pyrepo_mcda.normalizations
==========================

.. py:module:: pyrepo_mcda.normalizations


Functions
---------

.. autoapisummary::

   pyrepo_mcda.normalizations.linear_normalization
   pyrepo_mcda.normalizations.minmax_normalization
   pyrepo_mcda.normalizations.max_normalization
   pyrepo_mcda.normalizations.sum_normalization
   pyrepo_mcda.normalizations.vector_normalization
   pyrepo_mcda.normalizations.multimoora_normalization


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

.. py:function:: linear_normalization(matrix, types)

   Normalize decision matrix using linear normalization method.

   Parameters
   -----------
       matrix : ndarray
           Decision matrix with m alternatives in rows and n criteria in columns
       types : ndarray
           Criteria types. Profit criteria are represented by 1 and cost by -1.

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

   Examples
   ----------
   >>> nmatrix = linear_normalization(matrix, types)


.. py:function:: minmax_normalization(matrix, types)

   Normalize decision matrix using minimum-maximum normalization method.

   Parameters
   -----------
       matrix : ndarray
           Decision matrix with m alternatives in rows and n criteria in columns
       types : ndarray
           Criteria types. Profit criteria are represented by 1 and cost by -1.

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

   Examples
   ----------
   >>> nmatrix = minmax_normalization(matrix, types)


.. py:function:: max_normalization(matrix, types)

   Normalize decision matrix using maximum normalization method.

   Parameters
   -----------
       matrix : ndarray
           Decision matrix with m alternatives in rows and n criteria in columns
       types : ndarray
           Criteria types. Profit criteria are represented by 1 and cost by -1.

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

   Examples
   ----------
   >>> nmatrix = max_normalization(matrix, types)


.. py:function:: sum_normalization(matrix, types)

   Normalize decision matrix using sum normalization method.

   Parameters
   -----------
       matrix : ndarray
           Decision matrix with m alternatives in rows and n criteria in columns
       types : ndarray
           Criteria types. Profit criteria are represented by 1 and cost by -1.

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

   Examples
   ----------
   >>> nmatrix = sum_normalization(matrix, types)


.. py:function:: vector_normalization(matrix, types)

   Normalize decision matrix using vector normalization method.

   Parameters
   -----------
       matrix : ndarray
           Decision matrix with m alternatives in rows and n criteria in columns
       types : ndarray
           Criteria types. Profit criteria are represented by 1 and cost by -1.

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

   Examples
   -----------
   >>> nmatrix = vector_normalization(matrix, types)


.. py:function:: multimoora_normalization(matrix)

   Normalize decision matrix using vector normalization method as for profit criteria.

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

   Examples
   -----------
   >>> nmatrix = multimoora_normalization(matrix)