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 |
24.8409 |
| 3 feature(s) |
NaN |
24.8409 |
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 |
25.8443 |
| 3 feature(s) |
NaN |
25.8443 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000000 |
0.000000 |
0.000000 |
| 4 |
0.117672 |
0.024188 |
0.010953 |
| 8 |
0.138393 |
0.039542 |
0.022612 |
| 16 |
0.148688 |
0.049578 |
0.034594 |
| 32 |
0.152623 |
0.056138 |
0.045000 |
| 64 |
0.155750 |
0.062164 |
0.052629 |
| 128 |
0.157129 |
0.065070 |
0.057101 |
| 256 |
0.157215 |
0.066098 |
0.059301 |
| 512 |
0.158011 |
0.067215 |
0.060840 |
| 1024 |
0.158308 |
0.067700 |
0.061528 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188657 |
0.143915 |
0.134830 |
| 4 |
0.185212 |
0.129679 |
0.126156 |
| 8 |
0.178040 |
0.109539 |
0.110687 |
| 16 |
0.169579 |
0.091083 |
0.092437 |
| 32 |
0.164245 |
0.080648 |
0.079869 |
| 64 |
0.161599 |
0.074816 |
0.072154 |
| 128 |
0.160232 |
0.071609 |
0.067525 |
| 256 |
0.159401 |
0.069759 |
0.064811 |
| 512 |
0.159010 |
0.068913 |
0.063612 |
| 1024 |
0.158820 |
0.068478 |
0.062879 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.253080 |
0.162685 |
0.150890 |
| 4 |
0.205875 |
0.135427 |
0.130928 |
| 8 |
0.180165 |
0.109123 |
0.110977 |
| 16 |
0.169669 |
0.091016 |
0.092644 |
| 32 |
0.164228 |
0.080406 |
0.079681 |
| 64 |
0.161422 |
0.074712 |
0.071991 |
| 128 |
0.160318 |
0.071493 |
0.067377 |
| 256 |
0.159520 |
0.069635 |
0.064611 |
| 512 |
0.159099 |
0.068721 |
0.063162 |
| 1024 |
0.158832 |
0.068073 |
0.062495 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
7.614823 |
| THEORETICAL |
4.786579 |
| EMPIRICAL_TEST |
9.822461 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
4.403073 |
| 2 |
3.722268 |
| 3 |
3.578935 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
3.180857 |
| 1 |
2.498634 |
| 2 |
1.624229 |
| 3 |
1.170652 |
| 4 |
0.928141 |
| 5 |
0.645968 |
| 6 |
0.569007 |
| 7 |
0.652005 |
| 8 |
0.952672 |
| 9 |
2.043945 |
Iterations Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
11413 |
11413 |
11413 |
| 4 |
16000 |
16000 |
16000 |
| 8 |
8344 |
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 |
13334 |
17515 |
12204 |
| 4 |
11840 |
11680 |
8560 |
| 8 |
7392 |
10640 |
8736 |
| 16 |
5000 |
6880 |
6520 |
| 32 |
3808 |
3024 |
3080 |
| 64 |
2060 |
2160 |
2060 |
| 128 |
1582 |
1554 |
1750 |
| 256 |
1020 |
1080 |
1090 |
| 512 |
742 |
721 |
714 |
| 1024 |
510 |
505 |
505 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
22600 |
22600 |
21357 |
| 4 |
16000 |
16000 |
12880 |
| 8 |
9576 |
5656 |
7896 |
| 16 |
4880 |
5360 |
4360 |
| 32 |
2912 |
5600 |
3024 |
| 64 |
2020 |
2060 |
2140 |
| 128 |
1554 |
1414 |
1708 |
| 256 |
1120 |
1250 |
1110 |
| 512 |
861 |
1218 |
707 |
| 1024 |
670 |
580 |
625 |