public static double[,] calcProximityMetrics(List<double> input, Lattice neurons)
{
double[,] distances = new double[neurons.getSize(), neurons.getSize()];
int latticeSize = neurons.getSize();
for (int rowInd = 0; rowInd < latticeSize; ++rowInd)
{
for (int colInd = 0; colInd < latticeSize; ++colInd)
{
distances[rowInd, colInd] = calculateProximity(input, neurons.get(rowInd, colInd).weights_);
}
}
return distances;
}
public Algorithm(int latticeSize, int featuresCnt,
int epochsNumber = 1000, double learningRate = 0.01, int tau2 = 1000, double trainingError = 1e-3)
{
latticeSize_ = latticeSize;
featuresCnt_ = featuresCnt;
epochsNumber_ = epochsNumber;
learningRate_ = learningRate;
tau2_ = tau2;
trainingError_ = trainingError;
neurons_ = new Lattice(latticeSize, featuresCnt);
width_ = neurons_.getWidth();
tau1_ = epochsNumber_ / (width_ * 1.0);
}
public Algorithm(int latticeSize, int featuresCnt,
int epochsNumber = 1000, double learningRate = 0.01, int tau2 = 1000, double trainingError = 1e-3)
{
latticeSize_ = latticeSize;
featuresCnt_ = featuresCnt;
epochsNumber_ = epochsNumber;
learningRate_ = learningRate;
tau2_ = tau2;
trainingError_ = trainingError;
neurons_ = new Lattice(latticeSize, featuresCnt);
width_ = neurons_.getWidth();
tau1_ = epochsNumber_ / (width_ * 1.0);
}
public static double[,] calcProximityMetrics(List <double> input, Lattice neurons)
{
double[,] distances = new double[neurons.getSize(), neurons.getSize()];
int latticeSize = neurons.getSize();
for (int rowInd = 0; rowInd < latticeSize; ++rowInd)
{
for (int colInd = 0; colInd < latticeSize; ++colInd)
{
distances[rowInd, colInd] = calculateProximity(input, neurons.get(rowInd, colInd).weights_);
}
}
return(distances);
}
