Nonparametric nearest-neighbor-based estimators for divergences between distributions
C++ Matlab
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np-divs
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README.md

This is a C++ implementation of the nonparametric divergence estimators described by:

Barnabas Poczos, Liang Xiong, and Jeff Schneider (2011). Nonparametric divergence estimation with applications to machine learning on distributions. Uncertainty in Artificial Intelligence. http://autonlab.org/autonweb/20287.html

This code was written by Dougal J. Sutherland based on a pure-Matlab version by the authors above.

Requirements

Installation

mkdir build; cd build
cmake ..
make
make runtests # optional, requires HDF5
make install

This will install the npdivs command-line interface (run npdivs -h for help) as well as the shared library named e.g. libnp-divs.so (depending on platform) and header files. By default, these will be placed in /usr/local; to install to a different location, use something like:

cmake .. -DCMAKE_INSTALL_PREFIX=$HOME

Note that when testing, the Gaussians50DTest case is disabled by default, as it is computationally expensive and unlikely to reveal any installation problems not shown by the Gaussians2DTest. If you'd like to run it anyway, use:

make runtests GTEST_ALSO_RUN_DISABLED_TESTS=1