X-Git-Url: https://git.auder.net/?p=epclust.git;a=blobdiff_plain;f=epclust%2Ftests%2Ftestthat%2Ftest-assignMedoids.R;h=9b55d78d5029f716f1e56e38d0d7b7917ed8fe23;hp=0192563d48e431b278c69710ef9e33d33ffc0ddc;hb=282342bafdc9ff65c5df98c6e2304d63b33b9fb2;hpb=3c5a4b0880db63367a474a568e1322b3999932fe

diff --git a/epclust/tests/testthat/test-assignMedoids.R b/epclust/tests/testthat/test-assignMedoids.R
index 0192563..9b55d78 100644
--- a/epclust/tests/testthat/test-assignMedoids.R
+++ b/epclust/tests/testthat/test-assignMedoids.R
@@ -3,35 +3,17 @@ 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) )
+	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)),
+	require("MASS", quietly=TRUE)
+	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)
+	mi <- assignMedoids(series, medoids)
+	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
-}