Installation guideΒΆ

C++ supported compilers:

gcc version

clang version

msvc version

The rFBP project is written in C++ using a large amount of c++17 features. To enlarge the usability of our package we provide also a retro-compatibility of all the c++17 modules reaching an usability (tested) of our code from gcc 4.8.5+. The package installation can be performed via CMake or Makefile.

If you are using the CMake (recommended) installer the maximum version of C++ standard is automatic detected. The CMake installer provides also the export of the library: after the installation you can use this library into other CMake projects using a simple find_package function. The exported CMake library (rFBP::rfbp) is installed in the share/rFBP directory of the current project and the relative header files are available in the rFBP_INCLUDE_DIR variable.

The CMake installer provides also a rFBP.pc, useful if you want link to the rFBP using pkg-config.

You can also use the rFBP package in Python using the Cython wrap provided inside this project. The only requirements are the following:

  • numpy >= 1.15
  • cython >= 0.29
  • scipy >= 1.2.1
  • scikit-learn >= 0.20.3
  • requests >= 2.22.0

The Cython version can be built and installed via CMake enabling the -DPYWRAP variable. The Python wrap guarantees also a good integration with the other common Machine Learning tools provided by scikit-learn Python package; in this way you can use the rFBP algorithm as an equivalent alternative also in other pipelines. Like other Machine Learning algorithm also the rFBP one depends on many parameters, i.e its hyper-parameters, which has to be tuned according to the given problem. The Python wrap of the library was written according to scikit-optimize Python package to allow an easy hyper-parameters optimization using the already implemented classical methods.

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