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);
}
public static void main(String args[]) {
int inputNeurons = CHAR_WIDTH * CHAR_HEIGHT;
int outputNeurons = DIGITS.length;
BasicNetwork network = new BasicNetwork();
Layer inputLayer = new BasicLayer(new ActivationLinear(), false, inputNeurons);
Layer outputLayer = new BasicLayer(new ActivationLinear(), true, outputNeurons);
network.addLayer(inputLayer);
network.addLayer(outputLayer);
network.getStructure().finalizeStructure();
(new RangeRandomizer(-0.5, 0.5)).randomize(network);
// train it
NeuralDataSet training = generateTraining();
Train train = new TrainAdaline(network, training, 0.01);
int epoch = 1;
do {
train.iteration();
System.out.println("Epoch #" + epoch + " Error:" + train.getError());
epoch++;
} while (train.getError() > 0.01);
//
System.out.println("Error:" + network.calculateError(training));
// test it
for (int i = 0; i < DIGITS.length; i++) {
int output = network.winner(image2data(DIGITS[i]));
for (int j = 0; j < CHAR_HEIGHT; j++) {
if (j == CHAR_HEIGHT - 1) System.out.println(DIGITS[i][j] + " -> " + output);
else System.out.println(DIGITS[i][j]);
}
System.out.println();
}
}
