/// <summary>
/// Creates and returns neuron instance according to the given specification in neuronProperties.
/// </summary>
/// <param name="neuronProperties">specification of neuron properties</param>
/// <returns>returns instance of neuron with specified properties</returns>
public static Neuron CreateNeuron(NeuronProperties neuronProperties)
{
InputFunction inputFunction = null;
Type inputFunctionClass = neuronProperties.InputFunction;
if (!inputFunctionClass.Equals(" "))
{
inputFunction = CreateInputFunction(inputFunctionClass);
}
else
{
WeightsFunction weightsFunction = CreateWeightsFunction(neuronProperties.WeightsFunction);
SummingFunction summingFunction = CreateSummingFunction(neuronProperties.SummingFunction);
inputFunction = new InputFunction(weightsFunction, summingFunction);
}
TransferFunction transferFunction = CreateTransferFunction(neuronProperties.GetTransferFunctionProperties());
Neuron neuron = null;
Type neuronClass = neuronProperties.NeuronType;
// use two param constructor to create neuron
Type[] paramTypes = {
typeof(InputFunction),
typeof(TransferFunction) };
ConstructorInfo con = neuronClass.GetConstructor(paramTypes);
if (con != null)
{
Object[] paramList = new Object[2];
paramList[0] = inputFunction;
paramList[1] = transferFunction;
object o = con.Invoke(paramList);
neuron = (Neuron)o;
}
if (neuron == null)
{
neuron = (Neuron)Activator.CreateInstance(neuronClass);
}
if (neuronProperties.HasProperty("thresh"))
{
((ThresholdNeuron)neuron).Thresh = ((Double)neuronProperties.GetProperty("thresh"));
}
else if (neuronProperties.HasProperty("bias"))
{
((InputOutputNeuron)neuron).Bias = ((Double)neuronProperties.GetProperty("bias"));
}
return neuron;
}
/// <summary>
/// Creates MultiLayerPerceptron Network architecture - fully connected
/// feedforward with specified number of neurons in each layer
/// </summary>
/// <param name="neuronsInLayers">collection of neuron numbers in getLayersIterator</param>
/// <param name="neuronProperties">neuron propreties</param>
protected void CreateNetwork(IList<int> neuronsInLayers, NeuronProperties neuronProperties)
{
// set network type
this.NetworkType = NeuralNetworkType.MULTI_LAYER_PERCEPTRON;
// create input layer
NeuronProperties inputNeuronProperties = new NeuronProperties(TransferFunctionType.LINEAR);
Layer layer = LayerFactory.createLayer(neuronsInLayers[0], inputNeuronProperties);
bool useBias = true; // use bias neurons by default
if (neuronProperties.HasProperty("useBias"))
{
useBias = (Boolean)neuronProperties.GetProperty("useBias");
}
if (useBias)
{
layer.AddNeuron(new BiasNeuron());
}
this.AddLayer(layer);
// create layers
Layer prevLayer = layer;
//for(Integer neuronsNum : neuronsInLayers)
for (int layerIdx = 1; layerIdx < neuronsInLayers.Count; layerIdx++)
{
int neuronsNum = neuronsInLayers[layerIdx];
// createLayer layer
layer = LayerFactory.createLayer(neuronsNum, neuronProperties);
if (useBias && (layerIdx < (neuronsInLayers.Count - 1)))
{
layer.AddNeuron(new BiasNeuron());
}
// add created layer to network
this.AddLayer(layer);
// createLayer full connectivity between previous and this layer
if (prevLayer != null)
ConnectionFactory.FullConnect(prevLayer, layer);
prevLayer = layer;
}
// set input and output cells for network
NeuralNetworkFactory.SetDefaultIO(this);
// set learnng rule
//this.setLearningRule(new BackPropagation(this));
this.LearningRule = new MomentumBackpropagation();
// this.setLearningRule(new DynamicBackPropagation());
}