option to run sequentially. various fixes. R CMD check OK

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

diff --git a/epclust/tests/testthat/test.clustering.R b/epclust/tests/testthat/test.clustering.R
index b6231e2..49afe60 100644 (file)
--- a/epclust/tests/testthat/test.clustering.R
+++ b/epclust/tests/testthat/test.clustering.R
@@ -7,7 +7,8 @@ I = function(i, base)
 test_that("computeClusters1 behave as expected",
 {
        require("MASS", quietly=TRUE)
-       library("clue", quietly=TRUE)
+       if (!require("clue", quietly=TRUE))
+               skip("'clue' package not available")
 
        # 3 gaussian clusters, 300 items; and then 7 gaussian clusters, 490 items
        n = 300
@@ -62,10 +63,11 @@ test_that("computeSynchrones behave as expected",
        for (i in seq_len(n))
                series[i,] = s[[I(i,K)]] + rnorm(L,sd=0.01)
        getRefSeries = function(indices) {
-               indices = indices[indices < n]
+               indices = indices[indices <= n]
                if (length(indices)>0) series[indices,] else NULL
        }
-       synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, 100)
+       synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, n, 100,
+               verbose=TRUE, parll=FALSE)
 
        expect_equal(dim(synchrones), c(K,L))
        for (i in 1:K)
@@ -94,23 +96,23 @@ test_that("computeClusters2 behave as expected",
        for (i in seq_len(n))
                series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
        getRefSeries = function(indices) {
-               indices = indices[indices < n]
+               indices = indices[indices <= n]
                if (length(indices)>0) series[indices,] else NULL
        }
        # Artificially simulate 60 medoids - perfect situation, all equal to one of the refs
        medoids_K1 = do.call(rbind, lapply( 1:K1, function(i) s[[I(i,K1)]] ) )
-       medoids_K2 = computeClusters2(medoids_K1, K2, getRefSeries, 75)
+       medoids_K2 = computeClusters2(medoids_K1, K2, getRefSeries, n, 75,
+               verbose=TRUE, parll=FALSE)
 
        expect_equal(dim(medoids_K2), c(K2,L))
        # Not easy to evaluate result: at least we expect it to be better than random selection of
        # medoids within 1...K1 (among references)
-       
        distorGood = computeDistortion(series, medoids_K2)
        for (i in 1:3)
                expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
 })
 
-test_that("clusteringTask + computeClusters2 behave as expected",
+test_that("clusteringTask1 + computeClusters2 behave as expected",
 {
        n = 900
        x = seq(0,9.5,0.1)
@@ -126,9 +128,12 @@ test_that("clusteringTask + computeClusters2 behave as expected",
                if (length(indices)>0) series[indices,] else NULL
        }
        wf = "haar"
-       getCoefs = function(indices) curvesToCoefs(series[indices,],wf)
-       medoids_K1 = getSeries( clusteringTask(1:n, getCoefs, K1, 75, 4) )
-       medoids_K2 = computeClusters2(medoids_K1, K2, getSeries, 120)
+       ctype = "absolute"
+       getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype)
+       medoids_K1 = getSeries( clusteringTask1(1:n, getContribs, K1, 75,
+               verbose=TRUE, parll=FALSE) )
+       medoids_K2 = computeClusters2(medoids_K1, K2, getSeries, n, 120,
+               verbose=TRUE, parll=FALSE)
 
        expect_equal(dim(medoids_K1), c(K1,L))
        expect_equal(dim(medoids_K2), c(K2,L))