context("clustering")
-#shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3, ... (is base==3)
-I = function(i, base)
- (i-1) %% base + 1
-
-test_that("computeClusters1 behave as expected",
-{
- require("MASS", quietly=TRUE)
- library("clue", quietly=TRUE)
-
- # 3 gaussian clusters, 300 items; and then 7 gaussian clusters, 490 items
- n = 300
- d = 5
- K = 3
- for (ndK in list( c(300,5,3), c(490,10,7) ))
- {
- n = ndK[1] ; d = ndK[2] ; K = ndK[3]
- cs = n/K #cluster size
- Id = diag(d)
- coefs = do.call(rbind,
- lapply(1:K, function(i) MASS::mvrnorm(cs, c(rep(0,(i-1)),5,rep(0,d-i)), Id)))
- indices_medoids = computeClusters1(coefs, K)
- # Get coefs assignments (to medoids)
- assignment = sapply(seq_len(n), function(i)
- which.min( rowSums( sweep(coefs[indices_medoids,],2,coefs[i,],'-')^2 ) ) )
- for (i in 1:K)
- expect_equal(sum(assignment==i), cs, tolerance=5)
-
- costs_matrix = matrix(nrow=K,ncol=K)
- for (i in 1:K)
- {
- for (j in 1:K)
- {
- # assign i (in result) to j (order 1,2,3)
- costs_matrix[i,j] = abs( mean(assignment[((i-1)*cs+1):(i*cs)]) - j )
- }
- }
- permutation = as.integer( clue::solve_LSAP(costs_matrix) )
- for (i in 1:K)
- {
- expect_equal(
- mean(assignment[((i-1)*cs+1):(i*cs)]), permutation[i], tolerance=0.05)
- }
- }
-})
-
test_that("computeSynchrones behave as expected",
{
n = 300
#sum((s1-s3)^2) == 58
#sum((s2-s3)^2) == 38
s = list(s1, s2, s3)
- series = matrix(nrow=n, ncol=L)
+ series = matrix(nrow=L, ncol=n)
for (i in seq_len(n))
- series[i,] = s[[I(i,K)]] + rnorm(L,sd=0.01)
+ series[,i] = s[[I(i,K)]] + rnorm(L,sd=0.01)
getRefSeries = function(indices) {
- indices = indices[indices < n]
- if (length(indices)>0) series[indices,] else NULL
+ indices = indices[indices <= n]
+ if (length(indices)>0) series[,indices] else NULL
}
- synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, 100)
+ synchrones = computeSynchrones(bigmemory::as.big.matrix(cbind(s1,s2,s3)), getRefSeries,
+ n, 100, sync_mean=TRUE, verbose=TRUE, parll=FALSE)
- expect_equal(dim(synchrones), c(K,L))
+ expect_equal(dim(synchrones), c(L,K))
for (i in 1:K)
- expect_equal(synchrones[i,], s[[i]], tolerance=0.01)
+ expect_equal(synchrones[,i], s[[i]], tolerance=0.01)
})
-computeDistortion = function(series, medoids)
+# Helper function to divide indices into balanced sets
+test_that("Helper function to spread indices work properly",
{
- n = nrow(series) ; L = ncol(series)
- distortion = 0.
- for (i in seq_len(n))
- distortion = distortion + min( rowSums( sweep(medoids,2,series[i,],'-')^2 ) / L )
- distortion / n
-}
+ 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("computeClusters2 behave as expected",
+test_that("clusteringTask1 behave as expected",
{
n = 900
x = seq(0,9.5,0.1)
L = length(x) #96 1/4h
K1 = 60
- K2 = 3
- #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
- series = matrix(nrow=n, ncol=L)
+ series = matrix(nrow=L, ncol=n)
for (i in seq_len(n))
- series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
- getRefSeries = function(indices) {
- indices = indices[indices < n]
- if (length(indices)>0) series[indices,] else NULL
+ series[,i] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
+ getSeries = function(indices) {
+ 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)
+ wf = "haar"
+ ctype = "absolute"
+ getContribs = function(indices) curvesToContribs(series[,indices],wf,ctype)
+ require("cluster", quietly=TRUE)
+ algoClust1 = function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med
+ indices1 = clusteringTask1(1:n, getContribs, K1, algoClust1, 75, verbose=TRUE, parll=FALSE)
+ medoids_K1 = getSeries(indices1)
- expect_equal(dim(medoids_K2), c(K2,L))
+ expect_equal(dim(medoids_K1), c(L,K1))
# Not easy to evaluate result: at least we expect it to be better than random selection of
- # medoids within 1...K1 (among references)
-
- distorGood = computeDistortion(series, medoids_K2)
+ # medoids within initial series
+ distorGood = computeDistortion(series, medoids_K1)
for (i in 1:3)
- expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
+ expect_lte( distorGood, computeDistortion(series,series[,sample(1:n, K1)]) )
})
-test_that("clusteringTask + computeClusters2 behave as expected",
+test_that("clusteringTask2 behave as expected",
{
n = 900
x = seq(0,9.5,0.1)
L = length(x) #96 1/4h
K1 = 60
K2 = 3
+ #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
- series = matrix(nrow=n, ncol=L)
+ series = matrix(nrow=L, ncol=n)
for (i in seq_len(n))
- series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
- getSeries = function(indices) {
+ series[,i] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
+ getRefSeries = function(indices) {
indices = indices[indices <= n]
- if (length(indices)>0) series[indices,] else NULL
+ if (length(indices)>0) series[,indices] else NULL
}
- wf = "haar"
- getCoefs = function(indices) curvesToCoefs(series[indices,],wf)
- medoids_K1 = getSeries( clusteringTask(1:n, getCoefs, K1, 75, 4) )
- medoids_K2 = computeClusters2(medoids_K1, K2, getSeries, 120)
+ # Artificially simulate 60 medoids - perfect situation, all equal to one of the refs
+ medoids_K1 = bigmemory::as.big.matrix( sapply( 1:K1, function(i) s[[I(i,K1)]] ) )
+ algoClust2 = function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med
+ medoids_K2 = clusteringTask2(medoids_K1, K2, algoClust2, getRefSeries,
+ n, 75, sync_mean=TRUE, verbose=TRUE, parll=FALSE)
- expect_equal(dim(medoids_K1), c(K1,L))
- expect_equal(dim(medoids_K2), c(K2,L))
+ expect_equal(dim(medoids_K2), c(L,K2))
# 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),]) )
+ expect_lte( distorGood, computeDistortion(series,medoids_K1[,sample(1:K1, K2)]) )
})
projects / epclust.git / blobdiff