/**
* Builds a layer by cloning a prototype neuron and adding to it weights such that it is fully
* connected to the feeding layer.
*
* @param layerConfiguration
* @param previousLayerAbsoluteSize
* @return the built layer.
*/
@Override
public Layer buildLayer(LayerConfiguration layerConfiguration, int previousLayerAbsoluteSize) {
prototypeNeuron.setActivationFunction(layerConfiguration.getActivationFunction());
int layerSize = layerConfiguration.getSize();
boolean bias = layerConfiguration.isBias();
// determine correct domain registry
DomainRegistry domainRegistry = domainProvider.generateDomain(previousLayerAbsoluteSize);
// set domain for prototype neuron
prototypeNeuron.setDomain(domainRegistry.getDomainString());
// get prototype weight vector
Vector prototypeWeightVector = null;
try {
prototypeWeightVector = (Vector) domainRegistry.getBuiltRepresentation();
} catch (ClassCastException exception) {
throw new UnsupportedOperationException(
"The domain string of the neural network weights has to be real valued");
}
// add neurons to layer
Layer layer = new Layer();
for (int i = 0; i < layerSize; i++) {
Neuron newNeuron = prototypeNeuron.getClone();
Vector weights = prototypeWeightVector.getClone();
// TODO: initialisation should be done by training algorithm
this.getWeightInitialisationStrategy().initialise(weights);
newNeuron.setWeights(weights);
layer.add(newNeuron);
}
if (bias) {
layer.add(new BiasNeuron());
layer.setBias(true);
}
return layer;
}
