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 |
3.4810 |
| 2 feature(s) |
NaN |
NaN |
488.1202 |
| 3 feature(s) |
3.481 |
488.1202 |
NaN |
N* empirical test
| dim |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 1 feature(s) |
NaN |
NaN |
2.1122 |
| 2 feature(s) |
NaN |
NaN |
520.6060 |
| 3 feature(s) |
2.1122 |
520.606 |
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 |
0.029719 |
0.011969 |
| 8 |
0.139040 |
0.045993 |
0.027969 |
| 16 |
0.148383 |
0.057813 |
0.046180 |
| 32 |
0.153030 |
0.066858 |
0.058354 |
| 64 |
0.155469 |
0.072355 |
0.067867 |
| 128 |
0.157196 |
0.075424 |
0.072252 |
| 256 |
0.157289 |
0.076488 |
0.074939 |
| 512 |
0.158125 |
0.077698 |
0.076451 |
| 1024 |
0.158626 |
0.078304 |
0.077439 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.189002 |
0.149957 |
0.205002 |
| 4 |
0.185181 |
0.139318 |
0.181169 |
| 8 |
0.177935 |
0.119128 |
0.149678 |
| 16 |
0.169574 |
0.101940 |
0.118743 |
| 32 |
0.164453 |
0.091643 |
0.100465 |
| 64 |
0.161594 |
0.085348 |
0.089347 |
| 128 |
0.160165 |
0.082274 |
0.084068 |
| 256 |
0.159394 |
0.080448 |
0.081020 |
| 512 |
0.159013 |
0.079534 |
0.079492 |
| 1024 |
0.158837 |
0.079137 |
0.078840 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.251864 |
0.170644 |
0.252722 |
| 4 |
0.205648 |
0.143946 |
0.195607 |
| 8 |
0.180396 |
0.119730 |
0.151786 |
| 16 |
0.169826 |
0.102169 |
0.118810 |
| 32 |
0.164683 |
0.091582 |
0.100436 |
| 64 |
0.161636 |
0.085381 |
0.089362 |
| 128 |
0.160201 |
0.082180 |
0.083994 |
| 256 |
0.159605 |
0.080421 |
0.080997 |
| 512 |
0.159163 |
0.079576 |
0.079583 |
| 1024 |
0.158926 |
0.079108 |
0.078844 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.121662 |
| THEORETICAL |
0.074803 |
| EMPIRICAL_TEST |
0.146826 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.064507 |
| 2 |
0.051627 |
| 3 |
0.062757 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.041787 |
| 1 |
0.037186 |
| 2 |
0.023801 |
| 3 |
0.017201 |
| 4 |
0.013462 |
| 5 |
0.009929 |
| 6 |
0.008998 |
| 7 |
0.010436 |
| 8 |
0.017070 |
| 9 |
0.036884 |
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 |
7240 |
8000 |
| 32 |
5600 |
5600 |
5600 |
| 64 |
2300 |
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 |
11865 |
14464 |
14125 |
| 4 |
10880 |
16000 |
11280 |
| 8 |
6160 |
8120 |
7560 |
| 16 |
4040 |
4880 |
7440 |
| 32 |
4284 |
2912 |
4228 |
| 64 |
2180 |
2080 |
3000 |
| 128 |
1442 |
1456 |
1568 |
| 256 |
1100 |
1020 |
1090 |
| 512 |
721 |
707 |
805 |
| 1024 |
505 |
525 |
520 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
16837 |
13560 |
22035 |
| 4 |
16000 |
10880 |
15360 |
| 8 |
6272 |
7168 |
8624 |
| 16 |
4600 |
4360 |
6040 |
| 32 |
4172 |
2968 |
3472 |
| 64 |
2440 |
3100 |
2840 |
| 128 |
1834 |
1960 |
1512 |
| 256 |
1180 |
1180 |
1410 |
| 512 |
910 |
931 |
1029 |
| 1024 |
645 |
600 |
545 |