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Shuffle an array in Node.js with some rules


Shuffle an array in Node.js with some rules

By : user3100505
Date : January 12 2021, 07:00 PM
I hope this helps . The simple (but inelegant) solution would be to just shuffle until an output matching the rules is found:
code :
var array = [ 'num1', 'num2', 'num3', 'num4', 'num5', 'num6' , 'num7', 'num8', 'num9'];
function shuffle(a) {
    var j, x, i;
    for (i = a.length - 1; i > 0; i--) {
        j = Math.floor(Math.random() * (i + 1));
        x = a[i];
        a[i] = a[j];
        a[j] = x;
    }
    return a;
}

const numParts = 9;
const itemsInPositions = Array.from(
  { length: array.length },
  () => []
);

const parts = Array.from(
  { length: numParts },
  () => {
    let shuffled;
    do {
      shuffled = shuffle(array.slice());
    } while (shuffled.some((item, i) => itemsInPositions[i].includes(item)));
    shuffled.forEach((item, i) => {
      itemsInPositions[i].push(item);
      // reset to length 2:
      itemsInPositions[i] = itemsInPositions[i].slice(-2);
    });
    return shuffled;
  }
);
document.write(parts.join('<br>'));


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How to set the published content by the Drupal Rules module node as the frontpage node?

How to set the published content by the Drupal Rules module node as the frontpage node?


By : user2585317
Date : March 29 2020, 07:55 AM
wish of those help Rules comes with a rule for "Execute custom PHP code", which you could use to make this rule. The front page is saved as a variable with name "site_frontpage", so the custom rule code should be something very simple like this (untested):
code :
variable_set('site_frontpage', $node->path);
Deny creation of child node, if the node key is not the same as user id. Firebase Rules

Deny creation of child node, if the node key is not the same as user id. Firebase Rules


By : user7661405
Date : March 29 2020, 07:55 AM
With these it helps I changed my approach and it seems to have done the job. This basically says, you can write here as long as you're authenticated and you're not writing null... but if you do, then within the $owner_id tree...
The $owner_id must match your auth.uid, ensuring you only write to your location. Both a 'title' and 'link' child is required, ensuring you do not write arbitrary data within /favorites. The data type must be correct, ensuring good house keeping.
code :
{
    "rules": {
        "favorites": {
            ".write": "auth.uid != null && newData.val() != null",
            "$owner_id": {
                ".read": "$owner_id == auth.uid",
                ".validate": "$owner_id == auth.uid && 
                              newData.hasChildren(['title', 'link']) &&
                              newData.child('title').isString() && 
                              newData.child('link').isString()"
            }
        }
    }
}
Shuffle Function Doesn't Shuffle Array Elements (PHP)

Shuffle Function Doesn't Shuffle Array Elements (PHP)


By : Ubiratan - Brazil
Date : March 29 2020, 07:55 AM
help you fix your problem Your shuffleX function accepts the array by value. In order for the caller to see its modifications, it needs to accept the array by reference: function shuffleX(&$arr).
Given adjacency rules and unsorted array, return array conforming to rules

Given adjacency rules and unsorted array, return array conforming to rules


By : user3500749
Date : March 29 2020, 07:55 AM
With these it helps Since the characters are distinct and each character can only appear next to two other characters in the output, each character must appear in at most two rules. If we consider the rules as edges in a graph, then they form a disjoint set of paths.
The earliest solution in alphabetical order can be formed by finding all of the characters which either aren't in any path, or are the endpoints of paths, and inserting them into the output in alphabetical order. When a path endpoint is inserted, we have to insert the rest of that path in order before continuing.
code :
import java.util.*;

public class Solution {
    public static char[] solve(char[] letters, char[][] rules) {
        // convert to adjacency list
        Map<Character, List<Character>> neighbours = new HashMap<>();
        for(char[] edge : rules) {
            char a = edge[0], b = edge[1];
            neighbours.computeIfAbsent(a, ArrayList::new).add(b);
            neighbours.computeIfAbsent(b, ArrayList::new).add(a);
        }

        // find nodes with 0 or 1 edges, in order
        List<Character> endpoints = new ArrayList<>();
        for(char a : letters) {
            if(!neighbours.containsKey(a) || neighbours.get(a).size() <= 1) {
                endpoints.add(a);
            }
        }
        Collections.sort(endpoints);

        // build output
        char[] out = new char[letters.length];
        Set<Character> used = new HashSet<>();
        int i = 0;
        for(char a : endpoints) {
            if(used.contains(a)) { continue; }
            out[i++] = a;
            used.add(a);
            // if it's a path, iterate along path
            while(neighbours.containsKey(a) && !neighbours.get(a).isEmpty()) {
                char b = neighbours.get(a).get(0);
                out[i++] = b;
                used.add(b);
                // remove previous neighbour so next one guaranteed at index 0
                neighbours.get(b).remove((Character) a); // don't convert to int
                a = b;
            }
        }
        return out;
    }
}
>>> Solution.solve(
...     new char[] { 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H' },
...     new char[][] { {'A', 'B'} , {'D', 'B'}, {'G', 'E'}, {'H', 'A'} }
... )
... 
char[8] { 'C', 'D', 'B', 'A', 'H', 'E', 'G', 'F' }
Spark: Difference between Shuffle Write, Shuffle spill (memory), Shuffle spill (disk)?

Spark: Difference between Shuffle Write, Shuffle spill (memory), Shuffle spill (disk)?


By : Cherukuru Surya Teja
Date : March 29 2020, 07:55 AM
will help you The persist calls in your code are entirely wasted if you don't access the RDD multiple times. What's the point of storing something if you never access it? Caching has no bearing on shuffle behavior other than you can avoid re-doing shuffles by keeping their output cached.
Shuffle spill is controlled by the spark.shuffle.spill and spark.shuffle.memoryFraction configuration parameters. If spill is enabled (it is by default) then shuffle files will spill to disk if they start using more than given by memoryFraction (20% by default).
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