epclust/tests/testthat/test-computeSynchrones.R

   1 context("computeSynchrones")
   2
   3 test_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     getRefSeries = function(indices) {
  23         indices = indices[indices <= n]
  24         if (length(indices)>0) as.matrix(series[,indices]) else NULL
  25     }
  26
  27     synchrones = computeSynchrones(bigmemory::as.big.matrix(cbind(s1,s2,s3)), getRefSeries,
  28         n, 100, verbose=TRUE, parll=FALSE)
  29
  30     expect_equal(dim(synchrones), c(L,K))
  31     for (i in 1:K)
  32     {
  33         # Synchrones are (for each medoid) sums of closest curves.
  34         # Here, we expect exactly 100 curves of each kind to be assigned respectively to
  35         # synchrone 1, 2 and 3 => division by 100 should be very close to the ref curve
  36         expect_equal(synchrones[,i]/100, s[[i]], tolerance=0.01)
  37     }
  38 })