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

Example 11 with CyMatrix

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

the class HopachablePAMTest method testCollapse.

@Test
public void testCollapse() {
    Double[] data = { .9, .9, .8, .8, .4, .4, .5, .5, .1, .1, .0, .0 };
    int k = 3;
    CyMatrix mat = CyMatrixFactory.makeSmallMatrix(6, 2, data);
    HopachablePAM pam = new HopachablePAM(null, mat, DistanceMetric.CITYBLOCK);
    Clusters c1 = pam.cluster(k);
    Clusters c2 = pam.collapse(0, 1, c1);
    Clusters c3 = pam.collapse(1, 2, c1);
    Clusters c4 = pam.collapse(0, 2, c1);
    // check that the size has reduced
    --k;
    assertEquals(c2.getSizes().length, k);
    assertEquals(c3.getSizes().length, k);
    assertEquals(c4.getSizes().length, k);
    Clusters c5 = pam.collapse(0, 1, c2);
    --k;
    assertEquals(c5.getSizes().length, k);
}

Example 12 with CyMatrix

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

the class HopachablePAMTest method testSubset.

@Test
public void testSubset() {
    Double[] data = { .9, .9, .8, .8, .4, .4, .5, .5, .1, .1, .0, .0 };
    int k = 3;
    // new order
    int[] index = { 0, 1, 5, 2, 4, 3 };
    // expected results based on new order
    int[] ans = { 0, 0, 1, 2, 1, 2 };
    CyMatrix mat = CyMatrixFactory.makeSmallMatrix(6, 2, data);
    HopachablePAM pam = new HopachablePAM(null, mat, DistanceMetric.CITYBLOCK);
    // permute sample order
    Hopachable pamPermuted = pam.subset(index);
    Clusters c = pamPermuted.cluster(k);
    // the number of clusters should not change because it should always
    // return the specified number of clusters
    assertEquals(c.getNumberOfClusters(), k);
    // check that the clustering results match
    for (int i = 0; i < c.size(); ++i) {
        assertEquals(c.getClusterIndex(i), ans[i]);
    }
    // minor test case
    // subset the last 4 elements
    int[] subsetIndex = { 2, 3, 4, 5 };
    int[] subsetAns = { 0, 0, 1, 1 };
    int subsetK = 2;
    Hopachable pamSubset = pam.subset(subsetIndex);
    Clusters c2 = pamSubset.cluster(subsetK);
    // check number of clusters
    assertEquals(c2.getNumberOfClusters(), subsetK);
    // check cluster assignments
    for (int i = 0; i < c2.size(); ++i) {
        assertEquals(c2.getClusterIndex(i), subsetAns[i]);
    }
}

Example 13 with CyMatrix

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

the class HierarchicalCluster 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 && nodeAttributeList != null && nodeAttributeList.size() > 1 && CyTableUtil.getNodesInState(network, CyNetwork.SELECTED, true).size() < 3) {
        monitor.showMessage(TaskMonitor.Level.ERROR, "Must have at least three nodes to cluster");
        return;
    }
    // Get our attributes we're going to use for the cluster
    String[] attributeArray;
    if (nodeAttributeList != null && nodeAttributeList.size() > 0) {
        Collections.sort(nodeAttributeList);
        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.showMessage(TaskMonitor.Level.INFO, "Initializing");
    // System.out.println("Initializing");
    resetAttributes(network, SHORTNAME);
    // Create a new clusterer
    DistanceMetric metric = context.metric.getSelectedValue();
    RunHierarchical algorithm = new RunHierarchical(network, attributeArray, metric, clusterMethod, monitor, context);
    // Cluster the attributes, if requested
    if (context.clusterAttributes && (attributeArray.length > 1 || context.isAssymetric())) {
        monitor.setStatusMessage("Clustering attributes");
        // System.out.println("Clustering attributes");
        Integer[] rowOrder = algorithm.cluster(true);
        attributeTree = algorithm.getAttributeList();
        CyMatrix matrix = algorithm.getMatrix();
        updateAttributes(network, SHORTNAME, rowOrder, attributeArray, attributeTree, matrix);
        attributeOrder = new ArrayList<String>();
        for (int i = 0; i < rowOrder.length; i++) {
            attributeOrder.add(matrix.getRowLabel(rowOrder[i]));
        }
    }
    monitor.setStatusMessage("Clustering nodes");
    // Cluster the nodes
    // System.out.println("Clustering nodes");
    Integer[] rowOrder = algorithm.cluster(false);
    nodeTree = algorithm.getAttributeList();
    CyMatrix matrix = algorithm.getMatrix();
    updateAttributes(network, SHORTNAME, rowOrder, attributeArray, nodeTree, matrix);
    nodeOrder = new ArrayList<CyNode>();
    for (int i = 0; i < rowOrder.length; i++) {
        nodeOrder.add(matrix.getRowNode(rowOrder[i]));
    }
    // TODO: Deal with params!
    List<String> params = context.getParams(algorithm.getMatrix());
    updateParams(network, params);
    if (context.showUI) {
        insertTasksAfterCurrentTask(new TreeView(clusterManager));
    }
    monitor.setStatusMessage("Done");
}

Example 14 with CyMatrix

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

the class RunHierarchical method pclcluster.

/**
 * The pclcluster routine performs clustering, using pairwise centroid-linking
 * on a given set of gene expression data, using the distrance metric given by metric.
 *
 * @param matrix the data matrix containing the data and labels
 * @param distanceMatrix the distances that will be used to actually do the clustering.
 * @param metric the distance metric to be used.
 * @return the array of TreeNode's that describe the hierarchical clustering solution, or null if
 * it it files for some reason.
 */
private TreeNode[] pclcluster(CyMatrix matrix, double[][] distanceMatrix, DistanceMetric metric) {
    int nRows = matrix.nRows();
    int nColumns = matrix.nColumns();
    int nNodes = nRows - 1;
    double[][] mask = new double[matrix.nRows()][matrix.nColumns()];
    TreeNode[] nodeList = new TreeNode[nNodes];
    // Initialize
    CyMatrix newData = matrix.copy();
    // System.out.println("New matrix: ");
    // newData.printMatrix();
    int[] distID = new int[nRows];
    for (int row = 0; row < nRows; row++) {
        distID[row] = row;
        for (int col = 0; col < nColumns; col++) {
            if (newData.hasValue(row, col))
                mask[row][col] = 1.0;
            else
                mask[row][col] = 0.0;
        }
        if (row < nNodes)
            nodeList[row] = new TreeNode(Double.MAX_VALUE);
    }
    int[] pair = new int[2];
    for (int inode = 0; inode < nNodes; inode++) {
        // find the pair with the shortest distance
        pair[IS] = 1;
        pair[JS] = 0;
        double distance = findClosestPair(nRows - inode, distanceMatrix, pair);
        nodeList[inode].setDistance(distance);
        int is = pair[IS];
        int js = pair[JS];
        nodeList[inode].setLeft(distID[js]);
        nodeList[inode].setRight(distID[is]);
        // make node js the new node
        for (int col = 0; col < nColumns; col++) {
            double jsValue = newData.doubleValue(js, col);
            double isValue = newData.doubleValue(is, col);
            double newValue = 0.0;
            if (newData.hasValue(js, col))
                newValue = jsValue * mask[js][col];
            if (newData.hasValue(is, col))
                newValue += isValue * mask[is][col];
            if (newData.hasValue(js, col) || newData.hasValue(is, col)) {
                newData.setValue(js, col, newValue);
            }
            mask[js][col] += mask[is][col];
            if (mask[js][col] != 0) {
                newData.setValue(js, col, newValue / mask[js][col]);
            }
        }
        for (int col = 0; col < nColumns; col++) {
            mask[is][col] = mask[nNodes - inode][col];
            newData.setValue(is, col, newData.getValue(nNodes - inode, col));
        }
        // Fix the distances
        distID[is] = distID[nNodes - inode];
        for (int i = 0; i < is; i++) {
            distanceMatrix[is][i] = distanceMatrix[nNodes - inode][i];
        }
        for (int i = is + 1; i < nNodes - inode; i++) {
            distanceMatrix[i][is] = distanceMatrix[nNodes - inode][i];
        }
        distID[js] = -inode - 1;
        for (int i = 0; i < js; i++) {
            distanceMatrix[js][i] = metric.getMetric(newData, newData, js, i);
        }
        for (int i = js + 1; i < nNodes - inode; i++) {
            distanceMatrix[i][js] = metric.getMetric(newData, newData, js, i);
        }
    }
    return nodeList;
}

Example 15 with CyMatrix

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

the class RunAutoSOME method getNodeClusters.

private Map<NodeCluster, NodeCluster> getNodeClusters(clusterRun cr, Map<String, Integer> key, CyMatrix matrix, Settings s) {
    Map<NodeCluster, NodeCluster> cMap = new HashMap<NodeCluster, NodeCluster>();
    attrList = new ArrayList<String>();
    attrOrderList = new ArrayList<String>();
    nodeOrderList = new ArrayList<String>();
    for (int i = 0; i < matrix.nColumns(); i++) attrOrderList.add(matrix.getColumnLabel(i));
    for (int i = 0; i < clusterCount; i++) {
        if (cr.c[i].ids.isEmpty())
            continue;
        NodeCluster nc = new NodeCluster();
        nc.setClusterNumber(i);
        for (int j = 0; j < cr.c[i].ids.size(); j++) {
            int dataID = cr.c[i].ids.get(j).intValue();
            int nodeDataID = key.get(matrix.getRowLabels()[dataID]).intValue();
            CyNode cn = nodes.get(nodeDataID);
            nc.add(cn);
            attrList.add(ModelUtils.getNodeName(network, cn) + "\t" + i);
            nodeOrderList.add(ModelUtils.getNodeName(network, cn));
        }
        cMap.put(nc, nc);
    }
    return cMap;
}