Addressing the not-serializable issue

Addressing #12 („Classifier not serializable“)

Author: ptnplanet

README.md

@@ -26,7 +26,7 @@ Then, treat it as any other dependency.
 <dependency>
   <groupId>com.github.ptnplanet</groupId>
   <artifactId>Java-Naive-Bayes-Classifier</artifactId>
-  <version>1.0.6</version>
+  <version>1.0.7</version>
 </dependency>
 ```
 

pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.github.ptnplanet</groupId>
     <artifactId>Java-Naive-Bayes-Classifier</artifactId>
-    <version>1.0.6</version>
+    <version>1.0.7</version>
 
     <dependencies>
         <dependency>

src/main/java/de/daslaboratorium/machinelearning/classifier/Classification.java

@@ -1,17 +1,25 @@
 package de.daslaboratorium.machinelearning.classifier;
 
+import java.io.Serializable;
 import java.util.Collection;
 
 /**
- * A basic wrapper reflecting a classification.  It will store both featureset
+ * A basic wrapper reflecting a classification. It will store both featureset
  * and resulting classification.
  *
  * @author Philipp Nolte
  *
- * @param <T> The feature class.
- * @param <K> The category class.
+ * @param <T>
+ *            The feature class.
+ * @param <K>
+ *            The category class.
  */
-public class Classification<T, K> {
+public class Classification<T, K> implements Serializable {
+
+    /**
+     * Generated Serial Version UID (generated for v1.0.7).
+     */
+    private static final long serialVersionUID = -1210981535415341283L;
 
     /**
      * The classified featureset.
@@ -32,8 +40,10 @@ public class Classification<T, K> {
      * Constructs a new Classification with the parameters given and a default
      * probability of 1.
      *
-     * @param featureset The featureset.
-     * @param category The category.
+     * @param featureset
+     *            The featureset.
+     * @param category
+     *            The category.
      */
     public Classification(Collection<T> featureset, K category) {
         this(featureset, category, 1.0f);
@@ -42,12 +52,14 @@ public Classification(Collection<T> featureset, K category) {
     /**
      * Constructs a new Classification with the parameters given.
      *
-     * @param featureset The featureset.
-     * @param category The category.
-     * @param probability The probability.
+     * @param featureset
+     *            The featureset.
+     * @param category
+     *            The category.
+     * @param probability
+     *            The probability.
      */
-    public Classification(Collection<T> featureset, K category,
-            float probability) {
+    public Classification(Collection<T> featureset, K category, float probability) {
         this.featureset = featureset;
         this.category = category;
         this.probability = probability;
@@ -64,6 +76,7 @@ public Collection<T> getFeatureset() {
 
     /**
      * Retrieves the classification's probability.
+     * 
      * @return
      */
     public float getProbability() {
@@ -84,10 +97,8 @@ public K getCategory() {
      */
     @Override
     public String toString() {
-        return "Classification [category=" + this.category
-                + ", probability=" + this.probability
-                + ", featureset=" + this.featureset
-                + "]";
+        return "Classification [category=" + this.category + ", probability=" + this.probability + ", featureset="
+                + this.featureset + "]";
     }
 
 }

src/main/java/de/daslaboratorium/machinelearning/classifier/Classifier.java

@@ -24,7 +24,7 @@
 public abstract class Classifier<T, K> implements IFeatureProbability<T, K>, java.io.Serializable {
 
     /**
-     * Generated Serial Version UID (generated for v1.0.6).
+     * Generated Serial Version UID (generated for v1.0.7).
      */
     private static final long serialVersionUID = 5504911666956811966L;
 

src/test/java/de/daslaboratorium/machinelearning/classifier/bayes/BayesClassifierTest.java

@@ -1,5 +1,8 @@
 package de.daslaboratorium.machinelearning.classifier.bayes;
 
+import java.io.ByteArrayOutputStream;
+import java.io.IOException;
+import java.io.ObjectOutputStream;
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.Collection;
@@ -67,4 +70,9 @@ public void testStringClassificationInDetails() {
         Assert.assertEquals(0.0234375, list.get(1).getProbability(), EPSILON);
     }
 
-}
+    @Test
+    public void testSerialization() throws IOException {
+
+        new ObjectOutputStream(new ByteArrayOutputStream()).writeObject(bayes);
+    }
+}