CompClust

CompClust (pyMLX and IPlot) installation instructions

Welcome to CompClust! Hopefully this document should make it reasonably clear how to install the two components.

Prerequisites

pyMLX and IPlot are written in python and use require python modules.

Debian Linux Install

  apt-get install python python-numeric python-numeric-ext ipython \
                python-pmw python-scientific blt
  

Manual Linux Install

Download Most Recent Releases

• CompClust v1.1 Source (beta) 2005 Jun 22
• CompClust v1.0 Source 2005 May 16
• x86 Linux binaries of EM and KMeans (needed for compclust v0.2 and v1.0)

Previous releases

• CompClust v0.2 Source

Additional Python Module Installation

Obviously you will need python, we've used python 2.1 and later. Download Python

Next you will need Python Numeric and all of its extensions.

Download numpy from http://sourceforge.net/projects/numpy (Numarray is an upcoming version of numeric routines for python which we don't currently support.)

Python megawidgets (Pmw) wraps the core BLT graph widget for IPlot. It can be found at http://pmw.sourceforge.net/

Tk should have come with your installation of python, however you may also need to add BLT for its graph widget http://incrtcl.sourceforge.net/blt/

The following components, Scientific and IPython are optional. Scientific is used to provide a histogram plot in IPlot, and we just find IPython to be a convenient work environment.

Scientific is used by IPlot for it's histogram functionality and by compClust.visualize.SummaryViews for a least squares http://starship.python.net/~hinsen/ScientificPython/

Ipython can be downloaded from http://ipython.scipy.org/

Also for completeness, some archaic pyMLX code that predates IPlot uses gracePlot if you find yourself interested in that code. gracePlot is available from http://graceplot.sourceforge.net/

Algorithm Installation

Because of licensing issues we can't distribute the source to the command line c code that we use.

We provide binaries for our EM and KMeans clustering algorithms, which you can download using the link under the Download section above.

Included with that binary package is also a modified version of wmatch which was originally from ftp://dimacs.rutgers.edu/pub/netflow/matching/weighted/solver-1

It didn't seems to include a copy right so it appears we can redistribute it.

There are two packages that we can't redistribute.

We used the matlab dependent SOM toolbox from the Helsinki University of Technology for our SOM implementation. http://www.cis.hut.fi/projects/somtoolbox/

Also we used a modified version of XCluster which was based on the Gavin Sherlock's implementation. http://genetics.stanford.edu/~sherlock/cluster.html

For the binaries find a convenient location to store them.

For Xcluster you'll need to follow their build instructions.

The python code needs to know the location of these components for which we used environment variables to specify the paths to external programs. For instance the variable DIAGEM_COMMAND is used by the DiagEM wrapper to find the diagem executable.

  Wrapper    Required Environment variables
  DiagEM     DIAGEM_COMMAND=
  HutSOM     SOM_TOOLBOX_HOME=
  KMeans     KMEANS_COMMAND=
  KMedians   KMEDIANS_COMMAND=
  WMatch     WMATCH_COMMAND=
  XClust     XCLUST_COMMAND=
  

CompClust Package

Once you've finished installing all of the dependencies using their fine instructions, you'll need to install compClust. Thanks to the wonders of distutils, this should be relatively easy.

If it's not easy the DistUtils user manuals at http://www.python.org/doc/current/inst/inst.html might be useful.

    tar xzvf compClust-0.2.src.tar.gz      # extract the downloaded archive
    cd compClust-0.2                       # change to the subdirectory
  
    python setup.py install