'update'
[epclust.git] / pkg / tests / testthat / test-computeSynchrones.R
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1context("computeSynchrones")
2
3test_that("computeSynchrones behave as expected",
4{
5 # Generate 300 sinusoïdal series of 3 kinds: all series of indices == 0 mod 3 are the same
6 # (plus noise), all series of indices == 1 mod 3 are the same (plus noise) ...
7 n <- 300
8 x <- seq(0,9.5,0.1)
9 L <- length(x) #96 1/4h
10 K <- 3
11 s1 <- cos(x)
12 s2 <- sin(x)
13 s3 <- c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
14 #sum((s1-s2)^2) == 96
15 #sum((s1-s3)^2) == 58
16 #sum((s2-s3)^2) == 38
17 s <- list(s1, s2, s3)
18 series <- matrix(nrow=L, ncol=n)
19 for (i in seq_len(n))
20 series[,i] <- s[[I(i,K)]] + rnorm(L,sd=0.01)
21
22 getSeries <- function(indices) {
23 indices <- indices[indices <= n]
24 if (length(indices)>0) as.matrix(series[,indices]) else NULL
25 }
26
27 synchrones <- computeSynchrones(cbind(s1,s2,s3),getSeries,n,100,verbose=TRUE)
28
29 expect_equal(dim(synchrones), c(L,K))
30 for (i in 1:K)
31 {
32 # Synchrones are (for each medoid) sums of closest curves.
33 # Here, we expect exactly 100 curves of each kind to be assigned respectively to
34 # synchrone 1, 2 and 3 => division by 100 should be very close to the ref curve
35 expect_equal(synchrones[,i]/100, s[[i]], tolerance=0.01)
36 }
37})