r - 向量化函数 : cumsum with a decay parameter

Question

我写了 cumsum 的变体函数，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)
}

下面是一个demo，使用一个二元变量来说明效果:

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

编译这个函数会大大加快它的速度:

#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。有什么想法吗？

Answer 1

试试 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