Int" julia> gen1(1) "original definition" julia> gen2(1) "definition for Int" 生成函数的每个方法都有自己的已定义函数视图： julia> @generated gen1(x::Real) = f(x); julia> gen1(1) "definition for Type{Int}" 上例中的生成函数 foo 将输入索引向量存储在 SubArray 对象中， 该对象稍后可用于间接索引原始数组。通过将 @views 宏放在表达式或代码块之前，该表达式中的任 何 array [...] 切片将被转换为创建一个 SubArray 视图。 BitArray 是节省空间“压缩”的布尔数组，每个比特（bit）存储一个布尔值。它们可以类似于 Array{Bool} 数组（每个字节（byte）存储一个布尔值），并且可以分别通过 Array(bitarray) 列序优先方式访问数组的原因相同（请参见上文）。由于不按顺序访问内存，无规律的访问方式和不 连续的视图可能会大大减慢数组上的计算速度。 在对无规律访问的数据进行操作前，将其复制到连续的数组中可能带来巨大的加速，正如下例所示。 其中，矩阵和向量在相乘前会访问其 800,000 个已被随机混洗的索引处的值。将视图复制到普通数 组会加速乘法，即使考虑了复制操作的成本。 julia> using Random

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3);CHAPTER row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________ 4 = elements in each 4d slice (4,) ⇒ shape = ((2, 1, 1), (3, 1), (4,), IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3);CHAPTER row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________ 4 = elements in each 4d slice (4,) ⇒ shape = ((2, 1, 1), (3, 1), (4,), IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3);CHAPTER row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________ 4 = elements in each 4d slice (4,) ⇒ shape = ((2, 1, 1), (3, 1), (4,), IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3); julia> row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________ 4 = elements in each 4d slice (4,) ⇒ shape = ((2, 1, 1), (3, 1), (4,), IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3); julia> row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________CHAPTER 48. ARRAYS 1088 4 = elements in each 4d slice (4,) ⇒ shape = ((2 IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3); julia> row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________CHAPTER 48. ARRAYS 1087 4 = elements in each 4d slice (4,) ⇒ shape = ((2 IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3); julia> row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________CHAPTER 48. ARRAYS 1087 4 = elements in each 4d slice (4,) ⇒ shape = ((2 IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3); julia> row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________CHAPTER 47. ARRAYS 1079 4 = elements in each 4d slice (4,) ⇒ shape = ((2 IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

expression A[5] returns the value 5. Julia allows you to combine these styles of indexing: for example, a 3d array A3 can be indexed as A3[i,j], in which case i is interpreted as a cartesian index for the first terms of cartesian, rather than linear, indexing. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64,3}, :, 1,