public NeuralNetwork(int numberOfInputs, int numberOfHidden, int numberOfOutput, Matrix.Matrix inputs)
{
learningRate = 0.1;
momentumRate = 0.5;
numberOfHiddenNeurons = numberOfHidden;
inp = inputs;
hiddenOutput = new Matrix.Matrix(numberOfHidden, numberOfInputs);
outputWeights = new Matrix.Matrix(numberOfOutput, numberOfHidden);
centre = new Matrix.Matrix(numberOfHidden, numberOfInputs);
range = new Matrix.Matrix(numberOfHidden, 1);
//hiddenOutput.RandomizeMatrix(-1, 1);
outputWeights.RandomizeMatrix(-5, 5);
Random rnd = new Random();
for (int i = 0; i < numberOfHidden; ++i)
{
//wypełnianie macierzy c - centrów
int index = rnd.Next(0, inputs.row);
for (int j = 0; j < numberOfInputs; ++j)
{
centre.tab[i, j] = inputs.tab[index, j];
//Console.WriteLine(centre.tab[i, j]);
}
//wypełnianie macierzy r - zasięgu
range.tab[i, 0] = rnd.NextDouble() * 1;
}
}
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);
}
public NeuralNetwork(int inputAmount, int hiddenAmount, int outputAmount, bool bias)
{
learningRate = 0.5;
momentumRate = 0.5;
useBias = bias;
hiddenWeights = new Matrix.Matrix(hiddenAmount, inputAmount); // 2 x 3
outputWeights = new Matrix.Matrix(outputAmount, hiddenAmount); // 1 x 2
momentumMatrixHidden = new Matrix.Matrix(hiddenAmount, inputAmount);
momentumMatrixOutput = new Matrix.Matrix(outputAmount, hiddenAmount);
hiddenWeights.RandomizeMatrix(-1, 1);
outputWeights.RandomizeMatrix(-1, 1);
biasHidden = new Matrix.Matrix(hiddenAmount, 1);
biasOutput = new Matrix.Matrix(outputAmount, 1);
momentumMatrixHiddenBias = new Matrix.Matrix(hiddenAmount, 1);
momentumMatrixOutputBias = new Matrix.Matrix(outputAmount, 1);
biasHidden.RandomizeMatrix(-1, 1);
biasOutput.RandomizeMatrix(-1, 1);
}