pyrepo_mcda.mcda_methods.pvm
============================

.. py:module:: pyrepo_mcda.mcda_methods.pvm


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.pvm.PVM


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

.. py:class:: PVM

   Preference Vector Method (PVM) for evaluating and ranking alternatives
   based on their similarity to motivating and dissimilarity to demotivating
   preference vectors.


   .. py:method:: __call__(matrix, weights, types, psi=None, phi=None)

      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
              Attribute weights to the criteria. Sum of weights must be equal to 1.
          types: list
              Define the criteria character:
              Motivating criteria are represented by `m`
              Demotivating criteria are represented by `dm`
              Desirable criteria are represented by `d`
              Non-desirable criteria are represented by `nd`
          psi: ndarray
              Preference vector with motivating or desirable values. Providing of psi is optional.
          phi: ndarray
              Preference vector with demotivating or non-desirable values. Providing of psi is optional.

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

      Examples
      ----------
      >>> pvm = PVM()
      >>> pref = pvm(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _pvm(matrix, weights, types, psi, phi)
      :staticmethod: