public NetworkState(RecurrentNetwork network, NetworkState last)
{
this.Network = network;
states = new List <object>();
double[] input = new double[network.Layers[0].InputSize];
this.Input = input;
double[] output = null;
for (int i = 0; i < network.Layers.Count; i++)
{
Layer layer = network.Layers[i];
output = new double[layer.Size];
if (layer is RecurrentLayer)
{
RecurrentState lastState = last != null ? (RecurrentState)last.states[i] : null;
states.Add(new RecurrentState(input, output, lastState));
}
else
{
states.Add(new FeedForwardState(input, output));
}
// The input of the next layer is the output of the last.
input = output;
}
this.Output = output;
}
public void FeedForward(RecurrentState state)
{
double[] weightedSums = state.WeightedSums;
Array.Clear(weightedSums, 0, weightedSums.Length);
Sum(biasWeights, weightedSums);
WeightedInputSum(state.Inputs, inputWeights, weightedSums);
WeightedInputSum(state.LastOutputs, internalWeights, weightedSums);
OutputFromActivation(weightedSums, state.Outputs);
}
public RecurrentState(double[] inputs, double[] outputs, RecurrentState lastState)
{
Inputs = inputs;
Outputs = outputs;
if (lastState != null)
{
LastOutputs = lastState.Outputs;
}
else
{
LastOutputs = null;// new double[outputs.Length];
}
WeightedSums = new double[outputs.Length];
}
public void FeedForward()
{
for (int i = 0; i < states.Count; i++)
{
Layer layer = Network.Layers[i];
if (layer is RecurrentLayer)
{
RecurrentState state = (RecurrentState)states[i];
((RecurrentLayer)layer).FeedForward(state);
}
else
{
FeedForwardState state = (FeedForwardState)states[i];
((FeedForwardLayer)layer).FeedForward(state);
}
}
}