private void BackpropagationCalculation(int j) {
errorOutput = (idealOutput - actualOutput) * SupportFunction.derivativesigmoid(actualOutput);
for (int i = 0; i < matrix_weight_delta_HiddenOutput.getRows(); i++) {
matrix_weight_delta_HiddenOutput.set(
i, 0, argLearningRate * errorOutput * matrix_HiddenLayerOutputSignal.get(0, i));
}
for (int i = 1; i < matrix_weight_HiddenOutput.getRows(); i++) {
errorHidden =
(errorOutput * matrix_weight_HiddenOutput.get(i, 0))
* SupportFunction.derivativesigmoid(matrix_HiddenLayerOutputSignal.get(0, i));
for (int k = 0; k < matrix_weight_delta_InputHidden.getRows(); k++) {
matrix_weight_delta_InputHidden.set(
k, i - 1, argLearningRate * errorHidden * matrix_InputLayerInputSignal.get(0, k));
}
}
}
private void FeedForwardCalculation(int j) {
// [0,0] add a bias at the front [1,0,0]
matrix_InputLayerInputSignal = SupportFunction.createInputMatrix(inputPatterns[j]);
// input multiply with weight matrix
matrix_HiddenLayerInputSignal =
MatrixMath.multiply(matrix_InputLayerInputSignal, martix_weight_InputHidden);
// pass the Hidden Layers input through the activation function and add
// a bias at first
for (int i = 0; i < matrix_HiddenLayerInputSignal.getCols(); i++) {
matrix_HiddenLayerOutputSignal.set(
0, i + 1, SupportFunction.sigmoid(matrix_HiddenLayerInputSignal.get(0, i)));
matrix_HiddenLayerOutputSignal.set(0, 0, 1); // add bias at the
// front
}
// get output result
matrix_OutputLayerInputSignal =
MatrixMath.multiply(matrix_HiddenLayerOutputSignal, matrix_weight_HiddenOutput);
// pass the output result through a activation function
actualOutput = SupportFunction.sigmoid(matrix_OutputLayerInputSignal.get(0, 0));
}