kdtree

On the wikipedia entry for k-d trees , an algorithm is presented for doing a nearest neighbor search on a k-d tree. What I don't understand is the explanation of step 3.2. How do you know there isn't a closer point just because the difference between the splitting coordinate of the search point and the current node is greater than the difference between the splitting coordinate of the search point and the current best? Nearest neighbor search Animation of NN searching with a KD Tree in 2D The nearest neighbor (NN) algorithm aims to find the point in the tree which is nearest to a given input

I am currently writing a KDTree for a physics engine (Hobby project). The KDTree does not contain points. Instead it contains Axis Aligned bounding boxes which bound the different objects in the environment. My problem is deciding on how to split the KDTree nodes when they get full. I am trying 2 methods: Method1: Always split the node exactly in half on the biggest axis. This has the advantage of a pretty evenly spaced out tree. Big disadvantage: If objects are concentrated in small area of the node, redundant sub-divisions will be created. This is because all volumes are split exactly in

I looked at the definition of KD-tree and R-tree. It seems to me that they are almost the same. What's the difference between a KD-tree and an R-tree? R-trees and k d-trees are based on similar ideas (space partitioning based on axis-aligned regions), but the key differences are: Nodes in k d-trees represent separating planes, whereas nodes in R-trees represent bounding boxes. k d-trees partition the whole of space into regions whereas R-trees only partition the subset of space containing the points of interest. k d-trees represent a disjoint partition (points belong to only one region)

I have a catalogue of data and I want to use it in my MCMC code. What is crucial is the speed of implementation, in order to avoid slowing down my Markov chain monte carlo sampling. The problem : In the catalogue, I have in the first and second column two parameters called ra and dec which are sky coordinates : data=np.loadtxt('Final.Cluster.Shear.NegligibleShotNoise.Redshift.cat') ra=data[:,0] dec=data[:,1] then in the seven and eight columns X and Y positions, i.e. the grid coordinates, they are points in a grid space Xpos=data[:,6] Ypos=data[:,7] In the function that I have written and it

I'm looking for a KDTree implementation in Java. I've done a google search and the results seem pretty haphazard. There are actually lots of results, but they're mostly just little one-off implementations, and I'd rather find something with a little more "production value". Something like apache collections or the excellent C5 collection library for .NET. Something where I can see the public bug tracker and check to see when the last SVN commit happened. Also, in an ideal world, I'd find a nice well-designed API for spatial data structures, and the KDTree would be just one class in that