Source: https://learnxinyminutes.com/
# Single line comments start with a hash (pound) symbol. #= Multiline comments can be written by putting '#=' before the text and '=#' after the text. They can also be nested. =# #################################################### ## 1. Primitive Datatypes and Operators #################################################### # Everything in Julia is an expression. # There are several basic types of numbers. typeof(3) # => Int64 typeof(3.2) # => Float64 typeof(2 + 1im) # => Complex{Int64} typeof(2 // 3) # => Rational{Int64} # All of the normal infix operators are available. 1 + 1 # => 2 8 - 1 # => 7 10 * 2 # => 20 35 / 5 # => 7.0 10 / 2 # => 5.0 # dividing integers always results in a Float64 div(5, 2) # => 2 # for a truncated result, use div 5 \ 35 # => 7.0 2^2 # => 4 # power, not bitwise xor 12 % 10 # => 2 # Enforce precedence with parentheses (1 + 3) * 2 # => 8 # Julia (unlike Python for instance) has integer under/overflow 10^19 # => -8446744073709551616 # use bigint or floating point to avoid this big(10)^19 # => 10000000000000000000 1e19 # => 1.0e19 10.0^19 # => 1.0e19 # Bitwise Operators ~2 # => -3 # bitwise not 3 & 5 # => 1 # bitwise and 2 | 4 # => 6 # bitwise or xor(2, 4) # => 6 # bitwise xor 2 >>> 1 # => 1 # logical shift right 2 >> 1 # => 1 # arithmetic shift right 2 << 1 # => 4 # logical/arithmetic shift left # Use the bitstring function to see the binary representation of a number. bitstring(12345) # => "0000000000000000000000000000000000000000000000000011000000111001" bitstring(12345.0) # => "0100000011001000000111001000000000000000000000000000000000000000" # Boolean values are primitives true false # Boolean operators !true # => false !false # => true 1 == 1 # => true 2 == 1 # => false 1 != 1 # => false 2 != 1 # => true 1 < 10 # => true 1 > 10 # => false 2 <= 2 # => true 2 >= 2 # => true # Comparisons can be chained, like in Python but unlike many other languages 1 < 2 < 3 # => true 2 < 3 < 2 # => false # Strings are created with " "This is a string." # Character literals are written with ' 'a' # Strings are UTF8 encoded, so strings like "π" or "☃" are not directly equivalent # to an array of single characters. # Only if they contain only ASCII characters can they be safely indexed. ascii("This is a string")[1] # => 'T' # => 'T': ASCII/Unicode U+0054 (category Lu: Letter, uppercase) # Beware, Julia indexes everything from 1 (like MATLAB), not 0 (like most languages). # Otherwise, iterating over strings is recommended (map, for loops, etc). # String can be compared lexicographically, in dictionnary order: "good" > "bye" # => true "good" == "good" # => true "1 + 2 = 3" == "1 + 2 = $(1 + 2)" # => true # $(..) can be used for string interpolation: "2 + 2 = $(2 + 2)" # => "2 + 2 = 4" # You can put any Julia expression inside the parentheses. # Printing is easy println("I'm Julia. Nice to meet you!") # => I'm Julia. Nice to meet you! # Another way to format strings is the printf macro from the stdlib Printf. using Printf # this is how you load (or import) a module @printf "%d is less than %f\n" 4.5 5.3 # => 5 is less than 5.300000 #################################################### ## 2. Variables and Collections #################################################### # You don't declare variables before assigning to them. someVar = 5 # => 5 someVar # => 5 # Accessing a previously unassigned variable is an error try someOtherVar # => ERROR: UndefVarError: someOtherVar not defined catch e println(e) end # Variable names start with a letter or underscore. # After that, you can use letters, digits, underscores, and exclamation points. SomeOtherVar123! = 6 # => 6 # You can also use certain unicode characters # here ☃ is a Unicode 'snowman' characters, see http://emojipedia.org/%E2%98%83%EF%B8%8F if it displays wrongly here ☃ = 8 # => 8 # These are especially handy for mathematical notation, like the constant π 2 * π # => 6.283185307179586 # A note on naming conventions in Julia: # # * Word separation can be indicated by underscores ('_'), but use of # underscores is discouraged unless the name would be hard to read # otherwise. # # * Names of Types begin with a capital letter and word separation is shown # with CamelCase instead of underscores. # # * Names of functions and macros are in lower case, without underscores. # # * Functions that modify their inputs have names that end in !. These # functions are sometimes called mutating functions or in-place functions. # Arrays store a sequence of values indexed by integers 1 through n: a = Int64[] # => 0-element Array{Int64,1} # 1-dimensional array literals can be written with comma-separated values. b = [4, 5, 6] # => 3-element Array{Int64,1}: [4, 5, 6] b = [4; 5; 6] # => 3-element Array{Int64,1}: [4, 5, 6] b[1] # => 4 b[end] # => 6 # 2-dimensional arrays use space-separated values and semicolon-separated rows. matrix = [1 2; 3 4] # => 2×2 Array{Int64,2}: [1 2; 3 4] # Arrays of a particular type b = Int8[4, 5, 6] # => 3-element Array{Int8,1}: [4, 5, 6] # Add stuff to the end of a list with push! and append! # By convention, the exclamation mark '!' is appended to names of functions # that modify their arguments push!(a, 1) # => [1] push!(a, 2) # => [1,2] push!(a, 4) # => [1,2,4] push!(a, 3) # => [1,2,4,3] append!(a, b) # => [1,2,4,3,4,5,6] # Remove from the end with pop pop!(b) # => 6 b # => [4,5] # Let's put it back push!(b, 6) # => [4,5,6] b # => [4,5,6] a[1] # => 1 # remember that Julia indexes from 1, not 0! # end is a shorthand for the last index. It can be used in any # indexing expression a[end] # => 6 # we also have popfirst! and pushfirst! popfirst!(a) # => 1 a # => [2,4,3,4,5,6] pushfirst!(a, 7) # => [7,2,4,3,4,5,6] a # => [7,2,4,3,4,5,6] # Function names that end in exclamations points indicate that they modify # their argument. arr = [5,4,6] # => 3-element Array{Int64,1}: [5,4,6] sort(arr) # => [4,5,6] arr # => [5,4,6] sort!(arr) # => [4,5,6] arr # => [4,5,6] # Looking out of bounds is a BoundsError try a[0] # => ERROR: BoundsError: attempt to access 7-element Array{Int64,1} at # index [0] # => Stacktrace: # => [1] getindex(::Array{Int64,1}, ::Int64) at .\array.jl:731 # => [2] top-level scope at none:0 # => [3] ... # => in expression starting at ...\LearnJulia.jl:180 a[end + 1] # => ERROR: BoundsError: attempt to access 7-element Array{Int64,1} at # index [8] # => Stacktrace: # => [1] getindex(::Array{Int64,1}, ::Int64) at .\array.jl:731 # => [2] top-level scope at none:0 # => [3] ... # => in expression starting at ...\LearnJulia.jl:188 catch e println(e) end # Errors list the line and file they came from, even if it's in the standard # library. You can look in the folder share/julia inside the julia folder to # find these files. # You can initialize arrays from ranges a = [1:5;] # => 5-element Array{Int64,1}: [1,2,3,4,5] a2 = [1:5] # => 1-element Array{UnitRange{Int64},1}: [1:5] # You can look at ranges with slice syntax. a[1:3] # => [1, 2, 3] a[2:end] # => [2, 3, 4, 5] # Remove elements from an array by index with splice! arr = [3,4,5] splice!(arr, 2) # => 4 arr # => [3,5] # Concatenate lists with append! b = [1,2,3] append!(a, b) # => [1, 2, 3, 4, 5, 1, 2, 3] a # => [1, 2, 3, 4, 5, 1, 2, 3] # Check for existence in a list with in in(1, a) # => true # Examine the length with length length(a) # => 8 # Tuples are immutable. tup = (1, 2, 3) # => (1,2,3) typeof(tup) # => Tuple{Int64,Int64,Int64} tup[1] # => 1 try tup[1] = 3 # => ERROR: MethodError: no method matching # setindex!(::Tuple{Int64,Int64,Int64}, ::Int64, ::Int64) catch e println(e) end # Many array functions also work on tuples length(tup) # => 3 tup[1:2] # => (1,2) in(2, tup) # => true # You can unpack tuples into variables a, b, c = (1, 2, 3) # => (1,2,3) a # => 1 b # => 2 c # => 3 # Tuples are created even if you leave out the parentheses d, e, f = 4, 5, 6 # => (4,5,6) d # => 4 e # => 5 f # => 6 # A 1-element tuple is distinct from the value it contains (1,) == 1 # => false (1) == 1 # => true # Look how easy it is to swap two values e, d = d, e # => (5,4) d # => 5 e # => 4 # Dictionaries store mappings emptyDict = Dict() # => Dict{Any,Any} with 0 entries # You can create a dictionary using a literal filledDict = Dict("one" => 1, "two" => 2, "three" => 3) # => Dict{String,Int64} with 3 entries: # => "two" => 2, "one" => 1, "three" => 3 # Look up values with [] filledDict["one"] # => 1 # Get all keys keys(filledDict) # => Base.KeySet for a Dict{String,Int64} with 3 entries. Keys: # => "two", "one", "three" # Note - dictionary keys are not sorted or in the order you inserted them. # Get all values values(filledDict) # => Base.ValueIterator for a Dict{String,Int64} with 3 entries. Values: # => 2, 1, 3 # Note - Same as above regarding key ordering. # Check for existence of keys in a dictionary with in, haskey in(("one" => 1), filledDict) # => true in(("two" => 3), filledDict) # => false haskey(filledDict, "one") # => true haskey(filledDict, 1) # => false # Trying to look up a non-existent key will raise an error try filledDict["four"] # => ERROR: KeyError: key "four" not found catch e println(e) end # Use the get method to avoid that error by providing a default value # get(dictionary, key, defaultValue) get(filledDict, "one", 4) # => 1 get(filledDict, "four", 4) # => 4 # Use Sets to represent collections of unordered, unique values emptySet = Set() # => Set(Any[]) # Initialize a set with values filledSet = Set([1, 2, 2, 3, 4]) # => Set([4, 2, 3, 1]) # Add more values to a set push!(filledSet, 5) # => Set([4, 2, 3, 5, 1]) # Check if the values are in the set in(2, filledSet) # => true in(10, filledSet) # => false # There are functions for set intersection, union, and difference. otherSet = Set([3, 4, 5, 6]) # => Set([4, 3, 5, 6]) intersect(filledSet, otherSet) # => Set([4, 3, 5]) union(filledSet, otherSet) # => Set([4, 2, 3, 5, 6, 1]) setdiff(Set([1,2,3,4]), Set([2,3,5])) # => Set([4, 1]) #################################################### ## 3. Control Flow #################################################### # Let's make a variable someVar = 5 # Here is an if statement. Indentation is not meaningful in Julia. if someVar > 10 println("someVar is totally bigger than 10.") elseif someVar < 10 # This elseif clause is optional. println("someVar is smaller than 10.") else # The else clause is optional too. println("someVar is indeed 10.") end # => prints "some var is smaller than 10" # For loops iterate over iterables. # Iterable types include Range, Array, Set, Dict, and AbstractString. for animal = ["dog", "cat", "mouse"] println("$animal is a mammal") # You can use $ to interpolate variables or expression into strings. # In this special case, no need for parenthesis: $animal and $(animal) give the same end # => dog is a mammal # => cat is a mammal # => mouse is a mammal # You can use 'in' instead of '='. for animal in ["dog", "cat", "mouse"] println("$animal is a mammal") end # => dog is a mammal # => cat is a mammal # => mouse is a mammal for pair in Dict("dog" => "mammal", "cat" => "mammal", "mouse" => "mammal") from, to = pair println("$from is a $to") end # => mouse is a mammal # => cat is a mammal # => dog is a mammal for (k, v) in Dict("dog" => "mammal", "cat" => "mammal", "mouse" => "mammal") println("$k is a $v") end # => mouse is a mammal # => cat is a mammal # => dog is a mammal # While loops loop while a condition is true let x = 0 while x < 4 println(x) x += 1 # Shorthand for in place increment: x = x + 1 end end # => 0 # => 1 # => 2 # => 3 # Handle exceptions with a try/catch block try error("help") catch e println("caught it $e") end # => caught it ErrorException("help") #################################################### ## 4. Functions #################################################### # The keyword 'function' creates new functions # function name(arglist) # body... # end function add(x, y) println("x is $x and y is $y") # Functions return the value of their last statement x + y end add(5, 6) # => x is 5 and y is 6 # => 11 # Compact assignment of functions f_add(x, y) = x + y # => f_add (generic function with 1 method) f_add(3, 4) # => 7 # Function can also return multiple values as tuple fn(x, y) = x + y, x - y # => fn (generic function with 1 method) fn(3, 4) # => (7, -1) # You can define functions that take a variable number of # positional arguments function varargs(args...) return args # use the keyword return to return anywhere in the function end # => varargs (generic function with 1 method) varargs(1, 2, 3) # => (1,2,3) # The ... is called a splat. # We just used it in a function definition. # It can also be used in a function call, # where it will splat an Array or Tuple's contents into the argument list. add([5,6]...) # this is equivalent to add(5,6) x = (5, 6) # => (5,6) add(x...) # this is equivalent to add(5,6) # You can define functions with optional positional arguments function defaults(a, b, x=5, y=6) return "$a $b and $x $y" end # => defaults (generic function with 3 methods) defaults('h', 'g') # => "h g and 5 6" defaults('h', 'g', 'j') # => "h g and j 6" defaults('h', 'g', 'j', 'k') # => "h g and j k" try defaults('h') # => ERROR: MethodError: no method matching defaults(::Char) defaults() # => ERROR: MethodError: no method matching defaults() catch e println(e) end # You can define functions that take keyword arguments function keyword_args(;k1=4, name2="hello") # note the ; return Dict("k1" => k1, "name2" => name2) end # => keyword_args (generic function with 1 method) keyword_args(name2="ness") # => ["name2"=>"ness", "k1"=>4] keyword_args(k1="mine") # => ["name2"=>"hello", "k1"=>"mine"] keyword_args() # => ["name2"=>"hello", "k1"=>4] # You can combine all kinds of arguments in the same function function all_the_args(normalArg, optionalPositionalArg=2; keywordArg="foo") println("normal arg: $normalArg") println("optional arg: $optionalPositionalArg") println("keyword arg: $keywordArg") end # => all_the_args (generic function with 2 methods) all_the_args(1, 3, keywordArg=4) # => normal arg: 1 # => optional arg: 3 # => keyword arg: 4 # Julia has first class functions function create_adder(x) adder = function (y) return x + y end return adder end # => create_adder (generic function with 1 method) # This is "stabby lambda syntax" for creating anonymous functions (x -> x > 2)(3) # => true # This function is identical to create_adder implementation above. function create_adder(x) y -> x + y end # => create_adder (generic function with 1 method) # You can also name the internal function, if you want function create_adder(x) function adder(y) x + y end adder end # => create_adder (generic function with 1 method) add_10 = create_adder(10) # => (::getfield(Main, Symbol("#adder#11")){Int64}) # (generic function with 1 method) add_10(3) # => 13 # There are built-in higher order functions map(add_10, [1,2,3]) # => [11, 12, 13] filter(x -> x > 5, [3, 4, 5, 6, 7]) # => [6, 7] # We can use list comprehensions [add_10(i) for i = [1, 2, 3]] # => [11, 12, 13] [add_10(i) for i in [1, 2, 3]] # => [11, 12, 13] [x for x in [3, 4, 5, 6, 7] if x > 5] # => [6, 7] #################################################### ## 5. Types #################################################### # Julia has a type system. # Every value has a type; variables do not have types themselves. # You can use the `typeof` function to get the type of a value. typeof(5) # => Int64 # Types are first-class values typeof(Int64) # => DataType typeof(DataType) # => DataType # DataType is the type that represents types, including itself. # Types are used for documentation, optimizations, and dispatch. # They are not statically checked. # Users can define types # They are like records or structs in other languages. # New types are defined using the `struct` keyword. # struct Name # field::OptionalType # ... # end struct Tiger taillength::Float64 coatcolor # not including a type annotation is the same as `::Any` end # The default constructor's arguments are the properties # of the type, in the order they are listed in the definition tigger = Tiger(3.5, "orange") # => Tiger(3.5,"orange") # The type doubles as the constructor function for values of that type sherekhan = typeof(tigger)(5.6, "fire") # => Tiger(5.6,"fire") # These struct-style types are called concrete types # They can be instantiated, but cannot have subtypes. # The other kind of types is abstract types. # abstract Name abstract type Cat end # just a name and point in the type hierarchy # Abstract types cannot be instantiated, but can have subtypes. # For example, Number is an abstract type subtypes(Number) # => 2-element Array{Any,1}: # => Complex # => Real subtypes(Cat) # => 0-element Array{Any,1} # AbstractString, as the name implies, is also an abstract type subtypes(AbstractString) # => 4-element Array{Any,1}: # => String # => SubString # => SubstitutionString # => Test.GenericString # Every type has a super type; use the `supertype` function to get it. typeof(5) # => Int64 supertype(Int64) # => Signed supertype(Signed) # => Integer supertype(Integer) # => Real supertype(Real) # => Number supertype(Number) # => Any supertype(supertype(Signed)) # => Real supertype(Any) # => Any # All of these type, except for Int64, are abstract. typeof("fire") # => String supertype(String) # => AbstractString # Likewise here with String supertype(SubString) # => AbstractString # <: is the subtyping operator struct Lion <: Cat # Lion is a subtype of Cat maneColor roar::AbstractString end # You can define more constructors for your type # Just define a function of the same name as the type # and call an existing constructor to get a value of the correct type Lion(roar::AbstractString) = Lion("green", roar) # This is an outer constructor because it's outside the type definition struct Panther <: Cat # Panther is also a subtype of Cat eyeColor Panther() = new("green") # Panthers will only have this constructor, and no default constructor. end # Using inner constructors, like Panther does, gives you control # over how values of the type can be created. # When possible, you should use outer constructors rather than inner ones. #################################################### ## 6. Multiple-Dispatch #################################################### # In Julia, all named functions are generic functions # This means that they are built up from many small methods # Each constructor for Lion is a method of the generic function Lion. # For a non-constructor example, let's make a function meow: # Definitions for Lion, Panther, Tiger function meow(animal::Lion) animal.roar # access type properties using dot notation end function meow(animal::Panther) "grrr" end function meow(animal::Tiger) "rawwwr" end # Testing the meow function meow(tigger) # => "rawwwr" meow(Lion("brown", "ROAAR")) # => "ROAAR" meow(Panther()) # => "grrr" # Review the local type hierarchy Tiger <: Cat # => false Lion <: Cat # => true Panther <: Cat # => true # Defining a function that takes Cats function pet_cat(cat::Cat) println("The cat says $(meow(cat))") end # => pet_cat (generic function with 1 method) pet_cat(Lion("42")) # => The cat says 42 try pet_cat(tigger) # => ERROR: MethodError: no method matching pet_cat(::Tiger) catch e println(e) end # In OO languages, single dispatch is common; # this means that the method is picked based on the type of the first argument. # In Julia, all of the argument types contribute to selecting the best method. # Let's define a function with more arguments, so we can see the difference function fight(t::Tiger, c::Cat) println("The $(t.coatcolor) tiger wins!") end # => fight (generic function with 1 method) fight(tigger, Panther()) # => The orange tiger wins! fight(tigger, Lion("ROAR")) # => The orange tiger wins! # Let's change the behavior when the Cat is specifically a Lion fight(t::Tiger, l::Lion) = println("The $(l.maneColor)-maned lion wins!") # => fight (generic function with 2 methods) fight(tigger, Panther()) # => The orange tiger wins! fight(tigger, Lion("ROAR")) # => The green-maned lion wins! # We don't need a Tiger in order to fight fight(l::Lion, c::Cat) = println("The victorious cat says $(meow(c))") # => fight (generic function with 3 methods) fight(Lion("balooga!"), Panther()) # => The victorious cat says grrr try fight(Panther(), Lion("RAWR")) # => ERROR: MethodError: no method matching fight(::Panther, ::Lion) # => Closest candidates are: # => fight(::Tiger, ::Lion) at ... # => fight(::Tiger, ::Cat) at ... # => fight(::Lion, ::Cat) at ... # => ... catch e println(e) end # Also let the cat go first fight(c::Cat, l::Lion) = println("The cat beats the Lion") # => fight (generic function with 4 methods) # This warning is because it's unclear which fight will be called in: try fight(Lion("RAR"), Lion("brown", "rarrr")) # => ERROR: MethodError: fight(::Lion, ::Lion) is ambiguous. Candidates: # => fight(c::Cat, l::Lion) in Main at ... # => fight(l::Lion, c::Cat) in Main at ... # => Possible fix, define # => fight(::Lion, ::Lion) # => ... catch e println(e) end # The result may be different in other versions of Julia fight(l::Lion, l2::Lion) = println("The lions come to a tie") # => fight (generic function with 5 methods) fight(Lion("RAR"), Lion("brown", "rarrr")) # => The lions come to a tie # Under the hood # You can take a look at the llvm and the assembly code generated. square_area(l) = l * l # square_area (generic function with 1 method) square_area(5) # => 25 # What happens when we feed square_area an integer? code_native(square_area, (Int32,), syntax = :intel) # .text # ; Function square_area { # ; Location: REPL[116]:1 # Prologue # push rbp # mov rbp, rsp # ; Function *; { # ; Location: int.jl:54 # imul ecx, ecx # Square l and store the result in ECX # ;} # mov eax, ecx # pop rbp # Restore old base pointer # ret # Result will still be in EAX # nop dword ptr [rax + rax] # ;} code_native(square_area, (Float32,), syntax = :intel) # .text # ; Function square_area { # ; Location: REPL[116]:1 # push rbp # mov rbp, rsp # ; Function *; { # ; Location: float.jl:398 # vmulss xmm0, xmm0, xmm0 # Scalar single precision multiply (AVX) # ;} # pop rbp # ret # nop word ptr [rax + rax] # ;} code_native(square_area, (Float64,), syntax = :intel) # .text # ; Function square_area { # ; Location: REPL[116]:1 # push rbp # mov rbp, rsp # ; Function *; { # ; Location: float.jl:399 # vmulsd xmm0, xmm0, xmm0 # Scalar double precision multiply (AVX) # ;} # pop rbp # ret # nop word ptr [rax + rax] # ;} # Note that julia will use floating point instructions if any of the # arguments are floats. # Let's calculate the area of a circle circle_area(r) = pi * r * r # circle_area (generic function with 1 method) circle_area(5) # 78.53981633974483 code_native(circle_area, (Int32,), syntax = :intel) # .text # ; Function circle_area { # ; Location: REPL[121]:1 # push rbp # mov rbp, rsp # ; Function *; { # ; Location: operators.jl:502 # ; Function *; { # ; Location: promotion.jl:314 # ; Function promote; { # ; Location: promotion.jl:284 # ; Function _promote; { # ; Location: promotion.jl:261 # ; Function convert; { # ; Location: number.jl:7 # ; Function Type; { # ; Location: float.jl:60 # vcvtsi2sd xmm0, xmm0, ecx # Load integer (r) from memory # movabs rax, 497710928 # Load pi # ;}}}}} # ; Function *; { # ; Location: float.jl:399 # vmulsd xmm1, xmm0, qword ptr [rax] # pi * r # vmulsd xmm0, xmm1, xmm0 # (pi * r) * r # ;}} # pop rbp # ret # nop dword ptr [rax] # ;} code_native(circle_area, (Float64,), syntax = :intel) # .text # ; Function circle_area { # ; Location: REPL[121]:1 # push rbp # mov rbp, rsp # movabs rax, 497711048 # ; Function *; { # ; Location: operators.jl:502 # ; Function *; { # ; Location: promotion.jl:314 # ; Function *; { # ; Location: float.jl:399 # vmulsd xmm1, xmm0, qword ptr [rax] # ;}}} # ; Function *; { # ; Location: float.jl:399 # vmulsd xmm0, xmm1, xmm0 # ;} # pop rbp # ret # nop dword ptr [rax + rax] # ;}
# 单行注释只需要一个井号「#」 #= 多行注释 只需要以「#=」开始「=#」结束 还可以嵌套. =# #################################################### ## 1. 原始类型与操作符 #################################################### # Julia 中一切皆为表达式 # 这是一些基本数字类型 typeof(3) # => Int64 typeof(3.2) # => Float64 typeof(2 + 1im) # => Complex{Int64} typeof(2 // 3) # => Rational{Int64} # 支持所有的普通中缀操作符 1 + 1 # => 2 8 - 1 # => 7 10 * 2 # => 20 35 / 5 # => 7.0 10 / 2 # => 5.0 # 整数除法总是返回浮点数 div(5, 2) # => 2 # 使用 div 可以获得整除的结果 5 \ 35 # => 7.0 2^2 # => 4 # 幂运算，不是异或 (xor) 12 % 10 # => 2 # 用括号提高优先级 (1 + 3) * 2 # => 8 # 位操作符 ~2 # => -3 # 按位非 (not) 3 & 5 # => 1 # 按位与 (and) 2 | 4 # => 6 # 按位或 (or) xor(2, 4) # => 6 # 按位异或 (xor) 2 >>> 1 # => 1 # 逻辑右移 2 >> 1 # => 1 # 算术右移 2 << 1 # => 4 # 逻辑/算术左移 # 可以用函数 bitstring 查看二进制数。 bitstring(12345) # => "0000000000000000000000000000000000000000000000000011000000111001" bitstring(12345.0) # => "0100000011001000000111001000000000000000000000000000000000000000" # 布尔值是原始类型 true false # 布尔操作符 !true # => false !false # => true 1 == 1 # => true 2 == 1 # => false 1 != 1 # => false 2 != 1 # => true 1 < 10 # => true 1 > 10 # => false 2 <= 2 # => true 2 >= 2 # => true # 链式比较 1 < 2 < 3 # => true 2 < 3 < 2 # => false # 字符串可以由「"」创建 "This is a string." # 字符字面量可用「'」创建 'a' # 字符串使用 UTF-8 编码 # 可以像取数组取值一样用 index 取出对应字符 ascii("This is a string")[1] # => 'T': ASCII/Unicode U+0054 (category Lu: Letter, uppercase) # Julia 的 index 从 1 开始 :( # 但只有在字符串仅由 ASCII 字符构成时，字符串才能够被安全的引索 # 因此建议使用遍历器 (map, for loops, 等) # $ 可用于字符插值: "2 + 2 = $(2 + 2)" # => "2 + 2 = 4" # 可以将任何 Julia 表达式放入括号。 # 另一种输出格式化字符串的方法是使用标准库 Printf 中的 Printf 宏 using Printf @printf "%d is less than %f\n" 4.5 5.3 # => 5 is less than 5.300000 # 打印字符串很容易 println("I'm Julia. Nice to meet you!") # => I'm Julia. Nice to meet you! # 字符串可以按字典序进行比较 "good" > "bye" # => true "good" == "good" # => true "1 + 2 = 3" == "1 + 2 = $(1 + 2)" # => true #################################################### ## 2. 变量与集合 #################################################### # 给变量赋值就是声明变量 some_var = 5 # => 5 some_var # => 5 # 访问未声明变量会抛出异常 try some_other_var # => ERROR: UndefVarError: some_other_var not defined catch e println(e) end # 变量名必须以下划线或字母开头 # 之后任何字母，数字，下划线，叹号都是合法的。 SomeOtherVar123! = 6 # => 6 # 甚至可以用 unicode 字符 ☃ = 8 # => 8 # 用数学符号非常方便 2 * π # => 6.283185307179586 # 注意 Julia 的命名规约: # # * 名称可以用下划线「_」分割。 # 不过一般不推荐使用下划线，除非不用变量名就会变得难于理解 # # * 类型名以大写字母开头，单词以 CamelCase 方式连接，无下划线。 # # * 函数与宏的名字小写，无下划线。 # # * 会改变输入的函数名末位为「!」。 # 这类函数有时被称为 mutating functions 或 in-place functions. # 数组存储一列值，index 从 1 开始 a = Int64[] # => 0-element Array{Int64,1} # 一维数组可以以逗号分隔值的方式声明 b = [4, 5, 6] # => 3-element Array{Int64,1}: [4, 5, 6] b = [4; 5; 6] # => 3-element Array{Int64,1}: [4, 5, 6] b[1] # => 4 b[end] # => 6 # 二维数组以分号分隔维度 matrix = [1 2; 3 4] # => 2×2 Array{Int64,2}: [1 2; 3 4] # 指定数组的类型 b = Int8[4, 5, 6] # => 3-element Array{Int8,1}: [4, 5, 6] # 使用 push! 和 append! 往数组末尾添加元素 push!(a, 1) # => [1] push!(a, 2) # => [1,2] push!(a, 4) # => [1,2,4] push!(a, 3) # => [1,2,4,3] append!(a, b) # => [1,2,4,3,4,5,6] # 用 pop 弹出尾部的元素 pop!(b) # => 6 b # => [4,5] # 再放回去 push!(b, 6) # => [4,5,6] b # => [4,5,6] a[1] # => 1 # 永远记住 Julia 的引索从 1 开始！而不是 0！ # 用 end 可以直接取到最后索引。它可以用在任何索引表达式中 a[end] # => 6 # 数组还支持 popfirst! 和 pushfirst! popfirst!(a) # => 1 a # => [2,4,3,4,5,6] pushfirst!(a, 7) # => [7,2,4,3,4,5,6] a # => [7,2,4,3,4,5,6] # 以叹号结尾的函数名表示它会改变参数的值 arr = [5,4,6] # => 3-element Array{Int64,1}: [5,4,6] sort(arr) # => [4,5,6] arr # => [5,4,6] sort!(arr) # => [4,5,6] arr # => [4,5,6] # 数组越界会抛出 BoundsError try a[0] # => ERROR: BoundsError: attempt to access 7-element Array{Int64,1} at # index [0] # => Stacktrace: # => [1] getindex(::Array{Int64,1}, ::Int64) at .\array.jl:731 # => [2] top-level scope at none:0 # => [3] ... # => in expression starting at ...\LearnJulia.jl:203 a[end + 1] # => ERROR: BoundsError: attempt to access 7-element Array{Int64,1} at # index [8] # => Stacktrace: # => [1] getindex(::Array{Int64,1}, ::Int64) at .\array.jl:731 # => [2] top-level scope at none:0 # => [3] ... # => in expression starting at ...\LearnJulia.jl:211 catch e println(e) end # 报错时错误会指出出错的文件位置以及行号，标准库也一样 # 你可以在 Julia 安装目录下的 share/julia 文件夹里找到这些标准库 # 可以用 range 初始化数组 a = [1:5;] # => 5-element Array{Int64,1}: [1,2,3,4,5] # 注意！分号不可省略 a2 = [1:5] # => 1-element Array{UnitRange{Int64},1}: [1:5] # 可以切割数组 a[1:3] # => [1, 2, 3] a[2:end] # => [2, 3, 4, 5] # 用 splice! 切割原数组 arr = [3,4,5] splice!(arr, 2) # => 4 arr # => [3,5] # 用 append! 连接数组 b = [1,2,3] append!(a, b) # => [1, 2, 3, 4, 5, 1, 2, 3] a # => [1, 2, 3, 4, 5, 1, 2, 3] # 检查元素是否在数组中 in(1, a) # => true # 用 length 获得数组长度 length(a) # => 8 # 元组(Tuples)是不可变的 tup = (1, 2, 3) # => (1,2,3) typeof(tup) # => Tuple{Int64,Int64,Int64} tup[1] # => 1 try tup[1] = 3 # => ERROR: MethodError: no method matching # setindex!(::Tuple{Int64,Int64,Int64}, ::Int64, ::Int64) catch e println(e) end # 大多数组的函数同样支持元组 length(tup) # => 3 tup[1:2] # => (1,2) in(2, tup) # => true # 可以将元组的元素解包赋给变量 a, b, c = (1, 2, 3) # => (1,2,3) a # => 1 b # => 2 c # => 3 # 不用括号也可以 d, e, f = 4, 5, 6 # => (4,5,6) d # => 4 e # => 5 f # => 6 # 单元素 tuple 不等于其元素值 (1,) == 1 # => false (1) == 1 # => true # 交换值 e, d = d, e # => (5,4) d # => 5 e # => 4 # 字典用于储存映射(mappings)（键值对） empty_dict = Dict() # => Dict{Any,Any} with 0 entries # 也可以用字面量创建字典 filled_dict = Dict("one" => 1, "two" => 2, "three" => 3) # => Dict{String,Int64} with 3 entries: # => "two" => 2, "one" => 1, "three" => 3 # 用 [] 获得键值 filled_dict["one"] # => 1 # 获得所有键 keys(filled_dict) # => Base.KeySet for a Dict{String,Int64} with 3 entries. Keys: # => "two", "one", "three" # 注意，键的顺序不是插入时的顺序 # 获得所有值 values(filled_dict) # => Base.ValueIterator for a Dict{String,Int64} with 3 entries. Values: # => 2, 1, 3 # 注意，值的顺序也一样 # 用 in 检查键值是否已存在，用 haskey 检查键是否存在 in(("one" => 1), filled_dict) # => true in(("two" => 3), filled_dict) # => false haskey(filled_dict, "one") # => true haskey(filled_dict, 1) # => false # 获取不存在的键的值会抛出异常 try filled_dict["four"] # => ERROR: KeyError: key "four" not found catch e println(e) end # 使用 get 可以提供默认值来避免异常 # get(dictionary,key,default_value) get(filled_dict, "one", 4) # => 1 get(filled_dict, "four", 4) # => 4 # Set 表示无序不可重复的值的集合 empty_set = Set() # => Set(Any[]) # 初始化一个带初值的 Set filled_set = Set([1, 2, 2, 3, 4]) # => Set([4, 2, 3, 1]) # 新增值 push!(filled_set, 5) # => Set([4, 2, 3, 5, 1]) # 检查 Set 中是否存在某值 in(2, filled_set) # => true in(10, filled_set) # => false # 交集，并集，差集 other_set = Set([3, 4, 5, 6]) # => Set([4, 3, 5, 6]) intersect(filled_set, other_set) # => Set([4, 3, 5]) union(filled_set, other_set) # => Set([4, 2, 3, 5, 6, 1]) setdiff(Set([1,2,3,4]), Set([2,3,5])) # => Set([4, 1]) #################################################### ## 3. 控制语句 #################################################### # 声明一个变量 some_var = 5 # 这是一个 if 语句块，其中的缩进不是必须的 if some_var > 10 println("some_var is totally bigger than 10.") elseif some_var < 10 # elseif 是可选的 println("some_var is smaller than 10.") else # else 也是可选的 println("some_var is indeed 10.") end # => some_var is smaller than 10. # For 循环遍历 # 可迭代的类型包括：Range, Array, Set, Dict 和 AbstractString for animal = ["dog", "cat", "mouse"] println("$animal is a mammal") # 你可以用 $ 将变量或表达式插入字符串中 end # => dog is a mammal # => cat is a mammal # => mouse is a mammal # 你也可以不用「=」而使用「in」 for animal in ["dog", "cat", "mouse"] println("$animal is a mammal") end # => dog is a mammal # => cat is a mammal # => mouse is a mammal for pair in Dict("dog" => "mammal", "cat" => "mammal", "mouse" => "mammal") from, to = pair println("$from is a $to") end # => mouse is a mammal # => cat is a mammal # => dog is a mammal # 注意！这里的输出顺序和上面的不同 for (k, v) in Dict("dog" => "mammal", "cat" => "mammal", "mouse" => "mammal") println("$k is a $v") end # => mouse is a mammal # => cat is a mammal # => dog is a mammal # While 循环 let x = 0 while x < 4 println(x) x += 1 # x = x + 1 的缩写 end end # => 0 # => 1 # => 2 # => 3 # 用 try/catch 处理异常 try error("help") catch e println("caught it $e") end # => caught it ErrorException("help") #################################################### ## 4. 函数 #################################################### # 关键字 function 用于定义函数 # function name(arglist) # body... # end function add(x, y) println("x is $x and y is $y") # 函数会返回最后一行的值 x + y end add(5, 6) # => x is 5 and y is 6 # => 11 # 更紧凑的定义函数 f_add(x, y) = x + y # => f_add (generic function with 1 method) f_add(3, 4) # => 7 # 函数可以将多个值作为元组返回 fn(x, y) = x + y, x - y # => fn (generic function with 1 method) fn(3, 4) # => (7, -1) # 还可以定义接收可变长参数的函数 function varargs(args...) return args # 使用 return 可以在函数内的任何地方返回 end # => varargs (generic function with 1 method) varargs(1,2,3) # => (1,2,3) # 省略号「...」称为 splat # 刚刚用在了函数定义中 # 在调用函数时也可以使用它，此时它会把数组或元组解包为参数列表 add([5,6]...) # 等价于 add(5,6) x = (5, 6) # => (5,6) add(x...) # 等价于 add(5,6) # 可定义带可选参数的函数 function defaults(a, b, x=5, y=6) return "$a $b and $x $y" end # => defaults (generic function with 3 methods) defaults('h', 'g') # => "h g and 5 6" defaults('h', 'g', 'j') # => "h g and j 6" defaults('h', 'g', 'j', 'k') # => "h g and j k" try defaults('h') # => ERROR: MethodError: no method matching defaults(::Char) defaults() # => ERROR: MethodError: no method matching defaults() catch e println(e) end # 还可以定义带关键字参数的函数 function keyword_args(;k1=4, name2="hello") # 注意分号 ';' return Dict("k1" => k1, "name2" => name2) end # => keyword_args (generic function with 1 method) keyword_args(name2="ness") # => ["name2"=>"ness", "k1"=>4] keyword_args(k1="mine") # => ["name2"=>"hello", "k1"=>"mine"] keyword_args() # => ["name2"=>"hello", "k1"=>4] # 可以在一个函数中组合各种类型的参数 function all_the_args(normal_arg, optional_positional_arg=2; keyword_arg="foo") println("normal arg: $normal_arg") println("optional arg: $optional_positional_arg") println("keyword arg: $keyword_arg") end # => all_the_args (generic function with 2 methods) all_the_args(1, 3, keyword_arg=4) # => normal arg: 1 # => optional arg: 3 # => keyword arg: 4 # Julia 有一等函数 function create_adder(x) adder = function (y) return x + y end return adder end # => create_adder (generic function with 1 method) # 这是用 "stabby lambda syntax" 创建的匿名函数 (x -> x > 2)(3) # => true # 这个函数和上面的 create_adder 是等价的 function create_adder(x) y -> x + y end # => create_adder (generic function with 1 method) # 你也可以给内部函数起个名字 function create_adder(x) function adder(y) x + y end adder end # => create_adder (generic function with 1 method) add_10 = create_adder(10) # => (::getfield(Main, Symbol("#adder#11")){Int64}) # (generic function with 1 method) add_10(3) # => 13 # 内置的高阶函数有 map(add_10, [1,2,3]) # => [11, 12, 13] filter(x -> x > 5, [3, 4, 5, 6, 7]) # => [6, 7] # 还可以使用 list comprehensions 让 map 更美观 [add_10(i) for i = [1, 2, 3]] # => [11, 12, 13] [add_10(i) for i in [1, 2, 3]] # => [11, 12, 13] #################################################### ## 5. 类型 #################################################### # Julia 有类型系统 # 所有的值都有类型；但变量本身没有类型 # 你可以用 `typeof` 函数获得值的类型 typeof(5) # => Int64 # 类型是一等值 typeof(Int64) # => DataType typeof(DataType) # => DataType # DataType 是代表类型的类型，也代表他自己的类型 # 类型可用于文档化代码、执行优化以及多重派分(dispatch) # Julia 并不只是静态的检查类型 # 用户还可以自定义类型 # 就跟其它语言的 record 或 struct 一样 # 用 `struct` 关键字定义新的类型 # struct Name # field::OptionalType # ... # end struct Tiger taillength::Float64 coatcolor # 不带类型标注相当于 `::Any` end # 默认构造函数的参数是类型的属性，按类型定义中的顺序排列 tigger = Tiger(3.5, "orange") # => Tiger(3.5, "orange") # 用新类型作为构造函数还会创建一个类型 sherekhan = typeof(tigger)(5.6, "fire") # => Tiger(5.6, "fire") # 类似 struct 的类型被称为具体类型 # 它们可被实例化，但不能有子类型 # 另一种类型是抽象类型 # 抽象类型名 abstract type Cat end # 仅仅是指向类型结构层次的一个名称 # 抽象类型不能被实例化，但可以有子类型 # 例如，Number 就是抽象类型 subtypes(Number) # => 2-element Array{Any,1}: # => Complex # => Real subtypes(Cat) # => 0-element Array{Any,1} # AbstractString，类如其名，也是一个抽象类型 subtypes(AbstractString) # => 4-element Array{Any,1}: # => String # => SubString # => SubstitutionString # => Test.GenericString # 所有的类型都有父类型。可以用函数 `supertype` 得到父类型 typeof(5) # => Int64 supertype(Int64) # => Signed supertype(Signed) # => Integer supertype(Integer) # => Real supertype(Real) # => Number supertype(Number) # => Any supertype(supertype(Signed)) # => Real supertype(Any) # => Any # 除了 Int64 外，其余的类型都是抽象类型 typeof("fire") # => String supertype(String) # => AbstractString supertype(AbstractString) # => Any supertype(SubString) # => AbstractString # <: 是子类型化操作符 struct Lion <: Cat # Lion 是 Cat 的子类型 mane_color roar::AbstractString end # 可以继续为你的类型定义构造函数 # 只需要定义一个与类型同名的函数，并调用已有的构造函数得到正确的类型 Lion(roar::AbstractString) = Lion("green", roar) # => Lion # 这是一个外部构造函数，因为它在类型定义之外 struct Panther <: Cat # Panther 也是 Cat 的子类型 eye_color Panther() = new("green") # Panthers 只有这个构造函数，没有默认构造函数 end # 像 Panther 一样使用内置构造函数，让你可以控制如何构建类型的值 # 应该尽量使用外部构造函数，而不是内部构造函数 #################################################### ## 6. 多分派 #################################################### # Julia 中所有的函数都是通用函数，或者叫做泛型函数(generic functions) # 也就是说这些函数都是由许多小方法组合而成的 # Lion 的每一种构造函数都是通用函数 Lion 的一个方法 # 我们来看一个非构造函数的例子 # 首先，让我们定义一个函数 meow # Lion, Panther, Tiger 的 meow 定义分别为 function meow(animal::Lion) animal.roar # 使用点记号「.」访问属性 end # => meow (generic function with 1 method) function meow(animal::Panther) "grrr" end # => meow (generic function with 2 methods) function meow(animal::Tiger) "rawwwr" end # => meow (generic function with 3 methods) # 试试 meow 函数 meow(tigger) # => "rawwwr" meow(Lion("brown", "ROAAR")) # => "ROAAR" meow(Panther()) # => "grrr" # 回顾类型的层次结构 Tiger <: Cat # => false Lion <: Cat # => true Panther <: Cat # => true # 定义一个接收 Cat 类型的函数 function pet_cat(cat::Cat) println("The cat says $(meow(cat))") end # => pet_cat (generic function with 1 method) pet_cat(Lion("42")) # => The cat says 42 try pet_cat(tigger) # => ERROR: MethodError: no method matching pet_cat(::Tiger) catch e println(e) end # 在面向对象语言中，通常都是单分派 # 这意味着使用的方法取决于第一个参数的类型 # 而 Julia 中选择方法时会考虑到所有参数的类型 # 让我们定义一个有更多参数的函数，这样我们就能看出区别 function fight(t::Tiger, c::Cat) println("The $(t.coatcolor) tiger wins!") end # => fight (generic function with 1 method) fight(tigger, Panther()) # => The orange tiger wins! fight(tigger, Lion("ROAR")) # => fight(tigger, Lion("ROAR")) # 让我们修改一下传入 Lion 类型时的行为 fight(t::Tiger, l::Lion) = println("The $(l.mane_color)-maned lion wins!") # => fight (generic function with 2 methods) fight(tigger, Panther()) # => The orange tiger wins! fight(tigger, Lion("ROAR")) # => The green-maned lion wins! # 我们不需要一只老虎参与战斗 fight(l::Lion, c::Cat) = println("The victorious cat says $(meow(c))") # => fight (generic function with 3 methods) fight(Lion("balooga!"), Panther()) # => The victorious cat says grrr try fight(Panther(), Lion("RAWR")) # => ERROR: MethodError: no method matching fight(::Panther, ::Lion) # => Closest candidates are: # => fight(::Tiger, ::Lion) at ... # => fight(::Tiger, ::Cat) at ... # => fight(::Lion, ::Cat) at ... # => ... catch e println(e) end # 试试把 Cat 放在前面 fight(c::Cat, l::Lion) = println("The cat beats the Lion") # => fight (generic function with 4 methods) # 由于无法判断该使用哪个 fight 方法，而产生了错误 try fight(Lion("RAR"), Lion("brown", "rarrr")) # => ERROR: MethodError: fight(::Lion, ::Lion) is ambiguous. Candidates: # => fight(c::Cat, l::Lion) in Main at ... # => fight(l::Lion, c::Cat) in Main at ... # => Possible fix, define # => fight(::Lion, ::Lion) # => ... catch e println(e) end # 在不同版本的 Julia 中错误信息可能有所不同 fight(l::Lion, l2::Lion) = println("The lions come to a tie") # => fight (generic function with 5 methods) fight(Lion("RAR"), Lion("brown", "rarrr")) # => The lions come to a tie # 深入编译器之后 # 你还可以看看 llvm 以及它生成的汇编代码 square_area(l) = l * l # => square_area (generic function with 1 method) square_area(5) # => 25 # 当我们喂给 square_area 一个整数时会发生什么？ code_native(square_area, (Int32,), syntax = :intel) # .text # ; Function square_area { # ; Location: REPL[116]:1 # 函数序言 (Prologue) # push rbp # mov rbp, rsp # ; Function *; { # ; Location: int.jl:54 # imul ecx, ecx # 求 l 的平方，并把结果放在 ECX 中 # ;} # mov eax, ecx # pop rbp # 还原旧的基址指针(base pointer) # ret # 返回值放在 EAX 中 # nop dword ptr [rax + rax] # ;} # 使用 syntax 参数指定输出语法。默认为 AT&T 格式，这里指定为 Intel 格式 code_native(square_area, (Float32,), syntax = :intel) # .text # ; Function square_area { # ; Location: REPL[116]:1 # push rbp # mov rbp, rsp # ; Function *; { # ; Location: float.jl:398 # vmulss xmm0, xmm0, xmm0 # 标量双精度乘法 (AVX) # ;} # pop rbp # ret # nop word ptr [rax + rax] # ;} code_native(square_area, (Float64,), syntax = :intel) # .text # ; Function square_area { # ; Location: REPL[116]:1 # push rbp # mov rbp, rsp # ; Function *; { # ; Location: float.jl:399 # vmulsd xmm0, xmm0, xmm0 # 标量双精度乘法 (AVX) # ;} # pop rbp # ret # nop word ptr [rax + rax] # ;} # 注意！只要参数中有浮点数，Julia 就会使用浮点指令 # 让我们计算一下圆的面积 circle_area(r) = pi * r * r # => circle_area (generic function with 1 method) circle_area(5) # => 78.53981633974483 code_native(circle_area, (Int32,), syntax = :intel) # .text # ; Function circle_area { # ; Location: REPL[121]:1 # push rbp # mov rbp, rsp # ; Function *; { # ; Location: operators.jl:502 # ; Function *; { # ; Location: promotion.jl:314 # ; Function promote; { # ; Location: promotion.jl:284 # ; Function _promote; { # ; Location: promotion.jl:261 # ; Function convert; { # ; Location: number.jl:7 # ; Function Type; { # ; Location: float.jl:60 # vcvtsi2sd xmm0, xmm0, ecx # 从内存中读取整数 r # movabs rax, 497710928 # 读取 pi # ;}}}}} # ; Function *; { # ; Location: float.jl:399 # vmulsd xmm1, xmm0, qword ptr [rax] # pi * r # vmulsd xmm0, xmm1, xmm0 # (pi * r) * r # ;}} # pop rbp # ret # nop dword ptr [rax] # ;} code_native(circle_area, (Float64,), syntax = :intel) # .text # ; Function circle_area { # ; Location: REPL[121]:1 # push rbp # mov rbp, rsp # movabs rax, 497711048 # ; Function *; { # ; Location: operators.jl:502 # ; Function *; { # ; Location: promotion.jl:314 # ; Function *; { # ; Location: float.jl:399 # vmulsd xmm1, xmm0, qword ptr [rax] # ;}}} # ; Function *; { # ; Location: float.jl:399 # vmulsd xmm0, xmm1, xmm0 # ;} # pop rbp # ret # nop dword ptr [rax + rax] # ;}