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 |
2.6267 |
| 3 feature(s) |
NaN |
2.6267 |
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 |
278.3754 |
| 3 feature(s) |
NaN |
278.3754 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000000 |
0.000000 |
0.000000 |
| 4 |
0.117813 |
0.024531 |
0.013203 |
| 8 |
0.139319 |
0.039654 |
0.026261 |
| 16 |
0.149656 |
0.049891 |
0.039430 |
| 32 |
0.153348 |
0.056412 |
0.049900 |
| 64 |
0.155844 |
0.062242 |
0.057602 |
| 128 |
0.157068 |
0.064727 |
0.062123 |
| 256 |
0.157424 |
0.065822 |
0.064242 |
| 512 |
0.158115 |
0.066855 |
0.065716 |
| 1024 |
0.158474 |
0.067423 |
0.066423 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188538 |
0.144200 |
0.141970 |
| 4 |
0.185187 |
0.130165 |
0.133606 |
| 8 |
0.178058 |
0.109034 |
0.116894 |
| 16 |
0.169483 |
0.090936 |
0.098195 |
| 32 |
0.164307 |
0.080637 |
0.085240 |
| 64 |
0.161556 |
0.074873 |
0.077361 |
| 128 |
0.160205 |
0.071634 |
0.072643 |
| 256 |
0.159411 |
0.069762 |
0.069827 |
| 512 |
0.159013 |
0.068925 |
0.068614 |
| 1024 |
0.158823 |
0.068469 |
0.067851 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.251157 |
0.162411 |
0.160443 |
| 4 |
0.206513 |
0.134461 |
0.139334 |
| 8 |
0.180304 |
0.109157 |
0.117517 |
| 16 |
0.169584 |
0.090886 |
0.098234 |
| 32 |
0.164395 |
0.080351 |
0.084923 |
| 64 |
0.161338 |
0.074745 |
0.077183 |
| 128 |
0.160316 |
0.071421 |
0.072498 |
| 256 |
0.159352 |
0.069625 |
0.069667 |
| 512 |
0.159085 |
0.068445 |
0.068145 |
| 1024 |
0.158716 |
0.068073 |
0.067505 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.117732 |
| THEORETICAL |
0.073825 |
| EMPIRICAL_TEST |
0.140718 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.065255 |
| 2 |
0.053250 |
| 3 |
0.055044 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.041566 |
| 1 |
0.037214 |
| 2 |
0.025290 |
| 3 |
0.017321 |
| 4 |
0.013323 |
| 5 |
0.009919 |
| 6 |
0.008526 |
| 7 |
0.009937 |
| 8 |
0.016036 |
| 9 |
0.033743 |
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 |
1990 |
2000 |
2000 |
| 512 |
1400 |
1400 |
1400 |
| 1024 |
1000 |
1000 |
1000 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
13221 |
22035 |
13899 |
| 4 |
9040 |
16000 |
9040 |
| 8 |
6776 |
6160 |
6832 |
| 16 |
4240 |
6360 |
5160 |
| 32 |
3332 |
4256 |
4620 |
| 64 |
2720 |
2080 |
2020 |
| 128 |
1442 |
1526 |
1428 |
| 256 |
1050 |
1110 |
1090 |
| 512 |
742 |
735 |
714 |
| 1024 |
510 |
510 |
505 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
15142 |
17063 |
14464 |
| 4 |
16000 |
9920 |
14880 |
| 8 |
8288 |
5992 |
11200 |
| 16 |
4760 |
5280 |
4280 |
| 32 |
4480 |
3304 |
3808 |
| 64 |
2180 |
2640 |
2140 |
| 128 |
1442 |
1624 |
1638 |
| 256 |
1310 |
1060 |
1400 |
| 512 |
1246 |
819 |
784 |
| 1024 |
530 |
560 |
670 |