Examples with DenseLocalOnHeapMatrix org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix used on opensource projects

Example 11 with DenseLocalOnHeapMatrix

use of org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix in project ignite by apache.

the class MLPGroupTrainerExample method main.

/**
 * Executes example.
 *
 * @param args Command line arguments, none required.
 */
public static void main(String[] args) throws InterruptedException {
    // IMPL NOTE based on MLPGroupTrainerTest#testXOR
    System.out.println(">>> Distributed multilayer perceptron 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(), MLPGroupTrainerExample.class.getSimpleName(), () -> {
            int samplesCnt = 10000;
            Matrix xorInputs = new DenseLocalOnHeapMatrix(new double[][] { { 0.0, 0.0 }, { 0.0, 1.0 }, { 1.0, 0.0 }, { 1.0, 1.0 } }, StorageConstants.ROW_STORAGE_MODE).transpose();
            Matrix xorOutputs = new DenseLocalOnHeapMatrix(new double[][] { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } }, StorageConstants.ROW_STORAGE_MODE).transpose();
            MLPArchitecture conf = new MLPArchitecture(2).withAddedLayer(10, true, Activators.RELU).withAddedLayer(1, false, Activators.SIGMOID);
            IgniteCache<Integer, LabeledVector<Vector, Vector>> cache = LabeledVectorsCache.createNew(ignite);
            String cacheName = cache.getName();
            Random rnd = new Random(12345L);
            try (IgniteDataStreamer<Integer, LabeledVector<Vector, Vector>> streamer = ignite.dataStreamer(cacheName)) {
                streamer.perNodeBufferSize(100);
                for (int i = 0; i < samplesCnt; i++) {
                    int col = Math.abs(rnd.nextInt()) % 4;
                    streamer.addData(i, new LabeledVector<>(xorInputs.getCol(col), xorOutputs.getCol(col)));
                }
            }
            int totalCnt = 100;
            int failCnt = 0;
            MLPGroupUpdateTrainer<RPropParameterUpdate> trainer = MLPGroupUpdateTrainer.getDefault(ignite).withSyncPeriod(3).withTolerance(0.001).withMaxGlobalSteps(20);
            for (int i = 0; i < totalCnt; i++) {
                MLPGroupUpdateTrainerCacheInput trainerInput = new MLPGroupUpdateTrainerCacheInput(conf, new RandomInitializer(rnd), 6, cache, 10);
                MultilayerPerceptron mlp = trainer.train(trainerInput);
                Matrix predict = mlp.apply(xorInputs);
                System.out.println(">>> Prediction data at step " + i + " of total " + totalCnt + ":");
                Tracer.showAscii(predict);
                System.out.println("Difference estimate: " + xorOutputs.getRow(0).minus(predict.getRow(0)).kNorm(2));
                failCnt += closeEnough(xorOutputs.getRow(0), predict.getRow(0)) ? 0 : 1;
            }
            double failRatio = (double) failCnt / totalCnt;
            System.out.println("\n>>> Fail percentage: " + (failRatio * 100) + "%.");
            System.out.println("\n>>> Distributed multilayer perceptron example completed.");
        });
        igniteThread.start();
        igniteThread.join();
    }
}

Example 12 with DenseLocalOnHeapMatrix

use of org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix in project ignite by apache.

the class TracerExample method main.

/**
 * Executes example.
 *
 * @param args Command line arguments, none required.
 */
public static void main(String[] args) throws IOException {
    System.out.println(">>> Tracer utility example started.");
    // Tracer is a simple utility class that allows pretty-printing of matrices/vectors.
    DenseLocalOnHeapMatrix m = new DenseLocalOnHeapMatrix(new double[][] { { 1.12345, 2.12345 }, { 3.12345, 4.12345 } });
    System.out.println("\n>>> Tracer output to console in ASCII.");
    Tracer.showAscii(m, "%.3g");
    System.out.println("\n>>> Tracer output to browser in HTML.");
    Tracer.showHtml(m, COLOR_MAPPER, true);
    System.out.println("\n>>> Tracer utility example completed.");
}

Example 13 with DenseLocalOnHeapMatrix

use of org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix in project ignite by apache.

the class KMeansLocalClustererTest method testPerformClusterAnalysisDegenerate.

/**
 * Two points, one cluster, one iteration
 */
@Test
public void testPerformClusterAnalysisDegenerate() {
    KMeansLocalClusterer clusterer = new KMeansLocalClusterer(new EuclideanDistance(), 1, 1L);
    double[] v1 = new double[] { 1959, 325100 };
    double[] v2 = new double[] { 1960, 373200 };
    DenseLocalOnHeapMatrix points = new DenseLocalOnHeapMatrix(new double[][] { v1, v2 });
    KMeansModel mdl = clusterer.cluster(points, 1);
    Assert.assertEquals(1, mdl.centers().length);
    Assert.assertEquals(2, mdl.centers()[0].size());
}

Example 14 with DenseLocalOnHeapMatrix

use of org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix in project ignite by apache.

the class IgniteFuzzyCMeansLocalClustererBenchmark method test.

/**
 * {@inheritDoc}
 */
@Override
public boolean test(Map<Object, Object> ctx) throws Exception {
    // IMPL NOTE originally taken from FuzzyLocalCMeansExample.
    // Distance measure that computes distance between two points.
    DistanceMeasure distanceMeasure = new EuclideanDistance();
    // "Fuzziness" - specific constant that is used in membership calculation (1.0+-eps ~ K-Means).
    double exponentialWeight = 2.0;
    // Condition that indicated when algorithm must stop.
    // In this example algorithm stops if memberships have changed insignificantly.
    BaseFuzzyCMeansClusterer.StopCondition stopCond = BaseFuzzyCMeansClusterer.StopCondition.STABLE_MEMBERSHIPS;
    // Maximum difference between new and old membership values with which algorithm will continue to work.
    double maxDelta = 0.01;
    // The maximum number of FCM iterations.
    int maxIterations = 50;
    // Create new local clusterer with parameters described above.
    FuzzyCMeansLocalClusterer clusterer = new FuzzyCMeansLocalClusterer(distanceMeasure, exponentialWeight, stopCond, maxDelta, maxIterations, null);
    // Create sample data.
    double[][] points = shuffle((int) (DataChanger.next()));
    // Create the matrix that contains sample points.
    DenseLocalOnHeapMatrix pntMatrix = new DenseLocalOnHeapMatrix(points);
    // Call clusterization method with some number of centers.
    // It returns model that can predict results for new points.
    int numCenters = 4;
    FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, numCenters);
    // Get centers of clusters that is computed by Fuzzy C-Means algorithm.
    mdl.centers();
    return true;
}

Example 15 with DenseLocalOnHeapMatrix

use of org.apache.ignite.ml.math.impls.matrix.DenseLocalOnHeapMatrix in project ignite by apache.

the class IgniteEigenDecompositionBenchmark method runEigenDecomposition.

/**
 * Based on EigenDecompositionTest.
 */
private void runEigenDecomposition() {
    final DataChanger.Scale scale = new DataChanger.Scale();
    EigenDecomposition decomposition1 = new EigenDecomposition(new DenseLocalOnHeapMatrix(scale.mutate(new double[][] { { 1.0d, 0.0d, 0.0d, 1.0d }, { 0.0d, 1.0d, 0.0d, 1.0d }, { 0.0d, 0.0d, 2.0d, 0.0d }, { 1.0d, 1.0d, 0.0d, 2.0d } })));
    decomposition1.getD();
    decomposition1.getV();
    decomposition1.destroy();
    EigenDecomposition decomposition2 = new EigenDecomposition(new DenseLocalOnHeapMatrix(scale.mutate(new double[][] { { 1.0d, 0.0d, 0.0d }, { 0.0d, 1.0d, 0.0d }, { 0.0d, 0.0d, 2.0d }, { 1.0d, 1.0d, 0.0d } })));
    // TODO: IGNITE-5828, find out why decomposition of 3X4 matrix throws row index exception
    decomposition2.getD();
    decomposition2.getV();
    decomposition2.destroy();
}