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INSTALL.md

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Requirements

  • Boost >= 1.58 (earlier versions may suffice, but have not been tested)
  • CMake 2.8.12
  • libunittest++ 1.4.0
  • maven >= 3.3.9 (for Matlab/Java implementation)

Boost is used to provide big integer and decimal support.

You will also require a CPU with the AES-NI instruction set.

Installation instructions

The software has only been tested on Linux and OSX. We have not attempted to build in a Windows environment yet, but have not used any platform specific code and so a Windows build should hopefully be reasonably achievable.

To build the C++ examples and install the library files, run

cd build/
cmake -DCMAKE_BUILD_TYPE=Release .. && make install

The library files will be installed into the $root/include/ directory and the executable to run rank and search examples will be installed into the $root/bindirectory.

The library is header-only and can be used in your own projects by including the $root/include/ directory (and including boost_multiprecision).

Folder hierarchy

  • bin/ -- will contain the examples executable
  • build/ -- used for out-of-source CMake builds
  • examples/ -- C++ exemplar rank and parallel search examples
  • include/ -- will contain the header-only library files
  • matlab/rank -- contains a minimal implementation of the path count rank algorithm in Java. This is primarily intended to allow users to run rank estimations directly from Matlab.
  • matlab/ -- example usage of running rank estimations directly from Matlab using the Java implementation.
  • src/ -- C++ library source
  • test/ -- C++ unit tests

Using Matlab/Java implementation

To use the Matlab/Java implementation, compile the Java source code and move the library to the matlab folder.

cd matlab/rank/
mvn package
cp target/LabynkyrRankJ-1.0-jar-with-dependencies.jar ../

Now you should be ready do run the matlab examples.