Project description

CUR Decomposition

CUR Decomposition as described in Mining of Massive Datasets, page 406.

Currently it only works with Numpy arrays but the point of CUR is to keep C and R sparse if M is sparse so I plan to add support for Scipy Sparse arrays.

Usage

M = np.array([ ... ])
r = int
from cur import cur_decomposition
C, U, R = cur_decomposition(M, r)

Project details

These details have not been verified by PyPI

Project links

GitHub Statistics

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

Development Status
- 3 - Alpha
Environment
- Console
Intended Audience
- Developers
- Science/Research
License
- OSI Approved :: MIT License
Natural Language
- English
Programming Language
- Python :: 3 :: Only
Topic
- Scientific/Engineering

Release history Release notifications | RSS feed

This version

0.0.2

Apr 26, 2022

0.0.1

Jul 9, 2018

0.0.0

Jul 7, 2018

Download files

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

Source Distribution

cur-0.0.2.tar.gz (3.7 kB view hashes)

Uploaded Apr 26, 2022 Source

Built Distribution

cur-0.0.2-py3-none-any.whl (3.6 kB view hashes)

Uploaded Apr 26, 2022 Python 3

Hashes for cur-0.0.2.tar.gz

Hashes for cur-0.0.2.tar.gz
Algorithm	Hash digest
SHA256	`766463980be480c336f1598d7e2e3eafe1400b4e2aac5bd0592debb1f5343eb6`
MD5	`5e43d9a80e5e8fac25d8d7e31dcf9c87`
BLAKE2b-256	`2ce830ec73fe60516751405956db94582ef0f91e23836fbd2c1eb846e957e1a5`

Hashes for cur-0.0.2-py3-none-any.whl

Hashes for cur-0.0.2-py3-none-any.whl
Algorithm	Hash digest
SHA256	`a42dd0a224ee8dc223949e7c05573448c47f7f875f4b91e7ff20e693b2149871`
MD5	`c814d7a66990eb7ba57a977210729f55`
BLAKE2b-256	`806791a57cd9cb14c1fc559c956d67346d93018eb7d77c24e3ee8328871dfc2a`