inline double KDERules<MetricType, KernelType, TreeType>::
Score(const size_t queryIndex, TreeType& referenceNode)
{
double score, maxKernel, minKernel, bound;
const arma::vec& queryPoint = querySet.unsafe_col(queryIndex);
const double minDistance = referenceNode.MinDistance(queryPoint);
bool newCalculations = true;
if (tree::TreeTraits<TreeType>::FirstPointIsCentroid &&
lastQueryIndex == queryIndex &&
traversalInfo.LastReferenceNode() != NULL &&
traversalInfo.LastReferenceNode()->Point(0) == referenceNode.Point(0))
{
// Don't duplicate calculations.
newCalculations = false;
lastQueryIndex = queryIndex;
lastReferenceIndex = referenceNode.Point(0);
}
else
{
// Calculations are new.
maxKernel = kernel.Evaluate(minDistance);
minKernel = kernel.Evaluate(referenceNode.MaxDistance(queryPoint));
bound = maxKernel - minKernel;
}
if (newCalculations &&
bound <= (absError + relError * minKernel) / referenceSet.n_cols)
{
// Estimate values.
double kernelValue;
// Calculate kernel value based on reference node centroid.
if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
{
kernelValue = EvaluateKernel(queryIndex, referenceNode.Point(0));
}
else
{
kde::KDEStat& referenceStat = referenceNode.Stat();
kernelValue = EvaluateKernel(queryPoint, referenceStat.Centroid());
}
densities(queryIndex) += referenceNode.NumDescendants() * kernelValue;
// Don't explore this tree branch.
score = DBL_MAX;
}
else
{
score = minDistance;
}
++scores;
traversalInfo.LastReferenceNode() = &referenceNode;
traversalInfo.LastScore() = score;
return score;
}
inline double KDERules<MetricType, KernelType, TreeType>::
Score(TreeType& queryNode, TreeType& referenceNode)
{
double score, maxKernel, minKernel, bound;
const double minDistance = queryNode.MinDistance(referenceNode);
// Calculations are not duplicated.
bool newCalculations = true;
if (tree::TreeTraits<TreeType>::FirstPointIsCentroid &&
(traversalInfo.LastQueryNode() != NULL) &&
(traversalInfo.LastReferenceNode() != NULL) &&
(traversalInfo.LastQueryNode()->Point(0) == queryNode.Point(0)) &&
(traversalInfo.LastReferenceNode()->Point(0) == referenceNode.Point(0)))
{
// Don't duplicate calculations.
newCalculations = false;
lastQueryIndex = queryNode.Point(0);
lastReferenceIndex = referenceNode.Point(0);
}
else
{
// Calculations are new.
maxKernel = kernel.Evaluate(minDistance);
minKernel = kernel.Evaluate(queryNode.MaxDistance(referenceNode));
bound = maxKernel - minKernel;
}
// If possible, avoid some calculations because of the error tolerance.
if (newCalculations &&
bound <= (absError + relError * minKernel) / referenceSet.n_cols)
{
// Auxiliary variables.
double kernelValue;
kde::KDEStat& referenceStat = referenceNode.Stat();
kde::KDEStat& queryStat = queryNode.Stat();
// If calculating a center is not required.
if (tree::TreeTraits<TreeType>::FirstPointIsCentroid)
{
kernelValue = EvaluateKernel(queryNode.Point(0), referenceNode.Point(0));
}
// Sadly, we have no choice but to calculate the center.
else
{
kernelValue = EvaluateKernel(queryStat.Centroid(),
referenceStat.Centroid());
}
// Sum up estimations.
for (size_t i = 0; i < queryNode.NumDescendants(); ++i)
{
densities(queryNode.Descendant(i)) +=
referenceNode.NumDescendants() * kernelValue;
}
score = DBL_MAX;
}
else
{
score = minDistance;
}
++scores;
traversalInfo.LastQueryNode() = &queryNode;
traversalInfo.LastReferenceNode() = &referenceNode;
traversalInfo.LastScore() = score;
return score;
}