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PolyMath: version 1.0 -> v2.0

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PolyMath: version 1.0 -> v2.0

  1. 1. version 1.0 v2.0 Oleksandr Zaitsev, Sebastian Jordan Montaño, Serge Stinckwich, Hemal Varambhia, etc.
  2. 2. 2 PolyMath is a library for scientific computing in Pharo. (it’s a math library) In a nutshell:
  3. 3. 3 On ESUG 2019 in Cologne, Serge Stinckwich presented PolyMath v1.0
  4. 4. Time flies :) 2019 2020 2021 2022 …
  5. 5. 5 Now we are here
  6. 6. 6 1. Remind you what is PolyMath 2. Discuss what lies ahead 3. Tell you how to support us So today we will:
  7. 7. Part 1: PolyMath - Who are we?
  8. 8. 8 We are not mathematicians
  9. 9. 9 Contributors (between v1.0 and now) Serge 
 Stinckwich Hemal 
 Varambhia Oleksandr 
 Zaitsev Yvan 
 Guifo Sebastian 
 Jordan Montaño Hernan 
 Moralez Durand Rakshit
  10. 10. SVD 10 Some Packages & Algorithms Complex Numbers Probability Distributions Kolmogorov Smirnov Matrix Decomposition Dimensionality Reduction Differential 
 Equations Quaternion Statistics Polynomials Numerical Methods Matrix-Vector Algebra Numerical Optimisation DataFrame
  11. 11. 11 Getting Started with PolyMath Metacello new repository: 'github://PolyMathOrg/PolyMath'; baseline: 'PolyMath'; load.
  12. 12. 12 Two Data Structures Vector Matrix vector := PMVector withAll: #(1 4 3 -1). matrix := PMMatrix rows: #( (1 0 0) (0 1 0) (0 0 1)).
  13. 13. 13 Dimensionality Reduction with PCA
  14. 14. 14 Dimensionality Reduction with PCA - Used in machine learning, statistics to solve the dimensionality curse. - Dimensionality reduction approach that perform a linear mapping of the data to a lower-dimensional space, in such a way that the variance of the data in low-dimensional representation is maximised.
  15. 15. 15 Dimensionality Reduction with PCA
  16. 16. 16 Dimensionality Reduction with PCA
  17. 17. 17 Dimensionality Reduction with PCA Visualised with Roassal3 ❤
  18. 18. 18 DataFrame
  19. 19. 19 DataFrame
  20. 20. 20 DataFrame
  21. 21. 21 PolyMath Book “Object-Oriented Implementation of Numerical Methods. An Introduction with Java and Smalltalk” 
 by Didier B. Besset
  22. 22. Part 2: The future
  23. 23. 23 Goal 1: Decoupling Packages
  24. 24. 24 Goal 2: Making PolyMath Faster Time in seconds (less is better) Small Medium Pure Pharo vs Pharo & LAPACK 1820 times faster 2103 times faster Pharo Pharo & LAPACK 19min40sec 1h44min 0.6sec 2.9sec Help
  25. 25. 25 Goal 3: More Integration with pharo-ai LAPACK SciPy scikit-learn pharo-ai PolyMath LAPACK Pharo Python Fast Algebra Math Machine Learning
  26. 26. 26 Goal 4: Document Using Microdown ❤
  27. 27. 27 Goal 4: Document Using Microdown ❤
  28. 28. 28 Goal 5: Roassal Charting
  29. 29. 29 Goal 5: Roassal Charting
  30. 30. Goal 6: Notebooks 30
  31. 31. Goal 6: Notebooks 31
  32. 32. Part 3: How to support us
  33. 33. 33 Contribute!
  34. 34. 34 Sponsor our work https://github.com/ sponsors/SergeStinckwich

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