在 Python 中，鸭子类型（duck typing）是一种动态类型的风格。所谓鸭子类型，来自于 James Whitcomb Riley 的“鸭子测试”：
当看到一只鸟走起来像鸭子、游泳起来像鸭子、叫起来也像鸭子，那么这只鸟就可以被称为鸭子。
假设我们需要定义一个函数，这个函数使用一个类型为鸭子的参数，并调用它的走和叫方法。
在鸭子类型的语言中，这样的函数可以接受任何类型的对象，只要这个对象实现了走和叫的方法，否则就引发一个运行时错误。换句话说，任何拥有走和叫方法的参数都是合法的。
先看一个例子，父类：
In [1]:
class Leaf(object):
def __init__(self, color="green"):
self.color = color
def fall(self):
print "Splat!"子类：
In [2]:
class MapleLeaf(Leaf):
def fall(self):
self.color = 'brown'
super(MapleLeaf, self).fall()新的类：
In [3]:
class Acorn(object):
def fall(self):
print "Plunk!"这三个类都实现了 fall() 方法，因此可以这样使用：
In [4]:
objects = [Leaf(), MapleLeaf(), Acorn()]
for obj in objects:
obj.fall()Splat!
Splat!
Plunk!这里 fall() 方法就一种鸭子类型的体现。
不仅方法可以用鸭子类型，属性也可以：
In [5]:
import numpy as np
from scipy.ndimage.measurements import label
class Forest(object):
""" Forest can grow trees which eventually die."""
def __init__(self, size=(150,150), p_sapling=0.0025):
self.size = size
self.trees = np.zeros(self.size, dtype=bool)
self.p_sapling = p_sapling
def __repr__(self):
my_repr = "{}(size={})".format(self.__class__.__name__, self.size)
return my_repr
def __str__(self):
return self.__class__.__name__
@property
def num_cells(self):
"""Number of cells available for growing trees"""
return np.prod(self.size)
@property
def losses(self):
return np.zeros(self.size)
@property
def tree_fraction(self):
"""
Fraction of trees
"""
num_trees = self.trees.sum()
return float(num_trees) / self.num_cells
def _rand_bool(self, p):
"""
Random boolean distributed according to p, less than p will be True
"""
return np.random.uniform(size=self.trees.shape) < p
def grow_trees(self):
"""
Growing trees.
"""
growth_sites = self._rand_bool(self.p_sapling)
self.trees[growth_sites] = True
def advance_one_step(self):
"""
Advance one step
"""
self.grow_trees()
class BurnableForest(Forest):
"""
Burnable forest support fires
"""
def __init__(self, p_lightning=5.0e-6, **kwargs):
super(BurnableForest, self).__init__(**kwargs)
self.p_lightning = p_lightning
self.fires = np.zeros((self.size), dtype=bool)
def advance_one_step(self):
"""
Advance one step
"""
super(BurnableForest, self).advance_one_step()
self.start_fires()
self.burn_trees()
@property
def losses(self):
return self.fires
@property
def fire_fraction(self):
"""
Fraction of fires
"""
num_fires = self.fires.sum()
return float(num_fires) / self.num_cells
def start_fires(self):
"""
Start of fire.
"""
lightning_strikes = (self._rand_bool(self.p_lightning) &
self.trees)
self.fires[lightning_strikes] = True
def burn_trees(self):
pass
class SlowBurnForest(BurnableForest):
def burn_trees(self):
"""
Burn trees.
"""
fires = np.zeros((self.size[0] + 2, self.size[1] + 2), dtype=bool)
fires[1:-1, 1:-1] = self.fires
north = fires[:-2, 1:-1]
south = fires[2:, 1:-1]
east = fires[1:-1, :-2]
west = fires[1:-1, 2:]
new_fires = (north | south | east | west) & self.trees
self.trees[self.fires] = False
self.fires = new_fires
class InstantBurnForest(BurnableForest):
def burn_trees(self):
# 起火点
strikes = self.fires
# 找到连通区域
groves, num_groves = label(self.trees)
fires = set(groves[strikes])
self.fires.fill(False)
# 将与着火点相连的区域都烧掉
for fire in fires:
self.fires[groves == fire] = True
self.trees[self.fires] = False
self.fires.fill(False)测试：
In [6]:
forest = Forest()
b_forest = BurnableForest()
sb_forest = SlowBurnForest()
ib_forest = InstantBurnForest()
forests = [forest, b_forest, sb_forest, ib_forest]
losses_history = []
for i in xrange(1500):
for fst in forests:
fst.advance_one_step()
losses_history.append(tuple(fst.losses.sum() for fst in forests))显示结果：
In [7]:
import matplotlib.pyplot as plt
%matplotlib inline
plt.figure(figsize=(10,6))
plt.plot(losses_history)
plt.legend([f.__str__() for f in forests])
plt.show()