pyrepo_mcda.mcda_methods.multimoora
===================================

.. py:module:: pyrepo_mcda.mcda_methods.multimoora


Classes
-------

.. autoapisummary::

   pyrepo_mcda.mcda_methods.multimoora.MULTIMOORA_RS
   pyrepo_mcda.mcda_methods.multimoora.MULTIMOORA_RP
   pyrepo_mcda.mcda_methods.multimoora.MULTIMOORA_FMF
   pyrepo_mcda.mcda_methods.multimoora.MULTIMOORA


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

.. py:class:: MULTIMOORA_RS

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


   Ratio System component of the MULTIMOORA method for multi-criteria
   decision analysis.


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

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

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

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

      Examples
      ---------
      >>> multimoora_rs = MULTIMOORA_RS()
      >>> pref = multimoora_rs(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _multimoora_rs(matrix, weights, types)
      :staticmethod:



.. py:class:: MULTIMOORA_RP

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


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

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

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

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

      Examples
      ---------
      >>> multimoora_rp = MULTIMOORA_RP()
      >>> pref = multimoora_rp(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = False)



   .. py:method:: _multimoora_rp(matrix, weights, types)
      :staticmethod:



.. py:class:: MULTIMOORA_FMF

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


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

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

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

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

      Examples
      ---------
      >>> multimoora_fmf = MULTIMOORA_FMF()
      >>> pref = multimoora_fmf(matrix, weights, types)
      >>> rank = rank_preferences(pref, reverse = True)



   .. py:method:: _multimoora_fmf(matrix, weights, types)
      :staticmethod:



.. py:class:: MULTIMOORA(compromise_rank_method=dominance_directed_graph)

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


   Multi-Objective Optimization on the basis of Ratio Analysis
   (MULTIMOORA) method for ranking alternatives using the Ratio System,
   Reference Point, and Full Multiplicative Form approaches.


   .. py:attribute:: compromise_rank_method


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

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

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

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

      Examples
      ----------
      >>> multimoora = MULTIMOORA()
      >>> rank = multimoora(matrix, weights, types)



   .. py:method:: _multimoora(weights, types, compromise_rank_method)