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 |
NaN |
| 2 feature(s) |
NaN |
NaN |
NaN |
| 3 feature(s) |
NaN |
NaN |
NaN |
N* empirical test
| dim |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 1 feature(s) |
NaN |
NaN |
NaN |
| 2 feature(s) |
NaN |
NaN |
NaN |
| 3 feature(s) |
NaN |
NaN |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000000 |
0.000000 |
0.000000 |
| 4 |
0.118641 |
0.023969 |
0.004359 |
| 8 |
0.141462 |
0.039587 |
0.011138 |
| 16 |
0.149820 |
0.049984 |
0.017641 |
| 32 |
0.152963 |
0.057221 |
0.023103 |
| 64 |
0.156441 |
0.062504 |
0.028855 |
| 128 |
0.158373 |
0.065237 |
0.031914 |
| 256 |
0.157955 |
0.066227 |
0.034137 |
| 512 |
0.158185 |
0.066889 |
0.035006 |
| 1024 |
0.158246 |
0.067571 |
0.035639 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188291 |
0.143588 |
0.110937 |
| 4 |
0.185463 |
0.129380 |
0.095453 |
| 8 |
0.177913 |
0.108391 |
0.079675 |
| 16 |
0.170046 |
0.090927 |
0.063743 |
| 32 |
0.164529 |
0.080474 |
0.052534 |
| 64 |
0.161693 |
0.074720 |
0.045124 |
| 128 |
0.160202 |
0.071584 |
0.041098 |
| 256 |
0.159457 |
0.069785 |
0.038757 |
| 512 |
0.159024 |
0.068909 |
0.037655 |
| 1024 |
0.158849 |
0.068463 |
0.036993 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.253864 |
0.163232 |
0.119268 |
| 4 |
0.206350 |
0.133840 |
0.097879 |
| 8 |
0.181184 |
0.109286 |
0.079851 |
| 16 |
0.170014 |
0.090727 |
0.063800 |
| 32 |
0.164486 |
0.080639 |
0.052547 |
| 64 |
0.161523 |
0.074752 |
0.045094 |
| 128 |
0.160113 |
0.071616 |
0.041085 |
| 256 |
0.159512 |
0.069874 |
0.038608 |
| 512 |
0.159170 |
0.068886 |
0.037749 |
| 1024 |
0.158848 |
0.068543 |
0.037056 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.112773 |
| THEORETICAL |
0.072792 |
| EMPIRICAL_TEST |
0.136803 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.062331 |
| 2 |
0.054114 |
| 3 |
0.050886 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.042381 |
| 1 |
0.035441 |
| 2 |
0.024803 |
| 3 |
0.017886 |
| 4 |
0.012619 |
| 5 |
0.009751 |
| 6 |
0.008425 |
| 7 |
0.009245 |
| 8 |
0.014766 |
| 9 |
0.030615 |
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 |
12430 |
15142 |
22600 |
| 4 |
11440 |
14400 |
14640 |
| 8 |
7896 |
7336 |
5712 |
| 16 |
4400 |
5080 |
8000 |
| 32 |
2996 |
3640 |
3108 |
| 64 |
2200 |
2360 |
2020 |
| 128 |
1442 |
1442 |
1666 |
| 256 |
1010 |
1070 |
1060 |
| 512 |
707 |
749 |
728 |
| 1024 |
505 |
505 |
505 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
11865 |
22600 |
12769 |
| 4 |
10240 |
8160 |
15920 |
| 8 |
9856 |
5768 |
9184 |
| 16 |
6120 |
5640 |
4680 |
| 32 |
2968 |
4452 |
3108 |
| 64 |
2820 |
2280 |
2020 |
| 128 |
1582 |
1442 |
2016 |
| 256 |
1280 |
1080 |
1140 |
| 512 |
952 |
1281 |
770 |
| 1024 |
535 |
745 |
570 |