Load the Scaler for the pre-trained Neural Networks for Determining the Number of Factors

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.DNN training the Deep Neural Network (DNN) or the 1,000,000 datasets data.datasets.LSTM training the Long Short Term Memory (LSTM) Network. It serves as a tool for normalizing features in NN.

load.scaler(model = "DNN")

Arguments

model

A character string indicating the model type. Possible values are "DNN" (default) or "LSTM".

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 
#>