This code has been tested with Python 2.7.11
First create a virtual environment
$: pyenv versions
system
2.7.11rc1
3.5.0
$: pyenv virtualenv 2.7.11rc1 k_means
$: pip install -r requirements.txt
You can now load your KMeans code in a console:
>>> import imp
>>> k_means = imp.load_source('k_means', 'lib/k_means.py')
>>> data = np.array([
[1, 1],
[2, 2],
[3, 3],
[10, 10],
[11, 11],
[12, 12]
])
>>> k_means.KMeans(data, 2).fit()
[Cluster(center=[ 11. 11.], members=[array([10, 10]), array([11, 11]), array([12, 12])], converged=True), Cluster(center=[ 2. 2.], members=[array([1, 1]), array([2, 2]), array([3, 3])], converged=True)]
Alternatively you can download the tar in the dist folder, extract it and install it with
$: python setup.py install
Finally, check the experiments folder to see K means in action on the iris dataset in a Jupyter notebook.
py.test