/**
* 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.canopy;
import java.awt.Graphics;
import java.awt.Graphics2D;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import org.apache.mahout.clustering.dirichlet.DisplayDirichlet;
import org.apache.mahout.clustering.dirichlet.models.NormalModelDistribution;
import org.apache.mahout.matrix.DenseVector;
import org.apache.mahout.matrix.Vector;
import org.apache.mahout.common.distance.DistanceMeasure;
import org.apache.mahout.common.distance.ManhattanDistanceMeasure;
import org.apache.mahout.common.RandomUtils;
class DisplayCanopy extends DisplayDirichlet {
DisplayCanopy() {
initialize();
this.setTitle("Canopy Clusters (> 5% of population)");
}
private static List<Canopy> canopies;
private static final double t1 = 3.0;
private static final double t2 = 1.5;
@Override
public void paint(Graphics g) {
super.plotSampleData(g);
Graphics2D g2 = (Graphics2D) g;
Vector dv = new DenseVector(2);
for (Canopy canopy : canopies) {
if (canopy.getNumPoints() > sampleData.size() * 0.05) {
dv.assign(t1);
g2.setColor(colors[0]);
plotEllipse(g2, canopy.getCenter(), dv);
dv.assign(t2);
plotEllipse(g2, canopy.getCenter(), dv);
}
}
}
/**
* Iterate through the points, adding new canopies. Return the canopies.
*
* @param measure
* a DistanceMeasure to use
* @param points
* a list<Vector> defining the points to be clustered
* @param t1
* the T1 distance threshold
* @param t2
* the T2 distance threshold
* @return the List<Canopy> created
*/
static List<Canopy> populateCanopies(DistanceMeasure measure,
List<Vector> points, double t1, double t2) {
List<Canopy> canopies = new ArrayList<Canopy>();
Canopy.config(measure, t1, t2);
/**
* Reference Implementation: Given a distance metric, one can create
* canopies as follows: Start with a list of the data points in any order,
* and with two distance thresholds, T1 and T2, where T1 > T2. (These
* thresholds can be set by the user, or selected by cross-validation.) Pick
* a point on the list and measure its distance to all other points. Put all
* points that are within distance threshold T1 into a canopy. Remove from
* the list all points that are within distance threshold T2. Repeat until
* the list is empty.
*/
while (!points.isEmpty()) {
Iterator<Vector> ptIter = points.iterator();
Vector p1 = ptIter.next();
ptIter.remove();
Canopy canopy = new Canopy(p1);
canopies.add(canopy);
while (ptIter.hasNext()) {
Vector p2 = ptIter.next();
double dist = measure.distance(p1, p2);
// Put all points that are within distance threshold T1 into the canopy
if (dist < t1)
canopy.addPoint(p2);
// Remove from the list all points that are within distance threshold T2
if (dist < t2)
ptIter.remove();
}
}
return canopies;
}
public static void main(String[] args) {
RandomUtils.useTestSeed();
generateSamples();
List<Vector> points = new ArrayList<Vector>();
points.addAll(sampleData);
canopies = populateCanopies(new ManhattanDistanceMeasure(), points, t1, t2);
new DisplayCanopy();
}
static void generateResults() {
DisplayDirichlet.generateResults(new NormalModelDistribution());
}
}