// AnnBP.h: interface for the CAnnBP class.
//
//////////////////////////////////////////////////////////////////////


#define ABS(x)          (((x) > 0.0) ? (x) : (-(x)))

/* 宏定義：快速拷貝 */
#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++;\
}

#define BIGRND 0x7fffffff
/*** 神經網絡的數據結構。網絡被假定為一個全連接的3層前向結構，
	 每層的單元0是閾值，這意味著真正的神經元編號為1－n
 ***/

typedef struct {
	int input_n;                  /* 輸入層的神經元個數 */
	int hidden_n;                 /* 隱含層的神經元個數 */
	int output_n;                 /* 輸出層的神經元個數 */

	double *input_units;          /* 輸入層的神經元 */
	double *hidden_units;         /* 隱藏層的神經元 */
	double *output_units;         /* 輸出曾的神經元 */

	double *hidden_delta;         /* 隱藏層的誤差 */
	double *output_delta;         /* 輸出層的誤差 */

	double *target;               /* 目標向量 */

	double **input_weights;       /* 輸入層到隱藏層的連接權 */
	double **hidden_weights;      /* 隱藏層到輸出曾的連接權 */

                                /*** 下面兩個在迭代時使用 ***/
	double **input_prev_weights;  /* 前次輸入層導隱藏層權值的改變 */
	double **hidden_prev_weights; /* 前次隱藏層導輸出層權值的改變 */
} BPNN;


class CAnnBP  
{
public:
	void SetBParm(double eta,double momentum);

	void Initialize(int seed);
	void Read(char* filename);
	void Save(char *filename);
	void Identify(double *input_unit,int input_num,double *target,int target_num);
	void Train(double *input_unit,int input_num, double *target,int target_num, double *eo, double *eh);
	void FreeBP();
	void CreateBP(int n_in,int n_hidden,int n_out);
	CAnnBP();
	virtual ~CAnnBP();
	

	


	double momentum1;
	double eta1;
	BPNN* net;
	BPNN* bpnn_read(char *filename);
	void bpnn_save(BPNN *net, char *filename);
	void bpnn_train(BPNN *net, double eta, double momentum, double *eo, double *eh);
	void bpnn_feedforward(BPNN* net);
	BPNN* bpnn_create(int n_in, int n_hidden, int n_out);
	void bpnn_free(BPNN *net);
	void bpnn_initialize(int seed);
	double* alloc_1d_dbl(int n);
	void bpnn_adjust_weights(double *delta, int ndelta, double *ly, int nly, double** w, double **oldw, double eta, double momentum);
	void bpnn_hidden_error(double* delta_h, int nh, double *delta_o, int no, double **who, double *hidden, double *err);
	void bpnn_output_error(double *delta, double *target, double *output, int nj, double *err);
	void bpnn_layerforward(double *l1, double *l2, double **conn, int n1, int n2);
	BPNN* bpnn_internal_create(int n_in, int n_hidden, int n_out);
	void bpnn_zero_weights(double **w, int m, int n);
	void bpnn_randomize_weights(double **w, int m, int n);
	double ** alloc_2d_dbl(int m, int n);
	double dpn1();
	double drnd();
	double squash(double x);
};