Matineh Rahmatbakhsh
| Snapshot Date: 2024-11-01 13:40 -0400 (Fri, 01 Nov 2024) |
| git_url: https://git.bioconductor.org/packages/HPiP |
| git_branch: RELEASE_3_20 |
| git_last_commit: ce9e305 |
| git_last_commit_date: 2024-10-29 11:04:11 -0400 (Tue, 29 Oct 2024) |
| Back to Multiple platform build/check report for BioC 3.20: simplified long |
|
This page was generated on 2024-11-02 12:05 -0400 (Sat, 02 Nov 2024).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| teran2 | Linux (Ubuntu 24.04.1 LTS) | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4500 |
| nebbiolo2 | Linux (Ubuntu 24.04.1 LTS) | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4763 |
| palomino8 | Windows Server 2022 Datacenter | x64 | 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" | 4505 |
| lconway | macOS 12.7.1 Monterey | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4538 |
| kunpeng2 | Linux (openEuler 22.03 LTS-SP1) | aarch64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4493 |
| 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 979/2289 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| HPiP 1.12.0 (landing page) Matineh Rahmatbakhsh
| teran2 | Linux (Ubuntu 24.04.1 LTS) / x86_64 | OK | OK | OK | | ||||||||
| nebbiolo2 | Linux (Ubuntu 24.04.1 LTS) / x86_64 | OK | OK | OK | ||||||||||
| palomino8 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | | ||||||||
| lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | | ||||||||
| kunpeng2 | Linux (openEuler 22.03 LTS-SP1) / aarch64 | 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: 1.12.0 |
| Command: F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:HPiP.install-out.txt --library=F:\biocbuild\bbs-3.20-bioc\R\library --no-vignettes --timings HPiP_1.12.0.tar.gz |
| StartedAt: 2024-11-02 03:24:40 -0400 (Sat, 02 Nov 2024) |
| EndedAt: 2024-11-02 03:29:46 -0400 (Sat, 02 Nov 2024) |
| EllapsedTime: 306.5 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: HPiP.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### Running command:
###
### F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:HPiP.install-out.txt --library=F:\biocbuild\bbs-3.20-bioc\R\library --no-vignettes --timings HPiP_1.12.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory 'F:/biocbuild/bbs-3.20-bioc/meat/HPiP.Rcheck'
* using R version 4.4.1 (2024-06-14 ucrt)
* using platform: x86_64-w64-mingw32
* R was compiled by
gcc.exe (GCC) 13.2.0
GNU Fortran (GCC) 13.2.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* using option '--no-vignettes'
* checking for file 'HPiP/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'HPiP' version '1.12.0'
* 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 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
FSmethod 34.98 1.89 37.28
var_imp 35.05 1.33 36.39
corr_plot 33.37 1.99 35.36
pred_ensembel 15.60 0.67 11.86
enrichfindP 0.61 0.12 13.73
* 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
'F:/biocbuild/bbs-3.20-bioc/meat/HPiP.Rcheck/00check.log'
for details.
HPiP.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD INSTALL HPiP ### ############################################################################## ############################################################################## * installing to library 'F:/biocbuild/bbs-3.20-bioc/R/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 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64
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 95.909439
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 95.791256
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 100.367566
final value 94.484211
converged
Fitting Repeat 4
# weights: 103
initial value 102.122239
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 99.031826
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 97.339838
final value 94.484210
converged
Fitting Repeat 2
# weights: 305
initial value 108.279583
final value 94.466823
converged
Fitting Repeat 3
# weights: 305
initial value 120.463498
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 100.408020
final value 94.484211
converged
Fitting Repeat 5
# weights: 305
initial value 100.135760
final value 94.484211
converged
Fitting Repeat 1
# weights: 507
initial value 99.906263
iter 10 value 94.466823
iter 10 value 94.466823
iter 10 value 94.466823
final value 94.466823
converged
Fitting Repeat 2
# weights: 507
initial value 108.625200
iter 10 value 93.381482
iter 20 value 92.354114
iter 30 value 91.732081
iter 40 value 91.490917
final value 91.484072
converged
Fitting Repeat 3
# weights: 507
initial value 111.537717
final value 94.484211
converged
Fitting Repeat 4
# weights: 507
initial value 97.015929
final value 94.088889
converged
Fitting Repeat 5
# weights: 507
initial value 101.439986
iter 10 value 83.977566
iter 20 value 83.778691
iter 20 value 83.778691
iter 20 value 83.778691
final value 83.778691
converged
Fitting Repeat 1
# weights: 103
initial value 98.207205
iter 10 value 94.488708
iter 20 value 92.503826
iter 30 value 87.138413
iter 40 value 84.692263
iter 50 value 84.257339
iter 60 value 84.185914
iter 70 value 84.089316
iter 80 value 84.020573
iter 90 value 83.568860
final value 83.566587
converged
Fitting Repeat 2
# weights: 103
initial value 102.647601
iter 10 value 94.415490
iter 20 value 85.815485
iter 30 value 85.421110
iter 40 value 84.817873
iter 50 value 84.396606
iter 60 value 83.898017
iter 70 value 83.624911
iter 80 value 83.566590
final value 83.566587
converged
Fitting Repeat 3
# weights: 103
initial value 104.334706
iter 10 value 94.557489
iter 20 value 94.277650
iter 30 value 94.068112
iter 40 value 88.145371
iter 50 value 85.203632
iter 60 value 84.966545
iter 70 value 84.332747
iter 80 value 83.739768
iter 90 value 83.571088
final value 83.566587
converged
Fitting Repeat 4
# weights: 103
initial value 102.223325
iter 10 value 94.446507
iter 20 value 86.862348
iter 30 value 84.127642
iter 40 value 83.821991
iter 50 value 83.244970
iter 60 value 82.150569
iter 70 value 82.033728
iter 80 value 81.999959
final value 81.999955
converged
Fitting Repeat 5
# weights: 103
initial value 100.979544
iter 10 value 94.488678
iter 20 value 94.471236
iter 30 value 94.339302
iter 40 value 94.294790
iter 50 value 94.224745
iter 60 value 87.395951
iter 70 value 85.051082
iter 80 value 84.732628
iter 90 value 84.343698
iter 100 value 84.112151
final value 84.112151
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 103.089359
iter 10 value 94.436977
iter 20 value 92.275849
iter 30 value 92.119550
iter 40 value 91.183342
iter 50 value 85.430013
iter 60 value 84.700205
iter 70 value 82.618169
iter 80 value 82.006049
iter 90 value 81.586534
iter 100 value 81.107420
final value 81.107420
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 107.586520
iter 10 value 94.491932
iter 20 value 94.277595
iter 30 value 91.583156
iter 40 value 86.736005
iter 50 value 84.811537
iter 60 value 83.718373
iter 70 value 83.166286
iter 80 value 82.478606
iter 90 value 81.492535
iter 100 value 81.105647
final value 81.105647
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 131.335818
iter 10 value 94.471857
iter 20 value 86.739830
iter 30 value 84.889975
iter 40 value 83.482261
iter 50 value 82.758607
iter 60 value 82.536388
iter 70 value 82.038401
iter 80 value 81.207152
iter 90 value 80.476490
iter 100 value 80.421120
final value 80.421120
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 108.493241
iter 10 value 94.539280
iter 20 value 88.823289
iter 30 value 85.561727
iter 40 value 83.688356
iter 50 value 83.312278
iter 60 value 82.810319
iter 70 value 82.393806
iter 80 value 82.185724
iter 90 value 82.065942
iter 100 value 81.152681
final value 81.152681
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 114.883224
iter 10 value 94.515700
iter 20 value 90.539964
iter 30 value 85.674092
iter 40 value 84.901573
iter 50 value 84.556149
iter 60 value 83.880537
iter 70 value 82.567517
iter 80 value 82.024290
iter 90 value 81.751520
iter 100 value 81.033254
final value 81.033254
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 112.806350
iter 10 value 94.393144
iter 20 value 86.024793
iter 30 value 85.124169
iter 40 value 84.638008
iter 50 value 84.208409
iter 60 value 83.947158
iter 70 value 83.286244
iter 80 value 83.161830
iter 90 value 83.063652
iter 100 value 82.499356
final value 82.499356
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 124.257109
iter 10 value 94.866608
iter 20 value 86.061013
iter 30 value 85.564035
iter 40 value 83.828241
iter 50 value 81.968721
iter 60 value 81.361655
iter 70 value 81.163929
iter 80 value 80.499814
iter 90 value 80.216470
iter 100 value 80.091047
final value 80.091047
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 106.503885
iter 10 value 94.468762
iter 20 value 94.238144
iter 30 value 88.696853
iter 40 value 86.724077
iter 50 value 85.270726
iter 60 value 81.449559
iter 70 value 80.979562
iter 80 value 80.711026
iter 90 value 80.474847
iter 100 value 80.424052
final value 80.424052
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 113.336732
iter 10 value 95.226801
iter 20 value 90.060129
iter 30 value 88.821199
iter 40 value 87.853443
iter 50 value 87.508960
iter 60 value 86.461210
iter 70 value 86.324482
iter 80 value 86.006660
iter 90 value 85.035894
iter 100 value 82.749158
final value 82.749158
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 119.299699
iter 10 value 93.209837
iter 20 value 86.448775
iter 30 value 83.181549
iter 40 value 81.843344
iter 50 value 81.697998
iter 60 value 80.923546
iter 70 value 80.766493
iter 80 value 80.540319
iter 90 value 80.366796
iter 100 value 80.251130
final value 80.251130
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 97.778133
final value 94.486026
converged
Fitting Repeat 2
# weights: 103
initial value 96.348519
iter 10 value 94.478511
iter 20 value 94.468384
iter 30 value 94.467405
iter 40 value 94.466772
iter 50 value 91.958742
final value 91.791663
converged
Fitting Repeat 3
# weights: 103
initial value 95.884250
final value 94.485864
converged
Fitting Repeat 4
# weights: 103
initial value 98.231233
final value 94.485590
converged
Fitting Repeat 5
# weights: 103
initial value 113.531148
iter 10 value 94.485992
iter 20 value 94.484275
final value 94.484217
converged
Fitting Repeat 1
# weights: 305
initial value 95.890926
iter 10 value 94.487884
iter 20 value 94.279708
iter 30 value 94.253001
iter 30 value 94.253001
iter 30 value 94.253000
final value 94.253000
converged
Fitting Repeat 2
# weights: 305
initial value 96.216793
iter 10 value 86.531117
iter 20 value 85.041651
iter 30 value 84.700375
iter 40 value 84.154917
iter 50 value 84.005147
iter 60 value 84.003578
iter 70 value 84.002601
iter 70 value 84.002600
final value 84.002600
converged
Fitting Repeat 3
# weights: 305
initial value 114.501127
iter 10 value 94.448624
iter 20 value 94.367124
iter 30 value 87.706591
iter 40 value 85.889601
iter 50 value 83.927572
iter 60 value 83.161069
iter 70 value 82.846669
iter 80 value 82.436922
iter 90 value 82.159938
iter 100 value 82.156197
final value 82.156197
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 95.871776
iter 10 value 94.150402
iter 20 value 92.586506
iter 30 value 92.577494
iter 40 value 92.574713
iter 50 value 92.572977
final value 92.572782
converged
Fitting Repeat 5
# weights: 305
initial value 95.667039
iter 10 value 94.484869
iter 20 value 94.197517
iter 30 value 86.723955
iter 40 value 84.985392
iter 50 value 84.909875
iter 60 value 84.884570
iter 70 value 84.884423
final value 84.883942
converged
Fitting Repeat 1
# weights: 507
initial value 119.760315
iter 10 value 89.212643
iter 20 value 86.646969
iter 30 value 86.486561
iter 40 value 85.973519
iter 50 value 85.444591
iter 60 value 85.425896
iter 70 value 84.041539
final value 83.931078
converged
Fitting Repeat 2
# weights: 507
initial value 94.601163
iter 10 value 94.485048
iter 20 value 93.477472
iter 30 value 85.602689
iter 40 value 85.075276
iter 50 value 82.735241
iter 60 value 80.097167
iter 70 value 79.832590
iter 80 value 79.578703
iter 90 value 79.575642
iter 100 value 79.571987
final value 79.571987
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 96.875657
iter 10 value 94.480143
iter 20 value 94.474736
iter 30 value 94.472825
iter 40 value 94.470061
iter 50 value 93.269274
iter 60 value 92.822319
iter 70 value 92.550628
iter 80 value 92.547706
iter 90 value 92.547243
iter 100 value 92.546978
final value 92.546978
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 130.795225
iter 10 value 94.475385
iter 20 value 91.904846
iter 30 value 86.496771
iter 40 value 86.013205
iter 50 value 85.445426
iter 60 value 85.395830
iter 70 value 85.395493
iter 80 value 85.317190
iter 90 value 82.180305
iter 100 value 82.155623
final value 82.155623
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 97.922692
iter 10 value 94.467114
iter 20 value 94.436702
iter 30 value 93.630346
iter 40 value 92.147416
iter 50 value 91.946903
iter 60 value 91.908425
iter 70 value 91.423157
iter 80 value 91.422095
final value 91.422085
converged
Fitting Repeat 1
# weights: 103
initial value 105.548317
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 103.982641
final value 94.026542
converged
Fitting Repeat 3
# weights: 103
initial value 98.842139
final value 94.484211
converged
Fitting Repeat 4
# weights: 103
initial value 102.203195
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 94.820361
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 106.469741
iter 10 value 94.031618
iter 20 value 93.953752
final value 93.945326
converged
Fitting Repeat 2
# weights: 305
initial value 106.890894
final value 94.484211
converged
Fitting Repeat 3
# weights: 305
initial value 99.648738
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 103.410535
final value 93.809648
converged
Fitting Repeat 5
# weights: 305
initial value 110.357773
final value 94.484211
converged
Fitting Repeat 1
# weights: 507
initial value 96.694885
final value 94.484211
converged
Fitting Repeat 2
# weights: 507
initial value 98.676222
final value 94.484211
converged
Fitting Repeat 3
# weights: 507
initial value 102.358501
iter 10 value 94.484216
iter 10 value 94.484215
iter 10 value 94.484215
final value 94.484215
converged
Fitting Repeat 4
# weights: 507
initial value 101.437786
final value 94.484211
converged
Fitting Repeat 5
# weights: 507
initial value 98.177651
final value 94.026542
converged
Fitting Repeat 1
# weights: 103
initial value 101.864245
iter 10 value 93.417219
iter 20 value 87.390882
iter 30 value 87.083024
iter 40 value 86.786134
iter 50 value 86.091845
iter 60 value 85.951523
iter 70 value 85.937791
iter 80 value 85.932533
iter 90 value 85.875435
iter 100 value 85.805926
final value 85.805926
stopped after 100 iterations
Fitting Repeat 2
# weights: 103
initial value 101.682487
iter 10 value 94.565209
iter 20 value 92.871696
iter 30 value 89.388840
iter 40 value 87.326913
iter 50 value 87.101779
iter 60 value 87.028216
iter 70 value 87.020765
iter 80 value 86.912760
iter 90 value 86.900185
iter 90 value 86.900184
iter 90 value 86.900184
final value 86.900184
converged
Fitting Repeat 3
# weights: 103
initial value 99.539815
iter 10 value 94.114510
iter 20 value 92.291795
iter 30 value 85.593357
iter 40 value 85.118083
iter 50 value 84.886292
iter 60 value 84.626390
iter 70 value 84.232011
iter 80 value 83.899319
iter 90 value 83.895793
iter 100 value 83.883526
final value 83.883526
stopped after 100 iterations
Fitting Repeat 4
# weights: 103
initial value 111.107329
iter 10 value 94.455960
iter 20 value 93.631255
iter 30 value 91.120917
iter 40 value 90.481855
iter 50 value 88.365733
iter 60 value 88.341366
final value 88.341315
converged
Fitting Repeat 5
# weights: 103
initial value 97.345885
iter 10 value 94.488240
iter 20 value 94.224528
iter 30 value 93.900303
iter 40 value 93.896268
iter 50 value 93.829897
iter 60 value 89.575330
iter 70 value 89.352671
iter 80 value 88.339064
iter 90 value 87.539797
iter 100 value 87.340127
final value 87.340127
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 112.697427
iter 10 value 94.309442
iter 20 value 91.080677
iter 30 value 89.752443
iter 40 value 87.624187
iter 50 value 86.557551
iter 60 value 86.205113
iter 70 value 85.830669
iter 80 value 84.996741
iter 90 value 83.453208
iter 100 value 82.995299
final value 82.995299
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 123.601981
iter 10 value 94.038622
iter 20 value 88.251510
iter 30 value 87.108548
iter 40 value 86.946219
iter 50 value 85.464364
iter 60 value 84.729560
iter 70 value 83.845178
iter 80 value 83.216087
iter 90 value 82.652727
iter 100 value 82.554066
final value 82.554066
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 102.839908
iter 10 value 94.476340
iter 20 value 93.913513
iter 30 value 90.752344
iter 40 value 88.065488
iter 50 value 84.951626
iter 60 value 84.118206
iter 70 value 83.665560
iter 80 value 83.334028
iter 90 value 83.230720
iter 100 value 83.111477
final value 83.111477
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 99.306599
iter 10 value 92.657951
iter 20 value 87.313239
iter 30 value 84.922167
iter 40 value 83.473335
iter 50 value 83.207559
iter 60 value 83.103497
iter 70 value 82.951139
iter 80 value 82.802939
iter 90 value 82.709618
iter 100 value 82.704639
final value 82.704639
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 101.662270
iter 10 value 94.492324
iter 20 value 90.933371
iter 30 value 87.373781
iter 40 value 84.513719
iter 50 value 83.389959
iter 60 value 83.134093
iter 70 value 83.032961
iter 80 value 82.824720
iter 90 value 82.748829
iter 100 value 82.721730
final value 82.721730
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 110.016767
iter 10 value 95.154816
iter 20 value 93.495247
iter 30 value 93.126268
iter 40 value 92.897263
iter 50 value 88.585387
iter 60 value 86.022342
iter 70 value 85.498711
iter 80 value 85.180759
iter 90 value 84.375145
iter 100 value 84.227113
final value 84.227113
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 109.752350
iter 10 value 94.289901
iter 20 value 94.237601
iter 30 value 93.526479
iter 40 value 91.663438
iter 50 value 91.150396
iter 60 value 86.616660
iter 70 value 85.512430
iter 80 value 85.427709
iter 90 value 85.196851
iter 100 value 84.698219
final value 84.698219
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 112.831136
iter 10 value 95.124507
iter 20 value 92.551658
iter 30 value 89.717809
iter 40 value 87.476907
iter 50 value 85.912050
iter 60 value 84.547208
iter 70 value 83.740829
iter 80 value 83.495945
iter 90 value 83.144037
iter 100 value 83.031897
final value 83.031897
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 107.023409
iter 10 value 94.426751
iter 20 value 93.329341
iter 30 value 90.843039
iter 40 value 89.971867
iter 50 value 88.671513
iter 60 value 87.178429
iter 70 value 85.207645
iter 80 value 84.598401
iter 90 value 83.965731
iter 100 value 83.692211
final value 83.692211
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 121.662864
iter 10 value 94.188028
iter 20 value 93.157166
iter 30 value 88.031594
iter 40 value 85.802365
iter 50 value 85.330453
iter 60 value 83.893047
iter 70 value 83.276041
iter 80 value 83.129827
iter 90 value 83.088030
iter 100 value 83.001682
final value 83.001682
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 95.555859
final value 94.485794
converged
Fitting Repeat 2
# weights: 103
initial value 96.305503
final value 94.485595
converged
Fitting Repeat 3
# weights: 103
initial value 98.321396
final value 94.485903
converged
Fitting Repeat 4
# weights: 103
initial value 100.213209
final value 94.485966
converged
Fitting Repeat 5
# weights: 103
initial value 96.721829
final value 94.485904
converged
Fitting Repeat 1
# weights: 305
initial value 105.021566
iter 10 value 94.489479
iter 20 value 93.944665
iter 30 value 93.808019
final value 93.807962
converged
Fitting Repeat 2
# weights: 305
initial value 107.836135
iter 10 value 94.031812
iter 20 value 94.027207
final value 94.026759
converged
Fitting Repeat 3
# weights: 305
initial value 94.809578
iter 10 value 87.819084
iter 20 value 86.502554
iter 30 value 86.272679
iter 40 value 86.270635
iter 50 value 86.269376
iter 60 value 86.268851
final value 86.268829
converged
Fitting Repeat 4
# weights: 305
initial value 104.627275
iter 10 value 94.031421
iter 20 value 92.955426
iter 30 value 91.077598
iter 40 value 91.005909
iter 50 value 90.976844
final value 90.976552
converged
Fitting Repeat 5
# weights: 305
initial value 98.337412
iter 10 value 94.485142
iter 20 value 94.484223
final value 94.484221
converged
Fitting Repeat 1
# weights: 507
initial value 94.698578
iter 10 value 94.485175
iter 20 value 89.448150
iter 30 value 88.993199
iter 40 value 88.989947
final value 88.989944
converged
Fitting Repeat 2
# weights: 507
initial value 105.511036
iter 10 value 93.824228
iter 20 value 91.347331
iter 30 value 91.154438
iter 40 value 90.989282
final value 90.989024
converged
Fitting Repeat 3
# weights: 507
initial value 111.591956
iter 10 value 94.035309
iter 20 value 93.929693
iter 30 value 93.793964
final value 93.793911
converged
Fitting Repeat 4
# weights: 507
initial value 97.435019
iter 10 value 94.491930
iter 20 value 94.484307
iter 30 value 93.863865
final value 93.569767
converged
Fitting Repeat 5
# weights: 507
initial value 97.678891
iter 10 value 94.468957
iter 20 value 94.466311
iter 30 value 92.610147
iter 40 value 89.769052
iter 50 value 89.648590
iter 60 value 89.576479
iter 70 value 89.347843
final value 89.347828
converged
Fitting Repeat 1
# weights: 103
initial value 101.131443
iter 10 value 94.008696
iter 10 value 94.008696
iter 10 value 94.008696
final value 94.008696
converged
Fitting Repeat 2
# weights: 103
initial value 110.553498
final value 94.052910
converged
Fitting Repeat 3
# weights: 103
initial value 94.937509
final value 94.052910
converged
Fitting Repeat 4
# weights: 103
initial value 97.056792
final value 94.052910
converged
Fitting Repeat 5
# weights: 103
initial value 103.142865
final value 94.008696
converged
Fitting Repeat 1
# weights: 305
initial value 102.932737
final value 94.008696
converged
Fitting Repeat 2
# weights: 305
initial value 107.864366
final value 94.011561
converged
Fitting Repeat 3
# weights: 305
initial value 104.614668
final value 94.008696
converged
Fitting Repeat 4
# weights: 305
initial value 95.110663
iter 10 value 90.478438
iter 20 value 84.895105
iter 30 value 84.172545
iter 40 value 83.393418
iter 50 value 82.404720
iter 60 value 82.366007
iter 70 value 82.210414
iter 80 value 81.653475
final value 81.653444
converged
Fitting Repeat 5
# weights: 305
initial value 102.969619
final value 94.052910
converged
Fitting Repeat 1
# weights: 507
initial value 95.500126
iter 10 value 93.596277
final value 93.593182
converged
Fitting Repeat 2
# weights: 507
initial value 106.629856
final value 94.052929
converged
Fitting Repeat 3
# weights: 507
initial value 100.220146
final value 94.052910
converged
Fitting Repeat 4
# weights: 507
initial value 118.240162
iter 10 value 93.389846
iter 20 value 91.534105
iter 30 value 91.398879
iter 40 value 91.397294
final value 91.397286
converged
Fitting Repeat 5
# weights: 507
initial value 100.289694
final value 94.052910
converged
Fitting Repeat 1
# weights: 103
initial value 100.073981
iter 10 value 91.723532
iter 20 value 91.402919
iter 30 value 91.061382
iter 40 value 90.875385
iter 50 value 90.787094
final value 90.786875
converged
Fitting Repeat 2
# weights: 103
initial value 100.334721
iter 10 value 94.030111
iter 20 value 89.926137
iter 30 value 83.710300
iter 40 value 82.940130
iter 50 value 81.776956
iter 60 value 81.605014
iter 70 value 81.178012
iter 80 value 80.818134
iter 90 value 80.730723
final value 80.730719
converged
Fitting Repeat 3
# weights: 103
initial value 103.773790
iter 10 value 92.730552
iter 20 value 83.715124
iter 30 value 83.359649
iter 40 value 82.729269
iter 50 value 82.412704
iter 60 value 82.402441
iter 70 value 82.382575
final value 82.378513
converged
Fitting Repeat 4
# weights: 103
initial value 96.215817
iter 10 value 94.005711
iter 20 value 85.838493
iter 30 value 84.776323
iter 40 value 84.716163
iter 50 value 83.836755
iter 60 value 82.877575
iter 70 value 82.428746
iter 80 value 82.378624
final value 82.378513
converged
Fitting Repeat 5
# weights: 103
initial value 98.274954
iter 10 value 94.015416
iter 20 value 89.776191
iter 30 value 87.290386
iter 40 value 86.567538
iter 50 value 86.532344
iter 60 value 83.277478
iter 70 value 82.467374
iter 80 value 82.411319
iter 90 value 82.400206
iter 100 value 82.383615
final value 82.383615
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 105.551580
iter 10 value 94.259532
iter 20 value 92.466545
iter 30 value 83.577364
iter 40 value 81.194951
iter 50 value 79.998457
iter 60 value 79.572451
iter 70 value 79.462380
iter 80 value 79.379623
iter 90 value 79.237489
iter 100 value 79.124539
final value 79.124539
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 103.630437
iter 10 value 93.793085
iter 20 value 85.010358
iter 30 value 83.671135
iter 40 value 82.745692
iter 50 value 81.383054
iter 60 value 80.974079
iter 70 value 79.948052
iter 80 value 79.366854
iter 90 value 78.822474
iter 100 value 78.782783
final value 78.782783
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 103.464873
iter 10 value 94.026171
iter 20 value 84.439300
iter 30 value 83.057090
iter 40 value 82.326526
iter 50 value 82.230254
iter 60 value 82.143172
iter 70 value 82.117468
iter 80 value 82.098165
iter 90 value 82.074367
iter 100 value 82.061739
final value 82.061739
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 100.879164
iter 10 value 89.614442
iter 20 value 84.649812
iter 30 value 83.944614
iter 40 value 82.737713
iter 50 value 82.009538
iter 60 value 80.109888
iter 70 value 79.523518
iter 80 value 79.374770
iter 90 value 79.332794
iter 100 value 79.308529
final value 79.308529
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 106.982140
iter 10 value 92.948848
iter 20 value 88.028958
iter 30 value 84.273358
iter 40 value 83.542079
iter 50 value 82.840216
iter 60 value 82.324914
iter 70 value 82.038281
iter 80 value 81.847137
iter 90 value 81.803141
iter 100 value 81.515002
final value 81.515002
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 103.946067
iter 10 value 94.829278
iter 20 value 93.508982
iter 30 value 90.623064
iter 40 value 90.144151
iter 50 value 87.089226
iter 60 value 81.183239
iter 70 value 79.669568
iter 80 value 79.377438
iter 90 value 78.852648
iter 100 value 78.769163
final value 78.769163
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 106.677625
iter 10 value 94.479799
iter 20 value 93.287868
iter 30 value 87.867836
iter 40 value 83.410777
iter 50 value 82.583066
iter 60 value 81.455748
iter 70 value 80.408248
iter 80 value 79.975211
iter 90 value 79.398521
iter 100 value 79.243715
final value 79.243715
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 136.559664
iter 10 value 94.122746
iter 20 value 90.294914
iter 30 value 84.750312
iter 40 value 83.357055
iter 50 value 82.100244
iter 60 value 80.857567
iter 70 value 80.068629
iter 80 value 79.751963
iter 90 value 79.567857
iter 100 value 79.496226
final value 79.496226
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 109.698789
iter 10 value 93.803108
iter 20 value 90.198086
iter 30 value 84.127426
iter 40 value 81.721380
iter 50 value 81.350288
iter 60 value 80.654641
iter 70 value 80.180816
iter 80 value 79.746590
iter 90 value 79.649533
iter 100 value 79.390509
final value 79.390509
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 110.271578
iter 10 value 93.959236
iter 20 value 89.408713
iter 30 value 83.761590
iter 40 value 82.583136
iter 50 value 81.701177
iter 60 value 81.473754
iter 70 value 81.424902
iter 80 value 81.401562
iter 90 value 81.377766
iter 100 value 81.362264
final value 81.362264
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 98.363710
final value 94.054639
converged
Fitting Repeat 2
# weights: 103
initial value 106.256643
final value 94.010356
converged
Fitting Repeat 3
# weights: 103
initial value 103.317185
final value 94.054583
converged
Fitting Repeat 4
# weights: 103
initial value 97.106107
final value 94.054252
converged
Fitting Repeat 5
# weights: 103
initial value 95.798577
final value 94.054467
converged
Fitting Repeat 1
# weights: 305
initial value 100.108512
iter 10 value 94.056719
iter 20 value 84.629810
iter 30 value 84.150526
iter 40 value 84.149124
iter 50 value 84.143745
final value 84.143699
converged
Fitting Repeat 2
# weights: 305
initial value 124.039307
iter 10 value 93.633880
iter 20 value 93.630557
iter 30 value 93.629342
iter 40 value 91.434617
iter 50 value 85.349043
iter 60 value 82.426137
final value 82.422555
converged
Fitting Repeat 3
# weights: 305
initial value 108.167537
iter 10 value 94.057836
iter 20 value 93.639349
iter 30 value 93.636508
iter 40 value 93.444656
final value 93.444581
converged
Fitting Repeat 4
# weights: 305
initial value 105.801275
iter 10 value 94.013832
iter 20 value 89.065514
iter 30 value 85.261000
iter 40 value 83.847590
iter 50 value 83.434845
iter 60 value 83.433106
final value 83.432872
converged
Fitting Repeat 5
# weights: 305
initial value 96.378021
iter 10 value 93.967221
iter 20 value 93.963080
iter 30 value 91.850602
final value 91.208987
converged
Fitting Repeat 1
# weights: 507
initial value 114.104680
iter 10 value 94.060049
iter 20 value 93.015285
iter 30 value 84.129116
iter 40 value 84.079319
iter 50 value 84.078411
iter 60 value 83.133929
iter 70 value 81.558283
iter 80 value 81.555811
iter 90 value 81.553660
final value 81.552133
converged
Fitting Repeat 2
# weights: 507
initial value 105.471778
iter 10 value 94.019983
iter 20 value 93.926573
iter 30 value 93.830158
iter 40 value 93.827206
iter 50 value 93.333504
iter 60 value 84.845578
iter 70 value 83.485562
iter 80 value 82.913076
iter 90 value 81.865410
final value 81.865401
converged
Fitting Repeat 3
# weights: 507
initial value 101.035486
iter 10 value 94.059214
iter 20 value 94.017453
iter 30 value 84.790882
iter 40 value 83.089613
iter 50 value 83.017589
iter 60 value 82.783621
iter 70 value 82.046079
iter 80 value 82.035254
iter 90 value 81.997240
final value 81.996918
converged
Fitting Repeat 4
# weights: 507
initial value 97.565511
iter 10 value 94.020766
iter 20 value 93.997860
iter 30 value 87.701020
iter 40 value 85.930838
iter 50 value 84.127580
iter 60 value 84.126225
iter 70 value 84.124272
iter 80 value 83.817268
iter 90 value 83.416741
iter 100 value 83.415790
final value 83.415790
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 94.929705
iter 10 value 92.770704
iter 20 value 91.679255
iter 30 value 91.447134
iter 40 value 91.245946
iter 50 value 91.169614
final value 91.169567
converged
Fitting Repeat 1
# weights: 103
initial value 107.426794
final value 94.052910
converged
Fitting Repeat 2
# weights: 103
initial value 94.428833
final value 94.052910
converged
Fitting Repeat 3
# weights: 103
initial value 105.570719
final value 94.052910
converged
Fitting Repeat 4
# weights: 103
initial value 97.839591
final value 94.052910
converged
Fitting Repeat 5
# weights: 103
initial value 105.988871
final value 94.052910
converged
Fitting Repeat 1
# weights: 305
initial value 101.987030
final value 94.052910
converged
Fitting Repeat 2
# weights: 305
initial value 98.991708
iter 10 value 93.295189
final value 93.295187
converged
Fitting Repeat 3
# weights: 305
initial value 104.700960
iter 10 value 94.052710
iter 20 value 93.518975
final value 93.288889
converged
Fitting Repeat 4
# weights: 305
initial value 98.027006
final value 93.915746
converged
Fitting Repeat 5
# weights: 305
initial value 97.179217
final value 94.052910
converged
Fitting Repeat 1
# weights: 507
initial value 105.078792
iter 10 value 94.219318
iter 20 value 88.488430
iter 30 value 79.781767
iter 40 value 79.714911
iter 50 value 79.713297
final value 79.713285
converged
Fitting Repeat 2
# weights: 507
initial value 125.378303
final value 94.052910
converged
Fitting Repeat 3
# weights: 507
initial value 99.918185
iter 10 value 89.468348
iter 20 value 87.571367
final value 87.571361
converged
Fitting Repeat 4
# weights: 507
initial value 105.499157
iter 10 value 93.347922
final value 93.226190
converged
Fitting Repeat 5
# weights: 507
initial value 96.435551
iter 10 value 93.451987
iter 20 value 89.263456
iter 30 value 88.287445
iter 40 value 88.247081
final value 88.246928
converged
Fitting Repeat 1
# weights: 103
initial value 111.451623
iter 10 value 94.056386
iter 20 value 89.658493
iter 30 value 84.602292
iter 40 value 84.046600
iter 50 value 83.620054
iter 60 value 83.369235
final value 83.363023
converged
Fitting Repeat 2
# weights: 103
initial value 97.171673
iter 10 value 93.978662
iter 20 value 83.911190
iter 30 value 82.688713
iter 40 value 81.244047
iter 50 value 80.524918
iter 60 value 80.373185
iter 70 value 79.252508
iter 80 value 78.069507
iter 90 value 77.875164
iter 100 value 77.870274
final value 77.870274
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 103.644627
iter 10 value 94.056825
iter 20 value 93.257299
iter 30 value 90.003165
iter 40 value 86.938273
iter 50 value 85.282825
iter 60 value 85.005151
iter 70 value 81.408346
iter 80 value 81.293027
iter 80 value 81.293026
final value 81.293026
converged
Fitting Repeat 4
# weights: 103
initial value 98.880352
iter 10 value 94.054555
iter 20 value 93.250204
iter 30 value 92.405231
iter 40 value 87.204789
iter 50 value 86.679985
iter 60 value 82.342436
iter 70 value 80.451934
iter 80 value 80.374535
iter 90 value 80.354502
iter 100 value 78.939984
final value 78.939984
stopped after 100 iterations
Fitting Repeat 5
# weights: 103
initial value 99.917613
iter 10 value 94.051950
iter 20 value 91.140024
iter 30 value 87.726003
iter 40 value 84.815961
iter 50 value 84.307321
iter 60 value 84.182544
iter 70 value 84.022426
iter 80 value 83.469193
iter 90 value 83.187982
iter 100 value 83.181717
final value 83.181717
stopped after 100 iterations
Fitting Repeat 1
# weights: 305
initial value 102.695170
iter 10 value 94.063979
iter 20 value 86.933643
iter 30 value 83.536432
iter 40 value 82.197786
iter 50 value 80.698291
iter 60 value 80.306220
iter 70 value 79.532971
iter 80 value 79.127892
iter 90 value 78.256641
iter 100 value 77.510589
final value 77.510589
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 117.670071
iter 10 value 94.060550
iter 20 value 86.574181
iter 30 value 81.161560
iter 40 value 80.823477
iter 50 value 80.451584
iter 60 value 79.252850
iter 70 value 78.395804
iter 80 value 77.144648
iter 90 value 76.875186
iter 100 value 76.783780
final value 76.783780
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 99.000008
iter 10 value 93.866501
iter 20 value 88.513401
iter 30 value 85.844630
iter 40 value 81.760095
iter 50 value 78.588688
iter 60 value 78.276414
iter 70 value 78.008988
iter 80 value 77.689037
iter 90 value 77.257845
iter 100 value 76.844386
final value 76.844386
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 104.313369
iter 10 value 90.947376
iter 20 value 86.181453
iter 30 value 81.083535
iter 40 value 79.973993
iter 50 value 79.813658
iter 60 value 79.777264
iter 70 value 79.660684
iter 80 value 79.487054
iter 90 value 78.830310
iter 100 value 78.393527
final value 78.393527
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 104.798040
iter 10 value 94.068276
iter 20 value 90.105230
iter 30 value 85.582393
iter 40 value 84.599520
iter 50 value 81.964578
iter 60 value 78.643696
iter 70 value 78.177433
iter 80 value 77.840192
iter 90 value 77.483701
iter 100 value 77.308518
final value 77.308518
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 112.303260
iter 10 value 94.044938
iter 20 value 85.967062
iter 30 value 82.768125
iter 40 value 82.373207
iter 50 value 82.149936
iter 60 value 80.460359
iter 70 value 78.756594
iter 80 value 77.543896
iter 90 value 77.113813
iter 100 value 76.757392
final value 76.757392
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 123.697172
iter 10 value 94.131223
iter 20 value 89.429236
iter 30 value 86.081575
iter 40 value 81.488864
iter 50 value 80.089700
iter 60 value 78.705845
iter 70 value 77.631668
iter 80 value 77.225870
iter 90 value 76.919104
iter 100 value 76.770117
final value 76.770117
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 129.994042
iter 10 value 95.973309
iter 20 value 91.637767
iter 30 value 82.636216
iter 40 value 82.442615
iter 50 value 79.961909
iter 60 value 78.894210
iter 70 value 78.617088
iter 80 value 78.348341
iter 90 value 78.317169
iter 100 value 78.296813
final value 78.296813
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 109.798523
iter 10 value 94.189021
iter 20 value 93.311093
iter 30 value 85.437680
iter 40 value 80.949392
iter 50 value 78.664883
iter 60 value 77.940141
iter 70 value 77.214043
iter 80 value 76.992887
iter 90 value 76.919933
iter 100 value 76.906935
final value 76.906935
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 108.741469
iter 10 value 94.496038
iter 20 value 94.363016
iter 30 value 92.927192
iter 40 value 83.006739
iter 50 value 80.368238
iter 60 value 78.715101
iter 70 value 77.686848
iter 80 value 77.027607
iter 90 value 76.877573
iter 100 value 76.672584
final value 76.672584
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 103.079673
final value 94.054330
converged
Fitting Repeat 2
# weights: 103
initial value 100.573387
iter 10 value 94.054515
iter 20 value 94.052922
final value 94.052920
converged
Fitting Repeat 3
# weights: 103
initial value 98.718456
final value 94.054569
converged
Fitting Repeat 4
# weights: 103
initial value 97.501825
iter 10 value 93.719535
iter 20 value 91.794312
iter 30 value 91.772569
iter 40 value 84.461455
final value 84.418268
converged
Fitting Repeat 5
# weights: 103
initial value 99.052075
final value 94.054480
converged
Fitting Repeat 1
# weights: 305
initial value 96.027966
iter 10 value 83.839099
iter 20 value 81.260331
iter 30 value 80.977861
iter 40 value 80.875778
iter 50 value 80.873144
final value 80.871865
converged
Fitting Repeat 2
# weights: 305
initial value 95.055798
iter 10 value 94.057470
iter 20 value 92.926883
iter 30 value 86.491685
iter 40 value 86.472926
iter 50 value 86.472780
iter 50 value 86.472779
iter 50 value 86.472779
final value 86.472779
converged
Fitting Repeat 3
# weights: 305
initial value 105.006579
iter 10 value 93.920337
iter 20 value 88.584153
iter 30 value 79.342722
iter 40 value 79.295480
final value 79.295389
converged
Fitting Repeat 4
# weights: 305
initial value 102.329646
iter 10 value 93.929618
iter 20 value 93.924681
iter 30 value 93.924018
iter 40 value 93.916050
iter 50 value 88.098717
iter 60 value 81.573528
final value 81.573220
converged
Fitting Repeat 5
# weights: 305
initial value 109.411915
iter 10 value 94.058382
iter 20 value 93.801316
iter 30 value 93.226971
final value 93.226724
converged
Fitting Repeat 1
# weights: 507
initial value 99.313847
iter 10 value 92.553251
iter 20 value 92.541816
iter 30 value 91.538332
iter 40 value 83.002835
iter 50 value 81.646610
iter 60 value 81.552276
iter 70 value 81.552157
iter 80 value 81.551992
iter 90 value 79.818829
iter 100 value 79.511515
final value 79.511515
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 100.719166
iter 10 value 94.060936
iter 20 value 94.039052
iter 30 value 93.198950
iter 40 value 93.195199
iter 50 value 93.193274
iter 60 value 81.435217
iter 70 value 79.700846
iter 80 value 78.211607
iter 90 value 78.187882
iter 100 value 78.182696
final value 78.182696
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 99.572481
iter 10 value 92.947211
iter 20 value 85.653410
iter 30 value 84.827808
iter 40 value 84.080474
iter 50 value 82.419265
iter 60 value 82.416293
final value 82.416261
converged
Fitting Repeat 4
# weights: 507
initial value 96.973377
iter 10 value 93.924235
iter 20 value 93.533392
iter 30 value 91.244267
iter 40 value 83.401857
iter 50 value 78.562267
iter 60 value 78.494698
iter 70 value 78.383105
iter 80 value 78.341813
iter 90 value 78.336665
iter 100 value 78.331903
final value 78.331903
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 95.540568
iter 10 value 92.152529
iter 20 value 92.144658
iter 30 value 92.141571
iter 40 value 92.140677
iter 50 value 92.138945
iter 60 value 88.234231
iter 70 value 81.526456
iter 80 value 79.672078
iter 90 value 79.387574
iter 100 value 79.208031
final value 79.208031
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 101.804199
final value 94.484211
converged
Fitting Repeat 2
# weights: 103
initial value 95.601039
final value 94.484211
converged
Fitting Repeat 3
# weights: 103
initial value 95.179395
final value 94.484211
converged
Fitting Repeat 4
# weights: 103
initial value 96.537983
final value 94.484211
converged
Fitting Repeat 5
# weights: 103
initial value 98.499531
final value 94.484211
converged
Fitting Repeat 1
# weights: 305
initial value 95.216919
iter 10 value 84.560835
iter 20 value 83.459377
final value 83.457662
converged
Fitting Repeat 2
# weights: 305
initial value 108.502713
final value 94.484211
converged
Fitting Repeat 3
# weights: 305
initial value 103.343476
final value 94.484211
converged
Fitting Repeat 4
# weights: 305
initial value 104.192804
iter 10 value 94.219855
final value 94.212644
converged
Fitting Repeat 5
# weights: 305
initial value 97.924160
final value 94.484211
converged
Fitting Repeat 1
# weights: 507
initial value 97.094690
iter 10 value 93.912035
final value 93.911761
converged
Fitting Repeat 2
# weights: 507
initial value 114.522454
iter 10 value 93.214284
iter 20 value 93.211438
final value 93.211429
converged
Fitting Repeat 3
# weights: 507
initial value 103.491595
iter 10 value 94.252920
iter 10 value 94.252920
iter 10 value 94.252920
final value 94.252920
converged
Fitting Repeat 4
# weights: 507
initial value 97.655572
final value 94.484211
converged
Fitting Repeat 5
# weights: 507
initial value 99.303011
final value 94.484211
converged
Fitting Repeat 1
# weights: 103
initial value 98.078569
iter 10 value 94.218834
iter 20 value 91.745648
iter 30 value 87.544920
iter 40 value 86.527055
iter 50 value 84.386637
iter 60 value 84.201564
iter 70 value 84.139002
iter 80 value 84.109280
final value 84.109274
converged
Fitting Repeat 2
# weights: 103
initial value 105.046665
iter 10 value 94.471562
iter 20 value 94.186809
iter 30 value 94.031776
iter 40 value 92.337823
iter 50 value 90.565631
iter 60 value 85.646378
iter 70 value 84.772113
iter 80 value 84.300876
iter 90 value 83.870606
iter 100 value 83.836925
final value 83.836925
stopped after 100 iterations
Fitting Repeat 3
# weights: 103
initial value 101.904596
iter 10 value 94.488532
iter 20 value 86.590584
iter 30 value 85.878883
iter 40 value 85.457999
iter 50 value 84.837226
iter 60 value 83.955420
iter 70 value 83.873473
iter 80 value 83.810520
final value 83.807655
converged
Fitting Repeat 4
# weights: 103
initial value 97.244091
iter 10 value 87.159061
iter 20 value 84.171517
iter 30 value 83.919832
iter 40 value 83.837081
final value 83.836920
converged
Fitting Repeat 5
# weights: 103
initial value 105.868515
iter 10 value 94.483035
iter 20 value 88.367838
iter 30 value 87.951596
iter 40 value 87.670993
iter 50 value 85.162388
iter 60 value 83.885438
iter 70 value 83.808248
final value 83.807655
converged
Fitting Repeat 1
# weights: 305
initial value 117.133702
iter 10 value 94.668235
iter 20 value 89.510070
iter 30 value 86.344101
iter 40 value 85.738422
iter 50 value 84.577905
iter 60 value 83.965630
iter 70 value 83.918116
iter 80 value 83.835237
iter 90 value 83.005091
iter 100 value 82.299520
final value 82.299520
stopped after 100 iterations
Fitting Repeat 2
# weights: 305
initial value 112.608110
iter 10 value 94.681506
iter 20 value 94.344108
iter 30 value 93.128198
iter 40 value 92.822483
iter 50 value 90.652926
iter 60 value 83.569057
iter 70 value 82.256754
iter 80 value 81.759086
iter 90 value 81.343948
iter 100 value 80.441572
final value 80.441572
stopped after 100 iterations
Fitting Repeat 3
# weights: 305
initial value 109.054887
iter 10 value 91.055046
iter 20 value 85.947573
iter 30 value 85.667155
iter 40 value 83.938916
iter 50 value 83.595377
iter 60 value 82.340567
iter 70 value 81.739926
iter 80 value 81.610262
iter 90 value 81.427999
iter 100 value 81.363793
final value 81.363793
stopped after 100 iterations
Fitting Repeat 4
# weights: 305
initial value 109.737563
iter 10 value 94.430076
iter 20 value 91.977597
iter 30 value 87.694060
iter 40 value 84.067700
iter 50 value 80.861454
iter 60 value 80.511818
iter 70 value 80.298099
iter 80 value 80.039266
iter 90 value 79.976936
iter 100 value 79.937830
final value 79.937830
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 113.808289
iter 10 value 94.423510
iter 20 value 91.994602
iter 30 value 87.622471
iter 40 value 86.658683
iter 50 value 84.602913
iter 60 value 82.206610
iter 70 value 80.116487
iter 80 value 79.783977
iter 90 value 79.617153
iter 100 value 79.415175
final value 79.415175
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 132.653295
iter 10 value 95.153991
iter 20 value 87.620708
iter 30 value 86.494831
iter 40 value 84.824881
iter 50 value 83.698231
iter 60 value 83.370717
iter 70 value 83.328891
iter 80 value 83.271286
iter 90 value 83.136413
iter 100 value 82.278222
final value 82.278222
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 106.353074
iter 10 value 94.022986
iter 20 value 88.601106
iter 30 value 85.500368
iter 40 value 83.789571
iter 50 value 81.229075
iter 60 value 80.452399
iter 70 value 80.185999
iter 80 value 80.021371
iter 90 value 79.871699
iter 100 value 79.669295
final value 79.669295
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 115.247001
iter 10 value 94.765860
iter 20 value 94.291396
iter 30 value 86.722684
iter 40 value 85.716093
iter 50 value 83.358592
iter 60 value 81.726546
iter 70 value 81.594218
iter 80 value 80.467350
iter 90 value 79.546764
iter 100 value 79.354576
final value 79.354576
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 107.748124
iter 10 value 94.733215
iter 20 value 89.563501
iter 30 value 83.047709
iter 40 value 82.672825
iter 50 value 82.141710
iter 60 value 80.573994
iter 70 value 80.331582
iter 80 value 80.122895
iter 90 value 79.932240
iter 100 value 79.842656
final value 79.842656
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 105.706153
iter 10 value 94.100492
iter 20 value 85.097793
iter 30 value 84.554055
iter 40 value 83.535226
iter 50 value 81.739682
iter 60 value 80.394859
iter 70 value 79.963629
iter 80 value 79.904354
iter 90 value 79.858439
iter 100 value 79.855298
final value 79.855298
stopped after 100 iterations
Fitting Repeat 1
# weights: 103
initial value 95.166063
iter 10 value 94.486040
final value 94.484403
converged
Fitting Repeat 2
# weights: 103
initial value 99.383987
iter 10 value 94.485780
iter 20 value 94.484250
iter 30 value 94.444350
final value 94.443342
converged
Fitting Repeat 3
# weights: 103
initial value 95.378290
final value 94.485747
converged
Fitting Repeat 4
# weights: 103
initial value 104.288063
iter 10 value 94.485925
final value 94.484232
converged
Fitting Repeat 5
# weights: 103
initial value 106.886988
final value 94.486056
converged
Fitting Repeat 1
# weights: 305
initial value 105.789886
iter 10 value 94.489056
iter 20 value 94.471007
iter 30 value 93.914278
final value 93.911978
converged
Fitting Repeat 2
# weights: 305
initial value 101.856604
iter 10 value 94.444690
iter 20 value 94.170384
iter 30 value 94.168498
iter 40 value 93.914160
final value 93.913369
converged
Fitting Repeat 3
# weights: 305
initial value 95.293470
iter 10 value 94.487895
iter 20 value 86.171513
iter 30 value 85.830348
iter 40 value 85.827193
iter 50 value 85.609721
final value 85.575573
converged
Fitting Repeat 4
# weights: 305
initial value 113.860070
iter 10 value 94.489516
iter 20 value 94.484266
iter 30 value 93.949406
iter 40 value 90.365661
iter 50 value 85.906280
iter 60 value 83.478807
iter 70 value 81.159376
iter 80 value 80.551708
iter 90 value 80.370624
iter 100 value 80.318773
final value 80.318773
stopped after 100 iterations
Fitting Repeat 5
# weights: 305
initial value 100.229039
iter 10 value 94.488761
iter 20 value 94.439732
iter 30 value 92.553453
iter 40 value 88.461191
iter 50 value 85.534769
iter 60 value 85.449765
iter 70 value 84.683565
iter 80 value 81.893780
iter 90 value 81.685921
iter 100 value 81.643843
final value 81.643843
stopped after 100 iterations
Fitting Repeat 1
# weights: 507
initial value 101.724844
iter 10 value 94.451365
iter 20 value 94.323444
iter 30 value 87.239221
iter 40 value 85.191082
iter 50 value 85.029871
iter 60 value 84.886064
iter 60 value 84.886063
final value 84.886063
converged
Fitting Repeat 2
# weights: 507
initial value 105.124419
iter 10 value 94.451383
iter 20 value 94.209583
iter 30 value 86.233608
iter 40 value 85.084145
iter 50 value 84.896134
iter 50 value 84.896133
iter 50 value 84.896133
final value 84.896133
converged
Fitting Repeat 3
# weights: 507
initial value 97.857742
iter 10 value 93.511178
iter 20 value 92.959455
iter 30 value 85.196119
iter 40 value 83.749717
iter 50 value 83.728407
iter 60 value 83.646881
iter 70 value 83.644918
iter 80 value 83.635680
iter 90 value 82.975089
iter 100 value 82.881580
final value 82.881580
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 104.826606
iter 10 value 94.260979
iter 20 value 93.516536
iter 30 value 92.907731
final value 92.614659
converged
Fitting Repeat 5
# weights: 507
initial value 97.795726
iter 10 value 93.944326
iter 20 value 93.906221
iter 30 value 93.861310
iter 30 value 93.861309
final value 93.861309
converged
Fitting Repeat 1
# weights: 507
initial value 134.650493
iter 10 value 117.736979
iter 20 value 108.684001
iter 30 value 107.633595
iter 40 value 106.214003
iter 50 value 105.053606
iter 60 value 104.106467
iter 70 value 101.816403
iter 80 value 101.353751
iter 90 value 101.083828
iter 100 value 100.681713
final value 100.681713
stopped after 100 iterations
Fitting Repeat 2
# weights: 507
initial value 131.038735
iter 10 value 118.933997
iter 20 value 109.060955
iter 30 value 107.656644
iter 40 value 106.220782
iter 50 value 104.256830
iter 60 value 103.046525
iter 70 value 102.652723
iter 80 value 102.500252
iter 90 value 102.226663
iter 100 value 102.069702
final value 102.069702
stopped after 100 iterations
Fitting Repeat 3
# weights: 507
initial value 149.601983
iter 10 value 120.412845
iter 20 value 108.551167
iter 30 value 105.990823
iter 40 value 105.745963
iter 50 value 105.619040
iter 60 value 105.264919
iter 70 value 104.188766
iter 80 value 103.933368
iter 90 value 103.787723
iter 100 value 103.581662
final value 103.581662
stopped after 100 iterations
Fitting Repeat 4
# weights: 507
initial value 130.022378
iter 10 value 114.768911
iter 20 value 108.881771
iter 30 value 108.516588
iter 40 value 107.824389
iter 50 value 105.144421
iter 60 value 103.988597
iter 70 value 103.447674
iter 80 value 103.349519
iter 90 value 103.009255
iter 100 value 102.718281
final value 102.718281
stopped after 100 iterations
Fitting Repeat 5
# weights: 507
initial value 127.876271
iter 10 value 118.210453
iter 20 value 117.821850
iter 30 value 115.074965
iter 40 value 107.186670
iter 50 value 105.864376
iter 60 value 103.582126
iter 70 value 103.006375
iter 80 value 101.889327
iter 90 value 101.454398
iter 100 value 101.348446
final value 101.348446
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 -- Sat Nov 2 03:29:32 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
45.10 1.95 49.18
HPiP.Rcheck/HPiP-Ex.timings
| name | user | system | elapsed | |
| FSmethod | 34.98 | 1.89 | 37.28 | |
| FreqInteractors | 0.24 | 0.03 | 0.28 | |
| calculateAAC | 0.05 | 0.01 | 0.07 | |
| calculateAutocor | 0.80 | 0.04 | 0.82 | |
| calculateCTDC | 0.07 | 0.00 | 0.08 | |
| calculateCTDD | 0.91 | 0.06 | 0.97 | |
| calculateCTDT | 0.41 | 0.00 | 0.41 | |
| calculateCTriad | 0.45 | 0.05 | 0.50 | |
| calculateDC | 0.11 | 0.03 | 0.14 | |
| calculateF | 0.39 | 0.03 | 0.42 | |
| calculateKSAAP | 0.11 | 0.03 | 0.15 | |
| calculateQD_Sm | 2.30 | 0.16 | 2.45 | |
| calculateTC | 2.32 | 0.09 | 2.44 | |
| calculateTC_Sm | 0.43 | 0.00 | 0.42 | |
| corr_plot | 33.37 | 1.99 | 35.36 | |
| enrichfindP | 0.61 | 0.12 | 13.73 | |
| enrichfind_hp | 0.11 | 0.02 | 1.02 | |
| enrichplot | 0.5 | 0.0 | 0.5 | |
| filter_missing_values | 0 | 0 | 0 | |
| getFASTA | 0.03 | 0.02 | 2.31 | |
| getHPI | 0 | 0 | 0 | |
| get_negativePPI | 0 | 0 | 0 | |
| get_positivePPI | 0 | 0 | 0 | |
| impute_missing_data | 0 | 0 | 0 | |
| plotPPI | 0.10 | 0.01 | 0.11 | |
| pred_ensembel | 15.60 | 0.67 | 11.86 | |
| var_imp | 35.05 | 1.33 | 36.39 | |