Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/ZScoreNormalization.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/ZScoreNormalization.java	(revision 37)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/ZScoreNormalization.java	(revision 37)
@@ -0,0 +1,70 @@
+package de.ugoe.cs.cpdp.dataprocessing;
+
+import org.apache.commons.collections4.list.SetUniqueList;
+
+import weka.core.Attribute;
+import weka.core.Instance;
+import weka.core.Instances;
+
+/**
+ * N4 after "Transfer Defect Learning" by Jaechang Nam, Sinno Jialin Pan, and Sunghun Kim.
+ * 
+ * @author Steffen Herbold
+ */
+public class ZScoreNormalization implements ISetWiseProcessingStrategy, IProcessesingStrategy {
+
+	/**
+	 * Does not have parameters. String is ignored.
+	 * @param parameters ignored
+	 */
+	@Override
+	public void setParameter(String parameters) {
+		// dummy
+	}
+
+	/**
+	 * @see de.ugoe.cs.cpdp.dataprocessing.SetWiseProcessingStrategy#apply(weka.core.Instances, org.apache.commons.collections4.list.SetUniqueList)
+	 */
+	@Override
+	public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
+		normalize(testdata);
+		for( Instances instances : traindataSet ) {
+			normalize(instances);
+		}
+	}
+
+	/**
+	 * @see de.ugoe.cs.cpdp.dataprocessing.ProcessesingStrategy#apply(weka.core.Instances, weka.core.Instances)
+	 */
+	@Override
+	public void apply(Instances testdata, Instances traindata) {
+		normalize(testdata);
+		normalize(traindata);
+	}
+	
+	private void normalize(Instances instances) {
+		final Attribute classAttribute = instances.classAttribute();
+		
+		final double[] means = new double[instances.numAttributes()];
+		final double[] stddevs = new double[instances.numAttributes()];
+		
+		// get means and stddevs of data
+		for( int j=0 ; j<instances.numAttributes() ; j++ ) {
+			if( instances.attribute(j)!=classAttribute ) {
+				means[j] = instances.meanOrMode(j);
+				stddevs[j] = Math.sqrt(instances.variance(j));
+			}
+		}
+		
+		for( int i=0 ; i<instances.numAttributes(); i++) {
+			if( !instances.attribute(i).equals(classAttribute) ) {
+				for( int j=0 ; j<instances.numAttributes() ; j++ ) {
+					Instance inst = instances.get(i);
+					double newValue = (inst.value(j)-means[j])/stddevs[j];
+					inst.setValue(j, newValue);
+				}
+			}
+		}
+	}
+
+}
Index: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/ZScoreTargetNormalization.java
===================================================================
--- trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/ZScoreTargetNormalization.java	(revision 37)
+++ trunk/CrossPare/src/de/ugoe/cs/cpdp/dataprocessing/ZScoreTargetNormalization.java	(revision 37)
@@ -0,0 +1,104 @@
+package de.ugoe.cs.cpdp.dataprocessing;
+
+import org.apache.commons.collections4.list.SetUniqueList;
+
+import weka.core.Attribute;
+import weka.core.Instance;
+import weka.core.Instances;
+
+/**
+ * N4 after "Transfer Defect Learning" by Jaechang Nam, Sinno Jialin Pan, and Sunghun Kim.
+ * 
+ * @author Steffen Herbold
+ */
+public class ZScoreTargetNormalization implements ISetWiseProcessingStrategy, IProcessesingStrategy {
+
+	/**
+	 * Does not have parameters. String is ignored.
+	 * @param parameters ignored
+	 */
+	@Override
+	public void setParameter(String parameters) {
+		// dummy
+	}
+
+	/**
+	 * @see de.ugoe.cs.cpdp.dataprocessing.SetWiseProcessingStrategy#apply(weka.core.Instances, org.apache.commons.collections4.list.SetUniqueList)
+	 */
+	@Override
+	public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
+		final Attribute classAttribute = testdata.classAttribute();
+		
+		final double[] meanTest = new double[testdata.numAttributes()];
+		final double[] stddevTest = new double[testdata.numAttributes()];
+		
+		// get means of testdata
+		for( int j=0 ; j<testdata.numAttributes() ; j++ ) {
+			if( testdata.attribute(j)!=classAttribute ) {
+				meanTest[j] = testdata.meanOrMode(j);
+				stddevTest[j] = Math.sqrt(testdata.variance(j));
+			}
+		}
+		
+		// preprocess test data
+		for( int i=0 ; i<testdata.numInstances() ; i++ ) {
+			Instance instance = testdata.instance(i);
+			for( int j=0 ; j<testdata.numInstances() ; j++ ) {
+				if( testdata.attribute(j)!=classAttribute ) {
+					instance.setValue(j, instance.value(j)-meanTest[j]/stddevTest[j]);
+				}
+			}
+		}
+		
+		// preprocess training data
+		for( Instances traindata : traindataSet ) {			
+			for( int i=0 ; i<traindata.numInstances() ; i++ ) {
+				Instance instance = traindata.instance(i);
+				for( int j=0 ; j<testdata.numAttributes() ; j++ ) {
+					if( testdata.attribute(j)!=classAttribute ) {
+						instance.setValue(j, instance.value(j)-meanTest[j]/stddevTest[j]);
+					}
+				}
+			}
+		}
+	}
+
+	/**
+	 * @see de.ugoe.cs.cpdp.dataprocessing.ProcessesingStrategy#apply(weka.core.Instances, weka.core.Instances)
+	 */
+	@Override
+	public void apply(Instances testdata, Instances traindata) {
+		final Attribute classAttribute = testdata.classAttribute();
+		
+		final double[] meanTest = new double[testdata.numAttributes()];
+		final double[] stddevTest = new double[testdata.numAttributes()];
+		
+		// get means of testdata
+		for( int j=0 ; j<testdata.numAttributes() ; j++ ) {
+			if( testdata.attribute(j)!=classAttribute ) {
+				meanTest[j] = testdata.meanOrMode(j);
+				stddevTest[j] = Math.sqrt(testdata.variance(j));
+			}
+		}
+		
+		// preprocess test data
+		for( int i=0 ; i<testdata.numInstances() ; i++ ) {
+			Instance instance = testdata.instance(i);
+			for( int j=0 ; j<testdata.numInstances() ; j++ ) {
+				if( testdata.attribute(j)!=classAttribute ) {
+					instance.setValue(j, instance.value(j)-meanTest[j]/stddevTest[j]);
+				}
+			}
+		}
+		
+		// preprocess training data
+		for( int i=0 ; i<traindata.numInstances() ; i++ ) {
+			Instance instance = traindata.instance(i);
+			for( int j=0 ; j<testdata.numAttributes() ; j++ ) {
+				if( testdata.attribute(j)!=classAttribute ) {
+					instance.setValue(j, instance.value(j)-meanTest[j]/stddevTest[j]);
+				}
+			}
+		}
+	}
+}