我的程序似乎遭受线性访问字典，它的运行时间呈指数增长，即使算法是二次的。 我使用字典来记录值。这似乎是一个瓶颈。
我散列的值是元组元组。 每个点是：（x，y），0 <= x，y <= 50 字典中的每个键都是：一个2-5点的元组：（（x1 ，y1），（x2，y2），（x3，y3），（x4，y4））
这些键的读取次数比写入次数多出多倍。
我正确的说，python dicts遭受线性访问时间与这样的输入？
据我所知，集合保证了对数访问时间。 如何在Python中使用set（或类似的东西）模拟dicts？
编辑根据请求，这是一个（简化）版本的记忆功能：
$（$） 如果没有，则 key = args 密钥记录： memoized [key] = fun（* args） return memoized [key] return memo解决方案
请参阅时间复杂性。 python dict是一个哈希函数，其最坏的情况是O（n），如果哈希函数不好，并导致大量的冲突。然而，这是一个非常罕见的情况，其中每个添加的项目都具有相同的哈希值，因此被添加到同一个链中，对于主要的Python实现将非常不太可能。平均时间复杂度当然是O（1）。
最好的方法是检查并查看您正在使用的对象的哈希值。 CPython Dict 使用 int PyObject_Hash（PyObject * o），相当于哈希（o）。
快速检查后，我还没有设法找到两个散列到同一个值的元组，表示查找是O（1）
l = [] for x in range（0，50） ： for y in range（0，50）：如果hash（（x，y））in l： printFail：，（x，y） l.append（hash（（x，y））） printTest FinishedCodePad （24小时可用）
I am writing a simple Python program.
My program seems to suffer from linear access to dictionaries, its run-time grows exponentially even though the algorithm is quadratic. I use a dictionary to memoize values. That seems to be a bottleneck.
The values I'm hashing are tuples of points. Each point is: (x,y), 0 <= x,y <= 50 Each key in the dictionary is: A tuple of 2-5 points: ((x1,y1),(x2,y2),(x3,y3),(x4,y4))
The keys are read many times more often than they are written.
Am I correct that python dicts suffer from linear access times with such inputs?
As far as I know, sets have guaranteed logarithmic access times. How can I simulate dicts using sets(or something similar) in Python?
edit As per request, here's a (simplified) version of the memoization function:
def memoize(fun): memoized = {} def memo(*args): key = args if not key in memoized: memoized[key] = fun(*args) return memoized[key] return memo解决方案
See Time Complexity. The python dict is a hashmap, its worst case is therefore O(n) if the hash function is bad and results in a lot of collisions. However that is a very rare case where every item added has the same hash and so is added to the same chain which for a major Python implementation would be extremely unlikely. The average time complexity is of course O(1).
The best method would be to check and take a look at the hashs of the objects you are using. The CPython Dict uses int PyObject_Hash (PyObject *o) which is the equivalent of hash(o).
After a quick check, I have not yet managed to find two tuples that hash to the same value, which would indicate that the lookup is O(1)
l = [] for x in range(0, 50): for y in range(0, 50): if hash((x,y)) in l: print "Fail: ", (x,y) l.append(hash((x,y))) print "Test Finished"CodePad (Available for 24 hours)