C++ Neural Networks and Fuzzy Logic by Valluru B. Rao M&T Books, IDG Books Worldwide, Inc. ISBN: 1558515526   Pub Date: 06/01/95

Flow of the Program and the main() Function

The main() function is contained in a file called kohonen.cpp, which is shown in Listing 11.4. To compile this program, you need only compile and make this main file, kohonen.cpp. Other files are included in this.

Listing 11.4 The main implementation file, kohonen.cpp for the Kohonen Map program

// kohonen.cpp        V. Rao, H. Rao
// Program to simulate a Kohonen map

#include “layerk.cpp”

#define INPUT_FILE “input.dat”
#define OUTPUT_FILE “kohonen.dat”
#define dist_tol   0.05

void main()
{

int neighborhood_size, period;
float avg_dist_per_cycle=0.0;
float dist_last_cycle=0.0;
float avg_dist_per_pattern=100.0; // for the latest cycle
float dist_last_pattern=0.0;
float total_dist;
float alpha;
unsigned startup;
int max_cycles;
int patterns_per_cycle=0;

int total_cycles, total_patterns;
// create a network object
Kohonen_network knet;

FILE * input_file_ptr, * output_file_ptr;

// open input file for reading
if ((input_file_ptr=fopen(INPUT_FILE,”r”))==NULL)
               {
               cout << “problem opening input file\n”;
               exit(1);
               }

// open writing file for writing
if ((output_file_ptr=fopen(OUTPUT_FILE,”w”))==NULL)
               {
               cout << “problem opening output file\n”;
               exit(1);
               }

// ————————————————————-
//     Read in an initial values for alpha, and the
//  neighborhood size.
//  Both of these parameters are decreased with
//  time. The number of cycles to execute before
//  decreasing the value of these parameters is
//            called the period. Read in a value for the
//            period.
// ————————————————————-
              cout << “ Please enter initial values for:\n”;
              cout << “alpha (0.01-1.0),\n”;
              cout << “and the neighborhood size (integer between 0
              and
              50)\n”;
              cout << “separated by spaces, e.g. 0.3 5 \n “;

              cin >> alpha >> neighborhood_size ;

              cout << “\nNow enter the period, which is the\n”;
              cout << “number of cycles after which the values\n”;
              cout << “for alpha the neighborhood size are
                      decremented\n”;
              cout << “choose an integer between 1 and 500 , e.g. 50 \n”;

              cin >> period;

              // Read in the maximum number of cycles
              // each pass through the input data file is a cycle
              cout << “\nPlease enter the maximum cycles for the
                      simulation\n”;
              cout << “A cycle is one pass through the data set.\n”;
              cout << “Try a value of 500 to start with\n\n”;

              cin >> max_cycles;

// the main loop
//
//    continue looping until the average distance is less
//            than the tolerance specified at the top of this file
//            , or the maximum number of
//            cycles is exceeded;

// initialize counters
total_cycles=0; // a cycle is once through all the input data
total_patterns=0; // a pattern is one entry in the input data

// get layer information
knet.get_layer_info();

// set up the network connections
knet.set_up_network(neighborhood_size);
// initialize the weights

// randomize weights for the Kohonen layer
// note that the randomize function for the
// Kohonen simulator generates
// weights that are normalized to length = 1
knet.randomize_weights();

// write header to output file
fprintf(output_file_ptr,
       “cycle\tpattern\twin index\tneigh_size\tavg_dist_per_pa
               tern\n”);

fprintf(output_file_ptr,
       “———————————————————————————\n”);

// main loop

startup=1;
total_dist=0;

while (
                      (avg_dist_per_pattern > dist_tol)
                      && (total_cycles < max_cycles)

                      || (startup==1)
                      )
{
startup=0;
dist_last_cycle=0; // reset for each cycle
patterns_per_cycle=0;
// process all the vectors in the datafile

while (!feof(input_file_ptr))
        {
        knet.get_next_vector(input_file_ptr);
        // now apply it to the Kohonen network
        knet.process_next_pattern();

dist_last_pattern=knet.get_win_dist();

// print result to output file
fprintf(output_file_ptr,”%i\t%i\t%i\t\t%i\t\t%f\n”,
     total_cycles,total_patterns,knet.get_win_index(),
     neighborhood_size,avg_dist_per_pattern);

     total_patterns++;

     // gradually reduce the neighborhood size
     // and the gain, alpha
     if (((total_cycles+1) % period) == 0)
             {
             if (neighborhood_size > 0)
                    neighborhood_size —;
             knet.update_neigh_size(neighborhood_size);
             if (alpha>0.1)
                    alpha -= (float)0.1;
             }

     patterns_per_cycle++;
     dist_last_cycle += dist_last_pattern;
     knet.update_weights(alpha);
     dist_last_pattern = 0;
  }

avg_dist_per_pattern= dist_last_cycle/patterns_per_cycle;
total_dist += dist_last_cycle;
total_cycles++;

fseek(input_file_ptr, 0L, SEEK_SET); // reset the file
                              pointer
                              // to the beginning of
                              // the file
} // end main loop

cout << “\n\n\n\n\n\n\n\n\n\n\n”;
cout << “—————————————————————————-\n”;
cout << “    done \n”;

avg_dist_per_cycle= total_dist/total_cycles;

cout << “\n”;
cout << “——>average dist per cycle = “ << avg_dist_per_cycle
        << “ <—-\n”;
cout << “——>dist last cycle = “ << dist_last_cycle << “ <—-
        \n”;
cout << “->dist last cycle per pattern= “ <<
        avg_dist_per_pattern << “ <—-\n”;
cout << “——————>total cycles = “ << total_cycles << “ <—-\n”;
cout << “——————>total patterns = “ << total_patterns << “ <—-
        \n”;
cout << “—————————————————————————-\n”;
// close the input file
fclose(input_file_ptr);
}