pyrepo_mcda.mcda_methods.waspas
===============================

.. py:module:: pyrepo_mcda.mcda_methods.waspas


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.waspas.WASPAS


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

.. py:class:: WASPAS(normalization_method=linear_normalization, lambda_param=0.5)

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


   Weighted Aggregated Sum Product Assessment (WASPAS) method
   for ranking alternatives by combining the Weighted Sum Model
   and Weighted Product Model.


   .. py:attribute:: normalization_method


   .. py:attribute:: lambda_param
      :value: 0.5



   .. py:method:: __call__(matrix, weights, types)

      Score alternatives provided in decision matrix `matrix` with m alternatives and n criteria 
      using criteria `weights` and criteria `types`.

      Parameters
      -----------
          matrix : ndarray
              Decision matrix with m alternatives in rows and n criteria in columns.
          weights: ndarray
              Vector with criteria weights. Sum of weights must be equal to 1.
          types: ndarray
              Vector with criteria types. Profit criteria are represented by 1 and cost by -1.

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

      Examples
      ----------
      >>> waspas = WASPAS(normalization_method = linear_normalization, lambda_param = 0.5)
      >>> pref = waspas(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _waspas(matrix, weights, types, normalization_method, lambda_param)
      :staticmethod: