## Usage ```javascript var minstd = require( '@stdlib/random/base/minstd-shuffle' ); ``` #### minstd() Returns a pseudorandom integer on the interval `[1, 2147483646]`. ```javascript var v = minstd(); // returns ``` #### minstd.normalized() Returns a pseudorandom number on the interval `[0,1)`. ```javascript var v = minstd.normalized(); // returns ``` #### minstd.factory( \[options] ) Returns a linear congruential pseudorandom number generator ([LCG][lcg]) whose output is shuffled. ```javascript var rand = minstd.factory(); ``` The function accepts the following `options`: - **seed**: pseudorandom number generator seed. - **state**: an [`Int32Array`][@stdlib/array/int32] 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]` ```javascript 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 ```javascript 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. ```javascript 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. ```javascript var str = minstd.NAME; // returns 'minstd-shuffle' ``` #### minstd.MIN Minimum possible value. ```javascript var min = minstd.MIN; // returns 1 ``` #### minstd.MAX Maximum possible value. ```javascript var max = minstd.MAX; // returns 2147483646 ``` #### minstd.seed The value used to seed `minstd()`. ```javascript 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. ```javascript var len = minstd.seedLength; // returns ``` #### minstd.state Writable property for getting and setting the generator state. ```javascript var r = minstd(); // returns r = minstd(); // returns // ... // Get a copy of the current state: var state = minstd.state; // returns r = minstd(); // returns r = minstd(); // returns // Reset the state: minstd.state = state; // Replay the last two pseudorandom numbers: r = minstd(); // returns r = minstd(); // returns // ... ``` #### minstd.stateLength Length of generator state. ```javascript var len = minstd.stateLength; // returns ``` #### minstd.byteLength Size (in bytes) of generator state. ```javascript var sz = minstd.byteLength; // returns ``` #### minstd.toJSON() Serializes the pseudorandom number generator as a JSON object. ```javascript var o = minstd.toJSON(); // returns { 'type': 'PRNG', 'name': '...', 'state': {...}, 'params': [] } ```

## Notes - Before output from a simple linear congruential generator ([LCG][lcg]) is returned, the output is shuffled using the Bays-Durham algorithm. This additional step considerably strengthens the "randomness quality" of a simple [LCG][lcg]'s output. - The generator has a period of approximately `2.1e9` (see [Numerical Recipes in C, 2nd Edition](#references), p. 279). - An [LCG][lcg] is fast and uses little memory. On the other hand, because the generator is a simple [linear congruential generator][lcg], 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).

## 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][@park:1988]. - 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][@bays:1976]. - 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][@herzog:2002]. - 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.