Waikato · automenta · Jun 1, 2015 · Jun 1, 2015 · Jun 1, 2015 · Jun 1, 2015
diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,3 @@
+*.iml
+.idea/
+
diff --git a/moa/pom.xml b/moa/pom.xml
@@ -4,7 +4,7 @@
   <parent>
     <groupId>nz.ac.waikato.cms.moa</groupId>
     <artifactId>moa-pom</artifactId>
-    <version>2015.05-SNAPSHOT</version>
+    <version>2012.09-SNAPSHOT</version>
   </parent>
 
   <artifactId>moa</artifactId>
@@ -62,7 +62,7 @@
     <dependency>
       <groupId>com.googlecode.java-diff-utils</groupId>
       <artifactId>diffutils</artifactId>
-      <version>1.2.1</version>
+      <version>1.3.0</version>
       <scope>test</scope>
     </dependency>
   </dependencies>
@@ -73,8 +73,8 @@
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-compiler-plugin</artifactId>
         <configuration>
-          <source>1.6</source>
-          <target>1.6</target>
+          <source>1.7</source>
+          <target>1.7</target>
         </configuration>
       </plugin>
 

diff --git a/moa/src/main/java/com/yahoo/labs/samoa/instances/Instances.java b/moa/src/main/java/com/yahoo/labs/samoa/instances/Instances.java
@@ -507,12 +507,12 @@ public String toString() {
 
     StringBuffer text = new StringBuffer();
 
-    text.append(ARFF_RELATION).append(" ").
+    text.append(ARFF_RELATION).append(' ').
       append(Utils.quote( this.instanceInformation.getRelationName())).append("\n\n");
     for (int i = 0; i < numAttributes(); i++) {
-      text.append(attribute(i).toString()).append("\n");
+      text.append(attribute(i).toString()).append('\n');
     }
-    text.append("\n").append(ARFF_DATA).append("\n");
+    text.append('\n').append(ARFF_DATA).append('\n');
 
     text.append(stringWithoutHeader());
     return text.toString();

diff --git a/moa/src/main/java/moa/DoTask.java b/moa/src/main/java/moa/DoTask.java
@@ -127,7 +127,7 @@ public static void main(String[] args) {
                 // build a single string by concatenating cli options
                 StringBuilder cliString = new StringBuilder();
                 for (int i = 0; i < args.length; i++) {
-                    cliString.append(" ").append(args[i]);
+                    cliString.append(' ').append(args[i]);
                 }
                 // parse options
                 Task task = (Task) ClassOption.cliStringToObject(cliString.toString(), Task.class, extraOptions);
@@ -160,7 +160,7 @@ public static void main(String[] args) {
                         }
                         progressLine.append(taskThread.getCurrentActivityString());
                         while (progressLine.length() < MAX_STATUS_STRING_LENGTH) {
-                            progressLine.append(" ");
+                            progressLine.append(' ');
                         }
                         if (progressLine.length() > MAX_STATUS_STRING_LENGTH) {
                             progressLine.setLength(MAX_STATUS_STRING_LENGTH);

diff --git a/moa/src/main/java/moa/MakeObject.java b/moa/src/main/java/moa/MakeObject.java
@@ -53,7 +53,7 @@ public static void main(String[] args) {
                 // build a single string by concatenating cli options
                 StringBuilder cliString = new StringBuilder();
                 for (int i = 1; i < args.length; i++) {
-                    cliString.append(" " + args[i]);
+                    cliString.append(' ').append(args[i]);
                 }
                 // parse options
                 System.err.println("Making object...");

diff --git a/moa/src/main/java/moa/classifiers/active/ActiveClassifier.java b/moa/src/main/java/moa/classifiers/active/ActiveClassifier.java
@@ -20,6 +20,7 @@
  */
 package moa.classifiers.active;
 
+import java.util.Collections;
 import java.util.LinkedList;
 import java.util.List;
 import moa.classifiers.AbstractClassifier;
@@ -251,9 +252,7 @@ protected Measurement[] getModelMeasurementsImpl() {
         measurementList.add(new Measurement("accuracyBaseLearner (percent)", 100 * this.accuracyBaseLearner / this.costLabeling));
         Measurement[] modelMeasurements = ((AbstractClassifier) this.classifier).getModelMeasurements();
         if (modelMeasurements != null) {
-            for (Measurement measurement : modelMeasurements) {
-                measurementList.add(measurement);
-            }
+            Collections.addAll(measurementList, modelMeasurements);
         }
         return measurementList.toArray(new Measurement[measurementList.size()]);
     }

diff --git a/moa/src/main/java/moa/classifiers/bayes/NaiveBayes.java b/moa/src/main/java/moa/classifiers/bayes/NaiveBayes.java
@@ -95,7 +95,7 @@ public void getModelDescription(StringBuilder out, int indent) {
             out.append(getClassNameString());
             out.append(" = ");
             out.append(getClassLabelString(i));
-            out.append(":");
+            out.append(':');
             StringUtils.appendNewlineIndented(out, indent + 1,
                     "Total observed weight = ");
             out.append(this.observedClassDistribution.getValue(i));

diff --git a/moa/src/main/java/moa/classifiers/bayes/NaiveBayesMultinomial.java b/moa/src/main/java/moa/classifiers/bayes/NaiveBayesMultinomial.java
@@ -218,32 +218,32 @@ public void getModelDescription(StringBuilder result, int indent) {
         result.append("--------------------------------------\n");
 
         for (int c = 0; c < m_numClasses; c++) {
-            result.append(m_headerInfo.classAttribute().value(c)).append("\t").
-                    append(Double.toString(m_probOfClass[c])).append("\n");
+            result.append(m_headerInfo.classAttribute().value(c)).append('\t').
+                    append(Double.toString(m_probOfClass[c])).append('\n');
         }
 
         result.append("\nThe probability of a word given the class\n");
         result.append("-----------------------------------------\n\t");
 
         for (int c = 0; c < m_numClasses; c++) {
-            result.append(m_headerInfo.classAttribute().value(c)).append("\t");
+            result.append(m_headerInfo.classAttribute().value(c)).append('\t');
         }
 
-        result.append("\n");
+            result.append('\n');
 
         for (int w = 0; w < m_headerInfo.numAttributes(); w++) {
             if (w == m_headerInfo.classIndex()) {
                 continue;
             }
-            result.append(m_headerInfo.attribute(w).name()).append("\t");
+            result.append(m_headerInfo.attribute(w).name()).append('\t');
             for (int c = 0; c < m_numClasses; c++) {
                 double value = m_wordTotalForClass[c].getValue(w);
                 if (value == 0){
                     value = this.laplaceCorrectionOption.getValue();
                 }
-                result.append(value / m_classTotals[c]).append("\t");
+                result.append(value / m_classTotals[c]).append('\t');
             }
-            result.append("\n");
+            result.append('\n');
         }
         StringUtils.appendNewline(result);
     }

diff --git a/moa/src/main/java/moa/classifiers/drift/DriftDetectionMethodClassifier.java b/moa/src/main/java/moa/classifiers/drift/DriftDetectionMethodClassifier.java
@@ -19,6 +19,8 @@
 package moa.classifiers.drift;
 
 import com.yahoo.labs.samoa.instances.Instance;
+
+import java.util.Collections;
 import java.util.LinkedList;
 import java.util.List;
 import moa.classifiers.AbstractClassifier;
@@ -175,9 +177,7 @@ protected Measurement[] getModelMeasurementsImpl() {
         measurementList.add(new Measurement("Warning detected", this.warningDetected));
         Measurement[] modelMeasurements = ((AbstractClassifier) this.classifier).getModelMeasurements();
         if (modelMeasurements != null) {
-            for (Measurement measurement : modelMeasurements) {
-                measurementList.add(measurement);
-            }
+            Collections.addAll(measurementList, modelMeasurements);
         }
         this.changeDetected = 0;
         this.warningDetected = 0;

diff --git a/moa/src/main/java/moa/classifiers/functions/SGD.java b/moa/src/main/java/moa/classifiers/functions/SGD.java
@@ -351,18 +351,16 @@ public String toString() {
                 buff.append("   ");
             }
 
-            buff.append(Utils.doubleToString(m_weights.getValue(i), 12, 4) + " "
-                    // + m_data.attribute(i).name()
-                    + "\n");
+            buff.append(Utils.doubleToString(m_weights.getValue(i), 12, 4)).append(' ').append('\n');
 
             printed++;
             //}
         }
 
         if (m_bias > 0) {
-            buff.append(" + " + Utils.doubleToString(m_bias, 12, 4));
+            buff.append(" + ").append(Utils.doubleToString(m_bias, 12, 4));
         } else {
-            buff.append(" - " + Utils.doubleToString(-m_bias, 12, 4));
+            buff.append(" - ").append(Utils.doubleToString(-m_bias, 12, 4));
         }
 
         return buff.toString();

diff --git a/moa/src/main/java/moa/classifiers/functions/SGDMultiClass.java b/moa/src/main/java/moa/classifiers/functions/SGDMultiClass.java
@@ -373,18 +373,16 @@ public String toString() {
                 buff.append("   ");
             }
 
-            buff.append(Utils.doubleToString(m_weights[0].getValue(i), 12, 4) + " "
-                    // + m_data.attribute(i).name()
-                    + "\n");
+            buff.append(Utils.doubleToString(m_weights[0].getValue(i), 12, 4)).append(' ').append('\n');
 
             printed++;
             //}
         }
 
         if (m_bias[0] > 0) {
-            buff.append(" + " + Utils.doubleToString(m_bias[0], 12, 4));
+            buff.append(" + ").append(Utils.doubleToString(m_bias[0], 12, 4));
         } else {
-            buff.append(" - " + Utils.doubleToString(-m_bias[0], 12, 4));
+            buff.append(" - ").append(Utils.doubleToString(-m_bias[0], 12, 4));
         }
 
         return buff.toString();

diff --git a/moa/src/main/java/moa/classifiers/functions/SPegasos.java b/moa/src/main/java/moa/classifiers/functions/SPegasos.java
@@ -324,18 +324,16 @@ public String toString() {
                 buff.append("   ");
             }
 
-            buff.append(Utils.doubleToString(m_weights[i], 12, 4) + " "
-                    //+ m_data.attribute(i).name()
-                    + "\n");
+            buff.append(Utils.doubleToString(m_weights[i], 12, 4)).append(' ').append('\n');
 
             printed++;
         }
         //}
 
         if (m_weights[m_weights.length - 1] > 0) {
-            buff.append(" + " + Utils.doubleToString(m_weights[m_weights.length - 1], 12, 4));
+            buff.append(" + ").append(Utils.doubleToString(m_weights[m_weights.length - 1], 12, 4));
         } else {
-            buff.append(" - " + Utils.doubleToString(-m_weights[m_weights.length - 1], 12, 4));
+            buff.append(" - ").append(Utils.doubleToString(-m_weights[m_weights.length - 1], 12, 4));
         }
 
         return buff.toString();

diff --git a/moa/src/main/java/moa/classifiers/lazy/neighboursearch/KDTree.java b/moa/src/main/java/moa/classifiers/lazy/neighboursearch/KDTree.java
@@ -801,8 +801,7 @@ protected int[] refineOwners(KDTreeNode node, Instances centers,
       }
     }
     int[] result = new int[index];
-    for (int i = 0; i < index; i++)
-      result[i] = owners[i];
+    System.arraycopy(owners, 0, result, 0, index);
     return result;
   }
 

diff --git a/moa/src/main/java/moa/classifiers/lazy/neighboursearch/kdtrees/MedianOfWidestDimension.java b/moa/src/main/java/moa/classifiers/lazy/neighboursearch/kdtrees/MedianOfWidestDimension.java
@@ -161,27 +161,30 @@ protected int partition(int attIdx, int[] index, int l, int r) {
    * Implements computation of the kth-smallest element according
    * to Manber's "Introduction to Algorithms".
    *
-   * @param attIdx The dimension/attribute of the instances in 
-   * which to find the kth-smallest element.
-   * @param indices The master index array containing indices of 
-   * the instances.
-   * @param left The begining index of the portion of the master 
-   * index array in which to find the kth-smallest element.
-   * @param right The end index of the portion of the master index 
-   * array in which to find the kth-smallest element.
-   * @param k The value of k
+   * @param attIdx  The dimension/attribute of the instances in
+   *                which to find the kth-smallest element.
+   * @param indices The master index array containing indices of
+   *                the instances.
+   * @param left    The begining index of the portion of the master
+   *                index array in which to find the kth-smallest element.
+   * @param right   The end index of the portion of the master index
+   *                array in which to find the kth-smallest element.
+   * @param k       The value of k
    * @return The index of the kth-smallest element
    */
   public int select(int attIdx, int[] indices, int left, int right, int k) {
-
-    if (left == right) {
-      return left;
-    } else {
-      int middle = partition(attIdx, indices, left, right);
-      if ((middle - left + 1) >= k) {
-        return select(attIdx, indices, left, middle, k);
+    while (true) {
+
+      if (left == right) {
+        return left;
       } else {
-        return select(attIdx, indices, middle + 1, right, k - (middle - left + 1));
+        int middle = partition(attIdx, indices, left, right);
+        if ((middle - left + 1) >= k) {
+          right = middle;
+        } else {
+          left = middle + 1;
+          k = k - (middle - left + 1);
+        }
       }
     }
   }

diff --git a/moa/src/main/java/moa/classifiers/meta/ADACC.java b/moa/src/main/java/moa/classifiers/meta/ADACC.java
@@ -249,8 +249,7 @@ private Classifier getBestAdaptiveClassifier(){
 
 		//take a copy of the ensemble weights (excluding snapshots)
 		Pair[] newEnsembleWeights = new Pair[ensembleWeights.length-MAXPERMANENT];
-		for (int i = 0 ; i < newEnsembleWeights.length; i++)	
-			newEnsembleWeights[i]=ensembleWeights[i];
+		System.arraycopy(ensembleWeights, 0, newEnsembleWeights, 0, newEnsembleWeights.length);
 
 	    //sort the weight values	
 		Arrays.sort(newEnsembleWeights,Collections.reverseOrder());

diff --git a/moa/src/main/java/moa/classifiers/meta/LimAttClassifier.java b/moa/src/main/java/moa/classifiers/meta/LimAttClassifier.java
@@ -175,9 +175,7 @@ public int[] getNext() {
             if (numLeft.equals(total)) {
                 numLeft = numLeft.subtract(BigInteger.ONE);
                 int[] b = new int[a.length];
-                for (int k = 0; k < a.length; k++) {
-                    b[k] = a[k];
-                }
+                System.arraycopy(a, 0, b, 0, a.length);
                 return b;
             }
             int i = r - 1;
@@ -190,9 +188,7 @@ public int[] getNext() {
             }
             numLeft = numLeft.subtract(BigInteger.ONE);
             int[] b = new int[a.length];
-            for (int k = 0; k < a.length; k++) {
-                b[k] = a[k];
-            }
+            System.arraycopy(a, 0, b, 0, a.length);
             return b;
         }
     }

diff --git a/moa/src/main/java/moa/classifiers/meta/RandomRules.java b/moa/src/main/java/moa/classifiers/meta/RandomRules.java
@@ -104,7 +104,7 @@ public double[] getVotesForInstance(Instance inst) {
 		for (int i = 0; i < this.ensemble.length; i++) {
 			DoubleVector vote = new DoubleVector(this.ensemble[i].getVotesForInstance(transformInstance(inst,i)));
 			if (VerbosityOption.getValue()>1)
-					sb.append(vote.getValue(0) + ", ");
+					sb.append(vote.getValue(0)).append(", ");
 			if (this.isRegression == false && vote.sumOfValues() != 0.0){
 				vote.normalize();
 			}
@@ -114,7 +114,7 @@ public double[] getVotesForInstance(Instance inst) {
 			combinedVote.scaleValues(1.0/this.ensemble.length);
 		}
 		if (VerbosityOption.getValue()>1){
-			sb.append(combinedVote.getValue(0)  + ", ").append(inst.classValue());
+			sb.append(combinedVote.getValue(0)).append(", ").append(inst.classValue());
 			System.out.println(sb.toString());
 		}
 		return combinedVote.getArrayRef();
@@ -172,7 +172,7 @@ protected Instance transformInstance(Instance inst, int classifierIndex) {
 					System.out.print(this.listAttributes[attributeIndex][ensembleIndex]);
 				}
 				//System.out.println("Number of attributes: "+this.numAttributes+ ","+inst.numAttributes()); //JD
-				System.out.println("Number of attributes: "+this.numAttributes+ ","+(inst.numAttributes()-1));
+				System.out.println("Number of attributes: "+this.numAttributes+ ',' +(inst.numAttributes()-1));
 				attributes.addElement(inst.classAttribute());
 				this.dataset[ensembleIndex] =  new InstancesHeader(new Instances(
 						getCLICreationString(InstanceStream.class), attributes, 0));

diff --git a/moa/src/main/java/moa/classifiers/meta/TemporallyAugmentedClassifier.java b/moa/src/main/java/moa/classifiers/meta/TemporallyAugmentedClassifier.java
@@ -21,10 +21,9 @@
 
 import com.github.javacliparser.FlagOption;
 import com.github.javacliparser.IntOption;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.LinkedList;
-import java.util.List;
+
+import java.util.*;
+
 import moa.classifiers.AbstractClassifier;
 import moa.classifie
B98A
rs.Classifier;
 import moa.core.Measurement;
@@ -90,9 +89,7 @@ public void trainOnInstanceImpl(Instance instance) {
     public void addOldLabel(double newPrediction) {
         int numLabels = this.oldLabels.length;
         if (numLabels > 0) {
-            for (int i = 1; i < numLabels; i++) {
-                this.oldLabels[i - 1] = this.oldLabels[i];
-            }
+            System.arraycopy(this.oldLabels, 1, this.oldLabels, 0, numLabels - 1);
             this.oldLabels[ numLabels - 1] = newPrediction;
         }
     }
@@ -109,7 +106,7 @@ public void initHeader(Instances dataset) {
 
         ArrayList<Attribute> attrs = new ArrayList<Attribute>(numLabels + dataset.numAttributes());
         for (int i = 0; i < numLabels; i++) {
-            attrs.add(new Attribute(target.name() + "_" + i, possibleValues));
+            attrs.add(new Attribute(target.name() + '_' + i, possibleValues));
         }
         for (int i = 0; i < dataset.numAttributes(); i++) {
             Attribute attr = dataset.attribute(i);
@@ -165,9 +162,7 @@ protected Measurement[] getModelMeasurementsImpl() {
         List<Measurement> measurementList = new LinkedList<Measurement>();
         Measurement[] modelMeasurements = ((AbstractClassifier) this.baseLearner).getModelMeasurements();
         if (modelMeasurements != null) {
-            for (Measurement measurement : modelMeasurements) {
-                measurementList.add(measurement);
-            }
+            Collections.addAll(measurementList, modelMeasurements);
         }
         return measurementList.toArray(new Measurement[measurementList.size()]);
     }