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README.txt 2014-03-30 2.0 kB
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This is the script prepared to run monmlp package from R environment in a manner of 10-fold cross-validation mode. MONMLP is a very effective tool for modeling with artificial neural networks introducing monotonicity rules, ensembles and bagging/boosting enhancements. For more information please refer to its original manual at the CRAN repostitory:

http://cran.r-project.org/web/packages/monmlp/index.html

It requires data prepared in a manner of 10 pairs of training-testing datasets in tab-delimited TXT files, where the last column contains the known answer to the problem (dependent variable) and previous columns are features (regressors or independent variables). The assumption is that the model is of MISO type (multiple-input-single-output)

Script basic adjustable parameters are:

hid1
# number of nodes in the first hidden layer
hid2
# number of nodes in the second hidden layer
ens
# number of members of the ensemble
trials
# number of multistarts of the single neural network (ANN) training phase to find best model
max_iter
# number of iterations (epochs) of the ANN training

Please find an example of how to run the script in the example.tar.gz archive Runs on Linux and Mac 
Windows users must get rid of multicore library

##########################################################################
    This program comes with ABSOLUTELY NO WARRANTY
    This is free software, and you are welcome to redistribute it
    under certain conditions. Please find a LICENSE file to look 
    for a more detailed description of terms and conditions based on the 
    GNU GPLv3 license
##########################################################################

If you are satisfied with this script and find it useful please cite our last work:
Szlęk J, Pacławski A, Lau R, Jachowicz R, Mendyk A. Heuristic modeling of macromolecule release from PLGA microspheres. Int J Nanomedicine. 2013;8:4601-11. doi: 10.2147/IJN.S53364. Epub 2013 Dec 3.
Source: README.txt, updated 2014-03-30