### 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 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).
• 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 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:
1. First install pip
sudo easy_install pip
2. This command will also install pysparse (in case you don't have it):
sudo pip install pysparse
3. Now install Divisi2
sudo pip install divisi2 csc-pysparse
• For a further tutorial using 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: