Dear all,
you can find below my solution for sliding a window. Please find below the code
for the two alternatives and the benchmarks.
install.packages('caTools')
require(caTools)
do_sliding_for_a_window_duty_cycle <- function(DataToAnalyse, windowSize) {
data<-DataToAnalyse
out <- numeric()
elements<- numeric()
if (length(data[,1]) >= windowSize){
for (i in 1:(length(data[,1]) - windowSize +1 )) {
out[i] <- mean(data[i:(i + windowSize - 1), ])
elements[i]<-length(i:(i + windowSize - 1))
}
}
return (list(result=out , numberOfElements=elements, windowSize=windowSize ))
}
do_sliding_for_a_window_duty_cycle_alternative <- function(DataToAnalyse,
windowSize) {
result= runmean(rowMeans(DataToAnalyse),windowSize,endrule="trim",alg="C")
return( list(result= result, windowSize=windowSize))
}
DataToAnalyse<-matrix(data=round(seq(1:100000000)),nrow=10000,byrow=TRUE)
# How much time they need to runmean
system.time(a<-do_sliding_for_a_window_duty_cycle_alternative(DataToAnalyse,500))
system.time(b<-do_sliding_for_a_window_duty_cycle(DataToAnalyse,500))
# Are the results the same
# Print the difference of the resulted length.
print(length(a$result)-length(b$result))
# Difference of the returned values
boxplot(a$result-b$result)
My results on a normal dual core laptop cpu
system.time(a<-do_sliding_for_a_window_duty_cycle_alternative(DataToAnalyse,500))
user system elapsed
0.352 0.004 0.368
system.time(b<-do_sliding_for_a_window_duty_cycle(DataToAnalyse,500))
user system elapsed
2834.009 207.989 3080.434
> print(length(a$result)-length(b$result))
[1] 0
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