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 |
2.5595 |
| 3 feature(s) |
NaN |
2.5595 |
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 |
2.6555 |
| 3 feature(s) |
NaN |
2.6555 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000000 |
0.000000 |
0.000000 |
| 4 |
0.119125 |
0.029188 |
0.001266 |
| 8 |
0.141127 |
0.047846 |
0.003103 |
| 16 |
0.149023 |
0.060008 |
0.006266 |
| 32 |
0.153599 |
0.068225 |
0.009693 |
| 64 |
0.155684 |
0.073102 |
0.012305 |
| 128 |
0.157252 |
0.075592 |
0.013965 |
| 256 |
0.158039 |
0.076750 |
0.015152 |
| 512 |
0.158343 |
0.077854 |
0.015847 |
| 1024 |
0.158305 |
0.078135 |
0.016548 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.187808 |
0.150398 |
0.169366 |
| 4 |
0.184905 |
0.139186 |
0.104854 |
| 8 |
0.177600 |
0.120035 |
0.065882 |
| 16 |
0.169516 |
0.102747 |
0.043800 |
| 32 |
0.164600 |
0.091858 |
0.031209 |
| 64 |
0.161632 |
0.085641 |
0.024685 |
| 128 |
0.160263 |
0.082370 |
0.021223 |
| 256 |
0.159421 |
0.080465 |
0.019223 |
| 512 |
0.159014 |
0.079574 |
0.018305 |
| 1024 |
0.158840 |
0.079104 |
0.017698 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.252369 |
0.173360 |
0.199126 |
| 4 |
0.205699 |
0.146157 |
0.108919 |
| 8 |
0.180898 |
0.121066 |
0.065778 |
| 16 |
0.169790 |
0.102927 |
0.043805 |
| 32 |
0.164564 |
0.092027 |
0.030949 |
| 64 |
0.161696 |
0.085710 |
0.024632 |
| 128 |
0.160201 |
0.082333 |
0.021092 |
| 256 |
0.159288 |
0.080424 |
0.019258 |
| 512 |
0.158857 |
0.079485 |
0.018352 |
| 1024 |
0.158493 |
0.079157 |
0.017728 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.123255 |
| THEORETICAL |
0.075719 |
| EMPIRICAL_TEST |
0.158219 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.071392 |
| 2 |
0.062600 |
| 3 |
0.055672 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.052645 |
| 1 |
0.040318 |
| 2 |
0.027282 |
| 3 |
0.019170 |
| 4 |
0.014528 |
| 5 |
0.010961 |
| 6 |
0.009361 |
| 7 |
0.009421 |
| 8 |
0.015445 |
| 9 |
0.033336 |
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 |
16498 |
12656 |
17176 |
| 4 |
9600 |
8480 |
13840 |
| 8 |
5656 |
11200 |
5824 |
| 16 |
4280 |
4880 |
4320 |
| 32 |
3388 |
5320 |
2912 |
| 64 |
2040 |
2100 |
3220 |
| 128 |
1442 |
1596 |
1442 |
| 256 |
1040 |
1040 |
1130 |
| 512 |
707 |
742 |
707 |
| 1024 |
505 |
505 |
520 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
22600 |
22600 |
22600 |
| 4 |
12880 |
16000 |
16000 |
| 8 |
6832 |
8680 |
8456 |
| 16 |
4280 |
5520 |
4320 |
| 32 |
2856 |
4340 |
3752 |
| 64 |
2580 |
2140 |
2260 |
| 128 |
2506 |
1624 |
1568 |
| 256 |
1040 |
1050 |
1110 |
| 512 |
1015 |
1015 |
707 |
| 1024 |
505 |
700 |
510 |