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package org.apache.commons.math3.stat.clustering;

import java.util.ArrayList;
import java.util.Collection;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;

import org.apache.commons.math3.exception.NotPositiveException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.util.MathUtils;

DBSCAN (density-based spatial clustering of applications with noise) algorithm.

The DBSCAN algorithm forms clusters based on the idea of density connectivity, i.e.
a point p is density connected to another point q, if there exists a chain of
points pi, with i = 1 .. n and p1 = p and pn = q,
such that each pair <pi, pi+1> is directly density-reachable.
A point q is directly density-reachable from point p if it is in the ε-neighborhood
of this point.

Any point that is not density-reachable from a formed cluster is treated as noise, and
will thus not be present in the result.

The algorithm requires two parameters:

  
eps: the distance that defines the ε-neighborhood of a point
  
minPoints: the minimum number of density-connected points required to form a cluster


Note: as DBSCAN is not a centroid-based clustering algorithm, the resulting Cluster objects will have no defined center, i.e. Cluster.getCenter() will return null. 
Type parameters:
<T> – type of the points to cluster
See Also:
DBSCAN (wikipedia)

 * A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
Since:
3.1
Deprecated:
As of 3.2 (to be removed in 4.0), use DBSCANClusterer instead/**
 * DBSCAN (density-based spatial clustering of applications with noise) algorithm.
 * <p>
 * The DBSCAN algorithm forms clusters based on the idea of density connectivity, i.e.
 * a point p is density connected to another point q, if there exists a chain of
 * points p<sub>i</sub>, with i = 1 .. n and p<sub>1</sub> = p and p<sub>n</sub> = q,
 * such that each pair &lt;p<sub>i</sub>, p<sub>i+1</sub>&gt; is directly density-reachable.
 * A point q is directly density-reachable from point p if it is in the &epsilon;-neighborhood
 * of this point.
 * <p>
 * Any point that is not density-reachable from a formed cluster is treated as noise, and
 * will thus not be present in the result.
 * <p>
 * The algorithm requires two parameters:
 * <ul>
 *   <li>eps: the distance that defines the &epsilon;-neighborhood of a point
 *   <li>minPoints: the minimum number of density-connected points required to form a cluster
 * </ul>
 * <p>
 * <b>Note:</b> as DBSCAN is not a centroid-based clustering algorithm, the resulting
 * {@link Cluster} objects will have no defined center, i.e. {@link Cluster#getCenter()} will
 * return {@code null}.
 *
 * @param <T> type of the points to cluster
 * @see <a href="http://en.wikipedia.org/wiki/DBSCAN">DBSCAN (wikipedia)</a>
 * @see <a href="http://www.dbs.ifi.lmu.de/Publikationen/Papers/KDD-96.final.frame.pdf">
 * A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise</a>
 * @since 3.1
 * @deprecated As of 3.2 (to be removed in 4.0),
 * use {@link org.apache.commons.math3.ml.clustering.DBSCANClusterer} instead
 */
@Deprecated
public class DBSCANClusterer<T extends Clusterable<T>> {

    
Maximum radius of the neighborhood to be considered. /** Maximum radius of the neighborhood to be considered. */
    private final double              eps;

    
Minimum number of points needed for a cluster. /** Minimum number of points needed for a cluster. */
    private final int                 minPts;

    
Status of a point during the clustering process. /** Status of a point during the clustering process. */
    private enum PointStatus {
        
The point has is considered to be noise. /** The point has is considered to be noise. */
        NOISE,
        
The point is already part of a cluster. /** The point is already part of a cluster. */
        PART_OF_CLUSTER
    }

    
Creates a new instance of a DBSCANClusterer.
Params:
eps – maximum radius of the neighborhood to be considered
minPts – minimum number of points needed for a cluster
Throws:
NotPositiveException – if eps < 0.0 or minPts < 0/**
     * Creates a new instance of a DBSCANClusterer.
     *
     * @param eps maximum radius of the neighborhood to be considered
     * @param minPts minimum number of points needed for a cluster
     * @throws NotPositiveException if {@code eps < 0.0} or {@code minPts < 0}
     */
    public DBSCANClusterer(final double eps, final int minPts)
        throws NotPositiveException {
        if (eps < 0.0d) {
            throw new NotPositiveException(eps);
        }
        if (minPts < 0) {
            throw new NotPositiveException(minPts);
        }
        this.eps = eps;
        this.minPts = minPts;
    }

    
Returns the maximum radius of the neighborhood to be considered.
Returns:
maximum radius of the neighborhood/**
     * Returns the maximum radius of the neighborhood to be considered.
     *
     * @return maximum radius of the neighborhood
     */
    public double getEps() {
        return eps;
    }

    
Returns the minimum number of points needed for a cluster.
Returns:
minimum number of points needed for a cluster/**
     * Returns the minimum number of points needed for a cluster.
     *
     * @return minimum number of points needed for a cluster
     */
    public int getMinPts() {
        return minPts;
    }

    
Performs DBSCAN cluster analysis.

Note: as DBSCAN is not a centroid-based clustering algorithm, the resulting Cluster objects will have no defined center, i.e. Cluster.getCenter() will return null. 
Params:
points – the points to cluster
Throws:
NullArgumentException – if the data points are null
Returns:
the list of clusters/**
     * Performs DBSCAN cluster analysis.
     * <p>
     * <b>Note:</b> as DBSCAN is not a centroid-based clustering algorithm, the resulting
     * {@link Cluster} objects will have no defined center, i.e. {@link Cluster#getCenter()} will
     * return {@code null}.
     *
     * @param points the points to cluster
     * @return the list of clusters
     * @throws NullArgumentException if the data points are null
     */
    public List<Cluster<T>> cluster(final Collection<T> points) throws NullArgumentException {

        // sanity checks
        MathUtils.checkNotNull(points);

        final List<Cluster<T>> clusters = new ArrayList<Cluster<T>>();
        final Map<Clusterable<T>, PointStatus> visited = new HashMap<Clusterable<T>, PointStatus>();

        for (final T point : points) {
            if (visited.get(point) != null) {
                continue;
            }
            final List<T> neighbors = getNeighbors(point, points);
            if (neighbors.size() >= minPts) {
                // DBSCAN does not care about center points
                final Cluster<T> cluster = new Cluster<T>(null);
                clusters.add(expandCluster(cluster, point, neighbors, points, visited));
            } else {
                visited.put(point, PointStatus.NOISE);
            }
        }

        return clusters;
    }

    
Expands the cluster to include density-reachable items.
Params:
cluster – Cluster to expand
point – Point to add to cluster
neighbors – List of neighbors
points – the data set
visited – the set of already visited points
Returns:
the expanded cluster/**
     * Expands the cluster to include density-reachable items.
     *
     * @param cluster Cluster to expand
     * @param point Point to add to cluster
     * @param neighbors List of neighbors
     * @param points the data set
     * @param visited the set of already visited points
     * @return the expanded cluster
     */
    private Cluster<T> expandCluster(final Cluster<T> cluster,
                                     final T point,
                                     final List<T> neighbors,
                                     final Collection<T> points,
                                     final Map<Clusterable<T>, PointStatus> visited) {
        cluster.addPoint(point);
        visited.put(point, PointStatus.PART_OF_CLUSTER);

        List<T> seeds = new ArrayList<T>(neighbors);
        int index = 0;
        while (index < seeds.size()) {
            final T current = seeds.get(index);
            PointStatus pStatus = visited.get(current);
            // only check non-visited points
            if (pStatus == null) {
                final List<T> currentNeighbors = getNeighbors(current, points);
                if (currentNeighbors.size() >= minPts) {
                    seeds = merge(seeds, currentNeighbors);
                }
            }

            if (pStatus != PointStatus.PART_OF_CLUSTER) {
                visited.put(current, PointStatus.PART_OF_CLUSTER);
                cluster.addPoint(current);
            }

            index++;
        }
        return cluster;
    }

    
Returns a list of density-reachable neighbors of a point. 
Params:
point – the point to look for
points – possible neighbors
Returns:
the List of neighbors/**
     * Returns a list of density-reachable neighbors of a {@code point}.
     *
     * @param point the point to look for
     * @param points possible neighbors
     * @return the List of neighbors
     */
    private List<T> getNeighbors(final T point, final Collection<T> points) {
        final List<T> neighbors = new ArrayList<T>();
        for (final T neighbor : points) {
            if (point != neighbor && neighbor.distanceFrom(point) <= eps) {
                neighbors.add(neighbor);
            }
        }
        return neighbors;
    }

    
Merges two lists together.
Params:
one – first list
two – second list
Returns:
merged lists/**
     * Merges two lists together.
     *
     * @param one first list
     * @param two second list
     * @return merged lists
     */
    private List<T> merge(final List<T> one, final List<T> two) {
        final Set<T> oneSet = new HashSet<T>(one);
        for (T item : two) {
            if (!oneSet.contains(item)) {
                one.add(item);
            }
        }
        return one;
    }
}