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Is there a way to avoid number to string conversion & nested loops for performance?

I just took a coding test online and this one question really bothered me. My solution was correct but was rejected for being unoptimized. The question is as following:
Write a function combineTheGivenNumber taking two arguments:
numArray: number[]
num: a number
The function should check all the concatenation pairs that can result in making a number equal to num and return their count.
E.g. if numArray = [1, 212, 12, 12] & num = 1212 then we will have return value of 3 from combineTheGivenNumber
The pairs are as following:
numArray[0]+numArray[1]
numArray[2]+numArray[3]
numArray[3]+numArray[2]
The function I wrote for this purpose is as following:
function combineTheGivenNumber(numArray, num) {
//convert all numbers to strings for easy concatenation
numArray = numArray.map(e => e+'');
//also convert the `hay` to string for easy comparison
num = num+'';
let pairCounts = 0;
// itereate over the array to get pairs
numArray.forEach((e,i) => {
numArray.forEach((f,j) => {
if(i!==j && num === (e+f)) {
pairCounts++;
}
});
});
return pairCounts;
}
console.log('Test 1: ', combineTheGivenNumber([1,212,12,12],1212));
console.log('Test 2: ', combineTheGivenNumber([4,21,42,1],421));
From my experience, I know conversion of number to string is slow in JS, but I am not sure whether my approach is wrong/lack of knowledge or does the tester is ignorant of this fact. Can anyone suggest further optimization of the code snipped?
Elimination of string to number to string will be a significant speed boost but I am not sure how to check for concatenated numbers otherwise.

Elimination of string to number to string will be a significant speed boost
No, it won't.
Firstly, you're not converting strings to numbers anywhere, but more importantly the exercise asks for concatenation so working with strings is exactly what you should do. No idea why they're even passing numbers. You're doing fine already by doing the conversion only once for each number input, not every time your form a pair. And last but not least, avoiding the conversion will not be a significant improvement.
To get a significant improvement, you should use a better algorithm. #derpirscher is correct in his comment: "[It's] the nested loop checking every possible combination which hits the time limit. For instance for your example, when the outer loop points at 212 you don't need to do any checks, because regardless, whatever you concatenate to 212, it can never result in 1212".
So use
let pairCounts = 0;
numArray.forEach((e,i) => {
if (num.startsWith(e)) {
//^^^^^^^^^^^^^^^^^^^^^^
numArray.forEach((f,j) => {
if (i !== j && num === e+f) {
pairCounts++;
}
});
}
});
You might do the same with suffixes, but it becomes more complicated to rule out concatenation to oneself there.
Optimising further, you can even achieve a linear complexity solution by putting the strings in a lookup structure, then when finding a viable prefix just checking whether the missing part is an available suffix:
function combineTheGivenNumber(numArray, num) {
const strings = new Map();
for (const num of numArray) {
const str = String(num);
strings.set(str, 1 + (strings.get(str) ?? 0));
}
const whole = String(num);
let pairCounts = 0;
for (const [prefix, pCount] of strings) {
if (!whole.startsWith(prefix))
continue;
const suffix = whole.slice(prefix.length);
if (strings.has(suffix)) {
let sCount = strings.get(suffix);
if (suffix == prefix) sCount--; // no self-concatenation
pairCounts += pCount*sCount;
}
}
return pairCounts;
}
(the proper handling of duplicates is a bit difficile)

I like your approach of going to strings early. I can suggest a couple of simple optimizations.
You only need the numbers that are valid "first parts" and those that are valid "second parts"
You can use the javascript .startsWith and .endsWith to test for those conditions. All other strings can be thrown away.
The lengths of the strings must add up to the length of the desired answer
Suppose your target string is 8 digits long. If you have 2 valid 3-digit "first parts", then you only need to know how many valid 5-digit "second parts" you have. Suppose you have 9 of them. Those first parts can only combine with those second parts, and give you 2 * 9 = 18 valid pairs.
You don't actually need to keep the strings!
It struck me that if you know you have 2 valid 3-digit "first parts", you don't need to keep those actual strings. Knowing that they are valid 2-digit first parts is all you need to know.
So let's build an array containing:
How many valid 1-digit first parts do we have?,
How many valid 2-digit first parts do we have?,
How many valid 3-digit first parts do we have?,
etc.
And similarly an array containing the number of valid 1-digit second parts, etc.
X first parts and Y second parts can be combined in X * Y ways
Except if the parts are the same length, in which case we are reusing the same list, and so it is just X * (Y-1).
So not only do we not need to keep the strings, but we only need to do the multiplication of the appropriate elements of the arrays.
5 1-char first parts & 7 3-char second parts = 5 * 7 = 35 pairs
6 2-char first part & 4 2-char second parts = 6 * (4-1) = 18 pairs
etc
So this becomes extremely easy. One pass over the strings, tallying the "first part" and "second part" matches of each length. This can be done with an if and a ++ of the relevant array element.
Then one pass over the lengths, which will be very quick as the array of lengths will be very much shorter than the array of actual strings.
function combineTheGivenNumber(numArray, num) {
const sElements = numArray.map(e => "" + e);
const sTarget = "" + num;
const targetLength = sTarget.length
const startsByLen = (new Array(targetLength)).fill(0);
const endsByLen = (new Array(targetLength)).fill(0);
sElements.forEach(sElement => {
if (sTarget.startsWith(sElement)) {
startsByLen[sElement.length]++
}
if (sTarget.endsWith(sElement)) {
endsByLen[sElement.length]++
}
})
// We can now throw away the strings. We have two separate arrays:
// startsByLen[1] is the count of strings (without attempting to remove duplicates) which are the first character of the required answer
// startsByLen[2] similarly the count of strings which are the first 2 characters of the required answer
// etc.
// and endsByLen[1] is the count of strings which are the last character ...
// and endsByLen[2] is the count of strings which are the last 2 characters, etc.
let pairCounts = 0;
for (let firstElementLength = 1; firstElementLength < targetLength; firstElementLength++) {
const secondElementLength = targetLength - firstElementLength;
if (firstElementLength === secondElementLength) {
pairCounts += startsByLen[firstElementLength] * (endsByLen[secondElementLength] - 1)
} else {
pairCounts += startsByLen[firstElementLength] * endsByLen[secondElementLength]
}
}
return pairCounts;
}
console.log('Test 1: ', combineTheGivenNumber([1, 212, 12, 12], 1212));
console.log('Test 2: ', combineTheGivenNumber([4, 21, 42, 1], 421));

Depending on a setup, the integer slicing can be marginally faster
Although in the end it falls short
Also, when tested on higher N values, the previous answer exploded in jsfiddle. Possibly a memory error.
As far as I have tested with both random and hand-crafted values, my solution holds. It is based on an observation, that if X, Y concantenated == Z, then following must be true:
Z - Y == X * 10^(floor(log10(Y)) + 1)
an example of this:
1212 - 12 = 1200
12 * 10^(floor((log10(12)) + 1) = 12 * 10^(1+1) = 12 * 100 = 1200
Now in theory, this should be faster then manipulating strings. And in many other languages it most likely would be. However in Javascript as I just learned, the situation is a bit more complicated. Javascript does some weird things with casting that I haven't figured out yet. In short - when I tried storing the numbers(and their counts) in a map, the code got significantly slower making any possible gains from this logarithm shenanigans evaporate. Furthermore, storing them in a custom-crafted data structure isn't guaranteed to be faster since you have to build it etc. Also it would be quite a lot of work.
As it stands this log comparison is ~ 8 times faster in a case without(or with just a few) matches since the quadratic factor is yet to kick in. As long as the possible postfix count isn't too high, it will outperform the linear solution. Unfortunately it is still quadratic in nature with the breaking point depending on a total number of strings as well as their length.
So if you are searching for a needle in a haystack - for example you are looking for a few pairs in a huge heap of numbers, this can help. In the other case of searching for many matches, this won't help. Similarly, if the input array was sorted, you could use binary search to push the breaking point further up.
In the end, unless you manage to figure out how to store ints in a map(or some custom implementation of it) in a way that doesn't completely kill the performance, the linear solution of the previous answer will be faster. It can still be useful even with the performance hit if your computation is going to be memory heavy. Storing numbers takes less space then storing strings.
var log10 = Math.log(10)
function log10floored(num) {
return Math.floor(Math.log(num) / log10)
}
function combineTheGivenNumber(numArray, num) {
count = 0
for (var i=0; i!=numArray.length; i++) {
let portion = num - numArray[i]
let removedPart = Math.pow(10, log10floored(numArray[i]))
if (portion % (removedPart * 10) == 0) {
for (var j=0; j!=numArray.length; j++) {
if (j != i && portion / (removedPart * 10) == numArray[j] ) {
count += 1
}
}
}
}
return count
}
//The previous solution, that I used for timing, comparison and check purposes
function combineTheGivenNumber2(numArray, num) {
const strings = new Map();
for (const num of numArray) {
const str = String(num);
strings.set(str, 1 + (strings.get(str) ?? 0));
}
const whole = String(num);
let pairCounts = 0;
for (const [prefix, pCount] of strings) {
if (!whole.startsWith(prefix))
continue;
const suffix = whole.slice(prefix.length);
if (strings.has(suffix)) {
let sCount = strings.get(suffix);
if (suffix == prefix) sCount--; // no self-concatenation
pairCounts += pCount*sCount;
}
}
return pairCounts;
}
var myArray = []
for (let i =0; i!= 10000000; i++) {
myArray.push(Math.floor(Math.random() * 1000000))
}
var a = new Date()
t1 = a.getTime()
console.log('Test 1: ', combineTheGivenNumber(myArray,15285656));
var b = new Date()
t2 = b.getTime()
console.log('Test 2: ', combineTheGivenNumber2(myArray,15285656));
var c = new Date()
t3 = c.getTime()
console.log('Test1 time: ', t2 - t1)
console.log('test2 time: ', t3 - t2)
Small update
As long as you are willing to take a performance hit with the setup and settle for the ~2 times performance, using a simple "hashing" table can help.(Hashing tables are nice and tidy, this is a simple modulo lookup table. The principle is similar though.)
Technically this isn't linear, practicaly it is enough for the most cases - unless you are extremely unlucky and all your numbers fall in the same bucket.
function combineTheGivenNumber(numArray, num) {
count = 0
let size = 1000000
numTable = new Array(size)
for (var i=0; i!=numArray.length; i++) {
let idx = numArray[i] % size
if (numTable[idx] == undefined) {
numTable[idx] = [numArray[i]]
} else {
numTable[idx].push(numArray[i])
}
}
for (var i=0; i!=numArray.length; i++) {
let portion = num - numArray[i]
let removedPart = Math.pow(10, log10floored(numArray[i]))
if (portion % (removedPart * 10) == 0) {
if (numTable[portion / (removedPart * 10) % size] != undefined) {
let a = numTable[portion / (removedPart * 10) % size]
for (var j=0; j!=a.length; j++) {
if (j != i && portion / (removedPart * 10) == a[j] ) {
count += 1
}
}
}
}
}
return count
}

Here's a simplified, and partially optimised approach with 2 loops:
// let's optimise 'combineTheGivenNumber', where
// a=array of numbers AND n=number to match
const ctgn = (a, n) => {
// convert our given number to a string using `toString` for clarity
// this isn't entirely necessary but means we can use strict equality later
const ns = n.toString();
// reduce is an efficient mechanism to return a value based on an array, giving us
// _=[accumulator], na=[array number] and i=[index]
return a.reduce((_, na, i) => {
// convert our 'array number' to an 'array number string' for later concatenation
const nas = na.toString();
// iterate back over our array of numbers ... we're using an optimised/reverse loop
for (let ii = a.length - 1; ii >= 0; ii--) {
// skip the current array number
if (i === ii) continue;
// string + number === string, which lets us strictly compare our 'number to match'
// if there's a match we increment the accumulator
if (a[ii] + nas === ns) ++_;
}
// we're done
return _;
}, 0);
}

Trying to optimize my code to either remove nested loop or make it more efficient

A friend of mine takes a sequence of numbers from 1 to n (where n > 0)
Within that sequence, he chooses two numbers, a and b
He says that the product of a and b should be equal to the sum of all numbers in the sequence, excluding a and b
Given a number n, could you tell me the numbers he excluded from the sequence?
Have found the solution to this Kata from Code Wars but it times out (After 12 seconds) in the editor when I run it; any ideas as too how I should further optimize the nested for loop and or remove it?
function removeNb(n) {
var nArray = [];
var sum = 0;
var answersArray = [];
for (let i = 1; i <= n; i++) {
nArray.push(n - (n - i));
sum += i;
}
var length = nArray.length;
for (let i = Math.round(n / 2); i < length; i++) {
for (let y = Math.round(n / 2); y < length; y++) {
if (i != y) {
if (i * y === sum - i - y) {
answersArray.push([i, y]);
break;
}
}
}
}
return answersArray;
}
console.log(removeNb(102));
.as-console-wrapper { max-height: 100% !important; top: 0; }

I think there is no reason for calculating the sum after you fill the array, you can do that while filling it.
function removeNb(n) {
let nArray = [];
let sum = 0;
for(let i = 1; i <= n; i++) {
nArray.push(i);
sum += i;
}
}
And since there could be only two numbers a and b as the inputs for the formula a * b = sum - a - b, there could be only one possible value for each of them. So, there's no need to continue the loop when you find them.
if(i*y === sum - i - y) {
answersArray.push([i,y]);
break;
}
I recommend looking at the problem in another way.
You are trying to find two numbers a and b using this formula a * b = sum - a - b.
Why not reduce the formula like this:
a * b + a = sum - b
a ( b + 1 ) = sum - b
a = (sum - b) / ( b + 1 )
Then you only need one for loop that produces the value of b, check if (sum - b) is divisible by ( b + 1 ) and if the division produces a number that is less than n.
for(let i = 1; i <= n; i++) {
let eq1 = sum - i;
let eq2 = i + 1;
if (eq1 % eq2 === 0) {
let a = eq1 / eq2;
if (a < n && a != i) {
return [[a, b], [b, a]];
}
}
}

You can solve this in linear time with two pointers method (page 77 in the book).
In order to gain intuition towards a solution, let's start thinking about this part of your code:
for(let i = Math.round(n/2); i < length; i++) {
for(let y = Math.round(n/2); y < length; y++) {
...
You already figured out this is the part of your code that is slow. You are trying every combination of i and y, but what if you didn't have to try every single combination?
Let's take a small example to illustrate why you don't have to try every combination.
Suppose n == 10 so we have 1 2 3 4 5 6 7 8 9 10 where sum = 55.
Suppose the first combination we tried was 1*10.
Does it make sense to try 1*9 next? Of course not, since we know that 1*10 < 55-10-1 we know we have to increase our product, not decrease it.
So let's try 2*10. Well, 20 < 55-10-2 so we still have to increase.
3*10==30 < 55-3-10==42
4*10==40 < 55-4-10==41
But then 5*10==50 > 55-5-10==40. Now we know we have to decrease our product. We could either decrease 5 or we could decrease 10, but we already know that there is no solution if we decrease 5 (since we tried that in the previous step). So the only choice is to decrease 10.
5*9==45 > 55-5-9==41. Same thing again: we have to decrease 9.
5*8==40 < 55-5-8==42. And now we have to increase again...
You can think about the above example as having 2 pointers which are initialized to the beginning and end of the sequence. At every step we either
move the left pointer towards right
or move the right pointer towards left
In the beginning the difference between pointers is n-1. At every step the difference between pointers decreases by one. We can stop when the pointers cross each other (and say that no solution can be obtained if one was not found so far). So clearly we can not do more than n computations before arriving at a solution. This is what it means to say that the solution is linear with respect to n; no matter how large n grows, we never do more than n computations. Contrast this to your original solution, where we actually end up doing n^2 computations as n grows large.

Hassan is correct, here is a full solution:
function removeNb (n) {
var a = 1;
var d = 1;
// Calculate the sum of the numbers 1-n without anything removed
var S = 0.5 * n * (2*a + (d *(n-1)));
// For each possible value of b, calculate a if it exists.
var results = [];
for (let numB = a; numB <= n; numB++) {
let eq1 = S - numB;
let eq2 = numB + 1;
if (eq1 % eq2 === 0) {
let numA = eq1 / eq2;
if (numA < n && numA != numB) {
results.push([numA, numB]);
results.push([numB, numA]);
}
}
}
return results;
}

In case it's of interest, CY Aries pointed this out:
ab + a + b = n(n + 1)/2
add 1 to both sides
ab + a + b + 1 = (n^2 + n + 2) / 2
(a + 1)(b + 1) = (n^2 + n + 2) / 2
so we're looking for factors of (n^2 + n + 2) / 2 and have some indication about the least size of the factor. This doesn't necessarily imply a great improvement in complexity for the actual search but still it's kind of cool.

This is part comment, part answer.
In engineering terms, the original function posted is using "brute force" to solve the problem, iterating every (or more than needed) possible combinations. The number of iterations is n is large - if you did all possible it would be
n * (n-1) = bazillio n
Less is More
So lets look at things that can be optimized, first some minor things, I'm a little confused about the first for loop and nArray:
// OP's code
for(let i = 1; i <= n; i++) {
nArray.push(n - (n - i));
sum += i;
}
??? You don't really use nArray for anything? Length is just n .. am I so sleep deprived I'm missing something? And while you can sum a consecutive sequence of integers 1-n by using a for loop, there is a direct and easy way that avoids a loop:
sum = ( n + 1 ) * n * 0.5 ;
THE LOOPS
// OP's loops, not optimized
for(let i = Math.round(n/2); i < length; i++) {
for(let y = Math.round(n/2); y < length; y++) {
if(i != y) {
if(i*y === sum - i - y) {
Optimization Considerations:
I see you're on the right track in a way, cutting the starting i, y values in half since the factors . But you're iterating both of them in the same direction : UP. And also, the lower numbers look like they can go a little below half of n (perhaps not because the sequence start at 1, I haven't confirmed that, but it seems the case).
Plus we want to avoid division every time we start an instantiation of the loop (i.e set the variable once, and also we're going to change it). And finally, with the IF statements, i and y will never be equal to each other the way we're going to create the loops, so that's a conditional that can vanish.
But the more important thing is the direction of transversing the loops. The smaller factor low is probably going to be close to the lowest loop value (about half of n) and the larger factor hi is probably going to be near the value of n. If we has some solid math theory that said something like "hi will never be less than 0.75n" then we could make a couple mods to take advantage of that knowledge.
The way the loops are show below, they break and iterate before the hi and low loops meet.
Moreover, it doesn't matter which loop picks the lower or higher number, so we can use this to shorten the inner loop as number pairs are tested, making the loop smaller each time. We don't want to waste time checking the same pair of numbers more than once! The lower factor's loop will start a little below half of n and go up, and the higher factor's loop will start at n and go down.
// Code Fragment, more optimized:
let nHi = n;
let low = Math.trunc( n * 0.49 );
let sum = ( n + 1 ) * n * 0.5 ;
// While Loop for the outside (incrementing) loop
while( low < nHi ) {
// FOR loop for the inside decrementing loop
for(let hi = nHi; hi > low; hi--) {
// If we're higher than the sum, we exit, decrement.
if( hi * low + hi + low > sum ) {
continue;
}
// If we're equal, then we're DONE and we write to array.
else if( hi * low + hi + low === sum) {
answersArray.push([hi, low]);
low = nHi; // Note this is if we want to end once finding one pair
break; // If you want to find ALL pairs for large numbers then replace these low = nHi; with low++;
}
// And if not, we increment the low counter and restart the hi loop from the top.
else {
low++;
break;
}
} // close for
} // close while
Tutorial:
So we set the few variables. Note that low is set slightly less than half of n, as larger numbers look like they could be a few points less. Also, we don't round, we truncate, which is essentially "always rounding down", and is slightly better for performance, (though it dosenit matter in this instance with just the single assignment).
The while loop starts at the lowest value and increments, potentially all the way up to n-1. The hi FOR loop starts at n (copied to nHi), and then decrements until the factor are found OR it intercepts at low + 1.
The conditionals:
First IF: If we're higher than the sum, we exit, decrement, and continue at a lower value for the hi factor.
ELSE IF: If we are EQUAL, then we're done, and break for lunch. We set low = nHi so that when we break out of the FOR loop, we will also exit the WHILE loop.
ELSE: If we get here it's because we're less than the sum, so we need to increment the while loop and reset the hi FOR loop to start again from n (nHi).

Shuffling a poker deck in JavaScript with window.crypto.getRandomValues

A poker deck has 52 cards and thus 52! or roughly 2^226 possible permutations.
Now I want to shuffle such a deck of cards perfectly, with truly random results and a uniform distribution, so that you can reach every single one of those possible permutations and each is equally likely to appear.
Why is this actually necessary?
For games, perhaps, you don't really need perfect randomness, unless there's money to be won. Apart from that, humans probably won't even perceive the "differences" in randomness.
But if I'm not mistaken, if you use shuffling functions and RNG components commonly built into popular programming languages, you will often get no more than 32 bits of entropy and 2^32 states. Thus, you will never be able to reach all 52! possible permutations of the deck when shuffling, but only about ...
0.000000000000000000000000000000000000000000000000000000005324900157 %
... of the possible permutations. That means a whole lot of all the possible games that could be played or simulated in theory will never actually be seen in practice.
By the way, you can further improve the results if you don't reset to the default order every time before shuffling but instead start with the order from the last shuffle or keep the "mess" after a game has been played and shuffle from there.
Requirements:
So in order to do what is described above, one needs all of the following three components, as far as I have understood:
A good shuffling algorithm that ensures a uniform distribution.
A proper RNG with at least 226 bits of internal state. Since we're on deterministic machines, a PRNG will be all we'll get, and perhaps this should be a CSPRNG.
A random seed with at least 226 bits of entropy.
Solutions:
Now is this achievable? What do we have?
Fisher-Yates shuffle will be fine, as far as I can see.
The xorshift7 RNG has more than the required 226 bits of internal state and should suffice.
Using window.crypto.getRandomValues we can generate the required 226 bits of entropy to be used as our seed. If that still isn't enough, we can add some more entropy from other sources.
Question:
Are the solutions (and also the requirements) mentioned above correct? How can you implement shuffling using these solutions in JavaScript in practice then? How do you combine the three components to a working solution?
I guess I have to replace the usage of Math.random in the example of the Fisher-Yates shuffle with a call to xorshift7. But that RNG outputs a value in the [0, 1) float range and I need the [1, n] integer range instead. When scaling that range, I don't want to lose the uniform distribution. Moreover, I wanted about 226 bits of randomness. If my RNG outputs just a single Number, isn't that randomness effectively reduced to 2^53 (or 2^64) bits because there are no more possibilities for the output?
In order to generate the seed for the RNG, I wanted to do something like this:
var randomBytes = generateRandomBytes(226);
function generateRandomBytes(n) {
var data = new Uint8Array(
Math.ceil(n / 8)
);
window.crypto.getRandomValues(data);
return data;
}
Is this correct? I don't see how I could pass randomBytes to the RNG as a seed in any way, and I don't know how I could modify it to accep this.

Here's a function I wrote that uses Fisher-Yates shuffling based on random bytes sourced from window.crypto. Since Fisher-Yates requires that random numbers are generated over varying ranges, it starts out with a 6-bit mask (mask=0x3f), but gradually reduces the number of bits in this mask as the required range gets smaller (i.e., whenever i is a power of 2).
function shuffledeck() {
var cards = Array("A♣️","2♣️","3♣️","4♣️","5♣️","6♣️","7♣️","8♣️","9♣️","10♣️","J♣️","Q♣️","K♣️",
"A♦️","2♦️","3♦️","4♦️","5♦️","6♦️","7♦️","8♦️","9♦️","10♦️","J♦️","Q♦️","K♦️",
"A♥️","2♥️","3♥️","4♥️","5♥️","6♥️","7♥️","8♥️","9♥️","10♥️","J♥️","Q♥️","K♥️",
"A♠️","2♠️","3♠️","4♠️","5♠️","6♠️","7♠️","8♠️","9♠️","10♠️","J♠️","Q♠️","K♠️");
var rndbytes = new Uint8Array(100);
var i, j, r=100, tmp, mask=0x3f;
/* Fisher-Yates shuffle, using uniform random values from window.crypto */
for (i=51; i>0; i--) {
if ((i & (i+1)) == 0) mask >>= 1;
do {
/* Fetch random values in 100-byte blocks. (We probably only need to do */
/* this once.) The `mask` variable extracts the required number of bits */
/* for efficient discarding of random numbers that are too large. */
if (r == 100) {
window.crypto.getRandomValues(rndbytes);
r = 0;
}
j = rndbytes[r++] & mask;
} while (j > i);
/* Swap cards[i] and cards[j] */
tmp = cards[i];
cards[i] = cards[j];
cards[j] = tmp;
}
return cards;
}
An assessment of window.crypto libraries really deserves its own question, but anyway...
The pseudorandom stream provided by window.crypto.getRandomValues() should be sufficiently random for any purpose, but is generated by different mechanisms in different browsers. According to a 2013 survey:
Firefox (v. 21+) uses NIST SP 800-90 with a 440-bit seed. Note: This standard was updated in 2015 to remove the (possibly backdoored) Dual_EC_DRBG elliptic curve PRNG algorithm.
Internet Explorer (v. 11+) uses one of the algorithms supported by BCryptGenRandom (seed length = ?)
Safari, Chrome and Opera use an ARC4 stream cipher with a 1024-bit seed.
Edit:
A cleaner solution would be to add a generic shuffle() method to Javascript's array prototype:
// Add Fisher-Yates shuffle method to Javascript's Array type, using
// window.crypto.getRandomValues as a source of randomness.
if (Uint8Array && window.crypto && window.crypto.getRandomValues) {
Array.prototype.shuffle = function() {
var n = this.length;
// If array has <2 items, there is nothing to do
if (n < 2) return this;
// Reject arrays with >= 2**31 items
if (n > 0x7fffffff) throw "ArrayTooLong";
var i, j, r=n*2, tmp, mask;
// Fetch (2*length) random values
var rnd_words = new Uint32Array(r);
// Create a mask to filter these values
for (i=n, mask=0; i; i>>=1) mask = (mask << 1) | 1;
// Perform Fisher-Yates shuffle
for (i=n-1; i>0; i--) {
if ((i & (i+1)) == 0) mask >>= 1;
do {
if (r == n*2) {
// Refresh random values if all used up
window.crypto.getRandomValues(rnd_words);
r = 0;
}
j = rnd_words[r++] & mask;
} while (j > i);
tmp = this[i];
this[i] = this[j];
this[j] = tmp;
}
return this;
}
} else throw "Unsupported";
// Example:
deck = [ "A♣️","2♣️","3♣️","4♣️","5♣️","6♣️","7♣️","8♣️","9♣️","10♣️","J♣️","Q♣️","K♣️",
"A♦️","2♦️","3♦️","4♦️","5♦️","6♦️","7♦️","8♦️","9♦️","10♦️","J♦️","Q♦️","K♦️",
"A♥️","2♥️","3♥️","4♥️","5♥️","6♥️","7♥️","8♥️","9♥️","10♥️","J♥️","Q♥️","K♥️",
"A♠️","2♠️","3♠️","4♠️","5♠️","6♠️","7♠️","8♠️","9♠️","10♠️","J♠️","Q♠️","K♠️"];
deck.shuffle();

Combining this answer from here with this answer from another question, it seems the following could be a more general and modular (though less optimized) version:
// Fisher-Yates
function shuffle(array) {
var i, j;
for (i = array.length - 1; i > 0; i--) {
j = randomInt(0, i + 1);
swap(array, i, j);
}
}
// replacement for:
// Math.floor(Math.random() * (max - min)) + min
function randomInt(min, max) {
var range = max - min;
var bytesNeeded = Math.ceil(Math.log2(range) / 8);
var randomBytes = new Uint8Array(bytesNeeded);
var maximumRange = Math.pow(Math.pow(2, 8), bytesNeeded);
var extendedRange = Math.floor(maximumRange / range) * range;
var i, randomInteger;
while (true) {
window.crypto.getRandomValues(randomBytes);
randomInteger = 0;
for (i = 0; i < bytesNeeded; i++) {
randomInteger <<= 8;
randomInteger += randomBytes[i];
}
if (randomInteger < extendedRange) {
randomInteger %= range;
return min + randomInteger;
}
}
}
function swap(array, first, second) {
var temp;
temp = array[first];
array[first] = array[second];
array[second] = temp;
}

I personally think you could move outside the box a little bit. If you're that worried about randomness, you could look into an API key from random.org ( https://api.random.org/json-rpc/1/ ) or parse it out of a link like this: https://www.random.org/integer-sets/?sets=1&num=52&min=1&max=52&seqnos=on&commas=on&order=index&format=html&rnd=new .
Sure, your datasets could be intercepted, but if you get a few hundred thousand sets of them then shuffle those sets you would be fine.

Calculate maximum available rows and columns to fill with N amount of items

By reviewing this and this, I've come up with a function, that's probably more complex than it should be, but, man, my math sux:
function tablize(elements)
{
var root = Math.floor(Math.sqrt(elements));
var factors = [];
for (var i = 1; i <= root; i++)
{
if (elements % i === 0)
{
factors.push([i, elements / i]);
}
}
var smallest = null;
for (var f = 0; f < factors.length; f++)
{
var factor = factors[f];
var current = Math.abs(factor[0] - factor[1]);
if (!smallest || factors[smallest] > factor)
{
smallest = f;
}
}
return factors[smallest];
}
While this does work, it provides results I'm not satisfied with.
For instance - 7, it's divided in 1x7, where I'd like it to be 3x3. That's the minimum, optimal, grid size needed to fill with 7 elements.
Also - 3, it's divided in 1x3, where I'd like it to be 2x2.
I need this for a live camera feed frame distribution on a monitor, but I'm totally lost. The only way I can think of is building an extra function to feed with previously generated number and divide again, but that seems wrong.
What is the optimal solution to solve this?

For squares:
function squareNeeded(num) {
return Math.ceil(Math.sqrt(num));
}
http://jsfiddle.net/aKNVq/
(I think you mean the smallest square of a whole number that is bigger than the given amount, because if you meant a rectangle, then your example for seven would be 2*4 instead of 3*3.)

Calculating cubic root for negative number

So, to be short,
3√(-8) = (-8)1/3
console.log(Math.pow(-8,1/3));
//Should be -2
But when I test it out, it outputs
NaN
Why? Is it a bug or it is expected to be like this in the first place? I am using JavaScript to draw graphs, but this messes up the graph.

You can use this snippet to calculate it. It also works for other powers, e.g. 1/4, 1/5, etc.
function nthroot(x, n) {
try {
var negate = n % 2 == 1 && x < 0;
if(negate)
x = -x;
var possible = Math.pow(x, 1 / n);
n = Math.pow(possible, n);
if(Math.abs(x - n) < 1 && (x > 0 == n > 0))
return negate ? -possible : possible;
} catch(e){}
}
nthroot(-8, 3);
Source: http://gotochriswest.com/blog/2011/05/06/cube-root-an-beyond/
A faster approach for just calculating the cubic root:
Math.cbrt = function(x) {
var sign = x === 0 ? 0 : x > 0 ? 1 : -1;
return sign * Math.pow(Math.abs(x), 1 / 3);
}
Math.cbrt(-8);
Update
To find an integer based cubic root, you can use the following function, inspired by this answer:
// positive-only cubic root approximation
function cbrt(n)
{
var a = n; // note: this is a non optimized assumption
while (a * a * a > n) {
a = Math.floor((2 * a + (n / (a * a))) / 3);
}
return a;
}
It starts with an assumption that converges to the closest integer a for which a^3 <= n. This function can be adjusted in the same way to support a negative base.

There's no bug; you are raising a negative number to a fractional power; hence, the NaN.
The top hit on google for this is from Dr Math the explanation is pretty good. It says for for real numbers (not complex numbers anyway), a negative number raised to a fractional power may not be a real number. The simplest example is probably
-4 ^ (1/2)
which is essentially computing the square root of -4. Even though the cubic root of -8 does have real solutions, I think that most software libraries find it more efficient not to do all the complex arithmetic and return NaN only when the imaginary part is nonzero and give you the nice real answer otherwise.
EDIT
Just to make absolutely clear that NaN is the intended result, see the official ECMAScript 5.1 Specification, Section 15.8.2.13. It says:
If x<0 and x is finite and y is finite and y is not an integer, the result is NaN.
Again, even though SOME instances of raising negative numbers to fractional powers have exactly one real root, many languages just do the NaN thing for all cases of negative numbers to fractional roots.
Please do not think JavaScript is the only such language. C++ does the same thing:
If x is finite negative and y is finite but not an integer value, it causes a domain error.

Two key problems:
Mathematically, there are multiple cubic roots of a negative number: -2, but also 2 complex roots (see cube roots of unity).
Javascript's Math object (and most other standard math libraries) will not do fractional powers of negative numbers. It converts the fractional power to a float before the function receives it, so you are asking the function to compute a floating point power of a negative number, which may or may not have a real solution. So it does the pragmatic thing and refuses to attempt to calculate such a value.
If you want to get the correct answer, you'll need to decide how mathematically correct you want to be, and write those rules into a non-standard implementation of pow.
All library functions are limited to avoid excessive calculation times and unnecessary complexity.

I like the other answers, but how about overriding Math.pow so it would be able to work with all nth roots of negative numbers:
//keep the original method for proxying
Math.pow_ = Math.pow;
//redefine the method
Math.pow = function(_base, _exponent) {
if (_base < 0) {
if (Math.abs(_exponent) < 1) {
//we're calculating nth root of _base, where n === 1/_exponent
if (1 / _exponent % 2 === 0) {
//nth root of a negative number is imaginary when n is even, we could return
//a string like "123i" but this would completely mess up further computation
return NaN;
}/*else if (1 / _exponent % 2 !== 0)*/
//nth root of a negative number when n is odd
return -Math.pow_(Math.abs(_base), _exponent);
}
}/*else if (_base >=0)*/
//run the original method, nothing will go wrong
return Math.pow_(_base, _exponent);
};
Fiddled with some test cases, give me a shout if you spot a bug!

So I see a bunch of methods that revolve around Math.pow(...) which is cool, but based on the wording of the bounty I'm proposing a slightly different approach.
There are several computational approximations for solving roots, some taking quicker steps than others. Ultimately the stopping point comes down to the degree of precision desired(it's really up to you/the problem being solved).
I'm not going to explain the math in fine detail, but the following are implementations of cubed root approximations that passed the target test(bounty test - also added negative range, because of the question title). Each iteration in the loop (see the while(Math.abs(xi-xi0)>precision) loops in each method) gets a step closer to the desired precision. Once precision is achieved a format is applied to the number so it's as precise as the calculation derived from the iteration.
var precision = 0.0000000000001;
function test_cuberoot_fn(fn) {
var tested = 0,
failed = 0;
for (var i = -100; i < 100; i++) {
var root = fn(i*i*i);
if (i !== root) {
console.log(i, root);
failed++;
}
tested++;
}
if (failed) {
console.log("failed %d / %d", failed, tested);
}else{
console.log("Passed test");
}
}
test_cuberoot_fn(newtonMethod);
test_cuberoot_fn(halleysMethod);
Newton's approximation Implementation
function newtonMethod(cube){
if(cube == 0){//only John Skeet and Chuck Norris
return 0; //can divide by zero, we'll have
} //to settle for check and return
var xi = 1;
var xi0 = -1;
while(Math.abs(xi-xi0)>precision){//precision = 0.0000000000001
xi0=xi;
xi = (1/3)*((cube/(xi*xi))+2*xi);
}
return Number(xi.toPrecision(12));
}
Halley's approximation Implementation
note Halley's approximation takes quicker steps to solving the cube, so it's computationally faster than newton's approximation.
function halleysMethod(cube){
if(cube == 0){//only John Skeet and Chuck Norris
return 0; //can divide by zero, we'll have
} //to settle for check and return
var xi = 1;
var xi0 = -1;
while(Math.abs(xi-xi0)>precision){//precision = 0.0000000000001
xi0=xi;
xi = xi*((xi*xi*xi + 2*cube)/(2*xi*xi*xi+cube));
}
return Number(xi.toPrecision(12));
}

It's Working in Chrome Console
function cubeRoot(number) {
var num = number;
var temp = 1;
var inverse = 1 / 3;
if (num < 0) {
num = -num;
temp = -1;
}
var res = Math.pow(num, inverse);
var acc = res - Math.floor(res);
if (acc <= 0.00001)
res = Math.floor(res);
else if (acc >= 0.99999)
res = Math.ceil(res);
return (temp * res);
}
cubeRoot(-64) // -4
cubeRoot(64) // 4

As a heads up, in ES6 there is now a Math.cbrt function.
In my testing in Google chrome it appears to work almost twice as fast as Math.pow. Interestingly I had to add up the results otherwise chrome did a better job of optimizing away the pow function.
//do a performance test on the cube root function from es6
var start=0, end=0, k=0;
start = performance.now();
k=0;
for (var i=0.0; i<10000000.0; i+=1.0)
{
var j = Math.cbrt(i);
//k+=j;
}
end = performance.now();
console.log("cbrt took:" + (end-start),k);
k=0;
start = performance.now();
for (var i=0.0; i<10000000.0; i+=1.0)
{
var j = Math.pow(i,0.33333333);
//k+=j;
}
end = performance.now();
console.log("pow took:" + (end-start),k);
k=0;
start = performance.now();
for (var i=0.0; i<10000000.0; i+=1.0)
{
var j = Math.cbrt(i);
k+=j;
}
end = performance.now();
console.log("cbrt took:" + (end-start),k);
k=0;
start = performance.now();
for (var i=0.0; i<10000000.0; i+=1.0)
{
var j = Math.pow(i,0.33333333);
k+=j;
}
end = performance.now();
console.log("pow took:" + (end-start),k);
Result:
cbrt took:468.28200000163633 0
pow took:77.21999999921536 0
cbrt took:546.8039999977918 1615825909.5248165
pow took:869.1149999940535 1615825826.7510242

//aren't cube roots of negative numbers the same as positive, except for the sign?
Math.cubeRoot= function(n, r){
var sign= (n<0)? -1: 1;
return sign*Math.pow(Math.abs(n), 1/3);
}
Math.cubeRoot(-8)
/* returned value: (Number)
-2
*/

Just want to highlight that in ES6 there is a native cubic root function. So you can just do this (check the support here)
Math.cbrt(-8) will return you -2

this works with negative number and negative exponent:
function nthRoot(x = 0, r = 1) {
if (x < 0) {
if (r % 2 === 1) return -nthRoot(-x, r)
if (r % 2 === -1) return -1 / nthRoot(-x, -r)
}
return x ** (1 / r)
}
examples:
nthRoot( 16, 2) 4
nthRoot( 16, -2) 0.25
nthRoot(-16, 2) NaN
nthRoot(-16, -2) NaN
nthRoot( 27, 3) 3
nthRoot( 27, -3) 0.3333333333333333
nthRoot(-27, 3) -3
nthRoot(-27, -3) -0.3333333333333333