Matineh Rahmatbakhsh
| Snapshot Date: 2026-04-24 13:40 -0400 (Fri, 24 Apr 2026) |
| git_url: https://git.bioconductor.org/packages/HPiP |
| git_branch: devel |
| git_last_commit: 68bd9a1 |
| git_last_commit_date: 2025-12-28 18:34:02 -0400 (Sun, 28 Dec 2025) |
| Back to Multiple platform build/check report for BioC 3.23: simplified long |
|
This page was generated on 2026-04-25 11:36 -0400 (Sat, 25 Apr 2026).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo1 | Linux (Ubuntu 24.04.4 LTS) | x86_64 | 4.6.0 RC (2026-04-17 r89917) -- "Because it was There" | 4978 |
| kjohnson3 | macOS 13.7.7 Ventura | arm64 | 4.6.0 alpha (2026-04-08 r89818) | 4722 |
| Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X | ||||
| Package 1028/2415 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| HPiP 1.17.2 (landing page) Matineh Rahmatbakhsh
| nebbiolo1 | Linux (Ubuntu 24.04.4 LTS) / x86_64 | OK | OK | OK | | ||||||||
| kjohnson3 | macOS 13.7.7 Ventura / arm64 | OK | OK | OK | OK | | ||||||||
| See other builds for HPiP in R Universe. | ||||||||||||||
|
To the developers/maintainers of the HPiP package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/HPiP.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
| Package: HPiP |
| Version: 1.17.2 |
| Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:HPiP.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings HPiP_1.17.2.tar.gz |
| StartedAt: 2026-04-24 20:17:38 -0400 (Fri, 24 Apr 2026) |
| EndedAt: 2026-04-24 20:20:56 -0400 (Fri, 24 Apr 2026) |
| EllapsedTime: 198.3 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: HPiP.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### Running command:
###
### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:HPiP.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings HPiP_1.17.2.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/Users/biocbuild/bbs-3.23-bioc/meat/HPiP.Rcheck’
* using R version 4.6.0 alpha (2026-04-08 r89818)
* using platform: aarch64-apple-darwin23
* R was compiled by
Apple clang version 17.0.0 (clang-1700.3.19.1)
GNU Fortran (GCC) 14.2.0
* running under: macOS Tahoe 26.3.1
* using session charset: UTF-8
* current time: 2026-04-25 00:17:38 UTC
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.17.2’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
29 | then the Kronecker product is the code{(pm × qn)} block matrix
| ^
* checking Rd metadata ... OK
* checking Rd cross-references ... INFO
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
var_imp 17.211 0.150 17.483
FSmethod 17.240 0.099 17.439
corr_plot 17.059 0.099 17.231
pred_ensembel 6.227 0.203 5.719
enrichfindP 0.202 0.038 10.619
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
Running ‘runTests.R’
OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 2 NOTEs
See
‘/Users/biocbuild/bbs-3.23-bioc/meat/HPiP.Rcheck/00check.log’
for details.
HPiP.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL HPiP ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.6/Resources/library’ * installing *source* package ‘HPiP’ ... ** this is package ‘HPiP’ version ‘1.17.2’ ** using staged installation ** R ** data ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (HPiP)
HPiP.Rcheck/tests/runTests.Rout
R version 4.6.0 alpha (2026-04-08 r89818)
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin23
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> BiocGenerics:::testPackage('HPiP')
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
avNNet
Loading required package: ggplot2
Loading required package: lattice
Fitting Repeat 1
# weights: 103
initial value 113.088600
final value 94.052910
converged
Fitting Repeat 2
# weights: 103
initial value 97.247318
final value 94.052910
converged
Fitting Repeat 3
# weights: 103
initial value 98.498393
final value 94.052910
converged
Fitting Repeat 4
# weights: 103
initial value 96.106346
final value 94.052910
converged
Fitting Repeat 5
# weights: 103
initial value 98.575784
final value 94.043243
converged
Fitting Repeat 1
# weights: 305
initial value 104.184287
final value 94.043243
converged
Fitting Repeat 2
# weights: 305
initial value 97.422736
final value 94.052910
converged
Fitting Repeat 3
# weights: 305
initial value 98.611393
final value 94.043243
converged
Fitting Repeat 4
# weights: 305
initial value 107.480376
iter 10 value 94.052910
iter 10 value 94.052910
iter 10 value 94.052910
final value 94.052910
converged
Fitting Repeat 5
# weights: 305
initial value 94.707712
final value 94.033150
converged
Fitting Repeat 1
# weights: 507
initial value 102.803207
iter 10 value 84.988234
iter 20 value 83.068433
iter 30 value 82.987043
iter 40 value 82.974429
final value 82.973059
converged
Fitting Repeat 2
# weights: 507
initial value 99.882818
iter 10 value 94.043803
final value 94.043137
converged
Fitting Repeat 3
# weights: 507
initial value 103.346625
final value 94.052910
converged
Fitting Repeat 4
# weights: 507
initial value 102.687753
iter 10 value 93.860570
final value 92.005707
converged
Fitting Repeat 5
# weights: 507
initial value 118.507745
iter 10 value 94.054037
iter 20 value 94.052911
iter 20 value 94.052911
iter 20 value 94.052911
final value 94.052911
converged
Fitting Repeat 1
# weights: 103
initial value 96.427377
iter 10 value 93.857287
iter 20 value 89.646053
iter 30 value 89.454734
iter 40 value 88.713604
iter 50 value 88.238517
iter 60 value 87.845117
iter 70 value 87.760223
iter 80 value 86.732861
iter 90 value 86.069077
iter 100 value 85.154442
final value 85.154442
stopped after 100 iterations
Fitting Repeat 2
# weights: 103
initial value 104.517764
iter 10 value 94.049850
iter 20 value 92.827748
iter 30 value 88.562145
iter 40 value 88.371979
iter 50 value 87.743234
iter 60 value 87.632295
iter 70 value 87.563627
iter 80 value 87.557530
iter 90 value 85.177250
iter 100 value 84.913622
final value 84.913622
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 103.773025
iter 10 value 93.981966
iter 20 value 89.465610
iter 30 value 87.441560
iter 40 value 86.949225
iter 50 value 86.564000
iter 60 value 84.897798
iter 70 value 84.643615
iter 80 value 84.543668
iter 90 value 84.513016
final value 84.511939
converged
Fitting Repeat 4
# weights: 103
initial value 97.494362
iter 10 value 93.820021
iter 20 value 92.741095
iter 30 value 88.517772
iter 40 value 87.308943
iter 50 value 85.873840
iter 60 value 85.616953
iter 70 value 85.173745
iter 80 value 85.034641
iter 90 value 84.962561
iter 100 value 84.906887
final value 84.906887
stopped after 100 iterations
Fitting Repeat 5
# weights: 103
initial value 102.539116
iter 10 value 94.053697
iter 20 value 90.664397
iter 30 value 89.019055
iter 40 value 87.844696
iter 50 value 86.849780
iter 60 value 85.225767
iter 70 value 85.139873
iter 80 value 85.025379
iter 90 value 84.985236
iter 100 value 84.927425
final value 84.927425
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 104.467436
iter 10 value 94.835966
iter 20 value 94.041633
iter 30 value 93.222751
iter 40 value 92.888439
iter 50 value 92.439798
iter 60 value 92.175779
iter 70 value 91.584511
iter 80 value 88.112715
iter 90 value 87.189968
iter 100 value 85.968722
final value 85.968722
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 104.840442
iter 10 value 94.085612
iter 20 value 87.155746
iter 30 value 86.009461
iter 40 value 84.557839
iter 50 value 83.214436
iter 60 value 82.892954
iter 70 value 82.699990
iter 80 value 82.655281
iter 90 value 82.622369
iter 100 value 82.279651
final value 82.279651
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 102.725200
iter 10 value 93.567370
iter 20 value 85.853907
iter 30 value 85.739263
iter 40 value 85.543081
iter 50 value 84.793590
iter 60 value 84.664621
iter 70 value 84.487765
iter 80 value 83.921100
iter 90 value 83.687086
iter 100 value 83.005917
final value 83.005917
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 110.362993
iter 10 value 94.041776
iter 20 value 88.312617
iter 30 value 87.190980
iter 40 value 86.916802
iter 50 value 86.057779
iter 60 value 84.432668
iter 70 value 84.137036
iter 80 value 84.063989
iter 90 value 83.721798
iter 100 value 82.576230
final value 82.576230
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 118.261447
iter 10 value 94.192592
iter 20 value 87.716666
iter 30 value 86.076644
iter 40 value 85.687987
iter 50 value 84.304502
iter 60 value 83.586146
iter 70 value 82.759259
iter 80 value 82.271033
iter 90 value 82.081220
iter 100 value 81.963533
final value 81.963533
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 107.582859
iter 10 value 93.688297
iter 20 value 85.732126
iter 30 value 85.254754
iter 40 value 85.010270
iter 50 value 84.538316
iter 60 value 84.283528
iter 70 value 84.196714
iter 80 value 84.098270
iter 90 value 83.860741
iter 100 value 82.645307
final value 82.645307
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 120.384828
iter 10 value 94.100744
iter 20 value 92.268582
iter 30 value 91.366304
iter 40 value 90.861983
iter 50 value 84.788787
iter 60 value 83.490147
iter 70 value 83.045780
iter 80 value 82.683687
iter 90 value 82.574263
iter 100 value 82.495046
final value 82.495046
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 106.088583
iter 10 value 93.895424
iter 20 value 89.675280
iter 30 value 85.161093
iter 40 value 84.062071
iter 50 value 83.553997
iter 60 value 83.443550
iter 70 value 83.166073
iter 80 value 83.135397
iter 90 value 83.074464
iter 100 value 82.869379
final value 82.869379
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 120.877684
iter 10 value 94.320616
iter 20 value 93.917284
iter 30 value 87.830626
iter 40 value 86.765633
iter 50 value 85.570517
iter 60 value 84.506638
iter 70 value 83.976207
iter 80 value 83.589367
iter 90 value 83.203682
iter 100 value 82.850721
final value 82.850721
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 102.473287
iter 10 value 94.108769
iter 20 value 93.758445
iter 30 value 90.508138
iter 40 value 88.122033
iter 50 value 87.487702
iter 60 value 85.223304
iter 70 value 84.259028
iter 80 value 84.082972
iter 90 value 83.759426
iter 100 value 83.046426
final value 83.046426
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 94.499002
final value 94.054577
converged
Fitting Repeat 2
# weights: 103
initial value 98.867016
iter 10 value 91.344516
iter 20 value 91.149655
iter 30 value 91.004473
iter 40 value 87.169142
iter 50 value 86.843405
iter 60 value 86.832874
iter 70 value 86.832513
iter 70 value 86.832512
final value 86.832512
converged
Fitting Repeat 3
# weights: 103
initial value 98.459720
final value 94.044676
converged
Fitting Repeat 4
# weights: 103
initial value 94.558674
final value 94.044908
converged
Fitting Repeat 5
# weights: 103
initial value 103.530843
final value 94.054652
converged
Fitting Repeat 1
# weights: 305
initial value 103.965222
iter 10 value 94.055468
iter 20 value 94.050296
final value 94.050186
converged
Fitting Repeat 2
# weights: 305
initial value 103.986394
iter 10 value 94.037987
iter 20 value 93.978957
iter 30 value 89.559599
iter 40 value 89.400875
iter 50 value 88.061907
iter 60 value 87.612440
iter 70 value 87.611690
iter 80 value 87.126643
iter 90 value 86.281436
iter 100 value 86.281024
final value 86.281024
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 98.696817
iter 10 value 91.793699
iter 20 value 88.076305
iter 30 value 88.074853
iter 40 value 88.001535
iter 50 value 87.994849
iter 60 value 87.046108
iter 70 value 86.756887
final value 86.756886
converged
Fitting Repeat 4
# weights: 305
initial value 99.927473
iter 10 value 94.057431
iter 20 value 94.052941
final value 94.052937
converged
Fitting Repeat 5
# weights: 305
initial value 95.074359
iter 10 value 94.057345
iter 20 value 94.054465
final value 94.053768
converged
Fitting Repeat 1
# weights: 507
initial value 96.507053
iter 10 value 90.033597
iter 20 value 89.054810
iter 30 value 88.910588
iter 40 value 88.891161
iter 50 value 86.903652
iter 60 value 86.318505
iter 70 value 83.924231
iter 80 value 83.571085
iter 90 value 83.080503
iter 100 value 82.920021
final value 82.920021
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 94.452779
iter 10 value 94.051205
iter 20 value 92.853124
iter 30 value 86.798021
iter 40 value 86.729427
iter 50 value 86.728886
iter 60 value 86.728580
final value 86.728558
converged
Fitting Repeat 3
# weights: 507
initial value 95.454802
iter 10 value 94.059759
iter 20 value 93.999600
iter 30 value 86.638380
iter 40 value 86.500113
iter 50 value 85.639001
iter 60 value 85.386144
iter 70 value 85.336552
iter 70 value 85.336552
final value 85.336552
converged
Fitting Repeat 4
# weights: 507
initial value 111.608866
iter 10 value 93.722709
iter 20 value 93.678463
iter 30 value 93.434802
iter 40 value 86.650383
iter 50 value 86.640261
iter 60 value 86.639349
iter 70 value 86.639203
final value 86.639069
converged
Fitting Repeat 5
# weights: 507
initial value 108.769651
iter 10 value 94.060434
iter 20 value 93.526569
iter 30 value 89.152887
iter 40 value 87.719693
iter 50 value 84.559545
iter 60 value 81.959707
iter 70 value 81.947387
iter 80 value 81.946291
iter 90 value 81.777451
iter 100 value 81.670906
final value 81.670906
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 101.192336
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 96.471335
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 100.255464
final value 94.484211
converged
Fitting Repeat 4
# weights: 103
initial value 110.284036
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 95.453166
iter 10 value 93.888390
iter 20 value 92.522756
iter 30 value 92.506821
iter 40 value 92.499260
final value 92.499247
converged
Fitting Repeat 1
# weights: 305
initial value 96.014014
final value 94.466823
converged
Fitting Repeat 2
# weights: 305
initial value 98.400789
final value 94.484211
converged
Fitting Repeat 3
# weights: 305
initial value 94.656261
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 95.340840
final value 94.466823
converged
Fitting Repeat 5
# weights: 305
initial value 98.271880
final value 94.484211
converged
Fitting Repeat 1
# weights: 507
initial value 115.249946
final value 94.427725
converged
Fitting Repeat 2
# weights: 507
initial value 100.552975
final value 94.484211
converged
Fitting Repeat 3
# weights: 507
initial value 96.915776
final value 94.252920
converged
Fitting Repeat 4
# weights: 507
initial value 103.900880
iter 10 value 94.308684
iter 20 value 94.200092
final value 94.200001
converged
Fitting Repeat 5
# weights: 507
initial value 122.036811
iter 10 value 94.238807
final value 94.238672
converged
Fitting Repeat 1
# weights: 103
initial value 96.524136
iter 10 value 94.364885
iter 20 value 87.569577
iter 30 value 86.643302
iter 40 value 86.224743
iter 50 value 85.240635
iter 60 value 84.214478
iter 70 value 83.922600
final value 83.922382
converged
Fitting Repeat 2
# weights: 103
initial value 104.324336
iter 10 value 94.448258
iter 20 value 94.253576
iter 30 value 89.533378
iter 40 value 88.235125
iter 50 value 87.988840
iter 60 value 87.288029
iter 70 value 86.610160
iter 80 value 86.270528
final value 86.267047
converged
Fitting Repeat 3
# weights: 103
initial value 98.698826
iter 10 value 94.501816
iter 20 value 88.540094
iter 30 value 86.265687
iter 40 value 85.979156
iter 50 value 85.718609
iter 60 value 85.266450
iter 70 value 85.228227
final value 85.228225
converged
Fitting Repeat 4
# weights: 103
initial value 97.407204
iter 10 value 90.811937
iter 20 value 88.126796
iter 30 value 87.532512
iter 40 value 87.069978
iter 50 value 86.285267
iter 60 value 86.267049
final value 86.267048
converged
Fitting Repeat 5
# weights: 103
initial value 100.890416
iter 10 value 93.177208
iter 20 value 85.860694
iter 30 value 84.790312
iter 40 value 84.249186
iter 50 value 83.794258
iter 60 value 83.385651
iter 70 value 83.354775
final value 83.354754
converged
Fitting Repeat 1
# weights: 305
initial value 122.546816
iter 10 value 94.452286
iter 20 value 90.240158
iter 30 value 85.740079
iter 40 value 84.704174
iter 50 value 84.387026
iter 60 value 84.148455
iter 70 value 83.935787
iter 80 value 83.711298
iter 90 value 83.109189
iter 100 value 82.969595
final value 82.969595
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 111.451436
iter 10 value 94.479505
iter 20 value 92.118039
iter 30 value 89.607752
iter 40 value 88.781375
iter 50 value 86.147798
iter 60 value 85.464460
iter 70 value 84.298364
iter 80 value 84.189078
iter 90 value 84.086199
iter 100 value 84.047881
final value 84.047881
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 103.703424
iter 10 value 94.486966
iter 20 value 87.322328
iter 30 value 87.039886
iter 40 value 85.800080
iter 50 value 85.264596
iter 60 value 85.135343
iter 70 value 85.088700
iter 80 value 84.899907
iter 90 value 84.073497
iter 100 value 82.833825
final value 82.833825
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 123.037391
iter 10 value 94.349517
iter 20 value 87.426118
iter 30 value 86.814756
iter 40 value 85.614483
iter 50 value 85.070679
iter 60 value 84.786578
iter 70 value 83.476436
iter 80 value 82.980293
iter 90 value 82.675816
iter 100 value 82.409179
final value 82.409179
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 104.863345
iter 10 value 94.709972
iter 20 value 94.448953
iter 30 value 94.256042
iter 40 value 93.409285
iter 50 value 92.732824
iter 60 value 92.305212
iter 70 value 91.801566
iter 80 value 89.614572
iter 90 value 88.440604
iter 100 value 85.107639
final value 85.107639
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 109.244789
iter 10 value 95.067936
iter 20 value 94.365132
iter 30 value 88.100394
iter 40 value 86.285321
iter 50 value 84.721644
iter 60 value 83.809958
iter 70 value 83.727708
iter 80 value 83.705270
iter 90 value 83.703160
iter 100 value 83.666491
final value 83.666491
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 115.315598
iter 10 value 95.751743
iter 20 value 91.615104
iter 30 value 88.779363
iter 40 value 85.496287
iter 50 value 85.199647
iter 60 value 83.508127
iter 70 value 83.065298
iter 80 value 82.644434
iter 90 value 82.425423
iter 100 value 82.117677
final value 82.117677
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 104.093421
iter 10 value 94.416796
iter 20 value 91.880431
iter 30 value 85.597516
iter 40 value 84.333440
iter 50 value 83.924390
iter 60 value 83.501943
iter 70 value 83.376260
iter 80 value 83.191822
iter 90 value 83.071611
iter 100 value 82.752863
final value 82.752863
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 127.591519
iter 10 value 94.654974
iter 20 value 91.291007
iter 30 value 85.512376
iter 40 value 83.878581
iter 50 value 83.422015
iter 60 value 82.839825
iter 70 value 82.711526
iter 80 value 82.679075
iter 90 value 82.591784
iter 100 value 82.305578
final value 82.305578
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 112.153302
iter 10 value 94.697510
iter 20 value 94.381045
iter 30 value 90.289240
iter 40 value 86.584149
iter 50 value 85.727363
iter 60 value 85.254476
iter 70 value 84.285864
iter 80 value 84.063032
iter 90 value 83.986800
iter 100 value 83.381607
final value 83.381607
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 96.218511
iter 10 value 86.415659
iter 20 value 86.403182
iter 30 value 86.402049
iter 40 value 86.142199
final value 86.142132
converged
Fitting Repeat 2
# weights: 103
initial value 102.799484
final value 94.485793
converged
Fitting Repeat 3
# weights: 103
initial value 104.809953
final value 94.485944
converged
Fitting Repeat 4
# weights: 103
initial value 101.473810
final value 94.485704
converged
Fitting Repeat 5
# weights: 103
initial value 96.570217
iter 10 value 88.120588
iter 20 value 87.295138
iter 30 value 87.287330
iter 40 value 87.198147
iter 50 value 87.187368
iter 60 value 87.187106
iter 70 value 87.187001
iter 80 value 87.186332
iter 90 value 87.186057
iter 100 value 87.185916
final value 87.185916
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 98.460792
iter 10 value 94.488864
iter 20 value 94.352338
iter 30 value 90.798648
iter 40 value 90.791400
iter 50 value 90.791061
iter 60 value 90.790678
iter 70 value 90.790035
final value 90.789788
converged
Fitting Repeat 2
# weights: 305
initial value 96.068329
iter 10 value 94.432704
iter 20 value 93.461446
iter 30 value 87.684783
final value 87.684763
converged
Fitting Repeat 3
# weights: 305
initial value 104.686307
iter 10 value 94.489113
iter 20 value 94.484236
iter 20 value 94.484235
iter 20 value 94.484235
final value 94.484235
converged
Fitting Repeat 4
# weights: 305
initial value 94.758429
iter 10 value 94.432509
iter 20 value 94.307805
iter 30 value 90.196934
iter 40 value 87.000472
iter 50 value 86.764794
final value 86.759350
converged
Fitting Repeat 5
# weights: 305
initial value 105.662461
iter 10 value 94.475112
iter 20 value 94.470652
iter 30 value 93.401793
iter 40 value 89.044382
iter 50 value 86.909988
iter 60 value 84.632657
iter 70 value 83.697476
iter 80 value 83.579986
iter 90 value 83.485289
iter 100 value 83.426770
final value 83.426770
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 99.919603
iter 10 value 94.491851
iter 20 value 93.687301
iter 30 value 87.607494
iter 40 value 87.555918
iter 50 value 87.521711
iter 60 value 87.475996
iter 70 value 85.824621
iter 80 value 85.166812
iter 90 value 83.806483
iter 100 value 82.923001
final value 82.923001
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 126.601657
iter 10 value 94.723995
iter 20 value 91.679499
iter 30 value 90.843427
iter 40 value 90.553402
iter 50 value 90.528839
iter 60 value 90.525296
iter 70 value 89.404285
iter 80 value 89.399489
iter 90 value 89.397862
iter 100 value 89.396255
final value 89.396255
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 98.417721
iter 10 value 94.492366
iter 20 value 94.480176
iter 30 value 91.043243
iter 40 value 90.714422
iter 50 value 90.648266
iter 60 value 90.643103
final value 90.643067
converged
Fitting Repeat 4
# weights: 507
initial value 118.473133
iter 10 value 94.480433
iter 20 value 94.475201
iter 30 value 94.428192
iter 40 value 89.535802
iter 50 value 87.483606
iter 60 value 86.250582
iter 70 value 83.284534
iter 80 value 81.524609
iter 90 value 81.215480
iter 100 value 81.129307
final value 81.129307
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 107.097920
iter 10 value 94.492538
iter 20 value 94.484500
iter 30 value 87.561971
iter 40 value 87.456986
iter 50 value 87.212225
final value 87.212097
converged
Fitting Repeat 1
# weights: 103
initial value 100.390254
iter 10 value 93.836066
iter 10 value 93.836066
iter 10 value 93.836066
final value 93.836066
converged
Fitting Repeat 2
# weights: 103
initial value 96.479700
final value 94.052910
converged
Fitting Repeat 3
# weights: 103
initial value 94.449929
final value 93.836066
converged
Fitting Repeat 4
# weights: 103
initial value 95.878349
final value 94.052910
converged
Fitting Repeat 5
# weights: 103
initial value 97.927610
final value 94.052910
converged
Fitting Repeat 1
# weights: 305
initial value 95.833140
final value 94.052910
converged
Fitting Repeat 2
# weights: 305
initial value 98.285627
final value 93.836066
converged
Fitting Repeat 3
# weights: 305
initial value 101.932432
final value 93.836066
converged
Fitting Repeat 4
# weights: 305
initial value 103.539281
iter 10 value 85.007740
iter 20 value 83.751635
iter 30 value 83.746524
final value 83.746507
converged
Fitting Repeat 5
# weights: 305
initial value 95.234830
iter 10 value 93.562962
iter 20 value 93.410312
final value 93.410249
converged
Fitting Repeat 1
# weights: 507
initial value 104.699232
final value 93.836066
converged
Fitting Repeat 2
# weights: 507
initial value 96.111182
final value 93.991525
converged
Fitting Repeat 3
# weights: 507
initial value 97.802927
final value 93.836066
converged
Fitting Repeat 4
# weights: 507
initial value 108.628666
iter 10 value 83.088244
iter 20 value 82.950633
iter 30 value 82.855258
iter 40 value 82.811409
final value 82.811321
converged
Fitting Repeat 5
# weights: 507
initial value 112.903924
iter 10 value 94.053866
iter 20 value 94.052911
iter 20 value 94.052911
iter 20 value 94.052911
final value 94.052911
converged
Fitting Repeat 1
# weights: 103
initial value 105.404068
iter 10 value 93.988763
iter 20 value 88.939540
iter 30 value 83.902884
iter 40 value 83.768361
iter 50 value 83.632943
iter 60 value 80.324781
iter 70 value 79.554642
iter 80 value 79.419395
iter 90 value 75.764266
iter 100 value 75.159235
final value 75.159235
stopped after 100 iterations
Fitting Repeat 2
# weights: 103
initial value 101.541738
iter 10 value 94.056096
iter 20 value 94.055532
iter 30 value 92.273433
iter 40 value 89.564148
iter 50 value 81.185546
iter 60 value 79.141953
iter 70 value 78.976703
iter 80 value 78.646039
iter 90 value 78.003417
final value 77.993996
converged
Fitting Repeat 3
# weights: 103
initial value 102.491634
iter 10 value 81.343258
iter 20 value 80.543668
iter 30 value 80.389824
iter 40 value 80.022032
iter 50 value 79.962355
iter 60 value 79.764561
iter 70 value 79.721823
final value 79.718070
converged
Fitting Repeat 4
# weights: 103
initial value 98.242895
iter 10 value 94.054979
iter 20 value 94.032862
iter 30 value 93.732127
iter 40 value 89.468155
iter 50 value 88.031096
iter 60 value 87.983924
iter 70 value 85.260543
iter 80 value 81.432565
iter 90 value 79.362991
iter 100 value 78.231754
final value 78.231754
stopped after 100 iterations
Fitting Repeat 5
# weights: 103
initial value 110.931747
iter 10 value 93.970719
iter 20 value 89.109860
iter 30 value 88.373374
iter 40 value 88.064853
iter 50 value 88.050938
final value 88.050892
converged
Fitting Repeat 1
# weights: 305
initial value 108.013535
iter 10 value 94.916296
iter 20 value 92.542908
iter 30 value 90.899840
iter 40 value 80.124830
iter 50 value 76.751587
iter 60 value 76.207797
iter 70 value 75.968428
iter 80 value 75.836041
iter 90 value 75.685954
iter 100 value 75.344988
final value 75.344988
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 109.910330
iter 10 value 93.927580
iter 20 value 82.480287
iter 30 value 76.970445
iter 40 value 75.747100
iter 50 value 74.854957
iter 60 value 74.476293
iter 70 value 74.361140
iter 80 value 74.223472
iter 90 value 74.111743
iter 100 value 74.100685
final value 74.100685
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 111.152726
iter 10 value 93.713912
iter 20 value 87.197094
iter 30 value 81.254305
iter 40 value 80.239903
iter 50 value 79.617398
iter 60 value 79.465590
iter 70 value 79.372819
iter 80 value 78.337822
iter 90 value 75.579597
iter 100 value 74.283469
final value 74.283469
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 135.839469
iter 10 value 91.674892
iter 20 value 82.022457
iter 30 value 77.734497
iter 40 value 76.666610
iter 50 value 75.689175
iter 60 value 75.173498
iter 70 value 73.961532
iter 80 value 73.584258
iter 90 value 73.547078
iter 100 value 73.457722
final value 73.457722
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 105.581234
iter 10 value 93.985449
iter 20 value 91.301252
iter 30 value 89.009593
iter 40 value 86.444377
iter 50 value 81.768883
iter 60 value 78.620671
iter 70 value 75.893157
iter 80 value 75.364092
iter 90 value 75.145798
iter 100 value 74.334135
final value 74.334135
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 117.708600
iter 10 value 93.954580
iter 20 value 89.526719
iter 30 value 85.690820
iter 40 value 84.508422
iter 50 value 81.423053
iter 60 value 80.423085
iter 70 value 80.052414
iter 80 value 79.785211
iter 90 value 77.775101
iter 100 value 74.155782
final value 74.155782
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 119.181400
iter 10 value 94.851156
iter 20 value 94.012373
iter 30 value 87.037440
iter 40 value 86.528532
iter 50 value 84.999830
iter 60 value 77.716643
iter 70 value 76.259915
iter 80 value 75.008534
iter 90 value 74.918477
iter 100 value 74.560832
final value 74.560832
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 104.052980
iter 10 value 94.010245
iter 20 value 82.118671
iter 30 value 79.519810
iter 40 value 76.957393
iter 50 value 75.929956
iter 60 value 74.939665
iter 70 value 73.955588
iter 80 value 73.267822
iter 90 value 73.047657
iter 100 value 72.895495
final value 72.895495
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 106.262171
iter 10 value 94.014560
iter 20 value 92.897831
iter 30 value 77.461422
iter 40 value 77.039404
iter 50 value 76.375222
iter 60 value 75.203603
iter 70 value 74.477245
iter 80 value 74.283764
iter 90 value 74.084801
iter 100 value 73.634171
final value 73.634171
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 108.290348
iter 10 value 94.105127
iter 20 value 85.379825
iter 30 value 78.406432
iter 40 value 76.291162
iter 50 value 75.622075
iter 60 value 74.801515
iter 70 value 73.102797
iter 80 value 72.888405
iter 90 value 72.782870
iter 100 value 72.658558
final value 72.658558
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 104.984424
final value 94.054635
converged
Fitting Repeat 2
# weights: 103
initial value 94.588301
final value 94.054782
converged
Fitting Repeat 3
# weights: 103
initial value 102.824914
final value 94.054485
converged
Fitting Repeat 4
# weights: 103
initial value 117.568432
final value 94.054412
converged
Fitting Repeat 5
# weights: 103
initial value 101.525411
iter 10 value 93.838101
iter 20 value 93.836727
iter 30 value 92.787682
iter 40 value 76.290658
iter 50 value 75.763756
iter 60 value 75.659032
iter 70 value 75.658675
iter 80 value 75.657982
iter 90 value 75.646615
iter 100 value 75.478313
final value 75.478313
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 101.182676
iter 10 value 94.057777
iter 20 value 94.052932
iter 20 value 94.052931
iter 20 value 94.052931
final value 94.052931
converged
Fitting Repeat 2
# weights: 305
initial value 94.984876
iter 10 value 93.995684
iter 20 value 93.991862
final value 93.991840
converged
Fitting Repeat 3
# weights: 305
initial value 95.359898
iter 10 value 94.057216
iter 20 value 93.944307
iter 30 value 93.553201
iter 40 value 92.366985
iter 50 value 92.365010
final value 92.364978
converged
Fitting Repeat 4
# weights: 305
initial value 108.262204
iter 10 value 93.841437
iter 20 value 93.803806
iter 30 value 93.535908
iter 40 value 93.535577
final value 93.535489
converged
Fitting Repeat 5
# weights: 305
initial value 96.056716
iter 10 value 94.059704
iter 20 value 94.046199
iter 30 value 93.416235
iter 40 value 93.390590
final value 93.388954
converged
Fitting Repeat 1
# weights: 507
initial value 108.904370
iter 10 value 93.821017
iter 20 value 93.474556
iter 30 value 93.416266
iter 40 value 93.383232
iter 50 value 93.360704
iter 60 value 93.354862
iter 70 value 85.851892
iter 80 value 82.119352
iter 90 value 76.784458
iter 100 value 73.899158
final value 73.899158
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 95.567091
iter 10 value 89.191547
iter 20 value 88.601083
iter 30 value 88.599976
final value 88.599967
converged
Fitting Repeat 3
# weights: 507
initial value 126.895830
iter 10 value 93.844603
iter 20 value 92.405348
iter 30 value 78.848055
iter 40 value 77.600401
iter 50 value 74.287005
iter 60 value 74.083738
iter 70 value 74.081247
final value 74.081181
converged
Fitting Repeat 4
# weights: 507
initial value 103.669169
iter 10 value 93.843844
iter 20 value 93.504510
iter 30 value 82.817079
iter 40 value 82.789318
iter 50 value 82.788047
iter 60 value 75.310725
iter 70 value 75.243050
iter 80 value 75.069766
iter 90 value 74.703902
iter 100 value 73.444276
final value 73.444276
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 108.539434
iter 10 value 94.061299
iter 20 value 93.925224
iter 30 value 92.297524
iter 40 value 84.636150
iter 40 value 84.636150
iter 40 value 84.636150
final value 84.636150
converged
Fitting Repeat 1
# weights: 103
initial value 95.686741
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 100.910302
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 96.823762
final value 94.484211
converged
Fitting Repeat 4
# weights: 103
initial value 96.261750
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 97.805404
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 108.815349
final value 94.484211
converged
Fitting Repeat 2
# weights: 305
initial value 97.670159
final value 94.354396
converged
Fitting Repeat 3
# weights: 305
initial value 97.746234
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 99.620600
final value 94.484211
converged
Fitting Repeat 5
# weights: 305
initial value 108.738488
final value 94.484211
converged
Fitting Repeat 1
# weights: 507
initial value 108.440620
iter 10 value 92.407210
iter 20 value 88.220816
iter 30 value 88.033331
iter 40 value 88.013573
final value 88.013558
converged
Fitting Repeat 2
# weights: 507
initial value 125.723306
final value 94.484211
converged
Fitting Repeat 3
# weights: 507
initial value 102.749352
iter 10 value 93.701312
iter 20 value 91.722237
iter 30 value 90.570694
iter 40 value 90.526606
iter 50 value 90.526226
iter 60 value 90.465588
iter 70 value 90.459650
final value 90.459596
converged
Fitting Repeat 4
# weights: 507
initial value 101.571118
final value 94.484211
converged
Fitting Repeat 5
# weights: 507
initial value 100.870979
final value 94.484211
converged
Fitting Repeat 1
# weights: 103
initial value 107.933202
iter 10 value 94.573677
iter 20 value 94.485259
iter 30 value 94.146138
iter 40 value 94.135786
iter 50 value 94.031234
iter 60 value 94.017677
iter 70 value 88.206183
iter 80 value 87.297498
iter 90 value 85.117874
iter 100 value 85.013849
final value 85.013849
stopped after 100 iterations
Fitting Repeat 2
# weights: 103
initial value 109.699541
iter 10 value 94.525060
iter 20 value 94.430175
iter 30 value 89.620699
iter 40 value 88.051455
iter 50 value 85.610195
iter 60 value 85.506981
iter 70 value 85.233443
iter 80 value 83.879175
iter 90 value 83.269359
iter 100 value 83.004412
final value 83.004412
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 101.452535
iter 10 value 94.474440
iter 20 value 91.077449
iter 30 value 88.372555
iter 40 value 85.454640
iter 50 value 85.089385
iter 60 value 84.954324
iter 70 value 84.876189
final value 84.874307
converged
Fitting Repeat 4
# weights: 103
initial value 101.175873
iter 10 value 94.425824
iter 20 value 92.264801
iter 30 value 88.703240
iter 40 value 88.258950
iter 50 value 87.611672
iter 60 value 85.051678
iter 70 value 84.312274
iter 80 value 83.122706
iter 90 value 82.946638
iter 100 value 82.873151
final value 82.873151
stopped after 100 iterations
Fitting Repeat 5
# weights: 103
initial value 98.312622
iter 10 value 94.481444
iter 20 value 94.206142
iter 30 value 87.191016
iter 40 value 86.653726
iter 50 value 86.450861
iter 60 value 84.060815
iter 70 value 83.072816
iter 80 value 82.959067
iter 90 value 82.856325
final value 82.855999
converged
Fitting Repeat 1
# weights: 305
initial value 100.306135
iter 10 value 94.328606
iter 20 value 90.456666
iter 30 value 88.442096
iter 40 value 88.098914
iter 50 value 87.159779
iter 60 value 85.260209
iter 70 value 84.251756
iter 80 value 83.108208
iter 90 value 82.750838
iter 100 value 82.587447
final value 82.587447
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 116.699048
iter 10 value 94.381783
iter 20 value 91.249483
iter 30 value 86.550988
iter 40 value 83.258476
iter 50 value 83.033639
iter 60 value 82.511226
iter 70 value 82.256192
iter 80 value 82.063703
iter 90 value 81.931455
iter 100 value 81.539362
final value 81.539362
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 111.503994
iter 10 value 94.721387
iter 20 value 94.487402
iter 30 value 92.201994
iter 40 value 91.755002
iter 50 value 90.249448
iter 60 value 86.825465
iter 70 value 83.836243
iter 80 value 83.609382
iter 90 value 83.256500
iter 100 value 82.904763
final value 82.904763
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 102.351228
iter 10 value 93.822741
iter 20 value 88.219845
iter 30 value 85.511753
iter 40 value 84.868458
iter 50 value 84.725710
iter 60 value 84.660218
iter 70 value 84.599924
iter 80 value 84.574215
iter 90 value 84.281605
iter 100 value 83.314990
final value 83.314990
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 105.070603
iter 10 value 94.436170
iter 20 value 91.106083
iter 30 value 88.017248
iter 40 value 87.728355
iter 50 value 87.638116
iter 60 value 85.752978
iter 70 value 85.256391
iter 80 value 82.901240
iter 90 value 82.244637
iter 100 value 82.052932
final value 82.052932
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 109.335294
iter 10 value 94.619770
iter 20 value 85.438128
iter 30 value 83.263077
iter 40 value 82.798062
iter 50 value 82.622024
iter 60 value 82.561099
iter 70 value 82.281967
iter 80 value 81.905948
iter 90 value 81.703697
iter 100 value 81.675613
final value 81.675613
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 126.604228
iter 10 value 95.637390
iter 20 value 86.327959
iter 30 value 85.363526
iter 40 value 84.821214
iter 50 value 84.681625
iter 60 value 84.610308
iter 70 value 84.582240
iter 80 value 84.533073
iter 90 value 83.479996
iter 100 value 83.123929
final value 83.123929
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 104.624092
iter 10 value 94.497112
iter 20 value 92.851497
iter 30 value 88.140870
iter 40 value 87.376383
iter 50 value 85.685158
iter 60 value 84.130581
iter 70 value 82.877445
iter 80 value 82.508400
iter 90 value 82.433794
iter 100 value 82.370527
final value 82.370527
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 123.833120
iter 10 value 94.464247
iter 20 value 91.438965
iter 30 value 88.983373
iter 40 value 86.458749
iter 50 value 86.293091
iter 60 value 85.437201
iter 70 value 83.372302
iter 80 value 82.480338
iter 90 value 82.173950
iter 100 value 82.108645
final value 82.108645
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 107.189529
iter 10 value 94.604822
iter 20 value 91.864995
iter 30 value 89.086903
iter 40 value 85.970778
iter 50 value 85.817540
iter 60 value 83.463695
iter 70 value 82.525427
iter 80 value 82.097505
iter 90 value 81.576752
iter 100 value 81.249284
final value 81.249284
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 99.296497
final value 93.173476
converged
Fitting Repeat 2
# weights: 103
initial value 96.459356
final value 94.485723
converged
Fitting Repeat 3
# weights: 103
initial value 95.028188
final value 94.486136
converged
Fitting Repeat 4
# weights: 103
initial value 95.416052
final value 94.485834
converged
Fitting Repeat 5
# weights: 103
initial value 98.081020
final value 94.485616
converged
Fitting Repeat 1
# weights: 305
initial value 95.732240
iter 10 value 94.488903
iter 20 value 94.355069
final value 94.159680
converged
Fitting Repeat 2
# weights: 305
initial value 97.721717
iter 10 value 94.491175
final value 94.487065
converged
Fitting Repeat 3
# weights: 305
initial value 100.314028
iter 10 value 94.137138
iter 20 value 94.135821
iter 30 value 94.135359
iter 40 value 94.131363
iter 50 value 93.677330
iter 60 value 87.319273
iter 70 value 84.267800
iter 80 value 84.259131
iter 90 value 84.255249
final value 84.254802
converged
Fitting Repeat 4
# weights: 305
initial value 109.526589
iter 10 value 94.142866
iter 20 value 94.137186
iter 30 value 94.133826
iter 40 value 88.161359
iter 50 value 86.193190
iter 60 value 85.149779
iter 70 value 85.148996
final value 85.148987
converged
Fitting Repeat 5
# weights: 305
initial value 96.884261
iter 10 value 84.970260
iter 20 value 84.063318
iter 30 value 84.021867
iter 40 value 83.998921
iter 50 value 83.908951
iter 60 value 83.902171
iter 70 value 83.619615
iter 80 value 83.476019
iter 90 value 83.475352
iter 100 value 83.474239
final value 83.474239
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 121.463327
iter 10 value 93.023338
iter 20 value 91.117005
iter 30 value 88.820194
iter 40 value 88.305763
iter 50 value 86.854244
iter 60 value 86.789646
iter 70 value 86.121291
final value 86.120780
converged
Fitting Repeat 2
# weights: 507
initial value 101.486350
iter 10 value 92.164701
iter 20 value 91.396480
iter 30 value 91.386860
iter 40 value 91.082505
iter 50 value 90.978964
iter 60 value 90.974368
iter 70 value 90.970615
iter 80 value 90.967571
iter 90 value 90.962090
iter 100 value 85.697654
final value 85.697654
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 110.068763
iter 10 value 94.492366
iter 20 value 94.461974
iter 30 value 93.312822
iter 40 value 91.904451
iter 50 value 91.880774
iter 60 value 91.839063
iter 70 value 91.804779
iter 80 value 91.798010
iter 90 value 91.784017
iter 100 value 87.402440
final value 87.402440
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 126.179960
iter 10 value 94.491564
iter 20 value 94.434716
iter 30 value 84.345235
iter 40 value 84.314736
iter 50 value 84.299881
iter 60 value 84.298623
iter 70 value 84.082998
iter 80 value 83.358643
iter 90 value 83.162788
iter 100 value 83.142982
final value 83.142982
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 118.902380
iter 10 value 94.346415
iter 20 value 94.337038
iter 30 value 92.336150
iter 40 value 92.194466
iter 50 value 92.193888
final value 92.193885
converged
Fitting Repeat 1
# weights: 103
initial value 99.924151
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 98.800737
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 99.218962
iter 10 value 93.801436
final value 93.794996
converged
Fitting Repeat 4
# weights: 103
initial value 95.637052
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 95.139218
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 102.165713
final value 94.484211
converged
Fitting Repeat 2
# weights: 305
initial value 98.884154
final value 94.484211
converged
Fitting Repeat 3
# weights: 305
initial value 97.638838
iter 10 value 93.619052
final value 93.567525
converged
Fitting Repeat 4
# weights: 305
initial value 99.094133
final value 94.484211
converged
Fitting Repeat 5
# weights: 305
initial value 108.537011
final value 94.484211
converged
Fitting Repeat 1
# weights: 507
initial value 104.528211
final value 94.026542
converged
Fitting Repeat 2
# weights: 507
initial value 130.321695
iter 10 value 94.484212
final value 94.484211
converged
Fitting Repeat 3
# weights: 507
initial value 112.004318
iter 10 value 94.387500
iter 10 value 94.387500
iter 10 value 94.387500
final value 94.387500
converged
Fitting Repeat 4
# weights: 507
initial value 95.633114
final value 94.484211
converged
Fitting Repeat 5
# weights: 507
initial value 95.633389
iter 10 value 92.781323
iter 20 value 92.291925
final value 92.278966
converged
Fitting Repeat 1
# weights: 103
initial value 104.569077
iter 10 value 94.238918
iter 20 value 87.126315
iter 30 value 86.534358
iter 40 value 85.912829
iter 50 value 85.527037
iter 60 value 85.471524
final value 85.460777
converged
Fitting Repeat 2
# weights: 103
initial value 111.274674
iter 10 value 94.450493
iter 20 value 94.137285
iter 30 value 93.761838
iter 40 value 87.685060
iter 50 value 87.211713
iter 60 value 86.679166
iter 70 value 85.177262
iter 80 value 85.071173
iter 90 value 85.023090
final value 85.020768
converged
Fitting Repeat 3
# weights: 103
initial value 97.479729
iter 10 value 94.228496
iter 20 value 93.932561
iter 30 value 93.536866
iter 40 value 86.964146
iter 50 value 84.863415
iter 60 value 83.559581
iter 70 value 83.476558
iter 80 value 83.296465
iter 90 value 82.697123
iter 100 value 82.653497
final value 82.653497
stopped after 100 iterations
Fitting Repeat 4
# weights: 103
initial value 96.440338
iter 10 value 89.169015
iter 20 value 85.722561
iter 30 value 85.539358
iter 40 value 85.253687
iter 50 value 85.248163
final value 85.248139
converged
Fitting Repeat 5
# weights: 103
initial value 101.104056
iter 10 value 94.411979
iter 20 value 90.163111
iter 30 value 88.254054
iter 40 value 87.968615
iter 50 value 85.439486
iter 60 value 84.707507
iter 70 value 83.214448
iter 80 value 82.598407
iter 90 value 82.589403
iter 100 value 82.585542
final value 82.585542
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 105.951641
iter 10 value 93.708250
iter 20 value 87.500428
iter 30 value 84.610146
iter 40 value 84.147642
iter 50 value 82.972514
iter 60 value 81.754126
iter 70 value 81.674072
iter 80 value 81.630816
iter 90 value 81.572941
iter 100 value 81.426129
final value 81.426129
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 105.614447
iter 10 value 94.443522
iter 20 value 93.329781
iter 30 value 86.532034
iter 40 value 85.540346
iter 50 value 83.897063
iter 60 value 82.158834
iter 70 value 81.866945
iter 80 value 81.509858
iter 90 value 81.332530
iter 100 value 81.252727
final value 81.252727
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 101.729272
iter 10 value 94.863609
iter 20 value 94.375997
iter 30 value 87.081671
iter 40 value 85.570965
iter 50 value 84.945892
iter 60 value 83.516475
iter 70 value 82.959540
iter 80 value 82.365320
iter 90 value 82.243040
iter 100 value 82.087968
final value 82.087968
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 116.075672
iter 10 value 94.441360
iter 20 value 86.922383
iter 30 value 85.250557
iter 40 value 83.644928
iter 50 value 82.782608
iter 60 value 82.436304
iter 70 value 82.211521
iter 80 value 81.348549
iter 90 value 81.257910
iter 100 value 81.252234
final value 81.252234
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 107.630206
iter 10 value 94.431247
iter 20 value 90.548972
iter 30 value 87.462017
iter 40 value 84.749311
iter 50 value 83.974984
iter 60 value 83.080119
iter 70 value 81.637268
iter 80 value 81.304278
iter 90 value 80.968460
iter 100 value 80.934306
final value 80.934306
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 103.578497
iter 10 value 94.485793
iter 20 value 92.581133
iter 30 value 90.769252
iter 40 value 87.073418
iter 50 value 85.232836
iter 60 value 82.577124
iter 70 value 82.066271
iter 80 value 81.214217
iter 90 value 80.767558
iter 100 value 80.553701
final value 80.553701
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 112.934543
iter 10 value 98.375556
iter 20 value 93.292042
iter 30 value 92.658496
iter 40 value 92.404641
iter 50 value 92.030254
iter 60 value 90.809467
iter 70 value 86.144169
iter 80 value 84.975378
iter 90 value 83.075594
iter 100 value 82.343983
final value 82.343983
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 116.200743
iter 10 value 100.622268
iter 20 value 94.160796
iter 30 value 86.775463
iter 40 value 84.971528
iter 50 value 84.548678
iter 60 value 83.901343
iter 70 value 81.789420
iter 80 value 81.247297
iter 90 value 80.867386
iter 100 value 80.829915
final value 80.829915
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 103.919969
iter 10 value 95.030911
iter 20 value 87.670589
iter 30 value 86.564254
iter 40 value 83.903123
iter 50 value 83.292245
iter 60 value 83.060279
iter 70 value 82.919656
iter 80 value 82.333484
iter 90 value 81.554818
iter 100 value 81.155227
final value 81.155227
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 108.087588
iter 10 value 95.068839
iter 20 value 94.181490
iter 30 value 87.905136
iter 40 value 86.551472
iter 50 value 86.007878
iter 60 value 83.464242
iter 70 value 83.072574
iter 80 value 82.222300
iter 90 value 81.851725
iter 100 value 81.636857
final value 81.636857
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 112.568735
iter 10 value 94.486030
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 99.342210
final value 94.485831
converged
Fitting Repeat 3
# weights: 103
initial value 95.409987
final value 94.486084
converged
Fitting Repeat 4
# weights: 103
initial value 102.255104
final value 94.485811
converged
Fitting Repeat 5
# weights: 103
initial value 98.699466
iter 10 value 89.060216
iter 20 value 86.699497
iter 30 value 86.677017
iter 40 value 86.633478
iter 50 value 86.627520
iter 60 value 86.626245
iter 70 value 86.626037
iter 80 value 85.426236
iter 90 value 85.039381
final value 85.039124
converged
Fitting Repeat 1
# weights: 305
initial value 128.270692
iter 10 value 94.490476
iter 20 value 89.608156
iter 30 value 87.921840
iter 40 value 86.243091
iter 50 value 86.237216
iter 60 value 85.639168
iter 70 value 85.037650
final value 85.037470
converged
Fitting Repeat 2
# weights: 305
initial value 100.212148
iter 10 value 94.031838
iter 20 value 93.328108
iter 30 value 86.695933
final value 86.693231
converged
Fitting Repeat 3
# weights: 305
initial value 97.938486
iter 10 value 94.488764
iter 20 value 94.347244
iter 30 value 87.859675
iter 40 value 87.448157
iter 50 value 86.444353
iter 60 value 86.438405
iter 70 value 86.434289
iter 80 value 86.430920
iter 90 value 86.419578
final value 86.419460
converged
Fitting Repeat 4
# weights: 305
initial value 121.520652
iter 10 value 94.489059
iter 20 value 94.423129
final value 94.027026
converged
Fitting Repeat 5
# weights: 305
initial value 101.318172
iter 10 value 94.488481
iter 20 value 93.395381
iter 30 value 93.221811
iter 40 value 93.220375
final value 93.220362
converged
Fitting Repeat 1
# weights: 507
initial value 107.182017
iter 10 value 94.494176
iter 20 value 94.430168
iter 30 value 94.119896
iter 40 value 93.883075
iter 50 value 93.792496
iter 60 value 93.641335
iter 70 value 91.152964
iter 80 value 91.149468
iter 90 value 86.341229
iter 100 value 86.133561
final value 86.133561
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 115.205123
iter 10 value 94.495548
iter 20 value 94.491542
iter 30 value 94.485902
iter 40 value 87.067904
iter 50 value 84.668089
iter 60 value 84.399432
iter 70 value 84.398708
iter 80 value 84.299629
iter 90 value 83.774372
iter 100 value 83.762183
final value 83.762183
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 99.556911
iter 10 value 94.492015
iter 20 value 94.045210
iter 30 value 90.381206
iter 40 value 90.019103
iter 50 value 90.018447
iter 60 value 90.018172
iter 70 value 90.016771
iter 80 value 89.977802
iter 90 value 89.929213
final value 89.928386
converged
Fitting Repeat 4
# weights: 507
initial value 94.804335
iter 10 value 92.687335
iter 20 value 92.289033
iter 30 value 92.103389
iter 40 value 92.099585
final value 92.098937
converged
Fitting Repeat 5
# weights: 507
initial value 115.160193
iter 10 value 94.492518
iter 20 value 94.484831
iter 30 value 89.163766
iter 40 value 85.884282
iter 50 value 85.879825
final value 85.878551
converged
Fitting Repeat 1
# weights: 507
initial value 134.570282
iter 10 value 117.786586
iter 20 value 115.915059
iter 30 value 113.836846
iter 40 value 109.519419
iter 50 value 104.781187
iter 60 value 103.742342
iter 70 value 102.370992
iter 80 value 102.042249
iter 90 value 101.829382
iter 100 value 101.131990
final value 101.131990
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 130.427909
iter 10 value 118.416228
iter 20 value 108.892301
iter 30 value 105.831028
iter 40 value 102.458148
iter 50 value 101.924050
iter 60 value 101.427528
iter 70 value 100.822687
iter 80 value 100.734030
iter 90 value 100.600171
iter 100 value 100.351160
final value 100.351160
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 135.307142
iter 10 value 113.096737
iter 20 value 109.032450
iter 30 value 107.339954
iter 40 value 105.793987
iter 50 value 104.959755
iter 60 value 103.472664
iter 70 value 102.383221
iter 80 value 101.489366
iter 90 value 101.208937
iter 100 value 100.897463
final value 100.897463
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 126.031497
iter 10 value 117.119358
iter 20 value 109.848085
iter 30 value 106.500086
iter 40 value 105.164242
iter 50 value 103.841899
iter 60 value 102.793044
iter 70 value 102.413363
iter 80 value 101.508203
iter 90 value 101.125499
iter 100 value 100.839037
final value 100.839037
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 145.185780
iter 10 value 117.673333
iter 20 value 110.811509
iter 30 value 109.472453
iter 40 value 108.538425
iter 50 value 108.256960
iter 60 value 103.087060
iter 70 value 101.425437
iter 80 value 101.107948
iter 90 value 100.882898
iter 100 value 100.759459
final value 100.759459
stopped after 100 iterations
svmRadial
ranger
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
RUNIT TEST PROTOCOL -- Fri Apr 24 20:20:52 2026
***********************************************
Number of test functions: 7
Number of errors: 0
Number of failures: 0
1 Test Suite :
HPiP RUnit Tests - 7 test functions, 0 errors, 0 failures
Number of test functions: 7
Number of errors: 0
Number of failures: 0
Warning messages:
1: `repeats` has no meaning for this resampling method.
2: executing %dopar% sequentially: no parallel backend registered
>
>
>
>
> proc.time()
user system elapsed
20.108 0.714 78.702
HPiP.Rcheck/HPiP-Ex.timings
| name | user | system | elapsed | |
| FSmethod | 17.240 | 0.099 | 17.439 | |
| FreqInteractors | 0.158 | 0.006 | 0.164 | |
| calculateAAC | 0.014 | 0.001 | 0.014 | |
| calculateAutocor | 0.247 | 0.007 | 0.255 | |
| calculateCTDC | 0.031 | 0.005 | 0.036 | |
| calculateCTDD | 0.159 | 0.006 | 0.166 | |
| calculateCTDT | 0.057 | 0.002 | 0.058 | |
| calculateCTriad | 0.150 | 0.008 | 0.159 | |
| calculateDC | 0.035 | 0.003 | 0.039 | |
| calculateF | 0.101 | 0.002 | 0.102 | |
| calculateKSAAP | 0.037 | 0.003 | 0.040 | |
| calculateQD_Sm | 0.673 | 0.028 | 0.703 | |
| calculateTC | 0.565 | 0.050 | 0.620 | |
| calculateTC_Sm | 0.130 | 0.007 | 0.140 | |
| corr_plot | 17.059 | 0.099 | 17.231 | |
| enrichfindP | 0.202 | 0.038 | 10.619 | |
| enrichfind_hp | 0.024 | 0.003 | 1.057 | |
| enrichplot | 0.163 | 0.002 | 0.165 | |
| filter_missing_values | 0.001 | 0.000 | 0.001 | |
| getFASTA | 0.032 | 0.011 | 3.693 | |
| getHPI | 0 | 0 | 0 | |
| get_negativePPI | 0.001 | 0.000 | 0.001 | |
| get_positivePPI | 0 | 0 | 0 | |
| impute_missing_data | 0.001 | 0.000 | 0.000 | |
| plotPPI | 0.030 | 0.001 | 0.031 | |
| pred_ensembel | 6.227 | 0.203 | 5.719 | |
| var_imp | 17.211 | 0.150 | 17.483 | |