Methods

• Fuzzy ARAS
  • Method object
    • fARAS
      • fARAS.__call__()
      • fARAS.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy CoCoSo
  • Method object
    • fCOCOSO
      • fCOCOSO.__call__()
      • fCOCOSO.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy CODAS
  • Method object
    • fCODAS
      • fCODAS.__call__()
      • fCODAS.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy COPRAS
  • Method object
    • fCOPRAS
      • fCOPRAS.__call__()
      • fCOPRAS.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy EDAS
  • Method object
    • fEDAS
      • fEDAS.__call__()
      • fEDAS.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy MABAC
  • Method object
    • fMABAC
      • fMABAC.__call__()
      • fMABAC.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy MAIRCA
  • Method object
    • fMAIRCA
      • fMAIRCA.__call__()
      • fMAIRCA.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy MOORA
  • Method object
    • fMOORA
      • fMOORA.__call__()
      • fMOORA.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy OCRA
  • Method object
    • fOCRA
      • fOCRA.__call__()
      • fOCRA.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy SPOTIS
  • Method object
    • fSPOTIS
      • fSPOTIS.__call__()
      • fSPOTIS.make_bounds()
      • fSPOTIS.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy TOPSIS
  • Method object
    • fTOPSIS
      • fTOPSIS.__call__()
      • fTOPSIS.rank()
  • Fuzzy calculations
    • fuzzy()
• Utils package
  • Defuzzifications module
    • bisector_defuzzification()
    • graded_mean_average_defuzzification()
    • height_defuzzification()
    • lom_defuzzification()
    • mean_area_defuzzification()
    • mean_defuzzification()
    • som_defuzzification()
    • weighted_mean_defuzzification()
  • Distances module
    • canberra_distance()
    • chebyshev_distance()
    • euclidean_distance()
    • hamming_distance()
    • lr_distance()
    • mahdavi_distance()
    • tran_duckstein_distance()
    • vertex_distance()
    • weighted_euclidean_distance()
    • weighted_hamming_distance()
  • Normalizations module
    • cocoso_normalization()
    • linear_normalization()
    • max_normalization()
    • minmax_normalization()
    • saw_normalization()
    • sqrt_normalization()
    • sum_normalization()
    • vector_normalization()
    • waspas_normalization()
• Fuzzy VIKOR
  • Method object
    • fVIKOR
      • fVIKOR.__call__()
      • fVIKOR.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy WASPAS
  • fuzzy()
• Fuzzy WPM
  • Method object
    • fWPM
      • fWPM.__call__()
      • fWPM.rank()
  • Fuzzy calculations
    • fuzzy()
• Fuzzy WSM
  • Method object
    • fWSM
      • fWSM.__call__()
      • fWSM.rank()
  • Fuzzy calculations
    • fuzzy()

Validator

class pyfdm.methods.validator.Validator

Bases: object

static fuzzy_validation(matrix, weights, types=None, crisp_required=False)

Runs all validations for the fuzzy TFN extension

Parameters:

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

• weights (ndarray) – Vector of weights in a crisp form

• types (ndarray, default=None) – Types of criteria, 1 profit, -1 cost

• crisp_required (bool, default=False) – Flag representing the need to obtain crisp criteria weights as input data

Returns:

ValueError if one of validations do not pass

Return type:

raises

static validate_input(matrix, weights, types)

Checks if number of criteria, number of weights, and number of types are the same

Parameters:

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

• weights (ndarray) – Vector of weights in a crisp form

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

Returns:

ValueError if shapes of matrix, weights and types are not the same

Return type:

raises

static validate_tfn_matrix(matrix)

Checks if TFN matrix is defined properly, all elements should have length of 3

Parameters:

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

Returns:

ValueError if matrix elements has different length than 3

Return type:

raises

static validate_types(types)

Checks if all criteria types are same type

Parameters:

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

Returns:

ValueError if criteria types are the same

Return type:

raises

static validate_weights(weights, crisp_required=False)

For crisp weights checks if sum of weights equals 1 For fuzzy weights checks if given as Triangular Fuzzy Numbers

Parameters:

• weights (ndarray) – Vector of weights in a crisp form or as a TFNs

• crisp_required (bool, default=False) – Flag representing the need to obtain crisp criteria weights as input data

Returns:

ValueError if sum of weights is different than 1 or not in TFN form

Return type:

raises