private void modifyWeights(
DoubleMatrix trainingExample,
DoubleMatrix result,
DoubleMatrix output,
double learningFactor,
double momentum,
List<DoubleMatrix> previousModifications,
EvaluationContext evalCtx) {
List<DoubleMatrix> errors = countErrors(result, output);
List<Layer> layers = getLayers();
evalCtx.resetContext();
Iterator<ActivationFunction> activationFunctionIter = evalCtx.getActivationFunction();
DoubleMatrix temporalResult = trainingExample;
for (int i = 0; i < errors.size(); i++) {
DoubleMatrix error = errors.get(i);
int layerIndex = i + 1;
Layer layer = layers.get(layerIndex);
DoubleMatrix previousModification = previousModifications.get(i);
ActivationFunction activationFunc = activationFunctionIter.next();
ActivationFunctionDerivative derivative =
DerivativeFactory.getInstance().getDerivative(activationFunc);
if (layer.includeBias()) {
temporalResult = addBiasInput(temporalResult);
}
DoubleMatrix oldVal = temporalResult.dup();
temporalResult = layer.getWeights().mmul(temporalResult);
temporalResult = activationFunc.eval(temporalResult);
// dla kazdego neuronu w warstwie
for (int j = 0; j < layer.getWeights().rows; j++) {
double derVal = derivative.evaluate(temporalResult.get(j));
DoubleMatrix oldDelta = previousModification.getRow(j);
DoubleMatrix delta = oldVal.mul(derVal).mul(learningFactor).mul(error.get(j));
delta = delta.transpose();
delta = delta.add(oldDelta.mul(momentum));
previousModification.putRow(j, delta);
DoubleMatrix oldWeights = layer.getWeights().getRow(j);
DoubleMatrix newWeights = oldWeights.add(delta);
layer.getWeights().putRow(j, newWeights);
}
}
}
