package classifier;

import de.tu_darmstadt.tk.SmartHomeNetworkSim.core.Packet;
import weka.clusterers.SimpleKMeans;
import weka.core.Instance;
import weka.core.Instances;

/**
 * Unsupervised Example: K Means Clustering
 *
 * @author Andreas T. Meyer-Berg
 */
public class KMeansClustering extends BasicPacketClassifier {

	/**
	 * Clusterer
	 */
	private SimpleKMeans clusterer;

	/**
	 * Number of Clusters
	 */
	protected int NUMBER_OF_CLUSTERS = 16;
	
	/**
	 * Initializes the k means clusterer
	 */
	public KMeansClustering() {
		super();
		clusterer = new SimpleKMeans();
		clusterer.setSeed(42);/*
		clusterer.setCanopyPeriodicPruningRate(100);
		clusterer.setCanopyT1(0.5);
		clusterer.setCanopyT2(1.0);*/
		try {
			clusterer.setNumClusters(this.NUMBER_OF_CLUSTERS);
		} catch (Exception e) {
			System.out.println("Error while building cluster");
			e.printStackTrace();
		}
	}

	@Override
	public void trainModel(Instances instances) {
		try {
			clusterer.buildClusterer(instances);
		} catch (Exception e) {
			System.out.println("Failed while training the classifier");
			e.printStackTrace();
		}
	}

	@Override
	public double classifyInstance(Instance instance, Packet origin) throws Exception {
		/**
		 * Id of the closes cluster centroid
		 */
		int x = clusterer.clusterInstance(instance);
		/**
		 * centroid instance
		 */
		Instance center = clusterer.getClusterCentroids().get(x);
		return clusterer.getDistanceFunction().distance(center, instance);
	}

	@Override
	public long getClassificationStart() {
		return 3600000;
	}

	@Override
	public String getAlgoName() {
		return "KNN";
	}
}