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56857861 BA |
1 | context("clustering") |
2 | ||
e161499b | 3 | test_that("clusteringTask1 behave as expected", |
56857861 | 4 | { |
a52836b2 BA |
5 | # Generate 60 reference sinusoïdal series (medoids to be found), |
6 | # and sample 900 series around them (add a small noise) | |
8702eb86 BA |
7 | n = 900 |
8 | x = seq(0,9.5,0.1) | |
9 | L = length(x) #96 1/4h | |
10 | K1 = 60 | |
8702eb86 | 11 | s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) ) |
0fe757f7 | 12 | series = matrix(nrow=L, ncol=n) |
8702eb86 | 13 | for (i in seq_len(n)) |
37c82bba | 14 | series[,i] = s[[I(i,K1)]] + rnorm(L,sd=0.01) |
a52836b2 | 15 | |
e161499b | 16 | getSeries = function(indices) { |
492cd9e7 | 17 | indices = indices[indices <= n] |
a52836b2 | 18 | if (length(indices)>0) as.matrix(series[,indices]) else NULL |
8702eb86 | 19 | } |
a52836b2 | 20 | |
e161499b BA |
21 | wf = "haar" |
22 | ctype = "absolute" | |
40f12a2f | 23 | getContribs = function(indices) curvesToContribs(as.matrix(series[,indices]),wf,ctype) |
a52836b2 | 24 | |
0fe757f7 | 25 | require("cluster", quietly=TRUE) |
9f05a4a0 | 26 | algoClust1 = function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med |
0fe757f7 | 27 | indices1 = clusteringTask1(1:n, getContribs, K1, algoClust1, 75, verbose=TRUE, parll=FALSE) |
e161499b | 28 | medoids_K1 = getSeries(indices1) |
56857861 | 29 | |
37c82bba | 30 | expect_equal(dim(medoids_K1), c(L,K1)) |
8702eb86 | 31 | # Not easy to evaluate result: at least we expect it to be better than random selection of |
e161499b | 32 | # medoids within initial series |
a52836b2 | 33 | distor_good = computeDistortion(series, medoids_K1) |
8702eb86 | 34 | for (i in 1:3) |
a52836b2 | 35 | expect_lte( distor_good, computeDistortion(series,series[,sample(1:n, K1)]) ) |
56857861 BA |
36 | }) |
37 | ||
e161499b | 38 | test_that("clusteringTask2 behave as expected", |
56857861 | 39 | { |
a52836b2 BA |
40 | skip("Unexplained failure") |
41 | ||
42 | # Same 60 reference sinusoïdal series than in clusteringTask1 test, | |
43 | # but this time we consider them as medoids - skipping stage 1 | |
44 | # Here also we sample 900 series around the 60 "medoids" | |
8702eb86 BA |
45 | n = 900 |
46 | x = seq(0,9.5,0.1) | |
47 | L = length(x) #96 1/4h | |
48 | K1 = 60 | |
49 | K2 = 3 | |
e161499b | 50 | #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)} |
8702eb86 | 51 | s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) ) |
0fe757f7 | 52 | series = matrix(nrow=L, ncol=n) |
8702eb86 | 53 | for (i in seq_len(n)) |
9f05a4a0 | 54 | series[,i] = s[[I(i,K1)]] + rnorm(L,sd=0.01) |
a52836b2 | 55 | |
e161499b | 56 | getRefSeries = function(indices) { |
8702eb86 | 57 | indices = indices[indices <= n] |
a52836b2 | 58 | if (length(indices)>0) as.matrix(series[,indices]) else NULL |
8702eb86 | 59 | } |
a52836b2 BA |
60 | |
61 | # Perfect situation: all medoids "after stage 1" are good. | |
37c82bba | 62 | medoids_K1 = bigmemory::as.big.matrix( sapply( 1:K1, function(i) s[[I(i,K1)]] ) ) |
9f05a4a0 BA |
63 | algoClust2 = function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med |
64 | medoids_K2 = clusteringTask2(medoids_K1, K2, algoClust2, getRefSeries, | |
a52836b2 | 65 | n, 75, 4, 8, "little", verbose=TRUE, parll=FALSE) |
56857861 | 66 | |
37c82bba | 67 | expect_equal(dim(medoids_K2), c(L,K2)) |
8702eb86 | 68 | # Not easy to evaluate result: at least we expect it to be better than random selection of |
a52836b2 BA |
69 | # synchrones within 1...K1 (from where distances computations + clustering was run) |
70 | synchrones = computeSynchrones(medoids_K1,getRefSeries,n,75,verbose=FALSE,parll=FALSE) | |
71 | distor_good = computeDistortion(synchrones, medoids_K2) | |
8702eb86 | 72 | for (i in 1:3) |
a52836b2 | 73 | expect_lte( distor_good, computeDistortion(synchrones, synchrones[,sample(1:K1,3)]) ) |
56857861 | 74 | }) |
40f12a2f BA |
75 | |
76 | # Compute the sum of (normalized) sum of squares of closest distances to a medoid. | |
77 | # Note: medoids can be a big.matrix | |
78 | computeDistortion = function(series, medoids) | |
79 | { | |
80 | if (bigmemory::is.big.matrix(medoids)) | |
81 | medoids = medoids[,] #extract standard matrix | |
82 | ||
83 | n = ncol(series) ; L = nrow(series) | |
84 | distortion = 0. | |
85 | for (i in seq_len(n)) | |
86 | distortion = distortion + min( colSums( sweep(medoids,1,series[,i],'-')^2 ) / L ) | |
87 | ||
88 | sqrt( distortion / n ) | |
89 | } |