pyrepo_mcda.compromise_rankings
===============================

.. py:module:: pyrepo_mcda.compromise_rankings


Functions
---------

.. autoapisummary::

   pyrepo_mcda.compromise_rankings.copeland
   pyrepo_mcda.compromise_rankings.dominance_directed_graph
   pyrepo_mcda.compromise_rankings.rank_position_method
   pyrepo_mcda.compromise_rankings.improved_borda_rule


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

.. py:function:: copeland(matrix)

   Calculate the compromise ranking considering several rankings obtained using different
   methods using the Copeland compromise ranking methodology.

   Parameters
   -----------
       matrix : ndarray
           Two-dimensional matrix containing different rankings in columns.

   Returns
   --------
       ndarray
           Vector including compromise ranking.

   Examples
   ----------
   >>> rank = copeland(matrix)


.. py:function:: dominance_directed_graph(matrix)

   Calculate the compromise ranking considering several rankings obtained using different
   methods using Dominance Directed Graph methodology

   Parameters
   -----------
       matrix : ndarray
           Two-dimensional matrix containing different rankings in columns.

   Returns
   --------
       ndarray
           Vector including compromise ranking.

   Examples
   ----------
   >>> rank = dominance_directed_graph(matrix)


.. py:function:: rank_position_method(matrix)

   Calculate the compromise ranking considering several rankings obtained using different
   methods using Rank Position Method

   Parameters
   -----------
       matrix : ndarray
           Two-dimensional matrix containing different rankings in columns.

   Returns
   --------
       ndarray
           Vector including compromise ranking.

   Examples
   ---------
   >>> rank = rank_position_method(matrix)


.. py:function:: improved_borda_rule(prefs, ranks)

   Calculate the compromise ranking considering several rankings obtained using different
   methods using Improved Borda rule methodology

   Parameters
   -----------
       prefs : ndarray
           Two-dimensional matrix containing preferences calculated by different methods in columns.

       ranks : ndarray
           Two-dimensional matrix containing rankings determined by different methods in columns.

   Returns
   --------
       ndarray
           Vector including compromise ranking.

   Examples
   ----------
   >>> rank = improved_borda_rule(prefs, ranks)