pyrepo_mcda.mcda_methods.codas
==============================

.. py:module:: pyrepo_mcda.mcda_methods.codas


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.codas.CODAS


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

.. py:class:: CODAS(normalization_method=linear_normalization, distance_metric=euclidean, tau=0.02)

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


   Combinative Distance-based Assessment (CODAS) method for ranking
   alternatives based on their distances from the negative ideal solution.


   .. py:attribute:: normalization_method


   .. py:attribute:: distance_metric


   .. py:attribute:: tau
      :value: 0.02



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

      Score alternatives provided in decision matrix `matrix` with m alternatives and n criteria 
      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
      ----------
      >>> codas = CODAS(normalization_method = linear_normalization, distance_metric = euclidean, tau = 0.02)
      >>> pref = codas(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _psi(x)


   .. py:method:: _codas(self, matrix, weights, types, normalization_method, distance_metric)
      :staticmethod: