public void PropagateValuesForward(List <double> networkInputsData = null, int firstLayerNoToChangeInputs = 1)
{
if (networkInputsData == null)
{
networkInputsData = Topology.NetworkInputValues();
}
#region First layer
Layer firstLayer = Topology.Layers.First();
firstLayer.SetInputsDataAndOutputsForFirstLayer(networkInputsData);
#endregion
#region Next layers
for (int layerNo = firstLayerNoToChangeInputs; layerNo < Topology.Layers.Count; layerNo++)
{
Layer currentLayer = Topology.Layers.ElementAt(layerNo);
Layer previousLayer = Topology.Layers.ElementAt(layerNo - 1);
// Each neuron in current layer gets data from all neurons from previous layers and multiples it by his weight
for (int neuronNo = 0; neuronNo < currentLayer.Neurons.Count; neuronNo++)
{
Neuron neuron = currentLayer.Neurons.ElementAt(neuronNo);
// setting input values for neuron
for (int inputNo = 0; inputNo < neuron.Inputs.Count; inputNo++)
{
neuron.Inputs.ElementAt(inputNo).Value = previousLayer.Neurons.ElementAt(inputNo).GetOutputValue();
}
// let's neuron to count his output
neuron.CountOutput();
}
}
#endregion
}
public void SetInputsDataAndOutputsForFirstLayer(List <double> inputsDataForFirstLayer)
{
for (int neuronNo = 0; neuronNo < Neurons.Count; neuronNo++)
{
Neuron neuron = Neurons.ElementAt(neuronNo);
neuron.Inputs.ElementAt(0).Value = inputsDataForFirstLayer.ElementAt(neuronNo);
neuron.CountOutput();
}
}
