPNN *,double,double,double *,double *); 
void bpnn_trainrec(BPNN *,double,double,double *,double *,double *); 
void bpnn_feedforward(BPNN *); 
 
void bpnn_adjust_weights(double *,int,double *,int,double **,double **,double,double); 
void bpnn_context_error(double *,int,double *,int,double **,double *,double *); 
void bpnn_hidden_error(double *,int,double *,int,double **,double *,double *); 
void bpnn_output_error(double *,double *,double *,int,double *); 
void bpnn_layerforward(double *,double *,double **,int,int); 
void bpnn_layerrecforward(double *,double *,double **,int,int); 
 
void bpnn_save(BPNN *,char *); 
BPNN *bpnn_read(char *); 
 
double drnd(); 
double dpn1(); 
 
double squash(double); 
double *alloc_1d_dbl(int); 
double **alloc_2d_dbl(int,int); 
void bpnn_randomize_weights(double**,int,int); 
void bpnn_zero_weights(double **,int,int); 
 
 
 
 
 
#endif