memoization

问题 What I have learnt is that dynamic programming (DP) is of two kinds: top-down and bottom-up. In top-down , you use recursion along with memoization. In bottom-up , you just fill an array (a table). Also, both these methods use same time complexity. Personally, I find top-down approach more easier and natural to follow. Is it true that a given question of DP can be solved using either of the approaches? Or will I ever face a problem which can only be solved by one of the two methods? 回答1: Well

问题 I'm using a variant of the following decorator for memoization (found here): # note that this decorator ignores **kwargs def memoize(obj): cache = obj.cache = {} @functools.wraps(obj) def memoizer(*args, **kwargs): if args not in cache: cache[args] = obj(*args, **kwargs) return cache[args] return memoizer I'm wondering, is there a reasonable way to memoize based on both args and kwargs, particularly in cases where two function calls specified with arguments assigned differently positionally

问题 I'm trying to memoize a member function of a class, but every time the member is called (by another member) it makes a whole new cache and 'memoized' function. member x.internal_dec_rates = let cache = new Dictionary< Basis*(DateTime option), float*float>() fun (basis:Basis) (tl:DateTime option) -> match cache.TryGetValue((basis,tl)) with | true, (sgl_mux, sgl_lps) -> (sgl_mux, sgl_lps) | _ -> let (sgl_mux, sgl_lps) = (* Bunch of stuff *) cache.Add((basis,tl),(sgl_mux,sgl_lps)) sgl_mux,sgl

问题 So I have triangular mesh approximating a surface. It's like a graph with the following properties: The vertices on the graph border are trivially identifiable. (Number of neighbor vertices > number of containing triangles) You can trivially calculate the distance between any two vertices. (Euclidean distance) For any vertex v , any vertex that is not a neighbor of v must have a greater distance to v than at least one of v 's neighbors. In other words, no non-neighbor vertices may appear

问题 I have a set of rather complex ORM modules that inherit from Class::DBI. Since the data changes quite infrequently, I am considering using a Caching/Memoization layer on top of this to speed things up. I found a module: Class::DBI::Cacheable but no rating or any reviews on RT. I would appreciate hearing from people who have used this or any other Class::DBI caching scheme. Thanks a ton. 回答1: I too have rolled my own ORM plenty of times I hate to say! Caching/Memoization is pretty easy if all

问题 I am working on a homework assignment, and I have completely exhausted myself. I'm new to programming, and this is my first programming class. this is the problem: Consider the following recursive function in Collatz.java, which is related to a famous unsolved problem in number theory, known as the Collatz problem or the 3n + 1 problem. public static void collatz(int n) { StdOut.print(n + " "); if (n == 1) return; if (n % 2 == 0) collatz(n / 2); else collatz(3*n + 1);} For example, a call to

问题 Consider a below problem Given an infinite number of nickels (5 cents) and pennies (1 cent). Write a code to calculate a number of ways of representing n cents. My code def coins(n): if (n < 0): return 0 elif (n == 0): return 1 else: if (cnt_lst[n-1] == 0): cnt_lst[n-1] = coins(n-1) + coins(n-5) return cnt_lst[n-1] if __name__ == "__main__": cnt = int(input()) cnt_lst = [0] * cnt #Memiozation ret = coins(cnt) print(ret) Above approach counting repeated patterns more than one (obviously I'm

问题 Quite recently, I started playing around with tabling in Prolog; some experiments that I did with b-prolog and xsb can be found in this question. With the tables getting bigger and bigger, I realized that I needed to find some tabling options / parameters that would allow me to limit the amount of memory dedicated to tabling. So far, I didn't find anything suitable in the manuals of yap, b-prolog and xsb. Could you please pinpoint me to some useful information? 回答1: In the case of YAP, there

问题 I can't figure out why the following code is makes fib run in linear rather than exponential time. def memoize(obj): """Memoization decorator from PythonDecoratorLibrary. Ignores **kwargs""" cache = obj.cache = {} @functools.wraps(obj) def memoizer(*args, **kwargs): if args not in cache: cache[args] = obj(*args, **kwargs) return cache[args] return memoizer @memoize def fib(n): return n if n in (0, 1) else fib(n-1) + fib(n-2) For example, fib(100) doesn't completely blow up like I expected it

问题 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