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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:
class Leaf(object): def __init__(self, color="green"): self.color = color def fall(self): print "Splat!"
子类:
class MapleLeaf(Leaf): def fall(self): self.color = 'brown' super(MapleLeaf, self).fall()
新的类:
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:
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:
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:
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:
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()