Goal if this vignette is to measure the run-time speed of calc_act
, for
RBeast issue #10.
library(RBeast)
Create a trace:
trace <- sin(seq(from = 0.0, to = 2.0 * pi, length.out = 10))
Every size, the size of trace
is doubled.
n_sizes <- 6 n_types <- 2 elapseds <- data.frame( type = as.factor(rep(c("cpp", "r"), times = n_sizes)), size = rep(NA, times = n_sizes * n_types), t_sec = rep(NA, times = n_sizes * n_types) ) for (i in seq(1, n_sizes)) { # Duplicate input trace <- c(trace, trace) # Measure again t_r <- rbenchmark::benchmark( RBeast::calc_act_r(trace, sample_interval = 2), replications = 1, columns = c("elapsed") )$elapsed t_cpp <- rbenchmark::benchmark( RBeast::calc_act(trace, sample_interval = 2), replications = 1, columns = c("elapsed") )$elapsed elapseds$size[(i * 2) - 1] <- length(trace) elapseds$size[i * 2] <- length(trace) elapseds$t_sec[(i * 2) - 1] <- t_cpp elapseds$t_sec[i * 2] <- t_r }
In a plot:
ggplot2::ggplot( data = elapseds, ggplot2::aes(x = size, y = t_sec, color = type) ) + ggplot2::geom_line() + ggplot2::geom_point() + ggplot2::ggtitle("cpp version is faster")
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