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distance-metrics-for-mcda

Python 3 library for Multi-Criteria Decision Analysis based on distance metrics. The documentation is provided here

Installation

pip install distance-metrics-mcda

Methods

This is Python 3 library providing package distance_metrics_mcda that includes metrics that can measure alternatives distance from reference solutions in multi-criteria decision analysis. This library contains module weighting_methods with the following distance metrics:

  • Euclidean distance euclidean
  • Manhattan (Taxicab) distance manhattan
  • Hausdorff distance hausdorff
  • Correlation distance correlation
  • Chebyshev distance chebyshev
  • Standardized euclidean distance std_euclidean
  • Cosine distance cosine
  • Cosine similarity measure csm
  • Squared Euclidean distance squared_euclidean
  • Sorensen or Bray-Curtis distance bray_curtis
  • Canberra distance canberra
  • Lorentzian distance lorentzian
  • Jaccard distance jaccard
  • Dice distance dice
  • Bhattacharyya distance bhattacharyya
  • Hellinger distance hellinger
  • Matusita distance matusita
  • Squared-chord distance squared_chord
  • Pearson chi-square distance pearson_chi_square
  • Squared chi-square distance squared_chi_square

The library also provides other methods necessary for multi-criteria decision analysis, which are as follows: The TOPSIS method for multi-criteria decision analysis TOPSIS in module mcda_methods. The TOPSIS method is based on measuring the distance of alternatives from Positive Ideal Solution and Negative Ideal Solution using distance_metrics mentioned above.

Normalization techniques:

  • Linear linear_normalization
  • Minimum-Maximum minmax_normalization
  • Maximum max_normalization
  • Sum sum_normalization
  • Vector vector_normalization

Correlation coefficients:

  • Spearman rank correlation coefficient rs spearman
  • Weighted Spearman rank correlation coefficient rw weighted_spearman
  • Pearson coefficent pearson_coeff

Objective weighting methods:

  • Entropy weighting method entropy_weighting
  • CRITIC weighting method critic_weighting

Example of usage of distance-metrics-mcda are provided on GitHub in examples