/** * Copyright 2010 Neuroph Project http://neuroph.sourceforge.net * * 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. */ package org.neuroph.samples; import java.util.Arrays; import org.neuroph.core.learning.TrainingElement; import org.neuroph.core.learning.TrainingSet; import org.neuroph.nnet.Hopfield; /** * This sample shows how to create and train Hopfield neural network * @author Zoran Sevarac <sevarac@gmail.com> */ public class HopfieldSample { /** * Runs this sample */ public static void main(String args[]) { // create training set (H and T letter in 3x3 grid) TrainingSet trainingSet = new TrainingSet(); trainingSet.addElement(new TrainingElement(new double[]{1, 0, 1, 1, 1, 1, 1, 0, 1})); // H letter trainingSet.addElement(new TrainingElement(new double[]{1, 1, 1, 0, 1, 0, 0, 1, 0})); // T letter // create hopfield network Hopfield myHopfield = new Hopfield(9); // learn the training set myHopfield.learnInSameThread(trainingSet); // test hopfield network System.out.println("Testing network"); // add one more 'incomplete' H pattern for testing - it will be recognized as H trainingSet.addElement(new TrainingElement(new double[]{1, 0, 0, 1, 0, 1, 1, 0, 1})); // print network output for the each element from the specified training set. for(TrainingElement trainingElement : trainingSet.trainingElements()) { myHopfield.setInput(trainingElement.getInput()); myHopfield.calculate(); myHopfield.calculate(); double[] networkOutput = myHopfield.getOutput(); System.out.print("Input: " + Arrays.toString(trainingElement.getInput()) ); System.out.println(" Output: " + Arrays.toString(networkOutput) ); } } }