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/basics.html#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'

# 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,] 0.04645949 -0.1943898 -0.2372063 -0.07259125 -0.2371378 -0.2644288
#>             V15        V16        V17        V18        V19         V2
#> [1,] -0.1346865 -0.1426376 -0.3156689 -0.2171791 -0.1986703 -0.0418947
#>            V20        V21        V22       V23        V24        V25        V26
#> [1,] -0.289348 -0.3646039 -0.3868069 -0.273882 -0.4292337 -0.4278482 -0.3385878
#>             V27        V28        V29          V3        V30        V31
#> [1,] -0.2982136 -0.3753741 -0.3321043 -0.07686591 -0.2540266 -0.2104561
#>             V32       V33        V34        V35        V36        V37
#> [1,] -0.2433324 -0.320127 -0.1673504 -0.2769029 -0.1998029 -0.2190747
#>             V38        V39         V4        V40        V41        V42
#> [1,] -0.1955575 -0.1982753 0.00745181 -0.1768644 -0.1874402 -0.1931262
#>             V43        V44        V45         V46        V47          V48
#> [1,] -0.2410026 -0.0221978 -0.1413831 -0.08669861 -0.1028996 -0.008325467
#>              V49          V5         V50         V51        V52        V53
#> [1,] -0.04943444 -0.08704037 -0.05973566 -0.05053569 0.01661704 0.06754987
#>             V54        V55        V56         V57       V58        V59
#> [1,] 0.02121582 0.09263625 0.09124764 -0.08208621 0.0874445 0.04427101
#>              V6           V60         V7         V8         V9
#> [1,] 0.01408168 -0.0004100208 -0.1599613 -0.1765555 -0.2018661
#> 
#> $W[[2]]
#>            [,1]
#> [1,] 0.04448625
#> [2,] 0.04857819
#> 
#> 
#> $vW
#> $vW[[1]]
#>                V1          V10          V11          V12          V13
#> [1,] 3.223934e-08 2.224422e-07 3.322828e-07 2.297615e-07 4.078259e-08
#>                V14           V15           V16           V17           V18
#> [1,] -7.400633e-08 -2.252821e-07 -4.925902e-07 -5.457096e-07 -5.113957e-07
#>                V19           V2          V20           V21           V22
#> [1,] -1.511665e-07 4.736145e-08 5.590482e-08 -1.360114e-07 -4.046654e-07
#>               V23           V24           V25           V26           V27
#> [1,] -5.30852e-07 -7.881123e-07 -1.042739e-06 -1.120126e-06 -7.487733e-07
#>                V28           V29           V3           V30           V31
#> [1,] -3.299797e-07 -5.092292e-07 6.490266e-09 -6.232957e-07 -6.024531e-07
#>                V32           V33           V34           V35           V36
#> [1,] -3.421088e-07 -6.864229e-07 -8.006746e-07 -1.072347e-06 -1.227286e-06
#>                V37           V38           V39           V4          V40
#> [1,] -8.566664e-07 -2.897233e-07 -2.962798e-07 1.654941e-08 -5.59333e-07
#>                V41           V42           V43          V44           V45
#> [1,] -6.436839e-07 -3.866535e-07 -1.496804e-07 -1.02867e-07 -5.028328e-08
#>                V46           V47          V48           V49           V5
#> [1,] -3.421066e-08 -2.166156e-08 1.584516e-08 -6.095315e-09 2.584519e-08
#>                V50          V51          V52          V53          V54
#> [1,] -1.874739e-08 1.388216e-09 1.369141e-08 1.643534e-10 8.600737e-09
#>                V55           V56           V57           V58           V59
#> [1,] -1.025158e-08 -6.772726e-09 -9.126362e-09 -2.096442e-10 -1.352434e-08
#>                V6           V60            V7           V8           V9
#> [1,] 1.481784e-08 -7.649521e-09 -3.397576e-10 9.068818e-08 2.713144e-07
#> 
#> $vW[[2]]
#>               [,1]
#> [1,]  0.0009009274
#> [2,] -0.0009009274
#> 
#> 
#> $B
#> $B[[1]]
#> [1] -0.523084
#> 
#> $B[[2]]
#> [1]  0.19153799 -0.01503568
#> 
#> 
#> $vB
#> $vB[[1]]
#> [1] -1.368874e-06
#> 
#> $vB[[2]]
#> [1]  0.02156604 -0.02156604
#> 
#> 
#> $post
#> $post[[1]]
#>           V1    V10    V11    V12    V13    V14    V15    V16    V17    V18
#>  [1,] 0.0164 0.0780 0.1791 0.2681 0.1788 0.1039 0.1980 0.3234 0.3748 0.2586
#>  [2,] 0.0056 0.1859 0.2481 0.2712 0.2934 0.2637 0.1880 0.1405 0.2028 0.2613
#>  [3,] 0.0715 0.4186 0.4867 0.5249 0.5959 0.6855 0.8573 0.9718 0.8693 0.8711
#>  [4,] 0.0229 0.1631 0.2709 0.3358 0.4091 0.4400 0.5485 0.7213 0.8137 0.9185
#>  [5,] 0.0274 0.1808 0.2366 0.0906 0.1749 0.4012 0.5187 0.7312 0.9062 0.9260
#>  [6,] 0.0071 0.0898 0.0289 0.1554 0.1437 0.1035 0.1424 0.1227 0.0892 0.2047
#>  [7,] 0.0239 0.1376 0.0938 0.0259 0.1499 0.2851 0.5743 0.8278 0.8669 0.8131
#>  [8,] 0.0177 0.0613 0.1680 0.3476 0.4561 0.5188 0.6308 0.7201 0.5153 0.3818
#>  [9,] 0.0240 0.0389 0.1934 0.2434 0.2906 0.2606 0.3811 0.4997 0.3015 0.3655
#> [10,] 0.0201 0.1097 0.0841 0.0942 0.1204 0.0420 0.0031 0.0162 0.0624 0.2127
#> [11,] 0.0235 0.3199 0.2946 0.2484 0.2510 0.1806 0.1413 0.3019 0.3635 0.3887
#> [12,] 0.0131 0.2079 0.2295 0.1990 0.1184 0.1891 0.2949 0.5343 0.6850 0.7923
#> [13,] 0.0409 0.2259 0.2373 0.3323 0.3827 0.4840 0.6812 0.7555 0.9522 0.9826
#> [14,] 0.0119 0.0864 0.2143 0.3720 0.2665 0.2113 0.1103 0.1136 0.1934 0.4142
#> [15,] 0.0162 0.1334 0.2969 0.4754 0.5677 0.5690 0.6421 0.7487 0.8999 1.0000
#> [16,] 0.0129 0.2245 0.2431 0.3134 0.3206 0.2917 0.2249 0.2347 0.2143 0.2939
#> [17,] 0.0093 0.0640 0.0888 0.1599 0.1541 0.2768 0.2176 0.2799 0.3491 0.2824
#> [18,] 0.0188 0.1806 0.2139 0.1523 0.1975 0.4844 0.7298 0.7807 0.7906 0.6122
#> [19,] 0.0096 0.2952 0.4025 0.5148 0.4901 0.4127 0.3575 0.3447 0.3068 0.2945
#> [20,] 0.0231 0.2461 0.2245 0.1520 0.1732 0.3099 0.4380 0.5595 0.6820 0.6164
#> [21,] 0.0303 0.2354 0.2898 0.2812 0.1578 0.0273 0.0673 0.1444 0.2070 0.2645
#> [22,] 0.0108 0.0393 0.1106 0.1412 0.2202 0.2976 0.4116 0.4754 0.5390 0.6279
#> [23,] 0.0201 0.2571 0.2931 0.3108 0.3603 0.3002 0.2718 0.2007 0.1801 0.2234
#> [24,] 0.0197 0.1695 0.1734 0.2470 0.3141 0.3297 0.2759 0.2056 0.1162 0.1884
#> [25,] 0.0065 0.1109 0.0937 0.0827 0.0920 0.0911 0.1487 0.1666 0.1268 0.1374
#> [26,] 0.0039 0.0452 0.0492 0.0996 0.1424 0.1194 0.0628 0.0907 0.1177 0.1429
#> [27,] 0.0235 0.3674 0.2974 0.0837 0.1912 0.5040 0.6352 0.6804 0.7505 0.6595
#> [28,] 0.0180 0.2918 0.3273 0.3035 0.3033 0.2587 0.1682 0.1308 0.2803 0.4519
#> [29,] 0.0315 0.1533 0.2128 0.2536 0.2686 0.2803 0.1886 0.1485 0.2160 0.2417
#> [30,] 0.0388 0.2718 0.3645 0.3934 0.3843 0.4677 0.5364 0.4823 0.4835 0.5862
#> [31,] 0.0100 0.2668 0.3376 0.3282 0.2432 0.1268 0.1278 0.4441 0.6795 0.7051
#> [32,] 0.1083 0.5966 0.5304 0.2251 0.2402 0.2689 0.6646 0.6632 0.1674 0.0837
#> [33,] 0.0114 0.1799 0.2486 0.2973 0.3672 0.4394 0.5258 0.6755 0.7402 0.8284
#> [34,] 0.0202 0.1370 0.0843 0.0269 0.1254 0.3046 0.5584 0.7973 0.8341 0.8057
#> [35,] 0.0124 0.2120 0.1640 0.1901 0.3026 0.2019 0.0592 0.2390 0.3657 0.3809
#> [36,] 0.0089 0.2119 0.3003 0.3094 0.2743 0.2547 0.1870 0.1452 0.1457 0.2429
#> [37,] 0.0790 0.6609 0.5002 0.2583 0.1650 0.4347 0.4515 0.4579 0.3366 0.4000
#> [38,] 0.0116 0.2271 0.3171 0.2882 0.2657 0.2307 0.1889 0.1791 0.2298 0.3715
#> [39,] 0.0228 0.3517 0.3330 0.3643 0.4020 0.4731 0.5196 0.6573 0.8426 0.8476
#> [40,] 0.0100 0.2990 0.3242 0.3565 0.3951 0.5201 0.6953 0.8468 1.0000 0.9278
#>          V19     V2    V20    V21    V22    V23    V24    V25    V26    V27
#>  [1,] 0.3680 0.0627 0.3508 0.5606 0.5231 0.5469 0.6954 0.6352 0.6757 0.8499
#>  [2,] 0.2778 0.0267 0.3346 0.3830 0.4003 0.5114 0.6860 0.7490 0.7843 0.9021
#>  [3,] 0.8954 0.0849 0.9922 0.8980 0.8158 0.8373 0.7541 0.5893 0.5488 0.5643
#>  [4,] 1.0000 0.0369 0.9418 0.9116 0.9349 0.7484 0.5146 0.4106 0.3443 0.6981
#>  [5,] 0.7434 0.0242 0.4463 0.5103 0.6952 0.7755 0.8364 0.7283 0.6399 0.5759
#>  [6,] 0.0827 0.0103 0.1524 0.3031 0.1608 0.0667 0.1426 0.0395 0.1653 0.3399
#>  [7,] 0.9045 0.0189 0.9046 1.0000 0.9976 0.9872 0.9761 0.9009 0.9724 0.9675
#>  [8,] 0.2644 0.0300 0.3345 0.4865 0.6628 0.7389 0.9213 1.0000 0.7750 0.5593
#>  [9,] 0.6791 0.0218 0.7307 0.5053 0.4441 0.6987 0.8133 0.7781 0.8943 0.8929
#> [10,] 0.3436 0.0026 0.3813 0.3825 0.4764 0.6313 0.7523 0.8675 0.8788 0.7901
#> [11,] 0.2980 0.0220 0.2219 0.1624 0.1343 0.2046 0.3791 0.5771 0.7545 0.8406
#> [12,] 0.8220 0.0201 0.7290 0.7352 0.7918 0.8057 0.4898 0.1934 0.2924 0.6255
#> [13,] 0.8871 0.0421 0.8268 0.7561 0.8217 0.6967 0.6444 0.6948 0.8014 0.6053
#> [14,] 0.3279 0.0582 0.6222 0.7468 0.7676 0.7867 0.8253 1.0000 0.9481 0.7539
#> [15,] 0.9690 0.0253 0.9032 0.7685 0.6998 0.6644 0.5964 0.3711 0.0921 0.0481
#> [16,] 0.4898 0.0141 0.6127 0.7531 0.7718 0.7432 0.8673 0.9308 0.9836 1.0000
#> [17,] 0.2479 0.0185 0.3005 0.4300 0.4684 0.4520 0.5026 0.6217 0.6571 0.6632
#> [18,] 0.4200 0.0370 0.2807 0.5148 0.7569 0.8596 1.0000 0.8457 0.6797 0.6971
#> [19,] 0.4351 0.0404 0.7264 0.8147 0.8103 0.6665 0.6958 0.7748 0.8688 1.0000
#> [20,] 0.6803 0.0315 0.8435 0.9921 1.0000 0.7983 0.5426 0.3952 0.5179 0.5650
#> [21,] 0.2828 0.0353 0.4293 0.5685 0.6990 0.7246 0.7622 0.9242 1.0000 0.9979
#> [22,] 0.7060 0.0086 0.7918 0.9493 1.0000 0.9645 0.9432 0.8658 0.7895 0.6501
#> [23,] 0.3568 0.0165 0.5492 0.7209 0.8318 0.8864 0.9520 0.9637 1.0000 0.9673
#> [24,] 0.3390 0.0394 0.3926 0.4282 0.5418 0.6448 0.7223 0.7853 0.7984 0.8847
#> [25,] 0.1095 0.0122 0.1286 0.2146 0.2889 0.4238 0.6168 0.8167 0.9622 0.8280
#> [26,] 0.1223 0.0063 0.1104 0.1847 0.3715 0.4382 0.5707 0.6654 0.7476 0.7654
#> [27,] 0.4509 0.0291 0.2964 0.4019 0.6794 0.8297 1.0000 0.8240 0.7115 0.7726
#> [28,] 0.6641 0.0444 0.7683 0.6960 0.4393 0.2432 0.2886 0.4974 0.8172 1.0000
#> [29,] 0.2989 0.0252 0.3341 0.3786 0.3956 0.5232 0.6913 0.7868 0.8337 0.9199
#> [30,] 0.7579 0.0324 0.6997 0.6918 0.8633 0.9107 0.9346 0.7884 0.8585 0.9261
#> [31,] 0.7966 0.0275 0.9401 0.9857 0.8193 0.5789 0.6394 0.7043 0.6875 0.4081
#> [32,] 0.4331 0.1070 0.8718 0.7992 0.3712 0.1703 0.1611 0.2086 0.2847 0.2211
#> [33,] 0.9033 0.0222 0.9584 1.0000 0.9982 0.8899 0.7493 0.6367 0.6744 0.7207
#> [34,] 0.8616 0.0104 0.8769 0.9413 0.9403 0.9409 1.0000 0.9725 0.9309 0.9351
#> [35,] 0.5929 0.0433 0.6299 0.5801 0.4574 0.4449 0.3691 0.6446 0.8940 0.8978
#> [36,] 0.3259 0.0274 0.3679 0.3355 0.3100 0.3914 0.5280 0.6409 0.7707 0.8754
#> [37,] 0.5325 0.0707 0.9010 0.9939 0.3689 0.1012 0.0248 0.2318 0.3981 0.2259
#> [38,] 0.6223 0.0744 0.7260 0.7934 0.8045 0.8067 0.9173 0.9327 0.9562 1.0000
#> [39,] 0.8344 0.0853 0.8453 0.7999 0.8537 0.9642 1.0000 0.9357 0.9409 0.9070
#> [40,] 0.8510 0.0194 0.8010 0.8142 0.8825 0.7302 0.6107 0.7159 0.8458 0.6319
#>          V28    V29     V3    V30    V31    V32    V33    V34    V35    V36
#>  [1,] 0.8025 0.6563 0.0738 0.8591 0.6655 0.5369 0.3118 0.3763 0.2801 0.0875
#>  [2,] 1.0000 0.8888 0.0221 0.6511 0.6083 0.4463 0.2948 0.1729 0.1488 0.0801
#>  [3,] 0.5406 0.4783 0.0587 0.4439 0.3698 0.2574 0.1478 0.1743 0.1229 0.1588
#>  [4,] 0.8713 0.9013 0.0040 0.8014 0.4380 0.1319 0.1709 0.2484 0.3044 0.2312
#>  [5,] 0.4146 0.3495 0.0621 0.4437 0.2665 0.2024 0.1942 0.0765 0.3725 0.5843
#>  [6,] 0.4855 0.5206 0.0135 0.5508 0.6102 0.5989 0.6764 0.8897 1.0000 0.9517
#>  [7,] 0.7633 0.4434 0.0466 0.3822 0.4727 0.4007 0.3381 0.3172 0.2222 0.0733
#>  [8,] 0.6172 0.8635 0.0288 0.6592 0.4770 0.4983 0.3330 0.3076 0.2876 0.2226
#>  [9,] 0.8913 0.8610 0.0324 0.8063 0.5540 0.2446 0.3459 0.1615 0.2467 0.5564
#> [10,] 0.8357 0.9631 0.0138 0.9619 0.9236 0.8903 0.9708 0.9647 0.7892 0.5307
#> [11,] 0.8547 0.9036 0.0167 1.0000 0.9646 0.7912 0.6412 0.5986 0.6835 0.7771
#> [12,] 0.8546 0.8966 0.0045 0.7821 0.5168 0.4840 0.4038 0.3411 0.2849 0.2353
#> [13,] 0.6084 0.8877 0.0573 0.8557 0.5563 0.2897 0.3638 0.4786 0.2908 0.0899
#> [14,] 0.6008 0.5437 0.0623 0.5387 0.5619 0.5141 0.6084 0.5621 0.5956 0.6078
#> [15,] 0.0876 0.1040 0.0262 0.1714 0.3264 0.4612 0.3939 0.5050 0.4833 0.3511
#> [16,] 0.9595 0.8722 0.0309 0.6862 0.4901 0.3280 0.3115 0.1969 0.1019 0.0317
#> [17,] 0.7321 0.8534 0.0056 1.0000 0.8448 0.6354 0.6308 0.6211 0.6976 0.5868
#> [18,] 0.5843 0.4772 0.0953 0.5201 0.4241 0.1592 0.1668 0.0588 0.3967 0.7147
#> [19,] 0.9941 0.8793 0.0682 0.6482 0.5876 0.6408 0.4972 0.2755 0.0300 0.3356
#> [20,] 0.3042 0.1881 0.0170 0.3960 0.2286 0.3544 0.4187 0.2398 0.1847 0.3760
#> [21,] 0.8297 0.7032 0.0490 0.7141 0.6893 0.4961 0.2584 0.0969 0.0776 0.0364
#> [22,] 0.4492 0.4739 0.0058 0.6153 0.4929 0.3195 0.3735 0.3336 0.1052 0.0671
#> [23,] 0.8664 0.7896 0.0344 0.6345 0.5351 0.4056 0.2563 0.2894 0.3588 0.4296
#> [24,] 0.9582 0.8990 0.0384 0.6831 0.6108 0.5480 0.5058 0.4476 0.2401 0.1405
#> [25,] 0.5816 0.4667 0.0068 0.3539 0.2727 0.1410 0.1863 0.2176 0.2360 0.1725
#> [26,] 0.8555 0.9720 0.0152 0.9221 0.7502 0.7209 0.7757 0.6055 0.5021 0.4499
#> [27,] 0.6124 0.4936 0.0749 0.5648 0.4906 0.1820 0.1811 0.1107 0.4603 0.6650
#> [28,] 0.9238 0.8519 0.0476 0.7722 0.5772 0.5190 0.6824 0.6220 0.5054 0.3578
#> [29,] 1.0000 0.8990 0.0167 0.6456 0.5967 0.4355 0.2997 0.2294 0.1866 0.0922
#> [30,] 0.7080 0.5779 0.0688 0.5215 0.4505 0.3129 0.1448 0.1046 0.1820 0.1519
#> [31,] 0.1811 0.2064 0.0190 0.3917 0.3791 0.2042 0.2227 0.3341 0.3984 0.5077
#> [32,] 0.6134 0.5807 0.0257 0.6925 0.3825 0.4303 0.7791 0.8703 1.0000 0.9212
#> [33,] 0.6821 0.5512 0.0269 0.4789 0.3924 0.2533 0.1089 0.1390 0.2551 0.3301
#> [34,] 0.7317 0.4421 0.0325 0.3244 0.4161 0.4611 0.4031 0.3000 0.2459 0.1348
#> [35,] 0.4980 0.3333 0.0604 0.2350 0.1553 0.3666 0.4340 0.3082 0.3024 0.4109
#> [36,] 1.0000 0.9806 0.0248 0.6969 0.4973 0.5020 0.5359 0.3842 0.1848 0.1149
#> [37,] 0.5247 0.6898 0.0352 0.8316 0.4326 0.3741 0.5756 0.8043 0.7963 0.7174
#> [38,] 0.9818 0.8684 0.0367 0.6381 0.3997 0.3242 0.2835 0.2413 0.2321 0.1260
#> [39,] 0.7104 0.6320 0.1000 0.5667 0.3501 0.2447 0.1698 0.3290 0.3674 0.2331
#> [40,] 0.4808 0.6291 0.0155 0.7152 0.6005 0.4235 0.4106 0.3992 0.1730 0.1975
#>          V37    V38    V39     V4    V40    V41    V42    V43    V44    V45
#>  [1,] 0.3319 0.4237 0.1801 0.0608 0.3743 0.4627 0.1614 0.2494 0.3202 0.2265
#>  [2,] 0.1770 0.1382 0.2404 0.0561 0.2046 0.1970 0.2778 0.1377 0.0685 0.0664
#>  [3,] 0.1803 0.1436 0.1667 0.0218 0.2630 0.2234 0.1239 0.0869 0.2092 0.1499
#>  [4,] 0.1338 0.2056 0.2474 0.0375 0.2790 0.1610 0.0056 0.0351 0.1148 0.1331
#>  [5,] 0.4827 0.2347 0.0999 0.0560 0.3244 0.3990 0.2975 0.1684 0.1761 0.1683
#>  [6,] 0.8459 0.7073 0.6697 0.0494 0.6326 0.5102 0.4161 0.2816 0.1705 0.1421
#>  [7,] 0.2692 0.1888 0.0712 0.0440 0.1062 0.0694 0.0300 0.0893 0.1459 0.1348
#>  [8,] 0.0794 0.0603 0.1049 0.0394 0.0606 0.1530 0.0983 0.1643 0.1901 0.1107
#>  [9,] 0.4681 0.0979 0.1582 0.0569 0.0751 0.3321 0.3745 0.2666 0.1078 0.1418
#> [10,] 0.2718 0.1953 0.1374 0.0062 0.3105 0.3790 0.4105 0.3355 0.2998 0.2748
#> [11,] 0.8084 0.7426 0.6295 0.0516 0.5708 0.4433 0.3361 0.3795 0.4950 0.4373
#> [12,] 0.2699 0.4442 0.4323 0.0217 0.3314 0.1195 0.1669 0.3702 0.3072 0.0945
#> [13,] 0.2043 0.1707 0.0407 0.0130 0.1286 0.1581 0.2191 0.1701 0.0971 0.2217
#> [14,] 0.5025 0.2829 0.0477 0.0600 0.2811 0.3422 0.5147 0.4372 0.2470 0.1708
#> [15,] 0.2319 0.4029 0.3676 0.0386 0.1510 0.0745 0.1395 0.1552 0.0377 0.0636
#> [16,] 0.0756 0.0907 0.1066 0.0375 0.1380 0.0665 0.1475 0.2470 0.2788 0.2709
#> [17,] 0.4889 0.3683 0.2043 0.0064 0.1469 0.2220 0.1449 0.1490 0.1211 0.1144
#> [18,] 0.7319 0.3509 0.0589 0.0824 0.2690 0.4200 0.3874 0.2440 0.2000 0.2307
#> [19,] 0.3167 0.4133 0.6281 0.0688 0.4977 0.2613 0.4697 0.4806 0.4921 0.5294
#> [20,] 0.4331 0.3626 0.2519 0.0226 0.1870 0.1046 0.2339 0.1991 0.1100 0.0684
#> [21,] 0.1572 0.1823 0.1349 0.0608 0.0849 0.0492 0.1367 0.1552 0.1548 0.1319
#> [22,] 0.0379 0.0461 0.1694 0.0460 0.2169 0.1677 0.0644 0.0159 0.0778 0.0653
#> [23,] 0.4773 0.4516 0.3765 0.0330 0.3051 0.1921 0.1184 0.1984 0.1570 0.0660
#> [24,] 0.1772 0.1742 0.3326 0.0076 0.4021 0.3009 0.2075 0.1206 0.0255 0.0298
#> [25,] 0.0589 0.0621 0.1847 0.0108 0.2452 0.2984 0.3041 0.2275 0.1480 0.1102
#> [26,] 0.3947 0.4281 0.4427 0.0336 0.3749 0.1972 0.0511 0.0793 0.1269 0.1533
#> [27,] 0.6423 0.2166 0.1951 0.0519 0.4947 0.4925 0.4041 0.2402 0.1392 0.1779
#> [28,] 0.3809 0.3813 0.3359 0.0698 0.2771 0.3648 0.3834 0.3453 0.2096 0.1031
#> [29,] 0.1829 0.1743 0.2452 0.0479 0.2407 0.2518 0.3184 0.1685 0.0675 0.1186
#> [30,] 0.1017 0.1438 0.1986 0.0898 0.2039 0.2778 0.2879 0.1331 0.1140 0.1310
#> [31,] 0.5534 0.3352 0.2723 0.0371 0.2278 0.2044 0.1986 0.0835 0.0908 0.1380
#> [32,] 0.9386 0.9303 0.7314 0.0837 0.4791 0.2087 0.2016 0.1669 0.2872 0.4374
#> [33,] 0.2818 0.2142 0.2266 0.0384 0.2142 0.2354 0.2871 0.2596 0.1925 0.1256
#> [34,] 0.2541 0.2255 0.1598 0.0239 0.1485 0.0845 0.0569 0.0855 0.1262 0.1153
#> [35,] 0.5501 0.4129 0.5499 0.0449 0.5018 0.3132 0.2802 0.2351 0.2298 0.1155
#> [36,] 0.1570 0.1311 0.1583 0.0237 0.2631 0.3103 0.4512 0.3785 0.1269 0.1459
#> [37,] 0.7056 0.8148 0.7601 0.1660 0.6034 0.4554 0.4729 0.4478 0.3722 0.4693
#> [38,] 0.0693 0.0701 0.1439 0.0225 0.1475 0.0438 0.0469 0.1476 0.1742 0.1555
#> [39,] 0.2413 0.2556 0.1892 0.0428 0.1940 0.3074 0.2785 0.0308 0.1238 0.1854
#> [40,] 0.2370 0.1339 0.1583 0.0489 0.3151 0.1968 0.2054 0.1272 0.1129 0.1946
#>          V46    V47    V48    V49     V5    V50    V51    V52    V53    V54
#>  [1,] 0.1146 0.0476 0.0943 0.0824 0.0233 0.0171 0.0244 0.0258 0.0143 0.0226
#>  [2,] 0.1665 0.1807 0.1245 0.0516 0.0936 0.0044 0.0185 0.0072 0.0055 0.0074
#>  [3,] 0.0676 0.0899 0.0927 0.0658 0.0862 0.0086 0.0216 0.0153 0.0121 0.0096
#>  [4,] 0.0276 0.0763 0.0631 0.0309 0.0455 0.0240 0.0115 0.0064 0.0022 0.0122
#>  [5,] 0.0729 0.1190 0.1297 0.0748 0.1129 0.0067 0.0255 0.0113 0.0108 0.0085
#>  [6,] 0.0971 0.0879 0.0863 0.0355 0.0253 0.0233 0.0252 0.0043 0.0048 0.0076
#>  [7,] 0.0391 0.0546 0.0469 0.0201 0.0657 0.0095 0.0155 0.0091 0.0151 0.0080
#>  [8,] 0.1917 0.1467 0.0392 0.0356 0.0630 0.0270 0.0168 0.0102 0.0122 0.0044
#>  [9,] 0.1687 0.0738 0.0634 0.0144 0.0330 0.0226 0.0061 0.0162 0.0146 0.0093
#> [10,] 0.2024 0.1043 0.0453 0.0337 0.0133 0.0122 0.0072 0.0108 0.0070 0.0063
#> [11,] 0.2404 0.1128 0.1654 0.0933 0.0746 0.0225 0.0214 0.0221 0.0152 0.0083
#> [12,] 0.1545 0.1394 0.0772 0.0615 0.0230 0.0230 0.0111 0.0168 0.0086 0.0045
#> [13,] 0.2732 0.1874 0.1062 0.0665 0.0183 0.0405 0.0113 0.0028 0.0036 0.0105
#> [14,] 0.1343 0.0838 0.0755 0.0304 0.1397 0.0074 0.0069 0.0025 0.0103 0.0074
#> [15,] 0.0443 0.0264 0.0223 0.0187 0.0645 0.0077 0.0137 0.0071 0.0082 0.0232
#> [16,] 0.2283 0.1818 0.1185 0.0546 0.0767 0.0219 0.0204 0.0124 0.0093 0.0072
#> [17,] 0.0791 0.0365 0.0152 0.0085 0.0260 0.0120 0.0022 0.0069 0.0064 0.0129
#> [18,] 0.1886 0.1960 0.1701 0.1366 0.0249 0.0398 0.0143 0.0093 0.0033 0.0113
#> [19,] 0.2216 0.1401 0.1888 0.0947 0.0887 0.0134 0.0310 0.0237 0.0078 0.0144
#> [20,] 0.0303 0.0674 0.0785 0.0455 0.0410 0.0246 0.0151 0.0125 0.0036 0.0123
#> [21,] 0.0985 0.1258 0.0954 0.0489 0.0167 0.0241 0.0042 0.0086 0.0046 0.0126
#> [22,] 0.0210 0.0509 0.0387 0.0262 0.0752 0.0101 0.0161 0.0029 0.0078 0.0114
#> [23,] 0.1294 0.0797 0.0052 0.0233 0.0397 0.0152 0.0125 0.0054 0.0057 0.0137
#> [24,] 0.0691 0.0781 0.0777 0.0369 0.0251 0.0057 0.0091 0.0134 0.0097 0.0042
#> [25,] 0.1178 0.0608 0.0333 0.0276 0.0217 0.0100 0.0023 0.0069 0.0025 0.0027
#> [26,] 0.0690 0.0402 0.0534 0.0228 0.0310 0.0073 0.0062 0.0062 0.0120 0.0052
#> [27,] 0.1946 0.1723 0.1522 0.0929 0.0227 0.0179 0.0242 0.0083 0.0037 0.0095
#> [28,] 0.0798 0.0701 0.0526 0.0241 0.1615 0.0117 0.0122 0.0122 0.0114 0.0098
#> [29,] 0.1833 0.1878 0.1114 0.0310 0.0902 0.0143 0.0138 0.0108 0.0062 0.0044
#> [30,] 0.1433 0.0624 0.0100 0.0098 0.1267 0.0131 0.0152 0.0255 0.0071 0.0263
#> [31,] 0.1948 0.1211 0.0843 0.0589 0.0416 0.0247 0.0118 0.0088 0.0104 0.0036
#> [32,] 0.3097 0.1578 0.0553 0.0334 0.0748 0.0209 0.0172 0.0180 0.0110 0.0234
#> [33,] 0.1003 0.0951 0.1210 0.0728 0.1217 0.0174 0.0213 0.0269 0.0152 0.0257
#> [34,] 0.0570 0.0426 0.0425 0.0235 0.0807 0.0006 0.0188 0.0127 0.0081 0.0067
#> [35,] 0.0724 0.0621 0.0318 0.0450 0.0597 0.0167 0.0078 0.0083 0.0057 0.0174
#> [36,] 0.1092 0.1485 0.1385 0.0716 0.0224 0.0176 0.0199 0.0096 0.0103 0.0093
#> [37,] 0.3839 0.0768 0.1467 0.0777 0.1330 0.0469 0.0193 0.0298 0.0390 0.0294
#> [38,] 0.1651 0.1181 0.0720 0.0321 0.0076 0.0056 0.0202 0.0141 0.0103 0.0100
#> [39,] 0.1753 0.1079 0.0728 0.0242 0.1117 0.0191 0.0159 0.0172 0.0191 0.0260
#> [40,] 0.2195 0.1930 0.1498 0.0773 0.0839 0.0196 0.0122 0.0130 0.0073 0.0077
#>          V55    V56    V57    V58    V59     V6    V60     V7     V8     V9
#>  [1,] 0.0187 0.0185 0.0110 0.0094 0.0078 0.1048 0.0112 0.1338 0.0644 0.1522
#>  [2,] 0.0068 0.0084 0.0037 0.0024 0.0034 0.1146 0.0007 0.0706 0.0996 0.1673
#>  [3,] 0.0196 0.0042 0.0066 0.0099 0.0083 0.1801 0.0124 0.1916 0.1896 0.2960
#>  [4,] 0.0151 0.0056 0.0026 0.0029 0.0104 0.1452 0.0163 0.2211 0.1188 0.0750
#>  [5,] 0.0047 0.0074 0.0104 0.0161 0.0220 0.0973 0.0173 0.1823 0.1745 0.1440
#>  [6,] 0.0124 0.0105 0.0054 0.0032 0.0073 0.0806 0.0063 0.0701 0.0738 0.0117
#>  [7,] 0.0018 0.0078 0.0045 0.0026 0.0036 0.0742 0.0024 0.1380 0.1099 0.1384
#>  [8,] 0.0075 0.0124 0.0099 0.0057 0.0032 0.0526 0.0019 0.0688 0.0633 0.0624
#>  [9,] 0.0112 0.0094 0.0054 0.0019 0.0066 0.0513 0.0023 0.0897 0.0713 0.0569
#> [10,] 0.0030 0.0011 0.0007 0.0024 0.0057 0.0151 0.0044 0.0541 0.0210 0.0505
#> [11,] 0.0058 0.0023 0.0057 0.0052 0.0027 0.1121 0.0021 0.1258 0.1717 0.3074
#> [12,] 0.0062 0.0065 0.0030 0.0066 0.0029 0.0481 0.0053 0.0742 0.0333 0.1369
#> [13,] 0.0120 0.0087 0.0061 0.0061 0.0030 0.1019 0.0078 0.1054 0.1070 0.2302
#> [14,] 0.0123 0.0069 0.0076 0.0073 0.0030 0.1883 0.0138 0.1422 0.1447 0.0487
#> [15,] 0.0198 0.0074 0.0035 0.0100 0.0048 0.0472 0.0019 0.1056 0.1388 0.0598
#> [16,] 0.0019 0.0027 0.0054 0.0017 0.0024 0.0787 0.0029 0.0662 0.1108 0.1777
#> [17,] 0.0114 0.0054 0.0089 0.0050 0.0058 0.0458 0.0025 0.0470 0.0057 0.0425
#> [18,] 0.0030 0.0057 0.0090 0.0057 0.0068 0.0488 0.0024 0.1424 0.1972 0.1873
#> [19,] 0.0170 0.0012 0.0109 0.0036 0.0043 0.0932 0.0018 0.0955 0.2140 0.2546
#> [20,] 0.0043 0.0114 0.0052 0.0091 0.0008 0.0116 0.0092 0.0223 0.0805 0.2365
#> [21,] 0.0036 0.0035 0.0034 0.0079 0.0036 0.1354 0.0048 0.1465 0.1123 0.1945
#> [22,] 0.0083 0.0058 0.0003 0.0023 0.0026 0.0887 0.0027 0.1015 0.0494 0.0472
#> [23,] 0.0109 0.0035 0.0056 0.0105 0.0082 0.0443 0.0036 0.0684 0.0903 0.1739
#> [24,] 0.0058 0.0072 0.0041 0.0045 0.0047 0.0629 0.0054 0.0747 0.0578 0.1357
#> [25,] 0.0052 0.0036 0.0026 0.0036 0.0006 0.0284 0.0035 0.0527 0.0575 0.1054
#> [26,] 0.0056 0.0093 0.0042 0.0003 0.0053 0.0284 0.0036 0.0396 0.0272 0.0323
#> [27,] 0.0105 0.0030 0.0132 0.0068 0.0108 0.0834 0.0090 0.0677 0.2002 0.2876
#> [28,] 0.0027 0.0025 0.0026 0.0050 0.0073 0.0887 0.0022 0.0596 0.1071 0.3175
#> [29,] 0.0072 0.0007 0.0054 0.0035 0.0001 0.1057 0.0055 0.1024 0.1209 0.1241
#> [30,] 0.0079 0.0111 0.0107 0.0068 0.0097 0.1515 0.0067 0.2134 0.2613 0.2832
#> [31,] 0.0088 0.0047 0.0117 0.0020 0.0091 0.0201 0.0058 0.0314 0.0651 0.1896
#> [32,] 0.0276 0.0032 0.0084 0.0122 0.0082 0.1125 0.0143 0.3322 0.4590 0.5526
#> [33,] 0.0097 0.0041 0.0050 0.0145 0.0103 0.2062 0.0025 0.1489 0.0929 0.1350
#> [34,] 0.0043 0.0065 0.0049 0.0054 0.0073 0.1529 0.0054 0.1154 0.0608 0.1317
#> [35,] 0.0188 0.0054 0.0114 0.0196 0.0147 0.0355 0.0062 0.0531 0.0343 0.1052
#> [36,] 0.0025 0.0044 0.0021 0.0069 0.0060 0.0845 0.0018 0.1488 0.1224 0.1569
#> [37,] 0.0175 0.0249 0.0141 0.0073 0.0025 0.0226 0.0101 0.0771 0.2678 0.5664
#> [38,] 0.0034 0.0026 0.0037 0.0044 0.0057 0.0545 0.0035 0.1110 0.1069 0.1708
#> [39,] 0.0140 0.0125 0.0116 0.0093 0.0012 0.1651 0.0036 0.1597 0.2116 0.3295
#> [40,] 0.0075 0.0060 0.0080 0.0019 0.0053 0.1009 0.0019 0.1627 0.2071 0.2696
#> 
#> $post[[2]]
#>              [,1]
#>  [1,] 0.010675343
#>  [2,] 0.013077742
#>  [3,] 0.005312127
#>  [4,] 0.006201956
#>  [5,] 0.008409909
#>  [6,] 0.024072479
#>  [7,] 0.004242470
#>  [8,] 0.008580835
#>  [9,] 0.007072309
#> [10,] 0.005608910
#> [11,] 0.005565966
#> [12,] 0.007761831
#> [13,] 0.004417148
#> [14,] 0.005191715
#> [15,] 0.012503568
#> [16,] 0.006931676
#> [17,] 0.010163972
#> [18,] 0.005830761
#> [19,] 0.003142286
#> [20,] 0.010915525
#> [21,] 0.010910602
#> [22,] 0.008098986
#> [23,] 0.004924678
#> [24,] 0.009387496
#> [25,] 0.032670194
#> [26,] 0.012870172
#> [27,] 0.005397903
#> [28,] 0.006341014
#> [29,] 0.011871806
#> [30,] 0.005590075
#> [31,] 0.009846507
#> [32,] 0.006414521
#> [33,] 0.005069821
#> [34,] 0.004441773
#> [35,] 0.013641518
#> [36,] 0.012120065
#> [37,] 0.005852851
#> [38,] 0.006493066
#> [39,] 0.003165591
#> [40,] 0.004128700
#> 
#> $post[[3]]
#>            [,1]      [,2]
#>  [1,] 0.5620981 0.4379019
#>  [2,] 0.5620967 0.4379033
#>  [3,] 0.5621011 0.4378989
#>  [4,] 0.5621006 0.4378994
#>  [5,] 0.5620993 0.4379007
#>  [6,] 0.5620905 0.4379095
#>  [7,] 0.5621017 0.4378983
#>  [8,] 0.5620992 0.4379008
#>  [9,] 0.5621001 0.4378999
#> [10,] 0.5621009 0.4378991
#> [11,] 0.5621009 0.4378991
#> [12,] 0.5620997 0.4379003
#> [13,] 0.5621016 0.4378984
#> [14,] 0.5621011 0.4378989
#> [15,] 0.5620970 0.4379030
#> [16,] 0.5621002 0.4378998
#> [17,] 0.5620983 0.4379017
#> [18,] 0.5621008 0.4378992
#> [19,] 0.5621023 0.4378977
#> [20,] 0.5620979 0.4379021
#> [21,] 0.5620979 0.4379021
#> [22,] 0.5620995 0.4379005
#> [23,] 0.5621013 0.4378987
#> [24,] 0.5620988 0.4379012
#> [25,] 0.5620857 0.4379143
#> [26,] 0.5620968 0.4379032
#> [27,] 0.5621010 0.4378990
#> [28,] 0.5621005 0.4378995
#> [29,] 0.5620974 0.4379026
#> [30,] 0.5621009 0.4378991
#> [31,] 0.5620985 0.4379015
#> [32,] 0.5621005 0.4378995
#> [33,] 0.5621012 0.4378988
#> [34,] 0.5621016 0.4378984
#> [35,] 0.5620964 0.4379036
#> [36,] 0.5620972 0.4379028
#> [37,] 0.5621008 0.4378992
#> [38,] 0.5621004 0.4378996
#> [39,] 0.5621023 0.4378977
#> [40,] 0.5621017 0.4378983
#> 
#> 
#> $pre
#> $pre[[1]]
#> NULL
#> 
#> $pre[[2]]
#>            [,1]
#>  [1,] -4.529086
#>  [2,] -4.323680
#>  [3,] -5.232437
#>  [4,] -5.076669
#>  [5,] -4.769899
#>  [6,] -3.702319
#>  [7,] -5.458358
#>  [8,] -4.749606
#>  [9,] -4.944471
#> [10,] -5.177774
#> [11,] -5.185503
#> [12,] -4.850745
#> [13,] -5.417834
#> [14,] -5.255486
#> [15,] -4.369159
#> [16,] -4.964698
#> [17,] -4.578690
#> [18,] -5.138760
#> [19,] -5.759658
#> [20,] -4.506594
#> [21,] -4.507050
#> [22,] -4.807884
#> [23,] -5.308560
#> [24,] -4.658945
#> [25,] -3.388076
#> [26,] -4.339889
#> [27,] -5.216332
#> [28,] -5.054355
#> [29,] -4.421646
#> [30,] -5.181157
#> [31,] -4.610743
#> [32,] -5.042756
#> [33,] -5.279367
#> [34,] -5.412250
#> [35,] -4.280902
#> [36,] -4.400699
#> [37,] -5.134956
#> [38,] -5.030506
#> [39,] -5.752245
#> [40,] -5.485656
#> 
#> $pre[[3]]
#>            [,1]        [,2]
#>  [1,] 0.2135886 -0.03609275
#>  [2,] 0.2136976 -0.03597821
#>  [3,] 0.2133451 -0.03634845
#>  [4,] 0.2133855 -0.03630603
#>  [5,] 0.2134857 -0.03620076
#>  [6,] 0.2141966 -0.03545401
#>  [7,] 0.2132966 -0.03639945
#>  [8,] 0.2134935 -0.03619261
#>  [9,] 0.2134250 -0.03626453
#> [10,] 0.2133586 -0.03633430
#> [11,] 0.2133567 -0.03633635
#> [12,] 0.2134563 -0.03623166
#> [13,] 0.2133045 -0.03639112
#> [14,] 0.2133397 -0.03635419
#> [15,] 0.2136715 -0.03600558
#> [16,] 0.2134186 -0.03627124
#> [17,] 0.2135653 -0.03611713
#> [18,] 0.2133687 -0.03632373
#> [19,] 0.2132467 -0.03645190
#> [20,] 0.2135995 -0.03608130
#> [21,] 0.2135992 -0.03608153
#> [22,] 0.2134716 -0.03621558
#> [23,] 0.2133275 -0.03636693
#> [24,] 0.2135301 -0.03615415
#> [25,] 0.2145868 -0.03504409
#> [26,] 0.2136882 -0.03598811
#> [27,] 0.2133490 -0.03634436
#> [28,] 0.2133918 -0.03629940
#> [29,] 0.2136429 -0.03603571
#> [30,] 0.2133577 -0.03633520
#> [31,] 0.2135509 -0.03613227
#> [32,] 0.2133952 -0.03629589
#> [33,] 0.2133341 -0.03636001
#> [34,] 0.2133056 -0.03638995
#> [35,] 0.2137232 -0.03595133
#> [36,] 0.2136541 -0.03602387
#> [37,] 0.2133697 -0.03632267
#> [38,] 0.2133987 -0.03629215
#> [39,] 0.2132477 -0.03645079
#> [40,] 0.2132914 -0.03640488
#> 
#> 
#> $e
#>             [,1]       [,2]
#>  [1,]  0.4379019 -0.4379019
#>  [2,]  0.4379033 -0.4379033
#>  [3,]  0.4378989 -0.4378989
#>  [4,] -0.5621006  0.5621006
#>  [5,] -0.5620993  0.5620993
#>  [6,] -0.5620905  0.5620905
#>  [7,] -0.5621017  0.5621017
#>  [8,] -0.5620992  0.5620992
#>  [9,] -0.5621001  0.5621001
#> [10,] -0.5621009  0.5621009
#> [11,]  0.4378991 -0.4378991
#> [12,]  0.4379003 -0.4379003
#> [13,] -0.5621016  0.5621016
#> [14,] -0.5621011  0.5621011
#> [15,]  0.4379030 -0.4379030
#> [16,]  0.4378998 -0.4378998
#> [17,] -0.5620983  0.5620983
#> [18,] -0.5621008  0.5621008
#> [19,]  0.4378977 -0.4378977
#> [20,]  0.4379021 -0.4379021
#> [21,]  0.4379021 -0.4379021
#> [22,] -0.5620995  0.5620995
#> [23,]  0.4378987 -0.4378987
#> [24,]  0.4379012 -0.4379012
#> [25,] -0.5620857  0.5620857
#> [26,] -0.5620968  0.5620968
#> [27,] -0.5621010  0.5621010
#> [28,]  0.4378995 -0.4378995
#> [29,]  0.4379026 -0.4379026
#> [30,]  0.4378991 -0.4378991
#> [31,] -0.5620985  0.5620985
#> [32,]  0.4378995 -0.4378995
#> [33,]  0.4378988 -0.4378988
#> [34,] -0.5621016  0.5621016
#> [35,] -0.5620964  0.5620964
#> [36,]  0.4379028 -0.4379028
#> [37,]  0.4378992 -0.4378992
#> [38,]  0.4378996 -0.4378996
#> [39,]  0.4378977 -0.4378977
#> [40,] -0.5621017  0.5621017
#> 
#> $L
#> [1] 0.6928773 0.6919219 0.6850295 0.6941676 0.6816549 0.6946768
#> 


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.5217391