Back to Multiple platform build/check report for BioC 3.19: simplified long |
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This page was generated on 2024-06-11 14:43 -0400 (Tue, 11 Jun 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
---|---|---|---|---|
nebbiolo1 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.4.0 (2024-04-24) -- "Puppy Cup" | 4757 |
palomino3 | Windows Server 2022 Datacenter | x64 | 4.4.0 (2024-04-24 ucrt) -- "Puppy Cup" | 4491 |
lconway | macOS 12.7.1 Monterey | x86_64 | 4.4.0 (2024-04-24) -- "Puppy Cup" | 4522 |
kjohnson3 | macOS 13.6.5 Ventura | arm64 | 4.4.0 (2024-04-24) -- "Puppy Cup" | 4468 |
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 987/2300 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
HPiP 1.10.0 (landing page) Matineh Rahmatbakhsh
| nebbiolo1 | Linux (Ubuntu 22.04.3 LTS) / x86_64 | OK | OK | OK | |||||||||
palomino3 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | |||||||||
lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | |||||||||
kjohnson3 | macOS 13.6.5 Ventura / arm64 | OK | OK | OK | OK | |||||||||
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: 1.10.0 |
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_1.10.0.tar.gz |
StartedAt: 2024-06-10 18:45:08 -0400 (Mon, 10 Jun 2024) |
EndedAt: 2024-06-10 18:53:04 -0400 (Mon, 10 Jun 2024) |
EllapsedTime: 475.8 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_1.10.0.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/Users/biocbuild/bbs-3.19-bioc/meat/HPiP.Rcheck’ * using R version 4.4.0 (2024-04-24) * using platform: aarch64-apple-darwin20 * R was compiled by Apple clang version 14.0.0 (clang-1400.0.29.202) GNU Fortran (GCC) 12.2.0 * running under: macOS Ventura 13.6.5 * using session charset: UTF-8 * using option ‘--no-vignettes’ * checking for file ‘HPiP/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘HPiP’ version ‘1.10.0’ * 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 ... NOTE 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 41.905 1.825 64.093 corr_plot 39.491 1.760 60.890 FSmethod 39.330 1.647 61.633 pred_ensembel 13.616 0.382 17.826 enrichfindP 0.535 0.115 15.866 getFASTA 0.076 0.020 5.175 * 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: 3 NOTEs See ‘/Users/biocbuild/bbs-3.19-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/4.4-arm64/Resources/library’ * installing *source* package ‘HPiP’ ... ** 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 4.4.0 (2024-04-24) -- "Puppy Cup" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: aarch64-apple-darwin20 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 avNNet Loading required package: ggplot2 Loading required package: lattice Fitting Repeat 1 # weights: 103 initial value 101.892845 final value 94.484211 converged Fitting Repeat 2 # weights: 103 initial value 98.129033 final value 94.484211 converged Fitting Repeat 3 # weights: 103 initial value 99.688912 final value 94.484211 converged Fitting Repeat 4 # weights: 103 initial value 96.976505 final value 94.484211 converged Fitting Repeat 5 # weights: 103 initial value 95.336568 final value 94.484211 converged Fitting Repeat 1 # weights: 305 initial value 115.408499 final value 94.484211 converged Fitting Repeat 2 # weights: 305 initial value 106.060102 final value 94.484211 converged Fitting Repeat 3 # weights: 305 initial value 96.950535 final value 94.484211 converged Fitting Repeat 4 # weights: 305 initial value 97.182853 final value 94.484211 converged Fitting Repeat 5 # weights: 305 initial value 102.787371 final value 94.443243 converged Fitting Repeat 1 # weights: 507 initial value 98.743760 final value 94.484210 converged Fitting Repeat 2 # weights: 507 initial value 100.756441 iter 10 value 93.624515 iter 20 value 91.988225 iter 30 value 91.937748 final value 91.937593 converged Fitting Repeat 3 # weights: 507 initial value 99.250648 iter 10 value 93.922222 iter 10 value 93.922222 iter 10 value 93.922222 final value 93.922222 converged Fitting Repeat 4 # weights: 507 initial value 109.470992 final value 94.443243 converged Fitting Repeat 5 # weights: 507 initial value 107.436871 final value 94.443243 converged Fitting Repeat 1 # weights: 103 initial value 103.702050 iter 10 value 94.434607 iter 20 value 87.632980 iter 30 value 86.556315 iter 40 value 84.806419 iter 50 value 84.710967 final value 84.710850 converged Fitting Repeat 2 # weights: 103 initial value 98.845082 iter 10 value 94.486521 iter 20 value 88.920890 iter 30 value 85.627718 iter 40 value 85.146923 iter 50 value 85.125260 final value 85.121580 converged Fitting Repeat 3 # weights: 103 initial value 108.709807 iter 10 value 94.486659 iter 20 value 94.486443 iter 30 value 93.873960 iter 40 value 93.679027 iter 50 value 93.639440 iter 60 value 93.531603 iter 70 value 88.103192 iter 80 value 87.293656 iter 90 value 85.350368 iter 100 value 82.668403 final value 82.668403 stopped after 100 iterations Fitting Repeat 4 # weights: 103 initial value 103.718060 iter 10 value 94.340560 iter 20 value 93.397276 iter 30 value 84.990022 iter 40 value 84.819516 iter 50 value 84.719663 final value 84.710850 converged Fitting Repeat 5 # weights: 103 initial value 102.191699 iter 10 value 94.488524 iter 10 value 94.488524 iter 20 value 85.256008 iter 30 value 84.767267 iter 40 value 84.710886 final value 84.710850 converged Fitting Repeat 1 # weights: 305 initial value 108.122253 iter 10 value 94.433204 iter 20 value 87.765081 iter 30 value 86.033590 iter 40 value 83.441845 iter 50 value 82.546941 iter 60 value 82.199804 iter 70 value 81.929724 iter 80 value 81.899530 iter 90 value 81.173114 iter 100 value 80.752754 final value 80.752754 stopped after 100 iterations Fitting Repeat 2 # weights: 305 initial value 108.978214 iter 10 value 92.535698 iter 20 value 89.421602 iter 30 value 86.912893 iter 40 value 83.751242 iter 50 value 82.550518 iter 60 value 81.737779 iter 70 value 81.240869 iter 80 value 81.024015 iter 90 value 80.981349 iter 100 value 80.935487 final value 80.935487 stopped after 100 iterations Fitting Repeat 3 # weights: 305 initial value 113.287133 iter 10 value 94.399361 iter 20 value 88.670384 iter 30 value 84.860961 iter 40 value 83.796460 iter 50 value 82.236387 iter 60 value 82.170473 iter 70 value 81.928189 iter 80 value 81.760736 iter 90 value 81.483636 iter 100 value 81.291370 final value 81.291370 stopped after 100 iterations Fitting Repeat 4 # weights: 305 initial value 100.159204 iter 10 value 91.837633 iter 20 value 85.084655 iter 30 value 84.759017 iter 40 value 84.511886 iter 50 value 83.567529 iter 60 value 83.241648 iter 70 value 83.132718 iter 80 value 83.074957 iter 90 value 82.658863 iter 100 value 82.520215 final value 82.520215 stopped after 100 iterations Fitting Repeat 5 # weights: 305 initial value 113.415882 iter 10 value 93.354357 iter 20 value 88.725806 iter 30 value 88.388855 iter 40 value 87.025137 iter 50 value 85.678341 iter 60 value 81.796746 iter 70 value 81.061438 iter 80 value 80.743163 iter 90 value 80.630747 iter 100 value 80.616602 final value 80.616602 stopped after 100 iterations Fitting Repeat 1 # weights: 507 initial value 111.948165 iter 10 value 94.195542 iter 20 value 87.142600 iter 30 value 86.865950 iter 40 value 85.220041 iter 50 value 84.025362 iter 60 value 82.644498 iter 70 value 82.183280 iter 80 value 82.068645 iter 90 value 81.777416 iter 100 value 81.476502 final value 81.476502 stopped after 100 iterations Fitting Repeat 2 # weights: 507 initial value 111.499430 iter 10 value 94.411335 iter 20 value 89.345010 iter 30 value 85.483078 iter 40 value 83.022235 iter 50 value 82.196078 iter 60 value 81.737873 iter 70 value 81.196410 iter 80 value 81.112629 iter 90 value 81.079563 iter 100 value 80.997767 final value 80.997767 stopped after 100 iterations Fitting Repeat 3 # weights: 507 initial value 108.224371 iter 10 value 94.669884 iter 20 value 94.427874 iter 30 value 87.636642 iter 40 value 86.154032 iter 50 value 85.519824 iter 60 value 84.617312 iter 70 value 83.334967 iter 80 value 83.026622 iter 90 value 82.653470 iter 100 value 82.476272 final value 82.476272 stopped after 100 iterations Fitting Repeat 4 # weights: 507 initial value 103.363794 iter 10 value 93.465571 iter 20 value 87.613945 iter 30 value 84.722848 iter 40 value 84.283105 iter 50 value 82.600655 iter 60 value 81.733097 iter 70 value 81.020996 iter 80 value 80.545996 iter 90 value 80.242041 iter 100 value 80.137617 final value 80.137617 stopped after 100 iterations Fitting Repeat 5 # weights: 507 initial value 121.033481 iter 10 value 94.397821 iter 20 value 88.907300 iter 30 value 86.682761 iter 40 value 86.441604 iter 50 value 84.812362 iter 60 value 82.206796 iter 70 value 81.382909 iter 80 value 80.784236 iter 90 value 80.562305 iter 100 value 80.488282 final value 80.488282 stopped after 100 iterations Fitting Repeat 1 # weights: 103 initial value 95.706358 iter 10 value 94.485756 iter 20 value 92.004873 iter 30 value 84.036040 iter 30 value 84.036040 iter 30 value 84.036040 final value 84.036040 converged Fitting Repeat 2 # weights: 103 initial value 96.681536 final value 94.485699 converged Fitting Repeat 3 # weights: 103 initial value 97.289331 final value 94.485495 converged Fitting Repeat 4 # weights: 103 initial value 103.280914 final value 94.485644 converged Fitting Repeat 5 # weights: 103 initial value 104.210271 final value 94.486306 converged Fitting Repeat 1 # weights: 305 initial value 94.217307 iter 10 value 92.316097 iter 20 value 92.298660 iter 30 value 92.296688 iter 40 value 91.483579 iter 50 value 91.476563 iter 60 value 91.474176 final value 91.474097 converged Fitting Repeat 2 # weights: 305 initial value 100.648938 iter 10 value 94.488714 iter 20 value 94.356507 iter 30 value 92.587697 iter 40 value 92.532329 iter 50 value 92.532084 iter 60 value 92.531338 iter 70 value 91.611519 iter 80 value 91.611383 iter 90 value 91.609987 iter 90 value 91.609987 final value 91.609987 converged Fitting Repeat 3 # weights: 305 initial value 123.394534 iter 10 value 94.448919 iter 20 value 94.447845 iter 30 value 94.443897 final value 94.443493 converged Fitting Repeat 4 # weights: 305 initial value 94.586739 iter 10 value 88.783477 iter 20 value 88.648474 iter 30 value 88.647164 iter 40 value 87.815975 iter 50 value 87.813281 iter 60 value 84.959922 iter 70 value 84.042237 iter 80 value 84.036571 iter 90 value 84.035837 iter 100 value 83.926013 final value 83.926013 stopped after 100 iterations Fitting Repeat 5 # weights: 305 initial value 124.251421 iter 10 value 94.489188 iter 20 value 94.297190 iter 30 value 90.511372 iter 40 value 88.974763 iter 50 value 85.511077 final value 85.492460 converged Fitting Repeat 1 # weights: 507 initial value 113.541970 iter 10 value 94.491771 iter 20 value 93.910278 iter 30 value 93.742201 iter 40 value 90.800305 iter 50 value 83.787892 iter 60 value 82.496791 iter 70 value 82.459806 iter 80 value 82.457212 iter 90 value 82.456717 iter 100 value 82.410631 final value 82.410631 stopped after 100 iterations Fitting Repeat 2 # weights: 507 initial value 112.809771 iter 10 value 94.492926 iter 20 value 94.298877 iter 30 value 93.791185 iter 40 value 93.789330 iter 50 value 93.788702 iter 60 value 93.779793 iter 70 value 92.342241 iter 80 value 90.920408 iter 90 value 90.859404 iter 100 value 90.834778 final value 90.834778 stopped after 100 iterations Fitting Repeat 3 # weights: 507 initial value 95.534991 iter 10 value 94.490440 iter 20 value 92.506546 iter 30 value 86.708889 iter 40 value 86.708632 final value 86.708629 converged Fitting Repeat 4 # weights: 507 initial value 121.418055 iter 10 value 94.451442 iter 20 value 94.192583 iter 30 value 89.757375 iter 40 value 81.967826 iter 50 value 81.457548 iter 60 value 81.284531 iter 70 value 81.263894 iter 80 value 81.262508 iter 90 value 80.635644 iter 100 value 80.453879 final value 80.453879 stopped after 100 iterations Fitting Repeat 5 # weights: 507 initial value 97.936211 iter 10 value 94.271129 iter 20 value 93.901259 iter 30 value 91.499737 iter 40 value 91.499181 iter 50 value 91.449119 iter 60 value 82.546113 iter 70 value 82.214969 iter 80 value 81.219187 iter 90 value 81.209818 iter 100 value 81.207972 final value 81.207972 stopped after 100 iterations Fitting Repeat 1 # weights: 103 initial value 97.050615 final value 94.052910 converged Fitting Repeat 2 # weights: 103 initial value 100.468677 final value 94.052910 converged Fitting Repeat 3 # weights: 103 initial value 95.158030 final value 93.765896 converged Fitting Repeat 4 # weights: 103 initial value 110.673090 final value 94.052910 converged Fitting Repeat 5 # weights: 103 initial value 101.581286 iter 10 value 91.362122 iter 20 value 88.068256 iter 30 value 88.057354 final value 88.057344 converged Fitting Repeat 1 # weights: 305 initial value 97.566404 final value 94.052911 converged Fitting Repeat 2 # weights: 305 initial value 95.462418 final value 94.052910 converged Fitting Repeat 3 # weights: 305 initial value 100.969798 iter 10 value 93.582418 iter 10 value 93.582418 iter 10 value 93.582418 final value 93.582418 converged Fitting Repeat 4 # weights: 305 initial value 101.896162 final value 94.052910 converged Fitting Repeat 5 # weights: 305 initial value 107.226203 final value 94.052910 converged Fitting Repeat 1 # weights: 507 initial value 122.683019 final value 94.052910 converged Fitting Repeat 2 # weights: 507 initial value 108.962280 final value 93.582418 converged Fitting Repeat 3 # weights: 507 initial value 98.853995 final value 94.052910 converged Fitting Repeat 4 # weights: 507 initial value 100.560727 iter 10 value 93.599645 final value 93.582418 converged Fitting Repeat 5 # weights: 507 initial value 101.135503 iter 10 value 93.106566 final value 93.063302 converged Fitting Repeat 1 # weights: 103 initial value 103.791539 iter 10 value 94.057771 iter 20 value 93.728530 iter 30 value 86.140567 iter 40 value 84.795920 iter 50 value 84.504073 iter 60 value 83.937713 iter 70 value 83.192902 iter 80 value 82.530462 iter 90 value 82.503172 final value 82.503170 converged Fitting Repeat 2 # weights: 103 initial value 98.467408 iter 10 value 94.045829 iter 20 value 93.698697 iter 30 value 93.683537 iter 40 value 93.628237 iter 50 value 87.807858 iter 60 value 84.345158 iter 70 value 83.965084 iter 80 value 83.529880 iter 90 value 82.688949 iter 100 value 82.521700 final value 82.521700 stopped after 100 iterations Fitting Repeat 3 # weights: 103 initial value 101.603367 iter 10 value 94.601532 iter 20 value 94.021939 iter 30 value 93.420844 iter 40 value 87.129629 iter 50 value 83.807452 iter 60 value 83.503569 iter 70 value 83.467818 iter 80 value 83.267467 iter 90 value 83.215023 final value 83.214640 converged Fitting Repeat 4 # weights: 103 initial value 103.658137 iter 10 value 93.982351 iter 20 value 93.322601 iter 30 value 93.145886 iter 40 value 91.267490 iter 50 value 82.487219 iter 60 value 81.139660 iter 70 value 80.592349 iter 80 value 80.490066 final value 80.489084 converged Fitting Repeat 5 # weights: 103 initial value 98.008905 iter 10 value 93.987808 iter 20 value 90.715260 iter 30 value 87.756650 iter 40 value 85.927563 iter 50 value 85.577608 iter 60 value 81.382286 iter 70 value 80.798720 iter 80 value 80.500682 iter 90 value 79.997131 iter 100 value 79.922608 final value 79.922608 stopped after 100 iterations Fitting Repeat 1 # weights: 305 initial value 99.875582 iter 10 value 94.095082 iter 20 value 92.459976 iter 30 value 84.678904 iter 40 value 84.332310 iter 50 value 84.219475 iter 60 value 83.123247 iter 70 value 83.048223 iter 80 value 82.834875 iter 90 value 82.712089 iter 100 value 82.627878 final value 82.627878 stopped after 100 iterations Fitting Repeat 2 # weights: 305 initial value 111.370897 iter 10 value 94.185724 iter 20 value 90.418725 iter 30 value 85.415253 iter 40 value 83.883225 iter 50 value 82.857193 iter 60 value 80.919364 iter 70 value 80.417067 iter 80 value 79.935243 iter 90 value 79.670238 iter 100 value 79.432560 final value 79.432560 stopped after 100 iterations Fitting Repeat 3 # weights: 305 initial value 101.310041 iter 10 value 93.956640 iter 20 value 93.466274 iter 30 value 90.148118 iter 40 value 85.703916 iter 50 value 84.204054 iter 60 value 79.858724 iter 70 value 79.418510 iter 80 value 79.233377 iter 90 value 79.134880 iter 100 value 78.930616 final value 78.930616 stopped after 100 iterations Fitting Repeat 4 # weights: 305 initial value 101.216937 iter 10 value 93.286676 iter 20 value 85.967051 iter 30 value 85.192970 iter 40 value 84.900661 iter 50 value 84.386374 iter 60 value 83.739105 iter 70 value 81.971299 iter 80 value 80.310976 iter 90 value 79.442186 iter 100 value 79.331673 final value 79.331673 stopped after 100 iterations Fitting Repeat 5 # weights: 305 initial value 98.793137 iter 10 value 94.116434 iter 20 value 93.971706 iter 30 value 85.257987 iter 40 value 84.754592 iter 50 value 83.097259 iter 60 value 82.567085 iter 70 value 81.917835 iter 80 value 80.451336 iter 90 value 79.356336 iter 100 value 79.090231 final value 79.090231 stopped after 100 iterations Fitting Repeat 1 # weights: 507 initial value 103.389738 iter 10 value 94.020078 iter 20 value 93.504209 iter 30 value 92.292432 iter 40 value 88.915380 iter 50 value 83.429010 iter 60 value 81.292428 iter 70 value 80.756608 iter 80 value 79.770997 iter 90 value 79.563824 iter 100 value 79.523326 final value 79.523326 stopped after 100 iterations Fitting Repeat 2 # weights: 507 initial value 104.271116 iter 10 value 94.401034 iter 20 value 86.810188 iter 30 value 83.039400 iter 40 value 80.793560 iter 50 value 79.777537 iter 60 value 79.177438 iter 70 value 78.785276 iter 80 value 78.500350 iter 90 value 78.163800 iter 100 value 78.054050 final value 78.054050 stopped after 100 iterations Fitting Repeat 3 # weights: 507 initial value 107.713353 iter 10 value 94.094664 iter 20 value 88.974720 iter 30 value 81.952161 iter 40 value 80.986160 iter 50 value 80.896577 iter 60 value 80.551821 iter 70 value 80.447496 iter 80 value 80.001618 iter 90 value 79.435793 iter 100 value 78.877383 final value 78.877383 stopped after 100 iterations Fitting Repeat 4 # weights: 507 initial value 111.441143 iter 10 value 94.101689 iter 20 value 85.217586 iter 30 value 84.806434 iter 40 value 83.120111 iter 50 value 81.915735 iter 60 value 80.170992 iter 70 value 79.771866 iter 80 value 79.539041 iter 90 value 79.511952 iter 100 value 79.503927 final value 79.503927 stopped after 100 iterations Fitting Repeat 5 # weights: 507 initial value 128.451750 iter 10 value 94.293452 iter 20 value 92.781517 iter 30 value 89.875381 iter 40 value 87.285944 iter 50 value 86.439353 iter 60 value 83.064860 iter 70 value 82.135878 iter 80 value 81.487966 iter 90 value 80.538403 iter 100 value 79.734301 final value 79.734301 stopped after 100 iterations Fitting Repeat 1 # weights: 103 initial value 95.013055 iter 10 value 94.054394 final value 94.053286 converged Fitting Repeat 2 # weights: 103 initial value 103.833313 final value 93.811518 converged Fitting Repeat 3 # weights: 103 initial value 109.105881 final value 94.054675 converged Fitting Repeat 4 # weights: 103 initial value 101.140217 final value 94.054549 converged Fitting Repeat 5 # weights: 103 initial value 96.123870 final value 94.054763 converged Fitting Repeat 1 # weights: 305 initial value 101.159082 iter 10 value 94.057629 iter 20 value 94.052952 iter 30 value 93.585774 final value 93.582558 converged Fitting Repeat 2 # weights: 305 initial value 102.252416 iter 10 value 93.587637 iter 20 value 93.583011 iter 30 value 84.015168 iter 40 value 83.914695 iter 50 value 81.961210 iter 60 value 81.059707 iter 70 value 79.417086 iter 80 value 79.346323 iter 90 value 79.329314 iter 100 value 79.280780 final value 79.280780 stopped after 100 iterations Fitting Repeat 3 # weights: 305 initial value 106.765867 iter 10 value 90.304245 iter 20 value 84.814186 iter 30 value 84.168846 iter 40 value 84.101782 iter 50 value 83.598877 iter 60 value 83.579049 iter 70 value 83.578163 iter 80 value 82.595432 iter 90 value 82.171299 iter 100 value 82.170662 final value 82.170662 stopped after 100 iterations Fitting Repeat 4 # weights: 305 initial value 104.869078 iter 10 value 93.557806 iter 20 value 93.534196 iter 30 value 86.462591 iter 40 value 86.441038 iter 50 value 85.227932 iter 60 value 84.633579 iter 70 value 84.011674 iter 80 value 83.718322 iter 90 value 83.466643 iter 100 value 83.422126 final value 83.422126 stopped after 100 iterations Fitting Repeat 5 # weights: 305 initial value 105.424054 iter 10 value 94.057655 iter 20 value 94.041696 iter 30 value 84.618587 iter 40 value 84.004455 iter 50 value 83.183610 iter 60 value 79.982080 iter 70 value 79.927102 iter 80 value 79.865474 final value 79.864862 converged Fitting Repeat 1 # weights: 507 initial value 102.235643 iter 10 value 94.060993 iter 20 value 94.052496 iter 30 value 91.633280 iter 40 value 84.999289 iter 50 value 80.964204 iter 60 value 80.683261 iter 70 value 79.719881 final value 79.717924 converged Fitting Repeat 2 # weights: 507 initial value 100.241164 iter 10 value 93.590735 iter 20 value 93.585648 iter 30 value 93.394253 iter 40 value 85.453978 iter 50 value 82.049035 iter 60 value 81.895879 iter 70 value 80.358228 iter 80 value 78.034474 iter 90 value 77.315399 iter 100 value 77.277892 final value 77.277892 stopped after 100 iterations Fitting Repeat 3 # weights: 507 initial value 105.435260 iter 10 value 94.060640 iter 20 value 92.337215 final value 92.238719 converged Fitting Repeat 4 # weights: 507 initial value 108.450028 iter 10 value 94.060920 iter 20 value 87.604113 iter 30 value 82.555751 iter 40 value 82.294457 iter 50 value 82.293742 iter 60 value 82.284721 final value 82.284109 converged Fitting Repeat 5 # weights: 507 initial value 121.940872 iter 10 value 93.589693 iter 20 value 93.582118 final value 93.524477 converged Fitting Repeat 1 # weights: 103 initial value 101.606358 final value 94.052910 converged Fitting Repeat 2 # weights: 103 initial value 94.954519 final value 94.052910 converged Fitting Repeat 3 # weights: 103 initial value 98.397961 final value 94.052910 converged Fitting Repeat 4 # weights: 103 initial value 103.193848 iter 10 value 93.279366 iter 20 value 93.271931 final value 93.271928 converged Fitting Repeat 5 # weights: 103 initial value 123.551519 final value 94.052910 converged Fitting Repeat 1 # weights: 305 initial value 107.203993 iter 10 value 94.038252 iter 10 value 94.038251 iter 10 value 94.038251 final value 94.038251 converged Fitting Repeat 2 # weights: 305 initial value 115.052443 final value 94.052910 converged Fitting Repeat 3 # weights: 305 initial value 99.226297 final value 94.052910 converged Fitting Repeat 4 # weights: 305 initial value 96.318314 final value 94.052910 converged Fitting Repeat 5 # weights: 305 initial value 112.199504 final value 94.038251 converged Fitting Repeat 1 # weights: 507 initial value 103.169548 final value 94.038251 converged Fitting Repeat 2 # weights: 507 initial value 119.000746 iter 10 value 94.038250 iter 10 value 94.038250 iter 10 value 94.038250 final value 94.038250 converged Fitting Repeat 3 # weights: 507 initial value 99.149509 iter 10 value 93.649711 iter 10 value 93.649711 iter 10 value 93.649711 final value 93.649711 converged Fitting Repeat 4 # weights: 507 initial value 98.685456 iter 10 value 91.596438 iter 20 value 91.098485 iter 30 value 91.092707 final value 91.092705 converged Fitting Repeat 5 # weights: 507 initial value 119.721448 iter 10 value 94.052910 iter 10 value 94.052910 iter 10 value 94.052910 final value 94.052910 converged Fitting Repeat 1 # weights: 103 initial value 97.061124 iter 10 value 94.049277 iter 20 value 91.791622 iter 30 value 88.097436 iter 40 value 86.482629 iter 50 value 84.500713 iter 60 value 83.098317 iter 70 value 81.892445 iter 80 value 80.480881 iter 90 value 80.365451 iter 100 value 79.812048 final value 79.812048 stopped after 100 iterations Fitting Repeat 2 # weights: 103 initial value 98.701393 iter 10 value 93.378975 iter 20 value 83.662072 iter 30 value 83.280384 iter 40 value 82.756041 iter 50 value 82.086343 iter 60 value 82.043223 iter 70 value 81.887719 iter 80 value 81.852758 iter 90 value 80.810203 iter 100 value 79.705511 final value 79.705511 stopped after 100 iterations Fitting Repeat 3 # weights: 103 initial value 97.308143 iter 10 value 94.056666 iter 20 value 87.502025 iter 30 value 83.998327 iter 40 value 82.976929 iter 50 value 81.566629 iter 60 value 80.876871 iter 70 value 79.710771 iter 80 value 79.400675 final value 79.362614 converged Fitting Repeat 4 # weights: 103 initial value 99.616565 iter 10 value 94.150535 iter 20 value 94.056867 iter 30 value 87.981017 iter 40 value 85.828548 iter 50 value 85.570838 iter 60 value 85.475281 iter 70 value 85.110256 iter 80 value 82.873253 iter 90 value 82.752816 iter 100 value 82.751671 final value 82.751671 stopped after 100 iterations Fitting Repeat 5 # weights: 103 initial value 99.218303 iter 10 value 94.093703 iter 20 value 94.052357 iter 30 value 84.281543 iter 40 value 83.390489 iter 50 value 83.350597 iter 60 value 82.969230 iter 70 value 82.309724 iter 80 value 81.893385 iter 90 value 81.864975 iter 100 value 81.849650 final value 81.849650 stopped after 100 iterations Fitting Repeat 1 # weights: 305 initial value 114.721660 iter 10 value 94.096173 iter 20 value 90.058516 iter 30 value 86.727817 iter 40 value 83.658923 iter 50 value 83.051447 iter 60 value 82.621655 iter 70 value 81.944813 iter 80 value 80.305110 iter 90 value 78.961445 iter 100 value 78.616061 final value 78.616061 stopped after 100 iterations Fitting Repeat 2 # weights: 305 initial value 99.987104 iter 10 value 93.871535 iter 20 value 89.744385 iter 30 value 86.494232 iter 40 value 84.958517 iter 50 value 84.120673 iter 60 value 83.501349 iter 70 value 83.133246 iter 80 value 82.754829 iter 90 value 80.363636 iter 100 value 79.101245 final value 79.101245 stopped after 100 iterations Fitting Repeat 3 # weights: 305 initial value 101.729153 iter 10 value 94.096531 iter 20 value 84.810011 iter 30 value 83.951816 iter 40 value 83.208378 iter 50 value 83.090541 iter 60 value 82.914061 iter 70 value 82.102682 iter 80 value 80.705146 iter 90 value 79.518895 iter 100 value 78.826645 final value 78.826645 stopped after 100 iterations Fitting Repeat 4 # weights: 305 initial value 111.663859 iter 10 value 94.062378 iter 20 value 84.097442 iter 30 value 82.784777 iter 40 value 82.144058 iter 50 value 81.640811 iter 60 value 80.468082 iter 70 value 79.864583 iter 80 value 79.391666 iter 90 value 78.636893 iter 100 value 77.949300 final value 77.949300 stopped after 100 iterations Fitting Repeat 5 # weights: 305 initial value 104.561207 iter 10 value 93.584213 iter 20 value 87.581893 iter 30 value 86.203194 iter 40 value 83.869691 iter 50 value 83.360375 iter 60 value 82.260190 iter 70 value 82.191633 iter 80 value 81.774479 iter 90 value 80.024300 iter 100 value 78.546236 final value 78.546236 stopped after 100 iterations Fitting Repeat 1 # weights: 507 initial value 115.884455 iter 10 value 94.175680 iter 20 value 91.789476 iter 30 value 85.322130 iter 40 value 84.499491 iter 50 value 83.082727 iter 60 value 81.696499 iter 70 value 79.921458 iter 80 value 79.353551 iter 90 value 79.036772 iter 100 value 78.860445 final value 78.860445 stopped after 100 iterations Fitting Repeat 2 # weights: 507 initial value 104.143143 iter 10 value 94.155826 iter 20 value 87.350690 iter 30 value 85.282692 iter 40 value 83.430398 iter 50 value 81.399339 iter 60 value 79.013956 iter 70 value 78.240025 iter 80 value 78.143876 iter 90 value 78.018244 iter 100 value 77.808176 final value 77.808176 stopped after 100 iterations Fitting Repeat 3 # weights: 507 initial value 114.923710 iter 10 value 93.982772 iter 20 value 92.733855 iter 30 value 82.566490 iter 40 value 81.723342 iter 50 value 80.701480 iter 60 value 80.419659 iter 70 value 79.488596 iter 80 value 78.627055 iter 90 value 78.217585 iter 100 value 78.071329 final value 78.071329 stopped after 100 iterations Fitting Repeat 4 # weights: 507 initial value 126.188577 iter 10 value 95.400680 iter 20 value 89.349141 iter 30 value 83.844716 iter 40 value 82.164637 iter 50 value 80.236732 iter 60 value 79.314549 iter 70 value 78.888101 iter 80 value 78.784349 iter 90 value 78.693334 iter 100 value 78.428297 final value 78.428297 stopped after 100 iterations Fitting Repeat 5 # weights: 507 initial value 128.228177 iter 10 value 94.083310 iter 20 value 91.668713 iter 30 value 85.847181 iter 40 value 85.009882 iter 50 value 83.746848 iter 60 value 83.343139 iter 70 value 82.433133 iter 80 value 80.841044 iter 90 value 79.720078 iter 100 value 79.365994 final value 79.365994 stopped after 100 iterations Fitting Repeat 1 # weights: 103 initial value 94.932563 final value 94.054473 converged Fitting Repeat 2 # weights: 103 initial value 99.679586 final value 94.054406 converged Fitting Repeat 3 # weights: 103 initial value 116.954394 iter 10 value 94.054447 iter 20 value 93.782641 iter 30 value 89.826721 iter 40 value 88.158976 iter 50 value 83.699075 iter 60 value 83.241799 final value 83.233957 converged Fitting Repeat 4 # weights: 103 initial value 96.848674 final value 94.054765 converged Fitting Repeat 5 # weights: 103 initial value 95.190352 iter 10 value 94.054747 iter 20 value 94.050845 iter 30 value 90.775386 final value 90.726312 converged Fitting Repeat 1 # weights: 305 initial value 94.760903 iter 10 value 93.966618 iter 20 value 93.846135 iter 30 value 93.674238 iter 40 value 93.673572 iter 50 value 85.134278 final value 84.573122 converged Fitting Repeat 2 # weights: 305 initial value 97.897790 iter 10 value 94.043218 iter 20 value 94.038354 iter 30 value 91.912099 iter 40 value 83.139350 iter 50 value 79.313501 iter 60 value 79.028569 iter 70 value 78.923073 iter 80 value 78.903895 iter 90 value 78.903651 iter 100 value 78.895320 final value 78.895320 stopped after 100 iterations Fitting Repeat 3 # weights: 305 initial value 96.507609 iter 10 value 94.056092 iter 20 value 89.365308 iter 30 value 86.370896 iter 30 value 86.370895 final value 86.178815 converged Fitting Repeat 4 # weights: 305 initial value 95.672853 iter 10 value 94.057663 iter 20 value 93.976200 iter 30 value 90.761405 iter 40 value 90.757235 iter 50 value 90.690554 iter 60 value 90.685832 iter 70 value 90.685118 iter 80 value 90.562997 iter 90 value 90.557099 iter 100 value 90.556724 final value 90.556724 stopped after 100 iterations Fitting Repeat 5 # weights: 305 initial value 94.804946 iter 10 value 94.057044 iter 20 value 93.813056 iter 30 value 91.890597 iter 40 value 91.260930 iter 50 value 91.259694 iter 60 value 91.257900 iter 70 value 91.154301 iter 80 value 91.152367 final value 91.152325 converged Fitting Repeat 1 # weights: 507 initial value 95.121025 iter 10 value 93.109637 iter 20 value 93.108548 iter 30 value 93.108258 iter 40 value 93.078672 iter 50 value 93.050532 iter 60 value 92.662402 iter 70 value 90.816328 iter 80 value 90.755791 iter 90 value 90.749720 iter 100 value 90.717115 final value 90.717115 stopped after 100 iterations Fitting Repeat 2 # weights: 507 initial value 117.452454 iter 10 value 94.127176 iter 20 value 94.114361 iter 30 value 93.174838 iter 40 value 87.591008 iter 50 value 87.571846 iter 60 value 87.326149 iter 70 value 87.283631 iter 80 value 87.034043 iter 90 value 81.821216 iter 100 value 77.087206 final value 77.087206 stopped after 100 iterations Fitting Repeat 3 # weights: 507 initial value 94.762112 iter 10 value 94.046384 iter 20 value 93.999774 iter 30 value 89.606210 iter 40 value 84.571425 iter 50 value 84.415096 iter 60 value 84.413936 iter 70 value 84.413719 iter 80 value 84.413228 iter 90 value 84.412717 final value 84.412709 converged Fitting Repeat 4 # weights: 507 initial value 97.281389 iter 10 value 86.979458 iter 20 value 82.710749 iter 30 value 82.563566 final value 82.557463 converged Fitting Repeat 5 # weights: 507 initial value 95.189332 iter 10 value 94.046632 iter 20 value 94.045792 iter 30 value 94.044826 iter 40 value 94.042330 iter 50 value 94.041845 iter 60 value 94.017834 iter 70 value 85.582519 iter 80 value 84.344892 iter 90 value 83.241820 iter 100 value 81.899940 final value 81.899940 stopped after 100 iterations Fitting Repeat 1 # weights: 103 initial value 97.551936 final value 94.484211 converged Fitting Repeat 2 # weights: 103 initial value 99.260925 final value 94.484211 converged Fitting Repeat 3 # weights: 103 initial value 102.852732 final value 94.484211 converged Fitting Repeat 4 # weights: 103 initial value 95.060219 final value 94.484211 converged Fitting Repeat 5 # weights: 103 initial value 98.567432 final value 94.484211 converged Fitting Repeat 1 # weights: 305 initial value 95.964596 iter 10 value 94.275370 final value 94.275363 converged Fitting Repeat 2 # weights: 305 initial value 107.428721 final value 94.484211 converged Fitting Repeat 3 # weights: 305 initial value 104.613245 final value 94.484211 converged Fitting Repeat 4 # weights: 305 initial value 113.292842 final value 94.484211 converged Fitting Repeat 5 # weights: 305 initial value 95.902778 final value 94.275362 converged Fitting Repeat 1 # weights: 507 initial value 95.895838 iter 10 value 94.275363 iter 10 value 94.275362 iter 10 value 94.275362 final value 94.275362 converged Fitting Repeat 2 # weights: 507 initial value 113.133218 final value 94.275362 converged Fitting Repeat 3 # weights: 507 initial value 103.970925 final value 94.312038 converged Fitting Repeat 4 # weights: 507 initial value 115.605968 final value 94.275362 converged Fitting Repeat 5 # weights: 507 initial value 106.676806 iter 10 value 94.349765 final value 94.313817 converged Fitting Repeat 1 # weights: 103 initial value 116.547402 iter 10 value 94.429483 iter 20 value 93.535830 iter 30 value 93.497153 iter 40 value 92.568387 iter 50 value 84.554181 iter 60 value 83.861181 iter 70 value 83.050500 iter 80 value 82.277146 iter 90 value 81.781071 final value 81.774378 converged Fitting Repeat 2 # weights: 103 initial value 95.584539 iter 10 value 93.513213 iter 20 value 92.321333 iter 30 value 92.061494 iter 40 value 91.966135 iter 50 value 91.715854 iter 60 value 91.709553 final value 91.709470 converged Fitting Repeat 3 # weights: 103 initial value 98.988040 iter 10 value 94.368753 iter 20 value 92.914708 iter 30 value 90.120867 iter 40 value 83.823607 iter 50 value 83.539870 iter 60 value 82.670077 iter 70 value 82.600367 iter 80 value 82.555355 final value 82.553262 converged Fitting Repeat 4 # weights: 103 initial value 96.437583 iter 10 value 94.491669 iter 20 value 94.486620 iter 30 value 94.102907 iter 40 value 90.008215 iter 50 value 85.083465 iter 60 value 82.667599 iter 70 value 82.149787 iter 80 value 81.904822 iter 90 value 81.774380 final value 81.774378 converged Fitting Repeat 5 # weights: 103 initial value 96.342341 iter 10 value 94.409325 iter 20 value 94.081464 iter 30 value 91.910726 iter 40 value 85.421004 iter 50 value 84.001186 iter 60 value 83.958749 iter 70 value 83.597044 iter 80 value 83.289611 final value 83.287469 converged Fitting Repeat 1 # weights: 305 initial value 100.216370 iter 10 value 94.526055 iter 20 value 94.007079 iter 30 value 89.539325 iter 40 value 86.490362 iter 50 value 85.901412 iter 60 value 85.269507 iter 70 value 83.723983 iter 80 value 82.203658 iter 90 value 80.950392 iter 100 value 80.642673 final value 80.642673 stopped after 100 iterations Fitting Repeat 2 # weights: 305 initial value 104.239480 iter 10 value 94.312206 iter 20 value 86.357038 iter 30 value 83.807038 iter 40 value 82.704061 iter 50 value 82.275828 iter 60 value 80.898153 iter 70 value 80.494743 iter 80 value 80.098265 iter 90 value 79.869587 iter 100 value 79.768572 final value 79.768572 stopped after 100 iterations Fitting Repeat 3 # weights: 305 initial value 109.103972 iter 10 value 94.549400 iter 20 value 86.971099 iter 30 value 83.640188 iter 40 value 82.061401 iter 50 value 80.847398 iter 60 value 80.467717 iter 70 value 80.219064 iter 80 value 79.647993 iter 90 value 79.616339 iter 100 value 79.539527 final value 79.539527 stopped after 100 iterations Fitting Repeat 4 # weights: 305 initial value 102.897178 iter 10 value 94.478000 iter 20 value 94.108107 iter 30 value 93.755404 iter 40 value 87.351413 iter 50 value 84.763934 iter 60 value 84.026847 iter 70 value 82.988043 iter 80 value 82.410973 iter 90 value 82.321532 iter 100 value 81.835911 final value 81.835911 stopped after 100 iterations Fitting Repeat 5 # weights: 305 initial value 128.882521 iter 10 value 94.579498 iter 20 value 94.202625 iter 30 value 88.583422 iter 40 value 86.627388 iter 50 value 82.431410 iter 60 value 82.259227 iter 70 value 82.165959 iter 80 value 81.292716 iter 90 value 80.667339 iter 100 value 80.519618 final value 80.519618 stopped after 100 iterations Fitting Repeat 1 # weights: 507 initial value 111.435520 iter 10 value 88.317318 iter 20 value 87.449608 iter 30 value 85.796775 iter 40 value 84.953254 iter 50 value 83.864112 iter 60 value 81.613877 iter 70 value 80.809400 iter 80 value 80.590405 iter 90 value 80.482166 iter 100 value 80.431750 final value 80.431750 stopped after 100 iterations Fitting Repeat 2 # weights: 507 initial value 109.898452 iter 10 value 94.191995 iter 20 value 88.818189 iter 30 value 86.657353 iter 40 value 86.398433 iter 50 value 83.126247 iter 60 value 81.314511 iter 70 value 80.439320 iter 80 value 80.148460 iter 90 value 80.018131 iter 100 value 79.974753 final value 79.974753 stopped after 100 iterations Fitting Repeat 3 # weights: 507 initial value 103.650382 iter 10 value 94.515610 iter 20 value 93.226317 iter 30 value 85.811800 iter 40 value 85.153482 iter 50 value 84.068298 iter 60 value 83.683801 iter 70 value 82.947008 iter 80 value 82.585626 iter 90 value 82.312988 iter 100 value 82.216512 final value 82.216512 stopped after 100 iterations Fitting Repeat 4 # weights: 507 initial value 118.869071 iter 10 value 94.621830 iter 20 value 90.005905 iter 30 value 87.936450 iter 40 value 87.126988 iter 50 value 86.343120 iter 60 value 85.157166 iter 70 value 83.310460 iter 80 value 82.460005 iter 90 value 81.453809 iter 100 value 80.564074 final value 80.564074 stopped after 100 iterations Fitting Repeat 5 # weights: 507 initial value 110.959598 iter 10 value 94.322437 iter 20 value 93.299768 iter 30 value 84.451916 iter 40 value 83.412600 iter 50 value 82.996395 iter 60 value 81.696480 iter 70 value 80.932182 iter 80 value 79.918765 iter 90 value 79.458418 iter 100 value 79.316545 final value 79.316545 stopped after 100 iterations Fitting Repeat 1 # weights: 103 initial value 101.302124 final value 94.485813 converged Fitting Repeat 2 # weights: 103 initial value 105.468891 final value 94.485882 converged Fitting Repeat 3 # weights: 103 initial value 101.190985 final value 94.486200 converged Fitting Repeat 4 # weights: 103 initial value 100.227887 iter 10 value 94.485849 iter 20 value 94.484226 final value 94.484215 converged Fitting Repeat 5 # weights: 103 initial value 102.760550 iter 10 value 94.276925 iter 20 value 93.266159 iter 30 value 86.960129 final value 86.960123 converged Fitting Repeat 1 # weights: 305 initial value 97.892134 iter 10 value 89.996666 iter 20 value 83.342615 iter 30 value 82.878159 iter 40 value 82.737333 iter 50 value 82.718993 iter 60 value 82.708757 iter 70 value 82.704491 iter 80 value 82.700976 iter 90 value 81.977161 iter 100 value 81.903451 final value 81.903451 stopped after 100 iterations Fitting Repeat 2 # weights: 305 initial value 96.354301 iter 10 value 94.488684 iter 20 value 94.484223 final value 94.484214 converged Fitting Repeat 3 # weights: 305 initial value 98.175066 iter 10 value 94.280400 iter 20 value 94.130592 iter 30 value 94.038211 iter 40 value 94.028121 final value 94.027966 converged Fitting Repeat 4 # weights: 305 initial value 105.651906 iter 10 value 90.660556 iter 20 value 87.304849 iter 30 value 86.218367 iter 40 value 85.614419 iter 50 value 85.607112 iter 60 value 85.539258 iter 70 value 84.914947 iter 80 value 84.617591 iter 90 value 84.589168 iter 100 value 84.582064 final value 84.582064 stopped after 100 iterations Fitting Repeat 5 # weights: 305 initial value 101.263117 iter 10 value 94.269373 final value 94.267667 converged Fitting Repeat 1 # weights: 507 initial value 101.751654 iter 10 value 94.284364 iter 20 value 94.281586 iter 30 value 94.275524 iter 40 value 92.228271 iter 50 value 88.503238 iter 60 value 86.510757 iter 70 value 86.028504 final value 86.027589 converged Fitting Repeat 2 # weights: 507 initial value 102.543277 iter 10 value 94.092045 iter 20 value 94.088501 iter 30 value 94.057394 iter 40 value 92.621983 iter 50 value 87.514335 iter 60 value 85.827897 iter 70 value 82.810641 iter 80 value 82.588446 iter 90 value 82.588140 iter 100 value 82.587949 final value 82.587949 stopped after 100 iterations Fitting Repeat 3 # weights: 507 initial value 102.747051 iter 10 value 94.236900 iter 20 value 94.233257 final value 94.230216 converged Fitting Repeat 4 # weights: 507 initial value 101.509470 iter 10 value 94.491307 iter 20 value 94.243193 iter 30 value 91.125664 iter 40 value 91.030417 iter 50 value 91.027895 iter 60 value 91.027308 iter 70 value 87.896408 iter 80 value 86.476510 iter 90 value 83.784149 iter 100 value 81.654798 final value 81.654798 stopped after 100 iterations Fitting Repeat 5 # weights: 507 initial value 104.009970 iter 10 value 94.091643 iter 20 value 94.086142 iter 30 value 94.084752 iter 40 value 89.350792 iter 50 value 84.077268 iter 60 value 84.076360 iter 70 value 83.613591 iter 80 value 83.609749 iter 90 value 83.544678 iter 100 value 81.716808 final value 81.716808 stopped after 100 iterations Fitting Repeat 1 # weights: 103 initial value 110.085099 final value 94.484211 converged Fitting Repeat 2 # weights: 103 initial value 95.605095 final value 94.484211 converged Fitting Repeat 3 # weights: 103 initial value 103.123510 final value 94.484211 converged Fitting Repeat 4 # weights: 103 initial value 103.568605 final value 94.484211 converged Fitting Repeat 5 # weights: 103 initial value 95.797997 final value 94.354396 converged Fitting Repeat 1 # weights: 305 initial value 100.298806 final value 94.484211 converged Fitting Repeat 2 # weights: 305 initial value 122.012720 iter 10 value 92.752039 iter 20 value 89.037975 final value 88.780974 converged Fitting Repeat 3 # weights: 305 initial value 106.914481 iter 10 value 94.386587 final value 94.354396 converged Fitting Repeat 4 # weights: 305 initial value 97.106506 final value 94.484211 converged Fitting Repeat 5 # weights: 305 initial value 110.180316 final value 94.479532 converged Fitting Repeat 1 # weights: 507 initial value 96.288276 iter 10 value 92.478359 iter 20 value 92.068481 final value 92.068451 converged Fitting Repeat 2 # weights: 507 initial value 97.601032 iter 10 value 94.132626 final value 94.132577 converged Fitting Repeat 3 # weights: 507 initial value 94.498399 final value 94.484213 converged Fitting Repeat 4 # weights: 507 initial value 95.425764 final value 94.354396 converged Fitting Repeat 5 # weights: 507 initial value 100.587169 final value 94.479533 converged Fitting Repeat 1 # weights: 103 initial value 101.194720 iter 10 value 91.766781 iter 20 value 88.002359 iter 30 value 86.904864 iter 40 value 86.278227 iter 50 value 85.743008 final value 85.737149 converged Fitting Repeat 2 # weights: 103 initial value 103.110661 iter 10 value 94.546299 iter 20 value 94.436408 iter 30 value 94.343064 iter 40 value 94.157086 iter 50 value 89.818706 iter 60 value 88.522856 iter 70 value 87.649534 iter 80 value 87.385226 iter 90 value 86.198417 iter 100 value 85.739984 final value 85.739984 stopped after 100 iterations Fitting Repeat 3 # weights: 103 initial value 101.864791 iter 10 value 94.058717 iter 20 value 88.699283 iter 30 value 88.189608 iter 40 value 86.769027 iter 50 value 86.037242 iter 60 value 85.492606 iter 70 value 85.257181 iter 80 value 85.189688 iter 90 value 85.106467 final value 85.095471 converged Fitting Repeat 4 # weights: 103 initial value 98.841339 iter 10 value 94.349829 iter 20 value 93.337416 iter 30 value 87.915718 iter 40 value 87.549239 iter 50 value 87.152700 iter 60 value 86.599739 iter 70 value 86.246157 iter 80 value 85.738788 final value 85.737149 converged Fitting Repeat 5 # weights: 103 initial value 104.802817 iter 10 value 93.430271 iter 20 value 87.295846 iter 30 value 87.022541 iter 40 value 86.877026 iter 50 value 86.088656 iter 60 value 85.330558 iter 70 value 85.201125 iter 80 value 85.178653 iter 90 value 85.159657 iter 90 value 85.159657 iter 90 value 85.159657 final value 85.159657 converged Fitting Repeat 1 # weights: 305 initial value 115.056388 iter 10 value 94.597262 iter 20 value 93.773786 iter 30 value 87.848568 iter 40 value 87.069065 iter 50 value 86.809234 iter 60 value 86.400445 iter 70 value 85.630210 iter 80 value 85.550231 iter 90 value 85.058576 iter 100 value 83.770977 final value 83.770977 stopped after 100 iterations Fitting Repeat 2 # weights: 305 initial value 104.792721 iter 10 value 94.666350 iter 20 value 91.125198 iter 30 value 86.548645 iter 40 value 85.590685 iter 50 value 85.223163 iter 60 value 84.343619 iter 70 value 83.579367 iter 80 value 83.411083 iter 90 value 83.343697 iter 100 value 83.304113 final value 83.304113 stopped after 100 iterations Fitting Repeat 3 # weights: 305 initial value 108.250164 iter 10 value 94.400914 iter 20 value 93.215354 iter 30 value 92.765388 iter 40 value 86.504248 iter 50 value 85.535581 iter 60 value 84.224827 iter 70 value 83.350483 iter 80 value 83.264640 iter 90 value 83.192868 iter 100 value 83.033164 final value 83.033164 stopped after 100 iterations Fitting Repeat 4 # weights: 305 initial value 107.444605 iter 10 value 95.614736 iter 20 value 93.611006 iter 30 value 87.778051 iter 40 value 84.948567 iter 50 value 83.832160 iter 60 value 83.483845 iter 70 value 83.115906 iter 80 value 83.002204 iter 90 value 82.989161 iter 100 value 82.988103 final value 82.988103 stopped after 100 iterations Fitting Repeat 5 # weights: 305 initial value 119.002852 iter 10 value 94.126540 iter 20 value 89.321014 iter 30 value 87.908681 iter 40 value 85.383337 iter 50 value 83.639313 iter 60 value 83.493261 iter 70 value 83.390187 iter 80 value 83.251100 iter 90 value 82.977411 iter 100 value 82.779798 final value 82.779798 stopped after 100 iterations Fitting Repeat 1 # weights: 507 initial value 114.058353 iter 10 value 94.487188 iter 20 value 94.194605 iter 30 value 90.176644 iter 40 value 87.081318 iter 50 value 85.072988 iter 60 value 84.304643 iter 70 value 84.105571 iter 80 value 83.889055 iter 90 value 83.686380 iter 100 value 83.399072 final value 83.399072 stopped after 100 iterations Fitting Repeat 2 # weights: 507 initial value 136.828653 iter 10 value 94.512396 iter 20 value 93.720843 iter 30 value 91.852255 iter 40 value 87.117238 iter 50 value 84.795707 iter 60 value 83.661944 iter 70 value 83.269198 iter 80 value 83.067474 iter 90 value 82.981991 iter 100 value 82.977774 final value 82.977774 stopped after 100 iterations Fitting Repeat 3 # weights: 507 initial value 104.827124 iter 10 value 94.374497 iter 20 value 90.009030 iter 30 value 88.518227 iter 40 value 88.088637 iter 50 value 86.692251 iter 60 value 85.560845 iter 70 value 84.554913 iter 80 value 83.637917 iter 90 value 83.099676 iter 100 value 83.019206 final value 83.019206 stopped after 100 iterations Fitting Repeat 4 # weights: 507 initial value 127.913513 iter 10 value 91.580867 iter 20 value 88.928271 iter 30 value 87.916149 iter 40 value 86.574255 iter 50 value 85.042275 iter 60 value 83.670106 iter 70 value 83.524808 iter 80 value 83.472014 iter 90 value 83.267174 iter 100 value 82.968022 final value 82.968022 stopped after 100 iterations Fitting Repeat 5 # weights: 507 initial value 114.732950 iter 10 value 94.520328 iter 20 value 94.444988 iter 30 value 93.989455 iter 40 value 89.161734 iter 50 value 87.664284 iter 60 value 85.518080 iter 70 value 84.207580 iter 80 value 83.501250 iter 90 value 83.274246 iter 100 value 83.160008 final value 83.160008 stopped after 100 iterations Fitting Repeat 1 # weights: 103 initial value 94.965172 final value 94.485839 converged Fitting Repeat 2 # weights: 103 initial value 95.441723 final value 94.485926 converged Fitting Repeat 3 # weights: 103 initial value 98.727276 final value 94.485810 converged Fitting Repeat 4 # weights: 103 initial value 98.231830 iter 10 value 94.356207 iter 20 value 93.315809 iter 30 value 92.612590 final value 92.612308 converged Fitting Repeat 5 # weights: 103 initial value 98.074884 final value 94.485893 converged Fitting Repeat 1 # weights: 305 initial value 99.773490 iter 10 value 94.488738 iter 20 value 94.370762 iter 30 value 94.133024 final value 94.133006 converged Fitting Repeat 2 # weights: 305 initial value 114.313685 iter 10 value 94.489239 iter 20 value 94.483839 iter 30 value 89.208854 iter 40 value 87.148221 iter 50 value 86.557838 iter 60 value 86.160577 iter 70 value 86.064642 iter 80 value 85.378375 iter 90 value 83.617482 iter 100 value 82.361783 final value 82.361783 stopped after 100 iterations Fitting Repeat 3 # weights: 305 initial value 110.655311 iter 10 value 92.792176 iter 20 value 92.789782 iter 30 value 92.610551 iter 40 value 85.984462 iter 50 value 85.887781 iter 60 value 85.525914 final value 85.486401 converged Fitting Repeat 4 # weights: 305 initial value 114.200162 iter 10 value 94.359874 iter 20 value 94.354833 iter 30 value 92.289808 iter 40 value 89.426057 iter 50 value 89.360813 final value 89.359842 converged Fitting Repeat 5 # weights: 305 initial value 115.232309 iter 10 value 94.488861 iter 20 value 94.356343 final value 94.354712 converged Fitting Repeat 1 # weights: 507 initial value 102.543337 iter 10 value 94.331194 iter 20 value 94.275394 iter 30 value 92.150734 iter 40 value 88.964620 iter 50 value 88.883038 iter 60 value 88.380538 iter 70 value 88.293208 final value 88.293146 converged Fitting Repeat 2 # weights: 507 initial value 136.729968 iter 10 value 94.492108 iter 20 value 94.483338 iter 30 value 94.282832 iter 40 value 91.789770 iter 50 value 90.151983 iter 60 value 86.449477 iter 70 value 84.512927 iter 80 value 84.332366 iter 90 value 84.159647 iter 100 value 84.100328 final value 84.100328 stopped after 100 iterations Fitting Repeat 3 # weights: 507 initial value 103.065055 iter 10 value 94.362025 iter 20 value 94.361382 iter 30 value 94.354503 iter 40 value 91.747815 iter 50 value 85.684965 iter 60 value 85.463754 iter 70 value 85.462805 final value 85.462700 converged Fitting Repeat 4 # weights: 507 initial value 144.641602 iter 10 value 94.492410 iter 20 value 94.484326 iter 30 value 92.826358 iter 40 value 89.631614 final value 89.629160 converged Fitting Repeat 5 # weights: 507 initial value 115.008126 iter 10 value 94.330780 iter 20 value 94.284779 iter 30 value 88.209045 iter 40 value 87.344581 iter 50 value 85.190736 iter 60 value 83.514432 iter 70 value 82.695104 iter 80 value 82.627850 iter 90 value 82.627582 iter 100 value 82.626765 final value 82.626765 stopped after 100 iterations Fitting Repeat 1 # weights: 305 initial value 117.989129 iter 10 value 117.891488 iter 20 value 117.852664 iter 30 value 110.377588 iter 40 value 108.166941 iter 50 value 108.001897 iter 60 value 107.979656 final value 107.979652 converged Fitting Repeat 2 # weights: 305 initial value 134.701359 iter 10 value 117.763663 iter 20 value 117.645938 iter 30 value 105.358212 iter 40 value 105.342792 iter 50 value 105.341685 final value 105.341660 converged Fitting Repeat 3 # weights: 305 initial value 126.037504 iter 10 value 117.763784 iter 20 value 117.733687 iter 30 value 117.731402 final value 117.729959 converged Fitting Repeat 4 # weights: 305 initial value 119.365309 iter 10 value 117.894616 iter 20 value 117.850109 iter 30 value 115.358488 iter 40 value 109.326477 iter 50 value 107.715141 iter 60 value 107.692287 iter 70 value 106.695029 iter 80 value 106.678748 iter 90 value 104.305639 iter 100 value 104.270647 final value 104.270647 stopped after 100 iterations Fitting Repeat 5 # weights: 305 initial value 123.330763 iter 10 value 117.894729 iter 20 value 117.762071 iter 30 value 107.263788 iter 40 value 107.166250 iter 50 value 106.860185 iter 60 value 106.730781 iter 70 value 106.084579 iter 80 value 106.066537 final value 106.064389 converged 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 -- Mon Jun 10 18:52:46 2024 *********************************************** 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 40.066 1.470 67.700
HPiP.Rcheck/HPiP-Ex.timings
name | user | system | elapsed | |
FSmethod | 39.330 | 1.647 | 61.633 | |
FreqInteractors | 0.230 | 0.016 | 0.345 | |
calculateAAC | 0.043 | 0.009 | 0.079 | |
calculateAutocor | 0.392 | 0.056 | 0.663 | |
calculateCTDC | 0.091 | 0.007 | 0.133 | |
calculateCTDD | 0.685 | 0.021 | 1.018 | |
calculateCTDT | 0.236 | 0.009 | 0.380 | |
calculateCTriad | 0.411 | 0.018 | 0.594 | |
calculateDC | 0.094 | 0.011 | 0.149 | |
calculateF | 0.386 | 0.015 | 0.563 | |
calculateKSAAP | 0.090 | 0.011 | 0.101 | |
calculateQD_Sm | 2.078 | 0.101 | 3.238 | |
calculateTC | 1.910 | 0.131 | 3.113 | |
calculateTC_Sm | 0.333 | 0.010 | 0.512 | |
corr_plot | 39.491 | 1.760 | 60.890 | |
enrichfindP | 0.535 | 0.115 | 15.866 | |
enrichfind_hp | 0.091 | 0.022 | 1.177 | |
enrichplot | 0.359 | 0.009 | 0.369 | |
filter_missing_values | 0.002 | 0.000 | 0.001 | |
getFASTA | 0.076 | 0.020 | 5.175 | |
getHPI | 0 | 0 | 0 | |
get_negativePPI | 0.001 | 0.000 | 0.002 | |
get_positivePPI | 0 | 0 | 0 | |
impute_missing_data | 0.001 | 0.000 | 0.001 | |
plotPPI | 0.062 | 0.005 | 0.101 | |
pred_ensembel | 13.616 | 0.382 | 17.826 | |
var_imp | 41.905 | 1.825 | 64.093 | |