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/* =======================================================
Neural Network Classes for the NeXT Computer
Written by: Ralph Zazula
University of Arizona
zazula@bonehead.tucson.az.us (NeXT Mail)
==========================================================*/
//
// This feed-forward, back-prop network is setup to learn
// the identity function (output=input). The number N is the
// number of inputs (outputs) and H is the number of hidden
// nodes. The case (H < N) is most interesting - this is a form
// of data compression.
// The number P determines the number of random patterns to learn.
//
#import "BackPropEngine.h"
#import <stdio.h>
#define N 8
#define H 4
#define P 10
void main()
{
id bp = [[BackPropEngine alloc] initWithInputs:N
hidden:H
outputs:N];
id random = [[Random alloc] init];
double inputs[P][N];
double target[P][N];
int i,j;
[bp setEta:0.9];
for(j=0; j<P; j++)
for(i=0; i<N; i++)
inputs[j][i] = target[j][i] = [random percent]*0.8;
for(j=0; j<100000; j++) {
for(i=0; i<20; i++) {
[bp applyInput:inputs[j%P]];
[bp correctWithTarget:target[j%P]];
}
if(!(j % P)) // BPE doesn't average the errors
printf("%u %e\n",j,[bp getError]); // should do that here...
}
for(j=0; j<[[bp inputs] count]; j++) {
for(i=0; i<[[bp hidden] count]; i++)
printf("%10e ",[[[bp hidden] objectAt:i]
getWeightFor:[[bp inputs] objectAt:j]]);
printf("\n");
}
printf("-----\n");
for(j=0; j<[[bp hidden] count]; j++) {
for(i=0; i<[[bp outputs] count]; i++)
printf("%10e ",[[[bp outputs] objectAt:i]
getWeightFor:[[bp hidden] objectAt:j]]);
printf("\n");
}
}
These are the contents of the former NiCE NeXT User Group NeXTSTEP/OpenStep software archive, currently hosted by Netfuture.ch.