kdtree | K-d tree example code | Dataset library

 by   soniakeys Go Version: Current License: No License

kandi X-RAY | kdtree Summary

kandi X-RAY | kdtree Summary

kdtree is a Go library typically used in Artificial Intelligence, Dataset applications. kdtree has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

The code here passes some simple tests but has not been well tested or used for any real tasks. Pivot choice is median obtained by sorting, leaving tree construction time asymptotically slow. There is no support for modifying the tree after construction. It’s just a simple demonstration.
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              kdtree has a low active ecosystem.
              It has 12 star(s) with 2 fork(s). There are 4 watchers for this library.
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              It had no major release in the last 6 months.
              kdtree has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of kdtree is current.

            kandi-Quality Quality

              kdtree has no bugs reported.

            kandi-Security Security

              kdtree has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              kdtree does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              kdtree releases are not available. You will need to build from source code and install.

            Top functions reviewed by kandi - BETA

            kandi has reviewed kdtree and discovered the below as its top functions. This is intended to give you an instant insight into kdtree implemented functionality, and help decide if they suit your requirements.
            • nn returns the nearest distance between target and maxDistSqd
            • New creates a new KdTree .
            • Nearest returns the nearest point from the KdTree
            Get all kandi verified functions for this library.

            kdtree Key Features

            No Key Features are available at this moment for kdtree.

            kdtree Examples and Code Snippets

            No Code Snippets are available at this moment for kdtree.

            Community Discussions

            QUESTION

            How to multiply multiple lists together in python?
            Asked 2021-Apr-15 at 11:26

            I have been looking at producing a multiplication function to be used in a method called Conflation. The method can be found in the following article (An Optimal Method for Consolidating Data from Different Experiments). The Conflation equation can be found below:

            I know that 2 lists can be multiplied together using the following codes and functions:

            ...

            ANSWER

            Answered 2021-Apr-02 at 17:12

            In the second prod_pdf you are using computed PDFs while in the first you were using defined distributions. So, in the second prod_pdf you already have the PDF. Thus, in the for loop you simply need to do p_pdf = p_pdf * pdf

            From the paper you linked, we know that "For discrete input distributions, the analogous definition of conflation is the normalized product of the probability mass functions". So you need not only to take the product of PDFs but also to normalize it. Thus, rewriting the equation for a discrete distribution, we get

            where F is the number of distributions we need to conflate and N is the length of the discrete variable x.

            Source https://stackoverflow.com/questions/66870758

            QUESTION

            avoiding iterrows for querying local outlier
            Asked 2021-Feb-27 at 17:35

            For a dataframe containing coordinate columns (e.g. 'x', 'y') I would like to check if the associated value 'val' deviates from the mean of 'val' in the local (distance to coordinates < radius) neighbourhood. I found following approach which is often used (e.g. here or here), building a KDTree and querying for each row the local mean. However I'm wondering if there is a better solution which prevents the dataframe iteration leading to a faster execution?

            ...

            ANSWER

            Answered 2021-Feb-27 at 17:35

            There might be away to avoid looping all together that I haven't figured out yet, but an easy solution you can apply is to place your values needed into arrays, and then perform vectorized operations on those arrays. I did some tests and this averaged around 40% decrease in execution time.

            Source https://stackoverflow.com/questions/66323066

            QUESTION

            Inserting objects in scipy kdtree?
            Asked 2021-Feb-09 at 12:53

            I am trying to create a kd-tree through scipy's KD_tree class built by objects rather than pure coordinates. The objects has a (x,y) tuple, and the tree is based upon this, but i would like to include the object itself as the node/in the node.

            Is there some "easy" approach to this? Had a look on scipy kdtree with meta data, which says to use a third dimension as a object pointer(?). Wouldn't the tree then apply this value to the comparison of neighbors? I am also in the same boat as this gentleman, where creating my own kd-tree would be nice to skip for now.

            PS. This is my first post, so be gentle with me ;)

            ...

            ANSWER

            Answered 2021-Feb-09 at 12:53

            The API of scipy's KdTree wants a 2D array of coordinates as input and not any sort of object array. In this array the rows are the points and the cols the coordinates of those points.

            In the question you link to, he doesn't mean that there is a third dimension but a third index. Suppose you are looking for a single nearest neighbor and you query using some point, the function will return a distance and an index. The index is a reference into the array with which you built the tree. The distance is the difference in distance between your query point and the tree point.

            So to use this tree you could keep two arrays. One with the object coordinates and a second one with the objects. They should be in the same order, so that when a query returns an index, they mean the same thing in both arrays.

            PS. This is my first answer, so also be gentle :D

            Source https://stackoverflow.com/questions/66097608

            QUESTION

            Finding k nearest neighbors in 3d numpy array
            Asked 2021-Jan-25 at 21:20

            So I'm trying to find the k nearest neighbors in a pyvista numpy array from an example mesh. With the neighbors received, I want to implement some region growing in my 3d model.

            But unfortunaley I receive some weird output, which you can see in the following picture. It seems like I'm missing something on the KDTree implementation. I was following the answer on a similar question: https://stackoverflow.com/a/2486341/9812286

            ...

            ANSWER

            Answered 2021-Jan-25 at 21:18

            You're almost there :) The problem is that you are using the points in the mesh to build the tree, but then extracting cells. Of course these are unrelated in the sense that indices for points will give you nonsense when applied as indices of cells.

            Either you have to extract_points:

            Source https://stackoverflow.com/questions/65891163

            QUESTION

            ReadTheDocs trouble with sklearn/umap
            Asked 2021-Jan-25 at 13:26

            I've got a package which I've previously successfully built on ReadTheDocs, but this is no longer the case. My imports are as follows:

            ...

            ANSWER

            Answered 2021-Jan-25 at 13:26

            Based on feedback from a helpful user, I eventually arrived at a less hack'y solution. Unfortunately, the discussion went AWOL because of an unhelpful user, who responded with pip install -U numpy, waited for me to figure it out, edited their answer and requested I accept it. Upon being denied, the answer and comment thread vanished. I don't even remember your name, helpful user, so I can't credit you for the tip.

            Apparently ReadTheDocs uses an old pip, and requiring pip>=19.0 makes scikit-learn not install from source. As such, I added that line to docs/requirements.txt, which I had previously set up to be a ReadTheDocs requirement file. This resulted in some progress - now rather than scikit-learn complaining about numpy, it was numba. Still, some synapses connected, and I just handled any dependency problems that arose via docs/requirements.txt, the final contents of which are:

            Source https://stackoverflow.com/questions/65776380

            QUESTION

            Efficient nearby distance matrix in Python
            Asked 2021-Jan-18 at 19:40

            I need a memory & time efficient method to compute distances between about 50000 points in 1- to 10-dimensions, in Python. The methods I tried so far were not very good; so far, I tried:

            • scipy.spatial.distance.pdist computes the full distance matrix
            • scipy.spatial.KDTree.sparse_distance_matrix computes the sparse distance matrix up to a threshold

            To my surprise, the sparse_distance_matrix was badly underperforming. The example I used was 5000 points chosen uniformly from the unit 5-dimensional ball, where pdist returned me the result in 0.113 seconds and the sparse_distance_matrix returned me the result in 44.966 seconds, when I made it use the threshold 0.1 for the maximum distance cutoff.

            At this point, I would just stick with pdist, but with 50000 points, it will be using a numpy array of 2.5 x 10^9 entries, and I'm concerned if it will overload the runtime (?) memory.

            Does anyone know a better method, or sees a glaring mistake in my implementations? Thanks in advance!

            Here's what's needed to reproduce the output in Python3:

            ...

            ANSWER

            Answered 2021-Jan-18 at 19:40
            import numpy as np
            from sklearn.neighbors import BallTree
            
            tic = time.monotonic()
            
            tree = BallTree(sample, leaf_size=10)       
            d,i = tree.query(sample, k=1)
            
            toc = time.monotonic()
            
            print(f"Time taken from Sklearn BallTree = {toc-tic}")
            

            Source https://stackoverflow.com/questions/65778076

            QUESTION

            Ld undefined reference to OpenGL
            Asked 2021-Jan-10 at 22:26

            After many tries and a lot research, I still can't link OpenGL. The software I'm trying to compile was made on Ubuntu 18.04 and compiled fine while I'm now on Ubuntu 20.04.

            This is the CMakeLists used on Ubuntu 18.04:

            ...

            ANSWER

            Answered 2021-Jan-10 at 22:26

            It's hard to diagnose your problem without the minimal reproducible example. But what I see is that your system has both libGL and libOpenGL. This may mean that libGL is just a proxy for libglvnd and doesn't contain any of the GL API functions.

            But you shouldn't rush to link directly to libOpenGL. Different systems may be configured differently. Instead, the correct way of locating OpenGL objects with CMake is to use find_package(OpenGL) and then include OpenGL::GL in your target_link_libraries.

            Example dummy project:

            • CMakeLists.txt

            Source https://stackoverflow.com/questions/65655228

            QUESTION

            Stange behaviour using nanoflann
            Asked 2020-Dec-02 at 05:28

            Using the nanoflann-library for k-nearest-neighbor searches based on KDTrees I encountered a very strange behavior. My Code is a simple set of queries:

            ...

            ANSWER

            Answered 2020-Dec-02 at 05:28

            The result set appears to be stateful - it's always showing you the nearest overall neighbor of all the points. For instance, if you loop from 5 to 10 you get 5 50 for each iteration

            Reinitialize the result set each iteration and you'll get your desired behavior:

            Source https://stackoverflow.com/questions/65099735

            QUESTION

            Understanding `leafsize` in scipy.spatial.KDTree
            Asked 2020-Nov-25 at 19:30

            Problem statement:

            I have 150k points in a 3D space with their coordinates stored in a matrix with dimension [150k, 3] in mm.

            I want to find all the neighbors of a given point p that are within a radius r. And I want to do that in the most accurate way.

            How should I choose my leafsize parameter ?

            ...

            ANSWER

            Answered 2020-Nov-25 at 19:30

            The function query_ball_point will return the correct set of points for any version of the search tree. The leafsize parameter does not impact the results of the query, only the performance of the results.

            Imagine two trees shown below for the same data (but different leafsize parameters) and a query searching for all points inside the red circle.

            In both cases, the code will only return the two points that lie inside the red circle. This is done by checking all points in all boxes of the tree that intersect the circle. This leads to a different amount of work (i.e., different performance) in each case. For the left tree (corresponding to a larger leafsize), the algorithm has to check if 13 points are inside the circle (6 in the upper intersecting box and 7 in the lower intersecting box). In the right tree (which has a smaller leaf size), only three points get processed (one in the upper intersecting box and two in the lower intersecting box).

            Following this logic, you may think it just makes sense to always use a small leaf size: this will minimize the number of actual comparisons at the end of the algorithm (do decide if the points actually lie in the query region). But it isn't that simple: the smaller leaf size will generate a deeper tree adding cost to the construction time and to the tree traversal time. Getting the right balance of tree-traversal performance with the leaf-level comparisons really depends on the type of data going into the tree and the specific leaf-level comparisons you are doing. Which is why scipy provides the leafsize parameter as an argument so you can tune things to perform best on a particular algorithm.

            Source https://stackoverflow.com/questions/65003877

            QUESTION

            Compiling with Cython using OpenMP on macOS
            Asked 2020-Oct-15 at 15:58

            I'm on macOS Mojave 10.14.6 and I'm trying to compile some required extensions modules in c and c++ from this repository with:

            python setup.py build_ext --inplace

            which gives me the following error:

            ...

            ANSWER

            Answered 2020-Oct-15 at 15:58

            Here are a few hints:

            • Use gcc instead of llvm or clang for painless openmp-support on macOS. Note that apple's default gcc is just an alias for Apple clang as you'll see with gcc --version. You can install the real gcc with homebrew: brew install gcc.

            • Then use export CC='gcc-10' (the newest version should be gcc 10.x) inside the same terminal window to use homebrew's gcc temporarily as your C compiler.

            • There's no need to set CXXFLAGS or CFLAGS. The required flags are set by distutils/setuptools inside the setup.py.

            • You won't be able to compile dmc_cuda_module on macOS 10.14.6. The latest macOS version nvidia offers cuda drivers for is 10.13.6. So you might uncomment this part of the setup.py and hope for the best you don't need this module...

            • Some of the Extensions inside the setup.py aren't including the numpy headers while using the numpy C-API. On macOS it's necessary to include the numpy headers for each Extension, see this comment. So you have to add include_dirs=[numpy_include_dir] to those Extensions.

            • Edit: As discussed in the chat: The error was due to the conda env ignoring the CC variable. After installing python+pip via homebrew and the required python packages via pip, this answer's steps worked for the OP.

            All in all, here's a setup.py that worked for me (macOS 10.5.7, gcc-10):

            Source https://stackoverflow.com/questions/64350106

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