Examples with DistanceMetric edu.ucsf.rbvi.clusterMaker2.internal.api.DistanceMetric used on opensource projects

Example 6 with DistanceMetric

use of edu.ucsf.rbvi.clusterMaker2.internal.api.DistanceMetric in project clusterMaker2 by RBVI.

the class KMeansCluster method run.

public void run(TaskMonitor monitor) {
    this.monitor = monitor;
    monitor.setTitle("Performing " + getName());
    List<String> nodeAttributeList = context.attributeList.getNodeAttributeList();
    String edgeAttribute = context.attributeList.getEdgeAttribute();
    if (nodeAttributeList == null && edgeAttribute == null) {
        monitor.showMessage(TaskMonitor.Level.ERROR, "Must select either one edge column or two or more node columns");
        return;
    }
    if (nodeAttributeList != null && nodeAttributeList.size() > 0 && edgeAttribute != null) {
        monitor.showMessage(TaskMonitor.Level.ERROR, "Can't have both node and edge columns selected");
        return;
    }
    if (context.selectedOnly && CyTableUtil.getNodesInState(network, CyNetwork.SELECTED, true).size() < 3) {
        monitor.showMessage(TaskMonitor.Level.ERROR, "Must have at least three nodes to cluster");
        return;
    }
    createGroups = context.createGroups;
    if (nodeAttributeList != null && nodeAttributeList.size() > 0) {
        // To make debugging easier, sort the attribute list
        Collections.sort(nodeAttributeList);
    }
    // Get our attributes we're going to use for the cluster
    String[] attributeArray;
    if (nodeAttributeList != null && nodeAttributeList.size() > 0) {
        attributeArray = new String[nodeAttributeList.size()];
        int i = 0;
        for (String attr : nodeAttributeList) {
            attributeArray[i++] = "node." + attr;
        }
    } else {
        attributeArray = new String[1];
        attributeArray[0] = "edge." + edgeAttribute;
    }
    monitor.setStatusMessage("Initializing");
    // System.out.println("Initializing");
    resetAttributes(network, SHORTNAME);
    // Create a new clusterer
    RunKCluster algorithm = new RunKCluster(network, attributeArray, distanceMetric, monitor, context, this);
    // System.out.println("Algorithm defined");
    String resultsString = "K-Means results:";
    // Cluster the attributes, if requested
    if (context.clusterAttributes && attributeArray.length > 1) {
        monitor.setStatusMessage("Clustering attributes");
        // System.out.println("Clustering attributes: k="+context.kcluster.kNumber);
        Integer[] rowOrder = algorithm.cluster(clusterManager, context.kcluster.kNumber, context.iterations, true, "kmeans", context.kcluster, false);
        attributeList = algorithm.getAttributeList();
        updateAttributes(network, SHORTNAME, rowOrder, attributeArray, attributeList, algorithm.getMatrix());
        attributeSilhouette = algorithm.getSilhouettes();
        attributeOrder = getOrder(rowOrder, algorithm.getMatrix());
    }
    // Cluster the nodes
    monitor.setStatusMessage("Clustering nodes");
    Integer[] rowOrder = algorithm.cluster(clusterManager, context.kcluster.kNumber, context.iterations, false, "kmeans", context.kcluster, createGroups);
    nodeList = algorithm.getAttributeList();
    updateAttributes(network, SHORTNAME, rowOrder, attributeArray, nodeList, algorithm.getMatrix());
    nodeSilhouette = algorithm.getSilhouettes();
    nodeOrder = getOrder(rowOrder, algorithm.getMatrix());
    // System.out.println(resultsString);
    if (context.showUI) {
        insertTasksAfterCurrentTask(new KnnView(clusterManager));
    }
}

Example 7 with DistanceMetric

use of edu.ucsf.rbvi.clusterMaker2.internal.api.DistanceMetric in project clusterMaker2 by RBVI.

the class DBSCAN method run.

public void run(TaskMonitor monitor) {
    this.monitor = monitor;
    monitor.setTitle("Performing " + getName());
    List<String> nodeAttributeList = context.attributeList.getNodeAttributeList();
    String edgeAttribute = context.attributeList.getEdgeAttribute();
    if (nodeAttributeList == null && edgeAttribute == null) {
        monitor.showMessage(TaskMonitor.Level.ERROR, "Must select either one edge column or two or more node columns");
        return;
    }
    if (nodeAttributeList != null && nodeAttributeList.size() > 0 && edgeAttribute != null) {
        monitor.showMessage(TaskMonitor.Level.ERROR, "Can't have both node and edge columns selected");
        return;
    }
    if (context.selectedOnly && CyTableUtil.getNodesInState(network, CyNetwork.SELECTED, true).size() < 3) {
        monitor.showMessage(TaskMonitor.Level.ERROR, "Must have at least three nodes to cluster");
        return;
    }
    createGroups = context.createGroups;
    if (nodeAttributeList != null && nodeAttributeList.size() > 0) {
        // To make debugging easier, sort the attribute list
        Collections.sort(nodeAttributeList);
    }
    // Get our attributes we're going to use for the cluster
    String[] attributeArray;
    if (nodeAttributeList != null && nodeAttributeList.size() > 0) {
        attributeArray = new String[nodeAttributeList.size()];
        int i = 0;
        for (String attr : nodeAttributeList) {
            attributeArray[i++] = "node." + attr;
        }
    } else {
        attributeArray = new String[1];
        attributeArray[0] = "edge." + edgeAttribute;
    }
    monitor.setStatusMessage("Initializing");
    resetAttributes(network, SHORTNAME);
    distanceMetric = context.getDistanceMetric();
    // Create a new clusterer
    RunDBSCAN algorithm = new RunDBSCAN(network, attributeArray, distanceMetric, monitor, context);
    String resultsString = "DBSCAN results:";
    // Cluster the attributes, if requested
    if (context.clusterAttributes && attributeArray.length > 1) {
        monitor.setStatusMessage("Clustering attributes");
        int[] clusters = algorithm.cluster(true);
        if (!algorithm.getMatrix().isTransposed())
            createGroups(network, algorithm.getMatrix(), algorithm.getNClusters(), clusters, "dbscan");
        Integer[] rowOrder = MatrixUtils.indexSort(clusters, clusters.length);
        // Integer[] rowOrder = algorithm.cluster(context.kcluster.kNumber,1, true, "dbscan", context.kcluster);
        updateAttributes(network, SHORTNAME, rowOrder, attributeArray, getAttributeList(), algorithm.getMatrix());
    }
    // Cluster the nodes
    monitor.setStatusMessage("Clustering nodes");
    int[] clusters = algorithm.cluster(false);
    int nNodes = 0;
    for (int i = 0; i < clusters.length; i++) {
        if (clusters[i] >= 0)
            nNodes++;
    }
    monitor.setStatusMessage("Allocated " + nNodes + " nodes to " + algorithm.getNClusters() + " clusters");
    if (!algorithm.getMatrix().isTransposed()) {
        createGroups(network, algorithm.getMatrix(), algorithm.getNClusters(), clusters, "dbscan");
    }
    Integer[] rowOrder = MatrixUtils.indexSort(clusters, clusters.length);
    // In DBSCAN, not all nodes will be assigned to a cluster, so they will have a cluster # of -1.  Find
    // all of those and trim rowOrder accordingly.
    Integer[] newRowOrder = new Integer[nNodes];
    int newOrder = 0;
    for (int i = 0; i < rowOrder.length; i++) {
        int nodeIndex = rowOrder[i];
        if (clusters[nodeIndex] >= 0) {
            newRowOrder[newOrder] = nodeIndex;
            newOrder++;
        }
    }
    // Integer[] rowOrder = algorithm.cluster(context.kcluster.kNumber,1, false, "dbscan", context.kcluster);
    updateAttributes(network, SHORTNAME, newRowOrder, attributeArray, getAttributeList(), algorithm.getMatrix());
    // System.out.println(resultsString);
    if (context.showUI) {
        insertTasksAfterCurrentTask(new KnnView(clusterManager));
    }
}

Example 8 with DistanceMetric

use of edu.ucsf.rbvi.clusterMaker2.internal.api.DistanceMetric in project clusterMaker2 by RBVI.

the class RunFFT method kcluster.

@Override
public int kcluster(int nClusters, int nIterations, CyMatrix matrix, DistanceMetric metric, int[] clusterID) {
    random = null;
    int nelements = matrix.nRows();
    int[] tclusterid = new int[nelements];
    int[] saved = new int[nelements];
    int[] mapping = new int[nClusters];
    int[] counts = new int[nClusters];
    CyMatrix distanceMatrix = matrix.getDistanceMatrix(metric);
    HashMap<Integer, Integer> centers = new HashMap<Integer, Integer>();
    double error = Double.MAX_VALUE;
    if (monitor != null)
        monitor.setProgress(0);
    for (int i = 0; i < nelements; i++) clusterID[i] = 0;
    // the first center
    Random randomGenerator = new Random();
    centers.put(0, randomGenerator.nextInt(nelements));
    // now find the remaining centers
    for (int i = 1; i < nClusters; i++) {
        int y = getMaxMin(centers, distanceMatrix);
        centers.put(i, y);
        clusterID[y] = i;
    }
    /*
		for (int i = 0; i < nClusters; i++)
			System.out.printf("Center for %d = %d\n", i, centers.get(i));
		*/
    // assign clusters now
    int k = centers.get(0);
    for (int i = 0; i < nelements; i++) {
        // Is this one of our centers?
        if (centers.containsValue(i))
            continue;
        double minDistance = Double.MAX_VALUE;
        int center = 0;
        for (int cluster = 0; cluster < nClusters; cluster++) {
            double dist = distanceMatrix.doubleValue(i, centers.get(cluster));
            if (dist < minDistance) {
                center = cluster;
                minDistance = dist;
            }
        }
        clusterID[i] = center;
    }
    return nClusters;
}

Example 9 with DistanceMetric

use of edu.ucsf.rbvi.clusterMaker2.internal.api.DistanceMetric in project clusterMaker2 by RBVI.

the class CyColtMatrix method getDistanceMatrix.

public CyMatrix getDistanceMatrix(DistanceMetric metric) {
    CyColtMatrix dist = new CyColtMatrix(network, nRows, nRows);
    if (rowNodes != null) {
        dist.rowNodes = Arrays.copyOf(rowNodes, nRows);
        dist.columnNodes = Arrays.copyOf(rowNodes, nRows);
    }
    Matrix cMatrix = super.getDistanceMatrix(metric);
    return dist.copy(cMatrix);
}

Example 10 with DistanceMetric

use of edu.ucsf.rbvi.clusterMaker2.internal.api.DistanceMetric in project clusterMaker2 by RBVI.

the class CyOjAlgoMatrix method getDistanceMatrix.

public CyMatrix getDistanceMatrix(DistanceMetric metric) {
    CyOjAlgoMatrix dist = new CyOjAlgoMatrix(network, nRows, nRows);
    if (rowNodes != null) {
        dist.rowNodes = Arrays.copyOf(rowNodes, nRows);
        dist.columnNodes = Arrays.copyOf(rowNodes, nRows);
    }
    Matrix cMatrix = super.getDistanceMatrix(metric);
    return dist.copy(cMatrix);
}