public void Train(double[] inputArray, double[] targetArray)
{
/*Random rnd = new Random();
* for (int i = 0; i < 10; ++i)
* {
* //wypełnianie macierzy c - centrów
* int index = rnd.Next(0, inp.row);
* for (int j = 0; j < 1; ++j)
* {
* centre.tab[i, j] = inp.tab[index, j];
* }
* //wypełnianie macierzy r - zasięgu
* range.tab[i, 0] = rnd.NextDouble() * 50;
* }*/
//Matrix.Matrix inputMatrix = new Matrix.Matrix(inputArray);
FillHiddenNeurons(inputArray);
//hiddenOutput.DisplayMatrix();
Matrix.Matrix outputsOutput = outputWeights * hiddenOutput;
Matrix.Matrix targetMatrix = new Matrix.Matrix(targetArray);
Matrix.Matrix outputErrorsMatrix = targetMatrix - outputsOutput;
mapMatrixLinearry(outputsOutput); //!!! do zmiany w wariancie 2 na normalne mapowanie
Matrix.Matrix gradients_output = outputErrorsMatrix * learningRate;
gradients_output.HadamardProduct(outputsOutput);
//gradients_output.DisplayMatrix();
Matrix.Matrix hiddenTransposed = hiddenOutput.TransposeMatrix();
//hiddenTransposed.DisplayMatrix();
Matrix.Matrix outputs_deltas = gradients_output * hiddenTransposed;
//outputs_deltas.DisplayMatrix();
outputWeights += outputs_deltas;
//Console.WriteLine("outputWeights: ");
//outputWeights.DisplayMatrix();
}
public void train(double[] inputArray, double[] targetArray)
{
Matrix.Matrix inputMatrix = new Matrix.Matrix(inputArray); // 3 x 1
Matrix.Matrix hiddenOutput = hiddenWeights * inputMatrix;
if (useBias)
{
hiddenOutput += biasHidden;
}
activationFunction(hiddenOutput);
//showHiddenOutputs(hiddenOutput);
Matrix.Matrix outputsOutput = outputWeights * hiddenOutput;
if (useBias)
{
outputsOutput += biasOutput;
}
activationFunction(outputsOutput);
Matrix.Matrix targetMatrix = new Matrix.Matrix(targetArray);
Matrix.Matrix outputErrorsMatrix = targetMatrix - outputsOutput;
mapMatrix(outputsOutput);
Matrix.Matrix gradients_output = outputErrorsMatrix * learningRate;
gradients_output.HadamardProduct(outputsOutput);
Matrix.Matrix hiddenTransposed = hiddenOutput.TransposeMatrix();
Matrix.Matrix outputs_deltas = gradients_output * hiddenTransposed;
//momentumMatrixOutput += outputs_deltas;
//momentumMatrixOutput *= momentumRate;
outputWeights += outputs_deltas;
//outputWeights += momentumMatrixOutput;
biasOutput += gradients_output;
//biasOutput += outputs_deltas;
//hidden layer errors
Matrix.Matrix outputWeights_transposed = outputWeights.TransposeMatrix();
Matrix.Matrix hiddenErrorsMatrix = outputWeights_transposed * outputErrorsMatrix;
mapMatrix(hiddenOutput);
Matrix.Matrix gradients_hidden = hiddenErrorsMatrix * learningRate;
gradients_hidden.HadamardProduct(hiddenOutput);
Matrix.Matrix hidden_deltas = gradients_hidden * inputMatrix.TransposeMatrix();
hiddenWeights += hidden_deltas;
biasHidden += gradients_hidden;
//biasHidden += hidden_deltas;
//MOMENTUM
outputWeights += momentumMatrixOutput;
hiddenWeights += momentumMatrixHidden;
biasOutput += momentumMatrixOutputBias;
biasHidden += momentumMatrixHiddenBias;
momentumMatrixOutput = outputs_deltas * momentumRate;
momentumMatrixHidden = hidden_deltas * momentumRate;
momentumMatrixOutputBias = gradients_output * momentumRate;
momentumMatrixHiddenBias = gradients_hidden * momentumRate;
}
