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));
}
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);
}
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);
}