I am watching a tutorial on parsers in haskell https://www.youtube.com/watch?v=9FGThag0Fqs. The lecture starts with defining some really basic parsers. These are to be used together to create more complicated parsers later. One of the basic parsers is item. This is used to extract a character from the string we are parsing.
All parsers have the following type:
type Parser a = String -> [(a, String)]
The parser item is defined like this:
item :: Parser Char
item = \inp -> case inp of
[] -> []
(x:xs) -> [(x,xs)]
I am not so used to this syntax, so it looks strange to me. I would have written it:
item' :: Parser Char
item' [] = []
item' (x:xs) = [(x,xs)]
Testing it in ghci indicates that they are equal:
*Main> item ""
[]
*Main> item "abc"
[('a',"bc")]
*Main> item' ""
[]
*Main> item' "abc"
[('a',"bc")]
The lecturer makes a short comment about thinking it looks clearer, but I disagree. So my questions are:
Are they indeed completely identical?
Why is the lambda version clearer?
I believe this comes from the common practice of writing
f :: Type1 -> ... -> Typen -> Result
f x1 ... xn = someResult
where we have exactly n function arrows in the type, and exactly n arguments in the left hand side of the equation. This makes it easy to relate types and formal parameters.
If Result is a type alias for a function, then we may write
f :: Type1 -> ... -> Typen -> Result
f x1 ... xn y = something
or
f :: Type1 -> ... -> Typen -> Result
f x1 ... xn = \y -> something
The latter follows the convention above: n arrows, n variables in the left hand side. Also, on the right hand side we have something of type Result, making it easier to spot. The former instead does not, and one might miss the extra argument when reading the code quickly.
Further, this style makes it easy to convert Result to a newtype instead of a type alias:
newtype Result = R (... -> ...)
f :: Type1 -> ... -> Typen -> Result
f x1 ... xn = R $ \y -> something
The posted item :: Parser Char code is an instance of this style when n=0.
Why you should avoid equational function definitions (by Roman Cheplyaka):
http://ro-che.info/articles/2014-05-09-clauses
Major Points from the above link:
DRY: Function and argument names are repeated --> harder to refactor
Clearer shows which arguments the function decides upon
Easy to add pragmas (e.g. for profiling)
Syntactically closer to lower level code
This doesn't explain the lambda though..
I think they are absolutely equal. The lambda-style definition puts a name item to an anonymous lambda function which does pattern matching inside. The pattern-matching-style definition defines it directly. But in the end both are functions that do pattern matching. I think it's a matter of personal taste.
Also, the lambda-style definition could be considered to be in pointfree style, i.e. a function defined without explicitly writing down its arguments actually it is not very much pointfree since the argument is still written (but in a different place), but in this case you don't get anything with this.
Here is another possible definion, somewhere in between of those two:
item :: Parser Char
item inp = case inp of
[] -> []
(x:xs) -> [(x, xs)]
It's essentially identical to the lambda-style, but not pointfree.
Related
I'm trying to do this exercise:
I'm not sure how to use Type in F#, in F# interactive, I wrote type term = Term of float *int, Then I tried to create a value of type term by let x: term = (3.5,8);;But it gives an error.
Then I tried let x: term = Term (3.5,8);; and it worked. So Why is that?
For the first function, I tried:
let multiplyPolyByTerm (x:term, p:poly)=
match p with
|[]->[]
But that gives an error on the line |[]->[] saying that the expression is expecting a type poly, but poly is a in fact a list right? So why is it wrong here? I fixed it by |Poly[]->Poly[]. Then I tried to finish the function by giving the recursive definition of multiplying each term of the polynomial by the given term: |Poly a::af-> This gives an error so I'm stuck on trying to break down the Poly list.
If anyone has suggestion on good readings about Type in F#, please share it.
I got all the methods now, However,I find myself unable to throw an exception when the polynomial is an empty list as the base case of my recursive function is an empty list. Also, I don't know how to group common term together, Please help, Here are my codes:
type poly=Poly of (float*int) list
type term = Term of float *int
exception EmptyList
(*
let rec mergeCommonTerm(p:poly)=
let rec iterator ((a: float,b: int ), k: (float*int) list)=
match k with
|[]->(a,b)
|ki::kf-> if b= snd ki then (a+ fst ki,b)
match p with
|Poly [] -> Poly []
|Poly (a::af)-> match af with
|[]-> Poly [a]
|b::bf -> if snd a =snd b then Poly (fst a +fst b,snd a)::bf
else
*)
let rec multiplyPolyByTerm (x:term, p:poly)=
match x with
| Term (coe,deg) -> match p with
|Poly[] -> Poly []
|Poly (a::af) -> match multiplyPolyByTerm (x,Poly af) with
|Poly recusivep-> Poly ((fst a *coe,snd a + deg)::recusivep)
let rec addTermToPoly (x:term, p:poly)=
match x with
|Term (coe, deg)-> match p with
|Poly[] -> Poly [(coe,deg)]
|Poly (a::af)-> if snd a=deg then Poly ((fst a+coe,deg)::af)
else match addTermToPoly (x,Poly af) with
|Poly recusivep-> Poly (a::recusivep)
let rec addPolys (x:poly, y: poly)=
match x with
|Poly []->y
|Poly (xh::xt)-> addPolys(Poly xt,addTermToPoly(Term xh, y))
let rec multPolys (x:poly,y:poly)=
match x with
|Poly []-> Poly[]
|Poly (xh::xt)->addPolys (multiplyPolyByTerm(Term xh,y),multPolys(Poly xt,y))
let evalTerm (values:float) (termmm : term) :float=
match termmm with
|Term (coe,deg)->coe*(values**float(deg))
let rec evalPoly (polyn : poly, v: float) :float=
match polyn with
|Poly []->0.0
|Poly (ph::pt)-> (evalTerm v (Term ph)) + evalPoly (Poly pt,v)
let rec diffPoly (p:poly) :poly=
match p with
|Poly []->Poly []
|Poly (ah::at)-> match diffPoly (Poly at) with
|Poly [] -> if snd ah = 0 then Poly []
else Poly [(float(snd ah)*fst ah,snd ah - 1)]
|Poly (bh::bt)->Poly ((float(snd ah)*fst ah,snd ah - 1)::bh::bt)
As I mentioned in a comment, reading https://fsharpforfunandprofit.com/posts/discriminated-unions/ will be very helpful for you. But let me give you some quick help to get you unstuck and starting to solve your immediate problems. You're on the right track, you're just struggling a little with the syntax (and operator precedence, which is part of the syntax).
First, load the MSDN operator precedence documentation in another tab while you read the rest of this answer. You'll want to look at it later on, but first I'll explain a subtlety of how F# treats discriminated unions that you probably haven't understood yet.
When you define a discriminated union type like poly, the name Poly acts like a constructor for the type. In F#, constructors are functions. So when you write Poly (something), the F# parser interprets this as "take the value (something) and pass it to the function named Poly". Here, the function Poly isn't one you had to define explicitly; it was implicitly defined as part of your type definition. To really make this clear, consider this example:
type Example =
| Number of int
| Text of string
5 // This has type int
Number 5 // This has type Example
Number // This has type (int -> Example), i.e. a function
"foo" // This has type string
Text "foo" // This has type Example
Text // This has type (string -> Example), i.e. a function
Now look at the operator precedence list that you loaded in another tab. Lowest precedence is at the top of the table, and highest precedence is at the bottom; in other words, the lower something is on the table, the more "tightly" it binds. As you can see, function application (f x, calling f with parameter x) binds very tightly, more tightly than the :: operator. So when you write f a::b, that is not read as f (a::b), but rather as (f a)::b. In other words, f a::b reads as "Item b is a list of some type which we'll call T, and the function call f a produces an item of type T that should go in front of list b". If you instead meant "take the list formed by putting item a at the head of list b, and then call f with the resulting list", then that needs parentheses: you have to write f (a::b) to get that meaning.
So when you write Poly a::af, that's interpreted as (Poly a)::af, which means "Here is a list. The first item is a Poly a, which means that a is a (float * int) list. The rest of the list will be called af". And since the value your passing into it is not a list, but rather a poly type, that is a type mismatch. (Note that items of type poly contain lists, but they are not themselves lists). What you needed to write was Poly (a::af), which would have meant "Here is an item of type poly that contains a list. That list should be split into the head, a, and the rest, af."
I hope that helped rather than muddle the waters further. If you didn't understand any part of this, let me know and I'll try to make it clearer.
P.S. Another point of syntax you might want to know: F# gives you many ways to signal an error condition (like an empty list in this assignment), but your professor has asked you to use exception EmptyList when invalid input is given. That means he expects your code to "throw" or "raise" an exception when you encounter an error. In C# the term is "throw", but in F# the term is "raise", and the syntax looks like this:
if someErrorCondition then
raise EmptyList
// Or ...
match listThatShouldNotBeEmpty with
| [] -> raise EmptyList
| head::rest -> // Do something with head, etc.
That should take care of the next question you would have needed to ask. :-)
Update 2: You've edited your question to clarify another issue you're having, where your recursive function boils down to an empty list as the base case — yet your professor asked you to consider an empty list as an invalid input. There are two ways to solve this. I'll discuss the more complicated one first, then I'll discuss the easier one.
The more complicated way to solve this is to have two separate functions, an "outer" one and an "inner" one, for each of the functions you have been asked to define. In each case, the "outer" one checks whether the input is an empty list and throws an exception if that's the case. If the input is not an empty list, then it passes the input to the "inner" function, which does the recursive algorithm (and does NOT consider an empty list to be an error). So the "outer" function is basically only doing error-checking, and the "inner" function is doing all the work. This is a VERY common approach in professional programming, where all your error-checking is done at the "edges" of your code, while the "inner" code never has to deal with errors. It's therefore a good approach to know about — but in your particular case, I think it's more complicated than you need.
The easier solution is to rewrite your functions to consider a single-item list as the base case, so that your recursive functions never go all the way to an empty list. Then you can always consider an empty list to be an error. Since this is homework I won't give you an example based on your actual code, but rather an example based on a simple "take the sum of a list of integers" exercise where an empty list would be considered an error:
let rec sumNonEmptyList (input : int list) : int =
match input with
| [] -> raise EmptyList
| [x] -> x
| x::rest -> x + sumNonEmptyList rest
The syntax [x] in a match expression means "This matches a list with exactly one item in it, and assigns the name x to the value of that item". In your case, you'd probably be matching against Poly [] to raise an exception, Poly [a] as the base case, and Poly (a::af) as the "more than one item" case. (That's as much of a clue as I think I should give you; you'll learn better if you work out the rest yourself).
Being relatively new to functional programming, I am still unfamiliar with all the standard operators. The fact that their definition is allowed to be arbitrary in many languages and also that such definitions aren't available in nearby source code, if at all, makes reading functional code unnecessarily challenging.
Presently, I don't know what <*> as it occurs in WebSharper.UI.Next documentation.
It would be good if there was a place that listed all the conventional definition for the various operators of the various functional languages.
I agree with you, it would be good to have a place where all implicit conventions for operators used in F# are listed.
The <*> operator comes from Haskell, it's an operator for Applicative Functors, its general signature is: Applicative'<('A -> 'B)> -> Applicative'<'A> -> Applicative'<'B> which is an illegal signature in .NET as higher kinds are not supported.
Anyway nothing stops you from defining the operator for a specific Applicative Functor, here's the typical definition for option types:
let (<*>) f x =
match (f, x) with
| Some f, Some x -> Some (f x)
| _ -> None
Here the type is inferred as:
val ( <*> ) : f:('a -> 'b) option -> x:'a option -> 'b option
which is equivalent to:
val ( <*> ) : f: option<('a -> 'b)> -> x: option<'a> -> option<'b>
The intuitive explanation is that it takes a function in a context and an argument for that function in the context, then it executes the function inside the context.
In our example for option types it can be used for applying a function to a result value of an operation which may return a None value:
let tryParse x =
match System.Int32.TryParse "100" with
| (true, x) -> Some x
| _ -> None
Some ((+) 10) <*> tryParse "100"
You can take advantage of currying and write:
Some (+) <*> tryParse "100" <*> Some 10
Which represents something like:
(+) (System.Int32.Parse "100") 10
but without throwing exceptions, that's why it is also said that Applicatives are used to model side-effects, specially in pure functional languages like Haskell. Here's another sample of option applicatives.
But for different types it has different uses, for lists it may be used to zip them as shown in this post.
In F# it's not defined because .NET type system would not make it possible to define it in a generic way however it would be possible using overloads and static member constraints as in FsControl otherwise you will have to select different instances by hand by opening specific modules, which is the approach used in FSharpx.
Just discovered elsewhere in the documentation on another subject...
let ( <*> ) f x = View.Apply f x
where type of View.Apply and therefore ( <*> ) is:
View<'A * 'B> -> View<'A> -> View<'B>
From Brent Yorgey's 2013 Penn class, after getting help on defining a Functor Parser, I'm attempting to make an Applicative Parser:
--p1 <*> p2 represents the parser which first runs p1 (which will
--consume some input and produce a function), then passes the
--remaining input to p2 (which consumes more input and produces
--some value), then returns the result of applying the function to the
--value
Here's my attempt:
instance Applicative (Parser) where
pure x = Parser $ \_ -> Just (x, [])
(Parser f) <*> (Parser g) = case (\ys -> f ys) of Nothing -> Parser Nothing
Just (_, xs) -> Parser $ g xs
However, I'm getting compile-time errors on the apply (<*>) definition.
Intuitively, I believe that using <*> achieves AND functionality.
If I have a parser for foo and a parser for bar, then I should be able to use apply <*> to say: foo followed by bar. In other words, input of foobar should successfully match, whereas foobip would not. It would fail on the second parser.
However, I believe that the types are:
Parser (a -> b) -> Parser a -> Parser b
So, that makes me think that my intuition is not entirely correct.
Please give me a tip to guide me towards understanding how to implement apply.
Your code is predicated on a misunderstanding of what a Parser is. Don't worry, virtually everybody makes this mistake.
newtype Parser a = Parser { runParser :: String -> Maybe (a, String) }
Lets break down what this means.
String -> Maybe (a, String)
[1] [2] [3] [4]
[1]: I take a string and return Maybe (a, String)
[2]: I might not succeed in parsing the input into the desired datatype
[3]: The desired type I am parsing the String into
[4]: Remaining input after having consumed the amount of data required to parse a
Parser is a function of text input to Maybe a tuple of a value and the rest of the text. Parser is emphatically not a tuple, otherwise you wouldn't have a parser. Just data in a tuple.
I'm not going to tell you how to implement <*> and nobody else should either as it would deprive you of the experience.
However, I'll give you pure so you understand the basic pattern:
pure a = Parser (\s -> Just (a, s))
See? It's a function of s -> Maybe (a, s). I intentionally mimicked the type variables in my terms to make it more obvious.
Suppose that Parser x is a parser that parses an x. This parser probably possesses a many combinator, that parses zero or more occurrences of something (stopping when the item parser fails).
I can see how one might implement that if Parser forms a monad. I can't figure out how to do it if Parser is only an Applicative Functor. There doesn't seem to be any way to check the previous result and decide what to do next (precisely the notion that monads add). What am I missing?
The Alternative type class provides the many combinator:
class Applicative f => Alternative f where
empty :: f a
(<|>) :: f a -> f a -> f a
many :: f a -> f [a]
some :: f a -> f [a]
some = some'
many = many'
many' a = some' a <|> pure []
some' a = (:) <$> a <*> many' a
The many a combinator means “zero or more” a.
The some a combinator means “one or more” a.
Hence:
The some a combinator returns a list of one a followed by many a (i.e. 1 + (0 or more)).
The many a combinator returns either some a or an empty list (i.e. (1 or more) | 0).
The many combinator depends upon the (<|>) operator which can be viewed as the default operator in languages like JavaScript. For example, consider the Alternative instance of Maybe:
instance Alternative Maybe where
empty = Nothing
Nothing <|> r = r
l <|> _ = l
Essentially the (<|>) should return the left hand side value if it's truthy. Otherwise it should return the right hand side value.
A Parser is a data structure which is defined similarly to Maybe (the idea of applicative lexer combinators and parser combinators is essentially the same):
data Lexer a = Fail | Ok (Maybe a) (Vec (Lexer a))
If parsing fails, the Fail value is returned. Otherwise an Ok value is returned. Since Fail <|> pure [] is pure [], this is how the many combinator knows when to stop and return an empty list.
It can't be done just by using what is provided by Applicative. But Alternative has a function that gives you power beyond Applicative:
(<|>) :: f a -> f a -> f a
This function lets you "combine" two Alternatives without any restriction whatsoever on the a. But how? Something intrinsic to the particular functor f must give you a means to do that.
Typically, Alternatives require some notion of failure or emptiness. Like for parsers, where (<|>) means "try to parse this, if it fails, try this other thing". But this "dependence on a previous value" is hidden in the machinery implementing (<|>). It is not available to the external interface, so to speak.
From (<|>), one can implement a zero-or-one combinator:
optional :: Alternative f => f a -> f (Maybe a)
optional v = Just <$> v <|> pure Nothing
The definitions of some an many are similar but they require mutually recursive functions.
Notice that there are Applicatives that aren't Alternatives. You can't make the Identity functor an Alternative, for example. How would you implement empty?
many is a class method of the Alternative class (link) which suggests that an general applicative functor does not always have a many implementation.
Is it possible to use one of the parsing libraries (e.g. Parsec) for parsing something different than a String? And how would I do this?
For the sake of simplicity, let's assume the input is a list of ints [Int]. The task could be
drop leading zeros
parse the rest into the pattern (S+L+)*, where S is a number less than 10, and L is a number larger or equal to ten.
return a list of tuples (Int,Int), where fst is the product of the S and snd is the product of the L integers
It would be great if someone could show how to write such a parser (or something similar).
Yes, as user5402 points out, Parsec can parse any instance of Stream, including arbitrary lists. As there are no predefined token parsers (as there are for text) you have to roll your own, (myToken below) using e.g. tokenPrim
The only thing I find a bit awkward is the handling of "source positions". SourcePos is an abstract type (rather than a type class) and forces me to use its "filename/line/column" format, which feels a bit unnatural here.
Anyway, here is the code (without the skipping of leading zeroes, for brevity)
import Text.Parsec
myToken :: (Show a) => (a -> Bool) -> Parsec [a] () a
myToken test = tokenPrim show incPos $ justIf test where
incPos pos _ _ = incSourceColumn pos 1
justIf test x = if (test x) then Just x else Nothing
small = myToken (< 10)
large = myToken (>= 10)
smallLargePattern = do
smallints <- many1 small
largeints <- many1 large
let prod = foldl1 (*)
return (prod smallints, prod largeints)
myIntListParser :: Parsec [Int] () [(Int,Int)]
myIntListParser = many smallLargePattern
testMe :: [Int] -> [(Int, Int)]
testMe xs = case parse myIntListParser "your list" xs of
Left err -> error $ show err
Right result -> result
Trying it all out:
*Main> testMe [1,2,55,33,3,5,99]
[(2,1815),(15,99)]
*Main> testMe [1,2,55,33,3,5,99,1]
*** Exception: "your list" (line 1, column 9):
unexpected end of input
Note the awkward line/column format in the error message
Of course one could write a function sanitiseSourcePos :: SourcePos -> MyListPosition
There is very likely a way to get Parsec to use [a] as the stream type, but the idea behind parser combinators is actually very simple, and it's not very difficult to roll your own library.
A very accessible resource I would recommend is Monadic Parsing in Haskell by Graham Hutton and Erik Meijer.
Indeed, right now Erik Meijer is teaching an intro Haskell/functional programming course on edx.org (link) and Lecture 7 is all about functional parsers. As he states in the intro to the lecture:
"... No one can follow the path towards mastering functional programming without writing their own parser combinator library. We start by explaining what parsers are and how they can naturally be viewed as side-effecting functions. Next we define a number of basic parsers and higher-order functions for combining parsers. ..."