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pyXla documentation
pyXla documentation

Contents:

  • Guides
    • Installation
    • Loading and Sampling
    • Analysing Samples
    • Auxiliary Functions
    • Examples
      • Comparing Continuous Problems
      • Constraints Anaysis
      • Comparing Knapsack Instances
      • Comparing NK Problem Instances
  • Examples by Feature Category
    • Statistical
      • Objective Distribution (distr_F)
      • Violation Distribution (distr_V)
      • Correlation of Values (corr)
      • Correlation of Ranks (corr_ranks)
      • Variable importance (X_imp)
    • Rank-based
      • Pareto Rank Distribution (distr_Par)
      • Deb’s Feasibility Rule Ranking Distribution (distr_Deb)
    • Distance-based
      • Fitness Distance Correlation (FDC)
      • Violation Distance Correlation (VDC)
      • Rank Distance Correlation (RDC)
      • Pairwise Distance Correlation (PDC)
      • Dispersion of the Best Solutions (disp_best)
    • Neighbourhood-based
      • Neighbouring Solutions’ Objective (Fitness) Values Correlation (NFC)
      • Neighbouring Solutions’ Violation Values Correlation (NVC)
      • Neighbouring Solutions’ Ranks Correlation (NRC)
  • API Reference
    • pyxla
    • pyxla.util
    • pyxla.sampling
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Examples¶

Some example Jupyter notebook are provided:

  • Comparing Continuous Problems
  • Constraints Anaysis
  • Comparing Knapsack Instances
  • Comparing NK Problem Instances

These notebooks can be accessed from the pyXla repository.

Some of the examples such as Comparing Knapsack Instances and Comparing NK Problem Instances utilise some samples provided by the maintainers of pyXla.

For further experimentation these samples can be accessed here.

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Comparing Continuous Problems
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Auxiliary Functions
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