kohonen_training_data.java

som 算法实现。在别的网站上下载的

package dm.clustering.KNN;

import java.io.*;

public class Kohonen_Training_Data {

    //Number of dimensions contained in signal
	int signal_dimensions;
	
	double[] max_output_value;
	double[] min_output_value;
	 
    //Dimensions of test data output
	int nodes_in_output_layer;              
	
	//Pointer to the array containing signals
	sample_data[] number_of_samples;   
    //Number of signals in training set
	int sample_number; 
	
	String filename;
	
	void acquire_net_info(int signal){
		signal_dimensions = signal;
	}
	
	void normalize_data_in_array(){
	  int i, j, imax, imin;
	  int trigger;
	  double min=0, max=0;
	  max_output_value = new double[signal_dimensions];
	  min_output_value = new double[signal_dimensions];

	  for(j = 0; j < signal_dimensions; j++){
	    trigger = 1;
	    // identify minimum and maximum values for each dimension
	    for(i = 0; i < sample_number; i++){
	      if(i == 0){
		    max = number_of_samples[i].data_in_sample[j];
		    min = number_of_samples[i].data_in_sample[j];
	      }else{
		    if(number_of_samples[i].data_in_sample[j] < min){
		    	min = number_of_samples[i].data_in_sample[j];
		    }

            if(number_of_samples[i].data_in_sample[j] > max){
            	max = number_of_samples[i].data_in_sample[j];
            }
	      }
	    }

	    // normalize the values in each dimension of the signal
	    max_output_value[j] = max;
	    min_output_value[j] = min;

	    imax = (int)(max);
	    imin = (int)(min);

	    if((imax == 1) && (imin == 0) && (max <= 1.0) && (min <= 0.0))
	    {trigger = 0;}

	    if((imax == 1) && (imin == 1) && (max <= 1.0) && (min <= 1.0))
	    {trigger = 0;}

	    if((imax == 0) && (imin == 0) && (max <= 0.0) && (min <= 0.0))
	    {trigger = 0;}

	    if(trigger != 0)   //  do not normalize binary signals
	    {
	      for(i = 0; i < sample_number; i++)
	      {number_of_samples[i].data_in_sample[j] = (number_of_samples[i].data_in_sample[j] - min)/(max - min);}
	    }
	  }
	}
	
	void specify_signal_sample_size(){
	  String tchoice;
	  int dolock = 1;
	  do{
		  System.out.println();
		  System.out.println( "Please select the number of samples you wish to use" );
		  System.out.println( "	A.  All samples in the file" );
		  System.out.println( "	S.  Specific number of samples");
		  System.out.println( "	Your Selection: "); 
		  tchoice = MyInput.readString();
		  System.out.println();
	      tchoice = tchoice.toUpperCase();
	    if((tchoice.equalsIgnoreCase("A")) || (tchoice.equalsIgnoreCase("S"))) {dolock = 0;}
	  } while(dolock >= 1);
	  
	  System.out.println();
	  if(tchoice.equalsIgnoreCase("A")) {determine_sample_number();}
	  else{
		  System.out.println();
		  System.out.println( "please enter the number of testing samples you wish to use: ");
	      sample_number = MyInput.readInt();
	      System.out.println();
	  }
	  load_data_into_array();
	}	

	void request_Kohonen_data(int net_no){
		System.out.println( "Enter the file name containing the training data for Kohonen network no. " +net_no);
	    filename = MyInput.readString(); 
	    System.out.println();
	    specify_signal_sample_size();
	    normalize_data_in_array();
	}
	
	void determine_sample_number(){
	  FileReader dfile_ptr; 
	  try{
		  dfile_ptr = new FileReader(filename);
	      BufferedReader br = new BufferedReader(dfile_ptr);
		  String string;
		  sample_number = 0;
		  
		  while((string=br.readLine())!=null){
			 if(string.equalsIgnoreCase("")){
				 continue;
			 }
			 else if(!string.equalsIgnoreCase("")){
				 sample_number++;
			 }
		  }
		  br.close();
		  dfile_ptr.close();
	  }
	  catch(IOException exc) {   
			String str  = exc.toString();
			System.out.println(str);
	  }	 
	}
	
	void load_data_into_array(){
	  // open the file containing the data
	  FileReader file_ptr;  
	  int i;
	  try{
		  file_ptr = new FileReader(filename);
		  BufferedReader br = new BufferedReader(file_ptr);
		  String string;
		  
          //create dynamic array to hold the specified number of samples
		  number_of_samples = new sample_data[sample_number];
          
          //create a dynamic array to hold the dimensions of each signal
		  for(i = 0; i < sample_number; i++){   
			  number_of_samples[i] = new sample_data();
			  
			  number_of_samples[i].data_in_sample = new double[signal_dimensions + nodes_in_output_layer];
		  }

		  int dimensions = signal_dimensions + nodes_in_output_layer;
		  int row = 0;

		  while((string=br.readLine())!=null){
			  if(string.trim().equalsIgnoreCase("")){
				  //read in data from file and place in array
				  String[] s = string.split(",");
				  
				  System.out.println("s.length="+s.length);
				  
                  if(row<sample_number) {
                	  for(int j = 0; j < dimensions; j++){
                		if(s[j]!=null){
                			number_of_samples[row].data_in_sample[j] = Double.parseDouble(s[j]);
                		}		  
                	  }
                  }
				   
                  row++;
			  }        
		  }
		  
		  file_ptr.close();
		  System.out.println();
	  }
	  catch(IOException exc) {   
			String str  = exc.toString();
			System.out.println(str);
	  }
	  
	}
	
	public void delete_signal_array(){
		number_of_samples = null;
	}
}