Removed useless files

Further, GEPMTRv2 was renamed to GEPMTR

Author: i02momuj

src/ensemble/EGEPMTR_B.java

@@ -2,7 +2,6 @@
 import java.util.Random;
 
 import gep.GEPMTR;
-import gep.GEPMTRv2;
 import mulan.classifier.InvalidDataException;
 import mulan.classifier.MultiLabelLearnerBase;
 import mulan.classifier.MultiLabelOutput;
@@ -31,7 +30,7 @@ public class EGEPMTR_B extends MultiLabelLearnerBase {
 	/**
 	 * Stores the GEPMTR models.
 	 */
-	private GEPMTRv2[] ensemble;
+	private GEPMTR[] ensemble;
 
 	/**
 	 * Three types of sampling.
@@ -88,7 +87,7 @@ public EGEPMTR_B(int h, int numberOfIndividuals, int numberGenerations, int numO
 		this.sampling = sampling;
 		this.numOfModels = numOfModels;
 
-		ensemble = new GEPMTRv2[numOfModels];
+		ensemble = new GEPMTR[numOfModels];
 	}
 
 	public EGEPMTR_B(int h, int numberOfIndividuals, int numberGenerations, int numOfModels)
@@ -101,7 +100,7 @@ public EGEPMTR_B(int h, int numberOfIndividuals, int numberGenerations, int numO
 		this.sampling = SamplingMethod.WithReplacement;
 		this.numOfModels = numOfModels;
 
-		ensemble = new GEPMTRv2[numOfModels];
+		ensemble = new GEPMTR[numOfModels];
 	}
 
 	public void setSeed(long seed){
@@ -143,7 +142,7 @@ protected void buildInternal(MultiLabelInstances mlTrainSet) throws Exception {
 				sampledTrainingSet = mlTrainSet;
 			}
 
-			ensemble[i] = new GEPMTRv2(h, numberOfIndividuals, numberOfGenerations, seed*i);
+			ensemble[i] = new GEPMTR(h, numberOfIndividuals, numberOfGenerations, seed*i);
 
 			ensemble[i].build(sampledTrainingSet);
 		}

src/ensemble/EGEPMTR_OTA.java

@@ -9,7 +9,7 @@
 import java.util.concurrent.Executors;
 import java.util.concurrent.TimeUnit;
 
-import gep.GEPMTRv2;
+import gep.GEPMTR;
 import mulan.classifier.InvalidDataException;
 import mulan.classifier.MultiLabelLearnerBase;
 import mulan.classifier.MultiLabelOutput;
@@ -61,10 +61,10 @@ public class EGEPMTR_OTA extends MultiLabelLearnerBase {
 	Random rand;
 
 	// Full targets regressor
-	public GEPMTRv2 fullTargetsRegressor;
+	public GEPMTR fullTargetsRegressor;
 
 	// Rest of the ensemble
-	public GEPMTRv2[] ensemble;
+	public GEPMTR[] ensemble;
 
 
 	/**
@@ -99,12 +99,12 @@ protected void buildInternal(MultiLabelInstances trainingSet) throws Exception {
 		numLabels = trainingSet.getNumLabels();
 		numberOfModels = numLabels + 1;
 
-		fullTargetsRegressor = new GEPMTRv2(h, numberGenerations, numberOfIndividuals, seed);
+		fullTargetsRegressor = new GEPMTR(h, numberGenerations, numberOfIndividuals, seed);
 		fullTargetsRegressor.build(trainingSet);
 
-		ensemble = new GEPMTRv2[numLabels];
+		ensemble = new GEPMTR[numLabels];
 		for(int n=0; n<numLabels; n++){
-			ensemble[n] = new GEPMTRv2(h, numberGenerations, numberOfIndividuals, seed*n);
+			ensemble[n] = new GEPMTR(h, numberGenerations, numberOfIndividuals, seed*n);
 			ensemble[n].build(transformInstances(trainingSet, n));
 		}
 	}

src/ensemble/EGEPMTR_S.java

@@ -9,7 +9,7 @@
 import java.util.concurrent.Executors;
 import java.util.concurrent.TimeUnit;
 
-import gep.GEPMTRv2;
+import gep.GEPMTR;
 import mulan.classifier.InvalidDataException;
 import mulan.classifier.MultiLabelLearnerBase;
 import mulan.classifier.MultiLabelOutput;
@@ -67,10 +67,10 @@ public class EGEPMTR_S extends MultiLabelLearnerBase {
 	int [] votesPerLabel;
 
 	// Full targets regressor
-	public GEPMTRv2 fullTargetsRegressor;
+	public GEPMTR fullTargetsRegressor;
 
 	// Rest of the ensemble
-	public GEPMTRv2[] ensemble;
+	public GEPMTR[] ensemble;
 
 
 	/**
@@ -113,14 +113,14 @@ protected void buildInternal(MultiLabelInstances trainingSet) throws Exception {
 
 		subsetsMatrix = new byte[numberOfModels-1][numLabels];
 
-		fullTargetsRegressor = new GEPMTRv2(h, numberGenerations, numberOfIndividuals, seed);
+		fullTargetsRegressor = new GEPMTR(h, numberGenerations, numberOfIndividuals, seed);
 		fullTargetsRegressor.build(trainingSet);
 
 		for(int i=0; i<numLabels; i++){
 			votesPerLabel[i] = 1;
 		}
 
-		ensemble = new GEPMTRv2[numberOfModels-1];
+		ensemble = new GEPMTR[numberOfModels-1];
 		for(int n=0; n<(numberOfModels-1); n++){
 			//Select random subset
 			byte [] subset = selectRandomSubset(k, numLabels);
@@ -134,7 +134,7 @@ protected void buildInternal(MultiLabelInstances trainingSet) throws Exception {
 			}
 
 			//Generate ensemble member
-			ensemble[n] = new GEPMTRv2(h, numberGenerations, numberOfIndividuals, seed*n);
+			ensemble[n] = new GEPMTR(h, numberGenerations, numberOfIndividuals, seed*n);
 			ensemble[n].build(transformInstances(trainingSet, subsetsMatrix[n]));
 		}
 	}