Classification Stacked Autoencoder Deep Neural Network Learner

Calls deepnet::sae.dnn.train() from deepnet.

Initial parameter values

• output is set to "softmax" for probabilistic classification.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.saeDNN")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, deepnet

Parameters

Id	Type	Default	Levels	Range
hidden	untyped	10L		-
activationfun	character	sigm	sigm, linear, tanh	-
learningrate	numeric	0.8		\([0, \infty)\)
momentum	numeric	0.5		\([0, \infty)\)
learningrate_scale	numeric	1		\([0, \infty)\)
numepochs	integer	3		\([1, \infty)\)
batchsize	integer	100		\([1, \infty)\)
output	character	-	sigm, linear, softmax	-
sae_output	character	linear	sigm, linear, softmax	-
hidden_dropout	numeric	0		\([0, 1]\)
visible_dropout	numeric	0		\([0, 1]\)

References

Rong, Xiao (2022). “deepnet: Deep Learning Toolkit in R.” R package version 0.2.1. doi:10.32614/CRAN.package.deepnet , https://CRAN.R-project.org/package=deepnet.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

awinterstetter

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSaeDNN

Methods

Public methods

• LearnerClassifSaeDNN$new()

• LearnerClassifSaeDNN$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerClassif$predict_newdata_fast()

---

LearnerClassifSaeDNN$new()

Creates a new instance of this R6 class.

Usage

LearnerClassifSaeDNN$new()

---

LearnerClassifSaeDNN$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifSaeDNN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.saeDNN")
print(learner)
#> 
#> ── <LearnerClassifSaeDNN> (classif.saeDNN): Deep neural network with weights ini
#> • Model: -
#> • Parameters: output=softmax
#> • Packages: mlr3 and deepnet
#> • Predict Types: [response] and prob
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass and twoclass
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
#> begin to train sae ......
#> training layer 1 autoencoder ...
#> sae has been trained.
#> begin to train deep nn ......
#> deep nn has been trained.

print(learner$model)
#> $input_dim
#> [1] 60
#> 
#> $output_dim
#> [1] 2
#> 
#> $hidden
#> [1] 1
#> 
#> $size
#> [1] 60  1  2
#> 
#> $activationfun
#> [1] "sigm"
#> 
#> $learningrate
#> [1] 0.8
#> 
#> $momentum
#> [1] 0.5
#> 
#> $learningrate_scale
#> [1] 1
#> 
#> $hidden_dropout
#> [1] 0
#> 
#> $visible_dropout
#> [1] 0
#> 
#> $output
#> [1] "softmax"
#> 
#> $W
#> $W[[1]]
#>              V1        V10        V11        V12        V13        V14
#> [1,] -0.1049212 -0.1251303 -0.1002541 -0.1848406 -0.1908152 -0.2195758
#>             V15        V16        V17        V18        V19          V2
#> [1,] -0.1360601 -0.2617629 -0.1829674 -0.2400035 -0.1874371 -0.06699678
#>             V20        V21        V22        V23        V24        V25
#> [1,] -0.1933547 -0.3794436 -0.3875981 -0.3453898 -0.3354566 -0.3722823
#>             V26       V27        V28        V29          V3        V30
#> [1,] -0.3656538 -0.363434 -0.2895164 -0.2355845 -0.02092024 -0.3620106
#>             V31        V32        V33       V34        V35        V36
#> [1,] -0.2651074 -0.2691942 -0.2712251 -0.144578 -0.2185912 -0.2072005
#>             V37         V38        V39          V4        V40        V41
#> [1,] -0.2055409 -0.08188812 -0.1687008 -0.03627779 -0.1773278 -0.1635009
#>              V42         V43        V44        V45         V46        V47
#> [1,] -0.08228645 -0.04374209 -0.1821722 -0.1212138 -0.04455609 0.01056506
#>              V48         V49         V5        V50        V51          V52
#> [1,] -0.08349755 -0.05662546 0.04164302 -0.1001997 -0.0517596 -0.009980564
#>             V53         V54        V55        V56         V57         V58
#> [1,] 0.08113672 -0.04255851 0.07318475 0.09221357 -0.08227268 -0.02510994
#>              V59         V6         V60         V7          V8         V9
#> [1,] -0.06797514 -0.1094239 -0.09481433 -0.1070253 -0.02943077 -0.1014426
#> 
#> $W[[2]]
#>            [,1]
#> [1,] 0.07542375
#> [2,] 0.03928880
#> 
#> 
#> $vW
#> $vW[[1]]
#>                 V1           V10           V11           V12           V13
#> [1,] -3.973716e-06 -1.882209e-05 -2.806084e-05 -2.505037e-05 -2.301779e-05
#>                V14           V15           V16           V17           V18
#> [1,] -1.873846e-05 -1.723911e-05 -1.572365e-05 -1.254125e-05 -1.070643e-05
#>                V19            V2           V20          V21          V22
#> [1,] -1.016577e-05 -5.251588e-06 -1.307181e-05 -1.31826e-05 3.292244e-06
#>               V23          V24          V25          V26          V27
#> [1,] 2.274293e-05 2.841503e-05 3.063014e-05 3.171173e-05 3.306637e-05
#>               V28          V29           V3          V30          V31
#> [1,] 2.841928e-05 2.390815e-05 -5.53054e-06 1.552943e-05 2.382162e-05
#>               V32          V33          V34          V35          V36
#> [1,] 1.237562e-05 1.152555e-05 1.994691e-05 2.312068e-05 2.649678e-05
#>               V37          V38         V39            V4        V40
#> [1,] 1.684577e-05 6.727979e-06 4.26869e-06 -7.730242e-06 4.7763e-06
#>               V41           V42           V43           V44           V45
#> [1,] 1.892797e-06 -5.013368e-06 -9.789749e-06 -1.072479e-05 -1.631688e-05
#>                V46           V47           V48           V49            V5
#> [1,] -1.538593e-05 -9.947606e-06 -1.000354e-05 -5.312936e-06 -5.384203e-06
#>                V50          V51           V52           V53           V54
#> [1,] -1.036892e-06 -1.57103e-06 -1.645397e-06 -7.294453e-07 -4.265604e-07
#>                V55           V56           V57           V58           V59
#> [1,] -8.638199e-07 -7.293629e-07 -3.719056e-07 -7.577603e-07 -3.709603e-07
#>                 V6           V60            V7            V8            V9
#> [1,] -2.887694e-06 -2.413629e-07 -3.561848e-06 -9.965001e-06 -1.540035e-05
#> 
#> $vW[[2]]
#>               [,1]
#> [1,]  0.0004694238
#> [2,] -0.0004694238
#> 
#> 
#> $B
#> $B[[1]]
#> [1] -0.4392133
#> 
#> $B[[2]]
#> [1] -0.0378760 -0.0338965
#> 
#> 
#> $vB
#> $vB[[1]]
#> [1] 2.130679e-05
#> 
#> $vB[[2]]
#> [1] -0.001436109  0.001436109
#> 
#> 
#> $post
#> $post[[1]]
#>           V1    V10    V11    V12    V13    V14    V15    V16    V17    V18
#>  [1,] 0.0335 0.2660 0.3188 0.3553 0.3116 0.1965 0.1780 0.2794 0.2870 0.3969
#>  [2,] 0.0374 0.2597 0.3483 0.3999 0.4574 0.5950 0.7924 0.8272 0.8087 0.8977
#>  [3,] 0.0130 0.1483 0.1532 0.1100 0.0890 0.1236 0.1197 0.1145 0.2137 0.2838
#>  [4,] 0.0086 0.1185 0.0775 0.1101 0.1042 0.0853 0.0456 0.1304 0.2690 0.2947
#>  [5,] 0.0307 0.2792 0.3877 0.4992 0.4981 0.4972 0.5607 0.7339 0.8230 0.9173
#>  [6,] 0.0116 0.1006 0.2500 0.3988 0.3809 0.4753 0.6165 0.6464 0.8024 0.9208
#>  [7,] 0.0473 0.3759 0.3021 0.2909 0.2301 0.1411 0.1582 0.2430 0.4474 0.5964
#>  [8,] 0.0291 0.1146 0.0942 0.0794 0.0252 0.1191 0.1045 0.2050 0.1556 0.2690
#>  [9,] 0.0176 0.0474 0.0526 0.1854 0.1040 0.0948 0.0912 0.1688 0.1568 0.0375
#> [10,] 0.0731 0.1114 0.1739 0.3160 0.3249 0.2164 0.2031 0.2580 0.1796 0.2422
#> [11,] 0.0519 0.2838 0.2802 0.3086 0.2657 0.3801 0.5626 0.4376 0.2617 0.1199
#> [12,] 0.1088 0.5761 0.4733 0.2362 0.1023 0.2904 0.4713 0.4659 0.1415 0.0849
#> [13,] 0.0108 0.0393 0.1106 0.1412 0.2202 0.2976 0.4116 0.4754 0.5390 0.6279
#> [14,] 0.0715 0.4186 0.4867 0.5249 0.5959 0.6855 0.8573 0.9718 0.8693 0.8711
#> [15,] 0.0378 0.2053 0.3135 0.3118 0.3686 0.3885 0.5850 0.7868 0.9739 1.0000
#> [16,] 0.0262 0.6194 0.6333 0.7060 0.5544 0.5320 0.6479 0.6931 0.6759 0.7551
#> [17,] 0.0762 0.4459 0.4152 0.3952 0.4256 0.4135 0.4528 0.5326 0.7306 0.6193
#> [18,] 0.0190 0.0349 0.1459 0.3473 0.3197 0.2823 0.0166 0.0572 0.2164 0.4563
#> [19,] 0.0293 0.0751 0.0528 0.1209 0.1763 0.2039 0.2727 0.2321 0.2676 0.2934
#> [20,] 0.0239 0.1376 0.0938 0.0259 0.1499 0.2851 0.5743 0.8278 0.8669 0.8131
#> [21,] 0.0134 0.1250 0.2405 0.2325 0.2523 0.1472 0.0669 0.1100 0.2353 0.3282
#> [22,] 0.0411 0.2300 0.1370 0.1335 0.2137 0.1526 0.0775 0.1196 0.0903 0.0689
#> [23,] 0.0629 0.3597 0.5466 0.5205 0.5127 0.5395 0.6558 0.8705 0.9786 0.9335
#> [24,] 0.1371 0.2067 0.4257 0.5484 0.7131 0.7003 0.6777 0.7939 0.9382 0.8925
#> [25,] 0.0790 0.6609 0.5002 0.2583 0.1650 0.4347 0.4515 0.4579 0.3366 0.4000
#> [26,] 0.0530 0.0740 0.1610 0.2226 0.2703 0.3365 0.4266 0.4144 0.5655 0.6921
#> [27,] 0.0274 0.1808 0.2366 0.0906 0.1749 0.4012 0.5187 0.7312 0.9062 0.9260
#> [28,] 0.0201 0.0279 0.2251 0.2615 0.1770 0.3709 0.4533 0.5553 0.4616 0.3797
#> [29,] 0.0201 0.2720 0.2188 0.3037 0.2959 0.2059 0.0906 0.1610 0.1800 0.2180
#> [30,] 0.0335 0.4372 0.5533 0.5771 0.7022 0.7067 0.7367 0.7391 0.8622 0.9458
#> [31,] 0.0135 0.1908 0.1576 0.1112 0.1197 0.1174 0.1415 0.2215 0.2658 0.2713
#> [32,] 0.0126 0.4284 0.3015 0.1207 0.3299 0.5707 0.6962 0.9751 1.0000 0.9293
#> [33,] 0.0238 0.2048 0.2652 0.3100 0.2381 0.1918 0.1430 0.1735 0.1781 0.2852
#> [34,] 0.0087 0.0818 0.1315 0.1862 0.2789 0.2579 0.2240 0.2568 0.2933 0.2991
#> [35,] 0.0305 0.3127 0.2192 0.2621 0.2419 0.2179 0.1159 0.1237 0.0886 0.1755
#> [36,] 0.0151 0.1205 0.0847 0.1518 0.2305 0.2793 0.3404 0.4527 0.6950 0.8807
#> [37,] 0.0191 0.3134 0.4786 0.5239 0.4393 0.3440 0.2869 0.3889 0.4420 0.3892
#> [38,] 0.0968 0.5025 0.3488 0.1700 0.2076 0.3087 0.4224 0.5312 0.2436 0.1884
#> [39,] 0.0423 0.2696 0.3412 0.4292 0.3682 0.3940 0.2965 0.3172 0.2825 0.3050
#> [40,] 0.0100 0.1264 0.0881 0.1992 0.0184 0.2261 0.1729 0.2131 0.0693 0.2281
#>          V19     V2    V20    V21    V22    V23    V24    V25    V26    V27
#>  [1,] 0.5599 0.0258 0.6936 0.7969 0.7452 0.8203 0.9261 0.8810 0.8814 0.9301
#>  [2,] 0.9828 0.0586 0.8982 0.8890 0.9367 0.9122 0.7936 0.6718 0.6318 0.4865
#>  [3,] 0.3640 0.0006 0.5430 0.6673 0.7979 0.9273 0.9027 0.9192 1.0000 0.9821
#>  [4,] 0.3669 0.0215 0.4948 0.6275 0.8162 0.9237 0.8710 0.8052 0.8756 1.0000
#>  [5,] 0.9975 0.0523 0.9911 0.8240 0.6498 0.5980 0.4862 0.3150 0.1543 0.0989
#>  [6,] 0.9832 0.0179 0.9634 0.8646 0.8325 0.8276 0.8007 0.6102 0.4853 0.4355
#>  [7,] 0.6744 0.0509 0.7969 0.8319 0.7813 0.8626 0.7369 0.4122 0.2596 0.3392
#>  [8,] 0.3784 0.0400 0.4024 0.3470 0.1395 0.1208 0.2827 0.1500 0.2626 0.4468
#>  [9,] 0.1316 0.0172 0.2086 0.1976 0.0946 0.1965 0.1242 0.0616 0.2141 0.4642
#> [10,] 0.3609 0.1249 0.1810 0.2604 0.6572 0.9734 0.9757 0.8079 0.6521 0.4915
#> [11,] 0.6676 0.0548 0.9402 0.7832 0.5352 0.6809 0.9174 0.7613 0.8220 0.8872
#> [12,] 0.3257 0.1278 0.9007 0.9312 0.4856 0.1346 0.1604 0.2737 0.5609 0.3654
#> [13,] 0.7060 0.0086 0.7918 0.9493 1.0000 0.9645 0.9432 0.8658 0.7895 0.6501
#> [14,] 0.8954 0.0849 0.9922 0.8980 0.8158 0.8373 0.7541 0.5893 0.5488 0.5643
#> [15,] 0.9843 0.0318 0.8610 0.8443 0.9061 0.5847 0.4033 0.5946 0.6793 0.6389
#> [16,] 0.8929 0.0582 0.8619 0.7974 0.6737 0.4293 0.3648 0.5331 0.2413 0.5070
#> [17,] 0.2032 0.0666 0.4636 0.4148 0.4292 0.5730 0.5399 0.3161 0.2285 0.6995
#> [18,] 0.3819 0.0038 0.5627 0.6484 0.7235 0.8242 0.8766 1.0000 0.8582 0.6563
#> [19,] 0.3295 0.0378 0.4910 0.5402 0.6257 0.6826 0.7527 0.8504 0.8938 0.9928
#> [20,] 0.9045 0.0189 0.9046 1.0000 0.9976 0.9872 0.9761 0.9009 0.9724 0.9675
#> [21,] 0.4416 0.0172 0.5167 0.6508 0.7793 0.7978 0.7786 0.8587 0.9321 0.9454
#> [22,] 0.2071 0.0277 0.2975 0.2836 0.3353 0.3622 0.3202 0.3452 0.3562 0.3892
#> [23,] 0.7917 0.1065 0.7383 0.6908 0.3850 0.0671 0.0502 0.2717 0.2839 0.2234
#> [24,] 0.9146 0.1226 0.7832 0.7960 0.7983 0.7716 0.6615 0.4860 0.5572 0.4697
#> [25,] 0.5325 0.0707 0.9010 0.9939 0.3689 0.1012 0.0248 0.2318 0.3981 0.2259
#> [26,] 0.8547 0.0885 0.9234 0.9171 1.0000 0.9532 0.9101 0.8337 0.7053 0.6534
#> [27,] 0.7434 0.0242 0.4463 0.5103 0.6952 0.7755 0.8364 0.7283 0.6399 0.5759
#> [28,] 0.3450 0.0423 0.2665 0.2395 0.1127 0.2556 0.5169 0.3779 0.4082 0.5353
#> [29,] 0.2026 0.0178 0.1506 0.0521 0.2143 0.4333 0.5943 0.6926 0.7576 0.8787
#> [30,] 0.8782 0.0134 0.7913 0.5760 0.3061 0.0563 0.0239 0.2554 0.4862 0.5027
#> [31,] 0.3862 0.0045 0.5717 0.6797 0.8747 1.0000 0.8948 0.8420 0.9174 0.9307
#> [32,] 0.6210 0.0519 0.4586 0.5001 0.5032 0.7082 0.8420 0.8109 0.7690 0.8105
#> [33,] 0.5036 0.0318 0.6166 0.7616 0.8125 0.7793 0.8788 0.8813 0.9470 1.0000
#> [34,] 0.3924 0.0046 0.4691 0.5665 0.6464 0.6774 0.7577 0.8856 0.9419 1.0000
#> [35,] 0.1758 0.0363 0.1540 0.0512 0.1805 0.4039 0.5697 0.6577 0.7474 0.8543
#> [36,] 0.9154 0.0320 0.7542 0.6736 0.7146 0.8335 0.7701 0.6993 0.6543 0.5040
#> [37,] 0.4088 0.0173 0.5006 0.7271 0.9385 1.0000 0.9831 0.9932 0.9161 0.8237
#> [38,] 0.1908 0.0821 0.8321 1.0000 0.4076 0.0960 0.1928 0.2419 0.3790 0.2893
#> [39,] 0.2408 0.0321 0.5420 0.6802 0.6320 0.5824 0.6805 0.5984 0.8412 0.9911
#> [40,] 0.4060 0.0171 0.3973 0.2741 0.3690 0.5556 0.4846 0.3140 0.5334 0.5256
#>          V28    V29     V3    V30    V31    V32    V33    V34    V35    V36
#>  [1,] 0.9955 0.8576 0.0398 0.6069 0.3934 0.2464 0.1645 0.1140 0.0956 0.0080
#>  [2,] 0.3388 0.4832 0.0628 0.3822 0.3075 0.1267 0.0743 0.1510 0.1906 0.1817
#>  [3,] 0.9092 0.8184 0.0088 0.6962 0.5900 0.5447 0.5142 0.5389 0.5531 0.5318
#>  [4,] 0.9858 0.9427 0.0242 0.8114 0.6987 0.6810 0.6591 0.6954 0.7290 0.6680
#>  [5,] 0.0284 0.1008 0.0653 0.2636 0.2694 0.2930 0.2925 0.3998 0.3660 0.3172
#>  [6,] 0.4307 0.4399 0.0449 0.3833 0.3032 0.3035 0.3197 0.2292 0.2131 0.2347
#>  [7,] 0.3788 0.4488 0.0819 0.6281 0.7449 0.7328 0.7704 0.7870 0.6048 0.5860
#>  [8,] 0.7520 0.9036 0.0771 0.7812 0.4766 0.2483 0.5372 0.6279 0.3647 0.4572
#>  [9,] 0.6471 0.6340 0.0501 0.6107 0.7046 0.5376 0.5934 0.8443 0.9481 0.9705
#> [10,] 0.5363 0.7649 0.1665 0.5250 0.5101 0.4219 0.4160 0.1906 0.0223 0.4219
#> [11,] 0.6091 0.2967 0.0842 0.1103 0.1318 0.0624 0.0990 0.4006 0.3666 0.1050
#> [12,] 0.6139 0.5470 0.0926 0.8474 0.5638 0.5443 0.5086 0.6253 0.8497 0.8406
#> [13,] 0.4492 0.4739 0.0058 0.6153 0.4929 0.3195 0.3735 0.3336 0.1052 0.0671
#> [14,] 0.5406 0.4783 0.0587 0.4439 0.3698 0.2574 0.1478 0.1743 0.1229 0.1588
#> [15,] 0.5002 0.5578 0.0423 0.4831 0.4729 0.3318 0.3969 0.3894 0.2314 0.1036
#> [16,] 0.8533 0.6036 0.1099 0.8514 0.8512 0.5045 0.1862 0.2709 0.4232 0.3043
#> [17,] 1.0000 0.7262 0.0481 0.4724 0.5103 0.5459 0.2881 0.0981 0.1951 0.4181
#> [18,] 0.5087 0.4817 0.0642 0.4530 0.4521 0.4532 0.5385 0.5308 0.5356 0.5271
#> [19,] 0.9134 0.7080 0.0257 0.6318 0.6126 0.4638 0.2797 0.1721 0.1665 0.2561
#> [20,] 0.7633 0.4434 0.0466 0.3822 0.4727 0.4007 0.3381 0.3172 0.2222 0.0733
#> [21,] 0.8645 0.7220 0.0178 0.4850 0.1357 0.2951 0.4715 0.6036 0.8083 0.9870
#> [22,] 0.6622 0.9254 0.0604 1.0000 0.8528 0.6297 0.5250 0.4012 0.2901 0.2007
#> [23,] 0.1911 0.0408 0.1526 0.2531 0.1979 0.1891 0.2433 0.1956 0.2667 0.1340
#> [24,] 0.5640 0.4517 0.1385 0.3369 0.2684 0.2339 0.3052 0.3016 0.2753 0.1041
#> [25,] 0.5247 0.6898 0.0352 0.8316 0.4326 0.3741 0.5756 0.8043 0.7963 0.7174
#> [26,] 0.4483 0.2460 0.1997 0.2020 0.1446 0.0994 0.1510 0.2392 0.4434 0.5023
#> [27,] 0.4146 0.3495 0.0621 0.4437 0.2665 0.2024 0.1942 0.0765 0.3725 0.5843
#> [28,] 0.5116 0.4544 0.0554 0.4258 0.3869 0.3939 0.4661 0.3974 0.2194 0.1816
#> [29,] 0.9060 0.8528 0.0274 0.9087 0.9657 0.9306 0.7774 0.6643 0.6604 0.6884
#> [30,] 0.4402 0.2847 0.0696 0.1797 0.3560 0.3522 0.3321 0.3112 0.3638 0.0754
#> [31,] 0.9050 0.8228 0.0051 0.6986 0.5831 0.4924 0.4563 0.5159 0.5670 0.5284
#> [32,] 0.6203 0.2356 0.0621 0.2595 0.6299 0.6762 0.2903 0.4393 0.8529 0.7180
#> [33,] 0.9739 0.8446 0.0422 0.6151 0.4302 0.3165 0.2869 0.2017 0.1206 0.0271
#> [34,] 0.8564 0.6790 0.0081 0.5587 0.4147 0.2946 0.2025 0.0688 0.1171 0.2157
#> [35,] 0.9085 0.8668 0.0214 0.8892 0.9065 0.8522 0.7204 0.6200 0.6253 0.6848
#> [36,] 0.4926 0.4992 0.0599 0.4161 0.1631 0.0404 0.0637 0.2962 0.3609 0.1866
#> [37,] 0.6957 0.4536 0.0291 0.3281 0.2522 0.3964 0.4154 0.3308 0.1445 0.1923
#> [38,] 0.3451 0.3777 0.0629 0.5213 0.2316 0.3335 0.4781 0.6116 0.6705 0.7375
#> [39,] 0.9187 0.8005 0.0709 0.6713 0.5632 0.7332 0.6038 0.2575 0.0349 0.1799
#> [40,] 0.2520 0.2090 0.0623 0.3559 0.6260 0.7340 0.6120 0.3497 0.3953 0.3012
#>          V37    V38    V39     V4    V40    V41    V42    V43    V44    V45
#>  [1,] 0.0702 0.0936 0.0894 0.0570 0.1127 0.0873 0.1020 0.1964 0.2256 0.1814
#>  [2,] 0.1709 0.0946 0.2829 0.0534 0.3006 0.1602 0.1483 0.2875 0.2047 0.1064
#>  [3,] 0.4826 0.3790 0.1831 0.0456 0.1750 0.1679 0.0674 0.0609 0.0375 0.0533
#>  [4,] 0.5917 0.4899 0.3439 0.0445 0.2366 0.1716 0.1013 0.0766 0.0845 0.0260
#>  [5,] 0.4609 0.4374 0.1820 0.0521 0.3376 0.6202 0.4448 0.1863 0.1420 0.0589
#>  [6,] 0.3201 0.4455 0.3655 0.1096 0.2715 0.1747 0.1781 0.2199 0.1056 0.0573
#>  [7,] 0.6385 0.7279 0.6286 0.1252 0.5316 0.4069 0.1791 0.1625 0.2527 0.1903
#>  [8,] 0.6359 0.6474 0.5520 0.0809 0.3253 0.2292 0.0653 0.0000 0.0000 0.0000
#>  [9,] 0.7766 0.6313 0.5760 0.0285 0.6148 0.5450 0.4813 0.3406 0.1916 0.1134
#> [10,] 0.5496 0.2483 0.2034 0.1496 0.2729 0.2837 0.4463 0.3178 0.0807 0.1192
#> [11,] 0.1915 0.3930 0.4288 0.0319 0.2546 0.1151 0.2196 0.1879 0.1437 0.2146
#> [12,] 0.8420 0.9136 0.7713 0.1234 0.4882 0.3724 0.4469 0.4586 0.4491 0.5616
#> [13,] 0.0379 0.0461 0.1694 0.0460 0.2169 0.1677 0.0644 0.0159 0.0778 0.0653
#> [14,] 0.1803 0.1436 0.1667 0.0218 0.2630 0.2234 0.1239 0.0869 0.2092 0.1499
#> [15,] 0.1312 0.0864 0.2569 0.0350 0.3179 0.2649 0.2714 0.1713 0.0584 0.1230
#> [16,] 0.6116 0.6756 0.5375 0.1083 0.4719 0.4647 0.2587 0.2129 0.2222 0.2111
#> [17,] 0.4604 0.3217 0.2828 0.0394 0.2430 0.1979 0.2444 0.1847 0.0841 0.0692
#> [18,] 0.4260 0.2436 0.1205 0.0452 0.3845 0.4107 0.5067 0.4216 0.2479 0.1586
#> [19,] 0.2735 0.3209 0.2724 0.0062 0.1880 0.1552 0.2522 0.2121 0.1801 0.1473
#> [20,] 0.2692 0.1888 0.0712 0.0440 0.1062 0.0694 0.0300 0.0893 0.1459 0.1348
#> [21,] 0.8800 0.6411 0.4276 0.0363 0.2702 0.2642 0.3342 0.4335 0.4542 0.3960
#> [22,] 0.3356 0.4799 0.6147 0.0525 0.6246 0.4973 0.3492 0.2662 0.3137 0.4282
#> [23,] 0.1073 0.2023 0.1794 0.1229 0.0227 0.1313 0.1775 0.1549 0.1626 0.0708
#> [24,] 0.1757 0.3156 0.3603 0.1484 0.2736 0.1301 0.2458 0.3404 0.1753 0.0679
#> [25,] 0.7056 0.8148 0.7601 0.1660 0.6034 0.4554 0.4729 0.4478 0.3722 0.4693
#> [26,] 0.4441 0.4571 0.3927 0.2604 0.2900 0.3408 0.4990 0.3632 0.1387 0.1800
#> [27,] 0.4827 0.2347 0.0999 0.0560 0.3244 0.3990 0.2975 0.1684 0.1761 0.1683
#> [28,] 0.1023 0.2108 0.3253 0.0783 0.3697 0.2912 0.3010 0.2563 0.1927 0.2062
#> [29,] 0.6938 0.5932 0.5774 0.0232 0.6223 0.5841 0.4527 0.4911 0.5762 0.5013
#> [30,] 0.1834 0.1820 0.1815 0.1180 0.1593 0.0576 0.0954 0.1086 0.0812 0.0784
#> [31,] 0.5144 0.3742 0.2282 0.0289 0.1193 0.1088 0.0431 0.1070 0.0583 0.0046
#> [32,] 0.4801 0.5856 0.4993 0.0518 0.2866 0.0601 0.1167 0.2737 0.2812 0.2078
#> [33,] 0.0580 0.1262 0.1072 0.0399 0.1082 0.0360 0.1197 0.2061 0.2054 0.1878
#> [34,] 0.2216 0.2776 0.2309 0.0230 0.1444 0.1513 0.1745 0.1756 0.1424 0.0908
#> [35,] 0.7337 0.6281 0.5725 0.0227 0.6119 0.5597 0.4965 0.5027 0.5772 0.5907
#> [36,] 0.0476 0.1497 0.2405 0.1050 0.1980 0.3175 0.2379 0.1716 0.1559 0.1556
#> [37,] 0.3208 0.3367 0.5683 0.0301 0.5505 0.3231 0.0448 0.3131 0.3387 0.4130
#> [38,] 0.7356 0.7792 0.6788 0.0608 0.5259 0.2762 0.1545 0.2019 0.2231 0.4221
#> [39,] 0.3039 0.4760 0.5756 0.0108 0.4254 0.5046 0.7179 0.6163 0.5663 0.5749
#> [40,] 0.5408 0.8814 0.9857 0.0205 0.9167 0.6121 0.5006 0.3210 0.3202 0.4295
#>          V46    V47    V48    V49     V5    V50    V51    V52    V53    V54
#>  [1,] 0.2012 0.1688 0.1037 0.0501 0.0529 0.0136 0.0130 0.0120 0.0039 0.0053
#>  [2,] 0.1395 0.1065 0.0527 0.0395 0.0255 0.0183 0.0353 0.0118 0.0063 0.0237
#>  [3,] 0.0278 0.0179 0.0114 0.0073 0.0525 0.0116 0.0092 0.0078 0.0041 0.0013
#>  [4,] 0.0333 0.0205 0.0309 0.0101 0.0667 0.0095 0.0047 0.0072 0.0054 0.0022
#>  [5,] 0.0576 0.0672 0.0269 0.0245 0.0611 0.0190 0.0063 0.0321 0.0189 0.0137
#>  [6,] 0.0307 0.0237 0.0470 0.0102 0.1913 0.0057 0.0031 0.0163 0.0099 0.0084
#>  [7,] 0.1643 0.0604 0.0209 0.0436 0.1783 0.0175 0.0107 0.0193 0.0118 0.0064
#>  [8,] 0.0000 0.0000 0.0000 0.0000 0.0521 0.0000 0.0000 0.0056 0.0237 0.0204
#>  [9,] 0.0640 0.0911 0.0980 0.0563 0.0262 0.0187 0.0088 0.0042 0.0175 0.0171
#> [10,] 0.2134 0.3241 0.2945 0.1474 0.1443 0.0211 0.0361 0.0444 0.0230 0.0290
#> [11,] 0.2360 0.1125 0.0254 0.0285 0.1158 0.0178 0.0052 0.0081 0.0120 0.0045
#> [12,] 0.4305 0.0945 0.0794 0.0274 0.1276 0.0154 0.0140 0.0455 0.0213 0.0082
#> [13,] 0.0210 0.0509 0.0387 0.0262 0.0752 0.0101 0.0161 0.0029 0.0078 0.0114
#> [14,] 0.0676 0.0899 0.0927 0.0658 0.0862 0.0086 0.0216 0.0153 0.0121 0.0096
#> [15,] 0.2200 0.2198 0.1074 0.0423 0.1787 0.0162 0.0093 0.0046 0.0044 0.0078
#> [16,] 0.0176 0.1348 0.0744 0.0130 0.0974 0.0106 0.0033 0.0232 0.0166 0.0095
#> [17,] 0.0528 0.0357 0.0085 0.0230 0.0590 0.0046 0.0156 0.0031 0.0054 0.0105
#> [18,] 0.1124 0.0651 0.0789 0.0325 0.0333 0.0070 0.0026 0.0093 0.0118 0.0112
#> [19,] 0.0681 0.1091 0.0919 0.0397 0.0130 0.0093 0.0076 0.0065 0.0072 0.0108
#> [20,] 0.0391 0.0546 0.0469 0.0201 0.0657 0.0095 0.0155 0.0091 0.0151 0.0080
#> [21,] 0.2525 0.1084 0.0372 0.0286 0.0444 0.0099 0.0046 0.0094 0.0048 0.0047
#> [22,] 0.4262 0.3511 0.2458 0.1259 0.0489 0.0327 0.0181 0.0217 0.0038 0.0019
#> [23,] 0.0129 0.0795 0.0762 0.0117 0.1437 0.0061 0.0257 0.0089 0.0262 0.0108
#> [24,] 0.1062 0.0643 0.0532 0.0531 0.1776 0.0272 0.0171 0.0118 0.0129 0.0344
#> [25,] 0.3839 0.0768 0.1467 0.0777 0.1330 0.0469 0.0193 0.0298 0.0390 0.0294
#> [26,] 0.1299 0.0523 0.0817 0.0469 0.3225 0.0114 0.0299 0.0244 0.0199 0.0257
#> [27,] 0.0729 0.1190 0.1297 0.0748 0.1129 0.0067 0.0255 0.0113 0.0108 0.0085
#> [28,] 0.1751 0.0841 0.1035 0.0641 0.0620 0.0153 0.0081 0.0191 0.0182 0.0160
#> [29,] 0.4042 0.3123 0.2232 0.1085 0.0724 0.0414 0.0253 0.0131 0.0049 0.0104
#> [30,] 0.0487 0.0439 0.0586 0.0370 0.0348 0.0185 0.0302 0.0244 0.0232 0.0093
#> [31,] 0.0473 0.0408 0.0290 0.0192 0.0561 0.0094 0.0025 0.0037 0.0084 0.0102
#> [32,] 0.0660 0.0491 0.0345 0.0172 0.1072 0.0287 0.0027 0.0208 0.0048 0.0199
#> [33,] 0.2047 0.1716 0.1069 0.0477 0.0788 0.0170 0.0186 0.0096 0.0071 0.0084
#> [34,] 0.0138 0.0469 0.0480 0.0159 0.0586 0.0045 0.0015 0.0052 0.0038 0.0079
#> [35,] 0.4803 0.3877 0.2779 0.1427 0.0456 0.0424 0.0271 0.0200 0.0070 0.0070
#> [36,] 0.0422 0.0493 0.0476 0.0219 0.1163 0.0059 0.0086 0.0061 0.0015 0.0084
#> [37,] 0.3639 0.2069 0.0859 0.0600 0.0463 0.0267 0.0125 0.0040 0.0136 0.0137
#> [38,] 0.3067 0.1329 0.1349 0.1057 0.0617 0.0499 0.0206 0.0073 0.0081 0.0303
#> [39,] 0.3593 0.2526 0.2299 0.1271 0.1070 0.0356 0.0367 0.0176 0.0035 0.0093
#> [40,] 0.3654 0.2655 0.1576 0.0681 0.0205 0.0294 0.0241 0.0121 0.0036 0.0150
#>          V55    V56    V57    V58    V59     V6    V60     V7     V8     V9
#>  [1,] 0.0062 0.0046 0.0045 0.0022 0.0005 0.1091 0.0031 0.1709 0.1684 0.1865
#>  [2,] 0.0032 0.0087 0.0124 0.0113 0.0098 0.1422 0.0126 0.2072 0.2734 0.3070
#>  [3,] 0.0011 0.0045 0.0039 0.0022 0.0023 0.0778 0.0016 0.0931 0.0941 0.1711
#>  [4,] 0.0016 0.0029 0.0058 0.0050 0.0024 0.0771 0.0030 0.0499 0.0906 0.1229
#>  [5,] 0.0277 0.0152 0.0052 0.0121 0.0124 0.0577 0.0055 0.0665 0.0664 0.1460
#>  [6,] 0.0270 0.0277 0.0097 0.0054 0.0148 0.0924 0.0092 0.0761 0.1092 0.0757
#>  [7,] 0.0042 0.0054 0.0049 0.0082 0.0028 0.3070 0.0027 0.3008 0.2362 0.3830
#>  [8,] 0.0050 0.0137 0.0164 0.0081 0.0139 0.1051 0.0111 0.0145 0.0674 0.1294
#>  [9,] 0.0079 0.0050 0.0112 0.0179 0.0294 0.0351 0.0063 0.0362 0.0535 0.0258
#> [10,] 0.0141 0.0161 0.0177 0.0194 0.0207 0.2770 0.0057 0.2555 0.1712 0.0466
#> [11,] 0.0121 0.0097 0.0085 0.0047 0.0048 0.0922 0.0053 0.1027 0.0613 0.1465
#> [12,] 0.0124 0.0167 0.0103 0.0205 0.0178 0.1731 0.0187 0.1948 0.4262 0.6828
#> [13,] 0.0083 0.0058 0.0003 0.0023 0.0026 0.0887 0.0027 0.1015 0.0494 0.0472
#> [14,] 0.0196 0.0042 0.0066 0.0099 0.0083 0.1801 0.0124 0.1916 0.1896 0.2960
#> [15,] 0.0102 0.0065 0.0061 0.0062 0.0043 0.1635 0.0053 0.0887 0.0817 0.1779
#> [16,] 0.0180 0.0244 0.0316 0.0164 0.0095 0.2280 0.0078 0.2431 0.3771 0.5598
#> [17,] 0.0110 0.0015 0.0072 0.0048 0.0107 0.0649 0.0094 0.1209 0.2467 0.3564
#> [18,] 0.0094 0.0140 0.0072 0.0022 0.0055 0.0690 0.0122 0.0901 0.1454 0.0740
#> [19,] 0.0051 0.0102 0.0041 0.0055 0.0050 0.0612 0.0087 0.0895 0.1107 0.0973
#> [20,] 0.0018 0.0078 0.0045 0.0026 0.0036 0.0742 0.0024 0.1380 0.1099 0.1384
#> [21,] 0.0016 0.0008 0.0042 0.0024 0.0027 0.0744 0.0041 0.0800 0.0456 0.0368
#> [22,] 0.0065 0.0132 0.0108 0.0050 0.0085 0.0385 0.0044 0.0611 0.1117 0.1237
#> [23,] 0.0138 0.0187 0.0230 0.0057 0.0113 0.1190 0.0131 0.0884 0.0907 0.2107
#> [24,] 0.0065 0.0067 0.0022 0.0079 0.0146 0.1428 0.0051 0.1773 0.2161 0.1630
#> [25,] 0.0175 0.0249 0.0141 0.0073 0.0025 0.0226 0.0101 0.0771 0.2678 0.5664
#> [26,] 0.0082 0.0151 0.0171 0.0146 0.0134 0.2247 0.0056 0.0617 0.2287 0.0950
#> [27,] 0.0047 0.0074 0.0104 0.0161 0.0220 0.0973 0.0173 0.1823 0.1745 0.1440
#> [28,] 0.0290 0.0090 0.0242 0.0224 0.0190 0.0871 0.0096 0.1201 0.2707 0.1206
#> [29,] 0.0102 0.0092 0.0083 0.0020 0.0048 0.0833 0.0036 0.1232 0.1298 0.2085
#> [30,] 0.0159 0.0193 0.0032 0.0377 0.0126 0.1180 0.0156 0.1948 0.1607 0.3036
#> [31,] 0.0096 0.0024 0.0037 0.0028 0.0030 0.0929 0.0030 0.1031 0.0883 0.1596
#> [32,] 0.0126 0.0022 0.0037 0.0034 0.0114 0.2587 0.0077 0.2304 0.2067 0.3416
#> [33,] 0.0038 0.0026 0.0028 0.0013 0.0035 0.0766 0.0060 0.0881 0.1143 0.1594
#> [34,] 0.0114 0.0050 0.0030 0.0064 0.0058 0.0682 0.0030 0.0993 0.0717 0.0576
#> [35,] 0.0086 0.0089 0.0074 0.0042 0.0055 0.0665 0.0021 0.0939 0.0972 0.2535
#> [36,] 0.0128 0.0054 0.0011 0.0019 0.0023 0.1734 0.0062 0.1679 0.1119 0.0889
#> [37,] 0.0172 0.0132 0.0110 0.0122 0.0114 0.0690 0.0068 0.0576 0.1103 0.2423
#> [38,] 0.0190 0.0212 0.0126 0.0201 0.0210 0.1207 0.0041 0.0944 0.4223 0.5744
#> [39,] 0.0121 0.0075 0.0056 0.0021 0.0043 0.0973 0.0017 0.0961 0.1323 0.2462
#> [40,] 0.0085 0.0073 0.0050 0.0044 0.0040 0.0368 0.0117 0.1098 0.1276 0.0598
#> 
#> $post[[2]]
#>              [,1]
#>  [1,] 0.010700775
#>  [2,] 0.008827192
#>  [3,] 0.007700351
#>  [4,] 0.006073559
#>  [5,] 0.018428119
#>  [6,] 0.010402064
#>  [7,] 0.007651029
#>  [8,] 0.039667295
#>  [9,] 0.032303550
#> [10,] 0.015646186
#> [11,] 0.016419861
#> [12,] 0.009595001
#> [13,] 0.010116691
#> [14,] 0.007535410
#> [15,] 0.009203037
#> [16,] 0.005972602
#> [17,] 0.016874395
#> [18,] 0.010238211
#> [19,] 0.013095629
#> [20,] 0.005803892
#> [21,] 0.006985651
#> [22,] 0.023438847
#> [23,] 0.036791005
#> [24,] 0.009358743
#> [25,] 0.011054985
#> [26,] 0.009303309
#> [27,] 0.012164023
#> [28,] 0.036637390
#> [29,] 0.007606991
#> [30,] 0.024417001
#> [31,] 0.007700164
#> [32,] 0.005841064
#> [33,] 0.011310714
#> [34,] 0.015251805
#> [35,] 0.008782217
#> [36,] 0.016214223
#> [37,] 0.006939734
#> [38,] 0.019796063
#> [39,] 0.006904862
#> [40,] 0.022304909
#> 
#> $post[[3]]
#>            [,1]      [,2]
#>  [1,] 0.4983863 0.5016137
#>  [2,] 0.4983689 0.5016311
#>  [3,] 0.4983584 0.5016416
#>  [4,] 0.4983434 0.5016566
#>  [5,] 0.4984579 0.5015421
#>  [6,] 0.4983835 0.5016165
#>  [7,] 0.4983580 0.5016420
#>  [8,] 0.4986547 0.5013453
#>  [9,] 0.4985865 0.5014135
#> [10,] 0.4984321 0.5015679
#> [11,] 0.4984393 0.5015607
#> [12,] 0.4983760 0.5016240
#> [13,] 0.4983808 0.5016192
#> [14,] 0.4983569 0.5016431
#> [15,] 0.4983724 0.5016276
#> [16,] 0.4983424 0.5016576
#> [17,] 0.4984435 0.5015565
#> [18,] 0.4983820 0.5016180
#> [19,] 0.4984085 0.5015915
#> [20,] 0.4983409 0.5016591
#> [21,] 0.4983518 0.5016482
#> [22,] 0.4985043 0.5014957
#> [23,] 0.4986281 0.5013719
#> [24,] 0.4983738 0.5016262
#> [25,] 0.4983895 0.5016105
#> [26,] 0.4983733 0.5016267
#> [27,] 0.4983998 0.5016002
#> [28,] 0.4986266 0.5013734
#> [29,] 0.4983576 0.5016424
#> [30,] 0.4985134 0.5014866
#> [31,] 0.4983584 0.5016416
#> [32,] 0.4983412 0.5016588
#> [33,] 0.4983919 0.5016081
#> [34,] 0.4984284 0.5015716
#> [35,] 0.4983685 0.5016315
#> [36,] 0.4984374 0.5015626
#> [37,] 0.4983514 0.5016486
#> [38,] 0.4984706 0.5015294
#> [39,] 0.4983511 0.5016489
#> [40,] 0.4984938 0.5015062
#> 
#> 
#> $pre
#> $pre[[1]]
#> NULL
#> 
#> $pre[[2]]
#>            [,1]
#>  [1,] -4.526681
#>  [2,] -4.721052
#>  [3,] -4.858759
#>  [4,] -5.097718
#>  [5,] -3.975278
#>  [6,] -4.555294
#>  [7,] -4.865235
#>  [8,] -3.186753
#>  [9,] -3.399741
#> [10,] -4.141758
#> [11,] -4.092707
#> [12,] -4.636872
#> [13,] -4.583400
#> [14,] -4.880578
#> [15,] -4.678976
#> [16,] -5.114582
#> [17,] -4.064940
#> [18,] -4.571337
#> [19,] -4.322295
#> [20,] -5.143406
#> [21,] -4.956887
#> [22,] -3.729643
#> [23,] -3.265017
#> [24,] -4.662041
#> [25,] -4.493757
#> [26,] -4.668038
#> [27,] -4.397034
#> [28,] -3.269361
#> [29,] -4.871052
#> [30,] -3.687756
#> [31,] -4.858784
#> [32,] -5.136984
#> [33,] -4.470630
#> [34,] -4.167688
#> [35,] -4.726205
#> [36,] -4.105519
#> [37,] -4.963528
#> [38,] -3.902278
#> [39,] -4.968601
#> [40,] -3.780391
#> 
#> $pre[[3]]
#>              [,1]        [,2]
#>  [1,] -0.03849999 -0.03204500
#>  [2,] -0.03864218 -0.03211773
#>  [3,] -0.03872770 -0.03216147
#>  [4,] -0.03885117 -0.03222462
#>  [5,] -0.03791354 -0.03174502
#>  [6,] -0.03852266 -0.03205659
#>  [7,] -0.03873145 -0.03216338
#>  [8,] -0.03630163 -0.03092053
#>  [9,] -0.03686049 -0.03120639
#> [10,] -0.03812467 -0.03185302
#> [11,] -0.03806595 -0.03182298
#> [12,] -0.03858391 -0.03208792
#> [13,] -0.03854432 -0.03206767
#> [14,] -0.03874022 -0.03216787
#> [15,] -0.03861366 -0.03210314
#> [16,] -0.03885883 -0.03222854
#> [17,] -0.03803146 -0.03180534
#> [18,] -0.03853510 -0.03206295
#> [19,] -0.03831824 -0.03195203
#> [20,] -0.03887163 -0.03223509
#> [21,] -0.03878194 -0.03218921
#> [22,] -0.03753326 -0.03155051
#> [23,] -0.03651992 -0.03103219
#> [24,] -0.03860184 -0.03209709
#> [25,] -0.03847311 -0.03203125
#> [26,] -0.03860605 -0.03209924
#> [27,] -0.03838894 -0.03198819
#> [28,] -0.03653158 -0.03103815
#> [29,] -0.03873479 -0.03216509
#> [30,] -0.03745902 -0.03151254
#> [31,] -0.03872772 -0.03216148
#> [32,] -0.03886881 -0.03223365
#> [33,] -0.03845370 -0.03202132
#> [34,] -0.03815460 -0.03186833
#> [35,] -0.03864560 -0.03211947
#> [36,] -0.03808156 -0.03183097
#> [37,] -0.03878543 -0.03219100
#> [38,] -0.03780972 -0.03169192
#> [39,] -0.03878808 -0.03219235
#> [40,] -0.03761932 -0.03159453
#> 
#> 
#> $e
#>             [,1]       [,2]
#>  [1,]  0.5016137 -0.5016137
#>  [2,]  0.5016311 -0.5016311
#>  [3,] -0.4983584  0.4983584
#>  [4,] -0.4983434  0.4983434
#>  [5,]  0.5015421 -0.5015421
#>  [6,]  0.5016165 -0.5016165
#>  [7,] -0.4983580  0.4983580
#>  [8,] -0.4986547  0.4986547
#>  [9,] -0.4985865  0.4985865
#> [10,]  0.5015679 -0.5015679
#> [11,] -0.4984393  0.4984393
#> [12,]  0.5016240 -0.5016240
#> [13,] -0.4983808  0.4983808
#> [14,]  0.5016431 -0.5016431
#> [15,] -0.4983724  0.4983724
#> [16,] -0.4983424  0.4983424
#> [17,] -0.4984435  0.4984435
#> [18,] -0.4983820  0.4983820
#> [19,] -0.4984085  0.4984085
#> [20,] -0.4983409  0.4983409
#> [21,]  0.5016482 -0.5016482
#> [22,]  0.5014957 -0.5014957
#> [23,]  0.5013719 -0.5013719
#> [24,]  0.5016262 -0.5016262
#> [25,]  0.5016105 -0.5016105
#> [26,]  0.5016267 -0.5016267
#> [27,] -0.4983998  0.4983998
#> [28,]  0.5013734 -0.5013734
#> [29,]  0.5016424 -0.5016424
#> [30,]  0.5014866 -0.5014866
#> [31,] -0.4983584  0.4983584
#> [32,] -0.4983412  0.4983412
#> [33,]  0.5016081 -0.5016081
#> [34,] -0.4984284  0.4984284
#> [35,]  0.5016315 -0.5016315
#> [36,] -0.4984374  0.4984374
#> [37,]  0.5016486 -0.5016486
#> [38,]  0.5015294 -0.5015294
#> [39,]  0.5016489 -0.5016489
#> [40,] -0.4984938  0.4984938
#> 
#> $L
#> [1] 0.6906556 0.7064611 0.6923680 0.7056059 0.6938517 0.6933050
#> 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.5942029