--- /dev/null
+context("computeSynchrones")
+
+test_that("computeSynchrones behave as expected",
+{
+ # Generate 300 sinusoïdal series of 3 kinds: all series of indices == 0 mod 3 are the same
+ # (plus noise), all series of indices == 1 mod 3 are the same (plus noise) ...
+ n <- 300
+ x <- seq(0,9.5,0.1)
+ L <- length(x) #96 1/4h
+ K <- 3
+ s1 <- cos(x)
+ s2 <- sin(x)
+ s3 <- c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
+ #sum((s1-s2)^2) == 96
+ #sum((s1-s3)^2) == 58
+ #sum((s2-s3)^2) == 38
+ s <- list(s1, s2, s3)
+ series <- matrix(nrow=L, ncol=n)
+ for (i in seq_len(n))
+ series[,i] <- s[[I(i,K)]] + rnorm(L,sd=0.01)
+
+ getSeries <- function(indices) {
+ indices <- indices[indices <= n]
+ if (length(indices)>0) as.matrix(series[,indices]) else NULL
+ }
+
+ synchrones <- computeSynchrones(cbind(s1,s2,s3),getSeries,n,100,verbose=TRUE)
+
+ expect_equal(dim(synchrones), c(L,K))
+ for (i in 1:K)
+ {
+ # Synchrones are (for each medoid) sums of closest curves.
+ # Here, we expect exactly 100 curves of each kind to be assigned respectively to
+ # synchrone 1, 2 and 3 => division by 100 should be very close to the ref curve
+ expect_equal(synchrones[,i]/100, s[[i]], tolerance=0.01)
+ }
+})