Python實現的最近最少使用演算法

本文執行個體講述了Python實現的最近最少使用演算法。分享給大家供大家參考。具體如下：

# lrucache.py -- a simple LRU (Least-Recently-Used) cache class # Copyright 2004 Evan Prodromou  # Licensed under the Academic Free License 2.1 # Licensed for ftputil under the revised BSD license # with permission by the author, Evan Prodromou. Many # thanks, Evan! :-) # # The original file is available at # http://pypi.python.org/pypi/lrucache/0.2 . # arch-tag: LRU cache main module """a simple LRU (Least-Recently-Used) cache module This module provides very simple LRU (Least-Recently-Used) cache functionality. An *in-memory cache* is useful for storing the results of an 'expe\nsive' process (one that takes a lot of time or resources) for later re-use. Typical examples are accessing data from the filesystem, a database, or a network location. If you know you'll need to re-read the data again, it can help to keep it in a cache. You *can* use a Python dictionary as a cache for some purposes. However, if the results you're caching are large, or you have a lot of possible results, this can be impractical memory-wise. An *LRU cache*, on the other hand, only keeps _some_ of the results in memory, which keeps you from overusing resources. The cache is bounded by a maximum size; if you try to add more values to the cache, it will automatically discard the values that you haven't read or written to in the longest time. In other words, the least-recently-used items are discarded. [1]_ .. [1]: 'Discarded' here means 'removed from the cache'. """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."""  passclass 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 = self.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 

希望本文所述對大家的Python程式設計有所協助。

