X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2Ftests%2Ftestthat%2Ftest.clustering.R;h=c10f820c4770ac9b401d00e2c71c505418d8295b;hb=d9bb53c5e1392018bf67f92140edb10137f3423c;hp=527f6bda2544bbc80ce6330e25cecc03dda2fc23;hpb=56857861dc15088cf58e7438968fe5714b22168e;p=epclust.git

diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test.clustering.R
index 527f6bd..c10f820 100644
--- a/epclust/tests/testthat/test.clustering.R
+++ b/epclust/tests/testthat/test.clustering.R
@@ -1,25 +1,116 @@
 context("clustering")
 
-#TODO: load some dataset ASCII CSV
-#data_bin_file <<- "/tmp/epclust_test.bin"
-#unlink(data_bin_file)
-
-test_that("computeClusters1 behave as expected",
+test_that("computeSynchrones behave as expected",
 {
+	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) 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)
+		expect_equal(synchrones[,i]/100, s[[i]], tolerance=0.01)
 })
 
-test_that("computeSynchrones behave as expected",
+# Helper function to divide indices into balanced sets
+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("computeClusters2 behave as expected",
+test_that("clusteringTask1 behave as expected",
 {
+	n = 900
+	x = seq(0,9.5,0.1)
+	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=L, ncol=n)
+	for (i in seq_len(n))
+		series[,i] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
+	getSeries = function(indices) {
+		indices = indices[indices <= n]
+		if (length(indices)>0) series[,indices] else NULL
+	}
+	wf = "haar"
+	ctype = "absolute"
+	getContribs = function(indices) curvesToContribs(series[,indices],wf,ctype)
+	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))
+	# Not easy to evaluate result: at least we expect it to be better than random selection of
+	# medoids within initial series
+	distorGood = computeDistortion(series, medoids_K1)
+	for (i in 1:3)
+		expect_lte( distorGood, computeDistortion(series,series[,sample(1:n, K1)]) )
 })
 
-test_that("clusteringTask + computeClusters2 behave as expected",
+test_that("clusteringTask2 behave as expected",
 {
+	n = 900
+	x = seq(0,9.5,0.1)
+	L = length(x) #96 1/4h
+	K1 = 60
+	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=L, ncol=n)
+	for (i in seq_len(n))
+		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)]] ) )
+	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
+	# 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)]) )
 })