pyrepo_mcda.mcda_methods.copras
===============================

.. py:module:: pyrepo_mcda.mcda_methods.copras


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.copras.COPRAS


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

.. py:class:: COPRAS(normalization_method=sum_normalization)

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


   Complex Proportional Assessment (COPRAS) method for evaluating and
   ranking alternatives based on their proportional significance and utility degree.


   .. 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
              Criteria weights. Sum of weights must be equal to 1.
          types: ndarray
              Criteria types. Profit criteria are represented by 1 and cost by -1.

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

      Examples
      ----------
      >>> copras = COPRAS(normalization_method = sum_normalization)
      >>> pref = copras(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



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