/// <summary>
/// Creates MaxNet network architecture
/// </summary>
/// <param name="neuronsCount">neuron number in network</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);
// 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.SetDefaultIO(this);
}
/// <summary>
/// Creates Competitive network architecture
/// </summary>
/// <param name="inputNeuronsCount">input neurons number</param>
/// <param name="outputNeuronsCount">neuron properties</param>
private void CreateNetwork(int inputNeuronsCount, int outputNeuronsCount)
{
// set network type
this.NetworkType = NeuralNetworkType.COMPETITIVE;
// createLayer input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, new NeuronProperties());
this.AddLayer(inputLayer);
// createLayer properties for neurons in output layer
NeuronProperties neuronProperties = new NeuronProperties();
neuronProperties.SetProperty("neuronType", typeof(CompetitiveNeuron));
neuronProperties.SetProperty("weightsFunction", typeof(WeightedInput));
neuronProperties.SetProperty("summingFunction", typeof(Sum));
neuronProperties.SetProperty("transferFunction", TransferFunctionType.RAMP);
// createLayer full connectivity in competitive layer
CompetitiveLayer competitiveLayer = new CompetitiveLayer(outputNeuronsCount, neuronProperties);
// add competitive layer to network
this.AddLayer(competitiveLayer);
double competitiveWeight = -(1 / (double)outputNeuronsCount);
// createLayer full connectivity within competitive layer
ConnectionFactory.FullConnect(competitiveLayer, competitiveWeight, 1);
// createLayer full connectivity from input to competitive layer
ConnectionFactory.FullConnect(inputLayer, competitiveLayer);
// set input and output cells for this network
NeuralNetworkFactory.SetDefaultIO(this);
this.LearningRule = new CompetitiveLearning(this);
}