diff options
Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/algorithm/DependencyDerivator.java')
| -rw-r--r-- | src/de/lmu/ifi/dbs/elki/algorithm/DependencyDerivator.java | 26 |
1 files changed, 12 insertions, 14 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/DependencyDerivator.java b/src/de/lmu/ifi/dbs/elki/algorithm/DependencyDerivator.java index dca3649e..ee43ecf4 100644 --- a/src/de/lmu/ifi/dbs/elki/algorithm/DependencyDerivator.java +++ b/src/de/lmu/ifi/dbs/elki/algorithm/DependencyDerivator.java @@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.algorithm; This file is part of ELKI: Environment for Developing KDD-Applications Supported by Index-Structures - Copyright (C) 2013 + Copyright (C) 2014 Ludwig-Maximilians-Universität München Lehr- und Forschungseinheit für Datenbanksysteme ELKI Development Team @@ -33,11 +33,10 @@ import de.lmu.ifi.dbs.elki.data.type.TypeUtil; import de.lmu.ifi.dbs.elki.database.Database; import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil; import de.lmu.ifi.dbs.elki.database.ids.DBIDs; -import de.lmu.ifi.dbs.elki.database.ids.distance.KNNList; +import de.lmu.ifi.dbs.elki.database.ids.KNNList; import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery; import de.lmu.ifi.dbs.elki.database.relation.Relation; import de.lmu.ifi.dbs.elki.distance.distancefunction.PrimitiveDistanceFunction; -import de.lmu.ifi.dbs.elki.distance.distancevalue.Distance; import de.lmu.ifi.dbs.elki.logging.Logging; import de.lmu.ifi.dbs.elki.math.linearalgebra.Centroid; import de.lmu.ifi.dbs.elki.math.linearalgebra.LinearEquationSystem; @@ -72,12 +71,11 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter; * * @author Arthur Zimek * @param <V> the type of FeatureVector handled by this Algorithm - * @param <D> the type of Distance used by this Algorithm */ @Title("Dependency Derivator: Deriving numerical inter-dependencies on data") @Description("Derives an equality-system describing dependencies between attributes in a correlation-cluster") @Reference(authors = "E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, A. Zimek", title = "Deriving Quantitative Dependencies for Correlation Clusters", booktitle = "Proc. 12th Int. Conf. on Knowledge Discovery and Data Mining (KDD '06), Philadelphia, PA 2006.", url = "http://dx.doi.org/10.1145/1150402.1150408") -public class DependencyDerivator<V extends NumberVector<?>, D extends Distance<D>> extends AbstractPrimitiveDistanceBasedAlgorithm<V, D, CorrelationAnalysisSolution<V>> { +public class DependencyDerivator<V extends NumberVector> extends AbstractPrimitiveDistanceBasedAlgorithm<V, CorrelationAnalysisSolution<V>> { /** * The logger for this class. */ @@ -112,7 +110,7 @@ public class DependencyDerivator<V extends NumberVector<?>, D extends Distance<D /** * Holds the object performing the pca. */ - private final PCAFilteredRunner<V> pca; + private final PCAFilteredRunner pca; /** * Number format for output of solution. @@ -133,7 +131,7 @@ public class DependencyDerivator<V extends NumberVector<?>, D extends Distance<D * @param sampleSize sample size * @param randomsample flag for random sampling */ - public DependencyDerivator(PrimitiveDistanceFunction<V, D> distanceFunction, NumberFormat nf, PCAFilteredRunner<V> pca, int sampleSize, boolean randomsample) { + public DependencyDerivator(PrimitiveDistanceFunction<V> distanceFunction, NumberFormat nf, PCAFilteredRunner pca, int sampleSize, boolean randomsample) { super(distanceFunction); this.nf = nf; this.pca = pca; @@ -162,8 +160,8 @@ public class DependencyDerivator<V extends NumberVector<?>, D extends Distance<D ids = DBIDUtil.randomSample(relation.getDBIDs(), this.sampleSize, 1L); } else { - DistanceQuery<V, D> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction()); - KNNList<D> queryResults = database.getKNNQuery(distanceQuery, this.sampleSize).getKNNForObject(centroidDV, this.sampleSize); + DistanceQuery<V> distanceQuery = database.getDistanceQuery(relation, getDistanceFunction()); + KNNList queryResults = database.getKNNQuery(distanceQuery, this.sampleSize).getKNNForObject(centroidDV, this.sampleSize); ids = DBIDUtil.newHashSet(queryResults); } } @@ -221,7 +219,7 @@ public class DependencyDerivator<V extends NumberVector<?>, D extends Distance<D log.append("Transposed weak Eigenvectors:\n"); log.append(FormatUtil.format(transposedWeakEigenvectors, nf)).append('\n'); log.append("Eigenvalues:\n"); - log.append(FormatUtil.format(pcares.getEigenvalues(), " , ", 2)); + log.append(FormatUtil.format(pcares.getEigenvalues(), ", ", nf)); LOG.debugFine(log.toString()); } Vector b = transposedWeakEigenvectors.times(centroid); @@ -279,7 +277,7 @@ public class DependencyDerivator<V extends NumberVector<?>, D extends Distance<D * * @apiviz.exclude */ - public static class Parameterizer<V extends NumberVector<?>, D extends Distance<D>> extends AbstractPrimitiveDistanceBasedAlgorithm.Parameterizer<V, D> { + public static class Parameterizer<V extends NumberVector> extends AbstractPrimitiveDistanceBasedAlgorithm.Parameterizer<V> { /** * Output accuracy. */ @@ -298,7 +296,7 @@ public class DependencyDerivator<V extends NumberVector<?>, D extends Distance<D /** * Class to compute PCA with */ - protected PCAFilteredRunner<V> pca = null; + protected PCAFilteredRunner pca = null; @Override protected void makeOptions(Parameterization config) { @@ -321,12 +319,12 @@ public class DependencyDerivator<V extends NumberVector<?>, D extends Distance<D if(config.grab(randomSampleF)) { randomSample = randomSampleF.getValue(); } - Class<PCAFilteredRunner<V>> cls = ClassGenericsUtil.uglyCastIntoSubclass(PCAFilteredRunner.class); + Class<PCAFilteredRunner> cls = ClassGenericsUtil.uglyCastIntoSubclass(PCAFilteredRunner.class); pca = config.tryInstantiate(cls); } @Override - protected DependencyDerivator<V, D> makeInstance() { + protected DependencyDerivator<V> makeInstance() { NumberFormat nf = NumberFormat.getInstance(Locale.US); nf.setMaximumFractionDigits(outputAccuracy); nf.setMinimumFractionDigits(outputAccuracy); |