X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2Ftests%2Ftestthat%2Ftest.clustering.R;h=49afe601ad6a6b6eac7aed4cc216914ea8c2b8a4;hb=492cd9e74a79cbcc0ecde55fa3071a44b7e463dc;hp=a4d59d9d72ba0e62168f69e97fd988232e518770;hpb=8702eb86906bd6d59e07bb887e690a20f29be63f;p=epclust.git

diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test.clustering.R
index a4d59d9..49afe60 100644
--- a/epclust/tests/testthat/test.clustering.R
+++ b/epclust/tests/testthat/test.clustering.R
@@ -7,7 +7,8 @@ I = function(i, base)
 test_that("computeClusters1 behave as expected",
 {
 	require("MASS", quietly=TRUE)
-	require("clue", quietly=TRUE)
+	if (!require("clue", quietly=TRUE))
+		skip("'clue' package not available")
 
 	# 3 gaussian clusters, 300 items; and then 7 gaussian clusters, 490 items
 	n = 300
@@ -62,10 +63,11 @@ test_that("computeSynchrones behave as expected",
 	for (i in seq_len(n))
 		series[i,] = s[[I(i,K)]] + rnorm(L,sd=0.01)
 	getRefSeries = function(indices) {
-		indices = indices[indices < n]
+		indices = indices[indices <= n]
 		if (length(indices)>0) series[indices,] else NULL
 	}
-	synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, 100)
+	synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, n, 100,
+		verbose=TRUE, parll=FALSE)
 
 	expect_equal(dim(synchrones), c(K,L))
 	for (i in 1:K)
@@ -94,23 +96,23 @@ test_that("computeClusters2 behave as expected",
 	for (i in seq_len(n))
 		series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
 	getRefSeries = function(indices) {
-		indices = indices[indices < n]
+		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 = do.call(rbind, lapply( 1:K1, function(i) s[[I(i,K1)]] ) )
-	medoids_K2 = computeClusters2(medoids_K1, K2, getRefSeries, 75)
+	medoids_K2 = computeClusters2(medoids_K1, K2, getRefSeries, n, 75,
+		verbose=TRUE, parll=FALSE)
 
 	expect_equal(dim(medoids_K2), c(K2,L))
 	# Not easy to evaluate result: at least we expect it to be better than random selection of
 	# medoids within 1...K1 (among references)
-	
 	distorGood = computeDistortion(series, medoids_K2)
 	for (i in 1:3)
 		expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
 })
 
-test_that("clusteringTask + computeClusters2 behave as expected",
+test_that("clusteringTask1 + computeClusters2 behave as expected",
 {
 	n = 900
 	x = seq(0,9.5,0.1)
@@ -126,9 +128,12 @@ test_that("clusteringTask + computeClusters2 behave as expected",
 		if (length(indices)>0) series[indices,] else NULL
 	}
 	wf = "haar"
-	getCoefs = function(indices) curvesToCoefs(series[indices,],wf)
-	medoids_K1 = getSeries( clusteringTask(1:n, getCoefs, K1, 75, 4) )
-	medoids_K2 = computeClusters2(medoids_K1, K2, getSeries, 120)
+	ctype = "absolute"
+	getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype)
+	medoids_K1 = getSeries( clusteringTask1(1:n, getContribs, K1, 75,
+		verbose=TRUE, parll=FALSE) )
+	medoids_K2 = computeClusters2(medoids_K1, K2, getSeries, n, 120,
+		verbose=TRUE, parll=FALSE)
 
 	expect_equal(dim(medoids_K1), c(K1,L))
 	expect_equal(dim(medoids_K2), c(K2,L))