'update'

[epclust.git] / epclust / tests / testthat / test-clustering.R

diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test-clustering.R
similarity index 55%
rename from epclust/tests/testthat/test.clustering.R
rename to epclust/tests/testthat/test-clustering.R
index 2f24d08..fa22dff 100644 (file)
--- a/epclust/tests/testthat/test.clustering.R
+++ b/epclust/tests/testthat/test-clustering.R
@@ -1,68 +1,5 @@
 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),
@@ -83,7 +20,7 @@ test_that("clusteringTask1 behave as expected",
 
        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
@@ -135,3 +72,18 @@ test_that("clusteringTask2 behave as expected",
        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 )
+}