Related
I have found 3 methods to convert Uint8Array to BigInt and all of them give different results for some reason. Could you please tell me which one is correct and which one should I use?
Using bigint-conversion library. We can use bigintConversion.bufToBigint() function to get a BigInt. The implementation is as follows:
export function bufToBigint (buf: ArrayBuffer|TypedArray|Buffer): bigint {
let bits = 8n
if (ArrayBuffer.isView(buf)) bits = BigInt(buf.BYTES_PER_ELEMENT * 8)
else buf = new Uint8Array(buf)
let ret = 0n
for (const i of (buf as TypedArray|Buffer).values()) {
const bi = BigInt(i)
ret = (ret << bits) + bi
}
return ret
}
Using DataView:
let view = new DataView(arr.buffer, 0);
let result = view.getBigUint64(0, true);
Using a FOR loop:
let result = BigInt(0);
for (let i = arr.length - 1; i >= 0; i++) {
result = result * BigInt(256) + BigInt(arr[i]);
}
I'm honestly confused which one is right since all of them give different results but do give results.
I'm fine with either BE or LE but I'd just like to know why these 3 methods give a different result.
One reason for the different results is that they use different endianness.
Let's turn your snippets into a form where we can execute and compare them:
let source_array = new Uint8Array([
0xff, 0xee, 0xdd, 0xcc, 0xbb, 0xaa, 0x99, 0x88,
0x77, 0x66, 0x55, 0x44, 0x33, 0x22, 0x11]);
let buffer = source_array.buffer;
function method1(buf) {
let bits = 8n
if (ArrayBuffer.isView(buf)) {
bits = BigInt(buf.BYTES_PER_ELEMENT * 8)
} else {
buf = new Uint8Array(buf)
}
let ret = 0n
for (const i of buf.values()) {
const bi = BigInt(i)
ret = (ret << bits) + bi
}
return ret
}
function method2(buf) {
let view = new DataView(buf, 0);
return view.getBigUint64(0, true);
}
function method3(buf) {
let arr = new Uint8Array(buf);
let result = BigInt(0);
for (let i = arr.length - 1; i >= 0; i--) {
result = result * BigInt(256) + BigInt(arr[i]);
}
return result;
}
console.log(method1(buffer).toString(16));
console.log(method2(buffer).toString(16));
console.log(method3(buffer).toString(16));
Note that this includes a bug fix for method3: where you wrote for (let i = arr.length - 1; i >= 0; i++), you clearly meant i-- at the end.
For "method1" this prints: ffeeddccbbaa998877665544332211
Because method1 is a big-endian conversion (first byte of the array is most-significant part of the result) without size limit.
For "method2" this prints: 8899aabbccddeeff
Because method2 is a little-endian conversion (first byte of the array is least significant part of the result) limited to 64 bits.
If you switch the second getBigUint64 argument from true to false, you get big-endian behavior: ffeeddccbbaa9988.
To eliminate the size limitation, you'd have to add a loop: using getBigUint64 you can get 64-bit chunks, which you can assemble using shifts similar to method1 and method3.
For "method3" this prints: 112233445566778899aabbccddeeff
Because method3 is a little-endian conversion without size limit. If you reverse the for-loop's direction, you'll get the same big-endian behavior as method1: result * 256n gives the same value as result << 8n; the latter is a bit faster.
(Side note: BigInt(0) and BigInt(256) are needlessly verbose, just write 0n and 256n instead. Additional benefit: 123456789123456789n does what you'd expect, BigInt(123456789123456789) does not.)
So which method should you use? That depends on:
(1) Do your incoming arrays assume BE or LE encoding?
(2) Are your BigInts limited to 64 bits or arbitrarily large?
(3) Is this performance-critical code, or are all approaches "fast enough"?
Taking a step back: if you control both parts of the overall process (converting BigInts to Uint8Array, then transmitting/storing them, then converting back to BigInt), consider simply using hexadecimal strings instead: that'll be easier to code, easier to debug, and significantly faster. Something like:
function serialize(bigint) {
return "0x" + bigint.toString(16);
}
function deserialize(serialized_bigint) {
return BigInt(serialized_bigint);
}
If you need to store really big integers that isn't bound to any base64 or 128 and also keep negative numbers then this is a solution for you...
function encode(n) {
let hex, bytes
// shift all numbers 1 step to the left and xor if less then 0
n = (n << 1n) ^ (n < 0n ? -1n : 0n)
// convert to hex
hex = n.toString(16)
// pad if neccesseery
if (hex.length % 2) hex = '0' + hex
// convert hex to bytes
bytes = hex.match(/.{1,2}/g).map(byte => parseInt(byte, 16))
return bytes
}
function decode(bytes) {
let hex, n
// convert bytes back into hex
hex = bytes.map(e => e.toString(16).padStart(2, 0)).join('')
// Convert hex to BigInt
n = BigInt(`0x`+hex)
// Shift all numbers to right and xor if the first bit was signed
n = (n >> 1n) ^ (n & 1n ? -1n : 0n)
return n
}
const input = document.querySelector('input')
input.oninput = () => {
console.clear()
const bytes = encode(BigInt(input.value))
// TODO: Save or transmit this bytes
// new Uint8Array(bytes)
console.log(bytes.join(','))
const n = decode(bytes)
console.log(n.toString(10)+'n') // cuz SO can't render bigints...
}
input.oninput()
<input type="number" value="-39287498324798237498237498273323423" style="width: 100%">
Is it possible to seed the random number generator (Math.random) in JavaScript?
No, it is not possible to seed Math.random(). The ECMAScript specification is intentionally vague on the subject, providing no means for seeding nor require that browsers even use the same algorithm. So such a function must be externally provided, which thankfully isn't too difficult.
I've implemented a number of good, short and fast Pseudorandom number generator (PRNG) functions in plain JavaScript. All of them can be seeded and provide high quality numbers. These are not intended for security purposes--if you need a seedable CSPRNG, look into ISAAC.
First of all, take care to initialize your PRNGs properly. To keep things simple, the generators below have no built-in seed generating procedure, but accept one or more 32-bit numbers as the initial seed state of the PRNG. Similar or sparse seeds (e.g. a simple seed of 1 and 2) have low entropy, and can cause correlations or other randomness quality issues, sometimes resulting in the output having similar properties (such as randomly generated levels being similar). To avoid this, it is best practice to initialize PRNGs with a well-distributed, high entropy seed and/or advancing past the first 15 or so numbers.
There are many ways to do this, but here are two methods. Firstly, hash functions are very good at generating seeds from short strings. A good hash function will generate very different results even when two strings are similar, so you don't have to put much thought into the string. Here's an example hash function:
function cyrb128(str) {
let h1 = 1779033703, h2 = 3144134277,
h3 = 1013904242, h4 = 2773480762;
for (let i = 0, k; i < str.length; i++) {
k = str.charCodeAt(i);
h1 = h2 ^ Math.imul(h1 ^ k, 597399067);
h2 = h3 ^ Math.imul(h2 ^ k, 2869860233);
h3 = h4 ^ Math.imul(h3 ^ k, 951274213);
h4 = h1 ^ Math.imul(h4 ^ k, 2716044179);
}
h1 = Math.imul(h3 ^ (h1 >>> 18), 597399067);
h2 = Math.imul(h4 ^ (h2 >>> 22), 2869860233);
h3 = Math.imul(h1 ^ (h3 >>> 17), 951274213);
h4 = Math.imul(h2 ^ (h4 >>> 19), 2716044179);
return [(h1^h2^h3^h4)>>>0, (h2^h1)>>>0, (h3^h1)>>>0, (h4^h1)>>>0];
}
Calling cyrb128 will produce a 128-bit hash value from a string which can be used to seed a PRNG. Here's how you might use it:
// Create cyrb128 state:
var seed = cyrb128("apples");
// Four 32-bit component hashes provide the seed for sfc32.
var rand = sfc32(seed[0], seed[1], seed[2], seed[3]);
// Only one 32-bit component hash is needed for mulberry32.
var rand = mulberry32(seed[0]);
// Obtain sequential random numbers like so:
rand();
rand();
Note: If you want a slightly more robust 128-bit hash, consider MurmurHash3_x86_128, it's more thorough, but intended for use with large arrays.
Alternatively, simply choose some dummy data to pad the seed with, and advance the generator beforehand a few times (12-20 iterations) to mix the initial state thoroughly. This has the benefit of being simpler, and is often used in reference implementations of PRNGs, but it does limit the number of initial states:
var seed = 1337 ^ 0xDEADBEEF; // 32-bit seed with optional XOR value
// Pad seed with Phi, Pi and E.
// https://en.wikipedia.org/wiki/Nothing-up-my-sleeve_number
var rand = sfc32(0x9E3779B9, 0x243F6A88, 0xB7E15162, seed);
for (var i = 0; i < 15; i++) rand();
Note: the output of these PRNG functions produce a positive 32-bit number (0 to 232-1) which is then converted to a floating-point number between 0-1 (0 inclusive, 1 exclusive) equivalent to Math.random(), if you want random numbers of a specific range, read this article on MDN. If you only want the raw bits, simply remove the final division operation.
JavaScript numbers can only represent whole integers up to 53-bit resolution. And when using bitwise operations, this is reduced to 32. Modern PRNGs in other languages often use 64-bit operations, which require shims when porting to JS that can drastically reduce performance. The algorithms here only use 32-bit operations, as it is directly compatible with JS.
Now, onward to the the generators. (I maintain the full list with references and license info here)
sfc32 (Simple Fast Counter)
sfc32 is part of the PractRand random number testing suite (which it passes of course). sfc32 has a 128-bit state and is very fast in JS.
function sfc32(a, b, c, d) {
return function() {
a >>>= 0; b >>>= 0; c >>>= 0; d >>>= 0;
var t = (a + b) | 0;
a = b ^ b >>> 9;
b = c + (c << 3) | 0;
c = (c << 21 | c >>> 11);
d = d + 1 | 0;
t = t + d | 0;
c = c + t | 0;
return (t >>> 0) / 4294967296;
}
}
You may wonder what the | 0 and >>>= 0 are for. These are essentially 32-bit integer casts, used for performance optimizations. Number in JS are basically floats, but during bitwise operations, they switch into a 32-bit integer mode. This mode is processed faster by JS interpreters, but any multiplication or addition will cause it to switch back to a float, resulting in a performance hit.
Mulberry32
Mulberry32 is a simple generator with a 32-bit state, but is extremely fast and has good quality randomness (author states it passes all tests of gjrand testing suite and has a full 232 period, but I haven't verified).
function mulberry32(a) {
return function() {
var t = a += 0x6D2B79F5;
t = Math.imul(t ^ t >>> 15, t | 1);
t ^= t + Math.imul(t ^ t >>> 7, t | 61);
return ((t ^ t >>> 14) >>> 0) / 4294967296;
}
}
I would recommend this if you just need a simple but decent PRNG and don't need billions of random numbers (see Birthday problem).
xoshiro128**
As of May 2018, xoshiro128** is the new member of the Xorshift family, by Vigna & Blackman (professor Vigna was also responsible for the Xorshift128+ algorithm powering most Math.random implementations under the hood). It is the fastest generator that offers a 128-bit state.
function xoshiro128ss(a, b, c, d) {
return function() {
var t = b << 9, r = a * 5; r = (r << 7 | r >>> 25) * 9;
c ^= a; d ^= b;
b ^= c; a ^= d; c ^= t;
d = d << 11 | d >>> 21;
return (r >>> 0) / 4294967296;
}
}
The authors claim it passes randomness tests well (albeit with caveats). Other researchers have pointed out that it fails some tests in TestU01 (particularly LinearComp and BinaryRank). In practice, it should not cause issues when floats are used (such as in these implementations), but may cause issues if relying on the raw lowest order bit.
JSF (Jenkins' Small Fast)
This is JSF or 'smallprng' by Bob Jenkins (2007), who also made ISAAC and SpookyHash. It passes PractRand tests and should be quite fast, although not as fast as sfc32.
function jsf32(a, b, c, d) {
return function() {
a |= 0; b |= 0; c |= 0; d |= 0;
var t = a - (b << 27 | b >>> 5) | 0;
a = b ^ (c << 17 | c >>> 15);
b = c + d | 0;
c = d + t | 0;
d = a + t | 0;
return (d >>> 0) / 4294967296;
}
}
No, it is not possible to seed Math.random(), but it's fairly easy to write your own generator, or better yet, use an existing one.
Check out: this related question.
Also, see David Bau's blog for more information on seeding.
NOTE: Despite (or rather, because of) succinctness and apparent elegance, this algorithm is by no means a high-quality one in terms of randomness. Look for e.g. those listed in this answer for better results.
(Originally adapted from a clever idea presented in a comment to another answer.)
var seed = 1;
function random() {
var x = Math.sin(seed++) * 10000;
return x - Math.floor(x);
}
You can set seed to be any number, just avoid zero (or any multiple of Math.PI).
The elegance of this solution, in my opinion, comes from the lack of any "magic" numbers (besides 10000, which represents about the minimum amount of digits you must throw away to avoid odd patterns - see results with values 10, 100, 1000). Brevity is also nice.
It's a bit slower than Math.random() (by a factor of 2 or 3), but I believe it's about as fast as any other solution written in JavaScript.
No, but here's a simple pseudorandom generator, an implementation of Multiply-with-carry I adapted from Wikipedia (has been removed since):
var m_w = 123456789;
var m_z = 987654321;
var mask = 0xffffffff;
// Takes any integer
function seed(i) {
m_w = (123456789 + i) & mask;
m_z = (987654321 - i) & mask;
}
// Returns number between 0 (inclusive) and 1.0 (exclusive),
// just like Math.random().
function random()
{
m_z = (36969 * (m_z & 65535) + (m_z >> 16)) & mask;
m_w = (18000 * (m_w & 65535) + (m_w >> 16)) & mask;
var result = ((m_z << 16) + (m_w & 65535)) >>> 0;
result /= 4294967296;
return result;
}
Antti Sykäri's algorithm is nice and short. I initially made a variation that replaced JavaScript's Math.random when you call Math.seed(s), but then Jason commented that returning the function would be better:
Math.seed = function(s) {
return function() {
s = Math.sin(s) * 10000; return s - Math.floor(s);
};
};
// usage:
var random1 = Math.seed(42);
var random2 = Math.seed(random1());
Math.random = Math.seed(random2());
This gives you another functionality that JavaScript doesn't have: multiple independent random generators. That is especially important if you want to have multiple repeatable simulations running at the same time.
Please see Pierre L'Ecuyer's work going back to the late 1980s and early 1990s. There are others as well. Creating a (pseudo) random number generator on your own, if you are not an expert, is pretty dangerous, because there is a high likelihood of either the results not being statistically random or in having a small period. Pierre (and others) have put together some good (pseudo) random number generators that are easy to implement. I use one of his LFSR generators.
https://www.iro.umontreal.ca/~lecuyer/myftp/papers/handstat.pdf
Combining some of the previous answers, this is the seedable random function you are looking for:
Math.seed = function(s) {
var mask = 0xffffffff;
var m_w = (123456789 + s) & mask;
var m_z = (987654321 - s) & mask;
return function() {
m_z = (36969 * (m_z & 65535) + (m_z >>> 16)) & mask;
m_w = (18000 * (m_w & 65535) + (m_w >>> 16)) & mask;
var result = ((m_z << 16) + (m_w & 65535)) >>> 0;
result /= 4294967296;
return result;
}
}
var myRandomFunction = Math.seed(1234);
var randomNumber = myRandomFunction();
It's not possible to seed the builtin Math.random function, but it is possible to implement a high quality RNG in Javascript with very little code.
Javascript numbers are 64-bit floating point precision, which can represent all positive integers less than 2^53. This puts a hard limit to our arithmetic, but within these limits you can still pick parameters for a high quality Lehmer / LCG random number generator.
function RNG(seed) {
var m = 2**35 - 31
var a = 185852
var s = seed % m
return function () {
return (s = s * a % m) / m
}
}
Math.random = RNG(Date.now())
If you want even higher quality random numbers, at the cost of being ~10 times slower, you can use BigInt for the arithmetic and pick parameters where m is just able to fit in a double.
function RNG(seed) {
var m_as_number = 2**53 - 111
var m = 2n**53n - 111n
var a = 5667072534355537n
var s = BigInt(seed) % m
return function () {
return Number(s = s * a % m) / m_as_number
}
}
See this paper by Pierre l'Ecuyer for the parameters used in the above implementations:
https://www.ams.org/journals/mcom/1999-68-225/S0025-5718-99-00996-5/S0025-5718-99-00996-5.pdf
And whatever you do, avoid all the other answers here that use Math.sin!
To write your own pseudo random generator is quite simple.
The suggestion of Dave Scotese is useful but, as pointed out by others, it is not quite uniformly distributed.
However, it is not because of the integer arguments of sin. It's simply because of the range of sin, which happens to be a one dimensional projection of a circle. If you would take the angle of the circle instead it would be uniform.
So instead of sin(x) use arg(exp(i * x)) / (2 * PI).
If you don't like the linear order, mix it a bit up with xor. The actual factor doesn't matter that much either.
To generate n pseudo random numbers one could use the code:
function psora(k, n) {
var r = Math.PI * (k ^ n)
return r - Math.floor(r)
}
n = 42; for(k = 0; k < n; k++) console.log(psora(k, n))
Please also note that you cannot use pseudo random sequences when real entropy is needed.
Many people who need a seedable random-number generator in Javascript these days are using David Bau's seedrandom module.
Math.random no, but the ran library solves this. It has almost all distributions you can imagine and supports seeded random number generation. Example:
ran.core.seed(0)
myDist = new ran.Dist.Uniform(0, 1)
samples = myDist.sample(1000)
Here's the adopted version of Jenkins hash, borrowed from here
export function createDeterministicRandom(): () => number {
let seed = 0x2F6E2B1;
return function() {
// Robert Jenkins’ 32 bit integer hash function
seed = ((seed + 0x7ED55D16) + (seed << 12)) & 0xFFFFFFFF;
seed = ((seed ^ 0xC761C23C) ^ (seed >>> 19)) & 0xFFFFFFFF;
seed = ((seed + 0x165667B1) + (seed << 5)) & 0xFFFFFFFF;
seed = ((seed + 0xD3A2646C) ^ (seed << 9)) & 0xFFFFFFFF;
seed = ((seed + 0xFD7046C5) + (seed << 3)) & 0xFFFFFFFF;
seed = ((seed ^ 0xB55A4F09) ^ (seed >>> 16)) & 0xFFFFFFFF;
return (seed & 0xFFFFFFF) / 0x10000000;
};
}
You can use it like this:
const deterministicRandom = createDeterministicRandom()
deterministicRandom()
// => 0.9872818551957607
deterministicRandom()
// => 0.34880331158638
No, like they said it is not possible to seed Math.random()
but you can install external package which make provision for that. i used these package which can be install using these command
npm i random-seed
the example is gotten from the package documentation.
var seed = 'Hello World',
rand1 = require('random-seed').create(seed),
rand2 = require('random-seed').create(seed);
console.log(rand1(100), rand2(100));
follow the link for documentation https://www.npmjs.com/package/random-seed
SIN(id + seed) is a very interesting replacement for RANDOM functions that cannot be seeded like SQLite:
https://stackoverflow.com/a/75089040/7776828
Most of the answers here produce biased results. So here's a tested function based on seedrandom library from github:
!function(f,a,c){var s,l=256,p="random",d=c.pow(l,6),g=c.pow(2,52),y=2*g,h=l-1;function n(n,t,r){function e(){for(var n=u.g(6),t=d,r=0;n<g;)n=(n+r)*l,t*=l,r=u.g(1);for(;y<=n;)n/=2,t/=2,r>>>=1;return(n+r)/t}var o=[],i=j(function n(t,r){var e,o=[],i=typeof t;if(r&&"object"==i)for(e in t)try{o.push(n(t[e],r-1))}catch(n){}return o.length?o:"string"==i?t:t+"\0"}((t=1==t?{entropy:!0}:t||{}).entropy?[n,S(a)]:null==n?function(){try{var n;return s&&(n=s.randomBytes)?n=n(l):(n=new Uint8Array(l),(f.crypto||f.msCrypto).getRandomValues(n)),S(n)}catch(n){var t=f.navigator,r=t&&t.plugins;return[+new Date,f,r,f.screen,S(a)]}}():n,3),o),u=new m(o);return e.int32=function(){return 0|u.g(4)},e.quick=function(){return u.g(4)/4294967296},e.double=e,j(S(u.S),a),(t.pass||r||function(n,t,r,e){return e&&(e.S&&v(e,u),n.state=function(){return v(u,{})}),r?(c[p]=n,t):n})(e,i,"global"in t?t.global:this==c,t.state)}function m(n){var t,r=n.length,u=this,e=0,o=u.i=u.j=0,i=u.S=[];for(r||(n=[r++]);e<l;)i[e]=e++;for(e=0;e<l;e++)i[e]=i[o=h&o+n[e%r]+(t=i[e])],i[o]=t;(u.g=function(n){for(var t,r=0,e=u.i,o=u.j,i=u.S;n--;)t=i[e=h&e+1],r=r*l+i[h&(i[e]=i[o=h&o+t])+(i[o]=t)];return u.i=e,u.j=o,r})(l)}function v(n,t){return t.i=n.i,t.j=n.j,t.S=n.S.slice(),t}function j(n,t){for(var r,e=n+"",o=0;o<e.length;)t[h&o]=h&(r^=19*t[h&o])+e.charCodeAt(o++);return S(t)}function S(n){return String.fromCharCode.apply(0,n)}if(j(c.random(),a),"object"==typeof module&&module.exports){module.exports=n;try{s=require("crypto")}catch(n){}}else"function"==typeof define&&define.amd?define(function(){return n}):c["seed"+p]=n}("undefined"!=typeof self?self:this,[],Math);
function randIntWithSeed(seed, max=1) {
/* returns a random number between [0,max] including zero and max
seed can be either string or integer */
return Math.round(new Math.seedrandom('seed' + seed)()) * max
}
test for true randomness of this code: https://es6console.com/kkjkgur2/
There are plenty of good answers here but I had a similar issue with the additional requirement that I would like portability between Java's random number generator and whatever I ended up using in JavaScript.
I found the java-random package
These two pieces of code had identical output assuming the seed is the same:
Java:
Random randomGenerator = new Random(seed);
int randomInt;
for (int i=0; i<10; i++) {
randomInt = randomGenerator.nextInt(50);
System.out.println(randomInt);
}
JavaScript:
let Random = require('java-random');
let rng = new Random(seed);
for (let i=0; i<10; i++) {
let val = rng.nextInt(50);
console.log(val);
}
Do what bryc suggests ... but before you use his cyrb128 hash function to initialise, note that the return statement throws away 32 bits of entropy. Exclusive-or the four values together = 0. You should probably make the first element (h2^h3^h4) >>> 0.
I have written a function that returns a seeded random number, it uses Math.sin to have a long random number and uses the seed to pick numbers from that.
Use :
seedRandom("k9]:2#", 15)
it will return your seeded number
the first parameter is any string value ; your seed.
the second parameter is how many digits will return.
function seedRandom(inputSeed, lengthOfNumber){
var output = "";
var seed = inputSeed.toString();
var newSeed = 0;
var characterArray = ['0','1','2','3','4','5','6','7','8','9','a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','y','x','z','A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','U','R','S','T','U','V','W','X','Y','Z','!','#','#','$','%','^','&','*','(',')',' ','[','{',']','}','|',';',':',"'",',','<','.','>','/','?','`','~','-','_','=','+'];
var longNum = "";
var counter = 0;
var accumulator = 0;
for(var i = 0; i < seed.length; i++){
var a = seed.length - (i+1);
for(var x = 0; x < characterArray.length; x++){
var tempX = x.toString();
var lastDigit = tempX.charAt(tempX.length-1);
var xOutput = parseInt(lastDigit);
addToSeed(characterArray[x], xOutput, a, i);
}
}
function addToSeed(character, value, a, i){
if(seed.charAt(i) === character){newSeed = newSeed + value * Math.pow(10, a)}
}
newSeed = newSeed.toString();
var copy = newSeed;
for(var i=0; i<lengthOfNumber*9; i++){
newSeed = newSeed + copy;
var x = Math.sin(20982+(i)) * 10000;
var y = Math.floor((x - Math.floor(x))*10);
longNum = longNum + y.toString()
}
for(var i=0; i<lengthOfNumber; i++){
output = output + longNum.charAt(accumulator);
counter++;
accumulator = accumulator + parseInt(newSeed.charAt(counter));
}
return(output)
}
A simple approach for a fixed seed:
function fixedrandom(p){
const seed = 43758.5453123;
return (Math.abs(Math.sin(p)) * seed)%1;
}
In PHP, there is function srand(seed) which generate fixed random value for particular seed.
But, in JS, there is no such inbuilt function.
However, we can write simple and short function.
Step 1: Choose some Seed (Fix Number).
var seed = 100;
Number should be Positive Integer and greater than 1, further explanation in Step 2.
Step 2: Perform Math.sin() function on Seed, it will give sin value of that number. Store this value in variable x.
var x;
x = Math.sin(seed); // Will Return Fractional Value between -1 & 1 (ex. 0.4059..)
sin() method returns a Fractional value between -1 and 1.And we don't need Negative value, therefore, in first step choose number greater than 1.
Step 3: Returned Value is a Fractional value between -1 and 1. So mulitply this value with 10 for making it more than 1.
x = x * 10; // 10 for Single Digit Number
Step 4: Multiply the value with 10 for additional digits
x = x * 10; // Will Give value between 10 and 99 OR
x = x * 100; // Will Give value between 100 and 999
Multiply as per requirement of digits.
The result will be in decimal.
Step 5: Remove value after Decimal Point by Math's Round (Math.round()) Method.
x = Math.round(x); // This will give Integer Value.
Step 6: Turn Negative Values into Positive (if any) by Math.abs method
x = Math.abs(x); // Convert Negative Values into Positive(if any)
Explanation End.Final Code
var seed = 111; // Any Number greater than 1
var digit = 10 // 1 => single digit, 10 => 2 Digits, 100 => 3 Digits and so. (Multiple of 10)
var x; // Initialize the Value to store the result
x = Math.sin(seed); // Perform Mathematical Sin Method on Seed.
x = x * 10; // Convert that number into integer
x = x * digit; // Number of Digits to be included
x = Math.round(x); // Remove Decimals
x = Math.abs(x); // Convert Negative Number into Positive
Clean and Optimized Functional Code
function random_seed(seed, digit = 1) {
var x = Math.abs(Math.round(Math.sin(seed++) * 10 * digit));
return x;
}
Then Call this function using
random_seed(any_number, number_of_digits)any_number is must and should be greater than 1.number_of_digits is optional parameter and if nothing passed, 1 Digit will return.
random_seed(555); // 1 Digit
random_seed(234, 1); // 1 Digit
random_seed(7895656, 1000); // 4 Digit
For a number between 0 and 100.
Number.parseInt(Math.floor(Math.random() * 100))
I got this from GitHub.
function LittleEndianView(size) {
Object.defineProperty(this, 'native', {
value: new Uint8Array(size)
})
}
LittleEndianView.prototype.get = function(bits, offset) {
let available = (this.native.length * 8 - offset)
if (bits > available) {
throw new Error('Range error')
}
let value = 0
let i = 0
// why loop through like this?
while (i < bits) {
// remaining bits
const remaining = bits - i
const bitOffset = offset & 7
const currentByte = this.native[offset >> 3]
const read = Math.min(remaining, 8 - bitOffset)
const a = 0xFF << read
mask = ~a
const b = currentByte >> bitOffset
readBits = b & mask
const c = readBits << i
value = value | c
offset += read
i += read
}
return value >>> 0
}
LittleEndianView.prototype.set = function(bits, offset) {
const available = (this.native.length * 8 - offset)
if (bits > available) {
throw new Error('Range error')
}
let i = 0
while (i < bits) {
const remaining = bits - i
const bitOffset = offset & 7
const byteOffset = offset >> 3
const finished = Math.min(remaining, 8 - bitOffset)
const mask = ~(0xFF << finished)
const writeBits = value & mask
const value >>= finished
const destMask = ~(mask << bitOffset)
const byte = this.view[byteOffset]
this.native[byteOffset] = (byte & destMask) | (writeBits << bitOffset)
offset += finished
i += finished
}
}
After studying it for a few hours, I realize that the offset & 7 is only keeping the first 3 bits of the offset, which is for 1 byte. The offset >> 3 is simply dividing the offset by 8 to go from bits to bytes. The 0xFF << read is to get a bunch of 1s on the left like 11111000. Then negate it to get them on the right. I understand most of it just barely in pieces. But I don't see how they figured out how to implement this solution using these techniques (and I don't fully grasp the whole solution -- how or why it works -- after a couple days at this).
So that leads me to the question, I need to apply this same read/write functionality to a Uint32Array in JavaScript (rather than a Uint8Array like the above code would use). I need to read and write arbitrary bits (not bytes) to this Uint32Array. What needs to change in the existing get and set algorithms to accommodate this?
Seems like the offset & 7 might become something different, but I can't tell how to get it to be 32 bit representation. And the offset >> 3 might become divide by 32 or something like that. But I'm not sure. That may be mostly it.
This question can be answered using javascript, underscore or Jquery functions.
given 4 arrays:
[17,17,17,17,17,18,18,18,18,18,19,19,19,19,19,20,20,20,20,20] => x coordinate of unit
[11,12,13,14,15,11,12,13,14,15,11,12,13,14,15,11,12,13,14,15] => y coordinate of unit
[92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92] => x (unit moves to this direction x)
[35,36,37,38,39,35,36,37,38,39,35,36,37,38,39,35,36,37,38,39] => y (unit moves to this direction y)
They are very related to each other.
For example first element of all arrays: [17,11,92,35] is unit x/y coordinates and also x/y coordinates of this units target.
So here are totally 5*4 = 20 units. Every unit has slightly different stats.
These 4 arrays of units x/y coordinates visually looks like an army of 20 units "x" (targeting "o"):
xxxxx o
xxxxx o
xxxxx o
xxxxx o
There will always be 4 arrays. Even if 1 unit, there will be 4 arrays, but each size of 1. This is the simplest situation and most common.
In real situation, every unit has totally 20 different stats(keys) and 14 keys are mostly exact to other group of units - all 14 keys.
So they are grouped as an army with same stats. Difference is only coordinates of the unit and also coordinates of the units target.
I need to compress all this data into as small as possible data, which later can be decompressed.
There can also be more complex situations, when all these 14 keys are accidently same, but coordinates are totally different from pattern. Example:
[17,17,17,17,17,18,18,18, 215, 18,18,19,19,19,19,19,20,20,20,20,20] => x coordinate of unit
[11,12,13,14,15,11,12,13, 418, 14,15,11,12,13,14,15,11,12,13,14,15] => y coordinate of unit
[92,92,92,92,92,92,92,92, -78, 92,92,92,92,92,92,92,92,92,92,92,92] => x (unit moves to this direction x)
[35,36,37,38,39,35,36,37, -887, 38,39,35,36,37,38,39,35,36,37,38,39] => y (unit moves to this direction y)
In this situation i need to extract this array as for 2 different armies. When there are less than 3 units in army, i just simply write these units without the
pattern - as [215,418,-78,-887],[..] and if there are more than 2 units army, i need a compressed string with pattern, which can be decompressed later. In this example there are 21 units. It just has to be splitted into armies of 1 unit and (5x4 = 20) untis army.
In assumption that every n units has a pattern,
encode units with
n: sequence units count
ssx: start of source x
dsx: difference of source x
ssy: start of source y
dsy: difference of source y
stx: start of target x
dtx: difference of target x
sty: start of target y
dty: difference of target y
by the array: [n,ssx,dsx,ssy,dsy,stx,dtx,sty,dty]
so that the units:
[17,17,17,17,17],
[11,12,13,14,15],
[92,92,92,92,92],
[35,36,37,38,39]
are encoded:
[5,17,0,11,1,92,0,35,1]
of course if you know in advance that, for example the y targets are always the same for such a sequence you can give up the difference parameter, to have:
[n,ssx,dsx,ssy,---,stx,dtx,sty,---] => [n,ssx,dsx,ssy,stx,dtx,sty], and so on.
For interruption of a pattern like you mentioned in your last example, you can use other 'extra' arrays, and then insert them in the sequence, with:
exsx: extra unit starting x
exsy: extra unit starting y
extx: extra unit target x
exty: extra unit target y
m: insert extra unit at
so that the special case is encoded:
{
patterns:[
[5,17,0,11,1,92,0,35,1],
[5,18,0,11,1,92,0,35,1],
[5,19,0,11,1,92,0,35,1],
[5,17,0,11,1,92,0,35,1]
],
extras: [
[215,418,-78,-887,8] // 8 - because you want to insert this unit at index 8
]
}
Again, this is a general encoding. Any specific properties for the patterns may further reduce the encoded representation.
Hope this helps.
High compression using bitstreams
You can encode sets of values into a bit stream allowing you to remove unused bits. The numbers you have shown are not greater than -887 (ignoring the negative) and that means you can fit all the numbers into 10 bits saving 54 bits per number (Javascript uses 64 bit numbers).
Run length compression
You also have many repeated sets of numbers which you can use run length compression on. You set a flag in the bitstream that indicates that the following set of bits represents a repeated sequence of numbers, then you have the number of repeats and the value to repeat. For sequences of random numbers you just keep them as is.
If you use run-length compression you create a block type structure in the bit stream, this makes it possible to embed further compression. As you have many numbers that are below 128 many of the numbers can be encoded into 7 bits, or even less. For a small overhead (in this case 2 bits per block) you can select the smallest bit size to pack all the numbers in that block in.
Variable bit depth numbers
I have created a number type value that represent the number of bits used to store numbers in a block. Each block has a number type and all numbers in the block use that type. There are 4 number types that can be encoded into 2 bits.
00 = 4 bit numbers. Range 0-15
01 = 5 bit numbers. Range 0-31
10 = 7 bit numbers. Range 0-127
11 = 10 bit numbers. Range 0-1023
The bitstream
To make this easy you will need a bit stream read/write. It allows you to easily write and read bits from a stream of bits.
// Simple unsigned bit stream
// Read and write to and from a bit stream.
// Numbers are stored as Big endian
// Does not comprehend sign so wordlength should be less than 32 bits
// methods
// eof(); // returns true if read pos > buffer bit size
// read(numberBits); // number of bits to read as one number. No sign so < 32
// write(value,numberBits); // value to write, number of bits to write < 32
// getBuffer(); // return object with buffer and array of numbers, and bitLength the total number of bits
// setBuffer(buffer,bitLength); // the buffers as an array of numbers, and bitLength the total number of bits
// Properties
// wordLength; // read only length of a word.
function BitStream(){
var buffer = [];
var pos = 0;
var numBits = 0;
const wordLength = 16;
this.wordLength = wordLength;
// read a single bit
var readBit = function(){
var b = buffer[Math.floor(pos / wordLength)]; // get word
b = (b >> ((wordLength - 1) - (pos % wordLength))) & 1;
pos += 1;
return b;
}
// write a single bit. Will fill bits with 0 if wite pos is moved past buffer length
var writeBit = function(bit){
var rP = Math.floor(pos / wordLength);
if(rP >= buffer.length){ // check that the buffer has values at current pos.
var end = buffer.length; // fill buffer up to pos with zero
while(end <= rP){
buffer[end] = 0;
end += 1;
}
}
var b = buffer[rP];
bit &= 1; // mask out any unwanted bits
bit <<= (wordLength - 1) - (pos % wordLength);
b |= bit;
buffer[rP] = b;
pos += 1;
}
// returns true is past eof
this.eof = function(){
return pos >= numBits;
}
// reads number of bits as a Number
this.read = function(bits){
var v = 0;
while(bits > 0){
v <<= 1;
v |= readBit();
bits -= 1;
}
return v;
}
// writes value to bit stream
this.write = function(value,bits){
var v;
while(bits > 0){
bits -= 1;
writeBit( (value >> bits) & 1 );
}
}
// returns the buffer and length
this.getBuffer = function(){
return {
buffer : buffer,
bitLength : pos,
};
}
// set the buffer and length and returns read write pos to start
this.setBuffer = function(_buffer,bitLength){
buffer = _buffer;
numBits = bitLength;
pos = 0;
}
}
A format for your numbers
Now to design the format. The first bit read from a stream is a sequence flag, if 0 then the following block will be a repeated value, if 1 the following block will be a sequence of random numbers.
Block bits : description;
repeat block holds a repeated number
bit 0 : Val 0 = repeat
bit 1 : Val 0 = 4bit repeat count or 1 = 5bit repeat count
then either
bits 2,3,4,5 : 4 bit number of repeats - 1
bits 6,7 : 2 bit Number type
or
bits 2,3,4,5,6 : 5 bit number of repeats - 1
bits 7,8 : 2 bit Number type
Followed by
Then a value that will be repeated depending on the number type
End of block
sequence block holds a sequence of random numbers
bit 0 : Val 1 = sequence
bit 1 : Val 0 = positive sequence Val 1 = negative sequence
bits 2,3,4,5 : 4 bit number of numbers in sequence - 1
bits 6,7 : 2 bit Number type
then the sequence of numbers in the number format
End of block.
Keep reading blocks until the end of file.
Encoder and decoder
The following object will encode and decode the a flat array of numbers. It will only handles numbers upto 10 bits long, So no values over 1023 or under -1023.
If you want larger numbers you will have to change the number types that are used. To do this change the arrays
const numberSize = [0,0,0,0,0,1,2,2,3,3,3]; // the number bit depth
const numberBits = [4,5,7,10]; // the number bit depth lookup;
If you want max number to be 12 bits -4095 to 4095 ( the sign bit is in the block encoding). I have also shown the 7 bit number type changed to 8. The first array is used to look up the bit depth, if I have a 3 bit number you get the number type with numberSize[bitcount] and the bits used to store the number numberBits[numberSize[bitCount]]
const numberSize = [0,0,0,0,0,1,2,2,2,3,3,3,3]; // the number bit depth
const numberBits = [4,5,8,12]; // the number bit depth lookup;
function ArrayZip(){
var zipBuffer = 0;
const numberSize = [0,0,0,0,0,1,2,2,3,3,3]; // the number bit depth lookup;
const numberBits = [4,5,7,10]; // the number bit depth lookup;
this.encode = function(data){ // encodes the data
var pos = 0;
function getRepeat(){ // returns the number of repeat values
var p = pos + 1;
if(data[pos] < 0){
return 1; // ignore negative numbers
}
while(p < data.length && data[p] === data[pos]){
p += 1;
}
return p - pos;
}
function getNoRepeat(){ // returns the number of non repeat values
// if the sequence has negitive numbers then
// the length is returned as a negative
var p = pos + 1;
if(data[pos] < 0){ // negative numbers
while(p < data.length && data[p] !== data[p-1] && data[p] < 0){
p += 1;
}
return -(p - pos);
}
while(p < data.length && data[p] !== data[p-1] && data[p] >= 0){
p += 1;
}
return p - pos;
}
function getMax(count){
var max = 0;
var p = pos;
while(count > 0){
max = Math.max(Math.abs(data[p]),max);
p += 1;
count -= 1;
}
return max;
}
var out = new BitStream();
while(pos < data.length){
var reps = getRepeat();
if(reps > 1){
var bitCount = numberSize[Math.ceil(Math.log(getMax(reps) + 1) / Math.log(2))];
if(reps < 16){
out.write(0,1); // repeat header
out.write(0,1); // use 4 bit repeat count;
out.write(reps-1,4); // write 4 bit number of reps
out.write(bitCount,2); // write 2 bit number size
out.write(data[pos],numberBits[bitCount]);
pos += reps;
}else {
if(reps > 32){ // if more than can fit in one repeat block split it
reps = 32;
}
out.write(0,1); // repeat header
out.write(1,1); // use 5 bit repeat count;
out.write(reps-1,5); // write 5 bit number of reps
out.write(bitCount,2); // write 2 bit number size
out.write(data[pos],numberBits[bitCount]);
pos += reps;
}
}else{
var seq = getNoRepeat(); // get number no repeats
var neg = seq < 0 ? 1 : 0; // found negative numbers
seq = Math.min(16,Math.abs(seq));
// check if last value is the start of a repeating block
if(seq > 1){
var tempPos = pos;
pos += seq;
seq -= getRepeat() > 1 ? 1 : 0;
pos = tempPos;
}
// ge the max bit count to hold numbers
var bitCount = numberSize[Math.ceil(Math.log(getMax(seq) + 1) / Math.log(2))];
out.write(1,1); // sequence header
out.write(neg,1); // write negative flag
out.write(seq - 1,4); // write sequence length;
out.write(bitCount,2); // write 2 bit number size
while(seq > 0){
out.write(Math.abs(data[pos]),numberBits[bitCount]);
pos += 1;
seq -= 1;
}
}
}
// get the bit stream buffer
var buf = out.getBuffer();
// start bit stream with number of trailing bits. There are 4 bits used of 16 so plenty
// of room for aulturnative encoding flages.
var str = String.fromCharCode(buf.bitLength % out.wordLength);
// convert bit stream to charcters
for(var i = 0; i < buf.buffer.length; i ++){
str += String.fromCharCode(buf.buffer[i]);
}
// return encoded string
return str;
}
this.decode = function(zip){
var count,rSize,header,_in,i,data,endBits,numSize,val,neg;
data = []; // holds character codes
decompressed = []; // holds the decompressed array of numbers
endBits = zip.charCodeAt(0); // get the trailing bits count
for(i = 1; i < zip.length; i ++){ // convert string to numbers
data[i-1] = zip.charCodeAt(i);
}
_in = new BitStream(); // create a bitstream to read the bits
// set the buffer data and length
_in.setBuffer(data,(data.length - 1) * _in.wordLength + endBits);
while(!_in.eof()){ // do until eof
header = _in.read(1); // read header bit
if(header === 0){ // is repeat header
rSize = _in.read(1); // get repeat count size
if(rSize === 0){
count = _in.read(4); // get 4 bit repeat count
}else{
count = _in.read(5); // get 5 bit repeat count
}
numSize = _in.read(2); // get 2 bit number size type
val = _in.read(numberBits[numSize]); // get the repeated value
while(count >= 0){ // add to the data count + 1 times
decompressed.push(val);
count -= 1;
}
}else{
neg = _in.read(1); // read neg flag
count = _in.read(4); // get 4 bit seq count
numSize = _in.read(2); // get 2 bit number size type
while(count >= 0){
if(neg){ // if negative numbers convert to neg
decompressed.push(-_in.read(numberBits[numSize]));
}else{
decompressed.push(_in.read(numberBits[numSize]));
}
count -= 1;
}
}
}
return decompressed;
}
}
The best way to store a bit stream is as a string. Javascript has Unicode strings so we can pack 16 bits into every character
The results and how to use.
You need to flatten the array. If you need to add extra info to reinstate the multi/dimensional arrays just add that to the array and let the compressor compress it along with the rest.
// flatten the array
var data = [17,17,17,17,17,18,18,18,18,18,19,19,19,19,19,20,20,20,20,20,11,12,13,14,15,11,12,13,14,15,11,12,13,14,15,11,12,13,14,15,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,92,35,36,37,38,39,35,36,37,38,39,35,36,37,38,39,35,36,37,38,39];
var zipper = new ArrayZip();
var encoded = zipper.encode(data); // packs the 80 numbers in data into 21 characters.
// compression rate of the data array 5120 bits to 336 bits
// 93% compression.
// or as a flat 7bit ascii string as numbers 239 charcters (including ,)
// 239 * 7 bits = 1673 bits to 336 bits 80% compression.
var decoded = zipper.decode(encoded);
I did not notice the negative numbers at first so the compression does not do well with the negative values.
var data = [17,17,17,17,17,18,18,18, 215, 18,18,19,19,19,19,19,20,20,20,20,20, 11,12,13,14,15,11,12,13, 418, 14,15,11,12,13,14,15,11,12,13,14,15, 92,92,92,92,92,92,92,92, -78, 92,92,92,92,92,92,92,92,92,92,92,92, 35,36,37,38,39,35,36,37, -887, 38,39,35,36,37,38,39,35,36,37,38,39]
var encoded = zipper.encode(data); // packs the 84 numbers in data into 33 characters.
// compression rate of the data array 5376 bits to 528 bits
var decoded = zipper.decode(encoded);
Summary
As you can see this results in a very high compression rate (almost twice as good as LZ compression). The code is far from optimal and you could easily implement a multi pass compressor with various settings ( there are 12 spare bits at the start of the encoded string that can be used to select many options to improve compression.)
Also I did not see the negative numbers until I came back to post so the fix for negatives is not good, so you can some more out of it by modifying the bitStream to understand negatives (ie use the >>> operator)
Is it possible to seed the random number generator (Math.random) in JavaScript?
No, it is not possible to seed Math.random(). The ECMAScript specification is intentionally vague on the subject, providing no means for seeding nor require that browsers even use the same algorithm. So such a function must be externally provided, which thankfully isn't too difficult.
I've implemented a number of good, short and fast Pseudorandom number generator (PRNG) functions in plain JavaScript. All of them can be seeded and provide high quality numbers. These are not intended for security purposes--if you need a seedable CSPRNG, look into ISAAC.
First of all, take care to initialize your PRNGs properly. To keep things simple, the generators below have no built-in seed generating procedure, but accept one or more 32-bit numbers as the initial seed state of the PRNG. Similar or sparse seeds (e.g. a simple seed of 1 and 2) have low entropy, and can cause correlations or other randomness quality issues, sometimes resulting in the output having similar properties (such as randomly generated levels being similar). To avoid this, it is best practice to initialize PRNGs with a well-distributed, high entropy seed and/or advancing past the first 15 or so numbers.
There are many ways to do this, but here are two methods. Firstly, hash functions are very good at generating seeds from short strings. A good hash function will generate very different results even when two strings are similar, so you don't have to put much thought into the string. Here's an example hash function:
function cyrb128(str) {
let h1 = 1779033703, h2 = 3144134277,
h3 = 1013904242, h4 = 2773480762;
for (let i = 0, k; i < str.length; i++) {
k = str.charCodeAt(i);
h1 = h2 ^ Math.imul(h1 ^ k, 597399067);
h2 = h3 ^ Math.imul(h2 ^ k, 2869860233);
h3 = h4 ^ Math.imul(h3 ^ k, 951274213);
h4 = h1 ^ Math.imul(h4 ^ k, 2716044179);
}
h1 = Math.imul(h3 ^ (h1 >>> 18), 597399067);
h2 = Math.imul(h4 ^ (h2 >>> 22), 2869860233);
h3 = Math.imul(h1 ^ (h3 >>> 17), 951274213);
h4 = Math.imul(h2 ^ (h4 >>> 19), 2716044179);
return [(h1^h2^h3^h4)>>>0, (h2^h1)>>>0, (h3^h1)>>>0, (h4^h1)>>>0];
}
Calling cyrb128 will produce a 128-bit hash value from a string which can be used to seed a PRNG. Here's how you might use it:
// Create cyrb128 state:
var seed = cyrb128("apples");
// Four 32-bit component hashes provide the seed for sfc32.
var rand = sfc32(seed[0], seed[1], seed[2], seed[3]);
// Only one 32-bit component hash is needed for mulberry32.
var rand = mulberry32(seed[0]);
// Obtain sequential random numbers like so:
rand();
rand();
Note: If you want a slightly more robust 128-bit hash, consider MurmurHash3_x86_128, it's more thorough, but intended for use with large arrays.
Alternatively, simply choose some dummy data to pad the seed with, and advance the generator beforehand a few times (12-20 iterations) to mix the initial state thoroughly. This has the benefit of being simpler, and is often used in reference implementations of PRNGs, but it does limit the number of initial states:
var seed = 1337 ^ 0xDEADBEEF; // 32-bit seed with optional XOR value
// Pad seed with Phi, Pi and E.
// https://en.wikipedia.org/wiki/Nothing-up-my-sleeve_number
var rand = sfc32(0x9E3779B9, 0x243F6A88, 0xB7E15162, seed);
for (var i = 0; i < 15; i++) rand();
Note: the output of these PRNG functions produce a positive 32-bit number (0 to 232-1) which is then converted to a floating-point number between 0-1 (0 inclusive, 1 exclusive) equivalent to Math.random(), if you want random numbers of a specific range, read this article on MDN. If you only want the raw bits, simply remove the final division operation.
JavaScript numbers can only represent whole integers up to 53-bit resolution. And when using bitwise operations, this is reduced to 32. Modern PRNGs in other languages often use 64-bit operations, which require shims when porting to JS that can drastically reduce performance. The algorithms here only use 32-bit operations, as it is directly compatible with JS.
Now, onward to the the generators. (I maintain the full list with references and license info here)
sfc32 (Simple Fast Counter)
sfc32 is part of the PractRand random number testing suite (which it passes of course). sfc32 has a 128-bit state and is very fast in JS.
function sfc32(a, b, c, d) {
return function() {
a >>>= 0; b >>>= 0; c >>>= 0; d >>>= 0;
var t = (a + b) | 0;
a = b ^ b >>> 9;
b = c + (c << 3) | 0;
c = (c << 21 | c >>> 11);
d = d + 1 | 0;
t = t + d | 0;
c = c + t | 0;
return (t >>> 0) / 4294967296;
}
}
You may wonder what the | 0 and >>>= 0 are for. These are essentially 32-bit integer casts, used for performance optimizations. Number in JS are basically floats, but during bitwise operations, they switch into a 32-bit integer mode. This mode is processed faster by JS interpreters, but any multiplication or addition will cause it to switch back to a float, resulting in a performance hit.
Mulberry32
Mulberry32 is a simple generator with a 32-bit state, but is extremely fast and has good quality randomness (author states it passes all tests of gjrand testing suite and has a full 232 period, but I haven't verified).
function mulberry32(a) {
return function() {
var t = a += 0x6D2B79F5;
t = Math.imul(t ^ t >>> 15, t | 1);
t ^= t + Math.imul(t ^ t >>> 7, t | 61);
return ((t ^ t >>> 14) >>> 0) / 4294967296;
}
}
I would recommend this if you just need a simple but decent PRNG and don't need billions of random numbers (see Birthday problem).
xoshiro128**
As of May 2018, xoshiro128** is the new member of the Xorshift family, by Vigna & Blackman (professor Vigna was also responsible for the Xorshift128+ algorithm powering most Math.random implementations under the hood). It is the fastest generator that offers a 128-bit state.
function xoshiro128ss(a, b, c, d) {
return function() {
var t = b << 9, r = a * 5; r = (r << 7 | r >>> 25) * 9;
c ^= a; d ^= b;
b ^= c; a ^= d; c ^= t;
d = d << 11 | d >>> 21;
return (r >>> 0) / 4294967296;
}
}
The authors claim it passes randomness tests well (albeit with caveats). Other researchers have pointed out that it fails some tests in TestU01 (particularly LinearComp and BinaryRank). In practice, it should not cause issues when floats are used (such as in these implementations), but may cause issues if relying on the raw lowest order bit.
JSF (Jenkins' Small Fast)
This is JSF or 'smallprng' by Bob Jenkins (2007), who also made ISAAC and SpookyHash. It passes PractRand tests and should be quite fast, although not as fast as sfc32.
function jsf32(a, b, c, d) {
return function() {
a |= 0; b |= 0; c |= 0; d |= 0;
var t = a - (b << 27 | b >>> 5) | 0;
a = b ^ (c << 17 | c >>> 15);
b = c + d | 0;
c = d + t | 0;
d = a + t | 0;
return (d >>> 0) / 4294967296;
}
}
No, it is not possible to seed Math.random(), but it's fairly easy to write your own generator, or better yet, use an existing one.
Check out: this related question.
Also, see David Bau's blog for more information on seeding.
NOTE: Despite (or rather, because of) succinctness and apparent elegance, this algorithm is by no means a high-quality one in terms of randomness. Look for e.g. those listed in this answer for better results.
(Originally adapted from a clever idea presented in a comment to another answer.)
var seed = 1;
function random() {
var x = Math.sin(seed++) * 10000;
return x - Math.floor(x);
}
You can set seed to be any number, just avoid zero (or any multiple of Math.PI).
The elegance of this solution, in my opinion, comes from the lack of any "magic" numbers (besides 10000, which represents about the minimum amount of digits you must throw away to avoid odd patterns - see results with values 10, 100, 1000). Brevity is also nice.
It's a bit slower than Math.random() (by a factor of 2 or 3), but I believe it's about as fast as any other solution written in JavaScript.
No, but here's a simple pseudorandom generator, an implementation of Multiply-with-carry I adapted from Wikipedia (has been removed since):
var m_w = 123456789;
var m_z = 987654321;
var mask = 0xffffffff;
// Takes any integer
function seed(i) {
m_w = (123456789 + i) & mask;
m_z = (987654321 - i) & mask;
}
// Returns number between 0 (inclusive) and 1.0 (exclusive),
// just like Math.random().
function random()
{
m_z = (36969 * (m_z & 65535) + (m_z >> 16)) & mask;
m_w = (18000 * (m_w & 65535) + (m_w >> 16)) & mask;
var result = ((m_z << 16) + (m_w & 65535)) >>> 0;
result /= 4294967296;
return result;
}
Antti Sykäri's algorithm is nice and short. I initially made a variation that replaced JavaScript's Math.random when you call Math.seed(s), but then Jason commented that returning the function would be better:
Math.seed = function(s) {
return function() {
s = Math.sin(s) * 10000; return s - Math.floor(s);
};
};
// usage:
var random1 = Math.seed(42);
var random2 = Math.seed(random1());
Math.random = Math.seed(random2());
This gives you another functionality that JavaScript doesn't have: multiple independent random generators. That is especially important if you want to have multiple repeatable simulations running at the same time.
Please see Pierre L'Ecuyer's work going back to the late 1980s and early 1990s. There are others as well. Creating a (pseudo) random number generator on your own, if you are not an expert, is pretty dangerous, because there is a high likelihood of either the results not being statistically random or in having a small period. Pierre (and others) have put together some good (pseudo) random number generators that are easy to implement. I use one of his LFSR generators.
https://www.iro.umontreal.ca/~lecuyer/myftp/papers/handstat.pdf
Combining some of the previous answers, this is the seedable random function you are looking for:
Math.seed = function(s) {
var mask = 0xffffffff;
var m_w = (123456789 + s) & mask;
var m_z = (987654321 - s) & mask;
return function() {
m_z = (36969 * (m_z & 65535) + (m_z >>> 16)) & mask;
m_w = (18000 * (m_w & 65535) + (m_w >>> 16)) & mask;
var result = ((m_z << 16) + (m_w & 65535)) >>> 0;
result /= 4294967296;
return result;
}
}
var myRandomFunction = Math.seed(1234);
var randomNumber = myRandomFunction();
It's not possible to seed the builtin Math.random function, but it is possible to implement a high quality RNG in Javascript with very little code.
Javascript numbers are 64-bit floating point precision, which can represent all positive integers less than 2^53. This puts a hard limit to our arithmetic, but within these limits you can still pick parameters for a high quality Lehmer / LCG random number generator.
function RNG(seed) {
var m = 2**35 - 31
var a = 185852
var s = seed % m
return function () {
return (s = s * a % m) / m
}
}
Math.random = RNG(Date.now())
If you want even higher quality random numbers, at the cost of being ~10 times slower, you can use BigInt for the arithmetic and pick parameters where m is just able to fit in a double.
function RNG(seed) {
var m_as_number = 2**53 - 111
var m = 2n**53n - 111n
var a = 5667072534355537n
var s = BigInt(seed) % m
return function () {
return Number(s = s * a % m) / m_as_number
}
}
See this paper by Pierre l'Ecuyer for the parameters used in the above implementations:
https://www.ams.org/journals/mcom/1999-68-225/S0025-5718-99-00996-5/S0025-5718-99-00996-5.pdf
And whatever you do, avoid all the other answers here that use Math.sin!
To write your own pseudo random generator is quite simple.
The suggestion of Dave Scotese is useful but, as pointed out by others, it is not quite uniformly distributed.
However, it is not because of the integer arguments of sin. It's simply because of the range of sin, which happens to be a one dimensional projection of a circle. If you would take the angle of the circle instead it would be uniform.
So instead of sin(x) use arg(exp(i * x)) / (2 * PI).
If you don't like the linear order, mix it a bit up with xor. The actual factor doesn't matter that much either.
To generate n pseudo random numbers one could use the code:
function psora(k, n) {
var r = Math.PI * (k ^ n)
return r - Math.floor(r)
}
n = 42; for(k = 0; k < n; k++) console.log(psora(k, n))
Please also note that you cannot use pseudo random sequences when real entropy is needed.
Many people who need a seedable random-number generator in Javascript these days are using David Bau's seedrandom module.
Math.random no, but the ran library solves this. It has almost all distributions you can imagine and supports seeded random number generation. Example:
ran.core.seed(0)
myDist = new ran.Dist.Uniform(0, 1)
samples = myDist.sample(1000)
Here's the adopted version of Jenkins hash, borrowed from here
export function createDeterministicRandom(): () => number {
let seed = 0x2F6E2B1;
return function() {
// Robert Jenkins’ 32 bit integer hash function
seed = ((seed + 0x7ED55D16) + (seed << 12)) & 0xFFFFFFFF;
seed = ((seed ^ 0xC761C23C) ^ (seed >>> 19)) & 0xFFFFFFFF;
seed = ((seed + 0x165667B1) + (seed << 5)) & 0xFFFFFFFF;
seed = ((seed + 0xD3A2646C) ^ (seed << 9)) & 0xFFFFFFFF;
seed = ((seed + 0xFD7046C5) + (seed << 3)) & 0xFFFFFFFF;
seed = ((seed ^ 0xB55A4F09) ^ (seed >>> 16)) & 0xFFFFFFFF;
return (seed & 0xFFFFFFF) / 0x10000000;
};
}
You can use it like this:
const deterministicRandom = createDeterministicRandom()
deterministicRandom()
// => 0.9872818551957607
deterministicRandom()
// => 0.34880331158638
No, like they said it is not possible to seed Math.random()
but you can install external package which make provision for that. i used these package which can be install using these command
npm i random-seed
the example is gotten from the package documentation.
var seed = 'Hello World',
rand1 = require('random-seed').create(seed),
rand2 = require('random-seed').create(seed);
console.log(rand1(100), rand2(100));
follow the link for documentation https://www.npmjs.com/package/random-seed
SIN(id + seed) is a very interesting replacement for RANDOM functions that cannot be seeded like SQLite:
https://stackoverflow.com/a/75089040/7776828
Most of the answers here produce biased results. So here's a tested function based on seedrandom library from github:
!function(f,a,c){var s,l=256,p="random",d=c.pow(l,6),g=c.pow(2,52),y=2*g,h=l-1;function n(n,t,r){function e(){for(var n=u.g(6),t=d,r=0;n<g;)n=(n+r)*l,t*=l,r=u.g(1);for(;y<=n;)n/=2,t/=2,r>>>=1;return(n+r)/t}var o=[],i=j(function n(t,r){var e,o=[],i=typeof t;if(r&&"object"==i)for(e in t)try{o.push(n(t[e],r-1))}catch(n){}return o.length?o:"string"==i?t:t+"\0"}((t=1==t?{entropy:!0}:t||{}).entropy?[n,S(a)]:null==n?function(){try{var n;return s&&(n=s.randomBytes)?n=n(l):(n=new Uint8Array(l),(f.crypto||f.msCrypto).getRandomValues(n)),S(n)}catch(n){var t=f.navigator,r=t&&t.plugins;return[+new Date,f,r,f.screen,S(a)]}}():n,3),o),u=new m(o);return e.int32=function(){return 0|u.g(4)},e.quick=function(){return u.g(4)/4294967296},e.double=e,j(S(u.S),a),(t.pass||r||function(n,t,r,e){return e&&(e.S&&v(e,u),n.state=function(){return v(u,{})}),r?(c[p]=n,t):n})(e,i,"global"in t?t.global:this==c,t.state)}function m(n){var t,r=n.length,u=this,e=0,o=u.i=u.j=0,i=u.S=[];for(r||(n=[r++]);e<l;)i[e]=e++;for(e=0;e<l;e++)i[e]=i[o=h&o+n[e%r]+(t=i[e])],i[o]=t;(u.g=function(n){for(var t,r=0,e=u.i,o=u.j,i=u.S;n--;)t=i[e=h&e+1],r=r*l+i[h&(i[e]=i[o=h&o+t])+(i[o]=t)];return u.i=e,u.j=o,r})(l)}function v(n,t){return t.i=n.i,t.j=n.j,t.S=n.S.slice(),t}function j(n,t){for(var r,e=n+"",o=0;o<e.length;)t[h&o]=h&(r^=19*t[h&o])+e.charCodeAt(o++);return S(t)}function S(n){return String.fromCharCode.apply(0,n)}if(j(c.random(),a),"object"==typeof module&&module.exports){module.exports=n;try{s=require("crypto")}catch(n){}}else"function"==typeof define&&define.amd?define(function(){return n}):c["seed"+p]=n}("undefined"!=typeof self?self:this,[],Math);
function randIntWithSeed(seed, max=1) {
/* returns a random number between [0,max] including zero and max
seed can be either string or integer */
return Math.round(new Math.seedrandom('seed' + seed)()) * max
}
test for true randomness of this code: https://es6console.com/kkjkgur2/
There are plenty of good answers here but I had a similar issue with the additional requirement that I would like portability between Java's random number generator and whatever I ended up using in JavaScript.
I found the java-random package
These two pieces of code had identical output assuming the seed is the same:
Java:
Random randomGenerator = new Random(seed);
int randomInt;
for (int i=0; i<10; i++) {
randomInt = randomGenerator.nextInt(50);
System.out.println(randomInt);
}
JavaScript:
let Random = require('java-random');
let rng = new Random(seed);
for (let i=0; i<10; i++) {
let val = rng.nextInt(50);
console.log(val);
}
Do what bryc suggests ... but before you use his cyrb128 hash function to initialise, note that the return statement throws away 32 bits of entropy. Exclusive-or the four values together = 0. You should probably make the first element (h2^h3^h4) >>> 0.
I have written a function that returns a seeded random number, it uses Math.sin to have a long random number and uses the seed to pick numbers from that.
Use :
seedRandom("k9]:2#", 15)
it will return your seeded number
the first parameter is any string value ; your seed.
the second parameter is how many digits will return.
function seedRandom(inputSeed, lengthOfNumber){
var output = "";
var seed = inputSeed.toString();
var newSeed = 0;
var characterArray = ['0','1','2','3','4','5','6','7','8','9','a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','y','x','z','A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','U','R','S','T','U','V','W','X','Y','Z','!','#','#','$','%','^','&','*','(',')',' ','[','{',']','}','|',';',':',"'",',','<','.','>','/','?','`','~','-','_','=','+'];
var longNum = "";
var counter = 0;
var accumulator = 0;
for(var i = 0; i < seed.length; i++){
var a = seed.length - (i+1);
for(var x = 0; x < characterArray.length; x++){
var tempX = x.toString();
var lastDigit = tempX.charAt(tempX.length-1);
var xOutput = parseInt(lastDigit);
addToSeed(characterArray[x], xOutput, a, i);
}
}
function addToSeed(character, value, a, i){
if(seed.charAt(i) === character){newSeed = newSeed + value * Math.pow(10, a)}
}
newSeed = newSeed.toString();
var copy = newSeed;
for(var i=0; i<lengthOfNumber*9; i++){
newSeed = newSeed + copy;
var x = Math.sin(20982+(i)) * 10000;
var y = Math.floor((x - Math.floor(x))*10);
longNum = longNum + y.toString()
}
for(var i=0; i<lengthOfNumber; i++){
output = output + longNum.charAt(accumulator);
counter++;
accumulator = accumulator + parseInt(newSeed.charAt(counter));
}
return(output)
}
A simple approach for a fixed seed:
function fixedrandom(p){
const seed = 43758.5453123;
return (Math.abs(Math.sin(p)) * seed)%1;
}
In PHP, there is function srand(seed) which generate fixed random value for particular seed.
But, in JS, there is no such inbuilt function.
However, we can write simple and short function.
Step 1: Choose some Seed (Fix Number).
var seed = 100;
Number should be Positive Integer and greater than 1, further explanation in Step 2.
Step 2: Perform Math.sin() function on Seed, it will give sin value of that number. Store this value in variable x.
var x;
x = Math.sin(seed); // Will Return Fractional Value between -1 & 1 (ex. 0.4059..)
sin() method returns a Fractional value between -1 and 1.And we don't need Negative value, therefore, in first step choose number greater than 1.
Step 3: Returned Value is a Fractional value between -1 and 1. So mulitply this value with 10 for making it more than 1.
x = x * 10; // 10 for Single Digit Number
Step 4: Multiply the value with 10 for additional digits
x = x * 10; // Will Give value between 10 and 99 OR
x = x * 100; // Will Give value between 100 and 999
Multiply as per requirement of digits.
The result will be in decimal.
Step 5: Remove value after Decimal Point by Math's Round (Math.round()) Method.
x = Math.round(x); // This will give Integer Value.
Step 6: Turn Negative Values into Positive (if any) by Math.abs method
x = Math.abs(x); // Convert Negative Values into Positive(if any)
Explanation End.Final Code
var seed = 111; // Any Number greater than 1
var digit = 10 // 1 => single digit, 10 => 2 Digits, 100 => 3 Digits and so. (Multiple of 10)
var x; // Initialize the Value to store the result
x = Math.sin(seed); // Perform Mathematical Sin Method on Seed.
x = x * 10; // Convert that number into integer
x = x * digit; // Number of Digits to be included
x = Math.round(x); // Remove Decimals
x = Math.abs(x); // Convert Negative Number into Positive
Clean and Optimized Functional Code
function random_seed(seed, digit = 1) {
var x = Math.abs(Math.round(Math.sin(seed++) * 10 * digit));
return x;
}
Then Call this function using
random_seed(any_number, number_of_digits)any_number is must and should be greater than 1.number_of_digits is optional parameter and if nothing passed, 1 Digit will return.
random_seed(555); // 1 Digit
random_seed(234, 1); // 1 Digit
random_seed(7895656, 1000); // 4 Digit
For a number between 0 and 100.
Number.parseInt(Math.floor(Math.random() * 100))