bp.txt

这是一篇关于神经网络算法。此算法在原算法的基础上有了一定的提高。

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

#define N 8 /*学习样本个数(测试样本个数)*/
#define IN 3 /*输入层神经元数目*/
#define HN 2 /*隐层神经元数目*/
#define ON 1 /*输出层神经元数目*/
float P[IN]; /*单个样本输入数据*/
float T[ON]; /*单个样本教师数据*/
float W[HN][IN]; /*输入层至隐层权值*/
float V[ON][HN]; /*隐层至输出层权值*/
float X[HN]; /*隐层的输入*/
float Y[ON]; /*输出层的输入*/
float H[HN]; /*隐层的输出*/
float O[ON]; /*输出层的输出*/
float YU_HN[HN]; /*隐层的阈值*/
float YU_ON[ON]; /*输出层的阈值*/
float err_m[N]; /*第m个样本的总误差*/
float a; /*输出层至隐层学习效率*/
float b; /*隐层至输入层学习效率*/
float alpha;   /*/动量因子，改进型bp算法使用*/
float d_err[ON];/*δk*/
float e_err[HN];/*δj*/
FILE *fp;
/*定义一个放学习样本的结构*/
struct {
float input[IN];
float teach[ON];
        }Study_Data[N];
/*定义一个放测试样本的结构*/
struct {
float input[IN];
float expect[ON];
        }Test_Data[N];
/*改进型bp算法用来保存每次计算的权值*/
float old_W[HN][IN];
float old_V[ON][HN];


int Start_Show()
{
//clrscr();
printf("\n                        *********************** \n");
printf("                        *     Welcome to use    *\n");
printf("                        *   this program of     *\n");
printf("                        *   calculating the BP *\n");
printf("                        *       model!          *\n");
printf("                        *    Happy every day!   *\n");
printf("                        ***********************\n");
printf("\n\nBefore starting,please read the follows carefully:\n\n");
printf("     The program of BP can study itself for no more than 200000 times.\nAnd surpassing the number,the program will be ended by itself in\npreventing running infinitely because of error!\n");
printf("\n\n\n");
printf("Now press any key to start...\n");
getch();
//clrscr();
return 0;
}
int End_Show()
{
printf("\n\n---------------------------------------------------\n");
printf("The program has reached the end successfully!\n\nPress any key to exit!\n\n");
printf("\n                        ***********************\n");
printf("                        *     This is the end   *\n");
printf("                        * of the program which*\n");
printf("                        * can calculate the BP*\n");
printf("                        *       model!          *\n");
printf("                        ***********************\n");
printf("                        *   Thanks for using!   *\n");
printf("                        *    Happy every day!   *\n");
printf("                        ***********************\n");
getch();
exit(0);
}


/*读取训练样本*/
GetTrainingData()
{int i,j,m;
float datr;
if((fp=fopen("sample.txt","r"))==NULL)
      {
       printf("Cannot open file strike any key exit!");
       getch();
       exit(1);
      }
for(i=0;i<N;i++)
      {j=0;
       while(j!=(IN+ON)&&fscanf(fp,"%f",&datr)!=EOF)
        {if(j>IN-1) Study_Data[i].teach[j-IN]=datr;
         else        Study_Data[i].input[j]=datr;
/*printf("\nthe Study_Data[%d].input[%d]=%f\n %f",i,j,Study_Data[i].input[j],datr);getch();*/
/*use to check the loaded training datas*/
         j++;
        }
      }
fclose(fp);
printf("\nThere are [%d] sample datas that have been loaded successfully!\n",N*(IN+ON));
printf("\nShow the data which has been loaded as follows:\n");
for(m=0;m<N;m++)
    {for(i=0;i<IN;i++)
      {printf("Study_Data[%d].input[%d]=%f          ",m,i,Study_Data[m].input[i]);}
     for(j=0;j<ON;j++)
      {printf("Study_Data[%d].teach[%d]=%f          ",m,j,Study_Data[m].teach[j]);}
    }
printf("\n\n\nPress any key to begin Study...");
getch();
//clrscr();
return 1;
}


/*初始化权、阈值子程序*/
initial()
{int i;
int ii;
int j;
int jj;
int k;
int kk;
printf("\nRandsom Weight value and Bias value as follow:\n");
srand(time(NULL));/*随机函数种子*/
printf("\nWeight Value:\n");
for(i=0;i<HN;i++) {
   for(j=0;j<IN;j++)    {W[i][j]=(float)(((rand()/32767.0)*2-1)/2);
/*初始化输入层到隐层的权值，随机模拟0.5~-0.5 */
     printf("\nw[%d][%d]=%f",i,j,W[i][j]);
     }
   }
for(ii=0;ii<ON;ii++) {
   for(jj=0;jj<HN;jj++)    {V[ii][jj]= (float)(((rand()/32767.0)*2-1)/2);
/*初始化隐层到输出层的权值，随机模拟0.5~-0.5 */
     printf("\nV[%d][%d]=%f",ii,jj,V[ii][jj]);
     }
   }
printf("\n\nBias Value:\n");
for(k=0;k<HN;k++) {
   YU_HN[k] = 1.0;
/*隐层阈值初始化 ,-0.01 ~ 0.01 之间*/
     printf("\nYU_HN[%d]=%f",k,YU_HN[k]);
     }
for(kk=0;kk<ON;kk++) {
   YU_ON[kk] = 1.0;
/*输出层阈值初始化 ,-0.01 ~ 0.01 之间*/
     printf("\nYU_ON[%d]=%f\n",kk,YU_ON[kk]);
    }
printf("\n\n\n\n\nPress any key to start culculating...:\n");
getch();
//clrscr();
printf("Please wait...");
return 1;
}


/*第m个学习样本输入子程序*/
input_P(int m)
{
	int i,j=0;
   for(i=0;i<IN;i++)   {P[i]=Study_Data[m].input[i];}
   return 1;
}


/*第m个样本教师信号子程序*/
input_T(int m)
{int k;
for(k=0;k<ON;k++)    {T[k]=Study_Data[m].teach[k];}
return 1;
}


/*求净输入，输出*/
IN_OUT()
{
float sigma1,sigma2;
int i,j,ii,jj;
for(i=0;i<HN;i++)   {
    sigma1=0.0;
    for(j=0;j<IN;j++)
    {sigma1+=W[i][j]*P[j];}/*求隐层内积*/
     X[i]=sigma1+YU_HN[i];
     H[i]=1.0/(1.0+exp(-X[i]));
    }
for(ii=0;ii<ON;ii++) {
    sigma2=0.0;
    for(jj=0;jj<HN;jj++)
    {sigma2+=V[ii][jj]*H[jj];}
     Y[ii]=sigma2+YU_ON[ii];
     O[ii]=1.0/(1.0+exp(-Y[ii]));
    }
return 1;
}


/*误差分析*/
/*δk*/
int Err_O_H(int m)
{int k;
float abs_err[ON];
float sqr_err=0.0;
for (k=0;k<ON;k++)   {
    abs_err[k]=T[k]-O[k];
    sqr_err+=(abs_err[k])*(abs_err[k]);
    d_err[k]=abs_err[k]*O[k]*(1.0-O[k]);
    err_m[m]=sqr_err/2;
   }
return 1;
}

/*δj*/
int Err_H_I()
{
int j,k;
float sigma;
for(j=0;j<HN;j++) {
   sigma=0.0;
   for(k=0;k<ON;k++)
    {sigma+=d_err[k]*V[k][j];}
     e_err[j]=sigma*H[j]*(1-H[j]);
   }
return 1;
}

/*总误差*/
float Err_Sum()
{int m;
float total_err=0.0;
for(m=0;m<N;m++)
    {total_err+=err_m[m];}
return total_err;
}



/*新旧权值更新量交替--改进型Bp算法*/
saveWV()
{int i;
int ii;
int j;
int jj;
for(i=0;i<HN;i++)    {
    for(j=0;j<IN;j++)
      {old_W[i][j] =b*e_err[i]*P[j];}
    }
for(ii=0;ii<ON;ii++){
    for(jj=0;jj<HN;jj++)
      {old_V[ii][jj] =a*d_err[ii]*H[jj];}
    }
return 1;
}



/*更新权值，阈值*/
/*输出层*/
int Delta_O_H(int n,int study)
{int k,j;
if((n<=1)||(study=1))
   {
    for (k=0;k<ON;k++)   {
      for (j=0;j<HN;j++)
        {V[k][j]=V[k][j]+a*d_err[k]*H[j];}
      YU_ON[k]+=a*d_err[k];
     }
   }
else
   {
    for (k=0;k<ON;k++)   {
      for (j=0;j<HN;j++)
        {V[k][j]=V[k][j]+(1.0-alpha)*a*d_err[k]*H[j]+alpha*old_V[k][j];}
      YU_ON[k]+=a*d_err[k];
     }
   }
return 1;
}
/*隐层*/
Delta_H_I(int n,int study)
{int i,j;
if((n<=1)||(study=1))
{
   for (j=0;j<HN;j++)    {
      for (i=0;i<IN;i++)
        {W[j][i]=W[j][i]+b*e_err[j]*P[i];}
      YU_HN[j]+=b*e_err[j];
     }
   }
else
{
   for(j=0;j<HN;j++)     {
     for(i=0;i<IN;i++)
       {W[j][i]=W[j][i]+(1.0-alpha)*b*e_err[j]*P[i]+alpha*old_W[j][i];}
     YU_HN[j]+=b*e_err[j];
    }
}
return 1;
}


/*保存更新*/
void savequan()
{ int i,j,k;
   int ii,jj,kk;
   /*save weight*/
   if((fp=fopen("weight.txt","a"))==NULL)
    {
      printf("Cannot open file strike any key exit!");
      getch();
      exit(1);
    }
   for(i=0;i<HN;i++)
     {for(j=0;j<IN;j++)   fprintf(fp,"W[%d][%d]=%f\n",i,j,W[i][j]); }

   fprintf(fp,"\n");
   for(ii=0;ii<ON;ii++)
     {for(jj=0;jj<HN;jj++)   fprintf(fp,"V[%d][%d]=%f\n",ii,jj,V[ii][jj]);}

   fclose(fp);
   printf("\nThe result of weight.txt(quanzhi) has been saved successfully!");
   /*save limit*/
   if((fp=fopen("limit.txt","a"))==NULL)
    {
      printf("Cannot open file strike any key exit!");
      getch();
      exit(1);
    }
   for(k=0;k<ON;k++)    fprintf(fp,"YU_ON[%d]=%f\n",k,YU_ON[k]);
   for(kk=0;kk<HN;kk++)   fprintf(fp,"YU_HN[%d]=%f\n",kk,YU_HN[kk]);
   fclose(fp);
   printf("\nThe result of limit.txt(yuzhi) has been saved successfully!\n\n\n\n\nPress any key to Test...");
   getch();
//   clrscr();
}



/*读取测试样本*/
GetTestData()
{int i,j,m;
float datr;
if((fp=fopen("test.txt","r"))==NULL)
      {
       printf("Cannot open file strike any key exit!");
       getch();
       exit(1);
      }
for(i=0;i<N;i++)
      {j=0;
       while(j!=(IN+ON)&&fscanf(fp,"%f",&datr)!=EOF)
        {if(j>IN-1) Test_Data[i].expect[j-IN]=datr;
         else        Test_Data[i].input[j]=datr;
         j++;
        }
      }
fclose(fp);
printf("\nThere are [%d] test datas that have been loaded successfully!\n",N*(IN+ON));
printf("\nShow the data which has been loaded as follows:\n");
for(m=0;m<N;m++)
    {for(i=0;i<IN;i++)
      {printf("Test__Data[%d].input[%d]=%f          ",m,i,Test_Data[m].input[i]);}
     for(j=0;j<ON;j++)
      {printf("Test__Data[%d].expec[%d]=%f          ",m,j,Test_Data[m].expect[j]);}
    }
printf("\n\n\n\nPress any key to culculating...");
getch();
//clrscr();
return 1;
}


/*样本测试及结果*/
Test()
{int i,j,k,m;
float net1[HN],net2[ON],H_net[HN],O_net[ON];
for(m=0;m<N;m++)
     {for(i=0;i<HN;i++)
        {net1[i]=0.0;
         for(j=0;j<IN;j++)
            {net1[i]+=Test_Data[m].input[j]*W[i][j];
            }
         net1[i]=net1[i]+YU_HN[i];
         H_net[i]=1.0/(1.0+exp(-net1[i]));
        }
      for(k=0;k<ON;k++)
        {net2[k]=0.0;
         for(j=0;j<HN;j++)
            {net2[k]+=H_net[j]*V[k][j];
            }
         net2[k]=net2[k]+YU_ON[k];
         O_net[k]=1.0/(1.0+exp(-net2[k]));
         printf("\nTest result[%d]=%f\n",m,O_net[k]);
        }
     }
}



/**********************/
/**程序入口，即主程序**/
/**********************/
void main()
{float Pre_error;
float sum_err;
long int study;
long int flag;
int n=1;/*n=1:普通Bp算法；n=2:改进型Bp算法*/
flag=200000;
a=0.5;
b=0.5;
alpha=0.5;
study=0;
Pre_error=0.000001;/*可接受误差*/
Start_Show();
GetTrainingData();
initial();
do
   {int m;
    ++study;
    for(m=0;m<N;m++)
     {input_P(m);
      input_T(m);
      IN_OUT();
      Err_O_H(m);
      Err_H_I();
      Delta_O_H(n,study);
      Delta_H_I(n,study);
      saveWV();
     }
   sum_err=Err_Sum();
   if(study>flag)
    {
     printf("\n*******************************\n");
     printf("The program is ended by itself because of error!\nThe learning times is surpassed!\n");
     printf("*****************************\n");
     getch();
     break;
    }
}while (sum_err>Pre_error);
printf("\n****************\n");
printf("\nThe program have studyed for [%ld] times!\n",study);
printf("\n****************\n");
savequan();         /*save the results*/
GetTestData();
printf("\nThe Result of the Test As follows:\n");
Test();
End_Show();
}