84.

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
  

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The new member functions are shown in italic. The functions display_input_char() and display_winner_weights() are used to display the input and weight maps on the screen to watch weight character map converge to the input map.

The implementation of these functions is in the file, layerk.cpp. The portion of this file containing these functions is shown in Listing 12.2.

Listing 12.2 Additions to the layerk.cpp implementation file

 void Kohonen_network::display_input_char() { int i, num_inputs; unsigned char ch; float temp; int col=0; float * inputptr; num_inputs=layer_ptr[1]->num_inputs; inputptr = layer_ptr[1]->inputs; // we’ve got a 5x7 character to display for (i=0; i<num_inputs; i++)        {        temp = *(inputptr);        if (temp <= 0)               ch=255;// blank        else if ((temp > 0) && (temp <= 0.25))               ch=176; // dotted rectangle -light        else if ((temp > 0.25) && (temp <= 0.50))               ch=177; // dotted rectangle -medium        else if ((temp >0.50) && (temp <= 0.75))               ch=178; // dotted rectangle -dark        else if (temp > 0.75)               ch=219; // filled rectangle        printf(“%c”,ch); //fill a row        col++;        if ((col % 5)==0)               printf(“\n”); // new row        inputptr++;        } printf(“\n\n\n”); } void Kohonen_network::display_winner_weights() { int i, k; unsigned char ch; float temp; float * wmat; int col=0; int win_index; int num_inputs, num_outputs; num_inputs= layer_ptr[1]->num_inputs; wmat = ((Kohonen_layer*)layer_ptr[1])               ->weights; win_index=((Kohonen_layer*)layer_ptr[1])               ->winner_index; num_outputs=layer_ptr[1]->num_outputs; // we’ve got a 5x7 character to display for (i=0; i<num_inputs; i++)        {        k= i*num_outputs;        temp = wmat[k+win_index];        if (temp <= 0)               ch=255;// blank        else if ((temp > 0) && (temp <= 0.25))               ch=176; // dotted rectangle -light        else if ((temp > 0.25) && (temp <= 0.50))               ch=177; // dotted rectangle -medium        else if ((temp > 0.50) && (temp <= 0.75))               ch=178; // dotted rectangle -dark        else if (temp > 0.75)               ch=219; // filled rectangle        printf(“%c”,ch); //fill a row        col++;        if ((col % 5)==0)               printf(“\n”); // new row        } printf(“\n\n”); printf(“—————————-\n”); } 

The final change to make is to the kohonen.cpp file. The new file is called pattern.cpp and is shown in Listing 12.3.

Listing 12.3 The implementation file pattern.cpp

 // pattern.cpp      V. Rao, H. Rao // Kohonen map for pattern recognition #include “layerk.cpp” #define INPUT_FILE “input.dat” #define OUTPUT_FILE “kohonen.dat” #define dist_tol      0.001 #define wait_cycles   10000 // creates a pause to                       // view the character maps 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; int i; // 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_pattern\n”); fprintf(output_file_ptr,        “————————————————————————\n”); 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);        // display the input character and the        // weights for the winner to see match        knet.display_input_char();        knet.display_winner_weights();        // pause for a while to view the        // character maps        for (i=0; i<wait_cycles; i++)               {;}        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); } 

Changes to the program are indicated in italic. Compile this program by compiling and making the pattern.cpp file, after modifying the layerk.cpp and layerk.h files, as indicated previously.


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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

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