/** {@inheritDoc } */
public DomainRegistry generateDomain(int fanin) {
DomainRegistry domainRegistry = provider.generateDomain(fanin);
String domainString = domainRegistry.getDomainString();
domainRegistry.setDomainString(
domainString
+ ","
+ lambdaDomainPrototype.getDomainString()
+ ","
+ gammaDomainPrototype.getDomainString());
return domainRegistry;
}
public AngleModulation() {
precision = 3;
bitsPerDimension = 0;
domainRegistry = new StringBasedDomainRegistry();
domainRegistry.setDomainString("R(-1.0:1.0)^4");
}
/**
* @TODO: This needs to use an API for domain string manipulation
*
* @param domain
* @return
*/
public int getRequiredNumberOfBits(DomainRegistry domain) {
if (domain.getDomainString().contains("B")) {
return 1;
} else {
String range = domain.getDomainString();
// now remove all the irrelevant details from the domain provided
range = range.substring(range.indexOf('(') + 1);
range = range.substring(0, range.indexOf(')'));
String[] bounds = range.split(":");
lowerBound = Double.valueOf(bounds[0]).doubleValue();
upperBound = Double.valueOf(bounds[1]).doubleValue();
double greaterRange = Math.abs(lowerBound) + Math.abs(upperBound);
double expandedRange = greaterRange * Math.pow(10, getPrecision());
return Double.valueOf(Math.ceil(Math.log(expandedRange) / Math.log(2.0))).intValue();
}
}
/**
* 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;
}
