PKG-INFO

Metadata-Version: 1.0
Name: rbm.py
Version: 1.0
Summary: Pain-free Restricted Boltzmann Machines
Home-page: http://ynd.github.com/rbm.py/
Author: Yann N. Dauphin
Author-email: dhaemon@gmail.com
License: BSD
Description: 
        Introduction
        ============
        _rbm.py_ is the fastest and easiest way to use Restricted Boltzmann
        Machines (RBMs). RBMs are a class of probabilistic models that can discover
        hidden patterns in your data. _rbm.py_ provides all the necessary methods with
        a pythonic interface, and moreover, all methods call blazing fast C code. The
        code can also run transparently on GPU thanks to
        Theano (http://deeplearning.net/software/theano/).

        Here's an example usage

            $ python
            >>> import numpy, rbm
            >>> X = numpy.array([[0, 1, 0], \
                                 [0, 1, 1], \
                                 [1, 0, 1], \
                                 [1, 1, 1]]) # Improvised dataset
            >>> model = rbm.RBM(n_hiddens=2) # RBM with two hiddens units
            >>> model.fit(X) # Train using dataset X
            >>> model.sample_h(X) # Get hidden code
            array([[0, 0],
                   [0, 1],
                   [1, 1],
                   [1, 0]])
            >>> model.gibbs(X) # MCMC step
            array([[0, 1, 1],
                   [1, 0, 1],
                   [0, 0, 1],
                   [1, 0, 1]])

        Authors and Contributors
        ========================
        Yann N. Dauphin (@ynd)

        Support or Contact
        ==================
        Having trouble? Check out https://github.com/ynd/rbm.py/issues.
        
Keywords: machine learning restricted boltzmann machine
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: BSD License