time-to-botec/squiggle/node_modules/@stdlib/random/base/minstd-shuffle/lib/main.js
NunoSempere b6addc7f05 feat: add the node modules
Necessary in order to clearly see the squiggle hotwiring.
2022-12-03 12:44:49 +00:00

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/**
* @license Apache-2.0
*
* Copyright (c) 2018 The Stdlib Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
'use strict';
// MODULES //
var factory = require( './factory.js' );
var randint32 = require( './rand_int32.js' );
// MAIN //
/**
* Generates a pseudorandom integer on the interval \\( [1,2^{31}-1) \\).
*
* ## Method
*
* Linear congruential generators (LCGs) use the recurrence relation
*
* ```tex
* X_{n+1} = ( a \cdot X_n + c ) \operatorname{mod}(m)
* ```
*
* where the modulus \\( m \\) is a prime number or power of a prime number and \\( a \\) is a primitive root modulo \\( m \\).
*
* <!-- <note> -->
*
* For an LCG to be a Lehmer RNG, the seed \\( X_0 \\) must be coprime to \\( m \\).
*
* <!-- </note> -->
*
* In this implementation, the constants \\( a \\), \\( c \\), and \\( m \\) have the values
*
* ```tex
* \begin{align*}
* a &= 7^5 = 16807 \\
* c &= 0 \\
* m &= 2^{31} - 1 = 2147483647
* \end{align*}
* ```
*
* <!-- <note> -->
*
* The constant \\( m \\) is a Mersenne prime (modulo \\(31\\)).
*
* <!-- </note> -->
*
* <!-- <note> -->
*
* The constant \\( a \\) is a primitive root (modulo \\(31\\)).
*
* <!-- </note> -->
*
* Accordingly, the maximum possible product is
*
* ```tex
* 16807 \cdot (m - 1) \approx 2^{46}
* ```
*
* The values for \\( a \\), \\( c \\), and \\( m \\) are taken from Park and Miller, "Random Number Generators: Good Ones Are Hard To Find". Park's and Miller's article is also the basis for a recipe in the second edition of _Numerical Recipes in C_.
*
* This implementation subsequently shuffles the output of a linear congruential pseudorandom number generator (LCG) using a shuffle table in accordance with the Bays-Durham algorithm.
*
*
* ## Notes
*
* - The generator has a period of approximately \\(2.1\mbox{e}9\\) (see [Numerical Recipes in C, 2nd Edition](#references), p. 279).
*
*
* ## References
*
* - 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: 5964. doi:[10.1145/355666.355670](http://dx.doi.org/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](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.
*
*
* @function minstd
* @type {PRNG}
* @returns {PositiveInteger} pseudorandom integer
*
* @example
* var v = minstd();
* // returns <number>
*/
var minstd = factory({
'seed': randint32()
});
// EXPORTS //
module.exports = minstd;