如果在R中進行模擬后使用鼠標滾輪，Windows 7將無響應
[英]Windows 7 becomes unresponsive if mouse scroll wheel is used after a simulation in R
問題描述
我在Windows 7 64位中使用R 3.0.2。 在運行長度大於約100000模擬后，如果我在R控制台中使用鼠標滾輪，Windows將無限期凍結。
我曾經讓它坐了一個多星期沒有回應。 強制關閉是唯一的出路，它不會在Windows事件日志中注冊。 我試圖在其他程序中復制該問題，但它似乎只在R中出現。我已經嘗試了幾個版本的R，每個都卸載並重新安裝，使用了幾個不同的計算機鼠標和驅動程序，甚至重新安裝了Windows。 什么都沒有解決問題。
我能想到的其他一些共同方面（但尚未確定是因素）就是這樣
模擬通常在模擬期間向控制台打印迭代次數等
flush.console()例如使用flush.console()），和在模擬期間（但不是在完成時）內存使用很高。 計算機具有32GB RAM和兩個Intel Xeon E5-2687W CPU（8核，3.1GHz）。
可能導致此問題的一個示例是：
foo<-function(X, SD, N, sims){
output<-vector("list")
for(i in 1:sims){
output[[i]]<-rnorm(N, X, SD)
flush.console()
cat(paste("Iteration", i, ":", "\n",
"mean = ", round(mean(output[[i]]),1), "\n",
"sd = ", round(sd(output[[i]]), 1), "\n"))
}
return(output)
}
result<-foo(X=20, SD=2, N=100, sims=100) # but increase N or sims to > 100000
# Now used the mouse scroll wheel in the R console. Computer freezes.
# Can also do rm(list=ls()) after the simulation, then use scroll wheel... Computer still freezes.
1 個解決方案
解決方案1
0 2014-02-10 00:25:01
您的代碼在我的計算機上運行順暢（Rgui.exe和Tinn-R + Rterm.exe）。
Windows 7（64位）和R版本3.0.2已修補（2013-10-08 r64039）。
見下面的輸出：
R version 3.0.2 Patched (2013-10-08 r64039) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R é um software livre e vem sem GARANTIA ALGUMA.
Você pode redistribuí-lo sob certas circunstâncias.
Digite 'license()' ou 'licence()' para detalhes de distribuição.
R é um projeto colaborativo com muitos contribuidores.
Digite 'contributors()' para obter mais informações e
'citation()' para saber como citar o R ou pacotes do R em publicações.
Digite 'demo()' para demonstrações, 'help()' para o sistema on-line de ajuda,
ou 'help.start()' para abrir o sistema de ajuda em HTML no seu navegador.
Digite 'q()' para sair do R.
> foo<-function(X, SD, N, sims){
+ output<-vector("list")
+ for(i in 1:sims){
+ output[[i]]<-rnorm(N, X, SD)
+ flush.console()
+ cat(paste("Iteration", i, ":", "\n",
+ "mean = ", round(mean(output[[i]]),1), "\n",
+ "sd = ", round(sd(output[[i]]), 1), "\n"))
+ }
+ return(output)
+ }
>
> result<-foo(X=20, SD=2, N=100, sims=100) # but increase N or sims to > 100000
Iteration 1 :
mean = 20.1
sd = 2
Iteration 2 :
mean = 20
sd = 2
Iteration 3 :
mean = 19.9
sd = 2
Iteration 4 :
mean = 19.9
sd = 1.9
Iteration 5 :
mean = 19.5
sd = 2.1
Iteration 6 :
mean = 20
sd = 2.2
Iteration 7 :
mean = 20.2
sd = 2.2
Iteration 8 :
mean = 20
sd = 1.8
Iteration 9 :
mean = 19.5
sd = 2
Iteration 10 :
mean = 20.1
sd = 2.1
Iteration 11 :
mean = 19.8
sd = 2
Iteration 12 :
mean = 20
sd = 2
Iteration 13 :
mean = 20
sd = 1.8
Iteration 14 :
mean = 19.9
sd = 1.8
Iteration 15 :
mean = 20.2
sd = 2
Iteration 16 :
mean = 20.2
sd = 1.8
Iteration 17 :
mean = 20.5
sd = 2.2
Iteration 18 :
mean = 20
sd = 1.9
Iteration 19 :
mean = 19.8
sd = 1.8
Iteration 20 :
mean = 19.9
sd = 2.2
Iteration 21 :
mean = 20.2
sd = 2
Iteration 22 :
mean = 19.7
sd = 1.8
Iteration 23 :
mean = 19.8
sd = 2
Iteration 24 :
mean = 19.8
sd = 1.9
Iteration 25 :
mean = 19.9
sd = 2.1
Iteration 26 :
mean = 20.3
sd = 2.1
Iteration 27 :
mean = 19.6
sd = 2
Iteration 28 :
mean = 20
sd = 2.1
Iteration 29 :
mean = 20
sd = 2.2
Iteration 30 :
mean = 19.9
sd = 1.7
Iteration 31 :
mean = 19.9
sd = 1.8
Iteration 32 :
mean = 19.8
sd = 1.9
Iteration 33 :
mean = 20.1
sd = 2.1
Iteration 34 :
mean = 20.3
sd = 2.2
Iteration 35 :
mean = 20.2
sd = 2
Iteration 36 :
mean = 20.1
sd = 2
Iteration 37 :
mean = 19.8
sd = 2.1
Iteration 38 :
mean = 20
sd = 2
Iteration 39 :
mean = 20.1
sd = 1.9
Iteration 40 :
mean = 20.1
sd = 2
Iteration 41 :
mean = 19.8
sd = 2.1
Iteration 42 :
mean = 19.9
sd = 2
Iteration 43 :
mean = 19.8
sd = 1.8
Iteration 44 :
mean = 20.1
sd = 1.7
Iteration 45 :
mean = 20.1
sd = 1.8
Iteration 46 :
mean = 20.1
sd = 1.9
Iteration 47 :
mean = 20
sd = 2.2
Iteration 48 :
mean = 19.8
sd = 1.9
Iteration 49 :
mean = 19.9
sd = 2.1
Iteration 50 :
mean = 19.7
sd = 2
Iteration 51 :
mean = 19.9
sd = 2
Iteration 52 :
mean = 20.5
sd = 2
Iteration 53 :
mean = 20
sd = 2
Iteration 54 :
mean = 20.3
sd = 1.9
Iteration 55 :
mean = 19.9
sd = 1.9
Iteration 56 :
mean = 20.1
sd = 2.1
Iteration 57 :
mean = 20.3
sd = 2.2
Iteration 58 :
mean = 19.8
sd = 2.3
Iteration 59 :
mean = 20.2
sd = 2
Iteration 60 :
mean = 19.6
sd = 2.1
Iteration 61 :
mean = 19.9
sd = 1.9
Iteration 62 :
mean = 20.1
sd = 1.9
Iteration 63 :
mean = 20.1
sd = 2.3
Iteration 64 :
mean = 19.8
sd = 2.1
Iteration 65 :
mean = 20
sd = 2
Iteration 66 :
mean = 19.7
sd = 1.9
Iteration 67 :
mean = 20.1
sd = 2.1
Iteration 68 :
mean = 20.2
sd = 2
Iteration 69 :
mean = 20.1
sd = 2
Iteration 70 :
mean = 20.2
sd = 2.1
Iteration 71 :
mean = 20.1
sd = 2
Iteration 72 :
mean = 20.2
sd = 2.1
Iteration 73 :
mean = 20.1
sd = 2
Iteration 74 :
mean = 20
sd = 2
Iteration 75 :
mean = 19.8
sd = 2.2
Iteration 76 :
mean = 20.1
sd = 2.2
Iteration 77 :
mean = 20.2
sd = 1.5
Iteration 78 :
mean = 20.1
sd = 2.2
Iteration 79 :
mean = 20.2
sd = 2.1
Iteration 80 :
mean = 20.1
sd = 2.1
Iteration 81 :
mean = 20
sd = 1.8
Iteration 82 :
mean = 20.4
sd = 2.1
Iteration 83 :
mean = 20.1
sd = 1.9
Iteration 84 :
mean = 20.1
sd = 2.1
Iteration 85 :
mean = 20.2
sd = 2
Iteration 86 :
mean = 19.8
sd = 2.1
Iteration 87 :
mean = 20.1
sd = 2
Iteration 88 :
mean = 19.9
sd = 2.1
Iteration 89 :
mean = 20
sd = 1.9
Iteration 90 :
mean = 19.8
sd = 2.2
Iteration 91 :
mean = 19.9
sd = 2
Iteration 92 :
mean = 20
sd = 2
Iteration 93 :
mean = 19.9
sd = 2.2
Iteration 94 :
mean = 19.8
sd = 1.8
Iteration 95 :
mean = 19.7
sd = 1.8
Iteration 96 :
mean = 20.6
sd = 1.8
Iteration 97 :
mean = 20.1
sd = 2
Iteration 98 :
mean = 19.8
sd = 1.9
Iteration 99 :
mean = 19.9
sd = 1.9
Iteration 100 :
mean = 19.8
sd = 1.8
>
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