Python近期使用较少算法实现方法，python近期较少算法,这是一个简单python

Python近期使用较少算法实现方法，python近期较少算法,这是一个简单python

这是一个简单python最近比较少使用的算法，python最近最少使用算法代码会涉及到Python字典、文件系统的访问数据、Python类、Python for循环控制语句等等相关知识。

代码中有些英文注释，需要这个python实现的最近最少使用算法代码的朋友们，可以自行去翻译一下，python 注释还是很好用的。

下面我们一起来看下正式的代码段：

""" from __future__ import generators import time from heapq import heappush, heappop, heapify # the suffix after the hyphen denotes modifications by the #  ftputil project with respect to the original version __version__ = "0.2-1" __all__ = ['CacheKeyError', 'LRUCache', 'DEFAULT_SIZE'] __docformat__ = 'reStructuredText en' DEFAULT_SIZE = 16 """Default size of a new LRUCache object, if no 'size' argument is given.""" class CacheKeyError(KeyError):     """Error raised when cache requests fail     When a cache record is accessed which no longer exists (or never did),     this error is raised. To avoid it, you may want to check for the existence     of a cache record before reading or deleting it."""     pass class LRUCache(object):     """Least-Recently-Used (LRU) cache.     Instances of this class provide a least-recently-used (LRU) cache. They     emulate a Python mapping type. You can use an LRU cache more or less like     a Python dictionary, with the exception that objects you put into the     cache may be discarded before you take them out.     Some example usage::     cache = LRUCache(32) # new cache     cache['foo'] = get_file_contents('foo') # or whatever     if 'foo' in cache: # if it's still in cache...         # use cached version         contents = cache['foo']     else:         # recalculate         contents = get_file_contents('foo')         # store in cache for next time         cache['foo'] = contents     print cache.size # Maximum size     print len(cache) # 0 <= len(cache) <= cache.size     cache.size = 10 # Auto-shrink on size assignment     for i in range(50): # note: larger than cache size         cache[i] = i     if 0 not in cache: print 'Zero was discarded.'     if 42 in cache:         del cache[42] # Manual deletion     for j in cache:   # iterate (in LRU order)         print j, cache[j] # iterator produces keys, not values     """     class __Node(object):         """Record of a cached value. Not for public consumption."""         def __init__(self, key, obj, timestamp, sort_key):             object.__init__(self)             self.key = key             self.obj = obj             self.atime = timestamp             self.mtime = sel#www.iplaypy.comf.atime             self._sort_key = sort_key         def __cmp__(self, other):             return cmp(self._sort_key, other._sort_key)         def __repr__(self):             return "<%s %s => %s (%s)>" % \                    (self.__class__, self.key, self.obj, \                     time.asctime(time.localtime(self.atime)))     def __init__(self, size=DEFAULT_SIZE):         # Check arguments         if size <= 0:             raise ValueError, size         elif type(size) is not type(0):             raise TypeError, size         object.__init__(self)         self.__heap = []         self.__dict = {}                 """Maximum size of the cache.         If more than 'size' elements are added to the cache,         the least-recently-used ones will be discarded."""                 self.size = size         self.__counter = 0     def _sort_key(self):         """Return a new integer value upon every call.                 Cache nodes need a monotonically increasing time indicator.         time.time() and time.clock() don't guarantee this in a         platform-independent way.         """         self.__counter += 1         return self.__counter     def __len__(self):         return len(self.__heap)     def __contains__(self, key):         return self.__dict.has_key(key)     def __setitem__(self, key, obj):         if self.__dict.has_key(key):             node = self.__dict[key]             # update node object in-place             node.obj = obj             node.atime = time.time()             node.mtime = node.atime             node._sort_key = self._sort_key()             heapify(self.__heap)         else:             # size may have been reset, so we loop             while len(self.__heap) >= self.size:                 lru = heappop(self.__heap)                 del self.__dict[lru.key]             node = self.__Node(key, obj, time.time(), self._sort_key())             self.__dict[key] = node             heappush(self.__heap, node)     def __getitem__(self, key):         if not self.__dict.has_key(key):             raise CacheKeyError(key)         else:             node = self.__dict[key]             # update node object in-place             node.atime = time.time()             node._sort_key = self._sort_key()             heapify(self.__heap)             return node.obj     def __delitem__(self, key):         if not self.__dict.has_key(key):             raise CacheKeyError(key)         else:             node = self.__dict[key]             del self.__dict[key]             self.__heap.remove(node)             heapify(self.__heap)             return node.obj     def __iter__(self):         copy = self.__heap[:]         while len(copy) > 0:             node = heappop(copy)             yield node.key         raise StopIteration     def __setattr__(self, name, value):         object.__setattr__(self, name, value)         # automagically shrink heap on resize         if name == 'size':             while len(self.__heap) > value:                 lru = heappop(self.__heap)                 del self.__dict[lru.key]     def __repr__(self):         return "<%s (%d elements)>" % (str(self.__class__), len(self.__heap))     def mtime(self, key):         """Return the last modification time for the cache record with key.         May be useful for cache instances where the stored values can get         'stale', such as caching file or network resource contents."""         if not self.__dict.has_key(key):             raise CacheKeyError(key)         else:             node = self.__dict[key]             return node.mtime if __name__ == "__main__":     cache = LRUCache(25)     print cache     for i in range(50):         cache[i] = str(i)     print cache     if 46 in cache:         print "46 in cache"         del cache[46]     print cache     cache.size = 10     print cache     cache[46] = '46'     print cache     print len(cache)     for c in cache:         print c     print cache     print cache.mtime(46)     for c in cache:         print c 

浏览此文章用户还在关注：python from import使用方法。

编橙之家文章，