fixed class label parsing

src/main/java/meka/classifiers/multitarget/NSR.java

@@ -98,6 +98,10 @@ public void buildClassifier(Instances D) throws Exception {
 		}
 	}
 
+	protected double[] classLabelToDistribution(String label) {
+		return A.toDoubleArray(MLUtils.toIntArray("[" + label.replace("+", ",") + "]"));
+	}
+
 	@Override
 	public double[] distributionForInstance(Instance x) throws Exception {
 
@@ -114,15 +118,15 @@ public double[] distributionForInstance(Instance x) throws Exception {
 		//int max_j = (int)m_Classifier.classifyInstance(x_sl);			// where comb_i is selected
 		String y_max = m_InstancesTemplate.classAttribute().value(max_j);									// comb_i e.g. "0+3+0+0+1+2+0+0"
 
-		double y[] = Arrays.copyOf(A.toDoubleArray(MLUtils.toIntArray(y_max)),L*2);					// "0+3+0+0+1+2+0+0" -> [0.0,3.0,0.0,...,0.0]
+		double y[] = Arrays.copyOf(classLabelToDistribution(y_max),L*2);					// "0+3+0+0+1+2+0+0" -> [0.0,3.0,0.0,...,0.0]
 
 		HashMap<Double,Double> votes[] = new HashMap[L];
 		for(int j = 0; j < L; j++) {
 			votes[j] = new HashMap<Double,Double>();
 		}
 
 		for(int i = 0; i < w.length; i++) {
-			double y_i[] = A.toDoubleArray(MLUtils.toIntArray(m_InstancesTemplate.classAttribute().value(i)));
+			double y_i[] = classLabelToDistribution(m_InstancesTemplate.classAttribute().value(i));
 			for(int j = 0; j < y_i.length; j++) {
 				votes[j].put(y_i[j] , votes[j].containsKey(y_i[j]) ? votes[j].get(y_i[j]) + w[i] : w[i]);
 			}