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 |
4.6749 |
| 3 feature(s) |
NaN |
4.6749 |
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 |
4.6854 |
| 3 feature(s) |
NaN |
4.6854 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000044 |
0.000000 |
0.000000 |
| 4 |
0.119000 |
0.029141 |
0.004859 |
| 8 |
0.140525 |
0.047634 |
0.013393 |
| 16 |
0.149180 |
0.059773 |
0.022547 |
| 32 |
0.152907 |
0.067584 |
0.030647 |
| 64 |
0.156191 |
0.073004 |
0.036535 |
| 128 |
0.157171 |
0.075951 |
0.040273 |
| 256 |
0.157979 |
0.077053 |
0.041854 |
| 512 |
0.158052 |
0.077646 |
0.042913 |
| 1024 |
0.158630 |
0.078119 |
0.044190 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188042 |
0.150127 |
0.184442 |
| 4 |
0.185118 |
0.139178 |
0.142902 |
| 8 |
0.177780 |
0.120054 |
0.107223 |
| 16 |
0.169764 |
0.102461 |
0.080175 |
| 32 |
0.164662 |
0.091633 |
0.064227 |
| 64 |
0.161710 |
0.085452 |
0.054907 |
| 128 |
0.160257 |
0.082267 |
0.050169 |
| 256 |
0.159403 |
0.080487 |
0.047494 |
| 512 |
0.159014 |
0.079575 |
0.046291 |
| 1024 |
0.158827 |
0.079115 |
0.045528 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.253049 |
0.172839 |
0.219603 |
| 4 |
0.206424 |
0.146223 |
0.149969 |
| 8 |
0.180496 |
0.120701 |
0.108031 |
| 16 |
0.169951 |
0.102732 |
0.080110 |
| 32 |
0.164922 |
0.091752 |
0.064219 |
| 64 |
0.161877 |
0.085619 |
0.054989 |
| 128 |
0.160374 |
0.082399 |
0.050037 |
| 256 |
0.159412 |
0.080481 |
0.047508 |
| 512 |
0.159071 |
0.079578 |
0.046392 |
| 1024 |
0.158565 |
0.079041 |
0.045514 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.122655 |
| THEORETICAL |
0.079399 |
| EMPIRICAL_TEST |
0.151360 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.070316 |
| 2 |
0.053325 |
| 3 |
0.057311 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.048391 |
| 1 |
0.037895 |
| 2 |
0.024067 |
| 3 |
0.017732 |
| 4 |
0.012300 |
| 5 |
0.010115 |
| 6 |
0.008493 |
| 7 |
0.009781 |
| 8 |
0.016049 |
| 9 |
0.034072 |
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 |
7952 |
| 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 |
12543 |
22600 |
22600 |
| 4 |
9360 |
16000 |
16000 |
| 8 |
8960 |
6048 |
9072 |
| 16 |
4280 |
4920 |
8000 |
| 32 |
2856 |
4424 |
3192 |
| 64 |
2120 |
2480 |
2260 |
| 128 |
1540 |
1442 |
1456 |
| 256 |
1020 |
1100 |
1010 |
| 512 |
707 |
714 |
826 |
| 1024 |
505 |
505 |
515 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
22600 |
11865 |
22600 |
| 4 |
16000 |
12160 |
16000 |
| 8 |
11200 |
8344 |
5712 |
| 16 |
4440 |
4240 |
7960 |
| 32 |
3276 |
3332 |
3192 |
| 64 |
2320 |
2440 |
3000 |
| 128 |
1540 |
1582 |
1540 |
| 256 |
1300 |
1310 |
1050 |
| 512 |
1400 |
714 |
707 |
| 1024 |
620 |
600 |
885 |