bp.c

用C语言编写的遗传算法 可用于机器人的智能路径规划

#include <conio.h>
#include <math.h>
#include <stdlib.h>
#include <stdio.h>


#define Beta 1.0
#define NAME_MAX 80

double
bp(w_ih,w_ho,patt,targ,nn,hh,pp,mm,fin,fileW,
		resultF,crition,total,sa)
double **w_ih;	/* weight from input to hidden */
double **w_ho;	/* weight from hidden to output */
double **patt;	/* input patterns */
double **targ;	/* desired outputs */
int	nn;		/* input dim */
int 	hh;		/* hidden dim */
int 	pp;		/* output dim */
int 	mm;		/* training patterns number */
int	*fin;		/* flag for terminate */
char	*fileW;	/* weightFile */
char	*resultF;	/* resultFile Name */
double	crition;	/* minimum mean square */
int total;
int sa;		/* flag for saving weights */
{
	double *output;
	int i,j,s,num_p,num_d,mis,mis_tr,mis_te;
	double err;

	char Msg[NAME_MAX],Msg1[NAME_MAX],Msg2[NAME_MAX],
			Msg3[NAME_MAX],Msg4[NAME_MAX],Msg5[NAME_MAX];

	FILE *fp;
	FILE *cor;

	extern int maximum(double *,int);
	//extern void drawbp(double **,double **,int,int,int);

	void feedward(double **,double **,double *,double *,int,int,int);
	double errSqu(double *,double *,int);

	output=(double *)calloc(pp,sizeof(double));

	err=0.0;
	for (i=0;i<mm;i++)
	{
		feedward(w_ih,w_ho,patt[i],output,nn,hh,pp);

		err +=errSqu(output,targ[i],pp);
	 }

	/* err=sqrt(err/(pp*mm));  	 mean-square-root */

	err=err/(pp*mm);  	 /* mean-square */
	if ((err<=crition)||(sa==1))
	{
		/* draw bp */
	  //	drawbp(w_ih,w_ho,nn,hh,pp);

		if (err<=crition)
			*fin=1;
		if ((fp=fopen(fileW,"wt"))==NULL)
		{
			fprintf(stderr,"cannot open %s file for writing\n",fileW);
			exit(1);
		}

		sprintf(Msg,"weight from input to hidden");
		fprintf(fp,"%s\n",Msg);
		for (j=0;j<hh;j++)
		{
			for (s=0;s<(nn+1);s++)
				fprintf(fp,"%f\t",w_ih[j][s]);
			fprintf(fp,"\n");
		}

		sprintf(Msg,"weight from hidden to output");
		fprintf(fp,"%s\n",Msg);
		for (j=0;j<pp;j++)
		{
			for (s=0;s<(hh+1);s++)
				fprintf(fp,"%f\t",w_ho[j][s]);
			fprintf(fp,"\n");
		}
		fclose(fp);

		if ((cor=fopen(resultF,"wt"))==NULL)
		{
			fprintf(stderr,"cannot open %s for writing\n",resultF);
			exit(1);
		}

		sprintf(Msg,"Index");
		sprintf(Msg1,"Class_Get");
		sprintf(Msg2,"Class_Des");
		sprintf(Msg3,"Out_1");
		sprintf(Msg4,"Out_2");
		sprintf(Msg5,"Out_3");
		fprintf(cor,"%10s %10s %10s %10s %10s %10s\n",Msg,Msg1,Msg2,Msg3,Msg4,Msg5);
		mis=0;
		mis_tr=0;
		mis_te=0;
		for (i=0;i<total;i++)
		{
			feedward(w_ih,w_ho,patt[i],output,nn,hh,pp);
			num_p=maximum(output,pp);
			num_d=maximum(targ[i],pp);
			if (num_p!=num_d)
			{
				if (i<mm)
					mis_tr++;
				else
					mis_te++;
				mis++;
			}
			fprintf(cor,"%10.3d %10.1d %10.1d",i,num_p,num_d);
			for (j=0;j<pp;j++)
				fprintf(cor,"%10.6f",output[j]);
			fprintf(cor,"\n");
		}
		fprintf(cor,"Total number of misclassfication: %d\n",mis);
		fprintf(cor,"   for traing: %d\t for test: %d\n",mis_tr,mis_te);
		fclose(cor);
		gotoxy(57,2);
		printf("Error:%6.6f",err);
		gotoxy(57,3);
		printf("traing:%3.3d test:%3.3d",mis_tr,mis_te);
	}


	free(output);
	return(err);
}

void
feedward(wih,who,in,out,n1,h1,p1)
double **wih;	/* weight from input to hidden */
double **who;	/* weight from hidden to output */
double *in;		/* input vector */
double *out;	/* output vector */
int	n1;	/* input dim */
int 	h1;	/* hidden dim */
int 	p1;	/* output dim */
{
	int i,j;
	double sum;
	double *out_h;
	double sigmoid(double);

	out_h=(double *)calloc(h1,sizeof(double));

	/* calculate the outputs of hidden neurons */
	for (i=0;i<h1;i++)
	{
		sum=0.0;
		for (j=0;j<n1;j++)
			sum +=((wih[i][j])*(in[j]));
		sum +=wih[i][n1];
		out_h[i]=sigmoid(sum);
	}

	/* calculate the network output */
	for (i=0;i<p1;i++)
	{
		sum =0.0;
		for (j=0;j<h1;j++)
			sum +=who[i][j]*out_h[j];
		sum +=who[i][h1];
		out[i]=sigmoid(sum);
	}
	free(out_h);
}

double
errSqu(ou,ta,le)
double *ou;	/* practical output */
double *ta;	/* desired output */
int 	le;	/* output dim */
{
	int i;
	double dif,sum;

	sum=0.0;
	for (i=0;i<le;i++)
	{
		dif=ou[i]-ta[i];
		sum +=(dif*dif);
	}

	return(sum);
}

double
sigmoid(x)
double x;
{
	x=(tanh(x*Beta)+1.0)/2;
	return(x);
}