public SOMColors()
{
InitializeComponent();
network = CreateNetwork();
gaussian = new NeighborhoodRBF(RBFEnum.Gaussian, WIDTH, HEIGHT);
train = new BasicTrainSOM(network, 0.01, null, gaussian);
train.ForceWinner = false;
samples = AIFH.Alloc2D<double>(15, 3);
for (int i = 0; i < 15; i++)
{
samples[i][0] = rnd.NextDouble(-1, 1);
samples[i][1] = rnd.NextDouble(-1, 1);
samples[i][2] = rnd.NextDouble(-1, 1);
}
train.SetAutoDecay(100, 0.8, 0.003, 30, 5);
}
public SOMColors()
{
InitializeComponent();
network = CreateNetwork();
gaussian = new NeighborhoodRBF(RBFEnum.Gaussian, WIDTH, HEIGHT);
train = new BasicTrainSOM(network, 0.01, null, gaussian);
train.ForceWinner = false;
samples = new List<IMLData>();
for (int i = 0; i < 15; i++)
{
IMLData data = new BasicMLData(3);
data.Data[0] = RangeRandomizer.Randomize(-1, 1);
data.Data[1] = RangeRandomizer.Randomize(-1, 1);
data.Data[2] = RangeRandomizer.Randomize(-1, 1);
samples.Add(data);
}
train.SetAutoDecay(100, 0.8, 0.003, 30, 5);
}
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 CompetitiveTraining(BasicNetwork network,
double learningRate, INeuralDataSet training,
INeighborhoodFunction neighborhood)
{
this.neighborhood = neighborhood;
Training = training;
this.LearningRate = learningRate;
this.network = network;
this.inputLayer = network.GetLayer(BasicNetwork.TAG_INPUT);
this.outputLayer = network.GetLayer(BasicNetwork.TAG_OUTPUT);
this.synapses = network.Structure.GetPreviousSynapses(
this.outputLayer);
this.inputNeuronCount = this.inputLayer.NeuronCount;
this.outputNeuronCount = this.outputLayer.NeuronCount;
this.ForceWinner = false;
Error = 0;
// setup the correction matrix
foreach (ISynapse synapse in this.synapses)
{
Matrix matrix = new Matrix(synapse.WeightMatrix.Rows,
synapse.WeightMatrix.Cols);
this.correctionMatrix[synapse] = matrix;
}
// create the BMU class
this.bmuUtil = new BestMatchingUnit(this);
}
/// <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>
/// 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);
}
