/**
* Construct a network analyze class. Analyze the specified network.
*
* @param network The network to analyze.
*/
public AnalyzeNetwork(final BasicNetwork network) {
final int assignDisabled = 0;
final int assignedTotal = 0;
final List<Double> biasList = new ArrayList<Double>();
final List<Double> weightList = new ArrayList<Double>();
final List<Double> allList = new ArrayList<Double>();
for (int layerNumber = 0; layerNumber < network.getLayerCount() - 1; layerNumber++) {
final int fromCount = network.getLayerNeuronCount(layerNumber);
final int fromBiasCount = network.getLayerTotalNeuronCount(layerNumber);
final int toCount = network.getLayerNeuronCount(layerNumber + 1);
// weights
for (int fromNeuron = 0; fromNeuron < fromCount; fromNeuron++) {
for (int toNeuron = 0; toNeuron < toCount; toNeuron++) {
final double v = network.getWeight(layerNumber, fromNeuron, toNeuron);
weightList.add(v);
allList.add(v);
}
}
// bias
if (fromCount != fromBiasCount) {
final int biasNeuron = fromCount;
for (int toNeuron = 0; toNeuron < toCount; toNeuron++) {
final double v = network.getWeight(layerNumber, biasNeuron, toNeuron);
biasList.add(v);
allList.add(v);
}
}
}
for (final Layer layer : network.getStructure().getLayers()) {
if (layer.hasBias()) {
for (int i = 0; i < layer.getNeuronCount(); i++) {}
}
}
this.disabledConnections = assignDisabled;
this.totalConnections = assignedTotal;
this.weights = new NumericRange(weightList);
this.bias = new NumericRange(biasList);
this.weightsAndBias = new NumericRange(allList);
this.weightValues = EngineArray.listToDouble(weightList);
this.allValues = EngineArray.listToDouble(allList);
this.biasValues = EngineArray.listToDouble(biasList);
}
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);
}
}
}