/*
* Encog(tm) Examples v3.0 - Java Version
* http://www.heatonresearch.com/encog/
* http://code.google.com/p/encog-java/
* Copyright 2008-2011 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.xorsql;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.train.MLTrain;
import org.encog.ml.train.strategy.RequiredImprovementStrategy;
import org.encog.neural.networks.BasicNetwork;
import org.encog.neural.networks.layers.BasicLayer;
import org.encog.neural.networks.training.propagation.resilient.ResilientPropagation;
import org.encog.platformspecific.j2se.data.SQLNeuralDataSet;
/**
* XOR SQL: This program uses a SQL data source to train a neural network.
* This example is setup to use MySQL, but it could easily be adapted to
* other databases. It assumes that a database is already setup that contains
* XOR training data. A proper database can be setup with the following SQL:
*
* DROP TABLE IF EXISTS `xordata`;
* CREATE TABLE `xordata` (
* `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
* `input1` double NOT NULL,
* `input2` double NOT NULL,
* `ideal1` double NOT NULL,
* PRIMARY KEY (`id`)
* ) ENGINE=InnoDB AUTO_INCREMENT=5 DEFAULT CHARSET=latin1;
*
*
* INSERT INTO `xordata` VALUES ('1', '0', '0', '0');
* INSERT INTO `xordata` VALUES ('2', '1', '0', '1');
* INSERT INTO `xordata` VALUES ('3', '0', '1', '1');
* INSERT INTO `xordata` VALUES ('4', '1', '1', '0');
*
* @author $Author$
* @version $Revision$
*/
public class XORSQL {
public final static String SQL = "SELECT INPUT1,INPUT2,IDEAL1 FROM XORDATA ORDER BY ID";
public final static int INPUT_SIZE = 2;
public final static int IDEAL_SIZE = 1;
public final static String SQL_DRIVER = "com.mysql.jdbc.Driver";
public final static String SQL_URL = "jdbc:mysql://localhost/xor";
public final static String SQL_UID = "xoruser";
public final static String SQL_PWD = "xorpassword";
public static void main(final String args[]) {
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(2));
network.addLayer(new BasicLayer(2));
network.addLayer(new BasicLayer(1));
network.getStructure().finalizeStructure();
network.reset();
MLDataSet trainingSet = new SQLNeuralDataSet(
XORSQL.SQL,
XORSQL.INPUT_SIZE,
XORSQL.IDEAL_SIZE,
XORSQL.SQL_DRIVER,
XORSQL.SQL_URL,
XORSQL.SQL_UID,
XORSQL.SQL_PWD);
// train the neural network
final MLTrain train = new ResilientPropagation(network, trainingSet);
// reset if improve is less than 1% over 5 cycles
train.addStrategy(new RequiredImprovementStrategy(5));
int epoch = 1;
do {
train.iteration();
System.out
.println("Epoch #" + epoch + " Error:" + train.getError());
epoch++;
} while(train.getError() > 0.01);
// test the neural network
System.out.println("Neural Network Results:");
for(MLDataPair pair: trainingSet ) {
final MLData output = network.compute(pair.getInput());
System.out.println(pair.getInput().getData(0) + "," + pair.getInput().getData(1)
+ ", actual=" + output.getData(0) + ",ideal=" + pair.getIdeal().getData(0));
}
}
}