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Douglas M. Auclair (geophf): 'O' is for ontology.

Planet Haskell - Sun, 04/20/2014 - 2:09am

'O' is for Ontology.
What is an ontology? A knowledge-base? Sure, if that's simpler to grasp, but only insofar as 'knowledge-base' doesn't mean 'collection of facts' or 'data set.'
An ontology is more than that. But what, precisely, is an ontology?
Well, actually, there is a precise meaning to 'ontology.' And 'meaning,' itself, is central to ontology. Because what does these data mean is what an ontology is about. It's not a listing of facts, but it's also the relationship of the facts in the ontology that makes it what it is. The data, the facts, of an ontology have meaning, not only intrinsically, but also explicitly, or: the meaning is useable, or can be processed, itself, as information, and used in the handling of the underlying information.
An ontology? Absolutely! You hand that to your husband, and he knows exactly what it is and he knows exactly how to use it. He even, helpfully, penciled in the missing item (ho-hos, just as a 'fer instance') onto your shopping list for you.
Now, ontology, per se?  Not so much. But if you explicitly titled it "Shopping List," now you're talking!
Format it as XML or JSON or OWL and then your computer will do your shopping for you, just as well as your husband would.
Even better, as it won't add those pesky ho-hos your husband always 'helpfully' adds to your list for you.
Your computer does need arms and legs and artificial intelligence, but I'm sure Cyberdyne Systems will be happy to help you there ... and also hand your terminator that was a computer a fully automatic plasma rifle.
Whoopsie! Better give your husband the list, and live with the ho-hos ... Ho-hos are, relatively speaking, better than global thermonuclear war and the extinction of all mankind.
But I digress.
As always.
Categories: Offsite Blogs

Douglas M. Auclair (geophf): 'P' is for Predicate

Planet Haskell - Sun, 04/20/2014 - 2:08am
Hey, all, 'P' day.

'P' should be for π, but that's redundant, right? since π (pronounced 'pee' in math circles) is just the letter 'p' in Greek.

And it's 3.141592653589793238462643383279502884196939937510582097494459230

Yeah, ... I memorized that in high school. For real. People didn't believe me. Every time. So I'd recite it for them, after I wrote it down on a piece of paper so they could verify that I wasn't just spouting digits.



So, but, this post is not about π.

No. Wait. Stop using 22/7 to represent π. That's, like, way off. Way. Use 355/113 instead. And thank Chuck Moore whenever you do.

Okay, done with that aside. Onto this post. This post is about the predicate.

So, what's a predicate?

A predicate is a statement of truth, that you arrive at from other predicates.

Yeah, math and logic are like that: a thing is a thing from other things just like it. Take 'number' for example, any number, x, is just all the previous numbers before x with some starter seed (usually zero or some other representation of null).

Well, that's what a predicate is.

First of all, a predicate is more than a proposition. A proposition is something that you've been seeing already in the alphabet-soup I've been writing, so the proposition for the type of the continuation function is:

(p → r) → r

So, this says, 'given a function that takes a p and gives an r, I can get you an r,' right? Remember that?

Well, a predicate is of this form:

p |- q

Or, if you're feeling your Prolog:

p :- q

A p is true, depending on whether q is true.

(Here we go again with dependent types).

So, what's the difference? I mean, if you just reverse the arrows you could say you have:

q → p

And you could say that, but there is a difference, in that the propositional logic of

p → r

Is all very deterministic whereas the predicate logic of

p :- q

is all very non-deterministic. Predicate logic allows you to base the truth of your consequence on none, one, or several conditions.

So, you could have:

p :- q, r, s.

Which means that p is true if q and r and s are all true, as well.

These are universals, but you can get specific with it, too:

p(x) :- q(x, y), r(y, z).

Which says that there is some x in p, giving a truth, dependent upon the same x in q (associated with some y) being true, along with the same y in r being true with some z.

And, to prove that statement you have to find the (x, y, z) values that satisfy the conditions of that statement. And you keep looking until you find it.

What does that sound like? ... We'll get back to that in a moment.

The neater thing about predicate logic is that the clauses of a predicate can, themselves, be consequences of other predicates:

q(x, y) :- s(y), t(x)
r(a, b) :- t(a), w(b)

And predicates can have multiple statements to arrive at that truth:

q(1, 2).
q(2, 4).
q(3, 6).

The above is saying: "q(1, 2) is true, regardless of anything." And you can take that truth (that fact) and plug it in anywhere. The fact propagates throughout the system.

So, those three facts, along with the relation, q(x, y) :- s(y), t(x), form a predicate. A statement of truth throughout the system, covering all possibilities.

A predicate is a program.

Yeah. Wow.

In Java or Haskell or Ruby a program is thousands, tens of thousands of lines, but in a logic programming languages, like Prolog, for example, a program could be one line or several ... more than a few lines, actually, and you're going off the deep end. And how you program in Prolog is that you string these predicates, these programs, together to create an Universe of related truths, an ontology, in which you can ... play, by which I mean, you can query the system, get an answer (if there is one) or get back several answers, and explore this Universe you've created, or that someone has created for you, as an inquiry into truth.

*Sigh* I miss coding in Prolog. When done neatly, it was so neat to use.

But I don't miss coding in Prolog. It was all dynamically typed, so you could put in anything for your inquiry, and it would try it out, looking for the string "Mama meetzaballs!" even though this particular predicate is about arithmoquines. And I don't miss it for the fact that statements of fact were easy to formulate, but the functional constructs ... ugh, they were terrible.

I need me a programming language that does logic typefully and handles functional logic beautifully, like Haskell does. Maybe ... Idris? ... but Idris doesn't work on my decade-old laptop. Birthday present for moi, then?

Categories: Offsite Blogs

JSON validation combinators

Haskell on Reddit - Sun, 04/20/2014 - 1:21am
Categories: Incoming News

Bartosz Milewski | FP Complete - Sat, 04/19/2014 - 10:44pm
Categories: Offsite Blogs

Gabriel Gonzalez: How the continuation monad works

Planet Haskell - Sat, 04/19/2014 - 10:32pm

I remember the first time I read the Monad instance for ContT I was so confused. I couldn't fathom how it worked because it was hard to discern the pattern.

However, I later discovered that renaming things makes the pattern much more clear:

import Control.Applicative

newtype ContT x m r = ContT { (>>-) :: (r -> m x) -> m x }

instance Functor (ContT x m) where
fmap f m = ContT $ \_return -> -- fmap f m =
m >>- \a -> -- m >>= \a ->
_return (f a) -- return (f a)

instance Applicative (ContT x m) where
pure r = ContT $ \_return -> -- pure r =
_return r -- return r

mf <*> mx = ContT $ \_return -> -- mf <*> mx =
mf >>- \f -> -- mf >>= \f ->
mx >>- \x -> -- mx >>= \x ->
_return (f x) -- return (f x)

instance Monad (ContT x m) where
return r = ContT $ \_return -> -- return r =
_return r -- return r

m >>= f = ContT $ \_return -> -- m >>= f =
m >>- \a -> -- m >>= \a ->
f a >>- \b -> -- f a >>= \b ->
_return b -- return b
Categories: Offsite Blogs

Request: What to read for reification/hylomorphisms?

Haskell on Reddit - Sat, 04/19/2014 - 9:02pm

Reification into an intermediate data structure is often a neat little trick. Tree sort, where you foldl' and insert elements into a tree then flatten the tree is a neat little technique. Ornamented zippers for traversing trees makes it possible to pretend you have a tail recursive function.

What other interesting solutions are there that reify some computational structure into an intermediate data structure?

submitted by psygnisfive
[link] [2 comments]
Categories: Incoming News

Is there a way to modify the default implementation of Show?

Haskell on Reddit - Sat, 04/19/2014 - 8:13pm

I've got a complicated ADT, and want to be able to show it. The default implementation obtained from deriving Show on it is close to what I want, but I want different behavior on one of the (many) constructors the ADT has. Is there a way of telling Haskell "use the default implementation except for this one case which should be done differently," or do I need to write the whole thing out by hand?

This is an example of roughly what I'm aiming at:

data Foo = First Int Int | Second String Int | Third [Bool] String | Fourth Int Int Int | Fifth String | Sixth Foo Foo | Seventh Foo String | ... | Twentieth String [Bool] deriving Show

I'd like Foo's implementation of show to have the default behavior except that Twentieth shouldn't display its list of Bools. Is there a simpler way of doing this than writing the whole thing myself?

edit: made Foo recursive, as my actual ADT is recursive, too.

submitted by penguinland
[link] [20 comments]
Categories: Incoming News - Sat, 04/19/2014 - 7:46pm
Categories: Offsite Blogs - Sat, 04/19/2014 - 7:46pm
Categories: Offsite Blogs

Douglas M. Auclair (geophf): 'Q' is for Quine

Planet Haskell - Sat, 04/19/2014 - 4:47pm
So, yesterday ('P' is for Predicate) I forgot to mention to whom we owe our gratitude, and that is Gottlob Frege. After Wittgenstein, Frege is considered to have made the most important contributions to philosophy through his formalization of predicate logic.

I mention this, in fronting (as opposed to 'in passing') because this post is about 'quining.' And the word 'Quine' comes from the American philosopher Willard 'Van' Quine.

Quining is a neat thing. What a quine does is it's a program that outputs itself.

That sounds simple enough, but there's a problem here. If you were using BASIC, for example, then you might start off with:

10 PRINT "10 PRINT \"10 PRINT \" ...

but then, whoops! We've just run into a problem, haven't we?

So how do we solve that? With a GOTO statement? Even though the "GOTO Statement Considered Harmful"?

Crusty BASIC programmer: HECK! I've used GOTO zillions of times, and it never smacked me once!

Yeah. Okay. Whatevs.

But, okay, let's give it a try:

10 PRINT "10 PRINT \""
20 GOTO 10

The problem here is that it prints:

10 PRINT "
10 PRINT "
10 PRINT "
... blah, blah, blah ...

but it never gets to the

20 GOTO 10

That solved a whole ton of nothing.

Writing quines in non-functional languages is a big-ole pile-o-pain! But totally doable, in tons of languages (and since languages have no weight, that's saying a lot)!

Here's the ones for BASIC (the first one is totally cheating, by the way) (or totally clever) (I'm not sure which).

Now, in functional programming languages, writing a quine is trivial.

Here's one in combinatory logic:


because, as you recall,

M = λx → xx

So, MM is M where x is also M so MM = MM

Piece of cake!

Now, since, usually, functional languages are in an evaluator, that gives instant feedback to what you just entered, for example:

3 + 4



right away.

Well, then just simply evaluating a function that returns itself is easy enough:




(the 'unit' value)





(the empty list)

And even:




... and we can skip all this 3 + 4 nonsense.

So, there's that.

Same thing for any programming language that has an evaluator, like Prolog, (SWI, for example, or interpreted on the web) (ooh, look at the cute little 'witch' example on the online Prolog interpreter!):

? X = 3

X = 3

So, there are those that might consider this cheating, too, like that BASIC program that uses a BASIC command to print itself as its own list. I say P-SHAW!

But also, as the quine it to get you thinking about recursion and bootstrapping, maybe those blow-hards saying you can't use the evaluator to write a quine may be onto something.

Quine was a man who exercised his brain, and let to some serious reexamination of what it is to be true, that there may be things we consider 'obviously' true to be examined again under the light of our experiential knowledge.

A little quine program just may do the same thing for me and my ideas.

p.s. Happy Easter!
Categories: Offsite Blogs

Roman Cheplyaka: JSON validation combinators

Planet Haskell - Sat, 04/19/2014 - 3:00pm

At Signal Vine we have a JSON-based language for describing text messaging campaigns. We may design a better surface syntax for this language in the future, but the current one gets the job done and there are certain existing systems that depend on it.

Anyway, the problem with this language is that it is too easy to make mistakes — including errors in JSON syntax, structural errors (plugging an array where an object is expected), or name errors (making a typo in a field name).

So the first thing I did was write a validator for our JSON format.

There are several projects of «JSON schemas» around, but there were many reasons against using them.

  1. I don’t know about the quality of the tools that support such schemas (i.e. the quality of error messages they generate), and the expressivity of the schemas themselves (whether they’d let us to express the structure of our JSON structure and the constraints we’d like to enforce). So, though it may seem that using an existing solution is «free», it is not — I’d have to spend time learning and evaluating these existing solutions.

  2. I remember that we went through this in our team at Barclays, and eventually decided to create a custom JSON schema language, although I was not involved in the evaluation process, so can’t share the details.

  3. I was almost certain that no existing «generic JSON schema» solution can provide the power of a custom one. For instance, some of the JSON strings contain expressions in another in-house mini-language. Ideally, I’d like to parse those expressions while I am parsing the enclosing JSON structure, and give locations of possible errors as they appear in the JSON file.

  4. I’d need a parser for the language anyway. Maintaining a schema separately from the parser would mean one more thing to keep in sync and worry about.

I couldn’t use an existing JSON parsing library either. Of course, aeson was out of question, being notorious for its poor error messages (since it’s based on attoparsec and optimized for speed). json, though, is based on parsec, so its error messages are better.

But there is a deeper reason why a JSON parsing library is inadequate for validation. All of the existing JSON libraries first parse into a generic JSON structure, and only then do they try to recognize the specific format and convert to a value of the target Haskell type.

Which means that during parsing, only JSON syntax errors will be detected, but not the other kinds of errors described above. Granted, they all can be detected sooner or later. But what differentiates sooner from later is that once we’re out of the parsing monad, we no longer have access to the position information (unless, of course, our JSON parsing library does extra work to store locations in the parsed JSON structure — which it typically doesn’t). And not having such position information severely impacts our ability to produce good error messages.

To summarize, in order to provide good diagnostics, it is important to parse exactly the language we expect (and not some superset thereof), and to perform all the checks in the parsing monad, where we have access to location information.

JSON parsing combinators

Even though I couldn’t re-use an existing JSON parser or schema, I still wanted my parser to be high-level, and ideally to resemble a JSON schema, just embedded in Haskell.

The rest of this article describes the JSON schema combinators I wrote for this purpose.


As I mentioned before, the json package uses parsec underneath, so I was able to reuse some basic definitions from there — most notably, p_string, which parses a JSON string. This is fortunate, because handling escape sequences is not straightforward, and I’d rather use a well-tested existing implementation.

string :: Parser String string = {- copied from Text.JSON.Parsec -}

I introduced one other combinator, theString, which parses a given string:

theString :: String -> Parser () theString str = (<?> "\"" ++ str ++ "\"") $ try $ do str' <- string if str == str' then return () else empty Objects

Objects are an interesting case because we know what set of fields to expect, but not the order in which they come (it may be arbitrary). Such syntax is known as a «permutation phrase», and can be parsed as described in the classical paper Parsing Permutation Phrases by Arthur Baars, Andres Löh and Doaitse Swierstra.

There are surprisingly many implementations of permutation parsing on hackage, including one in parsec itself. Most of them suffer from one or both of the following issues:

  1. they use custom combinators, which, despite being similar to Applicative and Alternative operators, have their quirks and require learning

  2. they don’t support permutation phrases with separators, which is obviously required to parse JSON objects. (The technique to parse permutation phrases with separators was descibed in the original paper, too.)

On the other hand, the action-permutations library by Ross Paterson addresses both of these issues. It provides the familiar Applicative interface to combine permutation elements (or atoms, as it calls them), and includes the function runPermsSep to parse phrases with separators. The interface is also very generic, requiring the underlying functor to be just Alternative.

Below are the combinators for parsing JSON objects. field parses a single object field (or member, as it’s called in the JSON spec), using the supplied parser to parse the field’s value. optField is similar, except it returns Nothing if the field is absent (in which case field would produce an error message). Finally, theField is a shortcut to parse a field with the fixed contents. It is useful when there’s a tag-like field identifying the type/class of the object, for instance

data Item = Book String -- writer | Song String -- composer String -- singer item = (try . object $ Book <$ theField "type" "book" <*> field "writer" string) <|> (try . object $ Song <$ theField "type" "song" <*> field "composer" string <*> field "singer" string)

(Note: examples in this article have nothing to do with the JSON schema we actually use at Signal Vine.)

One thing to pay attention to is how field parsers (field, theField and optField) have a different type from the ordinary parsers. This makes it much easier to reason about what actually gets permuted.

object :: Perms Parser a -> Parser a object fields = (<?> "JSON object") $ between (tok (char '{')) (tok (char '}')) $ runPermsSep (tok (char ',')) fields -- common function used by field and optField field' :: String -- key name -> Parser a -- value parser -> Parser a field' key value = theString key *> tok (char ':') *> value field :: String -- key name -> Parser a -- value parser -> Perms Parser a field key value = atom $ field' key value theField :: String -- key name -> String -- expected value -> Perms Parser () theField key value = () <$ field key (theString value) optField :: String -- key name -> Parser a -- value parser -> Perms Parser (Maybe a) optField key value = maybeAtom $ field' key value Aside: correct separator parsing

There was only one issue I ran into with action-permutations, and it is interesting enough that I decided to describe it here in more detail.

Consider, for example, the expression runPermsSep sep (f <$> atom a <*> atom b <*> atom c)

It would expand to

(flip ($) <$> a <*> ( (sep *> (flip ($) <$> b <*> (sep *> (flip ($) <$> c <*> pure (\xc xb xa -> f xc xb xa))))) <|> (sep *> (flip ($) <$> c <*> (sep *> (flip ($) <$> b <*> pure (\xc xb xa -> f xb xc xa))))) )) <|> (flip ($) <$> b <*> ( ... )) <|> (flip ($) <$> c <*> ( ... ))

See the problem? Suppose the actual order of the atoms in the input stream is a, c, b. At the beginning the parser is lucky to enter the right branch (the one starting from flip ($) <$> a <*> ...) on the first guess. After that, it has two alternatives: b-then-c, or c-then-b. First it enters the b-then-c branch (i.e. the wrong one) and fails. However, it fails after having consumed some input (namely, the separator) — which in libraries like parsec and trifecta means that the other branch (the right one) won’t be considered.

We cannot even work around this outside of the library by using try, because we can’t insert it in the right place. E.g. wrapping the separator in try won’t work. The right place to insert try would be around the whole alternative

(sep *> (flip ($) <$> b <*> (sep *> (flip ($) <$> c <*> pure (\xc xb xa -> f xc xb xa)))))

but this piece is generated by the lirbary and, as a library user, we have no control over it.

The usage of try inside the library itself is unsatisfactory, too. Remember, the interface only assumes the Alternative instance, which has no notion of try. If we had to make it less generic by imposing a Parsing constraint, that would be really unfortunate.

Fortunately, once identified, this problem is not hard to fix properly — and no usage of try is required! All we need is to change runPermsSep so that it expands to the tree where separator parsing is factored out:

(flip ($) <$> a <*> sep *> ( (flip ($) <$> b <*> sep *> (flip ($) <$> c <*> pure (\xc xb xa -> f xc xb xa))))) <|> (flip ($) <$> c <*> sep *> (flip ($) <$> b <*> pure (\xc xb xa -> f xb xc xa))) )) <|> (flip ($) <$> b <*> sep *> ( ... )) <|> (flip ($) <$> c <*> sep *> ( ... ))

Now, all alternatives start with atoms, so we have full control over whether they consume any input.

Mathematically, this demonstrates that <*> does not distribute over <|> for some backtracking parsers. Note that such distributive property is not required by the Alternative class.

Even for parser monads that allow backtracking by default (attoparsec, polyparse) and for which there’s no semantic difference between the two versions, this change improves efficiency by sharing separator parsing across branches.

My patch fixing the issue has been incorporated into the version of action-permutations.


Arrays should be easier to parse than objects in that the order of the elements is fixed. Still, we need to handle separators (commas) between array elements.

If we interpreted arrays as lists, then the schema combinator for arrays might look like

array :: Parser a -- parser for a signle element -> Parser [a] -- parser for the array

Implementation would be straightforward, too.

However, in our JSON schema we use arrays as tuples rather than lists. That is, we typically expect an array of a fixed number of heterogeneous elements. Thus we’d like to combine these tuple elements into a single parser using the applicative interface.

Let’s say we expect a 2-tuple of a string (a person’s name) and an object (that person’s address).

data Address -- = ... data Person = Person String -- name Address

Written by hand, the parser may look like

address :: Parser Address address = _ personAddress = between (tok (char '[')) (tok (char ']')) $ Person <$> string <* sep <*> address where sep = tok $ char ','

It makes sense to move brackets parsing to the array combinator:

array :: Parser a -> Parser a array p = (<?> "JSON array") $ between (tok (char '[')) (tok (char ']')) p

But what should we do with the commas? Manually interspersing elements with separators is error-prone and doesn’t correspond to my view of a high-level JSON schema description.

Inserting comma parsers automatically isn’t impossible — after all, it is done in the action-permutations package and we used it to parse object fields, which are comma-separated, too. But it cannot be as easy as adding a separator to every element since there’s one less separators than elements. We have somehow to detect the last element and not to expect a separator after it.

A nice and simple way to achieve this is with a free applicative functor. A free applicative functor will allow us to capture the whole applicative expression and postpone the decision on where to insert separator parsers until we can tell which element is the last one. In this case we’ll use Twan van Laarhoven’s free applicative, as implemented in the free package.

element :: Parser a -> Ap Parser a element = liftAp theArray :: Ap Parser a -> Parser a theArray p = between (tok (char '[')) (tok (char ']')) $ go p where go :: Ap Parser a -> Parser a go (Ap p (Pure f)) = f <$> p go (Ap p1 pn) = flip id <$> p1 <* tok (char ',') <*> go pn

Like object fields, array elements have a special type which makes it clear which pieces exactly are comma-separated.

In fact, the applicative functor Perms is essentially the free applicative functor Ap plus branching.

Optional array elements

Now comes the twist. Some of the array elements may be optional — in the same way as positional function arguments in some languages may be optional. Since the elements are positional, if one of them is omitted, all subsequent ones have to be omitted, too — otherwise we won’t be able to tell which one was omitted, exactly.

For that reason, all optional elements should come after all the non-optional ones; if not, then we’ve made a mistake while designing (or describing) our schema. Ideally, our solution should catch such mistakes, too.

So, how can the above solution be adapted to handle optional arguments?

Attempt #1: optElement :: Parser a -> Ap Parser (Maybe a) optElement p = element $ optional p

Here optional is a combinator defined in Control.Applicative as

optional v = Just <$> v <|> pure Nothing

This won’t work at all, as it doesn’t give us any information about whether it’s an optional element or just a parser that happens to return a Maybe type.

Attempt #2:

Well, let’s just add a flag indicating whether the element was created using optElement, shall we?

data El a = El Bool -- is optional? (Parser a) element :: Parser a -> Ap El a element = liftAp . El False optElement :: Parser a -> Ap El (Maybe a) optElement = liftAp . El True . optional

Now we can check that optional arguments come after non-optional ones. If an element’s parse result is Nothing, we also know whether that element is an optional one, and whether we should stop trying to parse the subsequent elements.

Still, there are two related issues preventing this version from working:

  • How do we actually know when a parser returns Nothing? Once we lift a parser into the free applicative, its return type becomes existentially quantified, i.e. we should treat it as polymorphic and cannot assume it has form Maybe a (by pattern-matching on it), even if we can convince ourselves by looking at the Bool flag that it indeed must be of that form.

  • Similarly, once we’ve detected an absent optional element (assuming for a second that it is possible), we have to force all the remaining optional parsers to return Nothing without parsing anything. But again, we cannot convince the compiler that Nothing is an acceptable return value of those parsers.

Attempt #3:

So, we need certain run-time values (the optionality flag) to introduce type-level information (namely, that the parser’s return type has form Maybe a). That’s exactly what GADTs do!

data El a where El :: Parser a -> El a OptEl :: Parser a -> El (Maybe a)

El’s two constructors are a more powerful version of our old Bool flag. They let us see whether the element is optional, and if so, guarantee that its parser’s return type is Maybeish.

And here’s the code for the parsing functions:

element :: Parser a -> Ap El a element = liftAp . El optElement :: Parser a -> Ap El (Maybe a) optElement = liftAp . OptEl theArray :: Ap El a -> Parser a theArray p = between (tok (char '[')) (tok (char ']')) $ go True False False p where go :: Bool -> Bool -> Bool -> Ap El a -> Parser a go _ _ _ (Pure x) = pure x go isFirst optionalOccurred optionalOmitted (Ap el1 eln) = let eltSequenceError :: a eltSequenceError = error "theArray: a non-optional element after an optional one" !_check = case el1 of El {} | optionalOccurred -> eltSequenceError _ -> () in if optionalOmitted then case el1 of El {} -> eltSequenceError OptEl {} -> go False True True eln <*> pure Nothing else do let sep = if isFirst then pure () else () <$ tok (char ',') case el1 of El p1 -> flip id <$ sep <*> p1 <*> go False False False eln OptEl p1 -> do r1 <- optional $ sep *> p1 go False True (isNothing r1) eln <*> pure r1

theArray is a state machine with three pieces of state: isFirst, optionalOccurred and optionalOmitted. isFirst and optionalOmitted are used to guide actual parsing, while optionalOccurred is needed to check the proper arrangement of optional vs non-optional arguments.


Although the standard approach to JSON parsing is to parse into a generic JSON representation first, the article shows that an alternative approach — parsing the expected structure directly — is also viable and can be employed to improve error reporting.

Of course, the tricks and ideas described here are not specific to JSON. Understanding how they work and how to use them may become handy in a variety of parsing situations.

Categories: Offsite Blogs

My first haskell program, tell my why it's awful.

Haskell on Reddit - Sat, 04/19/2014 - 6:53am


I'm an experienced programmer (gamedev, 17years pro) but 95% of my work has been in the C/C++/C# family of languages, so to broaden my horizons I decided to learn some haskell. To which end I wrote a bot that plays 'coin fight':

Let me have it. I'm sure it's awful. Tell me why!



PS: I ran this learning experiment with Clojure as well, but the performance of Clojure for command line tools is just:

submitted by codemonkey_uk
[link] [54 comments]
Categories: Incoming News

Why are examples completely absent from hackage? Am I missing something?

Haskell on Reddit - Fri, 04/18/2014 - 2:59pm

As far as my learning goes, I still didn't find any problem with Haskell - that is, the language and its concepts themselves. But I am completely unable to use any library. What I do is:

  1. Google what I want

  2. Find it on Hackage

  3. Download the lib

  4. Read the main page

  5. Have no freaking idea on how to actually use the package for total lack of examples

  6. Give up.

Is there something I am missing?

submitted by SrPeixinho
[link] [68 comments]
Categories: Incoming News