这里没有一个答案显示了惯用的函数式编程。递归阶乘的答案很适合在FP中表示递归，但大多数代码不是递归的，所以我不认为这个答案是完全具有代表性的。

假设你有一个字符串数组，每个字符串表示一个整数，比如“5”或“-200”。您希望根据内部测试用例检查这个输入字符串数组(使用整数比较)。两种解决方案如下所示

程序上的

arr_equal(a : [Int], b : [Str]) -> Bool {
    if(a.len != b.len) {
        return false;
    }

    bool ret = true;
    for( int i = 0; i < a.len /* Optimized with && ret*/; i++ ) {
        int a_int = a[i];
        int b_int = parseInt(b[i]);
        ret &= a_int == b_int;  
    }
    return ret;
}

功能

eq = i, j => i == j # This is usually a built-in
toInt = i => parseInt(i) # Of course, parseInt === toInt here, but this is for visualization

arr_equal(a : [Int], b : [Str]) -> Bool =
    zip(a, b.map(toInt)) # Combines into [Int, Int]
   .map(eq)
   .reduce(true, (i, j) => i && j) # Start with true, and continuously && it with each value

虽然纯函数式语言通常是研究语言(因为现实世界喜欢免费的副作用)，但现实世界的过程式语言在适当的时候会使用更简单的函数式语法。

这通常是用Lodash这样的外部库实现的，或者是用Rust这样的新语言内置的。函数式编程的繁重工作是通过map、filter、reduce、currying、partial等函数/概念完成的，最后三个你可以查阅以进一步理解。

齿顶高

In order to be used in the wild, the compiler will normally have to work out how to convert the functional version into the procedural version internally, as function call overhead is too high. Recursive cases such as the factorial shown will use tricks such as tail call to remove O(n) memory usage. The fact that there are no side effects allows functional compilers to implement the && ret optimization even when the .reduce is done last. Using Lodash in JS, obviously does not allow for any optimization, so it is a hit to performance (Which isn't usually a concern with web development). Languages like Rust will optimize internally (And have functions such as try_fold to assist && ret optimization).

进一步阐述康拉德的评论:

求值的顺序不是 定义良好的

一些函数式语言有所谓的惰性求值。这意味着直到需要该值时才执行函数。在此之前，传递的是函数本身。

过程式语言是步骤1、步骤2、步骤3……如果在第二步你说加2 + 2，它马上就会做。在惰性求值中，你会说2 + 2，但如果结果从未被使用，它就永远不会做加法。

我相信过程式/函数式/目标式编程是关于如何处理问题的。

The first style would plan everything in to steps, and solves the problem by implementing one step (a procedure) at a time. On the other hand, functional programming would emphasize the divide-and-conquer approach, where the problem is divided into sub-problem, then each sub-problem is solved (creating a function to solve that sub problem) and the results are combined to create the answer for the whole problem. Lastly, Objective programming would mimic the real world by create a mini-world inside the computer with many objects, each of which has a (somewhat) unique characteristics, and interacts with others. From those interactions the result would emerge.

每种编程风格都有自己的优点和缺点。因此，做一些诸如“纯编程”(即纯粹的程序设计——顺便说一下，没有人会这样做，这有点奇怪——或纯粹的函数式或纯粹的目标)是非常困难的，如果不是不可能的话，除了一些专门设计来展示编程风格优势的基本问题(因此，我们称那些喜欢纯粹的人为“weenie”:D)。

Then, from those styles, we have programming languages that is designed to optimized for some each style. For example, Assembly is all about procedural. Okay, most early languages are procedural, not only Asm, like C, Pascal, (and Fortran, I heard). Then, we have all famous Java in objective school (Actually, Java and C# is also in a class called "money-oriented," but that is subject for another discussion). Also objective is Smalltalk. In functional school, we would have "nearly functional" (some considered them to be impure) Lisp family and ML family and many "purely functional" Haskell, Erlang, etc. By the way, there are many general languages such as Perl, Python, Ruby.

这里没有一个答案显示了惯用的函数式编程。递归阶乘的答案很适合在FP中表示递归，但大多数代码不是递归的，所以我不认为这个答案是完全具有代表性的。

假设你有一个字符串数组，每个字符串表示一个整数，比如“5”或“-200”。您希望根据内部测试用例检查这个输入字符串数组(使用整数比较)。两种解决方案如下所示

程序上的

arr_equal(a : [Int], b : [Str]) -> Bool {
    if(a.len != b.len) {
        return false;
    }

    bool ret = true;
    for( int i = 0; i < a.len /* Optimized with && ret*/; i++ ) {
        int a_int = a[i];
        int b_int = parseInt(b[i]);
        ret &= a_int == b_int;  
    }
    return ret;
}

功能

eq = i, j => i == j # This is usually a built-in
toInt = i => parseInt(i) # Of course, parseInt === toInt here, but this is for visualization

arr_equal(a : [Int], b : [Str]) -> Bool =
    zip(a, b.map(toInt)) # Combines into [Int, Int]
   .map(eq)
   .reduce(true, (i, j) => i && j) # Start with true, and continuously && it with each value

虽然纯函数式语言通常是研究语言(因为现实世界喜欢免费的副作用)，但现实世界的过程式语言在适当的时候会使用更简单的函数式语法。

这通常是用Lodash这样的外部库实现的，或者是用Rust这样的新语言内置的。函数式编程的繁重工作是通过map、filter、reduce、currying、partial等函数/概念完成的，最后三个你可以查阅以进一步理解。

齿顶高

In order to be used in the wild, the compiler will normally have to work out how to convert the functional version into the procedural version internally, as function call overhead is too high. Recursive cases such as the factorial shown will use tricks such as tail call to remove O(n) memory usage. The fact that there are no side effects allows functional compilers to implement the && ret optimization even when the .reduce is done last. Using Lodash in JS, obviously does not allow for any optimization, so it is a hit to performance (Which isn't usually a concern with web development). Languages like Rust will optimize internally (And have functions such as try_fold to assist && ret optimization).

进一步阐述康拉德的评论:

因此，纯函数式程序总是对输入产生相同的值，求值的顺序没有很好的定义;

因此，函数式代码通常更容易并行化。由于函数(通常)没有副作用，并且它们(通常)只是作用于它们的参数，因此许多并发问题都消失了。

当您需要能够证明您的代码是正确的时，也可以使用函数式编程。这在过程式编程中要困难得多(在函数式编程中不容易，但仍然容易)。

免责声明:我已经很多年没有使用函数式编程了，直到最近才开始重新研究它，所以我在这里可能不完全正确。：）

函数式编程与不使用全局变量的过程式编程相同。