@stdlib/random-iter-minstd-shuffle

v0.2.1

Published

10 months ago

Create an iterator for a linear congruential pseudorandom number generator (LCG) whose output is shuffled.

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MINSTD Shuffle

Create an iterator for a linear congruential pseudorandom number generator (LCG) whose output is shuffled.

Installation

npm install @stdlib/random-iter-minstd-shuffle

Usage

var iterator = require( '@stdlib/random-iter-minstd-shuffle' );

iterator( [options] )

Returns an iterator for generating pseudorandom numbers via a linear congruential pseudorandom number generator (LCG) whose output is shuffled.

var it = iterator();
// returns <Object>

var r = it.next().value;
// returns <number>

r = it.next().value;
// returns <number>

r = it.next().value;
// returns <number>

// ...

The function accepts the following options:

normalized: boolean indicating whether to return pseudorandom numbers on the interval [0,1).
seed: pseudorandom number generator seed.
state: an Int32Array containing pseudorandom number generator state. If provided, the function ignores the seed option.
copy: boolean 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.

var it = iterator({
    'normalized': true
});

var r = it.next().value;
// returns <number>

To return an iterator having a specific initial state, set the iterator state option.

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.

var it = iterator({
    'seed': 12345
});

var r = it.next().value;
// returns 1982386332

it = iterator({
    'seed': 12345
});

r = it.next().value;
// returns 1982386332

To limit the number of iterations, set the iter option.

var it = iterator({
    'iter': 2
});

var r = it.next().value;
// returns <number>

r = it.next().value;
// returns <number>

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.

Notes

If an environment supports Symbol.iterator, the returned iterator is iterable.
The generator has a period of approximately 2.1e9 (see Numerical Recipes in C, 2nd Edition, p. 279).
An LCG is fast and uses little memory. On the other hand, because the generator is a simple linear congruential generator, the generator has recognized shortcomings. By today's PRNG standards, the generator's period is relatively short. In general, this generator is unsuitable for Monte Carlo simulations and 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).

Examples

var iterator = require( '@stdlib/random-iter-minstd-shuffle' );

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 );
}

References

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.
Bays, Carter, and S. D. Durham. 1976. "Improving a Poor Random Number Generator." ACM Transactions on Mathematical Software 2 (1). New York, NY, USA: ACM: 59–64. doi:10.1145/355666.355670.
Herzog, T.N., and G. Lord. 2002. Applications of Monte Carlo Methods to Finance and Insurance. ACTEX Publications. https://books.google.com/books?id=vC7I\_gdX-A0C.
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.

Notice

This package is part of 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, see the main project repository.

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See LICENSE.

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