📄 bp.c
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#include <stdlib.h>
#include <math.h>
#include <conio.h>
#include <stdio.h>
#include <time.h>
#define N 1 /*学习样本个数(测试样本个数)*/
#define IN 20 /*输入层神经元数目*/
#define HN 4 /*隐层神经元数目*/
#define ON 3 /*输出层神经元数目*/
float P[IN]; /*单个样本输入数据*/
float T[ON]; /*单个样本输出数据*/
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 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];
/*读取训练样本*/
GetTrainingData()
{int i,j,m;
float datr;
printf("请输入训练样本\n");
for(i=0;i<N;i++)
{j=0;
while(j!=(IN+ON)&&scanf("%f",&datr)!=EOF)
{if(j>IN-1) Study_Data[i].teach[j-IN]=datr;
else Study_Data[i].input[j]=datr;
j++;
}
}
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 \n",m,i,Study_Data[m].input[i]);}
for(j=0;j<ON;j++)
{printf("Study_Data[%d].teach[%d]=%f \n",m,j,Study_Data[m].teach[j]);}
}
printf("\n\n\nPress any key to begin Study...");
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 1*/
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 1*/
printf("\nYU_ON[%d]=%f\n",kk,YU_ON[kk]);
}
printf("\n\n\n\nPress any key to start culculating...:\n");
getch();
printf("wait please...");
return 1;
}
/*第m个学习样本输入子程序*/
input_P(int m)
{ int i,j;
for(i=0;i<IN;i++) {P[i]=Study_Data[m].input[i];}
return 1;
}
/*第m个样本输出子程序*/
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;
}
/*更新权值,阈值*/
/*输出层*/
int Delta_O_H(int n)
{int k,j;
if(n<=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];
}
}
return 1;
}
/*隐层*/
Delta_H_I(int n)
{int i,j;
if(n<=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];
}
}
return 1;
}
/*读取测试样本*/
GetTestData()
{int i,j,m;
float datr;
printf("输入测试样本\n");
for(i=0;i<N;i++)
{j=0;
while(j!=(IN+ON)&&scanf("%f",&datr)!=EOF)
{if(j>IN-1) Test_Data[i].expect[j-IN]=datr;
else Test_Data[i].input[j]=datr;
j++;
}
}
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 \n ",m,i,Test_Data[m].input[i]);}
for(j=0;j<ON;j++)
{printf("Test__Data[%d].expec[%d]=%f \n ",m,j,Test_Data[m].expect[j]);}
}
printf("\n\n\n\nPress any key to culculating...\n");
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*/
flag=200000000;
a=0.5;
b=0.5;
study=0;
Pre_error=0.000001;/*可接受误差*/
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);
Delta_H_I(n);
}
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");
GetTestData();
printf("\nThe Result of the Test As follows:\n");
Test();
}
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