/*
madaline.c
Dwayne Phillips
February 1992
The functions in this file implement the multiple
adaptive linear element - Madaline - and the
Madaline I learning law.
Contents:
display_2d_weights
initialize_2d_weights
madaline_output
process_new_madaline
train_the_madaline
which_adaline
*/
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
/*
void initialize_2d_weights(w, N, A)
This function initializes the weights in the
2-dimensional weight vector. They are set
to 0 2 4 6 0 2 4 6 ...
*/
long initialize_2d_weights(w, N, A)
long **w, N, A;
{
int i, j;
for(i=0; i<A; i++)
for(j=0; j<N+1; j++)
w[i][j] = ((i+j+1) % 4) * 2;
} /* ends initialize_2d_weights */
/*
long display_2d_weights(w, N, A)
This function displays the 2-dimemsional
weights to the screen.
*/
long display_2d_weights(w, N, A)
long **w, N, A;
{
int i, j;
for(i=0; i<A; i++){
printf("\n>>");
for(j=0; j<N+1; j++)
printf("%10ld ", w[i] [j]);
}
} /* ends display_2d_weights */
/*
long train_the_madaline(inputs, weights, x, w,
nets, outs, N, A, choice)
x[N+2] nets[A] outs[A] w[A][N+1]
This function trains the Madaline network using
the Madaline I training law. It loops through
the input vectors. If an output is wrong, it
trains the weights for that input. If a single
output is wrong from all of the inputs, then
the training loop repeats. This continues until
all of the answers are correct, or until the training
goes beyond a limit.
*/
long train_the_madaline(inputs, weights, x, w,
nets, outs, N, A, choice)
char choice;
FILE *inputs, *weights;
long A, N, nets[], outs[], x[], **w;
{
char string[80];
float eta = 0.5;
int adjusting = 1,
counter = 0,
i,
j,
s;
long net,
one_net,
*output,
target,
this_one;
initialize_2d_weights(w, N, A);
printf("\nEnter number of input vectors >>");
gets(string);
s = atoi(string);
while(adjusting){
printf("\nwhile adjusting counter=%d", counter);
counter++;
adjusting = 0;
fseek(inputs, OL, SEEK_SET);
for(i=0; i<s; i++){
fread(x, (N+2)*sizeof(long), 1, inputs);
for(j=0; j<A; j++){
calculate_net(&one_net, N, w[j], x);
nets[j] = one_net;
outs[j] = calculate_output(nets[j]);
}
output = madaline output(outs, choice, A);
target = x[N+1];
if(output != target){
printf(" %d-t ", i);
adjusting = 1;
this one = which_adaline(target, outs, nets, A);
printf("%ldx ", this_one);
train_weights(target, nets[this_one], eta,
w[this one], x, N);
} /* ends if output != target */
else
printf(" %d-n ", i);
} /* ends loop over s vectors */
if(counter > 299){
printf("\n\nTOO MUCH TRAINING - quiting");
adjusting = 0;
}
} /* ends while adjusting */
printf("\n\nwent through the training cycle %d times",counter);
fclose(inputs);
for(i=0; i<A; i++)
fwrite(w[i], (N+1)*sizeof(long), 1, weights);
fclose(weights);
display_2d_weights(w, N, A);
} /* ends train_the_madaline */
/*
long madaline_output(outputs, choice, A)
This function sets the single output for the
Madaline network. It can use either the
AND, OR, or Majority vote.
*/
long madaline_output(outputs, choice, A)
long A, outputs[];
char choice;
{
int i,
minus = 0,
plus = 0;
long result = -1;
/* use the AND method */
if(choice == 'a' ||
choice == 'A'){
result = 1;
for(i=0; i<A; i++)
if(outputs[i] == -1)
result = -1;
}
/* use the OR method */
if(choice == 'o' ||
choice == '0'){
for(i=0; i<A; i++)
if(outputs[i] == 1)
result = 1;
}
/* use the Majority vote method */
if(choice == 'm' ||
choice == 'M'){
for(i=0; i<A; i++){
if(outputs[i] == 1) plus++;
if(outputs[i] == -1) minus++;
}
if(plus > minus) result = 1;
}
return(result);
} /* ends madaline_output */
/*
long which_adaline(target, outs, nets, A)
Find the nets that has the smallest absolute value
and the corresponding outs does not equal the target.
*/
long which_adaline(target, outs, nets, A)
long A, nets[], outs[], target;
{
int i;
long result, sum;
sum = 32000;
for(i=0; i<A; i++){
if(outs[i] != target){
if(abs(nets[i]) < sum){
result = i;
sum = abs(nets[i]);
}
}
}
return(result);
} /* ends which_adaline */
/*
long process_new_madaline(weights, x, w, nets,
outs, choice, N, A)
This function implements the working mode of the
network. It takes in a new input vector and
calculates the answer.
*/
long process_new_madaline(weights, x, w, nets,
outs, choice, N, A)
char choice;
FILE *weights;
long A, N, nets[], outs[], x[], **w;
{
int i, j;
long one_net, output;
for(i=0; i<A; i++)
fread(w[i], (N+1)*sizeof(long), 1, weights);
fclose(weights);
display_2d_weights(w, N, A);
get_straight_input_from_user(x, N);
for(j=0; j<A; j++){
calculate_net(&one_net, N, w[j], x);
nets[j] = one_net;
outs[j] = calculate_output(nets[j]);
}
output = madaline_output(outs, choice, A);
printf("\n\noutput is %ld \n", output);
} /* ends process_new_madaline */
/* End of File */