pyrepo_mcda.mcda_methods.spotis
===============================

.. py:module:: pyrepo_mcda.mcda_methods.spotis


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.spotis.SPOTIS


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

.. py:class:: SPOTIS

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


   Stable Preference Ordering Towards Ideal Solution (SPOTIS) method
   for ranking alternatives according to their distances from the ideal
   solution defined within criterion bounds.


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

      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
              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.
          bounds: ndarray
              Bounds is ndarray with 2 rows and number of columns equal to criteria number. 
              Bounds contain minimum values in the first row and maximum values in the second row 
              for each criterion. Minimum and maximum values for the same criterion cannot be 
              the same.

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

      Examples
      ----------
      >>> bounds_min = np.amin(matrix, axis = 0)
      >>> bounds_max = np.amax(matrix, axis = 0)
      >>> bounds = np.vstack((bounds_min, bounds_max))
      >>> spotis = SPOTIS()
      >>> pref = spotis(matrix, weights, types, bounds)
      >>> rank = rank_preferences(pref, reverse = False)



   .. py:method:: _spotis(matrix, weights, types, bounds)
      :staticmethod: