Welcome to distance_metrics_mcda documentation!

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

  • The TOPSIS method

  • Distance metrics:

    • euclidean (Euclidean distance)

    • manhattan (Manhattan distance)

    • hausdorff (Hausdorff distance)

    • correlation (Correlation distance)

    • chebyshev (Chebyshev distance)

    • std_euclidean (Standardized Euclidean distance)

    • cosine (Cosine distance)

    • csm (Cosine similarity measure)

    • squared_euclidean (Squared Euclidean distance)

    • bray_curtis (Sorensen or Bray-Curtis distance)

    • canberra (Canberra distance)

    • lorentzian (Lorentzian distance)

    • jaccard (Jaccard distance)

    • dice (Dice distance)

    • bhattacharyya (Bhattacharyya distance)

    • hellinger (Hellinger distance)

    • matusita (Matusita distance)

    • squared_chord (Squared-chord distance)

    • pearson_chi_square (Pearson chi square distance)

    • squared_chi_square (Sqaured chi square distance)

  • Correlation coefficients:

    • spearman (Spearman rank correlation coefficient)

    • weighted_spearman (Weighted Spearman rank correlation coefficient)

    • pearson_coeff (Pearson correlation coefficient)

  • Methods for normalization of decision matrix:

    • linear_normalization (Linear normalization)

    • minmax_normalization (Minimum-Maximum normalization)

    • max_normalization (Maximum normalization)

    • sum_normalization (Sum normalization)

    • vector_normalization (Vector normalization)

  • Methods for determination of criteria weights (weighting methods):

    • entropy_weighting (Entropy weighting method)

    • critic_weighting (CRITIC weighting method)

  • additions:

    • rank_preferences (Method for ordering alternatives according to their preference values obtained with MCDA methods)

Check out the Usage section for further information, including how to Installation the project.

Note

This project is under active development.

Contents