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Split Linearized Bregman Iteration

Project description

Citing libra_py_001_02
=============

The library libra_py is an academic project. The time and resources spent developing fastFM are therefore justified
by the number of citations of the software. If you publish scientific articles using libra_py, please cite the following article (bibtex entry `citation.bib <http://jmlr.org/papers/v17/15-355.bib>`_).

Bayer, I. "fastFM: A Library for Factorization Machines" Journal of Machine Learning Research 17, pp. 1-5 (2016)


libra_py: A Package for sparsity problem
============================================



Supported Operating Systems
---------------------------
fastFM has a continuous integration / testing servers (Travis) for **Linux (Ubuntu 14.04 LTS)**
and **OS X Mavericks**. Other OS are not actively supported.

Usage
-----
.. code-block:: python

from fastFM import als
fm = als.FMRegression(n_iter=1000, init_stdev=0.1, rank=2, l2_reg_w=0.1, l2_reg_V=0.5)
fm.fit(X_train, y_train)
y_pred = fm.predict(X_test)


Tutorials and other information are available `here <http://arxiv.org/abs/1505.00641>`_.
The C code is available as `subrepository <https://github.com/ibayer/fastFM-core>`_ and provides
a stand alone command line interface. If you have still **questions** after reading the documentation please open a issue at GitHub.

+----------------+------------------+-----------------------------+
| Family | Solver | Loss |
+================+==================+=============================+
| Gaussian | LBI_Linear | Square Loss |
+----------------+------------------+-----------------------------+
| Binomial | LBI_Logit | Logit Model |
+----------------+------------------+-----------------------------+

*Supported solvers and tasks*

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

**binary install**

``pip install libra_py``


Tests
-----

Project details


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