hartman-server/qhtcp-workflow/apps/java/weka-clustering/src/Information.java

/*
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 * and open the template in the editor.
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import java.io.BufferedWriter;
import java.io.FileWriter;
import java.util.HashMap;
import java.util.Iterator;

/**
 *
 * @author DTian
 */
public class Information {

    /**
     *
     * @param key  the key  of the dictionary
     * @return the entropy
     */
    public static double entropy(String [] data ) {
        double entropy = 0;

        // Frequency table
        HashMap freqDict = new HashMap();
        int one = 1;
        
        for(int i=0; i<data.length; i++){
            String newkey = data[i];
            if (freqDict.containsKey(newkey)) {
                int val = Integer.parseInt(freqDict.get(newkey).toString());
                freqDict.remove(newkey);
                val = val + 1;
                freqDict.put(newkey, val + "");
            } else {
                freqDict.put(newkey, (one + ""));
            }
        }

        // Probability table
        HashMap probDict = new HashMap();
        Iterator it = freqDict.keySet().iterator();
        String newkey = "";
        while (it.hasNext()) {
            newkey = (String) it.next();
            double value = 0.0;
            value = Double.parseDouble((String) freqDict.get(newkey)) / data.length;
            probDict.put(newkey, value + "");
        }

        // Calculate entropy
        it = probDict.keySet().iterator();
        while (it.hasNext()) {
            newkey = (String) it.next();
            double value = 0.0;
            value = Double.parseDouble((String) probDict.get(newkey));
            entropy = entropy - value * (Math.log(value) / Math.log(2));
        }
        return entropy;
    }

    public static double relativeEntropy(String[] data1, String[] data2) {

        double result = 0;
        // System.out.println(data1.length);

        // Frequency table
        HashMap freqDict1 = new HashMap();
        int one = 1;
        for(int i=0; i<data1.length; i++){
            Object key = data1[i];
            if(freqDict1.containsKey(key)){
                int val = Integer.parseInt( freqDict1.get(key).toString());
                //freqDict1.remove(key);
                val++;
                freqDict1.put(key, val + "");
            } else {
                freqDict1.put(key, (one + ""));
            }
        }

        // toFileHM(freqDict1, "FreqDict1.txt");
        HashMap freqDict2 = new HashMap();
        for (int i=0; i<data2.length; i++) {
            Object key = data2[i];
            if (freqDict2.containsKey(key)) {
                int val = Integer.parseInt(freqDict2.get(key).toString());
                //freqDict2.remove(key);
                val++;
                freqDict2.put(key, val + "");
            } else {
                freqDict2.put(key, (one + ""));
            }
        }

        // Probability table
        HashMap<Object, Object> probDict1 = new HashMap<Object, Object>();
        HashMap<Object, Object> probDict2 = new HashMap<Object, Object>();
        Iterator it = freqDict1.keySet().iterator();
        while (it.hasNext()) {
        	Object newkey = it.next();
            double value = 0;
            value = Double.parseDouble((String) freqDict1.get(newkey)) / data1.length;
            probDict1.put(newkey, value + "");
        }
        
        it = freqDict2.keySet().iterator();
        while (it.hasNext()) {
        	Object newkey = it.next();
            double value = 0;
            value = Double.parseDouble((String) freqDict2.get(newkey)) / data2.length;
            probDict2.put(newkey, value + "");
        }

        // Calculate the relative entropy
        it = probDict1.keySet().iterator();        
        while (it.hasNext()) {
            Object newkey = it.next();
            Object value1 = probDict1.get(newkey);
            //Object value2 = probDict2.get(newkey);
            double dValue1 = Double.parseDouble(probDict1.get(newkey).toString());
            double dValue2 = Double.parseDouble(probDict2.get(newkey).toString());
            if ( value1.toString().trim().compareToIgnoreCase("1.0") == 0) {
                result = result +  dValue1 * (Math.log(dValue1/dValue2) / Math.log(2));
            } else if (value1.toString().trim().compareToIgnoreCase("0") == 0){
                result = result +  (1-dValue1) * (Math.log((1-dValue1)/(1-dValue2)) / Math.log(2));
            } else {
                result = result +  dValue1 * (Math.log(dValue1/dValue2) / Math.log(2));
                result = result +  (1-dValue1) * (Math.log((1-dValue1)/(1-dValue2)) / Math.log(2));
            }
            // toFile(result+"", "probDict1.txt");            
            // toFile(result, "resultsOfresult.txt");//check point by Jingyu            
        }
        //toFile(probDict1.size()+ "*******************", "probDict1.txt");
        //System.out.println("relative entropy = " + result);
        return result;
    }
    
    private static void toFile(String data, String filename) {

        // Output to file
        try {
            BufferedWriter writer = new BufferedWriter(new FileWriter(filename,true));
            writer.write(data + "\n");
            writer.close();
        } catch (Exception e) {
            System.err.println(e.getStackTrace());
        }
    }

	private static void toFileHM(HashMap data, String filename) {

        // Output to file
        try {
            BufferedWriter writer = new BufferedWriter(new FileWriter(filename, true));
            for (Object key : data.keySet()) {
                writer.write(key.toString() +":"+ data.get(key)+"\n");
            }
            writer.close();
        } catch (Exception e) {
            System.err.println(e.getStackTrace());
        }
    }
}