package org.apache.samoa.moa.clusterers;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2014 - 2015 Apache Software Foundation
 * %%
 * Licensed 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.
 * #L%
 */

import java.util.ArrayList;
import java.util.List;

import org.apache.samoa.moa.cluster.CFCluster;
import org.apache.samoa.moa.cluster.Cluster;
import org.apache.samoa.moa.cluster.Clustering;
import org.apache.samoa.moa.cluster.SphereCluster;

/**
 * A kMeans implementation for microclusterings. For now it only uses the real centers of the groundtruthclustering for
 * implementation. There should also be an option to use random centers. TODO: random centers TODO: Create a macro
 * clustering interface to make different macro clustering algorithms available to micro clustering algorithms like
 * clustream, denstream and clustree
 * 
 */
public class KMeans {

  /**
   * This kMeans implementation clusters a big number of microclusters into a smaller amount of macro clusters. To make
   * it comparable to other algorithms it uses the real centers of the ground truth macro clustering to have the best
   * possible initialization. The quality of resulting macro clustering yields an upper bound for kMeans on the
   * underlying microclustering.
   * 
   * @param centers
   *          of the ground truth clustering
   * @param data
   *          list of microclusters
   * @return
   */
  public static Clustering kMeans(Cluster[] centers, List<? extends Cluster> data) {
    int k = centers.length;

    int dimensions = centers[0].getCenter().length;

    ArrayList<ArrayList<Cluster>> clustering =
        new ArrayList<ArrayList<Cluster>>();
    for (int i = 0; i < k; i++) {
      clustering.add(new ArrayList<Cluster>());
    }

    int repetitions = 100;
    while (repetitions-- >= 0) {
      // Assign points to clusters
      for (Cluster point : data) {
        double minDistance = distance(point.getCenter(), centers[0].getCenter());
        int closestCluster = 0;
        for (int i = 1; i < k; i++) {
          double distance = distance(point.getCenter(), centers[i].getCenter());
          if (distance < minDistance) {
            closestCluster = i;
            minDistance = distance;
          }
        }

        clustering.get(closestCluster).add(point);
      }

      // Calculate new centers and clear clustering lists
      SphereCluster[] newCenters = new SphereCluster[centers.length];
      for (int i = 0; i < k; i++) {
        newCenters[i] = calculateCenter(clustering.get(i), dimensions);
        clustering.get(i).clear();
      }
      centers = newCenters;
    }

    return new Clustering(centers);
  }

  private static double distance(double[] pointA, double[] pointB) {
    double distance = 0.0;
    for (int i = 0; i < pointA.length; i++) {
      double d = pointA[i] - pointB[i];
      distance += d * d;
    }
    return Math.sqrt(distance);
  }

  private static SphereCluster calculateCenter(ArrayList<Cluster> cluster, int dimensions) {
    double[] res = new double[dimensions];
    for (int i = 0; i < res.length; i++) {
      res[i] = 0.0;
    }

    if (cluster.size() == 0) {
      return new SphereCluster(res, 0.0);
    }

    for (Cluster point : cluster) {
      double[] center = point.getCenter();
      for (int i = 0; i < res.length; i++) {
        res[i] += center[i];
      }
    }

    // Normalize
    for (int i = 0; i < res.length; i++) {
      res[i] /= cluster.size();
    }

    // Calculate radius
    double radius = 0.0;
    for (Cluster point : cluster) {
      double dist = distance(res, point.getCenter());
      if (dist > radius) {
        radius = dist;
      }
    }

    return new SphereCluster(res, radius);
  }

  public static Clustering gaussianMeans(Clustering gtClustering, Clustering clustering) {
    ArrayList<CFCluster> microclusters = new ArrayList<CFCluster>();
    for (int i = 0; i < clustering.size(); i++) {
      if (clustering.get(i) instanceof CFCluster) {
        microclusters.add((CFCluster) clustering.get(i));
      }
      else {
        System.out.println("Unsupported Cluster Type:" + clustering.get(i).getClass()
            + ". Cluster needs to extend moa.cluster.CFCluster");
      }
    }
    Cluster[] centers = new Cluster[gtClustering.size()];
    for (int i = 0; i < centers.length; i++) {
      centers[i] = gtClustering.get(i);

    }

    int k = centers.length;
    if (microclusters.size() < k) {
      return new Clustering(new Cluster[0]);
    }

    Clustering kMeansResult = kMeans(centers, microclusters);

    k = kMeansResult.size();
    CFCluster[] res = new CFCluster[k];

    for (CFCluster microcluster : microclusters) {
      // Find closest kMeans cluster
      double minDistance = Double.MAX_VALUE;
      int closestCluster = 0;
      for (int i = 0; i < k; i++) {
        double distance = distance(kMeansResult.get(i).getCenter(), microcluster.getCenter());
        if (distance < minDistance) {
          closestCluster = i;
          minDistance = distance;
        }
      }

      // Add to cluster
      if (res[closestCluster] == null) {
        res[closestCluster] = (CFCluster) microcluster.copy();
      } else {
        res[closestCluster].add(microcluster);
      }
    }

    // Clean up res
    int count = 0;
    for (int i = 0; i < res.length; i++) {
      if (res[i] != null)
        ++count;
    }

    CFCluster[] cleaned = new CFCluster[count];
    count = 0;
    for (int i = 0; i < res.length; i++) {
      if (res[i] != null)
        cleaned[count++] = res[i];
    }

    return new Clustering(cleaned);
  }

}