k-D tree
This distribution contains the KDTREE, KDTREEIDX, and KDRANGEQUERY functions.
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KDTREE Find closest points using a k-D tree.
CP = KDTREE( REFERENCE, MODEL ) finds the closest points in
REFERENCE for each point in MODEL. The search is performed in an efficient manner by building a k-D tree from the datapoints in REFERENCE, and querying the tree for each datapoint in MODEL.
IDX = KDTREEIDX( REFERENCE, MODEL ) finds the closest points in REFERENCE for each point in MODEL. The search is performed in an efficient manner by building a k-D tree from the datapoints in REFERENCE, and querying the tree for each datapoint in MODEL.
PTS = KDRANGEQUERY( ROOT, QUERYPT, DISTLIM ) finds all the points stored in the k-D tree ROOT that are within DISTLIM units from the QUERYPT. Proximity is quantified using a D-dimensional Euclidean (2-norm) distance.
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Two demo scripts are provided (kdtree_demo.m & kdrange_demo.m).
You will need to compile the code in the kdtree/src library using the
MATLAB mex compiler. Place the compiled mex files in the kdtree/lib directory. Finally, add the kdtree/lib directory to your MATLAB path.
** Refer to the README file for more detailed instructions.
Zitieren als
Guy Shechter (2024). k-D tree (https://www.mathworks.com/matlabcentral/fileexchange/4586-k-d-tree), MATLAB Central File Exchange. Abgerufen .
Kompatibilität der MATLAB-Version
Plattform-Kompatibilität
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- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Cluster Analysis and Anomaly Detection >
Tags
Quellenangaben
Inspiriert: Iterative Closest Point Method, C++, ataiya/kdtree, Kdtree implementation in matlab, Measures of Analysis of Time Series toolkit (MATS)
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kdtree/
kdtree/lib/
Version | Veröffentlicht | Versionshinweise | |
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1.2.0.0 | More detailed instructions on how to create the mex runtimes. |
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1.1.0.0 | More detailed instructions on how to create the mex runtimes. |