data.scaler.RdThis dataset contains the means and standard deviations of the 10,000,000 datasets for training Pre-Trained Deep Neural Network (DNN), which can be used to determine the number of factors.
A list containing two vectors, each of length 54:
A numeric vector representing the means of the 54 features extracted from the 10,000,000 datasets.
A numeric vector representing the standard deviations of the 54 features extracted from the 10,000,000 datasets.
data(data.scaler)
print(data.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
#>
data.scaler <- load_scaler()
print(data.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
#>