Examples with NoDataException org.apache.ignite.ml.math.exceptions.NoDataException used on opensource projects

Example 1 with NoDataException

use of org.apache.ignite.ml.math.exceptions.NoDataException in project ignite by apache.

the class AbstractMultipleLinearRegression method newXSampleData.

/**
     * <p>Loads new x sample data, overriding any previous data.
     * </p>
     * The input <code>x</code> array should have one row for each sample
     * observation, with columns corresponding to independent variables.
     * For example, if <pre>
     * <code> x = new double[][] {{1, 2}, {3, 4}, {5, 6}} </code></pre>
     * then <code>setXSampleData(x) </code> results in a model with two independent
     * variables and 3 observations:
     * <pre>
     *   x[0]  x[1]
     *   ----------
     *     1    2
     *     3    4
     *     5    6
     * </pre>
     * </p>
     * <p>Note that there is no need to add an initial unitary column (column of 1's) when
     * specifying a model including an intercept term.
     * </p>
     *
     * @param x the rectangular array representing the x sample
     * @throws NullArgumentException if x is null
     * @throws NoDataException if x is empty
     * @throws CardinalityException if x is not rectangular
     */
protected void newXSampleData(Matrix x) {
    if (x == null)
        throw new NullArgumentException();
    if (x.rowSize() == 0)
        throw new NoDataException();
    if (noIntercept)
        // TODO: Should we copy here?
        xMatrix = x;
    else {
        // Augment design matrix with initial unitary column
        xMatrix = MatrixUtil.like(x, x.rowSize(), x.columnSize() + 1);
        xMatrix.viewColumn(0).map(Functions.constant(1.0));
        xMatrix.viewPart(0, x.rowSize(), 1, x.columnSize()).assign(x);
    }
}

Example 2 with NoDataException

use of org.apache.ignite.ml.math.exceptions.NoDataException in project ignite by apache.

the class LabeledDatasetLoader method loadFromTxtFile.

/**
 * Datafile should keep class labels in the first column.
 *
 * @param pathToFile Path to file.
 * @param separator Element to tokenize row on separate tokens.
 * @param isDistributed Generates distributed dataset if true.
 * @param isFallOnBadData Fall on incorrect data if true.
 * @return Labeled Dataset parsed from file.
 */
public static LabeledDataset loadFromTxtFile(Path pathToFile, String separator, boolean isDistributed, boolean isFallOnBadData) throws IOException {
    Stream<String> stream = Files.lines(pathToFile);
    List<String> list = new ArrayList<>();
    stream.forEach(list::add);
    final int rowSize = list.size();
    List<Double> labels = new ArrayList<>();
    List<Vector> vectors = new ArrayList<>();
    if (rowSize > 0) {
        final int colSize = getColumnSize(separator, list) - 1;
        if (colSize > 0) {
            for (int i = 0; i < rowSize; i++) {
                Double clsLb;
                String[] rowData = list.get(i).split(separator);
                try {
                    clsLb = Double.parseDouble(rowData[0]);
                    Vector vec = parseFeatures(pathToFile, isDistributed, isFallOnBadData, colSize, i, rowData);
                    labels.add(clsLb);
                    vectors.add(vec);
                } catch (NumberFormatException e) {
                    if (isFallOnBadData)
                        throw new FileParsingException(rowData[0], i, pathToFile);
                }
            }
            LabeledVector[] data = new LabeledVector[vectors.size()];
            for (int i = 0; i < vectors.size(); i++) data[i] = new LabeledVector(vectors.get(i), labels.get(i));
            return new LabeledDataset(data, colSize);
        } else
            throw new NoDataException("File should contain first row with data");
    } else
        throw new EmptyFileException(pathToFile.toString());
}