-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)]) )
-})