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package de.lmu.ifi.dbs.elki.visualization.visualizers.vis2d;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2011
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import java.util.Collection;
import java.util.Iterator;
import org.apache.batik.util.SVGConstants;
import org.w3c.dom.Element;
import de.lmu.ifi.dbs.elki.data.Cluster;
import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.data.model.MeanModel;
import de.lmu.ifi.dbs.elki.result.HierarchicalResult;
import de.lmu.ifi.dbs.elki.result.Result;
import de.lmu.ifi.dbs.elki.result.ResultUtil;
import de.lmu.ifi.dbs.elki.utilities.iterator.IterableUtil;
import de.lmu.ifi.dbs.elki.visualization.VisualizationTask;
import de.lmu.ifi.dbs.elki.visualization.css.CSSClass;
import de.lmu.ifi.dbs.elki.visualization.projector.ScatterPlotProjector;
import de.lmu.ifi.dbs.elki.visualization.style.StyleLibrary;
import de.lmu.ifi.dbs.elki.visualization.style.marker.MarkerLibrary;
import de.lmu.ifi.dbs.elki.visualization.svg.SVGPlot;
import de.lmu.ifi.dbs.elki.visualization.svg.SVGUtil;
import de.lmu.ifi.dbs.elki.visualization.visualizers.AbstractVisFactory;
import de.lmu.ifi.dbs.elki.visualization.visualizers.Visualization;
/**
* Visualize the mean of a KMeans-Clustering
*
* @author Heidi Kolb
*
* @apiviz.has MeanModel oneway - - visualizes
*
* @param <NV> Type of the DatabaseObject being visualized.
*/
public class ClusterMeanVisualization<NV extends NumberVector<NV, ?>> extends P2DVisualization<NV> {
/**
* A short name characterizing this Visualizer.
*/
private static final String NAME = "Cluster Means";
/**
* CSS class name for center of the means
*/
private final static String CSS_MEAN_CENTER = "mean-center";
/**
* CSS class name for center of the means
*/
private final static String CSS_MEAN = "mean-marker";
/**
* Clustering to visualize.
*/
Clustering<MeanModel<NV>> clustering;
public ClusterMeanVisualization(VisualizationTask task) {
super(task);
this.clustering = task.getResult();
context.addContextChangeListener(this);
incrementalRedraw();
}
@Override
protected void redraw() {
addCSSClasses(svgp);
MarkerLibrary ml = context.getStyleLibrary().markers();
double marker_size = context.getStyleLibrary().getSize(StyleLibrary.MARKERPLOT);
Iterator<Cluster<MeanModel<NV>>> ci = clustering.getAllClusters().iterator();
for(int cnum = 0; cnum < clustering.getAllClusters().size(); cnum++) {
Cluster<MeanModel<NV>> clus = ci.next();
double[] mean = proj.fastProjectDataToRenderSpace(clus.getModel().getMean());
// add a greater Marker for the mean
Element meanMarker = ml.useMarker(svgp, layer, mean[0], mean[1], cnum, marker_size * 3);
SVGUtil.setAtt(meanMarker, SVGConstants.SVG_CLASS_ATTRIBUTE, CSS_MEAN);
// Add a fine cross to mark the exact location of the mean.
Element meanMarkerCenter = svgp.svgLine(mean[0] - .7, mean[1], mean[0] + .7, mean[1]);
SVGUtil.setAtt(meanMarkerCenter, SVGConstants.SVG_CLASS_ATTRIBUTE, CSS_MEAN_CENTER);
Element meanMarkerCenter2 = svgp.svgLine(mean[0], mean[1] - .7, mean[0], mean[1] + .7);
SVGUtil.setAtt(meanMarkerCenter2, SVGConstants.SVG_CLASS_ATTRIBUTE, CSS_MEAN_CENTER);
layer.appendChild(meanMarkerCenter);
layer.appendChild(meanMarkerCenter2);
}
}
/**
* Adds the required CSS-Classes
*
* @param svgp SVG-Plot
*/
private void addCSSClasses(SVGPlot svgp) {
if(!svgp.getCSSClassManager().contains(CSS_MEAN_CENTER)) {
CSSClass center = new CSSClass(svgp, CSS_MEAN_CENTER);
center.setStatement(SVGConstants.CSS_STROKE_PROPERTY, context.getStyleLibrary().getTextColor(StyleLibrary.DEFAULT));
center.setStatement(SVGConstants.CSS_STROKE_WIDTH_PROPERTY, context.getStyleLibrary().getLineWidth(StyleLibrary.AXIS_TICK) / 2);
svgp.addCSSClassOrLogError(center);
}
if(!svgp.getCSSClassManager().contains(CSS_MEAN)) {
CSSClass center = new CSSClass(svgp, CSS_MEAN);
center.setStatement(SVGConstants.CSS_OPACITY_PROPERTY, "0.7");
svgp.addCSSClassOrLogError(center);
}
}
/**
* Factory for visualizers to generate an SVG-Element containing a marker for
* the mean in a KMeans-Clustering
*
* @author Heidi Kolb
*
* @apiviz.stereotype factory
* @apiviz.uses ClusterMeanVisualization oneway - - «create»
*
* @param <NV> Type of the NumberVector being visualized.
*/
public static class Factory<NV extends NumberVector<NV, ?>> extends AbstractVisFactory {
/**
* Constructor
*/
public Factory() {
super();
}
@Override
public Visualization makeVisualization(VisualizationTask task) {
return new ClusterMeanVisualization<NV>(task);
}
@Override
public void processNewResult(HierarchicalResult baseResult, Result result) {
// Find clusterings we can visualize:
Collection<Clustering<?>> clusterings = ResultUtil.filterResults(result, Clustering.class);
for(Clustering<?> c : clusterings) {
if(c.getAllClusters().size() > 0) {
// Does the cluster have a model with cluster means?
Clustering<MeanModel<NV>> mcls = findMeanModel(c);
if(mcls != null) {
Iterator<ScatterPlotProjector<?>> ps = ResultUtil.filteredResults(baseResult, ScatterPlotProjector.class);
for(ScatterPlotProjector<?> p : IterableUtil.fromIterator(ps)) {
final VisualizationTask task = new VisualizationTask(NAME, c, p.getRelation(), this);
task.put(VisualizationTask.META_LEVEL, VisualizationTask.LEVEL_DATA + 1);
baseResult.getHierarchy().add(c, task);
baseResult.getHierarchy().add(p, task);
}
}
}
}
}
/**
* Test if the given clustering has a mean model.
*
* @param <NV> Vector type
* @param c Clustering to inspect
* @return the clustering cast to return a mean model, null otherwise.
*/
@SuppressWarnings("unchecked")
private static <NV extends NumberVector<NV, ?>> Clustering<MeanModel<NV>> findMeanModel(Clustering<?> c) {
if(c.getAllClusters().get(0).getModel() instanceof MeanModel<?>) {
return (Clustering<MeanModel<NV>>) c;
}
return null;
}
}
}
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