Salve a tutti, sto muovendo i primi passi nel mondo Java ed avrei bisogno di provare questo codice ma mi dice che manca il main che non c'è.
Sto provando a metterlo ma non so come richiamarmi i metodi c'è qualcuno che mi può' suggerire come scrivere il main?..
codice:import java.util.ArrayList; import java.util.Collection; import java.util.Collections; import java.util.List; import org.apache.commons.math3.exception.MathIllegalArgumentException; import org.apache.commons.math3.exception.MathIllegalStateException; import org.apache.commons.math3.exception.NumberIsTooSmallException; import org.apache.commons.math3.linear.MatrixUtils; import org.apache.commons.math3.linear.RealMatrix; import org.apache.commons.math3.ml.clustering.CentroidCluster; import org.apache.commons.math3.ml.clustering.Clusterable; import org.apache.commons.math3.ml.clustering.Clusterer; import org.apache.commons.math3.ml.clustering.DoublePoint; import org.apache.commons.math3.ml.distance.DistanceMeasure; import org.apache.commons.math3.ml.distance.EuclideanDistance; import org.apache.commons.math3.random.JDKRandomGenerator; import org.apache.commons.math3.random.RandomGenerator; import org.apache.commons.math3.util.FastMath; import org.apache.commons.math3.util.MathArrays; import org.apache.commons.math3.util.MathUtils; public class FuzzyKMeansClusterer<T extends Clusterable> extends Clusterer<T> { private static final double DEFAULT_EPSILON = 1e-3; private final int k; private final int maxIterations; private final double fuzziness; private final double epsilon; private final RandomGenerator random; private double[][] membershipMatrix; private List<T> points; private List<CentroidCluster<T>> clusters; public FuzzyKMeansClusterer(final int k, final double fuzziness) throws NumberIsTooSmallException { this(k, fuzziness, -1, new EuclideanDistance()); } public FuzzyKMeansClusterer(final int k, final double fuzziness, final int maxIterations, final DistanceMeasure measure) throws NumberIsTooSmallException { this(k, fuzziness, maxIterations, measure, DEFAULT_EPSILON, new JDKRandomGenerator()); } public FuzzyKMeansClusterer(final int k, final double fuzziness, final int maxIterations, final DistanceMeasure measure, final double epsilon, final RandomGenerator random) throws NumberIsTooSmallException { super(measure); if (fuzziness <= 1.0d) { throw new NumberIsTooSmallException(fuzziness, 1.0, false); } this.k = k; this.fuzziness = fuzziness; this.maxIterations = maxIterations; this.epsilon = epsilon; this.random = random; this.membershipMatrix = null; this.points = null; this.clusters = null; } public int getK() { return k; } public double getFuzziness() { return fuzziness; } public int getMaxIterations() { return maxIterations; } public double getEpsilon() { return epsilon; } public RandomGenerator getRandomGenerator() { return random; } public RealMatrix getMembershipMatrix() { if (membershipMatrix == null) { throw new MathIllegalStateException(); } return MatrixUtils.createRealMatrix(membershipMatrix); } public List<T> getDataPoints() { return points; } public List<CentroidCluster<T>> getClusters() { return clusters; } public double getObjectiveFunctionValue() { if (points == null || clusters == null) { throw new MathIllegalStateException(); } int i = 0; double objFunction = 0.0; for (final T point : points) { int j = 0; for (final CentroidCluster<T> cluster : clusters) { final double dist = distance(point, cluster.getCenter()); objFunction += (dist * dist) * FastMath.pow(membershipMatrix[i][j], fuzziness); j++; } i++; } return objFunction; } // @Override public List<CentroidCluster<T>> cluster(final Collection<T> dataPoints) throws MathIllegalArgumentException { // sanity checks MathUtils.checkNotNull(dataPoints); final int size = dataPoints.size(); // number of clusters has to be smaller or equal the number of data points if (size < k) { throw new NumberIsTooSmallException(size, k, false); } // copy the input collection to an unmodifiable list with indexed access points = Collections.unmodifiableList(new ArrayList<T>(dataPoints)); clusters = new ArrayList<CentroidCluster<T>>(); membershipMatrix = new double[size][k]; final double[][] oldMatrix = new double[size][k]; // if no points are provided, return an empty list of clusters if (size == 0) { return clusters; } initializeMembershipMatrix(); // there is at least one point final int pointDimension = points.get(0).getPoint().length; for (int i = 0; i < k; i++) { clusters.add(new CentroidCluster<T>(new DoublePoint(new double[pointDimension]))); } int iteration = 0; final int max = (maxIterations < 0) ? Integer.MAX_VALUE : maxIterations; double difference = 0.0; do { saveMembershipMatrix(oldMatrix); updateClusterCenters(); updateMembershipMatrix(); difference = calculateMaxMembershipChange(oldMatrix); } while (difference > epsilon && ++iteration < max); return clusters; } private void updateClusterCenters() { int j = 0; final List<CentroidCluster<T>> newClusters = new ArrayList<CentroidCluster<T>>(k); for (final CentroidCluster<T> cluster : clusters) { final Clusterable center = cluster.getCenter(); int i = 0; double[] arr = new double[center.getPoint().length]; double sum = 0.0; for (final T point : points) { final double u = FastMath.pow(membershipMatrix[i][j], fuzziness); final double[] pointArr = point.getPoint(); for (int idx = 0; idx < arr.length; idx++) { arr[idx] += u * pointArr[idx]; } sum += u; i++; } MathArrays.scaleInPlace(1.0 / sum, arr); newClusters.add(new CentroidCluster<T>(new DoublePoint(arr))); j++; } clusters.clear(); clusters = newClusters; } private void updateMembershipMatrix() { for (int i = 0; i < points.size(); i++) { final T point = points.get(i); double maxMembership = Double.MIN_VALUE; int newCluster = -1; for (int j = 0; j < clusters.size(); j++) { double sum = 0.0; final double distA = FastMath.abs(distance(point, clusters.get(j).getCenter())); if (distA != 0.0) { for (final CentroidCluster<T> c : clusters) { final double distB = FastMath.abs(distance(point, c.getCenter())); if (distB == 0.0) { sum = Double.POSITIVE_INFINITY; break; } sum += FastMath.pow(distA / distB, 2.0 / (fuzziness - 1.0)); } } double membership; if (sum == 0.0) { membership = 1.0; } else if (sum == Double.POSITIVE_INFINITY) { membership = 0.0; } else { membership = 1.0 / sum; } membershipMatrix[i][j] = membership; if (membershipMatrix[i][j] > maxMembership) { maxMembership = membershipMatrix[i][j]; newCluster = j; } } clusters.get(newCluster).addPoint(point); } } private void initializeMembershipMatrix() { for (int i = 0; i < points.size(); i++) { for (int j = 0; j < k; j++) { membershipMatrix[i][j] = random.nextDouble(); } membershipMatrix[i] = MathArrays.normalizeArray(membershipMatrix[i], 1.0); } } private double calculateMaxMembershipChange(final double[][] matrix) { double maxMembership = 0.0; for (int i = 0; i < points.size(); i++) { for (int j = 0; j < clusters.size(); j++) { double v = FastMath.abs(membershipMatrix[i][j] - matrix[i][j]); maxMembership = FastMath.max(v, maxMembership); } } return maxMembership; } private void saveMembershipMatrix(final double[][] matrix) { for (int i = 0; i < points.size(); i++) { System.arraycopy(membershipMatrix[i], 0, matrix[i], 0, clusters.size()); } } }
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