Added runnable example

Classifier.java

@@ -35,7 +35,7 @@ public abstract class Classifier<T, K> implements IFeatureProbability<T, K> {
     /**
      * The initial memory capacity or how many classifications are memorized.
      */
-    private int memoryCapacity = 200;
+    private int memoryCapacity = 1000;
 
     /**
      * A dictionary mapping features to their number of occurrences in each

example/RunnableExample.java

@@ -0,0 +1,77 @@
+package de.daslaboratorium.machinelearning.bayes.example;
+
+import java.util.Arrays;
+
+import de.daslaboratorium.machinelearning.bayes.BayesClassifier;
+import de.daslaboratorium.machinelearning.bayes.Classifier;
+
+public class RunnableExample {
+
+    public static void main(String[] args) {
+
+        /*
+         * Create a new classifier instance. The context features are
+         * Strings and the context will be classified with a String according
+         * to the featureset of the context.
+         */
+        final Classifier<String, String> bayes =
+                new BayesClassifier<String, String>();
+
+        /*
+         * The classifier can learn from classifications that are handed over
+         * to the learn methods. Imagin a tokenized text as follows. The tokens
+         * are the text's features. The category of the text will either be
+         * positive or negative.
+         */
+        final String[] positiveText = "I love sunny days".split("\\s");
+        bayes.learn("positive", Arrays.asList(positiveText));
+
+        final String[] negativeText = "I hate rain".split("\\s");
+        bayes.learn("negative", Arrays.asList(negativeText));
+
+        /*
+         * Now that the classifier has "learned" two classifications, it will
+         * be able to classify similar sentences. The classify method returns
+         * a Classification Object, that contains the given featureset,
+         * classification probability and resulting category.
+         */
+        final String[] unknownText1 = "today is a sunny day".split("\\s");
+        final String[] unknownText2 = "there will be rain".split("\\s");
+
+        System.out.println( // will output "positive"
+                bayes.classify(Arrays.asList(unknownText1)).getCategory());
+        System.out.println( // will output "negative"
+                bayes.classify(Arrays.asList(unknownText2)).getCategory());
+
+        /*
+         * The BayesClassifier extends the abstract Classifier and provides
+         * detailed classification results that can be retrieved by calling
+         * the classifyDetailed Method.
+         *
+         * The classification with the highest probability is the resulting
+         * classification. The returned List will look like this.
+         * [
+         *   Classification [
+         *     category=negative,
+         *     probability=0.0078125,
+         *     featureset=[today, is, a, sunny, day]
+         *   ],
+         *   Classification [
+         *     category=positive,
+         *     probability=0.0234375,
+         *     featureset=[today, is, a, sunny, day]
+         *   ]
+         * ]
+         */
+        ((BayesClassifier<String, String>) bayes).classifyDetailed(
+                Arrays.asList(unknownText1));
+
+        /*
+         * Please note, that this particular classifier implementation will
+         * "forget" learned classifications after a few learning sessions. The
+         * number of learning sessions it will record can be set as follows:
+         */
+        bayes.setMemoryCapacity(500); // remember the last 500 learned classifications
+    }
+
+}