Beispiel #1
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  public void testHopfieldPersist() throws Exception {
    boolean input[] = {true, false, true, false};

    BasicNetwork network = new BasicNetwork();
    network.addLayer(new HopfieldLayer(4));

    NeuralData data = new BiPolarNeuralData(input);
    Train train = new TrainHopfield(data, network);
    train.iteration();

    EncogPersistedCollection encog = new EncogPersistedCollection();
    encog.add(network);
    encog.save("encogtest.xml");

    EncogPersistedCollection encog2 = new EncogPersistedCollection();
    encog2.load("encogtest.xml");
    new File("encogtest.xml").delete();

    BasicNetwork network2 = (BasicNetwork) encog2.getList().get(0);

    BiPolarNeuralData output = (BiPolarNeuralData) network2.compute(new BiPolarNeuralData(input));
    TestCase.assertTrue(output.getBoolean(0));
    TestCase.assertFalse(output.getBoolean(1));
    TestCase.assertTrue(output.getBoolean(2));
    TestCase.assertFalse(output.getBoolean(3));
  }
Beispiel #2
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  public void testFeedforwardPersist() throws Throwable {
    NeuralDataSet trainingData = new BasicNeuralDataSet(XOR.XOR_INPUT, XOR.XOR_IDEAL);

    BasicNetwork network = createNetwork();
    Train train = new Backpropagation(network, trainingData, 0.7, 0.9);

    for (int i = 0; i < 5000; i++) {
      train.iteration();
      network = (BasicNetwork) train.getNetwork();
    }

    TestCase.assertTrue("Error too high for backpropagation", train.getError() < 0.1);
    TestCase.assertTrue("XOR outputs not correct", XOR.verifyXOR(network, 0.1));

    EncogPersistedCollection encog = new EncogPersistedCollection();
    encog.add(network);
    encog.save("encogtest.xml");

    EncogPersistedCollection encog2 = new EncogPersistedCollection();
    encog2.load("encogtest.xml");
    new File("encogtest.xml").delete();

    BasicNetwork n = (BasicNetwork) encog2.getList().get(0);
    TestCase.assertTrue("Error too high for load", n.calculateError(trainingData) < 0.1);
  }
Beispiel #3
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  public void testSOMPersist() throws Exception {
    Matrix matrix = new Matrix(TestPersist.trainedData);
    double pattern1[] = {-0.5, -0.5, -0.5, -0.5};
    double pattern2[] = {0.5, 0.5, 0.5, 0.5};
    double pattern3[] = {-0.5, -0.5, -0.5, 0.5};
    double pattern4[] = {0.5, 0.5, 0.5, -0.5};

    NeuralData data1 = new BasicNeuralData(pattern1);
    NeuralData data2 = new BasicNeuralData(pattern2);
    NeuralData data3 = new BasicNeuralData(pattern3);
    NeuralData data4 = new BasicNeuralData(pattern4);

    SOMLayer layer;
    BasicNetwork network = new BasicNetwork();
    network.addLayer(layer = new SOMLayer(4, NormalizationType.MULTIPLICATIVE));
    network.addLayer(new BasicLayer(2));
    layer.setMatrix(matrix);

    EncogPersistedCollection encog = new EncogPersistedCollection();
    encog.add(network);
    encog.save("encogtest.xml");

    EncogPersistedCollection encog2 = new EncogPersistedCollection();
    encog2.load("encogtest.xml");
    new File("encogtest.xml").delete();

    BasicNetwork network2 = (BasicNetwork) encog2.getList().get(0);

    int data1Neuron = network2.winner(data1);
    int data2Neuron = network2.winner(data2);

    TestCase.assertTrue(data1Neuron != data2Neuron);

    int data3Neuron = network2.winner(data3);
    int data4Neuron = network2.winner(data4);

    TestCase.assertTrue(data3Neuron == data1Neuron);
    TestCase.assertTrue(data4Neuron == data2Neuron);
  }