Hey, as a part of my diploma thesis I developed a symbolic regression tool, finding _compact formulas_ for datasets. So what does this mean? E.g. it can find the formula for the surface of a circle providing samples to the software.
Maybe it makes sense or there is interest in integrating this method as an alternative to classic curve-fitting methods into libreoffice calc. AFAIK neither Microsoft Office nor gnumeric includes this method (that's why I've also posted a similar mail to gnumeri's ml). I've written a prototype in vala which works fairly well. It can be found at http://gitorious.org/pigp/libmlgp It includes a library doing all the work and a simple commandline interface to run a symbolic regression. Please handle this software with care, sometimes it does not know what it is doing :) The docs/ folder provides more informations on how to build and how to run a regression. More on the topic of symbolic regression (Schmidt and Lipson published a nice implementation in 2009 which raised my interest): http://ccsl.mae.cornell.edu/eureqa http://www.hakank.org/eureqa/ My implementation differs from different ones, as it uses multi-objective optimization (via NSGA-2) to find (several differing) compact formulas, classical symbolic regression is also fitting curves to data, but it creates very long formulas to fit the data optimal. Thoughts? - fabian _______________________________________________ LibreOffice mailing list LibreOffice@lists.freedesktop.org http://lists.freedesktop.org/mailman/listinfo/libreoffice