Neural Networks on C#

// AForge Neural Net Library
//
// Copyright � Andrew Kirillov, 2005-2006
// andrew.kirillov@gmail.com
//

namespace AForge.Neuro
{
	using System;

	/// <summary>
	/// Activation network
	/// </summary>
	/// 
	/// <remarks>Activation network is a base for multi-layer neural network
	/// with activation functions. It consists of <see cref="ActivationLayer">activation
	/// layers</see>.</remarks>
	///
	public class ActivationNetwork : Network
	{
		/// <summary>
		/// Network's layers accessor
		/// </summary>
		/// 
		/// <param name="index">Layer index</param>
		/// 
		/// <remarks>Allows to access network's layer.</remarks>
		/// 
		public new ActivationLayer this[int index]
		{
			get { return ( (ActivationLayer) layers[index] ); }
		}

		/// <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 );
			}
		}
	}
}