pyrepo_mcda.mcda_methods.prosa_c
================================

.. py:module:: pyrepo_mcda.mcda_methods.prosa_c


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.prosa_c.PROSA_C


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

.. py:class:: PROSA_C

   Bases: :py:obj:`pyrepo_mcda.mcda_methods.promethee.PROMETHEE_II`


   PROMETHEE for Sustainability Assessment - Criteria (PROSA-C)
   method for ranking alternatives using preference flows and a
   compensation coefficient.


   .. py:method:: __call__(matrix, weights, types, preference_functions=None, p=None, q=None, s=None)


   .. py:method:: _prosa_c(self, matrix, weights, types, preference_functions, p, q, s)
      :staticmethod:


      Score alternatives provided in the 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. The sum of weights must be equal to 1.
      types: ndarray
          Criteria types. Profit criteria are represented by 1 and cost by -1.
      preference_functions : list
          List with methods containing preference functions for calculating the
          preference degree for each criterion.
      p : ndarray
          Vector with values representing the threshold of absolute preference.
      q : ndarray
          Vector with values representing the threshold of indifference.
      s : ndarray
          Vector with values of the coefficient sj for the criteria

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

      Examples
      --------
      >>> prosa_c = PROSA_C()
      >>> preference_functions = [prosa_c._linear_function for pf in range(len(weights))]
      >>> u = np.sqrt(np.sum(np.square(np.mean(matrix, axis = 0) - matrix), axis = 0) / matrix.shape[0])
      >>> p = 2 * u
      >>> q = 0.5 * u
      >>> s = np.repeat(0.3, len(weights))
      >>> pref = prosa_c(matrix, weights, types, preference_functions, p = p, q = q, s = s)
      >>> rank = rank_preferences(pref, reverse = True)