pyrepo_mcda.mcda_methods.topsis
===============================

.. py:module:: pyrepo_mcda.mcda_methods.topsis


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.topsis.TOPSIS


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

.. py:class:: TOPSIS(normalization_method=minmax_normalization, distance_metric=euclidean)

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


   Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)
   method for ranking alternatives according to their distances from
   ideal and anti-ideal solutions.


   .. py:attribute:: normalization_method


   .. py:attribute:: distance_metric


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

      Score alternatives provided in decision matrix `matrix` with m alternatives in rows and 
      n criteria in columns 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
      ---------
      >>> topsis = TOPSIS(normalization_method = minmax_normalization, distance_metric = euclidean)
      >>> pref = topsis(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _topsis(matrix, weights, types, normalization_method, distance_metric)
      :staticmethod: