X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2Ftests%2Ftestthat%2Ftest.clustering.R;h=e22835aaf8bb65cdbbdd395a3dbb7cc81da90826;hb=9f05a4a0b703deffd7bdb9cd99b0aaa2246a5c83;hp=a5dc3bdffc3516767c8b4b9c68f0af109d1aae89;hpb=0486fbadb122cb4d78c5d9f248c29800a59eb24e;p=epclust.git

diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test.clustering.R
index a5dc3bd..e22835a 100644
--- a/epclust/tests/testthat/test.clustering.R
+++ b/epclust/tests/testthat/test.clustering.R
@@ -62,7 +62,7 @@ test_that("clusteringTask1 behave as expected",
 	L = length(x) #96 1/4h
 	K1 = 60
 	s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
-	series = matrix(nrow=n, ncol=L)
+	series = matrix(nrow=L, ncol=n)
 	for (i in seq_len(n))
 		series[,i] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
 	getSeries = function(indices) {
@@ -72,7 +72,9 @@ test_that("clusteringTask1 behave as expected",
 	wf = "haar"
 	ctype = "absolute"
 	getContribs = function(indices) curvesToContribs(series[,indices],wf,ctype)
-	indices1 = clusteringTask1(1:n, getContribs, K1, 75, verbose=TRUE, parll=FALSE)
+	require("cluster", quietly=TRUE)
+	algoClust1 = function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med
+	indices1 = clusteringTask1(1:n, getContribs, K1, algoClust1, 75, verbose=TRUE, parll=FALSE)
 	medoids_K1 = getSeries(indices1)
 
 	expect_equal(dim(medoids_K1), c(L,K1))
@@ -92,16 +94,18 @@ test_that("clusteringTask2 behave as expected",
 	K2 = 3
 	#for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
 	s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
-	series = matrix(nrow=n, ncol=L)
+	series = matrix(nrow=L, ncol=n)
 	for (i in seq_len(n))
-		series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
+		series[,i] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
 	getRefSeries = function(indices) {
 		indices = indices[indices <= n]
 		if (length(indices)>0) series[,indices] else NULL
 	}
 	# Artificially simulate 60 medoids - perfect situation, all equal to one of the refs
 	medoids_K1 = bigmemory::as.big.matrix( sapply( 1:K1, function(i) s[[I(i,K1)]] ) )
-	medoids_K2 = clusteringTask2(medoids_K1, K2, getRefSeries, n, 75, verbose=TRUE, parll=FALSE)
+	algoClust2 = function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med
+	medoids_K2 = clusteringTask2(medoids_K1, K2, algoClust2, getRefSeries,
+		n, 75, sync_mean=TRUE, verbose=TRUE, parll=FALSE)
 
 	expect_equal(dim(medoids_K2), c(L,K2))
 	# Not easy to evaluate result: at least we expect it to be better than random selection of