skcriteria.madm.closeness module

Methods based on an aggregating function representing “closeness to the ideal”.

class skcriteria.madm.closeness.TOPSIS(mnorm='vector', wnorm='sum')[source]

Bases: skcriteria.madm._dmaker.DecisionMaker

TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the ideal solution and the longest euclidean distance from the worst solution.

An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing, and also allow trade-offs between criteria, where a poor result in one criterion can be negated by a good result in another criterion.

Parameters:
mnorm : string, callable, optional (default=”vector”)

Normalization method for the alternative matrix.

wnorm : string, callable, optional (default=”sum”)

Normalization method for the weights array.
Returns:
Decision : skcriteria.madm.Decision

With values:

  • kernel_: None
  • rank_: A ranking (start at 1) where the i-nth element represent the position of the i-nth alternative.
  • best_alternative_: The index of the best alternative.
  • alpha_solution_: True
  • beta_solution_: False
  • gamma_solution_: True
  • e_: Particular data created by this method.
    • e_.closeness: Array where the i-nth element represent the closenees of the i-nth alternative to ideal and worst solution.

References

[1]Yoon, K., & Hwang, C. L. (1981). Multiple attribute decision making: methods and applications. SPRINGER-VERLAG BERLIN AN.
[2]TOPSIS. In Wikipedia, The Free Encyclopedia. Retrieved from https://en.wikipedia.org/wiki/TOPSIS
[3]Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
Attributes:
mnorm

Normalization function for the alternative matrix.

wnorm

Normalization function for the weights vector.

Methods

as_dict() Create a simply dict representation of the object.
decide(data[, criteria, weights]) Execute the Solver over the given data.
doc_inherit
make_result(data, kernel, rank, extra) Create a new skcriteria.madm.Decision
preprocess(data) Normalize the alternative matrix and weight vector.
solve(ndata) Execute the multi-criteria method.
doc_inherit = functools.partial(<function _doc_inherit>, (<class 'skcriteria.madm._dmaker.DecisionMaker'>, <class 'skcriteria.base.BaseSolver'>, <class 'object'>))
solve(ndata)[source]

Execute the multi-criteria method.

Parameters:
data : skcriteria.Data

Preprocessed Data.
Returns:
object

object or tuple of objects with the raw result data.