Metadata-Version: 2.1
Name: bayesKMeans
Version: 0.3.9
Summary: Finding optimal k in k-means using bayesian optimization
Home-page: https://github.com/ForseIKomar/BayesKMeans
Author: Aleksandr Burlakov
Author-email: samitist11@gmail.com
License-File: LICENSE
Requires-Dist: joblib (>=0.11)
Requires-Dist: pyaml (>=16.9)
Requires-Dist: numpy (>=1.13.3)
Requires-Dist: scipy (>=0.19.1)
Requires-Dist: scikit-learn (>=0.20.0)
Requires-Dist: scikit-optimize (>=0.9.0)

Example of work:

from bayeskmeans.bayes_kmeans import BayesKMeans
from bayeskmeans.bayes_visualize import BayesKMeansVisualize
from sklearn.datasets import make_blobs

data = make_blobs(n_samples=1000, n_features=2, centers=21, cluster_std=5, center_box=(-300, 300))
data = data[0]

bayesKMeans = BayesKMeans(data)

bayesKMeans.find_k()

print(bayesKMeans.found_k)

visual = BayesKMeansVisualize(bayesKMeans)
visual.show_bayesian_plot()
