2

我编写了该cumsum函数的一个变体,在添加当前值之前,我将先前的总和乘以衰减因子:

decay <- function(x, decay=0.5){
  for (i in 2:length(x)){
    x[i] <- x[i] + decay*x[(i-1)]
  }
  return(x)
}

这是一个演示,使用二进制变量使效果清晰:

set.seed(42)
Events <- sample(0:1, 50, replace=TRUE, prob=c(.7, .3))
plot(decay(Events), type='l')
points(Events)

绘图

编译这个函数会加快很多速度:

#Benchmark
library(compiler)
library(rbenchmark)
cumsum_decayCOMP <- cmpfun(cumsum_decay)
Events <- sample(0:1, 10000, replace=TRUE, prob=c(.7, .3))
benchmark(replications=rep(100, 1),
          cumsum_decay(Events),
          cumsum_decayCOMP(Events),
          columns=c('test', 'elapsed', 'replications', 'relative'))

                      test elapsed replications relative
1     cumsum_decay(Events)    3.28          100    6.979
2 cumsum_decayCOMP(Events)    0.47          100    1.000

但我怀疑矢量化会进一步改善它。有任何想法吗?

4

1 回答 1

3

试试这个filter功能:

filter.decay <- function(x, decay=0.5) filter(x, decay, method = "recursive")

它非常快:

#                       test elapsed replications relative
# 1     cumsum_decay(Events)    4.83          100    19.32
# 2 cumsum_decayCOMP(Events)    1.00          100     4.00
# 3     filter.decay(Events)    0.25          100     1.00
于 2012-11-02T18:52:19.860 回答