Functional programming in JuliaJulia Taiwan 發起人 杜岳華

讓我們來談談 programming

Imperative programming ( 指令式 )•How to achieve the goal

Sequential Looping Condition Function

Declarative programming ( 宣告式 )•What we want to achieve

Functional programming•What we want to achieve

FunctionFunction FunctionFunction

Function is from math

Function is from math

Function is from math

Function is from math

What does it do?…01234…

…-10123…

f

add_one[int]

01234…

[int]-10123…

f

Function is mapping[int]

01234…

[int]-10123…

f

Function is mapping[int]

01234…

[int]-10123…

if x == -1 return 0elseif x == 0 return 1elseif x == 1 return 2elseif x == 2 return 3elseif x == 3 return 4

Function is mapping

[Name]AliceBobChrisDavidEve…

[Customer]

Cus1Cus2Cus3Cus4Cus5

…

if x == Cus1 return “Alice”elseif x == Cus2 return “Bob”elseif x == Cus3 return “Chris”elseif x == Cus4 return “David”elseif x == Cus5 return “Eve”

Pure function is good

Impure• May change the global state (variable)

file = “/path/to/file”

function read_file() f = open(file) return readlines(f)end

read_file()

Pure• No side effect

function read_file(file) f = open(file) return readlines(f)end

filename = “/path/to/file”read_file(filename)

Impure• There are hidden and mutable states (variables) inside

the class• Sometimes, it is difficult to reason about.

customer.setName(newName)name = customer.getName()

Pure• Good to reason about

newCustomer = setCustomerName(aCustomer, newName)name = getCustomerName(aCustomer)

More practical benefits• Laziness

• Cacheable results

• No order dependencies

• Easy to parallelize

Types are not class

Types are not class

Types are not class[int]

0123…

[int]-1012… These are types.

“int” is type.“Customer” is type.“Function” is also type.

Types separate data from behavior

DataBehavior

Types are not class• Types are cheap and classes are expensive!• Types are static and classes are dynamic.

• Julia use Type, not Class.• One can perform object-oriented design in Julia and

easily write it in functional style.

Functions are things

Functions are things

f

Functions are things• Function as input

fg

Functions are things• Function as output

f g

Functions are things• Function as parameter

fg

Lambda expression (function)• Anonymous function• Function as first-class citizen

() -> println()

x -> x + 5

(x, y, z) -> x + y + z

func = x -> x * 2

First-class citizen• 可以被指定給變數• 可以存在 expression 內• 可以以參數的方式指定給 function• 可以以回傳值的方式由 function 回傳• Second-class• 可以如同 function (operator) 般被呼叫• 可以以參數的方式指定給 function

Higher-order function

• 那我們就可以做點有趣的事 !

Higher-order function• map

numbers = [1, 2, 3, 4]results = map(x -> x + 5, numbers)

numbers = [1, 2, 3, 4]results = []for i=numbers append!(results, i + 5)end

Higher-order function

1234

numbers map x -> x + 5

Higher-order function

1234

numbers resultsmap

x -> x + 5

Higher-order function

1234

numbers

6789

resultsmap

x -> x + 5

Higher-order function

map

Higher-order function

map

Higher-order function

map

Higher-order function• filter

numbers = [1, 2, 3, 4]results = filter(x -> x > 2, numbers)

numbers = [1, 2, 3, 4]results = []for i=numbers if i > 2 append!(results, i) endend

Higher-order function

1234

numbers

34

resultsfilter

x -> x > 2

Higher-order function

1234

numbers

10

resultreduce

(x, y) -> x + y

Higher-order function

1234

numbersreduce

(x, y) -> x + y 334

reduce

(x, y) -> x + y 64

Higher-order function

10

result

reduce

(x, y) -> x + y64

More higher-order functions• foldl(op, iter)• foldr(op, iter)• sum(f, iter)• count(f, iter)• any(f, iter)• all(f, iter)• foreach(f, iter)• mapreduce, mapfoldl, mapfoldr

Function composition

Function composition

f

𝑓 ∘𝑔 (𝑥 )

g

gf

Function composition

f ∘ g

Function composition

map

x -> x + 3

filter

x -> x > 2

Function composition

numbers = [1, 2, 3, 4]results = filter(x -> x > 2, map(x -> x + 3, numbers))

More functional properties• Recursion is better than iteration• Relies on the language design

• Lazy evaluation• Use Task to help you.

• Closure• OK

• Curry• OK

• Pattern matching• use multiple dispatch in Julia

Pipeline?

map

x -> x + 3

filter

x -> x > 2

reduce

(x, y) ->x + y

Reactive programming in Python

source = [1, 2, 3, 4, 5, 6, 7, 8]result = Observable.from(source) .map(lambda x: x ** 2) .filter(lambda x: x > 20) .reduce(lambda x, y: x+y) .subscribe()

Reactive programming

Reactive programming in Julia• Reactive.jl

• I hope…source = [1, 2, 3, 4, 5, 6, 7, 8]result = from(source) (map, x -> x ^ 2) (filter, x -> x > 20) (reduce, (x, y) -> x + y) (subscribe)

Thank you for attention