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 |
5.5543 |
| 2 feature(s) |
NaN |
NaN |
69.6505 |
| 3 feature(s) |
5.5543 |
69.6505 |
NaN |
N* empirical test
| dim |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 1 feature(s) |
NaN |
NaN |
5.7887 |
| 2 feature(s) |
NaN |
NaN |
71.5288 |
| 3 feature(s) |
5.7887 |
71.5288 |
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 |
0.009641 |
| 8 |
0.141016 |
0.047835 |
0.026540 |
| 16 |
0.148484 |
0.059250 |
0.043234 |
| 32 |
0.154319 |
0.066858 |
0.055737 |
| 64 |
0.156617 |
0.072348 |
0.064977 |
| 128 |
0.158072 |
0.075086 |
0.069076 |
| 256 |
0.157725 |
0.076871 |
0.071717 |
| 512 |
0.158092 |
0.077393 |
0.072768 |
| 1024 |
0.158658 |
0.078044 |
0.073814 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188731 |
0.149616 |
0.255296 |
| 4 |
0.185450 |
0.139319 |
0.205193 |
| 8 |
0.177809 |
0.119610 |
0.155866 |
| 16 |
0.169977 |
0.101823 |
0.117642 |
| 32 |
0.164718 |
0.091529 |
0.096562 |
| 64 |
0.161593 |
0.085341 |
0.085615 |
| 128 |
0.160153 |
0.082306 |
0.080337 |
| 256 |
0.159389 |
0.080505 |
0.077400 |
| 512 |
0.159020 |
0.079623 |
0.075985 |
| 1024 |
0.158809 |
0.079144 |
0.075291 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.252575 |
0.172077 |
0.347468 |
| 4 |
0.205634 |
0.145481 |
0.230298 |
| 8 |
0.180369 |
0.120396 |
0.158780 |
| 16 |
0.169966 |
0.102151 |
0.117958 |
| 32 |
0.164836 |
0.091385 |
0.096777 |
| 64 |
0.161582 |
0.085388 |
0.085752 |
| 128 |
0.160041 |
0.082241 |
0.080333 |
| 256 |
0.159438 |
0.080571 |
0.077249 |
| 512 |
0.158843 |
0.079627 |
0.075997 |
| 1024 |
0.158577 |
0.079015 |
0.075273 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.134247 |
| THEORETICAL |
0.088662 |
| EMPIRICAL_TEST |
0.169694 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.071670 |
| 2 |
0.063058 |
| 3 |
0.068352 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.049884 |
| 1 |
0.044260 |
| 2 |
0.027033 |
| 3 |
0.017988 |
| 4 |
0.013375 |
| 5 |
0.010644 |
| 6 |
0.009612 |
| 7 |
0.011790 |
| 8 |
0.019101 |
| 9 |
0.040324 |
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 |
11200 |
| 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 |
19323 |
| 4 |
9840 |
16000 |
10560 |
| 8 |
8064 |
6832 |
11200 |
| 16 |
4160 |
5800 |
4320 |
| 32 |
3192 |
4928 |
3164 |
| 64 |
2280 |
2480 |
2660 |
| 128 |
1540 |
1736 |
1470 |
| 256 |
1020 |
1090 |
1090 |
| 512 |
707 |
735 |
728 |
| 1024 |
505 |
505 |
505 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
21922 |
22600 |
12769 |
| 4 |
16000 |
14560 |
15840 |
| 8 |
7336 |
9744 |
11200 |
| 16 |
4920 |
7680 |
5360 |
| 32 |
3108 |
3808 |
2912 |
| 64 |
2240 |
2480 |
2260 |
| 128 |
1540 |
1428 |
2450 |
| 256 |
1090 |
1150 |
1230 |
| 512 |
861 |
805 |
819 |
| 1024 |
725 |
565 |
645 |