Fixes for ANN serialization and unit tests.

src/net/woodyfolsom/msproj/ann/MultiLayerPerceptron.java

@@ -122,7 +122,6 @@ public class MultiLayerPerceptron extends FeedforwardNetwork {
 			JAXBContext jc = JAXBContext
 					.newInstance(MultiLayerPerceptron.class);
 
-			// unmarshal from foo.xml
 			Unmarshaller u = jc.createUnmarshaller();
 			MultiLayerPerceptron mlp = (MultiLayerPerceptron) u.unmarshal(is);
 

src/net/woodyfolsom/msproj/ann/Neuron.java

@@ -2,12 +2,16 @@ package net.woodyfolsom.msproj.ann;
 
 import javax.xml.bind.annotation.XmlAttribute;
 import javax.xml.bind.annotation.XmlElement;
+import javax.xml.bind.annotation.XmlElements;
 import javax.xml.bind.annotation.XmlTransient;
 
 import net.woodyfolsom.msproj.ann.math.ActivationFunction;
+import net.woodyfolsom.msproj.ann.math.Linear;
 import net.woodyfolsom.msproj.ann.math.Sigmoid;
+import net.woodyfolsom.msproj.ann.math.Tanh;
 
 public class Neuron {
+	
 	private ActivationFunction activationFunction;
 	private int id;
 	private transient double input = 0.0;
@@ -26,7 +30,12 @@ public class Neuron {
 		input += value;
 	}
 	
-	@XmlElement(type=Sigmoid.class)
+
+	@XmlElements({
+		@XmlElement(name="LinearActivationFunction",type=Linear.class),
+		@XmlElement(name="SigmoidActivationFunction",type=Sigmoid.class),
+		@XmlElement(name="TanhActivationFunction",type=Tanh.class)
+	})
 	public ActivationFunction getActivationFunction() {
 		return activationFunction;
 	}

src/net/woodyfolsom/msproj/ann/math/ActivationFunction.java

@@ -1,7 +1,9 @@
 package net.woodyfolsom.msproj.ann.math;
 
 import javax.xml.bind.annotation.XmlAttribute;
+import javax.xml.bind.annotation.XmlTransient;
 
+@XmlTransient
 public abstract class ActivationFunction {
 	private String name;
 	

src/net/woodyfolsom/msproj/ann/math/Linear.java

@@ -1,5 +1,7 @@
 package net.woodyfolsom.msproj.ann.math;
 
+import javax.xml.bind.annotation.XmlRootElement;
+
 public class Linear extends ActivationFunction{
 	public static final Linear function = new Linear();
 	

src/net/woodyfolsom/msproj/ann/math/Sigmoid.java

@@ -1,5 +1,7 @@
 package net.woodyfolsom.msproj.ann.math;
 
+import javax.xml.bind.annotation.XmlRootElement;
+
 public class Sigmoid extends ActivationFunction{
 	public static final Sigmoid function = new Sigmoid();
 	
@@ -12,9 +14,7 @@ public class Sigmoid extends ActivationFunction{
 	}
 
 	public double derivative(double arg) {
-		//lol wth? oh, the next derivative formula is a function of s(x), not x.
 		double eX = Math.exp(arg);
 		return eX / (Math.pow((1+eX), 2));
-		//return arg - Math.pow(arg,2);
 	}
 }

src/net/woodyfolsom/msproj/ann/math/Tanh.java

@@ -1,5 +1,7 @@
 package net.woodyfolsom.msproj.ann.math;
 
+import javax.xml.bind.annotation.XmlRootElement;
+
 public class Tanh extends ActivationFunction{
 	public static final Tanh function = new Tanh();
 	

src/net/woodyfolsom/msproj/tictactoe/NNDataSetFactory.java

@@ -4,7 +4,6 @@ import java.util.ArrayList;
 import java.util.List;
 
 import net.woodyfolsom.msproj.GameBoard;
-import net.woodyfolsom.msproj.GameConfig;
 import net.woodyfolsom.msproj.GameResult;
 import net.woodyfolsom.msproj.GameState;
 import net.woodyfolsom.msproj.Player;

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

@@ -70,10 +70,10 @@ public class MultiLayerPerceptronTest {
 	@Test
 	public void testCompute() {
 		FeedforwardNetwork mlp = new MultiLayerPerceptron(true, 2, 2, 1);
-		NNDataPair expected = new NNDataPair(new NNData(new String[]{"x","y"}, new double[]{0.0,0.0}),new NNData(new String[]{"xor"}, new double[]{0.0}));
+		NNDataPair expected = new NNDataPair(new NNData(new String[]{"x","y"}, new double[]{0.0,0.0}),new NNData(new String[]{"xor"}, new double[]{0.5}));
 		NNDataPair actual = new NNDataPair(new NNData(new String[]{"x","y"}, new double[]{0.0,0.0}),new NNData(new String[]{"xor"}, new double[]{0.5}));
 		NNData actualOutput = mlp.compute(actual);
-		assertEquals(expected.getIdeal(), actualOutput);
+		assertEquals(expected.getIdeal().getValues()[0], actualOutput.getValues()[0], EPS);
 	}
 	
 	@Test
@@ -87,16 +87,9 @@ public class MultiLayerPerceptronTest {
 		for (Connection connection : mlp.getConnections()) {
 			System.out.println(connection);
 		}
-		NNDataPair expected = new NNDataPair(new NNData(new String[]{"x","y"}, new double[]{0.0,0.0}),new NNData(new String[]{"xor"}, new double[]{0.367610}));
+		NNDataPair expected = new NNDataPair(new NNData(new String[]{"x","y"}, new double[]{0.0,0.0}),new NNData(new String[]{"xor"}, new double[]{0.263932}));
 		NNDataPair actual = new NNDataPair(new NNData(new String[]{"x","y"}, new double[]{0.0,0.0}),new NNData(new String[]{"xor"}, new double[]{0.0}));
 		NNData actualOutput = mlp.compute(actual);
 		assertEquals(expected.getIdeal().getValues()[0], actualOutput.getValues()[0], EPS);		
 	}
-	/**
-	 * 
-Hidden Neuron 1:	w2(0,1) = 0.341232	w2(1,1) = 0.129952	w2(2,1) =-0.923123
-Hidden Neuron 2:	w2(0,2) =-0.115223	w2(1,2) = 0.570345	w2(2,2) =-0.328932
-Output Neuron:	w3(0,1) =-0.993423	w3(1,1) = 0.164732	w3(2,1) = 0.752621
-
-	 */
 }