context("clustering")
-#TODO: load some dataset ASCII CSV
-#data_bin_file <<- "/tmp/epclust_test.bin"
-#unlink(data_bin_file)
+#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)
+ require("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
+ 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=n, ncol=L)
+ 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) series[indices,] else NULL
+ }
+ synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, 100)
+ expect_equal(dim(synchrones), c(K,L))
+ for (i in 1:K)
+ expect_equal(synchrones[i,], s[[i]], tolerance=0.01)
})
+computeDistortion = function(series, medoids)
+{
+ 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
+}
+
test_that("computeClusters2 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)
+ 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
+ }
+ # 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)
+ 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",
{
+ n = 900
+ x = seq(0,9.5,0.1)
+ L = length(x) #96 1/4h
+ K1 = 60
+ K2 = 3
+ s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
+ series = matrix(nrow=n, ncol=L)
+ for (i in seq_len(n))
+ 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
+ }
+ 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)
+ expect_equal(dim(medoids_K1), c(K1,L))
+ 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),]) )
})
projects / epclust.git / blobdiff