Python实现的最近最少使用算法

本文实例讲述了Python实现的最近最少使用算法。分享给大家供大家参考。具体如下：

    # lrucache.py -- a simple LRU (Least-Recently-Used) cache class 
    # Copyright 2004 Evan Prodromou <evan@bad.dynu.ca> 
    # 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。