X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2Ftests%2Ftestthat%2Ftest-clustering.R;fp=pkg%2Ftests%2Ftestthat%2Ftest-clustering.R;h=0000000000000000000000000000000000000000;hp=2e3a4315f600766c7016ff2e5225071c2b17becd;hb=0eb161e3f3d018bce7d98fc85622d14910f89d43;hpb=2279a641f2bee1db586e7ab1e13726d111d5daaf

diff --git a/pkg/tests/testthat/test-clustering.R b/pkg/tests/testthat/test-clustering.R
deleted file mode 100644
index 2e3a431..0000000
--- a/pkg/tests/testthat/test-clustering.R
+++ /dev/null
@@ -1,72 +0,0 @@
-context("clustering")
-
-test_that("clusteringTask1 behave as expected",
-{
-	# Generate 60 reference sinusoïdal series (medoids to be found),
-	# and sample 900 series around them (add a small noise)
-	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) as.matrix(series[,indices]) else NULL
-	}
-
-	wf <- "haar"
-	ctype <- "absolute"
-	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)$id.med
-	indices1 <- clusteringTask1(1:n, getContribs, K1, algoClust1, 140, verbose=TRUE, parll=FALSE)
-	medoids_K1 <- getSeries(indices1)
-
-	expect_equal(dim(medoids_K1), c(L,K1))
-	# Not easy to evaluate result: at least we expect it to be better than random selection of
-	# medoids within initial series
-	distor_good <- computeDistortion(series, medoids_K1)
-	for (i in 1:3)
-		expect_lte( distor_good, computeDistortion(series,series[,sample(1:n, K1)]) )
-})
-
-test_that("clusteringTask2 behave as expected",
-{
-	# Same 60 reference sinusoïdal series than in clusteringTask1 test,
-	# but this time we consider them as medoids - skipping stage 1
-	# Here also we sample 900 series around the 60 "medoids"
-	n <- 900
-	x <- seq(0,9.5,0.1)
-	L <- length(x) #96 1/4h
-	K1 <- 60
-	K2 <- 3
-	#for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
-	s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
-	series <- matrix(nrow=L, ncol=n)
-	for (i in seq_len(n))
-		series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01)
-
-	getSeries <- function(indices) {
-		indices <- indices[indices <= n]
-		if (length(indices)>0) as.matrix(series[,indices]) else NULL
-	}
-
-	# Perfect situation: all medoids "after stage 1" are ~good
-	algoClust2 <- function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med
-	indices2 <- clusteringTask2(1:K1, getSeries, K2, algoClust2, 210, 3, 4, 8, "little",
-		verbose=TRUE, parll=FALSE)
-	medoids_K2 <- getSeries(indices2)
-
-	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
-	# synchrones within 1...K1 (from where distances computations + clustering was run)
-	distor_good <- computeDistortion(series, medoids_K2)
-#TODO: This fails; why?
-#	for (i in 1:3)
-#		expect_lte( distor_good, computeDistortion(series, series[,sample(1:K1,3)]) )
-})