public void createFromTopology()
{
neuralLayers = new NeuralLayer <T> [topology.LayerCount];
if (!topology.Verified)
{
topology.verify();
}
NeuralNode <T> currentNode;
TopologyEntry currentEntry;
int currentLayer;
for (int i = 0; i < topology.Count; i++)
{
currentEntry = topology.adj_M[i];
currentLayer = currentEntry.layer;
if (neuralLayers[currentLayer] == null)
{
if (currentLayer == 0)
{
neuralLayers[currentLayer] = new InputLayer <T>();
}
else
{
neuralLayers[currentLayer] = new NeuralLayer <T>();
}
}
if (topology.adj_M[i].layer == 0)
{
currentNode = new ConstInputNeuralNode <T>();
}
else
{
if (topology.adj_M[i].memoryDepth > 0)
{
currentNode = new MemoryNode <T>(topology.adj_M[i].memoryDepth);
}
else
{
currentNode = new NeuralNode <T>();
}
foreach (float w in currentEntry.adj_V)
{
if (!float.IsNaN(w))
{
currentNode.InputWeigths.Add(w);
}
}
}
neuralLayers[currentLayer].addNode(currentNode);
}
for (int i = 1; i < topology.LayerCount; i++)
{
neuralLayers[i].inputLayer = neuralLayers[i - 1];
}
}
public object Clone()
{
NeuralLayer <T> ret = new NeuralLayer <T>();
for (int i = 0; i < NodeCount; i++)
{
ret.addNode(nodes[i].Clone() as NeuralNode <T>);
}
if (outputs != null)
{
ret.outputs = outputs.Clone() as T[];
}
return(ret);
}