private void general(ICollection <NodeDistance <KDTreeNode <int> > > neighbors, double[] result)
{
Array.Clear(result, 0, result.Length);
double sum = 0;
double h = Bandwidth * Bandwidth;
// Compute weighted mean
foreach (var neighbor in neighbors)
{
double distance = neighbor.Distance; // ||(x-xi)||
double[] point = neighbor.Node.Position;
int weight = neighbor.Node.Value; // count
double u = distance * distance;
// Compute g = -k'(|| (x - xi) / h ||²)
double g = -kernel.Derivative(u / h) * weight;
for (int i = 0; i < result.Length; i++)
{
result[i] += g * point[i];
}
sum += g;
}
// Normalize
if (sum != 0)
{
for (int i = 0; i < result.Length; i++)
{
result[i] /= sum;
}
}
}
private void computeMeanShift(double[] x, double[] shift)
{
// Get points near the current point
ICollection <KDTreeNodeDistance <int> > neighbors;
if (maximum > 0)
{
neighbors = tree.Nearest(x, Bandwidth * 3, maximum);
}
else
{
neighbors = tree.Nearest(x, Bandwidth * 3);
}
double sum = 0;
Array.Clear(shift, 0, shift.Length);
// Compute weighted mean
foreach (KDTreeNodeDistance <int> neighbor in neighbors)
{
double distance = neighbor.Distance;
double[] p = neighbor.Node.Position;
double u = distance / Bandwidth;
// Compute g = -k'(||(x-xi)/h||²)
double g = -kernel.Derivative(u * u);
for (int i = 0; i < shift.Length; i++)
{
shift[i] += g * p[i];
}
sum += g;
}
// Normalize
if (sum != 0)
{
for (int i = 0; i < shift.Length; i++)
{
shift[i] /= sum;
}
}
}