First of all shuffling is the process of transfering data from the mappers to the reducers, so I think it is obvious that it is necessary for the reducers, since otherwise, they wouldn’t be able to have any input (or input from every mapper). Shuffling can start even before the map phase has finished, to … Read more
You are looking for a derangement of your entries. First of all, your algorithm works in the sense that it outputs a random derangement, ie a permutation with no fixed point. However it has a enormous flaw (which you might not mind, but is worth keeping in mind): some derangements cannot be obtained with your … Read more
import random random.shuffle(array)
You can use shuf. On some systems at least (doesn’t appear to be in POSIX). As jleedev pointed out: sort -R might also be an option. On some systems at least; well, you get the picture. It has been pointed out that sort -R doesn’t really shuffle but instead sort items according to their hash … Read more
Xcode 11 • Swift 5.1 extension Collection { func choose(_ n: Int) -> ArraySlice<Element> { shuffled().prefix(n) } } Playground testing var alphabet = [“A”,”B”,”C”,”D”,”E”,”F”,”G”,”H”,”I”,”J”,”K”,”L”,”M”,”N”,”O”,”P”,”Q”,”R”,”S”,”T”,”U”,”V”,”W”,”X”,”Y”,”Z”] let shuffledAlphabet = alphabet.shuffled() // “O”, “X”, “L”, “D”, “N”, “K”, “R”, “E”, “S”, “Z”, “I”, “T”, “H”, “C”, “U”, “B”, “W”, “M”, “Q”, “Y”, “V”, “A”, “G”, “P”, “F”, “J”] … Read more
The idiomatic way to do this with Pandas is to use the .sample method of your data frame to sample all rows without replacement: df.sample(frac=1) The frac keyword argument specifies the fraction of rows to return in the random sample, so frac=1 means to return all rows (in random order). Note: If you wish to … Read more
Use Fisher-Yates shuffle. Your C# code should look something like this: static public class FisherYates { static Random r = new Random(); // Based on Java code from wikipedia: // http://en.wikipedia.org/wiki/Fisher-Yates_shuffle static public void Shuffle(int[] deck) { for (int n = deck.Length – 1; n > 0; –n) { int k = r.Next(n+1); int temp … Read more
An Empirical Approach. Let’s implement the erroneous algorithm in Mathematica: p = 10; (* Range *) s = {} For[l = 1, l <= 30000, l++, (*Iterations*) a = Range[p]; For[k = 1, k <= p, k++, i = RandomInteger[{1, p}]; temp = a[[k]]; a[[k]] = a[[i]]; a[[i]] = temp ]; AppendTo[s, a]; ] Now … Read more
First, you should extract the code for generating a random number that’s equally distributed between 0 (inclusive) and n (exclusive) to a separate function. That’s a nice task of work that you will need elsewhere, too. Second, I would not call srand inside the shuffle function but depend on the caller on initializing the random … Read more
The de-facto unbiased shuffle algorithm is the Fisher-Yates (aka Knuth) Shuffle. You can see a great visualization here (and the original post linked to this) function shuffle(array) { let currentIndex = array.length, randomIndex; // While there remain elements to shuffle. while (currentIndex != 0) { // Pick a remaining element. randomIndex = Math.floor(Math.random() * currentIndex); … Read more