/// <summary>
/// Initializes a new instance of the <see cref="ActivationNetwork"/> class
/// </summary>
/// <param name="function">Activation function of neurons of the network</param>
/// <param name="inputsCount">Network's inputs count</param>
/// <param name="neuronsCount">Array, which specifies the amount of neurons in
/// each layer of the neural network</param>
///
/// <remarks>The new network will be randomized (see <see cref="ActivationNeuron.Randomize"/>
/// method) after it is created.</remarks>
///
/// <example>The following sample illustrates the usage of <c>ActivationNetwork</c> class:
/// <code>
/// // create activation network
/// ActivationNetwork network = new ActivationNetwork(
/// new SigmoidFunction( ), // sigmoid activation function
/// 3, // 3 inputs
/// 4, 1 ); // 2 layers:
/// // 4 neurons in the firs layer
/// // 1 neuron in the second layer
/// </code>
/// </example>
///
public ActivationNetwork(IActivationFunction function, int inputsCount, params int[] neuronsCount)
: base(inputsCount, neuronsCount.Length)
{
// create each layer
for (int i = 0; i < layersCount; i++)
{
layers[i] = new ActivationLayer(
// neurons count in the layer
neuronsCount[i],
// inputs count of the layer
(i == 0) ? inputsCount : neuronsCount[i - 1],
// activation function of the layer
function);
}
}
/// <summary>
/// Initializes a new instance of the <see cref="ActivationNetwork"/> class.
/// </summary>
///
/// <param name="function">Activation function of neurons of the network.</param>
/// <param name="inputsCount">Network's inputs count.</param>
/// <param name="neuronsCount">Array, which specifies the amount of neurons in
/// each layer of the neural network.</param>
///
/// <remarks>The new network is randomized (see <see cref="ActivationNeuron.Randomize"/>
/// method) after it is created.</remarks>
///
public ActivationNetwork(IActivationFunction function, int inputsCount, params int[] neuronsCount)
: base(inputsCount, neuronsCount.Length)
{
// create each layer
for (int i = 0; i < layers.Length; i++)
{
layers[i] = new ActivationLayer(
// neurons count in the layer
neuronsCount[i],
// inputs count of the layer
(i == 0) ? inputsCount : neuronsCount[i - 1],
// activation function of the layer
function);
}
}
