As other commenters have said, you're just going to learn the decoder ring from context, but I'm also curious. If I take off the keycaps to clean the keyboard and then put them back, do I need to relearn the defects? If I rip out all the switches and rearrange them, do the defects follow the switches or is it more about the position of the key? Is there some resonance that could change if I move the keyboard onto a desk pad. If I switch to Dvorak in software, obviously the key presses mean something different, but also I'll type completely differently. How much does user cadence/timing matter? To what extent is user cadence/timing identifying?
Anyway, I assume the answers aren't known because it would be an ethics/privacy nightmare to run these kinds of experiments on anyone but yourself.
Yeah, lots of different uses for a keyboard with wildly different frequency distributions. Writing an English letter != text chat in Spanish != data entry != league of legends. It's also not quite a substitution cipher due to modifiers (shift), toggles (caps lock), non-printable characters (escape, volume up/down), and deletion (backspace/delete).
It's an interesting problem you'd probably try to solve with Markov chains back in the day, but now you'd just throw machine learning at.
>> Hashmaps don't have guaranteed performance characteristics
>Of course they do
No, they do not, at least not worst case O(1). If you write a very poor (but functional!) hash function or an adversary is choosing your items, then congratulations, your hashmap is now a linked list with extra steps.
Sure, you could use a robust cryptographic hash function, but you didn't because (a) it's not the default in your language and (b) it's slow and you are "a lot more interested in a low-constant-factor O(1) best case".
I'm no expert, but there are approaches to reinforcement learning where you build a table of the expected payoff for each scenario, and these can recursively reference other entries in the table. Take a look at Q-learning, for example.