Changeset 135 for trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/TCAPlusNormalization.java

Timestamp:

07/18/16 12:26:03 (8 years ago)

Author:

sherbold

Message:

• code documentation and formatting

File:

• trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/TCAPlusNormalization.java (modified) (2 diffs)

trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/TCAPlusNormalization.java

@@ -19 +19 @@
 import weka.core.Instances;
 
-// normalization selected according to TCA+ rules (TCA has to be applied separately
+/**
+ * <p>
+ * Normalization selected according to the TCA+ rules after Nam et al. (Transfer Defect Learning).
+ * </p>
+ * 
+ * @author Steffen Herbold
+ */
 public class TCAPlusNormalization implements IProcessesingStrategy {
 
@@ -30 +36 @@
     @Override
     public void setParameter(String parameters) {
-        // TODO Auto-generated method stub
-        
+        // dummy, paramters not used
     }
 
+    /*
+     * (non-Javadoc)
+     * 
+     * @see de.ugoe.cs.cpdp.dataprocessing.IProcessesingStrategy#apply(weka.core.Instances,
+     * weka.core.Instances)
+     */
     @Override
     public void apply(Instances testdata, Instances traindata) {
         applyTCAPlus(testdata, traindata);
     }
-    
+
     private void applyTCAPlus(Instances testdata, Instances traindata) {
         DistChar dcTest = WekaUtils.datasetDistance(testdata);
         DistChar dcTrain = WekaUtils.datasetDistance(traindata);
-        
+
         // RULE 1:
-        if( 0.9*dcTrain.mean<=dcTest.mean && 1.1*dcTrain.mean>=dcTest.mean &&
-            0.9*dcTrain.std<=dcTest.std && 1.1*dcTrain.std>=dcTest.std) {
+        if (0.9 * dcTrain.mean <= dcTest.mean && 1.1 * dcTrain.mean >= dcTest.mean &&
+            0.9 * dcTrain.std <= dcTest.std && 1.1 * dcTrain.std >= dcTest.std)
+        {
             // do nothing
         }
         // RULE 2:
-        else if((0.4*dcTrain.min>dcTest.min || 1.6*dcTrain.min<dcTest.min) &&
-                (0.4*dcTrain.max>dcTest.max || 1.6*dcTrain.min<dcTest.max) &&
-                (0.4*dcTrain.min>dcTest.num || 1.6*dcTrain.min<dcTest.num)) {
+        else if ((0.4 * dcTrain.min > dcTest.min || 1.6 * dcTrain.min < dcTest.min) &&
+            (0.4 * dcTrain.max > dcTest.max || 1.6 * dcTrain.min < dcTest.max) &&
+            (0.4 * dcTrain.min > dcTest.num || 1.6 * dcTrain.min < dcTest.num))
+        {
             NormalizationUtil.minMax(testdata);
             NormalizationUtil.minMax(traindata);
         }
         // RULE 3:
-        else if((0.4*dcTrain.std>dcTest.std && dcTrain.num<dcTest.num) || 
-                (1.6*dcTrain.std<dcTest.std)&& dcTrain.num>dcTest.num) {
+        else if ((0.4 * dcTrain.std > dcTest.std && dcTrain.num < dcTest.num) ||
+            (1.6 * dcTrain.std < dcTest.std) && dcTrain.num > dcTest.num)
+        {
             NormalizationUtil.zScoreTraining(testdata, traindata);
         }
         // RULE 4:
-        else if((0.4*dcTrain.std>dcTest.std && dcTrain.num>dcTest.num) || 
-                (1.6*dcTrain.std<dcTest.std)&& dcTrain.num<dcTest.num) {
+        else if ((0.4 * dcTrain.std > dcTest.std && dcTrain.num > dcTest.num) ||
+            (1.6 * dcTrain.std < dcTest.std) && dcTrain.num < dcTest.num)
+        {
             NormalizationUtil.zScoreTarget(testdata, traindata);
         }
-        //RULE 5:
+        // RULE 5:
         else {
             NormalizationUtil.zScore(testdata);