?? nn-1.cpp
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// Neural Networks
// Written by Microsoft Visual C++
// Copyright by UTLab at Tsinghua University
#include "math.h"
#include "stdlib.h"
#include "stdio.h"
#include "conio.h"
#include "UTLab.h"
#define N 4 // number of input neurons (decision variables)。4個輸入神經元
#define H 10 // number of hidden neurons。10個隱藏神經元
#define O 1 // number of output neurons 。1個輸出神經元
#define D 3000 // number of training data
#define SIMU_PRINT_NUMBER 100
static double BOUND[N+O+1][2], WH[H+1][N+1], WO[O+1][H+1];
static void Simu(double x[D+1][N+1], double y[D+1][O+1]);
static void Input_Output(double x[N+1], double y[O+1]);
static void Train(double original_x[D+1][N+1], double original_y[D+1][O+1],
double standard_x[D+1][N+1], double standard_y[D+1][O+1]);
static void Standarlize(double original_x[D+1][N+1], double original_y[D+1][O+1],
double standard_x[D+1][N+1], double standard_y[D+1][O+1]);
static void BPA(double standard_x[D+1][N+1], double standard_y[D+1][O+1]);
static void Init_NN(void);
static void NN(double x[N+1], double y[O+1]);
static void Input_Output(double x[N+1], double y[O+1])
{
int i;
double pi=3.1415926;
for(i=1; i<=N; i++) x[i]=myu(0,2*pi);
y[1]=sin(x[1])+sin(x[2])+sin(x[3])+sin(x[4]);
}
int main( void )
{
double x[D+1][N+1], y[D+1][O+1], standard_x[D+1][N+1], standard_y[D+1][O+1];
srand(100);
// 產生輸入-輸出樣本
Simu(x,y);
Standarlize(x,y,standard_x,standard_y);
Train(x,y,standard_x,standard_y);
return 1;
}
static void BPA(double x[D+1][N+1],double y[D+1][O+1])
{
double E0 = 0.001;
double EE0 = 0.001;
double TOLERANCE=0.0001;// *((N+1+O)*H+O);
int i,j,k,step=0;
double ALPHA=0.05;
double BETA=4./3;
double ETA=0.5;
double MU=1.;
double DETA_WH[H+1][N+1],DETA_WO[O+1][H+1];
double E_WO[O+1],E_WH[H+1],h[H+1],NN_y[O+1];
double e[O+1],lambda,E,EE,TD,temp;
Init_NN();
for( i=1; i<=H; i++ )
for( j=0; j<=N; j++ )
DETA_WH[i][j] = 0.;
for( i=1; i<=O; i++ )
for( j=0; j<=H; j++ )
DETA_WO[i][j] = 0.;
lambda=1.;
do
{ step++;
E=0.;
TD=0.;
EE=0.;
for( k=1; k<=D; k++)
{ /*calculate the output of the hidden neurons*/
h[0]=1.;
for( i=1; i<=H; i++ )
{ h[i] = WH[i][0];
for( j=1; j<=N; j++ )
h[i] += WH[i][j] * x[k][j];
h[i] = tanh(h[i]);
}
/*calculate the output of the outlayer neurons*/
for( i=1; i<=O; i++ )
{ NN_y[i] = WO[i][0];
for( j=1; j<=H; j++)
NN_y[i] += WO[i][j] * h[j];
}
/*adjust the weights of WO:*/
for( i=1; i<=O; i++ )
{ e[i] = y[k][i] - NN_y[i];
E_WO[i] = lambda*e[i] + (1-lambda)*tanh(BETA*e[i]);
for( j=0; j<=H; j++ )
{ DETA_WO[i][j] = ETA*DETA_WO[i][j] + ALPHA*E_WO[i]*h[j];
TD += fabs(DETA_WO[i][j]);
WO[i][j] += DETA_WO[i][j];
}
}
/*adjust the weights of WH:*/
for( j=1; j<=H; j++ )
{ for( i=1,temp=0.; i<=O; i++ )
temp += E_WO[i] * WO[i][j];
E_WH[j] = ( 1-h[j]*h[j])*temp;
DETA_WH[j][0] = ETA*DETA_WH[j][0] +ALPHA*E_WH[j];
TD += fabs(DETA_WH[j][0]);
WH[j][0] += DETA_WH[j][0];
for( i=1; i<=N; i++ )
{ DETA_WH[j][i]= ETA*DETA_WH[j][i] +ALPHA*E_WH[j]*x[k][i];
TD += fabs(DETA_WH[j][i]);
WH[j][i] += DETA_WH[j][i];
}
}
/*calculate the total errors*/
for( i=1; i<=H; i++ )
{ h[i] = WH[i][0];
for( j=1; j<=N; j++ )
h[i] += WH[i][j]*x[k][j];
h[i] = tanh(h[i]);
}
for( i=1; i<=O; i++ )
{ NN_y[i] = WO[i][0];
for( j=1; j<=H; j++)
NN_y[i] += WO[i][j] * h[j];
}
for( i=1; i<=O; i++ )
{ e[i] = y[k][i] - NN_y[i];
EE += fabs(e[i])/D;
E += 0.5*e[i]*e[i];
}
}
lambda = exp( -MU/(E*E));
TD=TD/D;
if(step%100==0) printf("\nBPA_%d (Std)Sum-squared-E=%f Average-E=%f Gradient=%f;",step,E,EE,TD);
} while ( step<2000 && TD>TOLERANCE && E>E0 && EE>EE0);
printf("\nBPA_%d (Std)Sum-squared-E=%f Average-E=%f Gradient=%f;",step,E,EE,TD);
}
// 產生輸入-輸出樣本
static void Simu(double x[D+1][N+1], double y[D+1][O+1])
{
int i, j;
double xx[N+1], yy[O+1];
float read;
FILE *fp;
if((fp=fopen("TRAIN1.dat","r"))==NULL) {
mark:
fp=fopen("TRAIN1.dat","w");
for(i=1; i<=D; i++) {
// 產生一個輸入-輸出對，輸入、輸出值都從第2個位置開始放置！
Input_Output(xx, yy);
if(i%SIMU_PRINT_NUMBER==0)
printf("\n%d input-output Data have been generated",i);
for(j=1; j<=N; j++) {
x[i][j]=xx[j];
fprintf(fp,"%3.6f ",xx[j]);
}
for(j=1; j<=O; j++) {
y[i][j]=yy[j];
fprintf(fp,"%3.6f ",yy[j]);
}
fprintf(fp,"\n");
}
fclose(fp);
}
else {
printf("The training data <<TRAIN1.dat>> already exists. Renew it? (y/n)");
if(getche()=='y') goto mark;
for(i=1; i<=D; i++) {
for(j=1; j<=N; j++) {
fscanf(fp,"%f",&read);
x[i][j]=read;
}
for(j=1; j<=O; j++) {
fscanf(fp,"%f",&read);
y[i][j]=read;
}
}
}
fclose(fp);
}
static void Train(double x[D+1][N+1], double y[D+1][O+1],double standard_x[D+1][N+1], double standard_y[D+1][O+1])
{
int i, j, k;
double E1, E2, a, xx[N+1], yy[O+1];
float read;
FILE *fp;
if((fp=fopen("WEIGHT1.dat","r"))==NULL) {
mark:
BPA(standard_x,standard_y);
E1=0;
E2=0;
for(i=1; i<=D; i++) {
for(j=1; j<=N; j++) xx[j]=x[i][j];
NN(xx,yy);
a=0;
for(j=1; j<=O; j++) a += (yy[j]-y[i][j])*(yy[j]-y[i][j]);
E1=E1+a;
E2=E2+sqrt(a);
}
E1=E1/2;
E2=E2/D;
printf("\n\nSum-Squared Error = %3.4f",E1);
printf("\nAverage Error = %3.4f\n\n",E2);
for(i=1; i<=18; i++){
k=(int)myu(1,D);
for(j=1; j<=N; j++) xx[j]=x[k][j];
NN(xx,yy);
printf("Sample %2d: y=(",i);
for(j=1; j<=O; j++) {
if(j<O) printf("%3.4f,",y[k][j]);
else printf("%3.4f",y[k][j]);
}
printf(") Dif.=(");
for(j=1; j<=O; j++) {
if(j<O) printf("%3.4f,",yy[j]-y[k][j]);
else printf("%3.4f",yy[j]-y[k][j]);
}
printf(")\n");
}
printf("\nAre you satisfactory?(y/n)");
if(getche()=='n') goto mark;
printf("\n");
fp=fopen("WEIGHT1.dat","w");
for(i=1; i<=H; i++) {
for(j=0; j<=N; j++) fprintf(fp,"%3.4f ",WH[i][j]);
fprintf(fp,"\n");
}
for(i=1; i<=O; i++) {
for(j=0; j<=H; j++) fprintf(fp,"%3.4f ",WO[i][j]);
fprintf(fp,"\n");
}
fclose(fp);
}
else {
printf("\nThe weight data <<WEIGHT1.dat>> already exists. Renew it? (y/n)");
if(getche()=='y') goto mark;
for(i=1; i<=H; i++)
for(j=0; j<=N; j++) {
fscanf(fp, "%f", &read);
WH[i][j]=read;
}
for(i=1; i<=O; i++)
for(j=0; j<=H; j++) {
fscanf(fp,"%f", &read);
WO[i][j]=read;
}
fclose(fp);
}
}
static void Standarlize(double x[D+1][N+1],double y[D+1][O+1],double xx[D+1][N+1],double yy[D+1][O+1])
{
int i,k;
for(i=1; i<=N; i++) {
BOUND[i][0]=x[1][i];
BOUND[i][1]=x[1][i];
}
for( i=1; i<=O; i++ ) {
BOUND[N+i][0]=y[1][i];
BOUND[N+i][1]=y[1][i];
}
for( k=2; k<=D; k++ ) {
for( i=1; i<=N; i++ )
{ if(BOUND[i][0]>x[k][i])
BOUND[i][0]=x[k][i];
if(BOUND[i][1]<x[k][i])
BOUND[i][1]=x[k][i];
}
for( i=1; i<=O; i++ )
{ if(BOUND[N+i][0]>y[k][i])
BOUND[N+i][0]=y[k][i];
if(BOUND[N+i][1]<y[k][i])
BOUND[N+i][1]=y[k][i];
}
}
for( i=1; i<=N+O; i++ )
BOUND[i][1]=BOUND[i][1] - BOUND[i][0];
for( k=1; k<=D; k++ )
{ for( i=1; i<=N; i++ )
xx[k][i]=(x[k][i]-BOUND[i][0])/BOUND[i][1];
for( i=1; i<=O; i++ )
yy[k][i]=(y[k][i]-BOUND[N+i][0])/BOUND[N+i][1];
}
}
static void NN( double x[], double y[] )
{
double h[H+1];
int i,j;
for( i=1; i<=H; i++ )
{ h[i] = WH[i][0];
for( j=1; j<=N; j++ )
h[i] += WH[i][j] * (x[j]-BOUND[j][0])/BOUND[j][1];
h[i] = tanh( h[i] );
}
for( i=1; i<=O; i++ )
{ y[i] = WO[i][0];
for( j=1; j<=H; j++)
y[i] += WO[i][j] * h[j];
y[i] = y[i]*BOUND[N+i][1] + BOUND[N+i][0];
}
}
static void Init_NN(void)
{
int i, j;
for(i=1; i<=H; i++)
for(j=0; j<=N; j++)
WH[i][j]= myu(-0.3,0.3);
for(i=1; i<=O; i++)
for(j=0; j<=H; j++)
WO[i][j]= myu(-0.3,0.3);
}
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