annbp.h

关于汽车牌照检测与识别代码

// 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);
};