public void testFactoryFeedforward() {
String architecture = "?:B->TANH->3->LINEAR->?:B";
MLMethodFactory factory = new MLMethodFactory();
BasicNetwork network =
(BasicNetwork) factory.create(MLMethodFactory.TYPE_FEEDFORWARD, architecture, 1, 4);
Assert.assertTrue(network.isLayerBiased(0));
Assert.assertFalse(network.isLayerBiased(1));
Assert.assertTrue(network.isLayerBiased(2));
Assert.assertEquals(3, network.getLayerCount());
Assert.assertTrue(network.getActivation(0) instanceof ActivationLinear);
Assert.assertTrue(network.getActivation(1) instanceof ActivationTANH);
Assert.assertTrue(network.getActivation(2) instanceof ActivationLinear);
Assert.assertEquals(18, network.encodedArrayLength());
Assert.assertEquals(1, network.getLayerNeuronCount(0));
Assert.assertEquals(3, network.getLayerNeuronCount(1));
Assert.assertEquals(4, network.getLayerNeuronCount(2));
}
private void randomizeSynapse(BasicNetwork network, int fromLayer) {
int toLayer = fromLayer + 1;
int toCount = network.getLayerNeuronCount(toLayer);
int fromCount = network.getLayerNeuronCount(fromLayer);
int fromCountTotalCount = network.getLayerTotalNeuronCount(fromLayer);
ActivationFunction af = network.getActivation(toLayer);
double low = calculateRange(af, Double.MIN_VALUE);
double high = calculateRange(af, Double.MAX_VALUE);
double b = 0.7d * Math.pow(toCount, (1d / fromCount)) / (high - low);
for (int toNeuron = 0; toNeuron < toCount; toNeuron++) {
if (fromCount != fromCountTotalCount) {
double w = nextDouble(-b, b);
network.setWeight(fromLayer, fromCount, toNeuron, w);
}
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++) {
double w = nextDouble(0, b);
network.setWeight(fromLayer, fromNeuron, toNeuron, w);
}
}
}
