Quick Knn

Locality Sensitive hash functions

Uses MinHash to approximate Jaccard Similarity and Random Hyperplanes to approximate Cosine similarity.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for BLester125 from gravatar.com BLester125

Credits

Author: blester125

License

MIT License (MIT)

Requires

Python >=3.6

Tags

Data Mining

Classifiers

Report project as malware

Download files

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

Source Distribution

quick_knn-0.1.4.tar.gz (3.4 kB view details)

Uploaded Source

File details

Details for the file quick_knn-0.1.4.tar.gz.

File metadata

  • Download URL: quick_knn-0.1.4.tar.gz
  • Upload date:
  • Size: 3.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for quick_knn-0.1.4.tar.gz
Algorithm Hash digest
SHA256 0ef70d3d098c86af2a89739a5703daf81866b3bfa22143918e39d5d6ecd4725c
MD5 26f7d8bb2d3075660367882c35270e98
BLAKE2b-256 591c1b0389b1f0ffb67bc0b700781ae5f91be5cc40f0d440d91ba1c7aa211957

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