Changeset 41 for trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseKNNSelection.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/SetWiseKNNSelection.java (modified) (2 diffs)

trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseKNNSelection.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;
 
@@ -10 +24 @@
 
 /**
- * Filter based on the k-nearest neighbor (KNN) algorithm S. Herbold: Training data selection for cross-project defect prediction
+ * Filter based on the k-nearest neighbor (KNN) algorithm S. Herbold: Training data selection for
+ * cross-project defect prediction
+ * 
  * @author Steffen Herbold
  */
 public class SetWiseKNNSelection extends AbstractCharacteristicSelection {
-        
-        /**
-         * number of neighbors selected
-         */
-        private int k = 1;
-        
-        /**
-         * @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 Set<Integer> selected = new HashSet<Integer>();           
-                for( int i=0 ; i<k ; i++ ) {
-                        int closestIndex = getClosest(data);
-                        
-                        selected.add(closestIndex);
-                        data.delete(closestIndex);
-                }
-                
-                for( int i=traindataSet.size()-1; i>=0 ; i-- ) {
-                        if( selected.contains(i) ) {
-                                traindataSet.remove(i);
-                        }
-                }
-        }
-        
-        /**
-         * Helper method that determines the index of the instance with the smallest distance to the first instance (index 0).
-         * @param data data set
-         * @return index of the closest instance
-         */
-        private int getClosest(Instances data) {
-                double closestDistance = Double.MAX_VALUE;
-                int closestIndex = 1;
-                for( int i=1 ; i<data.numInstances() ; i++ ) {
-                        double distance = MathArrays.distance(data.instance(0).toDoubleArray(), data.instance(i).toDoubleArray());
-                        if( distance < closestDistance) {
-                                closestDistance = distance;
-                                closestIndex = i;
-                        }
-                }
-                return closestIndex;
-        }
 
-        /**
-         * Sets the number of neighbors followed by the distributional characteristics, the values are separated by blanks.
-         * @see AbstractCharacteristicSelection#setParameter(String) 
-         */
-        @Override
-        public void setParameter(String parameters) {
-                if( !"".equals(parameters) ) {
-                        final String[] split = parameters.split(" ");
-                        k = Integer.parseInt(split[0]);
-                        String str = "";
-                        for( int i=1 ; i<split.length; i++ ) {
-                                str += split[i];
-                                if( i<split.length-1 )  {
-                                        str += " ";
-                                }
-                        }
-                        super.setParameter(str);
-                }
-        }
+    /**
+     * number of neighbors selected
+     */
+    private int k = 1;
+
+    /**
+     * @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 Set<Integer> selected = new HashSet<Integer>();
+        for (int i = 0; i < k; i++) {
+            int closestIndex = getClosest(data);
+
+            selected.add(closestIndex);
+            data.delete(closestIndex);
+        }
+
+        for (int i = traindataSet.size() - 1; i >= 0; i--) {
+            if (selected.contains(i)) {
+                traindataSet.remove(i);
+            }
+        }
+    }
+
+    /**
+     * Helper method that determines the index of the instance with the smallest distance to the
+     * first instance (index 0).
+     * 
+     * @param data
+     *            data set
+     * @return index of the closest instance
+     */
+    private int getClosest(Instances data) {
+        double closestDistance = Double.MAX_VALUE;
+        int closestIndex = 1;
+        for (int i = 1; i < data.numInstances(); i++) {
+            double distance =
+                MathArrays.distance(data.instance(0).toDoubleArray(), data.instance(i)
+                    .toDoubleArray());
+            if (distance < closestDistance) {
+                closestDistance = distance;
+                closestIndex = i;
+            }
+        }
+        return closestIndex;
+    }
+
+    /**
+     * Sets the number of neighbors followed by the distributional characteristics, the values are
+     * separated by blanks.
+     * 
+     * @see AbstractCharacteristicSelection#setParameter(String)
+     */
+    @Override
+    public void setParameter(String parameters) {
+        if (!"".equals(parameters)) {
+            final String[] split = parameters.split(" ");
+            k = Integer.parseInt(split[0]);
+            String str = "";
+            for (int i = 1; i < split.length; i++) {
+                str += split[i];
+                if (i < split.length - 1) {
+                    str += " ";
+                }
+            }
+            super.setParameter(str);
+        }
+    }
 }