pyrepo_mcda.mcda_methods.promethee
==================================

.. py:module:: pyrepo_mcda.mcda_methods.promethee


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.promethee.PROMETHEE_II


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

.. py:class:: PROMETHEE_II

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


   Preference Ranking Organization Method for Enrichment Evaluation II
   (PROMETHEE II) for ranking alternatives based on positive, negative,
   and net preference flows.


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

      Score alternatives from decision matrix `matrix` using criteria weights `weights` and 
      criteria types `types`

      Parameters
      ----------
      matrix : ndarray
          decision matrix with performance values for m alternatives in rows and n criteria 
          in columns
      weights : ndarray
          matrix with criteria weights vectors with number of columns equal to 
          number of columns n of `matrix`
      types : ndarray
          vector with criteria types containing values of 1 for profit criteria and -1 for 
          cost criteria with size equal to number of columns n of `matrix`



   .. py:method:: _usual_function(d, p, q)


   .. py:method:: _ushape_function(d, p, q)


   .. py:method:: _vshape_function(d, p, q)


   .. py:method:: _level_function(d, p, q)


   .. py:method:: _linear_function(d, p, q)


   .. py:method:: _gaussian_function(d, p, q)


   .. py:method:: _promethee_II(self, matrix, weights, types, preference_functions, p, q)
      :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.
              If None, default PROMETHEE preference function (usual) is applied.
          p : ndarray
              Vector with values representing the threshold of absolute preference.
              If None, default PROMETHEE preference thresholds are applied.
          q : ndarray
              Vector with values representing the threshold of indifference.
              If None, default PROMETHEE indifference thresholds are applied.

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

      Examples
      ----------
      >>> promethee_II = PROMETHEE_II()
      >>> preference_functions = [promethee_II._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
      >>> pref = promethee_II(matrix, weights, types, preference_functions, p = p, q = q)
      >>> rank = rank_preferences(pref, reverse = True)