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 |
584.925 |
| 3 feature(s) |
NaN |
584.925 |
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 |
775.731 |
| 3 feature(s) |
NaN |
775.731 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000000 |
0.000000 |
0.000000 |
| 4 |
0.118313 |
0.000156 |
0.000000 |
| 8 |
0.138783 |
0.000402 |
0.000145 |
| 16 |
0.149305 |
0.000469 |
0.000242 |
| 32 |
0.152952 |
0.000564 |
0.000340 |
| 64 |
0.156066 |
0.000598 |
0.000410 |
| 128 |
0.157051 |
0.000558 |
0.000346 |
| 256 |
0.157891 |
0.000598 |
0.000512 |
| 512 |
0.157870 |
0.000628 |
0.000529 |
| 1024 |
0.158650 |
0.000664 |
0.000572 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188667 |
0.088814 |
0.175619 |
| 4 |
0.184981 |
0.029442 |
0.088609 |
| 8 |
0.177693 |
0.010560 |
0.031392 |
| 16 |
0.169540 |
0.004839 |
0.010838 |
| 32 |
0.164336 |
0.002595 |
0.004487 |
| 64 |
0.161683 |
0.001657 |
0.002430 |
| 128 |
0.160245 |
0.001321 |
0.001598 |
| 256 |
0.159426 |
0.001095 |
0.001155 |
| 512 |
0.158996 |
0.000979 |
0.000988 |
| 1024 |
0.158840 |
0.000895 |
0.000856 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.252077 |
0.096957 |
0.207431 |
| 4 |
0.205337 |
0.029744 |
0.090645 |
| 8 |
0.180350 |
0.010613 |
0.031324 |
| 16 |
0.169707 |
0.004823 |
0.010687 |
| 32 |
0.164671 |
0.002581 |
0.004478 |
| 64 |
0.161787 |
0.001645 |
0.002430 |
| 128 |
0.160162 |
0.001326 |
0.001595 |
| 256 |
0.159552 |
0.001055 |
0.001091 |
| 512 |
0.159333 |
0.000891 |
0.000941 |
| 1024 |
0.159160 |
0.000853 |
0.000819 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.101626 |
| THEORETICAL |
0.064192 |
| EMPIRICAL_TEST |
0.125722 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.066128 |
| 2 |
0.042338 |
| 3 |
0.044299 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.046840 |
| 1 |
0.034345 |
| 2 |
0.024820 |
| 3 |
0.017525 |
| 4 |
0.012445 |
| 5 |
0.009028 |
| 6 |
0.007225 |
| 7 |
0.007699 |
| 8 |
0.010830 |
| 9 |
0.021256 |
Iterations Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
11413 |
11413 |
11413 |
| 4 |
16000 |
16000 |
8080 |
| 8 |
11200 |
11200 |
11200 |
| 16 |
8000 |
8000 |
8000 |
| 32 |
5600 |
5600 |
5600 |
| 64 |
4000 |
4000 |
4000 |
| 128 |
2800 |
2030 |
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 |
11526 |
21244 |
22600 |
| 4 |
10480 |
8640 |
8400 |
| 8 |
5880 |
6440 |
5712 |
| 16 |
4600 |
4560 |
4400 |
| 32 |
3724 |
2884 |
3696 |
| 64 |
2260 |
2020 |
2080 |
| 128 |
1582 |
1414 |
1414 |
| 256 |
1010 |
1020 |
1030 |
| 512 |
721 |
707 |
707 |
| 1024 |
505 |
505 |
510 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
13899 |
21244 |
20114 |
| 4 |
13360 |
9680 |
10560 |
| 8 |
11200 |
5824 |
7280 |
| 16 |
6080 |
4600 |
5000 |
| 32 |
3192 |
2884 |
3892 |
| 64 |
2220 |
2060 |
2080 |
| 128 |
1414 |
1456 |
1414 |
| 256 |
1100 |
1040 |
1030 |
| 512 |
721 |
742 |
770 |
| 1024 |
610 |
520 |
515 |