pyrepo_mcda.mcda_methods.vmcm
=============================

.. py:module:: pyrepo_mcda.mcda_methods.vmcm


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.vmcm.VMCM


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

.. py:class:: VMCM

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


   Vector Measure Construction Method (VMCM) for evaluating and ranking
   alternatives using vector-based measures of their performance across
   multiple criteria.


   .. py:method:: _elimination(matrix)

      Calculate significance coefficient values for each criterion.
      Criteria with significance coefficient values between 0 and 0.1 are recommended to be eliminated from the considered criteria set.

      Parameters
      --------------
          matrix : ndarray
              Decision matrix with m alternatives in rows and n criteria in columns.

      Examples
      --------------
      >>> vmcm = VMCM()
      >>> vmcm._elimination(matrix)



   .. py:method:: _weighting(matrix)

      Calculate criteria weights

      Parameters
      -------------
          matrix : ndarray
              Decision matrix with m alternatives in rows and n criteria in columns.

      Returns
      -------------
          ndarray
              Vector with criteria weights

      Examples
      ------------
      >>> vmcm = VMCM()
      >>> weights = vmcm._weighting(matrix)



   .. py:method:: _normalization(matrix)

      Calculates normalized matrix

      Parameters
      --------------
          matrix : ndarray
              Decision matrix with m alternatives in rows and n criteria in columns.

      Returns
      ---------------
          ndarray
              Normalized matrix

      Examples
      ---------------
      >>> vmcm = VMCM()
      >>> norm_matrix = vmcm._normalization(matrix)



   .. py:method:: _pattern_determination(matrix, types)

      Automatic determination of pattern and anti-pattern

      Parameters
      --------------
          matrix : ndarray
              Decision matrix with m alternatives in rows and n criteria in columns.

      Returns
      --------------
          ndarray, ndarray
              Two vectors including values respectively of pattern and anti-pattern

      Examples
      --------------
      >>> vmcm = VMCM()
      >>> pattern, antipattern = vmcm._pattern_determination(matrix, types)



   .. py:method:: _classification(m)

      Assign evaluated objects to classes

      Parameters
      -------------
          m : ndarray
              Vector with values of synthetic measure

      Returns
      -------------
          ndarray
              Vector including classes assigned to evaluated objects

      Examples
      --------------
      >>> vmcm = VMCM()
      >>> pref = vmcm(matrix, weights, types)
      >>> classes = vmcm._classification(pref)



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

      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.
          pattern : ndarray
              Vector with values of pattern
          anti_pattern : ndarray
              Vector with values of anti-pattern

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

      Examples
      ---------
      >>> vmcm = VMCM()
      >>> pattern, antipattern = vmcm._pattern_determination(matrix, types)
      >>> pref = vmcm(matrix, weights, types, pattern, antipattern)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _vmcm(self, matrix, weights, types, pattern, anti_pattern)
      :staticmethod: