CRAN Package Check Results for Package gensvm

Last updated on 2020-08-13 06:47:27 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.1.3 12.40 32.05 44.45 OK
r-devel-linux-x86_64-debian-gcc 0.1.3 7.17 25.21 32.38 OK
r-devel-linux-x86_64-fedora-clang 0.1.3 61.44 OK
r-devel-linux-x86_64-fedora-gcc 0.1.3 50.01 OK
r-devel-windows-ix86+x86_64 0.1.3 128.00 56.00 184.00 OK
r-patched-linux-x86_64 0.1.3 9.15 31.78 40.93 OK
r-patched-solaris-x86 0.1.3 64.70 ERROR
r-release-linux-x86_64 0.1.3 7.72 32.00 39.72 OK
r-release-macos-x86_64 0.1.3 OK
r-release-windows-ix86+x86_64 0.1.3 98.00 59.00 157.00 OK
r-oldrel-macos-x86_64 0.1.3 OK
r-oldrel-windows-ix86+x86_64 0.1.3 123.00 75.00 198.00 OK

Check Details

Version: 0.1.3
Check: examples
Result: ERROR
    Running examples in ‘gensvm-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: gensvm
    > ### Title: Fit the GenSVM model
    > ### Aliases: gensvm
    >
    > ### ** Examples
    >
    > x <- iris[, -5]
    > y <- iris[, 5]
    >
    > # fit using the default parameters and show progress
    > fit <- gensvm(x, y, verbose=TRUE)
    Starting main loop.
    Dataset:
     n = 150
     m = 4
     K = 3
    Parameters:
     kappa = 0.000000
     p = 1.000000
     lambda = 0.0000000100000000
     epsilon = 1e-06
    
    iter = 0, L = 0.3355674184722912, Lbar = 5.3937517070403684, reldiff = 15.0735262427920897
    iter = 100, L = 0.0319142554519934, Lbar = 0.0320093975332437, reldiff = 0.0029811781569952
    iter = 200, L = 0.0276120729424826, Lbar = 0.0276320595902218, reldiff = 0.0007238372787425
    iter = 300, L = 0.0262702960526469, Lbar = 0.0262794295945235, reldiff = 0.0003476756355669
    iter = 400, L = 0.0255657735206489, Lbar = 0.0255714155427902, reldiff = 0.0002206865415892
    iter = 500, L = 0.0251049147711520, Lbar = 0.0251086514210743, reldiff = 0.0001488413705609
    iter = 600, L = 0.0247723707970373, Lbar = 0.0247754962368639, reldiff = 0.0001261663589741
    iter = 700, L = 0.0244762022037133, Lbar = 0.0244790051976307, reldiff = 0.0001145191518696
    iter = 800, L = 0.0242111802415812, Lbar = 0.0242136856712352, reldiff = 0.0001034823428282
    iter = 900, L = 0.0239744304796390, Lbar = 0.0239766674055901, reldiff = 0.0000933046544330
    iter = 1000, L = 0.0237631500663394, Lbar = 0.0237651454835360, reldiff = 0.0000839710724838
    iter = 1100, L = 0.0235747483781695, Lbar = 0.0235765271182299, reldiff = 0.0000754510729782
    iter = 1200, L = 0.0234069299782224, Lbar = 0.0234085062085074, reldiff = 0.0000673403255548
    iter = 1300, L = 0.0232604484940971, Lbar = 0.0232618092187424, reldiff = 0.0000584995016587
    iter = 1400, L = 0.0231340163454433, Lbar = 0.0231351906527558, reldiff = 0.0000507610652199
    iter = 1500, L = 0.0230249163602709, Lbar = 0.0230259295959000, reldiff = 0.0000440060503697
    iter = 1600, L = 0.0229307862595038, Lbar = 0.0229316604267088, reldiff = 0.0000381219900248
    iter = 1700, L = 0.0228495773693461, Lbar = 0.0228503315320077, reldiff = 0.0000330055409554
    iter = 1800, L = 0.0227795161021378, Lbar = 0.0227801667519348, reldiff = 0.0000285629332137
    iter = 1900, L = 0.0227190691207572, Lbar = 0.0227196305070698, reldiff = 0.0000247099170137
    iter = 2000, L = 0.0226667079856416, Lbar = 0.0226672022911132, reldiff = 0.0000218075545821
    iter = 2100, L = 0.0226196099798179, Lbar = 0.0226200588239216, reldiff = 0.0000198431407097
    iter = 2200, L = 0.0225768422264877, Lbar = 0.0225772498184046, reldiff = 0.0000180535396792
    iter = 2300, L = 0.0225380034912304, Lbar = 0.0225383736552502, reldiff = 0.0000164239933624
    iter = 2400, L = 0.0225027294173094, Lbar = 0.0225030656252171, reldiff = 0.0000149407612476
    iter = 2500, L = 0.0224706892994059, Lbar = 0.0224709947017381, reldiff = 0.0000135911421402
    iter = 2600, L = 0.0224415830818074, Lbar = 0.0224418605366709, reldiff = 0.0000123634265262
    iter = 2700, L = 0.0224151385952185, Lbar = 0.0224153906947787, reldiff = 0.0000112468436941
    iter = 2800, L = 0.0223911090202845, Lbar = 0.0223913381150503, reldiff = 0.0000102315059751
    iter = 2900, L = 0.0223692705646256, Lbar = 0.0223694787856675, reldiff = 0.0000093083518882
    iter = 3000, L = 0.0223494203395886, Lbar = 0.0223496096188286, reldiff = 0.0000084690894470
    iter = 3100, L = 0.0223313744227895, Lbar = 0.0223315465115011, reldiff = 0.0000077061406235
    iter = 3200, L = 0.0223149660927022, Lbar = 0.0223151225783570, reldiff = 0.0000070125876161
    iter = 3300, L = 0.0223000442219694, Lbar = 0.0223001865435612, reldiff = 0.0000063821215043
    iter = 3400, L = 0.0222864718166803, Lbar = 0.0222866012786496, reldiff = 0.0000058089934695
    iter = 3500, L = 0.0222741246895216, Lbar = 0.0222742424743995, reldiff = 0.0000052879688666
    iter = 3600, L = 0.0222628902554380, Lbar = 0.0222629974353186, reldiff = 0.0000048142841904
    iter = 3700, L = 0.0222526664391832, Lbar = 0.0222527639861275, reldiff = 0.0000043836069932
    iter = 3800, L = 0.0222433606848941, Lbar = 0.0222434494803594, reldiff = 0.0000039919986279
    iter = 3900, L = 0.0222348890585623, Lbar = 0.0222349699019463, reldiff = 0.0000036358798013
    iter = 4000, L = 0.0222271754349877, Lbar = 0.0222272490513665, reldiff = 0.0000033119988205
    iter = 4100, L = 0.0222201507614849, Lbar = 0.0222202178086198, reldiff = 0.0000030174023395
    iter = 4200, L = 0.0222137523912559, Lbar = 0.0222138134659363, reldiff = 0.0000027494085348
    iter = 4300, L = 0.0222079234799506, Lbar = 0.0222079791237361, reldiff = 0.0000025055825480
    iter = 4400, L = 0.0222026417495738, Lbar = 0.0222026919754526, reldiff = 0.0000022621577810
    iter = 4500, L = 0.0221978420933337, Lbar = 0.0221978879740708, reldiff = 0.0000020669007772
    iter = 4600, L = 0.0221934572013539, Lbar = 0.0221934991176016, reldiff = 0.0000018886758995
    iter = 4700, L = 0.0221894512546957, Lbar = 0.0221894895480162, reldiff = 0.0000017257443682
    iter = 4800, L = 0.0221857915954315, Lbar = 0.0221858265781490, reldiff = 0.0000015768072691
    iter = 4900, L = 0.0221824483623731, Lbar = 0.0221824803200484, reldiff = 0.0000014406739411
    iter = 5000, L = 0.0221793942546930, Lbar = 0.0221794234483880, reldiff = 0.0000013162530371
    iter = 5100, L = 0.0221766043150504, Lbar = 0.0221766309834058, reldiff = 0.0000012025445863
    iter = 5200, L = 0.0221740557306287, Lbar = 0.0221740800917694, reldiff = 0.0000010986326094
    iter = 5300, L = 0.0221717276506830, Lbar = 0.0221717499039652, reldiff = 0.0000010036783146
    
    Optimization finished, iter = 5306, loss = 0.0221716166858759, reldiff = 0.0000009991516780
    Number of support vectors: 16
    Training time: 0.463405
    >
    > # fit with some changed parameters
    > fit <- gensvm(x, y, lambda=1e-6)
    >
    > # Early stopping defined through epsilon
    > fit <- gensvm(x, y, epsilon=1e-3)
    >
    > # Early stopping defined through max.iter
    > fit <- gensvm(x, y, max.iter=1000)
    >
    > # Nonlinear training
    > fit <- gensvm(x, y, kernel='rbf', max.iter=1000)
    
     *** caught segfault ***
    address 0, cause 'memory not mapped'
    
    Traceback:
     1: gensvm(x, y, kernel = "rbf", max.iter = 1000)
    An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86