1 files changed, 199 insertions, 0 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/vis2d/ClusterMeanVisualization.java b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/vis2d/ClusterMeanVisualization.java
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+++ b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/vis2d/ClusterMeanVisualization.java
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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.database.relation.Relation;
+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.css.CSSClass;
+import de.lmu.ifi.dbs.elki.visualization.projections.Projection;
+import de.lmu.ifi.dbs.elki.visualization.projections.Projection2D;
+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;
+import de.lmu.ifi.dbs.elki.visualization.visualizers.VisualizationTask;
+import de.lmu.ifi.dbs.elki.visualization.visualizers.VisualizerUtil;
+
+/**
+ * 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) {
+      Iterator<Relation<? extends NumberVector<?, ?>>> reps = VisualizerUtil.iterateVectorFieldRepresentations(baseResult);
+      for(Relation<? extends NumberVector<?, ?>> rep : IterableUtil.fromIterator(reps)) {
+        // 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) {
+              final VisualizationTask task = new VisualizationTask(NAME, c, rep, this, P2DVisualization.class);
+              task.put(VisualizationTask.META_LEVEL, VisualizationTask.LEVEL_DATA + 1);
+              baseResult.getHierarchy().add(c, 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;
+    }
+
+    @Override
+    public Class<? extends Projection> getProjectionType() {
+      return Projection2D.class;
+    }
+  }
+}
+\ No newline at end of file