time-to-botec/squiggle/node_modules/@stdlib/random/base

Pseudorandom Number Generators

Standard library base pseudorandom number generators (PRNGs).

Usage

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

random

Standard library base pseudorandom number generators (PRNGs).

var ns = random;
// returns {...}

The namespace contains the following PRNGs:

• arcsine( a, b ): arcsine distributed pseudorandom numbers.
• bernoulli( p ): Bernoulli distributed pseudorandom numbers.
• beta( alpha, beta ): beta distributed pseudorandom numbers.
• betaprime( alpha, beta ): beta prime distributed pseudorandom numbers.
• binomial( n, p ): binomial distributed pseudorandom numbers.
• boxMuller(): standard normally distributed pseudorandom numbers using the Box-Muller transform.
• cauchy( x0, gamma ): Cauchy distributed pseudorandom numbers.
• chi( k ): Chi distributed pseudorandom numbers.
• chisquare( k ): Chi-square distributed pseudorandom numbers.
• cosine( mu, s ): raised cosine distributed pseudorandom numbers.
• discreteUniform( a, b ): discrete uniform distributed pseudorandom numbers.
• erlang( k, lambda ): Erlang distributed pseudorandom numbers.
• exponential( lambda ): exponentially distributed pseudorandom numbers.
• f( d1, d2 ): F distributed pseudorandom numbers.
• frechet( alpha, s, m ): Fréchet distributed pseudorandom numbers.
• gamma( alpha, beta ): gamma distributed pseudorandom numbers.
• geometric( p ): geometric distributed pseudorandom numbers.
• gumbel( mu, beta ): Gumbel distributed pseudorandom numbers.
• hypergeometric( N, K, n ): hypergeometric distributed pseudorandom numbers.
• improvedZiggurat(): standard normally distributed pseudorandom numbers using the Improved Ziggurat method.
• invgamma( alpha, beta ): inverse gamma distributed pseudorandom numbers.
• kumaraswamy( a, b ): Kumaraswamy's double bounded distributed pseudorandom numbers.
• laplace( mu, b ): Laplace (double exponential) distributed pseudorandom numbers.
• levy( mu, c ): Lévy distributed pseudorandom numbers.
• logistic( mu, s ): logistic distributed pseudorandom numbers.
• lognormal( mu, sigma ): lognormal distributed pseudorandom numbers.
• minstdShuffle(): A linear congruential pseudorandom number generator (LCG) whose output is shuffled.
• minstd(): A linear congruential pseudorandom number generator (LCG) based on Park and Miller.
• mt19937(): A 32-bit Mersenne Twister pseudorandom number generator.
• negativeBinomial( r, p ): negative binomially distributed pseudorandom numbers.
• normal( mu, sigma ): normally distributed pseudorandom numbers.
• pareto1( alpha, beta ): Pareto (Type I) distributed pseudorandom numbers.
• poisson( lambda ): Poisson distributed pseudorandom numbers.
• randi(): pseudorandom numbers having integer values.
• randn(): standard normally distributed pseudorandom numbers.
• randu(): uniformly distributed pseudorandom numbers between 0 and 1.
• rayleigh( sigma ): Rayleigh distributed pseudorandom numbers.
• reviveBasePRNG( key, value ): revive a JSON-serialized pseudorandom number generator (PRNG).
• t( v ): Student's t-distributed pseudorandom numbers.
• triangular( a, b, c ): triangular distributed pseudorandom numbers.
• uniform( a, b ): uniformly distributed pseudorandom numbers.
• weibull( k, lambda ): Weibull distributed pseudorandom numbers.

Attached to each PRNG are the following properties:

• NAME: the generator name.
• seed: the value used to seed the PRNG.
• seedLength: the length of the PRNG seed.
• state: the PRNG state.
• stateLength: the length of the PRNG state.
• byteLength: the size of the PRNG state.
• PRNG: the underlying pseudorandom number generator.

Additionally, attached to each PRNG is a .factory() method which supports creating a seeded PRNG and thus generating a reproducible sequence of pseudorandom numbers.

var rand;
var v;
var i;

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

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

For parameterized PRNGs, the .factory() method supports specifying parameters upon either PRNG creation or invocation. For example,

// Create a PRNG which requires providing parameters at each invocation:
var rand = random.normal.factory({
    'seed': 12345
});

var r = rand( 1.0, 2.0 );
// returns <number>

// Create a PRNG with fixed parameters:
rand = random.normal.factory( 1.0, 2.0, {
    'seed': 12345
});

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

Examples

var objectKeys = require( '@stdlib/utils/keys' );
var random = require( '@stdlib/random/base' );

console.log( objectKeys( random ) );