Import Debian changes 0.5.0~beta2-1

elki (0.5.0~beta2-1) unstable; urgency=low

  * New upstream beta release.
  * Needs GNU Trove 3, in NEW.
  * Build with OpenJDK7, as OpenJDK6 complains.

elki (0.5.0~beta1-1) unstable; urgency=low

  * New upstream beta release.
  * Needs GNU Trove 3, not yet in Debian (private package)
  * Build with OpenJDK7, as OpenJDK6 complains.

1 files changed, 281 insertions, 0 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterMeanVisualization.java b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterMeanVisualization.java
new file mode 100644
index 00000000..c9443a9f
--- /dev/null
+++ b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterMeanVisualization.java
@@ -0,0 +1,281 @@
+package de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster;

+

+/*

+ 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.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.ids.DBID;

+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.optionhandling.AbstractParameterizer;

+import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;

+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;

+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Flag;

+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.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.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.scatterplot.AbstractScatterplotVisualization;

+

+/**

+ * Visualize the mean of a KMeans-Clustering

+ * 

+ * @author Heidi Kolb

+ * 

+ * @apiviz.has MeanModel oneway - - visualizes

+ */

+public class ClusterMeanVisualization extends AbstractScatterplotVisualization {

+  /**

+   * 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";

+

+  /**

+   * CSS class name for center of the means

+   */

+  private final static String CSS_MEAN_STAR = "mean-star";

+

+  /**

+   * Clustering to visualize.

+   */

+  Clustering<MeanModel<? extends NumberVector<?, ?>>> clustering;

+

+  /**

+   * Draw stars

+   */

+  boolean stars;

+

+  /**

+   * Constructor.

+   * 

+   * @param task Visualization task

+   * @param stars Draw stars

+   */

+  public ClusterMeanVisualization(VisualizationTask task, boolean stars) {

+    super(task);

+    this.clustering = task.getResult();

+    this.stars = stars;

+    incrementalRedraw();

+  }

+

+  @Override

+  protected void redraw() {

+    addCSSClasses(svgp);

+

+    MarkerLibrary ml = context.getStyleLibrary().markers();

+    double marker_size = context.getStyleLibrary().getSize(StyleLibrary.MARKERPLOT);

+

+    Iterator<Cluster<MeanModel<? extends NumberVector<?, ?>>>> ci = clustering.getAllClusters().iterator();

+    for(int cnum = 0; ci.hasNext(); cnum++) {

+      Cluster<MeanModel<? extends NumberVector<?, ?>>> 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);

+

+      if(stars) {

+        SVGPath star = new SVGPath();

+        for(DBID id : clus.getIDs()) {

+          double[] obj = proj.fastProjectDataToRenderSpace(rel.get(id));

+          star.moveTo(mean);

+          star.drawTo(obj);

+        }

+        Element stare = star.makeElement(svgp);

+        SVGUtil.setCSSClass(stare, CSS_MEAN_STAR + "_" + cnum);

+        layer.appendChild(stare);

+      }

+    }

+  }

+

+  /**

+   * 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(this, 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(this, CSS_MEAN);

+      center.setStatement(SVGConstants.CSS_OPACITY_PROPERTY, "0.7");

+      svgp.addCSSClassOrLogError(center);

+    }

+    if(stars) {

+      ColorLibrary colors = context.getStyleLibrary().getColorSet(StyleLibrary.PLOT);

+

+      Iterator<Cluster<MeanModel<? extends NumberVector<?, ?>>>> ci = clustering.getAllClusters().iterator();

+      for(int cnum = 0; ci.hasNext(); cnum++) {

+        ci.next();

+        if(!svgp.getCSSClassManager().contains(CSS_MEAN_STAR + "_" + cnum)) {

+          CSSClass center = new CSSClass(this, CSS_MEAN_STAR + "_" + cnum);

+          center.setStatement(SVGConstants.CSS_STROKE_PROPERTY, colors.getColor(cnum));

+          center.setStatement(SVGConstants.CSS_STROKE_WIDTH_PROPERTY, context.getStyleLibrary().getLineWidth(StyleLibrary.PLOT));

+          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»

+   */

+  public static class Factory extends AbstractVisFactory {

+    /**

+     * Option ID for visualization of cluster means.

+     * 

+     * <pre>

+     * -cluster.stars

+     * </pre>

+     */

+    public static final OptionID STARS_ID = OptionID.getOrCreateOptionID("cluster.stars", "Visualize mean-based clusters using stars.");

+

+    /**

+     * Draw stars

+     */

+    private boolean stars;

+

+    /**

+     * Constructor.

+     * 

+     * @param stars Draw stars

+     */

+    public Factory(boolean stars) {

+      super();

+      this.stars = stars;

+    }

+

+    @Override

+    public Visualization makeVisualization(VisualizationTask task) {

+      return new ClusterMeanVisualization(task, stars);

+    }

+

+    @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<ScatterPlotProjector<?>> ps = ResultUtil.filteredResults(baseResult, ScatterPlotProjector.class);

+            while(ps.hasNext()) {

+              ScatterPlotProjector<?> 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;

+    }

+

+    /**

+     * Parameterization class.

+     * 

+     * @author Erich Schubert

+     * 

+     * @apiviz.exclude

+     */

+    public static class Parameterizer extends AbstractParameterizer {

+      protected boolean stars = false;

+

+      @Override

+      protected void makeOptions(Parameterization config) {

+        super.makeOptions(config);

+        Flag starsF = new Flag(STARS_ID);

+        if(config.grab(starsF)) {

+          stars = starsF.getValue();

+        }

+      }

+

+      @Override

+      protected Factory makeInstance() {

+        return new Factory(stars);

+      }

+    }

+  }

+}
\ No newline at end of file