Import Upstream version 0.6.5~20141030

1 files changed, 105 insertions, 91 deletions

diff --git a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
index 8e5fa627..adef67ff 100644
--- a/src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
+++ b/src/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/CASH.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.algorithm.clustering.correlation;
  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
@@ -57,8 +57,7 @@ import de.lmu.ifi.dbs.elki.database.query.distance.DistanceQuery;
 import de.lmu.ifi.dbs.elki.database.relation.MaterializedRelation;
 import de.lmu.ifi.dbs.elki.database.relation.Relation;
 import de.lmu.ifi.dbs.elki.datasource.filter.normalization.NonNumericFeaturesException;
-import de.lmu.ifi.dbs.elki.distance.distancefunction.WeightedDistanceFunction;
-import de.lmu.ifi.dbs.elki.distance.distancevalue.DoubleDistance;
+import de.lmu.ifi.dbs.elki.distance.distancefunction.MatrixWeightedDistanceFunction;
 import de.lmu.ifi.dbs.elki.logging.Logging;
 import de.lmu.ifi.dbs.elki.logging.progress.FiniteProgress;
 import de.lmu.ifi.dbs.elki.math.linearalgebra.LinearEquationSystem;
@@ -82,19 +81,20 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.Flag;
 import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
 
 /**
- * Provides the CASH algorithm, an subspace clustering algorithm based on the
- * Hough transform.
+ * The CASH algorithm is a subspace clustering algorithm based on the Hough
+ * transform.
  * 
+ * Reference:
  * <p>
- * Reference: E. Achtert, C. Böhm, J. David, P. Kröger, A. Zimek: Robust
- * clustering in arbitrarily oriented subspaces. <br>
+ * E. Achtert, C. Böhm, J. David, P. Kröger, A. Zimek:<br />
+ * Robust clustering in arbitrarily oriented subspaces. <br>
  * In Proc. 8th SIAM Int. Conf. on Data Mining (SDM'08), Atlanta, GA, 2008
  * </p>
  * 
  * @author Elke Achtert
  * 
  * @apiviz.has CASHInterval
- * @apiviz.uses ParameterizationFunction
+ * @apiviz.has ParameterizationFunction
  * @apiviz.has LinearEquationModel
  * 
  * @param <V> Vector type
@@ -102,8 +102,11 @@ import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.IntParameter;
 // todo elke hierarchy (later)
 @Title("CASH: Robust clustering in arbitrarily oriented subspaces")
 @Description("Subspace clustering algorithm based on the Hough transform.")
-@Reference(authors = "E. Achtert, C. Böhm, J. David, P. Kröger, A. Zimek", title = "Robust clustering in arbitraily oriented subspaces", booktitle = "Proc. 8th SIAM Int. Conf. on Data Mining (SDM'08), Atlanta, GA, 2008", url = "http://www.siam.org/proceedings/datamining/2008/dm08_69_AchtertBoehmDavidKroegerZimek.pdf")
-public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clustering<Model>> implements ClusteringAlgorithm<Clustering<Model>> {
+@Reference(authors = "E. Achtert, C. Böhm, J. David, P. Kröger, A. Zimek", //
+title = "Robust clustering in arbitraily oriented subspaces",//
+booktitle = "Proc. 8th SIAM Int. Conf. on Data Mining (SDM'08), Atlanta, GA, 2008",//
+url = "http://www.siam.org/proceedings/datamining/2008/dm08_69_AchtertBoehmDavidKroegerZimek.pdf")
+public class CASH<V extends NumberVector> extends AbstractAlgorithm<Clustering<Model>> implements ClusteringAlgorithm<Clustering<Model>> {
   /**
    * The logger for this class.
    */
@@ -224,7 +227,7 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
    */
   public Clustering<Model> run(Database database, Relation<V> vrel) {
     this.fulldatabase = preprocess(database, vrel);
-    if (LOG.isVerbose()) {
+    if(LOG.isVerbose()) {
       StringBuilder msg = new StringBuilder();
       msg.append("DB size: ").append(fulldatabase.size());
       msg.append("\nmin Dim: ").append(minDim);
@@ -236,17 +239,16 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
 
     FiniteProgress progress = LOG.isVerbose() ? new FiniteProgress("CASH Clustering", fulldatabase.size(), LOG) : null;
     Clustering<Model> result = doRun(fulldatabase, progress);
-    if (progress != null) {
-      progress.ensureCompleted(LOG);
-    }
+    LOG.ensureCompleted(progress);
 
-    if (LOG.isVerbose()) {
+    if(LOG.isVerbose()) {
       StringBuilder msg = new StringBuilder();
-      for (Cluster<Model> c : result.getAllClusters()) {
-        if (c.getModel() instanceof LinearEquationModel) {
+      for(Cluster<Model> c : result.getAllClusters()) {
+        if(c.getModel() instanceof LinearEquationModel) {
           LinearEquationModel s = (LinearEquationModel) c.getModel();
           msg.append("\n Cluster: Dim: " + s.getLes().subspacedim() + " size: " + c.size());
-        } else {
+        }
+        else {
           msg.append("\n Cluster: " + c.getModel().getClass().getName() + " size: " + c.size());
         }
       }
@@ -268,7 +270,7 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
     MaterializedRelation<ParameterizationFunction> prep = new MaterializedRelation<>(db, type, ids);
 
     // Project
-    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
       ParameterizationFunction f = new ParameterizationFunction(vrel.get(iter));
       prep.set(iter, f);
     }
@@ -294,51 +296,54 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
     ModifiableDBIDs noiseIDs = DBIDUtil.newHashSet(relation.getDBIDs());
     initHeap(heap, relation, dim, noiseIDs);
 
-    if (LOG.isDebugging()) {
+    if(LOG.isDebugging()) {
       StringBuilder msg = new StringBuilder();
       msg.append("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX");
       msg.append("\nXXXX dim ").append(dim);
       msg.append("\nXXXX database.size ").append(relation.size());
       msg.append("\nXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX");
       LOG.debugFine(msg.toString());
-    } else if (LOG.isVerbose()) {
+    }
+    else if(LOG.isVerbose()) {
       StringBuilder msg = new StringBuilder();
       msg.append("XXXX dim ").append(dim).append(" database.size ").append(relation.size());
       LOG.verbose(msg.toString());
     }
 
     // get the ''best'' d-dimensional intervals at max level
-    while (!heap.isEmpty()) {
+    while(!heap.isEmpty()) {
       CASHInterval interval = determineNextIntervalAtMaxLevel(heap);
-      if (LOG.isDebugging()) {
+      if(LOG.isDebugging()) {
         LOG.debugFine("next interval in dim " + dim + ": " + interval);
-      } else if (LOG.isVerbose()) {
+      }
+      else if(LOG.isVerbose()) {
         LOG.verbose("next interval in dim " + dim + ": " + interval);
       }
 
       // only noise left
-      if (interval == null) {
+      if(interval == null) {
         break;
       }
 
       // do a dim-1 dimensional run
       ModifiableDBIDs clusterIDs = DBIDUtil.newHashSet();
-      if (dim > minDim + 1) {
+      if(dim > minDim + 1) {
         ModifiableDBIDs ids;
         Matrix basis_dim_minus_1;
-        if (adjust) {
+        if(adjust) {
           ids = DBIDUtil.newHashSet();
           basis_dim_minus_1 = runDerivator(relation, dim, interval, ids);
-        } else {
+        }
+        else {
           ids = interval.getIDs();
           basis_dim_minus_1 = determineBasis(SpatialUtil.centroid(interval));
         }
 
-        if (ids.size() != 0) {
+        if(ids.size() != 0) {
           MaterializedRelation<ParameterizationFunction> db = buildDB(dim, basis_dim_minus_1, ids, relation);
           // add result of dim-1 to this result
           Clustering<Model> res_dim_minus_1 = doRun(db, progress);
-          for (Cluster<Model> cluster : res_dim_minus_1.getAllClusters()) {
+          for(Cluster<Model> cluster : res_dim_minus_1.getAllClusters()) {
             res.addToplevelCluster(cluster);
             noiseIDs.removeDBIDs(cluster.getIDs());
             clusterIDs.addDBIDs(cluster.getIDs());
@@ -358,30 +363,31 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
 
       // Rebuild heap
       ArrayList<IntegerPriorityObject<CASHInterval>> heapVector = new ArrayList<>(heap.size());
-      for (ObjectHeap.UnsortedIter<IntegerPriorityObject<CASHInterval>> iter = heap.unsortedIter(); iter.valid(); iter.advance()) {
+      for(ObjectHeap.UnsortedIter<IntegerPriorityObject<CASHInterval>> iter = heap.unsortedIter(); iter.valid(); iter.advance()) {
         heapVector.add(iter.get());
       }
       heap.clear();
-      for (IntegerPriorityObject<CASHInterval> pair : heapVector) {
+      for(IntegerPriorityObject<CASHInterval> pair : heapVector) {
         CASHInterval currentInterval = pair.getObject();
         currentInterval.removeIDs(clusterIDs);
-        if (currentInterval.getIDs().size() >= minPts) {
+        if(currentInterval.getIDs().size() >= minPts) {
           heap.add(new IntegerPriorityObject<>(currentInterval.priority(), currentInterval));
         }
       }
 
-      if (progress != null) {
+      if(progress != null) {
         progress.setProcessed(processedIDs.size(), LOG);
       }
     }
 
     // put noise to clusters
-    if (!noiseIDs.isEmpty()) {
-      if (dim == noiseDim) {
+    if(!noiseIDs.isEmpty()) {
+      if(dim == noiseDim) {
         Cluster<Model> c = new Cluster<Model>(noiseIDs, true, ClusterModel.CLUSTER);
         res.addToplevelCluster(c);
         processedIDs.addDBIDs(noiseIDs);
-      } else if (noiseIDs.size() >= minPts) {
+      }
+      else if(noiseIDs.size() >= minPts) {
         LinearEquationSystem les = runDerivator(fulldatabase, dim - 1, noiseIDs);
         Cluster<Model> c = new Cluster<Model>(noiseIDs, true, new LinearEquationModel(les));
         res.addToplevelCluster(c);
@@ -389,22 +395,23 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
       }
     }
 
-    if (LOG.isDebugging()) {
+    if(LOG.isDebugging()) {
       StringBuilder msg = new StringBuilder();
       msg.append("noise fuer dim ").append(dim).append(": ").append(noiseIDs.size());
 
-      for (Cluster<Model> c : res.getAllClusters()) {
-        if (c.getModel() instanceof LinearEquationModel) {
+      for(Cluster<Model> c : res.getAllClusters()) {
+        if(c.getModel() instanceof LinearEquationModel) {
           LinearEquationModel s = (LinearEquationModel) c.getModel();
           msg.append("\n Cluster: Dim: " + s.getLes().subspacedim() + " size: " + c.size());
-        } else {
+        }
+        else {
           msg.append("\n Cluster: " + c.getModel().getClass().getName() + " size: " + c.size());
         }
       }
       LOG.debugFine(msg.toString());
     }
 
-    if (progress != null) {
+    if(progress != null) {
       progress.setProcessed(processedIDs.size(), LOG);
     }
     return res;
@@ -442,14 +449,15 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
     double[] d_mins = new double[numDIntervals];
     double[] d_maxs = new double[numDIntervals];
 
-    if (LOG.isDebugging()) {
+    if(LOG.isDebugging()) {
       StringBuilder msg = new StringBuilder();
       msg.append("d_min ").append(d_min);
       msg.append("\nd_max ").append(d_max);
       msg.append("\nnumDIntervals ").append(numDIntervals);
       msg.append("\ndIntervalSize ").append(dIntervalSize);
       LOG.debugFine(msg.toString());
-    } else if (LOG.isVerbose()) {
+    }
+    else if(LOG.isVerbose()) {
       StringBuilder msg = new StringBuilder();
       msg.append("d_min ").append(d_min);
       msg.append("\nd_max ").append(d_max);
@@ -463,28 +471,30 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
     double[] alphaMax = new double[dim - 1];
     Arrays.fill(alphaMax, Math.PI);
 
-    for (int i = 0; i < numDIntervals; i++) {
-      if (i == 0) {
+    for(int i = 0; i < numDIntervals; i++) {
+      if(i == 0) {
         d_mins[i] = d_min;
-      } else {
+      }
+      else {
         d_mins[i] = d_maxs[i - 1];
       }
 
-      if (i < numDIntervals - 1) {
+      if(i < numDIntervals - 1) {
         d_maxs[i] = d_mins[i] + dIntervalSize;
-      } else {
+      }
+      else {
         d_maxs[i] = d_max - d_mins[i];
       }
 
       HyperBoundingBox alphaInterval = new HyperBoundingBox(alphaMin, alphaMax);
       ModifiableDBIDs intervalIDs = split.determineIDs(ids, alphaInterval, d_mins[i], d_maxs[i]);
-      if (intervalIDs != null && intervalIDs.size() >= minPts) {
+      if(intervalIDs != null && intervalIDs.size() >= minPts) {
         CASHInterval rootInterval = new CASHInterval(alphaMin, alphaMax, split, intervalIDs, -1, 0, d_mins[i], d_maxs[i]);
         heap.add(new IntegerPriorityObject<>(rootInterval.priority(), rootInterval));
       }
     }
 
-    if (LOG.isDebuggingFiner()) {
+    if(LOG.isDebuggingFiner()) {
       StringBuilder msg = new StringBuilder();
       msg.append("heap.size ").append(heap.size());
       LOG.debugFiner(msg.toString());
@@ -509,12 +519,12 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
     proxy.addRelation(prep);
 
     // Project
-    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
       ParameterizationFunction f = project(basis, relation.get(iter));
       prep.set(iter, f);
     }
 
-    if (LOG.isDebugging()) {
+    if(LOG.isDebugging()) {
       LOG.debugFine("db fuer dim " + (dim - 1) + ": " + ids.size());
     }
 
@@ -546,7 +556,7 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
    */
   private Matrix determineBasis(double[] alpha) {
     double[] nn = new double[alpha.length + 1];
-    for (int i = 0; i < nn.length; i++) {
+    for(int i = 0; i < nn.length; i++) {
       double alpha_i = i == alpha.length ? 0 : alpha[i];
       nn[i] = sinusProduct(0, i, alpha) * StrictMath.cos(alpha_i);
     }
@@ -566,7 +576,7 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
    */
   private double sinusProduct(int start, int end, double[] alpha) {
     double result = 1;
-    for (int j = start; j < end; j++) {
+    for(int j = start; j < end; j++) {
       result *= Math.sin(alpha[j]);
     }
     return result;
@@ -582,8 +592,8 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
   private CASHInterval determineNextIntervalAtMaxLevel(ObjectHeap<IntegerPriorityObject<CASHInterval>> heap) {
     CASHInterval next = doDetermineNextIntervalAtMaxLevel(heap);
     // noise path was chosen
-    while (next == null) {
-      if (heap.isEmpty()) {
+    while(next == null) {
+      if(heap.isEmpty()) {
         return null;
       }
       next = doDetermineNextIntervalAtMaxLevel(heap);
@@ -602,45 +612,48 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
   private CASHInterval doDetermineNextIntervalAtMaxLevel(ObjectHeap<IntegerPriorityObject<CASHInterval>> heap) {
     CASHInterval interval = heap.poll().getObject();
     int dim = interval.getDimensionality();
-    while (true) {
+    while(true) {
       // max level is reached
-      if (interval.getLevel() >= maxLevel && interval.getMaxSplitDimension() == (dim - 1)) {
+      if(interval.getLevel() >= maxLevel && interval.getMaxSplitDimension() == (dim - 1)) {
         return interval;
       }
 
-      if (heap.size() % 10000 == 0 && LOG.isVerbose()) {
+      if(heap.size() % 10000 == 0 && LOG.isVerbose()) {
         LOG.verbose("heap size " + heap.size());
       }
 
-      if (heap.size() >= 40000) {
+      if(heap.size() >= 40000) {
         LOG.warning("Heap size > 40.000!!!");
         heap.clear();
         return null;
       }
 
-      if (LOG.isDebuggingFiner()) {
+      if(LOG.isDebuggingFiner()) {
         LOG.debugFiner("split " + interval.toString() + " " + interval.getLevel() + "-" + interval.getMaxSplitDimension());
       }
       interval.split();
 
       // noise
-      if (!interval.hasChildren()) {
+      if(!interval.hasChildren()) {
         return null;
       }
 
       CASHInterval bestInterval;
-      if (interval.getLeftChild() != null && interval.getRightChild() != null) {
+      if(interval.getLeftChild() != null && interval.getRightChild() != null) {
         int comp = interval.getLeftChild().compareTo(interval.getRightChild());
-        if (comp < 0) {
+        if(comp < 0) {
           bestInterval = interval.getRightChild();
           heap.add(new IntegerPriorityObject<>(interval.getLeftChild().priority(), interval.getLeftChild()));
-        } else {
+        }
+        else {
           bestInterval = interval.getLeftChild();
           heap.add(new IntegerPriorityObject<>(interval.getRightChild().priority(), interval.getRightChild()));
         }
-      } else if (interval.getLeftChild() == null) {
+      }
+      else if(interval.getLeftChild() == null) {
         bestInterval = interval.getRightChild();
-      } else {
+      }
+      else {
         bestInterval = interval.getLeftChild();
       }
 
@@ -665,7 +678,7 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
 
     double d_min = Double.POSITIVE_INFINITY;
     double d_max = Double.NEGATIVE_INFINITY;
-    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+    for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
       ParameterizationFunction f = relation.get(iditer);
       HyperBoundingBox minMax = f.determineAlphaMinMax(box);
       double f_min = f.function(SpatialUtil.getMin(minMax));
@@ -694,25 +707,25 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
 
     // set the parameters
     ListParameterization parameters = new ListParameterization();
-    parameters.addParameter(PCAFilteredRunner.PCA_EIGENPAIR_FILTER, FirstNEigenPairFilter.class.getName());
+    parameters.addParameter(PCAFilteredRunner.Parameterizer.PCA_EIGENPAIR_FILTER, FirstNEigenPairFilter.class.getName());
     parameters.addParameter(FirstNEigenPairFilter.EIGENPAIR_FILTER_N, Integer.toString(dim - 1));
-    DependencyDerivator<DoubleVector, DoubleDistance> derivator = null;
-    Class<DependencyDerivator<DoubleVector, DoubleDistance>> cls = ClassGenericsUtil.uglyCastIntoSubclass(DependencyDerivator.class);
+    DependencyDerivator<DoubleVector> derivator = null;
+    Class<DependencyDerivator<DoubleVector>> cls = ClassGenericsUtil.uglyCastIntoSubclass(DependencyDerivator.class);
     derivator = parameters.tryInstantiate(cls);
 
     CorrelationAnalysisSolution<DoubleVector> model = derivator.run(derivatorDB);
 
     Matrix weightMatrix = model.getSimilarityMatrix();
     DoubleVector centroid = new DoubleVector(model.getCentroid());
-    DistanceQuery<DoubleVector, DoubleDistance> df = QueryUtil.getDistanceQuery(derivatorDB, new WeightedDistanceFunction(weightMatrix));
-    DoubleDistance eps = new DoubleDistance(0.25);
+    DistanceQuery<DoubleVector> df = QueryUtil.getDistanceQuery(derivatorDB, new MatrixWeightedDistanceFunction(weightMatrix));
+    double eps = .25;
 
     ids.addDBIDs(interval.getIDs());
     // Search for nearby vectors in original database
-    for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
-      DoubleVector v = new DoubleVector(relation.get(iditer).getColumnVector().getArrayRef());
-      DoubleDistance d = df.distance(v, centroid);
-      if (d.compareTo(eps) < 0) {
+    for(DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
+      DoubleVector v = new DoubleVector(relation.get(iditer).getColumnVector());
+      double d = df.distance(v, centroid);
+      if(d <= eps) {
         ids.add(iditer);
       }
     }
@@ -739,12 +752,12 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
     proxy.addRelation(prep);
 
     // Project
-    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
       DoubleVector v = new DoubleVector(relation.get(iter).getColumnVector().getArrayRef());
       prep.set(iter, v);
     }
 
-    if (LOG.isDebugging()) {
+    if(LOG.isDebugging()) {
       LOG.debugFine("db fuer derivator : " + prep.size());
     }
 
@@ -767,16 +780,17 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
       Database derivatorDB = buildDerivatorDB(relation, ids);
 
       ListParameterization parameters = new ListParameterization();
-      parameters.addParameter(PCAFilteredRunner.PCA_EIGENPAIR_FILTER, FirstNEigenPairFilter.class.getName());
+      parameters.addParameter(PCAFilteredRunner.Parameterizer.PCA_EIGENPAIR_FILTER, FirstNEigenPairFilter.class.getName());
       parameters.addParameter(FirstNEigenPairFilter.EIGENPAIR_FILTER_N, Integer.toString(dimensionality));
-      DependencyDerivator<DoubleVector, DoubleDistance> derivator = null;
-      Class<DependencyDerivator<DoubleVector, DoubleDistance>> cls = ClassGenericsUtil.uglyCastIntoSubclass(DependencyDerivator.class);
+      DependencyDerivator<DoubleVector> derivator = null;
+      Class<DependencyDerivator<DoubleVector>> cls = ClassGenericsUtil.uglyCastIntoSubclass(DependencyDerivator.class);
       derivator = parameters.tryInstantiate(cls);
 
       CorrelationAnalysisSolution<DoubleVector> model = derivator.run(derivatorDB);
       LinearEquationSystem les = model.getNormalizedLinearEquationSystem(null);
       return les;
-    } catch (NonNumericFeaturesException e) {
+    }
+    catch(NonNumericFeaturesException e) {
       throw new IllegalStateException("Error during normalization" + e);
     }
   }
@@ -798,7 +812,7 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
     proxy.addRelation(prep);
 
     // Project
-    for (DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
+    for(DBIDIter iter = ids.iter(); iter.valid(); iter.advance()) {
       DoubleVector v = new DoubleVector(relation.get(iter).getColumnVector().getArrayRef());
       prep.set(iter, v);
     }
@@ -839,32 +853,32 @@ public class CASH<V extends NumberVector<?>> extends AbstractAlgorithm<Clusterin
       super.makeOptions(config);
       IntParameter minptsP = new IntParameter(MINPTS_ID);
       minptsP.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
-      if (config.grab(minptsP)) {
+      if(config.grab(minptsP)) {
         minpts = minptsP.getValue();
       }
       IntParameter maxlevelP = new IntParameter(MAXLEVEL_ID);
       maxlevelP.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
-      if (config.grab(maxlevelP)) {
+      if(config.grab(maxlevelP)) {
         maxlevel = maxlevelP.getValue();
       }
       IntParameter mindimP = new IntParameter(MINDIM_ID, 1);
       mindimP.addConstraint(CommonConstraints.GREATER_EQUAL_ONE_INT);
-      if (config.grab(mindimP)) {
+      if(config.grab(mindimP)) {
         mindim = mindimP.getValue();
       }
       DoubleParameter jitterP = new DoubleParameter(JITTER_ID);
       jitterP.addConstraint(CommonConstraints.GREATER_THAN_ZERO_DOUBLE);
-      if (config.grab(jitterP)) {
+      if(config.grab(jitterP)) {
         jitter = jitterP.getValue();
       }
       Flag adjustF = new Flag(ADJUST_ID);
-      if (config.grab(adjustF)) {
+      if(config.grab(adjustF)) {
         adjust = adjustF.getValue();
       }
     }
 
     @Override
-    protected CASH<NumberVector<?>> makeInstance() {
+    protected CASH<NumberVector> makeInstance() {
       return new CASH<>(minpts, maxlevel, mindim, jitter, adjust);
     }
   }