/// <summary>
/// Creates a neural network with <paramref name="inputCount"/> number of inputs,
/// <paramref name="outputCount"/> number of outputs,
/// <paramref name="hiddenLayersCount"/> number of hidden layers,
/// where the number of neurons in each hidden layer can be selected using the <paramref name="hiddenLayerNeuronCountSelector"/>
/// The <paramref name="activationFunctionSelector"/> allows to select a different activation function used for each neuron in each hidden layer.
/// Each neuron in the hidden layer uses the default bias (0) and synapse weights are initialized to 0.5.
/// </summary>
/// <param name="inputCount">The ammount of inputs</param>
/// <param name="outputCount">The ammount of outputs</param>
/// <param name="hiddenLayersCount">The ammount of hidden layers</param>
/// <param name="hiddenLayerNeuronCountSelector">Selects the ammount of neurons in the given hidden layer index</param>
/// <param name="activationFunctionSelector">Selects an activation function for each neuron in each hidden layer</param>
public NeuralNetwork(int inputCount, int outputCount, int hiddenLayersCount, LayerNeuronCountSelector hiddenLayerNeuronCountSelector, ActivationFunctionSelector activationFunctionSelector) : this(inputCount, outputCount, hiddenLayersCount, hiddenLayerNeuronCountSelector, activationFunctionSelector, (l, n) => 0)
{
}
/// <summary>
/// Creates a neural network with <paramref name="inputCount"/> number of inputs,
/// <paramref name="outputCount"/> number of outputs,
/// <paramref name="hiddenLayersCount"/> number of hidden layers,
/// where the number of neurons in each hidden layer can be selected using the <paramref name="hiddenLayerNeuronCountSelector"/>
/// The <paramref name="activationFunctionSelector"/> allows to select a different activation function used for each neuron in each hidden layer.
/// The <paramref name="biasSelector"/> allows to select a different bias for each neuron in each hidden layer.
/// Each synapse weight is initialized to 0.5.
/// </summary>
/// <param name="inputCount">The ammount of inputs</param>
/// <param name="outputCount">The ammount of outputs</param>
/// <param name="hiddenLayersCount">The ammount of hidden layers</param>
/// <param name="hiddenLayerNeuronCountSelector">Selects the ammount of neurons in the given hidden layer index</param>
/// <param name="activationFunctionSelector">Selects an activation function for each neuron in each hidden layer</param>
/// <param name="biasSelector">Selects a bias for each neuron in each hidden layer</param>
public NeuralNetwork(int inputCount, int outputCount, int hiddenLayersCount, LayerNeuronCountSelector hiddenLayerNeuronCountSelector, ActivationFunctionSelector activationFunctionSelector, BiasSelector biasSelector) : this(inputCount, outputCount, hiddenLayersCount, hiddenLayerNeuronCountSelector, activationFunctionSelector, biasSelector, (l1, n1, l2, n2) => 0.5)
{
}
/// <summary>
/// Creates a neural network with <paramref name="inputCount"/> number of inputs,
/// <paramref name="outputCount"/> number of outputs,
/// <paramref name="hiddenLayersCount"/> number of hidden layers,
/// where the number of neurons in each hidden layer can be selected using the <paramref name="hiddenLayerNeuronCountSelector"/>
/// The <paramref name="activationFunctionSelector"/> allows to select a different activation function used for each neuron in each hidden layer.
/// The <paramref name="biasSelector"/> allows to select a different bias for each neuron in each hidden layer.
/// The <paramref name="synapseWeightSelector"/> allows to select a different weight for each synapse between each neuron. Starting from the input layer (Layer -1) to the output layer (Number of hidden layers + 1).
/// </summary>
/// <param name="inputCount">The ammount of inputs</param>
/// <param name="outputCount">The ammount of outputs</param>
/// <param name="hiddenLayersCount">The ammount of hidden layers</param>
/// <param name="hiddenLayerNeuronCountSelector">Selects the ammount of neurons in the given hidden layer index</param>
/// <param name="activationFunctionSelector">Selects an activation function for each neuron in each hidden layer</param>
/// <param name="biasSelector">Selects a bias for each neuron in each hidden layer</param>
/// <param name="synapseWeightSelector">Selects a synapse weight for each synapse between each neuron. Starting from the input layer (Layer -1) to the output layer (Number of hidden layers + 1).</param>
public NeuralNetwork(int inputCount, int outputCount, int hiddenLayersCount, LayerNeuronCountSelector hiddenLayerNeuronCountSelector, ActivationFunctionSelector activationFunctionSelector, BiasSelector biasSelector, SynapseWeightSelector synapseWeightSelector)
{
_hiddenLayers = new List <HiddenLayer>();
_inputLayer = new InputLayer(inputCount);
_outputLayer = new OutputLayer(outputCount, (i) => ActivationFunctions.Sigmoid, (i) => 0);
for (int layer = 0; layer < hiddenLayersCount; layer++)
{
var hiddenLayer = new HiddenLayer(hiddenLayerNeuronCountSelector(layer), (i) => activationFunctionSelector(layer, i), (i) => biasSelector(layer, i));
_hiddenLayers.Add(hiddenLayer);
if (layer == 0)
{
foreach (var inputNeuron in _inputLayer.Neurons.EnumerateWithIndex())
{
foreach (var hiddenNeuron in hiddenLayer.Neurons.EnumerateWithIndex())
{
inputNeuron.Item.AddSynapse(hiddenNeuron, synapseWeightSelector(-1, inputNeuron.Index, 0, hiddenNeuron.Index));
}
}
}
else
{
foreach (var previousLayerNeuron in _hiddenLayers[layer - 1].Neurons.EnumerateWithIndex())
{
foreach (var hiddenNeuron in hiddenLayer.Neurons.EnumerateWithIndex())
{
previousLayerNeuron.Item.AddSynapse(hiddenNeuron, synapseWeightSelector(layer - 1, previousLayerNeuron.Index, layer, hiddenNeuron.Index));
}
}
}
if (layer == hiddenLayersCount - 1)
{
foreach (var hiddenNeuron in hiddenLayer.Neurons.EnumerateWithIndex())
{
foreach (var outputNeuron in _outputLayer.Neurons.EnumerateWithIndex())
{
hiddenNeuron.Item.AddSynapse(outputNeuron, synapseWeightSelector(layer - 1, hiddenNeuron.Index, layer, outputNeuron.Index));
}
}
}
}
}
