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Lognormal Random Numbers

Fill a strided array with pseudorandom numbers drawn from a lognormal distribution.

Installation

npm install @stdlib/random-strided-lognormal

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var lognormal = require( '@stdlib/random-strided-lognormal' );

lognormal( N, mu, sm, sigma, ss, out, so[, options] )

Fills a strided array with pseudorandom numbers drawn from a lognormal distribution.

var Float64Array = require( '@stdlib/array-float64' );

// Create an array:
var out = new Float64Array( 10 );

// Fill the array with pseudorandom numbers:
lognormal( out.length, [ 2.0 ], 0, [ 5.0 ], 0, out, 1 );

The function has the following parameters:

• N: number of indexed elements.
• mu: location parameter.
• sm: index increment for mu.
• sigma: scale parameter.
• ss: index increment for sigma.
• out: output array.
• so: index increment for out.

The N and stride parameters determine which strided array elements are accessed at runtime. For example, to access every other value in out,

var out = [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ];

lognormal( 3, [ 2.0 ], 0, [ 5.0 ], 0, out, 2 );

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

var Float64Array = require( '@stdlib/array-float64' );

// Initial arrays...
var mu0 = new Float64Array( [ 0.0, 0.0, 0.0, 2.0, 2.0, 2.0 ] );
var sigma0 = new Float64Array( [ 5.0, 5.0, 5.0, 5.0, 5.0, 5.0 ] );

// Create offset views...
var mu1 = new Float64Array( mu0.buffer, mu0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var sigma1 = new Float64Array( sigma0.buffer, sigma0.BYTES_PER_ELEMENT*3 ); // start at 4th element

// Create an output array:
var out = new Float64Array( 3 );

// Fill the output array:
lognormal( out.length, mu1, -2, sigma1, 1, out, 1 );

The function accepts the following options:

• prng: pseudorandom number generator for generating uniformly distributed pseudorandom numbers on the interval [0,1). If provided, the function ignores both the state and seed options. In order to seed the underlying pseudorandom number generator, one must seed the provided prng (assuming the provided prng is seedable).
• seed: pseudorandom number generator seed.
• state: a Uint32Array 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 an underlying generator has exclusive control over its internal state. Default: true.

To use a custom PRNG as the underlying source of uniformly distributed pseudorandom numbers, set the prng option.

var Float64Array = require( '@stdlib/array-float64' );
var minstd = require( '@stdlib/random-base-minstd' );

var opts = {
    'prng': minstd.normalized
};

var out = new Float64Array( 10 );
lognormal( out.length, [ 2.0 ], 0, [ 5.0 ], 0, out, 1, opts );

To seed the underlying pseudorandom number generator, set the seed option.

var Float64Array = require( '@stdlib/array-float64' );

var opts = {
    'seed': 12345
};

var out = new Float64Array( 10 );
lognormal( out.length, [ 2.0 ], 0, [ 5.0 ], 0, out, 1, opts );

lognormal.ndarray( N, mu, sm, om, sigma, ss, os, out, so, oo[, options] )

Fills a strided array with pseudorandom numbers drawn from a lognormal distribution using alternative indexing semantics.

var Float64Array = require( '@stdlib/array-float64' );

// Create an array:
var out = new Float64Array( 10 );

// Fill the array with pseudorandom numbers:
lognormal.ndarray( out.length, [ 2.0 ], 0, 0, [ 5.0 ], 0, 0, out, 1, 0 );

The function has the following additional parameters:

• om: starting index for mu.
• os: starting index for sigma.
• oo: starting index for out.

While typed array views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, to access every other value in out starting from the second value,

var out = [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ];

lognormal.ndarray( 3, [ 2.0 ], 0, 0, [ 5.0 ], 0, 0, out, 2, 1 );

The function accepts the same options as documented above for lognormal().

Notes

• If N <= 0, both functions leave the output array unchanged.
• Both functions support array-like objects having getter and setter accessors for array element access.

Examples

var zeros = require( '@stdlib/array-zeros' );
var zeroTo = require( '@stdlib/array-base-zero-to' );
var logEach = require( '@stdlib/console-log-each' );
var lognormal = require( '@stdlib/random-strided-lognormal' );

// Specify a PRNG seed:
var opts = {
    'seed': 1234
};

// Create an array:
var x1 = zeros( 10, 'float64' );

// Create a list of indices:
var idx = zeroTo( x1.length );

// Fill the array with pseudorandom numbers:
lognormal( x1.length, [ 2.0 ], 0, [ 5.0 ], 0, x1, 1, opts );

// Create a second array:
var x2 = zeros( 10, 'generic' );

// Fill the array with the same pseudorandom numbers:
lognormal( x2.length, [ 2.0 ], 0, [ 5.0 ], 0, x2, 1, opts );

// Print the array contents:
logEach( 'x1[%d] = %.2f; x2[%d] = %.2f', idx, x1, idx, x2 );

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See Also

• @stdlib/random-base/lognormal: log-normally distributed pseudorandom numbers.
• @stdlib/random-array/lognormal: create an array containing pseudorandom numbers drawn from a lognormal distribution.

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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.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.