pyrepo_mcda.mcda_methods.saw
============================

.. py:module:: pyrepo_mcda.mcda_methods.saw


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.saw.SAW


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

.. py:class:: SAW(normalization_method=linear_normalization)

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


   Simple Additive Weighting (SAW) method for ranking alternatives based on
   the weighted sum of normalized criterion values.


   .. py:attribute:: normalization_method


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

      Score alternatives provided in decision matrix `matrix` with m alternatives in rows and 
      n criteria in columns 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
      ---------
      >>> saw = SAW(normalization_method = minmax_normalization)
      >>> pref = saw(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _saw(matrix, weights, types, normalization_method)
      :staticmethod: