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