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-rw-r--r--src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterHullVisualization.java15
-rw-r--r--src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterMeanVisualization.java55
-rw-r--r--src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterOrderVisualization.java3
-rw-r--r--src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/EMClusterVisualization.java5
-rw-r--r--src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/VoronoiVisualization.java113
5 files changed, 117 insertions, 74 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterHullVisualization.java b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterHullVisualization.java
index 430ab194..e1817b1e 100644
--- a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterHullVisualization.java
+++ b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterHullVisualization.java
@@ -36,7 +36,7 @@ import de.lmu.ifi.dbs.elki.data.Clustering;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.data.spatial.Polygon;
import de.lmu.ifi.dbs.elki.data.spatial.SpatialUtil;
-import de.lmu.ifi.dbs.elki.database.ids.DBID;
+import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.math.geometry.AlphaShape;
import de.lmu.ifi.dbs.elki.math.geometry.GrahamScanConvexHull2D;
@@ -44,7 +44,6 @@ import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector;
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.IterableIterator;
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;
@@ -71,7 +70,7 @@ import de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.AbstractScatter
* @author Erich Schubert
*
* @apiviz.has Clustering oneway - - visualizes
- * @apiviz.uses ConvexHull2D
+ * @apiviz.uses GrahamScanConvexHull2D
* @apiviz.uses AlphaShape
*/
public class ClusterHullVisualization extends AbstractScatterplotVisualization {
@@ -125,8 +124,8 @@ public class ClusterHullVisualization extends AbstractScatterplotVisualization {
if(alpha >= Double.POSITIVE_INFINITY) {
GrahamScanConvexHull2D hull = new GrahamScanConvexHull2D();
- for(DBID clpnum : ids) {
- double[] projP = proj.fastProjectDataToRenderSpace(rel.get(clpnum));
+ for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+ double[] projP = proj.fastProjectDataToRenderSpace(rel.get(iter));
hull.add(new Vector(projP));
}
Polygon chres = hull.getHull();
@@ -148,8 +147,8 @@ public class ClusterHullVisualization extends AbstractScatterplotVisualization {
}
else {
ArrayList<Vector> ps = new ArrayList<Vector>(ids.size());
- for(DBID clpnum : ids) {
- double[] projP = proj.fastProjectDataToRenderSpace(rel.get(clpnum));
+ for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+ double[] projP = proj.fastProjectDataToRenderSpace(rel.get(iter));
ps.add(new Vector(projP));
}
List<Polygon> polys = (new AlphaShape(ps, alpha * Projection.SCALE)).compute();
@@ -225,7 +224,7 @@ public class ClusterHullVisualization extends AbstractScatterplotVisualization {
// Find clusterings we can visualize:
Collection<Clustering<?>> clusterings = ResultUtil.filterResults(result, Clustering.class);
for(Clustering<?> c : clusterings) {
- IterableIterator<ScatterPlotProjector<?>> ps = ResultUtil.filteredResults(baseResult, ScatterPlotProjector.class);
+ Collection<ScatterPlotProjector<?>> ps = ResultUtil.filterResults(baseResult, ScatterPlotProjector.class);
for(ScatterPlotProjector<?> p : ps) {
final VisualizationTask task = new VisualizationTask(NAME, c, p.getRelation(), this);
task.put(VisualizationTask.META_LEVEL, VisualizationTask.LEVEL_DATA - 1);
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
index c9443a9f..4f14f4ef 100644
--- 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
@@ -23,6 +23,7 @@ package de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster;
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;
@@ -32,6 +33,8 @@ 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.data.model.MedoidModel;
+import de.lmu.ifi.dbs.elki.data.model.Model;
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;
@@ -59,6 +62,7 @@ import de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.AbstractScatter
* @author Heidi Kolb
*
* @apiviz.has MeanModel oneway - - visualizes
+ * @apiviz.has MedoidModel oneway - - visualizes
*/
public class ClusterMeanVisualization extends AbstractScatterplotVisualization {
/**
@@ -84,7 +88,7 @@ public class ClusterMeanVisualization extends AbstractScatterplotVisualization {
/**
* Clustering to visualize.
*/
- Clustering<MeanModel<? extends NumberVector<?, ?>>> clustering;
+ Clustering<Model> clustering;
/**
* Draw stars
@@ -111,10 +115,23 @@ public class ClusterMeanVisualization extends AbstractScatterplotVisualization {
MarkerLibrary ml = context.getStyleLibrary().markers();
double marker_size = context.getStyleLibrary().getSize(StyleLibrary.MARKERPLOT);
- Iterator<Cluster<MeanModel<? extends NumberVector<?, ?>>>> ci = clustering.getAllClusters().iterator();
+ Iterator<Cluster<Model>> 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());
+ Cluster<Model> clus = ci.next();
+ Model model = clus.getModel();
+ double[] mean;
+ if(model instanceof MeanModel) {
+ @SuppressWarnings("unchecked")
+ MeanModel<? extends NumberVector<?, ?>> mmodel = (MeanModel<? extends NumberVector<?, ?>>) model;
+ mean = proj.fastProjectDataToRenderSpace(mmodel.getMean());
+ }
+ else if(model instanceof MedoidModel) {
+ MedoidModel mmodel = (MedoidModel) model;
+ mean = proj.fastProjectDataToRenderSpace(rel.get(mmodel.getMedoid()));
+ }
+ else {
+ continue;
+ }
// add a greater Marker for the mean
Element meanMarker = ml.useMarker(svgp, layer, mean[0], mean[1], cnum, marker_size * 3);
@@ -163,7 +180,7 @@ public class ClusterMeanVisualization extends AbstractScatterplotVisualization {
if(stars) {
ColorLibrary colors = context.getStyleLibrary().getColorSet(StyleLibrary.PLOT);
- Iterator<Cluster<MeanModel<? extends NumberVector<?, ?>>>> ci = clustering.getAllClusters().iterator();
+ Iterator<Cluster<Model>> ci = clustering.getAllClusters().iterator();
for(int cnum = 0; ci.hasNext(); cnum++) {
ci.next();
if(!svgp.getCSSClassManager().contains(CSS_MEAN_STAR + "_" + cnum)) {
@@ -219,16 +236,13 @@ public class ClusterMeanVisualization extends AbstractScatterplotVisualization {
@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();
+ 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<? extends NumberVector<?, ?>>> mcls = findMeanModel(c);
- if(mcls != null) {
- Iterator<ScatterPlotProjector<?>> ps = ResultUtil.filteredResults(baseResult, ScatterPlotProjector.class);
- while(ps.hasNext()) {
- ScatterPlotProjector<?> p = ps.next();
+ if(testMeanModel(c)) {
+ Collection<ScatterPlotProjector<?>> ps = ResultUtil.filterResults(baseResult, ScatterPlotProjector.class);
+ for(ScatterPlotProjector<?> p : 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);
@@ -243,14 +257,17 @@ public class ClusterMeanVisualization extends AbstractScatterplotVisualization {
* 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.
+ * @return true when the clustering has a mean or medoid model.
*/
- @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;
+ private static boolean testMeanModel(Clustering<?> c) {
+ Model firstmodel = c.getAllClusters().get(0).getModel();
+ if(firstmodel instanceof MeanModel<?>) {
+ return true;
+ }
+ if(firstmodel instanceof MedoidModel) {
+ return true;
}
- return null;
+ return false;
}
/**
diff --git a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterOrderVisualization.java b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterOrderVisualization.java
index 1ac55851..0d43875c 100644
--- a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterOrderVisualization.java
+++ b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/ClusterOrderVisualization.java
@@ -35,7 +35,6 @@ import de.lmu.ifi.dbs.elki.result.Result;
import de.lmu.ifi.dbs.elki.result.ResultUtil;
import de.lmu.ifi.dbs.elki.result.optics.ClusterOrderEntry;
import de.lmu.ifi.dbs.elki.result.optics.ClusterOrderResult;
-import de.lmu.ifi.dbs.elki.utilities.iterator.IterableIterator;
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;
@@ -135,7 +134,7 @@ public class ClusterOrderVisualization extends AbstractScatterplotVisualization
public void processNewResult(HierarchicalResult baseResult, Result result) {
Collection<ClusterOrderResult<DoubleDistance>> cos = ResultUtil.filterResults(result, ClusterOrderResult.class);
for(ClusterOrderResult<DoubleDistance> co : cos) {
- IterableIterator<ScatterPlotProjector<?>> ps = ResultUtil.filteredResults(baseResult, ScatterPlotProjector.class);
+ Collection<ScatterPlotProjector<?>> ps = ResultUtil.filterResults(baseResult, ScatterPlotProjector.class);
for(ScatterPlotProjector<?> p : ps) {
final VisualizationTask task = new VisualizationTask(NAME, co, p.getRelation(), this);
task.put(VisualizationTask.META_VISIBLE_DEFAULT, false);
diff --git a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/EMClusterVisualization.java b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/EMClusterVisualization.java
index 6b2f43a3..6070361e 100644
--- a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/EMClusterVisualization.java
+++ b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/EMClusterVisualization.java
@@ -50,7 +50,6 @@ 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.ClassGenericsUtil;
-import de.lmu.ifi.dbs.elki.utilities.iterator.IterableIterator;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.EmptyParameterization;
import de.lmu.ifi.dbs.elki.visualization.VisualizationTask;
import de.lmu.ifi.dbs.elki.visualization.colors.ColorLibrary;
@@ -71,7 +70,7 @@ import de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.AbstractScatter
* @author Robert Rödler
*
* @apiviz.has EMModel oneway - - visualizes
- * @apiviz.uses ConvexHull2D
+ * @apiviz.uses GrahamScanConvexHull2D
*
* @param <NV> Type of the NumberVector being visualized.
*/
@@ -442,7 +441,7 @@ public class EMClusterVisualization<NV extends NumberVector<NV, ?>> extends Abst
// Does the cluster have a model with cluster means?
Clustering<MeanModel<NV>> mcls = findMeanModel(c);
if(mcls != null) {
- IterableIterator<ScatterPlotProjector<?>> ps = ResultUtil.filteredResults(baseResult, ScatterPlotProjector.class);
+ Collection<ScatterPlotProjector<?>> ps = ResultUtil.filterResults(baseResult, ScatterPlotProjector.class);
for(ScatterPlotProjector<?> p : ps) {
final VisualizationTask task = new VisualizationTask(NAME, c, p.getRelation(), this);
task.put(VisualizationTask.META_LEVEL, VisualizationTask.LEVEL_DATA + 3);
diff --git a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/VoronoiVisualization.java b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/VoronoiVisualization.java
index 37f125d4..d6ce810c 100644
--- a/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/VoronoiVisualization.java
+++ b/src/de/lmu/ifi/dbs/elki/visualization/visualizers/scatterplot/cluster/VoronoiVisualization.java
@@ -1,26 +1,27 @@
package de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster;
-/*
- This file is part of ELKI:
+
+/*
+ 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
+
+ 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
+
+ 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
+
+ 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/>.
- */
+
+ 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.ArrayList;
import java.util.Collection;
@@ -32,8 +33,8 @@ 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.EMModel;
import de.lmu.ifi.dbs.elki.data.model.MeanModel;
+import de.lmu.ifi.dbs.elki.data.model.MedoidModel;
import de.lmu.ifi.dbs.elki.data.model.Model;
import de.lmu.ifi.dbs.elki.math.geometry.SweepHullDelaunay2D;
import de.lmu.ifi.dbs.elki.math.geometry.SweepHullDelaunay2D.Triangle;
@@ -42,7 +43,6 @@ 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.DatabaseUtil;
-import de.lmu.ifi.dbs.elki.utilities.iterator.IterableIterator;
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;
@@ -62,13 +62,14 @@ import de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.AbstractScatter
/**
* Visualizer drawing Voronoi cells for k-means clusterings.
*
- * See also: {@link de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd KMeans
- * clustering}
+ * See also: {@link de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd
+ * KMeans clustering}
*
* @author Robert Rödler
* @author Erich Schubert
*
* @apiviz.has MeanModel oneway - - visualizes
+ * @apiviz.has MedoidModel oneway - - visualizes
*/
public class VoronoiVisualization extends AbstractScatterplotVisualization {
/**
@@ -95,7 +96,7 @@ public class VoronoiVisualization extends AbstractScatterplotVisualization {
/**
* The result we work on
*/
- Clustering<MeanModel<? extends NumberVector<?, ?>>> clustering;
+ Clustering<Model> clustering;
/**
* The Voronoi diagram
@@ -123,7 +124,7 @@ public class VoronoiVisualization extends AbstractScatterplotVisualization {
@Override
protected void redraw() {
addCSSClasses(svgp);
- final List<Cluster<MeanModel<? extends NumberVector<?, ?>>>> clusters = clustering.getAllClusters();
+ final List<Cluster<Model>> clusters = clustering.getAllClusters();
if(clusters.size() < 2) {
return;
@@ -133,8 +134,22 @@ public class VoronoiVisualization extends AbstractScatterplotVisualization {
if(clusters.size() == 2) {
ArrayList<double[]> means = new ArrayList<double[]>(clusters.size());
{
- for(Cluster<MeanModel<? extends NumberVector<?, ?>>> clus : clusters) {
- means.add(clus.getModel().getMean().getColumnVector().getArrayRef());
+ for(Cluster<Model> clus : clusters) {
+ Model model = clus.getModel();
+ double[] mean;
+ if(model instanceof MeanModel) {
+ @SuppressWarnings("unchecked")
+ MeanModel<? extends NumberVector<?, ?>> mmodel = (MeanModel<? extends NumberVector<?, ?>>) model;
+ mean = proj.fastProjectDataToRenderSpace(mmodel.getMean());
+ }
+ else if(model instanceof MedoidModel) {
+ MedoidModel mmodel = (MedoidModel) model;
+ mean = proj.fastProjectDataToRenderSpace(rel.get(mmodel.getMedoid()));
+ }
+ else {
+ continue;
+ }
+ means.add(mean);
}
}
if(mode == Mode.VORONOI || mode == Mode.V_AND_D) {
@@ -143,7 +158,7 @@ public class VoronoiVisualization extends AbstractScatterplotVisualization {
layer.appendChild(path);
}
if(mode == Mode.DELAUNAY || mode == Mode.V_AND_D) {
- Element path = new SVGPath(proj.fastProjectDataToRenderSpace(means.get(0))).drawTo(proj.fastProjectDataToRenderSpace(means.get(1))).makeElement(svgp);
+ Element path = new SVGPath(means.get(0)).drawTo(means.get(1)).makeElement(svgp);
SVGUtil.addCSSClass(path, KMEANSBORDER);
layer.appendChild(path);
}
@@ -152,10 +167,23 @@ public class VoronoiVisualization extends AbstractScatterplotVisualization {
ArrayList<Vector> vmeans = new ArrayList<Vector>(clusters.size());
ArrayList<double[]> means = new ArrayList<double[]>(clusters.size());
{
- for(Cluster<MeanModel<? extends NumberVector<?, ?>>> clus : clusters) {
- Vector v = clus.getModel().getMean().getColumnVector();
- vmeans.add(v);
- means.add(v.getArrayRef());
+ for(Cluster<Model> clus : clusters) {
+ Model model = clus.getModel();
+ Vector mean;
+ if(model instanceof MeanModel) {
+ @SuppressWarnings("unchecked")
+ MeanModel<? extends NumberVector<?, ?>> mmodel = (MeanModel<? extends NumberVector<?, ?>>) model;
+ mean = mmodel.getMean().getColumnVector();
+ }
+ else if(model instanceof MedoidModel) {
+ MedoidModel mmodel = (MedoidModel) model;
+ mean = rel.get(mmodel.getMedoid()).getColumnVector();
+ }
+ else {
+ continue;
+ }
+ vmeans.add(mean);
+ means.add(mean.getArrayRef());
}
}
// Compute Delaunay Triangulation
@@ -200,7 +228,7 @@ public class VoronoiVisualization extends AbstractScatterplotVisualization {
* @author Erich Schubert
*
* @apiviz.stereotype factory
- * @apiviz.uses VoronoiVisualisation oneway - - «create»
+ * @apiviz.uses VoronoiVisualization oneway - - «create»
*/
public static class Factory extends AbstractVisFactory {
/**
@@ -239,9 +267,8 @@ public class VoronoiVisualization extends AbstractScatterplotVisualization {
for(Clustering<?> c : clusterings) {
if(c.getAllClusters().size() > 0) {
// Does the cluster have a model with cluster means?
- Clustering<MeanModel<? extends NumberVector<?, ?>>> mcls = findMeanModel(c);
- if(mcls != null) {
- IterableIterator<ScatterPlotProjector<?>> ps = ResultUtil.filteredResults(baseResult, ScatterPlotProjector.class);
+ if(testMeanModel(c)) {
+ Collection<ScatterPlotProjector<?>> ps = ResultUtil.filterResults(baseResult, ScatterPlotProjector.class);
for(ScatterPlotProjector<?> p : ps) {
if(DatabaseUtil.dimensionality(p.getRelation()) == 2) {
final VisualizationTask task = new VisualizationTask(NAME, c, p.getRelation(), this);
@@ -259,15 +286,17 @@ public class VoronoiVisualization extends AbstractScatterplotVisualization {
* 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.
+ * @return true when the clustering has a mean or medoid model.
*/
- @SuppressWarnings("unchecked")
- private static Clustering<MeanModel<? extends NumberVector<?, ?>>> findMeanModel(Clustering<?> c) {
- final Model firstModel = c.getAllClusters().get(0).getModel();
- if(firstModel instanceof MeanModel<?> && !(firstModel instanceof EMModel<?>)) {
- return (Clustering<MeanModel<? extends NumberVector<?, ?>>>) c;
+ private static boolean testMeanModel(Clustering<?> c) {
+ Model firstmodel = c.getAllClusters().get(0).getModel();
+ if(firstmodel instanceof MeanModel<?>) {
+ return true;
+ }
+ if(firstmodel instanceof MedoidModel) {
+ return true;
}
- return null;
+ return false;
}
/**
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-01 12:11:22 +0000