##############################################################################
##############################################################################
###
### 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_1.13.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.21-bioc/meat/HPiP.Rcheck’
* using R Under development (unstable) (2024-11-20 r87352)
* 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 13.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.13.0’
* 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 ... INFO
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
var_imp       18.165  0.703  19.107
FSmethod      17.697  0.780  18.777
corr_plot     17.461  0.715  18.404
pred_ensembel  5.676  0.120   5.163
enrichfindP    0.167  0.028   7.625
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 2 NOTEs
See
  ‘/Users/biocbuild/bbs-3.21-bioc/meat/HPiP.Rcheck/00check.log’
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL HPiP
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-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)

R Under development (unstable) (2024-11-20 r87352) -- "Unsuffered Consequences"
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 104.313035 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.949666 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.646085 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 106.237857 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.377321 
iter  10 value 94.320360
final  value 94.320300 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.516669 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 112.842695 
iter  10 value 94.154675
final  value 94.145582 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.817186 
iter  10 value 93.700637
final  value 93.693222 
converged
Fitting Repeat 4 

# weights:  305
initial  value 112.309658 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 108.452261 
iter  10 value 94.215090
final  value 94.214007 
converged
Fitting Repeat 1 

# weights:  507
initial  value 106.653489 
iter  10 value 94.035716
final  value 94.035715 
converged
Fitting Repeat 2 

# weights:  507
initial  value 98.436833 
iter  10 value 94.090611
final  value 94.090583 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.029166 
final  value 94.466823 
converged
Fitting Repeat 4 

# weights:  507
initial  value 114.541863 
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  507
initial  value 94.906677 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.524192 
iter  10 value 94.431236
iter  20 value 92.341895
iter  30 value 91.796086
iter  40 value 89.669460
iter  50 value 87.889073
iter  60 value 87.774804
iter  70 value 85.266157
iter  80 value 85.129642
iter  90 value 85.019640
iter 100 value 84.995315
final  value 84.995315 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 97.328162 
iter  10 value 94.098691
iter  20 value 90.241815
iter  30 value 88.168317
iter  40 value 87.392173
iter  50 value 87.313104
iter  60 value 87.299891
iter  70 value 87.296154
iter  80 value 85.954020
iter  90 value 84.671419
iter 100 value 84.605414
final  value 84.605414 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 97.629898 
iter  10 value 94.492368
iter  20 value 93.840478
iter  30 value 86.374590
iter  40 value 86.015772
iter  50 value 85.678581
iter  60 value 85.100834
iter  70 value 84.996746
final  value 84.995311 
converged
Fitting Repeat 4 

# weights:  103
initial  value 109.999316 
iter  10 value 94.486625
iter  20 value 94.248782
iter  30 value 93.383795
iter  40 value 91.091141
iter  50 value 88.287117
iter  60 value 87.198169
iter  70 value 86.994698
iter  80 value 84.475673
iter  90 value 83.277195
iter 100 value 82.531010
final  value 82.531010 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 104.850747 
iter  10 value 94.501839
iter  20 value 92.643662
iter  30 value 87.390608
iter  40 value 86.604213
iter  50 value 86.177199
iter  60 value 85.395457
iter  70 value 84.919652
iter  80 value 84.476624
iter  90 value 84.438305
final  value 84.435354 
converged
Fitting Repeat 1 

# weights:  305
initial  value 108.144524 
iter  10 value 94.465172
iter  20 value 88.332967
iter  30 value 87.113047
iter  40 value 85.614177
iter  50 value 83.494446
iter  60 value 81.559981
iter  70 value 80.887642
iter  80 value 80.717872
iter  90 value 80.291003
iter 100 value 80.230702
final  value 80.230702 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.568591 
iter  10 value 94.326064
iter  20 value 94.148776
iter  30 value 92.348072
iter  40 value 89.698658
iter  50 value 86.341320
iter  60 value 82.652729
iter  70 value 81.909307
iter  80 value 81.711899
iter  90 value 81.519602
iter 100 value 81.405095
final  value 81.405095 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 105.302820 
iter  10 value 95.275389
iter  20 value 88.775752
iter  30 value 87.725153
iter  40 value 87.231139
iter  50 value 85.271155
iter  60 value 84.590308
iter  70 value 83.324178
iter  80 value 82.952365
iter  90 value 82.336516
iter 100 value 81.198525
final  value 81.198525 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 110.921537 
iter  10 value 93.855872
iter  20 value 88.576371
iter  30 value 84.513178
iter  40 value 81.984782
iter  50 value 81.275716
iter  60 value 80.876417
iter  70 value 80.819504
iter  80 value 80.725603
iter  90 value 80.686771
iter 100 value 80.603315
final  value 80.603315 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 118.077775 
iter  10 value 95.286557
iter  20 value 94.655931
iter  30 value 90.139564
iter  40 value 86.658628
iter  50 value 85.008728
iter  60 value 84.338814
iter  70 value 84.033735
iter  80 value 83.837730
iter  90 value 83.740720
iter 100 value 83.595540
final  value 83.595540 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 116.722912 
iter  10 value 94.683685
iter  20 value 93.559149
iter  30 value 90.207272
iter  40 value 85.574811
iter  50 value 85.019709
iter  60 value 83.711840
iter  70 value 83.200055
iter  80 value 82.299445
iter  90 value 81.733986
iter 100 value 81.246168
final  value 81.246168 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.672961 
iter  10 value 94.288719
iter  20 value 88.252538
iter  30 value 86.157825
iter  40 value 85.347980
iter  50 value 84.924738
iter  60 value 82.892538
iter  70 value 82.504865
iter  80 value 82.254695
iter  90 value 82.169269
iter 100 value 81.909070
final  value 81.909070 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.446959 
iter  10 value 97.426965
iter  20 value 87.375588
iter  30 value 83.412540
iter  40 value 82.705144
iter  50 value 82.365836
iter  60 value 81.498201
iter  70 value 81.336618
iter  80 value 81.106540
iter  90 value 80.952539
iter 100 value 80.929140
final  value 80.929140 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 109.761992 
iter  10 value 94.612048
iter  20 value 94.460511
iter  30 value 85.826690
iter  40 value 83.828626
iter  50 value 83.358705
iter  60 value 82.732114
iter  70 value 82.553035
iter  80 value 82.270611
iter  90 value 81.789920
iter 100 value 81.556099
final  value 81.556099 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 128.002527 
iter  10 value 95.125457
iter  20 value 94.373169
iter  30 value 91.925401
iter  40 value 89.499961
iter  50 value 84.551052
iter  60 value 83.537221
iter  70 value 81.821544
iter  80 value 81.595527
iter  90 value 81.110131
iter 100 value 80.949862
final  value 80.949862 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 104.082992 
final  value 94.485772 
converged
Fitting Repeat 2 

# weights:  103
initial  value 104.389379 
final  value 94.485732 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.631613 
final  value 94.485706 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.378704 
final  value 94.485727 
converged
Fitting Repeat 5 

# weights:  103
initial  value 112.634657 
iter  10 value 94.485846
iter  20 value 94.437009
iter  30 value 94.424704
iter  40 value 94.410321
iter  50 value 94.409567
iter  60 value 94.409373
final  value 94.409317 
converged
Fitting Repeat 1 

# weights:  305
initial  value 106.419548 
iter  10 value 94.488846
final  value 94.484237 
converged
Fitting Repeat 2 

# weights:  305
initial  value 119.424451 
iter  10 value 94.471646
iter  20 value 94.459097
iter  30 value 94.198761
final  value 94.167479 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.671752 
iter  10 value 94.488939
iter  20 value 94.484242
iter  30 value 94.424282
iter  40 value 87.419952
iter  50 value 87.283575
iter  60 value 87.046811
iter  70 value 87.042399
iter  80 value 87.041697
iter  90 value 86.738226
iter 100 value 86.737652
final  value 86.737652 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 99.350473 
iter  10 value 94.251670
iter  20 value 94.171397
iter  30 value 94.167627
iter  40 value 94.140074
iter  50 value 93.976080
iter  60 value 90.330548
iter  70 value 87.306076
iter  80 value 85.766708
iter  90 value 80.344167
iter 100 value 79.336002
final  value 79.336002 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 107.038905 
iter  10 value 94.489133
iter  20 value 94.472388
iter  30 value 94.121780
iter  40 value 91.373627
iter  50 value 84.337686
iter  60 value 84.061870
iter  70 value 84.057333
iter  80 value 83.042762
iter  90 value 82.822887
iter 100 value 82.777709
final  value 82.777709 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 107.442839 
iter  10 value 94.261785
iter  20 value 94.208006
iter  30 value 94.188711
iter  40 value 94.186702
iter  50 value 94.163639
iter  60 value 92.523470
iter  70 value 92.121586
iter  80 value 91.980852
iter  90 value 91.770942
final  value 91.770928 
converged
Fitting Repeat 2 

# weights:  507
initial  value 99.938642 
iter  10 value 94.491734
iter  20 value 94.484399
iter  30 value 94.179321
iter  40 value 92.294494
iter  50 value 92.287309
iter  60 value 92.214050
iter  70 value 92.211211
iter  80 value 88.392586
iter  90 value 88.214543
final  value 88.214434 
converged
Fitting Repeat 3 

# weights:  507
initial  value 116.662040 
iter  10 value 94.493855
iter  20 value 94.466157
iter  30 value 86.886368
iter  40 value 86.458220
iter  50 value 86.457903
iter  60 value 86.431366
iter  70 value 86.421416
iter  80 value 86.358725
iter  90 value 86.297362
final  value 86.297303 
converged
Fitting Repeat 4 

# weights:  507
initial  value 106.780680 
iter  10 value 94.437768
iter  20 value 94.400796
iter  30 value 94.236157
iter  40 value 94.200501
iter  50 value 94.199874
iter  50 value 94.199874
final  value 94.199874 
converged
Fitting Repeat 5 

# weights:  507
initial  value 131.842045 
iter  10 value 94.475477
iter  20 value 94.299264
iter  30 value 94.190455
iter  40 value 94.182971
iter  50 value 86.858378
iter  60 value 85.043257
iter  70 value 85.042982
iter  80 value 85.004765
iter  90 value 84.962980
iter 100 value 84.526860
final  value 84.526860 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 104.644508 
iter  10 value 90.798758
iter  20 value 90.785453
final  value 90.785354 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.532682 
iter  10 value 94.105275
final  value 94.105264 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.304913 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.170287 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.772223 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.314106 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 99.980847 
iter  10 value 94.321755
iter  20 value 94.055838
final  value 94.055815 
converged
Fitting Repeat 3 

# weights:  305
initial  value 110.151491 
iter  10 value 94.311285
final  value 94.275362 
converged
Fitting Repeat 4 

# weights:  305
initial  value 107.308092 
iter  10 value 94.275364
final  value 94.275362 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.091448 
iter  10 value 94.484218
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 110.269303 
final  value 94.057228 
converged
Fitting Repeat 2 

# weights:  507
initial  value 108.282159 
iter  10 value 94.055259
iter  20 value 93.238651
final  value 93.238631 
converged
Fitting Repeat 3 

# weights:  507
initial  value 102.295240 
iter  10 value 93.701710
iter  10 value 93.701709
iter  10 value 93.701709
final  value 93.701709 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.097192 
iter  10 value 94.057229
iter  10 value 94.057229
iter  10 value 94.057229
final  value 94.057229 
converged
Fitting Repeat 5 

# weights:  507
initial  value 98.408363 
iter  10 value 90.231955
iter  20 value 87.517813
iter  30 value 85.179589
iter  40 value 84.957214
iter  50 value 84.952012
iter  60 value 84.951900
final  value 84.951899 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.654461 
iter  10 value 94.426782
iter  20 value 94.097891
iter  30 value 94.041855
iter  40 value 92.155943
iter  50 value 87.540627
iter  60 value 85.868261
iter  70 value 85.640166
iter  80 value 85.519707
iter  90 value 85.507219
final  value 85.506820 
converged
Fitting Repeat 2 

# weights:  103
initial  value 110.023441 
iter  10 value 95.475143
iter  20 value 94.486808
iter  30 value 94.329970
iter  40 value 93.900114
iter  50 value 87.746193
iter  60 value 86.624865
iter  70 value 86.500139
iter  80 value 85.771848
iter  90 value 84.722208
iter 100 value 84.129685
final  value 84.129685 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 106.274955 
iter  10 value 94.486857
iter  20 value 94.422571
iter  30 value 92.261544
iter  40 value 87.660375
iter  50 value 84.689558
iter  60 value 84.178680
iter  70 value 84.053094
iter  80 value 83.880009
iter  90 value 83.495433
iter 100 value 83.374330
final  value 83.374330 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 117.230408 
iter  10 value 95.841113
iter  20 value 94.485818
iter  30 value 94.331106
iter  40 value 94.313203
iter  50 value 90.148517
iter  60 value 87.892266
iter  70 value 87.851464
iter  80 value 85.924647
iter  90 value 85.521845
iter 100 value 85.508293
final  value 85.508293 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 101.354711 
iter  10 value 94.354352
iter  20 value 89.605164
iter  30 value 86.350556
iter  40 value 85.912798
iter  50 value 85.737995
iter  60 value 85.575161
iter  70 value 85.509302
final  value 85.506821 
converged
Fitting Repeat 1 

# weights:  305
initial  value 108.011320 
iter  10 value 94.407002
iter  20 value 93.447353
iter  30 value 86.864361
iter  40 value 85.550563
iter  50 value 84.840114
iter  60 value 83.563941
iter  70 value 82.632013
iter  80 value 82.412869
iter  90 value 82.285495
iter 100 value 82.232269
final  value 82.232269 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 103.636148 
iter  10 value 94.473072
iter  20 value 93.886479
iter  30 value 92.834747
iter  40 value 91.710732
iter  50 value 90.368092
iter  60 value 90.155382
iter  70 value 87.383820
iter  80 value 84.711203
iter  90 value 84.295473
iter 100 value 83.426846
final  value 83.426846 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 105.066713 
iter  10 value 95.836409
iter  20 value 90.053235
iter  30 value 86.895164
iter  40 value 86.137628
iter  50 value 85.667196
iter  60 value 84.925318
iter  70 value 83.153128
iter  80 value 82.804412
iter  90 value 82.644434
iter 100 value 82.283749
final  value 82.283749 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 110.019740 
iter  10 value 94.342937
iter  20 value 89.739843
iter  30 value 86.813069
iter  40 value 85.976931
iter  50 value 83.053753
iter  60 value 82.502173
iter  70 value 82.253934
iter  80 value 82.175615
iter  90 value 82.108432
iter 100 value 82.057423
final  value 82.057423 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 98.946826 
iter  10 value 94.236473
iter  20 value 87.997522
iter  30 value 86.286688
iter  40 value 85.910069
iter  50 value 84.977047
iter  60 value 84.243271
iter  70 value 84.068625
iter  80 value 83.985980
iter  90 value 83.850244
iter 100 value 83.740244
final  value 83.740244 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.747816 
iter  10 value 94.691392
iter  20 value 91.050815
iter  30 value 87.621602
iter  40 value 87.140932
iter  50 value 83.351210
iter  60 value 82.777939
iter  70 value 82.541814
iter  80 value 82.459082
iter  90 value 82.452798
iter 100 value 82.269951
final  value 82.269951 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.446280 
iter  10 value 94.507012
iter  20 value 87.433719
iter  30 value 85.803881
iter  40 value 83.639568
iter  50 value 82.657178
iter  60 value 82.496246
iter  70 value 82.350680
iter  80 value 81.873858
iter  90 value 81.520399
iter 100 value 81.404179
final  value 81.404179 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 103.460586 
iter  10 value 94.167882
iter  20 value 92.899768
iter  30 value 88.493753
iter  40 value 83.845782
iter  50 value 82.758991
iter  60 value 81.852296
iter  70 value 81.697030
iter  80 value 81.430710
iter  90 value 81.292843
iter 100 value 81.250535
final  value 81.250535 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 106.490497 
iter  10 value 94.829175
iter  20 value 89.675836
iter  30 value 86.832896
iter  40 value 85.682331
iter  50 value 84.564727
iter  60 value 83.247779
iter  70 value 82.718865
iter  80 value 82.214581
iter  90 value 82.136865
iter 100 value 82.030972
final  value 82.030972 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 106.008798 
iter  10 value 94.780579
iter  20 value 93.784843
iter  30 value 92.656998
iter  40 value 86.438555
iter  50 value 85.646737
iter  60 value 84.976280
iter  70 value 84.124608
iter  80 value 83.983168
iter  90 value 83.782607
iter 100 value 83.600607
final  value 83.600607 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.802330 
iter  10 value 94.485812
iter  20 value 94.484226
final  value 94.484215 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.886259 
iter  10 value 94.256348
iter  20 value 94.230291
iter  30 value 94.228703
iter  40 value 91.989181
iter  50 value 88.294985
iter  60 value 87.912185
iter  70 value 87.910504
iter  80 value 86.999687
final  value 86.961706 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.265339 
final  value 94.485946 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.506757 
iter  10 value 89.279453
iter  20 value 89.279171
iter  30 value 89.006641
iter  40 value 89.005415
final  value 89.005397 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.859674 
final  value 94.486031 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.148478 
iter  10 value 94.280372
iter  20 value 94.120312
iter  30 value 94.034666
iter  40 value 94.027998
iter  50 value 93.823992
iter  60 value 89.650720
iter  70 value 85.172820
iter  80 value 83.774190
iter  90 value 80.597643
iter 100 value 79.963962
final  value 79.963962 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 96.632574 
iter  10 value 94.094361
iter  20 value 94.088656
iter  30 value 94.001672
iter  40 value 93.487744
iter  50 value 93.479200
final  value 93.479129 
converged
Fitting Repeat 3 

# weights:  305
initial  value 117.544347 
iter  10 value 94.257730
iter  20 value 94.056113
iter  30 value 94.034620
final  value 94.028067 
converged
Fitting Repeat 4 

# weights:  305
initial  value 111.187732 
iter  10 value 94.443077
iter  20 value 94.440806
iter  30 value 94.439807
iter  40 value 94.240987
iter  50 value 94.090734
iter  60 value 94.089218
final  value 94.089194 
converged
Fitting Repeat 5 

# weights:  305
initial  value 98.882731 
iter  10 value 94.488385
iter  20 value 94.484198
iter  30 value 94.050122
iter  40 value 86.911633
iter  50 value 85.085312
iter  60 value 84.827932
iter  70 value 83.803163
iter  80 value 83.800681
iter  90 value 83.798887
iter 100 value 83.794462
final  value 83.794462 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 114.525079 
iter  10 value 94.097311
iter  20 value 88.786694
iter  30 value 85.273127
iter  40 value 85.232841
iter  50 value 85.168666
iter  60 value 85.034746
iter  70 value 85.020686
iter  80 value 85.018835
iter  90 value 85.013373
iter 100 value 84.645575
final  value 84.645575 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 95.732696 
iter  10 value 94.492194
iter  20 value 94.032293
iter  30 value 87.356473
iter  40 value 87.322034
iter  50 value 87.191552
iter  60 value 87.052615
iter  70 value 87.048170
iter  80 value 86.468984
iter  90 value 82.911486
iter 100 value 81.281812
final  value 81.281812 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 105.229611 
iter  10 value 94.036896
iter  20 value 94.035312
iter  30 value 89.099571
iter  40 value 88.550969
iter  50 value 88.550007
final  value 88.549874 
converged
Fitting Repeat 4 

# weights:  507
initial  value 99.908862 
iter  10 value 94.492673
iter  20 value 94.064871
iter  30 value 94.058114
iter  40 value 91.284396
iter  50 value 88.157126
iter  60 value 88.107939
iter  70 value 82.701202
iter  80 value 81.320211
iter  90 value 81.285142
iter 100 value 81.281404
final  value 81.281404 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 102.678576 
iter  10 value 94.100555
iter  20 value 93.323536
iter  30 value 93.231485
iter  40 value 92.124137
iter  50 value 92.117120
iter  60 value 88.157705
iter  70 value 87.573904
iter  80 value 87.565590
iter  90 value 86.809276
iter 100 value 86.581578
final  value 86.581578 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.557284 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.648895 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 113.013000 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.352978 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.794407 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.190941 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 112.057096 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.453137 
iter  10 value 92.744555
final  value 92.701657 
converged
Fitting Repeat 4 

# weights:  305
initial  value 115.866019 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 107.033698 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 96.334719 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 97.662579 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 104.028921 
iter  10 value 93.220126
iter  20 value 90.793003
iter  30 value 90.767852
final  value 90.767807 
converged
Fitting Repeat 4 

# weights:  507
initial  value 110.671268 
iter  10 value 89.350797
iter  20 value 86.715619
iter  30 value 86.704649
iter  40 value 86.697221
iter  50 value 86.660546
final  value 86.660227 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.797026 
final  value 94.052911 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.366694 
iter  10 value 94.057132
iter  20 value 93.133794
iter  30 value 92.945308
iter  40 value 84.943308
iter  50 value 82.665354
iter  60 value 80.155401
iter  70 value 79.035474
iter  80 value 78.794795
final  value 78.777084 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.457885 
iter  10 value 93.768869
iter  20 value 85.720697
iter  30 value 84.272998
iter  40 value 84.112121
iter  50 value 83.980852
iter  60 value 83.952901
final  value 83.952895 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.168861 
iter  10 value 94.063089
iter  20 value 94.004019
iter  30 value 93.736939
iter  40 value 93.703227
iter  50 value 84.254097
iter  60 value 81.544635
iter  70 value 81.239772
iter  80 value 81.158470
iter  90 value 81.148601
iter 100 value 81.148362
final  value 81.148362 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 98.833836 
iter  10 value 93.832987
iter  20 value 87.560658
iter  30 value 87.047799
iter  40 value 86.034214
iter  50 value 85.052818
iter  60 value 78.960895
iter  70 value 78.876507
iter  80 value 78.798712
iter  90 value 78.779516
final  value 78.777084 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.244734 
iter  10 value 94.055881
iter  20 value 94.000186
iter  30 value 93.741754
iter  40 value 93.130803
iter  50 value 84.355090
iter  60 value 83.166034
iter  70 value 82.741445
iter  80 value 82.483064
iter  90 value 81.848558
final  value 81.800525 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.022668 
iter  10 value 91.486397
iter  20 value 84.342360
iter  30 value 83.302494
iter  40 value 81.880343
iter  50 value 80.397492
iter  60 value 79.922954
iter  70 value 79.415656
iter  80 value 79.185500
iter  90 value 78.980661
iter 100 value 78.696787
final  value 78.696787 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.220358 
iter  10 value 94.139250
iter  20 value 93.445544
iter  30 value 90.861538
iter  40 value 90.665804
iter  50 value 90.622294
iter  60 value 90.173288
iter  70 value 87.366875
iter  80 value 84.595554
iter  90 value 80.645256
iter 100 value 80.178313
final  value 80.178313 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 105.553427 
iter  10 value 92.176550
iter  20 value 90.530751
iter  30 value 84.461642
iter  40 value 83.930479
iter  50 value 82.688030
iter  60 value 81.162001
iter  70 value 80.692439
iter  80 value 80.142160
iter  90 value 79.718214
iter 100 value 79.627619
final  value 79.627619 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.215009 
iter  10 value 94.037418
iter  20 value 89.804910
iter  30 value 87.882512
iter  40 value 87.087225
iter  50 value 86.791171
iter  60 value 84.171097
iter  70 value 81.298339
iter  80 value 79.527740
iter  90 value 78.044760
iter 100 value 77.811235
final  value 77.811235 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.436838 
iter  10 value 93.193705
iter  20 value 89.306724
iter  30 value 88.576778
iter  40 value 83.561473
iter  50 value 82.568942
iter  60 value 81.076289
iter  70 value 80.762312
iter  80 value 80.723848
iter  90 value 80.667321
iter 100 value 80.534125
final  value 80.534125 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.872709 
iter  10 value 93.833437
iter  20 value 89.564510
iter  30 value 88.392976
iter  40 value 83.135710
iter  50 value 79.688508
iter  60 value 79.171303
iter  70 value 78.853284
iter  80 value 78.724259
iter  90 value 78.681610
iter 100 value 78.615028
final  value 78.615028 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 107.406262 
iter  10 value 94.083014
iter  20 value 93.790998
iter  30 value 84.295735
iter  40 value 83.936399
iter  50 value 83.625203
iter  60 value 81.813261
iter  70 value 81.551247
iter  80 value 81.266707
iter  90 value 80.524499
iter 100 value 80.362001
final  value 80.362001 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 113.207232 
iter  10 value 94.042245
iter  20 value 88.384189
iter  30 value 87.632171
iter  40 value 83.936526
iter  50 value 80.513071
iter  60 value 80.346318
iter  70 value 80.180879
iter  80 value 79.131862
iter  90 value 78.766008
iter 100 value 78.327509
final  value 78.327509 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 108.283154 
iter  10 value 94.601593
iter  20 value 89.949201
iter  30 value 88.085001
iter  40 value 86.690090
iter  50 value 82.235256
iter  60 value 80.543863
iter  70 value 79.951019
iter  80 value 79.714846
iter  90 value 79.669497
iter 100 value 79.624689
final  value 79.624689 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 107.838347 
iter  10 value 94.784739
iter  20 value 94.043924
iter  30 value 91.274658
iter  40 value 85.043365
iter  50 value 84.817627
iter  60 value 83.857328
iter  70 value 82.694484
iter  80 value 81.010362
iter  90 value 79.260588
iter 100 value 78.349420
final  value 78.349420 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.048220 
iter  10 value 93.723016
final  value 93.706567 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.949736 
final  value 94.054549 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.163402 
iter  10 value 92.704145
iter  20 value 92.703611
iter  30 value 92.665972
iter  40 value 92.665526
final  value 92.665465 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.937801 
final  value 94.054435 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.900825 
final  value 94.054600 
converged
Fitting Repeat 1 

# weights:  305
initial  value 129.166992 
iter  10 value 94.055802
iter  20 value 92.702956
iter  30 value 83.038272
iter  40 value 82.759257
iter  50 value 81.140369
iter  60 value 80.999010
iter  70 value 80.997732
iter  80 value 80.987063
iter  90 value 80.092049
iter 100 value 79.112378
final  value 79.112378 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 94.828050 
iter  10 value 91.803674
iter  20 value 91.768718
iter  30 value 91.708823
iter  40 value 91.708501
final  value 91.708120 
converged
Fitting Repeat 3 

# weights:  305
initial  value 96.338055 
iter  10 value 94.054116
iter  20 value 94.023931
iter  30 value 88.207409
iter  40 value 82.451736
iter  50 value 82.441693
iter  60 value 81.169693
iter  70 value 78.813190
iter  80 value 78.773352
iter  90 value 78.770892
iter  90 value 78.770891
final  value 78.770891 
converged
Fitting Repeat 4 

# weights:  305
initial  value 108.171577 
iter  10 value 93.920483
iter  20 value 93.916139
iter  30 value 92.978637
iter  40 value 87.265927
iter  50 value 77.683317
iter  60 value 76.367267
iter  70 value 76.268847
iter  80 value 76.253729
iter  90 value 76.241300
iter 100 value 76.236821
final  value 76.236821 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 111.066479 
iter  10 value 94.058611
iter  20 value 94.053422
iter  30 value 90.460490
iter  40 value 90.440460
iter  50 value 90.439438
iter  60 value 88.284153
iter  70 value 81.873373
iter  80 value 81.871475
iter  90 value 81.871052
iter 100 value 81.856107
final  value 81.856107 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 94.494915 
iter  10 value 94.054222
iter  20 value 84.682263
iter  30 value 79.695932
iter  40 value 79.662652
iter  50 value 79.662159
final  value 79.662152 
converged
Fitting Repeat 2 

# weights:  507
initial  value 114.550123 
iter  10 value 93.968459
iter  20 value 93.919106
iter  30 value 86.989882
iter  40 value 86.203505
iter  50 value 86.167587
iter  60 value 86.165874
iter  70 value 85.974222
iter  80 value 83.791618
iter  90 value 82.795947
iter 100 value 82.770681
final  value 82.770681 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 101.709980 
iter  10 value 94.069815
iter  20 value 92.928102
iter  30 value 83.438486
iter  40 value 79.963851
iter  50 value 79.760466
final  value 79.758992 
converged
Fitting Repeat 4 

# weights:  507
initial  value 101.373231 
iter  10 value 90.486096
iter  20 value 86.211645
iter  30 value 86.199519
iter  40 value 86.043055
iter  50 value 85.869676
iter  60 value 85.844001
iter  70 value 83.056065
iter  80 value 80.814941
iter  90 value 80.386127
iter 100 value 80.384410
final  value 80.384410 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 95.455703 
iter  10 value 94.061375
iter  20 value 93.926268
iter  30 value 92.559569
iter  40 value 88.591869
iter  50 value 78.850474
iter  60 value 78.713380
iter  70 value 78.487007
iter  80 value 78.160885
iter  90 value 78.154853
iter 100 value 78.152478
final  value 78.152478 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.212943 
final  value 94.443243 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.640097 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.338875 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.295559 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.547725 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.700437 
iter  10 value 93.019448
iter  20 value 92.912487
final  value 92.912281 
converged
Fitting Repeat 2 

# weights:  305
initial  value 107.658360 
final  value 94.443243 
converged
Fitting Repeat 3 

# weights:  305
initial  value 115.271139 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 98.548618 
final  value 94.443243 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.195832 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 124.710793 
iter  10 value 94.365463
iter  10 value 94.365462
iter  10 value 94.365462
final  value 94.365462 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.451742 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.255636 
iter  10 value 87.578860
iter  20 value 86.058665
iter  30 value 86.033319
iter  40 value 86.033035
iter  40 value 86.033035
final  value 86.033035 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.024298 
iter  10 value 94.362441
final  value 94.361958 
converged
Fitting Repeat 5 

# weights:  507
initial  value 115.404940 
iter  10 value 94.443370
final  value 94.442934 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.497529 
iter  10 value 94.437616
iter  20 value 89.243066
iter  30 value 86.934699
iter  40 value 85.608207
iter  50 value 85.088032
iter  60 value 84.966103
iter  70 value 84.783603
iter  80 value 84.100310
iter  90 value 83.590879
iter 100 value 83.563298
final  value 83.563298 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 100.303705 
iter  10 value 93.506483
iter  20 value 87.604010
iter  30 value 87.071994
iter  40 value 86.664755
iter  50 value 86.437785
iter  60 value 85.529992
iter  70 value 85.247732
final  value 85.244639 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.522542 
iter  10 value 93.743552
iter  20 value 89.489776
iter  30 value 85.799625
iter  40 value 85.379708
iter  50 value 85.061244
iter  60 value 84.796345
iter  70 value 84.566093
iter  80 value 84.277147
iter  90 value 83.838445
iter 100 value 83.724178
final  value 83.724178 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 100.334294 
iter  10 value 94.367889
iter  20 value 86.791119
iter  30 value 86.423964
iter  40 value 86.274197
iter  50 value 86.105070
iter  60 value 85.916472
iter  70 value 85.633197
iter  80 value 85.624702
iter  90 value 85.557890
iter 100 value 85.497082
final  value 85.497082 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 96.901194 
iter  10 value 94.488990
iter  20 value 94.449545
iter  30 value 94.314578
iter  40 value 94.293051
iter  50 value 94.289217
iter  60 value 91.618600
iter  70 value 86.892602
iter  80 value 86.257202
iter  90 value 85.920683
iter 100 value 85.649963
final  value 85.649963 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 111.776836 
iter  10 value 95.189700
iter  20 value 94.934920
iter  30 value 94.350821
iter  40 value 87.738149
iter  50 value 85.974500
iter  60 value 83.684582
iter  70 value 83.270795
iter  80 value 83.036009
iter  90 value 82.771671
iter 100 value 82.401310
final  value 82.401310 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.846239 
iter  10 value 94.484877
iter  20 value 93.235492
iter  30 value 90.728673
iter  40 value 87.914941
iter  50 value 86.931866
iter  60 value 84.056618
iter  70 value 83.413403
iter  80 value 83.173757
iter  90 value 82.988138
iter 100 value 82.826174
final  value 82.826174 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 121.687132 
iter  10 value 95.683455
iter  20 value 94.462039
iter  30 value 93.156947
iter  40 value 88.194031
iter  50 value 87.802362
iter  60 value 87.350970
iter  70 value 86.549710
iter  80 value 86.228586
iter  90 value 85.359848
iter 100 value 85.009290
final  value 85.009290 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 111.209024 
iter  10 value 94.368453
iter  20 value 89.558812
iter  30 value 88.043207
iter  40 value 86.554716
iter  50 value 85.804099
iter  60 value 85.491039
iter  70 value 85.257528
iter  80 value 85.153959
iter  90 value 84.491704
iter 100 value 84.296371
final  value 84.296371 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 99.309335 
iter  10 value 87.275266
iter  20 value 85.458644
iter  30 value 83.710026
iter  40 value 82.909573
iter  50 value 82.307925
iter  60 value 82.191268
iter  70 value 82.183953
iter  80 value 82.179983
iter  90 value 82.174588
iter 100 value 82.170340
final  value 82.170340 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.234362 
iter  10 value 94.487397
iter  20 value 90.093471
iter  30 value 86.900214
iter  40 value 86.450946
iter  50 value 84.883461
iter  60 value 84.462687
iter  70 value 83.773346
iter  80 value 83.601280
iter  90 value 82.876643
iter 100 value 82.534036
final  value 82.534036 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 116.734831 
iter  10 value 94.443761
iter  20 value 90.319878
iter  30 value 89.704823
iter  40 value 89.108636
iter  50 value 87.025401
iter  60 value 86.411013
iter  70 value 86.065443
iter  80 value 84.002225
iter  90 value 83.110594
iter 100 value 82.784018
final  value 82.784018 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 103.743264 
iter  10 value 93.947816
iter  20 value 88.038765
iter  30 value 86.028846
iter  40 value 85.810078
iter  50 value 85.601850
iter  60 value 85.331640
iter  70 value 85.137757
iter  80 value 83.348793
iter  90 value 82.808077
iter 100 value 82.717136
final  value 82.717136 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 114.102618 
iter  10 value 94.748405
iter  20 value 93.137931
iter  30 value 90.025524
iter  40 value 86.931859
iter  50 value 86.226719
iter  60 value 85.737439
iter  70 value 83.902434
iter  80 value 83.561098
iter  90 value 83.201232
iter 100 value 82.784299
final  value 82.784299 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 107.918171 
iter  10 value 94.534276
iter  20 value 89.898647
iter  30 value 86.727378
iter  40 value 85.878032
iter  50 value 83.636032
iter  60 value 83.342964
iter  70 value 83.258677
iter  80 value 83.106644
iter  90 value 83.033635
iter 100 value 82.868219
final  value 82.868219 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.075661 
final  value 94.485523 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.438312 
iter  10 value 94.486038
final  value 94.484281 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.795338 
final  value 94.485756 
converged
Fitting Repeat 4 

# weights:  103
initial  value 109.051678 
final  value 94.485879 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.869230 
final  value 94.485865 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.901555 
iter  10 value 94.258621
iter  20 value 94.256870
final  value 94.254287 
converged
Fitting Repeat 2 

# weights:  305
initial  value 98.194482 
iter  10 value 94.480811
iter  20 value 94.447072
iter  30 value 94.254077
iter  40 value 94.253645
final  value 94.253641 
converged
Fitting Repeat 3 

# weights:  305
initial  value 98.641146 
iter  10 value 94.487953
iter  20 value 93.432004
iter  30 value 87.939082
iter  40 value 86.524919
iter  50 value 86.437521
iter  60 value 85.399078
iter  70 value 85.354351
final  value 85.353974 
converged
Fitting Repeat 4 

# weights:  305
initial  value 105.765116 
iter  10 value 94.489507
iter  20 value 94.322342
iter  30 value 91.651545
iter  40 value 90.641432
iter  50 value 87.439588
iter  60 value 87.421882
iter  70 value 87.406597
iter  80 value 87.404661
iter  90 value 86.014676
iter 100 value 85.992931
final  value 85.992931 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 97.592136 
iter  10 value 94.489030
iter  20 value 94.402788
iter  30 value 88.501666
iter  40 value 86.686772
iter  50 value 86.476103
final  value 86.476085 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.109888 
iter  10 value 94.492668
iter  20 value 94.480553
iter  30 value 93.190439
iter  40 value 87.894669
iter  50 value 87.889592
iter  60 value 87.738831
iter  70 value 87.719844
iter  80 value 87.711695
iter  90 value 87.701865
iter 100 value 87.698896
final  value 87.698896 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 119.113203 
iter  10 value 94.558255
iter  20 value 86.789150
iter  30 value 85.908161
iter  40 value 85.904092
iter  50 value 84.932072
iter  60 value 84.177739
iter  70 value 84.165294
final  value 84.165260 
converged
Fitting Repeat 3 

# weights:  507
initial  value 95.049984 
iter  10 value 94.283310
iter  20 value 94.240737
iter  30 value 94.236168
iter  40 value 94.234985
iter  50 value 94.230717
final  value 94.230305 
converged
Fitting Repeat 4 

# weights:  507
initial  value 103.926099 
iter  10 value 94.492169
iter  20 value 91.308574
iter  30 value 87.829233
iter  40 value 87.712327
iter  50 value 87.710082
iter  60 value 87.707459
iter  70 value 87.255045
iter  80 value 86.591705
iter  90 value 86.270242
final  value 86.268495 
converged
Fitting Repeat 5 

# weights:  507
initial  value 118.964818 
iter  10 value 94.492592
iter  20 value 94.443483
iter  30 value 92.631335
iter  40 value 92.604163
iter  50 value 92.603723
final  value 92.603701 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.989272 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.685885 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.707441 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.133343 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.524086 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.396836 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.780741 
final  value 93.987879 
converged
Fitting Repeat 3 

# weights:  305
initial  value 108.482931 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 104.819430 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 112.816164 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.155690 
iter  10 value 94.051331
iter  20 value 92.430140
iter  30 value 87.217294
iter  40 value 86.586533
iter  50 value 85.751471
final  value 85.729814 
converged
Fitting Repeat 2 

# weights:  507
initial  value 111.180901 
iter  10 value 94.058208
iter  20 value 94.052915
final  value 94.052911 
converged
Fitting Repeat 3 

# weights:  507
initial  value 94.886611 
final  value 93.812866 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.820873 
final  value 93.836066 
converged
Fitting Repeat 5 

# weights:  507
initial  value 105.954217 
iter  10 value 81.477710
iter  20 value 80.885430
iter  30 value 80.883120
final  value 80.883117 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.566991 
iter  10 value 94.055011
iter  20 value 93.725370
iter  30 value 83.913934
iter  40 value 83.118536
iter  50 value 81.474667
iter  60 value 81.336451
iter  70 value 81.171778
iter  80 value 81.119867
iter  80 value 81.119867
final  value 81.119867 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.413297 
iter  10 value 94.056707
iter  10 value 94.056707
iter  20 value 94.029162
iter  30 value 93.925558
iter  40 value 87.975181
iter  50 value 86.186121
iter  60 value 83.735776
iter  70 value 81.331440
iter  80 value 80.244270
iter  90 value 79.985959
iter 100 value 79.434091
final  value 79.434091 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 100.617348 
iter  10 value 94.054829
iter  20 value 92.722782
iter  30 value 84.234175
iter  40 value 81.963667
iter  50 value 81.831718
iter  60 value 81.125858
final  value 81.119947 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.426609 
iter  10 value 92.006724
iter  20 value 85.546260
iter  30 value 83.511987
iter  40 value 82.837204
iter  50 value 80.419871
iter  60 value 80.188754
iter  70 value 80.158189
iter  80 value 79.920924
iter  90 value 79.749627
iter  90 value 79.749626
iter  90 value 79.749626
final  value 79.749626 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.551454 
iter  10 value 94.056789
iter  20 value 91.218618
iter  30 value 82.952118
iter  40 value 82.211150
iter  50 value 81.398495
iter  60 value 80.973830
iter  70 value 80.459263
iter  80 value 80.249179
iter  90 value 80.188740
final  value 80.188630 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.397433 
iter  10 value 87.480272
iter  20 value 85.951976
iter  30 value 85.275078
iter  40 value 83.082869
iter  50 value 80.407381
iter  60 value 79.598169
iter  70 value 78.869124
iter  80 value 78.491285
iter  90 value 77.977741
iter 100 value 77.879318
final  value 77.879318 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 109.953174 
iter  10 value 93.960694
iter  20 value 84.681153
iter  30 value 83.630057
iter  40 value 83.048321
iter  50 value 80.596019
iter  60 value 79.971192
iter  70 value 79.544956
iter  80 value 79.363869
iter  90 value 78.477353
iter 100 value 78.045244
final  value 78.045244 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 128.481895 
iter  10 value 94.064463
iter  20 value 89.281926
iter  30 value 84.726359
iter  40 value 84.282826
iter  50 value 83.849862
iter  60 value 82.402126
iter  70 value 80.372743
iter  80 value 79.542193
iter  90 value 79.221215
iter 100 value 79.062939
final  value 79.062939 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 99.828527 
iter  10 value 94.055705
iter  20 value 93.775374
iter  30 value 87.990858
iter  40 value 82.641931
iter  50 value 82.212907
iter  60 value 80.348448
iter  70 value 78.908544
iter  80 value 78.228735
iter  90 value 78.089662
iter 100 value 78.064525
final  value 78.064525 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.606218 
iter  10 value 93.789661
iter  20 value 91.260552
iter  30 value 82.986200
iter  40 value 80.153263
iter  50 value 79.783035
iter  60 value 79.642160
iter  70 value 79.423295
iter  80 value 78.774743
iter  90 value 78.393916
iter 100 value 78.268632
final  value 78.268632 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 106.043333 
iter  10 value 94.341518
iter  20 value 94.011776
iter  30 value 87.079141
iter  40 value 82.141440
iter  50 value 81.347300
iter  60 value 81.107784
iter  70 value 81.011133
iter  80 value 80.588492
iter  90 value 79.244596
iter 100 value 78.852378
final  value 78.852378 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 116.070438 
iter  10 value 94.154493
iter  20 value 93.849559
iter  30 value 88.897332
iter  40 value 82.135551
iter  50 value 80.226023
iter  60 value 78.703665
iter  70 value 77.946528
iter  80 value 77.883078
iter  90 value 77.670414
iter 100 value 77.609103
final  value 77.609103 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 113.928334 
iter  10 value 93.852099
iter  20 value 84.863225
iter  30 value 84.176233
iter  40 value 80.916531
iter  50 value 79.890028
iter  60 value 78.688930
iter  70 value 78.123768
iter  80 value 77.747843
iter  90 value 77.625828
iter 100 value 77.584328
final  value 77.584328 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 128.926903 
iter  10 value 96.611146
iter  20 value 94.087219
iter  30 value 87.305422
iter  40 value 81.619073
iter  50 value 81.324682
iter  60 value 80.093262
iter  70 value 79.775495
iter  80 value 79.431573
iter  90 value 78.726727
iter 100 value 78.210039
final  value 78.210039 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 110.608994 
iter  10 value 93.805072
iter  20 value 89.719372
iter  30 value 83.381510
iter  40 value 81.101277
iter  50 value 79.544596
iter  60 value 78.358203
iter  70 value 78.034679
iter  80 value 77.711446
iter  90 value 77.600002
iter 100 value 77.514557
final  value 77.514557 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 97.477371 
final  value 94.054758 
converged
Fitting Repeat 2 

# weights:  103
initial  value 114.606302 
iter  10 value 93.838024
iter  20 value 93.836643
iter  30 value 93.728591
iter  40 value 93.353287
iter  50 value 85.719684
final  value 83.650667 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.644771 
iter  10 value 93.724004
iter  20 value 93.722533
iter  30 value 93.722298
iter  30 value 93.722298
final  value 93.722298 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.883963 
iter  10 value 94.054721
iter  20 value 94.052979
final  value 94.052920 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.109030 
final  value 94.054553 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.512045 
iter  10 value 94.057530
iter  20 value 94.053027
iter  20 value 94.053027
final  value 93.836299 
converged
Fitting Repeat 2 

# weights:  305
initial  value 100.250970 
iter  10 value 94.057016
iter  20 value 94.050375
iter  30 value 93.670566
iter  40 value 93.192181
iter  50 value 85.988424
iter  60 value 84.598572
iter  70 value 80.675841
iter  80 value 80.299656
iter  90 value 80.283168
iter 100 value 80.282997
final  value 80.282997 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.184023 
iter  10 value 94.016252
iter  20 value 93.879841
iter  30 value 93.333924
iter  40 value 85.890440
iter  50 value 84.653897
iter  60 value 84.637296
iter  70 value 84.633843
final  value 84.633696 
converged
Fitting Repeat 4 

# weights:  305
initial  value 99.179909 
iter  10 value 93.841137
iter  20 value 93.836396
final  value 93.836267 
converged
Fitting Repeat 5 

# weights:  305
initial  value 94.156642 
iter  10 value 94.055460
iter  20 value 92.925916
iter  30 value 91.379567
iter  40 value 91.376021
final  value 91.376019 
converged
Fitting Repeat 1 

# weights:  507
initial  value 122.756970 
iter  10 value 94.033611
iter  20 value 93.996637
iter  30 value 90.523911
iter  40 value 89.758776
iter  50 value 89.756497
iter  60 value 88.762999
iter  70 value 82.543191
iter  80 value 81.118542
iter  90 value 80.166082
iter 100 value 79.013800
final  value 79.013800 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 94.887847 
iter  10 value 93.844354
iter  20 value 93.230223
iter  30 value 86.163984
iter  40 value 84.776284
iter  50 value 83.920372
iter  60 value 83.645473
iter  70 value 83.244572
iter  80 value 83.238241
iter  90 value 83.236601
iter 100 value 83.228507
final  value 83.228507 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 99.759275 
iter  10 value 93.844758
final  value 93.844752 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.527732 
iter  10 value 93.678086
iter  20 value 88.565437
iter  30 value 82.303246
iter  40 value 82.278374
final  value 82.277407 
converged
Fitting Repeat 5 

# weights:  507
initial  value 115.978242 
iter  10 value 93.852465
iter  20 value 93.847517
iter  30 value 88.146098
iter  40 value 84.550019
iter  50 value 84.214973
iter  60 value 84.208595
iter  70 value 83.866566
iter  80 value 81.423279
iter  90 value 79.018411
iter 100 value 79.017033
final  value 79.017033 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 134.698112 
iter  10 value 117.495880
iter  20 value 115.352520
iter  30 value 112.889095
iter  40 value 106.804514
iter  50 value 105.251583
iter  60 value 103.461846
iter  70 value 102.012786
iter  80 value 101.632415
iter  90 value 101.106301
iter 100 value 100.853607
final  value 100.853607 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 150.473072 
iter  10 value 118.027568
iter  20 value 113.969720
iter  30 value 108.398520
iter  40 value 104.970166
iter  50 value 104.245130
iter  60 value 101.465908
iter  70 value 101.066373
iter  80 value 100.870795
iter  90 value 100.661427
iter 100 value 100.515477
final  value 100.515477 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 133.556620 
iter  10 value 117.920532
iter  20 value 117.047308
iter  30 value 115.160538
iter  40 value 111.267014
iter  50 value 109.395981
iter  60 value 104.774506
iter  70 value 103.450116
iter  80 value 102.870843
iter  90 value 102.190737
iter 100 value 101.633380
final  value 101.633380 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 150.244508 
iter  10 value 117.821233
iter  20 value 110.801246
iter  30 value 108.806078
iter  40 value 108.322593
iter  50 value 108.161787
iter  60 value 106.098114
iter  70 value 105.076481
iter  80 value 104.591016
iter  90 value 103.642748
iter 100 value 102.911632
final  value 102.911632 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 163.422224 
iter  10 value 118.163156
iter  20 value 117.501896
iter  30 value 111.678211
iter  40 value 108.574183
iter  50 value 107.698771
iter  60 value 106.646876
iter  70 value 106.175311
iter  80 value 105.361811
iter  90 value 104.664397
iter 100 value 102.740171
final  value 102.740171 
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 -- Mon Dec 23 19:46:45 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 
 17.436   0.377  28.751