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 |
49.9069 |
| 3 feature(s) |
NaN |
49.9069 |
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 |
49.5394 |
| 3 feature(s) |
NaN |
49.5394 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000044 |
0.000000 |
0.000000 |
| 4 |
0.117766 |
0.011203 |
0.003031 |
| 8 |
0.139542 |
0.018326 |
0.007567 |
| 16 |
0.149492 |
0.023438 |
0.013664 |
| 32 |
0.153717 |
0.026881 |
0.019074 |
| 64 |
0.156258 |
0.030098 |
0.023062 |
| 128 |
0.156830 |
0.031621 |
0.025304 |
| 256 |
0.157521 |
0.032459 |
0.026824 |
| 512 |
0.158145 |
0.032983 |
0.027586 |
| 1024 |
0.158658 |
0.033487 |
0.028555 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188626 |
0.122088 |
0.186139 |
| 4 |
0.185121 |
0.093194 |
0.136171 |
| 8 |
0.177400 |
0.069849 |
0.092870 |
| 16 |
0.169376 |
0.053359 |
0.063333 |
| 32 |
0.164314 |
0.044550 |
0.046910 |
| 64 |
0.161729 |
0.039710 |
0.038389 |
| 128 |
0.160230 |
0.036946 |
0.033972 |
| 256 |
0.159377 |
0.035557 |
0.031651 |
| 512 |
0.158997 |
0.034776 |
0.030525 |
| 1024 |
0.158843 |
0.034384 |
0.029847 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.252295 |
0.133569 |
0.222313 |
| 4 |
0.205518 |
0.095876 |
0.142485 |
| 8 |
0.180496 |
0.070013 |
0.093327 |
| 16 |
0.169675 |
0.053295 |
0.062994 |
| 32 |
0.164598 |
0.044630 |
0.046847 |
| 64 |
0.161815 |
0.039612 |
0.038313 |
| 128 |
0.160353 |
0.036853 |
0.033859 |
| 256 |
0.159429 |
0.035560 |
0.031624 |
| 512 |
0.159148 |
0.034780 |
0.030430 |
| 1024 |
0.158952 |
0.034479 |
0.029826 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.130823 |
| THEORETICAL |
0.080184 |
| EMPIRICAL_TEST |
0.163274 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.078622 |
| 2 |
0.051372 |
| 3 |
0.062344 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.049158 |
| 1 |
0.044000 |
| 2 |
0.029078 |
| 3 |
0.019561 |
| 4 |
0.015592 |
| 5 |
0.011655 |
| 6 |
0.010029 |
| 7 |
0.011819 |
| 8 |
0.014382 |
| 9 |
0.030305 |
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 |
11200 |
| 16 |
8000 |
5440 |
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 |
18306 |
14916 |
15707 |
| 4 |
8400 |
10000 |
14240 |
| 8 |
11200 |
8792 |
9072 |
| 16 |
5080 |
5640 |
4440 |
| 32 |
3332 |
3668 |
3192 |
| 64 |
2220 |
2680 |
2300 |
| 128 |
1596 |
1484 |
1652 |
| 256 |
1020 |
1070 |
1030 |
| 512 |
721 |
749 |
707 |
| 1024 |
505 |
515 |
550 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
19323 |
13221 |
22600 |
| 4 |
16000 |
14160 |
16000 |
| 8 |
11200 |
5992 |
11200 |
| 16 |
5280 |
8000 |
7320 |
| 32 |
3780 |
2884 |
3248 |
| 64 |
2260 |
2080 |
2420 |
| 128 |
2296 |
1526 |
1680 |
| 256 |
1680 |
1040 |
1110 |
| 512 |
861 |
931 |
847 |
| 1024 |
670 |
645 |
520 |