Python实现的最近最少使用算法

# 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."""
  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 = 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 

希望本文所述对大家的Python程序设计有所帮助。