Changeset 135 for trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SynonymOutlierRemoval.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/SynonymOutlierRemoval.java (modified) (5 diffs)

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

@@ -19 +19 @@
 /**
  * <p>
- * Synonym outlier removal after Amasaki et al. (2015). 
+ * Synonym outlier removal after Amasaki et al. (2015).
  * </p>
  * 
@@ -26 +26 @@
 public class SynonymOutlierRemoval implements IPointWiseDataselectionStrategy {
 
-    /* (non-Javadoc)
+    /*
+     * (non-Javadoc)
+     * 
      * @see de.ugoe.cs.cpdp.IParameterizable#setParameter(java.lang.String)
      */
@@ -34 +36 @@
     }
 
-    /* (non-Javadoc)
-     * @see de.ugoe.cs.cpdp.dataselection.IPointWiseDataselectionStrategy#apply(weka.core.Instances, weka.core.Instances)
+    /*
+     * (non-Javadoc)
+     * 
+     * @see de.ugoe.cs.cpdp.dataselection.IPointWiseDataselectionStrategy#apply(weka.core.Instances,
+     * weka.core.Instances)
      */
     @Override
@@ -48 +53 @@
      * </p>
      *
-     * @param traindata data from which the outliers are removed.
+     * @param traindata
+     *            data from which the outliers are removed.
      */
     public void applySynonymRemoval(Instances traindata) {
-        double minDistance[][] = new double[traindata.size()][traindata.numAttributes()-1];
-        double minDistanceAttribute[] = new double[traindata.numAttributes()-1];
+        double minDistance[][] = new double[traindata.size()][traindata.numAttributes() - 1];
+        double minDistanceAttribute[] = new double[traindata.numAttributes() - 1];
         double distance;
-        for( int j=0; j<minDistanceAttribute.length; j++ ) {
+        for (int j = 0; j < minDistanceAttribute.length; j++) {
             minDistanceAttribute[j] = Double.MAX_VALUE;
         }
-        for (int i1 = traindata.size()-1; i1 < traindata.size(); i1++) {
-            int k=0;
+        for (int i1 = traindata.size() - 1; i1 < traindata.size(); i1++) {
+            int k = 0;
             for (int j = 0; j < traindata.numAttributes(); j++) {
-                if( j!=traindata.classIndex() ) {
+                if (j != traindata.classIndex()) {
                     minDistance[i1][k] = Double.MAX_VALUE;
                     for (int i2 = 0; i2 < traindata.size(); i2++) {
                         if (i1 != i2) {
-                            distance = Math.abs(traindata.get(i1).value(j) - traindata.get(i2).value(j));
+                            distance =
+                                Math.abs(traindata.get(i1).value(j) - traindata.get(i2).value(j));
                             if (distance < minDistance[i1][k]) {
                                 minDistance[i1][k] = distance;
                             }
-                            if( distance < minDistanceAttribute[k] ) {
+                            if (distance < minDistanceAttribute[k]) {
                                 minDistanceAttribute[k] = distance;
                             }
@@ -77 +84 @@
             }
         }
-        for( int i=traindata.size()-1; i>=0; i-- ) {
+        for (int i = traindata.size() - 1; i >= 0; i--) {
             boolean hasClosest = false;
-            for( int j=0; !hasClosest && j<traindata.numAttributes(); j++ ) {
-                hasClosest = minDistance[i][j]<=minDistanceAttribute[j];
+            for (int j = 0; !hasClosest && j < traindata.numAttributes(); j++) {
+                hasClosest = minDistance[i][j] <= minDistanceAttribute[j];
             }
-            if( !hasClosest ) {
+            if (!hasClosest) {
                 traindata.delete(i);
             }