This is an efficient C++ implementation of Information-Theoretic Metric Learning (Davis et al., ICML 2007),
based on the Python implementation by CJ Carey.
Building this package requires the Eigen library of version at least 3.1 and has been tested with gcc 4.8 and 5.3 and Eigen 3.3.
If you installed Eigen to a non-standard location, you need to point the environment variable CPLUS_INCLUDE_PATH to it.
Compiling the library and installing the Python package can then be done as follows:
python setup.py install
If only the C++ library is of interest, it can also be compiled manually using gcc:
g++ -march=native -Wall -O3 --shared -fPIC -o libitml.so libitml.cc -fopenmp
Make sure to have Eigen in your include path (see above) or add a corresponding -I flag.
The main itml() function is implemented in C++, but C-style interfaces itml_float() and itml_double() are available,
so that it can be easily used from any other programming language. A Python interface function is provided as well.