Intuitionistic Fuzzy VIKOR

Method object

class pyifdm.methods.if_vikor.ifVIKOR(distance=<function hamming_distance>, normalization=None, v=0.5)

Bases: object

__call__(matrix, weights, types)

Calculates the alternatives preferences

Parameters:

• matrix (ndarray) – Decision matrix / alternatives data. Alternatives are in rows and Criteria are in columns.

• weights (ndarray) – Vector of criteria weights in a crisp or Intuitionistic Fuzzy form

• types (ndarray) – Types of criteria, 1 profit, -1 cost

Returns:

Preference calculated for alternatives. Lower values are placed higher in ranking

Return type:

ndarray

__init__(distance=<function hamming_distance>, normalization=None, v=0.5)

Creates Intuitionistic Fuzzy VIKOR method object with normalization and distance functions

Parameters:

• distance (callable, default=hamming_distance) – Function used to calculate distance between two IFS

• normalization (callable, default=None) – Function used to normalize the decision matrix

• v (float, default=0.5) – Weight of the strategy (see VIKOR algorithm explanation).

rank()

Calculates the alternatives ranking based on the obtained preferences

Returns:

Ranking of alternatives for the S, R, Q approaches

Return type:

ndarray

Intuitionistic Fuzzy calculations

pyifdm.methods.vikor.ifs.ifs(matrix, weights, types, normalization, distance, v)

Calculates the alternatives preferences based on Intuitionistic Fuzzy Sets

Parameters:

• matrix (ndarray) – Decision matrix / alternatives data. Alternatives are in rows and Criteria are in columns.

• weights (ndarray) – Vector of criteria weights in a crisp or Intuitionistic Fuzzy form

• types (ndarray) – Types of criteria, 1 profit, -1 cost

• normalization (callable) – Function used to normalize the decision matrix

• distance (callable) – Function used to calculate distance between two IFS

• v (float) – Weights for the strategy of maximum group utility

Returns:

Crisp preferences of alternatives

Return type:

ndarray