crispyn.mcda_methods.vikor

Classes

VIKOR

Helper class that provides a standard way to create an ABC using

Module Contents

class crispyn.mcda_methods.vikor.VIKOR(normalization_method=None, v=0.5)

Bases: crispyn.mcda_methods.mcda_method.MCDA_method

Helper class that provides a standard way to create an ABC using inheritance.

v = 0.5

normalization_method = None

__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
Matrix containing vectors with criteria weights in subsequent rows. Sum of weights in each vector must be equal to 1.

types: ndarray
Vector with criteria types. Profit criteria are represented by 1 and cost by -1.

Returns

ndrarray
Matrix with vectors containing preference values of each alternative. The best alternative has the lowest preference value. Vectors are placed in subsequent columns of matrix.

Examples

>>> vikor = VIKOR(normalization_method = minmax_normalization)
>>> pref = vikor(matrix, weights, types)
>>> rank = np.zeros((pref.shape))
>>> for i in range(pref.shape[1]):
>>>     rank[:, i] = rank_preferences(pref[:, i], reverse = False)

static _vikor(matrix, weights, types, normalization_method, v)