epclust/tests/testthat/test-assignMedoids.R

   1 context("assignMedoids")
   2
   3 test_that("assignMedoids behave as expected",
   4 {
   5     # Generate a gaussian mixture
   6     n = 999
   7     L = 7
   8     medoids = cbind( rep(0,L), rep(-5,L), rep(5,L) )
   9     # short series...
  10     series = t( rbind( MASS::mvrnorm(n/3, medoids[,1], diag(L)),
  11         MASS::mvrnorm(n/3, medoids[,2], diag(L)),
  12         MASS::mvrnorm(n/3, medoids[,3], diag(L)) ) )
  13
  14     # With high probability, medoids indices should resemble 1,1,1,...,2,2,2,...,3,3,3,...
  15     mi = epclust:::assignMedoids(medoids, series)
  16     mi_ref = rep(1:3, each=n/3)
  17     expect_lt( mean(mi != mi_ref), 0.01 )
  18
  19     # Now with a random matrix, compare with (~trusted) R version
  20     series = matrix(runif(n*L, min=-7, max=7), nrow=L)
  21     mi = epclust:::assignMedoids(medoids, series)
  22     mi_ref = R_assignMedoids(medoids, series)
  23     expect_equal(mi, mi_ref)
  24 })
  25
  26 # R-equivalent of , requiring a matrix
  27 # (thus potentially breaking "fit-in-memory" hope)
  28 R_assignMedoids <- function(medoids, series)
  29 {
  30     nb_series = ncol(series) #series in columns
  31
  32     mi = rep(NA,nb_series)
  33     for (i in 1:nb_series)
  34         mi[i] <- which.min( colSums( sweep(medoids, 1, series[,i], '-')^2 ) )
  35
  36     mi
  37 }