Skip to main content
Help us improve PyPI by participating in user testing. All experience levels needed!

Assumption Free and Efficient K-Means Seeding

Project description

Documentation Status

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-Means
Olivier Bachem, Mario Lucic, S. Hamed Hassani and Andreas Krause
In 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

Support

You can reach out to me through https://adriangoe.me/.

License

The project is licensed under the MIT License.

Project details


Release history Release notifications

This version
History Node

0.1

Download files

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

Filename, size & hash SHA256 hash help File type Python version Upload date
afkmc2-0.1.tar.gz (4.1 kB) Copy SHA256 hash SHA256 Source None Apr 20, 2017

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging CloudAMQP CloudAMQP RabbitMQ AWS AWS Cloud computing Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page