glmnet_matlab
glmnet for MATLAB compiled for compatibility with 64-bit Windows 10 and Mac OS systems. Plug 'n play!
Background
glmnet is an extremely efficient toolbox for fitting lasso and elastic-net regularized generalized linear models. Unfortunately the glmnet for MATLAB files provided by the authors are not compatible with newer versions of MATLAB, Mac OS, and Windows 10. This is a shame, because glmnet for MATLAB is orders of magnitude faster than other similar packages. Thus I recompiled the mex files in this repository to be compatible with modern systems; I have tested them with MATLAB 2017a in Mac OS 12 and Windows 10.
glmnet for MATLAB is fast because the core function is implemented in FORTRAN, a language beloved for its use in weather prediction but divisive for its one-based indexing. MATLAB can call FORTRAN (and C/C++) functions via MEX tools, but doing so requires a FORTRAN (or C/C++) compiler, which can be a hassle to obtain.
Implementation
I installed Virtual Studio 2015 Community (necessary on Windows 10 only) and then the Intel Parallel Studio XE 2016 FORTRAN compiler (necessary on Windows 10 and Mac OS). I compiled the files in MATLAB on Windows 10 using the commmand:
mex -compatibleArrayDims glmnetMex.F GLMnet.f
and on Mac OS 12 using the command:
mex FFLAGS='-fdefault-real-8 -ffixed-form -compatibleArrayDims' glmnetMex.F GLMnet.f
Note that these commands are different from the author-recommended commands for Windows and Mac. The
-compatibleArrayDims
option compiles the FORTRAN code using the 32-bit API, which may be deprecated in future MATLAB releases. The permanent solution is to modify the FORTRAN source to be 64-bit compliant, which you should do because you're a good person, or I should do if I have something extremely imposing to procrastinate on.
Please remember to cite the authors if you use glmnet:
Glmnet for Matlab (2013) Qian, J., Hastie, T., Friedman, J., Tibshirani, R. and Simon, N.
http://www.stanford.edu/~hastie/glmnet_matlab/
Also check out this interesting speed comparison between MATLAB, Python, Julia, R, and a bunch of other languages.