'update'
[epclust.git] / epclust / tests / testthat / test.clustering.R
1 context("clustering")
2
3 #shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3, ... (is base==3)
4 I = function(i, base)
5 (i-1) %% base + 1
6
7 test_that("computeClusters1 behave as expected",
8 {
9 require("MASS", quietly=TRUE)
10 library("clue", quietly=TRUE)
11
12 # 3 gaussian clusters, 300 items; and then 7 gaussian clusters, 490 items
13 n = 300
14 d = 5
15 K = 3
16 for (ndK in list( c(300,5,3), c(490,10,7) ))
17 {
18 n = ndK[1] ; d = ndK[2] ; K = ndK[3]
19 cs = n/K #cluster size
20 Id = diag(d)
21 coefs = do.call(rbind,
22 lapply(1:K, function(i) MASS::mvrnorm(cs, c(rep(0,(i-1)),5,rep(0,d-i)), Id)))
23 indices_medoids = computeClusters1(coefs, K)
24 # Get coefs assignments (to medoids)
25 assignment = sapply(seq_len(n), function(i)
26 which.min( rowSums( sweep(coefs[indices_medoids,],2,coefs[i,],'-')^2 ) ) )
27 for (i in 1:K)
28 expect_equal(sum(assignment==i), cs, tolerance=5)
29
30 costs_matrix = matrix(nrow=K,ncol=K)
31 for (i in 1:K)
32 {
33 for (j in 1:K)
34 {
35 # assign i (in result) to j (order 1,2,3)
36 costs_matrix[i,j] = abs( mean(assignment[((i-1)*cs+1):(i*cs)]) - j )
37 }
38 }
39 permutation = as.integer( clue::solve_LSAP(costs_matrix) )
40 for (i in 1:K)
41 {
42 expect_equal(
43 mean(assignment[((i-1)*cs+1):(i*cs)]), permutation[i], tolerance=0.05)
44 }
45 }
46 })
47
48 test_that("computeSynchrones behave as expected",
49 {
50 n = 300
51 x = seq(0,9.5,0.1)
52 L = length(x) #96 1/4h
53 K = 3
54 s1 = cos(x)
55 s2 = sin(x)
56 s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
57 #sum((s1-s2)^2) == 96
58 #sum((s1-s3)^2) == 58
59 #sum((s2-s3)^2) == 38
60 s = list(s1, s2, s3)
61 series = matrix(nrow=n, ncol=L)
62 for (i in seq_len(n))
63 series[i,] = s[[I(i,K)]] + rnorm(L,sd=0.01)
64 getRefSeries = function(indices) {
65 indices = indices[indices < n]
66 if (length(indices)>0) series[indices,] else NULL
67 }
68 synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, 100)
69
70 expect_equal(dim(synchrones), c(K,L))
71 for (i in 1:K)
72 expect_equal(synchrones[i,], s[[i]], tolerance=0.01)
73 })
74
75 computeDistortion = function(series, medoids)
76 {
77 n = nrow(series) ; L = ncol(series)
78 distortion = 0.
79 for (i in seq_len(n))
80 distortion = distortion + min( rowSums( sweep(medoids,2,series[i,],'-')^2 ) / L )
81 distortion / n
82 }
83
84 test_that("computeClusters2 behave as expected",
85 {
86 n = 900
87 x = seq(0,9.5,0.1)
88 L = length(x) #96 1/4h
89 K1 = 60
90 K2 = 3
91 #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
92 s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
93 series = matrix(nrow=n, ncol=L)
94 for (i in seq_len(n))
95 series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
96 getRefSeries = function(indices) {
97 indices = indices[indices < n]
98 if (length(indices)>0) series[indices,] else NULL
99 }
100 # Artificially simulate 60 medoids - perfect situation, all equal to one of the refs
101 medoids_K1 = do.call(rbind, lapply( 1:K1, function(i) s[[I(i,K1)]] ) )
102 medoids_K2 = computeClusters2(medoids_K1, K2, getRefSeries, 75)
103
104 expect_equal(dim(medoids_K2), c(K2,L))
105 # Not easy to evaluate result: at least we expect it to be better than random selection of
106 # medoids within 1...K1 (among references)
107
108 distorGood = computeDistortion(series, medoids_K2)
109 for (i in 1:3)
110 expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
111 })
112
113 test_that("clusteringTask + computeClusters2 behave as expected",
114 {
115 n = 900
116 x = seq(0,9.5,0.1)
117 L = length(x) #96 1/4h
118 K1 = 60
119 K2 = 3
120 s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
121 series = matrix(nrow=n, ncol=L)
122 for (i in seq_len(n))
123 series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
124 getSeries = function(indices) {
125 indices = indices[indices <= n]
126 if (length(indices)>0) series[indices,] else NULL
127 }
128 wf = "haar"
129 getCoefs = function(indices) curvesToCoefs(series[indices,],wf)
130 medoids_K1 = getSeries( clusteringTask(1:n, getCoefs, K1, 75, 4) )
131 medoids_K2 = computeClusters2(medoids_K1, K2, getSeries, 120)
132
133 expect_equal(dim(medoids_K1), c(K1,L))
134 expect_equal(dim(medoids_K2), c(K2,L))
135 # Not easy to evaluate result: at least we expect it to be better than random selection of
136 # medoids within 1...K1 (among references)
137 distorGood = computeDistortion(series, medoids_K2)
138 for (i in 1:3)
139 expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
140 })