XORPartialAuto.java

/*
 * Encog(tm) Java Examples v3.4
 * http://www.heatonresearch.com/encog/
 * https://github.com/encog/encog-java-examples
 *
 * Copyright 2008-2016 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.
 *   
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 * and trademarks visit:
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 */
package org.encog.examples.neural.xorpartial;

import org.encog.neural.data.NeuralDataSet;
import org.encog.neural.data.basic.BasicNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.structure.AnalyzeNetwork;
import org.encog.util.Format;
import org.encog.util.simple.EncogUtility;


/**
 * Partial neural networks. Encog allows you to remove any neuron connection in
 * a fully connected neural network. This example creates a 2x10x10x1 neural
 * network to learn the XOR. Several connections are removed prior to training.
 */
public class XORPartialAuto {

	public static double XOR_INPUT[][] = { { 0.0, 0.0 }, { 1.0, 0.0 },
			{ 0.0, 1.0 }, { 1.0, 1.0 } };

	public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };

	public static void main(final String args[]) {

		BasicNetwork network = EncogUtility.simpleFeedForward(2, 10, 10, 1,
				false);
		network.reset();

		NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, XOR_IDEAL);

		EncogUtility.trainToError(network, trainingSet, 0.01);
		
		
		AnalyzeNetwork analyze = new AnalyzeNetwork(network);
		double remove = analyze.getWeights().getHigh()/50;		
		System.out.println(analyze.toString());
		System.out.println("Remove connections below:" + Format.formatDouble(remove,5));
		
		network.setProperty(BasicNetwork.TAG_LIMIT,remove);
		network.getStructure().finalizeLimit();

		analyze = new AnalyzeNetwork(network);
		System.out.println(analyze.toString());
		
		System.out.println("Final output:");
		EncogUtility.evaluate(network, trainingSet);
	}
}