context("computeMedoidsIndices")

test_that("computeMedoidsIndices 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:::.computeMedoidsIndices(medoids, series)
	mi_ref = rep(1:3, each=n/3)
	expect_that( 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:::.computeMedoidsIndices(medoids, series)
	mi_ref = R_computeMedoidsIndices(medoids, series)
	expect_equal(mi, mi_ref)
})