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)$id.med indices1 <- clusteringTask1(1:n, getContribs, K1, algoClust1, 140, verbose=TRUE) medoids_K1 <- getSeries(indices1) expect_equal(dim(medoids_K1), c(L,K1)) # Not easy to evaluate result: at least we expect it to be better than random selection of # medoids within initial series distor_good <- computeDistortion(series, medoids_K1) for (i in 1:3) expect_lte( distor_good, computeDistortion(series,series[,sample(1:n, K1)]) ) }) test_that("clusteringTask2 behave as expected", { # Same 60 reference sinusoïdal series than in clusteringTask1 test, # but this time we consider them as medoids - skipping stage 1 # Here also we sample 900 series around the 60 "medoids" n <- 900 x <- seq(0,9.5,0.1) L <- length(x) #96 1/4h K1 <- 60 K2 <- 3 #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)} s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) ) series <- matrix(nrow=L, ncol=n) for (i in seq_len(n)) series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01) getSeries <- function(indices) { indices <- indices[indices <= n] if (length(indices)>0) as.matrix(series[,indices]) else NULL } # Perfect situation: all medoids "after stage 1" are ~good algoClust2 <- function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med indices2 <- clusteringTask2(1:K1, getSeries, K2, algoClust2, 210, 3, 4, 8, "little", verbose=TRUE) 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)]) ) })