/// <summary>
/// Initializes a new instance of the <see cref="DeltaRuleLearning"/> class
/// </summary>
/// <param name="network">Network to teach</param>
public DeltaRuleLearning( ActivationNetwork network )
{
// check layers count
if ( network.LayersCount != 1 )
{
throw new ArgumentException( "Invalid nuaral network. It should have one layer only." );
}
this._network = network;
}
/// <summary>
/// Initializes a new instance of the <see cref="BackPropagationLearning"/> class
/// </summary>
/// <param name="network">Network to teach</param>
public BackPropagationLearning( ActivationNetwork network )
{
this._network = network;
// create error and deltas arrays
_neuronErrors = new double[network.LayersCount][];
_weightsUpdates = new double[network.LayersCount][][];
_thresholdsUpdates = new double[network.LayersCount][];
// initialize errors and deltas arrays for each layer
for ( int i = 0, n = network.LayersCount; i < n; i++ )
{
Layer layer = network[i];
_neuronErrors[i] = new double[layer.NeuronsCount];
_weightsUpdates[i] = new double[layer.NeuronsCount][];
_thresholdsUpdates[i] = new double[layer.NeuronsCount];
// for each neuron
for ( var j = 0; j < layer.NeuronsCount; j++ )
_weightsUpdates[i][j] = new double[layer.InputsCount];
}
}
