Intuitionistic Fuzzy EDAS

Method object

class pyifdm.methods.if_edas.ifEDAS(normalization=<function swap_normalization>, score=<function liu_wang_score>)[source]

Bases: object

__call__(matrix, weights, types)[source]

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. Greater values are placed higher in ranking

Return type:

ndarray

__init__(normalization=<function swap_normalization>, score=<function liu_wang_score>)[source]

Create Intuitionistic Fuzzy EDAS method object with normalization function

Parameters:

  • normalization (callable, default=swap_normalization) – Function used to calculate normalized decision matrix

  • score (callable, default=liu_wang_score) – Function used to calculate crisp score of IFS

rank()[source]

Calculates the alternatives ranking based on the obtained preferences

Returns:

Ranking of alternatives

Return type:

ndarray

Intuitionistic Fuzzy calculations

pyifdm.methods.edas.ifs.ifs(matrix, weights, types, normalization, score)[source]

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 calculate normalized decision matrix

  • score (callable) – Function used to calculate crisp score of IFS

Returns:

Crisp preferences of alternatives

Return type:

ndarray