Package org.encog.neural.networks

Examples of org.encog.neural.networks.BasicNetwork

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  }

  public void loadAndEvaluate() {
    System.out.println("Loading network");

    BasicNetwork network = (BasicNetwork)EncogDirectoryPersistence.loadObject(new File(FILENAME));

    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    double e = network.calculateError(trainingSet);
    System.out
        .println("Loaded network's error is(should be same as above): "
            + e);
  }
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  public static void main(final String args[]) {
   
    final TemporalXOR temp = new TemporalXOR();
    final MLDataSet trainingSet = temp.generate(120);

    final BasicNetwork elmanNetwork = ElmanXOR.createElmanNetwork();
    final BasicNetwork feedforwardNetwork = ElmanXOR
        .createFeedforwardNetwork();

    final double elmanError = ElmanXOR.trainNetwork("Elman", elmanNetwork,
        trainingSet);
    final double feedforwardError = ElmanXOR.trainNetwork("Feedforward",
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import org.junit.Assert;

public class TestTrainingContinuation extends TestCase {
  public void testContRPROP()
  {
    BasicNetwork network1 = NetworkUtil.createXORNetworkUntrained();
    BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    // train network 1, no continue
    ResilientPropagation rprop1 = new ResilientPropagation(network1,trainingData);
    rprop1.iteration();
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  }
 
  public void testContBackprop()
  {
    BasicNetwork network1 = NetworkUtil.createXORNetworkUntrained();
    BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    // train network 1, no continue
    Backpropagation rprop1 = new Backpropagation(network1,trainingData,0.4,0.4);
    rprop1.iteration();
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import junit.framework.TestCase;

public class TestAnalyzeNetwork extends TestCase {
  public void testAnalyze()
  {
    BasicNetwork network = EncogUtility.simpleFeedForward(2, 2, 0, 1, false);
    double[] weights = new double[network.encodedArrayLength()];
    EngineArray.fill(weights, 1.0);
    network.decodeFromArray(weights);
    AnalyzeNetwork analyze = new AnalyzeNetwork(network);
    Assert.assertEquals(weights.length, analyze.getWeightsAndBias().getSamples());
    Assert.assertEquals(3,analyze.getBias().getSamples());
    Assert.assertEquals(6,analyze.getWeights().getSamples());
  }
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  public void testCompleteTrain()
  {
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
   
    BasicNetwork network = EncogUtility.simpleFeedForward(2, 5, 7, 1, true);
    Randomizer randomizer = new ConsistentRandomizer(-1, 1, 19);
    //randomizer.randomize(network);
    System.out.println(network.dumpWeights());
    MLTrain rprop = new ResilientPropagation(network, trainingData);
    int iteration = 0;
    do {
      rprop.iteration();
      System.out.println(rprop.getError());
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  public void testGreedy()
  {
    FeedForwardPattern pattern = new FeedForwardPattern();
    pattern.setInputNeurons(1);
    pattern.setOutputNeurons(1);
    BasicNetwork network = (BasicNetwork)pattern.generate();
    MockTrain.setFirstElement(3.0,network);
   
    MockTrain mock = new MockTrain();
    mock.setNetwork(network);
    Greedy strategy = new Greedy();
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  public void testReset()
  {
    FeedForwardPattern pattern = new FeedForwardPattern();
    pattern.setInputNeurons(1);
    pattern.setOutputNeurons(1);
    BasicNetwork network = (BasicNetwork)pattern.generate();
   
    ResetStrategy strategy = new ResetStrategy(0.95,2);
    MockTrain mock = new MockTrain();
    mock.setNetwork(network);
    mock.addStrategy(strategy);
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  public void testSmart()
  {
    FeedForwardPattern pattern = new FeedForwardPattern();
    pattern.setInputNeurons(1);
    pattern.setOutputNeurons(1);
    BasicNetwork network = (BasicNetwork)pattern.generate();
   
    SmartLearningRate strategy1 = new SmartLearningRate();
    SmartMomentum strategy2 = new SmartMomentum();
    MockTrain mock = new MockTrain();
    mock.setNetwork(network);
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       for ( int i = 0; i < 1; i++ ) {
          

          
           BasicNetwork network3 = NetworkUtil.createXORNetworknNguyenWidrowUntrained();
          
           MLTrain bpropNguyen = new Backpropagation( network3, trainingData3, 0.9, 0.8 );    
           train(i, bpropNguyen, "NguyenWidrowRandomizer" );
          
           BasicNetwork network2 = NetworkUtil.createXORNetworkUntrained();
          
           MLTrain bpropRange = new Backpropagation( network2, trainingData2, 0.9, 0.8 );    
           train(i, bpropRange,  "RangeRandomizer       ");
       }
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Related Classes of org.encog.neural.networks.BasicNetwork

  • com.wiieditor.neuralnetwork.NeuralNetworkWrapper
  • mechanics.NeuralNetwork
  • org.encog.engine.network.activation.ActivationFunction
  • org.encog.ensemble.ml.mlp.factory.MultiLayerPerceptronFactory
  • org.encog.examples.neural.activation.CustomActivation
  • org.encog.examples.neural.adaline.AdalineDigits
  • org.encog.examples.neural.benchmark.MultiBench
  • org.encog.examples.neural.benchmark.SimpleBenchmark
  • org.encog.examples.neural.benchmark.TestConverge
  • org.encog.examples.neural.benchmark.ThreadCount
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