/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.commons.math4.legacy.ml.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 java.util.stream.Collectors;

import org.apache.commons.math4.legacy.exception.NullArgumentException;
import org.apache.commons.math4.legacy.exception.NotPositiveException;
import org.apache.commons.math4.legacy.ml.distance.DistanceMeasure;
import org.apache.commons.math4.legacy.ml.distance.EuclideanDistance;

/**
 * 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>
 *
 * @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 3.2
 */
public class DBSCANClusterer<T extends Clusterable> extends Clusterer<T> {

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

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

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

    /**
     * Creates a new instance of a DBSCANClusterer.
     * <p>
     * The euclidean distance will be used as default distance measure.
     *
     * @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) {
        this(eps, minPts, new EuclideanDistance());
    }

    /**
     * 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
     * @param measure the distance measure to use
     * @throws NotPositiveException if {@code eps < 0.0} or {@code minPts < 0}
     */
    public DBSCANClusterer(final double eps, final int minPts, final DistanceMeasure measure) {
        super(measure);

        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.
     * @return maximum radius of the neighborhood
     */
    public double getEps() {
        return eps;
    }

    /**
     * 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.
     *
     * @param points Points to cluster (cannot be {@code null}).
     * @return the list of clusters.
     */
    @Override
    public List<Cluster<T>> cluster(final Collection<T> points) {
        // sanity checks
        NullArgumentException.check(points);

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

        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<>();
                clusters.add(expandCluster(cluster, point, neighbors, points, visited));
            } else {
                visited.put(point, PointStatus.NOISE);
            }
        }

        return clusters;
    }

    /**
     * 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, PointStatus> visited) {
        cluster.addPoint(point);
        visited.put(point, PointStatus.PART_OF_CLUSTER);

        List<T> seeds = new ArrayList<>(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 {@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) {
        return points.stream().filter(neighbor -> point != neighbor && distance(neighbor, point) <= eps)
                              .collect(Collectors.toList());
    }

    /**
     * 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<>(one);
        two.stream().filter(item -> !oneSet.contains(item)).forEach(one::add);
        return one;
    }
}
