edu.iu.dsc.tws.examples.batch.kmeans

Class KMeansUtils

java.lang.Object

edu.iu.dsc.tws.examples.batch.kmeans.KMeansUtils

public final class KMeansUtils
extends java.lang.Object

This class has the utility methods to generate the datapoints and centroids. Also, it has the methods to parse the data partitions and data objects and store into the double arrays.

Method Summary

Modifier and Type	Method and Description
static double[][]	findNearestCenter(int dim, double[][] datapoints, double[][] centers) This method first invoke the euclidean distance between to calculate the distance between the data points assigned to the task (i.e from start index to end index) and the centroid values.
static void	generateDataPoints(Config config, int dim, int numFiles, int datasize, int centroidsize, java.lang.String dinputDirectory, java.lang.String cinputDirectory, java.lang.String type) This method is to generate the datapoints and centroids based on the user submitted values.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

generateDataPoints

public static void generateDataPoints(Config config,
                                      int dim,
                                      int numFiles,
                                      int datasize,
                                      int centroidsize,
                                      java.lang.String dinputDirectory,
                                      java.lang.String cinputDirectory,
                                      java.lang.String type)

This method is to generate the datapoints and centroids based on the user submitted values.

findNearestCenter

public static double[][] findNearestCenter(int dim,
                                           double[][] datapoints,
                                           double[][] centers)

This method first invoke the euclidean distance between to calculate the distance between the data points assigned to the task (i.e from start index to end index) and the centroid values. The calculated centroid values and the number of data points closer to the particular centroid values assigned to the centerSums array object.