kdtree

I've got a MongoDB with about 1 million documents in it. These documents all have a string that represents a 256 bit bin of 1s and 0s, like: 0110101010101010110101010101 Ideally, I'd like to query for near binary matches. This means, if the two documents have the following numbers. Yes, this is Hamming Distance. This is NOT currently supported in Mongo. So, I'm forced to do it in the application layer. So, given this, I am trying to find a way to avoid having to do individual Hamming distance comparisons between the documents. that makes the time to do this basically impossible. I have a LOT

I am having huge set of 2D line segments. So, I know; Line number, Begin (X,Y,Z) and End (x,Y,Z) of each line segment. I want to get proximity line segments for a given line segment. Likewise for all. To find the proximity I can apply this If I say my data it is as; So, at the end I want to get proximity lines as a vector for each line segment . I heard this type of vector of vector can be taken with r-tree data structures. I was searching it but still could not find the relevant one for me. Also I looked in opencv, there is a r-tree but it says something about classifier, and training phase..

What is the difference between these two algorithms? cKDTree is a subset of KDTree , implemented in C++ wrapped in Cython, so therefore faster. Each of them is a binary trie, each of whose nodes represents an axis-aligned hyperrectangle. Each node specifies an axis and splits the set of points based on whether their coordinate along that axis is greater than or less than a particular value. but KDTree also supports all-neighbors queries, both with arrays of points and with other kd-trees. These do use a reasonably efficient algorithm, but the kd-tree is not necessarily the best data structure

I am looking at the Wikipedia page for KD trees. As an example, I implemented, in python, the algorithm for building a kd tree listed. The algorithm for doing KNN search with a KD tree, however, switches languages and isn't totally clear. The English explanation starts making sense, but parts of it (such as the area where they "unwind recursion" to check other leaf nodes) don't really make any sense to me. How does this work, and how can one do a KNN search with a KD tree in python? This isn't meant to be a "send me the code!" type question, and I don't expect that. Just a brief explanation

I know kd-trees are traditionally used to store points, but I want to store lines instead. Would it be best to split the line at every intersection with the splitting of the kd-tree? or would storing just the end-points into kd-suffice for nearest neighbor finding? The kd-tree itself is designed for point objects. Not even for boxes, spheres or something like this. I believe you can somehow use a 6d tree that stores minx, maxx, miny, maxy, minz, maxz ; but I'm not entirely sure on how to query it correctly. The R*-tree (Wikipedia) might be a better choice here. It is really designed for

I am trying to build a Distributed KD tree using map-reduce. Description of Distributed KD tree can be found out here Dkd-Tree I have a feature vector of images having dimension 20. I have to build distributed kd tree according to above link, also Check out this image Kdtree I have set of millions of images. So what method I can use to build the top of the tree (2nd part of the image) ? I am confused in the image distribution among the various nodes. If the tree is built HDFS in first map-reduce operation,then how can I access it in next map-reduce operation? 来源： https://stackoverflow.com

I have a set of points for which I want to construct KD Tree. After some time I want to add few more points to this KDTree periodically. Is there any way to do this in scipy implementation The problem with k-d-trees is that they are not designed for updates . While you can somewhat easily insert objects (if you use a pointer based representation, which needs substantially more memory than an array-based tree), and do deletions with tricks such as tombstone messages, doing such changes will degrate the performance of the tree . I am not aware of a good method for incrementally rebalancing a k-d