/// <summary>
/// The Nguyen-Widrow initialization algorithm is the following :
///
/// 1. Initialize all weight of hidden layers with (ranged) random values
/// 2. For each hidden layer
/// 2.1 calculate beta value, 0.7 * Nth(#neurons of input layer) root of
/// #neurons of current layer
/// 2.2 for each synapse
/// 2.1.1 for each weight
/// 2.1.2 Adjust weight by dividing by norm of weight for neuron and
/// multiplying by beta value
/// </summary>
/// <param name="network">The network to randomize.</param>
public override void Randomize(BasicNetwork network)
{
base.Randomize(network);
int neuronCount = 0;
foreach (ILayer layer in network.Structure.Layers)
{
neuronCount += layer.NeuronCount;
}
ILayer inputLayer = network.GetLayer(BasicNetwork.TAG_INPUT);
ILayer outputLayer = network.GetLayer(BasicNetwork.TAG_OUTPUT);
if (inputLayer == null)
throw new EncogError("Must have an input layer for Nguyen-Widrow.");
if (outputLayer == null)
throw new EncogError("Must have an output layer for Nguyen-Widrow.");
int hiddenNeurons = neuronCount - inputLayer.NeuronCount
- outputLayer.NeuronCount;
if (hiddenNeurons < 1)
throw new EncogError("Must have hidden neurons for Nguyen-Widrow.");
double beta = 0.7 * Math.Pow(hiddenNeurons, 1.0 / inputLayer
.NeuronCount);
foreach (ISynapse synapse in network.Structure.Synapses)
{
Randomize(beta, synapse);
}
network.Structure.FlatUpdate = FlatUpdateNeeded.Flatten;
network.Structure.FlattenWeights();
}