问题：Python函数重载
我知道Python不支持方法重载，但是我遇到了一个似乎无法用一种很好的Pythonic方法解决的问题。
我正在制作一个角色需要射击各种子弹的游戏，但是如何编写用于创建这些子弹的不同功能？例如，假设我有一个函数，该函数创建一个以给定速度从A点移动到B点的子弹。我会写一个像这样的函数：
def add_bullet(sprite, start, headto, speed):
... Code ...
但是我想编写其他创建项目符号的功能，例如：
def add_bullet(sprite, start, direction, speed):
def add_bullet(sprite, start, headto, spead, acceleration):
def add_bullet(sprite, script): # For bullets that are controlled by a script
def add_bullet(sprite, curve, speed): # for bullets with curved paths
... And so on ...
以此类推。有没有一种更好的方法可以在不使用太多关键字参数的情况下实现快速更新呢？重命名各功能是非常糟糕的一点，因为你要么add_bullet1，add_bullet2或add_bullet_with_really_long_name。
要解决一些答案：
不，我无法创建Bullet类层次结构，因为那太慢了。管理项目符号的实际代码在C中，而我的函数是C API的包装器。
我知道关键字参数，但是检查各种参数组合变得很烦人，但是默认参数可以像 acceleration=0
I know that Python does not support method overloading, but I’ve run into a problem that I can’t seem to solve in a nice Pythonic way.
I am making a game where a character needs to shoot a variety of bullets, but how do I write different functions for creating these bullets? For example suppose I have a function that creates a bullet travelling from point A to B with a given speed. I would write a function like this:
def add_bullet(sprite, start, headto, speed):
... Code ...
But I want to write other functions for creating bullets like:
def add_bullet(sprite, start, direction, speed):
def add_bullet(sprite, start, headto, spead, acceleration):
def add_bullet(sprite, script): # For bullets that are controlled by a script
def add_bullet(sprite, curve, speed): # for bullets with curved paths
... And so on ...
And so on with many variations. Is there a better way to do it without using so many keyword arguments cause its getting kinda ugly fast. Renaming each function is pretty bad too because you get either add_bullet1, add_bullet2, or add_bullet_with_really_long_name.
To address some answers:
No I can’t create a Bullet class hierarchy because thats too slow. The actual code for managing bullets is in C and my functions are wrappers around C API.
I know about the keyword arguments but checking for all sorts of combinations of parameters is getting annoying, but default arguments help allot like acceleration=0
回答 0
您要的就是所谓的多重调度。请参阅Julia语言示例，该示例演示了不同类型的调度。
但是，在查看该内容之前，我们将首先解决为什么重载并不是python中真正想要的。
为什么不超载？
首先，需要了解重载的概念以及为什么它不适用于python。
当使用可在编译时区分数据类型的语言时，可以在编译时进行选择。创建此类用于编译时选择的替代函数的操作通常称为重载函数。（维基百科）
Python是一种动态类型的语言，因此重载的概念根本不适用于它。但是，一切都不会丢失，因为我们可以在运行时创建这样的替代函数：
在将数据类型标识推迟到运行时的编程语言中，必须根据功能参数的动态确定类型，在运行时进行其他功能的选择。以这种方式选择其替代实现的功能通常被称为多方法。（维基百科）
因此，我们应该能够做到多方法在python-或者，它也可称为：多分派。
多次派遣
多重方法也称为多重调度：
多种调度或多种方法是某些面向对象的编程语言的功能，其中可以基于多个参数的运行时（动态）类型来动态调度函数或方法。（维基百科）
Python不支持这个开箱1，但是，因为它发生，有一个优秀的Python包称为multipledispatch这正是这么做的。
解
这是我们可能如何使用multidispatch 2包来实现您的方法的方法：
>>> from multipledispatch import dispatch
>>> from collections import namedtuple
>>> from types import * # we can test for lambda type, e.g.:
>>> type(lambda a: 1) == LambdaType
True
>>> Sprite = namedtuple('Sprite', ['name'])
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Curve = namedtuple('Curve', ['x', 'y', 'z'])
>>> Vector = namedtuple('Vector', ['x','y','z'])
>>> @dispatch(Sprite, Point, Vector, int)
... def add_bullet(sprite, start, direction, speed):
... print("Called Version 1")
...
>>> @dispatch(Sprite, Point, Point, int, float)
... def add_bullet(sprite, start, headto, speed, acceleration):
... print("Called version 2")
...
>>> @dispatch(Sprite, LambdaType)
... def add_bullet(sprite, script):
... print("Called version 3")
...
>>> @dispatch(Sprite, Curve, int)
... def add_bullet(sprite, curve, speed):
... print("Called version 4")
...
>>> sprite = Sprite('Turtle')
>>> start = Point(1,2)
>>> direction = Vector(1,1,1)
>>> speed = 100 #km/h
>>> acceleration = 5.0 #m/s
>>> script = lambda sprite: sprite.x * 2
>>> curve = Curve(3, 1, 4)
>>> headto = Point(100, 100) # somewhere far away
>>> add_bullet(sprite, start, direction, speed)
Called Version 1
>>> add_bullet(sprite, start, headto, speed, acceleration)
Called version 2
>>> add_bullet(sprite, script)
Called version 3
>>> add_bullet(sprite, curve, speed)
Called version 4
1. Python 3当前支持单调度
。2.注意不要在多线程环境中使用 多调度，否则会出现奇怪的行为。
What you are asking for is called multiple dispatch. See Julia language examples which demonstrates different types of dispatches.
However, before looking at that, we’ll first tackle why overloading is not really what you want in python.
Why Not Overloading?
First, one needs to understand the concept of overloading and why it’s not applicable to python.
When working with languages that can discriminate data types at
compile-time, selecting among the alternatives can occur at
compile-time. The act of creating such alternative functions for
compile-time selection is usually referred to as overloading a
function. (Wikipedia)
Python is a dynamically typed language, so the concept of overloading simply does not apply to it. However, all is not lost, since we can create such alternative functions at run-time:
In programming languages that defer data type identification until
run-time the selection among alternative
functions must occur at run-time, based on the dynamically determined
types of function arguments. Functions whose alternative
implementations are selected in this manner are referred to most
generally as multimethods. (Wikipedia)
So we should be able to do multimethods in python—or, as it is alternatively called: multiple dispatch.
Multiple dispatch
The multimethods are also called multiple dispatch:
Multiple dispatch or multimethods is the feature of some
object-oriented programming languages in which a function or method
can be dynamically dispatched based on the run time (dynamic) type of
more than one of its arguments. (Wikipedia)
Python does not support this out of the box1, but, as it happens, there is an excellent python package called multipledispatch that does exactly that.
Solution
Here is how we might use multipledispatch2 package to implement your methods:
>>> from multipledispatch import dispatch
>>> from collections import namedtuple
>>> from types import * # we can test for lambda type, e.g.:
>>> type(lambda a: 1) == LambdaType
True
>>> Sprite = namedtuple('Sprite', ['name'])
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Curve = namedtuple('Curve', ['x', 'y', 'z'])
>>> Vector = namedtuple('Vector', ['x','y','z'])
>>> @dispatch(Sprite, Point, Vector, int)
... def add_bullet(sprite, start, direction, speed):
... print("Called Version 1")
...
>>> @dispatch(Sprite, Point, Point, int, float)
... def add_bullet(sprite, start, headto, speed, acceleration):
... print("Called version 2")
...
>>> @dispatch(Sprite, LambdaType)
... def add_bullet(sprite, script):
... print("Called version 3")
...
>>> @dispatch(Sprite, Curve, int)
... def add_bullet(sprite, curve, speed):
... print("Called version 4")
...
>>> sprite = Sprite('Turtle')
>>> start = Point(1,2)
>>> direction = Vector(1,1,1)
>>> speed = 100 #km/h
>>> acceleration = 5.0 #m/s
>>> script = lambda sprite: sprite.x * 2
>>> curve = Curve(3, 1, 4)
>>> headto = Point(100, 100) # somewhere far away
>>> add_bullet(sprite, start, direction, speed)
Called Version 1
>>> add_bullet(sprite, start, headto, speed, acceleration)
Called version 2
>>> add_bullet(sprite, script)
Called version 3
>>> add_bullet(sprite, curve, speed)
Called version 4
1. Python 3 currently supports single dispatch
2. Take care not to use multipledispatch in a multi-threaded environment or you will get weird behavior.
回答 1
演示时，Python确实支持“方法重载”。实际上，您刚才描述的内容在Python中以许多不同的方式实现都是微不足道的，但我会同意：
class Character(object):
# your character __init__ and other methods go here
def add_bullet(self, sprite=default, start=default,
direction=default, speed=default, accel=default,
curve=default):
# do stuff with your arguments
在上面的代码中，default是这些参数的合理默认值或None。然后，您可以仅使用您感兴趣的参数来调用该方法，Python将使用默认值。
您还可以执行以下操作：
class Character(object):
# your character __init__ and other methods go here
def add_bullet(self, **kwargs):
# here you can unpack kwargs as (key, values) and
# do stuff with them, and use some global dictionary
# to provide default values and ensure that ``key``
# is a valid argument...
# do stuff with your arguments
另一种选择是直接将所需函数直接挂接到类或实例上：
def some_implementation(self, arg1, arg2, arg3):
# implementation
my_class.add_bullet = some_implementation_of_add_bullet
另一种方法是使用抽象工厂模式：
class Character(object):
def __init__(self, bfactory, *args, **kwargs):
self.bfactory = bfactory
def add_bullet(self):
sprite = self.bfactory.sprite()
speed = self.bfactory.speed()
# do stuff with your sprite and speed
class pretty_and_fast_factory(object):
def sprite(self):
return pretty_sprite
def speed(self):
return 10000000000.0
my_character = Character(pretty_and_fast_factory(), a1, a2, kw1=v1, kw2=v2)
my_character.add_bullet() # uses pretty_and_fast_factory
# now, if you have another factory called "ugly_and_slow_factory"
# you can change it at runtime in python by issuing
my_character.bfactory = ugly_and_slow_factory()
# In the last example you can see abstract factory and "method
# overloading" (as you call it) in action
Python does support “method overloading” as you present it. In fact, what you just describe is trivial to implement in Python, in so many different ways, but I would go with:
class Character(object):
# your character __init__ and other methods go here
def add_bullet(self, sprite=default, start=default,
direction=default, speed=default, accel=default,
curve=default):
# do stuff with your arguments
In the above code, default is a plausible default value for those arguments, or None. You can then call the method with only the arguments you are interested in, and Python will use the default values.
You could also do something like this:
class Character(object):
# your character __init__ and other methods go here
def add_bullet(self, **kwargs):
# here you can unpack kwargs as (key, values) and
# do stuff with them, and use some global dictionary
# to provide default values and ensure that ``key``
# is a valid argument...
# do stuff with your arguments
Another alternative is to directly hook the desired function directly to the class or instance:
def some_implementation(self, arg1, arg2, arg3):
# implementation
my_class.add_bullet = some_implementation_of_add_bullet
Yet another way is to use an abstract factory pattern:
class Character(object):
def __init__(self, bfactory, *args, **kwargs):
self.bfactory = bfactory
def add_bullet(self):
sprite = self.bfactory.sprite()
speed = self.bfactory.speed()
# do stuff with your sprite and speed
class pretty_and_fast_factory(object):
def sprite(self):
return pretty_sprite
def speed(self):
return 10000000000.0
my_character = Character(pretty_and_fast_factory(), a1, a2, kw1=v1, kw2=v2)
my_character.add_bullet() # uses pretty_and_fast_factory
# now, if you have another factory called "ugly_and_slow_factory"
# you can change it at runtime in python by issuing
my_character.bfactory = ugly_and_slow_factory()
# In the last example you can see abstract factory and "method
# overloading" (as you call it) in action
回答 2
您可以使用“自己动手”解决方案进行函数重载。这是从Guido van Rossum关于多方法的文章中复制的（因为mm和python中的重载之间几乎没有区别）：
registry = {}
class MultiMethod(object):
def __init__(self, name):
self.name = name
self.typemap = {}
def __call__(self, *args):
types = tuple(arg.__class__ for arg in args) # a generator expression!
function = self.typemap.get(types)
if function is None:
raise TypeError("no match")
return function(*args)
def register(self, types, function):
if types in self.typemap:
raise TypeError("duplicate registration")
self.typemap[types] = function
def multimethod(*types):
def register(function):
name = function.__name__
mm = registry.get(name)
if mm is None:
mm = registry[name] = MultiMethod(name)
mm.register(types, function)
return mm
return register
用法是
from multimethods import multimethod
import unittest
# 'overload' makes more sense in this case
overload = multimethod
class Sprite(object):
pass
class Point(object):
pass
class Curve(object):
pass
@overload(Sprite, Point, Direction, int)
def add_bullet(sprite, start, direction, speed):
# ...
@overload(Sprite, Point, Point, int, int)
def add_bullet(sprite, start, headto, speed, acceleration):
# ...
@overload(Sprite, str)
def add_bullet(sprite, script):
# ...
@overload(Sprite, Curve, speed)
def add_bullet(sprite, curve, speed):
# ...
最严格的限制，目前主要有：
- 不支持方法，仅支持不是类成员的函数；
- 不处理继承；
- 不支持kwarg；
- 注册新功能应该在导入时完成，这不是线程安全的
You can use “roll-your-own” solution for function overloading. This one is copied from Guido van Rossum’s article about multimethods (because there is little difference between mm and overloading in python):
registry = {}
class MultiMethod(object):
def __init__(self, name):
self.name = name
self.typemap = {}
def __call__(self, *args):
types = tuple(arg.__class__ for arg in args) # a generator expression!
function = self.typemap.get(types)
if function is None:
raise TypeError("no match")
return function(*args)
def register(self, types, function):
if types in self.typemap:
raise TypeError("duplicate registration")
self.typemap[types] = function
def multimethod(*types):
def register(function):
name = function.__name__
mm = registry.get(name)
if mm is None:
mm = registry[name] = MultiMethod(name)
mm.register(types, function)
return mm
return register
The usage would be
from multimethods import multimethod
import unittest
# 'overload' makes more sense in this case
overload = multimethod
class Sprite(object):
pass
class Point(object):
pass
class Curve(object):
pass
@overload(Sprite, Point, Direction, int)
def add_bullet(sprite, start, direction, speed):
# ...
@overload(Sprite, Point, Point, int, int)
def add_bullet(sprite, start, headto, speed, acceleration):
# ...
@overload(Sprite, str)
def add_bullet(sprite, script):
# ...
@overload(Sprite, Curve, speed)
def add_bullet(sprite, curve, speed):
# ...
Most restrictive limitations at the moment are:
- methods are not supported, only functions that are not class members;
- inheritance is not handled;
- kwargs are not supported;
- registering new functions should be done at import time thing is not thread-safe
回答 3
一个可能的选择是使用Multipledispatch模块，如下所示：
http //matthewrocklin.com/blog/work/2014/02/25/Multiple-Dispatch
而不是这样做：
def add(self, other):
if isinstance(other, Foo):
...
elif isinstance(other, Bar):
...
else:
raise NotImplementedError()
你可以这样做：
from multipledispatch import dispatch
@dispatch(int, int)
def add(x, y):
return x + y
@dispatch(object, object)
def add(x, y):
return "%s + %s" % (x, y)
使用结果：
>>> add(1, 2)
3
>>> add(1, 'hello')
'1 + hello'
A possible option is to use the multipledispatch module as detailed here:
http://matthewrocklin.com/blog/work/2014/02/25/Multiple-Dispatch
Instead of doing this:
def add(self, other):
if isinstance(other, Foo):
...
elif isinstance(other, Bar):
...
else:
raise NotImplementedError()
You can do this:
from multipledispatch import dispatch
@dispatch(int, int)
def add(x, y):
return x + y
@dispatch(object, object)
def add(x, y):
return "%s + %s" % (x, y)
With the resulting usage:
>>> add(1, 2)
3
>>> add(1, 'hello')
'1 + hello'
回答 4
在Python 3.4中添加了PEP-0443。单调度通用函数。
这是来自PEP的简短API描述。
要定义通用函数，请使用@singledispatch装饰器对其进行装饰。请注意，调度是根据第一个参数的类型进行的。相应地创建函数：
from functools import singledispatch
@singledispatch
def fun(arg, verbose=False):
if verbose:
print("Let me just say,", end=" ")
print(arg)
要将重载的实现添加到函数中，请使用泛型函数的register（）属性。这是一个装饰器，接受一个类型参数，并装饰实现该类型的操作的函数：
@fun.register(int)
def _(arg, verbose=False):
if verbose:
print("Strength in numbers, eh?", end=" ")
print(arg)
@fun.register(list)
def _(arg, verbose=False):
if verbose:
print("Enumerate this:")
for i, elem in enumerate(arg):
print(i, elem)
In Python 3.4 was added PEP-0443. Single-dispatch generic functions.
Here is short API description from PEP.
To define a generic function, decorate it with the @singledispatch decorator. Note that the dispatch happens on the type of the first argument. Create your function accordingly:
from functools import singledispatch
@singledispatch
def fun(arg, verbose=False):
if verbose:
print("Let me just say,", end=" ")
print(arg)
To add overloaded implementations to the function, use the register() attribute of the generic function. This is a decorator, taking a type parameter and decorating a function implementing the operation for that type:
@fun.register(int)
def _(arg, verbose=False):
if verbose:
print("Strength in numbers, eh?", end=" ")
print(arg)
@fun.register(list)
def _(arg, verbose=False):
if verbose:
print("Enumerate this:")
for i, elem in enumerate(arg):
print(i, elem)
回答 5
通常使用多态来解决这种类型的行为（在OOP语言中）。每种类型的子弹都将负责知道它的运动方式。例如：
class Bullet(object):
def __init__(self):
self.curve = None
self.speed = None
self.acceleration = None
self.sprite_image = None
class RegularBullet(Bullet):
def __init__(self):
super(RegularBullet, self).__init__()
self.speed = 10
class Grenade(Bullet):
def __init__(self):
super(Grenade, self).__init__()
self.speed = 4
self.curve = 3.5
add_bullet(Grendade())
def add_bullet(bullet):
c_function(bullet.speed, bullet.curve, bullet.acceleration, bullet.sprite, bullet.x, bullet.y)
void c_function(double speed, double curve, double accel, char[] sprite, ...) {
if (speed != null && ...) regular_bullet(...)
else if (...) curved_bullet(...)
//..etc..
}
将尽可能多的参数传递给存在的c_function，然后执行基于初始c函数中的值确定要调用哪个c函数的工作。因此，python应该只调用一个c函数。一个c函数查看参数，然后可以适当地委派给其他c函数。
从本质上讲，您只是将每个子类用作不同的数据容器，但是通过在基类上定义所有可能的参数，这些子类可以随意忽略它们不执行的操作。
当出现一种新型的项目符号时，您可以简单地在基础上定义另一个属性，更改一个python函数以使其传递额外的属性，然后更改一个c_function来检查参数并适当地委派。我猜听起来还不错。
This type of behaviour is typically solved (in OOP languages) using Polymorphism. Each type of bullet would be responsible for knowing how it travels. For instance:
class Bullet(object):
def __init__(self):
self.curve = None
self.speed = None
self.acceleration = None
self.sprite_image = None
class RegularBullet(Bullet):
def __init__(self):
super(RegularBullet, self).__init__()
self.speed = 10
class Grenade(Bullet):
def __init__(self):
super(Grenade, self).__init__()
self.speed = 4
self.curve = 3.5
add_bullet(Grendade())
def add_bullet(bullet):
c_function(bullet.speed, bullet.curve, bullet.acceleration, bullet.sprite, bullet.x, bullet.y)
void c_function(double speed, double curve, double accel, char[] sprite, ...) {
if (speed != null && ...) regular_bullet(...)
else if (...) curved_bullet(...)
//..etc..
}
Pass as many arguments to the c_function that exist, then do the job of determining which c function to call based on the values in the initial c function. So, python should only ever be calling the one c function. That one c function looks at the arguments, and then can delegate to other c functions appropriately.
You’re essentially just using each subclass as a different data container, but by defining all the potential arguments on the base class, the subclasses are free to ignore the ones they do nothing with.
When a new type of bullet comes along, you can simply define one more property on the base, change the one python function so that it passes the extra property, and the one c_function that examines the arguments and delegates appropriately. Doesn’t sound too bad I guess.
回答 6
通过传递关键字args。
def add_bullet(**kwargs):
#check for the arguments listed above and do the proper things
By passing keyword args.
def add_bullet(**kwargs):
#check for the arguments listed above and do the proper things
回答 7
在定义中使用多个关键字参数，或创建一个将Bullet其实例传递给该函数的层次结构。
Either use multiple keyword arguments in the definition, or create a Bullet hierarchy whose instances are passed to the function.
回答 8
我认为您的基本要求是在python中使用C / C ++之类的语法，并尽可能减少麻烦。尽管我喜欢Alexander Poluektov的回答，但不适用于课堂。
以下内容适用于类。它通过按非关键字参数的数量区分来工作（但不支持按类型区分）：
class TestOverloading(object):
def overloaded_function(self, *args, **kwargs):
# Call the function that has the same number of non-keyword arguments.
getattr(self, "_overloaded_function_impl_" + str(len(args)))(*args, **kwargs)
def _overloaded_function_impl_3(self, sprite, start, direction, **kwargs):
print "This is overload 3"
print "Sprite: %s" % str(sprite)
print "Start: %s" % str(start)
print "Direction: %s" % str(direction)
def _overloaded_function_impl_2(self, sprite, script):
print "This is overload 2"
print "Sprite: %s" % str(sprite)
print "Script: "
print script
它可以像这样简单地使用：
test = TestOverloading()
test.overloaded_function("I'm a Sprite", 0, "Right")
print
test.overloaded_function("I'm another Sprite", "while x == True: print 'hi'")
输出：
这是过载3
雪碧：我是雪碧
开始：0
方向：正确
这是重载2
Sprite：我是另一个Sprite
脚本：
而x == True：print’hi’
I think your basic requirement is to have a C/C++ like syntax in python with the least headache possible. Although I liked Alexander Poluektov’s answer it doesn’t work for classes.
The following should work for classes. It works by distinguishing by the number of non keyword arguments (but doesn’t support distinguishing by type):
class TestOverloading(object):
def overloaded_function(self, *args, **kwargs):
# Call the function that has the same number of non-keyword arguments.
getattr(self, "_overloaded_function_impl_" + str(len(args)))(*args, **kwargs)
def _overloaded_function_impl_3(self, sprite, start, direction, **kwargs):
print "This is overload 3"
print "Sprite: %s" % str(sprite)
print "Start: %s" % str(start)
print "Direction: %s" % str(direction)
def _overloaded_function_impl_2(self, sprite, script):
print "This is overload 2"
print "Sprite: %s" % str(sprite)
print "Script: "
print script
And it can be used simply like this:
test = TestOverloading()
test.overloaded_function("I'm a Sprite", 0, "Right")
print
test.overloaded_function("I'm another Sprite", "while x == True: print 'hi'")
Output:
This is overload 3
Sprite: I’m a Sprite
Start: 0
Direction: Right
This is overload 2
Sprite: I’m another Sprite
Script:
while x == True: print ‘hi’
回答 9
在@overload用类型的提示（PEP 484）添加装饰器。尽管这不会改变python的行为，但确实可以更轻松地了解正在发生的事情，并让mypy检测错误。
请参阅：键入提示和PEP 484
The @overload decorator was added with type hints (PEP 484). While this doesn’t change the behaviour of python, it does make it easier to understand what is going on, and for mypy to detect errors.
See: Type hints and PEP 484
回答 10
我认为Bullet具有相关多态性的类层次结构是必经之路。您可以通过使用元类有效地重载基类构造函数，以便调用基类可导致创建适当的子类对象。下面是一些示例代码，以说明我的意思。
更新
该代码已经过修改，可以在Python 2和3下运行，以保持相关性。这样做的方式避免了使用Python的显式元类语法，该语法在两个版本之间有所不同。
为了实现这一目标，一个BulletMetaBase的实例BulletMeta类是由创建时显式调用元类来创建Bullet基类（而不是使用__metaclass__=类属性或通过metaclass取决于Python版本关键字参数）。
class BulletMeta(type):
def __new__(cls, classname, bases, classdict):
""" Create Bullet class or a subclass of it. """
classobj = type.__new__(cls, classname, bases, classdict)
if classname != 'BulletMetaBase':
if classname == 'Bullet': # Base class definition?
classobj.registry = {} # Initialize subclass registry.
else:
try:
alias = classdict['alias']
except KeyError:
raise TypeError("Bullet subclass %s has no 'alias'" %
classname)
if alias in Bullet.registry: # unique?
raise TypeError("Bullet subclass %s's alias attribute "
"%r already in use" % (classname, alias))
# Register subclass under the specified alias.
classobj.registry[alias] = classobj
return classobj
def __call__(cls, alias, *args, **kwargs):
""" Bullet subclasses instance factory.
Subclasses should only be instantiated by calls to the base
class with their subclass' alias as the first arg.
"""
if cls != Bullet:
raise TypeError("Bullet subclass %r objects should not to "
"be explicitly constructed." % cls.__name__)
elif alias not in cls.registry: # Bullet subclass?
raise NotImplementedError("Unknown Bullet subclass %r" %
str(alias))
# Create designated subclass object (call its __init__ method).
subclass = cls.registry[alias]
return type.__call__(subclass, *args, **kwargs)
class Bullet(BulletMeta('BulletMetaBase', (object,), {})):
# Presumably you'd define some abstract methods that all here
# that would be supported by all subclasses.
# These definitions could just raise NotImplementedError() or
# implement the functionality is some sub-optimal generic way.
# For example:
def fire(self, *args, **kwargs):
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
# Abstract base class's __init__ should never be called.
# If subclasses need to call super class's __init__() for some
# reason then it would need to be implemented.
def __init__(self, *args, **kwargs):
raise NotImplementedError("Bullet is an abstract base class")
# Subclass definitions.
class Bullet1(Bullet):
alias = 'B1'
def __init__(self, sprite, start, direction, speed):
print('creating %s object' % self.__class__.__name__)
def fire(self, trajectory):
print('Bullet1 object fired with %s trajectory' % trajectory)
class Bullet2(Bullet):
alias = 'B2'
def __init__(self, sprite, start, headto, spead, acceleration):
print('creating %s object' % self.__class__.__name__)
class Bullet3(Bullet):
alias = 'B3'
def __init__(self, sprite, script): # script controlled bullets
print('creating %s object' % self.__class__.__name__)
class Bullet4(Bullet):
alias = 'B4'
def __init__(self, sprite, curve, speed): # for bullets with curved paths
print('creating %s object' % self.__class__.__name__)
class Sprite: pass
class Curve: pass
b1 = Bullet('B1', Sprite(), (10,20,30), 90, 600)
b2 = Bullet('B2', Sprite(), (-30,17,94), (1,-1,-1), 600, 10)
b3 = Bullet('B3', Sprite(), 'bullet42.script')
b4 = Bullet('B4', Sprite(), Curve(), 720)
b1.fire('uniform gravity')
b2.fire('uniform gravity')
输出：
creating Bullet1 object
creating Bullet2 object
creating Bullet3 object
creating Bullet4 object
Bullet1 object fired with uniform gravity trajectory
Traceback (most recent call last):
File "python-function-overloading.py", line 93, in <module>
b2.fire('uniform gravity') # NotImplementedError: Bullet2.fire() method
File "python-function-overloading.py", line 49, in fire
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
NotImplementedError: Bullet2.fire() method
I think a Bullet class hierarchy with the associated polymorphism is the way to go. You can effectively overload the base class constructor by using a metaclass so that calling the base class results in the creation of the appropriate subclass object. Below is some sample code to illustrate the essence of what I mean.
Updated
The code has been modified to run under both Python 2 and 3 to keep it relevant. This was done in a way that avoids the use Python’s explicit metaclass syntax, which varies between the two versions.
To accomplish that objective, a BulletMetaBase instance of the BulletMeta class is created by explicitly calling the metaclass when creating the Bullet baseclass (rather than using the __metaclass__= class attribute or via a metaclass keyword argument depending on the Python version).
class BulletMeta(type):
def __new__(cls, classname, bases, classdict):
""" Create Bullet class or a subclass of it. """
classobj = type.__new__(cls, classname, bases, classdict)
if classname != 'BulletMetaBase':
if classname == 'Bullet': # Base class definition?
classobj.registry = {} # Initialize subclass registry.
else:
try:
alias = classdict['alias']
except KeyError:
raise TypeError("Bullet subclass %s has no 'alias'" %
classname)
if alias in Bullet.registry: # unique?
raise TypeError("Bullet subclass %s's alias attribute "
"%r already in use" % (classname, alias))
# Register subclass under the specified alias.
classobj.registry[alias] = classobj
return classobj
def __call__(cls, alias, *args, **kwargs):
""" Bullet subclasses instance factory.
Subclasses should only be instantiated by calls to the base
class with their subclass' alias as the first arg.
"""
if cls != Bullet:
raise TypeError("Bullet subclass %r objects should not to "
"be explicitly constructed." % cls.__name__)
elif alias not in cls.registry: # Bullet subclass?
raise NotImplementedError("Unknown Bullet subclass %r" %
str(alias))
# Create designated subclass object (call its __init__ method).
subclass = cls.registry[alias]
return type.__call__(subclass, *args, **kwargs)
class Bullet(BulletMeta('BulletMetaBase', (object,), {})):
# Presumably you'd define some abstract methods that all here
# that would be supported by all subclasses.
# These definitions could just raise NotImplementedError() or
# implement the functionality is some sub-optimal generic way.
# For example:
def fire(self, *args, **kwargs):
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
# Abstract base class's __init__ should never be called.
# If subclasses need to call super class's __init__() for some
# reason then it would need to be implemented.
def __init__(self, *args, **kwargs):
raise NotImplementedError("Bullet is an abstract base class")
# Subclass definitions.
class Bullet1(Bullet):
alias = 'B1'
def __init__(self, sprite, start, direction, speed):
print('creating %s object' % self.__class__.__name__)
def fire(self, trajectory):
print('Bullet1 object fired with %s trajectory' % trajectory)
class Bullet2(Bullet):
alias = 'B2'
def __init__(self, sprite, start, headto, spead, acceleration):
print('creating %s object' % self.__class__.__name__)
class Bullet3(Bullet):
alias = 'B3'
def __init__(self, sprite, script): # script controlled bullets
print('creating %s object' % self.__class__.__name__)
class Bullet4(Bullet):
alias = 'B4'
def __init__(self, sprite, curve, speed): # for bullets with curved paths
print('creating %s object' % self.__class__.__name__)
class Sprite: pass
class Curve: pass
b1 = Bullet('B1', Sprite(), (10,20,30), 90, 600)
b2 = Bullet('B2', Sprite(), (-30,17,94), (1,-1,-1), 600, 10)
b3 = Bullet('B3', Sprite(), 'bullet42.script')
b4 = Bullet('B4', Sprite(), Curve(), 720)
b1.fire('uniform gravity')
b2.fire('uniform gravity')
Output:
creating Bullet1 object
creating Bullet2 object
creating Bullet3 object
creating Bullet4 object
Bullet1 object fired with uniform gravity trajectory
Traceback (most recent call last):
File "python-function-overloading.py", line 93, in <module>
b2.fire('uniform gravity') # NotImplementedError: Bullet2.fire() method
File "python-function-overloading.py", line 49, in fire
raise NotImplementedError(self.__class__.__name__ + ".fire() method")
NotImplementedError: Bullet2.fire() method
回答 11
Python 3.8添加了functools.singledispatchmethod
将方法转换为单调度通用函数。
要定义通用方法，请使用@singledispatchmethod装饰器对其进行装饰。请注意，调度是根据第一个非自身或非cls参数的类型进行的，请相应地创建函数：
from functools import singledispatchmethod
class Negator:
@singledispatchmethod
def neg(self, arg):
raise NotImplementedError("Cannot negate a")
@neg.register
def _(self, arg: int):
return -arg
@neg.register
def _(self, arg: bool):
return not arg
negator = Negator()
for v in [42, True, "Overloading"]:
neg = negator.neg(v)
print(f"{v=}, {neg=}")
输出量
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
@singledispatchmethod支持与其他装饰器（例如，@ classmethod）嵌套。请注意，要允许dispatcher.register，singledispatchmethod必须是最外面的装饰器。这是Negator类，其中neg方法绑定了类：
from functools import singledispatchmethod
class Negator:
@singledispatchmethod
@staticmethod
def neg(arg):
raise NotImplementedError("Cannot negate a")
@neg.register
def _(arg: int) -> int:
return -arg
@neg.register
def _(arg: bool) -> bool:
return not arg
for v in [42, True, "Overloading"]:
neg = Negator.neg(v)
print(f"{v=}, {neg=}")
输出：
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
相同的模式可用于其他类似的修饰符：staticmethod，abstractmethod等。
Python 3.8 added functools.singledispatchmethod
Transform a method into a single-dispatch generic function.
To define a generic method, decorate it with the @singledispatchmethod
decorator. Note that the dispatch happens on the type of the first
non-self or non-cls argument, create your function accordingly:
from functools import singledispatchmethod
class Negator:
@singledispatchmethod
def neg(self, arg):
raise NotImplementedError("Cannot negate a")
@neg.register
def _(self, arg: int):
return -arg
@neg.register
def _(self, arg: bool):
return not arg
negator = Negator()
for v in [42, True, "Overloading"]:
neg = negator.neg(v)
print(f"{v=}, {neg=}")
Output
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
@singledispatchmethod supports nesting with other decorators such as
@classmethod. Note that to allow for dispatcher.register,
singledispatchmethod must be the outer most decorator. Here is the
Negator class with the neg methods being class bound:
from functools import singledispatchmethod
class Negator:
@singledispatchmethod
@staticmethod
def neg(arg):
raise NotImplementedError("Cannot negate a")
@neg.register
def _(arg: int) -> int:
return -arg
@neg.register
def _(arg: bool) -> bool:
return not arg
for v in [42, True, "Overloading"]:
neg = Negator.neg(v)
print(f"{v=}, {neg=}")
Output:
v=42, neg=-42
v=True, neg=False
NotImplementedError: Cannot negate a
The same pattern can be used for other similar decorators:
staticmethod, abstractmethod, and others.
回答 12
将关键字参数与默认值一起使用。例如
def add_bullet(sprite, start=default, direction=default, script=default, speed=default):
对于直子弹和弯曲子弹，我将添加两个函数：add_bullet_straight和add_bullet_curved。
Use keyword arguments with defaults. E.g.
def add_bullet(sprite, start=default, direction=default, script=default, speed=default):
In the case of a straight bullet versus a curved bullet, I’d add two functions: add_bullet_straight and add_bullet_curved.
回答 13
重载方法在python中很棘手。但是，可能会使用传递字典，列表或原始变量的用法。
我已经为用例尝试过一些方法，这可以帮助您了解人们如何重载方法。
让我们举个例子：
一个类重载方法，其中调用了来自不同类的方法。
def add_bullet(sprite=None, start=None, headto=None, spead=None, acceleration=None):
从远程类传递参数：
add_bullet(sprite = 'test', start=Yes,headto={'lat':10.6666,'long':10.6666},accelaration=10.6}
要么
add_bullet(sprite = 'test', start=Yes, headto={'lat':10.6666,'long':10.6666},speed=['10','20,'30']}
因此，正在通过方法重载实现列表，字典或原始变量的处理。
试试看您的代码。
overloading methods is tricky in python. However, there could be usage of passing the dict, list or primitive variables.
I have tried something for my use cases, this could help here to understand people to overload the methods.
Let’s take your example:
a class overload method with call the methods from different class.
def add_bullet(sprite=None, start=None, headto=None, spead=None, acceleration=None):
pass the arguments from remote class:
add_bullet(sprite = 'test', start=Yes,headto={'lat':10.6666,'long':10.6666},accelaration=10.6}
OR
add_bullet(sprite = 'test', start=Yes, headto={'lat':10.6666,'long':10.6666},speed=['10','20,'30']}
So, handling is being achieved for list, Dictionary or primitive variables from method overloading.
try it out for your codes.
回答 14
只是一个简单的装饰
class overload:
def __init__(self, f):
self.cases = {}
def args(self, *args):
def store_function(f):
self.cases[tuple(args)] = f
return self
return store_function
def __call__(self, *args):
function = self.cases[tuple(type(arg) for arg in args)]
return function(*args)
你可以这样使用
@overload
def f():
pass
@f.args(int, int)
def f(x, y):
print('two integers')
@f.args(float)
def f(x):
print('one float')
f(5.5)
f(1, 2)
对其进行修改以使其适应您的用例。
概念澄清
- 功能调度：具有相同名称的多个函数。应该叫哪一个？两种策略
- 静态/编译时调度（也称为“超载”）。根据编译时间确定要调用的函数参数类型的函数。在所有动态语言中，没有编译时类型，因此根据定义，重载是不可能的
- 动态/运行时分派：根据参数的运行时类型决定要调用的函数。这就是所有OOP语言所要做的：多个类具有相同的方法，并且该语言根据
self/this参数的类型决定要调用的是哪种。但是，大多数语言仅将其用于this参数。上面的装饰器将构思扩展到多个参数。
要清除，假定使用静态语言，然后定义功能
void f(Integer x):
print('integer called')
void f(Float x):
print('float called')
void f(Number x):
print('number called')
Number x = new Integer('5')
f(x)
x = new Number('3.14')
f(x)
在静态分派（超载）中，您将看到两次“被调用”，因为x已被声明为Number，这就是所有超载所关心的。使用动态分派，您将看到“整数调用，浮点调用”，因为这些x是调用函数时的实际类型。
Just a simple decorator
class overload:
def __init__(self, f):
self.cases = {}
def args(self, *args):
def store_function(f):
self.cases[tuple(args)] = f
return self
return store_function
def __call__(self, *args):
function = self.cases[tuple(type(arg) for arg in args)]
return function(*args)
You can use it like this
@overload
def f():
pass
@f.args(int, int)
def f(x, y):
print('two integers')
@f.args(float)
def f(x):
print('one float')
f(5.5)
f(1, 2)
Modify it to adapt it to your use case.
A clarification of concepts
- function dispatch: there are multiple functions with the same name. Which one should be called? two strategies
- static/compile-time dispatch (aka. “overloading”). decide which function to call based on the compile-time type of the arguments. In all dynamic languages, there is no compile-time type, so overloading is impossible by definition
- dynamic/run-time dispatch: decide which function to call based on the runtime type of the arguments. This is what all OOP languages do: multiple classes have the same methods, and the language decides which one to call based on the type of
self/this argument. However, most languages only do it for the this argument only. The above decorator extends the idea to multiple parameters.
To clear up, assume a static language, and define the functions
void f(Integer x):
print('integer called')
void f(Float x):
print('float called')
void f(Number x):
print('number called')
Number x = new Integer('5')
f(x)
x = new Number('3.14')
f(x)
With static dispatch (overloading) you will see “number called” twice, because x has been declared as Number, and that’s all overloading cares about. With dynamic dispatch you will see “integer called, float called”, because those are the actual types of x at the time the function is called.
