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6ad3f3fd BA |
1 | context("computeMedoidsIndices") |
2 | ||
0486fbad | 3 | test_that("computeMedoidsIndices behave as expected", |
6ad3f3fd | 4 | { |
0486fbad BA |
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))) ) ) | |
6ad3f3fd | 13 | |
0486fbad BA |
14 | # With high probability, medoids indices should resemble 1,1,1,...,2,2,2,...,3,3,3,... |
15 | mi = epclust:::.computeMedoidsIndices(medoids, series) | |
16 | mi_ref = rep(1:3, each=n/3) | |
17 | expect_that( mean(mi != mi_ref) < 0.01 ) | |
6ad3f3fd | 18 | |
0486fbad BA |
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:::.computeMedoidsIndices(medoids, series) | |
22 | mi_ref = R_computeMedoidsIndices(medoids, series) | |
23 | expect_equal(mi, mi_ref) | |
6ad3f3fd | 24 | }) |