An open-source C++ multiplex HMM library for making inferences on multiple data types
C++ Makefile
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
doc
example
include
src
test
.gitignore
LICENSE
README.md
makefile

README.md

muxstep

muxstep is an open-source C++ multiplex HMM library for making inferences on multiple data types.

Building

You will need clang++ to compile the library (on Mac OS X, this compiler is installed by default).

Simply run

$ make

while in the root directory of the repository, and the libraries (both static and dynamic) will be created in the lib/ folder. This command will also compile the documentation files (supplementary data as given in the original publication) in doc/muxstep-suppl.pdf (latexmk and pdflatex are required for compiling the documentation).

Usage example

The folder example/ contains basic_usage.cpp, a file that demonstrates a basic way in which the muxstep library might be used.

If you would like to compile it using the static library, run the following:

$ clang++ -std=c++11 -I../include basic_usage.cpp -L../lib -lmuxstep -o basic

If you would like to use the dynamic library instead, run the following:

$ clang++ -std=c++11 -I../include basic_usage.cpp -L../lib -lmuxstep.dyn -o basic
$ LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/muxstep/lib/
$ export LD_LIBRARY_PATH

where /path/to/muxstep refers to the (absolute) path to the root repository folder.

After doing this, you may run the example using

$ ./basic

(Note, it takes some time for training to complete!)

License

MIT

References

If you make advantage of muxstep or derive it within your research, please cite the following article:

Veličković, P. and Liò, P. (2016) muxstep: an open-source C++ multiplex HMM library for making inferences on multiple data types. Bioinformatics

The models described here were originally investigated in the following manuscript:

Veličković, P. and Liò, P. (2015) Molecular multiplex network inference using Gaussian mixture hidden Markov models. Journal of Complex Networks