Functional MLP for XOR toy problem.

src/net/woodyfolsom/msproj/ann2/AbstractNeuralNetFilter.java

@@ -0,0 +1,84 @@
+package net.woodyfolsom.msproj.ann2;
+
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.util.List;
+
+public abstract class AbstractNeuralNetFilter implements NeuralNetFilter {
+	private final FeedforwardNetwork neuralNetwork;
+	private final TrainingMethod trainingMethod;
+	
+	private double maxError;
+	private int actualTrainingEpochs = 0;
+	private int maxTrainingEpochs;
+	
+	AbstractNeuralNetFilter(FeedforwardNetwork neuralNetwork, TrainingMethod trainingMethod, int maxTrainingEpochs, double maxError) {
+		this.neuralNetwork = neuralNetwork;
+		this.trainingMethod = trainingMethod;
+		this.maxError = maxError;
+		this.maxTrainingEpochs = maxTrainingEpochs;
+	}
+	
+	@Override
+	public NNData compute(NNDataPair input) {
+		return this.neuralNetwork.compute(input);
+	}
+	
+	public int getActualTrainingEpochs() {
+		return actualTrainingEpochs;
+	}
+	
+	@Override
+	public int getInputSize() {
+		return 2;
+	}
+
+	public int getMaxTrainingEpochs() {
+		return maxTrainingEpochs;
+	}
+	
+	protected FeedforwardNetwork getNeuralNetwork() {
+		return neuralNetwork;
+	}
+	
+	@Override
+	public void learn(List<NNDataPair> trainingSet) {
+		actualTrainingEpochs = 0;
+		double error;
+		neuralNetwork.initWeights();
+		
+		error = trainingMethod.computeError(neuralNetwork,trainingSet);
+		
+		if (error <= maxError) {
+			System.out.println("Initial error: " + error);
+			return;	
+		}
+		
+		do {
+			trainingMethod.iterate(neuralNetwork,trainingSet);
+			error = trainingMethod.computeError(neuralNetwork,trainingSet);
+			System.out.println("Epoch #" + actualTrainingEpochs + " Error:"
+					+ error);
+			actualTrainingEpochs++;
+			System.out.println("MSSE after epoch " + actualTrainingEpochs + ": " + error);
+		} while (error > maxError && actualTrainingEpochs < maxTrainingEpochs);
+	}
+	
+	@Override
+	public boolean load(InputStream input) {
+		return neuralNetwork.load(input);
+	}
+
+	@Override
+	public boolean save(OutputStream output) {
+		return neuralNetwork.save(output);
+	}
+	
+	public void setMaxError(double maxError) {
+		this.maxError = maxError;
+	}
+	
+	public void setMaxTrainingEpochs(int max) {
+		this.maxTrainingEpochs = max;
+	}
+}

src/net/woodyfolsom/msproj/ann2/ActivationFunction.java

@@ -1,5 +0,0 @@
-package net.woodyfolsom.msproj.ann2;
-
-public interface ActivationFunction {
-	double calculate(double arg);
-}

src/net/woodyfolsom/msproj/ann2/BackPropagation.java

@@ -0,0 +1,120 @@
+package net.woodyfolsom.msproj.ann2;
+
+import java.util.List;
+
+import net.woodyfolsom.msproj.ann2.math.ErrorFunction;
+import net.woodyfolsom.msproj.ann2.math.MSSE;
+
+public class BackPropagation implements TrainingMethod {
+	private final ErrorFunction errorFunction;
+	private final double learningRate;
+	private final double momentum;
+
+	public BackPropagation(double learningRate, double momentum) {
+		this.errorFunction = MSSE.function;
+		this.learningRate = learningRate;
+		this.momentum = momentum;
+	}
+
+	@Override
+	public void iterate(FeedforwardNetwork neuralNetwork,
+			List<NNDataPair> trainingSet) {
+		System.out.println("Learningrate: " + learningRate);
+		System.out.println("Momentum: " + momentum);
+
+		//zeroErrors(neuralNetwork);
+		
+		for (NNDataPair trainingPair : trainingSet) {
+			zeroErrors(neuralNetwork);
+			
+			System.out.println("Training with: " + trainingPair.getInput());
+
+			NNData ideal = trainingPair.getIdeal();
+			NNData actual = neuralNetwork.compute(trainingPair);
+			
+			System.out.println("Updating weights.  Ideal Output: " + ideal);
+			System.out.println("Actual Output: " + actual);
+
+			updateErrors(neuralNetwork, ideal);
+			
+			updateWeights(neuralNetwork);
+		}
+		
+		//updateWeights(neuralNetwork);
+	}
+
+	@Override
+	public double computeError(FeedforwardNetwork neuralNetwork,
+			List<NNDataPair> trainingSet) {
+		int numDataPairs = trainingSet.size();
+		int outputSize = neuralNetwork.getOutput().length;
+		int totalOutputSize = outputSize * numDataPairs;
+
+		double[] actuals = new double[totalOutputSize];
+		double[] ideals = new double[totalOutputSize];
+		for (int dataPair = 0; dataPair < numDataPairs; dataPair++) {
+			NNDataPair nnDataPair = trainingSet.get(dataPair);
+			double[] actual = neuralNetwork.compute(nnDataPair.getInput()
+					.getValues());
+			double[] ideal = nnDataPair.getIdeal().getValues();
+			int offset = dataPair * outputSize;
+
+			System.arraycopy(actual, 0, actuals, offset, outputSize);
+			System.arraycopy(ideal, 0, ideals, offset, outputSize);
+		}
+
+		double MSSE = errorFunction.compute(ideals, actuals);
+		return MSSE;
+	}
+
+	private void updateErrors(FeedforwardNetwork neuralNetwork, NNData ideal) {
+		Neuron[] outputNeurons = neuralNetwork.getOutputNeurons();
+		double[] idealValues = ideal.getValues();
+		
+		for (int i = 0; i < idealValues.length; i++) {
+			double output = outputNeurons[i].getOutput();
+			double derivative = outputNeurons[i].getActivationFunction()
+					.derivative(output);
+			outputNeurons[i].setError(outputNeurons[i].getError() + derivative * (idealValues[i] - output));
+		}
+		// walking down the list of Neurons in reverse order, propagate the
+		// error
+		Neuron[] neurons = neuralNetwork.getNeurons();
+
+		for (int n = neurons.length - 1; n >= 0; n--) {
+			
+			Neuron neuron = neurons[n];
+			double error = neuron.getError();
+			
+			Connection[] connectionsFromN = neuralNetwork
+					.getConnectionsFrom(neuron.getId());
+			if (connectionsFromN.length > 0) {
+
+				double derivative = neuron.getActivationFunction().derivative(
+						neuron.getOutput());
+				for (Connection connection : connectionsFromN) {
+					error += derivative * connection.getWeight() * neuralNetwork.getNeuron(connection.getDest()).getError();
+				}
+			}
+			neuron.setError(error);
+		}
+	}
+	
+	private void updateWeights(FeedforwardNetwork neuralNetwork) {
+		for (Connection connection : neuralNetwork.getConnections()) {
+			Neuron srcNeuron = neuralNetwork.getNeuron(connection.getSrc());
+			Neuron destNeuron = neuralNetwork.getNeuron(connection.getDest());
+			double delta = learningRate * srcNeuron.getOutput() * destNeuron.getError();
+			//TODO allow for momentum
+			//double lastDelta = connection.getLastDelta();
+			connection.addDelta(delta); 
+		}
+	}
+	
+	private void zeroErrors(FeedforwardNetwork neuralNetwork) {
+		// Set output errors relative to ideals, all other errors to 0.
+		for (Neuron neuron : neuralNetwork.getNeurons()) {
+			neuron.setError(0.0);
+		}
+	}
+}

src/net/woodyfolsom/msproj/ann2/Connection.java

@@ -0,0 +1,94 @@
+package net.woodyfolsom.msproj.ann2;
+
+import javax.xml.bind.annotation.XmlAttribute;
+import javax.xml.bind.annotation.XmlTransient;
+
+public class Connection {
+	private int src;
+	private int dest;
+	private double weight;
+	private transient double lastDelta = 0.0;
+	
+	public Connection() {
+		//no-arg constructor for JAXB
+	}
+	
+	public Connection(int src, int dest, double weight) {
+		this.src = src;
+		this.dest = dest;
+		this.weight = weight;
+	}
+	
+	public void addDelta(double delta) {
+		this.weight += delta;
+		this.lastDelta = delta;
+	}
+	
+	@XmlAttribute
+	public int getDest() {
+		return dest;
+	}
+	
+	@XmlTransient
+	public double getLastDelta() {
+		return lastDelta;
+	}
+	
+	@XmlAttribute
+	public int getSrc() {
+		return src;
+	}
+
+	@XmlAttribute
+	public double getWeight() {
+		return weight;
+	}
+	
+	public void setDest(int dest) {
+		this.dest = dest;
+	}
+	
+	public void setSrc(int src) {
+		this.src = src;
+	}
+	
+	public void setWeight(double weight) {
+		this.weight = weight;
+	}
+
+	@Override
+	public int hashCode() {
+		final int prime = 31;
+		int result = 1;
+		result = prime * result + dest;
+		result = prime * result + src;
+		long temp;
+		temp = Double.doubleToLongBits(weight);
+		result = prime * result + (int) (temp ^ (temp >>> 32));
+		return result;
+	}
+
+	@Override
+	public boolean equals(Object obj) {
+		if (this == obj)
+			return true;
+		if (obj == null)
+			return false;
+		if (getClass() != obj.getClass())
+			return false;
+		Connection other = (Connection) obj;
+		if (dest != other.dest)
+			return false;
+		if (src != other.src)
+			return false;
+		if (Double.doubleToLongBits(weight) != Double
+				.doubleToLongBits(other.weight))
+			return false;
+		return true;
+	}
+	
+	@Override
+	public String toString() {
+		return "Connection(" + src + ", " + dest +"), weight: " + weight;
+	}
+}

src/net/woodyfolsom/msproj/ann2/FeedforwardNetwork.java

@@ -0,0 +1,291 @@
+package net.woodyfolsom.msproj.ann2;
+
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+import javax.xml.bind.annotation.XmlAttribute;
+import javax.xml.bind.annotation.XmlElement;
+
+import net.woodyfolsom.msproj.ann2.math.ActivationFunction;
+import net.woodyfolsom.msproj.ann2.math.Linear;
+import net.woodyfolsom.msproj.ann2.math.Sigmoid;
+
+public abstract class FeedforwardNetwork {
+	private ActivationFunction activationFunction;
+	private boolean biased;
+	private List<Connection> connections;
+	private List<Neuron> neurons;
+	private String name;
+
+	private transient int biasNeuronId;
+	private transient Map<Integer, List<Connection>> connectionsFrom;
+	private transient Map<Integer, List<Connection>> connectionsTo;
+	
+	public FeedforwardNetwork() {
+		this(false);
+	}
+	
+	public FeedforwardNetwork(boolean biased) {
+		//No-arg constructor for JAXB
+		this.activationFunction = Sigmoid.function;
+		this.connections = new ArrayList<Connection>();
+		this.connectionsFrom = new HashMap<Integer,List<Connection>>();
+		this.connectionsTo = new HashMap<Integer,List<Connection>>();
+		this.neurons = new ArrayList<Neuron>();
+		this.name = "UNDEFINED";
+		this.biasNeuronId = -1;
+		setBiased(biased);
+	}
+
+	public void addConnection(Connection connection) {	
+		connections.add(connection);
+		
+		int src = connection.getSrc();
+		int dest = connection.getDest();
+		
+		if (!connectionsFrom.containsKey(src)) {
+			connectionsFrom.put(src, new ArrayList<Connection>());
+		}
+		
+		if (!connectionsTo.containsKey(dest)) {
+			connectionsTo.put(dest, new ArrayList<Connection>());
+		}
+		
+		connectionsFrom.get(src).add(connection);
+		connectionsTo.get(dest).add(connection);
+	}
+	
+	public NNData compute(NNDataPair nnDataPair) {
+		NNData actual = new NNData(nnDataPair.getIdeal().getFields(),
+				compute(nnDataPair.getInput().getValues()));
+		return actual;
+	}
+
+	public double[] compute(double[] input) {
+		zeroInputs();
+		setInput(input);
+		feedforward();
+		return getOutput();
+	}
+
+	void createBiasConnection(int neuronId, double weight) {
+		if (!biased) {
+			throw new UnsupportedOperationException("Not a biased network");
+		}
+		addConnection(new Connection(biasNeuronId, neuronId, weight));
+	}
+	
+	/**
+	 * Adds a new neuron with a unique id to this FeedforwardNetwork.
+	 * @return
+	 */
+	Neuron createNeuron(boolean input) {
+		Neuron neuron;
+		if (input) {
+			neuron = new Neuron(Linear.function, neurons.size());
+		} else {
+			neuron = new Neuron(activationFunction, neurons.size());
+		}
+		neurons.add(neuron);
+		return neuron;
+	}
+	
+	@Override
+	public boolean equals(Object obj) {
+		if (this == obj)
+			return true;
+		if (obj == null)
+			return false;
+		if (getClass() != obj.getClass())
+			return false;
+		FeedforwardNetwork other = (FeedforwardNetwork) obj;
+		if (activationFunction == null) {
+			if (other.activationFunction != null)
+				return false;
+		} else if (!activationFunction.equals(other.activationFunction))
+			return false;
+		if (connections == null) {
+			if (other.connections != null)
+				return false;
+		} else if (!connections.equals(other.connections))
+			return false;
+		if (name == null) {
+			if (other.name != null)
+				return false;
+		} else if (!name.equals(other.name))
+			return false;
+		if (neurons == null) {
+			if (other.neurons != null)
+				return false;
+		} else if (!neurons.equals(other.neurons))
+			return false;
+		return true;
+	}
+	
+	protected void feedforward() {
+		for (int i = 0; i < neurons.size(); i++) {
+			Neuron src = neurons.get(i);
+			for (Connection connection : getConnectionsFrom(src.getId())) {
+				Neuron dest = getNeuron(connection.getDest());
+				dest.addInput(src.getOutput() * connection.getWeight());
+			}
+		}
+	}
+
+	@XmlElement(type = Sigmoid.class)
+	public ActivationFunction getActivationFunction() {
+		return activationFunction;
+	}
+	
+	protected abstract double[] getOutput();
+	protected abstract Neuron[] getOutputNeurons();
+	
+	@XmlAttribute
+	public String getName() {
+		return name;
+	}
+
+	protected Neuron getNeuron(int id) {
+		return neurons.get(id);
+	}
+
+	@XmlElement
+	protected Connection[] getConnections() {
+		return connections.toArray(new Connection[connections.size()]);
+	}
+
+	protected Connection[] getConnectionsFrom(int neuronId) {
+		List<Connection> connList = connectionsFrom.get(neuronId);
+		
+		if (connList == null) {
+			return new Connection[0];
+		} else {
+			return connList.toArray(new Connection[connList.size()]);
+		}
+	}
+	
+	protected Connection[] getConnectionsTo(int neuronId) {
+		List<Connection> connList = connectionsTo.get(neuronId);
+		
+		if (connList == null) {
+			return new Connection[0];
+		} else {
+			return connList.toArray(new Connection[connList.size()]);
+		}
+	}
+	
+	@XmlAttribute
+	public boolean isBiased() {
+		return biased;
+	}
+
+	@XmlElement
+	protected Neuron[] getNeurons() {
+		return neurons.toArray(new Neuron[neurons.size()]);
+	}
+	
+	@Override
+	public int hashCode() {
+		final int prime = 31;
+		int result = 1;
+		result = prime
+				* result
+				+ ((activationFunction == null) ? 0 : activationFunction
+						.hashCode());
+		result = prime * result
+				+ ((connections == null) ? 0 : connections.hashCode());
+		result = prime * result + ((name == null) ? 0 : name.hashCode());
+		result = prime * result + ((neurons == null) ? 0 : neurons.hashCode());
+		return result;
+	}
+	
+	public void initWeights() {
+		for (Connection connection : connections) {
+			connection.setWeight(1.0-Math.random()*2.0);
+		}
+	}
+
+	public abstract boolean load(InputStream is);
+
+	public abstract boolean save(OutputStream os);
+
+	public void setActivationFunction(ActivationFunction activationFunction) {
+		this.activationFunction = activationFunction;
+	}
+	
+	public void setBiased(boolean biased) {
+		
+		if (this.biased == biased) {
+			return;
+		}
+		
+		this.biased = biased;
+		
+		if (biased) {
+			Neuron biasNeuron = createNeuron(true);
+			biasNeuron.setInput(1.0);
+			biasNeuronId = biasNeuron.getId();
+		} else {
+			//This is an inefficient but concise way to remove all connections involving the bias Neuron
+			//from the global 
+			
+			//Remove all connections from biasId from this index
+			List<Connection> connectionsFromBias = connectionsFrom.remove(biasNeuronId);
+			
+			//Remove all connections to all nodes from biasId from this index
+			for (Map.Entry<Integer,List<Connection>> mapEntry : connectionsTo.entrySet()) {
+				mapEntry.getValue().removeAll(connectionsFromBias);
+			}
+			
+			//Finally, remove from the (serialized) list of non-indexed connections
+			connections.remove(connectionsFromBias);
+			
+			biasNeuronId = -1;
+		}
+	}
+	
+	protected void setConnections(Connection[] connections) {
+		this.connections.clear();
+		this.connectionsFrom.clear();
+		this.connectionsTo.clear();
+		for (Connection connection : connections) {
+			addConnection(connection);
+		}
+	}
+	
+	protected abstract void setInput(double[] input);
+
+	public void setName(String name) {
+		this.name = name;
+	}
+
+	protected void setNeurons(Neuron[] neurons) {
+		this.neurons.clear();
+		for (Neuron neuron : neurons) {
+			this.neurons.add(neuron);
+		}
+	}
+	
+	public void setWeights(double[] weights) {
+		if (weights.length != connections.size()) {
+			throw new IllegalArgumentException("# of weights must == # of connections");
+		}
+		
+		for (int i = 0; i < connections.size(); i++) {
+			connections.get(i).setWeight(weights[i]);
+		}
+	}
+	
+	protected void zeroInputs() {
+		for (Neuron neuron : neurons) {
+			if (neuron.getId() != biasNeuronId){
+				neuron.setInput(0.0);
+			}
+		}
+	}
+
+}

src/net/woodyfolsom/msproj/ann2/Layer.java

@@ -2,29 +2,48 @@ package net.woodyfolsom.msproj.ann2;
 
 import java.util.Arrays;
 
+import javax.xml.bind.annotation.XmlElement;
+
 public class Layer {
-	private Neuron[] neurons;
+	private int[] neuronIds;
 	
 	public Layer() {
-		//default constructor for JAXB
+		neuronIds = new int[0];
 	}
 	
-	public Layer(int numNeurons, int numWeights, ActivationFunction activationFunction) {
+	public Layer(int numNeurons) {
-		neurons = new Neuron[numNeurons];
+		neuronIds = new int[numNeurons];
-		for (int neuronIndex = 0; neuronIndex < numNeurons; neuronIndex++) {
-			neurons[neuronIndex] = new Neuron(activationFunction, numWeights);
-		}
 	}
 	
 	public int size() {
-		return neurons.length;
+		return neuronIds.length;
+	}
+
+	public int getNeuronId(int index) {
+		return neuronIds[index];
+	}
+	
+	@XmlElement
+	public int[] getNeuronIds() {
+		int[] safeCopy = new int[neuronIds.length];
+		System.arraycopy(neuronIds, 0, safeCopy, 0, neuronIds.length);
+		return safeCopy;
+	}
+
+	public void setNeuronId(int index, int id) {
+		neuronIds[index] = id;
+	}
+	
+	public void setNeuronIds(int[] neuronIds) {
+		this.neuronIds = new int[neuronIds.length];
+		System.arraycopy(neuronIds, 0, this.neuronIds, 0, neuronIds.length);
 	}
 
 	@Override
 	public int hashCode() {
 		final int prime = 31;
 		int result = 1;
-		result = prime * result + Arrays.hashCode(neurons);
+		result = prime * result + Arrays.hashCode(neuronIds);
 		return result;
 	}
 
@@ -37,17 +56,9 @@ public class Layer {
 		if (getClass() != obj.getClass())
 			return false;
 		Layer other = (Layer) obj;
-		if (!Arrays.equals(neurons, other.neurons))
+		if (!Arrays.equals(neuronIds, other.neuronIds))
 			return false;
 		return true;
 	}
 
-	public Neuron[] getNeurons() {
-		return neurons;
-	}
-
-	public void setNeurons(Neuron[] neurons) {
-		this.neurons = neurons;
-	}
-
 }

src/net/woodyfolsom/msproj/ann2/MultiLayerPerceptron.java

@@ -2,19 +2,16 @@ package net.woodyfolsom.msproj.ann2;
 
 import java.io.InputStream;
 import java.io.OutputStream;
-import java.util.Arrays;
 
 import javax.xml.bind.JAXBContext;
 import javax.xml.bind.JAXBException;
 import javax.xml.bind.Marshaller;
 import javax.xml.bind.Unmarshaller;
-import javax.xml.bind.annotation.XmlAttribute;
 import javax.xml.bind.annotation.XmlElement;
 import javax.xml.bind.annotation.XmlRootElement;
 
 @XmlRootElement
-public class MultiLayerPerceptron extends NeuralNetwork {
+public class MultiLayerPerceptron extends FeedforwardNetwork {
-	private ActivationFunction activationFunction;
 	private boolean biased;
 	private Layer[] layers;
 
@@ -23,18 +20,16 @@ public class MultiLayerPerceptron extends NeuralNetwork {
 	}
 
 	public MultiLayerPerceptron(boolean biased, int... layerSizes) {
+		super(biased);
+
 		int numLayers = layerSizes.length;
 
 		if (numLayers < 2) {
 			throw new IllegalArgumentException("# of layers must be >= 2");
 		}
 
-		this.activationFunction = Sigmoid.function;
-		this.biased = biased;
 		this.layers = new Layer[numLayers];
 
-		int numWeights;
-
 		for (int layerIndex = 0; layerIndex < numLayers; layerIndex++) {
 			int layerSize = layerSizes[layerIndex];
 
@@ -42,23 +37,31 @@ public class MultiLayerPerceptron extends NeuralNetwork {
 				throw new IllegalArgumentException("Layer size must be >= 1");
 			}
 
-			if (layerIndex == 0) {
+			Layer newLayer = createNewLayer(layerIndex, layerSize);
-				numWeights = 0;
+
+			if (layerIndex > 0) {
+				Layer prevLayer = layers[layerIndex - 1];
+					for (int j = 0; j < newLayer.size(); j++) {
 						if (biased) {
-					layerSize++;
+							createBiasConnection(newLayer.getNeuronId(j),0.0);
+						}
+						for (int i = 0; i < prevLayer.size(); i++) {
+								addConnection(new Connection(prevLayer.getNeuronId(i),
+							newLayer.getNeuronId(j), 0.0));
+					}
 				}
-			} else {
-				numWeights = layers[layerIndex - 1].size();
 			}
-
-			layers[layerIndex] = new Layer(layerSize, numWeights,
-					activationFunction);
 		}
 	}
 
-	@XmlElement(type=Sigmoid.class)
+	private Layer createNewLayer(int layerIndex, int layerSize) {
-	public ActivationFunction getActivationFunction() {
+		Layer layer = new Layer(layerSize);
-		return activationFunction;
+		layers[layerIndex] = layer;
+		for (int n = 0; n < layerSize; n++) {
+			Neuron neuron = createNeuron(layerIndex == 0);
+			layer.setNeuronId(n, neuron.getId());
+		}
+		return layer;
 	}
 
 	@XmlElement
@@ -68,33 +71,30 @@ public class MultiLayerPerceptron extends NeuralNetwork {
 
 	@Override
 	protected double[] getOutput() {
-		// TODO Auto-generated method stub
+		Layer outputLayer = layers[layers.length - 1];
-		return null;
+		double output[] = new double[outputLayer.size()];
+		for (int n = 0; n < outputLayer.size(); n++) {
+			output[n] = getNeuron(outputLayer.getNeuronId(n)).getOutput();
+		}
+		return output;
 	}
 	
 	@Override
-	protected Neuron[] getNeurons() {
+	public Neuron[] getOutputNeurons() {
-		// TODO Auto-generated method stub
+		Layer outputLayer = layers[layers.length - 1];
-		return null;
+		Neuron[] outputNeurons = new Neuron[outputLayer.size()];
+		for (int i = 0; i < outputLayer.size(); i++) {
+			outputNeurons[i] = getNeuron(outputLayer.getNeuronId(i));
 		}
-	
+		return outputNeurons;
-	@XmlAttribute
-	public boolean isBiased() {
-		return biased;
-	}
-	
-	public void setActivationFunction(ActivationFunction activationFunction) {
-		this.activationFunction = activationFunction;
 	}
 
 	@Override
 	protected void setInput(double[] input) {
-		// TODO Auto-generated method stub
+		Layer inputLayer = layers[0];
-
+		for (int n = 0; n < inputLayer.size(); n++) {
+			getNeuron(inputLayer.getNeuronId(n)).setInput(input[n]);
 		}
-
-	public void setBiased(boolean biased) {
-		this.biased = biased;
 	}
 
 	public void setLayers(Layer[] layers) {
@@ -111,7 +111,9 @@ public class MultiLayerPerceptron extends NeuralNetwork {
 			Unmarshaller u = jc.createUnmarshaller();
 			MultiLayerPerceptron mlp = (MultiLayerPerceptron) u.unmarshal(is);
 
-			this.activationFunction = mlp.activationFunction;
+			super.setActivationFunction(mlp.getActivationFunction());
+			super.setConnections(mlp.getConnections());
+			super.setNeurons(mlp.getNeurons());
 			this.biased = mlp.biased;
 			this.layers = mlp.layers;
 
@@ -138,38 +140,4 @@ public class MultiLayerPerceptron extends NeuralNetwork {
 			return false;
 		}
 	}
-
-	@Override
-	public int hashCode() {
-		final int prime = 31;
-		int result = 1;
-		result = prime
-				* result
-				+ ((activationFunction == null) ? 0 : activationFunction
-						.hashCode());
-		result = prime * result + (biased ? 1231 : 1237);
-		result = prime * result + Arrays.hashCode(layers);
-		return result;
-	}
-
-	@Override
-	public boolean equals(Object obj) {
-		if (this == obj)
-			return true;
-		if (obj == null)
-			return false;
-		if (getClass() != obj.getClass())
-			return false;
-		MultiLayerPerceptron other = (MultiLayerPerceptron) obj;
-		if (activationFunction == null) {
-			if (other.activationFunction != null)
-				return false;
-		} else if (!activationFunction.equals(other.activationFunction))
-			return false;
-		if (biased != other.biased)
-			return false;
-		if (!Arrays.equals(layers, other.layers))
-			return false;
-		return true;
-	}
 }

src/net/woodyfolsom/msproj/ann2/NNData.java

@@ -9,6 +9,15 @@ public class NNData {
 		this.values = values;
 	}
 	
+	public NNData(NNData that) {
+		this.fields = that.fields;
+		this.values = that.values;
+	}
+
+	public String[] getFields() {
+		return fields;
+	}
+	
 	public double[] getValues() {
 		return values;
 	}

src/net/woodyfolsom/msproj/ann2/NNDataPair.java

@@ -1,16 +1,16 @@
 package net.woodyfolsom.msproj.ann2;
 
 public class NNDataPair {
-	private final NNData actual;
+	private final NNData input;
 	private final NNData ideal;
 	
 	public NNDataPair(NNData actual, NNData ideal) {
-		this.actual = actual;
+		this.input = actual;
 		this.ideal = ideal;
 	}
 	
-	public NNData getActual() {
+	public NNData getInput() {
-		return actual;
+		return input;
 	}
 	
 	public NNData getIdeal() {

src/net/woodyfolsom/msproj/ann2/NeuralNetFilter.java

@@ -0,0 +1,25 @@
+package net.woodyfolsom.msproj.ann2;
+
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.util.List;
+
+public interface NeuralNetFilter {
+	int getActualTrainingEpochs();
+
+	int getInputSize();
+
+	int getMaxTrainingEpochs();
+
+	int getOutputSize();
+
+	boolean load(InputStream input);
+
+	boolean save(OutputStream output);
+
+	void setMaxTrainingEpochs(int max);
+
+	NNData compute(NNDataPair input);
+
+	void learn(List<NNDataPair> trainingSet);
+}

src/net/woodyfolsom/msproj/ann2/NeuralNetwork.java

@@ -1,53 +0,0 @@
-package net.woodyfolsom.msproj.ann2;
-
-import java.io.InputStream;
-import java.io.OutputStream;
-
-import javax.xml.bind.JAXBException;
-
-/**
- * A NeuralNetwork is simply an ordered set of Neurons.
- * 
- * Functions which rely on knowledge of input neurons, output neurons and layers
- * are delegated to MultiLayerPerception.
- * 
- * The primary function implemented in this abstract class is feedfoward.
- * This function depends only on getNeurons() returning Neurons in feedforward order
- * and the returned Neurons must have the correct number of weights for the NeuralNetwork
- * configuration.
- * 
- * @author Woody
- *
- */
-public abstract class NeuralNetwork {
-	public NeuralNetwork() {	
-	}
-	
-	public double[] calculate(double[] input) {
-		zeroInputs();
-		setInput(input);
-		feedforward();
-		return getOutput();
-	}
-	
-	protected void feedforward() {
-		Neuron[] neurons = getNeurons();
-	}
-	
-	protected abstract double[] getOutput();
-	
-	protected abstract Neuron[] getNeurons();
-	
-	public abstract boolean load(InputStream is);
-	public abstract boolean save(OutputStream os);
-	
-	protected abstract void setInput(double[] input);
-	
-	protected void zeroInputs() {
-		for (Neuron neuron : getNeurons()) {
-			neuron.setInput(0.0);
-		}
-	}
-	
-	
-}

src/net/woodyfolsom/msproj/ann2/Neuron.java

@@ -1,24 +1,29 @@
 package net.woodyfolsom.msproj.ann2;
 
-import java.util.Arrays;
+import javax.xml.bind.annotation.XmlAttribute;
-
-import javax.xml.bind.Unmarshaller;
 import javax.xml.bind.annotation.XmlElement;
 import javax.xml.bind.annotation.XmlTransient;
 
+import net.woodyfolsom.msproj.ann2.math.ActivationFunction;
+import net.woodyfolsom.msproj.ann2.math.Sigmoid;
+
 public class Neuron {
 	private ActivationFunction activationFunction;
-	private double[] weights;
+	private int id;
-	
 	private transient double input = 0.0;
+	private transient double error = 0.0;
 	
 	public Neuron() {
 		//no-arg constructor for JAXB
 	}
 	
-	public Neuron(ActivationFunction activationFunction, int numWeights) {
+	public Neuron(ActivationFunction activationFunction, int id) {
 		this.activationFunction = activationFunction;
-		this.weights = new double[numWeights];
+		this.id = id;
+	}
+	
+	public void addInput(double value) {
+		input += value;
 	}
 	
 	@XmlElement(type=Sigmoid.class)
@@ -26,11 +31,14 @@ public class Neuron {
 		return activationFunction;
 	}
 	
-	void afterUnmarshal(Unmarshaller aUnmarshaller, Object aParent)
+	@XmlAttribute
-    {
+	public int getId() {
-		if (weights == null) {
+		return id;
-			weights = new double[0];
 	}
+	
+	@XmlTransient
+	public double getError() {
+		return error;
 	}
 	
 	@XmlTransient
@@ -42,9 +50,8 @@ public class Neuron {
 		return activationFunction.calculate(input);
 	}
 	
-	@XmlElement
+	public void setError(double value) {
-	public double[] getWeights() {
+		this.error = value;
-		return weights;
 	}
 	
 	public void setInput(double input) {
@@ -59,7 +66,6 @@ public class Neuron {
 				* result
 				+ ((activationFunction == null) ? 0 : activationFunction
 						.hashCode());
-		result = prime * result + Arrays.hashCode(weights);
 		return result;
 	}
 
@@ -77,8 +83,6 @@ public class Neuron {
 				return false;
 		} else if (!activationFunction.equals(other.activationFunction))
 			return false;
-		if (!Arrays.equals(weights, other.weights))
-			return false;
 		return true;
 	}
 	
@@ -86,7 +90,9 @@ public class Neuron {
 		this.activationFunction = activationFunction;
 	}
 	
-	public void setWeights(double[] weights) {
+	@Override
-		this.weights = weights;
+	public String toString() {
+		return "Neuron #" + id +", input: " + input + ", error: " + error;
 	}
+
 }

src/net/woodyfolsom/msproj/ann2/Tanh.java

@@ -1,10 +0,0 @@
-package net.woodyfolsom.msproj.ann2;
-
-public class Tanh implements ActivationFunction{
-
-	@Override
-	public double calculate(double arg) {
-		return Math.tanh(arg);
-	}
-
-}

src/net/woodyfolsom/msproj/ann2/TemporalDifference.java

@@ -0,0 +1,19 @@
+package net.woodyfolsom.msproj.ann2;
+
+import java.util.List;
+
+public class TemporalDifference implements TrainingMethod {
+
+	@Override
+	public void iterate(FeedforwardNetwork neuralNetwork,
+			List<NNDataPair> trainingSet) {
+		throw new UnsupportedOperationException("Not implemented");
+	}
+
+	@Override
+	public double computeError(FeedforwardNetwork neuralNetwork,
+			List<NNDataPair> trainingSet) {
+		throw new UnsupportedOperationException("Not implemented");
+	}
+
+}

src/net/woodyfolsom/msproj/ann2/TrainingMethod.java

@@ -0,0 +1,10 @@
+package net.woodyfolsom.msproj.ann2;
+
+import java.util.List;
+
+public interface TrainingMethod {
+	
+	void iterate(FeedforwardNetwork neuralNetwork, List<NNDataPair> trainingSet);
+	double computeError(FeedforwardNetwork neuralNetwork, List<NNDataPair> trainingSet);
+	
+}

src/net/woodyfolsom/msproj/ann2/XORFilter.java

@@ -0,0 +1,44 @@
+package net.woodyfolsom.msproj.ann2;
+
+/**
+ * Based on sample code from http://neuroph.sourceforge.net
+ * 
+ * @author Woody
+ * 
+ */
+public class XORFilter extends AbstractNeuralNetFilter implements
+		NeuralNetFilter {
+	
+	private static final int INPUT_SIZE = 2;
+	private static final int OUTPUT_SIZE = 1;
+	
+	public XORFilter() {
+		this(0.8,0.7);
+	}
+	
+	public XORFilter(double learningRate, double momentum) {
+		super( new MultiLayerPerceptron(true, INPUT_SIZE, 2, OUTPUT_SIZE),
+				new BackPropagation(learningRate, momentum), 1000, 0.01);
+		super.getNeuralNetwork().setName("XORFilter");
+		
+		//TODO remove
+		//getNeuralNetwork().setWeights(new double[] {
+		//		0.341232, 0.129952, -0.923123,	//hidden neuron 1 from input0, input1, bias 
+		//		-0.115223, 0.570345, -0.328932, //hidden neuron 2 from input0, input1, bias
+		//		-0.993423, 0.164732, 0.752621}); //output
+	}
+
+	public double compute(double x, double y) {
+		return getNeuralNetwork().compute(new double[]{x,y})[0];
+	}
+	
+	@Override
+	public int getInputSize() {
+		return INPUT_SIZE;
+	}
+
+	@Override
+	public int getOutputSize() {
+		return OUTPUT_SIZE;
+	}
+}

src/net/woodyfolsom/msproj/ann2/math/ActivationFunction.java

@@ -1,17 +1,22 @@
-package net.woodyfolsom.msproj.ann2;
+package net.woodyfolsom.msproj.ann2.math;
 
-public class Sigmoid implements ActivationFunction{
+import javax.xml.bind.annotation.XmlAttribute;
-	public static final Sigmoid function = new Sigmoid();
+
+public abstract class ActivationFunction {
 	private String name;
 	
-	private Sigmoid() {	
+	public abstract double calculate(double arg);
-		this.name = "Sigmoid";
+	public abstract double derivative(double arg);
+	
+	public ActivationFunction() {
+		//no-arg constructor for JAXB
 	}
 	
-	public double calculate(double arg) {
+	public ActivationFunction(String name) {
-		return 1.0 / (1 + Math.pow(Math.E, -1.0 * arg));
+		this.name = name;
 	}
 	
+	@XmlAttribute
 	public String getName() {
 		return name;
 	}
@@ -19,7 +24,6 @@ public class Sigmoid implements ActivationFunction{
 	public void setName(String name) {
 		this.name = name;
 	}
-
 	@Override
 	public int hashCode() {
 		final int prime = 31;
@@ -27,7 +31,6 @@ public class Sigmoid implements ActivationFunction{
 		result = prime * result + ((name == null) ? 0 : name.hashCode());
 		return result;
 	}
-
 	@Override
 	public boolean equals(Object obj) {
 		if (this == obj)
@@ -36,7 +39,7 @@ public class Sigmoid implements ActivationFunction{
 			return false;
 		if (getClass() != obj.getClass())
 			return false;
-		Sigmoid other = (Sigmoid) obj;
+		ActivationFunction other = (ActivationFunction) obj;
 		if (name == null) {
 			if (other.name != null)
 				return false;

src/net/woodyfolsom/msproj/ann2/math/ErrorFunction.java

@@ -0,0 +1,5 @@
+package net.woodyfolsom.msproj.ann2.math;
+
+public interface ErrorFunction {
+	double compute(double[] ideal, double[] actual);
+}

src/net/woodyfolsom/msproj/ann2/math/Linear.java

@@ -0,0 +1,17 @@
+package net.woodyfolsom.msproj.ann2.math;
+
+public class Linear extends ActivationFunction{
+	public static final Linear function = new Linear();
+	
+	private Linear() {	
+		super("Linear");
+	}
+	
+	public double calculate(double arg) {
+		return arg;
+	}
+
+	public double derivative(double arg) {
+		return 0;
+	}
+}

src/net/woodyfolsom/msproj/ann2/math/MSSE.java

@@ -0,0 +1,22 @@
+package net.woodyfolsom.msproj.ann2.math;
+
+public class MSSE implements ErrorFunction{
+	public static final ErrorFunction function = new MSSE();
+	
+	public double compute(double[] ideal, double[] actual) {
+		int idealSize = ideal.length;
+		int actualSize = actual.length;
+		
+		if (idealSize != actualSize) {
+			throw new IllegalArgumentException("actualSize != idealSize");
+		}
+		
+		double SSE = 0.0;
+		
+		for (int i = 0; i < idealSize; i++) {
+			SSE += Math.pow(ideal[i] - actual[i], 2);
+		}
+		
+		return SSE / idealSize;
+	}
+}

src/net/woodyfolsom/msproj/ann2/math/Sigmoid.java

@@ -0,0 +1,20 @@
+package net.woodyfolsom.msproj.ann2.math;
+
+public class Sigmoid extends ActivationFunction{
+	public static final Sigmoid function = new Sigmoid();
+	
+	private Sigmoid() {	
+		super("Sigmoid");
+	}
+	
+	public double calculate(double arg) {
+		return 1.0 / (1 + Math.pow(Math.E, -1.0 * arg));
+	}
+
+	public double derivative(double arg) {
+		//lol wth?
+		//double eX = Math.exp(arg);
+		//return eX / (Math.pow((1+eX), 2));
+		return arg - Math.pow(arg,2);
+	}
+}

src/net/woodyfolsom/msproj/ann2/math/Tanh.java

@@ -0,0 +1,21 @@
+package net.woodyfolsom.msproj.ann2.math;
+
+public class Tanh extends ActivationFunction{
+	public static final Tanh function = new Tanh();
+	
+	public Tanh() {
+		super("Tanh");
+	}
+	
+	@Override
+	public double calculate(double arg) {
+		return Math.tanh(arg);
+	}
+
+	@Override
+	public double derivative(double arg) {
+		double tanh = Math.tanh(arg);
+		return 1 - Math.pow(tanh, 2);
+	}
+
+}

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

@@ -16,6 +16,7 @@ import org.junit.Test;
 
 public class MultiLayerPerceptronTest {
 	static final File TEST_FILE = new File("data/test/mlp.net");
+	static final double EPS = 0.001;
 	
 	@BeforeClass
 	public static void setUp() {
@@ -49,14 +50,47 @@ public class MultiLayerPerceptronTest {
 
 	@Test
 	public void testPersistence() throws JAXBException, IOException {
-		NeuralNetwork mlp = new MultiLayerPerceptron(true, 2, 4, 1);
+		FeedforwardNetwork 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();
+		FeedforwardNetwork mlp2 = new MultiLayerPerceptron();
 		assertTrue(mlp2.load(fis));
 		assertEquals(mlp, mlp2);
 		fis.close();
 	}
+	
+	@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 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);
+	}
+	
+	@Test
+	public void testXORnetwork() {
+		FeedforwardNetwork mlp = new MultiLayerPerceptron(true, 2, 2, 1);
+		mlp.setWeights(new double[] {
+				0.341232, 0.129952, -0.923123,	//hidden neuron 1 from input0, input1, bias 
+				-0.115223, 0.570345, -0.328932, //hidden neuron 2 from input0, input1, bias
+				-0.993423, 0.164732, 0.752621}); //output
+		
+		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 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
+
+	 */
 }

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

@@ -3,16 +3,27 @@ package net.woodyfolsom.msproj.ann2;
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
+import net.woodyfolsom.msproj.ann2.math.ActivationFunction;
+import net.woodyfolsom.msproj.ann2.math.Sigmoid;
+import net.woodyfolsom.msproj.ann2.math.Tanh;
+
 import org.junit.Test;
 
 public class SigmoidTest {
+	static double EPS = 0.001;
+	
 	@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
+	public void testDerivative() {
+		ActivationFunction sigmoid = new Tanh();
+		assertEquals(1.0,sigmoid.derivative(0.0), EPS);
+	}
 }

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

@@ -3,16 +3,26 @@ package net.woodyfolsom.msproj.ann2;
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
+import net.woodyfolsom.msproj.ann2.math.ActivationFunction;
+import net.woodyfolsom.msproj.ann2.math.Tanh;
+
 import org.junit.Test;
 
 public class TanhTest {
+	static double EPS = 0.001;
+	
 	@Test
 	public void testCalculate() {
-		double EPS = 0.001;
 		
-		ActivationFunction sigmoid = new Tanh();
+		ActivationFunction tanh = new Tanh();
-		assertEquals(0.0,sigmoid.calculate(0.0),EPS);
+		assertEquals(0.0,tanh.calculate(0.0),EPS);
-		assertTrue(sigmoid.calculate(100.0) > 0.5 - EPS);
+		assertTrue(tanh.calculate(100.0) > 0.5 - EPS);
-		assertTrue(sigmoid.calculate(-9000.0) < -0.5+EPS);
+		assertTrue(tanh.calculate(-9000.0) < -0.5 + EPS);
+	}
+	
+	@Test
+	public void testDerivative() {
+		ActivationFunction tanh = new Tanh();
+		assertEquals(1.0,tanh.derivative(0.0), EPS);
 	}
 }

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

@@ -0,0 +1,136 @@
+package net.woodyfolsom.msproj.ann2;
+
+import static org.junit.Assert.assertTrue;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+import org.junit.AfterClass;
+import org.junit.BeforeClass;
+import org.junit.Test;
+
+public class XORFilterTest {
+	private static final String FILENAME = "xorPerceptron.net";
+
+	@AfterClass
+	public static void deleteNewNet() {
+		File file = new File(FILENAME);
+		if (file.exists()) {
+			file.delete();
+		}
+	}
+
+	@BeforeClass
+	public static void deleteSavedNet() {
+		File file = new File(FILENAME);
+		if (file.exists()) {
+			file.delete();
+		}
+	}
+
+	@Test
+	public void testLearn() throws IOException {
+		NeuralNetFilter nnLearner = new XORFilter(0.05,0.0);
+		
+		// create training set (logical XOR function)
+		int size = 1;
+		double[][] trainingInput = new double[4 * size][];
+		double[][] trainingOutput = new double[4 * size][];
+		for (int i = 0; i < size; i++) {
+			trainingInput[i * 4 + 0] = new double[] { 0, 0 };
+			trainingInput[i * 4 + 1] = new double[] { 0, 1 };
+		    trainingInput[i * 4 + 2] = new double[] { 1, 0 };
+			trainingInput[i * 4 + 3] = new double[] { 1, 1 };
+			trainingOutput[i * 4 + 0] = new double[] { 0 };
+			trainingOutput[i * 4 + 1] = new double[] { 1 };
+			trainingOutput[i * 4 + 2] = new double[] { 1 };
+			trainingOutput[i * 4 + 3] = new double[] { 0 };
+		}
+
+		// create training data
+		List<NNDataPair> trainingSet = new ArrayList<NNDataPair>();
+		String[] inputNames = new String[] {"x","y"};
+		String[] outputNames = new String[] {"XOR"};
+		for (int i = 0; i < 4*size; i++) {
+			trainingSet.add(new NNDataPair(new NNData(inputNames,trainingInput[i]),new NNData(outputNames,trainingOutput[i])));
+		}
+		
+		nnLearner.setMaxTrainingEpochs(20000);
+		nnLearner.learn(trainingSet);
+		System.out.println("Learned network after " + nnLearner.getActualTrainingEpochs() + " training epochs.");
+		
+		double[][] validationSet = new double[4][2];
+
+		validationSet[0] = new double[] { 0, 0 };
+		validationSet[1] = new double[] { 0, 1 };
+		validationSet[2] = new double[] { 1, 0 };
+		validationSet[3] = new double[] { 1, 1 };
+
+		System.out.println("Output from eval set (learned network):");
+		testNetwork(nnLearner, validationSet, inputNames, outputNames);
+	}
+	
+	@Test
+	public void testLearnSaveLoad() throws IOException {
+		NeuralNetFilter nnLearner = new XORFilter(0.5,0.0);
+		
+		// create training set (logical XOR function)
+		int size = 2;
+		double[][] trainingInput = new double[4 * size][];
+		double[][] trainingOutput = new double[4 * size][];
+		for (int i = 0; i < size; i++) {
+			trainingInput[i * 4 + 0] = new double[] { 0, 0 };
+			trainingInput[i * 4 + 1] = new double[] { 0, 1 };
+			trainingInput[i * 4 + 2] = new double[] { 1, 0 };
+			trainingInput[i * 4 + 3] = new double[] { 1, 1 };
+			trainingOutput[i * 4 + 0] = new double[] { 0 };
+			trainingOutput[i * 4 + 1] = new double[] { 1 };
+			trainingOutput[i * 4 + 2] = new double[] { 1 };
+			trainingOutput[i * 4 + 3] = new double[] { 0 };
+		}
+
+		// create training data
+		List<NNDataPair> trainingSet = new ArrayList<NNDataPair>();
+		String[] inputNames = new String[] {"x","y"};
+		String[] outputNames = new String[] {"XOR"};
+		for (int i = 0; i < 4*size; i++) {
+			trainingSet.add(new NNDataPair(new NNData(inputNames,trainingInput[i]),new NNData(outputNames,trainingOutput[i])));
+		}
+		
+		nnLearner.setMaxTrainingEpochs(1);
+		nnLearner.learn(trainingSet);
+		System.out.println("Learned network after " + nnLearner.getActualTrainingEpochs() + " training epochs.");
+		
+		double[][] validationSet = new double[4][2];
+
+		validationSet[0] = new double[] { 0, 0 };
+		validationSet[1] = new double[] { 0, 1 };
+		validationSet[2] = new double[] { 1, 0 };
+		validationSet[3] = new double[] { 1, 1 };
+
+		System.out.println("Output from eval set (learned network, pre-serialization):");
+		testNetwork(nnLearner, validationSet, inputNames, outputNames);
+
+		FileOutputStream fos = new FileOutputStream(FILENAME);
+		assertTrue(nnLearner.save(fos));
+		fos.close();
+		
+		FileInputStream fis = new FileInputStream(FILENAME);
+		assertTrue(nnLearner.load(fis));
+		fis.close();
+		
+		System.out.println("Output from eval set (learned network, post-serialization):");
+		testNetwork(nnLearner, validationSet, inputNames, outputNames);
+	}
+
+	private void testNetwork(NeuralNetFilter nnLearner, double[][] validationSet, String[] inputNames, String[] outputNames) {
+		for (int valIndex = 0; valIndex < validationSet.length; valIndex++) {
+			NNDataPair dp = new NNDataPair(new NNData(inputNames,validationSet[valIndex]), new NNData(outputNames,validationSet[valIndex]));
+			System.out.println(dp + " => " + nnLearner.compute(dp));
+		}
+	}
+}