/*
* 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.som;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.neural.som.SOM;
import org.encog.neural.som.training.basic.BasicTrainSOM;
import org.encog.neural.som.training.basic.neighborhood.NeighborhoodSingle;
/**
* Implement a simple SOM using Encog. It learns to recognize two patterns.
* @author jeff
*
*/
public class SimpleSOM {
public static double SOM_INPUT[][] = {
{ -1.0, -1.0, 1.0, 1.0 },
{ 1.0, 1.0, -1.0, -1.0 } };
public static void main(String args[])
{
// create the training set
MLDataSet training = new BasicMLDataSet(SOM_INPUT,null);
// Create the neural network.
SOM network = new SOM(4,2);
network.reset();
BasicTrainSOM train = new BasicTrainSOM(
network,
0.7,
training,
new NeighborhoodSingle());
int iteration = 0;
for(iteration = 0;iteration<=10;iteration++)
{
train.iteration();
System.out.println("Iteration: " + iteration + ", Error:" + train.getError());
}
MLData data1 = new BasicMLData(SOM_INPUT[0]);
MLData data2 = new BasicMLData(SOM_INPUT[1]);
System.out.println("Pattern 1 winner: " + network.winner(data1));
System.out.println("Pattern 2 winner: " + network.winner(data2));
}
}