/// <summary>
/// Constructor for the neural network class
/// </summary>
/// <param name="numberInputs">
/// The number of nodes in the input layer
/// </param>
/// <param name="numberHiddenNodes">
/// An array of the number of hidden nodes in each hidden layer
/// </param>
/// <param name="numberOutputs">
/// The number of nodes in the output layer
/// </param>
/// <param name="learningRate">
/// The learning rate of the network
/// </param>
/// <param name="momentumFactor">
/// The momentum factor of the network
/// </param>
public NeuralNetwork(int numberInputs, int[] numberHiddenNodes, int numberOutputs, float learningRate, float momentumFactor)
{
this.numberHiddenLayers = numberHiddenNodes.Length;
allLayers = new NeuronLayer[numberHiddenLayers + 2];
for (int i = 0; i < numberHiddenLayers + 2; i++)
{
if (i == 0)
{
inputLayer = allLayers[i] = new NeuronLayer(numberInputs, numberHiddenNodes[i], 0, learningRate, momentumFactor);
}
else if (i < numberHiddenLayers)
{
allLayers[i] = new NeuronLayer(numberHiddenNodes[i - 1], numberHiddenNodes[i], allLayers[i - 1].GetNumberNodes(), learningRate, momentumFactor);
}
else if (i < numberHiddenLayers + 1)
{
allLayers[i] = new NeuronLayer(numberHiddenNodes[i - 1], numberOutputs, allLayers[i - 1].GetNumberNodes(), learningRate, momentumFactor);
}
else
{
outputLayer = allLayers[i] = new NeuronLayer(numberOutputs, 0, allLayers[i - 1].GetNumberNodes(), learningRate, momentumFactor);
}
}
inputLayer.Initialise(null, allLayers[1]);
for (int i = 1; i < numberHiddenLayers + 1; i++)
{
allLayers[i].Initialise(allLayers[i - 1], allLayers[i + 1]);
}
outputLayer.Initialise(allLayers[numberHiddenLayers], null);
}
/// <summary>
/// Constructor for the neural network class
/// </summary>
/// <param name="numberInputs">
/// The number of nodes in the input layer
/// </param>
/// <param name="numberHiddenNodes">
/// An array of the number of hidden nodes in each hidden layer
/// </param>
/// <param name="numberOutputs">
/// The number of nodes in the output layer
/// </param>
/// <param name="learningRate">
/// The learning rate of the network
/// </param>
/// <param name="momentumFactor">
/// The momentum factor of the network
/// </param>
public NeuralNetwork(int numberInputs, int[] numberHiddenNodes, int numberOutputs, float learningRate, float momentumFactor)
{
this.numberHiddenLayers = numberHiddenNodes.Length;
allLayers = new NeuronLayer[numberHiddenLayers + 2];
for (int i = 0; i < numberHiddenLayers + 2; i++)
{
if (i == 0)
inputLayer = allLayers[i] = new NeuronLayer(numberInputs, numberHiddenNodes[i], 0, learningRate, momentumFactor);
else if (i < numberHiddenLayers)
allLayers[i] = new NeuronLayer(numberHiddenNodes[i - 1], numberHiddenNodes[i], allLayers[i - 1].GetNumberNodes(), learningRate, momentumFactor);
else if (i < numberHiddenLayers + 1)
allLayers[i] = new NeuronLayer(numberHiddenNodes[i - 1], numberOutputs, allLayers[i - 1].GetNumberNodes(), learningRate, momentumFactor);
else
outputLayer = allLayers[i] = new NeuronLayer(numberOutputs, 0, allLayers[i - 1].GetNumberNodes(), learningRate, momentumFactor);
}
inputLayer.Initialise(null, allLayers[1]);
for (int i = 1; i < numberHiddenLayers + 1; i++)
allLayers[i].Initialise(allLayers[i - 1], allLayers[i + 1]);
outputLayer.Initialise(allLayers[numberHiddenLayers], null);
}
/// <summary>
/// Register the previous and next layers with the current layer
/// </summary>
/// <param name="parentLayer">
/// The previous layer
/// </param>
/// <param name="childLayer">
/// The next layer
/// </param>
public void Initialise(NeuronLayer parentLayer, NeuronLayer childLayer)
{
this.parentLayer = parentLayer;
this.childLayer = childLayer;
}
/// <summary>
/// Register the previous and next layers with the current layer
/// </summary>
/// <param name="parentLayer">
/// The previous layer
/// </param>
/// <param name="childLayer">
/// The next layer
/// </param>
public void Initialise(NeuronLayer parentLayer, NeuronLayer childLayer)
{
this.parentLayer = parentLayer;
this.childLayer = childLayer;
}
