I placed B as the safest snark, despite it being the 21st
In general, I completely missed the fact that the choice not to hunt a snark was very far from random, thus introducing a bias I neither noticed nor corrected for
The above is the output for B from adding a “normalized_sprobs” to abstractapplic’s eval_snark_probs as follows:
def eval_snark_probs(ptaste, mtaste, wakemins, fond, lin, phob): sprobs = eval_species_probs(ptaste, mtaste, wakemins, fond, lin, phob) Ybooj = sum([sprobs[name] for name in sprobs if snarks[name][“boojum”]==True]) Nbooj = sum([sprobs[name] for name in sprobs if snarks[name][“boojum”]==False]) normalized_sprobs = {name: prob / sum([sprobs[name] for name in sprobs]) for name, prob in sprobs.items()} return Ybooj/(Ybooj+Nbooj),normalized_sprobs
You’re absolutely right, thank you, the inaccurate positioning of B has nothing to do with the probability for a snark not to be hunted
Looking at the code, my model is actually not really spot on, it just kind of looks similar to the real one. I also assume that the snarks can be split into species, each with specific waking-times, phenotypes and probability of being a snark, but, in details, both are actually quite different.
So yes, I built a different model, and got a different ranking of snarks, what was I expecting ’^^
How inaccurate were your results? Maybe your expectations were just too high?
I placed B as the safest snark, despite it being the 21st
In general, I completely missed the fact that the choice not to hunt a snark was very far from random, thus introducing a bias I neither noticed nor corrected for
I’m not convinced that’s the issue…
If B is a boojum it’s almost certainly a Snippid, which should show up just fine.
(0.03386145617504304, {‘Vorpal’: 0.9114744863640762, ‘Frumious’: 0.00013955487845201242, ‘Slythy’: 0.012207182834474093, ‘Mimsy’: 0.0, ‘Manxome’: 0.0, ‘Whiffling’: 0.0, ‘Burbling’: 0.0, ‘Uffish’: 0.0, ‘Gyring’: 0.015702867032507836, ‘Gimbling’: 0.026614452715446928, ‘Cromulent’: 1.1153450923986715e-05, ‘Snippid’: 0.033850302724119055, ‘Scrumbling’: 0.0})
The above is the output for B from adding a “normalized_sprobs” to abstractapplic’s eval_snark_probs as follows:
def eval_snark_probs(ptaste, mtaste, wakemins, fond, lin, phob):
sprobs = eval_species_probs(ptaste, mtaste, wakemins, fond, lin, phob)
Ybooj = sum([sprobs[name] for name in sprobs if snarks[name][“boojum”]==True])
Nbooj = sum([sprobs[name] for name in sprobs if snarks[name][“boojum”]==False])
normalized_sprobs = {name: prob / sum([sprobs[name] for name in sprobs]) for name, prob in sprobs.items()}
return Ybooj/(Ybooj+Nbooj),normalized_sprobs
Sorry for the late response
You’re absolutely right, thank you, the inaccurate positioning of B has nothing to do with the probability for a snark not to be hunted
Looking at the code, my model is actually not really spot on, it just kind of looks similar to the real one. I also assume that the snarks can be split into species, each with specific waking-times, phenotypes and probability of being a snark, but, in details, both are actually quite different.
So yes, I built a different model, and got a different ranking of snarks, what was I expecting ’^^
Thank you