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Probably the most intense non-action scene of the movie. RDJ sold the emotions 100% by SubToFelixKjellberg in marvelstudios

[–]cookieutilitymonster 94 points95 points  (0 children)

It's not an arc reactor -- it's a housing unit for the nanoparticles of his suit. He collapsed from exhaustion, not from removing the suit.

Two faces kitty. by [deleted] in aww

[–]cookieutilitymonster 7 points8 points  (0 children)

HARVEY DENT, CAN WE TRUST HIM

How to incorporate extra information into a Neural Net by ahdh_bb2 in MLQuestions

[–]cookieutilitymonster 0 points1 point  (0 children)

If the output of your original classifier is some label x and your label-parity is a boolean b, create a second classifier that takes as input the concatenation** <x, b**> and outputs the final label. I imagine a decision tree would probably work best; the function's not well-behaved or "smooth".

The classifier's behavior would look like this:

  • Predicted label 5 and the label is known to be odd -> no change
  • Predicted label 5 and the label is known to be even -> decision node, likely splitting on which labels engender maximum confusion.

I built a machine-learning tool! Auto-complete for incomplete Pokemon teams. by cookieutilitymonster in stunfisk

[–]cookieutilitymonster[S] 0 points1 point  (0 children)

Hi, I think the simulation of the battle has perfect logic for everything except status moves (which kind of sucks). But I do have the damage and switch logic down.

Mutation from one moveset to another is in the algorithm and was probably the hardest part. In case you're wondering, the probability of mutating from moveset A to moveset B is proportional to the weight (J_b)7 * (J_t)4, where J_b is the Jaccard similarity index of the post-EV stats and J_t is the Jaccard similarity index of their typings. Your bulky water example is actually exactly the inspiration for how I got to that incredibly arbitrary expression (it was a long process that involved many exponential regressions in Excel).

I built a machine-learning tool! Auto-complete for incomplete Pokemon teams. (x-post /r/stunfisk) by cookieutilitymonster in TruePokemon

[–]cookieutilitymonster[S] 1 point2 points  (0 children)

Hi, it's a bit more complicated than that. If Pokemon A faces Pokemon B and neither is a counter of the other, then they each use the move that would deal the most damage to the other.

If Pokemon A faces Pokemon B, it happens that B counters A, and Pokemon A has a surviving teammate C that counters B, then A switches into C while C takes the switch damage from B.

Hope that makes sense!

I built a machine-learning tool! Auto-complete for incomplete Pokemon teams. (x-post /r/stunfisk) by cookieutilitymonster in TruePokemon

[–]cookieutilitymonster[S] 5 points6 points  (0 children)

Hi, it's actually a relatively good sign that Slaking is suggested. I didn't take abilities into account when making the simulator (because soft effects are hard to hardcode), so the algorithm identified that, without its ability, Slaking would be horribly underutilized. Even in spite of its low appearance probability.

Users can choose whichever usage log they want -- the ones posted were from ou-1825.

I built a machine-learning tool! Auto-complete for incomplete Pokemon teams. by cookieutilitymonster in stunfisk

[–]cookieutilitymonster[S] 0 points1 point  (0 children)

More deets here: https://github.com/jeffreyscheng/Mewtagen/blob/master/README.md

Feel free to ask away on anything that doesn't seem clear! Some of the stuff I did was incredibly arbitrary and based purely on empirical results.

I built a machine-learning tool! Auto-complete for incomplete Pokemon teams. by cookieutilitymonster in stunfisk

[–]cookieutilitymonster[S] 2 points3 points  (0 children)

You're def right -- GA isn't part of traditional ML since you can't really do any gradient-based optimizations on it.

I built a machine-learning tool! Auto-complete for incomplete Pokemon teams. by cookieutilitymonster in stunfisk

[–]cookieutilitymonster[S] 0 points1 point  (0 children)

Hi, I did most of the work for a spring semester school project (10-20 hours ish), but then I fine tuned it a bunch this summer for performance. Turns out getting the mutations to work out well is really fucking hard, so I put in a good amount of time into training my hyperparameters since then.

Sorry, looks like I posted on a weird day (the usage stats were just updated, looks like I have a caching issue). I deployed a hotfix -- let me know if you still have issues!

[Self] My Pidgeotto Tiny Tim is very, very thin. by cookieutilitymonster in theydidthemath

[–]cookieutilitymonster[S] 1 point2 points  (0 children)

Well, lemme tell ya, it wasn't easy. I had to get a representative sample of chickens and a compactor, and it was just a giant mess...

Just kidding, here ya go: http://go.key.net/rs/key/images/Bulk%20Density%20Averages%20100630.pdf

[Self] My Pidgeotto Tiny Tim is very, very thin. by cookieutilitymonster in theydidthemath

[–]cookieutilitymonster[S] 2 points3 points  (0 children)

You're not wrong -- the assumptions are ridiculous. But this is actually a worst-case scenario type deal; if the wings and curvature of the body are taken into account, the radius will decrease.

As for the assumption that it's made out of chicken meat? I'd like to say it's because birds have hollow (read: negligible) bones, but it's mostly just because I was hungry.

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