/// <summary>
/// Connect two layers.
/// </summary>
/// <param name="source">The source layer.</param>
/// <param name="target">The target layer.</param>
/// <param name="theActivationFunction">The activation function to use.</param>
/// <param name="biasActivation">The bias activation to use.</param>
/// <param name="isRecurrent">True, if this is a recurrent connection.</param>
public void ConnectLayers(IFreeformLayer source,
IFreeformLayer target,
IActivationFunction theActivationFunction,
double biasActivation, bool isRecurrent)
{
// create bias, if requested
if (biasActivation > EncogFramework.DefaultDoubleEqual)
{
// does the source already have a bias?
if (source.HasBias)
{
throw new FreeformNetworkError(
"The source layer already has a bias neuron, you cannot create a second.");
}
IFreeformNeuron biasNeuron = _neuronFactory
.FactorRegular(null);
biasNeuron.Activation = biasActivation;
biasNeuron.IsBias = true;
source.Add(biasNeuron);
}
// create connections
foreach (IFreeformNeuron targetNeuron in target.Neurons)
{
// create the summation for the target
IInputSummation summation = targetNeuron.InputSummation;
// do not create a second input summation
if (summation == null)
{
summation = _summationFactory.Factor(theActivationFunction);
targetNeuron.InputSummation = summation;
}
// connect the source neurons to the target neuron
foreach (IFreeformNeuron sourceNeuron in source.Neurons)
{
IFreeformConnection connection = _connectionFactory
.Factor(sourceNeuron, targetNeuron);
sourceNeuron.AddOutput(connection);
targetNeuron.AddInput(connection);
}
}
}
/// <inheritdoc/>
public IFreeformNeuron FactorRegular(IInputSummation o)
{
return new BasicFreeformNeuron(o);
}
/// <summary>
/// Construct a basic freeform network.
/// </summary>
/// <param name="theInputSummation">The input summation to use.</param>
public BasicFreeformNeuron(IInputSummation theInputSummation)
{
InputSummation = theInputSummation;
}
/// <summary>
/// Construct a basic freeform network.
/// </summary>
/// <param name="theInputSummation">The input summation to use.</param>
public BasicFreeformNeuron(IInputSummation theInputSummation)
{
InputSummation = theInputSummation;
}
/// <summary>
/// Craete a freeform network from a basic network.
/// </summary>
/// <param name="network">The basic network to use.</param>
public FreeformNetwork(BasicNetwork network)
{
if (network.LayerCount < 2)
{
throw new FreeformNetworkError(
"The BasicNetwork must have at least two layers to be converted.");
}
// handle each layer
IFreeformLayer previousLayer = null;
for (int currentLayerIndex = 0;
currentLayerIndex < network
.LayerCount;
currentLayerIndex++)
{
// create the layer
IFreeformLayer currentLayer = _layerFactory.Factor();
// Is this the input layer?
if (_inputLayer == null)
{
_inputLayer = currentLayer;
}
// Add the neurons for this layer
for (int i = 0; i < network.GetLayerNeuronCount(currentLayerIndex); i++)
{
// obtain the summation object.
IInputSummation summation = null;
if (previousLayer != null)
{
summation = _summationFactory.Factor(network
.GetActivation(currentLayerIndex));
}
// add the new neuron
currentLayer.Add(_neuronFactory.FactorRegular(summation));
}
// Fully connect this layer to previous
if (previousLayer != null)
{
ConnectLayersFromBasic(network, currentLayerIndex - 1,
previousLayer, currentLayer);
}
// Add the bias neuron
// The bias is added after connections so it has no inputs
if (network.IsLayerBiased(currentLayerIndex))
{
IFreeformNeuron biasNeuron = _neuronFactory
.FactorRegular(null);
biasNeuron.IsBias = true;
biasNeuron.Activation = network
.GetLayerBiasActivation(currentLayerIndex);
currentLayer.Add(biasNeuron);
}
// update previous layer
previousLayer = currentLayer;
}
// finally, set the output layer.
_outputLayer = previousLayer;
}
/// <inheritdoc/>
public IFreeformNeuron FactorRegular(IInputSummation o)
{
return(new BasicFreeformNeuron(o));
}
