Subpackages

• pymcdm_reidentify.methods package
  • pymcdm_reidentify.methods.sesp
  • pymcdm_reidentify.methods.sfn
  • pymcdm_reidentify.methods.sitcom
  • pymcdm_reidentify.methods.sitw
  • pymcdm_reidentify.methods.sitwlocal
  • pymcdm_reidentify.methods.stfn
  • pymcdm_reidentify.methods.stochastic_identification
  • pymcdm_reidentify.methods.strfn
• pymcdm_reidentify.visuals package
  • pymcdm_reidentify.visuals.fitness_plot
  • pymcdm_reidentify.visuals.model_contourf
  • pymcdm_reidentify.visuals.tfn_plot
  • pymcdm_reidentify.visuals.trfn_plot
  • pymcdm_reidentify.visuals.weights_diff_plot

Subpackages tools

pymcdm_reidentify.methods.comet_tools

class pymcdm_reidentify.methods.comet_tools.MLExpert(expert_function, reverse=False)

Bases: object

Create an object which will rate characteristic objects using ml expert function.

Parameters:

expert_function (Callable) – Function with a signature (co_i, co_j) -> float. If co_i < co_j this function should return 0.0. If co_i == co_j this function should return 0.5. If co_i > co_j this function should return 1.0.

__call__(co)

Evaluate characteristic objects using provided expert function.

Parameters:

co (np.ndarray) – Characteristic objects which should be compared.

Returns:

• sj (np.ndarray) – SJ vector (see the COMET procedure for more info).

• mej (None) – Because of how this method works MEJ matrix is not generated.

__init__(expert_function, reverse=False)

comparison(c1, c2)

Submodules

pymcdm_reidentify.normalizations module

class pymcdm_reidentify.normalizations.FuzzyNormalization(fuzzy_numbers)

Bases: object

Class for applying a series of fuzzy normalization functions to decision matrices.

Parameters:

fuzzy_numbers (list[callable]) – A list of functions representing the fuzzy numbers to be used for normalization.

__call__(matrix, *args, **kwargs)

Normalize the decision matrix using the next fuzzy number in the sequence.

Parameters:

matrix (ndarray) – The decision matrix to be normalized.

Returns:

The normalized decision matrix.

Return type:

ndarray

__init__(fuzzy_numbers)