Skip to content

Library which can be used for machine learning in C++.

License

Notifications You must be signed in to change notification settings

abhinavmalhotra01/slowmokit

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

slowmoKIT logo

Slomokit

ML kit in C++.

Library which can be used for machine learning in C++.

Steps

Following are the steps to start contributing to our beautiful library:

  1. Install an IDE, preferably CLion.
  2. Fork this repo.
  3. Clone the forked repo, using git clone https://github.com/PEC-CSS/slowmokit.git.

After this,

You can directly use the execuatable file by running ./create in the root folder of library, to do the required configurations.

More details here

If it does not work then you need to comfigure everything by your own.

  1. Make the model directory you want to implement in the src/slowmokit/methods/<model type>/<your_model>.
    • <model type> is basically the class of your model, for example linear_model or cluster.
  2. Create two mandatory files using IDE ONLY! As this will automatically add the file to CMakeLists.txt:
    • <model>.hpp : the header file for the model, create the class of model here and include all the stuff required for the model here.
      • Include src/slowmokit.core.hpp for all the basic functionalities already added.
      • Make sure to add doc comments above each function signature (refer other files for the format)
      • Variable and function names should be in camel case and class names should be in pascal case in all files.
    • <model>.cpp : implement the functions of the class here.
  3. If your IDE does not automatically add the files to Cmake, add them manually in the CMakeLists.txt, under the add_libraries.

This step is mandatory.

  1. Add the header file <model>.hpp in src/slowmokit.hpp under the documentation block as shown here. More on documentation block.
  2. Test the working by building in IDE.

NOTE: Do NOT use #include <bits/stdc++.h> in any file, all the major header files are already included in core.hpp.]

Example

Once the algorithm is ready, run and test it. After testing, push it in the examples directory, with proper comments and instructions. With same directory structure as in src.

Documentation

Push the docs of the model in a <model>.md file in same directory structure in docs folder.

Pull Request (PR) format

After code is ready, you can make PR to the main branch. PR will be accepted only if:

  • Code pass the CI tests.
  • Example is pushed.
  • Code has proper comments and instructions.

GOOD LUCK!

About

Library which can be used for machine learning in C++.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • C++ 84.9%
  • Rust 11.2%
  • CMake 3.9%