public double CalculateError(IMLDataSet data)
{
BestMatchingUnit unit = new BestMatchingUnit(this);
unit.Reset();
foreach (IMLDataPair pair in data)
{
IMLData input = pair.Input;
unit.CalculateBMU(input);
}
return (unit.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(SOMNetwork network, double learningRate,
IMLDataSet training, INeighborhoodFunction neighborhood)
: base(TrainingImplementationType.Iterative)
{
_neighborhood = neighborhood;
Training = training;
LearningRate = learningRate;
_network = network;
_inputNeuronCount = network.InputCount;
_outputNeuronCount = network.OutputCount;
ForceWinner = false;
// setup the correction matrix
_correctionMatrix = new Matrix(_outputNeuronCount, _inputNeuronCount);
// create the BMU class
_bmuUtil = new BestMatchingUnit(network);
}
public BasicTrainSOM(SOMNetwork network, double learningRate, IMLDataSet training, INeighborhoodFunction neighborhood)
: base(TrainingImplementationType.Iterative)
{
this._x3d0ce91dbd8e0b1b = neighborhood;
while (true)
{
this.Training = training;
while (0 == 0)
{
this._x9b481c22b6706459 = learningRate;
this._x87a7fc6a72741c2e = network;
this._x57202a8751db8895 = network.InputCount;
this._x0c37ff3adde9bc44 = network.OutputCount;
this._xf7c1f63ae5cd87f7 = false;
this.Error = 0.0;
this._x42cc0dadfb982948 = new Matrix(this._x57202a8751db8895, this._x0c37ff3adde9bc44);
this._x703660d5c67ed8fb = new BestMatchingUnit(network);
if (0 == 0)
{
return;
}
}
}
}
/// <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(SOMNetwork network, double learningRate,
IMLDataSet training, INeighborhoodFunction neighborhood)
: base(TrainingImplementationType.Iterative)
{
_neighborhood = neighborhood;
Training = training;
LearningRate = learningRate;
_network = network;
_inputNeuronCount = network.InputCount;
_outputNeuronCount = network.OutputCount;
ForceWinner = false;
// setup the correction matrix
_correctionMatrix = new Matrix(_outputNeuronCount, _inputNeuronCount);
// create the BMU class
_bmuUtil = new BestMatchingUnit(network);
}
/// <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;
}
