pyrepo_mcda.mcda_methods.codas

Classes

CODAS

Module Contents

class pyrepo_mcda.mcda_methods.codas.CODAS(normalization_method=linear_normalization, distance_metric=euclidean, tau=0.02)

Bases: pyrepo_mcda.mcda_methods.mcda_method.MCDA_method

normalization_method

distance_metric

tau

__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

>>> codas = CODAS(normalization_method = linear_normalization, distance_metric = euclidean, tau = 0.02)
>>> pref = codas(matrix, weights, types)
>>> rank = rank_preferences(pref, reverse = True)

_psi(x)

static _codas(self, matrix, weights, types, normalization_method, distance_metric)