memoization

问题 In this and this question I talked about (and proposed a solution) to write a multi-function memoizator. This is my solution: template <typename ReturnType, typename... Args> function<ReturnType(Args...)> memoize(function<ReturnType(Args...)> func) { return ([=](Args... args) mutable { static map<tuple<Args...>, ReturnType> cache; tuple<Args...> t(args...); auto result = cache.insert(make_pair(t, ReturnType{})); if (result.second) { // insertion succeeded so the value wasn't cached already

问题 I have something like this: function main() { function create_node() { console.log("create_node") (function() { var memo; console.log(memo); function f() { var value; console.log(value); if (memo) { value = memo.cloneNode(); console.log("clone node"); console.log(value); } else { var value = document.createElement("div"); var style = ""; value.setAttribute("style", style); value.innerHTML = "hello"; console.log("new node"); console.log(value); } return value; } return f; })(); } var colection

问题 This question already has answers here : Closed 9 years ago . Possible Duplicate: Combine memoization and tail-recursion So the following is the code that I wrote, tail call optimized using an accumulation variable let rec counter init count = if init = 1 then count + 1 else match init with | Even value -> (counter (value/2) (1 + count)) | Odd value -> (counter ((3 * value) + 1) (count+1)) let SeqBuilder (initval:int) : int = counter initval 0 How do I memoize this? the problem I ran into

问题 This question already has answers here : Closed 9 years ago . Possible Duplicate: Combine memoization and tail-recursion So the following is the code that I wrote, tail call optimized using an accumulation variable let rec counter init count = if init = 1 then count + 1 else match init with | Even value -> (counter (value/2) (1 + count)) | Odd value -> (counter ((3 * value) + 1) (count+1)) let SeqBuilder (initval:int) : int = counter initval 0 How do I memoize this? the problem I ran into

问题 I was particularly interested over the last few days (more from an algorithmic than mathematical perspective) in investigating the length of a given number's Hailstone sequence (Collatz conjecture). Implementing a recursive algorithm is probably the simplest way to calculate the length, but seemed to me like an unnecessary waste of calculation time. Many sequences overlap; take for example 3's Hailstone sequence: 3 -> 10 -> 5 -> 16 -> 8 -> 4 -> 2 -> 1 This has length 7; more specifically, it

问题 I was particularly interested over the last few days (more from an algorithmic than mathematical perspective) in investigating the length of a given number's Hailstone sequence (Collatz conjecture). Implementing a recursive algorithm is probably the simplest way to calculate the length, but seemed to me like an unnecessary waste of calculation time. Many sequences overlap; take for example 3's Hailstone sequence: 3 -> 10 -> 5 -> 16 -> 8 -> 4 -> 2 -> 1 This has length 7; more specifically, it

问题 I was particularly interested over the last few days (more from an algorithmic than mathematical perspective) in investigating the length of a given number's Hailstone sequence (Collatz conjecture). Implementing a recursive algorithm is probably the simplest way to calculate the length, but seemed to me like an unnecessary waste of calculation time. Many sequences overlap; take for example 3's Hailstone sequence: 3 -> 10 -> 5 -> 16 -> 8 -> 4 -> 2 -> 1 This has length 7; more specifically, it

问题 I was particularly interested over the last few days (more from an algorithmic than mathematical perspective) in investigating the length of a given number's Hailstone sequence (Collatz conjecture). Implementing a recursive algorithm is probably the simplest way to calculate the length, but seemed to me like an unnecessary waste of calculation time. Many sequences overlap; take for example 3's Hailstone sequence: 3 -> 10 -> 5 -> 16 -> 8 -> 4 -> 2 -> 1 This has length 7; more specifically, it

问题 I've got an idea for caching that I'm beginning to implement: Memoizing functions and storing the return along with a hash of the function signature in Velocity. Using PostSharp, I want to check the cache and return a rehydrated representation of the return value instead of calling the function again. I want to use attributes to control this behavior. Unfortunately, this could prove dangerous to other developers in my organization, if they fall in love with the performance gain and start

问题 Below is the code from an answer regarding memoization, showing a memoization function used in the State monad, where the state is updated with the result of the passed function if the key is not already in the map. type MyMemo a b = State (Map.Map a b) b myMemo :: Ord a => (a -> MyMemo a b) -> a -> MyMemo a b myMemo f x = do map <- get case Map.lookup x map of Just y -> return y Nothing -> do y <- f x modify $ \map' -> Map.insert x y map' return y It doesn't seem like idiomatic Haskell: it