context("clustering")
-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)
-
- getRefSeries = function(indices) {
- indices = indices[indices <= n]
- if (length(indices)>0) as.matrix(series[,indices]) else NULL
- }
-
- synchrones = computeSynchrones(bigmemory::as.big.matrix(cbind(s1,s2,s3)), getRefSeries,
- n, 100, verbose=TRUE, parll=FALSE)
-
- 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)
- }
-})
-
-test_that("Helper function to spread indices work properly",
-{
- indices <- 1:400
-
- # bigger nb_per_set than length(indices)
- expect_equal(epclust:::.spreadIndices(indices,500), list(indices))
-
- # nb_per_set == length(indices)
- expect_equal(epclust:::.spreadIndices(indices,400), list(indices))
-
- # length(indices) %% nb_per_set == 0
- expect_equal(epclust:::.spreadIndices(indices,200),
- c( list(indices[1:200]), list(indices[201:400]) ))
- expect_equal(epclust:::.spreadIndices(indices,100),
- c( list(indices[1:100]), list(indices[101:200]),
- list(indices[201:300]), list(indices[301:400]) ))
-
- # length(indices) / nb_per_set == 1, length(indices) %% nb_per_set == 100
- expect_equal(epclust:::.spreadIndices(indices,300), list(indices))
- # length(indices) / nb_per_set == 2, length(indices) %% nb_per_set == 42
- repartition <- epclust:::.spreadIndices(indices,179)
- expect_equal(length(repartition), 2)
- expect_equal(length(repartition[[1]]), 179 + 21)
- expect_equal(length(repartition[[1]]), 179 + 21)
-})
-
test_that("clusteringTask1 behave as expected",
{
# Generate 60 reference sinusoïdal series (medoids to be found),
wf = "haar"
ctype = "absolute"
- getContribs = function(indices) curvesToContribs(series[,indices],wf,ctype)
+ 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
for (i in 1:3)
expect_lte( distor_good, computeDistortion(synchrones, synchrones[,sample(1:K1,3)]) )
})
+
+# Compute the sum of (normalized) sum of squares of closest distances to a medoid.
+# Note: medoids can be a big.matrix
+computeDistortion = function(series, medoids)
+{
+ if (bigmemory::is.big.matrix(medoids))
+ medoids = medoids[,] #extract standard matrix
+
+ n = ncol(series) ; L = nrow(series)
+ distortion = 0.
+ for (i in seq_len(n))
+ distortion = distortion + min( colSums( sweep(medoids,1,series[,i],'-')^2 ) / L )
+
+ sqrt( distortion / n )
+}