I thought it might be interesting to see what the n-gram frequencies were for the tapes of various machines. This means the frequencies that various size n sequences of symbols appears on the tape. After hacking around a bit I ended up deciding to look at windows around the TM head of size 5 (the current symbol and 2 as context in each direction) in addition to the TM state. Then I decided to compute the entropy of that window over time. The entropy is roughly the number of bits needed to encode an average “window” in an optimal encoding. Basically, low entropy means that the window is more predictable (less variable) and high entropy means the windows are more “random” (more variable). Here are the results for Skelet’s list sorted low entropy to high:
$ time python Code/ngram_entropy.py Machines/5x2-skelet | sort -nk2
1RB0LD_1RC0RE_1LA0RA_0LA1LD_0RD1RZ 0.065170 2.624923 2.885602
1RB0LE_0RC0LC_1LA0RD_1RC1RD_1LB1RZ 0.069403 2.531400 2.809011
1RB1LE_0RC0RB_1LD1RA_1LC1RZ_1RC0LA 0.073719 1.753842 2.048718
1RB1RD_1LC0RC_1RA1LD_0RE0LB_1RZ1RC 0.210259 3.032768 3.873805
1RB0RD_0LC1RA_0RA1LB_1LB0RE_1RZ1RD 0.220661 2.875455 3.758101
1RB0LE_0RC1LD_0LD1RB_1LB0LA_1RZ1LA 0.220662 2.875586 3.758235
1RB1RA_1LC0LC_0LE0RD_0RA1LB_1LD1RZ 0.244216 2.480900 3.457762
1RB1RZ_0LC1RD_1LD1LC_1RE0RE_0RA0LB 0.244739 2.483247 3.462203
1RB0RB_0RC0LD_1RD1RZ_0LE1RA_1LA1LE 0.251298 2.520131 3.525323
1RB1RZ_0LC1RE_0LD1LC_1RA1LB_0RB0RA 0.267918 2.732411 3.804085
1RB0LD_1RC1RA_1LA0RC_1RZ1LE_0LA1LB 0.275482 2.612768 3.714697
1RB0LC_1LC1RB_1RD1LA_1RE0RC_1RZ0RD 0.285419 3.021045 4.162722
1RB0LA_1LC0RD_1LA1LB_1RZ1RE_0RB1RC 0.287281 2.632414 3.781536
1RB0LE_0RC1RB_1RD0RB_1LA0LB_0LD1RZ 0.350166 2.870047 4.270711
1RB0RA_1LC1RZ_0LC0LD_1RE0RB_0RE1RA 0.350893 2.079874 3.483446
1RB0LE_1RC1RZ_0RD0RC_1LD0LA_0LB0LE 0.372895 2.376806 3.868385
1RB1RZ_0RC0RB_1LC0LD_1RA0LE_0LA0LE 0.373058 2.377073 3.869306
1RB0LB_1LC0RD_1RB0LD_1LA1RE_0RC1RZ 0.424470 3.039838 4.737720
1RB0LC_1LA0RC_1LD1RE_1RB0LB_0RA1RZ 0.424470 3.039838 4.737720
1RB1LD_1RC0RB_1LA1RC_1LE0LA_1LC1RZ 0.451514 2.975599 4.781655
1RB1RZ_1RC1LB_1LD1RE_1LB0LD_1RA0RC 0.460049 2.976033 4.816229
1RB0LA_1RC1RZ_0RD1LE_1RE0RA_1LC0LA 0.463559 2.981931 4.836166
1RB0RD_0LC1RA_1LA0LD_1LE0RD_1LB1RZ 0.463577 2.981866 4.836176
1RB0LE_1RC0LA_1LD0RB_1LB1LD_1RZ0LC 0.481513 2.949394 4.875448
1RB0LD_1LC0RA_1LA1LC_1RA0LE_1RZ0LB 0.485308 2.923904 4.865136
1RB1RA_1LC0RD_1RA0LB_1LB0RE_1RZ0RC 0.485423 2.923645 4.865335
1RB1RZ_0RC0LD_1RD0LE_1RE0RA_1LC0LD 0.486168 2.928137 4.872809
1RB1RD_1LC1RZ_1LE1LD_1RE0LC_1RA0RD 0.520271 2.903459 4.984542
1RB1RD_1LC0LD_1LE1LD_1LB0RA_1RA1RZ 0.520280 2.903392 4.984510
1RB0RE_1RC1RE_1LD1RZ_1LA1LE_1RA0LD 0.520460 2.903689 4.985530
1RB1LC_0RC0RB_1LD0LA_1LE1RZ_1LA0LA 0.521675 3.001241 5.087942
1RB0RA_0LC1RA_0LD1LC_1RA0LE_1RZ0LA 0.522168 2.969088 5.057759
1RB1LC_0RC0RB_1LD0LA_1LE1RZ_1LA1RE 0.529447 3.039388 5.157176
1RB1LC_1LA1RB_1LD0LA_1RE0RD_1RZ0RB 0.533572 2.932802 5.067089
1RB0LE_1RC0RB_0LD1RB_0LA1LD_1RZ0LB 0.539007 2.967105 5.123134
1RB1LC_0RC0RB_1LD0LA_1LE1RZ_1LA1RA 0.560370 2.994851 5.236332
1RB0LA_0RC1RZ_1LC1RD_1RE1LA_0RB0LD 0.564641 2.954846 5.213408
1RB0LD_0RC0RE_1LC0LA_1LA1RC_0RB1RZ 0.621502 2.897843 5.383851
1RB0RA_0LC1RA_1RE1LD_1LC0LD_1RZ0RB 0.628101 3.065073 5.577475
1RB0RE_1LC0RA_0LC1LD_1LA1RZ_0RB0LD 0.660916 2.793557 5.437221
1RB1RZ_1LC0LE_1RD0LB_0RD1RA_0LC0RA 0.661757 2.793634 5.440663
1RB0LA_1LC1RD_1LA0LC_0RD0LE_1RA1RZ 0.681545 3.027715 5.753897
1RB1RZ_0RC1RD_0LD1RC_1LE0RA_1RA0LE 0.710158 2.916590 5.757222
real 0m4.159s
user 0m4.124s
sys 0m0.025s
Lowest entropy: https://bbchallenge.org/1RB0LD_1RC0RE_1LA0RA_0LA1LD_0RD1RZ
Highest entropy: https://bbchallenge.org/1RB1RZ_0RC1RD_0LD1RC_1LE0RA_1RA0LE
Details: There are 3 #s listed after each machine. The second number h1 is entropy for window size 1 (i.e. entropy of cur state and cur symbol) max is log2(9) = 3.16 (if it used all 9 non-halt transitions equally often). Third number h5 is the entropy of for window size 5 max is log2(9) + 4 = 7.16. The first number is the average entropy increase per symbol of window ((h5 - h1) / 4) and I think is the most interesting number since it’s not specifically tied to window size. It has a max of 1 (if knowing a window told you nothing about what comes next).
Code: busy-beaver/ngram_entropy.py at main · sligocki/busy-beaver · GitHub
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And here’s what it looks like for a random 20 machines from my holdouts:
$ time python Code/ngram_entropy.py holdout.sample.txt | sort -nk2
1RB1LC_1LA1RB_0LB1LD_1LA0RE_1RZ1RD 0.060360 2.632477 2.873915
1RB1RE_0LC1RZ_1RE1LD_1LE0LD_0RA1RC 0.090153 2.631444 2.992057
1RB1LD_0RC1RB_1LD1RE_1LA0LB_1RZ1RD 0.136317 2.922965 3.468234
1RB0LC_1LC1RD_1LA0LC_1LE1RZ_0RA1RE 0.144981 2.931490 3.511415
1RB0LC_1RC1RZ_1RD1LC_1LC0RE_1LA1RE 0.192518 1.386243 2.156317
1RB1LA_0LC1RD_1LA0RB_1RC0RE_1RZ1LD 0.331947 2.970354 4.298141
1RB0LD_1LC0LA_1RZ1LD_0RE1LB_1RB1RE 0.397143 2.847294 4.435864
1RB1RC_1RC0LA_1LB1RD_1RZ1RE_0LB0RA 0.415552 2.707798 4.370007
1RB1RA_1LC1LA_1RA0LD_0RA1LE_1RZ0LC 0.433755 2.856580 4.591600
1RB1LA_1LC1RD_1RZ0RB_0LA1RE_1RB0RB 0.441370 2.324444 4.089925
1RB0RC_1LA0LC_1RZ1LD_0RE1LB_1LC1RE 0.441386 2.323689 4.089232
1RB1LB_0RC1LA_1RD0RE_0LB1RC_1RZ1LD 0.469857 3.000775 4.880204
1RB1LC_0LA0RE_1LD1RZ_1LA1LD_0LC0RB 0.488202 3.038504 4.991313
1RB1RD_0LC1RZ_0RE1LD_1RA0LC_1LC1RE 0.521987 2.265647 4.353595
1RB0RD_1LC0RE_0RA1LD_1RA0LD_1RZ1RC 0.538217 2.868764 5.021631
1RB1LB_1RC1RD_0RD1RZ_0LE0RD_1LA1LE 0.563444 2.500963 4.754739
1RB1LC_0LA0RE_1LD1LC_1RE1RZ_0LC0RB 0.572818 3.069518 5.360791
1RB0LA_1LC0RB_1RD1LA_0RE1RD_0LA1RZ 0.645341 2.965075 5.546438
1RB1LA_1RC0RB_1LD1RC_1LE0LD_1LA1RZ 0.661506 3.022060 5.668085
1RB1LA_0LC1RE_1RZ1LD_0RB1LC_0LA0RE 0.709517 2.999051 5.837117
Which interestingly covers a similar range (~0.06 to ~0.70).
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Running on a sample of 20 TMs proven by CPS (with block size <=6):
$ time python Code/ngram_entropy.py cps6.inf.sample.txt | sort -nk2
1RB0LD_1RC0RA_1LD0RE_1LA0LD_0LA1RZ 0.044593 2.147155 2.325528
1RB0LA_1LC1RE_1RZ1LD_1RE0LA_1RA0RB 0.053115 2.661584 2.874043
1RB0RA_1LC1RA_1RZ1LD_0LE1LD_1RA1LC 0.058281 2.651637 2.884763
1RB1RB_0LC0RA_1RE1LD_0LE1RZ_1LB0RC 0.063748 3.033164 3.288157
1RB1LC_1LC0RD_0LA1LA_1RZ1RE_1RB1RE 0.070964 1.695725 1.979580
1RB0RA_1LC0RE_0LD1LC_1RA1LC_1RZ1LD 0.072521 3.016125 3.306209
1RB1RA_1LC1LB_1RE1LD_0RD0LC_0RA1RZ 0.080887 1.330488 1.654034
1RB1RZ_1LC1RB_0RE0LD_0LB1RE_0RD1RA 0.090932 2.627635 2.991363
1RB1RD_1LC0LB_1RD1LB_1RZ1RE_0RA1LA 0.096941 2.663687 3.051449
1RB0RC_0RC1RE_0LD1LC_1LA0LD_0RA1RZ 0.104231 3.016161 3.433085
1RB1RE_0LC0RE_0RA1LD_0LC0LD_1RC1RZ 0.106467 2.666964 3.092833
1RB0RD_1LC0LB_1RA0LB_1RE1RZ_0RC1RD 0.222764 2.926871 3.817928
1RB0LA_0RC0RC_0LD1RE_1LA0RB_0RD1RZ 0.326563 2.889040 4.195293
1RB0LA_0RC0RD_1LA1RE_0LC1LB_1RB1RZ 0.339396 2.837638 4.195224
1RB0LA_0RC1LA_1RD1RE_0LB1RE_0RC1RZ 0.404764 2.907282 4.526337
1RB1RZ_1LC0LC_0RD1LB_1RE1RD_1RA1RC 0.565805 2.725739 4.988961
1RB0LA_1RC1LA_1LC0RD_1RZ1RE_1LB0RB 0.598393 3.014867 5.408437
1RB1RZ_0RC0RE_1LD0RA_1RB0LD_0LC1RA 0.623808 2.868375 5.363605
1RB0LA_0RC1RE_0LD0RB_1RC1LA_1RA1RZ 0.651181 2.953644 5.558369
1RB0RD_1LC1LB_1RA0LB_0RE1RD_1RZ1RA 0.674834 2.929456 5.628790
It looks like the median entropy is waaay lower ~0.1 (CPS) vs ~0.4 (holdouts) which (I think) makes some sense because I think that CPS is taking advantage of lower entropy to create a limited model of possible configurations that doesn’t include halting configs. But CPS still proved several reasonably high entropy TMs.
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And to round things out, here’s the distribution for top 100 Halting 6-state TMs (5-state TMs are too small and halt before simulation is done):
$ time python Code/ngram_entropy.py halt6.txt | sort -nk2
1RB1RZ_1RC1RA_1RD0RB_1LE0RC_0LF0LD_0LB1LA 0.026431 2.053672 2.159395
1RB1RE_1RC0LD_1LB1RZ_0LE1LD_1RF0RA_1RA0LA 0.030465 2.096668 2.218530
1RB0RB_1LC1RE_0LD1LB_1LA0LF_0RA0RB_1LA1RZ 0.032283 2.079196 2.208327
1RB0RB_1LC1RE_0LD1LB_1LA0LF_0RA0RB_0RC1RZ 0.033201 2.095847 2.228653
1RB0RC_1LC0LD_1RA0LB_1LE0RA_0RA1LF_1LB1RZ 0.033496 2.125938 2.259924
1RB1RZ_1RC0RE_1LD0RB_1LB0LC_1RF0LD_0LD1RA 0.033689 2.125954 2.260709
1RB1RF_1RC0RA_1LD0RB_0LE0LC_0LA1LE_1RA1RZ 0.034318 2.121867 2.259138
1RB0RB_1LC1RE_0LD1LB_1LA0LF_0RA0RB_1LE1RZ 0.034960 2.071099 2.210939
1RB0LC_1LA1RZ_0LD1LC_1RE0RF_1RF0LF_1RA1RD 0.035018 2.109867 2.249940
1RB0LE_1RC0LB_1RD0RF_1RE1RB_1LA0RD_1RD1RZ 0.036139 2.132902 2.277456
1RB0LD_1RC0RF_1LC1LA_0LE1RZ_1LA0RB_0RC0RE 0.037035 1.844898 1.993040
1RB0RF_1LC0RA_1LD0LB_0RD1RE_1RF0LE_1RA1RZ 0.037217 2.045673 2.194543
1RB0RF_0LB1LC_1LD0RC_1LE1RZ_1LF0LD_1RA0LE 0.037219 2.045660 2.194534
1RB1LF_1LC0RD_1LA1LD_1LB0RE_0LA1RB_0LE1RZ 0.040646 2.630279 2.792863
1RB0LA_1RC1RZ_1RD0RB_1LE0RF_0LA0LD_1RD0RE 0.040797 2.047001 2.210191
1RB0LA_1RC1RZ_1RD0RB_1LE0RF_0LA0LD_1RD1RB 0.040797 2.047001 2.210191
1RB0RC_1RC1RB_1RD1RA_1RE0LF_1LD1RZ_0LA1LF 0.042673 1.298521 1.469212
1RB0LC_1LA1RF_1LD0LE_1RE0RA_1LC0RD_1RD1RZ 0.042831 2.166315 2.337641
1RB0RD_1LC0RA_1LA0LB_1RE0LC_0RA1RF_1RA1RZ 0.043483 2.155920 2.329851
1RB1RF_1RC0RA_1LD0RB_1LE0LC_1RA1LE_1RZ1RC 0.045629 2.162059 2.344574
1RB1RF_1RC0RA_1LD0RB_1RE0LC_1RA1LE_1RZ1RC 0.046920 2.177552 2.365232
1RB0RF_1RC0RA_1LD0RB_1LE0LC_1RA1LE_1RZ0LA 0.049461 2.167388 2.365232
1RB0RF_1RC0RA_1LD0RB_1RE0LC_1RA1LE_1RZ0LA 0.050305 2.182866 2.384088
1RB1RZ_1RC0RA_1LD0RE_0LF0LC_1RC0RD_1RA0LF 0.056691 2.152736 2.379500
1RB1RZ_1RC0RA_1LD0RE_0LF0LC_1RC1RA_1RA0LF 0.056777 2.151818 2.378927
1RB0RD_1LC0RA_1RD0LB_1RE0LD_1RA1RF_1RA1RZ 0.057247 2.076274 2.305261
1RB0RD_1LC0RA_1RD0LB_1RE0LD_1RA0RF_0LD1RZ 0.057972 2.076272 2.308159
1RB0RD_1LC0RA_1RD0LB_1RE0LD_0LE1RF_1RA1RZ 0.058512 2.102893 2.336941
1RB0LC_1LA1RZ_0LD1LC_1RE0RF_1RF1RE_1RA1RD 0.058782 1.488335 1.723465
1RB0LC_1RC0RA_1LA0LD_1LE0RB_0LC1LF_1LC1RZ 0.059025 2.203560 2.439659
1RB0RA_0LC0RC_1RA1LD_0LE1LC_1LB0RF_1RE1RZ 0.059073 2.670231 2.906524
1RB1RZ_1LC0RA_0LD0RD_1RF1LE_0LB1LD_1RC0RF 0.059073 2.670231 2.906524
1RB1LD_1RC0RB_0LA0RA_0LE1LA_1LC0RF_1RE1RZ 0.060692 2.689891 2.932661
1RB0LA_1RC1RZ_1RD0RB_1LE0RC_1LF0LD_0RF1RA 0.061039 2.159305 2.403462
1RB0LF_1RC0RA_0LC1LD_1LE0RD_1LF1RZ_1LA0LE 0.061039 2.159305 2.403462
1RB0RD_1LC0RA_1RD0LB_1RE0LD_1RA1RF_1LA1RZ 0.061044 2.081224 2.325400
1RB0LB_0RC1LB_1RD0LA_1LE1LF_1LA0LD_1RZ1LE 0.061607 2.088306 2.334734
1RB0LF_0RC0RD_1LD1RE_0LE0LD_0RA1RC_1LA1RZ 0.061995 2.758484 3.006466
1RB0LF_0RC0LC_1LD1RE_1LB0LD_0RA1RC_1LA1RZ 0.062605 2.682003 2.932424
1RB1RE_1RC0LD_1LB1RZ_0LE1LD_1RF0RA_1RA1RF 0.063947 1.491418 1.747206
1RB0LE_0RC0RA_0RD0RE_1LE0RF_1LA0LD_1RA1RZ 0.064137 2.151735 2.408284
1RB0LC_1LC1RF_1LE0LD_1LC0RE_1RD0RA_1RE1RZ 0.064524 2.211178 2.469272
1RB0RD_1LC0RA_1LA0LB_1RE0LC_1LC1RF_1RA1RZ 0.064524 2.211178 2.469272
1RB0LF_1LC0RA_0RD1LB_1RF1RE_1RZ1RF_1LA0RD 0.064733 2.588631 2.847565
1RB0LF_1RC0RA_1LD0RB_0LE0LC_0LA0LB_1LC1RZ 0.065176 2.159423 2.420128
1RB0LC_1RC0RA_1LA0LD_1LE0RB_1RB1LF_1LC1RZ 0.065932 2.210204 2.473932
1RB1LF_1RC0RD_1LD0LE_1RB0LC_1LA0RB_1LC1RZ 0.065932 2.210204 2.473932
1RB0RC_1RC0LC_1RD1RA_1RE0LF_1LD1RZ_0LA1LF 0.070030 2.212752 2.492873
1RB0RC_1LC0LD_1RA0LB_1LE0RA_1LB1LF_1LB1RZ 0.070354 2.218575 2.499991
1RB1RF_1RC0RE_1LD0RB_1LB0LC_1RA0LD_1RB1RZ 0.070354 2.218578 2.499996
1RB1RZ_1RC0RE_1LD0RB_1LB0LC_1RF0LD_1RB1RA 0.070354 2.218578 2.499996
1RB0RC_1LC0LD_1RA0LB_1LE0RA_0RE1LF_1LB1RZ 0.070355 2.218580 2.500000
1RB1RZ_1RC0RE_1LD0RB_1LB0LC_1RF0LD_0LF1RA 0.070356 2.218581 2.500004
1RB0LA_1RC0RA_1LD0RB_0LE0LC_1LA0LF_1LB1RZ 0.070526 2.235329 2.517434
1RB1RZ_1LC0LF_1RD0LB_0RE0RC_1RF0RA_1LB0RF 0.070526 2.235329 2.517434
1RB1RZ_1RC0RE_1LD0LE_0LF0LC_0LF0RA_1RA1LC 0.070626 2.661838 2.944342
1RB0RF_1RC0RE_1LD0RB_1LB0LC_1RA0LD_0LE1RZ 0.072765 2.270568 2.561630
1RB0RC_1LC0LD_1RA0LB_1LE0RA_1LB0LF_0RD1RZ 0.072766 2.270576 2.561640
1RB1RB_1LC0RA_1LE0RD_0LD1RC_1RB1LF_0LB1RZ 0.082939 3.028819 3.360577
1RB0LE_0RC1RF_1RD0RB_1LA1RB_1LA0LD_1RA1RZ 0.088249 2.808615 3.161613
1RB0RF_1LC0RA_1LD0LB_0RD0LE_0LF1RZ_1RA1LA 0.097094 2.233849 2.622226
1RB0LF_1RC0RA_0LC0RD_0RE1RZ_1LF1RF_1LA0LE 0.097885 2.303905 2.695446
1RB0RF_1LC0RA_1RD0LB_1RE0LD_1RA0LC_0RE1RZ 0.100928 2.377033 2.780746
1RB0LE_0RC0RF_1RD0LD_1LA0RB_1LC1LC_1RB1RZ 0.102999 2.700411 3.112409
1RB1RZ_0RC0RA_1RD0LD_1LE0RB_1RB0LF_1LC1LC 0.102999 2.700411 3.112409
1RB1LE_1RC1RF_1LD0RB_1RE0LC_1LA0RD_1RZ1RC 0.115459 2.672642 3.134479
1RB1RF_1LC1RF_1RZ1LD_1LE0LF_0RA1RC_1RE0LD 0.119426 3.091113 3.568817
1RB0LF_0RC1RE_1RD1RA_1LE1RA_1RZ1LF_1LB0LA 0.119441 3.091051 3.568814
1RB1LE_1RC0RF_1LD0RB_1RE0LC_1LA0RD_1RZ0LA 0.120785 2.693624 3.176763
1RB0LE_0RC1LD_1RD1RF_0LB1RA_1LA1LB_0RA1RZ 0.122799 3.133320 3.624516
1RB1RZ_1LC0RE_0LD0LB_1RE0LC_1RF1RD_1LD0RA 0.125159 2.758484 3.259120
1RB1RC_1LC0RF_1RA0LD_0LC0LE_1LD0RA_1RE1RZ 0.125174 2.760195 3.260890
1RB1RF_1LC0LE_1RD0LB_0RE0RC_0LA0RA_1RB1RZ 0.125625 2.479001 2.981499
1RB1RZ_1LC0LE_1RD0LB_0RE0RC_0LF0RF_1RB1RA 0.125625 2.479001 2.981499
1RB0RF_0RC0RA_1LD1RA_1LE1RZ_1LA0LF_0RC0LD 0.127971 2.814481 3.326366
1RB1LD_1RC1RZ_1RD0RF_1LE0LF_0LA0LD_0LA0RB 0.128984 2.806547 3.322482
1RB0LF_0RC1RD_1LD1RA_0RE1LF_1RZ1RC_1LB0LA 0.131986 3.138235 3.666179
1RB1LF_0LC1RD_1RZ1LA_1RE0RF_0LA1LB_1LE0RD 0.131986 3.138235 3.666179
1RB0LC_1RC1RA_0RD0LE_1LE1RF_1LB1LC_0RA1RZ 0.144468 2.392150 2.970023
1RB0LB_0LC0RD_1LA1LD_1RE0LF_0RB0LB_0LE1RZ 0.166164 2.738109 3.402765
1RB0LF_0RC0LC_0LD0RA_1LE1LA_1RC0LC_0LB1RZ 0.166470 2.738110 3.403991
1RB0LC_0LA1RE_0RD1LC_1RA1LB_1RF0LE_1RC1RZ 0.202301 2.808486 3.617691
1RB1LC_1RC0LD_0LB1RE_0RA1LD_1RF0LE_1RD1RZ 0.202301 2.808486 3.617691
1RB1RZ_0RC1LB_1RD1LE_1RE0LB_0LD1RF_1RA0LF 0.202301 2.808486 3.617691
1RB1RZ_0RC1RD_0LD0LA_1LE0RB_0LF1LD_1RA0LA 0.208159 2.528048 3.360684
1RB0LA_1RC1LD_1LB0RE_1LB0LB_0RF0RA_1RZ1LC 0.232516 3.372232 4.302297
1RB0LA_1RC1RZ_0RD1LC_1RE1LF_1RF0LC_0LE1RA 0.237930 2.864985 3.816705
1RB0LA_1RC1LD_1LB0RF_0RE0LB_1RZ1LC_0RE0RA 0.245105 3.376250 4.356670
1RB0LA_1RC1LD_1LB0RE_1LB0LB_0RF0RA_1RZ1LD 0.252166 3.293632 4.302297
1RB0LA_1RC1LE_1LD0RF_1RZ1LE_1LB0LB_0RD0RA 0.260386 3.306564 4.348107
1RB0RF_1LC0RB_0LF0LD_0LE1RZ_1LC1LA_0RA1LE 0.276841 2.748674 3.856037
1RB0LA_0RC0RF_0LD1RE_1LA0LC_1RB1RD_0RE1RZ 0.276901 2.748394 3.855998
1RB1RD_0RC0RF_0LD1RA_1LE0LC_1RB0LE_0RA1RZ 0.276901 2.748394 3.855998
1RB1LC_1LC0RB_0LF0LD_0LE1RZ_1LC1LA_0RA1LE 0.276960 2.748121 3.855960
1RB0LA_0RC0RF_0LD1RE_1LA1RB_1RB1RD_0RE1RZ 0.277003 2.747908 3.855918
1RB1RD_0RC0RF_0LD1RA_1LE1RB_1RB0LE_0RA1RZ 0.277003 2.747908 3.855918
1RB0LA_1RC1LD_1LB0RE_1LB0LB_0RF0RA_1RZ0RD 0.294786 3.308191 4.487336
1RB1LE_1LC0RB_0RA1LD_1LE1LA_0LC0LF_0LD1RZ 0.311036 2.953248 4.197392
1RB1RD_0RC0RF_0LD1RA_1LE1RB_1RC0LE_0RA1RZ 0.311523 2.954133 4.200225
1RB0LA_0LC1RE_1LA1RD_0RB0RF_1RD1RC_0RE1RZ 0.311635 2.954006 4.200547
Entropy is even lower. This is not surprising at all given that all of these machines spend 99.99% of their time doing some basic bouncer behavior which is quite predictable.
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