X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2Ftests%2Ftestthat%2Fhelper.clustering.R;h=f39257e2b61df891ec37d5f36caee93cd170e1c8;hb=a52836b23adb4bfa6722642ec6426fb7b5f39650;hp=21a791a4b7a3a42fe71618d50f63c5025f277e38;hpb=0486fbadb122cb4d78c5d9f248c29800a59eb24e;p=epclust.git

diff --git a/epclust/tests/testthat/helper.clustering.R b/epclust/tests/testthat/helper.clustering.R
index 21a791a..f39257e 100644
--- a/epclust/tests/testthat/helper.clustering.R
+++ b/epclust/tests/testthat/helper.clustering.R
@@ -1,15 +1,18 @@
-#shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3, ... (is base==3)
+# Shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3, ... (is base==3)
 I = function(i, base)
 	(i-1) %% base + 1
 
-# NOTE: medoids can be a big.matrix
+# Compute the sum of (normalized) sum of squares of closest distances to a medoid.
+# Note: medoids can be a big.matrix
 computeDistortion = function(series, medoids)
 {
-	n = nrow(series) ; L = ncol(series)
-	distortion = 0.
 	if (bigmemory::is.big.matrix(medoids))
-		medoids = medoids[,]
+		medoids = medoids[,] #extract standard matrix
+
+	n = ncol(series) ; L = nrow(series)
+	distortion = 0.
 	for (i in seq_len(n))
 		distortion = distortion + min( colSums( sweep(medoids,1,series[,i],'-')^2 ) / L )
-	distortion / n
+
+	sqrt( distortion / n )
 }