Welcome to evo-spotis documentation!

This is Python 3 library for multi-criteria decision analysis with decision-maker preference identification. This library includes:

• the SPOTIS method SPOTIS

• 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)

• Method for objective determination of criteria weights (weighting method)`entropy_weighting` (Entropy 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

• Usage
  • Installation
  • Usage examples
• Illustrative example
  • Numerical example of the SPOTIS method application
  • Example of mobile phones evaluation using the SPOTIS method and criteria weights identified by EVO-SPOTIS
• API Reference
  • evo_spotis