Simulation Visualizations
Loss vs log_2(n)
Loss vs log(n) EMPIRICAL_TRAIN

Loss vs log(n) THEORETICAL

Loss vs log(n) EMPIRICAL_TEST

Loss vs log(n) 1 features

Loss vs log(n) 2 features

Loss vs log(n) 3 features

Time consumption(n)
Iterations vs log_2(n)
Iterations vs log(n) EMPIRICAL_TRAIN

Iterations vs log(n) THEORETICAL

Iterations vs log(n) EMPIRICAL_TEST

Iterations vs log(n) 1 features

Iterations vs log(n) 2 features

Iterations vs log(n) 3 features

N* Relationship Matrixes
N* theoretical
| dim |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 1 feature(s) |
NaN |
NaN |
7.1022 |
| 2 feature(s) |
NaN |
NaN |
511.6782 |
| 3 feature(s) |
7.1022 |
511.6782 |
NaN |
N* empirical test
| dim |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 1 feature(s) |
NaN |
NaN |
7.5471 |
| 2 feature(s) |
NaN |
NaN |
451.5564 |
| 3 feature(s) |
7.5471 |
451.5564 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000000 |
0.000000 |
0.000000 |
| 4 |
0.118953 |
0.029484 |
0.009531 |
| 8 |
0.141016 |
0.047835 |
0.029018 |
| 16 |
0.148484 |
0.059250 |
0.046953 |
| 32 |
0.154319 |
0.066858 |
0.058984 |
| 64 |
0.156617 |
0.072348 |
0.068250 |
| 128 |
0.158072 |
0.075086 |
0.072637 |
| 256 |
0.157725 |
0.076871 |
0.075453 |
| 512 |
0.158092 |
0.077393 |
0.076505 |
| 1024 |
0.158658 |
0.078044 |
0.077367 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188731 |
0.149616 |
0.296983 |
| 4 |
0.185450 |
0.139319 |
0.234659 |
| 8 |
0.177809 |
0.119610 |
0.167607 |
| 16 |
0.169977 |
0.101823 |
0.122748 |
| 32 |
0.164718 |
0.091529 |
0.100177 |
| 64 |
0.161593 |
0.085341 |
0.089367 |
| 128 |
0.160153 |
0.082306 |
0.083987 |
| 256 |
0.159389 |
0.080505 |
0.081055 |
| 512 |
0.159020 |
0.079623 |
0.079622 |
| 1024 |
0.158809 |
0.079144 |
0.078889 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.252575 |
0.172077 |
0.464058 |
| 4 |
0.205634 |
0.145481 |
0.284769 |
| 8 |
0.180369 |
0.120396 |
0.173105 |
| 16 |
0.169966 |
0.102151 |
0.123092 |
| 32 |
0.164836 |
0.091385 |
0.100460 |
| 64 |
0.161582 |
0.085388 |
0.089444 |
| 128 |
0.160041 |
0.082241 |
0.084043 |
| 256 |
0.159438 |
0.080571 |
0.080958 |
| 512 |
0.158843 |
0.079627 |
0.079541 |
| 1024 |
0.158577 |
0.079015 |
0.078838 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.155809 |
| THEORETICAL |
0.099189 |
| EMPIRICAL_TEST |
0.183023 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.066505 |
| 2 |
0.058873 |
| 3 |
0.100585 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.050091 |
| 1 |
0.038625 |
| 2 |
0.025505 |
| 3 |
0.018950 |
| 4 |
0.013776 |
| 5 |
0.011685 |
| 6 |
0.011661 |
| 7 |
0.014798 |
| 8 |
0.024902 |
| 9 |
0.054165 |
Iterations Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
11413 |
11413 |
11413 |
| 4 |
16000 |
16000 |
16000 |
| 8 |
11200 |
11200 |
6272 |
| 16 |
8000 |
8000 |
8000 |
| 32 |
5600 |
5600 |
5600 |
| 64 |
4000 |
4000 |
4000 |
| 128 |
2800 |
2800 |
2800 |
| 256 |
2000 |
2000 |
2000 |
| 512 |
1400 |
1400 |
1400 |
| 1024 |
1000 |
1000 |
1000 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
15707 |
22600 |
22600 |
| 4 |
9840 |
16000 |
16000 |
| 8 |
8064 |
6832 |
9128 |
| 16 |
4160 |
5800 |
4880 |
| 32 |
3192 |
4928 |
3528 |
| 64 |
2280 |
2480 |
2040 |
| 128 |
1540 |
1736 |
1442 |
| 256 |
1020 |
1090 |
1040 |
| 512 |
707 |
735 |
714 |
| 1024 |
505 |
505 |
505 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
21922 |
22600 |
22600 |
| 4 |
16000 |
14560 |
16000 |
| 8 |
7336 |
9744 |
11200 |
| 16 |
4920 |
7680 |
6120 |
| 32 |
3108 |
3808 |
2884 |
| 64 |
2240 |
2480 |
2040 |
| 128 |
1540 |
1428 |
1708 |
| 256 |
1090 |
1150 |
1270 |
| 512 |
861 |
805 |
1085 |
| 1024 |
725 |
565 |
565 |