- incorporated Paolo Ciccarese's fixes for VPTrees in Vicino

- moved all clustering stuff in the vicino package space to simplify external collaboration on that code
- added "type" function to the GEL


git-svn-id: http://google-refine.googlecode.com/svn/trunk@292 7d457c2a-affb-35e4-300a-418c747d4874

src/main/java/com/metaweb/gridworks/clustering/knn/kNNClusterer.java

@@ -10,7 +10,6 @@ import java.util.List;
 import java.util.Map;
 import java.util.Properties;
 import java.util.Set;
-import java.util.TreeSet;
 import java.util.Map.Entry;
 
 import org.json.JSONException;
@@ -25,9 +24,9 @@ import com.metaweb.gridworks.clustering.Clusterer;
 import com.metaweb.gridworks.model.Cell;
 import com.metaweb.gridworks.model.Project;
 import com.metaweb.gridworks.model.Row;
-import com.wcohen.ss.api.Token;
-import com.wcohen.ss.tokens.SimpleTokenizer;
 
+import edu.mit.simile.vicino.clustering.NGramClusterer;
+import edu.mit.simile.vicino.clustering.VPTreeClusterer;
 import edu.mit.simile.vicino.distances.BZip2Distance;
 import edu.mit.simile.vicino.distances.Distance;
 import edu.mit.simile.vicino.distances.GZipDistance;
@@ -37,7 +36,6 @@ import edu.mit.simile.vicino.distances.JaroWinklerDistance;
 import edu.mit.simile.vicino.distances.JaroWinklerTFIDFDistance;
 import edu.mit.simile.vicino.distances.LevenshteinDistance;
 import edu.mit.simile.vicino.distances.PPMDistance;
-import edu.mit.simile.vicino.vptree.VPTreeBuilder;
 
 public class kNNClusterer extends Clusterer {
 
@@ -64,13 +62,13 @@ public class kNNClusterer extends Clusterer {
 
         Distance _distance;
         JSONObject _config;
-        VPTreeBuilder _treeBuilder;
+        VPTreeClusterer _clusterer;
         double _radius = 1.0f;
         
         public VPTreeClusteringRowVisitor(Distance d, JSONObject o) {
             _distance = d;
             _config = o;
-            _treeBuilder = new VPTreeBuilder(_distance);
+            _clusterer = new VPTreeClusterer(_distance);
             try {
                 JSONObject params = o.getJSONObject("params");
                 _radius = params.getDouble("radius");
@@ -84,14 +82,14 @@ public class kNNClusterer extends Clusterer {
             if (cell != null && cell.value != null) {
                 Object v = cell.value;
                 String s = (v instanceof String) ? ((String) v) : v.toString();
-                _treeBuilder.populate(s);
+                _clusterer.populate(s);
                 count(s);
             }
             return false;
         }
         
-        public Map<Serializable,Set<Serializable>> getClusters() {
+        public List<Set<Serializable>> getClusters() {
-            return _treeBuilder.getClusters(_radius);
+            return _clusterer.getClusters(_radius);
         }
     }
 
@@ -102,6 +100,7 @@ public class kNNClusterer extends Clusterer {
         double _radius = 1.0d;
         int _blockingNgramSize = 6;
         HashSet<String> _data;
+        NGramClusterer _clusterer;
         
         public BlockingClusteringRowVisitor(Distance d, JSONObject o) {
             _distance = d;
@@ -116,6 +115,7 @@ public class kNNClusterer extends Clusterer {
             } catch (JSONException e) {
                 Gridworks.warn("No parameters found, using defaults");
             }
+            _clusterer = new NGramClusterer(_distance, _blockingNgramSize);
         }
         
         public boolean visit(Project project, int rowIndex, Row row, boolean includeContextual, boolean includeDependent) {
@@ -123,78 +123,17 @@ public class kNNClusterer extends Clusterer {
             if (cell != null && cell.value != null) {
                 Object v = cell.value;
                 String s = (v instanceof String) ? ((String) v) : v.toString().intern();
-                _data.add(s);
+                _clusterer.populate(s);
                 count(s);
             }
             return false;
         }
         
-        public Map<Serializable,Set<Serializable>> getClusters() {
+        public List<Set<Serializable>> getClusters() {
-            NGramTokenizer tokenizer = new NGramTokenizer(_blockingNgramSize,_blockingNgramSize,false,SimpleTokenizer.DEFAULT_TOKENIZER);
+            return _clusterer.getClusters(_radius);
-
-            Map<String,List<String>> blocks = new HashMap<String,List<String>>();
-            
-            for (String s : _data) {
-                Token[] tokens = tokenizer.tokenize(s);
-                for (Token t : tokens) {
-                    String ss = t.getValue();
-                    List<String> l = null;
-                    if (!blocks.containsKey(ss)) {
-                        l = new ArrayList<String>(); 
-                        blocks.put(ss, l);
-                    } else {
-                        l = blocks.get(ss);
-                    }
-                    l.add(s);
-                }
-            }
-
-            int block_count = 0;
-                        
-            Map<Serializable,Set<Serializable>> clusters = new HashMap<Serializable,Set<Serializable>>();
-            
-            for (List<String> list : blocks.values()) {
-                if (list.size() < 2) continue;
-                block_count++;
-                for (String a : list) {
-                    for (String b : list) {
-                        if (a == b) continue;
-                        if (clusters.containsKey(a) && clusters.get(a).contains(b)) continue;
-                        if (clusters.containsKey(b) && clusters.get(b).contains(a)) continue;
-                        double d = _distance.d(a,b);
-                        if (d <= _radius || _radius < 0) {
-                            Set<Serializable> l = null; 
-                            if (!clusters.containsKey(a)) {
-                                l = new TreeSet<Serializable>();
-                                l.add(a);
-                                clusters.put(a, l);
-                            } else {
-                                l = clusters.get(a);
-                            }
-                            l.add(b);
-                        }
-                    }
-                }
-            }
-            
-            Gridworks.log("Calculated " + _distance.getCount() + " distances in " + block_count + " blocks.");
-            _distance.resetCounter();
-            return clusters;
         }
     }
-    
+        
-    public class SizeComparator implements Comparator<Set<Serializable>> {
-        public int compare(Set<Serializable> o1, Set<Serializable> o2) {
-            return o2.size() - o1.size();
-        }
-    }
-
-    public class ValuesComparator implements Comparator<Entry<Serializable,Integer>> {
-        public int compare(Entry<Serializable,Integer> o1, Entry<Serializable,Integer> o2) {
-            return o2.getValue() - o1.getValue();
-        }
-    }
-    
     public void initializeFromJSON(Project project, JSONObject o) throws Exception {
         super.initializeFromJSON(project, o);
         _distance = _distances.get(o.getString("function").toLowerCase());
@@ -206,9 +145,13 @@ public class kNNClusterer extends Clusterer {
         FilteredRows filteredRows = engine.getAllFilteredRows(true);
         filteredRows.accept(_project, visitor);
      
-        Map<Serializable,Set<Serializable>> clusters = visitor.getClusters();
+        _clusters = visitor.getClusters();
-        _clusters = new ArrayList<Set<Serializable>>(clusters.values());
+    }
-        Collections.sort(_clusters, new SizeComparator());
+
+    public class ValuesComparator implements Comparator<Entry<Serializable,Integer>> {
+        public int compare(Entry<Serializable,Integer> o1, Entry<Serializable,Integer> o2) {
+            return o2.getValue() - o1.getValue();
+        }
     }
     
     public void write(JSONWriter writer, Properties options) throws JSONException {

src/main/java/com/metaweb/gridworks/expr/functions/Type.java

@@ -0,0 +1,45 @@
+package com.metaweb.gridworks.expr.functions;
+
+import java.util.Calendar;
+import java.util.Properties;
+
+import org.json.JSONException;
+import org.json.JSONWriter;
+
+import com.metaweb.gridworks.expr.EvalError;
+import com.metaweb.gridworks.gel.ControlFunctionRegistry;
+import com.metaweb.gridworks.gel.Function;
+
+public class Type implements Function {
+
+    public Object call(Properties bindings, Object[] args) {
+        if (args.length == 1) {
+            Object v = args[0];
+            
+            if (v != null) {
+                if (v instanceof String) {
+                    return "string";
+                } else if (v instanceof Calendar) {
+                    return "date";
+                } else if (v instanceof Number) {
+                    return "number";
+                } else if (v.getClass().isArray()) {
+                    return "array";
+                } else {
+                    return v.getClass().getName();
+                }
+            }
+        }
+        return new EvalError(ControlFunctionRegistry.getFunctionName(this) + " expects a parameter");
+    }
+
+    public void write(JSONWriter writer, Properties options)
+        throws JSONException {
+    
+        writer.object();
+        writer.key("description"); writer.value("Returns the type of o");
+        writer.key("params"); writer.value("object o");
+        writer.key("returns"); writer.value("string");
+        writer.endObject();
+    }
+}

src/main/java/com/metaweb/gridworks/gel/ControlFunctionRegistry.java

@@ -11,6 +11,7 @@ import com.metaweb.gridworks.expr.functions.Slice;
 import com.metaweb.gridworks.expr.functions.ToDate;
 import com.metaweb.gridworks.expr.functions.ToNumber;
 import com.metaweb.gridworks.expr.functions.ToString;
+import com.metaweb.gridworks.expr.functions.Type;
 import com.metaweb.gridworks.expr.functions.arrays.Join;
 import com.metaweb.gridworks.expr.functions.arrays.Reverse;
 import com.metaweb.gridworks.expr.functions.arrays.Sort;
@@ -104,6 +105,8 @@ public class ControlFunctionRegistry {
     }
 
     static {
+        registerFunction("type", new Type());
+        
         registerFunction("toString", new ToString());
         registerFunction("toNumber", new ToNumber());
         registerFunction("toDate", new ToDate());

src/main/java/edu/mit/simile/vicino/Cluster.java

@@ -0,0 +1,61 @@
+package edu.mit.simile.vicino;
+
+import java.io.Serializable;
+import java.util.List;
+import java.util.Set;
+
+import edu.mit.simile.vicino.clustering.Clusterer;
+import edu.mit.simile.vicino.clustering.NGramClusterer;
+import edu.mit.simile.vicino.clustering.VPTreeClusterer;
+import edu.mit.simile.vicino.distances.Distance;
+
+public class Cluster extends Operator {
+
+    public static void main(String[] args) throws Exception {
+        (new Cluster()).init(args);
+    }
+    
+    public void init(String[] args) throws Exception {
+        Distance distance = getDistance(args[0]);
+        List<String> strings = getStrings(args[1]);
+        double radius = Double.parseDouble(args[2]);
+        int blocking_size = Integer.parseInt(args[3]);
+
+        long vptree_start = System.currentTimeMillis();
+        Clusterer vptree_clusterer = new VPTreeClusterer(distance);
+        for (String s: strings) {
+            vptree_clusterer.populate(s);
+        }
+        List<Set<Serializable>> vptree_clusters = vptree_clusterer.getClusters(radius);
+        long vptree_elapsed = System.currentTimeMillis() - vptree_start;
+        int vptree_distances = distance.getCount();
+        distance.resetCounter();
+        
+        long ngram_start = System.currentTimeMillis();
+        Clusterer ngram_clusterer = new NGramClusterer(distance,blocking_size);
+        for (String s: strings) {
+            ngram_clusterer.populate(s);
+        }
+        List<Set<Serializable>> ngram_clusters = ngram_clusterer.getClusters(radius);
+        long ngram_elapsed = System.currentTimeMillis() - ngram_start;
+        int ngram_distances = distance.getCount();
+        distance.resetCounter();
+        
+        log("VPTree found " + vptree_clusters.size() + " in " + vptree_elapsed + " ms with " + vptree_distances + " distances\n");
+        for (Set<Serializable> s : vptree_clusters) {
+            for (Serializable ss : s) {
+                log(" " + ss);
+            }
+            log("");
+        }
+
+        log("NGram  found " + ngram_clusters.size() + " in " + ngram_elapsed + " ms with " + ngram_distances + " distances\n");
+        for (Set<Serializable> s : ngram_clusters) {
+            for (Serializable ss : s) {
+                log(" " + ss);
+            }
+            log("");
+        }
+        
+    }
+}

src/main/java/edu/mit/simile/vicino/Clusterer.java

@@ -1,149 +0,0 @@
-package edu.mit.simile.vicino;
-
-import java.io.Serializable;
-import java.util.ArrayList;
-import java.util.Collections;
-import java.util.Comparator;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-import java.util.Set;
-import java.util.TreeSet;
-
-import com.metaweb.gridworks.clustering.knn.NGramTokenizer;
-import com.wcohen.ss.api.Token;
-import com.wcohen.ss.tokens.SimpleTokenizer;
-
-import edu.mit.simile.vicino.distances.Distance;
-import edu.mit.simile.vicino.vptree.VPTreeBuilder;
-
-public class Clusterer extends Operator {
-
-    public class SizeComparator implements Comparator<Set<Serializable>> {
-        public int compare(Set<Serializable> o1, Set<Serializable> o2) {
-            return o2.size() - o1.size();
-        }
-    }
-
-    public static void main(String[] args) throws Exception {
-        (new Clusterer()).init(args);
-    }
-    
-    public void init(String[] args) throws Exception {
-        Distance distance = getDistance(args[0]);
-        List<String> strings = getStrings(args[1]);
-        double radius = Double.parseDouble(args[2]);
-        int blocking_size = Integer.parseInt(args[3]);
-
-        vptree(strings, radius, distance);
-        ngram_blocking(strings, radius, distance, blocking_size);
-    }
-    
-    public void vptree(List<String> strings, double radius, Distance distance) {
-        long start = System.currentTimeMillis();
-        
-        VPTreeBuilder treeBuilder = new VPTreeBuilder(distance);
-        for (String s : strings) {
-            treeBuilder.populate(s);
-        }
-        Map<Serializable,Set<Serializable>> cluster_map = treeBuilder.getClusters(radius);
-        List<Set<Serializable>> clusters = new ArrayList<Set<Serializable>>(cluster_map.values());
-        Collections.sort(clusters, new SizeComparator());
-        
-        System.out.println("Calculated " + distance.getCount() + " distances.");
-        
-        distance.resetCounter();
-        
-        int found = 0;
-        
-        for (Set<Serializable> m : clusters) {
-            if (m.size() > 1) {
-                found++;
-                for (Serializable s : m) {
-                    System.out.println(s);
-                }
-                System.out.println();
-            }
-        }
-        
-        long stop = System.currentTimeMillis();
-        
-        System.out.println("Found " + found + " clusters in " + (stop - start) + " ms");
-    }
-    
-    public void ngram_blocking(List<String> strings, double radius, Distance distance, int blockSize) {
-        long start = System.currentTimeMillis();
-        
-        System.out.println("block size: " + blockSize);
-        
-        NGramTokenizer tokenizer = new NGramTokenizer(blockSize,blockSize,false,SimpleTokenizer.DEFAULT_TOKENIZER);
-
-        Map<String,Set<String>> blocks = new HashMap<String,Set<String>>();
-        
-        for (String s : strings) {
-            Token[] tokens = tokenizer.tokenize(s);
-            for (Token t : tokens) {
-                String ss = t.getValue();
-                Set<String> l = null;
-                if (!blocks.containsKey(ss)) {
-                    l = new TreeSet<String>(); 
-                    blocks.put(ss, l);
-                } else {
-                    l = blocks.get(ss);
-                }
-                l.add(s);
-            }
-        }
-
-        int block_count = 0;
-                    
-        Map<Serializable,Set<Serializable>> cluster_map = new HashMap<Serializable,Set<Serializable>>();
-        
-        for (Set<String> list : blocks.values()) {
-            if (list.size() < 2) continue;
-            block_count++;
-            for (String a : list) {
-                for (String b : list) {
-                    if (a == b) continue;
-                    if (cluster_map.containsKey(a) && cluster_map.get(a).contains(b)) continue;
-                    if (cluster_map.containsKey(b) && cluster_map.get(b).contains(a)) continue;
-                    double d = distance.d(a,b);
-                    if (d <= radius || radius < 0) {
-                        Set<Serializable> l = null; 
-                        if (!cluster_map.containsKey(a)) {
-                            l = new TreeSet<Serializable>();
-                            l.add(a);
-                            cluster_map.put(a, l);
-                        } else {
-                            l = cluster_map.get(a);
-                        }
-                        l.add(b);
-                    }
-                }
-            }
-        }
-        
-        System.out.println("Calculated " + distance.getCount() + " distances in " + block_count + " blocks.");
-        
-        distance.resetCounter();
-
-        List<Set<Serializable>> clusters = new ArrayList<Set<Serializable>>(cluster_map.values());
-        Collections.sort(clusters, new SizeComparator());
-        
-        int found = 0;
-        
-        for (Set<Serializable> m : clusters) {
-            if (m.size() > 1) {
-                found++;
-                for (Serializable s : m) {
-                    System.out.println(s);
-                }
-                System.out.println();
-            }
-        }
-        
-        long stop = System.currentTimeMillis();
-        
-        System.out.println("Found " + found + " clusters in " + (stop - start) + " ms");
-    }
-}

src/main/java/edu/mit/simile/vicino/NGramTokenizer.java

@@ -1,4 +1,4 @@
-package com.metaweb.gridworks.clustering.knn;
+package edu.mit.simile.vicino;
 
 import java.util.ArrayList;
 import java.util.Iterator;

src/main/java/edu/mit/simile/vicino/clustering/Clusterer.java

@@ -0,0 +1,20 @@
+package edu.mit.simile.vicino.clustering;
+
+import java.io.Serializable;
+import java.util.Comparator;
+import java.util.List;
+import java.util.Set;
+
+public abstract class Clusterer {
+
+    public class SizeComparator implements Comparator<Set<Serializable>> {
+        public int compare(Set<Serializable> o1, Set<Serializable> o2) {
+            return o2.size() - o1.size();
+        }
+    }
+    
+    public abstract void populate(String s);
+
+    public abstract List<Set<Serializable>> getClusters(double radius);
+    
+}

src/main/java/edu/mit/simile/vicino/clustering/NGramClusterer.java

@@ -0,0 +1,85 @@
+package edu.mit.simile.vicino.clustering;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+import java.util.TreeSet;
+import java.util.Map.Entry;
+
+import com.wcohen.ss.api.Token;
+import com.wcohen.ss.tokens.SimpleTokenizer;
+
+import edu.mit.simile.vicino.NGramTokenizer;
+import edu.mit.simile.vicino.distances.Distance;
+
+public class NGramClusterer extends Clusterer {
+    
+    NGramTokenizer _tokenizer;
+    Distance _distance;
+    
+    Map<String,Set<String>> blocks = new HashMap<String,Set<String>>();
+    
+    public NGramClusterer(Distance d, int blockSize) {
+        _tokenizer = new NGramTokenizer(blockSize,blockSize,false,SimpleTokenizer.DEFAULT_TOKENIZER);
+        _distance = d;
+    }
+    
+    public void populate(String s) {
+        Token[] tokens = _tokenizer.tokenize(s);
+        for (Token t : tokens) {
+            String ss = t.getValue();
+            Set<String> l = null;
+            if (!blocks.containsKey(ss)) {
+                l = new TreeSet<String>(); 
+                blocks.put(ss, l);
+            } else {
+                l = blocks.get(ss);
+            }
+            l.add(s);
+        }
+    }
+    
+    public List<Set<Serializable>> getClusters(double radius) {
+    
+        Map<Serializable,Set<Serializable>> cluster_map = new HashMap<Serializable,Set<Serializable>>();
+        
+        for (Set<String> set : blocks.values()) {
+            if (set.size() < 2) continue;
+            for (String a : set) {
+                for (String b : set) {
+                    if (a == b) continue;
+                    if (cluster_map.containsKey(a) && cluster_map.get(a).contains(b)) continue;
+                    if (cluster_map.containsKey(b) && cluster_map.get(b).contains(a)) continue;
+                    double d = _distance.d(a,b);
+                    if (d <= radius || radius < 0) {
+                        Set<Serializable> l = null; 
+                        if (!cluster_map.containsKey(a)) {
+                            l = new TreeSet<Serializable>();
+                            l.add(a);
+                            cluster_map.put(a, l);
+                        } else {
+                            l = cluster_map.get(a);
+                        }
+                        l.add(b);
+                    }
+                }
+            }
+        }
+        
+        List<Set<Serializable>> clusters = new ArrayList<Set<Serializable>>();
+        for (Entry<Serializable,Set<Serializable>> e : cluster_map.entrySet()) {
+            Set<Serializable> v = e.getValue();
+            if (v.size() > 1) {
+                clusters.add(v);
+            }
+        }
+        Collections.sort(clusters, new SizeComparator());
+        
+        return clusters;
+    }
+    
+}

src/main/java/edu/mit/simile/vicino/clustering/VPTreeClusterer.java

@@ -0,0 +1,62 @@
+package edu.mit.simile.vicino.clustering;
+
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Set;
+
+import edu.mit.simile.vicino.distances.Distance;
+import edu.mit.simile.vicino.vptree.Node;
+import edu.mit.simile.vicino.vptree.VPTree;
+import edu.mit.simile.vicino.vptree.VPTreeBuilder;
+import edu.mit.simile.vicino.vptree.VPTreeSeeker;
+
+public class VPTreeClusterer extends Clusterer {
+
+    VPTreeBuilder _treeBuilder;
+    Distance _distance;
+    
+    public VPTreeClusterer(Distance d) {
+        _distance = d;
+        _treeBuilder = new VPTreeBuilder(d);
+    }
+    
+    public void populate(String s) {
+        _treeBuilder.populate(s);
+    }
+    
+    public List<Set<Serializable>> getClusters(double radius) {
+        VPTree tree = _treeBuilder.buildVPTree();
+        Set<Node> nodes = _treeBuilder.getNodes();
+        
+        VPTreeSeeker seeker = new VPTreeSeeker(_distance,tree);
+        Map<Serializable,Boolean> flags = new HashMap<Serializable,Boolean>();
+        for (Node n : nodes) {
+            flags.put(n.get(), true);
+        }
+        
+        Map<Serializable,Set<Serializable>> map = new HashMap<Serializable,Set<Serializable>>();
+        for (Node n : nodes) {
+            Serializable s = n.get();
+            if (flags.get(s)) {
+                Set<Serializable> results = seeker.range(s, radius);
+                for (Serializable ss : results) {
+                    flags.put(ss, false);
+                }
+                if (results.size() > 1) {
+                    map.put(s, results);
+                }
+            }
+        }
+        
+        List<Set<Serializable>> clusters = new ArrayList<Set<Serializable>>(map.values());
+        Collections.sort(clusters, new SizeComparator());
+        
+        return clusters;
+    }
+    
+    
+}

src/main/java/edu/mit/simile/vicino/vptree/VPTreeBuilder.java

@@ -2,9 +2,7 @@ package edu.mit.simile.vicino.vptree;
 
 import java.io.Serializable;
 import java.util.Collection;
-import java.util.HashMap;
 import java.util.HashSet;
-import java.util.Map;
 import java.util.Random;
 import java.util.Set;
 
@@ -33,6 +31,10 @@ public class VPTreeBuilder {
         this.distance = distance;
     }
 
+    public Set<Node> getNodes() {
+        return this.nodes;
+    }
+    
     public void populate(Serializable s) {
         nodes.add(new Node(s));
     }
@@ -64,99 +66,58 @@ public class VPTreeBuilder {
         this.nodes.clear();
     }
     
-    public Map<Serializable,Set<Serializable>> getClusters(double radius) {
-        VPTree tree = buildVPTree();
-        
-        if (DEBUG) {
-            System.out.println();
-            printNode(tree.getRoot(),0);
-            System.out.println();
-        }
-        
-        VPTreeSeeker seeker = new VPTreeSeeker(distance,tree);
-        Map<Serializable,Boolean> flags = new HashMap<Serializable,Boolean>();
-        for (Node n : nodes) {
-            flags.put(n.get(), true);
-        }
-        
-        Map<Serializable,Set<Serializable>> map = new HashMap<Serializable,Set<Serializable>>();
-        for (Node n : nodes) {
-            Serializable s = n.get();
-            if (flags.get(s)) {
-                Set<Serializable> results = seeker.range(s, radius);
-                results.add(s);
-                for (Serializable ss : results) {
-                    flags.put(ss, false);
-                }
-                map.put(s, results);
-            }
-        }
-        
-        return map;
-    }
-
-    private void printNode(TNode node, int level) {
-        if (node != null) {
-            if (DEBUG) System.out.println(indent(level++) + node.get() + " [" + node.getMedian() + "]");
-            printNode(node.getLeft(),level);
-            printNode(node.getRight(),level);
-        }
-    }
-    
-    private String indent(int i) {
-        StringBuffer b = new StringBuffer();
-        for (int j = 0; j < i; j++) {
-            b.append(' ');
-        }
-        return b.toString();
-    }
-    
     private TNode makeNode(Node nodes[], int begin, int end) {
 
         int delta = end - begin;
-        int middle = begin + (delta / 2);
 
         if (DEBUG) System.out.println("\ndelta: " + delta);
         
+        if (delta == 0) {
+            TNode vpNode = new TNode(nodes[begin].get());
+            vpNode.setMedian(0);
+            return vpNode;
+        } else if(delta < 0) {
+            return null;
+        }
+                
         TNode vpNode = new TNode(nodes[begin + getRandomIndex(delta)].get());
-
+    
         if (DEBUG) System.out.println("\nvp-node: " + vpNode.get().toString());
-
+        
-        calculateDistances(vpNode, nodes, begin, end);
+        calculateDistances (vpNode , nodes, begin, end);
-        orderDistances(nodes, begin, end);
+        orderDistances (nodes, begin, end);
-
+        
         if (DEBUG) {
-            System.out.println("delta: " + delta);
-            System.out.println("middle: " + middle);
             for (int i = begin; i <= end; i++) {
                 System.out.println(" +-- " + nodes[i].getDistance() + " --> " + nodes[i].get());
             }
         }
         
-        TNode node = new TNode(nodes[middle].get());
+        float median = (float) median(nodes, begin, end);
-        node.setMedian(nodes[middle].getDistance());
+        vpNode.setMedian(median);
-               
-        if (DEBUG) System.out.println("\n-node: " + node.get().toString());
-               
-        if ((middle-1)-begin > 0) {
-            node.setLeft(makeNode(nodes, begin, middle-1));
-        } else if ((middle-1)-begin == 0) {
-           TNode nodeLeft = new TNode(nodes[begin].get());
-           nodeLeft.setMedian(nodes[begin].getDistance());
-           node.setLeft(nodeLeft);
-        }
         
-        if (end-(middle+1) > 0) {
+        int i = 0;
-            node.setRight(makeNode(nodes, middle+1, end));
+        for (i = begin + 1; i < end; i++) {
-        } else if (end-(middle+1) == 0) {
+            if (nodes[i].getDistance() >= median) {
-            TNode nodeRight = new TNode(nodes[end].get());
+                vpNode.setLeft(makeNode(nodes, begin+1, i-1));
-            nodeRight.setMedian(nodes[end].getDistance());
+                break;
-            node.setRight(new TNode(nodes[end].get()));
+            }
         }
-
+        vpNode.setRight(makeNode(nodes, i, end));
-        return node;
+        
+        return vpNode;
     }
-
+    
+    public double median(Node nodes[], int begin, int end) {
+        int middle = (end-begin) / 2;  // subscript of middle element
+        
+        if ((end-begin) % 2 == 0) {
+            return nodes[begin+middle].getDistance();
+        } else {
+           return (nodes[begin+middle].getDistance() + nodes[begin+middle+1].getDistance()) / 2.0d;
+        }
+    }
+    
     private void calculateDistances(TNode pivot, Node nodes[], int begin, int end) {
         for (int i = begin; i <= end; i++) {
             Serializable x = pivot.get();