memoization

I often do interactive work in Python that involves some expensive operations that I don't want to repeat often. I'm generally running whatever Python file I'm working on frequently. If I write: import functools32 @functools32.lru_cache() def square(x): print "Squaring", x return x*x I get this behavior: >>> square(10) Squaring 10 100 >>> square(10) 100 >>> runfile(...) >>> square(10) Squaring 10 100 That is, rerunning the file clears the cache. This works: try: safe_square except NameError: @functools32.lru_cache() def safe_square(x): print "Squaring", x return x*x but when the function is

I need help with the Pythonic looping overhead of the following problem: I'm writing a function that calculates a pixel flow algorithm that's a classic dynamic programming algorithm on a 2D Numpy array. It requires: 1) visiting all the elements of the array at least once like this: for x in range(xsize): for y in range(ysize): updateDistance(x,y) 2) potentially following a path of elements based on the values of the neighbors of an element which looks like this while len(workingList) > 0: x,y = workingList.pop() #if any neighbors of x,y need calculation, push x,y and neighbors on workingList

I'd like to pre-calculate some values that are then used when I need to do further lookups. I came up with the following: import qualified Data.Vector.Unboxed as V lcgen s = lc where lc 0 b = lengths V.! b lc a b = lengths V.! b - lengths V.! (a - 1) - 1 lengths = V.fromList $ scanl1 ((+) . (1 +)) $ map length $ words s The function essentially returns the number of characters used in-between two words. I use it as follows: let lc = lcgen "some sentence with a lot of words" lc 0 0 -- == 4 lc 0 1 -- == 13 In this implementation, would the lengths vector be memoized? Furthermore, how can I know

I've found examples such as factorial calculation to explain memoization . These are helpful but I'm looking for a deeper understanding. I'm wondering if someone can describe a real world application of this technique and why they used it instead of recursion or whatever else they felt using memoization might help them optimize. Memoization is a little more specific than just caching. Think about searching for an element in the DOM using a selector, like you might with jQuery. Say, $('.some-selector') . In this context, I'm calling the function $ , telling it to find for me all elements that