pyrepo_mcda.mcda_methods.mabac

Classes

MABAC

Module Contents

class pyrepo_mcda.mcda_methods.mabac.MABAC(normalization_method=minmax_normalization)

Bases: pyrepo_mcda.mcda_methods.mcda_method.MCDA_method

normalization_method

__call__(matrix, weights, types)

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

Parameters

matrixndarray
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.

Returns

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

Examples

>>> mabac = MABAC(normalization_method = minmax_normalization)
>>> pref = mabac(matrix, weights, types)
>>> rank = rank_preferences(pref, reverse = True)

_mabac(weights, types, normalization_method)