Merge pull request ptnplanet#6 from yuripourre/master

Change to maven project structure

.gitignore

@@ -0,0 +1,6 @@
+# Binary files
+*.class
+
+# Eclipse files
+.classpath
+.project

BayesClassifier.java → src/main/java/de/daslaboratorium/machinelearning/classifier/bayes/BayesClassifier.java

@@ -1,10 +1,13 @@
-package de.daslaboratorium.machinelearning.classifier;
+package de.daslaboratorium.machinelearning.classifier.bayes;
 
 import java.util.Collection;
 import java.util.Comparator;
 import java.util.SortedSet;
 import java.util.TreeSet;
 
+import de.daslaboratorium.machinelearning.classifier.Classification;
+import de.daslaboratorium.machinelearning.classifier.Classifier;
+
 /**
  * A concrete implementation of the abstract Classifier class.  The Bayes
  * classifier implements a naive Bayes approach to classifying a given set of

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

@@ -0,0 +1,70 @@
+package de.daslaboratorium.machinelearning.classifier.bayes;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.List;
+
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import de.daslaboratorium.machinelearning.classifier.Classification;
+import de.daslaboratorium.machinelearning.classifier.Classifier;
+
+public class BayesClassifierTest {
+
+	private static final double EPSILON = 0.001;
+	private static final String CATEGORY_NEGATIVE = "negative";
+	private static final String CATEGORY_POSITIVE = "positive";
+	private Classifier<String, String> bayes;
+
+	@Before
+	public void setUp() {
+		/*
+         * 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.
+         */
+		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(CATEGORY_POSITIVE, Arrays.asList(positiveText));
+
+        final String[] negativeText = "I hate rain".split("\\s");
+        bayes.learn(CATEGORY_NEGATIVE, Arrays.asList(negativeText));
+	}
+
+	@Test
+	public void testStringClassification() {
+		final String[] unknownText1 = "today is a sunny day".split("\\s");
+        final String[] unknownText2 = "there will be rain".split("\\s");
+
+        Assert.assertEquals(CATEGORY_POSITIVE, bayes.classify(Arrays.asList(unknownText1)).getCategory());
+        Assert.assertEquals(CATEGORY_NEGATIVE, bayes.classify(Arrays.asList(unknownText2)).getCategory());
+	}
+
+	@Test
+	public void testStringClassificationInDetails() {
+
+		final String[] unknownText1 = "today is a sunny day".split("\\s");
+
+		Collection<Classification<String, String>> classifications = ((BayesClassifier<String, String>) bayes).classifyDetailed(
+                Arrays.asList(unknownText1));
+
+		List<Classification<String, String>> list = new ArrayList<Classification<String,String>>(classifications);
+
+		Assert.assertEquals(CATEGORY_NEGATIVE, list.get(0).getCategory());
+		Assert.assertEquals(0.0078125, list.get(0).getProbability(), EPSILON);
+
+		Assert.assertEquals(CATEGORY_POSITIVE, list.get(1).getCategory());
+		Assert.assertEquals(0.0234375, list.get(1).getProbability(), EPSILON);
+	}
+
+}