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Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/algorithm/clustering/trivial/ByModelClustering.java')
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diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/trivial/ByModelClustering.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/trivial/ByModelClustering.java new file mode 100644 index 00000000..cd45cda2 --- /dev/null +++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/trivial/ByModelClustering.java @@ -0,0 +1,163 @@ +package de.lmu.ifi.dbs.elki.algorithm.clustering.trivial; + +/* + 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.HashMap; +import java.util.Map.Entry; +import java.util.regex.Pattern; + +import de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm; +import de.lmu.ifi.dbs.elki.algorithm.clustering.ClusteringAlgorithm; +import de.lmu.ifi.dbs.elki.data.Cluster; +import de.lmu.ifi.dbs.elki.data.Clustering; +import de.lmu.ifi.dbs.elki.data.model.Model; +import de.lmu.ifi.dbs.elki.data.synthetic.bymodel.GeneratorInterface; +import de.lmu.ifi.dbs.elki.data.type.TypeInformation; +import de.lmu.ifi.dbs.elki.data.type.TypeUtil; +import de.lmu.ifi.dbs.elki.database.ids.DBID; +import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil; +import de.lmu.ifi.dbs.elki.database.ids.ModifiableDBIDs; +import de.lmu.ifi.dbs.elki.database.relation.Relation; +import de.lmu.ifi.dbs.elki.logging.Logging; +import de.lmu.ifi.dbs.elki.utilities.documentation.Description; +import de.lmu.ifi.dbs.elki.utilities.documentation.Title; +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.PatternParameter; + +/** + * Pseudo clustering using annotated models. + * + * This "algorithm" puts elements into the same cluster when they agree in their + * model. I.e. it just uses a predefined clustering, and is mostly useful for + * testing and evaluation (e.g. comparing the result of a real algorithm to the + * reference result / golden standard used by the generator). + * + * @author Erich Schubert + * + * @apiviz.uses Model + */ +@Title("Clustering by model") +@Description("Cluster points by a (pre-assigned!) model. For comparing results with a reference clustering.") +public class ByModelClustering extends AbstractAlgorithm<Clustering<Model>> implements ClusteringAlgorithm<Clustering<Model>> { + /** + * The logger for this class. + */ + private static final Logging logger = Logging.getLogger(ByModelClustering.class); + + /** + * Pattern to recognize noise clusters with + */ + public static final OptionID NOISE_ID = OptionID.getOrCreateOptionID("bymodel.noise", "Pattern to recognize noise models by their label."); + + /** + * Holds the value of {@link #NOISE_ID}. + */ + private Pattern noisepattern = null; + + /** + * Constructor. + * + * @param noisepattern Noise pattern + */ + public ByModelClustering(Pattern noisepattern) { + super(); + this.noisepattern = noisepattern; + } + + /** + * Constructor without parameters + */ + public ByModelClustering() { + this(null); + } + + /** + * Run the actual clustering algorithm. + * + * @param relation The data input we use + */ + public Clustering<Model> run(Relation<Model> relation) { + // Build model mapping + HashMap<Model, ModifiableDBIDs> modelMap = new HashMap<Model, ModifiableDBIDs>(); + for(DBID id : relation.iterDBIDs()) { + Model model = relation.get(id); + ModifiableDBIDs modelids = modelMap.get(model); + if(modelids == null) { + modelids = DBIDUtil.newHashSet(); + modelMap.put(model, modelids); + } + modelids.add(id); + } + + Clustering<Model> result = new Clustering<Model>("By Model Clustering", "bymodel-clustering"); + for(Entry<Model, ModifiableDBIDs> entry : modelMap.entrySet()) { + final Model model = entry.getKey(); + final ModifiableDBIDs ids = entry.getValue(); + final String name = (model instanceof GeneratorInterface) ? ((GeneratorInterface) model).getName() : model.toString(); + Cluster<Model> c = new Cluster<Model>(name, ids, model); + if(noisepattern != null && noisepattern.matcher(name).find()) { + c.setNoise(true); + } + result.addCluster(c); + } + return result; + } + + @Override + public TypeInformation[] getInputTypeRestriction() { + return TypeUtil.array(TypeUtil.MODEL); + } + + @Override + protected Logging getLogger() { + return logger; + } + + /** + * Parameterization class. + * + * @author Erich Schubert + * + * @apiviz.exclude + */ + public static class Parameterizer extends AbstractParameterizer { + protected Pattern noisepat; + + @Override + protected void makeOptions(Parameterization config) { + super.makeOptions(config); + PatternParameter noisepatP = new PatternParameter(NOISE_ID, true); + if(config.grab(noisepatP)) { + noisepat = noisepatP.getValue(); + } + } + + @Override + protected ByModelClustering makeInstance() { + return new ByModelClustering(noisepat); + } + } +}
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