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

.. py:module:: crispyn.mcda_methods


Submodules
----------

.. toctree::
   :maxdepth: 1

   /autoapi/crispyn/mcda_methods/mcda_method/index
   /autoapi/crispyn/mcda_methods/vikor/index
   /autoapi/crispyn/mcda_methods/vikor_smaa/index


Classes
-------

.. autoapisummary::

   crispyn.mcda_methods.VIKOR
   crispyn.mcda_methods.VIKOR_SMAA


Package 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:



.. 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: