add some folders; more complete package structure

[valse.git] / pkg / tests / testthat / test-clustering.R

diff --git a/pkg/tests/testthat/test-clustering.R b/pkg/tests/testthat/test-clustering.R
new file mode 100644 (file)
index 0000000..2e3a431
--- /dev/null
+++ b/pkg/tests/testthat/test-clustering.R
@@ -0,0 +1,72 @@
+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)]) )
+})