'update'

[epclust.git] / epclust / tests / testthat / test-assignMedoids.R

diff --git a/epclust/tests/testthat/test-assignMedoids.R b/epclust/tests/testthat/test-assignMedoids.R
new file mode 100644 (file)
index 0000000..0192563
--- /dev/null
+++ b/epclust/tests/testthat/test-assignMedoids.R
@@ -0,0 +1,37 @@
+context("assignMedoids")
+
+test_that("assignMedoids behave as expected",
+{
+       # Generate a gaussian mixture
+       n = 999
+       L = 7
+       medoids = cbind( rep(0,L), rep(-5,L), rep(5,L) )
+       # short series...
+       series = t( rbind( MASS::mvrnorm(n/3, medoids[,1], diag(L)),
+               MASS::mvrnorm(n/3, medoids[,2], diag(L)),
+               MASS::mvrnorm(n/3, medoids[,3], diag(L)) ) )
+
+       # With high probability, medoids indices should resemble 1,1,1,...,2,2,2,...,3,3,3,...
+       mi = epclust:::assignMedoids(medoids, series)
+       mi_ref = rep(1:3, each=n/3)
+       expect_lt( mean(mi != mi_ref), 0.01 )
+
+       # Now with a random matrix, compare with (~trusted) R version
+       series = matrix(runif(n*L, min=-7, max=7), nrow=L)
+       mi = epclust:::assignMedoids(medoids, series)
+       mi_ref = R_assignMedoids(medoids, series)
+       expect_equal(mi, mi_ref)
+})
+
+# R-equivalent of , requiring a matrix
+# (thus potentially breaking "fit-in-memory" hope)
+R_assignMedoids <- function(medoids, series)
+{
+       nb_series = ncol(series) #series in columns
+
+       mi = rep(NA,nb_series)
+       for (i in 1:nb_series)
+               mi[i] <- which.min( colSums( sweep(medoids, 1, series[,i], '-')^2 ) )
+
+       mi
+}