public ProtoNet(int[] topology)
{
// Validate the input.
if (topology == null || topology.Length < 1) return;
this.topology = topology;
// NOTE: I seed my random for consistency when
// testing the network.
rand = new Random(424242);
// And now I'm cheating with a flattened
// list of nodes. This makes training easier.
flattened = new List<Neuron>();
// Create the neuron array.
inputs = new Neuron[topology[0]];
// Generate the input nodes and recursively construct the graph.
for (var i = 0; i < topology[0]; i++)
{
// The first layer of the topology arg will be
// representative of the inputs.
inputs[i] = new Neuron(topology[0], rand);
flattened.Add(inputs[i]);
}
// Recursively construct the graph.
recurse(inputs, topology, 1);
}
private void recurse(Neuron[] parents, int[] topology, int index)
{
if (parents == null || parents.Length == 0) return;
if (topology == null || topology.Length == 0) return;
if (index >= topology.Length) return;
// Construct the next layer.
Neuron[] layer = new Neuron[topology[index]];
for (var i = 0; i < layer.Length; i++)
{
// Create each node in this layer.
layer[i] = new Neuron(parents.Length, rand);
flattened.Add(layer[i]);
}
// "Wire up" the parents.
for (var i = 0; i < parents.Length; i++)
{
parents[i].Children = layer;
}
// Recurse.
recurse(layer, topology, ++index);
}
