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&2 behave as expected",
-{
- require("MASS", 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
- 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 = sapply(1:K, function(i) MASS::mvrnorm(cs, c(rep(0,(i-1)),5,rep(0,d-i)), Id))
- indices_medoids1 = computeClusters1(coefs, K, verbose=TRUE)
- indices_medoids2 = computeClusters2(dist(coefs), K, verbose=TRUE)
- # Get coefs assignments (to medoids)
- assignment1 = sapply(seq_len(n), function(i)
- which.min( colSums( sweep(coefs[,indices_medoids1],1,coefs[,i],'-')^2 ) ) )
- assignment2 = sapply(seq_len(n), function(i)
- which.min( colSums( sweep(coefs[,indices_medoids2],1,coefs[,i],'-')^2 ) ) )
- for (i in 1:K)
- {
- expect_equal(sum(assignment1==i), cs, tolerance=5)
- expect_equal(sum(assignment2==i), cs, tolerance=5)
- }
-
- costs_matrix1 = matrix(nrow=K,ncol=K)
- costs_matrix2 = 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_matrix1[i,j] = abs( mean(assignment1[((i-1)*cs+1):(i*cs)]) - j )
- costs_matrix2[i,j] = abs( mean(assignment2[((i-1)*cs+1):(i*cs)]) - j )
- }
- }
- permutation1 = as.integer( clue::solve_LSAP(costs_matrix1) )
- permutation2 = as.integer( clue::solve_LSAP(costs_matrix2) )
- for (i in 1:K)
- {
- expect_equal(
- mean(assignment1[((i-1)*cs+1):(i*cs)]), permutation1[i], tolerance=0.05)
- expect_equal(
- mean(assignment2[((i-1)*cs+1):(i*cs)]), permutation2[i], tolerance=0.05)
- }
- }
-})
-
test_that("computeSynchrones behave as expected",
{
n = 300
if (length(indices)>0) series[,indices] else NULL
}
synchrones = computeSynchrones(bigmemory::as.big.matrix(cbind(s1,s2,s3)), getRefSeries,
- n, 100, verbose=TRUE, parll=FALSE)
+ n, 100, sync_mean=TRUE, verbose=TRUE, parll=FALSE)
expect_equal(dim(synchrones), c(L,K))
for (i in 1:K)
expect_equal(synchrones[,i], s[[i]], tolerance=0.01)
})
-# NOTE: medoids can be a big.matrix
-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.
- if (bigmemory::is.big.matrix(medoids))
- medoids = medoids[,]
- for (i in seq_len(n))
- distortion = distortion + min( colSums( sweep(medoids,1,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("clusteringTask1 behave as expected",
{
for (i in 1:3)
expect_lte( distorGood, computeDistortion(series,medoids_K1[,sample(1:K1, K2)]) )
})
-
-#NOTE: rather redundant test
-#test_that("clusteringTask1 + clusteringTask2 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"
-# ctype = "absolute"
-# getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype)
-# require("bigmemory", quietly=TRUE)
-# indices1 = clusteringTask1(1:n, getContribs, K1, 75, verbose=TRUE, parll=FALSE)
-# medoids_K1 = bigmemory::as.big.matrix( getSeries(indices1) )
-# medoids_K2 = clusteringTask2(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))
-# # 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