79.

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

Previous Table of Contents Next


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); } 


Previous Table of Contents Next

Copyright © IDG Books Worldwide, Inc.



C++ Neural Networks and Fuzzy Logic
C++ Neural Networks and Fuzzy Logic
ISBN: 1558515526
EAN: 2147483647
Year: 1995
Pages: 139

Similar book on Amazon

flylib.com © 2008-2017.
If you may any questions please contact us: flylib@qtcs.net