pyrepo_mcda.mcda_methods.edas
=============================

.. py:module:: pyrepo_mcda.mcda_methods.edas


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.edas.EDAS


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

.. py:class:: EDAS

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


   Evaluation based on Distance from Average Solution (EDAS) method
   for ranking alternatives according to their positive and negative
   distances from the average solution.


   .. 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
              Vevtor 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
      ---------
      >>> edas = EDAS()
      >>> pref = edas(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _edas(weights, types)