Multilayer Perceptron

It is an implementation of Multilayer Perceptron Neuron Network. The concepts are based on Simon Haykin (2009). Neural Networks and Learning Machines, 3rd edition. ISBN: 0-13-147139-2.

To define a MLP use:

MultilayerPerceptron mlp = new MultilayerPerceptron();
		
mlp.setInputLayer(10, new IdentityFunction());
		
mlp.addHiddenLayer(2, new SigmoidFunction());
	
HiddenLayer hiddenLayer = new HiddenLayer();

hiddenLayer.addNeuron(new HiddenNeuron(new SigmoidFunction()));
	
mlp.addHiddenLayer(hiddenLayer);
	
mlp.setOutputLayer(1, new ThresholdFunction(0.5));

To traine that MLP use:

Backpropagation back = new Backpropagation(mlp);

/*
 * Creating training data
 */
List<Integer[]> inputs = new ArrayList<Integer[]>();
List<Integer[]> outputs = new ArrayList<Integer[]>();

final int AND = 0, OR = 1, XOR = 2;

for (int i = 0; i < 2; i++)
	for (int j = 0; j < 2; j++) {
		inputs.add(new Integer[] {i,  j,  AND});
		outputs.add(new Integer[] {i + j == 2 ? 1 : 0});
		
		inputs.add(new Integer[] {i,  j,  OR});
		outputs.add(new Integer[] {i + j > 0 ? 1 : 0});
		
		inputs.add(new Integer[] {i,  j,  XOR});
		outputs.add(new Integer[] {i + j == 1 ? 1 : 0});
	}

back.setTraineInput(inputs);

back.setTraineOutput(outputs);

back.traine();