/// <summary>
/// Constructs a new neural network.
/// </summary>
/// <param name="numberOfInputs">Number of inputs.</param>
/// <param name="layerSizes">
/// Number of neurons in each hidden layer.
/// The last layer is the output layer.
/// </param>
/// <param name="activationFunctions">Activation functions for every layer.</param>
/// <exception cref="ArgumentException"></exception>
public NeuralNetwork(int numberOfInputs, int[] layerSizes, ActivationFunction[] activationFunctions)
{
if (numberOfInputs < 1)
{
throw new ArgumentException("Number of input neurons less than one!");
}
if (layerSizes.Length < 1)
{
throw new ArgumentException("Number of layers less than one!");
}
if (activationFunctions.Length != layerSizes.Length)
{
throw new ArgumentException("Number of activation functions doesn't match that of layers");
}
foreach (var layerSize in layerSizes)
{
if (layerSize < 1)
{
throw new ArgumentException("Number of neurons in layer less than one!");
}
}
_numberOfInputs = numberOfInputs;
_layerSizes = new int[layerSizes.Length];
layerSizes.CopyTo(_layerSizes, 0);
_activationFunctions = new ActivationFunction[activationFunctions.Length];
for (int i = 0; i < activationFunctions.Length; i++)
{
_activationFunctions[i] = new ActivationFunction(activationFunctions[i]);
}
_weights = new Matrix[layerSizes.Length];
_weights[0] = new Matrix(numberOfInputs, layerSizes[0]);
for (int i = 1; i < layerSizes.Length; i++)
{
_weights[i] = new Matrix(layerSizes[i - 1], layerSizes[i]);
}
_activityA = new Matrix[layerSizes.Length];
_activityZ = new Matrix[layerSizes.Length];
_keepTrainingFunction = DefaultKeepTraining;
_errorFunction = DefaultError;
}
/// <summary>
/// Constructs a new neural network based on a given one.
/// </summary>
/// <param name="input">The given neural network.</param>
public NeuralNetwork(NeuralNetwork input)
{
_numberOfInputs = input._numberOfInputs;
_layerSizes = new int[input._layerSizes.Length];
input._layerSizes.CopyTo(_layerSizes, 0);
_activationFunctions = new ActivationFunction[input._activationFunctions.Length];
for (int i = 0; i < input._activationFunctions.Length; i++)
{
_activationFunctions[i] = new ActivationFunction(input._activationFunctions[i]);
}
_weights = new Matrix[input._layerSizes.Length];
for (int i = 0; i < input._activationFunctions.Length; i++)
{
_weights[i] = new Matrix(input._weights[i]);
}
_activityA = new Matrix[input._layerSizes.Length];
_activityZ = new Matrix[input._layerSizes.Length];
_keepTrainingFunction = input._keepTrainingFunction;
_errorFunction = input._errorFunction;
}