Top "Kdtree" questions

A k-d-tree (k-dimensional tree) is a data structure for storing points in multidimensional space.

Nearest Neighbor Search: Python

I have a 2 dimensional array: MyArray = array([6588252.24, 1933573.3, 212.79, 0, 0], [6588253.79, 1933602.89, 212.66, 0, 0], etc...) The first two elements MyArray[0] and MyArray[1] are the X and Y …

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What is the difference between a KD-tree and a R-tree?

I looked at the definition of KD-tree and R-tree. It seems to me that they are almost the same. What's …

data-structures kdtree r-tree
Why are KD-trees so damn slow for nearest neighbor search in point sets?

I am using CGAL's (the latest) KD-tree implementation for searching nearest neighbors in point sets. And also Wikipedia and other …

c++ data-structures raytracing kdtree cgal
nearest neighbour search kdTree

To a list of N points [(x_1,y_1), (x_2,y_2), ... ] I am trying to find the nearest neighbours to each …

python scikit-learn nearest-neighbor kdtree
nearest neighbor - k-d tree - wikipedia proof

On the wikipedia entry for k-d trees, an algorithm is presented for doing a nearest neighbor search on a k-d …

nearest-neighbor kdtree
How does the KD-tree nearest neighbor search work?

I am looking at the Wikipedia page for KD trees. As an example, I implemented, in python, the algorithm for …

python machine-learning nearest-neighbor kdtree
Difference between quadtree and kd-tree

What is the main difference between a quadtree and kd-tree? I understand they split points in many dimensions, but I …

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Efficient method for finding KNN of all nodes in a KD-Tree

I'm currently attempting to find K Nearest Neighbor of all nodes of a balanced KD-Tree (with K=2). My implementation is …

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K-d trees: nearest neighbor search algorithm

This is my understanding of it: 1. Recurse down the tree, taking the left or right subtree according as whether ELEMENT …

algorithm nearest-neighbor kdtree
KDTree Splitting

I am currently writing a KDTree for a physics engine (Hobby project). The KDTree does not contain points. Instead it …

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