Please visit our web page for information about MeTA!

MeTA is a modern C++ data sciences toolkit featuring

• text tokenization, including deep semantic features like parse trees

• inverted and forward indexes with compression and various caching strategies

• various ranking functions for the indexes

• topic modeling algorithms

• language modeling algorithms

• clustering and similarity algorithms

• classification algorithms

• wrappers for liblinear and slda

Doxygen documentation can be found here.

Our current goal for MeTA is to publish in JMLR's Machine Learning Open-Source Software.

• master:
• develop:

• This project requires a very well conforming C++11 compiler. Currently, clang is the de-facto compiler for use with this project

• Additionally, you will need a conformant implementation of the C++11 standard library and ABI---currently libc++ and libc++abi are the best options for this. See your distribution's package manager for more information on installing these dependencies.

• Windows users: YMMV. It is not currently supported, but things may work. You will likely need Visual Studio 2013 for the C++11 features.

• This project makes use of several git submodules. To initialize these, run

git submodule init
git submodule update

• Once the submodules are instantiated, go to deps/libsvm-modules and run make in the liblinear and libsvm directories if you plan on using the svm_wrapper class.

• To compile initially, run the following commands

mkdir build
cd build
# omit CXX=clang++ if you want to use your default compiler
CXX=clang++ cmake ../ -DCMAKE_BUILD_TYPE=Debug
make

• There are rules for clean, tidy, and doc. (Also, once you run the cmake command once, you should be able to just run make like usual as you're developing---it'll detect when the CMakeLists.txt file has changed and rebuild Makefiles if it needs to.)