/// <summary>
/// Initializes a new instance of the <see cref="SelfOptimizationMapLearning"/> class
/// </summary>
/// <param name="network">Neural network to train</param>
/// <param name="width">Neural network's width</param>
/// <param name="height">Neural network's height</param>
/// <remarks>The constructor allows to pass network of arbitrary rectangular shape.
/// The amount of neurons in the network should be equal to <b>width</b> * <b>height</b>.
/// </remarks>
public SelfOptimizationMapLearning( DistanceNetwork network, int width, int height )
{
// check network size
if ( network[0].NeuronsCount != width * height )
throw new ArgumentException( "Invalid network size" );
this._network = network;
this._width = width;
this._height = height;
}
/// <summary>
/// Initializes a new instance of the <see cref="SelfOptimizationMapLearning"/> class
/// </summary>
/// <param name="network">Neural network to train</param>
/// <remarks>This constructor supposes that a square network will be passed for training -
/// it should be possible to get square root of network's neurons amount.</remarks>
public SelfOptimizationMapLearning( DistanceNetwork network )
{
// network's dimension was not specified, let's try to guess
var neuronsCount = network[0].NeuronsCount;
_width = (int) System.Math.Sqrt( neuronsCount );
if ( _width * _width != neuronsCount )
throw new ArgumentException( "Invalid network size" );
// ok, we got it
this._network = network;
}
/// <summary>
/// Initializes a new instance of the <see cref="ElasticNetworkLearning"/> class
/// </summary>
/// <param name="network">Neural network to train</param>
public ElasticNetworkLearning( DistanceNetwork network )
{
this._network = network;
// precalculate distances array
var neurons = network[0].NeuronsCount;
var deltaAlpha = System.Math.PI * 2.0 / neurons;
var alpha = deltaAlpha;
_distance = new double[neurons];
_distance[0] = 0.0;
// calculate all distance values
for ( var i = 1; i < neurons; i++ )
{
var dx = 0.5 * System.Math.Cos( alpha ) - 0.5;
var dy = 0.5 * System.Math.Sin( alpha );
_distance[i] = dx * dx + dy * dy;
alpha += deltaAlpha;
}
}
