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# PolyMath: version 1.0 -> v2.0

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# PolyMath: version 1.0 -> v2.0

PolyMath: version 1.0 -> v2.0

Oleksandr Zaitsev, Sebastian Jordan Montaño, Serge Stinckwich, Hemal Varambhia, etc.

ESUG 2022

PolyMath: version 1.0 -> v2.0

Oleksandr Zaitsev, Sebastian Jordan Montaño, Serge Stinckwich, Hemal Varambhia, etc.

ESUG 2022

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## Weitere Verwandte Inhalte

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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!