Starting my own ANN implementation.

test/net/woodyfolsom/msproj/ann/WinFilterTest.java

@@ -50,7 +50,6 @@ public class WinFilterTest {
 		winFilter.learn(trainingData);
 
 		for (List<MLDataPair> trainingSequence : trainingData) {
-			//for (MLDataPair mlDataPair : trainingSequence) {
 			for (int stateIndex = 0; stateIndex < trainingSequence.size(); stateIndex++) {
 				if (stateIndex > 0 && stateIndex < trainingSequence.size()-1) {
 					continue;
@@ -58,9 +57,8 @@ public class WinFilterTest {
 				MLData input = trainingSequence.get(stateIndex).getInput();
 				
 				System.out.println("Turn " + stateIndex + ": " + input + " => "
-						+ winFilter.computeValue(input));
+						+ winFilter.compute(input));
 			}
-			//}
 		}
 	}
 }

test/net/woodyfolsom/msproj/ann/XORFilterTest.java

@@ -73,7 +73,7 @@ public class XORFilterTest {
 	private void testNetwork(NeuralNetFilter nnLearner, double[][] validationSet) {
 		for (int valIndex = 0; valIndex < validationSet.length; valIndex++) {
 			DoublePair dp = new DoublePair(validationSet[valIndex][0],validationSet[valIndex][1]);
-			System.out.println(dp + " => " + nnLearner.computeValue(dp));
+			System.out.println(dp + " => " + nnLearner.compute(dp));
 		}
 	}
 }

test/net/woodyfolsom/msproj/ann2/MultiLayerPerceptronTest.java

@@ -0,0 +1,62 @@
+package net.woodyfolsom.msproj.ann2;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+
+import javax.xml.bind.JAXBException;
+
+import org.junit.AfterClass;
+import org.junit.BeforeClass;
+import org.junit.Test;
+
+public class MultiLayerPerceptronTest {
+	static final File TEST_FILE = new File("data/test/mlp.net");
+
+	@BeforeClass
+	public static void setUp() {
+		if (TEST_FILE.exists()) {
+			TEST_FILE.delete();
+		}
+	}
+
+	@AfterClass
+	public static void tearDown() {
+		if (TEST_FILE.exists()) {
+			TEST_FILE.delete();
+		}
+	}
+
+	@Test
+	public void testConstructor() {
+		new MultiLayerPerceptron(true, 2, 4, 1);
+		new MultiLayerPerceptron(false, 2, 1);
+	}
+
+	@Test(expected = IllegalArgumentException.class)
+	public void testConstructorTooFewLayers() {
+		new MultiLayerPerceptron(true, 2);
+	}
+
+	@Test(expected = IllegalArgumentException.class)
+	public void testConstructorTooFewNeurons() {
+		new MultiLayerPerceptron(true, 2, 4, 0, 1);
+	}
+
+	@Test
+	public void testPersistence() throws JAXBException, IOException {
+		NeuralNetwork mlp = new MultiLayerPerceptron(true, 2, 4, 1);
+		FileOutputStream fos = new FileOutputStream(TEST_FILE);
+		assertTrue(mlp.save(fos));
+		fos.close();
+		FileInputStream fis = new FileInputStream(TEST_FILE);
+		NeuralNetwork mlp2 = new MultiLayerPerceptron();
+		assertTrue(mlp2.load(fis));
+		assertEquals(mlp, mlp2);
+		fis.close();
+	}
+}

test/net/woodyfolsom/msproj/ann2/SigmoidTest.java

@@ -0,0 +1,18 @@
+package net.woodyfolsom.msproj.ann2;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
+import org.junit.Test;
+
+public class SigmoidTest {
+	@Test
+	public void testCalculate() {
+		double EPS = 0.001;
+		
+		ActivationFunction sigmoid = Sigmoid.function;
+		assertEquals(0.5,sigmoid.calculate(0.0),EPS);
+		assertTrue(sigmoid.calculate(100.0) > 1.0 - EPS);
+		assertTrue(sigmoid.calculate(-9000.0) < EPS);
+	}
+}

test/net/woodyfolsom/msproj/ann2/TanhTest.java

@@ -0,0 +1,18 @@
+package net.woodyfolsom.msproj.ann2;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
+import org.junit.Test;
+
+public class TanhTest {
+	@Test
+	public void testCalculate() {
+		double EPS = 0.001;
+		
+		ActivationFunction sigmoid = new Tanh();
+		assertEquals(0.0,sigmoid.calculate(0.0),EPS);
+		assertTrue(sigmoid.calculate(100.0) > 0.5 - EPS);
+		assertTrue(sigmoid.calculate(-9000.0) < -0.5+EPS);
+	}
+}