2013年(1)
分类: C/C++
2013-08-09 09:38:33
要理解Haskell的Monad,需要对类型构造子和值构造子有一定的认识。参数化类型其行为本身是一个“自举”的行为,这就触及了 罗素悖论的观点,因此让人恐惧的范畴论就应势而出。不过这也让我们得以在程序语言中浅尝一番这艰涩难懂的数学概念。
以下部分来自stackoverflow,作者没去解释让人头疼的数学概念,而是试图建立一个直觉。通俗对于不同的人群通常是大不相同的,这段文字对码农而言,应该显得通俗。
stackoverflow.com/questions/15726733/simple-examples-to-illustrate-category-monoid-and-monad
-----------------------------------------------------------------------------------------------
This probably isn't the answer you're looking for, but here you go anyways:
One way of looking at abstract concepts like these is to link them with basic concepts, such as ordinary list processing operations. Then, you could say that,
A category consists of a set (or a class) of objects and bunch of arrows that each connect two of the objects. In addition, for each object, there should be an identity arrow connecting this object to itself. Further, if there is one arrow (f) that ends on an object, and another (g) that starts from the same object, there should then also be a composite arrow called g . f.
In Haskell this is modelled as a typeclass that represents the category of Haskell types as objects.
class Category cat where id :: cat a a (.) :: cat b c -> cat a b -> cat a cBasic examples of a category are functions. Each function connects two types, for all types, there is the function id :: a -> a that connects the type (and the value) to itself. The composition of functions is the ordinary function composition.
In short, categories in Haskell base are things that behave like functions, i.e. you can put one after another with a generalized version of (.).
A monoid is a set with an unit element and an associative operation. This is modelled in Haskell as:
class Monoid a where mempty :: a mappend :: a -> a -> aCommon examples of monoids include:
These are modelled in Haskell as
newtype Sum a = Sum {getSum :: a} instance (Num a) => Monoid (Sum a) where mempty = Sum 0 mappend (Sum a) (Sum b) = Sum (a + b) instance Monoid [a] where mempty = [] mappend = (++)Monoids are used to 'combine' and accumulate things. For example, the function mconcat :: Monoid a => [a] -> a, can be used to reduce a list of sums to single sum, or a nested list into a flat list. Consider this as a kind of generalization of (++) or (+) operations that in a way 'merge' two things.
A functor in Haskell is a thing that quite directly generalizes the operation map :: (a->b) -> [a] -> [b]. Instead of mapping over a list, it maps over some structure, such as a list, binary tree, or even an IO operation. Functors are modelled like this:
class Functor f where fmap :: (a->b) -> f a -> f bContrast this to the definition of the normal map function.
Applicative functors can be seen as things with a generalized zipWith operation. Functors map over general structures one at the time, but with an Applicative functor you can zip together two or more structures. For the simplest example, you can use applicatives to zip together two integers inside the Maybe type:
pure (+) <*> Just 1 <*> Just 2 -- gives Just 3Notice that the structure can affect the result, for example:
pure (+) <*> Nothing <*> Just 2 -- gives NothingContrast this to the usual zipWith function:
zipWith (+) [1] [2]Instead of of just lists, the applicative works for all kinds of structures. Additionally, the clever trickery with pure and (<*>) generalizes the zipping to work with any number of arguments. To see how this works, inspect the following types while keeping the concept of partially applied functions at hand:
instance (Functor f) => Applicative f where pure :: a -> f a (<*>) :: f (a -> b) -> f a -> f bNotice also the similarity between fmap and (<*>).
Monads are often used to model different computational contexts, such as non-deterministic, or side-effectful computations. Since there are already far too many monad tutorials, I will just recommend The best one, instead of writing yet another.
Relating to the ordinary list processing functions, monads generalize the function concat :: [[a]] -> [a] to work with many other sorts of structures besides lists. As a simple example, the monadic operation join can be used to flatten nested Maybe values:
join (Just (Just 42)) -- gives Just 42 join (Just (Nothing)) -- gives NothingHow is this related to the use of Monads as a means of structuring computations? Consider a toy example where you do two consecutive queries from some database. The first query returns you some key value, with which you wish to do another lookup. The problem here is that the first value is wrapped inside Maybe, so you can't query with that directly. Instead, as maybe is a Functor, you could instead fmap the return value with the new query. This would give you two nested Maybe values like above. Another query would result in three layers of Maybes. This would be quite difficult to program with, but a monadic join gives you a way to flatten this structure, and work with just a single level of Maybes.