CRAN Package Check Results for Package fastcluster

Last updated on 2017-07-25 07:46:41.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.1.22 3.03 22.09 25.12 ERROR
r-devel-linux-x86_64-debian-gcc 1.1.22 2.84 21.79 24.64 ERROR
r-devel-linux-x86_64-fedora-clang 1.1.22 37.02 ERROR
r-devel-linux-x86_64-fedora-gcc 1.1.22 31.47 ERROR
r-devel-windows-ix86+x86_64 1.1.22 16.00 118.00 134.00 OK
r-patched-linux-x86_64 1.1.22 2.84 35.34 38.18 NOTE
r-patched-solaris-x86 1.1.22 96.20 OK
r-release-linux-x86_64 1.1.22 2.81 37.67 40.48 NOTE
r-release-windows-ix86+x86_64 1.1.22 12.00 97.00 109.00 OK
r-release-osx-x86_64 1.1.22 OK
r-oldrel-windows-ix86+x86_64 1.1.22 16.00 106.00 122.00 OK
r-oldrel-osx-x86_64 1.1.22 OK

Check Details

Version: 1.1.22
Check: compiled code
Result: NOTE
    File ‘fastcluster/libs/fastcluster.so’:
     Found no call to: ‘R_useDynamicSymbols’
    
    It is good practice to register native routines and to disable symbol
    search.
    
    See ‘Writing portable packages’ in the ‘Writing R Extensions’ manual.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.1.22
Check: tests
Result: ERROR
     Running ‘test_fastcluster.R’ [2s/2s]
    Running the tests in ‘tests/test_fastcluster.R’ failed.
    Complete output:
     > # fastcluster: Fast hierarchical clustering routines for R and Python
     > #
     > # Copyright © 2011 Daniel Müllner
     > # <http://danifold.net>
     > #
     > # Test script for the R interface
     >
     > seed = as.integer(runif(1, 0, 1e9))
     > set.seed(seed)
     > cat(sprintf("Random seed: %d\n",seed))
     Random seed: 587073007
     >
     > print_seed <- function() {
     + return(sprintf('
     + Please send a report to the author of the \'fastcluster\' package, Daniel Müllner.
     + For contact details, see <http://danifold.net>. To make the error
     + reproducible, you must include the following number (the random seed value) in
     + your error report: %d.\n\n', seed))
     + }
     >
     > hasWardD2 = getRversion() >= '3.1.0'
     >
     > # Compare two dendrograms and check whether they are equal, except that
     > # ties may be resolved differently.
     > compare <- function(dg1, dg2) {
     + h1 <- dg1$height
     + h2 <- dg2$height
     + # "height" vectors may have small numerical errors.
     + rdiffs <- abs(h1-h2)/pmax(abs(h1),abs(h2))
     + rdiffs = rdiffs[complete.cases(rdiffs)]
     + rel_error <- max(rdiffs)
     + # We allow a relative error of 1e-13.
     + if (rel_error>1e-13) {
     + print(h1)
     + print(h2)
     + cat(sprintf('Height vectors differ! The maximum relative error is %e.\n', rel_error))
     + return(FALSE)
     + }
     + # Filter the indices where consecutive merging distances are distinct.
     + d = diff(dg1$height)
     + b = (c(d,1)!=0 & c(1,d)!=0)
     + #cat(sprintf("Percentage of indices where we can test: %g.\n",100.0*length(b[b])/length(b)))
     + if (any(b)) {
     + m1 = dg1$merge[b,]
     + m2 = dg2$merge[b,]
     +
     + r = function(i) {
     + if (i<0) {
     + return(1)
     + }
     + else {
     + return(b[i])
     + }
     + }
     +
     + f = sapply(m1,r)
     + fm1 = m1*f
     + fm2 = m2*f
     + # The "merge" matrices must be identical whereever indices are not ambiguous
     + # due to ties.
     + if (!identical(fm1,fm2)) {
     + cat('Merge matrices differ!\n')
     + return(FALSE)
     + }
     + # Compare the "order" vectors only if all merging distances were distinct.
     + if (all(b) && !identical(dg1$order,dg2$order)) {
     + cat('Order vectors differ!\n')
     + return(FALSE)
     + }
     + }
     + return(TRUE)
     + }
     >
     > # Generate uniformly distributed random data
     > generate.uniform <- function() {
     + n = sample(10:1000,1)
     + range_exp = runif(1,min=-10, max=10)
     + cat(sprintf("Number of sample points: %d\n",n))
     + cat(sprintf("Dissimilarity range: [0,%g]\n",10^range_exp))
     + d = runif(n*(n-1)/2, min=0, max=10^range_exp)
     + # Fake a compressed distance matrix
     + attributes(d) <- NULL
     + attr(d,"Size") <- n
     + attr(d, "call") <- 'N/A'
     + class(d) <- "dist"
     + return(d)
     + }
     >
     > # Generate normally distributed random data
     > generate.normal <- function() {
     + n = sample(10:1000,1)
     + dim = sample(2:20,1)
     +
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     +
     + pcd = matrix(rnorm(n*dim), c(n,dim))
     + d = dist(pcd)
     + return(d)
     + }
     >
     > # Test the clustering functions when a distance matrix is given.
     > test.dm <- function(d) {
     + d2 = d
     + if (hasWardD2) {
     + methods = c('single','complete','average','mcquitty','ward.D','ward.D2','centroid','median')
     + }
     + else {
     + methods = c('single','complete','average','mcquitty','ward','centroid','median')
     + }
     + for (method in methods) {
     + cat(paste('Method :', method, '\n'))
     + dg_stats = stats::hclust(d, method=method)
     + if (method == 'ward') {
     + method = 'ward.D'
     + }
     + dg_fastcluster = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_stats, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + cat('Passed.\n')
     + }
     >
     > # Test the clustering functions for vector input in Euclidean space.
     > test.vector <- function() {
     + # generate test data
     + n = sample(10:1000,1)
     + dim = sample(2:20,1)
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     +
     + range_exp = runif(1,min=-10, max=10)
     + pcd = matrix(rnorm(n*dim, sd=10^range_exp), c(n,dim))
     + pcd2 = pcd
     + # test
     + method='single'
     + cat(paste('Method:', method, '\n'))
     + for (metric in c('euclidean', 'maximum', 'manhattan', 'canberra', 'minkowski')) {
     + cat(paste(' Metric:', metric, '\n'))
     + if (metric=='minkowski') {
     + p = runif(1, min=1.0, max=10.0)
     + cat (sprintf(" p: %g\n",p));
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric, p=p)
     + d = dist(pcd, method=metric, p=p)
     + }
     + else {
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric)
     + d = dist(pcd, method=metric)
     + }
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2) || !identical(pcd,pcd2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + for (method in c('ward','centroid','median') ) {
     + cat(paste('Method:', method, '\n'))
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method)
     + if (!identical(pcd,pcd2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + d = dist(pcd)
     + if(method == "ward" && hasWardD2) {
     + method = "ward.D2"
     + }
     + else
     + {
     + # Workaround: fastcluster::hclust expects _squared_ euclidean distances.
     + d = d^2
     + }
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if(method != "ward.D2") {
     + dg_fastcluster_dist$height = sqrt(dg_fastcluster_dist$height)
     + }
     + # The Euclidean methods may have small numerical errors due to squaring/
     + # taking the root in the Euclidean distances.
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + cat('Passed.\n')
     + }
     >
     > # Test the single linkage function with the "binary" metric
     > test.vector.binary <- function() {
     + # generate test data
     + cat (sprintf("Uniform sampling for the 'binary' metric:\n"))
     + n = sample(10:400,1)
     + dim = sample(n:(2*n),1)
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     + pcd = matrix(sample(-1:2, n*dim, replace=T), c(n,dim))
     + pcd2 = pcd
     + # test
     + method='single'
     + metric='binary'
     + cat(paste('Method:', method, '\n'))
     + cat(paste(' Metric:', metric, '\n'))
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric)
     + d = dist(pcd, method=metric)
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2) || !identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + cat('Passed.\n')
     + }
     >
     >
     > N = 15
     > for (i in (1:N)) {
     + if (i%%2==1) {
     + cat(sprintf('Random test %d of %d (uniform distribution of distances):\n',i,2*N))
     + d = generate.uniform()
     + }
     + else {
     + cat(sprintf('Random test %d of %d (Gaussian density):\n',i,2*N))
     + d = generate.normal()
     + }
     + test.dm(d)
     + }
     Random test 1 of 30 (uniform distribution of distances):
     Number of sample points: 255
     Dissimilarity range: [0,4.58208e-09]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 2 of 30 (Gaussian density):
     Number of sample points: 410
     Dimension: 3
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 3 of 30 (uniform distribution of distances):
     Number of sample points: 24
     Dissimilarity range: [0,4072.89]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 4 of 30 (Gaussian density):
     Number of sample points: 857
     Dimension: 15
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 5 of 30 (uniform distribution of distances):
     Number of sample points: 244
     Dissimilarity range: [0,5668.84]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 6 of 30 (Gaussian density):
     Number of sample points: 374
     Dimension: 18
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 7 of 30 (uniform distribution of distances):
     Number of sample points: 478
     Dissimilarity range: [0,104373]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 8 of 30 (Gaussian density):
     Number of sample points: 339
     Dimension: 17
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 9 of 30 (uniform distribution of distances):
     Number of sample points: 74
     Dissimilarity range: [0,0.0221521]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 10 of 30 (Gaussian density):
     Number of sample points: 969
     Dimension: 20
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 11 of 30 (uniform distribution of distances):
     Number of sample points: 46
     Dissimilarity range: [0,3.70412e-05]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 12 of 30 (Gaussian density):
     Number of sample points: 335
     Dimension: 10
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 13 of 30 (uniform distribution of distances):
     Number of sample points: 490
     Dissimilarity range: [0,4.03254e-06]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 14 of 30 (Gaussian density):
     Number of sample points: 735
     Dimension: 10
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 15 of 30 (uniform distribution of distances):
     Number of sample points: 410
     Dissimilarity range: [0,19.5648]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     > for (i in (N+1:N)) {
     + cat(sprintf('Random test %d of %d (Gaussian density):\n',i,2*N))
     + test.vector()
     + test.vector.binary()
     + }
     Random test 16 of 30 (Gaussian density):
     Number of sample points: 883
     Dimension: 2
     Method: single
     Metric: euclidean
     Metric: maximum
     Metric: manhattan
     Metric: canberra
     [1] 0.0007985965 0.0054315016 0.0075979387 0.0114340260 0.0119185052
     [6] 0.0122745732 0.0125760762 0.0132506874 0.0137326360 0.0139697577
     [11] 0.0142791457 0.0158414436 0.0162910981 0.0177295054 0.0177456836
     [16] 0.0181442655 0.0191992182 0.0199630888 0.0204979127 0.0208976516
     [21] 0.0209690027 0.0211038862 0.0220051307 0.0221117356 0.0224798853
     [26] 0.0224926638 0.0229032746 0.0236884995 0.0237686024 0.0240926696
     [31] 0.0241047438 0.0245625455 0.0247807941 0.0249135776 0.0249255167
     [36] 0.0253240695 0.0253690171 0.0260517126 0.0261195196 0.0262902357
     [41] 0.0267124000 0.0270494041 0.0277176059 0.0277971240 0.0279661516
     [46] 0.0281000610 0.0281244145 0.0283887449 0.0284233188 0.0285938854
     [51] 0.0286916051 0.0292032570 0.0293129598 0.0295409737 0.0295524857
     [56] 0.0296074153 0.0296236178 0.0297426298 0.0299515708 0.0302453610
     [61] 0.0304664212 0.0307775886 0.0311566630 0.0312016538 0.0313061104
     [66] 0.0313164481 0.0318584782 0.0319068323 0.0322080125 0.0329527775
     [71] 0.0330430936 0.0331956834 0.0335697944 0.0336123958 0.0339802732
     [76] 0.0340409747 0.0345077684 0.0354316088 0.0362363533 0.0365753965
     [81] 0.0368540346 0.0368969870 0.0369116091 0.0371566606 0.0372247528
     [86] 0.0372634499 0.0373319775 0.0379249630 0.0381218819 0.0385883630
     [91] 0.0392798141 0.0395237342 0.0395526463 0.0396331162 0.0398093862
     [96] 0.0398820675 0.0402155149 0.0402159691 0.0402207627 0.0403639254
     [101] 0.0404069259 0.0404777598 0.0408440029 0.0410351220 0.0410391492
     [106] 0.0413548071 0.0414305253 0.0414731533 0.0421398886 0.0422590328
     [111] 0.0422667120 0.0424708396 0.0433399983 0.0433604434 0.0433729118
     [116] 0.0433983710 0.0434737796 0.0437387735 0.0442473658 0.0446158195
     [121] 0.0446960338 0.0447039961 0.0448194528 0.0448569599 0.0448726561
     [126] 0.0449959383 0.0451055394 0.0452218663 0.0459278898 0.0463250999
     [131] 0.0463825335 0.0466170086 0.0467627962 0.0468061451 0.0468608740
     [136] 0.0470709195 0.0471645640 0.0471655039 0.0472568315 0.0472670713
     [141] 0.0473421348 0.0475063600 0.0477210494 0.0477725259 0.0479353847
     [146] 0.0483223229 0.0483334085 0.0483359050 0.0484586501 0.0486817952
     [151] 0.0489262939 0.0490953538 0.0493452093 0.0494245890 0.0494754651
     [156] 0.0495751945 0.0496499633 0.0497107039 0.0499015028 0.0499056951
     [161] 0.0501190700 0.0502451057 0.0503537774 0.0504498380 0.0508806966
     [166] 0.0510834821 0.0511036065 0.0513142099 0.0516013120 0.0517840300
     [171] 0.0520248873 0.0524168411 0.0526860379 0.0527225642 0.0527726980
     [176] 0.0528084629 0.0531805372 0.0533407578 0.0533973313 0.0537184084
     [181] 0.0539195798 0.0540280128 0.0542804088 0.0548395218 0.0549350952
     [186] 0.0555119574 0.0555435883 0.0556312631 0.0557466307 0.0559327364
     [191] 0.0561813216 0.0562209139 0.0562437715 0.0566716084 0.0570486147
     [196] 0.0571245603 0.0573715906 0.0574337694 0.0575269176 0.0577246929
     [201] 0.0578520185 0.0578852378 0.0579803560 0.0580004543 0.0582905703
     [206] 0.0584091277 0.0584725449 0.0586924395 0.0587190491 0.0587335978
     [211] 0.0587880631 0.0587957051 0.0588736706 0.0591156255 0.0591411012
     [216] 0.0598297797 0.0598689606 0.0603460615 0.0604783175 0.0607809704
     [221] 0.0608117598 0.0609398352 0.0610250619 0.0613276637 0.0613692639
     [226] 0.0619474870 0.0621458842 0.0622474916 0.0623221166 0.0628609041
     [231] 0.0628951855 0.0631973321 0.0634402311 0.0634542202 0.0635059154
     [236] 0.0635512940 0.0635568531 0.0637385388 0.0638285459 0.0638460337
     [241] 0.0639703935 0.0641184607 0.0645335550 0.0645438241 0.0646346104
     [246] 0.0647333418 0.0651500679 0.0653063151 0.0654060224 0.0658472587
     [251] 0.0658881823 0.0660491340 0.0660584500 0.0663227069 0.0665390828
     [256] 0.0667538573 0.0668830180 0.0669034308 0.0671051525 0.0671607496
     [261] 0.0675204797 0.0679486196 0.0679536394 0.0680047316 0.0680176994
     [266] 0.0680240406 0.0680424827 0.0684611511 0.0685565366 0.0686070032
     [271] 0.0686392370 0.0688722319 0.0689752579 0.0690348764 0.0690419852
     [276] 0.0690654518 0.0691558097 0.0692226973 0.0692277056 0.0694408653
     [281] 0.0697807533 0.0704289998 0.0704380558 0.0704739657 0.0705397659
     [286] 0.0706467468 0.0706521949 0.0707178234 0.0709756134 0.0711185816
     [291] 0.0715303285 0.0715904302 0.0716123478 0.0719068583 0.0720880990
     [296] 0.0721288094 0.0722051327 0.0722107720 0.0722596287 0.0724921356
     [301] 0.0725833339 0.0726229128 0.0729986486 0.0730395418 0.0731374766
     [306] 0.0731717912 0.0733693669 0.0733931431 0.0734244460 0.0735207423
     [311] 0.0744669116 0.0745727117 0.0746682831 0.0748219019 0.0748502907
     [316] 0.0749570685 0.0750318551 0.0751216525 0.0751600456 0.0751924932
     [321] 0.0755987714 0.0756496204 0.0757518739 0.0758230486 0.0759086080
     [326] 0.0760184639 0.0760798672 0.0761131745 0.0761723895 0.0762941450
     [331] 0.0763329461 0.0766691158 0.0766830727 0.0770026373 0.0770026479
     [336] 0.0770561710 0.0771186834 0.0774510347 0.0777747712 0.0781694853
     [341] 0.0784765878 0.0786378004 0.0788219089 0.0788504040 0.0788583324
     [346] 0.0788644973 0.0789856389 0.0791346027 0.0791522640 0.0792339421
     [351] 0.0794085334 0.0794845985 0.0795892934 0.0795932960 0.0799837726
     [356] 0.0801224385 0.0803212644 0.0805506469 0.0806060713 0.0806334383
     [361] 0.0806643106 0.0807019580 0.0807409664 0.0808462526 0.0814524162
     [366] 0.0817089273 0.0819046189 0.0820020991 0.0820627034 0.0821489338
     [371] 0.0821693180 0.0822140928 0.0824646748 0.0824996765 0.0828537825
     [376] 0.0830263991 0.0830408686 0.0831046564 0.0833264993 0.0833879495
     [381] 0.0835439606 0.0835591308 0.0836222133 0.0840816708 0.0844594471
     [386] 0.0844737473 0.0845089476 0.0846224803 0.0846916849 0.0848529267
     [391] 0.0850170198 0.0852136137 0.0855336780 0.0858344385 0.0858486667
     [396] 0.0858782168 0.0859565090 0.0861353305 0.0863478501 0.0864416762
     [401] 0.0865537827 0.0866758896 0.0868271553 0.0868323958 0.0870257323
     [406] 0.0871265928 0.0873339100 0.0873928199 0.0874952822 0.0875237142
     [411] 0.0875368101 0.0876414006 0.0876827025 0.0877495012 0.0877770585
     [416] 0.0878672579 0.0879026006 0.0882469829 0.0883438836 0.0885985613
     [421] 0.0886152697 0.0887806845 0.0891932511 0.0892220060 0.0892533492
     [426] 0.0895260242 0.0897547553 0.0898178075 0.0899413346 0.0899430003
     [431] 0.0906235027 0.0906303241 0.0906650937 0.0908024626 0.0909118609
     [436] 0.0912801241 0.0913471691 0.0915846701 0.0916275319 0.0920474672
     [441] 0.0920563467 0.0925362321 0.0925421182 0.0925577065 0.0926586952
     [446] 0.0927866896 0.0927885871 0.0929770000 0.0930423561 0.0932814073
     [451] 0.0933290766 0.0934064173 0.0934771155 0.0934831131 0.0935275930
     [456] 0.0935753939 0.0938009455 0.0938288594 0.0941158617 0.0941565085
     [461] 0.0941666886 0.0944839715 0.0946417205 0.0947461574 0.0948306559
     [466] 0.0948352165 0.0950187162 0.0950192736 0.0951880048 0.0954746086
     [471] 0.0954824512 0.0955510180 0.0958122680 0.0958401114 0.0960301482
     [476] 0.0962957092 0.0971311878 0.0973036837 0.0975358208 0.0978308591
     [481] 0.0979161321 0.0979318586 0.0984220114 0.0986827128 0.0988096222
     [486] 0.0995888683 0.0997469988 0.0999417607 0.1000455957 0.1001137043
     [491] 0.1007182090 0.1007755528 0.1009227431 0.1011887968 0.1011989452
     [496] 0.1012257850 0.1012492811 0.1016626500 0.1016738429 0.1016841912
     [501] 0.1018359253 0.1024836935 0.1026216875 0.1027054439 0.1027826406
     [506] 0.1029552310 0.1030563762 0.1030735416 0.1031423141 0.1038211928
     [511] 0.1038516339 0.1038683476 0.1041352588 0.1048262116 0.1050677067
     [516] 0.1054396286 0.1054529114 0.1055668267 0.1061145454 0.1061867364
     [521] 0.1062958696 0.1063021010 0.1065454150 0.1065909046 0.1065912118
     [526] 0.1070705465 0.1070925094 0.1073711414 0.1077335391 0.1078143479
     [531] 0.1079868582 0.1080035184 0.1081096330 0.1088011842 0.1089429026
     [536] 0.1091733515 0.1093281504 0.1098851468 0.1101861995 0.1104092354
     [541] 0.1105870672 0.1106021538 0.1108451921 0.1110044285 0.1111024698
     [546] 0.1111570114 0.1113142032 0.1123589425 0.1124196881 0.1127655055
     [551] 0.1129871274 0.1132235759 0.1132579794 0.1132905764 0.1138555128
     [556] 0.1140433242 0.1141976289 0.1146634968 0.1149068570 0.1160588418
     [561] 0.1167717039 0.1169609328 0.1174408664 0.1175326593 0.1177888921
     [566] 0.1179023620 0.1179177773 0.1181703657 0.1181856413 0.1182931427
     [571] 0.1183223004 0.1186568651 0.1188263581 0.1192381167 0.1193924317
     [576] 0.1197365707 0.1197864887 0.1199323174 0.1199444518 0.1200760751
     [581] 0.1203990160 0.1204144302 0.1209063768 0.1210594315 0.1211454255
     [586] 0.1215265442 0.1216325412 0.1217272262 0.1219143751 0.1225307367
     [591] 0.1225746802 0.1226034791 0.1228726354 0.1228897329 0.1228941449
     [596] 0.1229611936 0.1232618731 0.1233953086 0.1235366585 0.1239876570
     [601] 0.1240200466 0.1244683231 0.1245006000 0.1245456963 0.1245593046
     [606] 0.1247446352 0.1248603896 0.1260201926 0.1261047475 0.1261120364
     [611] 0.1264838321 0.1268000685 0.1268309515 0.1277977386 0.1278629418
     [616] 0.1279591982 0.1286885543 0.1290225746 0.1291986031 0.1298478763
     [621] 0.1301074445 0.1302534318 0.1305770164 0.1311587134 0.1312168231
     [626] 0.1314027829 0.1319039882 0.1322609138 0.1326068124 0.1326273644
     [631] 0.1327700976 0.1334598923 0.1335086001 0.1336049432 0.1336885342
     [636] 0.1338303764 0.1343443491 0.1343447864 0.1344836834 0.1345275758
     [641] 0.1353504161 0.1354095541 0.1358907412 0.1361162787 0.1363822326
     [646] 0.1364069478 0.1364484935 0.1368565901 0.1371737618 0.1382214865
     [651] 0.1391933394 0.1393058995 0.1393569467 0.1394060438 0.1394173918
     [656] 0.1394498226 0.1398326667 0.1400613307 0.1408199785 0.1409398587
     [661] 0.1413370344 0.1414720743 0.1415405096 0.1424567194 0.1424959580
     [666] 0.1426308102 0.1426327507 0.1431948243 0.1435914617 0.1446982712
     [671] 0.1458354577 0.1465321277 0.1467395062 0.1470220599 0.1476046125
     [676] 0.1481429067 0.1484010621 0.1486908180 0.1487077287 0.1488703542
     [681] 0.1490891887 0.1491524749 0.1491807810 0.1493156434 0.1495123453
     [686] 0.1500219159 0.1509267137 0.1510083362 0.1516471428 0.1518572521
     [691] 0.1529013950 0.1533626967 0.1535132593 0.1539902776 0.1547162512
     [696] 0.1550748143 0.1552017825 0.1564427447 0.1565164733 0.1567750500
     [701] 0.1572163741 0.1574159699 0.1576770653 0.1599487967 0.1602278059
     [706] 0.1604979249 0.1608963710 0.1610665752 0.1611264554 0.1622096134
     [711] 0.1622847425 0.1632607056 0.1644687304 0.1646677599 0.1653685745
     [716] 0.1655812891 0.1664199718 0.1665003594 0.1668965479 0.1675935397
     [721] 0.1686588886 0.1694774478 0.1707107553 0.1710094521 0.1712954550
     [726] 0.1715293990 0.1719398448 0.1719898316 0.1722041450 0.1729614018
     [731] 0.1740600327 0.1740635252 0.1741270719 0.1752144047 0.1752774523
     [736] 0.1753470572 0.1755956385 0.1762308315 0.1775196799 0.1780071553
     [741] 0.1789112873 0.1791771463 0.1794713432 0.1819525905 0.1857345930
     [746] 0.1858163637 0.1860899613 0.1863112470 0.1871538538 0.1871870206
     [751] 0.1878213726 0.1882638233 0.1883934986 0.1884011760 0.1887752751
     [756] 0.1888223247 0.1906627438 0.1916231824 0.1917331207 0.1926448274
     [761] 0.1948710026 0.1967626661 0.1969546392 0.1983364954 0.2016603106
     [766] 0.2029472826 0.2030939908 0.2047391674 0.2052960847 0.2071915171
     [771] 0.2075334156 0.2079650863 0.2080929372 0.2099454523 0.2108057715
     [776] 0.2111239882 0.2112907852 0.2127557863 0.2150118773 0.2161492280
     [781] 0.2190136361 0.2195613472 0.2200171814 0.2209788222 0.2228439560
     [786] 0.2242652152 0.2256665940 0.2266136881 0.2269980640 0.2290636125
     [791] 0.2320543931 0.2321126575 0.2322939320 0.2343486871 0.2347978765
     [796] 0.2349421552 0.2362334664 0.2399000362 0.2404910626 0.2413944682
     [801] 0.2418353568 0.2427185898 0.2436187468 0.2447751146 0.2480110161
     [806] 0.2493369624 0.2530882167 0.2600242455 0.2614636752 0.2627126982
     [811] 0.2635184725 0.2680336600 0.2698851900 0.2715713680 0.2748198064
     [816] 0.2757881100 0.2784838346 0.2855279876 0.2863107734 0.2887210514
     [821] 0.2896646317 0.2976786330 0.2992746885 0.3092589585 0.3096050071
     [826] 0.3103011947 0.3169902937 0.3177060729 0.3188644176 0.3188742305
     [831] 0.3215035057 0.3280019788 0.3347465280 0.3376384286 0.3419526597
     [836] 0.3427932426 0.3511378175 0.3558970087 0.3577039897 0.3683013768
     [841] 0.3743958549 0.3815057757 0.3824813001 0.3829712610 0.3889922223
     [846] 0.3988711004 0.4019040146 0.4109571086 0.4110001578 0.4131699617
     [851] 0.4190413423 0.4258047693 0.4306699461 0.4473546188 0.4480377062
     [856] 0.4580904336 0.4603345948 0.4682210069 0.4794851467 0.4799522413
     [861] 0.4942559216 0.5035558020 0.5045875554 0.5533017802 0.5872109727
     [866] 0.5937493899 0.6052006933 0.6255090862 0.6650134629 0.6947763035
     [871] 0.7156687257 0.7235547088 0.7632860806 0.8135751053 0.8577636769
     [876] 0.8633550106 0.8881911713 0.9416696192 0.9893595469 1.0000067779
     [881] 1.0000123798 1.0000143717
     [1] 0.0007985965 0.0054315016 0.0075979387 0.0114340260 0.0119185052
     [6] 0.0122745732 0.0125760762 0.0132506874 0.0137326360 0.0139697577
     [11] 0.0142791457 0.0158414436 0.0162910981 0.0177295054 0.0177456836
     [16] 0.0181442655 0.0191992182 0.0199630888 0.0204979127 0.0208976516
     [21] 0.0209690027 0.0211038862 0.0220051307 0.0221117356 0.0224798853
     [26] 0.0224926638 0.0229032746 0.0236884995 0.0237686024 0.0240926696
     [31] 0.0241047438 0.0245625455 0.0247807941 0.0249135776 0.0249255167
     [36] 0.0253240695 0.0253690171 0.0260517126 0.0261195196 0.0262902357
     [41] 0.0267124000 0.0270494041 0.0277176059 0.0277971240 0.0279661516
     [46] 0.0281000610 0.0281244145 0.0283887449 0.0284233188 0.0285938854
     [51] 0.0286916051 0.0292032570 0.0293129598 0.0295409737 0.0295524857
     [56] 0.0296074153 0.0296236178 0.0297426298 0.0299515708 0.0302453610
     [61] 0.0304664212 0.0307775886 0.0311566630 0.0312016538 0.0313061104
     [66] 0.0313164481 0.0318584782 0.0319068323 0.0322080125 0.0329527775
     [71] 0.0330430936 0.0331956834 0.0335697944 0.0336123958 0.0339802732
     [76] 0.0340409747 0.0345077684 0.0354316088 0.0362363533 0.0365753965
     [81] 0.0368540346 0.0368969870 0.0369116091 0.0371566606 0.0372247528
     [86] 0.0372634499 0.0373319775 0.0379249630 0.0381218819 0.0385883630
     [91] 0.0392798141 0.0395237342 0.0395526463 0.0396331162 0.0398093862
     [96] 0.0398820675 0.0402155149 0.0402159691 0.0402207627 0.0403639254
     [101] 0.0404069259 0.0404777598 0.0408440029 0.0410351220 0.0410391492
     [106] 0.0413548071 0.0414305253 0.0414731533 0.0421398886 0.0422590328
     [111] 0.0422667120 0.0424708396 0.0433399983 0.0433604434 0.0433729118
     [116] 0.0433983710 0.0434737796 0.0437387735 0.0442473658 0.0446158195
     [121] 0.0446960338 0.0447039961 0.0448194528 0.0448569599 0.0448726561
     [126] 0.0449959383 0.0451055394 0.0452218663 0.0459278898 0.0463250999
     [131] 0.0463825335 0.0466170086 0.0467627962 0.0468061451 0.0468608740
     [136] 0.0470709195 0.0471645640 0.0471655039 0.0472568315 0.0472670713
     [141] 0.0473421348 0.0475063600 0.0477210494 0.0477725259 0.0479353847
     [146] 0.0483223229 0.0483334085 0.0483359050 0.0484586501 0.0486817952
     [151] 0.0489262939 0.0490953538 0.0493452093 0.0494245890 0.0494754651
     [156] 0.0495751945 0.0496499633 0.0497107039 0.0499015028 0.0499056951
     [161] 0.0501190700 0.0502451057 0.0503537774 0.0504498380 0.0508806966
     [166] 0.0510834821 0.0511036065 0.0513142099 0.0516013120 0.0517840300
     [171] 0.0520248873 0.0524168411 0.0526860379 0.0527225642 0.0527726980
     [176] 0.0528084629 0.0531805372 0.0533407578 0.0533973313 0.0537184084
     [181] 0.0539195798 0.0540280128 0.0542804088 0.0548395218 0.0549350952
     [186] 0.0555119574 0.0555435883 0.0556312631 0.0557466307 0.0559327364
     [191] 0.0561813216 0.0562209139 0.0562437715 0.0566716084 0.0570486147
     [196] 0.0571245603 0.0573715906 0.0574337694 0.0575269176 0.0577246929
     [201] 0.0578520185 0.0578852378 0.0579803560 0.0580004543 0.0582905703
     [206] 0.0584091277 0.0584725449 0.0586924395 0.0587190491 0.0587335978
     [211] 0.0587880631 0.0587957051 0.0588736706 0.0591156255 0.0591411012
     [216] 0.0598297797 0.0598689606 0.0603460615 0.0604783175 0.0607809704
     [221] 0.0608117598 0.0609398352 0.0610250619 0.0613276637 0.0613692639
     [226] 0.0619474870 0.0621458842 0.0622474916 0.0623221166 0.0628609041
     [231] 0.0628951855 0.0631973321 0.0634402311 0.0634542202 0.0635059154
     [236] 0.0635512940 0.0635568531 0.0637385388 0.0638285459 0.0638460337
     [241] 0.0639703935 0.0641184607 0.0645335550 0.0645438241 0.0646346104
     [246] 0.0647333418 0.0651500679 0.0653063151 0.0654060224 0.0658472587
     [251] 0.0658881823 0.0660491340 0.0660584500 0.0663227069 0.0665390828
     [256] 0.0667538573 0.0668830180 0.0669034308 0.0671051525 0.0671607496
     [261] 0.0675204797 0.0679486196 0.0679536394 0.0680047316 0.0680176994
     [266] 0.0680240406 0.0680424827 0.0684611511 0.0685565366 0.0686070032
     [271] 0.0686392370 0.0688722319 0.0689752579 0.0690348764 0.0690419852
     [276] 0.0690654518 0.0691558097 0.0692226973 0.0692277056 0.0694408653
     [281] 0.0697807533 0.0704289998 0.0704380558 0.0704739657 0.0705397659
     [286] 0.0706467468 0.0706521949 0.0707178234 0.0709756134 0.0711185816
     [291] 0.0715303285 0.0715904302 0.0716123478 0.0719068583 0.0720880990
     [296] 0.0721288094 0.0722051327 0.0722107720 0.0722596287 0.0724921356
     [301] 0.0725833339 0.0726229128 0.0729986486 0.0730395418 0.0731374766
     [306] 0.0731717912 0.0733693669 0.0733931431 0.0734244460 0.0735207423
     [311] 0.0744669116 0.0745727117 0.0746682831 0.0748219019 0.0748502907
     [316] 0.0749570685 0.0750318551 0.0751216525 0.0751600456 0.0751924932
     [321] 0.0755987714 0.0756496204 0.0757518739 0.0758230486 0.0759086080
     [326] 0.0760184639 0.0760798672 0.0761131745 0.0761723895 0.0762941450
     [331] 0.0763329461 0.0766691158 0.0766830727 0.0770026373 0.0770026479
     [336] 0.0770561710 0.0771186834 0.0774510347 0.0777747712 0.0781694853
     [341] 0.0784765878 0.0786378004 0.0788219089 0.0788504040 0.0788583324
     [346] 0.0788644973 0.0789856389 0.0791346027 0.0791522640 0.0792339421
     [351] 0.0794085334 0.0794845985 0.0795892934 0.0795932960 0.0799837726
     [356] 0.0801224385 0.0803212644 0.0805506469 0.0806060713 0.0806334383
     [361] 0.0806643106 0.0807019580 0.0807409664 0.0808462526 0.0814524162
     [366] 0.0817089273 0.0819046189 0.0820020991 0.0820627034 0.0821489338
     [371] 0.0821693180 0.0822140928 0.0824646748 0.0824996765 0.0828537825
     [376] 0.0830263991 0.0830408686 0.0831046564 0.0833264993 0.0833879495
     [381] 0.0835439606 0.0835591308 0.0836222133 0.0840816708 0.0844594471
     [386] 0.0844737473 0.0845089476 0.0846224803 0.0846916849 0.0848529267
     [391] 0.0850170198 0.0852136137 0.0855336780 0.0858344385 0.0858486667
     [396] 0.0858782168 0.0859565090 0.0861353305 0.0863478501 0.0864416762
     [401] 0.0865537827 0.0866758896 0.0868271553 0.0868323958 0.0870257323
     [406] 0.0871265928 0.0873339100 0.0873928199 0.0874952822 0.0875237142
     [411] 0.0875368101 0.0876414006 0.0876827025 0.0877495012 0.0877770585
     [416] 0.0878672579 0.0879026006 0.0882469829 0.0883438836 0.0885985613
     [421] 0.0886152697 0.0887806845 0.0891932511 0.0892220060 0.0892533492
     [426] 0.0895260242 0.0897547553 0.0898178075 0.0899413346 0.0899430003
     [431] 0.0906235027 0.0906303241 0.0906650937 0.0908024626 0.0909118609
     [436] 0.0912801241 0.0913471691 0.0915846701 0.0916275319 0.0920474672
     [441] 0.0920563467 0.0925362321 0.0925421182 0.0925577065 0.0926586952
     [446] 0.0927866896 0.0927885871 0.0929770000 0.0930423561 0.0932814073
     [451] 0.0933290766 0.0934064173 0.0934771155 0.0934831131 0.0935275930
     [456] 0.0935753939 0.0938009455 0.0938288594 0.0941158617 0.0941565085
     [461] 0.0941666886 0.0944839715 0.0946417205 0.0947461574 0.0948306559
     [466] 0.0948352165 0.0950187162 0.0950192736 0.0951880048 0.0954746086
     [471] 0.0954824512 0.0955510180 0.0958122680 0.0958401114 0.0960301482
     [476] 0.0962957092 0.0971311878 0.0973036837 0.0975358208 0.0978308591
     [481] 0.0979161321 0.0979318586 0.0984220114 0.0986827128 0.0988096222
     [486] 0.0995888683 0.0997469988 0.0999417607 0.1000455957 0.1001137043
     [491] 0.1007182090 0.1007755528 0.1009227431 0.1011887968 0.1011989452
     [496] 0.1012257850 0.1012492811 0.1016626500 0.1016738429 0.1016841912
     [501] 0.1018359253 0.1024836935 0.1026216875 0.1027054439 0.1027826406
     [506] 0.1029552310 0.1030563762 0.1030735416 0.1031423141 0.1038211928
     [511] 0.1038516339 0.1038683476 0.1041352588 0.1048262116 0.1050677067
     [516] 0.1054396286 0.1054529114 0.1055668267 0.1061145454 0.1061867364
     [521] 0.1062958696 0.1063021010 0.1065454150 0.1065909046 0.1065912118
     [526] 0.1070705465 0.1070925094 0.1073711414 0.1077335391 0.1078143479
     [531] 0.1079868582 0.1080035184 0.1081096330 0.1088011842 0.1089429026
     [536] 0.1091733515 0.1093281504 0.1098851468 0.1101861995 0.1104092354
     [541] 0.1105870672 0.1106021538 0.1108451921 0.1110044285 0.1111024698
     [546] 0.1111570114 0.1113142032 0.1123589425 0.1124196881 0.1127655055
     [551] 0.1129871274 0.1132235759 0.1132579794 0.1132905764 0.1138555128
     [556] 0.1140433242 0.1141976289 0.1146634968 0.1149068570 0.1160588418
     [561] 0.1167717039 0.1169609328 0.1174408664 0.1175326593 0.1177888921
     [566] 0.1179023620 0.1179177773 0.1181703657 0.1181856413 0.1182931427
     [571] 0.1183223004 0.1186568651 0.1188263581 0.1192381167 0.1193924317
     [576] 0.1197365707 0.1197864887 0.1199323174 0.1199444518 0.1200760751
     [581] 0.1203990160 0.1204144302 0.1209063768 0.1210594315 0.1211454255
     [586] 0.1215265442 0.1216325412 0.1217272262 0.1219143751 0.1225307367
     [591] 0.1225746802 0.1226034791 0.1228726354 0.1228897329 0.1228941449
     [596] 0.1229611936 0.1232618731 0.1233953086 0.1235366585 0.1239876570
     [601] 0.1240200466 0.1244683231 0.1245006000 0.1245456963 0.1245593046
     [606] 0.1247446352 0.1248603896 0.1260201926 0.1261047475 0.1261120364
     [611] 0.1264838321 0.1268000685 0.1268309515 0.1277977386 0.1278629418
     [616] 0.1279591982 0.1286885543 0.1290225746 0.1291986031 0.1298478763
     [621] 0.1301074445 0.1302534318 0.1305770164 0.1311587134 0.1312168231
     [626] 0.1314027829 0.1319039882 0.1322609138 0.1326068124 0.1326273644
     [631] 0.1327700976 0.1334598923 0.1335086001 0.1336049432 0.1336885342
     [636] 0.1338303764 0.1343443491 0.1343447864 0.1344836834 0.1345275758
     [641] 0.1353504161 0.1354095541 0.1358907412 0.1361162787 0.1363822326
     [646] 0.1364069478 0.1364484935 0.1368565901 0.1371737618 0.1382214865
     [651] 0.1391933394 0.1393058995 0.1393569467 0.1394060438 0.1394173918
     [656] 0.1394498226 0.1398326667 0.1400613307 0.1408199785 0.1409398587
     [661] 0.1413370344 0.1414720743 0.1415405096 0.1424567194 0.1424959580
     [666] 0.1426308102 0.1426327507 0.1431948243 0.1435914617 0.1446982712
     [671] 0.1458354577 0.1465321277 0.1467395062 0.1470220599 0.1476046125
     [676] 0.1481429067 0.1484010621 0.1486908180 0.1487077287 0.1488703542
     [681] 0.1490891887 0.1491524749 0.1491807810 0.1493156434 0.1495123453
     [686] 0.1500219159 0.1509267137 0.1510083362 0.1516471428 0.1518572521
     [691] 0.1529013950 0.1533626967 0.1535132593 0.1539902776 0.1547162512
     [696] 0.1550748143 0.1552017825 0.1564427447 0.1565164733 0.1567750500
     [701] 0.1572163741 0.1574159699 0.1576770653 0.1599487967 0.1602278059
     [706] 0.1604979249 0.1608963710 0.1610665752 0.1611264554 0.1622096134
     [711] 0.1622847425 0.1632607056 0.1644687304 0.1646677599 0.1653685745
     [716] 0.1655812891 0.1664199718 0.1665003594 0.1668965479 0.1675935397
     [721] 0.1686588886 0.1694774478 0.1707107553 0.1710094521 0.1712954550
     [726] 0.1715293990 0.1719398448 0.1719898316 0.1722041450 0.1729614018
     [731] 0.1740600327 0.1740635252 0.1741270719 0.1752144047 0.1752774523
     [736] 0.1753470572 0.1755956385 0.1762308315 0.1775196799 0.1780071553
     [741] 0.1789112873 0.1791771463 0.1794713432 0.1819525905 0.1857345930
     [746] 0.1858163637 0.1860899613 0.1863112470 0.1871538538 0.1871870206
     [751] 0.1878213726 0.1882638233 0.1883934986 0.1884011760 0.1887752751
     [756] 0.1888223247 0.1906627438 0.1916231824 0.1917331207 0.1926448274
     [761] 0.1948710026 0.1967626661 0.1969546392 0.1983364954 0.2016603106
     [766] 0.2029472826 0.2030939908 0.2047391674 0.2052960847 0.2071915171
     [771] 0.2075334156 0.2079650863 0.2080929372 0.2099454523 0.2108057715
     [776] 0.2111239882 0.2112907852 0.2127557863 0.2150118773 0.2161492280
     [781] 0.2190136361 0.2195613472 0.2200171814 0.2209788222 0.2228439560
     [786] 0.2242652152 0.2256665940 0.2266136881 0.2269980640 0.2290636125
     [791] 0.2320543931 0.2321126575 0.2322939320 0.2343486871 0.2347978765
     [796] 0.2349421552 0.2362334664 0.2399000362 0.2404910626 0.2413944682
     [801] 0.2418353568 0.2427185898 0.2436187468 0.2447751146 0.2480110161
     [806] 0.2493369624 0.2530882167 0.2600242455 0.2614636752 0.2627126982
     [811] 0.2635184725 0.2680336600 0.2698851900 0.2715713680 0.2748198064
     [816] 0.2757881100 0.2784838346 0.2855279876 0.2863107734 0.2887210514
     [821] 0.2896646317 0.2976786330 0.2992746885 0.3092589585 0.3096050071
     [826] 0.3103011947 0.3169902937 0.3177060729 0.3188644176 0.3188742305
     [831] 0.3215035057 0.3280019788 0.3347465280 0.3376384286 0.3419526597
     [836] 0.3427932426 0.3511378175 0.3558970087 0.3577039897 0.3683013768
     [841] 0.3743958549 0.3815057757 0.3824813001 0.3829712610 0.3889922223
     [846] 0.3988711004 0.4019040146 0.4109571086 0.4110001578 0.4131699617
     [851] 0.4190413423 0.4258047693 0.4306699461 0.4473546188 0.4480377062
     [856] 0.4580904336 0.4603345948 0.4682210069 0.4794851467 0.4799522413
     [861] 0.4942559216 0.5035558020 0.5045875554 0.5533017802 0.5872109727
     [866] 0.5937493899 0.6052006933 0.6255090862 0.6650134629 0.6947763035
     [871] 0.7156687257 0.7235547088 0.7632860806 0.8135751053 0.8577636769
     [876] 0.8633550106 0.8881911713 0.9416696192 0.9893595469 1.0039688985
     [881] 1.0074253902 1.0164522005
     Height vectors differ! The maximum relative error is 1.617177e-02.
     Error in test.vector() :
     Please send a report to the author of the 'fastcluster' package, Daniel Müllner.
     For contact details, see <http://danifold.net>. To make the error
     reproducible, you must include the following number (the random seed value) in
     your error report: 587073007.
    
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.1.22
Check: tests
Result: ERROR
     Running ‘test_fastcluster.R’ [2s/2s]
    Running the tests in ‘tests/test_fastcluster.R’ failed.
    Complete output:
     > # fastcluster: Fast hierarchical clustering routines for R and Python
     > #
     > # Copyright © 2011 Daniel Müllner
     > # <http://danifold.net>
     > #
     > # Test script for the R interface
     >
     > seed = as.integer(runif(1, 0, 1e9))
     > set.seed(seed)
     > cat(sprintf("Random seed: %d\n",seed))
     Random seed: 793022628
     >
     > print_seed <- function() {
     + return(sprintf('
     + Please send a report to the author of the \'fastcluster\' package, Daniel Müllner.
     + For contact details, see <http://danifold.net>. To make the error
     + reproducible, you must include the following number (the random seed value) in
     + your error report: %d.\n\n', seed))
     + }
     >
     > hasWardD2 = getRversion() >= '3.1.0'
     >
     > # Compare two dendrograms and check whether they are equal, except that
     > # ties may be resolved differently.
     > compare <- function(dg1, dg2) {
     + h1 <- dg1$height
     + h2 <- dg2$height
     + # "height" vectors may have small numerical errors.
     + rdiffs <- abs(h1-h2)/pmax(abs(h1),abs(h2))
     + rdiffs = rdiffs[complete.cases(rdiffs)]
     + rel_error <- max(rdiffs)
     + # We allow a relative error of 1e-13.
     + if (rel_error>1e-13) {
     + print(h1)
     + print(h2)
     + cat(sprintf('Height vectors differ! The maximum relative error is %e.\n', rel_error))
     + return(FALSE)
     + }
     + # Filter the indices where consecutive merging distances are distinct.
     + d = diff(dg1$height)
     + b = (c(d,1)!=0 & c(1,d)!=0)
     + #cat(sprintf("Percentage of indices where we can test: %g.\n",100.0*length(b[b])/length(b)))
     + if (any(b)) {
     + m1 = dg1$merge[b,]
     + m2 = dg2$merge[b,]
     +
     + r = function(i) {
     + if (i<0) {
     + return(1)
     + }
     + else {
     + return(b[i])
     + }
     + }
     +
     + f = sapply(m1,r)
     + fm1 = m1*f
     + fm2 = m2*f
     + # The "merge" matrices must be identical whereever indices are not ambiguous
     + # due to ties.
     + if (!identical(fm1,fm2)) {
     + cat('Merge matrices differ!\n')
     + return(FALSE)
     + }
     + # Compare the "order" vectors only if all merging distances were distinct.
     + if (all(b) && !identical(dg1$order,dg2$order)) {
     + cat('Order vectors differ!\n')
     + return(FALSE)
     + }
     + }
     + return(TRUE)
     + }
     >
     > # Generate uniformly distributed random data
     > generate.uniform <- function() {
     + n = sample(10:1000,1)
     + range_exp = runif(1,min=-10, max=10)
     + cat(sprintf("Number of sample points: %d\n",n))
     + cat(sprintf("Dissimilarity range: [0,%g]\n",10^range_exp))
     + d = runif(n*(n-1)/2, min=0, max=10^range_exp)
     + # Fake a compressed distance matrix
     + attributes(d) <- NULL
     + attr(d,"Size") <- n
     + attr(d, "call") <- 'N/A'
     + class(d) <- "dist"
     + return(d)
     + }
     >
     > # Generate normally distributed random data
     > generate.normal <- function() {
     + n = sample(10:1000,1)
     + dim = sample(2:20,1)
     +
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     +
     + pcd = matrix(rnorm(n*dim), c(n,dim))
     + d = dist(pcd)
     + return(d)
     + }
     >
     > # Test the clustering functions when a distance matrix is given.
     > test.dm <- function(d) {
     + d2 = d
     + if (hasWardD2) {
     + methods = c('single','complete','average','mcquitty','ward.D','ward.D2','centroid','median')
     + }
     + else {
     + methods = c('single','complete','average','mcquitty','ward','centroid','median')
     + }
     + for (method in methods) {
     + cat(paste('Method :', method, '\n'))
     + dg_stats = stats::hclust(d, method=method)
     + if (method == 'ward') {
     + method = 'ward.D'
     + }
     + dg_fastcluster = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_stats, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + cat('Passed.\n')
     + }
     >
     > # Test the clustering functions for vector input in Euclidean space.
     > test.vector <- function() {
     + # generate test data
     + n = sample(10:1000,1)
     + dim = sample(2:20,1)
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     +
     + range_exp = runif(1,min=-10, max=10)
     + pcd = matrix(rnorm(n*dim, sd=10^range_exp), c(n,dim))
     + pcd2 = pcd
     + # test
     + method='single'
     + cat(paste('Method:', method, '\n'))
     + for (metric in c('euclidean', 'maximum', 'manhattan', 'canberra', 'minkowski')) {
     + cat(paste(' Metric:', metric, '\n'))
     + if (metric=='minkowski') {
     + p = runif(1, min=1.0, max=10.0)
     + cat (sprintf(" p: %g\n",p));
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric, p=p)
     + d = dist(pcd, method=metric, p=p)
     + }
     + else {
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric)
     + d = dist(pcd, method=metric)
     + }
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2) || !identical(pcd,pcd2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + for (method in c('ward','centroid','median') ) {
     + cat(paste('Method:', method, '\n'))
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method)
     + if (!identical(pcd,pcd2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + d = dist(pcd)
     + if(method == "ward" && hasWardD2) {
     + method = "ward.D2"
     + }
     + else
     + {
     + # Workaround: fastcluster::hclust expects _squared_ euclidean distances.
     + d = d^2
     + }
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if(method != "ward.D2") {
     + dg_fastcluster_dist$height = sqrt(dg_fastcluster_dist$height)
     + }
     + # The Euclidean methods may have small numerical errors due to squaring/
     + # taking the root in the Euclidean distances.
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + cat('Passed.\n')
     + }
     >
     > # Test the single linkage function with the "binary" metric
     > test.vector.binary <- function() {
     + # generate test data
     + cat (sprintf("Uniform sampling for the 'binary' metric:\n"))
     + n = sample(10:400,1)
     + dim = sample(n:(2*n),1)
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     + pcd = matrix(sample(-1:2, n*dim, replace=T), c(n,dim))
     + pcd2 = pcd
     + # test
     + method='single'
     + metric='binary'
     + cat(paste('Method:', method, '\n'))
     + cat(paste(' Metric:', metric, '\n'))
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric)
     + d = dist(pcd, method=metric)
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2) || !identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + cat('Passed.\n')
     + }
     >
     >
     > N = 15
     > for (i in (1:N)) {
     + if (i%%2==1) {
     + cat(sprintf('Random test %d of %d (uniform distribution of distances):\n',i,2*N))
     + d = generate.uniform()
     + }
     + else {
     + cat(sprintf('Random test %d of %d (Gaussian density):\n',i,2*N))
     + d = generate.normal()
     + }
     + test.dm(d)
     + }
     Random test 1 of 30 (uniform distribution of distances):
     Number of sample points: 735
     Dissimilarity range: [0,52912.4]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 2 of 30 (Gaussian density):
     Number of sample points: 266
     Dimension: 10
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 3 of 30 (uniform distribution of distances):
     Number of sample points: 635
     Dissimilarity range: [0,3641.39]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 4 of 30 (Gaussian density):
     Number of sample points: 102
     Dimension: 4
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 5 of 30 (uniform distribution of distances):
     Number of sample points: 229
     Dissimilarity range: [0,67288.6]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 6 of 30 (Gaussian density):
     Number of sample points: 343
     Dimension: 3
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 7 of 30 (uniform distribution of distances):
     Number of sample points: 269
     Dissimilarity range: [0,19.2551]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 8 of 30 (Gaussian density):
     Number of sample points: 896
     Dimension: 13
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 9 of 30 (uniform distribution of distances):
     Number of sample points: 976
     Dissimilarity range: [0,0.19783]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 10 of 30 (Gaussian density):
     Number of sample points: 275
     Dimension: 13
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 11 of 30 (uniform distribution of distances):
     Number of sample points: 505
     Dissimilarity range: [0,17.6464]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 12 of 30 (Gaussian density):
     Number of sample points: 359
     Dimension: 14
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 13 of 30 (uniform distribution of distances):
     Number of sample points: 249
     Dissimilarity range: [0,2.91872e-05]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 14 of 30 (Gaussian density):
     Number of sample points: 617
     Dimension: 8
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 15 of 30 (uniform distribution of distances):
     Number of sample points: 692
     Dissimilarity range: [0,134958]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     > for (i in (N+1:N)) {
     + cat(sprintf('Random test %d of %d (Gaussian density):\n',i,2*N))
     + test.vector()
     + test.vector.binary()
     + }
     Random test 16 of 30 (Gaussian density):
     Number of sample points: 437
     Dimension: 11
     Method: single
     Metric: euclidean
     Metric: maximum
     Metric: manhattan
     Metric: canberra
     [1] 2.420464 3.084547 3.095124 3.099222 3.168251 3.251445 3.294600 3.321336
     [9] 3.441580 3.451499 3.477812 3.485996 3.492922 3.505859 3.522436 3.569971
     [17] 3.570024 3.577999 3.593140 3.615259 3.624414 3.648639 3.674140 3.685575
     [25] 3.688864 3.700161 3.713141 3.728765 3.774620 3.797921 3.802956 3.803059
     [33] 3.804132 3.809720 3.815427 3.825409 3.861561 3.865633 3.878721 3.891434
     [41] 3.905180 3.910830 3.910905 3.924163 3.930598 3.934873 3.935366 3.952446
     [49] 3.966538 3.976068 3.983696 3.986128 3.989965 3.994476 3.995169 3.997645
     [57] 4.004718 4.020440 4.022702 4.029571 4.039055 4.049493 4.049672 4.049854
     [65] 4.053715 4.065936 4.071910 4.073335 4.086984 4.099047 4.101273 4.101380
     [73] 4.103826 4.104571 4.113987 4.118044 4.127912 4.139427 4.146808 4.155600
     [81] 4.159277 4.160408 4.165955 4.167814 4.171723 4.175959 4.175962 4.178208
     [89] 4.179835 4.186169 4.192175 4.195366 4.196355 4.202502 4.204742 4.207984
     [97] 4.212522 4.219853 4.222143 4.226509 4.230365 4.232494 4.238125 4.239746
     [105] 4.245432 4.247004 4.249795 4.254619 4.258719 4.259418 4.261844 4.267456
     [113] 4.270608 4.276583 4.281857 4.282333 4.284362 4.284650 4.291893 4.296225
     [121] 4.302059 4.303804 4.309051 4.316046 4.316587 4.316931 4.325388 4.327722
     [129] 4.330831 4.330985 4.334406 4.336023 4.337949 4.342670 4.343329 4.344009
     [137] 4.358981 4.360550 4.361371 4.362899 4.364133 4.364602 4.369432 4.370121
     [145] 4.373372 4.378299 4.378697 4.381508 4.382079 4.383463 4.385106 4.386040
     [153] 4.389091 4.394576 4.399543 4.400015 4.409911 4.414688 4.419540 4.420380
     [161] 4.422127 4.423339 4.427410 4.428976 4.430900 4.434557 4.437012 4.437969
     [169] 4.442272 4.443239 4.443310 4.445515 4.445695 4.448828 4.450977 4.453683
     [177] 4.454955 4.455692 4.459014 4.460004 4.460312 4.461070 4.463287 4.463835
     [185] 4.464070 4.466358 4.470831 4.474533 4.475304 4.488645 4.489220 4.489869
     [193] 4.495121 4.500572 4.503972 4.504173 4.505438 4.505516 4.507200 4.507469
     [201] 4.508199 4.509925 4.514936 4.515525 4.519764 4.525684 4.527409 4.538272
     [209] 4.539597 4.545772 4.545868 4.546970 4.548086 4.550945 4.553265 4.553883
     [217] 4.553908 4.564323 4.564460 4.564584 4.568699 4.568711 4.570151 4.570941
     [225] 4.571316 4.576796 4.577041 4.582066 4.591236 4.592081 4.593140 4.593781
     [233] 4.596493 4.596761 4.597835 4.598717 4.600501 4.601253 4.602105 4.602811
     [241] 4.603699 4.607637 4.612380 4.612875 4.613819 4.614888 4.614999 4.617073
     [249] 4.618079 4.618478 4.619604 4.620315 4.627286 4.627976 4.628568 4.633699
     [257] 4.634500 4.637850 4.639567 4.640306 4.642848 4.643673 4.643720 4.648277
     [265] 4.650733 4.652310 4.653909 4.654436 4.654700 4.657473 4.663408 4.665396
     [273] 4.667225 4.669184 4.671584 4.674565 4.675680 4.675882 4.677592 4.678422
     [281] 4.680568 4.681103 4.685660 4.687909 4.688238 4.690743 4.691009 4.691223
     [289] 4.694114 4.696118 4.705211 4.706900 4.708405 4.715445 4.717678 4.720091
     [297] 4.727445 4.729685 4.730087 4.731271 4.735030 4.735619 4.738402 4.744426
     [305] 4.748582 4.749171 4.751084 4.752300 4.753208 4.757048 4.760762 4.765568
     [313] 4.773859 4.783662 4.783665 4.784613 4.791568 4.791687 4.795261 4.796643
     [321] 4.796827 4.797179 4.800999 4.802713 4.804845 4.804894 4.808778 4.812413
     [329] 4.814189 4.814366 4.815285 4.818836 4.823418 4.827949 4.829915 4.840704
     [337] 4.844281 4.847461 4.848568 4.848779 4.850853 4.851584 4.859943 4.862475
     [345] 4.866552 4.880514 4.892212 4.907671 4.908171 4.914859 4.915072 4.919670
     [353] 4.921600 4.924204 4.924627 4.927252 4.933657 4.935416 4.935881 4.942732
     [361] 4.946544 4.951972 4.954909 4.958410 4.958413 4.960570 4.996191 5.020043
     [369] 5.022056 5.027992 5.036415 5.038263 5.044374 5.048703 5.076895 5.081188
     [377] 5.082971 5.084079 5.085253 5.088643 5.092317 5.099884 5.108405 5.108755
     [385] 5.126617 5.127169 5.139702 5.148305 5.149523 5.158031 5.166208 5.172512
     [393] 5.177321 5.184318 5.185637 5.192531 5.205493 5.205651 5.214527 5.214614
     [401] 5.227373 5.227605 5.240838 5.243038 5.244834 5.262926 5.278117 5.286300
     [409] 5.291287 5.297659 5.305777 5.307073 5.308809 5.331557 5.359700 5.363750
     [417] 5.371511 5.391068 5.418075 5.419026 5.424976 5.435920 5.443409 5.448064
     [425] 5.490163 5.503735 5.504369 5.585823 5.591951 5.615620 5.625681 5.678372
     [433] 5.705380 5.903894 6.049378 6.161480
     [1] 2.420464 3.251445 3.451499 3.485996 3.615084 3.615259 3.624414 3.627823
     [9] 3.676783 3.685575 3.700161 3.728765 3.783262 3.797921 3.882332 3.930598
     [17] 4.000949 4.058107 4.086897 4.108222 4.127912 4.133682 4.155600 4.171723
     [25] 4.186169 4.194094 4.206356 4.212522 4.223419 4.229149 4.246595 4.364602
     [33] 4.369875 4.370414 4.372149 4.384235 4.435422 4.466358 4.481275 4.490446
     [41] 4.523366 4.541817 4.574892 4.599870 4.640013 4.641727 4.654436 4.664621
     [49] 4.678868 4.678899 4.682745 4.702065 4.709553 4.720242 4.723380 4.727191
     [57] 4.756955 4.788369 4.804580 4.806140 4.825245 4.847842 4.854332 4.861923
     [65] 4.897101 4.898575 4.900033 4.913715 4.914969 4.916630 4.917691 4.919874
     [73] 4.933896 4.933904 4.943055 4.969654 4.978105 4.990330 5.007765 5.010427
     [81] 5.022731 5.030071 5.030898 5.032060 5.032186 5.040733 5.042736 5.054290
     [89] 5.071162 5.081188 5.087617 5.091612 5.097190 5.104030 5.123998 5.125974
     [97] 5.132284 5.134110 5.139121 5.150050 5.154016 5.154781 5.161125 5.171538
     [105] 5.173730 5.184106 5.184318 5.202329 5.204964 5.233767 5.241031 5.243110
     [113] 5.244839 5.256434 5.257452 5.259275 5.266259 5.266361 5.267572 5.274468
     [121] 5.274982 5.276838 5.292901 5.293899 5.295546 5.298423 5.307144 5.308727
     [129] 5.308766 5.311732 5.312980 5.317643 5.323029 5.324072 5.329275 5.338053
     [137] 5.341785 5.341954 5.351961 5.373480 5.392529 5.394721 5.397332 5.400364
     [145] 5.405131 5.405225 5.422460 5.424342 5.425102 5.425777 5.430048 5.435077
     [153] 5.452862 5.462274 5.466962 5.469800 5.475281 5.478863 5.483241 5.492478
     [161] 5.506289 5.507896 5.516083 5.519433 5.521364 5.541021 5.547071 5.549295
     [169] 5.549486 5.551948 5.565586 5.565993 5.572402 5.574886 5.579933 5.590398
     [177] 5.597493 5.606247 5.618916 5.620931 5.625486 5.632705 5.637924 5.649807
     [185] 5.653986 5.656074 5.663265 5.663447 5.664183 5.669644 5.671170 5.682116
     [193] 5.688693 5.698287 5.699808 5.700583 5.708782 5.709351 5.722118 5.723184
     [201] 5.724503 5.725197 5.738573 5.739821 5.748408 5.749727 5.757940 5.758667
     [209] 5.764494 5.766246 5.766940 5.769141 5.769433 5.776929 5.789581 5.789928
     [217] 5.790094 5.790224 5.793428 5.796655 5.801505 5.808588 5.812416 5.824548
     [225] 5.826336 5.833574 5.840192 5.842035 5.849212 5.849860 5.851358 5.853233
     [233] 5.854078 5.854511 5.858755 5.860894 5.864636 5.867596 5.867653 5.868680
     [241] 5.878978 5.891732 5.897555 5.897666 5.898283 5.916849 5.917713 5.926032
     [249] 5.926621 5.929356 5.932730 5.937661 5.937959 5.943361 5.944109 5.953444
     [257] 5.956090 5.957210 5.959489 5.964971 5.982632 5.983339 5.989437 5.999003
     [265] 5.999274 6.002027 6.005189 6.013735 6.019169 6.022653 6.023095 6.024773
     [273] 6.028124 6.031038 6.035247 6.036343 6.046443 6.047796 6.056476 6.057506
     [281] 6.064434 6.069146 6.073567 6.075067 6.075783 6.080782 6.085120 6.086115
     [289] 6.089137 6.097404 6.101195 6.121572 6.125419 6.126116 6.128948 6.129357
     [297] 6.129650 6.132636 6.139093 6.140850 6.144463 6.152365 6.153094 6.159246
     [305] 6.160217 6.162059 6.163087 6.172874 6.178623 6.179648 6.185198 6.187143
     [313] 6.188823 6.195879 6.201839 6.208383 6.211100 6.221357 6.227526 6.233220
     [321] 6.235051 6.248801 6.251086 6.251377 6.252736 6.254524 6.263852 6.273768
     [329] 6.276017 6.281476 6.281765 6.291529 6.292122 6.292939 6.300334 6.303227
     [337] 6.309728 6.311837 6.316336 6.323657 6.334088 6.349033 6.353593 6.356961
     [345] 6.368647 6.371041 6.372118 6.379964 6.386018 6.390677 6.392169 6.393466
     [353] 6.409044 6.414913 6.416296 6.419891 6.429769 6.430266 6.466330 6.470787
     [361] 6.471781 6.480976 6.487789 6.500871 6.503658 6.514076 6.516373 6.518438
     [369] 6.536099 6.536405 6.548673 6.548823 6.550192 6.558179 6.564563 6.570723
     [377] 6.576038 6.578364 6.580692 6.608650 6.610719 6.619286 6.622294 6.661556
     [385] 6.668175 6.680712 6.689155 6.690961 6.702876 6.712232 6.716219 6.722000
     [393] 6.737366 6.753735 6.755070 6.756590 6.757283 6.787196 6.793745 6.794320
     [401] 6.797920 6.833434 6.849787 6.854875 6.866215 6.870516 6.884663 6.896268
     [409] 6.916128 6.923988 6.926975 6.946058 6.972692 6.987909 7.004827 7.021391
     [417] 7.045424 7.082474 7.096623 7.141030 7.142820 7.153860 7.175245 7.181208
     [425] 7.197392 7.208945 7.212655 7.220625 7.223249 7.227780 7.279230 7.422993
     [433] 7.490688 7.544202 7.651060 8.135963
     Height vectors differ! The maximum relative error is 2.426858e-01.
     Error in test.vector() :
     Please send a report to the author of the 'fastcluster' package, Daniel Müllner.
     For contact details, see <http://danifold.net>. To make the error
     reproducible, you must include the following number (the random seed value) in
     your error report: 793022628.
    
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.1.22
Check: tests
Result: ERROR
     Running ‘test_fastcluster.R’ [6s/11s]
    Running the tests in ‘tests/test_fastcluster.R’ failed.
    Complete output:
     > # fastcluster: Fast hierarchical clustering routines for R and Python
     > #
     > # Copyright © 2011 Daniel Müllner
     > # <http://danifold.net>
     > #
     > # Test script for the R interface
     >
     > seed = as.integer(runif(1, 0, 1e9))
     > set.seed(seed)
     > cat(sprintf("Random seed: %d\n",seed))
     Random seed: 226451080
     >
     > print_seed <- function() {
     + return(sprintf('
     + Please send a report to the author of the \'fastcluster\' package, Daniel Müllner.
     + For contact details, see <http://danifold.net>. To make the error
     + reproducible, you must include the following number (the random seed value) in
     + your error report: %d.\n\n', seed))
     + }
     >
     > hasWardD2 = getRversion() >= '3.1.0'
     >
     > # Compare two dendrograms and check whether they are equal, except that
     > # ties may be resolved differently.
     > compare <- function(dg1, dg2) {
     + h1 <- dg1$height
     + h2 <- dg2$height
     + # "height" vectors may have small numerical errors.
     + rdiffs <- abs(h1-h2)/pmax(abs(h1),abs(h2))
     + rdiffs = rdiffs[complete.cases(rdiffs)]
     + rel_error <- max(rdiffs)
     + # We allow a relative error of 1e-13.
     + if (rel_error>1e-13) {
     + print(h1)
     + print(h2)
     + cat(sprintf('Height vectors differ! The maximum relative error is %e.\n', rel_error))
     + return(FALSE)
     + }
     + # Filter the indices where consecutive merging distances are distinct.
     + d = diff(dg1$height)
     + b = (c(d,1)!=0 & c(1,d)!=0)
     + #cat(sprintf("Percentage of indices where we can test: %g.\n",100.0*length(b[b])/length(b)))
     + if (any(b)) {
     + m1 = dg1$merge[b,]
     + m2 = dg2$merge[b,]
     +
     + r = function(i) {
     + if (i<0) {
     + return(1)
     + }
     + else {
     + return(b[i])
     + }
     + }
     +
     + f = sapply(m1,r)
     + fm1 = m1*f
     + fm2 = m2*f
     + # The "merge" matrices must be identical whereever indices are not ambiguous
     + # due to ties.
     + if (!identical(fm1,fm2)) {
     + cat('Merge matrices differ!\n')
     + return(FALSE)
     + }
     + # Compare the "order" vectors only if all merging distances were distinct.
     + if (all(b) && !identical(dg1$order,dg2$order)) {
     + cat('Order vectors differ!\n')
     + return(FALSE)
     + }
     + }
     + return(TRUE)
     + }
     >
     > # Generate uniformly distributed random data
     > generate.uniform <- function() {
     + n = sample(10:1000,1)
     + range_exp = runif(1,min=-10, max=10)
     + cat(sprintf("Number of sample points: %d\n",n))
     + cat(sprintf("Dissimilarity range: [0,%g]\n",10^range_exp))
     + d = runif(n*(n-1)/2, min=0, max=10^range_exp)
     + # Fake a compressed distance matrix
     + attributes(d) <- NULL
     + attr(d,"Size") <- n
     + attr(d, "call") <- 'N/A'
     + class(d) <- "dist"
     + return(d)
     + }
     >
     > # Generate normally distributed random data
     > generate.normal <- function() {
     + n = sample(10:1000,1)
     + dim = sample(2:20,1)
     +
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     +
     + pcd = matrix(rnorm(n*dim), c(n,dim))
     + d = dist(pcd)
     + return(d)
     + }
     >
     > # Test the clustering functions when a distance matrix is given.
     > test.dm <- function(d) {
     + d2 = d
     + if (hasWardD2) {
     + methods = c('single','complete','average','mcquitty','ward.D','ward.D2','centroid','median')
     + }
     + else {
     + methods = c('single','complete','average','mcquitty','ward','centroid','median')
     + }
     + for (method in methods) {
     + cat(paste('Method :', method, '\n'))
     + dg_stats = stats::hclust(d, method=method)
     + if (method == 'ward') {
     + method = 'ward.D'
     + }
     + dg_fastcluster = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_stats, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + cat('Passed.\n')
     + }
     >
     > # Test the clustering functions for vector input in Euclidean space.
     > test.vector <- function() {
     + # generate test data
     + n = sample(10:1000,1)
     + dim = sample(2:20,1)
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     +
     + range_exp = runif(1,min=-10, max=10)
     + pcd = matrix(rnorm(n*dim, sd=10^range_exp), c(n,dim))
     + pcd2 = pcd
     + # test
     + method='single'
     + cat(paste('Method:', method, '\n'))
     + for (metric in c('euclidean', 'maximum', 'manhattan', 'canberra', 'minkowski')) {
     + cat(paste(' Metric:', metric, '\n'))
     + if (metric=='minkowski') {
     + p = runif(1, min=1.0, max=10.0)
     + cat (sprintf(" p: %g\n",p));
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric, p=p)
     + d = dist(pcd, method=metric, p=p)
     + }
     + else {
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric)
     + d = dist(pcd, method=metric)
     + }
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2) || !identical(pcd,pcd2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + for (method in c('ward','centroid','median') ) {
     + cat(paste('Method:', method, '\n'))
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method)
     + if (!identical(pcd,pcd2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + d = dist(pcd)
     + if(method == "ward" && hasWardD2) {
     + method = "ward.D2"
     + }
     + else
     + {
     + # Workaround: fastcluster::hclust expects _squared_ euclidean distances.
     + d = d^2
     + }
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if(method != "ward.D2") {
     + dg_fastcluster_dist$height = sqrt(dg_fastcluster_dist$height)
     + }
     + # The Euclidean methods may have small numerical errors due to squaring/
     + # taking the root in the Euclidean distances.
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + cat('Passed.\n')
     + }
     >
     > # Test the single linkage function with the "binary" metric
     > test.vector.binary <- function() {
     + # generate test data
     + cat (sprintf("Uniform sampling for the 'binary' metric:\n"))
     + n = sample(10:400,1)
     + dim = sample(n:(2*n),1)
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     + pcd = matrix(sample(-1:2, n*dim, replace=T), c(n,dim))
     + pcd2 = pcd
     + # test
     + method='single'
     + metric='binary'
     + cat(paste('Method:', method, '\n'))
     + cat(paste(' Metric:', metric, '\n'))
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric)
     + d = dist(pcd, method=metric)
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2) || !identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + cat('Passed.\n')
     + }
     >
     >
     > N = 15
     > for (i in (1:N)) {
     + if (i%%2==1) {
     + cat(sprintf('Random test %d of %d (uniform distribution of distances):\n',i,2*N))
     + d = generate.uniform()
     + }
     + else {
     + cat(sprintf('Random test %d of %d (Gaussian density):\n',i,2*N))
     + d = generate.normal()
     + }
     + test.dm(d)
     + }
     Random test 1 of 30 (uniform distribution of distances):
     Number of sample points: 768
     Dissimilarity range: [0,0.0169324]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 2 of 30 (Gaussian density):
     Number of sample points: 751
     Dimension: 11
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 3 of 30 (uniform distribution of distances):
     Number of sample points: 275
     Dissimilarity range: [0,6.92095e+07]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 4 of 30 (Gaussian density):
     Number of sample points: 149
     Dimension: 13
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 5 of 30 (uniform distribution of distances):
     Number of sample points: 68
     Dissimilarity range: [0,8.9086e-07]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 6 of 30 (Gaussian density):
     Number of sample points: 546
     Dimension: 7
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 7 of 30 (uniform distribution of distances):
     Number of sample points: 535
     Dissimilarity range: [0,4.31993e-06]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 8 of 30 (Gaussian density):
     Number of sample points: 495
     Dimension: 6
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 9 of 30 (uniform distribution of distances):
     Number of sample points: 513
     Dissimilarity range: [0,7.13969e-06]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 10 of 30 (Gaussian density):
     Number of sample points: 385
     Dimension: 15
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 11 of 30 (uniform distribution of distances):
     Number of sample points: 879
     Dissimilarity range: [0,1.17421e+06]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 12 of 30 (Gaussian density):
     Number of sample points: 622
     Dimension: 2
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 13 of 30 (uniform distribution of distances):
     Number of sample points: 626
     Dissimilarity range: [0,7.84548e-06]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 14 of 30 (Gaussian density):
     Number of sample points: 43
     Dimension: 12
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 15 of 30 (uniform distribution of distances):
     Number of sample points: 861
     Dissimilarity range: [0,2.07898e-10]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     > for (i in (N+1:N)) {
     + cat(sprintf('Random test %d of %d (Gaussian density):\n',i,2*N))
     + test.vector()
     + test.vector.binary()
     + }
     Random test 16 of 30 (Gaussian density):
     Number of sample points: 797
     Dimension: 17
     Method: single
     Metric: euclidean
     Metric: maximum
     Metric: manhattan
     Metric: canberra
     [1] 4.628343 5.598264 5.690935 5.735310 5.805928 5.947788 6.067555 6.073317
     [9] 6.074999 6.106500 6.134628 6.214693 6.229836 6.233907 6.250742 6.280422
     [17] 6.310336 6.334255 6.360771 6.369164 6.369326 6.381487 6.456774 6.458935
     [25] 6.476929 6.483318 6.492471 6.502147 6.508867 6.529397 6.552825 6.553467
     [33] 6.569160 6.575032 6.582677 6.583153 6.584506 6.614543 6.617269 6.617345
     [41] 6.633622 6.648250 6.674022 6.674522 6.711647 6.711838 6.717650 6.728384
     [49] 6.730974 6.733172 6.742384 6.754029 6.754333 6.774418 6.775984 6.779804
     [57] 6.811528 6.816045 6.816798 6.821130 6.821236 6.822259 6.825848 6.827278
     [65] 6.828687 6.830098 6.832690 6.832768 6.837059 6.840895 6.845123 6.851711
     [73] 6.852731 6.852921 6.866864 6.878303 6.881165 6.881308 6.882393 6.882590
     [81] 6.888812 6.893186 6.893840 6.898470 6.901793 6.902249 6.908235 6.909950
     [89] 6.912238 6.920311 6.920358 6.923474 6.932440 6.942192 6.946224 6.947131
     [97] 6.953378 6.966119 6.970540 6.972553 6.988495 7.009386 7.014485 7.014815
     [105] 7.015399 7.017946 7.019155 7.024350 7.025976 7.026582 7.028656 7.047689
     [113] 7.051465 7.053875 7.060211 7.061814 7.064072 7.067255 7.078377 7.080603
     [121] 7.080772 7.101122 7.103157 7.106782 7.110015 7.112365 7.112597 7.129260
     [129] 7.135462 7.136273 7.143664 7.146541 7.147359 7.150464 7.155481 7.159643
     [137] 7.167133 7.168267 7.175331 7.178109 7.187239 7.198685 7.200847 7.203073
     [145] 7.205186 7.208671 7.215548 7.226798 7.227301 7.228832 7.228847 7.237810
     [153] 7.246809 7.251890 7.254212 7.257276 7.257488 7.266507 7.271231 7.272041
     [161] 7.286628 7.288228 7.288398 7.299191 7.307092 7.308218 7.308671 7.308688
     [169] 7.309125 7.310094 7.312125 7.312821 7.313861 7.316098 7.317743 7.317980
     [177] 7.318941 7.322569 7.323031 7.324842 7.334268 7.335179 7.336955 7.339068
     [185] 7.339656 7.344590 7.345725 7.353903 7.364294 7.367752 7.372198 7.374902
     [193] 7.375072 7.375364 7.377439 7.378241 7.379248 7.381755 7.382744 7.383557
     [201] 7.386142 7.386387 7.389040 7.406289 7.406868 7.408078 7.408143 7.408545
     [209] 7.410156 7.414877 7.416262 7.416751 7.418144 7.424987 7.425195 7.425894
     [217] 7.429545 7.430037 7.431540 7.438172 7.438571 7.444809 7.445035 7.445674
     [225] 7.445870 7.447398 7.450439 7.452490 7.454870 7.455988 7.456627 7.458927
     [233] 7.462671 7.463038 7.466642 7.468358 7.471403 7.472681 7.474612 7.477335
     [241] 7.478280 7.479988 7.480797 7.487416 7.489056 7.490754 7.492067 7.492683
     [249] 7.494502 7.495855 7.497405 7.498043 7.498327 7.504776 7.505698 7.506303
     [257] 7.507092 7.511535 7.512516 7.512660 7.514112 7.519096 7.524430 7.524576
     [265] 7.527530 7.528188 7.530711 7.531036 7.532340 7.534517 7.536943 7.538817
     [273] 7.539148 7.539266 7.539840 7.540530 7.543096 7.544393 7.549605 7.550038
     [281] 7.550734 7.554515 7.555033 7.555144 7.556079 7.556580 7.556726 7.557040
     [289] 7.562786 7.563573 7.564158 7.565567 7.567268 7.567403 7.572992 7.573660
     [297] 7.578733 7.580892 7.582927 7.585278 7.586638 7.586948 7.587582 7.587880
     [305] 7.588640 7.592402 7.593031 7.593605 7.594273 7.596240 7.597852 7.598999
     [313] 7.599239 7.599470 7.601370 7.605054 7.605810 7.606501 7.609922 7.611715
     [321] 7.612017 7.612359 7.615992 7.616668 7.619918 7.620381 7.622122 7.627647
     [329] 7.629502 7.629793 7.630350 7.632277 7.635674 7.638826 7.640415 7.641401
     [337] 7.641988 7.642630 7.642870 7.645216 7.648240 7.648796 7.650969 7.651167
     [345] 7.651765 7.651930 7.653762 7.657168 7.657270 7.657592 7.658464 7.659302
     [353] 7.659436 7.659722 7.666216 7.668551 7.669635 7.671171 7.673000 7.673691
     [361] 7.675841 7.679041 7.679785 7.680086 7.682376 7.684546 7.688947 7.697501
     [369] 7.701651 7.703724 7.704989 7.706037 7.708142 7.709406 7.710183 7.711936
     [377] 7.713137 7.713787 7.713927 7.714982 7.715360 7.721342 7.722088 7.725976
     [385] 7.732035 7.732136 7.733844 7.737229 7.741343 7.742072 7.743336 7.749596
     [393] 7.752598 7.753510 7.755031 7.756300 7.756349 7.760769 7.761120 7.765650
     [401] 7.767301 7.767898 7.767996 7.768146 7.769034 7.769870 7.770808 7.771608
     [409] 7.772245 7.775110 7.775872 7.778880 7.779167 7.782019 7.782694 7.783476
     [417] 7.785289 7.789958 7.790243 7.790528 7.791157 7.792158 7.794304 7.796164
     [425] 7.797323 7.803744 7.807653 7.810257 7.810379 7.812761 7.814014 7.814317
     [433] 7.815181 7.817857 7.818483 7.818862 7.819257 7.819264 7.819408 7.820398
     [441] 7.821064 7.821686 7.824293 7.824882 7.828522 7.830448 7.833120 7.834502
     [449] 7.835410 7.837856 7.840731 7.843352 7.844581 7.845048 7.845761 7.849430
     [457] 7.851056 7.851579 7.852740 7.852962 7.853462 7.856141 7.857425 7.861765
     [465] 7.863873 7.865188 7.865596 7.875384 7.875795 7.877095 7.877737 7.880907
     [473] 7.881077 7.881092 7.881139 7.882173 7.882715 7.884711 7.889639 7.893833
     [481] 7.894062 7.895784 7.896466 7.899051 7.900036 7.902503 7.902604 7.902784
     [489] 7.905461 7.906349 7.907948 7.908279 7.909111 7.911305 7.913130 7.917903
     [497] 7.918999 7.921501 7.922528 7.925024 7.927385 7.928095 7.928179 7.928987
     [505] 7.929740 7.933610 7.936138 7.944245 7.945540 7.946490 7.948148 7.948824
     [513] 7.949692 7.950591 7.951887 7.953879 7.955059 7.957985 7.959185 7.959192
     [521] 7.960083 7.961173 7.962654 7.962831 7.963430 7.963494 7.965259 7.966323
     [529] 7.970276 7.970552 7.970684 7.971104 7.974275 7.974289 7.974476 7.978678
     [537] 7.978727 7.980585 7.980633 7.980785 7.981592 7.983634 7.986611 7.992571
     [545] 7.993546 7.993939 7.997501 8.000924 8.001157 8.009684 8.009952 8.011536
     [553] 8.015137 8.016786 8.018936 8.019602 8.020360 8.025587 8.027551 8.027746
     [561] 8.028115 8.028559 8.028765 8.029623 8.029931 8.031358 8.036947 8.037778
     [569] 8.037972 8.041443 8.043070 8.045426 8.047551 8.048942 8.051370 8.056545
     [577] 8.062456 8.063147 8.064461 8.068510 8.070122 8.070902 8.073339 8.074117
     [585] 8.075057 8.076392 8.078169 8.082491 8.083709 8.084507 8.087441 8.088904
     [593] 8.090762 8.092369 8.093635 8.095226 8.101874 8.107547 8.109191 8.109308
     [601] 8.111174 8.111512 8.113274 8.114933 8.119359 8.119386 8.126038 8.128872
     [609] 8.129884 8.130146 8.130442 8.130644 8.132040 8.133032 8.135331 8.143798
     [617] 8.151315 8.155218 8.155708 8.160511 8.165713 8.173212 8.174101 8.174938
     [625] 8.175306 8.176081 8.176762 8.180311 8.193128 8.194862 8.196806 8.197381
     [633] 8.199897 8.201940 8.203373 8.204466 8.205993 8.206536 8.216243 8.218070
     [641] 8.223056 8.224109 8.225111 8.226006 8.229404 8.229848 8.231529 8.234870
     [649] 8.238006 8.238998 8.239283 8.240888 8.243527 8.245584 8.247490 8.249350
     [657] 8.252424 8.253800 8.256924 8.265232 8.266845 8.269623 8.272072 8.273930
     [665] 8.276184 8.277986 8.282264 8.284422 8.285123 8.286895 8.288250 8.290974
     [673] 8.292915 8.293065 8.293245 8.301515 8.303448 8.304666 8.308408 8.309130
     [681] 8.312421 8.316201 8.317300 8.318871 8.326351 8.329383 8.330793 8.330854
     [689] 8.336173 8.340311 8.344321 8.345550 8.347504 8.352023 8.353575 8.354819
     [697] 8.357023 8.357736 8.361049 8.362195 8.363518 8.372869 8.373366 8.375454
     [705] 8.382272 8.382909 8.386169 8.389636 8.391740 8.394555 8.405915 8.407211
     [713] 8.424356 8.428707 8.430615 8.433478 8.435852 8.436430 8.442725 8.442995
     [721] 8.444691 8.451834 8.457990 8.470624 8.472709 8.476280 8.477944 8.484759
     [729] 8.485832 8.492886 8.493360 8.494152 8.499876 8.504130 8.509702 8.513925
     [737] 8.515396 8.520105 8.532207 8.538550 8.543386 8.544334 8.562684 8.564016
     [745] 8.569675 8.569928 8.571737 8.587889 8.589503 8.591802 8.597604 8.612766
     [753] 8.615516 8.647583 8.647933 8.654246 8.654668 8.655811 8.658013 8.658585
     [761] 8.666733 8.673606 8.680562 8.694861 8.702000 8.707403 8.717559 8.721096
     [769] 8.729180 8.729247 8.743459 8.746099 8.759427 8.768638 8.787240 8.831904
     [777] 8.865468 8.868739 8.881636 8.914242 8.925616 8.939177 8.951559 8.990707
     [785] 8.994971 9.002956 9.047151 9.048549 9.088798 9.102692 9.117204 9.133049
     [793] 9.151124 9.313512 9.405091 9.415321
     [1] 5.805928 6.447409 6.539953 6.968986 7.063967 7.083289 7.123563
     [8] 7.189731 7.237194 7.422214 7.464210 7.547102 7.634389 7.669162
     [15] 7.739389 7.837504 7.914400 7.925421 8.062324 8.063609 8.073956
     [22] 8.149788 8.158390 8.187448 8.220800 8.229478 8.241783 8.252346
     [29] 8.297694 8.313463 8.339742 8.350673 8.351613 8.362392 8.389225
     [36] 8.429147 8.432245 8.498295 8.564931 8.569780 8.579761 8.611806
     [43] 8.662844 8.663219 8.715854 8.730480 8.731545 8.732678 8.780104
     [50] 8.821566 8.830143 8.830411 8.843128 8.872155 8.874687 8.905132
     [57] 8.936636 8.964433 8.991322 9.048449 9.058120 9.084093 9.120001
     [64] 9.122210 9.133811 9.146399 9.146814 9.152819 9.162727 9.171407
     [71] 9.176113 9.186204 9.197594 9.197947 9.260892 9.261718 9.273615
     [78] 9.275717 9.304825 9.310306 9.358566 9.370999 9.380886 9.389072
     [85] 9.406990 9.417154 9.420835 9.427760 9.432982 9.435989 9.440422
     [92] 9.441173 9.446738 9.481840 9.484425 9.486320 9.490116 9.506068
     [99] 9.507651 9.521216 9.532046 9.534254 9.555445 9.563678 9.564516
     [106] 9.571821 9.576648 9.585661 9.593152 9.593591 9.593592 9.622983
     [113] 9.642442 9.648067 9.667410 9.672452 9.674088 9.686623 9.692315
     [120] 9.698069 9.698204 9.698777 9.709269 9.720608 9.731748 9.752724
     [127] 9.786366 9.788434 9.792479 9.798523 9.834552 9.838816 9.838877
     [134] 9.861950 9.865774 9.871982 9.874445 9.876653 9.880331 9.896395
     [141] 9.901619 9.920429 9.924445 9.943300 9.951560 9.960666 9.967746
     [148] 9.969121 9.969420 9.970255 9.972266 9.975283 9.976604 9.978890
     [155] 9.981795 9.984996 9.985285 9.992322 9.995315 10.014467 10.035289
     [162] 10.052100 10.059180 10.060353 10.070283 10.073068 10.075599 10.077538
     [169] 10.087018 10.091394 10.100244 10.100771 10.108920 10.123775 10.136845
     [176] 10.139818 10.160339 10.167492 10.170520 10.177386 10.185034 10.192845
     [183] 10.204936 10.211830 10.215673 10.219283 10.220136 10.227183 10.235988
     [190] 10.255806 10.262976 10.270704 10.272320 10.275365 10.278891 10.285072
     [197] 10.285506 10.288363 10.288451 10.292991 10.298118 10.309277 10.312999
     [204] 10.331992 10.338949 10.339089 10.339547 10.341627 10.344129 10.347646
     [211] 10.347900 10.355518 10.364780 10.366713 10.382085 10.382823 10.386110
     [218] 10.396410 10.398867 10.400626 10.411987 10.413736 10.415273 10.418653
     [225] 10.420511 10.423755 10.430669 10.431085 10.432570 10.434301 10.440305
     [232] 10.446195 10.448164 10.451189 10.455598 10.457287 10.460310 10.461750
     [239] 10.467482 10.469046 10.469805 10.472068 10.478580 10.479034 10.481225
     [246] 10.483243 10.484825 10.485507 10.485622 10.490044 10.497175 10.497185
     [253] 10.508227 10.509154 10.510454 10.526311 10.540415 10.540698 10.541688
     [260] 10.542278 10.542449 10.544325 10.545345 10.554552 10.561144 10.567381
     [267] 10.568390 10.569741 10.570213 10.570959 10.594491 10.597750 10.599725
     [274] 10.601102 10.604286 10.608839 10.610840 10.621067 10.624475 10.624800
     [281] 10.629629 10.630554 10.637204 10.638198 10.638840 10.646178 10.650122
     [288] 10.650584 10.654538 10.657285 10.661084 10.664950 10.674888 10.674890
     [295] 10.682312 10.683533 10.685071 10.688147 10.693594 10.694789 10.697274
     [302] 10.698903 10.700895 10.706380 10.712234 10.712888 10.718310 10.731616
     [309] 10.731742 10.743474 10.745017 10.752354 10.753716 10.756077 10.757748
     [316] 10.762737 10.771340 10.773617 10.777002 10.786197 10.786514 10.787085
     [323] 10.787926 10.792787 10.799963 10.802376 10.805040 10.805782 10.809762
     [330] 10.814037 10.821496 10.828065 10.829853 10.835334 10.835584 10.836951
     [337] 10.839365 10.843456 10.854676 10.861305 10.861465 10.862478 10.863344
     [344] 10.869504 10.871658 10.876728 10.879093 10.881071 10.886590 10.886637
     [351] 10.889686 10.894020 10.895134 10.909774 10.919919 10.921893 10.922162
     [358] 10.923645 10.930087 10.935707 10.937909 10.939343 10.939679 10.939878
     [365] 10.941417 10.946098 10.965155 10.966698 10.974343 10.977081 10.979513
     [372] 10.985689 10.990986 10.992257 10.994908 10.997965 10.999798 11.000949
     [379] 11.003151 11.009212 11.011494 11.013857 11.014954 11.020771 11.024836
     [386] 11.024920 11.029160 11.033944 11.034173 11.035344 11.039674 11.039934
     [393] 11.045038 11.047253 11.049363 11.053454 11.062540 11.063452 11.064213
     [400] 11.064760 11.066435 11.071659 11.077950 11.078686 11.083069 11.084211
     [407] 11.086334 11.091042 11.092750 11.093767 11.096985 11.110637 11.111321
     [414] 11.113498 11.114840 11.115492 11.115597 11.122663 11.126987 11.134013
     [421] 11.135774 11.136041 11.146521 11.147985 11.151405 11.152289 11.152468
     [428] 11.153073 11.160560 11.162311 11.169509 11.173969 11.176509 11.179548
     [435] 11.181333 11.190519 11.190967 11.194502 11.196544 11.197936 11.208089
     [442] 11.208897 11.209938 11.211051 11.215233 11.215735 11.216679 11.217027
     [449] 11.217320 11.221373 11.222310 11.225919 11.230141 11.231225 11.232681
     [456] 11.234608 11.234819 11.237388 11.242346 11.243371 11.250158 11.252314
     [463] 11.259384 11.262402 11.262609 11.265159 11.266103 11.267696 11.270791
     [470] 11.271006 11.275206 11.277603 11.282077 11.285815 11.286895 11.297870
     [477] 11.299618 11.304320 11.306095 11.306249 11.307090 11.308237 11.325184
     [484] 11.327207 11.329238 11.336025 11.345740 11.346407 11.346724 11.351402
     [491] 11.352609 11.355607 11.361211 11.362258 11.368629 11.373096 11.378551
     [498] 11.380779 11.382535 11.384410 11.387949 11.388058 11.388119 11.389152
     [505] 11.391806 11.395535 11.398875 11.400157 11.402929 11.415253 11.415557
     [512] 11.415934 11.417727 11.422572 11.424495 11.433606 11.437283 11.443202
     [519] 11.444219 11.448119 11.448799 11.452570 11.462959 11.464131 11.465329
     [526] 11.469251 11.469576 11.470511 11.472341 11.472800 11.473285 11.484309
     [533] 11.484611 11.487396 11.493293 11.494038 11.497927 11.502531 11.505029
     [540] 11.505903 11.507160 11.508014 11.508349 11.510598 11.511044 11.517705
     [547] 11.522070 11.523305 11.526149 11.528192 11.532279 11.532445 11.537474
     [554] 11.540197 11.550848 11.555816 11.561428 11.561857 11.562449 11.565683
     [561] 11.567923 11.569140 11.575625 11.575924 11.577375 11.582003 11.582062
     [568] 11.586306 11.587093 11.590823 11.593200 11.593242 11.593726 11.599399
     [575] 11.602675 11.602770 11.605789 11.606825 11.609734 11.614634 11.615126
     [582] 11.619810 11.622961 11.623951 11.625472 11.632498 11.634164 11.636249
     [589] 11.648037 11.655144 11.664943 11.669089 11.675421 11.675484 11.676453
     [596] 11.676929 11.680500 11.683098 11.688119 11.695109 11.696908 11.700327
     [603] 11.700967 11.701485 11.707142 11.715987 11.719612 11.720660 11.722154
     [610] 11.724511 11.724520 11.727970 11.728461 11.738772 11.740046 11.742143
     [617] 11.743442 11.744391 11.746698 11.753778 11.756617 11.761747 11.769376
     [624] 11.769859 11.773968 11.776024 11.777174 11.777778 11.780187 11.783401
     [631] 11.784531 11.788023 11.790897 11.790982 11.791403 11.800486 11.802897
     [638] 11.808431 11.811351 11.817865 11.818982 11.824299 11.829265 11.830302
     [645] 11.834332 11.848550 11.849648 11.855460 11.864672 11.871320 11.876825
     [652] 11.878229 11.878341 11.879874 11.890979 11.893840 11.896803 11.897928
     [659] 11.908657 11.909271 11.911092 11.919144 11.934759 11.943177 11.946058
     [666] 11.946763 11.948787 11.949040 11.962024 11.967857 11.968887 11.972708
     [673] 11.985298 12.005619 12.009939 12.023281 12.036057 12.038953 12.048948
     [680] 12.058302 12.065911 12.066748 12.068353 12.068797 12.071479 12.073272
     [687] 12.085494 12.086159 12.087036 12.090457 12.092747 12.093808 12.095329
     [694] 12.103928 12.113449 12.115684 12.116698 12.118708 12.122198 12.137082
     [701] 12.142168 12.145742 12.154418 12.166905 12.198467 12.198669 12.198675
     [708] 12.202741 12.211659 12.217218 12.218588 12.219684 12.222341 12.244477
     [715] 12.260279 12.262214 12.271315 12.279657 12.282815 12.287486 12.295405
     [722] 12.301164 12.301639 12.304852 12.310176 12.319779 12.330782 12.336805
     [729] 12.347700 12.347892 12.351977 12.364351 12.370589 12.393835 12.396501
     [736] 12.413232 12.443766 12.452854 12.456285 12.470122 12.473778 12.479265
     [743] 12.486049 12.501006 12.501387 12.506403 12.510233 12.525082 12.533762
     [750] 12.536790 12.543510 12.561102 12.570553 12.584968 12.588739 12.612972
     [757] 12.641047 12.641749 12.652324 12.659465 12.660238 12.684955 12.695648
     [764] 12.718475 12.720236 12.745007 12.758322 12.772414 12.778047 12.790613
     [771] 12.795777 12.801486 12.847094 12.862187 12.873449 12.900959 12.927212
     [778] 12.952824 12.968828 13.024684 13.155329 13.158228 13.189943 13.195289
     [785] 13.202265 13.226573 13.228569 13.291808 13.370143 13.458498 13.560576
     [792] 13.640208 13.796289 13.937065 14.195658 14.866116
     Height vectors differ! The maximum relative error is 3.666590e-01.
     Error in test.vector() :
     Please send a report to the author of the 'fastcluster' package, Daniel Müllner.
     For contact details, see <http://danifold.net>. To make the error
     reproducible, you must include the following number (the random seed value) in
     your error report: 226451080.
    
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.1.22
Check: tests
Result: ERROR
     Running ‘test_fastcluster.R’
    Running the tests in ‘tests/test_fastcluster.R’ failed.
    Complete output:
     > # fastcluster: Fast hierarchical clustering routines for R and Python
     > #
     > # Copyright © 2011 Daniel Müllner
     > # <http://danifold.net>
     > #
     > # Test script for the R interface
     >
     > seed = as.integer(runif(1, 0, 1e9))
     > set.seed(seed)
     > cat(sprintf("Random seed: %d\n",seed))
     Random seed: 869050511
     >
     > print_seed <- function() {
     + return(sprintf('
     + Please send a report to the author of the \'fastcluster\' package, Daniel Müllner.
     + For contact details, see <http://danifold.net>. To make the error
     + reproducible, you must include the following number (the random seed value) in
     + your error report: %d.\n\n', seed))
     + }
     >
     > hasWardD2 = getRversion() >= '3.1.0'
     >
     > # Compare two dendrograms and check whether they are equal, except that
     > # ties may be resolved differently.
     > compare <- function(dg1, dg2) {
     + h1 <- dg1$height
     + h2 <- dg2$height
     + # "height" vectors may have small numerical errors.
     + rdiffs <- abs(h1-h2)/pmax(abs(h1),abs(h2))
     + rdiffs = rdiffs[complete.cases(rdiffs)]
     + rel_error <- max(rdiffs)
     + # We allow a relative error of 1e-13.
     + if (rel_error>1e-13) {
     + print(h1)
     + print(h2)
     + cat(sprintf('Height vectors differ! The maximum relative error is %e.\n', rel_error))
     + return(FALSE)
     + }
     + # Filter the indices where consecutive merging distances are distinct.
     + d = diff(dg1$height)
     + b = (c(d,1)!=0 & c(1,d)!=0)
     + #cat(sprintf("Percentage of indices where we can test: %g.\n",100.0*length(b[b])/length(b)))
     + if (any(b)) {
     + m1 = dg1$merge[b,]
     + m2 = dg2$merge[b,]
     +
     + r = function(i) {
     + if (i<0) {
     + return(1)
     + }
     + else {
     + return(b[i])
     + }
     + }
     +
     + f = sapply(m1,r)
     + fm1 = m1*f
     + fm2 = m2*f
     + # The "merge" matrices must be identical whereever indices are not ambiguous
     + # due to ties.
     + if (!identical(fm1,fm2)) {
     + cat('Merge matrices differ!\n')
     + return(FALSE)
     + }
     + # Compare the "order" vectors only if all merging distances were distinct.
     + if (all(b) && !identical(dg1$order,dg2$order)) {
     + cat('Order vectors differ!\n')
     + return(FALSE)
     + }
     + }
     + return(TRUE)
     + }
     >
     > # Generate uniformly distributed random data
     > generate.uniform <- function() {
     + n = sample(10:1000,1)
     + range_exp = runif(1,min=-10, max=10)
     + cat(sprintf("Number of sample points: %d\n",n))
     + cat(sprintf("Dissimilarity range: [0,%g]\n",10^range_exp))
     + d = runif(n*(n-1)/2, min=0, max=10^range_exp)
     + # Fake a compressed distance matrix
     + attributes(d) <- NULL
     + attr(d,"Size") <- n
     + attr(d, "call") <- 'N/A'
     + class(d) <- "dist"
     + return(d)
     + }
     >
     > # Generate normally distributed random data
     > generate.normal <- function() {
     + n = sample(10:1000,1)
     + dim = sample(2:20,1)
     +
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     +
     + pcd = matrix(rnorm(n*dim), c(n,dim))
     + d = dist(pcd)
     + return(d)
     + }
     >
     > # Test the clustering functions when a distance matrix is given.
     > test.dm <- function(d) {
     + d2 = d
     + if (hasWardD2) {
     + methods = c('single','complete','average','mcquitty','ward.D','ward.D2','centroid','median')
     + }
     + else {
     + methods = c('single','complete','average','mcquitty','ward','centroid','median')
     + }
     + for (method in methods) {
     + cat(paste('Method :', method, '\n'))
     + dg_stats = stats::hclust(d, method=method)
     + if (method == 'ward') {
     + method = 'ward.D'
     + }
     + dg_fastcluster = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_stats, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + cat('Passed.\n')
     + }
     >
     > # Test the clustering functions for vector input in Euclidean space.
     > test.vector <- function() {
     + # generate test data
     + n = sample(10:1000,1)
     + dim = sample(2:20,1)
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     +
     + range_exp = runif(1,min=-10, max=10)
     + pcd = matrix(rnorm(n*dim, sd=10^range_exp), c(n,dim))
     + pcd2 = pcd
     + # test
     + method='single'
     + cat(paste('Method:', method, '\n'))
     + for (metric in c('euclidean', 'maximum', 'manhattan', 'canberra', 'minkowski')) {
     + cat(paste(' Metric:', metric, '\n'))
     + if (metric=='minkowski') {
     + p = runif(1, min=1.0, max=10.0)
     + cat (sprintf(" p: %g\n",p));
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric, p=p)
     + d = dist(pcd, method=metric, p=p)
     + }
     + else {
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric)
     + d = dist(pcd, method=metric)
     + }
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2) || !identical(pcd,pcd2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + for (method in c('ward','centroid','median') ) {
     + cat(paste('Method:', method, '\n'))
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method)
     + if (!identical(pcd,pcd2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + d = dist(pcd)
     + if(method == "ward" && hasWardD2) {
     + method = "ward.D2"
     + }
     + else
     + {
     + # Workaround: fastcluster::hclust expects _squared_ euclidean distances.
     + d = d^2
     + }
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if(method != "ward.D2") {
     + dg_fastcluster_dist$height = sqrt(dg_fastcluster_dist$height)
     + }
     + # The Euclidean methods may have small numerical errors due to squaring/
     + # taking the root in the Euclidean distances.
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + }
     + cat('Passed.\n')
     + }
     >
     > # Test the single linkage function with the "binary" metric
     > test.vector.binary <- function() {
     + # generate test data
     + cat (sprintf("Uniform sampling for the 'binary' metric:\n"))
     + n = sample(10:400,1)
     + dim = sample(n:(2*n),1)
     + cat (sprintf("Number of sample points: %d\n",n))
     + cat (sprintf("Dimension: %d\n",dim))
     + pcd = matrix(sample(-1:2, n*dim, replace=T), c(n,dim))
     + pcd2 = pcd
     + # test
     + method='single'
     + metric='binary'
     + cat(paste('Method:', method, '\n'))
     + cat(paste(' Metric:', metric, '\n'))
     + dg_fastcluster = fastcluster::hclust.vector(pcd, method=method, metric=metric)
     + d = dist(pcd, method=metric)
     + d2 = d
     + dg_fastcluster_dist = fastcluster::hclust(d, method=method)
     + if (!identical(d,d2) || !identical(d,d2)) {
     + cat('Input array was corrupted!\n')
     + stop(print_seed())
     + }
     + if (!compare(dg_fastcluster_dist, dg_fastcluster)) {
     + stop(print_seed())
     + }
     + cat('Passed.\n')
     + }
     >
     >
     > N = 15
     > for (i in (1:N)) {
     + if (i%%2==1) {
     + cat(sprintf('Random test %d of %d (uniform distribution of distances):\n',i,2*N))
     + d = generate.uniform()
     + }
     + else {
     + cat(sprintf('Random test %d of %d (Gaussian density):\n',i,2*N))
     + d = generate.normal()
     + }
     + test.dm(d)
     + }
     Random test 1 of 30 (uniform distribution of distances):
     Number of sample points: 656
     Dissimilarity range: [0,2.68964e-05]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 2 of 30 (Gaussian density):
     Number of sample points: 557
     Dimension: 16
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 3 of 30 (uniform distribution of distances):
     Number of sample points: 658
     Dissimilarity range: [0,34862.2]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 4 of 30 (Gaussian density):
     Number of sample points: 799
     Dimension: 6
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 5 of 30 (uniform distribution of distances):
     Number of sample points: 230
     Dissimilarity range: [0,2.61624e-06]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 6 of 30 (Gaussian density):
     Number of sample points: 543
     Dimension: 4
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 7 of 30 (uniform distribution of distances):
     Number of sample points: 12
     Dissimilarity range: [0,1.97487e+09]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 8 of 30 (Gaussian density):
     Number of sample points: 646
     Dimension: 13
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 9 of 30 (uniform distribution of distances):
     Number of sample points: 246
     Dissimilarity range: [0,1.86576e+07]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 10 of 30 (Gaussian density):
     Number of sample points: 906
     Dimension: 12
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 11 of 30 (uniform distribution of distances):
     Number of sample points: 151
     Dissimilarity range: [0,3.69539e-05]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 12 of 30 (Gaussian density):
     Number of sample points: 292
     Dimension: 17
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 13 of 30 (uniform distribution of distances):
     Number of sample points: 101
     Dissimilarity range: [0,18.0648]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 14 of 30 (Gaussian density):
     Number of sample points: 369
     Dimension: 18
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     Random test 15 of 30 (uniform distribution of distances):
     Number of sample points: 765
     Dissimilarity range: [0,3.12729e+06]
     Method : single
     Method : complete
     Method : average
     Method : mcquitty
     Method : ward.D
     Method : ward.D2
     Method : centroid
     Method : median
     Passed.
     > for (i in (N+1:N)) {
     + cat(sprintf('Random test %d of %d (Gaussian density):\n',i,2*N))
     + test.vector()
     + test.vector.binary()
     + }
     Random test 16 of 30 (Gaussian density):
     Number of sample points: 320
     Dimension: 4
     Method: single
     Metric: euclidean
     Metric: maximum
     Metric: manhattan
     Metric: canberra
     [1] 0.1210558 0.2401720 0.2604767 0.2746578 0.3564080 0.3612250 0.4434859
     [8] 0.4506113 0.4506271 0.4546131 0.4595725 0.4664745 0.4688598 0.4728897
     [15] 0.4734842 0.4842961 0.5046007 0.5097285 0.5388380 0.5397551 0.5451900
     [22] 0.5455253 0.5512819 0.5577838 0.5593887 0.5596767 0.5639085 0.5692731
     [29] 0.5726761 0.5754861 0.5782011 0.5832009 0.5888467 0.5895244 0.5933639
     [36] 0.5978763 0.6004472 0.6037002 0.6045499 0.6058400 0.6080115 0.6107288
     [43] 0.6249217 0.6255735 0.6267955 0.6419603 0.6429127 0.6488524 0.6522160
     [50] 0.6554369 0.6578615 0.6662959 0.6703459 0.6709416 0.6731697 0.6736341
     [57] 0.6760047 0.6762076 0.6817081 0.6875552 0.6897736 0.6924490 0.6925106
     [64] 0.6931641 0.6942245 0.6992834 0.7004730 0.7082330 0.7110069 0.7112657
     [71] 0.7152620 0.7176552 0.7184925 0.7189541 0.7199498 0.7220679 0.7260562
     [78] 0.7307325 0.7340400 0.7351775 0.7354546 0.7357529 0.7365147 0.7497946
     [85] 0.7524238 0.7545090 0.7680828 0.7698426 0.7725040 0.7747702 0.7764964
     [92] 0.7875625 0.7875951 0.7914108 0.7942075 0.7945695 0.7997973 0.8057354
     [99] 0.8061648 0.8087403 0.8098479 0.8106719 0.8186910 0.8211676 0.8303144
     [106] 0.8305985 0.8350592 0.8365644 0.8373273 0.8394896 0.8420892 0.8426069
     [113] 0.8430615 0.8431484 0.8445431 0.8456930 0.8461047 0.8476311 0.8488232
     [120] 0.8506715 0.8533545 0.8536081 0.8543236 0.8555812 0.8578833 0.8580044
     [127] 0.8582798 0.8583814 0.8615072 0.8655997 0.8662081 0.8690909 0.8742578
     [134] 0.8742909 0.8756252 0.8809479 0.8813170 0.8861636 0.8926286 0.8959148
     [141] 0.9011505 0.9090203 0.9125456 0.9135076 0.9151588 0.9153869 0.9192663
     [148] 0.9220151 0.9337749 0.9351556 0.9390318 0.9391229 0.9406016 0.9423299
     [155] 0.9517165 0.9580718 0.9623458 0.9651437 0.9733590 0.9742692 0.9793850
     [162] 0.9840885 0.9846279 0.9853935 0.9862372 0.9862667 0.9901963 0.9913007
     [169] 0.9916807 0.9940046 0.9967717 0.9988544 0.9990595 1.0015876 1.0017831
     [176] 1.0026342 1.0066788 1.0107227 1.0144174 1.0188694 1.0196093 1.0205385
     [183] 1.0261794 1.0266135 1.0300754 1.0346884 1.0366464 1.0370881 1.0386820
     [190] 1.0455582 1.0480091 1.0538114 1.0541181 1.0559257 1.0584414 1.0640884
     [197] 1.0668163 1.0682920 1.0716989 1.0724422 1.0729327 1.0753048 1.0841385
     [204] 1.0931877 1.0941084 1.0943288 1.0972404 1.0978408 1.0983494 1.1003896
     [211] 1.1011617 1.1013809 1.1051353 1.1139637 1.1141416 1.1153828 1.1178716
     [218] 1.1187661 1.1190995 1.1208781 1.1226905 1.1258278 1.1264999 1.1280049
     [225] 1.1287160 1.1361142 1.1364943 1.1385601 1.1393429 1.1430319 1.1445279
     [232] 1.1492293 1.1504991 1.1550934 1.1556261 1.1569867 1.1575183 1.1585807
     [239] 1.1609573 1.1621325 1.1639406 1.1685937 1.1708257 1.1724086 1.1728515
     [246] 1.1737687 1.1737904 1.1777900 1.1781479 1.1838354 1.1871677 1.1878547
     [253] 1.1930912 1.1947035 1.1961032 1.1963461 1.1992895 1.2004741 1.2017533
     [260] 1.2040374 1.2046256 1.2050623 1.2067190 1.2092729 1.2095870 1.2160441
     [267] 1.2201535 1.2335527 1.2358792 1.2400273 1.2403675 1.2426241 1.2485142
     [274] 1.2513105 1.2530630 1.2541652 1.2571964 1.2662943 1.2740626 1.2751370
     [281] 1.2761099 1.2805921 1.2808263 1.2811098 1.2881156 1.2890134 1.3049960
     [288] 1.3166887 1.3220696 1.3286816 1.3298910 1.3318819 1.3361685 1.3524448
     [295] 1.3680769 1.3728210 1.3735881 1.3879700 1.3906257 1.4180346 1.4409124
     [302] 1.4433972 1.4439257 1.4469072 1.4525766 1.4531153 1.4777424 1.4853834
     [309] 1.4880216 1.4915242 1.5101832 1.5150587 1.6069462 1.6105443 1.6154600
     [316] 1.6223764 1.6835637 1.7627906 1.8633882
     [1] 0.1210558 0.2401720 0.2604767 0.2746578 0.3564080 0.3612250 0.4434859
     [8] 0.4506113 0.4506271 0.4546131 0.4595725 0.4664745 0.4688598 0.4728897
     [15] 0.4734842 0.4842961 0.5046007 0.5097285 0.5388380 0.5397551 0.5451900
     [22] 0.5455253 0.5512819 0.5577838 0.5593887 0.5596767 0.5639085 0.5692731
     [29] 0.5726761 0.5754861 0.5782011 0.5832009 0.5888467 0.5895244 0.5933639
     [36] 0.5978763 0.6004472 0.6037002 0.6045499 0.6058400 0.6080115 0.6107288
     [43] 0.6249217 0.6255735 0.6267955 0.6419603 0.6429127 0.6488524 0.6522160
     [50] 0.6554369 0.6578615 0.6662959 0.6703459 0.6709416 0.6731697 0.6736341
     [57] 0.6760047 0.6762076 0.6817081 0.6875552 0.6897736 0.6924490 0.6925106
     [64] 0.6931641 0.6942245 0.6992834 0.7004730 0.7082330 0.7110069 0.7112657
     [71] 0.7152620 0.7176552 0.7184925 0.7189541 0.7199498 0.7220679 0.7260562
     [78] 0.7307325 0.7340400 0.7351775 0.7354546 0.7357529 0.7365147 0.7497946
     [85] 0.7524238 0.7545090 0.7680828 0.7698426 0.7725040 0.7747702 0.7764964
     [92] 0.7875625 0.7875951 0.7914108 0.7942075 0.7945695 0.7997973 0.8057354
     [99] 0.8061648 0.8087403 0.8098479 0.8106719 0.8186910 0.8211676 0.8303144
     [106] 0.8305985 0.8350592 0.8365644 0.8373273 0.8394896 0.8420892 0.8426069
     [113] 0.8430615 0.8431484 0.8445431 0.8456930 0.8461047 0.8476311 0.8488232
     [120] 0.8506715 0.8533545 0.8536081 0.8543236 0.8555812 0.8578833 0.8580044
     [127] 0.8582798 0.8583814 0.8615072 0.8655997 0.8662081 0.8690909 0.8742578
     [134] 0.8742909 0.8756252 0.8809479 0.8813170 0.8861636 0.8926286 0.8959148
     [141] 0.9011505 0.9090203 0.9125456 0.9135076 0.9151588 0.9153869 0.9192663
     [148] 0.9220151 0.9337749 0.9351556 0.9390318 0.9391229 0.9406016 0.9423299
     [155] 0.9517165 0.9580718 0.9623458 0.9651437 0.9733590 0.9742692 0.9793850
     [162] 0.9840885 0.9846279 0.9853935 0.9862372 0.9862667 0.9901963 0.9913007
     [169] 0.9916807 0.9940046 0.9967717 0.9988544 0.9990595 1.0015876 1.0017831
     [176] 1.0026342 1.0066788 1.0107227 1.0144174 1.0188694 1.0196093 1.0205385
     [183] 1.0261794 1.0266135 1.0300754 1.0346884 1.0366464 1.0370881 1.0386820
     [190] 1.0455582 1.0480091 1.0538114 1.0559257 1.0584414 1.0640884 1.0668163
     [197] 1.0682920 1.0716989 1.0724422 1.0729327 1.0753048 1.0841385 1.0941084
     [204] 1.0943288 1.0978408 1.1011617 1.1051353 1.1139637 1.1141416 1.1153828
     [211] 1.1190995 1.1226905 1.1264999 1.1280049 1.1287160 1.1361142 1.1364943
     [218] 1.1393429 1.1445279 1.1504991 1.1556261 1.1585807 1.1621325 1.1639406
     [225] 1.1685937 1.1708257 1.1722479 1.1724086 1.1728515 1.1737687 1.1737904
     [232] 1.1777900 1.1781479 1.1838354 1.1930912 1.1947035 1.1961032 1.1963461
     [239] 1.1992895 1.2017533 1.2046256 1.2050623 1.2092729 1.2095870 1.2335527
     [246] 1.2358792 1.2403675 1.2425100 1.2426241 1.2431383 1.2452673 1.2485142
     [253] 1.2530630 1.2541652 1.2542367 1.2571964 1.2662943 1.2680333 1.2751370
     [260] 1.2761099 1.2784214 1.2808263 1.2811098 1.2841401 1.2856707 1.2881156
     [267] 1.2890134 1.3030839 1.3044976 1.3086961 1.3114735 1.3166887 1.3220696
     [274] 1.3282519 1.3286816 1.3298910 1.3361685 1.3387057 1.3478519 1.3495943
     [281] 1.3524448 1.3617066 1.3680769 1.3728210 1.3730620 1.3783408 1.3795255
     [288] 1.3799444 1.3836677 1.3848145 1.3879700 1.3906257 1.3966183 1.4054453
     [295] 1.4168649 1.4180346 1.4356306 1.4409124 1.4433972 1.4439257 1.4531153
     [302] 1.4544954 1.4777424 1.4850504 1.4853834 1.4880216 1.4900513 1.4915242
     [309] 1.4919721 1.4982773 1.5101832 1.5164980 1.6105443 1.6154600 1.6204200
     [316] 1.6304231 1.6835637 1.7689717 1.9164065
     Height vectors differ! The maximum relative error is 6.680495e-02.
     Error in test.vector() :
     Please send a report to the author of the 'fastcluster' package, Daniel Müllner.
     For contact details, see <http://danifold.net>. To make the error
     reproducible, you must include the following number (the random seed value) in
     your error report: 869050511.
    
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc