我正在使用bigmemory和biganalytics个包,并专门尝试计算big.matrix
个对象的平均值。 biganalytics的文档(例如?biganalytics
)表明mean()
应该可用于big.matrix
个对象,但这会失败:
x <- big.matrix(5, 2, type="integer", init=0,
+ dimnames=list(NULL, c("alpha", "beta")))
x
# An object of class "big.matrix"
# Slot "address":
# <pointer: 0x00000000069a5200>
x[,1] <- 1:5
x[,]
# alpha beta
# [1,] 1 0
# [2,] 2 0
# [3,] 3 0
# [4,] 4 0
# [5,] 5 0
mean(x)
# [1] NA
# Warning message:
# In mean.default(x) : argument is not numeric or logical: returning NA
虽然有些事情可行但是:
colmean(x)
# alpha beta
# 3 0
sum(x)
# [1] 15
mean(x[])
# [1] 1.5
mean(colmean(x))
# [1] 1.5
没有mean()
,似乎mean(colmean(x))
是下一个最好的事情:
# try it on something bigger
x = big.matrix(nrow=10000, ncol=10000, type="integer")
x[] <- c(1:(10000*10000))
mean(colmean(x))
# [1] 5e+07
mean(x[])
# [1] 5e+07
system.time(mean(colmean(x)))
# user system elapsed
# 0.19 0.00 0.19
system.time(mean(x[]))
# user system elapsed
# 0.28 0.11 0.39
据推测,mean()
可能会更快,尤其是对于具有大量列的矩形矩阵。
为什么mean()
对我不起作用的任何想法?
答案 0 :(得分:0)
好的 - 重新安装biganalytics
似乎解决了这个问题。
我现在有:
library("biganalytics")
x = big.matrix(10000,10000, type="integer")
for(i in 1L:10000L) { j = c(1L:10000L) ; x[i,] <- i*10000L + j }
mean(x)
# [1] 50010001
mean(x[,])
# [1] 50010001
mean(colmean(x))
# [1] 50010001
system.time(replicate(100, mean(x)))
# user system elapsed
# 20.16 0.02 20.23
system.time(replicate(100, mean(colmean(x))))
# user system elapsed
# 20.08 0.00 20.24
system.time(replicate(100, mean(x[,])))
# user system elapsed
# 31.62 12.88 44.74
一切都很好。我的sessionInfo()
现在是:
R version 3.1.0 (2014-04-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United Kingdom.1252 LC_CTYPE=English_United Kingdom.1252 LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C LC_TIME=English_United Kingdom.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] biganalytics_1.1.12 biglm_0.9-1 DBI_0.3.1 foreach_1.4.2 bigmemory_4.5.8 bigmemory.sri_0.1.3
loaded via a namespace (and not attached):
[1] codetools_0.2-8 iterators_1.0.7 Rcpp_0.11.2