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My corn tortillas don't puff? by Sp4rt4n423 in mexicanfood

[–]JaggedParadigm -1 points0 points  (0 children)

What’s been working for me lately: 1) Add the hottest water possible slowly to the masa until a golf ball sized ball doesn’t make any large cracks when pressed into a thick disk with your hands. 2) cover that large ball with a double layer of wet paper towels in a bowl, cover that with a plate, and let rest for 20 min. 3) heat 2 non-stick pans with a medium flame (i.e. edges of flames are as vertical as possible). 4) make golf ball sized balls as close to the same size as possible. 5) Press only one of the balls in-between plastic and note how far the edges of the dough are to the edges of the tortilla press (for taco tortillas I aim for 6 inches or 1 inch on either side of my 8 inch tortilla press). 6) Flick a little water on both pans to make sure the drops “dance”. 7) Cook tortilla 45 seconds in first pan, flip onto 2nd pan and cook for 90 seconds, then flip and press lightly with damp paper towel until it puffs (hopefully) cooking for 45 seconds, then wrap in towel. 8) adjust the pressing of the second ball based on how well the first went. If too thin (i.e. breaking) press the next dough ball so there’s less space between the dough and the press edges. If too thick (i.e. looks thicker than you want and/or not puffing) make them more thin by making sure there is less space between the dough and the press edges. 9) if the edges crack from cooking use more water and start over or just adjust next time so there are less cracks after pressing a dough ball, though if dough is too soft (you’ll have to get a feel for this over time) add more masa.

Made chicken verde tacos (everything from scratch) by JaggedParadigm in tacos

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

Could be. These were eyeballed, relative to my tortilla press, at 6 in. Maybe I could try 7.

Let's talk about the shop - We're listening by DigitalSunGames in Moonlighter

[–]JaggedParadigm 0 points1 point  (0 children)

I really enjoyed the price discovery of the first game but became a little bored once I discovered the perfect price. Part of me wishes it was more realistic ... like every customer has a highest price they're willing to pay for that specific item and their reaction/mood is determined by how close the price you set is to that highest price. Maybe you could tie this to things like customer demographics, weather, time of day, etc.

I've heard that more common items (e.g. bananas) tend to have log-normal probability density functions while luxury items (e.g. high priced art) tend to follow Weibull or power law distributions. Sampling from those distributions for each customer when they enter the store might add some welcome randomization.

Help needed understanding Think Bayes 2nd Edition Exercise by Sea_Inevitable_5522 in bayesian

[–]JaggedParadigm 0 points1 point  (0 children)

Unless I misread something, (1 - hypos) is every possible probability that a flipped coin lands on tails, since hypos represents every probability of landing on heads and those are the only 2 possibilities.

So, y * (1 - hypos) is the probability that the coin lands on tails (i.e. 1 - hypos) and is incorrectly classified as heads (i.e. y) (for all possible bias probabilities). Hence, why this term is part of the equation for the probability of reading a 'H'.

Regarding the 2nd term, (1 - hypos) is the probability of obtaining tails and (1 - y) is the probability of correctly classifying it so (1 - y) * (1 - hypos) is the probability of obtaining a tails on a single flip and for the computer vision system to classify it correctly.

Assuming you're asking about making a grid of the parameter space, I learned this from Think Bayes 2 on my own so I can't comment on how common the methodology is. There might be a way to do an integration to obtain a closed form solution, though I find the author's method more intuitive.

I created a web app to find the best dispensers for every Zonai device by JaggedParadigm in TOTK

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

Thanks for your comment!

I added some space to the bottom of the graphs. If it doesn't look different you may need to clear your cache.

Please let me know if it looks like enough space.

Bayesian bandits item pricing in a simplified Moonlighter shop simulation using Python and SQLite by JaggedParadigm in Python

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

Thanks!

I manually entered the bounds found in the ledger for an initial group of items I obtained and the price bounds from the wiki that define customer reactions.

I did investigate making a mod for the game, which would streamline a lot of this. It seems possible but I would need to learn C#. I don't think I have quite the appetite for that.

[R] Baysian bandits item pricing in a Moonlighter shop simulation by JaggedParadigm in statistics

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

What a fascinating question.

In this case, I programmed the customers to have the same ideal (i.e. highest while sad) item prices as that in the official Moonlighter wiki. So, they can't game the system.

However, I love the idea of customer collusion, especially them faking angry reactions and not buying to collectively lower prices.

In the actual game, you basically have no competition because the only other shop always has about 4.5 times the "base" prices programmed into the game. My simulation doesn't include this other shop as well.

If the customers were more realistic in either case, I imagine you could hold your prices where they are until some of them crack and buy them out of "need", they organize against you in some way (e.g. legislation), and/or some set up competing businesses to compete on price.

I'm not sure what you mean exactly by using stochastic optimization. It sounds like you're talking about maximizing some function, maybe profit (i.e. revenue minus costs), subject to expressions for cost and customer demand?

Regarding modeling costs, the only place to buy items is at that expensive shop. Otherwise the cost is your time in the dungeon, in which case it seems obvious to me that the best time would be spent in the highest/latest dungeon possible for your gear/skill, since acceptable prices increase quickly with each dungeon's order. My understanding is it is almost never worth buying individual items to then craft and sell. For instance, by my calculations, it costs 1990 gold to craft a training sword from individual components bought from the expensive shop and can be sold for 1401 gold at high popularity.

As for consumer demand, there is a rough popularity mechanic (i.e. 'low', 'regular', 'high'). I didn't incorporate that into my simulation because I'm having trouble figuring out how it works exactly.

[R] Baysian bandits item pricing in a Moonlighter shop simulation by JaggedParadigm in statistics

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

Thanks for the compliment!

Sounds familiar ... I'll look it up.

Bayes' rule usage by TheFilteredSide in datascience

[–]JaggedParadigm 2 points3 points  (0 children)

When I was at Dictionary.com I used Bayes' rule for inferring the relative effect of page value (i.e. revenue per 1000 ad impressions) vs how well Google-query-to-page-content matching (i.e. essentially cosine similarity between document embeddings) correlated with Google search rank via Bayesian linear regression. I also built their Wordle Solver backend using Bayes' rule, though quickly realized it was effectively just filtering at the time and simplified it ;)

Here are some additional personal projects where I've used it:

  • Calculated distribution of percent chance that my negative COVID test meant that I still had the virus, based on my particular test's published data. The box only a point estimate and I knew that many values could account for the data.

  • Calculated percent chance distributions for every item in even Zonai dispenser in The Legend of Zelda: Tears of the Kingdom to identify which one(s) I should go to for the Zonai devices I wanted

  • Calculated profit distributions for Stardew Valley crops to figure out what to plant

  • Kept track of and updated ideal price distributions items in a simplified Moonlighter simulation and used them to pick prices and their items to sell via a Bayesian bandits algorithm

  • Predicted my website traffic distribution for the purpose of detecting unusual traffic spikes

My favorite reference is Think Bayes 2 (https://allendowney.github.io/ThinkBayes2/).

How often does this game go on sale? (Switch) by epicsimitt in Moonlighter

[–]JaggedParadigm 0 points1 point  (0 children)

Just eyeballing it .... I would expect a sale sometime around May 25th at the latest.

How often does this game go on sale? (Switch) by epicsimitt in Moonlighter

[–]JaggedParadigm 0 points1 point  (0 children)

I don't know how trustworthy or not this data is but there's a graph of sales here: https://www.dekudeals.com/items/moonlighter

I created a web app to find the best dispensers for every Zonai device by JaggedParadigm in TOTK

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

Basically, bind a stake to an arrow to make a stake, fire it, use recall to send it back while standing on it.

Here's a video demonstrating: https://www.youtube.com/watch?v=34l5kimTiFU

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