package de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.cluster;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2012
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.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.database.datastore.DataStoreListener;
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.visualization.VisualizationTask;
import de.lmu.ifi.dbs.elki.visualization.colors.ColorLibrary;
import de.lmu.ifi.dbs.elki.visualization.css.CSSClass;
import de.lmu.ifi.dbs.elki.visualization.projector.ParallelPlotProjector;
import de.lmu.ifi.dbs.elki.visualization.style.StyleLibrary;
import de.lmu.ifi.dbs.elki.visualization.svg.SVGPath;
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;
import de.lmu.ifi.dbs.elki.visualization.visualizers.parallel.AbstractParallelVisualization;
/**
* Generates a SVG-Element that visualizes cluster means.
*
* @author Robert Rödler
*/
public class ClusterParallelMeanVisualization extends AbstractParallelVisualization<NumberVector<?, ?>> implements DataStoreListener {
/**
* Generic tags to indicate the type of element. Used in IDs, CSS-Classes etc.
*/
public static final String CLUSTERMEAN = "Clustermean";
/**
* The result we visualize
*/
private Clustering<MeanModel<? extends NumberVector<?, ?>>> clustering;
/**
* Constructor.
*
* @param task VisualizationTask
*/
public ClusterParallelMeanVisualization(VisualizationTask task) {
super(task);
this.clustering = task.getResult();
context.addDataStoreListener(this);
context.addResultListener(this);
incrementalRedraw();
}
@Override
public void destroy() {
context.removeDataStoreListener(this);
context.removeResultListener(this);
super.destroy();
}
@Override
protected void redraw() {
addCSSClasses(svgp);
Iterator<Cluster<MeanModel<? extends NumberVector<?, ?>>>> ci = clustering.getAllClusters().iterator();
for(int cnum = 0; cnum < clustering.getAllClusters().size(); cnum++) {
Cluster<MeanModel<? extends NumberVector<?, ?>>> clus = ci.next();
NumberVector<?, ?> mean = clus.getModel().getMean();
if(mean == null) {
continue;
}
double[] pmean = proj.fastProjectDataToRenderSpace(mean);
SVGPath path = new SVGPath();
for(int i = 0; i < pmean.length; i++) {
path.drawTo(getVisibleAxisX(i), pmean[i]);
}
Element meanline = path.makeElement(svgp);
SVGUtil.addCSSClass(meanline, CLUSTERMEAN + cnum);
layer.appendChild(meanline);
}
}
/**
* Adds the required CSS-Classes
*
* @param svgp SVG-Plot
*/
private void addCSSClasses(SVGPlot svgp) {
if(!svgp.getCSSClassManager().contains(CLUSTERMEAN)) {
ColorLibrary colors = context.getStyleLibrary().getColorSet(StyleLibrary.PLOT);
int clusterID = 0;
for(@SuppressWarnings("unused")
Cluster<?> cluster : clustering.getAllClusters()) {
CSSClass cls = new CSSClass(this, CLUSTERMEAN + clusterID);
cls.setStatement(SVGConstants.CSS_STROKE_WIDTH_PROPERTY, context.getStyleLibrary().getLineWidth(StyleLibrary.PLOT) * 2);
final String color;
if(clustering.getAllClusters().size() == 1) {
color = SVGConstants.CSS_BLACK_VALUE;
}
else {
color = colors.getColor(clusterID);
}
cls.setStatement(SVGConstants.CSS_STROKE_PROPERTY, color);
cls.setStatement(SVGConstants.CSS_FILL_PROPERTY, SVGConstants.CSS_NONE_VALUE);
svgp.addCSSClassOrLogError(cls);
clusterID++;
}
}
}
/**
* Factory for axis visualizations
*
* @author Robert Rödler
*
* @apiviz.stereotype factory
* @apiviz.uses ClusterParallelMeanVisualization oneway - - «create»
*
*/
public static class Factory extends AbstractVisFactory {
/**
* A short name characterizing this Visualizer.
*/
private static final String NAME = "Cluster Means";
/**
* Constructor, adhering to
* {@link de.lmu.ifi.dbs.elki.utilities.optionhandling.Parameterizable}
*/
public Factory() {
super();
}
@Override
public Visualization makeVisualization(VisualizationTask task) {
return new ClusterParallelMeanVisualization(task);
}
@Override
public void processNewResult(HierarchicalResult baseResult, Result result) {
// Find clusterings we can visualize:
Iterator<Clustering<?>> clusterings = ResultUtil.filteredResults(result, Clustering.class);
while(clusterings.hasNext()) {
Clustering<?> c = clusterings.next();
if(c.getAllClusters().size() > 0) {
// Does the cluster have a model with cluster means?
Clustering<MeanModel<? extends NumberVector<?, ?>>> mcls = findMeanModel(c);
if(mcls != null) {
Iterator<ParallelPlotProjector<?>> ps = ResultUtil.filteredResults(baseResult, ParallelPlotProjector.class);
while(ps.hasNext()) {
ParallelPlotProjector<?> p = ps.next();
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 c Clustering to inspect
* @return the clustering cast to return a mean model, null otherwise.
*/
@SuppressWarnings("unchecked")
private static Clustering<MeanModel<? extends NumberVector<?, ?>>> findMeanModel(Clustering<?> c) {
if(c.getAllClusters().get(0).getModel() instanceof MeanModel<?>) {
return (Clustering<MeanModel<? extends NumberVector<?, ?>>>) c;
}
return null;
}
@Override
public boolean allowThumbnails(VisualizationTask task) {
// Don't use thumbnails
return false;
}
}
}