memoization

C# Memoization of functions with arbitrary number of arguments [closed]

由 限于喜欢 提交于 2019-11-27 10:11:45
I'm trying to create a memoization interface for functions with arbitrary number of arguments, but I'm failing miserably I feel like my solution is not very flexible. I tried to define an interface for a function which gets memoized automatically upon execution and each function will have to implement this interface. Here is an example with a two parameter Exponential Moving Average function: class EMAFunction:IFunction { Dictionary<List<object>, List<object>> map; class EMAComparer : IEqualityComparer<List<object>> { private int _multiplier = 97; public bool Equals(List<object> a, List<object

doubts on javascript apply - function memoization

由 笑着哭i 提交于 2019-11-27 08:13:35
问题 I'm struggling with an example of js memoization found on a book, here's the code: Function.prototype.memoized = function(key){ this._values = this._values || {}; return this._values[key] !== undefined ? this._values[key] : this._values[key] = this.apply(this, arguments); } here's a fiddle with a complete example what I don't really get is how this piece of code works and what it does, in particular the apply part: return this._values[key] !== undefined ? this._values[key] : this._values[key]

How to create a memoize function

由 这一生的挚爱 提交于 2019-11-27 07:07:58
问题 I am stumped with this memoize problem. I need to create a function that will check to see if a value has already been calculated for a given argument, return the previous result, or run the calculation and return that value. I have spent hours on this and while I am new to JS. I cannot get my head around how to do this. I cannot use any built in functions and would really like to understand what I need to do. Here is what I have so far, which is so wrong it feels like pseudo-code at this

Is there a generic way to memoize in Scala?

由 你。 提交于 2019-11-27 06:29:36
I wanted to memoize this: def fib(n: Int) = if(n <= 1) 1 else fib(n-1) + fib(n-2) println(fib(100)) // times out So I wrote this and this surprisingly compiles and works (I am surprised because fib references itself in its declaration): case class Memo[A,B](f: A => B) extends (A => B) { private val cache = mutable.Map.empty[A, B] def apply(x: A) = cache getOrElseUpdate (x, f(x)) } val fib: Memo[Int, BigInt] = Memo { case 0 => 0 case 1 => 1 case n => fib(n-1) + fib(n-2) } println(fib(100)) // prints 100th fibonacci number instantly But when I try to declare fib inside of a def , I get a

Factorial Memoization in R

由 醉酒当歌 提交于 2019-11-27 06:27:50
问题 I wrote this function to find a factorial of number fact <- function(n) { if (n < 0){ cat ("Sorry, factorial does not exist for negative numbers", "\n") } else if (n == 0){ cat ("The factorial of 0 is 1", "\n") } else { results = 1 for (i in 1:n){ results = results * i } cat(paste("The factorial of", n ,"is", results, "\n")) } } Now I want to implement Memoization in R. I have Basic idea on R and trying to implement using them. But I am not sure is this way forward. Could you please also

How to perform thread-safe function memoization in c#?

由 纵饮孤独 提交于 2019-11-27 05:56:57
问题 Here on stack overflow I've found the code that memoizes single-argument functions: static Func<A, R> Memoize<A, R>(this Func<A, R> f) { var d = new Dictionary<A, R>(); return a=> { R r; if (!d.TryGetValue(a, out r)) { r = f(a); d.Add(a, r); } return r; }; } While this code does its job for me, it fails sometimes when the memoized function is called from the multiple threads simultaneously: the Add method gets called twice with the same argument and throws an exception. How can I make the

When to use memoization in Ruby on Rails

由 不羁岁月 提交于 2019-11-27 05:07:39
问题 In mid July 2008 Memoization was added to Rails core. A demonstration of the usage is here. I have not been able to find any good examples on when methods should be memoized, and the performance implications of each. This blog post, for example, suggests that oftentimes, memoization should not be used at all. For something that could potentially have tremendous performance implications, there seem to be few resources that go beyond providing a simple tutorial. Has anyone seen memoization used

How does Data.MemoCombinators work?

由 北战南征 提交于 2019-11-27 03:13:08
I've been looking at the source for Data.MemoCombinators but I can't really see where the heart of it is. Please explain to me what the logic is behind all of these combinators and the mechanics of how they actually work to speed up your program in real world programming. I'm looking for specifics for this implementation, and optionally comparison/contrast with other Haskell approaches to memoization. I understand what memoization is and am not looking for a description of how it works in general. This library is a straightforward combinatorization of the well-known technique of memoization.

How do I write a generic memoize function?

由 淺唱寂寞╮ 提交于 2019-11-27 01:57:33
问题 I'm writing a function to find triangle numbers and the natural way to write it is recursively: function triangle (x) if x == 0 then return 0 end return x+triangle(x-1) end But attempting to calculate the first 100,000 triangle numbers fails with a stack overflow after a while. This is an ideal function to memoize, but I want a solution that will memoize any function I pass to it. 回答1: I bet something like this should work with variable argument lists in Lua: local function varg_tostring(...)

What is memoization good for and is it really all that helpful?

由 孤人 提交于 2019-11-27 00:43:28
问题 There are a few automatic memoization libraries available on the internet for various different languages; but without knowing what they are for, where to use them, and how they work, it can be difficult to see their value. What are some convincing arguments for using memoization, and what problem domain does memoization especially shine in? Information for the uninformed would be especially appreciated here. 回答1: The popular factorial answer here is something of a toy answer. Yes,
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