Intuitionistic Fuzzy WASPAS

Method object

class pyifdm.methods.if_waspas.ifWASPAS(score=<function chen_score_1>, normalization=None, v=0.5)[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. Criteria types cannot be all profit or all cost.

Returns:

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

Return type:

ndarray

__init__(score=<function chen_score_1>, normalization=None, v=0.5)[source]

Creates Intuitionistic Fuzzy WASPAS method object with normalization and score functions

Parameters:

  • score (callable, default=chen_score_1) – Function used to calculate score between two IFS

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

  • v (float, default=0.5) – The aggregating coefficient of decision precision

rank()[source]

Calculates the alternatives ranking based on the obtained preferences

Returns:

Ranking of alternatives

Return type:

ndarray

Intuitionistic Fuzzy calculations

pyifdm.methods.waspas.ifs.ifs(matrix, weights, types, normalization, score, v)[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 normalize the decision matrix

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

Returns:

Crisp preferences of alternatives

Return type:

ndarray