https://en.m.wikipedia.org/wiki/Monad_(functional_programming)
Tag Archives: FP
Functional Programing in Data Science Projects
“Functional Programing in Data Science Projects” by Nathanael Weill https://link.medium.com/UiysKbFl16
Algebraic Data Type vs Algebraic Data Structure
Algebraic Data Type:
- Product Type
- Sum Type: Either
Functor (map) :
Introduction to functional programming with Python examples
The “Bible of Functional Programming” 《Structure and Interpretation of Computer Programs》
Kevlin Henney 2017 – Functional C++
Very good presentation of Functional C++ by the guru Kevlin Henney.
Piping (Functional Composition) in Channels Asynchronously Concurrency:
h
…
Functional C++ for Fun and Profit
Functional Programming in C++
Functional Programming Python: Lambdas, Decorators, and Other Magic
https://dev.to/codemouse92/dead-simple-python-lambdas-decorators-and-other-magic-5gbf
Python is multi-paradigm: OO or FP.
Useful Functional Programming Techniques :
- Closure: local variables
- Resursion : stop unlimiting looping
- Lambdas : anonymous function for 1-time throw-away functions
- Nested Function: function returns a function as result.
- Decorators : wrap an existing function with additional features without modifying it.
OO vs FP in Java language
What the heck is polymorphism? – DEV Community
https://dev.to/jvanbruegge/what-the-heck-is-polymorphism-nmh
Examples: Generics
Quora: What is an ALGEBRA structure
What is an algebra? by Tikhon Jelvis https://www.quora.com/What-is-an-algebra/answer/Tikhon-Jelvis?ch=3&share=2dd8711d&srid=oZzP
“Basically, an algebra is just an algebraic structure. It’s some set A along with some number of functions closed over the set. It’s a generalization over the structures we normally study: a group is an algebra, a ring is an algebra, a lattice is an algebra… etc.
Algebras have different “signatures” which specify the functions it has. For example, a group is an algebra that has an identity element, a function of one argument and a function of two arguments.
That is, a group with a carrier set A is just a tuple:
⟨A, 0:A, −:A→A, +:A×A→A⟩
For uniformity, we can write all of these as functions in the form An→A, where n is the “arity” of a function—the number of arguments it has. The identity element is a function A0→A, which just identifies a single element from A. Thus, we can talk about the signature of an algebra as the arities of its functions.
A group would be (0, 1, 2) while a ring would be (0, 0, 1, 2, 2).
Generally, the functions of an algebra have to be associative. Sometimes, we also look at other laws—for example, we might want to study algebras with commutative operations like Abelian groups.
So the intuition for an algebra in general is that it’s any structure like a group, a ring or whatever else we like. As the name “structure” implies, these additional operations on a set expose the internal structure of its elements: a group describes symmetries, a lattice describes a partial order and so on.
The study of algebras, then, can be thought of as the study of “structured sets” in general.”
The Software War : Object-Oriented Programming (OOP) vs Functional Programming (FP)
The “war” of OOP vs FP is akin to Applied Math vs Pure Math.
The formers (OOP & Applied Math) are not “rigourous” but practical, compared to the laters (FP & Pure Math) which are elegant but too abstract for popular usage.
OOP: SmallTalk and its followers – C++, C#, Objective-C, Java…
FP: LISP and its followers – Haskell, Clojure, …
The “hybrid” (OOP&FP): Scala, Kotlin (Google: Java ‘cousin’), Swift (Apple: Objective -C ‘cousin’), F# (MicroSoft)
The “cons” of OOP, which are bad for concurrency / parallel computing in multi-cored CPU:
- State changed
- Side-effect
- Mutability of data
- Polymorphism
https://blog.cleancoder.com/uncle-bob/2014/11/24/FPvsOO.html
State in Functions: Cache, Lazy + Memoization, and Counter
How to keep state (counter, threshold, etc) in Functional Programming without BAD side effect ?
https://dzone.com/articles/functionalfun-states-in-functions-cache-lazy-and-c
Choosing a Programming Language
Crash course in Category Theory
Key Point:
Haskell & any FP compiler don’t check the Category Theory proof if your codes (eg. fmap) follow Functor’s Laws (eg. Preserve structure, identity) or Monad’s Laws !
Android Architecture Simplified with Kotlin
I hate Android Architecture since it is based on OO Java, Kotlin is FP cousin of Java, hopefully it could simplify the Android Architecture by getting rid of the complex OO architecture. Here Antonio attempts to simplify it with more terse Kotlin codes, albeit still in the OO architecture spirit.
Goodbye OO, Welcome FP
OO = Object-Oriented, eg. Smalltalk, C++, Java
FP = Functional Programming, aka “Mathematical” Programming Language eg. Lisp, Haskell, Scala (hybrid OO+FP), Kotlin (hybrid OO+FP).
Three “weaknesses” (also “strengths”) of OO :
1. Inheritance
2. Polymorphism
3. Encapsulation
Kotlin+FP = Kategory
Tail Recursion in Kotlin:
The Cost of Kotlin:
“Gang of Four” Design Pattern with Kotlin
FP Style: Collection Pipeline
Higher Order Components
Functional Programming in Javascript:
https://medium.freecodecamp.org/higher-order-components-the-ultimate-guide-b453a68bb851
Quora: Functional Programming / Side Effect / Monads
“Arrow “: Functional Kotlin
Λrrow
is a functional programming library for the Kotlin programming language born from the fusion of KΛTEGORY and funKTionale.
Kategory and Funktionale were the two most relevant FP libraries for Kotlin in 2017.
Functional Programming : Batch Data Processing
OOP 与 FP 笔记
Lambda Calculus – The Math Behind Functional Programming
Functional Programming (FP) Languages : Lisp, Haskell, Scala, Kotlin, etc.
Other non-FP influenced by Lambda Calculus: Python, Ruby, Javascript, Java 8, C++
Inventor of Lambda Calculus : Alonzo Church (1903 – 1995), whose student in Princeton University (1936-1938) was Alan Turing (The Father of Artificial Intelligence).
Lambda Calculus is not : another Differential Calculus !
Note: Calculus has a meaning of manipulating symbolic expressions : either in functions (differentiation, integration) or computations.
Lambda Calculus is almost programming!
I. Syntax of Lambda Calculus:
eg.
Notice: it has only one parameter “x”.
- Function definition:
- Identifier reference:
- Function application:
II. Currying 柯里化 : (named after Haskell Curry ) for multiple parameters.
eg.
written by “Currying” as :
Syntactic Sugar 语法糖 : a notational shorthand. Eg. “cubic”
cubic = λ x . x * x * x
III. Binding: Every parameter (aka variable) must be declared (syntactically binding).
eg.
here, x is bound, but z is FREE (error!)
IV. Two Execution Methods:
1.
- rename variables to avoid conflict
2.
- Eager evaluation strategy : Right to Left (innermost expression first to outermost) or
- Lazy evaluation strategy : Left to Right (outermost expression first to innermost) – don’t compute the value of an expression until you need to – (save memory space and computing time)
- Most FP are Lazy.
- Most Procedural (Imperative) languages (C, Fortran, Basic, …) are Eager.
V. Lambda Calculus fulfilling the 3 conditions for “Turing Complete” Computation :
- Unbounded “Storage” (not necessarily a physical device) – generate arbitrarily complicated values in a variable or many functions without bound.
- Arithmetic – Church numerals (Peano arithmetic using functions): eg z=0, s= z+ 1 => 1 = λ s z . s z => 2 = λ s z . s ( s z ) … => 7= λ s z . s (s(s(s(s(s(s(z )))))))
- Control Flow – TRUE = λ t f . t / FALSE = λ t f . f / BoolAnd = λ x y . x y FALSE / BoolOr = λ x y . x TRUE y / Repetition by Recursion (Y Combinator )
Conclusion: Lambda Calculus = “Computer on paper”
VI. Type – Consistent Model (notation “:“)
eg. λ x : I . x + 3 ( I is Integer Type)
=> The result (x + 3) is also Type I since by inference “+” is of Type I -> I
Reference: “Good Math” by Mark Chu-Carroll https://www.amazon.com/Good-Math-Computation-Pragmatic-Programmers/dp/1937785335/
Explore Kotlin’s Advanced Functional Programming
Since May 2017 Kotlin released by Google, 12.8% Java developers have converted to Kotlin, yet they still keep to the OO spirit of Java (for Interoperability) , not taking full advantage of FP capability of Kotlin. The OO Design Patterns of Android Java still being used instead of the FP more elegant “Monadic” Design.
1. Interview with Kotlin Designer:
2. Android Studio 3.0 Released : https://www.androidauthority.com/android-studio-3-released-810099/
2a. Android Studio v3.0 with Kotlin built-in & many improvements.
(The previous stable v2.3 needs seperate Kotlin plug-in)
2c: Gradle upgrade
https://zhuanlan.zhihu.com/p/30722989
3. Inner / Infix Function
http://thetechnocafe.com/more-about-functions-in-kotlin/
4. Test (Mockito)
https://proandroiddev.com/improve-your-tests-with-kotlin-in-android-pt-1-6d0b04017e80
https://proandroiddev.com/improve-your-tests-with-kotlin-in-android-pt-2-f3594e5e7bfd
Kluent library:
https://proandroiddev.com/improve-your-tests-with-kotlin-in-android-pt-3-104e1c9f77cc
5. Function vs Procedure vs Method – Kotlin simplifies all 3 into 1 : Function which always returns a value or UNIT.
6. Kotlin has which Java lacks … “with“, “?”, …
https://blog.simon-wirtz.de/kotlin-features-miss-java/
7. Kotlin Operator Overloading aka “Convention”: ‘plus’ / ‘+’
https://blog.simon-wirtz.de/kotlin-conventions-operator-overloading/
8. JVM Byte Code Generation:
https://blog.simon-wirtz.de/kotlin-on-the-jvm-byte-code-generation/
9. Reified Types
https://blog.simon-wirtz.de/kotlin-reified-types/
10. SICP: Sequence as conventional interfaces: eg. flatmap, map, reduce, fold
https://dzone.com/articles/conventional-interfaces-in-functional-programming
11.Kotlin中extensions的用法
https://zhuanlan.zhihu.com/weiweidaolai/27537731
12. Generic : Kotlin入门(11)江湖绝技之特殊函数
https://zhuanlan.zhihu.com/aqi00/28302792
13. Array <String>: Kotlin入门(4)声明与操作数组
https://zhuanlan.zhihu.com/aqi00/27744799
14. ViewPager (Horizontal Swipe)
https://www.raywenderlich.com/169774/viewpager-tutorial-android-getting-started-kotlin
15. Kotlin 1.2 Beta & Multiplatform iOS
https://blog.jetbrains.com/kotlin/2017/11/kotlinconf-keynote-recap/
16. Kotlin Edu ( Android Studio 3.0)
https://www.jetbrains.com/education/kotlin-edu/
17. Kotlin Style Guide
https://android.github.io/kotlin-guides/style.html
18. Android Layout Foundamental – ConstraintLayout
https://developer.android.com/guide/topics/ui/declaring-layout.html
19. Android SDK
https://www.raywenderlich.com/171148/android-sdk-versions-tutorial-2
20. Javalin v1.0 – Web Framework for Java + Kotlin
https://jaxenter.com/javalin-1-0-is-here-138750.html
21. Ten Modern Features (Kotlin, Clojure, Javascript, Swift…)
22. Function literals with receiver
https://blog.simon-wirtz.de/function-literals-with-receiver-quick-introduction/#page
23. Sealed classes: (restricted types no “else”)
https://mindorks.com/course/advance-kotlin/chapter/id/1
24. Android Intent
https://www.raywenderlich.com/171071/android-intents-tutorial-kotlin
25. Top kotlin tutorials
26. Kotlin Contexts & Shared Preferences
https://blog.stylingandroid.com/kotlin-contexts-sharedpreferences/
27. Delegation – but not Inheritance (which takes all)
https://blog.kotlin-academy.com/superpowers-of-kotlin-delegation-e6bd9ff8026c
28. Functions or Properties Reference (cool *)
29. Web Framework ktor:
What is a Monoid / Monad – in Scala Functional Programming
Monoid = an Algebraic structure (“Algebra” ) with: Associativity & Identity
Monad = Monoid + Endo-Functors
https://livebook.manning.com/#!/book/functional-programming-in-scala/chapter-10/37
Deep Learning is Functional Programming
Functional Programming “Clojure“-based DeepLearning :
https://medium.com/towards-data-science/functional-programming-for-deep-learning-bc7b80e347e9