Welcome to pydaria documentation!

pydaria is The Data Variability Based Multi-Criteria Assessment Method based on MCDA. This library includes:

  • DARIA class:

    • Data variability measures:

      • gini (Gini coefficient)

      • entropy (Entropy)

      • std (Standard deviation)

      • stat_var (Statistical variance)

      • coeff_var (Coefficient of variation)

    • Variability direction determination direction

    • Calculation of the final overall alternatives efficiency values update_efficiency

  • MCDA method TOPSIS

  • Correlation coefficients:

    • spearman (Spearman rank correlation coefficient)

    • weighted_spearman (Weighted Spearman rank correlation coefficient)

    • pearson_coeff (Pearson correlation coefficient)

    • WS_coeff (Similarity rank coefficient - WS 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):

    • equal_weighting (Equal weighting method)

    • entropy_weighting (Entropy weighting method)

    • std_weighting (Standard Deviation 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.

Note

This project is under active development.

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