julia

龙芯.NET正式发布 开源共享与开发者共成长

由 被刻印的时光 ゝ 提交于 2020-12-25 13:36:57
2020年12月19日，2020中国. NET开发者大会于苏州盛大开幕。本次大会以“开源、共享、创新”为主题，以线下城市苏州为中心，覆盖北京、上海、深圳、广州、长沙、成都、厦门、胶东等地区，是中国 .NET 开发者的大聚会，线上+线下参会人数达数十万人，覆盖城市达10+个。峰会共包含5大会场，近50场热点技术专题，数万名开发者将就各类“ .NET 开发和产品设计”相关的前沿技术话题展开深度交流。 此次开发者大会上，龙芯.NET项目及JVM负责人敖琪博士发表了《龙芯.NET到来》主题演讲，并正式发布龙芯.NET。这意味着国产龙芯已支持.NET，具备更为灵活的部署能力，也将进一步完善龙芯软件生态开发体系。会上，微软全球开发平台事业部资深副总裁Julia Liuson女士特别提到：“中国的.NET社区也积极为.NET开源项目做出了很多贡献，其中特别提一下，对龙芯平台的移植是一个非常大的工程，谢谢龙芯团队。” 此次发布的龙芯.NET 3基于.NET Core 3.1，支持该版本具备的所有主要功能，包括GC、AOT等。CoreCLR、CoreFX、ASP.NET Core等库的测试通过情况与x64/arm64相当。同时支持龙芯CPU家族，包括龙芯3A4000/3A3000/3A2000单路多路、龙芯2K1000等。支持多款操作系统，包括Loongnix、Debian、UOS、麒麟等。后续

Hacker News 简讯 2020-10-09

由 微笑、不失礼 提交于 2020-12-24 03:58:25
最后更新时间: 2020-10-09 21:00 Memory Safe ‘Curl’ for a More Secure Internet - (abetterinternet.org) 内存安全的“Curl”可实现更安全的互联网 得分:26 | 评论:9 Matt Levine makes sense of Wall Street like none other - (nytimes.com) 马特莱文对华尔街的理解是独一无二的 得分:119 | 评论:34 Why the Arabic world turned away from science (2011) - (thenewatlantis.com) 阿拉伯世界为何远离科学（2011） 得分:80 | 评论:39 Microsoft is letting employees work from home permanently - (theverge.com) 微软允许员工永久在家工作 得分:68 | 评论:38 Iron, How Did They Make It, Part IVa: Steel Yourself - (acoup.blog) 铁，他们是怎么做的，第四部分：钢铁你自己 得分:143 | 评论:20 Faith in government declines when mobile internet

Julia :　进制之间转换

由 浪尽此生 提交于 2020-12-16 15:24:18
一　基本逻辑 # 10进制的668 =>如何转换成8制的string string ( 668,base = 8 ) string ( 668,base = 2 ) string ( 668,base = 2, pad = 6 ) string ( 668,base = 2, pad = 16 ) parse ( Int64, "2019" ,base = 10 ) # 将10进制的“2019”转换成int64 parse ( Int16, "2019" ,base = 10 ) # 将10进制的“2019”转换成int16 二　转换 1、 2进制 101010 => 10进制是多少　（42） n_2 = 101010 parse ( Int64,string ( n_2 ) ,base = 2 ) 　 2、10进制42 =>2 进制是多少 （101010） n_10 = 42 string ( n_10,base = 2 ) 来源： oschina 链接： https://my.oschina.net/u/4373067/blog/4810164

JuMP constraints involving matrix inverse

由 眉间皱痕 提交于 2020-12-14 22:39:28
问题 I'm attempting to solve for an n*n matrix U , which satisfies a variety of constraints, including some involving inverses of its sub-matrices. However, it seems that JuMP can't handle inverses, at least without some additional specification of invertibility. Here's an example of the problem with n=2 . using JuMP, Ipopt m = Model(with_optimizer(Ipopt.Optimizer)) A = [5 7; 7 10] B = [9 13; 13 19] C = [3 4; 4 6] nnodes = 2 @variable(m, U[1:nnodes, 1:nnodes]) A1 = U * A * U' B1 = U * B * U' C1 =

Julia Dataframe combine specific calculations and tranpose

由 廉价感情. 提交于 2020-12-12 05:40:35
问题 I need to do something quite specific and i'm trying to do it the good way , especially i want it to be optimized . So i have a DataFrame that look like this : v = ["x","y","z"][rand(1:3, 10)] df = DataFrame(Any[collect(1:10), v, rand(10)], [:USER_ID, :GENRE_MAIN, :TOTAL_LISTENED]) Row │ USER_ID GENRE_MAIN TOTAL_LISTENED │ Int64 String Float64 ─────┼───────────────────────────────────── 1 │ 1 x 0.237186 12 │ 1 y 0.237186 13 │ 1 x 0.254486 2 │ 2 z 0.920804 3 │ 3 y 0.140626 4 │ 4 x 0.653306 5 │

Julia Dataframe combine specific calculations and tranpose

由 隐身守侯 提交于 2020-12-12 05:39:33
问题 I need to do something quite specific and i'm trying to do it the good way , especially i want it to be optimized . So i have a DataFrame that look like this : v = ["x","y","z"][rand(1:3, 10)] df = DataFrame(Any[collect(1:10), v, rand(10)], [:USER_ID, :GENRE_MAIN, :TOTAL_LISTENED]) Row │ USER_ID GENRE_MAIN TOTAL_LISTENED │ Int64 String Float64 ─────┼───────────────────────────────────── 1 │ 1 x 0.237186 12 │ 1 y 0.237186 13 │ 1 x 0.254486 2 │ 2 z 0.920804 3 │ 3 y 0.140626 4 │ 4 x 0.653306 5 │

Inline Interactive Plots with Julia in jupyter notebook

由 。_饼干妹妹 提交于 2020-12-11 23:56:54
问题 when I use %matplotlib notebook import matplotlib.pyplot as plt I get interactive plots, i.e. I can also zoom into the figure. For julia this command does not seem to exist. Any ideas? 回答1: You can get interactive plots by using Plotly, either directly with Plotly.jl or through its Plots backend. 回答2: Today I am thinking about how to enlarge the plot window interactively without referring to python or plotly or something else in Jupiter notebook. And I find a good solution. It is to use the

How to write to multiple indices of an array at the same time in Julia?

由 安稳与你 提交于 2020-12-10 05:03:49
问题 I would like to see something like this working in Julia: using Distributed addprocs(4) @everywhere arr = Array{Int}(undef, 10) for i = 1:10 @spawn arr[i] = i end What is the proper way of doing this? 回答1: You have the following ways to parallelize the process. Threads (requires setting JULIA_NUM_THREADS system variable) arr = Array{Int}(undef, 10) Threads.@threads for i = 1:10 arr[i] = i end SharedArrays using Distributed, SharedArrays addprocs(4) arr = SharedVector{Int}(10) @sync

How to write to multiple indices of an array at the same time in Julia?

由 佐手、 提交于 2020-12-10 05:03:43
问题 I would like to see something like this working in Julia: using Distributed addprocs(4) @everywhere arr = Array{Int}(undef, 10) for i = 1:10 @spawn arr[i] = i end What is the proper way of doing this? 回答1: You have the following ways to parallelize the process. Threads (requires setting JULIA_NUM_THREADS system variable) arr = Array{Int}(undef, 10) Threads.@threads for i = 1:10 arr[i] = i end SharedArrays using Distributed, SharedArrays addprocs(4) arr = SharedVector{Int}(10) @sync

How to write to multiple indices of an array at the same time in Julia?

由 ⅰ亾dé卋堺 提交于 2020-12-10 05:03:08
问题 I would like to see something like this working in Julia: using Distributed addprocs(4) @everywhere arr = Array{Int}(undef, 10) for i = 1:10 @spawn arr[i] = i end What is the proper way of doing this? 回答1: You have the following ways to parallelize the process. Threads (requires setting JULIA_NUM_THREADS system variable) arr = Array{Int}(undef, 10) Threads.@threads for i = 1:10 arr[i] = i end SharedArrays using Distributed, SharedArrays addprocs(4) arr = SharedVector{Int}(10) @sync
订阅 julia