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
}