8347fb6fbf501a01d4e631fdc58f62bbec195658
[epclust.git] / epclust / tests / testthat / test.computeMedoidsIndices.R
1 context("computeMedoidsIndices")
2
3 test_that("computeMedoidsIndices 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 require("bigmemory", quietly=TRUE)
16 mi = epclust:::computeMedoidsIndices(bigmemory::as.big.matrix(medoids)@address, series)
17 mi_ref = rep(1:3, each=n/3)
18 expect_lt( mean(mi != mi_ref), 0.01 )
19
20 # Now with a random matrix, compare with (trusted) R version
21 series = matrix(runif(n*L, min=-7, max=7), nrow=L)
22 mi = epclust:::computeMedoidsIndices(bigmemory::as.big.matrix(medoids)@address, series)
23 mi_ref = R_computeMedoidsIndices(medoids, series)
24 expect_equal(mi, mi_ref)
25 })