www.pudn.com > dip.rar > AnnBP.h

// AnnBP.h: interface for the CAnnBP class. 
// 
////////////////////////////////////////////////////////////////////// 
 
 
#define ABS(x)          (((x) > 0.0) ? (x) : (-(x))) 
 
/* 宏定义：快速拷贝 */ 
#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 
/*** 神经网络的数据结构。网络被假定为一个全连接的3层前向结构， 
	 每层的单元0是阈值，这意味着真正的神经元编号为1－n 
 ***/ 
 
typedef struct { 
	int input_n;                  /* 输入层的神经元个数 */ 
	int hidden_n;                 /* 隐含层的神经元个数 */ 
	int output_n;                 /* 输出层的神经元个数 */ 
 
	double *input_units;          /* 输入层的神经元 */ 
	double *hidden_units;         /* 隐藏层的神经元 */ 
	double *output_units;         /* 输出曾的神经元 */ 
 
	double *hidden_delta;         /* 隐藏层的误差 */ 
	double *output_delta;         /* 输出层的误差 */ 
 
	double *target;               /* 目标向量 */ 
 
	double **input_weights;       /* 输入层到隐藏层的连接权 */ 
	double **hidden_weights;      /* 隐藏层到输出曾的连接权 */ 
 
                                /*** 下面两个在迭代时使用 ***/ 
	double **input_prev_weights;  /* 前次输入层导隐藏层权值的改变 */ 
	double **hidden_prev_weights; /* 前次隐藏层导输出层权值的改变 */ 
} 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); 
};