pyrepo_mcda.mcda_methods.waspas

Classes

WASPAS

Module Contents

class pyrepo_mcda.mcda_methods.waspas.WASPAS(normalization_method=linear_normalization, lambda_param=0.5)

Bases: pyrepo_mcda.mcda_methods.mcda_method.MCDA_method

normalization_method

lambda_param

__call__(matrix, weights, types)

Score alternatives provided in decision matrix matrix with m alternatives and n criteria 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

>>> waspas = WASPAS(normalization_method = linear_normalization, lambda_param = 0.5)
>>> pref = waspas(matrix, weights, types)
>>> rank = rank_preferences(pref, reverse = True)

static _waspas(matrix, weights, types, normalization_method, lambda_param)