private void BTrain_Click(object sender, EventArgs e)
{
if (Data.Count != 0)
{
KMeans k = new KMeans((int)NiDCentroidsAmount.Value, Data, rnd, pB);
k.Train();
Error = k.Error;
}
}
public NeuralNetwork(int numberOfInputs, int numberOfHidden, int numberOfOutput, bool bias, Matrix.Matrix inputs)
{
learningRate = 0.001;
momentumRate = 0.002;
useBias = bias;
numberOfHiddenNeurons = numberOfHidden;
inp = inputs;
hiddenOutput = new Matrix.Matrix(numberOfHidden, 1);
outputWeights = new Matrix.Matrix(numberOfOutput, numberOfHidden);
range = new Matrix.Matrix(numberOfHidden, 1);
//hiddenOutput.RandomizeMatrix(-1, 1);
outputWeights.RandomizeMatrix(-1, 1);
Random rnd = new Random();
List <Kmeans.Point> Data = new List <Kmeans.Point>();
for (int i = 0; i < inputs.row; i++)
{
Data.Add(new Kmeans.Point(inputs.tab[i, 0], inputs.tab[i, 1], inputs.tab[i, 2], inputs.tab[i, 3]));
}
var km = new Kmeans.KMeans(numberOfHidden, Data, rnd);
km.Train();
Console.WriteLine("centroids done");
centre = km.Centroids;
for (int i = 0; i < numberOfHidden; ++i)
{
//wypełnianie macierzy r - zasięgu
range.tab[i, 0] =
setRange(centre[i]) * beta;
}
momentumMatrixOutput = new Matrix.Matrix(numberOfOutput, numberOfHidden);
biasOutput = new Matrix.Matrix(numberOfOutput, 1);
momentumMatrixOutputBias = new Matrix.Matrix(numberOfOutput, 1);
biasOutput.RandomizeMatrix(-1, 1);
}
