Changeset 141 for trunk/CrossPare/src

Timestamp:

08/31/16 10:42:32 (8 years ago)

Author:

sherbold

Message:

• code formatting

File:

• trunk/CrossPare/src/de/ugoe/cs/cpdp/training/MetricMatchingTraining.java (modified) (18 diffs)

trunk/CrossPare/src/de/ugoe/cs/cpdp/training/MetricMatchingTraining.java

@@ -46 +46 @@
  * Implements Heterogenous Defect Prediction after Nam et al. 2015.
  * 
- * We extend WekaBaseTraining because we have to Wrap the Classifier to use MetricMatching.
- * This also means we can use any Weka Classifier not just LogisticRegression.
+ * We extend WekaBaseTraining because we have to Wrap the Classifier to use MetricMatching. This
+ * also means we can use any Weka Classifier not just LogisticRegression.
  * 
- * Config: <setwisetestdataawaretrainer name="MetricMatchingTraining" param="Logistic weka.classifiers.functions.Logistic" threshold="0.05" method="spearman"/>
- * Instead of spearman metchod it also takes ks, percentile.
- * Instead of Logistic every other weka classifier can be chosen.
+ * Config: <setwisetestdataawaretrainer name="MetricMatchingTraining" param=
+ * "Logistic weka.classifiers.functions.Logistic" threshold="0.05" method="spearman"/> Instead of
+ * spearman metchod it also takes ks, percentile. Instead of Logistic every other weka classifier
+ * can be chosen.
  * 
- * Future work:
- * implement chisquare test in addition to significance for attribute selection
- * http://commons.apache.org/proper/commons-math/apidocs/org/apache/commons/math3/stat/inference/ChiSquareTest.html
- * use chiSquareTestDataSetsComparison
+ * Future work: implement chisquare test in addition to significance for attribute selection
+ * http://commons.apache.org/proper/commons-math/apidocs/org/apache/commons/math3/stat/inference/
+ * ChiSquareTest.html use chiSquareTestDataSetsComparison
  */
-public class MetricMatchingTraining extends WekaBaseTraining implements ISetWiseTestdataAwareTrainingStrategy {
+public class MetricMatchingTraining extends WekaBaseTraining
+    implements ISetWiseTestdataAwareTrainingStrategy
+{
 
     private MetricMatch mm = null;
     private Classifier classifier = null;
-    
+
     private String method;
     private float threshold;
-    
+
     /**
      * We wrap the classifier here because of classifyInstance with our MetricMatchingClassfier
+     * 
      * @return
      */
@@ -91 +94 @@
     }
 
-        /**
-         * We need the test data instances to do a metric matching, so in this special case we get this data
-         * before evaluation.
-         */
-        @Override
-        public void apply(SetUniqueList<Instances> traindataSet, Instances testdata) {
-            // reset these for each run
-            this.mm = null;
-            this.classifier = null;
-            
-                double score = 0; // matching score to select the best matching training data from the set
-                int num = 0;
-                int biggest_num = 0;
-                MetricMatch tmp;
-                for (Instances traindata : traindataSet) {
-                        num++;
-
-                        tmp = new MetricMatch(traindata, testdata);
-
-                        // metric selection may create error, continue to next training set
-                        try {
-                                tmp.attributeSelection();
-                                tmp.matchAttributes(this.method, this.threshold);
-                        }catch(Exception e) {
-                                e.printStackTrace();
-                                throw new RuntimeException(e);
-                        }
-                        
-                        // we only select the training data from our set with the most matching attributes
-                        if (tmp.getScore() > score && tmp.attributes.size() > 0) {
-                                score = tmp.getScore();
-                                this.mm = tmp;
-                                biggest_num = num;
-                        }
-                }
-                
-                // if we have found a matching instance we use it, log information about the match for additional eval later
-                Instances ilist = null;
-                if (this.mm != null) {
-                ilist = this.mm.getMatchedTrain();
-                Console.traceln(Level.INFO, "[MATCH FOUND] match: ["+biggest_num +"], score: [" + score + "], instances: [" + ilist.size() + "], attributes: [" + this.mm.attributes.size() + "], ilist attrs: ["+ilist.numAttributes()+"]");
-                for(Map.Entry<Integer, Integer> attmatch : this.mm.attributes.entrySet()) {
-                    Console.traceln(Level.INFO, "[MATCHED ATTRIBUTE] source attribute: [" + this.mm.train.attribute(attmatch.getKey()).name() + "], target attribute: [" + this.mm.test.attribute(attmatch.getValue()).name() + "]");
-                }
-            }else {
-                Console.traceln(Level.INFO, "[NO MATCH FOUND]");
-            }
-                
-                // if we have a match we build the MetricMatchingClassifier, if not we fall back to FixClass Classifier
-                try {
-                        if(this.mm != null) {
-                            this.classifier = new MetricMatchingClassifier();
-                            this.classifier.buildClassifier(ilist);
-                            ((MetricMatchingClassifier) this.classifier).setMetricMatching(this.mm);
-                        }else {
-                            this.classifier = new FixClass();
-                            this.classifier.buildClassifier(ilist);  // this is null, but the FixClass Classifier does not use it anyway
-                        }
-                }catch(Exception e) {
-                        e.printStackTrace();
-                        throw new RuntimeException(e);
-                }
-        }
-
-        
-        /**
-         * Encapsulates the classifier configured with WekaBase within but use metric matching.
-         * This allows us to use any Weka classifier with Heterogenous Defect Prediction.
-         */
-        public class MetricMatchingClassifier extends AbstractClassifier {
-
-                private static final long serialVersionUID = -1342172153473770935L;
-                private MetricMatch mm;
-                private Classifier classifier;
-                
-                @Override
-                public void buildClassifier(Instances traindata) throws Exception {
-                        this.classifier = setupClassifier();
-                        this.classifier.buildClassifier(traindata);
-                }
-                
-                /**
-                 * Sets the MetricMatch instance so that we can use matched test data later.
-                 * @param mm
-                 */
-                public void setMetricMatching(MetricMatch mm) {
-                        this.mm = mm;
-                }
-                
-                /**
-                 * Here we can not do the metric matching because we only get one instance.
-                 * Therefore we need a MetricMatch instance beforehand to use here.
-                 */
-                public double classifyInstance(Instance testdata) {
-                    // get a copy of testdata Instance with only the matched attributes
-                        Instance ntest = this.mm.getMatchedTestInstance(testdata);
-
-                        double ret = 0.0;
-                        try {
-                                ret = this.classifier.classifyInstance(ntest);
-                        }catch(Exception e) {
-                                e.printStackTrace();
-                                throw new RuntimeException(e);
-                        }
-                        
-                        return ret;
-                }
-        }
-        
-        /**
-         * Encapsulates one MetricMatching process.
-         * One source (train) matches against one target (test).
-         */
+    /**
+     * We need the test data instances to do a metric matching, so in this special case we get this
+     * data before evaluation.
+     */
+    @Override
+    public void apply(SetUniqueList<Instances> traindataSet, Instances testdata) {
+        // reset these for each run
+        this.mm = null;
+        this.classifier = null;
+
+        double score = 0; // matching score to select the best matching training data from the set
+        int num = 0;
+        int biggest_num = 0;
+        MetricMatch tmp;
+        for (Instances traindata : traindataSet) {
+            num++;
+
+            tmp = new MetricMatch(traindata, testdata);
+
+            // metric selection may create error, continue to next training set
+            try {
+                tmp.attributeSelection();
+                tmp.matchAttributes(this.method, this.threshold);
+            }
+            catch (Exception e) {
+                e.printStackTrace();
+                throw new RuntimeException(e);
+            }
+
+            // we only select the training data from our set with the most matching attributes
+            if (tmp.getScore() > score && tmp.attributes.size() > 0) {
+                score = tmp.getScore();
+                this.mm = tmp;
+                biggest_num = num;
+            }
+        }
+
+        // if we have found a matching instance we use it, log information about the match for
+        // additional eval later
+        Instances ilist = null;
+        if (this.mm != null) {
+            ilist = this.mm.getMatchedTrain();
+            Console.traceln(Level.INFO, "[MATCH FOUND] match: [" + biggest_num + "], score: [" +
+                score + "], instances: [" + ilist.size() + "], attributes: [" +
+                this.mm.attributes.size() + "], ilist attrs: [" + ilist.numAttributes() + "]");
+            for (Map.Entry<Integer, Integer> attmatch : this.mm.attributes.entrySet()) {
+                Console.traceln(Level.INFO, "[MATCHED ATTRIBUTE] source attribute: [" +
+                    this.mm.train.attribute(attmatch.getKey()).name() + "], target attribute: [" +
+                    this.mm.test.attribute(attmatch.getValue()).name() + "]");
+            }
+        }
+        else {
+            Console.traceln(Level.INFO, "[NO MATCH FOUND]");
+        }
+
+        // if we have a match we build the MetricMatchingClassifier, if not we fall back to FixClass
+        // Classifier
+        try {
+            if (this.mm != null) {
+                this.classifier = new MetricMatchingClassifier();
+                this.classifier.buildClassifier(ilist);
+                ((MetricMatchingClassifier) this.classifier).setMetricMatching(this.mm);
+            }
+            else {
+                this.classifier = new FixClass();
+                this.classifier.buildClassifier(ilist); // this is null, but the FixClass Classifier
+                                                        // does not use it anyway
+            }
+        }
+        catch (Exception e) {
+            e.printStackTrace();
+            throw new RuntimeException(e);
+        }
+    }
+
+    /**
+     * Encapsulates the classifier configured with WekaBase within but use metric matching. This
+     * allows us to use any Weka classifier with Heterogenous Defect Prediction.
+     */
+    public class MetricMatchingClassifier extends AbstractClassifier {
+
+        private static final long serialVersionUID = -1342172153473770935L;
+        private MetricMatch mm;
+        private Classifier classifier;
+
+        @Override
+        public void buildClassifier(Instances traindata) throws Exception {
+            this.classifier = setupClassifier();
+            this.classifier.buildClassifier(traindata);
+        }
+
+        /**
+         * Sets the MetricMatch instance so that we can use matched test data later.
+         * 
+         * @param mm
+         */
+        public void setMetricMatching(MetricMatch mm) {
+            this.mm = mm;
+        }
+
+        /**
+         * Here we can not do the metric matching because we only get one instance. Therefore we
+         * need a MetricMatch instance beforehand to use here.
+         */
+        public double classifyInstance(Instance testdata) {
+            // get a copy of testdata Instance with only the matched attributes
+            Instance ntest = this.mm.getMatchedTestInstance(testdata);
+
+            double ret = 0.0;
+            try {
+                ret = this.classifier.classifyInstance(ntest);
+            }
+            catch (Exception e) {
+                e.printStackTrace();
+                throw new RuntimeException(e);
+            }
+
+            return ret;
+        }
+    }
+
+    /**
+     * Encapsulates one MetricMatching process. One source (train) matches against one target
+     * (test).
+     */
     public class MetricMatch {
-            Instances train;
-                Instances test;
-                
-                // used to sum up the matching values of all attributes
-                protected double p_sum = 0;
-                
-                // attribute matching, train -> test
-                HashMap<Integer, Integer> attributes = new HashMap<Integer,Integer>();
-                
-                // used for similarity tests
-                protected ArrayList<double[]> train_values;
-                protected ArrayList<double[]> test_values;
-
-                
-                public MetricMatch(Instances train, Instances test) {
-                        // this is expensive but we need to keep the original data intact
-                    this.train = this.deepCopy(train);
-                        this.test = test; // we do not need a copy here because we do not drop attributes before the matching and after the matching we create a new Instances with only the matched attributes
-                        
-                        // convert metrics of testdata and traindata to later use in similarity tests
-                        this.train_values = new ArrayList<double[]>();
-                        for (int i = 0; i < this.train.numAttributes(); i++) {
-                            if(this.train.classIndex() != i) {
-                                this.train_values.add(this.train.attributeToDoubleArray(i));
-                            }
-                        }
-                        
-                        this.test_values = new ArrayList<double[]>();
-                        for (int i=0; i < this.test.numAttributes(); i++) {
-                            if(this.test.classIndex() != i) {
-                                this.test_values.add(this.test.attributeToDoubleArray(i));
-                            }
-                        }
-                }
-                 
-                /**
-                 * We have a lot of matching possibilities.
-                 * Here we try to determine the best one.
-                 * 
-                 * @return double matching score
-                 */
-            public double getScore() {
-                int as = this.attributes.size();  // # of attributes that were matched
-                
-                // we use thresholding ranking approach for numInstances to influence the matching score
-                int instances = this.train.numInstances();
-                int inst_rank = 0;
-                if(instances > 100) {
-                    inst_rank = 1;
-                }
-                if(instances > 500) {
+        Instances train;
+        Instances test;
+
+        // used to sum up the matching values of all attributes
+        protected double p_sum = 0;
+
+        // attribute matching, train -> test
+        HashMap<Integer, Integer> attributes = new HashMap<Integer, Integer>();
+
+        // used for similarity tests
+        protected ArrayList<double[]> train_values;
+        protected ArrayList<double[]> test_values;
+
+        public MetricMatch(Instances train, Instances test) {
+            // this is expensive but we need to keep the original data intact
+            this.train = this.deepCopy(train);
+            this.test = test; // we do not need a copy here because we do not drop attributes before
+                              // the matching and after the matching we create a new Instances with
+                              // only the matched attributes
+
+            // convert metrics of testdata and traindata to later use in similarity tests
+            this.train_values = new ArrayList<double[]>();
+            for (int i = 0; i < this.train.numAttributes(); i++) {
+                if (this.train.classIndex() != i) {
+                    this.train_values.add(this.train.attributeToDoubleArray(i));
+                }
+            }
+
+            this.test_values = new ArrayList<double[]>();
+            for (int i = 0; i < this.test.numAttributes(); i++) {
+                if (this.test.classIndex() != i) {
+                    this.test_values.add(this.test.attributeToDoubleArray(i));
+                }
+            }
+        }
+
+        /**
+         * We have a lot of matching possibilities. Here we try to determine the best one.
+         * 
+         * @return double matching score
+         */
+        public double getScore() {
+            int as = this.attributes.size(); // # of attributes that were matched
+
+            // we use thresholding ranking approach for numInstances to influence the matching score
+            int instances = this.train.numInstances();
+            int inst_rank = 0;
+            if (instances > 100) {
+                inst_rank = 1;
+            }
+            if (instances > 500) {
                 inst_rank = 2;
             }
-            
-                return this.p_sum + as + inst_rank;
-            }
-                 
-                public HashMap<Integer, Integer> getAttributes() {
-                    return this.attributes;
-                }
-                 
-                public int getNumInstances() {
-                    return this.train_values.get(0).length;
-                }
-
-                
-                /**
-                 * The test instance must be of the same dataset as the train data, otherwise WekaEvaluation will die.
-                 * This means we have to force the dataset of this.train (after matching) and only 
-                 * set the values for the attributes we matched but with the index of the traindata attributes we matched.
-                 * 
-                 * @param test
-                 * @return
-                 */
-                public Instance getMatchedTestInstance(Instance test) {
-            Instance ni = new DenseInstance(this.attributes.size()+1);
-            
+
+            return this.p_sum + as + inst_rank;
+        }
+
+        public HashMap<Integer, Integer> getAttributes() {
+            return this.attributes;
+        }
+
+        public int getNumInstances() {
+            return this.train_values.get(0).length;
+        }
+
+        /**
+         * The test instance must be of the same dataset as the train data, otherwise WekaEvaluation
+         * will die. This means we have to force the dataset of this.train (after matching) and only
+         * set the values for the attributes we matched but with the index of the traindata
+         * attributes we matched.
+         * 
+         * @param test
+         * @return
+         */
+        public Instance getMatchedTestInstance(Instance test) {
+            Instance ni = new DenseInstance(this.attributes.size() + 1);
+
             Instances inst = this.getMatchedTrain();
-            
+
             ni.setDataset(inst);
-            
+
             // assign only the matched attributes to new indexes
             double val;
             int k = 0;
-            for(Map.Entry<Integer, Integer> attmatch : this.attributes.entrySet()) {
+            for (Map.Entry<Integer, Integer> attmatch : this.attributes.entrySet()) {
                 // get value from matched attribute
                 val = test.value(attmatch.getValue());
-                
+
                 // set it to new index, the order of the attributes is the same
                 ni.setValue(k, val);
@@ -300 +315 @@
 
             return ni;
-                }
-
-                
+        }
+
         /**
          * returns a new instances array with the metric matched training data
@@ -308 +322 @@
          * @return instances
          */
-                public Instances getMatchedTrain() {
-                    return this.getMatchedInstances("train", this.train);
-                }
-                 
-        /**
-                 * returns a new instances array with the metric matched test data
-                 * 
-                 * @return instances
-                 */
-                public Instances getMatchedTest() {
-                    return this.getMatchedInstances("test", this.test);
-                }
-                
-                /**
-                 * We could drop unmatched attributes from our instances datasets.
-                 * Alas, that would not be nice for the following postprocessing jobs and would not work at all for evaluation.
-                 * We keep this as a warning for future generations.
-                 * 
-                 * @param name
-                 * @param data
-                 */
-                @SuppressWarnings("unused")
+        public Instances getMatchedTrain() {
+            return this.getMatchedInstances("train", this.train);
+        }
+
+        /**
+         * returns a new instances array with the metric matched test data
+         * 
+         * @return instances
+         */
+        public Instances getMatchedTest() {
+            return this.getMatchedInstances("test", this.test);
+        }
+
+        /**
+         * We could drop unmatched attributes from our instances datasets. Alas, that would not be
+         * nice for the following postprocessing jobs and would not work at all for evaluation. We
+         * keep this as a warning for future generations.
+         * 
+         * @param name
+         * @param data
+         */
+        @SuppressWarnings("unused")
         private void dropUnmatched(String name, Instances data) {
-                    for(int i = 0; i < data.numAttributes(); i++) {
-                        if(data.classIndex() == i) {
-                            continue;
-                        }
-                        
-                        if(name.equals("train") && !this.attributes.containsKey(i)) { 
-                            data.deleteAttributeAt(i);
-                        }
-                        
-                        if(name.equals("test") && !this.attributes.containsValue(i)) {
-                            data.deleteAttributeAt(i);
-                        }
-                    }
-                }
-
-        /**
-         * Deep Copy (well, reasonably deep, not sure about header information of attributes) Weka Instances.
-         * 
-         * @param data Instances
+            for (int i = 0; i < data.numAttributes(); i++) {
+                if (data.classIndex() == i) {
+                    continue;
+                }
+
+                if (name.equals("train") && !this.attributes.containsKey(i)) {
+                    data.deleteAttributeAt(i);
+                }
+
+                if (name.equals("test") && !this.attributes.containsValue(i)) {
+                    data.deleteAttributeAt(i);
+                }
+            }
+        }
+
+        /**
+         * Deep Copy (well, reasonably deep, not sure about header information of attributes) Weka
+         * Instances.
+         * 
+         * @param data
+         *            Instances
          * @return copy of Instances passed
          */
-                private Instances deepCopy(Instances data) {
-                    Instances newInst = new Instances(data);
-                    
-                    newInst.clear();
-                    
-            for (int i=0; i < data.size(); i++) {
+        private Instances deepCopy(Instances data) {
+            Instances newInst = new Instances(data);
+
+            newInst.clear();
+
+            for (int i = 0; i < data.size(); i++) {
                 Instance ni = new DenseInstance(data.numAttributes());
-                for(int j = 0; j < data.numAttributes(); j++) {
+                for (int j = 0; j < data.numAttributes(); j++) {
                     ni.setValue(newInst.attribute(j), data.instance(i).value(data.attribute(j)));
                 }
                 newInst.add(ni);
             }
-            
+
             return newInst;
-                }
-
-        /**
-         * Returns a deep copy of passed Instances data for Train or Test data.
-         * It only keeps attributes that have been matched.
+        }
+
+        /**
+         * Returns a deep copy of passed Instances data for Train or Test data. It only keeps
+         * attributes that have been matched.
          * 
          * @param name
@@ -376 +392 @@
          * @return matched Instances
          */
-                private Instances getMatchedInstances(String name, Instances data) {
-                    ArrayList<Attribute> attrs = new ArrayList<Attribute>();
-            
-                    // bug attr is a string, really!
-                    ArrayList<String> bug = new ArrayList<String>();
+        private Instances getMatchedInstances(String name, Instances data) {
+            ArrayList<Attribute> attrs = new ArrayList<Attribute>();
+
+            // bug attr is a string, really!
+            ArrayList<String> bug = new ArrayList<String>();
             bug.add("0");
             bug.add("1");
-            
+
             // add our matched attributes and last the bug
-                    for(Map.Entry<Integer, Integer> attmatch : this.attributes.entrySet()) {
-                        attrs.add(new Attribute(String.valueOf(attmatch.getValue())));
-                    }
-                    attrs.add(new Attribute("bug", bug));
-                    
-                    // create new instances object of the same size (at least for instances)
-                    Instances newInst = new Instances(name, attrs, data.size());
-                    
-                    // set last as class
-                    newInst.setClassIndex(newInst.numAttributes()-1);
-                    
-                    // copy data for matched attributes, this depends if we return train or test data
-            for (int i=0; i < data.size(); i++) {
-                Instance ni = new DenseInstance(this.attributes.size()+1);
-                
+            for (Map.Entry<Integer, Integer> attmatch : this.attributes.entrySet()) {
+                attrs.add(new Attribute(String.valueOf(attmatch.getValue())));
+            }
+            attrs.add(new Attribute("bug", bug));
+
+            // create new instances object of the same size (at least for instances)
+            Instances newInst = new Instances(name, attrs, data.size());
+
+            // set last as class
+            newInst.setClassIndex(newInst.numAttributes() - 1);
+
+            // copy data for matched attributes, this depends if we return train or test data
+            for (int i = 0; i < data.size(); i++) {
+                Instance ni = new DenseInstance(this.attributes.size() + 1);
+
                 int j = 0; // new indices!
-                for(Map.Entry<Integer, Integer> attmatch : this.attributes.entrySet()) {
-  
+                for (Map.Entry<Integer, Integer> attmatch : this.attributes.entrySet()) {
+
                     // test attribute match
                     int value = attmatch.getValue();
-                    
+
                     // train attribute match
-                    if(name.equals("train")) {
+                    if (name.equals("train")) {
                         value = attmatch.getKey();
                     }
-                    
+
                     ni.setValue(newInst.attribute(j), data.instance(i).value(value));
                     j++;
                 }
-                ni.setValue(ni.numAttributes()-1, data.instance(i).value(data.classAttribute()));
+                ni.setValue(ni.numAttributes() - 1, data.instance(i).value(data.classAttribute()));
                 newInst.add(ni);
             }
 
             return newInst;
-                }
-                 
-                /**
-                 * performs the attribute selection
-                 * we perform attribute significance tests and drop attributes 
-                 * 
-                 * attribute selection is only performed on the source dataset
-                 * we retain the top 15% attributes (if 15% is a float we just use the integer part)
-                 */
-                public void attributeSelection() throws Exception {
-                    
-                    // it is a wrapper, we may decide to implement ChiSquare or other means of selecting attributes
-                        this.attributeSelectionBySignificance(this.train);
-                }
-                
-                private void attributeSelectionBySignificance(Instances which) throws Exception {
-                        // Uses: http://weka.sourceforge.net/doc.packages/probabilisticSignificanceAE/weka/attributeSelection/SignificanceAttributeEval.html
-                        SignificanceAttributeEval et = new SignificanceAttributeEval();
-                        et.buildEvaluator(which);
-                        
-                        // evaluate all training attributes
-                        HashMap<String,Double> saeval = new HashMap<String,Double>();
-                        for(int i=0; i < which.numAttributes(); i++) { 
-                                if(which.classIndex() != i) {
-                                        saeval.put(which.attribute(i).name(), et.evaluateAttribute(i));
-                                }
-                        }
-                        
-                        // sort by significance
-                        HashMap<String, Double> sorted = (HashMap<String, Double>) sortByValues(saeval);
-                        
-                        // Keep the best 15%
-                        double last = ((double)saeval.size() / 100.0) * 15.0;
-                        int drop_first = saeval.size() - (int)last;
-                        
-                        // drop attributes above last
-                        Iterator<Entry<String, Double>> it = sorted.entrySet().iterator();
-                    while (drop_first > 0) {
-                        Map.Entry<String, Double> pair = (Map.Entry<String, Double>)it.next();
-                        if(which.attribute((String)pair.getKey()).index() != which.classIndex()) {
-                                which.deleteAttributeAt(which.attribute((String)pair.getKey()).index());
-                        }
-                        drop_first-=1;
-                    }
-                }
-                
-                /**
-                 * Helper method to sort a hashmap by its values.
-                 * 
-                 * @param map
-                 * @return sorted map
-                 */
-                private HashMap<String, Double> sortByValues(HashMap<String, Double> map) {
-               List<Map.Entry<String, Double>> list = new LinkedList<Map.Entry<String, Double>>(map.entrySet());
-
-               Collections.sort(list, new Comparator<Map.Entry<String, Double>>() {
-                    public int compare(Map.Entry<String, Double> o1, Map.Entry<String, Double> o2) {
-                       return (o1.getValue()).compareTo( o2.getValue() );
-                    }
-               });
-
-               HashMap<String, Double> sortedHashMap = new LinkedHashMap<String, Double>();
-               for(Map.Entry<String, Double> item : list) {
-                   sortedHashMap.put(item.getKey(), item.getValue());
-               }
-               return sortedHashMap;
-                }
-                
-                
+        }
+
+        /**
+         * performs the attribute selection we perform attribute significance tests and drop
+         * attributes
+         * 
+         * attribute selection is only performed on the source dataset we retain the top 15%
+         * attributes (if 15% is a float we just use the integer part)
+         */
+        public void attributeSelection() throws Exception {
+
+            // it is a wrapper, we may decide to implement ChiSquare or other means of selecting
+            // attributes
+            this.attributeSelectionBySignificance(this.train);
+        }
+
+        private void attributeSelectionBySignificance(Instances which) throws Exception {
+            // Uses:
+            // http://weka.sourceforge.net/doc.packages/probabilisticSignificanceAE/weka/attributeSelection/SignificanceAttributeEval.html
+            SignificanceAttributeEval et = new SignificanceAttributeEval();
+            et.buildEvaluator(which);
+
+            // evaluate all training attributes
+            HashMap<String, Double> saeval = new HashMap<String, Double>();
+            for (int i = 0; i < which.numAttributes(); i++) {
+                if (which.classIndex() != i) {
+                    saeval.put(which.attribute(i).name(), et.evaluateAttribute(i));
+                }
+            }
+
+            // sort by significance
+            HashMap<String, Double> sorted = (HashMap<String, Double>) sortByValues(saeval);
+
+            // Keep the best 15%
+            double last = ((double) saeval.size() / 100.0) * 15.0;
+            int drop_first = saeval.size() - (int) last;
+
+            // drop attributes above last
+            Iterator<Entry<String, Double>> it = sorted.entrySet().iterator();
+            while (drop_first > 0) {
+                Map.Entry<String, Double> pair = (Map.Entry<String, Double>) it.next();
+                if (which.attribute((String) pair.getKey()).index() != which.classIndex()) {
+                    which.deleteAttributeAt(which.attribute((String) pair.getKey()).index());
+                }
+                drop_first -= 1;
+            }
+        }
+
+        /**
+         * Helper method to sort a hashmap by its values.
+         * 
+         * @param map
+         * @return sorted map
+         */
+        private HashMap<String, Double> sortByValues(HashMap<String, Double> map) {
+            List<Map.Entry<String, Double>> list =
+                new LinkedList<Map.Entry<String, Double>>(map.entrySet());
+
+            Collections.sort(list, new Comparator<Map.Entry<String, Double>>() {
+                public int compare(Map.Entry<String, Double> o1, Map.Entry<String, Double> o2) {
+                    return (o1.getValue()).compareTo(o2.getValue());
+                }
+            });
+
+            HashMap<String, Double> sortedHashMap = new LinkedHashMap<String, Double>();
+            for (Map.Entry<String, Double> item : list) {
+                sortedHashMap.put(item.getKey(), item.getValue());
+            }
+            return sortedHashMap;
+        }
+
         /**
          * Executes the similarity matching between train and test data.
          * 
-         * After this function is finished we have this.attributes with the correct matching between train and test data attributes.
+         * After this function is finished we have this.attributes with the correct matching between
+         * train and test data attributes.
          * 
          * @param type
          * @param cutoff
          */
-                public void matchAttributes(String type, double cutoff) {
-                    
-                    MWBMatchingAlgorithm mwbm = new MWBMatchingAlgorithm(this.train.numAttributes(), this.test.numAttributes());
-                    
-                    if (type.equals("spearman")) {
-                        this.spearmansRankCorrelation(cutoff, mwbm);
-                    }else if(type.equals("ks")) {
-                        this.kolmogorovSmirnovTest(cutoff, mwbm);
-                    }else if(type.equals("percentile")) {
-                        this.percentiles(cutoff, mwbm);
-                    }else {
-                        throw new RuntimeException("unknown matching method");
-                    }
-                    
-                    // resulting maximal match gets assigned to this.attributes
+        public void matchAttributes(String type, double cutoff) {
+
+            MWBMatchingAlgorithm mwbm =
+                new MWBMatchingAlgorithm(this.train.numAttributes(), this.test.numAttributes());
+
+            if (type.equals("spearman")) {
+                this.spearmansRankCorrelation(cutoff, mwbm);
+            }
+            else if (type.equals("ks")) {
+                this.kolmogorovSmirnovTest(cutoff, mwbm);
+            }
+            else if (type.equals("percentile")) {
+                this.percentiles(cutoff, mwbm);
+            }
+            else {
+                throw new RuntimeException("unknown matching method");
+            }
+
+            // resulting maximal match gets assigned to this.attributes
             int[] result = mwbm.getMatching();
-            for( int i = 0; i < result.length; i++) {
-                
+            for (int i = 0; i < result.length; i++) {
+
                 // -1 means that it is not in the set of maximal matching
-                if( i != -1 && result[i] != -1) {
-                    this.p_sum += mwbm.weights[i][result[i]];  // we add the weight of the returned matching for scoring the complete match later
+                if (i != -1 && result[i] != -1) {
+                    this.p_sum += mwbm.weights[i][result[i]]; // we add the weight of the returned
+                                                              // matching for scoring the complete
+                                                              // match later
                     this.attributes.put(i, result[i]);
                 }
             }
         }
-        
-        
-                /**
-                 * Calculates the Percentiles of the source and target metrics.
-                 * 
-                 * @param cutoff
-                 */
-                public void percentiles(double cutoff, MWBMatchingAlgorithm mwbm) {
-                    for( int i = 0; i < this.train.numAttributes(); i++ ) {
-                for( int j = 0; j < this.test.numAttributes(); j++ ) {
-                    // negative infinity counts as not present, we do this so we don't have to map between attribute indexes in weka
+
+        /**
+         * Calculates the Percentiles of the source and target metrics.
+         * 
+         * @param cutoff
+         */
+        public void percentiles(double cutoff, MWBMatchingAlgorithm mwbm) {
+            for (int i = 0; i < this.train.numAttributes(); i++) {
+                for (int j = 0; j < this.test.numAttributes(); j++) {
+                    // negative infinity counts as not present, we do this so we don't have to map
+                    // between attribute indexes in weka
                     // and the result of the mwbm computation
                     mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);
-                    
-                    // class attributes are not relevant 
+
+                    // class attributes are not relevant
                     if (this.test.classIndex() == j) {
                         continue;
@@ -546 +571 @@
                     double train[] = this.train_values.get(i);
                     double test[] = this.test_values.get(j);
-                    
+
                     Arrays.sort(train);
                     Arrays.sort(test);
-                    
+
                     // percentiles
                     double train_p;
                     double test_p;
                     double score = 0.0;
-                    for( int p=1; p <= 9; p++ ) {
-                        train_p = train[(int)Math.ceil(train.length * (p/100))];
-                        test_p = test[(int)Math.ceil(test.length * (p/100))];
-                    
-                        if( train_p > test_p ) {
+                    for (int p = 1; p <= 9; p++) {
+                        train_p = train[(int) Math.ceil(train.length * (p / 100))];
+                        test_p = test[(int) Math.ceil(test.length * (p / 100))];
+
+                        if (train_p > test_p) {
                             score += test_p / train_p;
-                        }else {
+                        }
+                        else {
                             score += train_p / test_p;
                         }
                     }
-                    
-                    if( score > cutoff ) {
+
+                    if (score > cutoff) {
                         mwbm.setWeight(i, j, score);
                     }
                 }
             }
-                }
-                 
-                /**
-                 * Calculate Spearmans rank correlation coefficient as matching score.
-                 * The number of instances for the source and target needs to be the same so we randomly sample from the bigger one.
-                 * 
-                 * @param cutoff
-                 * @param mwbmatching
-                 */
-                public void spearmansRankCorrelation(double cutoff, MWBMatchingAlgorithm mwbm) {
-                    double p = 0;
-
-                        SpearmansCorrelation t = new SpearmansCorrelation();
-
-                        // size has to be the same so we randomly sample the number of the smaller sample from the big sample
-                        if (this.train.size() > this.test.size()) {
-                            this.sample(this.train, this.test, this.train_values);
-                        }else if (this.test.size() > this.train.size()) {
-                            this.sample(this.test, this.train, this.test_values);
-                        }
-                        
+        }
+
+        /**
+         * Calculate Spearmans rank correlation coefficient as matching score. The number of
+         * instances for the source and target needs to be the same so we randomly sample from the
+         * bigger one.
+         * 
+         * @param cutoff
+         * @param mwbmatching
+         */
+        public void spearmansRankCorrelation(double cutoff, MWBMatchingAlgorithm mwbm) {
+            double p = 0;
+
+            SpearmansCorrelation t = new SpearmansCorrelation();
+
+            // size has to be the same so we randomly sample the number of the smaller sample from
+            // the big sample
+            if (this.train.size() > this.test.size()) {
+                this.sample(this.train, this.test, this.train_values);
+            }
+            else if (this.test.size() > this.train.size()) {
+                this.sample(this.test, this.train, this.test_values);
+            }
+
             // try out possible attribute combinations
-            for (int i=0; i < this.train.numAttributes(); i++) {
-                for (int j=0; j < this.test.numAttributes(); j++) {
-                    // negative infinity counts as not present, we do this so we don't have to map between attribute indexs in weka
+            for (int i = 0; i < this.train.numAttributes(); i++) {
+                for (int j = 0; j < this.test.numAttributes(); j++) {
+                    // negative infinity counts as not present, we do this so we don't have to map
+                    // between attribute indexs in weka
                     // and the result of the mwbm computation
                     mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);
-                    
-                    // class attributes are not relevant 
+
+                    // class attributes are not relevant
                     if (this.test.classIndex() == j) {
                         continue;
@@ -605 +635 @@
                         continue;
                     }
-                    
-                                        p = t.correlation(this.train_values.get(i), this.test_values.get(j));
-                                        if (p > cutoff) {
+
+                    p = t.correlation(this.train_values.get(i), this.test_values.get(j));
+                    if (p > cutoff) {
                         mwbm.setWeight(i, j, p);
-                                        }
-                                }
-                    }
-        }
-
-                /**
-                 * Helper method to sample instances for the Spearman rank correlation coefficient method.
-                 * 
-                 * @param bigger
-                 * @param smaller
-                 * @param values
-                 */
+                    }
+                }
+            }
+        }
+
+        /**
+         * Helper method to sample instances for the Spearman rank correlation coefficient method.
+         * 
+         * @param bigger
+         * @param smaller
+         * @param values
+         */
         private void sample(Instances bigger, Instances smaller, ArrayList<double[]> values) {
             // we want to at keep the indices we select the same
@@ -627 +657 @@
             Random rand = new Random();
             while (indices_to_draw > 0) {
-                
-                int index = rand.nextInt(bigger.size()-1);
-                
+
+                int index = rand.nextInt(bigger.size() - 1);
+
                 if (!indices.contains(index)) {
                     indices.add(index);
@@ -635 +665 @@
                 }
             }
-            
+
             // now reduce our values to the indices we choose above for every attribute
-            for (int att=0; att < bigger.numAttributes()-1; att++) {
-                
+            for (int att = 0; att < bigger.numAttributes() - 1; att++) {
+
                 // get double for the att
                 double[] vals = values.get(att);
                 double[] new_vals = new double[indices.size()];
-                
+
                 int i = 0;
                 for (Iterator<Integer> it = indices.iterator(); it.hasNext();) {
@@ -648 +678 @@
                     i++;
                 }
-                
+
                 values.set(att, new_vals);
             }
-                }
-                
-                
-                /**
-                 * We run the kolmogorov-smirnov test on the data from our test an traindata
-                 * if the p value is above the cutoff we include it in the results 
-                 * p value tends to be 0 when the distributions of the data are significantly different
-                 * but we want them to be the same
-                 * 
-                 * @param cutoff
-                 * @return p-val
-                 */
-                public void kolmogorovSmirnovTest(double cutoff, MWBMatchingAlgorithm mwbm) {
-                        double p = 0;
-            
-                        KolmogorovSmirnovTest t = new KolmogorovSmirnovTest();
-                        for (int i=0; i < this.train.numAttributes(); i++) {
-                                for ( int j=0; j < this.test.numAttributes(); j++) {
-                    // negative infinity counts as not present, we do this so we don't have to map between attribute indexs in weka
+        }
+
+        /**
+         * We run the kolmogorov-smirnov test on the data from our test an traindata if the p value
+         * is above the cutoff we include it in the results p value tends to be 0 when the
+         * distributions of the data are significantly different but we want them to be the same
+         * 
+         * @param cutoff
+         * @return p-val
+         */
+        public void kolmogorovSmirnovTest(double cutoff, MWBMatchingAlgorithm mwbm) {
+            double p = 0;
+
+            KolmogorovSmirnovTest t = new KolmogorovSmirnovTest();
+            for (int i = 0; i < this.train.numAttributes(); i++) {
+                for (int j = 0; j < this.test.numAttributes(); j++) {
+                    // negative infinity counts as not present, we do this so we don't have to map
+                    // between attribute indexs in weka
                     // and the result of the mwbm computation
                     mwbm.setWeight(i, j, Double.NEGATIVE_INFINITY);
-                    
-                    // class attributes are not relevant 
+
+                    // class attributes are not relevant
                     if (this.test.classIndex() == j) {
                         continue;
@@ -680 +709 @@
                         continue;
                     }
-                    
-                    // this may invoke exactP on small sample sizes which will not terminate in all cases
-                                        //p = t.kolmogorovSmirnovTest(this.train_values.get(i), this.test_values.get(j), false);
+
+                    // this may invoke exactP on small sample sizes which will not terminate in all
+                    // cases
+                    // p = t.kolmogorovSmirnovTest(this.train_values.get(i),
+                    // this.test_values.get(j), false);
 
                     // this uses approximateP everytime
-                                        p = t.approximateP(t.kolmogorovSmirnovStatistic(this.train_values.get(i), this.test_values.get(j)), this.train_values.get(i).length, this.test_values.get(j).length);
-                                        if (p > cutoff) {
+                    p = t.approximateP(
+                                       t.kolmogorovSmirnovStatistic(this.train_values.get(i),
+                                                                    this.test_values.get(j)),
+                                       this.train_values.get(i).length,
+                                       this.test_values.get(j).length);
+                    if (p > cutoff) {
                         mwbm.setWeight(i, j, p);
-                                        }
-                                }
-                        }
-            }
+                    }
+                }
+            }
+        }
     }
 
     /*
-     * Copyright (c) 2007, Massachusetts Institute of Technology
-     * Copyright (c) 2005-2006, Regents of the University of California
-     * All rights reserved.
+     * Copyright (c) 2007, Massachusetts Institute of Technology Copyright (c) 2005-2006, Regents of
+     * the University of California All rights reserved.
      * 
-     * Redistribution and use in source and binary forms, with or without
-     * modification, are permitted provided that the following conditions
-     * are met:
+     * Redistribution and use in source and binary forms, with or without modification, are
+     * permitted provided that the following conditions are met:
      *
-     * * Redistributions of source code must retain the above copyright
-     *   notice, this list of conditions and the following disclaimer.
+     * * Redistributions of source code must retain the above copyright notice, this list of
+     * conditions and the following disclaimer.
      *
-     * * Redistributions in binary form must reproduce the above copyright
-     *   notice, this list of conditions and the following disclaimer in
-     *   the documentation and/or other materials provided with the
-     *   distribution.  
+     * * Redistributions in binary form must reproduce the above copyright notice, this list of
+     * conditions and the following disclaimer in the documentation and/or other materials provided
+     * with the distribution.
      *
-     * * Neither the name of the University of California, Berkeley nor
-     *   the names of its contributors may be used to endorse or promote
-     *   products derived from this software without specific prior 
-     *   written permission.
+     * * Neither the name of the University of California, Berkeley nor the names of its
+     * contributors may be used to endorse or promote products derived from this software without
+     * specific prior written permission.
      *
-     * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-     * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-     * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-     * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-     * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-     * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-     * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-     * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
-     * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
-     * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
-     * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
+     * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS
+     * OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
+     * MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+     * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+     * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+     * GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
+     * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+     * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
      * OF THE POSSIBILITY OF SUCH DAMAGE.
      */
 
-
-
     /**
-     * An engine for finding the maximum-weight matching in a complete
-     * bipartite graph.  Suppose we have two sets <i>S</i> and <i>T</i>,
-     * both of size <i>n</i>.  For each <i>i</i> in <i>S</i> and <i>j</i>
-     * in <i>T</i>, we have a weight <i>w<sub>ij</sub></i>.  A perfect
-     * matching <i>X</i> is a subset of <i>S</i> x <i>T</i> such that each
-     * <i>i</i> in <i>S</i> occurs in exactly one element of <i>X</i>, and
-     * each <i>j</i> in <i>T</i> occurs in exactly one element of
-     * <i>X</i>.  Thus, <i>X</i> can be thought of as a one-to-one
-     * function from <i>S</i> to <i>T</i>.  The weight of <i>X</i> is the
-     * sum, over (<i>i</i>, <i>j</i>) in <i>X</i>, of
-     * <i>w<sub>ij</sub></i>.  A BipartiteMatcher takes the number
-     * <i>n</i> and the weights <i>w<sub>ij</sub></i>, and finds a perfect
-     * matching of maximum weight.
+     * An engine for finding the maximum-weight matching in a complete bipartite graph. Suppose we
+     * have two sets <i>S</i> and <i>T</i>, both of size <i>n</i>. For each <i>i</i> in <i>S</i> and
+     * <i>j</i> in <i>T</i>, we have a weight <i>w<sub>ij</sub></i>. A perfect matching <i>X</i> is
+     * a subset of <i>S</i> x <i>T</i> such that each <i>i</i> in <i>S</i> occurs in exactly one
+     * element of <i>X</i>, and each <i>j</i> in <i>T</i> occurs in exactly one element of <i>X</i>.
+     * Thus, <i>X</i> can be thought of as a one-to-one function from <i>S</i> to <i>T</i>. The
+     * weight of <i>X</i> is the sum, over (<i>i</i>, <i>j</i>) in <i>X</i>, of <i>w
+     * <sub>ij</sub></i>. A BipartiteMatcher takes the number <i>n</i> and the weights <i>w
+     * <sub>ij</sub></i>, and finds a perfect matching of maximum weight.
      *
-     * It uses the Hungarian algorithm of Kuhn (1955), as improved and
-     * presented by E. L. Lawler in his book <cite>Combinatorial
-     * Optimization: Networks and Matroids</cite> (Holt, Rinehart and
-     * Winston, 1976, p. 205-206).  The running time is
-     * O(<i>n</i><sup>3</sup>).  The weights can be any finite real
-     * numbers; Lawler's algorithm assumes positive weights, so if
-     * necessary we add a constant <i>c</i> to all the weights before
-     * running the algorithm.  This increases the weight of every perfect
-     * matching by <i>nc</i>, which doesn't change which perfect matchings
+     * It uses the Hungarian algorithm of Kuhn (1955), as improved and presented by E. L. Lawler in
+     * his book <cite>Combinatorial Optimization: Networks and Matroids</cite> (Holt, Rinehart and
+     * Winston, 1976, p. 205-206). The running time is O(<i>n</i><sup>3</sup>). The weights can be
+     * any finite real numbers; Lawler's algorithm assumes positive weights, so if necessary we add
+     * a constant <i>c</i> to all the weights before running the algorithm. This increases the
+     * weight of every perfect matching by <i>nc</i>, which doesn't change which perfect matchings
      * have maximum weight.
      *
-     * If a weight is set to Double.NEGATIVE_INFINITY, then the algorithm will 
-     * behave as if that edge were not in the graph.  If all the edges incident on 
-     * a given node have weight Double.NEGATIVE_INFINITY, then the final result 
-     * will not be a perfect matching, and an exception will be thrown.  
+     * If a weight is set to Double.NEGATIVE_INFINITY, then the algorithm will behave as if that
+     * edge were not in the graph. If all the edges incident on a given node have weight
+     * Double.NEGATIVE_INFINITY, then the final result will not be a perfect matching, and an
+     * exception will be thrown.
      */
-     class MWBMatchingAlgorithm {
-        /**
-         * Creates a BipartiteMatcher without specifying the graph size.  Calling 
-         * any other method before calling reset will yield an 
-         * IllegalStateException.
-         */
-        
-         /**
-         * Tolerance for comparisons to zero, to account for
-         * floating-point imprecision.  We consider a positive number to
-         * be essentially zero if it is strictly less than TOL.
+    class MWBMatchingAlgorithm {
+        /**
+         * Creates a BipartiteMatcher without specifying the graph size. Calling any other method
+         * before calling reset will yield an IllegalStateException.
+         */
+
+        /**
+         * Tolerance for comparisons to zero, to account for floating-point imprecision. We consider
+         * a positive number to be essentially zero if it is strictly less than TOL.
          */
         private static final double TOL = 1e-10;
-        //Number of left side nodes
+        // Number of left side nodes
         int n;
 
-        //Number of right side nodes
+        // Number of right side nodes
         int m;
 
@@ -786 +803 @@
         double maxWeight;
 
-        // If (i, j) is in the mapping, then sMatches[i] = j and tMatches[j] = i.  
-        // If i is unmatched, then sMatches[i] = -1 (and likewise for tMatches). 
+        // If (i, j) is in the mapping, then sMatches[i] = j and tMatches[j] = i.
+        // If i is unmatched, then sMatches[i] = -1 (and likewise for tMatches).
         int[] sMatches;
         int[] tMatches;
@@ -799 +816 @@
         double[] u;
         double[] v;
-        
+
         double[] pi;
 
         List<Integer> eligibleS = new ArrayList<Integer>();
-        List<Integer> eligibleT = new ArrayList<Integer>(); 
-        
-        
+        List<Integer> eligibleT = new ArrayList<Integer>();
+
         public MWBMatchingAlgorithm() {
-        n = -1;
-        m = -1;
-        }
-
-        /**
-         * Creates a BipartiteMatcher and prepares it to run on an n x m graph.  
-         * All the weights are initially set to 1.  
+            n = -1;
+            m = -1;
+        }
+
+        /**
+         * Creates a BipartiteMatcher and prepares it to run on an n x m graph. All the weights are
+         * initially set to 1.
          */
         public MWBMatchingAlgorithm(int n, int m) {
-        reset(n, m);
-        }
-
-        /**
-         * Resets the BipartiteMatcher to run on an n x m graph.  The weights are 
-         * all reset to 1.
+            reset(n, m);
+        }
+
+        /**
+         * Resets the BipartiteMatcher to run on an n x m graph. The weights are all reset to 1.
          */
         private void reset(int n, int m) {
@@ -833 +848 @@
             for (int i = 0; i < n; i++) {
                 for (int j = 0; j < m; j++) {
-                weights[i][j] = 1;
+                    weights[i][j] = 1;
                 }
             }
@@ -846 +861 @@
             v = new double[m];
             pi = new double[m];
-            
-        }
-        /**
-         * Sets the weight w<sub>ij</sub> to the given value w. 
+
+        }
+
+        /**
+         * Sets the weight w<sub>ij</sub> to the given value w.
          *
-         * @throws IllegalArgumentException if i or j is outside the range [0, n).
+         * @throws IllegalArgumentException
+         *             if i or j is outside the range [0, n).
          */
         public void setWeight(int i, int j, double w) {
-        if (n == -1 || m == -1) {
-            throw new IllegalStateException("Graph size not specified.");
-        }
-        if ((i < 0) || (i >= n)) {
-            throw new IllegalArgumentException("i-value out of range: " + i);
-        }
-        if ((j < 0) || (j >= m)) {
-            throw new IllegalArgumentException("j-value out of range: " + j);
-        }
-        if (Double.isNaN(w)) {
-            throw new IllegalArgumentException("Illegal weight: " + w);
-        }
-
-        weights[i][j] = w;
-        if ((w > Double.NEGATIVE_INFINITY) && (w < minWeight)) {
-            minWeight = w;
-        }
-        if (w > maxWeight) {
-            maxWeight = w;
-        }
-        }
-
-        /**
-         * Returns a maximum-weight perfect matching relative to the weights 
-         * specified with setWeight.  The matching is represented as an array arr 
-         * of length n, where arr[i] = j if (i,j) is in the matching.
+            if (n == -1 || m == -1) {
+                throw new IllegalStateException("Graph size not specified.");
+            }
+            if ((i < 0) || (i >= n)) {
+                throw new IllegalArgumentException("i-value out of range: " + i);
+            }
+            if ((j < 0) || (j >= m)) {
+                throw new IllegalArgumentException("j-value out of range: " + j);
+            }
+            if (Double.isNaN(w)) {
+                throw new IllegalArgumentException("Illegal weight: " + w);
+            }
+
+            weights[i][j] = w;
+            if ((w > Double.NEGATIVE_INFINITY) && (w < minWeight)) {
+                minWeight = w;
+            }
+            if (w > maxWeight) {
+                maxWeight = w;
+            }
+        }
+
+        /**
+         * Returns a maximum-weight perfect matching relative to the weights specified with
+         * setWeight. The matching is represented as an array arr of length n, where arr[i] = j if
+         * (i,j) is in the matching.
          */
         public int[] getMatching() {
-        if (n == -1 || m == -1 ) {
-            throw new IllegalStateException("Graph size not specified.");
-        }
-        if (n == 0) {
-            return new int[0];
-        }
-        ensurePositiveWeights();
-
-        // Step 0: Initialization
-        eligibleS.clear();
-        eligibleT.clear();
-        for (Integer i = 0; i < n; i++) {
-            sMatches[i] = -1;
-
-            u[i] = maxWeight; // ambiguous on p. 205 of Lawler, but see p. 202
-
-            // this is really first run of Step 1.0
-            sLabels[i] = EMPTY_LABEL; 
-            eligibleS.add(i);
-        }
-
-        for (int j = 0; j < m; j++) {
-            tMatches[j] = -1;
-
-            v[j] = 0;
-            pi[j] = Double.POSITIVE_INFINITY;
-
-            // this is really first run of Step 1.0
-            tLabels[j] = NO_LABEL;
-        }
-        
-        while (true) {
-            // Augment the matching until we can't augment any more given the 
-            // current settings of the dual variables.  
+            if (n == -1 || m == -1) {
+                throw new IllegalStateException("Graph size not specified.");
+            }
+            if (n == 0) {
+                return new int[0];
+            }
+            ensurePositiveWeights();
+
+            // Step 0: Initialization
+            eligibleS.clear();
+            eligibleT.clear();
+            for (Integer i = 0; i < n; i++) {
+                sMatches[i] = -1;
+
+                u[i] = maxWeight; // ambiguous on p. 205 of Lawler, but see p. 202
+
+                // this is really first run of Step 1.0
+                sLabels[i] = EMPTY_LABEL;
+                eligibleS.add(i);
+            }
+
+            for (int j = 0; j < m; j++) {
+                tMatches[j] = -1;
+
+                v[j] = 0;
+                pi[j] = Double.POSITIVE_INFINITY;
+
+                // this is really first run of Step 1.0
+                tLabels[j] = NO_LABEL;
+            }
+
             while (true) {
-            // Steps 1.1-1.4: Find an augmenting path
-            int lastNode = findAugmentingPath();
-            if (lastNode == -1) {
-                break; // no augmenting path
-            }
-                    
-            // Step 2: Augmentation
-            flipPath(lastNode);
-            for (int i = 0; i < n; i++)
-                sLabels[i] = NO_LABEL;
-            
-            for (int j = 0; j < m; j++) {
-                pi[j] = Double.POSITIVE_INFINITY;
-                tLabels[j] = NO_LABEL;
-            }
-            
-            
-            
-            // This is Step 1.0
-            eligibleS.clear();
+                // Augment the matching until we can't augment any more given the
+                // current settings of the dual variables.
+                while (true) {
+                    // Steps 1.1-1.4: Find an augmenting path
+                    int lastNode = findAugmentingPath();
+                    if (lastNode == -1) {
+                        break; // no augmenting path
+                    }
+
+                    // Step 2: Augmentation
+                    flipPath(lastNode);
+                    for (int i = 0; i < n; i++)
+                        sLabels[i] = NO_LABEL;
+
+                    for (int j = 0; j < m; j++) {
+                        pi[j] = Double.POSITIVE_INFINITY;
+                        tLabels[j] = NO_LABEL;
+                    }
+
+                    // This is Step 1.0
+                    eligibleS.clear();
+                    for (int i = 0; i < n; i++) {
+                        if (sMatches[i] == -1) {
+                            sLabels[i] = EMPTY_LABEL;
+                            eligibleS.add(new Integer(i));
+                        }
+                    }
+
+                    eligibleT.clear();
+                }
+
+                // Step 3: Change the dual variables
+
+                // delta1 = min_i u[i]
+                double delta1 = Double.POSITIVE_INFINITY;
+                for (int i = 0; i < n; i++) {
+                    if (u[i] < delta1) {
+                        delta1 = u[i];
+                    }
+                }
+
+                // delta2 = min_{j : pi[j] > 0} pi[j]
+                double delta2 = Double.POSITIVE_INFINITY;
+                for (int j = 0; j < m; j++) {
+                    if ((pi[j] >= TOL) && (pi[j] < delta2)) {
+                        delta2 = pi[j];
+                    }
+                }
+
+                if (delta1 < delta2) {
+                    // In order to make another pi[j] equal 0, we'd need to
+                    // make some u[i] negative.
+                    break; // we have a maximum-weight matching
+                }
+
+                changeDualVars(delta2);
+            }
+
+            int[] matching = new int[n];
             for (int i = 0; i < n; i++) {
-                if (sMatches[i] == -1) {
-                sLabels[i] = EMPTY_LABEL;
-                eligibleS.add(new Integer(i));
-                }
-            }
-
-            
-            eligibleT.clear();
-            }
-
-            // Step 3: Change the dual variables
-
-            // delta1 = min_i u[i]
-            double delta1 = Double.POSITIVE_INFINITY;
-            for (int i = 0; i < n; i++) {
-            if (u[i] < delta1) {
-                delta1 = u[i];
-            }
-            }
-
-            // delta2 = min_{j : pi[j] > 0} pi[j]
-            double delta2 = Double.POSITIVE_INFINITY;
-            for (int j = 0; j < m; j++) {
-            if ((pi[j] >= TOL) && (pi[j] < delta2)) {
-                delta2 = pi[j];
-            }
-            }
-
-            if (delta1 < delta2) {
-            // In order to make another pi[j] equal 0, we'd need to 
-            // make some u[i] negative.  
-            break; // we have a maximum-weight matching
-            }
-                
-            changeDualVars(delta2);
-        }
-
-        int[] matching = new int[n];
-        for (int i = 0; i < n; i++) {
-            matching[i] = sMatches[i];
-        }
-        return matching;
-        }
-
-        /**
-         * Tries to find an augmenting path containing only edges (i,j) for which 
-         * u[i] + v[j] = weights[i][j].  If it succeeds, returns the index of the 
-         * last node in the path.  Otherwise, returns -1.  In any case, updates 
-         * the labels and pi values.
+                matching[i] = sMatches[i];
+            }
+            return matching;
+        }
+
+        /**
+         * Tries to find an augmenting path containing only edges (i,j) for which u[i] + v[j] =
+         * weights[i][j]. If it succeeds, returns the index of the last node in the path. Otherwise,
+         * returns -1. In any case, updates the labels and pi values.
          */
         int findAugmentingPath() {
-        while ((!eligibleS.isEmpty()) || (!eligibleT.isEmpty())) {
-            if (!eligibleS.isEmpty()) {
-            int i = ((Integer) eligibleS.get(eligibleS.size() - 1)).
-                intValue();
-            eligibleS.remove(eligibleS.size() - 1);
-            for (int j = 0; j < m; j++) {
-                // If pi[j] has already been decreased essentially
-                // to zero, then j is already labeled, and we
-                // can't decrease pi[j] any more.  Omitting the 
-                // pi[j] >= TOL check could lead us to relabel j
-                // unnecessarily, since the diff we compute on the
-                // next line may end up being less than pi[j] due
-                // to floating point imprecision.
-                if ((tMatches[j] != i) && (pi[j] >= TOL)) {
-                double diff = u[i] + v[j] - weights[i][j];
-                if (diff < pi[j]) {
-                    tLabels[j] = i;
-                    pi[j] = diff;
-                    if (pi[j] < TOL) {
-                    eligibleT.add(new Integer(j));
-                    }
-                }
-                }
-            }
-            } else {
-            int j = ((Integer) eligibleT.get(eligibleT.size() - 1)).
-                intValue();
-            eligibleT.remove(eligibleT.size() - 1);
-            if (tMatches[j] == -1) {
-                return j; // we've found an augmenting path
-            } 
-
-            int i = tMatches[j];
-            sLabels[i] = j;
-            eligibleS.add(new Integer(i)); // ok to add twice
-            }
-        }
-
-        return -1;
-        }
-
-        /**
-         * Given an augmenting path ending at lastNode, "flips" the path.  This 
-         * means that an edge on the path is in the matching after the flip if 
-         * and only if it was not in the matching before the flip.  An augmenting 
-         * path connects two unmatched nodes, so the result is still a matching. 
-         */ 
+            while ((!eligibleS.isEmpty()) || (!eligibleT.isEmpty())) {
+                if (!eligibleS.isEmpty()) {
+                    int i = ((Integer) eligibleS.get(eligibleS.size() - 1)).intValue();
+                    eligibleS.remove(eligibleS.size() - 1);
+                    for (int j = 0; j < m; j++) {
+                        // If pi[j] has already been decreased essentially
+                        // to zero, then j is already labeled, and we
+                        // can't decrease pi[j] any more. Omitting the
+                        // pi[j] >= TOL check could lead us to relabel j
+                        // unnecessarily, since the diff we compute on the
+                        // next line may end up being less than pi[j] due
+                        // to floating point imprecision.
+                        if ((tMatches[j] != i) && (pi[j] >= TOL)) {
+                            double diff = u[i] + v[j] - weights[i][j];
+                            if (diff < pi[j]) {
+                                tLabels[j] = i;
+                                pi[j] = diff;
+                                if (pi[j] < TOL) {
+                                    eligibleT.add(new Integer(j));
+                                }
+                            }
+                        }
+                    }
+                }
+                else {
+                    int j = ((Integer) eligibleT.get(eligibleT.size() - 1)).intValue();
+                    eligibleT.remove(eligibleT.size() - 1);
+                    if (tMatches[j] == -1) {
+                        return j; // we've found an augmenting path
+                    }
+
+                    int i = tMatches[j];
+                    sLabels[i] = j;
+                    eligibleS.add(new Integer(i)); // ok to add twice
+                }
+            }
+
+            return -1;
+        }
+
+        /**
+         * Given an augmenting path ending at lastNode, "flips" the path. This means that an edge on
+         * the path is in the matching after the flip if and only if it was not in the matching
+         * before the flip. An augmenting path connects two unmatched nodes, so the result is still
+         * a matching.
+         */
         void flipPath(int lastNode) {
             while (lastNode != EMPTY_LABEL) {
                 int parent = tLabels[lastNode];
-    
-                // Add (parent, lastNode) to matching.  We don't need to 
-                // explicitly remove any edges from the matching because: 
-                //  * We know at this point that there is no i such that 
-                //    sMatches[i] = lastNode.  
-                //  * Although there might be some j such that tMatches[j] =
-                //    parent, that j must be sLabels[parent], and will change 
-                //    tMatches[j] in the next time through this loop.  
+
+                // Add (parent, lastNode) to matching. We don't need to
+                // explicitly remove any edges from the matching because:
+                // * We know at this point that there is no i such that
+                // sMatches[i] = lastNode.
+                // * Although there might be some j such that tMatches[j] =
+                // parent, that j must be sLabels[parent], and will change
+                // tMatches[j] in the next time through this loop.
                 sMatches[parent] = lastNode;
                 tMatches[lastNode] = parent;
-                            
+
                 lastNode = sLabels[parent];
             }
@@ -1057 +1069 @@
             for (int i = 0; i < n; i++) {
                 if (sLabels[i] != NO_LABEL) {
-                u[i] -= delta;
-                }
-            }
-                
+                    u[i] -= delta;
+                }
+            }
+
             for (int j = 0; j < m; j++) {
                 if (pi[j] < TOL) {
-                v[j] += delta;
-                } else if (tLabels[j] != NO_LABEL) {
-                pi[j] -= delta;
-                if (pi[j] < TOL) {
-                    eligibleT.add(new Integer(j));
-                }
-                }
-            }
-        }
-
-        /**
-         * Ensures that all weights are either Double.NEGATIVE_INFINITY, 
-         * or strictly greater than zero.
+                    v[j] += delta;
+                }
+                else if (tLabels[j] != NO_LABEL) {
+                    pi[j] -= delta;
+                    if (pi[j] < TOL) {
+                        eligibleT.add(new Integer(j));
+                    }
+                }
+            }
+        }
+
+        /**
+         * Ensures that all weights are either Double.NEGATIVE_INFINITY, or strictly greater than
+         * zero.
          */
         private void ensurePositiveWeights() {
@@ -1081 +1094 @@
             if (minWeight < TOL) {
                 for (int i = 0; i < n; i++) {
-                for (int j = 0; j < m; j++) {
-                    weights[i][j] = weights[i][j] - minWeight + 1;
-                }
-                }
-    
+                    for (int j = 0; j < m; j++) {
+                        weights[i][j] = weights[i][j] - minWeight + 1;
+                    }
+                }
+
                 maxWeight = maxWeight - minWeight + 1;
                 minWeight = 1;
@@ -1095 +1108 @@
             for (int i = 0; i < n; i++) {
                 for (int j = 0; j < m; j++) {
-                System.out.print(weights[i][j] + " ");
+                    System.out.print(weights[i][j] + " ");
                 }
                 System.out.println("");