Introduction to Functional Programming

An Introduction to Functional Programming

Andreas Pauley – @apauley

Pattern Matched TechnologiesLambda Luminaries @lambdaluminary

September 2, 2013

Andreas Pauley – @apauley (Pattern Matched TechnologiesLambda Luminaries @lambdaluminary)Functional Programming September 2, 2013 1 / 58

Pattern Matched Technologies

Developing financial applications in Erlang.http://www.patternmatched.com/

Lambda Luminaries – @lambdaluminaryLocal functional programming user group

We meet once a month, on the second Monday of the month.http://www.meetup.com/lambda-luminaries/

Introduction

Table of Contents

1 Introduction

2 Definition of Functional Programming

3 Function Recap

4 Common FP IdiomsRecursionPattern MatchingHigher-order Functions

5 Higher-order Functions and Lists

6 Advantages of Functional Programming

7 Disdvantages of Functional Programming

8 Companies using Functional Programming

9 Where do we go from here?

Introduction

A word from the wise

“ No matter what language you work in, programming ina functional style provides benefits. You should do itwhenever it is convenient, and you should think hardabout the decision when it isn’t convenient. ”

— John Carmack, ID Software [2]

Introduction

A Few Functional Programming Languages

Haskell Strong focus on functional purity. Lazy evaluation.Advanced static type system. Native-code compiler.

Erlang Focused around concurrency and distributed programming.Strict evaluation. Dynamic typing. Runs on the BEAM VM.

Clojure A modern Lisp language. Focus on concurrency. Lazyevaluation. Dynamic typing. Runs on the JVM.

Scala FP and OO. Strict evaluation by default, but supports lazyevaluation. Advanced static type system. Runs on the JVM.

OCaml FP and OO. Part of the ML family. Sometimes claimed tobe “faster than C”. Strict evaluation. Advanced static typesystem. Native-code compiler.

F# FP and OO. Based on OCaml. Strict evaluation by default,but supports lazy evaluation. Advanced static type system.Runs on the .NET CLR.

Introduction

Definition of Functional Programming

Table of Contents

1 Introduction

3 Function Recap

So what exactly is Functional Programming?

Functional Programming, noun:

“ Functional Programming is a listof things you CAN’T do. ”

When programming in a functional style/language:

You can’t vary your variables.

You can’t mutate or change your state.

No while/for loops, sorry.

You can’t have side-effects.

You can’t control the order of execution (lazy evaluated languages).

Are you kidding me?

How can anyone program like this???

GOTO 10

This sounds like

“You can’t have GOTO statements”

See Hughes and Dijkstra [1, 3]

Also framed in the negative (you can’t).

In hindsight we don’t really need GOTO’s.

In hindsight it is not about what you cannot do.

GOTO 10

This sounds like

GOTO 10

This sounds like

Structured Programming and Functional Programming

Structured Programming introduced subroutines with fixed entry/exitpoints (instead of GOTO’s), resulting in improvedmodularity.

Functional Programming provides similar important benefits – more aboutthis later.

Structured Programming and Functional Programming

Structured Programming introduced subroutines with fixed entry/exitpoints (instead of GOTO’s), resulting in improvedmodularity.

Functional Programming provides similar important benefits – more aboutthis later.

We need a better definition for Functional Programming.

Functional Programming, noun:

“ Functional programming is so called because a programconsists entirely of functions. ”

— John Hughes, Why Functional Programming Matters [1, p. 1]

OK... so what exactly is a function?

Function Recap

Table of Contents

1 Introduction

3 Function Recap

Function Recap

An example function

f (x) = 2x + 3

Function Recap

Variables in functions

f (x) = 2x + 3When we evaluate the function:

f (4) = 2 ∗ 4 + 3 = 11

The value of x will not change inside the function body.

No x = 4 and later x = 21 in the same function body.

Same input, same output. Every time.

In other words, we can replace any occurrence of f (4) with 11(Referential Transparency)

Function Recap

f (4) = 2 ∗ 4 + 3 = 11

Function Recap

f (4) = 2 ∗ 4 + 3 = 11

Function Recap

f (4) = 2 ∗ 4 + 3 = 11

Function Recap

f (4) = 2 ∗ 4 + 3 = 11

Function Recap

Functions can call other functions

g(x) = f (x) + 1

Function Recap

Values are functions

Constant values are just functions with no input parameters

k = 42def k():

return 42

Function Recap

Functions can be combined

h(x) = f (g(x))

Function Recap

Higher-order Functions

Functions can take functions as input and/or returnfunctions as the result.

h(p, q, x) = p(x) + q(2)

h(f , g , 3) = f (3) + g(2)

Function Recap

Functions can take functions as input and/or returnfunctions as the result.

h(p, q, x) = p(x) + q(2)h(f , g , 3) = f (3) + g(2)

Function Recap

The derivative of cos(x) returns another function.

ddx cos(x) = − sin(x)

Function Recap

A functional program consists entirely of functions

def main(args):

result = do_something_with_args(args)

print result

def do_something_with_args(args):

return ’-’.join(args[1:])

./justfunctions.py hello functional programming

hello-functional-programming

Function Recap

A functional program consists entirely of functions

main :: IO()

main = do

args <- getArgs

let result = do_something_with_args args

putStrLn result

do_something_with_args :: [String] -> String

do_something_with_args args = intercalate "-" args

./justfunctions hello functional programming

hello-functional-programming

Common FP Idioms

Table of Contents

1 Introduction

3 Function Recap

Common FP Idioms Recursion

Recursive function: Haskell

doubleAll :: Num a => [a] -> [a]

doubleAll [] = []

doubleAll (x:xs) = x*2 : doubleAll xs

Example use in the interactive interpreter:

Prelude Main> doubleAll [8,2,3]

[16,4,6]

Recursive function: Python

def doubleAll(numbers):

if numbers == []:

return []

first = numbers[0]

rest = numbers[1:]

return [first * 2] + doubleAll(rest)

Example use in the interactive interpreter:

>>> doubleAll([8,2,3])

[16, 4, 6]

An iterative version in Python

def doubleAll(numbers):

doubled = []

for num in numbers:

doubled.append(num * 2)

return doubled

Common FP Idioms Pattern Matching

Pattern Matching: Haskell

doubleAll [] = []

Common FP Idioms Higher-order Functions

Higher-order functions: Python

f (x) = 2x + 3g(x) = f (x) + 1h(p, q, x) = p(x) + q(2)

def f(x):

return (2*x) + 3

def g(x):

return f(x) + 1

def h(func_a, func_b, x):

return func_a(x) + func_b(2)

Common FP Idioms Higher-order Functions

Higher-order functions: Haskell

f (x) = 2x + 3g(x) = f (x) + 1h(p, q, x) = p(x) + q(2)

f x = (2*x) + 3

g x = (f x) + 1

h :: (Int->Int) -> (Int->Int) -> Int -> Int

h func_a func_b x = (func_a x) + (func_b 2)

Higher-order Functions and Lists

Table of Contents

1 Introduction

3 Function Recap

3 Basic List Operations

Map Convert each element of a list into some other value.Example: Convert a list of students to a list of exam scores.

Filter Get a subset of a list based on some condition. Example:Filter the entire list of students down to only those thatpassed the exam.

Fold Reduce a list of items to a single value. Example: Reducethe list of students to a single string of all names.

doubleAll [] = []

Looks very similar to the builtin map function:

map :: (a -> b) -> [a] -> [b]

map _ [] = []

map f (x:xs) = f x : map f xs

So our doubleAll can actually be simplified as:

doubleAll = map (*2)

Student Data

data Student = Student { firstName :: String

, lastName :: String

, finalExamScore :: Double}

Student Data

[Student {firstName="John",

lastName="Deer",

finalExamScore=60},

Student {firstName="Billy",

lastName="Bob",

finalExamScore=49.1},

Student {firstName="Jane",

lastName="Doe",

finalExamScore=89},

Student {firstName="Jack",

lastName="Johnson",

finalExamScore=29.3}]

Map type signature:

map :: (a -> b) -> [a] -> [b]

Map on student data:

allscores :: [Student] -> [Double]

allscores students = map finalExamScore students

finalExamScore type signature:

finalExamScore :: Student -> Double

Output:

Final Exam Scores: [60.0,49.1,89.0,29.3]

Filter

Filter type signature:

filter :: (a -> Bool) -> [a] -> [a]

Filter on student data:

passed :: [Student] -> [Student]

passed students = filter has_passed students

has_passed :: Student -> Bool

has_passed student = finalExamScore student >= 60

Output:

Students that have passed:

[John Deer (60.0),Jane Doe (89.0)]

Fold type signature:

foldl :: (a -> b -> a) -> a -> [b] -> a

Fold on student data:

namecat :: [Student] -> String

namecat students = foldl catfun "" students

catfun :: String -> Student -> String

catfun acc student = acc ++ (firstName student) ++ "\n"

Output:

"John\nBilly\nJane\nJack\n"

Shortened Map, Filter, Fold

allscores = map finalExamScore

passed = filter has_passed

has_passed student = finalExamScore student >= 60

namecat = foldl catfun ""

where catfun = (\acc student ->

acc ++ (firstName student) ++ "\n")

Imperative Filter Example

class Student:

def __init__(self, firstname, lastname, finalexamscore):

self.firstname = firstname

self.lastname = lastname

self.finalexamscore = finalexamscore

def has_passed(self):

return self.finalexamscore >= 60

def passed(students):

passed_students = []

for student in students:

if student.has_passed():

passed_students.append(student)

return passed_students

Advantages of Functional Programming

Table of Contents

1 Introduction

3 Function Recap

Perlisism #19

“ A language that doesn’t affect the way you think aboutprogramming, is not worth knowing. ”

— Alan Perlis[5]

Changes the way you think about programming and problem solving.

Improvements on the types of abstractions we can do with eg.higher-order functions.

Lock-free concurrency.

Improved ways of testing - QuickCheck.

More effective reasoning about code.

Declare possible Null values explicitly if you need them. GoodbyeNullPointerException (mostly).

Disdvantages of Functional Programming

Table of Contents

1 Introduction

3 Function Recap

Disadvantages of Functional Programming

Steep learning curve.

There are some very cryptic concepts.

Tools/IDE’s are not as advanced as the mainstream equivalents.

Order of execution may be difficult to reason about in a lazy language.

Unfamiliar Territory

Functional Programming is unfamiliar territory for most.

“ If you want everything to be familiar you will never learnanything new. ”

— Rich Hickey, author of Clojure[6]

Companies using Functional Programming

Table of Contents

1 Introduction

3 Function Recap

Companies In South Africa

Pattern Matched Technologies, Midrand Using Erlang for all systems, eg.processing high volumes of financial transactions.

Eldo Energy, Johannesburg Using Clojure for automated meter readingand intelligent monitoring of consumer energy.

Rheo Systems, Pretoria Using Clojure for supply chain integration.

Yuppiechef, Cape Town Using Clojure for their Warehouse ManagementSystem.

Effective Control Systems, Kyalami Using Erlang for printer management.

Mira Networks, Somerset West Using Erlang for billing administration andmobile development.

Amazon.com, Cape Town Using Scala in EC2.

Where do we go from here?

Table of Contents

1 Introduction

3 Function Recap

Lambda Luminaries – @lambdaluminaryLocal functional programming user group

We meet once a month, on the second Monday of the month.http://www.meetup.com/lambda-luminaries/

Online Courses

Functional Programming Principles in ScalaEPFL Universityhttps://www.coursera.org/course/progfun

School of HaskellFP Completehttps://www.fpcomplete.com/school

Programming LanguagesUniversity of Washingtonhttps://www.coursera.org/course/proglang

Miran LipovacaLearn You a Haskell for Great Good!http://learnyouahaskell.com/

Fred HebertLearn You Some Erlang for Great Good!http://learnyousomeerlang.com/

Yaron Minski, Anil Madhavapeddy, Jason HickeyReal World OCamlhttps://realworldocaml.org/

Paul Chiusano, Runar BjarnasonFunctional Programming in Scalahttp://www.manning.com/bjarnason/

References I

John HughesWhy Functional Programming Mattershttp:

//www.cs.kent.ac.uk/people/staff/dat/miranda/whyfp90.pdf

John CarmackFunctional Programming in C++http://www.altdevblogaday.com/2012/04/26/

functional-programming-in-c/

Edsger W. DijkstraGo To Statement Considered Harmfulhttp://www.u.arizona.edu/~rubinson/copyright_violations/

Go_To_Considered_Harmful.html

References II

Tweet by Michael Feathershttps://twitter.com/mfeathers/status/29581296216

Alan Jay Perlishttp://www.cs.yale.edu/quotes.html

Rich Hickeyhttp://www.infoq.com/presentations/Simple-Made-Easy

Andreas PauleyAn Introduction to Functional Programminghttps://github.com/apauley/fp_presentation

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