Using it you can do the following: Random rnd = new Random(); int randomNumberFromArray = array[rnd. nextInt(3)]; Hope this helps!
Random(direct) access implies the ability to access any entry in a array in constant time (independent of its position in the array and of array's size). And that is big advantage. It is typically contrasted to sequential access.
Use the numpy. random. choice() function to generate the random choices and samples from a NumPy multidimensional array. Using this function we can get single or multiple random numbers from the n-dimensional array with or without replacement.
To find the position of an element in an array, you use the indexOf() method. This method returns the index of the first occurrence the element that you want to find, or -1 if the element is not found. The following illustrates the syntax of the indexOf() method.
Just two lines :
// Shuffle array
const shuffled = array.sort(() => 0.5 - Math.random());
// Get sub-array of first n elements after shuffled
let selected = shuffled.slice(0, n);
Try this non-destructive (and fast) function:
function getRandom(arr, n) {
var result = new Array(n),
len = arr.length,
taken = new Array(len);
if (n > len)
throw new RangeError("getRandom: more elements taken than available");
while (n--) {
var x = Math.floor(Math.random() * len);
result[n] = arr[x in taken ? taken[x] : x];
taken[x] = --len in taken ? taken[len] : len;
}
return result;
}
There is a one-liner unique solution here
array.sort(() => Math.random() - Math.random()).slice(0, n)
Porting .sample
from the Python standard library:
function sample(population, k){
/*
Chooses k unique random elements from a population sequence or set.
Returns a new list containing elements from the population while
leaving the original population unchanged. The resulting list is
in selection order so that all sub-slices will also be valid random
samples. This allows raffle winners (the sample) to be partitioned
into grand prize and second place winners (the subslices).
Members of the population need not be hashable or unique. If the
population contains repeats, then each occurrence is a possible
selection in the sample.
To choose a sample in a range of integers, use range as an argument.
This is especially fast and space efficient for sampling from a
large population: sample(range(10000000), 60)
Sampling without replacement entails tracking either potential
selections (the pool) in a list or previous selections in a set.
When the number of selections is small compared to the
population, then tracking selections is efficient, requiring
only a small set and an occasional reselection. For
a larger number of selections, the pool tracking method is
preferred since the list takes less space than the
set and it doesn't suffer from frequent reselections.
*/
if(!Array.isArray(population))
throw new TypeError("Population must be an array.");
var n = population.length;
if(k < 0 || k > n)
throw new RangeError("Sample larger than population or is negative");
var result = new Array(k);
var setsize = 21; // size of a small set minus size of an empty list
if(k > 5)
setsize += Math.pow(4, Math.ceil(Math.log(k * 3) / Math.log(4)))
if(n <= setsize){
// An n-length list is smaller than a k-length set
var pool = population.slice();
for(var i = 0; i < k; i++){ // invariant: non-selected at [0,n-i)
var j = Math.random() * (n - i) | 0;
result[i] = pool[j];
pool[j] = pool[n - i - 1]; // move non-selected item into vacancy
}
}else{
var selected = new Set();
for(var i = 0; i < k; i++){
var j = Math.random() * n | 0;
while(selected.has(j)){
j = Math.random() * n | 0;
}
selected.add(j);
result[i] = population[j];
}
}
return result;
}
Implementation ported from Lib/random.py.
Notes:
setsize
is set based on characteristics in Python for efficiency. Although it has not been adjusted for JavaScript, the algorithm will still function as expected.Array.prototype.sort
. This algorithm however is guaranteed to terminate in finite time.Set
implemented, the set can be replaced with an Array
and .has(j)
replaced with .indexOf(j) > -1
.Performance against the accepted answer:
lodash _.sample
and _.sampleSize
.
Gets one or n random elements at unique keys from collection up to the size of collection.
_.sample([1, 2, 3, 4]);
// => 2
_.sampleSize([1, 2, 3], 2);
// => [3, 1]
_.sampleSize([1, 2, 3], 3);
// => [2, 3, 1]
create a funcion which does that:
var getMeRandomElements = function(sourceArray, neededElements) {
var result = [];
for (var i = 0; i < neededElements; i++) {
result.push(sourceArray[Math.floor(Math.random()*sourceArray.length)]);
}
return result;
}
you should also check if the sourceArray has enough elements to be returned. and if you want unique elements returned, you should remove selected element from the sourceArray.
Getting 5 random items without changing the original array:
const n = 5;
const sample = items
.map(x => ({ x, r: Math.random() }))
.sort((a, b) => a.r - b.r)
.map(a => a.x)
.slice(0, n);
(Don't use this for big lists)
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