public static void PreparePerceptionLayerForPulse(NeuralNet net, double[] input)
{
int i;
if (input.Length != net.m_inputLayer.Count)
{
throw new ArgumentException(string.Format("Expecting {0} inputs for this net", net.m_inputLayer.Count));
}
// initialize data
for (i = 0; i < net.m_inputLayer.Count; i++)
{
net.m_inputLayer[i].Output = input[i];
}
}
private static void Initialize(NeuralNet net, int randomSeed,
int inputNeuronCount, int hiddenNeuronCount, int outputNeuronCount, IActivationFunction aktivationFunction)
{
int i, j;
Random rand;
rand = new Random(randomSeed);
net.m_inputLayer = new NeuralLayer();
net.m_outputLayer = new NeuralLayer();
net.m_hiddenLayer = new NeuralLayer();
for (i = 0; i < inputNeuronCount; i++)
{
net.m_inputLayer.Add(new Neuron(0, aktivationFunction));
}
for (i = 0; i < outputNeuronCount; i++)
{
net.m_outputLayer.Add(new Neuron(rand.NextDouble(), aktivationFunction));
}
for (i = 0; i < hiddenNeuronCount; i++)
{
net.m_hiddenLayer.Add(new Neuron(rand.NextDouble(), aktivationFunction));
}
// wire-up input layer to hidden layer
for (i = 0; i < net.m_hiddenLayer.Count; i++)
{
for (j = 0; j < net.m_inputLayer.Count; j++)
{
net.m_hiddenLayer[i].Input.Add(net.m_inputLayer[j], new NeuralFactor(rand.NextDouble()));
}
}
// wire-up output layer to hidden layer
for (i = 0; i < net.m_outputLayer.Count; i++)
{
for (j = 0; j < net.m_hiddenLayer.Count; j++)
{
net.m_outputLayer[i].Input.Add(net.HiddenLayer[j], new NeuralFactor(rand.NextDouble()));
}
}
}
