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 |
9.2057 |
| 2 feature(s) |
NaN |
NaN |
12.0414 |
| 3 feature(s) |
9.2057 |
12.0414 |
NaN |
N* empirical test
| dim |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 1 feature(s) |
NaN |
NaN |
9.5899 |
| 2 feature(s) |
NaN |
NaN |
12.4056 |
| 3 feature(s) |
9.5899 |
12.4056 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000000 |
0.000000 |
0.000000 |
| 4 |
0.119594 |
0.076313 |
0.027813 |
| 8 |
0.140815 |
0.103962 |
0.056719 |
| 16 |
0.148852 |
0.122086 |
0.082453 |
| 32 |
0.152606 |
0.131540 |
0.098850 |
| 64 |
0.156074 |
0.138184 |
0.109664 |
| 128 |
0.156981 |
0.141501 |
0.116110 |
| 256 |
0.157971 |
0.144213 |
0.118717 |
| 512 |
0.157944 |
0.144849 |
0.120230 |
| 1024 |
0.158446 |
0.145647 |
0.121292 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.187981 |
0.183357 |
0.212626 |
| 4 |
0.185476 |
0.180587 |
0.201630 |
| 8 |
0.177818 |
0.172506 |
0.181151 |
| 16 |
0.169954 |
0.162839 |
0.156830 |
| 32 |
0.164484 |
0.156227 |
0.140960 |
| 64 |
0.161608 |
0.151898 |
0.132157 |
| 128 |
0.160234 |
0.149393 |
0.127313 |
| 256 |
0.159427 |
0.147786 |
0.124600 |
| 512 |
0.159030 |
0.146808 |
0.123115 |
| 1024 |
0.158848 |
0.146405 |
0.122440 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.251791 |
0.237196 |
0.267608 |
| 4 |
0.205761 |
0.196857 |
0.223372 |
| 8 |
0.180068 |
0.174684 |
0.184535 |
| 16 |
0.170026 |
0.163122 |
0.157409 |
| 32 |
0.164762 |
0.156548 |
0.141266 |
| 64 |
0.161726 |
0.152111 |
0.132351 |
| 128 |
0.160334 |
0.149498 |
0.127513 |
| 256 |
0.159442 |
0.147801 |
0.124629 |
| 512 |
0.159225 |
0.147154 |
0.123155 |
| 1024 |
0.159047 |
0.146842 |
0.122583 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.144581 |
| THEORETICAL |
0.087475 |
| EMPIRICAL_TEST |
0.165736 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.067867 |
| 2 |
0.067212 |
| 3 |
0.074506 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.048049 |
| 1 |
0.039356 |
| 2 |
0.026959 |
| 3 |
0.019147 |
| 4 |
0.014366 |
| 5 |
0.010916 |
| 6 |
0.010435 |
| 7 |
0.013135 |
| 8 |
0.021632 |
| 9 |
0.047804 |
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 |
11865 |
15255 |
16046 |
| 4 |
9680 |
9280 |
12640 |
| 8 |
10080 |
6664 |
7896 |
| 16 |
4240 |
5160 |
5560 |
| 32 |
3444 |
3584 |
5124 |
| 64 |
2240 |
2300 |
2220 |
| 128 |
1442 |
1596 |
2562 |
| 256 |
1090 |
1200 |
1100 |
| 512 |
714 |
707 |
707 |
| 1024 |
505 |
505 |
545 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
15142 |
18419 |
22600 |
| 4 |
13520 |
13440 |
10560 |
| 8 |
7224 |
6720 |
9744 |
| 16 |
5040 |
4600 |
4680 |
| 32 |
3472 |
3220 |
3892 |
| 64 |
2040 |
2120 |
2180 |
| 128 |
1470 |
1918 |
1554 |
| 256 |
1230 |
1590 |
1120 |
| 512 |
833 |
861 |
805 |
| 1024 |
535 |
675 |
610 |