Last updated on 2021-04-29 10:50:36 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.2.1 | 11.41 | 60.70 | 72.11 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.2.1 | 7.98 | 46.50 | 54.48 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.2.1 | 93.75 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.2.1 | 84.25 | ERROR | |||
| r-devel-windows-ix86+x86_64 | 0.2.1 | 15.00 | 80.00 | 95.00 | ERROR | |
| r-devel-windows-x86_64-gcc10-UCRT | 0.2.1 | ERROR | ||||
| r-patched-linux-x86_64 | 0.2.1 | 9.73 | 60.05 | 69.78 | ERROR | |
| r-patched-solaris-x86 | 0.2.1 | 113.10 | OK | |||
| r-release-linux-x86_64 | 0.2.1 | 10.93 | 58.21 | 69.14 | OK | |
| r-release-macos-x86_64 | 0.2.1 | OK | ||||
| r-release-windows-ix86+x86_64 | 0.2.1 | 16.00 | 61.00 | 77.00 | OK | |
| r-oldrel-macos-x86_64 | 0.2.1 | OK |
Version: 0.2.1
Check: LazyData
Result: NOTE
'LazyData' is specified without a 'data' directory
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-devel-windows-x86_64-gcc10-UCRT, r-patched-linux-x86_64
Version: 0.2.1
Check: examples
Result: ERROR
Running examples in 'funGp-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: fgpm
> ### Title: Gaussian process models for scalar and functional inputs
> ### Aliases: fgpm
>
> ### ** Examples
>
> # creating funGp model using default fgpm arguments________________________________________
> # generating input data for training
> set.seed(100)
> n.tr <- 25
> sIn <- expand.grid(x1 = seq(0,1,length = sqrt(n.tr)), x2 = seq(0,1,length = sqrt(n.tr)))
> fIn <- list(f1 = matrix(runif(n.tr*10), ncol = 10), f2 = matrix(runif(n.tr*22), ncol = 22))
>
> # generating output data for training
> sOut <- fgp_BB3(sIn, fIn, n.tr)
>
> # building a scalar-input funGp model
> ms <- fgpm(sIn = sIn, sOut = sOut)
** Presampling...
** Optimising hyperparameters...
iter 10 value 26.756350
final value 26.743784
converged
** Hyperparameters done!
>
> # building a functional-input funGp model
> mf <- fgpm(fIn = fIn, sOut = sOut)
** Presampling...
** Optimising hyperparameters...
final value 40.291940
converged
** Hyperparameters done!
>
> # building a hybrid-input funGp model
> msf <- fgpm(sIn = sIn, fIn = fIn, sOut = sOut)
** Presampling...
** Optimising hyperparameters...
final value 2.841058
converged
** Hyperparameters done!
>
> # plotting the three models
> plotLOO(ms)
> plotLOO(mf)
> plotLOO(msf)
>
> # printing the three models
> ms # equivalent to show(ms)
Gaussian Process Model__
* Scalar inputs: 2
* Functional inputs: 0
* Total data points: 25
* Trained with: 25
* Kernel type: matern5_2
* Hyperparameters:
-> variance: 4.0588
-> length-scale:
ls(X1): 0.7555
ls(X2): 0.2091
________________________> mf # equivalent to show(mf)
Gaussian Process Model____________________________________
* Scalar inputs: 0
* Functional inputs: 2
| Input | Orig. dim | Proj. dim | Basis | Distance |
|:-----:|:---------:|:---------:|:---------:|:----------:|
| F1 | 10 | 3 | B-splines | L2_bygroup |
| F2 | 22 | 3 | B-splines | L2_bygroup |
* Total data points: 25
* Trained with: 25
* Kernel type: matern5_2
* Hyperparameters:
-> variance: 5.0745
-> length-scale:
ls(F1): 0.4093
ls(F2): 3.0370
__________________________________________________________> msf # equivalent to show(msf)
Gaussian Process Model____________________________________
* Scalar inputs: 2
* Functional inputs: 2
| Input | Orig. dim | Proj. dim | Basis | Distance |
|:-----:|:---------:|:---------:|:---------:|:----------:|
| F1 | 10 | 3 | B-splines | L2_bygroup |
| F2 | 22 | 3 | B-splines | L2_bygroup |
* Total data points: 25
* Trained with: 25
* Kernel type: matern5_2
* Hyperparameters:
-> variance: 1.6404
-> length-scale:
ls(X1): 2.0000
ls(X2): 2.0000
ls(F1): 2.5804
ls(F2): 3.0370
__________________________________________________________>
>
> # recovering useful information from a funGp model_________________________________________
> # building the model
> set.seed(100)
> n.tr <- 25
> sIn <- expand.grid(x1 = seq(0,1,length = sqrt(n.tr)), x2 = seq(0,1,length = sqrt(n.tr)))
> fIn <- list(f1 = matrix(runif(n.tr*10), ncol = 10), f2 = matrix(runif(n.tr*22), ncol = 22))
> sOut <- fgp_BB3(sIn, fIn, n.tr)
> m1 <- fgpm(sIn = sIn, fIn = fIn, sOut = sOut)
** Presampling...
** Optimising hyperparameters...
final value 2.841058
converged
** Hyperparameters done!
>
> # recovering data from model slots
> m1@f_proj@coefs # list of projection coefficients for the functional inputs
[[1]]
[,1] [,2] [,3]
[1,] 0.3237129 0.21255838 0.5346522
[2,] 0.2223468 1.34947571 0.4559333
[3,] 0.5909821 0.55327055 0.4265835
[4,] 0.2446155 0.67055154 0.4551564
[5,] 0.3870073 0.41419577 0.3815046
[6,] 0.4415457 0.41894317 0.6668334
[7,] 0.6494271 0.91240638 0.5792249
[8,] 0.2678789 0.95232360 0.7711043
[9,] 0.8276012 0.20151182 0.4172618
[10,] 0.2734095 0.54643429 0.3847912
[11,] 0.7067003 0.56329358 0.3014265
[12,] 0.6468009 0.59221739 0.4309373
[13,] 0.6229271 -0.03915762 0.4759646
[14,] 0.7804757 -0.11358585 0.6696015
[15,] 0.5372785 0.56704494 0.6119392
[16,] 0.5013464 0.51404137 0.4817981
[17,] 0.5460831 0.11155402 0.7273778
[18,] 0.5480458 0.31391318 0.6452983
[19,] 0.4162956 0.62130199 0.7310317
[20,] 0.6518674 0.93601117 0.1772725
[21,] 0.5286249 0.29027465 0.6395710
[22,] 0.7234143 0.31068935 0.5257543
[23,] 0.6418424 0.33005655 0.3487010
[24,] 0.5947669 0.33128887 0.4093980
[25,] 0.3747970 0.80801968 0.3211958
[[2]]
[,1] [,2] [,3]
[1,] 0.6062311 0.30341235 0.43462080
[2,] 0.6307970 0.31383736 0.72598551
[3,] 0.4873021 0.60904740 0.63901236
[4,] 0.4311773 0.58477244 0.36711484
[5,] 0.7072156 0.08218531 0.58237092
[6,] 0.4650037 0.86995420 0.36261238
[7,] 0.2116793 1.14510095 0.08505487
[8,] 0.2677710 0.70653857 0.35718808
[9,] 0.5498281 0.44441875 0.55291115
[10,] 0.7557775 -0.22431841 0.74606600
[11,] 0.3784137 0.61684573 0.44460131
[12,] 0.8184529 0.18063618 0.55994024
[13,] 0.3666109 0.99749267 0.54820428
[14,] 0.4563043 0.97305441 0.42286358
[15,] 0.4834991 0.74113335 0.64067963
[16,] 0.7010682 0.29187240 0.52339393
[17,] 0.3832942 0.32088375 0.63379726
[18,] 0.3934434 0.72451950 0.42451127
[19,] 0.6153849 0.61048910 0.27888426
[20,] 0.6064385 0.81107253 0.49298452
[21,] 0.3397964 0.95946994 0.32982844
[22,] 0.5816670 0.48594555 0.45196211
[23,] 0.6198225 0.35582662 0.48754169
[24,] 0.4933785 0.83987795 0.19927211
[25,] 0.6043914 0.09276635 0.69666106
> m1@f_proj@basis # list of projection basis functions for the functional inputs
[[1]]
[,1] [,2] [,3]
[1,] 1.00000000 0.0000000 0.00000000
[2,] 0.79012346 0.1975309 0.01234568
[3,] 0.60493827 0.3456790 0.04938272
[4,] 0.44444444 0.4444444 0.11111111
[5,] 0.30864198 0.4938272 0.19753086
[6,] 0.19753086 0.4938272 0.30864198
[7,] 0.11111111 0.4444444 0.44444444
[8,] 0.04938272 0.3456790 0.60493827
[9,] 0.01234568 0.1975309 0.79012346
[10,] 0.00000000 0.0000000 1.00000000
[[2]]
[,1] [,2] [,3]
[1,] 1.000000000 0.00000000 0.000000000
[2,] 0.907029478 0.09070295 0.002267574
[3,] 0.818594104 0.17233560 0.009070295
[4,] 0.734693878 0.24489796 0.020408163
[5,] 0.655328798 0.30839002 0.036281179
[6,] 0.580498866 0.36281179 0.056689342
[7,] 0.510204082 0.40816327 0.081632653
[8,] 0.444444444 0.44444444 0.111111111
[9,] 0.383219955 0.47165533 0.145124717
[10,] 0.326530612 0.48979592 0.183673469
[11,] 0.274376417 0.49886621 0.226757370
[12,] 0.226757370 0.49886621 0.274376417
[13,] 0.183673469 0.48979592 0.326530612
[14,] 0.145124717 0.47165533 0.383219955
[15,] 0.111111111 0.44444444 0.444444444
[16,] 0.081632653 0.40816327 0.510204082
[17,] 0.056689342 0.36281179 0.580498866
[18,] 0.036281179 0.30839002 0.655328798
[19,] 0.020408163 0.24489796 0.734693878
[20,] 0.009070295 0.17233560 0.818594104
[21,] 0.002267574 0.09070295 0.907029478
[22,] 0.000000000 0.00000000 1.000000000
> Map(function(a, b) a %*% t(b), m1@f_proj@coefs, m1@f_proj@basis) # list of projected
[[1]]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.3237129 0.3043607 0.2957059 0.2977486 0.3104888 0.3339265 0.3680617
[2,] 0.2223468 0.4478734 0.6235068 0.7492470 0.8250942 0.8510483 0.8271092
[3,] 0.5909821 0.5815033 0.5698275 0.5559549 0.5398853 0.5216188 0.5011554
[4,] 0.2446155 0.3313503 0.4022497 0.4573138 0.4965426 0.5199360 0.5274941
[5,] 0.3870073 0.3923099 0.3961341 0.3984797 0.3993468 0.3987353 0.3966454
[6,] 0.4415457 0.4398624 0.4448578 0.4565321 0.4748852 0.4999172 0.5316280
[7,] 0.6494271 0.7005069 0.7368667 0.7585065 0.7654263 0.7576261 0.7351058
[8,] 0.2678789 0.4092905 0.5293277 0.6279905 0.7052788 0.7611928 0.7957323
[9,] 0.8276012 0.6988633 0.5909116 0.5037460 0.4373666 0.3917733 0.3669662
[10,] 0.2734095 0.3287154 0.3732888 0.4071296 0.4302379 0.4426136 0.4442568
[11,] 0.7067003 0.6733697 0.6371141 0.5979336 0.5558281 0.5107977 0.4628423
[12,] 0.6468009 0.6333540 0.6172726 0.5985567 0.5772063 0.5532215 0.5266022
[13,] 0.6229271 0.4903306 0.3868009 0.3123380 0.2669420 0.2506129 0.2633505
[14,] 0.7804757 0.6025022 0.4659421 0.3707957 0.3170628 0.3047434 0.3338376
[15,] 0.5372785 0.5440801 0.5512551 0.5588037 0.5667258 0.5750214 0.5836906
[16,] 0.5013464 0.5036127 0.5047694 0.5048166 0.5037541 0.5015821 0.4983005
[17,] 0.5460831 0.4624884 0.4048284 0.3731029 0.3673121 0.3874560 0.4335345
[18,] 0.5480458 0.5029981 0.4719137 0.4547927 0.4516351 0.4624410 0.4872102
[19,] 0.4162956 0.4606763 0.5027045 0.5423802 0.5797034 0.6146741 0.6472923
[20,] 0.6518674 0.7021354 0.7266532 0.7254208 0.6984382 0.6457054 0.5672225
[21,] 0.5286249 0.4829131 0.4517111 0.4350188 0.4328364 0.4451637 0.4720008
[22,] 0.7234143 0.6394482 0.5709830 0.5180188 0.4805556 0.4585933 0.4521321
[23,] 0.6418424 0.5766360 0.5195884 0.4706996 0.4299696 0.3973983 0.3729859
[24,] 0.5947669 0.5404334 0.4945341 0.4570690 0.4280382 0.4074417 0.3952794
[25,] 0.3747970 0.4597101 0.5219060 0.5613847 0.5781462 0.5721905 0.5435176
[,8] [,9] [,10]
[1,] 0.4128944 0.4684245 0.5346522
[2,] 0.7532770 0.6295517 0.4559333
[3,] 0.4784950 0.4536377 0.4265835
[4,] 0.5192169 0.4951043 0.4551564
[5,] 0.3930770 0.3880300 0.3815046
[6,] 0.5700176 0.6150861 0.6668334
[7,] 0.6978655 0.6459053 0.5792249
[8,] 0.8088974 0.8006881 0.7711043
[9,] 0.3629452 0.3797104 0.4172618
[10,] 0.4351675 0.4153456 0.3847912
[11,] 0.4119620 0.3581567 0.3014265
[12,] 0.4973484 0.4654601 0.4309373
[13,] 0.3051550 0.3760264 0.4759646
[14,] 0.4043454 0.5162667 0.6696015
[15,] 0.5927333 0.6021495 0.6119392
[16,] 0.4939092 0.4884084 0.4817981
[17,] 0.5055476 0.6034954 0.7273778
[18,] 0.5259428 0.5786388 0.6452983
[19,] 0.6775579 0.7054711 0.7310317
[20,] 0.4629893 0.3330060 0.1772725
[21,] 0.5133478 0.5692045 0.6395710
[22,] 0.4611719 0.4857126 0.5257543
[23,] 0.3567321 0.3486372 0.3487010
[24,] 0.3915514 0.3962576 0.4093980
[25,] 0.4921276 0.4180203 0.3211958
[[2]]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.6062311 0.5783754 0.5524881 0.5285691 0.5066186 0.4866364 0.4686226
[2,] 0.6307970 0.6022637 0.5770370 0.5551169 0.5365034 0.5211965 0.5091962
[3,] 0.4873021 0.4986888 0.5096592 0.5202134 0.5303514 0.5400731 0.5493786
[4,] 0.4311773 0.4449636 0.4570662 0.4674851 0.4762203 0.4832718 0.4886396
[5,] 0.7072156 0.6502404 0.5983683 0.5515991 0.5099330 0.4733699 0.4419098
[6,] 0.4650037 0.5015018 0.5338624 0.5620857 0.5861715 0.6061200 0.6219312
[7,] 0.2116793 0.2960563 0.3713926 0.4376882 0.4949431 0.5431574 0.5823310
[8,] 0.2677710 0.3077712 0.3441973 0.3770491 0.4063267 0.4320300 0.4541591
[9,] 0.5498281 0.5402742 0.5316903 0.5240765 0.5174328 0.5117591 0.5070556
[10,] 0.7557775 0.6668579 0.5867840 0.5155558 0.4531734 0.3996366 0.3549456
[11,] 0.3784137 0.4001903 0.4201044 0.4381560 0.4543451 0.4686718 0.4811360
[12,] 0.8184529 0.7600149 0.7061896 0.6569772 0.6123775 0.5723906 0.5370165
[13,] 0.3666109 0.4242456 0.4769814 0.5248186 0.5677570 0.6057967 0.6389376
[14,] 0.4563043 0.5030992 0.5450554 0.5821729 0.6144516 0.6418916 0.6644928
[15,] 0.4834991 0.5072237 0.5293243 0.5498009 0.5686536 0.5858823 0.6014870
[16,] 0.7010682 0.6635501 0.6289376 0.5972310 0.5684301 0.5425349 0.5195455
[17,] 0.3832942 0.3782014 0.3748108 0.3731223 0.3731360 0.3748518 0.3782698
[18,] 0.3934434 0.4235435 0.4507814 0.4751573 0.4966712 0.5153230 0.5311127
[19,] 0.6153849 0.6141778 0.6114890 0.6073186 0.6016664 0.5945326 0.5859172
[20,] 0.6064385 0.6247422 0.6406752 0.6542376 0.6654294 0.6742505 0.6807011
[21,] 0.3397964 0.3959800 0.4464978 0.4913498 0.5305359 0.5640562 0.5919107
[22,] 0.5816670 0.5726906 0.5639943 0.5555779 0.5474416 0.5395852 0.5320088
[23,] 0.6198225 0.5955773 0.5731267 0.5524708 0.5336095 0.5165427 0.5012706
[24,] 0.4933785 0.5241401 0.5504250 0.5722333 0.5895649 0.6024199 0.6107981
[25,] 0.6043914 0.5581947 0.5170571 0.4809785 0.4499590 0.4239985 0.4030971
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 0.4525772 0.4385001 0.4263914 0.4162511 0.4080792 0.4018757 0.3976405
[2,] 0.5005026 0.4951155 0.4930351 0.4942612 0.4987940 0.5066334 0.5177794
[3,] 0.5582678 0.5667408 0.5747976 0.5824381 0.5896624 0.5964705 0.6028623
[4,] 0.4923238 0.4943242 0.4946410 0.4932741 0.4902235 0.4854892 0.4790713
[5,] 0.4155527 0.3942987 0.3781477 0.3670997 0.3611547 0.3603127 0.3645738
[6,] 0.6336049 0.6411413 0.6445402 0.6438018 0.6389261 0.6299129 0.6167624
[7,] 0.6124640 0.6335563 0.6456079 0.6486188 0.6425891 0.6275187 0.6034076
[8,] 0.4727140 0.4876947 0.4991011 0.5069333 0.5111912 0.5118750 0.5089845
[9,] 0.5033221 0.5005587 0.4987653 0.4979420 0.4980889 0.4992057 0.5012927
[10,] 0.3191003 0.2921007 0.2739468 0.2646386 0.2641762 0.2725594 0.2897884
[11,] 0.4917377 0.5004769 0.5073537 0.5123679 0.5155197 0.5168090 0.5162359
[12,] 0.5062552 0.4801067 0.4585710 0.4416480 0.4293379 0.4216406 0.4185560
[13,] 0.6671799 0.6905234 0.7089681 0.7225142 0.7311615 0.7349100 0.7337599
[14,] 0.6822554 0.6951792 0.7032642 0.7065105 0.7049181 0.6984870 0.6872171
[15,] 0.6154677 0.6278244 0.6385572 0.6476660 0.6551507 0.6610116 0.6652484
[16,] 0.4994618 0.4822839 0.4680117 0.4566453 0.4481846 0.4426297 0.4399805
[17,] 0.3833899 0.3902122 0.3987366 0.4089632 0.4208919 0.4345227 0.4498558
[18,] 0.5440403 0.5541059 0.5613095 0.5656510 0.5671304 0.5657477 0.5615030
[19,] 0.5758200 0.5642412 0.5511807 0.5366386 0.5206147 0.5031092 0.4841220
[20,] 0.6847810 0.6864903 0.6858289 0.6827970 0.6773944 0.6696212 0.6594774
[21,] 0.6140993 0.6306221 0.6414791 0.6466703 0.6461956 0.6400551 0.6282488
[22,] 0.5247125 0.5176961 0.5109597 0.5045033 0.4983268 0.4924304 0.4868140
[23,] 0.4877931 0.4761102 0.4662219 0.4581282 0.4518291 0.4473246 0.4446148
[24,] 0.6146997 0.6141247 0.6090730 0.5995446 0.5855395 0.5670578 0.5440994
[25,] 0.3872547 0.3764713 0.3707470 0.3700818 0.3744756 0.3839284 0.3984403
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] 0.3953737 0.3950753 0.3967453 0.4003836 0.4059904 0.4135655 0.4231089
[2,] 0.5322321 0.5499913 0.5710571 0.5954296 0.6231087 0.6540943 0.6883866
[3,] 0.6088379 0.6143973 0.6195404 0.6242672 0.6285779 0.6324723 0.6359504
[4,] 0.4709696 0.4611843 0.4497152 0.4365625 0.4217261 0.4052061 0.3870023
[5,] 0.3739378 0.3884049 0.4079751 0.4326482 0.4624243 0.4973035 0.5372857
[6,] 0.5994745 0.5780492 0.5524865 0.5227864 0.4889490 0.4509742 0.4088620
[7,] 0.5702558 0.5280634 0.4768303 0.4165566 0.3472422 0.2688871 0.1814913
[8,] 0.5025197 0.4924808 0.4788676 0.4616801 0.4409185 0.4165826 0.3886724
[9,] 0.5043497 0.5083769 0.5133741 0.5193413 0.5262787 0.5341861 0.5430636
[10,] 0.3158631 0.3507835 0.3945496 0.4471615 0.5086190 0.5789223 0.6580713
[11,] 0.5138002 0.5095021 0.5033415 0.4953184 0.4854328 0.4736848 0.4600743
[12,] 0.4200843 0.4262253 0.4369792 0.4523458 0.4723252 0.4969174 0.5261224
[13,] 0.7277110 0.7167634 0.7009170 0.6801719 0.6545281 0.6239856 0.5885443
[14,] 0.6711085 0.6501611 0.6243750 0.5937502 0.5582867 0.5179844 0.4728433
[15,] 0.6678612 0.6688501 0.6682150 0.6659559 0.6620728 0.6565657 0.6494347
[16,] 0.4402371 0.4433994 0.4494674 0.4584412 0.4703208 0.4851061 0.5027971
[17,] 0.4668909 0.4856282 0.5060677 0.5282093 0.5520531 0.5775990 0.6048471
[18,] 0.5543963 0.5444275 0.5315966 0.5159036 0.4973486 0.4759316 0.4516525
[19,] 0.4636532 0.4417026 0.4182704 0.3933565 0.3669610 0.3390837 0.3097248
[20,] 0.6469630 0.6320779 0.6148222 0.5951959 0.5731990 0.5488315 0.5220933
[21,] 0.6107767 0.5876387 0.5588349 0.5243652 0.4842298 0.4384285 0.3869614
[22,] 0.4814775 0.4764211 0.4716446 0.4671481 0.4629316 0.4589951 0.4553386
[23,] 0.4436995 0.4445789 0.4472528 0.4517214 0.4579845 0.4660423 0.4758947
[24,] 0.5166643 0.4847526 0.4483642 0.4074991 0.3621573 0.3123389 0.2580439
[25,] 0.4180112 0.4426412 0.4723302 0.5070783 0.5468854 0.5917516 0.6416768
[,22]
[1,] 0.43462080
[2,] 0.72598551
[3,] 0.63901236
[4,] 0.36711484
[5,] 0.58237092
[6,] 0.36261238
[7,] 0.08505487
[8,] 0.35718808
[9,] 0.55291115
[10,] 0.74606600
[11,] 0.44460131
[12,] 0.55994024
[13,] 0.54820428
[14,] 0.42286358
[15,] 0.64067963
[16,] 0.52339393
[17,] 0.63379726
[18,] 0.42451127
[19,] 0.27888426
[20,] 0.49298452
[21,] 0.32982844
[22,] 0.45196211
[23,] 0.48754169
[24,] 0.19927211
[25,] 0.69666106
> # functional inputs
> tcrossprod(m1@preMats$L) # training auto-covariance matrix
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1.6404312 1.353128 1.408579 1.426799 1.337221 1.504611 1.277256 1.324538
[2,] 1.3531276 1.640431 1.493215 1.405305 1.275562 1.431691 1.389219 1.473720
[3,] 1.4085786 1.493215 1.640431 1.517888 1.440802 1.477615 1.409830 1.431642
[4,] 1.4267986 1.405305 1.517888 1.640431 1.552524 1.397232 1.362230 1.493991
[5,] 1.3372209 1.275562 1.440802 1.552524 1.640431 1.221276 1.148343 1.301065
[6,] 1.5046113 1.431691 1.477615 1.397232 1.221276 1.640431 1.475849 1.441653
[7,] 1.2772563 1.389219 1.409830 1.362230 1.148343 1.475849 1.640431 1.504811
[8,] 1.3245375 1.473720 1.431642 1.493991 1.301065 1.441653 1.504811 1.640431
[9,] 1.3340750 1.317342 1.552564 1.489639 1.505073 1.364151 1.338750 1.356668
[10,] 1.2839967 1.254791 1.363113 1.484974 1.594336 1.168538 1.085179 1.288684
[11,] 1.4134142 1.351688 1.439762 1.316637 1.197134 1.507062 1.454748 1.356971
[12,] 1.4142625 1.420078 1.462590 1.368550 1.332386 1.474275 1.389749 1.368481
[13,] 1.3031886 1.212329 1.469310 1.358100 1.286829 1.431855 1.269642 1.269313
[14,] 1.2383815 1.194134 1.459248 1.389662 1.341036 1.392554 1.311023 1.311672
[15,] 1.1292851 1.302382 1.461720 1.415293 1.376802 1.301206 1.288928 1.389307
[16,] 1.4153246 1.329613 1.345673 1.272679 1.192758 1.490919 1.333129 1.321976
[17,] 1.3776469 1.267095 1.358217 1.323436 1.242380 1.431508 1.295576 1.389289
[18,] 1.3206882 1.293891 1.428691 1.409326 1.303663 1.465748 1.404893 1.450479
[19,] 1.2158968 1.271803 1.329034 1.404245 1.319626 1.356409 1.366254 1.455045
[20,] 0.9988652 1.176824 1.337056 1.267145 1.254139 1.178846 1.234981 1.213783
[21,] 1.2596179 1.162631 1.241131 1.169223 1.018902 1.459400 1.330685 1.279480
[22,] 1.2606114 1.202205 1.302843 1.225537 1.158141 1.392095 1.318668 1.283076
[23,] 1.2382875 1.157319 1.302221 1.274866 1.262531 1.303545 1.231043 1.243909
[24,] 1.1443856 1.075254 1.259220 1.296337 1.246537 1.267583 1.257796 1.249411
[25,] 1.0574377 1.137962 1.229070 1.283294 1.323806 1.105924 1.095492 1.242143
[,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
[1,] 1.334075 1.283997 1.413414 1.414262 1.303189 1.238381 1.129285 1.415325
[2,] 1.317342 1.254791 1.351688 1.420078 1.212329 1.194134 1.302382 1.329613
[3,] 1.552564 1.363113 1.439762 1.462590 1.469310 1.459248 1.461720 1.345673
[4,] 1.489639 1.484974 1.316637 1.368550 1.358100 1.389662 1.415293 1.272679
[5,] 1.505073 1.594336 1.197134 1.332386 1.286829 1.341036 1.376802 1.192758
[6,] 1.364151 1.168538 1.507062 1.474275 1.431855 1.392554 1.301206 1.490919
[7,] 1.338750 1.085179 1.454748 1.389749 1.269642 1.311023 1.288928 1.333129
[8,] 1.356668 1.288684 1.356971 1.368481 1.269313 1.311672 1.389307 1.321976
[9,] 1.640431 1.448521 1.424030 1.481892 1.477430 1.530941 1.489119 1.349729
[10,] 1.448521 1.640431 1.182072 1.336698 1.244488 1.285491 1.367842 1.225398
[11,] 1.424030 1.182072 1.640431 1.532287 1.407465 1.349524 1.264224 1.545914
[12,] 1.481892 1.336698 1.532287 1.640431 1.377762 1.389749 1.356222 1.578841
[13,] 1.477430 1.244488 1.407465 1.377762 1.640431 1.580239 1.450506 1.365046
[14,] 1.530941 1.285491 1.349524 1.389749 1.580239 1.640431 1.531764 1.326714
[15,] 1.489119 1.367842 1.264224 1.356222 1.450506 1.531764 1.640431 1.260366
[16,] 1.349729 1.225398 1.545914 1.578841 1.365046 1.326714 1.260366 1.640431
[17,] 1.390914 1.296584 1.486160 1.463380 1.424465 1.389984 1.332307 1.528379
[18,] 1.475670 1.316968 1.481134 1.492355 1.511995 1.532986 1.487424 1.507368
[19,] 1.402312 1.337723 1.315601 1.447024 1.336316 1.445683 1.479857 1.411409
[20,] 1.426565 1.246247 1.258295 1.350287 1.361522 1.443288 1.522149 1.244691
[21,] 1.237753 1.034840 1.476401 1.397187 1.404506 1.356310 1.242020 1.529027
[22,] 1.382343 1.190010 1.505391 1.526358 1.400678 1.405954 1.321870 1.575300
[23,] 1.432929 1.306267 1.444638 1.497900 1.416887 1.423449 1.355282 1.515421
[24,] 1.403982 1.252758 1.339291 1.386425 1.416308 1.479417 1.402836 1.388656
[25,] 1.364156 1.428247 1.225135 1.338257 1.259366 1.310255 1.427827 1.306598
[,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
[1,] 1.377647 1.320688 1.215897 0.9988652 1.259618 1.260611 1.238288 1.144386
[2,] 1.267095 1.293891 1.271803 1.1768238 1.162631 1.202205 1.157319 1.075254
[3,] 1.358217 1.428691 1.329034 1.3370558 1.241131 1.302843 1.302221 1.259220
[4,] 1.323436 1.409326 1.404245 1.2671455 1.169223 1.225537 1.274866 1.296337
[5,] 1.242380 1.303663 1.319626 1.2541389 1.018902 1.158141 1.262531 1.246537
[6,] 1.431508 1.465748 1.356409 1.1788462 1.459400 1.392095 1.303545 1.267583
[7,] 1.295576 1.404893 1.366254 1.2349815 1.330685 1.318668 1.231043 1.257796
[8,] 1.389289 1.450479 1.455045 1.2137829 1.279480 1.283076 1.243909 1.249411
[9,] 1.390914 1.475670 1.402312 1.4265653 1.237753 1.382343 1.432929 1.403982
[10,] 1.296584 1.316968 1.337723 1.2462475 1.034840 1.190010 1.306267 1.252758
[11,] 1.486160 1.481134 1.315601 1.2582954 1.476401 1.505391 1.444638 1.339291
[12,] 1.463380 1.492355 1.447024 1.3502869 1.397187 1.526358 1.497900 1.386425
[13,] 1.424465 1.511995 1.336316 1.3615224 1.404506 1.400678 1.416887 1.416308
[14,] 1.389984 1.532986 1.445683 1.4432878 1.356310 1.405954 1.423449 1.479417
[15,] 1.332307 1.487424 1.479857 1.5221489 1.242020 1.321870 1.355282 1.402836
[16,] 1.528379 1.507368 1.411409 1.2446906 1.529027 1.575300 1.515421 1.388656
[17,] 1.640431 1.566621 1.411995 1.2063658 1.502863 1.530833 1.499653 1.389719
[18,] 1.566621 1.640431 1.551825 1.4088072 1.529600 1.563333 1.549505 1.542463
[19,] 1.411995 1.551825 1.640431 1.4250179 1.377721 1.463112 1.468441 1.523597
[20,] 1.206366 1.408807 1.425018 1.6404312 1.212606 1.348407 1.407139 1.465897
[21,] 1.502863 1.529600 1.377721 1.2126060 1.640431 1.556620 1.461930 1.424798
[22,] 1.530833 1.563333 1.463112 1.3484067 1.556620 1.640431 1.598463 1.517011
[23,] 1.499653 1.549505 1.468441 1.4071394 1.461930 1.598463 1.640431 1.569898
[24,] 1.389719 1.542463 1.523597 1.4658974 1.424798 1.517011 1.569898 1.640431
[25,] 1.370233 1.439172 1.452190 1.4373175 1.224460 1.392564 1.497675 1.462687
[,25]
[1,] 1.057438
[2,] 1.137962
[3,] 1.229070
[4,] 1.283294
[5,] 1.323806
[6,] 1.105924
[7,] 1.095492
[8,] 1.242143
[9,] 1.364156
[10,] 1.428247
[11,] 1.225135
[12,] 1.338257
[13,] 1.259366
[14,] 1.310255
[15,] 1.427827
[16,] 1.306598
[17,] 1.370233
[18,] 1.439172
[19,] 1.452190
[20,] 1.437318
[21,] 1.224460
[22,] 1.392564
[23,] 1.497675
[24,] 1.462687
[25,] 1.640431
>
>
> # making predictions based on a funGp model________________________________________________
> # building the model
> set.seed(100)
> n.tr <- 25
> sIn <- expand.grid(x1 = seq(0,1,length = sqrt(n.tr)), x2 = seq(0,1,length = sqrt(n.tr)))
> fIn <- list(f1 = matrix(runif(n.tr*10), ncol = 10), f2 = matrix(runif(n.tr*22), ncol = 22))
> sOut <- fgp_BB3(sIn, fIn, n.tr)
> m1 <- fgpm(sIn = sIn, fIn = fIn, sOut = sOut)
** Presampling...
** Optimising hyperparameters...
final value 2.841058
converged
** Hyperparameters done!
>
> # generating input data for prediction
> n.pr <- 100
> sIn.pr <- as.matrix(expand.grid(x1 = seq(0,1,length = sqrt(n.pr)),
+ x2 = seq(0,1,length = sqrt(n.pr))))
> fIn.pr <- list(f1 = matrix(runif(n.pr*10), ncol = 10), matrix(runif(n.pr*22), ncol = 22))
>
> # making predictions
> m1.preds <- predict(m1, sIn.pr = sIn.pr, fIn.pr = fIn.pr)
>
> # plotting predictions
> plotPreds(m1, preds = m1.preds)
Error in assign("last.warning", NULL, envir = baseenv()) :
cannot add binding of 'last.warning' to the base environment
Calls: plotPreds -> plotPreds -> .local -> plotPreds.fgpm -> assign
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64
Version: 0.2.1
Check: examples
Result: ERROR
Running examples in ‘funGp-Ex.R’ failed
The error most likely occurred in:
> ### Name: fgpm
> ### Title: Gaussian process models for scalar and functional inputs
> ### Aliases: fgpm
>
> ### ** Examples
>
> # creating funGp model using default fgpm arguments________________________________________
> # generating input data for training
> set.seed(100)
> n.tr <- 25
> sIn <- expand.grid(x1 = seq(0,1,length = sqrt(n.tr)), x2 = seq(0,1,length = sqrt(n.tr)))
> fIn <- list(f1 = matrix(runif(n.tr*10), ncol = 10), f2 = matrix(runif(n.tr*22), ncol = 22))
>
> # generating output data for training
> sOut <- fgp_BB3(sIn, fIn, n.tr)
>
> # building a scalar-input funGp model
> ms <- fgpm(sIn = sIn, sOut = sOut)
** Presampling...
** Optimising hyperparameters...
iter 10 value 26.756350
final value 26.743784
converged
** Hyperparameters done!
>
> # building a functional-input funGp model
> mf <- fgpm(fIn = fIn, sOut = sOut)
** Presampling...
** Optimising hyperparameters...
final value 40.291940
converged
** Hyperparameters done!
>
> # building a hybrid-input funGp model
> msf <- fgpm(sIn = sIn, fIn = fIn, sOut = sOut)
** Presampling...
** Optimising hyperparameters...
final value 2.841058
converged
** Hyperparameters done!
>
> # plotting the three models
> plotLOO(ms)
> plotLOO(mf)
> plotLOO(msf)
>
> # printing the three models
> ms # equivalent to show(ms)
Gaussian Process Model__
* Scalar inputs: 2
* Functional inputs: 0
* Total data points: 25
* Trained with: 25
* Kernel type: matern5_2
* Hyperparameters:
-> variance: 4.0588
-> length-scale:
ls(X1): 0.7555
ls(X2): 0.2091
________________________> mf # equivalent to show(mf)
Gaussian Process Model____________________________________
* Scalar inputs: 0
* Functional inputs: 2
| Input | Orig. dim | Proj. dim | Basis | Distance |
|:-----:|:---------:|:---------:|:---------:|:----------:|
| F1 | 10 | 3 | B-splines | L2_bygroup |
| F2 | 22 | 3 | B-splines | L2_bygroup |
* Total data points: 25
* Trained with: 25
* Kernel type: matern5_2
* Hyperparameters:
-> variance: 5.0745
-> length-scale:
ls(F1): 0.4093
ls(F2): 3.0370
__________________________________________________________> msf # equivalent to show(msf)
Gaussian Process Model____________________________________
* Scalar inputs: 2
* Functional inputs: 2
| Input | Orig. dim | Proj. dim | Basis | Distance |
|:-----:|:---------:|:---------:|:---------:|:----------:|
| F1 | 10 | 3 | B-splines | L2_bygroup |
| F2 | 22 | 3 | B-splines | L2_bygroup |
* Total data points: 25
* Trained with: 25
* Kernel type: matern5_2
* Hyperparameters:
-> variance: 1.6404
-> length-scale:
ls(X1): 2.0000
ls(X2): 2.0000
ls(F1): 2.5804
ls(F2): 3.0370
__________________________________________________________>
>
> # recovering useful information from a funGp model_________________________________________
> # building the model
> set.seed(100)
> n.tr <- 25
> sIn <- expand.grid(x1 = seq(0,1,length = sqrt(n.tr)), x2 = seq(0,1,length = sqrt(n.tr)))
> fIn <- list(f1 = matrix(runif(n.tr*10), ncol = 10), f2 = matrix(runif(n.tr*22), ncol = 22))
> sOut <- fgp_BB3(sIn, fIn, n.tr)
> m1 <- fgpm(sIn = sIn, fIn = fIn, sOut = sOut)
** Presampling...
** Optimising hyperparameters...
final value 2.841058
converged
** Hyperparameters done!
>
> # recovering data from model slots
> m1@f_proj@coefs # list of projection coefficients for the functional inputs
[[1]]
[,1] [,2] [,3]
[1,] 0.3237129 0.21255838 0.5346522
[2,] 0.2223468 1.34947571 0.4559333
[3,] 0.5909821 0.55327055 0.4265835
[4,] 0.2446155 0.67055154 0.4551564
[5,] 0.3870073 0.41419577 0.3815046
[6,] 0.4415457 0.41894317 0.6668334
[7,] 0.6494271 0.91240638 0.5792249
[8,] 0.2678789 0.95232360 0.7711043
[9,] 0.8276012 0.20151182 0.4172618
[10,] 0.2734095 0.54643429 0.3847912
[11,] 0.7067003 0.56329358 0.3014265
[12,] 0.6468009 0.59221739 0.4309373
[13,] 0.6229271 -0.03915762 0.4759646
[14,] 0.7804757 -0.11358585 0.6696015
[15,] 0.5372785 0.56704494 0.6119392
[16,] 0.5013464 0.51404137 0.4817981
[17,] 0.5460831 0.11155402 0.7273778
[18,] 0.5480458 0.31391318 0.6452983
[19,] 0.4162956 0.62130199 0.7310317
[20,] 0.6518674 0.93601117 0.1772725
[21,] 0.5286249 0.29027465 0.6395710
[22,] 0.7234143 0.31068935 0.5257543
[23,] 0.6418424 0.33005655 0.3487010
[24,] 0.5947669 0.33128887 0.4093980
[25,] 0.3747970 0.80801968 0.3211958
[[2]]
[,1] [,2] [,3]
[1,] 0.6062311 0.30341235 0.43462080
[2,] 0.6307970 0.31383736 0.72598551
[3,] 0.4873021 0.60904740 0.63901236
[4,] 0.4311773 0.58477244 0.36711484
[5,] 0.7072156 0.08218531 0.58237092
[6,] 0.4650037 0.86995420 0.36261238
[7,] 0.2116793 1.14510095 0.08505487
[8,] 0.2677710 0.70653857 0.35718808
[9,] 0.5498281 0.44441875 0.55291115
[10,] 0.7557775 -0.22431841 0.74606600
[11,] 0.3784137 0.61684573 0.44460131
[12,] 0.8184529 0.18063618 0.55994024
[13,] 0.3666109 0.99749267 0.54820428
[14,] 0.4563043 0.97305441 0.42286358
[15,] 0.4834991 0.74113335 0.64067963
[16,] 0.7010682 0.29187240 0.52339393
[17,] 0.3832942 0.32088375 0.63379726
[18,] 0.3934434 0.72451950 0.42451127
[19,] 0.6153849 0.61048910 0.27888426
[20,] 0.6064385 0.81107253 0.49298452
[21,] 0.3397964 0.95946994 0.32982844
[22,] 0.5816670 0.48594555 0.45196211
[23,] 0.6198225 0.35582662 0.48754169
[24,] 0.4933785 0.83987795 0.19927211
[25,] 0.6043914 0.09276635 0.69666106
> m1@f_proj@basis # list of projection basis functions for the functional inputs
[[1]]
[,1] [,2] [,3]
[1,] 1.00000000 0.0000000 0.00000000
[2,] 0.79012346 0.1975309 0.01234568
[3,] 0.60493827 0.3456790 0.04938272
[4,] 0.44444444 0.4444444 0.11111111
[5,] 0.30864198 0.4938272 0.19753086
[6,] 0.19753086 0.4938272 0.30864198
[7,] 0.11111111 0.4444444 0.44444444
[8,] 0.04938272 0.3456790 0.60493827
[9,] 0.01234568 0.1975309 0.79012346
[10,] 0.00000000 0.0000000 1.00000000
[[2]]
[,1] [,2] [,3]
[1,] 1.000000000 0.00000000 0.000000000
[2,] 0.907029478 0.09070295 0.002267574
[3,] 0.818594104 0.17233560 0.009070295
[4,] 0.734693878 0.24489796 0.020408163
[5,] 0.655328798 0.30839002 0.036281179
[6,] 0.580498866 0.36281179 0.056689342
[7,] 0.510204082 0.40816327 0.081632653
[8,] 0.444444444 0.44444444 0.111111111
[9,] 0.383219955 0.47165533 0.145124717
[10,] 0.326530612 0.48979592 0.183673469
[11,] 0.274376417 0.49886621 0.226757370
[12,] 0.226757370 0.49886621 0.274376417
[13,] 0.183673469 0.48979592 0.326530612
[14,] 0.145124717 0.47165533 0.383219955
[15,] 0.111111111 0.44444444 0.444444444
[16,] 0.081632653 0.40816327 0.510204082
[17,] 0.056689342 0.36281179 0.580498866
[18,] 0.036281179 0.30839002 0.655328798
[19,] 0.020408163 0.24489796 0.734693878
[20,] 0.009070295 0.17233560 0.818594104
[21,] 0.002267574 0.09070295 0.907029478
[22,] 0.000000000 0.00000000 1.000000000
> Map(function(a, b) a %*% t(b), m1@f_proj@coefs, m1@f_proj@basis) # list of projected
[[1]]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.3237129 0.3043607 0.2957059 0.2977486 0.3104888 0.3339265 0.3680617
[2,] 0.2223468 0.4478734 0.6235068 0.7492470 0.8250942 0.8510483 0.8271092
[3,] 0.5909821 0.5815033 0.5698275 0.5559549 0.5398853 0.5216188 0.5011554
[4,] 0.2446155 0.3313503 0.4022497 0.4573138 0.4965426 0.5199360 0.5274941
[5,] 0.3870073 0.3923099 0.3961341 0.3984797 0.3993468 0.3987353 0.3966454
[6,] 0.4415457 0.4398624 0.4448578 0.4565321 0.4748852 0.4999172 0.5316280
[7,] 0.6494271 0.7005069 0.7368667 0.7585065 0.7654263 0.7576261 0.7351058
[8,] 0.2678789 0.4092905 0.5293277 0.6279905 0.7052788 0.7611928 0.7957323
[9,] 0.8276012 0.6988633 0.5909116 0.5037460 0.4373666 0.3917733 0.3669662
[10,] 0.2734095 0.3287154 0.3732888 0.4071296 0.4302379 0.4426136 0.4442568
[11,] 0.7067003 0.6733697 0.6371141 0.5979336 0.5558281 0.5107977 0.4628423
[12,] 0.6468009 0.6333540 0.6172726 0.5985567 0.5772063 0.5532215 0.5266022
[13,] 0.6229271 0.4903306 0.3868009 0.3123380 0.2669420 0.2506129 0.2633505
[14,] 0.7804757 0.6025022 0.4659421 0.3707957 0.3170628 0.3047434 0.3338376
[15,] 0.5372785 0.5440801 0.5512551 0.5588037 0.5667258 0.5750214 0.5836906
[16,] 0.5013464 0.5036127 0.5047694 0.5048166 0.5037541 0.5015821 0.4983005
[17,] 0.5460831 0.4624884 0.4048284 0.3731029 0.3673121 0.3874560 0.4335345
[18,] 0.5480458 0.5029981 0.4719137 0.4547927 0.4516351 0.4624410 0.4872102
[19,] 0.4162956 0.4606763 0.5027045 0.5423802 0.5797034 0.6146741 0.6472923
[20,] 0.6518674 0.7021354 0.7266532 0.7254208 0.6984382 0.6457054 0.5672225
[21,] 0.5286249 0.4829131 0.4517111 0.4350188 0.4328364 0.4451637 0.4720008
[22,] 0.7234143 0.6394482 0.5709830 0.5180188 0.4805556 0.4585933 0.4521321
[23,] 0.6418424 0.5766360 0.5195884 0.4706996 0.4299696 0.3973983 0.3729859
[24,] 0.5947669 0.5404334 0.4945341 0.4570690 0.4280382 0.4074417 0.3952794
[25,] 0.3747970 0.4597101 0.5219060 0.5613847 0.5781462 0.5721905 0.5435176
[,8] [,9] [,10]
[1,] 0.4128944 0.4684245 0.5346522
[2,] 0.7532770 0.6295517 0.4559333
[3,] 0.4784950 0.4536377 0.4265835
[4,] 0.5192169 0.4951043 0.4551564
[5,] 0.3930770 0.3880300 0.3815046
[6,] 0.5700176 0.6150861 0.6668334
[7,] 0.6978655 0.6459053 0.5792249
[8,] 0.8088974 0.8006881 0.7711043
[9,] 0.3629452 0.3797104 0.4172618
[10,] 0.4351675 0.4153456 0.3847912
[11,] 0.4119620 0.3581567 0.3014265
[12,] 0.4973484 0.4654601 0.4309373
[13,] 0.3051550 0.3760264 0.4759646
[14,] 0.4043454 0.5162667 0.6696015
[15,] 0.5927333 0.6021495 0.6119392
[16,] 0.4939092 0.4884084 0.4817981
[17,] 0.5055476 0.6034954 0.7273778
[18,] 0.5259428 0.5786388 0.6452983
[19,] 0.6775579 0.7054711 0.7310317
[20,] 0.4629893 0.3330060 0.1772725
[21,] 0.5133478 0.5692045 0.6395710
[22,] 0.4611719 0.4857126 0.5257543
[23,] 0.3567321 0.3486372 0.3487010
[24,] 0.3915514 0.3962576 0.4093980
[25,] 0.4921276 0.4180203 0.3211958
[[2]]
[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,] 0.6062311 0.5783754 0.5524881 0.5285691 0.5066186 0.4866364 0.4686226
[2,] 0.6307970 0.6022637 0.5770370 0.5551169 0.5365034 0.5211965 0.5091962
[3,] 0.4873021 0.4986888 0.5096592 0.5202134 0.5303514 0.5400731 0.5493786
[4,] 0.4311773 0.4449636 0.4570662 0.4674851 0.4762203 0.4832718 0.4886396
[5,] 0.7072156 0.6502404 0.5983683 0.5515991 0.5099330 0.4733699 0.4419098
[6,] 0.4650037 0.5015018 0.5338624 0.5620857 0.5861715 0.6061200 0.6219312
[7,] 0.2116793 0.2960563 0.3713926 0.4376882 0.4949431 0.5431574 0.5823310
[8,] 0.2677710 0.3077712 0.3441973 0.3770491 0.4063267 0.4320300 0.4541591
[9,] 0.5498281 0.5402742 0.5316903 0.5240765 0.5174328 0.5117591 0.5070556
[10,] 0.7557775 0.6668579 0.5867840 0.5155558 0.4531734 0.3996366 0.3549456
[11,] 0.3784137 0.4001903 0.4201044 0.4381560 0.4543451 0.4686718 0.4811360
[12,] 0.8184529 0.7600149 0.7061896 0.6569772 0.6123775 0.5723906 0.5370165
[13,] 0.3666109 0.4242456 0.4769814 0.5248186 0.5677570 0.6057967 0.6389376
[14,] 0.4563043 0.5030992 0.5450554 0.5821729 0.6144516 0.6418916 0.6644928
[15,] 0.4834991 0.5072237 0.5293243 0.5498009 0.5686536 0.5858823 0.6014870
[16,] 0.7010682 0.6635501 0.6289376 0.5972310 0.5684301 0.5425349 0.5195455
[17,] 0.3832942 0.3782014 0.3748108 0.3731223 0.3731360 0.3748518 0.3782698
[18,] 0.3934434 0.4235435 0.4507814 0.4751573 0.4966712 0.5153230 0.5311127
[19,] 0.6153849 0.6141778 0.6114890 0.6073186 0.6016664 0.5945326 0.5859172
[20,] 0.6064385 0.6247422 0.6406752 0.6542376 0.6654294 0.6742505 0.6807011
[21,] 0.3397964 0.3959800 0.4464978 0.4913498 0.5305359 0.5640562 0.5919107
[22,] 0.5816670 0.5726906 0.5639943 0.5555779 0.5474416 0.5395852 0.5320088
[23,] 0.6198225 0.5955773 0.5731267 0.5524708 0.5336095 0.5165427 0.5012706
[24,] 0.4933785 0.5241401 0.5504250 0.5722333 0.5895649 0.6024199 0.6107981
[25,] 0.6043914 0.5581947 0.5170571 0.4809785 0.4499590 0.4239985 0.4030971
[,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 0.4525772 0.4385001 0.4263914 0.4162511 0.4080792 0.4018757 0.3976405
[2,] 0.5005026 0.4951155 0.4930351 0.4942612 0.4987940 0.5066334 0.5177794
[3,] 0.5582678 0.5667408 0.5747976 0.5824381 0.5896624 0.5964705 0.6028623
[4,] 0.4923238 0.4943242 0.4946410 0.4932741 0.4902235 0.4854892 0.4790713
[5,] 0.4155527 0.3942987 0.3781477 0.3670997 0.3611547 0.3603127 0.3645738
[6,] 0.6336049 0.6411413 0.6445402 0.6438018 0.6389261 0.6299129 0.6167624
[7,] 0.6124640 0.6335563 0.6456079 0.6486188 0.6425891 0.6275187 0.6034076
[8,] 0.4727140 0.4876947 0.4991011 0.5069333 0.5111912 0.5118750 0.5089845
[9,] 0.5033221 0.5005587 0.4987653 0.4979420 0.4980889 0.4992057 0.5012927
[10,] 0.3191003 0.2921007 0.2739468 0.2646386 0.2641762 0.2725594 0.2897884
[11,] 0.4917377 0.5004769 0.5073537 0.5123679 0.5155197 0.5168090 0.5162359
[12,] 0.5062552 0.4801067 0.4585710 0.4416480 0.4293379 0.4216406 0.4185560
[13,] 0.6671799 0.6905234 0.7089681 0.7225142 0.7311615 0.7349100 0.7337599
[14,] 0.6822554 0.6951792 0.7032642 0.7065105 0.7049181 0.6984870 0.6872171
[15,] 0.6154677 0.6278244 0.6385572 0.6476660 0.6551507 0.6610116 0.6652484
[16,] 0.4994618 0.4822839 0.4680117 0.4566453 0.4481846 0.4426297 0.4399805
[17,] 0.3833899 0.3902122 0.3987366 0.4089632 0.4208919 0.4345227 0.4498558
[18,] 0.5440403 0.5541059 0.5613095 0.5656510 0.5671304 0.5657477 0.5615030
[19,] 0.5758200 0.5642412 0.5511807 0.5366386 0.5206147 0.5031092 0.4841220
[20,] 0.6847810 0.6864903 0.6858289 0.6827970 0.6773944 0.6696212 0.6594774
[21,] 0.6140993 0.6306221 0.6414791 0.6466703 0.6461956 0.6400551 0.6282488
[22,] 0.5247125 0.5176961 0.5109597 0.5045033 0.4983268 0.4924304 0.4868140
[23,] 0.4877931 0.4761102 0.4662219 0.4581282 0.4518291 0.4473246 0.4446148
[24,] 0.6146997 0.6141247 0.6090730 0.5995446 0.5855395 0.5670578 0.5440994
[25,] 0.3872547 0.3764713 0.3707470 0.3700818 0.3744756 0.3839284 0.3984403
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] 0.3953737 0.3950753 0.3967453 0.4003836 0.4059904 0.4135655 0.4231089
[2,] 0.5322321 0.5499913 0.5710571 0.5954296 0.6231087 0.6540943 0.6883866
[3,] 0.6088379 0.6143973 0.6195404 0.6242672 0.6285779 0.6324723 0.6359504
[4,] 0.4709696 0.4611843 0.4497152 0.4365625 0.4217261 0.4052061 0.3870023
[5,] 0.3739378 0.3884049 0.4079751 0.4326482 0.4624243 0.4973035 0.5372857
[6,] 0.5994745 0.5780492 0.5524865 0.5227864 0.4889490 0.4509742 0.4088620
[7,] 0.5702558 0.5280634 0.4768303 0.4165566 0.3472422 0.2688871 0.1814913
[8,] 0.5025197 0.4924808 0.4788676 0.4616801 0.4409185 0.4165826 0.3886724
[9,] 0.5043497 0.5083769 0.5133741 0.5193413 0.5262787 0.5341861 0.5430636
[10,] 0.3158631 0.3507835 0.3945496 0.4471615 0.5086190 0.5789223 0.6580713
[11,] 0.5138002 0.5095021 0.5033415 0.4953184 0.4854328 0.4736848 0.4600743
[12,] 0.4200843 0.4262253 0.4369792 0.4523458 0.4723252 0.4969174 0.5261224
[13,] 0.7277110 0.7167634 0.7009170 0.6801719 0.6545281 0.6239856 0.5885443
[14,] 0.6711085 0.6501611 0.6243750 0.5937502 0.5582867 0.5179844 0.4728433
[15,] 0.6678612 0.6688501 0.6682150 0.6659559 0.6620728 0.6565657 0.6494347
[16,] 0.4402371 0.4433994 0.4494674 0.4584412 0.4703208 0.4851061 0.5027971
[17,] 0.4668909 0.4856282 0.5060677 0.5282093 0.5520531 0.5775990 0.6048471
[18,] 0.5543963 0.5444275 0.5315966 0.5159036 0.4973486 0.4759316 0.4516525
[19,] 0.4636532 0.4417026 0.4182704 0.3933565 0.3669610 0.3390837 0.3097248
[20,] 0.6469630 0.6320779 0.6148222 0.5951959 0.5731990 0.5488315 0.5220933
[21,] 0.6107767 0.5876387 0.5588349 0.5243652 0.4842298 0.4384285 0.3869614
[22,] 0.4814775 0.4764211 0.4716446 0.4671481 0.4629316 0.4589951 0.4553386
[23,] 0.4436995 0.4445789 0.4472528 0.4517214 0.4579845 0.4660423 0.4758947
[24,] 0.5166643 0.4847526 0.4483642 0.4074991 0.3621573 0.3123389 0.2580439
[25,] 0.4180112 0.4426412 0.4723302 0.5070783 0.5468854 0.5917516 0.6416768
[,22]
[1,] 0.43462080
[2,] 0.72598551
[3,] 0.63901236
[4,] 0.36711484
[5,] 0.58237092
[6,] 0.36261238
[7,] 0.08505487
[8,] 0.35718808
[9,] 0.55291115
[10,] 0.74606600
[11,] 0.44460131
[12,] 0.55994024
[13,] 0.54820428
[14,] 0.42286358
[15,] 0.64067963
[16,] 0.52339393
[17,] 0.63379726
[18,] 0.42451127
[19,] 0.27888426
[20,] 0.49298452
[21,] 0.32982844
[22,] 0.45196211
[23,] 0.48754169
[24,] 0.19927211
[25,] 0.69666106
> # functional inputs
> tcrossprod(m1@preMats$L) # training auto-covariance matrix
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1.6404312 1.353128 1.408579 1.426799 1.337221 1.504611 1.277256 1.324538
[2,] 1.3531276 1.640431 1.493215 1.405305 1.275562 1.431691 1.389219 1.473720
[3,] 1.4085786 1.493215 1.640431 1.517888 1.440802 1.477615 1.409830 1.431642
[4,] 1.4267986 1.405305 1.517888 1.640431 1.552524 1.397232 1.362230 1.493991
[5,] 1.3372209 1.275562 1.440802 1.552524 1.640431 1.221276 1.148343 1.301065
[6,] 1.5046113 1.431691 1.477615 1.397232 1.221276 1.640431 1.475849 1.441653
[7,] 1.2772563 1.389219 1.409830 1.362230 1.148343 1.475849 1.640431 1.504811
[8,] 1.3245375 1.473720 1.431642 1.493991 1.301065 1.441653 1.504811 1.640431
[9,] 1.3340750 1.317342 1.552564 1.489639 1.505073 1.364151 1.338750 1.356668
[10,] 1.2839967 1.254791 1.363113 1.484974 1.594336 1.168538 1.085179 1.288684
[11,] 1.4134142 1.351688 1.439762 1.316637 1.197134 1.507062 1.454748 1.356971
[12,] 1.4142625 1.420078 1.462590 1.368550 1.332386 1.474275 1.389749 1.368481
[13,] 1.3031886 1.212329 1.469310 1.358100 1.286829 1.431855 1.269642 1.269313
[14,] 1.2383815 1.194134 1.459248 1.389662 1.341036 1.392554 1.311023 1.311672
[15,] 1.1292851 1.302382 1.461720 1.415293 1.376802 1.301206 1.288928 1.389307
[16,] 1.4153246 1.329613 1.345673 1.272679 1.192758 1.490919 1.333129 1.321976
[17,] 1.3776469 1.267095 1.358217 1.323436 1.242380 1.431508 1.295576 1.389289
[18,] 1.3206882 1.293891 1.428691 1.409326 1.303663 1.465748 1.404893 1.450479
[19,] 1.2158968 1.271803 1.329034 1.404245 1.319626 1.356409 1.366254 1.455045
[20,] 0.9988652 1.176824 1.337056 1.267145 1.254139 1.178846 1.234981 1.213783
[21,] 1.2596179 1.162631 1.241131 1.169223 1.018902 1.459400 1.330685 1.279480
[22,] 1.2606114 1.202205 1.302843 1.225537 1.158141 1.392095 1.318668 1.283076
[23,] 1.2382875 1.157319 1.302221 1.274866 1.262531 1.303545 1.231043 1.243909
[24,] 1.1443856 1.075254 1.259220 1.296337 1.246537 1.267583 1.257796 1.249411
[25,] 1.0574377 1.137962 1.229070 1.283294 1.323806 1.105924 1.095492 1.242143
[,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
[1,] 1.334075 1.283997 1.413414 1.414262 1.303189 1.238381 1.129285 1.415325
[2,] 1.317342 1.254791 1.351688 1.420078 1.212329 1.194134 1.302382 1.329613
[3,] 1.552564 1.363113 1.439762 1.462590 1.469310 1.459248 1.461720 1.345673
[4,] 1.489639 1.484974 1.316637 1.368550 1.358100 1.389662 1.415293 1.272679
[5,] 1.505073 1.594336 1.197134 1.332386 1.286829 1.341036 1.376802 1.192758
[6,] 1.364151 1.168538 1.507062 1.474275 1.431855 1.392554 1.301206 1.490919
[7,] 1.338750 1.085179 1.454748 1.389749 1.269642 1.311023 1.288928 1.333129
[8,] 1.356668 1.288684 1.356971 1.368481 1.269313 1.311672 1.389307 1.321976
[9,] 1.640431 1.448521 1.424030 1.481892 1.477430 1.530941 1.489119 1.349729
[10,] 1.448521 1.640431 1.182072 1.336698 1.244488 1.285491 1.367842 1.225398
[11,] 1.424030 1.182072 1.640431 1.532287 1.407465 1.349524 1.264224 1.545914
[12,] 1.481892 1.336698 1.532287 1.640431 1.377762 1.389749 1.356222 1.578841
[13,] 1.477430 1.244488 1.407465 1.377762 1.640431 1.580239 1.450506 1.365046
[14,] 1.530941 1.285491 1.349524 1.389749 1.580239 1.640431 1.531764 1.326714
[15,] 1.489119 1.367842 1.264224 1.356222 1.450506 1.531764 1.640431 1.260366
[16,] 1.349729 1.225398 1.545914 1.578841 1.365046 1.326714 1.260366 1.640431
[17,] 1.390914 1.296584 1.486160 1.463380 1.424465 1.389984 1.332307 1.528379
[18,] 1.475670 1.316968 1.481134 1.492355 1.511995 1.532986 1.487424 1.507368
[19,] 1.402312 1.337723 1.315601 1.447024 1.336316 1.445683 1.479857 1.411409
[20,] 1.426565 1.246247 1.258295 1.350287 1.361522 1.443288 1.522149 1.244691
[21,] 1.237753 1.034840 1.476401 1.397187 1.404506 1.356310 1.242020 1.529027
[22,] 1.382343 1.190010 1.505391 1.526358 1.400678 1.405954 1.321870 1.575300
[23,] 1.432929 1.306267 1.444638 1.497900 1.416887 1.423449 1.355282 1.515421
[24,] 1.403982 1.252758 1.339291 1.386425 1.416308 1.479417 1.402836 1.388656
[25,] 1.364156 1.428247 1.225135 1.338257 1.259366 1.310255 1.427827 1.306598
[,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
[1,] 1.377647 1.320688 1.215897 0.9988652 1.259618 1.260611 1.238288 1.144386
[2,] 1.267095 1.293891 1.271803 1.1768238 1.162631 1.202205 1.157319 1.075254
[3,] 1.358217 1.428691 1.329034 1.3370558 1.241131 1.302843 1.302221 1.259220
[4,] 1.323436 1.409326 1.404245 1.2671455 1.169223 1.225537 1.274866 1.296337
[5,] 1.242380 1.303663 1.319626 1.2541389 1.018902 1.158141 1.262531 1.246537
[6,] 1.431508 1.465748 1.356409 1.1788462 1.459400 1.392095 1.303545 1.267583
[7,] 1.295576 1.404893 1.366254 1.2349815 1.330685 1.318668 1.231043 1.257796
[8,] 1.389289 1.450479 1.455045 1.2137829 1.279480 1.283076 1.243909 1.249411
[9,] 1.390914 1.475670 1.402312 1.4265653 1.237753 1.382343 1.432929 1.403982
[10,] 1.296584 1.316968 1.337723 1.2462475 1.034840 1.190010 1.306267 1.252758
[11,] 1.486160 1.481134 1.315601 1.2582954 1.476401 1.505391 1.444638 1.339291
[12,] 1.463380 1.492355 1.447024 1.3502869 1.397187 1.526358 1.497900 1.386425
[13,] 1.424465 1.511995 1.336316 1.3615224 1.404506 1.400678 1.416887 1.416308
[14,] 1.389984 1.532986 1.445683 1.4432878 1.356310 1.405954 1.423449 1.479417
[15,] 1.332307 1.487424 1.479857 1.5221489 1.242020 1.321870 1.355282 1.402836
[16,] 1.528379 1.507368 1.411409 1.2446906 1.529027 1.575300 1.515421 1.388656
[17,] 1.640431 1.566621 1.411995 1.2063658 1.502863 1.530833 1.499653 1.389719
[18,] 1.566621 1.640431 1.551825 1.4088072 1.529600 1.563333 1.549505 1.542463
[19,] 1.411995 1.551825 1.640431 1.4250179 1.377721 1.463112 1.468441 1.523597
[20,] 1.206366 1.408807 1.425018 1.6404312 1.212606 1.348407 1.407139 1.465897
[21,] 1.502863 1.529600 1.377721 1.2126060 1.640431 1.556620 1.461930 1.424798
[22,] 1.530833 1.563333 1.463112 1.3484067 1.556620 1.640431 1.598463 1.517011
[23,] 1.499653 1.549505 1.468441 1.4071394 1.461930 1.598463 1.640431 1.569898
[24,] 1.389719 1.542463 1.523597 1.4658974 1.424798 1.517011 1.569898 1.640431
[25,] 1.370233 1.439172 1.452190 1.4373175 1.224460 1.392564 1.497675 1.462687
[,25]
[1,] 1.057438
[2,] 1.137962
[3,] 1.229070
[4,] 1.283294
[5,] 1.323806
[6,] 1.105924
[7,] 1.095492
[8,] 1.242143
[9,] 1.364156
[10,] 1.428247
[11,] 1.225135
[12,] 1.338257
[13,] 1.259366
[14,] 1.310255
[15,] 1.427827
[16,] 1.306598
[17,] 1.370233
[18,] 1.439172
[19,] 1.452190
[20,] 1.437318
[21,] 1.224460
[22,] 1.392564
[23,] 1.497675
[24,] 1.462687
[25,] 1.640431
>
>
> # making predictions based on a funGp model________________________________________________
> # building the model
> set.seed(100)
> n.tr <- 25
> sIn <- expand.grid(x1 = seq(0,1,length = sqrt(n.tr)), x2 = seq(0,1,length = sqrt(n.tr)))
> fIn <- list(f1 = matrix(runif(n.tr*10), ncol = 10), f2 = matrix(runif(n.tr*22), ncol = 22))
> sOut <- fgp_BB3(sIn, fIn, n.tr)
> m1 <- fgpm(sIn = sIn, fIn = fIn, sOut = sOut)
** Presampling...
** Optimising hyperparameters...
final value 2.841058
converged
** Hyperparameters done!
>
> # generating input data for prediction
> n.pr <- 100
> sIn.pr <- as.matrix(expand.grid(x1 = seq(0,1,length = sqrt(n.pr)),
+ x2 = seq(0,1,length = sqrt(n.pr))))
> fIn.pr <- list(f1 = matrix(runif(n.pr*10), ncol = 10), matrix(runif(n.pr*22), ncol = 22))
>
> # making predictions
> m1.preds <- predict(m1, sIn.pr = sIn.pr, fIn.pr = fIn.pr)
>
> # plotting predictions
> plotPreds(m1, preds = m1.preds)
Error in assign("last.warning", NULL, envir = baseenv()) :
cannot add binding of 'last.warning' to the base environment
Calls: plotPreds -> plotPreds -> .local -> plotPreds.fgpm -> assign
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-devel-windows-x86_64-gcc10-UCRT