/*******************************************************************************
* Copyright (c) 2010 Haifeng Li
*
* 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.
*******************************************************************************/
package smile.demo.clustering;
import java.awt.Dimension;
import javax.swing.JComponent;
import javax.swing.JFrame;
import smile.clustering.KMeans;
import smile.plot.Palette;
import smile.plot.PlotCanvas;
import smile.plot.ScatterPlot;
/**
*
* @author Haifeng Li
*/
@SuppressWarnings("serial")
public class KMeansDemo extends ClusteringDemo {
public KMeansDemo() {
}
@Override
public JComponent learn() {
long clock = System.currentTimeMillis();
KMeans kmeans = new KMeans(dataset[datasetIndex], clusterNumber, 100, 4);
System.out.format("K-Means clusterings %d samples in %dms\n", dataset[datasetIndex].length, System.currentTimeMillis()-clock);
PlotCanvas plot = ScatterPlot.plot(dataset[datasetIndex], kmeans.getClusterLabel(), pointLegend, Palette.COLORS);
plot.points(kmeans.centroids(), '@');
return plot;
}
@Override
public String toString() {
return "K-Means";
}
public static void main(String argv[]) {
ClusteringDemo demo = new KMeansDemo();
JFrame f = new JFrame("K-Means");
f.setSize(new Dimension(1000, 1000));
f.setLocationRelativeTo( null );
f.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
f.getContentPane().add(demo);
f.setVisible(true);
}
}
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