as a shape tuple. At most one dimension may be specified with a `:`, in which case its length is computed such that its product with all the specified dimensions is equal to the length of the original array `A`. The total number of elements must not change.#...
通过给array函数传递Python的序列对象创建数组,如果传递的是多层嵌套的序列,将创建多 维数组 import numpy as np a = np.array([1, 2, 3, 4]) b =...下面的例子将数组c的shape改为(4,3),注意从(3,4)改为(4,3)并 不是对数组进行转置,而只是改变每个轴的大小,数组元素在内存中的位置并没有改变:...
function Base.similar(bc::Broadcast.Broadcasted{Broadcast.ArrayStyle{ArrayAndChar}}, ::Type{ElType}) where ElType # Scan the inputs for the ArrayAndChar: A = find_aac(bc) # Use the char field of A to create the output ArrayAndChar(similar(Array{ElType}, axes(bc)), A.char)...
function Base.similar(bc::Broadcast.Broadcasted{Broadcast.ArrayStyle{ArrayAndChar}}, ::Type{ElType}) where ElType # Scan the inputs for the ArrayAndChar: A = find_aac(bc) # Use the char field of A to create the output ArrayAndChar(similar(Array{ElType}, axes(bc)), A.char) end ...
, takebuf_array, takebuf_string, tan, tand, tanh, Task, task_local_storage, tempdir, tempname, TextDisplay, throw, tic, time, timedwait, Timer, time_ns, toc, toq, touch, trace, trailing_ones, trailing_zeros, transpose, transpose!, Tridiagonal, trigamma, tril, tril!, triu, triu!, ...
functionbench_match_smem(cfg, phaL, phaR, w, h, winSize, pha_dif)texarr2D = CuTextureArray(phaR) tex2D = CuTexture(texarr2D; interpolation = CUDA.LinearInterpolation())cp, minv, maxv = cfg.cpdiff, cfg.minv, cfg.maxv colstart, colend = cfg.colstart, cfg.colend ...
Breadcrumbs julia /base / slicearray.jlTop File metadata and controls Code Blame 245 lines (192 loc) · 7.24 KB Raw """ AbstractSlices{S,N} <: AbstractArray{S,N} Supertype for arrays of slices into a parent array over some dimension(s), returning views that select all the da...
texarr2D = CuTextureArray(phaR) tex2D = CuTexture(texarr2D; interpolation = CUDA.LinearInterpolation()) cp, minv, maxv = cfg.cpdiff, cfg.minv, cfg.maxv colstart, colend = cfg.colstart, cfg.colend rowstart, rowend = cfg.rowstart, cfg.rowend ...
a = rand(3,3)typeof(a)Array(Float64,2) 矩阵操作(Julia在矩阵操作方面跟MATLAB基本类似,函数用法也基本一致) 矩阵合并 x = np.ones((2,3))y = np.zeros((2,3))np.hstack((x,y))np.vstack((x,y)) x = ones(2,3)y = zeros(2,3)[x,y]或[x y][x;y] x = ones(2,3)y = zer...
深度学习库 Flux 和 GPU 计算库 CuArray 基本稳定下来 ,周边项目更新迅速,比如常用的预训练模型也都可以在 Julia 社区中找到靠谱的库了(如 MetalHead )。当然, 周边项目 的快速迭代也会导致一些库 动不动就报错 (甚至在安装时都要费一番功夫)。另外比较有特点的是,大部分常用的 Python 库都有 PyCall 封装 ...