/*
* 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.Graphics;
import java.awt.Graphics2D;
import java.io.File;
import java.io.Writer;
import com.google.common.base.Charsets;
import com.google.common.io.Closeables;
import com.google.common.io.Files;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.mahout.clustering.spectral.kmeans.SpectralKMeansDriver;
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.ManhattanDistanceMeasure;
public class DisplaySpectralKMeans extends DisplayClustering {
protected static final String SAMPLES = "samples";
protected static final String OUTPUT = "output";
protected static final String TEMP = "tmp";
protected static final String AFFINITIES = "affinities";
DisplaySpectralKMeans() {
initialize();
setTitle("Spectral k-Means Clusters (>" + (int) (significance * 100) + "% of population)");
}
public static void main(String[] args) throws Exception {
DistanceMeasure measure = new ManhattanDistanceMeasure();
Path samples = new Path(SAMPLES);
Path output = new Path(OUTPUT);
Path tempDir = new Path(TEMP);
Configuration conf = new Configuration();
HadoopUtil.delete(conf, samples);
HadoopUtil.delete(conf, output);
RandomUtils.useTestSeed();
DisplayClustering.generateSamples();
writeSampleData(samples);
Path affinities = new Path(output, AFFINITIES);
FileSystem fs = FileSystem.get(output.toUri(), conf);
if (!fs.exists(output)) {
fs.mkdirs(output);
}
Writer writer = null;
try {
writer = Files.newWriter(new File(affinities.toString()), Charsets.UTF_8);
for (int i = 0; i < SAMPLE_DATA.size(); i++) {
for (int j = 0; j < SAMPLE_DATA.size(); j++) {
writer.write(i + "," + j + ',' + measure.distance(SAMPLE_DATA.get(i).get(), SAMPLE_DATA.get(j).get()) + '\n');
}
}
} finally {
Closeables.close(writer, false);
}
int maxIter = 10;
double convergenceDelta = 0.001;
SpectralKMeansDriver.run(new Configuration(), affinities, output, SAMPLE_DATA.size(), 3, measure, convergenceDelta,
maxIter, tempDir, false);
new DisplaySpectralKMeans();
}
@Override
public void paint(Graphics g) {
plotClusteredSampleData((Graphics2D) g, new Path(new Path(OUTPUT), "kmeans_out"));
}
}