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Logs on 2024-07-28 (liberachat/#haskell)

00:02:07 <geekosaur> `main` isn't special. you can always access some other module's `main` qualified
00:02:20 <geekosaur> module `Main` is special (check the Report)
00:02:52 <geekosaur> (and `main` in some other module can have any type)
00:04:58 <Inst> no, but let's say you have some package that has Main somewhere, under Main is, with module Main (iirc this is compulsory)
00:05:23 <Inst> there's no way to differentiate between this module Main and the actual module Main you're using, no?
00:06:03 <geekosaur> no, but module Main is considered reserved — libraries are expected not to use it
00:06:49 <Inst> ancient
00:06:57 <geekosaur> ghc allows you to change this, by using `-main-is` to point to a different module as the main module (and optionally a different binding within that module, provided its type unifies with `IO a`)
00:06:58 <Inst> oh well
00:07:00 <Inst> https://stackoverflow.com/questions/27591266/telling-cabal-where-the-main-module-is
00:07:30 <geekosaur> also note that a cabal file's "main-is" is subtly different from ghc's -main-is
00:08:43 <Inst> main-is afaik has to point to a file containing module Main, right?
00:09:45 <Inst> yeah, there are more recent responses on that stackexchange
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00:12:39 <geekosaur> (libraries *could* meaningfully do that kind of thing, though, specifically to allow use of Haskell plugins via FFI from C etc.; see hs_init() (https://downloads.haskell.org/ghc/9.10.1/docs/users_guide/exts/ffi.html#using-your-own-main)
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05:10:00 <Guest36> I was working through the Functional Pearl, Probabilistic Functional Programming. It's a nice paper. There is a `selectP :: (Num prob, Ord prob) => T prob a -> prob -> a` which seems reasonable to me. For this question let's say `prob` is `Float` and `T prob a` is `[(Float, a)]`. The implementation of `selectP` looks at the head of the distribution
05:10:01 <Guest36> and if the outcome's probability, say `q`, is greater than the 2nd arg (ie, probability as a `float`), say `p`, it recurses over the tail of the distribution but with `p-q`. Can someone give some intuition on what that is achieving (there is no specified ordering of distribution elements by their probability, afaics, the `Ord` constraint seems to
05:10:01 <Guest36> be only for the `p<=q` test)?
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05:16:57 <Guest36> If it was the case that the distribution is known to be unaggregated (ie the same outcome can appear more than once), and the list grouped equivalent outcomes together, then maybe it would make a bit more sense to me, but still not porperly, because it would always return the first outcome that is more likely to occur than `p`.
05:17:29 <mauke> https://web.engr.oregonstate.edu/~erwig/papers/PFP_JFP06.pdf
05:17:35 <Guest36> yes
05:17:55 <Guest36> and I am looking at the code for probability-0.2.8
05:19:05 <glguy> If all the probabilities in the list add up to N and your random number is in the range 0-N you're getting a weighted sampling of the elements in the list
05:19:07 <Guest36> (which is newer than the paper's downloadable code tar)
05:19:19 <Guest36> OK
05:20:38 <Guest36> if the list is not ordered, you would eat up `p` at each step and then choose some outcome which might have nothing to do with the ones that ate up most of your `p`
05:20:55 <glguy> The order wouldn't matter
05:21:09 <Guest36> that's the bit I don't see, I suppose
05:21:10 <glguy> Because of the p-q part you mentioned
05:21:27 <Guest36> `p-q` is what I mean by eat
05:21:41 <Guest36> *eat up
05:22:03 <Guest36> am I thinking incorrectly?
05:22:20 <glguy> It's the sum of the probabilities that matters, not the order
05:23:08 <Guest36> right, but you are jumping over outcomes that did some of the eating up of p
05:23:14 <glguy> So if you've got probabilities 1,1,2,3. Pick a number between 0 and 7
05:23:28 <glguy> The first 0-1 are for the first element
05:23:54 <glguy> 1-2 for the next, 2-4 will pick the third, 4-7 the last
05:24:09 <glguy> With a tie breaking rule for exact matches
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05:24:41 <Guest36> right, but if the order was different you would get a different outcome returned, no?
05:25:12 <glguy> You'd get the same distribution
05:25:53 <glguy> You'd get different values for different random indexes, but that doesn't matter
05:25:55 <Guest36> so, we are returning an `a` (outcome)
05:26:16 <Guest36> not a distrubution
05:26:37 <glguy> But you'd have the same likelihood of each outcome
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05:26:51 <glguy> It's a distribution of outcomes
05:27:22 <Guest36> I was trying it out in VSCode with HLS
05:27:22 <Guest36> ```
05:27:23 <Guest36> -- >>> [scanP x [(1, 0.6), (2, 0.1), (3, 0.2)] | x <- [0.5, 0.6, 0.7, 0.8, 0.9]]
05:27:23 <Guest36> -- [1,1,2,3,3]
05:27:24 <Guest36> ```
05:27:39 <Guest36> where `selectP` just calls `scanP`
05:28:09 <Guest36> but I still don't understand what it is acheiving
05:29:03 <glguy> It's choosing one element from a finite list of elements
05:29:13 <glguy> Not necessarily uniformly
05:29:25 <Guest36> if we had to write a 1 line doc comment, or whatever we call that in haskell, for `selectP` what precisely would it say?
05:29:58 <glguy> Element an element from a weighted finite distribution
05:30:00 <Guest36> there is something that, to me, is not apparent just from the types
05:30:42 <glguy> Select an element from*
05:31:18 <Guest36> but what does the `p` signify, if you are just traversing the list until you at up `p`?
05:31:40 <glguy> The weight
05:32:22 <glguy> If one element's p is twice as much as another's then it's twice as likely to be selected
05:32:36 <Guest36> let's say the distribution is both aggregated and normalised (ie each outcome is unique and the total probability is 1(
05:33:03 <glguy> Ok
05:33:53 <Guest36> "If one element's p is twice as much as another's then it's twice as likely to be selected" that might be what I don't find obvious, because that "big one" ,ight be at  the end of the list, and it will never get reached if the complement is > p
05:34:41 <mauke> imagine selectP's second argument ranges from 0 to 1, continuously, uniformly
05:34:57 <Guest36> so the accidental ordering would be significant
05:34:58 <Guest36> OK
05:35:24 <glguy> Just think about the simple case : 1/3 and 2/3
05:35:31 <Guest36> OK
05:35:46 <glguy> Order then ascendinh
05:36:10 <Guest36> but that is what is missing, iiuc - there is no implied ordering
05:36:10 <glguy> 1/3 of the time you pick the first element, leaving the second for the other 2/3
05:36:26 <glguy> Ok now order them the other way
05:36:35 <Guest36> ohh!!!
05:36:37 <glguy> 2/3 of the time you pick the first element
05:36:38 <Guest36> yes
05:36:45 <Guest36> nice example, thanks
05:37:22 <mauke> I need some wooden blocks to illustrate this
05:37:42 <glguy> Yeah, pictures > text here
05:37:45 <Guest36> so, as you previously said, the distribution of returned outcomes is the same
05:38:32 <mauke> each outcome gets an associated color and a block of that color
05:38:46 <mauke> the probability of the outcome corresponds to the length of the block
05:38:57 <Guest36> nice
05:39:08 <mauke> you stack the blocks up (and because probabilities are normalized, the stack has total length 1)
05:39:41 <mauke> then you pick a random distance between 0 and 1 and see which color you land on
05:39:56 <Guest36> not normailsed and not aggregated would not change anything, if my intuition is now better!
05:40:27 <Guest36> precisely because the orsed does not matter, as you have both demonstrated
05:42:45 <Guest36> mauke I think the next sentence is the key but is missing (because it is "probably" obvious to most) - if you repeat this repeatedly, you will get a distribution of outcomes consistent with the original distribution
05:44:40 <mauke> yes, that's important
05:44:52 <mauke> that's why I started with "imagine selectP's second argument ranges from 0 to 1, continuously, uniformly"
05:44:59 <Guest36> right
05:45:18 <mauke> if you only look at an individual result, it makes no sense
05:45:53 <Guest36> exactly - so I can only see a meaningful use for it in the _random_ sampling
05:46:44 <Guest36> where you want to approximate, for example, because the transitions explode exponentially
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05:59:42 <Guest36> btw, also just watched the first few minutes of https://www.youtube.com/watch?v=LHlZNYFraAs which seems to start off covering similar material to the paper
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14:58:02 <sssgemooo> Are there specific libraries to optimize audio Graphs? Which Graph library would allow some flexibility to adapt it?
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15:02:36 <sssgemooo> https://hackage.haskell.org/package/hsc3-0.20/docs/Sound-Sc3-Ugen-Graph-Transform.html
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All times are in UTC on 2024-07-28.