Changeset 41 for trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseEMClusterSelection.java

Timestamp:

09/24/15 10:59:05 (9 years ago)

Author:

sherbold

Message:

• formatted code and added copyrights

File:

• trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseEMClusterSelection.java (modified) (2 diffs)

trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseEMClusterSelection.java

@@ +1 @@
+// Copyright 2015 Georg-August-Universität Göttingen, Germany
+//
+//   Licensed under the Apache License, Version 2.0 (the "License");
+//   you may not use this file except in compliance with the License.
+//   You may obtain a copy of the License at
+//
+//       http://www.apache.org/licenses/LICENSE-2.0
+//
+//   Unless required by applicable law or agreed to in writing, software
+//   distributed under the License is distributed on an "AS IS" BASIS,
+//   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+//   See the License for the specific language governing permissions and
+//   limitations under the License.
+
 package de.ugoe.cs.cpdp.dataselection;
 
@@ -11 +25 @@
 
 /**
- * Filter based on EM clustering after S. Herbold: Training data selection for cross-project defect prediction
+ * Filter based on EM clustering after S. Herbold: Training data selection for cross-project defect
+ * prediction
+ * 
  * @author Steffen Herbold
  */
 public class SetWiseEMClusterSelection extends AbstractCharacteristicSelection {
-        
-        /**
-         * @see de.ugoe.cs.cpdp.dataselection.SetWiseDataselectionStrategy#apply(weka.core.Instances, org.apache.commons.collections4.list.SetUniqueList)
-         */
-        @Override
-        public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
-                final Instances data = normalizedCharacteristicInstances(testdata, traindataSet); 
-                final Instance targetInstance = data.instance(0);
-                final List<Instance> candidateInstances = new LinkedList<Instance>();
-                for( int i=1; i<data.numInstances(); i++ ) {
-                        candidateInstances.add(data.instance(i));
-                }
-                
-                // cluster and select
-                try {
-                        final EM emeans = new EM();
-                        boolean onlyTarget = true;
-                        int targetCluster;
-                        int maxNumClusters = candidateInstances.size();
-                        do { // while(onlyTarget)
-                                emeans.setMaximumNumberOfClusters(maxNumClusters);
-                                emeans.buildClusterer(data);
-                                                        
-                                targetCluster = emeans.clusterInstance(targetInstance);
-                                
-                                // check if cluster only contains target project
-                                for( int i=0 ; i<candidateInstances.size() && onlyTarget; i++ ) {
-                                        onlyTarget &= !(emeans.clusterInstance(candidateInstances.get(i))==targetCluster);
-                                }
-                                maxNumClusters = emeans.numberOfClusters()-1;
-                        } while(onlyTarget);
-                        
-                        int numRemoved = 0;
-                        for( int i=0 ; i<candidateInstances.size() ; i++ ) {
-                                if( emeans.clusterInstance(candidateInstances.get(i))!=targetCluster ) {
-                                        traindataSet.remove(i-numRemoved++);
-                                }
-                        }
-                } catch(Exception e) {
-                        throw new RuntimeException("error applying setwise EM clustering training data selection", e);
-                }
-        }       
+
+    /**
+     * @see de.ugoe.cs.cpdp.dataselection.SetWiseDataselectionStrategy#apply(weka.core.Instances,
+     *      org.apache.commons.collections4.list.SetUniqueList)
+     */
+    @Override
+    public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
+        final Instances data = normalizedCharacteristicInstances(testdata, traindataSet);
+        final Instance targetInstance = data.instance(0);
+        final List<Instance> candidateInstances = new LinkedList<Instance>();
+        for (int i = 1; i < data.numInstances(); i++) {
+            candidateInstances.add(data.instance(i));
+        }
+
+        // cluster and select
+        try {
+            final EM emeans = new EM();
+            boolean onlyTarget = true;
+            int targetCluster;
+            int maxNumClusters = candidateInstances.size();
+            do { // while(onlyTarget)
+                emeans.setMaximumNumberOfClusters(maxNumClusters);
+                emeans.buildClusterer(data);
+
+                targetCluster = emeans.clusterInstance(targetInstance);
+
+                // check if cluster only contains target project
+                for (int i = 0; i < candidateInstances.size() && onlyTarget; i++) {
+                    onlyTarget &=
+                        !(emeans.clusterInstance(candidateInstances.get(i)) == targetCluster);
+                }
+                maxNumClusters = emeans.numberOfClusters() - 1;
+            }
+            while (onlyTarget);
+
+            int numRemoved = 0;
+            for (int i = 0; i < candidateInstances.size(); i++) {
+                if (emeans.clusterInstance(candidateInstances.get(i)) != targetCluster) {
+                    traindataSet.remove(i - numRemoved++);
+                }
+            }
+        }
+        catch (Exception e) {
+            throw new RuntimeException(
+                                       "error applying setwise EM clustering training data selection",
+                                       e);
+        }
+    }
 }