private static void Initialize(NeuralNet net, int randomSeed,
int inputNeuronCount, int hiddenNeuronCount, int outputNeuronCount)
{
#region Declarations
int i, j;
Random rand;
#endregion
#region Initialization
rand = new Random(randomSeed);
#endregion
#region Execution
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));
}
for (i = 0; i < outputNeuronCount; i++)
{
net.m_outputLayer.Add(new Neuron(rand.NextDouble()));
}
for (i = 0; i < hiddenNeuronCount; i++)
{
net.m_hiddenLayer.Add(new Neuron(rand.NextDouble()));
}
// 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()));
}
}
#endregion
}
public static void CalculateAndAppendTransformation(NeuralNet net)
{
#region Declarations
int i, j;
INeuron outputNode, inputNode, hiddenNode;
#endregion
#region Execution
// adjust output layer weight change
for (j = 0; j < net.m_outputLayer.Count; j++)
{
outputNode = net.m_outputLayer[j];
for (i = 0; i < net.m_hiddenLayer.Count; i++)
{
hiddenNode = net.m_hiddenLayer[i];
outputNode.Input[hiddenNode].H_Vector += outputNode.Error * hiddenNode.Output;
}
outputNode.Bias.H_Vector += outputNode.Error * outputNode.Bias.Weight;
}
// adjust hidden layer weight change
for (j = 0; j < net.m_hiddenLayer.Count; j++)
{
hiddenNode = net.m_hiddenLayer[j];
for (i = 0; i < net.m_inputLayer.Count; i++)
{
inputNode = net.m_inputLayer[i];
hiddenNode.Input[inputNode].H_Vector += hiddenNode.Error * inputNode.Output;
}
hiddenNode.Bias.H_Vector += hiddenNode.Error * hiddenNode.Bias.Weight;
}
#endregion
}
private static void CalculateErrors(NeuralNet net, double[] desiredResults)
{
#region Declarations
int i, j;
double temp, error;
INeuron outputNode, hiddenNode;
#endregion
#region Execution
// Calcualte output error values
for (i = 0; i < net.m_outputLayer.Count; i++)
{
outputNode = net.m_outputLayer[i];
temp = outputNode.Output;
outputNode.Error = (desiredResults[i] - temp) * SigmoidDerivative(temp); //* temp * (1.0F - temp);
}
// calculate hidden layer error values
for (i = 0; i < net.m_hiddenLayer.Count; i++)
{
hiddenNode = net.m_hiddenLayer[i];
temp = hiddenNode.Output;
error = 0;
for (j = 0; j < net.m_outputLayer.Count; j++)
{
outputNode = net.m_outputLayer[j];
error += (outputNode.Error * outputNode.Input[hiddenNode].Weight) * SigmoidDerivative(temp);// *(1.0F - temp);
}
hiddenNode.Error = error;
}
#endregion
}
public static void PreparePerceptionLayerForPulse(NeuralNet net, double[] input)
{
#region Declarations
int i;
#endregion
#region Execution
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];
}
#endregion
}