Assumption Free KMeans Monte Carlo
This package contains sklearn compatible python implementations of various K-Means seeding algorithms.
The package was inspired by the AFKMC^2 algorithm detailed in
Fast and Provably Good Seedings for k-MeansOlivier Bachem, Mario Lucic, S. Hamed Hassani and Andreas KrauseIn Neural Information Processing Systems (NIPS), 2016.
The algorithm uses Monte Carlo Markov Chain to quickly find good seedings for KMeans and offers a runtime improvement over the common K-Means++ algorithm.
Usage
Using this package to get seedings for KMeans in sklearn is as simple as:
import afkmc2
X = np.array([[1, 2], [1, 4], [1, 0],
[4, 2], [4, 4], [4, 0]])
seeds = afkmc2.afkmc2(X, 2)
from sklearn.custer import KMeans
model = KMeans(n_clusters=2, init=seeds).fit(X)
print model.cluster_centers_
Installation
Quickly install afkmc2 by running (coming soon):
pip install afkmc2
Contribute
Issue Tracker: https://github.com/adriangoe/afkmc2/issues
Support
You can reach out to me through https://adriangoe.me/.
License
The project is licensed under the MIT License.
1 maintainer
Metadata
1 maintainer
Release files for afkmc2 0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| afkmc2-0.1.tar.gz | 4.1 kB | Without provenance | Details |
Release files / afkmc2-0.1.tar.gz
| Download URL | afkmc2-0.1.tar.gz |
|---|---|
| Size | 4.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
4a6a48948c64f2bc580cd780087162ff3ae675ae3f3beb98b001b94628ff9b8a
|
|
BLAKE2b-256 checksum How to use checksums |
4f9f4abca0daf62c08bac4cd751d6bf4dd6e1ee8f2962df9f7f89b0cfb21d86d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
Security for afkmc2 0.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
