/**
* 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 java.awt.Color;
import java.awt.Graphics;
import java.awt.Graphics2D;
import java.awt.geom.AffineTransform;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.mahout.clustering.Cluster;
import org.apache.mahout.clustering.kernel.IKernelProfile;
import org.apache.mahout.clustering.kernel.TriangularKernelProfile;
import org.apache.mahout.clustering.meanshift.MeanShiftCanopy;
import org.apache.mahout.clustering.meanshift.MeanShiftCanopyDriver;
import org.apache.mahout.common.HadoopUtil;
import org.apache.mahout.common.RandomUtils;
import org.apache.mahout.common.distance.DistanceMeasure;
import org.apache.mahout.common.distance.EuclideanDistanceMeasure;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
import org.apache.mahout.math.function.Functions;
public final class DisplayMeanShift extends DisplayClustering {
private static double t1;
private static double t2;
private DisplayMeanShift() {
initialize();
this.setTitle("Mean Shift Canopy Clusters (>" + (int) (significance * 100)
+ "% of population)");
}
@Override
public void paint(Graphics g) {
Graphics2D g2 = (Graphics2D) g;
double sx = (double) res / DS;
g2.setTransform(AffineTransform.getScaleInstance(sx, sx));
// plot the axes
g2.setColor(Color.BLACK);
Vector dv = new DenseVector(2).assign(SIZE / 2.0);
Vector dv1 = new DenseVector(2).assign(t1);
Vector dv2 = new DenseVector(2).assign(t2);
DisplayClustering.plotRectangle(g2, new DenseVector(2).assign(2), dv);
DisplayClustering.plotRectangle(g2, new DenseVector(2).assign(-2), dv);
// plot the sample data
g2.setColor(Color.DARK_GRAY);
dv.assign(0.03);
for (VectorWritable v : SAMPLE_DATA) {
DisplayClustering.plotRectangle(g2, v.get(), dv);
}
int i = 0;
for (Cluster cluster : CLUSTERS.get(CLUSTERS.size() - 1)) {
MeanShiftCanopy canopy = (MeanShiftCanopy) cluster;
if (canopy.getMass() >= significance
* DisplayClustering.SAMPLE_DATA.size()) {
g2.setColor(COLORS[Math.min(i++, DisplayClustering.COLORS.length - 1)]);
int count = 0;
Vector center = new DenseVector(2);
for (int vix : canopy.getBoundPoints().toList()) {
Vector v = SAMPLE_DATA.get(vix).get();
count++;
center.assign(v, Functions.PLUS);
DisplayClustering.plotRectangle(g2, v, dv);
}
center = center.divide(count);
DisplayClustering.plotEllipse(g2, center, dv1);
DisplayClustering.plotEllipse(g2, center, dv2);
}
}
}
public static void main(String[] args) throws Exception {
t1 = 1.5;
t2 = 0.5;
DistanceMeasure measure = new EuclideanDistanceMeasure();
IKernelProfile kernelProfile = new TriangularKernelProfile();
significance = 0.02;
Path samples = new Path("samples");
Path output = new Path("output");
Configuration conf = new Configuration();
HadoopUtil.delete(conf, samples);
HadoopUtil.delete(conf, output);
RandomUtils.useTestSeed();
DisplayClustering.generateSamples();
writeSampleData(samples);
// boolean b = true;
// if (b) {
MeanShiftCanopyDriver.run(conf, samples, output, measure, kernelProfile,
t1, t2, 0.005, 20, false, true, true);
loadClustersWritable(output);
// } else {
// Collection<Vector> points = new ArrayList<Vector>();
// for (VectorWritable sample : SAMPLE_DATA) {
// points.add(sample.get());
// }
// List<MeanShiftCanopy> canopies =
// MeanShiftCanopyClusterer.clusterPoints(points, measure, 0.005, t1, t2,
// 20);
// for (MeanShiftCanopy canopy : canopies) {
// log.info(canopy.toString());
// }
// }
new DisplayMeanShift();
}
}