Examples with FuzzyCMeansModel org.apache.ignite.ml.clustering.FuzzyCMeansModel used on opensource projects

Example 1 with FuzzyCMeansModel

use of org.apache.ignite.ml.clustering.FuzzyCMeansModel 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 2 with FuzzyCMeansModel

use of org.apache.ignite.ml.clustering.FuzzyCMeansModel 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 3 with FuzzyCMeansModel

use of org.apache.ignite.ml.clustering.FuzzyCMeansModel in project ignite by apache.

the class FuzzyCMeansExample method main.

/**
 * Executes example.
 *
 * @param args Command line arguments, none required.
 */
public static void main(String[] args) throws InterruptedException {
    System.out.println(">>> Fuzzy C-Means usage example started.");
    // Start ignite grid.
    try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
        System.out.println(">>> Ignite grid started.");
        // Start new Ignite thread.
        IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(), FuzzyCMeansExample.class.getSimpleName(), () -> {
            // 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;
            // Value that is used to initialize random numbers generator. You can choose it randomly.
            Long seed = null;
            // 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.
            System.out.println(">>> Create new Distributed Fuzzy C-Means clusterer.");
            FuzzyCMeansDistributedClusterer clusterer = new FuzzyCMeansDistributedClusterer(distanceMeasure, exponentialWeight, stopCond, maxDelta, maxIterations, seed, initializationSteps, kMeansMaxIterations);
            // Create sample data.
            double[][] points = new double[][] { { -10, -10 }, { -9, -11 }, { -10, -9 }, { -11, -9 }, { 10, 10 }, { 9, 11 }, { 10, 9 }, { 11, 9 }, { -10, 10 }, { -9, 11 }, { -10, 9 }, { -11, 9 }, { 10, -10 }, { 9, -11 }, { 10, -9 }, { 11, -9 } };
            // Initialize matrix of data points. Each row contains one point.
            int rows = points.length;
            int cols = points[0].length;
            System.out.println(">>> 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.
            System.out.println(">>> Perform clusterization.");
            int numCenters = 4;
            FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, numCenters);
            // You can also get centers of clusters that is computed by Fuzzy C-Means algorithm.
            Vector[] centers = mdl.centers();
            String res = ">>> Results:\n" + ">>> 1st center: " + centers[0].get(0) + " " + centers[0].get(1) + "\n" + ">>> 2nd center: " + centers[1].get(0) + " " + centers[1].get(1) + "\n" + ">>> 3rd center: " + centers[2].get(0) + " " + centers[2].get(1) + "\n" + ">>> 4th center: " + centers[3].get(0) + " " + centers[3].get(1) + "\n";
            System.out.println(res);
            pntMatrix.destroy();
        });
        igniteThread.start();
        igniteThread.join();
    }
}

Example 4 with FuzzyCMeansModel

use of org.apache.ignite.ml.clustering.FuzzyCMeansModel in project ignite by apache.

the class FuzzyCMeansLocalExample method main.

/**
 * Executes example.
 *
 * @param args Command line arguments, none required.
 */
public static void main(String[] args) {
    System.out.println(">>> Local Fuzzy C-Means usage example started.");
    // 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;
    // Value that is used to initialize random numbers generator. You can choose it randomly.
    Long seed = null;
    // Create new distributed clusterer with parameters described above.
    System.out.println(">>> Create new Local Fuzzy C-Means clusterer.");
    FuzzyCMeansLocalClusterer clusterer = new FuzzyCMeansLocalClusterer(distanceMeasure, exponentialWeight, stopCond, maxDelta, maxIterations, seed);
    // Create sample data.
    double[][] points = new double[][] { { -10, -10 }, { -9, -11 }, { -10, -9 }, { -11, -9 }, { 10, 10 }, { 9, 11 }, { 10, 9 }, { 11, 9 }, { -10, 10 }, { -9, 11 }, { -10, 9 }, { -11, 9 }, { 10, -10 }, { 9, -11 }, { 10, -9 }, { 11, -9 } };
    // Initialize matrix of data points. Each row contains one point.
    System.out.println(">>> Create the matrix that contains sample points.");
    // Store points into matrix.
    DenseLocalOnHeapMatrix pntMatrix = new DenseLocalOnHeapMatrix(points);
    // Call clusterization method with some number of centers.
    // It returns model that can predict results for new points.
    System.out.println(">>> Perform clusterization.");
    int numCenters = 4;
    FuzzyCMeansModel mdl = clusterer.cluster(pntMatrix, numCenters);
    // You can also get centers of clusters that is computed by Fuzzy C-Means algorithm.
    Vector[] centers = mdl.centers();
    String res = ">>> Results:\n" + ">>> 1st center: " + centers[0].get(0) + " " + centers[0].get(1) + "\n" + ">>> 2nd center: " + centers[1].get(0) + " " + centers[1].get(1) + "\n" + ">>> 3rd center: " + centers[2].get(0) + " " + centers[2].get(1) + "\n" + ">>> 4th center: " + centers[3].get(0) + " " + centers[3].get(1) + "\n";
    System.out.println(res);
}