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,] -0.02758831 -0.07979884 -0.123371 -0.1894519 -0.0550611 -0.1555208
#>              V15        V16        V17        V18        V19          V2
#> [1,] -0.08421417 -0.1989506 -0.1966503 -0.2186331 -0.2742969 -0.02130233
#>             V20        V21        V22        V23        V24        V25
#> [1,] -0.2614896 -0.3481392 -0.2236044 -0.2078709 -0.3096186 -0.2107787
#>             V26        V27        V28        V29          V3        V30
#> [1,] -0.2141846 -0.4033377 -0.3796173 -0.3096947 0.005819075 -0.1891596
#>             V31        V32       V33        V34        V35        V36
#> [1,] -0.3116087 -0.2396177 -0.295279 -0.1995558 -0.1518857 -0.2217397
#>             V37        V38       V39          V4        V40        V41
#> [1,] -0.2366786 -0.2595172 -0.108967 -0.04093487 -0.2482227 -0.1682584
#>             V42         V43        V44        V45         V46         V47
#> [1,] -0.1676904 -0.04464789 -0.1899078 -0.1150964 -0.07253784 -0.05492658
#>              V48        V49         V5        V50        V51        V52
#> [1,] -0.05308694 -0.0921315 0.02693524 -0.1080072 0.04488399 0.06070939
#>              V53        V54         V55        V56        V57        V58
#> [1,] -0.05478199 0.06972583 -0.07850924 0.04858062 -0.0875962 -0.0319417
#>              V59          V6        V60           V7          V8         V9
#> [1,] -0.05080149 -0.06755769 -0.0755964 -0.009145783 -0.01198545 -0.1244758
#> 
#> $W[[2]]
#>             [,1]
#> [1,] -0.07462052
#> [2,]  0.04280250
#> 
#> 
#> $vW
#> $vW[[1]]
#>               V1          V10          V11          V12           V13
#> [1,] 1.00455e-05 4.511712e-06 2.512837e-05 1.295355e-05 -9.978799e-06
#>                V14           V15           V16           V17           V18
#> [1,] -3.936106e-05 -4.456222e-05 -6.081636e-05 -6.551081e-05 -5.664394e-05
#>                V19           V2           V20           V21          V22
#> [1,] -2.902977e-05 1.474962e-05 -3.859582e-05 -7.848255e-05 -9.37029e-05
#>                V23           V24           V25           V26           V27
#> [1,] -0.0001511033 -0.0001905513 -0.0002569679 -0.0003202034 -0.0002653684
#>                V28           V29           V3           V30           V31
#> [1,] -0.0002008257 -0.0001850739 1.754543e-05 -0.0001212771 -0.0001250456
#>                V32           V33           V34           V35           V36
#> [1,] -9.727011e-05 -0.0001009439 -0.0001385702 -0.0001692893 -0.0001775906
#>                V37          V38           V39           V4           V40
#> [1,] -0.0001186428 -4.50867e-05 -3.616963e-05 2.026484e-05 -6.044031e-05
#>                V41           V42           V43           V44          V45
#> [1,] -5.269592e-05 -3.258272e-05 -2.988913e-05 -1.146463e-05 3.166261e-05
#>               V46          V47          V48          V49           V5
#> [1,] 2.666103e-05 2.674564e-05 1.626132e-05 4.390148e-06 1.800915e-05
#>              V50           V51          V52          V53           V54
#> [1,] -1.6406e-06 -3.215339e-07 -5.18833e-07 1.907335e-07 -5.373322e-07
#>                V55          V56           V57           V58           V59
#> [1,] -1.399511e-06 2.187159e-07 -1.121703e-06 -8.383579e-07 -2.210595e-07
#>                V6          V60            V7            V8           V9
#> [1,] -1.29646e-05 6.959608e-08 -2.043247e-05 -1.939446e-05 2.040185e-06
#> 
#> $vW[[2]]
#>              [,1]
#> [1,]  0.003111612
#> [2,] -0.003111612
#> 
#> 
#> $B
#> $B[[1]]
#> [1] -0.3988432
#> 
#> $B[[2]]
#> [1]  0.23092015 -0.03919281
#> 
#> 
#> $vB
#> $vB[[1]]
#> [1] -0.0003284343
#> 
#> $vB[[2]]
#> [1]  0.04655696 -0.04655696
#> 
#> 
#> $post
#> $post[[1]]
#>           V1    V10    V11    V12    V13    V14    V15    V16    V17    V18
#>  [1,] 0.0329 0.2672 0.3056 0.3161 0.2314 0.2067 0.1804 0.2808 0.4423 0.5947
#>  [2,] 0.0335 0.2660 0.3188 0.3553 0.3116 0.1965 0.1780 0.2794 0.2870 0.3969
#>  [3,] 0.0286 0.3039 0.2988 0.4250 0.6343 0.8198 1.0000 0.9988 0.9508 0.9025
#>  [4,] 0.0116 0.1006 0.2500 0.3988 0.3809 0.4753 0.6165 0.6464 0.8024 0.9208
#>  [5,] 0.0225 0.1452 0.1442 0.0948 0.0618 0.1641 0.0708 0.0844 0.2590 0.2679
#>  [6,] 0.0629 0.3597 0.5466 0.5205 0.5127 0.5395 0.6558 0.8705 0.9786 0.9335
#>  [7,] 0.0126 0.5920 0.5832 0.5419 0.5472 0.5314 0.4981 0.6985 0.8292 0.7839
#>  [8,] 0.0346 0.1508 0.1809 0.2390 0.2947 0.2866 0.4010 0.5325 0.5486 0.5823
#>  [9,] 0.0036 0.0888 0.0937 0.1245 0.1599 0.1542 0.1846 0.1732 0.1477 0.1748
#> [10,] 0.0201 0.2720 0.2188 0.3037 0.2959 0.2059 0.0906 0.1610 0.1800 0.2180
#> [11,] 0.0411 0.2300 0.1370 0.1335 0.2137 0.1526 0.0775 0.1196 0.0903 0.0689
#> [12,] 0.0308 0.2795 0.3336 0.2940 0.1608 0.3335 0.4985 0.7295 0.7350 0.8253
#> [13,] 0.0152 0.1504 0.1911 0.2115 0.2249 0.2573 0.1701 0.2023 0.2538 0.3417
#> [14,] 0.0294 0.3473 0.4231 0.5044 0.5237 0.4398 0.3236 0.2956 0.3286 0.3231
#> [15,] 0.0047 0.3469 0.3265 0.3263 0.2301 0.1253 0.2102 0.2401 0.1928 0.1673
#> [16,] 0.0192 0.1186 0.1237 0.1601 0.3520 0.4479 0.3769 0.5761 0.6426 0.6790
#> [17,] 0.0115 0.0734 0.0740 0.0622 0.1055 0.1183 0.1721 0.2584 0.3232 0.3817
#> [18,] 0.0206 0.1573 0.2327 0.1785 0.1507 0.1916 0.2061 0.2307 0.2360 0.1299
#> [19,] 0.0188 0.1806 0.2139 0.1523 0.1975 0.4844 0.7298 0.7807 0.7906 0.6122
#> [20,] 0.0079 0.1240 0.1097 0.1215 0.1874 0.3383 0.3227 0.2723 0.3943 0.6432
#> [21,] 0.0100 0.2990 0.3242 0.3565 0.3951 0.5201 0.6953 0.8468 1.0000 0.9278
#> [22,] 0.0117 0.2169 0.1180 0.0754 0.2782 0.3758 0.5093 0.6592 0.7071 0.7532
#> [23,] 0.0189 0.2522 0.2607 0.3710 0.3906 0.2672 0.2716 0.4183 0.6988 0.5733
#> [24,] 0.0114 0.1799 0.2486 0.2973 0.3672 0.4394 0.5258 0.6755 0.7402 0.8284
#> [25,] 0.0156 0.1567 0.2476 0.2783 0.2896 0.2956 0.3189 0.1892 0.1730 0.2226
#> [26,] 0.0162 0.1779 0.2164 0.2568 0.3089 0.3829 0.4393 0.5335 0.5996 0.6728
#> [27,] 0.0530 0.0740 0.1610 0.2226 0.2703 0.3365 0.4266 0.4144 0.5655 0.6921
#> [28,] 0.0283 0.1767 0.1995 0.2869 0.3275 0.3769 0.4169 0.5036 0.6180 0.8025
#> [29,] 0.0025 0.1430 0.0994 0.2250 0.2444 0.3239 0.3039 0.2410 0.0367 0.1672
#> [30,] 0.0180 0.2918 0.3273 0.3035 0.3033 0.2587 0.1682 0.1308 0.2803 0.4519
#> [31,] 0.0310 0.4432 0.5222 0.5611 0.5379 0.4048 0.2245 0.1784 0.2297 0.2720
#> [32,] 0.0299 0.0998 0.0523 0.0904 0.2655 0.3099 0.3520 0.3892 0.3962 0.2449
#> [33,] 0.0216 0.0973 0.1203 0.1102 0.1192 0.1762 0.2390 0.2138 0.1929 0.1765
#> [34,] 0.1313 0.2231 0.2907 0.2259 0.3136 0.3302 0.3660 0.3956 0.4386 0.4670
#> [35,] 0.0124 0.2120 0.1640 0.1901 0.3026 0.2019 0.0592 0.2390 0.3657 0.3809
#> [36,] 0.0408 0.2176 0.2459 0.3332 0.3087 0.2613 0.3232 0.3731 0.4203 0.5364
#> [37,] 0.0163 0.2822 0.3691 0.3750 0.3927 0.3308 0.1085 0.1139 0.3446 0.5441
#> [38,] 0.0123 0.0475 0.1152 0.0520 0.1192 0.1943 0.1840 0.2077 0.1956 0.1630
#> [39,] 0.0090 0.1895 0.1896 0.2547 0.4073 0.2988 0.2901 0.5326 0.4022 0.1571
#> [40,] 0.0201 0.1097 0.0841 0.0942 0.1204 0.0420 0.0031 0.0162 0.0624 0.2127
#>          V19     V2    V20    V21    V22    V23    V24    V25    V26    V27
#>  [1,] 0.6601 0.0216 0.5844 0.4539 0.4789 0.5646 0.5281 0.7115 1.0000 0.9564
#>  [2,] 0.5599 0.0258 0.6936 0.7969 0.7452 0.8203 0.9261 0.8810 0.8814 0.9301
#>  [3,] 0.7234 0.0453 0.5122 0.2074 0.3985 0.5890 0.2872 0.2043 0.5782 0.5389
#>  [4,] 0.9832 0.0179 0.9634 0.8646 0.8325 0.8276 0.8007 0.6102 0.4853 0.4355
#>  [5,] 0.3094 0.0019 0.4678 0.5958 0.7245 0.8773 0.9214 0.9282 0.9942 1.0000
#>  [6,] 0.7917 0.1065 0.7383 0.6908 0.3850 0.0671 0.0502 0.2717 0.2839 0.2234
#>  [7,] 0.8215 0.0149 0.9363 1.0000 0.9224 0.7839 0.5470 0.4562 0.5922 0.5448
#>  [8,] 0.6041 0.0509 0.6749 0.7084 0.7890 0.9284 0.9781 0.9738 1.0000 0.9702
#>  [9,] 0.1455 0.0078 0.1579 0.2257 0.1975 0.3368 0.5828 0.8505 1.0000 0.8457
#> [10,] 0.2026 0.0178 0.1506 0.0521 0.2143 0.4333 0.5943 0.6926 0.7576 0.8787
#> [11,] 0.2071 0.0277 0.2975 0.2836 0.3353 0.3622 0.3202 0.3452 0.3562 0.3892
#> [12,] 0.8793 0.0339 0.9657 1.0000 0.8707 0.6471 0.5973 0.8218 0.7755 0.6111
#> [13,] 0.4026 0.0102 0.4553 0.5525 0.5991 0.5854 0.7114 0.9500 0.9858 1.0000
#> [14,] 0.4528 0.0123 0.6339 0.7044 0.8314 0.8449 0.8512 0.9138 0.9985 1.0000
#> [15,] 0.1228 0.0059 0.0902 0.1557 0.3291 0.5268 0.6740 0.7906 0.8938 0.9395
#> [16,] 0.7157 0.0607 0.5466 0.5399 0.6362 0.7849 0.7756 0.5780 0.4862 0.4181
#> [17,] 0.4243 0.0150 0.4217 0.4449 0.4075 0.3306 0.4012 0.4466 0.5218 0.7552
#> [18,] 0.3812 0.0132 0.5858 0.4497 0.4876 1.0000 0.8675 0.4718 0.5341 0.6197
#> [19,] 0.4200 0.0370 0.2807 0.5148 0.7569 0.8596 1.0000 0.8457 0.6797 0.6971
#> [20,] 0.7271 0.0086 0.8673 0.9674 0.9847 0.9480 0.8036 0.6833 0.5136 0.3090
#> [21,] 0.8510 0.0194 0.8010 0.8142 0.8825 0.7302 0.6107 0.7159 0.8458 0.6319
#> [22,] 0.8357 0.0069 0.8593 0.9615 0.9838 0.8705 0.6403 0.5067 0.5395 0.6934
#> [23,] 0.2226 0.0308 0.2631 0.7473 0.7263 0.3393 0.2824 0.6053 0.5897 0.4967
#> [24,] 0.9033 0.0222 0.9584 1.0000 0.9982 0.8899 0.7493 0.6367 0.6744 0.7207
#> [25,] 0.2427 0.0210 0.3149 0.4102 0.3808 0.4896 0.6292 0.7519 0.7985 0.8830
#> [26,] 0.7309 0.0041 0.8092 0.8941 0.9668 1.0000 0.9893 0.9376 0.8991 0.9184
#> [27,] 0.8547 0.0885 0.9234 0.9171 1.0000 0.9532 0.9101 0.8337 0.7053 0.6534
#> [28,] 0.9333 0.0599 0.9399 0.9275 0.9450 0.8328 0.7773 0.7007 0.6154 0.5810
#> [29,] 0.3038 0.0309 0.4069 0.3613 0.1994 0.4611 0.6849 0.7272 0.7152 0.7102
#> [30,] 0.6641 0.0444 0.7683 0.6960 0.4393 0.2432 0.2886 0.4974 0.8172 1.0000
#> [31,] 0.5209 0.0221 0.6898 0.8202 0.8780 0.7600 0.7616 0.7152 0.7288 0.8686
#> [32,] 0.2355 0.0688 0.3045 0.3112 0.4698 0.5534 0.4532 0.4464 0.4670 0.4621
#> [33,] 0.0746 0.0215 0.1265 0.2005 0.1571 0.2605 0.5386 0.8440 1.0000 0.8684
#> [34,] 0.5255 0.2339 0.3735 0.2243 0.1973 0.4337 0.6532 0.5070 0.2796 0.4163
#> [35,] 0.5929 0.0433 0.6299 0.5801 0.4574 0.4449 0.3691 0.6446 0.8940 0.8978
#> [36,] 0.7062 0.0653 0.8196 0.8835 0.8299 0.7609 0.7605 0.8367 0.8905 0.7652
#> [37,] 0.6470 0.0198 0.7276 0.7894 0.8264 0.8697 0.7836 0.7140 0.5698 0.2908
#> [38,] 0.1218 0.0022 0.1017 0.1354 0.3157 0.4645 0.5906 0.6776 0.8119 0.8594
#> [39,] 0.3024 0.0062 0.3907 0.3542 0.4438 0.6414 0.4601 0.6009 0.8690 0.8345
#> [40,] 0.3436 0.0026 0.3813 0.3825 0.4764 0.6313 0.7523 0.8675 0.8788 0.7901
#>          V28    V29     V3    V30    V31    V32    V33    V34    V35    V36
#>  [1,] 0.6090 0.5112 0.0386 0.4000 0.0482 0.1852 0.2186 0.1436 0.1757 0.1428
#>  [2,] 0.9955 0.8576 0.0398 0.6069 0.3934 0.2464 0.1645 0.1140 0.0956 0.0080
#>  [3,] 0.3750 0.3411 0.0277 0.5067 0.5580 0.4778 0.3299 0.2198 0.1407 0.2856
#>  [4,] 0.4307 0.4399 0.0449 0.3833 0.3032 0.3035 0.3197 0.2292 0.2131 0.2347
#>  [5,] 0.9071 0.8545 0.0075 0.7293 0.6499 0.6071 0.5588 0.5967 0.6275 0.5459
#>  [6,] 0.1911 0.0408 0.1526 0.2531 0.1979 0.1891 0.2433 0.1956 0.2667 0.1340
#>  [7,] 0.3971 0.0882 0.0641 0.2385 0.2005 0.0587 0.2544 0.2009 0.0329 0.1547
#>  [8,] 0.9956 0.8235 0.0079 0.6020 0.5342 0.4867 0.3526 0.1566 0.0946 0.1613
#>  [9,] 0.6624 0.5564 0.0092 0.3925 0.3233 0.2054 0.1920 0.2227 0.3147 0.2268
#> [10,] 0.9060 0.8528 0.0274 0.9087 0.9657 0.9306 0.7774 0.6643 0.6604 0.6884
#> [11,] 0.6622 0.9254 0.0604 1.0000 0.8528 0.6297 0.5250 0.4012 0.2901 0.2007
#> [12,] 0.4195 0.2990 0.0202 0.1354 0.2438 0.5624 0.5555 0.6963 0.7298 0.7022
#> [13,] 0.9578 0.8642 0.0113 0.7128 0.5893 0.4323 0.2897 0.1744 0.0770 0.2297
#> [14,] 0.7544 0.4661 0.0117 0.3924 0.3849 0.4674 0.4245 0.3095 0.0752 0.2885
#> [15,] 0.9493 0.9040 0.0080 0.9151 0.8828 0.8086 0.7180 0.6720 0.6447 0.6879
#> [16,] 0.2457 0.0716 0.0378 0.0613 0.1816 0.4493 0.5976 0.3785 0.2495 0.5771
#> [17,] 0.9503 1.0000 0.0136 0.9084 0.8283 0.7571 0.7262 0.6152 0.5680 0.5757
#> [18,] 0.7143 0.5605 0.0533 0.3728 0.2481 0.1921 0.1386 0.3325 0.2883 0.3228
#> [19,] 0.5843 0.4772 0.0953 0.5201 0.4241 0.1592 0.1668 0.0588 0.3967 0.7147
#> [20,] 0.0832 0.4019 0.0055 0.2344 0.1905 0.1235 0.1717 0.2351 0.2489 0.3649
#> [21,] 0.4808 0.6291 0.0155 0.7152 0.6005 0.4235 0.4106 0.3992 0.1730 0.1975
#> [22,] 0.8487 0.8213 0.0279 0.5962 0.2950 0.2758 0.2885 0.1893 0.1446 0.0955
#> [23,] 0.8616 0.8339 0.0197 0.4084 0.2268 0.1745 0.0507 0.1588 0.3040 0.1369
#> [24,] 0.6821 0.5512 0.0269 0.4789 0.3924 0.2533 0.1089 0.1390 0.2551 0.3301
#> [25,] 0.9915 0.9223 0.0282 0.6981 0.6167 0.5069 0.3921 0.3524 0.2183 0.1245
#> [26,] 0.9128 0.7811 0.0239 0.6018 0.3765 0.3300 0.2280 0.0212 0.1117 0.1788
#> [27,] 0.4483 0.2460 0.1997 0.2020 0.1446 0.0994 0.1510 0.2392 0.4434 0.5023
#> [28,] 0.4454 0.3707 0.0656 0.2891 0.2185 0.1711 0.3578 0.3947 0.2867 0.2401
#> [29,] 0.8516 1.0000 0.0171 0.7690 0.4841 0.3717 0.6096 0.5110 0.2586 0.0916
#> [30,] 0.9238 0.8519 0.0476 0.7722 0.5772 0.5190 0.6824 0.6220 0.5054 0.3578
#> [31,] 0.9509 0.8348 0.0433 0.5730 0.4363 0.4289 0.4240 0.3156 0.1287 0.1477
#> [32,] 0.6988 0.7626 0.0992 0.7025 0.7382 0.7446 0.7927 0.5227 0.3967 0.3042
#> [33,] 0.6742 0.5537 0.0273 0.4638 0.3609 0.2055 0.1620 0.2092 0.3100 0.2344
#> [34,] 0.5950 0.5242 0.3059 0.4178 0.3714 0.2375 0.0863 0.1437 0.2896 0.4577
#> [35,] 0.4980 0.3333 0.0604 0.2350 0.1553 0.3666 0.4340 0.3082 0.3024 0.4109
#> [36,] 0.5897 0.3037 0.0397 0.0823 0.2787 0.7241 0.8032 0.8050 0.7676 0.7468
#> [37,] 0.4636 0.6409 0.0202 0.7405 0.8069 0.8420 1.0000 0.9536 0.6755 0.3905
#> [38,] 0.9228 0.8387 0.0196 0.7238 0.6292 0.5181 0.4629 0.5255 0.5147 0.3929
#> [39,] 0.7669 0.5081 0.0253 0.4620 0.5380 0.5375 0.3844 0.3601 0.7402 0.7761
#> [40,] 0.8357 0.9631 0.0138 0.9619 0.9236 0.8903 0.9708 0.9647 0.7892 0.5307
#>          V37    V38    V39     V4    V40    V41    V42    V43    V44    V45
#>  [1,] 0.1644 0.3089 0.3648 0.0627 0.4441 0.3859 0.2813 0.1238 0.0953 0.1201
#>  [2,] 0.0702 0.0936 0.0894 0.0570 0.1127 0.0873 0.1020 0.1964 0.2256 0.1814
#>  [3,] 0.3807 0.4158 0.4054 0.0174 0.3296 0.2707 0.2650 0.0723 0.1238 0.1192
#>  [4,] 0.3201 0.4455 0.3655 0.1096 0.2715 0.1747 0.1781 0.2199 0.1056 0.0573
#>  [5,] 0.4786 0.3965 0.2087 0.0097 0.1651 0.1836 0.0652 0.0758 0.0486 0.0353
#>  [6,] 0.1073 0.2023 0.1794 0.1229 0.0227 0.1313 0.1775 0.1549 0.1626 0.0708
#>  [7,] 0.1212 0.2446 0.3171 0.1732 0.3195 0.3051 0.0836 0.1266 0.1381 0.1136
#>  [8,] 0.2824 0.3390 0.3019 0.0243 0.2945 0.2978 0.2676 0.2055 0.2069 0.1625
#>  [9,] 0.0795 0.0748 0.1166 0.0387 0.1969 0.2619 0.2507 0.1983 0.0948 0.0931
#> [10,] 0.6938 0.5932 0.5774 0.0232 0.6223 0.5841 0.4527 0.4911 0.5762 0.5013
#> [11,] 0.3356 0.4799 0.6147 0.0525 0.6246 0.4973 0.3492 0.2662 0.3137 0.4282
#> [12,] 0.5468 0.1421 0.4738 0.0889 0.6410 0.4375 0.3178 0.2377 0.2808 0.1374
#> [13,] 0.2459 0.3101 0.3312 0.0263 0.2220 0.0871 0.2064 0.1808 0.1624 0.1120
#> [14,] 0.4072 0.3170 0.2863 0.0113 0.2634 0.0541 0.1874 0.3459 0.4646 0.4366
#> [15,] 0.6241 0.4936 0.4144 0.0554 0.4240 0.4546 0.4392 0.4323 0.4921 0.4710
#> [16,] 0.8852 0.8409 0.3570 0.0774 0.3133 0.6096 0.6378 0.2709 0.1419 0.1260
#> [17,] 0.5324 0.3672 0.1669 0.0076 0.0866 0.0646 0.1891 0.2683 0.2887 0.2341
#> [18,] 0.2607 0.2040 0.2396 0.0569 0.1319 0.0683 0.0334 0.0716 0.0976 0.0787
#> [19,] 0.7319 0.3509 0.0589 0.0824 0.2690 0.4200 0.3874 0.2440 0.2000 0.2307
#> [20,] 0.3382 0.1589 0.0989 0.0250 0.1089 0.1043 0.0839 0.1391 0.0819 0.0678
#> [21,] 0.2370 0.1339 0.1583 0.0489 0.3151 0.1968 0.2054 0.1272 0.1129 0.1946
#> [22,] 0.0888 0.0836 0.0894 0.0583 0.1547 0.2318 0.2225 0.1035 0.1721 0.2017
#> [23,] 0.1605 0.2061 0.0734 0.0622 0.0202 0.1638 0.1583 0.1830 0.1886 0.1008
#> [24,] 0.2818 0.2142 0.2266 0.0384 0.2142 0.2354 0.2871 0.2596 0.1925 0.1256
#> [25,] 0.1592 0.1626 0.2356 0.0596 0.2483 0.2437 0.2715 0.1184 0.1157 0.1449
#> [26,] 0.2373 0.2843 0.2241 0.0441 0.2715 0.3363 0.2546 0.1867 0.2160 0.1278
#> [27,] 0.4441 0.4571 0.3927 0.2604 0.2900 0.3408 0.4990 0.3632 0.1387 0.1800
#> [28,] 0.3619 0.3314 0.3763 0.0229 0.4767 0.4059 0.3661 0.2320 0.1450 0.1017
#> [29,] 0.0947 0.2287 0.3480 0.0228 0.2095 0.1901 0.2941 0.2211 0.1524 0.0746
#> [30,] 0.3809 0.3813 0.3359 0.0698 0.2771 0.3648 0.3834 0.3453 0.2096 0.1031
#> [31,] 0.2062 0.2400 0.5173 0.0191 0.5168 0.1491 0.2407 0.3415 0.4494 0.4624
#> [32,] 0.1309 0.2408 0.1780 0.1021 0.1598 0.5657 0.6443 0.4241 0.4567 0.5760
#> [33,] 0.1058 0.0383 0.0528 0.0139 0.1291 0.2241 0.1915 0.1587 0.0942 0.0840
#> [34,] 0.3725 0.3372 0.3803 0.4264 0.4181 0.3603 0.2711 0.1653 0.1951 0.2811
#> [35,] 0.5501 0.4129 0.5499 0.0449 0.5018 0.3132 0.2802 0.2351 0.2298 0.1155
#> [36,] 0.6253 0.1730 0.2916 0.0604 0.5003 0.5220 0.4824 0.4004 0.3877 0.1651
#> [37,] 0.1249 0.3629 0.6356 0.0386 0.8116 0.7664 0.5417 0.2614 0.1723 0.2814
#> [38,] 0.1279 0.0411 0.0859 0.0206 0.1131 0.1306 0.1757 0.2648 0.1955 0.0656
#> [39,] 0.3858 0.0667 0.3684 0.0489 0.6114 0.3510 0.2312 0.2195 0.3051 0.1937
#> [40,] 0.2718 0.1953 0.1374 0.0062 0.3105 0.3790 0.4105 0.3355 0.2998 0.2748
#>          V46    V47    V48    V49     V5    V50    V51    V52    V53    V54
#>  [1,] 0.0825 0.0618 0.0141 0.0108 0.1158 0.0124 0.0104 0.0095 0.0151 0.0059
#>  [2,] 0.2012 0.1688 0.1037 0.0501 0.0529 0.0136 0.0130 0.0120 0.0039 0.0053
#>  [3,] 0.1089 0.0623 0.0494 0.0264 0.0384 0.0081 0.0104 0.0045 0.0014 0.0038
#>  [4,] 0.0307 0.0237 0.0470 0.0102 0.1913 0.0057 0.0031 0.0163 0.0099 0.0084
#>  [5,] 0.0297 0.0241 0.0379 0.0119 0.0445 0.0073 0.0051 0.0034 0.0129 0.0100
#>  [6,] 0.0129 0.0795 0.0762 0.0117 0.1437 0.0061 0.0257 0.0089 0.0262 0.0108
#>  [7,] 0.0516 0.0073 0.0278 0.0372 0.2565 0.0121 0.0153 0.0092 0.0035 0.0098
#>  [8,] 0.1216 0.1013 0.0744 0.0386 0.0432 0.0050 0.0146 0.0040 0.0122 0.0107
#>  [9,] 0.0965 0.0381 0.0435 0.0336 0.0530 0.0055 0.0079 0.0119 0.0055 0.0035
#> [10,] 0.4042 0.3123 0.2232 0.1085 0.0724 0.0414 0.0253 0.0131 0.0049 0.0104
#> [11,] 0.4262 0.3511 0.2458 0.1259 0.0489 0.0327 0.0181 0.0217 0.0038 0.0019
#> [12,] 0.1136 0.1034 0.0688 0.0422 0.1570 0.0117 0.0070 0.0167 0.0127 0.0138
#> [13,] 0.0815 0.1117 0.0950 0.0412 0.0097 0.0120 0.0048 0.0049 0.0041 0.0036
#> [14,] 0.2581 0.1319 0.0505 0.0112 0.0497 0.0059 0.0041 0.0056 0.0104 0.0079
#> [15,] 0.3196 0.2241 0.1806 0.0990 0.0883 0.0251 0.0129 0.0095 0.0126 0.0069
#> [16,] 0.1288 0.0790 0.0829 0.0520 0.1388 0.0216 0.0360 0.0331 0.0131 0.0120
#> [17,] 0.1668 0.1015 0.1195 0.0704 0.0211 0.0167 0.0107 0.0091 0.0016 0.0084
#> [18,] 0.0522 0.0500 0.0231 0.0221 0.0647 0.0144 0.0307 0.0386 0.0147 0.0018
#> [19,] 0.1886 0.1960 0.1701 0.1366 0.0249 0.0398 0.0143 0.0093 0.0033 0.0113
#> [20,] 0.0663 0.1202 0.0692 0.0152 0.0344 0.0266 0.0174 0.0176 0.0127 0.0088
#> [21,] 0.2195 0.1930 0.1498 0.0773 0.0839 0.0196 0.0122 0.0130 0.0073 0.0077
#> [22,] 0.1787 0.1112 0.0398 0.0305 0.0915 0.0084 0.0039 0.0053 0.0029 0.0020
#> [23,] 0.0663 0.0183 0.0404 0.0108 0.0080 0.0143 0.0091 0.0038 0.0096 0.0142
#> [24,] 0.1003 0.0951 0.1210 0.0728 0.1217 0.0174 0.0213 0.0269 0.0152 0.0257
#> [25,] 0.1883 0.1954 0.1492 0.0511 0.0462 0.0155 0.0189 0.0150 0.0060 0.0082
#> [26,] 0.0768 0.1070 0.0946 0.0636 0.0630 0.0227 0.0128 0.0173 0.0135 0.0114
#> [27,] 0.1299 0.0523 0.0817 0.0469 0.3225 0.0114 0.0299 0.0244 0.0199 0.0257
#> [28,] 0.1111 0.0655 0.0271 0.0244 0.0839 0.0179 0.0109 0.0147 0.0170 0.0158
#> [29,] 0.0606 0.0692 0.0446 0.0344 0.0434 0.0082 0.0108 0.0149 0.0077 0.0036
#> [30,] 0.0798 0.0701 0.0526 0.0241 0.1615 0.0117 0.0122 0.0122 0.0114 0.0098
#> [31,] 0.2001 0.0775 0.1232 0.0783 0.0964 0.0089 0.0249 0.0204 0.0059 0.0053
#> [32,] 0.5293 0.3287 0.1283 0.0698 0.0800 0.0334 0.0342 0.0459 0.0277 0.0172
#> [33,] 0.0670 0.0342 0.0469 0.0357 0.0357 0.0136 0.0082 0.0140 0.0044 0.0052
#> [34,] 0.2246 0.1921 0.1500 0.0665 0.4010 0.0193 0.0156 0.0362 0.0210 0.0154
#> [35,] 0.0724 0.0621 0.0318 0.0450 0.0597 0.0167 0.0078 0.0083 0.0057 0.0174
#> [36,] 0.0442 0.0663 0.0418 0.0475 0.0496 0.0235 0.0066 0.0062 0.0129 0.0184
#> [37,] 0.2764 0.1985 0.1502 0.1219 0.0752 0.0493 0.0027 0.0077 0.0026 0.0031
#> [38,] 0.0580 0.0319 0.0301 0.0272 0.0180 0.0074 0.0149 0.0125 0.0134 0.0026
#> [39,] 0.1570 0.0479 0.0538 0.0146 0.1197 0.0068 0.0187 0.0059 0.0095 0.0194
#> [40,] 0.2024 0.1043 0.0453 0.0337 0.0133 0.0122 0.0072 0.0108 0.0070 0.0063
#>          V55    V56    V57    V58    V59     V6    V60     V7     V8     V9
#>  [1,] 0.0015 0.0053 0.0016 0.0042 0.0053 0.1482 0.0074 0.2054 0.1605 0.2532
#>  [2,] 0.0062 0.0046 0.0045 0.0022 0.0005 0.1091 0.0031 0.1709 0.1684 0.1865
#>  [3,] 0.0013 0.0089 0.0057 0.0027 0.0051 0.0990 0.0062 0.1201 0.1833 0.2105
#>  [4,] 0.0270 0.0277 0.0097 0.0054 0.0148 0.0924 0.0092 0.0761 0.1092 0.0757
#>  [5,] 0.0044 0.0057 0.0030 0.0035 0.0021 0.0906 0.0027 0.0889 0.0655 0.1624
#>  [6,] 0.0138 0.0187 0.0230 0.0057 0.0113 0.1190 0.0131 0.0884 0.0907 0.2107
#>  [7,] 0.0121 0.0006 0.0181 0.0094 0.0116 0.2559 0.0063 0.2947 0.4110 0.4983
#>  [8,] 0.0112 0.0102 0.0052 0.0024 0.0079 0.0735 0.0031 0.0938 0.1134 0.1228
#>  [9,] 0.0036 0.0004 0.0018 0.0049 0.0024 0.1197 0.0016 0.1243 0.1026 0.1239
#> [10,] 0.0102 0.0092 0.0083 0.0020 0.0048 0.0833 0.0036 0.1232 0.1298 0.2085
#> [11,] 0.0065 0.0132 0.0108 0.0050 0.0085 0.0385 0.0044 0.0611 0.1117 0.1237
#> [12,] 0.0090 0.0051 0.0029 0.0122 0.0056 0.1750 0.0020 0.0920 0.1353 0.1593
#> [13,] 0.0013 0.0046 0.0037 0.0011 0.0034 0.0391 0.0033 0.0857 0.0915 0.0949
#> [14,] 0.0014 0.0054 0.0015 0.0006 0.0081 0.0998 0.0043 0.1326 0.1117 0.2984
#> [15,] 0.0039 0.0068 0.0060 0.0045 0.0002 0.1278 0.0029 0.1674 0.1373 0.2922
#> [16,] 0.0108 0.0024 0.0045 0.0037 0.0112 0.0809 0.0075 0.0568 0.0219 0.1037
#> [17,] 0.0064 0.0026 0.0029 0.0037 0.0070 0.1058 0.0041 0.1023 0.0440 0.0931
#> [18,] 0.0100 0.0096 0.0077 0.0180 0.0109 0.1432 0.0070 0.1344 0.2041 0.1571
#> [19,] 0.0030 0.0057 0.0090 0.0057 0.0068 0.0488 0.0024 0.1424 0.1972 0.1873
#> [20,] 0.0098 0.0019 0.0059 0.0058 0.0059 0.0546 0.0032 0.0528 0.0958 0.1009
#> [21,] 0.0075 0.0060 0.0080 0.0019 0.0053 0.1009 0.0019 0.1627 0.2071 0.2696
#> [22,] 0.0013 0.0029 0.0020 0.0062 0.0026 0.1267 0.0052 0.1577 0.1927 0.2361
#> [23,] 0.0190 0.0140 0.0099 0.0092 0.0052 0.0789 0.0075 0.1440 0.1451 0.1789
#> [24,] 0.0097 0.0041 0.0050 0.0145 0.0103 0.2062 0.0025 0.1489 0.0929 0.1350
#> [25,] 0.0091 0.0038 0.0056 0.0056 0.0048 0.0779 0.0024 0.1365 0.0780 0.1038
#> [26,] 0.0062 0.0157 0.0088 0.0036 0.0053 0.0921 0.0030 0.1368 0.1078 0.1552
#> [27,] 0.0082 0.0151 0.0171 0.0146 0.0134 0.2247 0.0056 0.0617 0.2287 0.0950
#> [28,] 0.0046 0.0073 0.0054 0.0033 0.0045 0.1673 0.0079 0.1154 0.1098 0.1370
#> [29,] 0.0114 0.0085 0.0101 0.0016 0.0028 0.1224 0.0014 0.1947 0.1661 0.1368
#> [30,] 0.0027 0.0025 0.0026 0.0050 0.0073 0.0887 0.0022 0.0596 0.1071 0.3175
#> [31,] 0.0079 0.0037 0.0015 0.0056 0.0067 0.1827 0.0054 0.1106 0.1702 0.2804
#> [32,] 0.0087 0.0046 0.0203 0.0130 0.0115 0.0629 0.0015 0.0130 0.0813 0.1761
#> [33,] 0.0073 0.0021 0.0047 0.0024 0.0009 0.0785 0.0017 0.0906 0.0908 0.1151
#> [34,] 0.0180 0.0013 0.0106 0.0127 0.0178 0.1791 0.0231 0.1853 0.0055 0.1929
#> [35,] 0.0188 0.0054 0.0114 0.0196 0.0147 0.0355 0.0062 0.0531 0.0343 0.1052
#> [36,] 0.0069 0.0198 0.0199 0.0102 0.0070 0.1817 0.0055 0.1178 0.1024 0.0583
#> [37,] 0.0083 0.0020 0.0084 0.0108 0.0083 0.1444 0.0033 0.1487 0.1484 0.2442
#> [38,] 0.0038 0.0018 0.0113 0.0058 0.0047 0.0492 0.0071 0.0033 0.0398 0.0791
#> [39,] 0.0080 0.0152 0.0158 0.0053 0.0189 0.1589 0.0102 0.1392 0.0987 0.0955
#> [40,] 0.0030 0.0011 0.0007 0.0024 0.0057 0.0151 0.0044 0.0541 0.0210 0.0505
#> 
#> $post[[2]]
#>              [,1]
#>  [1,] 0.025683712
#>  [2,] 0.016988899
#>  [3,] 0.021278337
#>  [4,] 0.015186078
#>  [5,] 0.011711323
#>  [6,] 0.048209084
#>  [7,] 0.019443338
#>  [8,] 0.009246571
#>  [9,] 0.052461628
#> [10,] 0.009011959
#> [11,] 0.026465037
#> [12,] 0.008714542
#> [13,] 0.017496920
#> [14,] 0.011992112
#> [15,] 0.009277188
#> [16,] 0.018091775
#> [17,] 0.015922308
#> [18,] 0.037575089
#> [19,] 0.014150662
#> [20,] 0.032893547
#> [21,] 0.010180120
#> [22,] 0.014821063
#> [23,] 0.036320008
#> [24,] 0.011703246
#> [25,] 0.022361777
#> [26,] 0.009428231
#> [27,] 0.014350842
#> [28,] 0.014010320
#> [29,] 0.026234006
#> [30,] 0.011933299
#> [31,] 0.010781278
#> [32,] 0.020485062
#> [33,] 0.056378780
#> [34,] 0.035755358
#> [35,] 0.024547216
#> [36,] 0.008276343
#> [37,] 0.007342411
#> [38,] 0.032175866
#> [39,] 0.018557021
#> [40,] 0.011035418
#> 
#> $post[[3]]
#>            [,1]      [,2]
#>  [1,] 0.5891302 0.4108698
#>  [2,] 0.5893642 0.4106358
#>  [3,] 0.5892488 0.4107512
#>  [4,] 0.5894127 0.4105873
#>  [5,] 0.5895062 0.4104938
#>  [6,] 0.5885238 0.4114762
#>  [7,] 0.5892982 0.4107018
#>  [8,] 0.5895726 0.4104274
#>  [9,] 0.5884092 0.4115908
#> [10,] 0.5895789 0.4104211
#> [11,] 0.5891092 0.4108908
#> [12,] 0.5895869 0.4104131
#> [13,] 0.5893505 0.4106495
#> [14,] 0.5894987 0.4105013
#> [15,] 0.5895717 0.4104283
#> [16,] 0.5893345 0.4106655
#> [17,] 0.5893929 0.4106071
#> [18,] 0.5888101 0.4111899
#> [19,] 0.5894406 0.4105594
#> [20,] 0.5889361 0.4110639
#> [21,] 0.5895474 0.4104526
#> [22,] 0.5894226 0.4105774
#> [23,] 0.5888439 0.4111561
#> [24,] 0.5895065 0.4104935
#> [25,] 0.5892196 0.4107804
#> [26,] 0.5895677 0.4104323
#> [27,] 0.5894352 0.4105648
#> [28,] 0.5894444 0.4105556
#> [29,] 0.5891154 0.4108846
#> [30,] 0.5895003 0.4104997
#> [31,] 0.5895313 0.4104687
#> [32,] 0.5892701 0.4107299
#> [33,] 0.5883037 0.4116963
#> [34,] 0.5888591 0.4111409
#> [35,] 0.5891608 0.4108392
#> [36,] 0.5895987 0.4104013
#> [37,] 0.5896238 0.4103762
#> [38,] 0.5889554 0.4110446
#> [39,] 0.5893220 0.4106780
#> [40,] 0.5895244 0.4104756
#> 
#> 
#> $pre
#> $pre[[1]]
#> NULL
#> 
#> $pre[[2]]
#>            [,1]
#>  [1,] -3.635879
#>  [2,] -4.058060
#>  [3,] -3.828558
#>  [4,] -4.172074
#>  [5,] -4.435419
#>  [6,] -2.982798
#>  [7,] -3.920616
#>  [8,] -4.674213
#>  [9,] -2.893785
#> [10,] -4.700150
#> [11,] -3.605109
#> [12,] -4.734009
#> [13,] -4.028079
#> [14,] -4.411442
#> [15,] -4.670876
#> [16,] -3.994040
#> [17,] -4.123984
#> [18,] -3.243115
#> [19,] -4.243742
#> [20,] -3.381032
#> [21,] -4.577086
#> [22,] -4.196774
#> [23,] -3.278390
#> [24,] -4.436117
#> [25,] -3.777787
#> [26,] -4.654574
#> [27,] -4.229492
#> [28,] -4.253852
#> [29,] -3.614115
#> [30,] -4.416418
#> [31,] -4.519104
#> [32,] -3.867362
#> [33,] -2.817632
#> [34,] -3.294645
#> [35,] -3.682303
#> [36,] -4.786043
#> [37,] -4.906718
#> [38,] -3.403834
#> [39,] -3.968176
#> [40,] -4.495549
#> 
#> $pre[[3]]
#>            [,1]        [,2]
#>  [1,] 0.2756405 -0.08473036
#>  [2,] 0.2762623 -0.08507547
#>  [3,] 0.2759555 -0.08490521
#>  [4,] 0.2763912 -0.08514702
#>  [5,] 0.2766396 -0.08528494
#>  [6,] 0.2740297 -0.08383631
#>  [7,] 0.2760867 -0.08497805
#>  [8,] 0.2768159 -0.08538277
#>  [9,] 0.2737256 -0.08366752
#> [10,] 0.2768327 -0.08539208
#> [11,] 0.2755846 -0.08469935
#> [12,] 0.2768539 -0.08540388
#> [13,] 0.2762259 -0.08505530
#> [14,] 0.2766196 -0.08527379
#> [15,] 0.2768137 -0.08538155
#> [16,] 0.2761834 -0.08503169
#> [17,] 0.2763385 -0.08511780
#> [18,] 0.2747902 -0.08425838
#> [19,] 0.2764652 -0.08518812
#> [20,] 0.2751249 -0.08444420
#> [21,] 0.2767491 -0.08534571
#> [22,] 0.2764173 -0.08516151
#> [23,] 0.2748799 -0.08430820
#> [24,] 0.2766402 -0.08528526
#> [25,] 0.2758780 -0.08486221
#> [26,] 0.2768029 -0.08537556
#> [27,] 0.2764509 -0.08518017
#> [28,] 0.2764752 -0.08519369
#> [29,] 0.2756011 -0.08470852
#> [30,] 0.2766238 -0.08527613
#> [31,] 0.2767062 -0.08532185
#> [32,] 0.2760122 -0.08493670
#> [33,] 0.2734455 -0.08351205
#> [34,] 0.2749203 -0.08433061
#> [35,] 0.2757218 -0.08477547
#> [36,] 0.2768853 -0.08542127
#> [37,] 0.2769521 -0.08545834
#> [38,] 0.2751762 -0.08447268
#> [39,] 0.2761501 -0.08501323
#> [40,] 0.2766880 -0.08531176
#> 
#> 
#> $e
#>             [,1]       [,2]
#>  [1,]  0.4108698 -0.4108698
#>  [2,]  0.4106358 -0.4106358
#>  [3,] -0.5892488  0.5892488
#>  [4,]  0.4105873 -0.4105873
#>  [5,] -0.5895062  0.5895062
#>  [6,]  0.4114762 -0.4114762
#>  [7,] -0.5892982  0.5892982
#>  [8,]  0.4104274 -0.4104274
#>  [9,] -0.5884092  0.5884092
#> [10,]  0.4104211 -0.4104211
#> [11,]  0.4108908 -0.4108908
#> [12,] -0.5895869  0.5895869
#> [13,] -0.5893505  0.5893505
#> [14,]  0.4105013 -0.4105013
#> [15,]  0.4104283 -0.4104283
#> [16,] -0.5893345  0.5893345
#> [17,] -0.5893929  0.5893929
#> [18,] -0.5888101  0.5888101
#> [19,] -0.5894406  0.5894406
#> [20,] -0.5889361  0.5889361
#> [21,] -0.5895474  0.5895474
#> [22,]  0.4105774 -0.4105774
#> [23,] -0.5888439  0.5888439
#> [24,]  0.4104935 -0.4104935
#> [25,]  0.4107804 -0.4107804
#> [26,]  0.4104323 -0.4104323
#> [27,]  0.4105648 -0.4105648
#> [28,]  0.4105556 -0.4105556
#> [29,] -0.5891154  0.5891154
#> [30,]  0.4104997 -0.4104997
#> [31,]  0.4104687 -0.4104687
#> [32,]  0.4107299 -0.4107299
#> [33,] -0.5883037  0.5883037
#> [34,]  0.4111409 -0.4111409
#> [35,] -0.5891608  0.5891608
#> [36,] -0.5895987  0.5895987
#> [37,]  0.4103762 -0.4103762
#> [38,] -0.5889554  0.5889554
#> [39,] -0.5893220  0.5893220
#> [40,] -0.5895244  0.5895244
#> 
#> $L
#> [1] 0.6933904 0.6935419 0.6950175 0.6904371 0.6884994 0.7091670
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.4637681