Neural Network implementation in java

I am attempting to implement a FFNN in Java with backpropagation and have no idea what I am doing wrong. It worked when I had only a single neuron in the network, but I wrote another class to handle larger networks and nothing converges. It seems like a problem in the math - or rather my implementation of the math - but I've checked it several times and I can't find anything wrong. This should be working.
Node class:

package arr;

import util.ActivationFunction;
import util.Functions;

public class Node {
    public ActivationFunction f;
    public double output;
    public double error;

    private double sumInputs;
    private double sumErrors;
    public Node(){
        sumInputs = 0;
        sumErrors = 0;
        f = Functions.SIG;
        output = 0;
        error = 0;
    }
    public Node(ActivationFunction func){
        this();
        this.f = func;
    }

    public void addIW(double iw){
        sumInputs += iw;
    }
    public void addIW(double input, double weight){
        sumInputs += (input*weight);
    }
    public double calculateOut(){
        output = f.eval(sumInputs);
        return output;
    }

    public void addEW(double ew){
        sumErrors+=ew;
    }
    public void addEW(double error, double weight){
        sumErrors+=(error*weight);
    }
    public double calculateError(){
        error = sumErrors * f.deriv(sumInputs);
        return error;
    }   
    public void resetValues(){
        sumErrors = 0;
        sumInputs = 0;
    }
}

LineNetwork class:

package arr;
import util.Functions;

public class LineNetwork {
public double[][][] weights;    //layer of node to, # of node to, # of node from
public Node[][] nodes;          //layer, #
public double lc;
public LineNetwork(){
    weights = new double[2][][];
    weights[0] = new double[2][1];
    weights[1] = new double[1][3];
    initializeWeights();
    nodes = new Node[2][];
    nodes[0] = new Node[2];
    nodes[1] = new Node[1];
    initializeNodes();
    lc = 1;
}
private void initializeWeights(){
    for(double[][] layer: weights)
        for(double[] curNode: layer)
            for(int i=0; i<curNode.length; i++)
                curNode[i] = Math.random()/10;
}
private void initializeNodes(){
    for(Node[] layer: nodes)
        for(int i=0; i<layer.length; i++)
            layer[i] = new Node();
    nodes[nodes.length-1][0].f = Functions.HSF;
}
public double feedForward(double[] inputs) {
    for(int j=0; j<nodes[0].length; j++)
        nodes[0][j].addIW(inputs[j], weights[0][j][0]);
    double[] outputs = new double[nodes[0].length];
    for(int i=0; i<nodes[0].length; i++)
        outputs[i] = nodes[0][i].calculateOut();
    for(int l=1; l<nodes.length; l++){
        for(int i=0; i<nodes[l].length; i++){
            for(int j=0; j<nodes[l-1].length; j++)
                nodes[l][i].addIW(
                        outputs[j], 
                        weights[l][i][j]);
            nodes[l][i].addIW(weights[l][i][weights[l][i].length-1]);
        }
        outputs = new double[nodes[l].length];
        for(int i=0; i<nodes[l].length; i++)
            outputs[i] = nodes[l][i].calculateOut();
    }
    return outputs[0];
}

public void backpropagate(double[] inputs, double expected) {
    nodes[nodes.length-1][0].addEW(expected-nodes[nodes.length-1][0].output);
    for(int l=nodes.length-2; l>=0; l--){
        for(Node n: nodes[l+1])
            n.calculateError();
        for(int i=0; i<nodes[l].length; i++)
            for(int j=0; j<nodes[l+1].length; j++)
                nodes[l][i].addEW(nodes[l+1][j].error, weights[l+1][j][i]);
        for(int j=0; j<nodes[l+1].length; j++){
            for(int i=0; i<nodes[l].length; i++)
                weights[l+1][j][i] += nodes[l][i].output*lc*nodes[l+1][j].error;
            weights[l+1][j][nodes[l].length] += lc*nodes[l+1][j].error;
        }
    }
    for(int i=0; i<nodes[0].length; i++){
        weights[0][i][0] += inputs[i]*lc*nodes[0][i].calculateError();
    }
}
public double train(double[] inputs, double expected) {
    double r = feedForward(inputs);
    backpropagate(inputs, expected);
    return r;
}
public void resetValues() {
    for(Node[] layer: nodes)
        for(Node n: layer)
            n.resetValues();
}

public static void main(String[] args) {
    LineNetwork ln = new LineNetwork();
    System.out.println(str2d(ln.weights[0]));
    for(int i=0; i<10000; i++){
        double[] in = {Math.round(Math.random()),Math.round(Math.random())};
        int out = 0;
        if(in[1]==1 ^ in[0] ==1) out = 1;
        ln.resetValues();
        System.out.print(i+": {"+in[0]+", "+in[1]+"}: "+out+" ");
        System.out.println((int)ln.train(in, out));
    }
    System.out.println(str2d(ln.weights[0]));
}
private static String str2d(double[][] a){
    String str = "[";
    for(double[] arr: a)
        str = str + str1d(arr) + ",\n";
    str = str.substring(0, str.length()-2)+"]";
    return str;
}
private static String str1d(double[] a){
    String str = "[";
    for(double d: a)
        str = str+d+", ";
    str = str.substring(0, str.length()-2)+"]";
    return str;
}
}

Quick explanation of structure: every node has an activation function f; f.eval evaluates the function and f.deriv evaluates its derivative. Functions.SIG is the standard sigmoidal function and Functions.HSF is the Heaviside step function. In order to set the inputs of a function, you call addIW with a value that already includes the weight of the previous output. A similar thing is done in backpropagation with addEW. Nodes are organized in a 2d array and weights are organized separately in a 3d array as described.

I realize this may be a bit much to ask - and I certainly realize how many Java conventions this code breaks - but I appreciate any help anyone can offer.

EDIT: Since this question and my code are such giant walls of text, if there's a line involving lots of complicated expressions in brackets that you don't want to figure out, add a comment or something asking me and I'll try to answer it as quickly as I can.

EDIT 2: The specific problem here is that this network doesn't converge on XOR. Here's some output to illustrate this:

9995: {1.0, 0.0}: 1 1
9996: {0.0, 1.0}: 1 1
9997: {0.0, 0.0}: 0 1
9998: {0.0, 1.0}: 1 0
9999: {0.0, 1.0}: 1 1
Each line is of the format TEST NUMBER: {INPUTS}: EXPECTED ACTUAL The network calls train with each test, so this network is backpropagating 10000 times.

Here are the two extra classes if anyone wants to run it:

package util;

public class Functions {
public static final ActivationFunction LIN = new ActivationFunction(){
            public double eval(double x) {
                return x;
            }

            public double deriv(double x) {
                return 1;
            }
};
public static final ActivationFunction SIG = new ActivationFunction(){
            public double eval(double x) {
                return 1/(1+Math.exp(-x));
            }

            public double deriv(double x) {
                double ev = eval(x);
                return ev * (1-ev);
            }
};
public static final ActivationFunction HSF = new ActivationFunction(){
            public double eval(double x) {
                if(x>0) return 1;
                return 0;
            }

            public double deriv(double x) {
                return (1);
            }
};
}

package util;

public interface ActivationFunction {
public double eval(double x);
public double deriv(double x);
}

Now it's even longer. Darn.