IrisKNNClassifier example

IrisKNNClassifier.java

public class IrisKNNClassifier extends VoltProcedure {
    // The query to extract the data from a VoltDB table.
    public final SQLStmt selectData =
            new SQLStmt("SELECT sepal_length, sepal_width, petal_length, petal_width, class FROM iris;");
    // Number of attributes.
    private static final int attributeCount = 4;
    private static Classifier knnc;

    // The train method (Java stored procedure).
    public long run() {
        // Run the SELECT query to get the data.
        voltQueueSQL(selectData);
        VoltTable[] queryResults = voltExecuteSQL(true);
        VoltTable dataTable = queryResults[0];

        // Build the data set that can be used by the machine learning library.
        Dataset dataset = new DefaultDataset();
        while (dataTable.advanceRow()) {
            DenseInstance dataRow = new DenseInstance(attributeCount);
            for (int i = 0; i < attributeCount; i++) {
                dataRow.put(i, dataTable.getDouble(i));
            }
            dataRow.setClassValue(dataTable.getString(attributeCount));
            dataset.add(dataRow);
        }

        // Train classifier.
        knnc = new KNearestNeighbors(5);
        knnc.buildClassifier(dataset);
        return ClientResponse.SUCCESS;
    }
}