?? adaline.c
字號(hào):
/******************************************************************************
===============
Network: Adaline Network
===============
Application: Pattern Recognition
Classification of Digits 0-9
Author: Karsten Kutza
Date: 15.4.96
Reference: B. Widrow, M.E. Hoff
Adaptive Switching Circuits
1960 IRE WESCON Convention Record, IRE, New York, NY,
pp. 96-104, 1960
******************************************************************************/
/******************************************************************************
D E C L A R A T I O N S
******************************************************************************/
#include <stdlib.h>
#include <stdio.h>
typedef int BOOL;
typedef char CHAR;
typedef int INT;
typedef double REAL;
#define FALSE 0
#define TRUE 1
#define NOT !
#define AND &&
#define OR ||
#define MIN(x,y) ((x)<(y) ? (x) : (y))
#define MAX(x,y) ((x)>(y) ? (x) : (y))
#define LO -1
#define HI +1
#define BIAS 1
#define sqr(x) ((x)*(x))
typedef struct { /* A LAYER OF A NET: */
INT Units; /* - number of units in this layer */
REAL* Activation; /* - activation of ith unit */
INT* Output; /* - output of ith unit */
REAL* Error; /* - error term of ith unit */
REAL** Weight; /* - connection weights to ith unit */
} LAYER;
typedef struct { /* A NET: */
LAYER* InputLayer; /* - input layer */
LAYER* OutputLayer; /* - output layer */
REAL Eta; /* - learning rate */
REAL Error; /* - total net error */
REAL Epsilon; /* - net error to terminate training */
} NET;
/******************************************************************************
R A N D O M S D R A W N F R O M D I S T R I B U T I O N S
******************************************************************************/
void InitializeRandoms()
{
srand(4711);
}
INT RandomEqualINT(INT Low, INT High)
{
return rand() % (High-Low+1) + Low;
}
REAL RandomEqualREAL(REAL Low, REAL High)
{
return ((REAL) rand() / RAND_MAX) * (High-Low) + Low;
}
/******************************************************************************
A P P L I C A T I O N - S P E C I F I C C O D E
******************************************************************************/
#define NUM_DATA 10
#define X 5
#define Y 7
#define N (X * Y)
#define M 10
CHAR Pattern[NUM_DATA][Y][X] = { { " OOO ",
"O O",
"O O",
"O O",
"O O",
"O O",
" OOO " },
{ " O ",
" OO ",
"O O ",
" O ",
" O ",
" O ",
" O " },
{ " OOO ",
"O O",
" O",
" O ",
" O ",
" O ",
"OOOOO" },
{ " OOO ",
"O O",
" O",
" OOO ",
" O",
"O O",
" OOO " },
{ " O ",
" OO ",
" O O ",
"O O ",
"OOOOO",
" O ",
" O " },
{ "OOOOO",
"O ",
"O ",
"OOOO ",
" O",
"O O",
" OOO " },
{ " OOO ",
"O O",
"O ",
"OOOO ",
"O O",
"O O",
" OOO " },
{ "OOOOO",
" O",
" O",
" O ",
" O ",
" O ",
"O " },
{ " OOO ",
"O O",
"O O",
" OOO ",
"O O",
"O O",
" OOO " },
{ " OOO ",
"O O",
"O O",
" OOOO",
" O",
"O O",
" OOO " } };
INT Input [NUM_DATA][N];
INT Output[NUM_DATA][M] =
{ {HI, LO, LO, LO, LO, LO, LO, LO, LO, LO},
{LO, HI, LO, LO, LO, LO, LO, LO, LO, LO},
{LO, LO, HI, LO, LO, LO, LO, LO, LO, LO},
{LO, LO, LO, HI, LO, LO, LO, LO, LO, LO},
{LO, LO, LO, LO, HI, LO, LO, LO, LO, LO},
{LO, LO, LO, LO, LO, HI, LO, LO, LO, LO},
{LO, LO, LO, LO, LO, LO, HI, LO, LO, LO},
{LO, LO, LO, LO, LO, LO, LO, HI, LO, LO},
{LO, LO, LO, LO, LO, LO, LO, LO, HI, LO},
{LO, LO, LO, LO, LO, LO, LO, LO, LO, HI} };
FILE* f;
void InitializeApplication(NET* Net)
{
INT n,i,j;
Net->Eta = 0.001;
Net->Epsilon = 0.0001;
for (n=0; n<NUM_DATA; n++) {
for (i=0; i<Y; i++) {
for (j=0; j<X; j++) {
Input[n][i*X+j] = (Pattern[n][i][j] == 'O') ? HI : LO;
}
}
}
f = fopen("ADALINE.txt", "w");
}
void WriteInput(NET* Net, INT* Input)
{
INT i;
for (i=0; i<N; i++) {
if (i%X == 0) {
fprintf(f, "\n");
}
fprintf(f, "%c", (Input[i] == HI) ? 'O' : ' ');
}
fprintf(f, " -> ");
}
void WriteOutput(NET* Net, INT* Output)
{
INT i;
INT Count, Index;
Count = 0;
for (i=0; i<M; i++) {
if (Output[i] == HI) {
Count++;
Index = i;
}
}
if (Count == 1)
fprintf(f, "%i\n", Index);
else
fprintf(f, "%s\n", "invalid");
}
void FinalizeApplication(NET* Net)
{
fclose(f);
}
/******************************************************************************
I N I T I A L I Z A T I O N
******************************************************************************/
void GenerateNetwork(NET* Net)
{
INT i;
Net->InputLayer = (LAYER*) malloc(sizeof(LAYER));
Net->OutputLayer = (LAYER*) malloc(sizeof(LAYER));
Net->InputLayer->Units = N;
Net->InputLayer->Output = (INT*) calloc(N+1, sizeof(INT));
Net->InputLayer->Output[0] = BIAS;
Net->OutputLayer->Units = M;
Net->OutputLayer->Activation = (REAL*) calloc(M+1, sizeof(REAL));
Net->OutputLayer->Output = (INT*) calloc(M+1, sizeof(INT));
Net->OutputLayer->Error = (REAL*) calloc(M+1, sizeof(REAL));
Net->OutputLayer->Weight = (REAL**) calloc(M+1, sizeof(REAL*));
for (i=1; i<=M; i++) {
Net->OutputLayer->Weight[i] = (REAL*) calloc(N+1, sizeof(REAL));
}
Net->Eta = 0.1;
Net->Epsilon = 0.01;
}
void RandomWeights(NET* Net)
{
INT i,j;
for (i=1; i<=Net->OutputLayer->Units; i++) {
for (j=0; j<=Net->InputLayer->Units; j++) {
Net->OutputLayer->Weight[i][j] = RandomEqualREAL(-0.5, 0.5);
}
}
}
void SetInput(NET* Net, INT* Input, BOOL Protocoling)
{
INT i;
for (i=1; i<=Net->InputLayer->Units; i++) {
Net->InputLayer->Output[i] = Input[i-1];
}
if (Protocoling) {
WriteInput(Net, Input);
}
}
void GetOutput(NET* Net, INT* Output, BOOL Protocoling)
{
INT i;
for (i=1; i<=Net->OutputLayer->Units; i++) {
Output[i-1] = Net->OutputLayer->Output[i];
}
if (Protocoling) {
WriteOutput(Net, Output);
}
}
/******************************************************************************
P R O P A G A T I N G S I G N A L S
******************************************************************************/
void PropagateNet(NET* Net)
{
INT i,j;
REAL Sum;
for (i=1; i<=Net->OutputLayer->Units; i++) {
Sum = 0;
for (j=0; j<=Net->InputLayer->Units; j++) {
Sum += Net->OutputLayer->Weight[i][j] * Net->InputLayer->Output[j];
}
Net->OutputLayer->Activation[i] = Sum;
if (Sum >= 0)
Net->OutputLayer->Output[i] = HI;
else
Net->OutputLayer->Output[i] = LO;
}
}
/******************************************************************************
A D J U S T I N G W E I G H T S
******************************************************************************/
void ComputeOutputError(NET* Net, INT* Target)
{
INT i;
REAL Err;
Net->Error = 0;
for (i=1; i<=Net->OutputLayer->Units; i++) {
Err = Target[i-1] - Net->OutputLayer->Activation[i];
Net->OutputLayer->Error[i] = Err;
Net->Error += 0.5 * sqr(Err);
}
}
void AdjustWeights(NET* Net)
{
INT i,j;
INT Out;
REAL Err;
for (i=1; i<=Net->OutputLayer->Units; i++) {
for (j=0; j<=Net->InputLayer->Units; j++) {
Out = Net->InputLayer->Output[j];
Err = Net->OutputLayer->Error[i];
Net->OutputLayer->Weight[i][j] += Net->Eta * Err * Out;
}
}
}
/******************************************************************************
S I M U L A T I N G T H E N E T
******************************************************************************/
void SimulateNet(NET* Net, INT* Input, INT* Target, BOOL Training, BOOL Protocoling)
{
INT Output[M];
SetInput(Net, Input, Protocoling);
PropagateNet(Net);
GetOutput(Net, Output, Protocoling);
ComputeOutputError(Net, Target);
if (Training)
AdjustWeights(Net);
}
/******************************************************************************
M A I N
******************************************************************************/
void main()
{
NET Net;
REAL Error;
BOOL Stop;
INT n,m;
InitializeRandoms();
GenerateNetwork(&Net);
RandomWeights(&Net);
InitializeApplication(&Net);
do {
Error = 0;
Stop = TRUE;
for (n=0; n<NUM_DATA; n++) {
SimulateNet(&Net, Input[n], Output[n], FALSE, FALSE);
Error = MAX(Error, Net.Error);
Stop = Stop AND (Net.Error < Net.Epsilon);
}
Error = MAX(Error, Net.Epsilon);
printf("Training %0.0f%% completed ...\n", (Net.Epsilon / Error) * 100);
if (NOT Stop) {
for (m=0; m<10*NUM_DATA; m++) {
n = RandomEqualINT(0, NUM_DATA-1);
SimulateNet(&Net, Input[n], Output[n], TRUE, FALSE);
}
}
} while (NOT Stop);
for (n=0; n<NUM_DATA; n++) {
SimulateNet(&Net, Input[n], Output[n], FALSE, TRUE);
}
FinalizeApplication(&Net);
}
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