Skip to main content

No project description provided

Project description

toha_nearest_neighbor

Serial and parallel bindings to brute force and kd-tree methods for low dimensional (<16) nearest neighbor problems in python

documentation

Benchmarks

Some basic benchmarks have been carried out against sklearn.neighbors to show the relative performance of this library. These preliminary results show better performance and better scaling in every function. However, keep in mind that sklearn handles a generic n-dimensional space while this package has been simplified to work with 2D data. Moreover, sklearn also has an additional algorithm ball_tree that scales to higher dimensions (N > 15) much better than kd-trees.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

toha_nearest_neighbor-0.4.0.tar.gz (332.2 kB view details)

Uploaded Source

File details

Details for the file toha_nearest_neighbor-0.4.0.tar.gz.

File metadata

  • Download URL: toha_nearest_neighbor-0.4.0.tar.gz
  • Upload date:
  • Size: 332.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for toha_nearest_neighbor-0.4.0.tar.gz
Algorithm Hash digest
SHA256 f9282b3cc5f0ef39c600ec72c70139cb2e3afe819fcfc9c8bb369f6d45a80875
MD5 51d7cd648bc4fa378736471c14843570
BLAKE2b-256 6d254fec20366fab523055540004ad04f7079f77512bc231ef00ebdc54fca2eb

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page