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 |
4.0229 |
| 2 feature(s) |
NaN |
NaN |
48.0505 |
| 3 feature(s) |
4.0229 |
48.0505 |
NaN |
N* empirical test
| dim |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 1 feature(s) |
NaN |
NaN |
3.2862 |
| 2 feature(s) |
NaN |
NaN |
47.9160 |
| 3 feature(s) |
3.2862 |
47.916 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000022 |
0.000000 |
0.000000 |
| 4 |
0.117453 |
0.029437 |
0.012266 |
| 8 |
0.139364 |
0.046250 |
0.028359 |
| 16 |
0.148492 |
0.057977 |
0.043945 |
| 32 |
0.152757 |
0.065670 |
0.054509 |
| 64 |
0.155641 |
0.072188 |
0.062750 |
| 128 |
0.157252 |
0.074919 |
0.066755 |
| 256 |
0.157625 |
0.076525 |
0.069213 |
| 512 |
0.158326 |
0.077701 |
0.070785 |
| 1024 |
0.158438 |
0.078319 |
0.071604 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188928 |
0.150010 |
0.211203 |
| 4 |
0.185208 |
0.139226 |
0.185456 |
| 8 |
0.177841 |
0.119472 |
0.148049 |
| 16 |
0.169509 |
0.102161 |
0.113966 |
| 32 |
0.164232 |
0.091581 |
0.094657 |
| 64 |
0.161614 |
0.085420 |
0.083252 |
| 128 |
0.160193 |
0.082275 |
0.078025 |
| 256 |
0.159402 |
0.080433 |
0.074967 |
| 512 |
0.159014 |
0.079555 |
0.073505 |
| 1024 |
0.158824 |
0.079134 |
0.072858 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.251376 |
0.171558 |
0.265308 |
| 4 |
0.205844 |
0.145674 |
0.200329 |
| 8 |
0.180299 |
0.120247 |
0.149618 |
| 16 |
0.169705 |
0.101973 |
0.114224 |
| 32 |
0.164437 |
0.091500 |
0.094506 |
| 64 |
0.161547 |
0.085302 |
0.083147 |
| 128 |
0.160168 |
0.082038 |
0.077869 |
| 256 |
0.159336 |
0.080363 |
0.075122 |
| 512 |
0.158884 |
0.079306 |
0.073501 |
| 1024 |
0.158754 |
0.078903 |
0.072887 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.127974 |
| THEORETICAL |
0.073324 |
| EMPIRICAL_TEST |
0.148174 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.067381 |
| 2 |
0.057138 |
| 3 |
0.058843 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.046423 |
| 1 |
0.038543 |
| 2 |
0.024220 |
| 3 |
0.017090 |
| 4 |
0.012863 |
| 5 |
0.010206 |
| 6 |
0.008975 |
| 7 |
0.010835 |
| 8 |
0.016443 |
| 9 |
0.036175 |
Iterations Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
22600 |
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 |
11413 |
22600 |
12543 |
| 4 |
8400 |
11680 |
8480 |
| 8 |
6608 |
5656 |
7560 |
| 16 |
4880 |
4560 |
8000 |
| 32 |
2968 |
2968 |
2912 |
| 64 |
2080 |
3140 |
2200 |
| 128 |
1442 |
1512 |
1540 |
| 256 |
1020 |
1070 |
1090 |
| 512 |
742 |
728 |
749 |
| 1024 |
510 |
515 |
520 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
15142 |
22600 |
17967 |
| 4 |
16000 |
16000 |
16000 |
| 8 |
8624 |
7896 |
7448 |
| 16 |
4800 |
4320 |
4520 |
| 32 |
3696 |
3668 |
3892 |
| 64 |
2960 |
2200 |
2160 |
| 128 |
1974 |
1778 |
1512 |
| 256 |
1830 |
1330 |
1040 |
| 512 |
721 |
756 |
1029 |
| 1024 |
520 |
530 |
670 |