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()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifSaeDNN$new()

---

Method 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,] 0.01967645 -0.1024042 -0.03643572 -0.05327515 -0.198025 -0.258982
#>              V15        V16       V17        V18        V19         V2
#> [1,] -0.09800692 -0.2541741 -0.225172 -0.1671067 -0.2323397 -0.1179358
#>             V20        V21        V22        V23        V24        V25
#> [1,] -0.3900021 -0.4177883 -0.4123051 -0.3586506 -0.3462892 -0.3519365
#>             V26        V27        V28        V29           V3        V30
#> [1,] -0.3098056 -0.4553629 -0.4033757 -0.3335439 -0.006636055 -0.3912546
#>             V31        V32        V33        V34        V35        V36
#> [1,] -0.3477385 -0.2551691 -0.2801521 -0.2195929 -0.1521091 -0.2254651
#>             V37        V38        V39          V4        V40        V41
#> [1,] -0.1656798 -0.2187773 -0.2611818 -0.08187994 -0.1597652 -0.1656594
#>             V42        V43        V44        V45         V46        V47
#> [1,] -0.1843945 -0.1244392 -0.1791647 -0.1683786 -0.06875338 -0.1324674
#>              V48        V49         V5        V50          V51        V52
#> [1,] -0.04162103 -0.1160493 -0.1175289 0.03148992 -0.004872011 0.06293737
#>             V53        V54         V55        V56       V57        V58
#> [1,] 0.04874799 0.07525437 -0.04899415 -0.0657944 0.0701257 -0.1012433
#>              V59         V6        V60         V7          V8          V9
#> [1,] -0.02907245 -0.1008288 0.07955412 -0.1062388 -0.02563918 -0.06656004
#> 
#> $W[[2]]
#>             [,1]
#> [1,]  0.03867719
#> [2,] -0.02184378
#> 
#> 
#> $vW
#> $vW[[1]]
#>                 V1           V10           V11           V12           V13
#> [1,] -1.120966e-06 -8.090293e-06 -1.321355e-05 -1.288191e-05 -7.127688e-06
#>                V14           V15           V16           V17           V18
#> [1,] -3.699334e-06 -2.117604e-06 -1.837128e-06 -1.554417e-06 -7.588112e-07
#>                V19           V2           V20          V21          V22
#> [1,] -8.396957e-06 -1.54752e-06 -1.403203e-05 -1.20885e-05 2.950967e-06
#>               V23          V24         V25          V26          V27
#> [1,] 1.729505e-05 2.866488e-05 4.96568e-05 5.929445e-05 5.713598e-05
#>               V28          V29            V3          V30          V31
#> [1,] 5.173824e-05 4.006341e-05 -1.644957e-06 3.078239e-05 3.292399e-05
#>               V32          V33          V34          V35          V36
#> [1,] 2.336562e-05 2.355943e-05 3.092875e-05 4.455271e-05 4.582471e-05
#>              V37          V38          V39            V4          V40
#> [1,] 2.90325e-05 1.187046e-05 3.989586e-06 -2.832262e-06 1.308642e-05
#>               V41          V42          V43           V44          V45
#> [1,] 1.481229e-05 9.151227e-06 5.424687e-06 -1.424564e-08 -6.84866e-06
#>                V46           V47           V48           V49            V5
#> [1,] -3.109479e-06 -3.001814e-06 -4.208996e-06 -2.372591e-06 -7.785749e-07
#>               V50           V51           V52          V53          V54
#> [1,] 1.672989e-07 -2.558745e-07 -1.290697e-07 2.041069e-07 1.134429e-07
#>                V55           V56           V57           V58           V59
#> [1,] -1.237663e-07 -8.741181e-08 -5.020774e-08 -3.573404e-07 -1.264794e-07
#>                V6           V60            V7            V8            V9
#> [1,] 3.191633e-07 -5.199702e-08 -1.793962e-06 -7.326617e-06 -8.121227e-06
#> 
#> $vW[[2]]
#>               [,1]
#> [1,]  0.0008658975
#> [2,] -0.0008658975
#> 
#> 
#> $B
#> $B[[1]]
#> [1] -0.4936759
#> 
#> $B[[2]]
#> [1]  0.07564226 -0.05827396
#> 
#> 
#> $vB
#> $vB[[1]]
#> [1] 5.371924e-05
#> 
#> $vB[[2]]
#> [1] -0.002974075  0.002974075
#> 
#> 
#> $post
#> $post[[1]]
#>           V1    V10    V11    V12    V13    V14    V15    V16    V17    V18
#>  [1,] 0.0109 0.1036 0.0972 0.0501 0.1546 0.3404 0.4804 0.6570 0.7738 0.7827
#>  [2,] 0.0408 0.2176 0.2459 0.3332 0.3087 0.2613 0.3232 0.3731 0.4203 0.5364
#>  [3,] 0.0707 0.1247 0.2340 0.1764 0.2284 0.3115 0.4725 0.5543 0.5386 0.3746
#>  [4,] 0.0130 0.1483 0.1532 0.1100 0.0890 0.1236 0.1197 0.1145 0.2137 0.2838
#>  [5,] 0.0201 0.0279 0.2251 0.2615 0.1770 0.3709 0.4533 0.5553 0.4616 0.3797
#>  [6,] 0.0139 0.1015 0.1261 0.0828 0.0493 0.0848 0.1514 0.1396 0.1066 0.1923
#>  [7,] 0.0430 0.7106 0.7342 0.5033 0.3000 0.1951 0.2767 0.3737 0.2507 0.2507
#>  [8,] 0.0293 0.0860 0.0414 0.0472 0.0835 0.0938 0.1466 0.0809 0.1179 0.2179
#>  [9,] 0.0209 0.4125 0.3943 0.1334 0.4622 0.9970 0.9137 0.8292 0.6994 0.7825
#> [10,] 0.0228 0.3517 0.3330 0.3643 0.4020 0.4731 0.5196 0.6573 0.8426 0.8476
#> [11,] 0.0202 0.1370 0.0843 0.0269 0.1254 0.3046 0.5584 0.7973 0.8341 0.8057
#> [12,] 0.0209 0.1853 0.1748 0.1556 0.1476 0.1378 0.2584 0.3827 0.4784 0.5360
#> [13,] 0.0216 0.0866 0.1564 0.0780 0.0997 0.0915 0.0662 0.1134 0.1740 0.2573
#> [14,] 0.0093 0.0640 0.0888 0.1599 0.1541 0.2768 0.2176 0.2799 0.3491 0.2824
#> [15,] 0.0394 0.2558 0.3078 0.3404 0.3400 0.3951 0.3352 0.2252 0.2086 0.2248
#> [16,] 0.0366 0.1847 0.2222 0.2648 0.2508 0.2291 0.1555 0.1863 0.2387 0.3345
#> [17,] 0.0163 0.2822 0.3691 0.3750 0.3927 0.3308 0.1085 0.1139 0.3446 0.5441
#> [18,] 0.0123 0.0835 0.0548 0.0847 0.2026 0.2557 0.1870 0.2032 0.1463 0.2849
#> [19,] 0.0094 0.1725 0.2228 0.3106 0.4144 0.5157 0.5369 0.5107 0.6441 0.7326
#> [20,] 0.0126 0.4284 0.3015 0.1207 0.3299 0.5707 0.6962 0.9751 1.0000 0.9293
#> [21,] 0.0715 0.4186 0.4867 0.5249 0.5959 0.6855 0.8573 0.9718 0.8693 0.8711
#> [22,] 0.0235 0.3674 0.2974 0.0837 0.1912 0.5040 0.6352 0.6804 0.7505 0.6595
#> [23,] 0.0096 0.2952 0.4025 0.5148 0.4901 0.4127 0.3575 0.3447 0.3068 0.2945
#> [24,] 0.0115 0.0734 0.0740 0.0622 0.1055 0.1183 0.1721 0.2584 0.3232 0.3817
#> [25,] 0.0352 0.2912 0.2328 0.2237 0.2470 0.1560 0.3491 0.3308 0.2299 0.2203
#> [26,] 0.0126 0.5920 0.5832 0.5419 0.5472 0.5314 0.4981 0.6985 0.8292 0.7839
#> [27,] 0.0599 0.0351 0.1582 0.2023 0.2268 0.2829 0.3819 0.4665 0.6687 0.8647
#> [28,] 0.0107 0.2936 0.3104 0.3431 0.2456 0.1887 0.1184 0.2080 0.2736 0.3274
#> [29,] 0.0454 0.1675 0.2799 0.3323 0.4012 0.4296 0.5350 0.5411 0.6870 0.8045
#> [30,] 0.0228 0.2227 0.2621 0.3109 0.2859 0.3316 0.3755 0.4499 0.4765 0.6254
#> [31,] 0.0094 0.5079 0.3350 0.0834 0.3004 0.3957 0.3769 0.3828 0.1247 0.1363
#> [32,] 0.0164 0.0251 0.0801 0.1056 0.1266 0.0890 0.0198 0.1133 0.2826 0.3234
#> [33,] 0.0211 0.1345 0.0785 0.0367 0.1227 0.2614 0.4280 0.6122 0.7435 0.8130
#> [34,] 0.0414 0.1975 0.2309 0.3025 0.3938 0.5050 0.5872 0.6610 0.7417 0.8006
#> [35,] 0.0123 0.0475 0.1152 0.0520 0.1192 0.1943 0.1840 0.2077 0.1956 0.1630
#> [36,] 0.0091 0.0837 0.1579 0.0898 0.0309 0.1856 0.2969 0.2032 0.1264 0.1655
#> [37,] 0.0412 0.2058 0.1546 0.2671 0.3141 0.2904 0.3531 0.5079 0.4639 0.1859
#> [38,] 0.0208 0.1093 0.1063 0.1179 0.1291 0.1591 0.1680 0.1918 0.1615 0.1647
#> [39,] 0.0217 0.2110 0.2343 0.2087 0.1645 0.1689 0.1650 0.1967 0.2934 0.3709
#> [40,] 0.0117 0.2169 0.1180 0.0754 0.2782 0.3758 0.5093 0.6592 0.7071 0.7532
#>          V19     V2    V20    V21    V22    V23    V24    V25    V26    V27
#>  [1,] 0.8152 0.0093 0.8129 0.8297 0.8535 0.8870 0.8894 0.8980 0.9667 1.0000
#>  [2,] 0.7062 0.0653 0.8196 0.8835 0.8299 0.7609 0.7605 0.8367 0.8905 0.7652
#>  [3,] 0.4583 0.1252 0.5961 0.7464 0.7644 0.5711 0.6257 0.6695 0.7131 0.7567
#>  [4,] 0.3640 0.0006 0.5430 0.6673 0.7979 0.9273 0.9027 0.9192 1.0000 0.9821
#>  [5,] 0.3450 0.0423 0.2665 0.2395 0.1127 0.2556 0.5169 0.3779 0.4082 0.5353
#>  [6,] 0.2991 0.0222 0.3247 0.3797 0.5658 0.7483 0.8757 0.9048 0.7511 0.6858
#>  [7,] 0.3292 0.0902 0.4871 0.6527 0.8454 0.9739 1.0000 0.6665 0.5323 0.4024
#>  [8,] 0.3326 0.0644 0.3258 0.2111 0.2302 0.3361 0.4259 0.4609 0.2606 0.0874
#>  [9,] 0.8789 0.0261 0.8501 0.8920 0.9473 1.0000 0.8975 0.7806 0.8321 0.6502
#> [10,] 0.8344 0.0853 0.8453 0.7999 0.8537 0.9642 1.0000 0.9357 0.9409 0.9070
#> [11,] 0.8616 0.0104 0.8769 0.9413 0.9403 0.9409 1.0000 0.9725 0.9309 0.9351
#> [12,] 0.6192 0.0278 0.7912 0.9264 1.0000 0.9080 0.7435 0.5557 0.3172 0.1295
#> [13,] 0.3294 0.0124 0.3910 0.5438 0.6115 0.7022 0.7610 0.7973 0.9105 0.8807
#> [14,] 0.2479 0.0185 0.3005 0.4300 0.4684 0.4520 0.5026 0.6217 0.6571 0.6632
#> [15,] 0.3382 0.0420 0.4578 0.6474 0.6708 0.7007 0.7619 0.7745 0.6767 0.7373
#> [16,] 0.5233 0.0421 0.6684 0.7766 0.7928 0.7940 0.9129 0.9498 0.9835 1.0000
#> [17,] 0.6470 0.0198 0.7276 0.7894 0.8264 0.8697 0.7836 0.7140 0.5698 0.2908
#> [18,] 0.5824 0.0309 0.7728 0.7852 0.8515 0.5312 0.3653 0.5973 0.8275 1.0000
#> [19,] 0.8164 0.0333 0.8856 0.9891 1.0000 0.8750 0.8631 0.9074 0.8674 0.7750
#> [20,] 0.6210 0.0519 0.4586 0.5001 0.5032 0.7082 0.8420 0.8109 0.7690 0.8105
#> [21,] 0.8954 0.0849 0.9922 0.8980 0.8158 0.8373 0.7541 0.5893 0.5488 0.5643
#> [22,] 0.4509 0.0291 0.2964 0.4019 0.6794 0.8297 1.0000 0.8240 0.7115 0.7726
#> [23,] 0.4351 0.0404 0.7264 0.8147 0.8103 0.6665 0.6958 0.7748 0.8688 1.0000
#> [24,] 0.4243 0.0150 0.4217 0.4449 0.4075 0.3306 0.4012 0.4466 0.5218 0.7552
#> [25,] 0.2493 0.0116 0.4128 0.3158 0.6191 0.5854 0.3395 0.2561 0.5599 0.8145
#> [26,] 0.8215 0.0149 0.9363 1.0000 0.9224 0.7839 0.5470 0.4562 0.5922 0.5448
#> [27,] 0.9361 0.0474 0.9367 0.9144 0.9162 0.9311 0.8604 0.7327 0.5763 0.4162
#> [28,] 0.2344 0.0453 0.1260 0.0576 0.1241 0.3239 0.4357 0.5734 0.7825 0.9252
#> [29,] 0.9194 0.0472 0.9169 1.0000 0.9972 0.9093 0.7918 0.6705 0.5324 0.3572
#> [30,] 0.7304 0.0106 0.8702 0.9349 0.9614 0.9126 0.9443 1.0000 0.9455 0.8815
#> [31,] 0.2678 0.0611 0.9188 0.9779 0.3236 0.1944 0.1874 0.0885 0.3443 0.2953
#> [32,] 0.3238 0.0173 0.4333 0.6068 0.7652 0.9203 0.9719 0.9207 0.7545 0.8289
#> [33,] 0.9006 0.0128 0.9603 0.9162 0.9140 0.7851 0.5134 0.3439 0.3290 0.2571
#> [34,] 0.8456 0.0436 0.7939 0.8804 0.8384 0.7852 0.8479 0.7434 0.6433 0.5514
#> [35,] 0.1218 0.0022 0.1017 0.1354 0.3157 0.4645 0.5906 0.6776 0.8119 0.8594
#> [36,] 0.1661 0.0213 0.2091 0.2310 0.4460 0.6634 0.6933 0.7663 0.8206 0.7049
#> [37,] 0.4474 0.1135 0.4079 0.5400 0.4786 0.4332 0.6113 0.5091 0.4606 0.7243
#> [38,] 0.1397 0.0186 0.1426 0.2429 0.2816 0.4290 0.6443 0.9061 1.0000 0.8087
#> [39,] 0.4309 0.0152 0.4161 0.5116 0.6501 0.7717 0.8491 0.9104 0.8912 0.8189
#> [40,] 0.8357 0.0069 0.8593 0.9615 0.9838 0.8705 0.6403 0.5067 0.5395 0.6934
#>          V28    V29     V3    V30    V31    V32    V33    V34    V35    V36
#>  [1,] 0.9134 0.6762 0.0121 0.4659 0.2895 0.2959 0.1746 0.2112 0.2569 0.2276
#>  [2,] 0.5897 0.3037 0.0397 0.0823 0.2787 0.7241 0.8032 0.8050 0.7676 0.7468
#>  [3,] 0.8077 0.8477 0.1447 0.9289 0.9513 0.7995 0.4362 0.4048 0.4952 0.1712
#>  [4,] 0.9092 0.8184 0.0088 0.6962 0.5900 0.5447 0.5142 0.5389 0.5531 0.5318
#>  [5,] 0.5116 0.4544 0.0554 0.4258 0.3869 0.3939 0.4661 0.3974 0.2194 0.1816
#>  [6,] 0.7043 0.5864 0.0089 0.3773 0.2206 0.2628 0.2672 0.2907 0.1982 0.2288
#>  [7,] 0.3444 0.4239 0.0833 0.4182 0.4393 0.1162 0.4336 0.6553 0.6172 0.4373
#>  [8,] 0.2862 0.5606 0.0390 0.8344 0.8096 0.7250 0.8048 0.9435 1.0000 0.8960
#>  [9,] 0.4548 0.4732 0.0120 0.3391 0.2747 0.0978 0.0477 0.1403 0.1834 0.2148
#> [10,] 0.7104 0.6320 0.1000 0.5667 0.3501 0.2447 0.1698 0.3290 0.3674 0.2331
#> [11,] 0.7317 0.4421 0.0325 0.3244 0.4161 0.4611 0.4031 0.3000 0.2459 0.1348
#> [12,] 0.0598 0.2722 0.0115 0.3616 0.3293 0.4855 0.3936 0.1845 0.0342 0.2489
#> [13,] 0.7949 0.7990 0.0174 0.7180 0.6407 0.6312 0.5929 0.6168 0.6498 0.6764
#> [14,] 0.7321 0.8534 0.0056 1.0000 0.8448 0.6354 0.6308 0.6211 0.6976 0.5868
#> [15,] 0.7834 0.9619 0.0446 1.0000 0.8086 0.5558 0.5409 0.4988 0.3108 0.2897
#> [16,] 0.9471 0.8237 0.0504 0.6252 0.4181 0.3209 0.2658 0.2196 0.1588 0.0561
#> [17,] 0.4636 0.6409 0.0202 0.7405 0.8069 0.8420 1.0000 0.9536 0.6755 0.3905
#> [18,] 0.8673 0.6301 0.0169 0.4591 0.3940 0.2576 0.2817 0.2641 0.2757 0.2698
#> [19,] 0.6600 0.5615 0.0306 0.4016 0.2331 0.1164 0.1095 0.0431 0.0619 0.1956
#> [20,] 0.6203 0.2356 0.0621 0.2595 0.6299 0.6762 0.2903 0.4393 0.8529 0.7180
#> [21,] 0.5406 0.4783 0.0587 0.4439 0.3698 0.2574 0.1478 0.1743 0.1229 0.1588
#> [22,] 0.6124 0.4936 0.0749 0.5648 0.4906 0.1820 0.1811 0.1107 0.4603 0.6650
#> [23,] 0.9941 0.8793 0.0682 0.6482 0.5876 0.6408 0.4972 0.2755 0.0300 0.3356
#> [24,] 0.9503 1.0000 0.0136 0.9084 0.8283 0.7571 0.7262 0.6152 0.5680 0.5757
#> [25,] 0.6941 0.6985 0.0191 0.8660 0.5930 0.3664 0.6750 0.8697 0.7837 0.7552
#> [26,] 0.3971 0.0882 0.0641 0.2385 0.2005 0.0587 0.2544 0.2009 0.0329 0.1547
#> [27,] 0.4113 0.4146 0.0498 0.3149 0.2936 0.3169 0.3149 0.4132 0.3994 0.4195
#> [28,] 0.9349 0.9348 0.0289 1.0000 0.9308 0.8478 0.7605 0.7040 0.7539 0.7990
#> [29,] 0.2484 0.3161 0.0697 0.3775 0.3138 0.1713 0.2937 0.5234 0.5926 0.5437
#> [30,] 0.7520 0.7068 0.0130 0.5986 0.3857 0.2510 0.2162 0.0968 0.1323 0.1344
#> [31,] 0.5908 0.4564 0.1136 0.7334 0.1969 0.2790 0.6212 0.8681 0.8621 0.9380
#> [32,] 0.8907 0.7309 0.0347 0.6896 0.5829 0.4935 0.3101 0.0306 0.0244 0.1108
#> [33,] 0.3685 0.5765 0.0015 0.6190 0.4613 0.3615 0.4434 0.3864 0.3093 0.2138
#> [34,] 0.3519 0.3168 0.0447 0.3346 0.2056 0.1032 0.3168 0.4040 0.4282 0.4538
#> [35,] 0.9228 0.8387 0.0196 0.7238 0.6292 0.5181 0.4629 0.5255 0.5147 0.3929
#> [36,] 0.7560 0.7466 0.0206 0.6387 0.4846 0.3328 0.5356 0.8741 0.8573 0.6718
#> [37,] 0.8987 0.8826 0.0518 0.9201 0.8005 0.6033 0.2120 0.2866 0.4033 0.2803
#> [38,] 0.6119 0.5260 0.0131 0.3677 0.2746 0.1020 0.1339 0.1582 0.1952 0.1787
#> [39,] 0.6779 0.5368 0.0346 0.5207 0.5651 0.5749 0.5250 0.4255 0.3330 0.2331
#> [40,] 0.8487 0.8213 0.0279 0.5962 0.2950 0.2758 0.2885 0.1893 0.1446 0.0955
#>          V37    V38    V39     V4    V40    V41    V42    V43    V44    V45
#>  [1,] 0.2149 0.1601 0.0371 0.0378 0.0117 0.0488 0.0288 0.0597 0.0431 0.0369
#>  [2,] 0.6253 0.1730 0.2916 0.0604 0.5003 0.5220 0.4824 0.4004 0.3877 0.1651
#>  [3,] 0.3652 0.3763 0.2841 0.1644 0.0427 0.5331 0.6952 0.4288 0.3063 0.5835
#>  [4,] 0.4826 0.3790 0.1831 0.0456 0.1750 0.1679 0.0674 0.0609 0.0375 0.0533
#>  [5,] 0.1023 0.2108 0.3253 0.0783 0.3697 0.2912 0.3010 0.2563 0.1927 0.2062
#>  [6,] 0.3186 0.2871 0.2921 0.0108 0.2806 0.2682 0.2112 0.1513 0.1789 0.1850
#>  [7,] 0.4118 0.3641 0.4572 0.0813 0.4367 0.2964 0.4312 0.4155 0.1824 0.1487
#>  [8,] 0.5516 0.3037 0.2338 0.0173 0.2382 0.3318 0.3821 0.1575 0.2228 0.1582
#>  [9,] 0.1271 0.1912 0.3391 0.0768 0.3444 0.2369 0.1195 0.2665 0.2587 0.1393
#> [10,] 0.2413 0.2556 0.1892 0.0428 0.1940 0.3074 0.2785 0.0308 0.1238 0.1854
#> [11,] 0.2541 0.2255 0.1598 0.0239 0.1485 0.0845 0.0569 0.0855 0.1262 0.1153
#> [12,] 0.3837 0.3514 0.2654 0.0445 0.1760 0.1599 0.0866 0.0590 0.0813 0.0492
#> [13,] 0.6253 0.5117 0.3890 0.0152 0.3273 0.2509 0.1530 0.1323 0.1657 0.1215
#> [14,] 0.4889 0.3683 0.2043 0.0064 0.1469 0.2220 0.1449 0.1490 0.1211 0.1144
#> [15,] 0.2244 0.0960 0.2287 0.0551 0.3228 0.3454 0.3882 0.3240 0.0926 0.1173
#> [16,] 0.0948 0.1700 0.1215 0.0250 0.1282 0.0386 0.1329 0.2331 0.2468 0.1960
#> [17,] 0.1249 0.3629 0.6356 0.0386 0.8116 0.7664 0.5417 0.2614 0.1723 0.2814
#> [18,] 0.3994 0.4576 0.3940 0.0313 0.2522 0.1782 0.1354 0.0516 0.0337 0.0894
#> [19,] 0.2120 0.3242 0.4102 0.0376 0.2939 0.1911 0.1702 0.1010 0.1512 0.1427
#> [20,] 0.4801 0.5856 0.4993 0.0518 0.2866 0.0601 0.1167 0.2737 0.2812 0.2078
#> [21,] 0.1803 0.1436 0.1667 0.0218 0.2630 0.2234 0.1239 0.0869 0.2092 0.1499
#> [22,] 0.6423 0.2166 0.1951 0.0519 0.4947 0.4925 0.4041 0.2402 0.1392 0.1779
#> [23,] 0.3167 0.4133 0.6281 0.0688 0.4977 0.2613 0.4697 0.4806 0.4921 0.5294
#> [24,] 0.5324 0.3672 0.1669 0.0076 0.0866 0.0646 0.1891 0.2683 0.2887 0.2341
#> [25,] 0.5789 0.4713 0.1252 0.0469 0.6087 0.7322 0.5977 0.3431 0.1803 0.2378
#> [26,] 0.1212 0.2446 0.3171 0.1732 0.3195 0.3051 0.0836 0.1266 0.1381 0.1136
#> [27,] 0.4532 0.4419 0.4737 0.0387 0.3431 0.3194 0.3370 0.2493 0.2650 0.1748
#> [28,] 0.7673 0.5955 0.4731 0.0713 0.4840 0.4340 0.3954 0.4837 0.5379 0.4485
#> [29,] 0.4516 0.3379 0.3215 0.1021 0.2178 0.1674 0.2634 0.2980 0.2037 0.1155
#> [30,] 0.2250 0.3244 0.3939 0.0842 0.3806 0.3258 0.3654 0.2983 0.1779 0.1535
#> [31,] 0.8327 0.9480 0.6721 0.1203 0.4436 0.5163 0.3809 0.1557 0.1449 0.2662
#> [32,] 0.1594 0.1371 0.0696 0.0070 0.0452 0.0620 0.1421 0.1597 0.1384 0.0372
#> [33,] 0.1112 0.1386 0.1523 0.0450 0.0996 0.1644 0.1902 0.1313 0.1776 0.2000
#> [34,] 0.3704 0.3741 0.3839 0.0844 0.3494 0.4380 0.4265 0.2854 0.2808 0.2395
#> [35,] 0.1279 0.0411 0.0859 0.0206 0.1131 0.1306 0.1757 0.2648 0.1955 0.0656
#> [36,] 0.3446 0.3150 0.2702 0.0505 0.2598 0.2742 0.3594 0.4382 0.2460 0.0758
#> [37,] 0.3087 0.3550 0.2545 0.0232 0.1432 0.5869 0.6431 0.5826 0.4286 0.4894
#> [38,] 0.0429 0.1096 0.1762 0.0211 0.2481 0.3150 0.2920 0.1902 0.0696 0.0758
#> [39,] 0.1451 0.1648 0.2694 0.0346 0.3730 0.4467 0.4133 0.3743 0.3021 0.2069
#> [40,] 0.0888 0.0836 0.0894 0.0583 0.1547 0.2318 0.2225 0.1035 0.1721 0.2017
#>          V46    V47    V48    V49     V5    V50    V51    V52    V53    V54
#>  [1,] 0.0025 0.0327 0.0257 0.0182 0.0679 0.0108 0.0124 0.0077 0.0023 0.0117
#>  [2,] 0.0442 0.0663 0.0418 0.0475 0.0496 0.0235 0.0066 0.0062 0.0129 0.0184
#>  [3,] 0.5692 0.2630 0.1196 0.0983 0.1693 0.0374 0.0291 0.0156 0.0197 0.0135
#>  [4,] 0.0278 0.0179 0.0114 0.0073 0.0525 0.0116 0.0092 0.0078 0.0041 0.0013
#>  [5,] 0.1751 0.0841 0.1035 0.0641 0.0620 0.0153 0.0081 0.0191 0.0182 0.0160
#>  [6,] 0.1717 0.0898 0.0656 0.0445 0.0215 0.0110 0.0024 0.0062 0.0072 0.0113
#>  [7,] 0.0138 0.1164 0.2052 0.1069 0.0165 0.0199 0.0208 0.0176 0.0197 0.0210
#>  [8,] 0.1433 0.1634 0.1133 0.0567 0.0476 0.0133 0.0170 0.0035 0.0052 0.0083
#>  [9,] 0.1083 0.1383 0.1321 0.1069 0.1064 0.0325 0.0316 0.0057 0.0159 0.0085
#> [10,] 0.1753 0.1079 0.0728 0.0242 0.1117 0.0191 0.0159 0.0172 0.0191 0.0260
#> [11,] 0.0570 0.0426 0.0425 0.0235 0.0807 0.0006 0.0188 0.0127 0.0081 0.0067
#> [12,] 0.0417 0.0495 0.0367 0.0115 0.0427 0.0118 0.0133 0.0096 0.0014 0.0049
#> [13,] 0.0978 0.0452 0.0273 0.0179 0.0608 0.0092 0.0018 0.0052 0.0049 0.0096
#> [14,] 0.0791 0.0365 0.0152 0.0085 0.0260 0.0120 0.0022 0.0069 0.0064 0.0129
#> [15,] 0.0566 0.0766 0.0969 0.0588 0.0597 0.0050 0.0118 0.0146 0.0040 0.0114
#> [16,] 0.1985 0.1570 0.0921 0.0549 0.0596 0.0194 0.0166 0.0132 0.0027 0.0022
#> [17,] 0.2764 0.1985 0.1502 0.1219 0.0752 0.0493 0.0027 0.0077 0.0026 0.0031
#> [18,] 0.0861 0.0872 0.0445 0.0134 0.0358 0.0217 0.0188 0.0133 0.0265 0.0224
#> [19,] 0.1097 0.1173 0.0972 0.0703 0.1296 0.0281 0.0216 0.0153 0.0112 0.0241
#> [20,] 0.0660 0.0491 0.0345 0.0172 0.1072 0.0287 0.0027 0.0208 0.0048 0.0199
#> [21,] 0.0676 0.0899 0.0927 0.0658 0.0862 0.0086 0.0216 0.0153 0.0121 0.0096
#> [22,] 0.1946 0.1723 0.1522 0.0929 0.0227 0.0179 0.0242 0.0083 0.0037 0.0095
#> [23,] 0.2216 0.1401 0.1888 0.0947 0.0887 0.0134 0.0310 0.0237 0.0078 0.0144
#> [24,] 0.1668 0.1015 0.1195 0.0704 0.0211 0.0167 0.0107 0.0091 0.0016 0.0084
#> [25,] 0.3424 0.2303 0.0689 0.0216 0.0737 0.0469 0.0426 0.0346 0.0158 0.0154
#> [26,] 0.0516 0.0073 0.0278 0.0372 0.2565 0.0121 0.0153 0.0092 0.0035 0.0098
#> [27,] 0.0932 0.0530 0.0081 0.0342 0.1026 0.0137 0.0028 0.0013 0.0005 0.0227
#> [28,] 0.2674 0.1541 0.1359 0.0941 0.1075 0.0261 0.0079 0.0164 0.0120 0.0113
#> [29,] 0.0919 0.0882 0.0228 0.0380 0.1397 0.0142 0.0137 0.0120 0.0042 0.0238
#> [30,] 0.1199 0.0959 0.0765 0.0649 0.1117 0.0313 0.0185 0.0098 0.0178 0.0077
#> [31,] 0.1806 0.1699 0.2559 0.1129 0.0403 0.0201 0.0480 0.0234 0.0175 0.0352
#> [32,] 0.0688 0.0867 0.0513 0.0092 0.0187 0.0198 0.0118 0.0090 0.0223 0.0179
#> [33,] 0.0765 0.0727 0.0749 0.0449 0.0711 0.0134 0.0174 0.0117 0.0023 0.0047
#> [34,] 0.0369 0.0805 0.0541 0.0177 0.0419 0.0065 0.0222 0.0045 0.0136 0.0113
#> [35,] 0.0580 0.0319 0.0301 0.0272 0.0180 0.0074 0.0149 0.0125 0.0134 0.0026
#> [36,] 0.0187 0.0797 0.0748 0.0367 0.0657 0.0155 0.0300 0.0112 0.0112 0.0102
#> [37,] 0.5777 0.4315 0.2640 0.1794 0.0646 0.0772 0.0798 0.0376 0.0143 0.0272
#> [38,] 0.0910 0.0441 0.0244 0.0265 0.0610 0.0095 0.0140 0.0074 0.0063 0.0081
#> [39,] 0.1790 0.1689 0.1341 0.0769 0.0484 0.0222 0.0205 0.0123 0.0067 0.0011
#> [40,] 0.1787 0.1112 0.0398 0.0305 0.0915 0.0084 0.0039 0.0053 0.0029 0.0020
#>          V55    V56    V57    V58    V59     V6    V60     V7     V8     V9
#>  [1,] 0.0053 0.0077 0.0076 0.0056 0.0055 0.0863 0.0039 0.1004 0.0664 0.0941
#>  [2,] 0.0069 0.0198 0.0199 0.0102 0.0070 0.1817 0.0055 0.1178 0.1024 0.0583
#>  [3,] 0.0127 0.0138 0.0133 0.0131 0.0154 0.0844 0.0218 0.0715 0.0947 0.1583
#>  [4,] 0.0011 0.0045 0.0039 0.0022 0.0023 0.0778 0.0016 0.0931 0.0941 0.1711
#>  [5,] 0.0290 0.0090 0.0242 0.0224 0.0190 0.0871 0.0096 0.1201 0.2707 0.1206
#>  [6,] 0.0012 0.0022 0.0025 0.0059 0.0039 0.0136 0.0048 0.0659 0.0954 0.0786
#>  [7,] 0.0141 0.0049 0.0027 0.0162 0.0059 0.0277 0.0021 0.0569 0.2057 0.3887
#>  [8,] 0.0078 0.0075 0.0105 0.0160 0.0095 0.0816 0.0011 0.0993 0.0315 0.0736
#>  [9,] 0.0372 0.0101 0.0127 0.0288 0.0129 0.1680 0.0023 0.3016 0.3460 0.3314
#> [10,] 0.0140 0.0125 0.0116 0.0093 0.0012 0.1651 0.0036 0.1597 0.2116 0.3295
#> [11,] 0.0043 0.0065 0.0049 0.0054 0.0073 0.1529 0.0054 0.1154 0.0608 0.1317
#> [12,] 0.0039 0.0029 0.0078 0.0047 0.0021 0.0766 0.0011 0.1458 0.1430 0.1894
#> [13,] 0.0134 0.0122 0.0047 0.0018 0.0006 0.1026 0.0023 0.1139 0.0877 0.1160
#> [14,] 0.0114 0.0054 0.0089 0.0050 0.0058 0.0458 0.0025 0.0470 0.0057 0.0425
#> [15,] 0.0032 0.0062 0.0101 0.0068 0.0053 0.1416 0.0087 0.0956 0.0802 0.1618
#> [16,] 0.0059 0.0016 0.0025 0.0017 0.0027 0.0252 0.0027 0.0958 0.0991 0.1419
#> [17,] 0.0083 0.0020 0.0084 0.0108 0.0083 0.1444 0.0033 0.1487 0.1484 0.2442
#> [18,] 0.0074 0.0118 0.0026 0.0092 0.0009 0.0102 0.0044 0.0182 0.0579 0.1122
#> [19,] 0.0164 0.0055 0.0078 0.0055 0.0091 0.1795 0.0067 0.1909 0.1692 0.1870
#> [20,] 0.0126 0.0022 0.0037 0.0034 0.0114 0.2587 0.0077 0.2304 0.2067 0.3416
#> [21,] 0.0196 0.0042 0.0066 0.0099 0.0083 0.1801 0.0124 0.1916 0.1896 0.2960
#> [22,] 0.0105 0.0030 0.0132 0.0068 0.0108 0.0834 0.0090 0.0677 0.2002 0.2876
#> [23,] 0.0170 0.0012 0.0109 0.0036 0.0043 0.0932 0.0018 0.0955 0.2140 0.2546
#> [24,] 0.0064 0.0026 0.0029 0.0037 0.0070 0.1058 0.0041 0.1023 0.0440 0.0931
#> [25,] 0.0109 0.0048 0.0095 0.0015 0.0073 0.1185 0.0067 0.1683 0.1541 0.1466
#> [26,] 0.0121 0.0006 0.0181 0.0094 0.0116 0.2559 0.0063 0.2947 0.4110 0.4983
#> [27,] 0.0209 0.0081 0.0117 0.0114 0.0112 0.0773 0.0100 0.0853 0.0447 0.1094
#> [28,] 0.0021 0.0097 0.0072 0.0060 0.0017 0.1019 0.0036 0.1606 0.2119 0.3061
#> [29,] 0.0129 0.0084 0.0218 0.0321 0.0154 0.1493 0.0053 0.1487 0.0771 0.1171
#> [30,] 0.0074 0.0095 0.0055 0.0045 0.0063 0.1506 0.0039 0.1776 0.0997 0.1428
#> [31,] 0.0158 0.0326 0.0201 0.0168 0.0245 0.1227 0.0154 0.2495 0.4566 0.6587
#> [32,] 0.0084 0.0068 0.0032 0.0035 0.0056 0.0671 0.0040 0.1056 0.0697 0.0962
#> [33,] 0.0049 0.0031 0.0024 0.0039 0.0051 0.1563 0.0015 0.1518 0.1206 0.1666
#> [34,] 0.0053 0.0165 0.0141 0.0077 0.0246 0.1215 0.0198 0.2002 0.1516 0.0818
#> [35,] 0.0038 0.0018 0.0113 0.0058 0.0047 0.0492 0.0071 0.0033 0.0398 0.0791
#> [36,] 0.0026 0.0097 0.0098 0.0043 0.0071 0.0795 0.0108 0.0970 0.0872 0.0743
#> [37,] 0.0127 0.0166 0.0095 0.0225 0.0098 0.1124 0.0085 0.1787 0.2407 0.2682
#> [38,] 0.0087 0.0044 0.0028 0.0019 0.0049 0.0613 0.0023 0.0612 0.0506 0.0989
#> [39,] 0.0026 0.0049 0.0029 0.0022 0.0022 0.0526 0.0032 0.0773 0.0862 0.1451
#> [40,] 0.0013 0.0029 0.0020 0.0062 0.0026 0.1267 0.0052 0.1577 0.1927 0.2361
#> 
#> $post[[2]]
#>              [,1]
#>  [1,] 0.004720906
#>  [2,] 0.003455885
#>  [3,] 0.002866037
#>  [4,] 0.004466987
#>  [5,] 0.025101126
#>  [6,] 0.015992516
#>  [7,] 0.007615619
#>  [8,] 0.017707962
#>  [9,] 0.003968939
#> [10,] 0.002922810
#> [11,] 0.003558574
#> [12,] 0.015077491
#> [13,] 0.005333582
#> [14,] 0.008090578
#> [15,] 0.004936392
#> [16,] 0.005729137
#> [17,] 0.002721504
#> [18,] 0.008716158
#> [19,] 0.004377312
#> [20,] 0.003754723
#> [21,] 0.004855307
#> [22,] 0.005626449
#> [23,] 0.002451209
#> [24,] 0.007470315
#> [25,] 0.006592178
#> [26,] 0.007982992
#> [27,] 0.005151631
#> [28,] 0.004541114
#> [29,] 0.005384015
#> [30,] 0.003227507
#> [31,] 0.008054562
#> [32,] 0.009409136
#> [33,] 0.008854832
#> [34,] 0.005203425
#> [35,] 0.015425524
#> [36,] 0.009701724
#> [37,] 0.005279952
#> [38,] 0.028242500
#> [39,] 0.006748975
#> [40,] 0.005296046
#> 
#> $post[[3]]
#>            [,1]      [,2]
#>  [1,] 0.5320216 0.4679784
#>  [2,] 0.5320020 0.4679980
#>  [3,] 0.5319929 0.4680071
#>  [4,] 0.5320177 0.4679823
#>  [5,] 0.5323375 0.4676625
#>  [6,] 0.5321963 0.4678037
#>  [7,] 0.5320665 0.4679335
#>  [8,] 0.5322229 0.4677771
#>  [9,] 0.5320099 0.4679901
#> [10,] 0.5319937 0.4680063
#> [11,] 0.5320036 0.4679964
#> [12,] 0.5321821 0.4678179
#> [13,] 0.5320311 0.4679689
#> [14,] 0.5320738 0.4679262
#> [15,] 0.5320249 0.4679751
#> [16,] 0.5320372 0.4679628
#> [17,] 0.5319906 0.4680094
#> [18,] 0.5320835 0.4679165
#> [19,] 0.5320163 0.4679837
#> [20,] 0.5320066 0.4679934
#> [21,] 0.5320237 0.4679763
#> [22,] 0.5320356 0.4679644
#> [23,] 0.5319864 0.4680136
#> [24,] 0.5320642 0.4679358
#> [25,] 0.5320506 0.4679494
#> [26,] 0.5320722 0.4679278
#> [27,] 0.5320283 0.4679717
#> [28,] 0.5320188 0.4679812
#> [29,] 0.5320319 0.4679681
#> [30,] 0.5319985 0.4680015
#> [31,] 0.5320733 0.4679267
#> [32,] 0.5320943 0.4679057
#> [33,] 0.5320857 0.4679143
#> [34,] 0.5320291 0.4679709
#> [35,] 0.5321875 0.4678125
#> [36,] 0.5320988 0.4679012
#> [37,] 0.5320303 0.4679697
#> [38,] 0.5323862 0.4676138
#> [39,] 0.5320530 0.4679470
#> [40,] 0.5320305 0.4679695
#> 
#> 
#> $pre
#> $pre[[1]]
#> NULL
#> 
#> $pre[[2]]
#>            [,1]
#>  [1,] -5.351023
#>  [2,] -5.664215
#>  [3,] -5.851955
#>  [4,] -5.406564
#>  [5,] -3.659421
#>  [6,] -4.119513
#>  [7,] -4.869909
#>  [8,] -4.015874
#>  [9,] -5.525280
#> [10,] -5.832283
#> [11,] -5.634830
#> [12,] -4.179360
#> [13,] -5.228384
#> [14,] -4.808932
#> [15,] -5.306172
#> [16,] -5.156445
#> [17,] -5.903846
#> [18,] -4.733822
#> [19,] -5.426934
#> [20,] -5.580979
#> [21,] -5.322816
#> [22,] -5.174634
#> [23,] -6.008720
#> [24,] -4.889320
#> [25,] -5.015257
#> [26,] -4.822427
#> [27,] -5.263277
#> [28,] -5.390032
#> [29,] -5.218922
#> [30,] -5.732813
#> [31,] -4.813429
#> [32,] -4.656620
#> [33,] -4.717898
#> [34,] -5.253221
#> [35,] -4.156186
#> [36,] -4.625703
#> [37,] -5.238544
#> [38,] -3.538278
#> [39,] -4.991593
#> [40,] -5.235485
#> 
#> $pre[[3]]
#>             [,1]        [,2]
#>  [1,] 0.07285487 -0.05540709
#>  [2,] 0.07280484 -0.05537836
#>  [3,] 0.07278152 -0.05536497
#>  [4,] 0.07284482 -0.05540133
#>  [5,] 0.07366076 -0.05586992
#>  [6,] 0.07330058 -0.05566307
#>  [7,] 0.07296933 -0.05547283
#>  [8,] 0.07336841 -0.05570202
#>  [9,] 0.07282513 -0.05539002
#> [10,] 0.07278376 -0.05536626
#> [11,] 0.07280890 -0.05538070
#> [12,] 0.07326440 -0.05564229
#> [13,] 0.07287909 -0.05542101
#> [14,] 0.07298811 -0.05548362
#> [15,] 0.07286339 -0.05541199
#> [16,] 0.07289473 -0.05542999
#> [17,] 0.07277580 -0.05536169
#> [18,] 0.07301285 -0.05549782
#> [19,] 0.07284128 -0.05539929
#> [20,] 0.07281666 -0.05538515
#> [21,] 0.07286018 -0.05541014
#> [22,] 0.07289067 -0.05542766
#> [23,] 0.07276511 -0.05535555
#> [24,] 0.07296359 -0.05546953
#> [25,] 0.07292886 -0.05544959
#> [26,] 0.07298386 -0.05548117
#> [27,] 0.07287190 -0.05541687
#> [28,] 0.07284776 -0.05540301
#> [29,] 0.07288109 -0.05542215
#> [30,] 0.07279581 -0.05537318
#> [31,] 0.07298669 -0.05548280
#> [32,] 0.07304025 -0.05551356
#> [33,] 0.07301833 -0.05550097
#> [34,] 0.07287395 -0.05541805
#> [35,] 0.07327816 -0.05565019
#> [36,] 0.07305182 -0.05552021
#> [37,] 0.07287697 -0.05541979
#> [38,] 0.07378498 -0.05594126
#> [39,] 0.07293506 -0.05545315
#> [40,] 0.07287761 -0.05542015
#> 
#> 
#> $e
#>             [,1]       [,2]
#>  [1,] -0.5320216  0.5320216
#>  [2,] -0.5320020  0.5320020
#>  [3,]  0.4680071 -0.4680071
#>  [4,] -0.5320177  0.5320177
#>  [5,]  0.4676625 -0.4676625
#>  [6,] -0.5321963  0.5321963
#>  [7,]  0.4679335 -0.4679335
#>  [8,] -0.5322229  0.5322229
#>  [9,]  0.4679901 -0.4679901
#> [10,]  0.4680063 -0.4680063
#> [11,] -0.5320036  0.5320036
#> [12,]  0.4678179 -0.4678179
#> [13,] -0.5320311  0.5320311
#> [14,] -0.5320738  0.5320738
#> [15,]  0.4679751 -0.4679751
#> [16,]  0.4679628 -0.4679628
#> [17,]  0.4680094 -0.4680094
#> [18,] -0.5320835  0.5320835
#> [19,]  0.4679837 -0.4679837
#> [20,] -0.5320066  0.5320066
#> [21,]  0.4679763 -0.4679763
#> [22,] -0.5320356  0.5320356
#> [23,]  0.4680136 -0.4680136
#> [24,] -0.5320642  0.5320642
#> [25,] -0.5320506  0.5320506
#> [26,] -0.5320722  0.5320722
#> [27,]  0.4679717 -0.4679717
#> [28,]  0.4679812 -0.4679812
#> [29,]  0.4679681 -0.4679681
#> [30,]  0.4680015 -0.4680015
#> [31,]  0.4679267 -0.4679267
#> [32,] -0.5320943  0.5320943
#> [33,]  0.4679143 -0.4679143
#> [34,]  0.4679709 -0.4679709
#> [35,] -0.5321875  0.5321875
#> [36,] -0.5320988  0.5320988
#> [37,]  0.4679697 -0.4679697
#> [38,] -0.5323862  0.5323862
#> [39,]  0.4679470 -0.4679470
#> [40,]  0.4679695 -0.4679695
#> 
#> $L
#> [1] 0.6942485 0.6869124 0.6931954 0.6883722 0.6952141 0.6888298
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.4492754