distance-metrics-mcda 0.0.5

Package for Multi-Criteria Decision Analysis with distance-based TOPSIS method

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