利用者:Fumiexcel/モナド (関数型プログラミング)

In functional programming, a monad is a structure that represents computations. A type with a monad structure defines what it means to chain operations of that type together. This allows the programmer to build pipelines that process data in steps, in which each action is decorated with additional processing rules provided by the monad. A pithy description, referring to the syntax for statements in several languages, is that monads are a "programmable semicolon"—that is, a monad specifies what a statement is.

関数型プログラミングにおいて、モナドとは計算を表現する構造である。モナドの構造を持つ型は、その型の操作の連鎖が何を意味するかを定義する。これは、データを処理するパイプラインを作ることを可能にし、それぞれのアクションが、モナドによって与えられた処理の規則によって修飾される。簡潔な説明として、いくつかの言語における文に例え、モナドは「プログラマブルなセミコロン」である;モナドは、何が文であるかを指定する。

Purely functional programs can use monads to structure procedures that include sequenced operations. Many common programming concepts can be described in terms of a monad structure, including side effects such as input/output, variable assignment, exception handling, parsing, nondeterminism, concurrency, and continuations. This allows these concepts to be defined in purely functional manner, without major extensions to the language's semantics. Because a monad can insert additional operations around a program's domain logic, monads can be considered a sort of aspect-oriented programming.

純粋関数型なプログラムは、順序のついた操作を含む手続きを構造化するのにモナドを用いることができる。入出力、代入、例外処理、構文解析、非決定性、並行、継続などの副作用を含む、ほとんどのプログラミングの概念はモナドの構造によって記述できる。これらの概念は、言語の意味論を拡張することなく、純粋関数型の中で定義できる。なぜなら、モナドはドメインロジックに追加の操作を含めることが出来るからである。モナドはアスペクト指向プログラミングの一種と考えることもできる。

Formally, a monad consists of a type constructor M and two operations, bind and return. The operations must fulfill several properties to allow the correct composition of monadic functions (i.e. functions that use values from the monad as their arguments or return value). In most contexts, a value of type M a can be thought of as an action that returns a value of type a. The return operation takes a value from a plain type a and puts it into a monadic container of type M a; the bind operation chains a monadic value of type M a with a function of type a → M b to create a monadic value of type M b, effectively creating an action that chooses the next action based on the results of previous actions.

形式的には、モナドは型コンストラクタMと、bindとreturnの二つの演算によって定義される。これらの操作は、モナディックな関数を正しく合成さ競るために、いくつかの要件を満たす必要がある。ほとんどの場合、型M  aを持つ値は、型aの値を返すアクションであると考えることができる。returnは型aの値をモナディックなコンテナM aに入れる。bindは型M aのモナディックな値と、型M bの値を作る関数

a → M b'を繋げるー事実上、前回のアクションを元に、次のアクションを作るアクションである。

The name is taken from the mathematical monad construct in category theory, though the two concepts are not identical.

この名前は、圏論におけるモナドから来ている。しかし、圏論のモナドとは同一ではない。

歴史

The concept of monad programming appeared already in the 1980s in the programming language Opal even though it was called "commands" and never formally specified. Eugenio Moggi first described the general use of monads to structure programs in 1991. Several people built on his work, including programming language researchers Philip Wadler and Simon Peyton Jones (both of whom were involved in the specification of Haskell). Early versions of Haskell used a problematic "lazy list" model for I/O, and Haskell 1.3 introduced monads as a more flexible way to combine I/O with lazy evaluation.

In addition to I/O, scientific articles and Haskell libraries have successfully applied monads to topics including parsers and programming language interpreters. The concept of monads along with the Haskell do-notation for them has also been generalized to form arrows.

Haskell and its derivatives have been for a long time the only major users of monads in programming. There also exist formulations in Scheme, Perl, Racket, Clojure and Scala, and monads have been an option in the design of a new ML standard. Recently F# has included a feature called computation expressions or workflows, which are an attempt to introduce monadic constructs within a syntax more palatable to programmers with an imperative background.

Effect systems are an alternative way of describing side effects as types.

背景

The Haskell programming language is a functional language that makes heavy use of monads, and includes syntactic sugar to make monadic composition more convenient. All of the code samples in this article are written in Haskell unless noted otherwise.

The name monad derives from category theory, a branch of mathematics that describes patterns applicable to many mathematical fields. (As a minor terminological mismatch, the term monad in functional programming contexts is usually used with a meaning corresponding to that of the term strong monad in category theory, a specific kind of category-theoretical monad.)

例

The two most common examples given when introducing monads are the I/O monad and the Maybe monad.

IOモナド

In a purely functional language, such as Haskell, functions cannot have any externally visible side effects. Although a function cannot directly cause a side effect, it can construct a value describing a desired side effect, that the caller should apply at a convenient time. In the Haskell notation, a value of type IO a represents an action that, when performed, produces a value of type a.

We can think of a value of type IO as a function that takes as its argument the current state of the world, and will return a new world where the state has been changed according to the function's return value. The state created in this way can be passed to another function, thus defining a series of functions which will apply in order as steps of state changes. This process is similar to how a temporal logic represents the passage of time using only declarative propositions.

We would like to be able to describe all of the basic types of I/O operations, e.g. write text to standard output, read text from standard input, read and write files, send data over networks, etc. In addition, we need to be able to compose these primitives to form larger programs. For example, we would like to be able to write:

main :: IO ()
main = do
  putStrLn "What is your name?"
  name <- getLine
  putStrLn ("Nice to meet you, " ++ name ++ "!")

How can we formalize this intuitive notation? To do this, we need to be able to perform some basic operations with I/O actions:

• We should be able to sequence two I/O operations together. In Haskell, this is written as an infix operator >>, so that putStrLn "abc" >> putStrLn "def" is an I/O action that prints two lines of text to the console. The type of >> is IO a → IO b → IO b, meaning that the operator takes two I/O operations and returns a third that sequences the two together and returns the value of the second.
• We should have an I/O action which does nothing. That is, it returns a value but has no side effects. In Haskell, this action constructor is called return; it has type a → IO a.
• More subtly, we should be able to determine our next action based on the results of previous actions. To do this, Haskell has an operator >>= (pronounced bind) with type IO a → (a → IO b) → IO b. That is, the operand on the left is an I/O action that returns a value of type a; the operand on the right is a function that can pick an I/O action based on the value produced by the action on the left. The resulting combined action, when performed, performs the first action, then evaluates the function with the first action's return value, then performs the second action, and finally returns the second action's value.

An example of the use of this operator in Haskell would be getLine >>= putStrLn, which reads a single line of text from standard input and echos it to standard output. Note that the first operator, >>, is just a special case of this operator in which the return value of the first action is ignored and the selected second action is always the same.

It is not necessarily obvious that the three preceding operations, along with a suitable primitive set of I/O operations, allow us to define any program action whatsoever, including data transformations (using lambda expressions), if/then control flow, and looping control flows (using recursion). We can write the above example as one long expression:

main =
  putStrLn "What is your name?" >> 
  getLine >>= \name ->
  putStrLn ("Nice to meet you, " ++ name ++ "!")

The pipeline structure of the bind operator ensures that the getLine and putStrLn operations get evaluated only once and in the given order, so that the side-effects of extracting text from the input stream and writing to the output stream are correctly handled in the functional pipeline. This remains true even if the language performs out-of-order or lazy evaluation of functions.

Maybeモナド

Now consider the option type Maybe a, representing a value that is either a single value of type a, or no value at all. To distinguish these, we have two algebraic data type constructors: Just x, containing the value x, or Nothing, containing no value.

data Maybe t = Just t | Nothing

We would like to be able to use this type as a simple sort of checked exception: at any point in a computation, the computation may fail, which causes the rest of the computation to be skipped and the final result to be Nothing. If all steps of the calculation succeed, the final result is Just x for some value x.

add :: Maybe Int -> Maybe Int -> Maybe Int
add x y = do
  x' <- x
  y' <- y
  return (x' + y')

In this example, if both x and y have Just values, we want to return Just their sum; but if either x or y is Nothing, we want to return Nothing.

If we naïvely attempt to write functions with this kind of behavior, we'll end up with a nested series of "if Nothing then Nothing else do something with the x in Just x" cases that will quickly become unwieldy. To alleviate this, we can define operations for chaining these computations together:

• We should be able to sequence two operations that could fail together, into a single operation that fails if either sub-operation fails and otherwise succeeds and returns the value of the second sub-operation.

(>>) :: Maybe a -> Maybe b -> Maybe b
(Just _) >> y = y
Nothing >> _ = Nothing

• We already have a value constructor that returns a value without affecting the computation's additional state: Just.

return x = Just x

• We should be able to chain the results of one computation that could fail into a function that chooses another computation that could fail. If the first argument is Nothing, the second argument (the function) is ignored and the entire operation simply fails. If the first argument is Just x, we pass x to the function to get a new Maybe value, which may or may not result in a Just value.

(>>=) :: Maybe a -> (a -> Maybe b) -> Maybe b
(Just x) >>= f = f x
Nothing >>= _ = Nothing

We can then write the example as:

add x y =
  x >>= (\x' ->
  y >>= (\y' ->
  return (x' + y')))

Clearly, there is some common structure between the I/O definitions and the Maybe definitions, even though they are different in many ways. Monads are an abstraction upon the structures described above, and many similar structures, that finds and exploits the commonalities. The general monad concept includes any situation where the programmer wants to carry out a purely functional computation while a related computation is carried out on the side.

形式的定義

A monad is a construction that, given an underlying type system, embeds a corresponding type system (called the monadic type system) into it (that is, each monadic type acts as the underlying type). This monadic type system preserves all significant aspects of the underlying type system, while adding features particular to the monad.

The usual formulation of a monad for programming is known as a Kleisli triple, and has the following components:

1. A type constructor that defines, for every underlying type, how to obtain a corresponding monadic type. In Haskell's notation, the name of the monad represents the type constructor. If M is the name of the monad and t is a data type, then M t is the corresponding type in the monad.
2. A unit function that maps a value in an underlying type to a value in the corresponding monadic type. The unit function has the polymorphic type t→M t. The result is normally the "simplest" value in the corresponding type that completely preserves the original value (simplicity being understood appropriately to the monad). In Haskell, this function is called return due to the way it is used in the do-notation described later.
3. A binding operation of polymorphic type (M t)→(t→M u)→(M u), which Haskell represents by the infix operator >>=. Its first argument is a value in a monadic type, its second argument is a function that maps from the underlying type of the first argument to another monadic type, and its result is in that other monadic type. Typically, the binding operation can be understood as having four stages:
   1. The monad-related structure on the first argument is "pierced" to expose any number of values in the underlying type t.
   2. The given function is applied to all of those values to obtain values of type (M u).
   3. The monad-related structure on those values is also pierced, exposing values of type u.
   4. Finally, the monad-related structure is reassembled over all of the results, giving a single value of type (M u).

公理

For a monad to behave correctly, the definitions must obey a few axioms. (The ≡ symbol is not Haskell code, but indicates an equivalence between two Haskell expressions.)

• "return" acts approximately as a neutral element of >>=.

(return x) >>= f ≡ f x
m >>= return ≡ m

• Binding two functions in succession is the same as binding one function that can be determined from them.

(m >>= f) >>= g ≡ m >>= ( \x -> (f x >>= g) )

In the last rule, the notation \x -> defines an anonymous function that maps any value x to the expression that follows.

The axioms can also be expressed in do-block style (see below):

do { f x } ≡ do { v <- return x; f v }

do { m }   ≡ do { v <- m; return v }

do { x <- m; 
     y <- f x;
     g y }
≡
do { y <- do { x <- m; f x };
     g y }

モナドのゼロ

A monad can optionally define a "zero" value for every type. Binding a zero with any function produces the zero for the result type, just as 0 multiplied by any number is 0.

mzero >>= f ≡ mzero

Similarly, binding any m with a function that always returns a zero results in a zero

m >>= (\x -> mzero) ≡ mzero

Intuitively, the zero represents a value in the monad that has only monad-related structure and no values from the underlying type. In the Maybe monad, "Nothing" is a zero. In the List monad, "[]" (the empty list) is a zero.

An additive monad is a monad endowed with a monadic zero and an operation (called mplus) satisfying the monoid laws, with the monadic zero as unit. The operation has type M t → M t → M t (where M is the monad constructor and t is the underlying data type), satisfies the associative law and has the zero as both left and right identity. (Thus, an additive monad is also a monoid.)

do記法

Although there are times when it makes sense to use the bind operator >>= directly in a program, it is more typical to use a format called do-notation (perform-notation in OCaml, computation expressions in F#), that mimics the appearance of imperative languages. The compiler translates do-notation to expressions involving >>=. For example, the following code:

a = do x <- [3..4]
       [1..2]
       return (x, 42)

is transformed during compilation into:

a = [3..4] >>= (\x -> [1..2] >>= (\_ -> return (x, 42)))

It is helpful to see the implementation of the list monad, and to know that concatMap maps a function over a list and concatenates (flattens) the resulting lists:

instance Monad [] where
  m >>= f  = concatMap f m
  return x = [x]
  fail s   = []

Therefore, the following transformations hold and all the following expressions are equivalent:

a = [3..4] >>= (\x -> [1..2] >>= (\_ -> return (x, 42)))
a = [3..4] >>= (\x -> concatMap (\_ -> return (x, 42)) [1..2] )
a = [3..4] >>= (\x -> [(x,42),(x,42)] )
a = concatMap (\x -> [(x,42),(x,42)] ) [3..4]
a = [(3,42),(3,42),(4,42),(4,42)]

Notice that the list [1..2] is not used. The lack of a left-pointing arrow, translated into a binding to a function that ignores its argument, indicates that only the monadic structure is of interest, not the values inside it, e.g. for a state monad this might be used for changing the state without producing any more result values. The do-block notation can be used with any monad as it is simply syntactic sugar for >>=.

The following definitions for safe division for values in the Maybe monad are also equivalent:

x // y = do
  a <- x  -- Extract the values "inside" x and y, if there are any.
  b <- y
  if b == 0 then Nothing else Just (a / b)

x // y = x >>= (\a -> y >>= (\b -> if b == 0 then Nothing else Just (a / b)))

A similar example in F# using a computation expression:

let readNum () =
  let s = Console.ReadLine()
  let succ,v = Int32.TryParse(s)
  if (succ) then Some(v) else None

let secure_div = 
  maybe { 
    let! x = readNum()
    let! y = readNum()
    if (y = 0) 
    then None
    else return (x / y)
  }

The syntactic sugar of the maybe block would get translated internally to the following expression:

maybe.Delay(fun () ->
  maybe.Bind(readNum(), fun x ->
    maybe.Bind(readNum(), fun y ->
      if (y=0) then None else maybe.Return( x/y ))))

Generic monadic functions

Given values produced by safe division, we might want to carry on doing calculations without having to check manually if they are Nothing (i.e. resulted from an attempted division by zero). We can do this using a "lifting" function, which we can define not only for Maybe but for arbitrary monads. In Haskell this is called liftM2:

liftM2 :: Monad m => (a -> b -> c) -> m a -> m b -> m c
liftM2 op mx my = do
    x <- mx
    y <- my
    return (op x y)

Recall that arrows in a type associate to the right, so liftM2 is a function that takes a binary function as an argument and returns another binary function. The type signature says: If m is a monad, we can "lift" any binary function into it. For example:

(.*.) :: (Monad m, Num a) => m a -> m a -> m a
x .*. y = liftM2 (*) x y

defines an operator (.*.) which multiplies two numbers, unless one of them is Nothing (in which case it again returns Nothing). The advantage here is that we need not dive into the details of the implementation of the monad; if we need to do the same kind of thing with another function, or in another monad, using liftM2 makes it immediately clear what is meant (see Code reuse).

Mathematically, the liftM2 operator is defined by:

${\text{liftM2}}\colon \forall M\colon {\text{monad}},\;(A_{1}\to A_{2}\to R)\to M\,A_{1}\to M\,A_{2}\to M\,R=$$op\mapsto m_{1}\mapsto m_{2}\mapsto {\text{bind}}\;m_{1}\;(a_{1}\mapsto {\text{bind}}\;m_{2}\;(a_{2}\mapsto {\text{return}}\;(op\,a_{1}\,a_{2})))$

他の例

Identityモナド

The simplest monad is the identity monad, which attaches no information to values.

Id t = t
return x = x
x >>= f = f x

A do-block in this monad performs variable substitution; do {x <- 2; return 3*x} results in 6.

From the category theory point of view, the identity monad is derived from the adjunction between identity functors.

コレクション

Some familiar collection types, including lists, sets, and multisets, are monads. The definition for lists is given here.

-- "return" constructs a one-item list.
return x = [x]
-- "bind" concatenates the lists obtained by applying f to each item in list xs.
xs >>= f = concat (map f xs)
-- The zero object is an empty list.
mzero = []

List comprehensions are a special application of the list monad. For example, the list comprehension [ 2*x | x <- [1..n], isOkay x] corresponds to the computation in the list monad do {x <- [1..n]; if isOkay x then return () else mzero; return (2*x)}.

The notation of list comprehensions is similar to the set-builder notation, but sets can't be made into a monad, since there's a restriction on the type of computation to be comparable for equality, whereas a monad does not put any constraints on the types of computations. Actually, the Set is a restricted monad. The monads for collections naturally represent nondeterministic computation. The list (or other collection) represents all the possible results from different nondeterministic paths of computation at that given time. For example, when one executes x <- [1,2,3,4,5], one is saying that the variable x can non-deterministically take on any of the values of that list. If one were to return x, it would evaluate to a list of the results from each path of computation. Notice that the bind operator above follows this theme by performing f on each of the current possible results, and then it concatenates the result lists together.

Statements like if condition x y then return () else mzero are also often seen; if the condition is true, the non-deterministic choice is being performed from one dummy path of computation, which returns a value we are not assigning to anything; however, if the condition is false, then the mzero = [] monad value non-deterministically chooses from 0 values, effectively terminating that path of computation. Other paths of computations might still succeed. This effectively serves as a "guard" to enforce that only paths of computation that satisfy certain conditions can continue. So collection monads are very useful for solving logic puzzles, Sudoku, and similar problems.

In a language with lazy evaluation, like Haskell, a list is evaluated only to the degree that its elements are requested: for example, if one asks for the first element of a list, only the first element will be computed. With respect to usage of the list monad for non-deterministic computation that means that we can non-deterministically generate a lazy list of all results of the computation and ask for the first of them, and only as much work will be performed as is needed to get that first result. The process roughly corresponds to backtracking: a path of computation is chosen, and then if it fails at some point (if it evaluates mzero), then it backtracks to the last branching point, and follows the next path, and so on. If the second element is then requested, it again does just enough work to get the second solution, and so on. So the list monad is a simple way to implement a backtracking algorithm in a lazy language.

From the category theory point of view, collection monads are derived from adjunctions between a free functor and an underlying functor between the category of sets and a category of monoids. Taking different types of monoids, we obtain different types of collections.

type of collections	type of monoids
list	monoid
finite multiset	commutative monoid
finite set	idempotent commutative monoid
finite permutation	idempotent non-commutative monoid

State monads

A state monad allows a programmer to attach state information of any type to a calculation. Given any value type, the corresponding type in the state monad is a function which accepts a state, then outputs a new state along with a return value.

type State s t = s -> (t, s)

Note that this monad, unlike those already seen, takes a type parameter, the type of the state information. The monad operations are defined as follows:

-- "return" produces the given value without changing the state.
return x = \s -> (x, s)
-- "bind" modifies m so that it applies f to its result.
m >>= f = \r -> let (x, s) = m r in (f x) s

Useful state operations include:

get = \s -> (s, s) -- Examine the state at this point in the computation.
put s = \_ -> ((), s) -- Replace the state.
modify f = \s -> ((), f s) -- Update the state

Another operation applies a state monad to a given initial state:

runState :: State s a -> s -> (a, s)
runState t s = t s

do-blocks in a state monad are sequences of operations that can examine and update the state data.

Informally, a state monad of state type S maps the type of return values T into functions of type $S\rightarrow T\times S$, where S is the underlying state. The return function is simply:

${\text{return}}\colon T\rightarrow S\rightarrow T\times S=t\mapsto s\mapsto (t,s)$

The bind function is:

${\text{bind}}\colon (S\rightarrow T\times S)\rightarrow (T\rightarrow S\rightarrow T'\times S)\rightarrow S\rightarrow T'\times S$$\ =m\mapsto k\mapsto s\mapsto (k\ t\ s')\quad {\text{where}}\;(t,s')=m\,s$.

From the category theory point of view, a state monad is derived from the adjunction between the product functor and the exponential functor, which exists in any cartesian closed category by definition.

環境モナド

The environment monad (also called the reader monad and the function monad) allows a computation to depend on values from a shared environment. The monad type constructor maps a type T to functions of type E → T, where E is the type of the shared environment. The monad functions are:

${\text{return}}\colon T\rightarrow E\rightarrow T=t\mapsto e\mapsto t$

${\text{bind}}\colon (E\rightarrow T)\rightarrow (T\rightarrow E\rightarrow T')\rightarrow E\rightarrow T'=r\mapsto f\mapsto e\mapsto f\,(r\,e)\,e$

The following monadic operations are useful:

${\text{ask}}\colon E\rightarrow E={\text{id}}_{E}$

${\text{local}}\colon (E\rightarrow E)\rightarrow (E\rightarrow T)\rightarrow E\rightarrow T=f\mapsto c\mapsto e\mapsto c\,(f\,e)$

The ask operation is used to retrieve the current context, while local executes a computation in a modified subcontext. As in the state monad, computations in the environment monad may be invoked by simply providing an environment value and applying it to an instance of the monad.

Writerモナド

The writer monad allows a program to compute various kinds of auxiliary output which can be "composed" or "accumulated" step-by-step, in addition to the main result of a computation. It is often used for logging or profiling. Given the underlying type T, a value in the writer monad has type W × T, where W is a type endowed with an operation satisfying the monoid laws. The monad functions are simply:

${\text{return}}\colon T\rightarrow W\times T=t\mapsto (\epsilon ,t)$

${\text{bind}}\colon (W\times T)\rightarrow (T\rightarrow W\times T')\rightarrow W\times T'=(w,t)\mapsto f\mapsto (w*w',\,t')\quad {\text{where}}\;(w',t')=f\,t$

where ε and * are the identity element of the monoid W and its associative operation, respectively.

The tell monadic operation is defined by:

${\text{tell}}\colon W\rightarrow (W\times 1)=w\mapsto (w,())$

where 1 and () denote the unit type and its trivial element. It is used in combination with bind to update the auxiliary value without affecting the main computation.

継続モナド

A continuation monad with return type $R$ maps type $T$ into functions of type $\left(T\rightarrow R\right)\rightarrow R$. It is used to model continuation-passing style. The return and bind functions are as follows:

${\text{return}}\colon T\rightarrow \left(T\rightarrow R\right)\rightarrow R=t\mapsto f\mapsto f\,t$

${\text{bind}}\colon \left(\left(T\rightarrow R\right)\rightarrow R\right)\rightarrow \left(T\rightarrow \left(T'\rightarrow R\right)\rightarrow R\right)\rightarrow \left(T'\rightarrow R\right)\rightarrow R$$=c\mapsto f\mapsto k\mapsto c\,\left(t\mapsto f\,t\,k\right)$

The call-with-current-continuation function is defined as follows:

${\text{call/cc}}\colon \left(\left(T\rightarrow \left(T'\rightarrow R\right)\rightarrow R\right)\rightarrow \left(T\rightarrow R\right)\rightarrow R\right)\rightarrow \left(T\rightarrow R\right)\rightarrow R$$=f\mapsto k\mapsto \left(f\left(t\mapsto x\mapsto k\,t\right)\,k\right)$

その他

Other concepts that researchers have expressed as monads include:

• Exception handling
• Graphical user interfaces
• Interprocess communication
• Parsers
• Interpreters
• Strict evaluation
• Interfaces to code written in other languages

fmapとjoin

Although Haskell defines monads in terms of the return and bind functions, it is also possible to define a monad in terms of return and two other operations, join and fmap. This formulation fits more closely with the definition of monads in category theory. The fmap operation, with type (t→u) → (M t→M u), takes a function between two types and produces a function that does the "same thing" to values in the monad. The join operation, with type M (M t)→M t, "flattens" two layers of monadic information into one.

The two formulations are related as follows. As before, the ≡ symbol indicates equivalence between two Haskell expressions.

(fmap f) m ≡ m >>= (\x -> return (f x))
join n ≡ n >>= id

m >>= g ≡ join ((fmap g) m)

Here, m has the type M t, n has the type M (M r), f has the type t → u, and g has the type t → M v, where t, r, u and v are underlying types.

The fmap function is defined for any functor in the category of types and functions, not just for monads. It is expected to satisfy the functor laws:

fmap id = id
fmap (f . g) = (fmap f) . (fmap g)

The return function characterizes pointed functors in the same category, by accounting for the ability to "lift" values into the functor. It should satisfy the following law:

return . f = fmap f . return

In addition, the join function characterizes monads:

join . fmap join = join . join
join . fmap return = join . return = id
join . fmap (fmap f) = fmap f . join

コモナド

Comonads are the categorical dual of monads. They are defined by a type constructor W T and two operations: extract with type W T → T for any T, and extend with type (W T → T' ) → W T → W T' . The operations extend and extract are expected to satisfy these laws:

${\text{extend}}\,\,{\text{extract}}={\text{id}}$

${\text{extract}}\circ ({\text{extend}}\,f)=f$

$({\text{extend}}\,f)\circ ({\text{extend}}\,g)={\text{extend}}\,(f\circ ({\text{extend}}\,g))$

Alternatively, comonads may be defined in terms of operations fmap, extract and duplicate. The fmap and extract operations define W as a copointed functor. The duplicate operation characterizes comonads: it has type W T → W (W T) and satisfies the following laws:

${\text{extract}}\circ {\text{duplicate}}={\text{id}}$

${\text{fmap}}\,{\text{extract}}\circ {\text{duplicate}}={\text{id}}$

${\text{duplicate}}\circ {\text{duplicate}}={\text{fmap}}\,{\text{duplicate}}\circ {\text{duplicate}}$

The two formulations are related as follows:

${\text{fmap}}:(A\rightarrow B)\rightarrow \mathrm {W} \,A\rightarrow \mathrm {W} \,B=f\mapsto {\text{extend}}\,(f\circ {\text{extract}})$

${\text{duplicate}}:\mathrm {W} \,A\rightarrow \mathrm {W} \,\mathrm {W} \,A={\text{extend}}\,{\text{id}}$

${\text{extend}}:(\mathrm {W} \,A\rightarrow B)\rightarrow \mathrm {W} \,A\rightarrow \mathrm {W} \,B=f\mapsto ({\text{fmap}}\,f)\circ {\text{duplicate}}$

Whereas monads could be said to represent side-effects, a comonads W represents a kind of context. The extract functions extracts a value from its context, while the extend function may be used to compose a pipeline of "context-dependent functions" of type W A → B.

Identityコモナド

The identity comonad is the simplest comonad: it maps type T to itself. The extract operator is the identity and the extend operator is function application.

直積コモナド

The product comonad maps type $T$ into tuples of type $C\times T$, where $C$ is the context type of the comonad. The comonad operations are:

${\text{extract}}:(C\times T)\rightarrow T=(c,t)\mapsto t$

${\text{extend}}:((C\times A)\rightarrow B)\rightarrow C\times A\rightarrow C\times B=f\mapsto (c,a)\mapsto (c,f\,(c,a))$

${\text{fmap}}:(A\rightarrow B)\rightarrow (C\times A)\rightarrow (C\times B)=(c,a)\mapsto (c,f\,a)$

${\text{duplicate}}:(C\times A)\rightarrow (C\times (C\times A))=(c,a)\mapsto (c,(c,a))$

関数コモナド

The function comonad maps type $T$ into functions of type $M\rightarrow T$, where $M$ is a type endowed with a monoid structure. The comonad operations are:

${\text{extract}}:(M\rightarrow T)\rightarrow T=f\mapsto f\,\varepsilon$

${\text{extend}}:((M\rightarrow A)\rightarrow B)\rightarrow (M\rightarrow A)\rightarrow M\rightarrow B=f\mapsto g\mapsto m\mapsto f\,(m'\mapsto g\,(m*m'))$

${\text{fmap}}:(A\rightarrow B)\rightarrow (M\rightarrow A)\rightarrow M\rightarrow B=f\mapsto g\mapsto (f\circ g)$

${\text{duplicate}}:(M\rightarrow A)\rightarrow M\rightarrow (M\rightarrow A)=f\mapsto m\mapsto m'\mapsto f\,(m*m')$

where ε is the identity element of $M$ and * is its associative operation.

Costateコモナド

The costate comonad maps a type $T$ into type $(S\rightarrow T)\times S$, where S is the base type of the store. The comonad operations are:

${\text{extract}}:((S\rightarrow T)\times S)\rightarrow T=(f,s)\mapsto f\,s$

${\text{extend}}:(((S\rightarrow A)\times S)\rightarrow B)\rightarrow ((S\rightarrow A)\times S)\rightarrow (S\rightarrow B)\times S\,$$=f\mapsto (g,s)\mapsto ((s'\mapsto f\,(g,s')),s)$

${\text{fmap}}:(A\rightarrow B)\rightarrow ((S\rightarrow A)\times S)\rightarrow (S\rightarrow B)\times S=f\mapsto (f',s)\mapsto (f\circ f',s)$

${\text{duplicate}}:((S\rightarrow A)\times S)\rightarrow (S\rightarrow ((S\rightarrow A)\times S))\times S=(f,s)\mapsto (s'\mapsto (f,s'),s)$

関連項目

• Arrows in functional programming — whereas monads generalize the results of a computation to effects, arrows further generalize the inputs similarly.
• Monad transformers — which allow monads to be composed in a modular and convenient way.
• Inversion of control — the abstract principle of calling specific functions from a reusable software entity.
• Aspect-oriented programming, a paradigm to increase modularity by isolating secondary or supporting functionality.
• Uniqueness types - an alternative way of dealing with side-effects in functional languages

外部リンク

Haskell monad tutorials

• Monad Tutorials Timeline Probably the most comprehensive collection of links to monad tutorials, ordered by date.
• Piponi, Dan (August 7, 2006). “You Could Have Invented Monads! (And Maybe You Already Have.)”. A Neighborhood of Infinity. — The most famous "blog post" tutorial.
• Yorgey, Brent (12 March 2009). “The Typeclassopedia”. The Monad.Reader (13): 17–68. http://www.haskell.org/wikiupload/8/85/TMR-Issue13.pdf.  — An attempt to explain all of the leading typeclasses in Haskell in an elementary way, with monadic functors considered as only one form, best understood by comparison with others: e.g., the more general idea of a "Functor" as something you can map over; "Applicative" functors, and so forth; contains an extensive bibliography.
• Yorgey, Brent (January 12, 2009). “Abstraction, intuition, and the "monad tutorial fallacy"”. blog :: Brent -> [String]. WordPress.com. — Opposes the idea of making a tutorial about monads in particular.
• What a Monad is not deals with common misconceptions and oversimplifications in a humorous way.
• beelsebob (March 31, 2009). “How you should(n’t) use Monad”. No Ordering. WordPress.com. — Takes a similar point of view, locating monads in a much wider array of Haskell functor classes, of use only in special circumstances.
• Vanier, Mike (July 25, 2010). “Yet Another Monad Tutorial (part 1: basics)”. Mike's World-O-Programming. LiveJournal. — An extremely detailed set of tutorials, deriving monads from first principles.
• “A Fistful of Monads”. An explanation of Monads, building on the concepts of Functors, Applicative Functors and Monoids discussed in the previous chapter.

Older tutorials

• All About Monads
• Haskell Wiki: Monads as Computation
• Haskell Wiki: Monads as Containers
• Norvell, Theodore. “Monads for the Working Haskell Programmer”. Memorial University of Newfoundland.
• Klinger, Stefan (15 December 2005). The Haskell Programmer’s Guide to the IO Monad — Don’t Panic. Centre for Telematics and Information Technology, University of Twente. ISSN 1381-3625. http://stefan-klinger.de/files/monadGuide.pdf. 
• Turoff, Adam (August 2, 2007). “Introduction to Haskell, Part 3: Monads”. ONLamp. O'Reilly Media.
• Monads A monad tutorial providing examples of non-trivial monads apart from the conventional IO/Maybe/List/State monads.
• Söylemez, Ertugrul (2010年7月11日). “Understanding Haskell Monads”.

Other documentation

• van Tuyl, Henk-Jan (2010年2月27日). “A tour of the Haskell monad functions”.
• Moggi, Eugenio. Notions of computation and monads. http://www.disi.unige.it/person/MoggiE/ftp/ic91.pdf.  — The original paper suggesting use of monads for programming
• Wadler, Philip (August 2001). Monads for functional programming. University of Glasgow. http://homepages.inf.ed.ac.uk/wadler/papers/marktoberdorf/baastad.pdf.  — Describes monads in Haskell (before they were implemented)

Scala monad tutorials

• League, Chris (12 July 2010). Monadologie: Professional Help for Type Anxiety (flv) (Tech talk). New York City: New York Scala Enthusiasts.
• Morris, Tony (June 22, 2010). “Understanding Monads using Scala (Part 1)”. λ Tony’s blog λ.
• “Monads are Elephants” (September 18, 2007).
• “Pro Scala: Monadic Design Patterns for the Web”. pp. 300 (April 25, 2010).

Monads in other languages

• Monads in Perl
• Monads in Ruby
• Monads in Python
• Monads in Scala
• Monads in Clojure
• Monads in JavaScript
• Monads in C# and LINQ
• Monads in Ocaml