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一个具有很好编程风格的代码
/******************************************************************************
===========================
Network: Adaptive Resonance Theory 1
===========================
Application: Brain Modeling
Stability-Plasticity Demonstration
Author: Karsten Kutza
Date: 27.6.96
Reference: G.A. Carpenter, S. Grossberg
A Massively Parallel Architecture
for a Self-Organizing Neural Pattern Recognition Machine
Computer Vision, Graphics, and Image Processing, 37,
pp. 54-115, 1987
******************************************************************************/
/******************************************************************************
D E C L A R A T I O N S
******************************************************************************/
#include <stdlib.h>
#include <stdio.h>
#include <math.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_REAL -HUGE_VAL
#define MAX_REAL +HUGE_VAL
typedef struct { /* A LAYER OF A NET: */
INT Units; /* - number of units in this layer */
BOOL* Output; /* - output of ith unit */
REAL** Weight; /* - connection weights to ith unit */
BOOL* Inhibited; /* - inhibition status of ith F2 unit */
} LAYER;
typedef struct { /* A NET: */
LAYER* F1; /* - F1 layer */
LAYER* F2; /* - F2 layer */
INT Winner; /* - last winner in F2 layer */
REAL A1; /* - A parameter for F1 layer */
REAL B1; /* - B parameter for F1 layer */
REAL C1; /* - C parameter for F1 layer */
REAL D1; /* - D parameter for F1 layer */
REAL L; /* - L parameter for net */
REAL Rho; /* - vigilance parameter */
} NET;
/******************************************************************************
A P P L I C A T I O N - S P E C I F I C C O D E
******************************************************************************/
#define N 10
#define M 10
#define NO_WINNER M
#define NUM_DATA 30
CHAR Pattern[NUM_DATA][N] = { " O ",
" O O",
" O",
" O O",
" O",
" O O",
" O",
" OO O",
" OO ",
" OO O",
" OO ",
"OOO ",
"OO ",
"O ",
"OO ",
"OOO ",
"OOOO ",
"OOOOO",
"O ",
" O ",
" O ",
" O ",
" O",
" O O",
" OO O",
" OO ",
"OOO ",
"OO ",
"OOOO ",
"OOOOO" };
BOOL Input [NUM_DATA][N];
BOOL Output[NUM_DATA][M];
FILE* f;
void InitializeApplication(NET* Net)
{
INT n,i,j;
for (n=0; n<NUM_DATA; n++) {
for (i=0; i<N; i++) {
Input[n]
= (Pattern[n]
== 'O');
}
}
Net->A1 = 1;
Net->B1 = 1.5;
Net->C1 = 5;
Net->D1 = 0.9;
Net->L = 3;
Net->Rho = 0.9;
for (i=0; i<Net->F1->Units; i++) {
for (j=0; j<Net->F2->Units; j++) {
Net->F1->Weight
[j] = (Net->B1 - 1) / Net->D1 + 0.2;
Net->F2->Weight[j]
= Net->L / (Net->L - 1 + N) - 0.1;
}
}
f = fopen("c:\ART1.txt", "w");
}
void WriteInput(NET* Net, BOOL* Input)
{
INT i;
for (i=0; i<N; i++) {
fprintf(f, "%c", (Input
) ? 'O' : ' ');
}
fprintf(f, " -> ");
}
void WriteOutput(NET* Net, BOOL* Output)
{
if (Net->Winner != NO_WINNER)
fprintf(f, "Class %i\n", Net->Winner);
else
fprintf(f, "new Input and all Classes exhausted\n");
}
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->F1 = (LAYER*) malloc(sizeof(LAYER));
Net->F2 = (LAYER*) malloc(sizeof(LAYER));
Net->F1->Units = N;
Net->F1->Output = (BOOL*) calloc(N, sizeof(BOOL));
Net->F1->Weight = (REAL**) calloc(N, sizeof(REAL*));
Net->F2->Units = M;
Net->F2->Output = (BOOL*) calloc(M, sizeof(BOOL));
Net->F2->Weight = (REAL**) calloc(M, sizeof(REAL*));
Net->F2->Inhibited = (BOOL*) calloc(M, sizeof(BOOL));
for (i=0; i<N; i++) {
Net->F1->Weight
= (REAL*) calloc(M, sizeof(REAL));
}
for (i=0; i<M; i++) {
Net->F2->Weight
= (REAL*) calloc(N, sizeof(REAL));
}
}
void SetInput(NET* Net, BOOL* Input)
{
INT i;
REAL Activation;
for (i=0; i<Net->F1->Units; i++) {
Activation = Input
/ (1 + Net->A1 * (Input
+ Net->B1) + Net->C1);
Net->F1->Output
= (Activation > 0);
}
}
void GetOutput(NET* Net, BOOL* Output)
{
INT i;
for (i=0; i<Net->F2->Units; i++) {
Output
= Net->F2->Output
;
}
}
/******************************************************************************
P R O P A G A T I N G S I G N A L S
******************************************************************************/
void PropagateToF2(NET* Net)
{
INT i,j;
REAL Sum, MaxOut;
MaxOut = MIN_REAL;
Net->Winner = NO_WINNER;
for (i=0; i<Net->F2->Units; i++) {
if (NOT Net->F2->Inhibited
) {
Sum = 0;
for (j=0; j<Net->F1->Units; j++) {
Sum += Net->F2->Weight
[j] * Net->F1->Output[j];
}
if (Sum > MaxOut) {
MaxOut = Sum;
Net->Winner = i;
}
}
Net->F2->Output
= FALSE;
}
if (Net->Winner != NO_WINNER)
Net->F2->Output[Net->Winner] = TRUE;
}
void PropagateToF1(NET* Net, BOOL* Input)
{
INT i;
REAL Sum, Activation;
for (i=0; i<Net->F1->Units; i++) {
Sum = Net->F1->Weight
[Net->Winner] * Net->F2->Output[Net->Winner];
Activation = (Input
+ Net->D1 * Sum - Net->B1) /
(1 + Net->A1 * (Input
+ Net->D1 * Sum) + Net->C1);
Net->F1->Output
= (Activation > 0);
}
}
/******************************************************************************
A D J U S T I N G W E I G H T S
******************************************************************************/
REAL Magnitude(NET* Net, BOOL* Input)
{
INT i;
REAL Magnitude;
Magnitude = 0;
for (i=0; i<Net->F1->Units; i++) {
Magnitude += Input
;
}
return Magnitude;
}
void AdjustWeights(NET* Net)
{
INT i;
REAL MagnitudeInput_;
for (i=0; i<Net->F1->Units; i++) {
if (Net->F1->Output
) {
MagnitudeInput_ = Magnitude(Net, Net->F1->Output);
Net->F1->Weight
[Net->Winner] = 1;
Net->F2->Weight[Net->Winner]
= Net->L / (Net->L - 1 + MagnitudeInput_);
}
else {
Net->F1->Weight
[Net->Winner] = 0;
Net->F2->Weight[Net->Winner]
= 0;
}
}
}
/******************************************************************************
S I M U L A T I N G T H E N E T
******************************************************************************/
void SimulateNet(NET* Net, BOOL* Input, BOOL* Output)
{
INT i;
BOOL Resonance, Exhausted;
REAL MagnitudeInput, MagnitudeInput_;
WriteInput(Net, Input);
for (i=0; i<Net->F2->Units; i++) {
Net->F2->Inhibited
= FALSE;
}
Resonance = FALSE;
Exhausted = FALSE;
do {
SetInput(Net, Input);
PropagateToF2(Net);
GetOutput(Net, Output);
if (Net->Winner != NO_WINNER) {
PropagateToF1(Net, Input);
MagnitudeInput = Magnitude(Net, Input);
MagnitudeInput_ = Magnitude(Net, Net->F1->Output);
if ((MagnitudeInput_ / MagnitudeInput) < Net->Rho)
Net->F2->Inhibited[Net->Winner] = TRUE;
else
Resonance = TRUE;
}
else Exhausted = TRUE;
} while (NOT (Resonance OR Exhausted));
if (Resonance)
AdjustWeights(Net);
WriteOutput(Net, Output);
}
/******************************************************************************
M A I N
******************************************************************************/
void main()
{
NET Net;
INT n;
GenerateNetwork(&Net);
InitializeApplication(&Net);
for (n=0; n<NUM_DATA; n++) {
SimulateNet(&Net, Input[n], Output[n]);
}
FinalizeApplication(&Net);
}
Posted: 2010-11-07 17:41 |
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