/// <summary>
/// Calculate the error for the specified data set. The error is the largest distance.
/// </summary>
/// <param name="data">The data set to check.</param>
/// <returns>The error.</returns>
public double CalculateError(IMLDataSet data)
{
var bmu = new BestMatchingUnit(this);
bmu.Reset();
// Determine the BMU for each training element.
foreach (IMLDataPair pair in data)
{
IMLData input = pair.Input;
bmu.CalculateBMU(input);
}
// update the error
return(bmu.WorstDistance / 100.0);
}
/// <summary>
/// Create an instance of competitive training.
/// </summary>
/// <param name="network">The network to train.</param>
/// <param name="learningRate">The learning rate, how much to apply per iteration.</param>
/// <param name="training">The training set (unsupervised).</param>
/// <param name="neighborhood">The neighborhood function to use.</param>
public BasicTrainSOM(SelfOrganizingMap network, double learningRate,
IList <BasicData> training, INeighborhoodFunction neighborhood)
{
_neighborhood = neighborhood;
_training = training;
LearningRate = learningRate;
_network = network;
_inputNeuronCount = network.InputCount;
_outputNeuronCount = network.OutputCount;
ForceWinner = false;
_error = 0;
// setup the correction matrix
_correctionMatrix = DenseMatrix.Create(_outputNeuronCount, _inputNeuronCount, 0);
// create the BMU class
_bmuUtil = new BestMatchingUnit(network);
}
/// <summary>
/// Create an instance of competitive training.
/// </summary>
/// <param name="network">The network to train.</param>
/// <param name="learningRate">The learning rate, how much to apply per iteration.</param>
/// <param name="training">The training set (unsupervised).</param>
/// <param name="neighborhood">The neighborhood function to use.</param>
public BasicTrainSOM(SelfOrganizingMap network, double learningRate,
IList<BasicData> training, INeighborhoodFunction neighborhood)
{
_neighborhood = neighborhood;
_training = training;
LearningRate = learningRate;
_network = network;
_inputNeuronCount = network.InputCount;
_outputNeuronCount = network.OutputCount;
ForceWinner = false;
_error = 0;
// setup the correction matrix
_correctionMatrix = DenseMatrix.Create(_outputNeuronCount, _inputNeuronCount, 0);
// create the BMU class
_bmuUtil = new BestMatchingUnit(network);
}
