From leet code question: https://leetcode.com/problems/lru-cache/
Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and put.
get(key) - Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1. put(key, value) - Set or insert the value if the key is not already present. When the cache reached its capacity, it should invalidate the least recently used item before inserting a new item.
Follow up: Could you do both operations in O(1) time complexity?
Example:
LRUCache cache = new LRUCache( 2 /* capacity */ ); cache.put(1, 1); cache.put(2, 2); cache.get(1); // returns 1 cache.put(3, 3); // evicts key 2 cache.get(2); // returns -1 (not found) cache.put(4, 4); // evicts key 1 cache.get(1); // returns -1 (not found) cache.get(3); // returns 3 cache.get(4); // returns 4
Code: Filename: lru_cache.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
class Node:
def __init__(self, key: int, val: int, prv=None, nxt=None):
self.prv = prv
self.nxt = nxt
self.val = val
self.key = key
def __str__(self):
nx = str(self.nxt)
return "{}:{} -> {}".format(self.key, self.val, nx)
class LRUCache:
def __init__(self, capacity: int):
"""
:type capacity: int
"""
self.first = None
self.last = None
self.cap = capacity
self.cache = {}
self.size = 0
def get(self, key):
"""
:type key: int
:rtype: int
"""
if key not in self.cache:
return -1
node = self.cache[key]
if self.first is self.last:
return node.val
if node is self.last:
return node.val
if node is self.first:
nxt = node.nxt
nxt.prv = None
self.first = nxt
node.nxt = None
else:
# In the middle
nxt = node.nxt
prv = node.prv
node.nxt = None
prv.nxt = nxt
nxt.prv = prv
self.last.nxt = node
node.prv = self.last
self.last = node
return node.val
def put(self, key, value):
"""
:type key: int
:type value: int
:rtype: void
"""
if self.get(key) != -1:
# Already have
self.cache[key].val = value
return
node = Node(key, value)
if not self.first:
self.size = 1
self.first = node
self.last = node
self.cache[key] = node
return
self.cache[key] = node
self.last.nxt = node
node.prv = self.last
self.last = node
self.size = len(self.cache)
if self.size > self.cap:
# Need to remove
first = self.first
nxt = first.nxt
nxt.prv = None
self.first = nxt
del self.cache[first.key]
self.size = self.cap
def __str__(self):
return "LRUCache:: {}".format(self.first)
def _test():
cache = LRUCache(2)
cache.put(1, 1)
cache.put(2, 2)
assert cache.get(1) == 1 # returns 1
cache.put(3, 3) # evicts key 2
assert str(cache) == "LRUCache:: 1:1 -> 3:3 -> None"
assert cache.get(2) == -1 # returns -1 (not found)
cache.put(4, 4) # evicts key 1
assert str(cache) == "LRUCache:: 3:3 -> 4:4 -> None"
assert cache.get(1) == -1 # returns -1 (not found)
assert cache.get(3) == 3 # returns 3
assert cache.get(4) == 4 # returns 4
assert str(cache) == "LRUCache:: 3:3 -> 4:4 -> None"
if __name__ == "__main__":
_test()
What I want reviewed:
- How pythonic the code is? How can I improve it.
- Is there any area I can optimise performance ? (This passes all tests on leet-code)
- Readability improvements?
- Structure of the code file? Can we place things better?
You are welcome to be strict and brutal.
Additional info:
- I've used black to format code.