==== General ====

  * Divisi2 is embedded in a larger project on knowledge representation (semantic networks and the like) in the MIT, so it is intended to be used in applications in AI and not in linguistics.
  * It is a SVD (singular value decomposition) library for **Python**.
  * Divisi requires numpy (numerical python, a standard library for scientifical computing with py) and [[http://pysparse.sourceforge.net/|pysparse]] (a further extension for matrix processing). From my own experience, installing numpy is not trivial.
  * As far as I can see right now, it is necessary to develop or use another application to collect the context vectors, etc..
  * Easy installation (around 5 minutes if you have numpy).
  * [[http://csc.media.mit.edu/docs/_static/divisi_slides.pdf|Quick overview.]]
  * [[http://csc.media.mit.edu/docs/divisi2/|Documentation]]
  * **Note:** In a first run, Divisi2 didn't work.
==== Installation ====

  * I presuppose that Python is already installed in your computer. Most distributions of linux include in their standard version Python. If you haven't install python yet, please check [[http://www.python.org/|here]].
  * The installation process is by itself quite simple -you will need just two commands. However a number of libraries are necessary to run Divisi2 and their installation is not quite simple, at least that is what I remember from installing numpy. In my case, I had to install (and maybe build) several libraries, in particular, ATLAS.
  * To install:
    - First install **pip**<file bash>sudo easy_install pip</file> 
    - This command will also install pysparse (in case you don't have it): <file bash>sudo pip install pysparse</file>
    - Now install **Divisi2**<file bash>sudo pip install divisi2 csc-pysparse</file>
  * For a further tutorial using [[http://csc.media.mit.edu/docs/divisi2/intro.html|Divisi2]]

==== Technical Issues =====

  * Divisi2 utilizes Pysparce by means of a classes called SparseMatrix- Pysparce is can be seen as an extension of Numpy. It supports three formats for saving sparse matrices:
    - Linked-list format
    - Compressed sparse row format 
    - Sparse Skyline format


==== Testing ====



 --- //[[eapontep@uos.de|Eduardo Aponte]] 2010/11/16 10:38//