/// <summary>
/// Creates MaxNet network architecture
/// </summary>
/// <param name="neuronNum">
/// neuron number in network </param>
/// <param name="neuronProperties">
/// neuron properties </param>
private void createNetwork(int neuronsCount)
{
// set network type
this.NetworkType = NeuralNetworkType.MAXNET;
// createLayer input layer in layer
Layer inputLayer = LayerFactory.createLayer(neuronsCount, new NeuronProperties());
this.addLayer(inputLayer);
// createLayer properties for neurons in output layer
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.setProperty("neuronType", typeof(CompetitiveNeuron));
neuronProperties.setProperty("transferFunction", TransferFunctionType.Ramp.ToString());
// createLayer full connectivity in competitive layer
CompetitiveLayer competitiveLayer = new CompetitiveLayer(neuronsCount, neuronProperties);
// add competitive layer to network
this.addLayer(competitiveLayer);
double competitiveWeight = -(1 / (double)neuronsCount);
// createLayer full connectivity within competitive layer
ConnectionFactory.fullConnect(competitiveLayer, competitiveWeight, 1);
// createLayer forward connectivity from input to competitive layer
ConnectionFactory.forwardConnect(inputLayer, competitiveLayer, 1);
// set input and output cells for this network
NeuralNetworkFactory.DefaultIO = this;
}
// three layers: input, hidden, output
// as mlp add context layer
// elman connect output of hidden layer to input of context layer
// output of context to input of hidden layer
private void createNetwork(int inputNeuronsCount, int hiddenNeuronsCount, int contextNeuronsCount, int outputNeuronsCount)
{
// create input layer
InputLayer inputLayer = new InputLayer(inputNeuronsCount);
inputLayer.addNeuron(new BiasNeuron());
addLayer(inputLayer);
NeuronProperties neuronProperties = new NeuronProperties();
// neuronProperties.setProperty("useBias", true);
neuronProperties.setProperty("transferFunction", TransferFunctionType.Sigmoid.ToString());
Layer hiddenLayer = new Layer(hiddenNeuronsCount, neuronProperties);
hiddenLayer.addNeuron(new BiasNeuron());
addLayer(hiddenLayer);
ConnectionFactory.fullConnect(inputLayer, hiddenLayer);
Layer contextLayer = new Layer(contextNeuronsCount, neuronProperties);
addLayer(contextLayer); // we might also need bias for context neurons?
Layer outputLayer = new Layer(outputNeuronsCount, neuronProperties);
addLayer(outputLayer);
ConnectionFactory.fullConnect(hiddenLayer, outputLayer);
ConnectionFactory.forwardConnect(hiddenLayer, contextLayer); // forward or full connectivity?
ConnectionFactory.fullConnect(contextLayer, hiddenLayer);
// set input and output cells for network
NeuralNetworkFactory.DefaultIO = this;
// set learnng rule
this.LearningRule = new BackPropagation();
}
