memoization

问题 Promises are my preferred way of managing my asynchronous code in Javascript. Memoize (memoizee on npm) is a Javascript library for easily caching & pre-fetching results of functions. Ideally I want to combine the best of both, and have the ability to "expire" a Promise and pre-fetch a new Promise result (when the cache is touched and near to expiring). Memoize can do this, but it wasn't built with Promises in mind. (I understand that Promises have a built-in "forever-cache" as is their

问题 Problem Statement: There is a broken calculator. Only a few of the digits [ 0 to 9 ] and operators [ +, -, *, / ] are working. A req no. needs to be formed using the working digits and the operators. Each press on the keyboard is called an operation. = operator is always working and is used when the req no. is formed using operators. -1 needs to be printed in case the req no. cannot be formed using the digits and the operators provided OR exceeds the max no. of operations allowed. At no point

问题 Given an array or object with n keys, I need to find all combinations with length x . Given X is variable. binomial_coefficient(n,x) . Currently I'm using this: function combine(items) { var result = []; var f = function(prefix, items) { for (var i = 0; i < items.length; i++) { result.push(prefix + items[i]); f(prefix + items[i], items.slice(i + 1)); } } f('', items); return result; } var combinations = combine(["a", "b", "c", "d"]); The output is: ["a", "ab", "abc", "abcd", "abd", "ac", "acd

问题 I've and Zope utility with a method that perform network processes. As the result of the is valid for a while, I'm using plone.memoize.ram to cache the result. MyClass(object): @cache(cache_key) def do_auth(self, adapter, data): # performing expensive network process here ...and the cache function: def cache_key(method, utility, data): return time() // 60 * 60)) But I want to prevent the memoization to take place when the do_auth call returns empty results (or raise network errors). Looking

问题 I have several TextField columns on my UserProfile object which contain JSON objects. I've also defined a setter/getter property for each column which encapsulates the logic for serializing and deserializing the JSON into python datastructures. The nature of this data ensures that it will be accessed many times by view and template logic within a single Request. To save on deserialization costs, I would like to memoize the python datastructures on read, invalidating on direct write to the

问题 I have been searching a bit for a python module that offers a memoize decorator with the following capabilities: Stores cache on disk to be reused among subsequent program runs. Works for any pickle-able arguments, most importantly numpy arrays. (Bonus) checks whether arguments are mutated in function calls. I found a few small code snippets for this task and could probably implement one myself, but I would prefer having an established package for this task. I also found incpy, but that does

问题 I found an article that contains this code: template <typename ReturnType, typename... Args> std::function<ReturnType (Args...)> memoize(std::function<ReturnType (Args...)> func) { std::map<std::tuple<Args...>, ReturnType> cache; return ([=](Args... args) mutable { std::tuple<Args...> t(args...); if (cache.find(t) == cache.end()) cache[t] = func(args...); return cache[t]; }); } Can you explain this please? I can't understand many things here, but the weirdest thing is that cache is local and

问题 I have a computing map (with soft values) that I am using to cache the results of an expensive computation. Now I have a situation where I know that a particular key is likely to be looked up within the next few seconds. That key is also more expensive to compute than most. I would like to compute the value in advance, in a minimum-priority thread, so that when the value is eventually requested it will already be cached, improving the response time. What is a good way to do this such that: I

问题 The blog post Automatic Memoization in c++0x provides an function for producing a memoized version of an existing function. The blog post and the associated code have been discussed previously on stackoverflow (e.g. What does this C++11 code do?), however, none of these solutions is able to provide a fully universal memoizer which is able to correctly memoize recursive functions as well. Sure, there is the trick of changing the recursive call by using something like this (assuming we have a

问题 After finding the difflib.SequenceMatcher class in Python's standard library to be unsuitable for my needs, a generic "diff"-ing module was written to solve a problem space. After having several months to think more about what it is doing, the recursive algorithm appears to be searching more than in needs to by re-searching the same areas in a sequence that a separate "search thread" may have also examined. The purpose of the diff module is to compute the difference and similarities between a