Java Cluster.getPoints方法代码示例

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
/** {@inheritDoc} */
@Override
public double score(final List<? extends Cluster<T>> clusters) {
    double varianceSum = 0.0;
    for (final Cluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            final Clusterable center = centroidOf(cluster);

            // compute the distance variance of the current cluster
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(distance(point, center));
            }
            varianceSum += stat.getResult();

        }
    }
    return varianceSum;
}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
/**
 * Computes the centroid for a cluster.
 *
 * @param cluster the cluster
 * @return the computed centroid for the cluster,
 * or {@code null} if the cluster does not contain any points
 */
protected Clusterable centroidOf(final Cluster<T> cluster) {
    final List<T> points = cluster.getPoints();
    if (points.isEmpty()) {
        return null;
    }

    // in case the cluster is of type CentroidCluster, no need to compute the centroid
    if (cluster instanceof CentroidCluster) {
        return ((CentroidCluster<T>) cluster).getCenter();
    }

    final int dimension = points.get(0).getPoint().length;
    final double[] centroid = new double[dimension];
    for (final T p : points) {
        final double[] point = p.getPoint();
        for (int i = 0; i < centroid.length; i++) {
            centroid[i] += point[i];
        }
    }
    for (int i = 0; i < centroid.length; i++) {
        centroid[i] /= points.size();
    }
    return new DoublePoint(centroid);
}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
@Override
public double score(final List<? extends Cluster<T>> clusters) {
    double varianceSum = 0.0;
    for (final Cluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            final Clusterable center = centroidOf(cluster);

            // compute the distance variance of the current cluster
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(distance(point, center));
            }
            varianceSum += stat.getResult();

        }
    }
    return varianceSum;
}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
private Envelope getBoundingRectangle(Cluster<ClusterableLocation> cluster) {
  double xmin = cluster.getPoints().get(0).getPoint()[0];
  double xmax = xmin;
  double ymin = cluster.getPoints().get(0).getPoint()[1];
  double ymax = ymin;
  for (ClusterableLocation p : cluster.getPoints()) {
    if (p.getPoint()[0] < xmin) {
      xmin = p.getPoint()[0];
    }
    if (p.getPoint()[0] > xmax) {
      xmax = p.getPoint()[0];
    }
    if (p.getPoint()[1] < ymin) {
      ymin = p.getPoint()[1];
    }
    if (p.getPoint()[1] > ymax) {
      ymax = p.getPoint()[1];
    }
  }
  Envelope boundingRectangle = new Envelope(xmin, ymin, xmax, ymax);
  return boundingRectangle;
}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestVarianceCluster(final Collection<CentroidCluster<T>> clusters)
        throws ConvergenceException {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final CentroidCluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final Clusterable center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(point.getWeight() * distance(point, center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
/**
 * Get a random point from the {@link Cluster} with the largest number of points
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 * @throws ConvergenceException if clusters are all empty
 */
private T getPointFromLargestNumberCluster(final Collection<? extends Cluster<T>> clusters)
        throws ConvergenceException {

    int maxNumber = 0;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {

        // get the number of points of the current cluster
        final int number = cluster.getPoints().size();

        // select the cluster with the largest number of points
        if (number > maxNumber) {
            maxNumber = number;
            selected = cluster;
        }

    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
/**
 * Gets the total number of locations which fall inside at least one protected area.
 * If at least one point in the cluster falls inside, then the location is counted.
 * @return
 */
public int getNumberOfLocationsInsideProtectedAreas() {
    int count = 0;
    for(Cluster<Point2D> cl : clusters) {
        for(Point2D p : cl.getPoints()) {
            if(!pointsInPolygons.get(p).iterator().next().isNullPolygon()) {    // exists at least in one protected area
                count++;
                break;
            }
        }
    }
    return count;
}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
@operator (
		value = "dbscan",
		can_be_const = false,
		type = IType.LIST,
		category = { IOperatorCategory.STATISTICAL },
		concept = { IConcept.STATISTIC, IConcept.CLUSTERING })
@doc (
		value = "returns the list of clusters (list of instance indices) computed with the dbscan (density-based spatial clustering of applications with noise) algorithm from the first operand data according to the maximum radius of the neighborhood to be considered (eps) and the minimum number of points needed for a cluster (minPts). Usage: dbscan(data,eps,minPoints)",
		special_cases = "if the lengths of two vectors in the right-hand aren't equal, returns 0",
		examples = { @example (value = "dbscan ([[2,4,5], [3,8,2], [1,1,3], [4,3,4]],10,2)", equals = "[]") })
public static IList<GamaList> DBscanApache(final IScope scope, final GamaList data, final Double eps,
		final Integer minPts) throws GamaRuntimeException {

	final DBSCANClusterer<DoublePoint> dbscan = new DBSCANClusterer(eps, minPts);
	final List<DoublePoint> instances = new ArrayList<DoublePoint>();
	for (int i = 0; i < data.size(); i++) {
		final GamaList d = (GamaList) data.get(i);
		final double point[] = new double[d.size()];
		for (int j = 0; j < d.size(); j++) {
			point[j] = Cast.asFloat(scope, d.get(j));
		}
		instances.add(new Instance(i, point));
	}
	final List<Cluster<DoublePoint>> clusters = dbscan.cluster(instances);
	final GamaList results = (GamaList) GamaListFactory.create();
	for (final Cluster<DoublePoint> cl : clusters) {
		final GamaList clG = (GamaList) GamaListFactory.create();
		for (final DoublePoint pt : cl.getPoints()) {
			clG.addValue(scope, ((Instance) pt).getId());
		}
		results.addValue(scope, clG);
	}
	return results;
}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
private Point getCenter(Cluster<ClusterableLocation> cluster) {
  double sumx = 0;
  double sumy = 0;
  for (ClusterableLocation p : cluster.getPoints()) {
    sumx += p.getPoint()[0];
    sumy += p.getPoint()[1];
  }
  Point center = new Point(sumx / cluster.getPoints().size(), sumy/ cluster.getPoints().size());
  return center;
}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
private Polygon getConvexHull(Cluster<ClusterableLocation> cluster) {
  List<ClusterableLocation> pointsList = cluster.getPoints();
  ClusterableLocation[] pointsArray = pointsList.toArray(new ClusterableLocation[pointsList.size()]);
  ClusterableLocation[] pointsConvexHull = ConvexHullGenerator.generateHull(pointsArray);
  Polygon convexHull = new Polygon();
  convexHull.startPath(pointsConvexHull[0].getPoint()[0], pointsConvexHull[0].getPoint()[1]);
  for (int i = 1; i < pointsConvexHull.length; i++) {
    ClusterableLocation p = pointsConvexHull[i];
    convexHull.lineTo(p.getPoint()[0], p.getPoint()[1]);
  }
  convexHull.closePathWithLine();
  return convexHull;
}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
public MerescoCluster cluster(int docId) {
    for (Cluster<MerescoVector> c : this.clusters) {
        List<MerescoVector> points = c.getPoints();
        for (MerescoVector oc : points) {
            if (oc.docId == docId) {
                return rankCluster(c.getPoints());
            }
        }
    }
    return null;
}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
@Override
public MerescoVector centroidOf(Cluster<MerescoVector> arg0) {
    MerescoVector centroid = new MerescoVector();
    for (MerescoVector v : arg0.getPoints()) {
        centroid.combineToSelf(1.0, 1.0/arg0.getPoints().size(), v);
    }
    return centroid;
}
 

import org.apache.commons.math3.ml.clustering.Cluster; //导入方法依赖的package包/类
private void print_cluster(Cluster<MerescoVector> cl) {
    for (MerescoVector v : cl.getPoints()) {
        v.printVector(this.ords);
    }
}