ó
‡ˆ\c           @   s)  d  Z  d d l m Z m Z d d l m Z d d l m Z d d l m	 Z	 d d l
 m Z m Z d d l m Z d d l m Z m Z d d	 l m Z m Z m Z m Z m Z m Z d d
 l m Z d d l m Z d d l m Z d d d d d d d d d d d d d d d d d d d  g Z d! S("   sÙ   
The :mod:`sklearn.decomposition` module includes matrix decomposition
algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
this module can be regarded as dimensionality reduction techniques.
i   (   t   NMFt   non_negative_factorization(   t   PCA(   t   IncrementalPCA(   t	   KernelPCA(   t	   SparsePCAt   MiniBatchSparsePCA(   t   TruncatedSVD(   t   FastICAt   fastica(   t   dict_learningt   dict_learning_onlinet   sparse_encodet   DictionaryLearningt   MiniBatchDictionaryLearningt   SparseCoder(   t   FactorAnalysisi   (   t   randomized_svd(   t   LatentDirichletAllocationR   R   R   R   R   R   R    R   R   R   R
   R   R	   R   R   R   R   R   R   N(   t   __doc__t   nmfR    R   t   pcaR   t   incremental_pcaR   t
   kernel_pcaR   t
   sparse_pcaR   R   t   truncated_svdR   t   fastica_R   R	   R
   R   R   R   R   R   t   factor_analysisR   t   utils.extmathR   t
   online_ldaR   t   __all__(    (    (    s=   lib/python2.7/site-packages/sklearn/decomposition/__init__.pyt   <module>   s<   .