- it
- BP简单神经网络分类器实现a+b
#include <stdio.h>
#include <time.h>
#include <math.h>
#include <stdlib.h>
#define Data 820
#define In 2
#define Out 1
#define Neuron 45
#define TrainC 20000
#define A 0.2
#define B 0.4
#define a 0.2
#define b 0.3
double d_in
[Data
][In
],d_out
[Data
][Out
];
double w
[Neuron
][In
],o
[Neuron
],v
[Out
][Neuron
];
double Maxin
[In
],Minin
[In
],Maxout
[Out
],Minout
[Out
];
double OutputData
[Out
];
double dv
[Out
][Neuron
],dw
[Neuron
][In
];
double e
;
void writeTest(){
FILE
*fp1
,*fp2
;
double r1
,r2
;
int i
;
srand((unsigned)time(NULL));
if((fp1
=fopen("D:\\in.txt","w"))==NULL){
printf("can not open the in file\n");
exit(0);
}
if((fp2
=fopen("D:\\out.txt","w"))==NULL){
printf("can not open the out file\n");
exit(0);
}
for(i
=0;i
<Data
;i
++){
r1
=rand()%1000/100.0;
r2
=rand()%1000/100.0;
fprintf(fp1
,"%lf %lf\n",r1
,r2
);
fprintf(fp2
,"%lf \n",r1
+r2
);
}
fclose(fp1
);
fclose(fp2
);
}
void readData(){
FILE
*fp1
,*fp2
;
int i
,j
;
if((fp1
=fopen("D:\\in.txt","r"))==NULL){
printf("can not open the in file\n");
exit(0);
}
for(i
=0;i
<Data
;i
++)
for(j
=0; j
<In
; j
++)
fscanf(fp1
,"%lf",&d_in
[i
][j
]);
fclose(fp1
);
if((fp2
=fopen("D:\\out.txt","r"))==NULL){
printf("can not open the out file\n");
exit(0);
}
for(i
=0;i
<Data
;i
++)
for(j
=0; j
<Out
; j
++)
fscanf(fp1
,"%lf",&d_out
[i
][j
]);
fclose(fp2
);
}
void initBPNework(){
int i
,j
;
for(i
=0; i
<In
; i
++){
Minin
[i
]=Maxin
[i
]=d_in
[0][i
];
for(j
=0; j
<Data
; j
++)
{
Maxin
[i
]=Maxin
[i
]>d_in
[j
][i
]?Maxin
[i
]:d_in
[j
][i
];
Minin
[i
]=Minin
[i
]<d_in
[j
][i
]?Minin
[i
]:d_in
[j
][i
];
}
}
for(i
=0; i
<Out
; i
++){
Minout
[i
]=Maxout
[i
]=d_out
[0][i
];
for(j
=0; j
<Data
; j
++)
{
Maxout
[i
]=Maxout
[i
]>d_out
[j
][i
]?Maxout
[i
]:d_out
[j
][i
];
Minout
[i
]=Minout
[i
]<d_out
[j
][i
]?Minout
[i
]:d_out
[j
][i
];
}
}
for (i
= 0; i
< In
; i
++)
for(j
= 0; j
< Data
; j
++)
d_in
[j
][i
]=(d_in
[j
][i
]-Minin
[i
]+1)/(Maxin
[i
]-Minin
[i
]+1);
for (i
= 0; i
< Out
; i
++)
for(j
= 0; j
< Data
; j
++)
d_out
[j
][i
]=(d_out
[j
][i
]-Minout
[i
]+1)/(Maxout
[i
]-Minout
[i
]+1);
for (i
= 0; i
< Neuron
; ++i
)
for (j
= 0; j
< In
; ++j
){
w
[i
][j
]=rand()*2.0/RAND_MAX
-1;
dw
[i
][j
]=0;
}
for (i
= 0; i
< Neuron
; ++i
)
for (j
= 0; j
< Out
; ++j
){
v
[j
][i
]=rand()*2.0/RAND_MAX
-1;
dv
[j
][i
]=0;
}
}
void computO(int var
){
int i
,j
;
double sum
,y
;
for (i
= 0; i
< Neuron
; ++i
){
sum
=0;
for (j
= 0; j
< In
; ++j
)
sum
+=w
[i
][j
]*d_in
[var
][j
];
o
[i
]=1/(1+exp(-1*sum
));
}
for (i
= 0; i
< Out
; ++i
){
sum
=0;
for (j
= 0; j
< Neuron
; ++j
)
sum
+=v
[i
][j
]*o
[j
];
OutputData
[i
]=sum
;
}
}
void backUpdate(int var
)
{
int i
,j
;
double t
;
for (i
= 0; i
< Neuron
; ++i
)
{
t
=0;
for (j
= 0; j
< Out
; ++j
){
t
+=(OutputData
[j
]-d_out
[var
][j
])*v
[j
][i
];
dv
[j
][i
]=A
*dv
[j
][i
]+B
*(OutputData
[j
]-d_out
[var
][j
])*o
[i
];
v
[j
][i
]-=dv
[j
][i
];
}
for (j
= 0; j
< In
; ++j
){
dw
[i
][j
]=a
*dw
[i
][j
]+b
*t
*o
[i
]*(1-o
[i
])*d_in
[var
][j
];
w
[i
][j
]-=dw
[i
][j
];
}
}
}
double result(double var1
,double var2
)
{
int i
,j
;
double sum
,y
;
var1
=(var1
-Minin
[0]+1)/(Maxin
[0]-Minin
[0]+1);
var2
=(var2
-Minin
[1]+1)/(Maxin
[1]-Minin
[1]+1);
for (i
= 0; i
< Neuron
; ++i
){
sum
=0;
sum
=w
[i
][0]*var1
+w
[i
][1]*var2
;
o
[i
]=1/(1+exp(-1*sum
));
}
sum
=0;
for (j
= 0; j
< Neuron
; ++j
)
sum
+=v
[0][j
]*o
[j
];
return sum
*(Maxout
[0]-Minout
[0]+1)+Minout
[0]-1;
}
void writeNeuron()
{
FILE
*fp1
;
int i
,j
;
if((fp1
=fopen("D:\\neuron.txt","w"))==NULL)
{
printf("can not open the neuron file\n");
exit(0);
}
for (i
= 0; i
< Neuron
; ++i
)
for (j
= 0; j
< In
; ++j
){
fprintf(fp1
,"%lf ",w
[i
][j
]);
}
fprintf(fp1
,"\n\n\n\n");
for (i
= 0; i
< Neuron
; ++i
)
for (j
= 0; j
< Out
; ++j
){
fprintf(fp1
,"%lf ",v
[j
][i
]);
}
fclose(fp1
);
}
void trainNetwork(){
int i
,c
=0,j
;
do{
e
=0;
for (i
= 0; i
< Data
; ++i
){
computO(i
);
for (j
= 0; j
< Out
; ++j
)
e
+=fabs((OutputData
[j
]-d_out
[i
][j
])/d_out
[i
][j
]);
backUpdate(i
);
}
printf("%d %lf\n",c
,e
/Data
);
c
++;
}while(c
<TrainC
&& e
/Data
>0.01);
}
int main(int argc
, char const *argv
[])
{
writeTest();
readData();
initBPNework();
trainNetwork();
printf("%lf \n",result(6,8) );
printf("%lf \n",result(2.1,7) );
printf("%lf \n",result(4.3,8) );
writeNeuron();
return 0;
}
