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  • System.Interactive: Difference between Memoize() and MemoizeAll()?

    - by Joel Mueller
    In System.Interactive.dll (v1.0.2521.0) from Reactive Extensions, EnumerableEx has both a Memoize method and a MemoizeAll method. The API documentation is identical for both of them: Creates an enumerable that enumerates the original enumerable only once and caches its results. However, these methods are clearly not identical. If I use Memoize, my enumerable has values the first time I enumerate it, and seems to be empty the second time. If I use MemoizeAll then I get the behavior I would expect from the description of either method - I can enumerate the result as many times as I want and get the same results each time, but the source is only enumerated once. Can anyone tell me what the intended difference between these methods is? What is the use-case for Memoize? It seems like a fairly useless method with really confusing documentation.

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  • What's an easy way to remember what the term 'memoization' means

    - by Evan Plaice
    I know this sounds like a strange question. Intuitively, I know what the concept of memoization means because I have used it in my code before I ever heard of the term. The problem is, I use it so rarely that I lose the association and have to look it up; and, it feels like technobabble (read. gibberish) every time I use it. I might as well be a 'turboenacabulator'. Is there an easy/simple way to describe how memoization works that relates to the word itself.

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  • How do I memoize expensive calculations on Django model objects?

    - by David Eyk
    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 property or save signal from the model object. Where/How do I store the memo? I'm nervous about using instance variables, as I don't understand the magic behind how any particular UserProfile is instantiated by a query. Is __init__ safe to use, or do I need to check the existence of the memo attribute via hasattr() at each read? Here's an example of my current implementation: class UserProfile(Model): text_json = models.TextField(default=text_defaults) @property def text(self): if not hasattr(self, "text_memo"): self.text_memo = None self.text_memo = self.text_memo or simplejson.loads(self.text_json) return self.text_memo @text.setter def text(self, value=None): self.text_memo = None self.text_json = simplejson.dumps(value)

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  • How can I memoize a method that may return true or false in Ruby?

    - by Seamus Abshere
    Obviously ||= won't work def x? @x_query ||= expensive_way_to_calculate_x end because if it turns out to be false, then expensive_way_to_calculate_x will get run over and over. Currently the best way I know is to put the memoized true or false into an Array: def x? return @x_query.first if @x_query.is_a?(Array) @x_query = [expensive_way_to_calculate_x] @x_query.first end Is there a more conventional or efficient way of doing this?

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  • Writing an auto-memoizer in Scheme. Help with macro and a wrapper.

    - by kunjaan
    I am facing a couple of problems while writing an auto-memoizer in Scheme. I have a working memoizer function, which creats a hash table and checks if the value is already computed. If it has been computed before then it returns the value else it calls the function. (define (memoizer fun) (let ((a-table (make-hash))) (?(n) (define false-if-fail (?() #f)) (let ((return-val (hash-ref a-table n false-if-fail))) (if return-val return-val (begin (hash-set! a-table n (fun n)) (hash-ref a-table n))))))) Now I want to create a memoize-wrapper function like this: (define (memoize-wrapper function) (set! function (memoizer function))) And hopefully create a macro called def-memo which defines the function with the memoize-wrapper. eg. the macro could expand to (memoizer (define function-name arguments body ...) or something like that. So that I should be able to do : (def-memo (factorial n) (cond ((= n 1) 1) (else (* n (factorial (- n 1)))))) which should create a memoized version of the factorial instead of the normal slow one. My problem is that the The memoize-wrapper is not working properly, it doesnt call the memoized function but the original function. I have no idea how to write a define inside of the macro. How do I make sure that I can get variable lenght arguments and variable length body? How do I then define the function and wrap it around with the memoizer? Thanks a lot.

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  • How do I resolve not fully installed package (python3-setuptools)?

    - by user3737693
    I was trying to install python3-setuptools, and when i run $ sudo apt-get install python3-setuptools I get this error: Reading package lists... Done Building dependency tree Reading state information... Done 0 upgraded, 0 newly installed, 0 to remove and 6 not upgraded. 1 not fully installed or removed. After this operation, 0 B of additional disk space will be used. Setting up python3-setuptools (0.6.34-0ubuntu1) ... Traceback (most recent call last): File "/usr/bin/py3compile", line 36, in <module> from debpython import files as dpf File "/usr/share/python3/debpython/files.py", line 25, in <module> from debpython.pydist import PUBLIC_DIR_RE File "/usr/share/python3/debpython/pydist.py", line 28, in <module> from debpython.tools import memoize File "/usr/share/python3/debpython/tools.py", line 25, in <module> from datetime import datetime ImportError: /usr/bin/datetime.so: undefined symbol: _Py_ZeroStruct dpkg: error processing python3-setuptools (--configure): subprocess installed post-installation script returned error exit status 1 Errors were encountered while processing: python3-setuptools E: Sub-process /usr/bin/dpkg returned an error code (1) I tried apt-get clean, apt-get autoclean, apt-get remove python3-setuptools, dpkg --remove python3-setuptools, apt-get install -f, dpkg -P --force-remove-reinstreq, dpkg -P --force-all --force-remove-reinstreq and dpkg --purge, but none of them worked. Output of sudo dpkg -P --force-all --force-remove-reinstreq python3-setuptools (Reading database ... 225309 files and directories currently installed.) Removing python3-setuptools ... Traceback (most recent call last): File "/usr/bin/py3clean", line 32, in <module> from debpython import files as dpf File "/usr/share/python3/debpython/files.py", line 25, in <module> from debpython.pydist import PUBLIC_DIR_RE File "/usr/share/python3/debpython/pydist.py", line 28, in <module> from debpython.tools import memoize File "/usr/share/python3/debpython/tools.py", line 25, in <module> from datetime import datetime ImportError: /usr/bin/datetime.so: undefined symbol: _Py_ZeroStruct dpkg: error processing python3-setuptools (--purge): subprocess installed pre-removal script returned error exit status 1 Traceback (most recent call last): File "/usr/bin/py3compile", line 36, in <module> from debpython import files as dpf File "/usr/share/python3/debpython/files.py", line 25, in <module> from debpython.pydist import PUBLIC_DIR_RE File "/usr/share/python3/debpython/pydist.py", line 28, in <module> from debpython.tools import memoize File "/usr/share/python3/debpython/tools.py", line 25, in <module> from datetime import datetime ImportError: /usr/bin/datetime.so: undefined symbol: _Py_ZeroStruct dpkg: error while cleaning up: subprocess installed post-installation script returned error exit status 1 Errors were encountered while processing: python3-setuptools

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  • add a decorate function to a class

    - by wiso
    I have a decorated function (simplified version): class Memoize: def __init__(self, function): self.function = function self.memoized = {} def __call__(self, *args, **kwds): hash = args try: return self.memoized[hash] except KeyError: self.memoized[hash] = self.function(*args) return self.memoized[hash] @Memoize def _DrawPlot(self, options): do something... now I want to add this method to a pre-esisting class. ROOT.TChain.DrawPlot = _DrawPlot when I call this method: chain = TChain() chain.DrawPlot(opts) I got: self.memoized[hash] = self.function(*args) TypeError: _DrawPlot() takes exactly 2 arguments (1 given) why doesn't it propagate self?

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  • How do I silence strace's message "[ Process PID=15733 runs in 64 bit mode. ]" ?

    - by Ross Rogers
    I'm using memoize.py, but strace keeps injecting the following into the program output each time a process is executed: [ Process PID=15733 runs in 64 bit mode. ] or [ Process PID=16503 runs in 32 bit mode. ] How can I silence strace such that it doesn't inject these statements into the log file? At the very least, I'd like these statements to only go into the output file that memoize.py is instructing strace to use. It's already telling strace to put its output into a specific file ithrough arguments -o /tmp/OUTFILE. Note that strace is being called with the -f parameter to follow child processes.

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  • F# - Facebook Hacker Cup - Double Squares

    - by Jacob
    I'm working on strengthening my F#-fu and decided to tackle the Facebook Hacker Cup Double Squares problem. I'm having some problems with the run-time and was wondering if anyone could help me figure out why it is so much slower than my C# equivalent. There's a good description from another post; Source: Facebook Hacker Cup Qualification Round 2011 A double-square number is an integer X which can be expressed as the sum of two perfect squares. For example, 10 is a double-square because 10 = 3^2 + 1^2. Given X, how can we determine the number of ways in which it can be written as the sum of two squares? For example, 10 can only be written as 3^2 + 1^2 (we don't count 1^2 + 3^2 as being different). On the other hand, 25 can be written as 5^2 + 0^2 or as 4^2 + 3^2. You need to solve this problem for 0 = X = 2,147,483,647. Examples: 10 = 1 25 = 2 3 = 0 0 = 1 1 = 1 My basic strategy (which I'm open to critique on) is to; Create a dictionary (for memoize) of the input numbers initialzed to 0 Get the largest number (LN) and pass it to count/memo function Get the LN square root as int Calculate squares for all numbers 0 to LN and store in dict Sum squares for non repeat combinations of numbers from 0 to LN If sum is in memo dict, add 1 to memo Finally, output the counts of the original numbers. Here is the F# code (See code changes at bottom) I've written that I believe corresponds to this strategy (Runtime: ~8:10); open System open System.Collections.Generic open System.IO /// Get a sequence of values let rec range min max = seq { for num in [min .. max] do yield num } /// Get a sequence starting from 0 and going to max let rec zeroRange max = range 0 max /// Find the maximum number in a list with a starting accumulator (acc) let rec maxNum acc = function | [] -> acc | p::tail when p > acc -> maxNum p tail | p::tail -> maxNum acc tail /// A helper for finding max that sets the accumulator to 0 let rec findMax nums = maxNum 0 nums /// Build a collection of combinations; ie [1,2,3] = (1,1), (1,2), (1,3), (2,2), (2,3), (3,3) let rec combos range = seq { let count = ref 0 for inner in range do for outer in Seq.skip !count range do yield (inner, outer) count := !count + 1 } let rec squares nums = let dict = new Dictionary<int, int>() for s in nums do dict.[s] <- (s * s) dict /// Counts the number of possible double squares for a given number and keeps track of other counts that are provided in the memo dict. let rec countDoubleSquares (num: int) (memo: Dictionary<int, int>) = // The highest relevent square is the square root because it squared plus 0 squared is the top most possibility let maxSquare = System.Math.Sqrt((float)num) // Our relevant squares are 0 to the highest possible square; note the cast to int which shouldn't hurt. let relSquares = range 0 ((int)maxSquare) // calculate the squares up front; let calcSquares = squares relSquares // Build up our square combinations; ie [1,2,3] = (1,1), (1,2), (1,3), (2,2), (2,3), (3,3) for (sq1, sq2) in combos relSquares do let v = calcSquares.[sq1] + calcSquares.[sq2] // Memoize our relevant results if memo.ContainsKey(v) then memo.[v] <- memo.[v] + 1 // return our count for the num passed in memo.[num] // Read our numbers from file. //let lines = File.ReadAllLines("test2.txt") //let nums = [ for line in Seq.skip 1 lines -> Int32.Parse(line) ] // Optionally, read them from straight array let nums = [1740798996; 1257431873; 2147483643; 602519112; 858320077; 1048039120; 415485223; 874566596; 1022907856; 65; 421330820; 1041493518; 5; 1328649093; 1941554117; 4225; 2082925; 0; 1; 3] // Initialize our memoize dictionary let memo = new Dictionary<int, int>() for num in nums do memo.[num] <- 0 // Get the largest number in our set, all other numbers will be memoized along the way let maxN = findMax nums // Do the memoize let maxCount = countDoubleSquares maxN memo // Output our results. for num in nums do printfn "%i" memo.[num] // Have a little pause for when we debug let line = Console.Read() And here is my version in C# (Runtime: ~1:40: using System; using System.Collections.Generic; using System.Diagnostics; using System.IO; using System.Linq; using System.Text; namespace FBHack_DoubleSquares { public class TestInput { public int NumCases { get; set; } public List<int> Nums { get; set; } public TestInput() { Nums = new List<int>(); } public int MaxNum() { return Nums.Max(); } } class Program { static void Main(string[] args) { // Read input from file. //TestInput input = ReadTestInput("live.txt"); // As example, load straight. TestInput input = new TestInput { NumCases = 20, Nums = new List<int> { 1740798996, 1257431873, 2147483643, 602519112, 858320077, 1048039120, 415485223, 874566596, 1022907856, 65, 421330820, 1041493518, 5, 1328649093, 1941554117, 4225, 2082925, 0, 1, 3, } }; var maxNum = input.MaxNum(); Dictionary<int, int> memo = new Dictionary<int, int>(); foreach (var num in input.Nums) { if (!memo.ContainsKey(num)) memo.Add(num, 0); } DoMemoize(maxNum, memo); StringBuilder sb = new StringBuilder(); foreach (var num in input.Nums) { //Console.WriteLine(memo[num]); sb.AppendLine(memo[num].ToString()); } Console.Write(sb.ToString()); var blah = Console.Read(); //File.WriteAllText("out.txt", sb.ToString()); } private static int DoMemoize(int num, Dictionary<int, int> memo) { var highSquare = (int)Math.Floor(Math.Sqrt(num)); var squares = CreateSquareLookup(highSquare); var relSquares = squares.Keys.ToList(); Debug.WriteLine("Starting - " + num.ToString()); Debug.WriteLine("RelSquares.Count = {0}", relSquares.Count); int sum = 0; var index = 0; foreach (var square in relSquares) { foreach (var inner in relSquares.Skip(index)) { sum = squares[square] + squares[inner]; if (memo.ContainsKey(sum)) memo[sum]++; } index++; } if (memo.ContainsKey(num)) return memo[num]; return 0; } private static TestInput ReadTestInput(string fileName) { var lines = File.ReadAllLines(fileName); var input = new TestInput(); input.NumCases = int.Parse(lines[0]); foreach (var lin in lines.Skip(1)) { input.Nums.Add(int.Parse(lin)); } return input; } public static Dictionary<int, int> CreateSquareLookup(int maxNum) { var dict = new Dictionary<int, int>(); int square; foreach (var num in Enumerable.Range(0, maxNum)) { square = num * num; dict[num] = square; } return dict; } } } Thanks for taking a look. UPDATE Changing the combos function slightly will result in a pretty big performance boost (from 8 min to 3:45): /// Old and Busted... let rec combosOld range = seq { let rangeCache = Seq.cache range let count = ref 0 for inner in rangeCache do for outer in Seq.skip !count rangeCache do yield (inner, outer) count := !count + 1 } /// The New Hotness... let rec combos maxNum = seq { for i in 0..maxNum do for j in i..maxNum do yield i,j }

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  • Lazy Sequences that "Look Ahead" for Project Euler Problem 14

    - by ivar
    I'm trying to solve Project Euler Problem 14 in a lazy way. Unfortunately, I may be trying to do the impossible: create a lazy sequence that is both lazy, yet also somehow 'looks ahead' for values it hasn't computed yet. The non-lazy version I wrote to test correctness was: (defn chain-length [num] (loop [len 1 n num] (cond (= n 1) len (odd? n) (recur (inc len) (+ 1 (* 3 n))) true (recur (inc len) (/ n 2))))) Which works, but is really slow. Of course I could memoize that: (def memoized-chain (memoize (fn [n] (cond (= n 1) 1 (odd? n) (+ 1 (memoized-chain (+ 1 (* 3 n)))) true (+ 1 (memoized-chain (/ n 2))))))) However, what I really wanted to do was scratch my itch for understanding the limits of lazy sequences, and write a function like this: (def lazy-chain (letfn [(chain [n] (lazy-seq (cons (if (odd? n) (+ 1 (nth lazy-chain (dec (+ 1 (* 3 n))))) (+ 1 (nth lazy-chain (dec (/ n 2))))) (chain (+ n 1)))))] (chain 1))) Pulling elements from this will cause a stack overflow for n2, which is understandable if you think about why it needs to look 'into the future' at n=3 to know the value of the tenth element in the lazy list because (+ 1 (* 3 n)) = 10. Since lazy lists have much less overhead than memoization, I would like to know if this kind of thing is possible somehow via even more delayed evaluation or queuing?

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  • How can get dtrace to run the traced command with non-root priviledges ?

    - by Gyom
    OS X lacks linux's strace, but it has dtrace which is supposed to be so much better. However, I miss the ability to do simple tracing on individual commands. For example, on linux I can write strace -f gcc hello.c to caputre all system calls, which gives me the list of all the filenames needed by the compiler to compile my program (the excellent memoize script is built upon this trick) I want to port memoize on the mac, so I need some kind of strace. What I actually need is the list of files gcc reads and writes into, so what I need is more of a truss. Sure enough can I say dtruss -f gcc hello.c and get somewhat the same functionality, but then the compiler is run with root priviledges, which is obviously undesirable (apart from the massive security risk, one issue is that the a.out file is now owned by root :-) I then tried dtruss -f sudo -u myusername gcc hello.c, but this feels a bit wrong, and does not work anyway (I get no a.out file at all this time, not sure why) All that long story tries to motivate my original question: how do I get dtrace to run my command with normal user priviledges, just like strace does in linux ? Edit: is seems that I'm not the only one wondering how to do this: question #1204256 is pretty much the same as mine (and has the same suboptimal sudo answer :-)

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  • Type classe, generic memoization

    - by nicolas
    Something quite odd is happening with y types and I quite dont understand if this is justified or not. I would tend to think not. This code works fine : type DictionarySingleton private () = static let mutable instance = Dictionary<string*obj, obj>() static member Instance = instance let memoize (f:'a -> 'b) = fun (x:'a) -> let key = f.ToString(), (x :> obj) if (DictionarySingleton.Instance).ContainsKey(key) then let r = (DictionarySingleton.Instance).[key] r :?> 'b else let res = f x (DictionarySingleton.Instance).[key] <- (res :> obj) res And this ones complains type DictionarySingleton private () = static let mutable instance = Dictionary<string*obj, _>() static member Instance = instance let memoize (f:'a -> 'b) = fun (x:'a) -> let key = f.ToString(), (x :> obj) if (DictionarySingleton.Instance).ContainsKey(key) then let r = (DictionarySingleton.Instance).[key] r :?> 'b else let res = f x (DictionarySingleton.Instance).[key] <- (res :> obj) res The difference is only the underscore in the dictionary definition. The infered types are the same, but the dynamic cast from r to type 'b exhibits an error. 'this runtime coercition ... runtime type tests are not allowed on some types, etc..' Am I missing something or is it a rough edge ?

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  • Optimizing a memoization decorator not increase call stack

    - by Tyler Crompton
    I have a very, very basic memoization decorator that I need to optimize below: def memoize(function): memos = {} def wrapper(*args): try: return memos[args] except KeyError: pass result = function(*args) memos[args] = result return result return wrapper The goal is to make this so that it doesn't add on to the call stack. It actually doubles it right now. I realize that I can embed this on a function by function basis, but that is not desired as I would like a global solution for memoizing. Any ideas?

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  • 0/1 Knapsack with irrational weights

    - by user356106
    Consider the 0/1 knapsack problem. The standard Dynamic Programming algorithm applies only when the capacity as well as the weights to fill the knapsack with are integers/ rational numbers. What do you do when the capacity/weights are irrational? The issue is that we can't memoize like we do for integer weights because we may need potentially infinite decimal places for irrational weights - leading to an infinitely large number of columns for the Dynamic Programming Table . Is there any standard method for solving this? Any comments on the complexity of this problem? Any heuristics? What about associated recurrences like (for example): f(x)=1, for x< sqrt(2) f(x)=f(x-sqrt(2))+sqrt(3)

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  • Hashing a python function to regenerate output when the function is modified

    - by Seth Johnson
    I have a python function that has a deterministic result. It takes a long time to run and generates a large output: def time_consuming_function(): # lots_of_computing_time to come up with the_result return the_result I modify time_consuming_function from time to time, but I would like to avoid having it run again while it's unchanged. [time_consuming_function only depends on functions that are immutable for the purposes considered here; i.e. it might have functions from Python libraries but not from other pieces of my code that I'd change.] The solution that suggests itself to me is to cache the output and also cache some "hash" of the function. If the hash changes, the function will have been modified, and we have to re-generate the output. Is this possible or ridiculous? Updated: based on the answers, it looks like what I want to do is to "memoize" time_consuming_function, except instead of (or in addition to) arguments passed into an invariant function, I want to account for a function that itself will change.

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  • In Ruby, how do I make a hash from an array?

    - by Wizzlewott
    I have a simple array: arr = ["apples", "bananas", "coconuts", "watermelons"] I also have a function f that will perform an operation on a single string input and return a value. This operation is very expensive, so I would like to memoize the results in the hash. I know I can make the desired hash with something like this: h = {} arr.each { |a| h[a] = f(a) } What I'd like to do is not have to initialize h, so that I can just write something like this: h = arr.(???) { |a| a => f(a) } Can that be done?

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  • Project Euler #15

    - by Aistina
    Hey everyone, Last night I was trying to solve challenge #15 from Project Euler: Starting in the top left corner of a 2×2 grid, there are 6 routes (without backtracking) to the bottom right corner. How many routes are there through a 20×20 grid? I figured this shouldn't be so hard, so I wrote a basic recursive function: const int gridSize = 20; // call with progress(0, 0) static int progress(int x, int y) { int i = 0; if (x < gridSize) i += progress(x + 1, y); if (y < gridSize) i += progress(x, y + 1); if (x == gridSize && y == gridSize) return 1; return i; } I verified that it worked for a smaller grids such as 2×2 or 3×3, and then set it to run for a 20×20 grid. Imagine my surprise when, 5 hours later, the program was still happily crunching the numbers, and only about 80% done (based on examining its current position/route in the grid). Clearly I'm going about this the wrong way. How would you solve this problem? I'm thinking it should be solved using an equation rather than a method like mine, but that's unfortunately not a strong side of mine. Update: I now have a working version. Basically it caches results obtained before when a n×m block still remains to be traversed. Here is the code along with some comments: // the size of our grid static int gridSize = 20; // the amount of paths available for a "NxM" block, e.g. "2x2" => 4 static Dictionary<string, long> pathsByBlock = new Dictionary<string, long>(); // calculate the surface of the block to the finish line static long calcsurface(long x, long y) { return (gridSize - x) * (gridSize - y); } // call using progress (0, 0) static long progress(long x, long y) { // first calculate the surface of the block remaining long surface = calcsurface(x, y); long i = 0; // zero surface means only 1 path remains // (we either go only right, or only down) if (surface == 0) return 1; // create a textual representation of the remaining // block, for use in the dictionary string block = (gridSize - x) + "x" + (gridSize - y); // if a same block has not been processed before if (!pathsByBlock.ContainsKey(block)) { // calculate it in the right direction if (x < gridSize) i += progress(x + 1, y); // and in the down direction if (y < gridSize) i += progress(x, y + 1); // and cache the result! pathsByBlock[block] = i; } // self-explanatory :) return pathsByBlock[block]; } Calling it 20 times, for grids with size 1×1 through 20×20 produces the following output: There are 2 paths in a 1 sized grid 0,0110006 seconds There are 6 paths in a 2 sized grid 0,0030002 seconds There are 20 paths in a 3 sized grid 0 seconds There are 70 paths in a 4 sized grid 0 seconds There are 252 paths in a 5 sized grid 0 seconds There are 924 paths in a 6 sized grid 0 seconds There are 3432 paths in a 7 sized grid 0 seconds There are 12870 paths in a 8 sized grid 0,001 seconds There are 48620 paths in a 9 sized grid 0,0010001 seconds There are 184756 paths in a 10 sized grid 0,001 seconds There are 705432 paths in a 11 sized grid 0 seconds There are 2704156 paths in a 12 sized grid 0 seconds There are 10400600 paths in a 13 sized grid 0,001 seconds There are 40116600 paths in a 14 sized grid 0 seconds There are 155117520 paths in a 15 sized grid 0 seconds There are 601080390 paths in a 16 sized grid 0,0010001 seconds There are 2333606220 paths in a 17 sized grid 0,001 seconds There are 9075135300 paths in a 18 sized grid 0,001 seconds There are 35345263800 paths in a 19 sized grid 0,001 seconds There are 137846528820 paths in a 20 sized grid 0,0010001 seconds 0,0390022 seconds in total I'm accepting danben's answer, because his helped me find this solution the most. But upvotes also to Tim Goodman and Agos :) Bonus update: After reading Eric Lippert's answer, I took another look and rewrote it somewhat. The basic idea is still the same but the caching part has been taken out and put in a separate function, like in Eric's example. The result is some much more elegant looking code. // the size of our grid const int gridSize = 20; // magic. static Func<A1, A2, R> Memoize<A1, A2, R>(this Func<A1, A2, R> f) { // Return a function which is f with caching. var dictionary = new Dictionary<string, R>(); return (A1 a1, A2 a2) => { R r; string key = a1 + "x" + a2; if (!dictionary.TryGetValue(key, out r)) { // not in cache yet r = f(a1, a2); dictionary.Add(key, r); } return r; }; } // calculate the surface of the block to the finish line static long calcsurface(long x, long y) { return (gridSize - x) * (gridSize - y); } // call using progress (0, 0) static Func<long, long, long> progress = ((Func<long, long, long>)((long x, long y) => { // first calculate the surface of the block remaining long surface = calcsurface(x, y); long i = 0; // zero surface means only 1 path remains // (we either go only right, or only down) if (surface == 0) return 1; // calculate it in the right direction if (x < gridSize) i += progress(x + 1, y); // and in the down direction if (y < gridSize) i += progress(x, y + 1); // self-explanatory :) return i; })).Memoize(); By the way, I couldn't think of a better way to use the two arguments as a key for the dictionary. I googled around a bit, and it seems this is a common solution. Oh well.

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  • ClassLoader exceptions being memoized

    - by Jim
    Hello, I am writing a classloader for a long-running server instance. If a user has not yet uploaded a class definition, I through a ClassNotFoundException; seems reasonable. The problem is this: There are three classes (C1, C2, and C3). C1 depends on C2, C2 depends on C3. C1 and C2 are resolvable, C3 isn't (yet). C1 is loaded. C1 subsequently performs an action that requires C2, so C2 is loaded. C2 subsequently performs an action that requires C3, so the classloader attempts to load C3, but can't resolve it, and an exception is thrown. Now C3 is added to the classpath, and the process is restarted (starting from the originally-loaded C1). The issue is, C2 seems to remember that C3 couldn't be loaded, and doesn't bother asking the classloader to find the class... it just re-throws the memoized exception. Clearly I can't reload C1 or C2 because other classes may have linked to them (as C1 has already linked to C2). I tried throwing different types of errors, hoping the class might not memoize them. Unfortunately, no such luck. Is there a way to prevent the loaded class from binding to the exception? That is, I want the classloader to be allowed to keep trying if it didn't succeed the first time. Thanks!

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  • Tail recursion and memoization with C#

    - by Jay
    I'm writing a function that finds the full path of a directory based on a database table of entries. Each record contains a key, the directory's name, and the key of the parent directory (it's the Directory table in an MSI if you're familiar). I had an iterative solution, but it started looking a little nasty. I thought I could write an elegant tail recursive solution, but I'm not sure anymore. I'll show you my code and then explain the issues I'm facing. Dictionary<string, string> m_directoryKeyToFullPathDictionary = new Dictionary<string, string>(); ... private string ExpandDirectoryKey(Database database, string directoryKey) { // check for terminating condition string fullPath; if (m_directoryKeyToFullPathDictionary.TryGetValue(directoryKey, out fullPath)) { return fullPath; } // inductive step Record record = ExecuteQuery(database, "SELECT DefaultDir, Directory_Parent FROM Directory where Directory.Directory='{0}'", directoryKey); // null check string directoryName = record.GetString("DefaultDir"); string parentDirectoryKey = record.GetString("Directory_Parent"); return Path.Combine(ExpandDirectoryKey(database, parentDirectoryKey), directoryName); } This is how the code looked when I realized I had a problem (with some minor validation/massaging removed). I want to use memoization to short circuit whenever possible, but that requires me to make a function call to the dictionary to store the output of the recursive ExpandDirectoryKey call. I realize that I also have a Path.Combine call there, but I think that can be circumvented with a ... + Path.DirectorySeparatorChar + .... I thought about using a helper method that would memoize the directory and return the value so that I could call it like this at the end of the function above: return MemoizeHelper( m_directoryKeyToFullPathDictionary, Path.Combine(ExpandDirectoryKey(database, parentDirectoryKey)), directoryName); But I feel like that's cheating and not going to be optimized as tail recursion. Any ideas? Should I be using a completely different strategy? This doesn't need to be a super efficient algorithm at all, I'm just really curious. I'm using .NET 4.0, btw. Thanks!

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  • Project Euler #14 and memoization in Clojure

    - by dbyrne
    As a neophyte clojurian, it was recommended to me that I go through the Project Euler problems as a way to learn the language. Its definitely a great way to improve your skills and gain confidence. I just finished up my answer to problem #14. It works fine, but to get it running efficiently I had to implement some memoization. I couldn't use the prepackaged memoize function because of the way my code was structured, and I think it was a good experience to roll my own anyways. My question is if there is a good way to encapsulate my cache within the function itself, or if I have to define an external cache like I have done. Also, any tips to make my code more idiomatic would be appreciated. (use 'clojure.test) (def mem (atom {})) (with-test (defn chain-length ([x] (chain-length x x 0)) ([start-val x c] (if-let [e (last(find @mem x))] (let [ret (+ c e)] (swap! mem assoc start-val ret) ret) (if (<= x 1) (let [ret (+ c 1)] (swap! mem assoc start-val ret) ret) (if (even? x) (recur start-val (/ x 2) (+ c 1)) (recur start-val (+ 1 (* x 3)) (+ c 1))))))) (is (= 10 (chain-length 13)))) (with-test (defn longest-chain ([] (longest-chain 2 0 0)) ([c max start-num] (if (>= c 1000000) start-num (let [l (chain-length c)] (if (> l max) (recur (+ 1 c) l c) (recur (+ 1 c) max start-num)))))) (is (= 837799 (longest-chain))))

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  • How to store visited states in iterative deepening / depth limited search?

    - by colinfang
    Update: Search for the first solution. for a normal Depth First Search it is simple, just use a hashset bool DFS (currentState) = { if (myHashSet.Contains(currentState)) { return; } else { myHashSet.Add(currentState); } if (IsSolution(currentState) return true; else { for (var nextState in GetNextStates(currentState)) if (DFS(nextState)) return true; } return false; } However, when it becomes depth limited, i cannot simply do this bool DFS (currentState, maxDepth) = { if (maxDepth = 0) return false; if (myHashSet.Contains(currentState)) { return; } else { myHashSet.Add(currentState); } if (IsSolution(currentState) return true; else { for (var nextState in GetNextStates(currentState)) if (DFS(nextState, maxDepth - 1)) return true; } return false; } Because then it is not going to do a complete search (in a sense of always be able to find a solution if there is any) before maxdepth How should I fix it? Would it add more space complexity to the algorithm? Or it just doesn't require to memoize the state at all. Update: for example, a decision tree is the following: A - B - C - D - E - A | F - G (Goal) Starting from state A. and G is a goal state. Clearly there is a solution under depth 3. However, using my implementation under depth 4, if the direction of search happens to be A(0) -> B(1) -> C(2) -> D(3) -> E(4) -> F(5) exceeds depth, then it would do back track to A, however E is visited, it would ignore the check direction A - E - F - G

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