Library was originally created as part of a semester project on Operational research and complexity theory - fourth semester subject on Computer Science. Currently, the library allows you to build complex AHP decision structures, save them to a file, re-read them, as well as calculating the ranking with three methods:

• Eigenvector method
• Geometric mean method
• Normalized columns method

To use library, it is necessary to have Python 3.5 installed along with the basic libraries supporting the calculation of the linear albebra. If you are a Ubuntu distribution user you are lucky, you can install all dependencies using the simple script contained inside repository. setup_python_with_env.sh For Windows users, the easiest way will be to install Anaconda and downloading the source code.

Check how EasyAHPTool handles the calculation of the ranking based on the data read from the file.

The library requires that the load file that defines the AHP decision tree has the following structure.

{
  "alternatives": [
    "Tom",
    "Dick",
    ...
  ],

  "goal": {
    "name": "Most Suitable Leader",
    "preferences": [
      [1, 4, ... ], ... ],
    "children": [
      {
        "name": "Experience",
        "preferences": [...],
        "children": [...]
      },
      ... ]
   }
}