Examples with Clusters edu.ucsf.rbvi.clusterMaker2.internal.algorithms.attributeClusterers.Clusters used on opensource projects

Example 1 with Clusters

use of edu.ucsf.rbvi.clusterMaker2.internal.algorithms.attributeClusterers.Clusters in project clusterMaker2 by RBVI.

the class Fuzzifier method run.

/**
 * The method run creates an instance of the RunFuzzifier and creates the fuzzy clusters
 * by calling the fuzzifier algorithm.
 * Also creates fuzzy groups and the Fuzzy Cluster Table
 *
 * @param Task Monitor
 */
public void run(TaskMonitor monitor) {
    monitor.setTitle("Performing Fuzzifier clustering");
    this.monitor = monitor;
    if (network == null)
        network = clusterManager.getNetwork();
    super.network = network;
    this.Clusters = getClusters();
    this.cNumber = Clusters.size();
    // Make sure to update the context
    context.setNetwork(network);
    Long networkID = network.getSUID();
    CyTable nodeAttributes = network.getDefaultNodeTable();
    CyMatrix distanceMatrix = context.edgeAttributeHandler.getMatrix();
    if (distanceMatrix == null) {
        monitor.showMessage(TaskMonitor.Level.ERROR, "Can't get distance matrix: no attribute value?");
        return;
    }
    // Update our tunable results
    clusterAttributeName = context.getClusterAttribute();
    runFuzzifier = new RunFuzzifier(Clusters, distanceMatrix, cNumber, context.membershipThreshold.getValue(), context.maxThreads, monitor);
    runFuzzifier.setDebug(debug);
    if (canceled)
        return;
    monitor.showMessage(TaskMonitor.Level.INFO, "Clustering...");
    List<FuzzyNodeCluster> FuzzyClusters = runFuzzifier.run(network, monitor);
    // Canceled?
    if (FuzzyClusters == null)
        return;
    monitor.showMessage(TaskMonitor.Level.INFO, "Removing groups");
    // Remove any leftover groups from previous runs
    removeGroups(network, GROUP_ATTRIBUTE);
    monitor.showMessage(TaskMonitor.Level.INFO, "Creating groups");
    params = new ArrayList<String>();
    context.edgeAttributeHandler.setParams(params);
    List<List<CyNode>> nodeClusters = createFuzzyGroups(network, FuzzyClusters, GROUP_ATTRIBUTE);
    results = new AbstractClusterResults(network, FuzzyClusters);
    monitor.showMessage(TaskMonitor.Level.INFO, "Done.  Fuzzifier results:\n" + results);
    if (context.vizProperties.showUI) {
        monitor.showMessage(TaskMonitor.Level.INFO, "Creating network");
        insertTasksAfterCurrentTask(new NewNetworkView(network, clusterManager, true, context.vizProperties.restoreEdges, !context.edgeAttributeHandler.selectedOnly));
    } else {
        monitor.showMessage(TaskMonitor.Level.INFO, "Done.  Fizzifier results:\n" + results);
    }
    System.out.println("Creating fuzzy table");
    createFuzzyTable(FuzzyClusters);
    System.out.println("Done");
}

Example 2 with Clusters

use of edu.ucsf.rbvi.clusterMaker2.internal.algorithms.attributeClusterers.Clusters in project clusterMaker2 by RBVI.

the class RunFuzzifier method run.

/**
 * The method run has the actual implementation of the fuzzifier code
 * @param monitor, Task monitor for the process
 * @return List of FuzzyNodeCLusters
 */
public List<FuzzyNodeCluster> run(CyNetwork network, TaskMonitor monitor) {
    Long networkID = network.getSUID();
    long startTime = System.currentTimeMillis();
    int nelements = distanceMatrix.nRows();
    nodeList = distanceMatrix.getRowNodes();
    // Matrix to store the temporary cluster membership values of elements
    double[][] ClusterMemberships = new double[nelements][number_clusters];
    // Initializing all membership values to 0
    for (int i = 0; i < nelements; i++) {
        for (int j = 0; j < number_clusters; j++) {
            ClusterMemberships[i][j] = 0;
        }
    }
    // This matrix will store the centroid data
    CyMatrix cData = CyMatrixFactory.makeSmallMatrix(network, number_clusters, nelements);
    getFuzzyCenters(cData);
    for (CyNode node : nodeList) {
        int nodeIndex = nodeList.indexOf(node);
        double sumDistances = 0;
        for (int i = 0; i < Clusters.size(); i++) {
            sumDistances += cData.doubleValue(i, nodeIndex);
        }
        for (int i = 0; i < Clusters.size(); i++) {
            ClusterMemberships[nodeIndex][i] = cData.doubleValue(i, nodeIndex) / sumDistances;
        }
    }
    HashMap<CyNode, double[]> membershipMap = createMembershipMap(ClusterMemberships);
    List<FuzzyNodeCluster> fuzzyClusters = new ArrayList<FuzzyNodeCluster>();
    // Adding the nodes which have memberships greater than the threshold to fuzzy node clusters
    List<CyNode> fuzzyNodeList;
    for (int i = 0; i < number_clusters; i++) {
        fuzzyNodeList = new ArrayList<CyNode>();
        HashMap<CyNode, Double> clusterMembershipMap = new HashMap<CyNode, Double>();
        for (CyNode node : nodeList) {
            if (membershipMap.get(node)[i] > membershipThreshold) {
                fuzzyNodeList.add(node);
                clusterMembershipMap.put(node, membershipMap.get(node)[i]);
            }
        }
        fuzzyClusters.add(new FuzzyNodeCluster(fuzzyNodeList, clusterMembershipMap));
    }
    return fuzzyClusters;
}

Example 3 with Clusters

use of edu.ucsf.rbvi.clusterMaker2.internal.algorithms.attributeClusterers.Clusters in project clusterMaker2 by RBVI.

the class RunTransClust method getClusterMap.

private Map<Integer, NodeCluster> getClusterMap(String[] clusters, HashMap<String, CyNode> nodeHash) {
    HashMap<Integer, NodeCluster> clusterMap = new HashMap<Integer, NodeCluster>();
    for (int i = 0; i < clusters.length; i++) {
        String[] elements = clusters[i].split(",");
        NodeCluster nc = new NodeCluster();
        for (int j = 0; j < elements.length; j++) {
            if (nodeHash.containsKey(elements[j].trim())) {
                nc.add(nodeHash.get(elements[j].trim()));
            }
        }
        clusterCount++;
        updateClusters(nc, clusterMap);
    }
    return clusterMap;
}

Example 4 with Clusters

use of edu.ucsf.rbvi.clusterMaker2.internal.algorithms.attributeClusterers.Clusters in project clusterMaker2 by RBVI.

the class RunTransClust method run.

public List<NodeCluster> run(TaskMonitor monitor, CyNetwork network) {
    DoubleMatrix2D matrix = this.distanceMatrix.getColtMatrix();
    nodes = distanceMatrix.getRowNodes();
    HashMap<String, CyNode> nodeHash = new HashMap<String, CyNode>();
    for (CyNode node : nodes) {
        nodeHash.put(ModelUtils.getNodeName(network, node), node);
    }
    HashMap<String, Integer> integers2proteins = new HashMap<String, Integer>();
    HashMap<Integer, String> proteins2integers = new HashMap<Integer, String>();
    int count = 0;
    for (CyNode node : this.nodes) {
        integers2proteins.put(ModelUtils.getNodeName(network, node), count);
        proteins2integers.put(count, ModelUtils.getNodeName(network, node));
        count++;
    }
    Edges es = new Edges(this.nodes.size() * this.nodes.size(), this.nodes.size());
    count = 0;
    for (int i = 0; i < this.nodes.size(); i++) {
        CyNode cyNodeI = this.nodes.get(i);
        es.startPositions[integers2proteins.get(cyNodeI.getSUID())] = count;
        for (int j = 0; j < this.nodes.size(); j++) {
            CyNode cyNodeJ = this.nodes.get(j);
            es.sources[count] = i;
            es.targets[count] = j;
            Double val = distanceMatrix.getValue(i, j);
            if (val != null) {
                es.values[count] = val.floatValue();
                count++;
            }
        }
        es.endPositions[integers2proteins.get(cyNodeI.getSUID())] = count - 1;
    }
    Semaphore s = new Semaphore(1);
    TaskConfig.mode = TaskConfig.COMPARISON_MODE;
    TaskConfig.monitor = monitor;
    IteratorThread it = new IteratorThread(es, integers2proteins, proteins2integers, s);
    TaskConfig.minThreshold = threshold;
    TaskConfig.maxThreshold = threshold;
    try {
        s.acquire();
    } catch (InterruptedException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
    it.start();
    monitor.showMessage(TaskMonitor.Level.INFO, "Executing TransClust Clustering...");
    try {
        s.acquire();
    } catch (InterruptedException e) {
        // TODO Auto-generated catch block
        e.printStackTrace();
    }
    monitor.showMessage(TaskMonitor.Level.INFO, "Assigning nodes to clusters");
    String result = it.resultsStringBuffer.toString();
    String[] clusters = result.split("\t")[2].split(";");
    Map<Integer, NodeCluster> clusterMap = getClusterMap(clusters, nodeHash);
    // Update node attributes in network to include clusters. Create cygroups from clustered nodes
    monitor.showMessage(TaskMonitor.Level.INFO, "Created " + clusterMap.size() + " clusters");
    if (clusterCount == 0) {
        monitor.showMessage(TaskMonitor.Level.ERROR, "Created 0 clusters!!!!");
        return null;
    }
    int clusterNumber = 1;
    Map<NodeCluster, NodeCluster> cMap = new HashMap();
    for (NodeCluster cluster : NodeCluster.sortMap(clusterMap)) {
        if (cMap.containsKey(cluster))
            continue;
        cMap.put(cluster, cluster);
        cluster.setClusterNumber(clusterNumber);
        clusterNumber++;
    }
    Set<NodeCluster> clusters2 = cMap.keySet();
    return new ArrayList<NodeCluster>(clusters2);
}

Example 5 with Clusters

use of edu.ucsf.rbvi.clusterMaker2.internal.algorithms.attributeClusterers.Clusters in project clusterMaker2 by RBVI.

the class TransClustCluster method run.

/**
 * Perform the actual clustering.  For TransClust, there are really
 * two steps:
 * 	1) Assign all of the connected components
 * 	2) Do the TransClust clustering.
 *
 * There is also an optional approach called evolutionary parameter
 * tuning, which takes a lot longer and is probably less relevant for
 * the Cytoscape integration.
 *
 * @param monitor the TaskMonitor to use
 */
public void run(TaskMonitor monitor) {
    monitor.setTitle("Performing Transitivity clustering");
    this.monitor = monitor;
    if (network == null)
        network = clusterManager.getNetwork();
    // Make sure to update the context
    context.setNetwork(network);
    NodeCluster.init();
    CyMatrix matrix = context.edgeAttributeHandler.getMatrix();
    if (matrix == null) {
        monitor.showMessage(TaskMonitor.Level.ERROR, "Can't get distance matrix: no attribute value?");
        return;
    }
    updateSettings();
    runTransClust = new RunTransClust(matrix, context.edgeAttributeHandler.edgeCutOff.getValue(), monitor);
    if (canceled)
        return;
    monitor.showMessage(TaskMonitor.Level.INFO, "Clustering...");
    createGroups = context.advancedAttributes.createGroups;
    // Cluster the nodes
    List<NodeCluster> clusters = runTransClust.run(monitor, network);
    // Canceled?
    if (clusters == null)
        return;
    monitor.showMessage(TaskMonitor.Level.INFO, "Removing groups");
    // Remove any leftover groups from previous runs
    removeGroups(network, GROUP_ATTRIBUTE);
    monitor.showMessage(TaskMonitor.Level.INFO, "Creating groups");
    params = new ArrayList<String>();
    context.edgeAttributeHandler.setParams(params);
    List<List<CyNode>> nodeClusters = createGroups(network, clusters, GROUP_ATTRIBUTE);
    results = new AbstractClusterResults(network, clusters);
    monitor.setStatusMessage("Done.  TransClust results:\n" + results);
    if (context.vizProperties.showUI) {
        monitor.showMessage(TaskMonitor.Level.INFO, "Creating network");
        insertTasksAfterCurrentTask(new NewNetworkView(network, clusterManager, true, context.vizProperties.restoreEdges, !context.edgeAttributeHandler.selectedOnly));
    }
}