Changeset 135 for trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/LACE2.java

Timestamp:

07/18/16 12:26:03 (8 years ago)

Author:

sherbold

Message:

• code documentation and formatting

File:

• trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/LACE2.java (modified) (5 diffs)

trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/LACE2.java

@@ -12 +12 @@
 //   See the License for the specific language governing permissions and
 //   limitations under the License.
-
 
 package de.ugoe.cs.cpdp.dataselection;
@@ -39 +38 @@
 public class LACE2 implements ISetWiseDataselectionStrategy {
 
+    /**
+     * percentage of data selected by the internal CLIFF.
+     */
     private double percentage = 0.10;
-    
+
+    /*
+     * (non-Javadoc)
+     * 
+     * @see de.ugoe.cs.cpdp.IParameterizable#setParameter(java.lang.String)
+     */
     @Override
     public void setParameter(String parameters) {
-        if( parameters!=null && !parameters.isEmpty()) {
+        if (parameters != null && !parameters.isEmpty()) {
             percentage = Double.parseDouble(parameters);
         }
     }
 
+    /*
+     * (non-Javadoc)
+     * 
+     * @see de.ugoe.cs.cpdp.dataselection.ISetWiseDataselectionStrategy#apply(weka.core.Instances,
+     * org.apache.commons.collections4.list.SetUniqueList)
+     */
     @Override
     public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
         Instances selectedData = new Instances(testdata);
         selectedData.clear();
-        
+
         LinkedList<Instances> traindataCopy = new LinkedList<>(traindataSet);
         Collections.shuffle(traindataCopy);
-        
+
         CLIFF cliff = new CLIFF();
         cliff.setParameter(Double.toString(percentage));
@@ -61 +74 @@
         Median median = new Median();
         double minDist = Double.MIN_VALUE;
-        
-        for( Instances traindata : traindataCopy ) {
+
+        for (Instances traindata : traindataCopy) {
             Instances cliffedData = cliff.applyCLIFF(traindata);
-            if( minDist==Double.MIN_VALUE ) {
+            if (minDist == Double.MIN_VALUE) {
                 // determine distance for leader-follower algorithm
                 Instances sample;
-                if( traindata.size()>100 ) {
+                if (traindata.size() > 100) {
                     Resample resample = new Resample();
-                    resample.setSampleSizePercent(100.0/traindata.size()*100.0);
+                    resample.setSampleSizePercent(100.0 / traindata.size() * 100.0);
                     resample.setBiasToUniformClass(0.0);
                     resample.setNoReplacement(true);
@@ -79 +92 @@
                         throw new RuntimeException(e);
                     }
-                } else {
+                }
+                else {
                     sample = new Instances(traindata);
                 }
                 double[] distances = new double[sample.size()];
-                for( int i=0; i<sample.size(); i++ ) {
+                for (int i = 0; i < sample.size(); i++) {
                     Instance unlikeNeighbor = morph.getNearestUnlikeNeighbor(sample.get(i), sample);
-                    distances[i] = MathArrays.distance(WekaUtils.instanceValues(sample.get(i)), WekaUtils.instanceValues(unlikeNeighbor));
+                    distances[i] = MathArrays.distance(WekaUtils.instanceValues(sample.get(i)),
+                                                       WekaUtils.instanceValues(unlikeNeighbor));
                 }
                 minDist = median.evaluate(distances);
             }
-            for( int i=0; i<cliffedData.size(); i++ ) {
-                Instance unlikeNeighbor = morph.getNearestUnlikeNeighbor(cliffedData.get(i), selectedData);
-                if( unlikeNeighbor==null ) {
+            for (int i = 0; i < cliffedData.size(); i++) {
+                Instance unlikeNeighbor =
+                    morph.getNearestUnlikeNeighbor(cliffedData.get(i), selectedData);
+                if (unlikeNeighbor == null) {
                     selectedData.add(cliffedData.get(i));
-                } else {
-                    double distance = MathArrays.distance(WekaUtils.instanceValues(cliffedData.get(i)), WekaUtils.instanceValues(unlikeNeighbor));
-                    if( distance>minDist ) {
+                }
+                else {
+                    double distance =
+                        MathArrays.distance(WekaUtils.instanceValues(cliffedData.get(i)),
+                                            WekaUtils.instanceValues(unlikeNeighbor));
+                    if (distance > minDist) {
                         morph.morphInstance(cliffedData.get(i), cliffedData);
                         selectedData.add(cliffedData.get(i));
@@ -103 +122 @@
         }
     }
-    
+
 }