The goal of this vignette is to show how to use custom asymptotic references. As an example, we explore the differences in asymptotic time complexity between different implementations of binary segmentation.

The code below uses the following arguments:

`N`

is a numeric vector of data sizes,`setup`

is an R expression to create the data,- the other arguments have names to identify them in the results, and values which are R expressions to time,

```
library(data.table)
seg.result <- atime::atime(
N=2^seq(2, 20),
setup={
max.segs <- as.integer(N/2)
max.changes <- max.segs-1L
set.seed(1)
data.vec <- 1:N
},
"changepoint\n::cpt.mean"={
cpt.fit <- changepoint::cpt.mean(data.vec, method="BinSeg", Q=max.changes)
sort(c(N,cpt.fit@cpts.full[max.changes,]))
},
"binsegRcpp\nmultiset"={
binseg.fit <- binsegRcpp::binseg(
"mean_norm", data.vec, max.segs, container.str="multiset")
sort(binseg.fit$splits$end)
},
"fpop::\nmultiBinSeg"={
mbs.fit <- fpop::multiBinSeg(data.vec, max.changes)
sort(c(mbs.fit$t.est, N))
},
"wbs::sbs"={
wbs.fit <- wbs::sbs(data.vec)
split.dt <- data.table(wbs.fit$res)[order(-min.th, scale)]
sort(split.dt[, c(N, cpt)][1:max.segs])
},
"binsegRcpp\nlist"={
binseg.fit <- binsegRcpp::binseg(
"mean_norm", data.vec, max.segs, container.str="list")
sort(binseg.fit$splits$end)
})
plot(seg.result)
#> Warning in ggplot2::scale_y_log10("median line, min/max band"): log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
```

The plot method creates a log-log plot of median time and memory vs
data size, for each of the specified R expressions.
The plot above shows some speed differences between binary
segmentation algorithms, but they could be even easier to see for
larger data sizes (exercise for the reader: try modifying the `N`

and
`seconds.limit`

arguments). You can also see that memory usage is much
larger for changepoint than for the other packages.

You can use
`references_best`

to get a tall/long data table that can be plotted to
show both empirical time and memory complexity:

```
seg.best <- atime::references_best(seg.result)
plot(seg.best)
#> Warning in ggplot2::scale_y_log10(""): log-10 transformation introduced
#> infinite values.
```

The figure above shows asymptotic references which are best fit for each expression. We do the best fit by adjusting each reference to the largest N, and then ranking each reference by distance to the measurement of the second to largest N.

```
(seg.pred <- predict(seg.best))
#> atime_prediction object
#> unit expr.name unit.value N
#> <char> <char> <num> <num>
#> 1: seconds changepoint\n::cpt.mean 0.01 521.5553
#> 2: seconds binsegRcpp\nmultiset 0.01 13118.4553
#> 3: seconds fpop::\nmultiBinSeg 0.01 20115.8351
#> 4: seconds wbs::sbs 0.01 18492.6648
#> 5: seconds binsegRcpp\nlist 0.01 3389.8913
plot(seg.pred)
#> Warning in ggplot2::scale_x_log10("N", breaks = meas[,
#> 10^seq(ceiling(min(log10(N))), : log-10 transformation introduced infinite
#> values.
```

If you have one or more expected time complexity classes that you want
to compare with your empirical measurements, you can use the
`fun.list`

argument. Note that each function in that list should take
as input the data size `N`

and output log base 10 of the reference
function, as below:

```
my.refs <- list(
"N \\log N"=function(N)log10(N) + log10(log(N)),
"N^2"=function(N)2*log10(N),
"N^3"=function(N)3*log10(N))
my.best <- atime::references_best(seg.result, fun.list=my.refs)
plot(my.best)
#> Warning in ggplot2::scale_y_log10(""): log-10 transformation introduced
#> infinite values.
```

From the plot above you should be able to see the asymptotic time
complexity class of each algorithm. Keep in mind that the plot method
displays only the next largest and next smallest reference lines, from those specified in `fun.list`

.

- the fastest packages are log-linear (fpop, wbs, binsegRcpp),
- that the binsegRcpp implementation that uses the list container is much slower (quadratic).
- the changepoint package uses a super-quadratic algorithm (actually cubic, which you could see if you increase N even larger).

- increase
`seconds.limit`

to see the differences more clearly. - compute and plot asymptotic references for memory instead of time (including expected memory complexity, linear).