crispyn.mcda_methods

Submodules

• crispyn.mcda_methods.mcda_method
• crispyn.mcda_methods.vikor
• crispyn.mcda_methods.vikor_smaa

Classes

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

Package Contents

class crispyn.mcda_methods.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)

class crispyn.mcda_methods.VIKOR_SMAA(normalization_method=None, v=0.5)

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.

weightsndarray

Matrix with i vectors in rows of n weights in columns. i means number of iterations of SMAA

typesndarray

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

Returns

ndrarray, ndarray, ndarray

Matrix with acceptability indexes values for each alternative in rows in relation to each rank in columns, Matrix with central weight vectors for each alternative in rows Matrix with final ranking of alternatives

Examples

>>> vikor_smaa = VIKOR_SMAA(normalization_method = minmax_normalization)
>>> rank_acceptability_index, central_weight_vector, rank_scores = vikor_smaa(matrix, weights, types)

_generate_weights(n, iterations)

Function to generate multiple weight vectors

Parameters

nint

Number of criteria

iterationsint

Number of weight vector to generate

Returns

ndarray

Matrix containing in rows vectors with weights for n criteria

static _vikor_smaa(self, matrix, weights, types, normalization_method, v)