pyrepo_mcda.mcda_methods.prosa_c

Classes

PROSA_C

Module Contents

class pyrepo_mcda.mcda_methods.prosa_c.PROSA_C

Bases: pyrepo_mcda.mcda_methods.promethee.PROMETHEE_II

__call__(matrix, weights, types, preference_functions=None, p=None, q=None, s=None)

static _prosa_c(self, matrix, weights, types, preference_functions, p, q, s)

Score alternatives provided in the 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

Criteria weights. The sum of weights must be equal to 1.

types: ndarray

Criteria types. Profit criteria are represented by 1 and cost by -1.

preference_functionslist

List with methods containing preference functions for calculating the preference degree for each criterion.

pndarray

Vector with values representing the threshold of absolute preference.

qndarray

Vector with values representing the threshold of indifference.

sndarray

Vector with values of the coefficient sj for the criteria

Returns

ndrarray

Preference values of each alternative. The best alternative has the highest preference value.

Examples

>>> prosa_c = PROSA_C()
>>> preference_functions = [prosa_c._linear_function for pf in range(len(weights))]
>>> u = np.sqrt(np.sum(np.square(np.mean(matrix, axis = 0) - matrix), axis = 0) / matrix.shape[0])
>>> p = 2 * u
>>> q = 0.5 * u
>>> s = np.repeat(0.3, len(weights))
>>> pref = promethee_II(matrix, weights, types, preference_functions, p = p, q = q, s = s)
>>> rank = rank_preferences(pref, reverse = True)