crispyn.mcda_methods.vikor_smaa
===============================

.. py:module:: crispyn.mcda_methods.vikor_smaa


Classes
-------

.. autoapisummary::

   crispyn.mcda_methods.vikor_smaa.VIKOR_SMAA


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

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

   .. 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 with i vectors in rows of n weights in columns. i means number of
              iterations of SMAA
          types : ndarray
              Vector with criteria types. Profit criteria are represented by 1 and cost by -1.

      Returns
      --------
          ndrarray, ndarray, ndarray
              Matrix with acceptability indexes values for each alternative in rows in relation to each rank in columns,
              Matrix with central weight vectors for each alternative in rows
              Matrix with final ranking of alternatives

      Examples
      ---------
      >>> vikor_smaa = VIKOR_SMAA(normalization_method = minmax_normalization)
      >>> rank_acceptability_index, central_weight_vector, rank_scores = vikor_smaa(matrix, weights, types)



   .. py:method:: _generate_weights(n, iterations)

      Function to generate multiple weight vectors

      Parameters
      -----------
          n : int
              Number of criteria
          iterations : int
              Number of weight vector to generate

      Returns
      ----------
          ndarray
              Matrix containing in rows vectors with weights for n criteria




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