X-Git-Url: https://git.auder.net/?a=blobdiff_plain;ds=sidebyside;f=epclust%2Ftests%2Ftestthat%2Ftest.clustering.R;h=c10f820c4770ac9b401d00e2c71c505418d8295b;hb=d9bb53c5e1392018bf67f92140edb10137f3423c;hp=77faeb97822b42967fd96b819be59151ef2b2d22;hpb=0fe757f750f51e580d2c5a7b7f7df87cc405d12d;p=epclust.git

diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test.clustering.R
index 77faeb9..c10f820 100644
--- a/epclust/tests/testthat/test.clustering.R
+++ b/epclust/tests/testthat/test.clustering.R
@@ -21,11 +21,11 @@ test_that("computeSynchrones behave as expected",
 		if (length(indices)>0) series[,indices] else NULL
 	}
 	synchrones = computeSynchrones(bigmemory::as.big.matrix(cbind(s1,s2,s3)), getRefSeries,
-		n, 100, sync_mean=TRUE, verbose=TRUE, parll=FALSE)
+		n, 100, verbose=TRUE, parll=FALSE)
 
 	expect_equal(dim(synchrones), c(L,K))
 	for (i in 1:K)
-		expect_equal(synchrones[,i], s[[i]], tolerance=0.01)
+		expect_equal(synchrones[,i]/100, s[[i]], tolerance=0.01)
 })
 
 # Helper function to divide indices into balanced sets
@@ -73,8 +73,7 @@ test_that("clusteringTask1 behave as expected",
 	ctype = "absolute"
 	getContribs = function(indices) curvesToContribs(series[,indices],wf,ctype)
 	require("cluster", quietly=TRUE)
-	browser()
-	algoClust1 = function(contribs,K) cluster::pam(contribs,K,diss=FALSE)$id.med
+	algoClust1 = function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med
 	indices1 = clusteringTask1(1:n, getContribs, K1, algoClust1, 75, verbose=TRUE, parll=FALSE)
 	medoids_K1 = getSeries(indices1)
 
@@ -97,14 +96,16 @@ test_that("clusteringTask2 behave as expected",
 	s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
 	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, 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