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;
	}
}