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 |
2.5484 |
| 2 feature(s) |
NaN |
NaN |
3.7951 |
| 3 feature(s) |
2.5484 |
3.7951 |
NaN |
N* empirical test
| dim |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 1 feature(s) |
NaN |
NaN |
2.4748 |
| 2 feature(s) |
NaN |
NaN |
3.9097 |
| 3 feature(s) |
2.4748 |
3.9097 |
NaN |
Loss Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.000000 |
0.000000 |
0.0 |
| 4 |
0.120375 |
0.030203 |
0.0 |
| 8 |
0.141217 |
0.047065 |
0.0 |
| 16 |
0.150008 |
0.059789 |
0.0 |
| 32 |
0.154046 |
0.066462 |
0.0 |
| 64 |
0.156090 |
0.072199 |
0.0 |
| 128 |
0.157377 |
0.075296 |
0.0 |
| 256 |
0.158309 |
0.076822 |
0.0 |
| 512 |
0.158491 |
0.077785 |
0.0 |
| 1024 |
0.158451 |
0.078148 |
0.0 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.188263 |
0.150368 |
2.159073e-01 |
| 4 |
0.185113 |
0.139065 |
1.336852e-01 |
| 8 |
0.178046 |
0.120117 |
4.488830e-02 |
| 16 |
0.169725 |
0.102247 |
5.040113e-03 |
| 32 |
0.164411 |
0.091981 |
2.218743e-04 |
| 64 |
0.161667 |
0.085602 |
2.794638e-06 |
| 128 |
0.160187 |
0.082326 |
3.099264e-07 |
| 256 |
0.159400 |
0.080430 |
1.230080e-08 |
| 512 |
0.158997 |
0.079580 |
2.186880e-09 |
| 1024 |
0.158820 |
0.079111 |
7.110094e-10 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
0.250977 |
0.171888 |
2.795665e-01 |
| 4 |
0.207368 |
0.146602 |
1.429324e-01 |
| 8 |
0.181411 |
0.120820 |
4.512535e-02 |
| 16 |
0.169628 |
0.102102 |
5.098302e-03 |
| 32 |
0.164377 |
0.091475 |
2.111318e-04 |
| 64 |
0.161512 |
0.085415 |
3.625851e-06 |
| 128 |
0.160014 |
0.082037 |
3.453191e-07 |
| 256 |
0.159185 |
0.080138 |
0.000000e+00 |
| 512 |
0.158985 |
0.079248 |
0.000000e+00 |
| 1024 |
0.158759 |
0.078725 |
0.000000e+00 |
Time Consumption Tables
Table EMPIRICAL_TRAIN
| loss type |
time (min) |
| EMPIRICAL_TRAIN |
0.101006 |
| THEORETICAL |
0.071191 |
| EMPIRICAL_TEST |
0.137958 |
Table THEORETICAL
| # features |
time (min) |
| 1 |
0.064439 |
| 2 |
0.056167 |
| 3 |
0.039272 |
Table EMPIRICAL_TEST
| n |
time (min) |
| 0 |
0.044595 |
| 1 |
0.034953 |
| 2 |
0.023375 |
| 3 |
0.016233 |
| 4 |
0.012534 |
| 5 |
0.008981 |
| 6 |
0.007689 |
| 7 |
0.008115 |
| 8 |
0.013030 |
| 9 |
0.028018 |
Iterations Tables
Table EMPIRICAL_TRAIN
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
11413 |
11413 |
11413 |
| 4 |
16000 |
16000 |
8080 |
| 8 |
11200 |
11200 |
5656 |
| 16 |
8000 |
8000 |
4040 |
| 32 |
5600 |
5600 |
2828 |
| 64 |
4000 |
4000 |
2020 |
| 128 |
2800 |
2800 |
1414 |
| 256 |
2000 |
2000 |
1010 |
| 512 |
1400 |
1400 |
707 |
| 1024 |
1000 |
1000 |
505 |
Table THEORETICAL
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
19323 |
20792 |
21018 |
| 4 |
9200 |
8080 |
11120 |
| 8 |
6776 |
5880 |
11200 |
| 16 |
5800 |
4720 |
4760 |
| 32 |
3472 |
2912 |
2828 |
| 64 |
2160 |
2060 |
2020 |
| 128 |
1456 |
1414 |
1414 |
| 256 |
1050 |
1080 |
1010 |
| 512 |
714 |
728 |
707 |
| 1024 |
505 |
510 |
505 |
Table EMPIRICAL_TEST
| n |
1 feature(s) |
2 feature(s) |
3 feature(s) |
| 2 |
17402 |
17628 |
17515 |
| 4 |
8880 |
15440 |
16000 |
| 8 |
5936 |
8064 |
8176 |
| 16 |
4720 |
5080 |
4840 |
| 32 |
4900 |
4508 |
3136 |
| 64 |
2700 |
2040 |
2020 |
| 128 |
1778 |
1652 |
1414 |
| 256 |
1010 |
1010 |
1010 |
| 512 |
770 |
1218 |
707 |
| 1024 |
815 |
810 |
505 |