Manjaro Linux上安装Julia包管理与案例测试

原创

不知名站长

修改于 2021-04-01 17:57:30

8960

修改于 2021-04-01 17:57:30

参考了参考链接1中的案例，我们来测试一下julia执行简单的张量网络缩并的功能。关于张量网络计算的背景知识，这里用julia来计算张量网络的话会依赖于Einsum这个第三方包，需要我们来手动安装。首先我们测试一下直接调用这个包的指令，如果这个包已经被安装了，那么调用就不会报错：

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julia> using Einsum
ERROR: ArgumentError: Package Einsum not found in current path:
- Run `import Pkg; Pkg.add("Einsum")` to install the Einsum package.

Stacktrace:
 [1] run_repl(::REPL.AbstractREPL, ::Any) at /build/julia/src/julia-1.5.4/usr/share/julia/stdlib/v1.5/REPL/src/REPL.jl:288

这里我们发现系统中是没有这个库的，而这里调用的时候也已经提示了我们安装这个包的方法，我们可以尝试直接按照这个指令来安装：

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julia> import Pkg

julia> Pkg.add("Einsum")
 Installing known registries into `~/.julia`
######################################################################## 100.0%
      Added registry `General` to `~/.julia/registries/General`
  Resolving package versions...
  Installed Compat ─ v3.25.0
  Installed Einsum ─ v0.4.1
Updating `~/.julia/environments/v1.5/Project.toml`
  [b7d42ee7] + Einsum v0.4.1
Updating `~/.julia/environments/v1.5/Manifest.toml`
  [34da2185] + Compat v3.25.0
  [b7d42ee7] + Einsum v0.4.1
  [2a0f44e3] + Base64
  [ade2ca70] + Dates
  [8bb1440f] + DelimitedFiles
  [8ba89e20] + Distributed
  [b77e0a4c] + InteractiveUtils
  [76f85450] + LibGit2
  [8f399da3] + Libdl
  [37e2e46d] + LinearAlgebra
  [56ddb016] + Logging
  [d6f4376e] + Markdown
  [a63ad114] + Mmap
  [44cfe95a] + Pkg
  [de0858da] + Printf
  [3fa0cd96] + REPL
  [9a3f8284] + Random
  [ea8e919c] + SHA
  [9e88b42a] + Serialization
  [1a1011a3] + SharedArrays
  [6462fe0b] + Sockets
  [2f01184e] + SparseArrays
  [10745b16] + Statistics
  [8dfed614] + Test
  [cf7118a7] + UUIDs
  [4ec0a83e] + Unicode

安装过程没有什么问题，那我们再次调用看看：

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julia> using Einsum
[ Info: Precompiling Einsum [b7d42ee7-0b51-5a75-98ca-779d3107e4c0]

调用没有问题，说明我们这个包是安装成功了。接下来正式测试一下张量网络缩并的案例：无损音乐下载

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julia> A = zeros(5,6,7)
5×6×7 Array{Float64,3}:
[:, :, 1] =
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0

[:, :, 2] =
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0

[:, :, 3] =
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0

[:, :, 4] =
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0

[:, :, 5] =
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0

[:, :, 6] =
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0

[:, :, 7] =
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0
 0.0  0.0  0.0  0.0  0.0  0.0

julia> X = randn(5,2)
5×2 Array{Float64,2}:
 -0.573591   0.550235
 -0.893529  -1.25679
 -0.338177   0.632082
 -0.304742   2.67068
 -0.171912  -0.714813

julia> Y = randn(6,2)
6×2 Array{Float64,2}:
 -0.609149  -0.815229
  0.199472   0.554751
 -0.562527   0.259988
 -1.65124    1.08916
 -0.625242  -0.0391435
 -0.943587  -0.695565

julia> Z = randn(7,2)
7×2 Array{Float64,2}:
  0.311165   0.555719
 -0.486201  -1.26421
 -1.90713    0.738125
 -1.26129   -0.274261
 -0.570305  -0.295527
 -0.182373  -0.0410972
 -0.213648  -0.12244

julia> @einsum A[i,j,k] = X[i,r]*Y[j,r]*Z[k,r]
5×6×7 Array{Float64,3}:
[:, :, 1] =
 -0.140556   0.134027   0.179899    0.627755  0.0996249   -0.0442743
  0.738739  -0.442911  -0.0251791  -0.30159   0.201178     0.748148
 -0.222257   0.173872   0.150518    0.556337  0.052044    -0.145031
 -1.15216    0.804416   0.439202    1.77305   0.00119409  -0.942843
  0.356423  -0.231037  -0.0731852  -0.344323  0.0489952    0.326778

[:, :, 2] =
  0.397202  -0.330261  -0.337728  -1.21813   -0.147139    0.220694
 -1.5599     0.968069   0.168698   1.01314   -0.333819   -1.51507
  0.551277  -0.410494  -0.300243  -1.14183   -0.0715246   0.400667
  2.66219   -1.84344   -0.96114   -3.92197    0.0395199   2.20862
 -0.787613   0.517985   0.187925   0.846224  -0.0876327  -0.70743

[:, :, 3] =
 -0.997453   0.443513  -0.509762  -1.36396  -0.699856  -1.3147
 -0.281771  -0.174709  -1.19977   -3.82422  -1.02915   -0.962687
 -0.773218   0.387471  -0.241501  -0.55681  -0.42151   -0.933083
 -1.96108    1.20951    0.185581   1.18738  -0.440543  -1.91956
  0.230419  -0.2273    -0.321604  -1.11604  -0.184337   0.0576331

[:, :, 4] =
 -0.317672   0.0605948  -0.446202   -1.35898   -0.446433  -0.577685
 -0.967509   0.416021   -0.544352   -1.48553   -0.718138  -1.30317
 -0.118501  -0.0110862  -0.28501    -0.89313   -0.259904  -0.281897
  0.362986  -0.329663   -0.406649   -1.43245   -0.211652   0.146789
 -0.291903   0.152008   -0.0710035  -0.144515  -0.143245  -0.34096

[:, :, 5] =
 -0.0667018  -0.0249559  -0.226291     -0.717264   -0.198165   -0.195562
 -0.6132      0.307691   -0.190091     -0.436916   -0.333152   -0.73918
  0.0347997  -0.0651548  -0.157056     -0.521917   -0.113275   -0.0520544
  0.537557   -0.403173   -0.302962     -1.1466     -0.0777703   0.384987
 -0.231936    0.136746   -0.000229789   0.0681898  -0.0695689  -0.239447

[:, :, 6] =
 -0.0452868   0.00832171  -0.0647238   -0.197362   -0.06452    -0.0829776
 -0.141371    0.0611584   -0.0782386   -0.212824   -0.103909   -0.189689
 -0.0163919  -0.00210828  -0.0414473   -0.130132   -0.0375447  -0.0401267
  0.0556227  -0.0498019   -0.0597991   -0.211314   -0.0304527   0.0239016
 -0.0430469   0.0225507   -0.00999881  -0.0197739  -0.0207526  -0.0500169

[:, :, 7] =
 -0.0197261  -0.0129295  -0.0864512   -0.275731   -0.0739839  -0.0687722
 -0.241735    0.123445   -0.0673792   -0.147621   -0.125382   -0.287166
  0.019081   -0.0285214  -0.0607641   -0.203596   -0.0421448  -0.0143435
  0.226918   -0.168415   -0.12164     -0.463661   -0.0279081   0.166014
 -0.0937235   0.0558792   0.00209385   0.0346776  -0.0263901  -0.0955337

在上面这个案例中，我们事先定义好了一个张量A用于存放计算结果，如果我们不事先定义的话，就需要按照以下示例来使用：

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julia> @einsum B[i,j,k] := X[i,r]*Y[j,r]*Z[k,r]
5×6×7 Array{Float64,3}:
[:, :, 1] =
 -0.140556   0.134027   0.179899    0.627755  0.0996249   -0.0442743
  0.738739  -0.442911  -0.0251791  -0.30159   0.201178     0.748148
 -0.222257   0.173872   0.150518    0.556337  0.052044    -0.145031
 -1.15216    0.804416   0.439202    1.77305   0.00119409  -0.942843
  0.356423  -0.231037  -0.0731852  -0.344323  0.0489952    0.326778

[:, :, 2] =
  0.397202  -0.330261  -0.337728  -1.21813   -0.147139    0.220694
 -1.5599     0.968069   0.168698   1.01314   -0.333819   -1.51507
  0.551277  -0.410494  -0.300243  -1.14183   -0.0715246   0.400667
  2.66219   -1.84344   -0.96114   -3.92197    0.0395199   2.20862
 -0.787613   0.517985   0.187925   0.846224  -0.0876327  -0.70743

[:, :, 3] =
 -0.997453   0.443513  -0.509762  -1.36396  -0.699856  -1.3147
 -0.281771  -0.174709  -1.19977   -3.82422  -1.02915   -0.962687
 -0.773218   0.387471  -0.241501  -0.55681  -0.42151   -0.933083
 -1.96108    1.20951    0.185581   1.18738  -0.440543  -1.91956
  0.230419  -0.2273    -0.321604  -1.11604  -0.184337   0.0576331

[:, :, 4] =
 -0.317672   0.0605948  -0.446202   -1.35898   -0.446433  -0.577685
 -0.967509   0.416021   -0.544352   -1.48553   -0.718138  -1.30317
 -0.118501  -0.0110862  -0.28501    -0.89313   -0.259904  -0.281897
  0.362986  -0.329663   -0.406649   -1.43245   -0.211652   0.146789
 -0.291903   0.152008   -0.0710035  -0.144515  -0.143245  -0.34096

[:, :, 5] =
 -0.0667018  -0.0249559  -0.226291     -0.717264   -0.198165   -0.195562
 -0.6132      0.307691   -0.190091     -0.436916   -0.333152   -0.73918
  0.0347997  -0.0651548  -0.157056     -0.521917   -0.113275   -0.0520544
  0.537557   -0.403173   -0.302962     -1.1466     -0.0777703   0.384987
 -0.231936    0.136746   -0.000229789   0.0681898  -0.0695689  -0.239447

[:, :, 6] =
 -0.0452868   0.00832171  -0.0647238   -0.197362   -0.06452    -0.0829776
 -0.141371    0.0611584   -0.0782386   -0.212824   -0.103909   -0.189689
 -0.0163919  -0.00210828  -0.0414473   -0.130132   -0.0375447  -0.0401267
  0.0556227  -0.0498019   -0.0597991   -0.211314   -0.0304527   0.0239016
 -0.0430469   0.0225507   -0.00999881  -0.0197739  -0.0207526  -0.0500169

[:, :, 7] =
 -0.0197261  -0.0129295  -0.0864512   -0.275731   -0.0739839  -0.0687722
 -0.241735    0.123445   -0.0673792   -0.147621   -0.125382   -0.287166
  0.019081   -0.0285214  -0.0607641   -0.203596   -0.0421448  -0.0143435
  0.226918   -0.168415   -0.12164     -0.463661   -0.0279081   0.166014
 -0.0937235   0.0558792   0.00209385   0.0346776  -0.0263901  -0.0955337

这里我们可以发现，julia的变量定义形式跟python是类似的，并不需要事先声明变量的具体类型。

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

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原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

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