函子是对象和态射的映射，它保留了一个类别的组成和身份。

让我们定义什么是类别?

是一堆东西! 在a内部画几个点(现在是两个点，一个是“a”，另一个是“b”) 圆圈，并命名为圆圈A(类别)。

这个类别包含什么?

对象之间的组合和每个对象的恒等函数。

因此，在应用Functor之后，我们必须映射对象并保存组合。

让我们想象‘A’是我们的范畴，它有对象[' A'， 'b']，并且存在一个态射A -> b

现在，我们必须定义一个函子，它可以将这些对象和态射映射到另一个类别“B”。

假设这个函子叫做Maybe

data Maybe a = Nothing | Just a

B类是这样的。

请再画一个圆，但这次用“也许a”和“也许b”代替“a”和“b”。

一切看起来都很好，所有的对象都被映射了

“a”变成了“也许a”，“b”变成了“也许b”。

但问题是我们也要把态射从a映射到b。

这意味着' a'中的态态a -> b应该映射到'可能a' -> '可能b'

来自a -> b的形态称为f，然后来自'Maybe a' -> 'Maybe b'的形态称为'fmap f'

现在让我们看看函数f在A中做了什么，看看我们能否在B中复制它

A中f的函数定义:

f :: a -> b

F取a并返回b

f在B中的函数定义:

f :: Maybe a -> Maybe b

f取也许a，返回也许b

让我们看看如何使用fmap将函数'f'从'A'映射到'B'中的函数'fmap f'

fmap的定义

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

那么，我们在这里做什么?

我们将函数“f”应用于类型为“a”的“x”。“Nothing”的特殊模式匹配来自于Functor Maybe的定义。

因此，我们将对象[a, b]和形态[f]从类别' a '映射到类别' b '。

那是富克托！

简单地说，函子或函数对象是可以像函数一样调用的类对象。

在c++中:

这就是写函数的方法

void foo()
{
    cout << "Hello, world! I'm a function!";
}

这就是写函子的方法

class FunctorClass
{
    public:
    void operator ()
    {
        cout << "Hello, world! I'm a functor!";
    }
};

现在你可以这样做:

foo(); //result: Hello, World! I'm a function!

FunctorClass bar;
bar(); //result: Hello, World! I'm a functor!

这样做的好处是可以将state保存在类中——想象一下，如果您想询问一个函数被调用了多少次。没有办法以简洁、封装的方式做到这一点。对于函数对象，它就像任何其他类一样:你有一些实例变量，你在operator()中增加它，还有一些方法来检查这个变量，一切都很整齐。

Functor与函数式编程没有特别的关系。它只是一个指向函数或某种对象的“指针”，可以像调用函数一样调用它。

这是一篇关于函子的编程文章，后面更具体地介绍了它们是如何在编程语言中出现的。

函子的实际使用是在单子中，如果你想找的话，你可以找到很多关于单子的教程。

“函子”这个词来自范畴论，范畴论是数学中一个非常普遍、非常抽象的分支。函数式语言的设计者至少以两种不同的方式借用了它。

In the ML family of languages, a functor is a module that takes one or more other modules as a parameter. It's considered an advanced feature, and most beginning programmers have difficulty with it. As an example of implementation and practical use, you could define your favorite form of balanced binary search tree once and for all as a functor, and it would take as a parameter a module that provides: The type of key to be used in the binary tree A total-ordering function on keys Once you've done this, you can use the same balanced binary tree implementation forever. (The type of value stored in the tree is usually left polymorphic—the tree doesn't need to look at values other than to copy them around, whereas the tree definitely needs to be able to compare keys, and it gets the comparison function from the functor's parameter.) Another application of ML functors is layered network protocols. The link is to a really terrific paper by the CMU Fox group; it shows how to use functors to build more complex protocol layers (like TCP) on type of simpler layers (like IP or even directly over Ethernet). Each layer is implemented as a functor that takes as a parameter the layer below it. The structure of the software actually reflects the way people think about the problem, as opposed to the layers existing only in the mind of the programmer. In 1994 when this work was published, it was a big deal. For a wild example of ML functors in action, you could see the paper ML Module Mania, which contains a publishable (i.e., scary) example of functors at work. For a brilliant, clear, pellucid explanation of the ML modules system (with comparisons to other kinds of modules), read the first few pages of Xavier Leroy's brilliant 1994 POPL paper Manifest Types, Modules, and Separate Compilation. In Haskell, and in some related pure functional language, Functor is a type class. A type belongs to a type class (or more technically, the type "is an instance of" the type class) when the type provides certain operations with certain expected behavior. A type T can belong to class Functor if it has certain collection-like behavior: The type T is parameterized over another type, which you should think of as the element type of the collection. The type of the full collection is then something like T Int, T String, T Bool, if you are containing integers, strings, or Booleans respectively. If the element type is unknown, it is written as a type parameter a, as in T a. Examples include lists (zero or more elements of type a), the Maybe type (zero or one elements of type a), sets of elements of type a, arrays of elements of type a, all kinds of search trees containing values of type a, and lots of others you can think of. The other property that T has to satisfy is that if you have a function of type a -> b (a function on elements), then you have to be able to take that function and product a related function on collections. You do this with the operator fmap, which is shared by every type in the Functor type class. The operator is actually overloaded, so if you have a function even with type Int -> Bool, then fmap even is an overloaded function that can do many wonderful things: Convert a list of integers to a list of Booleans Convert a tree of integers to a tree of Booleans Convert Nothing to Nothing and Just 7 to Just False In Haskell, this property is expressed by giving the type of fmap: fmap :: (Functor t) => (a -> b) -> t a -> t b where we now have a small t, which means "any type in the Functor class." To make a long story short, in Haskell a functor is a kind of collection for which if you are given a function on elements, fmap will give you back a function on collections. As you can imagine, this is an idea that can be widely reused, which is why it is blessed as part of Haskell's standard library.

像往常一样，人们继续发明新的、有用的抽象，您可能想要研究应用函子，对此最好的参考可能是Conor McBride和Ross Paterson撰写的一篇名为《带效果的应用编程》的论文。

这里的其他答案是完整的，但我将尝试另一种解释FP使用函子。我们来做个类比:

函子是类型为A的容器，当它受到从A→b映射的函数的作用时，将产生类型为b的容器。

与c++中使用的抽象函数指针不同，这里函子不是函数;相反，它是一种在受到某个函数影响时表现一致的东西。