Automatic Window Width Determination for CoCoNAD
Download
32 bit | |
64 bit | |
(32/64 bit only for executable) |
width.pdf |
(3909 kb) |
| |
window width result diagrams |
width.zip |
(57 kb) |
width.tar.gz |
(50 kb) |
scripts and other source files |
Description
The document width.pdf contains some result diagrams for
experiments in which it was tried to find the analysis window width
to be used with CoCoNAD automatically from
the given data set. The ideas underlying the approach are described
in the paper
[Picado-Muiño
and Borgelt 2015].
The archives width.{zip,tar.gz} contain scripts and other
source files, with which the experiments were conducted and the document
with the result diagrams was created.
Note that the scripts etc. were developed on/for a GNU/Linux
system (Ubuntu 14.04) and thus are directly executable on such a
system or a similar one (that is, some other GNU/Linux distribution).
Although at least most of the Python scripts should also be working
on a Windows system (with the possible exception of the parallelization
scripts), most of the other scripts (like the run script,
which is the main control script, and the makefile, which
controls generating the diagrams from the result data) may need
porting to batch files or something similar.
On a GNU/Linux system, the following software needs to be
installed to run the experiments:
- Python, preferably version 2.7.x (although version 3.x can be
made to work as well),
- the PyCoCo extension module for Python,
which makes a fast C implementation of the CoCoNAD algorithm
available as a Python function (the scripts in the cocoexp
package will also work without this extension module, namely by
falling back on a pure Python replacement, which, however, is
slower by a factor of about 40 or more),
- a LaTeX system, preferably TeX Live (which is the standard LaTeX
system for Ubuntu and many other Debian based GNU/Linux systems
and may be installed directly through the package manager), in
particular the pdflatex program,
- the MetaPost program for the LaTeX system (which is usually
already contained in standard LaTeX installations, but on some
systems may require separate installation), in particular the
mptopdf command,
- some standard Unix tools, which are usually available on all Unix
systems even with a very basic installation (like bash,
awk, tar etc.) and are easy to install
otherwise.
On such a system the experiments can be run by simply calling
the main script run (in the directory width) on the
command line, which does everything. The execution of the experiments
exploits 4-fold parallelization, thus making full use of the quadcore
processors basically all modern computers are equipped with.
The progress of the experiments can be followed on the command line,
to which regular progress messages are written. Once all experiments
are completed (which should take less than an hour on a modern computer
system), the result diagrams are created and compiled into the final
documents, which are also directly available above.
References
- Automatic
Learning of Synchrony in Neuronal Electrode Recordings
David Picado-Muiño and Christian Borgelt
Proc. 16th Congress Int. Fuzzy Systems Association (IFSA)
and 9th Conf. Europ. Society for Fuzzy Logic and Technology
(EUSFLAT)
(IFSA-EUSFLAT 2015, Gijón, Spain),
accepted, to appear.
- Finding Frequent Patterns in Parallel Point Processes
Christian Borgelt and David Picado-Muiño
Proc. 12th Int. Symposium on Intelligent Data Analysis,
116-126.
Springer-Verlag, Berlin/Heidelberg, Germany 2013
ida_13.pdf (443 kb)
ida_13.ps.gz (491 kb)
(11 pages)
- Frequent
Item Set Mining for Sequential Data:
Synchrony in Neuronal Spike Trains
David Picado-Muiño and Christian Borgelt
Intelligent Data Analysis 18(6):997-1012.
IOS Press, Amsterdam, Netherlands 2014
doi:10.3233/IDA-140681
iospress.com
(16 pages)
- Finding Neural Assemblies
with Frequent Item Set Mining
David Picado-Muiño, Christian Borgelt, Denise Berger,
George Gerstein, and Sonja Grün
Frontiers in Neuroinformatics 7:article 9
Frontiers Media, Lausanne, Switzerland 2013
doi:10.3389/fninf.2013.00009
frontiersin.org
accfim.pdf (1797 kb)
accfim.ps.gz (772 kb)
(14 pages)
- Statistical Evaluation
of Synchronous Spike Patterns extracted by Frequent Item
Set Mining
Emiliano Torre, David Picado-Muiño, Michael Denker,
Christian Borgelt, and Sonja Grün
Frontiers in Computational Neuroscience,
7:article 132
Frontiers Media, Lausanne, Switzerland 2013
doi:10.3389/fncom.2013.00132
frontiersin.org
(13 pages)