Create an iterator for a linear congruential pseudorandom number generator (LCG) based on Park and Miller.
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> Create an iterator for a linear congruential pseudorandom number generator ([LCG][lcg]) based on Park and Miller.
``bash`
npm install @stdlib/random-iter-minstd
`javascript`
var iterator = require( '@stdlib/random-iter-minstd' );
#### iterator( \[options] )
Returns an iterator for generating pseudorandom numbers via a linear congruential pseudorandom number generator ([LCG][lcg]) based on Park and Miller.
`javascript
var it = iterator();
// returns
var r = it.next().value;
// returns
r = it.next().value;
// returns
r = it.next().value;
// returns
// ...
`
The function accepts the following options:
- normalized: boolean indicating whether to return pseudorandom numbers on the interval [0,1).Int32Array
- seed: pseudorandom number generator seed.
- state: an [][@stdlib/array/int32] containing pseudorandom number generator state. If provided, the function ignores the seed option.boolean
- copy: indicating whether to copy a provided pseudorandom number generator state. Setting this option to false allows sharing state between two or more pseudorandom number generators. Setting this option to true ensures that a returned iterator has exclusive control over its internal pseudorandom number generator state. Default: true.
- iter: number of iterations.
To return pseudorandom numbers on the interval 0,1), set the normalized option.
`javascript
var it = iterator({
'normalized': true
});
var r = it.next().value;
// returns
`
To return an iterator having a specific initial state, set the iterator state option.
`javascript
var bool;
var it1;
var it2;
var r;
var i;
it1 = iterator();
// Generate pseudorandom numbers, thus progressing the generator state:
for ( i = 0; i < 1000; i++ ) {
r = it1.next().value;
}
// Create a new iterator initialized to the current state of it1:
it2 = iterator({
'state': it1.state
});
// Test that the generated pseudorandom numbers are the same:
bool = ( it1.next().value === it2.next().value );
// returns true
`
To seed the iterator, set the seed option.
`javascript
var it = iterator({
'seed': 12345
});
var r = it.next().value;
// returns 207482415
it = iterator({
'seed': 12345
});
r = it.next().value;
// returns 207482415
`
To limit the number of iterations, set the iter option.
`javascript
var it = iterator({
'iter': 2
});
var r = it.next().value;
// returns
r = it.next().value;
// returns
r = it.next().done;
// returns true
`
The returned iterator protocol-compliant object has the following properties:
- next: function which returns an iterator protocol-compliant object containing the next iterated value (if one exists) assigned to a value property and a done property having a boolean value indicating whether the iterator is finished.
- return: function which closes an iterator and returns a single (optional) argument in an iterator protocol-compliant object.
- seed: pseudorandom number generator seed.
- seedLength: length of generator seed.
- state: writable property for getting and setting the generator state.
- stateLength: length of generator state.
- byteLength: size (in bytes) of generator state.
- If an environment supports Symbol.iterator, the returned iterator is iterable.2.1e9
- The generator has a period of approximately (see [Numerical Recipes in C, 2nd Edition, p. 279).
- An [LCG][lcg] is fast and uses little memory. On the other hand, because the generator is a simple [linear congruential generator][lcg], the generator has recognized shortcomings. By today's PRNG standards, the generator's period is relatively short. More importantly, the "randomness quality" of the generator's output is lacking. These defects make the generator unsuitable, for example, in Monte Carlo simulations and in cryptographic applications.
- If PRNG state is "shared" (meaning a state array was provided during iterator creation and not copied) and one sets the underlying generator state to a state array having a different length, the iterator does not update the existing shared state and, instead, points to the newly provided state array. In order to synchronize the output of the underlying generator according to the new shared state array, the state array for each relevant iterator and/or PRNG must be explicitly set.
- If PRNG state is "shared" and one sets the underlying generator state to a state array of the same length, the PRNG state is updated (along with the state of all other iterator and/or PRNGs sharing the PRNG's state array).
`javascript
var iterator = require( '@stdlib/random-iter-minstd' );
var it;
var r;
// Create a seeded iterator for generating pseudorandom numbers:
it = iterator({
'seed': 1234,
'iter': 10
});
// Perform manual iteration...
while ( true ) {
r = it.next();
if ( r.done ) {
break;
}
console.log( r.value );
}
`
*
- Park, S. K., and K. W. Miller. 1988. "Random Number Generators: Good Ones Are Hard to Find." _Communications of the ACM_ 31 (10). New York, NY, USA: ACM: 1192–1201. doi:[10.1145/63039.63042][@park:1988].
- Press, William H., Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling. 1992. _Numerical Recipes in C: The Art of Scientific Computing, Second Edition_. Cambridge University Press.
*
- [@stdlib/random-base/minstd][@stdlib/random/base/minstd]: A linear congruential pseudorandom number generator (LCG) based on Park and Miller.
- [@stdlib/random-iter/minstd-shuffle][@stdlib/random/iter/minstd-shuffle]: create an iterator for a linear congruential pseudorandom number generator (LCG) whose output is shuffled.
- [@stdlib/random-iter/mt19937][@stdlib/random/iter/mt19937]: create an iterator for a 32-bit Mersenne Twister pseudorandom number generator.
- [@stdlib/random-iter/randi][@stdlib/random/iter/randi]: create an iterator for generating pseudorandom numbers having integer values.
- [@stdlib/random-iter/randu][@stdlib/random/iter/randu]: create an iterator for generating uniformly distributed pseudorandom numbers between 0 and 1.
*
This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].
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[@park:1988]: http://dx.doi.org/10.1145/63039.63042
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[@stdlib/random/iter/randi]: https://www.npmjs.com/package/@stdlib/random-iter-randi
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