diff options
Diffstat (limited to 'src/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java')
| -rw-r--r-- | src/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java | 22 |
1 files changed, 12 insertions, 10 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java b/src/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java index 1643d378..76e5ef66 100644 --- a/src/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java +++ b/src/de/lmu/ifi/dbs/elki/algorithm/statistics/EvaluateRankingQuality.java @@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.algorithm.statistics; This file is part of ELKI: Environment for Developing KDD-Applications Supported by Index-Structures - Copyright (C) 2012 + Copyright (C) 2013 Ludwig-Maximilians-Universität München Lehr- und Forschungseinheit für Datenbanksysteme ELKI Development Team @@ -41,22 +41,24 @@ import de.lmu.ifi.dbs.elki.database.Database; import de.lmu.ifi.dbs.elki.database.ids.DBIDIter; import de.lmu.ifi.dbs.elki.database.ids.DBIDUtil; import de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDPair; +import de.lmu.ifi.dbs.elki.database.ids.distance.KNNList; import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery; import de.lmu.ifi.dbs.elki.database.query.knn.KNNQuery; import de.lmu.ifi.dbs.elki.database.relation.Relation; import de.lmu.ifi.dbs.elki.distance.distancefunction.DistanceFunction; -import de.lmu.ifi.dbs.elki.distance.distanceresultlist.KNNResult; import de.lmu.ifi.dbs.elki.distance.distancevalue.NumberDistance; import de.lmu.ifi.dbs.elki.evaluation.roc.ROC; import de.lmu.ifi.dbs.elki.logging.Logging; import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress; import de.lmu.ifi.dbs.elki.math.MathUtil; +import de.lmu.ifi.dbs.elki.math.MeanVariance; import de.lmu.ifi.dbs.elki.math.linearalgebra.CovarianceMatrix; import de.lmu.ifi.dbs.elki.math.linearalgebra.Matrix; import de.lmu.ifi.dbs.elki.math.linearalgebra.Vector; import de.lmu.ifi.dbs.elki.result.CollectionResult; import de.lmu.ifi.dbs.elki.result.HistogramResult; import de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.MeanVarianceStaticHistogram; +import de.lmu.ifi.dbs.elki.utilities.datastructures.histogram.ObjHistogram; 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.OptionID; @@ -126,8 +128,8 @@ public class EvaluateRankingQuality<V extends NumberVector<?>, D extends NumberD Collection<Cluster<Model>> split = (new ByLabelOrAllInOneClustering()).run(database).getAllClusters(); // Compute cluster averages and covariance matrix - HashMap<Cluster<?>, Vector> averages = new HashMap<Cluster<?>, Vector>(split.size()); - HashMap<Cluster<?>, Matrix> covmats = new HashMap<Cluster<?>, Matrix>(split.size()); + HashMap<Cluster<?>, Vector> averages = new HashMap<>(split.size()); + HashMap<Cluster<?>, Matrix> covmats = new HashMap<>(split.size()); for (Cluster<?> clus : split) { CovarianceMatrix covmat = CovarianceMatrix.make(relation, clus.getIDs()); averages.put(clus, covmat.getMeanVector()); @@ -143,7 +145,7 @@ public class EvaluateRankingQuality<V extends NumberVector<?>, D extends NumberD // sort neighbors for (Cluster<?> clus : split) { - ArrayList<DoubleDBIDPair> cmem = new ArrayList<DoubleDBIDPair>(clus.size()); + ArrayList<DoubleDBIDPair> cmem = new ArrayList<>(clus.size()); Vector av = averages.get(clus); Matrix covm = covmats.get(clus); @@ -154,7 +156,7 @@ public class EvaluateRankingQuality<V extends NumberVector<?>, D extends NumberD Collections.sort(cmem); for (int ind = 0; ind < cmem.size(); ind++) { - KNNResult<D> knn = knnQuery.getKNNForDBID(cmem.get(ind), relation.size()); + KNNList<D> knn = knnQuery.getKNNForDBID(cmem.get(ind), relation.size()); double result = ROC.computeROCAUCDistanceResult(relation.size(), clus, knn); hist.put(((double) ind) / clus.size(), result); @@ -170,12 +172,12 @@ public class EvaluateRankingQuality<V extends NumberVector<?>, D extends NumberD // Collections.sort(results); // Transform Histogram into a Double Vector array. - Collection<DoubleVector> res = new ArrayList<DoubleVector>(relation.size()); - for (MeanVarianceStaticHistogram.Iter iter = hist.iter(); iter.valid(); iter.advance()) { + Collection<DoubleVector> res = new ArrayList<>(relation.size()); + for (ObjHistogram.Iter<MeanVariance> iter = hist.iter(); iter.valid(); iter.advance()) { DoubleVector row = new DoubleVector(new double[] { iter.getCenter(), iter.getValue().getCount(), iter.getValue().getMean(), iter.getValue().getSampleVariance() }); res.add(row); } - return new HistogramResult<DoubleVector>("Ranking Quality Histogram", "ranking-histogram", res); + return new HistogramResult<>("Ranking Quality Histogram", "ranking-histogram", res); } @Override @@ -213,7 +215,7 @@ public class EvaluateRankingQuality<V extends NumberVector<?>, D extends NumberD @Override protected EvaluateRankingQuality<V, D> makeInstance() { - return new EvaluateRankingQuality<V, D>(distanceFunction, numbins); + return new EvaluateRankingQuality<>(distanceFunction, numbins); } } } |