compute_microbenchmark_univariate: Launch the benchmark to compare computational performances...

In bastienchassagnol/RGMMBench: A benchmark on Gaussian mixtures in R

Launch the benchmark to compare computational performances between packages

Description

Launch the benchmark to compare computational performances between packages

Usage

compute_microbenchmark_univariate(
  mixture_functions,
  id_scenario = NULL,
  sigma_values,
  mean_values,
  proportions,
  cores = getOption("mc.cores", parallel::detectCores()),
  prop_outliers = 0,
  nobservations = c(100, 1000, 10000),
  Nbootstrap = 100,
  epsilon = 10^-4,
  itmax = 500,
  nstart = 10L,
  short_iter = 200,
  short_eps = 10^-2,
  prior_prob = 0.05,
  initialisation_algorithms = c("kmeans", "quantiles", "random", "hc", "rebmix")
)

compute_microbenchmark_multivariate(
  mixture_functions,
  id_scenario = NULL,
  sigma_values,
  mean_values,
  proportions,
  cores = getOption("mc.cores", parallel::detectCores()),
  nobservations = c(50, 100, 200, 500, 1000, 2000, 5000, 10000),
  Nbootstrap = 100,
  epsilon = 10^-4,
  itmax = 500,
  nstart = 10L,
  short_iter = 200,
  short_eps = 10^-2,
  prior_prob = 0.05,
  initialisation_algorithms = c("kmeans", "random", "hc", "rebmix")
)

Arguments

mixture_functions	List of the packages to be compared (Id:name of the package, value: its options)
id_scenario	Possibility to set it to another number than one, to uniquely identify them
sigma_values, mean_values, proportions	the true parameters to be retrieved
cores	the number of cores to be used, by default all the available cores
prop_outliers	the proportion of outliers added in the simulation
nobservations	the number of observations drawn to generate the random sample
Nbootstrap	the number of bootstrap simulations and repetitions to perform
epsilon, itmax	respectively criterion threshold and maximal number of iterations to reach it
nstart, short_iter, short_eps	hyper-parameters to control the initialisation step
prior_prob	add minimal uncertainty on the cluster assignment returned by hierarchical clustering method
initialisation_algorithms	among 6 methods, which algorithms to be chosen for the initialisation phase

Value

a list with the running time of the initialisation and the EM estimation itself, as well as corresponding time curve representations

Author(s)

Bastien CHASSAGNOL

bastienchassagnol/RGMMBench documentation built on Oct. 26, 2023, 5:58 p.m.