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

Example 6 with EuclideanDistance

use of org.apache.ignite.ml.math.distances.EuclideanDistance in project ignite by apache.

the class IgniteFuzzyCMeansDistributedClustererBenchmark method test.

/**
 * {@inheritDoc}
 */
@Override
public boolean test(Map<Object, Object> ctx) throws Exception {
    // Create IgniteThread, we must work with SparseDistributedMatrix inside IgniteThread
    // because we create ignite cache internally.
    IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), this.getClass().getSimpleName(), new Runnable() {

        /**
         * {@inheritDoc}
         */
        @Override
        public void run() {
            // IMPL NOTE originally taken from FuzzyCMeansExample.
            // 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;
            // Number of steps of primary centers selection (more steps more candidates).
            int initializationSteps = 2;
            // Number of K-Means iteration that is used to choose required number of primary centers from candidates.
            int kMeansMaxIterations = 50;
            // Create new distributed clusterer with parameters described above.
            FuzzyCMeansDistributedClusterer clusterer = new FuzzyCMeansDistributedClusterer(distanceMeasure, exponentialWeight, stopCond, maxDelta, maxIterations, null, initializationSteps, kMeansMaxIterations);
            // Create sample data.
            double[][] points = shuffle((int) (DataChanger.next()));
            // Initialize matrix of data points. Each row contains one point.
            int rows = points.length;
            int cols = points[0].length;
            // Create the matrix that contains sample points.
            SparseDistributedMatrix pntMatrix = new SparseDistributedMatrix(rows, cols, StorageConstants.ROW_STORAGE_MODE, StorageConstants.RANDOM_ACCESS_MODE);
            // Store points into matrix.
            pntMatrix.assign(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();
            pntMatrix.destroy();
        }
    });
    igniteThread.start();
    igniteThread.join();
    return true;
}

Example 7 with EuclideanDistance

use of org.apache.ignite.ml.math.distances.EuclideanDistance 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 8 with EuclideanDistance

use of org.apache.ignite.ml.math.distances.EuclideanDistance in project ignite by apache.

the class KNNClassificationTest method testBinaryClassificationWithSmallestKTest.

/**
 */
public void testBinaryClassificationWithSmallestKTest() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    double[][] mtx = new double[][] { { 1.0, 1.0 }, { 1.0, 2.0 }, { 2.0, 1.0 }, { -1.0, -1.0 }, { -1.0, -2.0 }, { -2.0, -1.0 } };
    double[] lbs = new double[] { 1.0, 1.0, 1.0, 2.0, 2.0, 2.0 };
    LabeledDataset training = new LabeledDataset(mtx, lbs);
    KNNModel knnMdl = new KNNModel(1, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
    Vector firstVector = new DenseLocalOnHeapVector(new double[] { 2.0, 2.0 });
    assertEquals(knnMdl.apply(firstVector), 1.0);
    Vector secondVector = new DenseLocalOnHeapVector(new double[] { -2.0, -2.0 });
    assertEquals(knnMdl.apply(secondVector), 2.0);
}

Example 9 with EuclideanDistance

use of org.apache.ignite.ml.math.distances.EuclideanDistance in project ignite by apache.

the class KNNClassificationTest method testBinaryClassificationFarPointsWithSimpleStrategy.

/**
 */
public void testBinaryClassificationFarPointsWithSimpleStrategy() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    double[][] mtx = new double[][] { { 10.0, 10.0 }, { 10.0, 20.0 }, { -1, -1 }, { -2, -2 }, { -1.0, -2.0 }, { -2.0, -1.0 } };
    double[] lbs = new double[] { 1.0, 1.0, 1.0, 2.0, 2.0, 2.0 };
    LabeledDataset training = new LabeledDataset(mtx, lbs);
    KNNModel knnMdl = new KNNModel(3, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
    Vector vector = new DenseLocalOnHeapVector(new double[] { -1.01, -1.01 });
    assertEquals(knnMdl.apply(vector), 2.0);
}

Example 10 with EuclideanDistance

use of org.apache.ignite.ml.math.distances.EuclideanDistance in project ignite by apache.

the class KNNClassificationTest method testLargeKValue.

/**
 */
public void testLargeKValue() {
    IgniteUtils.setCurrentIgniteName(ignite.configuration().getIgniteInstanceName());
    double[][] mtx = new double[][] { { 10.0, 10.0 }, { 10.0, 20.0 }, { -1, -1 }, { -2, -2 }, { -1.0, -2.0 }, { -2.0, -1.0 } };
    double[] lbs = new double[] { 1.0, 1.0, 1.0, 2.0, 2.0, 2.0 };
    LabeledDataset training = new LabeledDataset(mtx, lbs);
    try {
        new KNNModel(7, new EuclideanDistance(), KNNStrategy.SIMPLE, training);
        fail("SmallTrainingDatasetSizeException");
    } catch (SmallTrainingDatasetSizeException e) {
        return;
    }
    fail("SmallTrainingDatasetSizeException");
}