typeof(), eltype(),length(),ndims(),size() typeof()返回输出对象的类型(Int64, Float64, Bool or String);eltype()一般使用在Vector 或者Matrix里面,返回矩阵的数据类型; lenghth()返回matrix(tuple)里面元素的总个数; ndims()返回matrix(tuple)里面的维度数
正因为一维数组和二维数组都太常用了,所以 Julia 为它们的类型提供了别名。别名Vector{T}代表类型Array{T,1},也就是一维数组的类型。而别名Matrix{T}则代表了Array{T,2},即二维数组的类型。其中的 vector(向量)和 matrix(矩阵)都是线性代数中最核心的概念。从形状上来讲,向量就是由一个个值组成的纵队,而矩...
b = rand(10); #created a random vector of size 10x = A\b; #x is the solutions to the linear system Ax=b@show norm(A*x-b); 结果如下: 需要说一下,以上的A是矩阵类型(Matrix),b是向量(Vector),转置是所谓伴随类型(Adjoint),从中可见Julia的强类型特色,用typeof再看一下各自的类型 @show ...
1.1 一维数组(vector/list) julia> v = [1,2,3,4]# 逗号分隔的语法用于创建一维数组4-elementArray{Int64,1}:1234 向量,指列向量,Julia 使用的是 Fortran Order,各种操作都是列优先于行的。(和 numpy 相反,numpy 是 C Order 的,行优先于列) 1.2. 二维数组(table/matrix) julia> mat = [1234]# 空...
[1, 2, 3]: Vector{Int64}, (3,) [1 2 3]: Matrix{Int64}, (1, 3) [[1], [2], [3]]: Vector{Vector{Int64}}, (3,) 1. 2. 3. 除了逗号的这个区别之外,Julia中的size函数返回的是这个数组的shape,而Python的Numpy中的size函数返回的是这个数组的元素的总个数,而shape就是通过shape参量来...
The author studies matrix valued functions analytic in the unit disk and such that 1-S * (z)S(z)>0 for any z, |z|<1. The main result is a generalization of a theorem due to Carathéodory: let f be a vector and let lim r→1-0 f * (1-S * (r)S(r))(1-r 2 ) -1 f...
4A1D = cu([1, 2, 3]) # cl for CLArrays 5A1D = fill(CuArray{Int}, 0, (100,)) # CLArray for CLArrays 6# Float32 array - Float32 is usually preferred and can be up to 30x faster on most GPUs than Float64 7diagonal_matrix = CuArray{Float32}(I, 100, 100) 8filled = fi...
Julia Furst submitted Solution 7203545 to Problem 6. Select every other element of a vector on 13 Dec 2021Julia Furst submitted Solution 7203490 to Problem 26. Determine if input is odd on 13 Dec 2021Julia Furst received Commenter badge for Problem 1702. Maximum value in a matrix on 13...
functionlossfunc(x::AbstractVector, y::AbstractVector) return-sum(x.*log.(y) + (-x.+1).*log.(-y.+1))/length(x) end #renormalize to 0 <= data <= 1 globaldata =readbylines("./sklearn_mnist_784_data.txt")/255 #target = 1 iff digit is 0 ...
function public_preprocess(batch) ka = OffsetArray(0:7, 0:7) # Create feature extracted matrix I = [[batch[i′*3 .+ (1:7), j′*3 .+ (1:7), 1, k] for i′=ka, j′=ka] for k = 1:64] # Reshape into the ciphertext I = [[I[k][l...][i,j] for k=1:64, l=...