crispyn.mcda_methods.vikor
==========================

.. py:module:: crispyn.mcda_methods.vikor


Classes
-------

.. autoapisummary::

   crispyn.mcda_methods.vikor.VIKOR


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

.. py:class:: VIKOR(normalization_method=None, v=0.5)

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


   Helper class that provides a standard way to create an ABC using
   inheritance.


   .. py:attribute:: v
      :value: 0.5



   .. py:attribute:: normalization_method
      :value: None



   .. 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
              Matrix containing vectors with criteria weights in subsequent rows. 
              Sum of weights in each vector must be equal to 1.
          types: ndarray
              Vector with criteria types. Profit criteria are represented by 1 and cost by -1.

      Returns
      --------
          ndrarray
              Matrix with vectors containing preference values of each alternative. 
              The best alternative has the lowest preference value. Vectors are placed
              in subsequent columns of matrix.

      Examples
      ---------
      >>> vikor = VIKOR(normalization_method = minmax_normalization)
      >>> pref = vikor(matrix, weights, types)
      >>> rank = np.zeros((pref.shape))
      >>> for i in range(pref.shape[1]):
      >>>     rank[:, i] = rank_preferences(pref[:, i], reverse = False)



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