package ca.pfv.spmf.test;
import java.io.IOException;
import java.io.UnsupportedEncodingException;
import java.net.URL;
import java.util.List;
import ca.pfv.spmf.algorithms.clustering.distanceFunctions.DistanceEuclidian;
import ca.pfv.spmf.algorithms.clustering.distanceFunctions.DistanceFunction;
import ca.pfv.spmf.algorithms.clustering.hierarchical_clustering.AlgoHierarchicalClustering;
import ca.pfv.spmf.patterns.cluster.ClusterWithMean;
import ca.pfv.spmf.patterns.cluster.DoubleArray;
/**
* Example of how to use the hierarchical clustering algorithm
* from the source code.
*/
public class MainTestHierarchicalClustering_saveToMemory {
public static void main(String []args) throws NumberFormatException, IOException{
String input = fileToPath("configKmeans.txt");
String output = ".//output.txt";
int maxdistance = 4;
// Here we specify that we want to use the euclidian distance
DistanceFunction distanceFunction = new DistanceEuclidian();
// Alternative distance functions are also available such as:
// DistanceFunction distanceFunction = new DistanceManathan();
// DistanceFunction distanceFunction = new DistanceCosine();
// DistanceFunction distanceFunction = new DistanceCorrelation();
// DistanceFunction distanceFunction = new DistanceJaccard();
// Apply the algorithm
AlgoHierarchicalClustering algo = new AlgoHierarchicalClustering(); // we request 3 clusters
List<ClusterWithMean> clusters = algo.runAlgorithm(input, maxdistance, distanceFunction);
algo.printStatistics();
// Print the clusters found by the algorithm
// For each cluster:
int i=0;
for(ClusterWithMean cluster : clusters) {
System.out.println("Cluster " + i++);
// For each data point:
for(DoubleArray dataPoint : cluster.getVectors()) {
System.out.println(" " + dataPoint);
}
}
}
public static String fileToPath(String filename) throws UnsupportedEncodingException{
URL url = MainTestHierarchicalClustering_saveToMemory.class.getResource(filename);
return java.net.URLDecoder.decode(url.getPath(),"UTF-8");
}
}