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 |
4.3937 |
| 2 feature(s) |
NaN |
NaN |
14.1055 |
| 3 feature(s) |
4.3937 |
14.1055 |
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 |
14.6146 |
| 3 feature(s) |
NaN |
14.6146 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000000 |
0.000000 |
0.000000 |
| 4 |
0.119641 |
0.048641 |
0.016516 |
| 8 |
0.140592 |
0.073761 |
0.037578 |
| 16 |
0.148609 |
0.092188 |
0.060539 |
| 32 |
0.152863 |
0.102137 |
0.075820 |
| 64 |
0.156238 |
0.109004 |
0.086289 |
| 128 |
0.157358 |
0.111908 |
0.091842 |
| 256 |
0.157891 |
0.114002 |
0.094568 |
| 512 |
0.158163 |
0.114782 |
0.095806 |
| 1024 |
0.158135 |
0.115676 |
0.096999 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188192 |
0.169568 |
0.205118 |
| 4 |
0.185457 |
0.166815 |
0.187597 |
| 8 |
0.177617 |
0.153928 |
0.163953 |
| 16 |
0.169529 |
0.138871 |
0.136643 |
| 32 |
0.164616 |
0.128688 |
0.118857 |
| 64 |
0.161568 |
0.122826 |
0.108506 |
| 128 |
0.160241 |
0.119737 |
0.103485 |
| 256 |
0.159444 |
0.117854 |
0.100415 |
| 512 |
0.159056 |
0.116882 |
0.098975 |
| 1024 |
0.158852 |
0.116457 |
0.098288 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.252869 |
0.205788 |
0.251593 |
| 4 |
0.205588 |
0.178422 |
0.204256 |
| 8 |
0.179990 |
0.154839 |
0.166427 |
| 16 |
0.169757 |
0.138823 |
0.137081 |
| 32 |
0.164752 |
0.129061 |
0.118946 |
| 64 |
0.161627 |
0.122917 |
0.108662 |
| 128 |
0.160335 |
0.119895 |
0.103621 |
| 256 |
0.159449 |
0.117945 |
0.100482 |
| 512 |
0.158941 |
0.116968 |
0.099037 |
| 1024 |
0.159037 |
0.116807 |
0.098437 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.134279 |
| THEORETICAL |
0.078726 |
| EMPIRICAL_TEST |
0.165724 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.067413 |
| 2 |
0.065221 |
| 3 |
0.064919 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.042337 |
| 1 |
0.036815 |
| 2 |
0.028497 |
| 3 |
0.018937 |
| 4 |
0.014464 |
| 5 |
0.012065 |
| 6 |
0.010016 |
| 7 |
0.011886 |
| 8 |
0.020056 |
| 9 |
0.042738 |
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 |
6400 |
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 |
12995 |
11413 |
14351 |
| 4 |
9200 |
10560 |
10560 |
| 8 |
8848 |
5824 |
11200 |
| 16 |
6160 |
7520 |
5440 |
| 32 |
3248 |
3108 |
3360 |
| 64 |
2460 |
2280 |
2500 |
| 128 |
1456 |
1624 |
1568 |
| 256 |
1050 |
1030 |
1030 |
| 512 |
707 |
728 |
714 |
| 1024 |
505 |
505 |
520 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
13899 |
22600 |
12882 |
| 4 |
11920 |
16000 |
9040 |
| 8 |
11200 |
10696 |
11200 |
| 16 |
6840 |
5360 |
4080 |
| 32 |
3444 |
3752 |
5600 |
| 64 |
2680 |
2060 |
3420 |
| 128 |
1722 |
2016 |
1428 |
| 256 |
1320 |
1010 |
1730 |
| 512 |
938 |
973 |
1351 |
| 1024 |
565 |
555 |
520 |