Analyze Big-O algorithmic complexity of a network function

This function times a network function on generated networks of different sizes and densities. It returns an object of class bigo, which contains a tibble of runtime results, the name of the function being measured, and the number of runs over which the results are averaged.

Usage

bigo_net(fun, gen = "random", n, p, num_runs = 1)

Arguments

fun

The function to be investigated. It must take a network as its first argument.

gen

A character vector of the kinds of network to generate, each of which names a {manynet} function generate_*(), for example "random", "smallworld", or "scalefree". By default "random".

n

A numeric vector of the numbers of nodes in the generated networks.

p

A numeric vector of the values of the p argument of the generating function, for example the density for "random".

num_runs

The number of runs over which each runtime is averaged. By default 1.

Value

An object of class bigo containing the results of the runtime experiments: a tibble of runtimes, the function name, and the number of runs that the results are averaged over.

Examples

bigo_net(fun = manynet::net_nodes, gen = "random",
         n = c(10, 20), p = 0.1)
#> $runtimes
#> # A tibble: 2 × 4
#>   gen        n     p elapsed
#>   <chr>  <dbl> <dbl>   <dbl>
#> 1 random    10   0.1 0.00200
#> 2 random    20   0.1 0      
#> 
#> $function_name
#> [1] "manynet::net_nodes"
#> 
#> $num_runs
#> [1] 1
#> 
#> attr(,"class")
#> [1] "bigo"