Last updated on 2015-02-01 20:48:31.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.9.45 | 18.46 | 21.70 | 40.16 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.9.45 | 23.80 | 21.47 | 45.28 | OK | |
| r-devel-linux-x86_64-fedora-clang | 0.9.45 | 79.04 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 0.9.45 | 82.99 | OK | |||
| r-devel-osx-x86_64-clang | 0.9.45 | 72.88 | OK | |||
| r-devel-windows-ix86+x86_64 | 0.9.45 | 149.00 | 50.00 | 199.00 | OK | |
| r-patched-linux-x86_64 | 0.9.45 | 24.30 | 23.87 | 48.17 | OK | |
| r-patched-solaris-sparc | 0.9.45 | 370.60 | ERROR | |||
| r-patched-solaris-x86 | 0.9.45 | 119.60 | ERROR | |||
| r-release-linux-ix86 | 0.9.45 | 31.62 | 31.44 | 63.06 | OK | |
| r-release-linux-x86_64 | 0.9.45 | 24.05 | 23.74 | 47.79 | OK | |
| r-release-osx-x86_64-mavericks | 0.9.45 | OK | ||||
| r-release-osx-x86_64-snowleopard | 0.9.45 | OK | ||||
| r-release-windows-ix86+x86_64 | 0.9.45 | 119.00 | 59.00 | 178.00 | OK | |
| r-oldrel-windows-ix86+x86_64 | 0.9.45 | 122.00 | 59.00 | 181.00 | OK |
Memtest notes: valgrind
Version: 0.9.45
Check: examples
Result: ERROR
Running examples in ‘CORElearn-Ex.R’ failed
The error most likely occurred in:
> ### Name: CORElearn-package
> ### Title: R port of CORElearn
> ### Aliases: CORElearn-package CORElearn
> ### Keywords: package datasets models regression nonlinear tree
> ### multivariate loess classif
>
> ### ** Examples
>
> # load the package
> library(CORElearn)
> cat(versionCore(),"\n")
CORElearn, Linux R version 0.9.45, built on Jan 27 2015 at 09:27:50 with OpenMP support
>
> # use iris data set
>
> # build random forests model with certain parameters
> # setting maxThreads to 0 or more than 1 uses several processor cores
> model <- CoreModel(Species ~ ., iris, model="rf",
+ selectionEstimator="MDL",minNodeWeightRF=5,
+ rfNoTrees=100, maxThreads=1)
>
> print(model)
$modelID
[1] 0
$terms
Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width
attr(,"variables")
list(Species, Sepal.Length, Sepal.Width, Petal.Length, Petal.Width)
attr(,"factors")
Sepal.Length Sepal.Width Petal.Length Petal.Width
Species 0 0 0 0
Sepal.Length 1 0 0 0
Sepal.Width 0 1 0 0
Petal.Length 0 0 1 0
Petal.Width 0 0 0 1
attr(,"term.labels")
[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
attr(,"order")
[1] 1 1 1 1
attr(,"response")
[1] 1
attr(,".Environment")
<environment: cca51c>
$class.lev
[1] "setosa" "versicolor" "virginica"
$model
[1] "rf"
$formula
Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width
<environment: cca51c>
$noClasses
[1] 3
$priorClassProb
[1] 0.3333333 0.3333333 0.3333333
$avgTrainPrediction
[1] 0
$noNumeric
[1] 4
$noDiscrete
[1] 1
$discAttrNames
[1] "Species"
$discValNames
$discValNames[[1]]
[1] "setosa" "versicolor" "virginica"
$numAttrNames
[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
$discmap
[1] 1
$nummap
[1] 2 3 4 5
$skipmap
integer(0)
attr(,"class")
[1] "CoreModel"
> plot(model, iris, rfGraphType="prototypes")
>
> # prediction with node distribution
> pred <- predict(model, iris, rfPredictClass=FALSE)
> print(pred)
$class
[1] setosa setosa setosa setosa setosa setosa
[7] setosa setosa setosa setosa setosa setosa
[13] setosa setosa setosa setosa setosa setosa
[19] setosa setosa setosa setosa setosa setosa
[25] setosa setosa setosa setosa setosa setosa
[31] setosa setosa setosa setosa setosa setosa
[37] setosa setosa setosa setosa setosa setosa
[43] setosa setosa setosa setosa setosa setosa
[49] setosa setosa versicolor versicolor versicolor versicolor
[55] versicolor versicolor versicolor versicolor versicolor versicolor
[61] versicolor versicolor versicolor versicolor versicolor versicolor
[67] versicolor versicolor versicolor versicolor virginica versicolor
[73] versicolor versicolor versicolor versicolor versicolor virginica
[79] versicolor versicolor versicolor versicolor versicolor versicolor
[85] versicolor versicolor versicolor versicolor versicolor versicolor
[91] versicolor versicolor versicolor versicolor versicolor versicolor
[97] versicolor versicolor versicolor versicolor virginica virginica
[103] virginica virginica virginica virginica versicolor virginica
[109] virginica virginica virginica virginica virginica virginica
[115] virginica virginica virginica virginica virginica versicolor
[121] virginica virginica virginica virginica virginica virginica
[127] virginica virginica virginica virginica virginica virginica
[133] virginica versicolor versicolor virginica virginica virginica
[139] virginica virginica virginica virginica virginica virginica
[145] virginica virginica virginica virginica virginica virginica
Levels: setosa versicolor virginica
$probabilities
setosa versicolor virginica
[1,] 0.9992307692 0.0005769231 0.0001923077
[2,] 0.9992307692 0.0005769231 0.0001923077
[3,] 0.9992307692 0.0005769231 0.0001923077
[4,] 0.9992307692 0.0005769231 0.0001923077
[5,] 0.9992307692 0.0005769231 0.0001923077
[6,] 0.9992307692 0.0005769231 0.0001923077
[7,] 0.9992307692 0.0005769231 0.0001923077
[8,] 0.9992307692 0.0005769231 0.0001923077
[9,] 0.9992307692 0.0005769231 0.0001923077
[10,] 0.9992307692 0.0005769231 0.0001923077
[11,] 0.9992307692 0.0005769231 0.0001923077
[12,] 0.9992307692 0.0005769231 0.0001923077
[13,] 0.9992307692 0.0005769231 0.0001923077
[14,] 0.9992307692 0.0005769231 0.0001923077
[15,] 0.8707996297 0.1149900456 0.0142103247
[16,] 0.9098187684 0.0829709068 0.0072103247
[17,] 0.9992307692 0.0005769231 0.0001923077
[18,] 0.9992307692 0.0005769231 0.0001923077
[19,] 0.9098187684 0.0829709068 0.0072103247
[20,] 0.9992307692 0.0005769231 0.0001923077
[21,] 0.9992307692 0.0005769231 0.0001923077
[22,] 0.9992307692 0.0005769231 0.0001923077
[23,] 0.9992307692 0.0005769231 0.0001923077
[24,] 0.9992307692 0.0005769231 0.0001923077
[25,] 0.9992307692 0.0005769231 0.0001923077
[26,] 0.9992307692 0.0005769231 0.0001923077
[27,] 0.9992307692 0.0005769231 0.0001923077
[28,] 0.9992307692 0.0005769231 0.0001923077
[29,] 0.9992307692 0.0005769231 0.0001923077
[30,] 0.9992307692 0.0005769231 0.0001923077
[31,] 0.9992307692 0.0005769231 0.0001923077
[32,] 0.9992307692 0.0005769231 0.0001923077
[33,] 0.9992307692 0.0005769231 0.0001923077
[34,] 0.9673191919 0.0280296296 0.0046511785
[35,] 0.9992307692 0.0005769231 0.0001923077
[36,] 0.9992307692 0.0005769231 0.0001923077
[37,] 0.9673191919 0.0280296296 0.0046511785
[38,] 0.9992307692 0.0005769231 0.0001923077
[39,] 0.9992307692 0.0005769231 0.0001923077
[40,] 0.9992307692 0.0005769231 0.0001923077
[41,] 0.9992307692 0.0005769231 0.0001923077
[42,] 0.9678021978 0.0285206460 0.0036771562
[43,] 0.9992307692 0.0005769231 0.0001923077
[44,] 0.9992307692 0.0005769231 0.0001923077
[45,] 0.9992307692 0.0005769231 0.0001923077
[46,] 0.9992307692 0.0005769231 0.0001923077
[47,] 0.9992307692 0.0005769231 0.0001923077
[48,] 0.9992307692 0.0005769231 0.0001923077
[49,] 0.9992307692 0.0005769231 0.0001923077
[50,] 0.9992307692 0.0005769231 0.0001923077
[51,] 0.0044359933 0.9370048424 0.0585591643
[52,] 0.0044359933 0.9468084138 0.0487555929
[53,] 0.0037314479 0.7651157792 0.2311527730
[54,] 0.0009555556 0.9640509116 0.0349935328
[55,] 0.0044359933 0.9361000805 0.0594639262
[56,] 0.0034551321 0.9644810056 0.0320638624
[57,] 0.0044359933 0.8847340470 0.1108299597
[58,] 0.0178021978 0.9450267765 0.0371710257
[59,] 0.0044359933 0.9485256757 0.0470383309
[60,] 0.0178021978 0.9367281654 0.0454696368
[61,] 0.0178021978 0.9322911298 0.0499066724
[62,] 0.0044359933 0.9646125195 0.0309514872
[63,] 0.0044359933 0.9527746902 0.0427893165
[64,] 0.0044359933 0.9550232337 0.0405407730
[65,] 0.0034551321 0.9667131484 0.0298317195
[66,] 0.0044359933 0.9480465091 0.0475174976
[67,] 0.0034551321 0.9591387437 0.0374061243
[68,] 0.0044359933 0.9693297814 0.0262342253
[69,] 0.0044359933 0.9344943941 0.0610696126
[70,] 0.0034551321 0.9647687040 0.0317761639
[71,] 0.0000000000 0.2110933225 0.7889066775
[72,] 0.0044359933 0.9615024004 0.0340616063
[73,] 0.0037314479 0.7706424232 0.2256261290
[74,] 0.0044359933 0.9578357337 0.0377282730
[75,] 0.0044359933 0.9485256757 0.0470383309
[76,] 0.0044359933 0.9457131757 0.0498508309
[77,] 0.0037314479 0.8153793641 0.1808891880
[78,] 0.0018862915 0.4812353638 0.5168783447
[79,] 0.0044359933 0.9627375195 0.0328264872
[80,] 0.0034551321 0.9653937040 0.0311511639
[81,] 0.0009555556 0.9640509116 0.0349935328
[82,] 0.0009555556 0.9640509116 0.0349935328
[83,] 0.0044359933 0.9693297814 0.0262342253
[84,] 0.0018862915 0.5084237205 0.4896899880
[85,] 0.0392307692 0.9214530932 0.0393161375
[86,] 0.0044359933 0.8976631526 0.0979008541
[87,] 0.0044359933 0.9444750805 0.0510889262
[88,] 0.0044359933 0.9565812313 0.0389827754
[89,] 0.0034551321 0.9667131484 0.0298317195
[90,] 0.0009555556 0.9640509116 0.0349935328
[91,] 0.0009555556 0.9640509116 0.0349935328
[92,] 0.0044359933 0.9586899004 0.0368741063
[93,] 0.0044359933 0.9673853369 0.0281786698
[94,] 0.0178021978 0.9450267765 0.0371710257
[95,] 0.0034551321 0.9667131484 0.0298317195
[96,] 0.0034551321 0.9673381484 0.0292067195
[97,] 0.0034551321 0.9673381484 0.0292067195
[98,] 0.0044359933 0.9616000805 0.0339639262
[99,] 0.0178021978 0.9450267765 0.0371710257
[100,] 0.0034551321 0.9673381484 0.0292067195
[101,] 0.0000000000 0.0219424873 0.9780575127
[102,] 0.0000000000 0.0453141990 0.9546858010
[103,] 0.0000000000 0.0112671210 0.9887328790
[104,] 0.0000000000 0.0147373591 0.9852626409
[105,] 0.0000000000 0.0099754544 0.9900245456
[106,] 0.0000000000 0.0112671210 0.9887328790
[107,] 0.0178021978 0.7082743911 0.2739234111
[108,] 0.0000000000 0.0160290258 0.9839709742
[109,] 0.0000000000 0.0213989238 0.9786010762
[110,] 0.0063636364 0.0179960588 0.9756403049
[111,] 0.0000000000 0.0249944354 0.9750055646
[112,] 0.0000000000 0.0141659306 0.9858340694
[113,] 0.0000000000 0.0112671210 0.9887328790
[114,] 0.0000000000 0.1324084080 0.8675915920
[115,] 0.0000000000 0.0411237228 0.9588762772
[116,] 0.0000000000 0.0219424873 0.9780575127
[117,] 0.0000000000 0.0147373591 0.9852626409
[118,] 0.0063636364 0.0179960588 0.9756403049
[119,] 0.0000000000 0.0141242639 0.9858757361
[120,] 0.0018862915 0.6206249591 0.3774887494
[121,] 0.0000000000 0.0142341540 0.9857658460
[122,] 0.0000000000 0.1758655129 0.8241344871
[123,] 0.0000000000 0.0112671210 0.9887328790
[124,] 0.0000000000 0.1159274593 0.8840725407
[125,] 0.0000000000 0.0166091540 0.9833908460
[126,] 0.0000000000 0.0189960588 0.9810039412
[127,] 0.0000000000 0.1673676315 0.8326323685
[128,] 0.0000000000 0.1047651216 0.8952348784
[129,] 0.0000000000 0.0099754544 0.9900245456
[130,] 0.0018862915 0.4292972484 0.5688164601
[131,] 0.0000000000 0.0126004544 0.9873995456
[132,] 0.0063636364 0.0179960588 0.9756403049
[133,] 0.0000000000 0.0099754544 0.9900245456
[134,] 0.0018862915 0.5349845661 0.4631291424
[135,] 0.0018862915 0.5082363768 0.4898773317
[136,] 0.0000000000 0.0112671210 0.9887328790
[137,] 0.0000000000 0.0259424873 0.9740575127
[138,] 0.0000000000 0.0147373591 0.9852626409
[139,] 0.0000000000 0.1879358134 0.8120641866
[140,] 0.0000000000 0.0112671210 0.9887328790
[141,] 0.0000000000 0.0106421210 0.9893578790
[142,] 0.0000000000 0.0143190691 0.9856809309
[143,] 0.0000000000 0.0453141990 0.9546858010
[144,] 0.0000000000 0.0142341540 0.9857658460
[145,] 0.0000000000 0.0166091540 0.9833908460
[146,] 0.0000000000 0.0106421210 0.9893578790
[147,] 0.0000000000 0.0636915083 0.9363084917
[148,] 0.0000000000 0.0099754544 0.9900245456
[149,] 0.0000000000 0.0266091540 0.9733908460
[150,] 0.0000000000 0.0587061404 0.9412938596
>
> # Model evaluation
> mEval <- modelEval(model, iris[["Species"]], pred$class, pred$prob)
> # print(mEval)
>
> # Clean up. otherwise the memory is still taken
> destroyModels(model) # clean up
>
>
> # evaluate features in given data set with selected method
> estReliefF <- attrEval(Species ~ ., iris,
+ estimator="ReliefFexpRank", ReliefIterations=30)
*** caught segfault ***
address 0, cause 'memory not mapped'
Traceback:
1: .C("estimateCore", noInst = aux$noInst, noDiscrete = ncol(discdata), noDiscreteValues = as.integer(discnumvalues), discreteData = as.integer(discdata), noNumeric = ncol(numdata), numericData = as.double(numdata), costs = as.double(costMatrix), discAttrNames = as.character(discAttrNames), discValNames = as.character(discValCompressed), numAttrNames = as.character(numAttrNames), numOptions = length(options), optionsName = names(options), optionsVal = options, selEst = estIndex, estDisc = double(ncol(discdata)), estNum = double(ncol(numdata)), NAOK = TRUE, PACKAGE = "CORElearn")
2: attrEval(Species ~ ., iris, estimator = "ReliefFexpRank", ReliefIterations = 30)
aborting ...
Flavor: r-patched-solaris-sparc
Version: 0.9.45
Check: examples
Result: ERROR
Running examples in ‘CORElearn-Ex.R’ failed
The error most likely occurred in:
> ### Name: testCore
> ### Title: Verification of the CORElearn installation
> ### Aliases: testCore testCoreClass testCoreAttrEval testCoreReg
> ### testCoreOrdEval testCoreNA testCoreRPORT testCoreRand allTests
> ### Keywords: classif
>
> ### ** Examples
>
> allTests() # run all tests and generate an error, if any of the tests fails
testCoreClass() : OK
Comparison FAILED for testCoreAttrEval/estReliefF/stored
stored
[1] 0.074131090 0.085205400 0.050185750 0.026567790 0.065219700
[6] 0.030826570 -0.008773201 0.100277400 0.082634870 -0.004818440
difference
a1 a2 a3 a4 a5 a6 a7 x1
2.271002 1.679700 2.541132 1.474514 1.449294 2.834697 2.525920 9.554145
x2 x3
8.444995 8.808747
estReliefF0
a1 a2 a3 a4 a5 a6 a7 x1
2.345133 1.764905 2.591318 1.501082 1.514514 2.865524 2.517147 9.654422
x2 x3
8.527630 8.803928
comparison FAILED
testCoreAttrEval() : FAIL
testCoreReg() : OK
testCoreOrdEval() : OK
testCoreNA() : OK
testCoreRPORT() : OK
testCoreRand() : OK
Error in outputResult("allTests", result, "", continue = FALSE) :
Test FAILED: allTests
Calls: allTests -> outputResult
Execution halted
Flavor: r-patched-solaris-x86