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
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)
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)
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))