-PlayerModel classes, including a function to get the target score for a player's game and a neural network to predict a player's moves.

src/model/playerModel/node/SigmoidNode.java

@@ -0,0 +1,58 @@
+package model.playerModel.node;
+
+import java.util.Hashtable;
+
+import model.playerModel.Node;
+
+
+public class SigmoidNode implements Node {
+
+	// Training rate.
+	private final double A = .15;
+
+	private final Hashtable<Node, Double> dendrites = new Hashtable<Node, Double>();
+
+	private final double s = 1; // Slope paramater for the activation function.
+								// It's doing nothing right now, but I figured
+								// I'd implement it as a tweaking option.
+	private final double THRESHOLD = .5;
+
+	public void addNode(Node n, double weight) {
+		dendrites.put(n, weight);
+	}
+
+	@Override
+	public boolean axon() {
+		double sum = 0;
+
+		for (Node n : dendrites.keySet()) {
+			sum += n.axon() ? dendrites.get(n) : 0;
+		}
+
+		return (activation(sum) > THRESHOLD);
+
+	}
+
+	@Override
+	public void learn(boolean correct) {
+		for (Node n : dendrites.keySet()) {
+			if (correct && n.axon()) {
+				dendrites.put(n, dendrites.get(n) + A);
+
+				n.learn(correct);
+			}
+		}
+	}
+
+	private double activation(double sum) {
+		// Sigmoid function:
+		// 1/(1+(e^(-bt)))
+
+		double a = -1 * s * sum;
+		a = Math.exp(a);
+		a = 1 + a;
+		a = 1 / a;
+
+		return a;
+	}
+}