memoization

问题 I have made a simple, but relatively computationally complex, UDF that queries a rarely changing table. In typical usage this function is called many many times from a WHERE clauses over a very small domain of parameters. What can I do to make my usage of the UDF faster? My thoughts are that there should be some way to tell SQL Server that my function returns the same result with the same parameters and thus should be memoized. There doesn't seem a way to do it within the UDF because they are

问题 I'm going through "Intermediate Perl" and it's pretty cool. I just finished the section on "The Schwartzian Transform" and after it sunk in I started to wonder why the transform doesn't use a cache. In lists that have several repeated values the transform recomputes the value for each one so I thought why not use a hash to cache results. Here' some code: # a place to keep our results my %cache; # the transformation we are interested in sub foo { # expensive operations } # some data my

问题 var lookup = {}; function memoized(n) { if(n <= 1) { return 1; } if(lookup[n]) { return lookup[n]; } lookup[n] = n * memoized(n - 1); return lookup[n]; } vs. function fact(n) { if(n <= 1) { return 1; } return n * fact(n-1); } If we call fact(3) With the second method we get --> 3 * (2 * (1)) What is the efficiency gain of storing the result in a hash. Is it only for subsequent calls to the same function? I can't see how you would gain anything if you are only calling the function once. With

问题 Using the method presented here: http://cslibrary.stanford.edu/110/BinaryTrees.html#java 12. countTrees() Solution (Java) /** For the key values 1...numKeys, how many structurally unique binary search trees are possible that store those keys? Strategy: consider that each value could be the root. Recursively find the size of the left and right subtrees. */ public static int countTrees(int numKeys) { if (numKeys <=1) { return(1); } else { // there will be one value at the root, with whatever

问题 In the current exercise assignment of the Functional Programming course I'm doing, we've got to make a memoized version of a given function. To explain memoization, the following example is given: fiblist = [ fibm x | x <- [0..]] fibm 0 = 0 fibm 1 = 1 fibm n = fiblist !! (n-1) + fiblist !! (n-2) But I don't fully understand how this works. Let's call fibm 3 . fibm 3 --> fiblist !! 2 + fibList 1 --> [fibm 0, fibm 1, fibm 2] !! 2 + [fibm 0, fibm 1] !! 1 --> fibm 2 + fibm 1 --> (fiblist !! 1 +

问题 In the current exercise assignment of the Functional Programming course I'm doing, we've got to make a memoized version of a given function. To explain memoization, the following example is given: fiblist = [ fibm x | x <- [0..]] fibm 0 = 0 fibm 1 = 1 fibm n = fiblist !! (n-1) + fiblist !! (n-2) But I don't fully understand how this works. Let's call fibm 3 . fibm 3 --> fiblist !! 2 + fibList 1 --> [fibm 0, fibm 1, fibm 2] !! 2 + [fibm 0, fibm 1] !! 1 --> fibm 2 + fibm 1 --> (fiblist !! 1 +

问题 Not sure what exactly to google for this question, so I'll post it directly to SO: Variables in Haskell are immutable Pure functions should result in same values for same arguments From these two points it's possible to deduce that if you call somePureFunc somevar1 somevar2 in your code twice, it only makes sense to compute the value during the first call. The resulting value can be stored in some sort of a giant hash table (or something like that) and looked up during subsequent calls to the

问题 I am looking for a way of building a decorator @memoize that I can use in functions as follows: @memoize my_function(a, b, c): # Do stuff # result may not always be the same for fixed (a,b,c) return result Then, if I do: result1 = my_function(a=1,b=2,c=3) # The function f runs (slow). We cache the result for later result2 = my_function(a=1, b=2, c=3) # The decorator reads the cache and returns the result (fast) Now say that I want to force a cache update : result3 = my_function(a=1, b=2, c=3,

问题 I'm developing my own functional-programming library, and now referring the underscore . memoize _.memoize(function, [hashFunction]) Memoizes a given function by caching the computed result. Useful for speeding up slow-running computations. If passed an optional hashFunction, it will be used to compute the hash key for storing the result, based on the arguments to the original function. The default hashFunction just uses the first argument to the memoized function as the key. var fibonacci =

问题 I have lengthy computations which I repeat many times. Therefore, I would like to use memoization (packages such as jug and joblib), in concert with Pandas. The problem is whether the package would memoize well Pandas DataFrames as method arguments. Has anyone tried it? Is there any other recommended package/way to do this? 回答1: Author of jug here: jug works fine. I just tried the following and it works: from jug import TaskGenerator import pandas as pd import numpy as np @TaskGenerator def