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

Example 71 with Matrix

use of org.apache.ignite.ml.math.Matrix in project ignite by apache.

the class DistributedLinearRegressionWithQRTrainerExample method main.

/**
 * Run example.
 */
public static void main(String[] args) throws InterruptedException {
    System.out.println();
    System.out.println(">>> Linear regression model over sparse distributed matrix API usage example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
        // because we create ignite cache internally.
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), SparseDistributedMatrixExample.class.getSimpleName(), () -> {
            // Create SparseDistributedMatrix, new cache will be created automagically.
            System.out.println(">>> Create new SparseDistributedMatrix inside IgniteThread.");
            SparseDistributedMatrix distributedMatrix = new SparseDistributedMatrix(data);
            System.out.println(">>> Create new linear regression trainer object.");
            Trainer<LinearRegressionModel, Matrix> trainer = new LinearRegressionQRTrainer();
            System.out.println(">>> Perform the training to get the model.");
            LinearRegressionModel model = trainer.train(distributedMatrix);
            System.out.println(">>> Linear regression model: " + model);
            System.out.println(">>> ---------------------------------");
            System.out.println(">>> | Prediction\t| Ground Truth\t|");
            System.out.println(">>> ---------------------------------");
            for (double[] observation : data) {
                Vector inputs = new SparseDistributedVector(Arrays.copyOfRange(observation, 1, observation.length));
                double prediction = model.apply(inputs);
                double groundTruth = observation[0];
                System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", prediction, groundTruth);
            }
            System.out.println(">>> ---------------------------------");
        });
        igniteThread.start();
        igniteThread.join();
    }
}

Example 72 with Matrix

use of org.apache.ignite.ml.math.Matrix in project ignite by apache.

the class LinearRegressionQRTrainer method train.

/**
 * {@inheritDoc}
 */
@Override
public LinearRegressionModel train(Matrix data) {
    Vector groundTruth = extractGroundTruth(data);
    Matrix inputs = extractInputs(data);
    QRDecomposition decomposition = new QRDecomposition(inputs);
    QRDSolver solver = new QRDSolver(decomposition.getQ(), decomposition.getR());
    Vector variables = solver.solve(groundTruth);
    Vector weights = variables.viewPart(1, variables.size() - 1);
    double intercept = variables.get(0);
    return new LinearRegressionModel(weights, intercept);
}

Example 73 with Matrix

use of org.apache.ignite.ml.math.Matrix in project ignite by apache.

the class LocalBatchTrainer method train.

/**
 * {@inheritDoc}
 */
@Override
public M train(LocalBatchTrainerInput<M> data) {
    int i = 0;
    M mdl = data.mdl();
    double err;
    ParameterUpdateCalculator<? super M, P> updater = updaterSupplier.get();
    P updaterParams = updater.init(mdl, loss);
    while (i < maxIterations) {
        IgniteBiTuple<Matrix, Matrix> batch = data.batchSupplier().get();
        Matrix input = batch.get1();
        Matrix truth = batch.get2();
        updaterParams = updater.calculateNewUpdate(mdl, updaterParams, i, input, truth);
        // Update mdl with updater parameters.
        mdl = updater.update(mdl, updaterParams);
        Matrix predicted = mdl.apply(input);
        int batchSize = input.columnSize();
        err = MatrixUtil.zipFoldByColumns(predicted, truth, (predCol, truthCol) -> loss.apply(truthCol).apply(predCol)).sum() / batchSize;
        debug("Error: " + err);
        if (err < errorThreshold)
            break;
        i++;
    }
    return mdl;
}

Example 74 with Matrix

use of org.apache.ignite.ml.math.Matrix in project ignite by apache.

the class TestUtils method assertEquals.

/**
 * Verifies that two matrices are close (1-norm).
 *
 * @param msg The identifying message for the assertion error.
 * @param exp Expected matrix.
 * @param actual Actual matrix.
 * @param tolerance Comparison tolerance value.
 */
public static void assertEquals(String msg, Matrix exp, Matrix actual, double tolerance) {
    Assert.assertNotNull(msg + "\nObserved should not be null", actual);
    if (exp.columnSize() != actual.columnSize() || exp.rowSize() != actual.rowSize()) {
        String msgBuff = msg + "\nObserved has incorrect dimensions." + "\nobserved is " + actual.rowSize() + " x " + actual.columnSize() + "\nexpected " + exp.rowSize() + " x " + exp.columnSize();
        Assert.fail(msgBuff);
    }
    Matrix delta = exp.minus(actual);
    if (TestUtils.maximumAbsoluteRowSum(delta) >= tolerance) {
        String msgBuff = msg + "\nExpected: " + exp + "\nObserved: " + actual + "\nexpected - observed: " + delta;
        Assert.fail(msgBuff);
    }
}

Example 75 with Matrix

use of org.apache.ignite.ml.math.Matrix in project ignite by apache.

the class VectorToMatrixTest method assertCross.

/**
 */
private void assertCross(Vector v1, Vector v2, String desc) {
    assertNotNull(v1);
    assertNotNull(v2);
    final Matrix res = v1.cross(v2);
    assertNotNull("Cross matrix is expected to be not null in " + desc, res);
    assertEquals("Unexpected number of rows in cross Matrix in " + desc, v1.size(), res.rowSize());
    assertEquals("Unexpected number of cols in cross Matrix in " + desc, v2.size(), res.columnSize());
    for (int row = 0; row < v1.size(); row++) for (int col = 0; col < v2.size(); col++) {
        final Metric metric = new Metric(v1.get(row) * v2.get(col), res.get(row, col));
        assertTrue("Not close enough cross " + metric + " at row " + row + " at col " + col + " in " + desc, metric.closeEnough());
    }
}