private protected override double[] GetGradient(IChannel ch)
{
Contracts.AssertValue(ch);
_previousGradient = _currentGradient;
_currentGradient = ObjectiveFunction.GetGradient(ch, TrainingScores.Scores);
// We need to make a copy of gradient coz the reference returned is private structare of ObejctiveFunctionBase is valid only till next GetGradient call
_currentGradient = (double[])_currentGradient.Clone();
double[] previousDk = _currentDk;
//First iteration
if (_previousGradient == null)
{
_previousGradient = _currentGradient;
}
#if !POLAK_RIBIERE_STEP
// Compute Beta[k] = curG[k] * (curG[k] - prevG[k])
// TODO: this can be optimized for speed. Keeping it slow but simple for now
double beta = VectorUtils.GetDotProduct(_currentGradient, VectorUtils.Subtract(_currentGradient, _previousGradient)) / VectorUtils.GetDotProduct(_previousGradient, _previousGradient);
#else //Fletcher Reeves step
// Compute Beta[k] = (curG[k]*cutG[k]) / (prevG[k] * prevG[k])
double beta = VectorUtils.GetDotProduct(currentGradient, currentGradient) / VectorUtils.GetDotProduct(previousGradient, previousGradient);
#endif
if (beta < 0)
{
beta = 0;
}
ch.Info("beta: {0}", beta);
VectorUtils.MutiplyInPlace(previousDk, beta);
VectorUtils.AddInPlace(previousDk, _currentGradient);
_currentDk = previousDk; // Reallay no-op opration
// We know that LeastSquaresRegressionTreeLearner does not destroy gradients so we can return our reference that we will need in next iter.
if (TreeLearner is LeastSquaresRegressionTreeLearner)
{
return(_currentDk);
}
// Assume that other treLearners destroy the gradient array so return a copy.
else
{
return((double[])_currentDk.Clone());
}
}