Code reorganization

moa/src/main/java/com/yahoo/labs/samoa/instances/MultiLabelPrediction.java

@@ -28,6 +28,10 @@ public MultiLabelPrediction(int numOutputAttributes) {
 		for (int i=0; i<numOutputAttributes;i++)
 			prediction[i]= new DoubleVector();
 	}
+
+	public MultiLabelPrediction(DoubleVector[] prediction) {
+		this.prediction = prediction;
+	}
 
 	public MultiLabelPrediction(DoubleVector[] prediction) {
 		this.prediction = prediction;

moa/src/main/java/moa/classifiers/core/conditionaltests/NumericAttributeBinaryTest.java

@@ -52,7 +52,11 @@ public int branchForInstance(Instance inst) {
         if (inst.isMissing(instAttIndex)) {
             return -1;
         }
+<<<<<<< HEAD
         double v = inst.valueInputAttribute(instAttIndex);
+=======
+        double v = inst.valueInputAttribute(instAttIndex); // if the attIndex is not calculated above this is the correct method call
+>>>>>>> 5c49882b22b3a8314c6dfcc8c026456c887262fa
         if (v == this.attValue) {
             return this.equalsPassesTest ? 0 : 1;
         }

moa/src/main/java/moa/classifiers/multilabel/LocalMultiTargetClassifier.java

@@ -1,6 +1,9 @@
 package moa.classifiers.multilabel;
 
+<<<<<<< HEAD
 import java.util.ArrayList;
+=======
+>>>>>>> 5c49882b22b3a8314c6dfcc8c026456c887262fa
 import java.util.LinkedList;
 import java.util.List;
 import java.util.Vector;
@@ -35,8 +38,13 @@ public class LocalMultiTargetClassifier extends AbstractMultiLabelLearner
 			Classifier.class,
 			"moa.classifiers.trees.FIMTDD");
 
+<<<<<<< HEAD
 	ArrayList<Classifier> classifiers = null;
 	ArrayList<InstancesHeader> headers = null;
+=======
+	LinkedList<Classifier> classifiers = null;
+	LinkedList<InstancesHeader> headers = null;
+>>>>>>> 5c49882b22b3a8314c6dfcc8c026456c887262fa
 
 	@Override
 	public boolean isRandomizable() {
@@ -53,7 +61,11 @@ private InstancesHeader getHeader(int target) {
 
 	private void makeHeaders() {
 		if (headers == null) {
+<<<<<<< HEAD
 			headers = new ArrayList<InstancesHeader>();
+=======
+			headers = new LinkedList<InstancesHeader>();
+>>>>>>> 5c49882b22b3a8314c6dfcc8c026456c887262fa
 			for (int target = 0; target < getModelContext().numOutputAttributes(); target++) {
 				List<Attribute> attributes = new LinkedList<Attribute>();
 				List<Integer> indexValues = new LinkedList<Integer>();
@@ -100,6 +112,10 @@ public Prediction getPredictionForInstance(MultiLabelInstance inst) {
 		MultiLabelPrediction prediction = new MultiLabelPrediction(getModelContext().numOutputAttributes());
 		for (int j = 0; j < inst.numberOutputTargets(); j++) {
 			Instance STInst = getSTInstance(inst, j);
+<<<<<<< HEAD
+=======
+
+>>>>>>> 5c49882b22b3a8314c6dfcc8c026456c887262fa
 			prediction.setVote(j, 1, classifiers.get(j).getVotesForInstance(STInst)[0]);
 		}
 		return prediction;
@@ -115,9 +131,15 @@ public void initializeClassifiers() {
 		this.classifierRandom.setSeed(1234);
 		if (classifiers == null) {
 			makeHeaders();
+<<<<<<< HEAD
 			classifiers = new ArrayList<Classifier>();
 			for (int i = 0; i < getModelContext().numOutputAttributes(); i++) {
 				Classifier learner = ((Classifier) getPreparedClassOption(this.classifierOption)).copy();
+=======
+			classifiers = new LinkedList<Classifier>();
+			for (int i = 0; i < getModelContext().numOutputAttributes(); i++) {
+				Classifier learner = (Classifier) getPreparedClassOption(this.classifierOption);
+>>>>>>> 5c49882b22b3a8314c6dfcc8c026456c887262fa
 				classifiers.add(learner);
 				learner.resetLearning();
 				((AbstractClassifier) learner).classifierRandom = this.classifierRandom;

moa/src/main/java/moa/classifiers/multilabel/meta/MTOzaBag.java

@@ -93,6 +93,7 @@ public Prediction getPredictionForInstance(MultiLabelInstance inst) {
 		}
 		return new MultiLabelPrediction(prediction);
 	}
+<<<<<<< HEAD
 
 	public Prediction getPredictionForInstanceUsingN(MultiLabelInstance inst, int n) {
 		int actual = Math.max(1, Math.min(n, this.ensemble.length));
@@ -111,6 +112,8 @@ public Prediction getPredictionForInstanceUsingN(MultiLabelInstance inst, int n)
 		}
 		return new MultiLabelPrediction(prediction);
 	}
+=======
+>>>>>>> 5c49882b22b3a8314c6dfcc8c026456c887262fa
 
     @Override
     public void trainOnInstanceImpl(MultiLabelInstance instance) {