Intuitionistic Fuzzy CODAS

Method object

class pyifdm.methods.if_codas.ifCODAS(normalization=<function swap_normalization>, distance_1=<function euclidean_distance>, distance_2=<function hamming_distance>, tau=0.05)[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__(normalization=<function swap_normalization>, distance_1=<function euclidean_distance>, distance_2=<function hamming_distance>, tau=0.05)[source]

Create Intuitionistic Fuzzy CODAS method object with normalization function and distances metrics

Parameters:

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

  • distance_1 (callable, default=euclidean_distance) – Function used to calculate distance between two IFS

  • distance_2 (callable, default=hamming_distance) – Function used to calculate distance between two IFS

  • tau (float, default=0.05) – Threshold parameter

rank()[source]

Calculates the alternatives ranking based on the obtained preferences

Returns:

Ranking of alternatives

Return type:

ndarray

Intuitionistic Fuzzy calculations

pyifdm.methods.codas.ifs.ifs(matrix, weights, types, normalization, distance_1, distance_2, tau)[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

  • distance_1 (callable) – Function used to calculate distance between two IFS

  • distance_2 (callable) – Function used to calculate distance between two IFS

  • tau (float) – Threshold parameter

Returns:

Crisp preferences of alternatives

Return type:

ndarray