X-Git-Url: https://git.auder.net/game/current/gitweb.css?a=blobdiff_plain;f=epclust%2Ftests%2Ftestthat%2Ftest-clustering.R;fp=epclust%2Ftests%2Ftestthat%2Ftest.clustering.R;h=fa22dff3cae04d3f605f6f2d5c3a01fff76e6f34;hb=40f12a2f66d06fd77183ea02b996f5c66f90761c;hp=2f24d0844695aa6f65fdfb3bf6a7b44be9ffab32;hpb=a52836b23adb4bfa6722642ec6426fb7b5f39650;p=epclust.git

diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test-clustering.R
similarity index 55%
rename from epclust/tests/testthat/test.clustering.R
rename to epclust/tests/testthat/test-clustering.R
index 2f24d08..fa22dff 100644
--- a/epclust/tests/testthat/test.clustering.R
+++ b/epclust/tests/testthat/test-clustering.R
@@ -1,68 +1,5 @@
 context("clustering")
 
-test_that("computeSynchrones behave as expected",
-{
-	# Generate 300 sinusoïdal series of 3 kinds: all series of indices == 0 mod 3 are the same
-	# (plus noise), all series of indices == 1 mod 3 are the same (plus noise) ...
-	n = 300
-	x = seq(0,9.5,0.1)
-	L = length(x) #96 1/4h
-	K = 3
-	s1 = cos(x)
-	s2 = sin(x)
-	s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
-	#sum((s1-s2)^2) == 96
-	#sum((s1-s3)^2) == 58
-	#sum((s2-s3)^2) == 38
-	s = list(s1, s2, s3)
-	series = matrix(nrow=L, ncol=n)
-	for (i in seq_len(n))
-		series[,i] = s[[I(i,K)]] + rnorm(L,sd=0.01)
-
-	getRefSeries = function(indices) {
-		indices = indices[indices <= n]
-		if (length(indices)>0) as.matrix(series[,indices]) else NULL
-	}
-
-	synchrones = computeSynchrones(bigmemory::as.big.matrix(cbind(s1,s2,s3)), getRefSeries,
-		n, 100, verbose=TRUE, parll=FALSE)
-
-	expect_equal(dim(synchrones), c(L,K))
-	for (i in 1:K)
-	{
-		# Synchrones are (for each medoid) sums of closest curves.
-		# Here, we expect exactly 100 curves of each kind to be assigned respectively to
-		# synchrone 1, 2 and 3 => division by 100 should be very close to the ref curve
-		expect_equal(synchrones[,i]/100, s[[i]], tolerance=0.01)
-	}
-})
-
-test_that("Helper function to spread indices work properly",
-{
-	indices <- 1:400
-
-	# bigger nb_per_set than length(indices)
-	expect_equal(epclust:::.spreadIndices(indices,500), list(indices))
-
-	# nb_per_set == length(indices)
-	expect_equal(epclust:::.spreadIndices(indices,400), list(indices))
-
-	# length(indices) %% nb_per_set == 0
-	expect_equal(epclust:::.spreadIndices(indices,200),
-		c( list(indices[1:200]), list(indices[201:400]) ))
-	expect_equal(epclust:::.spreadIndices(indices,100),
-		c( list(indices[1:100]), list(indices[101:200]),
-			list(indices[201:300]), list(indices[301:400]) ))
-
-	# length(indices) / nb_per_set == 1, length(indices) %% nb_per_set == 100
-	expect_equal(epclust:::.spreadIndices(indices,300), list(indices))
-	# length(indices) / nb_per_set == 2, length(indices) %% nb_per_set == 42
-	repartition <- epclust:::.spreadIndices(indices,179)
-	expect_equal(length(repartition), 2)
-	expect_equal(length(repartition[[1]]), 179 + 21)
-	expect_equal(length(repartition[[1]]), 179 + 21)
-})
-
 test_that("clusteringTask1 behave as expected",
 {
 	# Generate 60 reference sinusoïdal series (medoids to be found),
@@ -83,7 +20,7 @@ test_that("clusteringTask1 behave as expected",
 
 	wf = "haar"
 	ctype = "absolute"
-	getContribs = function(indices) curvesToContribs(series[,indices],wf,ctype)
+	getContribs = function(indices) curvesToContribs(as.matrix(series[,indices]),wf,ctype)
 
 	require("cluster", quietly=TRUE)
 	algoClust1 = function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med
@@ -135,3 +72,18 @@ test_that("clusteringTask2 behave as expected",
 	for (i in 1:3)
 		expect_lte( distor_good, computeDistortion(synchrones, synchrones[,sample(1:K1,3)]) )
 })
+
+# 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)
+{
+	if (bigmemory::is.big.matrix(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 )
+
+	sqrt( distortion / n )
+}