pyrepo_mcda.mcda_methods.cocoso
===============================

.. py:module:: pyrepo_mcda.mcda_methods.cocoso


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.cocoso.COCOSO


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

.. py:class:: COCOSO(normalization_method=minmax_normalization, lambda_param=0.5)

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


   Combined Compromise Solution (CoCoSo) method for evaluating and ranking
   alternatives using a combination of additive and multiplicative aggregation strategies.


   .. py:attribute:: normalization_method


   .. py:attribute:: lambda_param
      :value: 0.5



   .. 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
      ---------
      >>> cocoso = COCOSO(lambda_param = lambda_param)
      >>> pref = cocoso(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _cocoso(matrix, weights, types, normalization_method=minmax_normalization, lambda_param=0.5)
      :staticmethod: