Python中的生成器和函数之间的区别

Question

我想了解函数和生成器之间的区别，并使用下面的代码来实现该目的。但是，我不明白产出。

class GeneratorsSample(object): 

    def DoReturn(self): 
     counter, maxCounter = 0, 5 
     listResults = [] 
     while counter < maxCounter: 
      print "tic" 
      listResults.append(counter*2) 
      counter += 1 
     return listResults 

    def DoYield(self): 
     counter, maxCounter = 0, 5 
     listResults = [] 
     while counter < maxCounter: 
      print "tic" 
      listResults.append(counter*2) 
      yield listResults #to return the entire list 
      #yield listResults[counter] #you can only return one item at a time 
      counter += 1 
     return 


generatorSample = GeneratorsSample() 

我不明白为什么，为DoReturn()输出是从的DoYield()不同。例如，

returnResults = generatorSample.DoReturn() 
for r in returnResults: 
    print "toc", r 

输出：

tic 
tic 
tic 
tic 
tic 
toc 0 
toc 2 
toc 4 
toc 6 
toc 8 

而且，

yieldResults = generatorSample.DoYield() 
for r in yieldResults:  
    print "toc", r 

输出：

tic 
toc [0] 
tic 
toc [0, 2] 
tic 
toc [0, 2, 4] 
tic 
toc [0, 2, 4, 6] 
tic 
toc [0, 2, 4, 6, 8] 

Answer 1

这可能是一个更好的例子：

class GeneratorsSample(object): 
    def DoReturn(self): 
     counter, maxCounter = 0, 5 
     listResults = [] 
     while counter < maxCounter: 
      print "tic" 
      listResults.append(counter*2) 
      counter += 1 
     return listResults 

    def DoYield(self): 
     counter, maxCounter = 0, 5 
     while counter < maxCounter: 
      print "tic" 
      yield counter*2 
      counter += 1 
     return 


generatorSample = GeneratorsSample() 

ret = generatorSample.DoReturn() 
yld = generatorSample.DoYield() 

for r in ret: print "toc", r 
for r in yld: print "toc", r 

print ret 
print yld 

首先看看这些行：

for r in ret: print "toc", r 
for r in yld: print "toc", r 

产生相同的数值，但在“回归”的版本，抽动所有放在第一位，那么所有的TOC的。在“良率”版本中，抽搐和toc是散布的。

但是这两种方法之间的主要差异是由这些线所示：

print ret # prints: [0, 2, 4, 6, 8] 
print yld # prints: <generator object DoYield at 0x0000000002202630> 

这里，ret是所产生的所有的值的列表。也就是说，当这项任务是由：产生

ret = generatorSample.DoReturn() 

完整列表然后，并返回的完整列表。

随着发生器的方法，整个列表不是生成的，事实上，没有元素的计算。只需提及一台发电机，根据需要即可生成元件“只有苍蝇”。

换句话说，“返回”的方法：

 
generates a number 
generates a number 
generates a number 
... 
uses that number 
uses that number 
uses that number 
... 

而产生办法：

 
generates a number 
uses that number 
generates a number 
uses that number 
... 

发电机的效率是一个事实，即他们只花时间生成单一元素，因为他们需要（如果他们需要）。如果maxCounter比方说是100万，并且计算比counter * 2更复杂，那么获得第一个输出所花费的时间会有明显的改善。

事实上，你可以看到，通过添加人工延迟（这里，与time.sleep(1)：

import time 

class GeneratorsSample(object): 
    def DoReturn(self): 
     counter, maxCounter = 0, 5 
     listResults = [] 
     while counter < maxCounter: 
      v = counter * 2 
      time.sleep(1) 
      print "[DoReturn] computed %d" % v 
      listResults.append(v) 
      counter += 1 
     return listResults 

    def DoYield(self): 
     counter, maxCounter = 0, 5 
     while counter < maxCounter: 
      v = counter * 2 
      time.sleep(1) 
      print "[DoYield] computed %d" % v 
      yield counter*2 
      counter += 1 

     return 


generatorSample = GeneratorsSample() 

ret = generatorSample.DoReturn() 
yld = generatorSample.DoYield() 

for r in ret: print "[return loop] using", r 
print("") 
for r in yld: print "[yield loop] using", r 

输出是：

 
[DoReturn] computed 0 
[DoReturn] computed 2 
[DoReturn] computed 4 
[DoReturn] computed 6 
[DoReturn] computed 8 
[return loop] using 0 
[return loop] using 2 
[return loop] using 4 
[return loop] using 6 
[return loop] using 8 

[DoYield] computed 0 
[yield loop] using 0 
[DoYield] computed 2 
[yield loop] using 2 
[DoYield] computed 4 
[yield loop] using 4 
[DoYield] computed 6 
[yield loop] using 6 
[DoYield] computed 8 
[yield loop] using 8