Load the Scaler for the Pre-Trained Deep Neural Network (DNN)

Loads the scaler object within the EFAfactors package. This object is a list containing a mean vector and a standard deviation vector, which were computed from the 10,000,000 datasets data.datasets used for training the Pre-Trained Deep Neural Network (DNN). It serves as a tool for normalizing features in DNN_predictor.

load_scaler()

Value

scaler objective.

Examples

library(EFAfactors)

scaler <- load_scaler()
print(scaler)
#> $means
#>          F1          F2          F3          F4          F5          F6 
#>  5.30944906  3.12764568  2.61623422  2.23013860  1.92889636  1.66541659 
#>          F7          F8          F9         F10         F11         F12 
#>  1.41336814  1.21937797  0.98545843  0.81144705  2.65555214  1.72245781 
#>         F13         F14         F15         F16         F17         F18 
#>  1.49378423  1.30028456  1.14222512  0.99044045  0.82598154  0.70080639 
#>         F19         F20         F21         F22         F23         F24 
#>  0.52144888  0.39034846  0.10957591  0.17578280  0.22999972  0.24403705 
#>         F25         F26         F27         F28         F29         F30 
#>  0.24966909  0.23364432  0.17975784  0.14605361  0.03995398 -0.03099673 
#>         F31         F32         F33         F34         F35         F36 
#>  0.86496859  0.75373405  0.66129613  0.56386260  0.44239338  0.34976915 
#>         F37         F38         F39         F40         F41         F42 
#>  0.19429303  0.07923934  0.82068124  0.68417412  0.57879501  0.47442970 
#>         F43         F44         F45         F46         F47         F48 
#>  0.34957684  0.25555389  0.09989640 -0.01469139  0.93120246  0.84768835 
#>         F49         F50         F51         F52         F53         F54 
#>  0.76975271  0.68115987  0.56541846  0.47393347  0.32151155  0.20556088 
#> 
#> $sds
#>         F1         F2         F3         F4         F5         F6         F7 
#> 3.22786405 1.57056545 1.36810129 1.37059154 1.40421364 1.52385909 1.76122401 
#>         F8         F9        F10        F11        F12        F13        F14 
#> 1.90333468 2.22116175 2.41365253 1.23676638 0.68215560 0.60219162 0.81037285 
#>        F15        F16        F17        F18        F19        F20        F21 
#> 0.98902550 1.21974457 1.53488654 1.72233781 2.06846959 2.27831324 0.06115850 
#>        F22        F23        F24        F25        F26        F27        F28 
#> 0.09081988 0.11610040 0.57161284 0.81987790 1.08863206 1.42134428 1.62427530 
#>        F29        F30        F31        F32        F33        F34        F35 
#> 1.97261763 2.18873691 0.12781481 0.60300212 0.85464007 1.12466718 1.45859099 
#>        F36        F37        F38        F39        F40        F41        F42 
#> 1.65685046 2.00301430 2.21280649 0.14102631 0.60161477 0.84910277 1.11544100 
#>        F43        F44        F45        F46        F47        F48        F49 
#> 1.44550522 1.64140775 1.98394272 2.19155854 0.05174170 0.59507749 0.85491681 
#>        F50        F51        F52        F53        F54 
#> 1.13195143 1.47248738 1.67491212 2.02679089 2.23997174 
#>