gmm

My implementation of the Gaussian mixture model (GMM).

Pull requests, comments, and suggestions are welcomed!

Installation

I'm assuming you are wise and you're using virtualenv and virtualenvwrapper. If not, go and install them now.

In order to install the package, run in the terminal:

$ python setup.py sdist

Then:

$ pip install dist/GMM-0.1.0.tar.gz

And you're done!

Basic usage

A typical usage would look as follows:

from pprint import pprint

import numpy as np

from gmm.algorithm import GMM


# Read in dataset from file
with open('faithful.txt', 'rt') as f:
    data = []
    for row in f:
        cols = row.strip('\r\n').split(' ')
        data.append(np.fromiter(map(lambda x: float(x), cols), np.float))
    data = np.array(data)

# Initialize GMM algorithm
n_components = 2
gmm_model = GMM(n_components)

# Fit GMM to the data
gmm_model.fit(data)

# Print the results
for mean, cov, prob in zip(gmm_model.means, gmm_model.covariances, gmm_model.mixing_probs):
    print("Fitted mean vector:")
    pprint(mean)

    print("Fitted covariance matrix:")
    pprint(cov)

    print("Fitted mixing probability:")
    pprint(prob)

More examples

You can find more examples in the examples/ folder.

License

License information can be found in License.txt.