@stdlib/random-base-minstd-shuffle

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MINSTD Shuffle

A linear congruential pseudorandom number generator (LCG) whose output is shuffled.

Installation

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

Usage

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

minstd()

Returns a pseudorandom integer on the interval [1, 2147483646].

var v = minstd();
// returns <number>

minstd.normalized()

Returns a pseudorandom number on the interval [0,1).

var v = minstd.normalized();
// returns <number>

minstd.factory( [options] )

Returns a linear congruential pseudorandom number generator (LCG) whose output is shuffled.

var rand = minstd.factory();

The function accepts the following options:

• 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 generator has exclusive control over its internal state. Default: true.

By default, a random integer is used to seed the returned generator. To seed the generator, provide either an integer on the interval [1, 2147483646]

var rand = minstd.factory({
    'seed': 1234
});

var v = rand();
// returns 1421600654

or, for arbitrary length seeds, an array-like object containing signed 32-bit integers

var Int32Array = require( '@stdlib/array-int32' );

var rand = minstd.factory({
    'seed': new Int32Array( [ 1234 ] )
});

var r = rand();
// returns 20739838

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

var rand;
var bool;
var r;
var i;

// Generate pseudorandom numbers, thus progressing the generator state:
for ( i = 0; i < 1000; i++ ) {
    r = minstd();
}

// Create a new PRNG initialized to the current state of `minstd`:
rand = minstd.factory({
    'state': minstd.state
});

// Test that the generated pseudorandom numbers are the same:
bool = ( rand() === minstd() );
// returns true

minstd.NAME

The generator name.

var str = minstd.NAME;
// returns 'minstd-shuffle'

minstd.MIN

Minimum possible value.

var min = minstd.MIN;
// returns 1

minstd.MAX

Maximum possible value.

var max = minstd.MAX;
// returns 2147483646

minstd.seed

The value used to seed minstd().

var rand;
var v;
var i;

// Generate pseudorandom values...
for ( i = 0; i < 100; i++ ) {
    v = minstd();
}

// Generate the same pseudorandom values...
rand = minstd.factory({
    'seed': minstd.seed
});
for ( i = 0; i < 100; i++ ) {
    v = rand();
}

minstd.seedLength

Length of generator seed.

var len = minstd.seedLength;
// returns <number>

minstd.state

Writable property for getting and setting the generator state.

var r = minstd();
// returns <number>

r = minstd();
// returns <number>

// ...

// Get a copy of the current state:
var state = minstd.state;
// returns <Int32Array>

r = minstd();
// returns <number>

r = minstd();
// returns <number>

// Reset the state:
minstd.state = state;

// Replay the last two pseudorandom numbers:
r = minstd();
// returns <number>

r = minstd();
// returns <number>

// ...

minstd.stateLength

Length of generator state.

var len = minstd.stateLength;
// returns <number>

minstd.byteLength

Size (in bytes) of generator state.

var sz = minstd.byteLength;
// returns <number>

minstd.toJSON()

Serializes the pseudorandom number generator as a JSON object.

var o = minstd.toJSON();
// returns { 'type': 'PRNG', 'name': '...', 'state': {...}, 'params': [] }

Notes

• Before output from a simple linear congruential generator (LCG) is returned, the output is shuffled using the Bays-Durham algorithm. This additional step considerably strengthens the "randomness quality" of a simple LCG's output.
• 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 PRNG creation and not copied) and one sets the generator state to a state array having a different length, the PRNG does not update the existing shared state and, instead, points to the newly provided state array. In order to synchronize PRNG output according to the new shared state array, the state array for each relevant PRNG must be explicitly set.
• If PRNG state is "shared" and one sets the generator state to a state array of the same length, the PRNG state is updated (along with the state of all other PRNGs sharing the PRNG's state array).

Examples

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

var seed;
var rand;
var i;

// Generate pseudorandom numbers...
for ( i = 0; i < 100; i++ ) {
    console.log( minstd() );
}

// Create a new pseudorandom number generator...
seed = 1234;
rand = minstd.factory({
    'seed': seed
});
for ( i = 0; i < 100; i++ ) {
    console.log( rand() );
}

// Create another pseudorandom number generator using a previous seed...
rand = minstd.factory({
    'seed': minstd.seed
});
for ( i = 0; i < 100; i++ ) {
    console.log( rand() );
}

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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.

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See Also

• @stdlib/random-base/minstd: A linear congruential pseudorandom number generator (LCG) based on Park and Miller.
• @stdlib/random-base/mt19937: A 32-bit Mersenne Twister pseudorandom number generator.
• @stdlib/random-base/randi: pseudorandom numbers having integer values.

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Notice

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For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

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License

See LICENSE.

Copyright

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