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freefire 6c0061699c add 结构调整
2025-01-12 04:36:52 +08:00

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R version 4.0.3 Patched (2021-01-18 r79850) -- "Bunny-Wunnies Freak Out"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(cluster)
> ## Compare on these:
> nms <- c("clustering", "objective", "isolation", "clusinfo", "silinfo")
> nm2 <- c("medoids", "id.med", nms)
> nm3 <- nm2[- pmatch("obj", nm2)]
>
> (x <- x0 <- cbind(V1 = (-3:4)^2, V2 = c(0:6,NA), V3 = c(1,2,NA,7,NA,8:9,8)))
V1 V2 V3
[1,] 9 0 1
[2,] 4 1 2
[3,] 1 2 NA
[4,] 0 3 7
[5,] 1 4 NA
[6,] 4 5 8
[7,] 9 6 9
[8,] 16 NA 8
> (px <- pam(x,2, metric="manhattan"))
Medoids:
ID V1 V2 V3
[1,] 2 4 1 2
[2,] 6 4 5 8
Clustering vector:
[1] 1 1 1 2 2 2 2 2
Objective function:
build swap
6.375 6.375
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call" "data"
> stopifnot(identical(x,x0))# DUP=FALSE ..
> pd <- pam(dist(x,"manhattan"), 2)
> px2 <- pam(x,2, metric="manhattan", keep.diss=FALSE, keep.data=FALSE)
> pdC <- pam(x,2, metric="manhattan", cluster.only = TRUE)
> p1 <- pam(x,1, metric="manhattan")
>
> stopifnot(identical(px[nms], pd[nms]),
+ identical(px[nms], px2[nms]),
+ identical(pdC, px2$clustering),
+ ## and for default dist "euclidean":
+ identical(pam(x, 2)[nms],
+ pam(dist(x),2)[nms]),
+ identical(p1[c("id.med", "objective", "clusinfo")],
+ list(id.med = 6L, objective = c(build=9.25, swap=9.25),
+ clusinfo = array(c(8, 18, 9.25, 45, 0), dim = c(1, 5),
+ dimnames=list(NULL, c("size", "max_diss", "av_diss",
+ "diameter", "separation"))))),
+ p1$clustering == 1, is.null(p1$silinfo)
+ )
>
> set.seed(253)
> ## generate 250 objects, divided into 2 clusters.
> x <- rbind(cbind(rnorm(120, 0,8), rnorm(120, 0,8)),
+ cbind(rnorm(130,50,8), rnorm(130,10,8)))
>
> .proctime00 <- proc.time()
>
> summary(px2 <- pam(x, 2))
Medoids:
ID
[1,] 61 -0.7697828 -0.2330187
[2,] 163 49.1392167 9.4097259
Clustering vector:
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Objective function:
build swap
13.25843 10.20817
Numerical information per cluster:
size max_diss av_diss diameter separation
[1,] 120 31.04843 10.18584 53.22082 9.419035
[2,] 130 26.94337 10.22878 47.86442 9.419035
Isolated clusters:
L-clusters: character(0)
L*-clusters: character(0)
Silhouette plot information:
cluster neighbor sil_width
117 1 2 0.80638966
75 1 2 0.80600824
81 1 2 0.80556624
107 1 2 0.80535252
6 1 2 0.80526675
100 1 2 0.80385505
68 1 2 0.80369702
113 1 2 0.80331774
61 1 2 0.80315322
57 1 2 0.80313945
12 1 2 0.80161573
59 1 2 0.80047745
82 1 2 0.79630964
67 1 2 0.79559589
63 1 2 0.79488886
47 1 2 0.79458809
21 1 2 0.79379540
9 1 2 0.79343081
95 1 2 0.79332153
4 1 2 0.79136081
3 1 2 0.79130879
39 1 2 0.79052367
120 1 2 0.78877423
90 1 2 0.78767224
85 1 2 0.78588359
106 1 2 0.78504452
92 1 2 0.78303000
83 1 2 0.78245915
19 1 2 0.78228359
14 1 2 0.78139236
10 1 2 0.77825678
49 1 2 0.77597087
64 1 2 0.77482761
44 1 2 0.77397394
89 1 2 0.77297318
119 1 2 0.77238705
108 1 2 0.77137189
104 1 2 0.76871378
32 1 2 0.76856251
115 1 2 0.76843312
27 1 2 0.76811698
88 1 2 0.76810713
109 1 2 0.76681303
62 1 2 0.76655954
36 1 2 0.76547988
66 1 2 0.76535606
74 1 2 0.76491406
26 1 2 0.76441455
24 1 2 0.76436188
65 1 2 0.76381352
40 1 2 0.76061109
52 1 2 0.75748679
54 1 2 0.75746436
13 1 2 0.75594073
56 1 2 0.75353784
96 1 2 0.75268786
116 1 2 0.75267215
110 1 2 0.75266614
112 1 2 0.75150872
78 1 2 0.75083708
7 1 2 0.74905187
86 1 2 0.74190424
18 1 2 0.74162144
111 1 2 0.74085474
69 1 2 0.74044653
76 1 2 0.73911707
50 1 2 0.73847075
93 1 2 0.73616384
31 1 2 0.73462007
33 1 2 0.73455252
43 1 2 0.73396232
102 1 2 0.72930751
118 1 2 0.72778023
15 1 2 0.72588122
53 1 2 0.72542363
8 1 2 0.72535191
77 1 2 0.72467809
16 1 2 0.72446952
48 1 2 0.72331213
105 1 2 0.72325095
37 1 2 0.72055248
101 1 2 0.71783562
22 1 2 0.71217552
23 1 2 0.71078375
84 1 2 0.70573352
17 1 2 0.70221946
38 1 2 0.69947240
2 1 2 0.69718780
98 1 2 0.69601237
1 1 2 0.69373841
35 1 2 0.69179546
70 1 2 0.69074915
28 1 2 0.68434091
97 1 2 0.68351978
5 1 2 0.67662675
72 1 2 0.67420722
34 1 2 0.67315267
11 1 2 0.67226046
103 1 2 0.67188668
87 1 2 0.67172802
58 1 2 0.67090513
46 1 2 0.66835116
60 1 2 0.66565445
80 1 2 0.65983842
73 1 2 0.65093947
55 1 2 0.64709226
20 1 2 0.64439401
45 1 2 0.63403361
51 1 2 0.63303101
42 1 2 0.62906268
94 1 2 0.60916406
91 1 2 0.59905996
41 1 2 0.57245485
29 1 2 0.55594781
99 1 2 0.55035955
79 1 2 0.50808544
71 1 2 0.46663954
25 1 2 0.43797346
114 1 2 0.16645003
30 1 2 0.08928664
121 2 1 0.80353953
137 2 1 0.80253721
146 2 1 0.80106653
173 2 1 0.80039417
216 2 1 0.79969919
124 2 1 0.79964913
163 2 1 0.79901674
157 2 1 0.79779188
242 2 1 0.79744315
227 2 1 0.79708130
207 2 1 0.79653829
130 2 1 0.79574204
188 2 1 0.79496670
250 2 1 0.79302877
145 2 1 0.79190501
126 2 1 0.79156003
166 2 1 0.79068795
222 2 1 0.78986170
232 2 1 0.78839216
176 2 1 0.78819086
198 2 1 0.78782877
225 2 1 0.78747329
230 2 1 0.78689375
205 2 1 0.78683641
160 2 1 0.78643596
150 2 1 0.78484046
136 2 1 0.78455577
228 2 1 0.78198238
206 2 1 0.78137390
152 2 1 0.78044944
200 2 1 0.77843458
149 2 1 0.77822272
221 2 1 0.77758324
226 2 1 0.77611981
129 2 1 0.77531368
199 2 1 0.77491451
154 2 1 0.77136276
241 2 1 0.77076783
179 2 1 0.77010597
174 2 1 0.76893758
214 2 1 0.76776510
181 2 1 0.76763087
213 2 1 0.76683151
215 2 1 0.76639087
236 2 1 0.76637552
218 2 1 0.76563050
182 2 1 0.76450873
219 2 1 0.76370712
208 2 1 0.76090426
151 2 1 0.75957536
164 2 1 0.75914844
248 2 1 0.75849775
224 2 1 0.75826151
168 2 1 0.75782023
189 2 1 0.75555083
128 2 1 0.75550519
125 2 1 0.75510766
177 2 1 0.75128941
147 2 1 0.75086382
158 2 1 0.75029192
245 2 1 0.74993652
186 2 1 0.74741247
165 2 1 0.74681005
156 2 1 0.74478894
122 2 1 0.74315425
247 2 1 0.74107328
220 2 1 0.74054057
183 2 1 0.73818743
184 2 1 0.73743259
169 2 1 0.73712431
180 2 1 0.73419669
240 2 1 0.73390938
134 2 1 0.73382823
190 2 1 0.73379720
217 2 1 0.73311931
171 2 1 0.73110365
143 2 1 0.72986022
153 2 1 0.72891371
223 2 1 0.72887340
238 2 1 0.72789416
175 2 1 0.72311665
138 2 1 0.72290131
235 2 1 0.72157157
237 2 1 0.71591233
132 2 1 0.71549875
204 2 1 0.71381083
201 2 1 0.71263881
170 2 1 0.70812568
191 2 1 0.70747428
243 2 1 0.70588929
193 2 1 0.70499170
141 2 1 0.70489885
161 2 1 0.70303117
249 2 1 0.69300988
229 2 1 0.69231982
196 2 1 0.69162302
211 2 1 0.69128644
246 2 1 0.68757678
159 2 1 0.68619850
133 2 1 0.68605444
194 2 1 0.68538064
155 2 1 0.68278455
234 2 1 0.68202095
127 2 1 0.68111027
144 2 1 0.67559517
131 2 1 0.65959281
139 2 1 0.65895024
209 2 1 0.65844942
148 2 1 0.65180336
185 2 1 0.64989675
212 2 1 0.63954685
192 2 1 0.63470144
123 2 1 0.63005333
202 2 1 0.61735843
135 2 1 0.61493992
210 2 1 0.60680456
140 2 1 0.58410004
187 2 1 0.58193543
239 2 1 0.57088679
203 2 1 0.56761998
244 2 1 0.55321123
231 2 1 0.55043439
197 2 1 0.52364031
195 2 1 0.51955678
142 2 1 0.47466260
162 2 1 0.46155841
172 2 1 0.45167576
178 2 1 0.42686872
233 2 1 0.37013099
167 2 1 0.32442373
Average silhouette width per cluster:
[1] 0.7196104 0.7148520
Average silhouette width of total data set:
[1] 0.717136
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call" "data"
> pdx <- pam(dist(x), 2)
> all.equal(px2[nms], pdx[nms], tol = 1e-12) ## TRUE
[1] TRUE
> pdxK <- pam(dist(x), 2, keep.diss = TRUE)
> stopifnot(identical(pdx[nm2], pdxK[nm2]))
>
> spdx <- silhouette(pdx)
> summary(spdx)
Silhouette of 250 units in 2 clusters from pam(x = dist(x), k = 2) :
Cluster sizes and average silhouette widths:
120 130
0.7196104 0.7148520
Individual silhouette widths:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.08929 0.69137 0.74397 0.71714 0.77806 0.80639
> spdx
cluster neighbor sil_width
117 1 2 0.80638966
75 1 2 0.80600824
81 1 2 0.80556624
107 1 2 0.80535252
6 1 2 0.80526675
100 1 2 0.80385505
68 1 2 0.80369702
113 1 2 0.80331774
61 1 2 0.80315322
57 1 2 0.80313945
12 1 2 0.80161573
59 1 2 0.80047745
82 1 2 0.79630964
67 1 2 0.79559589
63 1 2 0.79488886
47 1 2 0.79458809
21 1 2 0.79379540
9 1 2 0.79343081
95 1 2 0.79332153
4 1 2 0.79136081
3 1 2 0.79130879
39 1 2 0.79052367
120 1 2 0.78877423
90 1 2 0.78767224
85 1 2 0.78588359
106 1 2 0.78504452
92 1 2 0.78303000
83 1 2 0.78245915
19 1 2 0.78228359
14 1 2 0.78139236
10 1 2 0.77825678
49 1 2 0.77597087
64 1 2 0.77482761
44 1 2 0.77397394
89 1 2 0.77297318
119 1 2 0.77238705
108 1 2 0.77137189
104 1 2 0.76871378
32 1 2 0.76856251
115 1 2 0.76843312
27 1 2 0.76811698
88 1 2 0.76810713
109 1 2 0.76681303
62 1 2 0.76655954
36 1 2 0.76547988
66 1 2 0.76535606
74 1 2 0.76491406
26 1 2 0.76441455
24 1 2 0.76436188
65 1 2 0.76381352
40 1 2 0.76061109
52 1 2 0.75748679
54 1 2 0.75746436
13 1 2 0.75594073
56 1 2 0.75353784
96 1 2 0.75268786
116 1 2 0.75267215
110 1 2 0.75266614
112 1 2 0.75150872
78 1 2 0.75083708
7 1 2 0.74905187
86 1 2 0.74190424
18 1 2 0.74162144
111 1 2 0.74085474
69 1 2 0.74044653
76 1 2 0.73911707
50 1 2 0.73847075
93 1 2 0.73616384
31 1 2 0.73462007
33 1 2 0.73455252
43 1 2 0.73396232
102 1 2 0.72930751
118 1 2 0.72778023
15 1 2 0.72588122
53 1 2 0.72542363
8 1 2 0.72535191
77 1 2 0.72467809
16 1 2 0.72446952
48 1 2 0.72331213
105 1 2 0.72325095
37 1 2 0.72055248
101 1 2 0.71783562
22 1 2 0.71217552
23 1 2 0.71078375
84 1 2 0.70573352
17 1 2 0.70221946
38 1 2 0.69947240
2 1 2 0.69718780
98 1 2 0.69601237
1 1 2 0.69373841
35 1 2 0.69179546
70 1 2 0.69074915
28 1 2 0.68434091
97 1 2 0.68351978
5 1 2 0.67662675
72 1 2 0.67420722
34 1 2 0.67315267
11 1 2 0.67226046
103 1 2 0.67188668
87 1 2 0.67172802
58 1 2 0.67090513
46 1 2 0.66835116
60 1 2 0.66565445
80 1 2 0.65983842
73 1 2 0.65093947
55 1 2 0.64709226
20 1 2 0.64439401
45 1 2 0.63403361
51 1 2 0.63303101
42 1 2 0.62906268
94 1 2 0.60916406
91 1 2 0.59905996
41 1 2 0.57245485
29 1 2 0.55594781
99 1 2 0.55035955
79 1 2 0.50808544
71 1 2 0.46663954
25 1 2 0.43797346
114 1 2 0.16645003
30 1 2 0.08928664
121 2 1 0.80353953
137 2 1 0.80253721
146 2 1 0.80106653
173 2 1 0.80039417
216 2 1 0.79969919
124 2 1 0.79964913
163 2 1 0.79901674
157 2 1 0.79779188
242 2 1 0.79744315
227 2 1 0.79708130
207 2 1 0.79653829
130 2 1 0.79574204
188 2 1 0.79496670
250 2 1 0.79302877
145 2 1 0.79190501
126 2 1 0.79156003
166 2 1 0.79068795
222 2 1 0.78986170
232 2 1 0.78839216
176 2 1 0.78819086
198 2 1 0.78782877
225 2 1 0.78747329
230 2 1 0.78689375
205 2 1 0.78683641
160 2 1 0.78643596
150 2 1 0.78484046
136 2 1 0.78455577
228 2 1 0.78198238
206 2 1 0.78137390
152 2 1 0.78044944
200 2 1 0.77843458
149 2 1 0.77822272
221 2 1 0.77758324
226 2 1 0.77611981
129 2 1 0.77531368
199 2 1 0.77491451
154 2 1 0.77136276
241 2 1 0.77076783
179 2 1 0.77010597
174 2 1 0.76893758
214 2 1 0.76776510
181 2 1 0.76763087
213 2 1 0.76683151
215 2 1 0.76639087
236 2 1 0.76637552
218 2 1 0.76563050
182 2 1 0.76450873
219 2 1 0.76370712
208 2 1 0.76090426
151 2 1 0.75957536
164 2 1 0.75914844
248 2 1 0.75849775
224 2 1 0.75826151
168 2 1 0.75782023
189 2 1 0.75555083
128 2 1 0.75550519
125 2 1 0.75510766
177 2 1 0.75128941
147 2 1 0.75086382
158 2 1 0.75029192
245 2 1 0.74993652
186 2 1 0.74741247
165 2 1 0.74681005
156 2 1 0.74478894
122 2 1 0.74315425
247 2 1 0.74107328
220 2 1 0.74054057
183 2 1 0.73818743
184 2 1 0.73743259
169 2 1 0.73712431
180 2 1 0.73419669
240 2 1 0.73390938
134 2 1 0.73382823
190 2 1 0.73379720
217 2 1 0.73311931
171 2 1 0.73110365
143 2 1 0.72986022
153 2 1 0.72891371
223 2 1 0.72887340
238 2 1 0.72789416
175 2 1 0.72311665
138 2 1 0.72290131
235 2 1 0.72157157
237 2 1 0.71591233
132 2 1 0.71549875
204 2 1 0.71381083
201 2 1 0.71263881
170 2 1 0.70812568
191 2 1 0.70747428
243 2 1 0.70588929
193 2 1 0.70499170
141 2 1 0.70489885
161 2 1 0.70303117
249 2 1 0.69300988
229 2 1 0.69231982
196 2 1 0.69162302
211 2 1 0.69128644
246 2 1 0.68757678
159 2 1 0.68619850
133 2 1 0.68605444
194 2 1 0.68538064
155 2 1 0.68278455
234 2 1 0.68202095
127 2 1 0.68111027
144 2 1 0.67559517
131 2 1 0.65959281
139 2 1 0.65895024
209 2 1 0.65844942
148 2 1 0.65180336
185 2 1 0.64989675
212 2 1 0.63954685
192 2 1 0.63470144
123 2 1 0.63005333
202 2 1 0.61735843
135 2 1 0.61493992
210 2 1 0.60680456
140 2 1 0.58410004
187 2 1 0.58193543
239 2 1 0.57088679
203 2 1 0.56761998
244 2 1 0.55321123
231 2 1 0.55043439
197 2 1 0.52364031
195 2 1 0.51955678
142 2 1 0.47466260
162 2 1 0.46155841
172 2 1 0.45167576
178 2 1 0.42686872
233 2 1 0.37013099
167 2 1 0.32442373
attr(,"Ordered")
[1] TRUE
attr(,"call")
pam(x = dist(x), k = 2)
attr(,"class")
[1] "silhouette"
> postscript("pam-tst.ps")
> if(FALSE)
+ plot(spdx)# the silhouette
> ## is now identical :
> plot(pdx)# failed in 1.7.0 -- now only does silhouette
>
> par(mfrow = 2:1)
> ## new 'dist' argument for clusplot():
> plot(pdx, dist=dist(x))
> ## but this should work automagically (via eval()) as well:
> plot(pdx)
> ## or this
> clusplot(pdx)
>
> data(ruspini)
> summary(pr4 <- pam(ruspini, 4))
Medoids:
ID x y
10 10 19 65
32 32 44 149
52 52 99 119
70 70 69 21
Clustering vector:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Objective function:
build swap
17.22898 11.48637
Numerical information per cluster:
size max_diss av_diss diameter separation
[1,] 20 24.04163 12.55362 40.24922 40.49691
[2,] 23 26.92582 10.44238 36.61967 24.04163
[3,] 17 33.97058 13.84800 47.63402 24.04163
[4,] 15 17.02939 8.98767 27.07397 40.49691
Isolated clusters:
L-clusters: character(0)
L*-clusters: [1] 1 4
Silhouette plot information:
cluster neighbor sil_width
10 1 4 0.8056096
6 1 4 0.7954977
9 1 4 0.7923048
11 1 4 0.7831672
8 1 2 0.7811793
12 1 4 0.7658171
3 1 4 0.7587961
14 1 4 0.7569107
2 1 4 0.7456150
16 1 4 0.7436018
13 1 4 0.7398841
4 1 2 0.7361533
18 1 4 0.7080079
15 1 4 0.7006854
19 1 4 0.7000938
1 1 4 0.6798381
5 1 4 0.6646571
20 1 4 0.6619626
17 1 4 0.6148541
7 1 2 0.5900575
26 2 3 0.8357433
32 2 3 0.8332753
27 2 3 0.8290271
25 2 3 0.8285547
28 2 3 0.8192636
35 2 3 0.8186309
33 2 3 0.8175087
23 2 3 0.8089969
22 2 3 0.8025389
34 2 3 0.8013310
31 2 3 0.7949677
36 2 3 0.7943536
24 2 3 0.7930770
29 2 3 0.7897346
30 2 3 0.7892027
21 2 3 0.7698024
37 2 3 0.7684502
39 2 3 0.7631648
38 2 3 0.7438848
40 2 3 0.7083130
42 2 3 0.5291270
43 2 3 0.4931623
41 2 3 0.4290814
54 3 2 0.7741745
57 3 2 0.7703455
55 3 2 0.7641810
50 3 2 0.7619943
52 3 2 0.7616220
56 3 2 0.7575313
59 3 2 0.7327828
49 3 2 0.7317002
51 3 2 0.7209864
60 3 2 0.7206840
58 3 2 0.7019611
53 3 2 0.6775322
45 3 2 0.5974787
46 3 2 0.5740823
47 3 2 0.4835635
48 3 2 0.4247331
44 3 2 0.4196093
70 4 1 0.8548947
67 4 1 0.8527439
65 4 1 0.8503105
69 4 1 0.8391810
71 4 1 0.8381065
66 4 1 0.8229841
62 4 1 0.8153092
64 4 1 0.8061254
73 4 1 0.7950213
63 4 1 0.7795369
72 4 1 0.7748121
61 4 1 0.7701103
68 4 1 0.7620559
74 4 1 0.7596815
75 4 1 0.7425538
Average silhouette width per cluster:
[1] 0.7262347 0.7548344 0.6691154 0.8042285
Average silhouette width of total data set:
[1] 0.737657
2775 dissimilarities, summarized :
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.414 40.106 75.591 71.538 99.169 154.500
Metric : euclidean
Number of objects : 75
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call" "data"
> (pr3 <- pam(ruspini, 3))
Medoids:
ID x y
17 17 30 52
32 32 44 149
52 52 99 119
Clustering vector:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Objective function:
build swap
25.68229 21.59293
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call" "data"
> (pr5 <- pam(ruspini, 5))
Medoids:
ID x y
10 10 19 65
32 32 44 149
52 52 99 119
47 47 78 94
70 70 69 21
Clustering vector:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 4 4 4 3 3 3 3
53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
3 3 3 3 3 3 3 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
Objective function:
build swap
12.09864 10.39579
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call" "data"
>
> data(votes.repub)
> summary(pv3 <- pam(votes.repub, 3))
Medoids:
ID X1856 X1860 X1864 X1868 X1872 X1876 X1880 X1884 X1888 X1892 X1896
Alabama 1 NA NA NA 51.44 53.19 40.02 36.98 38.44 32.28 3.95 28.13
Alaska 2 NA NA NA NA NA NA NA NA NA NA NA
New Mexico 31 NA NA NA NA NA NA NA NA NA NA NA
X1900 X1904 X1908 X1912 X1916 X1920 X1924 X1928 X1932 X1936 X1940
Alabama 34.67 20.65 24.38 8.26 21.97 30.98 27.01 48.49 14.15 12.82 14.34
Alaska NA NA NA NA NA NA NA NA NA NA NA
New Mexico NA NA NA 35.91 46.53 54.68 48.52 59.01 35.76 36.50 43.28
X1944 X1948 X1952 X1956 X1960 X1964 X1968 X1972 X1976
Alabama 18.20 19.04 35.02 39.39 41.75 69.5 14.0 72.4 43.48
Alaska NA NA NA NA 50.94 34.1 45.3 58.1 62.91
New Mexico 46.44 42.93 55.39 57.81 49.41 41.0 51.8 61.0 51.04
Clustering vector:
Alabama Alaska Arizona Arkansas California
1 2 3 1 2
Colorado Connecticut Delaware Florida Georgia
2 2 3 1 1
Hawaii Idaho Illinois Indiana Iowa
2 3 2 3 3
Kansas Kentucky Louisiana Maine Maryland
2 3 1 2 3
Massachusetts Michigan Minnesota Mississippi Missouri
3 2 3 1 3
Montana Nebraska Nevada New Hampshire New Jersey
3 3 2 2 2
New Mexico New York North Carolina North Dakota Ohio
3 3 3 2 3
Oklahoma Oregon Pennsylvania Rhode Island South Carolina
3 3 2 3 2
South Dakota Tennessee Texas Utah Vermont
3 3 2 3 2
Virginia Washington West Virginia Wisconsin Wyoming
2 3 3 3 3
Objective function:
build swap
38.32548 38.32548
Numerical information per cluster:
size max_diss av_diss diameter separation
[1,] 6 78.92731 51.59134 116.7048 50.14189
[2,] 18 86.54675 38.47068 271.2455 19.42184
[3,] 26 60.03879 35.16361 124.8324 19.42184
Isolated clusters:
L-clusters: character(0)
L*-clusters: character(0)
Silhouette plot information:
cluster neighbor sil_width
Louisiana 1 3 0.54689535
Alabama 1 3 0.52839272
Georgia 1 3 0.52730253
Mississippi 1 2 0.52454810
Florida 1 3 0.25211631
Arkansas 1 3 0.24131701
Alaska 2 3 0.15699268
Hawaii 2 3 0.08479842
Vermont 2 3 -0.02620975
Maine 2 3 -0.03284950
Michigan 2 3 -0.11524982
Pennsylvania 2 3 -0.15341477
New Hampshire 2 3 -0.17099889
Connecticut 2 3 -0.19095000
New Jersey 2 3 -0.19281567
Kansas 2 3 -0.19719316
California 2 3 -0.24006293
Illinois 2 3 -0.25236336
North Dakota 2 3 -0.25464430
Virginia 2 3 -0.26262534
Nevada 2 3 -0.27016336
Colorado 2 3 -0.27885043
Texas 2 1 -0.47297583
South Carolina 2 1 -0.50899710
New Mexico 3 2 0.39555584
Washington 3 2 0.32989454
Oklahoma 3 2 0.30953823
Wyoming 3 2 0.30163169
Idaho 3 2 0.29915132
Montana 3 2 0.29105494
Missouri 3 2 0.29038462
Oregon 3 2 0.27710695
Maryland 3 2 0.27437520
West Virginia 3 2 0.27089938
Utah 3 2 0.26964380
Tennessee 3 2 0.26846440
Arizona 3 2 0.25968564
Delaware 3 2 0.25920434
Kentucky 3 2 0.25868341
South Dakota 3 2 0.25615670
Indiana 3 2 0.25031548
Wisconsin 3 2 0.21808013
Ohio 3 2 0.21477474
Nebraska 3 2 0.20965953
North Carolina 3 2 0.19201537
Minnesota 3 2 0.18955165
New York 3 2 0.18820394
Iowa 3 2 0.17296046
Rhode Island 3 2 0.12599915
Massachusetts 3 2 0.12106770
Average silhouette width per cluster:
[1] 0.4367620 -0.1876985 0.2497715
Average silhouette width of total data set:
[1] 0.1147212
1225 dissimilarities, summarized :
Min. 1st Qu. Median Mean 3rd Qu. Max.
17.20 48.34 64.68 82.23 105.49 281.95
Metric : euclidean
Number of objects : 50
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call" "data"
> (pv4 <- pam(votes.repub, 4))
Medoids:
ID X1856 X1860 X1864 X1868 X1872 X1876 X1880 X1884 X1888 X1892 X1896
Alabama 1 NA NA NA 51.44 53.19 40.02 36.98 38.44 32.28 3.95 28.13
Alaska 2 NA NA NA NA NA NA NA NA NA NA NA
New Mexico 31 NA NA NA NA NA NA NA NA NA NA NA
Iowa 15 49.13 54.87 64.23 61.92 64.18 58.58 56.85 52.42 52.36 49.60 55.46
X1900 X1904 X1908 X1912 X1916 X1920 X1924 X1928 X1932 X1936 X1940
Alabama 34.67 20.65 24.38 8.26 21.97 30.98 27.01 48.49 14.15 12.82 14.34
Alaska NA NA NA NA NA NA NA NA NA NA NA
New Mexico NA NA NA 35.91 46.53 54.68 48.52 59.01 35.76 36.50 43.28
Iowa 57.99 63.37 55.62 24.30 54.06 70.91 55.06 61.80 39.98 42.70 52.03
X1944 X1948 X1952 X1956 X1960 X1964 X1968 X1972 X1976
Alabama 18.20 19.04 35.02 39.39 41.75 69.5 14.0 72.4 43.48
Alaska NA NA NA NA 50.94 34.1 45.3 58.1 62.91
New Mexico 46.44 42.93 55.39 57.81 49.41 41.0 51.8 61.0 51.04
Iowa 51.99 47.58 63.76 59.06 56.71 38.1 53.0 57.6 50.51
Clustering vector:
Alabama Alaska Arizona Arkansas California
1 2 3 1 2
Colorado Connecticut Delaware Florida Georgia
2 2 3 1 1
Hawaii Idaho Illinois Indiana Iowa
2 3 4 3 4
Kansas Kentucky Louisiana Maine Maryland
4 3 1 2 3
Massachusetts Michigan Minnesota Mississippi Missouri
4 2 4 1 3
Montana Nebraska Nevada New Hampshire New Jersey
3 4 2 2 2
New Mexico New York North Carolina North Dakota Ohio
3 3 3 4 4
Oklahoma Oregon Pennsylvania Rhode Island South Carolina
3 3 4 4 2
South Dakota Tennessee Texas Utah Vermont
4 3 2 3 2
Virginia Washington West Virginia Wisconsin Wyoming
2 3 3 4 3
Objective function:
build swap
35.84182 35.84182
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call" "data"
> (pv6 <- pam(votes.repub, 6, trace = 3))
C pam(): computing 1226 dissimilarities from 50 x 31 matrix: [Ok]
pam()'s bswap(*, s=281.951, pamonce=0): build 6 medoids:
new repr. 2
new repr. 1
new repr. 31
new repr. 15
new repr. 46
new repr. 40
after build: medoids are 1 2 15 31 40 46
and min.dist dysma[1:n] are
0 0 37.7 56 35.8 28.5 28.6 31.7 54.1 48.2
51.7 33.2 27.3 30.5 0 35.1 25.4 60.9 36.9 26.7
48.4 28.1 33.2 63.1 21.1 28.6 37.5 35.8 29.8 31.3
0 32 29.9 35.7 30.9 35.1 27.8 35.7 50.2 0
26.2 30.2 45.2 34.1 33.8 0 28.5 35.1 34.2 28.8
swp new 10 <-> 1 old; decreasing diss. 1579.03 by -2.57067
end{bswap()}, end{cstat()}
Medoids:
ID X1856 X1860 X1864 X1868 X1872 X1876 X1880 X1884 X1888 X1892
Georgia 10 NA NA NA 35.72 43.77 27.94 34.33 33.84 28.33 21.80
Alaska 2 NA NA NA NA NA NA NA NA NA NA
Virginia 46 0.19 1.15 NA NA 50.48 40.62 39.52 48.90 49.47 38.75
New Mexico 31 NA NA NA NA NA NA NA NA NA NA
Iowa 15 49.13 54.87 64.23 61.92 64.18 58.58 56.85 52.42 52.36 49.60
South Carolina 40 NA NA NA 57.93 75.95 50.26 33.97 23.72 17.27 18.99
X1896 X1900 X1904 X1908 X1912 X1916 X1920 X1924 X1928 X1932
Georgia 36.82 28.56 18.32 31.40 4.27 7.07 28.57 18.19 43.37 7.77
Alaska NA NA NA NA NA NA NA NA NA NA
Virginia 45.90 43.81 36.67 38.36 17.00 32.05 37.85 32.79 53.91 30.09
New Mexico NA NA NA NA 35.91 46.53 54.68 48.52 59.01 35.76
Iowa 55.46 57.99 63.37 55.62 24.30 54.06 70.91 55.06 61.80 39.98
South Carolina 13.51 7.04 4.63 5.97 1.06 2.43 3.90 2.21 8.54 1.89
X1936 X1940 X1944 X1948 X1952 X1956 X1960 X1964 X1968 X1972
Georgia 12.60 14.84 18.25 18.31 30.34 33.22 37.44 54.1 30.4 75.0
Alaska NA NA NA NA NA NA 50.94 34.1 45.3 58.1
Virginia 29.39 31.55 37.39 41.04 56.32 55.37 52.44 46.5 41.4 67.8
New Mexico 36.50 43.28 46.44 42.93 55.39 57.81 49.41 41.0 51.8 61.0
Iowa 42.70 52.03 51.99 47.58 63.76 59.06 56.71 38.1 53.0 57.6
South Carolina 1.43 4.37 4.46 3.78 49.28 25.18 48.76 58.9 38.1 70.8
X1976
Georgia 33.02
Alaska 62.91
Virginia 50.73
New Mexico 51.04
Iowa 50.51
South Carolina 43.54
Clustering vector:
Alabama Alaska Arizona Arkansas California
1 2 3 3 2
Colorado Connecticut Delaware Florida Georgia
2 2 4 3 1
Hawaii Idaho Illinois Indiana Iowa
2 4 5 4 5
Kansas Kentucky Louisiana Maine Maryland
5 4 1 2 4
Massachusetts Michigan Minnesota Mississippi Missouri
5 2 5 6 4
Montana Nebraska Nevada New Hampshire New Jersey
4 5 2 2 2
New Mexico New York North Carolina North Dakota Ohio
4 4 3 5 5
Oklahoma Oregon Pennsylvania Rhode Island South Carolina
4 4 5 5 6
South Dakota Tennessee Texas Utah Vermont
5 3 2 4 2
Virginia Washington West Virginia Wisconsin Wyoming
3 4 4 5 4
Objective function:
build swap
31.58067 31.52926
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call" "data"
>
> cat('Time elapsed: ', proc.time() - .proctime00,'\n')
Time elapsed: 0.392 0.016 0.41 0 0
>
> ## re-starting with medoids from pv6 shouldn't change:
> pv6. <- pam(votes.repub, 6, medoids = pv6$id.med, trace = 3)
C pam(): computing 1226 dissimilarities from 50 x 31 matrix: [Ok]
pam()'s bswap(*, s=281.951, pamonce=0): medoids given; after build: medoids are 2 10 15 31 40 46
and min.dist dysma[1:n] are
48.2 0 37.7 56 35.8 28.5 28.6 31.7 54.1 0
51.7 33.2 27.3 30.5 0 35.1 25.4 58.3 36.9 26.7
48.4 28.1 33.2 63.1 21.1 28.6 37.5 35.8 29.8 31.3
0 32 29.9 35.7 30.9 35.1 27.8 35.7 50.2 0
26.2 30.2 45.2 34.1 33.8 0 28.5 35.1 34.2 28.8
end{bswap()}, end{cstat()}
> identical(pv6[nm3], pv6.[nm3])
[1] TRUE
>
> ## This example seg.faulted at some point:
> d.st <- data.frame(V1= c(9, 12, 12, 15, 9, 9, 13, 11, 15, 10, 13, 13,
+ 13, 15, 8, 13, 13, 10, 7, 9, 6, 11, 3),
+ V2= c(5, 9, 3, 5, 1, 1, 2, NA, 10, 1, 4, 7,
+ 4, NA, NA, 5, 2, 4, 3, 3, 6, 1, 1),
+ V3 = c(63, 41, 59, 50, 290, 226, 60, 36, 32, 121, 70, 51,
+ 79, 32, 42, 39, 76, 60, 56, 88, 57, 309, 254),
+ V4 = c(146, 43, 78, 88, 314, 149, 78, NA, 238, 153, 159, 222,
+ 203, NA, NA, 74, 100, 111, 9, 180, 50, 256, 107))
> dd <- daisy(d.st, stand = TRUE)
> (r0 <- pam(dd, 5))# cluster 5 = { 23 } -- on single observation
Medoids:
ID
[1,] 15 15
[2,] 8 8
[3,] 14 14
[4,] 22 22
[5,] 23 23
Clustering vector:
[1] 1 2 2 3 4 4 2 2 3 2 2 2 2 3 1 2 2 2 1 1 1 4 5
Objective function:
build swap
0.9368049 0.8621860
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call"
> ## pam doing the "daisy" computation internally:
> r0s <- pam(d.st, 5, stand=TRUE, keep.diss=FALSE, keep.data=FALSE)
> (ii <- which(names(r0) %in% c("call","medoids")))
[1] 1 9
> stopifnot(all.equal(r0[-ii], r0s[-ii], tol=1e-14),
+ identical(r0s$medoids, data.matrix(d.st)[r0$medoids, ]))
>
> ## This gave only 3 different medoids -> and seg.fault:
> (r5 <- pam(dd, 5, medoids = c(1,3,20,2,5), trace = 2)) # now "fine"
pam()'s bswap(*, s=8.51931, pamonce=0): medoids given; after build: medoids are 1 2 3 5 20
swp new 14 <-> 2 old; decreasing diss. 29.8745 by -5.50096
swp new 15 <-> 1 old; decreasing diss. 24.3735 by -2.20162
swp new 6 <-> 20 old; decreasing diss. 22.1719 by -2.12745
swp new 8 <-> 3 old; decreasing diss. 20.0444 by -0.201608
end{bswap()}, end{cstat()}
Medoids:
ID
[1,] 15 15
[2,] 8 8
[3,] 14 14
[4,] 5 5
[5,] 6 6
Clustering vector:
[1] 1 2 2 3 4 5 2 2 3 5 2 2 2 3 1 2 2 2 1 1 1 4 5
Objective function:
build swap
1.2988899 0.8627319
Available components:
[1] "medoids" "id.med" "clustering" "objective" "isolation"
[6] "clusinfo" "silinfo" "diss" "call"
>
> dev.off()
null device
1
>
> ##------------------------ Testing pam() with new "pamonce" argument:
>
> ## This is from "next version of Matrix" test-tools-1.R:
> showSys.time <- function(expr) {
+ ## prepend 'Time' for R CMD Rdiff
+ st <- system.time(expr)
+ writeLines(paste("Time", capture.output(print(st))))
+ invisible(st)
+ }
> show6Ratios <- function(...) {
+ stopifnot(length(rgs <- list(...)) == 6,
+ nchar(ns <- names(rgs)) > 0)
+ r <- round(cbind(..1, ..2, ..3, ..4, ..5, ..6)[c(1,5),], 5)
+ dimnames(r) <- list(paste("Time ", rownames(r)), ns)
+ r
+ }
>
>
> n <- 1000
> ## If not enough cases, all CPU times equals 0.
> n <- 500 # for now, and automatic testing
>
> sd <- 0.5
> set.seed(13)
> n2 <- as.integer(round(n * 1.5))
> x <- rbind(cbind(rnorm( n,0,sd), rnorm( n,0,sd)),
+ cbind(rnorm(n2,5,sd), rnorm(n2,5,sd)),
+ cbind(rnorm(n2,7,sd), rnorm(n2,7,sd)),
+ cbind(rnorm(n2,9,sd), rnorm(n2,9,sd)))
>
>
> ## original algorithm
> st0 <- showSys.time(pamx <- pam(x, 4, trace.lev=2))# 8.157 0.024 8.233
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=0): build 4 medoids:
new repr. 1268
new repr. 414
new repr. 2153
new repr. 915
after build: medoids are 414 915 1268 2153
swp new 1793 <-> 1268 old; decreasing diss. 1862.37 by -129.13
end{bswap()}, end{cstat()}
Time user system elapsed
Time 1.163 0.005 1.176
> ## bswapPamOnce algorithm
> st1 <- showSys.time(pamxonce <- pam(x, 4, pamonce=TRUE, trace.lev=2))# 6.122 0.024 6.181
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=1): build 4 medoids:
new repr. 1268
new repr. 414
new repr. 2153
new repr. 915
after build: medoids are 414 915 1268 2153
swp new 1793 <-> 1268 old; decreasing diss. 1862.37 by -129.13
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.882 0.015 0.908
> ## bswapPamOnceDistIndice
> st2 <- showSys.time(pamxonce2 <- pam(x, 4, pamonce = 2, trace.lev=2))# 4.101 0.024 4.151
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=2): build 4 medoids:
new repr. 1268
new repr. 414
new repr. 2153
new repr. 915
after build: medoids are 414 915 1268 2153
swp new 1793 <-> 1268 old; decreasing diss. 1862.37 by -129.13
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.741 0.000 0.746
> ## bswapPamSchubert FastPAM1
> st3 <- showSys.time(pamxonce3 <- pam(x, 4, pamonce = 3, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=3): build 4 medoids:
new repr. 1268
new repr. 414
new repr. 2153
new repr. 915
after build: medoids are 414 915 1268 2153
swp new 1793 <-> 1268 old; decreasing diss. 1862.37 by -129.13
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.560 0.001 0.566
> ## bswapPamSchubert FastPAM2
> st4 <- showSys.time(pamxonce4 <- pam(x, 4, pamonce = 4, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=4): build 4 medoids:
new repr. 1268
new repr. 414
new repr. 2153
new repr. 915
after build: medoids are 414 915 1268 2153
swp new 1793 <-> 1268 old; decreasing diss. 1862.37 by -129.13
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.637 0.000 0.643
> ## bswapPamSchubert FastPAM2 with linearized memory access
> st5 <- showSys.time(pamxonce5 <- pam(x, 4, pamonce = 5, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=5): build 4 medoids:
new repr. 1268
new repr. 414
new repr. 2153
new repr. 915
after build: medoids are 414 915 1268 2153
swp new 1793 <-> 1268 old; decreasing diss. 1862.37 by -129.13
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.494 0.000 0.498
> ## bswapPamSchubert FasterPAM
> st6 <- showSys.time(pamxonce6 <- pam(x, 4, pamonce = 6, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=6): build 4 medoids:
new repr. 1268
new repr. 414
new repr. 2153
new repr. 915
after build: medoids are 414 915 1268 2153
swp new 1251 <-> 1268 old; decreasing diss. 1862.37 by -27.6945
swp new 1255 <-> 1251 old; decreasing diss. 1834.68 by -81.213
swp new 1259 <-> 1255 old; decreasing diss. 1753.46 by -4.71205
swp new 1265 <-> 1259 old; decreasing diss. 1748.75 by -1.5194
swp new 1280 <-> 1265 old; decreasing diss. 1747.23 by -1.34867
swp new 1300 <-> 1280 old; decreasing diss. 1745.88 by -9.69283
swp new 1421 <-> 1300 old; decreasing diss. 1736.19 by -2.94296
swp new 1793 <-> 1421 old; decreasing diss. 1733.25 by -0.00639415
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.483 0.001 0.488
> show6Ratios('6:orig' = st6/st0, '5:orig' = st5/st0, '4:orig' = st4/st0, '3:orig' = st3/st0, '2:orig' = st2/st0, '1:orig' = st1/st0)
6:orig 5:orig 4:orig 3:orig 2:orig 1:orig
Time user.self 0.41531 0.42476 0.54772 0.48151 0.63715 0.75838
Time sys.child NaN NaN NaN NaN NaN NaN
>
> ## only call element is not equal
> (icall <- which(names(pamx) == "call"))
[1] 9
> pamx[[icall]]
pam(x = x, k = 4, trace.lev = 2)
> stopifnot(all.equal(pamx [-icall], pamxonce [-icall]),
+ all.equal(pamxonce[-icall], pamxonce2[-icall]),
+ all.equal(pamxonce[-icall], pamxonce3[-icall]),
+ all.equal(pamxonce[-icall], pamxonce4[-icall]),
+ all.equal(pamxonce[-icall], pamxonce5[-icall]),
+ all.equal(pamxonce[-icall], pamxonce6[-icall]))
>
> ## Same using specified medoids
> (med0 <- 1 + round(n* c(0,1, 2.5, 4)))# lynne (~ 2010, AMD Phenom II X4 925)
[1] 1 501 1251 2001
> st0 <- showSys.time(pamxst <- pam(x, 4, medoids = med0, trace.lev=2))# 13.071 0.024 13.177
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=0): medoids given; after build: medoids are 1 501 1251 2001
swp new 915 <-> 501 old; decreasing diss. 2126.83 by -197.507
swp new 1793 <-> 1251 old; decreasing diss. 1929.32 by -101.336
swp new 414 <-> 1 old; decreasing diss. 1827.98 by -86.3404
swp new 2153 <-> 2001 old; decreasing diss. 1741.64 by -8.40201
end{bswap()}, end{cstat()}
Time user system elapsed
Time 1.720 0.000 1.732
> st1 <- showSys.time(pamxoncest <- pam(x, 4, medoids = med0, pamonce=TRUE, trace.lev=2))# 8.503 0.024 8.578
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=1): medoids given; after build: medoids are 1 501 1251 2001
swp new 915 <-> 501 old; decreasing diss. 2126.83 by -197.507
swp new 1793 <-> 1251 old; decreasing diss. 1929.32 by -101.336
swp new 414 <-> 1 old; decreasing diss. 1827.98 by -86.3404
swp new 2153 <-> 2001 old; decreasing diss. 1741.64 by -8.40201
end{bswap()}, end{cstat()}
Time user system elapsed
Time 1.319 0.000 1.329
> st2 <- showSys.time(pamxonce2st <- pam(x, 4, medoids = med0, pamonce=2, trace.lev=2))# 5.587 0.025 5.647
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=2): medoids given; after build: medoids are 1 501 1251 2001
swp new 915 <-> 501 old; decreasing diss. 2126.83 by -197.507
swp new 1793 <-> 1251 old; decreasing diss. 1929.32 by -101.336
swp new 414 <-> 1 old; decreasing diss. 1827.98 by -86.3404
swp new 2153 <-> 2001 old; decreasing diss. 1741.64 by -8.40201
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.915 0.001 0.922
> st3 <- showSys.time(pamxonce3st <- pam(x, 4, medoids = med0, pamonce=3, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=3): medoids given; after build: medoids are 1 501 1251 2001
swp new 915 <-> 501 old; decreasing diss. 2126.83 by -197.507
swp new 1793 <-> 1251 old; decreasing diss. 1929.32 by -101.336
swp new 414 <-> 1 old; decreasing diss. 1827.98 by -86.3404
swp new 2153 <-> 2001 old; decreasing diss. 1741.64 by -8.40201
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.442 0.001 0.445
> st4 <- showSys.time(pamxonce4st <- pam(x, 4, medoids = med0, pamonce=4, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=4): medoids given; after build: medoids are 1 501 1251 2001
swp new 915 <-> 501 old; decreasing diss. 2126.83 by -197.507
fswp new 1421 <-> 1251 old; decreasing diss. 1929.32 by -101.326
fswp new 414 <-> 1 old; decreasing diss. 1827.99 by -86.3404
fswp new 2153 <-> 2001 old; decreasing diss. 1741.65 by -8.40546
swp new 1793 <-> 1421 old; decreasing diss. 1733.25 by -0.00639415
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.329 0.000 0.331
> st5 <- showSys.time(pamxonce5st <- pam(x, 4, medoids = med0, pamonce=5, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=5): medoids given; after build: medoids are 1 501 1251 2001
swp new 915 <-> 501 old; decreasing diss. 2126.83 by -197.507
fswp new 1421 <-> 1251 old; decreasing diss. 1929.32 by -101.326
fswp new 414 <-> 1 old; decreasing diss. 1827.99 by -86.3404
fswp new 2153 <-> 2001 old; decreasing diss. 1741.65 by -8.40546
swp new 1793 <-> 1421 old; decreasing diss. 1733.25 by -0.00639415
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.289 0.000 0.290
> st6 <- showSys.time(pamxonce6st <- pam(x, 4, medoids = med0, pamonce=6, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=6): medoids given; after build: medoids are 1 501 1251 2001
swp new 8 <-> 1 old; decreasing diss. 2126.83 by -33.2528
swp new 12 <-> 8 old; decreasing diss. 2093.57 by -32.0203
swp new 15 <-> 12 old; decreasing diss. 2061.55 by -2.88573
swp new 16 <-> 15 old; decreasing diss. 2058.67 by -3.50571
swp new 33 <-> 16 old; decreasing diss. 2055.16 by -0.382726
swp new 56 <-> 33 old; decreasing diss. 2054.78 by -0.660581
swp new 70 <-> 56 old; decreasing diss. 2054.12 by -9.63432
swp new 86 <-> 70 old; decreasing diss. 2044.48 by -2.56554
swp new 123 <-> 86 old; decreasing diss. 2041.92 by -1.30247
swp new 414 <-> 123 old; decreasing diss. 2040.62 by -0.130313
swp new 502 <-> 501 old; decreasing diss. 2040.49 by -36.5109
swp new 507 <-> 502 old; decreasing diss. 2003.97 by -131.351
swp new 509 <-> 507 old; decreasing diss. 1872.62 by -14.3528
swp new 530 <-> 509 old; decreasing diss. 1858.27 by -7.60641
swp new 542 <-> 530 old; decreasing diss. 1850.66 by -2.78128
swp new 574 <-> 542 old; decreasing diss. 1847.88 by -4.18202
swp new 913 <-> 574 old; decreasing diss. 1843.7 by -0.343729
swp new 915 <-> 913 old; decreasing diss. 1843.36 by -0.378301
swp new 1255 <-> 1251 old; decreasing diss. 1842.98 by -81.0222
swp new 1259 <-> 1255 old; decreasing diss. 1761.96 by -4.71205
swp new 1265 <-> 1259 old; decreasing diss. 1757.24 by -1.6954
swp new 1280 <-> 1265 old; decreasing diss. 1755.55 by -1.25118
swp new 1300 <-> 1280 old; decreasing diss. 1754.3 by -9.70566
swp new 1421 <-> 1300 old; decreasing diss. 1744.59 by -2.93951
swp new 1793 <-> 1421 old; decreasing diss. 1741.65 by -0.00984953
swp new 2089 <-> 2001 old; decreasing diss. 1741.64 by -3.18091
swp new 2153 <-> 2089 old; decreasing diss. 1738.46 by -5.2211
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.227 0.000 0.228
> show6Ratios('6:orig' = st6/st0, '5:orig' = st5/st0, '4:orig' = st4/st0, '3:orig' = st3/st0, '2:orig' = st2/st0, '1:orig' = st1/st0)
6:orig 5:orig 4:orig 3:orig 2:orig 1:orig
Time user.self 0.13198 0.16802 0.19128 0.25698 0.53198 0.76686
Time sys.child NaN NaN NaN NaN NaN NaN
>
> ## only call element is not equal
> stopifnot(all.equal(pamxst [-icall], pamxoncest [-icall]),
+ all.equal(pamxoncest[-icall], pamxonce2st[-icall]),
+ all.equal(pamxoncest[-icall], pamxonce3st[-icall]),
+ all.equal(pamxoncest[-icall], pamxonce4st[-icall]),
+ all.equal(pamxoncest[-icall], pamxonce5st[-icall]),
+ all.equal(pamxoncest[-icall], pamxonce6st[-icall]))
>
> ## Different starting values
> med0 <- 1:4 # lynne (~ 2010, AMD Phenom II X4 925)
> st0 <- showSys.time(pamxst <- pam(x, 4, medoids = med0, trace.lev=2))# 13.416 0.023 13.529
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=0): medoids given; after build: medoids are 1 2 3 4
swp new 1421 <-> 4 old; decreasing diss. 21009.4 by -15939.9
swp new 2153 <-> 3 old; decreasing diss. 5069.52 by -1657.88
swp new 915 <-> 2 old; decreasing diss. 3411.65 by -1592.06
swp new 414 <-> 1 old; decreasing diss. 1819.59 by -86.3404
swp new 1793 <-> 1421 old; decreasing diss. 1733.25 by -0.00639415
end{bswap()}, end{cstat()}
Time user system elapsed
Time 1.727 0.000 1.732
> st1 <- showSys.time(pamxoncest <- pam(x, 4, medoids = med0, pamonce=TRUE, trace.lev=2))# 8.384 0.024 8.459
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=1): medoids given; after build: medoids are 1 2 3 4
swp new 1421 <-> 4 old; decreasing diss. 21009.4 by -15939.9
swp new 2153 <-> 3 old; decreasing diss. 5069.52 by -1657.88
swp new 915 <-> 2 old; decreasing diss. 3411.65 by -1592.06
swp new 414 <-> 1 old; decreasing diss. 1819.59 by -86.3404
swp new 1793 <-> 1421 old; decreasing diss. 1733.25 by -0.00639415
end{bswap()}, end{cstat()}
Time user system elapsed
Time 1.362 0.000 1.366
> st2 <- showSys.time(pamxonce2st <- pam(x, 4, medoids = med0, pamonce=2, trace.lev=2))# 5.455 0.030 5.520
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=2): medoids given; after build: medoids are 1 2 3 4
swp new 1421 <-> 4 old; decreasing diss. 21009.4 by -15939.9
swp new 2153 <-> 3 old; decreasing diss. 5069.52 by -1657.88
swp new 915 <-> 2 old; decreasing diss. 3411.65 by -1592.06
swp new 414 <-> 1 old; decreasing diss. 1819.59 by -86.3404
swp new 1793 <-> 1421 old; decreasing diss. 1733.25 by -0.00639415
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.926 0.000 0.929
> st3 <- showSys.time(pamxonce3st <- pam(x, 4, medoids = med0, pamonce=3, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=3): medoids given; after build: medoids are 1 2 3 4
swp new 1421 <-> 4 old; decreasing diss. 21009.4 by -15939.9
swp new 2153 <-> 3 old; decreasing diss. 5069.52 by -1657.88
swp new 915 <-> 2 old; decreasing diss. 3411.65 by -1592.06
swp new 414 <-> 1 old; decreasing diss. 1819.59 by -86.3404
swp new 1793 <-> 1421 old; decreasing diss. 1733.25 by -0.00639415
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.504 0.000 0.505
> st4 <- showSys.time(pamxonce4st <- pam(x, 4, medoids = med0, pamonce=4, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=4): medoids given; after build: medoids are 1 2 3 4
swp new 1421 <-> 4 old; decreasing diss. 21009.4 by -15939.9
swp new 2153 <-> 3 old; decreasing diss. 5069.52 by -1657.88
swp new 915 <-> 2 old; decreasing diss. 3411.65 by -1592.06
swp new 414 <-> 1 old; decreasing diss. 1819.59 by -86.3404
fswp new 1793 <-> 1421 old; decreasing diss. 1733.25 by -0.00639415
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.449 0.000 0.451
> st5 <- showSys.time(pamxonce5st <- pam(x, 4, medoids = med0, pamonce=5, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=5): medoids given; after build: medoids are 1 2 3 4
swp new 1421 <-> 4 old; decreasing diss. 21009.4 by -15939.9
swp new 2153 <-> 3 old; decreasing diss. 5069.52 by -1657.88
swp new 915 <-> 2 old; decreasing diss. 3411.65 by -1592.06
swp new 414 <-> 1 old; decreasing diss. 1819.59 by -86.3404
fswp new 1793 <-> 1421 old; decreasing diss. 1733.25 by -0.00639415
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.338 0.000 0.340
> st6 <- showSys.time(pamxonce6st <- pam(x, 4, medoids = med0, pamonce=6, trace.lev=2))#
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=6): medoids given; after build: medoids are 1 2 3 4
swp new 7 <-> 3 old; decreasing diss. 21009.4 by -4.9986
swp new 8 <-> 2 old; decreasing diss. 21004.4 by -16.4723
swp new 11 <-> 7 old; decreasing diss. 20987.9 by -12.0798
swp new 12 <-> 8 old; decreasing diss. 20975.9 by -3.31491
swp new 13 <-> 1 old; decreasing diss. 20972.5 by -4.88251
swp new 14 <-> 13 old; decreasing diss. 20967.7 by -1.90148
swp new 15 <-> 14 old; decreasing diss. 20965.8 by -9.59363
swp new 22 <-> 11 old; decreasing diss. 20956.2 by -10.0478
swp new 24 <-> 15 old; decreasing diss. 20946.1 by -7.99457
swp new 26 <-> 12 old; decreasing diss. 20938.1 by -3.0991
swp new 44 <-> 22 old; decreasing diss. 20935 by -880.436
swp new 45 <-> 4 old; decreasing diss. 20054.6 by -9.44638
swp new 55 <-> 26 old; decreasing diss. 20045.1 by -1.77816
swp new 57 <-> 24 old; decreasing diss. 20043.4 by -1.76138
swp new 58 <-> 44 old; decreasing diss. 20041.6 by -178.122
swp new 63 <-> 45 old; decreasing diss. 19863.5 by -0.34634
swp new 71 <-> 57 old; decreasing diss. 19863.1 by -1.49747
swp new 75 <-> 63 old; decreasing diss. 19861.6 by -1.31804
swp new 76 <-> 71 old; decreasing diss. 19860.3 by -1.42789
swp new 80 <-> 55 old; decreasing diss. 19858.9 by -5.29615
swp new 81 <-> 75 old; decreasing diss. 19853.6 by -0.164308
swp new 83 <-> 76 old; decreasing diss. 19853.4 by -0.0877432
swp new 115 <-> 81 old; decreasing diss. 19853.3 by -0.303527
swp new 171 <-> 58 old; decreasing diss. 19853 by -971.874
swp new 185 <-> 115 old; decreasing diss. 18881.2 by -0.471238
swp new 192 <-> 80 old; decreasing diss. 18880.7 by -0.294662
swp new 257 <-> 185 old; decreasing diss. 18880.4 by -0.466733
swp new 290 <-> 257 old; decreasing diss. 18879.9 by -0.105762
swp new 309 <-> 83 old; decreasing diss. 18879.8 by -0.0395709
swp new 419 <-> 192 old; decreasing diss. 18879.8 by -0.0439214
swp new 425 <-> 309 old; decreasing diss. 18879.7 by -0.136105
swp new 471 <-> 425 old; decreasing diss. 18879.6 by -0.244642
swp new 501 <-> 171 old; decreasing diss. 18879.4 by -11830.7
swp new 502 <-> 471 old; decreasing diss. 7048.64 by -273.113
swp new 503 <-> 290 old; decreasing diss. 6775.52 by -813.133
swp new 504 <-> 502 old; decreasing diss. 5962.39 by -20.8894
swp new 507 <-> 504 old; decreasing diss. 5941.5 by -42.7153
swp new 515 <-> 501 old; decreasing diss. 5898.79 by -0.453931
swp new 523 <-> 515 old; decreasing diss. 5898.33 by -67.1248
swp new 526 <-> 507 old; decreasing diss. 5831.21 by -0.673248
swp new 527 <-> 526 old; decreasing diss. 5830.53 by -2.26904
swp new 537 <-> 503 old; decreasing diss. 5828.26 by -3.1636
swp new 542 <-> 527 old; decreasing diss. 5825.1 by -2.0623
swp new 545 <-> 542 old; decreasing diss. 5823.04 by -17.9136
swp new 547 <-> 523 old; decreasing diss. 5805.13 by -194.143
swp new 573 <-> 547 old; decreasing diss. 5610.98 by -607.192
swp new 576 <-> 545 old; decreasing diss. 5003.79 by -0.801911
swp new 579 <-> 573 old; decreasing diss. 5002.99 by -347.734
swp new 592 <-> 576 old; decreasing diss. 4655.26 by -0.997177
swp new 604 <-> 592 old; decreasing diss. 4654.26 by -0.808458
swp new 617 <-> 604 old; decreasing diss. 4653.45 by -1.02162
swp new 813 <-> 537 old; decreasing diss. 4652.43 by -0.254896
swp new 883 <-> 617 old; decreasing diss. 4652.17 by -1.83048
swp new 955 <-> 813 old; decreasing diss. 4650.34 by -0.591944
swp new 1015 <-> 883 old; decreasing diss. 4649.75 by -0.0303283
swp new 1016 <-> 955 old; decreasing diss. 4649.72 by -0.343593
swp new 1086 <-> 1016 old; decreasing diss. 4649.38 by -0.0942057
swp new 1088 <-> 1015 old; decreasing diss. 4649.28 by -0.0747132
swp new 1111 <-> 1086 old; decreasing diss. 4649.21 by -0.996659
swp new 1131 <-> 1088 old; decreasing diss. 4648.21 by -0.773913
swp new 1134 <-> 1111 old; decreasing diss. 4647.44 by -0.174449
swp new 1151 <-> 1131 old; decreasing diss. 4647.26 by -0.319467
swp new 1251 <-> 579 old; decreasing diss. 4646.94 by -1367.16
swp new 1252 <-> 1251 old; decreasing diss. 3279.79 by -27.203
swp new 1253 <-> 1151 old; decreasing diss. 3252.58 by -137.657
swp new 1255 <-> 1252 old; decreasing diss. 3114.93 by -52.4651
swp new 1257 <-> 1253 old; decreasing diss. 3062.46 by -42.3678
swp new 1259 <-> 1255 old; decreasing diss. 3020.09 by -1.87135
swp new 1266 <-> 1257 old; decreasing diss. 3018.22 by -90.6385
swp new 1280 <-> 1259 old; decreasing diss. 2927.58 by -20.3614
swp new 1283 <-> 1266 old; decreasing diss. 2907.22 by -272.98
swp new 1288 <-> 1280 old; decreasing diss. 2634.24 by -1.69952
swp new 1300 <-> 1288 old; decreasing diss. 2632.54 by -9.58469
swp new 1325 <-> 1300 old; decreasing diss. 2622.96 by -7.37653
swp new 1612 <-> 1283 old; decreasing diss. 2615.58 by -2.38886
swp new 2001 <-> 1612 old; decreasing diss. 2613.19 by -788.545
swp new 2089 <-> 2001 old; decreasing diss. 1824.65 by -3.28836
swp new 2153 <-> 2089 old; decreasing diss. 1821.36 by -5.16445
swp new 12 <-> 419 old; decreasing diss. 1816.19 by -28.3676
swp new 15 <-> 12 old; decreasing diss. 1787.83 by -2.88573
swp new 16 <-> 15 old; decreasing diss. 1784.94 by -3.50571
swp new 33 <-> 16 old; decreasing diss. 1781.43 by -0.382726
swp new 56 <-> 33 old; decreasing diss. 1781.05 by -0.660581
swp new 70 <-> 56 old; decreasing diss. 1780.39 by -9.63432
swp new 86 <-> 70 old; decreasing diss. 1770.76 by -2.56554
swp new 123 <-> 86 old; decreasing diss. 1768.19 by -1.30247
swp new 414 <-> 123 old; decreasing diss. 1766.89 by -0.130313
swp new 507 <-> 1134 old; decreasing diss. 1766.76 by -1.67967
swp new 509 <-> 507 old; decreasing diss. 1765.08 by -11.8255
swp new 530 <-> 509 old; decreasing diss. 1753.25 by -9.54887
swp new 542 <-> 530 old; decreasing diss. 1743.7 by -2.59694
swp new 574 <-> 542 old; decreasing diss. 1741.11 by -3.50085
swp new 913 <-> 574 old; decreasing diss. 1737.61 by -0.356354
swp new 915 <-> 913 old; decreasing diss. 1737.25 by -0.471447
swp new 1300 <-> 1325 old; decreasing diss. 1736.78 by -0.589135
swp new 1421 <-> 1300 old; decreasing diss. 1736.19 by -2.94296
swp new 1793 <-> 1421 old; decreasing diss. 1733.25 by -0.00639415
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.258 0.000 0.259
> show6Ratios('6:orig' = st6/st0, '5:orig' = st5/st0, '4:orig' = st4/st0, '3:orig' = st3/st0, '2:orig' = st2/st0, '1:orig' = st1/st0)
6:orig 5:orig 4:orig 3:orig 2:orig 1:orig
Time user.self 0.14939 0.19572 0.25999 0.29184 0.53619 0.78865
Time sys.child NaN NaN NaN NaN NaN NaN
>
> ## only call element is not equal
> stopifnot(all.equal(pamxst [-icall], pamxoncest [-icall]),
+ all.equal(pamxoncest[-icall], pamxonce2st[-icall]),
+ all.equal(pamxoncest[-icall], pamxonce3st[-icall]),
+ all.equal(pamxoncest[-icall], pamxonce4st[-icall]),
+ all.equal(pamxoncest[-icall], pamxonce5st[-icall]),
+ all.equal(pamxoncest[-icall], pamxonce6st[-icall]))
>
>
> ## Medoid bug --- MM: Fixed, well "0L+ hack", in my pam.q, on 2012-01-31
> ## ----------
> med0 <- (1:6)
> st0 <- showSys.time(pamxst <- pam(x, 6, medoids = med0 , trace.lev=2))
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=0): medoids given; after build: medoids are 1 2 3 4 5 6
swp new 1421 <-> 6 old; decreasing diss. 20991.1 by -15949.5
swp new 2153 <-> 4 old; decreasing diss. 5041.66 by -1676.25
swp new 915 <-> 3 old; decreasing diss. 3365.41 by -1671.37
swp new 325 <-> 2 old; decreasing diss. 1694.04 by -53.8582
swp new 2720 <-> 5 old; decreasing diss. 1640.18 by -26.6572
swp new 2696 <-> 2153 old; decreasing diss. 1613.53 by -19.0531
swp new 52 <-> 1 old; decreasing diss. 1594.47 by -13.965
swp new 2709 <-> 2720 old; decreasing diss. 1580.51 by -5.81848
swp new 199 <-> 325 old; decreasing diss. 1574.69 by -2.65496
swp new 438 <-> 52 old; decreasing diss. 1572.03 by -1.77054
swp new 2082 <-> 2696 old; decreasing diss. 1570.26 by -0.187256
end{bswap()}, end{cstat()}
Time user system elapsed
Time 4.345 0.000 4.357
> stopifnot(identical(med0, 1:6))
> med0 <- (1:6)
> st1 <- showSys.time(pamxst.1 <- pam(x, 6, medoids = med0 , pamonce=1, trace.lev=2))
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=1): medoids given; after build: medoids are 1 2 3 4 5 6
swp new 1421 <-> 6 old; decreasing diss. 20991.1 by -15949.5
swp new 2153 <-> 4 old; decreasing diss. 5041.66 by -1676.25
swp new 915 <-> 3 old; decreasing diss. 3365.41 by -1671.37
swp new 325 <-> 2 old; decreasing diss. 1694.04 by -53.8582
swp new 2720 <-> 5 old; decreasing diss. 1640.18 by -26.6572
swp new 2696 <-> 2153 old; decreasing diss. 1613.53 by -19.0531
swp new 52 <-> 1 old; decreasing diss. 1594.47 by -13.965
swp new 2709 <-> 2720 old; decreasing diss. 1580.51 by -5.81848
swp new 199 <-> 325 old; decreasing diss. 1574.69 by -2.65496
swp new 438 <-> 52 old; decreasing diss. 1572.03 by -1.77054
swp new 2082 <-> 2696 old; decreasing diss. 1570.26 by -0.187256
end{bswap()}, end{cstat()}
Time user system elapsed
Time 3.753 0.000 3.763
> stopifnot(identical(med0, 1:6))
> med0 <- (1:6)
> st2 <- showSys.time(pamxst.2 <- pam(x, 6, medoids = med0 , pamonce=2, trace.lev=2))
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=2): medoids given; after build: medoids are 1 2 3 4 5 6
swp new 1421 <-> 6 old; decreasing diss. 20991.1 by -15949.5
swp new 2153 <-> 4 old; decreasing diss. 5041.66 by -1676.25
swp new 915 <-> 3 old; decreasing diss. 3365.41 by -1671.37
swp new 325 <-> 2 old; decreasing diss. 1694.04 by -53.8582
swp new 2720 <-> 5 old; decreasing diss. 1640.18 by -26.6572
swp new 2696 <-> 2153 old; decreasing diss. 1613.53 by -19.0531
swp new 52 <-> 1 old; decreasing diss. 1594.47 by -13.965
swp new 2709 <-> 2720 old; decreasing diss. 1580.51 by -5.81848
swp new 199 <-> 325 old; decreasing diss. 1574.69 by -2.65496
swp new 438 <-> 52 old; decreasing diss. 1572.03 by -1.77054
swp new 2082 <-> 2696 old; decreasing diss. 1570.26 by -0.187256
end{bswap()}, end{cstat()}
Time user system elapsed
Time 2.519 0.001 2.530
> stopifnot(identical(med0, 1:6))
> med0 <- (1:6)
> st3 <- showSys.time(pamxst.3 <- pam(x, 6, medoids = med0 , pamonce=3, trace.lev=2))
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=3): medoids given; after build: medoids are 1 2 3 4 5 6
swp new 1421 <-> 6 old; decreasing diss. 20991.1 by -15949.5
swp new 2153 <-> 4 old; decreasing diss. 5041.66 by -1676.25
swp new 915 <-> 3 old; decreasing diss. 3365.41 by -1671.37
swp new 325 <-> 2 old; decreasing diss. 1694.04 by -53.8582
swp new 2720 <-> 5 old; decreasing diss. 1640.18 by -26.6572
swp new 2696 <-> 2153 old; decreasing diss. 1613.53 by -19.0531
swp new 52 <-> 1 old; decreasing diss. 1594.47 by -13.965
swp new 2709 <-> 2720 old; decreasing diss. 1580.51 by -5.81848
swp new 199 <-> 325 old; decreasing diss. 1574.69 by -2.65496
swp new 438 <-> 52 old; decreasing diss. 1572.03 by -1.77054
swp new 2082 <-> 2696 old; decreasing diss. 1570.26 by -0.187256
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.854 0.000 0.857
> stopifnot(identical(med0, 1:6))
> med0 <- (1:6)
> st4 <- showSys.time(pamxst.4 <- pam(x, 6, medoids = med0 , pamonce=4, trace.lev=2))
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=4): medoids given; after build: medoids are 1 2 3 4 5 6
swp new 1421 <-> 6 old; decreasing diss. 20991.1 by -15949.5
swp new 2153 <-> 4 old; decreasing diss. 5041.66 by -1676.25
swp new 915 <-> 3 old; decreasing diss. 3365.41 by -1671.37
swp new 325 <-> 2 old; decreasing diss. 1694.04 by -53.8582
fswp new 2720 <-> 5 old; decreasing diss. 1640.18 by -26.6572
swp new 2696 <-> 2153 old; decreasing diss. 1613.53 by -19.0531
fswp new 52 <-> 1 old; decreasing diss. 1594.47 by -13.965
swp new 2709 <-> 2720 old; decreasing diss. 1580.51 by -5.81848
fswp new 199 <-> 325 old; decreasing diss. 1574.69 by -2.65496
swp new 438 <-> 52 old; decreasing diss. 1572.03 by -1.77054
fswp new 2082 <-> 2696 old; decreasing diss. 1570.26 by -0.187256
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.619 0.000 0.620
> stopifnot(identical(med0, 1:6))
> med0 <- (1:6)
> st5 <- showSys.time(pamxst.5 <- pam(x, 6, medoids = med0 , pamonce=5, trace.lev=2))
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=5): medoids given; after build: medoids are 1 2 3 4 5 6
swp new 1421 <-> 6 old; decreasing diss. 20991.1 by -15949.5
swp new 2153 <-> 4 old; decreasing diss. 5041.66 by -1676.25
swp new 915 <-> 3 old; decreasing diss. 3365.41 by -1671.37
swp new 325 <-> 2 old; decreasing diss. 1694.04 by -53.8582
fswp new 2720 <-> 5 old; decreasing diss. 1640.18 by -26.6572
swp new 2696 <-> 2153 old; decreasing diss. 1613.53 by -19.0531
fswp new 52 <-> 1 old; decreasing diss. 1594.47 by -13.965
swp new 2709 <-> 2720 old; decreasing diss. 1580.51 by -5.81848
fswp new 199 <-> 325 old; decreasing diss. 1574.69 by -2.65496
swp new 438 <-> 52 old; decreasing diss. 1572.03 by -1.77054
fswp new 2082 <-> 2696 old; decreasing diss. 1570.26 by -0.187256
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.473 0.001 0.476
> stopifnot(identical(med0, 1:6))
> med0 <- (1:6)
> st6 <- showSys.time(pamxst.6 <- pam(x, 6, medoids = med0 , pamonce=6, trace.lev=2))
C pam(): computing 3779876 dissimilarities from 2750 x 2 matrix: [Ok]
pam()'s bswap(*, s=15.7788, pamonce=6): medoids given; after build: medoids are 1 2 3 4 5 6
swp new 7 <-> 6 old; decreasing diss. 20991.1 by -3.78677
swp new 8 <-> 5 old; decreasing diss. 20987.4 by -25.0122
swp new 9 <-> 2 old; decreasing diss. 20962.3 by -6.53934
swp new 10 <-> 7 old; decreasing diss. 20955.8 by -0.133503
swp new 11 <-> 9 old; decreasing diss. 20955.7 by -14.9294
swp new 12 <-> 10 old; decreasing diss. 20940.7 by -2.35016
swp new 13 <-> 12 old; decreasing diss. 20938.4 by -12.1267
swp new 14 <-> 11 old; decreasing diss. 20926.3 by -2.99664
swp new 15 <-> 13 old; decreasing diss. 20923.3 by -11.5871
swp new 20 <-> 1 old; decreasing diss. 20911.7 by -0.0423835
swp new 22 <-> 3 old; decreasing diss. 20911.6 by -8.76936
swp new 23 <-> 20 old; decreasing diss. 20902.9 by -0.779336
swp new 39 <-> 15 old; decreasing diss. 20902.1 by -0.285867
swp new 44 <-> 4 old; decreasing diss. 20901.8 by -889.012
swp new 45 <-> 14 old; decreasing diss. 20012.8 by -0.586001
swp new 46 <-> 45 old; decreasing diss. 20012.2 by -1.1479
swp new 57 <-> 39 old; decreasing diss. 20011.1 by -3.67274
swp new 58 <-> 44 old; decreasing diss. 20007.4 by -189.36
swp new 62 <-> 23 old; decreasing diss. 19818 by -0.626154
swp new 66 <-> 46 old; decreasing diss. 19817.4 by -1.29082
swp new 68 <-> 22 old; decreasing diss. 19816.1 by -0.580804
swp new 75 <-> 62 old; decreasing diss. 19815.5 by -0.860328
swp new 76 <-> 57 old; decreasing diss. 19814.7 by -1.97434
swp new 171 <-> 58 old; decreasing diss. 19812.7 by -968.625
swp new 185 <-> 75 old; decreasing diss. 18844.1 by -0.12972
swp new 198 <-> 68 old; decreasing diss. 18843.9 by -0.533904
swp new 204 <-> 198 old; decreasing diss. 18843.4 by -0.00928895
swp new 218 <-> 185 old; decreasing diss. 18843.4 by -0.0287215
swp new 280 <-> 204 old; decreasing diss. 18843.4 by -0.913559
swp new 329 <-> 280 old; decreasing diss. 18842.5 by -0.101916
swp new 361 <-> 8 old; decreasing diss. 18842.3 by -0.0966188
swp new 402 <-> 361 old; decreasing diss. 18842.3 by -0.0394578
swp new 501 <-> 171 old; decreasing diss. 18842.2 by -11833
swp new 502 <-> 66 old; decreasing diss. 7009.24 by -309.206
swp new 503 <-> 329 old; decreasing diss. 6700.03 by -887.644
swp new 504 <-> 76 old; decreasing diss. 5812.39 by -28.554
swp new 506 <-> 504 old; decreasing diss. 5783.83 by -14.2809
swp new 507 <-> 506 old; decreasing diss. 5769.55 by -19.8604
swp new 508 <-> 507 old; decreasing diss. 5749.69 by -7.88586
swp new 515 <-> 501 old; decreasing diss. 5741.8 by -3.36477
swp new 519 <-> 502 old; decreasing diss. 5738.44 by -1.16993
swp new 523 <-> 515 old; decreasing diss. 5737.27 by -71.9147
swp new 526 <-> 508 old; decreasing diss. 5665.35 by -1.56708
swp new 533 <-> 526 old; decreasing diss. 5663.79 by -1.652
swp new 537 <-> 519 old; decreasing diss. 5662.14 by -0.283073
swp new 540 <-> 537 old; decreasing diss. 5661.85 by -2.35314
swp new 545 <-> 533 old; decreasing diss. 5659.5 by -2.94604
swp new 547 <-> 503 old; decreasing diss. 5656.55 by -221.229
swp new 573 <-> 547 old; decreasing diss. 5435.32 by -574.75
swp new 575 <-> 523 old; decreasing diss. 4860.57 by -17.1525
swp new 579 <-> 573 old; decreasing diss. 4843.42 by -346.495
swp new 593 <-> 575 old; decreasing diss. 4496.93 by -2.83188
swp new 594 <-> 540 old; decreasing diss. 4494.1 by -0.50804
swp new 618 <-> 545 old; decreasing diss. 4493.59 by -0.137577
swp new 660 <-> 618 old; decreasing diss. 4493.45 by -4.45459
swp new 663 <-> 593 old; decreasing diss. 4489 by -0.912682
swp new 709 <-> 663 old; decreasing diss. 4488.08 by -1.44419
swp new 848 <-> 594 old; decreasing diss. 4486.64 by -0.00137738
swp new 991 <-> 709 old; decreasing diss. 4486.64 by -0.173764
swp new 1242 <-> 660 old; decreasing diss. 4486.46 by -0.244432
swp new 1251 <-> 579 old; decreasing diss. 4486.22 by -1369.29
swp new 1252 <-> 402 old; decreasing diss. 3116.93 by -58.5165
swp new 1253 <-> 1252 old; decreasing diss. 3058.42 by -169.451
swp new 1255 <-> 1251 old; decreasing diss. 2888.97 by -30.8092
swp new 1257 <-> 848 old; decreasing diss. 2858.16 by -70.3794
swp new 1262 <-> 1255 old; decreasing diss. 2787.78 by -29.3154
swp new 1266 <-> 1253 old; decreasing diss. 2758.46 by -64.8733
swp new 1282 <-> 1262 old; decreasing diss. 2693.59 by -0.803303
swp new 1283 <-> 1257 old; decreasing diss. 2692.79 by -238.314
swp new 1284 <-> 1266 old; decreasing diss. 2454.47 by -6.79018
swp new 1289 <-> 1284 old; decreasing diss. 2447.68 by -6.25715
swp new 1291 <-> 1289 old; decreasing diss. 2441.42 by -1.33251
swp new 1293 <-> 1291 old; decreasing diss. 2440.09 by -4.73682
swp new 1299 <-> 1293 old; decreasing diss. 2435.35 by -5.10123
swp new 1301 <-> 1282 old; decreasing diss. 2430.25 by -3.83834
swp new 1322 <-> 1301 old; decreasing diss. 2426.41 by -1.61521
swp new 1332 <-> 1322 old; decreasing diss. 2424.8 by -1.66893
swp new 1335 <-> 1299 old; decreasing diss. 2423.13 by -9.28429
swp new 1374 <-> 1332 old; decreasing diss. 2413.85 by -1.14706
swp new 1469 <-> 1374 old; decreasing diss. 2412.7 by -0.0376604
swp new 1583 <-> 1335 old; decreasing diss. 2412.66 by -0.0266074
swp new 1612 <-> 1283 old; decreasing diss. 2412.64 by -1.78413
swp new 2001 <-> 1612 old; decreasing diss. 2410.85 by -794.078
swp new 2089 <-> 2001 old; decreasing diss. 1616.77 by -3.16676
swp new 2153 <-> 2089 old; decreasing diss. 1613.61 by -5.2914
swp new 12 <-> 218 old; decreasing diss. 1608.32 by -9.02144
swp new 15 <-> 12 old; decreasing diss. 1599.29 by -2.88573
swp new 16 <-> 15 old; decreasing diss. 1596.41 by -3.50571
swp new 33 <-> 16 old; decreasing diss. 1592.9 by -0.382726
swp new 56 <-> 33 old; decreasing diss. 1592.52 by -0.660581
swp new 70 <-> 56 old; decreasing diss. 1591.86 by -9.63432
swp new 86 <-> 70 old; decreasing diss. 1582.22 by -2.56554
swp new 123 <-> 86 old; decreasing diss. 1579.66 by -1.30247
swp new 414 <-> 123 old; decreasing diss. 1578.36 by -0.130313
swp new 507 <-> 1242 old; decreasing diss. 1578.23 by -16.7114
swp new 513 <-> 991 old; decreasing diss. 1561.51 by -2.15127
swp new 518 <-> 507 old; decreasing diss. 1559.36 by -7.23052
swp new 556 <-> 518 old; decreasing diss. 1552.13 by -1.79073
swp new 622 <-> 513 old; decreasing diss. 1550.34 by -0.947204
swp new 822 <-> 556 old; decreasing diss. 1549.4 by -0.320612
swp new 926 <-> 622 old; decreasing diss. 1549.07 by -1.31521
swp new 1106 <-> 926 old; decreasing diss. 1547.76 by -0.187491
swp new 1124 <-> 822 old; decreasing diss. 1547.57 by -0.261824
swp new 1194 <-> 1124 old; decreasing diss. 1547.31 by -0.0387596
swp new 1256 <-> 1583 old; decreasing diss. 1547.27 by -1.04235
swp new 1262 <-> 1469 old; decreasing diss. 1546.23 by -2.25214
swp new 1429 <-> 1256 old; decreasing diss. 1543.98 by -0.359624
swp new 1592 <-> 1429 old; decreasing diss. 1543.62 by -0.641983
swp new 2286 <-> 2153 old; decreasing diss. 1542.98 by -0.109986
swp new 2482 <-> 2286 old; decreasing diss. 1542.87 by -0.0744393
end{bswap()}, end{cstat()}
Time user system elapsed
Time 0.282 0.001 0.284
> stopifnot(identical(med0, 1:6))
> stopifnot(all.equal(pamxst[-icall], pamxst.1 [-icall]),
+ all.equal(pamxst[-icall], pamxst.2 [-icall]),
+ all.equal(pamxst[-icall], pamxst.3 [-icall]),
+ all.equal(pamxst[-icall], pamxst.4 [-icall]),
+ all.equal(pamxst[-icall], pamxst.5 [-icall]))
> # FasterPAM finds a better solution here, by chance
> stopifnot(pamxst$objective >= pamxst.6$objective)
>
>
> ## Last Line:
> cat('Time elapsed: ', proc.time() - .proctime00,'\n')
Time elapsed: 29.923 0.049 30.113 0 0
>
>
> proc.time()
user system elapsed
30.026 0.082 30.241
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