Quick start
===========

Installation
------------

Decompose demands python 3.6 and tensorflow 1.7. The newest gibhub code of decompose can be installed using pip:

.. code-block:: bash

   pip3 install git+https://github.com/bethgelab/decompose


Example
-------

Decompose provides an interface that is similar to the interface of scikit-learn:

.. code-block:: python

   import numpy as np
   from sklearn.datasets import make_low_rank_matrix

   from decompose.sklearn import DECOMPOSE
   from decompose.distributions.cenNormal import CenNormal


   # create a numpy array containing a synthetic low rank dataset
   X = make_low_rank_matrix(n_samples=1000, n_features=1000,
                            effective_rank=3, tail_strength=0.5)

   # create an instance of a decompose model
   model = DECOMPOSE(modelDirectory="/tmp/myNewModel",
                     priors=[CenNormal(), CenNormal()],
                     n_components=3)

   # train the model and transform the training data
   U0 = model.fit_transform(X)

   # learned filter bank
   U1 = model.components_

   # variance ratio of the sources
   varianceRatio = model.variance_ratio_

   # reconstruction of the data
   XHat = np.dot(U0.T, U1)