pyrepo_mcda.mcda_methods.mabac
==============================

.. py:module:: pyrepo_mcda.mcda_methods.mabac


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.mabac.MABAC


Module Contents
---------------

.. py:class:: MABAC(normalization_method=minmax_normalization)

   Bases: :py:obj:`pyrepo_mcda.mcda_methods.mcda_method.MCDA_method`


   Multi-Attributive Border Approximation Area Comparison (MABAC) method
   for ranking alternatives according to their distances from the border
   approximation area.


   .. py:attribute:: normalization_method


   .. py:method:: __call__(matrix, weights, types)

      Score alternatives provided in decision matrix `matrix` using criteria `weights` and criteria `types`.

      Parameters
      -----------
          matrix : ndarray
              Decision matrix with m alternatives in rows and n criteria in columns.
          weights: ndarray
              Vector with criteria weights. Sum of weights must be equal to 1.
          types: ndarray
              Vector with criteria types. Profit criteria are represented by 1 and cost by -1.

      Returns
      --------
          ndrarray
              Vector with preference values of each alternative. The best alternative has the highest preference value. 

      Examples
      ---------
      >>> mabac = MABAC(normalization_method = minmax_normalization)
      >>> pref = mabac(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _mabac(weights, types, normalization_method)