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,] -0.04289269 -0.2108201 -0.1269913 -0.09145007 -0.06836667 -0.1342985
#>            V15        V16        V17        V18        V19          V2
#> [1,] -0.124833 -0.3236921 -0.2046152 -0.3198712 -0.3224484 -0.05563651
#>             V20        V21        V22        V23        V24       V25
#> [1,] -0.3375718 -0.3907106 -0.4042721 -0.2711029 -0.3012147 -0.429048
#>             V26        V27        V28        V29          V3        V30
#> [1,] -0.4591505 -0.3975009 -0.3355368 -0.3010127 -0.05195441 -0.2688198
#>             V31        V32        V33        V34        V35        V36
#> [1,] -0.3439952 -0.1699586 -0.3302591 -0.3143355 -0.1552842 -0.3421992
#>             V37        V38        V39         V4        V40        V41
#> [1,] -0.1250478 -0.2599968 -0.1614275 0.02203493 -0.2947246 -0.1840318
#>             V42       V43        V44        V45       V46         V47
#> [1,] -0.0919618 -0.161972 -0.1663669 -0.1048725 -0.017341 -0.01195135
#>             V48        V49           V5        V50         V51        V52
#> [1,] -0.1395885 0.03004407 -0.008467348 0.02988882 -0.06461014 -0.1078479
#>            V53       V54        V55        V56         V57         V58
#> [1,] 0.0518703 -0.101707 0.01182926 0.01302043 -0.04249675 -0.03896818
#>            V59           V6          V60         V7         V8         V9
#> [1,] 0.0858618 -0.003697136 -0.008128301 -0.1628407 -0.0666307 -0.1141115
#> 
#> $W[[2]]
#>            [,1]
#> [1,] 0.02925719
#> [2,] 0.08553955
#> 
#> 
#> $vW
#> $vW[[1]]
#>                V1          V10          V11          V12          V13
#> [1,] 1.762021e-06 9.217936e-06 1.397094e-05 8.716791e-06 4.955244e-06
#>                V14           V15           V16           V17        V18
#> [1,] -1.154534e-06 -5.259667e-06 -9.682211e-06 -1.458265e-05 -1.414e-05
#>                V19           V2           V20           V21           V22
#> [1,] -7.608163e-06 2.954639e-06 -4.930079e-06 -1.451575e-05 -1.840537e-05
#>                V23           V24           V25           V26           V27
#> [1,] -2.059958e-05 -2.580686e-05 -2.773551e-05 -3.329834e-05 -3.281458e-05
#>                V28           V29           V3           V30           V31
#> [1,] -2.171204e-05 -2.367694e-05 3.558905e-06 -2.231242e-05 -2.657214e-05
#>                V32           V33           V34           V35           V36
#> [1,] -1.143525e-05 -1.780291e-05 -3.326461e-05 -4.353026e-05 -4.635791e-05
#>                V37           V38           V39           V4         V40
#> [1,] -3.730377e-05 -2.252481e-05 -2.153615e-05 5.861841e-06 -2.8015e-05
#>                V41           V42           V43          V44         V45
#> [1,] -1.918499e-05 -1.179694e-05 -6.001227e-06 1.685757e-06 9.82354e-06
#>               V46          V47          V48          V49           V5
#> [1,] 8.048559e-06 3.816274e-06 2.668694e-06 1.500768e-06 3.488975e-06
#>              V50          V51          V52           V53           V54
#> [1,] -2.3169e-07 4.528265e-07 8.536426e-07 -7.130362e-08 -6.790175e-08
#>               V55           V56           V57          V58           V59
#> [1,] -2.02587e-07 -1.312764e-07 -9.986355e-08 2.167707e-07 -2.210589e-07
#>                V6           V60           V7           V8           V9
#> [1,] 1.949164e-06 -6.469176e-09 2.501384e-06 3.092503e-06 9.892524e-06
#> 
#> $vW[[2]]
#>              [,1]
#> [1,]  0.001079794
#> [2,] -0.001079794
#> 
#> 
#> $B
#> $B[[1]]
#> [1] -0.5162633
#> 
#> $B[[2]]
#> [1]  0.2527213 -0.1519036
#> 
#> 
#> $vB
#> $vB[[1]]
#> [1] -5.641398e-05
#> 
#> $vB[[2]]
#> [1]  0.01896076 -0.01896076
#> 
#> 
#> $post
#> $post[[1]]
#>           V1    V10    V11    V12    V13    V14    V15    V16    V17    V18
#>  [1,] 0.0522 0.2529 0.2716 0.2374 0.1878 0.0983 0.0683 0.1503 0.1723 0.2339
#>  [2,] 0.0519 0.2838 0.2802 0.3086 0.2657 0.3801 0.5626 0.4376 0.2617 0.1199
#>  [3,] 0.0096 0.2952 0.4025 0.5148 0.4901 0.4127 0.3575 0.3447 0.3068 0.2945
#>  [4,] 0.0721 0.0795 0.2534 0.3920 0.3375 0.1610 0.1889 0.3308 0.2282 0.2177
#>  [5,] 0.0225 0.1452 0.1442 0.0948 0.0618 0.1641 0.0708 0.0844 0.2590 0.2679
#>  [6,] 0.0071 0.0898 0.0289 0.1554 0.1437 0.1035 0.1424 0.1227 0.0892 0.2047
#>  [7,] 0.0050 0.2282 0.2521 0.3484 0.3309 0.2614 0.1782 0.2055 0.2298 0.3545
#>  [8,] 0.0329 0.2672 0.3056 0.3161 0.2314 0.2067 0.1804 0.2808 0.4423 0.5947
#>  [9,] 0.1083 0.5966 0.5304 0.2251 0.2402 0.2689 0.6646 0.6632 0.1674 0.0837
#> [10,] 0.0089 0.2119 0.3003 0.3094 0.2743 0.2547 0.1870 0.1452 0.1457 0.2429
#> [11,] 0.0163 0.2822 0.3691 0.3750 0.3927 0.3308 0.1085 0.1139 0.3446 0.5441
#> [12,] 0.0039 0.0452 0.0492 0.0996 0.1424 0.1194 0.0628 0.0907 0.1177 0.1429
#> [13,] 0.0231 0.0347 0.0575 0.1382 0.2274 0.4038 0.5223 0.6847 0.7521 0.7760
#> [14,] 0.0202 0.1370 0.0843 0.0269 0.1254 0.3046 0.5584 0.7973 0.8341 0.8057
#> [15,] 0.0235 0.3674 0.2974 0.0837 0.1912 0.5040 0.6352 0.6804 0.7505 0.6595
#> [16,] 0.0210 0.0686 0.1125 0.1741 0.2710 0.3087 0.3575 0.4998 0.6011 0.6470
#> [17,] 0.0079 0.1240 0.1097 0.1215 0.1874 0.3383 0.3227 0.2723 0.3943 0.6432
#> [18,] 0.1088 0.5761 0.4733 0.2362 0.1023 0.2904 0.4713 0.4659 0.1415 0.0849
#> [19,] 0.0209 0.3914 0.3504 0.3669 0.3943 0.3311 0.3331 0.3002 0.2324 0.1381
#> [20,] 0.0107 0.2936 0.3104 0.3431 0.2456 0.1887 0.1184 0.2080 0.2736 0.3274
#> [21,] 0.0257 0.0561 0.0891 0.0861 0.1531 0.1524 0.1849 0.2871 0.2009 0.2748
#> [22,] 0.0294 0.3473 0.4231 0.5044 0.5237 0.4398 0.3236 0.2956 0.3286 0.3231
#> [23,] 0.0100 0.2668 0.3376 0.3282 0.2432 0.1268 0.1278 0.4441 0.6795 0.7051
#> [24,] 0.0094 0.5079 0.3350 0.0834 0.3004 0.3957 0.3769 0.3828 0.1247 0.1363
#> [25,] 0.0274 0.1808 0.2366 0.0906 0.1749 0.4012 0.5187 0.7312 0.9062 0.9260
#> [26,] 0.0335 0.4372 0.5533 0.5771 0.7022 0.7067 0.7367 0.7391 0.8622 0.9458
#> [27,] 0.0086 0.1185 0.0775 0.1101 0.1042 0.0853 0.0456 0.1304 0.2690 0.2947
#> [28,] 0.0201 0.1199 0.1742 0.1387 0.2042 0.2580 0.2616 0.2097 0.2532 0.3213
#> [29,] 0.0265 0.0779 0.0327 0.2060 0.1908 0.1065 0.1457 0.2232 0.2070 0.1105
#> [30,] 0.0323 0.2154 0.3085 0.3425 0.2990 0.1402 0.1235 0.1534 0.1901 0.2429
#> [31,] 0.0109 0.1036 0.0972 0.0501 0.1546 0.3404 0.4804 0.6570 0.7738 0.7827
#> [32,] 0.0423 0.2696 0.3412 0.4292 0.3682 0.3940 0.2965 0.3172 0.2825 0.3050
#> [33,] 0.0366 0.1847 0.2222 0.2648 0.2508 0.2291 0.1555 0.1863 0.2387 0.3345
#> [34,] 0.0176 0.0474 0.0526 0.1854 0.1040 0.0948 0.0912 0.1688 0.1568 0.0375
#> [35,] 0.0235 0.3199 0.2946 0.2484 0.2510 0.1806 0.1413 0.3019 0.3635 0.3887
#> [36,] 0.0131 0.2079 0.2295 0.1990 0.1184 0.1891 0.2949 0.5343 0.6850 0.7923
#> [37,] 0.0299 0.0998 0.0523 0.0904 0.2655 0.3099 0.3520 0.3892 0.3962 0.2449
#> [38,] 0.0065 0.1109 0.0937 0.0827 0.0920 0.0911 0.1487 0.1666 0.1268 0.1374
#> [39,] 0.0047 0.3469 0.3265 0.3263 0.2301 0.1253 0.2102 0.2401 0.1928 0.1673
#> [40,] 0.1313 0.2231 0.2907 0.2259 0.3136 0.3302 0.3660 0.3956 0.4386 0.4670
#>          V19     V2    V20    V21    V22    V23    V24    V25    V26    V27
#>  [1,] 0.1962 0.0437 0.1395 0.3164 0.5888 0.7631 0.8473 0.9424 0.9986 0.9699
#>  [2,] 0.6676 0.0548 0.9402 0.7832 0.5352 0.6809 0.9174 0.7613 0.8220 0.8872
#>  [3,] 0.4351 0.0404 0.7264 0.8147 0.8103 0.6665 0.6958 0.7748 0.8688 1.0000
#>  [4,] 0.1853 0.1574 0.5167 0.5342 0.6298 0.8437 0.6756 0.5825 0.6141 0.8809
#>  [5,] 0.3094 0.0019 0.4678 0.5958 0.7245 0.8773 0.9214 0.9282 0.9942 1.0000
#>  [6,] 0.0827 0.0103 0.1524 0.3031 0.1608 0.0667 0.1426 0.0395 0.1653 0.3399
#>  [7,] 0.6218 0.0017 0.7265 0.8346 0.8268 0.8366 0.9408 0.9510 0.9801 0.9974
#>  [8,] 0.6601 0.0216 0.5844 0.4539 0.4789 0.5646 0.5281 0.7115 1.0000 0.9564
#>  [9,] 0.4331 0.1070 0.8718 0.7992 0.3712 0.1703 0.1611 0.2086 0.2847 0.2211
#> [10,] 0.3259 0.0274 0.3679 0.3355 0.3100 0.3914 0.5280 0.6409 0.7707 0.8754
#> [11,] 0.6470 0.0198 0.7276 0.7894 0.8264 0.8697 0.7836 0.7140 0.5698 0.2908
#> [12,] 0.1223 0.0063 0.1104 0.1847 0.3715 0.4382 0.5707 0.6654 0.7476 0.7654
#> [13,] 0.7708 0.0351 0.8627 1.0000 0.8873 0.8057 0.8760 0.9066 0.9430 0.8846
#> [14,] 0.8616 0.0104 0.8769 0.9413 0.9403 0.9409 1.0000 0.9725 0.9309 0.9351
#> [15,] 0.4509 0.0291 0.2964 0.4019 0.6794 0.8297 1.0000 0.8240 0.7115 0.7726
#> [16,] 0.8067 0.0121 0.9008 0.8906 0.9338 1.0000 0.9102 0.8496 0.7867 0.7688
#> [17,] 0.7271 0.0086 0.8673 0.9674 0.9847 0.9480 0.8036 0.6833 0.5136 0.3090
#> [18,] 0.3257 0.1278 0.9007 0.9312 0.4856 0.1346 0.1604 0.2737 0.5609 0.3654
#> [19,] 0.3450 0.0191 0.4428 0.4890 0.3677 0.4379 0.4864 0.6207 0.7256 0.6624
#> [20,] 0.2344 0.0453 0.1260 0.0576 0.1241 0.3239 0.4357 0.5734 0.7825 0.9252
#> [21,] 0.5017 0.0447 0.2172 0.4978 0.5265 0.3647 0.5768 0.5161 0.5715 0.4006
#> [22,] 0.4528 0.0123 0.6339 0.7044 0.8314 0.8449 0.8512 0.9138 0.9985 1.0000
#> [23,] 0.7966 0.0275 0.9401 0.9857 0.8193 0.5789 0.6394 0.7043 0.6875 0.4081
#> [24,] 0.2678 0.0611 0.9188 0.9779 0.3236 0.1944 0.1874 0.0885 0.3443 0.2953
#> [25,] 0.7434 0.0242 0.4463 0.5103 0.6952 0.7755 0.8364 0.7283 0.6399 0.5759
#> [26,] 0.8782 0.0134 0.7913 0.5760 0.3061 0.0563 0.0239 0.2554 0.4862 0.5027
#> [27,] 0.3669 0.0215 0.4948 0.6275 0.8162 0.9237 0.8710 0.8052 0.8756 1.0000
#> [28,] 0.4327 0.0116 0.4760 0.5328 0.6057 0.6696 0.7476 0.8930 0.9405 1.0000
#> [29,] 0.1078 0.0440 0.1165 0.2224 0.0689 0.2060 0.2384 0.0904 0.2278 0.5872
#> [30,] 0.2120 0.0101 0.2395 0.3272 0.5949 0.8302 0.9045 0.9888 0.9912 0.9448
#> [31,] 0.8152 0.0093 0.8129 0.8297 0.8535 0.8870 0.8894 0.8980 0.9667 1.0000
#> [32,] 0.2408 0.0321 0.5420 0.6802 0.6320 0.5824 0.6805 0.5984 0.8412 0.9911
#> [33,] 0.5233 0.0421 0.6684 0.7766 0.7928 0.7940 0.9129 0.9498 0.9835 1.0000
#> [34,] 0.1316 0.0172 0.2086 0.1976 0.0946 0.1965 0.1242 0.0616 0.2141 0.4642
#> [35,] 0.2980 0.0220 0.2219 0.1624 0.1343 0.2046 0.3791 0.5771 0.7545 0.8406
#> [36,] 0.8220 0.0201 0.7290 0.7352 0.7918 0.8057 0.4898 0.1934 0.2924 0.6255
#> [37,] 0.2355 0.0688 0.3045 0.3112 0.4698 0.5534 0.4532 0.4464 0.4670 0.4621
#> [38,] 0.1095 0.0122 0.1286 0.2146 0.2889 0.4238 0.6168 0.8167 0.9622 0.8280
#> [39,] 0.1228 0.0059 0.0902 0.1557 0.3291 0.5268 0.6740 0.7906 0.8938 0.9395
#> [40,] 0.5255 0.2339 0.3735 0.2243 0.1973 0.4337 0.6532 0.5070 0.2796 0.4163
#>          V28    V29     V3    V30    V31    V32    V33    V34    V35    V36
#>  [1,] 1.0000 0.8630 0.0180 0.6979 0.7717 0.7305 0.5197 0.1786 0.1098 0.1446
#>  [2,] 0.6091 0.2967 0.0842 0.1103 0.1318 0.0624 0.0990 0.4006 0.3666 0.1050
#>  [3,] 0.9941 0.8793 0.0682 0.6482 0.5876 0.6408 0.4972 0.2755 0.0300 0.3356
#>  [4,] 0.8375 0.3869 0.1112 0.5051 0.5455 0.4241 0.1534 0.4950 0.6983 0.7109
#>  [5,] 0.9071 0.8545 0.0075 0.7293 0.6499 0.6071 0.5588 0.5967 0.6275 0.5459
#>  [6,] 0.4855 0.5206 0.0135 0.5508 0.6102 0.5989 0.6764 0.8897 1.0000 0.9517
#>  [7,] 1.0000 0.9036 0.0270 0.6409 0.3857 0.2908 0.2040 0.1653 0.1769 0.1140
#>  [8,] 0.6090 0.5112 0.0386 0.4000 0.0482 0.1852 0.2186 0.1436 0.1757 0.1428
#>  [9,] 0.6134 0.5807 0.0257 0.6925 0.3825 0.4303 0.7791 0.8703 1.0000 0.9212
#> [10,] 1.0000 0.9806 0.0248 0.6969 0.4973 0.5020 0.5359 0.3842 0.1848 0.1149
#> [11,] 0.4636 0.6409 0.0202 0.7405 0.8069 0.8420 1.0000 0.9536 0.6755 0.3905
#> [12,] 0.8555 0.9720 0.0152 0.9221 0.7502 0.7209 0.7757 0.6055 0.5021 0.4499
#> [13,] 0.6500 0.2970 0.0030 0.2423 0.2992 0.2285 0.2277 0.1529 0.1037 0.0352
#> [14,] 0.7317 0.4421 0.0325 0.3244 0.4161 0.4611 0.4031 0.3000 0.2459 0.1348
#> [15,] 0.6124 0.4936 0.0749 0.5648 0.4906 0.1820 0.1811 0.1107 0.4603 0.6650
#> [16,] 0.7718 0.6268 0.0203 0.4301 0.2077 0.1198 0.1660 0.2618 0.3862 0.3958
#> [17,] 0.0832 0.4019 0.0055 0.2344 0.1905 0.1235 0.1717 0.2351 0.2489 0.3649
#> [18,] 0.6139 0.5470 0.0926 0.8474 0.5638 0.5443 0.5086 0.6253 0.8497 0.8406
#> [19,] 0.7689 0.7981 0.0411 0.8577 0.9273 0.7009 0.4851 0.3409 0.1406 0.1147
#> [20,] 0.9349 0.9348 0.0289 1.0000 0.9308 0.8478 0.7605 0.7040 0.7539 0.7990
#> [21,] 0.3650 0.6685 0.0388 0.8659 0.8052 0.4082 0.3379 0.5092 0.6776 0.7313
#> [22,] 0.7544 0.4661 0.0117 0.3924 0.3849 0.4674 0.4245 0.3095 0.0752 0.2885
#> [23,] 0.1811 0.2064 0.0190 0.3917 0.3791 0.2042 0.2227 0.3341 0.3984 0.5077
#> [24,] 0.5908 0.4564 0.1136 0.7334 0.1969 0.2790 0.6212 0.8681 0.8621 0.9380
#> [25,] 0.4146 0.3495 0.0621 0.4437 0.2665 0.2024 0.1942 0.0765 0.3725 0.5843
#> [26,] 0.4402 0.2847 0.0696 0.1797 0.3560 0.3522 0.3321 0.3112 0.3638 0.0754
#> [27,] 0.9858 0.9427 0.0242 0.8114 0.6987 0.6810 0.6591 0.6954 0.7290 0.6680
#> [28,] 0.9785 0.8473 0.0123 0.7639 0.6701 0.4989 0.3718 0.2196 0.1416 0.2680
#> [29,] 0.8457 0.8467 0.0137 0.7679 0.8055 0.6260 0.6545 0.8747 0.9885 0.9348
#> [30,] 1.0000 0.9092 0.0298 0.7412 0.7691 0.7117 0.5304 0.2131 0.0928 0.1297
#> [31,] 0.9134 0.6762 0.0121 0.4659 0.2895 0.2959 0.1746 0.2112 0.2569 0.2276
#> [32,] 0.9187 0.8005 0.0709 0.6713 0.5632 0.7332 0.6038 0.2575 0.0349 0.1799
#> [33,] 0.9471 0.8237 0.0504 0.6252 0.4181 0.3209 0.2658 0.2196 0.1588 0.0561
#> [34,] 0.6471 0.6340 0.0501 0.6107 0.7046 0.5376 0.5934 0.8443 0.9481 0.9705
#> [35,] 0.8547 0.9036 0.0167 1.0000 0.9646 0.7912 0.6412 0.5986 0.6835 0.7771
#> [36,] 0.8546 0.8966 0.0045 0.7821 0.5168 0.4840 0.4038 0.3411 0.2849 0.2353
#> [37,] 0.6988 0.7626 0.0992 0.7025 0.7382 0.7446 0.7927 0.5227 0.3967 0.3042
#> [38,] 0.5816 0.4667 0.0068 0.3539 0.2727 0.1410 0.1863 0.2176 0.2360 0.1725
#> [39,] 0.9493 0.9040 0.0080 0.9151 0.8828 0.8086 0.7180 0.6720 0.6447 0.6879
#> [40,] 0.5950 0.5242 0.3059 0.4178 0.3714 0.2375 0.0863 0.1437 0.2896 0.4577
#>          V37    V38    V39     V4    V40    V41    V42    V43    V44    V45
#>  [1,] 0.1066 0.1440 0.1929 0.0292 0.0325 0.1490 0.0328 0.0537 0.1309 0.0910
#>  [2,] 0.1915 0.3930 0.4288 0.0319 0.2546 0.1151 0.2196 0.1879 0.1437 0.2146
#>  [3,] 0.3167 0.4133 0.6281 0.0688 0.4977 0.2613 0.4697 0.4806 0.4921 0.5294
#>  [4,] 0.5647 0.4870 0.5515 0.1085 0.4433 0.5250 0.6075 0.5251 0.1359 0.4268
#>  [5,] 0.4786 0.3965 0.2087 0.0097 0.1651 0.1836 0.0652 0.0758 0.0486 0.0353
#>  [6,] 0.8459 0.7073 0.6697 0.0494 0.6326 0.5102 0.4161 0.2816 0.1705 0.1421
#>  [7,] 0.0740 0.0941 0.0621 0.0450 0.0426 0.0572 0.1068 0.1909 0.2229 0.2203
#>  [8,] 0.1644 0.3089 0.3648 0.0627 0.4441 0.3859 0.2813 0.1238 0.0953 0.1201
#>  [9,] 0.9386 0.9303 0.7314 0.0837 0.4791 0.2087 0.2016 0.1669 0.2872 0.4374
#> [10,] 0.1570 0.1311 0.1583 0.0237 0.2631 0.3103 0.4512 0.3785 0.1269 0.1459
#> [11,] 0.1249 0.3629 0.6356 0.0386 0.8116 0.7664 0.5417 0.2614 0.1723 0.2814
#> [12,] 0.3947 0.4281 0.4427 0.0336 0.3749 0.1972 0.0511 0.0793 0.1269 0.1533
#> [13,] 0.1073 0.1373 0.1331 0.0304 0.1454 0.1115 0.0440 0.0762 0.1381 0.0831
#> [14,] 0.2541 0.2255 0.1598 0.0239 0.1485 0.0845 0.0569 0.0855 0.1262 0.1153
#> [15,] 0.6423 0.2166 0.1951 0.0519 0.4947 0.4925 0.4041 0.2402 0.1392 0.1779
#> [16,] 0.3248 0.2302 0.3250 0.1036 0.4022 0.4344 0.4008 0.3370 0.2518 0.2101
#> [17,] 0.3382 0.1589 0.0989 0.0250 0.1089 0.1043 0.0839 0.1391 0.0819 0.0678
#> [18,] 0.8420 0.9136 0.7713 0.1234 0.4882 0.3724 0.4469 0.4586 0.4491 0.5616
#> [19,] 0.1433 0.1820 0.3605 0.0321 0.5529 0.5988 0.5077 0.5512 0.5027 0.7034
#> [20,] 0.7673 0.5955 0.4731 0.0713 0.4840 0.4340 0.3954 0.4837 0.5379 0.4485
#> [21,] 0.6062 0.7040 0.8849 0.0239 0.8979 0.7751 0.7247 0.7733 0.7762 0.6009
#> [22,] 0.4072 0.3170 0.2863 0.0113 0.2634 0.0541 0.1874 0.3459 0.4646 0.4366
#> [23,] 0.5534 0.3352 0.2723 0.0371 0.2278 0.2044 0.1986 0.0835 0.0908 0.1380
#> [24,] 0.8327 0.9480 0.6721 0.1203 0.4436 0.5163 0.3809 0.1557 0.1449 0.2662
#> [25,] 0.4827 0.2347 0.0999 0.0560 0.3244 0.3990 0.2975 0.1684 0.1761 0.1683
#> [26,] 0.1834 0.1820 0.1815 0.1180 0.1593 0.0576 0.0954 0.1086 0.0812 0.0784
#> [27,] 0.5917 0.4899 0.3439 0.0445 0.2366 0.1716 0.1013 0.0766 0.0845 0.0260
#> [28,] 0.2630 0.3104 0.3392 0.0245 0.2123 0.1170 0.2655 0.2203 0.1541 0.1464
#> [29,] 0.6960 0.5733 0.5872 0.0084 0.6663 0.5651 0.5247 0.3684 0.1997 0.1512
#> [30,] 0.1159 0.1226 0.1768 0.0564 0.0345 0.1562 0.0824 0.1149 0.1694 0.0954
#> [31,] 0.2149 0.1601 0.0371 0.0378 0.0117 0.0488 0.0288 0.0597 0.0431 0.0369
#> [32,] 0.3039 0.4760 0.5756 0.0108 0.4254 0.5046 0.7179 0.6163 0.5663 0.5749
#> [33,] 0.0948 0.1700 0.1215 0.0250 0.1282 0.0386 0.1329 0.2331 0.2468 0.1960
#> [34,] 0.7766 0.6313 0.5760 0.0285 0.6148 0.5450 0.4813 0.3406 0.1916 0.1134
#> [35,] 0.8084 0.7426 0.6295 0.0516 0.5708 0.4433 0.3361 0.3795 0.4950 0.4373
#> [36,] 0.2699 0.4442 0.4323 0.0217 0.3314 0.1195 0.1669 0.3702 0.3072 0.0945
#> [37,] 0.1309 0.2408 0.1780 0.1021 0.1598 0.5657 0.6443 0.4241 0.4567 0.5760
#> [38,] 0.0589 0.0621 0.1847 0.0108 0.2452 0.2984 0.3041 0.2275 0.1480 0.1102
#> [39,] 0.6241 0.4936 0.4144 0.0554 0.4240 0.4546 0.4392 0.4323 0.4921 0.4710
#> [40,] 0.3725 0.3372 0.3803 0.4264 0.4181 0.3603 0.2711 0.1653 0.1951 0.2811
#>          V46    V47    V48    V49     V5    V50    V51    V52    V53    V54
#>  [1,] 0.0757 0.1059 0.1005 0.0535 0.0351 0.0235 0.0155 0.0160 0.0029 0.0051
#>  [2,] 0.2360 0.1125 0.0254 0.0285 0.1158 0.0178 0.0052 0.0081 0.0120 0.0045
#>  [3,] 0.2216 0.1401 0.1888 0.0947 0.0887 0.0134 0.0310 0.0237 0.0078 0.0144
#>  [4,] 0.4442 0.2193 0.0900 0.1200 0.0666 0.0628 0.0234 0.0309 0.0127 0.0082
#>  [5,] 0.0297 0.0241 0.0379 0.0119 0.0445 0.0073 0.0051 0.0034 0.0129 0.0100
#>  [6,] 0.0971 0.0879 0.0863 0.0355 0.0253 0.0233 0.0252 0.0043 0.0048 0.0076
#>  [7,] 0.2265 0.1766 0.1097 0.0558 0.0958 0.0142 0.0281 0.0165 0.0056 0.0010
#>  [8,] 0.0825 0.0618 0.0141 0.0108 0.1158 0.0124 0.0104 0.0095 0.0151 0.0059
#>  [9,] 0.3097 0.1578 0.0553 0.0334 0.0748 0.0209 0.0172 0.0180 0.0110 0.0234
#> [10,] 0.1092 0.1485 0.1385 0.0716 0.0224 0.0176 0.0199 0.0096 0.0103 0.0093
#> [11,] 0.2764 0.1985 0.1502 0.1219 0.0752 0.0493 0.0027 0.0077 0.0026 0.0031
#> [12,] 0.0690 0.0402 0.0534 0.0228 0.0310 0.0073 0.0062 0.0062 0.0120 0.0052
#> [13,] 0.0654 0.0844 0.0595 0.0497 0.0339 0.0313 0.0154 0.0106 0.0097 0.0022
#> [14,] 0.0570 0.0426 0.0425 0.0235 0.0807 0.0006 0.0188 0.0127 0.0081 0.0067
#> [15,] 0.1946 0.1723 0.1522 0.0929 0.0227 0.0179 0.0242 0.0083 0.0037 0.0095
#> [16,] 0.1181 0.1150 0.0550 0.0293 0.1675 0.0183 0.0104 0.0117 0.0101 0.0061
#> [17,] 0.0663 0.1202 0.0692 0.0152 0.0344 0.0266 0.0174 0.0176 0.0127 0.0088
#> [18,] 0.4305 0.0945 0.0794 0.0274 0.1276 0.0154 0.0140 0.0455 0.0213 0.0082
#> [19,] 0.5904 0.4069 0.2761 0.1584 0.0698 0.0510 0.0054 0.0078 0.0201 0.0104
#> [20,] 0.2674 0.1541 0.1359 0.0941 0.1075 0.0261 0.0079 0.0164 0.0120 0.0113
#> [21,] 0.4514 0.3096 0.1859 0.0956 0.1315 0.0206 0.0206 0.0096 0.0153 0.0096
#> [22,] 0.2581 0.1319 0.0505 0.0112 0.0497 0.0059 0.0041 0.0056 0.0104 0.0079
#> [23,] 0.1948 0.1211 0.0843 0.0589 0.0416 0.0247 0.0118 0.0088 0.0104 0.0036
#> [24,] 0.1806 0.1699 0.2559 0.1129 0.0403 0.0201 0.0480 0.0234 0.0175 0.0352
#> [25,] 0.0729 0.1190 0.1297 0.0748 0.1129 0.0067 0.0255 0.0113 0.0108 0.0085
#> [26,] 0.0487 0.0439 0.0586 0.0370 0.0348 0.0185 0.0302 0.0244 0.0232 0.0093
#> [27,] 0.0333 0.0205 0.0309 0.0101 0.0667 0.0095 0.0047 0.0072 0.0054 0.0022
#> [28,] 0.1044 0.1225 0.0745 0.0490 0.0547 0.0224 0.0032 0.0076 0.0045 0.0056
#> [29,] 0.0508 0.0931 0.0982 0.0524 0.0305 0.0188 0.0100 0.0038 0.0187 0.0156
#> [30,] 0.0080 0.0790 0.1255 0.0647 0.0760 0.0179 0.0051 0.0061 0.0093 0.0135
#> [31,] 0.0025 0.0327 0.0257 0.0182 0.0679 0.0108 0.0124 0.0077 0.0023 0.0117
#> [32,] 0.3593 0.2526 0.2299 0.1271 0.1070 0.0356 0.0367 0.0176 0.0035 0.0093
#> [33,] 0.1985 0.1570 0.0921 0.0549 0.0596 0.0194 0.0166 0.0132 0.0027 0.0022
#> [34,] 0.0640 0.0911 0.0980 0.0563 0.0262 0.0187 0.0088 0.0042 0.0175 0.0171
#> [35,] 0.2404 0.1128 0.1654 0.0933 0.0746 0.0225 0.0214 0.0221 0.0152 0.0083
#> [36,] 0.1545 0.1394 0.0772 0.0615 0.0230 0.0230 0.0111 0.0168 0.0086 0.0045
#> [37,] 0.5293 0.3287 0.1283 0.0698 0.0800 0.0334 0.0342 0.0459 0.0277 0.0172
#> [38,] 0.1178 0.0608 0.0333 0.0276 0.0217 0.0100 0.0023 0.0069 0.0025 0.0027
#> [39,] 0.3196 0.2241 0.1806 0.0990 0.0883 0.0251 0.0129 0.0095 0.0126 0.0069
#> [40,] 0.2246 0.1921 0.1500 0.0665 0.4010 0.0193 0.0156 0.0362 0.0210 0.0154
#>          V55    V56    V57    V58    V59     V6    V60     V7     V8     V9
#>  [1,] 0.0062 0.0089 0.0140 0.0138 0.0077 0.1171 0.0031 0.1257 0.1178 0.1258
#>  [2,] 0.0121 0.0097 0.0085 0.0047 0.0048 0.0922 0.0053 0.1027 0.0613 0.1465
#>  [3,] 0.0170 0.0012 0.0109 0.0036 0.0043 0.0932 0.0018 0.0955 0.2140 0.2546
#>  [4,] 0.0281 0.0117 0.0092 0.0147 0.0157 0.1800 0.0129 0.1108 0.2794 0.1408
#>  [5,] 0.0044 0.0057 0.0030 0.0035 0.0021 0.0906 0.0027 0.0889 0.0655 0.1624
#>  [6,] 0.0124 0.0105 0.0054 0.0032 0.0073 0.0806 0.0063 0.0701 0.0738 0.0117
#>  [7,] 0.0027 0.0062 0.0024 0.0063 0.0017 0.0830 0.0028 0.0879 0.1220 0.1977
#>  [8,] 0.0015 0.0053 0.0016 0.0042 0.0053 0.1482 0.0074 0.2054 0.1605 0.2532
#>  [9,] 0.0276 0.0032 0.0084 0.0122 0.0082 0.1125 0.0143 0.3322 0.4590 0.5526
#> [10,] 0.0025 0.0044 0.0021 0.0069 0.0060 0.0845 0.0018 0.1488 0.1224 0.1569
#> [11,] 0.0083 0.0020 0.0084 0.0108 0.0083 0.1444 0.0033 0.1487 0.1484 0.2442
#> [12,] 0.0056 0.0093 0.0042 0.0003 0.0053 0.0284 0.0036 0.0396 0.0272 0.0323
#> [13,] 0.0052 0.0072 0.0056 0.0038 0.0043 0.0860 0.0030 0.1738 0.1351 0.1063
#> [14,] 0.0043 0.0065 0.0049 0.0054 0.0073 0.1529 0.0054 0.1154 0.0608 0.1317
#> [15,] 0.0105 0.0030 0.0132 0.0068 0.0108 0.0834 0.0090 0.0677 0.2002 0.2876
#> [16,] 0.0031 0.0099 0.0080 0.0107 0.0161 0.0418 0.0133 0.0723 0.0828 0.0494
#> [17,] 0.0098 0.0019 0.0059 0.0058 0.0059 0.0546 0.0032 0.0528 0.0958 0.1009
#> [18,] 0.0124 0.0167 0.0103 0.0205 0.0178 0.1731 0.0187 0.1948 0.4262 0.6828
#> [19,] 0.0039 0.0031 0.0062 0.0087 0.0070 0.1579 0.0042 0.1438 0.1402 0.3048
#> [20,] 0.0021 0.0097 0.0072 0.0060 0.0017 0.1019 0.0036 0.1606 0.2119 0.3061
#> [21,] 0.0131 0.0198 0.0025 0.0199 0.0255 0.1323 0.0180 0.1608 0.2145 0.0847
#> [22,] 0.0014 0.0054 0.0015 0.0006 0.0081 0.0998 0.0043 0.1326 0.1117 0.2984
#> [23,] 0.0088 0.0047 0.0117 0.0020 0.0091 0.0201 0.0058 0.0314 0.0651 0.1896
#> [24,] 0.0158 0.0326 0.0201 0.0168 0.0245 0.1227 0.0154 0.2495 0.4566 0.6587
#> [25,] 0.0047 0.0074 0.0104 0.0161 0.0220 0.0973 0.0173 0.1823 0.1745 0.1440
#> [26,] 0.0159 0.0193 0.0032 0.0377 0.0126 0.1180 0.0156 0.1948 0.1607 0.3036
#> [27,] 0.0016 0.0029 0.0058 0.0050 0.0024 0.0771 0.0030 0.0499 0.0906 0.1229
#> [28,] 0.0075 0.0037 0.0045 0.0029 0.0008 0.0208 0.0018 0.0891 0.0836 0.1335
#> [29,] 0.0068 0.0097 0.0073 0.0081 0.0086 0.0438 0.0095 0.0341 0.0780 0.0844
#> [30,] 0.0063 0.0063 0.0034 0.0032 0.0062 0.0958 0.0067 0.0990 0.1018 0.1030
#> [31,] 0.0053 0.0077 0.0076 0.0056 0.0055 0.0863 0.0039 0.1004 0.0664 0.0941
#> [32,] 0.0121 0.0075 0.0056 0.0021 0.0043 0.0973 0.0017 0.0961 0.1323 0.2462
#> [33,] 0.0059 0.0016 0.0025 0.0017 0.0027 0.0252 0.0027 0.0958 0.0991 0.1419
#> [34,] 0.0079 0.0050 0.0112 0.0179 0.0294 0.0351 0.0063 0.0362 0.0535 0.0258
#> [35,] 0.0058 0.0023 0.0057 0.0052 0.0027 0.1121 0.0021 0.1258 0.1717 0.3074
#> [36,] 0.0062 0.0065 0.0030 0.0066 0.0029 0.0481 0.0053 0.0742 0.0333 0.1369
#> [37,] 0.0087 0.0046 0.0203 0.0130 0.0115 0.0629 0.0015 0.0130 0.0813 0.1761
#> [38,] 0.0052 0.0036 0.0026 0.0036 0.0006 0.0284 0.0035 0.0527 0.0575 0.1054
#> [39,] 0.0039 0.0068 0.0060 0.0045 0.0002 0.1278 0.0029 0.1674 0.1373 0.2922
#> [40,] 0.0180 0.0013 0.0106 0.0127 0.0178 0.1791 0.0231 0.1853 0.0055 0.1929
#> 
#> $post[[2]]
#>              [,1]
#>  [1,] 0.009042476
#>  [2,] 0.009363211
#>  [3,] 0.002775647
#>  [4,] 0.006252465
#>  [5,] 0.004398427
#>  [6,] 0.020404849
#>  [7,] 0.005533691
#>  [8,] 0.010540288
#>  [9,] 0.005559878
#> [10,] 0.011478120
#> [11,] 0.002612386
#> [12,] 0.010572410
#> [13,] 0.005721959
#> [14,] 0.003215785
#> [15,] 0.005036067
#> [16,] 0.003856313
#> [17,] 0.012983241
#> [18,] 0.004584001
#> [19,] 0.006604362
#> [20,] 0.004435915
#> [21,] 0.005505303
#> [22,] 0.004508072
#> [23,] 0.007793357
#> [24,] 0.007422966
#> [25,] 0.007116837
#> [26,] 0.014899552
#> [27,] 0.003348785
#> [28,] 0.006265498
#> [29,] 0.012542120
#> [30,] 0.007751390
#> [31,] 0.004448499
#> [32,] 0.004063819
#> [33,] 0.006151456
#> [34,] 0.018625598
#> [35,] 0.004418788
#> [36,] 0.006005226
#> [37,] 0.010680262
#> [38,] 0.029013709
#> [39,] 0.004024966
#> [40,] 0.019618654
#> 
#> $post[[3]]
#>            [,1]      [,2]
#>  [1,] 0.6087491 0.3912509
#>  [2,] 0.6087449 0.3912551
#>  [3,] 0.6088298 0.3911702
#>  [4,] 0.6087850 0.3912150
#>  [5,] 0.6088089 0.3911911
#>  [6,] 0.6086026 0.3913974
#>  [7,] 0.6087943 0.3912057
#>  [8,] 0.6087297 0.3912703
#>  [9,] 0.6087939 0.3912061
#> [10,] 0.6087177 0.3912823
#> [11,] 0.6088319 0.3911681
#> [12,] 0.6087293 0.3912707
#> [13,] 0.6087919 0.3912081
#> [14,] 0.6088242 0.3911758
#> [15,] 0.6088007 0.3911993
#> [16,] 0.6088159 0.3911841
#> [17,] 0.6086983 0.3913017
#> [18,] 0.6088065 0.3911935
#> [19,] 0.6087805 0.3912195
#> [20,] 0.6088084 0.3911916
#> [21,] 0.6087946 0.3912054
#> [22,] 0.6088075 0.3911925
#> [23,] 0.6087652 0.3912348
#> [24,] 0.6087699 0.3912301
#> [25,] 0.6087739 0.3912261
#> [26,] 0.6086735 0.3913265
#> [27,] 0.6088224 0.3911776
#> [28,] 0.6087848 0.3912152
#> [29,] 0.6087039 0.3912961
#> [30,] 0.6087657 0.3912343
#> [31,] 0.6088083 0.3911917
#> [32,] 0.6088132 0.3911868
#> [33,] 0.6087863 0.3912137
#> [34,] 0.6086255 0.3913745
#> [35,] 0.6088087 0.3911913
#> [36,] 0.6087882 0.3912118
#> [37,] 0.6087279 0.3912721
#> [38,] 0.6084916 0.3915084
#> [39,] 0.6088137 0.3911863
#> [40,] 0.6086127 0.3913873
#> 
#> 
#> $pre
#> $pre[[1]]
#> NULL
#> 
#> $pre[[2]]
#>            [,1]
#>  [1,] -4.696739
#>  [2,] -4.661560
#>  [3,] -5.884092
#>  [4,] -5.068507
#>  [5,] -5.422100
#>  [6,] -3.871367
#>  [7,] -5.191351
#>  [8,] -4.541954
#>  [9,] -5.186604
#> [10,] -4.455768
#> [11,] -5.944875
#> [12,] -4.538879
#> [13,] -5.157706
#> [14,] -5.736463
#> [15,] -5.286081
#> [16,] -5.554180
#> [17,] -4.331028
#> [18,] -5.380589
#> [19,] -5.013399
#> [20,] -5.413576
#> [21,] -5.196523
#> [22,] -5.397368
#> [23,] -4.846660
#> [24,] -4.895726
#> [25,] -4.938150
#> [26,] -4.191412
#> [27,] -5.695803
#> [28,] -5.066412
#> [29,] -4.366041
#> [30,] -4.852102
#> [31,] -5.410730
#> [32,] -5.501560
#> [33,] -5.084896
#> [34,] -3.964417
#> [35,] -5.417461
#> [36,] -5.109102
#> [37,] -4.528620
#> [38,] -3.510544
#> [39,] -5.511206
#> [40,] -3.911461
#> 
#> $pre[[3]]
#>            [,1]       [,2]
#>  [1,] 0.2719563 -0.1701006
#>  [2,] 0.2719661 -0.1700735
#>  [3,] 0.2717662 -0.1706299
#>  [4,] 0.2718717 -0.1703363
#>  [5,] 0.2718154 -0.1704929
#>  [6,] 0.2723010 -0.1691410
#>  [7,] 0.2718499 -0.1703970
#>  [8,] 0.2720018 -0.1699741
#>  [9,] 0.2718507 -0.1703948
#> [10,] 0.2720302 -0.1698949
#> [11,] 0.2717613 -0.1706437
#> [12,] 0.2720027 -0.1699714
#> [13,] 0.2718556 -0.1703811
#> [14,] 0.2717796 -0.1705927
#> [15,] 0.2718348 -0.1704390
#> [16,] 0.2717990 -0.1705386
#> [17,] 0.2720759 -0.1697678
#> [18,] 0.2718211 -0.1704772
#> [19,] 0.2718824 -0.1703065
#> [20,] 0.2718166 -0.1704897
#> [21,] 0.2718490 -0.1703994
#> [22,] 0.2718188 -0.1704836
#> [23,] 0.2719184 -0.1702061
#> [24,] 0.2719072 -0.1702374
#> [25,] 0.2718979 -0.1702633
#> [26,] 0.2721340 -0.1696059
#> [27,] 0.2717836 -0.1705815
#> [28,] 0.2718721 -0.1703352
#> [29,] 0.2720625 -0.1698050
#> [30,] 0.2719172 -0.1702097
#> [31,] 0.2718170 -0.1704886
#> [32,] 0.2718053 -0.1705211
#> [33,] 0.2718686 -0.1703448
#> [34,] 0.2722471 -0.1692912
#> [35,] 0.2718161 -0.1704911
#> [36,] 0.2718642 -0.1703571
#> [37,] 0.2720060 -0.1699623
#> [38,] 0.2725622 -0.1684139
#> [39,] 0.2718041 -0.1705244
#> [40,] 0.2722772 -0.1692074
#> 
#> 
#> $e
#>             [,1]       [,2]
#>  [1,]  0.3912509 -0.3912509
#>  [2,] -0.6087449  0.6087449
#>  [3,]  0.3911702 -0.3911702
#>  [4,]  0.3912150 -0.3912150
#>  [5,] -0.6088089  0.6088089
#>  [6,] -0.6086026  0.6086026
#>  [7,]  0.3912057 -0.3912057
#>  [8,]  0.3912703 -0.3912703
#>  [9,]  0.3912061 -0.3912061
#> [10,]  0.3912823 -0.3912823
#> [11,]  0.3911681 -0.3911681
#> [12,] -0.6087293  0.6087293
#> [13,] -0.6087919  0.6087919
#> [14,] -0.6088242  0.6088242
#> [15,] -0.6088007  0.6088007
#> [16,]  0.3911841 -0.3911841
#> [17,] -0.6086983  0.6086983
#> [18,]  0.3911935 -0.3911935
#> [19,]  0.3912195 -0.3912195
#> [20,]  0.3911916 -0.3911916
#> [21,] -0.6087946  0.6087946
#> [22,]  0.3911925 -0.3911925
#> [23,] -0.6087652  0.6087652
#> [24,]  0.3912301 -0.3912301
#> [25,] -0.6087739  0.6087739
#> [26,]  0.3913265 -0.3913265
#> [27,] -0.6088224  0.6088224
#> [28,] -0.6087848  0.6087848
#> [29,] -0.6087039  0.6087039
#> [30,]  0.3912343 -0.3912343
#> [31,] -0.6088083  0.6088083
#> [32,]  0.3911868 -0.3911868
#> [33,]  0.3912137 -0.3912137
#> [34,] -0.6086255  0.6086255
#> [35,]  0.3911913 -0.3911913
#> [36,]  0.3912118 -0.3912118
#> [37,]  0.3912721 -0.3912721
#> [38,] -0.6084916  0.6084916
#> [39,]  0.3911863 -0.3911863
#> [40,]  0.3913873 -0.3913873
#> 
#> $L
#> [1] 0.6876533 0.6838576 0.6914940 0.6610425 0.6811842 0.6841891
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.5507246