/**
 * 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.mahout.clustering.display;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.fs.PathFilter;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.canopy.CanopyDriver;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.common.distance.ManhattanDistanceMeasure;
import org.apache.mahout.math.DenseVector;

import java.awt.BasicStroke;
import java.awt.Color;
import java.awt.Graphics;
import java.awt.Graphics2D;
import java.util.List;

/**
 * Java desktop graphics class that runs canopy clustering and displays the results.
 * This class generates random data and clusters it.
 */
public class DisplayCanopy extends DisplayClustering {

  DisplayCanopy() {
    initialize();
    this.setTitle("Canopy Clusters (>" + (int) (significance * 100) + "% of population)");
  }

  @Override
  public void paint(Graphics g) {
    plotSampleData((Graphics2D) g);
    plotClusters((Graphics2D) g);
  }

  protected static void plotClusters(Graphics2D g2) {
    int cx = CLUSTERS.size() - 1;
    for (List<Cluster> clusters : CLUSTERS) {
      for (Cluster cluster : clusters) {
        if (isSignificant(cluster)) {
          g2.setStroke(new BasicStroke(1));
          g2.setColor(Color.BLUE);
          double[] t1 = {T1, T1};
          plotEllipse(g2, cluster.getCenter(), new DenseVector(t1));
          double[] t2 = {T2, T2};
          plotEllipse(g2, cluster.getCenter(), new DenseVector(t2));
          g2.setColor(COLORS[Math.min(DisplayClustering.COLORS.length - 1, cx)]);
          g2.setStroke(new BasicStroke(cx == 0 ? 3 : 1));
          plotEllipse(g2, cluster.getCenter(), cluster.getRadius().times(3));
        }
      }
      cx--;
    }
  }

  public static void main(String[] args) throws Exception {
    Path samples = new Path("samples");
    Path output = new Path("output");
    Configuration conf = new Configuration();
    HadoopUtil.delete(conf, samples);
    HadoopUtil.delete(conf, output);
    RandomUtils.useTestSeed();
    generateSamples();
    writeSampleData(samples);
    //boolean b = true;
    //if (b) {
    CanopyDriver.buildClusters(conf, samples, output, new ManhattanDistanceMeasure(), T1, T2, 0, true);
    loadClusters(output, new PathFilter() {
      @Override
      public boolean accept(Path path) {
        String pathString = path.toString();
        return pathString.contains("/clusters-");
      }
    });
    //} else {
    //  List<Vector> points = new ArrayList<Vector>();
    //  for (VectorWritable sample : SAMPLE_DATA) {
    //    points.add(sample.get());
    //  }
    //  List<Canopy> canopies = CanopyClusterer.createCanopies(points, new ManhattanDistanceMeasure(), T1, T2);
    //  CanopyClusterer.updateCentroids(canopies);
    //  List<Cluster> clusters = new ArrayList<Cluster>();
    //  for (Canopy canopy : canopies) {
    //    clusters.add(canopy);
    //  }
    //  CLUSTERS.add(clusters);
    //}

    new DisplayCanopy();
  }

}