julia

问题 I create an optimization model in Julia-JuMP using the symbolic variables and constraints e.g. below using JuMP using CPLEX # model Mod = Model(CPLEX.Optimizer) # sets I = 1:2; # Variables x = @variable( Mod , [I] , base_name = "x" ) y = @variable( Mod , [I] , base_name = "y" ) # constraints Con1 = @constraint( Mod , [i in I] , 2 * x[i] + 3 * y[i] <= 100 ) # objective ObjFun = @objective( Mod , Max , sum( x[i] + 2 * y[i] for i in I) ) ; # solve optimize!(Mod) I guess JuMP creates the problem

问题 Hooking into rand has been easier in the old days... I think I followed the description in the docs, but it doesn't seem to like a sampler returning an array: using Random struct Shell{N} r0::Float64 r1::Float64 end Base.eltype(::Type{Shell{N}}) where {N} = Array{Float64, N} function Random.rand(rng::Random.AbstractRNG, d::Random.SamplerTrivial{Shell{N}}) where {N} # ignore the correctness of the sampling algorithm for now :) shell = d[] Δ = shell.r1 - shell.r0 θ = Δ .* randn(N) r = shell.r0

问题 I want to overwrite a function in Julia using its old definition. It seems the way to do this would be to clone the function and overwrite the original using the copy — something like the following. However, it appears deepcopy(f) just returns a reference to f , so this doesn't work. f(x) = x f_old = deepcopy(f) f(x) = 1 + f_old(x) How can I clone a function? Background: I'm interesting in writing a macro @override that allows me to override functions pointwise (or maybe even piecewise). fib

问题 I tried running the following code across multiple Julia REPL sessions, on MacOSX and Linux, and the outputs were always exactly the same (in the same order): julia> (Set([1, 2, 3, 4]), Dict(1=>2, 3=>4, 5=>6)) (Set([4, 2, 3, 1]), Dict(3 => 4,5 => 6,1 => 2)) Question: is this behavior guaranteed by the language, across sessions, platforms and versions, and for all hash-based collections? Notes: I understand that the output order may vary depending on the insertion order, especially if there

问题 I tried running the following code across multiple Julia REPL sessions, on MacOSX and Linux, and the outputs were always exactly the same (in the same order): julia> (Set([1, 2, 3, 4]), Dict(1=>2, 3=>4, 5=>6)) (Set([4, 2, 3, 1]), Dict(3 => 4,5 => 6,1 => 2)) Question: is this behavior guaranteed by the language, across sessions, platforms and versions, and for all hash-based collections? Notes: I understand that the output order may vary depending on the insertion order, especially if there

问题 Is there any way in Julia to execute an existing script step by step in REPL meanwhile being able to modify/plot arrays? (As in the case of Matlab debugging) Note: I am especially asking this for version 0.4 for which hopefully a new debugging system is being implemented. 回答1: You have a few options: The Debug package provides step-by-step debugging. It hasn't yet been updated for 0.4, however. Juno provides some nice functionality for executing specific lines, and includes integrated

问题 I want to find a maximum of several images: load them into an array and find a maximum along first dimension. Python code for example: import cv2 import sys import numpy as np imgs_paths = sys.argv[1:] imgs = list(map(cv2.imread, imgs_paths)) imgs_arr = np.array(imgs, dtype=np.float32) imgs_max = np.max(imgs_arr, 0) What I did is the following: using Colors, Images function im_to_array(im) img_array = permutedims(channelview(im), (2,3,1)) img_array = Float32.(img_array) return img_array end

问题 I want to find a maximum of several images: load them into an array and find a maximum along first dimension. Python code for example: import cv2 import sys import numpy as np imgs_paths = sys.argv[1:] imgs = list(map(cv2.imread, imgs_paths)) imgs_arr = np.array(imgs, dtype=np.float32) imgs_max = np.max(imgs_arr, 0) What I did is the following: using Colors, Images function im_to_array(im) img_array = permutedims(channelview(im), (2,3,1)) img_array = Float32.(img_array) return img_array end

问题 When I run my code one error occurs with my code setindex! not defined for WeakRefStrings.StringArray{String,1} CSV File here. using CSV EVDdata =CSV.read(raw"wikipediaEVDdatesconverted.csv") EVDdata[end-9:end,:] And the Error Code is here rows, cols = size(EVDdata) for j =1:cols for i = 1:rows if !isdigit(string(EVDdata[i, j])[1]) EVDdata[i,j] = 0 end end end I am working with Julia 1.4.1 on Jupter Notebook 回答1: setindex!(collection, item, inds...) is the function that colection[inds...] =

问题 When I run my code one error occurs with my code setindex! not defined for WeakRefStrings.StringArray{String,1} CSV File here. using CSV EVDdata =CSV.read(raw"wikipediaEVDdatesconverted.csv") EVDdata[end-9:end,:] And the Error Code is here rows, cols = size(EVDdata) for j =1:cols for i = 1:rows if !isdigit(string(EVDdata[i, j])[1]) EVDdata[i,j] = 0 end end end I am working with Julia 1.4.1 on Jupter Notebook 回答1: setindex!(collection, item, inds...) is the function that colection[inds...] =