/*
 * Encog(tm) Java Examples v3.3
 * http://www.heatonresearch.com/encog/
 * https://github.com/encog/encog-java-examples
 *
 * Copyright 2008-2014 Heaton Research, Inc.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *   
 * For more information on Heaton Research copyrights, licenses 
 * and trademarks visit:
 * http://www.heatonresearch.com/copyright
 */
package org.encog.examples.neural.freeform;

import org.encog.Encog;
import org.encog.engine.network.activation.ActivationSigmoid;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.freeform.FreeformLayer;
import org.encog.neural.freeform.FreeformNetwork;
import org.encog.util.simple.EncogUtility;


public class SkipNeuralNetwork {

	/**
	 * The input necessary for XOR.
	 */
	public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
			{ 0.0, 1.0 }, { 1.0, 1.0 } };

	/**
	 * The ideal data necessary for XOR.
	 */
	public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };
	
	/**
	 * The main method.
	 * @param args No arguments are used.
	 */
	public static void main(final String args[]) {
		
		// create a neural network, without using a factory
		FreeformNetwork network = new FreeformNetwork();
		FreeformLayer inputLayer = network.createInputLayer(2);
		FreeformLayer hiddenLayer1 = network.createLayer(3);
		FreeformLayer outputLayer = network.createOutputLayer(1);
		
		network.connectLayers(inputLayer, hiddenLayer1, new ActivationSigmoid(), 1.0, false);
		network.connectLayers(hiddenLayer1, outputLayer, new ActivationSigmoid(), 1.0, false);
		network.connectLayers(inputLayer, outputLayer, new ActivationSigmoid(), 0.0, false);
		
		network.reset();

		// create training data
		MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
		//MLTrain train = new FreeformBackPropagation(network, trainingSet, 0.7, 0.3);
		//EncogUtility.trainToError(train, 0.01);
		EncogUtility.trainToError(network, trainingSet, 0.01);
		EncogUtility.evaluate(network, trainingSet);

		Encog.getInstance().shutdown();
	}
}
