Example usage for weka.clusterers Canopy nonEmptyCanopySetIntersection

Introduction

In this page you can find the example usage for weka.clusterers Canopy nonEmptyCanopySetIntersection.

Prototype

public static boolean nonEmptyCanopySetIntersection(long[] first, long[] second) throws Exception 

Source Link

Document

Tests if two sets of canopies have a non-empty intersection

Usage

From source file:clusterer.SimpleKMeansWithSilhouette.java

License:Open Source License

/**
 * clusters an instance that has been through the filters.
 * //from   w  w  w.  j  a v  a 2s  .c o  m
 * @param instance the instance to assign a cluster to
 * @param updateErrors if true, update the within clusters sum of errors
 * @param useFastDistCalc whether to use the fast distance calculation or not
 * @param instanceCanopies the canopies covering the instance to be clustered,
 *          or null if not using the option to reduce the number of distance
 *          computations via canopies
 * @return a cluster number
 */
private int clusterProcessedInstance(Instance instance, boolean updateErrors, boolean useFastDistCalc,
        long[] instanceCanopies) {
    double minDist = Integer.MAX_VALUE;
    int bestCluster = 0;
    for (int i = 0; i < m_NumClusters; i++) {
        double dist;
        if (useFastDistCalc) {
            if (m_speedUpDistanceCompWithCanopies && instanceCanopies != null && instanceCanopies.length > 0) {
                try {
                    if (!Canopy.nonEmptyCanopySetIntersection(m_centroidCanopyAssignments.get(i),
                            instanceCanopies)) {
                        continue;
                    }
                } catch (Exception ex) {
                    ex.printStackTrace();
                }
                dist = m_DistanceFunction.distance(instance, m_ClusterCentroids.instance(i), minDist);
            } else {
                dist = m_DistanceFunction.distance(instance, m_ClusterCentroids.instance(i), minDist);
            }
        } else {
            dist = m_DistanceFunction.distance(instance, m_ClusterCentroids.instance(i));
        }
        if (dist < minDist) {
            minDist = dist;
            bestCluster = i;
        }
    }
    if (updateErrors) {
        if (m_DistanceFunction instanceof EuclideanDistance) {
            // Euclidean distance to Squared Euclidean distance
            minDist *= minDist * instance.weight();
        }
        m_squaredErrors[bestCluster] += minDist;
    }
    return bestCluster;
}

---