In nsdumont/jDirichletMixtureModels: Dirichlet Mixture Models for Clustering using Julia backend

knitr::opts_chunk$set(echo = TRUE)

library(tictoc)
library(jDirichletMixtureModels)
dmm.setup()

Time for Number of Data points

Getting computation times for this package for different numbers of data points.

model <- dmm.BaseModel("UnivariateNormalModel")

nums <- c(50,100,200,400)
times <- data.frame()
for (i in 1:length(nums)) {
  data <- rnorm(nums[i])
  t <- dmm.benchmark(model, data)
  times <- rbind(times, data.frame("N"=nums[i],t))
}

times

Comparing Packages

Getting computation times for this package and two other R packages: dpmixsim and dirichletprocess. These must be installed to run this.

library(tictoc)
library(dpmixsim)
library(dirichletprocess)


nums <- c(50,100,200,400)
times <- data.frame()
for (i in 1:length(nums)) {
  data <- rnorm(nums[i])

  model <- dmm.BaseModel("UnivariateNormalModel", data = data)
  jDMMTime <- dmm.benchmark(model, data, iters = 100, burnin=0)

  tic()
  dpmixsim(data, M=1.0, a=1.0,  b=1.0, upalpha = FALSE, maxiter = 100)
  dpmixTime <- toc()
  dpmixTime <- dpmixTime$toc-dpmixTime$tic

  dpObj <- DirichletProcessGaussian(data, g0Priors = c(0, 1, 1, 1), alphaPriors = c(2, 4))
  tic()
  Fit(dpObj, its=100, updatePrior = FALSE, progressBar = FALSE)
  dpTime <- toc()
  dpTime <- dpTime$toc-dpTime$tic

  times <- rbind(times, data.frame("N"=nums[i],jDMMTime, 
                                   "dpmixsim"= dpmixTime, "DirichletProcess"=dpTime))
}

times

nsdumont/jDirichletMixtureModels documentation built on May 23, 2019, 2:51 p.m.