Examples with KDTreeBuilder org.knime.base.util.kdtree.KDTreeBuilder used on opensource projects

Example 1 with KDTreeBuilder

use of org.knime.base.util.kdtree.KDTreeBuilder in project knime-core by knime.

the class KnnNodeModel method createRearranger.

/*
     * Creates a column rearranger. NOTE: This call possibly involves heavier calculations since the kd-tree is determined here based on the training data.
     * @param numRowsTable2 - can be -1 if can't be determined (streaming)
     */
private ColumnRearranger createRearranger(final BufferedDataTable trainData, final DataTableSpec inSpec2, final ExecutionContext exec, final long numRowsTable2) throws CanceledExecutionException, InvalidSettingsException {
    int classColIndex = trainData.getDataTableSpec().findColumnIndex(m_settings.classColumn());
    if (classColIndex == -1) {
        throw new InvalidSettingsException("Invalid class column chosen.");
    }
    List<Integer> featureColumns = new ArrayList<Integer>();
    Map<Integer, Integer> firstToSecond = new HashMap<Integer, Integer>();
    checkInputTables(new DataTableSpec[] { trainData.getDataTableSpec(), inSpec2 }, featureColumns, firstToSecond);
    KDTreeBuilder<DataCell> treeBuilder = new KDTreeBuilder<DataCell>(featureColumns.size());
    int count = 0;
    for (DataRow currentRow : trainData) {
        exec.checkCanceled();
        exec.setProgress(0.1 * count * trainData.size(), "Reading row " + currentRow.getKey());
        double[] features = createFeatureVector(currentRow, featureColumns);
        if (features == null) {
            setWarningMessage("Input table contains missing values, the " + "affected rows are ignored.");
        } else {
            DataCell thisClassCell = currentRow.getCell(classColIndex);
            // and finally add data
            treeBuilder.addPattern(features, thisClassCell);
            // compute the majority class for breaking possible ties later
            MutableInteger t = m_classDistribution.get(thisClassCell);
            if (t == null) {
                m_classDistribution.put(thisClassCell, new MutableInteger(1));
            } else {
                t.inc();
            }
        }
    }
    // and now use it to classify the test data...
    DataColumnSpec classColumnSpec = trainData.getDataTableSpec().getColumnSpec(classColIndex);
    exec.setMessage("Building kd-tree");
    KDTree<DataCell> tree = treeBuilder.buildTree(exec.createSubProgress(0.3));
    if (tree.size() < m_settings.k()) {
        setWarningMessage("There are only " + tree.size() + " patterns in the input table, but " + m_settings.k() + " nearest neighbours were requested for classification." + " The prediction will be the majority class for all" + " input patterns.");
    }
    exec.setMessage("Classifying");
    ColumnRearranger c = createRearranger(inSpec2, classColumnSpec, featureColumns, firstToSecond, tree, numRowsTable2);
    return c;
}