Examples with MedianSummarizer edu.ucsf.rbvi.clusterMaker2.internal.algorithms.numeric.MedianSummarizer used on opensource projects

Example 1 with MedianSummarizer

use of edu.ucsf.rbvi.clusterMaker2.internal.algorithms.numeric.MedianSummarizer in project clusterMaker2 by RBVI.

the class HopachablePAMTest method testSplit.

@Test
public void testSplit() {
    Double[] data = { .2, .2, .8, .8, .82, .82, .4, .5, .5, .4, .15, .15, .81, .81, .14, .14, .45, .45 };
    int k = 3;
    int[] ans = { 0, 1, 1, 2, 2, 0, 1, 0, 2 };
    CyMatrix mat = CyMatrixFactory.makeSmallMatrix(9, 2, data);
    HopachablePAM pam = new HopachablePAM(null, mat, DistanceMetric.CITYBLOCK);
    pam.setParameters(9, 9, SplitCost.AVERAGE_SPLIT_SILHOUETTE, new MedianSummarizer());
    Clusters c = pam.split(false);
    // check that data are split into expected number of clusters
    assertEquals(c.getNumberOfClusters(), k);
    // check cluster assignments
    for (int i = 0; i < c.size(); ++i) {
        assertEquals(c.getClusterIndex(i), ans[i]);
    }
}

Example 2 with MedianSummarizer

use of edu.ucsf.rbvi.clusterMaker2.internal.algorithms.numeric.MedianSummarizer in project clusterMaker2 by RBVI.

the class RunHopachPAM method kcluster.

@Override
public int kcluster(int nClusters, int nIterations, CyMatrix matrix, DistanceMetric metric, int[] clusterId) {
    monitor.setProgress(0);
    Summarizer summarizer;
    PrimitiveSummarizer psummarizer;
    switch(summaryMethod) {
        case MEDIAN:
            summarizer = new MedianSummarizer();
            psummarizer = new PrimitiveMedianSummarizer();
            break;
        case MEAN:
        default:
            summarizer = new MeanSummarizer();
            psummarizer = new PrimitiveMeanSummarizer();
            break;
    }
    HopachablePAM partitioner = new HopachablePAM(network, matrix, metric);
    partitioner.setParameters(K, L, splitCost, summarizer);
    HopachPAM hopachPam = new HopachPAM(partitioner);
    hopachPam.setParameters(maxLevel, minCostReduction, forceInitSplit, psummarizer);
    Clusters c = hopachPam.run();
    // copy results into clusterId
    for (int i = 0; i < c.size(); ++i) {
        clusterId[i] = c.getClusterIndex(i);
    }
    return c.getNumberOfClusters();
}