Julia beginner memo

println("hello!")
println("hello!")
print("hello!")
print("hello!")
hello!
hello!
hello!hello!

Wrap the difference between the two functions.

using Pkg

abstract type Real <: Number end

Real is a subclass of Number of

(1+2)::Int

::Symbol type declaration, as in Python3 :, such asdef uniquePaths(self, m: int, n: int) -> int:

:: Operator can be used to additional variables and expressions in the program type annotation.

Example, let's parameters for the specified type,

#指定参数 M 为Matrix类型，这里T是参数模板比如整数Int，Float64等，T <: Number表示参数得是Number子类型
function restructure_matrix(M::Matrix{T}) where {T <: Number}
#Matrix其实是二维数组
Matrix
Array{T,2} where T
#Vector是维数为1的数组
Vector
Array{T,1} where T

Example, let's return value is the specified type,

function sinc(x)::Float64
    if x == 0
        return 1
    end
    return sin(pi*x)/(pi*x)
end

Lightly trial,

Strings, symbols and dictionaries

sym = Symbol("haha") #:haha
str = String("haha") #"haha"

dic = Dict([("C",7),("D",8),("A", 3), ("B", 2),("E",1)])
tem =keys(dic)
typeof(tem) #Base.KeySet

collect(tem)
typeof(collect(tem)) #Array{String,1}

collect(dic)
sort(collect(dic)) #按键排序
dic["B"]
dic[collect(keys(dic))[1]] #取字典第一个元素，字典键是无序（不按你加入顺序）的且“B”为第一个
sort(collect(keys(dic)))  #这时A才在前



#collect后是Pair的向量
dic
Dict{String,Int64} with 5 entries:
  "B" => 2
  "A" => 3
  "C" => 7
  "D" => 8
  "E" => 1
collect(dic)
5-element Array{Pair{String,Int64},1}:
 "B" => 2
 "A" => 3
 "C" => 7
 "D" => 8
 "E" => 1

Example, declare a complex type member type,

struct Foo
     bar #默认Any类型
     baz::Int
     qux::Float64
 end
#摸索一下
typeof(Foo)
Foo.bar #type DataType has no field bar
a = Foo(1,2,3)
#atom ctrl+/注释  atom 选中提示 设为 enter
typeof(a)   #Foo
a.bar
a.baz

An array of very important, ;a separate line, increase the number of lines ; spaces from one another, increasing the number of columns ; correspond in numpy np.r_[]and np.c_[](there is a good time to sum up, easy to forget that the official statement is to increase the first dimension and a second dimension, which is not clear, who knows you where the number of edges ..)

Xtest = [0 4 1;
         2 2 0;
         1 1 1]
3×3 Array{Int64,2}:
 0  4  1
 2  2  0
 1  1  1

For more examples, no time to explain, running, think about it, we know that (waste for a long time, with a look, remember this for the record)

[1 3 2 4]
[[1 3] [2 4]]
1×4 Array{Int64,2}:
 1  3  2  4
[1 ;3; 2; 4]
[1 ,3 ,2, 4]
[[1, 3] ;[2, 4]] 
4-element Array{Int64,1}:
 1
 3
 2
 4
[1 3; 2 4]
[[1 3] ;[2 4]]
2×2 Array{Int64,2}:
 1  3
 2  4
[[1 ,3] [2 ,4]] #[[1, 3] ;[2, 4]] add row
2×2 Array{Int64,2}:
 1  2
 3  4

Attach python example,

np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]
[[1 2 3 0 0 4 5 6]]
np.c_[np.array([1,2,3]), np.array([4,5,6])]
[[1 4]
 [2 5]
 [3 6]]
np.r_[np.array([1,2,3]), np.array([4,5,6])]
[0 1 2 3 4 5]
np.r_[np.array([1,2,3]), 0, 0, np.array([4,5,6])]
[1 2 3 0 0 4 5 6]

The above may be a bit lost, in a two-dimensional array, the law clearer,

x= np.c_[np.array([11,12]), np.array([14,15])]
y = np.arange(4).reshape(2,2)
y
array([[0, 1],
       [2, 3]])
x
array([[11, 14],
       [12, 15]])
np.c_[x,y] #列数增加
array([[11, 14,  0,  1],
       [12, 15,  2,  3]])

np.r_[x,y] #行数增加
array([[11, 14],
       [12, 15],
       [ 0,  1],
       [ 2,  3]])

Composite type

#复合类型 Composite Types
julia> 
julia> foo = Foo("Hello, world.", 23, 1.5)
Foo("Hello, world.", 23, 1.5)

julia> typeof(foo)
Foo
#类型联合
julia> IntOrString = Union{Int,AbstractString}
Union{Int64, AbstractString}

julia> 1 :: IntOrString
1

julia> "Hello!" :: IntOrString
"Hello!"

julia> 1.0 :: IntOrString
ERROR: TypeError: in typeassert, expected Union{Int64, AbstractString}, got Float64
#有参数复合类型 Parametric Composite Types
julia> struct Point{T}
           x::T
           y::T
       end
#NTuple{N,T} is a convenient alias for Tuple{Vararg{T,N}}, i.e. a tuple type containing exactly N elements of type T.

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